From 7d7b2c26792c85993ff41895144815a1e5bda439 Mon Sep 17 00:00:00 2001 From: Work Date: Tue, 29 May 2018 11:31:52 +0200 Subject: [PATCH 001/116] initial test commit --- qdev_wrappers/fitting/Fitclasses.py | 67 +++++++++++++++++++++++++++++ 1 file changed, 67 insertions(+) create mode 100644 qdev_wrappers/fitting/Fitclasses.py diff --git a/qdev_wrappers/fitting/Fitclasses.py b/qdev_wrappers/fitting/Fitclasses.py new file mode 100644 index 00000000..e6f6ac02 --- /dev/null +++ b/qdev_wrappers/fitting/Fitclasses.py @@ -0,0 +1,67 @@ + +import qcodes as qc +import numpy as np +import scipy.fftpack as fftpack + + +class Fitclass: + def __init__(self): + self.name = 'NoName' + self.fun_str = 'None' + self.p_names = ['none'] + self.p_units = ['none'] + + + +class T1(Fitclass): + + def __init__(self): + self.name = 'T1fit' + self.fun_str = r'$f(x) = a \exp(-x/T) + c$' + self.p_names = ['$a$', '$T$', '$c$'] + self.p_labels= ['a', 'T', 'c'] + self.p_units = ['y', 'x', 'y'] #This units thing doesn't seem to quite work + #and I think its only used for plotting, so we might consider if this is really how we want to do this + def fun(self, x, a, T, c): + val = a*np.exp(-x/T)+c + return val + + def guess(self, x, y): + l = len(y) + val_init = y[0:round(l/20)].mean() + val_fin = y[-round(l/20):].mean() + a = val_init - val_fin + c = val_fin + # guess T1 as point where data has fallen to 1/e of init value + idx = (np.abs(y-a/np.e-c)).argmin() + T = x[idx] + return [a, T, c] + + + +class T2(Fitclass): + + def __init__(self): + self.name = 'T2fit' + self.fun_str = r'$f(x) = a \sin(\omega x +\phi)\exp(-x/T) + c$' + self.p_names = ['$a$', '$T$', '$\omega$', '$\phi$', '$c$'] + self.p_labels = ['a', 'T', 'w', 'p', 'c'] + self.p_units = ['y', 'x', '1/x', '', 'y'] + + def fun(self, x, a, T, w, p, c): + val = a*np.exp(-x/T)*np.sin(w*x+p)+c + return val + + def guess(self,x,y): + a = y.max() - y.min() + c = y.mean() + # guess T2 as point half way point in data + T = x[round(len(x)/2)] + # Get initial guess for frequency from a fourier transform + yhat = fftpack.rfft(y-y.mean()) + idx = (yhat**2).argmax() + freqs = fftpack.rfftfreq(len(x), d = (x[1]-x[0])/(2*np.pi)) + w = freqs[idx] + p = 0 + return [a,T,w,p,c] + From e96e6201e5f74b4e474320b1371caecf9c952fb5 Mon Sep 17 00:00:00 2001 From: lillian542 <38584660+lillian542@users.noreply.github.com> Date: Tue, 29 May 2018 13:17:53 +0200 Subject: [PATCH 002/116] Update Fitclasses.py --- qdev_wrappers/fitting/Fitclasses.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/qdev_wrappers/fitting/Fitclasses.py b/qdev_wrappers/fitting/Fitclasses.py index e6f6ac02..7a12cce4 100644 --- a/qdev_wrappers/fitting/Fitclasses.py +++ b/qdev_wrappers/fitting/Fitclasses.py @@ -20,8 +20,8 @@ def __init__(self): self.fun_str = r'$f(x) = a \exp(-x/T) + c$' self.p_names = ['$a$', '$T$', '$c$'] self.p_labels= ['a', 'T', 'c'] - self.p_units = ['y', 'x', 'y'] #This units thing doesn't seem to quite work - #and I think its only used for plotting, so we might consider if this is really how we want to do this + self.p_units = ['y', 'x', 'y'] + def fun(self, x, a, T, c): val = a*np.exp(-x/T)+c return val From 55c2081d92d5095e3c6dca1716495faa7e7cb2e1 Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Tue, 5 Jun 2018 19:25:44 +0200 Subject: [PATCH 003/116] converter for legacy and SQL data to dictionary, fitter to find fit from dictionary --- qdev_wrappers/fitting/Converter.py | 210 +++++++++++++++++++++++++++++ qdev_wrappers/fitting/Fitter.py | 187 +++++++++++++++++++++++++ 2 files changed, 397 insertions(+) create mode 100644 qdev_wrappers/fitting/Converter.py create mode 100644 qdev_wrappers/fitting/Fitter.py diff --git a/qdev_wrappers/fitting/Converter.py b/qdev_wrappers/fitting/Converter.py new file mode 100644 index 00000000..9839e116 --- /dev/null +++ b/qdev_wrappers/fitting/Converter.py @@ -0,0 +1,210 @@ +import qcodes as qc +import numpy as np +from os.path import sep +from qcodes.dataset.data_export import get_data_by_id +from qcodes.dataset.sqlite_base import connect +from qdev_wrappers.file_setup import CURRENT_EXPERIMENT +from qdev_wrappers.show_num import check_experiment_is_initialized + + + +class DataConverter(): + + def convert(self, id, samplefolder = None): + + data = self.find_data(id, samplefolder) + data_dict = self.make_data_dictionary(data) + + dependencies = self.find_dependencies(id, data) #SQL needs only id number to find dependencies, Legacy needs only data + all_variables = self.find_variables(data) + + data_dict['data_id'] = id + data_dict['dependencies'] = dependencies + data_dict['variables'] = all_variables + + return data_dict + + + +class SQL_Converter(DataConverter): + + def find_data(self, id, samplefolder): + + '''sample folder does nothing except act as placeholder. + This function currently proceeds under the assumption that the experiment is set up, and doesn't + check anything to confirm that. Also, I think this doesn't return any error if it can't find + the dataset with that id number.''' + + data = get_data_by_id(id) + + return data + + + def find_dependencies(self, id, data): #SQL converter only needs id, not data + conn = connect('experiments.db') #the name of the file to access can't be set manually right now + cur = conn.cursor() + + names = {} + dependencies = {} + + for row in cur.execute('SELECT layout_id, parameter FROM layouts WHERE run_id is ?', str(id)): + names[row[0]] = row[1] + + for row in cur.execute('SELECT dependent, independent FROM dependencies'): + if row[0] in names.keys(): + dependencies[names[row[0]]] = [] + + for row in cur.execute('SELECT dependent, independent FROM dependencies'): + if row[0] in names.keys(): + dependencies[names[row[0]]].append(names[row[1]]) + + conn.close() + + return dependencies + + + def find_variables(self, data): + + all_variables = [] + + for data_subset in data: + for variable in data_subset: + if variable['name'] not in all_variables: + all_variables.append(variable['name']) + + return all_variables + + + def make_data_dictionary(self, data): + + data_dict = {} + + for data_subset in data: + for variable in data_subset: + + name = variable['name'] + + if name in data_dict.keys(): + print(name) + if not np.array_equal(variable['data'], data_dict[name]['data']): + raise RuntimeError('Variables with identical names contain non-identical data arrays!') + + data_dict[name] = variable + + return data_dict + + + +class Legacy_Converter(DataConverter): + + def find_data(self, id, samplefolder): + + str_id = '{0:03d}'.format(id) + + if samplefolder==None: + check_experiment_is_initialized() + #check_experiment_is_initialized() doesn't do anything at the moment + + path = qc.DataSet.location_provider.fmt.format(counter=str_id) + data = qc.load_data(path) + + else: + path = '{}{}{}'.format(samplefolder,sep,str_id) + data = qc.load_data(path) + + return data + + + def find_dependencies(self, id, data): #legacy only needs data, not id + + dep_vars = [key for key in data.arrays.keys() if "_set" not in key[-4:]] + indep_vars = [key for key in data.arrays.keys() if "_set" in key[-4:]] + + dependencies = {} + + for variable in dep_vars: + dependencies[variable] = indep_vars + + return dependencies + + + def find_variables(self, data): + + all_variables = [variable for variable in data.arrays.keys()] + return all_variables + + + def resize_data(self, data1, data2): + + scan_size = len(data1[0]) + + new_data2 = [] + + for setvalue in data2: + array = np.array([setvalue for datapoint in range(scan_size)]) + new_data2.append(array) + + return np.array(new_data2) + + + def make_data_dictionary(self, data): + + data_dict = {} + + all_variables = self.find_variables(data) + + for variable in all_variables: + qc_data = getattr(data, variable) + name = getattr(qc_data, 'name', 'No Name') + label = getattr(qc_data, 'label', '') + unit = getattr(qc_data, 'unit', '') + np_data = qc_data.ndarray + + data_dict[variable] = {'name': name, + 'label': label, + 'var_name': variable, + 'unit': unit, + 'data': np_data} + + + '''checks if the data set used set points - if so, it reformats the data + arrays so that it looks the same as it would look if it were imported + from SQL. All data is stored as arrays of the same length, rather than + set_points being stored as single numbers + (i.e. for setpoints = [1, 2, 3] , data = [[a, b, c], [d, e, f], [g, h, i]] + becomes setpoints = [[1, 1, 1], [2, 2, 2], [3, 3, 3]], data = data)''' + dependencies = self.find_dependencies(id, data) + + dep_vars = [key for key in dependencies.keys()] + indep_vars = dependencies[dep_vars[0]] + + if len(indep_vars) == 2: + data1 = data_dict[dep_vars[0]]['data'] + data2 = data_dict[indep_vars[0]]['data'] + data3 = data_dict[indep_vars[1]]['data'] + + if type(data2[0]) != np.ndarray: + data_dict[indep_vars[0]]['data'] = self.resize_data(data1, data2) + if type(data3[0]) != np.ndarray: + data_dict[indep_vars[1]]['data'] = self.resize_data(data1, data3) + + + + '''reformatting nested arrays as a single 1d array, i.e. + setpoints = [[1, 1, 1], [2, 2, 2], [3, 3, 3]], data = [[a, b, c], [d, e, f], [g, h, i]] + becomes setpoints [1, 1, 1, 2, 2, 2, 3, 3, 3], data = [a, b, c, d, e, f, g, h, i]''' + + for variable in all_variables: + data1 = data_dict[variable]['data'] + + new_data = [] + for array in data1: + for datapoint in array: + new_data.append(datapoint) + + data_dict[variable]['data'] = np.array(new_data) + + + return data_dict + + diff --git a/qdev_wrappers/fitting/Fitter.py b/qdev_wrappers/fitting/Fitter.py new file mode 100644 index 00000000..b4e6ad94 --- /dev/null +++ b/qdev_wrappers/fitting/Fitter.py @@ -0,0 +1,187 @@ +import qcodes as qc +import numpy as np + +from Fitclasses import T1, T2 +from Converter import SQL_Converter, Legacy_Converter +from scipy.optimize import curve_fit + + + + + + + +class Fitter(): + + def do_1d(self, xdata, ydata, fitclass, p0,**kwargs): + + if (p0==None and hasattr(fitclass,'guess')): + p0 = getattr(fitclass,'guess')(xdata,ydata) + popt, pcov = curve_fit(fitclass.fun, xdata, ydata, p0=p0, **kwargs) + return popt, pcov + elif p0!=None: + popt, pcov = curve_fit(fitclass.fun, xdata, ydata, p0=p0, **kwargs) + return popt, pcov + else: + return "Could not find guess parameters for fit." + + + def fit(self, data, fitclass, x=None, y=None, z=None, default_dependencies=False, dataname = None, cut='vertical', p0=None,**kwargs): + + if type(fitclass) == type: + #Maybe I'm just an idiot, and this isn't necessary for the world-at-large, but + #I spent about 45 minutes trying to figure out what I broke before I realized + #that I just forgot the parentheses after the fitclass. So this is here for now. + raise RuntimeError('It looks like there is something wrong with your fitclass(). Possibly you forgot the parentheses?') + + if (x==None or y==None) and default_dependencies==False: + raise RuntimeError('Please either specify data for x, y (and optionally z) or set default_dependencies = True') + + if default_dependencies == True: + dep_var = [key for key in data['dependencies'].keys()] + indep_vars = [value for value in data['dependencies'].values()] + + if dataname==None: + if len(dep_var) != 1: + raise RuntimeError('Dataset has more than one dataname. Choose dataname to be fitted from: {}.'.format(', '.join(dep_var))) + + indep_vars = indep_vars[0] + + else: + if dataname not in dep_var: + raise RuntimeError('Dataname not in dataset. Input dataname was: \'{}\' while dataname(s) in dataset are: {}.'.format(dataname,', '.join(dep_var))) + + indep_vars = indep_vars[dep_var.index(dataname)] + dep_var = [dataname] + + if len(indep_vars) > 2: + print('independent variables: {}'.format(indep_vars)) + raise RuntimeError('That dataset seems to contain {} independent variables, which is {} too many.'.format(len(indep_vars), len(indep_vars)-2)) + + + if len(indep_vars) == 1: + dimensions = 1 + x_dict = data[indep_vars[0]] + y_dict = data[dep_var[0]] + + if len(indep_vars) == 2: + dimensions = 2 + x_dict = data[indep_vars[0]] + y_dict = data[indep_vars[1]] + z_dict = data[dep_var[0]] + + + elif default_dependencies == False: + + for dataname in [x, y, z]: + if dataname not in data['variables']: + raise RuntimeError('The specified variable "{}" is not found in the variables for this data dictionary. Variables are {}'.format(dataname, data['variables'])) + + x_dict = data[x] + y_dict = data[y] + dimensions = 1 + + if z != None: + z_dict = data[z] + dimensions = 2 + + + #Do fit for 1D data + if dimensions == 1: + + xdata = x_dict['data'] + ydata = y_dict['data'] + + fit1d = {} + fit1d['parameters'] = {} + + popt, pcov = self.do_1d(xdata, ydata, fitclass, p0,**kwargs) + + for parameter in fitclass.p_labels: #parameters currently missing units, use fitclass.p_units + fit1d['parameters'][parameter] = {'value': popt[fitclass.p_labels.index(parameter)]} + fit1d['parameters'][parameter]['cov'] = pcov[fitclass.p_labels.index(parameter)] + + fit1d['estimator'] = {'method': 'Least squared fit', + 'type': fitclass.name, + 'function': 'save all text from fitter function and class function here?', + 'more?' : 'perhaps'} + + fit1d['inferred_from'] = {'xdata': x_dict['name'], + 'ydata': y_dict['name'], + 'dataset': data['data_id'], + 'dependencies': data['dependencies']} #missing sample name + + + return fit1d + + + #Do fit for 2D data + if dimensions == 2: + + xdata = x_dict['data'] + ydata = y_dict['data'] + zdata = z_dict['data'] + + fits2d = {} + + if cut == 'horizontal': + setarray = ydata + xarray = xdata + + if cut == 'vertical': + setarray = xdata + xarray = ydata + + setpoints = np.unique(setarray) + yarray = zdata + + #reformats as array of set points, with a y-array and z-array corresponding to each set point + + xdata_lst = [] + ydata_lst = [] + + for set_point in setpoints: + x_dat = [] + y_dat = [] + for setpoint, x, y in zip(setarray, xarray, yarray): + if setpoint == set_point: + x_dat.append(x) + y_dat.append(y) + xdata_lst.append(np.array(x_dat)) + ydata_lst.append(np.array(y_dat)) + + + xdata = np.array(xdata_lst) + ydata = np.array(ydata_lst) + + + #fitting as a sequence of 1D plots for different set_values + + for set_value, xdata_1d, ydata_1d in zip(setpoints, xdata, ydata): + + popt, pcov = self.do_1d(xdata_1d, ydata_1d, fitclass, p0,**kwargs) + + fits2d[set_value] = {} + fits2d[set_value]['parameters'] = {} + + for parameter in fitclass.p_labels: #parameters currently missing units, use fitclass.p_units + fits2d[set_value]['parameters'][parameter] = {'value': popt[fitclass.p_labels.index(parameter)]} + fits2d[set_value]['parameters'][parameter]['cov'] = pcov[fitclass.p_labels.index(parameter)] + + + fits2d['estimator'] = {'method': 'Least squared fit', + 'type': fitclass.name, + 'function': 'save all text from fitter function and class function here?', + 'more?' : 'perhaps'} + + + #does this needs to be moved so that it specifies which cut the individual sets of parameters are inferred from? + fits2d['inferred_from'] = {'xdata': x_dict['name'], + 'ydata': y_dict['name'], + 'zdata': z_dict['name'], + 'dataset': data['data_id'], + 'dependencies': data['dependencies']} #missing sample name + + return fits2d + + From 7b6bd3a4d6d2d2fd7008a5aab40cca2916a80d5b Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Wed, 6 Jun 2018 10:39:58 +0200 Subject: [PATCH 004/116] Readd file removed somehow --- .../PulseBuilder_Instr.py | 467 ++++++++++++++++++ 1 file changed, 467 insertions(+) create mode 100644 qdev_wrappers/customised_instruments/PulseBuilder_Instr.py diff --git a/qdev_wrappers/customised_instruments/PulseBuilder_Instr.py b/qdev_wrappers/customised_instruments/PulseBuilder_Instr.py new file mode 100644 index 00000000..754ec1da --- /dev/null +++ b/qdev_wrappers/customised_instruments/PulseBuilder_Instr.py @@ -0,0 +1,467 @@ +import numpy as np +from qcodes.instrument.base import Instrument +from qcodes import validators as vals + + +#%% Create funciton for two-qubit readout and spectroscopy + +class MultiQ_PulseBuilder(Instrument): + def __init__(self,name,number_read_freqs,alazar,alazar_ctrl,awg,qubit,cavity,**kwargs): + super().__init__(name, **kwargs) + self.awg = awg + self.qubit = qubit + self.cavity = cavity + self.alazar = alazar + self.alazar_ctrl = alazar_ctrl + self.filename = 'Waveforms' + self.x_val = lambda: np.linspace(0,1,10) + self.SR = 2.5e9 + self.number_read_freqs = number_read_freqs + + self.add_parameter('cycle_time', + label='Pulse Cycle Time', + unit='s', + set_cmd= lambda x : x, + vals=vals.Numbers(0,1e-3)) + self.add_parameter('int_time', + label='Integration time', + unit='s', + set_cmd= lambda x : x, + vals=vals.Numbers(0,0.2e-3)) + self.add_parameter('int_delay', + label='Readout Delay', + unit='s', + set_cmd= lambda x : x, + vals=vals.Numbers(0,0.2e-3)) + self.add_parameter('readout_dur', + label='Readout Duration', + unit='s', + set_cmd= lambda x : x, + vals=vals.Numbers(0,0.2e-3)) + self.add_parameter('marker_offset', + label='Marker Offset', + unit='s', + set_cmd= lambda x : x, + vals=vals.Numbers(-1e-5,1e-5)) + self.add_parameter('averages', + label='Averages', + unit='', + set_cmd=self.num_averages, + vals=vals.Numbers(1,1e5)) + for i in range(number_read_freqs): + self.add_parameter('readout_freq_{}'.format(i+1), + label='Readout Frequency {}'.format(i+1), + unit='Hz', + set_cmd= lambda x : x, + vals=vals.Numbers(0,12.5e9)) + + + def MultiQ_SSB_Spectroscopy(self, start, stop, npts): + qubitf = np.mean([start,stop]) + self.qubit.frequency(qubitf) + self.x_val = lambda: np.linspace(start - qubitf,stop - qubitf,npts) + self.qubit.frequency() + + # Clear AWG + self.awg.clearSequenceList() + self.awg.clearWaveformList() + + N = int((self.cycle_time()*self.SR+64) - self.cycle_time()*self.SR%64) + N_offset = int(self.marker_offset()*self.SR) + time = np.linspace(N/self.SR-self.readout_dur(), N/self.SR, int(self.readout_dur()*self.SR), endpoint=False) + + # Create triggers + ZerosMarker = np.zeros(int(N)) + TriggerMarker = np.zeros(int(N)) + TriggerMarker[-int(self.readout_dur()*self.SR-N_offset):-int(self.readout_dur()*self.SR-N_offset-500e-9*self.SR)] = 1 + + # Create Drive tones + wfms = [[], []] + for i , f in enumerate(self.x_val()-qubitf): + # SSB drive tone + #sine_signal = np.concatenate((0.5*np.sin(f*2*np.pi*time),np.zeros(N-len(time)))) + #cosine_signal = np.concatenate((0.5*np.cos(f*2*np.pi*time),np.zeros(N-len(time)))) + sine_signal = np.concatenate((np.zeros(N-len(time)), 0.5*np.sin(f*2*np.pi*time))) + cosine_signal = np.concatenate((np.zeros(N-len(time)), 0.5*np.cos(f*2*np.pi*time))) + if i == 0: + wfm_ch1 = np.array([cosine_signal,TriggerMarker,TriggerMarker]) + wfm_ch2 = np.array([sine_signal,TriggerMarker,TriggerMarker]) + else: + wfm_ch1 = np.array([cosine_signal,ZerosMarker,ZerosMarker]) + wfm_ch2 = np.array([sine_signal,ZerosMarker,ZerosMarker]) + wfms[0].append(wfm_ch1) + wfms[1].append(wfm_ch2) + + trig_waits = [0 for _ in range(npts)] + nreps = [1 for _ in range(npts)] + event_jumps = [0 for _ in range(npts)] + event_jump_to = [0 for _ in range(npts)] + go_to = [0 for _ in range(npts)] + go_to[-1] = 1 # Make the sequence loop back to first step + + seqx = self.awg.makeSEQXFile(trig_waits, + nreps, + event_jumps, + event_jump_to, + go_to, + wfms, + [1, 1], + self.filename) + self.awg.sendSEQXFile(seqx, self.filename + '.seqx') + self.awg.loadSEQXFile(self.filename + '.seqx') + + # Create Readout tones + self.update_readout_freqs() + + # Create sequence for readout tones SLISt:SEQuence:NEW , [,] + self.awg.write('SLISt:SEQuence:NEW \"Readout_Seq\", {}, 2'.format(npts)) + self.awg.write('SLISt:SEQuence:STEP{}:GOTO \"Readout_Seq\", 1'.format(npts)) + # Fill Readout waveforms into sequence + for i in range(npts): + self.awg.write('SLISt:SEQuence:STEP{}:TASSet1:WAVeform \"Readout_Seq\", \"Readout_I\"'.format(str(i+1))) + self.awg.write('SLISt:SEQuence:STEP{}:TASSet2:WAVeform \"Readout_Seq\", \"Readout_Q\"'.format(str(i+1))) + + # Assign waveforms + self.awg.ch1.setSequenceTrack(self.filename, 1) + self.awg.ch2.setSequenceTrack(self.filename, 2) + self.awg.ch3.setSequenceTrack('Readout_Seq', 1) + self.awg.ch4.setSequenceTrack('Readout_Seq', 2) + self.awg.ch2.state(1) + self.awg.play() + + # ALazar labels + self.alazar.seq_mode('on') + for ala_chan in self.alazar_ctrl.channels[2:4]: + ala_chan.records_per_buffer(npts) + ala_chan.data.setpoint_labels = ('SSB Drive frequency',ala_chan.data.setpoint_labels[1]) + ala_chan.data.setpoint_units = ('Hz',ala_chan.data.setpoint_units[1]) + + for n, ala_chan in enumerate(self.alazar_ctrl.channels[12:20]): + ala_chan.records_per_buffer(npts) + ala_chan.data.setpoint_labels = ('SSB Drive frequency',) + ala_chan.data.setpoint_units = ('Hz',) + + # prepare channels + self.num_averages(self._averages) + + def MultiQ_SSB_Spec_NoOverlap(self, start, stop, npts, pulse_length = 2e-6): + qubitf = np.mean([start,stop]) + self.qubit.frequency(qubitf) + self.x_val = lambda: np.linspace(start - qubitf,stop - qubitf,npts) + self.qubit.frequency() + + # Clear AWG + self.awg.clearSequenceList() + self.awg.clearWaveformList() + + N = int((self.cycle_time()*self.SR+64) - self.cycle_time()*self.SR%64) + N_offset = int(self.marker_offset()*self.SR) + time = np.linspace(0, pulse_length, int(pulse_length*self.SR), endpoint=False) + + # Create triggers + ZerosMarker = np.zeros(int(N)) + TriggerMarker = np.zeros(int(N)) + TriggerMarker[-int(self.readout_dur()*self.SR-N_offset):-int(self.readout_dur()*self.SR-N_offset-500e-9*self.SR)] = 1 + + # Create Drive tones + wfms = [[], []] + for i , f in enumerate(self.x_val()-qubitf): + # SSB drive tone + sine_signal = np.concatenate((np.zeros(N-len(time)-int(self.readout_dur()*self.SR)), 0.5*np.sin(f*2*np.pi*time),np.zeros(int(self.readout_dur()*self.SR)))) + cosine_signal = np.concatenate((np.zeros(N-len(time)-int(self.readout_dur()*self.SR)), 0.5*np.cos(f*2*np.pi*time),np.zeros(int(self.readout_dur()*self.SR)))) + if i == 0: + wfm_ch1 = np.array([cosine_signal,TriggerMarker,TriggerMarker]) + wfm_ch2 = np.array([sine_signal,TriggerMarker,TriggerMarker]) + else: + wfm_ch1 = np.array([cosine_signal,ZerosMarker,ZerosMarker]) + wfm_ch2 = np.array([sine_signal,ZerosMarker,ZerosMarker]) + wfms[0].append(wfm_ch1) + wfms[1].append(wfm_ch2) + + trig_waits = [0 for _ in range(npts)] + nreps = [1 for _ in range(npts)] + event_jumps = [0 for _ in range(npts)] + event_jump_to = [0 for _ in range(npts)] + go_to = [0 for _ in range(npts)] + go_to[-1] = 1 # Make the sequence loop back to first step + + seqx = self.awg.makeSEQXFile(trig_waits, + nreps, + event_jumps, + event_jump_to, + go_to, + wfms, + [1, 1], + self.filename) + self.awg.sendSEQXFile(seqx, self.filename + '.seqx') + self.awg.loadSEQXFile(self.filename + '.seqx') + + # Create Readout tones + self.update_readout_freqs() + + # Create sequence for readout tones SLISt:SEQuence:NEW , [,] + self.awg.write('SLISt:SEQuence:NEW \"Readout_Seq\", {}, 2'.format(npts)) + self.awg.write('SLISt:SEQuence:STEP{}:GOTO \"Readout_Seq\", 1'.format(npts)) + # Fill Readout waveforms into sequence + for i in range(npts): + self.awg.write('SLISt:SEQuence:STEP{}:TASSet1:WAVeform \"Readout_Seq\", \"Readout_I\"'.format(str(i+1))) + self.awg.write('SLISt:SEQuence:STEP{}:TASSet2:WAVeform \"Readout_Seq\", \"Readout_Q\"'.format(str(i+1))) + + # Assign waveforms + self.awg.ch1.setSequenceTrack(self.filename, 1) + self.awg.ch2.setSequenceTrack(self.filename, 2) + self.awg.ch3.setSequenceTrack('Readout_Seq', 1) + self.awg.ch4.setSequenceTrack('Readout_Seq', 2) + self.awg.ch2.state(1) + self.awg.play() + + # ALazar labels + self.alazar.seq_mode('on') + for ala_chan in self.alazar_ctrl.channels[2:4]: + ala_chan.records_per_buffer(npts) + ala_chan.data.setpoint_labels = ('SSB Drive frequency',ala_chan.data.setpoint_labels[1]) + ala_chan.data.setpoint_units = ('Hz',ala_chan.data.setpoint_units[1]) + + for n, ala_chan in enumerate(self.alazar_ctrl.channels[12:20]): + ala_chan.records_per_buffer(npts) + ala_chan.data.setpoint_labels = ('SSB Drive frequency',) + ala_chan.data.setpoint_units = ('Hz',) + + # prepare channels + self.num_averages(self._averages) + + def MultiQ_Rabi(self, start, stop, npts): + + self.x_val = lambda: np.linspace(start ,stop ,npts) + + # Clear AWG + self.awg.ch2.state(0) + self.awg.clearSequenceList() + self.awg.clearWaveformList() + + N = int((self.cycle_time()*self.SR+64) - self.cycle_time()*self.SR%64) + N_offset = int(self.marker_offset()*self.SR) + + # Create triggers + ZerosMarker = np.zeros(int(N)) + TriggerMarker = np.zeros(int(N)) + TriggerMarker[-int(self.readout_dur()*self.SR-N_offset):-int(self.readout_dur()*self.SR-N_offset-500e-9*self.SR)] = 1 + + # Create Drive tones + wfms = [[]] + for i , t in enumerate(self.x_val()): + # SSB drive tone + drive = np.zeros(int(N)) + drive[-int((self.readout_dur() + t)*self.SR):-int(self.readout_dur()*self.SR)] = 0.5 + if i == 0: + wfm_ch1 = np.array([drive,TriggerMarker,TriggerMarker]) + else: + wfm_ch1 = np.array([drive,ZerosMarker,ZerosMarker]) + wfms[0].append(wfm_ch1) + + trig_waits = [0 for _ in range(npts)] + nreps = [1 for _ in range(npts)] + event_jumps = [0 for _ in range(npts)] + event_jump_to = [0 for _ in range(npts)] + go_to = [0 for _ in range(npts)] + go_to[-1] = 1 # Make the sequence loop back to first step + + seqx = self.awg.makeSEQXFile(trig_waits, + nreps, + event_jumps, + event_jump_to, + go_to, + wfms, + [1], + self.filename) + self.awg.sendSEQXFile(seqx, self.filename + '.seqx') + self.awg.loadSEQXFile(self.filename + '.seqx') + + # Create Readout tones + self.update_readout_freqs() + + # Create sequence for readout tones SLISt:SEQuence:NEW , [,] + self.awg.write('SLISt:SEQuence:NEW \"Readout_Seq\", {}, 2'.format(npts)) + self.awg.write('SLISt:SEQuence:STEP{}:GOTO \"Readout_Seq\", 1'.format(npts)) + # Fill Readout waveforms into sequence + for i in range(npts): + self.awg.write('SLISt:SEQuence:STEP{}:TASSet1:WAVeform \"Readout_Seq\", \"Readout_I\"'.format(str(i+1))) + self.awg.write('SLISt:SEQuence:STEP{}:TASSet2:WAVeform \"Readout_Seq\", \"Readout_Q\"'.format(str(i+1))) + + # Assign waveforms + self.awg.ch1.setSequenceTrack(self.filename, 1) + self.awg.ch3.setSequenceTrack('Readout_Seq', 1) + self.awg.ch4.setSequenceTrack('Readout_Seq', 2) + self.awg.play() + + # ALazar labels + self.alazar.seq_mode('on') + for ala_chan in self.alazar_ctrl.channels[2:4]: + ala_chan.records_per_buffer(npts) + ala_chan.data.setpoint_labels = ('Drive time',ala_chan.data.setpoint_labels[1]) + ala_chan.data.setpoint_units = ('s',ala_chan.data.setpoint_units[1]) + + for n, ala_chan in enumerate(self.alazar_ctrl.channels[12:20]): + ala_chan.records_per_buffer(npts) + ala_chan.data.setpoint_labels = ('Drive time',) + ala_chan.data.setpoint_units = ('s',) + + # prepare channels + self.num_averages(self._averages) + + def MultiQ_Lifetime(self, start, stop, npts, pipulse = 10e-9): + + self.x_val = lambda: np.linspace(start ,stop ,npts) + + # Clear AWG + self.awg.ch2.state(0) + self.awg.clearSequenceList() + self.awg.clearWaveformList() + + N = int((self.cycle_time()*self.SR+64) - self.cycle_time()*self.SR%64) + N_offset = int(self.marker_offset()*self.SR) + + # Create triggers + ZerosMarker = np.zeros(int(N)) + TriggerMarker = np.zeros(int(N)) + TriggerMarker[-int(self.readout_dur()*self.SR-N_offset):-int(self.readout_dur()*self.SR-N_offset-500e-9*self.SR)] = 1 + + # Create Drive tones + wfms = [[]] + for i , t in enumerate(self.x_val()): + # SSB drive tone + drive = np.zeros(int(N)) + drive[-int((self.readout_dur() + t + pipulse)*self.SR):-int((self.readout_dur() + t)*self.SR)] = 0.5 + if i == 0: + wfm_ch1 = np.array([drive,TriggerMarker,TriggerMarker]) + else: + wfm_ch1 = np.array([drive,ZerosMarker,ZerosMarker]) + wfms[0].append(wfm_ch1) + + trig_waits = [0 for _ in range(npts)] + nreps = [1 for _ in range(npts)] + event_jumps = [0 for _ in range(npts)] + event_jump_to = [0 for _ in range(npts)] + go_to = [0 for _ in range(npts)] + go_to[-1] = 1 # Make the sequence loop back to first step + + seqx = self.awg.makeSEQXFile(trig_waits, + nreps, + event_jumps, + event_jump_to, + go_to, + wfms, + [1], + self.filename) + self.awg.sendSEQXFile(seqx, self.filename + '.seqx') + self.awg.loadSEQXFile(self.filename + '.seqx') + + # Create Readout tones + self.update_readout_freqs() + + # Create sequence for readout tones SLISt:SEQuence:NEW , [,] + self.awg.write('SLISt:SEQuence:NEW \"Readout_Seq\", {}, 2'.format(npts)) + self.awg.write('SLISt:SEQuence:STEP{}:GOTO \"Readout_Seq\", 1'.format(npts)) + # Fill Readout waveforms into sequence + for i in range(npts): + self.awg.write('SLISt:SEQuence:STEP{}:TASSet1:WAVeform \"Readout_Seq\", \"Readout_I\"'.format(str(i+1))) + self.awg.write('SLISt:SEQuence:STEP{}:TASSet2:WAVeform \"Readout_Seq\", \"Readout_Q\"'.format(str(i+1))) + + # Assign waveforms + self.awg.ch1.setSequenceTrack(self.filename, 1) + self.awg.ch3.setSequenceTrack('Readout_Seq', 1) + self.awg.ch4.setSequenceTrack('Readout_Seq', 2) + self.awg.play() + + + # ALazar labels + self.alazar.seq_mode('on') + for ala_chan in self.alazar_ctrl.channels[2:4]: + ala_chan.records_per_buffer(npts) + ala_chan.data.setpoint_labels = ('Wait time',ala_chan.data.setpoint_labels[1]) + ala_chan.data.setpoint_units = ('s',ala_chan.data.setpoint_units[1]) + + for n, ala_chan in enumerate(self.alazar_ctrl.channels[12:20]): + ala_chan.records_per_buffer(npts) + ala_chan.data.setpoint_labels = ('Wait time',) + ala_chan.data.setpoint_units = ('s',) + + # prepare channels + self.num_averages(self._averages) + + def set_readout_freqs(self,readout_frequencies): + if len(readout_frequencies) != self.number_read_freqs: + raise ValueError('Number of given readout frequencies has to \ + be {}.'.format(self.number_read_freqs)) + for i in range(self.number_read_freqs): + getattr(self,'readout_freq_{}'.format(i+1))(readout_frequencies[i]) + self.update_readout_freqs() + + def get_readout_freqs(self): + ret = [] + for i in range(self.number_read_freqs): + ret.append(getattr(self,'readout_freq_{}'.format(i+1))()) + return np.array(ret) + + def update_readout_freqs(self): + readout_freqs = self.get_readout_freqs() + N = int((self.cycle_time()*self.SR+64) - self.cycle_time()*self.SR%64) + + N_offset = int(self.marker_offset()*self.SR) + # Create trigger + TriggerMarker = np.zeros(N) + TriggerMarker[-int(self.readout_dur()*self.SR-N_offset):-int(self.readout_dur()*self.SR-N_offset-500e-9*self.SR)] = 1 + + # Readout tones + time = np.linspace(0, self.readout_dur(), int(self.readout_dur()*self.SR), endpoint=False) + cosine_readout = np.zeros(N) + sine_readout = np.zeros(N) + for fr in readout_freqs: + fi = fr - self.cavity.frequency() + cosine_readout += (0.5/len(readout_freqs))*np.concatenate((np.zeros(N-len(time)),np.cos(fi*2*np.pi*time))) + sine_readout += (0.5/len(readout_freqs))*np.concatenate((np.zeros(N-len(time)),np.sin(fi*2*np.pi*time))) + wfm_ch3 = np.array([cosine_readout,TriggerMarker,TriggerMarker]) + wfm_ch4 = np.array([sine_readout,TriggerMarker,TriggerMarker]) + + state = self.awg.run_state() + self.awg.stop() + wfm_ch3_file = self.awg.makeWFMXFile(wfm_ch3, 1) + wfm_ch4_file = self.awg.makeWFMXFile(wfm_ch4, 1) + self.awg.sendWFMXFile(wfm_ch3_file, 'Readout_I.wfmx') + self.awg.sendWFMXFile(wfm_ch4_file, 'Readout_Q.wfmx') + self.awg.loadWFMXFile('Readout_I.wfmx') + self.awg.loadWFMXFile('Readout_Q.wfmx') + # Only start play if running to begin with + if state == 'Running': + self.awg.play() + + # Set demod frequencies + for ala_chan in self.alazar_ctrl.channels[2:4]: + ala_chan.demod_freq(abs(readout_freqs[0]-self.cavity.frequency())) + + for n, ala_chan in enumerate(self.alazar_ctrl.channels[4:12]): + try: + ala_chan.demod_freq(abs(readout_freqs[n//2]-self.cavity.frequency())) + except: + pass + for n, ala_chan in enumerate(self.alazar_ctrl.channels[12:20]): + try: + ala_chan.demod_freq(abs(readout_freqs[n//2]-self.cavity.frequency())) + except: + pass + + def num_averages(self,value): + self._averages = value + self.alazar_ctrl.int_time(self.int_time()) + self.alazar_ctrl.int_delay(self.int_delay()) + for ala_chan in self.alazar_ctrl.channels[2:4]: + ala_chan.num_averages(value) + ala_chan.prepare_channel() + ala_chan.data.setpoints = (tuple(self.x_val()),ala_chan.data.setpoints[1]) + + for ala_chan in self.alazar_ctrl.channels[4:12]: + ala_chan.num_averages(value) + ala_chan.prepare_channel() + + for ala_chan in self.alazar_ctrl.channels[12:20]: + ala_chan.num_averages(value) + ala_chan.prepare_channel() + ala_chan.data.setpoints = (tuple(self.x_val()),) \ No newline at end of file From 03545847df9729006973f199b235d4b6da6092ea Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Wed, 6 Jun 2018 11:51:53 +0200 Subject: [PATCH 005/116] fixing location to import from --- qdev_wrappers/fitting/Fitter.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/qdev_wrappers/fitting/Fitter.py b/qdev_wrappers/fitting/Fitter.py index b4e6ad94..ea6a81f6 100644 --- a/qdev_wrappers/fitting/Fitter.py +++ b/qdev_wrappers/fitting/Fitter.py @@ -1,8 +1,8 @@ import qcodes as qc import numpy as np -from Fitclasses import T1, T2 -from Converter import SQL_Converter, Legacy_Converter +from qdev_wrappers.fitting.Converter import SQL_Converter, Legacy_Converter +from qdev_wrappers.fitting.Fitclasses import T1, T2 from scipy.optimize import curve_fit From e2387ad3207858e84ed129e86fb9b2482d94beaf Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Wed, 6 Jun 2018 12:06:43 +0200 Subject: [PATCH 006/116] add cut direction to fit metadata --- qdev_wrappers/fitting/Fitter.py | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/qdev_wrappers/fitting/Fitter.py b/qdev_wrappers/fitting/Fitter.py index ea6a81f6..6738dd45 100644 --- a/qdev_wrappers/fitting/Fitter.py +++ b/qdev_wrappers/fitting/Fitter.py @@ -178,10 +178,17 @@ def fit(self, data, fitclass, x=None, y=None, z=None, default_dependencies=False #does this needs to be moved so that it specifies which cut the individual sets of parameters are inferred from? fits2d['inferred_from'] = {'xdata': x_dict['name'], 'ydata': y_dict['name'], - 'zdata': z_dict['name'], + 'zdata': z_dict['name'], 'dataset': data['data_id'], 'dependencies': data['dependencies']} #missing sample name - + + if cut == 'horizontal': + fits2d['inferred_from']['setpoints'] = 'ydata' + + if cut == 'vertical': + fits2d['inferred_from']['setpoints'] = 'xdata' + + return fits2d From 4472120ac631f62d6976cf6dcad129cdd111753c Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Wed, 6 Jun 2018 13:04:06 +0200 Subject: [PATCH 007/116] renaming metadata for legacy converter to match pattern of SQL converter --- qdev_wrappers/fitting/Converter.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/qdev_wrappers/fitting/Converter.py b/qdev_wrappers/fitting/Converter.py index 9839e116..c78defcf 100644 --- a/qdev_wrappers/fitting/Converter.py +++ b/qdev_wrappers/fitting/Converter.py @@ -160,9 +160,9 @@ def make_data_dictionary(self, data): unit = getattr(qc_data, 'unit', '') np_data = qc_data.ndarray - data_dict[variable] = {'name': name, + data_dict[variable] = {'name': variable, 'label': label, - 'var_name': variable, + 'var_name': name, 'unit': unit, 'data': np_data} From 86f8b24e56509e0888c8ce8f5c8256a6223590b8 Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Wed, 6 Jun 2018 14:22:14 +0200 Subject: [PATCH 008/116] takes data + fit, dictionaries, saves as new SQL dataset --- qdev_wrappers/fitting/AnalysisToSQL.py | 114 +++++++++++++++++++++++++ 1 file changed, 114 insertions(+) create mode 100644 qdev_wrappers/fitting/AnalysisToSQL.py diff --git a/qdev_wrappers/fitting/AnalysisToSQL.py b/qdev_wrappers/fitting/AnalysisToSQL.py new file mode 100644 index 00000000..d50facc6 --- /dev/null +++ b/qdev_wrappers/fitting/AnalysisToSQL.py @@ -0,0 +1,114 @@ + + +import sqlite3 +from qdev_wrappers.fitting.Fitclasses import T1, T2 + + + +def fit_to_SQL(data, fitclass, fit): #it would be an improvement if it were able to get the fitclass from the fit information + + if fit['estimator']['type'] != fitclass.name: #maybe this should be more general in this function, like 'analysis_class? - so it makes sense e.g. in the case of Baysian inference + raise RuntimeError('The data given was analyzed using {}. This does not seem to match {} specified in this function'.format(fit['estimator']['type'], fitclass.name)) + + dim = 1 + if 'zdata' in fit['inferred_from'].keys(): + dim = 2 + + xname = fit['inferred_from']['xdata'] + xdata = data[xname]['data'] + + yname = fit['inferred_from']['ydata'] + ydata = data[yname]['data'] + + est = [] #estimated/predicted value for the measured data given the fitted parameters + est_name = '{}_estimate'.format(yname) + + p_values = [] + + if dim == 2: + zname = fit['inferred_from']['zdata'] + zdata = data[zname]['data'] + est_name = '{}_estimate'.format(zname) + + + #make array of estimated values based on parameters and model used + if dim == 1: + + params = list(fitclass.p_labels) + for index, parameter in enumerate(params): + if parameter not in fit['parameters']: + raise KeyError('The list of parameters for the fitclass {} contains a parameter, {}, which is not present in the fit dictionary.'.format(fitclass.name, parameter)) + params[index] = fit['parameters'][parameter]['value'] + + for datapoint in xdata: + y = fitclass.fun(datapoint, *params) + est.append(y) + p_values.append(params) + + if dim == 2: + + for xpoint, ypoint in zip(xdata, ydata): + + if xpoint in fit.keys(): + setpoint = xpoint + datapoint = ypoint + elif ypoint in fit.keys(): + setpoint = ypoint + datapoint = xpoint + + params = list(fitclass.p_labels) + for index, parameter in enumerate(params): + if parameter not in fit[setpoint]['parameters']: + raise KeyError('The list of parameters for the fitclass {} contains a parameter, {}, which is not present in the fit dictionary.'.format(fitclass.name, parameter)) + params[index] = fit[setpoint]['parameters'][parameter]['value'] + + z = fitclass.fun(datapoint, *params) + est.append(z) + p_values.append(params) + + + + + + tablename = 'data_{}_{}'.format(data['data_id'], fitclass.name) + #this is not a good plan for naming the table! + #It doesn't allow to save multiple fit analyses for the same data set + #TODO: figure out how to generate a unique table name each time in a way that makes sense + + if dim == 1: + table_columns = [xname, yname, est_name] + elif dim == 2: + table_columns = [xname, yname, zname, est_name] + for parameter in fitclass.p_labels: + table_columns.append(parameter) + + table_rows = [] + if dim == 1: + for xpoint, ypoint, estimate in zip(xdata, ydata, est): + id_nr = est.index(estimate) + 1 + row = (id_nr, xpoint, ypoint, estimate, *params) + table_rows.append(row) + elif dim == 2: + for xpoint, ypoint, zpoint, estimate, parameters in zip(xdata, ydata, zdata, est, p_values): + id_nr = est.index(estimate) + 1 + row = (id_nr, xpoint, ypoint, zpoint, estimate, *parameters) + table_rows.append(row) + + + + + conn = sqlite3.connect('analysis.db') #should this go in a separate analysis database, or just go in experiments.db? + cur = conn.cursor() + + cur.execute('CREATE TABLE {} (id INTEGER)'.format(tablename)) + + for column in table_columns: + cur.execute('ALTER TABLE {} ADD {}'.format(tablename, column)) + + num_cols = len(table_rows[0]) + placeholder = ('?,'*num_cols).strip(',') + + cur.executemany('INSERT INTO {} VALUES ({})'.format(tablename, placeholder), table_rows) + + conn.commit() + conn.close() \ No newline at end of file From 135fcd9ee3be751b18853380a5359455a309dfce Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Thu, 7 Jun 2018 12:13:14 +0200 Subject: [PATCH 009/116] Create umbreall function for conversion to dict --- qdev_wrappers/fitting/Converter.py | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/qdev_wrappers/fitting/Converter.py b/qdev_wrappers/fitting/Converter.py index c78defcf..a29df0dd 100644 --- a/qdev_wrappers/fitting/Converter.py +++ b/qdev_wrappers/fitting/Converter.py @@ -7,6 +7,15 @@ from qdev_wrappers.show_num import check_experiment_is_initialized +def data_to_dict(id, samplefolder=None, datatype='SQL'): + if datatype == 'qcodes_legacy': + converter = Legacy_Converter() + + if datatype == 'SQL': + converter = SQL_Converter() + + return converter.convert(id, samplefolder) + class DataConverter(): From f217daeada051b7b125207be3ef9c3bf5fc8de74 Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Thu, 7 Jun 2018 13:29:01 +0200 Subject: [PATCH 010/116] add units to parameters in fit --- qdev_wrappers/fitting/Fitter.py | 23 ++++++++++++++++++++++- 1 file changed, 22 insertions(+), 1 deletion(-) diff --git a/qdev_wrappers/fitting/Fitter.py b/qdev_wrappers/fitting/Fitter.py index 6738dd45..cfd447cc 100644 --- a/qdev_wrappers/fitting/Fitter.py +++ b/qdev_wrappers/fitting/Fitter.py @@ -84,7 +84,26 @@ def fit(self, data, fitclass, x=None, y=None, z=None, default_dependencies=False if z != None: z_dict = data[z] dimensions = 2 - + + # find parameter units + unit_template = fitclass.p_units + param_units = [] + x = x_dict['unit'] + y = y_dict['unit'] + if dimensions == 2: + z = z_dict['unit'] + + for item in unit_template: + template = list(item) + for i in range(len(template)): + if template[i] == 'x': + template[i] = x + if template[i] == 'y': + template[i] = y + if template[i] == 'z': + template[i] = z + unit = "".join(template) + param_units.append(unit) #Do fit for 1D data if dimensions == 1: @@ -100,6 +119,7 @@ def fit(self, data, fitclass, x=None, y=None, z=None, default_dependencies=False for parameter in fitclass.p_labels: #parameters currently missing units, use fitclass.p_units fit1d['parameters'][parameter] = {'value': popt[fitclass.p_labels.index(parameter)]} fit1d['parameters'][parameter]['cov'] = pcov[fitclass.p_labels.index(parameter)] + fit1d['parameters'][parameter]['unit'] = param_units[fitclass.p_labels.index(parameter)] fit1d['estimator'] = {'method': 'Least squared fit', 'type': fitclass.name, @@ -167,6 +187,7 @@ def fit(self, data, fitclass, x=None, y=None, z=None, default_dependencies=False for parameter in fitclass.p_labels: #parameters currently missing units, use fitclass.p_units fits2d[set_value]['parameters'][parameter] = {'value': popt[fitclass.p_labels.index(parameter)]} fits2d[set_value]['parameters'][parameter]['cov'] = pcov[fitclass.p_labels.index(parameter)] + fits2d[set_value]['parameters'][parameter]['unit'] = param_units[fitclass.p_labels.index(parameter)] fits2d['estimator'] = {'method': 'Least squared fit', From e65b2174ba44c06745c5942f895e390309893973 Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Thu, 7 Jun 2018 14:06:36 +0200 Subject: [PATCH 011/116] fix bug when manually specifying axes for 1D data --- qdev_wrappers/fitting/Fitter.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/qdev_wrappers/fitting/Fitter.py b/qdev_wrappers/fitting/Fitter.py index cfd447cc..fcc666ae 100644 --- a/qdev_wrappers/fitting/Fitter.py +++ b/qdev_wrappers/fitting/Fitter.py @@ -74,8 +74,8 @@ def fit(self, data, fitclass, x=None, y=None, z=None, default_dependencies=False elif default_dependencies == False: for dataname in [x, y, z]: - if dataname not in data['variables']: - raise RuntimeError('The specified variable "{}" is not found in the variables for this data dictionary. Variables are {}'.format(dataname, data['variables'])) + if (dataname not in data['variables']) and (dataname is not None): + raise RuntimeError('X! The specified variable "{}" is not found in the variables for this data dictionary. Variables are {}'.format(dataname, data['variables'])) x_dict = data[x] y_dict = data[y] From c54ad41430c59e73414af077fdf430c6487fd99a Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Thu, 7 Jun 2018 15:14:34 +0200 Subject: [PATCH 012/116] Add plotting from fit and data dictionaries --- qdev_wrappers/fitting/RoughPlotting.py | 96 ++++++++++++++++++++++++++ 1 file changed, 96 insertions(+) create mode 100644 qdev_wrappers/fitting/RoughPlotting.py diff --git a/qdev_wrappers/fitting/RoughPlotting.py b/qdev_wrappers/fitting/RoughPlotting.py new file mode 100644 index 00000000..0942f9c2 --- /dev/null +++ b/qdev_wrappers/fitting/RoughPlotting.py @@ -0,0 +1,96 @@ +import numpy as np +import matplotlib.pyplot as plt + + +def plot_fit1d(data, fit1d, fitclass, plottype='scatter'): + + plot = plt.figure() + axes = plot.add_axes([0.1, 0.1, 0.8, 0.8]) + + xname = fit1d['inferred_from']['xdata'] + xdata = data[xname]['data'] + xlabel = data[xname]['label'] + xunit = data[xname]['unit'] + axes.set_xlabel('{} [{}]'.format(xlabel, xunit)) + + yname = fit1d['inferred_from']['ydata'] + ydata = data[yname]['data'] + ylabel = data[yname]['label'] + yunit = data[yname]['unit'] + axes.set_ylabel('{} [{}]'.format(ylabel, yunit)) + + axes.set_title('{} vs. {}'.format(ylabel, xlabel)) + + parameters = list(fitclass.p_labels) + for index, parameter in enumerate(parameters): + parameters[index] = fit1d['parameters'][parameter]['value'] + + x = np.linspace(xdata.min(), xdata.max(), len(xdata)*10) # Parameters are currently without units!!! + axes.plot(x, fitclass.fun(x, *parameters), color='C0') + + if plottype == 'line': + axes.plot(xdata, ydata, color='C0') + elif plottype == 'scatter': + axes.plot(xdata, ydata, marker='.', markersize=5, linestyle='', color='C0') + else: + raise ValueError('Unknown plottype. Please choose point or line plot.') + + +def plot_fit2d_slice(data, fits2d, fitclass, setvalue, plottype='scatter'): + + plot = plt.figure() + axes = plot.add_axes([0.1, 0.1, 0.8, 0.8]) + axes.set_title('Title') + + setpoint = fits2d['inferred_from']['setpoints'] + setname = fits2d['inferred_from'][setpoint] + setdata = data[setname]['data'] + + if setvalue not in setdata: + print('{}: {}'.format(setname, np.unique(setdata))) + raise RuntimeError('Setvalue not in set points for this fit. Choose from setvalues above.') + + if setpoint == 'xdata': + xname = fits2d['inferred_from']['ydata'] + xdata = data[xname]['data'] + xlabel = data[xname]['label'] + xunit = data[xname]['unit'] + axes.set_xlabel('{} [{}]'.format(xlabel, xunit)) + + elif setpoint == 'ydata': + xname = fits2d['inferred_from']['xdata'] + xdata = data[xname]['data'] + xlabel = data[xname]['label'] + xunit = data[xname]['unit'] + axes.set_xlabel('{} [{}]'.format(xlabel, xunit)) + + yname = fits2d['inferred_from']['zdata'] + ydata = data[yname]['data'] + ylabel = data[yname]['label'] + yunit = data[yname]['unit'] + axes.set_ylabel('{} [{}]'.format(ylabel, yunit)) + + axes.set_title('{} vs. {}'.format(ylabel, xlabel)) + + x_dat = [] + y_dat = [] + for xpoint, ypoint, setpoint in zip(xdata, ydata, setdata): + if setpoint == setvalue: + x_dat.append(xpoint) + y_dat.append(ypoint) + xdata = np.array(x_dat) + ydata = np.array(y_dat) + + parameters = list(fitclass.p_labels) + for index, parameter in enumerate(parameters): # Parameters are currently without units!!! + parameters[index] = fits2d[setvalue]['parameters'][parameter]['value'] + + x = np.linspace(xdata.min(), xdata.max(), len(xdata)*10) + axes.plot(x, fitclass.fun(x, *parameters), color='C0') + + if plottype == 'line': + axes.plot(xdata, ydata, color='C0') + elif plottype == 'scatter': + axes.plot(xdata, ydata, marker='.', markersize=5, linestyle='', color='C0') + else: + raise ValueError('Unknown plottype. Please choose point or line plot.') \ No newline at end of file From f533b2e229858700d52aba11dc67bb012676af07 Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Thu, 7 Jun 2018 15:16:41 +0200 Subject: [PATCH 013/116] Remove typo --- qdev_wrappers/fitting/Fitter.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/qdev_wrappers/fitting/Fitter.py b/qdev_wrappers/fitting/Fitter.py index fcc666ae..fca6688d 100644 --- a/qdev_wrappers/fitting/Fitter.py +++ b/qdev_wrappers/fitting/Fitter.py @@ -75,7 +75,7 @@ def fit(self, data, fitclass, x=None, y=None, z=None, default_dependencies=False for dataname in [x, y, z]: if (dataname not in data['variables']) and (dataname is not None): - raise RuntimeError('X! The specified variable "{}" is not found in the variables for this data dictionary. Variables are {}'.format(dataname, data['variables'])) + raise RuntimeError('The specified variable "{}" is not found in the variables for this data dictionary. Variables are {}'.format(dataname, data['variables'])) x_dict = data[x] y_dict = data[y] From 7cdf83d6c732637bc84df20c7ba38ae57420cae5 Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Mon, 25 Jun 2018 12:51:52 +0200 Subject: [PATCH 014/116] Add numbering to tables if identical table names are created --- qdev_wrappers/fitting/AnalysisToSQL.py | 49 +++++++++++++++++--------- 1 file changed, 33 insertions(+), 16 deletions(-) diff --git a/qdev_wrappers/fitting/AnalysisToSQL.py b/qdev_wrappers/fitting/AnalysisToSQL.py index d50facc6..bea2e8c4 100644 --- a/qdev_wrappers/fitting/AnalysisToSQL.py +++ b/qdev_wrappers/fitting/AnalysisToSQL.py @@ -4,6 +4,29 @@ from qdev_wrappers.fitting.Fitclasses import T1, T2 +def is_table(tablename): # Checks if table already exists. Assumes database connection and cursor already established + + table_count = "SELECT count(*) FROM sqlite_master WHERE type = 'table' AND name='{}'".format(tablename) + execute = cur.execute(table_count) + count = execute.fetchone()[0] + + if count == 0: + return False + + if count == 1: + return True + + +def make_table(tablename): + name = tablename + n = 0 + + while is_table(name): + n += 1 + name = "{}_{}".format(tablename, n) + else: + cur.execute('CREATE TABLE {} (id INTEGER)'.format(name)) + def fit_to_SQL(data, fitclass, fit): #it would be an improvement if it were able to get the fitclass from the fit information @@ -65,15 +88,10 @@ def fit_to_SQL(data, fitclass, fit): #it would be an improvement if it were a z = fitclass.fun(datapoint, *params) est.append(z) p_values.append(params) - - - - - - tablename = 'data_{}_{}'.format(data['data_id'], fitclass.name) - #this is not a good plan for naming the table! - #It doesn't allow to save multiple fit analyses for the same data set - #TODO: figure out how to generate a unique table name each time in a way that makes sense + + + + tablename = 'data_{}_{}'.format(data['data_id'], fitclass.name) if dim == 1: table_columns = [xname, yname, est_name] @@ -93,14 +111,13 @@ def fit_to_SQL(data, fitclass, fit): #it would be an improvement if it were a id_nr = est.index(estimate) + 1 row = (id_nr, xpoint, ypoint, zpoint, estimate, *parameters) table_rows.append(row) - - - - - conn = sqlite3.connect('analysis.db') #should this go in a separate analysis database, or just go in experiments.db? + + + + conn = sqlite3.connect('analysis.db') # should this go in a separate analysis database, or just go in experiments.db? cur = conn.cursor() - - cur.execute('CREATE TABLE {} (id INTEGER)'.format(tablename)) + + make_table(tablename) for column in table_columns: cur.execute('ALTER TABLE {} ADD {}'.format(tablename, column)) From 88528e16245ea17af6942d71e67824985418b318 Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Mon, 25 Jun 2018 13:22:35 +0200 Subject: [PATCH 015/116] Fix bug - add missing SQL cursor in table functions --- qdev_wrappers/fitting/AnalysisToSQL.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/qdev_wrappers/fitting/AnalysisToSQL.py b/qdev_wrappers/fitting/AnalysisToSQL.py index bea2e8c4..a245708a 100644 --- a/qdev_wrappers/fitting/AnalysisToSQL.py +++ b/qdev_wrappers/fitting/AnalysisToSQL.py @@ -4,10 +4,10 @@ from qdev_wrappers.fitting.Fitclasses import T1, T2 -def is_table(tablename): # Checks if table already exists. Assumes database connection and cursor already established +def is_table(tablename, cursor): # Checks if table already exists. Assumes database connection and cursor already established table_count = "SELECT count(*) FROM sqlite_master WHERE type = 'table' AND name='{}'".format(tablename) - execute = cur.execute(table_count) + execute = cursor.execute(table_count) count = execute.fetchone()[0] if count == 0: @@ -17,15 +17,15 @@ def is_table(tablename): # Checks if table already exists. Assumes database con return True -def make_table(tablename): +def make_table(tablename, cursor): name = tablename n = 0 - while is_table(name): + while is_table(name, cursor): n += 1 name = "{}_{}".format(tablename, n) else: - cur.execute('CREATE TABLE {} (id INTEGER)'.format(name)) + cursor.execute('CREATE TABLE {} (id INTEGER)'.format(name)) def fit_to_SQL(data, fitclass, fit): #it would be an improvement if it were able to get the fitclass from the fit information @@ -117,7 +117,7 @@ def fit_to_SQL(data, fitclass, fit): #it would be an improvement if it were a conn = sqlite3.connect('analysis.db') # should this go in a separate analysis database, or just go in experiments.db? cur = conn.cursor() - make_table(tablename) + make_table(tablename, cur) for column in table_columns: cur.execute('ALTER TABLE {} ADD {}'.format(tablename, column)) From d7a996cfaf86d598e858d21eb360cfc7a5e30395 Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Mon, 25 Jun 2018 13:39:42 +0200 Subject: [PATCH 016/116] Fix bug in table name that inserts data into incorrect table --- qdev_wrappers/fitting/AnalysisToSQL.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/qdev_wrappers/fitting/AnalysisToSQL.py b/qdev_wrappers/fitting/AnalysisToSQL.py index a245708a..bc307dcb 100644 --- a/qdev_wrappers/fitting/AnalysisToSQL.py +++ b/qdev_wrappers/fitting/AnalysisToSQL.py @@ -27,6 +27,8 @@ def make_table(tablename, cursor): else: cursor.execute('CREATE TABLE {} (id INTEGER)'.format(name)) + return name + def fit_to_SQL(data, fitclass, fit): #it would be an improvement if it were able to get the fitclass from the fit information @@ -117,15 +119,15 @@ def fit_to_SQL(data, fitclass, fit): #it would be an improvement if it were a conn = sqlite3.connect('analysis.db') # should this go in a separate analysis database, or just go in experiments.db? cur = conn.cursor() - make_table(tablename, cur) + table = make_table(tablename, cur) for column in table_columns: - cur.execute('ALTER TABLE {} ADD {}'.format(tablename, column)) + cur.execute('ALTER TABLE {} ADD {}'.format(table, column)) num_cols = len(table_rows[0]) placeholder = ('?,'*num_cols).strip(',') - cur.executemany('INSERT INTO {} VALUES ({})'.format(tablename, placeholder), table_rows) + cur.executemany('INSERT INTO {} VALUES ({})'.format(table, placeholder), table_rows) conn.commit() conn.close() \ No newline at end of file From c265ad24f09321ffae54e3ac44b78e3ce4055a19 Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Mon, 25 Jun 2018 14:08:17 +0200 Subject: [PATCH 017/116] Remove redundancies between data and analysis tables --- qdev_wrappers/fitting/AnalysisToSQL.py | 14 ++++++-------- 1 file changed, 6 insertions(+), 8 deletions(-) diff --git a/qdev_wrappers/fitting/AnalysisToSQL.py b/qdev_wrappers/fitting/AnalysisToSQL.py index bc307dcb..97fac7f8 100644 --- a/qdev_wrappers/fitting/AnalysisToSQL.py +++ b/qdev_wrappers/fitting/AnalysisToSQL.py @@ -95,23 +95,21 @@ def fit_to_SQL(data, fitclass, fit): #it would be an improvement if it were a tablename = 'data_{}_{}'.format(data['data_id'], fitclass.name) - if dim == 1: - table_columns = [xname, yname, est_name] - elif dim == 2: - table_columns = [xname, yname, zname, est_name] + + table_columns = [est_name] for parameter in fitclass.p_labels: table_columns.append(parameter) table_rows = [] if dim == 1: - for xpoint, ypoint, estimate in zip(xdata, ydata, est): + for estimate in est: id_nr = est.index(estimate) + 1 - row = (id_nr, xpoint, ypoint, estimate, *params) + row = (id_nr, estimate, *params) table_rows.append(row) elif dim == 2: - for xpoint, ypoint, zpoint, estimate, parameters in zip(xdata, ydata, zdata, est, p_values): + for estimate, parameters in zip(est, p_values): id_nr = est.index(estimate) + 1 - row = (id_nr, xpoint, ypoint, zpoint, estimate, *parameters) + row = (id_nr, estimate, *parameters) table_rows.append(row) From 00c1464bb517cbc1d782fbf7416179a04fc969bb Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Mon, 25 Jun 2018 14:36:31 +0200 Subject: [PATCH 018/116] Print confirmation after table creation --- qdev_wrappers/fitting/AnalysisToSQL.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/qdev_wrappers/fitting/AnalysisToSQL.py b/qdev_wrappers/fitting/AnalysisToSQL.py index 97fac7f8..84683f32 100644 --- a/qdev_wrappers/fitting/AnalysisToSQL.py +++ b/qdev_wrappers/fitting/AnalysisToSQL.py @@ -128,4 +128,6 @@ def fit_to_SQL(data, fitclass, fit): #it would be an improvement if it were a cur.executemany('INSERT INTO {} VALUES ({})'.format(table, placeholder), table_rows) conn.commit() - conn.close() \ No newline at end of file + conn.close() + + print("Table {} created in analysis.db".format(table)) \ No newline at end of file From b0d59a11aa4504f2986ef4a47498a8a3f8370ab7 Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Mon, 25 Jun 2018 17:39:18 +0200 Subject: [PATCH 019/116] Store metadata about data location and analysis in SQL database --- qdev_wrappers/fitting/AnalysisToSQL.py | 73 +++++++++++++++++++++++--- qdev_wrappers/fitting/Converter.py | 21 +++++++- 2 files changed, 85 insertions(+), 9 deletions(-) diff --git a/qdev_wrappers/fitting/AnalysisToSQL.py b/qdev_wrappers/fitting/AnalysisToSQL.py index 84683f32..8f602d9f 100644 --- a/qdev_wrappers/fitting/AnalysisToSQL.py +++ b/qdev_wrappers/fitting/AnalysisToSQL.py @@ -47,6 +47,9 @@ def fit_to_SQL(data, fitclass, fit): #it would be an improvement if it were a est = [] #estimated/predicted value for the measured data given the fitted parameters est_name = '{}_estimate'.format(yname) + est_label = data[yname]['label'] + est_unit = data[yname]['unit'] + p_values = [] @@ -54,12 +57,15 @@ def fit_to_SQL(data, fitclass, fit): #it would be an improvement if it were a zname = fit['inferred_from']['zdata'] zdata = data[zname]['data'] est_name = '{}_estimate'.format(zname) - + est_label = data[zname]['label'] + est_unit = data[zname]['unit'] #make array of estimated values based on parameters and model used if dim == 1: - - params = list(fitclass.p_labels) + + param_units = [fit['parameters'][param]['unit'] for param in list(fitclass.p_labels)] + + params = list(fitclass.p_labels) for index, parameter in enumerate(params): if parameter not in fit['parameters']: raise KeyError('The list of parameters for the fitclass {} contains a parameter, {}, which is not present in the fit dictionary.'.format(fitclass.name, parameter)) @@ -69,15 +75,19 @@ def fit_to_SQL(data, fitclass, fit): #it would be an improvement if it were a y = fitclass.fun(datapoint, *params) est.append(y) p_values.append(params) + if dim == 2: + + setpoints = [key for key in fit.keys()] + param_units = [fit[setpoints[0]]['parameters'][param]['unit'] for param in list(fitclass.p_labels)] for xpoint, ypoint in zip(xdata, ydata): - if xpoint in fit.keys(): + if xpoint in setpoints: setpoint = xpoint datapoint = ypoint - elif ypoint in fit.keys(): + elif ypoint in setpoints: setpoint = ypoint datapoint = xpoint @@ -114,11 +124,59 @@ def fit_to_SQL(data, fitclass, fit): #it would be an improvement if it were a + data_id = data['data_id'] + exp_id = data['exp_id'] + analysis_id = 1 + predicts = est_name.strip("estimate").strip("_") + estimator = "{}, {}".format(fit['estimator']['method'], fit['estimator']['type']) + analysis = fit['estimator']['function'] + parameters = fitclass.p_labels + param_labels = fitclass.p_names + + conn = sqlite3.connect('analysis.db') # should this go in a separate analysis database, or just go in experiments.db? cur = conn.cursor() + #Alternative: have a separate function that sets up the general analysis database with tables, so that this doesn't have to be here + if not is_table('analyses', cur): + cur.execute('''CREATE TABLE analyses + (run_id INTEGER, exp_id INTEGER, analysis_id INTEGER, result_table_name TEXT, + predicts TEXT, estimator TEXT, function TEXT)''') + if not is_table('overview', cur): + cur.execute('''CREATE TABLE overview + (id INTEGER, analysis_id INTEGER, + parameter TEXT, label TEXT, unit TEXT, inferred_from TEXT)''') + + table = make_table(tablename, cur) - + + + sql_analyses = 'INSERT INTO analyses VALUES (?,?,?,?,?,?,?)' + + max_id = cur.execute('SELECT MAX(analysis_id) FROM analyses').fetchone()[0] + if max_id != None: + analysis_id += max_id + + cur.execute(sql_analyses, (data_id, exp_id, analysis_id, table, predicts, estimator, analysis)) + + + sql_overview = 'INSERT INTO overview VALUES (?,?,?,?,?,?)' + + result_id = 1 + max_id = cur.execute('SELECT MAX(id) FROM overview').fetchone()[0] + if max_id != None: + result_id += max_id + + overview_rows = [] + for parameter, label, unit in zip(parameters, param_labels, param_units): + row = (result_id, analysis_id, parameter, label, unit, "inferred_from") + overview_rows.append(row) + result_id += 1 + overview_rows.append((result_id, analysis_id, est_name, est_label, est_unit, "inferred_from")) + + cur.executemany(sql_overview, (overview_rows)) + + for column in table_columns: cur.execute('ALTER TABLE {} ADD {}'.format(table, column)) @@ -127,7 +185,8 @@ def fit_to_SQL(data, fitclass, fit): #it would be an improvement if it were a cur.executemany('INSERT INTO {} VALUES ({})'.format(table, placeholder), table_rows) + conn.commit() conn.close() - print("Table {} created in analysis.db".format(table)) \ No newline at end of file + print("Table {} created in analysis.db".format(table)) diff --git a/qdev_wrappers/fitting/Converter.py b/qdev_wrappers/fitting/Converter.py index a29df0dd..3835e69d 100644 --- a/qdev_wrappers/fitting/Converter.py +++ b/qdev_wrappers/fitting/Converter.py @@ -23,10 +23,12 @@ def convert(self, id, samplefolder = None): data = self.find_data(id, samplefolder) data_dict = self.make_data_dictionary(data) - + + exp_id = self.find_experiment(id) dependencies = self.find_dependencies(id, data) #SQL needs only id number to find dependencies, Legacy needs only data all_variables = self.find_variables(data) - + + data_dict['exp_id'] = exp_id data_dict['data_id'] = id data_dict['dependencies'] = dependencies data_dict['variables'] = all_variables @@ -83,6 +85,16 @@ def find_variables(self, data): return all_variables + def find_experiment(self, id): + + conn = connect('experiments.db') # the name of the file to access can't be set manually right now + cur = conn.cursor() + + execute = cur.execute('SELECT exp_id FROM runs WHERE run_id is {}'.format(id)) + exp_id = execute.fetchone()[0] + + return exp_id + def make_data_dictionary(self, data): @@ -141,6 +153,11 @@ def find_variables(self, data): all_variables = [variable for variable in data.arrays.keys()] return all_variables + + def find_experiment(self, id): + + exp_id = id + return exp_id def resize_data(self, data1, data2): From 02c1d18cfbb4fb14e0ffb618bae19bcc851a13ff Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Tue, 26 Jun 2018 11:47:27 +0200 Subject: [PATCH 020/116] Create Jupyter notebook example document for fitter --- qdev_wrappers/fitting/Example.ipynb | 4609 +++++++++++++++++++++++++++ 1 file changed, 4609 insertions(+) create mode 100644 qdev_wrappers/fitting/Example.ipynb diff --git a/qdev_wrappers/fitting/Example.ipynb b/qdev_wrappers/fitting/Example.ipynb new file mode 100644 index 00000000..b399fc83 --- /dev/null +++ b/qdev_wrappers/fitting/Example.ipynb @@ -0,0 +1,4609 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import qcodes as qc\n", + "\n", + "from qdev_wrappers.fitting.Converter import SQL_Converter, Legacy_Converter, data_to_dict\n", + "from qdev_wrappers.fitting.Fitclasses import T1, T2\n", + "from qdev_wrappers.fitting.Fitter import Fitter\n", + "from qdev_wrappers.fitting.AnalysisToSQL import is_table, make_table, fit_to_SQL \n", + "from qdev_wrappers.fitting.RoughPlotting import plot_fit1d, plot_fit2d_slice\n", + "\n", + "from qdev_wrappers.show_num import show_num\n", + "from qcodes.dataset.plotting import plot_by_id" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Fitting, step 1: convert your dataset into a dictionary\n", + "\n", + "Convert dataset with run id 4 from SQL, store in 'data'. This, incidentally, is a 1D dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'pulse_readout_delay': {'name': 'pulse_readout_delay',\n", + " 'label': 'Pi Pulse Readout Delaraby',\n", + " 'unit': 'S',\n", + " 'data': array([ 0.00000000e+00, 1.00000000e-08, 2.00000000e-08,\n", + " 3.00000000e-08, 4.00000000e-08, 5.00000000e-08,\n", + " 6.00000000e-08, 7.00000000e-08, 8.00000000e-08,\n", + " 9.00000000e-08, 1.00000000e-07, 1.10000000e-07,\n", + " 1.20000000e-07, 1.30000000e-07, 1.40000000e-07,\n", + " 1.50000000e-07, 1.60000000e-07, 1.70000000e-07,\n", + " 1.80000000e-07, 1.90000000e-07, 2.00000000e-07,\n", + " 2.10000000e-07, 2.20000000e-07, 2.30000000e-07,\n", + " 2.40000000e-07, 2.50000000e-07, 2.60000000e-07,\n", + " 2.70000000e-07, 2.80000000e-07, 2.90000000e-07,\n", + " 3.00000000e-07, 3.10000000e-07, 3.20000000e-07,\n", + " 3.30000000e-07, 3.40000000e-07, 3.50000000e-07,\n", + " 3.60000000e-07, 3.70000000e-07, 3.80000000e-07,\n", + " 3.90000000e-07, 4.00000000e-07, 4.10000000e-07,\n", + " 4.20000000e-07, 4.30000000e-07, 4.40000000e-07,\n", + " 4.50000000e-07, 4.60000000e-07, 4.70000000e-07,\n", + " 4.80000000e-07, 4.90000000e-07, 5.00000000e-07,\n", + " 5.10000000e-07, 5.20000000e-07, 5.30000000e-07,\n", + " 5.40000000e-07, 5.50000000e-07, 5.60000000e-07,\n", + " 5.70000000e-07, 5.80000000e-07, 5.90000000e-07,\n", + " 6.00000000e-07, 6.10000000e-07, 6.20000000e-07,\n", + " 6.30000000e-07, 6.40000000e-07, 6.50000000e-07,\n", + " 6.60000000e-07, 6.70000000e-07, 6.80000000e-07,\n", + " 6.90000000e-07, 7.00000000e-07, 7.10000000e-07,\n", + " 7.20000000e-07, 7.30000000e-07, 7.40000000e-07,\n", + " 7.50000000e-07, 7.60000000e-07, 7.70000000e-07,\n", + " 7.80000000e-07, 7.90000000e-07, 8.00000000e-07,\n", + " 8.10000000e-07, 8.20000000e-07, 8.30000000e-07,\n", + " 8.40000000e-07, 8.50000000e-07, 8.60000000e-07,\n", + " 8.70000000e-07, 8.80000000e-07, 8.90000000e-07,\n", + " 9.00000000e-07, 9.10000000e-07, 9.20000000e-07,\n", + " 9.30000000e-07, 9.40000000e-07, 9.50000000e-07,\n", + " 9.60000000e-07, 9.70000000e-07, 9.80000000e-07,\n", + " 9.90000000e-07, 1.00000000e-06])},\n", + " 'data': {'name': 'data',\n", + " 'label': 'Cavity Response',\n", + " 'unit': 'V',\n", + " 'data': array([ 0.00190226, 0.00184296, 0.00187694, 0.0018484 , 0.00187253,\n", + " 0.00183823, 0.00181451, 0.00182108, 0.00181803, 0.0018168 ,\n", + " 0.00180962, 0.00178911, 0.00175819, 0.00180917, 0.0017232 ,\n", + " 0.00175071, 0.00178156, 0.00173019, 0.00174053, 0.00171546,\n", + " 0.00175654, 0.00173921, 0.00172067, 0.00168142, 0.00176445,\n", + " 0.00175339, 0.0017317 , 0.00176039, 0.00175976, 0.00172069,\n", + " 0.00173343, 0.00173168, 0.00169639, 0.00170679, 0.00168139,\n", + " 0.00170459, 0.00174184, 0.00168564, 0.00170346, 0.00174795,\n", + " 0.00167056, 0.0017 , 0.00170594, 0.00169131, 0.00167176,\n", + " 0.00170925, 0.00172371, 0.00169057, 0.00172341, 0.00171353,\n", + " 0.00170708, 0.00174262, 0.00169639, 0.0016967 , 0.00167803,\n", + " 0.00167778, 0.00167651, 0.00171114, 0.00166005, 0.00169204,\n", + " 0.00165937, 0.0016763 , 0.00166531, 0.00162962, 0.00168796,\n", + " 0.00173675, 0.00163938, 0.00167601, 0.00167918, 0.00167402,\n", + " 0.00170181, 0.00168008, 0.00170246, 0.00164879, 0.00166086,\n", + " 0.00166087, 0.00169208, 0.00166155, 0.00169739, 0.00165514,\n", + " 0.00166041, 0.00162791, 0.00169263, 0.0016809 , 0.00169787,\n", + " 0.00171273, 0.00167757, 0.00170379, 0.00171149, 0.00169174,\n", + " 0.00167011, 0.00167183, 0.00165937, 0.00166659, 0.00167763,\n", + " 0.0016915 , 0.00170809, 0.0016675 , 0.00168928, 0.00171038,\n", + " 0.00167808])},\n", + " 'exp_id': 1,\n", + " 'data_id': 4,\n", + " 'dependencies': {'data': ['pulse_readout_delay']},\n", + " 'variables': ['pulse_readout_delay', 'data']}" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = data_to_dict(4)\n", + "data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This can also be done with the 'old' datasets. In this case, this is a 2D dataset, containing multiple measurements at each point (mag0, phase0 and raw output):" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'qubit_rs_frequency_set': {'name': 'qubit_rs_frequency_set',\n", + " 'label': 'Frequency',\n", + " 'var_name': 'frequency',\n", + " 'unit': 'Hz',\n", + " 'data': array([ 5.60000000e+09, 5.60000000e+09, 5.60000000e+09, ...,\n", + " 5.70000000e+09, 5.70000000e+09, 5.70000000e+09])},\n", + " 'pi_pulse_dur_set': {'name': 'pi_pulse_dur_set',\n", + " 'label': 'pi_pulse_dur',\n", + " 'var_name': 'pi_pulse_dur',\n", + " 'unit': 's',\n", + " 'data': array([ 0.00000000e+00, 1.00000000e-09, 2.00000000e-09, ...,\n", + " 9.80000000e-08, 9.90000000e-08, 1.00000000e-07])},\n", + " 'rec_ctrl_demod_freq_0_mag': {'name': 'rec_ctrl_demod_freq_0_mag',\n", + " 'label': 'demod freq 0 mag',\n", + " 'var_name': 'demod_freq_0_mag',\n", + " 'unit': 'v',\n", + " 'data': array([ 0.00355048, 0.00357386, 0.00344386, ..., 0.00335167,\n", + " 0.00345182, 0.00341498])},\n", + " 'rec_ctrl_demod_freq_0_phase': {'name': 'rec_ctrl_demod_freq_0_phase',\n", + " 'label': 'demod freq 0 phase',\n", + " 'var_name': 'demod_freq_0_phase',\n", + " 'unit': 'v',\n", + " 'data': array([-44.00183506, -43.77336984, -42.36852509, ..., -40.35980814,\n", + " -40.97471696, -40.95782035])},\n", + " 'rec_ctrl_raw_output': {'name': 'rec_ctrl_raw_output',\n", + " 'label': 'raw output',\n", + " 'var_name': 'raw_output',\n", + " 'unit': 'v',\n", + " 'data': array([-0.00071378, -0.00065171, -0.0006484 , ..., -0.00077737,\n", + " -0.00077076, -0.00070182])},\n", + " 'exp_id': 621,\n", + " 'data_id': 621,\n", + " 'dependencies': {'rec_ctrl_demod_freq_0_mag': ['qubit_rs_frequency_set',\n", + " 'pi_pulse_dur_set'],\n", + " 'rec_ctrl_demod_freq_0_phase': ['qubit_rs_frequency_set',\n", + " 'pi_pulse_dur_set'],\n", + " 'rec_ctrl_raw_output': ['qubit_rs_frequency_set', 'pi_pulse_dur_set']},\n", + " 'variables': ['qubit_rs_frequency_set',\n", + " 'pi_pulse_dur_set',\n", + " 'rec_ctrl_demod_freq_0_mag',\n", + " 'rec_ctrl_demod_freq_0_phase',\n", + " 'rec_ctrl_raw_output']}" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data2 = data_to_dict(621, datatype = 'qcodes_legacy')\n", + "data2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Fitting step 2: Performing fit, getting fit dictionary\n", + "\n", + "To get a fit, the data dictionary goes into the fitter along with a fitclass (options are currently T1() and T2()). \n", + "\n", + "In doing the fit, you have to decide if you want to manually specify what data you want on which axis, or if you want if to use the dependencies stored in the dataset and set the axes automatically.\n", + "\n", + "To use the dependencies from the data, set *default_dependencies = True*\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'parameters': {'a': {'value': 0.00020686194829005843,\n", + " 'cov': array([ 1.12188937e-10, -8.65732231e-14, -2.94102847e-12]),\n", + " 'unit': 'V'},\n", + " 'T': {'value': 1.7792117433091448e-07,\n", + " 'cov': array([ -8.65732231e-14, 3.48328903e-16, -5.40259460e-14]),\n", + " 'unit': 'S'},\n", + " 'c': {'value': 0.0016790061428729915,\n", + " 'cov': array([ -2.94102847e-12, -5.40259460e-14, 1.68224887e-11]),\n", + " 'unit': 'V'}},\n", + " 'estimator': {'method': 'Least squared fit',\n", + " 'type': 'T1fit',\n", + " 'function': 'save all text from fitter function and class function here?',\n", + " 'more?': 'perhaps'},\n", + " 'inferred_from': {'xdata': 'pulse_readout_delay',\n", + " 'ydata': 'data',\n", + " 'dataset': 4,\n", + " 'dependencies': {'data': ['pulse_readout_delay']}}}" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fit = Fitter().fit(data, T1(), default_dependencies=True)\n", + "fit" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you want to specify the axes yourself, set *x = 'data_name_for_x'*, *y = 'data_name_for_y\"* (and optionally *z = \"data_name_for_z\"*). If you are uncertain of the names of the variables in the data, they can be read in the data dictionary, or you can enter something random for x and y and it will throw an error that gives you a list of your options." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'parameters': {'a': {'value': 0.00020686194829005843,\n", + " 'cov': array([ 1.12188937e-10, -8.65732231e-14, -2.94102847e-12]),\n", + " 'unit': 'V'},\n", + " 'T': {'value': 1.7792117433091448e-07,\n", + " 'cov': array([ -8.65732231e-14, 3.48328903e-16, -5.40259460e-14]),\n", + " 'unit': 'S'},\n", + " 'c': {'value': 0.0016790061428729915,\n", + " 'cov': array([ -2.94102847e-12, -5.40259460e-14, 1.68224887e-11]),\n", + " 'unit': 'V'}},\n", + " 'estimator': {'method': 'Least squared fit',\n", + " 'type': 'T1fit',\n", + " 'function': 'save all text from fitter function and class function here?',\n", + " 'more?': 'perhaps'},\n", + " 'inferred_from': {'xdata': 'pulse_readout_delay',\n", + " 'ydata': 'data',\n", + " 'dataset': 4,\n", + " 'dependencies': {'data': ['pulse_readout_delay']}}}" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Fitter().fit(data, T1(), x='pulse_readout_delay', y='data')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For the 2D datasets, at the moment, have only one option, which is to get back fits for slices of the data. Each slice is taken at a particular setpoint, and the fit for that setpoint is listed in the fit dictionary.\n", + "\n", + "If the dataset contains multiple plots (fx. data2 in this example contains measurements for 0 mag, 0 phase, and raw output) and you chose to use the default dependencies , you will need to specify a dataname." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{5600000000.0: {'parameters': {'a': {'value': 0.00018995671288537621,\n", + " 'cov': array([ 1.03060699e-09, -4.34010572e-13, -4.25402654e-01,\n", + " 5.38756557e-08, -1.75998153e-11]),\n", + " 'unit': 's'},\n", + " 'T': {'value': 6.098906943811456e-08,\n", + " 'cov': array([ -4.34010572e-13, 3.37578482e-16, -8.71382197e-04,\n", + " 1.83709703e-12, 8.12292258e-15]),\n", + " 'unit': 'Hz'},\n", + " 'w': {'value': 411814046.42546773,\n", + " 'cov': array([ -4.25402654e-01, -8.71382197e-04, 2.74305385e+13,\n", + " -7.50002488e+05, 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'perhaps'},\n", + " 'inferred_from': {'xdata': 'qubit_rs_frequency_set',\n", + " 'ydata': 'pi_pulse_dur_set',\n", + " 'zdata': 'rec_ctrl_demod_freq_0_mag',\n", + " 'dataset': 621,\n", + " 'dependencies': {'rec_ctrl_demod_freq_0_mag': ['qubit_rs_frequency_set',\n", + " 'pi_pulse_dur_set'],\n", + " 'rec_ctrl_demod_freq_0_phase': ['qubit_rs_frequency_set',\n", + " 'pi_pulse_dur_set'],\n", + " 'rec_ctrl_raw_output': ['qubit_rs_frequency_set', 'pi_pulse_dur_set']},\n", + " 'setpoints': 'xdata'}}" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fit2 = Fitter().fit(data2, T2(), default_dependencies=True, dataname='rec_ctrl_demod_freq_0_mag')\n", + "fit2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As with the 1D example, you can also choose to specify the axes yourself. 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1.67455723e-04,\n", + " -1.82520579e-11, 5.60316921e-16]),\n", + " 'unit': 's'},\n", + " 'w': {'value': 437544201.43309355,\n", + " 'cov': array([ -3.43249182e-01, 1.67455723e-04, 1.21026217e+12,\n", + " -4.23897209e+04, -3.11492424e-01]),\n", + " 'unit': '1/s'},\n", + " 'p': {'value': 1.5435027044156295,\n", + " 'cov': array([ 2.29025281e-08, -1.82520579e-11, -4.23897209e+04,\n", + " 2.32210573e-03, 1.30406028e-08]),\n", + " 'unit': ''},\n", + " 'c': {'value': 0.0033567968968173018,\n", + " 'cov': array([ -4.54732677e-13, 5.60316921e-16, -3.11492424e-01,\n", + " 1.30406028e-08, 6.98267040e-12]),\n", + " 'unit': 'Hz'}}},\n", + " 'estimator': {'method': 'Least squared fit',\n", + " 'type': 'T2fit',\n", + " 'function': 'save all text from fitter function and class function here?',\n", + " 'more?': 'perhaps'},\n", + " 'inferred_from': {'xdata': 'pi_pulse_dur_set',\n", + " 'ydata': 'qubit_rs_frequency_set',\n", + " 'zdata': 'rec_ctrl_demod_freq_0_mag',\n", + " 'dataset': 621,\n", + " 'dependencies': {'rec_ctrl_demod_freq_0_mag': ['qubit_rs_frequency_set',\n", + " 'pi_pulse_dur_set'],\n", + " 'rec_ctrl_demod_freq_0_phase': ['qubit_rs_frequency_set',\n", + " 'pi_pulse_dur_set'],\n", + " 'rec_ctrl_raw_output': ['qubit_rs_frequency_set', 'pi_pulse_dur_set']},\n", + " 'setpoints': 'ydata'}}" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Fitter().fit(data2, T2(), x='pi_pulse_dur_set', y='qubit_rs_frequency_set', z='rec_ctrl_demod_freq_0_mag', cut='horizontal')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Saving your fit to an SQL database (in progress)\n", + "\n", + "If you want to save the analysis information, you can use this (in-progress) function, which will create (if such a thing does not already exist) a new SQL database called *analysis.db* in the same folder as your experiments. \n", + "\n", + "It will then save an SQL table with the parameters and an estimate based on the fit, as well as an id that matches the id for the corresponding data point in the experiments database.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Table data_4_T1fit created in analysis.db\n", + "Table data_621_T2fit created in analysis.db\n" + ] + } + ], + "source": [ + "fit_to_SQL(data, T1(), fit)\n", + "fit_to_SQL(data2, T2(), fit2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plotting your fit to make sure nothing weird has happened\n", + "\n", + "To see how your fit looks compared to the data, there is currently only rough plotting from the dictionary. Pretty plotting from the saved dataset and analysis is for the near future.\n", + "\n", + "The first dataset was 1D, so:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "([], [None])" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_fit1d(data, fit, T1())\n", + "\n", + "#for comparison to original data:\n", + "plot_by_id(4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For the 2D dataset, this rough plotting can only plot a cut, so it is necessary to tell it which setpoint you want to see the fit from. If you aren't sure of the setpoints, you can either find them in the fit dictionary (long and annoying to read), or just suggest something, and if that isn't on the list of setpoints, then you will get a list of options to chose from." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_fit2d_slice(data2, fit2, T2(), setvalue = 5.65600000e+09)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "([DataSet:\n", + " location = '/Users/Lillian/Qcodes/My stuff/mainfolder/data/621'\n", + " | | | \n", + " Setpoint | qubit_rs_frequency_set | frequency | (101,)\n", + " Setpoint | pi_pulse_dur_set | pi_pulse_dur | (101, 101)\n", + " Measured | rec_ctrl_demod_freq_0_mag | demod_freq_0_mag | (101, 101)\n", + " Measured | rec_ctrl_demod_freq_0_phase | demod_freq_0_phase | (101, 101)\n", + " Measured | rec_ctrl_raw_output | raw_output | (101, 101)],\n", + " [,\n", + " ,\n", + " ])" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#for comparison to original data:\n", + "show_num(621)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 7e095653417e92a524949af0abf2edac3e6fef66 Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Tue, 26 Jun 2018 12:08:33 +0200 Subject: [PATCH 021/116] Fix bug reversing units in vertical-cut 2D fits --- qdev_wrappers/fitting/Fitter.py | 23 ++++++++++++++++------- 1 file changed, 16 insertions(+), 7 deletions(-) diff --git a/qdev_wrappers/fitting/Fitter.py b/qdev_wrappers/fitting/Fitter.py index fca6688d..0f493e9f 100644 --- a/qdev_wrappers/fitting/Fitter.py +++ b/qdev_wrappers/fitting/Fitter.py @@ -95,13 +95,22 @@ def fit(self, data, fitclass, x=None, y=None, z=None, default_dependencies=False for item in unit_template: template = list(item) - for i in range(len(template)): - if template[i] == 'x': - template[i] = x - if template[i] == 'y': - template[i] = y - if template[i] == 'z': - template[i] = z + if cut == 'horizontal': + for i in range(len(template)): + if template[i] == 'x': + template[i] = x + if template[i] == 'y': + template[i] = y + if template[i] == 'z': + template[i] = z + elif cut == 'vertical': + for i in range(len(template)): + if template[i] == 'x': + template[i] = y + if template[i] == 'y': + template[i] = x + if template[i] == 'z': + template[i] = z unit = "".join(template) param_units.append(unit) From 8b90524e3b4c10c473e1d2d3aa5f9c95b7399af7 Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Tue, 26 Jun 2018 12:15:45 +0200 Subject: [PATCH 022/116] Remove long and unnecessary outputs from Example notebook --- qdev_wrappers/fitting/Example.ipynb | 4145 +-------------------------- 1 file changed, 32 insertions(+), 4113 deletions(-) diff --git a/qdev_wrappers/fitting/Example.ipynb b/qdev_wrappers/fitting/Example.ipynb index b399fc83..f6b32915 100644 --- a/qdev_wrappers/fitting/Example.ipynb +++ b/qdev_wrappers/fitting/Example.ipynb @@ -2,12 +2,20 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "User schema at /Users/Lillian/qcodesrc_schema.json not found.User settings won't be validated\n", + "no config file found at /Users/Lillian/Qcodes/My stuff/mainfolder/data/local_scripts/instr.config, will try general\n", + "no config file found at /Users/Lillian/Qcodes/My stuff/instr.config\n" + ] + } + ], "source": [ - "import qcodes as qc\n", - "\n", "from qdev_wrappers.fitting.Converter import SQL_Converter, Legacy_Converter, data_to_dict\n", "from qdev_wrappers.fitting.Fitclasses import T1, T2\n", "from qdev_wrappers.fitting.Fitter import Fitter\n", @@ -29,7 +37,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -102,7 +110,7 @@ " 'variables': ['pulse_readout_delay', 'data']}" ] }, - "execution_count": 3, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -121,7 +129,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -171,7 +179,7 @@ " 'rec_ctrl_raw_output']}" ] }, - "execution_count": 4, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -197,7 +205,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -205,13 +213,13 @@ "text/plain": [ "{'parameters': {'a': {'value': 0.00020686194829005843,\n", " 'cov': array([ 1.12188937e-10, -8.65732231e-14, -2.94102847e-12]),\n", - " 'unit': 'V'},\n", + " 'unit': 'S'},\n", " 'T': {'value': 1.7792117433091448e-07,\n", " 'cov': array([ -8.65732231e-14, 3.48328903e-16, -5.40259460e-14]),\n", - " 'unit': 'S'},\n", + " 'unit': 'V'},\n", " 'c': {'value': 0.0016790061428729915,\n", " 'cov': array([ -2.94102847e-12, -5.40259460e-14, 1.68224887e-11]),\n", - " 'unit': 'V'}},\n", + " 'unit': 'S'}},\n", " 'estimator': {'method': 'Least squared fit',\n", " 'type': 'T1fit',\n", " 'function': 'save all text from fitter function and class function here?',\n", @@ -222,7 +230,7 @@ " 'dependencies': {'data': ['pulse_readout_delay']}}}" ] }, - "execution_count": 5, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -241,7 +249,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -249,13 +257,13 @@ "text/plain": [ "{'parameters': {'a': {'value': 0.00020686194829005843,\n", " 'cov': array([ 1.12188937e-10, -8.65732231e-14, -2.94102847e-12]),\n", - " 'unit': 'V'},\n", + " 'unit': 'S'},\n", " 'T': {'value': 1.7792117433091448e-07,\n", " 'cov': array([ -8.65732231e-14, 3.48328903e-16, -5.40259460e-14]),\n", - " 'unit': 'S'},\n", + " 'unit': 'V'},\n", " 'c': {'value': 0.0016790061428729915,\n", " 'cov': array([ -2.94102847e-12, -5.40259460e-14, 1.68224887e-11]),\n", - " 'unit': 'V'}},\n", + " 'unit': 'S'}},\n", " 'estimator': {'method': 'Least squared fit',\n", " 'type': 'T1fit',\n", " 'function': 'save all text from fitter function and class function here?',\n", @@ -266,7 +274,7 @@ " 'dependencies': {'data': ['pulse_readout_delay']}}}" ] }, - "execution_count": 6, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -286,2056 +294,11 @@ 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'pi_pulse_dur_set'],\n", - " 'rec_ctrl_demod_freq_0_phase': ['qubit_rs_frequency_set',\n", - " 'pi_pulse_dur_set'],\n", - " 'rec_ctrl_raw_output': ['qubit_rs_frequency_set', 'pi_pulse_dur_set']},\n", - " 'setpoints': 'xdata'}}" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "fit2 = Fitter().fit(data2, T2(), default_dependencies=True, dataname='rec_ctrl_demod_freq_0_mag')\n", - "fit2" + "fit2 = Fitter().fit(data2, T2(), default_dependencies=True, dataname='rec_ctrl_demod_freq_0_mag')" ] }, { @@ -2347,2055 +310,11 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 8, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{5600000000.0: {'parameters': {'a': {'value': 0.00018995671288537621,\n", - " 'cov': array([ 1.03060699e-09, -4.34010572e-13, -4.25402654e-01,\n", - " 5.38756557e-08, -1.75998153e-11]),\n", - " 'unit': 'Hz'},\n", - " 'T': {'value': 6.098906943811456e-08,\n", 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0.0033636103901789503,\n", - " 'cov': array([ -8.62438659e-13, 1.35156186e-15, -2.07840106e-01,\n", - " 9.77369943e-09, 6.44765391e-12]),\n", - " 'unit': 'Hz'}}},\n", - " 5700000000.0: {'parameters': {'a': {'value': 0.00019470436138841713,\n", - " 'cov': array([ 8.07941852e-11, -7.93070460e-14, -3.43249182e-01,\n", - " 2.29025281e-08, -4.54732677e-13]),\n", - " 'unit': 'Hz'},\n", - " 'T': {'value': 1.0252387734542798e-07,\n", - " 'cov': array([ -7.93070460e-14, 1.26121540e-16, 1.67455723e-04,\n", - " -1.82520579e-11, 5.60316921e-16]),\n", - " 'unit': 's'},\n", - " 'w': {'value': 437544201.43309355,\n", - " 'cov': array([ -3.43249182e-01, 1.67455723e-04, 1.21026217e+12,\n", - " -4.23897209e+04, -3.11492424e-01]),\n", - " 'unit': '1/s'},\n", - " 'p': {'value': 1.5435027044156295,\n", - " 'cov': array([ 2.29025281e-08, -1.82520579e-11, -4.23897209e+04,\n", - " 2.32210573e-03, 1.30406028e-08]),\n", - " 'unit': ''},\n", - " 'c': {'value': 0.0033567968968173018,\n", - " 'cov': array([ -4.54732677e-13, 5.60316921e-16, -3.11492424e-01,\n", - " 1.30406028e-08, 6.98267040e-12]),\n", - " 'unit': 'Hz'}}},\n", - " 'estimator': {'method': 'Least squared fit',\n", - " 'type': 'T2fit',\n", - " 'function': 'save all text from fitter function and class function here?',\n", - " 'more?': 'perhaps'},\n", - " 'inferred_from': {'xdata': 'pi_pulse_dur_set',\n", - " 'ydata': 'qubit_rs_frequency_set',\n", - " 'zdata': 'rec_ctrl_demod_freq_0_mag',\n", - " 'dataset': 621,\n", - " 'dependencies': {'rec_ctrl_demod_freq_0_mag': ['qubit_rs_frequency_set',\n", - " 'pi_pulse_dur_set'],\n", - " 'rec_ctrl_demod_freq_0_phase': ['qubit_rs_frequency_set',\n", - " 'pi_pulse_dur_set'],\n", - " 'rec_ctrl_raw_output': ['qubit_rs_frequency_set', 'pi_pulse_dur_set']},\n", - " 'setpoints': 'ydata'}}" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "Fitter().fit(data2, T2(), x='pi_pulse_dur_set', y='qubit_rs_frequency_set', z='rec_ctrl_demod_freq_0_mag', cut='horizontal')" + "fit2a = Fitter().fit(data2, T2(), x='pi_pulse_dur_set', y='qubit_rs_frequency_set', z='rec_ctrl_demod_freq_0_mag', cut='horizontal')" ] }, { From 5a51be80285da9e2803899d941e620ca8313284f Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Tue, 26 Jun 2018 12:26:24 +0200 Subject: [PATCH 023/116] Fix bug in setting 1D plot units --- qdev_wrappers/fitting/Fitter.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/qdev_wrappers/fitting/Fitter.py b/qdev_wrappers/fitting/Fitter.py index 0f493e9f..4fb690b1 100644 --- a/qdev_wrappers/fitting/Fitter.py +++ b/qdev_wrappers/fitting/Fitter.py @@ -26,7 +26,7 @@ def do_1d(self, xdata, ydata, fitclass, p0,**kwargs): return "Could not find guess parameters for fit." - def fit(self, data, fitclass, x=None, y=None, z=None, default_dependencies=False, dataname = None, cut='vertical', p0=None,**kwargs): + def fit(self, data, fitclass, x=None, y=None, z=None, default_dependencies=False, dataname = None, cut='horizontal', p0=None,**kwargs): if type(fitclass) == type: #Maybe I'm just an idiot, and this isn't necessary for the world-at-large, but @@ -86,6 +86,8 @@ def fit(self, data, fitclass, x=None, y=None, z=None, default_dependencies=False dimensions = 2 # find parameter units + if dimensions == 1: + cut = 'horizontal' #Must be horizontal for 1D plots for units to work. Now you can't mess it up. unit_template = fitclass.p_units param_units = [] x = x_dict['unit'] From 73e70921d6497fd8db32e7a3bc2f01bc3795d30f Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Tue, 26 Jun 2018 12:34:24 +0200 Subject: [PATCH 024/116] Fix bug I made fixing last bug --- qdev_wrappers/fitting/Fitter.py | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/qdev_wrappers/fitting/Fitter.py b/qdev_wrappers/fitting/Fitter.py index 4fb690b1..7fb8c780 100644 --- a/qdev_wrappers/fitting/Fitter.py +++ b/qdev_wrappers/fitting/Fitter.py @@ -29,8 +29,8 @@ def do_1d(self, xdata, ydata, fitclass, p0,**kwargs): def fit(self, data, fitclass, x=None, y=None, z=None, default_dependencies=False, dataname = None, cut='horizontal', p0=None,**kwargs): if type(fitclass) == type: - #Maybe I'm just an idiot, and this isn't necessary for the world-at-large, but - #I spent about 45 minutes trying to figure out what I broke before I realized + #Maybe I'm just an idiot, and this isn't necessary for the world-at-large, but + #I spent about 45 minutes trying to figure out what I broke before I realized #that I just forgot the parentheses after the fitclass. So this is here for now. raise RuntimeError('It looks like there is something wrong with your fitclass(). Possibly you forgot the parentheses?') @@ -38,6 +38,11 @@ def fit(self, data, fitclass, x=None, y=None, z=None, default_dependencies=False raise RuntimeError('Please either specify data for x, y (and optionally z) or set default_dependencies = True') if default_dependencies == True: + cut = 'vertical' + '''so far in the examples, the new data sets switch out the x and y axes, + but I don't know if this is an artifact of the conversion function for old to new datasets, + or if its how the new datasets are going to be. So this may be the incorrect default.''' + #TODO: Ask someone about this!!! dep_var = [key for key in data['dependencies'].keys()] indep_vars = [value for value in data['dependencies'].values()] From 27b8a19c710b80bfca8557a32196d057477b1f39 Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Tue, 26 Jun 2018 12:35:40 +0200 Subject: [PATCH 025/116] Update Example notebook after bug fixes --- qdev_wrappers/fitting/Example.ipynb | 44 +++++++++++------------------ 1 file changed, 17 insertions(+), 27 deletions(-) diff --git a/qdev_wrappers/fitting/Example.ipynb b/qdev_wrappers/fitting/Example.ipynb index f6b32915..0e1eedbf 100644 --- a/qdev_wrappers/fitting/Example.ipynb +++ b/qdev_wrappers/fitting/Example.ipynb @@ -2,19 +2,9 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 9, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "User schema at /Users/Lillian/qcodesrc_schema.json not found.User settings won't be validated\n", - "no config file found at /Users/Lillian/Qcodes/My stuff/mainfolder/data/local_scripts/instr.config, will try general\n", - "no config file found at /Users/Lillian/Qcodes/My stuff/instr.config\n" - ] - } - ], + "outputs": [], "source": [ "from qdev_wrappers.fitting.Converter import SQL_Converter, Legacy_Converter, data_to_dict\n", "from qdev_wrappers.fitting.Fitclasses import T1, T2\n", @@ -37,7 +27,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -110,7 +100,7 @@ " 'variables': ['pulse_readout_delay', 'data']}" ] }, - "execution_count": 2, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -129,7 +119,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -179,7 +169,7 @@ " 'rec_ctrl_raw_output']}" ] }, - "execution_count": 3, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -205,7 +195,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -213,13 +203,13 @@ "text/plain": [ "{'parameters': {'a': {'value': 0.00020686194829005843,\n", " 'cov': array([ 1.12188937e-10, -8.65732231e-14, -2.94102847e-12]),\n", - " 'unit': 'S'},\n", + " 'unit': 'V'},\n", " 'T': {'value': 1.7792117433091448e-07,\n", " 'cov': array([ -8.65732231e-14, 3.48328903e-16, -5.40259460e-14]),\n", - " 'unit': 'V'},\n", + " 'unit': 'S'},\n", " 'c': {'value': 0.0016790061428729915,\n", " 'cov': array([ -2.94102847e-12, -5.40259460e-14, 1.68224887e-11]),\n", - " 'unit': 'S'}},\n", + " 'unit': 'V'}},\n", " 'estimator': {'method': 'Least squared fit',\n", " 'type': 'T1fit',\n", " 'function': 'save all text from fitter function and class function here?',\n", @@ -230,7 +220,7 @@ " 'dependencies': {'data': ['pulse_readout_delay']}}}" ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -249,7 +239,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -257,13 +247,13 @@ "text/plain": [ "{'parameters': {'a': {'value': 0.00020686194829005843,\n", " 'cov': array([ 1.12188937e-10, -8.65732231e-14, -2.94102847e-12]),\n", - " 'unit': 'S'},\n", + " 'unit': 'V'},\n", " 'T': {'value': 1.7792117433091448e-07,\n", " 'cov': array([ -8.65732231e-14, 3.48328903e-16, -5.40259460e-14]),\n", - " 'unit': 'V'},\n", + " 'unit': 'S'},\n", " 'c': {'value': 0.0016790061428729915,\n", " 'cov': array([ -2.94102847e-12, -5.40259460e-14, 1.68224887e-11]),\n", - " 'unit': 'S'}},\n", + " 'unit': 'V'}},\n", " 'estimator': {'method': 'Least squared fit',\n", " 'type': 'T1fit',\n", " 'function': 'save all text from fitter function and class function here?',\n", @@ -274,7 +264,7 @@ " 'dependencies': {'data': ['pulse_readout_delay']}}}" ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -294,7 +284,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ From c0774c4724d820c1772d705d235225d4ca3a1e33 Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Wed, 27 Jun 2018 13:02:31 +0200 Subject: [PATCH 026/116] Change variable name from data_id to run_id --- qdev_wrappers/fitting/AnalysisToSQL.py | 6 +++--- qdev_wrappers/fitting/Converter.py | 2 +- qdev_wrappers/fitting/Fitter.py | 4 ++-- 3 files changed, 6 insertions(+), 6 deletions(-) diff --git a/qdev_wrappers/fitting/AnalysisToSQL.py b/qdev_wrappers/fitting/AnalysisToSQL.py index 8f602d9f..0eaff7c2 100644 --- a/qdev_wrappers/fitting/AnalysisToSQL.py +++ b/qdev_wrappers/fitting/AnalysisToSQL.py @@ -103,7 +103,7 @@ def fit_to_SQL(data, fitclass, fit): #it would be an improvement if it were a - tablename = 'data_{}_{}'.format(data['data_id'], fitclass.name) + tablename = 'data_{}_{}'.format(data['run_id'], fitclass.name) table_columns = [est_name] @@ -124,7 +124,7 @@ def fit_to_SQL(data, fitclass, fit): #it would be an improvement if it were a - data_id = data['data_id'] + run_id = data['run_id'] exp_id = data['exp_id'] analysis_id = 1 predicts = est_name.strip("estimate").strip("_") @@ -157,7 +157,7 @@ def fit_to_SQL(data, fitclass, fit): #it would be an improvement if it were a if max_id != None: analysis_id += max_id - cur.execute(sql_analyses, (data_id, exp_id, analysis_id, table, predicts, estimator, analysis)) + cur.execute(sql_analyses, (run_id, exp_id, analysis_id, table, predicts, estimator, analysis)) sql_overview = 'INSERT INTO overview VALUES (?,?,?,?,?,?)' diff --git a/qdev_wrappers/fitting/Converter.py b/qdev_wrappers/fitting/Converter.py index 3835e69d..70b21e06 100644 --- a/qdev_wrappers/fitting/Converter.py +++ b/qdev_wrappers/fitting/Converter.py @@ -29,7 +29,7 @@ def convert(self, id, samplefolder = None): all_variables = self.find_variables(data) data_dict['exp_id'] = exp_id - data_dict['data_id'] = id + data_dict['run_id'] = id data_dict['dependencies'] = dependencies data_dict['variables'] = all_variables diff --git a/qdev_wrappers/fitting/Fitter.py b/qdev_wrappers/fitting/Fitter.py index 7fb8c780..bc360300 100644 --- a/qdev_wrappers/fitting/Fitter.py +++ b/qdev_wrappers/fitting/Fitter.py @@ -144,7 +144,7 @@ def fit(self, data, fitclass, x=None, y=None, z=None, default_dependencies=False fit1d['inferred_from'] = {'xdata': x_dict['name'], 'ydata': y_dict['name'], - 'dataset': data['data_id'], + 'dataset': data['run_id'], 'dependencies': data['dependencies']} #missing sample name @@ -216,7 +216,7 @@ def fit(self, data, fitclass, x=None, y=None, z=None, default_dependencies=False fits2d['inferred_from'] = {'xdata': x_dict['name'], 'ydata': y_dict['name'], 'zdata': z_dict['name'], - 'dataset': data['data_id'], + 'dataset': data['run_id'], 'dependencies': data['dependencies']} #missing sample name if cut == 'horizontal': From 8c5a2676f73dc2143c0fda39231be0ee803b650f Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Wed, 27 Jun 2018 14:00:30 +0200 Subject: [PATCH 027/116] Add dill of fitclass to fit dictionary --- qdev_wrappers/fitting/Fitter.py | 56 +++++++++++++++------------------ 1 file changed, 26 insertions(+), 30 deletions(-) diff --git a/qdev_wrappers/fitting/Fitter.py b/qdev_wrappers/fitting/Fitter.py index bc360300..ad93d6c2 100644 --- a/qdev_wrappers/fitting/Fitter.py +++ b/qdev_wrappers/fitting/Fitter.py @@ -1,5 +1,6 @@ import qcodes as qc import numpy as np +import dill from qdev_wrappers.fitting.Converter import SQL_Converter, Legacy_Converter from qdev_wrappers.fitting.Fitclasses import T1, T2 @@ -127,28 +128,23 @@ def fit(self, data, fitclass, x=None, y=None, z=None, default_dependencies=False xdata = x_dict['data'] ydata = y_dict['data'] - fit1d = {} - fit1d['parameters'] = {} + fit = {} + fit['parameters'] = {} popt, pcov = self.do_1d(xdata, ydata, fitclass, p0,**kwargs) for parameter in fitclass.p_labels: #parameters currently missing units, use fitclass.p_units - fit1d['parameters'][parameter] = {'value': popt[fitclass.p_labels.index(parameter)]} - fit1d['parameters'][parameter]['cov'] = pcov[fitclass.p_labels.index(parameter)] - fit1d['parameters'][parameter]['unit'] = param_units[fitclass.p_labels.index(parameter)] - - fit1d['estimator'] = {'method': 'Least squared fit', - 'type': fitclass.name, - 'function': 'save all text from fitter function and class function here?', - 'more?' : 'perhaps'} + fit['parameters'][parameter] = {'value': popt[fitclass.p_labels.index(parameter)]} + fit['parameters'][parameter]['cov'] = pcov[fitclass.p_labels.index(parameter)] + fit['parameters'][parameter]['unit'] = param_units[fitclass.p_labels.index(parameter)] + - fit1d['inferred_from'] = {'xdata': x_dict['name'], + fit['inferred_from'] = {'xdata': x_dict['name'], 'ydata': y_dict['name'], 'dataset': data['run_id'], 'dependencies': data['dependencies']} #missing sample name - - return fit1d + #Do fit for 2D data @@ -158,7 +154,7 @@ def fit(self, data, fitclass, x=None, y=None, z=None, default_dependencies=False ydata = y_dict['data'] zdata = z_dict['data'] - fits2d = {} + fit = {} if cut == 'horizontal': setarray = ydata @@ -197,35 +193,35 @@ def fit(self, data, fitclass, x=None, y=None, z=None, default_dependencies=False popt, pcov = self.do_1d(xdata_1d, ydata_1d, fitclass, p0,**kwargs) - fits2d[set_value] = {} - fits2d[set_value]['parameters'] = {} + fit[set_value] = {} + fit[set_value]['parameters'] = {} for parameter in fitclass.p_labels: #parameters currently missing units, use fitclass.p_units - fits2d[set_value]['parameters'][parameter] = {'value': popt[fitclass.p_labels.index(parameter)]} - fits2d[set_value]['parameters'][parameter]['cov'] = pcov[fitclass.p_labels.index(parameter)] - fits2d[set_value]['parameters'][parameter]['unit'] = param_units[fitclass.p_labels.index(parameter)] - + fit[set_value]['parameters'][parameter] = {'value': popt[fitclass.p_labels.index(parameter)]} + fit[set_value]['parameters'][parameter]['cov'] = pcov[fitclass.p_labels.index(parameter)] + fit[set_value]['parameters'][parameter]['unit'] = param_units[fitclass.p_labels.index(parameter)] + - fits2d['estimator'] = {'method': 'Least squared fit', - 'type': fitclass.name, - 'function': 'save all text from fitter function and class function here?', - 'more?' : 'perhaps'} - - #does this needs to be moved so that it specifies which cut the individual sets of parameters are inferred from? - fits2d['inferred_from'] = {'xdata': x_dict['name'], + fit['inferred_from'] = {'xdata': x_dict['name'], 'ydata': y_dict['name'], 'zdata': z_dict['name'], 'dataset': data['run_id'], 'dependencies': data['dependencies']} #missing sample name if cut == 'horizontal': - fits2d['inferred_from']['setpoints'] = 'ydata' + fit['inferred_from']['setpoints'] = 'ydata' if cut == 'vertical': - fits2d['inferred_from']['setpoints'] = 'xdata' + fit['inferred_from']['setpoints'] = 'xdata' + + dill_obj = dill.dumps(fitclass) + fit['estimator'] = {'method': 'Least squared fit', + 'type': fitclass.name, + 'function': 'save all text from fitter function and class function here?', + 'dill': dill_obj} - return fits2d + return fit From bb2cc9b9b8ccf007f0bebebe9aa629f8249ff7c6 Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Wed, 27 Jun 2018 14:27:09 +0200 Subject: [PATCH 028/116] Change Fitter from class to function --- qdev_wrappers/fitting/Fitter.py | 396 ++++++++++++++++---------------- 1 file changed, 195 insertions(+), 201 deletions(-) diff --git a/qdev_wrappers/fitting/Fitter.py b/qdev_wrappers/fitting/Fitter.py index ad93d6c2..39a958d0 100644 --- a/qdev_wrappers/fitting/Fitter.py +++ b/qdev_wrappers/fitting/Fitter.py @@ -7,221 +7,215 @@ from scipy.optimize import curve_fit + +def fit(data, fitclass, x=None, y=None, z=None, default_dependencies=False, dataname = None, cut='horizontal', p0=None,**kwargs): + def do_1d(xdata, ydata, fitclass, p0, **kwargs): - - -class Fitter(): - - def do_1d(self, xdata, ydata, fitclass, p0,**kwargs): - - if (p0==None and hasattr(fitclass,'guess')): - p0 = getattr(fitclass,'guess')(xdata,ydata) + if (p0 == None and hasattr(fitclass, 'guess')): + p0 = getattr(fitclass, 'guess')(xdata, ydata) popt, pcov = curve_fit(fitclass.fun, xdata, ydata, p0=p0, **kwargs) - return popt, pcov - elif p0!=None: + return popt, pcov + elif p0 != None: popt, pcov = curve_fit(fitclass.fun, xdata, ydata, p0=p0, **kwargs) - return popt, pcov + return popt, pcov else: return "Could not find guess parameters for fit." - - def fit(self, data, fitclass, x=None, y=None, z=None, default_dependencies=False, dataname = None, cut='horizontal', p0=None,**kwargs): - - if type(fitclass) == type: - #Maybe I'm just an idiot, and this isn't necessary for the world-at-large, but - #I spent about 45 minutes trying to figure out what I broke before I realized - #that I just forgot the parentheses after the fitclass. So this is here for now. - raise RuntimeError('It looks like there is something wrong with your fitclass(). Possibly you forgot the parentheses?') - - if (x==None or y==None) and default_dependencies==False: - raise RuntimeError('Please either specify data for x, y (and optionally z) or set default_dependencies = True') - - if default_dependencies == True: - cut = 'vertical' - '''so far in the examples, the new data sets switch out the x and y axes, - but I don't know if this is an artifact of the conversion function for old to new datasets, - or if its how the new datasets are going to be. So this may be the incorrect default.''' - #TODO: Ask someone about this!!! - dep_var = [key for key in data['dependencies'].keys()] - indep_vars = [value for value in data['dependencies'].values()] - - if dataname==None: - if len(dep_var) != 1: - raise RuntimeError('Dataset has more than one dataname. Choose dataname to be fitted from: {}.'.format(', '.join(dep_var))) - - indep_vars = indep_vars[0] + if type(fitclass) == type: + #Maybe I'm just an idiot, and this isn't necessary for the world-at-large, but + #I spent about 45 minutes trying to figure out what I broke before I realized + #that I just forgot the parentheses after the fitclass. So this is here for now. + raise RuntimeError('It looks like there is something wrong with your fitclass(). Possibly you forgot the parentheses?') - else: - if dataname not in dep_var: - raise RuntimeError('Dataname not in dataset. Input dataname was: \'{}\' while dataname(s) in dataset are: {}.'.format(dataname,', '.join(dep_var))) + if (x==None or y==None) and default_dependencies==False: + raise RuntimeError('Please either specify data for x, y (and optionally z) or set default_dependencies = True') - indep_vars = indep_vars[dep_var.index(dataname)] - dep_var = [dataname] - - if len(indep_vars) > 2: - print('independent variables: {}'.format(indep_vars)) - raise RuntimeError('That dataset seems to contain {} independent variables, which is {} too many.'.format(len(indep_vars), len(indep_vars)-2)) + if default_dependencies == True: + cut = 'vertical' + '''so far in the examples, the new data sets switch out the x and y axes, + but I don't know if this is an artifact of the conversion function for old to new datasets, + or if its how the new datasets are going to be. So this may be the incorrect default.''' + #TODO: Ask someone about this!!! + dep_var = [key for key in data['dependencies'].keys()] + indep_vars = [value for value in data['dependencies'].values()] - - if len(indep_vars) == 1: - dimensions = 1 - x_dict = data[indep_vars[0]] - y_dict = data[dep_var[0]] - - if len(indep_vars) == 2: - dimensions = 2 - x_dict = data[indep_vars[0]] - y_dict = data[indep_vars[1]] - z_dict = data[dep_var[0]] - - - elif default_dependencies == False: - - for dataname in [x, y, z]: - if (dataname not in data['variables']) and (dataname is not None): - raise RuntimeError('The specified variable "{}" is not found in the variables for this data dictionary. Variables are {}'.format(dataname, data['variables'])) - - x_dict = data[x] - y_dict = data[y] + if dataname==None: + if len(dep_var) != 1: + raise RuntimeError('Dataset has more than one dataname. Choose dataname to be fitted from: {}.'.format(', '.join(dep_var))) + + indep_vars = indep_vars[0] + + else: + if dataname not in dep_var: + raise RuntimeError('Dataname not in dataset. Input dataname was: \'{}\' while dataname(s) in dataset are: {}.'.format(dataname,', '.join(dep_var))) + + indep_vars = indep_vars[dep_var.index(dataname)] + dep_var = [dataname] + + if len(indep_vars) > 2: + print('independent variables: {}'.format(indep_vars)) + raise RuntimeError('That dataset seems to contain {} independent variables, which is {} too many.'.format(len(indep_vars), len(indep_vars)-2)) + + + if len(indep_vars) == 1: dimensions = 1 - - if z != None: - z_dict = data[z] - dimensions = 2 - - # find parameter units - if dimensions == 1: - cut = 'horizontal' #Must be horizontal for 1D plots for units to work. Now you can't mess it up. - unit_template = fitclass.p_units - param_units = [] - x = x_dict['unit'] - y = y_dict['unit'] - if dimensions == 2: - z = z_dict['unit'] - - for item in unit_template: - template = list(item) - if cut == 'horizontal': - for i in range(len(template)): - if template[i] == 'x': - template[i] = x - if template[i] == 'y': - template[i] = y - if template[i] == 'z': - template[i] = z - elif cut == 'vertical': - for i in range(len(template)): - if template[i] == 'x': - template[i] = y - if template[i] == 'y': - template[i] = x - if template[i] == 'z': - template[i] = z - unit = "".join(template) - param_units.append(unit) - - #Do fit for 1D data - if dimensions == 1: - - xdata = x_dict['data'] - ydata = y_dict['data'] - - fit = {} - fit['parameters'] = {} - - popt, pcov = self.do_1d(xdata, ydata, fitclass, p0,**kwargs) - - for parameter in fitclass.p_labels: #parameters currently missing units, use fitclass.p_units - fit['parameters'][parameter] = {'value': popt[fitclass.p_labels.index(parameter)]} - fit['parameters'][parameter]['cov'] = pcov[fitclass.p_labels.index(parameter)] - fit['parameters'][parameter]['unit'] = param_units[fitclass.p_labels.index(parameter)] - - - fit['inferred_from'] = {'xdata': x_dict['name'], - 'ydata': y_dict['name'], - 'dataset': data['run_id'], - 'dependencies': data['dependencies']} #missing sample name - + x_dict = data[indep_vars[0]] + y_dict = data[dep_var[0]] - - - #Do fit for 2D data - if dimensions == 2: - - xdata = x_dict['data'] - ydata = y_dict['data'] - zdata = z_dict['data'] + if len(indep_vars) == 2: + dimensions = 2 + x_dict = data[indep_vars[0]] + y_dict = data[indep_vars[1]] + z_dict = data[dep_var[0]] - fit = {} - if cut == 'horizontal': - setarray = ydata - xarray = xdata - - if cut == 'vertical': - setarray = xdata - xarray = ydata - - setpoints = np.unique(setarray) - yarray = zdata - - #reformats as array of set points, with a y-array and z-array corresponding to each set point - - xdata_lst = [] - ydata_lst = [] - - for set_point in setpoints: - x_dat = [] - y_dat = [] - for setpoint, x, y in zip(setarray, xarray, yarray): - if setpoint == set_point: - x_dat.append(x) - y_dat.append(y) - xdata_lst.append(np.array(x_dat)) - ydata_lst.append(np.array(y_dat)) - - - xdata = np.array(xdata_lst) - ydata = np.array(ydata_lst) - - - #fitting as a sequence of 1D plots for different set_values - - for set_value, xdata_1d, ydata_1d in zip(setpoints, xdata, ydata): - - popt, pcov = self.do_1d(xdata_1d, ydata_1d, fitclass, p0,**kwargs) - - fit[set_value] = {} - fit[set_value]['parameters'] = {} - - for parameter in fitclass.p_labels: #parameters currently missing units, use fitclass.p_units - fit[set_value]['parameters'][parameter] = {'value': popt[fitclass.p_labels.index(parameter)]} - fit[set_value]['parameters'][parameter]['cov'] = pcov[fitclass.p_labels.index(parameter)] - fit[set_value]['parameters'][parameter]['unit'] = param_units[fitclass.p_labels.index(parameter)] - - - #does this needs to be moved so that it specifies which cut the individual sets of parameters are inferred from? - fit['inferred_from'] = {'xdata': x_dict['name'], - 'ydata': y_dict['name'], - 'zdata': z_dict['name'], - 'dataset': data['run_id'], - 'dependencies': data['dependencies']} #missing sample name - - if cut == 'horizontal': - fit['inferred_from']['setpoints'] = 'ydata' - - if cut == 'vertical': - fit['inferred_from']['setpoints'] = 'xdata' - - - dill_obj = dill.dumps(fitclass) - fit['estimator'] = {'method': 'Least squared fit', - 'type': fitclass.name, - 'function': 'save all text from fitter function and class function here?', - 'dill': dill_obj} - - return fit + elif default_dependencies == False: + + for dataname in [x, y, z]: + if (dataname not in data['variables']) and (dataname is not None): + raise RuntimeError('The specified variable "{}" is not found in the variables for this data dictionary. Variables are {}'.format(dataname, data['variables'])) + + x_dict = data[x] + y_dict = data[y] + dimensions = 1 + + if z != None: + z_dict = data[z] + dimensions = 2 + + # find parameter units + if dimensions == 1: + cut = 'horizontal' #Must be horizontal for 1D plots for units to work. Now you can't mess it up. + unit_template = fitclass.p_units + param_units = [] + x = x_dict['unit'] + y = y_dict['unit'] + if dimensions == 2: + z = z_dict['unit'] + + for item in unit_template: + template = list(item) + if cut == 'horizontal': + for i in range(len(template)): + if template[i] == 'x': + template[i] = x + if template[i] == 'y': + template[i] = y + if template[i] == 'z': + template[i] = z + elif cut == 'vertical': + for i in range(len(template)): + if template[i] == 'x': + template[i] = y + if template[i] == 'y': + template[i] = x + if template[i] == 'z': + template[i] = z + unit = "".join(template) + param_units.append(unit) + + #Do fit for 1D data + if dimensions == 1: + + xdata = x_dict['data'] + ydata = y_dict['data'] + + fit = {} + fit['parameters'] = {} + + popt, pcov = do_1d(xdata, ydata, fitclass, p0,**kwargs) + + for parameter in fitclass.p_labels: #parameters currently missing units, use fitclass.p_units + fit['parameters'][parameter] = {'value': popt[fitclass.p_labels.index(parameter)]} + fit['parameters'][parameter]['cov'] = pcov[fitclass.p_labels.index(parameter)] + fit['parameters'][parameter]['unit'] = param_units[fitclass.p_labels.index(parameter)] + + + fit['inferred_from'] = {'xdata': x_dict['name'], + 'ydata': y_dict['name'], + 'dataset': data['run_id'], + 'dependencies': data['dependencies']} #missing sample name + + + + + #Do fit for 2D data + if dimensions == 2: + + xdata = x_dict['data'] + ydata = y_dict['data'] + zdata = z_dict['data'] + + fit = {} + + if cut == 'horizontal': + setarray = ydata + xarray = xdata + + if cut == 'vertical': + setarray = xdata + xarray = ydata + + setpoints = np.unique(setarray) + yarray = zdata + + #reformats as array of set points, with a y-array and z-array corresponding to each set point + + xdata_lst = [] + ydata_lst = [] + + for set_point in setpoints: + x_dat = [] + y_dat = [] + for setpoint, x, y in zip(setarray, xarray, yarray): + if setpoint == set_point: + x_dat.append(x) + y_dat.append(y) + xdata_lst.append(np.array(x_dat)) + ydata_lst.append(np.array(y_dat)) + + + xdata = np.array(xdata_lst) + ydata = np.array(ydata_lst) + + + #fitting as a sequence of 1D plots for different set_values + + for set_value, xdata_1d, ydata_1d in zip(setpoints, xdata, ydata): + + popt, pcov = do_1d(xdata_1d, ydata_1d, fitclass, p0,**kwargs) + + fit[set_value] = {} + fit[set_value]['parameters'] = {} + + for parameter in fitclass.p_labels: #parameters currently missing units, use fitclass.p_units + fit[set_value]['parameters'][parameter] = {'value': popt[fitclass.p_labels.index(parameter)]} + fit[set_value]['parameters'][parameter]['cov'] = pcov[fitclass.p_labels.index(parameter)] + fit[set_value]['parameters'][parameter]['unit'] = param_units[fitclass.p_labels.index(parameter)] + + + #does this needs to be moved so that it specifies which cut the individual sets of parameters are inferred from? + fit['inferred_from'] = {'xdata': x_dict['name'], + 'ydata': y_dict['name'], + 'zdata': z_dict['name'], + 'dataset': data['run_id'], + 'dependencies': data['dependencies']} #missing sample name + + if cut == 'horizontal': + fit['inferred_from']['setpoints'] = 'ydata' + + if cut == 'vertical': + fit['inferred_from']['setpoints'] = 'xdata' + + + dill_obj = dill.dumps(fitclass) + fit['estimator'] = {'method': 'Least squared fit', + 'type': fitclass.name, + 'function': 'save all text from fitter function and class function here?', + 'dill': dill_obj} + + return fit From 357dc3d5db90d157b41bce6c67b469c24a82c4f0 Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Wed, 27 Jun 2018 14:36:11 +0200 Subject: [PATCH 029/116] Remove default_dependencies option, require user to specify explicitly --- qdev_wrappers/fitting/Fitter.py | 67 +++++++-------------------------- 1 file changed, 14 insertions(+), 53 deletions(-) diff --git a/qdev_wrappers/fitting/Fitter.py b/qdev_wrappers/fitting/Fitter.py index 39a958d0..cf5b5c73 100644 --- a/qdev_wrappers/fitting/Fitter.py +++ b/qdev_wrappers/fitting/Fitter.py @@ -9,7 +9,7 @@ -def fit(data, fitclass, x=None, y=None, z=None, default_dependencies=False, dataname = None, cut='horizontal', p0=None,**kwargs): +def fit(data, fitclass, x=None, y=None, z=None, cut='horizontal', p0=None,**kwargs): def do_1d(xdata, ydata, fitclass, p0, **kwargs): @@ -22,7 +22,8 @@ def do_1d(xdata, ydata, fitclass, p0, **kwargs): return popt, pcov else: return "Could not find guess parameters for fit." - + + if type(fitclass) == type: #Maybe I'm just an idiot, and this isn't necessary for the world-at-large, but #I spent about 45 minutes trying to figure out what I broke before I realized @@ -31,63 +32,23 @@ def do_1d(xdata, ydata, fitclass, p0, **kwargs): if (x==None or y==None) and default_dependencies==False: raise RuntimeError('Please either specify data for x, y (and optionally z) or set default_dependencies = True') - - if default_dependencies == True: - cut = 'vertical' - '''so far in the examples, the new data sets switch out the x and y axes, - but I don't know if this is an artifact of the conversion function for old to new datasets, - or if its how the new datasets are going to be. So this may be the incorrect default.''' - #TODO: Ask someone about this!!! - dep_var = [key for key in data['dependencies'].keys()] - indep_vars = [value for value in data['dependencies'].values()] - - if dataname==None: - if len(dep_var) != 1: - raise RuntimeError('Dataset has more than one dataname. Choose dataname to be fitted from: {}.'.format(', '.join(dep_var))) - - indep_vars = indep_vars[0] - - else: - if dataname not in dep_var: - raise RuntimeError('Dataname not in dataset. Input dataname was: \'{}\' while dataname(s) in dataset are: {}.'.format(dataname,', '.join(dep_var))) - - indep_vars = indep_vars[dep_var.index(dataname)] - dep_var = [dataname] - - if len(indep_vars) > 2: - print('independent variables: {}'.format(indep_vars)) - raise RuntimeError('That dataset seems to contain {} independent variables, which is {} too many.'.format(len(indep_vars), len(indep_vars)-2)) - - - if len(indep_vars) == 1: - dimensions = 1 - x_dict = data[indep_vars[0]] - y_dict = data[dep_var[0]] - - if len(indep_vars) == 2: - dimensions = 2 - x_dict = data[indep_vars[0]] - y_dict = data[indep_vars[1]] - z_dict = data[dep_var[0]] - - - elif default_dependencies == False: - for dataname in [x, y, z]: - if (dataname not in data['variables']) and (dataname is not None): - raise RuntimeError('The specified variable "{}" is not found in the variables for this data dictionary. Variables are {}'.format(dataname, data['variables'])) + for dataname in [x, y, z]: + if (dataname not in data['variables']) and (dataname is not None): + raise RuntimeError('The specified variable "{}" is not found in the variables for this data dictionary. Variables are {}'.format(dataname, data['variables'])) - x_dict = data[x] - y_dict = data[y] - dimensions = 1 + # specify x, y and z + x_dict = data[x] + y_dict = data[y] + dimensions = 1 - if z != None: - z_dict = data[z] - dimensions = 2 + if z != None: + z_dict = data[z] + dimensions = 2 # find parameter units if dimensions == 1: - cut = 'horizontal' #Must be horizontal for 1D plots for units to work. Now you can't mess it up. + cut = 'horizontal' # Must be horizontal for 1D plots for units to work. Now you can't mess it up. unit_template = fitclass.p_units param_units = [] x = x_dict['unit'] From 765c7502b50dd341233e0c4a8f896ae022e9319b Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Wed, 27 Jun 2018 14:39:21 +0200 Subject: [PATCH 030/116] Update Example notebook to reflect changes --- qdev_wrappers/fitting/Example.ipynb | 2155 +++++++++++++++++++++++++-- 1 file changed, 2068 insertions(+), 87 deletions(-) diff --git a/qdev_wrappers/fitting/Example.ipynb b/qdev_wrappers/fitting/Example.ipynb index 0e1eedbf..0238a124 100644 --- a/qdev_wrappers/fitting/Example.ipynb +++ b/qdev_wrappers/fitting/Example.ipynb @@ -2,18 +2,18 @@ "cells": [ { "cell_type": "code", - "execution_count": 9, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from qdev_wrappers.fitting.Converter import SQL_Converter, Legacy_Converter, data_to_dict\n", "from qdev_wrappers.fitting.Fitclasses import T1, T2\n", - "from qdev_wrappers.fitting.Fitter import Fitter\n", + "from qdev_wrappers.fitting.Fitter import fit\n", "from qdev_wrappers.fitting.AnalysisToSQL import is_table, make_table, fit_to_SQL \n", "from qdev_wrappers.fitting.RoughPlotting import plot_fit1d, plot_fit2d_slice\n", "\n", "from qdev_wrappers.show_num import show_num\n", - "from qcodes.dataset.plotting import plot_by_id" + "from qcodes.dataset.plotting import plot_by_id\n" ] }, { @@ -95,7 +95,7 @@ " 0.0016915 , 0.00170809, 0.0016675 , 0.00168928, 0.00171038,\n", " 0.00167808])},\n", " 'exp_id': 1,\n", - " 'data_id': 4,\n", + " 'run_id': 4,\n", " 'dependencies': {'data': ['pulse_readout_delay']},\n", " 'variables': ['pulse_readout_delay', 'data']}" ] @@ -156,7 +156,7 @@ " 'data': array([-0.00071378, -0.00065171, -0.0006484 , ..., -0.00077737,\n", " -0.00077076, -0.00070182])},\n", " 'exp_id': 621,\n", - " 'data_id': 621,\n", + " 'run_id': 621,\n", " 'dependencies': {'rec_ctrl_demod_freq_0_mag': ['qubit_rs_frequency_set',\n", " 'pi_pulse_dur_set'],\n", " 'rec_ctrl_demod_freq_0_phase': ['qubit_rs_frequency_set',\n", @@ -187,15 +187,12 @@ "\n", "To get a fit, the data dictionary goes into the fitter along with a fitclass (options are currently T1() and T2()). \n", "\n", - "In doing the fit, you have to decide if you want to manually specify what data you want on which axis, or if you want if to use the dependencies stored in the dataset and set the axes automatically.\n", - "\n", - "To use the dependencies from the data, set *default_dependencies = True*\n", - "\n" + "Set *x = 'data_name_for_x'*, *y = 'data_name_for_y\"* (and optionally *z = \"data_name_for_z\"*). If you are uncertain of the names of the variables in the data, they can be read in the data dictionary, or you can enter something random for x and y and it will throw an error that gives you a list of your options.\n" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -210,101 +207,2085 @@ " 'c': {'value': 0.0016790061428729915,\n", " 'cov': array([ -2.94102847e-12, -5.40259460e-14, 1.68224887e-11]),\n", " 'unit': 'V'}},\n", - " 'estimator': {'method': 'Least squared fit',\n", - " 'type': 'T1fit',\n", - " 'function': 'save all text from fitter function and class function here?',\n", - " 'more?': 'perhaps'},\n", " 'inferred_from': {'xdata': 'pulse_readout_delay',\n", " 'ydata': 'data',\n", " 'dataset': 4,\n", - " 'dependencies': {'data': ['pulse_readout_delay']}}}" + " 'dependencies': {'data': ['pulse_readout_delay']}},\n", + " 'estimator': {'method': 'Least squared fit',\n", + " 'type': 'T1fit',\n", + " 'function': 'save all text from fitter function and class function here?',\n", + " 'dill': b'\\x80\\x03cqdev_wrappers.fitting.Fitclasses\\nT1\\nq\\x00)\\x81q\\x01}q\\x02(X\\x04\\x00\\x00\\x00nameq\\x03X\\x05\\x00\\x00\\x00T1fitq\\x04X\\x07\\x00\\x00\\x00fun_strq\\x05X\\x19\\x00\\x00\\x00$f(x) = a \\\\exp(-x/T) + c$q\\x06X\\x07\\x00\\x00\\x00p_namesq\\x07]q\\x08(X\\x03\\x00\\x00\\x00$a$q\\tX\\x03\\x00\\x00\\x00$T$q\\nX\\x03\\x00\\x00\\x00$c$q\\x0beX\\x08\\x00\\x00\\x00p_labelsq\\x0c]q\\r(X\\x01\\x00\\x00\\x00aq\\x0eX\\x01\\x00\\x00\\x00Tq\\x0fX\\x01\\x00\\x00\\x00cq\\x10eX\\x07\\x00\\x00\\x00p_unitsq\\x11]q\\x12(X\\x01\\x00\\x00\\x00yq\\x13X\\x01\\x00\\x00\\x00xq\\x14h\\x13eub.'}}" ] }, - "execution_count": 5, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "fit = Fitter().fit(data, T1(), default_dependencies=True)\n", - "fit" + "fit(data, T1(), x='pulse_readout_delay', y='data')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "If you want to specify the axes yourself, set *x = 'data_name_for_x'*, *y = 'data_name_for_y\"* (and optionally *z = \"data_name_for_z\"*). If you are uncertain of the names of the variables in the data, they can be read in the data dictionary, or you can enter something random for x and y and it will throw an error that gives you a list of your options." + "For the 2D datasets, at the moment, have only one option, which is to get back fits for slices of the data. Each slice is taken at a particular setpoint, and the fit for that setpoint is listed in the fit dictionary.\n", + "\n", + "As with the 1D example, you currently specify the axes yourself. You should also choose along which direction you want the cuts to be taken.\n" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'parameters': {'a': {'value': 0.00020686194829005843,\n", - " 'cov': array([ 1.12188937e-10, -8.65732231e-14, -2.94102847e-12]),\n", - " 'unit': 'V'},\n", - " 'T': {'value': 1.7792117433091448e-07,\n", - " 'cov': array([ -8.65732231e-14, 3.48328903e-16, -5.40259460e-14]),\n", - " 'unit': 'S'},\n", - " 'c': {'value': 0.0016790061428729915,\n", - " 'cov': array([ -2.94102847e-12, -5.40259460e-14, 1.68224887e-11]),\n", - " 'unit': 'V'}},\n", + "{5600000000.0: {'parameters': {'a': {'value': 0.00018995671288537621,\n", + " 'cov': array([ 1.03060699e-09, -4.34010572e-13, -4.25402654e-01,\n", + " 5.38756557e-08, -1.75998153e-11]),\n", + " 'unit': 'Hz'},\n", + " 'T': {'value': 6.098906943811456e-08,\n", + " 'cov': array([ -4.34010572e-13, 3.37578482e-16, -8.71382197e-04,\n", + " 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'inferred_from': {'xdata': 'pi_pulse_dur_set',\n", + " 'ydata': 'qubit_rs_frequency_set',\n", + " 'zdata': 'rec_ctrl_demod_freq_0_mag',\n", + " 'dataset': 621,\n", + " 'dependencies': {'rec_ctrl_demod_freq_0_mag': ['qubit_rs_frequency_set',\n", + " 'pi_pulse_dur_set'],\n", + " 'rec_ctrl_demod_freq_0_phase': ['qubit_rs_frequency_set',\n", + " 'pi_pulse_dur_set'],\n", + " 'rec_ctrl_raw_output': ['qubit_rs_frequency_set', 'pi_pulse_dur_set']},\n", + " 'setpoints': 'ydata'},\n", " 'estimator': {'method': 'Least squared fit',\n", - " 'type': 'T1fit',\n", + " 'type': 'T2fit',\n", " 'function': 'save all text from fitter function and class function here?',\n", - " 'more?': 'perhaps'},\n", - " 'inferred_from': {'xdata': 'pulse_readout_delay',\n", - " 'ydata': 'data',\n", - " 'dataset': 4,\n", - " 'dependencies': {'data': ['pulse_readout_delay']}}}" + " 'dill': b'\\x80\\x03cqdev_wrappers.fitting.Fitclasses\\nT2\\nq\\x00)\\x81q\\x01}q\\x02(X\\x04\\x00\\x00\\x00nameq\\x03X\\x05\\x00\\x00\\x00T2fitq\\x04X\\x07\\x00\\x00\\x00fun_strq\\x05X-\\x00\\x00\\x00$f(x) = a \\\\sin(\\\\omega x +\\\\phi)\\\\exp(-x/T) + c$q\\x06X\\x07\\x00\\x00\\x00p_namesq\\x07]q\\x08(X\\x03\\x00\\x00\\x00$a$q\\tX\\x03\\x00\\x00\\x00$T$q\\nX\\x08\\x00\\x00\\x00$\\\\omega$q\\x0bX\\x06\\x00\\x00\\x00$\\\\phi$q\\x0cX\\x03\\x00\\x00\\x00$c$q\\reX\\x08\\x00\\x00\\x00p_labelsq\\x0e]q\\x0f(X\\x01\\x00\\x00\\x00aq\\x10X\\x01\\x00\\x00\\x00Tq\\x11X\\x01\\x00\\x00\\x00wq\\x12X\\x01\\x00\\x00\\x00pq\\x13X\\x01\\x00\\x00\\x00cq\\x14eX\\x07\\x00\\x00\\x00p_unitsq\\x15]q\\x16(X\\x01\\x00\\x00\\x00yq\\x17X\\x01\\x00\\x00\\x00xq\\x18X\\x03\\x00\\x00\\x001/xq\\x19X\\x00\\x00\\x00\\x00q\\x1ah\\x17eub.'}}" ] }, - "execution_count": 6, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "Fitter().fit(data, T1(), x='pulse_readout_delay', y='data')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For the 2D datasets, at the moment, have only one option, which is to get back fits for slices of the data. Each slice is taken at a particular setpoint, and the fit for that setpoint is listed in the fit dictionary.\n", - "\n", - "If the dataset contains multiple plots (fx. data2 in this example contains measurements for 0 mag, 0 phase, and raw output) and you chose to use the default dependencies , you will need to specify a dataname." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "fit2 = Fitter().fit(data2, T2(), default_dependencies=True, dataname='rec_ctrl_demod_freq_0_mag')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As with the 1D example, you can also choose to specify the axes yourself. For 2D data, if you are specifying axes manually, you should also choose along which direction you want the cuts to be taken." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "fit2a = Fitter().fit(data2, T2(), x='pi_pulse_dur_set', y='qubit_rs_frequency_set', z='rec_ctrl_demod_freq_0_mag', cut='horizontal')" + "fit(data2, T2(), x='pi_pulse_dur_set', y='qubit_rs_frequency_set', z='rec_ctrl_demod_freq_0_mag', cut='horizontal')" ] }, { @@ -320,15 +2301,15 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Table data_4_T1fit created in analysis.db\n", - "Table data_621_T2fit created in analysis.db\n" + "Table data_4_T1fit_1 created in analysis.db\n", + "Table data_621_T2fit_1 created in analysis.db\n" ] } ], @@ -356,7 +2337,7 @@ { "data": { "text/plain": [ - "([], [None])" + "([], [None])" ] }, "execution_count": 10, @@ -367,7 +2348,7 @@ "data": { "image/png": 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\n", 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\n", 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rB88YrSBXXdAAwIdf3bkqbywj0zF+eqCfd1zaQlnGew/6fbzn8jaePDbCsaFph0boPmKJJE8cHeb0xAyZ0UtP5u1JuVcTq+tuaTObG8upKAkwNB1bdYYI4EfPnSKeVG7Y1brg9nftbOHLDx7m+/v6+Ivrtqz6tYCR6Ri/d/seXj49CUBzVSn/etMu1teXeTJvE6854+rB84xWEJ9PuKyjht3HR50eiiP85KV+tjSVs6lx4dlrQ3kJuzpq+MEzfQxOzKzqtYBkSvnofzxD19A0t73nUu66aRez8SQfunMvM3PJVR+eSvcKLSm3Z4gKG88YrTCXddbQNRRheDrm9FByymhkjr3do7xxa+Oi+0RiCQ6cnqR/MsY1f/9zJlbxzfb7+/p4qmuUL7x9O2+5cA2v3ljPre+5hOPDEb722LFVHZ7yRAurE88YrTC7OmsA2LPKvKOfvTxASuGarU2L7nOwf4p4MgVAJBanNOhflTfb2XiSv3vwEJe2VfHOHS3zz79yfR2/edFa/vmxY8zGk6tW2LDavcLVimeMVpjtayspCvh4rnfc6aHklJ+9PMiayhK2N1csus+WpnIqS4P4BHzi44E/efWqvNk+8NJpBiZj/Ok1m34t5+qPrtpALJHiP3b3rNrw1GJe4WouXXY2CkHssrq+5TmgKODjgjUVvNC3eoxRKqX8smuEa7c1LpnQai1Gf/qHL/JfL5xatWVd/vWX3ayrD3Plhrpf27axsZzXbKrnrqe6+W+v34B/FZaWyhQtDEzO8qkfvMieE6NsbiznC2/fzs6OGqeH6RoyxS4//uir6B2byTvBh+cZ2cCFzZW8dHKSVGp1zOQO9k8xMRPPqvZcuDjAu3a0kkjB7uMjORiduzgxHOHZnnHefVnboob7hstaGZyK8ctjq+/6WFgTlanZBO/+xlMcHpjipld1Ep1LcuM3d/NMz5jTQ3QNv1qKa443ffXnebne5hkjG3hFSyXTsQRdwxGnh5ITftll3DSzLYS6s6Oa4oCPJ46svpvtAy/1A/DmC9csus9VWxooKw7w4+dO5mpYrkRV+bPvPsf0bIJvf+QKPvPWrfzoo6+ioaKYP7vneWbjSaeH6ArSw5qhogAz8fysAuMZIxu4sKUSgBdPro5Q3VNdI7TXhlhbVZrV/iVBP5e0VbG3e3WJPMBYL7q4tYrmJa5VSdDPG7c18pP9/SRMwcdq5MEDA/zi6Ah/cd0WtjQZa5E14SK++FsXcnw4wu1PHC+ItZLzJb2KxwN/8moq81SF6RkjG9hQX0ZJ0McLfRNOD8V2Uillz/FRLu88txj+jvZqDpyaZGZu9cxue0ejvNA3wZtfsbji0OLqCxqZnE3w7CoTwlioKv/4yFE668LceHnbr2y7cmMdr99czzd/3sUb/s6TgMOZsGZDRUneqjA9Y2QDAb+PrWsq2H9q0umh2M6JkQgTM/FzFiLsaK8mkdJVJfR4/MgQAG+4YPFcLIsrN9bh9wmPHFydhWWfPjHGC30T3HRlJ4EFGjP+4es2MBaNMxqZy8uQ1EqjqvzrL09w5Zce5rX/+xF+sr+fgD+/xC+eMbKJzU0VHOqfKng56tNmPtXGhnMLB1zSahivfatoIfqJI8OsrSxhXV34rPtWlATZ2V7NI4eGcjAy93H30z2UlwR4x6UtC26/rKOaTQ1lKJqXIamV5ssPHuazP95Pc1Upl3fW8vXHu/jDf3smr0RUnjGyiS1N5UzMxBmYLNxKDJFYgs/fdwCAj/7HM+cUJqkOF7G+Pswz3avDGCVTypPHRnjVhrqs+zm9fksDL5+eZGBy1ubRuYvZeJKf7h/gum1NlBb5F9xHRPidy1qJJ5W/+a1X5F1IaiV54sgwtz58lN/Z2cK3P3wFt914KZ/7zW08fHCQ//v4MaeHlzWeMbKJzeYs7WB/4YbqDvZPMWMqmpYTJrm0rZpne8YL3nsE2H9qgomZOFdu/PXcosV45XpDnbjaqnk8cnCQ6ViC37x47ZL7vf2SZnwCx4cjq9YQJZIpPvOjF1lXH+bz12/HZ+alve832rluWxO3/uxo3kxmPGNkE1bI4FABx7HX14dJKQR8sqwwyfbmSkYicwXtPVr84qghY/+N9dnJ3wG2rqkgXORfdcbovhdOU1dWzG+cJVWgrqyYHe3V/PTAQI5G5j5++OxJToxE+eR1WygJnvEiRYRPv/kCEqkU//jwUQdHmD2eMbKJqlARjRXFBW2M+sZmAPijN2xcVpjEKh300snCVx0+0zNGZ12YhvKSrF8T8PvY0VGzqoxRPJni8cNDXLO1YUHhQiZv3NrEy6cn6R2N5mB07kJV+ebPj3PBmgquWaBAcVttiLdf3Mz39vUxEY07MMJzwzNGNrK5qaKgFT6WdP3tF69dVphkS1MFIhS86lBVebZnjEvaqs75tZd31nBoYIqxyJwNI3Mfz3SPMRVL8NpNDVntb92EHzwwsOpyjp7rHefQwBTvvaJ90XXID7yqk5l4knv29uZ4dOeOZ4xsZEtTOUeHpgs2cfGlUxNUlARoqwkt6/Xh4gDr6sK8dKqwPaPe0RmGp+e4pO3c6/BZVeCfPrE6vKPHDg8R8Amv3JBdOLOjLsz6+jAPHxpcdW0nvvN0L6Ei/5Jra1vXVnBxaxU/fNb91Tw8Y2QjmxvLmUukODFSmGWBDvVPsWVNRdbqsIXYtraS/QUeprPqqF26DM/oFc2VBHzC86skH+uxw0Nc2l5NRUkw69e8akMdTx8fXVX9sWbjSe597iRXdNaw1K8vEktwSVsVB05PcmTA3dfEVmMkIteJyCEROSoin1xge7GIfMfcvltEOtK2fcp8/pCIXGs+VyIie0TkeRHZLyKfW+CYt4rItJ3vK1ssRd2RAVcMZ0VJpZRD/VNccJ65HdubKzg1MctoAYehnu0ZI1TkZ/MiHXCXoiToZ3NT+aqo5jEWmWP/qUlecw6KQzBUh7FEalX1x3r44ADReIpfHBtZ1BO0qnnfvccI0X1vX1+uh3lO2GaMRMQP3Aa8CdgKvFtEtmbsdhMwpqobgK8AXzJfuxW4AdgGXAf8k3m8GHCVql4EXAxcJyJXpJ1zJ3Du00+b6DSTGwuxYOrJ8RmmYwm2rFm8f1E2bFtr1PHbX8Chumd6xrmwpZJYIrWsNY0LW6p4vrfwJfB7zZyzXZ3ZKw7BKNArAjdc1paXZXCWww+eMcJusURqUU/QquY9E0/iE7jvhVO5HuY5YadntAs4qqpdqjoH3A1cn7HP9cCd5t/fA94gRszneuBuVY2p6nHgKLBLDSw3I2g+FOaN3/8G/sLG93ROhIsDNFWUcGyw8Dyjl08booPN5zkD3bbWMGaFKmKYjSd5+fQk29dWLntN46KWSiZnE7x8erKgF+h/eWyYgE9YX3/2ChXpVIWK2La2gr3do6uiGWEyZQhiSoK+JT3B9GreZcUBTo7Pulp1aKcxagbSJRx95nML7qOqCWACqF3qtSLiF5HngEHgQVXdbe7zMeBeVT291KBE5CMisldE9g4N2VtqJRJLUF9exOECNEaWZH05oad0qkJFNJQXc9jl8ezlcqh/ikRKqQ4Fl91K+8IWw9l/zzd3F+wCfSSW4K6nukmp8tZbnzjn97ero5bnesfn29oXMs/3jTMSifPX129f0hNMr+b97Q8bAaRHD7m31qGdxmihdbXMOMNi+yz6WlVNqurFQAuwS0S2i8ha4F3ArWcblKp+XVV3qurO+vr6s+2+bKx47cunp9h/coLpWffr/M+Fg/1TtNeGVmQWurmpvCDX1eCMx/eGCxoXbKWdDZsayyjyC9OziYJdoH+hb5x4UknpMqt5tFcRS6TmPfZC5smjw4DxnTqbJ2hV8966toL22pCrax3aaYz6gNa0/7cAmUHL+X1EJABUAqPZvFZVx4FHMdaULgE2AEdF5AQQEhFH046teG0ipSjwVIElLr7cP3neXpHFxoZyjgxO5VVRx2w5cHqC8uIAmxrLl13aP+D3sXVtJSIU7AL9XMLwaIoDvmW9P0s2X8i1Dq08qp8fGeaCNRXUhIuyfq2I8PrNDTx5bJhYwp1tW+w0Rk8DG0WkU0SKMAQJ92bscy/wfvPvdwIPq7FKey9wg6m26wQ2AntEpF5EqgBEpBS4Gjioqv+lqk2q2qGqHUDUFEU4hhWvLQ4Ylzjgy69y7ksxG09yYjiyYjfEzU1lzMZT9I65N569XPafmuSCtRX4fDI/S12ON3lxaxV+Ef71g7sKcoH+JdOD/Pp7dyzr/a2tLKGxophnegpTAm9FWt57+252Hx/lso5zz1l75fpaZuMpnu91p1ho0U9cRP4hi9dPqupnFtqgqgkR+RjwE8AP3KGq+0Xk88BeVb0XuB24y/RiRjEMFuZ+9wAHgATwUVVNisga4E5TrOAD7lHV+7J+tznEitf+/MgQf/Bvz3BqPD+KFWZD11CElMKmFTJGG00P6/DANO2157Z47WaSKeXg6Slu2NV69p3PwtY1FcwmUtSWFRecIQKjmsC6+jCv3Zxd5YVMRIRL26rnc7oKDSvSEjWbUbZUZ9dVOZ3LOowE6t1dI/PJ1G5iqW/19cBnz/L6TwILGiMAVb0fuD/juc+m/T2Lsdaz0GtvAW7JeO4FjJDckqhq2dn2yQXh4gBv3NpESdDHsaHCWROx3sv6+pW5zBsbjOMcHphasMZWvnJ8OMJMPMnW85S/A2xZY1aBPz05nzJQSLzYN8EV687vBnlpWzUPvNTP4NTsOdUAzAesSMtcMkU8qVx/0dIVzReiOlzElqZy9ri0msdSxugrqnrnEtsRkXP3FVcZPp/QWVdGV4EZIxFW7KZYXhKkuaq04BR1B8zFdCuX6nzY1FiOT+Dl/ine9Io15308NzE4NUv/5Czbm8/vOl3abqgOn+0Z59ptZ2/tnk9YkZZ3fO1Jgn4fjZXn7hmBUevwu/v6iCdTBLMoRJtLFh2Nqv49gIgsKjmz9vFYmnX14YJKfD02FKGluvRXStafLxsbyzhcYIq6A6cmCfhgajZ+3lLskqCfzrpwQarFrKrtloR9uWxbW4nfJ7xYoNUqgn4fXcMRLj+PENuuzlqic0ledGEJrmxM45Mi8lMRucnzhJbH+voyekejrlWxnCtHB6fZsEIhOovOujBHBoy4eKFgVJUQPvitp1ckN+iCNRUF2azxxb5JRM4kQC+XkqCfDfVlBVvN48DpSeYSKS5tX/5t2For2t3lvlDdWY2Rqm7EWBfaBuwTkftE5PdsH1kBsa7OaELXM5L/arFUSukaml6x9SIwlEI/eOYkiZQWVELnwf5JFFYsN+iCNRX0js4wVWA5ay+eHGd9fdmKCDO2NVfMK/MKjWdNccZyWpFY1JcX01kXdqXQI6ugoaruUdWPY5T4GeVMCR+PLGirNVosdBeAMTo5PkMskWJ9w8oZo4P9U/NeY6EkdEZiCYam5gitYPHOC9YUZvfgF09O8IrzXC+y2La2kqGpGIN50mr7XHi2Z5w1lSWsWeZ6kcXFrVU858Jah2c1RiJSISLvF5EHgCeB0xhGySNL2s1+P90urguVLSutpANDKVRZarQMKA74CiKh86hZAuqv3750yZZzYUuTEcYqpHWjwalZBiZj5y1esNhewLUOn1lmg8ZMLmoxDHa/ywx2Np7R8xgVsj+vqptU9ROqus/mcRUUNeEiyooD9BRAX6NjQ8Z7ONdilksRLg7ws4+/lnCRn+u2NxVEHs0R0xhd2FK5YsU711SWUF4cmD92IXDwtOHlrYT8HYxmclB4VeAHp2bpG5vh0mU0aLSwKjhY+YHP97orQTibX8g6dZs/l2eICG01oYLxjKpCwXMqRZINZSVBNjWV0zs6s6LHdYojA1MU+X3L7oK7ECLC+oayea+rELBCjivlDZeXBOmoDfHSycLyjKyqCRe3Ls8zsio4TM7EKS8NEvDBc70TXLfdPWkCi3pGInIzwFKGyNrH4+y014YKQsBwbNAQL5xPd9fFWFdXRtdwYdxoDw9Msa4+TGCFczk2NpQVlGf0cv8kjRXFVK/g5GZOVIpZAAAgAElEQVRbcyUvnBwvqHYbL52cwCfLz1mzKjhE5pJMzsTpqA3zXK+7RAxLeUYfEpGlpheCUb7n5hUdUYHSVhvioZcHSKYUfx7XqTs2FOGqLfZUO19XH+b7z/QxHUtQluehuiOD0+cVUlmMjY1lfHdfHxPROJWh7Ftzu5UDpyZZU1lKJJZYsfDshvoy/uuF07z39t1UlgYLopbf/lMTrKsvo7Roebl9VgUHBSpLg1zWWcO9z51y1f1oqWnbN4DyJR5l5j4eWdBeEyaeVE5P5G8Yamo2zvB0jM46e6otWetQx4fye20tEkvQNzYzX+ZoJdlgHvPoUP4r6iaicxzsN1qsrKSkP2TesKMF1G5j/6nJ88rDSu9t9NDHX8vO9hoic0lXVYZZdLqgqp/L5UAKnXZT3t0zEqWleuXWEXKJJU1PpVIrOpO1WGcq9LqGp3lFy8qoq5zAWtPZuEItNtLZ2GAc88jANDva3Vfs8lx47LDRWyee0nmjseM8EjotXre5ni8+cJAi//LaUbiN0cgcpydmzzsp2KoaD0Y+FhiJtHZ8T5eDu4oTFTBtBSDvthabb33kqC3Jqe21IUTOKPbyFWtNZ1PjyntGzVWllAR9BSFisLqylgRX1mhsbCinNOjj6q0NBROiA9i+AjUOLdbXl1Hk93HARRL4/P6U8oi1VaUE/ZLXia/7zKzt2XhqRWeyFsUBPy3Vpa4KHSyHY0PTBP2yoko6C59PWF9fGCKG48NRfALf+sBlvKK5asWMhs8nbG6qYCwSz3tDBMwrA7eep2eUzlwiRUt1iatq1HmeUY7w+4SW6hA9o/k765+ZS+CzudvouroyuvLcM+oamqatJrTiSjqLDQUi7z7YP8X6+jKuWFe34kZjc2N5wVSB339qguaqUqpCK6M4tGTePaMzPNU1wrRLykud9RuwSJO9CYwGeT9e+SEVLm01obz2jE6Nz3JRSxWfeetWtjSV2zLr7KwLs/fEKKpqi3w8Fxwfjsyvf9nBxoYyfvzcKVvW7XLJoYFJLjrPSt2LsampnO/s7WVoKkZ9ebEt58gVB85TvJCJJfNOpIysnae6RrnaBX3Espm6lWBUYDhiPi4EaoCbRMRrIXEOWLlG+ZpD3D0SZV192YpVFFiI9toQkbkkI5E5W45vN8mUcmIkyjobG+BtMEUM+dywMTqXoHd0hs02LZ5bXnu+e0fTsQTHRyIr0hPLwpJ5lwSM2/9c0h3dBLIxRhuAq1T1VlW9FbgauAD4LeCNdg6ukIjEjBDXVCzBWNQdbvG5MDOXpH9ylo5ae5WA7XleVPbU+AxziZSt3Vjn5d15HKqzQrEbbJC/g9GMEMh7Wfeh/ilUV3a9yJJ5f+P9OwFcExbPZnrbDIQxQnOYf69V1aSIxGwbWQFhxWhHzdn+wf5JXrm+zuFRnRs9pgqw3eaW1201xvEPm3177AoH2sVxs4mincaorSaET+BEHjdstAypXcaovryY2nARh/PcGFme3Uqvz4aLA7x6Yz3ttaH5jsROk82v/H8Bz4nIoxhVF14D/I2IhIGHbBxbwWDFaGMJQ8q65/ho3hmjE2aRV7s9o9Yaozz+LfcfRFWpyLMM+nljtIKFZDMpCvhoqQ7ldffgo4PT+H1Ce61912lTYzkH8zxMd6h/itKgn+aq82sbsRhb11S4Rt6dTXO924FXAj8yH1eq6jdVNaKqf273AAsBK0YbWmYpDzfQbRqj9hp7PaPigJ+6siJm48kVa0qXS7qGpgkX+ekdidpaF62zLjw/QchHjg1N014Toihgn6B3Y2MZXYPTebtGC3BkcIpNjWX4bCrZs3VNBSds/q5mS7bfhFmMPkajwAYReY19Qyo80ktxVIeCDE7lX3TzxEiU6lAwJ/XQ1tWVITZLyO3iyOA0c8kU77tjj61dazvrwhwfiuTtjfbo4PSKNmhciPX1ZUzFEgzl4e/N4uDpKWrDRbZ9j6x2Em7IW8umud6HgMeBnwCfM/+92d5hFR5WKY7WmhB9Y/lXn657JEKbjSGVdNY3hCkvDqxYU7pccmRwipSuXKvxxWiuLiEyl5z3WPOJRDLFiZHIijZoXAjr+Pkq9OgbizISmePnR4dtm9hYakY3rK1l4xn9CXAZ0K2qrwcuAYZsHVUB01JdSl8elgTqHonavl5k0VYTZjQaZ1NjWV4Zotl4kqGpOUpXsNX4QkRiCb72aBcA7/jaL10RYjkXekajxJNqm3jBwjp+vkrgHz44CEA8qbZNbFprQhQHfK6QwGdjjGZVdRZARIpV9SCw2d5hFS4t1SH6xmdIpfInvBJLJDk1PmPrYnM6+SrvthSHn33rBbZ6dQf7p5iZM3JDpmYTebWmBvYr6SwaK4oJF/nzttbhXML4jEuD9k1s/D5hY2MZh1xgjLL5pfSJSBWGeOFBERkDTtk7rMKltbqUuUSK4ekYDRUlTg8nK/rGZkgptNtQa20h5iucj0bZ3pw/1butmnoXrKm0ter4lqZyqkJBZiaSBAOSV2tqAEfN67TORsUhnOmMm6+e0YmRKGXFfu78wC62rKmwLUqwqbGcXxwdtuXY50I2arrfUtVxVb0Z+EvgduDtdg+sULHaR/Tm0bpRrznjb82ZMTJuUvmmFrOk1h119l4nSxDTUl3Krs6avAplguEZNVYUU1FivxhmfX3+1vE73D/NlqYKdnTY+xlvbixnYDLGhMPJ+Fmp6USkWkQuBKaAPmC7raMqYFqqjXyBvrH8CUFZggsrB8huyooD1IaL8q5N+/GhCPXlxZTn4CYbLg6wdU0FfaP5M6mxODYUsT1EZ7GhoYzTE7NM59m6mqpyeHAqJ72GrGoVhwedDdVlo6b7a+AF4Fbg78zH/7F5XAVL87wxyp+bSO9YlCK/j8by3IUV22vzr6hs92juRB5gJNZ2j0RJ5tH6o6pybHDadiWdRb52Dx6aijEejbPZhp5YmVjy7kMOrz1m4/v9DrBeVfOzcqXLCBUZs/58MkZ9ozM0V5falni3EO21YXZ3jeTsfCtBz0iUV23IXWWNdXVh5pIpTo3P5CyEer4MTsWYjiVyaIzOKOryqXvw4QGrQaP9ntHayhLKigOOK+qyCdO9BNhT532V0lJdmldhut6x6Hx4MVe01YQ4PTlLLOGOisJnYzZuFJJtz6Fn1GGureVTWSBLTJArY9ReG8bvk7xbN7LUbZtyIE4RETY1luWFMfoi8KyI/ERE7rUedg+skGmpzq/E197R6LzwIld01IVQhd48WROxRB65NEZW/bt8Kph6Yti4TnaLPCyKAj7aa0J5p6g7MmBUXqgrs78XUySWoCZcxMHTk45W9MgmTHcn8CXgRSBl73BWBy3VpTx4YIBUSnMa+loO02bLi1yJFyys6t3dI7lb7D4frPUtO1qNL0Z9WTFlxYH54qz5wImRCEG/0D8xS3WoKCdKwHX1+SfvzkW5JDjTUWBkOsZcUukejdBR68zvLRvPaFhV/0FVH1HVx6yH7SMrYFpqQswlUwxNu79mlhVObM2xZ2Td1HvypFpF97xnlJvEYDDCKx11+VW9+8ig0Z/n/TbX7kunrbaUrqEIE9H8WfbuGo7Miy/sxOooMJc0PKJHDzlXXCcbY7RPRL4oIr8hIpdaD9tHVsDkk7zbCpPleoG8rqyI0qA/b8J0PSMRyosDVOegkGw6nXVleRWmOzIwjeagdp9FJJbg+/tOkkgpV3/l8bwonTQWmWM0MpeTdTWro0Bp0OgokEg6F/zKxhhdAlwB/A3nKO0WketE5JCIHBWRTy6wvVhEvmNu3y0iHWnbPmU+f0hErjWfKxGRPSLyvIjsF5HPpe3/7+a+L4nIHSKS27vCOdCaR/Lu+YTXHAsYRISW6lJ688Bgg+EZtdWGEMlt2LW9JsTJ8RniDt5EsiWZUgYmZikp8uWsIvvB/ql5EcxknrQj6RrOTYUKOJNA/W837SIU9NE3Nmv7ORfjrAFbszjqOSMifuA24BqMRNmnReReVT2QtttNwJiqbhCRGzDWpn5XRLYCNwDbgLXAQyKyCYhhtECfNo3NEyLygKo+Bfw78Hvmcf8D+BDwteWM3W6aqwwvIy+M0ViUUJGfmnBRzs/dWhOaN4Zup2ckypY1uS/L01YbIpnSnNYOXC6nxmeIp5T/ee1mXtFclZMuvluayqksDTIbj1Ec8OVF6SSrlt66utys3YSLA+zoqGFDY7mjqkP7OlvBLuCoqnaZOUp3A9dn7HM9hkAC4HvAG8SYWl4P3K2qMVU9DhwFdqmBdbWC5kMBVPV+c7sCe4AWG9/beVFaZDSQy5cwXUt1ac5n/GB4Y31jM67v2ZNMKb1j0XnRRS6x6gU+9PKA60NQVnmnzY0V7Givzol4IVwc4OE/ex1lxX6u3d6UF6WTjg1NE/RLztMpNjgs9LDTGDUDvWn/7zOfW3AfVU0AE0DtUq8VEb+IPAcMAg+q6u70A5oe03uB/7di78QGmvNE3t03Fs25eMGitSbEdCzBuMM1s87G6YkZ4knNqazboqHCkP5+6YFDORMELBdrbauzLrdGO1wcYGNjed6UTuoaitBRGybgt/P2/Ousd7h0kp3vdqGpdOYUd7F9Fn2tqiZV9WIMz2eXiGTWyfsn4HFV/fmCgxL5iIjsFZG9Q0POKUdazFm/m1FV+sZmaKosYV/3WM5vdGeKyrrbg7Rq6OWqqnk6o9OGQmwumXJ9i/bjw1FKg34aK+zPnckkn9q0dw1N52S9KBNLMNHlkHe0pDESkS0i8gkR+QcR+ar59wVZHrsPaE37fwu/3npifh8RCQCVGK3Nz/paVR0HHgWuSxvvXwH1wMcXG5Sqfl1Vd6rqzvr6+izfysrTWh3i5Ji7+xqNR+NMxxL85/OneN/tu3M+87bk3W5X1Fmy7jYHPKMtayoI+AS/iOtbtJ8YidDugMgDoLM2zOmJWaJz7vUcAeLJFD2jUdblqEJFOhsaDAPo1LrRosZIRD6Bsc4jGGswT5t/f3shZdwCPA1sFJFOESnCECRkVm64F3i/+fc7gYfNNZ97gRtMtV0nsBHYIyL1Zm8lRKQUuBo4aP7/Q8C1wLtV1fXSopbqUuaSKQan3JtrZHkksUQqZ1LcdKxEW7d7Rt0jUYJ+YU1lbmP8YISgrtxYx9qqEte3aD8xHMl5iM7iTLUKd3+Xes0uuOscuE7ttWECPnFs3Wipb+5NwDZV/ZWAvYh8GdgP/O1SB1bVhIh8DPgJ4AfuUNX9IvJ5YK+q3ovRG+kuETmK4RHdYL52v4jcAxwAEsBHVTUpImuAO02lng+4R1XvM0/5f4Fu4JfmzOsHqvr5rK9EjrGqd58cj9JU6c4me5ZHUl4SxD+XyPnMu7wkSFUo6HpFXc9ohNbqEH6HqmlsqC/jqa4RQkV+R86fDQlzxn/d9iZHzm8ZwRMjEbaurXBkDNnQZSrpEiklEkvkdHIR9Ptoqw055hkt9U5TGLLq7ozn15BlWSBVvR+4P+O5z6b9PQu8a5HX3gLckvHcCxh5Twvt794p4QKk5xrtaHd4MItgeST3/dGrODk+mxMpbiat1SHXNyLsHok6EqKzaK8NMRs3vOxGl3YP7hubIZFSOhzyjKyism4vnXSwfxKAv77vAP/wsyM593YNRZ0z12ipd/nfgZ+JyBHOKNvagA3Ax+weWCETiSUYnDTCc24WMfSNRaksDdJUWUqTAyEoMEJ1B0+7d1FeVekZibKzvdqxMbTVWnX8oq41RsdHnFHSWYSLAzRWFLveGD3bMw5AdM5I1D3YP8WOHH631jeU8fDBQeLJFMEcq/kWNUaq+v/MRNNdGLJqwUxeVdX8qOvvQqzChJMzcXzi7pla7+hMzgukZtJaHeKhA4OuLSo7Fo0zFUvMGwQnsFR8J0Yi7OqscWwcS2HJujscvE6ddWFX/94AxqJzFPl9BPzOCFI21JeRSCk9o9GctfmwWNL/M4UAT+VoLKsCqzBhZC6JT5xTrmRD71iUzTlo7rUUVlHZwamYK9fWus0ZvxOybovm6lL8PnF1m/YTwxHKigPUleW+kodFZ12Yn+wfcOz82dA9EuVtF63hPZe3OxIWtyqFH81hN16L3PphHvOFCUNFfooCPsZn3FlJOJUycoyc7iBqra25tcJAjwN9jDIJ+n00V5XOS8zdyPGRKJ11YUdk3RaddWFGI3NMuDSJejw6x0hkjs1N5TmrUJGJVSncCUWdZ4xyjFWY8K6bLueGy1o5PT7rynI3Q9Mx5hKpnBdIzcRqLvbX9x1wZYUBq4+R00a7vTZEj4uTOo8PTzsmXrCYFzG49DrluibdQpSXBGmsKHYkYuMZIwcIFwfY0V5NZ10ZsUSK4Wn3eUeWnDrXHV4zmZo1ZrGxhDsrDHSPRGmqKKEk6Kysur02xAmXhunmEilOjs3Q6aD3CGeqYLu15YZV+SAXTfWWYkODM4q6sxojEZkSkckFHlMiMpmLQRYqzVXu7WtkqfycFjBc2FKFT8Dvc2eFgZ7RCM3VpY6US0qnvSbMxEzclSGontEoKcVxz6i1JoRPcG0zwq5howuu09GI9fVlHBucznnEJhvP6CvAJzEUdS3AJ4AvqGq5qro3eywPaKmxEl/dJ+92i2cULg5wUUsVmxrLXFlh4PhwhAOnJh0pl5SOlefUPeq+G+28ks5hY1Qc8NNcXepaRd2xwWnaakI5L5CayYaGMqZjiZxXh8nmXV+rqv+kqlOqOqmqXwPeYffAVgNnPCP3GaO+sRnqyoodDz+BcRObiMZdZ4hm5pIMT8+RSDlTLikdS0DR7cJQnVWgtNMF/Zbc3Bm3azjiSE26TCwVXa7XjbIxRkkRudFs3eATkRsBL89oBSgvCVJZGuSkC41R92iEmnDQFYKB1upSTk/OMpdwV8lBK7waLg7krHPpYlhFZbtduDh/fDhCVShItQMNGjNprS7l6OA007PuCmcmkim6RyI5l1MvxIYG9xqj9wC/AwyYj3eZz3msAM1Vpa5bM4rEEjx9YoxjQxFXKNhaa0KoGp1C3YTlhXztxh3cddPljoYRQ0UBGsqLXesZOZnsahExK9DPxJO8wQXf63T6xoyeWE60jsikobyY8uKA+4yRqp5Q1etVtU5V61X17ap6IgdjWxW0VJe6bs3owOlJkiklmVJXKNgs2bTbqndbOUZO5oWk014bcmWu0YnhqGNlgNI52D9FzPSuJ6LOf6/TsfJ61rvAGIkI6+rDdA27zBiJyCYR+ZmIvGT+/0IR+Yz9Q1sdNLuwtXZNOAhAkd/nCgVbq0v7GvWMRikrDlAdCjo9FADaasKuC9PNxpOcmphxhWe0pamcylLjsyoO+h3/XqfT5YIcI4tILEFVKOg+zwj4BvApIA7zlbNvsHNQq4mW6hDRuaSrWmuPRoyxfOJNm12hYGuqKCHoF9d5Rr2jUVprnGkWtxAdtSEGJmPMxt2zpNs7GkUVOuqcVWXCmYTzgE94544Wx7/X6XQNT1MTLnJ8Xc2qnfnksREGJmMMTs7m7NzZGKOQqu7JeM49wdY8x42KOmsN63WbG1zxg/X7hLVVpa7ra9QzGqXN4TysdCx5d4+LrtNxFxRITaeiNEhHXdh167THBiOONNTLxKqdGU8akZrHDg3l7NzZGKNhEVkPKICIvBM4beuoVhEtaU323EKfGQ6zDKUbcFtfI1U1jZHzM36L9rRWEm7BGotbjBEYNerc1vG1a3jaFeIFq3ZmSdAwDUrulg+yMUYfBf4Z2CIiJzH6HP2BraNaRbRUu9EzmqG+3B05RhatNaX0uWjGPzQVI5ZIucsYuVDefXzEkHUH/OJ4lQqLzrowx0cipFLuWKediMYZnp5zhazbCmV+6wOXGT2DcnhfWjIGIyI+YKeqXi0iYcCnqu6RoBQAlaVByooD7jJG49F5I+kWWqpDjETmct6KeTEs1ZqTfYwyqQoFqSgJuMwzMlqyWz28KkqDjq9DdtaFmUukODUx43iFEYBjpmpNwRXf73BxgCvW1dFeG8ppjbolPSOzn9HHzL8jniFaeUTEzDVykTEac8ePNB23ybut3kFu8oxEhPbasKvk3SeGo1SHgvM9vNyQKmDJzN0Sqnv5tFHi86sPHXZFXp/F+vqynLaSyCZM96CI/A8RaRWRGuth+8hWEW7KNUqmlFPjM67zjKzikW6Rd/eMRhFx17oaGCIGt7SSsGTd25sr53t4uSFVwDJGuc6jWYx9J8YAmIm7qzL9unqjM24yR+HMbPzBD5r/fjTtOQXWrfxwVifN1aXsOTHq9DAAGJicJZ5U9xmj+Vwjd8xme0ejrK0spSjgri4sHbUhfvJSP4lkyvGCm31jhqx7U2M5D338tRzsn3Kke2kmDeXFhIv8rimYOjkTx+8TigPuyOuzWF9vtLg5NZ6bJpuLfitE5F2q+l3gDaraZftIVjEt1aVMzSaYmInPJ+U5hRUudFuYrjZcRGnQ754w3WjU8fYaC9FeEyaRUk6Nz85LvZ3i+PCZLrhWDy83ICJ01ofnE02dpns0yqs31vFHV210hbG2sPoqHRuazokxWmrq9Cnz3+/ZPopVTnOV8UG7oWCqlX/hdE+VTESEtpqQq8J0blovsmh3USsJS9XnhlJAmXTWlbnCM0qmlO6RKJsb3VFSKh1L3ZcrEcNSxmhERB4BOkXk3sxHTka3SjiTa+T8jdbyjNa6bC0EjFCdG5IVZ+aSDE7FXGmMrJ5Bbuj6emIkQmVpkKqQ89W6M1lnJr7GEs5Wq+gbizKXTLkixyiTmnARH79mE5e2VeXkfEuZ4bcAlwJ3AX+Xk9GsUpqr3dPxtW8sSoPLcowsWmtKefLYMKrqaAkeK1ToJlm3hfHZ+VzRs+fEcNTxhnqLsa4+TEoNVeTGRufWaKxQoRtyjBbij9+wMWfnWtQYqeoc8JSIvFJVc1cTYhVSGy6iJOhzSZjOfUo6i7Yao47fSGSOurJix8bhRlm3RXQuSWN5CceGnFdknRiJuGadKJMzirqIo8bIkk67oame02TTQsIzRDbjplwjN+YYWVg3f6drr1nnd5sxsopc9o3P8MSREUfzVWKJJKfGZ+ZLFLmNeWPksIjh2JBRoaLGBY0HncZdutRVTEt1yPE1I7fmGFlYN//HDw85eqPtGY0a0uChadckKMKZIpfJlJJIKQfMZEon6B2dIaXQ6YJq3QtRXhKkvryY4w7nGnUNTbuiQKob8IyRSzD6Gjk74x+YnCWRUtd6RtXmQvhtjxx1NFP9+PA0sUSK992xx1UZ81aRyyIzv6jKwT5L1prVbDzlmuuTSWdd2HFFXdewO1qNu4FFjZGI3Coi/7DYI5eDXA20VJcyFo07+sM9k2PkTs+oezSKAPGksx1ojw1FjDpiLilvY2EVufz0m7cAMDQZc2wshweMa/LX9x1wlcFOZ12ds7lGk7NxhqZi3nqRyVKe0V5gH1CCoao7Yj4uBtzTvatAsLwRJ0N1lmfmVmO0pamcgF/wCY5lqqdSyuDkLCVBn2vK26QTLg5wzbYmwFl59wt9E4AhqHCTwU5nXX2YkcgcEw41tpzv7upCWbcTLKWmuxNARH4feL2qxs3//1/gpzkZ3SriTJO9KJscUve4OccIjBvtm7Y18WTXiGOVn4emY8wllb940yYuaa12Vca8xZqKEooCPkdbSUzOxgn6haBLWtcvRKfZ4vv4SISLQ7nJpUmny1TSrfeMEZBdbbq1QDlgFU8rM5/zWEGsigdOyrvdnGNk0VFfxn++eJqgQ3XXLCXdxoZy18qWfT6hvSbECQeNUe9YlGsuaOSmV69zpcGGdEXdNBe35t4YHRuaxu8T2mo8YwTZCRj+FnhWRL4lIt8CngH+xtZRrULqyoop8vsclXe7OcfIoq0mhKpz4Uw35xil014bdqyv0VwixcmxGTY0lLmuxE06bTUh/D5xTMTQNRShrSbkumK7TpFNntG/AJcDPwR+APyGFcLzWDl8PjEUdY6uGbk3x8jC6Vwjt7aOyKSj1vCMnOhm2jsWJaW4tvqCRVHAR2t1qWMihq6hiCfrTiNbk7wLeDXwGuAy+4az+ojEEvPtmJ1MfHV7jpGF08bIra0jMmmvCzMbTzE4lXtFnSXrdmvCazqddWG6HPCMkinl+EjEEy+kcdZflIj8LfAnwAHz8cci8sVsDi4i14nIIRE5KiKfXGB7sYh8x9y+W0Q60rZ9ynz+kIhcaz5XIiJ7ROR5EdkvIp9L27/TPMYR85iuT2m2Mubfd/turv7yYzRWFDu2ZuT2HCOLhvJiigI+x/oaubV1RCYdZvVuJ9aNLBWfG6t1pxOJJQgXBTg+NJ1zD/Lk2AxziZTrc4zSJ8t2k8307s3ANap6h6reAVyHUUR1SUTED9wGvAnYCrxbRLZm7HYTMKaqG4CvAF8yX7sVuAHYZp7vn8zjxYCrVPUiDIn5dSJyhXmsLwFfUdWNwJh5bFdjZcxb+SpFfh/D0zFm47lXzrs9x8jC5xNaq0vn125yTfdolPY8WHDuML0SJxR13SMRyksCVDuYdHs2rIngTw8MMJtI5bzr67Fh99eky5ws222Qso01pEtNKrN8zS7gqKp2mUVX7wauz9jnesBaf/oe8AYxyjFfD9ytqjFVPQ4cBXapgfWtCZoPNV9zFWd6L90JvD3LcTqGlTFv5atc1GpcWicW592eY5ROW03IkTDdzFySoamY403rsmFNZQlBvziSa3R8OEJHbdjRyupnw5oIziVTADxxZDin58+HHKPMybLduWLZyFy+iKGmewQQjHWjTy39EgCagd60//dhCCEW3EdVEyIyAdSazz+V8dpmmPe49gEbgNtUdbeI1AHjqprI3D8TEfkI8BGAtra2LN6GfVgZ81Y75v2njFpifWMzOXff3Z5jlE5bTYi9J8Zy3krCah2Ri66X50vA76O1OuSQZxTlIgek0ueCNRFMKczEkyQ0t2G6rqFpyov9dA9HKA36Xak4tK6Rkpsk87NeAfndFnkAACAASURBVFX9tog8iiFcEOATqtqfxbEXuktkfuKL7bPoa1U1CVwsIlXAD0VkOzCQxbkwX/914OsAO3fuzL3UKIP0dswtDuYa5UOOkUVrTYipmNGmPZeN2/JF1m3RXhvixHBuPaNYIknfWJS3X+zuVMT5ieDpSW785u6c/+YOD0wxa9Y3rCgNOpbEvRSZk2W7x5dtmK7e/NcPvFJEfjuL1/QBrWn/bwFOLbaPiAQwQoCj2bxWVceBRzHWlIaBKvMYi53L9TRWlBDwiSMFU/Mhx8ii1SFFXbdLW0cshpFrFEFzOOvvGTFk3W5eC7EIFwfY0VHDuvrctyA/OjiNqvvqG2ZiTZZzYSizUdPdAdwBvAN4m/l4axbHfhrYaKrcijAECZntyu8F3m/+/U7gYTV+OfcCN5hqu05gI7BHROpNjwgRKQWuBg6ar3nEPAbmMX+cxRhdhd8nrKkqcWjNaIZmlyvpLJySd+fDwnw6HbUhInNJhqfncnbOY3mwFpJJZ31uq3dPzcYZi8YpLfK7sr6hU2Rj7q5Q1UwV3Fkx14A+BvwEw6O6Q1X3i8jngb2qei9wO3CXiBzF8IhuMF+7X0TuwZCSJ4CPqmpSRNYAd5rrRj7gHlW9zzzlJ4C7ReQLwLPmsfMOJ3KNEskUJ8dneNtFa3J63uXilGd0dHCahvJionNJ14VUFqK97oyirr48N51xLVWa22Xd6ayvC/PAi6eJJZIUB+wPUx8dNK7RLW/fTnN1yLXlknJNNlfglyKyVVUPnOvBVfV+4P6M5z6b9vcs8K5FXnsLcEvGcy8AlyyyfxeGgi+vaakO5VzZc3pilmRK8yb8VFYcoDZclNNco0gswe7jRnnGq7/8mCtj/Jl0mvLuEyNRdnbU5OScXUMRGsqLKS/JD+8RjJBiSg3hRS6KFB8xjdErWqryymjbTTZrRndiGKRDIvKCiLwoIi/YPbDVSnNVKQNTs8wlUjk7p+VhNJSX5CzB7XxpzbG8+6WTEyRTSjLlbC+lc6G5uhS/T+YrIuSCrqHpvArRAWxoMNa3jgzkJtfo6OA0RQFf3kz+ckU2U7s7gPcCLwK5u0OuUlqqS1GF0xMzOSunYhXU/MT3XyASS7hW3ZNOW02IZ3vHcnY+61oUBdzbEiGToN9HS3VpTtdDuoYjvPkV+RHutdjQUIaIoXB7C/aP/fDAFOvry/D73JuH5QTZeEY9qnqvqh5X1W7rYfvIVinN1VZfo9ytG/WMRvH7YDqWcL26x6KtJsSp8VniydzMjwanZgG4+W3bXG+oLSKxBLXhIo4M5OazHI3MMR6N513xz5Kgn7aa0Pxajt0cGZhmY4P71Ya5Jptf1EER+Q/gPzHK8QCgqj+wbVSrmFar42sOjVHvaJSW6tB8aDAfZv5tNaH54q658CCPm/k6125rzBtDdPWXH2NoKkYipUzNxCkvtXcd50yzuPy70W5sKOPIoP1GOxJLcHJ8hhsuaz37zquMbH5VpRhG6I1pzylGOwmPFaapsgSfkNNco57RKB21Yf7pxktzluB2vrTPFwKN5sQYWbLumrDr6+8CZ0q5JMwCoL84Nsx12+0NQeVDiZvF2NBQzmOHh4gnU7Y2brS8r40OdXN2M9lUYPhALgbiYRD0+2iqKMlpX6Oe0SgXt1b9SjUIt2OpkE4MR3jtpvqz7H3+HB+O0Fnn7npr6VilXJIpZTaRysm4jw1PE/SL6yu/L8SmxjLiSaV7JMKGBvsMxZF5Y5R/3qPdZJP0uklEfiYiL5n/v1BEPmP/0FYvzdW5yzWaiMaZmInnnbKnvryYcJE/Z4vz3TnywFYKq5TLbTdeCsCpHExuuoYitNeG83JhfqNpgOxW1B0ZnKLI76M9z35vuSAbf/QbGIVR4zCf63ODnYNa7bRUh3K2ZmTJo/Oh+Gc6IkJ7bTgn/XrmEin6xqJ05kG17nTCxQGu2tJAeUkgJ91Mu4am8068YLG+wRj3Y4eHbE1tODpgSN8DNoYC85VsrkhIVfdkPOf+RJQ8prmqlP7JWRI5UIr15Fm9tXQ668I5yaGx2mjnk2dkISKsry/j2JC9M/5EMkXPaDQvatIthCr4ffDdfX229u45PDg1n9fk8atkY4yGRWQ9ZhVsEXkncNrWUa1yWqpLSaaU/slZ2881b4zybNYPsLaqhJ7RKONRe2uvWW0YOvJ01r+uPmy7Z9Q7NkM8qXkpXgBD8IGKrUnN0bkEfWMz8yFBj18lG2P0UeCfgS0ichL478Af2jqqVY61ANw7an+ormc0Sm24iDKXq+cyicQSfHdvHymFa77yuK2hFUvW3ZGHBhsMqXX/5CzTNl4jS9adSKbyooJHJluayikpMm6HdqU2dA1FUDXEEh6/zlmNkdmp9WqMNhJbVPVKVT1h+8hWKZFYgsmZOEBOaq/1jEbybr0IjJnsbMJozz5pc5Juvsm6M1lveivHbfSODp42rv8X/uvlnLSoXmnCxQE+/eYLAPjm+3baktrw8mmjeWY8lZ8G224WveIi8vFFngdAVb9s05hWLVai4oRpjHKROd8zGuWS1vyQc6ezpamcytIgs/EYxQGfrUm6+dBGeymsdZyu4Wle0VJpyzn2dRtFZKNzxgThYP9U3qQJWGxfa1ybvvEZtjWv/HV68eQEAJ/8/otUlh7Mm0oeuWIpz6jcfOzECMs1m48/AM65pYTH2bESFaNzSQTm25DbRTyZ4tT47HwCaT4RLg7ws4+/llCRn7dcuMbWH3X3SDRv14vASBD2CRyzsdzN4PQcRX5fXvfnOVMw1Z5J4LM9Y/jEMNj5UHIr1yz6C1bVzwGIyE+BS1V1yvz/zcB3czK6VUZ6z/lkShm1eWH+9LjROiIfw3QAZSVBNjSU0Tc2QySWsKV6xGw8P9poL0VxwE9rTYhjNikPVZXjQ9O8a2cLv31pS15U8FiIcHGAlupSW4yEqtI7GqUkaPRLyleDbSfZfGPagPS74hzQYctoVjnpPed/8EwfP37uFKpqW3gon2XdFh21Yfb1jHL1lx9jcia+4hXHT4xESCmsz3M57vr6Mts8o5PjM0TmkmxdW5F3oblMLlhTMb+2s5IMTMYYn0nw6TdvYUd7Td4abDvJRk13F0bL75tF5K+A3Rg9jjxswCrJs6GhjOlYgpGIfd5R96gxU85rY1QX5tTYLBMzcVsqjh8bNK5RvueGbGosp2sosuJVziOxBA+82D9/jnxn65oKjg9HmDHXvlaKl/sNA3dRSxU72qs9Q7QA2ajpbgE+AIwB48AHVPWLdg9stdNhJlj+dH+/bcqb40MRigNGLbx8pbMuhGIYcTvWK6xk0XV1+W2MNjeVMZdMzedMrQSW4OZ//eQgwP/f3pmHx3WWh/73jkabNdrlRbZky2tseYm3OI4dbLIQTICEtllpNgjQhqSQBfrApZcLoaXQEOjT3vRCiikEKNkagpMmOCUJJHESJ94XeZNs2ZJt2bKtfZ+Z9/5xzlHGspaRNIvmzPd7Hj3PzJxzvvN+Z0bnPd+7UpKXGbGx40X55ByCCgci7DdyVltzi3MiOq6bCEs9q+o2YFuUZTGEMCE7HYBvv1DBv75WGZXIm+qzVvFPTwLWEnNwlPY3P1HO5LzMiJs/qupbmZKXSWZaSsTGjAfOquVAXWvECoE6ATc9AUWAE02dFCe4Qiq3lUXFiWYWl+ZFbNx9J1uYkpdJbpTbeCQypkDSGMVJUOzyB6MWeXPYrkSdyDi+nNqGjqiYP6rqWxPeXwQwKScD4YPw4kjgBNx4BFKjHF4fK0ryM8nO8FJxMnLXCWD/yWbmFSf+9YkmRhmNURZMySVFIMUjUYm88QeCHDvbzpS8TLYebUjYJLycjFQm5qRHpUtnMKhUnW5jVoLWW3No6/LziX99C4CfbzoSse86K93LK/evwevxcNPyElf4QUSEecU5VEQwraKzJ8DhM23MMya6QTHKaIySle5lxfRCphdlRcVEd7yxA39QeWZrLXes35yQWfMOsydkUxmFLp0nmzvp6An0VnROVBxzmmKttCO5ym7s6KE7EGT+5Ogk08aD8uIc9te1ELQbE46W/XUtBILaawI09I9RRmOYGeOzONPaFZUnzsN2zkmXPxCVKLRYMmuCj6r6NlQjc/NwcEKhE7GNdiiOOS01RVAiW2PvgP2bcVO9tfLJObR3BzgaoXJcW6rPAjDLRdcoGhhlNIYpK8yisb0nKlWpnTpluZmpCZ01D5bfqLXLH/Eq504kXaIrIyd/7ctXzQbgZFPkrpMTJXbRJPc89YcGMYyWti4/j75yCIA71r+XsNaHWGCU0RjGKdNz9GzkC6ZW28U/X31wLb+8+9KErpM1u7eMS2T9RlX1reRkeCnyJWaB1FCy0r2sW1AMwMEIhi1XnGymrHBcwlV9H4zZE32kpkhEgj1CC/omsvUhFhhlNIZx6qFFo5vpkTNtzCjKwpeRmvBJeE5CaqSDGCpPt1JWlMW2Y42ueKItKxxHWoqn17QWCSpONlM+2T2rIrDKJ5UX57CjpmHUY00tyEQVUlOiE4jkJowyGsNMLRiHiKU4Is3h+raELv4ZSmFWGnnjUqmMYDdTVeVAXQuHTrUmfICHgzfFw6wJPioiVO6mpbOHo2fbXemYv7g0j921TQRGGcRQbVs1vnLNRQltfYgFRhmNYTJSUyjJz6Qqwn1oOnsCnGjqSPgcIwcRYfYEH5URNNPVt3bR0N5DIBhM+ACPUOZPtsKWIxHs4VwPt62MABaX5tHWHRjRaruty9+bLrGr1jL1/dmSKUYRDYG5OmOcWeN9ETc/HTvXjiquUUZgmeo27j0VsfEcU1Z2RiodPQHXmFgWluTyzNZa6po7Kc4dXbUEx8FfXuyesG4Hp/rCjpoGLhrG9+6USHKK9i6dmseknAwmJHDJrVhhlNEYZ+Z4H29XnSUQVFIiVLbnsEvqrYUyc7yPc201nG3totCXPurxHGX0/L2rON3S7Zoqy04+0J7jzRFRRvnjrKRjtzHel864tBTerz7HzZdMDfs4J6errTuAApuPnGPF9ILoCeoijJlujDNrgo8uf5ATjR0RG3PvceuJdlKue24ivbXXIhQpdqCuhSJfOqUFWQkf4BHKvOJsPAJ7IhAp5gQvJGoH3IFo6/JzzT+/QWdPgOe3nxiWr9DJ6RqXloIv3cuZ1m4uKTPKKByMMhrjzIxwpFhbl59/f+swAlz3fzclvFPewSm1su/k6JVRW5efbccaekPG3cS4NC8zx/vYe2J0yqizJ8D+umYWRKE9d7xxVjdBBX9QebvqTNgls5ycrl/efSlfuWYOgFFGYWKU0RjHqYtWFaFIsf11LXT5gyjuynsYn51OkS991I3RHJt/VX0bu2rdEdLdlwVTctlzfHTXqeJkMz0BZUkEK1uPFZzVTbrXuj3+7bO7hhVR6fQk23W8CV+619SkCxOjjMY4+VlpFGalRWxlNHuCD1XwRqkAazyZV5w9amW0v66FxvYeAHoC6hplHcr8yTnUNXdS39I14jF2HGsEYHFpYnd27Q9ndfMfd11CikBrp39EEZXvH2lg6bT8iPl63Y5RRgnAzPG+iK2MztmdY/9q7QzX5T2UF+dw6FTrqLqZzp2UTUaq9W+Rk+l1lbJ2WGib1nbVNo54jB01jUzKyWBSrjujxLLSvayaVcTFpXkgDLtkVlN7DwdOtXBJgrdhjyVRVUYisk5EDohIpYh8rZ/t6SLylL19s4iUhWz7uv35ARH5qP1ZqYi8LiL7RGSviHw5ZP/FIvKuiOwQkS0isiKac4slMydELrz7kD3O1fMmukoRgeU36g4ER6W4s9K93LislBSBjfevcd01AlhUkofXI2w9OvIKAztqGiPafG6scvmsIvwB5Se3LxvWw9u7R6ziqCaSLnyipoxEJAV4DPgYUA7cKiLlfXa7G2hQ1VnAj4Dv28eWA7cA84F1wL/Z4/mBh1R1HrASuDdkzH8Cvq2qi4Fv2u9dwawJPhrae3pXNaPhkN1qYZYLnfOObX7HsYZR9Wjaf6qFOZNyIhIiPhbJTEth/pRctoxQGZ1r6+bYuXYWT3W/Mlo5oxAF/AEdVBGFJroC/OlgPb50L0ummpVRuERzZbQCqFTVw6raDTwJXN9nn+uBX9ivnwWuEitO9HrgSVXtUtUjQCWwQlVP2i3QUdUWYB8wxT5eAcdTmAuciNK8Ys7M8VZy6ku7To7aoV55qpXi3AyyM9zX/njG+CxSPcJ3/nvfiEv4qCp7jjexcIq7nc7Lpuazs6ZxRCbNnTWOv8j9ymjptHzSUjxsqjwz4D5O0Ivzm2vt7OGNg/VcNrOQNK/xhIRLNK/UFKAm5H0tHyiOC/ZRVT/QBBSGc6xt0lsCbLY/uh94RERqgB8AX4/AHMYEJflWcuLDL1aMukbawdMtrlwVAaSmeCgtyKSje+Q9mo43dnCurZuFJe6+0S4vy6fLH2TvCNokvF99Dq9Hen1PbiYjNYVLZxTw2oHTA+4Tmuja1NHDawdOU9vQwdo542MoaeITTWXUXwhJ34JYA+0z6LEi4gP+C7hfVZ3/pnuAB1S1FHgAWN+vUCJfsH1KW+rr64eYwtigyY7u6g4ERxWO3RMIcvBUq6tDTZeVFaA6fIezg5MM6vYb7TLbsT4Sv9G7h8+yqCTXlf60/vhI+UQO17dd4It0THOl+Zm9ia65makcb7D6RRllNDyiqYxqgdKQ9yVcaDrr3UdEvFjmtXODHSsiqViK6Neq+lzIPncCzvtnsMyEF6Cqj6vqclVdPn58YvxY5hbnkJbiwSOMKhz7cH0b3f6gK6ssO1xSVoAC3/vzhSOKFtx9vAmvR1wZRefQ1uWntqGDyXkZbD16btjH7qxppKwwy5U5WP1x1byJALy674Pah6Gmuesf28Tv7l3d2xfsD/tOMXdSNqUFkeuomwxEUxm9D8wWkekikoYVkLChzz4bsJQIwA3Aa2qVE94A3GJH200HZgPv2f6k9cA+Vf1hn7FOAGvt11cChyI+oziRle7lpktK8Ho8I47wauvy89Ju61nAjVWWHZwkzC5/cETXaVdtE3MmZpORmhJp0cYEoTfRhrYe3qk6S3AYbRI2VZ4hoPDS7pOuaKsRDlPyMikvzjmvEG9f01xNQwfLpuXT1NHD1qMNfGJRcRwlTkyipoxsH9B9wEasQIOnVXWviDwsItfZu60HCkWkEngQ+Jp97F7gaaAC+D1wr6oGgNXA7cCVdgj3DhG51h7r88CjIrIT+C7whWjNLR4sKsmjOxDkTOvwExWdG9Bjr1cBMDHbnVFiYOVkZad72VEzvByati4/W6rPsau2kUUl7jXRhd5EA6o0tPcMy2/08p46ADr9ozMZJxofX1TM1qMNvb3FQmvQhVorXtp90t5/ctxkTVSiavRV1ZeAl/p89s2Q153AjQMc+w/AP/T57C369yc525aNUuQxy7xJH9RemzF+eAEIzg3IH1Q8ApX1bSyblvittPvD4xEWleYOSxk5yrqxvZuOniDzXRxJ59xEFcjO8HKquYs3DtWzMEwFvO9kM2kpgjfF47oKHoPxF0tLePSVAzy7tYavfnRub5WG/XUtvRXdVZXnth1n/uQcV7VniRUm7jBBmD3RR4pHRlTuxrkBgRUd5PYbyOLSPPbXtdDRHQhrf0dZd/RYYc7549ypqOH8Qp6vPfRhyotz+OOB0705Mn3zZUI52dTB/roW7rtydq9/JFmCGCblZrBmznie2VLLe0fO0tbl761B51yDbccaqDjZzKcvDb/lhOEDkuOX5AIyUlOYUZQ1qDJq6/Kf96TmkJXu5VefW8FVj77BAx+Z7fobyOLSfAJBZffxprAy4B1l3ekPoqp82OVRUM5NFGDlzAJ+9lY1t6/fTHaGF0Fo6bQaw/VVNq/us8KbP7ZgErMnuvuBpj/+YmkJfzywndvXv0dBVtoF1+enbx4hO93Lpxb3zWAxhINZGSUQ5ZNzBrTv90286/tkW32mHYAlLixs2Zfl9o128+Gzg+7nrAIA/vDgWoqy0rjyogn4XJgQPBCzJ1hKpb07QGN7D40d3QPmaL267xRTC8a5Nk9tKIpzMxCxgmP6Xp9dtY28vKeOz1w+3fUPe9HCKKMEYlFJHnXNnZxq7rxgW9/onr43kp21TXjEChN3O/lZaZQX5/B21cDKqK/yPtXcyamWLlbOLIyhpPHnk4uKSRFIESFvXCp5mWm9TvnS/Mxek11Tew+bKs9y9byJrmumFy7zinPIH2c9qHjkg/D/zp4AX39uNwVZaXz+Q9PjKWJCY1R4AuGUX9lR08hH5086b1uoY7o/x/L2Yw3MmZiNL0me2lbNLOSJd49ytrWL6rPtF5gu+7aH/q9ttfZxRXGSOD74MlK5Y1UZT7xTzfNfXE1OZir761oozc/k+sc20dxhmezuXDWN7kCQdX1+d8lEVrqXN//2Su759VbeOHiGJ9+vYd2CSfz9ixXsPdHMT+9Y7soyW7HCrIwSiPmTc/B6pN9IsVDHdF9bdjCo7KhpZGkSlbNfNauQbn+Qa370Rr+my76huVWnWxmfnc684uT0hQSCsHFvXa8/qaaho1dZN7Z384ONBxGBLz+1PSlyiwYiK93L47cv58q5E/jOixWs/t5r/H5vHd+4dh5Xl0+Mt3gJTXI8JruEjNQU5hXn9DY2gwuDFpb1o3AOn2mlpdPvyq6cA7FieiEegebOHnoCimKthpzrExqaO3uCjzWPvJ60JqgFU3JZMjWPJ945yh2XleGxK1A4K22vR+josRSQYwLu73eWLGSkpvDTO5bzp4P1HD3bxqpZRcxJwoCOSGNWRgnG4tI8dtU2EgjqkEELDtts5ZVM5ex96V5WTC8gOEidOkd5V9W30tjewxqXR9ENxmdWT+fwmTZe2mMlbYautBeV5iGMvN6fG/F4hCvmTuCu1dONIooQRhklGEun5dHWHWB/XfOQQQsO2481kJPhZUaSJeJdu7CYQFB769QB/ebQvFJxihSP8KFZyeUvCuXjC4uZOymb7728n/Zu6/pkpXtp6ezhrUNn+NJVyZdbZIgtRhklGCtnWNFez2ypuaBa8EBPrG9XnbXMVp7kMkFdU24522saOgD6XUWqKi/uOsHqWUXkZ7k32XUoUjzCt66bz4nGDh54agdd/gCH61v56rO7mDE+iy9eMfO8BE+DIdKYX1aCkZORSopH+OU7x9i49xS/u3c1NQ0dF0SLOdQ2tHP0bDt3XlYWe2HjzKTcDJZMzeOFnSdYOb3gvOg5x++xo6aRmnMd/M2Vs+MtbtxZOaOQv/t4OQ+/WMHK775KW1eArPQUfnzbMtK97iwcaxg7GGWUYOyva0EAv+p51YIH4u1KK9dmdZKaoG5cVsr/+u1uOgPBfkPf/3PzMTJTU1i3IHlDlkP57OXTmTXBx/M7jlMwLo3PXj6dyXmZ8RbLkAQYZZRgzJ2UjS/DS2N7D+PShq4z93bVGYp86cyZmJxZ89ctnsx3X9rHM+/XXFDY8lxbN7/beYIbl5WQY/JDelkzZ3xSB3MY4oPxGSUYWeleXrhvNQA3LS8d1IYfCCpvVZ5h1czCpAxZBiuq7tOXTmXDzhMcb+w4z+/x2OuV+ANB7lpVFl8hDQaDUUaJSGlBFivKCnht/+lB99t2rIEzrd1Jn4x3z9qZZKV7+bvf7qEnYFXmrjjRzBPvVHPzJaVJWfTTYBhrGGWUoHx0wST217VQbTf76ktbl58n3q4m1SNccVFym1zys9L4zvULeK/6HF/6zXae21bLZ35uVV5+6JqL4i2ewWDAKKOExXG4/35v3QXbnGTYF3edxOMRPElqogvlU0um8LWPzeWVilM8+PRO0rwefvHZFRT53Nv11mBIJEwAQ4IyJS+TpVPzeHpLDX+1ZsZ5PqH9dS2ca+tGQ94nc/kWh79eO5MblpVworGDecU5pKaYZzGDYaxg/hsTmNtWTuNwfRubKs9vlRAaYVeQlWbKt4RQ5EtnUUmeUUQGwxjD/EcmMNcuLKbIl84//+Egqtr7eeXpVrr8QW5eXmrKtxgMhoTAKKMEJiM1hQc/MoctRxv47fbjAPgDQb71wl4KstL4358sN4rIYDAkBOZOleDcfEkpz22r5evP7eZkUydbqs+x/Vgj/3LrkqRppGcwGBIfszJKcFI8wk9uX8aSqXk8svEA7xw+y7evm891F0+Ot2gGg8EQNubR2QUU+tJ58guXcbyxg5wMr2l9bDAYEg6jjFzEFFPQ0mAwJCjGTGcwGAyGuGOUkcFgMBjijlFGBoPBYIg7RhkZDAaDIe4YZWQwGAyGuGOUkcFgMBjijlFGBoPBYIg7RhkZDAaDIe5IaLXnZENE6oGjoxiiCDgTIXESiWSdN5i5m7knH6OZ+zRVDavVdFIro9EiIltUdXm85Yg1yTpvMHM3c08+YjV3Y6YzGAwGQ9wxyshgMBgMcccoo9HxeLwFiBPJOm8wc09WzNyjjPEZGQwGgyHumJWRwWAwGOKOUUYGg8FgiDtGGQ2BiKwTkQMiUikiX+tne7qIPGVv3ywiZbGXMjqEMfcHRaRCRHaJyKsiMi0eckaDoeYest8NIqIi4pqw33DmLiI32d/9XhH5z1jLGC3C+M1PFZHXRWS7/bu/Nh5yRhoR+ZmInBaRPQNsFxH5F/u67BKRpREXQlXN3wB/QApQBcwA0oCdQHmffb4I/Nh+fQvwVLzljuHcrwDG2a/vSaa52/tlA28A7wLL4y13DL/32cB2IN9+PyHecsdw7o8D99ivy4HqeMsdobmvAZYCewbYfi3wMiDASmBzpGUwK6PBWQFUquphVe0GngSu77PP9cAv7NfPAleJiMRQxmgx5NxV9XVVbbffvguUxFjGaBHO9w7wHeCfgM5YChdlwpn754HHVLUBQFVPx1jGaBHO3BXIsV/nAidiKF/UUNU3gHOD7HI98IRavAvkiUhxJGUwymhwpgA1Ie9r7c/63UdVgmftrAAABhxJREFU/UATUBgT6aJLOHMP5W6sJyc3MOTcRWQJUKqqL8ZSsBgQzvc+B5gjIptE5F0RWRcz6aJLOHP/FnCbiNQCLwF/ExvR4s5w7wfDxhvJwVxIfyucvrHw4eyTiIQ9LxG5DVgOrI2qRLFj0LmLiAf4EXBXrASKIeF8714sU92HsVbDb4rIAlVtjLJs0Sacud8K/FxVHxWRy4Bf2nMPRl+8uBL1+5xZGQ1OLVAa8r6EC5flvfuIiBdr6T7YcjdRCGfuiMjVwDeA61S1K0ayRZuh5p4NLAD+KCLVWDb0DS4JYgj3N/87Ve1R1SPAASzllOiEM/e7gacBVPUdIAOrkKjbCet+MBqMMhqc94HZIjJdRNKwAhQ29NlnA3Cn/foG4DW1PX4JzpBzt01VP8FSRG7xG8AQc1fVJlUtUtUyVS3D8pddp6pb4iNuRAnnN/88VvAKIlKEZbY7HFMpo0M4cz8GXAUgIvOwlFF9TKWMDxuAO+youpVAk6qejOQJjJluEFTVLyL3ARuxIm1+pqp7ReRhYIuqbgDWYy3VK7FWRLfET+LIEebcHwF8wDN2zMYxVb0ubkJHiDDn7krCnPtG4BoRqQACwFdV9Wz8pI4MYc79IeDfReQBLDPVXW54+BSR32CZXYtsf9j/AVIBVPXHWP6xa4FKoB34TMRlcMF1NBgMBkOCY8x0BoPBYIg7RhkZDAaDIe4YZWQwGAyGuGOUkcFgMBjijlFGBoPBkGQMVRh1mGNdISI7Qv46ReRTwx7HRNMZDAZDciEia4BWrHpzCyI4bgFW+HdJSN3KsDArI4MhTETkpyJSHsHxPiwiUaltJyLfEpGvjOL4MhHpEJEdQ+z3axE5JyI3jPRchtjTX2FUEZkpIr8Xka0i8qaIzB3B0DcALw9XEYFJejUYwkZVPxdvGaKFiHjtQr+hVKnq4sGOU9W/FJGfR08yQwx5HPhrVT0kIpcC/wZcOcwxbgF+OJKTm5WRwRCCvSLYLyK/sJuIPSsi4+xtfxys/pyItIrIoyKyTaxmg+P7HiciRXY9u77Hrg2xuW8XkWz786+KyPu2LN8eQvZviNUY7g/ARSGf93t+EblLRJ4RkReAV4YYO0tE/ltEdorIHhG5ebD9DYmFiPiAVVjVVHZglfkqtrf9uf2d9/3b2GeMYmAhVgWLYWNWRgbDhVwE3K2qm0TkZ1gNFH8QxnFZwDZVfUhEvolVUuW+MM/5FeBe+5w+oFNErsEqQLoCq2ryBhFZY5tYzkNElmE9lS7B+r/eBmwN47yXAYtUdajivuuAE6r6cft8uWHOy5AYeIDG/lbCqvoc8FwYY9wE/FZVe0YqgMFgOJ8aVd1kv/4VcHmYxwWBp0ZwHMAm4Ici8iUgzzaZXWP/bcdSLnMZuDr2h7BuBO2q2syFBT4H4n/CUEQAu4GrReT7IvIhVW0Kc3xDAmD/Zo6IyI3Q22b84mEOcyvwm5HKYJSRwXAhfUNMRxpy6hzn54P/tYx+d1T9HvA5IBN413YeC/CPqrrY/pulquuHIbfDYOdvG2IOjnwHgWVYSukf7ZWfIUGxC6O+A1wkIrUicjfwl8DdIrIT2Ev/3Y0HGq8Mq8XEn0Yqk1FGBsOFTBWrcRpYT3tvhXmcByuaCODTIcdVY93ICdl+HiIyU1V3q+r3gS1Yq6CNwGdtsx0iMkVEJgxw7jeAPxORTNvf9MmQbUOefyhEZDLQrqq/wjJZLh3JOIaxgareqqrFqpqqqiWqul5Vj6jqOlW9WFXLVfXhYYxXrapTRtNk0PiMDIYL2QfcKSI/AQ4B/y/M49qA+SKyFav9vOPk/wHwtIjcDrw2wLH3i8gVWC0ZKrDCY7vE6pnzjlgtOlqB24ALekep6jYReQrYARwF3gzZHM75h2Ih8IiIBIEe4J4RjmMw9ItJejUYQrDNDS+OJBFQRFpV1RdxoeLAcK6DHdr9oqo+G2WxDC7GmOkMBkN/BIDccJJegbVAZ0ykMrgWszIyGIaJiGwG0vt8fLuq7o7BuQuBV/vZdJUbuq0akhejjAwGg8EQd4yZzmAwGAxxxygjg8FgMMQdo4wMBoPBEHeMMjIYDAZD3Pn/0R3ElI4rcnkAAAAASUVORK5CYII=\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -434,9 +2415,9 @@ " Measured | rec_ctrl_demod_freq_0_mag | demod_freq_0_mag | (101, 101)\n", " Measured | rec_ctrl_demod_freq_0_phase | demod_freq_0_phase | (101, 101)\n", " Measured | rec_ctrl_raw_output | raw_output | (101, 101)],\n", - " [,\n", - " ,\n", - " ])" + " [,\n", + " ,\n", + " ])" ] }, "execution_count": 12, @@ -445,9 +2426,9 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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O7TiOeuqTLZ7J9hxL0ihnPN+TNGnQn9hGIR/rmP0i8mK7UeaoM8oU9/DZvCeTc666iXtYXt7PzaUDrPuQj+VgOXcM/fbJMbzh8XXSRndfmzx+ZJx55pm+ceOcBg1ddtlln3H3M+fzrL2JvbVBzsPd/QYzu4ekC8zsCnf/XHLdEyT962xzB3UwOVeSDjHzx9T0MVzzs/V3PdI24Pjw+svOF7t2/zvS+DGKvSm54S13GL8luSeirB2GtGOT8w9L8pEGHyDmeVX9/W6SJg1eYsq2BceRFzeW5e7pUQ/3Q9rqRI7VOL68/l6JNH54vl1/WbbIiy8z2zDa6GikMWrdpGZi29CvNBispLKv4rAcN+A4Oh3LSUQ9HY60g5I03h/twcGSpCPakW20qf6uQdqNOI5+y/7Nj3/0dW5UHPdkfYEyUc7rcRx1w+dE3bJd+E6doJnYhON19ZflYNvEBuX8GIT8+ydpgT8o22jOCxs3btRXvvKVOa8zs0Pn+6y9ib0yILj7DfX3ZjP7iMrG2dmA8BTtgrlohwadgS9kdCrucM4OFB1sM9Ki8x4ylP9wnux8ZEbxQvFjlGkVEzgOmW9M0qTBC8eP2mT95SBAJhZl4ou5LbmWmy/zAxUvKeshW81FmePDxQ865YsBkS87ZcoQdU85j8Bx5EUaGB84DlaUIxvY+AEMmdjGLFMMeKzb+JixrzD/kI+bclO7iQGPecYAyo/n/skx6/NWHG9JzgcB4Md3CscxgPPjzgEp0tkXQj5+fdkXL66/bA/WQ+RFckPCtHroVxr074xcDB/PD665e+nSxx6fQzCzCTNbFceSfl7SN5PrDlbZRP28PSthQ0NDwzBcxcA119/Sxt7QEA6T9BEzi+e/x90/bWbPlSSEHfglSee7+9Y8m5kYV5fxBILZ9JkjQqXenJynKeXrOA7mQ9ZE9hpyZMyFDIcMKrQBysFr11YquJn0sYJM7hgcB9PrY0rBeZhlxrJpSgltgdoL6zPKTNn5/Kiz45AWLJjaD9srnk/Z1uE4Y/OrkjTKHOlTSGN7hPaWmRKlQTtROwq2znuoAYT8LOeByXmy7NCoWIcs0w/qL/s+uSy1iUCwefZZmlFDZmpMzD/KnNUNtUn25ZCfL/SwvV/qvnPZvAWxbuhX2jXNc3TEgLC8sccHBHe/Sok5cTj+jLu/XdLb94xUDQ0NDbNhp34aTEZ7a1J5UbBdA8ZDlhIMi82Z2ZU7jLfStjtB5chcgvlw0jmbYyATDBZDhkSGFddyApYMKR6wGjR5a30QmTPZfIAslHIGW2fdrEiuZVrInNnjpUE98p5scpxlD62GE9FktncO/Ur5JD/viXY/KLlOGtQZ64M28Yztsz2iTqk17J9cR+0t6oFsnHUfdcc8QwPoK8dk/WW7s+5Dm2AbBfrmyKIe2G5k4Yck5+N+tkGmUTGNE8RRTyxbNjlPrTybA8u8meaPpiE0NDQ0NEyjDQhLCqYBiyB7jBlreizQhS6YU8dGWSnSVlB8MvdgQZNI+zaOgw1lLqJkZWR1wRRPRRptxTuqAGNwNwm2Re0lY6TX9pyfqr/UZKjBXJfkH/XAOQDWXdTNGKjiWtDs0BA47xBlZ938GMeZpwzrLlwqWd9RJmpPnTmZ+ks2TfYZclJrmMJxsNvMRZT38PlRTywH+2W0E7WSzLuGbRj2/D4tccXQdXw+NRX233gWZaPM0U6UI8pMLY9zCOGmy3bnXEnITIZPDSLKQU1l/6Ffqatx8Vnzw071r6xZPlhWAwIdw/iBi5eLLz7PZ26S0+YZJNFEEh9FvmT8mEU6/f/DxY4fXLoSxv1TSOMHLl6OtUjcUgvCD9n3cRwfCX60+MJmVlF+iLOPZtyTLfKSBh+OMSTSvTAG42wQopw0u9yQpLHMISfLFm3XZ0KIQYQmDH70QnwOhjuS8xxsQ/6+VVBxDz/YmVsq0w5M0tgeGaHhBzKexfUvIWefqSWexT5P8018yDlgRJ+nnLwn3gX2lcx9lu/MIcl5vnMB9gXes7Bup01DaGhoaGiQ1AaEJYYVGrAcMtJI46Qcmcu0Ox0pVFUXVoKyUr0O5kGG/1UcT9ZfqtQZGycjDqbH56RsB2rLqngAaP2xeFAwST6HrC5u40ImqunZpF0wZrJcriCeXvAFmrwd1Pu0+juFe4Kt/xvSyNYD1OLILsM0wXaNuqNZgua40FCoBZJpRjtwoVS2Ujoz35AFUyOMa+mKSmYfGlDmwMA82UemNUek0awSbcf2CvMNtUGaubIJYvahkI9OFafUX2ovNBmFJkbWfjmOQ37eQ2RsP2TiMw9Jzs8fzcuooaGhoUFSMxktQbjygGbBKsnEspAS46Qg9aYxULXNNKgmzyGr+2L9zWLFUI7JRCaycdq3J0lFA4nReccPZ56mbZ1MMnOPzcJc8NGZ/ThbjLQOtG2c2ld92GpUTpSTslFTmRySV8rdd7OFaZSNmNY2oCJw8dXWzTPT6IwQ7ZjNAVCroFZzZ5LGuo/7swlzsn72oRCfk8Ksx81D1w0/P5AtZut7Z6KeqanEtZyLYHiRaC++MyfhOPoo63MKx1n8pLiWsvH9yYJd7h7agNDQ0NDQIOmnJZbRshoQ2GRZvAvaJrMFN+Ns76AxoEhkn1k00y/jOHOHC1ZGRsg5iGBWZHL0qFhfBViVubqAlpH9ZfZnenHMFeY7qoS29biHTDANLnZblqjpSqEGEHKyjilz1FO26E4aMGLKEfNIffM48cwd6BiZR1K2KE8aaA5MIzsdlkMaOC5S2aR80bSU80dDv1K3j2QhHVgPmYa8NbmOiDLxnaGGknk2ZQEd2exxLb3gqDjGM9ltZpONx6zD63DMCK/zQ9MQGhoaGhoktQFhCeIu5cw90shG0pjtK5NE0MNJ0P0tSYA5suhgSVnQNz6bS12CXZKJEcHAViW75nDeIMufaZnvd19IiCjH1MxHitHheX94+mwH3c9CZtPTJrxeqDGRBUfdZIvZpAGrJGO9bujc8HEE1MqCv0l5aAo+MzQt1hfnkQKZ9tS3Jia8rGibjzkuysH2jDzpYZVpmVlI7r5AitmcDNsm+iL7amhMrCPa8EPmByGNZY85qb7gdJlmGoo8taesjeaPxTcZmdmrVfaA2a4SseS33H1zPXe2pN9WacYXuPtnFkOGpbCFZkNDQ8Nexh4Jf32BpOPc/YGSvifpbEkys2NV9oZ5gKQzJb3BzBZuETawrDSE/TRgLGR9YcMlgx9PzncoUhirOWSCCq6rFIo2fjKXYFaZT3WfvT4YUt/S+2nxEu1kDK4020CXTk6eSa+ZLEQ02VYW/iEL401ffzLVFFtm5hnPZxtlgQIyzY+ykMUGs2a7kFlvTtIylnxTkkawHNEfuOsc6zby4mZNXAMSmgHvCc2AHjuUI1hy39qGTOPMVlKzr0U5WHdZ4L9sjqBvM6aQjzb+TE72JcoZz8y0AV7Hd4pa1fyw+CYjdz8f/14q6cn1+EmS/tHd75B0tZl9X2VTsUsWWoZlNSA0NDQ0LA5GjmV0qJnRmnpu357xc+BZkt5Xj49QGSAC16nLDxYMy2pA2KGc9QXz6bNNBqMZp7N+Vt2gjwdWR/M+BpLFpAmlo2/Hn2zFKa+NVZ20L68OmeDcvZIXVNenk3CeMgcTpA87tatgdfS/z7YCzYKgMc8NXHNQG2ccqsSaetNK0L9MDtqcyaKzOo36pt87V+ZmNm+y22OrAIzJtDLRxKglTtZfehtxNW/0S9Zn3/MDWdyhk3GcBVrMtAJ6O4WnD1dek5lHt2IdU85oY2pk0W9Zx9SUoo9kGyNJA02J8zBcQ5KtU4i657vNPrKwNvGRNISN7n5y30kzu1B587zE3c+r17ykPuzdcVtyvY8izK5iWQ0IDQ0NDYuDhTEZufsZs503s/8m6fGSHuPu8dG/Tl1L773U74E9LyyrAcGUM4JgEZl9WJImwlCaxSnmWA5j70SlKQeCNmUrKOlzHVlSDt4TcmbhfZlnJ6ZLFl6ThauazmrQS9qaQ3w+k7eHxsXs4x6yMtrZ4x5u5Xk8jndUmceSUJoToPOTmICJuqNfORlrpiFEPWWrl6WB/OwzlPmqWhG0o49Bg4mosxMQJPLkPWTJgT6tIN5yMuPIk18EbkIefYTMOAuhzn4TbJ0Mn5pUyEfth80V7cGv0tjQ73CeoQ30bdsZeR6cpEnSQ+vvN5Pz7L9U9DONa/ew+LGMzOxMSX8s6VHuzub8mKT3mNlrVN6UDZK+tBgyLKsBoaGhoWFxsEfWIbxeha9dUPecv9Tdn+vu3zKz96uMqXdJ+j13z9YjzhvLdkDIbJt9m9t/u47Fx5JOZHtL0shaaWW2kYk0YHWM3x/MaXOSxvv7YtBnPuzTtLDPQT8JUr9y28xLs8XPvI2aTrZxDD1lgsmS5X4dxycFpaUBO1lIkPV4FjNZ/NxhpFHPrK/smNrP13AcLLtj8OVilti+FAXdXDUIekuR6mVrTdjekVW2v0TffgjB9rP+J6F86PRXVaHIxqk9xRzHWM/5bFvObJU138PIi5oO6yYL1cX8QznL3om+9SsLF+1U2gNeRveb5dwrJb1yUQXQMh4QGhoaGhYOLfz1kgO30CRTDOZBFkHb/DErhi6UBkyQRtKH4jgoLygOGVx48pAphm2zb9VwdLeDkzQej2c7ydNthctDw2UDhtXO/MkscvD4kCSN5SUTCxZMJkjPkU1VgDXXJRdAfVoNNn5EnaRgc7CeghXOtTI1ixFEDYFzFOEdRHv8alDmbbWTreRWofWX2s0VOA7NgAyejDebn4kuRq2WGkaUgx47pJrRvdkXp9sDjXgbKifas495j7oSP9uDgYo2Fa6oO5aNcxjxrFH3FVlYtNAVDQ0NDQ2S2oCwBLFDA7Z0AtKDEe+fpEkaGMMzNwtSY1Kfmn4KKNSduD/z7AiG17cjVDBqstxVyfHXIPyauh5iPd01SHNDJtDg4+EYf0iln2SprIapRKZgYGRqnEOYTO4hptkltYGox56JnvDoOQsa2VWg0cGOuQPXLUO/Urfus/hHZLSZL/6puGB9UG+sWVkdKhceehba6/paeLLkjFFTpizaaeZhNVfs/++j7oKtr0NG69FH1t4yUw7KHHXC+o6+3ufZsyU5T4+jrL/QIy52ZMvmkejFlnmSzR8t/HVDQ0NDg6SmISxBuAbsg0wvSBsXnqcx12mkDbpCJ/IkiP1W0OlsHwL6xweRpLk/k2kiSSOodUwz+wN7LghaSaqEB6y8baacWfgmZv/doXNSvrq6b5e2UGYmso0GWIlZRaAcVN6CSbIccUyWm3nNZO1GmfvWr0xXAGny4UPnhgSIIpFrZiG0MhY9lqQxT3p9sblDM6bWvC5bsENPtJhkQEYHQ1XKYh1l4b+o1WS792Ur/fnKUfGNOmF9rU066BY8NNvfYvfQBoSGhoaGBkllQBgpltGSxrIaEFwDMkZWGAwpi5w44+JA0BQaJBmwpxqtJ5Dp4WBTseKVdtGwc9NGz0cHs6LNeqznOBBMc4LUNnPQ/wWkIabimprBsXgoY9FkcfFjroNsPItz0+cXHxrG0YyvFFSRrkOkd7FmAWrBamxcfFIVkIzz4vpLxY4eLFP1N9ufWBpoC/S06dinq9by0EyloksPOtvqWqE/hwUPm1APUd+s2+hi9LjJ5sP62iOu5Qrf23Z285aktawILqCpOAIRetbU8G30oDqx/nItx8rkmNoNNaF4PLUKxoE6dug6SdpSC3oYCrwKjbjqMHU3uN5tNA2hoaGhoUFSGxAWAGY2pUIud0i6K4sCaGaPlvS3KkRmo7s/qqb/T0nPVmmJy1V2D5p1mt80YNGZCZesiqRue6Up47wpDLJ94UxjTcJ5eZ6ZVpIxPdrBs0exwMHSOa8wTS7JSElzg3FT26WRtgq9FiyKHkfBrilnyE95M5Lct9tWaAt8zkS2IW/f1nGzgFpU5E/GmcXfZ3tkK2f7FoFPy0+VL1say3KE2oHKWwOhTqoCcH/uyJLZZCuyKef+yflsBW8nRhUm3lZG4XuWsMcDg8xkAAAgAElEQVT9VJpDA+FzOK8xldyTrWTm/fReyzyGor3YpTfRe63PpW+X0RamLRROd/eN2QkzWy3pDZLOdPdrzOweNf0ISS+QdKy7317jeDxF0ttne9CYBh8ufqzimCo1X6hpZDt78C1k701mgDO3P5oYsm0Hp5Is2bl5HC8E74kXehX7ajZh2Leqqb7Z43gL1+MligVO/C6ESYnvWhYkjaYnIh7Fj9G6KNwkEmlbCJMRBz74Gq6pM5Xb8NXILIFfSdLIAzjw3ZCczzbToZjr4yY2HGdRDx/6lTqdcW299hScvmLmZZ1BLluwlQX7oxhZWA92i221X2zIYlNIWhsz1BhETqqZXov3iB/xqFs6fHAwjtumkMY+EuSEZY/2oKWR9bRwwe2kpiEsPp4q6cPufo0kufvNOLefpAPM7E6Vz9mihHttaGhomBvNZLQQcEnnm5lLelOyc9D9Je1vZhepDPavdfd3uPv1ZvZXkq6RdLuk84e2l+t9WDYJGmlkG52tATPKGmvqSQmz2Uf4xa3C+SPrKp4p3BKTsUxL1rp1VGaynbg/3S6wLyJeRh9JpyJTULW1oP7BGjkaR91RdjLNTPUn8w6yT+a9uTbS6j732TB5HdZzvtoWjkAbjFcqSbb8szjO5hrZb9YmaZnrZycoWxSUtqdsuxS2Bxa2raqZ7o/zMb+bKUzMKvOaJih7lK1vw6HpPkbbE6l59Bs+tPabvvDucSmfw5AmmYc071+TpGUh4fn8vs2odh3NZLQQeLi731BNQReY2RXu/rmh5z9Y0mMkHSDpEjO7VKVPPEnSfVS62QfM7Onu/q7hB5jZcyQ9R+raKxsaGhoWDk1DmDfc/Yb6e7OZfURlY2gOCNepTCRvlbTVzD6nwfqZq939Fkkysw+rxBibMSBUreNcSZowm551ptdckLa+NU9bKrNZdWByAY3jJAhfrb9gpFuxtj9bYBTHnN+lBpAtTKOd/gf1l2JOMyxGkuNNQfFJy0j3Q34IktmaGTgtquGHyXXSQEFh1ZHYx+QjYwVOawakh4xHEA8lPeSMYw2UtxmCRFYM3c2yhVJElszso2q4OIrawLqh64oA9ZftwX4Tjc9Gnpr5UGoDUfdsQpYjWDCblRpZyEfFMZsU3pDFWSGFZ9sEnScLSwz22XwB+wrnmSJLvqdsm2yRZNa9Wc7V++dy7R6WxoBQCfjDVaridpVX7ivunk6dEgu75WhXqAkzWxXHkn5eXVdoqfjonGZm+5nZgSrfiO+omIoeZmYHWtkp4jE1vaGhoWEvIExGc/3tPZjZ6Wb2GUmfVFl5dE+VMf+lki43s5eb2awhrxZTQzhM0kfqzj/7SXqPu3/azJ4rSe7+Rnf/jpl9WtI3VGr8ze7+TUkysw+qrEu5S4WPD88/zAAXptFjIogNWRWJz/qgfdcmF5DCMKZwMD1uqwlfwRPqWEw2FCyGnhUM3hVZ0u5J0pYFY5vWNpgp5wim6i/Z9iSOI44GKNbxEDq8Tej1EvWYhT6Wcrst7fVZxIco3AmghGM0lIcBmRSG1L3W92q8k6vrqqatYIhk+FFM9ovM9k4XUD4yHjWJtB9XOR5KasxGjHKw8k7CcVVRNkDmI2vlcZqICkjULesz83TlJkUHDv1K0mbU3c56fK9/G6Qdka0y44tUVb5VqNCTkljqV4KnTuF0REPnYrcMX8RxlJ3TNGzPExdMO1gSJqOzJP1OOOkQZrafyl7Nj5X0ob4MFm1AcPer1A2fEulvHPr/1ZJenVz3MkkvWyz5GhoaGkbHvj8guPsLJcnMxoa32HT3uyR9dK489rbb6YKCC9NoUwySQntpJwxE/EMf98jgfkmaNGDhjFSHB4xV4xgVjLBPUxGhjTSYNb2IppL7sw3Fj6Js9AYJQ32f7T0exspBV1pfy0HCGyybmgAfn9kheT5s+pkZvWOvp/E/KooGYpYp/P5Jg2tFTaLCO+Eb6i+JLwnt9jnOB/Nme8ZcyRVgpsew8HHTMUjLqD/aMDZEWoM8s/ag1sD5my1D10mDsjM0BKs7un1nnQIKPxYnMq26s9oNx1X9OgRp1CzjlaIyy7mrkJmv6Yqhc1J3ymAubWN0LKlYRldXy8v7JP2Lu/uoNy7aHEJDQ0PD8kFoCHP97RM4WtKFkn5PZXB4vZk9YpQbl5WGsE0DmyJZQpBKmtmncHy/ytrGafAPOsV5ebLTbJncTTNPk0VnS/tJ5uPxfCTN6LMF7iMJZXCvdDceuqCEzGR6oNHB0KZwOurx+0malG9IRJnDbp1NwXWaIHMdoWpH9SvkR8PvuLZ7SuoqR5F+Vc/5yCrbN0karEzPFrhjaUGXvsbmRaTw9NRhpQZqRV6JB2Uby9B2nm2DyuoMZk6bbrZ1K7Wjjla9c+hCaVARmdor6ZZtM+XgaxRlYl9h9pGebSBFTYLTGnO61YyMfd9kFHD32yW9X9L7zexnJL1WJdZjFh+zg2U1IDQ0NDQsDpbWjmlm9ihJv67ibfRlSb82yn3LakAwDbx2MmZDtkB7ajCO9aR6QW+PQxrpaxhse2IIhRmVSkWYuWk3zRbekv1lq2QpZojUmUHKbNLkBjSFJpvb05B/bKWdJH1xO8uReXX1xQjKwjVHmancrIRMa6OR0uhy6tLCirHqvXMS6OMK3B9mfDjSdLIP76Is5qE06EO8Jx7FNjoW5ZiMiQlOoLAcYbxnJ6htRzZPO3uEm87iMEkDZYT9P7oD2Xa24RG7EkU+shZ6LIt7zkVAqIi1tXFXoD7YbPHMvqU/WbzJKDO7bxZSe/5YOhqCmV2t0i3eL+mFdZ3XSFhWA0JDQ0PD4mDxBwQze7WkJ6gMqT9QifC82cwmVdZhhUX8Und/7ixZneDut85yvhfLakDYT10Hh0Ck9XmLrA9tgB5FpyQX0gUmKDNpGa5dW2nnaZgkCNJHh56LcRxZcaZ/CsfBprgBSdh96YN+GjMIysogPpxeCpmpqqBM66qD9+PfN0iLjW2ybTWlwXxC30rmYKp0CAr222N+1trLhy6UumpJyEzqHIKgwk9EhW+vDcLqugTHseHLD5BG9hrZTyJtrgiMt9UCHsuOyo4Z9Pc0pFVNZ4IUH2rNabWiTrohPT1dp59HWpSDc1hEdBtWJ8sW4nPfpTtrfa7mxBfLUeWkl9Cam2aep1DroYYG82e/4bxJgFlenpzfbexYOH2jBxdIOtvd7zKz/yPpbEl/XM/9wN1P7L9VMrOXSnqDu6fNamY/J+lAd/9EXx7LakBoaGhoWBRw1etiPaIbwPNSSU/exSwul/RxM9um4lF8iwrd2KDCby6U9L9ny2DZDgj0cAkWzVXB9NmeZmj0WgkKRbaTxQuihpBslLLiC4OkUDCuw2Uk7sF8aAOljTW8WujBktnzOwj7NGUj9Q7DLCuMtDCEwmraVXVTASpM1ABClsxrRcqjoQaLJaPrxA2q9bmybz1F5r0TAnL+BNrReF3yehpUGVbTVP3t83qJcrLs+w/9Sl2bd6QfiWdOZLv6EFk0Xqo1VagJeIc9AvMmccilD5EV+002x0bvMSJeBc4xTPfVbN2FNGgHqpZ8AUIFAcU/hdpTXfOzEoKGYksvI87z9Mm/yxh9QDjUzLjtxrlJlOdR8CyVdQSB+5jZV1V6/Uvd/fPDN7j7eZLOM7MNKrGM7lmvf5ek51Tvo1mxbAeEhoaGhgUDY+vPjo3ZzpABM7tQ+V6AL6kfdJnZS1QmLN5dz/2HpHu7+yYze7Ckj5rZA/rmCdz9SuXWtDmxrAaEvi00ox17ozrFTdk+kAQZQlDazF4vTVMsMrC4hQSJrZbtvpgZA3ldMM6OxkOqF+yyT5MJ1yd2dgodDwNrW1fL/F3kwyxDJrLpnckxFZVglyxHxwMr2oMZTeI4GpdqSfj087XhxEYtM23jPM40AJ6PKmN7ZqCWFwrABNUf7q4W6Wyv6DjsDOwE1Xa/Ff3v6zNPd5SnSGOW1GoCbA8qKPEqUMubiAyysKrSYC4nq2SmTyEN/S68vtge0cR9C/WzOcXdxgIsanD3M2Y7b2b/TSXm0GNihbG736FqDHD3y8zsByp7yWQbAM4Ly2pAaGhoaFgU7IE5BDM7U2US+VHufhvS10r6kbvvMLOjVIyhV/VkMy+MPCDUENbbhoMm7UugVkfbYZAuMgu6eR8TLJ/uD4FseSczpdGbzKjSmUkkBYFjBZIlB9tieCR63Uwk50N06qg7UNCxzEmdD42K4vxJtoAAadt3dp89fBzlo+xkksFUWbUhBrUKaggRxXQdg9PwoeF/wbIFdb81SZOm6e9hYKxklFHPJPNkzNmeF5nNmjQulrVMoPDrSeej0shGQ8XgdUTtGNQ61iP/kI/9P+zsffMjQfb51WE5o+oZCTaiyh7D94R+MSEA3xO2R9zHRqBmmsgRl7KJ2W/YB+eF0U1G88HrVaY6L6hRosO99JGSXmFmd6k02XP7PInmi94BwcxWqGxs/zQVJ8w7JN3NzG6R9CmVyZLdslM1NDQ0LDksvpfR/XrSP6RZQlYPw8zuL+nvJB3m7seZ2QMlPdHd/3yue2fTED6r4qZ0tqRvxm47ZnaIpNMlvcrMPpJta7m3sEMD1tnZ7wDnA/T8mB75aWQNOtLZfgnHQU3IcLiooHq4rMSQuaGyPrIukqFIJ0HPnINoJo9y0DROVvS4oH19axXDTYnlYJlDAAg1XvM8DarKtyFoVB0ZJ83GIR/9xePahyGNYX2inOt4E72hQn42cghCdyhOJFXGOgFBTwcLj7ahOweniYJFZyvPWd1UakKD6OxHDa53RPQXqiJRJlJfugyFuotOvx5hTNfXRSIHopyZcxm7f5SJpJgaXYj04+T8KnTG1fSminbgWhKqLdHHnoC0UweHEXl3PbTy4+tDP45b6MXXec/ngz1gMlpA/L2kF0p6kyS5+zfM7D2S5jUgnOHuM5Skqqp8SNKHzCybh2poaGhYXtgzJqOFwoHu/qVqdgqMtMy6d0CIwcDM3iLpde4+vUDWzM5x93OyAWNvwjSwbZJ5Z0sKOk5EUQo6MGfrDDj8xYN4D1ldME0sGhirTI7kjuwy2NZcvtNkZXEt2R3DL02XjS1F62PIz9XLnGAJ5k12WpnoNlzHMgURJZumn11UI9lbiMRV2Nm6Ec6fHEG1Ix5GbSA0OlYoy5F4xUyABR5VGSnrll3gq0laNgVATSmLEcSmuaNWxFE0xsYDqBGtSM5zPQProWIdtJ8sTFvmZJctJJYG8rMNQ2RqPxTj6HrBOLXRSRxHJ+KDWKZkrcmqWpBfhibCHdWoOc8bCxc6dbGx0czuqzKMycyerOK6OidGmVR+nKQHm9lr3P0dNe2Jks7ZDUEXFQxdQfU2+l9f8K+T7pdckO1Gk0VoY+fmlyHeBKju8cKxv9O8E6Ygfr/4ksaLxsdk65gYu257/cBw3OqYvuIDmc3U8WHJXqArk8ukwUeCAzA/gPFeZQvwWMXMPwaHTvxeVlTUNyskPu7copKDXYxCfNAtMw/pa8ANdjJnhagHWhcY6y3anv2TZT4q+lMWU5vsgTdl5jLaqaqAWV8hN2C/zLp337q4wPokje01Hn2NsnP0iA897b3sOLck5y8sP33bzvJR88LSMhn9nsqWw8eY2fWSrpb09FFuHGVAuFnSoyW928weKun3Vch4Q0NDw08HlpDJqG5ffEb1DF3h7tkSpxSjDAhWV8Q9wczOUZk6PXj2W/YOVmjADsjKMne1znR+UFpSi2DR1K2zzdKzxWo8hi0l3PGuxXUMdPep+ku2nbFwhq7ItFjePx6LnpgRmXWEDiD7vBeOoyuRXsa2nMjzJOjpkRXJ3/7JMdsoRCIjZXWmoTkycwJVv2CSbHjS2NuS89Cejv1M+d2MPMlEoxxkyyFnn8tttFdmLpOkr9UbT+SDItNHIY39MjQgtis126nyM/bZQdL6yrxXIh+6kGbrMrNNd6hwRXuyvBRpfbU/reozvcS70uc4EP2O3ga1D6xBBzl1anA8xbzmgyWkIZjZ70t6m8rb+/dmdpKkFw3FSkoxyhaaH4sDdz9H0l+ou5awoaGhYfljxwh/+waeVUn8z0u6h6TfkvSqUW6cU0Nw95cN/f8JSb3hU/cm7tKAbdFsnE2gpW6n2SodZrQa05y3V0s9DfY0sgZNOmxm2hGgj5z0ypbZU/aYTOZ8Y7AyMnBO8E4zZt7Emb4oO4OMccIhykGmlQi6EhN9D61ujrTfMsswb2dl69scKLQNThg+CqxwTQQRZHtFLGsGC6CmE/VwQfIgHNN18tTzBsdR52TE4RHJeQVWXbQXJ6o5UR7ij+GZx0cbfRAXPh7HUZGc7GAbR3sli93WopLPgNt0zNFTU6A5PxSYTCnOIp9IgzpZiwtO+youiHeJajPfydDuqNHFRBVVEdTd5HfV7TS7iyVkMtLApH+WpLe5+9dtyOWoD7MtTNuiOkudPMzdvTc0UENDQ8OygmspeRldZmbnS7qPpLPNbJVGlH42t9NprmRmX3X3B81bzEXGDg1YCAlDTHhka30kDShNZ9ec+ss18augDhxQLfk7we9IiTNWVkE7HRlUaC1kYolzT0cDiOxpTmfZbqysZl2fYTeoIJnx6uSY54PGkwqCSkbRGWmBjDhkpbk/ysY2ohYX19LBhO63a0KmhyIxKDzLy0By0d60t1OV2TR0XQ/I8KMaeAunA1iNGULDJUmelp9uW+xXwYj/S89DY2KG2lNoSmDP41AzD6r9hsrgLckx54GizOy/rNpodyov29HBx6fqAeOwsJNEX6Q6HHXD+qCauVv7hvVg3zEJzYXfVgkacpW732Zma1TMRnNi1FhGmabQ0NDQ8NOBJTSp7O47677K9zezzD+gF8sq2um4ButcJobSpa69s1NLwexJP7P4vmPk4RFR7IGDpE3fGBxniwYq/V0J1tPZVH5IHKnL/KNMZJ9BeMk8WbY0uBdpeNA1UsEsyB/df+KhXAGHl2Wsah2rYHMlcQ9ffr5fUSUsRxKHrlMfR7Hyop6pckXZWF669ER7Z7spSWl4kpWYEzripu5jpMFWoux/WSSQNUkaH9Vpt7iAbJmqY7QHy8ZOFPWUxZlgZ4EGsrY2Egk2HzlVf2k3phYYyLzLmEazfIRE6ZSD8kU7Zvuxso3Zia5Ud/HI7mIJzSGY2bNVlgfcS2Wt58NUZtR+bq57Z5tD+GX8u3rof7n7h3dL2oaGhoaliCWiIagMBqeoREs93cyOkfTyUW6cTUNgiKmLh/53SfvcgDCmATmgbTNIH5laZ7APBkW6ExcfRE76OBwnmti94d5wZ6Vjv4Hz/1R+JmHTfhRuCeLDfpfsg9IhQJEVWVe2neVRjMGcLSEmzeUkx0GVfjJiWTBrVg0ZWk0/9cJB0v4QKrxuKHMQPZY9i+/L8xcgz4iyvDaLVcCGJ0cKAR4NtWA7aHQWjO2sweFRdYPDo1DOcNSZwi0M4RHMn2ZuKjrRTOyfUczw3pKkMXrOBLNmu1JDiLkDqmnZDjn/NjhcVbWRE+F5dF+sug/lic5roahQu+H0zbahX2mI2FdZTuWXhYtuoh2PTtK4dobHD1VZsztfLK1J5W3uvs3MZGZ3c/crzOzouW+bfVJ5pEmIhoaGhmWPJWQyknSdma2W9FGVvRX+U12DYy9mMxndS9Kku3+h/v8Hku5eT7/H3b/fd+/eAr2M6EwSXJ5eEh0vjqAsU0h7ZBzcF4l3x3FYiclZGcmuMs1sa0ooF1n032xXQWmgLfB8sEsSqc7mP3FA2zgpXCzZZiS5g/hPLecYanRrpeFUVWifDkAFyOZKsng/zCbzcKFSwjDH0UprScejGHwQK296zgjtOgYJQui+SHUhDOoh/PrJnHlLKF9jc5xnvwgxybaPYDkiA7YrHxBaADWlmFijGxA5ZPL5yPoq2yhM9VNIoxYYYlI0OkNFpOvNKMdqaqubh36lwYvMjkO1YyFZ/RIxGbn7L9XDc8zssypv/6dHuXe2lcqvVvc9/u8q7eca0R5lZlNmdrmZfc3M0v0/zezR9fy3zOxipK82sw+a2RVm9h0z+9ns/oaGhoZFR3gZLY2VyjKzMTM7XCWw3dfUHfp7MdscwtF1VXLgNnf/6/qwz/fck+F0d9+YnahqzRsknenu15jZPXD6tZI+7e5PNrNxzb2XecczjMQ8CytN2/zKTJmKJQe3f2+QdgDdalYO/Q4dT1Y/klvgexFsHHbZLN5P38bgmct1MOsppDFOU6ycXQ/b+irYitNwkONYb7EuONwDBmkTPym/x/xkkLYZ90RLocI3gNHGtAXneUKToXZD9pgpIAyVHczm22CPj492fT8u5LLhWMG8ASsJJh45OF5TOe1ZKOeVUEHC4wgNdmxt25UoCFnVVP3t22c+SDz3Woqmo1Z7HOYTToxjbCbTmRsIKsUKD5nvAb6+P75ok/UX8ZPWY94iNt2ZDsAlabK+dAy0yjUJ8c7xPL2t4mPD6axDUM7j6/F6XhCvJGN58ktBz8H5YAmZjMzs+ZJeptLb43Phkh7Ye1PFbAPC8KzpY3CcOSbuDp4q6cPufo0kufvNkmRmB6kYbZ5Z07er250bGhoa9hyW0DoEFS+jo9098wSeFbMNCFvM7P7u/j1peqc0VRemn8xyH+GSzjczl/Qmdx+e77+/pP3N7CIVsvzauufCUSrE6W1mdoKkyyT9vrvP2AjSzJ4j6TlSMYtmgUuDeU8maZKGQp9WxPBzACkGFZ3wOKK9nWuI/6H8HAJaFawMrGsVNiANBtgXoz7KRA0hNAyah0mCw3Gqs1qW6tFtQ7/S0NAbsxPkuQ+vv+g+x8M1JCq3Z4fzx9UqWZHYdykaCVlMgdC/n7boYNmsh62VUU5MMhHHYdTu6J4sZ7Q3NpvacP/B8Y7vdfORpgvAXrM/tIV4FIhvRyOMKmOvirea2lPmy3/KFBKZQTyAFfaAqFFqtXxr6gzNcfgKUtDo1lBvTqrhUvdHw9FOESvX+4h2lD2bN5MG4h+OCZqxKCcN0lzpvJB7Oi4dL6NrlSvVc2K2AeFlkj5hZq/UYJuXB0t6scoINAoe7u43VFPQBWZ2hbt/buj5D1bRPg6QdImZXVrTT5L0fHf/NzN7raQXSfqT4QfUQeZcSbp7GXgaGhoaFhZLwGRUHX+kYqm7yMw+KYTfdPfXzJXHbG6nn66L0f5I0gtq8jcl/bK7j7T2z91vqL83m9lHJD1EEgeE6yRtrMx/q5l9TsXr+/OSrnP3sHZ/UGVAmP15ygOXBkgWOt4LQWNoT582ilHrotVsqv7Ctt6J3vOtmQKEWwwY5Tqw069UikSCnjHmzMuIrIrzElEk5rOOXhhBCsmS700eHkZ5hm0NWnbZIGkCVsSYN8Fm6B1tIWFaoRWRwFPm4LNcOUvCOpakTURF0OWMCkDIxAcdROe5WN2ActKfK4QlF4uqQf8is4+2oxmcjlEBelAlC6bTrTk66hOpefjlswmnc+AngL5qVTvaghZh5YeKk0SHHYOKy766OUljOQ4c+pW672yIP8abOqF9kwdk28TtDpaGySha45r6N66hzRLnwqyhK+qH/xm7Ixl366nHPy/pFUOXnSfp9Wa2n4rgD5X0N+5+o5lda2ZHu/t3Vb7E2XvT0NDQsGewjw8I7t7x/qxzsb4gO6aZ2bmSXufulyfnJiT9uqQ73P3dPVkcJukjNQz3fiprFz5tZs+twr/R3b9jZp+W9A0V3vhmaB/PV9m2c1xFBdqlhXI0lwazppfRVrDUiXCFoJfGNNUkHSHtykJlUpuoEwW3fWyQFOM3mueWxM7eZ0ffP0mLa0mCidAcOssUKWYQQE48bAYrXB0U7MG44F/r76FIo/9+fQBX+II1bq4GSJYj2ouMkGbwqKZsIzFpQMipuq6vlbMGK2zTIEMd9wk+IXoMNcN/HRwGzacrTWggyJMKWeRIje7A5Hy2RoNzS/TqiOY8krZ1doh4GMu5tjb4vdnpMVcSKzvGEJ+L6ld491D9mSOMWrwpLBu7Xebsx0eGUjIGDfv4WCVOjZ83jfwpnAN7wGRkZn8m6Ukq38KbJT2zmtxNxevyLJVX4Znu/u+z5HOyyo5pq+r/P1bZNOeyvnsCs2kIb5D0J2Z2vMp7dotKk29Qmat8q6S+wSD29TwhSX/j0P+vVlnzMHzd19SdHmpoaGjYO9gzoSte7e5/Iklm9gJJfyrpuZJ+QeW7u0HFivJ36gYiGcZbJf2uu3++5vUIlQFi991O6wf518zs7iof5ntKul3Sd6oZZ5/DCg3YFk2bwfmScDsFYWjP3Fo6lJIGaK5gDjwcx28pP2Rq8RzYctdiacPhld5O4haywiBjzDIL8kmiFvXQMSTSKyYUALrFrCb/nKq/ZM4hNA24YM6xxdgkTsOWu7oWYBJrI+KQRI/KU7BHsmkeZ7GQ0m0MViUXjFMX4ZLuYMw0vr94cHjQh8rvY780SIsGA/VdDw1i253dy4aR6Z2RxjamJhXpLPsY39CgZdwPYfoF+AES+Y2pjTRBDytYbbNV3LVBjsWucjtwPkTqW18SbxrrhrMa2X7VsZ/COKd++MrOtQHFrmCRTUZ128vAhAbbDjxJ0jvc3SVdWhft3tPd/6Mnqy0xGNR8v1A3PJsTo2yh+RNJF42SWUNDQ8OyxB7yMqpenc9QGStPr8lHqGucu66m9Q0IXzKzN0l6r4rkv67idXSSJM1mblpW+yHs1IAtkUUE2+KsNLntyqBgdO8JynoM98PKwjdRK6DWUG3uB4EGr6u0kbQHkTKDRJP90fFpqv6SYQUBo+7CnlNdw7uxm2i4jeUDpOYbMMmwLsrHADLhA0MeC73kmMout6LsLMhklQlG4831ZWMQT5Ypnk7th+UMSUiDwudrLXzUx6n8BPH/F6yy/jmGS1099MubpGnqvRrLck6uvYxsEnW79iP1Mpxmr8o8yaIvswUyssp7TkXlrI+9mPlBi7Uwj35YTuAAACAASURBVEBnuAe0vOlPAyQ9CG/NytpxnoZbkk2mj8eq+HW1aqiDsT3j/aRmx5hQkS01iOipR+ETdwyXsE9pYTC6l9GhQ2F6zuX6KzO7UHkYiZe4+3nu/hJJLzGzsyU9T8X9P9sPeTYX+3CPe9lQ+qn1vt59EZbVgNDQ0NCwKBh9QNjo7r1zn+5+Rt+5IbxH0idVPurXqWvxvpdmiV7q7qf3nZsLcw4IZnbcqOsO9jZ2asAuyOuznZqmcHx8ULTHIjHsvsdwnQGZYkTIZGAgWvcr89oMBham6h7//CB1ZLl0YAl7Ky3eUSZagk/EcTCwKaSdAAo2FqyRkyod6h1+LyxneByR21LtqXZndlnadSuVvR6Mdap7SlLXGSrqhOyR5ussEGY8vhNBlRUaRm1qDTtw9VhoC/Sm4hMqHLpnlJONCKFXJ65i9H6bqr+Z8w6zhE4zrf0xH9bdilqMI7IJKT7oHqzdkKSn7DHvcickiQ9mz4RW3E0x2EWy7y214ag6KvIXD52TpHXoGKt3yQt/FuwZL6MN7h5K0RM1CPv0MUnPM7N/VJno+fEs8wfzwigawhur6+fbVVxHkzdi38D+Gphd6HkWxxxij2cPijlSpk2P8VRq6ZY3w4FqCPWjuZof0jpMcVUSBofVtWY5D0bdMsYOvvjxHefnONt1kO8FzWmr4mtCSwhj+E1LQLfTGBi5EwnDetSvIiequWavln81vqPxlL71a5kpkB+GbHvR6d1BaQ6j7SwqmjaMMQod7qZsBfaB+voYZmsPr7YL2iT50Y1uA6MC++pk/WXZY6aRYyqbKyTmGixSlyPiATTbRdm5KVQnvHu2mo3Oy/UJh58/SDql/nJCGyNTyMc2yjbQ4cCQhaBhX46y94bynDMk5i5g8b2MXlU3stmpYrR+bk3/lIrL6fdVXoFF26tmlEnlR5jZBknPkvQVM/uSpLe5+wWLJVRDQ0PDPoU9sFLZ3X+lJ90l/d7iPr1gpDkEd7/SzF6qwmv+r6QH1cUSL96X9lbeoYE6mgVfIBPbCvVvIvTWLKxBhwVzwjGUVS5aSqJ8b4LxKqxHSTA0Kd0/J43NRaIXYh7Yc34sSVvFiohOTlMKafgBMYFMdhimIvIyKveVFmar6nA62zxlCmkk1kHOqBWQ3B6cpE2TU5rDaEt8UP2lyWgrKmIioh7SbEITYkzCosWiUFTzKFRtb7Jcdoe4nWbByJ3VyeyvHbpu+Hj6xmxz+k44i0T76ZSd/b9qvnRQiApn/+JiOc0Eqz5eQ9YHv8Gh7bIeoh5ZXr7GC8bql0AsI2k6+OiTVIwGrlKtH3P374xy/2wb5MQDHmhmfyPpOyqz009w9/9Sj/9mdwVvaGhoWFLYxzfIMbM/lvSPKl5JX1JxMjRJ7zWzOWPBSaNpCK+X9Pcq2sDtkViXVL90l6VeRIxpwITJmMOWTJY8QRoR5JfUZDI4J62c/w3HMYHMjJhBZVtrwIGOqhQqsadL0iGVdPXF5gpJeD7KuS5JIzrrc0ingkHRtt5hQlE+xgiP+QSWl5lWn8ZVUB6TPTTHMQd/VCWfVE7IooN8kpDyiaFoUYOI+7ejPONkjB0qWTFBHhvsmFogV/8/dOg6SQfUV+pgxHCkclVp7pGws3MONkg854RiqoVsOlu8ldnjO5kquYCzugcdnFzIT8TRM9PXweX2CZ8pv3dCW0RnXVmFnoSLKB8fr8KU8vNRZXTdyDRLlv34hYqAtjSC2/22pAe4e+cNNrPXqETbfNVcGYwyIJwl6XZ331EzXyFppbvf5u7v3HWZGxoaGpYYlobJaKcKh/zhUPo9NaLxbJQB4UKVzQaDChwo6Xx1Q8HtE9hPA9KXbU3ZCRdAY1lQzc7uKtX9cILeNeRtwQq5GI1zCOFO8l8HSYe/o/xmNnyI0adzhBbAfhm304LPcoZ0Hftttmiqz0A9Xc7Mq4pl/2cc19Df9yWXQx8NzyqY6ydnJqUhOOhCymIEt2XdBTntuJ3ypmDOaYwLaWBTZyhzLkSM14eSVvWPXkZUe6pD0gSY82HQGMNlmPUQWg+bKFuchX2XOsx6et8bqlfx/M5LwcqJslH3TDSEjsZUz/8CVIBEO1kFVeY4CJqFru/bEGk4exajs+pqIfdZ3Pc3yPkfkv7ZzK7UYGrp3irq/fNGyWCUAWFlDV8hqYSyMLOFdOZqaGho2LexBExGNZr0/VX2nTlCZf7gOklfDgvPXBhlQNhqZidF/Asze7BKkLt9Dls1cO9mmK4s+Nd2UIrxoGNUtIJBTZA+0kUl2BK3MqGXxmeGfjVgopwEgByrktPZ+iaeDynICKmAxD0kattw8cp4QLaTiSRNBBdgWIOoXU6G0Ic9XFdQN8wzjPuYP9lS24CMkEpciNzXq6MYNBk/ov6yaGsZCyGcpUitN+EJa2JdELWCKRyzvQOV/mYTHFI6wZMFrcsW4NG9n2tqQgFh2dkH1tXMjqLaEcobu/QOvADTMaap5dElKT4drLy6fpVUng26cmYa50WCzHPuaArH2RqzKCdf3X/C8RnDxpPdxdIwGcndd0q6dHfvH2VA+B+SPmBm8U7eUyVYUkNDQ8NPD/ZxDWEhMMrCtC9X39ajVVSQK4ZnsfcVcKVyFiCOrKnjy5xNPEzTNnI1agNRdQwiwGsjU3CgVdWIy7kKHN9WGRz7HcuRrbEIs3AqugaEtLNtIVlqsLmeUAsDjkaKH1oD51cYPCPqBlrDTljy4/mY+AjySDlpso3y0RzP8AzBmElOx5PrOhlkG7qs4QqAmD85MkmTBhXF1ejhkYSQ2Nl+mTDNr4OiFbXMPhB1wvkRNhf7dYDXTvchFu3a5EJmGjHKO1oQZ2Minf2i8sTxtw6SDocKEhM9cPfj3Fe8KdR0qBWExsjuGVoFPQiz9S3zxhIwGS0ERg1ud4rKvN9+KovS5O7vWDSpGhoaGvYlLBGT0XwxSnC7d6q4k3xNgzHSJe1zAwLbjF4H2bra07LlwCQ70yFtMlYkDWzq9FFndVbjJQOfhYLBJbg4nU1lUCfpMN2KiHOTOU1JAw2Ce6PsAEseC7ZOraVDOcPvhV5GyT6RHUlDg0B9kV1FAZPAfgR3Cwl75W0950MzoG09uGkn9DfLGTKxibNtJDtzJZydivbmPdXTjH2JhQsPKzjikMVGObNF3lxnwCaKtucj021YWc7T6i8rdDVViCgb3aWw5mB69opeRp8sPzdDKyDdjw7eE6czQrVTTGoQ0evoLBUaBP3/iNUrtDDeQXtmx7QFQd0MJwuPbSqRMA5KzkkaTUM4WdKxNZ5GQ0NDw08nlo7J6G9U+MM7VQaBp0la5e5/OdeNowwI31Qh14sSbnUhsVMDRsE1l2Fn7JiM2bgx8pOWBYM7jfyNx8HX34M0PrXqJYa0WNtAVkaqV9Uazgdke4fTzj6epFETCr8Qah00aY8F3erb7fygyvo6MXyiHrjughnEfAIM1NeC6wXlvWKQFM2ROSNJeQhozqVk3jnx9I5mldFoet9cg45x72C/nDegt1WwZGZay06jdlYoVAcfH22f9Tp2m0wDYLuzD0274nPuaCp50CbMh60JrYd6CY/j08EM7j7z4TwOtR1VnPVLvoZcRjA2dB2zYhVzDuHan7JYRhWPc3eqsn9nZv8maUEGhEMlfbtGOZ3uMe7+xF0Ws6GhoWEpYmlNKu8ws6epxDVySb+hEaUfZUA4Z/fl2rNgm9F5Io47NcJ/gq2RrU8bo7kJDHMNz1tW4ZtwnKxinfzxzGdjsiP4Jl3lyfoincw4+BkJKaUMUsNIRCtpR8+CP3Vi/NAHPxAaAOdPkiWpU7Als26TTRy21cKR3ZGPZvv4MMuwfmebek5SJhqbI4MHqQfhJXVXkiYNVgZQO6rzDbSdZx5c6BbHgfq/t/5SawgxWXZmGc3JeSL2h+npMLL1U4ZPqht3a7rWWHbq2NE67P+XzRQumy9AV6EGfOfQr9Rtz9CCs9X77AvsvusP0dBq7Hlg6QwIT5X02vrnKmrtU0e5cRS304vN7EhJG9z9wrpKOVtF3tDQ0LA8sYRMRu4+pRICe5cxipfR70h6jgrpuK8Kx3qjuvRwnwPZZbAlMuvrYVs8IoY3GuKDok3Sz4FsKfZD4OrNZHe1rWCPcQs61nY8M+ypJFgws0/zULLoZEfGDpEJNtUxpe5IjknwO/FfQgJS69A3uHaW2tHKzo+krqdNlHlqkBQkmTZl3p4F7OTKh1DoWDds72nQNSmeT8rZcVeJuQPOlVBDSJbBep2xIDOmtlDnprb9aEaSpEE9kMxHVp0F5DiOtqU0ZMxh5x3nTdlkRceLKJtDIPcOzytkMFWf9EVcxsLV9G3QiKgJxfGUcsR7wbLF/BA9yTqLwRdqIcLS8jK6v6S/k3SYux9nZg+U9ER3//O57p1zPwSVnXoervoq1T0/7zEPeRsaGhqWHvbx/RCAv5d0tipPdPdvSHrKKDeOModwh7tvLxukSWa2n3If172OHRr4MNMTIRhSFspFUllhIXWdhIKeHgU7+OTXcEEwp0mkIW5R8DkuYk28Wkg6gvGSkxHBzxh+JkzAJKRZpNfOLlRQWsaCvXLBNV3P76zCroTQRwQfo/cNMr2yctV/wWmyxqn6A7Um0wBo+l2VXJdssdBRRKLsnD/pIMrOZQZcyHBU3c/hAPrnYz3G1roamW5MwX6/iTTmf2P30bxFGkxnsezRbWjhJ5O729Av72Geq6g+RUWys12GTvDg88rvDnzl+NDoUBQ02pt9HmUPrehinGY9RJbZLoHSQAvOdgSkVtAh8uPqVvDuYgmZjCQd6O5fim92RRZ4awZGGRAuNrMXSzrAzB4r6XclfXzXZWxoaGhYolhaXkYbzey+qsTdzJ6sEZcNjDIgvEhlJ57LJf13SZ+S9Obdk3NxYRqQGLKEMBvfoh7cMfQrDWg4GdSB3xscj9fju2FnrGyJ5ZeRFvbUywdJnCOItb59ce+z/RBCM8gch6Q8RtAYjfPhF38c0mhnD8p9PNJur7zuAPgWbMXbEuVj3VGFSRxx4jEk2xQjqpbvZLaHMNll6DEdJxP+E57abDfKGcxyNTrGIYhRFJT2C8k9bHc08pX1+WTw1FyzyLxB5jtRW3EcmgP7BfvDtMaIfjetCVEjosp1Y5WgzwYfQnHiIo6pEaGRoxpYXr6nUT42AbWiQGd/iyFxpCENYxSj+ChYWgPC70k6V9IxZna9pKtVFqfNiVG8jHaq2KT+fj4SNjQ0NCxZLBGTUd3R8mR3P8PMJiStcPctc90XGMXL6GolcwbuflRy+fC9UyqkZ4eku9z95OSaR0v6W5WBfaO7PwrnxlS2OLje3R8/1/NWKHeeOHjoV+p6jh8TNIOGyPA2IS2jcT7o+gFw3bgDHklBh2iPD2oDarseTZXth5DtAEYGFPdkYeelQTE4p5JuNcYuQ6eayIDOVvHQ25ERjfehojBiShKCdQy23ZA/i3lP0N+ZdRMisbqDJXe8jVgR0e7UjiaSa/vo59eTtChnz+bQ0S+pCWWm+YyY05+NVRsvIufFqEFMRHs8DImh8bESqVoG+pYN3zj0Kw2oPdo6W1HN/sl6CG2B1UktMZqD9RCvIX0sJ/genyrpE1oYLAEvI3ffaWbPk/R+d9865w1DGDWWUWClpF9V3nX6cLq7b8xOmNlqSW+QdKa7X2Nmw95Lvy/pO+r2/16s0KCz8ZsXnYbtyX58S10UtZYdPm7iKMLeHSPKYahzvsXx8ecMcHxY0GHX4O04tt5PMfhyTCVp8T5zEKF1Z3ty/ii+5KHm80vKtzAqKovAxo8ehQ5vVG7TAQFurPfxAxZZZvP60mAgyBYcSl0TyfA9XNyUzlhyJSAfGm1Hswo7zlT9ZeXGhCpMRju4IVH9peUqC8HBD3o26PMFjDqbRNpkRl446xwZZPHVpYH5lC8SbTnxJafdL45hIsu8jdm9WLXxevCDT/NQmADZFWMs74Q0YaYL6Xa6yCYjM/szlbFtp6SbJT3T3W+opPk8FdOPJH3Y3V8xS1YXmNn/kvQ+gcq5+5xL9EYxGQ0H2fxbM/uCpD+d694R8FSVwl1Tn3VznDCze6lsSPxKSX+wAM9qaGho2D3sGZPRq939TyTJzF6g8o19bj33+VGsJBXPqr+/hzRXd116ilFMRlwRv0JFY1jVc/kwXNL5ZuaS3uTu5w6dv7+k/c3soprna7HPwt9K+qNdeJbGcDEZVAyRJENkYGvjBJ8UzII2ClZn5ufIa4NNsBOFbQAMZhMm5YIhcSKN3ouZ91w8hmw7C5PccTvF8Viwxz47VRzTrhHlZEbfwXEwxB69LgtUF1n17b4YdUMdmCIF9SE5jD6wKvNT5ANY4WzPULUyn1hp0CCZKaHnnmgbdjXOy25Jzmf7+PB8tDGZdaecUX62a7B93sTKi3pgPlM4jn7NDlo1BAaUo+KYTY7TRBhKCcvGVy6LshJmpE7ohOEYHhdqYbDIGoK7U3ma0C6695vZr7r7ByQ9xt2vmvOGBKOYjP4ax3epdItfGzH/h1eV5x4qaswV7g63HO2nsu3WYyQdIOkSM7tUZaC42d0vq+pSL8zsOSorqTsacUNDQ8OCYQ95GZnZKyU9Q8VadjpO/ayZfV1lKP9f7v6t5PazJX1A0gfVHRZHxigmo9PnumaWe2+ovzeb2UckPUQSB4TrVCaSt0raamafU1n5c5KkJ5rZWSoE6SAze5e7Pz15xrkqLlY62MyD9JG0ZSEM0kkQMtqg3H2dIIzWVDVIeYNhUUOI/MEe10BtObBeS1PtXC6kwZYoOklfBMyjHX2MXSXkZxoN8lEPpGBxnE00S4O1Wz3MLGRm1USW1GRIWOMetiurPpgkSfARK5ILj8bxcUkahQrjPC2vWdgPqmTRtqD1V0K1C5s4H5NtBcrHhPgsG5WamP5Zwwt+FsfHDl0oDco8hTQKFe1OnkkVNeZK2AcST1U6b0T2bA7OEcQCwr5IIiFyJ3z7CUMnpe5eTpOS/p/mj9FNRoea2Vfw/7m0jJjZhRqa1qp4ibuf5+4vkfQSMztb0vMkvUzFYf1Id/9J/SZ+VEN+IhWbzOyzku5jZh+bUYQRIlSPYjKa1X7v7q/puW/a5ake/7yk4YmQ8yS9vq5+HlfxDP+bqvacXfN5tMqIOGMwaGhoaNhjGM3LaGPmTRlw9zNGfNp7VLagexlNSe7+KTN7g5kdmjjr/FcVavdOdS07I2NUL6NTJMWI8wQVlp/tekgcJukjdfn0fpLe4+6fNrPnSpK7v9Hdv2Nmn5b0DZXqfrO7f7M3xzlArY725WCPfabkdOQPVkiXHTKkoDFczJbFWqDBMxamZf6Sko6o91NDIJsKWzOznEzSSPCjaB37MisnmCArJ9vUhA/I5h06blv196b8fGTJOZ0Qg1mSfG5IzpMURj2dirRpGk3GSLUjVEe2Pys8HkbNjx0nvKko6Jah36FbohqpFVC8+OawiabXxyGNu5+sjYv7pgzDUJ/FNCFXZRvHIjbujMpVlOGBhbc1mx/JFOwsyLY0qDKmsTmOiYwpc1QeKyebP5kv9oyX0YYaK06Snqha42a2TtJN7u5m9hCV7jFjR1133y7pUjM71d171+HOhlE3yDkpFjeY2TmSPuDuz57tpjqpcUKS/sah/18t6dWz5HORpItGkLOhoaFhcbBnvIxeZWZHq/CCH2rgYfRkSf+fmd0l6XZJT5ltS+PdHQyk0QaEe6vrLLBdQ3uO7Cugl1HWdrRXdhxyg0XQCT6LGkdFMGgb6Tgpb4zz3F8n7MpgVZsxn5CpXJQzC8+wKUmjZhvkdYKUkxQuykFWlcXXpiDB/LONX6QBs0a3vB7aVRwy3l0oWn3eUFlgM84NRXOtJMuNOYJ1SRozpRsP779h6Ffq1kP43aPhbqk2d1YNlzmEST5bsCUNmiaLKMEpgLVZv2P/5PlQm+j5lJk/GHJiqv5m5ZWm64RJ0RfpNcWyZRoEqz6mOij6ugOTC0gzY+6LNzHTkdfojoDF9zL6lZ7010t6/eI+vWCUAeGdkr5UJ4Vd0i9JesfstzQ0NDQsIyytWEa7jVG8jF5pZv8k6bSa9Fvu/tXFFWv3QK2OtsdgnbRddkIiZ/7oQcvo4E8bbTyITCvbD5DG+6n6ey/IBtaWaQBkzJmnTaSRdZFFT5MpsmQ6fwezYjm520gwRLKuoLzUKsg+gwZj3oBELWLB0TMqTNKdzU2AeBfZrhRz3YwDDdqQdJuI9mS7U42MiiTFZ4NE/rCtRzm5eU+2WLYvBEdUc2buPypzwJcGjdy3j2RopPQ8Cq2HFJ5tGFogN7gB9Z9KmHcWWoXIvKXYNNEV1/GdYTlj4mQSaVG52b6yUnd+Z57YsY+HrjCz12mWtQvu/oK58hg1FuCBkm5199dKus7M7jPifQ0NDQ1LHqEg7OP743xFZWPrlSrGtCvr34kaUbxR3E5fpmKdPFrS21QIwLuU776+V9E3hxCEoXf0C0pDFhwZ9a1iDZbdF+sorr1p5vltmEOgeToINe2yJDhB4Miwgkz12WWnWXhf9Lu4MfOukQZUluzxtiSNcyXVQ4XzBtnWmHxMVB2ZM8uU+eeTMK+MC+h+E+ySKhM1iKiTKaSR3oaHDT2o6ANX2fMWFCQ0AzZ7tmkP+yLLkcXbO2J6syYk0o4e7laZQZ7pWcztvrKFVoQ5hC8mQevYP7OZTFZ3lP0RPM/CR4NPIo1uY1FO9tWMtS/SHMI+riDI3f9BkszsmSox5O6s/79R0vmj5DHKHMIvSXqQavevK49HDifR0NDQsNSxRKJfBw5XGV5jOL+78viPMzDKgLC9+r/G7jvp3uX7AnZoQAjITDKC1VmHcOTQL0G6/iAcxxLKKaTRIyPo0qaZ5/s8TEKT4XmSoSB11BDifhLbjgIQ7JjdIVvFShZNehsCkOYGe+QqVtDDHVUzoNKQRb3M3PcpO0UKIkmbc2e5Z7aiOgsdygdklccyhVrDPR+/Ozi8pQr95eR0tqCZovSFyApPoo77/MlDJ6WhrT6TTKnZxgRN1obss5gv2FYbiQoEL430LCpr30Y9q4euk9TVXCM896OQxng08bBs3QhXxbPCqZrOA0tsTvlVkr5aVy1LpUbPGeXGUQaE95vZmyStNrPfUYmk1zbLaWho+KnBUhoQ3P1t1RHooSqiv8jd+7Zq72AUL6O/qnsp36pixftTd79gPgLvCZD0BaHY1nN+mlll1J22WrKZoLmbkzTmCTazvTIwdiyGNwzySqJHVhYm8U600vpLk3HHqSZkpucGjaEhP5kxmWiwy8y7hmWfGhx+PTmdaQPMcuXQr5TvEzCJtA79jBM0vsd8Aikp2eVtQ7/DmKq/2Y73GhBu9qWoZipZ1GSizBSJ5VwbqlC26phlYwZZ9Fm+/tG27J9RD9Rg0SDRdiw62zPaKesWfYuGpxUZloMqRmiuzDRT63l+qv7y3WVnmyuewohYYiYjqcSNC89Ql/TxUW6adUCoO5Z9psbf2OcHgYaGhobFwlLREMzsVSrhht5dk15Qw1mcPde9sw4I7r7DzG4zs4Pd/cezXbsvYIUGbIuEITOxduyY0dKkcrFQgV4Q2QpehmPM9iMC5R2v1H0tWEtWqX0+6sFQsrhFJP2djcmDgWWeRdLA5YOLArIt2TIPFbK7SWSf1APLFOVg2bItKagAhIPJKl7A42xBRniCsV1ZjngAV9WwQaJtMYeQaTr0FAscklwnDcpM4ttRBrJ4PaH+9W13GcesZAq1PkkLlg1VZisE/dHQZVK3C4W2kJnz2QSMNzUW9X18zwWZl1EWgTWrcPZFrhtZQA1hX/cyAs6SdKK775QkM/sHlV4+vwGhYpuky83sAnW3Y5tzkUNDQ0PDcsASNBmt1mBcP3i2C4lRBoRP1r99Hjs0YC50GIo02tYZpHQ81AraWIMVcp8AGnujisko6ZF0ZZJW7fFc/Jn5cdNHnUQw2Fo2BdDn1TIWF/TtLRu+57yJ5Qzm/VmkJV5GV6JQ2XqK7GVi2eORJMEUaZqdskI4cRLskxmEfZwPgpfQdDoN/lfOPL4e5xn8M9or29eBc0NEFnl3Nan3fSMRacGc+1bdRpmYKbW0iFxKZv0v5edGdCaGMoqqY/fOQj5lDlwk/eOUKeZ0sp0HiS09x9HHWA/RB7kDAeZPrlrAr/hSMRlJ+gsNvIxM0iM1gnYgzTIgmNm93f2aWOzQ0NDQ8NOKJeZl9N66LfEpKgPCHy+El9FHVfmxmX2oLxLfvgTTgHBk/v0kyZ1Vy1P1l4wzW3rHm8K/mVoF7ZXB0MBwtu7snhq+PUBySPIaxIqk6cT6y3mDscxHfRJpLEcwLDI5sujAtckxKCXJZ7w4nOvIQgSR6MWUDU3nDGOzJhqWGhu9oeJGMs6YmKAXEc+HCvPFJE3SLbUTcT0F2ytYNPtaPJKyk0WH+XwlJ0i4ujoyYBtG47ISsxhElyMNnWz7J8rvOOaGLq99kW76fD9C06FzWs+U0TTWx8UnIpHGiih8tiG0NOg4LAddm6KTsb1qg0ztnJEkaeEWKi9BkxGXvJxqZnL3D89102wDguG4b+uNhoaGhmWPpaQhmNlbJT1Q0rc0sDC7pHkNCN5zvM9iTANWmgW9JPOeoCdN2G3Z4kHxeB1t65kbRrZVFBS1LKY/mX1cyiwzbxWSqtioAgFUuxdERZCOZyyalJYVdWOSliwaOBJCh6k32ymM6ZkHVd+i4mnDNNsg88tnhUYbMLor2WdUaEbxNdAQCJYjqjSb96CY2XqK3h3q4jgL5cq+mG060LPd3Hilc5sw55N5dWVRSvti1ExvuEZtMxqPlXQajkOmyZ5MQz5qT9nanmSxyuFQBdhs2RTF7mIJeRk9zN2PnfuymZhtQDjBzG5V0RQOqMeq/7u7H9R/a0NDQ8PywRIzGV1iZse6+7fnvrSL3gHB3cf6zu2rGNeAfLDxgjx04sNkoablawAAIABJREFU8dMzf3W6TPCeSOeEAG3a0RSgKNnqz2z1dJ/HUHhOUYwQeTyjd9KA4p2CNIabnM11ScpXcVeb/C24juWIrCgSswwiSeIeJnPaJieyNSB0HyN9nay/pISxyprTabTNh2cKnrMd8w1RJmot3FY4NAO2USgofAzt8GMhP+kUGzTs7xl1ZxtQJYw5nb4vVjKdGG3E6uJxFquos5NZaAYs6IahX6nbMYKzUk4eR5noMcSXJTzAkmimmSIxnP18sJRMRpL+QWVQuFHFoTJI/APnunEUt9Mlg+0avBtZVN2Oypd9dfnVio7InsaOGOotg299OznGbGuWZfbt/VGSxqz4Yk5/NzjaccPT+ADxQfQvjJeXsmcbpfADVV/MbI5dGky2Zuv0pMFLmplVOiyE9R1ysvIYbiM+TFNIy0JTZLPCmKzNBmsOAvxARrYc+LINfjplCtLQN7Ealcqyx4M4IPAjH+Wgyyzac1sM4DgdTUxngMyttOMei+PpRXtZOHFmxJtCJnbgjTi+cehXyuO/w9Niqv72BYxcSCyhAeGtkn5TxUC6S5auZTUgNDQ0NCwGlpjJ6Bp3/9ju3LisBoSdGhCKLEActzW8FyjFeDASMslg2XT1o40jriXF+vbMa7Owz2TWJEA3JefXJcecNw3StO7/b+/cgyy7qjL+W+mZsZOmpU0yJDIZaIKBIESSEAPhIW8KkKeFpSCIUYtSUUGNFIKWYmkJBfKq8IoBJcobEkErBJLwCCoJhgkkMRMhhibJkJDMwIRhwjBkZvnH2eve79xeZ7p7um/33Mv+qrru6X1eaz/u3d9ae+21dKedquxRz9mkDPoyJ2G6gb42ITGed9/RPgXtekbTdJkj4lWZptNyB9YLgp7pg5T7RD9kAdy+kFwn1+4QZq2aTjSTuv5m0TL0h+LY5LpJNasEtI/UphSdq30QKoiG4dYBHiqMuGPukLaJvtGqZ3HidCxmHqKTKnPY9tSEF/2lsum4Cg1CG1S10Vjw10Q92/qH15U66RgJ+bt+rLuypy4VI2Yyut7M3k8T0K73y7Bct9OKioqKioIR8jI6nGYieIqULdvtdOSgal2WqF6JSyt0RdC6XckFurEmyxaiWoXSrcKMlGHF4zX8QRZzTpmaspK58qlEbTYO1Nib0SVlYkqbQijVhJSdhm+ZCBJtq+ZhPc6S3eiyRrZuGkRzg8qmC6/xbdRVZ3Wsy4KYZbEl9Dpdgxi4BfrtresCOkRCVGWs0Q4btfK6mS7Q5QSQxceOcafqorrPJklgtAtjiGZeIjpsZuU4fCYmVA5dL4sbVV3NkkZpf4Ympv2hgsYOQBmrO+VXOF6Z5bzRZZguT/DlYJRMRu5+5sHe25lmuKKioqKiQZiMFvpbCZjZWWbmZnZ0+d/M7K1mdoOZXW1mGcXQ+x9gZpea2bXl/58zsz9fzLvHSkMwctfNYNzKrJXs9Gihpu4LyvHD5Dro0xC1ke6af60SoJh9NfCZ3pJpMhkLV5Nzj4hq5dSuO8t8HJYcqyaUpJHc8+35pzMHEJVJ66aPz6Iz9DyOtHI/I8dZaAp9QTBVsTn3BkEWLhl6lE+7VV8fXaumApU5RFGW3dMM1F1ZK78xOa+NF5RWNZk7k7LEE+dmETQZii3mPMt8tNxjQw4NVa0hNqJO2jjRsXrPbcl5FUT7K1kQUIYf3yVt74nkOv1uT03SHdhxiViNNQQz2ww8GbhJip9G8w0/gaYX3kG7NwbxD8CfAu8CcPery5rC3yz0/qohVFRUVCyAyIew0N8K4E3AK2hHh3g2cJ43uJwmnfFPH+AZR7j7lwbK7l7My8dKQ9hPn9BniTvUTXtzV4zpQBYRb6GITppopXgZ7ZM1hmDBahrP9hxkIR2gb8tuZXwM2dVIryw65Fe2rTQ3vIyybDXQ8xyZFBYbCXDUP19fGXVSkZTBhUitIO3xTlWPZuU41gu6Yi1E52ZpTjN7vJZ3hD0I6BpCFgG9NSyiIWalTH8plD0HtD+ChuqYjHpkMUug57WzWQaTagihPGbeOVqfo7SiIVMWjgX6HneZq5g+R9WvkF/Xq/T5IaA8c0Lun941//FR5Y1aqO19X/JgjUuE027yYcDMngVsc/evmmkoOTbR1g9vKWW3djxqu5ndnzKpmNnzDnBtC2M1IVRUVFQMC4vUAI42M91rfY67nxP/mNkltN0DAq8GXkXbM6h3W1J2oPhyLwXOAU40s23AN4AXLiQ4jNmEoLO4EobwSmiRs2yHpdqajxz4hDY1DwO62tuFgu0tjDoz+2qZMrmQuSsPSmg/rYBdewZOahn02VbGnKHfOCqIMsEi7B5hn1EPJXpKvOOV2lx6HESxtQQQTFFZfxZ0TtdsVNUKGqw+7AHVOkRNjOQwXRHMo8lUJL32+IHrgL4BWwdgxpizaH+Qu9JEf+pag54v/aWyZ7vI9ZWhEOoj56STZmMMqBqnlX9C+92tF2SDGvr7D/Sl6nJX2mGffKeyZbss3MsOkb2VQnaF4l8vYR/Cdnc/rfM5TX76eTCzk4D7AaEdHAdsMbPTaVpJ9cjj6F4Zw91vBJ5kZlPAYe6+6FYY6oRgZnM0XbIPuDtrKDN7HPBmmr7d7u6PLQsr59H8bO+nmWXfMkxZKyoqKrowbLdTd78GuFf8X347T3P37Wb2CeD3zeyDNIvJd7r7PBOQmf1x9uwwP7n7GxeSYzU0hMe7+/bshJnNAG8HnuruN5lZNMjdwJ+4+xYzmwa+bGYXLxS9z+ibNpV0ZYlldsr8OhPsNItVpLuTlZ0GHdNdl0nCmCxisZpNlSyFfMpw1PMjHFNatszw/VbXCuUSV5RPDRWtdH3fwHXQ8nGfKxVR9hn1UNmVPQWrUxU7cxiazbZkq0E+i7+kDaLaWfTTZ6SsqC07RWvQR8Zg0i+6dndoatofj5TjjdG56jEUodRnpSxbTNGxpnsKdDwFLiyfMmYzLVPHt74y2y8RCtWEeqdpwLzNA5+Q746eSsoS7aUloOaQFbq/rQwYbRp9ZTT3JinrjessUiI0dXoby8Ya71S+EHg6zU6Nu4CufQYxuh5IE84ywlc8E7hsMS9aa5PRC4Dz3f0mAHe/vXzeSlkEcfddZraVZhwsOZxrRUVFxUpgNXcqu/usHDvNusBC97wGwMw+DZwapiIz+yvgI4t577AnBAc+bWYOvEsXVwoeAKwv+T+ngbe4+3l6gZnNAqfQ5rB6/iXAS6AxV4bJM3O0aUVuVLoULCeLKaM2UKUIwbI7PCbidmVyQX6VIGUMSG2kWRTHls36iKRQ2VIY6VQtyUa27l0Q3/DZwrJvlHvilS2vreTxrb0eCfbIMyejclm2GYCHlk+l81qP6HBlvGXdY0b68EdqJy+f2kdZKGuth7rN37PIskGtuRkd35wc6wDVl0a5up/F4pese20WanRj4mevjwxRVNPpeRlliz/QX+hRlUqpeTxMVb8s3pQivH10W5UsEmwqmsU+qc+CyW7ifpUti8W+TKyGl9EK4j60xd1Ld1qiFoY9ITzK3b9VTEEXm9n17q6qyzrgYcATaeJvfNHMLnf3rwGY2T2AjwEvd/fvDT4coEwy5wAc1Uw8FRUVFSuK2IcwIvhn4EtmdgGN6M+lyZGwIIY6Ibj7t8rn7UW402nbsm6hWUjeDew2s8touODXzGw9zWTwvsVE6YNm4SELMRTMQs2z10jvnhRzqd6UbdnLUh12BE6JR6qZO0hd14bTILf6SGVFYdNuLW7FP6pq6AtCEGWp+oIsb6d4fuxKvgWhtaijlsoUhDndiSzHk5lMqiGoxhZyqldKFrBKG3znwDnaHlxxqbLpjJFqH6hjUy8pkWolway7cjAEC9/RcT4aTzd5hBOjCHJ9ohV0ueIHk9Lm6j1K1z+yBa9HdpyPRlMjbubLkg120aj2yvGGyfajId9kPKkxrKLDtB6Z19YKYFSinbr735rZJ+knMD3T3a860D2BoU0I6vJUjp8C/PXAZR8HzjazdTSGjocDb7JmWfzdwNbFrIxXVFRUDBOjFNwOwN230LZOLwrD1BCOAS4oLk/rgPe7+0Vm9jsA7v5Od99qZhcBV9OQmnPd/VozezQl44+ZfaU871XufuH81/Rh9JmPkpk4VgbV2l0aVFfZ6caBc5CzOqUwwoZiaSFLHqWkJfOo0EfqcTBr3bDaoy1aqOsBwcKVGme0qyPo/2E724+BPsHLdoxC26wcSO2/2kkRmUXt8WrTDpkfJGWqLUTfqFBF9r1Cl1XOZKNymk5Bh4WKlO6uDlu2drJqOlG/LNEF9PdRqIYRzxfhdPzGpbqmoxpZDI3W1oeoiKZ9VQocLFwXUPYn16pHUdQ5i8ME/QaVPm5FYC0CbhDtfINeEGNAx3JoZ/prrdpC68ty8BixfAgHjaFNCGVzxEOT8ncO/P964PUDZf9BvjuvoqKiYk0wQmsIB421djsdGpQEZ+kOWsw7bM3KbIJldyldM0mZsLow3Wc7LRUZc+5yGAr5W8sW8QB9UZZxXNn4nclxh007TMS6AXjn/MvSRGZKjHX5pcdus13gSn2VzmceLHp81cB1wN5y/w1ymS4xRB/pJm/VAKKLlcBPajsGO9Wbgr1q5RXRH7pGoKnnovNlLG7bOV9OHQNRZf3BUuegEE/HUq9S2olnyHHUM9ubA31GroJEPe5IyqC/E1mL5Pn7vjP/kVPa3qGmqgY8MXBOZYPuxN5LxH5GysvooDG2E0JFRUXFSqJqCCOGCfpMNLPTq8NEi+AHu1D/6I3JhcqWwmB736QMmC4s6dFyOlLiqpk8yyqmhFPtvlGnjVq5qLAa5zRASNRDR7NSsNCA1HNDNJ1Tr5t/S5iNlQTrEkRcq37vaW5blSnkVDkyDxFV85Q9RkgwiWy5Ya48WoTT7sry8WZaT4sZaufEjeqRFvZrpeNZUgsVRK8NzUEarOdir/GkpEOyHNZZdNmWbT1be9K23Zicb6lK5VO16thxrSqArlHMNR8T0l57pB5xmy6fTGnbhdqjZTFeskVDaLfzMlDXECoqKioqgNHzMjpYjNWEcBh94pKFjlf2t1npVDAPpQBZbHe1vQebUsOuMLhNhaVcKaMoZFKSqaQsCLE6Viip65nXs+S4Xcljw+6rrEkp2K4DlNH/Eujjox5zUpY5qGTZxwA2BvvVymXIEgNneQKgv1AgGsLOohl0BVANBaUrk1gQ2VasYr04kkPrTaE+qcamBv2ZgeugvRklhNI+Lvfv7Mj8FW3flbpgU4x1rXzsftbvgap0Ma618urdE2sD2bjR78Te5LxohlqlbFmu9QWJuEWZ559GZdU6tZKHLA9VQxgx7Kc/ZjPtNw0bDf1Bo4Mni3ymX9K5gU9o/VjtuGP+LfE90EVj/bEK04B+737UcdxDfImv67gwJrSuJCGh5v9Xv0hDSsRjdT0xmk6bSy092WLssWozinbKkqdcImU6YcS1am9LflR3i6BxqU5GWczgrr1LMQHP6AXaeREMLpts9UU61qJcA7HoYC12xR0/mn9aX6O/udnexGOVEcUY0E7alZTpxBT3n0uOMDWq2S/aXsPAy6SehV6Z0S9lYRBTU/PLgDyLQMjZ9Wt9Y0f5EjFiO5UPGmM1IVRUVFQMAyMWy+igMVYTgoauyGK5dWaJiIUn7fEsuN2sHIcZoMMNMtYJlbgE81cipuQx1j51Y1fmSnhvYdYT8QC1Q2Uuj7rQnIVYlmdOyvkjy0sz79skj07rUarpTMgFadjoeJiGgVATRpZfNFG1poSR/myRXT08M41Ly7Iw3tPyzA06sILtZ77BytC1IcLsOCtlqi0UFnxUEhddu00fGc2pw/cO6c+eKJktUjWZLOCj9oHWPfpzIjkvKtkGVSP3tD4A2CX/7C0a3yb9Tql6FxsRdbNcLFp3uZ2u0MY0qBpCRUVFRQXVy2jkoeQxiI+SfWVYU1kI3aCV6oqqsQFidChDEntllkkxC8Ott4eiooRTyf5JUZHZ5KHXJWXQ11rU3q71KHXfIbIrEw0imC3M6h4yJXVhllZyNpUlYsnYvnaMxpoOpqn1lGv3lZ1zajKOLurSDON8FvUZ+uR4Q1YIfUa8MymTNZk0nrk2svTH7tIf2t6Z1qvHPUeKrsCAYXvXemRpOfX8vw3IC22KHOqI7PqLsNXqVqr3RDDAPcLgW/UIrUUrr9EEo8NUs4zKqxqoffR5VgTVy6iioqKiooeqIYwY1pHvJwvyqTP8VJZxXJlJFvBO7a1Bg9VTRrwg7tw1//ZQQLKsggpl3q1galnl4mHqgaHumnGPuuUlCeBVK5lMLtWyLKabmnBDlEmVU9MzZvE6otKqyej9u5IyYbcThWrukgYN2bPo013QOk2GKqeVy3KB6sAKdtwVJyVknsvPR9NkiZNU9gkdq9EmmScX9Gm4DrzQfFVOPQ6Wraq2Ln5FP4nsIdM+aY9WTqlSPqPaYrahTM9r2x458Al9DUS/NEvp8EWiehlVVFRUVADVy2hkkdmFg2S0dumL98N0sLaMWSsrujk5nyWjIc+lnrlMZznMley0NuvEP8r2s1SH+gINLlawR1yG4jbdpqCPirZTZ5F4vRK1VlSDYMkdtuQeO1UDcrxUv3W6IBDsVdp7jwgdpFAJZyh0XcECQ4NoRZbIGl/DH2Sb5bQe1zAfsmngtrJe0LW/JJLZqJxZLqZJuWki02S0HWOTnGoAsW6hD9X1mWi0rD7AzvL+Ob2llOl3bypLX6rjUz2GQuvONo1Cv6GyTTHaL/qd7HQtXBqqhlBRUVFR0UNdQxhBBMtWYhDEoxU2OgsulqXNVOj58HDR4F0S2mKyMJZ9IkhGnDOSrI42LQ0hKEprMaR86mhV6h5eGv8nsgmNDnuvNkfL7puUZYy1tTM882FXxEPXd5wPKNUMTafjmdGmWvVsV7K2bdRjpivmQxYCOnu/PnQ2eY4w72OLgF8XRqt9HPXIUsAqQ53I4jyoVqDqcLZFPrQJlVPPBwvvSBA1U9rmZBE0lAkdFxNyfir6M9uhrrJkA1Cv1TWwzGvrJDm+kRXREqqXUUVFRUUFUPchjCSMXEMIgtaKhKt2xtnymcVozrLDQ994qjRUXrqnHKsbdbYlINvo3LVzdiIYYJZcXpmc2l2DiSqDkheEfOrGre+MrQAqZxC8+0tZS7mK5+uDMhqs+z6CZSsN1mCCUQ+hn1l6Ue2O0Ba+k1wH0l3antnOVq2HVjQGm2oyIYCu3SQB4Doyr/agmk70gSqjLdUyy72qixDRYfdOynSwqYaxe37ZXqlHdJNuaQl0pYDdFG2jX0Rl+4OyQT+gHfTbNhv/WqYNqmsUy8RqrSGY2Vk0WSQ3uvt2M3scTf75b5RLznf3wfz0K4KxmhAqKioqhoHV8jIys83Ak4GbBk59wd2fMez3j9WEsJ98A2Z4xSg7bNmCgyVl/svq0aCsLNjS9/LzwajVISg2dSqZyXLEtMhfZmdX9hdydMURjp2aUrfMnTxjevrYjHCqhqDNubfctEHlUCYYTE+ZczBSVUUuTwQS4VUbCBKvXRhNp4RUlZJek3VtAY5KdXm9RKOod06ycPF1jT2VPEYVoWwZa312Tvs7diVr5XTghXao2kDIOSdl2njRDqJeZSk8sy0BKkarPeP9XWsyEwOf0FanY4xoPUIQDbYlz987el5GbwJeQaMRrDoWWkatqKioqKAhHQv9LQdm9ixgm7snzuKcYWZfNbNPmtmDl/mqToyVhmD0ZzhlhUFUW2Shi70GgrnoDlu1w8duyiySpZ7XtJrlU82aSpBiQB3VlQtxc1IWFVV2pxEikyQ009IQUT1xQmoR92gmZUexXKHsUJnFhtCutB5ZTlNlufHQrvyiwRRFON00HIEw9Za58qmyT8sF01kOTb04KqVyZqlG9ZfgtoFzwAkyvraVcl2q0OPoxmzJZYP2ZRZjqGvNJsaL1jM6XjtO7yma8V6NOyT12JkknolHtda9sqivqqpnO4m7IsVG4+j9IYfeI1rkSnkGLcHL6Ggzu1L+P8fdz4l/zOwS8swOrwZeRT8ZrGILcF93/76ZPR34V9r7uVcMYzUhVFRUVAwDS/Ay2u7up3WddPcnZeVmdhJwP+CrZgbNlL3FzE5399vk/gvN7O1mdrS7b198DRaHsZoQdA1BgjD2dv62thEIc58KdpHFnFEmpiPi+uS8MPfYDazm0GC0anLW23sEUNm07pyN5+u6Qtixld2pthD7IWQtRN+pJtqBW1qiZARfr2s5aMUFGnFzIcf6jH5pfyR7Am6Q86EUaXvHGkcrI5q2XQh9RFIGfTt8wpxbL9NGjHqIlrblrvmntYvU3B8ao1a9J5K+R5l1aIk6PrOwr9rGIYAuKOnA0PKCbVKPaJJsS8CEpg9VNp9t1Vdmn20cUa07SzISK70yGDXj39R6VkxNGOYagrtfA9wr/jezOeC04mV0LPBtd3czO53mp2xH/qTlYawmhIqKiophYD9rGsvoecDvmtndwA+AX3V3H8aLxmpCWE/fOJeFeVcC1Ip2muU2CA1CGaM+INiKMhxhKZPF1jypghS2dLL6SatdOKhsK5GAHGeMNmRSTULZeGkQteVOCD0NkbsSSwUrUi+jYIeqAEyqTMHClaWqfFE+mZQJM90j7R3anWolevuJA9eBrMWo3V/XX6Jc+1grle2nUA0j2Ks+M6i99PGp2jalb3RYzIjQwW4ns4igqqnoIln0t47PbH9MEhtKq6a3hMhHSX03ZdnRRKaZkEn7WrWaWLfIvIQg36atqlRsyFA54n5Rsya1DzfRzk2xDKxmLCN3n5Xjs4GzV+O9YzUhVFRUVAwDdafyCOKH9EmQkpQgnUrUNgoLmQ3aOScXhKqhtlRlaMFI1P858bxQ1ravULCJLpt0lKvD96wcZ7bggFLnOTkuTHWvMC1dw4hHKjvURwVpU0Z7THLdrKoQcXFH7oIWKxx8kXzr1MEk5FNyqIQ4rlVi3dsPoReekby7y1MsbARKDa+V46jTdfPLNCvYDjmOx6smc7M8P4j3rMqXeWXpO0O9y9YIoDfGVKYY1lmaZBDvMrnnCDmeCs1Bv2jRXq3AVnKcVV6/MyGUasVqKde+iVclY2lanr8vWyQ7CNRYRhUVFRUVPVQNYcRg9MmUEo9gl+rz32K3QY2ULgVL6aKkwdCyrF7QZ3MSeXEi3qMMSRnWY5JnqotMsLLMM0Pp3bHzz2fmXxVFSbLiQEslLUdo1QBiXURl0gbfM/BJn0V3eWCF/CqnKiXxylYytlBl1O6vjDH6RiunDZUx3iyOjr60rEfsk1wNm6S/N0XwqK4toSclZbHWoWqcagBRJ+0DYdbbigaiiudXyqfGR8rGhVatZUOPi1WOuDjxUGqdVy99HUTx0s9I2QKhYKezjhcNY2Ia+FSHPEtAzYcwRBSXql00k+7dmd9uCej0Zpqv4HZ3f+xqylhRUVERqBnTho/Hd22sMLMZ4O3AU939JjO7V3bdIPbTZ5BK5IJEKBnXIIq9C3Trq4YpDSjLDWajbEhVkGBOqmdmAY6UzcezlOmpN0loC0rlgh2uT66Td04IIz1GqE4w8ixDF/QJmpbF47UaadLlJ0iZUvtoW8nGNVPs3BoHJ9t8rX2YOZu0BI1KadsoI83aM/MYypJWQJ+x6+JUGSNT8p49suel10xKN3XcZV5jYbzWvQeZnEckZcBUeaYOz0xBUa06s5dPK1sPrUW1q3inrgFoPebK5+OlTHf/h1D6HvUYijWE7J2zKihDQdUQ1g4voAnxehOAu9++mJsOoz8WWwnJy2eHhyjHfjO5IH60NM6EfouynI36YxG/bPrM6eS6LMemuknOJOdnpSwqp+aZ7MdG6M2MLIzGo/T2bELQ73X87kxp3TN3TW1kpVfRdnP9orCw6BqhNn00k/5+aNP2vHdVpqi7/kCpnDEh6Myiv4Tx45xtqlMBWhlhyqcGOtT+jGCIsx3PjOMs9oqSjzuS47mOZ+6af0s0k1Zd0SMC2l4Za8jSzur4zsw1mWlVj7VM+yPqdIqUhR1M2YnawdYDH01kWCJ+XLyMbEj7Gw78UrNvAN+laed3aayPcj5MRQ+mGXpvcffzOp71EuAl5d+H0PYDGXUcDaz49vQ1xrjVadzqA+NXpwe6+7L0BjO7iKZdFsJ2d3/qct61llirCeHe7v6tYgq6GPgDd79Mzp8NnAY8ETgc+CLwi+7+tQWee+WB4oiMGsatPjB+dRq3+sD41Wnc6jNMrEn4a3f/Vvm8HbgAOH3gkluAi9x9d1lnuAx4KBUVFRUVQ8OqTwhmNmVm03FME+510MzzceAxZrbOzI4AHg5sXV1JKyoqKn68sBaLyscAF5QQr+uA97v7RWb2OwDu/k5331psdlfTLJGe6+6LWRs4Z+FLRgrjVh8YvzqNW31g/Oo0bvUZGtZkDaGioqKi4tBDTaFZUVFRUQHUCaGioqKiomAsJgQze6qZ/a+Z3WBmr1xreQ4GZrbZzD5rZlvN7H/M7GWl/Egzu9jMvl4+f2qtZV0KzGzCzK4ys38v/9/PzK4o9fmQmW1Y6BmHEsxsxsw+ambXl746Y5T7yMz+qIy3a83sA2Y2OWp9ZGbvMbPbzexaKUv7xBq8tfxWXG1mp66d5IceRn5CMLMJ4G3A02j2+D7fzH72wHcdkrgb+BN3fxDwCOClpR6vBC519xOAS8v/o4SX0fYQex3wplKf7wK/tSZSHTzeQuMSfSKNK/RWRrSPzGwT8Ic0qRofQrNx+VcZvT76J2BwM1hXnzyNJoDJCTQbWt+xSjKOBEZ+QqDZw3CDu9/o7nuBDwLPXmOZlgx3v9Xdt5TjXTQ/NJto6vLectl7geesjYRLh5kdB/wicG7532giHEUwgVGrz08CvwC8G8Dd97r7Tka4j2g8/Q43s3U0UUluZcT6qGxq/c5AcVefPBs4zxtcDsyY2U+vjqSHPsZhQthEOxTPLbRjpI1gXBwRAAAFsklEQVQczGyWJmLLFcAx7n4rNJMGkoh7BPBm4BX0oysdBex097vL/6PWV8fThAT6x2IGO7fspRnJPnL3bcAbgJtoJoI7gS8z2n0U6OqTsfu9WEmMw4RgSdnI+tKa2T2AjwEvd/fvLXT9oQozewZwu7t/WYuTS0epr9YBpwLvcPdTaGK4joR5KEOxqz8buB9NPMgpGpPKIEapjxbCqI/BoWIcJoRbaKdJP452KpGRgZmtp5kM3ufu55fib4dKWz4XFfn1EMCjgGeV3BcfpDFDvJlGRY8NkaPWV7cAt7j7FeX/j9JMEKPaR08CvuHud7j7j4DzgUcy2n0U6OqTsfm9GAbGYUL4b+CE4hmxgWZR7BNrLNOSUezr7wa2uvsb5dQngBeX4xfThPU45OHuf+bux7n7LE2ffMbdfw34LPC8ctnI1AfA3W8DbjaziLb9RJqUEiPZRzSmokeY2RFl/EV9RraPBF198gng14u30SOAO8O0VDEmO5XN7Ok07HMCeI+7/+0ai7RkmNmjgS/QpI0Jm/uraNYRPgzch+YL/MvuPriAdkijZL87y92fYWbH02gMRwJXAS909x8e6P5DCWZ2Ms0i+Qaa9PVn0hCrkewjM3sN8Cs0Xm5XAb9NY1MfmT4ysw8Aj6MJT/1t4C+BfyXpkzLxnU3jlXQXcKa7X5k998cRYzEhVFRUVFQsH+NgMqqoqKioWAHUCaGioqKiAqgTQkVFRUVFQZ0QKioqKiqAOiFUVFRUVBTUCaFiRVDCOKxYUEEze1xESF1pmNlfmdlZy3zG4Wb2+RJccan3/r6Znbmc91dUDAN1QqhYEbj7b7v7dWstxzAgu3YVvwmc7+77DuKR76GJMlpRcUihTggVi4aZzZY8AO8tseQ/amZHlHOfM7PTDnDv983s781si5ldamYbB+8zs6NLqIvBex9rZl8pf1eZ2XQp/1Mz++8iy2sWkP3V1uTMuAR4oJSn7zez3zCzj5jZvwGfTh75a5Tdr0Wb+Zz18yS8r2yAwsxea2bXFRnfAODudwFzZnb6gWSuqFht1AmhYql4IHCOu/8c8D3g9xZ53xSwxd1PBT5Ps5t0sTgLeKm7nww8BviBmT2FJqb96cDJwMPM7Beym83sYTThM04Bfgn4+UW+9wz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\n", + "image/png": 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IKqljO023/kzpqkaUzBrMYA86y5FU8rGniqLnxZWmAc0tRd2qyHnVrJl/2ZD9VgSb8BXp+qSUtTAh4oNS+ATqSFNstcQ8/Usy5r5go/pBP/VYUbcRxzglrppRmD8Tee7aZw5Ssv/2LUjX1udJe/a/Sel/oJh/rruuH9dI149zyHvLnOOfpe8BRcp75rLT+K1MV8/neaKiTrMc/yOunEt9jpjPfrl3E/frpnHVOeHe1D3Gz+xDF/VF6eJ/i1t3SB15c816HfP4VDzbRlrHcTxZURZzqMtN4uNsEPe+tRfL3H1iRdNhaZ999vFly5YN2+7WW2+9yt33GbZhNR0AYLf4fA5Sro1Pl9rsDWAuJRxmNhfAPmZ2PYBNmJfczM4F8FYkx85G+lXaAcDf3H1h9PXT6OPuNXyuIWmsDofXufsDZrY5gLlmNt/df1vRbn8Avx9KpBQHy5kAMOsV5t2M23lLXJl0UxN7tsf1Uil7dVz5A1wkdZfFdUcpW1m6TpG67eL6irhuIXWR7udGSeDZVeqq86VFXU8kMu2sOGBqL9xXYwCtvCZdF0rZdGbQ/R8p/FNcg4e8W15K02POLpfZ348/4TholvyuqJvEMX4mrl+T+7wzrpq4dCnqBykZnm/ZK1131HTxfJnuUfofKOaf66734cTuUlHWE9cuqbu41AYAjokr99dSqXt/XB+I65+k7qi46t6hNi1e1HXAJRKJLl9RFLVz73IONRP1JXHVVytf+Dwo/yJ1c9Jl5VvSteUtUkf+XPdTT7osjmebPE3qCDYWVpTFHC7BQOI52Rb3th/h3opmDdGyZcvQ3d09bDszm2Zm2vDMwUBpBW3h7g8CgLs/GO+tMk1G/a5bFGWTUf82Yflw/b7WzP6EtKv+y93vGuQeurNGlMbkcHD3B+L6sJldinQiVh0OB2F1REqPIvnTAsDH49q688B2PfELPPzDUkjVR4j3thZIVetC4OJOcVjzx39LUVV7qbBOs9q+LV120ZfLbaUxf6WomsQP75X28d2lcbhNfLyo6o1H6zg4XacfIN9jwkk9avnY91a0vy5d9pskZXy2eHlN+oXUfSSuJ8ZVXzJ8ketLjLDyC3H9z6Jqx1Pig64+U71zDPoS5ouZ86oHB4NxLJCyeXElx/E3qds1rhUHxrxY9tlax5/rVXE9UOpYJvdeGH1M5YtUxxo/9Xbh1mrPxFeorh/XfjspYwbrw+Kqh1wcZC2smyp1+8VVPUt4KPw0/v9MUdUfc9L08qJseZS1x29mknJwM6P7s+L/OzAC5Kh/wEFpWZV+k2Rm10B+bkL/3eBArKLMhygfim4DsI27P2Zm+wL4OdJuXJO+1pjW+eFgZq0ANnD3FfF5LxSvE233AqSf6aHreIiZMmV6zpCjXsa3hr247zFYnZk9ZGZbBrrfEsDDFc0WoRARAcBWSGKiRfFZywllKvt190dlXFeY2XfMbLPoa8ogfY04jQXnsAWAS0NhvyGA893912b2IQCQUAlvA3C1u/dVd1NBWxpwPKEg4R8FzQLnOilgfl1Rdn2cQeQSmrcp6gjHlUf+RlyJ4naVOnIRRMkiJqpJB98lZRQr/EdcX1RUtZD7UOFtoNCJ3M7C1neETLgvEHeriCdq20hFCRfG9ci4qolCzMV15xVFuxN7EXHeIO05VorrZkhdleCAgO/KuCripihPV58iL/48tH+KgqaW2gAFZ6ZEERMRvT43RYvfk7JYo9lU/6m+iqIWzrUg6VofM4uiqZxrrrM+4+y43iRlnH/i2t2ljoIS5U4pPjw1riqRpt1LFXfHcV0gZRzjSXF9e1HVxD0vnG47GW5yPntLX9QjBfcMcqlHYC1oZA6HYegXAA4HcHJcL6tocxWAL4kSei8Ax7n7I2a2wsx2QnozHIZCgFrZr5lNAvCQu7uZ7YC0O3uRdl2Xmb0YwGIkycohI/2wpHV+OIQyZYCUvBw/x93PBnD2uhlVpkyZnpv0DBoUK60NnQzgQjN7P4D7ALwDAMxsFoAPufuRcQh8AYXA8kTRlX4Y6V02AQkKXTlUv0hH8IfN7Gkk84aDPIWyeNrMjkY6iJoA/DB0EaNC4yp8xqytzbs/Ef8cE/C4J4TinTtIy0AafQIpW0Mw3R+C+aukOdGTyPZBxo9oTrkKHnOUwauMOOT4UNXY7aW+hHOoyZvbpYzoj1tM5fi0zuLv5QypI2JTboLybqJwVZ5Tf6Nyf3I5b46rcjRE65y7I6XutLjOlrL+UtmmUsfnUF0OORJyKKpo5bjJHaksnePRuK78LuXfClf4jKqjICLmXC+WurJA4uri48JA/VN1/bhHycCq/om6DOU+qASPvbNAuLAu3lvl99Q/MPKUcq7kWGN/LP9iUdXOvo6T9lTEx3P3STSr1l/Fh/dIe9oVcv7/o6jqC26rlXsnnt8uxa1D6QOGolmzXuXd3VcM285syhrfY32lsbJWypQpU6YRolEXK62XNL4Oh34UqPiy4Bh64v9jbi3aLQ/IqrLoKSX0ociT5pqC8FaE1UYb1eXKORC1f1bGRaJc/UopI8Km7kDu0xsosePj0p4y5NeW7gMUljlkbt8odUTQagzHfsPyZ7noF9qJkpUz6YwrZeJq2tibLrfHc7SKZUvXx+LDN6U9TTM59SLPrsnL1XqKXAq5nflF1cLQOdQQusjlV4Y5cItYQ9XqyUF8TOpogqu+D+TmqGtQzo9z9l9xnVdUTWUf+0l7onyuc6fUce/IOtTahTqtS/cC525aRXsidN3n3Mtxn3ad31g/fEDKuPZhPtuqHNAJ6bJQOOOp1P1Qj7JXUddKjof7d0SMMJ9BvaNFppGi8XU4tKMQnZTFkH3yhm6PN/Tu2ihMkef+eWC/3OAiQml7fXygCELFDHwB8kd6itTxRa7ORCV7g14x0eygQk8Uv30hXmn9ehSo2IqHFL+nYiVOgfpU8uW4dGBX7VQ4tkkh+6d4QsU+cTDP5GGnh9DlcVXDQB5WFEfpPHD8KraiKSsPVrE2nsqD5o9xlQO2hS9JVXjH4d8X69GqD877qJKXY+TLUsU+nB8CEz3keDio4p7Ag9buqjDmC1PsIWp7hSInFb9xjdTJjs/LfaQis/CzqR1yKtLiXKuCnM9NFawemDEXU18vZVw3KrX/KHW02eE69GIEaJ0opNdLGl+HQ6ZMmdZDyofDaND4OhweAfCT+PylcCudFFCp9fPS8PdxFVnQ34Nj2DNg9QUC54iONZwClZVEnMoiEwHTpFC5BJrKiuihpmwOMUmHmmHuXV8HAK1UOFK5rb58RIRUtKpzFFGvigbI8QRqnKqImONQJElOiWh5akUdHZ9UuU0mTZXI7INz+Tapo4mtuiUROZMjUcXpV+NK72PlmGjsp+g90HXr/1XUUTmv4huKlajAPaqoWhGcXE3EqAp/mrIqN0GUT0WxmtFyTdXJjma3nFflKiiqUdPX2sDiKnunJsKi8vk6qesrtQEKER73ke5NcqJqQEHOOMY4X5Tn08ixkmOkKE85tNWmdWKttF7S+DocMmXKtJ5RFiuNFo2vw6EdhYPXWcExHBkw7YkTinZEM9NE89YRnAPjI6jRG1GZylupVCSyVbNHKvmoLKxyQlJT2eAsFoaSc6pyIYyDpIpl9kckpuiPXAF1Aj1F1YJQFnYp58CwHOyzKiyEPPfdoTtYEVdVR0wnh0R0qbJujl9RImXcnGuNdEPuS1FyyK9XxXo0680Z5I+6DQ2ESNm2cjJE1fvHVZy1VsUYmzWsBUN8MM6RqLDauKZUwB8s3wvFd7/EmWri/iCiV30HuTvlNrk25Bw0Ug8V12+WMnIA3LfKyRBkzy3dTz+rTirWfl5wYrPfKXU0La6KKxZcwbRPSl1PXPlsynGsMeXDYbRofB0OmTJlWs+o4dhKmVaTxtfhsBEKJE+0e0HAqHcJhOmn+dDfi7Ieto+rImhahyjKouPWn0ptgHqnI6A+dMTNA9svCMTcRWSrZrEh019wYVHURRk1uRX5bSwJ2fmkQIv3ixljjTl4f1FGk9EbQp+yq0YIJWoXefx0BmzrjKsGEqD+gUhYLZloJaModo9SOwlBcn6YXB6i3FrMT3PocuaLqew0WhjxIdUyqcpiiGg3nvsq4Wj2JsejHBx1GLTuEWewGgfA8asZaiDppk9IGZ0LO+Oqc06djO416oU4dxo+g/cUXc78QPTTuB4StHF5hM1oJ3egYV84Ll23mDtayt4m+3A7cm4SvoXMytQj0lV/FrWINDQDlr7WnDLnMFo0vg6HTJkyrWeUD4fRovEVPuNF5t0fjH+YpK/zo+l6qyQL2J4ZSUT43hdCa6JwyVMwwDIJKOSl1D2ofJr3ptNVp9RRD6FhJIayfKJuQmTvq4ITIUieqfoRcgpEhGrHHshzhXAybeyXsmLlcqLfVaILaCaipYxbLbiIIDm9aqHDPipk3PMiPIcaJk35VHyoSmNCpP2olJHTo+5BETHXSNePzCMDyqkVVTnBBlCgauq0tH+G++Zcq86BVmlqNUZuiwhaw7Fz/pXLIUdJbudiqWOoDtUj0TmSY9Sw2eQY6H/wbqmjnuaDUsaQI/QtUd8V9n9MUbQw9sNU+rPoXHBvUi8U+9GuXJvwGS/x7u4vDdvO7KAcPmM1KXMOmTJleg5T5hxGi8bX4dCBwqa9Zu0R8G97NVqPQIbzJULatBDktoTpjyJDWn+oVUbZx0BDFJweV8q9FekRSU2WMsp4yTEoYt2jdAXQHDLlmbynIk/KgSNkhFro1EJxqAUQbe6rvGnDNaRZk+pQiEzUq1nAiFhpjXOn1BHZKicTn2dTNv5rqQsV0fLLi6L2H9T323dqUddKtoNhKlRuTsun10oZrZT4vDonFJwrB0e/hooQJzXuhmhffT+I2jXcBsfKOVFujfqFHikjt8V9oePi3lILJu4B+qxo/+SIycmoNRG5Nc2g8u248jl0valrmVsUTWUZ/R2U0yD39f24no4RoHw4jBaNr8MhU6ZM6xnl2EqjReP3cKjJwoM7eFTg0yYB21VPcGuwAtsHhOyT9ocEvJ4rrsLfC+GtB2xUeTY9bGcHbH/0oaKujBqBQkZPG3pFnkTq6gNA1MeYNiqXpzctOShx7+ggopcE9UvDYoSi8X3VvOSguGrgQMqlqU9QJEnESk5DLY2IdnWs1NuQAzpvYF37r6SMoauDK2xV3w+idnIa35A66hw0dhPr6b+gnAN9HjQpEpPYlmM4AYUPxy/jeqPUcQ4UjZPj4b1Vv0UrKN1P3KecO9VfUBeg46dfBzkOzRnOsPPkjJWhpu5E/UdeE1daFin3zD7UfyQ81VfF+Jsl/lXtebmvGINLg1CuEY0u52BmmyLZMXYi8XTvdPd/VrQ7HMDx8e9J7n5OlG+PIp/DFQCOiUQ+lf2a2W5Imql/RF+XuPuJ0VcPkramH8DTo6lH2WD4JpkyZcr0bCWKlYb7Wys6FsC17t4F4Nr4v47iRX8CEgzaAcAJkhXuDCTBZFf87dNAvze6+8z4K6dRfmOUj6qCfXxxDv9CYd1TQ1fBAUxS184wF1Gb++2DE/h8wLMTBF5fEhyDxq+5JdoRGVYl6Lk8OAb1hqa9u9jVXxbNDiDHIb4Dy0NW3a7b4O76a7+g8SYiyD3jqiZArFMdQtC+rNO0joFYl84piiYSXf4hXVYKU9RCVEnkquGglVOqdRZXGpJJ4pkamtZI6uQ+qOf5kNTxOambubeiTrkW6iToM6Dxh7he6nlOKx/GSuqUOvq1cO7USovckCZMYnytiCpfS4QEFFynrjc5NyJ6Re/UZagOhFwKORTlDqhDIGei0Vyj/1XyKmqO514cczdZuYqwsrv/lUXRlNhHzdR3aLRicn4xP6ozWnNaJ7GVDkCRH/ocpNzQny612RvAXGZ/M7O5APYxs+sBbOLuN0f5uQDeisQvNdLvmFHmHDJlyvQcpoY5h83MrFv+jhqkwyrawt0fBIC4bl7RZjLq1e+Lomwy6s0FWD5cv681sz+Z2ZVmtm3pga82s1tX8xlWm8YX5/AMCssiggmoXAcQAAAgAElEQVTKs6eLO2ZnJGj4nVgrdQc00qT1JMqxNSEMERgRnnjr1snaAfSKDX1HyLqvEi6kBhIZf0dkuLWcCrrtSp7FTTtLHdudE1f1pyB6FRQ/MTyelwcn064xomLcE1WezXvFvVs0Iqx6dgNYIGi5i8hbraHIwRBBau4GplpVPQERNBHo2VJXZvRVT0JdjkaXbSuV6TzNjKv6B5Bj4nqoLJ0cDbkPTRtKz2rlnOhHQbQ/U+qq4mWxP1p/KcfLPr4gZawPfdAq4chqiJ7PptxUcADXSNG+MU+T+T3Zh4tjD0zRaALkmqh3EX+NJRExeGWsXyfbqjXfGlFDYqNlQ4lhzOwa1PPZpP+uKKvsoqLMhygfim4DsI27P2Zm+wL4OYpd8zp3f8DMNgcw18zmu/tvB+1pLWh8HQ6ZMmVaz2hkxEruXs4EXiMze8jMtnT3B81sSwAPVzRbhEJEBKTURtdH+ValckKeyn7dvebe6e5XmNl3zGwzd1/m7g9E+cNmdimSfiMfDsPSpiiikRJ5vY8G1xoONKD90SLs7Q83z6aI1Hrf94s62n+rXPf7AfE+E9CrR+D1fSHwDrTZoSgwrEX2rrKrJ3pViyFyHWq9Qm6InsLqdUxkzu2nehLKqtXeP2IetfN7NxdVKwIltinaJ0oml6AyaMqeY3q79DdLq6blUsZnIlreV+qIkpUL48+X9vGKOIloiUZV1k2UrwidYyv7bQDAR+KqMahokURsqR7VRMm8j3odk9P4m5Rx/XriqilEyWHomlI/Qo5S54n0I/nMtQm036x+MHxecsOqm4nosvuqeo7jifldLJZFkzlnR0h7hiujj4/o2yZRN8O9UOI014zWiZ/DL5DiHpwc18sq2lwF4EuihN4LwHHu/oiZrTCznZBW8jAUWrbKfs1sEoCHwqJpByTxf6+ZtQLYwN1XxOe9AJSV1SNG4+twyJQp03pG6+RwOBnAhWb2fgD3AXgHAJjZLAAfcvcj4xD4Agqr8BOpnEYyoTgbyZT1ShTGu5X9IkGcD5vZ0wCeAHBQHBRbALjUzID07j7f3dV1dERpfB0OT6FAIzXUGlD3URHKbsJKgTXkGP4zOIY50u/WAZH+JCLLBcExEF1+TyAY0fVX4qoZzsIKZ4nI4yfRginQ3EpBpS1Ezqp64neJwiWHb3/kr25iljQ1OaDUUq2nyA3RaqenqKrlKRAP2BripKxaES6zsdHHoioOj3AOt8VPZDvau2sOA+pM1HqKiJu2+ioc571oTfSpompeIOHZh0l7ziHXSuX4zAGuOgo+r3JwJC59VU5o6mg0wx51K/ye2vlTh6OomvoTrvMdUkeuTv1NwjdhfqjZpv1M6qgDCIumuz9bVE3nz0K4x9q8xs9nsnJMtBarktRz/d4lZRw3QyHNqfjeatPoh+x2914Ab6oo70bhrQF3/yGAHw7S7pUV5YP1ezoq/MfdfSHqtXajSuPrcMiUKdN6Rjl8xmjR+DocmlDISGtexmGIXxe/JsrUXvyG4BgqrDLwyeAY1F6cfpBEydoXuYlA/SvFHr8lrJwmCSqdH1ZKxD+axGx69LHim0VZGzEIkaToFQhaZ9BK5jVFXc3mXvIHrAzdxLyo20VxCaWZqh8hwqZ/wy5Sx5sHt9InsZVaA+3fKAh3F+oaOBcaD4n30eikXENaBdXpgOJKKzIxTpv9YHzYQdpTf0H5vXIO5NaUK4pxLAyUP1W92MmR0ZP5SKkjN/VfUkaZfpUhOcehNjKMKUXdVRUaF+6RSH4a76Non34NwYVNV89t6jRU18Kc7OT4lIOlrkR1LORAGaFAuShyfFT/aTyyNaZ8OIwWja/DIVOmTOsZOXJspdGh8XU4rESBVOix/IeAQeq/MCOg82ViDE8EtUlAwnME4l4UV/VyJWoietX4MkSvwRG0qPc097Gg6mkhC18YiErt3noDsXVo/5RjczwSI2oGdQ2BWE+TjGXHkAMQf4KWkPPvQrSsHFD01XtQUcRh70qrILXE4lwEImxVlNk9sHum66aR1kpBmS3BwfSKN3dHZ3wgKlVZN+ec1jhqycQ+VOfA9hXRa/tjrE3q4R0c1lRybWp9RGTPnMrK7RwdV/UFIHonklaLLHJuX5EytifH1yN11Feo2J3onb4MGrOKY2UcKM3HwfVTroibkT44GteJXI74xiwIf58uroNy27Fe/aHe0wR4a06ZcxgtGl+HQ6ZMmdYzyofDaNGoHg6NRBCMCITfQMK/y9x91yj/OJL01pEkq+9196HNEtRaiRZC/MYEDVwfxtibXlIUEUH9MDgGlb0zIqrKTymzJnJTlHlyXCnvV/0FbRAUnYXce2pYwvRIDoNOQm1BbMzMVvN2VXQW47kjuINj1Ks7OI1+kdU3Ed1TbyFy/4Uxjqli7bIrvZnJrchYa9zUDaU2QM1LuVMiqXYSEYevRJ9yDgErO9TSi5wC7eWr4if1xPUnUse11JhS4SRfQ7+PF1VNRL3/Lu25r2ioqLJ69ntWXFVXwbnukbLz40ouUuZpSWzTSerrwmfTPRO0Mvpv0XtyjOSW1eqK+gTOiXrEc+2Vk+Fncl3Ti6ql4ZWuri5dtBKL9btDrKFmxNo38Tk4LonPtfq0TmIrrZe0LjiHN7r7sqoKM2sH8B0A+7j7feESDjObjBSsYrq7P2FmFyIF+j17yDv1o0hkb7ErJ4S/yuJTinaTQyawi/4K4k37Pv7SRe6z8K/pqs5BVPJR+ahdUUHHF5S+vPmSUbEBX8zRR6eKY3g4yI+/mT9oXtW0MQLizWD4CX15R2rPJk3Cw3vz5ShjnXp7fFARDV+mfF59YXGMMSfLJVR0Ow8FTaJEpXkcZB0/kDqGI3mzlPEwua70P1CIkzRsBolzrQdlOZS2zhNNatXvlOtA50IVK9HJjGaoYjxQm9dOKeNBz+CCnyuqJhHDaDhyggvuOREFtdBCW01ZKT6kk9qPpY5zR5mOpvFkmYYQ5x7gCSDPPZEHge4BjjEOrRnqgMdxUcTGn5r+FtaIMucwGjTWYqVDkGKV3wckl3Cp2xDABDN7Cunn+kDF9zNlyrReUxYrjRaN9uHACIIO4Hvufmap/mUANoqwtm0ATnP3c919sZl9Fclr8AkAV7v71RiOWiGhIUJ01PPndO3cXRoGj/yo2IBuQo1kbLS+3qKObL0G16OikWhZw0IQ2TEMg6JShjlQ7oAImmahqsCmqEUd16jBJVuuzmBEpRzrNKmjuGETKeO9giNYLs9YQ/uqhCTq4z2Voye6Do6mXYPysQ9V1jJVJzkyEe3U7qNl5dASQwV8+4DUfTmuioiJtDm/mnqTz6SmshTHUHSmIiqKnziXv5A6iuQU2TMaPKWaKq4kUlcTZCq6aUigjoEUb35Nyhi6guusnB+5gxAr3S0JjaaTS1MRKZ+Jc6HOfNyTKiwm1xXip1WyDs3kctin7uk1pixWGi0a7cNhuAiCGwLYHslLcAKAm83sD0hb8QAAL0Z67V5kZoe6+3ml/hFha48CgK23KNdmypRpfFPmHEaLRvVwaCCC4CIkJXQfgD4z+y0KVdk/3H0pAJjZJUgGnAMOh+BGzgSAWe3mtfSPE0PrV5PVS8zkvwdkU7Q/+aH6MkXvlGNrgDTKrGlOqkpnImZyBGq/yaebLGVErbyP6hDItaiCnAiPill1HiNRdyAKxxXBKPWKArAznnNVKHfbBTXeEuqaHdWMnPdk3MiTpY5ms1ReavIeci0qn+Zzcs5VH8E50xWnzoBzIsxdLfhb9LFEOI5J1AHoWDtLY7xH6shhaNgQrj25QJWlMyER9Reqj2Afiva5XnSg/LLU8dk6pYzPXQ5DDwDUUeje3CmuEUuybi9rgD4A01WRTRNpjVFJ5zpyL2pkQK5FTYR5r+irWUOVl40eKpJOrRnlw2E0aNSS/ZhZq5m18TOSfcidpWaXAdjFzDY0s42RXpP3IImTdjKzjS1FmXoT6n++mTJlyoRCrDTcX6bVpdHkHCojCJrZhwDA3b/r7veY2a8B/Blplc9y9zsBwMwuRsJITyOlcy/rKwbSZigQ3bYhXL094PjmPUW7l4T+4XviwbV9CMUnhZC+//yijshe01ISmdJ6R00PiSRphqkIusqRiVxEoLLFgu4oNlYjnPYY/vxw5FLw10HETaut24u6thhPm5ovBmonM7FEghHvGO1vurQo2zn0HPfHMT9FLLjYbReRrZpQdsZVVT+0bFFbSBID3Ikpa38YoDWRK1ILMVqBBTcx6etSx1Bo6rDHMR5W+h8oOBodKzkmOoUp10JkT9PRI2TMwZE1qbMZETMXTsdK/YNa8KjOByg4NKAIYqeh2RmJnlyOmuRyHHxu3Vhsr0H/2C85vhdIHRdcAwcS/pFb04RJfN4yF6acympTFiuNFo3a4TBYBEF3/27p/1MAnFLR7gSkhN2ZMmXKNAjlw2G0aKxNWUeWNoZYeYTwuS3g4hUCDfcNKFaX9J4wlLETxLn/FwGvPijNiXZpvaFWNZSTs3+VN9NqRY9NIsGQ105We3w6VqnVUaDLaVtU1AWa6wnE16lsRZUxcFj8TAnOeyMN0hbj2fnbUha+CFMoe/5GUdVFtE9EqXoYolG1MCqHHFf0zs9iRdREGTc5ALWqoSUMkbEiVgaqU+sjolUuu9rClUOjAAXKpRPYGVKnlk5AnW9GEwP7nSP17J/zo/6Z3Cu6B+jsR05Mg/LRl0TzmHGsDFCoVlr8LtOqaiBpWnwpR8Z7kpv9vNTRCkqt5ahzIKelOgpytdQ5KDe1xjT6sZXMbFMAFyDxvz0A3unu/6xodziKkJwnufs5Ub49inwOVwA4JvIzDNrvEM7B+yDxhk1IkhbVpI0ojZrOIVOmTJlGn8g5DPe3VnQsgGvdvQvAtRiYsZwHyAlIUGEHACdIVrgzkCwqu+Jvn6H6Fefgt7j7tiiSCzUhRQV7MxJMOdjMxGd9ZGl8cQ7/QiH//OBj6doZ/79EhaUh9FVZ7uKA2kS4qjonKlMERoRHu3rVR5DpoAxakwoSJasVDvUW1JupPJteqIrOaNFCD2HlCIJTuDfKOkXuvyoQW7OGfoj6VcEx1In/iZzFT2NeIPrZ9HbVMBXkUpi86D1SR9t7DV4YiHtp+BBM1FDX9NxVhF72sFXZOP0n+FNRq6iK3E41WfuecdW0olxvCftd0wUQ9epPkslxyHUpx8TnVYs1Ejkt9R0gV6EaNi4KoZzOIbkv1UPQ14d6MNUxxVwsibmYpD+L8j4EahxxTzxbp+zb/tgXTeplTY6MPjLqw8G541rp99aY1olY6QAU+aHPQcoN/elSm70BzGX2NzObC2Cf8OHaxN1vjvJzAbwV6U01WL+DOQfvAOBvIbKHmf00+tBZHjHKnEOmTJmew8RMcMNaK21mZt3yd9QgHVbRFu7+IADEdfOKNpNRDwsWRdnk+FwuH6rflwF4oZldb2a3mhnNBwa7x6jQ+OIcXgCR94aHdBPdSiV63NERg/r044qyBSG8pRfGxw8p6nBBumwqph2spmWIqtSpa6AsejOpo5xVM/pQLku0q7boRGJy6yXBwdTSi6rsNuTFu5K7+FVR1UwLkgukfSDP5tAdNKvOgSRc1GzOL7ki3arUAdAaR+33KRvXn2RweRPpF/BRqSMWkuyuNVt7omW1hqKPBOdJrcf4WRMTdcS1J65qmcT5nCdl1B1wzjV+EjksIm71KyAnoF7T1BFxrOrBzD6Um+D4yR2ofiE40X6xGmviXHCNPiLtYz4nsY2Eb6+Ne7aUxRzUdFdXFFW1oI1vl/bUV0yvqCMHSu55xHQODXEOy6oCf5LM7BpUJzz974qyyi4qynyI8qFoMOfgNelrjWl8HQ6ZMmVaz2hkxEruvsdgdWb2kJlt6e4PmtmWAB6uaLYIhYgISEbP10f5VqVyCoIH63cw5+BFqBdQal8jTuPrcGjeFNiauh5CsOAY5knGmtNpxiHcXldAtVcfEQUdRd3ygHh7CrzcI0xmiOw1eihl2x0hJF8hsaUpL1YrIiI8onZNy0k7f7H6mERUTRmxxjAiOmMfEjKZUVLbVbYfUVxrKjZF3ESVKnzkT4hcjlpiEY1Sxq16EjJpar1CTuMNpe8DBbMsPhY1vQD1BOrhTj0NEavGMuJcqyydyJyRS495XlF3wZP19wGKn+CcuKq+g2P9j7iqvopj1HnlGCl7173AdlWInk4v6rcQXFqTWpTReIffU26QVnKMgaT34bopV0uOiUblX5I6WtKpTm1OXDmvypFVeb2PBPVXheIdUfoFgMORfOwPR/0Tk64C8CVRQu8F4Dh3f8TMVpjZTkhal8MAfGuYfi8DcLqZbYi0GjsCOBXJ/qzLzF6M9Gs4CIUMY8RpfB0OmTJlWr/IUR2mfWTpZAAXmtn7kaI30HpoFoAPufuRcQh8AYUw8kQqp5Hg29lIIqIrUZjNVPY7jHPw0UgHUROAH7r7XaP10OY+aiKrdU6zZm3s3d1hynJ9RGMlqut6g7R8U1y3lbLwEf57CGi7paoc8RMo0NgHw+RpphjpEyXThvxfRRVVIXWRUemM/ZK4KromYtWkN9RJEJV+QupoUUWO5rVSR/m3Wrswng45DpW6EkwfI2W8J5G6yo3pf0CmS+9NNCoAvaYGYuKdmzGQ9pLP9Bpm2tb/kDpyFcyNoGZXfF5dh7bSVa2bKryzazoTzq/qBGj1Rl3OaVLHtVLLHCZ8Iseg/jbkHFRYQL0O97L6cHD+3y9lfPWUI6oCAzkr9S1he50ntuM9D5c6RpXdWcrYB38/e1TU0Tcm5txOxa1D6QOGolnbmXf/fvh2tvGa32N9pcw5ZMqU6blLjnrRWKYRo/F1ODzxBHBHcAy7BRTuI1xWgTbhe09RdEdAvBkR4H6imJdQFrubCFAvDJh8UUAvjY9D2Sp1Dxr3hnGBVO5KjkERYdDykOu2K8IjGqPcW23iydGQ+1AOpSqGDZEtEataEzHfglpRcxqJoBWVEk1z6sSypWa5pYZ3VNPxnhrplPb34rncF9Y6rYxVpXky4t4rwr6+TTOpEe3qnFPfwfHrGpHUkp3zSpSvaJmcBv1Ovip1fHGpDoScFVG8PkdwIatkTWt+KVwj1UnR50M5XUquuV3Vg5v3IqekkVE5fvHhuDHmbBf6s+gcUiczt6KMXJE+G+eMe0itutaGRsTqKVOZxtfhkClTpvWL1o3OYb2khg+HCLu90t2fvUuxCgUzcEdwDDVrGjGYbw34epm4xR7wsnT9TMDeTxVVNXT9ZUmcQL8Gqi8UzREdE+mplQwRtyLJhaWrIKp29rtC2lP2Xrbt1+/SEkbufXdYD03XmESsp0GV5H2uPceBUkYugveUvM+rwmKrmb4i4lHeGxnmOlRWT8RNfwXlnPhdMdxrpcSYegKNTxVxptqmV9Rx/dQIkAZrtBRTnQO5CdX9UA5PFK6ImDopcgmaWyF8GPrFh4PqhClE4+JuQ26weUAoShScqK43De7Ueop7hc+hcZ04B7Qk01wMHI/4wexCKyhufd3LHI8GkyB3ynVQjoy/B/4m1f9iTSmLlUaNBj0czGwDJFOpdyO5xTwJ4HlmthRJYHCmuy8Y7PuZMmXKtE7o2QtXn9M0FOfwGyR8cRyAO939GaAWYOqNAE42s0urUneOGbWg8D5lVEgin6O/Jw3Dbfj1GlAnBOCf+mu6tgucmxNwuk9cconMGRNRY9qTiDI1Dg9tw9VenFAyonP2iIy/842lNgDw/bjSs1bVKbRHp9WOxI+aLrkdakQZOtG4cihEemdJGaeFXI5YPjUTeVJeLtE9O6iT6ZG+GIOKqFRjVxH5V7WnVZTKrDvjShRf5TugGdrUsxuoR96cV5XVcxzUhei9iczpS6txtmJ+m8QXZQrzRZTHABR+KepZTD0V50fcZmr+CuWcDwCWBsc0UWNpEWWTYxLOr0YaEZa5qbmHj5Y6crCSh7pmgUZuQj3cuWcYhZbWbFXz0ChlsdKo0VCHwx7uPoBhC9vdnwH4mZltNPBrmTJlyrSOKIuVRo0GPRx4MJjZDwB8y91ruNPM5rj7nKrDY0xpFQp7d1rC1GLOqBlOGEbXjT58Hpj/AdcWVVUZy8hEELmpHJ/RSHl0Khpn3ofPSBn7DR+CTkWstCBR/wPyarT+UH/NfevbXCYe0geQm7hB2vM5KEuW434FI7UKil0RsnOK7GcrN0LuhkhQ0SzRtSJu3pNWShqNlnOu8ZAoJ6+Yk8sCxR7A9VBbffpKaNh/PufjpStQWDcp9KHVF/Uij0hdWMssDs/lyaqv4vwK2u8NDqCDVjZqpUVLL02JxbnjvlB9FX1cKriuiVxvRfbk0mjldInUUVciwQHqPP+Beg6ZHIlGr6VHO3UPrVJHyzNyhZo3Ym0oWyuNCg3rBGdmiwAsA/B1dz83ym5z9wpGdmxp1kzzbm7AzUPBjJlxVa0cf6kqamJ0PGrZNBY13xaqdeYhEm/5JyQTEH8g/BGoGSrzceqLZ2Lpqj9Aiio0BALbUdGqbDUPQzqU6Sr1xFXNTzkVfBwNh0Hxhzpw8QCgU5sqKCnKo3mkKoCXV5TxcKYyWEVnnDMVydEpjYeEzhNfnByDHqZUsmsgQC4z10HFUJ1x1YOPz0kQoAc+xV1tpbZAobjWIIF8YVLcpeE2yuG5gWILPxpXPeSY8EnDjFBUyPnSg4NOiFRkq/KcB/d/ShnnhaImFc3RVFlfzjxMOAYVOnOeOK44HGyHtXCCe6V598XDt7NXZCe41aVGQnY/jBT95h1m9u2I91EVHTBTpkyZ1i1RrDTcX6bVpkZMWc3dHwWwv5nNQRJKvGDor4wRbdgEbE7IQr6WaF+1tuS3NfsLs9AHdHaBuMY+RB7TG3C948R0layiOCRCdTwcNp0aHoHtVDRADoOIUp3OqNhTURMRPZV9GkaCnALRu3ItVG5rWkoic4oxdpL4Fu+oSL9YDvanIiqKtIh0lUOhwldDUhD5q/ks6YrYYreLfIjiKiJchTYbo55U1EamrkfKuLyUj+n88j4PSdnRfLiAziv+WtQxmg45Bt0L5IqU86M5L7kW5Y5eEpvgf/oGtmfR1vKFm2KzVIXNJmeiYqXyGHVc/FV3Shn3IjlRNTemWEmRO1/EfDYNn8E9z3srR7OmlBXSQ1IEAnwRgCcA9NCwqBFq5HCouQq7+xwz60Y945kpU6ZMY0f5cKgjM3sBkhfJwUgC4KVIkGGLyAvxHXf/zRBdAGjgcHD3E0r/X456F6BnERlqj3TXTum67Q5Rp0JoaoNFBNkb7avQ36QQ7P5dtIpEPU+FBlwDxG29LF05/Zo0hv2q7J2hD1SOTSKa0/DUROFkjjQ0Nh2LyK2omoQIT00b+UjUVUwXboFjvEfasz+aMaoiniav5AQ0YB9l2yqDJndDlY5yU6cGx/ColPE5yfgph0V9xS9Dm3qDaLdPjavqU4iAiZx1Pdivytd/HAJ2rtFMqWNZJwYS9RaqIKdD3Lviquv3lmAP1HyYnAD1EVvIg1NfofuDHCI5IA31cURc6eCoMgDOgXJh5AR4S9WV0WRZdFIr/i2Gyv2kDDs5ZKZ+HYnsx9laqYouRno77OLuujNgZtsDeI+ZTXX3KkPmGg3lBLcC1VmGDIC7+yYVdZkyZcq07siRrZVK5O57DlF3K4BbG+mnoZDdZvZHd39N48MbG5r1YvPuOfEPkQtRxSYiaL8oYhMoctk2vnBJ2OWpPJRpDdU6hqiMqEnN8r5ZavM5qbupVAcUqIxB14Qva6YsWeXGfyx9T0Nj01qL6E/Q+NL4PLFL2lNfEdZHvR8oqjpoqaKyfRp4sX9FuDQ75fjVf54ck4YXZz3l8mpOSq5CQyxoqk2gXqdBVE19jDpycTyCMFeEpVcbORq12iEnp+HFifx5H0XQ5Eg4F2qtRD2S6gTi2S4JDutATdRDLuTege1re0CD5dE0VcOFkDi/YkhXC41R1ksAhQOp6muo7+B41Jyb+i3ljKmLirVaIJxiVzhv3h97bApDdi9cC2ulaebd3x++nb1hze8Rjr8XIPGGPQDe6e7/rGh3OIDj49+T3P2cKN8eRT6HKwAc4+4+VL9mthtScPONkLLC7RrlPUi7pB/A08OkPv0FgJ8CuCyyyq0WNWKtBIxintJMmTJlWmOiQnq4v7WjYwFc6+5dSA5Qx5YbxIv+BCTThh0AnCBZ4c5AcrTqij+mq6zs18zaAXwHwFvcfVtEEiChN7r7zAYOu68hQdu7zewiM3u7mTXsXTK+orL2oUAxDCg2IeI93CQmMe8gyyD5FleF6c+BEWVuY/EOItuq6JIojg5A86WOiVcoB1dZOuXBy6SMqCwcq5oVIRMZqg/A8tJV7eTL0a7ECmciOSBxTOoNZN4RdR3qpMZE84oW44e2MnQsLWqNwntz7k+Uuip/DaLdMrcDFImSNDER+x8KLXPr65xT1yLOZm1z4gPl5vocxFh/L4pWhP6oxmnoC4ecCbkiXT9yiGo1Fv0fSAsxVYedFFcNBEj9CPfhdVIXz7tK/ByayTUT2as7D1VKhIWqW6ty2gzd1Xmxzw9Vqy7qDjR4H8Nt9KRLlz5HhDKfEjqQ26rCpK8urRudwwEo8kOfg5Qb+tOlNnsDmMvsb2Y2F8A+ZnY9gE3c/eYoPxfAW5HkEIP1ewiAS9z9PgBw96qc1cOSu98A4AYza0IK4P4BJLPMhlQCQ+kcNBZne+l/uPslyJQpU6axpsY4g83C0pJ0prufOWjretrC3R8EAHd/0Mw2r2gzGfXC4kVRNhl1yepr5UP1+zIAG8XB0gbgNDogIx2HV5uZA/jecM9gZhOQMrO8CwkunDNUe6WhOIf95fMNpf8d9Y73zw7aEIVHZ81WPTgGRTAPh7B+czWlCLokHkuRJ1Gs2m0xFSStVjThCWXQgTbvF9Q4hehdEXSg1iWBkieJ+8WSkEerCHpHomNeRQZ9f8i7p8SY+xeYjVYAACAASURBVER/QeCpgG3v4BROCh3L8cKorgjZs4qgDw4ZMg1sthJLm03i81SiUgnD0BPoshZIEKghz68Gx6RRJDgFhyh3EGu6MNqfJ8Z4VE2QwZqhFkBEu+qTQR1CWOicKr4i7Ev1nG0cHLmQGVpZqlPdCfehJgAKw7gVsbZtwjH1xHcnymusFqo8rK7uF10Lb91+pPRP3QfnWq20qEsjh6iWTOxXBQ/BZR7KPvRFXKGL643fTQc5MbUCI+cXnMl2tKirs6dZTWpcIb1sGPn8Najn4Uj/XVFW2UVFmQ9RPhRtCGB7pIQAEwDcbGZ/cPe/Aniduz8QB8lcM5vv7r+t6sTMLkDa/b9GCkF6/Yj4Obj7ewery5QpU6ZnBY2QWMnd9xiszsweMrMtA91viRQ1okyLUIiIgBTd7foo36pUTgwzWL+LkA6zPgB9ZvZbJCHqX939gRjvw2Z2KZJ+o/JwQAqwc8ia5uAZ1FrJzLYC0Onuv4v//xPA86P6fHf/W+UXx5DqLBeIEo8Pz9YrJGflvmGGcqPY9JcR/TRxEDgwcLsel0RZXaX/gXrHa6A+jSf7UM6BS0cEpZudCFVl1pQl8xlVJ8AQ4uHKsVxYDjZr0nhLJTSnhjYtRLSHSGEg4ZviGXfWeIb8MmXWiq5Pj2unlHGKOR71nibqVSsfzkEwfMu/UlS1M6YPf3aKRkN3Ml88eacdVV9Xx2zT/0AtsbjO1CdpLCNypVw3FS5wPO+UMlqXc8xqBccyXQiuM72UlRPgHKrAhBZ01INNk7rgJBfE+nV9Q+qou1LWkvoX4k210uqJi2DRTnIDfNWqZRzXhD/FUP3ZlWthrfRS8+6vDN/O/n2trJVOAdDr7ieb2bEANnX3T5XabIpkIsrdfBuA7d39ETObh+TdcgvS03/L3a8YrF8zewXSL2ZvJDvC/0PKrfMPABu4+4pIvjYXwInu/uvSWF7P9/Ygz7MJgK3d/c7B2gBDWyudgvrADx9E2ioO4PNDdSqD6DGzO8zs9pK8T9vsFvV3mdkNUt5uZheb2Xwzu8fMXlv1/UyZMq3HtG6slU4GsKeZLUDKkn0yAJjZLDM7C6ilMvgCkknMPKSXNmP3fhgJavwNyczhyqH6dfd7kERBf0Y6GM6KF/kWAH5nZn+K8l+VD4agfzezm8zsc2b2b2a2g5m9wczeZ2Y/RoIIE4Z76KE4h7rIq+rrYGY3uvsulV+s76MHwCx3XzZIfTsSTtnH3e8zs82pmTezcwDc6O5nmVkzgI3L3n5lmrWteTeth3hu0htYNwhR/kvEbIc6ovkRM0fTWVoYb994RlFGdMh+VVpJCxsiXUXQJNVRENkRZSmXEMhQrVFoYNJE/YriIT4b0aWGU6bdvj52eFvPDw5jmsqgafmlUCCQ56pAl81qYUSTBeoJfiR1RMKKqskpUC6tVj5EnmqdTRS+e1xVB8Qxkjt4vdRxnu6QMqJd6pZeKXXk/NSTnHGWOH5FxJxXWlGJzH557MN25TrbS+2q9FXKWZKboI5F54RzrWG26UHP8Ws8A+4P+uJoMh6u5c1Sxr64L3qkjroVteJbUqqT9a4lH+J+jTWw29aCc5hq3n3S8O3s3etXVNYwo307gNcB2BIpttI9SAdKQ1GthlJIl+1h3ySfOzAyVGmyFWzPGxDO/u6+CvUhvzJlypQpB94bhMKZ7vso8kauNg11OKwws5eFhpxsE8xsGoDHGh0jhja7GsxkayoSxvmRmb0aSZZ3TJWXn5kdhcjks/WWKBAhjzYKqvadXHxpYcRDuki6eyQ4BjIHt4sB+K1RqHGBGKSTXegGJZqjB3NPUbUqzuxmzZHAW0UIpxXCObRF7JtmVZeRE2H4II2K2RlXMq5ypC4P/UL7HGkfXM40ys1VpkwZusq/457NIS9eKmF+lsb8TK/yQ6CcWb3FiTL5/OqjwXGopzPRNJ9XZe+h01gR3EWb6lU4F8o5EO2SY9A4TdQ5KDzaoHTVxET/FVcibrFWamcf6k9Ar2Qieo1PFeNYKWvawnuS41Od1EOlK1BYIkWco16xvOugKRZ9E5SrIMpXIz7eizy7emfzPup3wTHGfuoT7nHiy+vrapzW2lIOnzEqNJTO4QQAl5vZ4WY2I/6OQGL+Txjie0qvC9HUmwF8xMzeUKqnyda/If3UP2tmL4vy7QCcEaKsPlR4JQKAu5/p7rPcfdbEF1a1yJQp07ilnM9h1GgoU9Zfh+Pbp1DgtzsBHDicllv6GM7sajCTrRsBLHJ3YouLMcjhMIB43B0eJjZ+fhRIAMKZ8dgzxQUW/y9dPrh9us77v6KKSE9RLJEaTYBUlk5kRNtzSV7ffMDAshoyD71Fm+oJiJZ1g5NbYTa2U6SO9v3kPkRf0B7RQPvnFGVNv4oPPO6VS2CyeLUGYQTVkJNrnKaJXCEiaPUhDUf1OssqIn/qaJ6WOsZ4UrTPe3E9tK+wummj/kb1NtwTmu2Na0ldjvbF/tXX5ei40h9CdQLkcqhXUP6WepS9KsqWlq5ATbbfojGomJSQXIhG2qW+RnN6cI/FmDs0RhQ5Y3WcIZFjUMsq/lqZflf1C+T0dPzkmoLTaFULNHK8fA6u5xcrxtIoZbHSoGRmz3P3J4crG4yGjK3k7ne6+2Huvn38Hd7owWBmrWbWxs9IP4/ydy8DsIuZbWhmGyM5bNzj7ksA3G9mZETfhHrGP1OmTJkSjb610nOVbm6wrJKGCp9xJpI97h0Vda1Iktkn3f0nA76caAsAl5oZ73N+cCMfAgB3/66732NmNNl6BoXJFpDsgn8SlkoLUe9lUE0TNge2pcA4oL39PP7/mTRkouEeKQu41R8e0ookKS9Ww14iVMr01TacCJVoS2doTqkNUGRmoy+mRlmnz4B6eNPjlz7qGpOfugwiY+VCwsqn6aVSxh8On21fqSPKVC9XRvVk9E3lgDhuch8ah4fWMVtIGa1vqJvYR+rof6BRQ4Oz6g3uqEMtbYjk58RVrc24Nl+qaF/lW8JABd+VMs4FmU01IGQ8IfpO7CL6rb/EJtDgMzTq5hppLCOuh/o+0IqI86q/SM6h+vFy73KP6hoRtVPnoBzQ1+OqqbxopUQeXm0U6TqvKk8+G3UZunf4uYrbXlPK+RwGkJlNQjK/nGBmr0Hhpb0JBuZMHJSGUkh/B0kHMAMJ8TObUFfc5Ieof+3VkbsvREVoNHf/bun/U1AvGGH57ag30syUKVOmesr5HKpobyRLz61QHPlAgiGfqfpCFQ2bz8HMno/0kq7Zyrr7X4b80hjRrFebd9NGnWhpJk2Mfl80vCxg4AFi1E/DfaIztcrg06obHvkbdq9yV1oWET2pruKz6dIvUVZpUEXmQO2E2wJpzxdrlGmU41KurlnGmHWDCFGYyNsCJW+nwkRyRWF5Mk9k0bPDTn6VWGk1kxsIefwd8twziNY51g8XdTX0riiWaDrG3Cey7lZ+fqQouyI4hn0/MrCuZjFT5clL3YZad5MP5VyoxzPXT71quB8646qcCREx9Tbq80I5vvZF/Qt/php5lh7hPVLG1C3MKqd7jVyHzgXXiPfUvCXkyLjplOtkmUZxJcfE+VU/GHITikXZP++t4yLHytgKsRfWKp/DFPPuY4ZvZ59cv/wcAMDM/t3dfzZ8y2pqJE3oY0gxQjJlypTp2UVZrDQUvdLMti0XuvuJVY3LNL7yOfSi8MqtxZjnPAj0pn12rygKyrkCVA7M9mpVQXkp5d5il74w2k2lDb1kVyOqVO367hS+hdz/NIkeekwg4Wlie35VoNy9qSdQ/UWgyqV3DnwMPuJ2iiSDU7gurgoMOUgNMnpLzE9n/K+uALcFh7VdIPWzJIUGjVwUSE6nVU/I+FtFh3B5cFj7iWXO7iiR+kVweWmpo3kpyE2q3oZxk8oxsgD0xPp1SnO6NXSQCxHb/lUh92+mLL1KR6OJG1WmDxRxpIBiH+maEr2z/youVeJ59QTH2kkv8XOlPb8bc7Hka0XVJO4x5RziOW8KTndnzVsSOpaVny2KWmjhxTnQ5+BmjP23ZKTyOay/CufhSP3RWpB+hvcM0nYAja/DIVOmTOsX5cNhUHL3r+n/ZvZVDEy2OygNeziY2SsbNV8dc2pCIUOljHhJeD6rdwVRpVofkYg8NbIqLX7U+oYybWaEe3NRVQOt4YdwjijMpgYC20WtcEpRWRUh9wTSaxNuYm+OgzqHRzGAJgY6myg/nKmhzFgsiG1yDLY1UF2P9DEp+peIUjguWIXLQjejQtwaMA85/pEVKp1JavfOerImYqGzH1GvsBotbE+5tlp10VIo7nObJKHYjuhVdQ7x3Kviue8Uv4jt+CCy3veHXqCD6FdiEjdH/32xVq0aYjLQ/jyJ8Dqba0/dhHA5tUCqojNZFf0283uiJ7gtuITthPPpZLyseF5VpxzHOeB6yBqdEXtM3SjY7c4xJ/3yJlgSnyfL+JfG/p7Idesp6ubF74Bqru00fumaUhYrrQ5tjHr+eUhqhHP4bpiTno1kjro2qTlGlx5D8QLgG7atol1PXNV9n09VdTjQwUjtrD4UV95PfjQtFNuE2GN2UVXzj1ogL73++ExJlYp22L3+YDnujngpXSViBurTD6DCW8xW58ULQUU7k+MF1RPtVSLCqOUT1WEqbnBAvKiuk87+GNfXxEt+snTWzJe99nU66klMG2+Iz6r35fvy8XgOlXDsGeOaF+swWyasP+6pzz0xXij0I9xOwpOcF4rvQ0UmN5NhPxjaW8NzxFhbOfmqoA1l6Wx52V8cpstv5/wISJlW4bDXzJAUFFN2FnXb0VhAxWiRPWBaGCocJy/f3tgr3DuaqvPDPOnF3ISHZ3+Ii5rEyGByiJx6pT3XZEn8njTnEkWLkbMIX16bQ0EpWytVkpndgSKxUBPSrmpI3wA0ppB+vZl1AXgfgG4z+z8AP3L3ucN8NVOmTJlGl7JYaShSf/anATzk7k8P1rhMw5qy1hqmJNVvRXJnehTJseIzz6Zc0rNmNXl3d4QpnxsYnXyxQm+ad2rwNKI+Qh21DibnoGGgifroBKciC2ppTy21BQqFoITxXhJcyiQ6Mp0q7anI1JAMwf6vDPTbIqayi8PZbDKRrZp0UlkoyJbJgMg4dWpgPI5DuK95wWEQ5B+o4rFAuGdEpeq9dw0RzUrh1giSm2m2KnX3x7imyJwvjDnmUumU0N+rLcRW80SWMpvjl8REd4Q0tpynB5CUm/LcvTEXHcFZrhSxVQvngKyJOtsRSmtwRDrzMaWmKqSZbVj3K/cM1+/bUhdhNm4S7nHn4CKWxFpNUtPlnVBPGqaDYVKUC+FzhizodpmomeS21OqBFO3mC3cwjb+pGGtfGCw8H2thyrqFefe7h29np65Vsp9NkUwLOpHkDu+MqKfldocDOD7+Pcndz4ny7ZEkLxOQQlAe4+4+WL9m9kkAfKoNAbwCwMRIHLQPUpLiJiSnYab3Gmzs2yG9uRzA79z9j0O1VxoyfEZ0/iozOxVJy707gP3d/RXx+dQhv5wpU6ZMo02jHz7jWADXunsXgGtREectXvQnIGmqdgBwQuRUAJLa7igkqNqFIhZAZb/ufoq7z3T3mUheMTfEwdCEBA3ejIS9DjYzxWDlMX0OSRDaAWAzAGeb2fGDtR/w/Qac4H6L5CB/sbs/Uap7j7v/uNGbjTbN6jTvJvKlnJYoaBfJcHNqYE4NyMFjktoydUwi+lObTm44wlhxaqsp2Cif1UDlMZ7F4mRHvernAiUvEC5EIz2XuqjdcqoqKWgjy2BrmpQmZMPXCcqkPqSNuhblpojk1YkvtNPzYi7UUrGTcxjRI+4QHcIMcjIa2I+hNDgXmoSIaFrdLalH+mV0pXqecnpNmd95gejV/qCLyJwOhBpsjopx1R0wLAnDgOje4XNeHVfJGDw3xqhRJ1poMsr+FXnzs+4nzgXDpakjYczr/WI1MCVeF/2xFzTaxkwqcahsl0lZGM+mEU7IKLSznerwiMNlj133lfqiKTpPTB7FZ4zO7YG14Bw2N+9+5/Dt7NtrxTn8BcBukuv5end/eanNwdHmg/H/95D8w64H8Bt3n1Zu12C/58f3vx/ZMOe4+95RdxwAuPv/oILM7B4Ar3H3lfH/BAC3BbgflhpRSO8L4AkmqTazDQC0uPvjz6aDIVOmTOshrRtrpS3c/UEAiBf55hVtJqM+WtSiKJuMQlio5cP2G8FI90ERE7jqHmpDWaYeJCE3IdPzUEQHG5YaORyuQXK1oUPFxkgYaedBvzFWtAkKpyDqDnahB5rYgr47zFvbFXJE/anhTaVyYIY00HSERJKU0ypCIuojylJkGAhssqSl/ByRZOhCutQSJqyNlksAPTImO2saS1LJUqpfHptgenc1JyWXUk51CdR0JavEWpq7fDbl8qLTWEz0Gtt3hsj4KWfuF13AGYFsj6bzm5oT0RFrmpT9MK4vSZcW2YG9gfY7OCcy57Op31E0HibI/fFsTZKUZkW8bNpE9r5kTrpOov5Ijbs5x9wDwpntydeA6p2opyJXpOtBjlW5Fq4JOVfdtmFSO6XCKq8p1JEzlVtjX3zFiE5garCBK2T87Qz1wbnXREDBCSyWNa2ZYQf7sUrmtbn8xiB7cSnWjhqzVtqslMf+TE0+ZmbXoD7wCem/K8qqyCrKfIjyRmh/AL+XXNSr29eTAO4ys7nRbk+kHNTfBAB3/9gQ323ocGiJEBqIDh+LEy1TpkyZxpYat1ZaNpRYyd33GKzOzB4ysy1F/PNwRbNFAHaT/7dCEiktQp2gEVuhgCnD9XsQ6gXci1APM7SvKroU9Ufv9UO0HUCNHA59Zradu98G1DTvTwzznbGhpSh8EeicdmPEE1brj0gMg48JHKfNPVGWOpbtWqoD0BvbrIPWHopJSAys1illDPmgS0xLFioAVNEQQ2z/VlE0NZL21ITJymjyu9F/kzz3drynWlYRzdFvUhFo4JVmsUhqi7L54dTVKc0pEp/Kn5luS47nsKLo6ODI5sVazVZrMArpb5eyY0plEhGlg0Z7RPFiyrQ8xlznzBeItokCdtUnUUwh4cvpEFhT+Gi4jbA2a4obqEpgNjlEjTPC0BtPla5AoQMQRcHKQMYtrBN9ylmxlipbmB4c2Ylx/by8EvuCo2qNebpKUDe3giK/pSHNnkgOUX4D9wfno9tvMr8cHFOzcH4rbqhvP71sObUmtG7ESr9ASnN1clwvq2hzFYAviRJ6LwDHhSJ5hZnthMS3HQaAv+ZB+zWzFyC9eVQ+MA9Al5m9GCkhwEEADhli3O3ufpoWmNkx5bLBaFhrJSRDzovM7EYzuxHJ9OroYb6TKVOmTOuGRt9a6WQAe5rZAiTRzMkAYGazzOwsAAjRzxeQXuDzAJwo4qAPI0Xz+huSzP/KofoNehuAqyNLJuIeTyO9e69Csh690N3vGmLch1eUHdHgMzfm52BmGyE5qxqA+e7+rHRYn9Vl3k1baqI+ynevloYM1X2JuIcSHROCqWsn/SLU/pvwh13c+KqibsKf05WMiaBlWi+vElt12vnPj/YqZidg1VOcIb2nUC+i6SxpffP+uEpIilWBJFWtQA9hPrZatswgklefeCJmInRNDhRfviOQrYq6CXq7JHPHkvAUnkRnAzXKo2WLInr+dGgipWvEuaath4Zc58tBXYLI1ZCbEp3RjeFDqmi8mc9NBK2RE9kw9kevcEwd5MQ0Jgr3Dv1nVDdDq6vLpYzPy/nRl93+cVUDRbIAMQe3iw5hJpE9+xQutSf2UaeEgqnpboKT6xe7GK5v6xxpz9ch951M4nXhzb07y2itdNtaWCttat695/Dt7ML1J2R3WEQdguTfcKNUtQHoH0qEptRo4L3ZSBKEDQG8xszg7ucO/ZVMmTJlGmXKsZWq6CYADyL5NmjwvRVIWTcbokb8HH6MZB9yOwrM4sNpuseCZk0y7yYjRRkvZb4q4ycKnCdllLkzJLOicQqRNa8drTCIVDcpqlYGuiLIbPqUfC+QVb8EYiPxlgqIP0yJo8je54elzDT2K8L0JYH0JgWKvVvUVTVgrl7TMS8XxM0VlFIcLYZVmElu4ncVlXyAkDdfJyG1dy/JogHUkif1MWS3IntiG40EQ5aKqP01UkedT/iW0N4eAHaPvu6QiZ0R6zw/9AXTxN6/Nzgl3QKzOf7gZHTrzI65ZgKnpoqAg82iT7mFegL6lqiQnzG91D+FaJz6I41PFVlIbxcOiyY3Ncsq5XhjjKcH96JTzubaPWUa3EZqhP+euGrGVC7NbLK6kh51bujWOD2tsXfszrXgHNrNu3cbvp1dtv5wDiNFjXAOswBM90bjbGTKlCnTuqQcW6mSzGwFClPXZiRY2Ofumwz+Lfl+A5zDRQA+RmeNZzPVeUueTnfbYCH+Lq6/1AF8oyiqQSLKWFUwT/T6UymjhXRV+sSwgFk+J13b1VIq5Ou3/KYoCoffmhuF7vUm6hVkPN8LlDgz/t9RFRJliyex2lkQMuguDdobKPnAQKffkSrqO/YVuHh7WKgQXUqIKEwl9IwHOFC4li/HVUMMUWZNEbSqL2qhq9Xng5ZFoe/oF7RM1Q+NnNS1pI1cgbr+dsaVnKJyKBESe6VA6JpXM7lI1cNwEqhLUM6MIg+F6H8pXXuKqlXRXgyxMIX6gRjrSknb2sI5F0xMnQf1SW3KhZD4HGqdxnVWK6LYWysjAZLO60Ra6ol+Z1VwZ83U4agLPY0yGXI8FsvOWAvO4QXm3a8dvp1dlTkHM3srgB3cvaE80o1wDpsBuDuisdJaEe7+lsG/kilTpkzrgHJU1obJ3X9uZgPiQg1GjXAOu1aVu/sNVeVjSbNmbejd3QETbw1OgTLuSS8rGt4VHtKaLIbwlehPfQ2IBBV3EI2Sg1CERHhMjkHTWRJJdhRFfQF7qSZR34TeQK+KJAn8W+hZqzb0jKcTaE7l5mQi/iVlHyaiJfpbLJW0AhGU3MM0oeSU1BKLyJDzpaifN39SymgaRd2JOghQeK2+EjSgpv2F2OivCFl920eiQK2ceuKqXs1UwPDZ1LSKQnv1uyBrwj7E92NxcACTuYe0r+j/JuFCdv54fKAXuCT8oEe76lhbuOBcTImkuiLUjbps08La6P6wGJqikXPJKdDDXc1KqMOROe+LgbRynfXZyHWJm9by4BzaeR/dm/EmuTx+H/vFOO3KteAcNjHvbuCb9pv1j3MwM9H4YAOkN9iu7t4Ar9VYPocbzGwbAF3ufk14R1fFg8uUKVOmdUvZWmko2l8+P40Ekw6objqQGkkT+gEkS+xNkayWJiP5Ib9pdUa5TujJfmBBcAxM48hj7JC/Fu0Yb//rkm/xD4GniYzU1puROMVnoIY8KfNV1BtWTSvDoqlFZNDLYyOrGfuh5ELIffQUdeQYpqtsODiGywL1HaBcS8iNZwSKnSGOCz2BpjuPK8oYU5/AdrqM9fZA4zOLosLoi8hePcPJHRDRq6yb7TV+Ej8zcr1m2uOAZKz4dFw5F4K422jdRKSqyg3eR612yM2RwxAu4f5YnCnK8dEKjroiQePkGPqDq2rSMYd+YGedi+j/irj3vuIL8MXgHD6nOgrqm8hB3FxUtcWC3C9uyjTqYi6MH4lH/Hv5e6CFm1q889lk/K3nlOrUF4V6GskO1869GL+H/VW3Fjqo/cgZjwTEdORMcIOQu793+FaDUyMe0h8B8DpEQAl3XwCgKiphpkyZMq17Gn0P6eckmdlWZnapmT0c8aF+ZmZbDf/NRI0opJ9091VmxhtuiMajCq5bakJhCUF5PBPwni1mOzX4K0b3E4Nz0JwCJCLOaRVlNOnRADPBdSwNzkFlvu0hcz9UYzfRV4DoWjbzdMqX1ZUluKIDiEaVy6H8l0hYOIFODlm8XAkECbjna6avsO7pE4VHK/vjdEqUmb6l9UVPfqCoI0Cdoug9EPPFgdD3PkL6iusktazqRD0Jt7YgOJj2kHmrGwmn8GKR+9Nhme4qxwlnRsTUL9ZWv2OMofh/G8kz0apWUADOkPn9cMzXLTKvO8ba7E7ORBB0LUKvjGdVzGszByaZz24MHYuGE10SHC5v+V7VGnIvkptQnQD1L8oxUe/EQM/KIdM0TIM+8PcXHtu/1LBwXEu+npTjXVPKYqWh6EcAzgfwjvj/0ChrwKe8Mc7hBjP7DIAJZrYngItQWF9mypQp09gRrZUy51BFE939R+7+dPydjdU4khuxVtoAKVLPXkixla5Cyl36rOMeZs0y7+4OGNYfO6Lp7HR94oii4YSwXFosegjKnolu1AWW3IF6WROFErXMlToyJIxv9CGpYx8qew/J4JJA2pM0DiMN/jWNGce6V1xFgTEvEPTsn0WB5ohiH4q0wjv3jkCbMzS3QDz3LYK4d2TsKjouqMczuQrKlDW+EZ9X82TEPe8P4/kpGjmfXJQiT9rhk2NSxQ1ZIHJwnVJHVkY93OmfQnsOfYFQZfdNKXtp6Z7NUsefW0CtS4SrODDiCPRLX02BtJeEUaEaEzWTC5R4SAu764fYJT41PWFF1VmRy7v2vPI6qOnBgntZLPoIOpBrTKlpsZZLYg8oU8FhNGmE2mjQyf+lfSc7pg4l/rdb1sJaaYJ590uHb7c2XtjPVYocFWejkCccDOC97t6QvrgRa6VnkNKEfn8Nx5gpU6ZMo0NZrDQUvQ8pGcGpSDN1U5Q1RI1wDv9AhY7B3adWNC9/twfJqbIfwNNVJ7eZ7Ybkq7wRUkKOXaWuCckeZrG771f+bplmvcq8m8hua0qV/1LRkmEwLyiK+gKqEhqpB+xudEoQ4fJN0Z4Z1H4r7RkHifb76m1N654qe3HmlFAugWhZnROCE1kcszlZLUi4KrTLf5fUhSXMUokoOpFcCuP7aL5rRs+qiM1T4wTUZZbyZj6vRGCtIW61UCGXwr5ELVQz0+qRMrYjUlV3HsYkIkpWroWfdStw3ThGtbriHNodagAAIABJREFU9lCLJ1pG0cpH9wc5plrCZanjemh7WtJ9PxbuMlkQ6opUx0Thb4U+oqZs+KiUUcHDMV8hdeyDXkrq4s45VG9/cl3k6pSbYl9VKe7J5ohlVY0lCe6xP55rQ6wF59Bi3r318O1swfrHOawtNRpbidSCpNzYdJC2VfRGd19WVWFm7UgRG/Zx9/sqcrMegxS3vKFYIFiJQhzxeMgNpsUhsfi6ot3/b+/r47Qq6/SvbxiNTqPkOIKNuBM1NmEUIYhhrEbrS2xFmUWaZRartr2YbWUvW1pZkdkLZWu4lOEaLWa4lj/SUFIplZjQFkNaiiYBRRBlJdxpcvz+/jjf6znX88yZmYcZBmaG+/p85nOeOed+7nOf+9zPOdf3vZFPwIX5vtoTsu3fDsy2uuB3hXvseknBQQMdfzQSmFR6KfAHrtZCPoz1wcOHF39sGvFGgVC9k6OsZqnU6Bw5xutnaVNVj8V4DlIDNh/kLHv6PjnGvtSFl85xDMjSQDeqQjh3mpKC6htNLUF1HftUtRLbibtq6UXBMes8Uc2zs2KrY1R9SWXpTX1xvCu2qvrjPeQ90pTd5AysoapqDj4ctWgRX/Q/ik70xcQXrKrfuFb4YtXAQL58NHkfjcwco64/fpcPb8laX/qeqLRK65XrUNvzRayRllQpMpGlvvC/Htt4iY7gmtNaZ7uLvRAhbWaHImOSTchW4Vvd/YmCducgT55+mbsvjP3HIlPvHIjsVX2hu3t3/ZrZR5G7HRwA4CXI7AePV0O49xR6NUi7+3b52+zu30B5dvr+4CwAS9z9oThXqUxeuFz9I8odTxISEhJyUK3U21//8HEAt7t7M4DbUS6zAii9QC5BRkGOA3CJVIW7Cpk82hx/p/XUr7t/xd0nuvtEZFEnd0rhICAj3BMHWhKqJghOuSFDsAvKmRfCAfzczBzAfC3oHTgawLPN7I7oc57UifgGMg5e7bkyZkWRnsyxMyQGVY0w58PvJAKIap622CrjYcoEXWRvju0PYqtpCKh6ISNWQzazeHwk37UsjMYnU3GmaihKDMp6o0hOieVLoBENsguClc9RS1GMWe24s4P9rQmWP0FUCouDJc4WqWV7pOwqZf9QBs12PIH6RVCC0facc6o41Mh7b2yVVbJAEu/tMXKMdIXM9vtyjJKPqvIomZBJa0IBqmg0cI2FechS1T2U4+G1KZPurGij36W0otIUDcTqMsr1x/Hr+qA7qczdllA7jqFBWiVLXhMFaVmba0NlNl5TzNPAz3Wh+eT5e+hJklHnCo4/kviVDOb9kRyAveGNNAt5feiFyGoxX1zR5lQAy/gQN7NlAE6LZ9vB7n5P7L8WwBuRrcpq+j0T/Z+hPqEatZJqPxmC/dbipl1wgrs/HOqiZWa2zt1VO38AgGORRVsfCOAeM7sX2SN0q7v/JmwS3cLMzkNoiY8a01PLhISEYYe9k3hvNLNSu/sjBepvIPPb02inTbGvEbnCUff32m+kKjoN5WWZeyPc+v0/IqNZKwDc5e5ru2tbhGq8lV7dW5sevvtwbLea2Y3IxC19OWxCZoTeBWCXmd2FjE9MAvAGM5uJTBN6sJld5+5nowIxOVcDwOSJ5iVmRPZGV9BjRAC6N6j68ZL7eEd03RT/KzsjC3q3RMitCdGCeuAidzp2rwyaBlDxCTyZEgZlJDX1s381KvJSyNxU3xx9zGEKBGWli7LN7PmyL+wdTLehctrsc8vbAEB9rIYdIa2M0nPHT+P+GNfEsV2PldlyuFR/X9EGyI2qmiyPLrVk4Wrcpk4/rGHb35wfqqceXFcPJUzO6zo5RqYt594ZjJ5WpyaRIunLUAqG0xAjzn+T7KOthWtVU1hcUTE+IDeesw+VQtpiK4x+DPtj/1IMqmRX4HxJarbxXDOHFrTn70HvKdewuj/TPjKm4n/ZtyVsGmNUCukrqvdWOszM1LpztT5Yw+2ziF5+qmBfEayb0XW3vxq8HsCvKlRKvRFuxXhkOofpAK4wsxYAv3X3N3XTvgzVqJU+3NNxd/9a0X4zqwXwLHffGZ9PQbmJEsh8Ia6MqOuRyC7k6+7+I0SGl5AcPlL0YkhISEioMrfSYz3p6HuqqxypJ44Idn8EgK0FzTYhVxEBWRz4HbH/yIr9VDz31u/bUKFSqoJwKzqRvTo7kc3So92MvRDVeitNQe5/8PoYzMZuv5FhNIAbI+3GAQAWufstZnYBALj7d9z9QTO7BVld02eQBdc90G2PveEvyHW2ZLRTgqresTpvRyZ8vJAC6njJYmeJlHBTSAlXiCFC2RtQzojJ1KL5RuErY4PNbRHJoURXqKxT4Y/9yvmWh8vhjLikDvEuGUlmV5R6nO6UyoU4X7xukQS2xHmUxNY0lHc/SljbxmCJE8lL9IucfrX90FMo5n6t9DWe9he1eFF9EGPtFBvCCKbBjtj9+kvle6Qk6tpAby7q0jUBffwcbxDbwRkhAdTRXiCslytle3hP1XdIX5TExGua7qMdMZ6R2p4Tq1zxxIpjym+5VlTa5GMurmmXrKdaSkWUlFQSWBJbtc1QWot1u17WbTPX663SPlynt4dnUr1KljHXpYwdKp33FXtHrfQTZKkX58b2poI2twL4ohihTwHwifAw2mlmxyO74ncC+FZv/ZrZIcju/NmyrxrCrXgSWWL8rwH4d3ff3kPbLqi22M8kd98ZA7wUwI/cfU5PX3L3DSiPSeX+71T8/xWUe8RXtr8D2Rs4ISEhoRx7JwhuLoDrzew9AB5C5Coys8kALnD3OfES+Dxy14vPiTrovchdWX+G3EWisN/AmwD8PFTuRCHh7mHcZyKLnPlnAHPM7G5ktofbq7noaoLg1gF4ubv/Nf5/DjK9VUuPX9wHmDzZvJVsZMRZ8YFbcSdacH18Qb48MRS7f4zMeMreK+MQgNzbg6YlZUHR747wGhmlAislgDbZR48TMjAtX/pRdAWpF9m1eCttDmbXSEYozG19MO5mtYEES/xSjEsFjY+FDn2jxBNw5Z9OPqOssbIcp6Rm4GUr5TqbUkRIDstFPTCDc60+92ThFMoladyuuO5aXrc4aS8O9jv7V9IXvdFZfEhtM5QYhFXvCC+oko1FqRE119TVK/OOMbdJCpImzlNlOgkA7SHd1WgsCieP86XxHag4BuQ2LrJ8DXSj9MjAy0vkGOezSfZRcqXUpXEnXNdXyj4uID6wdS5ol2PUUjz27LZ+BME9y7y1Coprf9t/g+DC1vBaZD53h7v7gdV8rxrJ4T8A/Dr0W47sjXZtz19JSEhI2AtIZUK7hZn9GFk5lj8g81h6J3ZDmder5BAnmYSc397l7vft/lAHHpMnmrdSYCq5qAQ9++n1ecPX0ylePMwWRhI+si0t2EKTkAp4l8aW7F2de+mPT/arTJ0J+jTqmEN9oOJ/IJdMlEmGkrszXAU0M0M9x0P9tPrck10Kiy0p/qjHl84oPE1Qf38yeZ6nKOEbCwxp1DGjudVDhedmn5o+mj94VRnQoY+xDGqqo22CtgQN5qB+XZWc9NKhFlbpDq9NpUeyd9pTlL1z/JQmm+RYZflZIGfTBcWdSgEkelN5bs6rRstzjtU2w+h9RmLreuI5+T2dc4qFp8o+rhVehxSPKtlr1PuN4+B91pgJ2p0oAYZXl/1TPyQHM6/madefFB1DFWY2BcBqd+/T67MayQHIlBNPuvs1ZtZgZi9w9z/15YQJCQkJewpJcOge7r7KzF5qZuMhNEACjXtENa6slyDTor8YWaGIZyPz4D+hTyMeSGxHzgDPC8pDNjtdG0aqpw5J2X1OKFf/WJGAD8jz3CiTpJ6Z9ghNMEY9LVN2a1I02ge+JfsiwrQ9JIcaLThEaUVTbwc7GxGMsL5ejpEdB3NbIzrfCaEnXyN2jAlktMGW10roxwQ6KauvOnXuK2J7oRybF1uW1FSWyaWp+m8KcLRbSH6qXSGJ1Wpk+OUVY1BbDvviPdI06YwkP0f28Zz0UlObAz2LhFW3x3Kq4fjVU/wd2WZjmB/HnpkfWhMhpBp5XrLJcK6V9VPSU+mAXkfh3VUrnkk3h/RUJ854J3INcCvHSpIJx6A1YCulTiCXxFgVqSg3lvTBQkNj2L9KWLRXUKrTc/cDqUpoMeLZfRKyJ9JSZPqHX6JKs0A1xX7eBOANiMdU+NlWn9IiISEhYYCwd1IrDVmcgSz7xJaoJ/1y5CGVvaIab6Vfu/txZrba3SeFf+097v6y/ox6IDD5YPNWahW/F1uyvzGSK9BDOaxpoxYHDaIOWpke2aLqsd8YW+pUVS/PqNtYlRvle0VlmGqot39+12PzQpqQGu6lxV5LPb76jQVb3/z78rYA0BTpv1eILWB6MPOdobjVlUNhSN3wGfxLNb6614+Mub8q4jreKzr+dSFhtRRF33KQqh+gR0+b7AvJZ1FIbWe9WI6RrhSl/45J3yYRNA2hV98eEkS9eh/RpUpjAGKuO4JzjdRzc6whtayWmJRJnAO9burxmZ5bChq1hcSnbHgcJSpKnWfIQdo75Jzbw7OoPqSPbeIi1kDmz2I/4unWyPGohMw5qMwHBeRzrAWZWCo2xjNLpbuNFe3jvtiGvtsDXmHmv+i9GZ63f9oc+Oz+DTL9x04AD7j7Mb18FUB1NofrzWw+gFFm9k/IikWkwj8JCQn7HMnm0CNaoyzCvwP4DbIw4V9X++VqvZVOhpQJdfdlvXxln2Byi3krJYbNsX0LBRx10YkqefcuyXeR4VVE7QLIo2e/IPvYnnpsLS5Ddkbbg5SSKEkHQnd2Rf+11PGr9wehEbMhpbSH106NsjPaGKJgzeZ35ocaya7FE2tnZFmtixoRZcV+qHRUVs1ob0pY6lVDCWBmbGV6SwxU2TjZNyUftV9QWhNvl/Yoi1pDe42U49gR4xrFRAIaDc1xqY8+7QrkkpoajZ5nKnaxftZnYqt2HkoFXGLqPkPPIdWv82nGSGdV7tIDSxW3lErZvk2OcV18XvbRm4txJiqRcm1SatPI8CkVx2Qc6yPiWQWg+rh/u0SaKJnLeG/VfhHS+JqQjiaEpGK/7Durf7mZ39p7Mxyxn0kOlkXKHenuG+P/JmTZYf+72j56lByiEtutkXdkUL4QEhIS9m8kyaEropjQfyHLeg13b9vdPnp8Obh7p5k9ZWaHuPv/9tR20OEtx8WHoLbrZPgtUafx+Eb5AkWFWGrHCDV8NGiyehiRXdE3XLOWkOmR0qielsxZ4g9qye4ZrSq5c0oF28UDhmyuJvTg24TtN4Q+emf02Sj5jUrRt5LhtY4MNc5zszC9110afX1a2pdyVsW2ScZF/XfokjeLsNYY5p1tEpvQEBLJtjhPg9ptyNrfle+qob9+eFutFE+sUlJcZmMVmxF17g2qS+d8Uv+t3mmsWKZZvig5xFihNocYx6q43ilqiKEHkHhu7QqpsZTFVb3TOIfqDUXjD5eoxCF0hjQ7oshFhDRfq7edH1vm/9II9Jjz1bI2J4Wk1Mw4G7Ev7AppuVZzaLEuRlNs9Vh43E2gNMjrkUj63YUjeSv1gHvNbIq7r+q9aVdUY3NoB7AmileUzHzu/sHuv5KQkJAw8Ng7qZWGLF4N4Hwz+zOyZ7chEyqqciaqxlvpnKL9rI86mDB5tHnr2+KfeZQKgvLdIUpZsh/1uafnD9miSgI/j+3rZB89QMjKtC96jpBdqm849dEarUu9L12BNKaBbkoqfQQD3hFsd5TmfAomeHe0mSY6/i0hFSiZ49BGkLWLDYHZXkcqi43j9HJpPK/ruddF3EaLSjuU/TVCmsyf1/h1OcZr0mRPZNgrCo6RrRfVTWYW2m/KPrpg6bwSlDo0LxBpESUmkb46wpd/5Bdjh+bJpD1I9f5cf9HHZqG+TDtUJ+5pG4Llj+M1qtTCtSaSw/qINWhmez03rzeeqO1yv2u4BrQ2CcfKudDsw1zLag/judhepSJKpZR0Yy3YtX23B0ww8yW9N8PR+5nNAQDM7O+K9rv7n4v2V6JbycHMjnL3hwbjSyAhISEBSN5KPaHal0B36FZyYFxDfP6xu7+5sOEgwuRx5q2XxT9keM2HxAd9D4ZG7ElJSVnpvXG5NKduVBk6GXmYL0o1hoFcn1vk+RT63G3CWBvIxshO5ZaS4I4syrcUdZbXCvMcTz0zq7h9Nz+2MljiVGWSIQ1tDjbXqO4o4Se/+Zp8VyMlHrJr1WZS6mLGVok16Axbg/6QGRdRYplPyUFer3rOENSJny/7KE0wsljzLlEq1Dpg1Ntz/EX5o1T64HXT5qJSC73YeB6VFOkFJV5jpfVBxq3tyeTVq4uRyGThyn+5VjbLPrajjUz7Z9HfYPFrxA4zgWtG7S8E781LZR+lQL2pUVdj88ezbaPYR0oxJZzrmFO7oe+s/hgz/88q2r1sP5Qc+oueIqS1vN24blslJCQk7CNQcujtrz8ws0PNbJmZrY/t87ppd060Wa/qeDM71szWmNkfzOyb4Wbabb9mdoiZ/dTMfmtmvzOzc3s7x0CgWsmh9HkwY/LR5q3UK58W9HpBUKo5Evu7NJL4KwuiIp4sXPMCUa+rli8yLrK6i+QY7RBkhlqfmIxTGXGIB2uu73qo5CWiWVwJ+uNLjMW8kEgupM5a8iKtD0bcLK/6tmDhJOMzRKq4OfTNr5N962NfM3XcykqDsa4LVqrR4FQ3Hyz7xsWFLmkv+zqAXK2uTji8RSNiPB3iOcMyhWfEPV0nnkbsd6xKTNFZZ9xHTV7bTAlRJb7KWgeH5IdWRtQ0r1HpaVPQr3Ui3bXQSykY9C7x1qmlXUuurT3WGL211olk2cJJVvrGe8JzKgWMtbIh1vQ4dSuJSP52mQwKf/XR5xb5DYyhxKuxLrS9xbh2Sq6uUogFpdOQKuyHfWf14838ut6b4dh+SA5mdjmAx919rpl9HMDz3P3iijaHIpNNJyN7Z/0GwLHu/oSZ/RpZFM+9yCxN33T3n3XXr5l9EsAh8bkBmaVoDIDndneOvlxXb+hJcni5mT1pZjsBvCw+Pxkl754ciMEkJCQk7A72Um6lWchDLhciT56jOBXAMnd/PB7WywCcFrWhD3b3ezxj4tfK97vr1wHUhYTxXGQ08+nuztH/yytGtwZpdx/R3bFBi3bk3hhPhcRAJrb+r3k76ss1rI8sa2FFGyC3NXxV9pGp8fWqnjbUqVKXrlkxg15uFyZZHyx2AuMD1PecenJ5jW+M7x4UkoY6i1zIcVHnPjo/1hzy9Tphhi1B0Z+JebtfGGuJZskJSoIC2d/M/BijjsnU60WqqA/PpU7JB9kR83N69LVezsM+1LNqBK8tWPxIYaxn0D4Qc/+/Ijm00NtM7BBtYUdpiijdZp1zYrZ8pvcaJTjxTpsa0spU2rlU6gx23KIxE8w0y/nSWiDsV6SJkiQZoTctmmgrItzL6o/Q/hLraa3EwYyPtTyON1fmZGfc+7qz8301NHpFBZcxYo9YvrZ8yADQHn1w2dVpVcPwYivNpeYq6yP2kkF6tLs/AgDu/oiZHV7QphHl8eCbYl8jyuPvub+nfq9EJgwzyelsd3/GzLo7x4Cg2noOQwPPRm5E5IODi1l/sPzRv0Vk8Xvjibk0ZPevi+xOVZW6EFKVwxeAGBDnhyvq+exefQbCMFmvTz32FaJ+o1KdEN0vkTQHn41zbY9l0qxusfFKvzUu59RT8kMd8TJR9U1HvBTGxUP1fvnxj6lMtQDgKV4vxyjG6u1xjKqgzXIdDUxYJ1M+InQW68MVVG3CNGpfIzqDc6nmiZfjOnE13RDXOzMexlPVoEsVoKQxaeKHz8b2HdKe90bGvzgKOM2mgVXUSvx5ro25GP8qOUaV5P2yj09O9i+G8mVxj05WmZ4vYt4bDeajakeJDl1MnypvAqBkbD8/iM58GWtdzOcKmXNmeaHWVE/Nd2GdnqCihP0icdTgS2RCqEo3PIA9gipfDoeZmbokXO3updemmd2GchcE4lMF+4pgBfu8h/094VRkK2YGgBcCWGZmK/rYV58xvF4OCQkJ+xV2IwjusZ5sDpEiqBBm9qiZHRHs/ggAWwuabUJWO4E4EsAdsf/Iiv2Uz7vr91wAc0MN9Qcz+xOyDGTdnWNAUFXivaGCssR7ZNoMxFJXUDIrNaaSAVOq0AR0FOM1IV7Q3M2RWqJRxXoagduyjRrxSHY1N9tsUuYQ4XeJpFHLJSuc55qQiihfqpaB+9hlnV5jqNgYpAbk2jDm1psi6rSbNe1CgL8uChOaGbvSbq+MbmKM42aZi8oaSipgnUH2LgFZa2IOKMCNFAMz1Rk1lEzEmLw+GLTK4zPojPD62EpQW+kESpOZTSUu6kui9iH5nh5SRbtkgquhGKVPMLrD0sVD3YfDNfpuUUVOoxqKfel9iettExbOZqyzM/4gaR9FrzbGGMeKU8bO6KNO24eYuSIkDvU25tLUtTyNUhHVdJLscHs0rI9z3hnnO6kfxuKjzfzbvTfDKf0zSH8FwHYxHB/q7h+raHMoMgMx7+pqZMbix81sFbKSXyuRrbRvufvS7vo1s6sAPOrul5rZ6Ojr5chk5sJz9OW6ekOSHBISEoY09kJupbnIShe8B8BDAN4CAGY2GcAF7j4nXgKfRx458zl5aL8XwPcBHIjMavWznvpFlmP3+2a2Bpkq6WJ3fyzO2d059jiGl+RwiHkrE39xSwFO0zzQIKvuoaS9ZM6qI2Z7LapTmQRNHduCnZFB1ohP57ZgVA3io/nuODeLZGgK4pm0JwjzZGoM0ocaZZ6Bm2PJqFsZ48OU6ZHdk+hpNokKj0MA+TRRzaxVIGnWoW5Z3VCZ4uB02dcQrHfjhvLzAQBz/WldGx6n9KXn5nWwpFON6NLXBssvijVrQlfcWbCPQgSXkcaJUagYX+GaCwCnh8S6XMQixszdHu23SHsKnVK7p+RTUVqiYgNa1N61PaXGGVwgKgGFdLoinqjXyyEK12qu4XyWTGty7kti52ekPS+TJhP9iXEpc/21xfab/WD1zWb+9d6b4fUpCG63kSSHhISEIYuUPmPgMLwkh6PNW6mApF6dTEeTwJE/KK0hnflsuPucJkuOlEcV7FS+hqdRu+iIGay0I2jUKPFM2hCUVeOxyNrJtFU3rvFFxI0V/6vXLfXAZKDilVjycNQ6NSSVbK+pCCi7avQjPR/p1vE6EQ/mB33lrj/kh/CZoP0dIgTTq5UqfnVeoT2iRaSulTFRU3lPhXGzdg+lA80vz/71Omhiojpf1ewct0pdZ1WMf6Rcd3tcN1eMsniaJs5Q7zTe1Fgn20WqGBnbOun/quiQS3jU+/Jjq75d3iWQBx9OjItaLYYCjo1OReoHSXNB01TZSREpxl+Wzjv23SAn1wwzQHmdK0oklHw64/4d0A9W/yIzv7z3Znhzkhx2G0lySEhIGLJI9RwGDsNLcjjIvPXo+IeKV1JC9WAm81TKQbZExauWlAwKvVMCuOpI1+k5oopagvrmAs+WC6QZh8YuVUxmTN6/yj6yPtaAUZsAK21+J7YTJQbiypByPi7tfxxbSi+aLZyJWzREhKYPOn+rOpvSEK9HHcRoH9BqqnQso7eVSkCVtegBYEYospfF00AtcbPj2I449mE5Rp241pShU9M0Mnph6pfHyVW64337QGx/JMcYFjAn1twSMUhQElAphHEmXKLjC7yuFJWSpc4hl62uGQoKDRqXE9gcRiXm/lMph/YgzeHH+3ZRzNN8kRK4FtRWRLMcl51KwbRvUPChpHh8P1j9ODO/rPdmeHuSHHYbSXJISEgY0kiSw8BgeEkOY8xbSXeZx5C0tyh9gTIrKtNJX7UozRWx1dLcVPxTka/hvaTONxUcK0gZsDzYGJlxp6x2FuHpUDZKWkbaroreoI3bQ6Kp76l8JJDTP7oYiSjQGbRvhIgma8KVisXh1YCxLq63hemp1egQc90hEdUjaVigi5QmLwzpbuMH8l1jyfsYT/AFac4EhXTtUTGB9FVFE95vzt0KOcaiS1rJhO3YV5McI5umwUP08qWUFBJF3BZzzNQdZU83xnWoSxn7Z4pyjXOgUl/C3neFOFAbxocNkhqEXkRNXAMiId8d93aaRmfTgMGMiRI/si7Oo0JzLfuNdbtSrmMqRUkahOI89njfWf0LzPzSKtq9K0kOu40BlRzMrA2Z5NoJ4Omim2NmJwH4BrKf1mPufqKZjUVmrxyD7KdztbvPG8ixJiQkDD2kMqEDhwGVHOLlMJkBHAXHRyHjQae5+0Nmdri7b41Q8iPcfbWZ1SGLCnyju68t6oeYfIx5KxWbVCqTQZ6sZVOfzjZbpbvDg7Y+FF7TkqevpOzVeoQ8D5PxacIislI6zGvuGUbiqk0j2u0Mhlenidgi4VlZAH6wuB3B1Nvk0EQqdOPS1v8iP9Z8RvkxADkz5Fjl3P8a13iZvNI7Ikp5ZLRbK47yVJ2PChepTsnRM4JSms4Ff9U8d1lIdWzVcEHvMt4PiZ4ulS2t8BQDcrOT3tJSTR3aHOQ8HaHU12xp4yjlkCaLdLAy1tjUglxUhaHkDB7g9ev9YP+vyHftCo+kWvalZVspGav0wbXPa5P+V4dX3STmOJ8r39NAEIIuTExeqEWteO/1HvHecN1KrqUtYSQaw3KqEUtk1/ed1f+dmX+y92a4IEkOu42eUnbvDZwFYIm7PwQA7r41to+4++r4vBPAgxjA7IMJCQlDF89U8Zew+xhoyeFPAJ5AJv3N1yyIcZzqpGOQcY557n5tRZsmAHcBeKm7d6kjYWbnIQJXj2rEsX+mPpY0sZlFfqTK6ZWLsu17pCPqvRmvoPkbqc9WPTb1uLQnLJJj3EfPE2WNdF8RNre5IspVC8/MDPa3VrxEmPVzaYxnpiRXar8KZahRB362E7vHlrhuev6o5wylih3iHjMqmGRbuLE0qU2DTPgNsX2q4Jjo9jviOE0TqrsuVeNUVsr5pK5ePdA4aZRQ1A2Hc6fAwaARAAAgAElEQVTh3zwZE9NrJlWycbWZsI+wabSL7aSmMsmQXgjHo4EIEUFfkkRF6uyIPkZq5h6OJ2wCG+RpV0q9PVLac23y3Fq0KG70ypivqbo+wq7SIfE/lcmHVfCrD2mqQ6RTSlvjYt3ukuuuDSq6PMY/I9aSPdp3Vn+Umf9LFe0+lCSH3cZAeyud4O4PR57yZWa2zt3vqjj/sQBegyzvyD1mdq+7/w8AmNlzkXlbfqjoxQAA8cK5GgAmv8yGj3U9ISGhV6Q4h4HDgL4c3P3h2G41sxsBHIdMCiA2ITNC7wKwy8zuQuYi/T9m9mxkL4YfuPsSVINHkZm2gdxZ+4shQrzwprwd9ezCeEpsn95Oqjemh4oyMDJVeg5pghnaIchstdgP2XtHvqsxWPhVwfrfIM1ZN2CLSg4h1VC1vF2kBZLjacy2qcV4WJdCIovHBHsbRYd2CR5YHEx9tiY4Ci8uXto2YegUkJro+bRAvkfWL3aFkeE1Np4Xoh46Ma+bJRycDmevjvumWVlLUgo9kiQPVkcopUveUUAeJMK1IOLammDOE0QqKpXVvLPrUE+kZ9ssdMGWGP8YZegx1va4IC2BQEFmjKyZDdE/S0joZZfGLfeNS39EeLip4x2/Wwo8F6llW1x3g3izTYk5aAsbTpm5Ku6DphwbxxPE/avVJ3fYNB4PyXWdJsfqB1L6jIHBgNkczKw2jMkws1oAp6DcoRTInD2nm9kBZnYQMifTB6M83ncBPOjuXxuoMSYkJAxt7KUyofslBlJyGA3gxuw5jwMALHL3W8zsAgBw9++4+4NmdguyCIJnACxw9wfM7FXIanOtMTPmR/2kuy/tehpBHXLdMfXAZMkLRfFPnawyvXWxjWjo7W/PD9WzVkOTtCcjD3a8U+gZmVvLpfHh3vxYicVqQfiQUmjakHRCpWLyM+Q1ziyeFHbURlHyUSejX5wfa4/vaYTtc4K9TaQ2VnIAzabHj8ZmhCdSTSj5auR1v4W0MryUNovHUGNIMpulfSMHzvvxuvwY/fcbxeawM/qjENL0fmnPSmi0aQjzHkmpTik6YwDC42aclFMtEWexQzxJPXyM+US5f7ti320hnM6S+BkKfHeL/WVMiB2UvsZp3q+CMqTjghpTtz9SPYy+m23aRXJgFww3Oftb0j7mZ11c9w6JgWjgApSn6fJY1zNCIlgvEdwcv0ZUs4rhxhhEUcgEhYsWxo4UpcGtEinx3sBhwF4O7r4BudJF93+n4v+vAPhKxb5forgkXkJCQkIZks1hYDC8IqRHm7e+Lf4hK6mr2AK5P7em6STz+qfYzpFjlD6U4bDoApMMqRKanixUIMt5ros+NDh7SkRz33xN+ekAqWcgFGxd/BpayPZFFFgQzK2gxENJ8JHiX6VL4g9MTS00sWiANy/zvdFwmQyWl1kf3jpnSWIdLaJXGmtsLwrpYJVIGiS0M4VetMV1NtHPX+b8imC0vO1TJF5jVbDkKUWeT+F+c4PM4Rlxc24Q7yY6INErQpOs0oOHy2qk2heCXreLeMfAa+rqtbkuI+L9sXbXhzTYLBHry2Mdziio6EYHOs29RTU/2b4G13PO1QmMAigz9OpaoKCoQjAFpBNjglaKrYznpFDEW3BSPzyJjjDz9/TeDF/oXyW4Q5HJ4E3Iwore6u5PFLQ7B3katMvcfWHsPxZ5sZ+lAC50d++uXzM7BJn8fRQyAn+Fu18TfXUi9197yN3LTJR7Evs6ziEhISGhX9gLcQ4fB3C7uzcDuB3luSsBlF4glyCzmx4H4BIze14cvgqZu31z/J3WS7/vA7DW3V+OrGb0V82Mzsr/5+4T42/AXgzAcJMcjjFvZUGCCTFvuyIUVP3MG+nSIrEPW8NbmvENSt+pc1flF0uVkUo9R46RfpM5a7kt2jmUvjPqlNRT/eSjD83WWcojFDRtVYHOliz5ZmHjJZW++LHvjL5+Gv+fdW5+rHTd4h6zORg2L1Eljdpo1xFj1ZrQzcxnpZSb+aI4TzPkWHTcIU5mI+npFfaEXUKzaynBca61JgEjtT8l++i5RRFuohwjtW+SfbxflCZkTjaGXn4sqZbmaQrbyfZP57vqmQqXEqauTd4kjfahPYTn1jmkhKSuHpxPigBKAZkLi6KlujKFCLNL4nlqKaXxeg+W9hQZ5HqZuZiGwukqVtA4wPGFp5Tt7DurH2Pm5/TeDJf3T3L4PYCT3P2RyN5wh7u/uKLNmdHm/Ph/PoA74u8X7t5S2a67fs3sE8ieAu9DtgqXATja3Z8xs7+4+3P7ch27iyQ5JCQkDFnshrfSYWbWKn/nFXZYjNHu/giQZW8AcHhBm0aUZyjfFPsaUZ6Jhft76vdKAC9Bpnlbg0wNVdL8xvjvNbM37sY17DaGV8rujcgT+R8aEgNLoals+ekIo66R9JZUHJPNqimdTO0U2Uf9NdmrRkGHfnlzrMpGdUwPf6s7pXDCiaFTns9KZyIlUC+tDJ3+6By9BvcyZc7jQdtVn00p5/7X5ruoauflXiGRvyTf08V7hf2/N+Zrpzi+T49xr4jB/lKkL7J8TUHFw02xrZPMs0tiDjXw98mI53ghxye5gK6JDK0kqpOkLyYu3SVZXJnaamVIilMlIr6Z9ikNuwwp6qqYi/dovqLYro019qwT8mNcVmpL2BgCK/X/SmenBmtvlBu+IM41J27m/bI+ai+NMYukND9ORmFI613TxkC3P7Uh0MZ0ltjIFkecxmz+BsSYdUPcI/Wu47CZePVksbXQBsI4ijYtyNEPVOmt9FhPkoOZ3YZycwvxqYJ9hV0U7PMe9veEU5EJXzOQLfdlZrYiAoGPisDicQCWm9kad/9jlWPcLQyvl8NI5CoZrtirKv6HtFG9B39wfGJJcNe2yMXXIAnPdsSPZhTVDRqEFA8Q0oVt8mNujc9qJOSL5fz48b9e3B75/Du9a/PCbA1tHF9sLxLZ8OB4SoiXJD4fW74k9CFGcf1Lovagnf5d8cCSzB34SGwXtpePHcjzsK2TfXww8d25UR4kHM9nZfy3xsOX179WXlo8190V/wPArHjYbdTAstieHf6em0WNsz3u/U9kDfCl8954ccyTY9QU8n2hMY8c4umiCmJZTfIWzdLBeWqUIMk5fHPHurhcnvb/Edt1cuO4fPgkVPUAvXm5nvR+HB/bRXIBZ8XTfkWM+bcFJUG12i49wPl+1SJVvDdL47fyZfQfeypC2t3/obtjZvaomR0h6p+tBc02IbMPEEciUyltQnnazCORP2266/dcAHM90/n/IdIQtQD4tQQWbzCzO5ClaByQl0NSKyUkJAxZOLJkA7399RM/Qc6VzkFeqUVxK4BTzOx5YYg+BcCtoS7aaWbHR3DvO+X73fX7ELKUQjCz0cgsYxui7+fE/sMAnIDyoPU9iuFlkG4wbyUlooBYVMuwyC+PdJrGSNV/UOqQgKHSa5UGOi3AQpBlqQExvnfnA112ldQr2hUvQ+VhKjaZWWKEHGPgE23Uqh1rCdXDUmGZHBrts5oNuihWsC2200PKuUmT6wXIGtXOSjWDxqFxbCfHh3niTsrboRLA9PDr3XhDtlWGTtBKqIIis6ToXPAXNSsmf5HQTy6Zc1QkC6Pw3aF+miZusQz2433QJwft+yPlQihZ8t6qorqJYpS4Xu8KQzS7UIGXUtREMY+uifa0Net6IlsfxwmSG7I05kCzyjADPKdCpYS22KpltjIaWf06aGv/XGwpvf2wH8biBjOvRvG+oH8G6Xpkrg5HIXtwv8XdHzezyQAucPc50e7dAJhB/AvifjoZuSvrzwB8IFxZu+v3+dH+CGRqqbnufp2ZTUPmFf4MssfGN9z9u325pmowvNRKCQkJ+xX2RoS0u29HMPmK/a2QiCh3/x7y0uiV7V5asL+7fh9GuYWT++9GuVZ4QDG8Xg5PI6e7s2NLNzs1CrPYiCR1w8WxpRJX6UhRvmLSPUZ3qWRCyx5p1K1yLKj6iWJw3NVe3qXmyuPC1wziJwZrf1OwdmXQZKO0TzbJeVaHxDBTUkWQ7q4OLahmsKDn5Fi5trHUtQfzfJXQWJ6KClW17o2Pc3ZIsrVS8Z1gyRp3WBvz1CHiR2dIDGNjkAdJWo96DjwmbJ1Q3DNCQtwouvpZYURYHvT1LJEiO9lO7Arb4+ZM41yIi+aImAPebs1TSGHoKaHUnP7GuMYaucZlYp8iOI8TYoJHCB2n4LZBAvYmxPr4WxzU6qjjYtw74rpV6qz0yQDypU9hU3NPFmW/oB2FS14dKS6NLe3dHNcP0XekSnADh+H1ckhISNjvkHIrDQyGl83hUPNWFodfHFFplwU/VcX5BLoZP53v2xqUlpKHShWkZ+ra8W+xvTy2qjgOFjov3FUv1IAsejWJlYwlLUcFG18htg3WhSlzCwpatirYn2YBGRF0bFEomtUtkcaN7SKGkBw3xRjXij1ifNC+TmGqvMxSkR9xi6V4szbGpdkqyO603g7JdwNZu+jZN4c41KjGA0p/7ET9S3gy2g7UTNdWcQxAZ1zICGqh5RqXhj1opp6bCn8K9eLKsSOkFLLwOvEfXhJrR73TSuU+2YdIpPNjDs9Xe0eFtNYh1J6uvupJXcvvRv+dIt1xKdfF/d6h9qeCQkkLQpKhu7R6n3JpFQg7ZTGhBCXFcTGX7bEoDuyHPeBQM++ifynA4lTsZ7eRJIeEhIQhC3orJex5DC/JYbR5Kx2tyc6YreQGaUhdtaZ8pmsHzUbqEkK3ikMK9jXFVqWK+G5nsFfV65KCXSEpCkgEycTOluYclgaznVzht685BTks7tN6O18O5n2dUL3K7B8TRUm8KNi0JmejyWBSnOBLwjJJy+iZdIbYeRjroQy3siKr2jumBfvdLhJZffR3U/Q1S1NYxD5WhdJMHJUVQQGgLr5LW4taC8mONYEOH0CLwmCwSGwnjA9gNrRV+aGS/Ugd1jhl7FNTrlOaUsc4jofXofeD91cFY+r7Ka0VJVrkuHTtUIhQKYcqG/5k5siFLApbic71mLDJbI9BaxYQ2isY2MnaSy/oB6sfZeYnVdHupiQ57DaS5JCQkDCkkWwOA4Ph9XJoR87gqV+m07nSLdJldfOhEzylgw/LMXoyaRwiC7SEpNIuLhMk32SE6mfOiBdlyS0UCyK24mKhjZQmVAW9JsY9IXjQR8WGQNMEh6/F1094uLwNkNsQOG0fEN07GaHkjMOvY3tN0F/1Rjk5WCWjaS8TCYW6cZWAeBsYC6BeL0tjYMpiN0R/NOGslP6vjC1vi9YnouPaPNk3Mr7LvHVqYpoduvq5YqOgoHfzo13HxTlgmo7L5GbtiOu4Wtp/LHTuS2LNqHca78Ppsl6viYnhramRmInHow/1MKJ0xuFr3AKFrUkharRLUAYllMulPeeT66RNPKsYCqTr41PRCde+xqlQcmMqUXXi6yuSt9LAYXi9HBISEvYrpEpwA4fh9XKoRU4rqfimjlQpDJW+qpCnWxDZ6MVyjEnylIYzwjm8g2qE9lLnTAKpUsLNQUEXyj7GK0yKrQYdzw5auk0YPUM3bgyJQSqalpg57RYamsHPqs8m6ISjEgqnRG0g9TGfm4NBniXH3hb7XlnxfSCXCjSDM1ksx6y6a6YQV1ZI56RGao5FWb/l8fL+NRqaOa5UYmqJ+3Z/iHUaWdQelFsjvEnkKSXUSWfXRP4uzt1KsZNMjfl6tXS2LS6KjnVaZrOUObwt33cuxcdQ2q8WiekzIX6tEdGB95nSmsbUfC7W0eqQGCaJS9nosJW9Ot+FSdFZXdjP1D5C56Y1so8SFoejMR+0adTHb3H6HqL8qRLcwGB4vRwSEhL2KyRvpYHD8Ho5/BW5D3zJiT62SpeLciuRndF9Q72bQj+7WNxXZv97fCCtFq+dKfwclHiV5AxidKx6l5wVtHTLneVdArnE0CBeIo8HC30fuoLu/SSq9eLRU1fgmUOmNyto7zXCejl1ymwvjo6/HOx9g9g7Kr2Visqd3i/7qINm+/ESV3Bh6P1vFq8g2hHOj+0Scbo/PcSKBeHepZLA1IjF2ClR0ytCYuCyOP1MORYhu3JL0RI3bmGMZ+pV+THmsxrBuIX6/BhFH5WY6qPdxUF59X7TbvFs8ZabWUGNJ4kUzGhuDcmYwHUdksa/yoXQU6gU9N6YH2uKG3aVzHlDLChGaWvKMa4dDaXhcY5Ho+TnxG9rV4xnCfqPPZWVNaErhtfLISEhYb9DsjkMDIZXnMMLzFsvjX9ICansLogULgv3pEKbksPP5diLYqulGKnUvqDifyBXuJKxqbKbrkvCLpeF28bJtJeIlMNoY/UKIiurK6glQba4PehUvVBWFrnXWAPtFwBqPyb/BIO+WaQJ6pmb44srRTzg8NlchTVKTKpmbgrKvCzKeKqNgj94NRWVSmFxAj6TH7vwA9mWev+PyP3eHt5d6lXzb3G/dv6263lqYn1sFEo6lmycCn3x8qE0NJEnl/u9MXJ0an4q0uudMVF1klRqWdjBND6A0cm8lSrwUnBTaZDCAG+NSn6cf96bmboAYs7WiHsd7/fIGP8Nstba0BUUOr4SUnNbQdbeptHlja0fMQi1Zt7SezOsTnEOu40kOSQkJAxZJG+lgcPwkhwOMG9lnMKPYkslvKaTpHSgiX4oOVA6EE+m9qBZNZojifQtpIO7hUmStN5d3qQMGvvQULHVqFXq2XWoNCPQC0p1rpVlICUQuxTW8YmC9lfEVm0hJJXXyT6aPkj+VNfNdowr0HOTsmnKI5Jw2gfmyzHOoTJ6tmNMgurqaSLieLR06rSYsAvFy4cE+D/CW2el6Php4NTaBbQrfDS2GjPBpcV7pXNI28+p4jb27rXl49f55f1QNsx7yeWnen8KqRqyQyHlsmDv1wl7532gJ5POL73gXij72D/vu8ap8LtqV+AcXBQ34vJtXdtTymH0/+X9YPUHmfmLem+GNf2r53AogMXI8iG0AXiruz9R0O4c5MXvLnP3hbH/WOT1HJYiqwnt3fUbxYK+h+xWtAN4t7s/EH2dhmz5jQCwwN3n9uWaqkGqBJeQkDBk8Qz2SiW4jwO43d2bAdyO8swqAEovkEuQvcOPA3BJPOSBrFjxecgoZTOA03rp95MA7nf3lyHjJfPiHCMAfBtZusvxAM40M/VW36MYXpLDkeatoXsuhXZeE1tVdjO8VaUJuszQGVupPaWEgjoIpTBPTU1J5XAwRNW90+tol/i9syuaRzTWYHxQyaUSR8HVUKoapjaHoHbMbaN1IDgO9T2vCTFlSYg34rxSYqqLZR/Hxuk8UehFyc4RtL9D6CylG2W4VAc0sW+d37gfq8WmMSmubW0wYVXjj4lJ2RZzrg+ExhjjKhGxKIWwEJo46JQkM83F1BCTsTHWh+ZK4ryOo7giY2bluCLzVk14Ud0pIgrvrVbM49LkNTXJMQqs4myF+ouy7YqofT5dxKjVIc6Rxeva5LCLJFF6UWmNcZpKdK7ZHy/prXKM0i9jJShFXtcPVl9j5kdV0W59/ySH3wM4SWo93+HuL65oc2a0OT/+n4+shvQdAH7hnplGtF13/ZrZ/wPwJXf/ZXznj8giqsYBuNTdT439nwAAd/8SBgBJckhISBiyoM2htz8Ah5lZq/ydtxunGR21oBHbwwvaNKI8cf+m2NeIPA5T9/fU728BnA4AZnYcsoT0R/ZwjgHB8DJIPwu5QpbUhSG8bdKOJahUcuDnV8T2XDlGResbZF8ojNcHDWpWRT6pZ0gTB+l7PQwFraJoppmEbE5zDI2Pf2ZKvMLy6LcpuMsWSZ7zt6B/ZGeaUopYKp9ZcpvsVBk0JYfZQiE2B61sDNp+t1DiaRV0dGR+qCQxTBO6T+nmnvh/rFL1EG8mLc93sUoaddxjCpT7DSHarNOfUIx5ik5GUHP+shp1sFRiC6PfGZSZ0k6diA519Jq6LNuskDmZTilVjS30bIt7qxJQQ/zToLUqeDMpnYpn3PspamgW4Yjjmf7B+F/mcFJIFSUxoS0/RgnmRA2Rjns5K4wgs9RViiKlzGtLnGs6DVbSfiLzLoVUPi7sPdfp2HcTu5Fb6bGeJAczuw3l5hPiU1UOxQr2eQ/7e8JcAPPM7H5kAej3ISs+05e++ozh9XJISEjY77AnvJXc/R+6O2Zmj5rZEaL+2VrQbBOAk+T/I5GplDbFZ93P13xhv+7+JIKempkB+FP8HYRyrbP2tccxvF4OHciFri+HCLEivPpVuUq3DGVBZIJkVOpixFDOV8q+oNXN3y4Yx/WxLVDabQmJQe/wuGCLzA+kGUKp450qLiqULJ4fEkOLKHbXxblPZM4cCRVeHq4kZUwrpKIT43o1qplkt04rqPFD2CqmiSfMkvjy6bw4qX8xNrzA7hRWTbPLLP4shV13xjyNEM0uh0Ehqk08xJqC7a6K+69ZSls4F7ozJMXF0alGME8J1tsha4AuKMz/1KQTtSi28b3p78wP3R8BCxMlEGFn3AfGH0wQl6HlMT8z1CUpfv5bYjtCHgcNjJfRtUZDU9zoNonP+Vt8bo5z7pDFcGLsU0l0TMxdR7iDqXDHdThVEi7xI6f6KXGz423g8ljTD4mB2EsR0j9BllB5bmxvKmhzK4AvihH6FACfcPfHzWynmR2P7Of8TgDf6qlfMxsF4Cl370Bm2rnL3Z80s1UAms3sBciWz9tQnt5sj2KfvBzMrA3Z47cTwNNF4p6ZnQTgG8i0LY+5e5GGJCEhYT/GXsqtNBfA9Wb2HgAPAXgLAJjZZAAXuPuceAl8Hnlyns+5O6nQe5G7sv4MucKysF8ALwFwrZl1IqNM7wEAd3/azN6P7EU0AsD33P13A3TN+8ZbKV4Ok939sW6Oj0LmL3Sauz9kZoe7e5EoV4bJLzJv/Wr8QzZK9qdhwfREeodk0m8LTXxF/V0AudShIbZUyFPi+KEcYy4l6oPVC4digTqMR//bgoUvkkMXklWKx8lNweza2Eac2dqDfdfQiV7YcmeIIZpFk5dbH/O16rb8GIndGRLfsT76YIT0emHQzQxOYK1jSdhP8jpJI3IjknheeNVcKAEeOwrqRdSQQkTq0gUSizIn7Eg3xL0VEw2mcf7V6S8ubkdQ3VGSOnd9MPtmab8x5nVsjGF1QR1u3qppIjlsCMlBb/eY8FLqZO1pFSPDFrJDJLJSbWfeBylWQbtNvdhAWHOhiWt4Wn5sY3grjeW1aWh1zMkucUmqDamWEo1O4RiOR0vfHZxt7o4xTBPJb11IES10rYqx2Ma+exIdYOZ1vTfDjhQhvdsYrGqlswAscfeHAKCaFwOAzFzDHwRVR/xhqIHyHSGz/0hMs28JC/Q54fuqeob7Yqspu6mSCiNkhyTq40N1WzyYp8nqXRtPEtVw8FTUJGjq6sUh9t8tov48/qCphhGVWQ0fNFSFiXpiRJzoHlEDTORTK/ZNEevolHjYd8oLozl+Xsvj4ThDH2zxQuqMSkMauDeJDxJJFYFl2eZCXo+8q0cxsZ1GszXFNvQZc/SJG+M6g09oje6iQVYj9sIKPooPckm30VwQ3TWW+rSY60mah53zWcrZnR8ax3tfVis2A5dkk9y/pfGynSkvmDIPhYq+6jk/sr6fTX1dyNrzZe2cH2/b6xiIpwr7x8s2AIDaeOlMiJ1lhnJGOcobfD1fCtQhics2h9gQRKpe70cfkSKkBw77SnL4E4AnkN3b+e5+dcVxqpOOQabdnufu13bpKGt7HrIAEyDjQQ8UtRsCOAxAoSQ1RJDGv+8wlMcOAC92r0oA6AIzuwXZ9feGx9z9tN6bJRD76uXwfHd/2MwOR8YfP+Dud8nxK5Fxwdcg09PdA+Af3f1/eum3daiKjkN57EAa/77EUB47MPTHP1yxT4Lg3P3h2G5FpoU/rqLJJgC3uPuusEvchfK8pwkJCQkJA4i9/nIws1ozq+NnZC5flaqgmwBMN7MDzOwgZKa4B/fuSBMSEhL2X+wLg/RoADdmsR04AMAid7/FzC4AAHf/jrs/GLrE/0bmxryAWQl7wdW9Nxm0GMpjB9L49yWG8tiBoT/+YYlhlXgvISEhIWHPICXeS0hISEjogvRySEhISEjogmHxcjCz08zs92b2BzPrUohjsMHMxprZL8zsQTP7nZldGPsPNbNlZrY+ts/rra99BTMbYWb3mdnN8f8LzGxljH2xmY3srY99BTMbZWY3mNm6uAevHGJzf1GsmwfM7IdmVjOY59/MvmdmW83sAdlXON+W4ZvxW/5vM5vUfc8JA4kh/3LY29WR9hCeBvAv7v4SAMcDeF+MudeKU4MIF6Lcg+zLAL4eY38CkQ9mkGIeMlfpFmQu0g9iiMy9mTUC+CCy9DMvRRYv/TYM7vn/PvLqZ0R38/1a5BXTzkNWRS1hH2DIvxyQxUj8wd03RBbD/0R5Gd9BB3d/xN1Xx+edyB5OjcjGvTCaLQTwxn0zwp5hZkcC+EdEpe1IKzwDeSnnwTz2gwH8PYDvAoC7d7j7DgyRuQ8cAOBAMzsAWdqmRzCI5z8CXB+v2N3dfM8CcK1nuBfAqEhnnbCXMRxeDnu1OtKehpk1ISsxtBLVVZwaDPgGgI8hz5ZcD2CHuz8d/w/mezAOWYaka0IttiDibYbE3Lv7ZgBXIMvi+QiyOk2/wdCZf6K7+R7Sv+fhhOHwctir1ZH2JMzsuQB+DOBDUeBj0MPMXgdgq7v/RncXNB2s9+AAZGn2rnL3VyDL1zsoVUhFCN38LAAvQJZ8thaZKqYSg3X+e8NQWkvDGsPh5bAJe7E60p6CmT0b2YvhB+7OHKqPUoTuoeLUvsYJAN4Qadf/E5k64xvIxH8GVQ7me7AJwCZ3Z+7UG5C9LIbC3ANZMvo/ufs2d/8bsvy70zB05p/obr6H5O95OGI4vBxK1ZHCQ+NtyCosDVqEjv67AB5096/JIVaGArqvOLVP4e6fcPcj3b0J2Vwvd/e3A/gFSpWfB+fYAcDdty/UuMAAAAR+SURBVADYaGasNPAaZMnPB/3cBx4CcLyZHRTriOMfEvMv6G6+fwLgneG1dDyA/6X6KWHvYlhESJvZTGTsldWRvrCPh9QjzOxVyCpOrEGut/8kMrvD9QCOQlSGkmpSgw6WVev7iLu/zszGIZMkDkVWAeNsd//rvhxfdzCziciM6SORVWM4FxlRGhJzb2afBTAbmdfbfciqZDRikM6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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, From 1394ccaeb047656b5e5cfef413e0826e0cfae783 Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Wed, 27 Jun 2018 14:56:21 +0200 Subject: [PATCH 031/116] Rename fit function to avoid confusion between function and its result --- qdev_wrappers/fitting/Example.ipynb | 75 ++++++++++++++++++++--------- qdev_wrappers/fitting/Fitter.py | 2 +- 2 files changed, 52 insertions(+), 25 deletions(-) diff --git a/qdev_wrappers/fitting/Example.ipynb b/qdev_wrappers/fitting/Example.ipynb index 0238a124..f46f33dd 100644 --- a/qdev_wrappers/fitting/Example.ipynb +++ b/qdev_wrappers/fitting/Example.ipynb @@ -2,13 +2,36 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "User schema at /Users/Lillian/qcodesrc_schema.json not found.User settings won't be validated\n", + "no config file found at /Users/Lillian/Qcodes/My stuff/mainfolder/data/local_scripts/instr.config, will try general\n", + "no config file found at /Users/Lillian/Qcodes/My stuff/instr.config\n" + ] + } + ], + "source": [ + "import qcodes as qc\n", + "from qdev_wrappers.file_setup import my_init\n", + "qc.config.add(\"mainfolder\", \"./mainfolder\", value_type=\"string\", description=\"Location of data\", default=\"./mainfolder\")\n", + "station = qc.Station()\n", + "my_init('data', station)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "from qdev_wrappers.fitting.Converter import SQL_Converter, Legacy_Converter, data_to_dict\n", "from qdev_wrappers.fitting.Fitclasses import T1, T2\n", - "from qdev_wrappers.fitting.Fitter import fit\n", + "from qdev_wrappers.fitting.Fitter import do_fit\n", "from qdev_wrappers.fitting.AnalysisToSQL import is_table, make_table, fit_to_SQL \n", "from qdev_wrappers.fitting.RoughPlotting import plot_fit1d, plot_fit2d_slice\n", "\n", @@ -27,7 +50,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -100,7 +123,7 @@ " 'variables': ['pulse_readout_delay', 'data']}" ] }, - "execution_count": 3, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -119,7 +142,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -169,7 +192,7 @@ " 'rec_ctrl_raw_output']}" ] }, - "execution_count": 4, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -223,7 +246,8 @@ } ], "source": [ - "fit(data, T1(), x='pulse_readout_delay', y='data')" + "fit = do_fit(data, T1(), x='pulse_readout_delay', y='data')\n", + "fit" ] }, { @@ -238,7 +262,9 @@ { "cell_type": "code", "execution_count": 8, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [ { "data": { @@ -2285,7 +2311,8 @@ } ], "source": [ - "fit(data2, T2(), x='pi_pulse_dur_set', y='qubit_rs_frequency_set', z='rec_ctrl_demod_freq_0_mag', cut='horizontal')" + "fit2 = do_fit(data2, T2(), x='pi_pulse_dur_set', y='qubit_rs_frequency_set', z='rec_ctrl_demod_freq_0_mag', cut='horizontal')\n", + "fit2" ] }, { @@ -2301,15 +2328,15 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Table data_4_T1fit_1 created in analysis.db\n", - "Table data_621_T2fit_1 created in analysis.db\n" + "Table data_4_T1fit created in analysis.db\n", + "Table data_621_T2fit created in analysis.db\n" ] } ], @@ -2337,7 +2364,7 @@ { "data": { "text/plain": [ - "([], [None])" + "([], [None])" ] }, "execution_count": 10, @@ -2348,7 +2375,7 @@ "data": { "image/png": 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\n", 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\n", 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -2415,9 +2442,9 @@ " Measured | rec_ctrl_demod_freq_0_mag | demod_freq_0_mag | (101, 101)\n", " Measured | rec_ctrl_demod_freq_0_phase | demod_freq_0_phase | (101, 101)\n", " Measured | rec_ctrl_raw_output | raw_output | (101, 101)],\n", - " [,\n", - " ,\n", - " ])" + " [,\n", + " ,\n", + " ])" ] }, "execution_count": 12, @@ -2426,9 +2453,9 @@ }, { "data": { - "image/png": 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O7TiOeuqTLZ7J9hxL0ihnPN+TNGnQn9hGIR/rmP0i8mK7UeaoM8oU9/DZvCeTc666iXtYXt7PzaUDrPuQj+VgOXcM/fbJMbzh8XXSRndfmzx+ZJx55pm+ceOcBg1ddtlln3H3M+fzrL2JvbVBzsPd/QYzu4ekC8zsCnf/XHLdEyT962xzB3UwOVeSDjHzx9T0MVzzs/V3PdI24Pjw+svOF7t2/zvS+DGKvSm54S13GL8luSeirB2GtGOT8w9L8pEGHyDmeVX9/W6SJg1eYsq2BceRFzeW5e7pUQ/3Q9rqRI7VOL68/l6JNH54vl1/WbbIiy8z2zDa6GikMWrdpGZi29CvNBispLKv4rAcN+A4Oh3LSUQ9HY60g5I03h/twcGSpCPakW20qf6uQdqNOI5+y/7Nj3/0dW5UHPdkfYEyUc7rcRx1w+dE3bJd+E6doJnYhON19ZflYNvEBuX8GIT8+ydpgT8o22jOCxs3btRXvvKVOa8zs0Pn+6y9ib0yILj7DfX3ZjP7iMrG2dmA8BTtgrlohwadgS9kdCrucM4OFB1sM9Ki8x4ylP9wnux8ZEbxQvFjlGkVEzgOmW9M0qTBC8eP2mT95SBAJhZl4ou5LbmWmy/zAxUvKeshW81FmePDxQ865YsBkS87ZcoQdU85j8Bx5EUaGB84DlaUIxvY+AEMmdjGLFMMeKzb+JixrzD/kI+bclO7iQGPecYAyo/n/skx6/NWHG9JzgcB4Md3CscxgPPjzgEp0tkXQj5+fdkXL66/bA/WQ+RFckPCtHroVxr074xcDB/PD665e+nSxx6fQzCzCTNbFceSfl7SN5PrDlbZRP28PSthQ0NDwzBcxcA119/Sxt7QEA6T9BEzi+e/x90/bWbPlSSEHfglSee7+9Y8m5kYV5fxBILZ9JkjQqXenJynKeXrOA7mQ9ZE9hpyZMyFDIcMKrQBysFr11YquJn0sYJM7hgcB9PrY0rBeZhlxrJpSgltgdoL6zPKTNn5/Kiz45AWLJjaD9srnk/Z1uE4Y/OrkjTKHOlTSGN7hPaWmRKlQTtROwq2znuoAYT8LOeByXmy7NCoWIcs0w/qL/s+uSy1iUCwefZZmlFDZmpMzD/KnNUNtUn25ZCfL/SwvV/qvnPZvAWxbuhX2jXNc3TEgLC8sccHBHe/Sok5cTj+jLu/XdLb94xUDQ0NDbNhp34aTEZ7a1J5UbBdA8ZDlhIMi82Z2ZU7jLfStjtB5chcgvlw0jmbYyATDBZDhkSGFddyApYMKR6wGjR5a30QmTPZfIAslHIGW2fdrEiuZVrInNnjpUE98p5scpxlD62GE9FktncO/Ur5JD/viXY/KLlOGtQZ64M28Yztsz2iTqk17J9cR+0t6oFsnHUfdcc8QwPoK8dk/WW7s+5Dm2AbBfrmyKIe2G5k4Yck5+N+tkGmUTGNE8RRTyxbNjlPrTybA8u8meaPpiE0NDQ0NEyjDQhLCqYBiyB7jBlreizQhS6YU8dGWSnSVlB8MvdgQZNI+zaOgw1lLqJkZWR1wRRPRRptxTuqAGNwNwm2Re0lY6TX9pyfqr/UZKjBXJfkH/XAOQDWXdTNGKjiWtDs0BA47xBlZ938GMeZpwzrLlwqWd9RJmpPnTmZ+ks2TfYZclJrmMJxsNvMRZT38PlRTywH+2W0E7WSzLuGbRj2/D4tccXQdXw+NRX233gWZaPM0U6UI8pMLY9zCOGmy3bnXEnITIZPDSLKQU1l/6Ffqatx8Vnzw071r6xZPlhWAwIdw/iBi5eLLz7PZ26S0+YZJNFEEh9FvmT8mEU6/f/DxY4fXLoSxv1TSOMHLl6OtUjcUgvCD9n3cRwfCX60+MJmVlF+iLOPZtyTLfKSBh+OMSTSvTAG42wQopw0u9yQpLHMISfLFm3XZ0KIQYQmDH70QnwOhjuS8xxsQ/6+VVBxDz/YmVsq0w5M0tgeGaHhBzKexfUvIWefqSWexT5P8018yDlgRJ+nnLwn3gX2lcx9lu/MIcl5vnMB9gXes7Bup01DaGhoaGiQ1AaEJYYVGrAcMtJI46Qcmcu0Ox0pVFUXVoKyUr0O5kGG/1UcT9ZfqtQZGycjDqbH56RsB2rLqngAaP2xeFAwST6HrC5u40ImqunZpF0wZrJcriCeXvAFmrwd1Pu0+juFe4Kt/xvSyNYD1OLILsM0wXaNuqNZgua40FCoBZJpRjtwoVS2Ujoz35AFUyOMa+mKSmYfGlDmwMA82UemNUek0awSbcf2CvMNtUGaubIJYvahkI9OFafUX2ovNBmFJkbWfjmOQ37eQ2RsP2TiMw9Jzs8fzcuooaGhoUFSMxktQbjygGbBKsnEspAS46Qg9aYxULXNNKgmzyGr+2L9zWLFUI7JRCaycdq3J0lFA4nReccPZ56mbZ1MMnOPzcJc8NGZ/ThbjLQOtG2c2ld92GpUTpSTslFTmRySV8rdd7OFaZSNmNY2oCJw8dXWzTPT6IwQ7ZjNAVCroFZzZ5LGuo/7swlzsn72oRCfk8Ksx81D1w0/P5AtZut7Z6KeqanEtZyLYHiRaC++MyfhOPoo63MKx1n8pLiWsvH9yYJd7h7agNDQ0NDQIOmnJZbRshoQ2GRZvAvaJrMFN+Ns76AxoEhkn1k00y/jOHOHC1ZGRsg5iGBWZHL0qFhfBViVubqAlpH9ZfZnenHMFeY7qoS29biHTDANLnZblqjpSqEGEHKyjilz1FO26E4aMGLKEfNIffM48cwd6BiZR1K2KE8aaA5MIzsdlkMaOC5S2aR80bSU80dDv1K3j2QhHVgPmYa8NbmOiDLxnaGGknk2ZQEd2exxLb3gqDjGM9ltZpONx6zD63DMCK/zQ9MQGhoaGhoktQFhCeIu5cw90shG0pjtK5NE0MNJ0P0tSYA5suhgSVnQNz6bS12CXZKJEcHAViW75nDeIMufaZnvd19IiCjH1MxHitHheX94+mwH3c9CZtPTJrxeqDGRBUfdZIvZpAGrJGO9bujc8HEE1MqCv0l5aAo+MzQt1hfnkQKZ9tS3Jia8rGibjzkuysH2jDzpYZVpmVlI7r5AitmcDNsm+iL7amhMrCPa8EPmByGNZY85qb7gdJlmGoo8taesjeaPxTcZmdmrVfaA2a4SseS33H1zPXe2pN9WacYXuPtnFkOGpbCFZkNDQ8Nexh4Jf32BpOPc/YGSvifpbEkys2NV9oZ5gKQzJb3BzBZuETawrDSE/TRgLGR9YcMlgx9PzncoUhirOWSCCq6rFIo2fjKXYFaZT3WfvT4YUt/S+2nxEu1kDK4020CXTk6eSa+ZLEQ02VYW/iEL401ffzLVFFtm5hnPZxtlgQIyzY+ykMUGs2a7kFlvTtIylnxTkkawHNEfuOsc6zby4mZNXAMSmgHvCc2AHjuUI1hy39qGTOPMVlKzr0U5WHdZ4L9sjqBvM6aQjzb+TE72JcoZz8y0AV7Hd4pa1fyw+CYjdz8f/14q6cn1+EmS/tHd75B0tZl9X2VTsUsWWoZlNSA0NDQ0LA5GjmV0qJnRmnpu357xc+BZkt5Xj49QGSAC16nLDxYMy2pA2KGc9QXz6bNNBqMZp7N+Vt2gjwdWR/M+BpLFpAmlo2/Hn2zFKa+NVZ20L68OmeDcvZIXVNenk3CeMgcTpA87tatgdfS/z7YCzYKgMc8NXHNQG2ccqsSaetNK0L9MDtqcyaKzOo36pt87V+ZmNm+y22OrAIzJtDLRxKglTtZfehtxNW/0S9Zn3/MDWdyhk3GcBVrMtAJ6O4WnD1dek5lHt2IdU85oY2pk0W9Zx9SUoo9kGyNJA02J8zBcQ5KtU4i657vNPrKwNvGRNISN7n5y30kzu1B587zE3c+r17ykPuzdcVtyvY8izK5iWQ0IDQ0NDYuDhTEZufsZs503s/8m6fGSHuPu8dG/Tl1L773U74E9LyyrAcGUM4JgEZl9WJImwlCaxSnmWA5j70SlKQeCNmUrKOlzHVlSDt4TcmbhfZlnJ6ZLFl6ThauazmrQS9qaQ3w+k7eHxsXs4x6yMtrZ4x5u5Xk8jndUmceSUJoToPOTmICJuqNfORlrpiFEPWWrl6WB/OwzlPmqWhG0o49Bg4mosxMQJPLkPWTJgT6tIN5yMuPIk18EbkIefYTMOAuhzn4TbJ0Mn5pUyEfth80V7cGv0tjQ73CeoQ30bdsZeR6cpEnSQ+vvN5Pz7L9U9DONa/ew+LGMzOxMSX8s6VHuzub8mKT3mNlrVN6UDZK+tBgyLKsBoaGhoWFxsEfWIbxeha9dUPecv9Tdn+vu3zKz96uMqXdJ+j13z9YjzhvLdkDIbJt9m9t/u47Fx5JOZHtL0shaaWW2kYk0YHWM3x/MaXOSxvv7YtBnPuzTtLDPQT8JUr9y28xLs8XPvI2aTrZxDD1lgsmS5X4dxycFpaUBO1lIkPV4FjNZ/NxhpFHPrK/smNrP13AcLLtj8OVilti+FAXdXDUIekuR6mVrTdjekVW2v0TffgjB9rP+J6F86PRXVaHIxqk9xRzHWM/5bFvObJU138PIi5oO6yYL1cX8QznL3om+9SsLF+1U2gNeRveb5dwrJb1yUQXQMh4QGhoaGhYOLfz1kgO30CRTDOZBFkHb/DErhi6UBkyQRtKH4jgoLygOGVx48pAphm2zb9VwdLeDkzQej2c7ydNthctDw2UDhtXO/MkscvD4kCSN5SUTCxZMJkjPkU1VgDXXJRdAfVoNNn5EnaRgc7CeghXOtTI1ixFEDYFzFOEdRHv8alDmbbWTreRWofWX2s0VOA7NgAyejDebn4kuRq2WGkaUgx47pJrRvdkXp9sDjXgbKifas495j7oSP9uDgYo2Fa6oO5aNcxjxrFH3FVlYtNAVDQ0NDQ2S2oCwBLFDA7Z0AtKDEe+fpEkaGMMzNwtSY1Kfmn4KKNSduD/z7AiG17cjVDBqstxVyfHXIPyauh5iPd01SHNDJtDg4+EYf0iln2SprIapRKZgYGRqnEOYTO4hptkltYGox56JnvDoOQsa2VWg0cGOuQPXLUO/Urfus/hHZLSZL/6puGB9UG+sWVkdKhceehba6/paeLLkjFFTpizaaeZhNVfs/++j7oKtr0NG69FH1t4yUw7KHHXC+o6+3ufZsyU5T4+jrL/QIy52ZMvmkejFlnmSzR8t/HVDQ0NDg6SmISxBuAbsg0wvSBsXnqcx12mkDbpCJ/IkiP1W0OlsHwL6xweRpLk/k2kiSSOodUwz+wN7LghaSaqEB6y8baacWfgmZv/doXNSvrq6b5e2UGYmso0GWIlZRaAcVN6CSbIccUyWm3nNZO1GmfvWr0xXAGny4UPnhgSIIpFrZiG0MhY9lqQxT3p9sblDM6bWvC5bsENPtJhkQEYHQ1XKYh1l4b+o1WS792Ur/fnKUfGNOmF9rU066BY8NNvfYvfQBoSGhoaGBkllQBgpltGSxrIaEFwDMkZWGAwpi5w44+JA0BQaJBmwpxqtJ5Dp4WBTseKVdtGwc9NGz0cHs6LNeqznOBBMc4LUNnPQ/wWkIabimprBsXgoY9FkcfFjroNsPItz0+cXHxrG0YyvFFSRrkOkd7FmAWrBamxcfFIVkIzz4vpLxY4eLFP1N9ufWBpoC/S06dinq9by0EyloksPOtvqWqE/hwUPm1APUd+s2+hi9LjJ5sP62iOu5Qrf23Z285aktawILqCpOAIRetbU8G30oDqx/nItx8rkmNoNNaF4PLUKxoE6dug6SdpSC3oYCrwKjbjqMHU3uN5tNA2hoaGhoUFSGxAWAGY2pUIud0i6K4sCaGaPlvS3KkRmo7s/qqb/T0nPVmmJy1V2D5p1mt80YNGZCZesiqRue6Up47wpDLJ94UxjTcJ5eZ6ZVpIxPdrBs0exwMHSOa8wTS7JSElzg3FT26WRtgq9FiyKHkfBrilnyE95M5Lct9tWaAt8zkS2IW/f1nGzgFpU5E/GmcXfZ3tkK2f7FoFPy0+VL1say3KE2oHKWwOhTqoCcH/uyJLZZCuyKef+yflsBW8nRhUm3lZG4XuWsMcDg8xkAAAgAElEQVT9VJpDA+FzOK8xldyTrWTm/fReyzyGor3YpTfRe63PpW+X0RamLRROd/eN2QkzWy3pDZLOdPdrzOweNf0ISS+QdKy7317jeDxF0ttne9CYBh8ufqzimCo1X6hpZDt78C1k701mgDO3P5oYsm0Hp5Is2bl5HC8E74kXehX7ajZh2Leqqb7Z43gL1+MligVO/C6ESYnvWhYkjaYnIh7Fj9G6KNwkEmlbCJMRBz74Gq6pM5Xb8NXILIFfSdLIAzjw3ZCczzbToZjr4yY2HGdRDx/6lTqdcW299hScvmLmZZ1BLluwlQX7oxhZWA92i221X2zIYlNIWhsz1BhETqqZXov3iB/xqFs6fHAwjtumkMY+EuSEZY/2oKWR9bRwwe2kpiEsPp4q6cPufo0kufvNOLefpAPM7E6Vz9mihHttaGhomBvNZLQQcEnnm5lLelOyc9D9Je1vZhepDPavdfd3uPv1ZvZXkq6RdLuk84e2l+t9WDYJGmlkG52tATPKGmvqSQmz2Uf4xa3C+SPrKp4p3BKTsUxL1rp1VGaynbg/3S6wLyJeRh9JpyJTULW1oP7BGjkaR91RdjLNTPUn8w6yT+a9uTbS6j732TB5HdZzvtoWjkAbjFcqSbb8szjO5hrZb9YmaZnrZycoWxSUtqdsuxS2Bxa2raqZ7o/zMb+bKUzMKvOaJih7lK1vw6HpPkbbE6l59Bs+tPabvvDucSmfw5AmmYc071+TpGUh4fn8vs2odh3NZLQQeLi731BNQReY2RXu/rmh5z9Y0mMkHSDpEjO7VKVPPEnSfVS62QfM7Onu/q7hB5jZcyQ9R+raKxsaGhoWDk1DmDfc/Yb6e7OZfURlY2gOCNepTCRvlbTVzD6nwfqZq939Fkkysw+rxBibMSBUreNcSZowm551ptdckLa+NU9bKrNZdWByAY3jJAhfrb9gpFuxtj9bYBTHnN+lBpAtTKOd/gf1l2JOMyxGkuNNQfFJy0j3Q34IktmaGTgtquGHyXXSQEFh1ZHYx+QjYwVOawakh4xHEA8lPeSMYw2UtxmCRFYM3c2yhVJElszso2q4OIrawLqh64oA9ZftwX4Tjc9Gnpr5UGoDUfdsQpYjWDCblRpZyEfFMZsU3pDFWSGFZ9sEnScLSwz22XwB+wrnmSJLvqdsm2yRZNa9Wc7V++dy7R6WxoBQCfjDVaridpVX7ivunk6dEgu75WhXqAkzWxXHkn5eXVdoqfjonGZm+5nZgSrfiO+omIoeZmYHWtkp4jE1vaGhoWEvIExGc/3tPZjZ6Wb2GUmfVFl5dE+VMf+lki43s5eb2awhrxZTQzhM0kfqzj/7SXqPu3/azJ4rSe7+Rnf/jpl9WtI3VGr8ze7+TUkysw+qrEu5S4WPD88/zAAXptFjIogNWRWJz/qgfdcmF5DCMKZwMD1uqwlfwRPqWEw2FCyGnhUM3hVZ0u5J0pYFY5vWNpgp5wim6i/Z9iSOI44GKNbxEDq8Tej1EvWYhT6Wcrst7fVZxIco3AmghGM0lIcBmRSG1L3W92q8k6vrqqatYIhk+FFM9ovM9k4XUD4yHjWJtB9XOR5KasxGjHKw8k7CcVVRNkDmI2vlcZqICkjULesz83TlJkUHDv1K0mbU3c56fK9/G6Qdka0y44tUVb5VqNCTkljqV4KnTuF0REPnYrcMX8RxlJ3TNGzPExdMO1gSJqOzJP1OOOkQZrafyl7Nj5X0ob4MFm1AcPer1A2fEulvHPr/1ZJenVz3MkkvWyz5GhoaGkbHvj8guPsLJcnMxoa32HT3uyR9dK489rbb6YKCC9NoUwySQntpJwxE/EMf98jgfkmaNGDhjFSHB4xV4xgVjLBPUxGhjTSYNb2IppL7sw3Fj6Js9AYJQ32f7T0exspBV1pfy0HCGyybmgAfn9kheT5s+pkZvWOvp/E/KooGYpYp/P5Jg2tFTaLCO+Eb6i+JLwnt9jnOB/Nme8ZcyRVgpsew8HHTMUjLqD/aMDZEWoM8s/ag1sD5my1D10mDsjM0BKs7un1nnQIKPxYnMq26s9oNx1X9OgRp1CzjlaIyy7mrkJmv6Yqhc1J3ymAubWN0LKlYRldXy8v7JP2Lu/uoNy7aHEJDQ0PD8kFoCHP97RM4WtKFkn5PZXB4vZk9YpQbl5WGsE0DmyJZQpBKmtmncHy/ytrGafAPOsV5ebLTbJncTTNPk0VnS/tJ5uPxfCTN6LMF7iMJZXCvdDceuqCEzGR6oNHB0KZwOurx+0malG9IRJnDbp1NwXWaIHMdoWpH9SvkR8PvuLZ7SuoqR5F+Vc/5yCrbN0karEzPFrhjaUGXvsbmRaTw9NRhpQZqRV6JB2Uby9B2nm2DyuoMZk6bbrZ1K7Wjjla9c+hCaVARmdor6ZZtM+XgaxRlYl9h9pGebSBFTYLTGnO61YyMfd9kFHD32yW9X9L7zexnJL1WJdZjFh+zg2U1IDQ0NDQsDpbWjmlm9ihJv67ibfRlSb82yn3LakAwDbx2MmZDtkB7ajCO9aR6QW+PQxrpaxhse2IIhRmVSkWYuWk3zRbekv1lq2QpZojUmUHKbNLkBjSFJpvb05B/bKWdJH1xO8uReXX1xQjKwjVHmancrIRMa6OR0uhy6tLCirHqvXMS6OMK3B9mfDjSdLIP76Is5qE06EO8Jx7FNjoW5ZiMiQlOoLAcYbxnJ6htRzZPO3uEm87iMEkDZYT9P7oD2Xa24RG7EkU+shZ6LIt7zkVAqIi1tXFXoD7YbPHMvqU/WbzJKDO7bxZSe/5YOhqCmV2t0i3eL+mFdZ3XSFhWA0JDQ0PD4mDxBwQze7WkJ6gMqT9QifC82cwmVdZhhUX8Und/7ixZneDut85yvhfLakDYT10Hh0Ck9XmLrA9tgB5FpyQX0gUmKDNpGa5dW2nnaZgkCNJHh56LcRxZcaZ/CsfBprgBSdh96YN+GjMIysogPpxeCpmpqqBM66qD9+PfN0iLjW2ybTWlwXxC30rmYKp0CAr222N+1trLhy6UumpJyEzqHIKgwk9EhW+vDcLqugTHseHLD5BG9hrZTyJtrgiMt9UCHsuOyo4Z9Pc0pFVNZ4IUH2rNabWiTrohPT1dp59HWpSDc1hEdBtWJ8sW4nPfpTtrfa7mxBfLUeWkl9Cam2aep1DroYYG82e/4bxJgFlenpzfbexYOH2jBxdIOtvd7zKz/yPpbEl/XM/9wN1P7L9VMrOXSnqDu6fNamY/J+lAd/9EXx7LakBoaGhoWBRw1etiPaIbwPNSSU/exSwul/RxM9um4lF8iwrd2KDCby6U9L9ny2DZDgj0cAkWzVXB9NmeZmj0WgkKRbaTxQuihpBslLLiC4OkUDCuw2Uk7sF8aAOljTW8WujBktnzOwj7NGUj9Q7DLCuMtDCEwmraVXVTASpM1ABClsxrRcqjoQaLJaPrxA2q9bmybz1F5r0TAnL+BNrReF3yehpUGVbTVP3t83qJcrLs+w/9Sl2bd6QfiWdOZLv6EFk0Xqo1VagJeIc9AvMmccilD5EV+002x0bvMSJeBc4xTPfVbN2FNGgHqpZ8AUIFAcU/hdpTXfOzEoKGYksvI87z9Mm/yxh9QDjUzLjtxrlJlOdR8CyVdQSB+5jZV1V6/Uvd/fPDN7j7eZLOM7MNKrGM7lmvf5ek51Tvo1mxbAeEhoaGhgUDY+vPjo3ZzpABM7tQ+V6AL6kfdJnZS1QmLN5dz/2HpHu7+yYze7Ckj5rZA/rmCdz9SuXWtDmxrAaEvi00ox17ozrFTdk+kAQZQlDazF4vTVMsMrC4hQSJrZbtvpgZA3ldMM6OxkOqF+yyT5MJ1yd2dgodDwNrW1fL/F3kwyxDJrLpnckxFZVglyxHxwMr2oMZTeI4GpdqSfj087XhxEYtM23jPM40AJ6PKmN7ZqCWFwrABNUf7q4W6Wyv6DjsDOwE1Xa/Ff3v6zNPd5SnSGOW1GoCbA8qKPEqUMubiAyysKrSYC4nq2SmTyEN/S68vtge0cR9C/WzOcXdxgIsanD3M2Y7b2b/TSXm0GNihbG736FqDHD3y8zsByp7yWQbAM4Ly2pAaGhoaFgU7IE5BDM7U2US+VHufhvS10r6kbvvMLOjVIyhV/VkMy+MPCDUENbbhoMm7UugVkfbYZAuMgu6eR8TLJ/uD4FseSczpdGbzKjSmUkkBYFjBZIlB9tieCR63Uwk50N06qg7UNCxzEmdD42K4vxJtoAAadt3dp89fBzlo+xkksFUWbUhBrUKaggRxXQdg9PwoeF/wbIFdb81SZOm6e9hYKxklFHPJPNkzNmeF5nNmjQulrVMoPDrSeej0shGQ8XgdUTtGNQ61iP/kI/9P+zsffMjQfb51WE5o+oZCTaiyh7D94R+MSEA3xO2R9zHRqBmmsgRl7KJ2W/YB+eF0U1G88HrVaY6L6hRosO99JGSXmFmd6k02XP7PInmi94BwcxWqGxs/zQVJ8w7JN3NzG6R9CmVyZLdslM1NDQ0LDksvpfR/XrSP6RZQlYPw8zuL+nvJB3m7seZ2QMlPdHd/3yue2fTED6r4qZ0tqRvxm47ZnaIpNMlvcrMPpJta7m3sEMD1tnZ7wDnA/T8mB75aWQNOtLZfgnHQU3IcLiooHq4rMSQuaGyPrIukqFIJ0HPnINoJo9y0DROVvS4oH19axXDTYnlYJlDAAg1XvM8DarKtyFoVB0ZJ83GIR/9xePahyGNYX2inOt4E72hQn42cghCdyhOJFXGOgFBTwcLj7ahOweniYJFZyvPWd1UakKD6OxHDa53RPQXqiJRJlJfugyFuotOvx5hTNfXRSIHopyZcxm7f5SJpJgaXYj04+T8KnTG1fSminbgWhKqLdHHnoC0UweHEXl3PbTy4+tDP45b6MXXec/ngz1gMlpA/L2kF0p6kyS5+zfM7D2S5jUgnOHuM5Skqqp8SNKHzCybh2poaGhYXtgzJqOFwoHu/qVqdgqMtMy6d0CIwcDM3iLpde4+vUDWzM5x93OyAWNvwjSwbZJ5Z0sKOk5EUQo6MGfrDDj8xYN4D1ldME0sGhirTI7kjuwy2NZcvtNkZXEt2R3DL02XjS1F62PIz9XLnGAJ5k12WpnoNlzHMgURJZumn11UI9lbiMRV2Nm6Ec6fHEG1Ix5GbSA0OlYoy5F4xUyABR5VGSnrll3gq0laNgVATSmLEcSmuaNWxFE0xsYDqBGtSM5zPQProWIdtJ8sTFvmZJctJJYG8rMNQ2RqPxTj6HrBOLXRSRxHJ+KDWKZkrcmqWpBfhibCHdWoOc8bCxc6dbGx0czuqzKMycyerOK6OidGmVR+nKQHm9lr3P0dNe2Jks7ZDUEXFQxdQfU2+l9f8K+T7pdckO1Gk0VoY+fmlyHeBKju8cKxv9O8E6Ygfr/4ksaLxsdk65gYu257/cBw3OqYvuIDmc3U8WHJXqArk8ukwUeCAzA/gPFeZQvwWMXMPwaHTvxeVlTUNyskPu7copKDXYxCfNAtMw/pa8ANdjJnhagHWhcY6y3anv2TZT4q+lMWU5vsgTdl5jLaqaqAWV8hN2C/zLp337q4wPokje01Hn2NsnP0iA897b3sOLck5y8sP33bzvJR88LSMhn9nsqWw8eY2fWSrpb09FFuHGVAuFnSoyW928weKun3Vch4Q0NDw08HlpDJqG5ffEb1DF3h7tkSpxSjDAhWV8Q9wczOUZk6PXj2W/YOVmjADsjKMne1znR+UFpSi2DR1K2zzdKzxWo8hi0l3PGuxXUMdPep+ku2nbFwhq7ItFjePx6LnpgRmXWEDiD7vBeOoyuRXsa2nMjzJOjpkRXJ3/7JMdsoRCIjZXWmoTkycwJVv2CSbHjS2NuS89Cejv1M+d2MPMlEoxxkyyFnn8tttFdmLpOkr9UbT+SDItNHIY39MjQgtis126nyM/bZQdL6yrxXIh+6kGbrMrNNd6hwRXuyvBRpfbU/reozvcS70uc4EP2O3ga1D6xBBzl1anA8xbzmgyWkIZjZ70t6m8rb+/dmdpKkFw3FSkoxyhaaH4sDdz9H0l+ou5awoaGhYfljxwh/+waeVUn8z0u6h6TfkvSqUW6cU0Nw95cN/f8JSb3hU/cm7tKAbdFsnE2gpW6n2SodZrQa05y3V0s9DfY0sgZNOmxm2hGgj5z0ypbZU/aYTOZ8Y7AyMnBO8E4zZt7Emb4oO4OMccIhykGmlQi6EhN9D61ujrTfMsswb2dl69scKLQNThg+CqxwTQQRZHtFLGsGC6CmE/VwQfIgHNN18tTzBsdR52TE4RHJeQVWXbQXJ6o5UR7ij+GZx0cbfRAXPh7HUZGc7GAbR3sli93WopLPgNt0zNFTU6A5PxSYTCnOIp9IgzpZiwtO+youiHeJajPfydDuqNHFRBVVEdTd5HfV7TS7iyVkMtLApH+WpLe5+9dtyOWoD7MtTNuiOkudPMzdvTc0UENDQ8OygmspeRldZmbnS7qPpLPNbJVGlH42t9NprmRmX3X3B81bzEXGDg1YCAlDTHhka30kDShNZ9ec+ss18augDhxQLfk7we9IiTNWVkE7HRlUaC1kYolzT0cDiOxpTmfZbqysZl2fYTeoIJnx6uSY54PGkwqCSkbRGWmBjDhkpbk/ysY2ohYX19LBhO63a0KmhyIxKDzLy0By0d60t1OV2TR0XQ/I8KMaeAunA1iNGULDJUmelp9uW+xXwYj/S89DY2KG2lNoSmDP41AzD6r9hsrgLckx54GizOy/rNpodyov29HBx6fqAeOwsJNEX6Q6HHXD+qCauVv7hvVg3zEJzYXfVgkacpW732Zma1TMRnNi1FhGmabQ0NDQ8NOBJTSp7O47677K9zezzD+gF8sq2um4ButcJobSpa69s1NLwexJP7P4vmPk4RFR7IGDpE3fGBxniwYq/V0J1tPZVH5IHKnL/KNMZJ9BeMk8WbY0uBdpeNA1UsEsyB/df+KhXAGHl2Wsah2rYHMlcQ9ffr5fUSUsRxKHrlMfR7Hyop6pckXZWF669ER7Z7spSWl4kpWYEzripu5jpMFWoux/WSSQNUkaH9Vpt7iAbJmqY7QHy8ZOFPWUxZlgZ4EGsrY2Egk2HzlVf2k3phYYyLzLmEazfIRE6ZSD8kU7Zvuxso3Zia5Ud/HI7mIJzSGY2bNVlgfcS2Wt58NUZtR+bq57Z5tD+GX8u3rof7n7h3dL2oaGhoaliCWiIagMBqeoREs93cyOkfTyUW6cTUNgiKmLh/53SfvcgDCmATmgbTNIH5laZ7APBkW6ExcfRE76OBwnmti94d5wZ6Vjv4Hz/1R+JmHTfhRuCeLDfpfsg9IhQJEVWVe2neVRjMGcLSEmzeUkx0GVfjJiWTBrVg0ZWk0/9cJB0v4QKrxuKHMQPZY9i+/L8xcgz4iyvDaLVcCGJ0cKAR4NtWA7aHQWjO2sweFRdYPDo1DOcNSZwi0M4RHMn2ZuKjrRTOyfUczw3pKkMXrOBLNmu1JDiLkDqmnZDjn/NjhcVbWRE+F5dF+sug/lic5roahQu+H0zbahX2mI2FdZTuWXhYtuoh2PTtK4dobHD1VZsztfLK1J5W3uvs3MZGZ3c/crzOzouW+bfVJ5pEmIhoaGhmWPJWQyknSdma2W9FGVvRX+U12DYy9mMxndS9Kku3+h/v8Hku5eT7/H3b/fd+/eAr2M6EwSXJ5eEh0vjqAsU0h7ZBzcF4l3x3FYiclZGcmuMs1sa0ooF1n032xXQWmgLfB8sEsSqc7mP3FA2zgpXCzZZiS5g/hPLecYanRrpeFUVWifDkAFyOZKsng/zCbzcKFSwjDH0UprScejGHwQK296zgjtOgYJQui+SHUhDOoh/PrJnHlLKF9jc5xnvwgxybaPYDkiA7YrHxBaADWlmFijGxA5ZPL5yPoq2yhM9VNIoxYYYlI0OkNFpOvNKMdqaqubh36lwYvMjkO1YyFZ/RIxGbn7L9XDc8zssypv/6dHuXe2lcqvVvc9/u8q7eca0R5lZlNmdrmZfc3M0v0/zezR9fy3zOxipK82sw+a2RVm9h0z+9ns/oaGhoZFR3gZLY2VyjKzMTM7XCWw3dfUHfp7MdscwtF1VXLgNnf/6/qwz/fck+F0d9+YnahqzRsknenu15jZPXD6tZI+7e5PNrNxzb2XecczjMQ8CytN2/zKTJmKJQe3f2+QdgDdalYO/Q4dT1Y/klvgexFsHHbZLN5P38bgmct1MOsppDFOU6ycXQ/b+irYitNwkONYb7EuONwDBmkTPym/x/xkkLYZ90RLocI3gNHGtAXneUKToXZD9pgpIAyVHczm22CPj492fT8u5LLhWMG8ASsJJh45OF5TOe1ZKOeVUEHC4wgNdmxt25UoCFnVVP3t22c+SDz3Woqmo1Z7HOYTToxjbCbTmRsIKsUKD5nvAb6+P75ok/UX8ZPWY94iNt2ZDsAlabK+dAy0yjUJ8c7xPL2t4mPD6axDUM7j6/F6XhCvJGN58ktBz8H5YAmZjMzs+ZJeptLb43Phkh7Ye1PFbAPC8KzpY3CcOSbuDp4q6cPufo0kufvNkmRmB6kYbZ5Z07er250bGhoa9hyW0DoEFS+jo9098wSeFbMNCFvM7P7u/j1peqc0VRemn8xyH+GSzjczl/Qmdx+e77+/pP3N7CIVsvzauufCUSrE6W1mdoKkyyT9vrvP2AjSzJ4j6TlSMYtmgUuDeU8maZKGQp9WxPBzACkGFZ3wOKK9nWuI/6H8HAJaFawMrGsVNiANBtgXoz7KRA0hNAyah0mCw3Gqs1qW6tFtQ7/S0NAbsxPkuQ+vv+g+x8M1JCq3Z4fzx9UqWZHYdykaCVlMgdC/n7boYNmsh62VUU5MMhHHYdTu6J4sZ7Q3NpvacP/B8Y7vdfORpgvAXrM/tIV4FIhvRyOMKmOvirea2lPmy3/KFBKZQTyAFfaAqFFqtXxr6gzNcfgKUtDo1lBvTqrhUvdHw9FOESvX+4h2lD2bN5MG4h+OCZqxKCcN0lzpvJB7Oi4dL6NrlSvVc2K2AeFlkj5hZq/UYJuXB0t6scoINAoe7u43VFPQBWZ2hbt/buj5D1bRPg6QdImZXVrTT5L0fHf/NzN7raQXSfqT4QfUQeZcSbp7GXgaGhoaFhZLwGRUHX+kYqm7yMw+KYTfdPfXzJXHbG6nn66L0f5I0gtq8jcl/bK7j7T2z91vqL83m9lHJD1EEgeE6yRtrMx/q5l9TsXr+/OSrnP3sHZ/UGVAmP15ygOXBkgWOt4LQWNoT582ilHrotVsqv7Ctt6J3vOtmQKEWwwY5Tqw069UikSCnjHmzMuIrIrzElEk5rOOXhhBCsmS700eHkZ5hm0NWnbZIGkCVsSYN8Fm6B1tIWFaoRWRwFPm4LNcOUvCOpakTURF0OWMCkDIxAcdROe5WN2ActKfK4QlF4uqQf8is4+2oxmcjlEBelAlC6bTrTk66hOpefjlswmnc+AngL5qVTvaghZh5YeKk0SHHYOKy766OUljOQ4c+pW672yIP8abOqF9kwdk28TtDpaGySha45r6N66hzRLnwqyhK+qH/xm7Ixl366nHPy/pFUOXnSfp9Wa2n4rgD5X0N+5+o5lda2ZHu/t3Vb7E2XvT0NDQsGewjw8I7t7x/qxzsb4gO6aZ2bmSXufulyfnJiT9uqQ73P3dPVkcJukjNQz3fiprFz5tZs+twr/R3b9jZp+W9A0V3vhmaB/PV9m2c1xFBdqlhXI0lwazppfRVrDUiXCFoJfGNNUkHSHtykJlUpuoEwW3fWyQFOM3mueWxM7eZ0ffP0mLa0mCidAcOssUKWYQQE48bAYrXB0U7MG44F/r76FIo/9+fQBX+II1bq4GSJYj2ouMkGbwqKZsIzFpQMipuq6vlbMGK2zTIEMd9wk+IXoMNcN/HRwGzacrTWggyJMKWeRIje7A5Hy2RoNzS/TqiOY8krZ1doh4GMu5tjb4vdnpMVcSKzvGEJ+L6ld491D9mSOMWrwpLBu7Xebsx0eGUjIGDfv4WCVOjZ83jfwpnAN7wGRkZn8m6Ukq38KbJT2zmtxNxevyLJVX4Znu/u+z5HOyyo5pq+r/P1bZNOeyvnsCs2kIb5D0J2Z2vMp7dotKk29Qmat8q6S+wSD29TwhSX/j0P+vVlnzMHzd19SdHmpoaGjYO9gzoSte7e5/Iklm9gJJfyrpuZJ+QeW7u0HFivJ36gYiGcZbJf2uu3++5vUIlQFi991O6wf518zs7iof5ntKul3Sd6oZZ5/DCg3YFk2bwfmScDsFYWjP3Fo6lJIGaK5gDjwcx28pP2Rq8RzYctdiacPhld5O4haywiBjzDIL8kmiFvXQMSTSKyYUALrFrCb/nKq/ZM4hNA24YM6xxdgkTsOWu7oWYBJrI+KQRI/KU7BHsmkeZ7GQ0m0MViUXjFMX4ZLuYMw0vr94cHjQh8rvY780SIsGA/VdDw1i253dy4aR6Z2RxjamJhXpLPsY39CgZdwPYfoF+AES+Y2pjTRBDytYbbNV3LVBjsWucjtwPkTqW18SbxrrhrMa2X7VsZ/COKd++MrOtQHFrmCRTUZ128vAhAbbDjxJ0jvc3SVdWhft3tPd/6Mnqy0xGNR8v1A3PJsTo2yh+RNJF42SWUNDQ8OyxB7yMqpenc9QGStPr8lHqGucu66m9Q0IXzKzN0l6r4rkv67idXSSJM1mblpW+yHs1IAtkUUE2+KsNLntyqBgdO8JynoM98PKwjdRK6DWUG3uB4EGr6u0kbQHkTKDRJP90fFpqv6SYQUBo+7CnlNdw7uxm2i4jeUDpOYbMMmwLsrHADLhA0MeC73kmMout6LsLMhklQlG4831ZWMQT5Ypnk7th+UMSUiDwudrLXzUx6n8BPH/F6yy/jmGS1099MubpGnqvRrLck6uvYxsEnW79iP1Mpxmr8o8yaIvswUyssp7TkXlrI+9mPlBi7Uwj35YTuAAACAASURBVEBnuAe0vOlPAyQ9CG/NytpxnoZbkk2mj8eq+HW1aqiDsT3j/aRmx5hQkS01iOipR+ETdwyXsE9pYTC6l9GhQ2F6zuX6KzO7UHkYiZe4+3nu/hJJLzGzsyU9T8X9P9sPeTYX+3CPe9lQ+qn1vt59EZbVgNDQ0NCwKBh9QNjo7r1zn+5+Rt+5IbxH0idVPurXqWvxvpdmiV7q7qf3nZsLcw4IZnbcqOsO9jZ2asAuyOuznZqmcHx8ULTHIjHsvsdwnQGZYkTIZGAgWvcr89oMBham6h7//CB1ZLl0YAl7Ky3eUSZagk/EcTCwKaSdAAo2FqyRkyod6h1+LyxneByR21LtqXZndlnadSuVvR6Mdap7SlLXGSrqhOyR5ussEGY8vhNBlRUaRm1qDTtw9VhoC/Sm4hMqHLpnlJONCKFXJ65i9H6bqr+Z8w6zhE4zrf0xH9bdilqMI7IJKT7oHqzdkKSn7DHvcickiQ9mz4RW3E0x2EWy7y214ag6KvIXD52TpHXoGKt3yQt/FuwZL6MN7h5K0RM1CPv0MUnPM7N/VJno+fEs8wfzwigawhur6+fbVVxHkzdi38D+Gphd6HkWxxxij2cPijlSpk2P8VRq6ZY3w4FqCPWjuZof0jpMcVUSBofVtWY5D0bdMsYOvvjxHefnONt1kO8FzWmr4mtCSwhj+E1LQLfTGBi5EwnDetSvIiequWavln81vqPxlL71a5kpkB+GbHvR6d1BaQ6j7SwqmjaMMQod7qZsBfaB+voYZmsPr7YL2iT50Y1uA6MC++pk/WXZY6aRYyqbKyTmGixSlyPiATTbRdm5KVQnvHu2mo3Oy/UJh58/SDql/nJCGyNTyMc2yjbQ4cCQhaBhX46y94bynDMk5i5g8b2MXlU3stmpYrR+bk3/lIrL6fdVXoFF26tmlEnlR5jZBknPkvQVM/uSpLe5+wWLJVRDQ0PDPoU9sFLZ3X+lJ90l/d7iPr1gpDkEd7/SzF6qwmv+r6QH1cUSL96X9lbeoYE6mgVfIBPbCvVvIvTWLKxBhwVzwjGUVS5aSqJ8b4LxKqxHSTA0Kd0/J43NRaIXYh7Yc34sSVvFiohOTlMKafgBMYFMdhimIvIyKveVFmar6nA62zxlCmkk1kHOqBWQ3B6cpE2TU5rDaEt8UP2lyWgrKmIioh7SbEITYkzCosWiUFTzKFRtb7Jcdoe4nWbByJ3VyeyvHbpu+Hj6xmxz+k44i0T76ZSd/b9qvnRQiApn/+JiOc0Eqz5eQ9YHv8Gh7bIeoh5ZXr7GC8bql0AsI2k6+OiTVIwGrlKtH3P374xy/2wb5MQDHmhmfyPpOyqz009w9/9Sj/9mdwVvaGhoWFLYxzfIMbM/lvSPKl5JX1JxMjRJ7zWzOWPBSaNpCK+X9Pcq2sDtkViXVL90l6VeRIxpwITJmMOWTJY8QRoR5JfUZDI4J62c/w3HMYHMjJhBZVtrwIGOqhQqsadL0iGVdPXF5gpJeD7KuS5JIzrrc0ingkHRtt5hQlE+xgiP+QSWl5lWn8ZVUB6TPTTHMQd/VCWfVE7IooN8kpDyiaFoUYOI+7ejPONkjB0qWTFBHhvsmFogV/8/dOg6SQfUV+pgxHCkclVp7pGws3MONkg854RiqoVsOlu8ldnjO5kquYCzugcdnFzIT8TRM9PXweX2CZ8pv3dCW0RnXVmFnoSLKB8fr8KU8vNRZXTdyDRLlv34hYqAtjSC2/22pAe4e+cNNrPXqETbfNVcGYwyIJwl6XZ331EzXyFppbvf5u7v3HWZGxoaGpYYlobJaKcKh/zhUPo9NaLxbJQB4UKVzQaDChwo6Xx1Q8HtE9hPA9KXbU3ZCRdAY1lQzc7uKtX9cILeNeRtwQq5GI1zCOFO8l8HSYe/o/xmNnyI0adzhBbAfhm304LPcoZ0Hftttmiqz0A9Xc7Mq4pl/2cc19Df9yWXQx8NzyqY6ydnJqUhOOhCymIEt2XdBTntuJ3ypmDOaYwLaWBTZyhzLkSM14eSVvWPXkZUe6pD0gSY82HQGMNlmPUQWg+bKFuchX2XOsx6et8bqlfx/M5LwcqJslH3TDSEjsZUz/8CVIBEO1kFVeY4CJqFru/bEGk4exajs+pqIfdZ3Pc3yPkfkv7ZzK7UYGrp3irq/fNGyWCUAWFlDV8hqYSyMLOFdOZqaGho2LexBExGNZr0/VX2nTlCZf7gOklfDgvPXBhlQNhqZidF/Asze7BKkLt9Dls1cO9mmK4s+Nd2UIrxoGNUtIJBTZA+0kUl2BK3MqGXxmeGfjVgopwEgByrktPZ+iaeDynICKmAxD0kattw8cp4QLaTiSRNBBdgWIOoXU6G0Ic9XFdQN8wzjPuYP9lS24CMkEpciNzXq6MYNBk/ov6yaGsZCyGcpUitN+EJa2JdELWCKRyzvQOV/mYTHFI6wZMFrcsW4NG9n2tqQgFh2dkH1tXMjqLaEcobu/QOvADTMaap5dElKT4drLy6fpVUng26cmYa50WCzHPuaArH2RqzKCdf3X/C8RnDxpPdxdIwGcndd0q6dHfvH2VA+B+SPmBm8U7eUyVYUkNDQ8NPD/ZxDWEhMMrCtC9X39ajVVSQK4ZnsfcVcKVyFiCOrKnjy5xNPEzTNnI1agNRdQwiwGsjU3CgVdWIy7kKHN9WGRz7HcuRrbEIs3AqugaEtLNtIVlqsLmeUAsDjkaKH1oD51cYPCPqBlrDTljy4/mY+AjySDlpso3y0RzP8AzBmElOx5PrOhlkG7qs4QqAmD85MkmTBhXF1ejhkYSQ2Nl+mTDNr4OiFbXMPhB1wvkRNhf7dYDXTvchFu3a5EJmGjHKO1oQZ2Minf2i8sTxtw6SDocKEhM9cPfj3Fe8KdR0qBWExsjuGVoFPQiz9S3zxhIwGS0ERg1ud4rKvN9+KovS5O7vWDSpGhoaGvYlLBGT0XwxSnC7d6q4k3xNgzHSJe1zAwLbjF4H2bra07LlwCQ70yFtMlYkDWzq9FFndVbjJQOfhYLBJbg4nU1lUCfpMN2KiHOTOU1JAw2Ce6PsAEseC7ZOraVDOcPvhV5GyT6RHUlDg0B9kV1FAZPAfgR3Cwl75W0950MzoG09uGkn9DfLGTKxibNtJDtzJZydivbmPdXTjH2JhQsPKzjikMVGObNF3lxnwCaKtucj021YWc7T6i8rdDVViCgb3aWw5mB69opeRp8sPzdDKyDdjw7eE6czQrVTTGoQ0evoLBUaBP3/iNUrtDDeQXtmx7QFQd0MJwuPbSqRMA5KzkkaTUM4WdKxNZ5GQ0NDw08nlo7J6G9U+MM7VQaBp0la5e5/OdeNowwI31Qh14sSbnUhsVMDRsE1l2Fn7JiM2bgx8pOWBYM7jfyNx8HX34M0PrXqJYa0WNtAVkaqV9Uazgdke4fTzj6epFETCr8Qah00aY8F3erb7fygyvo6MXyiHrjughnEfAIM1NeC6wXlvWKQFM2ROSNJeQhozqVk3jnx9I5mldFoet9cg45x72C/nDegt1WwZGZay06jdlYoVAcfH22f9Tp2m0wDYLuzD0274nPuaCp50CbMh60JrYd6CY/j08EM7j7z4TwOtR1VnPVLvoZcRjA2dB2zYhVzDuHan7JYRhWPc3eqsn9nZv8maUEGhEMlfbtGOZ3uMe7+xF0Ws6GhoWEpYmlNKu8ws6epxDVySb+hEaUfZUA4Z/fl2rNgm9F5Io47NcJ/gq2RrU8bo7kJDHMNz1tW4ZtwnKxinfzxzGdjsiP4Jl3lyfoincw4+BkJKaUMUsNIRCtpR8+CP3Vi/NAHPxAaAOdPkiWpU7Als26TTRy21cKR3ZGPZvv4MMuwfmebek5SJhqbI4MHqQfhJXVXkiYNVgZQO6rzDbSdZx5c6BbHgfq/t/5SawgxWXZmGc3JeSL2h+npMLL1U4ZPqht3a7rWWHbq2NE67P+XzRQumy9AV6EGfOfQr9Rtz9CCs9X77AvsvusP0dBq7Hlg6QwIT5X02vrnKmrtU0e5cRS304vN7EhJG9z9wrpKOVtF3tDQ0LA8sYRMRu4+pRICe5cxipfR70h6jgrpuK8Kx3qjuvRwnwPZZbAlMuvrYVs8IoY3GuKDok3Sz4FsKfZD4OrNZHe1rWCPcQs61nY8M+ypJFgws0/zULLoZEfGDpEJNtUxpe5IjknwO/FfQgJS69A3uHaW2tHKzo+krqdNlHlqkBQkmTZl3p4F7OTKh1DoWDds72nQNSmeT8rZcVeJuQPOlVBDSJbBep2xIDOmtlDnprb9aEaSpEE9kMxHVp0F5DiOtqU0ZMxh5x3nTdlkRceLKJtDIPcOzytkMFWf9EVcxsLV9G3QiKgJxfGUcsR7wbLF/BA9yTqLwRdqIcLS8jK6v6S/k3SYux9nZg+U9ER3//O57p1zPwSVnXoervoq1T0/7zEPeRsaGhqWHvbx/RCAv5d0tipPdPdvSHrKKDeOModwh7tvLxukSWa2n3If172OHRr4MNMTIRhSFspFUllhIXWdhIKeHgU7+OTXcEEwp0mkIW5R8DkuYk28Wkg6gvGSkxHBzxh+JkzAJKRZpNfOLlRQWsaCvXLBNV3P76zCroTQRwQfo/cNMr2yctV/wWmyxqn6A7Um0wBo+l2VXJdssdBRRKLsnD/pIMrOZQZcyHBU3c/hAPrnYz3G1roamW5MwX6/iTTmf2P30bxFGkxnsezRbWjhJ5O729Av72Geq6g+RUWys12GTvDg88rvDnzl+NDoUBQ02pt9HmUPrehinGY9RJbZLoHSQAvOdgSkVtAh8uPqVvDuYgmZjCQd6O5fim92RRZ4awZGGRAuNrMXSzrAzB4r6XclfXzXZWxoaGhYolhaXkYbzey+qsTdzJ6sEZcNjDIgvEhlJ57LJf13SZ+S9Obdk3NxYRqQGLKEMBvfoh7cMfQrDWg4GdSB3xscj9fju2FnrGyJ5ZeRFvbUywdJnCOItb59ce+z/RBCM8gch6Q8RtAYjfPhF38c0mhnD8p9PNJur7zuAPgWbMXbEuVj3VGFSRxx4jEk2xQjqpbvZLaHMNll6DEdJxP+E57abDfKGcxyNTrGIYhRFJT2C8k9bHc08pX1+WTw1FyzyLxB5jtRW3EcmgP7BfvDtMaIfjetCVEjosp1Y5WgzwYfQnHiIo6pEaGRoxpYXr6nUT42AbWiQGd/iyFxpCENYxSj+ChYWgPC70k6V9IxZna9pKtVFqfNiVG8jHaq2KT+fj4SNjQ0NCxZLBGTUd3R8mR3P8PMJiStcPctc90XGMXL6GolcwbuflRy+fC9UyqkZ4eku9z95OSaR0v6W5WBfaO7PwrnxlS2OLje3R8/1/NWKHeeOHjoV+p6jh8TNIOGyPA2IS2jcT7o+gFw3bgDHklBh2iPD2oDarseTZXth5DtAEYGFPdkYeelQTE4p5JuNcYuQ6eayIDOVvHQ25ERjfehojBiShKCdQy23ZA/i3lP0N+ZdRMisbqDJXe8jVgR0e7UjiaSa/vo59eTtChnz+bQ0S+pCWWm+YyY05+NVRsvIufFqEFMRHs8DImh8bESqVoG+pYN3zj0Kw2oPdo6W1HN/sl6CG2B1UktMZqD9RCvIX0sJ/genyrpE1oYLAEvI3ffaWbPk/R+d9865w1DGDWWUWClpF9V3nX6cLq7b8xOmNlqSW+QdKa7X2Nmw95Lvy/pO+r2/16s0KCz8ZsXnYbtyX58S10UtZYdPm7iKMLeHSPKYahzvsXx8ecMcHxY0GHX4O04tt5PMfhyTCVp8T5zEKF1Z3ty/ii+5KHm80vKtzAqKovAxo8ehQ5vVG7TAQFurPfxAxZZZvP60mAgyBYcSl0TyfA9XNyUzlhyJSAfGm1Hswo7zlT9ZeXGhCpMRju4IVH9peUqC8HBD3o26PMFjDqbRNpkRl446xwZZPHVpYH5lC8SbTnxJafdL45hIsu8jdm9WLXxevCDT/NQmADZFWMs74Q0YaYL6Xa6yCYjM/szlbFtp6SbJT3T3W+opPk8FdOPJH3Y3V8xS1YXmNn/kvQ+gcq5+5xL9EYxGQ0H2fxbM/uCpD+d694R8FSVwl1Tn3VznDCze6lsSPxKSX+wAM9qaGho2D3sGZPRq939TyTJzF6g8o19bj33+VGsJBXPqr+/hzRXd116ilFMRlwRv0JFY1jVc/kwXNL5ZuaS3uTu5w6dv7+k/c3soprna7HPwt9K+qNdeJbGcDEZVAyRJENkYGvjBJ8UzII2ClZn5ufIa4NNsBOFbQAMZhMm5YIhcSKN3ouZ91w8hmw7C5PccTvF8Viwxz47VRzTrhHlZEbfwXEwxB69LgtUF1n17b4YdUMdmCIF9SE5jD6wKvNT5ANY4WzPULUyn1hp0CCZKaHnnmgbdjXOy25Jzmf7+PB8tDGZdaecUX62a7B93sTKi3pgPlM4jn7NDlo1BAaUo+KYTY7TRBhKCcvGVy6LshJmpE7ohOEYHhdqYbDIGoK7U3ma0C6695vZr7r7ByQ9xt2vmvOGBKOYjP4ax3epdItfGzH/h1eV5x4qaswV7g63HO2nsu3WYyQdIOkSM7tUZaC42d0vq+pSL8zsOSorqTsacUNDQ8OCYQ95GZnZKyU9Q8VadjpO/ayZfV1lKP9f7v6t5PazJX1A0gfVHRZHxigmo9PnumaWe2+ovzeb2UckPUQSB4TrVCaSt0raamafU1n5c5KkJ5rZWSoE6SAze5e7Pz15xrkqLlY62MyD9JG0ZSEM0kkQMtqg3H2dIIzWVDVIeYNhUUOI/MEe10BtObBeS1PtXC6kwZYoOklfBMyjHX2MXSXkZxoN8lEPpGBxnE00S4O1Wz3MLGRm1USW1GRIWOMetiurPpgkSfARK5ILj8bxcUkahQrjPC2vWdgPqmTRtqD1V0K1C5s4H5NtBcrHhPgsG5WamP5Zwwt+FsfHDl0oDco8hTQKFe1OnkkVNeZK2AcST1U6b0T2bA7OEcQCwr5IIiFyJ3z7CUMnpe5eTpOS/p/mj9FNRoea2Vfw/7m0jJjZhRqa1qp4ibuf5+4vkfQSMztb0vMkvUzFYf1Id/9J/SZ+VEN+IhWbzOyzku5jZh+bUYQRIlSPYjKa1X7v7q/puW/a5ake/7yk4YmQ8yS9vq5+HlfxDP+bqvacXfN5tMqIOGMwaGhoaNhjGM3LaGPmTRlw9zNGfNp7VLagexlNSe7+KTN7g5kdmjjr/FcVavdOdS07I2NUL6NTJMWI8wQVlp/tekgcJukjdfn0fpLe4+6fNrPnSpK7v9Hdv2Nmn5b0DZXqfrO7f7M3xzlArY725WCPfabkdOQPVkiXHTKkoDFczJbFWqDBMxamZf6Sko6o91NDIJsKWzOznEzSSPCjaB37MisnmCArJ9vUhA/I5h06blv196b8fGTJOZ0Qg1mSfG5IzpMURj2dirRpGk3GSLUjVEe2Pys8HkbNjx0nvKko6Jah36FbohqpFVC8+OawiabXxyGNu5+sjYv7pgzDUJ/FNCFXZRvHIjbujMpVlOGBhbc1mx/JFOwsyLY0qDKmsTmOiYwpc1QeKyebP5kv9oyX0YYaK06Snqha42a2TtJN7u5m9hCV7jFjR1133y7pUjM71d171+HOhlE3yDkpFjeY2TmSPuDuz57tpjqpcUKS/sah/18t6dWz5HORpItGkLOhoaFhcbBnvIxeZWZHq/CCH2rgYfRkSf+fmd0l6XZJT5ltS+PdHQyk0QaEe6vrLLBdQ3uO7Cugl1HWdrRXdhxyg0XQCT6LGkdFMGgb6Tgpb4zz3F8n7MpgVZsxn5CpXJQzC8+wKUmjZhvkdYKUkxQuykFWlcXXpiDB/LONX6QBs0a3vB7aVRwy3l0oWn3eUFlgM84NRXOtJMuNOYJ1SRozpRsP779h6Ffq1kP43aPhbqk2d1YNlzmEST5bsCUNmiaLKMEpgLVZv2P/5PlQm+j5lJk/GHJiqv5m5ZWm64RJ0RfpNcWyZRoEqz6mOij6ugOTC0gzY+6LNzHTkdfojoDF9zL6lZ7010t6/eI+vWCUAeGdkr5UJ4Vd0i9JesfstzQ0NDQsIyytWEa7jVG8jF5pZv8k6bSa9Fvu/tXFFWv3QK2OtsdgnbRddkIiZ/7oQcvo4E8bbTyITCvbD5DG+6n6ey/IBtaWaQBkzJmnTaSRdZFFT5MpsmQ6fwezYjm520gwRLKuoLzUKsg+gwZj3oBELWLB0TMqTNKdzU2AeBfZrhRz3YwDDdqQdJuI9mS7U42MiiTFZ4NE/rCtRzm5eU+2WLYvBEdUc2buPypzwJcGjdy3j2RopPQ8Cq2HFJ5tGFogN7gB9Z9KmHcWWoXIvKXYNNEV1/GdYTlj4mQSaVG52b6yUnd+Z57YsY+HrjCz12mWtQvu/oK58hg1FuCBkm5199dKus7M7jPifQ0NDQ1LHqEg7OP743xFZWPrlSrGtCvr34kaUbxR3E5fpmKdPFrS21QIwLuU776+V9E3hxCEoXf0C0pDFhwZ9a1iDZbdF+sorr1p5vltmEOgeToINe2yJDhB4Miwgkz12WWnWXhf9Lu4MfOukQZUluzxtiSNcyXVQ4XzBtnWmHxMVB2ZM8uU+eeTMK+MC+h+E+ySKhM1iKiTKaSR3oaHDT2o6ANX2fMWFCQ0AzZ7tmkP+yLLkcXbO2J6syYk0o4e7laZQZ7pWcztvrKFVoQ5hC8mQevYP7OZTFZ3lP0RPM/CR4NPIo1uY1FO9tWMtS/SHMI+riDI3f9BkszsmSox5O6s/79R0vmj5DHKHMIvSXqQavevK49HDifR0NDQsNSxRKJfBw5XGV5jOL+78viPMzDKgLC9+r/G7jvp3uX7AnZoQAjITDKC1VmHcOTQL0G6/iAcxxLKKaTRIyPo0qaZ5/s8TEKT4XmSoSB11BDifhLbjgIQ7JjdIVvFShZNehsCkOYGe+QqVtDDHVUzoNKQRb3M3PcpO0UKIkmbc2e5Z7aiOgsdygdklccyhVrDPR+/Ozi8pQr95eR0tqCZovSFyApPoo77/MlDJ6WhrT6TTKnZxgRN1obss5gv2FYbiQoEL430LCpr30Y9q4euk9TVXCM896OQxng08bBs3QhXxbPCqZrOA0tsTvlVkr5aVy1LpUbPGeXGUQaE95vZmyStNrPfUYmk1zbLaWho+KnBUhoQ3P1t1RHooSqiv8jd+7Zq72AUL6O/qnsp36pixftTd79gPgLvCZD0BaHY1nN+mlll1J22WrKZoLmbkzTmCTazvTIwdiyGNwzySqJHVhYm8U600vpLk3HHqSZkpucGjaEhP5kxmWiwy8y7hmWfGhx+PTmdaQPMcuXQr5TvEzCJtA79jBM0vsd8Aikp2eVtQ7/DmKq/2Y73GhBu9qWoZipZ1GSizBSJ5VwbqlC26phlYwZZ9Fm+/tG27J9RD9Rg0SDRdiw62zPaKesWfYuGpxUZloMqRmiuzDRT63l+qv7y3WVnmyuewohYYiYjqcSNC89Ql/TxUW6adUCoO5Z9psbf2OcHgYaGhobFwlLREMzsVSrhht5dk15Qw1mcPde9sw4I7r7DzG4zs4Pd/cezXbsvYIUGbIuEITOxduyY0dKkcrFQgV4Q2QpehmPM9iMC5R2v1H0tWEtWqX0+6sFQsrhFJP2djcmDgWWeRdLA5YOLArIt2TIPFbK7SWSf1APLFOVg2bItKagAhIPJKl7A42xBRniCsV1ZjngAV9WwQaJtMYeQaTr0FAscklwnDcpM4ttRBrJ4PaH+9W13GcesZAq1PkkLlg1VZisE/dHQZVK3C4W2kJnz2QSMNzUW9X18zwWZl1EWgTWrcPZFrhtZQA1hX/cyAs6SdKK775QkM/sHlV4+vwGhYpuky83sAnW3Y5tzkUNDQ0PDcsASNBmt1mBcP3i2C4lRBoRP1r99Hjs0YC50GIo02tYZpHQ81AraWIMVcp8AGnujisko6ZF0ZZJW7fFc/Jn5cdNHnUQw2Fo2BdDn1TIWF/TtLRu+57yJ5Qzm/VmkJV5GV6JQ2XqK7GVi2eORJMEUaZqdskI4cRLskxmEfZwPgpfQdDoN/lfOPL4e5xn8M9or29eBc0NEFnl3Nan3fSMRacGc+1bdRpmYKbW0iFxKZv0v5edGdCaGMoqqY/fOQj5lDlwk/eOUKeZ0sp0HiS09x9HHWA/RB7kDAeZPrlrAr/hSMRlJ+gsNvIxM0iM1gnYgzTIgmNm93f2aWOzQ0NDQ8NOKJeZl9N66LfEpKgPCHy+El9FHVfmxmX2oLxLfvgTTgHBk/v0kyZ1Vy1P1l4wzW3rHm8K/mVoF7ZXB0MBwtu7snhq+PUBySPIaxIqk6cT6y3mDscxHfRJpLEcwLDI5sujAtckxKCXJZ7w4nOvIQgSR6MWUDU3nDGOzJhqWGhu9oeJGMs6YmKAXEc+HCvPFJE3SLbUTcT0F2ytYNPtaPJKyk0WH+XwlJ0i4ujoyYBtG47ISsxhElyMNnWz7J8rvOOaGLq99kW76fD9C06FzWs+U0TTWx8UnIpHGiih8tiG0NOg4LAddm6KTsb1qg0ztnJEkaeEWKi9BkxGXvJxqZnL3D89102wDguG4b+uNhoaGhmWPpaQhmNlbJT1Q0rc0sDC7pHkNCN5zvM9iTANWmgW9JPOeoCdN2G3Z4kHxeB1t65kbRrZVFBS1LKY/mX1cyiwzbxWSqtioAgFUuxdERZCOZyyalJYVdWOSliwaOBJCh6k32ymM6ZkHVd+i4mnDNNsg88tnhUYbMLor2WdUaEbxNdAQCJYjqjSb96CY2XqK3h3q4jgL5cq+mG060LPd3Hilc5sw55N5dWVRSvti1ExvuEZtMxqPlXQajkOmyZ5MQz5qT9nanmSxyuFQBdhs2RTF7mIJeRk9zN2PnfuymZhtQDjBzG5V0RQOqMeq/7u7H9R/a0NDQ8PywRIzGV1iZse6+7fnvrSL3gHB3cf6zu2rGNeAfLDxgjx04sNkoablawAAIABJREFU8dMzf3W6TPCeSOeEAG3a0RSgKNnqz2z1dJ/HUHhOUYwQeTyjd9KA4p2CNIabnM11ScpXcVeb/C24juWIrCgSswwiSeIeJnPaJieyNSB0HyN9nay/pISxyprTabTNh2cKnrMd8w1RJmot3FY4NAO2USgofAzt8GMhP+kUGzTs7xl1ZxtQJYw5nb4vVjKdGG3E6uJxFquos5NZaAYs6IahX6nbMYKzUk4eR5noMcSXJTzAkmimmSIxnP18sJRMRpL+QWVQuFHFoTJI/APnunEUt9Mlg+0avBtZVN2Oypd9dfnVio7InsaOGOotg299OznGbGuWZfbt/VGSxqz4Yk5/NzjaccPT+ADxQfQvjJeXsmcbpfADVV/MbI5dGky2Zuv0pMFLmplVOiyE9R1ysvIYbiM+TFNIy0JTZLPCmKzNBmsOAvxARrYc+LINfjplCtLQN7Ealcqyx4M4IPAjH+Wgyyzac1sM4DgdTUxngMyttOMei+PpRXtZOHFmxJtCJnbgjTi+cehXyuO/w9Niqv72BYxcSCyhAeGtkn5TxUC6S5auZTUgNDQ0NCwGlpjJ6Bp3/9ju3LisBoSdGhCKLEActzW8FyjFeDASMslg2XT1o40jriXF+vbMa7Owz2TWJEA3JefXJcecNw3StO7/b+/cgyy7qjL+W+mZsZOmpU0yJDIZaIKBIESSEAPhIW8KkKeFpSCIUYtSUUGNFIKWYmkJBfKq8IoBJcobEkErBJLwCCoJhgkkMRMhhibJkJDMwIRhwjBkZvnH2eve79xeZ7p7um/33Mv+qrru6X1eaz/u3d9ae+21dKedquxRz9mkDPoyJ2G6gb42ITGed9/RPgXtekbTdJkj4lWZptNyB9YLgp7pg5T7RD9kAdy+kFwn1+4QZq2aTjSTuv5m0TL0h+LY5LpJNasEtI/UphSdq30QKoiG4dYBHiqMuGPukLaJvtGqZ3HidCxmHqKTKnPY9tSEF/2lsum4Cg1CG1S10Vjw10Q92/qH15U66RgJ+bt+rLuypy4VI2Yyut7M3k8T0K73y7Bct9OKioqKioIR8jI6nGYieIqULdvtdOSgal2WqF6JSyt0RdC6XckFurEmyxaiWoXSrcKMlGHF4zX8QRZzTpmaspK58qlEbTYO1Nib0SVlYkqbQijVhJSdhm+ZCBJtq+ZhPc6S3eiyRrZuGkRzg8qmC6/xbdRVZ3Wsy4KYZbEl9Dpdgxi4BfrtresCOkRCVGWs0Q4btfK6mS7Q5QSQxceOcafqorrPJklgtAtjiGZeIjpsZuU4fCYmVA5dL4sbVV3NkkZpf4Ympv2hgsYOQBmrO+VXOF6Z5bzRZZguT/DlYJRMRu5+5sHe25lmuKKioqKiQZiMFvpbCZjZWWbmZnZ0+d/M7K1mdoOZXW1mGcXQ+x9gZpea2bXl/58zsz9fzLvHSkMwctfNYNzKrJXs9Gihpu4LyvHD5Dro0xC1ke6af60SoJh9NfCZ3pJpMhkLV5Nzj4hq5dSuO8t8HJYcqyaUpJHc8+35pzMHEJVJ66aPz6Iz9DyOtHI/I8dZaAp9QTBVsTn3BkEWLhl6lE+7VV8fXaumApU5RFGW3dMM1F1ZK78xOa+NF5RWNZk7k7LEE+dmETQZii3mPMt8tNxjQw4NVa0hNqJO2jjRsXrPbcl5FUT7K1kQUIYf3yVt74nkOv1uT03SHdhxiViNNQQz2ww8GbhJip9G8w0/gaYX3kG7NwbxD8CfAu8CcPery5rC3yz0/qohVFRUVCyAyIew0N8K4E3AK2hHh3g2cJ43uJwmnfFPH+AZR7j7lwbK7l7My8dKQ9hPn9BniTvUTXtzV4zpQBYRb6GITppopXgZ7ZM1hmDBahrP9hxkIR2gb8tuZXwM2dVIryw65Fe2rTQ3vIyybDXQ8xyZFBYbCXDUP19fGXVSkZTBhUitIO3xTlWPZuU41gu6Yi1E52ZpTjN7vJZ3hD0I6BpCFgG9NSyiIWalTH8plD0HtD+ChuqYjHpkMUug57WzWQaTagihPGbeOVqfo7SiIVMWjgX6HneZq5g+R9WvkF/Xq/T5IaA8c0Lun941//FR5Y1aqO19X/JgjUuE027yYcDMngVsc/evmmkoOTbR1g9vKWW3djxqu5ndnzKpmNnzDnBtC2M1IVRUVFQMC4vUAI42M91rfY67nxP/mNkltN0DAq8GXkXbM6h3W1J2oPhyLwXOAU40s23AN4AXLiQ4jNmEoLO4EobwSmiRs2yHpdqajxz4hDY1DwO62tuFgu0tjDoz+2qZMrmQuSsPSmg/rYBdewZOahn02VbGnKHfOCqIMsEi7B5hn1EPJXpKvOOV2lx6HESxtQQQTFFZfxZ0TtdsVNUKGqw+7AHVOkRNjOQwXRHMo8lUJL32+IHrgL4BWwdgxpizaH+Qu9JEf+pag54v/aWyZ7vI9ZWhEOoj56STZmMMqBqnlX9C+92tF2SDGvr7D/Sl6nJX2mGffKeyZbss3MsOkb2VQnaF4l8vYR/Cdnc/rfM5TX76eTCzk4D7AaEdHAdsMbPTaVpJ9cjj6F4Zw91vBJ5kZlPAYe6+6FYY6oRgZnM0XbIPuDtrKDN7HPBmmr7d7u6PLQsr59H8bO+nmWXfMkxZKyoqKrowbLdTd78GuFf8X347T3P37Wb2CeD3zeyDNIvJd7r7PBOQmf1x9uwwP7n7GxeSYzU0hMe7+/bshJnNAG8HnuruN5lZNMjdwJ+4+xYzmwa+bGYXLxS9z+ibNpV0ZYlldsr8OhPsNItVpLuTlZ0GHdNdl0nCmCxisZpNlSyFfMpw1PMjHFNatszw/VbXCuUSV5RPDRWtdH3fwHXQ8nGfKxVR9hn1UNmVPQWrUxU7cxiazbZkq0E+i7+kDaLaWfTTZ6SsqC07RWvQR8Zg0i+6dndoatofj5TjjdG56jEUodRnpSxbTNGxpnsKdDwFLiyfMmYzLVPHt74y2y8RCtWEeqdpwLzNA5+Q746eSsoS7aUloOaQFbq/rQwYbRp9ZTT3JinrjessUiI0dXoby8Ya71S+EHg6zU6Nu4CufQYxuh5IE84ywlc8E7hsMS9aa5PRC4Dz3f0mAHe/vXzeSlkEcfddZraVZhwsOZxrRUVFxUpgNXcqu/usHDvNusBC97wGwMw+DZwapiIz+yvgI4t577AnBAc+bWYOvEsXVwoeAKwv+T+ngbe4+3l6gZnNAqfQ5rB6/iXAS6AxV4bJM3O0aUVuVLoULCeLKaM2UKUIwbI7PCbidmVyQX6VIGUMSG2kWRTHls36iKRQ2VIY6VQtyUa27l0Q3/DZwrJvlHvilS2vreTxrb0eCfbIMyejclm2GYCHlk+l81qP6HBlvGXdY0b68EdqJy+f2kdZKGuth7rN37PIskGtuRkd35wc6wDVl0a5up/F4pese20WanRj4mevjwxRVNPpeRlliz/QX+hRlUqpeTxMVb8s3pQivH10W5UsEmwqmsU+qc+CyW7ifpUti8W+TKyGl9EK4j60xd1Ld1qiFoY9ITzK3b9VTEEXm9n17q6qyzrgYcATaeJvfNHMLnf3rwGY2T2AjwEvd/fvDT4coEwy5wAc1Uw8FRUVFSuK2IcwIvhn4EtmdgGN6M+lyZGwIIY6Ibj7t8rn7UW402nbsm6hWUjeDew2s8touODXzGw9zWTwvsVE6YNm4SELMRTMQs2z10jvnhRzqd6UbdnLUh12BE6JR6qZO0hd14bTILf6SGVFYdNuLW7FP6pq6AtCEGWp+oIsb6d4fuxKvgWhtaijlsoUhDndiSzHk5lMqiGoxhZyqldKFrBKG3znwDnaHlxxqbLpjJFqH6hjUy8pkWolway7cjAEC9/RcT4aTzd5hBOjCHJ9ohV0ueIHk9Lm6j1K1z+yBa9HdpyPRlMjbubLkg120aj2yvGGyfajId9kPKkxrKLDtB6Z19YKYFSinbr735rZJ+knMD3T3a860D2BoU0I6vJUjp8C/PXAZR8HzjazdTSGjocDb7JmWfzdwNbFrIxXVFRUDBOjFNwOwN230LZOLwrD1BCOAS4oLk/rgPe7+0Vm9jsA7v5Od99qZhcBV9OQmnPd/VozezQl44+ZfaU871XufuH81/Rh9JmPkpk4VgbV2l0aVFfZ6caBc5CzOqUwwoZiaSFLHqWkJfOo0EfqcTBr3bDaoy1aqOsBwcKVGme0qyPo/2E724+BPsHLdoxC26wcSO2/2kkRmUXt8WrTDpkfJGWqLUTfqFBF9r1Cl1XOZKNymk5Bh4WKlO6uDlu2drJqOlG/LNEF9PdRqIYRzxfhdPzGpbqmoxpZDI3W1oeoiKZ9VQocLFwXUPYn16pHUdQ5i8ME/QaVPm5FYC0CbhDtfINeEGNAx3JoZ/prrdpC68ty8BixfAgHjaFNCGVzxEOT8ncO/P964PUDZf9BvjuvoqKiYk0wQmsIB421djsdGpQEZ+kOWsw7bM3KbIJldyldM0mZsLow3Wc7LRUZc+5yGAr5W8sW8QB9UZZxXNn4nclxh007TMS6AXjn/MvSRGZKjHX5pcdus13gSn2VzmceLHp81cB1wN5y/w1ymS4xRB/pJm/VAKKLlcBPajsGO9Wbgr1q5RXRH7pGoKnnovNlLG7bOV9OHQNRZf3BUuegEE/HUq9S2olnyHHUM9ubA31GroJEPe5IyqC/E1mL5Pn7vjP/kVPa3qGmqgY8MXBOZYPuxN5LxH5GysvooDG2E0JFRUXFSqJqCCOGCfpMNLPTq8NEi+AHu1D/6I3JhcqWwmB736QMmC4s6dFyOlLiqpk8yyqmhFPtvlGnjVq5qLAa5zRASNRDR7NSsNCA1HNDNJ1Tr5t/S5iNlQTrEkRcq37vaW5blSnkVDkyDxFV85Q9RkgwiWy5Ya48WoTT7sry8WZaT4sZaufEjeqRFvZrpeNZUgsVRK8NzUEarOdir/GkpEOyHNZZdNmWbT1be9K23Zicb6lK5VO16thxrSqArlHMNR8T0l57pB5xmy6fTGnbhdqjZTFeskVDaLfzMlDXECoqKioqgNHzMjpYjNWEcBh94pKFjlf2t1npVDAPpQBZbHe1vQebUsOuMLhNhaVcKaMoZFKSqaQsCLE6Viip65nXs+S4Xcljw+6rrEkp2K4DlNH/Eujjox5zUpY5qGTZxwA2BvvVymXIEgNneQKgv1AgGsLOohl0BVANBaUrk1gQ2VasYr04kkPrTaE+qcamBv2ZgeugvRklhNI+Lvfv7Mj8FW3flbpgU4x1rXzsftbvgap0Ma618urdE2sD2bjR78Te5LxohlqlbFmu9QWJuEWZ559GZdU6tZKHLA9VQxgx7Kc/ZjPtNw0bDf1Bo4Mni3ymX9K5gU9o/VjtuGP+LfE90EVj/bEK04B+737UcdxDfImv67gwJrSuJCGh5v9Xv0hDSsRjdT0xmk6bSy092WLssWozinbKkqdcImU6YcS1am9LflR3i6BxqU5GWczgrr1LMQHP6AXaeREMLpts9UU61qJcA7HoYC12xR0/mn9aX6O/udnexGOVEcUY0E7alZTpxBT3n0uOMDWq2S/aXsPAy6SehV6Z0S9lYRBTU/PLgDyLQMjZ9Wt9Y0f5EjFiO5UPGmM1IVRUVFQMAyMWy+igMVYTgoauyGK5dWaJiIUn7fEsuN2sHIcZoMMNMtYJlbgE81cipuQx1j51Y1fmSnhvYdYT8QC1Q2Uuj7rQnIVYlmdOyvkjy0sz79skj07rUarpTMgFadjoeJiGgVATRpZfNFG1poSR/myRXT08M41Ly7Iw3tPyzA06sILtZ77BytC1IcLsOCtlqi0UFnxUEhddu00fGc2pw/cO6c+eKJktUjWZLOCj9oHWPfpzIjkvKtkGVSP3tD4A2CX/7C0a3yb9Tql6FxsRdbNcLFp3uZ2u0MY0qBpCRUVFRQXVy2jkoeQxiI+SfWVYU1kI3aCV6oqqsQFidChDEntllkkxC8Ott4eiooRTyf5JUZHZ5KHXJWXQ11rU3q71KHXfIbIrEw0imC3M6h4yJXVhllZyNpUlYsnYvnaMxpoOpqn1lGv3lZ1zajKOLurSDON8FvUZ+uR4Q1YIfUa8MymTNZk0nrk2svTH7tIf2t6Z1qvHPUeKrsCAYXvXemRpOfX8vw3IC22KHOqI7PqLsNXqVqr3RDDAPcLgW/UIrUUrr9EEo8NUs4zKqxqoffR5VgTVy6iioqKiooeqIYwY1pHvJwvyqTP8VJZxXJlJFvBO7a1Bg9VTRrwg7tw1//ZQQLKsggpl3q1galnl4mHqgaHumnGPuuUlCeBVK5lMLtWyLKabmnBDlEmVU9MzZvE6otKqyej9u5IyYbcThWrukgYN2bPo013QOk2GKqeVy3KB6sAKdtwVJyVknsvPR9NkiZNU9gkdq9EmmScX9Gm4DrzQfFVOPQ6Wraq2Ln5FP4nsIdM+aY9WTqlSPqPaYrahTM9r2x458Al9DUS/NEvp8EWiehlVVFRUVADVy2hkkdmFg2S0dumL98N0sLaMWSsrujk5nyWjIc+lnrlMZznMley0NuvEP8r2s1SH+gINLlawR1yG4jbdpqCPirZTZ5F4vRK1VlSDYMkdtuQeO1UDcrxUv3W6IBDsVdp7jwgdpFAJZyh0XcECQ4NoRZbIGl/DH2Sb5bQe1zAfsmngtrJe0LW/JJLZqJxZLqZJuWki02S0HWOTnGoAsW6hD9X1mWi0rD7AzvL+Ob2llOl3bypLX6rjUz2GQuvONo1Cv6GyTTHaL/qd7HQtXBqqhlBRUVFR0UNdQxhBBMtWYhDEoxU2OgsulqXNVOj58HDR4F0S2mKyMJZ9IkhGnDOSrI42LQ0hKEprMaR86mhV6h5eGv8nsgmNDnuvNkfL7puUZYy1tTM882FXxEPXd5wPKNUMTafjmdGmWvVsV7K2bdRjpivmQxYCOnu/PnQ2eY4w72OLgF8XRqt9HPXIUsAqQ53I4jyoVqDqcLZFPrQJlVPPBwvvSBA1U9rmZBE0lAkdFxNyfir6M9uhrrJkA1Cv1TWwzGvrJDm+kRXREqqXUUVFRUUFUPchjCSMXEMIgtaKhKt2xtnymcVozrLDQ994qjRUXrqnHKsbdbYlINvo3LVzdiIYYJZcXpmc2l2DiSqDkheEfOrGre+MrQAqZxC8+0tZS7mK5+uDMhqs+z6CZSsN1mCCUQ+hn1l6Ue2O0Ba+k1wH0l3antnOVq2HVjQGm2oyIYCu3SQB4Doyr/agmk70gSqjLdUyy72qixDRYfdOynSwqYaxe37ZXqlHdJNuaQl0pYDdFG2jX0Rl+4OyQT+gHfTbNhv/WqYNqmsUy8RqrSGY2Vk0WSQ3uvt2M3scTf75b5RLznf3wfz0K4KxmhAqKioqhoHV8jIys83Ak4GbBk59wd2fMez3j9WEsJ98A2Z4xSg7bNmCgyVl/svq0aCsLNjS9/LzwajVISg2dSqZyXLEtMhfZmdX9hdydMURjp2aUrfMnTxjevrYjHCqhqDNubfctEHlUCYYTE+ZczBSVUUuTwQS4VUbCBKvXRhNp4RUlZJek3VtAY5KdXm9RKOod06ycPF1jT2VPEYVoWwZa312Tvs7diVr5XTghXao2kDIOSdl2njRDqJeZSk8sy0BKkarPeP9XWsyEwOf0FanY4xoPUIQDbYlz987el5GbwJeQaMRrDoWWkatqKioqKAhHQv9LQdm9ixgm7snzuKcYWZfNbNPmtmDl/mqToyVhmD0ZzhlhUFUW2Shi70GgrnoDlu1w8duyiySpZ7XtJrlU82aSpBiQB3VlQtxc1IWFVV2pxEikyQ009IQUT1xQmoR92gmZUexXKHsUJnFhtCutB5ZTlNlufHQrvyiwRRFON00HIEw9Za58qmyT8sF01kOTb04KqVyZqlG9ZfgtoFzwAkyvraVcl2q0OPoxmzJZYP2ZRZjqGvNJsaL1jM6XjtO7yma8V6NOyT12JkknolHtda9sqivqqpnO4m7IsVG4+j9IYfeI1rkSnkGLcHL6Ggzu1L+P8fdz4l/zOwS8swOrwZeRT8ZrGILcF93/76ZPR34V9r7uVcMYzUhVFRUVAwDS/Ay2u7up3WddPcnZeVmdhJwP+CrZgbNlL3FzE5399vk/gvN7O1mdrS7b198DRaHsZoQdA1BgjD2dv62thEIc58KdpHFnFEmpiPi+uS8MPfYDazm0GC0anLW23sEUNm07pyN5+u6Qtixld2pthD7IWQtRN+pJtqBW1qiZARfr2s5aMUFGnFzIcf6jH5pfyR7Am6Q86EUaXvHGkcrI5q2XQh9RFIGfTt8wpxbL9NGjHqIlrblrvmntYvU3B8ao1a9J5K+R5l1aIk6PrOwr9rGIYAuKOnA0PKCbVKPaJJsS8CEpg9VNp9t1Vdmn20cUa07SzISK70yGDXj39R6VkxNGOYagrtfA9wr/jezOeC04mV0LPBtd3czO53mp2xH/qTlYawmhIqKiophYD9rGsvoecDvmtndwA+AX3V3H8aLxmpCWE/fOJeFeVcC1Ip2muU2CA1CGaM+INiKMhxhKZPF1jypghS2dLL6SatdOKhsK5GAHGeMNmRSTULZeGkQteVOCD0NkbsSSwUrUi+jYIeqAEyqTMHClaWqfFE+mZQJM90j7R3anWolevuJA9eBrMWo3V/XX6Jc+1grle2nUA0j2Ks+M6i99PGp2jalb3RYzIjQwW4ns4igqqnoIln0t47PbH9MEhtKq6a3hMhHSX03ZdnRRKaZkEn7WrWaWLfIvIQg36atqlRsyFA54n5Rsya1DzfRzk2xDKxmLCN3n5Xjs4GzV+O9YzUhVFRUVAwDdafyCOKH9EmQkpQgnUrUNgoLmQ3aOScXhKqhtlRlaMFI1P858bxQ1ravULCJLpt0lKvD96wcZ7bggFLnOTkuTHWvMC1dw4hHKjvURwVpU0Z7THLdrKoQcXFH7oIWKxx8kXzr1MEk5FNyqIQ4rlVi3dsPoReekby7y1MsbARKDa+V46jTdfPLNCvYDjmOx6smc7M8P4j3rMqXeWXpO0O9y9YIoDfGVKYY1lmaZBDvMrnnCDmeCs1Bv2jRXq3AVnKcVV6/MyGUasVqKde+iVclY2lanr8vWyQ7CNRYRhUVFRUVPVQNYcRg9MmUEo9gl+rz32K3QY2ULgVL6aKkwdCyrF7QZ3MSeXEi3qMMSRnWY5JnqotMsLLMM0Pp3bHzz2fmXxVFSbLiQEslLUdo1QBiXURl0gbfM/BJn0V3eWCF/CqnKiXxylYytlBl1O6vjDH6RiunDZUx3iyOjr60rEfsk1wNm6S/N0XwqK4toSclZbHWoWqcagBRJ+0DYdbbigaiiudXyqfGR8rGhVatZUOPi1WOuDjxUGqdVy99HUTx0s9I2QKhYKezjhcNY2Ia+FSHPEtAzYcwRBSXql00k+7dmd9uCej0Zpqv4HZ3f+xqylhRUVERqBnTho/Hd22sMLMZ4O3AU939JjO7V3bdIPbTZ5BK5IJEKBnXIIq9C3Trq4YpDSjLDWajbEhVkGBOqmdmAY6UzcezlOmpN0loC0rlgh2uT66Td04IIz1GqE4w8ixDF/QJmpbF47UaadLlJ0iZUvtoW8nGNVPs3BoHJ9t8rX2YOZu0BI1KadsoI83aM/MYypJWQJ+x6+JUGSNT8p49suel10xKN3XcZV5jYbzWvQeZnEckZcBUeaYOz0xBUa06s5dPK1sPrUW1q3inrgFoPebK5+OlTHf/h1D6HvUYijWE7J2zKihDQdUQ1g4voAnxehOAu9++mJsOoz8WWwnJy2eHhyjHfjO5IH60NM6EfouynI36YxG/bPrM6eS6LMemuknOJOdnpSwqp+aZ7MdG6M2MLIzGo/T2bELQ73X87kxp3TN3TW1kpVfRdnP9orCw6BqhNn00k/5+aNP2vHdVpqi7/kCpnDEh6Myiv4Tx45xtqlMBWhlhyqcGOtT+jGCIsx3PjOMs9oqSjzuS47mOZ+6af0s0k1Zd0SMC2l4Za8jSzur4zsw1mWlVj7VM+yPqdIqUhR1M2YnawdYDH01kWCJ+XLyMbEj7Gw78UrNvAN+laed3aayPcj5MRQ+mGXpvcffzOp71EuAl5d+H0PYDGXUcDaz49vQ1xrjVadzqA+NXpwe6+7L0BjO7iKZdFsJ2d3/qct61llirCeHe7v6tYgq6GPgDd79Mzp8NnAY8ETgc+CLwi+7+tQWee+WB4oiMGsatPjB+dRq3+sD41Wnc6jNMrEn4a3f/Vvm8HbgAOH3gkluAi9x9d1lnuAx4KBUVFRUVQ8OqTwhmNmVm03FME+510MzzceAxZrbOzI4AHg5sXV1JKyoqKn68sBaLyscAF5QQr+uA97v7RWb2OwDu/k5331psdlfTLJGe6+6LWRs4Z+FLRgrjVh8YvzqNW31g/Oo0bvUZGtZkDaGioqKi4tBDTaFZUVFRUQHUCaGioqKiomAsJgQze6qZ/a+Z3WBmr1xreQ4GZrbZzD5rZlvN7H/M7GWl/Egzu9jMvl4+f2qtZV0KzGzCzK4ys38v/9/PzK4o9fmQmW1Y6BmHEsxsxsw+ambXl746Y5T7yMz+qIy3a83sA2Y2OWp9ZGbvMbPbzexaKUv7xBq8tfxWXG1mp66d5IceRn5CMLMJ4G3A02j2+D7fzH72wHcdkrgb+BN3fxDwCOClpR6vBC519xOAS8v/o4SX0fYQex3wplKf7wK/tSZSHTzeQuMSfSKNK/RWRrSPzGwT8Ic0qRofQrNx+VcZvT76J2BwM1hXnzyNJoDJCTQbWt+xSjKOBEZ+QqDZw3CDu9/o7nuBDwLPXmOZlgx3v9Xdt5TjXTQ/NJto6vLectl7geesjYRLh5kdB/wicG7532giHEUwgVGrz08CvwC8G8Dd97r7Tka4j2g8/Q43s3U0UUluZcT6qGxq/c5AcVefPBs4zxtcDsyY2U+vjqSHPsZhQthEOxTPLbRjpI1gXBwRAAAFsklEQVQczGyWJmLLFcAx7n4rNJMGkoh7BPBm4BX0oysdBex097vL/6PWV8fThAT6x2IGO7fspRnJPnL3bcAbgJtoJoI7gS8z2n0U6OqTsfu9WEmMw4RgSdnI+tKa2T2AjwEvd/fvLXT9oQozewZwu7t/WYuTS0epr9YBpwLvcPdTaGK4joR5KEOxqz8buB9NPMgpGpPKIEapjxbCqI/BoWIcJoRbaKdJP452KpGRgZmtp5kM3ufu55fib4dKWz4XFfn1EMCjgGeV3BcfpDFDvJlGRY8NkaPWV7cAt7j7FeX/j9JMEKPaR08CvuHud7j7j4DzgUcy2n0U6OqTsfm9GAbGYUL4b+CE4hmxgWZR7BNrLNOSUezr7wa2uvsb5dQngBeX4xfThPU45OHuf+bux7n7LE2ffMbdfw34LPC8ctnI1AfA3W8DbjaziLb9RJqUEiPZRzSmokeY2RFl/EV9RraPBF198gng14u30SOAO8O0VDEmO5XN7Ok07HMCeI+7/+0ai7RkmNmjgS/QpI0Jm/uraNYRPgzch+YL/MvuPriAdkijZL87y92fYWbH02gMRwJXAS909x8e6P5DCWZ2Ms0i+Qaa9PVn0hCrkewjM3sN8Cs0Xm5XAb9NY1MfmT4ysw8Aj6MJT/1t4C+BfyXpkzLxnU3jlXQXcKa7X5k998cRYzEhVFRUVFQsH+NgMqqoqKioWAHUCaGioqKiAqgTQkVFRUVFQZ0QKioqKiqAOiFUVFRUVBTUCaFiRVDCOKxYUEEze1xESF1pmNlfmdlZy3zG4Wb2+RJccan3/r6Znbmc91dUDAN1QqhYEbj7b7v7dWstxzAgu3YVvwmc7+77DuKR76GJMlpRcUihTggVi4aZzZY8AO8tseQ/amZHlHOfM7PTDnDv983s781si5ldamYbB+8zs6NLqIvBex9rZl8pf1eZ2XQp/1Mz++8iy2sWkP3V1uTMuAR4oJSn7zez3zCzj5jZvwGfTh75a5Tdr0Wb+Zz18yS8r2yAwsxea2bXFRnfAODudwFzZnb6gWSuqFht1AmhYql4IHCOu/8c8D3g9xZ53xSwxd1PBT5Ps5t0sTgLeKm7nww8BviBmT2FJqb96cDJwMPM7Beym83sYTThM04Bfgn4+UW+9wzgxe7+hIHnbQCOd/c5KT4FeDlNTo7jgUeZ2ZHAc4EHl/b6G7n+ylKXiopDBnVCqFgqbnb3/yzH/wI8epH37Qc+dBD3Afwn8EYz+0NgpoRmfkr5uwrYApxIM0FkeAxwgbvfVSLILjbW1cUdISiOBnYOlH3J3W9x9/3AV4BZmglzD3Cumf0STaiEwO00EUYrKg4Z1AmhYqkYjHVysLFP4r676Y/DyfRC99fSxNg5HLjczE6kCWP8d+5+cvn7GXd/9xLkDhzo/bs77vlBcq3G+tkHrCsT1+k0EWyfA1wk10yW51RUHDKoE0LFUnEfMzujHD8f+I9F3ncY/QiaL5D75oCHlePnkcDM7u/u17j762hMLScCnwJ+s+SPwMw2mVlXYprLgOcWz6Bp4JlybsH3D8LdvwtMmFk6gYnc9wDu6e4X0piTTpbTDwCuTW+sqFgjZN4TFRUHwlbgxWb2LuDrLD4n7W7gwWb2ZZrMXL9Syt8AfNjMXgR8puPel5vZ42mY93XAJ939h2b2IOCLZf32+8ALSXIRuPsWM/sQjSnnmzRRZQOLeX+GT9OYvS45wDXTwMfLxGHAH8m5RwEHXAivqFht1GinFYtGSe357yUh+1Lv/b6732PFhVojmNkpwB+7+4tW896KimGimowqKg4C7n4V8NmD2ZhGsyj9FyssUkXFslE1hIoVhZldAfzEQPGL3P2aVXj3UcClyaknuvuOYb+/omLUUSeEioqKigqgmowqKioqKgrqhFBRUVFRAdQJoaKioqKioE4IFRUVFRVAnRAqKioqKgr+H6ODWR1TJ6qDAAAAAElFTkSuQmCC\n", + "image/png": 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\n", 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, diff --git a/qdev_wrappers/fitting/Fitter.py b/qdev_wrappers/fitting/Fitter.py index cf5b5c73..13d61759 100644 --- a/qdev_wrappers/fitting/Fitter.py +++ b/qdev_wrappers/fitting/Fitter.py @@ -9,7 +9,7 @@ -def fit(data, fitclass, x=None, y=None, z=None, cut='horizontal', p0=None,**kwargs): +def do_fit(data, fitclass, x=None, y=None, z=None, cut='horizontal', p0=None,**kwargs): def do_1d(xdata, ydata, fitclass, p0, **kwargs): From 72606527958332183a39a091afbf542f4c9ef83a Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Wed, 27 Jun 2018 14:58:49 +0200 Subject: [PATCH 032/116] Remove unnecessary header from Example notebook --- qdev_wrappers/fitting/Example.ipynb | 23 ----------------------- 1 file changed, 23 deletions(-) diff --git a/qdev_wrappers/fitting/Example.ipynb b/qdev_wrappers/fitting/Example.ipynb index f46f33dd..6d8806b0 100644 --- a/qdev_wrappers/fitting/Example.ipynb +++ b/qdev_wrappers/fitting/Example.ipynb @@ -1,28 +1,5 @@ { "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "User schema at /Users/Lillian/qcodesrc_schema.json not found.User settings won't be validated\n", - "no config file found at /Users/Lillian/Qcodes/My stuff/mainfolder/data/local_scripts/instr.config, will try general\n", - "no config file found at /Users/Lillian/Qcodes/My stuff/instr.config\n" - ] - } - ], - "source": [ - "import qcodes as qc\n", - "from qdev_wrappers.file_setup import my_init\n", - "qc.config.add(\"mainfolder\", \"./mainfolder\", value_type=\"string\", description=\"Location of data\", default=\"./mainfolder\")\n", - "station = qc.Station()\n", - "my_init('data', station)" - ] - }, { "cell_type": "code", "execution_count": 4, From d7c0df107967635ff117c64030f466e10ff0ed34 Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Wed, 27 Jun 2018 15:31:03 +0200 Subject: [PATCH 033/116] Add initial guess for parameters to fit dictionary --- qdev_wrappers/fitting/Fitter.py | 21 +++++++++++++-------- 1 file changed, 13 insertions(+), 8 deletions(-) diff --git a/qdev_wrappers/fitting/Fitter.py b/qdev_wrappers/fitting/Fitter.py index 13d61759..c786498d 100644 --- a/qdev_wrappers/fitting/Fitter.py +++ b/qdev_wrappers/fitting/Fitter.py @@ -11,15 +11,14 @@ def do_fit(data, fitclass, x=None, y=None, z=None, cut='horizontal', p0=None,**kwargs): - def do_1d(xdata, ydata, fitclass, p0, **kwargs): + def find_start_parameters(fitclass, p0, xdata, ydata): if (p0 == None and hasattr(fitclass, 'guess')): p0 = getattr(fitclass, 'guess')(xdata, ydata) - popt, pcov = curve_fit(fitclass.fun, xdata, ydata, p0=p0, **kwargs) - return popt, pcov + return p0 elif p0 != None: - popt, pcov = curve_fit(fitclass.fun, xdata, ydata, p0=p0, **kwargs) - return popt, pcov + p0 = p0 + return p0 else: return "Could not find guess parameters for fit." @@ -85,13 +84,16 @@ def do_1d(xdata, ydata, fitclass, p0, **kwargs): fit = {} fit['parameters'] = {} + fit['start_params'] = {} - popt, pcov = do_1d(xdata, ydata, fitclass, p0,**kwargs) + guess = find_start_parameters(fitclass, p0, xdata, ydata) + popt, pcov = curve_fit(fitclass.fun, xdata, ydata, p0=guess, **kwargs) - for parameter in fitclass.p_labels: #parameters currently missing units, use fitclass.p_units + for parameter in fitclass.p_labels: fit['parameters'][parameter] = {'value': popt[fitclass.p_labels.index(parameter)]} fit['parameters'][parameter]['cov'] = pcov[fitclass.p_labels.index(parameter)] fit['parameters'][parameter]['unit'] = param_units[fitclass.p_labels.index(parameter)] + fit['start_params'][parameter] = guess[fitclass.p_labels.index(parameter)] fit['inferred_from'] = {'xdata': x_dict['name'], @@ -146,15 +148,18 @@ def do_1d(xdata, ydata, fitclass, p0, **kwargs): for set_value, xdata_1d, ydata_1d in zip(setpoints, xdata, ydata): - popt, pcov = do_1d(xdata_1d, ydata_1d, fitclass, p0,**kwargs) + guess = find_start_parameters(fitclass, p0, xdata_1d, ydata_1d) + popt, pcov = curve_fit(fitclass.fun, xdata_1d, ydata_1d, p0=guess, **kwargs) fit[set_value] = {} fit[set_value]['parameters'] = {} + fit[set_value]['start_params'] = {} for parameter in fitclass.p_labels: #parameters currently missing units, use fitclass.p_units fit[set_value]['parameters'][parameter] = {'value': popt[fitclass.p_labels.index(parameter)]} fit[set_value]['parameters'][parameter]['cov'] = pcov[fitclass.p_labels.index(parameter)] fit[set_value]['parameters'][parameter]['unit'] = param_units[fitclass.p_labels.index(parameter)] + fit[set_value]['start_params'][parameter] = guess[fitclass.p_labels.index(parameter)] #does this needs to be moved so that it specifies which cut the individual sets of parameters are inferred from? From 506f3ddd4e8a240e62e5b46c33d637380f4e8e41 Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Wed, 27 Jun 2018 15:39:06 +0200 Subject: [PATCH 034/116] Add function fitted to fit dictionary metadata --- qdev_wrappers/fitting/Fitclasses.py | 2 ++ qdev_wrappers/fitting/Fitter.py | 2 +- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/qdev_wrappers/fitting/Fitclasses.py b/qdev_wrappers/fitting/Fitclasses.py index 4daad50d..aecd5234 100644 --- a/qdev_wrappers/fitting/Fitclasses.py +++ b/qdev_wrappers/fitting/Fitclasses.py @@ -18,6 +18,7 @@ class T1(Fitclass): def __init__(self): self.name = 'T1fit' self.fun_str = r'$f(x) = a \exp(-x/T) + c$' + self.fun_np = 'a*np.exp(-x/T)+c' self.p_names = ['$a$', '$T$', '$c$'] self.p_labels= ['a', 'T', 'c'] self.p_units = ['y', 'x', 'y'] @@ -44,6 +45,7 @@ class T2(Fitclass): def __init__(self): self.name = 'T2fit' self.fun_str = r'$f(x) = a \sin(\omega x +\phi)\exp(-x/T) + c$' + self.fun_np = 'a*np.exp(-x/T)*np.sin(w*x+p)+c' self.p_names = ['$a$', '$T$', '$\omega$', '$\phi$', '$c$'] self.p_labels = ['a', 'T', 'w', 'p', 'c'] self.p_units = ['y', 'x', '1/x', '', 'y'] diff --git a/qdev_wrappers/fitting/Fitter.py b/qdev_wrappers/fitting/Fitter.py index c786498d..5318b120 100644 --- a/qdev_wrappers/fitting/Fitter.py +++ b/qdev_wrappers/fitting/Fitter.py @@ -179,7 +179,7 @@ def find_start_parameters(fitclass, p0, xdata, ydata): dill_obj = dill.dumps(fitclass) fit['estimator'] = {'method': 'Least squared fit', 'type': fitclass.name, - 'function': 'save all text from fitter function and class function here?', + 'function used': str(fitclass.fun_np), 'dill': dill_obj} return fit From f7c472e88664c993a759a23bbcfa9908d2ec34a7 Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Wed, 27 Jun 2018 15:40:42 +0200 Subject: [PATCH 035/116] Remove overlooked remnants of default_dependencies --- qdev_wrappers/fitting/Fitter.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/qdev_wrappers/fitting/Fitter.py b/qdev_wrappers/fitting/Fitter.py index 5318b120..ffe82d57 100644 --- a/qdev_wrappers/fitting/Fitter.py +++ b/qdev_wrappers/fitting/Fitter.py @@ -29,8 +29,8 @@ def find_start_parameters(fitclass, p0, xdata, ydata): #that I just forgot the parentheses after the fitclass. So this is here for now. raise RuntimeError('It looks like there is something wrong with your fitclass(). Possibly you forgot the parentheses?') - if (x==None or y==None) and default_dependencies==False: - raise RuntimeError('Please either specify data for x, y (and optionally z) or set default_dependencies = True') + if (x==None or y==None): + raise RuntimeError('Please either specify data for x, y (and optionally z)') for dataname in [x, y, z]: if (dataname not in data['variables']) and (dataname is not None): From 1c50681c9ee7452e3412bff13d795a978d896580 Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Wed, 27 Jun 2018 17:24:07 +0200 Subject: [PATCH 036/116] Put analysis data in experiments.db instead of separate file. --- qdev_wrappers/fitting/AnalysisToSQL.py | 75 +++++++++++++++----------- qdev_wrappers/fitting/Fitter.py | 2 +- 2 files changed, 44 insertions(+), 33 deletions(-) diff --git a/qdev_wrappers/fitting/AnalysisToSQL.py b/qdev_wrappers/fitting/AnalysisToSQL.py index 0eaff7c2..657fe2e4 100644 --- a/qdev_wrappers/fitting/AnalysisToSQL.py +++ b/qdev_wrappers/fitting/AnalysisToSQL.py @@ -103,7 +103,7 @@ def fit_to_SQL(data, fitclass, fit): #it would be an improvement if it were a - tablename = 'data_{}_{}'.format(data['run_id'], fitclass.name) + tablename = 'analysis_{}_{}'.format(data['run_id'], fitclass.name) table_columns = [est_name] @@ -124,69 +124,80 @@ def fit_to_SQL(data, fitclass, fit): #it would be an improvement if it were a - run_id = data['run_id'] + data_run_id = data['run_id'] exp_id = data['exp_id'] - analysis_id = 1 predicts = est_name.strip("estimate").strip("_") estimator = "{}, {}".format(fit['estimator']['method'], fit['estimator']['type']) - analysis = fit['estimator']['function'] + analysis = fit['estimator']['function used'] + dill_obj = fit['estimator']['dill'] parameters = fitclass.p_labels param_labels = fitclass.p_names - conn = sqlite3.connect('analysis.db') # should this go in a separate analysis database, or just go in experiments.db? + conn = sqlite3.connect('experiments.db') # should this go in a separate analysis database, or just go in experiments.db? cur = conn.cursor() #Alternative: have a separate function that sets up the general analysis database with tables, so that this doesn't have to be here if not is_table('analyses', cur): cur.execute('''CREATE TABLE analyses - (run_id INTEGER, exp_id INTEGER, analysis_id INTEGER, result_table_name TEXT, - predicts TEXT, estimator TEXT, function TEXT)''') - if not is_table('overview', cur): - cur.execute('''CREATE TABLE overview - (id INTEGER, analysis_id INTEGER, - parameter TEXT, label TEXT, unit TEXT, inferred_from TEXT)''') + (data_run_id INTEGER, exp_id INTEGER, run_id INTEGER, analysis_table_name TEXT, + predicts TEXT, estimator TEXT, function TEXT, dill TEXT)''') + + table = make_table(tablename, cur) + for column in table_columns: + cur.execute('ALTER TABLE {} ADD {}'.format(table, column)) + + num_cols = len(table_rows[0]) + placeholder = ('?,' * num_cols).strip(',') + + cur.executemany('INSERT INTO {} VALUES ({})'.format(table, placeholder), table_rows) + + + - sql_analyses = 'INSERT INTO analyses VALUES (?,?,?,?,?,?,?)' - max_id = cur.execute('SELECT MAX(analysis_id) FROM analyses').fetchone()[0] + sql_analyses = 'INSERT INTO analyses VALUES (?,?,?,?,?,?,?,?)' + + run_id = 1 + max_id = cur.execute('SELECT MAX(run_id) FROM layouts').fetchone()[0] if max_id != None: - analysis_id += max_id + run_id += max_id + + cur.execute(sql_analyses, (data_run_id, exp_id, run_id, table, predicts, estimator, analysis, dill_obj)) + - cur.execute(sql_analyses, (run_id, exp_id, analysis_id, table, predicts, estimator, analysis)) - sql_overview = 'INSERT INTO overview VALUES (?,?,?,?,?,?)' + sql_layout = 'INSERT INTO layouts VALUES (?,?,?,?,?,?)' - result_id = 1 - max_id = cur.execute('SELECT MAX(id) FROM overview').fetchone()[0] + layout_id = 1 + max_id = cur.execute('SELECT MAX(layout_id) FROM layouts').fetchone()[0] if max_id != None: - result_id += max_id + layout_id += max_id - overview_rows = [] + layout_rows = [] for parameter, label, unit in zip(parameters, param_labels, param_units): - row = (result_id, analysis_id, parameter, label, unit, "inferred_from") - overview_rows.append(row) - result_id += 1 - overview_rows.append((result_id, analysis_id, est_name, est_label, est_unit, "inferred_from")) + row = (layout_id, run_id, parameter, label, unit, "inferred_from") + layout_rows.append(row) + layout_id += 1 + layout_rows.append((layout_id, run_id, est_name, est_label, est_unit, "inferred_from")) - cur.executemany(sql_overview, (overview_rows)) + cur.executemany(sql_layout, (layout_rows)) - for column in table_columns: - cur.execute('ALTER TABLE {} ADD {}'.format(table, column)) - - num_cols = len(table_rows[0]) - placeholder = ('?,'*num_cols).strip(',') - cur.executemany('INSERT INTO {} VALUES ({})'.format(table, placeholder), table_rows) + + sql_runs = 'INSERT INTO runs VALUES (?,?,?,?,?,?,?,?,?,?)' + cur.execute(sql_runs, (run_id, exp_id, 'analysis', table, "", "", "", 1, "", "")) + + conn.commit() conn.close() - print("Table {} created in analysis.db".format(table)) + print("Table {} created".format(table)) diff --git a/qdev_wrappers/fitting/Fitter.py b/qdev_wrappers/fitting/Fitter.py index ffe82d57..e46d81c0 100644 --- a/qdev_wrappers/fitting/Fitter.py +++ b/qdev_wrappers/fitting/Fitter.py @@ -30,7 +30,7 @@ def find_start_parameters(fitclass, p0, xdata, ydata): raise RuntimeError('It looks like there is something wrong with your fitclass(). Possibly you forgot the parentheses?') if (x==None or y==None): - raise RuntimeError('Please either specify data for x, y (and optionally z)') + raise RuntimeError('Please specify data for x, y (and optionally z)') for dataname in [x, y, z]: if (dataname not in data['variables']) and (dataname is not None): From d3c98b4444a2abe2aefdca497e4d11a52102f9a4 Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Thu, 28 Jun 2018 11:31:56 +0200 Subject: [PATCH 037/116] Move calculation of estimated function output from SQL conversion to fitter --- qdev_wrappers/fitting/AnalysisToSQL.py | 102 ++++++------------------- qdev_wrappers/fitting/Fitter.py | 77 ++++++++++++++++++- 2 files changed, 98 insertions(+), 81 deletions(-) diff --git a/qdev_wrappers/fitting/AnalysisToSQL.py b/qdev_wrappers/fitting/AnalysisToSQL.py index 657fe2e4..2cdb3a83 100644 --- a/qdev_wrappers/fitting/AnalysisToSQL.py +++ b/qdev_wrappers/fitting/AnalysisToSQL.py @@ -1,6 +1,7 @@ import sqlite3 +import dill from qdev_wrappers.fitting.Fitclasses import T1, T2 @@ -30,102 +31,49 @@ def make_table(tablename, cursor): return name -def fit_to_SQL(data, fitclass, fit): #it would be an improvement if it were able to get the fitclass from the fit information +def fit_to_SQL(fit): #it would be an improvement if it were able to get the fitclass from the fit information - if fit['estimator']['type'] != fitclass.name: #maybe this should be more general in this function, like 'analysis_class? - so it makes sense e.g. in the case of Baysian inference - raise RuntimeError('The data given was analyzed using {}. This does not seem to match {} specified in this function'.format(fit['estimator']['type'], fitclass.name)) - - dim = 1 - if 'zdata' in fit['inferred_from'].keys(): - dim = 2 - - xname = fit['inferred_from']['xdata'] - xdata = data[xname]['data'] - - yname = fit['inferred_from']['ydata'] - ydata = data[yname]['data'] - - est = [] #estimated/predicted value for the measured data given the fitted parameters - est_name = '{}_estimate'.format(yname) - est_label = data[yname]['label'] - est_unit = data[yname]['unit'] - - - p_values = [] - - if dim == 2: - zname = fit['inferred_from']['zdata'] - zdata = data[zname]['data'] - est_name = '{}_estimate'.format(zname) - est_label = data[zname]['label'] - est_unit = data[zname]['unit'] - - #make array of estimated values based on parameters and model used - if dim == 1: + fitclass_pckl = fit['estimator']['dill'] + fitclass = dill.loads(fitclass_pckl) - param_units = [fit['parameters'][param]['unit'] for param in list(fitclass.p_labels)] - - params = list(fitclass.p_labels) - for index, parameter in enumerate(params): - if parameter not in fit['parameters']: - raise KeyError('The list of parameters for the fitclass {} contains a parameter, {}, which is not present in the fit dictionary.'.format(fitclass.name, parameter)) - params[index] = fit['parameters'][parameter]['value'] - - for datapoint in xdata: - y = fitclass.fun(datapoint, *params) - est.append(y) - p_values.append(params) + est_values = fit['estimate']['data'] + est_name = '{}_estimate'.format(fit['estimate']['name']) + est_label = fit['estimate']['label'] + est_unit = fit['estimate']['unit'] + param_values = fit['estimate']['parameters'] - - if dim == 2: + dim = fit['inferred_from']['dimensions'] + if dim == 1: + param_units = [fit['parameters'][param]['unit'] for param in list(fitclass.p_labels)] + elif dim == 2: setpoints = [key for key in fit.keys()] param_units = [fit[setpoints[0]]['parameters'][param]['unit'] for param in list(fitclass.p_labels)] - - for xpoint, ypoint in zip(xdata, ydata): - - if xpoint in setpoints: - setpoint = xpoint - datapoint = ypoint - elif ypoint in setpoints: - setpoint = ypoint - datapoint = xpoint - - params = list(fitclass.p_labels) - for index, parameter in enumerate(params): - if parameter not in fit[setpoint]['parameters']: - raise KeyError('The list of parameters for the fitclass {} contains a parameter, {}, which is not present in the fit dictionary.'.format(fitclass.name, parameter)) - params[index] = fit[setpoint]['parameters'][parameter]['value'] - - z = fitclass.fun(datapoint, *params) - est.append(z) - p_values.append(params) - - - - tablename = 'analysis_{}_{}'.format(data['run_id'], fitclass.name) + + + tablename = 'analysis_{}_{}'.format(fit['inferred_from']['run_id'], fitclass.name) table_columns = [est_name] for parameter in fitclass.p_labels: table_columns.append(parameter) - + table_rows = [] if dim == 1: - for estimate in est: - id_nr = est.index(estimate) + 1 + for estimate, params in zip(est_values, param_values): + id_nr = est_values.index(estimate) + 1 row = (id_nr, estimate, *params) table_rows.append(row) elif dim == 2: - for estimate, parameters in zip(est, p_values): - id_nr = est.index(estimate) + 1 + for estimate, parameters in zip(est_values, param_values): + id_nr = est_values.index(estimate) + 1 row = (id_nr, estimate, *parameters) table_rows.append(row) - data_run_id = data['run_id'] - exp_id = data['exp_id'] + data_run_id = fit['inferred_from']['run_id'] + exp_id = fit['inferred_from']['exp_id'] predicts = est_name.strip("estimate").strip("_") estimator = "{}, {}".format(fit['estimator']['method'], fit['estimator']['type']) analysis = fit['estimator']['function used'] @@ -139,8 +87,8 @@ def fit_to_SQL(data, fitclass, fit): #it would be an improvement if it were a #Alternative: have a separate function that sets up the general analysis database with tables, so that this doesn't have to be here if not is_table('analyses', cur): - cur.execute('''CREATE TABLE analyses - (data_run_id INTEGER, exp_id INTEGER, run_id INTEGER, analysis_table_name TEXT, + cur.execute('''CREATE TABLE analyses + (data_run_id INTEGER, exp_id INTEGER, run_id INTEGER, analysis_table_name TEXT, predicts TEXT, estimator TEXT, function TEXT, dill TEXT)''') diff --git a/qdev_wrappers/fitting/Fitter.py b/qdev_wrappers/fitting/Fitter.py index e46d81c0..f40eb947 100644 --- a/qdev_wrappers/fitting/Fitter.py +++ b/qdev_wrappers/fitting/Fitter.py @@ -98,8 +98,10 @@ def find_start_parameters(fitclass, p0, xdata, ydata): fit['inferred_from'] = {'xdata': x_dict['name'], 'ydata': y_dict['name'], - 'dataset': data['run_id'], - 'dependencies': data['dependencies']} #missing sample name + 'run_id': data['run_id'], + 'exp_id': data['exp_id'], + 'dependencies': data['dependencies'], + 'dimensions': 1} #missing sample name @@ -166,8 +168,10 @@ def find_start_parameters(fitclass, p0, xdata, ydata): fit['inferred_from'] = {'xdata': x_dict['name'], 'ydata': y_dict['name'], 'zdata': z_dict['name'], - 'dataset': data['run_id'], - 'dependencies': data['dependencies']} #missing sample name + 'run_id': data['run_id'], + 'exp_id': data['exp_id'], + 'dependencies': data['dependencies'], + 'dimensions' : 2} #missing sample name if cut == 'horizontal': fit['inferred_from']['setpoints'] = 'ydata' @@ -182,6 +186,71 @@ def find_start_parameters(fitclass, p0, xdata, ydata): 'function used': str(fitclass.fun_np), 'dill': dill_obj} + # Find estimated values for the function output based on fitted parameters + + fit['estimate'] = {} + + xdata = x_dict['data'] + output = y_dict + + if dimensions == 2: + ydata = y_dict['data'] + output = z_dict + + est_values = [] # estimated/predicted value for the measured data given the fitted parameters + est_name = '{}_estimate'.format(output['name']) + est_label = output['label'] + est_unit = output['unit'] + + p_values = [] + + # make array of estimated values based on parameters and model used + if dimensions == 1: + + params = list(fitclass.p_labels) + for index, parameter in enumerate(params): + if parameter not in fit['parameters']: + raise KeyError( + 'The list of parameters for the fitclass {} contains a parameter, {}, which is not present in the fit dictionary.'.format( + fitclass.name, parameter)) + params[index] = fit['parameters'][parameter]['value'] + + for datapoint in xdata: + y = fitclass.fun(datapoint, *params) + est_values.append(y) + p_values.append(params) + + if dimensions == 2: + + for xpoint, ypoint in zip(xdata, ydata): + + if xpoint in setpoints: + setpoint = xpoint + datapoint = ypoint + elif ypoint in setpoints: + setpoint = ypoint + datapoint = xpoint + + params = list(fitclass.p_labels) + for index, parameter in enumerate(params): + if parameter not in fit[setpoint]['parameters']: + raise KeyError( + 'The list of parameters for the fitclass {} contains a parameter, {}, which is not present in the fit dictionary.'.format( + fitclass.name, parameter)) + params[index] = fit[setpoint]['parameters'][parameter]['value'] + + z = fitclass.fun(datapoint, *params) + est_values.append(z) + p_values.append(params) + + + fit['estimate']['name'] = est_name + fit['estimate']['label'] = est_label + fit['estimate']['unit'] = est_unit + fit['estimate']['data'] = est_values + fit['estimate']['parameters'] = p_values + + return fit From 02dd66009f526d9940238e3269addd61e891e05d Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Thu, 28 Jun 2018 14:55:28 +0200 Subject: [PATCH 038/116] Add fun_vars attribute to fitclasses - list of variables function takes --- qdev_wrappers/fitting/Fitclasses.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/qdev_wrappers/fitting/Fitclasses.py b/qdev_wrappers/fitting/Fitclasses.py index aecd5234..8942b1e8 100644 --- a/qdev_wrappers/fitting/Fitclasses.py +++ b/qdev_wrappers/fitting/Fitclasses.py @@ -19,6 +19,7 @@ def __init__(self): self.name = 'T1fit' self.fun_str = r'$f(x) = a \exp(-x/T) + c$' self.fun_np = 'a*np.exp(-x/T)+c' + self.fun_vars = ['x'] self.p_names = ['$a$', '$T$', '$c$'] self.p_labels= ['a', 'T', 'c'] self.p_units = ['y', 'x', 'y'] @@ -46,6 +47,7 @@ def __init__(self): self.name = 'T2fit' self.fun_str = r'$f(x) = a \sin(\omega x +\phi)\exp(-x/T) + c$' self.fun_np = 'a*np.exp(-x/T)*np.sin(w*x+p)+c' + self.fun_vars = ['x'] self.p_names = ['$a$', '$T$', '$\omega$', '$\phi$', '$c$'] self.p_labels = ['a', 'T', 'w', 'p', 'c'] self.p_units = ['y', 'x', '1/x', '', 'y'] @@ -66,4 +68,3 @@ def guess(self,x,y): w = freqs[idx] p = 0 return [a,T,w,p,c] - From 384fff70969be0073e432e4465002df6dddffd39 Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Thu, 28 Jun 2018 15:16:49 +0200 Subject: [PATCH 039/116] Change fitter back to class --- qdev_wrappers/fitting/Fitter.py | 357 ++++++++++++++++---------------- 1 file changed, 179 insertions(+), 178 deletions(-) diff --git a/qdev_wrappers/fitting/Fitter.py b/qdev_wrappers/fitting/Fitter.py index f40eb947..2a936b78 100644 --- a/qdev_wrappers/fitting/Fitter.py +++ b/qdev_wrappers/fitting/Fitter.py @@ -7,11 +7,9 @@ from scipy.optimize import curve_fit - - -def do_fit(data, fitclass, x=None, y=None, z=None, cut='horizontal', p0=None,**kwargs): +class Fitter(): - def find_start_parameters(fitclass, p0, xdata, ydata): + def find_start_parameters(self, fitclass, p0, xdata, ydata): if (p0 == None and hasattr(fitclass, 'guess')): p0 = getattr(fitclass, 'guess')(xdata, ydata) @@ -23,234 +21,237 @@ def find_start_parameters(fitclass, p0, xdata, ydata): return "Could not find guess parameters for fit." - if type(fitclass) == type: - #Maybe I'm just an idiot, and this isn't necessary for the world-at-large, but - #I spent about 45 minutes trying to figure out what I broke before I realized - #that I just forgot the parentheses after the fitclass. So this is here for now. - raise RuntimeError('It looks like there is something wrong with your fitclass(). Possibly you forgot the parentheses?') - - if (x==None or y==None): - raise RuntimeError('Please specify data for x, y (and optionally z)') - - for dataname in [x, y, z]: - if (dataname not in data['variables']) and (dataname is not None): - raise RuntimeError('The specified variable "{}" is not found in the variables for this data dictionary. Variables are {}'.format(dataname, data['variables'])) - - # specify x, y and z - x_dict = data[x] - y_dict = data[y] - dimensions = 1 - - if z != None: - z_dict = data[z] - dimensions = 2 - - # find parameter units - if dimensions == 1: - cut = 'horizontal' # Must be horizontal for 1D plots for units to work. Now you can't mess it up. - unit_template = fitclass.p_units - param_units = [] - x = x_dict['unit'] - y = y_dict['unit'] - if dimensions == 2: - z = z_dict['unit'] - - for item in unit_template: - template = list(item) - if cut == 'horizontal': - for i in range(len(template)): - if template[i] == 'x': - template[i] = x - if template[i] == 'y': - template[i] = y - if template[i] == 'z': - template[i] = z - elif cut == 'vertical': - for i in range(len(template)): - if template[i] == 'x': - template[i] = y - if template[i] == 'y': - template[i] = x - if template[i] == 'z': - template[i] = z - unit = "".join(template) - param_units.append(unit) - - #Do fit for 1D data - if dimensions == 1: + def do_fit(self, data, fitclass, x=None, y=None, z=None, cut='horizontal', p0=None,**kwargs): + + if type(fitclass) == type: + #Maybe I'm just an idiot, and this isn't necessary for the world-at-large, but + #I spent about 45 minutes trying to figure out what I broke before I realized + #that I just forgot the parentheses after the fitclass. So this is here for now. + raise RuntimeError('It looks like there is something wrong with your fitclass(). Possibly you forgot the parentheses?') - xdata = x_dict['data'] - ydata = y_dict['data'] + if (x==None or y==None): + raise RuntimeError('Please specify data for x, y (and optionally z)') - fit = {} - fit['parameters'] = {} - fit['start_params'] = {} + for dataname in [x, y, z]: + if (dataname not in data['variables']) and (dataname is not None): + raise RuntimeError('The specified variable "{}" is not found in the variables for this data dictionary. Variables are {}'.format(dataname, data['variables'])) - guess = find_start_parameters(fitclass, p0, xdata, ydata) - popt, pcov = curve_fit(fitclass.fun, xdata, ydata, p0=guess, **kwargs) + # specify x, y and z + x_dict = data[x] + y_dict = data[y] + dimensions = 1 + + if z != None: + z_dict = data[z] + dimensions = 2 - for parameter in fitclass.p_labels: - fit['parameters'][parameter] = {'value': popt[fitclass.p_labels.index(parameter)]} - fit['parameters'][parameter]['cov'] = pcov[fitclass.p_labels.index(parameter)] - fit['parameters'][parameter]['unit'] = param_units[fitclass.p_labels.index(parameter)] - fit['start_params'][parameter] = guess[fitclass.p_labels.index(parameter)] + # find parameter units + if dimensions == 1: + cut = 'horizontal' # Must be horizontal for 1D plots for units to work. Now you can't mess it up. + unit_template = fitclass.p_units + param_units = [] + x = x_dict['unit'] + y = y_dict['unit'] + if dimensions == 2: + z = z_dict['unit'] + for item in unit_template: + template = list(item) + if cut == 'horizontal': + for i in range(len(template)): + if template[i] == 'x': + template[i] = x + if template[i] == 'y': + template[i] = y + if template[i] == 'z': + template[i] = z + elif cut == 'vertical': + for i in range(len(template)): + if template[i] == 'x': + template[i] = y + if template[i] == 'y': + template[i] = x + if template[i] == 'z': + template[i] = z + unit = "".join(template) + param_units.append(unit) - fit['inferred_from'] = {'xdata': x_dict['name'], - 'ydata': y_dict['name'], - 'run_id': data['run_id'], - 'exp_id': data['exp_id'], - 'dependencies': data['dependencies'], - 'dimensions': 1} #missing sample name + #Do fit for 1D data + if dimensions == 1: + xdata = x_dict['data'] + ydata = y_dict['data'] + fit = {} + fit['parameters'] = {} + fit['start_params'] = {} - #Do fit for 2D data - if dimensions == 2: + guess = self.find_start_parameters(fitclass, p0, xdata, ydata) + popt, pcov = curve_fit(fitclass.fun, xdata, ydata, p0=guess, **kwargs) + + for parameter in fitclass.p_labels: + fit['parameters'][parameter] = {'value': popt[fitclass.p_labels.index(parameter)]} + fit['parameters'][parameter]['cov'] = pcov[fitclass.p_labels.index(parameter)] + fit['parameters'][parameter]['unit'] = param_units[fitclass.p_labels.index(parameter)] + fit['start_params'][parameter] = guess[fitclass.p_labels.index(parameter)] - xdata = x_dict['data'] - ydata = y_dict['data'] - zdata = z_dict['data'] - fit = {} + fit['inferred_from'] = {'xdata': x_dict['name'], + 'ydata': y_dict['name'], + 'run_id': data['run_id'], + 'exp_id': data['exp_id'], + 'dependencies': data['dependencies'], + 'dimensions': 1} #missing sample name - if cut == 'horizontal': - setarray = ydata - xarray = xdata - if cut == 'vertical': - setarray = xdata - xarray = ydata - setpoints = np.unique(setarray) - yarray = zdata - #reformats as array of set points, with a y-array and z-array corresponding to each set point + #Do fit for 2D data + if dimensions == 2: - xdata_lst = [] - ydata_lst = [] + xdata = x_dict['data'] + ydata = y_dict['data'] + zdata = z_dict['data'] - for set_point in setpoints: - x_dat = [] - y_dat = [] - for setpoint, x, y in zip(setarray, xarray, yarray): - if setpoint == set_point: - x_dat.append(x) - y_dat.append(y) - xdata_lst.append(np.array(x_dat)) - ydata_lst.append(np.array(y_dat)) + fit = {} + if cut == 'horizontal': + setarray = ydata + xarray = xdata - xdata = np.array(xdata_lst) - ydata = np.array(ydata_lst) + if cut == 'vertical': + setarray = xdata + xarray = ydata + setpoints = np.unique(setarray) + yarray = zdata - #fitting as a sequence of 1D plots for different set_values + #reformats as array of set points, with a y-array and z-array corresponding to each set point - for set_value, xdata_1d, ydata_1d in zip(setpoints, xdata, ydata): + xdata_lst = [] + ydata_lst = [] - guess = find_start_parameters(fitclass, p0, xdata_1d, ydata_1d) - popt, pcov = curve_fit(fitclass.fun, xdata_1d, ydata_1d, p0=guess, **kwargs) + for set_point in setpoints: + x_dat = [] + y_dat = [] + for setpoint, x, y in zip(setarray, xarray, yarray): + if setpoint == set_point: + x_dat.append(x) + y_dat.append(y) + xdata_lst.append(np.array(x_dat)) + ydata_lst.append(np.array(y_dat)) - fit[set_value] = {} - fit[set_value]['parameters'] = {} - fit[set_value]['start_params'] = {} - for parameter in fitclass.p_labels: #parameters currently missing units, use fitclass.p_units - fit[set_value]['parameters'][parameter] = {'value': popt[fitclass.p_labels.index(parameter)]} - fit[set_value]['parameters'][parameter]['cov'] = pcov[fitclass.p_labels.index(parameter)] - fit[set_value]['parameters'][parameter]['unit'] = param_units[fitclass.p_labels.index(parameter)] - fit[set_value]['start_params'][parameter] = guess[fitclass.p_labels.index(parameter)] + xdata = np.array(xdata_lst) + ydata = np.array(ydata_lst) - #does this needs to be moved so that it specifies which cut the individual sets of parameters are inferred from? - fit['inferred_from'] = {'xdata': x_dict['name'], - 'ydata': y_dict['name'], - 'zdata': z_dict['name'], - 'run_id': data['run_id'], - 'exp_id': data['exp_id'], - 'dependencies': data['dependencies'], - 'dimensions' : 2} #missing sample name + #fitting as a sequence of 1D plots for different set_values - if cut == 'horizontal': - fit['inferred_from']['setpoints'] = 'ydata' + for set_value, xdata_1d, ydata_1d in zip(setpoints, xdata, ydata): - if cut == 'vertical': - fit['inferred_from']['setpoints'] = 'xdata' + guess = self.find_start_parameters(fitclass, p0, xdata_1d, ydata_1d) + popt, pcov = curve_fit(fitclass.fun, xdata_1d, ydata_1d, p0=guess, **kwargs) + fit[set_value] = {} + fit[set_value]['parameters'] = {} + fit[set_value]['start_params'] = {} - dill_obj = dill.dumps(fitclass) - fit['estimator'] = {'method': 'Least squared fit', - 'type': fitclass.name, - 'function used': str(fitclass.fun_np), - 'dill': dill_obj} + for parameter in fitclass.p_labels: #parameters currently missing units, use fitclass.p_units + fit[set_value]['parameters'][parameter] = {'value': popt[fitclass.p_labels.index(parameter)]} + fit[set_value]['parameters'][parameter]['cov'] = pcov[fitclass.p_labels.index(parameter)] + fit[set_value]['parameters'][parameter]['unit'] = param_units[fitclass.p_labels.index(parameter)] + fit[set_value]['start_params'][parameter] = guess[fitclass.p_labels.index(parameter)] - # Find estimated values for the function output based on fitted parameters - fit['estimate'] = {} + #does this needs to be moved so that it specifies which cut the individual sets of parameters are inferred from? + fit['inferred_from'] = {'xdata': x_dict['name'], + 'ydata': y_dict['name'], + 'zdata': z_dict['name'], + 'run_id': data['run_id'], + 'exp_id': data['exp_id'], + 'dependencies': data['dependencies'], + 'dimensions' : 2} #missing sample name - xdata = x_dict['data'] - output = y_dict + if cut == 'horizontal': + fit['inferred_from']['setpoints'] = 'ydata' - if dimensions == 2: - ydata = y_dict['data'] - output = z_dict + if cut == 'vertical': + fit['inferred_from']['setpoints'] = 'xdata' - est_values = [] # estimated/predicted value for the measured data given the fitted parameters - est_name = '{}_estimate'.format(output['name']) - est_label = output['label'] - est_unit = output['unit'] - p_values = [] + dill_obj = dill.dumps(fitclass) + fit['estimator'] = {'method': 'Least squared fit', + 'type': fitclass.name, + 'function used': str(fitclass.fun_np), + 'dill': dill_obj} - # make array of estimated values based on parameters and model used - if dimensions == 1: + # Find estimated values for the function output based on fitted parameters - params = list(fitclass.p_labels) - for index, parameter in enumerate(params): - if parameter not in fit['parameters']: - raise KeyError( - 'The list of parameters for the fitclass {} contains a parameter, {}, which is not present in the fit dictionary.'.format( - fitclass.name, parameter)) - params[index] = fit['parameters'][parameter]['value'] + fit['estimate'] = {} - for datapoint in xdata: - y = fitclass.fun(datapoint, *params) - est_values.append(y) - p_values.append(params) + xdata = x_dict['data'] + output = y_dict - if dimensions == 2: + if dimensions == 2: + ydata = y_dict['data'] + output = z_dict - for xpoint, ypoint in zip(xdata, ydata): + est_values = [] # estimated/predicted value for the measured data given the fitted parameters + est_name = '{}_estimate'.format(output['name']) + est_label = output['label'] + est_unit = output['unit'] - if xpoint in setpoints: - setpoint = xpoint - datapoint = ypoint - elif ypoint in setpoints: - setpoint = ypoint - datapoint = xpoint + p_values = [] + + # make array of estimated values based on parameters and model used + if dimensions == 1: params = list(fitclass.p_labels) for index, parameter in enumerate(params): - if parameter not in fit[setpoint]['parameters']: + if parameter not in fit['parameters']: raise KeyError( 'The list of parameters for the fitclass {} contains a parameter, {}, which is not present in the fit dictionary.'.format( fitclass.name, parameter)) - params[index] = fit[setpoint]['parameters'][parameter]['value'] + params[index] = fit['parameters'][parameter]['value'] + + for datapoint in xdata: + y = fitclass.fun(datapoint, *params) + est_values.append(y) + p_values.append(params) + + if dimensions == 2: + + for xpoint, ypoint in zip(xdata, ydata): + + if xpoint in setpoints: + setpoint = xpoint + datapoint = ypoint + elif ypoint in setpoints: + setpoint = ypoint + datapoint = xpoint + + params = list(fitclass.p_labels) + for index, parameter in enumerate(params): + if parameter not in fit[setpoint]['parameters']: + raise KeyError( + 'The list of parameters for the fitclass {} contains a parameter, {}, which is not present in the fit dictionary.'.format( + fitclass.name, parameter)) + params[index] = fit[setpoint]['parameters'][parameter]['value'] - z = fitclass.fun(datapoint, *params) - est_values.append(z) - p_values.append(params) + z = fitclass.fun(datapoint, *params) + est_values.append(z) + p_values.append(params) - fit['estimate']['name'] = est_name - fit['estimate']['label'] = est_label - fit['estimate']['unit'] = est_unit - fit['estimate']['data'] = est_values - fit['estimate']['parameters'] = p_values + fit['estimate']['name'] = est_name + fit['estimate']['label'] = est_label + fit['estimate']['unit'] = est_unit + fit['estimate']['data'] = est_values + fit['estimate']['parameters'] = p_values - return fit + return fit From 720040b30e4e916ca7e16baa0edbbba719482c86 Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Sat, 30 Jun 2018 21:32:39 +0200 Subject: [PATCH 040/116] Modify fitter to use function based rather than graphic/visualization based input, generalize, and move estimate calculation based on fit from SQL conversion to fitter --- qdev_wrappers/fitting/AnalysisToSQL.py | 2 +- qdev_wrappers/fitting/Fitclasses.py | 2 + qdev_wrappers/fitting/Fitter.py | 410 +++++++++++++------------ 3 files changed, 224 insertions(+), 190 deletions(-) diff --git a/qdev_wrappers/fitting/AnalysisToSQL.py b/qdev_wrappers/fitting/AnalysisToSQL.py index 2cdb3a83..c8256026 100644 --- a/qdev_wrappers/fitting/AnalysisToSQL.py +++ b/qdev_wrappers/fitting/AnalysisToSQL.py @@ -36,7 +36,7 @@ def fit_to_SQL(fit): #it would be an improvement if it were able to get the f fitclass_pckl = fit['estimator']['dill'] fitclass = dill.loads(fitclass_pckl) - est_values = fit['estimate']['data'] + est_values = fit['estimate']['values'] est_name = '{}_estimate'.format(fit['estimate']['name']) est_label = fit['estimate']['label'] est_unit = fit['estimate']['unit'] diff --git a/qdev_wrappers/fitting/Fitclasses.py b/qdev_wrappers/fitting/Fitclasses.py index 8942b1e8..718d5a6a 100644 --- a/qdev_wrappers/fitting/Fitclasses.py +++ b/qdev_wrappers/fitting/Fitclasses.py @@ -20,6 +20,7 @@ def __init__(self): self.fun_str = r'$f(x) = a \exp(-x/T) + c$' self.fun_np = 'a*np.exp(-x/T)+c' self.fun_vars = ['x'] + self.fun_output = ['y'] self.p_names = ['$a$', '$T$', '$c$'] self.p_labels= ['a', 'T', 'c'] self.p_units = ['y', 'x', 'y'] @@ -48,6 +49,7 @@ def __init__(self): self.fun_str = r'$f(x) = a \sin(\omega x +\phi)\exp(-x/T) + c$' self.fun_np = 'a*np.exp(-x/T)*np.sin(w*x+p)+c' self.fun_vars = ['x'] + self.fun_output = ['y'] self.p_names = ['$a$', '$T$', '$\omega$', '$\phi$', '$c$'] self.p_labels = ['a', 'T', 'w', 'p', 'c'] self.p_units = ['y', 'x', '1/x', '', 'y'] diff --git a/qdev_wrappers/fitting/Fitter.py b/qdev_wrappers/fitting/Fitter.py index 2a936b78..8b729e45 100644 --- a/qdev_wrappers/fitting/Fitter.py +++ b/qdev_wrappers/fitting/Fitter.py @@ -7,251 +7,283 @@ from scipy.optimize import curve_fit -class Fitter(): +def fit_data(data, fitclass, fun_inputs, fun_output, setpoint_params=None, p0=None, **kwargs): - def find_start_parameters(self, fitclass, p0, xdata, ydata): - - if (p0 == None and hasattr(fitclass, 'guess')): - p0 = getattr(fitclass, 'guess')(xdata, ydata) - return p0 - elif p0 != None: - p0 = p0 - return p0 - else: - return "Could not find guess parameters for fit." + fun_dim = len(fitclass.fun_vars) + num_inputs = len(fun_inputs) + if setpoint_params is not None: + num_setpoints = len(setpoint_params) + else: num_setpoints = 0 - def do_fit(self, data, fitclass, x=None, y=None, z=None, cut='horizontal', p0=None,**kwargs): + if fun_dim == 1 and num_inputs == 1 and num_setpoints == 0: + fitter = Fitter_1D(data, fitclass, fun_output) + elif fun_dim == 1 and num_inputs == 1 and num_setpoints == 1: + fitter.Fitter_2Ddata_1Dfunction(data, fitclass, fun_output) #what does the fitter actually need?? + else: + print('The specified function has ') - if type(fitclass) == type: - #Maybe I'm just an idiot, and this isn't necessary for the world-at-large, but - #I spent about 45 minutes trying to figure out what I broke before I realized - #that I just forgot the parentheses after the fitclass. So this is here for now. - raise RuntimeError('It looks like there is something wrong with your fitclass(). Possibly you forgot the parentheses?') + fitter.find_fit(data, fitclass, fun_inputs, fun_output, setpoint_params, p0, **kwargs) - if (x==None or y==None): - raise RuntimeError('Please specify data for x, y (and optionally z)') +class Fitter1D: - for dataname in [x, y, z]: - if (dataname not in data['variables']) and (dataname is not None): - raise RuntimeError('The specified variable "{}" is not found in the variables for this data dictionary. Variables are {}'.format(dataname, data['variables'])) + def __init__(self, data, fitclass, output): - # specify x, y and z - x_dict = data[x] - y_dict = data[y] - dimensions = 1 + self.fitclass = fitclass #'UNNECESSARY??' + self.fun_dim = len(fitclass.fun_vars) + self.data_dim = 'undefined' + self.input_vars = [] + self.output_var = [] + self.all_datanames = [] + self.full_parameter_dict = 'undefined' + self.output_dataname = [] - if z != None: - z_dict = data[z] - dimensions = 2 - - # find parameter units - if dimensions == 1: - cut = 'horizontal' # Must be horizontal for 1D plots for units to work. Now you can't mess it up. - unit_template = fitclass.p_units - param_units = [] - x = x_dict['unit'] - y = y_dict['unit'] - if dimensions == 2: - z = z_dict['unit'] - - for item in unit_template: - template = list(item) - if cut == 'horizontal': - for i in range(len(template)): - if template[i] == 'x': - template[i] = x - if template[i] == 'y': - template[i] = y - if template[i] == 'z': - template[i] = z - elif cut == 'vertical': - for i in range(len(template)): - if template[i] == 'x': - template[i] = y - if template[i] == 'y': - template[i] = x - if template[i] == 'z': - template[i] = z - unit = "".join(template) - param_units.append(unit) + self.input_dataname = [] + self.setpoint_params = [] + self.input_data = [] + self.output_data = [] - #Do fit for 1D data - if dimensions == 1: + def find_fit(self, data, fitclass, fun_inputs, fun_output, setpoint_params=None, p0=None, **kwargs): - xdata = x_dict['data'] - ydata = y_dict['data'] + self.check_input_matches_fitclass(fitclass, fun_inputs, fun_output, setpoint_params, p0) + data_dict, label_dict, name_dict, unit_dict = self.organize_data(data, fun_inputs, fun_output, setpoint_params) + param_units = self.find_parameter_units(fitclass, unit_dict) - fit = {} - fit['parameters'] = {} - fit['start_params'] = {} + fit = self.perform_fit(data_dict, data, fitclass, setpoint_params, p0, param_units, **kwargs) - guess = self.find_start_parameters(fitclass, p0, xdata, ydata) - popt, pcov = curve_fit(fitclass.fun, xdata, ydata, p0=guess, **kwargs) + fit['estimate'] = self.estimate_function_values(fitclass, data, data_dict) - for parameter in fitclass.p_labels: - fit['parameters'][parameter] = {'value': popt[fitclass.p_labels.index(parameter)]} - fit['parameters'][parameter]['cov'] = pcov[fitclass.p_labels.index(parameter)] - fit['parameters'][parameter]['unit'] = param_units[fitclass.p_labels.index(parameter)] - fit['start_params'][parameter] = guess[fitclass.p_labels.index(parameter)] - - - fit['inferred_from'] = {'xdata': x_dict['name'], - 'ydata': y_dict['name'], - 'run_id': data['run_id'], - 'exp_id': data['exp_id'], - 'dependencies': data['dependencies'], - 'dimensions': 1} #missing sample name + dill_obj = dill.dumps(fitclass) + fit['inferred_from'] = {'inputs': fun_inputs, + 'output': fun_output, + 'setpoints': setpoint_params, + 'run_id': data['run_id'], + 'exp_id': data['exp_id'], + 'dependencies': data['dependencies'], + 'data_dimensions': self.data_dim, + 'estimator' : {'method': 'Least squared fit', + 'type': fitclass.name, + 'function used': str(fitclass.fun_np), + 'dill': dill_obj} + } + return fit + def check_input_matches_fitclass(self, fitclass, inputs, output, setpoints, p0): + + # check input and output are given, and that input, output and setpoints are in correct format + if (type(inputs) != dict or type(output) != dict): + raise RuntimeError('''Please specify both input and output variables for the function you wish to fit in + # the format fun_inputs = {'x':'name', 'y':'other_name'}, fun_output = {z: 'another_name'} ''') + if (setpoints is not None) and (type(setpoints) != list): + raise RuntimeError('''Please specify setpoints as a list ['setpoint_name', ...], even if there is only one.''') + + # check inputs specified match inputs function takes + if len(inputs) != len(fitclass.fun_vars): + raise RuntimeError('''The function you are fitting to takes {} variables, + and you have specified {}'''.format(len(fitclass.fun_vars), len(inputs))) + for variable in inputs.keys(): + if variable not in fitclass.fun_vars: + raise RuntimeError('''You have specified a variable {}. + The fit function takes variables {}'''.format(variable, fitclass.fun_vars)) + for variable in output.keys(): + if variable not in fitclass.fun_output: + raise RuntimeError('''You have specified a variable {}. + The fit function returns variables {}'''.format(variable, fitclass.fun_output)) + + # check that if a guess p0 is specified, the number of parameters is correct for the fit function + if (p0 is not None) and (len(p0) != len(fitclass.p_labels)): + raise RuntimeError('''You have specified {} start parameters for the fit function: {}. The function takes + {} start parameters: {}'''.format(len(p0), p0, len(fitclass.p_labels), fitclass.p_labels)) + + + def organize_data(self, data, inputs, output, setpoints): + + all_vars = [dataname for dataname in inputs.values()] + for dataname in output.values(): + all_vars.append(dataname) + if setpoints is not None: + for dataname in setpoints: + all_vars.append(dataname) + # confirm all variables involved are in the dataset + for dataname in all_vars: + if dataname not in data.keys(): + raise RuntimeError('''Variable {} not found in data dictionary. Data dictionary contains + variables: {}'''.format(dataname, data['variables'])) + self.all_datanames = all_vars + self.data_dim = len(self.all_datanames) - 1 + + data_dict = {} + label_dict = {} + name_dict = {} + unit_dict = {} + + for variable, name in inputs.items(): + data_dict[variable] = data[name]['data'] + label_dict[variable] = data[name]['label'] + name_dict[variable] = data[name]['name'] + unit_dict[variable] = data[name]['unit'] + + for variable, name in output.items(): + data_dict[variable] = data[name]['data'] + label_dict[variable] = data[name]['label'] + name_dict[variable] = data[name]['name'] + unit_dict[variable] = data[name]['unit'] + + self.input_vars = [variable for variable in inputs.keys()] + self.output_var = [variable for variable in output.keys()] + self.input_dataname = [dataname for dataname in inputs.values()] + self.output_dataname = [dataname for dataname in output.values()] + + return data_dict, label_dict, name_dict, unit_dict + + def find_start_parameters(self, fitclass, p0, data_dict): + if (p0 == None and hasattr(fitclass, 'guess')): + p0 = getattr(fitclass, 'guess')(**data_dict) + return p0 + elif p0 != None: + p0 = p0 + return p0 + else: + return "Could not find guess parameters for fit." - #Do fit for 2D data - if dimensions == 2: + def find_parameter_units(self, fitclass, unit_dict): - xdata = x_dict['data'] - ydata = y_dict['data'] - zdata = z_dict['data'] + unit_templates = fitclass.p_units + param_units = [] - fit = {} + for template in unit_templates: + template = list(template) + for i in range(len(template)): + for variable in unit_dict.keys(): + if template[i] == variable: + template[i] = unit_dict[variable] + unit = "".join(template) + param_units.append(unit) - if cut == 'horizontal': - setarray = ydata - xarray = xdata + return param_units - if cut == 'vertical': - setarray = xdata - xarray = ydata + def perform_fit(self, data_dict, data, fitclass, setpoints, p0, param_units, **kwargs): - setpoints = np.unique(setarray) - yarray = zdata + fit = {} + fit['parameters'] = {} + fit['start_params'] = {} - #reformats as array of set points, with a y-array and z-array corresponding to each set point + xdata = data_dict[self.input_vars[0]] + ydata = data_dict[self.output_var[0]] - xdata_lst = [] - ydata_lst = [] + p_guess = self.find_start_parameters(fitclass, p0, data_dict) + popt, pcov = curve_fit(fitclass.fun, xdata, ydata, p0=p_guess, **kwargs) - for set_point in setpoints: - x_dat = [] - y_dat = [] - for setpoint, x, y in zip(setarray, xarray, yarray): - if setpoint == set_point: - x_dat.append(x) - y_dat.append(y) - xdata_lst.append(np.array(x_dat)) - ydata_lst.append(np.array(y_dat)) + for parameter in fitclass.p_labels: + fit['parameters'][parameter] = {'value': popt[fitclass.p_labels.index(parameter)]} + fit['parameters'][parameter]['cov'] = pcov[fitclass.p_labels.index(parameter)] + fit['parameters'][parameter]['unit'] = param_units[fitclass.p_labels.index(parameter)] + fit['start_params'][parameter] = p_guess[fitclass.p_labels.index(parameter)] + # make an dictionary containing arrays of each parameter value with same dimension as data (for estimate) + all_params = {} + for parameter in fitclass.p_labels: + all_params[parameter] = [] + for datapoint in xdata: + all_params[parameter].append(fit['parameters'][parameter]['value']) + all_params[parameter] = np.array(all_params[parameter]) + self.full_parameter_dict = all_params - xdata = np.array(xdata_lst) - ydata = np.array(ydata_lst) + return fit + def estimate_function_values(self, fitclass, data, data_dict): - #fitting as a sequence of 1D plots for different set_values + estimate = {} - for set_value, xdata_1d, ydata_1d in zip(setpoints, xdata, ydata): + input_data = data_dict + for variable in self.output_var: + del input_data[variable] - guess = self.find_start_parameters(fitclass, p0, xdata_1d, ydata_1d) - popt, pcov = curve_fit(fitclass.fun, xdata_1d, ydata_1d, p0=guess, **kwargs) + input_parameters = self.full_parameter_dict - fit[set_value] = {} - fit[set_value]['parameters'] = {} - fit[set_value]['start_params'] = {} + # Calculate estimated values for output based on function and store in estimate['values'] + estimate['values'] = fitclass.fun(**input_data, **input_parameters) - for parameter in fitclass.p_labels: #parameters currently missing units, use fitclass.p_units - fit[set_value]['parameters'][parameter] = {'value': popt[fitclass.p_labels.index(parameter)]} - fit[set_value]['parameters'][parameter]['cov'] = pcov[fitclass.p_labels.index(parameter)] - fit[set_value]['parameters'][parameter]['unit'] = param_units[fitclass.p_labels.index(parameter)] - fit[set_value]['start_params'][parameter] = guess[fitclass.p_labels.index(parameter)] + # Save estimate + estimate['name'] = '{}_estimate'.format(self.output_dataname[0]) + estimate['label'] = '{} estimate'.format(data[self.output_dataname[0]]['label']) + estimate['unit'] = data[self.output_dataname[0]]['unit'] + estimate['parameters'] = self.full_parameter_dict #not sure this is used for anything + return estimate - #does this needs to be moved so that it specifies which cut the individual sets of parameters are inferred from? - fit['inferred_from'] = {'xdata': x_dict['name'], - 'ydata': y_dict['name'], - 'zdata': z_dict['name'], - 'run_id': data['run_id'], - 'exp_id': data['exp_id'], - 'dependencies': data['dependencies'], - 'dimensions' : 2} #missing sample name - if cut == 'horizontal': - fit['inferred_from']['setpoints'] = 'ydata' +class Fitter_2Ddata_1Dfunction(Fitter1D): - if cut == 'vertical': - fit['inferred_from']['setpoints'] = 'xdata' + def perform_fit(self, data_dict, data, fitclass, setpoints, p0, param_units, **kwargs): + #Retrieve data + xarray = data_dict[self.input_vars[0]] + yarray = data_dict[self.output_var[0]] + setpoint_array = data[setpoints[0]]['data'] - dill_obj = dill.dumps(fitclass) - fit['estimator'] = {'method': 'Least squared fit', - 'type': fitclass.name, - 'function used': str(fitclass.fun_np), - 'dill': dill_obj} + # Reorganize data by setpoint + unique_setpoints = np.unique(setpoint_array) - # Find estimated values for the function output based on fitted parameters + xdata_lst = [] + ydata_lst = [] - fit['estimate'] = {} + for set_point in unique_setpoints: + x_dat = [] + y_dat = [] + for setpoint, x, y in zip(setpoint_array, xarray, yarray): + if setpoint == set_point: + x_dat.append(x) + y_dat.append(y) + xdata_lst.append(np.array(x_dat)) + ydata_lst.append(np.array(y_dat)) - xdata = x_dict['data'] - output = y_dict + xdata = np.array(xdata_lst) + ydata = np.array(ydata_lst) - if dimensions == 2: - ydata = y_dict['data'] - output = z_dict + #perform 1D fit for cross section at each setpoint and save to fit dictionary + fit = {} - est_values = [] # estimated/predicted value for the measured data given the fitted parameters - est_name = '{}_estimate'.format(output['name']) - est_label = output['label'] - est_unit = output['unit'] + for set_value, xdata_1d, ydata_1d in zip(unique_setpoints, xdata, ydata): - p_values = [] + data_dict_1d = {self.input_vars[0]:xdata_1d, self.output_var[0]:ydata_1d} - # make array of estimated values based on parameters and model used - if dimensions == 1: + p_guess = self.find_start_parameters(fitclass, p0, data_dict_1d) + popt, pcov = curve_fit(fitclass.fun, xdata_1d, ydata_1d, p0=p_guess, **kwargs) - params = list(fitclass.p_labels) - for index, parameter in enumerate(params): - if parameter not in fit['parameters']: - raise KeyError( - 'The list of parameters for the fitclass {} contains a parameter, {}, which is not present in the fit dictionary.'.format( - fitclass.name, parameter)) - params[index] = fit['parameters'][parameter]['value'] + fit[set_value] = {} + fit[set_value]['parameters'] = {} + fit[set_value]['start_params'] = {} - for datapoint in xdata: - y = fitclass.fun(datapoint, *params) - est_values.append(y) - p_values.append(params) + for parameter in fitclass.p_labels: # parameters currently missing units, use fitclass.p_units + fit[set_value]['parameters'][parameter] = {'value': popt[fitclass.p_labels.index(parameter)]} + fit[set_value]['parameters'][parameter]['cov'] = pcov[fitclass.p_labels.index(parameter)] + fit[set_value]['parameters'][parameter]['unit'] = param_units[fitclass.p_labels.index(parameter)] + fit[set_value]['start_params'][parameter] = p_guess[fitclass.p_labels.index(parameter)] - if dimensions == 2: - for xpoint, ypoint in zip(xdata, ydata): - if xpoint in setpoints: - setpoint = xpoint - datapoint = ypoint - elif ypoint in setpoints: - setpoint = ypoint - datapoint = xpoint + # Make an array of parameters, specified in the same order as the original setpoint array, before data + # was rearranged to do the fit. This is for running through the estimation function later. + all_params = {} + for parameter in fitclass.p_labels: + all_params[parameter] = [] + for setpoint in setpoint_array: + all_params[parameter].append(fit[setpoint]['parameters'][parameter]['value']) + all_params[parameter] = np.array(all_params[parameter]) + self.full_parameter_dict = all_params - params = list(fitclass.p_labels) - for index, parameter in enumerate(params): - if parameter not in fit[setpoint]['parameters']: - raise KeyError( - 'The list of parameters for the fitclass {} contains a parameter, {}, which is not present in the fit dictionary.'.format( - fitclass.name, parameter)) - params[index] = fit[setpoint]['parameters'][parameter]['value'] + return fit - z = fitclass.fun(datapoint, *params) - est_values.append(z) - p_values.append(params) - fit['estimate']['name'] = est_name - fit['estimate']['label'] = est_label - fit['estimate']['unit'] = est_unit - fit['estimate']['data'] = est_values - fit['estimate']['parameters'] = p_values + #def fit_2d_function_to_2d_data(self): + # print('This does not work yet.') - return fit - + #is data_dict helpful? What are all theses things I've defined now, and how many of them are just reworks + #the same stuff? \ No newline at end of file From 352b73653bae88413c0cb74e1f7ea34414ebb04e Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Tue, 10 Jul 2018 18:31:21 -0300 Subject: [PATCH 041/116] Add guess parameters to SQL database --- qdev_wrappers/fitting/AnalysisToSQL.py | 51 ++++++++++++-------------- qdev_wrappers/fitting/Fitter.py | 33 +++++++++-------- 2 files changed, 41 insertions(+), 43 deletions(-) diff --git a/qdev_wrappers/fitting/AnalysisToSQL.py b/qdev_wrappers/fitting/AnalysisToSQL.py index c8256026..649fc2bd 100644 --- a/qdev_wrappers/fitting/AnalysisToSQL.py +++ b/qdev_wrappers/fitting/AnalysisToSQL.py @@ -33,7 +33,7 @@ def make_table(tablename, cursor): def fit_to_SQL(fit): #it would be an improvement if it were able to get the fitclass from the fit information - fitclass_pckl = fit['estimator']['dill'] + fitclass_pckl = fit['inferred_from']['estimator']['dill'] fitclass = dill.loads(fitclass_pckl) est_values = fit['estimate']['values'] @@ -41,44 +41,41 @@ def fit_to_SQL(fit): #it would be an improvement if it were able to get the f est_label = fit['estimate']['label'] est_unit = fit['estimate']['unit'] param_values = fit['estimate']['parameters'] - - dim = fit['inferred_from']['dimensions'] - - if dim == 1: - param_units = [fit['parameters'][param]['unit'] for param in list(fitclass.p_labels)] - elif dim == 2: - setpoints = [key for key in fit.keys()] - param_units = [fit[setpoints[0]]['parameters'][param]['unit'] for param in list(fitclass.p_labels)] - + param_units = fit['parameter units'] + data_length = len(est_values) tablename = 'analysis_{}_{}'.format(fit['inferred_from']['run_id'], fitclass.name) - + # make SQL table columns list table_columns = [est_name] for parameter in fitclass.p_labels: table_columns.append(parameter) + # reformat parameters as a list of table rows + param_list = [] + for i in range(data_length): + params = [] + for parameter in fitclass.p_labels: + params.append(param_values[parameter][i]) + param_list.append(params) + + # make list of table row contents table_rows = [] - if dim == 1: - for estimate, params in zip(est_values, param_values): - id_nr = est_values.index(estimate) + 1 - row = (id_nr, estimate, *params) - table_rows.append(row) - elif dim == 2: - for estimate, parameters in zip(est_values, param_values): - id_nr = est_values.index(estimate) + 1 - row = (id_nr, estimate, *parameters) - table_rows.append(row) + for estimate, parameters, index in zip(est_values, param_list, range(data_length)): + id_nr = index + 1 + row = (id_nr, estimate, *parameters) + table_rows.append(row) data_run_id = fit['inferred_from']['run_id'] exp_id = fit['inferred_from']['exp_id'] predicts = est_name.strip("estimate").strip("_") - estimator = "{}, {}".format(fit['estimator']['method'], fit['estimator']['type']) - analysis = fit['estimator']['function used'] - dill_obj = fit['estimator']['dill'] + estimator = "{}, {}".format(fit['inferred_from']['estimator']['method'], fit['inferred_from']['estimator']['type']) + analysis = str(fit['inferred_from']['estimator']['function used']) + dill_obj = fit['inferred_from']['estimator']['dill'] parameters = fitclass.p_labels + start_params = str(fit['start_params']) param_labels = fitclass.p_names @@ -89,7 +86,7 @@ def fit_to_SQL(fit): #it would be an improvement if it were able to get the f if not is_table('analyses', cur): cur.execute('''CREATE TABLE analyses (data_run_id INTEGER, exp_id INTEGER, run_id INTEGER, analysis_table_name TEXT, - predicts TEXT, estimator TEXT, function TEXT, dill TEXT)''') + predicts TEXT, estimator TEXT, function TEXT, start_parameters TEXT, dill TEXT)''') @@ -108,14 +105,14 @@ def fit_to_SQL(fit): #it would be an improvement if it were able to get the f - sql_analyses = 'INSERT INTO analyses VALUES (?,?,?,?,?,?,?,?)' + sql_analyses = 'INSERT INTO analyses VALUES (?,?,?,?,?,?,?,?,?)' run_id = 1 max_id = cur.execute('SELECT MAX(run_id) FROM layouts').fetchone()[0] if max_id != None: run_id += max_id - cur.execute(sql_analyses, (data_run_id, exp_id, run_id, table, predicts, estimator, analysis, dill_obj)) + cur.execute(sql_analyses, (data_run_id, exp_id, run_id, table, predicts, estimator, analysis, start_params, dill_obj)) diff --git a/qdev_wrappers/fitting/Fitter.py b/qdev_wrappers/fitting/Fitter.py index 8b729e45..ff499cb3 100644 --- a/qdev_wrappers/fitting/Fitter.py +++ b/qdev_wrappers/fitting/Fitter.py @@ -17,13 +17,13 @@ def fit_data(data, fitclass, fun_inputs, fun_output, setpoint_params=None, p0=No if fun_dim == 1 and num_inputs == 1 and num_setpoints == 0: - fitter = Fitter_1D(data, fitclass, fun_output) + fitter = Fitter1D(data, fitclass, fun_output) elif fun_dim == 1 and num_inputs == 1 and num_setpoints == 1: - fitter.Fitter_2Ddata_1Dfunction(data, fitclass, fun_output) #what does the fitter actually need?? - else: - print('The specified function has ') + fitter =Fitter_2Ddata_1Dfunction(data, fitclass, fun_output) #what does the fitter actually need?? - fitter.find_fit(data, fitclass, fun_inputs, fun_output, setpoint_params, p0, **kwargs) + fit = fitter.find_fit(data, fitclass, fun_inputs, fun_output, setpoint_params, p0, **kwargs) + + return fit class Fitter1D: @@ -48,9 +48,10 @@ def find_fit(self, data, fitclass, fun_inputs, fun_output, setpoint_params=None, self.check_input_matches_fitclass(fitclass, fun_inputs, fun_output, setpoint_params, p0) data_dict, label_dict, name_dict, unit_dict = self.organize_data(data, fun_inputs, fun_output, setpoint_params) - param_units = self.find_parameter_units(fitclass, unit_dict) - fit = self.perform_fit(data_dict, data, fitclass, setpoint_params, p0, param_units, **kwargs) + fit = self.perform_fit(data_dict, data, fitclass, setpoint_params, p0, **kwargs) + + fit['parameter units']= self.find_parameter_units(fitclass, unit_dict) fit['estimate'] = self.estimate_function_values(fitclass, data, data_dict) @@ -152,20 +153,21 @@ def find_start_parameters(self, fitclass, p0, data_dict): def find_parameter_units(self, fitclass, unit_dict): unit_templates = fitclass.p_units - param_units = [] + unit_labels = fitclass.p_labels + param_units = {} - for template in unit_templates: + for template, label in zip(unit_templates, unit_labels): template = list(template) for i in range(len(template)): for variable in unit_dict.keys(): if template[i] == variable: template[i] = unit_dict[variable] unit = "".join(template) - param_units.append(unit) + param_units[label] = unit return param_units - def perform_fit(self, data_dict, data, fitclass, setpoints, p0, param_units, **kwargs): + def perform_fit(self, data_dict, data, fitclass, setpoints, p0, **kwargs): fit = {} fit['parameters'] = {} @@ -180,7 +182,6 @@ def perform_fit(self, data_dict, data, fitclass, setpoints, p0, param_units, **k for parameter in fitclass.p_labels: fit['parameters'][parameter] = {'value': popt[fitclass.p_labels.index(parameter)]} fit['parameters'][parameter]['cov'] = pcov[fitclass.p_labels.index(parameter)] - fit['parameters'][parameter]['unit'] = param_units[fitclass.p_labels.index(parameter)] fit['start_params'][parameter] = p_guess[fitclass.p_labels.index(parameter)] # make an dictionary containing arrays of each parameter value with same dimension as data (for estimate) @@ -218,7 +219,7 @@ def estimate_function_values(self, fitclass, data, data_dict): class Fitter_2Ddata_1Dfunction(Fitter1D): - def perform_fit(self, data_dict, data, fitclass, setpoints, p0, param_units, **kwargs): + def perform_fit(self, data_dict, data, fitclass, setpoints, p0, **kwargs): #Retrieve data xarray = data_dict[self.input_vars[0]] @@ -246,6 +247,7 @@ def perform_fit(self, data_dict, data, fitclass, setpoints, p0, param_units, **k #perform 1D fit for cross section at each setpoint and save to fit dictionary fit = {} + fit['start_params'] = {} for set_value, xdata_1d, ydata_1d in zip(unique_setpoints, xdata, ydata): @@ -256,13 +258,12 @@ def perform_fit(self, data_dict, data, fitclass, setpoints, p0, param_units, **k fit[set_value] = {} fit[set_value]['parameters'] = {} - fit[set_value]['start_params'] = {} + fit['start_params'][set_value] = {} for parameter in fitclass.p_labels: # parameters currently missing units, use fitclass.p_units fit[set_value]['parameters'][parameter] = {'value': popt[fitclass.p_labels.index(parameter)]} fit[set_value]['parameters'][parameter]['cov'] = pcov[fitclass.p_labels.index(parameter)] - fit[set_value]['parameters'][parameter]['unit'] = param_units[fitclass.p_labels.index(parameter)] - fit[set_value]['start_params'][parameter] = p_guess[fitclass.p_labels.index(parameter)] + fit['start_params'][set_value][parameter] = p_guess[fitclass.p_labels.index(parameter)] From 2d798f3ce5afc2b434a0fc39d617470e128efaf0 Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Tue, 10 Jul 2018 18:46:28 -0300 Subject: [PATCH 042/116] Add dill of fitter to SQL (previously only contained fitclass --- qdev_wrappers/fitting/AnalysisToSQL.py | 4 ++-- qdev_wrappers/fitting/Fitter.py | 8 ++++++-- 2 files changed, 8 insertions(+), 4 deletions(-) diff --git a/qdev_wrappers/fitting/AnalysisToSQL.py b/qdev_wrappers/fitting/AnalysisToSQL.py index 649fc2bd..b81014b2 100644 --- a/qdev_wrappers/fitting/AnalysisToSQL.py +++ b/qdev_wrappers/fitting/AnalysisToSQL.py @@ -33,7 +33,7 @@ def make_table(tablename, cursor): def fit_to_SQL(fit): #it would be an improvement if it were able to get the fitclass from the fit information - fitclass_pckl = fit['inferred_from']['estimator']['dill'] + fitclass_pckl = fit['inferred_from']['estimator']['dill']['fitclass'] fitclass = dill.loads(fitclass_pckl) est_values = fit['estimate']['values'] @@ -73,7 +73,7 @@ def fit_to_SQL(fit): #it would be an improvement if it were able to get the f predicts = est_name.strip("estimate").strip("_") estimator = "{}, {}".format(fit['inferred_from']['estimator']['method'], fit['inferred_from']['estimator']['type']) analysis = str(fit['inferred_from']['estimator']['function used']) - dill_obj = fit['inferred_from']['estimator']['dill'] + dill_obj = str(fit['inferred_from']['estimator']['dill']) parameters = fitclass.p_labels start_params = str(fit['start_params']) param_labels = fitclass.p_names diff --git a/qdev_wrappers/fitting/Fitter.py b/qdev_wrappers/fitting/Fitter.py index ff499cb3..29f359fd 100644 --- a/qdev_wrappers/fitting/Fitter.py +++ b/qdev_wrappers/fitting/Fitter.py @@ -23,6 +23,11 @@ def fit_data(data, fitclass, fun_inputs, fun_output, setpoint_params=None, p0=No fit = fitter.find_fit(data, fitclass, fun_inputs, fun_output, setpoint_params, p0, **kwargs) + dill_fitclass = dill.dumps(fitclass) + dill_fitter = dill.dumps(fitter) + + fit['inferred_from']['estimator']['dill'] = {'fitclass' : dill_fitclass, 'fitter' : dill_fitter} + return fit class Fitter1D: @@ -55,7 +60,6 @@ def find_fit(self, data, fitclass, fun_inputs, fun_output, setpoint_params=None, fit['estimate'] = self.estimate_function_values(fitclass, data, data_dict) - dill_obj = dill.dumps(fitclass) fit['inferred_from'] = {'inputs': fun_inputs, 'output': fun_output, 'setpoints': setpoint_params, @@ -66,7 +70,7 @@ def find_fit(self, data, fitclass, fun_inputs, fun_output, setpoint_params=None, 'estimator' : {'method': 'Least squared fit', 'type': fitclass.name, 'function used': str(fitclass.fun_np), - 'dill': dill_obj} + } } return fit From f79b2d06f080211131eae4f9f43d592e7f617060 Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Tue, 17 Jul 2018 15:39:04 -0300 Subject: [PATCH 043/116] Throw error if count in is_table does not return 1 or 0. --- qdev_wrappers/fitting/AnalysisToSQL.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/qdev_wrappers/fitting/AnalysisToSQL.py b/qdev_wrappers/fitting/AnalysisToSQL.py index b81014b2..63aa47d6 100644 --- a/qdev_wrappers/fitting/AnalysisToSQL.py +++ b/qdev_wrappers/fitting/AnalysisToSQL.py @@ -14,9 +14,13 @@ def is_table(tablename, cursor): # Checks if table already exists. Assumes data if count == 0: return False - if count == 1: + elif count == 1: return True + else: + raise RuntimeError('''Attempt to check existing table names failed. Count of tables with + name {} returned {} instead of 1 or 0.'''.format(tablename, count)) + def make_table(tablename, cursor): name = tablename From cc9375a9b28eab417c8cc289fad8dee60341f368 Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Tue, 17 Jul 2018 15:42:45 -0300 Subject: [PATCH 044/116] Fix while... else statement --- qdev_wrappers/fitting/AnalysisToSQL.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/qdev_wrappers/fitting/AnalysisToSQL.py b/qdev_wrappers/fitting/AnalysisToSQL.py index 63aa47d6..301bfcb2 100644 --- a/qdev_wrappers/fitting/AnalysisToSQL.py +++ b/qdev_wrappers/fitting/AnalysisToSQL.py @@ -29,8 +29,8 @@ def make_table(tablename, cursor): while is_table(name, cursor): n += 1 name = "{}_{}".format(tablename, n) - else: - cursor.execute('CREATE TABLE {} (id INTEGER)'.format(name)) + + cursor.execute('CREATE TABLE {} (id INTEGER)'.format(name)) return name From f7d51e1783a6937f3eba96acd43f0a7efe6b92a9 Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Tue, 17 Jul 2018 17:45:35 -0300 Subject: [PATCH 045/116] Organize document and add comments for ease of reading --- qdev_wrappers/fitting/AnalysisToSQL.py | 107 +++++++++++++------------ 1 file changed, 57 insertions(+), 50 deletions(-) diff --git a/qdev_wrappers/fitting/AnalysisToSQL.py b/qdev_wrappers/fitting/AnalysisToSQL.py index 301bfcb2..499e78c5 100644 --- a/qdev_wrappers/fitting/AnalysisToSQL.py +++ b/qdev_wrappers/fitting/AnalysisToSQL.py @@ -5,7 +5,11 @@ from qdev_wrappers.fitting.Fitclasses import T1, T2 -def is_table(tablename, cursor): # Checks if table already exists. Assumes database connection and cursor already established +def is_table(tablename, cursor): + + """Takes table name (string), and a connection to an SQL database. Returns True if a table with that name + exists, and False if it does not. Throws an error if it finds multiple tables with the same name, or if the + sql count function returns something unexpected.""" table_count = "SELECT count(*) FROM sqlite_master WHERE type = 'table' AND name='{}'".format(tablename) execute = cursor.execute(table_count) @@ -23,6 +27,11 @@ def is_table(tablename, cursor): # Checks if table already exists. Assumes data def make_table(tablename, cursor): + + """Takes suggested table name, and a connection to an SQL database. If the name is already in use, it goes + through numbered iterations of the name until it finds an unused table name. Otherwise, it leaves the name as + suggested. It creates a table with the name in the database, and returns the name.""" + name = tablename n = 0 @@ -31,11 +40,17 @@ def make_table(tablename, cursor): name = "{}_{}".format(tablename, n) cursor.execute('CREATE TABLE {} (id INTEGER)'.format(name)) - return name -def fit_to_SQL(fit): #it would be an improvement if it were able to get the fitclass from the fit information +def fit_to_sql(fit): + + """Takes the fit (dictionary output by Fitter) and stores the information to an SQL database. + In the first part of this function, the data from the fit dictionary is retrieved and organized + for storage in the SQL file. In the second part, a connection to the SQL database is established a + series of SQL commands are executed to store the data.""" + + # Part1 : Information from the fit dictionary is retrieved and organized fitclass_pckl = fit['inferred_from']['estimator']['dill']['fitclass'] fitclass = dill.loads(fitclass_pckl) @@ -44,57 +59,52 @@ def fit_to_SQL(fit): #it would be an improvement if it were able to get the f est_name = '{}_estimate'.format(fit['estimate']['name']) est_label = fit['estimate']['label'] est_unit = fit['estimate']['unit'] + param_values = fit['estimate']['parameters'] param_units = fit['parameter units'] - data_length = len(est_values) - tablename = 'analysis_{}_{}'.format(fit['inferred_from']['run_id'], fitclass.name) - - # make SQL table columns list - table_columns = [est_name] - for parameter in fitclass.p_labels: - table_columns.append(parameter) + data_run_id = fit['inferred_from']['run_id'] + exp_id = fit['inferred_from']['exp_id'] + predicts = est_name.strip("estimate").strip("_") + estimator = "{}, {}".format(fit['inferred_from']['estimator']['method'], fit['inferred_from']['estimator']['type']) + analysis = str(fit['inferred_from']['estimator']['function used']) + dill_obj = str(fit['inferred_from']['estimator']['dill']) + start_params = str(fit['start_params']) + param_labels = fitclass.p_names - # reformat parameters as a list of table rows + # reorganize parameters to fit into rows, i.e. [a1, a2, a3...] and [b1, b2, b3...] --> [ [a1, b1], [a2, b2]...] param_list = [] + data_length = len(est_values) for i in range(data_length): params = [] for parameter in fitclass.p_labels: params.append(param_values[parameter][i]) param_list.append(params) - # make list of table row contents + # make list of table row contents, i.e. [(row1), (row2), (row3)...], row1 = (id_nr1, estimate1, a1, b1...) table_rows = [] for estimate, parameters, index in zip(est_values, param_list, range(data_length)): id_nr = index + 1 row = (id_nr, estimate, *parameters) table_rows.append(row) + # make list of column titles (estimated output, param1, param2...) + table_columns = [est_name] + for parameter in fitclass.p_labels: + table_columns.append(parameter) - data_run_id = fit['inferred_from']['run_id'] - exp_id = fit['inferred_from']['exp_id'] - predicts = est_name.strip("estimate").strip("_") - estimator = "{}, {}".format(fit['inferred_from']['estimator']['method'], fit['inferred_from']['estimator']['type']) - analysis = str(fit['inferred_from']['estimator']['function used']) - dill_obj = str(fit['inferred_from']['estimator']['dill']) - parameters = fitclass.p_labels - start_params = str(fit['start_params']) - param_labels = fitclass.p_names + # Part2 : A connection to the SQL database is created and the fit data is saved to the database - conn = sqlite3.connect('experiments.db') # should this go in a separate analysis database, or just go in experiments.db? + #create a connection to the SQL database + file = 'experiments.db' + #TODO: instead of being named explicitly, this should come from the config + conn = sqlite3.connect(file) cur = conn.cursor() - #Alternative: have a separate function that sets up the general analysis database with tables, so that this doesn't have to be here - if not is_table('analyses', cur): - cur.execute('''CREATE TABLE analyses - (data_run_id INTEGER, exp_id INTEGER, run_id INTEGER, analysis_table_name TEXT, - predicts TEXT, estimator TEXT, function TEXT, start_parameters TEXT, dill TEXT)''') - - - - + # Create table for fit parameter and predicted output values, store data + tablename = 'analysis_{}_{}'.format(fit['inferred_from']['run_id'], fitclass.name) table = make_table(tablename, cur) for column in table_columns: @@ -102,50 +112,47 @@ def fit_to_SQL(fit): #it would be an improvement if it were able to get the f num_cols = len(table_rows[0]) placeholder = ('?,' * num_cols).strip(',') - cur.executemany('INSERT INTO {} VALUES ({})'.format(table, placeholder), table_rows) - - - - sql_analyses = 'INSERT INTO analyses VALUES (?,?,?,?,?,?,?,?,?)' - + # Create 'analyses' table if it does not already exist, store info about analysis in 'analyses' + if not is_table('analyses', cur): + cur.execute('''CREATE TABLE analyses + (data_run_id INTEGER, exp_id INTEGER, run_id INTEGER, analysis_table_name TEXT, + predicts TEXT, estimator TEXT, function TEXT, start_parameters TEXT, dill TEXT)''') run_id = 1 max_id = cur.execute('SELECT MAX(run_id) FROM layouts').fetchone()[0] - if max_id != None: + if max_id is not None: run_id += max_id - cur.execute(sql_analyses, (data_run_id, exp_id, run_id, table, predicts, estimator, analysis, start_params, dill_obj)) - - - + sql_analyses = 'INSERT INTO analyses VALUES (?,?,?,?,?,?,?,?,?)' + cur.execute(sql_analyses, + (data_run_id, exp_id, run_id, table, predicts, estimator, analysis, start_params, dill_obj)) - sql_layout = 'INSERT INTO layouts VALUES (?,?,?,?,?,?)' + # Store unit and label info in 'layouts' table layout_id = 1 max_id = cur.execute('SELECT MAX(layout_id) FROM layouts').fetchone()[0] - if max_id != None: + if max_id is not None: layout_id += max_id layout_rows = [] - for parameter, label, unit in zip(parameters, param_labels, param_units): + for parameter, label, unit in zip(fitclass.p_labels, param_labels, param_units): row = (layout_id, run_id, parameter, label, unit, "inferred_from") layout_rows.append(row) layout_id += 1 layout_rows.append((layout_id, run_id, est_name, est_label, est_unit, "inferred_from")) - cur.executemany(sql_layout, (layout_rows)) - - + sql_layout = 'INSERT INTO layouts VALUES (?,?,?,?,?,?)' + cur.executemany(sql_layout, layout_rows) + # Store summary in 'runs' SQL table, with run type 'analysis' instead of 'result' sql_runs = 'INSERT INTO runs VALUES (?,?,?,?,?,?,?,?,?,?)' cur.execute(sql_runs, (run_id, exp_id, 'analysis', table, "", "", "", 1, "", "")) - - + # save changes to SQL database and close connection conn.commit() conn.close() From 2072321427a1e564666299c1d97aac752481f612 Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Tue, 17 Jul 2018 18:32:45 -0300 Subject: [PATCH 046/116] Remove hard coded database name --- qdev_wrappers/fitting/AnalysisToSQL.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/qdev_wrappers/fitting/AnalysisToSQL.py b/qdev_wrappers/fitting/AnalysisToSQL.py index 499e78c5..f1a5885a 100644 --- a/qdev_wrappers/fitting/AnalysisToSQL.py +++ b/qdev_wrappers/fitting/AnalysisToSQL.py @@ -43,7 +43,7 @@ def make_table(tablename, cursor): return name -def fit_to_sql(fit): +def fit_to_sql(fit, database=None): """Takes the fit (dictionary output by Fitter) and stores the information to an SQL database. In the first part of this function, the data from the fit dictionary is retrieved and organized @@ -98,8 +98,11 @@ def fit_to_sql(fit): # Part2 : A connection to the SQL database is created and the fit data is saved to the database #create a connection to the SQL database - file = 'experiments.db' - #TODO: instead of being named explicitly, this should come from the config + if database is not None: + file = database + else: + raise NotImplementedError('Retrieving database from qc.config not implemented') + #TODO: make it get database from the config conn = sqlite3.connect(file) cur = conn.cursor() From 7c918ac3e7f7643921a20c8ccfce0e00e8499731 Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Tue, 17 Jul 2018 18:54:53 -0300 Subject: [PATCH 047/116] Add clarifying comment about the new database argument --- qdev_wrappers/fitting/AnalysisToSQL.py | 17 +++++++++++++---- 1 file changed, 13 insertions(+), 4 deletions(-) diff --git a/qdev_wrappers/fitting/AnalysisToSQL.py b/qdev_wrappers/fitting/AnalysisToSQL.py index f1a5885a..97b7f803 100644 --- a/qdev_wrappers/fitting/AnalysisToSQL.py +++ b/qdev_wrappers/fitting/AnalysisToSQL.py @@ -45,10 +45,19 @@ def make_table(tablename, cursor): def fit_to_sql(fit, database=None): - """Takes the fit (dictionary output by Fitter) and stores the information to an SQL database. - In the first part of this function, the data from the fit dictionary is retrieved and organized + """Takes the fit (dictionary output by Fitter) and an optional database. If no database name/location + is given, it gets the database from qc.config. Stores the information from the fit dictionary in the SQL + database. In the first part of this function, the data from the fit dictionary is retrieved and organized for storage in the SQL file. In the second part, a connection to the SQL database is established a - series of SQL commands are executed to store the data.""" + series of SQL commands are executed to store the data. + + Note: It does not accept file locations of the format './file.db'. It seems you either need to specify + the full location of the file (e.g. /Users/Username/Desktop/file.db), or have the file in the same folder + you are running from and just specify the file name. """ + + # Note: the function uses SQL commands to establish the connection. It does not understand './file.db' as a + # location. If the database in question is in the same folder that you are running from, it just takes the + # file name, 'file.db'. Otherwise it seems to want a full""" # Part1 : Information from the fit dictionary is retrieved and organized @@ -102,7 +111,7 @@ def fit_to_sql(fit, database=None): file = database else: raise NotImplementedError('Retrieving database from qc.config not implemented') - #TODO: make it get database from the config + #TODO: make it get database from the config. conn = sqlite3.connect(file) cur = conn.cursor() From ee64f56d3a105d26966609f69bbb64c191301d2b Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Tue, 17 Jul 2018 19:45:38 -0300 Subject: [PATCH 048/116] Define functions with NotImplementedError in base class, add comments --- qdev_wrappers/fitting/Converter.py | 69 ++++++++++++++++++++++++------ 1 file changed, 56 insertions(+), 13 deletions(-) diff --git a/qdev_wrappers/fitting/Converter.py b/qdev_wrappers/fitting/Converter.py index 70b21e06..6dce7f65 100644 --- a/qdev_wrappers/fitting/Converter.py +++ b/qdev_wrappers/fitting/Converter.py @@ -18,14 +18,58 @@ def data_to_dict(id, samplefolder=None, datatype='SQL'): class DataConverter(): + + """This is the base class for converters that convert data stored in files into a python dictionary.""" + + def find_data(self, id, samplefolder): + + """ Takes data run id as argument, as well as a samplefolder, which can be set + to None if the run is part of the current configuration. Retrieves the data from the file + it is stored in, based on either the sample folder or qc.config. Returns data. """ + + raise NotImplementedError('find_data not implemented in the base class.') + + def make_data_dictionary(self, data): + + """ Takes data (as returned by find_data). Organizes data into as a dictionary. Each key + is one of the variables from the data, and its corresponding value is a dictionary in + the format {'name': str, 'label': str, 'unit': str, 'data': np.array }. Returns data dict + containing name, label, unit and data array for each variable in the dataset. """ + + raise NotImplementedError('make_data_dictionary not implemented in the base class.') + + def find_experiment(self, run_id): + + """ Takes run_id, and returns corresponding experiment id """ + + raise NotImplementedError('find_experiment not implemented in the base class.') + + def find_dependencies(self, run_id, data): + + """ Takes run_id and data dict. Finds dependencies of the variables, and returns them as a + dependencies dict. E.g. if A and B were measured, and they both depend on C and D: + dependencies = {A: [C, D], B: [C, D]} """ + + raise NotImplementedError('find_dependencies not implemented in the base class.') + + def find_variables(self, data): + + """ Takes data dict. Returns list of all variables contained in the data dict.""" + + raise NotImplementedError('find_variables not implemented in the base class.') - def convert(self, id, samplefolder = None): + def convert(self, id, samplefolder=None): + + """ Takes run_id and optionally a samplefolder name. Sample folder name should only be + necessary if the run in question isn't part of the current experimental configuration. + Returns a dictionary containing all the data from the specified run as numpy arrays, + plus relevant metadata (experiment id, run id, dependencies, names, labels, and units). """ data = self.find_data(id, samplefolder) data_dict = self.make_data_dictionary(data) exp_id = self.find_experiment(id) - dependencies = self.find_dependencies(id, data) #SQL needs only id number to find dependencies, Legacy needs only data + dependencies = self.find_dependencies(id, data) # SQL needs only id, Legacy needs only data all_variables = self.find_variables(data) data_dict['exp_id'] = exp_id @@ -34,25 +78,26 @@ def convert(self, id, samplefolder = None): data_dict['variables'] = all_variables return data_dict - class SQL_Converter(DataConverter): - def find_data(self, id, samplefolder): + def find_data(self, id, samplefolder): - '''sample folder does nothing except act as placeholder. + """sample folder does nothing except act as placeholder. This function currently proceeds under the assumption that the experiment is set up, and doesn't check anything to confirm that. Also, I think this doesn't return any error if it can't find - the dataset with that id number.''' + the dataset with that id number.""" - data = get_data_by_id(id) + data = get_data_by_id(id) + + if len(data) == 0: + raise RuntimeError('No data found for run id {}'.format(id)) return data - - def find_dependencies(self, id, data): #SQL converter only needs id, not data - conn = connect('experiments.db') #the name of the file to access can't be set manually right now + def find_dependencies(self, id, data): # SQL converter only needs id, not data + conn = connect('experiments.db') # the name of the file to access can't be set manually right now cur = conn.cursor() names = {} @@ -73,7 +118,6 @@ def find_dependencies(self, id, data): #SQL converter only needs id, not da return dependencies - def find_variables(self, data): all_variables = [] @@ -95,7 +139,6 @@ def find_experiment(self, id): return exp_id - def make_data_dictionary(self, data): data_dict = {} @@ -106,8 +149,8 @@ def make_data_dictionary(self, data): name = variable['name'] if name in data_dict.keys(): - print(name) if not np.array_equal(variable['data'], data_dict[name]['data']): + print(name) raise RuntimeError('Variables with identical names contain non-identical data arrays!') data_dict[name] = variable From a17cb5885f358b11a8f8404f090e392181337bd3 Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Tue, 17 Jul 2018 20:06:42 -0300 Subject: [PATCH 049/116] Throw error if SQL converter find_data method does not find data --- qdev_wrappers/fitting/Converter.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/qdev_wrappers/fitting/Converter.py b/qdev_wrappers/fitting/Converter.py index 6dce7f65..cbb67302 100644 --- a/qdev_wrappers/fitting/Converter.py +++ b/qdev_wrappers/fitting/Converter.py @@ -84,10 +84,8 @@ class SQL_Converter(DataConverter): def find_data(self, id, samplefolder): - """sample folder does nothing except act as placeholder. - This function currently proceeds under the assumption that the experiment is set up, and doesn't - check anything to confirm that. Also, I think this doesn't return any error if it can't find - the dataset with that id number.""" + """This isn't actually set up to use the samplefolder if one is given - it can only use + the qc.config via the get_data_by_id function""" data = get_data_by_id(id) From 676a82193e8bb9688380a02062a0238574d0cdd6 Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Tue, 17 Jul 2018 20:14:29 -0300 Subject: [PATCH 050/116] Make it possible to get database from qc.config --- qdev_wrappers/fitting/AnalysisToSQL.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/qdev_wrappers/fitting/AnalysisToSQL.py b/qdev_wrappers/fitting/AnalysisToSQL.py index 97b7f803..1f458713 100644 --- a/qdev_wrappers/fitting/AnalysisToSQL.py +++ b/qdev_wrappers/fitting/AnalysisToSQL.py @@ -2,6 +2,8 @@ import sqlite3 import dill +import qcodes as qc +from os.path import expanduser from qdev_wrappers.fitting.Fitclasses import T1, T2 @@ -110,8 +112,7 @@ def fit_to_sql(fit, database=None): if database is not None: file = database else: - raise NotImplementedError('Retrieving database from qc.config not implemented') - #TODO: make it get database from the config. + file = expanduser(qc.config['core']['db_location']) conn = sqlite3.connect(file) cur = conn.cursor() From f55d723e77cdb82f11e6de57ec7f2a0f8c32d4fa Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Tue, 17 Jul 2018 20:22:23 -0300 Subject: [PATCH 051/116] Remove hard coded database name, replace with qc.config retrieval of database --- qdev_wrappers/fitting/Converter.py | 17 +++++++++++++---- 1 file changed, 13 insertions(+), 4 deletions(-) diff --git a/qdev_wrappers/fitting/Converter.py b/qdev_wrappers/fitting/Converter.py index cbb67302..a2d757cb 100644 --- a/qdev_wrappers/fitting/Converter.py +++ b/qdev_wrappers/fitting/Converter.py @@ -1,6 +1,6 @@ import qcodes as qc import numpy as np -from os.path import sep +from os.path import sep, expanduser from qcodes.dataset.data_export import get_data_by_id from qcodes.dataset.sqlite_base import connect from qdev_wrappers.file_setup import CURRENT_EXPERIMENT @@ -85,7 +85,7 @@ class SQL_Converter(DataConverter): def find_data(self, id, samplefolder): """This isn't actually set up to use the samplefolder if one is given - it can only use - the qc.config via the get_data_by_id function""" + the qc.config via the get_data_by_id function. """ data = get_data_by_id(id) @@ -95,7 +95,12 @@ def find_data(self, id, samplefolder): return data def find_dependencies(self, id, data): # SQL converter only needs id, not data - conn = connect('experiments.db') # the name of the file to access can't be set manually right now + + """Again, this only works if the database is the one indicated by qc.config. It is not + possible to set another database manually at the moment.""" + + file = expanduser(qc.config['core']['db_location']) + conn = connect(file) cur = conn.cursor() names = {} @@ -129,7 +134,11 @@ def find_variables(self, data): def find_experiment(self, id): - conn = connect('experiments.db') # the name of the file to access can't be set manually right now + """Again, this only works if the database is the one indicated by qc.config. It is not + possible to set another database manually at the moment.""" + + file = expanduser(qc.config['core']['db_location']) + conn = connect(file) cur = conn.cursor() execute = cur.execute('SELECT exp_id FROM runs WHERE run_id is {}'.format(id)) From d93753b280f7c0fd4daf925e794908ef83d2dc59 Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Tue, 17 Jul 2018 20:43:17 -0300 Subject: [PATCH 052/116] Add manual specification of sample folder to find_experiment and find_dependencies --- qdev_wrappers/fitting/Converter.py | 46 +++++++++++++++++------------- 1 file changed, 26 insertions(+), 20 deletions(-) diff --git a/qdev_wrappers/fitting/Converter.py b/qdev_wrappers/fitting/Converter.py index a2d757cb..0f41b2a3 100644 --- a/qdev_wrappers/fitting/Converter.py +++ b/qdev_wrappers/fitting/Converter.py @@ -38,13 +38,13 @@ def make_data_dictionary(self, data): raise NotImplementedError('make_data_dictionary not implemented in the base class.') - def find_experiment(self, run_id): + def find_experiment(self, run_id, samplefolder): """ Takes run_id, and returns corresponding experiment id """ raise NotImplementedError('find_experiment not implemented in the base class.') - def find_dependencies(self, run_id, data): + def find_dependencies(self, run_id, data, samplefolder): """ Takes run_id and data dict. Finds dependencies of the variables, and returns them as a dependencies dict. E.g. if A and B were measured, and they both depend on C and D: @@ -68,8 +68,8 @@ def convert(self, id, samplefolder=None): data = self.find_data(id, samplefolder) data_dict = self.make_data_dictionary(data) - exp_id = self.find_experiment(id) - dependencies = self.find_dependencies(id, data) # SQL needs only id, Legacy needs only data + exp_id = self.find_experiment(id, samplefolder) + dependencies = self.find_dependencies(id, data, samplefolder) # SQL needs only id, Legacy needs only data all_variables = self.find_variables(data) data_dict['exp_id'] = exp_id @@ -84,22 +84,28 @@ class SQL_Converter(DataConverter): def find_data(self, id, samplefolder): - """This isn't actually set up to use the samplefolder if one is given - it can only use - the qc.config via the get_data_by_id function. """ - - data = get_data_by_id(id) + """ This isn't actually set up to use the samplefolder if one is given - it can only use + the qc.config via the get_data_by_id function. If this were set up to retrieve the data as + get_data_by_id does, but with get_DB_location() returning expanduser(sampefolder) instead + of expanduser(qcodes.config["core"]["db_location"]), then I think everything would work + for manual specification of database location """ + + if samplefolder is None: + data = get_data_by_id(id) + else: + raise NotImplementedError('Manual specification of database location not implemented for SQL') if len(data) == 0: raise RuntimeError('No data found for run id {}'.format(id)) return data - def find_dependencies(self, id, data): # SQL converter only needs id, not data - - """Again, this only works if the database is the one indicated by qc.config. It is not - possible to set another database manually at the moment.""" + def find_dependencies(self, id, data, samplefolder): # SQL converter only needs id, not data - file = expanduser(qc.config['core']['db_location']) + if samplefolder is None: + file = expanduser(qc.config['core']['db_location']) + else: + file = expanduser(samplefolder) conn = connect(file) cur = conn.cursor() @@ -132,12 +138,12 @@ def find_variables(self, data): return all_variables - def find_experiment(self, id): - - """Again, this only works if the database is the one indicated by qc.config. It is not - possible to set another database manually at the moment.""" + def find_experiment(self, id, samplefolder): - file = expanduser(qc.config['core']['db_location']) + if samplefolder is None: + file = expanduser(qc.config['core']['db_location']) + else: + file = expanduser(samplefolder) conn = connect(file) cur = conn.cursor() @@ -186,7 +192,7 @@ def find_data(self, id, samplefolder): return data - def find_dependencies(self, id, data): #legacy only needs data, not id + def find_dependencies(self, id, data, samplefolder): # legacy only needs data, not id or sample folder dep_vars = [key for key in data.arrays.keys() if "_set" not in key[-4:]] indep_vars = [key for key in data.arrays.keys() if "_set" in key[-4:]] @@ -204,7 +210,7 @@ def find_variables(self, data): all_variables = [variable for variable in data.arrays.keys()] return all_variables - def find_experiment(self, id): + def find_experiment(self, id, samplefolder): # samplefolder only needed in sql converter exp_id = id return exp_id From f7c992bcb9134a50d0c20d5c8d93446f972562cb Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Tue, 17 Jul 2018 21:34:49 -0300 Subject: [PATCH 053/116] Fix bug created by addition of manual samplefolder specification. --- qdev_wrappers/fitting/Converter.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/qdev_wrappers/fitting/Converter.py b/qdev_wrappers/fitting/Converter.py index 0f41b2a3..5d2e1e43 100644 --- a/qdev_wrappers/fitting/Converter.py +++ b/qdev_wrappers/fitting/Converter.py @@ -191,8 +191,7 @@ def find_data(self, id, samplefolder): return data - - def find_dependencies(self, id, data, samplefolder): # legacy only needs data, not id or sample folder + def find_dependencies(self, id, data, samplefolder=None): # legacy only needs data, not id or sample folder dep_vars = [key for key in data.arrays.keys() if "_set" not in key[-4:]] indep_vars = [key for key in data.arrays.keys() if "_set" in key[-4:]] @@ -210,7 +209,7 @@ def find_variables(self, data): all_variables = [variable for variable in data.arrays.keys()] return all_variables - def find_experiment(self, id, samplefolder): # samplefolder only needed in sql converter + def find_experiment(self, id, samplefolder=None): # samplefolder only needed in sql converter exp_id = id return exp_id @@ -228,7 +227,6 @@ def resize_data(self, data1, data2): return np.array(new_data2) - def make_data_dictionary(self, data): data_dict = {} From edfcd3da60e212132c9dca7deb33cf4e5162cf46 Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Tue, 17 Jul 2018 21:46:38 -0300 Subject: [PATCH 054/116] Tidy document up --- qdev_wrappers/fitting/Converter.py | 34 ++++++++++++------------------ 1 file changed, 14 insertions(+), 20 deletions(-) diff --git a/qdev_wrappers/fitting/Converter.py b/qdev_wrappers/fitting/Converter.py index 5d2e1e43..f8c7c92e 100644 --- a/qdev_wrappers/fitting/Converter.py +++ b/qdev_wrappers/fitting/Converter.py @@ -11,13 +11,16 @@ def data_to_dict(id, samplefolder=None, datatype='SQL'): if datatype == 'qcodes_legacy': converter = Legacy_Converter() - if datatype == 'SQL': + elif datatype == 'SQL': converter = SQL_Converter() + else: + raise NotImplementedError('datatype {} is not currently supported'.format(datatype)) + return converter.convert(id, samplefolder) -class DataConverter(): +class DataConverter: """This is the base class for converters that convert data stored in files into a python dictionary.""" @@ -169,7 +172,6 @@ def make_data_dictionary(self, data): data_dict[name] = variable return data_dict - class Legacy_Converter(DataConverter): @@ -178,22 +180,22 @@ def find_data(self, id, samplefolder): str_id = '{0:03d}'.format(id) - if samplefolder==None: + if samplefolder is None: check_experiment_is_initialized() - #check_experiment_is_initialized() doesn't do anything at the moment + # check_experiment_is_initialized() doesn't do anything at the moment path = qc.DataSet.location_provider.fmt.format(counter=str_id) data = qc.load_data(path) else: - path = '{}{}{}'.format(samplefolder,sep,str_id) + path = '{}{}{}'.format(samplefolder, sep, str_id) data = qc.load_data(path) return data def find_dependencies(self, id, data, samplefolder=None): # legacy only needs data, not id or sample folder - dep_vars = [key for key in data.arrays.keys() if "_set" not in key[-4:]] + dep_vars = [key for key in data.arrays.keys() if "_set" not in key[-4:]] indep_vars = [key for key in data.arrays.keys() if "_set" in key[-4:]] dependencies = {} @@ -203,7 +205,6 @@ def find_dependencies(self, id, data, samplefolder=None): # legacy only needs d return dependencies - def find_variables(self, data): all_variables = [variable for variable in data.arrays.keys()] @@ -214,7 +215,6 @@ def find_experiment(self, id, samplefolder=None): # samplefolder only needed in exp_id = id return exp_id - def resize_data(self, data1, data2): scan_size = len(data1[0]) @@ -241,12 +241,11 @@ def make_data_dictionary(self, data): np_data = qc_data.ndarray data_dict[variable] = {'name': variable, - 'label': label, + 'label': label, 'var_name': name, - 'unit': unit, - 'data': np_data} - - + 'unit': unit, + 'data': np_data} + '''checks if the data set used set points - if so, it reformats the data arrays so that it looks the same as it would look if it were imported from SQL. All data is stored as arrays of the same length, rather than @@ -267,8 +266,6 @@ def make_data_dictionary(self, data): data_dict[indep_vars[0]]['data'] = self.resize_data(data1, data2) if type(data3[0]) != np.ndarray: data_dict[indep_vars[1]]['data'] = self.resize_data(data1, data3) - - '''reformatting nested arrays as a single 1d array, i.e. setpoints = [[1, 1, 1], [2, 2, 2], [3, 3, 3]], data = [[a, b, c], [d, e, f], [g, h, i]] @@ -283,8 +280,5 @@ def make_data_dictionary(self, data): new_data.append(datapoint) data_dict[variable]['data'] = np.array(new_data) - - + return data_dict - - From 79b5f698282ed84b9cdaf2accea89e8015e2d289 Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Tue, 17 Jul 2018 22:02:54 -0300 Subject: [PATCH 055/116] Add 'fun' and 'guess' functions with NotImplementedError in base class --- qdev_wrappers/fitting/Fitclasses.py | 12 ++++++++---- 1 file changed, 8 insertions(+), 4 deletions(-) diff --git a/qdev_wrappers/fitting/Fitclasses.py b/qdev_wrappers/fitting/Fitclasses.py index 718d5a6a..afab08b7 100644 --- a/qdev_wrappers/fitting/Fitclasses.py +++ b/qdev_wrappers/fitting/Fitclasses.py @@ -4,16 +4,21 @@ import scipy.fftpack as fftpack -class Fitclass: +class LeastSquaresFit: def __init__(self): self.name = 'NoName' self.fun_str = 'None' self.p_names = ['none'] self.p_units = ['none'] + def fun(self, x, a, T, c): + raise NotImplementedError('This is not implemented in the base class.') + + def guess(self, x, y): + raise NotImplementedError('This is not implemented in the base class.') -class T1(Fitclass): +class T1(LeastSquaresFit): def __init__(self): self.name = 'T1fit' @@ -41,8 +46,7 @@ def guess(self, x, y): return [a, T, c] - -class T2(Fitclass): +class T2(LeastSquaresFit): def __init__(self): self.name = 'T2fit' From ddb32908d4f7821ffc587671dc50cc93499083ba Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Tue, 17 Jul 2018 22:20:11 -0300 Subject: [PATCH 056/116] Add docstring to Fitclasses --- qdev_wrappers/fitting/Fitclasses.py | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/qdev_wrappers/fitting/Fitclasses.py b/qdev_wrappers/fitting/Fitclasses.py index afab08b7..54502fb8 100644 --- a/qdev_wrappers/fitting/Fitclasses.py +++ b/qdev_wrappers/fitting/Fitclasses.py @@ -5,6 +5,16 @@ class LeastSquaresFit: + + """ This is the base class for the other classes in this file. Each class specifies + a particular mathematical function and information that can be used to perform a + Least Squares Fit to the data. + + Each class holds the mathematical function itself ('fun'), a function for determining + an initial guess for the fit parameters ('guess'), and a list of attributes that set + the function inputs and outputs, the fit parameter labels and names, and the relationship + between the units on the input and output variables and the units on the parameters. """ + def __init__(self): self.name = 'NoName' self.fun_str = 'None' From a12750b005b73171005cb97ffac8e3efc7240c5d Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Wed, 18 Jul 2018 02:18:34 -0300 Subject: [PATCH 057/116] Reorganize Fitter as parent and child classes --- qdev_wrappers/fitting/Fitter.py | 53 +++++++++++++++++++-------------- 1 file changed, 30 insertions(+), 23 deletions(-) diff --git a/qdev_wrappers/fitting/Fitter.py b/qdev_wrappers/fitting/Fitter.py index 29f359fd..498e3cdb 100644 --- a/qdev_wrappers/fitting/Fitter.py +++ b/qdev_wrappers/fitting/Fitter.py @@ -30,7 +30,7 @@ def fit_data(data, fitclass, fun_inputs, fun_output, setpoint_params=None, p0=No return fit -class Fitter1D: +class Fitter: def __init__(self, data, fitclass, output): @@ -171,6 +171,34 @@ def find_parameter_units(self, fitclass, unit_dict): return param_units + def perform_fit(self, data_dict, data, fitclass, setpoints, p0, **kwargs): + + raise NotImplementedError("A generic fitter function for the Fitter base class has yet to be implemented.") + + def estimate_function_values(self, fitclass, data, data_dict): + + estimate = {} + + input_data = data_dict + for variable in self.output_var: + del input_data[variable] + + input_parameters = self.full_parameter_dict + + # Calculate estimated values for output based on function and store in estimate['values'] + estimate['values'] = fitclass.fun(**input_data, **input_parameters) + + # Save estimate + estimate['name'] = '{}_estimate'.format(self.output_dataname[0]) + estimate['label'] = '{} estimate'.format(data[self.output_dataname[0]]['label']) + estimate['unit'] = data[self.output_dataname[0]]['unit'] + estimate['parameters'] = self.full_parameter_dict #not sure this is used for anything + + return estimate + + +class Fitter1D(Fitter): + def perform_fit(self, data_dict, data, fitclass, setpoints, p0, **kwargs): fit = {} @@ -199,29 +227,8 @@ def perform_fit(self, data_dict, data, fitclass, setpoints, p0, **kwargs): return fit - def estimate_function_values(self, fitclass, data, data_dict): - - estimate = {} - - input_data = data_dict - for variable in self.output_var: - del input_data[variable] - - input_parameters = self.full_parameter_dict - - # Calculate estimated values for output based on function and store in estimate['values'] - estimate['values'] = fitclass.fun(**input_data, **input_parameters) - - # Save estimate - estimate['name'] = '{}_estimate'.format(self.output_dataname[0]) - estimate['label'] = '{} estimate'.format(data[self.output_dataname[0]]['label']) - estimate['unit'] = data[self.output_dataname[0]]['unit'] - estimate['parameters'] = self.full_parameter_dict #not sure this is used for anything - - return estimate - -class Fitter_2Ddata_1Dfunction(Fitter1D): +class Fitter_2Ddata_1Dfunction(Fitter): def perform_fit(self, data_dict, data, fitclass, setpoints, p0, **kwargs): From 96f29a7a8b8761fbb16d75a38f0a396994dde94c Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Wed, 18 Jul 2018 02:30:16 -0300 Subject: [PATCH 058/116] Add description to Fitter class --- qdev_wrappers/fitting/Fitter.py | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/qdev_wrappers/fitting/Fitter.py b/qdev_wrappers/fitting/Fitter.py index 498e3cdb..c063dd36 100644 --- a/qdev_wrappers/fitting/Fitter.py +++ b/qdev_wrappers/fitting/Fitter.py @@ -32,10 +32,19 @@ def fit_data(data, fitclass, fun_inputs, fun_output, setpoint_params=None, p0=No class Fitter: + """ The fitter has a method, 'find_fit', which takes the fitclass, data, function inputs, function outputs, + setpoints when relevant, and optionally, guess parameters, and returns a fit dictionary. It does this by + using all the other methods in the Fitter to fit a particular function (as defined in a LeastSquaresFit + class) to the specified data. + + Much of the fitting is generic and does not depend on the number of inputs, outputs or setpoints in the + specified LeastSquaresFit class; however, depending on the dimensionality of the function to be fitted + and the measured data, the function 'perform_fit' varies """ + def __init__(self, data, fitclass, output): self.fitclass = fitclass #'UNNECESSARY??' - self.fun_dim = len(fitclass.fun_vars) + self.fun_dim = len(fitclass.fun_vars) #'UNNECESSARY??' self.data_dim = 'undefined' self.input_vars = [] self.output_var = [] From ec1c358ee9b36f48a17501da1b56d1344f0b87b2 Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Wed, 18 Jul 2018 04:06:46 -0300 Subject: [PATCH 059/116] Update Example notebook to reflect changes --- qdev_wrappers/fitting/Example.ipynb | 2869 +++++++-------------------- 1 file changed, 712 insertions(+), 2157 deletions(-) diff --git a/qdev_wrappers/fitting/Example.ipynb b/qdev_wrappers/fitting/Example.ipynb index 6d8806b0..e29a2a72 100644 --- a/qdev_wrappers/fitting/Example.ipynb +++ b/qdev_wrappers/fitting/Example.ipynb @@ -2,18 +2,41 @@ "cells": [ { "cell_type": "code", - "execution_count": 4, + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "User schema at /Users/Lillian/qcodesrc_schema.json not found.User settings won't be validated\n", + "no config file found at /Users/Lillian/Qcodes/My stuff/mainfolder/data/local_scripts/instr.config, will try general\n", + "no config file found at /Users/Lillian/Qcodes/My stuff/instr.config\n" + ] + } + ], + "source": [ + "import qcodes as qc\n", + "from qdev_wrappers.file_setup import my_init\n", + "qc.config.add(\"mainfolder\", \"./mainfolder\", value_type=\"string\", description=\"Location of data\", default=\"./mainfolder\")\n", + "station = qc.Station()\n", + "my_init('data', station)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from qdev_wrappers.fitting.Converter import SQL_Converter, Legacy_Converter, data_to_dict\n", "from qdev_wrappers.fitting.Fitclasses import T1, T2\n", - "from qdev_wrappers.fitting.Fitter import do_fit\n", - "from qdev_wrappers.fitting.AnalysisToSQL import is_table, make_table, fit_to_SQL \n", + "from qdev_wrappers.fitting.Fitter import Fitter1D, Fitter_2Ddata_1Dfunction, fit_data\n", + "from qdev_wrappers.fitting.AnalysisToSQL import is_table, make_table, fit_to_sql\n", "from qdev_wrappers.fitting.RoughPlotting import plot_fit1d, plot_fit2d_slice\n", "\n", "from qdev_wrappers.show_num import show_num\n", - "from qcodes.dataset.plotting import plot_by_id\n" + "from qcodes.dataset.plotting import plot_by_id" ] }, { @@ -27,7 +50,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -100,7 +123,7 @@ " 'variables': ['pulse_readout_delay', 'data']}" ] }, - "execution_count": 5, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -119,7 +142,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -169,13 +192,13 @@ " 'rec_ctrl_raw_output']}" ] }, - "execution_count": 6, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "data2 = data_to_dict(621, datatype = 'qcodes_legacy')\n", + "data2 = data_to_dict(621, datatype = 'qcodes_legacy', samplefolder = None)\n", "data2" ] }, @@ -192,38 +215,151 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "fit = fit_data(data, T1(), {'x':'pulse_readout_delay'}, {'y':'data'})" + ] + }, + { + "cell_type": "code", + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'parameters': {'a': {'value': 0.00020686194829005843,\n", - " 'cov': array([ 1.12188937e-10, -8.65732231e-14, -2.94102847e-12]),\n", - " 'unit': 'V'},\n", + " 'cov': array([ 1.12188937e-10, -8.65732231e-14, -2.94102847e-12])},\n", " 'T': {'value': 1.7792117433091448e-07,\n", - " 'cov': array([ -8.65732231e-14, 3.48328903e-16, -5.40259460e-14]),\n", - " 'unit': 'S'},\n", + " 'cov': array([ -8.65732231e-14, 3.48328903e-16, -5.40259460e-14])},\n", " 'c': {'value': 0.0016790061428729915,\n", - " 'cov': array([ -2.94102847e-12, -5.40259460e-14, 1.68224887e-11]),\n", - " 'unit': 'V'}},\n", - " 'inferred_from': {'xdata': 'pulse_readout_delay',\n", - " 'ydata': 'data',\n", - " 'dataset': 4,\n", - " 'dependencies': {'data': ['pulse_readout_delay']}},\n", - " 'estimator': {'method': 'Least squared fit',\n", - " 'type': 'T1fit',\n", - " 'function': 'save all text from fitter function and class function here?',\n", - " 'dill': 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0.00020686,\n", + " 0.00020686, 0.00020686, 0.00020686, 0.00020686, 0.00020686,\n", + " 0.00020686, 0.00020686, 0.00020686, 0.00020686, 0.00020686,\n", + " 0.00020686, 0.00020686, 0.00020686, 0.00020686, 0.00020686,\n", + " 0.00020686, 0.00020686, 0.00020686, 0.00020686, 0.00020686,\n", + " 0.00020686, 0.00020686, 0.00020686, 0.00020686, 0.00020686,\n", + " 0.00020686, 0.00020686, 0.00020686, 0.00020686, 0.00020686,\n", + " 0.00020686, 0.00020686, 0.00020686, 0.00020686, 0.00020686,\n", + " 0.00020686, 0.00020686, 0.00020686, 0.00020686, 0.00020686,\n", + " 0.00020686, 0.00020686, 0.00020686, 0.00020686, 0.00020686,\n", + " 0.00020686, 0.00020686, 0.00020686, 0.00020686, 0.00020686,\n", + " 0.00020686, 0.00020686, 0.00020686, 0.00020686, 0.00020686,\n", + " 0.00020686]),\n", + " 'T': array([ 1.77921174e-07, 1.77921174e-07, 1.77921174e-07,\n", + " 1.77921174e-07, 1.77921174e-07, 1.77921174e-07,\n", + " 1.77921174e-07, 1.77921174e-07, 1.77921174e-07,\n", + " 1.77921174e-07, 1.77921174e-07, 1.77921174e-07,\n", + " 1.77921174e-07, 1.77921174e-07, 1.77921174e-07,\n", + " 1.77921174e-07, 1.77921174e-07, 1.77921174e-07,\n", + " 1.77921174e-07, 1.77921174e-07, 1.77921174e-07,\n", + " 1.77921174e-07, 1.77921174e-07, 1.77921174e-07,\n", + " 1.77921174e-07, 1.77921174e-07, 1.77921174e-07,\n", + " 1.77921174e-07, 1.77921174e-07, 1.77921174e-07,\n", + " 1.77921174e-07, 1.77921174e-07, 1.77921174e-07,\n", + " 1.77921174e-07, 1.77921174e-07, 1.77921174e-07,\n", + " 1.77921174e-07, 1.77921174e-07, 1.77921174e-07,\n", + " 1.77921174e-07, 1.77921174e-07, 1.77921174e-07,\n", + " 1.77921174e-07, 1.77921174e-07, 1.77921174e-07,\n", + " 1.77921174e-07, 1.77921174e-07, 1.77921174e-07,\n", + " 1.77921174e-07, 1.77921174e-07, 1.77921174e-07,\n", + " 1.77921174e-07, 1.77921174e-07, 1.77921174e-07,\n", + " 1.77921174e-07, 1.77921174e-07, 1.77921174e-07,\n", + " 1.77921174e-07, 1.77921174e-07, 1.77921174e-07,\n", + " 1.77921174e-07, 1.77921174e-07, 1.77921174e-07,\n", + " 1.77921174e-07, 1.77921174e-07, 1.77921174e-07,\n", + " 1.77921174e-07, 1.77921174e-07, 1.77921174e-07,\n", + " 1.77921174e-07, 1.77921174e-07, 1.77921174e-07,\n", + " 1.77921174e-07, 1.77921174e-07, 1.77921174e-07,\n", + " 1.77921174e-07, 1.77921174e-07, 1.77921174e-07,\n", + " 1.77921174e-07, 1.77921174e-07, 1.77921174e-07,\n", + " 1.77921174e-07, 1.77921174e-07, 1.77921174e-07,\n", + " 1.77921174e-07, 1.77921174e-07, 1.77921174e-07,\n", + " 1.77921174e-07, 1.77921174e-07, 1.77921174e-07,\n", + " 1.77921174e-07, 1.77921174e-07, 1.77921174e-07,\n", + " 1.77921174e-07, 1.77921174e-07, 1.77921174e-07,\n", + " 1.77921174e-07, 1.77921174e-07, 1.77921174e-07,\n", + " 1.77921174e-07, 1.77921174e-07]),\n", + " 'c': array([ 0.00167901, 0.00167901, 0.00167901, 0.00167901, 0.00167901,\n", + " 0.00167901, 0.00167901, 0.00167901, 0.00167901, 0.00167901,\n", + " 0.00167901, 0.00167901, 0.00167901, 0.00167901, 0.00167901,\n", + " 0.00167901, 0.00167901, 0.00167901, 0.00167901, 0.00167901,\n", + " 0.00167901, 0.00167901, 0.00167901, 0.00167901, 0.00167901,\n", + " 0.00167901, 0.00167901, 0.00167901, 0.00167901, 0.00167901,\n", + " 0.00167901, 0.00167901, 0.00167901, 0.00167901, 0.00167901,\n", + " 0.00167901, 0.00167901, 0.00167901, 0.00167901, 0.00167901,\n", + " 0.00167901, 0.00167901, 0.00167901, 0.00167901, 0.00167901,\n", + " 0.00167901, 0.00167901, 0.00167901, 0.00167901, 0.00167901,\n", + " 0.00167901, 0.00167901, 0.00167901, 0.00167901, 0.00167901,\n", + " 0.00167901, 0.00167901, 0.00167901, 0.00167901, 0.00167901,\n", + " 0.00167901, 0.00167901, 0.00167901, 0.00167901, 0.00167901,\n", + " 0.00167901, 0.00167901, 0.00167901, 0.00167901, 0.00167901,\n", + " 0.00167901, 0.00167901, 0.00167901, 0.00167901, 0.00167901,\n", + " 0.00167901, 0.00167901, 0.00167901, 0.00167901, 0.00167901,\n", + " 0.00167901, 0.00167901, 0.00167901, 0.00167901, 0.00167901,\n", + " 0.00167901, 0.00167901, 0.00167901, 0.00167901, 0.00167901,\n", + " 0.00167901, 0.00167901, 0.00167901, 0.00167901, 0.00167901,\n", + " 0.00167901, 0.00167901, 0.00167901, 0.00167901, 0.00167901,\n", + " 0.00167901])}},\n", + " 'inferred_from': {'inputs': {'x': 'pulse_readout_delay'},\n", + " 'output': {'y': 'data'},\n", + " 'setpoints': None,\n", + " 'run_id': 4,\n", + " 'exp_id': 1,\n", + " 'dependencies': {'data': ['pulse_readout_delay']},\n", + " 'data_dimensions': 1,\n", + " 'estimator': {'method': 'Least squared fit',\n", + " 'type': 'T1fit',\n", + " 'function used': 'a*np.exp(-x/T)+c',\n", + " 'dill': {'fitclass': b'\\x80\\x03cqdev_wrappers.fitting.Fitclasses\\nT1\\nq\\x00)\\x81q\\x01}q\\x02(X\\x04\\x00\\x00\\x00nameq\\x03X\\x05\\x00\\x00\\x00T1fitq\\x04X\\x07\\x00\\x00\\x00fun_strq\\x05X\\x19\\x00\\x00\\x00$f(x) = a \\\\exp(-x/T) + c$q\\x06X\\x06\\x00\\x00\\x00fun_npq\\x07X\\x10\\x00\\x00\\x00a*np.exp(-x/T)+cq\\x08X\\x08\\x00\\x00\\x00fun_varsq\\t]q\\nX\\x01\\x00\\x00\\x00xq\\x0baX\\n\\x00\\x00\\x00fun_outputq\\x0c]q\\rX\\x01\\x00\\x00\\x00yq\\x0eaX\\x07\\x00\\x00\\x00p_namesq\\x0f]q\\x10(X\\x03\\x00\\x00\\x00$a$q\\x11X\\x03\\x00\\x00\\x00$T$q\\x12X\\x03\\x00\\x00\\x00$c$q\\x13eX\\x08\\x00\\x00\\x00p_labelsq\\x14]q\\x15(X\\x01\\x00\\x00\\x00aq\\x16X\\x01\\x00\\x00\\x00Tq\\x17X\\x01\\x00\\x00\\x00cq\\x18eX\\x07\\x00\\x00\\x00p_unitsq\\x19]q\\x1a(h\\x0eh\\x0bh\\x0eeub.',\n", + " 'fitter': b'\\x80\\x03cqdev_wrappers.fitting.Fitter\\nFitter1D\\nq\\x00)\\x81q\\x01}q\\x02(X\\x08\\x00\\x00\\x00fitclassq\\x03cqdev_wrappers.fitting.Fitclasses\\nT1\\nq\\x04)\\x81q\\x05}q\\x06(X\\x04\\x00\\x00\\x00nameq\\x07X\\x05\\x00\\x00\\x00T1fitq\\x08X\\x07\\x00\\x00\\x00fun_strq\\tX\\x19\\x00\\x00\\x00$f(x) = a \\\\exp(-x/T) + 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] }, - "execution_count": 7, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "fit = do_fit(data, T1(), x='pulse_readout_delay', y='data')\n", "fit" ] }, @@ -238,2058 +374,527 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": true - }, + "execution_count": 7, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{5600000000.0: {'parameters': {'a': {'value': 0.00018995671288537621,\n", - " 'cov': array([ 1.03060699e-09, -4.34010572e-13, -4.25402654e-01,\n", - " 5.38756557e-08, -1.75998153e-11]),\n", - " 'unit': 'Hz'},\n", - " 'T': {'value': 6.098906943811456e-08,\n", - " 'cov': array([ -4.34010572e-13, 3.37578482e-16, -8.71382197e-04,\n", - " 1.83709703e-12, 8.12292258e-15]),\n", - " 'unit': 's'},\n", - " 'w': {'value': 411814046.42546773,\n", - " 'cov': array([ -4.25402654e-01, -8.71382197e-04, 2.74305385e+13,\n", - " -7.50002488e+05, -7.39551159e-01]),\n", - " 'unit': '1/s'},\n", - " 'p': {'value': 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4.9999999999999998e-08,\n", + " 'w': 373258533.09977734,\n", + " 'p': 0,\n", + " 'c': 0.0033379871682528055},\n", + " 5694000000.0: {'a': 0.00049551373951694025,\n", + " 'T': 4.9999999999999998e-08,\n", + " 'w': 373258533.09977734,\n", + " 'p': 0,\n", + " 'c': 0.0033527539176898503},\n", + " 5695000000.0: {'a': 0.00046562011883022033,\n", + " 'T': 4.9999999999999998e-08,\n", + " 'w': 435468288.61640692,\n", + " 'p': 0,\n", + " 'c': 0.0033495911540175356},\n", + " 5696000000.0: {'a': 0.00045117647374506983,\n", + " 'T': 4.9999999999999998e-08,\n", + " 'w': 435468288.61640692,\n", + " 'p': 0,\n", + " 'c': 0.0033614787518780245},\n", + " 5697000000.0: {'a': 0.00041106319770999001,\n", + " 'T': 4.9999999999999998e-08,\n", + " 'w': 435468288.61640692,\n", + " 'p': 0,\n", + " 'c': 0.0033458565117449246},\n", + " 5698000000.0: {'a': 0.00042985410466427976,\n", + " 'T': 4.9999999999999998e-08,\n", + " 'w': 435468288.61640692,\n", + " 'p': 0,\n", + " 'c': 0.0033499994076264085},\n", + " 5699000000.0: {'a': 0.00044181886593302024,\n", + " 'T': 4.9999999999999998e-08,\n", + " 'w': 435468288.61640692,\n", + " 'p': 0,\n", + " 'c': 0.0033632548294878356},\n", + " 5700000000.0: {'a': 0.00037406460006786987,\n", + " 'T': 4.9999999999999998e-08,\n", + " 'w': 435468288.61640692,\n", + " 'p': 0,\n", + " 'c': 0.003357881264637656}}" ] }, - "execution_count": 8, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "fit2 = do_fit(data2, T2(), x='pi_pulse_dur_set', y='qubit_rs_frequency_set', z='rec_ctrl_demod_freq_0_mag', cut='horizontal')\n", - "fit2" + "fit2 = fit_data(data2, T2(), {'x':'pi_pulse_dur_set'}, {'y':'rec_ctrl_demod_freq_0_mag'}, ['qubit_rs_frequency_set'])\n", + "fit2['start_params']" ] }, { @@ -2303,6 +908,26 @@ "It will then save an SQL table with the parameters and an estimate based on the fit, as well as an id that matches the id for the corresponding data point in the experiments database.\n" ] }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'a*np.exp(-x/T)+c'" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fit['inferred_from']['estimator']['function used']" + ] + }, { "cell_type": "code", "execution_count": 9, @@ -2312,14 +937,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "Table data_4_T1fit created in analysis.db\n", - "Table data_621_T2fit created in analysis.db\n" + "Table analysis_4_T1fit_4 created\n", + "Table analysis_621_T2fit_4 created\n" ] } ], "source": [ - "fit_to_SQL(data, T1(), fit)\n", - "fit_to_SQL(data2, T2(), fit2)" + "fit_to_sql(fit)\n", + "fit_to_sql(fit2)" ] }, { @@ -2339,30 +964,22 @@ "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "([], [None])" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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S1h9aSss551xmU1TwcbHKysrMnyfmnHOpI2memZW1Nx0fscM551zG8iDmnHMuY3kQc845l7E8iDnnnMtYHsSSbP57m/jvW2s6OxvOOdct+B25SXbTf95m+foKTty3P1lZjQ0W4pxzLlm8JJZk50wcTPmmSuau2NTZWXHOuS7Pg1iSnbr/QIrysnn0lfLOzopzznV5HsSSrCgvh9MOGMg/Xv+AqhofGMQ551LJg1gKfHLiELZV1fKkd/BwzrmU8iCWAkeO6sPePQt49JX3d02rqK5l3opN/rRn55xLIu+dmALZWWLyhMH8fvYy1m2rpigvm0k3zWJrZQ2lhbk8ec3x/qgW55xLAi+Jpcg5EwdTV2889uoqFq3extbKGip21rGlsoZFq7d1dvacc65L8CCWImMHlHDg4J48Or+ccQNLKC3MpSgvm56FuYwbWNLZ2XPOuS7B67RS6JyJg/nhYwt5f3MlT15zPItWb2PcwJI9qhIrqmubnOecc65pXhJLobMOHkROlvjzvHKK83M4dHjvRgPYpJtmceHUl5h00yzv+OGcc63gQSyF+vbI56Rx/fnzK+XsrK1vdBlvL3POubbzIJZiUw4fyvrtO5m5qPF7xry9zDnn2s4bYFLsuDH9GFhawLQ5KzntgL33mF+cn9Nse5lzzrmmeUksxXKys/hM2RBmvb2OVZsrG12mqfYy55xzzfMg1gE+XTYUgIfn+qDAzjmXTB7EOsDQvYr4yOi+TJ+7krp66+zsOOdcl+FBrIOce9hQ3t9cyXNL1nd2VpxzrsvwINZBThk/gN5FuUyb815nZ8U557oMD2IdJD8nm3MmDuE/C9ewfnt1Z2fHOee6BA9iHei8w4dSU2dMn7uys7PinHNdggexDjS6fwlHjerDn158zzt4OOdcEngQ62AXHjWc9zdXMnPR2s7OinPOZTwPYh3slPEDGFhawH0vvNvZWXHOuYznQayD5WRncf4Rw5j9znqWrdve2dlxzrmMltIgJuk0SYslLZF0bSPz8yU9FOa/JGlEzLzrwvTFkk6NmX63pLWS3ohLa4KkFyUtkDRX0uFh+gmStoTpCyRdn7o9TsyUw4eSmy3++KJ3t3fOufZIWRCTlA3cBpwOjAfOkzQ+brFLgE1mNhr4NXBjWHc8MAXYHzgNuD2kB3BPmBbv58APzWwCcH343GC2mU0IrxuSsX/t0b+kgNMO2JuH561kx05/fphzzrVVKktihwNLzGyZme0EpgGT45aZDNwb3j8CnCxJYfo0M6s2s+XAkpAeZvYMsLGR7RlQGt73BFYlc2eSqaK6liNG7sW2qlr+tiBts+mcc2kvlcOmDwZib4gqB45oahkzq5W0BegTpr8Yt+7gFrZ3FfCEpF8SBeejY+YdJelVosD2TTN7M35lSZcBlwEMGzashU21XcOTnLdW1pCTJf7w7HKmHDaUKHY755xrjVSWxBo7K8ffHNXUMomsG+/LwNVmNhS4Gpgapr8CDDezg4HfAn9tbGUzu9PMysysrF+/fi1squ1in+ScJfH22u28uKyxgqVzzrmWpDKIlQNDYz4PYc8qvl3LSMohqgbcmOC68S4CHg3vH+bD6setZrY9vP8nkCupb2t3Jllin+S8V4889irK5a7ZyzorO845l9FSGcTmAGMkjZSUR9RRY0bcMjOIgg/Ap4CZZmZh+pTQe3EkMAZ4uYXtrQKOD+9PAt4BkDQwtLMReixmARvatWcJqqiuZd6KTVRUf9h5o+FJzvdfcgT/veZ4PnfUCP67aC1Lvbu9c861WsqCmJnVAlcCTwBvAdPN7E1JN0g6Oyw2FegjaQlwDXBtWPdNYDqwEPgXcIWZ1QFIehB4AdhXUrmkS0JalwK/Cm1fPyW0bxEFxzfC9FuAKSFQplRD29eFU19i0k2z9ghkDU9y/txRw8nLyWLqs8tTnSXnnOty1AHn84xTVlZmc+fObVca81Zs4sKpL1Gxs46ivGzuv+QIDh3eu9Flr/3za/xl/vu8cN3J7FWc167tOudcJpA0z8zK2puOj9iRIrFtXz0Lcxk3sKTJZS/5yEiqa+v504srOjCHzjmX+VLZxb5ba2j7WrR6G+MGllCc3/RXPWZACSfs2497X1jB+UcM490NO1pcxznnnAexlGpo+0rEpceO4oK7XuKkX82itq6e0sJcnrzmeA9kzjnXDK9OTBNH79OH4X2K2FoV3UO2pbKGRau3dXa2nHMurXkQSxOSuPLE0ZhBfk5Wi+1ozjnnPIillXMmDmHYXoUM7lXIf64+zqsSnXOuBR7E0kh2lvjKCaNZtr6CV97b3NnZcc65tOdBLM2cM3EIe/cs4NanlnR2VpxzLu15EEszeTlZXHrsKF5evpE57/rAwM451xwPYmloyuFD2as4j9u8NOacc83yIJaGivJyuOQjI3l68TreeH9LZ2fHOefSVpNBTNLWFl7bJL3dkZntTj575HBKCnK4+b/vdHZWnHMubTVXEltqZqXNvEqAio7KaHfTszCXL35kFP9ZuIbXy7005pxzjWkuiH0ygfUTWca10Rc+MoKehbnc9J/FnZ0V55xLS80FsWskHdPcymbmjyROoZKCXC47bhRPLV7Hs0vW7/GATeec6+6aGxLiHeCXkvYGHgIeNLMFHZMt1+Dio0cwdfYyvnDPHHKz5AMDO+dcjCZLYmZ2s5kdBRwPbAT+IOktSddLGtthOezmivNzOOvgQeysrd81MPD8lZu9VOacc7Tyyc6SDgHuBg4ys+yU5aqTJePJzsm0fls1h//0SQD6leQjxLaqGkoLc/nbFcewclOlP3/MOZdROuzJzpJyJZ0l6U/A48DbeIeODtW3JJ9vnzaOeouqF7eFx7Vs3rGT02+ezYVTX2LSTbO8ZOac63aau0/sFEl3A+XAZcA/gX3M7Fwz+2tHZbA7q6iu3VVtePExIxjSu5C/zl9FaWEuRXnZFOXlUFlT588fc851W83VP30HeAD4ppn5IH4drKK6lkk3zWJrZc2uzhzfOnVfvj5tAf/3iQMYO7CUob0LmXzbcwD+/DHnXLfUZBAzsxM7MiNud4tWb2NrZVRtaOHzWQcN4q7Zy/ntzCXM/OYJFORm8+Q1x7No9TZvE3POdUvNVSe+0tLKiSzj2mbcwJJd1YYNpaysLHHdGeNYtaWKe59/F4h6Lx46vLcHMOdct9TcmW8/Sa81M19AzyTnxwXF+TmNlrKO3qcvJ+7bj1ufWsJnyobSuzhv1zoV1bUJlcoSXc4559Jdc2ewcQmsX5esjLg9NZSy4n379HGcfvNsbntqCd87czzQeBtaYwEq0eWccy4TNHez84oEXuUdmVkXGTewlE9NHMK9L7zL8vXRGMyxbWjN9VRMdDnnnMsE/jyxDPWtU/clLzuLH/99IdB4G1pjEl3OOecygdcjZaj+pQV87eQx/N/ji3hq8VpO3Ld/Qj0Vm2prc865TJRQSUzScEmTwvtCSX75ngY+f8xIRvYt5kePLWRnbX3CPRW9R6NzrqtIZNipS4FHgP8XJg0BEhqxQ9JpkhZLWiLp2kbm50t6KMx/SdKImHnXhemLJZ0aM/1uSWslvRGX1gRJL0paIGmupMPDdEm6JaT1mqSJieQ9E+TlZHH9meNZtr6Ce55f3tnZcc65DpdISewK4BhgK4CZvQP0b2klSdnAbcDpwHjgPEnj4xa7BNhkZqOBXwM3hnXHA1OA/YHTgNtDegD3hGnxfg780MwmANeHz4Ttjwmvy4DftbjHGeTEcf05aVx/bvnvEtZuq+rs7DjnXIdKJIhVm9nOhg+ScoBEhr4/HFhiZsvC+tOAyXHLTAbuDe8fAU6WpDB9mplVm9lyYElIDzN7hujRMPEMKA3vewKrYrZxn0VeBHqFZ6R1Gf975niqa+v42T8XdXZWnHOuQyUSxGZJ+g5QKOkU4GHgsQTWGwysjPlcHqY1uoyZ1QJbgD4JrhvvKuAXklYCvwSua0U+kHRZqIacu27duhY2lV5G9i3msuNG8ej893l+6frOzo5zznWYRILYtcA64HXgS0Sj2X8vgfXUyLT4ElxTyySybrwvA1eb2VDgamBqK/KBmd1pZmVmVtavX78WNpV+vnrSGIbtVcT3/vIG1bV+D7pzrntoMYiZWb2Z/d7MPk3UpvSSJfYkzXJgaMznIXxYxbfHMqGasidRVWEi68a7CHg0vH+YUP3YxrQyTkFuNj/++AEsW1/B755e2tnZcc65DpFI78SnJZVK2gtYAPxB0k0JpD0HGCNppKQ8oo4aM+KWmUEUfAA+BcwMAXIGMCX0XhxJ1Cnj5Ra2two4Prw/CXgnZhsXhl6KRwJbzOyDBPKfcY4b24/JEwZx+1NLWbpue2dnxznnUi6R6sSeZrYVOAf4g5kdCkxqaaXQxnUl8ATwFjDdzN6UdIOks8NiU4E+kpYA1xBVXWJmbwLTgYXAv4ArzKwOQNKDwAvAvpLKJV0S0roU+JWkV4GfEpUaIar+XEbUOeT3wFcS2OeM9b2PjacgN4vv/uV1EiswO+dc5lJLJzpJrwMfJepF+F0zmyPpNTM7qCMy2BnKysps7ty5nZ2NNnvw5fe47tHX+fknD+Izhw1teQXnnOtgkuaZWVl700mkJHYDUWlqSQhgo/iwqs6loXPLhnLEyL244bE3+febq6moru3sLDnnXEq0WBLrjjK9JAaw6IOtnHbzbLIEA0oL/JErzrm0kqySWItnNUn9iNqbRsQub2ZfaO/GXepU7KwjLzuLnXX1bNi+k0WrtzX6bDLnnMtkiVya/w2YDTyJPwQzY4wbWEKfHnms2VpFTV09pYVeCnPOdT2JtIkVmdm3zWy6mf254ZXynLl2aXjkyq3nTaQgN4sbHlvovRWdc11OIkHs75LOSHlOXNIV5+dwxkF7850z9mP2O+v500vvdXaWnHMuqRIJYl8nCmRVkraF19ZUZ8wlzwVHDOfYMX358T8W8s6abQmtU1Fdy7wVm7xno3MurSUy7FSJmWWZWUF4X2JmpS2t59JHVpb41acPpigvh69NW0BVTfNNmxXVtUy6aRYXTn2JSTfN8kDmnEtbiT7Z+WxJvwyvM1OdKZd8/UsL+MWnDuKtD7by838tbnbZRau3sbWyhoqddWyprGHR6sRKb84519ESGTvxZ0RVigvD6+thmsswJ+83gIuOGs7dzy3nqcVrm1xu3MASSgtzKcrLpmdhLuMGlnRgLp1zLnGJDDv1GjDBzOrD52xgvg87lZmqauqYfOtzbKio5vGvH0e/kvxGl6uormXR6m2MG1jiN0k755KuI4edAugV875nezfqOk9Bbja/Pf8QtlXV8vVp86mrb/wipjg/h0OH9/YA5pxLa4kEsf8D5ku6R9K9wDyiUeJdhho7oIQfffwAnl+6gZv+03z7mHPOpbMWL7PN7EFJTwOHhUnfNrPVKc2VS7nPlA3llRWbuO2ppRwytDeTxg/o7Cw551yrJVqdeBRwAtFDJ49KWW5ch/rB2ftzwOBSrp6+gPc27Ojs7DjnXKsl0jvxduBy4HXgDeBLkm5LdcZc6hXkZvO7Cw4lS+LyP85r8f4x55xLN4mUxI4HTjWzP5jZH4AziEplrgsYulcRvzl3Ags/2Mp1j/rToJ1zmSWRILYYGBbzeSjwWmqy4zrDieP6c80pY/nL/Pe5Y9ayzs6Oc84lLJH+032AtyS9HD4fBrwgaQaAmZ2dqsy5jvPVk0bzztrt/PyJRezTr5iP7j+ws7PknHMtSiSIXZ/yXLhOJ4lffOog3ttQwVUPLeCRy49m/KDUDpHpN1Q759orkerEucBsM5sFfEB0s/PzZjYrTHNdREFuNndeWEZpQS6X3jeXdduqU7YtH2TYOZcMiQSxZ4ACSYOB/wKfB+5JZaZc5xlQWsDvLyxjQ0U1X7xvLjt2pia4+CDDzrlkSCSIycx2AOcAvzWzTwD7pzZbrjMdOKQnN085hNfLN3PlA/OpratP+jZaM8iwP9vMOdeURBoiJOko4ALgkjAtO3VZcung1P0HcsPkA/jeX9/gO395nRu8Kns0AAAgAElEQVQ/eRCSkpZ+cX4OT15zfIttYg3VjlsraygtzOVvVxzDyk2V3o7mnAMSC2JXAdcBfzGzNyWNAp5KbbZcsrWlE8VnjxzOmq1V/HbmEgaWFnDNR/dNap4aBhluTmy1Y70Zp988m6qaOkoLc3nymuM9kDnXzSUyduIsYJak4vB5GfC1VGfMJU98aaY1J/9rThnLmq1V3DJzCf1KC/jckcOb3U6yexs2VDsaUJibTWVNHTt21mFEAa6lIOic69paPNOEqsSpQA9gmKSDgS+Z2VdSnTmXHLGlmdae/CXx008cyIbtO7n+b29QlJvNJw8dssdy7QmUzYmtdhzau5DJtz0H4A/rdM4BiVUn/gY4FWi4uflVScelNFcuqWJLM205+edkZ3HbBRO55N45fOuRV8nPzeLMgwbttkx7AmVLYqsdE2lHc851HwmdBcxsZVyjvo8Um0ES7UTRnILcbH5/YRkX3f0yV01bQH5ONqfEPL6lvYESEquOTKQdzTnXfSTSxX6lpKMBk5Qn6ZvAW4kkLuk0SYslLZF0bSPz8yU9FOa/JGlEzLzrwvTFkk6NmX63pLWS3ohL6yFJC8LrXUkLwvQRkipj5t2RSN67mmQ8qbkoL4e7Lz6M/QeVcsWfXmHW2+t2S//Ja47n/kuOaFNVot/87Jxri0SC2OXAFcBgoByYED43S1I2cBtwOjAeOE/S+LjFLgE2mdlo4NfAjWHd8cAUovvRTgNuD+lBdKP1afHbM7NzzWyCmU0A/gw8GjN7acM8M7s8gX3ulhK5H6ukIJd7v3A4o/v34NL75vLft9bsmteeQOk3Pzvn2qLFIGZm683sAjMbYGb9zeyzQFUCaR8OLDGzZWa2E5gGTI5bZjJwb3j/CHCyonrLycA0M6s2s+XAkpAeZvYMsLGpjYb1PwM8mEAeXdCaklCvojweuPQIxg0s4fI/zuPx1z9o9/Zbc/Ozc841aDaISRosqUxSXvjcX9JPgXcSSHswsDLmc3mY1ugyZlYLbCEaNT+RdZtyLLDGzGLzOFLSfEmzJB3b2EqSLpM0V9LcdevWNbZIl9baklCvojz++MUjOGhIL658cD5/nf9+u7bf3urIzuAjiTjX+ZoMYpKuAhYAvwVelHQRUVtYIXBoAmk3NrxD/BMXm1omkXWbch67l8I+AIaZ2SHANcADkvYYnt3M7jSzMjMr69evX4Kb6jraUhIqLcjlvi8czmEjenP19AU8NOe9duUhGe12HcXb8JxLD82dLS4D9jWzjZKGEVXpHWdmLyaYdjnRAzQbDAFWNbFMuaQcohHyNya47h5CGucQE2TNrBqoDu/nSVoKjCUand8Fbe3BWJyfwz2fP5zL7p/Ht//8Ohsrarj8+FFJHaIqHaXylgLnXOKaq06sMrONAGb2HvB2KwIYwBxgjKSRoTpyCuFesxgzgIvC+08BM83MwvQpoffiSGAM8DItmwQsMrPyhgmS+jV0CglDZo0B/PHFjWhrSSjqfn8oZx88iBv/tYgfPraQuvpEC86ZydvwnEsPzZ2thki6JeZz/9jPZtbs0FNmVivpSuAJogGD7w5jL94AzDWzGUQjgdwvaQlRCWxKWPdNSdOBhUAtcIWZ1QFIehA4AegrqRz4vplNDZudwp4dOo4DbpBUS3R/2+UNwdklT35ONr85dwIDSvP5/ezlrN1WxU2fmUBBbuaNFZ3o/WrpeOO1P2jUdTeKCj6NzIjawJpkZvc2Nz+TlZWV2dy5XtvYlJZOlHfNXsaP//EWh4/Yi99fWEbPotxOyGXbpGr4rI6QyXl33Y+keWZW1t50mjzCu3KQcm2XyInyi8eOomdhLtc9+jpn3/YsUy86jNH9e3RSjlsnk9u6MjnvzrVVIjc7O7dLIl3xK6pruek/b5OTLVZu3MHHb3uWpxev7YTctl4mt3Vlct6dayuva3CtksgYiQ2BrqqmnsLcbPr0yOcL98zhO2fsxyUfGZnWPRfTta0rEZmYd2/Dc+2VyKNY9vKOEK5BIifK+ED38JeO4vq/vcmP//EWCz/Yyk8+fiCFeenb4SOTBxnOpLx7G55LhkSOmJfCYLp/AB63pnqCuG6jpRNlY4Hu9gsmcvN/3+GWme+wcNVWbrtgIvv0y4x2Mpca3obnkiGRNrGxwJ3A54Alkn4qaWxqs+UyXfw9Z1lZ4upTxvKHiw9jzdYqzv7ts/z9tRbvX0+YDwGVebwNzyVDk13sG11YOhH4I1AMvApca2YvpChvnca72KfWqs2VfPXB+cxbsYmLjhrOdz62H/k5u1cvtqatJL5a6m9XHMPKTZXezpIBvE2s+0p5F/uYDfUBPktUElsDfJVoRI0JwMPAyPZmwqW3ZJ9oBvUqZNplR3Lj44u469nlvPzuJm6eMoGxA0p2ba81bSWx1VL1Zpx+82yqauq8nSUDZFIbnktPiVQnvgCUAh83s4+Z2aNmVmtmc4Fu+YDJ7iRVA93mZmfxvTPHM/WiMtZureLM3z7L1GeXU19vrR5RP7Zaqigvh8qaOn8umXPdRCJB7Htm9qO48Qg/DWBmN6YsZy4tpPphlSfvN4Anrj6O48b05Ud/X8iFd79M76LcVrWVxD7G5fGvH0tPb2dxrttosU1M0itmNrGlaV2Jt4l9qKEktqWyhp4prJ4zM6bNWckNjy0kN1v8z2n7st/AUsbtXdrq7Xk7i3PpL1ltYs2NnXg6cAbRU5IfiplVCow3s8Pbu/F05UFsdx0ZFJavr+B/HnmVOe9u4tgxffnpJw5k6F5FKd2mc67jJSuINVeduIromVtVwLyY1wzg1PZu2GWOjnxY5ci+xTx02VH86OMHMP+9zXz0189w1+xlXf7RLrGaul3AbyNwbk+JVCfmmFm3+tV0p5JYOle9rdpcyf/+9Q3+u2gtBw7uyQ8n78/EYV27J1tTPTN9dAvX1aS8JBae5wUwX9Jr8a/2bth1vlT1PEyWQb0KueuiMn573iGs3VbFObc/zzcffpV126qBrlkyaaojTao72DiXqZq7lPt6+HtmR2TEdbyOGvanPaU9SZx18CBOHNefW2cuYeqzy3jijdV85cTR3PfCu12uZNLUAMuJDLzsXHfU3PPEPghvzwGmm9n7HZMl11E64sSYrGqwHvk5XHv6OD5dNoQbHlvIjf9ahAALr64y7l5TAyxn4gj1znWERH4JpcC/JW0EpgGPmNma1GbLdYSOODEmu7S3T78e3PP5w/j7ax9w1UMLqKs3aurq2bGz61QpNjWKhY9u4dyeWrzZ2cx+aGb7A1cAg4BZkp5Mec5ch2hvz8OW2qVSMchrQxXjK9+bxOXHR0+R/tzUl/nivXN5e423FTnXnSQ8ALCkgcCngSlAiZkdlMqMdabu1DuxPRKtKkx1D8jKnXXc/dxy7nh6Kdt31nLGAXtz5Umj2W/v0qRvyzmXHB1xn1jDhr4s6Wngv0Bf4NKuHMBc4hLtMZfq+8wK87K54sTRPPM/J3LFCaOZ9fY6Tr95Nl+6fy5vvL8lJdvsbF2xZ6ZzbZHIWWU4cJWZLUh1ZlxmSbcec72L8/jmqfty6bGjuPu55dz93HKeeHMNJ4/rz6XHjeKIkXshqVPzmAx+z1jXkM73aGaS1lQn9gcKGj6b2XupylRn8+rExKXzD3FrVQ33PPcuf3huOZt21HDg4J588diRnHHg3uRmJzL2dXqat2ITF059iYqddRTlZXP/JUek3a0RrnmtuRBJl/9DsvPRkc8TOwu4iahTx1qiktlbwP7t3bjLfOncY660IJevnTyGS48dxaPzy5k6ezlfn7aAGx9fxMXHjODcw4bRszC3s7PZ6pNDJt0a4RqXaK/ddPk/pEs+GpNILn4MHAk8aWaHhKc7n5fabDmXPIV52VxwxHDOO2wYTy1ey+9nL+On/1zETf95m7MPHsT5Rwzn4CE9O6WqsS0nh0y8NcLtLtELkXQZkCCdj4dEjv4aM9sgKUtSlpk9JcmfI+YyTlaWOHJUH3oV5VFfbzw6v5y/LVjF9Lnl7D+olPOPGMbkCYPp0YFXmG09OcSWgFNR3ZRu7Z1dTaIXIulS6k7n4yGRAYCfBD4O/B9R78S1wGFmdnTqs9c5vE2sa2rsx1pvxl8XrOKBl97jrQ+2UpyXzekH7s05hwzmyFF9yMpqX+mspQDT3ue1pbKaJ13aYrq7VP8fEm1jzdg2MWAyUAlcDVwA9ARuaO+GnetoTZV6PnfkcD4xYRCPvfYBLy/fwL/eWM0j88oZ1LOAyYcM5pxDBjNmQOuvPBMJMO2tGkxlNU86t3d2ZfHBItX/h0RLWel6PDT5i5E0GhhgZs+FSfXAvZKOA3oBGzogf84lTVM/1orqWk759TO7gs2sb53A80s38Ogr5dz5zDJ+9/RSDhhcyin7DWR0/2JO2Ld/QsEm0QDTnpNDOlfzdKZMLUV2RgeKTB+Xs7nc/gb4TiPTd4R5Z7WUuKTTgJuBbOAuM/tZ3Px84D7gUKKgeK6ZvRvmXQdcAtQBXzOzJ8L0u4lG1l9rZgfEpPUQsG/42AvYbGYTmkvLdS9N/Vjjg827G3Zw1sGDOOvgQazbVs1jr67i0VfK+fWTbwOQkyW+csI+nD1hMKP792hyex0RYDL9BNRejQWrdO5J15LO6kCRrqWsRDT3nx1hZns8N8zM5koa0VLCkrKB24BTgHJgjqQZZrYwZrFLgE1mNlrSFOBG4FxJ44mGt9qfqGv/k5LGmlkdcA9wK1Hwi83XuTHb/hWwJbxvLi3XzTT2Y20u2PQryecLHxnJwUN78dm7XqKypo56M26ZuYRbZi5h7IAeTNpvACeN688hw3qTnaXdTqwdEWAy+QTUHk0Fq3TuSdcSL1m3XnO/qoJm5hUmkPbhwBIzWwYgaRpR+1psEJsM/CC8fwS4VVE/58nANDOrBpZLWhLSe8HMnmkuiIb1PwOcFLONRtNKYB9cN5BIaWbcwBJ6FeWiyujk8qcvHsEzb6/j8TdW8/+eWcbtTy+lV1Eux+zTh+eWbmBnbf2ujhqZcgLNNE0Fq0wOBN29ZN0WzX1DcyRdama/j50o6RJgXgJpDwZWxnwuB45oahkzq5W0BegTpr8Yt+7gBLYJcCywxszeidlGi2lJugy4DGDYsGEJbsp1FS2VZho7uYzq14OLjxnJlsoaZr+zjpmL1vLkwjVsrYrGM6yqqeP7M97gnIlDmDisNwW52R21O91CU8Eq2YEgme1riaTVXUvWbdXcf+Qq4C+SLuDDoFUG5AGfSCDtxvomx/fnb2qZRNZtynnAg63MB2Z2J3AnRF3sE9yWywDJOgk1dXLpWZjLmQcN4syDBrGtsoYTf/U0WyprAHj0lfd5ZN775OdkUTaiN0fv05ej9+nDgYN7kpPioa/i9ztTOzs0pblglaxAkMz2tUxuq0tnzT3ZeQ1wdBiho6EDxT/MbGaCaZcDQ2M+DwFWNbFMuaQcou77GxNcdw8hjXOIOoq0Jh+ui+roE0dJYS6zvnXirhNrnRlzlm/kuSUbeH7pen7xxGIAivKymTC0F2XDezNxeG8OGdY7qUNgxe/33644hsm3PdflTqCpLrUks30tk9vq0lmLR7GZPQU81Ya05wBjJI0E3ifqXHF+3DIzgIuI2qc+Bcw0M5M0A3hAUsOYjWOAlxPY5iRgkZmVx22jLWm5LqAzThzxJ9aT9xvAyfsNAGDD9mpeWLaBOcs3Mu+9Tdz29FLq6g0JxvYvYeLw3hw6vDcHDenJPv16kN3Gm63j9/vJt9b6CbQNktm+lsltdeksZZdioY3rSuAJoi72d5vZm5JuAOaa2QxgKnB/6GyxkSjQEZabTtQJpBa4oqE3oaQHgROAvpLKge+b2dSw2SnsXpXYbFqu60u3E0efHvm7qh4hKjG9unIz81ZsYt57m/jHa6t48OXoARGFudmMH1TKgYN7csDgnhw4uCf79CtOqBoyfr8n7def3858JynfQ1erlmzQ2H61tX0tmWm55iX8KJbuxIed6lrS5aSbSD7q642l67bz+vtbeP39Lby6cjNvrtpKdW09AAW5WYwbWMq4gSXsO7CEfQeUMHZgCX175Le4vWR8D121XSdT2r46upNJKnXksFPOZbR06O2V6IktK0uMGVDCmAElnLr/QCbdNIvsLNGvJJ9vnDKGt9dU8OaqLTzx5mqmzfmw82+f4jzGDogC29gBJezTr5iRfYuZOKzXrtH5k/E9dNV2nUxo++qMQNvZgS4R6Zkr57qYtpzYGtbZsTOq/R4zoJQphw8HwMxYt72at1dvZ/Gabby9ehuL12zj4bkrqdj5YW15cV42I/sVM6JPMaP6FjOibxTcRvXtQc+i1nckSUb1bDqeGDOh7aujA22mlLrTL0fOdUFtObE1t44k+pcU0L+kgI+M6btren298f7mSpavr9jt9Vr5Fv75+gfUx7QelBbkMLh3EUN6F4bX7u8b6y3Z3naddD0xJrO9KlVtXx0daDOl1O1tYo3wNjHXoLPbIJK5/eraOlZubAhw2ynfVBleOyjfVLmrxNegJD+Hwb0L2btnAQN7RgFzQGkBA3vm73rfpzivVY+rSfSxH65xHXk8tvcxQS1JVpuYB7FGeBBzkL6lhlQwMzbtqOH9mKDW8Hf11irWbK1mQ0U18aeLnCzRvySfAT0LGFBSQJ8eefTpkU+f4rzofXF++JtHr6I8qmrqUnpidMmVyqpf79jhXIplSnVKMkhir+I89irO48AhPRtdpqaunnXbqlm9tYq1IbBFAS56LVm3nZeWV7O5smaPYAeQJehdlEevwlz2Ks5lSK8ifvLPt+hZmLvr1Sv8LS3MpVdR9L5Hfs6uzimuY6VDp6iWeBBzHSqdGvVbyku63WPW2XKzsxjUq5BBvZof/7u2rp5NO2rYWLGTDdur2RD+bqzYyfqKnWzcvpMNFdW8s247897bxOYdNdTWN10jlJ0lSgty6FWUR2lhLqUFORTnRQ+LLCnIoTg/O3ofHiBZnJ9Dj/Da9b4gh6Lc7HY/qdulHw9irsOkU/VcRzx1ubvKyc6iX0k+/UrygZYDv5mxY2cdWypr2FJZw+Yd0d+t4fOWyho2V+5kS2UtWypr2FZVw5qtVVRU17G9upbt1bXUNRMEYxXlZVOYm01BbjaF4X1hbjYFedkU5mZFn/PC/NyYvzHLFuZlkZedTV5OVvTKjv7mh1fs9JzsrLS6cOuK/Bt1HSadquc64qnLLjGSdpWgmirlNRcIzIyNFTs545bZbK2spTg/m199+mDqzNhWVUtFdR0V1bVsq65lR3UtVbV1VO6sp6qmjsqaOip31rG1soa1Wz/8XFlTR1VNHTV17eszID4cbTxLMKC0gILc7A+DXQiAudlZ5GaLnKwssrNFbpbICdOys6Lp0fsPl8vJFjkxy+VkZYXPYVpWtG5udrRsdpbIVvQ3KytaNyt8zo55nxPmZ0tkZUG2orSzsthjuXSo5vUg5jpMOlXPpVNeOkNsUADSuqTQUqlZEu9u2MH2qloqa+qQoEdBblIuPmrqPgx2VTvroyBXU8fO2vroVRe9rw6vD6dHf1dsqGDGglXU1BtZEmMH9KCkIHe3ZXbW1rO1sobt1bXkZmVRh1FbZ9TW1VNbb9TWGzV19dTWGXX1Rk19faNtjh0tJ1u8ev1HO/2YSb8j1nVZ6VQ9l0556WixQaFHQQ5CbKv6cLT7lZsq2/ydpKLqLJFSc6ouSqISUhYlBW17wkBFdS3PL92wqzfm7Rccusf30pZq9roQ2Orqo4BXUx8FudrYvw3z6urZVl3L1dMWUFFdS1F+Dj/5xAHkZWdRW2/U1xt1FgXIeovWqTejrh7qLMxveJlRubOOu59dTlVt1NO0s3uYdp9frksL6VQ9l0556UixQaE2jKBfVVNPvRmn3zybqpq6NrVZJrvNsyEgDu1d2GKASteLkkTy1ZZq9qgKMPGHrM5bsYmqmjqqauvJyqqjf0lBm4/9eSs2cdfsZdTUGVsqazq91256/Kedcx0mttRSEkpiW1VDYW42lTV17Ghjm2Uy2zwbex5aQwkRohNpqh6EmWwt5asjqrYzYVittvIg5lw3E186AHaVeCbf9hzQtpNTMk9u8QFx5aZKDh3eO616uCZLR5QiM2FYrbbK7P++cxmqs7tdx5cOGt635+SUzJNbUwExnXq4JlN7S5GJHE/JLKmmU6nXg5hzSdCaoJTOpYn2npySdXJrKiCmW1VWZ4ptM5x823NpeTx1hO6zp86lSGuDUlctTSRbYwEx3aqyOkvsMVfQzrbMTNfyc86dc82KDUoNvbWa01CaKMrL7valibZoCG7dNYDB7sfcjp21FOZmJ+14qqiuZd6KTVRU1yYpt6nVfY8C55KktVVcXppw7RV/zDV1f19r217Tuaq7KemdO+cyQFuCUjo1jKdKZ3de6coaO+b6lxbstkxbAlImVnX7keVcEnSHoNQamXhFn2laOubaEpAyseOMH1XOuaTLxCv6rqYtASkTq7rTP4fOuYyTzlf0iQx+3BWqQtsakDKtViEz/zvOuQ7V3Em9sXnpekXf3ODHDVWeiVaFpmugi89XJgWktkifb945l5aaO6k3Ny8dT6BNDX4cW+WZSFVourb5pWu+UsnvE3PONau5++Bae49cZ4u9R69XUS69CvP2uL8qkfv40nW/0zVfqdS1Q7Rzrt2aa99K57avxjQ1+HFrq0LTdb/TNV+pJEuHR4SmmbKyMps7d25nZ8O5tNHaNrGuKhOeiJ0p/w9J88ysrN3peBDbkwcx51y87tjelErJCmIpbROTdJqkxZKWSLq2kfn5kh4K81+SNCJm3nVh+mJJp8ZMv1vSWklvNJLeV8Pyb0r6eZg2QlKlpAXhdUdq9tY515V1x/amTJCyywhJ2cBtwClAOTBH0gwzWxiz2CXAJjMbLWkKcCNwrqTxwBRgf2AQ8KSksWZWB9wD3ArcF7e9E4HJwEFmVi2pf8zspWY2ISU76pzrFrpje1MmSGVZ+HBgiZktA5A0jSjIxAaxycAPwvtHgFslKUyfZmbVwHJJS0J6L5jZM7ElthhfBn4W1sHM1iZ9j5xzGSdZbUTpeu9bd5fK6sTBwMqYz+VhWqPLmFktsAXok+C68cYCx4ZqyVmSDouZN1LS/DD92MZWlnSZpLmS5q5bt66lfXPOZYCGdqwLp77EpJtmtfvxIv4YmPSTyiCmRqbF9yJpaplE1o2XA/QGjgS+BUwPpboPgGFmdghwDfCApNI9Eje708zKzKysX79+LWzKOZcJvB2r60tlECsHhsZ8HgKsamoZSTlAT2Bjgus2tr1HLfIyUA/0NbNqM9sAYGbzgKVEpTbnXBfnDyDt+lJZJp4DjJE0EnifqKPG+XHLzAAuAl4APgXMNDOTNIOoxHQTUceOMcDLLWzvr8BJwNOSxgJ5wHpJ/YCNZlYnaVRIa1lS9tA5l9a8HavrS9l/1MxqJV0JPAFkA3eb2ZuSbgDmmtkMYCpwf+i4sZEo0BGWm07UCaQWuCL0TETSg8AJQF9J5cD3zWwqcDdwd+h6vxO4KATE44AbJNUCdcDlZrYxVfvtnEsv6TiGo0sev9m5EX6zs3POpVZG3OzsnHPOpZIHMedcWqiormXeik3t7gbvuhdv5XTOdTofl9C1lZfEnHOdzu/ncm3llzrOuU7n4xK6tvIg5pzrdH4/l2srP1Kcc2nB7+dybeFtYs455zKWBzHnXMp593mXKl6d6JxLqe7SfT5Zzy1zrePftHMupWK7z1v43NXavrpLoE5HXp3onEup7vA4FL/PrfP4pYJzLqW6Q/d5v8+t83S9o8k5l3a6evf57hCo05V/0845lwRdPVCnK28Tc845l7E8iDnnnMtYHsScc85lLA9izjnnMpYHMeeccxnLg5hzzrmM5UHMOedcxpKZdXYe0o6kdcCKdiTRF1ifpOxkGt/37qm77nt33W9o/74PN7N+7c2EB7EUkDTXzMo6Ox+dwffd97076a77Demz716d6JxzLmN5EHPOOZexPIilxp2dnYFO5PvePXXXfe+u+w1psu/eJuaccy5jeUnMOedcxvIg5pxzLmN1uyAm6TRJiyUtkXRtI/PzJT0U5r8kaUTMvOvC9MWSTm0pTUkjQxrvhDTzkr2Nrr7vkoZKekrSW5LelPT17rLvMfOyJc2X9Pfust+Sekl6RNKi8L8/qhvt+9XhWH9D0oOSCrrSvkvqo+g3vV3SrXH5OlTS62GdWySpxZ01s27zArKBpcAoIA94FRgft8xXgDvC+ynAQ+H9+LB8PjAypJPdXJrAdGBKeH8H8OVkb6Mb7PvewMSwTAnwdnfZ95i8XQM8APy9u+w3cC/wxfA+D+jVHfYdGAwsBwpj0r24i+17MfAR4HLg1rh8vQwcBQh4HDi9xf1tzZeT6a/w5TwR8/k64Lq4ZZ4Ajgrvc4juSFf8sg3LNZVmWGc9kBO/7WRtozvseyP78TfglO6y78AQ4L/ASbQ+iGXkfgOlRCdydcPf+mBgJbBXWP7vwEe70r7HpHExMUGM6IJ1Uczn84D/19L+drfqxIYDpEF5mNboMmZWC2wB+jSzblPT+wCbQxrx20rWNlojU/d9l1AdcQjwUot728R+NZV2G/LVUfv+G+B/gPpEd7axfWok3T2WSaP9HgWsA/4QqlHvklTcmh1vJu1Gl0mXfTez94FfAu8BHwBbzOzfrdjv3bbZSH72WKYT9r25fJe3kO89dLcg1lj9qiW4TLKmJ3MbrZGp+x6tJPUA/gxcZWZbG1m2ORm575LOBNaa2bxG5iciI/eb6Kp9IvA7MzsEqABa2w6ckfsuqTcwmagqbxBQLOmzjSzbnHTf96a06TzX3YJYOTA05vMQYFVTy0jKAXoCG5tZt6np64FeIY34bSVrG62RqfuOpFyiAPYnM3u0Ffu8x341kp89lkmjfT8GOFvSu8A04CRJf0x8tzN2v8uBcjNrKHE/QnZIpYIAAAcpSURBVBTUWiNT930SsNzM1plZDfAocHQr9nu3bTaSnz2W6YR9by7fQ1rI957aWueciS+iK7xlRFc5DY2T+8ctcwW7N0ZOD+/3Z/cGz2VEjZ1Npgk8zO4Nnl9J9ja6wb4LuA/4TXf7v8fl7wRa3yaWsfsNzAb2De9/APyiO+w7cATwJlBEdOzfC3y1K+17TB4uZs+OHXOAI/mwY8cZLe5vW08MmfoCziDq4bYU+G6YdgNwdnhfEP4pS4h6yoyKWfe7Yb3FxPSaaSzNMH1USGNJSDM/2dvo6vtO1IvJgNeABeHV4oHdFfY9Lv8n0Moglsn7DUwA5ob/+1+B3t1o338ILALeAO5vSKuL7fu7RKWy7UQlsIaejmVhv5cCt5JA5x4fdso551zG6m5tYs4557oQD2LOOecylgcx55xzGcuDmHPOuYzlQcw551zG8iDm0p6kOkkLwqjeD0sqCtOfb83yzaR/j6RPtTOPJ0jaEoZJWiTpl+1JL1n5C/lq9GZZSRdLWhfy/I6kJ5paNm69H0j6Zlvy00Qebm15yUbXHSGpUtKCmGnfDSPAvxaOgSPC9D9J2tje/7NLPx7EXCaoNLMJZnYAsJNo9GvMrKkTbqPLd4DZFg2TdAhwpqRjOmi7zTmB5kd8eMjMDjGzMcDPgEcl7ZfMDMSM5pAKS81sQtjOUcCZRE89OIho9IuGsfsuAGakMB+uk3gQc5lmNjAaQNL2RJcPV+1vNEyU9E1JP4hfWNLPJC0MV/K/DNP6SfqzpDnh1WxwMrNKopuyB4f1iyXdHdadL2lymD5C0mxJr4TX0WG6JN0a8vEPoH9M/k4Oabwe0swP09+V1De8L5P0dBgw+XLg6lAqObaFfD8F3AlcFtLZR9K/JM0L+RzXyPd1adivV8N31FBKvkfSTZKeAm6UdLik50Pen5e0b0wyQ8N2Fkv6flj/R4p5dpykn0j6WnP5JxoFfb2ZVYf9WW9mrR2ezWUYD2IuY4Qr+tOB11O0/F7AJ4iG0zkI+HGYdTPwazM7DPgkcFcL6fQGxgDPhEnfBWaG9U8EfqFoVPa1RI+VmQicC9wSlv8EsC9wIHApoSSl6OGI9wDnmtmBREMBfbmpfJjZu0TDAP06lExnJ/A1vAI0BKv/397ZhGhVRnH895+QEhwnCvpwEQqlMAijjLqQnFlk0kJIkaFgEA23FkpBERSlkdFKISYpjXEhEZazSIKScmQUWkT5AUrjQl2UuXCThUM4nRbn3Lzz9vq+1zDy6vmt7r3Pee59nvvCPe85z8P5f4CXPOoFXgKGmtjvN7PFZtYDnAY2lNrmAsvN7EW8AkVfRKqvA2+X7JYAg3iVjgFJi4DdwLqYdwdetmhvm7F/hTvEcUlDkvorzDepOf9lmJ8kN4vppXWPMfwDd6P2syo851dgAtgVEVChpLwc6NY1kdmZkjrN7HJD/2WSTuAO6B0z+yWur8AL+RbrSPcAj+DFTd+TtACYxD/6AH3Ax2Y2Cfws6Zu4Pg8vDjse53vw+nTbK8ytKoK/VQOWAvtK8767if18SW8B9wIzcA2pgn0xB/Dir3skPYaXEZtWsjtoZpfiufuBx81su6RLkhYCDwI/FDbXw8x+k9QLLMP/LHwi6RUzG64496SGpBNL6sCVYt3j39pLusrUzMM/JN/N7KqkJcAT+D//jbgYZQcu7nelzXPHzGylpLnAEUkjZnYMdwxrzOzHhjG9AVwEeuIZE+XhNLl/K6n28vxuWM6+xEI8ourAdaLavfdhYJWZHZe0Hl+DK/i9dLwVOGRmqyPNOVpqa5xrcb4LLxL7EPBRlcGH0xwFRiWdxKO54Sp9k3qS6cTkTuEi8ICk+2MdaWWjQUQfXWb2BbAJT2+Bp6k2luxaftgjUtoGvByXvgSeV4Q0EV2ARycXzOxPYC1eLRw8DfmspLskPYxHFeApudmSHo3ztcDhOD4H9MbxmtJwLgOdrcZbmlc/vh72oblm21lJA9EmST1NunUCF+RyOYMtbt8F/BTH6xvanpR0n6TpwCrgaFwfAZ4CFjM1wrve+OdFpFewADjfrl9Sb9KJJXcE5tpMW3BV6AO4Q2ikEzgQKcHDwOa4/gKwKDZ7nKLabsedQJ+kOXgUMg04Id9csjVshoB1kr7FU4lF5DICnMHX8t6PsWBmE8BzeIrvJK72vDP6vAnskDSGpyYLPgdWt9jY8Uy0jQOv4hHj6WgbBDZIOo7LgzzdpP9r+Ds9SPN3WvAusE3SUa4564IjeLX2Y8BnZvZdzPcP4BAu4TFJe2bgKctT8Rt24zIuyW1MVrFPkuSWJDZ0fA8MmNmZJu2zcXma+RXvNxz2n97EYSb/MxmJJUlyyyGpG9eh+rqZAwsmga7SJp5W99sL9DN13TG5DchILEmSJKktGYklSZIktSWdWJIkSVJb0oklSZIktSWdWJIkSVJb0oklSZIkteUvEdilLpGwjQMAAAAASUVORK5CYII=\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" + "ename": "KeyError", + "evalue": "'xdata'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplot_fit1d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mT1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;31m#for comparison to original data:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m 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\u001b[0mfit1d\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'inferred_from'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'xdata'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 11\u001b[0m \u001b[0mxdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mxname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'data'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0mxlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mxname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'label'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mKeyError\u001b[0m: 'xdata'" + ] }, { "data": { - "image/png": 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\n", 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -2385,80 +1002,18 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plot_fit2d_slice(data2, fit2, T2(), setvalue = 5.65600000e+09)" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "([DataSet:\n", - " location = '/Users/Lillian/Qcodes/My stuff/mainfolder/data/621'\n", - " | | | \n", - " Setpoint | qubit_rs_frequency_set | frequency | (101,)\n", - " Setpoint | pi_pulse_dur_set | pi_pulse_dur | (101, 101)\n", - " Measured | rec_ctrl_demod_freq_0_mag | demod_freq_0_mag | (101, 101)\n", - " Measured | rec_ctrl_demod_freq_0_phase | demod_freq_0_phase | (101, 101)\n", - " Measured | rec_ctrl_raw_output | raw_output | (101, 101)],\n", - " [,\n", - " ,\n", - " ])" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "#for comparison to original data:\n", "show_num(621)" From 98ef6d15cb9ea2866d5675ea429b99157b6329cf Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Wed, 18 Jul 2018 04:12:57 -0300 Subject: [PATCH 060/116] Remove unnecessary printing of data from Example notebook --- qdev_wrappers/fitting/Example.ipynb | 824 +--------------------------- 1 file changed, 9 insertions(+), 815 deletions(-) diff --git a/qdev_wrappers/fitting/Example.ipynb b/qdev_wrappers/fitting/Example.ipynb index e29a2a72..11e1c19d 100644 --- a/qdev_wrappers/fitting/Example.ipynb +++ b/qdev_wrappers/fitting/Example.ipynb @@ -50,87 +50,11 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 11, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'pulse_readout_delay': {'name': 'pulse_readout_delay',\n", - " 'label': 'Pi Pulse Readout Delaraby',\n", - " 'unit': 'S',\n", - " 'data': array([ 0.00000000e+00, 1.00000000e-08, 2.00000000e-08,\n", - " 3.00000000e-08, 4.00000000e-08, 5.00000000e-08,\n", - " 6.00000000e-08, 7.00000000e-08, 8.00000000e-08,\n", - " 9.00000000e-08, 1.00000000e-07, 1.10000000e-07,\n", - " 1.20000000e-07, 1.30000000e-07, 1.40000000e-07,\n", - " 1.50000000e-07, 1.60000000e-07, 1.70000000e-07,\n", - " 1.80000000e-07, 1.90000000e-07, 2.00000000e-07,\n", - " 2.10000000e-07, 2.20000000e-07, 2.30000000e-07,\n", - " 2.40000000e-07, 2.50000000e-07, 2.60000000e-07,\n", - " 2.70000000e-07, 2.80000000e-07, 2.90000000e-07,\n", - " 3.00000000e-07, 3.10000000e-07, 3.20000000e-07,\n", - " 3.30000000e-07, 3.40000000e-07, 3.50000000e-07,\n", - " 3.60000000e-07, 3.70000000e-07, 3.80000000e-07,\n", - " 3.90000000e-07, 4.00000000e-07, 4.10000000e-07,\n", - " 4.20000000e-07, 4.30000000e-07, 4.40000000e-07,\n", - " 4.50000000e-07, 4.60000000e-07, 4.70000000e-07,\n", - " 4.80000000e-07, 4.90000000e-07, 5.00000000e-07,\n", - " 5.10000000e-07, 5.20000000e-07, 5.30000000e-07,\n", - " 5.40000000e-07, 5.50000000e-07, 5.60000000e-07,\n", - " 5.70000000e-07, 5.80000000e-07, 5.90000000e-07,\n", - " 6.00000000e-07, 6.10000000e-07, 6.20000000e-07,\n", - " 6.30000000e-07, 6.40000000e-07, 6.50000000e-07,\n", - " 6.60000000e-07, 6.70000000e-07, 6.80000000e-07,\n", - " 6.90000000e-07, 7.00000000e-07, 7.10000000e-07,\n", - " 7.20000000e-07, 7.30000000e-07, 7.40000000e-07,\n", - " 7.50000000e-07, 7.60000000e-07, 7.70000000e-07,\n", - " 7.80000000e-07, 7.90000000e-07, 8.00000000e-07,\n", - " 8.10000000e-07, 8.20000000e-07, 8.30000000e-07,\n", - " 8.40000000e-07, 8.50000000e-07, 8.60000000e-07,\n", - " 8.70000000e-07, 8.80000000e-07, 8.90000000e-07,\n", - " 9.00000000e-07, 9.10000000e-07, 9.20000000e-07,\n", - " 9.30000000e-07, 9.40000000e-07, 9.50000000e-07,\n", - " 9.60000000e-07, 9.70000000e-07, 9.80000000e-07,\n", - " 9.90000000e-07, 1.00000000e-06])},\n", - " 'data': {'name': 'data',\n", - " 'label': 'Cavity Response',\n", - " 'unit': 'V',\n", - " 'data': array([ 0.00190226, 0.00184296, 0.00187694, 0.0018484 , 0.00187253,\n", - " 0.00183823, 0.00181451, 0.00182108, 0.00181803, 0.0018168 ,\n", - " 0.00180962, 0.00178911, 0.00175819, 0.00180917, 0.0017232 ,\n", - " 0.00175071, 0.00178156, 0.00173019, 0.00174053, 0.00171546,\n", - " 0.00175654, 0.00173921, 0.00172067, 0.00168142, 0.00176445,\n", - " 0.00175339, 0.0017317 , 0.00176039, 0.00175976, 0.00172069,\n", - " 0.00173343, 0.00173168, 0.00169639, 0.00170679, 0.00168139,\n", - " 0.00170459, 0.00174184, 0.00168564, 0.00170346, 0.00174795,\n", - " 0.00167056, 0.0017 , 0.00170594, 0.00169131, 0.00167176,\n", - " 0.00170925, 0.00172371, 0.00169057, 0.00172341, 0.00171353,\n", - " 0.00170708, 0.00174262, 0.00169639, 0.0016967 , 0.00167803,\n", - " 0.00167778, 0.00167651, 0.00171114, 0.00166005, 0.00169204,\n", - " 0.00165937, 0.0016763 , 0.00166531, 0.00162962, 0.00168796,\n", - " 0.00173675, 0.00163938, 0.00167601, 0.00167918, 0.00167402,\n", - " 0.00170181, 0.00168008, 0.00170246, 0.00164879, 0.00166086,\n", - " 0.00166087, 0.00169208, 0.00166155, 0.00169739, 0.00165514,\n", - " 0.00166041, 0.00162791, 0.00169263, 0.0016809 , 0.00169787,\n", - " 0.00171273, 0.00167757, 0.00170379, 0.00171149, 0.00169174,\n", - " 0.00167011, 0.00167183, 0.00165937, 0.00166659, 0.00167763,\n", - " 0.0016915 , 0.00170809, 0.0016675 , 0.00168928, 0.00171038,\n", - " 0.00167808])},\n", - " 'exp_id': 1,\n", - " 'run_id': 4,\n", - " 'dependencies': {'data': ['pulse_readout_delay']},\n", - " 'variables': ['pulse_readout_delay', 'data']}" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "data = data_to_dict(4)\n", - "data" + "data = data_to_dict(4)" ] }, { @@ -142,64 +66,11 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 13, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'qubit_rs_frequency_set': {'name': 'qubit_rs_frequency_set',\n", - " 'label': 'Frequency',\n", - " 'var_name': 'frequency',\n", - " 'unit': 'Hz',\n", - " 'data': array([ 5.60000000e+09, 5.60000000e+09, 5.60000000e+09, ...,\n", - " 5.70000000e+09, 5.70000000e+09, 5.70000000e+09])},\n", - " 'pi_pulse_dur_set': {'name': 'pi_pulse_dur_set',\n", - " 'label': 'pi_pulse_dur',\n", - " 'var_name': 'pi_pulse_dur',\n", - " 'unit': 's',\n", - " 'data': array([ 0.00000000e+00, 1.00000000e-09, 2.00000000e-09, ...,\n", - " 9.80000000e-08, 9.90000000e-08, 1.00000000e-07])},\n", - " 'rec_ctrl_demod_freq_0_mag': {'name': 'rec_ctrl_demod_freq_0_mag',\n", - " 'label': 'demod freq 0 mag',\n", - " 'var_name': 'demod_freq_0_mag',\n", - " 'unit': 'v',\n", - " 'data': array([ 0.00355048, 0.00357386, 0.00344386, ..., 0.00335167,\n", - " 0.00345182, 0.00341498])},\n", - " 'rec_ctrl_demod_freq_0_phase': {'name': 'rec_ctrl_demod_freq_0_phase',\n", - " 'label': 'demod freq 0 phase',\n", - " 'var_name': 'demod_freq_0_phase',\n", - " 'unit': 'v',\n", - " 'data': array([-44.00183506, -43.77336984, -42.36852509, ..., -40.35980814,\n", - " -40.97471696, -40.95782035])},\n", - " 'rec_ctrl_raw_output': {'name': 'rec_ctrl_raw_output',\n", - " 'label': 'raw output',\n", - " 'var_name': 'raw_output',\n", - " 'unit': 'v',\n", - " 'data': array([-0.00071378, -0.00065171, -0.0006484 , ..., -0.00077737,\n", - " -0.00077076, -0.00070182])},\n", - " 'exp_id': 621,\n", - " 'run_id': 621,\n", - " 'dependencies': {'rec_ctrl_demod_freq_0_mag': ['qubit_rs_frequency_set',\n", - " 'pi_pulse_dur_set'],\n", - " 'rec_ctrl_demod_freq_0_phase': ['qubit_rs_frequency_set',\n", - " 'pi_pulse_dur_set'],\n", - " 'rec_ctrl_raw_output': ['qubit_rs_frequency_set', 'pi_pulse_dur_set']},\n", - " 'variables': ['qubit_rs_frequency_set',\n", - " 'pi_pulse_dur_set',\n", - " 'rec_ctrl_demod_freq_0_mag',\n", - " 'rec_ctrl_demod_freq_0_phase',\n", - " 'rec_ctrl_raw_output']}" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "data2 = data_to_dict(621, datatype = 'qcodes_legacy', samplefolder = None)\n", - "data2" + "data2 = data_to_dict(621, datatype = 'qcodes_legacy', samplefolder = None)" ] }, { @@ -222,147 +93,6 @@ "fit = fit_data(data, T1(), {'x':'pulse_readout_delay'}, {'y':'data'})" ] }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'parameters': {'a': {'value': 0.00020686194829005843,\n", - " 'cov': array([ 1.12188937e-10, -8.65732231e-14, -2.94102847e-12])},\n", - " 'T': {'value': 1.7792117433091448e-07,\n", - " 'cov': array([ -8.65732231e-14, 3.48328903e-16, -5.40259460e-14])},\n", - " 'c': {'value': 0.0016790061428729915,\n", - " 'cov': array([ -2.94102847e-12, -5.40259460e-14, 1.68224887e-11])}},\n", - " 'start_params': {'a': 0.00017795331091539001,\n", - " 'T': 1.9999999999999999e-07,\n", - " 'c': 0.001690665726187212},\n", - " 'parameter units': {'a': 'V', 'T': 'S', 'c': 'V'},\n", - " 'estimate': {'values': array([ 0.00188587, 0.00187456, 0.00186387, 0.00185377, 0.00184422,\n", - " 0.00183519, 0.00182665, 0.00181858, 0.00181095, 0.00180374,\n", - " 0.00179693, 0.00179048, 0.00178439, 0.00177863, 0.00177318,\n", - " 0.00176804, 0.00176317, 0.00175857, 0.00175422, 0.00175011,\n", - " 0.00174623, 0.00174255, 0.00173908, 0.0017358 , 0.00173269,\n", - " 0.00172976, 0.00172698, 0.00172436, 0.00172188, 0.00171954,\n", - " 0.00171732, 0.00171523, 0.00171325, 0.00171138, 0.00170961,\n", - " 0.00170794, 0.00170636, 0.00170486, 0.00170345, 0.00170211,\n", - " 0.00170085, 0.00169966, 0.00169853, 0.00169746, 0.00169645,\n", - " 0.0016955 , 0.0016946 , 0.00169374, 0.00169294, 0.00169218,\n", - " 0.00169146, 0.00169078, 0.00169013, 0.00168953, 0.00168895,\n", - " 0.00168841, 0.00168789, 0.00168741, 0.00168695, 0.00168651,\n", - " 0.0016861 , 0.00168572, 0.00168535, 0.001685 , 0.00168467,\n", - " 0.00168436, 0.00168407, 0.0016838 , 0.00168353, 0.00168329,\n", - " 0.00168305, 0.00168283, 0.00168262, 0.00168242, 0.00168224,\n", - " 0.00168206, 0.00168189, 0.00168174, 0.00168159, 0.00168145,\n", - " 0.00168131, 0.00168119, 0.00168107, 0.00168095, 0.00168085,\n", - " 0.00168075, 0.00168065, 0.00168056, 0.00168048, 0.0016804 ,\n", - " 0.00168032, 0.00168025, 0.00168018, 0.00168012, 0.00168006,\n", - " 0.00168 , 0.00167994, 0.00167989, 0.00167984, 0.0016798 ,\n", - " 0.00167976]),\n", - " 'name': 'data_estimate',\n", - " 'label': 'Cavity Response estimate',\n", - " 'unit': 'V',\n", - " 'parameters': {'a': array([ 0.00020686, 0.00020686, 0.00020686, 0.00020686, 0.00020686,\n", - " 0.00020686, 0.00020686, 0.00020686, 0.00020686, 0.00020686,\n", - " 0.00020686, 0.00020686, 0.00020686, 0.00020686, 0.00020686,\n", - " 0.00020686, 0.00020686, 0.00020686, 0.00020686, 0.00020686,\n", - " 0.00020686, 0.00020686, 0.00020686, 0.00020686, 0.00020686,\n", - " 0.00020686, 0.00020686, 0.00020686, 0.00020686, 0.00020686,\n", - " 0.00020686, 0.00020686, 0.00020686, 0.00020686, 0.00020686,\n", - " 0.00020686, 0.00020686, 0.00020686, 0.00020686, 0.00020686,\n", - " 0.00020686, 0.00020686, 0.00020686, 0.00020686, 0.00020686,\n", - " 0.00020686, 0.00020686, 0.00020686, 0.00020686, 0.00020686,\n", - " 0.00020686, 0.00020686, 0.00020686, 0.00020686, 0.00020686,\n", - " 0.00020686, 0.00020686, 0.00020686, 0.00020686, 0.00020686,\n", - " 0.00020686, 0.00020686, 0.00020686, 0.00020686, 0.00020686,\n", - " 0.00020686, 0.00020686, 0.00020686, 0.00020686, 0.00020686,\n", - " 0.00020686, 0.00020686, 0.00020686, 0.00020686, 0.00020686,\n", - " 0.00020686, 0.00020686, 0.00020686, 0.00020686, 0.00020686,\n", - " 0.00020686, 0.00020686, 0.00020686, 0.00020686, 0.00020686,\n", - " 0.00020686, 0.00020686, 0.00020686, 0.00020686, 0.00020686,\n", - " 0.00020686, 0.00020686, 0.00020686, 0.00020686, 0.00020686,\n", - " 0.00020686, 0.00020686, 0.00020686, 0.00020686, 0.00020686,\n", - " 0.00020686]),\n", - " 'T': array([ 1.77921174e-07, 1.77921174e-07, 1.77921174e-07,\n", - " 1.77921174e-07, 1.77921174e-07, 1.77921174e-07,\n", - " 1.77921174e-07, 1.77921174e-07, 1.77921174e-07,\n", - " 1.77921174e-07, 1.77921174e-07, 1.77921174e-07,\n", - " 1.77921174e-07, 1.77921174e-07, 1.77921174e-07,\n", - " 1.77921174e-07, 1.77921174e-07, 1.77921174e-07,\n", - " 1.77921174e-07, 1.77921174e-07, 1.77921174e-07,\n", - " 1.77921174e-07, 1.77921174e-07, 1.77921174e-07,\n", - " 1.77921174e-07, 1.77921174e-07, 1.77921174e-07,\n", - " 1.77921174e-07, 1.77921174e-07, 1.77921174e-07,\n", - " 1.77921174e-07, 1.77921174e-07, 1.77921174e-07,\n", - " 1.77921174e-07, 1.77921174e-07, 1.77921174e-07,\n", - " 1.77921174e-07, 1.77921174e-07, 1.77921174e-07,\n", - " 1.77921174e-07, 1.77921174e-07, 1.77921174e-07,\n", - " 1.77921174e-07, 1.77921174e-07, 1.77921174e-07,\n", - " 1.77921174e-07, 1.77921174e-07, 1.77921174e-07,\n", - " 1.77921174e-07, 1.77921174e-07, 1.77921174e-07,\n", - " 1.77921174e-07, 1.77921174e-07, 1.77921174e-07,\n", - " 1.77921174e-07, 1.77921174e-07, 1.77921174e-07,\n", - " 1.77921174e-07, 1.77921174e-07, 1.77921174e-07,\n", - " 1.77921174e-07, 1.77921174e-07, 1.77921174e-07,\n", - " 1.77921174e-07, 1.77921174e-07, 1.77921174e-07,\n", - " 1.77921174e-07, 1.77921174e-07, 1.77921174e-07,\n", - " 1.77921174e-07, 1.77921174e-07, 1.77921174e-07,\n", - " 1.77921174e-07, 1.77921174e-07, 1.77921174e-07,\n", - " 1.77921174e-07, 1.77921174e-07, 1.77921174e-07,\n", - " 1.77921174e-07, 1.77921174e-07, 1.77921174e-07,\n", - " 1.77921174e-07, 1.77921174e-07, 1.77921174e-07,\n", - " 1.77921174e-07, 1.77921174e-07, 1.77921174e-07,\n", - " 1.77921174e-07, 1.77921174e-07, 1.77921174e-07,\n", - " 1.77921174e-07, 1.77921174e-07, 1.77921174e-07,\n", - " 1.77921174e-07, 1.77921174e-07, 1.77921174e-07,\n", - " 1.77921174e-07, 1.77921174e-07, 1.77921174e-07,\n", - " 1.77921174e-07, 1.77921174e-07]),\n", - " 'c': array([ 0.00167901, 0.00167901, 0.00167901, 0.00167901, 0.00167901,\n", - " 0.00167901, 0.00167901, 0.00167901, 0.00167901, 0.00167901,\n", - " 0.00167901, 0.00167901, 0.00167901, 0.00167901, 0.00167901,\n", - " 0.00167901, 0.00167901, 0.00167901, 0.00167901, 0.00167901,\n", - " 0.00167901, 0.00167901, 0.00167901, 0.00167901, 0.00167901,\n", - " 0.00167901, 0.00167901, 0.00167901, 0.00167901, 0.00167901,\n", - " 0.00167901, 0.00167901, 0.00167901, 0.00167901, 0.00167901,\n", - " 0.00167901, 0.00167901, 0.00167901, 0.00167901, 0.00167901,\n", - " 0.00167901, 0.00167901, 0.00167901, 0.00167901, 0.00167901,\n", - " 0.00167901, 0.00167901, 0.00167901, 0.00167901, 0.00167901,\n", - " 0.00167901, 0.00167901, 0.00167901, 0.00167901, 0.00167901,\n", - " 0.00167901, 0.00167901, 0.00167901, 0.00167901, 0.00167901,\n", - " 0.00167901, 0.00167901, 0.00167901, 0.00167901, 0.00167901,\n", - " 0.00167901, 0.00167901, 0.00167901, 0.00167901, 0.00167901,\n", - " 0.00167901, 0.00167901, 0.00167901, 0.00167901, 0.00167901,\n", - " 0.00167901, 0.00167901, 0.00167901, 0.00167901, 0.00167901,\n", - " 0.00167901, 0.00167901, 0.00167901, 0.00167901, 0.00167901,\n", - " 0.00167901, 0.00167901, 0.00167901, 0.00167901, 0.00167901,\n", - " 0.00167901, 0.00167901, 0.00167901, 0.00167901, 0.00167901,\n", - " 0.00167901, 0.00167901, 0.00167901, 0.00167901, 0.00167901,\n", - " 0.00167901])}},\n", - " 'inferred_from': {'inputs': {'x': 'pulse_readout_delay'},\n", - " 'output': {'y': 'data'},\n", - " 'setpoints': None,\n", - " 'run_id': 4,\n", - " 'exp_id': 1,\n", - " 'dependencies': {'data': ['pulse_readout_delay']},\n", - " 'data_dimensions': 1,\n", - " 'estimator': {'method': 'Least squared fit',\n", - " 'type': 'T1fit',\n", - " 'function used': 'a*np.exp(-x/T)+c',\n", - " 'dill': {'fitclass': b'\\x80\\x03cqdev_wrappers.fitting.Fitclasses\\nT1\\nq\\x00)\\x81q\\x01}q\\x02(X\\x04\\x00\\x00\\x00nameq\\x03X\\x05\\x00\\x00\\x00T1fitq\\x04X\\x07\\x00\\x00\\x00fun_strq\\x05X\\x19\\x00\\x00\\x00$f(x) = a \\\\exp(-x/T) + 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0.00037406460006786987,\n", - " 'T': 4.9999999999999998e-08,\n", - " 'w': 435468288.61640692,\n", - " 'p': 0,\n", - " 'c': 0.003357881264637656}}" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "fit2 = fit_data(data2, T2(), {'x':'pi_pulse_dur_set'}, {'y':'rec_ctrl_demod_freq_0_mag'}, ['qubit_rs_frequency_set'])\n", - "fit2['start_params']" + "fit2 = fit_data(data2, T2(), {'x':'pi_pulse_dur_set'}, {'y':'rec_ctrl_demod_freq_0_mag'}, ['qubit_rs_frequency_set'])" ] }, { @@ -908,26 +122,6 @@ "It will then save an SQL table with the parameters and an estimate based on the fit, as well as an id that matches the id for the corresponding data point in the experiments database.\n" ] }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'a*np.exp(-x/T)+c'" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fit['inferred_from']['estimator']['function used']" - ] - }, { "cell_type": "code", "execution_count": 9, From a5df7bf100c2a661c452ee29cbf24ba6c2420a51 Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Thu, 19 Jul 2018 01:29:28 -0300 Subject: [PATCH 061/116] Move function checking for correct inputs out of Fitter class --- qdev_wrappers/fitting/Fitter.py | 58 ++++++++++++++++----------------- 1 file changed, 29 insertions(+), 29 deletions(-) diff --git a/qdev_wrappers/fitting/Fitter.py b/qdev_wrappers/fitting/Fitter.py index c063dd36..730802b9 100644 --- a/qdev_wrappers/fitting/Fitter.py +++ b/qdev_wrappers/fitting/Fitter.py @@ -9,6 +9,8 @@ def fit_data(data, fitclass, fun_inputs, fun_output, setpoint_params=None, p0=None, **kwargs): + check_input_matches_fitclass(fitclass, fun_inputs, fun_output, setpoint_params, p0) + fun_dim = len(fitclass.fun_vars) num_inputs = len(fun_inputs) if setpoint_params is not None: @@ -30,6 +32,33 @@ def fit_data(data, fitclass, fun_inputs, fun_output, setpoint_params=None, p0=No return fit +def check_input_matches_fitclass(fitclass, inputs, output, setpoints, p0): + + # check input and output are given, and that input, output and setpoints are in correct format + if (type(inputs) != dict or type(output) != dict): + raise RuntimeError('''Please specify both input and output variables for the function you wish to fit in + # the format fun_inputs = {'x':'name', 'y':'other_name'}, fun_output = {z: 'another_name'} ''') + if (setpoints is not None) and (type(setpoints) != list): + raise RuntimeError('''Please specify setpoints as a list ['setpoint_name', ...], even if there is only one.''') + + # check inputs specified match inputs function takes + if len(inputs) != len(fitclass.fun_vars): + raise RuntimeError('''The function you are fitting to takes {} variables, + and you have specified {}'''.format(len(fitclass.fun_vars), len(inputs))) + for variable in inputs.keys(): + if variable not in fitclass.fun_vars: + raise RuntimeError('''You have specified a variable {}. + The fit function takes variables {}'''.format(variable, fitclass.fun_vars)) + for variable in output.keys(): + if variable not in fitclass.fun_output: + raise RuntimeError('''You have specified a variable {}. + The fit function returns variables {}'''.format(variable, fitclass.fun_output)) + + # check that if a guess p0 is specified, the number of parameters is correct for the fit function + if (p0 is not None) and (len(p0) != len(fitclass.p_labels)): + raise RuntimeError('''You have specified {} start parameters for the fit function: {}. The function takes + {} start parameters: {}'''.format(len(p0), p0, len(fitclass.p_labels), fitclass.p_labels)) + class Fitter: """ The fitter has a method, 'find_fit', which takes the fitclass, data, function inputs, function outputs, @@ -60,7 +89,6 @@ def __init__(self, data, fitclass, output): def find_fit(self, data, fitclass, fun_inputs, fun_output, setpoint_params=None, p0=None, **kwargs): - self.check_input_matches_fitclass(fitclass, fun_inputs, fun_output, setpoint_params, p0) data_dict, label_dict, name_dict, unit_dict = self.organize_data(data, fun_inputs, fun_output, setpoint_params) fit = self.perform_fit(data_dict, data, fitclass, setpoint_params, p0, **kwargs) @@ -84,34 +112,6 @@ def find_fit(self, data, fitclass, fun_inputs, fun_output, setpoint_params=None, return fit - def check_input_matches_fitclass(self, fitclass, inputs, output, setpoints, p0): - - # check input and output are given, and that input, output and setpoints are in correct format - if (type(inputs) != dict or type(output) != dict): - raise RuntimeError('''Please specify both input and output variables for the function you wish to fit in - # the format fun_inputs = {'x':'name', 'y':'other_name'}, fun_output = {z: 'another_name'} ''') - if (setpoints is not None) and (type(setpoints) != list): - raise RuntimeError('''Please specify setpoints as a list ['setpoint_name', ...], even if there is only one.''') - - # check inputs specified match inputs function takes - if len(inputs) != len(fitclass.fun_vars): - raise RuntimeError('''The function you are fitting to takes {} variables, - and you have specified {}'''.format(len(fitclass.fun_vars), len(inputs))) - for variable in inputs.keys(): - if variable not in fitclass.fun_vars: - raise RuntimeError('''You have specified a variable {}. - The fit function takes variables {}'''.format(variable, fitclass.fun_vars)) - for variable in output.keys(): - if variable not in fitclass.fun_output: - raise RuntimeError('''You have specified a variable {}. - The fit function returns variables {}'''.format(variable, fitclass.fun_output)) - - # check that if a guess p0 is specified, the number of parameters is correct for the fit function - if (p0 is not None) and (len(p0) != len(fitclass.p_labels)): - raise RuntimeError('''You have specified {} start parameters for the fit function: {}. The function takes - {} start parameters: {}'''.format(len(p0), p0, len(fitclass.p_labels), fitclass.p_labels)) - - def organize_data(self, data, inputs, output, setpoints): all_vars = [dataname for dataname in inputs.values()] From eab25c6b5ac8657f307417e7d46c9bd143bbf14a Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Thu, 19 Jul 2018 01:50:18 -0300 Subject: [PATCH 062/116] Fix references to fit dicionary to reflect fit dict restructuring --- qdev_wrappers/fitting/RoughPlotting.py | 34 ++++++++++---------------- 1 file changed, 13 insertions(+), 21 deletions(-) diff --git a/qdev_wrappers/fitting/RoughPlotting.py b/qdev_wrappers/fitting/RoughPlotting.py index 0942f9c2..2e04a277 100644 --- a/qdev_wrappers/fitting/RoughPlotting.py +++ b/qdev_wrappers/fitting/RoughPlotting.py @@ -7,13 +7,13 @@ def plot_fit1d(data, fit1d, fitclass, plottype='scatter'): plot = plt.figure() axes = plot.add_axes([0.1, 0.1, 0.8, 0.8]) - xname = fit1d['inferred_from']['xdata'] + xname = fit1d['inferred_from']['inputs'][fitclass.fun_vars[0]] xdata = data[xname]['data'] xlabel = data[xname]['label'] xunit = data[xname]['unit'] axes.set_xlabel('{} [{}]'.format(xlabel, xunit)) - yname = fit1d['inferred_from']['ydata'] + yname = fit1d['inferred_from']['output'][fitclass.fun_output[0]] ydata = data[yname]['data'] ylabel = data[yname]['label'] yunit = data[yname]['unit'] @@ -42,29 +42,21 @@ def plot_fit2d_slice(data, fits2d, fitclass, setvalue, plottype='scatter'): axes = plot.add_axes([0.1, 0.1, 0.8, 0.8]) axes.set_title('Title') - setpoint = fits2d['inferred_from']['setpoints'] - setname = fits2d['inferred_from'][setpoint] - setdata = data[setname]['data'] + setpoint = fits2d['inferred_from']['setpoints'][0] + #setname = fits2d['inferred_from'][setpoint] + setdata = data[setpoint]['data'] if setvalue not in setdata: print('{}: {}'.format(setname, np.unique(setdata))) raise RuntimeError('Setvalue not in set points for this fit. Choose from setvalues above.') - - if setpoint == 'xdata': - xname = fits2d['inferred_from']['ydata'] - xdata = data[xname]['data'] - xlabel = data[xname]['label'] - xunit = data[xname]['unit'] - axes.set_xlabel('{} [{}]'.format(xlabel, xunit)) - - elif setpoint == 'ydata': - xname = fits2d['inferred_from']['xdata'] - xdata = data[xname]['data'] - xlabel = data[xname]['label'] - xunit = data[xname]['unit'] - axes.set_xlabel('{} [{}]'.format(xlabel, xunit)) - - yname = fits2d['inferred_from']['zdata'] + + xname = fits2d['inferred_from']['inputs'][fitclass.fun_vars[0]] + xdata = data[xname]['data'] + xlabel = data[xname]['label'] + xunit = data[xname]['unit'] + axes.set_xlabel('{} [{}]'.format(xlabel, xunit)) + + yname = fits2d['inferred_from']['output'][fitclass.fun_output[0]] ydata = data[yname]['data'] ylabel = data[yname]['label'] yunit = data[yname]['unit'] From 4112f24ee4cf2f6e95a013d710fbc4c60c06fe09 Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Thu, 19 Jul 2018 01:55:31 -0300 Subject: [PATCH 063/116] Update explanatory text in Example doc to reflect changes --- qdev_wrappers/fitting/Example.ipynb | 203 +++++++++++++++++++++++----- 1 file changed, 167 insertions(+), 36 deletions(-) diff --git a/qdev_wrappers/fitting/Example.ipynb b/qdev_wrappers/fitting/Example.ipynb index 11e1c19d..409c3d41 100644 --- a/qdev_wrappers/fitting/Example.ipynb +++ b/qdev_wrappers/fitting/Example.ipynb @@ -43,14 +43,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Fitting, step 1: convert your dataset into a dictionary\n", + "### Fitting, step 1: Take saved dataset and convert it to a python dictionary\n", "\n", - "Convert dataset with run id 4 from SQL, store in 'data'. This, incidentally, is a 1D dataset." + "The *data_to_dict* function will load data from the database currently set up in qc.config. It takes the run id of the data as an argument. Here we load a dataset with run id 4 from the SQL database, and store it in variable 'data'." ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -61,27 +61,41 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "This can also be done with the 'old' datasets. In this case, this is a 2D dataset, containing multiple measurements at each point (mag0, phase0 and raw output):" + "This can also be done with the 'old' datasets. To do this, the *data_to_dict* function must also be given *datatype = 'qcodes_legacy'*. This also takes the optional argument *samplefolder*. If no samplefolder is given, then it uses the file path from *qc.Dataset.location_provider*." ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ - "data2 = data_to_dict(621, datatype = 'qcodes_legacy', samplefolder = None)" + "data2 = data_to_dict(621, datatype = 'qcodes_legacy')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Fitting step 2: Performing fit, getting fit dictionary\n", + "### Fitting, step 2: Perform fit, get fitted parameters and function estimate in another dictionary\n", + "\n", + "To get a fit, the *fit_data* function takes the following arguments:\n", + "\n", + "#### Required arguments\n", + "**data**: data dictionary returned by *data_to_dict*
\n", + "**fitclass**: fitclass for the function to be fit (options are currently T1() and T2())
\n", + "**function input(s)**: inputs of the function as a dictionary {'variable_letter' : 'data_name_for_variable'}
\n", + "**function outputs**: output(s) of the function, in the same format as inputs
\n", + "\n", + "#### Optional arguments: \n", + "**setpoint_params**: data name of setpoint parameters in list format.
\n", + "**p0**: initial guesses for the parameters, if values other than the estimate found by the fitclass 'guess' method should be used\n", "\n", - "To get a fit, the data dictionary goes into the fitter along with a fitclass (options are currently T1() and T2()). \n", "\n", - "Set *x = 'data_name_for_x'*, *y = 'data_name_for_y\"* (and optionally *z = \"data_name_for_z\"*). If you are uncertain of the names of the variables in the data, they can be read in the data dictionary, or you can enter something random for x and y and it will throw an error that gives you a list of your options.\n" + "
\n", + "If you are uncertain of the names of the variables in the data, they can be read in the data dictionary, or you can enter some random string and it will throw an error that gives you a list of all the data names in the data dictionary. Similarly, if you aren't sure which variables the function takes, just guess, and it will provide a list of options if you are incorrect.
\n", + "\n", + "An example where the function to be fitted has one input variable, *x*, and one output variable, *y*, with no setpoints used in the measurement, is stored in variable 'fit':\n" ] }, { @@ -97,14 +111,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "For the 2D datasets, at the moment, have only one option, which is to get back fits for slices of the data. Each slice is taken at a particular setpoint, and the fit for that setpoint is listed in the fit dictionary.\n", - "\n", - "As with the 1D example, you currently specify the axes yourself. You should also choose along which direction you want the cuts to be taken.\n" + "The second example is a case where data was taken at multiple setpoints, but the fuction to be fitted still takes one variable *x* and outputs one variable, *y*." ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -115,11 +127,61 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Saving your fit to an SQL database (in progress)\n", + "The input variables, output variables, and function to be fitted are all attributes of the fitclass, and the variable names stored in the data dictionary, so it is also possible to check which variables are used in the fitclass and which are available in the data:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['x']\n", + "['y']\n" + ] + } + ], + "source": [ + "print(T2().fun_vars) #inputs\n", + "print(T2().fun_output) #outputs" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['qubit_rs_frequency_set',\n", + " 'pi_pulse_dur_set',\n", + " 'rec_ctrl_demod_freq_0_mag',\n", + " 'rec_ctrl_demod_freq_0_phase',\n", + " 'rec_ctrl_raw_output']" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data2['variables'] #all data names contained in the data dictionary" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Saving your fit to an SQL database \n", "\n", - "If you want to save the analysis information, you can use this (in-progress) function, which will create (if such a thing does not already exist) a new SQL database called *analysis.db* in the same folder as your experiments. \n", + "If you want to save the analysis information, you can use this function, which will create a new run in the experiment database with the type *analysis* instead of *result*. It will save the run in *runs*, save the relevant units and labels in *layouts*, save an analysis summary in a table called *analyses*. It takes the *fit* dictionary produced by the *fit_data* function as an argument. \n", "\n", - "It will then save an SQL table with the parameters and an estimate based on the fit, as well as an id that matches the id for the corresponding data point in the experiments database.\n" + "It will also save a new SQL table in the experiment with the fitted parameters values and an 'estimate' for each data point based on the fit, as well as an id that matches the id for the corresponding data point in the corresponding measurement run.\n" ] }, { @@ -131,8 +193,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Table analysis_4_T1fit_4 created\n", - "Table analysis_621_T2fit_4 created\n" + "Table analysis_4_T1fit created\n", + "Table analysis_621_T2fit created\n" ] } ], @@ -147,7 +209,9 @@ "source": [ "### Plotting your fit to make sure nothing weird has happened\n", "\n", - "To see how your fit looks compared to the data, there is currently only rough plotting from the dictionary. Pretty plotting from the saved dataset and analysis is for the near future.\n", + "To see how your fit looks compared to the data, there is currently only rough plotting from the dictionary. Rough plotting only does 1D plots. The function *plot_fit1d* plots a 1D fit on a 1D dataset, while *plot_fit2d_slice* makes a 1D plot of a slice through a 2D dataset at a particular set value. Other dimensionalities of data and plotting are not options for rough plotting. \n", + "\n", + "Pretty plotting from the saved data and analysis is for the near future, and will be less picky about which types of plots it will perform.\n", "\n", "The first dataset was 1D, so:" ] @@ -158,22 +222,20 @@ "metadata": {}, "outputs": [ { - "ename": "KeyError", - "evalue": "'xdata'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplot_fit1d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mT1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;31m#for comparison to original data:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mplot_by_id\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/qdev-wrappers/qdev_wrappers/fitting/RoughPlotting.py\u001b[0m in \u001b[0;36mplot_fit1d\u001b[0;34m(data, fit1d, fitclass, plottype)\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0maxes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_axes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.8\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mxname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfit1d\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'inferred_from'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'xdata'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 11\u001b[0m \u001b[0mxdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mxname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'data'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0mxlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mxname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'label'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mKeyError\u001b[0m: 'xdata'" - ] + "data": { + "image/png": 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\n", 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -184,7 +246,7 @@ "plot_fit1d(data, fit, T1())\n", "\n", "#for comparison to original data:\n", - "plot_by_id(4)" + "plot = plot_by_id(4)" ] }, { @@ -196,18 +258,80 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plot_fit2d_slice(data2, fit2, T2(), setvalue = 5.65600000e+09)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "([DataSet:\n", + " location = '/Users/Lillian/Qcodes/My stuff/mainfolder/data/621'\n", + " | | | \n", + " Setpoint | qubit_rs_frequency_set | frequency | (101,)\n", + " Setpoint | pi_pulse_dur_set | pi_pulse_dur | (101, 101)\n", + " Measured | rec_ctrl_demod_freq_0_mag | demod_freq_0_mag | (101, 101)\n", + " Measured | rec_ctrl_demod_freq_0_phase | demod_freq_0_phase | (101, 101)\n", + " Measured | rec_ctrl_raw_output | raw_output | (101, 101)],\n", + " [,\n", + " ,\n", + " ])" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "#for comparison to original data:\n", "show_num(621)" @@ -220,6 +344,13 @@ "outputs": [], "source": [] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null, From 5dbdb4bb3e641a7eb5f85c4d74a279e0498162d7 Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Thu, 19 Jul 2018 02:10:47 -0300 Subject: [PATCH 064/116] Add clarifying comments to rough plotting functions --- qdev_wrappers/fitting/RoughPlotting.py | 46 +++++++++++++++++--------- 1 file changed, 30 insertions(+), 16 deletions(-) diff --git a/qdev_wrappers/fitting/RoughPlotting.py b/qdev_wrappers/fitting/RoughPlotting.py index 2e04a277..f7882e4b 100644 --- a/qdev_wrappers/fitting/RoughPlotting.py +++ b/qdev_wrappers/fitting/RoughPlotting.py @@ -6,7 +6,8 @@ def plot_fit1d(data, fit1d, fitclass, plottype='scatter'): plot = plt.figure() axes = plot.add_axes([0.1, 0.1, 0.8, 0.8]) - + + # get data, label and units for the two axes, and set appropriate axis labels xname = fit1d['inferred_from']['inputs'][fitclass.fun_vars[0]] xdata = data[xname]['data'] xlabel = data[xname]['label'] @@ -18,16 +19,20 @@ def plot_fit1d(data, fit1d, fitclass, plottype='scatter'): ylabel = data[yname]['label'] yunit = data[yname]['unit'] axes.set_ylabel('{} [{}]'.format(ylabel, yunit)) - + + # set plot title axes.set_title('{} vs. {}'.format(ylabel, xlabel)) - + + # make list of fitted parameter values in the order the fitclass method 'fun' takes them parameters = list(fitclass.p_labels) for index, parameter in enumerate(parameters): parameters[index] = fit1d['parameters'][parameter]['value'] - x = np.linspace(xdata.min(), xdata.max(), len(xdata)*10) # Parameters are currently without units!!! + # add the fit to the plot, using the fitted parameter list to find points on the fit + x = np.linspace(xdata.min(), xdata.max(), len(xdata)*10) axes.plot(x, fitclass.fun(x, *parameters), color='C0') + # plot the data if plottype == 'line': axes.plot(xdata, ydata, color='C0') elif plottype == 'scatter': @@ -37,19 +42,23 @@ def plot_fit1d(data, fit1d, fitclass, plottype='scatter'): def plot_fit2d_slice(data, fits2d, fitclass, setvalue, plottype='scatter'): + + """ Takes a 2D dataset and fit, and plots a 1D cross-section of the data at the setpoint specified by + 'setvalue', along with the fit at that setpoint. """ plot = plt.figure() axes = plot.add_axes([0.1, 0.1, 0.8, 0.8]) - axes.set_title('Title') - + + # get setpoint name and data setpoint = fits2d['inferred_from']['setpoints'][0] - #setname = fits2d['inferred_from'][setpoint] setdata = data[setpoint]['data'] - + + # confirm tha the setvalue tha the cross-section should be taken at is in fact one of the setpoints from the data if setvalue not in setdata: print('{}: {}'.format(setname, np.unique(setdata))) raise RuntimeError('Setvalue not in set points for this fit. Choose from setvalues above.') + # get data, label and units for the two axes, and set appropriate axis labels xname = fits2d['inferred_from']['inputs'][fitclass.fun_vars[0]] xdata = data[xname]['data'] xlabel = data[xname]['label'] @@ -61,9 +70,11 @@ def plot_fit2d_slice(data, fits2d, fitclass, setvalue, plottype='scatter'): ylabel = data[yname]['label'] yunit = data[yname]['unit'] axes.set_ylabel('{} [{}]'.format(ylabel, yunit)) - - axes.set_title('{} vs. {}'.format(ylabel, xlabel)) - + + # set plot title + axes.set_title('{} vs. {} at {}={}'.format(ylabel, xlabel, setpoint, setvalue)) + + # select, from the full data for the two axes, only the data that corresponds to the chosen setpoint x_dat = [] y_dat = [] for xpoint, ypoint, setpoint in zip(xdata, ydata, setdata): @@ -72,17 +83,20 @@ def plot_fit2d_slice(data, fits2d, fitclass, setvalue, plottype='scatter'): y_dat.append(ypoint) xdata = np.array(x_dat) ydata = np.array(y_dat) - + + # make list of fitted parameter values in the order the fitclass method 'fun' takes them parameters = list(fitclass.p_labels) - for index, parameter in enumerate(parameters): # Parameters are currently without units!!! + for index, parameter in enumerate(parameters): parameters[index] = fits2d[setvalue]['parameters'][parameter]['value'] - + + # add the fit to the plot, using the fitted parameter list to find points on the fit x = np.linspace(xdata.min(), xdata.max(), len(xdata)*10) axes.plot(x, fitclass.fun(x, *parameters), color='C0') - + + # plot the data if plottype == 'line': axes.plot(xdata, ydata, color='C0') elif plottype == 'scatter': axes.plot(xdata, ydata, marker='.', markersize=5, linestyle='', color='C0') else: - raise ValueError('Unknown plottype. Please choose point or line plot.') \ No newline at end of file + raise ValueError('Unknown plottype. Please choose point or line plot.') From ea61e8938ec8004b375dfae34ccca519adb54848 Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Thu, 19 Jul 2018 19:40:53 -0300 Subject: [PATCH 065/116] Clean up Fitter and add explanatory comments --- qdev_wrappers/fitting/Fitter.py | 271 +++++++++++++++++++++++--------- 1 file changed, 194 insertions(+), 77 deletions(-) diff --git a/qdev_wrappers/fitting/Fitter.py b/qdev_wrappers/fitting/Fitter.py index 730802b9..e7c74761 100644 --- a/qdev_wrappers/fitting/Fitter.py +++ b/qdev_wrappers/fitting/Fitter.py @@ -2,29 +2,53 @@ import numpy as np import dill -from qdev_wrappers.fitting.Converter import SQL_Converter, Legacy_Converter from qdev_wrappers.fitting.Fitclasses import T1, T2 from scipy.optimize import curve_fit def fit_data(data, fitclass, fun_inputs, fun_output, setpoint_params=None, p0=None, **kwargs): + """ This takes as arguments the data dictionary, the desired fit class as a LeastSquaresFit + class (which defines the function to be fitted to the data), function inputs and outputs + as a dictionary of the form {'x' : 'variable_name'}, and optionally setpoint parameters as + a list. + + It returns a dictionary containing the fitted parameters and an array of "estimated" + outputs given the fit (i.e. what this fit and parameters predict the measurement would have + given at each point), as well as some metadata about the fit. + + It first checks that the arguments given for setpoints, inputs and outputs matches the input + needed for fitting to the specified fit class. Then it creates an instance of the appropriate + fitter class, and calls the 'find_fit' method of the fitter, saving it in the variable 'fit'. + This method creates a dictionary with the fit. + + Finally, it preserves the fitter and fitclass used to perform the fit by pickling them via the + dill module, and adds the binary to the fit dictionary.""" + check_input_matches_fitclass(fitclass, fun_inputs, fun_output, setpoint_params, p0) + # this is used to decide which fitter class to create an instance of. If they are combined, this won't be needed. fun_dim = len(fitclass.fun_vars) num_inputs = len(fun_inputs) if setpoint_params is not None: num_setpoints = len(setpoint_params) else: num_setpoints = 0 - + # creates an instance of the relevant fitter based on the number of setpoints and inputs and the dimensionality + # of the function to be fitted. If there were just one fitter class, the if statements wouldn't be needed. if fun_dim == 1 and num_inputs == 1 and num_setpoints == 0: - fitter = Fitter1D(data, fitclass, fun_output) + fitter = Fitter1D(data, fun_inputs, fun_output) elif fun_dim == 1 and num_inputs == 1 and num_setpoints == 1: - fitter =Fitter_2Ddata_1Dfunction(data, fitclass, fun_output) #what does the fitter actually need?? + fitter =Fitter_2Ddata_1Dfunction(data, fun_inputs, fun_output) #what does the fitter actually need?? + else: + raise NotImplementedError('''You have specified a {}-dimensional function, {} function inputs, and + {} setpoints. This process is currently so specific that your only options are to fit to 1D functions, + provide 1 input, and have either 0 or 1 setpoint types. Sorry.'''.format(fun_dim, num_inputs, num_setpoints)) + # the fitter performs the fit and saves a dictionary containing the results in variable 'fit' fit = fitter.find_fit(data, fitclass, fun_inputs, fun_output, setpoint_params, p0, **kwargs) + # the fitter and fit class used for the fit are preserved via dill and added to the fit dictionary dill_fitclass = dill.dumps(fitclass) dill_fitter = dill.dumps(fitter) @@ -32,7 +56,19 @@ def fit_data(data, fitclass, fun_inputs, fun_output, setpoint_params=None, p0=No return fit -def check_input_matches_fitclass(fitclass, inputs, output, setpoints, p0): +def check_input_matches_fitclass(fitclass, inputs, output, setpoints, p0=None): + + """ This function takes the same arguments as the 'fit_data' function, and confirms that the + given arguments are in the correct format for the 'fit_data' function to proceed, and that + they match the expected inputs for the specified fit class. + + Each fit class describes a mathematical function to be fitted to, with a set of attributes, + including which variables the function has as inputs, and what outputs it has. This function + makes sure that the correct inputs and outputs are specified compared to what inputs and + outputs the mathematical function has, and that they are specified in the correct format. It + also checks that setpoints are given in the correct format (a list). If guess parameters are + specified, it checks that the length of the parameter list is the expected number of + parameters for the function chosen. """ # check input and output are given, and that input, output and setpoints are in correct format if (type(inputs) != dict or type(output) != dict): @@ -41,7 +77,7 @@ def check_input_matches_fitclass(fitclass, inputs, output, setpoints, p0): if (setpoints is not None) and (type(setpoints) != list): raise RuntimeError('''Please specify setpoints as a list ['setpoint_name', ...], even if there is only one.''') - # check inputs specified match inputs function takes + # check inputs/outputs specified match inputs/outputs function takes if len(inputs) != len(fitclass.fun_vars): raise RuntimeError('''The function you are fitting to takes {} variables, and you have specified {}'''.format(len(fitclass.fun_vars), len(inputs))) @@ -59,51 +95,67 @@ def check_input_matches_fitclass(fitclass, inputs, output, setpoints, p0): raise RuntimeError('''You have specified {} start parameters for the fit function: {}. The function takes {} start parameters: {}'''.format(len(p0), p0, len(fitclass.p_labels), fitclass.p_labels)) + class Fitter: """ The fitter has a method, 'find_fit', which takes the fitclass, data, function inputs, function outputs, - setpoints when relevant, and optionally, guess parameters, and returns a fit dictionary. It does this by - using all the other methods in the Fitter to fit a particular function (as defined in a LeastSquaresFit - class) to the specified data. + setpoints when relevant, and optionally, guess parameters, and returns a fit dictionary. The fit class is + a LeastSqauresFit class describing a mathematical function that can be fitted to data, while the Fitter + class performs the fitting. All other methods in the fitter are com + + Much of the fitting is generic and does not depend on the number of inputs, outputs or setpoints in the + specified LeastSquaresFit class; however, depending on the dimensionality of the function to be fitted + and the measured data, the function 'perform_fit' varies. - Much of the fitting is generic and does not depend on the number of inputs, outputs or setpoints in the - specified LeastSquaresFit class; however, depending on the dimensionality of the function to be fitted - and the measured data, the function 'perform_fit' varies """ + Currently, Fitter is a parent class with no 'perform_fit' method of its own, and two child classes inherit + from it: a class for 1D fits to 1D data (i.e. one variable, one output and no setpoints) and a class for + 1D fits to 2D data (i.e. one variable, one setpoint and one output). """ - def __init__(self, data, fitclass, output): + #TODO: perform_fit and find_fit are two different functions with deceptively similar names. This could be improved. + #TODO: I think with some changes, perform_fit could be made generic, so only one Fitter class was needed - self.fitclass = fitclass #'UNNECESSARY??' - self.fun_dim = len(fitclass.fun_vars) #'UNNECESSARY??' - self.data_dim = 'undefined' - self.input_vars = [] - self.output_var = [] + def __init__(self, data, inputs, output): + + self.input_vars = [variable for variable in inputs.keys()] + self.output_var = [variable for variable in output.keys()] + self.output_dataname = [dataname for dataname in output.values()] self.all_datanames = [] - self.full_parameter_dict = 'undefined' - self.output_dataname = [] + self.full_parameter_dict = {} - self.input_dataname = [] self.setpoint_params = [] - self.input_data = [] - self.output_data = [] - def find_fit(self, data, fitclass, fun_inputs, fun_output, setpoint_params=None, p0=None, **kwargs): - data_dict, label_dict, name_dict, unit_dict = self.organize_data(data, fun_inputs, fun_output, setpoint_params) + """ find_fit combines other Fitter methods to organize the data, find fitted parameters and save + them to a fit dictionary. It then adds parameter units to the fit dictionary, uses the fitted + parameters and the given data to calculate estimated outputs given the fitten function, and adds + metadata about the data and the fit to fit['inferred_from'] + + As arguments, it takes the data dictionary produced by the Converter, a LeastSquaresFit class + that defines the mathematical function to be fitted to the data, the inputs and outputs for the + mathematical function in the format {'x' : 'dataname', ...}. Optionally, it also takes a list of + setpoint parameters if any in the format ['dataname1', ... ], and a list of guess parameters if + they are being specified manually instead of estimated by the LeastSquaresFit 'guess' function.""" + + # organize data in a format needed later (see 'organize_data' function) + data_kwargs, unit_dict = self.organize_data(data, fun_inputs, fun_output, setpoint_params) - fit = self.perform_fit(data_dict, data, fitclass, setpoint_params, p0, **kwargs) + # fit parameters via SciPy curve_fit and save to fit dictionary + fit = self.perform_fit(data_kwargs, data, fitclass, setpoint_params, p0, **kwargs) + # find parameter units and save to fit dictionary fit['parameter units']= self.find_parameter_units(fitclass, unit_dict) - fit['estimate'] = self.estimate_function_values(fitclass, data, data_dict) + # use fitted parameters and data to find the estimated output of the fitted function + fit['estimate'] = self.estimate_function_values(fitclass, data, data_kwargs) + # save metadata fit['inferred_from'] = {'inputs': fun_inputs, 'output': fun_output, 'setpoints': setpoint_params, 'run_id': data['run_id'], 'exp_id': data['exp_id'], 'dependencies': data['dependencies'], - 'data_dimensions': self.data_dim, 'estimator' : {'method': 'Least squared fit', 'type': fitclass.name, 'function used': str(fitclass.fun_np), @@ -114,50 +166,66 @@ def find_fit(self, data, fitclass, fun_inputs, fun_output, setpoint_params=None, def organize_data(self, data, inputs, output, setpoints): + """ This function organizes the data based on the inputs, outputs and setpoints specified. + + First, it makes a list of all data names from inputs, outputs and setpoints, and confirms + that those data names are all present in the actual data dictionary. That is, it makes + sure that if you have said that inputs are {'x': 'frequency'}, that 'frequency' is among + the variables present in the data. It makes a list of all variables that will be used, and + saves them as Fitter attribute self.all_datanames + + Second, it makes and returns two dictionaries, for units and for data arrays: + data_kwargs = {'x': np.array, 'y': np.array ...} and + unit_dict = {'x': 'unit', 'y': 'unit' ...}. + + The data_kwargs dict will be used later as **kwargs in a variety of functions that need + inputs like 'x = np.array'. The unit dictionary specifies the unit for each input variable + of the mathematical function being fitted, and will be used to find the parameter units + in find_parameter_units. """ + + # make a list of all datanames specefied by the inputs, outputs and setpoints all_vars = [dataname for dataname in inputs.values()] for dataname in output.values(): all_vars.append(dataname) if setpoints is not None: for dataname in setpoints: all_vars.append(dataname) - # confirm all variables involved are in the dataset + # confirm all variables involved are in the data dictionary for dataname in all_vars: if dataname not in data.keys(): raise RuntimeError('''Variable {} not found in data dictionary. Data dictionary contains variables: {}'''.format(dataname, data['variables'])) self.all_datanames = all_vars - self.data_dim = len(self.all_datanames) - 1 - data_dict = {} - label_dict = {} - name_dict = {} + # make a dictionary of data arrays and units in format {'x' : np.array...} and {'x' : 'unit', ...} + data_kwargs = {} unit_dict = {} for variable, name in inputs.items(): - data_dict[variable] = data[name]['data'] - label_dict[variable] = data[name]['label'] - name_dict[variable] = data[name]['name'] + data_kwargs[variable] = data[name]['data'] unit_dict[variable] = data[name]['unit'] for variable, name in output.items(): - data_dict[variable] = data[name]['data'] - label_dict[variable] = data[name]['label'] - name_dict[variable] = data[name]['name'] + data_kwargs[variable] = data[name]['data'] unit_dict[variable] = data[name]['unit'] - self.input_vars = [variable for variable in inputs.keys()] - self.output_var = [variable for variable in output.keys()] - self.input_dataname = [dataname for dataname in inputs.values()] - self.output_dataname = [dataname for dataname in output.values()] + return data_kwargs, unit_dict + + def find_start_parameters(self, fitclass, p0, data_kwargs): - return data_dict, label_dict, name_dict, unit_dict + """ Takes as arguments the fit class, the guess parameters if any, and the "data_kwargs", + which is not the data as originally given, but the data arrays organized by variables + by the organize_data function. It has the format {"x": np.array, ...}, so that it can + unpacked as keyword arguments for the mathematical functions. - def find_start_parameters(self, fitclass, p0, data_dict): + If no guess parameters are specified, the guess function from the fit class is given + key word arguments from the unpacked data_kwargs, and returns a set of guess parameters. + If guess parameters have been specified explicitly, then those are used instead. """ - if (p0 == None and hasattr(fitclass, 'guess')): - p0 = getattr(fitclass, 'guess')(**data_dict) + if (p0 is None and hasattr(fitclass, 'guess')): + p0 = getattr(fitclass, 'guess')(**data_kwargs) return p0 - elif p0 != None: + elif p0 is not None: p0 = p0 return p0 else: @@ -165,61 +233,121 @@ def find_start_parameters(self, fitclass, p0, data_dict): def find_parameter_units(self, fitclass, unit_dict): + """ This uses the relationship between the units stored in the fitclass attribute 'p_units' and + the unit labels for the data to determine the unit labels for each parameter. + + For example: according to the p_units attribute, the unit for the first parameter in the list + is 1/y. Based on the data that has been organized by organize_data and stored in the unit_dict, + the unit for y are 'S', so this function concludes that the units for the first parameter are 1/S + (it does not know that this is Hz, its not that clever). + + It returns a dictionary in the format {"p1" : unit, "p2" : unit ...} of all function parameters, + where the key is the parameters as specified in the fitclass attribute p_labels. """ + + # unit labels could fx. be [a, T, w, p, c], and unit templates could be ['y', 'x', '1/x', '', 'y'] unit_templates = fitclass.p_units unit_labels = fitclass.p_labels param_units = {} + # zip the two lists together and take the elements one at a time for template, label in zip(unit_templates, unit_labels): + # for each element, make the template a list. 'y' --> ['y'], '1/x' --> ['1', '/', 'x'] template = list(template) + # go through each element of the template list, and compare to the variables in the unit_dict for i in range(len(template)): for variable in unit_dict.keys(): + # if the i'th element of the template is one of the variables in unit_dict, replace with the unit + # f.x., if {'x': 'S', ...}, ['x', ...] --> ['S', ...] if template[i] == variable: template[i] = unit_dict[variable] + # join the list again, fx. ['1', '/', 'S'] --> '1/S' unit = "".join(template) + #add the unit to the param_units dictionary param_units[label] = unit return param_units - def perform_fit(self, data_dict, data, fitclass, setpoints, p0, **kwargs): + def perform_fit(self, data_kwargs, data, fitclass, setpoints, p0, **kwargs): + + """ A suitably generic version of this function isn't implemented yet. See specific versions in + child classes. + + The purpose of this function is to find the fitted values of the parameters via SciPy's curve_fit + function. The complication in making it generic lies in the need to sort the data by setpoints and + then do multiple fits to subsets of the data if the fitted function is of lower dimensionality than + the measurement. + + It does two things: + + 1. It sets the Fitter attribute self.full_parameter_dict, which is a dictionary that contains all + of the parameters as keys, and for each key, the value is an array with the same dimension as + the original data, with the parameter's value for each datapoint in the original data. For + example, if parameter 1 were found to be 2.5 for the first 5 datapoints, and then the measurement + was moved to a new setpoint, and the next 5 datapoints had p1 = 3.5, you would have + + self.full_parameter_dict = {"p1" : [[2.5, 2.5, 2.5, 2.5, 2.5, 3.5, 3.5, 3.5 ... ]], + "p2" : [[ ... ]], + ... } + + Thereby saving all the parameters with indicies matching their corresponding datapoints, which + will be needed for calculating the estimated measurement based on the fit. + + 2. It returns a fit dictionary containing the start_params (initial guess), and the value and cov + matrix for each fitted parameter (organized by setpoint if there were setpoints). """ raise NotImplementedError("A generic fitter function for the Fitter base class has yet to be implemented.") - def estimate_function_values(self, fitclass, data, data_dict): + def estimate_function_values(self, fitclass, data, data_kwargs): + + """ Takes fitclass and data, as well as the data_kwargs produced by organize_data, and references the + Fitter attribute self.full_parameter_dict created by perform_fit. It uses the numpy function saved + in the LeastSquaresFit class attribute 'fun', giving it the input data and input parameters as numpy + arrays. It returns a numpy array of 'estimates', i.e. what the predicted output of the fitted function + would be given the parameter values found by perform_fit and function inputs. + + It returns an estimate dictionary containing the estimate numpy array and information about labels, + units and parameters used. The parameter array and estimate array are what will be saved in the SQL + table later.""" estimate = {} - input_data = data_dict + # obtain a dictionary containing only input variables in format {"x": np.array, ...}, for **kwargs + input_data = data_kwargs for variable in self.output_var: del input_data[variable] + #obtain parameters, also in the format {"p1" : np.array, ...} for **kwargs input_parameters = self.full_parameter_dict # Calculate estimated values for output based on function and store in estimate['values'] estimate['values'] = fitclass.fun(**input_data, **input_parameters) + # FIXME : PyCharm says this duplicate ** expression is incompatible with versions < 3.5. Is that a problem? - # Save estimate + # Save other info about the estimate estimate['name'] = '{}_estimate'.format(self.output_dataname[0]) estimate['label'] = '{} estimate'.format(data[self.output_dataname[0]]['label']) estimate['unit'] = data[self.output_dataname[0]]['unit'] - estimate['parameters'] = self.full_parameter_dict #not sure this is used for anything + estimate['parameters'] = self.full_parameter_dict return estimate class Fitter1D(Fitter): - def perform_fit(self, data_dict, data, fitclass, setpoints, p0, **kwargs): + def perform_fit(self, data_kwargs, data, fitclass, setpoints, p0, **kwargs): - fit = {} - fit['parameters'] = {} - fit['start_params'] = {} + #make fit dictionary + fit = { 'parameters': {}, 'start_params': {} } - xdata = data_dict[self.input_vars[0]] - ydata = data_dict[self.output_var[0]] + #define data arrays + xdata = data_kwargs[self.input_vars[0]] + ydata = data_kwargs[self.output_var[0]] - p_guess = self.find_start_parameters(fitclass, p0, data_dict) + #find guess for start parameters, run curve_fit function to perform SciPy least squares fit + p_guess = self.find_start_parameters(fitclass, p0, data_kwargs) popt, pcov = curve_fit(fitclass.fun, xdata, ydata, p0=p_guess, **kwargs) + # save fit information for parameter in fitclass.p_labels: fit['parameters'][parameter] = {'value': popt[fitclass.p_labels.index(parameter)]} fit['parameters'][parameter]['cov'] = pcov[fitclass.p_labels.index(parameter)] @@ -239,11 +367,11 @@ def perform_fit(self, data_dict, data, fitclass, setpoints, p0, **kwargs): class Fitter_2Ddata_1Dfunction(Fitter): - def perform_fit(self, data_dict, data, fitclass, setpoints, p0, **kwargs): + def perform_fit(self, data_kwargs, data, fitclass, setpoints, p0, **kwargs): #Retrieve data - xarray = data_dict[self.input_vars[0]] - yarray = data_dict[self.output_var[0]] + xarray = data_kwargs[self.input_vars[0]] + yarray = data_kwargs[self.output_var[0]] setpoint_array = data[setpoints[0]]['data'] # Reorganize data by setpoint @@ -265,9 +393,8 @@ def perform_fit(self, data_dict, data, fitclass, setpoints, p0, **kwargs): xdata = np.array(xdata_lst) ydata = np.array(ydata_lst) - #perform 1D fit for cross section at each setpoint and save to fit dictionary - fit = {} - fit['start_params'] = {} + #perform 1D fit for cross section at each setpoint and save to fit dictionary (as in Fitter1D) + fit = { 'start_params' : {} } for set_value, xdata_1d, ydata_1d in zip(unique_setpoints, xdata, ydata): @@ -280,7 +407,7 @@ def perform_fit(self, data_dict, data, fitclass, setpoints, p0, **kwargs): fit[set_value]['parameters'] = {} fit['start_params'][set_value] = {} - for parameter in fitclass.p_labels: # parameters currently missing units, use fitclass.p_units + for parameter in fitclass.p_labels: fit[set_value]['parameters'][parameter] = {'value': popt[fitclass.p_labels.index(parameter)]} fit[set_value]['parameters'][parameter]['cov'] = pcov[fitclass.p_labels.index(parameter)] fit['start_params'][set_value][parameter] = p_guess[fitclass.p_labels.index(parameter)] @@ -298,13 +425,3 @@ def perform_fit(self, data_dict, data, fitclass, setpoints, p0, **kwargs): self.full_parameter_dict = all_params return fit - - - - - #def fit_2d_function_to_2d_data(self): - # print('This does not work yet.') - - - #is data_dict helpful? What are all theses things I've defined now, and how many of them are just reworks - #the same stuff? \ No newline at end of file From cab0b62045a34f91f1dfa4b756bd963bff516dfe Mon Sep 17 00:00:00 2001 From: nataliejpg Date: Thu, 16 Aug 2018 14:09:55 +0200 Subject: [PATCH 066/116] pep8 and comments updated, Fitclasses to have more generic names and slightly refactored structure --- qdev_wrappers/fitting/AnalysisToSQL.py | 70 ++++--- qdev_wrappers/fitting/Converter.py | 268 ++++++++++++++---------- qdev_wrappers/fitting/Example.ipynb | 24 ++- qdev_wrappers/fitting/Fitclasses.py | 104 +++++----- qdev_wrappers/fitting/Fitter.py | 272 +++++++++++++++---------- qdev_wrappers/fitting/RoughPlotting.py | 31 +-- 6 files changed, 445 insertions(+), 324 deletions(-) diff --git a/qdev_wrappers/fitting/AnalysisToSQL.py b/qdev_wrappers/fitting/AnalysisToSQL.py index 1f458713..f150724b 100644 --- a/qdev_wrappers/fitting/AnalysisToSQL.py +++ b/qdev_wrappers/fitting/AnalysisToSQL.py @@ -4,14 +4,20 @@ import dill import qcodes as qc from os.path import expanduser -from qdev_wrappers.fitting.Fitclasses import T1, T2 def is_table(tablename, cursor): - - """Takes table name (string), and a connection to an SQL database. Returns True if a table with that name - exists, and False if it does not. Throws an error if it finds multiple tables with the same name, or if the - sql count function returns something unexpected.""" + """ + Args: + tablename (str) + cursor: connection to an SQL database + Returns: + is_table (bool): True if a table with that name exists, + False if not. + + Error if it finds multiple tables with the same name, or if the + sql count function returns something unexpected. + """ table_count = "SELECT count(*) FROM sqlite_master WHERE type = 'table' AND name='{}'".format(tablename) execute = cursor.execute(table_count) @@ -24,16 +30,21 @@ def is_table(tablename, cursor): return True else: - raise RuntimeError('''Attempt to check existing table names failed. Count of tables with - name {} returned {} instead of 1 or 0.'''.format(tablename, count)) + raise RuntimeError( + 'Attempt to check existing table names failed.' + ' Count of tables with name {} returned {} ' + 'instead of 1 or 0.'.format(tablename, count)) def make_table(tablename, cursor): - - """Takes suggested table name, and a connection to an SQL database. If the name is already in use, it goes - through numbered iterations of the name until it finds an unused table name. Otherwise, it leaves the name as - suggested. It creates a table with the name in the database, and returns the name.""" - + """ + Args: + tablename (str) + cursor: connection to an SQL database. + Returns: + tablename (str): same as arg if this name was available, + otherwise a numbered variant + """ name = tablename n = 0 @@ -46,12 +57,12 @@ def make_table(tablename, cursor): def fit_to_sql(fit, database=None): - - """Takes the fit (dictionary output by Fitter) and an optional database. If no database name/location - is given, it gets the database from qc.config. Stores the information from the fit dictionary in the SQL - database. In the first part of this function, the data from the fit dictionary is retrieved and organized - for storage in the SQL file. In the second part, a connection to the SQL database is established a - series of SQL commands are executed to store the data. + """ + Organises and saves the fit dictionary in SQL database + Args: + fit (dict): as output by fitter + database (str or None, default None): defualt uses database from + qc.config Note: It does not accept file locations of the format './file.db'. It seems you either need to specify the full location of the file (e.g. /Users/Username/Desktop/file.db), or have the file in the same folder @@ -77,7 +88,8 @@ def fit_to_sql(fit, database=None): data_run_id = fit['inferred_from']['run_id'] exp_id = fit['inferred_from']['exp_id'] predicts = est_name.strip("estimate").strip("_") - estimator = "{}, {}".format(fit['inferred_from']['estimator']['method'], fit['inferred_from']['estimator']['type']) + estimator = "{}, {}".format( + fit['inferred_from']['estimator']['method'], fit['inferred_from']['estimator']['type']) analysis = str(fit['inferred_from']['estimator']['function used']) dill_obj = str(fit['inferred_from']['estimator']['dill']) start_params = str(fit['start_params']) @@ -104,11 +116,9 @@ def fit_to_sql(fit, database=None): for parameter in fitclass.p_labels: table_columns.append(parameter) - - # Part2 : A connection to the SQL database is created and the fit data is saved to the database - #create a connection to the SQL database + # create a connection to the SQL database if database is not None: file = database else: @@ -117,7 +127,8 @@ def fit_to_sql(fit, database=None): cur = conn.cursor() # Create table for fit parameter and predicted output values, store data - tablename = 'analysis_{}_{}'.format(fit['inferred_from']['run_id'], fitclass.name) + tablename = 'analysis_{}_{}'.format( + fit['inferred_from']['run_id'], fitclass.name) table = make_table(tablename, cur) for column in table_columns: @@ -125,8 +136,8 @@ def fit_to_sql(fit, database=None): num_cols = len(table_rows[0]) placeholder = ('?,' * num_cols).strip(',') - cur.executemany('INSERT INTO {} VALUES ({})'.format(table, placeholder), table_rows) - + cur.executemany('INSERT INTO {} VALUES ({})'.format( + table, placeholder), table_rows) # Create 'analyses' table if it does not already exist, store info about analysis in 'analyses' if not is_table('analyses', cur): @@ -142,7 +153,6 @@ def fit_to_sql(fit, database=None): cur.execute(sql_analyses, (data_run_id, exp_id, run_id, table, predicts, estimator, analysis, start_params, dill_obj)) - # Store unit and label info in 'layouts' table layout_id = 1 max_id = cur.execute('SELECT MAX(layout_id) FROM layouts').fetchone()[0] @@ -154,16 +164,16 @@ def fit_to_sql(fit, database=None): row = (layout_id, run_id, parameter, label, unit, "inferred_from") layout_rows.append(row) layout_id += 1 - layout_rows.append((layout_id, run_id, est_name, est_label, est_unit, "inferred_from")) + layout_rows.append((layout_id, run_id, est_name, + est_label, est_unit, "inferred_from")) sql_layout = 'INSERT INTO layouts VALUES (?,?,?,?,?,?)' cur.executemany(sql_layout, layout_rows) - # Store summary in 'runs' SQL table, with run type 'analysis' instead of 'result' sql_runs = 'INSERT INTO runs VALUES (?,?,?,?,?,?,?,?,?,?)' - cur.execute(sql_runs, (run_id, exp_id, 'analysis', table, "", "", "", 1, "", "")) - + cur.execute(sql_runs, (run_id, exp_id, 'analysis', + table, "", "", "", 1, "", "")) # save changes to SQL database and close connection conn.commit() diff --git a/qdev_wrappers/fitting/Converter.py b/qdev_wrappers/fitting/Converter.py index f8c7c92e..59faccae 100644 --- a/qdev_wrappers/fitting/Converter.py +++ b/qdev_wrappers/fitting/Converter.py @@ -8,6 +8,12 @@ def data_to_dict(id, samplefolder=None, datatype='SQL'): + """ + Args: + id (int) + sample_folder (str or None, default None) + datatype (str, default 'SQL'): 'SQL' or 'qcodes_legacy' + """ if datatype == 'qcodes_legacy': converter = Legacy_Converter() @@ -15,114 +21,160 @@ def data_to_dict(id, samplefolder=None, datatype='SQL'): converter = SQL_Converter() else: - raise NotImplementedError('datatype {} is not currently supported'.format(datatype)) + raise NotImplementedError( + 'datatype {} is not currently supported'.format(datatype)) return converter.convert(id, samplefolder) class DataConverter: - - """This is the base class for converters that convert data stored in files into a python dictionary.""" - + """ + Base class for converters that convert data into a python dictionary. + """ def find_data(self, id, samplefolder): - - """ Takes data run id as argument, as well as a samplefolder, which can be set - to None if the run is part of the current configuration. Retrieves the data from the file - it is stored in, based on either the sample folder or qc.config. Returns data. """ - - raise NotImplementedError('find_data not implemented in the base class.') + """ + Args: + id (int) + samplefolder (str or None): None implies the run is part of the + current configuration and the samplefolder in qc.config is used + Returns: + data + """ + + raise NotImplementedError( + 'find_data not implemented in the base class.') def make_data_dictionary(self, data): - - """ Takes data (as returned by find_data). Organizes data into as a dictionary. Each key - is one of the variables from the data, and its corresponding value is a dictionary in - the format {'name': str, 'label': str, 'unit': str, 'data': np.array }. Returns data dict - containing name, label, unit and data array for each variable in the dataset. """ - - raise NotImplementedError('make_data_dictionary not implemented in the base class.') + """ + Args: + data as returned by find_data + Returns: + data (dict): keys are variables from the data, values are + dictionaries in the format + {'name': str, 'label': str, 'unit': str, 'data': np.array } + """ + + raise NotImplementedError( + 'make_data_dictionary not implemented in the base class.') def find_experiment(self, run_id, samplefolder): + """ + Args: + run_id - """ Takes run_id, and returns corresponding experiment id """ + Returns: + corresponding experiment id + """ - raise NotImplementedError('find_experiment not implemented in the base class.') + raise NotImplementedError( + 'find_experiment not implemented in the base class.') def find_dependencies(self, run_id, data, samplefolder): - - """ Takes run_id and data dict. Finds dependencies of the variables, and returns them as a - dependencies dict. E.g. if A and B were measured, and they both depend on C and D: - dependencies = {A: [C, D], B: [C, D]} """ - - raise NotImplementedError('find_dependencies not implemented in the base class.') + """ + Args: + run_id (int) + data (dict) + samplefolder (str) + Returns: + dependencies (dict): eg if A and B were measured, and they both + depend on C and D + dependencies = {A: [C, D], B: [C, D]} + """ + + raise NotImplementedError( + 'find_dependencies not implemented in the base class.') def find_variables(self, data): + """ + Args: + data (dict) + Returns: + list of all variables contained in the data dict + """ - """ Takes data dict. Returns list of all variables contained in the data dict.""" + raise NotImplementedError( + 'find_variables not implemented in the base class.') - raise NotImplementedError('find_variables not implemented in the base class.') - def convert(self, id, samplefolder=None): - - """ Takes run_id and optionally a samplefolder name. Sample folder name should only be - necessary if the run in question isn't part of the current experimental configuration. - Returns a dictionary containing all the data from the specified run as numpy arrays, - plus relevant metadata (experiment id, run id, dependencies, names, labels, and units). """ - - data = self.find_data(id, samplefolder) + """ + Args: + run_id (int) + samplefolder (str or None, default None): only necessary if the run + isn't part of the current experimental configuration. + Returns: + data (dict): containing all the data from the specified + run as numpy arrays, plus relevant metadata + (experiment id, run id, dependencies, names, labels, and units) + """ + + data = self.find_data(id, samplefolder) data_dict = self.make_data_dictionary(data) exp_id = self.find_experiment(id, samplefolder) - dependencies = self.find_dependencies(id, data, samplefolder) # SQL needs only id, Legacy needs only data + # SQL needs only id, Legacy needs only data + dependencies = self.find_dependencies(id, data, samplefolder) all_variables = self.find_variables(data) data_dict['exp_id'] = exp_id data_dict['run_id'] = id data_dict['dependencies'] = dependencies data_dict['variables'] = all_variables - + return data_dict class SQL_Converter(DataConverter): - + def find_data(self, id, samplefolder): - - """ This isn't actually set up to use the samplefolder if one is given - it can only use - the qc.config via the get_data_by_id function. If this were set up to retrieve the data as - get_data_by_id does, but with get_DB_location() returning expanduser(sampefolder) instead - of expanduser(qcodes.config["core"]["db_location"]), then I think everything would work - for manual specification of database location """ + """ + Uses the database location specified in qc.config to + get the data using get_data_by_id function. - if samplefolder is None: - data = get_data_by_id(id) - else: - raise NotImplementedError('Manual specification of database location not implemented for SQL') + If this were set up to retrieve the data as get_data_by_id does, + but with get_DB_location() returning expanduser(sampefolder) + instead of expanduser(qcodes.config["core"]["db_location"]), + then I think everything would work for manual specification + of database location + """ - if len(data) == 0: - raise RuntimeError('No data found for run id {}'.format(id)) - - return data + if samplefolder is None: + data = get_data_by_id(id) + else: + raise NotImplementedError( + 'Manual specification of database ' + 'location not implemented for SQL') - def find_dependencies(self, id, data, samplefolder): # SQL converter only needs id, not data + if len(data) == 0: + raise RuntimeError('No data found for run id {}'.format(id)) + return data + + # SQL converter only needs id, not data + def find_dependencies(self, id, data, samplefolder): if samplefolder is None: file = expanduser(qc.config['core']['db_location']) else: file = expanduser(samplefolder) + + # connect to SQL database conn = connect(file) cur = conn.cursor() - names = {} dependencies = {} - for row in cur.execute('SELECT layout_id, parameter FROM layouts WHERE run_id is ?', str(id)): + # extract names from layouts table + for row in cur.execute( + 'SELECT layout_id, parameter FROM layouts WHERE run_id is ?', str(id)): names[row[0]] = row[1] - for row in cur.execute('SELECT dependent, independent FROM dependencies'): + # extract dependencies from dependencies table + for row in cur.execute( + 'SELECT dependent, independent FROM dependencies'): if row[0] in names.keys(): dependencies[names[row[0]]] = [] - - for row in cur.execute('SELECT dependent, independent FROM dependencies'): + + for row in cur.execute( + 'SELECT dependent, independent FROM dependencies'): if row[0] in names.keys(): dependencies[names[row[0]]].append(names[row[1]]) @@ -131,18 +183,16 @@ def find_dependencies(self, id, data, samplefolder): # SQL converter only needs return dependencies def find_variables(self, data): - all_variables = [] - + for data_subset in data: for variable in data_subset: if variable['name'] not in all_variables: all_variables.append(variable['name']) - + return all_variables def find_experiment(self, id, samplefolder): - if samplefolder is None: file = expanduser(qc.config['core']['db_location']) else: @@ -150,96 +200,96 @@ def find_experiment(self, id, samplefolder): conn = connect(file) cur = conn.cursor() - execute = cur.execute('SELECT exp_id FROM runs WHERE run_id is {}'.format(id)) + execute = cur.execute( + 'SELECT exp_id FROM runs WHERE run_id is {}'.format(id)) exp_id = execute.fetchone()[0] return exp_id def make_data_dictionary(self, data): - data_dict = {} - + for data_subset in data: for variable in data_subset: - + name = variable['name'] - + if name in data_dict.keys(): - if not np.array_equal(variable['data'], data_dict[name]['data']): + if not np.array_equal(variable['data'], + data_dict[name]['data']): print(name) - raise RuntimeError('Variables with identical names contain non-identical data arrays!') - + raise RuntimeError( + 'Variables with identical names contain ' + 'non-identical data arrays!') + data_dict[name] = variable - + return data_dict class Legacy_Converter(DataConverter): - + def find_data(self, id, samplefolder): - str_id = '{0:03d}'.format(id) - + if samplefolder is None: - check_experiment_is_initialized() + check_experiment_is_initialized() # check_experiment_is_initialized() doesn't do anything at the moment - + path = qc.DataSet.location_provider.fmt.format(counter=str_id) data = qc.load_data(path) else: path = '{}{}{}'.format(samplefolder, sep, str_id) data = qc.load_data(path) - + return data - - def find_dependencies(self, id, data, samplefolder=None): # legacy only needs data, not id or sample folder - - dep_vars = [key for key in data.arrays.keys() if "_set" not in key[-4:]] + + # legacy only needs data, not id or sample folder + def find_dependencies(self, id, data, samplefolder=None): + dep_vars = [key for key in data.arrays.keys() + if "_set" not in key[-4:]] indep_vars = [key for key in data.arrays.keys() if "_set" in key[-4:]] - + dependencies = {} - + for variable in dep_vars: dependencies[variable] = indep_vars - + return dependencies - + def find_variables(self, data): - all_variables = [variable for variable in data.arrays.keys()] return all_variables - def find_experiment(self, id, samplefolder=None): # samplefolder only needed in sql converter - + # samplefolder only needed in sql converter + def find_experiment(self, id, samplefolder=None): exp_id = id return exp_id - + def resize_data(self, data1, data2): - scan_size = len(data1[0]) - + new_data2 = [] - + for setvalue in data2: array = np.array([setvalue for datapoint in range(scan_size)]) new_data2.append(array) - + return np.array(new_data2) def make_data_dictionary(self, data): - data_dict = {} - + all_variables = self.find_variables(data) - + for variable in all_variables: qc_data = getattr(data, variable) name = getattr(qc_data, 'name', 'No Name') label = getattr(qc_data, 'label', '') unit = getattr(qc_data, 'unit', '') np_data = qc_data.ndarray - + data_dict[variable] = {'name': variable, 'label': label, 'var_name': name, @@ -253,32 +303,34 @@ def make_data_dictionary(self, data): (i.e. for setpoints = [1, 2, 3] , data = [[a, b, c], [d, e, f], [g, h, i]] becomes setpoints = [[1, 1, 1], [2, 2, 2], [3, 3, 3]], data = data)''' dependencies = self.find_dependencies(id, data) - + dep_vars = [key for key in dependencies.keys()] indep_vars = dependencies[dep_vars[0]] - + if len(indep_vars) == 2: - data1 = data_dict[dep_vars[0]]['data'] - data2 = data_dict[indep_vars[0]]['data'] + data1 = data_dict[dep_vars[0]]['data'] + data2 = data_dict[indep_vars[0]]['data'] data3 = data_dict[indep_vars[1]]['data'] - + if type(data2[0]) != np.ndarray: - data_dict[indep_vars[0]]['data'] = self.resize_data(data1, data2) + data_dict[indep_vars[0]]['data'] = self.resize_data( + data1, data2) if type(data3[0]) != np.ndarray: - data_dict[indep_vars[1]]['data'] = self.resize_data(data1, data3) - + data_dict[indep_vars[1]]['data'] = self.resize_data( + data1, data3) + '''reformatting nested arrays as a single 1d array, i.e. setpoints = [[1, 1, 1], [2, 2, 2], [3, 3, 3]], data = [[a, b, c], [d, e, f], [g, h, i]] becomes setpoints [1, 1, 1, 2, 2, 2, 3, 3, 3], data = [a, b, c, d, e, f, g, h, i]''' - + for variable in all_variables: data1 = data_dict[variable]['data'] - + new_data = [] for array in data1: for datapoint in array: new_data.append(datapoint) - + data_dict[variable]['data'] = np.array(new_data) return data_dict diff --git a/qdev_wrappers/fitting/Example.ipynb b/qdev_wrappers/fitting/Example.ipynb index 409c3d41..69cc0ba8 100644 --- a/qdev_wrappers/fitting/Example.ipynb +++ b/qdev_wrappers/fitting/Example.ipynb @@ -9,16 +9,15 @@ "name": "stderr", "output_type": "stream", "text": [ - "User schema at /Users/Lillian/qcodesrc_schema.json not found.User settings won't be validated\n", - "no config file found at /Users/Lillian/Qcodes/My stuff/mainfolder/data/local_scripts/instr.config, will try general\n", - "no config file found at /Users/Lillian/Qcodes/My stuff/instr.config\n" + "no config file found at /Users/natalie/Documents/PhD/Qdev/QdevWrappers/qdev_wrappers/fitting/data/local_scripts/instr.config, will try general\n", + "no config file found at /Users/natalie/Documents/PhD/Qdev/QdevWrappers/qdev_wrappers/fitting/instr.config\n" ] } ], "source": [ "import qcodes as qc\n", "from qdev_wrappers.file_setup import my_init\n", - "qc.config.add(\"mainfolder\", \"./mainfolder\", value_type=\"string\", description=\"Location of data\", default=\"./mainfolder\")\n", + "# qc.config.add(\"mainfolder\", \"./mainfolder\", value_type=\"string\", description=\"Location of data\", default=\"./mainfolder\")\n", "station = qc.Station()\n", "my_init('data', station)" ] @@ -30,7 +29,7 @@ "outputs": [], "source": [ "from qdev_wrappers.fitting.Converter import SQL_Converter, Legacy_Converter, data_to_dict\n", - "from qdev_wrappers.fitting.Fitclasses import T1, T2\n", + "from qdev_wrappers.fitting.Fitclasses import ExpDecay, ExpDecaySin\n", "from qdev_wrappers.fitting.Fitter import Fitter1D, Fitter_2Ddata_1Dfunction, fit_data\n", "from qdev_wrappers.fitting.AnalysisToSQL import is_table, make_table, fit_to_sql\n", "from qdev_wrappers.fitting.RoughPlotting import plot_fit1d, plot_fit2d_slice\n", @@ -104,7 +103,11 @@ "metadata": {}, "outputs": [], "source": [ - "fit = fit_data(data, T1(), {'x':'pulse_readout_delay'}, {'y':'data'})" + "t1_fitclass = ExpDecay('T1fit')\n", + "fit = fit_data(data,\n", + " fitclass=t1_fitclass,\n", + " fun_inputs={'x':'pulse_readout_delay'},\n", + " fun_output={'y':'data'})" ] }, { @@ -120,7 +123,12 @@ "metadata": {}, "outputs": [], "source": [ - "fit2 = fit_data(data2, T2(), {'x':'pi_pulse_dur_set'}, {'y':'rec_ctrl_demod_freq_0_mag'}, ['qubit_rs_frequency_set'])" + "t2_fitclass = ExpDecaySin('T2fit')\n", + "fit2 = fit_data(data2,\n", + " fitclass=t2_fitclass,\n", + " fun_inputs={'x':'pi_pulse_dur_set'},\n", + " fun_output={'y':'rec_ctrl_demod_freq_0_mag'},\n", + " setpoint_params=['qubit_rs_frequency_set'])" ] }, { @@ -375,7 +383,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.6.6" } }, "nbformat": 4, diff --git a/qdev_wrappers/fitting/Fitclasses.py b/qdev_wrappers/fitting/Fitclasses.py index 54502fb8..8439d5f1 100644 --- a/qdev_wrappers/fitting/Fitclasses.py +++ b/qdev_wrappers/fitting/Fitclasses.py @@ -1,25 +1,29 @@ - -import qcodes as qc import numpy as np import scipy.fftpack as fftpack class LeastSquaresFit: + """ + Base class for fit functions to be used with curve_fit. Specifies + a particular mathematical function and information that can be used to + perform a least squares fit to the data. - """ This is the base class for the other classes in this file. Each class specifies - a particular mathematical function and information that can be used to perform a - Least Squares Fit to the data. - - Each class holds the mathematical function itself ('fun'), a function for determining - an initial guess for the fit parameters ('guess'), and a list of attributes that set - the function inputs and outputs, the fit parameter labels and names, and the relationship - between the units on the input and output variables and the units on the parameters. """ + Each class holds the mathematical function itself ('fun'), a function + for determining an initial guess for the fit parameters ('guess'), and + a list of attributes that set the function inputs and outputs, the fit + parameter labels and names, and the relationship between the units on the + input and output variables and the units on the parameters. + """ - def __init__(self): - self.name = 'NoName' - self.fun_str = 'None' - self.p_names = ['none'] - self.p_units = ['none'] + def __init__(self, name, fun_str, fun_np, fun_vars, + fun_output, p_names, p_labels, p_units): + self.name = name + self.fun_str = fun_str + self.fun_np = fun_np + self.fun_output = fun_output + self.p_names = p_names + self.p_units = p_labels + self.p_units = p_units def fun(self, x, a, T, c): raise NotImplementedError('This is not implemented in the base class.') @@ -28,59 +32,57 @@ def guess(self, x, y): raise NotImplementedError('This is not implemented in the base class.') -class T1(LeastSquaresFit): - - def __init__(self): - self.name = 'T1fit' - self.fun_str = r'$f(x) = a \exp(-x/T) + c$' - self.fun_np = 'a*np.exp(-x/T)+c' - self.fun_vars = ['x'] - self.fun_output = ['y'] - self.p_names = ['$a$', '$T$', '$c$'] - self.p_labels= ['a', 'T', 'c'] - self.p_units = ['y', 'x', 'y'] - +class ExpDecay(LeastSquaresFit): + def __init__(self, name='ExpDecayFit'): + super().__init__( + name=name, + fun_str=r'$f(x) = a \exp(-x/T) + c$', + fun_np='a*np.exp(-x/T)+c', + fun_vars=['x'], + fun_output=['y'], + p_names=['$a$', '$T$', '$c$'], + p_labels=['a', 'T', 'c'], + p_units=['y', 'x', 'y']) + def fun(self, x, a, T, c): - val = a*np.exp(-x/T)+c - return val + return eval(self.fun_np) def guess(self, x, y): - l = len(y) - val_init = y[0:round(l/20)].mean() - val_fin = y[-round(l/20):].mean() + length = len(y) + val_init = y[0:round(length / 20)].mean() + val_fin = y[-round(length / 20):].mean() a = val_init - val_fin c = val_fin # guess T1 as point where data has fallen to 1/e of init value - idx = (np.abs(y-a/np.e-c)).argmin() + idx = (np.abs(y - a / np.e - c)).argmin() T = x[idx] return [a, T, c] -class T2(LeastSquaresFit): - - def __init__(self): - self.name = 'T2fit' - self.fun_str = r'$f(x) = a \sin(\omega x +\phi)\exp(-x/T) + c$' - self.fun_np = 'a*np.exp(-x/T)*np.sin(w*x+p)+c' - self.fun_vars = ['x'] - self.fun_output = ['y'] - self.p_names = ['$a$', '$T$', '$\omega$', '$\phi$', '$c$'] - self.p_labels = ['a', 'T', 'w', 'p', 'c'] - self.p_units = ['y', 'x', '1/x', '', 'y'] +class ExpDecaySin(LeastSquaresFit): + def __init__(self, name='ExpDecaySinFit'): + super().__init__( + name=name, + fun_str=r'$f(x) = a \sin(\omega x +\phi)\exp(-x/T) + c$', + fun_np='a*np.exp(-x/T)*np.sin(w*x+p)+c', + fun_vars=['x'], + fun_output=['y'], + p_names=['$a$', '$T$', '$\omega$', '$\phi$', '$c$'], + p_labels=['a', 'T', 'w', 'p', 'c'], + p_units=['y', 'x', '1/x', '', 'y']) def fun(self, x, a, T, w, p, c): - val = a*np.exp(-x/T)*np.sin(w*x+p)+c - return val + return eval(self.fun_np) - def guess(self,x,y): + def guess(self, x, y): a = y.max() - y.min() c = y.mean() # guess T2 as point half way point in data - T = x[round(len(x)/2)] + T = x[round(len(x) / 2)] # Get initial guess for frequency from a fourier transform - yhat = fftpack.rfft(y-y.mean()) + yhat = fftpack.rfft(y - y.mean()) idx = (yhat**2).argmax() - freqs = fftpack.rfftfreq(len(x), d = (x[1]-x[0])/(2*np.pi)) + freqs = fftpack.rfftfreq(len(x), d=(x[1] - x[0]) / (2 * np.pi)) w = freqs[idx] p = 0 - return [a,T,w,p,c] + return [a, T, w, p, c] diff --git a/qdev_wrappers/fitting/Fitter.py b/qdev_wrappers/fitting/Fitter.py index e7c74761..e64391f6 100644 --- a/qdev_wrappers/fitting/Fitter.py +++ b/qdev_wrappers/fitting/Fitter.py @@ -2,108 +2,146 @@ import numpy as np import dill -from qdev_wrappers.fitting.Fitclasses import T1, T2 from scipy.optimize import curve_fit -def fit_data(data, fitclass, fun_inputs, fun_output, setpoint_params=None, p0=None, **kwargs): - - """ This takes as arguments the data dictionary, the desired fit class as a LeastSquaresFit - class (which defines the function to be fitted to the data), function inputs and outputs - as a dictionary of the form {'x' : 'variable_name'}, and optionally setpoint parameters as - a list. - - It returns a dictionary containing the fitted parameters and an array of "estimated" - outputs given the fit (i.e. what this fit and parameters predict the measurement would have - given at each point), as well as some metadata about the fit. - - It first checks that the arguments given for setpoints, inputs and outputs matches the input - needed for fitting to the specified fit class. Then it creates an instance of the appropriate - fitter class, and calls the 'find_fit' method of the fitter, saving it in the variable 'fit'. - This method creates a dictionary with the fit. - - Finally, it preserves the fitter and fitclass used to perform the fit by pickling them via the - dill module, and adds the binary to the fit dictionary.""" - - check_input_matches_fitclass(fitclass, fun_inputs, fun_output, setpoint_params, p0) - - # this is used to decide which fitter class to create an instance of. If they are combined, this won't be needed. +def fit_data(data, fitclass, fun_inputs, fun_output, + setpoint_params=None, p0=None, **kwargs): + """ + Fits the data in the data dictionary using the fit class provided and the + mappings provided to generate a fit dictionary. + + Args: + data (dict) + fitclass (currently limited to LeastSquaresFit) + fun_inputs (dict): mapping between data names in data dictionary and + arguments of fit class function eg {'x': 'pulse_readout_delay'} + fun_output (dict: mapping between data names in data dictionary and + return of fit class function eg {'y': 'data'} + setpoint_params (list or None, default None): other data names in + dictionary which are not part of the fit funetion input or output + p0 (list or None, default None): initial estimation of parameters of + the fit, overwrites those generated by the fit class guess + **kwargs (optional): passed to find_fit function of fit class + + Returns: + fit (dict): containing the fitted parameters and an array of + "estimated" outputs given the fit as well as some metadata about + the fit. + + """ + + check_input_matches_fitclass( + fitclass, fun_inputs, fun_output, setpoint_params, p0) + + # this is used to decide which fitter class to create an instance of. + # If they are combined, this won't be needed. fun_dim = len(fitclass.fun_vars) num_inputs = len(fun_inputs) if setpoint_params is not None: num_setpoints = len(setpoint_params) - else: num_setpoints = 0 + else: + num_setpoints = 0 - # creates an instance of the relevant fitter based on the number of setpoints and inputs and the dimensionality - # of the function to be fitted. If there were just one fitter class, the if statements wouldn't be needed. + # creates an instance of the relevant fitter based on the number of + # setpoints and inputs and the dimensionality + # of the function to be fitted. If there were just one fitter class, + # the if statements wouldn't be needed. if fun_dim == 1 and num_inputs == 1 and num_setpoints == 0: fitter = Fitter1D(data, fun_inputs, fun_output) elif fun_dim == 1 and num_inputs == 1 and num_setpoints == 1: - fitter =Fitter_2Ddata_1Dfunction(data, fun_inputs, fun_output) #what does the fitter actually need?? + # what does the fitter actually need?? + fitter = Fitter_2Ddata_1Dfunction(data, fun_inputs, fun_output) else: - raise NotImplementedError('''You have specified a {}-dimensional function, {} function inputs, and - {} setpoints. This process is currently so specific that your only options are to fit to 1D functions, - provide 1 input, and have either 0 or 1 setpoint types. Sorry.'''.format(fun_dim, num_inputs, num_setpoints)) - - # the fitter performs the fit and saves a dictionary containing the results in variable 'fit' - fit = fitter.find_fit(data, fitclass, fun_inputs, fun_output, setpoint_params, p0, **kwargs) - - # the fitter and fit class used for the fit are preserved via dill and added to the fit dictionary + raise NotImplementedError( + 'You have specified a {}-dimensional function, {} function ' + 'inputs, and {} setpoints. This process is currently so ' + 'specific that your only options are to fit to 1D functions,' + ' provide 1 input, and have either 0 or 1 setpoint types. ' + 'Sorry.'format(fun_dim, num_inputs, num_setpoints)) # TODO: DON'T SAY SORRY!!! + + # the fitter performs the fit and saves a dictionary containing the + # results in variable 'fit' + fit = fitter.find_fit(data, fitclass, fun_inputs, + fun_output, setpoint_params, p0, **kwargs) + + # the fitter and fit class used for the fit are preserved via dill + # and added to the fit dictionary dill_fitclass = dill.dumps(fitclass) dill_fitter = dill.dumps(fitter) - fit['inferred_from']['estimator']['dill'] = {'fitclass' : dill_fitclass, 'fitter' : dill_fitter} + fit['inferred_from']['estimator']['dill'] = { + 'fitclass': dill_fitclass, 'fitter': dill_fitter} return fit -def check_input_matches_fitclass(fitclass, inputs, output, setpoints, p0=None): - - """ This function takes the same arguments as the 'fit_data' function, and confirms that the - given arguments are in the correct format for the 'fit_data' function to proceed, and that - they match the expected inputs for the specified fit class. - - Each fit class describes a mathematical function to be fitted to, with a set of attributes, - including which variables the function has as inputs, and what outputs it has. This function - makes sure that the correct inputs and outputs are specified compared to what inputs and - outputs the mathematical function has, and that they are specified in the correct format. It - also checks that setpoints are given in the correct format (a list). If guess parameters are - specified, it checks that the length of the parameter list is the expected number of - parameters for the function chosen. """ - # check input and output are given, and that input, output and setpoints are in correct format +def check_input_matches_fitclass(fitclass, inputs, output, setpoints, p0=None): + """ + Checks that the given arguments are in the correct format for the + 'fit_data' function to proceed, and that they match the expected inputs for + the specified fit class. + + Each fit class describes a mathematical function to be fitted to, + with a set of attributes, including which variables the function has as + inputs, and what outputs it has. This function makes sure that the correct + inputs and outputs are specified compared to what inputs and outputs the + mathematical function has, and that they are specified in the correct + format. It also checks that setpoints are given in the correct format + (a list). If guess parameters are specified, it checks that the length of + the parameter list is the expected number of parameters for the function + chosen. + + Args: + same arguments as the 'fit_data' function + """ + + # check input and output are given, and that input, + # output and setpoints are in correct format if (type(inputs) != dict or type(output) != dict): - raise RuntimeError('''Please specify both input and output variables for the function you wish to fit in - # the format fun_inputs = {'x':'name', 'y':'other_name'}, fun_output = {z: 'another_name'} ''') + raise RuntimeError( + 'Please specify both input and output variables for the function ' + 'you wish to fit in the format fun_inputs = ' + '{"x": "name", "y": "other_name"}, fun_output =' + ' {"z": "another_name"}') if (setpoints is not None) and (type(setpoints) != list): - raise RuntimeError('''Please specify setpoints as a list ['setpoint_name', ...], even if there is only one.''') + raise RuntimeError( + 'Please specify setpoints as a list ["setpoint_name", ...],' + ' even if there is only one.') # check inputs/outputs specified match inputs/outputs function takes if len(inputs) != len(fitclass.fun_vars): - raise RuntimeError('''The function you are fitting to takes {} variables, - and you have specified {}'''.format(len(fitclass.fun_vars), len(inputs))) + raise RuntimeError( + 'The function you are fitting to takes {} variables, and you ' + 'have specified {}'.format(len(fitclass.fun_vars), len(inputs))) for variable in inputs.keys(): if variable not in fitclass.fun_vars: - raise RuntimeError('''You have specified a variable {}. - The fit function takes variables {}'''.format(variable, fitclass.fun_vars)) + raise RuntimeError( + 'You have specified a variable {}. The fit function takes' + 'variables {}'.format(variable, fitclass.fun_vars)) for variable in output.keys(): if variable not in fitclass.fun_output: - raise RuntimeError('''You have specified a variable {}. - The fit function returns variables {}'''.format(variable, fitclass.fun_output)) + raise RuntimeError( + 'You have specified a variable {}. The fit function returns ' + 'variables {}'.format(variable, fitclass.fun_output)) - # check that if a guess p0 is specified, the number of parameters is correct for the fit function + # check that if a guess p0 is specified, the number of parameters + # is correct for the fit function if (p0 is not None) and (len(p0) != len(fitclass.p_labels)): - raise RuntimeError('''You have specified {} start parameters for the fit function: {}. The function takes - {} start parameters: {}'''.format(len(p0), p0, len(fitclass.p_labels), fitclass.p_labels)) + raise RuntimeError( + 'You have specified {} start parameters for the fit function' + '. The function takes {} start parameters: ' + '{}'.format(len(p0), len(fitclass.p_labels), fitclass.p_labels)) class Fitter: - - """ The fitter has a method, 'find_fit', which takes the fitclass, data, function inputs, function outputs, - setpoints when relevant, and optionally, guess parameters, and returns a fit dictionary. The fit class is - a LeastSqauresFit class describing a mathematical function that can be fitted to data, while the Fitter - class performs the fitting. All other methods in the fitter are com - - Much of the fitting is generic and does not depend on the number of inputs, outputs or setpoints in the + """ + The fitter has a method, 'find_fit', which takes the fitclass, data, + function inputs, function outputs, setpoints when relevant, and optionally, + guess parameters, and returns a fit dictionary. The fit class is a + LeastSqauresFit class describing a mathematical function that can be fitted + to data, while the Fitter class performs the fitting. All other methods in the fitter are com + Much of the fitting is generic and does not depend on the number of inputs, outputs or setpoints in the specified LeastSquaresFit class; however, depending on the dimensionality of the function to be fitted and the measured data, the function 'perform_fit' varies. @@ -111,11 +149,10 @@ class performs the fitting. All other methods in the fitter are com from it: a class for 1D fits to 1D data (i.e. one variable, one output and no setpoints) and a class for 1D fits to 2D data (i.e. one variable, one setpoint and one output). """ - #TODO: perform_fit and find_fit are two different functions with deceptively similar names. This could be improved. - #TODO: I think with some changes, perform_fit could be made generic, so only one Fitter class was needed + # TODO: perform_fit and find_fit are two different functions with deceptively similar names. This could be improved. + # TODO: I think with some changes, perform_fit could be made generic, so only one Fitter class was needed def __init__(self, data, inputs, output): - self.input_vars = [variable for variable in inputs.keys()] self.output_var = [variable for variable in output.keys()] self.output_dataname = [dataname for dataname in output.values()] @@ -125,8 +162,8 @@ def __init__(self, data, inputs, output): self.setpoint_params = [] def find_fit(self, data, fitclass, fun_inputs, fun_output, setpoint_params=None, p0=None, **kwargs): - - """ find_fit combines other Fitter methods to organize the data, find fitted parameters and save + """ + find_fit combines other Fitter methods to organize the data, find fitted parameters and save them to a fit dictionary. It then adds parameter units to the fit dictionary, uses the fitted parameters and the given data to calculate estimated outputs given the fitten function, and adds metadata about the data and the fit to fit['inferred_from'] @@ -138,16 +175,19 @@ def find_fit(self, data, fitclass, fun_inputs, fun_output, setpoint_params=None, they are being specified manually instead of estimated by the LeastSquaresFit 'guess' function.""" # organize data in a format needed later (see 'organize_data' function) - data_kwargs, unit_dict = self.organize_data(data, fun_inputs, fun_output, setpoint_params) + data_kwargs, unit_dict = self.organize_data( + data, fun_inputs, fun_output, setpoint_params) # fit parameters via SciPy curve_fit and save to fit dictionary - fit = self.perform_fit(data_kwargs, data, fitclass, setpoint_params, p0, **kwargs) + fit = self.perform_fit(data_kwargs, data, fitclass, + setpoint_params, p0, **kwargs) # find parameter units and save to fit dictionary - fit['parameter units']= self.find_parameter_units(fitclass, unit_dict) + fit['parameter units'] = self.find_parameter_units(fitclass, unit_dict) # use fitted parameters and data to find the estimated output of the fitted function - fit['estimate'] = self.estimate_function_values(fitclass, data, data_kwargs) + fit['estimate'] = self.estimate_function_values( + fitclass, data, data_kwargs) # save metadata fit['inferred_from'] = {'inputs': fun_inputs, @@ -156,16 +196,15 @@ def find_fit(self, data, fitclass, fun_inputs, fun_output, setpoint_params=None, 'run_id': data['run_id'], 'exp_id': data['exp_id'], 'dependencies': data['dependencies'], - 'estimator' : {'method': 'Least squared fit', - 'type': fitclass.name, - 'function used': str(fitclass.fun_np), - } + 'estimator': {'method': 'Least squared fit', + 'type': fitclass.name, + 'function used': str(fitclass.fun_np), + } } return fit def organize_data(self, data, inputs, output, setpoints): - """ This function organizes the data based on the inputs, outputs and setpoints specified. First, it makes a list of all data names from inputs, outputs and setpoints, and confirms @@ -203,7 +242,7 @@ def organize_data(self, data, inputs, output, setpoints): for variable, name in inputs.items(): data_kwargs[variable] = data[name]['data'] - unit_dict[variable] = data[name]['unit'] + unit_dict[variable] = data[name]['unit'] for variable, name in output.items(): data_kwargs[variable] = data[name]['data'] @@ -212,7 +251,6 @@ def organize_data(self, data, inputs, output, setpoints): return data_kwargs, unit_dict def find_start_parameters(self, fitclass, p0, data_kwargs): - """ Takes as arguments the fit class, the guess parameters if any, and the "data_kwargs", which is not the data as originally given, but the data arrays organized by variables by the organize_data function. It has the format {"x": np.array, ...}, so that it can @@ -232,7 +270,6 @@ def find_start_parameters(self, fitclass, p0, data_kwargs): return "Could not find guess parameters for fit." def find_parameter_units(self, fitclass, unit_dict): - """ This uses the relationship between the units stored in the fitclass attribute 'p_units' and the unit labels for the data to determine the unit labels for each parameter. @@ -262,13 +299,12 @@ def find_parameter_units(self, fitclass, unit_dict): template[i] = unit_dict[variable] # join the list again, fx. ['1', '/', 'S'] --> '1/S' unit = "".join(template) - #add the unit to the param_units dictionary + # add the unit to the param_units dictionary param_units[label] = unit return param_units def perform_fit(self, data_kwargs, data, fitclass, setpoints, p0, **kwargs): - """ A suitably generic version of this function isn't implemented yet. See specific versions in child classes. @@ -295,10 +331,10 @@ def perform_fit(self, data_kwargs, data, fitclass, setpoints, p0, **kwargs): 2. It returns a fit dictionary containing the start_params (initial guess), and the value and cov matrix for each fitted parameter (organized by setpoint if there were setpoints). """ - raise NotImplementedError("A generic fitter function for the Fitter base class has yet to be implemented.") + raise NotImplementedError( + "A generic fitter function for the Fitter base class has yet to be implemented.") def estimate_function_values(self, fitclass, data, data_kwargs): - """ Takes fitclass and data, as well as the data_kwargs produced by organize_data, and references the Fitter attribute self.full_parameter_dict created by perform_fit. It uses the numpy function saved in the LeastSquaresFit class attribute 'fun', giving it the input data and input parameters as numpy @@ -314,9 +350,9 @@ def estimate_function_values(self, fitclass, data, data_kwargs): # obtain a dictionary containing only input variables in format {"x": np.array, ...}, for **kwargs input_data = data_kwargs for variable in self.output_var: - del input_data[variable] + del input_data[variable] - #obtain parameters, also in the format {"p1" : np.array, ...} for **kwargs + # obtain parameters, also in the format {"p1" : np.array, ...} for **kwargs input_parameters = self.full_parameter_dict # Calculate estimated values for output based on function and store in estimate['values'] @@ -325,7 +361,8 @@ def estimate_function_values(self, fitclass, data, data_kwargs): # Save other info about the estimate estimate['name'] = '{}_estimate'.format(self.output_dataname[0]) - estimate['label'] = '{} estimate'.format(data[self.output_dataname[0]]['label']) + estimate['label'] = '{} estimate'.format( + data[self.output_dataname[0]]['label']) estimate['unit'] = data[self.output_dataname[0]]['unit'] estimate['parameters'] = self.full_parameter_dict @@ -336,29 +373,34 @@ class Fitter1D(Fitter): def perform_fit(self, data_kwargs, data, fitclass, setpoints, p0, **kwargs): - #make fit dictionary - fit = { 'parameters': {}, 'start_params': {} } + # make fit dictionary + fit = {'parameters': {}, 'start_params': {}} - #define data arrays + # define data arrays xdata = data_kwargs[self.input_vars[0]] ydata = data_kwargs[self.output_var[0]] - #find guess for start parameters, run curve_fit function to perform SciPy least squares fit + # find guess for start parameters, run curve_fit function to perform SciPy least squares fit p_guess = self.find_start_parameters(fitclass, p0, data_kwargs) - popt, pcov = curve_fit(fitclass.fun, xdata, ydata, p0=p_guess, **kwargs) + popt, pcov = curve_fit(fitclass.fun, xdata, + ydata, p0=p_guess, **kwargs) # save fit information for parameter in fitclass.p_labels: - fit['parameters'][parameter] = {'value': popt[fitclass.p_labels.index(parameter)]} - fit['parameters'][parameter]['cov'] = pcov[fitclass.p_labels.index(parameter)] - fit['start_params'][parameter] = p_guess[fitclass.p_labels.index(parameter)] + fit['parameters'][parameter] = { + 'value': popt[fitclass.p_labels.index(parameter)]} + fit['parameters'][parameter]['cov'] = pcov[fitclass.p_labels.index( + parameter)] + fit['start_params'][parameter] = p_guess[fitclass.p_labels.index( + parameter)] # make an dictionary containing arrays of each parameter value with same dimension as data (for estimate) all_params = {} for parameter in fitclass.p_labels: all_params[parameter] = [] for datapoint in xdata: - all_params[parameter].append(fit['parameters'][parameter]['value']) + all_params[parameter].append( + fit['parameters'][parameter]['value']) all_params[parameter] = np.array(all_params[parameter]) self.full_parameter_dict = all_params @@ -369,7 +411,7 @@ class Fitter_2Ddata_1Dfunction(Fitter): def perform_fit(self, data_kwargs, data, fitclass, setpoints, p0, **kwargs): - #Retrieve data + # Retrieve data xarray = data_kwargs[self.input_vars[0]] yarray = data_kwargs[self.output_var[0]] setpoint_array = data[setpoints[0]]['data'] @@ -393,26 +435,29 @@ def perform_fit(self, data_kwargs, data, fitclass, setpoints, p0, **kwargs): xdata = np.array(xdata_lst) ydata = np.array(ydata_lst) - #perform 1D fit for cross section at each setpoint and save to fit dictionary (as in Fitter1D) - fit = { 'start_params' : {} } + # perform 1D fit for cross section at each setpoint and save to fit dictionary (as in Fitter1D) + fit = {'start_params': {}} for set_value, xdata_1d, ydata_1d in zip(unique_setpoints, xdata, ydata): - data_dict_1d = {self.input_vars[0]:xdata_1d, self.output_var[0]:ydata_1d} + data_dict_1d = { + self.input_vars[0]: xdata_1d, self.output_var[0]: ydata_1d} p_guess = self.find_start_parameters(fitclass, p0, data_dict_1d) - popt, pcov = curve_fit(fitclass.fun, xdata_1d, ydata_1d, p0=p_guess, **kwargs) + popt, pcov = curve_fit( + fitclass.fun, xdata_1d, ydata_1d, p0=p_guess, **kwargs) fit[set_value] = {} fit[set_value]['parameters'] = {} fit['start_params'][set_value] = {} for parameter in fitclass.p_labels: - fit[set_value]['parameters'][parameter] = {'value': popt[fitclass.p_labels.index(parameter)]} - fit[set_value]['parameters'][parameter]['cov'] = pcov[fitclass.p_labels.index(parameter)] - fit['start_params'][set_value][parameter] = p_guess[fitclass.p_labels.index(parameter)] - - + fit[set_value]['parameters'][parameter] = { + 'value': popt[fitclass.p_labels.index(parameter)]} + fit[set_value]['parameters'][parameter]['cov'] = pcov[fitclass.p_labels.index( + parameter)] + fit['start_params'][set_value][parameter] = p_guess[fitclass.p_labels.index( + parameter)] # Make an array of parameters, specified in the same order as the original setpoint array, before data # was rearranged to do the fit. This is for running through the estimation function later. @@ -420,7 +465,8 @@ def perform_fit(self, data_kwargs, data, fitclass, setpoints, p0, **kwargs): for parameter in fitclass.p_labels: all_params[parameter] = [] for setpoint in setpoint_array: - all_params[parameter].append(fit[setpoint]['parameters'][parameter]['value']) + all_params[parameter].append( + fit[setpoint]['parameters'][parameter]['value']) all_params[parameter] = np.array(all_params[parameter]) self.full_parameter_dict = all_params diff --git a/qdev_wrappers/fitting/RoughPlotting.py b/qdev_wrappers/fitting/RoughPlotting.py index f7882e4b..6870d9e0 100644 --- a/qdev_wrappers/fitting/RoughPlotting.py +++ b/qdev_wrappers/fitting/RoughPlotting.py @@ -13,7 +13,7 @@ def plot_fit1d(data, fit1d, fitclass, plottype='scatter'): xlabel = data[xname]['label'] xunit = data[xname]['unit'] axes.set_xlabel('{} [{}]'.format(xlabel, xunit)) - + yname = fit1d['inferred_from']['output'][fitclass.fun_output[0]] ydata = data[yname]['data'] ylabel = data[yname]['label'] @@ -29,23 +29,23 @@ def plot_fit1d(data, fit1d, fitclass, plottype='scatter'): parameters[index] = fit1d['parameters'][parameter]['value'] # add the fit to the plot, using the fitted parameter list to find points on the fit - x = np.linspace(xdata.min(), xdata.max(), len(xdata)*10) + x = np.linspace(xdata.min(), xdata.max(), len(xdata) * 10) axes.plot(x, fitclass.fun(x, *parameters), color='C0') # plot the data - if plottype == 'line': + if plottype == 'line': axes.plot(xdata, ydata, color='C0') elif plottype == 'scatter': - axes.plot(xdata, ydata, marker='.', markersize=5, linestyle='', color='C0') - else: + axes.plot(xdata, ydata, marker='.', + markersize=5, linestyle='', color='C0') + else: raise ValueError('Unknown plottype. Please choose point or line plot.') - -def plot_fit2d_slice(data, fits2d, fitclass, setvalue, plottype='scatter'): +def plot_fit2d_slice(data, fits2d, fitclass, setvalue, plottype='scatter'): """ Takes a 2D dataset and fit, and plots a 1D cross-section of the data at the setpoint specified by 'setvalue', along with the fit at that setpoint. """ - + plot = plt.figure() axes = plot.add_axes([0.1, 0.1, 0.8, 0.8]) @@ -56,7 +56,8 @@ def plot_fit2d_slice(data, fits2d, fitclass, setvalue, plottype='scatter'): # confirm tha the setvalue tha the cross-section should be taken at is in fact one of the setpoints from the data if setvalue not in setdata: print('{}: {}'.format(setname, np.unique(setdata))) - raise RuntimeError('Setvalue not in set points for this fit. Choose from setvalues above.') + raise RuntimeError( + 'Setvalue not in set points for this fit. Choose from setvalues above.') # get data, label and units for the two axes, and set appropriate axis labels xname = fits2d['inferred_from']['inputs'][fitclass.fun_vars[0]] @@ -72,7 +73,8 @@ def plot_fit2d_slice(data, fits2d, fitclass, setvalue, plottype='scatter'): axes.set_ylabel('{} [{}]'.format(ylabel, yunit)) # set plot title - axes.set_title('{} vs. {} at {}={}'.format(ylabel, xlabel, setpoint, setvalue)) + axes.set_title('{} vs. {} at {}={}'.format( + ylabel, xlabel, setpoint, setvalue)) # select, from the full data for the two axes, only the data that corresponds to the chosen setpoint x_dat = [] @@ -90,13 +92,14 @@ def plot_fit2d_slice(data, fits2d, fitclass, setvalue, plottype='scatter'): parameters[index] = fits2d[setvalue]['parameters'][parameter]['value'] # add the fit to the plot, using the fitted parameter list to find points on the fit - x = np.linspace(xdata.min(), xdata.max(), len(xdata)*10) + x = np.linspace(xdata.min(), xdata.max(), len(xdata) * 10) axes.plot(x, fitclass.fun(x, *parameters), color='C0') # plot the data - if plottype == 'line': + if plottype == 'line': axes.plot(xdata, ydata, color='C0') elif plottype == 'scatter': - axes.plot(xdata, ydata, marker='.', markersize=5, linestyle='', color='C0') - else: + axes.plot(xdata, ydata, marker='.', + markersize=5, linestyle='', color='C0') + else: raise ValueError('Unknown plottype. Please choose point or line plot.') From 3abe329362ee1a6fde92f1371646f83a2ba615ca Mon Sep 17 00:00:00 2001 From: nataliejpg Date: Thu, 16 Aug 2018 14:21:26 +0200 Subject: [PATCH 067/116] remove samplefolder concept from converter, converter now assumes that the run corresponds to the current experiment, also find_dependencies to only need run_id for both converters --- qdev_wrappers/fitting/Converter.py | 90 ++++++++---------------------- 1 file changed, 23 insertions(+), 67 deletions(-) diff --git a/qdev_wrappers/fitting/Converter.py b/qdev_wrappers/fitting/Converter.py index 59faccae..1809f563 100644 --- a/qdev_wrappers/fitting/Converter.py +++ b/qdev_wrappers/fitting/Converter.py @@ -1,17 +1,15 @@ import qcodes as qc import numpy as np -from os.path import sep, expanduser +from os.path import expanduser from qcodes.dataset.data_export import get_data_by_id from qcodes.dataset.sqlite_base import connect -from qdev_wrappers.file_setup import CURRENT_EXPERIMENT from qdev_wrappers.show_num import check_experiment_is_initialized -def data_to_dict(id, samplefolder=None, datatype='SQL'): +def data_to_dict(id, datatype='SQL'): """ Args: id (int) - sample_folder (str or None, default None) datatype (str, default 'SQL'): 'SQL' or 'qcodes_legacy' """ if datatype == 'qcodes_legacy': @@ -24,19 +22,17 @@ def data_to_dict(id, samplefolder=None, datatype='SQL'): raise NotImplementedError( 'datatype {} is not currently supported'.format(datatype)) - return converter.convert(id, samplefolder) + return converter.convert(id) class DataConverter: """ Base class for converters that convert data into a python dictionary. """ - def find_data(self, id, samplefolder): + def find_data(self, id): """ Args: id (int) - samplefolder (str or None): None implies the run is part of the - current configuration and the samplefolder in qc.config is used Returns: data """ @@ -57,7 +53,7 @@ def make_data_dictionary(self, data): raise NotImplementedError( 'make_data_dictionary not implemented in the base class.') - def find_experiment(self, run_id, samplefolder): + def find_experiment(self, run_id): """ Args: run_id @@ -69,12 +65,11 @@ def find_experiment(self, run_id, samplefolder): raise NotImplementedError( 'find_experiment not implemented in the base class.') - def find_dependencies(self, run_id, data, samplefolder): + def find_dependencies(self, run_id, data): """ Args: run_id (int) data (dict) - samplefolder (str) Returns: dependencies (dict): eg if A and B were measured, and they both depend on C and D @@ -95,24 +90,22 @@ def find_variables(self, data): raise NotImplementedError( 'find_variables not implemented in the base class.') - def convert(self, id, samplefolder=None): + def convert(self, id): """ Args: run_id (int) - samplefolder (str or None, default None): only necessary if the run - isn't part of the current experimental configuration. Returns: data (dict): containing all the data from the specified run as numpy arrays, plus relevant metadata (experiment id, run id, dependencies, names, labels, and units) """ - data = self.find_data(id, samplefolder) + data = self.find_data(id) data_dict = self.make_data_dictionary(data) - exp_id = self.find_experiment(id, samplefolder) + exp_id = self.find_experiment(id) # SQL needs only id, Legacy needs only data - dependencies = self.find_dependencies(id, data, samplefolder) + dependencies = self.find_dependencies(id, data) all_variables = self.find_variables(data) data_dict['exp_id'] = exp_id @@ -125,36 +118,20 @@ def convert(self, id, samplefolder=None): class SQL_Converter(DataConverter): - def find_data(self, id, samplefolder): + def find_data(self, id): """ Uses the database location specified in qc.config to get the data using get_data_by_id function. - - If this were set up to retrieve the data as get_data_by_id does, - but with get_DB_location() returning expanduser(sampefolder) - instead of expanduser(qcodes.config["core"]["db_location"]), - then I think everything would work for manual specification - of database location """ - if samplefolder is None: - data = get_data_by_id(id) - else: - raise NotImplementedError( - 'Manual specification of database ' - 'location not implemented for SQL') - + data = get_data_by_id(id) if len(data) == 0: raise RuntimeError('No data found for run id {}'.format(id)) - return data # SQL converter only needs id, not data - def find_dependencies(self, id, data, samplefolder): - if samplefolder is None: - file = expanduser(qc.config['core']['db_location']) - else: - file = expanduser(samplefolder) + def find_dependencies(self, id, data): + file = expanduser(qc.config['core']['db_location']) # connect to SQL database conn = connect(file) @@ -192,11 +169,8 @@ def find_variables(self, data): return all_variables - def find_experiment(self, id, samplefolder): - if samplefolder is None: - file = expanduser(qc.config['core']['db_location']) - else: - file = expanduser(samplefolder) + def find_experiment(self, id): + file = expanduser(qc.config['core']['db_location']) conn = connect(file) cur = conn.cursor() @@ -229,60 +203,42 @@ def make_data_dictionary(self, data): class Legacy_Converter(DataConverter): - def find_data(self, id, samplefolder): + def find_data(self, id): str_id = '{0:03d}'.format(id) - - if samplefolder is None: - check_experiment_is_initialized() - # check_experiment_is_initialized() doesn't do anything at the moment - - path = qc.DataSet.location_provider.fmt.format(counter=str_id) - data = qc.load_data(path) - - else: - path = '{}{}{}'.format(samplefolder, sep, str_id) - data = qc.load_data(path) - + check_experiment_is_initialized() # TODO: what does this do? + path = qc.DataSet.location_provider.fmt.format(counter=str_id) + data = qc.load_data(path) return data - # legacy only needs data, not id or sample folder - def find_dependencies(self, id, data, samplefolder=None): + def find_dependencies(self, id): + data = self.find_data(id) dep_vars = [key for key in data.arrays.keys() if "_set" not in key[-4:]] indep_vars = [key for key in data.arrays.keys() if "_set" in key[-4:]] - dependencies = {} - for variable in dep_vars: dependencies[variable] = indep_vars - return dependencies def find_variables(self, data): all_variables = [variable for variable in data.arrays.keys()] return all_variables - # samplefolder only needed in sql converter - def find_experiment(self, id, samplefolder=None): + def find_experiment(self, id): exp_id = id return exp_id def resize_data(self, data1, data2): scan_size = len(data1[0]) - new_data2 = [] - for setvalue in data2: array = np.array([setvalue for datapoint in range(scan_size)]) new_data2.append(array) - return np.array(new_data2) def make_data_dictionary(self, data): data_dict = {} - all_variables = self.find_variables(data) - for variable in all_variables: qc_data = getattr(data, variable) name = getattr(qc_data, 'name', 'No Name') From a0cf79bb2e5a2c7364c6c12643b9c63fbdb57415 Mon Sep 17 00:00:00 2001 From: nataliejpg Date: Thu, 16 Aug 2018 14:29:01 +0200 Subject: [PATCH 068/116] id -> run_id everywhere --- qdev_wrappers/fitting/Converter.py | 61 +++++++++++++----------------- 1 file changed, 26 insertions(+), 35 deletions(-) diff --git a/qdev_wrappers/fitting/Converter.py b/qdev_wrappers/fitting/Converter.py index 1809f563..9ea402cd 100644 --- a/qdev_wrappers/fitting/Converter.py +++ b/qdev_wrappers/fitting/Converter.py @@ -4,12 +4,13 @@ from qcodes.dataset.data_export import get_data_by_id from qcodes.dataset.sqlite_base import connect from qdev_wrappers.show_num import check_experiment_is_initialized +from qdev_wrappers.transmon.file_helpers import get_data_num -def data_to_dict(id, datatype='SQL'): +def data_to_dict(run_id, datatype='SQL'): """ Args: - id (int) + run_id (int) datatype (str, default 'SQL'): 'SQL' or 'qcodes_legacy' """ if datatype == 'qcodes_legacy': @@ -22,21 +23,20 @@ def data_to_dict(id, datatype='SQL'): raise NotImplementedError( 'datatype {} is not currently supported'.format(datatype)) - return converter.convert(id) + return converter.convert(run_id) class DataConverter: """ Base class for converters that convert data into a python dictionary. """ - def find_data(self, id): + def find_data(self, run_id): """ Args: - id (int) + run_id (int) Returns: data """ - raise NotImplementedError( 'find_data not implemented in the base class.') @@ -49,7 +49,6 @@ def make_data_dictionary(self, data): dictionaries in the format {'name': str, 'label': str, 'unit': str, 'data': np.array } """ - raise NotImplementedError( 'make_data_dictionary not implemented in the base class.') @@ -61,11 +60,10 @@ def find_experiment(self, run_id): Returns: corresponding experiment id """ - raise NotImplementedError( 'find_experiment not implemented in the base class.') - def find_dependencies(self, run_id, data): + def find_dependencies(self, run_id): """ Args: run_id (int) @@ -75,7 +73,6 @@ def find_dependencies(self, run_id, data): depend on C and D dependencies = {A: [C, D], B: [C, D]} """ - raise NotImplementedError( 'find_dependencies not implemented in the base class.') @@ -86,11 +83,10 @@ def find_variables(self, data): Returns: list of all variables contained in the data dict """ - raise NotImplementedError( 'find_variables not implemented in the base class.') - def convert(self, id): + def convert(self, run_id): """ Args: run_id (int) @@ -99,17 +95,15 @@ def convert(self, id): run as numpy arrays, plus relevant metadata (experiment id, run id, dependencies, names, labels, and units) """ - - data = self.find_data(id) + data = self.find_data(run_id) data_dict = self.make_data_dictionary(data) - exp_id = self.find_experiment(id) - # SQL needs only id, Legacy needs only data - dependencies = self.find_dependencies(id, data) + exp_id = self.find_experiment(run_id) + dependencies = self.find_dependencies(run_id) all_variables = self.find_variables(data) data_dict['exp_id'] = exp_id - data_dict['run_id'] = id + data_dict['run_id'] = run_id data_dict['dependencies'] = dependencies data_dict['variables'] = all_variables @@ -118,19 +112,17 @@ def convert(self, id): class SQL_Converter(DataConverter): - def find_data(self, id): + def find_data(self, run_id): """ Uses the database location specified in qc.config to get the data using get_data_by_id function. """ - - data = get_data_by_id(id) + data = get_data_by_id(run_id) if len(data) == 0: - raise RuntimeError('No data found for run id {}'.format(id)) + raise RuntimeError('No data found for run id {}'.format(run_id)) return data - # SQL converter only needs id, not data - def find_dependencies(self, id, data): + def find_dependencies(self, run_id): file = expanduser(qc.config['core']['db_location']) # connect to SQL database @@ -141,7 +133,7 @@ def find_dependencies(self, id, data): # extract names from layouts table for row in cur.execute( - 'SELECT layout_id, parameter FROM layouts WHERE run_id is ?', str(id)): + 'SELECT layout_id, parameter FROM layouts WHERE run_id is ?', str(run_id)): names[row[0]] = row[1] # extract dependencies from dependencies table @@ -169,13 +161,13 @@ def find_variables(self, data): return all_variables - def find_experiment(self, id): + def find_experiment(self, run_id): file = expanduser(qc.config['core']['db_location']) conn = connect(file) cur = conn.cursor() execute = cur.execute( - 'SELECT exp_id FROM runs WHERE run_id is {}'.format(id)) + 'SELECT exp_id FROM runs WHERE run_id is {}'.format(run_id)) exp_id = execute.fetchone()[0] return exp_id @@ -203,15 +195,15 @@ def make_data_dictionary(self, data): class Legacy_Converter(DataConverter): - def find_data(self, id): - str_id = '{0:03d}'.format(id) + def find_data(self, run_id): + str_id = '{0:03d}'.format(run_id) check_experiment_is_initialized() # TODO: what does this do? path = qc.DataSet.location_provider.fmt.format(counter=str_id) data = qc.load_data(path) return data - def find_dependencies(self, id): - data = self.find_data(id) + def find_dependencies(self, run_id): + data = self.find_data(run_id) dep_vars = [key for key in data.arrays.keys() if "_set" not in key[-4:]] indep_vars = [key for key in data.arrays.keys() if "_set" in key[-4:]] @@ -224,9 +216,8 @@ def find_variables(self, data): all_variables = [variable for variable in data.arrays.keys()] return all_variables - def find_experiment(self, id): - exp_id = id - return exp_id + def find_experiment(self, run_id): + return None # TODO: this will never work for legacy datasets right? def resize_data(self, data1, data2): scan_size = len(data1[0]) @@ -258,7 +249,7 @@ def make_data_dictionary(self, data): set_points being stored as single numbers (i.e. for setpoints = [1, 2, 3] , data = [[a, b, c], [d, e, f], [g, h, i]] becomes setpoints = [[1, 1, 1], [2, 2, 2], [3, 3, 3]], data = data)''' - dependencies = self.find_dependencies(id, data) + dependencies = self.find_dependencies(get_data_num(data)) dep_vars = [key for key in dependencies.keys()] indep_vars = dependencies[dep_vars[0]] From 22de21a0befc4159b15ce0bf15218e87fb9febe9 Mon Sep 17 00:00:00 2001 From: nataliejpg Date: Thu, 16 Aug 2018 15:01:45 +0200 Subject: [PATCH 069/116] restructure fit_to_sql into two functions, one creates named_tuple of fit_info and the other saves it in the database --- qdev_wrappers/fitting/AnalysisToSQL.py | 101 ++++++++++++++++--------- 1 file changed, 64 insertions(+), 37 deletions(-) diff --git a/qdev_wrappers/fitting/AnalysisToSQL.py b/qdev_wrappers/fitting/AnalysisToSQL.py index f150724b..282030a4 100644 --- a/qdev_wrappers/fitting/AnalysisToSQL.py +++ b/qdev_wrappers/fitting/AnalysisToSQL.py @@ -1,9 +1,16 @@ - import sqlite3 import dill import qcodes as qc from os.path import expanduser +from collections import namedtuple + + +FitInfo = namedtuple( + 'FitInfo', + 'fitclass est_name est_label est_unit param_units data_run_id ' + 'exp_id predicts estimator analysis dill_obj start_params ' + 'param_labels table_rows table_columns tablename') def is_table(tablename, cursor): @@ -56,24 +63,15 @@ def make_table(tablename, cursor): return name -def fit_to_sql(fit, database=None): +def reorganise_fit_dict(fit): """ - Organises and saves the fit dictionary in SQL database + Information from the fit dictionary is retrieved and organized + Args: fit (dict): as output by fitter - database (str or None, default None): defualt uses database from - qc.config - - Note: It does not accept file locations of the format './file.db'. It seems you either need to specify - the full location of the file (e.g. /Users/Username/Desktop/file.db), or have the file in the same folder - you are running from and just specify the file name. """ - - # Note: the function uses SQL commands to establish the connection. It does not understand './file.db' as a - # location. If the database in question is in the same folder that you are running from, it just takes the - # file name, 'file.db'. Otherwise it seems to want a full""" - - # Part1 : Information from the fit dictionary is retrieved and organized - + Returns: + FitInfo (NamedTuple) + """ fitclass_pckl = fit['inferred_from']['estimator']['dill']['fitclass'] fitclass = dill.loads(fitclass_pckl) @@ -89,13 +87,15 @@ def fit_to_sql(fit, database=None): exp_id = fit['inferred_from']['exp_id'] predicts = est_name.strip("estimate").strip("_") estimator = "{}, {}".format( - fit['inferred_from']['estimator']['method'], fit['inferred_from']['estimator']['type']) + fit['inferred_from']['estimator']['method'], + fit['inferred_from']['estimator']['type']) analysis = str(fit['inferred_from']['estimator']['function used']) dill_obj = str(fit['inferred_from']['estimator']['dill']) start_params = str(fit['start_params']) param_labels = fitclass.p_names - # reorganize parameters to fit into rows, i.e. [a1, a2, a3...] and [b1, b2, b3...] --> [ [a1, b1], [a2, b2]...] + # reorganize parameters to fit into rows, + # [a1, a2, a3...] and [b1, b2, b3...] --> [ [a1, b1], [a2, b2]...] param_list = [] data_length = len(est_values) for i in range(data_length): @@ -104,7 +104,8 @@ def fit_to_sql(fit, database=None): params.append(param_values[parameter][i]) param_list.append(params) - # make list of table row contents, i.e. [(row1), (row2), (row3)...], row1 = (id_nr1, estimate1, a1, b1...) + # make list of table row contents, + # [(row1), (row2), (row3)...], row1 = (id_nr1, estimate1, a1, b1...) table_rows = [] for estimate, parameters, index in zip(est_values, param_list, range(data_length)): id_nr = index + 1 @@ -116,32 +117,45 @@ def fit_to_sql(fit, database=None): for parameter in fitclass.p_labels: table_columns.append(parameter) - # Part2 : A connection to the SQL database is created and the fit data is saved to the database + tablename = 'analysis_{}_{}'.format( + fit['inferred_from']['run_id'], fitclass.name) + + return FitInfo( + fitclass, est_name, est_label, est_unit, param_units, data_run_id, + exp_id, predicts, estimator, analysis, dill_obj, start_params, + param_labels, table_rows, table_columns, tablename) + +def save_fit_dict(fit_info): + """ + A connection to the SQL database is created and the fit data is + saved to the database. + + Args: + fit_info (FitInfo named_tuple) + """ # create a connection to the SQL database - if database is not None: - file = database - else: - file = expanduser(qc.config['core']['db_location']) + file = expanduser(qc.config['core']['db_location']) conn = sqlite3.connect(file) cur = conn.cursor() # Create table for fit parameter and predicted output values, store data - tablename = 'analysis_{}_{}'.format( - fit['inferred_from']['run_id'], fitclass.name) - table = make_table(tablename, cur) - for column in table_columns: + table = make_table(fit_info.tablename, cur) + + for column in fit_info.table_columns: cur.execute('ALTER TABLE {} ADD {}'.format(table, column)) - num_cols = len(table_rows[0]) + num_cols = len(fit_info.table_rows[0]) placeholder = ('?,' * num_cols).strip(',') cur.executemany('INSERT INTO {} VALUES ({})'.format( - table, placeholder), table_rows) + table, placeholder), fit_info.table_rows) - # Create 'analyses' table if it does not already exist, store info about analysis in 'analyses' + # Create 'analyses' table if it does not already exist, + # store info about analysis in 'analyses' if not is_table('analyses', cur): - cur.execute('''CREATE TABLE analyses + cur.execute( + '''CREATE TABLE analyses (data_run_id INTEGER, exp_id INTEGER, run_id INTEGER, analysis_table_name TEXT, predicts TEXT, estimator TEXT, function TEXT, start_parameters TEXT, dill TEXT)''') run_id = 1 @@ -151,7 +165,9 @@ def fit_to_sql(fit, database=None): sql_analyses = 'INSERT INTO analyses VALUES (?,?,?,?,?,?,?,?,?)' cur.execute(sql_analyses, - (data_run_id, exp_id, run_id, table, predicts, estimator, analysis, start_params, dill_obj)) + (fit_info.data_run_id, fit_info.exp_id, fit_info.run_id, + table, fit_info.predicts, fit_info.estimator, + fit_info.analysis, fit_info.start_params, fit_info.dill_obj)) # Store unit and label info in 'layouts' table layout_id = 1 @@ -160,17 +176,17 @@ def fit_to_sql(fit, database=None): layout_id += max_id layout_rows = [] - for parameter, label, unit in zip(fitclass.p_labels, param_labels, param_units): + for parameter, label, unit in zip(fit_info.fitclass.p_labels, fit_info.param_labels, fit_info.param_units): row = (layout_id, run_id, parameter, label, unit, "inferred_from") layout_rows.append(row) layout_id += 1 - layout_rows.append((layout_id, run_id, est_name, - est_label, est_unit, "inferred_from")) + layout_rows.append((layout_id, run_id, fit_info.est_name, + fit_info.est_label, fit_info.est_unit, "inferred_from")) sql_layout = 'INSERT INTO layouts VALUES (?,?,?,?,?,?)' cur.executemany(sql_layout, layout_rows) - # Store summary in 'runs' SQL table, with run type 'analysis' instead of 'result' + # Store summary in 'runs' SQL table, with run type 'analysis' sql_runs = 'INSERT INTO runs VALUES (?,?,?,?,?,?,?,?,?,?)' cur.execute(sql_runs, (run_id, exp_id, 'analysis', table, "", "", "", 1, "", "")) @@ -180,3 +196,14 @@ def fit_to_sql(fit, database=None): conn.close() print("Table {} created".format(table)) + + +def fit_to_sql(fit): + """ + Organises and saves the fit dictionary in SQL database + Args: + fit (dict): as output by fitter + """ + fit_info_tuple = reorganise_fit_dict(fit) + + save_fit_dict(fit_info_tuple) From 9a2579b14ecac5726f2e8bd853c8ebb7885cdd96 Mon Sep 17 00:00:00 2001 From: nataliejpg Date: Thu, 16 Aug 2018 15:11:30 +0200 Subject: [PATCH 070/116] update docstrings --- qdev_wrappers/fitting/Converter.py | 2 -- qdev_wrappers/fitting/Fitclasses.py | 23 ++++++++++++----------- 2 files changed, 12 insertions(+), 13 deletions(-) diff --git a/qdev_wrappers/fitting/Converter.py b/qdev_wrappers/fitting/Converter.py index 9ea402cd..8ec0481e 100644 --- a/qdev_wrappers/fitting/Converter.py +++ b/qdev_wrappers/fitting/Converter.py @@ -177,9 +177,7 @@ def make_data_dictionary(self, data): for data_subset in data: for variable in data_subset: - name = variable['name'] - if name in data_dict.keys(): if not np.array_equal(variable['data'], data_dict[name]['data']): diff --git a/qdev_wrappers/fitting/Fitclasses.py b/qdev_wrappers/fitting/Fitclasses.py index 8439d5f1..55a9fc35 100644 --- a/qdev_wrappers/fitting/Fitclasses.py +++ b/qdev_wrappers/fitting/Fitclasses.py @@ -5,14 +5,9 @@ class LeastSquaresFit: """ Base class for fit functions to be used with curve_fit. Specifies - a particular mathematical function and information that can be used to - perform a least squares fit to the data. - - Each class holds the mathematical function itself ('fun'), a function - for determining an initial guess for the fit parameters ('guess'), and - a list of attributes that set the function inputs and outputs, the fit - parameter labels and names, and the relationship between the units on the - input and output variables and the units on the parameters. + a function for fitting, function for guessing initial parameters for fit + and attributes specifying inputs and outputs of fit function as + well as and names, labels and units of fit parameters. """ def __init__(self, name, fun_str, fun_np, fun_vars, @@ -22,13 +17,19 @@ def __init__(self, name, fun_str, fun_np, fun_vars, self.fun_np = fun_np self.fun_output = fun_output self.p_names = p_names - self.p_units = p_labels + self.p_labes = p_labels self.p_units = p_units - def fun(self, x, a, T, c): + def fun(self, *args): + """ + The mathematical function to fit to + """ raise NotImplementedError('This is not implemented in the base class.') - def guess(self, x, y): + def guess(self, *args): + """ + Function for determining an initial guess for the fit parameters + """ raise NotImplementedError('This is not implemented in the base class.') From 918ab59b8c68bb33ca07ba7d52dc6cc6805a439d Mon Sep 17 00:00:00 2001 From: nataliejpg Date: Thu, 16 Aug 2018 16:17:07 +0200 Subject: [PATCH 071/116] rename converter and data saver files to better represent their functionality --- qdev_wrappers/fitting/{Converter.py => data_extraction.py} | 0 qdev_wrappers/fitting/{AnalysisToSQL.py => fit_saving.py} | 0 2 files changed, 0 insertions(+), 0 deletions(-) rename qdev_wrappers/fitting/{Converter.py => data_extraction.py} (100%) rename qdev_wrappers/fitting/{AnalysisToSQL.py => fit_saving.py} (100%) diff --git a/qdev_wrappers/fitting/Converter.py b/qdev_wrappers/fitting/data_extraction.py similarity index 100% rename from qdev_wrappers/fitting/Converter.py rename to qdev_wrappers/fitting/data_extraction.py diff --git a/qdev_wrappers/fitting/AnalysisToSQL.py b/qdev_wrappers/fitting/fit_saving.py similarity index 100% rename from qdev_wrappers/fitting/AnalysisToSQL.py rename to qdev_wrappers/fitting/fit_saving.py From 43000efa5d1dc8a56b867fb5101fabe5c115dc96 Mon Sep 17 00:00:00 2001 From: nataliejpg Date: Thu, 16 Aug 2018 16:20:11 +0200 Subject: [PATCH 072/116] rename key functions for data extraction and fit saving --- qdev_wrappers/fitting/data_extraction.py | 3 +-- qdev_wrappers/fitting/fit_saving.py | 3 +-- 2 files changed, 2 insertions(+), 4 deletions(-) diff --git a/qdev_wrappers/fitting/data_extraction.py b/qdev_wrappers/fitting/data_extraction.py index 8ec0481e..da1793fa 100644 --- a/qdev_wrappers/fitting/data_extraction.py +++ b/qdev_wrappers/fitting/data_extraction.py @@ -7,7 +7,7 @@ from qdev_wrappers.transmon.file_helpers import get_data_num -def data_to_dict(run_id, datatype='SQL'): +def load_data_dict(run_id, datatype='SQL'): """ Args: run_id (int) @@ -56,7 +56,6 @@ def find_experiment(self, run_id): """ Args: run_id - Returns: corresponding experiment id """ diff --git a/qdev_wrappers/fitting/fit_saving.py b/qdev_wrappers/fitting/fit_saving.py index 282030a4..370d640b 100644 --- a/qdev_wrappers/fitting/fit_saving.py +++ b/qdev_wrappers/fitting/fit_saving.py @@ -198,12 +198,11 @@ def save_fit_dict(fit_info): print("Table {} created".format(table)) -def fit_to_sql(fit): +def save_fit_dict(fit): """ Organises and saves the fit dictionary in SQL database Args: fit (dict): as output by fitter """ fit_info_tuple = reorganise_fit_dict(fit) - save_fit_dict(fit_info_tuple) From cc7e053ecfab94077651ca4a7453e7e338ff0408 Mon Sep 17 00:00:00 2001 From: nataliejpg Date: Thu, 16 Aug 2018 16:23:20 +0200 Subject: [PATCH 073/116] fix duplicate naming bug I just introduced to fit_saving file and make all but actual fit saving function private --- qdev_wrappers/fitting/fit_saving.py | 33 +++++++++++++++-------------- 1 file changed, 17 insertions(+), 16 deletions(-) diff --git a/qdev_wrappers/fitting/fit_saving.py b/qdev_wrappers/fitting/fit_saving.py index 370d640b..464d0537 100644 --- a/qdev_wrappers/fitting/fit_saving.py +++ b/qdev_wrappers/fitting/fit_saving.py @@ -13,7 +13,17 @@ 'param_labels table_rows table_columns tablename') -def is_table(tablename, cursor): +def save_fit_dict(fit): + """ + Organises and saves the fit dictionary in SQL database + Args: + fit (dict): as output by fitter + """ + fit_info_tuple = _reorganise_fit_dict(fit) + _save_to_database(fit_info_tuple) + + +def _is_table(tablename, cursor): """ Args: tablename (str) @@ -43,7 +53,7 @@ def is_table(tablename, cursor): 'instead of 1 or 0.'.format(tablename, count)) -def make_table(tablename, cursor): +def _make_table(tablename, cursor): """ Args: tablename (str) @@ -55,7 +65,7 @@ def make_table(tablename, cursor): name = tablename n = 0 - while is_table(name, cursor): + while _is_table(name, cursor): n += 1 name = "{}_{}".format(tablename, n) @@ -63,7 +73,7 @@ def make_table(tablename, cursor): return name -def reorganise_fit_dict(fit): +def _reorganise_fit_dict(fit): """ Information from the fit dictionary is retrieved and organized @@ -126,7 +136,7 @@ def reorganise_fit_dict(fit): param_labels, table_rows, table_columns, tablename) -def save_fit_dict(fit_info): +def _save_to_database(fit_info): """ A connection to the SQL database is created and the fit data is saved to the database. @@ -141,7 +151,7 @@ def save_fit_dict(fit_info): # Create table for fit parameter and predicted output values, store data - table = make_table(fit_info.tablename, cur) + table = _make_table(fit_info.tablename, cur) for column in fit_info.table_columns: cur.execute('ALTER TABLE {} ADD {}'.format(table, column)) @@ -153,7 +163,7 @@ def save_fit_dict(fit_info): # Create 'analyses' table if it does not already exist, # store info about analysis in 'analyses' - if not is_table('analyses', cur): + if not _is_table('analyses', cur): cur.execute( '''CREATE TABLE analyses (data_run_id INTEGER, exp_id INTEGER, run_id INTEGER, analysis_table_name TEXT, @@ -197,12 +207,3 @@ def save_fit_dict(fit_info): print("Table {} created".format(table)) - -def save_fit_dict(fit): - """ - Organises and saves the fit dictionary in SQL database - Args: - fit (dict): as output by fitter - """ - fit_info_tuple = reorganise_fit_dict(fit) - save_fit_dict(fit_info_tuple) From 3c80e44c5db357b31501358e39aeab0148bb3067 Mon Sep 17 00:00:00 2001 From: nataliejpg Date: Thu, 16 Aug 2018 16:25:11 +0200 Subject: [PATCH 074/116] rename Fitclasses --- qdev_wrappers/fitting/{Fitclasses.py => least_squares_fit.py} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename qdev_wrappers/fitting/{Fitclasses.py => least_squares_fit.py} (100%) diff --git a/qdev_wrappers/fitting/Fitclasses.py b/qdev_wrappers/fitting/least_squares_fit.py similarity index 100% rename from qdev_wrappers/fitting/Fitclasses.py rename to qdev_wrappers/fitting/least_squares_fit.py From 0569bb3c04adda79dcf1ca2772c13f1242c5ed02 Mon Sep 17 00:00:00 2001 From: nataliejpg Date: Wed, 22 Aug 2018 11:40:45 +0200 Subject: [PATCH 075/116] rewrite fitter to do 1 dimensional fits for any number of setpoints and return fitter object rather than dictionary --- qdev_wrappers/fitting/Fitter.py | 620 ++++++++++---------------------- 1 file changed, 180 insertions(+), 440 deletions(-) diff --git a/qdev_wrappers/fitting/Fitter.py b/qdev_wrappers/fitting/Fitter.py index e64391f6..05768061 100644 --- a/qdev_wrappers/fitting/Fitter.py +++ b/qdev_wrappers/fitting/Fitter.py @@ -1,12 +1,12 @@ import qcodes as qc import numpy as np -import dill from scipy.optimize import curve_fit +from itertools import product +from qcodes.config.config import DotDict -def fit_data(data, fitclass, fun_inputs, fun_output, - setpoint_params=None, p0=None, **kwargs): +def fit_data(data, fitclass, fun_inputs, fun_output, **kwargs): """ Fits the data in the data dictionary using the fit class provided and the mappings provided to generate a fit dictionary. @@ -14,460 +14,200 @@ def fit_data(data, fitclass, fun_inputs, fun_output, Args: data (dict) fitclass (currently limited to LeastSquaresFit) - fun_inputs (dict): mapping between data names in data dictionary and - arguments of fit class function eg {'x': 'pulse_readout_delay'} - fun_output (dict: mapping between data names in data dictionary and + fun_input (dict): mapping between data name in data dictionary and + argument of fitclass function eg {'x': 'pulse_readout_delay'} + fun_output (dict: mapping between data name in data dictionary and return of fit class function eg {'y': 'data'} - setpoint_params (list or None, default None): other data names in - dictionary which are not part of the fit funetion input or output - p0 (list or None, default None): initial estimation of parameters of - the fit, overwrites those generated by the fit class guess **kwargs (optional): passed to find_fit function of fit class Returns: fit (dict): containing the fitted parameters and an array of "estimated" outputs given the fit as well as some metadata about the fit. - - """ - - check_input_matches_fitclass( - fitclass, fun_inputs, fun_output, setpoint_params, p0) - - # this is used to decide which fitter class to create an instance of. - # If they are combined, this won't be needed. - fun_dim = len(fitclass.fun_vars) - num_inputs = len(fun_inputs) - if setpoint_params is not None: - num_setpoints = len(setpoint_params) - else: - num_setpoints = 0 - - # creates an instance of the relevant fitter based on the number of - # setpoints and inputs and the dimensionality - # of the function to be fitted. If there were just one fitter class, - # the if statements wouldn't be needed. - if fun_dim == 1 and num_inputs == 1 and num_setpoints == 0: - fitter = Fitter1D(data, fun_inputs, fun_output) - elif fun_dim == 1 and num_inputs == 1 and num_setpoints == 1: - # what does the fitter actually need?? - fitter = Fitter_2Ddata_1Dfunction(data, fun_inputs, fun_output) - else: - raise NotImplementedError( - 'You have specified a {}-dimensional function, {} function ' - 'inputs, and {} setpoints. This process is currently so ' - 'specific that your only options are to fit to 1D functions,' - ' provide 1 input, and have either 0 or 1 setpoint types. ' - 'Sorry.'format(fun_dim, num_inputs, num_setpoints)) # TODO: DON'T SAY SORRY!!! - - # the fitter performs the fit and saves a dictionary containing the - # results in variable 'fit' - fit = fitter.find_fit(data, fitclass, fun_inputs, - fun_output, setpoint_params, p0, **kwargs) - - # the fitter and fit class used for the fit are preserved via dill - # and added to the fit dictionary - dill_fitclass = dill.dumps(fitclass) - dill_fitter = dill.dumps(fitter) - - fit['inferred_from']['estimator']['dill'] = { - 'fitclass': dill_fitclass, 'fitter': dill_fitter} - - return fit - - -def check_input_matches_fitclass(fitclass, inputs, output, setpoints, p0=None): - """ - Checks that the given arguments are in the correct format for the - 'fit_data' function to proceed, and that they match the expected inputs for - the specified fit class. - - Each fit class describes a mathematical function to be fitted to, - with a set of attributes, including which variables the function has as - inputs, and what outputs it has. This function makes sure that the correct - inputs and outputs are specified compared to what inputs and outputs the - mathematical function has, and that they are specified in the correct - format. It also checks that setpoints are given in the correct format - (a list). If guess parameters are specified, it checks that the length of - the parameter list is the expected number of parameters for the function - chosen. - - Args: - same arguments as the 'fit_data' function """ - # check input and output are given, and that input, - # output and setpoints are in correct format - if (type(inputs) != dict or type(output) != dict): - raise RuntimeError( - 'Please specify both input and output variables for the function ' - 'you wish to fit in the format fun_inputs = ' - '{"x": "name", "y": "other_name"}, fun_output =' - ' {"z": "another_name"}') - if (setpoints is not None) and (type(setpoints) != list): - raise RuntimeError( - 'Please specify setpoints as a list ["setpoint_name", ...],' - ' even if there is only one.') - - # check inputs/outputs specified match inputs/outputs function takes - if len(inputs) != len(fitclass.fun_vars): - raise RuntimeError( - 'The function you are fitting to takes {} variables, and you ' - 'have specified {}'.format(len(fitclass.fun_vars), len(inputs))) - for variable in inputs.keys(): - if variable not in fitclass.fun_vars: - raise RuntimeError( - 'You have specified a variable {}. The fit function takes' - 'variables {}'.format(variable, fitclass.fun_vars)) - for variable in output.keys(): - if variable not in fitclass.fun_output: - raise RuntimeError( - 'You have specified a variable {}. The fit function returns ' - 'variables {}'.format(variable, fitclass.fun_output)) + check_input_matches_fitclass(fitclass, fun_input, fun_output) - # check that if a guess p0 is specified, the number of parameters - # is correct for the fit function - if (p0 is not None) and (len(p0) != len(fitclass.p_labels)): - raise RuntimeError( - 'You have specified {} start parameters for the fit function' - '. The function takes {} start parameters: ' - '{}'.format(len(p0), len(fitclass.p_labels), fitclass.p_labels)) + setpoints = [v for v in data['variables'] if + v not in set([*fun_input.values(), + *fun_output.values()])] + return Fitter(data, fitclass, fun_input, fun_output, setpoints) class Fitter: """ - The fitter has a method, 'find_fit', which takes the fitclass, data, - function inputs, function outputs, setpoints when relevant, and optionally, - guess parameters, and returns a fit dictionary. The fit class is a - LeastSqauresFit class describing a mathematical function that can be fitted - to data, while the Fitter class performs the fitting. All other methods in the fitter are com - Much of the fitting is generic and does not depend on the number of inputs, outputs or setpoints in the - specified LeastSquaresFit class; however, depending on the dimensionality of the function to be fitted - and the measured data, the function 'perform_fit' varies. - - Currently, Fitter is a parent class with no 'perform_fit' method of its own, and two child classes inherit - from it: a class for 1D fits to 1D data (i.e. one variable, one output and no setpoints) and a class for - 1D fits to 2D data (i.e. one variable, one setpoint and one output). """ - - # TODO: perform_fit and find_fit are two different functions with deceptively similar names. This could be improved. - # TODO: I think with some changes, perform_fit could be made generic, so only one Fitter class was needed - - def __init__(self, data, inputs, output): - self.input_vars = [variable for variable in inputs.keys()] - self.output_var = [variable for variable in output.keys()] - self.output_dataname = [dataname for dataname in output.values()] - self.all_datanames = [] - self.full_parameter_dict = {} - - self.setpoint_params = [] + Class which performs fit for data based on data, fitclass, mapping between + paramter names and fit function arguments/returns and setpoints + """ - def find_fit(self, data, fitclass, fun_inputs, fun_output, setpoint_params=None, p0=None, **kwargs): + def __ init__(self, data, fitclass, fun_input, fun_output, setpoints): + """ + Args: + data (dict) + """ + self.fitclass = fitclass + self.fun_input = fun_input + self.fun_output = fun_output + self.setpoints = {setpoint: set(data[setpoint].flatten()) for + setpoint in setpoints} + self.estimator = {'method': 'LSF', + 'type': fitclass.name, + 'fit_function_str': fitclass.fun_str} + self.inferred_from = data.run_id + self.fit_results = [] + self._do_fitting_procedure() + + def _do_fitting_procedure(self): + """ + Populates fit_results list with one dictionary per combination + of setpoints. Dictionaries are of the form: + + for self.setpoints = {'temperature': [0.01, 0.02, 0.03]} + + { + 'temperature': 0.02, + 'param_names': ['a', 'b'], + 'param_labels': {'a': 'T1', 'b': 'b'}, + 'param_units': {'a': 's', 'b': ''}, + 'param_start_values': {'a': 3, 'b': 10}, + 'param_values': {'a': 2.5, 'b': 11}, + 'param_variance': {'a': 0.1, 'b': 2}, + 'input': [0.1, 0.2, 0.3, 0.4], + 'output': [1.15, 1.26, 1.34, 1.23] + 'estimate' [1.1, 1.2, 1.3, 1.4] + } """ - find_fit combines other Fitter methods to organize the data, find fitted parameters and save - them to a fit dictionary. It then adds parameter units to the fit dictionary, uses the fitted - parameters and the given data to calculate estimated outputs given the fitten function, and adds - metadata about the data and the fit to fit['inferred_from'] - - As arguments, it takes the data dictionary produced by the Converter, a LeastSquaresFit class - that defines the mathematical function to be fitted to the data, the inputs and outputs for the - mathematical function in the format {'x' : 'dataname', ...}. Optionally, it also takes a list of - setpoint parameters if any in the format ['dataname1', ... ], and a list of guess parameters if - they are being specified manually instead of estimated by the LeastSquaresFit 'guess' function.""" - - # organize data in a format needed later (see 'organize_data' function) - data_kwargs, unit_dict = self.organize_data( - data, fun_inputs, fun_output, setpoint_params) - - # fit parameters via SciPy curve_fit and save to fit dictionary - fit = self.perform_fit(data_kwargs, data, fitclass, - setpoint_params, p0, **kwargs) - - # find parameter units and save to fit dictionary - fit['parameter units'] = self.find_parameter_units(fitclass, unit_dict) - - # use fitted parameters and data to find the estimated output of the fitted function - fit['estimate'] = self.estimate_function_values( - fitclass, data, data_kwargs) - - # save metadata - fit['inferred_from'] = {'inputs': fun_inputs, - 'output': fun_output, - 'setpoints': setpoint_params, - 'run_id': data['run_id'], - 'exp_id': data['exp_id'], - 'dependencies': data['dependencies'], - 'estimator': {'method': 'Least squared fit', - 'type': fitclass.name, - 'function used': str(fitclass.fun_np), - } - } - - return fit - - def organize_data(self, data, inputs, output, setpoints): - """ This function organizes the data based on the inputs, outputs and setpoints specified. - - First, it makes a list of all data names from inputs, outputs and setpoints, and confirms - that those data names are all present in the actual data dictionary. That is, it makes - sure that if you have said that inputs are {'x': 'frequency'}, that 'frequency' is among - the variables present in the data. It makes a list of all variables that will be used, and - saves them as Fitter attribute self.all_datanames - - Second, it makes and returns two dictionaries, for units and for data arrays: - data_kwargs = {'x': np.array, 'y': np.array ...} and - unit_dict = {'x': 'unit', 'y': 'unit' ...}. - - The data_kwargs dict will be used later as **kwargs in a variety of functions that need - inputs like 'x = np.array'. The unit dictionary specifies the unit for each input variable - of the mathematical function being fitted, and will be used to find the parameter units - in find_parameter_units. """ - - # make a list of all datanames specefied by the inputs, outputs and setpoints - all_vars = [dataname for dataname in inputs.values()] - for dataname in output.values(): - all_vars.append(dataname) - if setpoints is not None: - for dataname in setpoints: - all_vars.append(dataname) - # confirm all variables involved are in the data dictionary - for dataname in all_vars: - if dataname not in data.keys(): - raise RuntimeError('''Variable {} not found in data dictionary. Data dictionary contains - variables: {}'''.format(dataname, data['variables'])) - self.all_datanames = all_vars - - # make a dictionary of data arrays and units in format {'x' : np.array...} and {'x' : 'unit', ...} - data_kwargs = {} - unit_dict = {} - - for variable, name in inputs.items(): - data_kwargs[variable] = data[name]['data'] - unit_dict[variable] = data[name]['unit'] - - for variable, name in output.items(): - data_kwargs[variable] = data[name]['data'] - unit_dict[variable] = data[name]['unit'] - - return data_kwargs, unit_dict - - def find_start_parameters(self, fitclass, p0, data_kwargs): - """ Takes as arguments the fit class, the guess parameters if any, and the "data_kwargs", - which is not the data as originally given, but the data arrays organized by variables - by the organize_data function. It has the format {"x": np.array, ...}, so that it can - unpacked as keyword arguments for the mathematical functions. - - If no guess parameters are specified, the guess function from the fit class is given - key word arguments from the unpacked data_kwargs, and returns a set of guess parameters. - If guess parameters have been specified explicitly, then those are used instead. """ - if (p0 is None and hasattr(fitclass, 'guess')): - p0 = getattr(fitclass, 'guess')(**data_kwargs) - return p0 - elif p0 is not None: - p0 = p0 - return p0 + # if setpoints then perform fit for all + if len(self.setpoints) > 0: + setpoint_combinations = product(*self.setpoints.values()) + setpoint_names = self.setpints.keys() + for setpoint_combination in setpoint_combinations: + # find indices where where setpoing combination is satisfied + setpoint_0_indices = list(np.argwhere( + data[setpoint_names[0]] == setpoint_combination[0])) + for i in range(1, len(setpoint_names)): + new_setpoint_indices = list(np.argwhere( + data[setpoint_names[i]] == setpoint_combination[i])) + union_setpoint_indices = [] + for index in indices: + for new_index in new_indices: + if np.array_equal(index, new_index): + union_setpoint_indices.append(index) + index = union_setpoint_indices[0] + try: + result = {} + input_data_array = {k: data[v][index] + for k, v in fun_input.items()} + output_data_array = {k: data[v][index] + for k, v in fun_output.items()} + param_dict = self._perform_fit(input_data_array, + output_data_array) + result.update(dict(zip(self.setpoints.keys(), + setpoint_combination))) + result.update({'input': input_data_array, + 'output': output_data_array}) + result.update( + {'estimate': + self.find_estimate(input_data_array, + param_dict['param_values'])}) + result.update(result) + self.fit_results.append(result) + except Exception: # TODO: what kind of exception + print('no data for setpoint combination ', dict( + zip(self.setpoints.keys(), setpoint_combination))) else: - return "Could not find guess parameters for fit." - - def find_parameter_units(self, fitclass, unit_dict): - """ This uses the relationship between the units stored in the fitclass attribute 'p_units' and - the unit labels for the data to determine the unit labels for each parameter. - - For example: according to the p_units attribute, the unit for the first parameter in the list - is 1/y. Based on the data that has been organized by organize_data and stored in the unit_dict, - the unit for y are 'S', so this function concludes that the units for the first parameter are 1/S - (it does not know that this is Hz, its not that clever). - - It returns a dictionary in the format {"p1" : unit, "p2" : unit ...} of all function parameters, - where the key is the parameters as specified in the fitclass attribute p_labels. """ - - # unit labels could fx. be [a, T, w, p, c], and unit templates could be ['y', 'x', '1/x', '', 'y'] - unit_templates = fitclass.p_units - unit_labels = fitclass.p_labels - param_units = {} - - # zip the two lists together and take the elements one at a time - for template, label in zip(unit_templates, unit_labels): - # for each element, make the template a list. 'y' --> ['y'], '1/x' --> ['1', '/', 'x'] - template = list(template) - # go through each element of the template list, and compare to the variables in the unit_dict - for i in range(len(template)): - for variable in unit_dict.keys(): - # if the i'th element of the template is one of the variables in unit_dict, replace with the unit - # f.x., if {'x': 'S', ...}, ['x', ...] --> ['S', ...] - if template[i] == variable: - template[i] = unit_dict[variable] - # join the list again, fx. ['1', '/', 'S'] --> '1/S' - unit = "".join(template) - # add the unit to the param_units dictionary - param_units[label] = unit - - return param_units - - def perform_fit(self, data_kwargs, data, fitclass, setpoints, p0, **kwargs): - """ A suitably generic version of this function isn't implemented yet. See specific versions in - child classes. - - The purpose of this function is to find the fitted values of the parameters via SciPy's curve_fit - function. The complication in making it generic lies in the need to sort the data by setpoints and - then do multiple fits to subsets of the data if the fitted function is of lower dimensionality than - the measurement. - - It does two things: - - 1. It sets the Fitter attribute self.full_parameter_dict, which is a dictionary that contains all - of the parameters as keys, and for each key, the value is an array with the same dimension as - the original data, with the parameter's value for each datapoint in the original data. For - example, if parameter 1 were found to be 2.5 for the first 5 datapoints, and then the measurement - was moved to a new setpoint, and the next 5 datapoints had p1 = 3.5, you would have - - self.full_parameter_dict = {"p1" : [[2.5, 2.5, 2.5, 2.5, 2.5, 3.5, 3.5, 3.5 ... ]], - "p2" : [[ ... ]], - ... } - - Thereby saving all the parameters with indicies matching their corresponding datapoints, which - will be needed for calculating the estimated measurement based on the fit. - - 2. It returns a fit dictionary containing the start_params (initial guess), and the value and cov - matrix for each fitted parameter (organized by setpoint if there were setpoints). """ - - raise NotImplementedError( - "A generic fitter function for the Fitter base class has yet to be implemented.") - - def estimate_function_values(self, fitclass, data, data_kwargs): - """ Takes fitclass and data, as well as the data_kwargs produced by organize_data, and references the - Fitter attribute self.full_parameter_dict created by perform_fit. It uses the numpy function saved - in the LeastSquaresFit class attribute 'fun', giving it the input data and input parameters as numpy - arrays. It returns a numpy array of 'estimates', i.e. what the predicted output of the fitted function - would be given the parameter values found by perform_fit and function inputs. - - It returns an estimate dictionary containing the estimate numpy array and information about labels, - units and parameters used. The parameter array and estimate array are what will be saved in the SQL - table later.""" - - estimate = {} - - # obtain a dictionary containing only input variables in format {"x": np.array, ...}, for **kwargs - input_data = data_kwargs - for variable in self.output_var: - del input_data[variable] - - # obtain parameters, also in the format {"p1" : np.array, ...} for **kwargs - input_parameters = self.full_parameter_dict - - # Calculate estimated values for output based on function and store in estimate['values'] - estimate['values'] = fitclass.fun(**input_data, **input_parameters) - # FIXME : PyCharm says this duplicate ** expression is incompatible with versions < 3.5. Is that a problem? - - # Save other info about the estimate - estimate['name'] = '{}_estimate'.format(self.output_dataname[0]) - estimate['label'] = '{} estimate'.format( - data[self.output_dataname[0]]['label']) - estimate['unit'] = data[self.output_dataname[0]]['unit'] - estimate['parameters'] = self.full_parameter_dict - - return estimate - - -class Fitter1D(Fitter): - - def perform_fit(self, data_kwargs, data, fitclass, setpoints, p0, **kwargs): - - # make fit dictionary - fit = {'parameters': {}, 'start_params': {}} - - # define data arrays - xdata = data_kwargs[self.input_vars[0]] - ydata = data_kwargs[self.output_var[0]] - - # find guess for start parameters, run curve_fit function to perform SciPy least squares fit - p_guess = self.find_start_parameters(fitclass, p0, data_kwargs) - popt, pcov = curve_fit(fitclass.fun, xdata, - ydata, p0=p_guess, **kwargs) - - # save fit information - for parameter in fitclass.p_labels: - fit['parameters'][parameter] = { - 'value': popt[fitclass.p_labels.index(parameter)]} - fit['parameters'][parameter]['cov'] = pcov[fitclass.p_labels.index( - parameter)] - fit['start_params'][parameter] = p_guess[fitclass.p_labels.index( - parameter)] - - # make an dictionary containing arrays of each parameter value with same dimension as data (for estimate) - all_params = {} - for parameter in fitclass.p_labels: - all_params[parameter] = [] - for datapoint in xdata: - all_params[parameter].append( - fit['parameters'][parameter]['value']) - all_params[parameter] = np.array(all_params[parameter]) - self.full_parameter_dict = all_params - - return fit - - -class Fitter_2Ddata_1Dfunction(Fitter): - - def perform_fit(self, data_kwargs, data, fitclass, setpoints, p0, **kwargs): - - # Retrieve data - xarray = data_kwargs[self.input_vars[0]] - yarray = data_kwargs[self.output_var[0]] - setpoint_array = data[setpoints[0]]['data'] - - # Reorganize data by setpoint - unique_setpoints = np.unique(setpoint_array) - - xdata_lst = [] - ydata_lst = [] - - for set_point in unique_setpoints: - x_dat = [] - y_dat = [] - for setpoint, x, y in zip(setpoint_array, xarray, yarray): - if setpoint == set_point: - x_dat.append(x) - y_dat.append(y) - xdata_lst.append(np.array(x_dat)) - ydata_lst.append(np.array(y_dat)) - - xdata = np.array(xdata_lst) - ydata = np.array(ydata_lst) - - # perform 1D fit for cross section at each setpoint and save to fit dictionary (as in Fitter1D) - fit = {'start_params': {}} - - for set_value, xdata_1d, ydata_1d in zip(unique_setpoints, xdata, ydata): - - data_dict_1d = { - self.input_vars[0]: xdata_1d, self.output_var[0]: ydata_1d} - - p_guess = self.find_start_parameters(fitclass, p0, data_dict_1d) - popt, pcov = curve_fit( - fitclass.fun, xdata_1d, ydata_1d, p0=p_guess, **kwargs) - - fit[set_value] = {} - fit[set_value]['parameters'] = {} - fit['start_params'][set_value] = {} - - for parameter in fitclass.p_labels: - fit[set_value]['parameters'][parameter] = { - 'value': popt[fitclass.p_labels.index(parameter)]} - fit[set_value]['parameters'][parameter]['cov'] = pcov[fitclass.p_labels.index( - parameter)] - fit['start_params'][set_value][parameter] = p_guess[fitclass.p_labels.index( - parameter)] + input_data_array = {k: data.get_data(v) for + k, v in fun_input.items()} + output_data_array = {k: data.get_data(v) for + k, v in fun_output.items()} + param_dict = self.perform_fit(input_data_array, + output_data_array) + result.update({'input': input_data_array, + 'output': output_data_array}) + result.update( + {'estimate': + self.find_estimate(input_data_array, + param_dict['param_values'])}) + self.fit_dict['fit_results'].append(result) + + def get_result(**setpoint_values): + if len(setpoint_values) != len(self.setpoints): + raise RuntimeError('Must specify a value for each setpoint') + return next(res for res in self.fit_results if + all(res[setpoint] == value for + setpoint, value in setpoint_values.items())) + + def _perform_fit(self, input_data_array, output_data_array): + + # make fit params dictionary + params_dict = {'param_names': [], 'param_labels': {}, + 'param_units': {}, 'param_start_values': {}, + 'param_variance': {}, 'param_values': {}} + for i, param_name in enumerate(fitcass.params_names): + params_dict['param_names'].append(param_name) + params_dict['param_labels'][param_name] = fitclass.param_labels[i] + params_dict['param_units'][param_name] = fitclass.param_units[i] + + # find start parameters, run curve_fit function to perform fit + p_guess = self.fitclass.guess(input_data_array, output_data_array) + popt, pcov = curve_fit(fitclass.fun, + input_data_array, + output_data_array, + p0=p_guess) + + # add guess and fit results to dict + for i, param_name in enumerate(self.fitclass.p_names): + params_dict['param_start_values'][param_name] = p_guess[i] + params_dict['param_values'][param_name] = popt[i] + params_dict['param_variance'][param_name] = pcov[i, i] + + return params_dict + + def find_estimate(self, input_data_array, params_values_dict): + return fitlass.fun(input_data_array, **params_values_dict) + + + def check_input_matches_fitclass(self): + """ + Checks that the given arguments are in the correct format for the + 'fit_data' function to proceed, and that they match the expected inputs for + the specified fit class. + + Each fit class describes a mathematical function to be fitted to, + with a set of attributes, including which variables the function has as + inputs, and what outputs it has. This function makes sure that the correct + inputs and outputs are specified compared to what inputs and outputs the + mathematical function has, and that they are specified in the correct + format. + + Args: + fitclass (currently only LeastSquaresFit implemented) + inputs (dict) + output (dict) + """ - # Make an array of parameters, specified in the same order as the original setpoint array, before data - # was rearranged to do the fit. This is for running through the estimation function later. - all_params = {} - for parameter in fitclass.p_labels: - all_params[parameter] = [] - for setpoint in setpoint_array: - all_params[parameter].append( - fit[setpoint]['parameters'][parameter]['value']) - all_params[parameter] = np.array(all_params[parameter]) - self.full_parameter_dict = all_params + # check input and output are given, and that input, + # output and setpoints are in correct format + if (type(inputs) != dict or type(output) != dict): + raise RuntimeError( + 'Please specify both input and output variables for the function ' + 'you wish to fit in the format fun_inputs = ' + '{"x": "name", "y": "other_name"}, fun_output =' + ' {"z": "another_name"}') - return fit + # check inputs/outputs specified match inputs/outputs function takes + if len(inputs) != len(fitclass.fun_vars): + raise RuntimeError( + 'The function you are fitting to takes {} variables, and you ' + 'have specified {}'.format(len(fitclass.fun_vars), len(inputs))) + for variable in inputs.keys(): + if variable not in fitclass.fun_vars: + raise RuntimeError( + 'You have specified a variable {}. The fit function takes' + 'variables {}'.format(variable, fitclass.fun_vars)) + for variable in output.keys(): + if variable not in fitclass.fun_output: + raise RuntimeError( + 'You have specified a variable {}. The fit function returns ' + 'variables {}'.format(variable, fitclass.fun_output)) From 9bfe64546813283e6743ede5235c21e5fd96727f Mon Sep 17 00:00:00 2001 From: nataliejpg Date: Wed, 22 Aug 2018 11:41:48 +0200 Subject: [PATCH 076/116] rewrite least suares to recognise that only 1d fits are supported and swap names and labels --- qdev_wrappers/fitting/least_squares_fit.py | 20 +++++++++++++------- 1 file changed, 13 insertions(+), 7 deletions(-) diff --git a/qdev_wrappers/fitting/least_squares_fit.py b/qdev_wrappers/fitting/least_squares_fit.py index 55a9fc35..f8569d31 100644 --- a/qdev_wrappers/fitting/least_squares_fit.py +++ b/qdev_wrappers/fitting/least_squares_fit.py @@ -6,8 +6,13 @@ class LeastSquaresFit: """ Base class for fit functions to be used with curve_fit. Specifies a function for fitting, function for guessing initial parameters for fit - and attributes specifying inputs and outputs of fit function as + and attributes specifying inputs and output of fit function as well as and names, labels and units of fit parameters. + + Only one output is currently allowed. + + Order of p_names, p_labels, p_units must match function guess output and + order of appearance in fun """ def __init__(self, name, fun_str, fun_np, fun_vars, @@ -15,6 +20,7 @@ def __init__(self, name, fun_str, fun_np, fun_vars, self.name = name self.fun_str = fun_str self.fun_np = fun_np + self.fun_vars = fun_vars self.fun_output = fun_output self.p_names = p_names self.p_labes = p_labels @@ -40,9 +46,9 @@ def __init__(self, name='ExpDecayFit'): fun_str=r'$f(x) = a \exp(-x/T) + c$', fun_np='a*np.exp(-x/T)+c', fun_vars=['x'], - fun_output=['y'], - p_names=['$a$', '$T$', '$c$'], - p_labels=['a', 'T', 'c'], + fun_output='y', + p_labels=['$a$', '$T$', '$c$'], + p_names=['a', 'T', 'c'], p_units=['y', 'x', 'y']) def fun(self, x, a, T, c): @@ -67,9 +73,9 @@ def __init__(self, name='ExpDecaySinFit'): fun_str=r'$f(x) = a \sin(\omega x +\phi)\exp(-x/T) + c$', fun_np='a*np.exp(-x/T)*np.sin(w*x+p)+c', fun_vars=['x'], - fun_output=['y'], - p_names=['$a$', '$T$', '$\omega$', '$\phi$', '$c$'], - p_labels=['a', 'T', 'w', 'p', 'c'], + fun_output='y', + p_labels=['$a$', '$T$', '$\omega$', '$\phi$', '$c$'], + p_names=['a', 'T', 'w', 'p', 'c'], p_units=['y', 'x', '1/x', '', 'y']) def fun(self, x, a, T, w, p, c): From c09c527abd99b95b946b92f50890e391e2bf6672 Mon Sep 17 00:00:00 2001 From: nataliejpg Date: Tue, 28 Aug 2018 17:12:16 +0200 Subject: [PATCH 077/116] add logic for saving fits --- qdev_wrappers/fitting/Fitter.py | 245 +++++++++--------- qdev_wrappers/fitting/data_extraction.py | 19 +- qdev_wrappers/fitting/fit_saving.py | 281 ++++++--------------- qdev_wrappers/fitting/least_squares_fit.py | 32 +-- 4 files changed, 211 insertions(+), 366 deletions(-) diff --git a/qdev_wrappers/fitting/Fitter.py b/qdev_wrappers/fitting/Fitter.py index 05768061..deaabe02 100644 --- a/qdev_wrappers/fitting/Fitter.py +++ b/qdev_wrappers/fitting/Fitter.py @@ -1,94 +1,97 @@ import qcodes as qc import numpy as np - from scipy.optimize import curve_fit from itertools import product -from qcodes.config.config import DotDict +from least_squares_fit import LeastSquaresFit -def fit_data(data, fitclass, fun_inputs, fun_output, **kwargs): +class Fitter: """ - Fits the data in the data dictionary using the fit class provided and the - mappings provided to generate a fit dictionary. + Class which performs fit for data based on data, fitclass and names of Args: data (dict) fitclass (currently limited to LeastSquaresFit) - fun_input (dict): mapping between data name in data dictionary and - argument of fitclass function eg {'x': 'pulse_readout_delay'} - fun_output (dict: mapping between data name in data dictionary and - return of fit class function eg {'y': 'data'} - **kwargs (optional): passed to find_fit function of fit class - - Returns: - fit (dict): containing the fitted parameters and an array of - "estimated" outputs given the fit as well as some metadata about - the fit. - """ - - check_input_matches_fitclass(fitclass, fun_input, fun_output) - - setpoints = [v for v in data['variables'] if - v not in set([*fun_input.values(), - *fun_output.values()])] - return Fitter(data, fitclass, fun_input, fun_output, setpoints) - - -class Fitter: - """ - Class which performs fit for data based on data, fitclass, mapping between - paramter names and fit function arguments/returns and setpoints + indept_var (str): name of parameter in data dict to be used as + independent variable in fit procedure. eg 'pulse_readout_delay' + dept_var (str): name of parameter in data dict which represents + dependent variable for fitting. eg 'cavity_magnitude_result' """ - def __ init__(self, data, fitclass, fun_input, fun_output, setpoints): - """ - Args: - data (dict) - """ + def __ init__(self, data: dict, fitclass: LeastSquaresFit, + indept_var: str, dept_var: str): self.fitclass = fitclass - self.fun_input = fun_input - self.fun_output = fun_output - self.setpoints = {setpoint: set(data[setpoint].flatten()) for - setpoint in setpoints} + self.experiment_info = {'exp_id': data['exp_id'], + 'run_id': data['run_id'], + 'sample_name': data['sample_name']} + self.indept_var = data[indept_var] + self.dept_var = data[dept_var] + self.setpoints = {k: data[k] for k in data.keys() if + k not in [indept_var, dept_var]} + self.fit_parameters = { + fitclass.param_names[i]: + {'name': fitclass.param_names[i], + 'label': fitclass.param_labels[i], + 'unit': fitclass.param_units[i]} for + i in len(fitclass.param_names)} self.estimator = {'method': 'LSF', 'type': fitclass.name, - 'fit_function_str': fitclass.fun_str} - self.inferred_from = data.run_id - self.fit_results = [] - self._do_fitting_procedure() + 'fit_function_str': fitclass.fun_str} # TODO: add dill here + self.fit_results = self._do_fitting_procedure() def _do_fitting_procedure(self): """ Populates fit_results list with one dictionary per combination - of setpoints. Dictionaries are of the form: - - for self.setpoints = {'temperature': [0.01, 0.02, 0.03]} - + of setpoints. + + eg + for self.setpoints = {'frequency': + {'label': '', + 'unit': 'Hz', + 'data': [1e9, 2e9, 3e9]}, + 'power': + {'label': '', + 'unit': 'dBm', + 'data': [-10, -30]}} + + will make 6 dictionaries of the form { - 'temperature': 0.02, + 'setpoint_names': ['frequency', 'power'], + 'setpoint_labels': {'frequency': '' , 'power': ''}, + 'setpoint_units': {'frequency': 'Hz' , 'power': 'dBm'}, + 'setpoint_values': {'frequency': 1e9 , 'power': -10}, 'param_names': ['a', 'b'], 'param_labels': {'a': 'T1', 'b': 'b'}, 'param_units': {'a': 's', 'b': ''}, 'param_start_values': {'a': 3, 'b': 10}, 'param_values': {'a': 2.5, 'b': 11}, 'param_variance': {'a': 0.1, 'b': 2}, - 'input': [0.1, 0.2, 0.3, 0.4], - 'output': [1.15, 1.26, 1.34, 1.23] - 'estimate' [1.1, 1.2, 1.3, 1.4] + 'indept_var_name': 'pulse_readout_delay', + 'indept_var_label': 'Pulse Readout Delay', + 'indept_var_unit': 's', + 'indept_var_values': [0.1, 0.2, 0.3, 0.4], + 'dept_var_name': 'cavity_magnitude_response', + 'dept_var_label': 'Cavity Response', + 'dept_var_unit': '', + 'dept_var_values': [1.15, 1.26, 1.34, 1.23], + 'estimate_values' [1.1, 1.2, 1.3, 1.4] } """ - + fit_results = [] # if setpoints then perform fit for all if len(self.setpoints) > 0: - setpoint_combinations = product(*self.setpoints.values()) - setpoint_names = self.setpints.keys() + setpoint_combinations = product( + *[v['data'] for v in self.setpoints.values()]) + setpoint_names = list(self.setpoints.keys()) + setpoint_labels = [v['label'] for v in self.setpoints.values()] + setpoint_units = [v['unit'] for v in self.setpoints.values()] for setpoint_combination in setpoint_combinations: # find indices where where setpoing combination is satisfied setpoint_0_indices = list(np.argwhere( - data[setpoint_names[0]] == setpoint_combination[0])) + data[setpoint_names[0]['data']] == setpoint_combination[0])) for i in range(1, len(setpoint_names)): new_setpoint_indices = list(np.argwhere( - data[setpoint_names[i]] == setpoint_combination[i])) + data[setpoint_names[i]['data']] == setpoint_combination[i])) union_setpoint_indices = [] for index in indices: for new_index in new_indices: @@ -96,47 +99,75 @@ def _do_fitting_procedure(self): union_setpoint_indices.append(index) index = union_setpoint_indices[0] try: - result = {} - input_data_array = {k: data[v][index] - for k, v in fun_input.items()} - output_data_array = {k: data[v][index] - for k, v in fun_output.items()} - param_dict = self._perform_fit(input_data_array, - output_data_array) - result.update(dict(zip(self.setpoints.keys(), - setpoint_combination))) - result.update({'input': input_data_array, - 'output': output_data_array}) + input_data_array = data[self.indept_var['name'] + ]['data'][index] + output_data_array = data[self.dept_var['name'] + ]['data'][index] + result = self._perform_fit(input_data_array, + output_data_array) result.update( - {'estimate': + {'setpoint_names': setpoint_names, + 'setpoint_labels': dict(zip(setpoint_names, + setpoint_labels)), + 'setpoint_units': dict(zip(setpoint_names, + setpoint_units)), + 'setpoint_values': dict(zip(setpoint_names, + setpoint_combination)), + 'indept_var_values': input_data_array, + 'dept_var_values': output_data_array, + 'indept_var_name': self.indept_var['name'], + 'dept_var_name': self.dept_var['name'], + 'indept_var_label': self.indept_var['label'], + 'dept_var_label': data[self.dept_var]['label'], + 'indept_var_unit': data[self.indept_var]['unit'], + 'dept_var_unit': data[self.dept_var]['unit'], + 'estimate_values': self.find_estimate(input_data_array, param_dict['param_values'])}) - result.update(result) - self.fit_results.append(result) + fit_results.append(result) except Exception: # TODO: what kind of exception print('no data for setpoint combination ', dict( zip(self.setpoints.keys(), setpoint_combination))) else: - input_data_array = {k: data.get_data(v) for - k, v in fun_input.items()} - output_data_array = {k: data.get_data(v) for - k, v in fun_output.items()} - param_dict = self.perform_fit(input_data_array, - output_data_array) - result.update({'input': input_data_array, - 'output': output_data_array}) + input_data_array = data[self.indept_var]['data'][index] + output_data_array = data[self.dept_var]['data'][index] + result = self._perform_fit(input_data_array, + output_data_array) result.update( - {'estimate': + {'setpoint_names': [], + 'indept_var_values': input_data_array, + 'dept_var_values': output_data_array, + 'indept_var_name': self.indept_var['name'], + 'dept_var_name': self.dept_var['name'], + 'indept_var_label': data[self.indept_var]['label'], + 'dept_var_label': data[self.dept_var]['label'], + 'indept_var_unit': data[self.indept_var]['unit'], + 'dept_var_unit': data[self.dept_var]['unit'], + 'estimate_values': self.find_estimate(input_data_array, param_dict['param_values'])}) - self.fit_dict['fit_results'].append(result) + fit_results.append(result) + except Exception: # TODO: what kind of exception + print('no data for setpoint combination ', dict( + zip(self.setpoints.keys(), setpoint_combination))) + return fit_results def get_result(**setpoint_values): + """ + Args: + kwargs for each setpoints + eg 'frequency=1e9, power=-10' + Returns: + dict for fit where these conditions are satisfiedxw + """ if len(setpoint_values) != len(self.setpoints): raise RuntimeError('Must specify a value for each setpoint') - return next(res for res in self.fit_results if - all(res[setpoint] == value for - setpoint, value in setpoint_values.items())) + elif len(self.setpoints) == 0: + return self.fit_results[0] + else: + return next(res for res in self.fit_results if + all(res[setpoint] == value for + setpoint, value in setpoint_values.items())) def _perform_fit(self, input_data_array, output_data_array): @@ -157,57 +188,15 @@ def _perform_fit(self, input_data_array, output_data_array): p0=p_guess) # add guess and fit results to dict - for i, param_name in enumerate(self.fitclass.p_names): + for i, param_name in enumerate(self.fitclass.param_names): params_dict['param_start_values'][param_name] = p_guess[i] params_dict['param_values'][param_name] = popt[i] params_dict['param_variance'][param_name] = pcov[i, i] return params_dict - def find_estimate(self, input_data_array, params_values_dict): + def _find_estimate(self, input_data_array, params_values_dict): return fitlass.fun(input_data_array, **params_values_dict) - - def check_input_matches_fitclass(self): - """ - Checks that the given arguments are in the correct format for the - 'fit_data' function to proceed, and that they match the expected inputs for - the specified fit class. - - Each fit class describes a mathematical function to be fitted to, - with a set of attributes, including which variables the function has as - inputs, and what outputs it has. This function makes sure that the correct - inputs and outputs are specified compared to what inputs and outputs the - mathematical function has, and that they are specified in the correct - format. - - Args: - fitclass (currently only LeastSquaresFit implemented) - inputs (dict) - output (dict) - """ - - # check input and output are given, and that input, - # output and setpoints are in correct format - if (type(inputs) != dict or type(output) != dict): - raise RuntimeError( - 'Please specify both input and output variables for the function ' - 'you wish to fit in the format fun_inputs = ' - '{"x": "name", "y": "other_name"}, fun_output =' - ' {"z": "another_name"}') - - # check inputs/outputs specified match inputs/outputs function takes - if len(inputs) != len(fitclass.fun_vars): - raise RuntimeError( - 'The function you are fitting to takes {} variables, and you ' - 'have specified {}'.format(len(fitclass.fun_vars), len(inputs))) - for variable in inputs.keys(): - if variable not in fitclass.fun_vars: - raise RuntimeError( - 'You have specified a variable {}. The fit function takes' - 'variables {}'.format(variable, fitclass.fun_vars)) - for variable in output.keys(): - if variable not in fitclass.fun_output: - raise RuntimeError( - 'You have specified a variable {}. The fit function returns ' - 'variables {}'.format(variable, fitclass.fun_output)) + def plot(self): + raise NotImplementedError diff --git a/qdev_wrappers/fitting/data_extraction.py b/qdev_wrappers/fitting/data_extraction.py index da1793fa..e8283279 100644 --- a/qdev_wrappers/fitting/data_extraction.py +++ b/qdev_wrappers/fitting/data_extraction.py @@ -47,7 +47,7 @@ def make_data_dictionary(self, data): Returns: data (dict): keys are variables from the data, values are dictionaries in the format - {'name': str, 'label': str, 'unit': str, 'data': np.array } + {'label': str, 'unit': str, 'data': np.array } """ raise NotImplementedError( 'make_data_dictionary not implemented in the base class.') @@ -62,7 +62,7 @@ def find_experiment(self, run_id): raise NotImplementedError( 'find_experiment not implemented in the base class.') - def find_dependencies(self, run_id): + def find_dependencies(self, run_id): # TODO: why do we need this? """ Args: run_id (int) @@ -97,14 +97,11 @@ def convert(self, run_id): data = self.find_data(run_id) data_dict = self.make_data_dictionary(data) - exp_id = self.find_experiment(run_id) - dependencies = self.find_dependencies(run_id) - all_variables = self.find_variables(data) - - data_dict['exp_id'] = exp_id + data_dict['exp_id'] = self.find_experiment(run_id) data_dict['run_id'] = run_id - data_dict['dependencies'] = dependencies - data_dict['variables'] = all_variables + data_dict['sample_name'] = self.find_sample_name(run_id) + data_dict['dependencies'] = self.find_dependencies(run_id) + data_dict['variables'] = self.find_variables(data) return data_dict @@ -171,9 +168,11 @@ def find_experiment(self, run_id): return exp_id + def find_sample_name(self, run_id): + return None # TODO + def make_data_dictionary(self, data): data_dict = {} - for data_subset in data: for variable in data_subset: name = variable['name'] diff --git a/qdev_wrappers/fitting/fit_saving.py b/qdev_wrappers/fitting/fit_saving.py index 464d0537..cec901ee 100644 --- a/qdev_wrappers/fitting/fit_saving.py +++ b/qdev_wrappers/fitting/fit_saving.py @@ -1,209 +1,72 @@ - -import sqlite3 -import dill -import qcodes as qc -from os.path import expanduser -from collections import namedtuple - - -FitInfo = namedtuple( - 'FitInfo', - 'fitclass est_name est_label est_unit param_units data_run_id ' - 'exp_id predicts estimator analysis dill_obj start_params ' - 'param_labels table_rows table_columns tablename') - - -def save_fit_dict(fit): - """ - Organises and saves the fit dictionary in SQL database - Args: - fit (dict): as output by fitter - """ - fit_info_tuple = _reorganise_fit_dict(fit) - _save_to_database(fit_info_tuple) - - -def _is_table(tablename, cursor): - """ - Args: - tablename (str) - cursor: connection to an SQL database - Returns: - is_table (bool): True if a table with that name exists, - False if not. - - Error if it finds multiple tables with the same name, or if the - sql count function returns something unexpected. - """ - - table_count = "SELECT count(*) FROM sqlite_master WHERE type = 'table' AND name='{}'".format(tablename) - execute = cursor.execute(table_count) - count = execute.fetchone()[0] - - if count == 0: - return False - - elif count == 1: - return True - - else: - raise RuntimeError( - 'Attempt to check existing table names failed.' - ' Count of tables with name {} returned {} ' - 'instead of 1 or 0.'.format(tablename, count)) - - -def _make_table(tablename, cursor): - """ - Args: - tablename (str) - cursor: connection to an SQL database. - Returns: - tablename (str): same as arg if this name was available, - otherwise a numbered variant - """ - name = tablename - n = 0 - - while _is_table(name, cursor): - n += 1 - name = "{}_{}".format(tablename, n) - - cursor.execute('CREATE TABLE {} (id INTEGER)'.format(name)) - return name - - -def _reorganise_fit_dict(fit): - """ - Information from the fit dictionary is retrieved and organized - - Args: - fit (dict): as output by fitter - Returns: - FitInfo (NamedTuple) - """ - fitclass_pckl = fit['inferred_from']['estimator']['dill']['fitclass'] - fitclass = dill.loads(fitclass_pckl) - - est_values = fit['estimate']['values'] - est_name = '{}_estimate'.format(fit['estimate']['name']) - est_label = fit['estimate']['label'] - est_unit = fit['estimate']['unit'] - - param_values = fit['estimate']['parameters'] - param_units = fit['parameter units'] - - data_run_id = fit['inferred_from']['run_id'] - exp_id = fit['inferred_from']['exp_id'] - predicts = est_name.strip("estimate").strip("_") - estimator = "{}, {}".format( - fit['inferred_from']['estimator']['method'], - fit['inferred_from']['estimator']['type']) - analysis = str(fit['inferred_from']['estimator']['function used']) - dill_obj = str(fit['inferred_from']['estimator']['dill']) - start_params = str(fit['start_params']) - param_labels = fitclass.p_names - - # reorganize parameters to fit into rows, - # [a1, a2, a3...] and [b1, b2, b3...] --> [ [a1, b1], [a2, b2]...] - param_list = [] - data_length = len(est_values) - for i in range(data_length): - params = [] - for parameter in fitclass.p_labels: - params.append(param_values[parameter][i]) - param_list.append(params) - - # make list of table row contents, - # [(row1), (row2), (row3)...], row1 = (id_nr1, estimate1, a1, b1...) - table_rows = [] - for estimate, parameters, index in zip(est_values, param_list, range(data_length)): - id_nr = index + 1 - row = (id_nr, estimate, *parameters) - table_rows.append(row) - - # make list of column titles (estimated output, param1, param2...) - table_columns = [est_name] - for parameter in fitclass.p_labels: - table_columns.append(parameter) - - tablename = 'analysis_{}_{}'.format( - fit['inferred_from']['run_id'], fitclass.name) - - return FitInfo( - fitclass, est_name, est_label, est_unit, param_units, data_run_id, - exp_id, predicts, estimator, analysis, dill_obj, start_params, - param_labels, table_rows, table_columns, tablename) - - -def _save_to_database(fit_info): - """ - A connection to the SQL database is created and the fit data is - saved to the database. - - Args: - fit_info (FitInfo named_tuple) - """ - # create a connection to the SQL database - file = expanduser(qc.config['core']['db_location']) - conn = sqlite3.connect(file) - cur = conn.cursor() - - # Create table for fit parameter and predicted output values, store data - - table = _make_table(fit_info.tablename, cur) - - for column in fit_info.table_columns: - cur.execute('ALTER TABLE {} ADD {}'.format(table, column)) - - num_cols = len(fit_info.table_rows[0]) - placeholder = ('?,' * num_cols).strip(',') - cur.executemany('INSERT INTO {} VALUES ({})'.format( - table, placeholder), fit_info.table_rows) - - # Create 'analyses' table if it does not already exist, - # store info about analysis in 'analyses' - if not _is_table('analyses', cur): - cur.execute( - '''CREATE TABLE analyses - (data_run_id INTEGER, exp_id INTEGER, run_id INTEGER, analysis_table_name TEXT, - predicts TEXT, estimator TEXT, function TEXT, start_parameters TEXT, dill TEXT)''') - run_id = 1 - max_id = cur.execute('SELECT MAX(run_id) FROM layouts').fetchone()[0] - if max_id is not None: - run_id += max_id - - sql_analyses = 'INSERT INTO analyses VALUES (?,?,?,?,?,?,?,?,?)' - cur.execute(sql_analyses, - (fit_info.data_run_id, fit_info.exp_id, fit_info.run_id, - table, fit_info.predicts, fit_info.estimator, - fit_info.analysis, fit_info.start_params, fit_info.dill_obj)) - - # Store unit and label info in 'layouts' table - layout_id = 1 - max_id = cur.execute('SELECT MAX(layout_id) FROM layouts').fetchone()[0] - if max_id is not None: - layout_id += max_id - - layout_rows = [] - for parameter, label, unit in zip(fit_info.fitclass.p_labels, fit_info.param_labels, fit_info.param_units): - row = (layout_id, run_id, parameter, label, unit, "inferred_from") - layout_rows.append(row) - layout_id += 1 - layout_rows.append((layout_id, run_id, fit_info.est_name, - fit_info.est_label, fit_info.est_unit, "inferred_from")) - - sql_layout = 'INSERT INTO layouts VALUES (?,?,?,?,?,?)' - cur.executemany(sql_layout, layout_rows) - - # Store summary in 'runs' SQL table, with run type 'analysis' - sql_runs = 'INSERT INTO runs VALUES (?,?,?,?,?,?,?,?,?,?)' - cur.execute(sql_runs, (run_id, exp_id, 'analysis', - table, "", "", "", 1, "", "")) - - # save changes to SQL database and close connection - conn.commit() - conn.close() - - print("Table {} created".format(table)) - +from qcodes import ParamSpec, new_data_set, new_experiment +from qdev_wrappers.fitting.fitter import Fitter +from qcodes.dataset.database import initialise_database +from qcodes.dataset.data_set import load_by_id + + +def save_fit_result(fitter: Fitter): + paramspecs = [] + setpoint_paramspecs = {} + for setpoint, setpoint_dict in fitter.setpoints.items(): + paramspec = ParamSpec(setpoint, 'numeric', + label=setpoint_dict['label'], + unit=setpoint_dict['unit']) + setpoint_paramspecs[setpoint] = paramspec + paramspecs.append(paramspec) + indept_var_paramspec = ParamSpec( + **fitter['indept_var'], paramtype='numeric', + depends_on=list(setpoint_paramspecs.values())) + paramspecs.append(indept_var_paramspec) + dept_var_paramspec = ParamSpec( + **fitter['dept_var'], paramtype='numeric', + depends_on=list(setpoint_paramspecs.values())) + paramspecs.append(dept_var_paramspec) + for param, param_dict in fitter.fit_parameters.items(): + paramspec = ParamSpec( + param, 'numeric', + label=param_dict['label'], + unit=param_dict['unit'], + inferred_from=[indept_var_paramspec, + dept_var_paramspec]) # TODO: should also have depends on? + paramspec_initial_val = ParamSpec( + param + '_initial_val', 'numeric', + label=param_dict['label'] + ' Initial Value', + unit=param_dict['unit']) + paramspec_variance = ParamSpec( + param + '_variance', 'numeric', + label=param_dict['label'] + ' Variance', + inferred_from=[indept_var_paramspec, + dept_var_paramspec]) # TODO: should also have depends on? + paramspecs.extend( + [paramspec, paramspec_initial_val, paramspec_variance]) + if fitter.experiment_info['exp_id'] is None: + initialise_database() + exp = new_experiment(fitter.experiment_info['sample_name'], + sample_name=fitter.experiment_info['sample_name']) + fitter.experiment_info['exp_id'] = exp.exp_id + metadata = {'estimator': fitter.estimator, + 'dept_var': fitter.dept_var['name'], + 'indept_var': fitter.indept_var['name'], + 'inferred_from': fitter.experiment_info['run_id']} + dataset = new_data_set('analysis', + exp_id=fitter.experiment_info['exp_id'], + specs=paramspecs + metadata=metadata) + results = [] + for r in fitter.results: + result.update({r.setpoint_names[i]: r.setpoint_values[i] + for i in range(len(r.setpoint_names))}) + result.update( + {r.param_names[i]: r.param_values[i], + r.param_names[i] + '_initial_val': r.param_start_values[i], + r.param_names[i] + '_variance': r.param_variance[i], + for i in range(len(param_names))}) + for j in range(len(r.indept_var_values)): + result.update( + {r.indept_var_name: r.indept_var_values[j], + r.dept_var_name: r.dept_var_values[j], + r.dept_var_name + '_estimate': r.estimate_values[j]}) + results.append(result) + dataset.add_results(results) + dataset.mark_complete() + return dataset diff --git a/qdev_wrappers/fitting/least_squares_fit.py b/qdev_wrappers/fitting/least_squares_fit.py index f8569d31..45b2301c 100644 --- a/qdev_wrappers/fitting/least_squares_fit.py +++ b/qdev_wrappers/fitting/least_squares_fit.py @@ -9,22 +9,20 @@ class LeastSquaresFit: and attributes specifying inputs and output of fit function as well as and names, labels and units of fit parameters. - Only one output is currently allowed. + Only one input and one output is currently allowed. - Order of p_names, p_labels, p_units must match function guess output and + Order of param_names, param_labels, param_units must match function guess output and order of appearance in fun """ - def __init__(self, name, fun_str, fun_np, fun_vars, - fun_output, p_names, p_labels, p_units): + def __init__(self, name, fun_str, fun_np, + fun_output, param_names, param_labels, param_units): self.name = name self.fun_str = fun_str self.fun_np = fun_np - self.fun_vars = fun_vars - self.fun_output = fun_output - self.p_names = p_names - self.p_labes = p_labels - self.p_units = p_units + self.param_names = param_names + self.param_labels = param_labels + self.param_units = param_units def fun(self, *args): """ @@ -45,11 +43,9 @@ def __init__(self, name='ExpDecayFit'): name=name, fun_str=r'$f(x) = a \exp(-x/T) + c$', fun_np='a*np.exp(-x/T)+c', - fun_vars=['x'], - fun_output='y', - p_labels=['$a$', '$T$', '$c$'], - p_names=['a', 'T', 'c'], - p_units=['y', 'x', 'y']) + param_labels=['$a$', '$T$', '$c$'], + param_names=['a', 'T', 'c'], + param_units=['', 's', '']) def fun(self, x, a, T, c): return eval(self.fun_np) @@ -72,11 +68,9 @@ def __init__(self, name='ExpDecaySinFit'): name=name, fun_str=r'$f(x) = a \sin(\omega x +\phi)\exp(-x/T) + c$', fun_np='a*np.exp(-x/T)*np.sin(w*x+p)+c', - fun_vars=['x'], - fun_output='y', - p_labels=['$a$', '$T$', '$\omega$', '$\phi$', '$c$'], - p_names=['a', 'T', 'w', 'p', 'c'], - p_units=['y', 'x', '1/x', '', 'y']) + param_labels=['$a$', '$T$', '$\omega$', '$\phi$', '$c$'], + param_names=['a', 'T', 'w', 'p', 'c'], + param_units=['', 's', 'Hz', '', '']) def fun(self, x, a, T, w, p, c): return eval(self.fun_np) From d523c37301dccf61ab06af9b7eb0c3b30b503aa1 Mon Sep 17 00:00:00 2001 From: nataliejpg Date: Tue, 28 Aug 2018 17:52:49 +0200 Subject: [PATCH 078/116] make it possible to specify guess params for fit --- qdev_wrappers/fitting/Fitter.py | 2 +- qdev_wrappers/fitting/least_squares_fit.py | 33 ++++++++++++++++------ 2 files changed, 25 insertions(+), 10 deletions(-) diff --git a/qdev_wrappers/fitting/Fitter.py b/qdev_wrappers/fitting/Fitter.py index deaabe02..21aa27d8 100644 --- a/qdev_wrappers/fitting/Fitter.py +++ b/qdev_wrappers/fitting/Fitter.py @@ -198,5 +198,5 @@ def _perform_fit(self, input_data_array, output_data_array): def _find_estimate(self, input_data_array, params_values_dict): return fitlass.fun(input_data_array, **params_values_dict) - def plot(self): + def plot(self, **setpoint_config): raise NotImplementedError diff --git a/qdev_wrappers/fitting/least_squares_fit.py b/qdev_wrappers/fitting/least_squares_fit.py index 45b2301c..c451ae2d 100644 --- a/qdev_wrappers/fitting/least_squares_fit.py +++ b/qdev_wrappers/fitting/least_squares_fit.py @@ -1,5 +1,6 @@ import numpy as np import scipy.fftpack as fftpack +from typing import List class LeastSquaresFit: @@ -11,18 +12,20 @@ class LeastSquaresFit: Only one input and one output is currently allowed. - Order of param_names, param_labels, param_units must match function guess output and - order of appearance in fun + Order of param_names, param_labels, param_units must match function guess + output and order of appearance in fun """ def __init__(self, name, fun_str, fun_np, - fun_output, param_names, param_labels, param_units): + fun_output, param_names, param_labels, param_units, + guess: List[float]=None): self.name = name self.fun_str = fun_str self.fun_np = fun_np self.param_names = param_names self.param_labels = param_labels self.param_units = param_units + self.guess = guess def fun(self, *args): """ @@ -31,6 +34,16 @@ def fun(self, *args): raise NotImplementedError('This is not implemented in the base class.') def guess(self, *args): + """ + Function for determining an initial guess for the fit parameters from + given values or based on perform guess function + """ + if guess is not None: + return guess + else: + return self.perform_guess(*args) + + def perform_guess(self, *args): """ Function for determining an initial guess for the fit parameters """ @@ -38,19 +51,20 @@ def guess(self, *args): class ExpDecay(LeastSquaresFit): - def __init__(self, name='ExpDecayFit'): + def __init__(self, name='ExpDecayFit', guess=None): super().__init__( name=name, fun_str=r'$f(x) = a \exp(-x/T) + c$', fun_np='a*np.exp(-x/T)+c', param_labels=['$a$', '$T$', '$c$'], param_names=['a', 'T', 'c'], - param_units=['', 's', '']) + param_units=['', 's', ''], + guess=guess) def fun(self, x, a, T, c): return eval(self.fun_np) - def guess(self, x, y): + def perform_guess(self, x, y): length = len(y) val_init = y[0:round(length / 20)].mean() val_fin = y[-round(length / 20):].mean() @@ -63,19 +77,20 @@ def guess(self, x, y): class ExpDecaySin(LeastSquaresFit): - def __init__(self, name='ExpDecaySinFit'): + def __init__(self, name='ExpDecaySinFit', guess=None): super().__init__( name=name, fun_str=r'$f(x) = a \sin(\omega x +\phi)\exp(-x/T) + c$', fun_np='a*np.exp(-x/T)*np.sin(w*x+p)+c', param_labels=['$a$', '$T$', '$\omega$', '$\phi$', '$c$'], param_names=['a', 'T', 'w', 'p', 'c'], - param_units=['', 's', 'Hz', '', '']) + param_units=['', 's', 'Hz', '', ''], + guess=guess) def fun(self, x, a, T, w, p, c): return eval(self.fun_np) - def guess(self, x, y): + def perform_guess(self, x, y): a = y.max() - y.min() c = y.mean() # guess T2 as point half way point in data From 8b77a66eb60e15bd1455627cc529b2d2dbe92205 Mon Sep 17 00:00:00 2001 From: Natalie Pearson Date: Tue, 4 Sep 2018 17:33:41 +0200 Subject: [PATCH 079/116] measuring on t2 edits --- qdev_wrappers/fitting/Fitter.py | 125 ++++++++++----------- qdev_wrappers/fitting/data_extraction.py | 2 +- qdev_wrappers/fitting/fit_saving.py | 60 ++++++---- qdev_wrappers/fitting/least_squares_fit.py | 38 +++---- 4 files changed, 116 insertions(+), 109 deletions(-) diff --git a/qdev_wrappers/fitting/Fitter.py b/qdev_wrappers/fitting/Fitter.py index 21aa27d8..6eb5ea92 100644 --- a/qdev_wrappers/fitting/Fitter.py +++ b/qdev_wrappers/fitting/Fitter.py @@ -2,7 +2,8 @@ import numpy as np from scipy.optimize import curve_fit from itertools import product -from least_squares_fit import LeastSquaresFit +from qdev_wrappers.fitting.least_squares_fit import LeastSquaresFit +from typing import Dict class Fitter: @@ -18,7 +19,7 @@ class Fitter: dependent variable for fitting. eg 'cavity_magnitude_result' """ - def __ init__(self, data: dict, fitclass: LeastSquaresFit, + def __init__(self, data: Dict, fitclass: LeastSquaresFit, indept_var: str, dept_var: str): self.fitclass = fitclass self.experiment_info = {'exp_id': data['exp_id'], @@ -27,19 +28,19 @@ def __ init__(self, data: dict, fitclass: LeastSquaresFit, self.indept_var = data[indept_var] self.dept_var = data[dept_var] self.setpoints = {k: data[k] for k in data.keys() if - k not in [indept_var, dept_var]} + (k not in [indept_var, dept_var]) and (k in data['variables'])} self.fit_parameters = { fitclass.param_names[i]: {'name': fitclass.param_names[i], 'label': fitclass.param_labels[i], 'unit': fitclass.param_units[i]} for - i in len(fitclass.param_names)} + i in range(len(fitclass.param_names))} self.estimator = {'method': 'LSF', 'type': fitclass.name, 'fit_function_str': fitclass.fun_str} # TODO: add dill here - self.fit_results = self._do_fitting_procedure() + self.fit_results = self._do_fitting_procedure(data) - def _do_fitting_procedure(self): + def _do_fitting_procedure(self, data): """ Populates fit_results list with one dictionary per combination of setpoints. @@ -80,76 +81,72 @@ def _do_fitting_procedure(self): fit_results = [] # if setpoints then perform fit for all if len(self.setpoints) > 0: +# raise RuntimeError('many setpoints is currently broken, apologies') + # find all possible combinations of setpoint values setpoint_combinations = product( - *[v['data'] for v in self.setpoints.values()]) + *[set(v['data']) for v in self.setpoints.values()]) setpoint_names = list(self.setpoints.keys()) setpoint_labels = [v['label'] for v in self.setpoints.values()] setpoint_units = [v['unit'] for v in self.setpoints.values()] for setpoint_combination in setpoint_combinations: - # find indices where where setpoing combination is satisfied - setpoint_0_indices = list(np.argwhere( - data[setpoint_names[0]['data']] == setpoint_combination[0])) + # find indices where where setpoint combination is satisfied + indices = set(np.argwhere( + data[setpoint_names[0]]['data'] == setpoint_combination[0]).flatten()) for i in range(1, len(setpoint_names)): - new_setpoint_indices = list(np.argwhere( - data[setpoint_names[i]['data']] == setpoint_combination[i])) - union_setpoint_indices = [] - for index in indices: - for new_index in new_indices: - if np.array_equal(index, new_index): - union_setpoint_indices.append(index) - index = union_setpoint_indices[0] - try: - input_data_array = data[self.indept_var['name'] - ]['data'][index] - output_data_array = data[self.dept_var['name'] - ]['data'][index] - result = self._perform_fit(input_data_array, + new_indices = np.argwhere( + data[setpoint_names[0]]['data'] == setpoint_combination[0]).flatten() + indices = indices.intesection(new_indices) + indices = list(indices) + input_data_array = data[self.indept_var['name'] + ]['data'][indices] + output_data_array = data[self.dept_var['name'] + ]['data'][indices] + param_dict = self._perform_fit(input_data_array, output_data_array) - result.update( - {'setpoint_names': setpoint_names, - 'setpoint_labels': dict(zip(setpoint_names, - setpoint_labels)), - 'setpoint_units': dict(zip(setpoint_names, - setpoint_units)), - 'setpoint_values': dict(zip(setpoint_names, - setpoint_combination)), - 'indept_var_values': input_data_array, - 'dept_var_values': output_data_array, - 'indept_var_name': self.indept_var['name'], - 'dept_var_name': self.dept_var['name'], - 'indept_var_label': self.indept_var['label'], - 'dept_var_label': data[self.dept_var]['label'], - 'indept_var_unit': data[self.indept_var]['unit'], - 'dept_var_unit': data[self.dept_var]['unit'], - 'estimate_values': - self.find_estimate(input_data_array, - param_dict['param_values'])}) - fit_results.append(result) - except Exception: # TODO: what kind of exception - print('no data for setpoint combination ', dict( - zip(self.setpoints.keys(), setpoint_combination))) + result = param_dict.copy() + result.update( + {'setpoint_names': setpoint_names, + 'setpoint_labels': dict(zip(setpoint_names, + setpoint_labels)), + 'setpoint_units': dict(zip(setpoint_names, + setpoint_units)), + 'setpoint_values': dict(zip(setpoint_names, + setpoint_combination)), + 'indept_var_values': input_data_array, + 'dept_var_values': output_data_array, + 'indept_var_name': self.indept_var['name'], + 'dept_var_name': self.dept_var['name'], + 'indept_var_label': self.indept_var['label'], + 'dept_var_label': self.dept_var['label'], + 'indept_var_unit': self.indept_var['unit'], + 'dept_var_unit': self.dept_var['unit'], + 'estimate_values': + self._find_estimate(input_data_array, + param_dict['param_values'])}) + fit_results.append(result) +# except Exception: # TODO: what kind of exception +# print('no data for setpoint combination ', dict( +# zip(self.setpoints.keys(), setpoint_combination))) else: - input_data_array = data[self.indept_var]['data'][index] - output_data_array = data[self.dept_var]['data'][index] - result = self._perform_fit(input_data_array, + input_data_array = data[self.indept_var['name']]['data'] + output_data_array = data[self.dept_var['name']]['data'] + param_dict = self._perform_fit(input_data_array, output_data_array) + result = param_dict.copy() result.update( {'setpoint_names': [], 'indept_var_values': input_data_array, 'dept_var_values': output_data_array, 'indept_var_name': self.indept_var['name'], 'dept_var_name': self.dept_var['name'], - 'indept_var_label': data[self.indept_var]['label'], - 'dept_var_label': data[self.dept_var]['label'], - 'indept_var_unit': data[self.indept_var]['unit'], - 'dept_var_unit': data[self.dept_var]['unit'], + 'indept_var_label': data[self.indept_var['name']]['label'], + 'dept_var_label': data[self.dept_var['name']]['label'], + 'indept_var_unit': data[self.indept_var['name']]['unit'], + 'dept_var_unit': data[self.dept_var['name']]['unit'], 'estimate_values': - self.find_estimate(input_data_array, - param_dict['param_values'])}) + self._find_estimate(input_data_array, + param_dict['param_values'])}) fit_results.append(result) - except Exception: # TODO: what kind of exception - print('no data for setpoint combination ', dict( - zip(self.setpoints.keys(), setpoint_combination))) return fit_results def get_result(**setpoint_values): @@ -175,14 +172,14 @@ def _perform_fit(self, input_data_array, output_data_array): params_dict = {'param_names': [], 'param_labels': {}, 'param_units': {}, 'param_start_values': {}, 'param_variance': {}, 'param_values': {}} - for i, param_name in enumerate(fitcass.params_names): + for i, param_name in enumerate(self.fitclass.param_names): params_dict['param_names'].append(param_name) - params_dict['param_labels'][param_name] = fitclass.param_labels[i] - params_dict['param_units'][param_name] = fitclass.param_units[i] + params_dict['param_labels'][param_name] = self.fitclass.param_labels[i] + params_dict['param_units'][param_name] = self.fitclass.param_units[i] # find start parameters, run curve_fit function to perform fit p_guess = self.fitclass.guess(input_data_array, output_data_array) - popt, pcov = curve_fit(fitclass.fun, + popt, pcov = curve_fit(self.fitclass.fun, input_data_array, output_data_array, p0=p_guess) @@ -196,7 +193,7 @@ def _perform_fit(self, input_data_array, output_data_array): return params_dict def _find_estimate(self, input_data_array, params_values_dict): - return fitlass.fun(input_data_array, **params_values_dict) + return self.fitclass.fun(input_data_array, **params_values_dict) def plot(self, **setpoint_config): raise NotImplementedError diff --git a/qdev_wrappers/fitting/data_extraction.py b/qdev_wrappers/fitting/data_extraction.py index e8283279..71a347a0 100644 --- a/qdev_wrappers/fitting/data_extraction.py +++ b/qdev_wrappers/fitting/data_extraction.py @@ -100,7 +100,7 @@ def convert(self, run_id): data_dict['exp_id'] = self.find_experiment(run_id) data_dict['run_id'] = run_id data_dict['sample_name'] = self.find_sample_name(run_id) - data_dict['dependencies'] = self.find_dependencies(run_id) +# data_dict['dependencies'] = self.find_dependencies(run_id) data_dict['variables'] = self.find_variables(data) return data_dict diff --git a/qdev_wrappers/fitting/fit_saving.py b/qdev_wrappers/fitting/fit_saving.py index cec901ee..3986fbb5 100644 --- a/qdev_wrappers/fitting/fit_saving.py +++ b/qdev_wrappers/fitting/fit_saving.py @@ -14,31 +14,41 @@ def save_fit_result(fitter: Fitter): setpoint_paramspecs[setpoint] = paramspec paramspecs.append(paramspec) indept_var_paramspec = ParamSpec( - **fitter['indept_var'], paramtype='numeric', - depends_on=list(setpoint_paramspecs.values())) + fitter.indept_var['name'], + 'numeric', + label=fitter.indept_var['label'], + unit=fitter.indept_var['unit'], + depends_on=list(setpoint_paramspecs.values()) or None) paramspecs.append(indept_var_paramspec) dept_var_paramspec = ParamSpec( - **fitter['dept_var'], paramtype='numeric', - depends_on=list(setpoint_paramspecs.values())) + fitter.dept_var['name'], + 'numeric', + label=fitter.dept_var['label'], + unit=fitter.dept_var['unit'], + depends_on=[indept_var_paramspec]+list(setpoint_paramspecs.values())) paramspecs.append(dept_var_paramspec) for param, param_dict in fitter.fit_parameters.items(): paramspec = ParamSpec( param, 'numeric', label=param_dict['label'], unit=param_dict['unit'], - inferred_from=[indept_var_paramspec, - dept_var_paramspec]) # TODO: should also have depends on? + depends_on=list(setpoint_paramspecs.values()) or None) # TODO: should also have inferred from, depends on? paramspec_initial_val = ParamSpec( param + '_initial_val', 'numeric', label=param_dict['label'] + ' Initial Value', unit=param_dict['unit']) paramspec_variance = ParamSpec( param + '_variance', 'numeric', - label=param_dict['label'] + ' Variance', - inferred_from=[indept_var_paramspec, - dept_var_paramspec]) # TODO: should also have depends on? + label=param_dict['label'] + ' Variance') # TODO: should also have inferred from, depends on? paramspecs.extend( [paramspec, paramspec_initial_val, paramspec_variance]) + estimate_paramspec = ParamSpec( + fitter.dept_var['name'] + '_estimate', + 'numeric', + label = fitter.dept_var['label'] + ' Fit Estimate', + unit = fitter.dept_var['unit'], + depends_on=[indept_var_paramspec]+list(setpoint_paramspecs.values())) + paramspecs.append(estimate_paramspec) if fitter.experiment_info['exp_id'] is None: initialise_database() exp = new_experiment(fitter.experiment_info['sample_name'], @@ -49,23 +59,25 @@ def save_fit_result(fitter: Fitter): 'indept_var': fitter.indept_var['name'], 'inferred_from': fitter.experiment_info['run_id']} dataset = new_data_set('analysis', - exp_id=fitter.experiment_info['exp_id'], - specs=paramspecs - metadata=metadata) +# exp_id=fitter.experiment_info['exp_id'], + specs=paramspecs) +# metadata=metadata) results = [] - for r in fitter.results: - result.update({r.setpoint_names[i]: r.setpoint_values[i] - for i in range(len(r.setpoint_names))}) - result.update( - {r.param_names[i]: r.param_values[i], - r.param_names[i] + '_initial_val': r.param_start_values[i], - r.param_names[i] + '_variance': r.param_variance[i], - for i in range(len(param_names))}) - for j in range(len(r.indept_var_values)): + for r in fitter.fit_results: + result = {} + for setpoint_name in r['setpoint_names']: + result[setpoint_name] = r['setpoint_values'][setpoint_name] + for param_name in r['param_names']: result.update( - {r.indept_var_name: r.indept_var_values[j], - r.dept_var_name: r.dept_var_values[j], - r.dept_var_name + '_estimate': r.estimate_values[j]}) + {param_name: r['param_values'][param_name], + param_name + '_initial_val': r['param_start_values'][param_name], + param_name + '_variance': r['param_variance'][param_name]}) + for j in range(len(r['indept_var_values'])): + result = result.copy() + result.update( + {r['indept_var_name']: r['indept_var_values'][j], + r['dept_var_name']: r['dept_var_values'][j], + r['dept_var_name'] + '_estimate': r['estimate_values'][j]}) results.append(result) dataset.add_results(results) dataset.mark_complete() diff --git a/qdev_wrappers/fitting/least_squares_fit.py b/qdev_wrappers/fitting/least_squares_fit.py index c451ae2d..8239cb2c 100644 --- a/qdev_wrappers/fitting/least_squares_fit.py +++ b/qdev_wrappers/fitting/least_squares_fit.py @@ -17,15 +17,13 @@ class LeastSquaresFit: """ def __init__(self, name, fun_str, fun_np, - fun_output, param_names, param_labels, param_units, - guess: List[float]=None): + param_names, param_labels, param_units): self.name = name self.fun_str = fun_str self.fun_np = fun_np self.param_names = param_names self.param_labels = param_labels self.param_units = param_units - self.guess = guess def fun(self, *args): """ @@ -38,33 +36,31 @@ def guess(self, *args): Function for determining an initial guess for the fit parameters from given values or based on perform guess function """ - if guess is not None: - return guess - else: - return self.perform_guess(*args) + raise NotImplementedError +# if guess is not None: +# return guess +# else: +# return self.perform_guess(*args) - def perform_guess(self, *args): - """ - Function for determining an initial guess for the fit parameters - """ - raise NotImplementedError('This is not implemented in the base class.') class ExpDecay(LeastSquaresFit): - def __init__(self, name='ExpDecayFit', guess=None): + def __init__(self, name='ExpDecayFit', guess: List=None): super().__init__( name=name, fun_str=r'$f(x) = a \exp(-x/T) + c$', fun_np='a*np.exp(-x/T)+c', param_labels=['$a$', '$T$', '$c$'], param_names=['a', 'T', 'c'], - param_units=['', 's', ''], - guess=guess) + param_units=['', 's', '']) + self.guess_params = guess def fun(self, x, a, T, c): return eval(self.fun_np) - def perform_guess(self, x, y): + def guess(self, x, y): + if self.guess_params is not None: + return self.guess_params length = len(y) val_init = y[0:round(length / 20)].mean() val_fin = y[-round(length / 20):].mean() @@ -77,20 +73,22 @@ def perform_guess(self, x, y): class ExpDecaySin(LeastSquaresFit): - def __init__(self, name='ExpDecaySinFit', guess=None): + def __init__(self, name='ExpDecaySinFit', guess: List=None): super().__init__( name=name, fun_str=r'$f(x) = a \sin(\omega x +\phi)\exp(-x/T) + c$', fun_np='a*np.exp(-x/T)*np.sin(w*x+p)+c', param_labels=['$a$', '$T$', '$\omega$', '$\phi$', '$c$'], param_names=['a', 'T', 'w', 'p', 'c'], - param_units=['', 's', 'Hz', '', ''], - guess=guess) + param_units=['', 's', 'Hz', '', '']) + self.guess_params = guess def fun(self, x, a, T, w, p, c): return eval(self.fun_np) - def perform_guess(self, x, y): + def guess(self, x, y): + if self.guess_params is not None: + return self.guess_params a = y.max() - y.min() c = y.mean() # guess T2 as point half way point in data From d164ef82d91cbaf4e937e19f2b9eebc1d7f93319 Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Tue, 11 Sep 2018 20:24:49 +0200 Subject: [PATCH 080/116] Add 1d plotting to fitter --- qdev_wrappers/fitting/Example.ipynb | 2 +- qdev_wrappers/fitting/Fitter.py | 93 ++++++++++++++++++++++++++++- qdev_wrappers/fitting/fit_saving.py | 2 +- 3 files changed, 93 insertions(+), 4 deletions(-) diff --git a/qdev_wrappers/fitting/Example.ipynb b/qdev_wrappers/fitting/Example.ipynb index 69cc0ba8..35a8555c 100644 --- a/qdev_wrappers/fitting/Example.ipynb +++ b/qdev_wrappers/fitting/Example.ipynb @@ -383,7 +383,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.6" + "version": "3.6.5" } }, "nbformat": 4, diff --git a/qdev_wrappers/fitting/Fitter.py b/qdev_wrappers/fitting/Fitter.py index 6eb5ea92..9f74857e 100644 --- a/qdev_wrappers/fitting/Fitter.py +++ b/qdev_wrappers/fitting/Fitter.py @@ -5,6 +5,14 @@ from qdev_wrappers.fitting.least_squares_fit import LeastSquaresFit from typing import Dict +import matplotlib.pyplot as plt +from qcodes.dataset.data_set import load_by_id +from qcodes.dataset.data_export import get_data_by_id +from qcodes.dataset.data_export import (datatype_from_setpoints_1d, + datatype_from_setpoints_2d, reshape_2D_data) +from qcodes.dataset.plotting import _rescale_ticks_and_units + + class Fitter: """ @@ -195,5 +203,86 @@ def _perform_fit(self, input_data_array, output_data_array): def _find_estimate(self, input_data_array, params_values_dict): return self.fitclass.fun(input_data_array, **params_values_dict) - def plot(self, **setpoint_config): - raise NotImplementedError + def plot(self, rescale_axes: bool=True): + + run_id = self.experiment_info['run_id'] + + data_info = load_by_id(run_id) + experiment_name = data_info.exp_name + sample_name = data_info.sample_name + title = f"Run #{run_id} fitted, Experiment {experiment_name} ({sample_name})" + + alldata = get_data_by_id(run_id) + data = alldata[0] + # ToDo: fix so that this always accesses the data that matches the dependent and independent variables + + fig, ax = plt.subplots(1, 1) + axes = [ax] + + if len(self.setpoints) == 0: # 1D PLOTTING + + parameter_values = self.fit_results[0]['param_values'] + + # sort data for plotting + order = self.indept_var['data'].argsort() + xpoints = self.indept_var['data'][order] + ypoints = self.dept_var['data'][order] + + # returns 'point' if all xpoints are identical, otherwise returns 'line' + plottype = datatype_from_setpoints_1d(xpoints) + + if plottype == 'line': + # or maybe it should just always be a scatter plot anyway? Thorvald's is a scatter plot with a fit line. + ax.plot(xpoints, ypoints, marker='.',markersize=5,linestyle='',color='C0') + + x = np.linspace(xpoints.min(), xpoints.max(), len(xpoints) * 10) + ax.plot(x, self.fitclass.fun(x, **parameter_values), color='C1') + + elif plottype == 'point': + # ToDo: does this even make sense? Would we ever do a fit of this type for a scatter plot? What even would that be? + x = xpoints[0] + ax.scatter(xpoints, ypoints) + ax.scatter(x, fitclass.fun(x, **parameter_values)) + else: + raise ValueError('Unknown plottype. Something is way wrong.') + + # axes labels and title + ax.set_xlabel(f"{self.indept_var['label']} ({self.indept_var['unit']})") + ax.set_ylabel(f"{self.dept_var['label']} ({self.dept_var['unit']})") + + if rescale_axes: + data_lst = [self.indept_var, self.dept_var] + _rescale_ticks_and_units(ax, data_lst) + + ax.set_title(title) + + # fit result box + p_label_list = [self.fitclass.fun_str] + for parameter in parameter_values: + value = parameter_values[parameter] + unit = self.fit_results[0]['param_units'][parameter] + p_label_list.append('{} = {:.3g} {}'.format(parameter, value, unit)) + textstr = '\n'.join(p_label_list) + + ax.text(1.05, 0.7, textstr, transform=ax.transAxes, fontsize=14, + verticalalignment='top', bbox={'ec':'k','fc':'w'}) + # Todo: scaling for fit result numbers and units + + elif len(self.setpoints) == 1: # 2D PLOTTING + + xdata = self.indept_var + ydata = self.setpoints + zdata = self.dept_var + + # Don't understand this fully. Seems to make color scale-bar for 2D plots. + colorbars = len(axes) * [None] # ToDo: what does this do? + new_colorbars: List[matplotlib.colorbar.Colorbar] = [] # ToDo: what does this do? + + raise NotImplementedError + + elif len(self.setpoints) > 1: # Are we even going to do this? + raise NotImplementedError + + + + diff --git a/qdev_wrappers/fitting/fit_saving.py b/qdev_wrappers/fitting/fit_saving.py index 3986fbb5..2ffeb539 100644 --- a/qdev_wrappers/fitting/fit_saving.py +++ b/qdev_wrappers/fitting/fit_saving.py @@ -1,5 +1,5 @@ from qcodes import ParamSpec, new_data_set, new_experiment -from qdev_wrappers.fitting.fitter import Fitter +from qdev_wrappers.fitting.Fitter import Fitter from qcodes.dataset.database import initialise_database from qcodes.dataset.data_set import load_by_id From 6f755f742b3d84a6193dc774649bad7edbc413a7 Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Tue, 18 Sep 2018 11:46:15 +0200 Subject: [PATCH 081/116] 2D heat map fit plots, error bars on plots, R2 fitting threshhold and power function fitclass --- qdev_wrappers/fitting/Fitter.py | 267 ++++++++++++++++----- qdev_wrappers/fitting/fit_saving.py | 11 +- qdev_wrappers/fitting/least_squares_fit.py | 23 ++ 3 files changed, 235 insertions(+), 66 deletions(-) diff --git a/qdev_wrappers/fitting/Fitter.py b/qdev_wrappers/fitting/Fitter.py index 9f74857e..1be30729 100644 --- a/qdev_wrappers/fitting/Fitter.py +++ b/qdev_wrappers/fitting/Fitter.py @@ -7,11 +7,20 @@ import matplotlib.pyplot as plt from qcodes.dataset.data_set import load_by_id -from qcodes.dataset.data_export import get_data_by_id +from qcodes.dataset.data_export import get_data_by_id, flatten_1D_data_for_plot from qcodes.dataset.data_export import (datatype_from_setpoints_1d, datatype_from_setpoints_2d, reshape_2D_data) -from qcodes.dataset.plotting import _rescale_ticks_and_units +from qcodes.dataset.plotting import _rescale_ticks_and_units, plot_on_a_plain_grid, plot_2d_scatterplot +import warnings +warnings.simplefilter("always") + +#alldata = get_data_by_id(run_id) +#data_info = load_by_id(run_id) +#data_info = load_by_id(run_id) +#experiment_name = data_info.exp_name +#sample_name = data_info.sample_name +# ToDo: use this to get data instead of making data dictionary class Fitter: @@ -28,7 +37,7 @@ class Fitter: """ def __init__(self, data: Dict, fitclass: LeastSquaresFit, - indept_var: str, dept_var: str): + indept_var: str, dept_var: str, r2_limit = 0.6): self.fitclass = fitclass self.experiment_info = {'exp_id': data['exp_id'], 'run_id': data['run_id'], @@ -36,7 +45,7 @@ def __init__(self, data: Dict, fitclass: LeastSquaresFit, self.indept_var = data[indept_var] self.dept_var = data[dept_var] self.setpoints = {k: data[k] for k in data.keys() if - (k not in [indept_var, dept_var]) and (k in data['variables'])} + (k not in [indept_var, dept_var]) and (k in data['variables'])} # Todo: this will mark phase as a setpoint if magnitude is chosen as a variable self.fit_parameters = { fitclass.param_names[i]: {'name': fitclass.param_names[i], @@ -45,7 +54,8 @@ def __init__(self, data: Dict, fitclass: LeastSquaresFit, i in range(len(fitclass.param_names))} self.estimator = {'method': 'LSF', 'type': fitclass.name, - 'fit_function_str': fitclass.fun_str} # TODO: add dill here + 'fit_function_str': fitclass.fun_str, + 'r2_limit': r2_limit} # TODO: add dill here self.fit_results = self._do_fitting_procedure(data) def _do_fitting_procedure(self, data): @@ -174,6 +184,17 @@ def get_result(**setpoint_values): all(res[setpoint] == value for setpoint, value in setpoint_values.items())) + def get_r2(self, estimate, data): + """ + Finds residual and total sum of squares, calculates the R^2 value + Args: + A np.array of data, and a corresponding np.array of data estimate based off a fit to the data. + """ + ss_res = np.sum((data - estimate) ** 2) + ss_tot = np.sum((data - np.mean(data)) ** 2) + r2 = 1 - (ss_res / ss_tot) + return r2 + def _perform_fit(self, input_data_array, output_data_array): # make fit params dictionary @@ -198,90 +219,214 @@ def _perform_fit(self, input_data_array, output_data_array): params_dict['param_values'][param_name] = popt[i] params_dict['param_variance'][param_name] = pcov[i, i] + # r2 test to approve fit + est_data = self._find_estimate(input_data_array, params_dict['param_values']) + r2 = self.get_r2(est_data, output_data_array) + if r2 < self.estimator['r2_limit']: + params_dict['param_values'] = None + params_dict['param_variance'] = None + warnings.warn('Unsuccessful fit') + return params_dict def _find_estimate(self, input_data_array, params_values_dict): - return self.fitclass.fun(input_data_array, **params_values_dict) + if params_values_dict is None: + return [None] * len(input_data_array) + else: + return self.fitclass.fun(input_data_array, **params_values_dict) - def plot(self, rescale_axes: bool=True): + def _plot_1d(self, ax, xdata, ydata, parameter_values, parameter_variance, rescale_axes): - run_id = self.experiment_info['run_id'] + ax.plot(xdata, ydata, marker='.', markersize=5, linestyle='', color='C0') + if parameter_values is None: + pass + else: + x = np.linspace(xdata.min(), xdata.max(), len(xdata) * 10) + ax.plot(x, self.fitclass.fun(x, **parameter_values), color='C1') - data_info = load_by_id(run_id) - experiment_name = data_info.exp_name - sample_name = data_info.sample_name - title = f"Run #{run_id} fitted, Experiment {experiment_name} ({sample_name})" + # axes labels and title + ax.set_xlabel(f"{self.indept_var['label']} ({self.indept_var['unit']})") + ax.set_ylabel(f"{self.dept_var['label']} ({self.dept_var['unit']})") + + if rescale_axes: + data_lst = [self.indept_var, self.dept_var] + _rescale_ticks_and_units(ax, data_lst) + + # fit result box + if parameter_values is None: + textstr = '{} \n Unsuccessful fit'.format(self.fitclass.fun_str) + else: + p_label_list = [self.fitclass.fun_str] + for parameter in parameter_values: + value = parameter_values[parameter] + unit = self.fit_results[0]['param_units'][parameter] + standard_dev = np.sqrt(parameter_variance[parameter]) + p_label_list.append('{} = {:.3g} $\pm$ {:.3g} {}'.format(parameter, value, standard_dev, unit)) + textstr = '\n'.join(p_label_list) + + ax.text(1.05, 0.7, textstr, transform=ax.transAxes, fontsize=14, + verticalalignment='top', bbox={'ec': 'k', 'fc': 'w'}) + # Todo: scaling for fit result numbers and units, units based on input - alldata = get_data_by_id(run_id) - data = alldata[0] - # ToDo: fix so that this always accesses the data that matches the dependent and independent variables + def plot(self, setpoint=None, rescale_axes: bool=True): + # setpoint should be None or a list of the form {setpoint_name: value, setpoint_name2: value, ...} + #Todo: should this really be part of the fitter? What about if you want to plot the fit from the fit_id later? + #Todo: wouldn't it be better to save the fit first, and then plot from the experiment database? + # make figure, axes fig, ax = plt.subplots(1, 1) - axes = [ax] + colorbar = None + + # make title + run_id = self.experiment_info['run_id'] + experiment_name = self.experiment_info['exp_id'] + sample_name = self.experiment_info['sample_name'] + title = f"Run #{run_id} fitted, Experiment {experiment_name} ({sample_name})" + ax.set_title(title) if len(self.setpoints) == 0: # 1D PLOTTING parameter_values = self.fit_results[0]['param_values'] + parameter_variance = self.fit_results[0]['param_variance'] + xpoints = self.indept_var['data'] + ypoints = self.dept_var['data'] - # sort data for plotting - order = self.indept_var['data'].argsort() - xpoints = self.indept_var['data'][order] - ypoints = self.dept_var['data'][order] + self._plot_1d(ax, xpoints, ypoints, parameter_values, parameter_variance, rescale_axes) - # returns 'point' if all xpoints are identical, otherwise returns 'line' - plottype = datatype_from_setpoints_1d(xpoints) - if plottype == 'line': - # or maybe it should just always be a scatter plot anyway? Thorvald's is a scatter plot with a fit line. - ax.plot(xpoints, ypoints, marker='.',markersize=5,linestyle='',color='C0') + elif len(self.setpoints) >= 1 : # 2D PLOTTING - x = np.linspace(xpoints.min(), xpoints.max(), len(xpoints) * 10) - ax.plot(x, self.fitclass.fun(x, **parameter_values), color='C1') + # make copy of fit_results + plot_results = self.fit_results.copy() - elif plottype == 'point': - # ToDo: does this even make sense? Would we ever do a fit of this type for a scatter plot? What even would that be? - x = xpoints[0] - ax.scatter(xpoints, ypoints) - ax.scatter(x, fitclass.fun(x, **parameter_values)) - else: - raise ValueError('Unknown plottype. Something is way wrong.') + # if setpoints are specified, remove all results from plot_results that are not at the specified setpoints + if setpoint is not None: + setpoint_names = [key for key in setpoint.keys()] + for name in setpoint_names: + for result in self.fit_results: + if result['setpoint_values'][name] != setpoint[name]: + plot_results.remove(result) - # axes labels and title - ax.set_xlabel(f"{self.indept_var['label']} ({self.indept_var['unit']})") - ax.set_ylabel(f"{self.dept_var['label']} ({self.dept_var['unit']})") - if rescale_axes: - data_lst = [self.indept_var, self.dept_var] - _rescale_ticks_and_units(ax, data_lst) + # if, after removing all results not at specified setpoint, only 1 fit is left, plot the 1D fit of this cut + if len(plot_results) == 1: + + title += '\n' + str(setpoint) + ax.set_title(title) + + parameter_values = plot_results[0]['param_values'] + parameter_variance = plot_results[0]['param_variance'] + xpoints = plot_results[0]['indept_var_values'] + ypoints = plot_results[0]['dept_var_values'] + + self._plot_1d(ax, xpoints, ypoints, parameter_values, parameter_variance, rescale_axes) + + + # if, after removing all results, there are many fits left in plot_results, attempt 2D plot + params plots + elif len(plot_results) > 1: + + # confirm that there is only 1 setpoint type besides the independent variable i.e. that data is 2D + if setpoint is None: + setpoint_list = [name for name in self.setpoints] + else: + setpoint_list = [name for name in self.setpoints if name not in setpoint] + if len(setpoint_list) > 1: + raise NotImplementedError('Function has too many ({}) unspecified setpoints.'.format(len(setpoint_list))) + + + # get setpoint info for plot labels + setpoint_name = setpoint_list[0] + setpoint_unit = self.setpoints[setpoint_name]['unit'] + setpoint_label = self.setpoints[setpoint_name]['label'] + + # retrieve independent variable and setpoint data, output estimate, parameter values + x = [] + y = [] + z = [] + params = {'param_setpoints' : []} + for param_name in self.fit_parameters: + params[param_name] = [] + params[param_name + '_variance'] = [] + + + for result in plot_results: + setpoint_value = result['setpoint_values'][setpoint_name] + for xvalue in result['indept_var_values']: + x.append(xvalue) + y.append(setpoint_value) + for est_value in result['estimate_values']: + z.append(est_value) + if result['param_values'] is not None: + params['param_setpoints'].append(setpoint_value) + for parameter in result['param_values']: + params[parameter].append( result['param_values'][parameter] ) + params[parameter + '_variance'].append( result['param_variance'][parameter] ) + + + + + xdata = np.array(x) + ydata = np.array(y) + zdata = np.array(z) + for item in params: params[item] = np.array(params[item]) + + + + # 2D heatmap plot + # how to plot - is this necessary? Don't we always want to plot on a grid? + how_to_plot = {'grid': plot_on_a_plain_grid, + 'equidistant': plot_on_a_plain_grid, + 'point': plot_2d_scatterplot, + 'unknown': plot_2d_scatterplot} + + plottype = datatype_from_setpoints_2d([xdata ,ydata]) + + xpoints = flatten_1D_data_for_plot(xdata) + ypoints = flatten_1D_data_for_plot(ydata) + zpoints = flatten_1D_data_for_plot(zdata) + plot_func = how_to_plot[plottype] + + ax, colorbar = plot_func(xpoints, ypoints, zpoints, ax, colorbar) + + ax.set_xlabel(f"{self.indept_var['label']} ({self.indept_var['unit']})") + ax.set_ylabel(f"{setpoint_label} ({setpoint_unit})") + colorbar.set_label(f"{self.dept_var['label']} ({self.dept_var['unit']})") + + if rescale_axes: + # create list of dictionaries for x, y and z containing units and labels + data_lst = [self.indept_var, self.setpoints[setpoint_name], self.dept_var] + #run rescale function to create new, scaled axes + _rescale_ticks_and_units(ax, data_lst, colorbar) + - ax.set_title(title) - # fit result box - p_label_list = [self.fitclass.fun_str] - for parameter in parameter_values: - value = parameter_values[parameter] - unit = self.fit_results[0]['param_units'][parameter] - p_label_list.append('{} = {:.3g} {}'.format(parameter, value, unit)) - textstr = '\n'.join(p_label_list) - ax.text(1.05, 0.7, textstr, transform=ax.transAxes, fontsize=14, - verticalalignment='top', bbox={'ec':'k','fc':'w'}) - # Todo: scaling for fit result numbers and units + # Parameter vs setpoint plots + order = params['param_setpoints'].argsort() + setpoint_data_dict = self.setpoints[setpoint_name] + setpoint_data_dict['data'] = params['param_setpoints'][order] - elif len(self.setpoints) == 1: # 2D PLOTTING + for param in self.fit_parameters: + # Todo: this should be like in plot_by_id, containing n subplots where ax holds all of them + fig, ax = plt.subplots(1, 1) - xdata = self.indept_var - ydata = self.setpoints - zdata = self.dept_var + param_data_dict = self.fit_parameters[param] + param_data_dict['data'] = params[param][order] + param_variance = params[param + '_variance'][order] + param_standard_dev = np.array( [np.sqrt(variance) for variance in param_variance] ) - # Don't understand this fully. Seems to make color scale-bar for 2D plots. - colorbars = len(axes) * [None] # ToDo: what does this do? - new_colorbars: List[matplotlib.colorbar.Colorbar] = [] # ToDo: what does this do? + ax.errorbar(setpoint_data_dict['data'], param_data_dict['data'], param_standard_dev, None, 'g.-') - raise NotImplementedError + ax.set_title(title) + ax.set_xlabel(f"{setpoint_data_dict['label']} ({setpoint_data_dict['unit']})") + ax.set_ylabel(f"{param_data_dict['label']} ({param_data_dict['unit']})") + if rescale_axes: + # create list of dictionaries for x, y and z containing units and labels + data_lst = [setpoint_data_dict, param_data_dict] + # run rescale function to create new, scaled axes + _rescale_ticks_and_units(ax, data_lst) - elif len(self.setpoints) > 1: # Are we even going to do this? - raise NotImplementedError + return ax, colorbar diff --git a/qdev_wrappers/fitting/fit_saving.py b/qdev_wrappers/fitting/fit_saving.py index 2ffeb539..eb45bd2a 100644 --- a/qdev_wrappers/fitting/fit_saving.py +++ b/qdev_wrappers/fitting/fit_saving.py @@ -1,5 +1,5 @@ from qcodes import ParamSpec, new_data_set, new_experiment -from qdev_wrappers.fitting.Fitter import Fitter +from qdev_wrappers.fitting.fitter import Fitter from qcodes.dataset.database import initialise_database from qcodes.dataset.data_set import load_by_id @@ -68,10 +68,11 @@ def save_fit_result(fitter: Fitter): for setpoint_name in r['setpoint_names']: result[setpoint_name] = r['setpoint_values'][setpoint_name] for param_name in r['param_names']: - result.update( - {param_name: r['param_values'][param_name], - param_name + '_initial_val': r['param_start_values'][param_name], - param_name + '_variance': r['param_variance'][param_name]}) + if r['param_values'] is not None: + result.update( + {param_name: r['param_values'][param_name], + param_name + '_initial_val': r['param_start_values'][param_name], + param_name + '_variance': r['param_variance'][param_name]}) for j in range(len(r['indept_var_values'])): result = result.copy() result.update( diff --git a/qdev_wrappers/fitting/least_squares_fit.py b/qdev_wrappers/fitting/least_squares_fit.py index 8239cb2c..9128b942 100644 --- a/qdev_wrappers/fitting/least_squares_fit.py +++ b/qdev_wrappers/fitting/least_squares_fit.py @@ -100,3 +100,26 @@ def guess(self, x, y): w = freqs[idx] p = 0 return [a, T, w, p, c] + + +class PowerDecay(LeastSquaresFit): + def __init__(self, name='PowerFit', guess: List=None): + super().__init__( + name=name, + fun_str=r'$f(x) = A p^x + B$', + fun_np='a * p**x + b', + param_labels=['$A$', '$p$', '$B$'], + param_names=['a', 'p', 'b'], + param_units=['V', '', 'V']) + self.guess_params = guess + + def fun(self, x, a, p, b): + return eval(self.fun_np) + + def guess(self, x, y): # NOTE: This guess is only valid for a decaying power function (i.e. 0 < p < 1) + if self.guess_params is not None: + return self.guess_params + a = y.max() - y.min() + b = y.min() + p = 1 + return [a, p, b] From 724ef5f2b24f36673099df615c09723bb32009f2 Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Tue, 18 Sep 2018 11:54:42 +0200 Subject: [PATCH 082/116] file name changing part 1 --- qdev_wrappers/fitting/{Fitter.py => fitteraw.py} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename qdev_wrappers/fitting/{Fitter.py => fitteraw.py} (100%) diff --git a/qdev_wrappers/fitting/Fitter.py b/qdev_wrappers/fitting/fitteraw.py similarity index 100% rename from qdev_wrappers/fitting/Fitter.py rename to qdev_wrappers/fitting/fitteraw.py From 46d8e02d5f6f1e95282f117afe4a538f71bbc185 Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Tue, 18 Sep 2018 11:54:57 +0200 Subject: [PATCH 083/116] file name changing part 2 --- qdev_wrappers/fitting/{fitteraw.py => fitter.py} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename qdev_wrappers/fitting/{fitteraw.py => fitter.py} (100%) diff --git a/qdev_wrappers/fitting/fitteraw.py b/qdev_wrappers/fitting/fitter.py similarity index 100% rename from qdev_wrappers/fitting/fitteraw.py rename to qdev_wrappers/fitting/fitter.py From 4409a8f233adcd0e67d8c799efa17ace14bc666a Mon Sep 17 00:00:00 2001 From: Natalie Pearson Date: Mon, 24 Sep 2018 16:43:09 +0200 Subject: [PATCH 084/116] fit from run id --- qdev_wrappers/fitting/fitter.py | 235 +++++++++++---------- qdev_wrappers/fitting/least_squares_fit.py | 53 ++++- 2 files changed, 168 insertions(+), 120 deletions(-) diff --git a/qdev_wrappers/fitting/fitter.py b/qdev_wrappers/fitting/fitter.py index 1be30729..2721a2dc 100644 --- a/qdev_wrappers/fitting/fitter.py +++ b/qdev_wrappers/fitting/fitter.py @@ -10,17 +10,11 @@ from qcodes.dataset.data_export import get_data_by_id, flatten_1D_data_for_plot from qcodes.dataset.data_export import (datatype_from_setpoints_1d, datatype_from_setpoints_2d, reshape_2D_data) -from qcodes.dataset.plotting import _rescale_ticks_and_units, plot_on_a_plain_grid, plot_2d_scatterplot +from qcodes.dataset.plotting import _rescale_ticks_and_units, plot_on_a_plain_grid import warnings warnings.simplefilter("always") -#alldata = get_data_by_id(run_id) -#data_info = load_by_id(run_id) -#data_info = load_by_id(run_id) -#experiment_name = data_info.exp_name -#sample_name = data_info.sample_name -# ToDo: use this to get data instead of making data dictionary class Fitter: @@ -36,16 +30,17 @@ class Fitter: dependent variable for fitting. eg 'cavity_magnitude_result' """ - def __init__(self, data: Dict, fitclass: LeastSquaresFit, + def __init__(self, run_id, fitclass: LeastSquaresFit, indept_var: str, dept_var: str, r2_limit = 0.6): self.fitclass = fitclass - self.experiment_info = {'exp_id': data['exp_id'], - 'run_id': data['run_id'], - 'sample_name': data['sample_name']} - self.indept_var = data[indept_var] - self.dept_var = data[dept_var] - self.setpoints = {k: data[k] for k in data.keys() if - (k not in [indept_var, dept_var]) and (k in data['variables'])} # Todo: this will mark phase as a setpoint if magnitude is chosen as a variable + self.experiment_info = {'exp_id': load_by_id(run_id).exp_name, + 'run_id': run_id, + 'sample_name': load_by_id(run_id).sample_name} + self.all_data = self._select_relevant_data(run_id, indept_var, dept_var) + self.indept_var = [ data for data in self.all_data if data['name'] == indept_var ][0] + self.dept_var = [ data for data in self.all_data if data['name'] == dept_var ][0] + self.setpoints = {data['name']: data for data in self.all_data if + (data['name'] != dept_var) and (data['name'] != indept_var)} self.fit_parameters = { fitclass.param_names[i]: {'name': fitclass.param_names[i], @@ -56,9 +51,44 @@ def __init__(self, data: Dict, fitclass: LeastSquaresFit, 'type': fitclass.name, 'fit_function_str': fitclass.fun_str, 'r2_limit': r2_limit} # TODO: add dill here - self.fit_results = self._do_fitting_procedure(data) + self.fit_results = self._do_fitting_procedure() + + def _select_relevant_data(self, run_id, indept_var, dept_var): + """ + This function goes through the all the data retrieved by + get_data_by_id(run_id), and returns the data that contains + the dependent variable, or throws an error if no matching + data is found. + + Calling get_data_by_id(run_id) returns data as a list of lists + of dictionaries: + + [ + # each element in this list refers + # to one dependent (aka measured) parameter + [ + # each element in this list is a dictionary referring + # to one independent (aka setpoint) parameter + # that the dependent parameter depends on; + # a dictionary with the data and metadata of the dependent + # parameter is in the *last* element in this list + + ] + ] + """ - def _do_fitting_procedure(self, data): + index = None + for idx, measured_data in enumerate(get_data_by_id(run_id)): + if measured_data[-1]['name'] == dept_var: + index = idx + if index == None: + raise RuntimeError(f"No data found with dependent variable {dept_var}") + all_data = get_data_by_id(run_id)[index] + if indept_var not in [data['name'] for data in all_data]: + raise RuntimeError(f"Specified independent variable not found in data") + return all_data + + def _do_fitting_procedure(self): """ Populates fit_results list with one dictionary per combination of setpoints. @@ -106,6 +136,7 @@ def _do_fitting_procedure(self, data): setpoint_names = list(self.setpoints.keys()) setpoint_labels = [v['label'] for v in self.setpoints.values()] setpoint_units = [v['unit'] for v in self.setpoints.values()] + data = {data['name'] : data for data in self.all_data} for setpoint_combination in setpoint_combinations: # find indices where where setpoint combination is satisfied indices = set(np.argwhere( @@ -115,10 +146,8 @@ def _do_fitting_procedure(self, data): data[setpoint_names[0]]['data'] == setpoint_combination[0]).flatten() indices = indices.intesection(new_indices) indices = list(indices) - input_data_array = data[self.indept_var['name'] - ]['data'][indices] - output_data_array = data[self.dept_var['name'] - ]['data'][indices] + input_data_array = self.indept_var['data'][indices] + output_data_array = self.dept_var['data'][indices] param_dict = self._perform_fit(input_data_array, output_data_array) result = param_dict.copy() @@ -146,8 +175,8 @@ def _do_fitting_procedure(self, data): # print('no data for setpoint combination ', dict( # zip(self.setpoints.keys(), setpoint_combination))) else: - input_data_array = data[self.indept_var['name']]['data'] - output_data_array = data[self.dept_var['name']]['data'] + input_data_array = self.indept_var['data'] + output_data_array = self.dept_var['data'] param_dict = self._perform_fit(input_data_array, output_data_array) result = param_dict.copy() @@ -157,10 +186,10 @@ def _do_fitting_procedure(self, data): 'dept_var_values': output_data_array, 'indept_var_name': self.indept_var['name'], 'dept_var_name': self.dept_var['name'], - 'indept_var_label': data[self.indept_var['name']]['label'], - 'dept_var_label': data[self.dept_var['name']]['label'], - 'indept_var_unit': data[self.indept_var['name']]['unit'], - 'dept_var_unit': data[self.dept_var['name']]['unit'], + 'indept_var_label': self.indept_var['label'], + 'dept_var_label': self.dept_var['label'], + 'indept_var_unit': self.indept_var['unit'], + 'dept_var_unit': self.dept_var['unit'], 'estimate_values': self._find_estimate(input_data_array, param_dict['param_values'])}) @@ -187,8 +216,6 @@ def get_result(**setpoint_values): def get_r2(self, estimate, data): """ Finds residual and total sum of squares, calculates the R^2 value - Args: - A np.array of data, and a corresponding np.array of data estimate based off a fit to the data. """ ss_res = np.sum((data - estimate) ** 2) ss_tot = np.sum((data - np.mean(data)) ** 2) @@ -208,10 +235,17 @@ def _perform_fit(self, input_data_array, output_data_array): # find start parameters, run curve_fit function to perform fit p_guess = self.fitclass.guess(input_data_array, output_data_array) - popt, pcov = curve_fit(self.fitclass.fun, + try: + popt, pcov = curve_fit(self.fitclass.fun, input_data_array, output_data_array, p0=p_guess) + except RuntimeError: + params_dict['param_values'] = None + params_dict['param_variance'] = None + warnings.warn('Unsuccessful fit - curve_fit did not find optimal parameters') + + return params_dict # add guess and fit results to dict for i, param_name in enumerate(self.fitclass.param_names): @@ -222,10 +256,13 @@ def _perform_fit(self, input_data_array, output_data_array): # r2 test to approve fit est_data = self._find_estimate(input_data_array, params_dict['param_values']) r2 = self.get_r2(est_data, output_data_array) - if r2 < self.estimator['r2_limit']: + if self.estimator['r2_limit'] == None: + pass + elif r2 < self.estimator['r2_limit']: params_dict['param_values'] = None params_dict['param_variance'] = None - warnings.warn('Unsuccessful fit') + warnings.warn('Unsuccessful fit - r2 for fit is below limit {}' + .format(self.estimator['r2_limit'])) return params_dict @@ -237,6 +274,7 @@ def _find_estimate(self, input_data_array, params_values_dict): def _plot_1d(self, ax, xdata, ydata, parameter_values, parameter_variance, rescale_axes): + # plot data, and fit if successful ax.plot(xdata, ydata, marker='.', markersize=5, linestyle='', color='C0') if parameter_values is None: pass @@ -244,7 +282,7 @@ def _plot_1d(self, ax, xdata, ydata, parameter_values, parameter_variance, resca x = np.linspace(xdata.min(), xdata.max(), len(xdata) * 10) ax.plot(x, self.fitclass.fun(x, **parameter_values), color='C1') - # axes labels and title + # set axes labels and title ax.set_xlabel(f"{self.indept_var['label']} ({self.indept_var['unit']})") ax.set_ylabel(f"{self.dept_var['label']} ({self.dept_var['unit']})") @@ -252,7 +290,7 @@ def _plot_1d(self, ax, xdata, ydata, parameter_values, parameter_variance, resca data_lst = [self.indept_var, self.dept_var] _rescale_ticks_and_units(ax, data_lst) - # fit result box + # add fit result summary box if parameter_values is None: textstr = '{} \n Unsuccessful fit'.format(self.fitclass.fun_str) else: @@ -261,45 +299,42 @@ def _plot_1d(self, ax, xdata, ydata, parameter_values, parameter_variance, resca value = parameter_values[parameter] unit = self.fit_results[0]['param_units'][parameter] standard_dev = np.sqrt(parameter_variance[parameter]) - p_label_list.append('{} = {:.3g} $\pm$ {:.3g} {}'.format(parameter, value, standard_dev, unit)) + p_label_list.append(r'{} = {:.3g} +/- {:.3g} {}' + .format(parameter, value, standard_dev, unit)) textstr = '\n'.join(p_label_list) ax.text(1.05, 0.7, textstr, transform=ax.transAxes, fontsize=14, verticalalignment='top', bbox={'ec': 'k', 'fc': 'w'}) - # Todo: scaling for fit result numbers and units, units based on input def plot(self, setpoint=None, rescale_axes: bool=True): - # setpoint should be None or a list of the form {setpoint_name: value, setpoint_name2: value, ...} - #Todo: should this really be part of the fitter? What about if you want to plot the fit from the fit_id later? - #Todo: wouldn't it be better to save the fit first, and then plot from the experiment database? - - # make figure, axes + """ + setpoint : None or a dict of form {name: value, name2: value, ...} + """ + # make figure, axes, title fig, ax = plt.subplots(1, 1) + axes = [ax] colorbar = None + title = "Run #{} fitted, Experiment {} ({})".format(self.experiment_info['run_id'], + self.experiment_info['exp_id'], self.experiment_info['sample_name']) - # make title - run_id = self.experiment_info['run_id'] - experiment_name = self.experiment_info['exp_id'] - sample_name = self.experiment_info['sample_name'] - title = f"Run #{run_id} fitted, Experiment {experiment_name} ({sample_name})" - ax.set_title(title) if len(self.setpoints) == 0: # 1D PLOTTING + ax.set_title(title) + parameter_values = self.fit_results[0]['param_values'] parameter_variance = self.fit_results[0]['param_variance'] xpoints = self.indept_var['data'] ypoints = self.dept_var['data'] self._plot_1d(ax, xpoints, ypoints, parameter_values, parameter_variance, rescale_axes) - + elif len(self.setpoints) >= 1 : # 2D PLOTTING - # make copy of fit_results + # make copy of fit_results, remove all results not at specified setpoints plot_results = self.fit_results.copy() - # if setpoints are specified, remove all results from plot_results that are not at the specified setpoints if setpoint is not None: setpoint_names = [key for key in setpoint.keys()] for name in setpoint_names: @@ -307,8 +342,7 @@ def plot(self, setpoint=None, rescale_axes: bool=True): if result['setpoint_values'][name] != setpoint[name]: plot_results.remove(result) - - # if, after removing all results not at specified setpoint, only 1 fit is left, plot the 1D fit of this cut + # if only 1 fit is left, plot the 1D fit of this cut if len(plot_results) == 1: title += '\n' + str(setpoint) @@ -321,112 +355,83 @@ def plot(self, setpoint=None, rescale_axes: bool=True): self._plot_1d(ax, xpoints, ypoints, parameter_values, parameter_variance, rescale_axes) - - # if, after removing all results, there are many fits left in plot_results, attempt 2D plot + params plots + # if there are many fits left in plot_results, make 2D plot + params plots elif len(plot_results) > 1: - # confirm that there is only 1 setpoint type besides the independent variable i.e. that data is 2D + # confirm that there is at most 1 variable setpoint if setpoint is None: - setpoint_list = [name for name in self.setpoints] + unspecified_setpoints = [name for name in self.setpoints] else: - setpoint_list = [name for name in self.setpoints if name not in setpoint] - if len(setpoint_list) > 1: - raise NotImplementedError('Function has too many ({}) unspecified setpoints.'.format(len(setpoint_list))) - + unspecified_setpoints = [name for name in self.setpoints if name not in setpoint] + if len(unspecified_setpoints) > 1: + raise NotImplementedError('Function has too many ({}) unspecified setpoints.' + .format(len(setpoint_list))) # get setpoint info for plot labels - setpoint_name = setpoint_list[0] + setpoint_name = unspecified_setpoints[0] setpoint_unit = self.setpoints[setpoint_name]['unit'] setpoint_label = self.setpoints[setpoint_name]['label'] - # retrieve independent variable and setpoint data, output estimate, parameter values - x = [] - y = [] - z = [] + # organize all data for plotting + x, y, z = [], [], [] params = {'param_setpoints' : []} for param_name in self.fit_parameters: params[param_name] = [] params[param_name + '_variance'] = [] - for result in plot_results: setpoint_value = result['setpoint_values'][setpoint_name] - for xvalue in result['indept_var_values']: - x.append(xvalue) - y.append(setpoint_value) - for est_value in result['estimate_values']: - z.append(est_value) + data_length = len(result['indept_var_values']) + x.append(list(result['indept_var_values'])) + y.append([setpoint_value] * data_length) + z.append(list(result['estimate_values'])) + if result['param_values'] is not None: params['param_setpoints'].append(setpoint_value) for parameter in result['param_values']: params[parameter].append( result['param_values'][parameter] ) params[parameter + '_variance'].append( result['param_variance'][parameter] ) - - - - xdata = np.array(x) - ydata = np.array(y) - zdata = np.array(z) + xpoints = np.array(x).flatten() + ypoints = np.array(y).flatten() + zpoints = np.array(z).flatten() for item in params: params[item] = np.array(params[item]) - - - # 2D heatmap plot - # how to plot - is this necessary? Don't we always want to plot on a grid? - how_to_plot = {'grid': plot_on_a_plain_grid, - 'equidistant': plot_on_a_plain_grid, - 'point': plot_2d_scatterplot, - 'unknown': plot_2d_scatterplot} - - plottype = datatype_from_setpoints_2d([xdata ,ydata]) - - xpoints = flatten_1D_data_for_plot(xdata) - ypoints = flatten_1D_data_for_plot(ydata) - zpoints = flatten_1D_data_for_plot(zdata) - plot_func = how_to_plot[plottype] - - ax, colorbar = plot_func(xpoints, ypoints, zpoints, ax, colorbar) + # Make 2D heatmap plot + ax, colorbar = plot_on_a_plain_grid(xpoints, ypoints, zpoints, ax, colorbar) ax.set_xlabel(f"{self.indept_var['label']} ({self.indept_var['unit']})") ax.set_ylabel(f"{setpoint_label} ({setpoint_unit})") + ax.set_title(title) colorbar.set_label(f"{self.dept_var['label']} ({self.dept_var['unit']})") if rescale_axes: - # create list of dictionaries for x, y and z containing units and labels data_lst = [self.indept_var, self.setpoints[setpoint_name], self.dept_var] - #run rescale function to create new, scaled axes _rescale_ticks_and_units(ax, data_lst, colorbar) - - - - # Parameter vs setpoint plots + # Select data for parameter vs setpoint plots order = params['param_setpoints'].argsort() - setpoint_data_dict = self.setpoints[setpoint_name] - setpoint_data_dict['data'] = params['param_setpoints'][order] + xdata = self.setpoints[setpoint_name] + xpoints = params['param_setpoints'][order] for param in self.fit_parameters: - # Todo: this should be like in plot_by_id, containing n subplots where ax holds all of them - fig, ax = plt.subplots(1, 1) - - param_data_dict = self.fit_parameters[param] - param_data_dict['data'] = params[param][order] + ydata = self.fit_parameters[param] + ydata['data'] = params[param][order] param_variance = params[param + '_variance'][order] param_standard_dev = np.array( [np.sqrt(variance) for variance in param_variance] ) - ax.errorbar(setpoint_data_dict['data'], param_data_dict['data'], param_standard_dev, None, 'g.-') - + #Make parameter vs setpoint plot + fig, ax = plt.subplots(1, 1) + ax.errorbar(xpoints, ydata['data'], param_standard_dev, None, 'g.-') ax.set_title(title) - ax.set_xlabel(f"{setpoint_data_dict['label']} ({setpoint_data_dict['unit']})") - ax.set_ylabel(f"{param_data_dict['label']} ({param_data_dict['unit']})") + ax.set_xlabel(f"{xdata['label']} ({xdata['unit']})") + ax.set_ylabel(f"{ydata['label']} ({ydata['unit']})") if rescale_axes: - # create list of dictionaries for x, y and z containing units and labels - data_lst = [setpoint_data_dict, param_data_dict] - # run rescale function to create new, scaled axes + data_lst = [xdata, ydata] _rescale_ticks_and_units(ax, data_lst) + axes.append(ax) - return ax, colorbar + return axes, colorbar diff --git a/qdev_wrappers/fitting/least_squares_fit.py b/qdev_wrappers/fitting/least_squares_fit.py index 9128b942..032fb33b 100644 --- a/qdev_wrappers/fitting/least_squares_fit.py +++ b/qdev_wrappers/fitting/least_squares_fit.py @@ -78,7 +78,7 @@ def __init__(self, name='ExpDecaySinFit', guess: List=None): name=name, fun_str=r'$f(x) = a \sin(\omega x +\phi)\exp(-x/T) + c$', fun_np='a*np.exp(-x/T)*np.sin(w*x+p)+c', - param_labels=['$a$', '$T$', '$\omega$', '$\phi$', '$c$'], + param_labels=['$a$', '$T$', r'$\omega$', r'$\phi$', '$c$'], param_names=['a', 'T', 'w', 'p', 'c'], param_units=['', 's', 'Hz', '', '']) self.guess_params = guess @@ -116,10 +116,53 @@ def __init__(self, name='PowerFit', guess: List=None): def fun(self, x, a, p, b): return eval(self.fun_np) - def guess(self, x, y): # NOTE: This guess is only valid for a decaying power function (i.e. 0 < p < 1) + def guess(self, x, y): if self.guess_params is not None: return self.guess_params - a = y.max() - y.min() - b = y.min() - p = 1 + + length = len(y) + val_init = y[0:round(length / 20)].mean() + val_fin = y[-round(length / 20):].mean() + a = val_init - val_fin + b = val_fin + + # guess T as point where data has fallen to 1/e of init value + idx = (np.abs(y - a / np.e - b)).argmin() + T = x[idx] + #guess p as e^(-1/T): + p = np.e**(-1/T) + + return [a, p, b] + + +class FirstOrderBM(LeastSquaresFit): + def __init__(self, name='1st order benchmarking', guess: List=None): + super().__init__( + name=name, + fun_str=r'$f(x) = A p^x + C(x-1)p^{x-2} + B$', + fun_np='a * p**x + c*(x-1)*p**(x-2) + b', + param_labels=['$A$', '$p$', '$B$'], + param_names=['a', 'p', 'b'], + param_units=['V', '', 'V']) + self.guess_params = guess + + def fun(self, x, a, p, b): + return eval(self.fun_np) + + def guess(self, x, y): + if self.guess_params is not None: + return self.guess_params + + length = len(y) + val_init = y[0:round(length / 20)].mean() + val_fin = y[-round(length / 20):].mean() + a = val_init - val_fin + b = val_fin + + # guess T as point where data has fallen to 1/e of init value + idx = (np.abs(y - a / np.e - b)).argmin() + T = x[idx] + #guess p as e^(-1/T): + p = np.e**(-1/T) + return [a, p, b] From a6c80a4f37b46159a612cc91e1f3926a68ea3776 Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Tue, 25 Sep 2018 14:21:36 +0200 Subject: [PATCH 085/116] Add perform_fit function to fitclass --- qdev_wrappers/fitting/least_squares_fit.py | 53 ++++++++++++++++------ 1 file changed, 39 insertions(+), 14 deletions(-) diff --git a/qdev_wrappers/fitting/least_squares_fit.py b/qdev_wrappers/fitting/least_squares_fit.py index 9128b942..7248a127 100644 --- a/qdev_wrappers/fitting/least_squares_fit.py +++ b/qdev_wrappers/fitting/least_squares_fit.py @@ -1,9 +1,11 @@ import numpy as np import scipy.fftpack as fftpack from typing import List +from qcodes import Instrument +from scipy.optimize import curve_fit -class LeastSquaresFit: +class LeastSquaresFit(Instrument): """ Base class for fit functions to be used with curve_fit. Specifies a function for fitting, function for guessing initial parameters for fit @@ -18,12 +20,32 @@ class LeastSquaresFit: def __init__(self, name, fun_str, fun_np, param_names, param_labels, param_units): - self.name = name + super().__init__(name) self.fun_str = fun_str self.fun_np = fun_np self.param_names = param_names - self.param_labels = param_labels - self.param_units = param_units + + for idx, parameter in enumerate(self.param_names): + self.add_parameter(name=parameter, + unit=param_units[idx], + label=param_labels[idx], + set_cmd=False) + del self.parameters['IDN'] + + def perform_fit(self, input_data_array, output_data_array): + + # find start parameters, run curve_fit function to perform fit + p_guess = self.guess(input_data_array, output_data_array) + popt, pcov = curve_fit(self.fun, + input_data_array, + output_data_array, + p0=p_guess) + + # update guess and fit results in fit parameters + for i, param in enumerate(self.param_names): + self.parameters[param]._save_val(popt[i]) + self.parameters[param].start_value = p_guess[i] + self.parameters[param].variance = pcov[i, i] def fun(self, *args): """ @@ -83,9 +105,6 @@ def __init__(self, name='ExpDecaySinFit', guess: List=None): param_units=['', 's', 'Hz', '', '']) self.guess_params = guess - def fun(self, x, a, T, w, p, c): - return eval(self.fun_np) - def guess(self, x, y): if self.guess_params is not None: return self.guess_params @@ -113,13 +132,19 @@ def __init__(self, name='PowerFit', guess: List=None): param_units=['V', '', 'V']) self.guess_params = guess - def fun(self, x, a, p, b): - return eval(self.fun_np) - - def guess(self, x, y): # NOTE: This guess is only valid for a decaying power function (i.e. 0 < p < 1) + def guess(self, x, y): if self.guess_params is not None: return self.guess_params - a = y.max() - y.min() - b = y.min() - p = 1 + + length = len(y) + val_init = y[0:round(length / 20)].mean() + val_fin = y[-round(length / 20):].mean() + a = val_init - val_fin + c = val_fin + # guess T1 as point where data has fallen to 1/e of init value + idx = (np.abs(y - a / np.e - c)).argmin() + T = x[idx] + # guess p as e^(-1/T) + p = np.e**(-1/T) + return [a, p, b] From 0cb1c7444dc78a9c5c5e70ff3f54f2db649da034 Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Tue, 25 Sep 2018 14:51:52 +0200 Subject: [PATCH 086/116] Add missing func to ExpDexaySin --- qdev_wrappers/fitting/least_squares_fit.py | 8 +++----- 1 file changed, 3 insertions(+), 5 deletions(-) diff --git a/qdev_wrappers/fitting/least_squares_fit.py b/qdev_wrappers/fitting/least_squares_fit.py index 8d55c6a1..d4b0e176 100644 --- a/qdev_wrappers/fitting/least_squares_fit.py +++ b/qdev_wrappers/fitting/least_squares_fit.py @@ -59,11 +59,6 @@ def guess(self, *args): given values or based on perform guess function """ raise NotImplementedError -# if guess is not None: -# return guess -# else: -# return self.perform_guess(*args) - class ExpDecay(LeastSquaresFit): @@ -105,6 +100,9 @@ def __init__(self, name='ExpDecaySinFit', guess: List=None): param_units=['', 's', 'Hz', '', '']) self.guess_params = guess + def fun(self, x, a, T, w, p, c): + return eval(self.fun_np) + def guess(self, x, y): if self.guess_params is not None: return self.guess_params From ef4e7f825071bb77374b660e37cb914badafb223 Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Tue, 25 Sep 2018 16:04:44 +0200 Subject: [PATCH 087/116] Transferring stuff from QInfer notebook --- qdev_wrappers/fitting/least_squares_fit.py | 34 ++++++++++++++++++++++ 1 file changed, 34 insertions(+) diff --git a/qdev_wrappers/fitting/least_squares_fit.py b/qdev_wrappers/fitting/least_squares_fit.py index d4b0e176..74e0ad2f 100644 --- a/qdev_wrappers/fitting/least_squares_fit.py +++ b/qdev_wrappers/fitting/least_squares_fit.py @@ -5,6 +5,40 @@ from scipy.optimize import curve_fit +class MockInstrument(qc.Instrument): + parameters = OrderedDict() + + def __init__(self, name, **kwargs): + super().__init__(name, **kwargs) + for param_name, param_kwargs in self.parameters.items(): + self.add_parameter(param_name, set_cmd=False, **kwargs) + + def funtion(self): + raise NotImplementedError + + def guess(self): + raise NotImplementedError + + def perform_fit(self): + # TODO + + +class ExpDecay(MockInstrument): + parameters = {'a': {'unit': 'V', 'label': 'A'}, } + + def __init__(self, guess=None): + self.guess = guess + super().__init__() + + def guess(self): + if self.guess is not None: + return self.guess + else: + # TODO + + def function(self): + # TODO + class LeastSquaresFit(Instrument): """ Base class for fit functions to be used with curve_fit. Specifies From 360fc8191ebad80a18c2f835cd82527fdceeb447 Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Tue, 25 Sep 2018 16:53:16 +0200 Subject: [PATCH 088/116] Use ordered dictionary to define parameter info in __init__ --- qdev_wrappers/fitting/least_squares_fit.py | 183 ++++++--------------- 1 file changed, 54 insertions(+), 129 deletions(-) diff --git a/qdev_wrappers/fitting/least_squares_fit.py b/qdev_wrappers/fitting/least_squares_fit.py index 74e0ad2f..94ec3a89 100644 --- a/qdev_wrappers/fitting/least_squares_fit.py +++ b/qdev_wrappers/fitting/least_squares_fit.py @@ -3,42 +3,9 @@ from typing import List from qcodes import Instrument from scipy.optimize import curve_fit +from collections import OrderedDict -class MockInstrument(qc.Instrument): - parameters = OrderedDict() - - def __init__(self, name, **kwargs): - super().__init__(name, **kwargs) - for param_name, param_kwargs in self.parameters.items(): - self.add_parameter(param_name, set_cmd=False, **kwargs) - - def funtion(self): - raise NotImplementedError - - def guess(self): - raise NotImplementedError - - def perform_fit(self): - # TODO - - -class ExpDecay(MockInstrument): - parameters = {'a': {'unit': 'V', 'label': 'A'}, } - - def __init__(self, guess=None): - self.guess = guess - super().__init__() - - def guess(self): - if self.guess is not None: - return self.guess - else: - # TODO - - def function(self): - # TODO - class LeastSquaresFit(Instrument): """ Base class for fit functions to be used with curve_fit. Specifies @@ -48,40 +15,19 @@ class LeastSquaresFit(Instrument): Only one input and one output is currently allowed. - Order of param_names, param_labels, param_units must match function guess - output and order of appearance in fun + Order of parameters in model_parameters ordered dictionary must match function guess + output and order of appearance in func """ - - def __init__(self, name, fun_str, fun_np, - param_names, param_labels, param_units): - super().__init__(name) - self.fun_str = fun_str - self.fun_np = fun_np - self.param_names = param_names - - for idx, parameter in enumerate(self.param_names): - self.add_parameter(name=parameter, - unit=param_units[idx], - label=param_labels[idx], - set_cmd=False) + model_parameters = OrderedDict({}) + def __init__(self, name, **kwargs): + super().__init__(name, **kwargs) + for param_name, param_kwargs in self.model_parameters.items(): + self.add_parameter(param_name, set_cmd=False, **kwargs) del self.parameters['IDN'] - def perform_fit(self, input_data_array, output_data_array): - # find start parameters, run curve_fit function to perform fit - p_guess = self.guess(input_data_array, output_data_array) - popt, pcov = curve_fit(self.fun, - input_data_array, - output_data_array, - p0=p_guess) - # update guess and fit results in fit parameters - for i, param in enumerate(self.param_names): - self.parameters[param]._save_val(popt[i]) - self.parameters[param].start_value = p_guess[i] - self.parameters[param].variance = pcov[i, i] - - def fun(self, *args): + def func(self, *args): """ The mathematical function to fit to """ @@ -94,19 +40,34 @@ def guess(self, *args): """ raise NotImplementedError + def perform_fit(self, input_data_array, output_data_array): + + # find start parameters, run curve_fit function to perform fit + p_guess = self.guess(input_data_array, output_data_array) + popt, pcov = curve_fit(self.func, + input_data_array, + output_data_array, + p0=p_guess) + + # update guess and fit results in fit parameters + for i, param in enumerate(self.model_parameters): + self.parameters[param]._save_val(popt[i]) + self.parameters[param].start_value = p_guess[i] + self.parameters[param].variance = pcov[i, i] + class ExpDecay(LeastSquaresFit): - def __init__(self, name='ExpDecayFit', guess: List=None): - super().__init__( - name=name, - fun_str=r'$f(x) = a \exp(-x/T) + c$', - fun_np='a*np.exp(-x/T)+c', - param_labels=['$a$', '$T$', '$c$'], - param_names=['a', 'T', 'c'], - param_units=['', 's', '']) + + model_parameters = OrderedDict({'a': {'label':'$a$', 'unit':''}, + 'T': {'label':'$T$', 'unit':'s'}, + 'c': {'label':'$c$', 'unit':''} }) + def __init__(self, name, guess=None): + super().__init__(name) self.guess_params = guess + self.fun_str=r'$f(x) = a \exp(-x/T) + c$' + self.fun_np='a*np.exp(-x/T)+c' - def fun(self, x, a, T, c): + def func(self, x, a, T, c): return eval(self.fun_np) def guess(self, x, y): @@ -124,17 +85,19 @@ def guess(self, x, y): class ExpDecaySin(LeastSquaresFit): - def __init__(self, name='ExpDecaySinFit', guess: List=None): - super().__init__( - name=name, - fun_str=r'$f(x) = a \sin(\omega x +\phi)\exp(-x/T) + c$', - fun_np='a*np.exp(-x/T)*np.sin(w*x+p)+c', - param_labels=['$a$', '$T$', r'$\omega$', r'$\phi$', '$c$'], - param_names=['a', 'T', 'w', 'p', 'c'], - param_units=['', 's', 'Hz', '', '']) + + model_parameters = OrderedDict({'a': {'label': '$a$', 'unit': ''}, + 'T': {'label': '$T$', 'unit': 's'}, + 'w': {'label': r'$\omega$', 'unit': 'Hz'}, + 'p': {'label': r'$\phi$', 'unit': ''}, + 'c': {'label': '$c$', 'unit': ''}}) + def __init__(self, name, guess=None): + super().__init__(name) self.guess_params = guess + self.fun_str = r'$f(x) = a \sin(\omega x +\phi)\exp(-x/T) + c$' + self.fun_np = 'a*np.exp(-x/T)*np.sin(w*x+p)+c' - def fun(self, x, a, T, w, p, c): + def func(self, x, a, T, w, p, c): return eval(self.fun_np) def guess(self, x, y): @@ -154,65 +117,27 @@ def guess(self, x, y): class PowerDecay(LeastSquaresFit): - def __init__(self, name='PowerFit', guess: List=None): - super().__init__( - name=name, - fun_str=r'$f(x) = A p^x + B$', - fun_np='a * p**x + b', - param_labels=['$A$', '$p$', '$B$'], - param_names=['a', 'p', 'b'], - param_units=['V', '', 'V']) - self.guess_params = guess - - def fun(self, x, a, p, b): - return eval(self.fun_np) - - def guess(self, x, y): - if self.guess_params is not None: - return self.guess_params - - length = len(y) - val_init = y[0:round(length / 20)].mean() - val_fin = y[-round(length / 20):].mean() - a = val_init - val_fin - b = val_fin - - # guess T as point where data has fallen to 1/e of init value - idx = (np.abs(y - a / np.e - b)).argmin() - T = x[idx] - #guess p as e^(-1/T): - p = np.e**(-1/T) - return [a, p, b] - - -class FirstOrderBM(LeastSquaresFit): - def __init__(self, name='1st order benchmarking', guess: List=None): - super().__init__( - name=name, - fun_str=r'$f(x) = A p^x + C(x-1)p^{x-2} + B$', - fun_np='a * p**x + c*(x-1)*p**(x-2) + b', - param_labels=['$A$', '$p$', '$B$'], - param_names=['a', 'p', 'b'], - param_units=['V', '', 'V']) + model_parameters = OrderedDict({'a': {'label': '$a$', 'unit': 'V'}, + 'p': {'label': '$p$', 'unit': ''}, + 'b': {'label': '$c$', 'unit': 'V'}}) + def __init__(self, name, guess=None): + super().__init__(name) self.guess_params = guess + self.fun_str = r'$f(x) = A p^x + B$' + self.fun_np = 'a * p**x + b' - def fun(self, x, a, p, b): + def func(self, x, a, p, b): return eval(self.fun_np) def guess(self, x, y): if self.guess_params is not None: return self.guess_params - + length = len(y) val_init = y[0:round(length / 20)].mean() val_fin = y[-round(length / 20):].mean() a = val_init - val_fin - c = val_fin - # guess T1 as point where data has fallen to 1/e of init value - idx = (np.abs(y - a / np.e - c)).argmin() - T = x[idx] - # guess p as e^(-1/T) b = val_fin # guess T as point where data has fallen to 1/e of init value @@ -221,4 +146,4 @@ def guess(self, x, y): #guess p as e^(-1/T): p = np.e**(-1/T) - return [a, p, b] + return [a, p, b] \ No newline at end of file From 96a398c550e77ae0ae5415914ecf3de47b979b1d Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Thu, 27 Sep 2018 09:43:44 +0200 Subject: [PATCH 089/116] reorganization into fitter, guesses and models --- qdev_wrappers/fitting/fitter.py | 6 +- qdev_wrappers/fitting/guess.py | 92 +++++++++++++++++++ qdev_wrappers/fitting/least_squares_fit.py | 2 - qdev_wrappers/fitting/least_squares_fitter.py | 27 ++++++ qdev_wrappers/fitting/least_squares_models.py | 83 +++++++++++++++++ 5 files changed, 205 insertions(+), 5 deletions(-) create mode 100644 qdev_wrappers/fitting/guess.py create mode 100644 qdev_wrappers/fitting/least_squares_fitter.py create mode 100644 qdev_wrappers/fitting/least_squares_models.py diff --git a/qdev_wrappers/fitting/fitter.py b/qdev_wrappers/fitting/fitter.py index 2721a2dc..c947c8bd 100644 --- a/qdev_wrappers/fitting/fitter.py +++ b/qdev_wrappers/fitting/fitter.py @@ -202,7 +202,7 @@ def get_result(**setpoint_values): kwargs for each setpoints eg 'frequency=1e9, power=-10' Returns: - dict for fit where these conditions are satisfiedxw + dict for fit where these conditions are satisfied """ if len(setpoint_values) != len(self.setpoints): raise RuntimeError('Must specify a value for each setpoint') @@ -270,7 +270,7 @@ def _find_estimate(self, input_data_array, params_values_dict): if params_values_dict is None: return [None] * len(input_data_array) else: - return self.fitclass.fun(input_data_array, **params_values_dict) + return self.fitclass.func(input_data_array, **params_values_dict) def _plot_1d(self, ax, xdata, ydata, parameter_values, parameter_variance, rescale_axes): @@ -280,7 +280,7 @@ def _plot_1d(self, ax, xdata, ydata, parameter_values, parameter_variance, resca pass else: x = np.linspace(xdata.min(), xdata.max(), len(xdata) * 10) - ax.plot(x, self.fitclass.fun(x, **parameter_values), color='C1') + ax.plot(x, self.fitclass.func(x, **parameter_values), color='C1') # set axes labels and title ax.set_xlabel(f"{self.indept_var['label']} ({self.indept_var['unit']})") diff --git a/qdev_wrappers/fitting/guess.py b/qdev_wrappers/fitting/guess.py new file mode 100644 index 00000000..93be54d6 --- /dev/null +++ b/qdev_wrappers/fitting/guess.py @@ -0,0 +1,92 @@ +import numpy as np +import scipy.fftpack as fftpack + + +class Guess: + def __init__(self): + pass + + + +class ExpDecayGuess(Guess): + + """Guess for f(x) = a * e^(-x/b) + c""" + + def make_guess(self, x, y): + + length = len(y) + val_init = y[0:round(length / 20)].mean() + val_fin = y[-round(length / 20):].mean() + + a = val_init - val_fin + + c = val_fin + + # guess b as point where data has fallen to 1/e of init value + idx = (np.abs(y - a / np.e - c)).argmin() + b = x[idx] + + return [a, b, c] + + +class ExpDecaySinGuess(Guess): + """Guess for f(x) = a * e^(-x/b) sin(wx+p) + c""" + + def make_guess(self, x, y): + + a = y.max() - y.min() + + c = y.mean() + + # guess b as point half way point in data + b = x[round(len(x) / 2)] + + # Get initial guess for frequency from a fourier transform + yhat = fftpack.rfft(y - y.mean()) + idx = (yhat**2).argmax() + freqs = fftpack.rfftfreq(len(x), d=(x[1] - x[0]) / (2 * np.pi)) + w = freqs[idx] + + p = 0 + + return [a, b, w, p, c] + + +class PowerDecayGuess(Guess): + + """Guess for f(x) = a * b^x + c""" + + def make_guess(self, x, y): + + length = len(y) + val_init = y[0:round(length / 20)].mean() + val_fin = y[-round(length / 20):].mean() + + a = val_init - val_fin + + c = val_fin + + # find index where data has fallen to 1/e of init value + idx = (np.abs(y - a / np.e - c)).argmin() + # guess b as e^(-1/x[idx]): + b = np.e ** (-1 / x[idx]) + + return [a, b, c] + + +class RabiT2Guess(Guess): + + """Guess for f(x) = e^(-x/T) cos^2(wx/2)""" + + def make_guess(self, x, y): + + # guess b as point half way point in data + b = x[round(len(x) / 2)] + + # Get initial guess for frequency from a fourier transform + yhat = fftpack.rfft(y - y.mean()) + idx = (yhat ** 2).argmax() + freqs = fftpack.rfftfreq(len(x), d=(x[1] - x[0]) / (2 * np.pi)) + w = freqs[idx] * 2 + + return [b, w] diff --git a/qdev_wrappers/fitting/least_squares_fit.py b/qdev_wrappers/fitting/least_squares_fit.py index 94ec3a89..ed0461fa 100644 --- a/qdev_wrappers/fitting/least_squares_fit.py +++ b/qdev_wrappers/fitting/least_squares_fit.py @@ -25,8 +25,6 @@ def __init__(self, name, **kwargs): self.add_parameter(param_name, set_cmd=False, **kwargs) del self.parameters['IDN'] - - def func(self, *args): """ The mathematical function to fit to diff --git a/qdev_wrappers/fitting/least_squares_fitter.py b/qdev_wrappers/fitting/least_squares_fitter.py new file mode 100644 index 00000000..5ef71aff --- /dev/null +++ b/qdev_wrappers/fitting/least_squares_fitter.py @@ -0,0 +1,27 @@ +from qcodes.instrument.base import Instrument +from scipy.optimize import curve_fit + +class LeastSquaresFitter(Instrument): + + def __init__(self, name, model, guess): + super().__init__(name) + self.model = model + self.guess = guess + + for param_name, param_kwargs in self.model.model_parameters.items(): + self.add_parameter(param_name, set_cmd=False, **param_kwargs) + + def update(self, input_data_array, output_data_array): + + # find start parameters, run curve_fit function to perform fit + p_guess = self.guess.make_guess(input_data_array, output_data_array) + popt, pcov = curve_fit(self.model.func, + input_data_array, + output_data_array, + p0=p_guess) + + # update guess and fit results in fitter parameters + for i, param in enumerate(self.model.modelparam_names): + self.parameters[param]._save_val(popt[i]) + self.parameters[param].start_value = p_guess[i] + self.parameters[param].variance = pcov[i, i] diff --git a/qdev_wrappers/fitting/least_squares_models.py b/qdev_wrappers/fitting/least_squares_models.py new file mode 100644 index 00000000..e0b7ad56 --- /dev/null +++ b/qdev_wrappers/fitting/least_squares_models.py @@ -0,0 +1,83 @@ +from collections import OrderedDict +import numpy as np + + +class LeastSquaresModel: + + def __init__(self, model_parameters, model_function): + self.modelparam_names = [key for key in model_parameters] + self.model_parameters = model_parameters + self.func_str = model_function['str'] + self.func_np = model_function['np'] + + def func(self, *args): + + """The mathematical function to fit to""" + + raise NotImplementedError('This is not implemented in the base class.') + + + +class DecayT1(LeastSquaresModel): + + def __init__(self): + model_parameters = OrderedDict({'a': {'label': '$a$', 'unit': '' }, + 'T': {'label': '$T$', 'unit': 's'}, + 'c': {'label': '$c$', 'unit': '' } }) + + model_function = {'str': r'$f(x) = a \exp(-x/T) + c$', + 'np': 'a*np.exp(-x/T)+c'} + + super().__init__(model_parameters, model_function) + + def func(self, x, a, T, c): + return eval(self.func_np) + + +class DecayingRabis(LeastSquaresModel): + + def __init__(self): + model_parameters = OrderedDict({'a': {'label': '$a$', 'unit': '' }, + 'T': {'label': '$T$', 'unit': 's' }, + 'w': {'label': r'$\omega$', 'unit': 'Hz'}, + 'p': {'label': r'$\phi$', 'unit': '' }, + 'c': {'label': '$c$', 'unit': '' } }) + + model_function = {'str': r'$f(x) = a \exp(-x/T) \sin(\omega x +\phi) + c$', + 'np': 'a*np.exp(-x/T)*np.sin(w*x+p)+c'} + + super().__init__(model_parameters, model_function) + + def func(self, x, a, T, w, p, c): + return eval(self.func_np) + + +class Benchmarking(LeastSquaresModel): + + def __init__(self): + model_parameters = OrderedDict({'a': {'label': '$a$', 'unit': 'V'}, + 'p': {'label': '$p$', 'unit': '' }, + 'b': {'label': '$b$', 'unit': 'V'} }) + model_function = {'str': r'$f(x) = A p^x + B$', + 'np': 'a * p**x + b'} + + super().__init__(model_parameters, model_function) + + def func(self, x, a, p, b): + return eval(self.func_np) + + +class RabiT2(LeastSquaresModel): + + def __init__(self): + model_parameters = OrderedDict({'T': {'label': '$T_2$', 'unit': 's' }, + 'w': {'label': '$w_{rabi}$', 'unit': 'Hz' } }) + model_function = {'str': r'$f(x) = e^(-x/T_2) \cos^2(\omega x / 2)$', + 'np': 'np.exp(-x/T) * np.cos(w * x / 2) ** 2'} + + super().__init__(model_parameters, model_function) + + def func(self, x, T, w): + return eval(self.func_np) + + From ff9579c6ec13168492536fe392922e927fad396e Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Thu, 27 Sep 2018 11:17:13 +0200 Subject: [PATCH 090/116] Correct T2 to T1 in class names --- qdev_wrappers/fitting/guess.py | 2 +- qdev_wrappers/fitting/least_squares_models.py | 8 +++++--- 2 files changed, 6 insertions(+), 4 deletions(-) diff --git a/qdev_wrappers/fitting/guess.py b/qdev_wrappers/fitting/guess.py index 93be54d6..fdc5238c 100644 --- a/qdev_wrappers/fitting/guess.py +++ b/qdev_wrappers/fitting/guess.py @@ -74,7 +74,7 @@ def make_guess(self, x, y): return [a, b, c] -class RabiT2Guess(Guess): +class RabiT1Guess(Guess): """Guess for f(x) = e^(-x/T) cos^2(wx/2)""" diff --git a/qdev_wrappers/fitting/least_squares_models.py b/qdev_wrappers/fitting/least_squares_models.py index e0b7ad56..cff5cd7d 100644 --- a/qdev_wrappers/fitting/least_squares_models.py +++ b/qdev_wrappers/fitting/least_squares_models.py @@ -2,14 +2,16 @@ import numpy as np -class LeastSquaresModel: - +class LeastSquaresModel(object): def __init__(self, model_parameters, model_function): self.modelparam_names = [key for key in model_parameters] self.model_parameters = model_parameters self.func_str = model_function['str'] self.func_np = model_function['np'] + arg_list = ", ".join(self.modelparam_names) + self.func = eval(f"lambda {arg_list}: {self.func_np}") + def func(self, *args): """The mathematical function to fit to""" @@ -67,7 +69,7 @@ def func(self, x, a, p, b): return eval(self.func_np) -class RabiT2(LeastSquaresModel): +class RabiT1(LeastSquaresModel): def __init__(self): model_parameters = OrderedDict({'T': {'label': '$T_2$', 'unit': 's' }, From 37359f857e3f469ec50406facb7f9a253c7bed9a Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Thu, 27 Sep 2018 11:37:02 +0200 Subject: [PATCH 091/116] Update RabiT1 model to include second term in equation --- qdev_wrappers/fitting/guess.py | 2 +- qdev_wrappers/fitting/least_squares_models.py | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/qdev_wrappers/fitting/guess.py b/qdev_wrappers/fitting/guess.py index fdc5238c..1bc87dd1 100644 --- a/qdev_wrappers/fitting/guess.py +++ b/qdev_wrappers/fitting/guess.py @@ -76,7 +76,7 @@ def make_guess(self, x, y): class RabiT1Guess(Guess): - """Guess for f(x) = e^(-x/T) cos^2(wx/2)""" + """Guess for f(x) = e^(-x/b) cos^2(wx/2)""" def make_guess(self, x, y): diff --git a/qdev_wrappers/fitting/least_squares_models.py b/qdev_wrappers/fitting/least_squares_models.py index cff5cd7d..63ea3343 100644 --- a/qdev_wrappers/fitting/least_squares_models.py +++ b/qdev_wrappers/fitting/least_squares_models.py @@ -74,8 +74,8 @@ class RabiT1(LeastSquaresModel): def __init__(self): model_parameters = OrderedDict({'T': {'label': '$T_2$', 'unit': 's' }, 'w': {'label': '$w_{rabi}$', 'unit': 'Hz' } }) - model_function = {'str': r'$f(x) = e^(-x/T_2) \cos^2(\omega x / 2)$', - 'np': 'np.exp(-x/T) * np.cos(w * x / 2) ** 2'} + model_function = {'str': r'$f(x) = e^(-x/T_1) \cos^2(\omega x / 2) + (1 - e^(-x/T_1)) / 2$', + 'np': 'np.exp(-x/T) * np.cos(w * x / 2) ** 2 + (1 - np.exp(-x/T_1)) / 2'} super().__init__(model_parameters, model_function) From bdd5586f462ec9625e96f7faf8f98a11ad7ab139 Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Thu, 27 Sep 2018 14:40:35 +0200 Subject: [PATCH 092/116] Add rough plotting from analysis instrument and dataset --- qdev_wrappers/fitting/least_squares_fitter.py | 2 +- qdev_wrappers/fitting/least_squares_models.py | 6 +-- qdev_wrappers/fitting/rough_plot.py | 49 +++++++++++++++++++ 3 files changed, 53 insertions(+), 4 deletions(-) create mode 100644 qdev_wrappers/fitting/rough_plot.py diff --git a/qdev_wrappers/fitting/least_squares_fitter.py b/qdev_wrappers/fitting/least_squares_fitter.py index 5ef71aff..c1c77cb9 100644 --- a/qdev_wrappers/fitting/least_squares_fitter.py +++ b/qdev_wrappers/fitting/least_squares_fitter.py @@ -9,7 +9,7 @@ def __init__(self, name, model, guess): self.guess = guess for param_name, param_kwargs in self.model.model_parameters.items(): - self.add_parameter(param_name, set_cmd=False, **param_kwargs) + self.add_parameter(param_name, **param_kwargs) #set_cmd=False, def update(self, input_data_array, output_data_array): diff --git a/qdev_wrappers/fitting/least_squares_models.py b/qdev_wrappers/fitting/least_squares_models.py index 63ea3343..72f2ab08 100644 --- a/qdev_wrappers/fitting/least_squares_models.py +++ b/qdev_wrappers/fitting/least_squares_models.py @@ -9,8 +9,8 @@ def __init__(self, model_parameters, model_function): self.func_str = model_function['str'] self.func_np = model_function['np'] - arg_list = ", ".join(self.modelparam_names) - self.func = eval(f"lambda {arg_list}: {self.func_np}") + #arg_list = ", ".join(self.modelparam_names) + #self.func = eval(f"lambda {arg_list}: {self.func_np}") def func(self, *args): @@ -75,7 +75,7 @@ def __init__(self): model_parameters = OrderedDict({'T': {'label': '$T_2$', 'unit': 's' }, 'w': {'label': '$w_{rabi}$', 'unit': 'Hz' } }) model_function = {'str': r'$f(x) = e^(-x/T_1) \cos^2(\omega x / 2) + (1 - e^(-x/T_1)) / 2$', - 'np': 'np.exp(-x/T) * np.cos(w * x / 2) ** 2 + (1 - np.exp(-x/T_1)) / 2'} + 'np': 'np.exp(-x/T) * np.cos(w * x / 2) ** 2 + (1 - np.exp(-x/T)) / 2'} super().__init__(model_parameters, model_function) diff --git a/qdev_wrappers/fitting/rough_plot.py b/qdev_wrappers/fitting/rough_plot.py new file mode 100644 index 00000000..2f6d1fd1 --- /dev/null +++ b/qdev_wrappers/fitting/rough_plot.py @@ -0,0 +1,49 @@ +# take least_squares_fitter or baysian_analyzer, and data +#output plot of both +import matplotlib.pyplot as plt +import numpy as np + + +def plot_1d(xdata, ydata, instrument, rescale_axes=True): + + fig, ax = plt.subplots(1, 1) + + params = instrument.model.modelparam_names + parameter_values = [instrument.get(param) for param in params] + #parameter_variance = [instrument.param.variance] + + title = "Run #{} fitted, Experiment {} ({}) \n {} results".format('No run_id', #self.experiment_info['run_id'], + 'No exp_id',#self.experiment_info['exp_id'], + 'No sample',#self.experiment_info['sample_name'], + instrument.name) + ax.set_title(title) + + # plot data + ax.plot(xdata['data'], ydata['data'], marker='.', markersize=5, linestyle='', color='C0') + + # plot model + x = np.linspace(xdata['data'].min(), xdata['data'].max(), len(xdata['data']) * 10) + ax.plot(x, instrument.model.func(x, *parameter_values), color='C1') + + # set axes labels and title + ax.set_xlabel(f"{xdata['label']} ({xdata['unit']})") + ax.set_ylabel(f"{ydata['label']} ({ydata['unit']})") + if rescale_axes: + pass + #data_lst = [xdata, ydata] + #_rescale_ticks_and_units(ax, data_lst) + + # add fit result summary box + p_label_list = [instrument.model.func_str] + for param in params: + value = instrument.get(param) + unit = instrument.model.model_parameters[param]['unit'] + p_label_list.append(r'{} = {:.3g} {}' + .format(param, value, unit)) + #standard_dev = np.sqrt(parameter_variance[parameter]) + #p_label_list.append(r'{} = {:.3g} +/- {:.3g} {}' + # .format(parameter, value, standard_dev, unit)) + textstr = '\n'.join(p_label_list) + + ax.text(1.05, 0.7, textstr, transform=ax.transAxes, fontsize=14, + verticalalignment='top', bbox={'ec': 'k', 'fc': 'w'}) \ No newline at end of file From 28dff4ed007cd29e875c0c9e25ad9bf2b786fa7d Mon Sep 17 00:00:00 2001 From: nataliejpg Date: Wed, 5 Dec 2018 17:28:46 +0100 Subject: [PATCH 093/116] plot saving to be specified in config.user --- qdev_wrappers/dataset/doNd.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/qdev_wrappers/dataset/doNd.py b/qdev_wrappers/dataset/doNd.py index 2d60c35a..6486afa8 100644 --- a/qdev_wrappers/dataset/doNd.py +++ b/qdev_wrappers/dataset/doNd.py @@ -274,8 +274,10 @@ def _save_image(datasaver) -> AxesTupleList: os.makedirs(png_dir, exist_ok=True) os.makedirs(pdf_dif, exist_ok=True) - save_pdf = True - save_png = True + save_settings = config.user.get( + 'plot_save', {'pdf': True, 'png': True}) + save_pdf = save_settings['pdf'] + save_png = save_settings['png'] for i, ax in enumerate(axes): if save_pdf: From af2bf27da31e0068e565c3fccf922b299c77bafd Mon Sep 17 00:00:00 2001 From: nataliejpg Date: Wed, 5 Dec 2018 17:42:21 +0100 Subject: [PATCH 094/116] make requirement for config.user section to be flatter --- qdev_wrappers/dataset/doNd.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/qdev_wrappers/dataset/doNd.py b/qdev_wrappers/dataset/doNd.py index 6486afa8..fb264b91 100644 --- a/qdev_wrappers/dataset/doNd.py +++ b/qdev_wrappers/dataset/doNd.py @@ -274,10 +274,8 @@ def _save_image(datasaver) -> AxesTupleList: os.makedirs(png_dir, exist_ok=True) os.makedirs(pdf_dif, exist_ok=True) - save_settings = config.user.get( - 'plot_save', {'pdf': True, 'png': True}) - save_pdf = save_settings['pdf'] - save_png = save_settings['png'] + save_pdf = config.user.get('save_pdf', True) + save_png = config.user.get('save_png', True) for i, ax in enumerate(axes): if save_pdf: From d47932bf118fe6ea406a176661fa5279fbebc53f Mon Sep 17 00:00:00 2001 From: nataliejpg Date: Wed, 5 Dec 2018 17:47:30 +0100 Subject: [PATCH 095/116] remove plottr import from doNd example notebook --- examples/dataset/Example of doND wrappers.ipynb | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/examples/dataset/Example of doND wrappers.ipynb b/examples/dataset/Example of doND wrappers.ipynb index 3d977ab3..cd08d90f 100644 --- a/examples/dataset/Example of doND wrappers.ipynb +++ b/examples/dataset/Example of doND wrappers.ipynb @@ -24,7 +24,6 @@ "from qcodes.dataset.database import initialise_database\n", "from qcodes.dataset.experiment_container import new_experiment\n", "from qcodes.dataset.plotting import plot_by_id\n", - "import plottr\n", "import qcodes.config\n", "from qcodes.tests.instrument_mocks import DummyChannelInstrument" ] @@ -377,7 +376,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.7" + "version": "3.6.6" } }, "nbformat": 4, From 4eaa5c86d5db842369491cdcd0bc73622838f865 Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Mon, 21 Jan 2019 18:16:43 +0100 Subject: [PATCH 096/116] Restructure and change many things --- Untitled.ipynb | 102 ++ qdev_wrappers/fitting/Example.ipynb | 391 -------- .../fitting/Fitting from saved data.ipynb | 930 ++++++++++++++++++ .../fitting/Fitting while measuring.ipynb | 579 +++++++++++ qdev_wrappers/fitting/RoughPlotting.py | 105 -- qdev_wrappers/fitting/analysis.py | 372 +++++++ qdev_wrappers/fitting/data_extraction.py | 279 ------ qdev_wrappers/fitting/fit_saving.py | 85 -- qdev_wrappers/fitting/fitter.py | 464 ++------- qdev_wrappers/fitting/guess.py | 33 +- qdev_wrappers/fitting/least_squares_fit.py | 147 --- qdev_wrappers/fitting/least_squares_fitter.py | 27 - qdev_wrappers/fitting/least_squares_models.py | 168 +++- qdev_wrappers/fitting/models.py | 94 ++ qdev_wrappers/fitting/plot_analysis_by_id.py | 263 +++++ qdev_wrappers/fitting/rough_plot.py | 49 - 16 files changed, 2570 insertions(+), 1518 deletions(-) create mode 100644 Untitled.ipynb delete mode 100644 qdev_wrappers/fitting/Example.ipynb create mode 100644 qdev_wrappers/fitting/Fitting from saved data.ipynb create mode 100644 qdev_wrappers/fitting/Fitting while measuring.ipynb delete mode 100644 qdev_wrappers/fitting/RoughPlotting.py create mode 100644 qdev_wrappers/fitting/analysis.py delete mode 100644 qdev_wrappers/fitting/data_extraction.py delete mode 100644 qdev_wrappers/fitting/fit_saving.py delete mode 100644 qdev_wrappers/fitting/least_squares_fit.py delete mode 100644 qdev_wrappers/fitting/least_squares_fitter.py create mode 100644 qdev_wrappers/fitting/models.py create mode 100644 qdev_wrappers/fitting/plot_analysis_by_id.py delete mode 100644 qdev_wrappers/fitting/rough_plot.py diff --git a/Untitled.ipynb b/Untitled.ipynb new file mode 100644 index 00000000..eed06f6c --- /dev/null +++ b/Untitled.ipynb @@ -0,0 +1,102 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "User schema at /Users/Lillian/qcodesrc_schema.json not found.User settings won't be validated\n" + ] + } + ], + "source": [ + "from qdev_wrappers.fitting.least_squares_fitter import LeastSquaresFitter\n", + "from qdev_wrappers.fitting.least_squares_models import DecayT1\n", + "from qdev_wrappers.fitting.guess import ExpDecayGuess" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "fitter = LeastSquaresFitter('test', DecayT1(), ExpDecayGuess())" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "OrderedDict([('a', {'label': '$a$', 'unit': ''}),\n", + " ('T', {'label': '$T$', 'unit': 's'}),\n", + " ('c', {'label': '$c$', 'unit': ''})])" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fitter.model.model_parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "odict_items([('a', {'label': '$a$', 'unit': ''}), ('T', {'label': '$T$', 'unit': 's'}), ('c', {'label': '$c$', 'unit': ''})])" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fitter.model.model_parameters.items()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/qdev_wrappers/fitting/Example.ipynb b/qdev_wrappers/fitting/Example.ipynb deleted file mode 100644 index 35a8555c..00000000 --- a/qdev_wrappers/fitting/Example.ipynb +++ /dev/null @@ -1,391 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "no config file found at /Users/natalie/Documents/PhD/Qdev/QdevWrappers/qdev_wrappers/fitting/data/local_scripts/instr.config, will try general\n", - "no config file found at /Users/natalie/Documents/PhD/Qdev/QdevWrappers/qdev_wrappers/fitting/instr.config\n" - ] - } - ], - "source": [ - "import qcodes as qc\n", - "from qdev_wrappers.file_setup import my_init\n", - "# qc.config.add(\"mainfolder\", \"./mainfolder\", value_type=\"string\", description=\"Location of data\", default=\"./mainfolder\")\n", - "station = qc.Station()\n", - "my_init('data', station)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from qdev_wrappers.fitting.Converter import SQL_Converter, Legacy_Converter, data_to_dict\n", - "from qdev_wrappers.fitting.Fitclasses import ExpDecay, ExpDecaySin\n", - "from qdev_wrappers.fitting.Fitter import Fitter1D, Fitter_2Ddata_1Dfunction, fit_data\n", - "from qdev_wrappers.fitting.AnalysisToSQL import is_table, make_table, fit_to_sql\n", - "from qdev_wrappers.fitting.RoughPlotting import plot_fit1d, plot_fit2d_slice\n", - "\n", - "from qdev_wrappers.show_num import show_num\n", - "from qcodes.dataset.plotting import plot_by_id" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Fitting, step 1: Take saved dataset and convert it to a python dictionary\n", - "\n", - "The *data_to_dict* function will load data from the database currently set up in qc.config. It takes the run id of the data as an argument. Here we load a dataset with run id 4 from the SQL database, and store it in variable 'data'." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "data = data_to_dict(4)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This can also be done with the 'old' datasets. To do this, the *data_to_dict* function must also be given *datatype = 'qcodes_legacy'*. This also takes the optional argument *samplefolder*. If no samplefolder is given, then it uses the file path from *qc.Dataset.location_provider*." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "data2 = data_to_dict(621, datatype = 'qcodes_legacy')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Fitting, step 2: Perform fit, get fitted parameters and function estimate in another dictionary\n", - "\n", - "To get a fit, the *fit_data* function takes the following arguments:\n", - "\n", - "#### Required arguments\n", - "**data**: data dictionary returned by *data_to_dict*
\n", - "**fitclass**: fitclass for the function to be fit (options are currently T1() and T2())
\n", - "**function input(s)**: inputs of the function as a dictionary {'variable_letter' : 'data_name_for_variable'}
\n", - "**function outputs**: output(s) of the function, in the same format as inputs
\n", - "\n", - "#### Optional arguments: \n", - "**setpoint_params**: data name of setpoint parameters in list format.
\n", - "**p0**: initial guesses for the parameters, if values other than the estimate found by the fitclass 'guess' method should be used\n", - "\n", - "\n", - "
\n", - "If you are uncertain of the names of the variables in the data, they can be read in the data dictionary, or you can enter some random string and it will throw an error that gives you a list of all the data names in the data dictionary. Similarly, if you aren't sure which variables the function takes, just guess, and it will provide a list of options if you are incorrect.
\n", - "\n", - "An example where the function to be fitted has one input variable, *x*, and one output variable, *y*, with no setpoints used in the measurement, is stored in variable 'fit':\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "t1_fitclass = ExpDecay('T1fit')\n", - "fit = fit_data(data,\n", - " fitclass=t1_fitclass,\n", - " fun_inputs={'x':'pulse_readout_delay'},\n", - " fun_output={'y':'data'})" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The second example is a case where data was taken at multiple setpoints, but the fuction to be fitted still takes one variable *x* and outputs one variable, *y*." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "t2_fitclass = ExpDecaySin('T2fit')\n", - "fit2 = fit_data(data2,\n", - " fitclass=t2_fitclass,\n", - " fun_inputs={'x':'pi_pulse_dur_set'},\n", - " fun_output={'y':'rec_ctrl_demod_freq_0_mag'},\n", - " setpoint_params=['qubit_rs_frequency_set'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The input variables, output variables, and function to be fitted are all attributes of the fitclass, and the variable names stored in the data dictionary, so it is also possible to check which variables are used in the fitclass and which are available in the data:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['x']\n", - "['y']\n" - ] - } - ], - "source": [ - "print(T2().fun_vars) #inputs\n", - "print(T2().fun_output) #outputs" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['qubit_rs_frequency_set',\n", - " 'pi_pulse_dur_set',\n", - " 'rec_ctrl_demod_freq_0_mag',\n", - " 'rec_ctrl_demod_freq_0_phase',\n", - " 'rec_ctrl_raw_output']" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data2['variables'] #all data names contained in the data dictionary" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Saving your fit to an SQL database \n", - "\n", - "If you want to save the analysis information, you can use this function, which will create a new run in the experiment database with the type *analysis* instead of *result*. It will save the run in *runs*, save the relevant units and labels in *layouts*, save an analysis summary in a table called *analyses*. It takes the *fit* dictionary produced by the *fit_data* function as an argument. \n", - "\n", - "It will also save a new SQL table in the experiment with the fitted parameters values and an 'estimate' for each data point based on the fit, as well as an id that matches the id for the corresponding data point in the corresponding measurement run.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Table analysis_4_T1fit created\n", - "Table analysis_621_T2fit created\n" - ] - } - ], - "source": [ - "fit_to_sql(fit)\n", - "fit_to_sql(fit2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plotting your fit to make sure nothing weird has happened\n", - "\n", - "To see how your fit looks compared to the data, there is currently only rough plotting from the dictionary. Rough plotting only does 1D plots. The function *plot_fit1d* plots a 1D fit on a 1D dataset, while *plot_fit2d_slice* makes a 1D plot of a slice through a 2D dataset at a particular set value. Other dimensionalities of data and plotting are not options for rough plotting. \n", - "\n", - "Pretty plotting from the saved data and analysis is for the near future, and will be less picky about which types of plots it will perform.\n", - "\n", - "The first dataset was 1D, so:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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S1h9aSss551xmU1TwcbHKysrMnyfmnHOpI2memZW1Nx0fscM551zG8iDmnHMuY3kQc845l7E8iDnnnMtYHsSSbP57m/jvW2s6OxvOOdct+B25SXbTf95m+foKTty3P1lZjQ0W4pxzLlm8JJZk50wcTPmmSuau2NTZWXHOuS7Pg1iSnbr/QIrysnn0lfLOzopzznV5HsSSrCgvh9MOGMg/Xv+AqhofGMQ551LJg1gKfHLiELZV1fKkd/BwzrmU8iCWAkeO6sPePQt49JX3d02rqK5l3opN/rRn55xLIu+dmALZWWLyhMH8fvYy1m2rpigvm0k3zWJrZQ2lhbk8ec3x/qgW55xLAi+Jpcg5EwdTV2889uoqFq3extbKGip21rGlsoZFq7d1dvacc65L8CCWImMHlHDg4J48Or+ccQNLKC3MpSgvm56FuYwbWNLZ2XPOuS7B67RS6JyJg/nhYwt5f3MlT15zPItWb2PcwJI9qhIrqmubnOecc65pXhJLobMOHkROlvjzvHKK83M4dHjvRgPYpJtmceHUl5h00yzv+OGcc63gQSyF+vbI56Rx/fnzK+XsrK1vdBlvL3POubbzIJZiUw4fyvrtO5m5qPF7xry9zDnn2s4bYFLsuDH9GFhawLQ5KzntgL33mF+cn9Nse5lzzrmmeUksxXKys/hM2RBmvb2OVZsrG12mqfYy55xzzfMg1gE+XTYUgIfn+qDAzjmXTB7EOsDQvYr4yOi+TJ+7krp66+zsOOdcl+FBrIOce9hQ3t9cyXNL1nd2VpxzrsvwINZBThk/gN5FuUyb815nZ8U557oMD2IdJD8nm3MmDuE/C9ewfnt1Z2fHOee6BA9iHei8w4dSU2dMn7uys7PinHNdggexDjS6fwlHjerDn158zzt4OOdcEngQ62AXHjWc9zdXMnPR2s7OinPOZTwPYh3slPEDGFhawH0vvNvZWXHOuYznQayD5WRncf4Rw5j9znqWrdve2dlxzrmMltIgJuk0SYslLZF0bSPz8yU9FOa/JGlEzLzrwvTFkk6NmX63pLWS3ohLa4KkFyUtkDRX0uFh+gmStoTpCyRdn7o9TsyUw4eSmy3++KJ3t3fOufZIWRCTlA3cBpwOjAfOkzQ+brFLgE1mNhr4NXBjWHc8MAXYHzgNuD2kB3BPmBbv58APzWwCcH343GC2mU0IrxuSsX/t0b+kgNMO2JuH561kx05/fphzzrVVKktihwNLzGyZme0EpgGT45aZDNwb3j8CnCxJYfo0M6s2s+XAkpAeZvYMsLGR7RlQGt73BFYlc2eSqaK6liNG7sW2qlr+tiBts+mcc2kvlcOmDwZib4gqB45oahkzq5W0BegTpr8Yt+7gFrZ3FfCEpF8SBeejY+YdJelVosD2TTN7M35lSZcBlwEMGzashU21XcOTnLdW1pCTJf7w7HKmHDaUKHY755xrjVSWxBo7K8ffHNXUMomsG+/LwNVmNhS4Gpgapr8CDDezg4HfAn9tbGUzu9PMysysrF+/fi1squ1in+ScJfH22u28uKyxgqVzzrmWpDKIlQNDYz4PYc8qvl3LSMohqgbcmOC68S4CHg3vH+bD6setZrY9vP8nkCupb2t3Jllin+S8V4889irK5a7ZyzorO845l9FSGcTmAGMkjZSUR9RRY0bcMjOIgg/Ap4CZZmZh+pTQe3EkMAZ4uYXtrQKOD+9PAt4BkDQwtLMReixmARvatWcJqqiuZd6KTVRUf9h5o+FJzvdfcgT/veZ4PnfUCP67aC1Lvbu9c861WsqCmJnVAlcCTwBvAdPN7E1JN0g6Oyw2FegjaQlwDXBtWPdNYDqwEPgXcIWZ1QFIehB4AdhXUrmkS0JalwK/Cm1fPyW0bxEFxzfC9FuAKSFQplRD29eFU19i0k2z9ghkDU9y/txRw8nLyWLqs8tTnSXnnOty1AHn84xTVlZmc+fObVca81Zs4sKpL1Gxs46ivGzuv+QIDh3eu9Flr/3za/xl/vu8cN3J7FWc167tOudcJpA0z8zK2puOj9iRIrFtXz0Lcxk3sKTJZS/5yEiqa+v504srOjCHzjmX+VLZxb5ba2j7WrR6G+MGllCc3/RXPWZACSfs2497X1jB+UcM490NO1pcxznnnAexlGpo+0rEpceO4oK7XuKkX82itq6e0sJcnrzmeA9kzjnXDK9OTBNH79OH4X2K2FoV3UO2pbKGRau3dXa2nHMurXkQSxOSuPLE0ZhBfk5Wi+1ozjnnPIillXMmDmHYXoUM7lXIf64+zqsSnXOuBR7E0kh2lvjKCaNZtr6CV97b3NnZcc65tOdBLM2cM3EIe/cs4NanlnR2VpxzLu15EEszeTlZXHrsKF5evpE57/rAwM451xwPYmloyuFD2as4j9u8NOacc83yIJaGivJyuOQjI3l68TreeH9LZ2fHOefSVpNBTNLWFl7bJL3dkZntTj575HBKCnK4+b/vdHZWnHMubTVXEltqZqXNvEqAio7KaHfTszCXL35kFP9ZuIbXy7005pxzjWkuiH0ygfUTWca10Rc+MoKehbnc9J/FnZ0V55xLS80FsWskHdPcymbmjyROoZKCXC47bhRPLV7Hs0vW7/GATeec6+6aGxLiHeCXkvYGHgIeNLMFHZMt1+Dio0cwdfYyvnDPHHKz5AMDO+dcjCZLYmZ2s5kdBRwPbAT+IOktSddLGtthOezmivNzOOvgQeysrd81MPD8lZu9VOacc7Tyyc6SDgHuBg4ys+yU5aqTJePJzsm0fls1h//0SQD6leQjxLaqGkoLc/nbFcewclOlP3/MOZdROuzJzpJyJZ0l6U/A48DbeIeODtW3JJ9vnzaOeouqF7eFx7Vs3rGT02+ezYVTX2LSTbO8ZOac63aau0/sFEl3A+XAZcA/gX3M7Fwz+2tHZbA7q6iu3VVtePExIxjSu5C/zl9FaWEuRXnZFOXlUFlT588fc851W83VP30HeAD4ppn5IH4drKK6lkk3zWJrZc2uzhzfOnVfvj5tAf/3iQMYO7CUob0LmXzbcwD+/DHnXLfUZBAzsxM7MiNud4tWb2NrZVRtaOHzWQcN4q7Zy/ntzCXM/OYJFORm8+Q1x7No9TZvE3POdUvNVSe+0tLKiSzj2mbcwJJd1YYNpaysLHHdGeNYtaWKe59/F4h6Lx46vLcHMOdct9TcmW8/Sa81M19AzyTnxwXF+TmNlrKO3qcvJ+7bj1ufWsJnyobSuzhv1zoV1bUJlcoSXc4559Jdc2ewcQmsX5esjLg9NZSy4n379HGcfvNsbntqCd87czzQeBtaYwEq0eWccy4TNHez84oEXuUdmVkXGTewlE9NHMK9L7zL8vXRGMyxbWjN9VRMdDnnnMsE/jyxDPWtU/clLzuLH/99IdB4G1pjEl3OOecygdcjZaj+pQV87eQx/N/ji3hq8VpO3Ld/Qj0Vm2prc865TJRQSUzScEmTwvtCSX75ngY+f8xIRvYt5kePLWRnbX3CPRW9R6NzrqtIZNipS4FHgP8XJg0BEhqxQ9JpkhZLWiLp2kbm50t6KMx/SdKImHnXhemLJZ0aM/1uSWslvRGX1gRJL0paIGmupMPDdEm6JaT1mqSJieQ9E+TlZHH9meNZtr6Ce55f3tnZcc65DpdISewK4BhgK4CZvQP0b2klSdnAbcDpwHjgPEnj4xa7BNhkZqOBXwM3hnXHA1OA/YHTgNtDegD3hGnxfg780MwmANeHz4Ttjwmvy4DftbjHGeTEcf05aVx/bvnvEtZuq+rs7DjnXIdKJIhVm9nOhg+ScoBEhr4/HFhiZsvC+tOAyXHLTAbuDe8fAU6WpDB9mplVm9lyYElIDzN7hujRMPEMKA3vewKrYrZxn0VeBHqFZ6R1Gf975niqa+v42T8XdXZWnHOuQyUSxGZJ+g5QKOkU4GHgsQTWGwysjPlcHqY1uoyZ1QJbgD4JrhvvKuAXklYCvwSua0U+kHRZqIacu27duhY2lV5G9i3msuNG8ej893l+6frOzo5zznWYRILYtcA64HXgS0Sj2X8vgfXUyLT4ElxTyySybrwvA1eb2VDgamBqK/KBmd1pZmVmVtavX78WNpV+vnrSGIbtVcT3/vIG1bV+D7pzrntoMYiZWb2Z/d7MPk3UpvSSJfYkzXJgaMznIXxYxbfHMqGasidRVWEi68a7CHg0vH+YUP3YxrQyTkFuNj/++AEsW1/B755e2tnZcc65DpFI78SnJZVK2gtYAPxB0k0JpD0HGCNppKQ8oo4aM+KWmUEUfAA+BcwMAXIGMCX0XhxJ1Cnj5Ra2two4Prw/CXgnZhsXhl6KRwJbzOyDBPKfcY4b24/JEwZx+1NLWbpue2dnxznnUi6R6sSeZrYVOAf4g5kdCkxqaaXQxnUl8ATwFjDdzN6UdIOks8NiU4E+kpYA1xBVXWJmbwLTgYXAv4ArzKwOQNKDwAvAvpLKJV0S0roU+JWkV4GfEpUaIar+XEbUOeT3wFcS2OeM9b2PjacgN4vv/uV1EiswO+dc5lJLJzpJrwMfJepF+F0zmyPpNTM7qCMy2BnKysps7ty5nZ2NNnvw5fe47tHX+fknD+Izhw1teQXnnOtgkuaZWVl700mkJHYDUWlqSQhgo/iwqs6loXPLhnLEyL244bE3+febq6moru3sLDnnXEq0WBLrjjK9JAaw6IOtnHbzbLIEA0oL/JErzrm0kqySWItnNUn9iNqbRsQub2ZfaO/GXepU7KwjLzuLnXX1bNi+k0WrtzX6bDLnnMtkiVya/w2YDTyJPwQzY4wbWEKfHnms2VpFTV09pYVeCnPOdT2JtIkVmdm3zWy6mf254ZXynLl2aXjkyq3nTaQgN4sbHlvovRWdc11OIkHs75LOSHlOXNIV5+dwxkF7850z9mP2O+v500vvdXaWnHMuqRIJYl8nCmRVkraF19ZUZ8wlzwVHDOfYMX358T8W8s6abQmtU1Fdy7wVm7xno3MurSUy7FSJmWWZWUF4X2JmpS2t59JHVpb41acPpigvh69NW0BVTfNNmxXVtUy6aRYXTn2JSTfN8kDmnEtbiT7Z+WxJvwyvM1OdKZd8/UsL+MWnDuKtD7by838tbnbZRau3sbWyhoqddWyprGHR6sRKb84519ESGTvxZ0RVigvD6+thmsswJ+83gIuOGs7dzy3nqcVrm1xu3MASSgtzKcrLpmdhLuMGlnRgLp1zLnGJDDv1GjDBzOrD52xgvg87lZmqauqYfOtzbKio5vGvH0e/kvxGl6uormXR6m2MG1jiN0k755KuI4edAugV875nezfqOk9Bbja/Pf8QtlXV8vVp86mrb/wipjg/h0OH9/YA5pxLa4kEsf8D5ku6R9K9wDyiUeJdhho7oIQfffwAnl+6gZv+03z7mHPOpbMWL7PN7EFJTwOHhUnfNrPVKc2VS7nPlA3llRWbuO2ppRwytDeTxg/o7Cw551yrJVqdeBRwAtFDJ49KWW5ch/rB2ftzwOBSrp6+gPc27Ojs7DjnXKsl0jvxduBy4HXgDeBLkm5LdcZc6hXkZvO7Cw4lS+LyP85r8f4x55xLN4mUxI4HTjWzP5jZH4AziEplrgsYulcRvzl3Ags/2Mp1j/rToJ1zmSWRILYYGBbzeSjwWmqy4zrDieP6c80pY/nL/Pe5Y9ayzs6Oc84lLJH+032AtyS9HD4fBrwgaQaAmZ2dqsy5jvPVk0bzztrt/PyJRezTr5iP7j+ws7PknHMtSiSIXZ/yXLhOJ4lffOog3ttQwVUPLeCRy49m/KDUDpHpN1Q759orkerEucBsM5sFfEB0s/PzZjYrTHNdREFuNndeWEZpQS6X3jeXdduqU7YtH2TYOZcMiQSxZ4ACSYOB/wKfB+5JZaZc5xlQWsDvLyxjQ0U1X7xvLjt2pia4+CDDzrlkSCSIycx2AOcAvzWzTwD7pzZbrjMdOKQnN085hNfLN3PlA/OpratP+jZaM8iwP9vMOdeURBoiJOko4ALgkjAtO3VZcung1P0HcsPkA/jeX9/gO395nRu8Kns0AAAgAElEQVQ/eRCSkpZ+cX4OT15zfIttYg3VjlsraygtzOVvVxzDyk2V3o7mnAMSC2JXAdcBfzGzNyWNAp5KbbZcsrWlE8VnjxzOmq1V/HbmEgaWFnDNR/dNap4aBhluTmy1Y70Zp988m6qaOkoLc3nymuM9kDnXzSUyduIsYJak4vB5GfC1VGfMJU98aaY1J/9rThnLmq1V3DJzCf1KC/jckcOb3U6yexs2VDsaUJibTWVNHTt21mFEAa6lIOic69paPNOEqsSpQA9gmKSDgS+Z2VdSnTmXHLGlmdae/CXx008cyIbtO7n+b29QlJvNJw8dssdy7QmUzYmtdhzau5DJtz0H4A/rdM4BiVUn/gY4FWi4uflVScelNFcuqWJLM205+edkZ3HbBRO55N45fOuRV8nPzeLMgwbttkx7AmVLYqsdE2lHc851HwmdBcxsZVyjvo8Um0ES7UTRnILcbH5/YRkX3f0yV01bQH5ONqfEPL6lvYESEquOTKQdzTnXfSTSxX6lpKMBk5Qn6ZvAW4kkLuk0SYslLZF0bSPz8yU9FOa/JGlEzLzrwvTFkk6NmX63pLWS3ohL6yFJC8LrXUkLwvQRkipj5t2RSN67mmQ8qbkoL4e7Lz6M/QeVcsWfXmHW2+t2S//Ja47n/kuOaFNVot/87Jxri0SC2OXAFcBgoByYED43S1I2cBtwOjAeOE/S+LjFLgE2mdlo4NfAjWHd8cAUovvRTgNuD+lBdKP1afHbM7NzzWyCmU0A/gw8GjN7acM8M7s8gX3ulhK5H6ukIJd7v3A4o/v34NL75vLft9bsmteeQOk3Pzvn2qLFIGZm683sAjMbYGb9zeyzQFUCaR8OLDGzZWa2E5gGTI5bZjJwb3j/CHCyonrLycA0M6s2s+XAkpAeZvYMsLGpjYb1PwM8mEAeXdCaklCvojweuPQIxg0s4fI/zuPx1z9o9/Zbc/Ozc841aDaISRosqUxSXvjcX9JPgXcSSHswsDLmc3mY1ugyZlYLbCEaNT+RdZtyLLDGzGLzOFLSfEmzJB3b2EqSLpM0V9LcdevWNbZIl9baklCvojz++MUjOGhIL658cD5/nf9+u7bf3urIzuAjiTjX+ZoMYpKuAhYAvwVelHQRUVtYIXBoAmk3NrxD/BMXm1omkXWbch67l8I+AIaZ2SHANcADkvYYnt3M7jSzMjMr69evX4Kb6jraUhIqLcjlvi8czmEjenP19AU8NOe9duUhGe12HcXb8JxLD82dLS4D9jWzjZKGEVXpHWdmLyaYdjnRAzQbDAFWNbFMuaQcohHyNya47h5CGucQE2TNrBqoDu/nSVoKjCUand8Fbe3BWJyfwz2fP5zL7p/Ht//8Ohsrarj8+FFJHaIqHaXylgLnXOKaq06sMrONAGb2HvB2KwIYwBxgjKSRoTpyCuFesxgzgIvC+08BM83MwvQpoffiSGAM8DItmwQsMrPyhgmS+jV0CglDZo0B/PHFjWhrSSjqfn8oZx88iBv/tYgfPraQuvpEC86ZydvwnEsPzZ2thki6JeZz/9jPZtbs0FNmVivpSuAJogGD7w5jL94AzDWzGUQjgdwvaQlRCWxKWPdNSdOBhUAtcIWZ1QFIehA4AegrqRz4vplNDZudwp4dOo4DbpBUS3R/2+UNwdklT35ONr85dwIDSvP5/ezlrN1WxU2fmUBBbuaNFZ3o/WrpeOO1P2jUdTeKCj6NzIjawJpkZvc2Nz+TlZWV2dy5XtvYlJZOlHfNXsaP//EWh4/Yi99fWEbPotxOyGXbpGr4rI6QyXl33Y+keWZW1t50mjzCu3KQcm2XyInyi8eOomdhLtc9+jpn3/YsUy86jNH9e3RSjlsnk9u6MjnvzrVVIjc7O7dLIl3xK6pruek/b5OTLVZu3MHHb3uWpxev7YTctl4mt3Vlct6dayuva3CtksgYiQ2BrqqmnsLcbPr0yOcL98zhO2fsxyUfGZnWPRfTta0rEZmYd2/Dc+2VyKNY9vKOEK5BIifK+ED38JeO4vq/vcmP//EWCz/Yyk8+fiCFeenb4SOTBxnOpLx7G55LhkSOmJfCYLp/AB63pnqCuG6jpRNlY4Hu9gsmcvN/3+GWme+wcNVWbrtgIvv0y4x2Mpca3obnkiGRNrGxwJ3A54Alkn4qaWxqs+UyXfw9Z1lZ4upTxvKHiw9jzdYqzv7ts/z9tRbvX0+YDwGVebwNzyVDk13sG11YOhH4I1AMvApca2YvpChvnca72KfWqs2VfPXB+cxbsYmLjhrOdz62H/k5u1cvtqatJL5a6m9XHMPKTZXezpIBvE2s+0p5F/uYDfUBPktUElsDfJVoRI0JwMPAyPZmwqW3ZJ9oBvUqZNplR3Lj44u469nlvPzuJm6eMoGxA0p2ba81bSWx1VL1Zpx+82yqauq8nSUDZFIbnktPiVQnvgCUAh83s4+Z2aNmVmtmc4Fu+YDJ7iRVA93mZmfxvTPHM/WiMtZureLM3z7L1GeXU19vrR5RP7Zaqigvh8qaOn8umXPdRCJB7Htm9qO48Qg/DWBmN6YsZy4tpPphlSfvN4Anrj6O48b05Ud/X8iFd79M76LcVrWVxD7G5fGvH0tPb2dxrttosU1M0itmNrGlaV2Jt4l9qKEktqWyhp4prJ4zM6bNWckNjy0kN1v8z2n7st/AUsbtXdrq7Xk7i3PpL1ltYs2NnXg6cAbRU5IfiplVCow3s8Pbu/F05UFsdx0ZFJavr+B/HnmVOe9u4tgxffnpJw5k6F5FKd2mc67jJSuINVeduIromVtVwLyY1wzg1PZu2GWOjnxY5ci+xTx02VH86OMHMP+9zXz0189w1+xlXf7RLrGaul3AbyNwbk+JVCfmmFm3+tV0p5JYOle9rdpcyf/+9Q3+u2gtBw7uyQ8n78/EYV27J1tTPTN9dAvX1aS8JBae5wUwX9Jr8a/2bth1vlT1PEyWQb0KueuiMn573iGs3VbFObc/zzcffpV126qBrlkyaaojTao72DiXqZq7lPt6+HtmR2TEdbyOGvanPaU9SZx18CBOHNefW2cuYeqzy3jijdV85cTR3PfCu12uZNLUAMuJDLzsXHfU3PPEPghvzwGmm9n7HZMl11E64sSYrGqwHvk5XHv6OD5dNoQbHlvIjf9ahAALr64y7l5TAyxn4gj1znWERH4JpcC/JW0EpgGPmNma1GbLdYSOODEmu7S3T78e3PP5w/j7ax9w1UMLqKs3aurq2bGz61QpNjWKhY9u4dyeWrzZ2cx+aGb7A1cAg4BZkp5Mec5ch2hvz8OW2qVSMchrQxXjK9+bxOXHR0+R/tzUl/nivXN5e423FTnXnSQ8ALCkgcCngSlAiZkdlMqMdabu1DuxPRKtKkx1D8jKnXXc/dxy7nh6Kdt31nLGAXtz5Umj2W/v0qRvyzmXHB1xn1jDhr4s6Wngv0Bf4NKuHMBc4hLtMZfq+8wK87K54sTRPPM/J3LFCaOZ9fY6Tr95Nl+6fy5vvL8lJdvsbF2xZ6ZzbZHIWWU4cJWZLUh1ZlxmSbcec72L8/jmqfty6bGjuPu55dz93HKeeHMNJ4/rz6XHjeKIkXshqVPzmAx+z1jXkM73aGaS1lQn9gcKGj6b2XupylRn8+rExKXzD3FrVQ33PPcuf3huOZt21HDg4J588diRnHHg3uRmJzL2dXqat2ITF059iYqddRTlZXP/JUek3a0RrnmtuRBJl/9DsvPRkc8TOwu4iahTx1qiktlbwP7t3bjLfOncY660IJevnTyGS48dxaPzy5k6ezlfn7aAGx9fxMXHjODcw4bRszC3s7PZ6pNDJt0a4RqXaK/ddPk/pEs+GpNILn4MHAk8aWaHhKc7n5fabDmXPIV52VxwxHDOO2wYTy1ey+9nL+On/1zETf95m7MPHsT5Rwzn4CE9O6WqsS0nh0y8NcLtLtELkXQZkCCdj4dEjv4aM9sgKUtSlpk9JcmfI+YyTlaWOHJUH3oV5VFfbzw6v5y/LVjF9Lnl7D+olPOPGMbkCYPp0YFXmG09OcSWgFNR3ZRu7Z1dTaIXIulS6k7n4yGRAYCfBD4O/B9R78S1wGFmdnTqs9c5vE2sa2rsx1pvxl8XrOKBl97jrQ+2UpyXzekH7s05hwzmyFF9yMpqX+mspQDT3ue1pbKaJ13aYrq7VP8fEm1jzdg2MWAyUAlcDVwA9ARuaO+GnetoTZV6PnfkcD4xYRCPvfYBLy/fwL/eWM0j88oZ1LOAyYcM5pxDBjNmQOuvPBMJMO2tGkxlNU86t3d2ZfHBItX/h0RLWel6PDT5i5E0GhhgZs+FSfXAvZKOA3oBGzogf84lTVM/1orqWk759TO7gs2sb53A80s38Ogr5dz5zDJ+9/RSDhhcyin7DWR0/2JO2Ld/QsEm0QDTnpNDOlfzdKZMLUV2RgeKTB+Xs7nc/gb4TiPTd4R5Z7WUuKTTgJuBbOAuM/tZ3Px84D7gUKKgeK6ZvRvmXQdcAtQBXzOzJ8L0u4lG1l9rZgfEpPUQsG/42AvYbGYTmkvLdS9N/Vjjg827G3Zw1sGDOOvgQazbVs1jr67i0VfK+fWTbwOQkyW+csI+nD1hMKP792hyex0RYDL9BNRejQWrdO5J15LO6kCRrqWsRDT3nx1hZns8N8zM5koa0VLCkrKB24BTgHJgjqQZZrYwZrFLgE1mNlrSFOBG4FxJ44mGt9qfqGv/k5LGmlkdcA9wK1Hwi83XuTHb/hWwJbxvLi3XzTT2Y20u2PQryecLHxnJwUN78dm7XqKypo56M26ZuYRbZi5h7IAeTNpvACeN688hw3qTnaXdTqwdEWAy+QTUHk0Fq3TuSdcSL1m3XnO/qoJm5hUmkPbhwBIzWwYgaRpR+1psEJsM/CC8fwS4VVE/58nANDOrBpZLWhLSe8HMnmkuiIb1PwOcFLONRtNKYB9cN5BIaWbcwBJ6FeWiyujk8qcvHsEzb6/j8TdW8/+eWcbtTy+lV1Eux+zTh+eWbmBnbf2ujhqZcgLNNE0Fq0wOBN29ZN0WzX1DcyRdama/j50o6RJgXgJpDwZWxnwuB45oahkzq5W0BegTpr8Yt+7gBLYJcCywxszeidlGi2lJugy4DGDYsGEJbsp1FS2VZho7uYzq14OLjxnJlsoaZr+zjpmL1vLkwjVsrYrGM6yqqeP7M97gnIlDmDisNwW52R21O91CU8Eq2YEgme1riaTVXUvWbdXcf+Qq4C+SLuDDoFUG5AGfSCDtxvomx/fnb2qZRNZtynnAg63MB2Z2J3AnRF3sE9yWywDJOgk1dXLpWZjLmQcN4syDBrGtsoYTf/U0WyprAHj0lfd5ZN775OdkUTaiN0fv05ej9+nDgYN7kpPioa/i9ztTOzs0pblglaxAkMz2tUxuq0tnzT3ZeQ1wdBiho6EDxT/MbGaCaZcDQ2M+DwFWNbFMuaQcou77GxNcdw8hjXOIOoq0Jh+ui+roE0dJYS6zvnXirhNrnRlzlm/kuSUbeH7pen7xxGIAivKymTC0F2XDezNxeG8OGdY7qUNgxe/33644hsm3PdflTqCpLrUks30tk9vq0lmLR7GZPQU81Ya05wBjJI0E3ifqXHF+3DIzgIuI2qc+Bcw0M5M0A3hAUsOYjWOAlxPY5iRgkZmVx22jLWm5LqAzThzxJ9aT9xvAyfsNAGDD9mpeWLaBOcs3Mu+9Tdz29FLq6g0JxvYvYeLw3hw6vDcHDenJPv16kN3Gm63j9/vJt9b6CbQNktm+lsltdeksZZdioY3rSuAJoi72d5vZm5JuAOaa2QxgKnB/6GyxkSjQEZabTtQJpBa4oqE3oaQHgROAvpLKge+b2dSw2SnsXpXYbFqu60u3E0efHvm7qh4hKjG9unIz81ZsYt57m/jHa6t48OXoARGFudmMH1TKgYN7csDgnhw4uCf79CtOqBoyfr8n7def3858JynfQ1erlmzQ2H61tX0tmWm55iX8KJbuxIed6lrS5aSbSD7q642l67bz+vtbeP39Lby6cjNvrtpKdW09AAW5WYwbWMq4gSXsO7CEfQeUMHZgCX175Le4vWR8D121XSdT2r46upNJKnXksFPOZbR06O2V6IktK0uMGVDCmAElnLr/QCbdNIvsLNGvJJ9vnDKGt9dU8OaqLTzx5mqmzfmw82+f4jzGDogC29gBJezTr5iRfYuZOKzXrtH5k/E9dNV2nUxo++qMQNvZgS4R6Zkr57qYtpzYGtbZsTOq/R4zoJQphw8HwMxYt72at1dvZ/Gabby9ehuL12zj4bkrqdj5YW15cV42I/sVM6JPMaP6FjOibxTcRvXtQc+i1nckSUb1bDqeGDOh7aujA22mlLrTL0fOdUFtObE1t44k+pcU0L+kgI+M6btren298f7mSpavr9jt9Vr5Fv75+gfUx7QelBbkMLh3EUN6F4bX7u8b6y3Z3naddD0xJrO9KlVtXx0daDOl1O1tYo3wNjHXoLPbIJK5/eraOlZubAhw2ynfVBleOyjfVLmrxNegJD+Hwb0L2btnAQN7RgFzQGkBA3vm73rfpzivVY+rSfSxH65xHXk8tvcxQS1JVpuYB7FGeBBzkL6lhlQwMzbtqOH9mKDW8Hf11irWbK1mQ0U18aeLnCzRvySfAT0LGFBSQJ8eefTpkU+f4rzofXF++JtHr6I8qmrqUnpidMmVyqpf79jhXIplSnVKMkhir+I89irO48AhPRtdpqaunnXbqlm9tYq1IbBFAS56LVm3nZeWV7O5smaPYAeQJehdlEevwlz2Ks5lSK8ifvLPt+hZmLvr1Sv8LS3MpVdR9L5Hfs6uzimuY6VDp6iWeBBzHSqdGvVbyku63WPW2XKzsxjUq5BBvZof/7u2rp5NO2rYWLGTDdur2RD+bqzYyfqKnWzcvpMNFdW8s247897bxOYdNdTWN10jlJ0lSgty6FWUR2lhLqUFORTnRQ+LLCnIoTg/O3ofHiBZnJ9Dj/Da9b4gh6Lc7HY/qdulHw9irsOkU/VcRzx1ubvKyc6iX0k+/UrygZYDv5mxY2cdWypr2FJZw+Yd0d+t4fOWyho2V+5kS2UtWypr2FZVw5qtVVRU17G9upbt1bXUNRMEYxXlZVOYm01BbjaF4X1hbjYFedkU5mZFn/PC/NyYvzHLFuZlkZedTV5OVvTKjv7mh1fs9JzsrLS6cOuK/Bt1HSadquc64qnLLjGSdpWgmirlNRcIzIyNFTs545bZbK2spTg/m199+mDqzNhWVUtFdR0V1bVsq65lR3UtVbV1VO6sp6qmjsqaOip31rG1soa1Wz/8XFlTR1VNHTV17eszID4cbTxLMKC0gILc7A+DXQiAudlZ5GaLnKwssrNFbpbICdOys6Lp0fsPl8vJFjkxy+VkZYXPYVpWtG5udrRsdpbIVvQ3KytaNyt8zo55nxPmZ0tkZUG2orSzsthjuXSo5vUg5jpMOlXPpVNeOkNsUADSuqTQUqlZEu9u2MH2qloqa+qQoEdBblIuPmrqPgx2VTvroyBXU8fO2vroVRe9rw6vD6dHf1dsqGDGglXU1BtZEmMH9KCkIHe3ZXbW1rO1sobt1bXkZmVRh1FbZ9TW1VNbb9TWGzV19dTWGXX1Rk19faNtjh0tJ1u8ev1HO/2YSb8j1nVZ6VQ9l0556WixQaFHQQ5CbKv6cLT7lZsq2/ydpKLqLJFSc6ouSqISUhYlBW17wkBFdS3PL92wqzfm7Rccusf30pZq9roQ2Orqo4BXUx8FudrYvw3z6urZVl3L1dMWUFFdS1F+Dj/5xAHkZWdRW2/U1xt1FgXIeovWqTejrh7qLMxveJlRubOOu59dTlVt1NO0s3uYdp9frksL6VQ9l0556UixQaE2jKBfVVNPvRmn3zybqpq6NrVZJrvNsyEgDu1d2GKASteLkkTy1ZZq9qgKMPGHrM5bsYmqmjqqauvJyqqjf0lBm4/9eSs2cdfsZdTUGVsqazq91256/Kedcx0mttRSEkpiW1VDYW42lTV17Ghjm2Uy2zwbex5aQwkRohNpqh6EmWwt5asjqrYzYVittvIg5lw3E186AHaVeCbf9hzQtpNTMk9u8QFx5aZKDh3eO616uCZLR5QiM2FYrbbK7P++cxmqs7tdx5cOGt635+SUzJNbUwExnXq4JlN7S5GJHE/JLKmmU6nXg5hzSdCaoJTOpYn2npySdXJrKiCmW1VWZ4ptM5x823NpeTx1hO6zp86lSGuDUlctTSRbYwEx3aqyOkvsMVfQzrbMTNfyc86dc82KDUoNvbWa01CaKMrL7valibZoCG7dNYDB7sfcjp21FOZmJ+14qqiuZd6KTVRU1yYpt6nVfY8C55KktVVcXppw7RV/zDV1f19r217Tuaq7KemdO+cyQFuCUjo1jKdKZ3de6coaO+b6lxbstkxbAlImVnX7keVcEnSHoNQamXhFn2laOubaEpAyseOMH1XOuaTLxCv6rqYtASkTq7rTP4fOuYyTzlf0iQx+3BWqQtsakDKtViEz/zvOuQ7V3Em9sXnpekXf3ODHDVWeiVaFpmugi89XJgWktkifb945l5aaO6k3Ny8dT6BNDX4cW+WZSFVourb5pWu+UsnvE3PONau5++Bae49cZ4u9R69XUS69CvP2uL8qkfv40nW/0zVfqdS1Q7Rzrt2aa99K57avxjQ1+HFrq0LTdb/TNV+pJEuHR4SmmbKyMps7d25nZ8O5tNHaNrGuKhOeiJ0p/w9J88ysrN3peBDbkwcx51y87tjelErJCmIpbROTdJqkxZKWSLq2kfn5kh4K81+SNCJm3nVh+mJJp8ZMv1vSWklvNJLeV8Pyb0r6eZg2QlKlpAXhdUdq9tY515V1x/amTJCyywhJ2cBtwClAOTBH0gwzWxiz2CXAJjMbLWkKcCNwrqTxwBRgf2AQ8KSksWZWB9wD3ArcF7e9E4HJwEFmVi2pf8zspWY2ISU76pzrFrpje1MmSGVZ+HBgiZktA5A0jSjIxAaxycAPwvtHgFslKUyfZmbVwHJJS0J6L5jZM7ElthhfBn4W1sHM1iZ9j5xzGSdZbUTpeu9bd5fK6sTBwMqYz+VhWqPLmFktsAXok+C68cYCx4ZqyVmSDouZN1LS/DD92MZWlnSZpLmS5q5bt66lfXPOZYCGdqwLp77EpJtmtfvxIv4YmPSTyiCmRqbF9yJpaplE1o2XA/QGjgS+BUwPpboPgGFmdghwDfCApNI9Eje708zKzKysX79+LWzKOZcJvB2r60tlECsHhsZ8HgKsamoZSTlAT2Bjgus2tr1HLfIyUA/0NbNqM9sAYGbzgKVEpTbnXBfnDyDt+lJZJp4DjJE0EnifqKPG+XHLzAAuAl4APgXMNDOTNIOoxHQTUceOMcDLLWzvr8BJwNOSxgJ5wHpJ/YCNZlYnaVRIa1lS9tA5l9a8HavrS9l/1MxqJV0JPAFkA3eb2ZuSbgDmmtkMYCpwf+i4sZEo0BGWm07UCaQWuCL0TETSg8AJQF9J5cD3zWwqcDdwd+h6vxO4KATE44AbJNUCdcDlZrYxVfvtnEsv6TiGo0sev9m5EX6zs3POpVZG3OzsnHPOpZIHMedcWqiormXeik3t7gbvuhdv5XTOdTofl9C1lZfEnHOdzu/ncm3llzrOuU7n4xK6tvIg5pzrdH4/l2srP1Kcc2nB7+dybeFtYs455zKWBzHnXMp593mXKl6d6JxLqe7SfT5Zzy1zrePftHMupWK7z1v43NXavrpLoE5HXp3onEup7vA4FL/PrfP4pYJzLqW6Q/d5v8+t83S9o8k5l3a6evf57hCo05V/0845lwRdPVCnK28Tc845l7E8iDnnnMtYHsScc85lLA9izjnnMpYHMeeccxnLg5hzzrmM5UHMOedcxpKZdXYe0o6kdcCKdiTRF1ifpOxkGt/37qm77nt33W9o/74PN7N+7c2EB7EUkDTXzMo6Ox+dwffd97076a77Demz716d6JxzLmN5EHPOOZexPIilxp2dnYFO5PvePXXXfe+u+w1psu/eJuaccy5jeUnMOedcxvIg5pxzLmN1uyAm6TRJiyUtkXRtI/PzJT0U5r8kaUTMvOvC9MWSTm0pTUkjQxrvhDTzkr2Nrr7vkoZKekrSW5LelPT17rLvMfOyJc2X9Pfust+Sekl6RNKi8L8/qhvt+9XhWH9D0oOSCrrSvkvqo+g3vV3SrXH5OlTS62GdWySpxZ01s27zArKBpcAoIA94FRgft8xXgDvC+ynAQ+H9+LB8PjAypJPdXJrAdGBKeH8H8OVkb6Mb7PvewMSwTAnwdnfZ95i8XQM8APy9u+w3cC/wxfA+D+jVHfYdGAwsBwpj0r24i+17MfAR4HLg1rh8vQwcBQh4HDi9xf1tzZeT6a/w5TwR8/k64Lq4ZZ4Ajgrvc4juSFf8sg3LNZVmWGc9kBO/7WRtozvseyP78TfglO6y78AQ4L/ASbQ+iGXkfgOlRCdydcPf+mBgJbBXWP7vwEe70r7HpHExMUGM6IJ1Uczn84D/19L+drfqxIYDpEF5mNboMmZWC2wB+jSzblPT+wCbQxrx20rWNlojU/d9l1AdcQjwUot728R+NZV2G/LVUfv+G+B/gPpEd7axfWok3T2WSaP9HgWsA/4QqlHvklTcmh1vJu1Gl0mXfTez94FfAu8BHwBbzOzfrdjv3bbZSH72WKYT9r25fJe3kO89dLcg1lj9qiW4TLKmJ3MbrZGp+x6tJPUA/gxcZWZbG1m2ORm575LOBNaa2bxG5iciI/eb6Kp9IvA7MzsEqABa2w6ckfsuqTcwmagqbxBQLOmzjSzbnHTf96a06TzX3YJYOTA05vMQYFVTy0jKAXoCG5tZt6np64FeIY34bSVrG62RqfuOpFyiAPYnM3u0Ffu8x341kp89lkmjfT8GOFvSu8A04CRJf0x8tzN2v8uBcjNrKHE/QnZIpYIAAAcpSURBVBTUWiNT930SsNzM1plZDfAocHQr9nu3bTaSnz2W6YR9by7fQ1rI957aWueciS+iK7xlRFc5DY2T+8ctcwW7N0ZOD+/3Z/cGz2VEjZ1Npgk8zO4Nnl9J9ja6wb4LuA/4TXf7v8fl7wRa3yaWsfsNzAb2De9/APyiO+w7cATwJlBEdOzfC3y1K+17TB4uZs+OHXOAI/mwY8cZLe5vW08MmfoCziDq4bYU+G6YdgNwdnhfEP4pS4h6yoyKWfe7Yb3FxPSaaSzNMH1USGNJSDM/2dvo6vtO1IvJgNeABeHV4oHdFfY9Lv8n0Moglsn7DUwA5ob/+1+B3t1o338ILALeAO5vSKuL7fu7RKWy7UQlsIaejmVhv5cCt5JA5x4fdso551zG6m5tYs4557oQD2LOOecylgcx55xzGcuDmHPOuYzlQcw551zG8iDm0p6kOkkLwqjeD0sqCtOfb83yzaR/j6RPtTOPJ0jaEoZJWiTpl+1JL1n5C/lq9GZZSRdLWhfy/I6kJ5paNm69H0j6Zlvy00Qebm15yUbXHSGpUtKCmGnfDSPAvxaOgSPC9D9J2tje/7NLPx7EXCaoNLMJZnYAsJNo9GvMrKkTbqPLd4DZFg2TdAhwpqRjOmi7zTmB5kd8eMjMDjGzMcDPgEcl7ZfMDMSM5pAKS81sQtjOUcCZRE89OIho9IuGsfsuAGakMB+uk3gQc5lmNjAaQNL2RJcPV+1vNEyU9E1JP4hfWNLPJC0MV/K/DNP6SfqzpDnh1WxwMrNKopuyB4f1iyXdHdadL2lymD5C0mxJr4TX0WG6JN0a8vEPoH9M/k4Oabwe0swP09+V1De8L5P0dBgw+XLg6lAqObaFfD8F3AlcFtLZR9K/JM0L+RzXyPd1adivV8N31FBKvkfSTZKeAm6UdLik50Pen5e0b0wyQ8N2Fkv6flj/R4p5dpykn0j6WnP5JxoFfb2ZVYf9WW9mrR2ezWUYD2IuY4Qr+tOB11O0/F7AJ4iG0zkI+HGYdTPwazM7DPgkcFcL6fQGxgDPhEnfBWaG9U8EfqFoVPa1RI+VmQicC9wSlv8EsC9wIHApoSSl6OGI9wDnmtmBREMBfbmpfJjZu0TDAP06lExnJ/A1vAI0BKv/397ZhGhVRnH895+QEhwnCvpwEQqlMAijjLqQnFlk0kJIkaFgEA23FkpBERSlkdFKISYpjXEhEZazSIKScmQUWkT5AUrjQl2UuXCThUM4nRbn3Lzz9vq+1zDy6vmt7r3Pee59nvvCPe85z8P5f4CXPOoFXgKGmtjvN7PFZtYDnAY2lNrmAsvN7EW8AkVfRKqvA2+X7JYAg3iVjgFJi4DdwLqYdwdetmhvm7F/hTvEcUlDkvorzDepOf9lmJ8kN4vppXWPMfwDd6P2syo851dgAtgVEVChpLwc6NY1kdmZkjrN7HJD/2WSTuAO6B0z+yWur8AL+RbrSPcAj+DFTd+TtACYxD/6AH3Ax2Y2Cfws6Zu4Pg8vDjse53vw+nTbK8ytKoK/VQOWAvtK8767if18SW8B9wIzcA2pgn0xB/Dir3skPYaXEZtWsjtoZpfiufuBx81su6RLkhYCDwI/FDbXw8x+k9QLLMP/LHwi6RUzG64496SGpBNL6sCVYt3j39pLusrUzMM/JN/N7KqkJcAT+D//jbgYZQcu7nelzXPHzGylpLnAEUkjZnYMdwxrzOzHhjG9AVwEeuIZE+XhNLl/K6n28vxuWM6+xEI8ourAdaLavfdhYJWZHZe0Hl+DK/i9dLwVOGRmqyPNOVpqa5xrcb4LLxL7EPBRlcGH0xwFRiWdxKO54Sp9k3qS6cTkTuEi8ICk+2MdaWWjQUQfXWb2BbAJT2+Bp6k2luxaftgjUtoGvByXvgSeV4Q0EV2ARycXzOxPYC1eLRw8DfmspLskPYxHFeApudmSHo3ztcDhOD4H9MbxmtJwLgOdrcZbmlc/vh72oblm21lJA9EmST1NunUCF+RyOYMtbt8F/BTH6xvanpR0n6TpwCrgaFwfAZ4CFjM1wrve+OdFpFewADjfrl9Sb9KJJXcE5tpMW3BV6AO4Q2ikEzgQKcHDwOa4/gKwKDZ7nKLabsedQJ+kOXgUMg04Id9csjVshoB1kr7FU4lF5DICnMHX8t6PsWBmE8BzeIrvJK72vDP6vAnskDSGpyYLPgdWt9jY8Uy0jQOv4hHj6WgbBDZIOo7LgzzdpP9r+Ds9SPN3WvAusE3SUa4564IjeLX2Y8BnZvZdzPcP4BAu4TFJe2bgKctT8Rt24zIuyW1MVrFPkuSWJDZ0fA8MmNmZJu2zcXma+RXvNxz2n97EYSb/MxmJJUlyyyGpG9eh+rqZAwsmga7SJp5W99sL9DN13TG5DchILEmSJKktGYklSZIktSWdWJIkSVJb0oklSZIktSWdWJIkSVJb0oklSZIkteUvEdilLpGwjQMAAAAASUVORK5CYII=\n", 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xAQv2mIuIF8v09QZgK3CbiGRbFLwPGDHGbAA+iTXCHfu8W4FtWPupPm/fD+Cr9rFs/hn4O2PMTuBj9s/Yn7/R/vMB4AsLrf18pCbkp6M+xIunc0dMqZRhJGql8aJTMyu5NEVRlEVRiDA9IiL/HQiKyK8A3wJ+UMB1VwFHjTGdtrDdDdyUdc5NwNfs198G3iQiYh+/2xgzZYw5Dhy174cx5lFgOMfnGaDKfl0N9KR9xtftER5PAjUisrqA9Z93XNZazb48qbyx2LQ7FTeS0HSeoiilSyHCdAcwAOwDfg+4D/irAq5rBU6n/dxlH8t5jjEmCYwB9QVem80fA/9TRE4D/wJ8dBHruCDY0VZD92iMwcmpOe8NRdygl4jWmRRFKWEWFCZjTMoY8x/GmFuwUmFPFWjcKrluV+A5hVybzQeBjxhj2rGGGX5pEetARD5g16b2DAwMLPBRpYlTZ3oxR51pKE2stAFCUZRSppCuvIdFpEpE6oDnga+IyL8VcO8urKm3Dm3MptfmnGPXrqqx0nSFXJvNe4Dv2q+/hZ36K/RexpgvGmN2G2N2NzY2LvBRpcn21mo8Ai/m2EQ7nBYxRRNaY1IUpXQpJJVXbYwZB94JfMUYswt4cwHXPQNsFJF1IuLHama4N+uce5l1KL8ZeNCOxu4FbrW79tZhNS48vcDn9QCvs1+/Ects1vmM37K7814JjBljegtY/3lHOOBjQ1NFTmFKT+VpxKQoSikz3zwm9xy7WeDXgL8s9MbGmKSIfAhr4KAX+LIx5oCI3AnsMcbci5Vu+08ROYoVKd1qX3tARO4BDgJJ4HZjzAyAiNwFvB5oEJEu4G+MMV8C3g/8ux15xbHSjmDVxG7EaqCIAr9T6O9wPnJZaw2PHO7HGIPVR2KRGTGpMCmKUroUIkx3YonLL4wxz4jIemajkXkxxtyHJQzpxz6W9joO3JLn2o8DH89x/LY85/8C2JXjuMHai3VRsKO9mu8820XvWJyWmqB7PLPGpKk8RVFKl0KaH75ljLncGPMH9s+dxph3FX9pylJwHCCyGyCGIglWVQUA7cpTFKW0WTBiEpFGrDTZ2vTzjTHvLd6ylKVy6epKyrzCC11jXL99drvWcCRBa02Q/okpoipMiqKUMIWk8v4v8BjwANZEW6WECfi8bG6unDPeYjiSoL0uRKjMq6k8RVFKmkKEKWSM+Yuir0RZNjY2VfL08UxzjMHJBFesqSEc8Gnzg6IoJU0h7eI/FJEbi74SZdlYUxeiZyzGVNKKjCyfvAR1YT8VAZ+2iyuKUtIUIkwfxhKnuIhM2H/Gi70wZel01IcwBrpGYgCMxy2fvLpwgFDAqxtsFUUpaQrpyqs0xniMMeX260pjTNVC1ynnjo76EACnhqLA7Oba+rCfsF8jJkVRSptCJ9i+XUT+xf7ztmIvSjk71tSFATg5FAFmN9fWhf2EA75528W7RqL84IWF3J8URVGKRyFeef+Elc47aP/5sH1MKVEaKvyE/F5ODtsRk725tr7Cbzc/5E/lfeXxE/zR3c/pMEFFUc4ZhXTl3QjsNMakAETka8BzWOMwlBJERFhTF8qRygtQEfDOm8o7ORTBGBiJJGiqKl+R9SqKoqRTUCoPqEl7XV2MhSjLS0d9yI2YhictYaoNlxHy++bdYHvSFrPByUTecxRFUYpJIRHTPwLPichDWLONXsvsED6lROmoD/PQoQFSKcNQJEFlwEfA57VqTIkZUimDx5M5qiqVMpxyxCyiwqQoyrlhQWEyxtwlIg8Dr7AP/YUx5kxRV6WcNWvqQiSSKfom4gxFEtRX+AEI+70AxKZnCAcy//P3T0wxlbRqS0ORuVNwFUVRVoJCIiaAVwGvwZr86gW+V7QVKcuC0zJ+cijKcGSKurAtTLYYRaaSc4TJ6eIDjZgURTl3FNKV93ng94F9wH7g90Tkc8VemHJ2dNgt46eGogxNJqgLW87i4YAVMeVqgHBqUqDCpCjKuaOQiOl1wHZ7rpHTlbevqKtSzpqWmnJ8HuHkcIThSIId9jiMsN/6T56rZfzUUBSvR6gI+DIm3iqKoqwkhXTlHQLWpP3cDrxYnOUoy4XP66G1NsiJoSjDkQR1do2pwk7f5YuYWmuCNFUG3E4+RVGUlaaQiKkeeElEnrZ/fgXwhIjcC2CMeXuxFqecHWvqQhzoHiOZMtTbNaZQwImY5grTqaEIHfVW04Sm8hRFOVcUIkwfW/gUpRTpqA/x2JFBALcrr8KtMc1N5Z0cjvLWy1YzEk1w6MzEyi1UURQljUKEaQ8QM8akRGQTsAX4sTFmurhLU84WpwECcJsfQv7Zrrx0xmLTjEan6agPIVLc5oczY3Gaq9VVQlGU3BRSY3oUKBeRVuDnwO8AXy3mopTlYY3dMg64qbz0dvF0HPuiNXVh6sIBRmPWqIzlpmc0xqv+6ed8/7nuZb+3oigXBoUIkxhjosA7gc8YY/4bsK24y1KWgzV1acKUtcE2kpXKOzls7WHqqA9RH/ZbfnnR5Y+a+sbjGAPfeOrkst9bUZQLg4KESUReBbwb+JF9zFu8JSnLRbowORtsfV4PAZ9nTvPDSTdiCrnnFiOd5wjiMydG6ByYzHve4b4JXuodJ6ZDDRXloqOQGtMfY3njfc8Yc0BE1gMPFXdZynIQDvhoqAgQn54h4Jv9LpFrvPqpoSgNFQHCAZ+b9huaTMCq5V1T+ud+e28X/+/1W+acE5lK8rZP/4KEPXqjpbqcv/9v23njlmVejKIoJUkhE2wfsVvCP2v/3GmM+aOir0xZFjrqZyMgh1zj1U/YreKAu+epOBGTJUwbmir4zrNdJHPMfTo+GCExk+K916zjT39lEyPRaR56eWDZ16IoSmlSiCXRq0TkIPCS/fMO26ZIOQ+47ao1/D9Xr8k4lmu8+qnhKB126s8RskKMXKOJJN985hSpAhslInYK8bdfvZa+8Sm3nT2d44NWveuW3W384Zs20lEfoncsXtD9FUU5/ymkxvQp4DpgCMAY8wLW6AvlPODmXW38/usuyThWkTVePT49w5nxuNvFVxtKS+UtwE/2n+EvvrOPp44PF7QeRxDfvrOFurCfe/acnnPOCVuY1tZb7e7N1eX0jaswKcrFQkGDAo0x2U+PgirSInK9iBwSkaMiMmfirYgEROSb9vtPicjatPc+ah8/JCLXpR3/soj0i8j+rHt9U0Set/+cEJHn7eNrRSSW9t7/KmTtFzIheyaTQ9dIFGNmHcnLvB6qg2UFpfKcSOap40MFfXZkKolHoDLg479d0coDL/XN+ZzjgxFaqssJ2h2EzVXlGjEpykVEIcJ0WkReDRgR8YvIn2Gn9eZDRLzA54AbgK3AbSKyNeu09wEjxpgNwCeBT9jXbgVuxWpLvx74vH0/sPZQXZ/9ecaYXzfG7DTG7AS+A3w37e1jznvGmN8v4He+oKkIeDMippNpe5gc6sP+goSp345kni4wYopMWXOgRISbd7UxPWO4/0DmeK/OwQhrG2bX0lxdzuDkFInk3HqUoigXHoUI0+8DtwOtQBew0/55Ia4CjtrNEgngbuCmrHNuAr5mv/428CYREfv43caYKWPMceCofT+MMY8CeZ+C9vW/BtxVwBovSrLHqzvC1FGfue+pkBrTGVuYnj01UpBwTE4lXSPZLc2VVJb72Nc9lnHOiaEI69KEabXtEtE/oVGTolwMFNKVN2iMebcxZpUxpskY8xtAIU+IViA9BdhlH8t5jjEmCYxhmcYWcm0+rgX6jDFH0o6tE5HnROQREbm2wPtcsGS3i58cilCR1iYOVgNEIRFT3/gUPo8Qn06xr3t0wfPTBxSKCNtbqtnfM+6+PxJJMBqdzhCmVVWWMJ3RdJ6iXBTMK0wi0ioiu0XEb//cJCL/AByZ7zrn8hzHslu38p1TyLX5uI3MaKkXWGOMuQL4E+C/RKQq+yIR+YCI7BGRPQMDF3ZrcthuF7dHbNE5GGF9Yxgr2LSoCwcKTuW9ZmMDQEENEJNZk3O3t1bxUu8403bbeKfd+LC+MT1iCgKc0zpTZCrJ0X41tlWUlSCvMInIHwPPA58BnhSR92DVloLArgLu3YU1u8mhDejJd46I+IBqrDRdIdfmWrMPyzrpm84xOx3odBTuBY4Bm7KvNcZ80Riz2xizu7GxccFf7nwm5PeRTBmm7NRb50CE9WkRClg1ppHo9Lxt4KmUoX9iiktXV7GxqYKnOhcWpshU0nU4B9jWUk0imeKY7QJxPKsjD3ANX89lZ95XHj/O2z/7eMFt8YqiLJ35IqYPAJuNMa8C3gH8B/BWY8xHjDG9Bdz7GWCjiKyzI65bgXuzzrkXeI/9+mbgQXtS7r3ArXbX3jpgI/A0C/Nm4GVjTJdzQEQancYJ27ViI9BZwL0uWCrSjFxjiRm6R2Osb6zIOKcu7GcmZRiL5TeRH4okSKYMzVXlXL2+jr0nR3JumE0nMjXjTtEFK2IC2N9tpfNODEbweoT2NDulqnIfwTLvOY2YTg1HiSZmiE6rRZKiFJv5hClujBkGMMacAg4bY54s9MZ2zehDwE+xIq17bEujO0XEGS74JaBeRI5ipdnusK89ANwDHAR+AtxujJkBEJG7gCeAzSLSJSLvS/vYW5nb9PBa4EUReQGrweL3nd/rYiVkt2FHEzN0DlqRyiVZwuSYvs43Yt2JYFZVBbhqXT2TU0kO9o7nPR8ymx8A1jVUECzzst9ugDg+GGFNXYgy7+xfTRFhdXX5Oa0xDUxYjSCT8bkDFhVFWV7m88prE5FPp/3clP5zIbZExpj7gPuyjn0s7XUcuCXPtR8HPp7j+G3zfN5v5zj2Haz2ccUmfbx658Dcmg5QkJGr0yXXVFVOa41VB3r6+DCXt9XkvSaSyKwxeT3C1pYqDtoNEJ2DmR15Ds3V5W4H4LlgYNIWpqlpoDRmST10qJ+tq6vc5hBFuVCYL2L6c2Bv2p/sn5XzlPTx6p0DEUSYIwazwpS/ZfzMmPVec1U5q6rKWVsf4skF6kyRrOYHgO0tVRzoGWMmZTgxGMmoLzk0VxUvYvr6Eye44zsvznuOEzFNlEjElEim+N2v7eErj58410tRlGUnb8RkjPlavveU85v08erHBiZprQlSXpY5yaTennjrpPLuP3CGf/7pIX7wode4jgxOKq+x0jr3qnV1/PRAH6mUweOZ21g5lZxhesZkND8AbGut5mtPnOSp40PEpmdY15g7Yuobj+e999nws4N9HOgZ55/elfv9mZRh0LZnyvYYPFf0jsWYSRl3g7OiXEgUZEmkXFikT7HtHJyc0/gAUBsuA2DYfiB//uFjHO2f5FDfbMt0/0Schgq/Ww+6el09Y7HpjHPScWYxzY2YqgH4wQtWT012hyBYm2yTKcNgAZt+F0vvWJzRaCJvx91INOFO8y2VGlP3aAyYTTEqyoWECtNFiNMV59SYcglBwOelMuBjKJJgf/cYz5+2Ns8eThOdM2PxjPrGK9bWAbD35EjOz3Ue6tnCtHFVBX6vh5/st4QpV42pWJtsjTH0jsZImfxpOieNBzBRIhFT94gtTBOlL0yplOH0cPRcL0M5jyhk7EXdSixEWTkcYegciBBNzHBJ09yICay5TEORBN946hTlZR78Pg9H0oSpb3wqQ5ja64I0VAR4Np8w2Q/1iixhKvN62NxcyUh0moDPQ3OOYn6xNtmOx5Ouoe1wnlHy/WkP/1KJmHpGrX8PgwU4wJ9r7j94hjf8y8PnhYgqpUEhEdNTIvItEblR0q0BlPMWp13csRC6JEeEAlYDxKmhCP/3+W7evqOFjU0VHO6bHYfeP5EZMYkIuzpq2JNHmJxZTNkRE8zuZ1rXEM5ZQyrWJtvesZj7eiSPMKU/UEulxtQ9akUgw5EpN81YqpwYipJMmaIMnlQuTAoRpk3AF4HfBI6KyD+IyBznBOX8IeDz4PMI+7qsvUO5akxguT+80DVGNDHDu6/uYNOqSjdiSiRTDE4mWFUVyLhmd0cdp4ajOQ1XZyMm75z3ttl1plxpPGctZV7JiJj+7Fsv8KH/epZkpmuaAAAgAElEQVQnO4dce6XF0js6e7/RBYTJ55GSESYnYkqZ4kwaXk6G7DqY88VEURaiEBNXY4z5mb1/6HexnBqetg1RX1X0FSrLjogQDvgYjycJ+71zxMXB6cy7rLWaHe01bFxVQc9YnPH4tFt0z95Dc2VHLQDPnpxr6OqM2sgdMc0vTB6P0FQ52zJ+fDDCt/d28eP9Z7j1i0/ylk8+mre2NR896RFTJLfLRf9EnIqAj9qwv2TaxbtHY/h91v++pZ4ic9KNsYS6ZiiFUUiNqV5EPiwie4A/A/4QaAD+FPivIq9PKRJhO523vrGCfBnaOtv94d32aPZNTZUAHOmbdFNq2fWg7a1V+L0e9p6cu5/JFSb/XGG6dHUlr93UyBu3NOVdc7r7w337rEaJn33ktfzzzZczODnFFx4+lvfafKRHTPOl8horA1QG5o6kPxcYY+gejbGtxUp/DpZ4Z56zvqgKk1Ig8zk/ODwB/CfwjnQPOmCPToM9f3GilmzHh3SuXFPL1tVVvH1nCwCbVjnCNEF10Gonb8qKtgI+L5e1VeeMXibtdvHs5gfnuq+/96p517yqutx1iPjRi71cuaaG9Y0VrG+s4Kf7z7gt1IuhZyzGanuP1ELCFJ+eYTKe3ztwpRiKJEgkU+xoq+G5U6MlL0xDdsQU1VSeUiCF1Jj+yhjzP7KMUW8BMMZ8omgrU4qK4/6Q7ZGXzq9sXcV9H76WkB3htNUGCZZ5OZwWMeWyw9ndUcv+7nHiWYan86XyCmF1VTm9YzFODEY42DvOjZetdt9rqQnSPbL4luTe0TgtNUFqQpabei4cYcqeY3WucFrFd7Rb6c9ST+U5Ayc1lacUSiHCdEeOYx9d7oUoK4vTgDBfxJSNxyNsaKrgSP8EfRNTlHmFupB/znlXdtSSmElxoCdzMm1kKonf63FrI4ulubqc+HSKu545BcANacLUWhtkPJ5kYpERTa8dMdWGyuZtfmissISpFGpMPXZkuLGpkvIyT0lHTMaYtIhJhUkpjLxfXUXkBuBGoDXLzLUKOPf/dypnhVPnWd+QP2LKxcZVFTx+dJDGigBNleU5W7uvXGM1QOw5McKujtltcJNTSSrKlxYtwWzL+F1PnWJne41rHAu4r3tG42xuLivofsYYesfivGVbM2fG4jmbH2KJGSamkjRWBhiPT5dGxGQLU1ttkMbKQElHTOOxJEm7nT2mI0OUApnvq2sPsAdrjHq6eeu9wHXFX5pSTMIBX07z1oXYvKqSvvEpDvdPzKkvOTRWBlhbH5pTZ7IMXOe2ihfKaluYxuNJ3poWLYGVyoPZ/T2FMBxJMJVMsbq63E7lzY2YnGikqQipvMHJKW774pNzXBFmUoZ/vf9Q3ppZ92iMsN9LdbCMhopASW+yTbeQ0hqTUijzmbi+ALwgIt+wZyspFxCXtVYzODnlGrIWitMAcaBnnOu3Nec978qOWh49PIAxxu36m8waErhYmqtnI6QbLsv87LZaR5gK34Dr7IlaXR2kLlzGvu65D3hnP5ZbY4onM36ns+HH+8/wROcQTxwbyhiMeLR/ks88eJTesTj/csuOOdd1j8RoqQkiIjRUBEra7mdwIl2YNGJSCmO+0er32C+fE5EXs/+s0PqUIvHe16zjP9939aKv27jKSv0Zk7vxwWFXRy2DkwlODs0+NCNZQwIXS1NlABHY0V5DW20o473GigBlXnEbAwrBqdW01JRTazc/ZG/UddJkjZUBKsozR9KfLQ+93A9AV1bThiM0977Q425OzVj3WIxWW4hLPZWXPmhSmx+UQpnvKfFh+59vW4mFKOcHrTVBwn4vkcTMvMLkODkc6Z9krZ0ujCSS1OZoliiUMq+Hd1+9hms3Ns55z+MRVlcHF9Uynh4x1YT8JJIpYtMzbhcizApTU2U5lbaoTsSTc8aELJb49Ay/PDYIwOksMT1tC1UimeLuZ05z+xs2ZLzfPRJzhzE2VAQYjiZIzqTweUvPk9kR1oqAz/UkVJSFyPs32RjTa798J5A0xpxM/7Myy1NKDRFhg53Oy+cYAbP1oPSps9lj1ZfC37/jMq7Lk0JsrQm6UVAh9IzF8Hs91If91IbsMR9Z9j79E1N4xPINdBo3lqPO9MSxIeLTKcrLPHNScaeHYwTLvLxmQwP/58mTJGdmI7RoIslIdNpt9misDGBK2JZocDKBiPXfJqY1JqVACvmKVQXcLyKPicjtIrKq2ItSSptNthv5fBFTQ0UAr0c4k2b5c7bNDwth7WVaRMQ0Gqe52uosrLUn9o5m7WUamJii3v5dKgKWeC2Hw/iDL/cTLPPylq3NdOWImNrrgvz2q9fSOxbn/oN97nuOR54rTLY7R6nOZRqKTFEbskRda0xKoRTilfd3xphtwO1AC/CIiDxQ9JUpJcvmZidiyi9MXo/QVBlwx6+DNShwqZtrC6G1NkjfRJxEgTUgZw8T4KYYszvznD1MMOtYMTF1du4PxhgefLmfazY0cEljBX0TcaaSsw/t08NR2mtDvGFLE+11Qb6aNj7dSVU6NaYGe22l2pk3NJmgocJPyO9VYVIKZjFJ6X7gDDAE5Dc0Uy543nllG3/11ku5ZIHNuc3V5ZwZtx6kxhgiibNP5c1HW00QYwofjdFjuz4Abiov2/2hf2LKbYuvdFJ5ZxkxHemfpHs0xhu2NNJWa63ZifSMMXSNxGirDeL1CO951VqePjHsblaebdiYTeVB6bo/DE5OUR8OECzzavODUjCFmLh+UEQeBn6OZd76fmPM5cVemFK61IX9/O616xdsmW6umjVdjSZmMGbpdkSF4Dyss1NjuZhJGfrG427EVONETJGFI6azrTE9aHfjvWFzk9sm7qx5NGpt4nWO37K7ncpyH3f+4CCplKF7JIbXI6yyBWk2YipNYRqaTFDvREzTF0+N6Ycv9nDzF35JaoFZWUsd13KhU0jE1AH8sTFmmzHmb4wxB4u9KOXCoDnNDfxsffIKodXdy7SwMA1OTpFMGVbbYlbjRkyzwpRKGQYnp9yoZLmaHx58uZ8tzZW01ARpr7M+3+nEc/7ptMNXB8v467du5anjw3zj6VP0jMZorip3O/DCAR8hv/esIqZvPHWS//1Y59n8SnkZnJyioSJAKOC7qCKmu58+zZ6TIxnt8tn88MUedvzd/ec82u0fj7v/f5YKhdSY7jDGPC8iTSKyxvmzEotTzm+aq8qJJGaYSLPyyTUkcLlwop9COvPclJh9TZnXQ2W5L6P5YSSaIJkyNFVm1ZjOIpU3Fptm78kRd7xHU2U5ZV7h9LC1HidycgQL4JbdbVy7sYF/uu8lnu8apaUms7ZnuT8s/eF299OnuWfP6SVfn49EMsV4PEl92E+o7OKpMUWmkjx93Br7ku9LUnx6hn/40UuMx5M5R8SsFMYY3vmFX/KJn7x8ztaQi0JSeb8qIkeA48AjwAngx0Vel3IB4HjbnRmLE7FHXpyN88NClJd5aagIFNSZl76HyaE2y5bI6XRrrLR+j4DPQ5n37KbYvtg1ykzKcM2GBsBqEmmtCbqbbJ3W8XQnCBHhH995GQCdA5EMj0CAhgr/WQlT92isKO3mzj3rKwKE/F5i0zMLprYuBJ7sHCJht/jn+7v4lcdP0DMWRwSeOz13qOZyYIzhq48f5+RQJO85feNTdI3EOHRmoihrWCqFpPL+HnglcNgYsw54E/B4UVelXBA4QwTPjMfTIqbiCRNY6bz0b6nff66bH+/rnXNeuuuDQ22oLKP5Id31ASyBcGyJ8rHQg9f53DVpwtNeF3I32Z4eiVIdLKOqPNOItq02xF/csMVec6YwnY37QzSRZDiSYCQ6veyi4YhlQ4WfoN+HMRBPXvhR08OHBlwH/VzR+3AkwecfOsqbtjRxeWs1z58qjjCdGo7ytz84OG80vK/baqoppC67khQiTNPGmCHAIyIeY8xDwM4ir0u5AHCikd6x+IrUmMDqzHMeBpNTSf7ye/v49INH55zXOxYnWOZ1Bx4C1Ib9Gc0P2cIEVp0pX8R099On2HHn/fTP0xXYM2p9S05vtW+rDdI17ERMsYw0Xjq/cXUHf37dZm7e1ZZx/GyMXJ1v9DMpw1hseYcgOsLkRExw4fvlGWN4+HA/r93YQGXAlzOV95kHjxBJJLnjhi3sbK9hX/cYM0WIJJ84NgSQsWUjm31dlij2jMUK3maxEhQiTKMiUgE8CnxDRP6dAsdeiMj1InJIRI6KyJy5TiISEJFv2u8/JSJr0977qH38kIhcl3b8yyLSLyL7s+71TRF53v5zQkSeX+heSnFx2qz7xuJEEisjTC015XSPxjDG8H+f7yaSmOHYwOSc//G7R2KsrinP6CzMTuX1T8w6iztUBMpy1piO9k/wN/ceYCKedLvucnFmLE5DRSBjJlVbbYihSIJoImltrs3yAXTweITb37CB9VnDHRsrAwxHEkzPLP7B0pX24ByKLG8R3pnDZEVMljBd6A0QnYMRTg/HeN3mJlprg3MikVNDUf7Pkyf59Ve0s3FVJTvX1BBNzHCkf/lTaU90WsI03/YJJ2IyprDa7EpRiDDdBESBjwA/AY4Bv7rQRSLiBT4H3ABsBW4Tka1Zp70PGDHGbAA+CXzCvnYrcCuwDbge+Lx9P4Cv2scyMMb8ujFmpzFmJ/Ad4LsF3EspIuVlXurCfnpXMpVXE2QqmWJwMsH/efIUIlYR/lSW7c/hvgk2NmU+4GtCZRnND10jUSoCvgwxrQz4mMzaYDuVnOGP7nqecMBHY2WAhw7lF6aesZjbcOHg1JNOD8foGoll1JcKwWkZX0qdKL0GMrTMm3QdoTvbiClXS7Uxhm8+c8p1fy8VHjk0AMDrNzVaTiRZD/sf7etlesbw4TdtAmBnuzW7bLnTecYYfnlsfmEyxrCve9xNK2f/P3Iumc9dfIOIXGOMiRhjUsaYpDHma8DzQE0B974KOGqM6TTGJIC7sUQunZuAr9mvvw28SayvsDcBdxtjpowxx4Gj9v0wxjwK5G1jsa//NeCutM/IeS+l+DRXlVsRk5vKK+53glY72rhvXy8v9Y5zi532OtI3+400lpjh+FCELc1VGdfWhvxMTiXdlMbek6PsbM/8q54rlfdv9x/mYO84//yuy3nzpav4xZHBvGmR3rF4RsMFzI7sePbUCIlkivba3Km8fDjCtJQ6U/qDc7kbIIYmEwR8HsJ+b5owLa5xpH88zra/+amblnI4NRzlL76zj+89271s610OHj48wPrGMO11oZzejUf6J1hVFXAbg9bWh6gOlvH8MjdAHBuIMDAxRWW5L8OvMp2+8SkGJ6fcETKnR84DYQI+BeSKL6P2ewvRCqRX3brsYznPsWc+jQH1BV6bj2uBPmPMkUWsQykSzdXl9I7FmVyBrjyYbWb4zINHCfu9/NlbNgOW24LDob4JjIFLV2cJk+uXl2AsNs3LZ8Z5xdq6jHOymx/2nhzmi4918huvXMObt67iDZsbiSRm2JOjBdgYQ++olUJMx0ndOd9w2xYZMbnuD0vozOseibmiMd+em6UwOJmgoSKAiLiO7YtN5e3vGSOamOEXRwcyjh/sGQdKy7w2lpjhyc4hXr/J2grQWhtkLJY59fhY/yQb0iJ1EWFHe82yC5OTxrtx+2om4smcXwhetOtLb750FX6v5/yImIC1xpg5c5eMMXuAtQXcO5ctQHZMnu+cQq7Nx23MRkuFrgMR+YCI7BGRPQMDAzkuUZZCc3U5ffYGvpDfm3MU+3LSVmM91Acnp3jHFa00VZXTWhPkcFrE9FKv9VDbmi1MabZEz54cwRh4xbrajHOyI6ZHDlujKz56w6UAXLOhgTKv8PChuX+HxuNJIokZWqrntnuXl3ncqCBfjSkfjjPF4BIipq6RKNtarH8Pyx4xRaZosE1ml5rK6xywWp33d49nHD9QgsL0ZOcQiWSK12+2xrI4bf3pdlPHBiJsyKoR7myv4XDfxLJucn3y2BCrq8u5er31xapvfO7fjf3dY3gEtrdU2w0450eNKb9DJxSSa+gC2tN+bsMa157zHBHxAdVYabpCrp2DfY93At9c5DowxnzRGLPbGLO7sXHuvB9laTRXlTMUSTASSRS98QGgKuhz61jvvroDsIYbHumbjZhe7h0n7Pe6KTSHdCPXZ04M4/MIV7RnClNlwJfR/HBi0NpX5Pxu4YCPq9fVu0MA0+m1ndabs2pMIkJbbcjtYste10I0VFrrfu70KD872Md39nYVXMjuHo3RUR+msty37A/5wUnLmR3ShGl6kcI0aAnTgZ6xjFrTQfvLRbbp7lL4x/te4j8ePXvni0ePDFBe5uGqdZYYOG393aNWJOJsm3DGxjhc0V5Dysw2IpwtqZThyc4hXrW+fnbLxtjcdN6+7jE2NlUS9HtpqwudNxHTMyLy/uyDIvI+YG8B934G2Cgi60TEj9WAcG/WOfcC77Ff3ww8aKy/ffcCt9pde+uAjcDTBXzmm4GXjTFdWZ+xlHspy4DzED42GCl64wNYD/l1DWGuXFPDVjsS2NhUkdGZ91LvBFtWV82J3hxbolFbmLa3Vs8ZPV8R8DGVTLk1pBNDEdY1ZJrZvn5zI0f6J+fMWeq1R1ZkOzcAbl2psTKw6CGEIb+P2lAZ//XUKd7/9T386bde4B9/vPBO/kQyRf/EFG21wbN2j8jF0GSCejs9GnRTeYuLCjoHrC8Ug5MJt0sSljeVd+8LPdx/8MxZ3+fJzmF2ddS6//3aXIss67+78+UoO2K6vM0aqrlc6bzD/RMMRRK88pJ6mmxhym6AsBofxtjean32mrrgeVNj+mPgd0TkYRH5V/vPI8DvMjvdNi92zehDwE+Bl4B7jDEHROROEXm7fdqXgHoROQr8CXCHfe0B4B7gIFYn4O3GmBkAEbkLeALYLCJdtlA63EpmGm/eeynFx/nG1tk/WfTGB4fPv/tK/tdv7nJ/3riqkqlkitPDUYwxvHRmnC3NlXOuq7MfomfG4rxwesz95puO45cXmUpijOH4YISO+szU2xtsu6GHD2em83K5TTg43niLbXxwuOsDr+Tr772Kez90DW++tIm9Jxa2uekdi2GMlXKqC/uXNWIyxtgGrnbEVLb0VJ4j/PvtiGJocsot6Ge7wS+W6ZkUfePxnKmuxTAaTfDymXFeua7ePdZYEcDv9bipvKN2nXNDVjdofUWANXWhZevMc1LCr76k3v1imC1MZ8bjDE4mXFFsrw0xGp1mPL68e9mWSt6vsMaYPuDVIvIGYLt9+EfGmAcLvbkx5j7gvqxjH0t7HQduyXPtx4GP5zh+2zyf99uLuZdSfBz/uompZNEbHxyy262dtvDDfRP4vMJEPDmn8QFmU3mPHB4gMZNid0ftnHPSHcYNlm/e2vrMiGl9Q5g1dSEefrmf33xlh3u8dyyGRzL3Rc2uOZhz7YWypbmKLfZg32s2NPDAS/30jMbmuESk4zwwW2stYcqO8M6GiakkiZmUW2MKLqHGNBGfpn9iig/tbudzDx9lf/c4b7p0lZvG29hUUfCIk3z0jcdJGeifiGOMWdAxH+D4YIT+8ThXr58VoaeOD2MMvPKS2WMej7Da3lcHcHRgkupgmfvvJJ2d7TU8U8CXiUJ44tgQ7XVB98tORWBuZ96+LkvkZyMmZ8tClG0t1cuyjrOhEBPXh4wxn7H/FCxKigKwKq2eshKpvFxstHP6R/oneanXaoLIJUzlZV7Kyzw8ftT6xpndkQezM5km4kmO2/WP7FSeiPCGzY08fmyQeFpNpWc0zqo0Z/B02t2IaWnClM4uW1CfPTUy73nO5s+2mhD1Yf+crrxPPXCY3/zSU0tag7Mnqt5+CAd8HjyyuHZx59/v9tZq1jeE2W/PpHIaH67Z0MB4PFnwxuJ3feGX/PsDRzKOOROB49OW4WwhfObnR/jtrzyT0azwVOcwAZ/HjUAcWqqDdNspsqP9k2xsqsgpfjvba+gdi5+10M6kDE8dH+ZVaaLZVBWYc999duOD0wCUvpeuFFjMoEBFWTSVAR9h+9vySjQ/5KIi4KOlupwjfRNuR97mHKk8sKKmxEyKjU0Vbvt45r3s8epTSdccc23D3IGJ125sJD6dyqgbpE/MzcZ5MKxZYsSUzqWrqygv87D35ALCNGpFcM3V5dTZdkzpfnlPdg7xVOfwkjz0hhw7ovCsz2DIv7jx6k5H3iWNYba1VLt1pYM947TWBFlvD6ocLSCdl0imeO7UyJw2fqcxAZjXSiqdoUiC2PQMP0sbef9k5xC7OmoJ+DLT1a21QVf8jma1iqezo90SNCeSWSrPnx5lLDbtmgSDvZcwK1WZ3vgA6cJUGnUmFSalqIiIm+c+V8IEVtR0pH+Sl8+M01Efyhu9Oem8V+SoL0H6TKZpTgxG8EjuKOeKNdbG3Exhirvzn7LZ1lLFJ951GW/bsbrwXyoPZV4PO9pqeHYBYeoeibGqqhy/z0Nd2E8yZTJqDKeHYyRmUhlNB4Uya+A6m7YM+Rc3xbZzYBKPwJr6ENtbq1wX9IO941y6usqtCRbSmdc9GiNlrGaVdBzRAAr+PR1Pwe8+Z23uHYtO89KZcV6ZFqU4tNYE6ZuI0z8eZziSyCtMl66uQgQ3KlwqD7zUh88j7l4qyBzYCVb9b39a4wNgGwf7SqYBQoVJKTqOMBVzFtNCbGyq4Gj/JAd6cjc+ONSGrYjoFWvn1pcgcybT8aEorbXBDN87h/qKAB31swVtYww9ozFWV+WOmESEX3/FGncj6tmyq6OWAz3j8wpB92jU3WvjCIiTzkskU257+1IeVoNZqTywhGkxEdOxwQhttSECPi/b7brHnhPDdA5MsrWlijr7S0QhTRtOdNs9kmlWmu58UWgazRHvXxwZoH8iztMn7PpSLmGqDWIMPHbE2u92SR5hCvl9XNJYMWe/1mJ54GAfV62rozo0a07cVFVO/0TcjXwtx4cEl7VmprPX1JdOy7gKk1J0mqush9+5jJg22Z15J4eiOetLDs6I9Vz1JZitMU1OJTkxGJnT+JDOzvYanjttRS2j0Wmmkqm8EdNys6ujlmTKuLv7c9E1EnOn/jrRh/OQ77EjDFhaemfUjmJq0h6QwSWk8px0nVOQ/86zXaSMVRtxUq0jBQiT88BNmUwx6hmNsd5OxRbamTcem+aqdXWkDPzghV6e7Bwi4PO46bh0HOF/xO7QzPZnTGd7SxUHziJiOjEY4Uj/JG++dFXG8eaqANMzxo0snc9Ij5jAivw1ladcNDRXZ06APRdsWDX7QJhPmLa1VLG9tcrtaMomPWLKtYcpnZ3tNfSNT9E7FqNnLHNibrG5Yo0V8e3N0wAxkzKcGYu7e20cYXKaFtK/OS/lW/RodJqQ35tRc7GGBRbWYJBKGY4PTrK+wfrvVh0qo70uyM9fsjYub2uZTeUNF5DKOzU0+zukD87rGY2xoamCioCvIENYY6zxIFeuqeWy1mq+/1w3T3YOceWaufUlmBWmx44MECzzznH9SGd7azW9Y/El7yd74CWr5jVHmKpn56KB5aIhMvf/gzX2XLBSGOaowqQUnebqcx8xpX9TvbQ5vzD9wes38IMPvSbv+yG/FxHrYT0RT9IxT8TkiMPzp0bdzbUrFTHVhf2sbwznrTP1jcdJpgyttoWTk3JzIiZHjPw+z5I6tUai0269zmExqbwz43Hi0yk3YgLYtrqaZMpQWe6jrTboRmOFREwnh6OukJ20RcoYQ7cdNTZVBegvIGKKT6eYnjFUBX2844pW9nWPcaAnd30JcH0RR6LTXNIUnteSy4kKna7DxfLAS31sXlXJmqx9ddmbbPf3jLGuITzn/8e2uhCJZGpJnovLjQqTUnScTbbnUpgqy8toqS6nIuBb0PJnvr0szhRbZ7Pnuob8XXSXrq7E7/Xw3OlRt16zUhETwK41tew9OZJzbISTzpqbyrMeSqeHo/h9Hra3VC2pxjQWS2Sk8QCCZYU3PzgdeenCtN2uiWxdXYWIEPB5qQj4CjKfPTUU5co1NQTLvK4wjccs78LWmiCrKssLipicxofqYBm/umM1js44nnTZBHxed99atuNDNo5Tyf4lWBNZbiUjvHlr05z3ml1hsv7bHuwZd2t26ZTS+AsVJqXobGm2HtDr50l7rQS719Zx1bq6szaSrQz4eNneDzVfjSng87K1pYrnT43SMxanzCsZXWrFZldHLSPRaXc/UDru5lo7ggv4vFQGfG7TwumRKG21QTrqw+503cUwEp2eI0yLiZg6By2XhEvSHubb7JpI+gbQ2nDZghGTMYZTw1HW1IXpqA/NNkLY4txSE7T3+iwcKaQLU1NlOa/Z2Ijf55kzHiUdZ5PzxlX5m26ce3bUh5ZUZ3r40AAzKTMnjQeWzZWI5WgyEknQPRpzjXvTcVxHSqHOpMKkFJ32uhAH7rxuTrF1pfnXX9vB/5dmVbRUKsp9JGZSeD2StxblcMUaa3T26eEoq6rKi+6uno6z0TbXfqYuOwpKjx7rKvwZqbw1dSHa60L0jscXPXZ7NJpwG0kcrOaH3DWmPSeG+cDX93DM9sbrHIgQ9nszXDJ2ttVQWe7jNRtn02Z14QDDC+xjGpicIjY9Q0d9yBIm+8HbkyZMq6osF/xc0WU6TkdeVbklune+fRv/8Vu75/U3dKLSSxaImMBy+l5KZ97PXuqjoSLAjra5Alnm9VAftjbZOmnCXP8vttYG3TT1uebc5VaUi4qyHG4H5+sanAaI1prcreLp7Gyv4SuPn+AXRwfZ1DT/N+bl5pLGCqrKffzNvQf47ENHqSovY0NTBW/c0sThvkl73MbsAzXdL89KfdXSbrc794zGcm4kzsdodJqaYGbEFM4RMaVShi8+1sn//OkhZlKGo/2TfO8PruHYwCTrGzNdEmrDfvb97XUZ19eFytwoLx9O48Oa+hDdo2EeOjRAKmVmG1JqymmqDDCVtNwfqrPWnc5YdDZiAmtz9UL/XpaPbakAABlISURBVNrsiCnfHqZ0trVW8aN9vYxFpzNavucjkUzxyKEB3nb56rxffJqrLWFy9knlipgCPi/NVeUl4f6gwqQoi6SifPahtBDO2IzR6PSccRfFxuMR/uldl/PLY4NMxJOMRqd5+FA/37M3hu7Iss+pD/vpHo0zFp1mPJ50IyawUnvO7/v08WF+cXSQP/mVTTk/1xjDaCx380Nsesb1pItPz/AH33iWB1/u58bLmrl5Vxu/9597+cO7n+NY/yS787Tsp1Mb9nM4baRJLpwIYE1diJ5Rax/TmfE43aMx/F4PDeGA2yDQPx6fX5himcJUCK/b3MihvgnW1i/s6uHUfg70jvHqSxoWONviwZf7mJxKct225rznNFeV0z0aZ3/3GK01wTnRrMP7XrNuxf+e5kKFSVEWibNReF0BD5r2ulnn7uzJtSvBjZet5sbLZt0kZlKG506N8NChfnZmzZqqC/vZ1z3mPsjbakM5PdT+92Od3H+wj+u3NbsF+3QmppLMpMzc5ge/D2Oszrag38sjhwd48OV+7rhhC7/32vWICHfetJ2PfncfkNn4kI+6kH9B54eTQ1FErLRlR13YPdY9EqOlxkqvrrJThv0TU/PWgpxU3mKE6dWXNBQsMk4kc6B7vOBr7tnTxaqqANduzH9+U1U5z54aZWp6xm0iycXvXru+oM8sNuc+v6Io5xlOKq+QiElEuMIujM+3h2Wl8HqE3Wvr+PPrtvArWzML5XXhAMORREaE0VxVTplX3M68lG0SCnDPntM5P2M0Yj28s7+Vz06xtepMTgPGr+1ud1N2t121ht96leXIXkjqqzbsJ5qYyTDLzebUcJSW6iABn9cdUXJyKJLhvp5vblE2TsTkbLRebuorArRUlxdsTdQ3HufhQ/2868q2nObADs1V5QxHEhwfiuTsyCs1VJgUZZE4Rq7zdeSl43Rs5TNwLRUaKvxMzxi3K6y9LojXI7TUBN1OrZfPTDAWm6aq3Mf3n+9mKjlXEEZjtutDMDtiyhx90TMaI+DzuCPtHf76bVv5zG1XzBHOXGQ7VuTi1HDUHSuyutoS2pPDUXpG47PCZEdMC3XmjcWmqQj45hWBs2Vba7XbMn5yKMJdT5/K23ziOGHcsrs95/sOq6qs388Yq45V6qgwKcoicYxcC20GeP3mJsJ+77yOE6WA85B//vQodWE/lXYtrb3WcgQAy0Ub4L/feCmj0ekMh20HZ3if4zvo4ERMMTu66R2L01oTnLNvrMzr4Vd3tOR0Usi35vmE6eRQ1E3h+bwe2mpDHO2fpG9iVpjCAV9B7g9jselFpfGWwvaWajoHI/zml57idf/zYT763X18364LpmOM4dt7urhqbd28DiQAq9I8GjViUpQLkN0dtVy9rq7gabOXtVVz4M7rlzwEcKVwHvIvdo1lrLW9LujuZXqy0xpCd8vudlprgtyzp2vOfRyfvOpgvlSeJUzdCwwyXMya89WZIlNJBienMtwQOupDPGMbr7am1f0KcX8YjyWpKrIw7eqoxRhrTMZH3ryJttogP3ixZ855e0+O0DkY4ZbdbQve02loaKiYbfQoZbT5QVEWyWs3NfLaTY3nehnLjjM7aXIqmSG6bbUhhiIJJqeSPH1imF+5dBVej/CuXW185sEjdI/G3I26MDsfKTtFFyyzHjdOjal3LMZrN57dv8faBRzGndpY+pyrjroQDx+yTFUdSyagIPeHcTuNWUyu2VDP/R95Lesbwvi8HuLJGb74aCfDkYQrxGDV+MJ+b0ZzSz5WVVpiNF/jQymhEZOiKIC1wdYh/UHuvP75S32MRqddX7hbdrVhDHxnb2bUNBrN3bkWtrsZo1MzJJLWnKdli5jyCJNjP9SRETHNpr1asiKmQmpMxU7liQibVlW6day3Xb6amZThJ/vPuOdMxKf54Yu9vO3yloKsvmpCZayuLufVl+T29Cs1VJgURQGsfUwOazJSedbrb9lpO8cXrr0uxDUb6vnW3tMZjgkj0QSV5XMbBNxU3vSM7bKQKQxLoTpYhgh53R+czbVOjQkyRSpdGAtxfxiPF1+Ystm6uor1DWF+mJbO++yDR4kmZvhNu4NxIUSEh/7s9bzvNaXRDr4QKkyKogBQXuYlbItHhjDZab3Hjw3SVhvMsGF6/aYmTg/HGI/NWg2Nxeb65IG1jwkglkhm2AGdDV6PUBPM75d3ajhKVbkvw0XBiZjqw5nOF+nuD/kYi00XvcaUjYjwtstX82TnEAMTU3QOTPLlx49zy662Rdl8lZd58a6gJdbZoMKkKIqLk85Lb36oC/sJ+b05p7Q6PnDpw/dGook5rg8AobLZ5odee9T36mXY21Ub9uedyXRyODpnNEl7neUJly2K6e4PuZieSRFNzKx4xATwth0tpAz8eH8v/+OHBwn4vPz59ZtXfB0rhTY/KIriUhcO0Dsaz9hzJSK014Y41DcxV5jsh3vPaMx1gRiNTue0vEnfxxRNzPrUnS31YT/DaX55X338OMfssRn7ukZ59YZMR4SAz0tbbTAjKgRc94e+ccv9YTSaIJkyriP8+BLsiJaLTasq2dhUwad/foTByQR/eeOlNFWWfnfdUlFhUhTFpbEiQGttcE59qL0uyKG+Ca5el+lf50Qd6RHTaDQx56EPEPB58AjEEjOM2vOaQv6zfwTVhvyuW8XxwQh/+4ODVAR8+H0evB7h9Tk6KL/w7l1zBMaNmCbijEYTvP2zj9NcXc49v/cqYGk+ecvJ2y5v4ZMPHGZ9Y5j3vHrtOVnDSqHCpCiKy5++ZZMbGaRzzYYGIlMzc/Zi1Yf9+H0et2YEzvTauQ9vESHk9xFNzFiuC8tk0VQX9vP86VEA7n2+BxH42Z+89v9v78yjrKquPPz9CiioAhkKxFhlEUARBaOgSBuJMybGtiW2sSVLXWJMXJrEJNqubo3dtomxY6aOuujE2MYW04mKiobYJpgoMSR2HBnEAQVBU4oyyGwx7/7jnFvcerxXg/Ve1Xuv9rfWW+vWufsM+75Xd999zrl7tzhNmG1tJon+sHLDVi6/Zz5vvf8Bm7c1XzsD6F/VNbfNvz+yjl8+8yY3TDms1aj2pY4bJsdxmsgVneKiSSO4aNKIvcorKkTdwKomj2nXbgs713JEr66q7EHjjrD5obVMwm1lUN8QyNXM+NXCt5k4vOZDrV0l0R9++uQyNm7dyWF1/Vn89kY2b9tJv949mzZFdJXHVF9TzdPfmNwlfXc25W12HccpOLUD+zQZpo2NOzDb++XahCQn0zt5iPqQUFMdYvw9vfx93li9hTPH1X7otob2783GrTs5/5hhXHrCgcCejK5dPZXXnSioYZJ0mqQlkpZKujrL+d6S7ovnn5Y0PHXumli+RNKnUuV3SlolaXGW9i6P8i9J+l4sGy6pUdKC+LmtMNo6TvekdkBV01Te+sYksnj2m3dVZU9Wb9rGxq0787IjD4LHBDDjqRX0rBCnH9Z6JIRcjDtgIJMOGsx1Z4ylflCS8qO5Yers7eLdkYJN5UnqAfwncCrQADwrabaZvZwSuxhYZ2YHSZoKfBc4V9IYYCowFqgFfi/pYDPbBdwFTAfuzujvJGAKcLiZbZM0NHV6mZmNK4iijtPNqR1YxapN29i+c3dTzLpcieiqK3uwdNXmWC8/u8pqYrDYOS+9y0mjhzYZqg/DD//hCCDuRIzracnGimTtLUmr7hSOQnpME4GlZvaGmW0H7iUYjjRTgBnx+AHgFIVQw1OAe81sm5ktB5bG9jCzPwLvZ+nvMuAmM9sW5VblWyHHcfamLqZff3fD1qbU45kpLxKqK3uwalMI+5O3qbwY42+30aFpPAgGKYl2Pqi6F30re9Cwbs80Ze+eFc1eynUKQyENUx2QziTWEMuyypjZTmADMLiNdTM5GDguTgk+Keno1LkRkubH8uPar4rjOLmoS20ZTzymbC/YAlSlbur5XGNK2p58aOs5nNpK4jWlp/J8falzKOSuvGyxLzKDUOWSaUvdTHoCg4BjgKOBmZJGAiuBYWa2VtJRwMOSxprZxmYDkS4BLgEYNmxYK105jpNQm3rJdkMra0xJvLwK7XmhtaMkeZ8mj9mvTQFN20N9TTUr1oSXdbsiHFF3pZAeUwOQTqt4AJCZVKRJRlJPYABhmq4tdbP1N8sCzwC7gSFxOnAtgJk9DywjeFfNMLPbzWyCmU3Yd9/yS2ngOIUiiRLx9vpG1n+wHSn3OkwSL2+//n3ylgV2nz69+Je/PZQrJo/KS3tp6gdV07CuETPrkgCu3ZVCGqZngVGSRkiqJGxmmJ0hMxu4MB5/FnjCQmjf2cDUuGtvBDAKeKaV/h4GTgaQdDBQCayRtG/ciEH0oEYBb3RYO8dxgBAcdEi/3ryzvpH1cbqrIkew0MRjytc0XsIXjhvJyH375bVNgGE1VTTu2MWazdt9Kq8TKZhhimtGXwHmAK8AM83sJUnfknRmFPsZMFjSUuBK4OpY9yVgJvAy8Fvgy3FHHpLuAf4PGC2pQdLFsa07gZFxG/m9wIXRyB0PLJK0kLDB4lIzy7Z5wnGcD0ldfJcpRH3IvSsuMUzpWHzFTHpnnhumzqOgkR/M7FHg0Yyy61LHW4FzctS9EbgxS/nncshvB87PUv4g8GC7Bu44TruoHVjFa+9tAnKvLwFNsfHq8uwxFYrEMDWs+yCkVS9w9lon4JEfHMfpMElYovUf7Mi5VRxKz2NKwia9tfYDX2PqRNwwOY7TYWoHVrF1x26Wr9nS4lReVYHWmApFdWVPhvTrzcsrN2LmUR86CzdMjuN0mMTQbN62s1m22EwSb2rY4L3TYhQr9TVVLH5nA+Bx8joLN0yO43SY9JpRSx7TSYcM5e7PT+SQj2SPYl6M1A+q5q/vh+gP7jF1Dm6YHMfpMHWpFBYtbX7o1aOC47Mk7itm6mv26OYeU+fghslxnA4zqLoXfXqF20muAK6lSjobrxumzsENk+M4HUZS0zpTS7vySpEk/QX4VF5n4YbJcZy8kKwztbTGVIrUu8fU6fjbYo7j5IXEMLW0xlSK7D+gDz1iiKW+lZ7yojNww+Q4Tl6or6mmQnQoUV8x0rNHBbUD+7Bl266mXE1OYXHD5DhOXjj/mI8yrn4g/fKceqIYqB9U3ZQ+3ik85fcLchynSxhQ1YtJBw3p6mEUhEtPOJC1W7Z19TC6DW6YHMdxWqHU3r0qdXxXnuM4jlNUuGFyHMdxigo3TI7jOE5R4YbJcRzHKSrcMDmO4zhFhRsmx3Ecp6hww+Q4juMUFW6YHMdxnKJCZtbVYyg6JK0G3uxAE0OANXkaTinQ3fQF17m74Dq3j4+aWYffRnbDVAAkPWdmE7p6HJ1Fd9MXXOfuguvcNfhUnuM4jlNUuGFyHMdxigo3TIXh9q4eQCfT3fQF17m74Dp3Ab7G5DiO4xQV7jE5juM4xYWZdasPcBqwBFgKXJ3lfG/gvnj+aWB46tw1sXwJ8KnW2gRGxDZej21W5ruPctQXqAfmAq8ALwFf6w7fcTzXA5gPPNIddAYGAg8Ar8bv++PdQOcr4u96MXAP0KccdAYGE/5vNwPTM8Z1FPBirHMrcbYup57t+fGX+ofwT78MGAlUAguBMRkyXwJui8dTgfvi8Zgo3zt+YctieznbBGYCU+PxbcBl+e6jTPXdHzgyyuwDvNYWfUtZ59TYrgR+STsMUynrDMwAvhCPK4GB5awzUAcsB6pS7U4rE537Ap8ALmVvw/QM8HFAwG+AT7eoa1t//OXwiRdmTurva4BrMmTmEJ/aCBl+18SL2Uw2kcvVZqyzBuiZ2Xe++ihXfbPo8Svg1HL+juPxAcDjwMm0zzCVpM5Af8JNusWn5zLTuQ74K1AT5R8BPlkOOqfamEbKMBEeNF9N/f054Kct6drd1piSH0VCQyzLKmNmO4ENBBc1V91c5YOB9bGNzL7y1Ue56tuEpOHAeMKUQVsoZZ1vBv4J2N1GXffSJ0ube8kUkc4jgdXAf0uaL+kOSX3LWWczexv4AfAWsBLYYGaPlYnOLY27oZVxN6O7GSZlKbM2yuSrPJ99tEap6hsqSf2AB4Gvm9nGLLLZKEmdJZ0BrDKz57Ocb42S1JnwtH0k8BMzGw9sAa7OIpuNktRZ0iBgCmE6rRboK+n8LLLZKHadc9Hu+1d3M0wNhIX1hAOAd3LJSOoJDADeb6FurvI1wMDYRmZf+eqjXPVFUi+CUfqFmc1qg66lrvMk4ExJK4B7gZMl/U+Z69wANJhZ4g0/QDBU5azzZGC5ma02sx3ALODYMtG5pXEf0Mq4m9Peud1S/hCe0N4gPK0kC31jM2S+TPOFvZnxeCzNFw/fICwc5mwTuJ/mi4dfyncfZaqvgLuBm7vLd5wxvhNp3xpTyeoMzANGx+Prge+Xs87A3xB25FUTfuczgMvLQefUGKax9+aHZ4Fj2LP54fQWdW3vP36pf4DTCbu8lgHXxrJvAWfG4z7xC1lK2EkyMlX32lhvCaldJdnajOUjYxtLY5u9891HOepL2NljwCJgQfy0+EMudZ0zxn8i7d8uXpI6A+OA5+J3/TAwqBvo/E3C9vjFwM+TtspE5xUE72kzwVNKdvhNiPouA6bTyoYXj/zgOI7jFBXdbY3JcRzHKXLcMDmO4zhFhRsmx3Ecp6hww+Q4juMUFW6YHMdxnKLCDZNTNEjaJWmBpMWS7pdUHcufao98C+3fJemzHRzjiZI2xBA6r0r6QUfay9f44riyvqgpaZqk1XHMr0uak0s2o971kq76MOPJMYbpHahfJelJST0kVUi6NX7vL0p6VtKIKPf7GF3BKWHcMDnFRKOZjTOzw4DthCjFmFmum2hW+U5gnoUQOuOBMyRN6qR+W+JEWo4gcJ+ZjTezUcBNwCxJh+ZzAKkoAYXg88AsM9sFnEsI53O4mX0MOAtYH+V+Toh+7ZQwbpicYmUecBCApM1tlZc0XNLipFDSVZKuzxSWdJOklyUtSrweSftKejA+gT/bmsExs0bCy791sX5fSXfGuvMlTYnlwyXNk/RC/BwbyyVpehzH/wJDU+M7JbbxYmyzdyxfIWlIPJ4g6Q8x0O2lwBXRgzyulXHPJaTPviS2c6Ck30p6Po7zkCzX64tRr4XxGiXe7F2S/kPSXOC7kiZKeiqO/SlJo1PN1Md+lkj6t1j/BklfS/Vzo6SvZhn2eYQo8xCiVa80s91RnwYzWxfPzSZEr3ZKGDdMTtERn7w/TUgsVgj5GsJT9lgzOxz4djx1C/AjMzsaOBu4o5V2BgGjgD/GomuBJ2L9k4DvK0TLXkVI23Ek4Wn/1ih/FjAa+BjwRaLHI6kPcBdwbvQIegKX5RqHma0ghIz5UfQg57XhMrwAJAbodkJYnKOAq4AfZ5GfZWZHm9kRhIR+F6fOHQxMNrN/JEQ0OD56lNcB/56Sm0gwMOOAcyRNAH4GXBj1riCEuPlFumNJlYToAiti0Uzg76IR/qGk8alrsQ7oLamlaNdOkVNI19tx2kuVpAXxeB7hptVe+do29LMR2ArcET2VR2L5ZGCM1BQMub+kfcxsU0b94yQtIhiVm8zs3Vj+SUIg1mRdpg8wjBCwcrqkccAuwo0c4Hjgnjg99Y6kJ2L5aEKgz9fi3zMI8cluboNubUXQFMH9WOD+lN69s8gfJunbhIyz/Qg5eRLujzpACOg5Q9IoQlipXim535nZ2tjvLOATZnazpLXRuOwHzE9kUgxhz1QdZtYQPbGT4+dxSeeY2eNRZBXhd5DZjlMiuGFyiolGMxvXEXlJO2k+E9Ans5KZ7ZQ0ETiF8IT+FcINroKQAK2xlX7nmdkZkg4G/iTpITNbQLjZn21mSzLGdD3wHnBE7GNrejhZ2s+WJiAhrd9eurWD8QTPp4KQd6e1634X8BkzWyhpGmFNK2FL6vgGYK6ZnRWnGP+QOpepa/L3HYTAnx8B7szSdyMZuprZNkIw0N9Ieg/4DCHJIlG2te/QKWJ8Ks8pN94DhkoaHNdlzsgUiF7CADN7FPg6YWoJ4DGCkUrkWrxZR4/mO8A/x6I5wOWKrkdqimkAe9ZELiBEdYYwBTg17jTbnzD9B2E6bLikg+LfFwBPxuMVwFHx+OzUcDYR0tC3iqQTCOtL/2Uhz9VySefEc5J0RJZq+wArFdKRnNdC8wOAt+PxtIxzp0qqkVRFMCR/juUPAacBR9PcEwOapud6xClOJB0pqTYeVwCHA28m4ycYuBUtjNEpctwwOWWFhRw33yJkvH2EcJPPZB/gkTgd9yRwRSz/KjAhboh4mbbt8rsNOF5hu/INhKmrRQobMG6IMj8GLpT0F8I0XuJhPAS8Tlgb+0kcC2a2FbiIML32IiGj7W2xzjeBWyTNI0wLJvwaOKuFzQ/nxnOvAd8geHavxHPnARdLWkhIyTAlS/1/JVzT35H9miZ8D/iOpD+zxwAn/Imwa24B8KCZPRf13Q7MJaRP2EV2HiNEnYewSeTX8RovIniRyVb0o4C/2J7Mq04J4tHFHcfpUqLX8wJwjpm9nkNmPHClmV3QSlu3ALNT601OCeIek+M4XYakMYS8Po/nMkoAZjYfmCsp0wvLZLEbpdLHPSbHcRynqHCPyXEcxykq3DA5juM4RYUbJsdxHKeocMPkOI7jFBVumBzHcZyiwg2T4ziOU1T8P++vajUsErauAAAAAElFTkSuQmCC\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_fit1d(data, fit, T1())\n", - "\n", - "#for comparison to original data:\n", - "plot = plot_by_id(4)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For the 2D dataset, this rough plotting can only plot a cut, so it is necessary to tell it which setpoint you want to see the fit from. If you aren't sure of the setpoints, you can either find them in the fit dictionary (long and annoying to read), or just suggest something, and if that isn't on the list of setpoints, then you will get a list of options to chose from." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_fit2d_slice(data2, fit2, T2(), setvalue = 5.65600000e+09)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "([DataSet:\n", - " location = '/Users/Lillian/Qcodes/My stuff/mainfolder/data/621'\n", - " | | | \n", - " Setpoint | qubit_rs_frequency_set | frequency | (101,)\n", - " Setpoint | pi_pulse_dur_set | pi_pulse_dur | (101, 101)\n", - " Measured | rec_ctrl_demod_freq_0_mag | demod_freq_0_mag | (101, 101)\n", - " Measured | rec_ctrl_demod_freq_0_phase | demod_freq_0_phase | (101, 101)\n", - " Measured | rec_ctrl_raw_output | raw_output | (101, 101)],\n", - " [,\n", - " ,\n", - " ])" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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WK7eq8hbD1HXz7aSQS3boe8k2lLqsid+6+jDroONoC/k9PlMvJhhZI2Wb8SR9EP7WGwav1AXVck3IU/Qe/K3vGsdMrv1q/ZXud8cdKxe5+5TM0SNi3rx5vmjRiAoL3HHHHVe7+7zR3OPFjA21UM8r3f0JM5sK4Fozu9/db8wcdwyAnw5nW4hJ5VwA2GefHf322///2HN7bC+uDr47XroH5ALXxXZbKdNvGV8mtYJwKM44SAqPjG2uSX8qv69Om8dl3fQnYntD5tSF8psv3eulbHps+2ZJIb8Mp0gZH1BvckfarPxVVaSLgT4S28VSxm/AzVK2V6ZenCQ6Xi6FbBtd0pdryt8lZdFe98q6Rnz238hhd8d2mpQxjZ7257yozAJ5kGkHyAHs5AVSxq+/1Ot38YXT0cgJ8iEp048dMTNTL7bR5FOlkOvc6+yzf2zvkPv+Im2XyGHsL40B1xWNd4/tmkyZEoIu9omOZf7WtabiRVn566qITaj3YBtl5n5o2sPJsc1NnrX+SoPA7JFHMEosWrQIt99++4jHmdk2Ix7UhtggE4O7PxHbJ83sMqQFvnMTw4lYKzXSZqgo0itje021e/sY1Drw+EI/lSkD0oq0QP2DyQ8hdOlZflxeKWWcEHQCiY/2uEurIr7cM+Ww3BLwW8dW3sPqo6gfVn6MlTqyrn1StmXadMowmPiL6jclAX12XkY/cPwYqNz3xtjO+J0U8s3XNtoltj8ffNweUq8n46OuHxcKacrW+zEYd8a5ypin6YGcVPWAw2L7p1XRjO/GVj6OCx5PWx0/nMT0o3dbbHfL3Lbno1VZB9toDznw3qYTAHRHu3U/WJVtH32nH3kdy7kVwClA6jk7/zZtJ0sZ35nO10hhkIzOl1VFO3DyFXb1aFxPJUqOYa0f2027oY/XkPdYhYdRw1F/fwsUL7iNwcy6zWwifwN4DYB7MsdthfRF/d4LW8OCgoL2hyNJZSP9bZrYEBLDtgAuMzPe/yJ3v8rM3gEAkuLg9QCucfdl+cvk4KgUmrfEVjo3tDhZUV/Jg6qSyIZVJG9gvvzmkgH9UkZJ4Wopi/rklGPK6EhK9XJkSirxUNW0QnS/s0m5dGD3xVb1qnvHVlRdU4UR3h4sTZke1TdqqmDbqcTD/ctEZbZZ/O7aXw4khVb10pZN+wBM/XTaPijPSenhiaqowTpVY0bmriqnmpGEbaJqL3a81B9zmvYB2OpxtASq/JQC0byiGqydg3FPVQmQYqtIB41Gv2tw0avkMJVaOFYeyezXMr4fqr3vDEl8QJh7x67xQ9uAfSdjj+NH3zFeW1WDOs4ISs5alzszx601ODGsO8xsHlKEfgfS+h+faNq/BYALkF64xQBOcPf+2PcBJJ3vAID3uPvVkc7lRiT1x+YALnH30+N4A/AxAMfHOV9y9/8akwcRvOATg7s/hPobyPJzmv7/KoCvvjC1Kigo2LSwBmOhSop1Q74A4AgkpnibmV3u7qrwPQXAH919FzM7EWlVwhPMbHckdfkeALYHcJ2Z7YqkHD3U3Z+NtDA3mdkP3P0WAG9Fohmz3H1N2GnHHBvK+LyeoCyATE/oPxmtkjESRzXyKsjwlHmRXc1UCkqdsNIeDjxlJqGcnyX1eioqsbUcRlXtbCkjk1N2x9vVploy4G9LGXXlu0jZlbHdW8rEwMlrKvumMJKTsLSMkoVKNw2J4gIppFFRvbrYlmp3iKG6i3rFZMC6qvTCV1Q9Zh4QljuTlF2lKfat2Bga4tv3qyIaWXUocCypUELpRvXnZMPTpYz9OVXHDLWpr8wcKP3ZFRLF42In6pVTKMWp9EspVccP+/GXUrZjGHJ6xZ7Ad8zFqG+U8sS+NSmkjUlbVmWr4hw1v1ESULsD3wk1Zo8Jxkxi2A/Ag0F4YWbfQhLxdGI4Fim3FwBcAuDzwfyPBfAtd38ewMNm9iCA/dz9ZlQNOC7+6Kb2NwBOcvc1QLLTjsVDNOPFEMdQUFBQsB7Qko1hGzO7Xf5Oa7pIL+pK4PmoT8e1Y9x9NZKiePJw55pZh5ndCeBJANe6O1nSDCRp43Yz+4GZKQUaM7SZxLAVZB2XgCh1yRhzHjWqw1QPDbrzqedLYzpVN1QyOGXfZCR/LmXfiO1hVdHcsEGskJtQb67sqflWQOXRpGaMvvACmfa6zMnqavKW2KpIIHXtjRu9/kdVGYen2mnI9JQNs/6qouf+acL6u+kyKO3REJdOkrL4PVVccKcGqbpfrkdW3C+n0l6jbsrK5pfF+XNUFMvZDti3H66K6BU0V9swKPej0nk3xXZPOYycUm0klCwekgqS4Y+7siqbQe87bbd4nXv/SspkjE6Lei2TelG60c8T2yk3zn4jrs0HskyOm52LDeBnRiSe3jiuVwwsO0a9dBxRWFJBUW2Ao8Ya1F/qIbGoOWVPE3JRF83BYUMdM+S57j4AYI6Z9SDZZPd093uQ7A4r3H0fM3sDgPNRtyqNCdpsYngawOXxuz9tVspLwI/8tXIKX0o1AKpKJOd62LCdavM9mCnjxKAXp+ws6pQFMUA1XoD3y7n46UtCDYy+LPxQz9PZgrqCY6WMH0J1jRRVElUrOW9xVRHxw6UfOLahhlfQ4L69Xoj30xmEbaQNwvZ9o5RdlDZLMv4JOdWOGsy1rn2xvVc+FHvQl1+N4mwIHSB0c++TsnBK0D4h8VCVJfv4vkxdVd3DIXyIlPVHXafLZNExN37knA6ARhvqcNQ4CIJtp23EJtZ6sT/1Q94fpKRP2RfVS0qk2YbS5pPY5lKpWRo3Eci53a41xkyVNB91ZeBLUW85PWa+mW2O9NY+1cq57r7EzK4HMA+J5c4HEH7TuAxpzZIxR1ElFRQUbKIYE3fV2wDMNLOdYjGoE1GxU+JyAH8Zv48D8CNPKScuB3CimW1hZjshWcZuNbMpISnAzLoAHI7KIvW/AJg37iAAv12rR24RbSYxdKKKpA1apME3K0IMzoXtKxPKGaI1sIpahl2EqXaROf5QDjw/tkrLqHIQsXrrKwfXIecwQdaZc3VVIzW9aH8mbGwuqbvSfw58zZuggZ5h2Jx1b1U0IcQVNUzmJAZqPpVNfi22KvjuH4bSPg3GI7NUyyRprL6s0YYHSJt3RP1UOuBvlfrult8k3btLWU+8b70qjbAf1d2W7aUqlGi3yVLXI0IiekCkIEp2b5NTL8rUlQz/OimjRlAlyh5KX0dKoYpOMTBmqKE/PgFPSr0ooWh7UJuo7wbrrxlOOG6XiAS4W/x+WoziDTWnqjY5WESymB1jT6PgaxLnaDE2XknuvtrM3oWkzO0AcL6732tmZwK43d0vR/JlvzCMy08hTR6I4y5G0gWsBvBOdx8ws+0AfC08njYDcLG7XxG3/ASAb5jZPyAZqDVsfszQZhNDQUFBQSsYuzgGd78SFb1g2Yfl9wqkuIPcuWcBOKup7FcAXjHE8UtQd5VbL2iziaETlU6V7EnTPcRW3UKpr1dVZs6Iqu58JOLKnnZg6gfNefDJ2KpBmpY6YZg05OYMtWoDJfNVokN2pyyKDF6r0nCx1PwwtDfow2XcM12MGpRWVMhge6hgxLpqvSjxKAlvXFoLGUj200yZ5gph386oiiYE01dnQULtw0qk2dYafEaJp0c6oJsPrXYHWuPVCJyxLS2PCmn1eV+tFzXOqkfns7wBg6FmmCXREX3flcKezG8d7NFp6g5K6VPrlQtS46P3SRm7UccCbS363jVO1guqXYIIF9dpUue7MoetNcZuYmhHtNnEUFBQUNAKSq6k4dBmE8MiAF+O32QYQqnINFTNTtam7F/ZGtmOMlAyco2/6omLTurL1CsXkSZBUmRXB8ph32s6HBBXRimjulvrwiR/6kY4l1KBihFkvlo/FVuC/tmOVdHCOFY9bn6WqRdVx/ruUWetEhn3Dwgz76BLrzJzButp/cMusUzsb3ReUvJJ90t1sdU+pjOP6vWpz1fmu38w8qkaOHhCbC+SMkowIhVyfKnXOeulLiB0a1X9PttSxyCfTyVKmpGeEZfSSbvKAX2x1YYI1qzu2tRma1PfhMHgOeoll0trwf2173C0zTNyciMATg5ckkmAOJzzaMsoEsNwaLOJoaCgoKAVlIlhOLTZxNCDSm8e4sHABYMPU2ZInbkyQ9W3Uid8hJRR8nitlDUYtF5om+adqPTmopOeHOzpd0IJh/NAUvZEhqzPRGe5N0nZA+FWUkvjQaV6n5Spdw1pqTwTHeVqprbMZXL59vksP5OynIfYHvSCUvrJemtgRDDLbhnGB/5ocP2oM9dHVz01dekiGDV0/Zq3jpJih3h7TWbbqAcV21A6KhfHwFgQbQMKdsqu+2OrZhiaL1QCoRSkMSbqzTiJ0rPGE8RFNRaEj/KMHEZTqHbJFRgMSo1aBz67eortGJKC9skkShHSvj0cg+JJtWwsPDSLKmk4tNnEUFBQUNAKisQwHNpsYlgBgN5B3x28m4xVmQs9JTRdgjJyqtyV6VHHqRH1ZGt9NcNDbNXvnXEuZ1ZFi0NSUAJDJqVePVzsRetHRxnV8/I66gFD1tYvuvA+Ktc12rmWmzogVHrJrwbvJpNWMv/1pvsCVRtrvEZ/bNUm0BvMtkfpOu0Nmuq8L7bisUL1uS74xXZTXb62K5mx9jH169rW2rXE28NWtFI6oJN6fRkgXeFStIMsL/JcnKMxMqy/RiezXZWFcyxrnSg96CqvtXOiXaeI3a07mPhkjW2IAbRQ6v+Hpi1QSYO596VPyhYOc5xKS8wAoALNY3HgGrlxbonStUaZGIZDm00MBQUFBa2g5VxJmyTabGIYh4p6npw2HUIvdgv9cy4/jDLbnGu1eoSQbapfNj2KlkvCua5/jR+ai4g3F5ePycEwe0R3Sr23OpDQlf9LUkadtNoiyBJ13JNZqudTI0J2UaZM6yruTZOjPXe+tSpj22hsA4UkbUsy49xSoZoxhpLOXtLonfScUkMAGZ/0cU+4ZPWIp9W4YMjSNbXs47k02fdkyvoyVbgoWP9JKvJQgtGoXl7wPVXRbp9J29ySrnpfPoqOUUo5OpbZrio96rjguFXJqZsR5xq/EnawmRKdP5OSmjD3B+I5Nd6B74RKXxzDizNl2myUJLXOfE120MHFDlhXW0ORGIZCm00MBQUFBa2gqJKGQ5tNDJ2odNGkMbIYPfW2mieHkc2q61RGS/aiTie0Uaj3Ekn1XPVA4sBTRs68yxopG+iQ2IYJwZaVUdGbR/3Lc94/DG7WAFfm41FmPnB63FcXeO+T37s1bYHGkJkpEgMdwZQ5Mi2OBlWTqSojpBShDJPkWgn3VvGgtQVsOHzV7kCWK4vC9IWX02yhrGpTyq39Q/NQbpEibWtKPD/R3ECxnapjgRcUo4BFiviO/qqsO/pd4xjIpDU1G/XwaoZhvTSeRNuVfaHjm3aOWZqJlw8q+aseZ+6ouVXZihiQGldA6UbfMfaxprNnu+UWwFLJiM26VMTfvrHwShqbXEntijabGAoKCgpaQZEYhkObTQxPoYqQzbB1MhdloiRy/VImWUJXhHQwXj2ZyHh10ZWG94cw1QbjkgXUGxKNZjGNcxaITp11VIZPCAtcEXUdr8yLel5laP2xVQZMZjtDsqfW6s8cTxpCHR5MqtumtKUEjKYKbTdCz6UTlOYpogpZpaWj+UPbkgv5qF2E/a3ZWqNi6oOv3jokyOoddEumDpTElHEzRkIlkEaCX6Hu09ih6nKjbR3I6eg53rbNlKmdixKseu2od9OEzH7WX2MHJvHdkcjzXh0DAdpptI85BjQimxKPJFdtZGRVSZ37+zLX074bs2U+y8QwFNpsYigoKChoBUWVNBzabGLowOBkLfL/hNBTK0uh2ln13rJ/PJnlbbKfXiQ6rhosOLe0odoTGPmsyvdg7NPEl3xcGAiU8ZHUCUscTzW2RqkSyqwoBSkTpc5/hrJYdbkhS1RmG8dOfXVVtPTGtO2Tw5hbR9uabaTrNrD+ysJZL9WzUwXeJ1R0FqPadTnXXIR0SA8ThtBNsyt0vQM2ibY/x0Ic4HCrAAAgAElEQVTOA0z9+yn9aP0bkbvalksGl02KZ9ktE1qek1o17IQeTSoRqyTT0bQF0hpgQD2Kf068MwOXyrlcP0FsEV0xVu4SDzD2p0olHAtqy6EdQT3FKPVqW9aiuANjsrxYUSUNhzabGAoKCgpaQZkYhkObTQxbo8p2+Z+xFWY+PdimRr2SXQm7WyKqYZKYDhVEyIJV7UpbQC1bJJnUn8uBrI8q1elJI65Pk8PfvVeuxxgErX8wwhXCdsdTolC2zsto1DF/PyoeRjsoXSNLVHsIXZCEuc+IdZhnaLR53FAlAXqvXCJl9KrSKG3WW6Ub6sJV0No+6jpJaSelB1XIh8TWK776x59f/V4c0pnac9g9mteJLPfuzHFqz+H40fo/EYU7y+BiFe3dciCf6dCqaN/4gO154+BzFWxfdTDSlf3YZbmYHc17xFXmVOhaHC5R2kYzQvQ4QKTMgRjzs0UsOTTGgjJ9esypxLO4aR9QSUb9UjYmcWklV9JwaLOJoaCgoKAVFIlhOLTZxLAcFYWj3ra/2k32oeyIulBxCOoRvebCYK9T1JOGzEcZIc/XpQ1mMy5BIkhr7Lu5EprnP3L6q16cXiWSGXRlMFoVDsY/1XQ8UHkgqZ6aEk/NLKNK9dVNW6CKCzlWymIVw36RbvhT2TX13XqLsIMsEI+gaWxL9fpi++fW467RZ9ZVH4peV5pYSL2WArlV33Jlqve+M7Of3amEtLFOuJQZ662iJztK7E0r/zttuyT6d03QZrUh5HT5WgcycfW+4ng9MFOmtgpKaur91hD3RGKgoLBSI89jq/1+cdM+oPL6Uk8r1l8lRQ1bGTXKxDAc2mxiKCgoKGgFjpIraWi02cQwDpXSksl6Lh58mDrekHEJs1oq9JvkZYV6KtEHWz0v6AGkSSobfuDKTPhbF/ClclXDWIPd9kkRGdchVVEnK3jF4MMmCjPsphSRi+StrYilXjOUDlTZzCGjDxrxBH0iygxovEGANg11ggqV+zRtS0oHau7QIGKCiUqPEhtJY30Epeakmxq+LjaeyeHddJokKGJ7vkVOuTm2ynwpganIxjGlXmF8dn2m6fHP4dJW3WwIuUknx4pkwT0wGLk+5pSmLQD8QH7z3jlPHxVacus7szoaBN8ZFxwvDzUzjHWd+1VlvfFMvVL/zmhrjdLeN7b9UsbYBq2fSuWjRpEYhkObTQwFBQUFraBMDMNhvU4MZtaPxKkGAKx290GrtZrZwQA+i8RjFrn7QVH+DwBORerBuwG8zd1HcCNYhkqPTHYixoFchC4ZnzBSVdGS/KnzxHgS/JzHja6N+6agNqbeOv8WW001yTBbtT8ENdtawkXvi61m3lTf+0COxHaTZSnj64+tssrdpF5dHB5KkUlRtf6stzRsX2x1pTS2l9oJ2D36TLlsnBRQ1NZD7yyNK1gaNpk+NVBwLKiNQds6st8+JZJHX2zVxsA6qAcSB4bWi6Rf68/n07E3sWkfAHSzwWZIIest63pQau0Q+sw0RioVTsn8VulscdMWqJ5FhUJWQetPjye1CfRHg6nnXGcYK5bpg2LwcayD3qOvqU5A5RmlHmNrjRLgNhxeCInhEHfPRX3BzHoAfBHAPHd/1MymRnkvUn7i3d19uZldDOBEAF8d/lZdqFJc/zC2YmTkt0JdNikFywdM7WucGNRetxfHk34vadhTMZ1i8gxVz9CXcG8p46yioz/e7smid3lFWDDVeB5akk4xKtN4qzFv98elZ6nozg+wPvBl8vvICLKarPWiTP9GKWODvE+uHedMlIV91HWSoFE2EzRWU9/xx15yHAPlfiJlr4rtMgnO6uZJasHUnA3hxtorg+DujP6ZHqQ6v3DiUFs3J8BOKePHUxkG+0In97nxoZ+iQWP0pVara3RebyZp4+5Sd+06jv9fShkHtjolcO7XFBYc39pPNE7r5M8lQHPutN1izd46ZoFeGd/Lok904iJp0QlOAyTXCUViGAobWpV0EoBL3f1RAHD3J2Xf5gC6zGwVkn/NmA2HgoKCTR1FlTQc1vfE4ACuMTMH8GV3P7dp/64AxpnZ9Ui89Wx3v8DdHzez/wDwKJIP6jXunrFmNmMVKhrPxGr91e5Lgr0q8QrislKmHc0YoB57xMIwKk5RMYJumSoakwzNUJGVAXhSr4YeS5Z9ZGqNfmG2ZPZqC2Yd5NRcFukGUVUGuThzoJ5Mlc/kXGJAtQDSaK71j/37ShHvrQ1Mdjhz8HHjtdnIVJXFkuUqw6TE9he6sAspraqPRrCsHhpjRQUHtk3OYKrqG7oJqy6Pt1M1GlmwsnDG6qlkNIdjQB+ULtDisNAfdVa2ru1KqODEptHgM9ZxUqauqkZjXbVavLca3reldCBllLAWy/hmN+hLx4Gr4m9OGllrFFXScFjfE8Mr3f2JUBFda2b3u7uEb2JzJJ3KYUh6oJvN7BakoXksgJ2QPhPfMbM3u/vXm64PMzsNwGkAsMMOuc94QUFBQTOKxDAc1uvE4O5PxPZJM7sMwH4AdGKYj2RwXgZgmZndiIrPPOzuCwHAzC5FMq0NmhhCCjkXAPbZp9crF8tgA8+IjpvMTHXqwf46RUf868zSn6riJLlaKXrZTrJJZYS0ZawUo2bnf8UPXe6TdVZFdQxaZfWUdFTHHUaQJcpOA7mMGDUGyUJl8IpGgJy60TKwTVwPGxKP+BTeGUppFSzYNpmyFVLWH1slmKxqj879rLd2Dg2T/bqwC62oahRS6SHovqY9p2SiTJUjV1lz2HsGMuRTBUr28ByVKFktZdwcM/pMDSlNJDcPG4pJ53Hc6vjQ9Bgcr5p+hDY2HXr9sVXzBV281XmBA0xtR5QU1PbPwDyXPmGHqoKYthmVCtle10rZmBH9MjEMhTHJU5iDmXWb2UT+BvAa1BMEAUn38Coz29zMJiC5XdyHpEI6wMwmmJkhSRT3oaCgoGBMQFXSSH+bJtanxLAtgMvSdx2bA7jI3a8ys3cAgLuf4+73mdlVAH6F1FPnufs9AGBmlyBxh9VIfhTN9okMxqEKKgt6PUkUkrsHPfofOYWMRFILqAqZpE49U0nqlOgx1qd2INnaXyolJ2vV5TTZDX1SFq6Je8vgXBiMVtZPIbvrEYa5Mli4ZktoeKsKKaY6frJ6UuUSyU2WFum+KH5o5BdbQha6nxO0dY7YHb4TNFHdIMPTZ7ykzpgVqRFUWCKxXCEPtSp+T9f2yEluy0J62UOvOG3w72lSdlTQ70eFvpJVq+dWiAUdCwaXjZM69PGH2iK4Xz1T2Ta69lBfPNRcsZsY6bzIVXNCopgtdVZbBb2ij5ayXzRtFSoZUdrQQLNVTfuAfPu/5vn68fpbJRC+WBqAyuucKmUcwz/O1LllFFXScFhvE4O7P4S6mYvl5zT9/ykAn8ocdzqA09dX/QoKCjZljN3EYGbzAJyNpNw8z90/0bR/CwAXINlTFwM4wd37Y98HAJyCNKW+x92vNrPxSIrLLZC+0ZfE91Cv+Tmk2K7MMoDrjg3trjrG6EYVH8CEdBLHQB2sxjGQqYrP+e5C0vtjm8vYMFO9I2i3UPbN/bVU3KRI6v7DwB91l6LjuHQR2bzaCchehQxTeFHHEN5NiW0ff+htNYM1T1IpYja9YTQOgKxV6R8bUWwpR/yqvguofPg1kCwUnAvlAVgFlYIaZFiDCmlH0g5jGMByqXOXKtB5sCY7jDTTO8hxq6Kfcon8VEf/i8FVaMS3yTNtFhJZp16PD6W6dwqcT2bywXfJM/XHXfp2rcqmSBLGA2KrNoacnYb9o+k7yPA1Ro1DVBXSvIf2J21Val9h4+TSW9RE8dhq+6qwN2qMTa4kM+sA8AUARyC9SbeZ2eXuri1wCoA/uvsuZnYigH8HcIKZ7Y4Un7UHUutcZ2a7RsUOdfdnzWwcgJvM7Afufkvccx8MXpEsV7epSIN6e1QZRm939xEXR11vNoaCgoKCjReUGEb6GxH7AXjQ3R9y95UAvoV66mHE/1+L35cAOCxsp8cC+Ja7P+/uDyN5eeznCWS04+LPgcZE9CnUoknrMLNDzOxqAN9HWptvOyR69CEAd5vZR8xs0lDnA20nMTyHKg9ycON7heJ0y2FEkLBlqsqX3SRU6qhE1esyYVSr4nePKs9oLp8rZQ3DgyqbqVBW5Wqw8GUXVUWstzLuYHqLhWHyiTVTAXmlcvqGcKMHqnSwKrO/0RIauU0Pn8uljLKJUELquNXJiSRYPMUWxnMqp6dgp05JbA5NFtiAnszn6NK1NtXDJ7zGVokHW2eIe5rGgQND2fyCpi2QjQ/hOBqv7ctLa/1zixRxWEwVEfXRGHBPS/1m0wbxu6pMGT6HngqrbFgd4JlU4wOZNu6IPhsQl5IO2kv0HrS7qSRA1q9SN59dxUK+bCqg6v5RY8xUSb2oWxbno5a7pH6Mu682s6eRhnIvqreC5/YCjQngDqQ49C+4O33N3gXgcnf/fdhvczgKwF8xcFhhZpsjWZmOAPDd5v1Em00MBQUFBa2g5RXctjGz2+X/c5sCdXNfZ2/6f6hjhjzX3QcAzIm0QZeZ2Z5IitLjARw8XIXd/b1AmlziOrpvNYD/He58oO0mhs1QMcGgJHuIQvIPETytcQyxu1sch6YIW+OS7KqCpcpUA1YbxEfZFk/SZF/zSH1OkEIO0B9KWRzX/bKq6OXBaNVPPdj8ZBEFxofniMokfGQl66zzNFFd96hDMRme2h3mxj8zcum5lRIyxbW4scwLGj9eaCyZtjDpCd9MWyWxubROJObPyIG9vJ62Eb1mHpPOUU43lTYSpaXhHtQtr0hH7FcbDyuky5XSTiBt2RNd/LhwS44fNXl159g1D1wlIiqHtertl4fOXG0DKsn8SWx16cxcriReW3IgUTqo5X+K5uhQQYxN/Aopo9CoXkl8JpWuKOWtzBynsRIcK5/EOqBliWFRLvmnYD7qcvZLMTh9D4+ZH4x9K6QnG/Fcd18SmSHmIekgdgHwYEgLE8zsQXfX5OuKh8Pr89sAfuTuzRPWkCg2hoKCgk0QY2ZjuA3ATDPbycw6kYzJlzcdczmAv4zfx6H6SF8O4EQz28LMdkKiHLea2ZSQFGBmXQAOB3C/u3/f3ae5e5+79wF4bphJAUia4+sAvBNpkvi8mR04zPENtJnE0IUq5JKM9fvVbrKe2iLtabNSWKeSGBIyFTpp0lfFYkNiyEVyKhtrOBNoXC+74e1SRromoaudQdEOkhry5xeqou6o9OFSF7p8q3qWgoK6tS8R6YYst/bwZMsTZexPPSt+qLsIG1RpXTzLvnIujQYfk/qH6W6miDw9cTltSkpuqv6+M1j6HA1GYT/oc6j32F7RrlPFmweHxVbo974/ja1YOu4OiqxsmKxZKvZQJuMtu0erMjkErCk6uDhUtP78HGi8A8d3nxhY+tToFfm3lounEqUIlbD4CqlBiqKmRCAP8EXI2CRWyUAbzyooN6ZUpf1EKU4/dfNoNxEJdUkuydcoMJDLKrZ2CJvBu5AGdweA8939XjM7E8kD6HIAXwFwoZk9iCQpnBjn3huZo3+NNAu9090HzGw7AF8LO8NmAC529ysG333Eui1HWqnsYjN7CZJL7Q0YOtdBA202MRQUFBS0AEc+2+RoLuV+JSqtM8s+LL9XINkGcueeBeCsprJfoa6MG+q+I8YwmNlBSHrr1yJJN28c/oyENpsYVqBy0g72OvCz+m6grr+N5l8qUZQqUFAdO0HKyOVVTU3216eDLbdizuJgoJNzq5GoMp/eakqfQi6ZKIyJulel/ZFRSi1mlGhyjFvV40pAe+ixokyPcQdvljJ8JXM2xfA+KQsKmlusRh38YqVNPY4/xd+mYcJRlXmj+dUYwSGghiL1tCLzfa0waeMg0ZxKH4ztmVURuyfnyiZjYecQC5aJ3p68XvuEpz4u0sbuwb47Vdzg/fSZKN4+KjacThn/00L06JJw6IEIupgj12FjaxZf6vrl1I4Y9APC+lktlYIei2eeqdI0I61zubtqWXXDbjJFYg5qeaRGiTGcGDZWmNnDSG6aFwN4b+SkawltNjEUFBQUtABHnbC1J17u7s+MfNhgtNnEsBzAvfE79Nkdkl9mYbAOZQqhO1UPI9WUc+yoypfELJcE8gFx259JSqiePmRck3XZqxw7JW36qZQFN1bmRUKoet643HgZ+KyCqnQpRag1SkfRsji/W+kf207VvH0cRsrd6dCuflDRN/1SRIqpjUlPGvFxX/DI4KuRaWsIKKv6kJgGdqbOfOvMgUClS7fcQgBKX7ksq0TT0+lKV5FjI+ugiuOeyBymHiBUzaujD7lypzLlrZp2ApU5RMNhpuZiY0SKoClIjU98Jo1KpvQoL8L9FH4xGLmQhRrTp0S3c+5AAaVVfYyx+qCPGP/74oSZfQjAF939qSH2HwpgwnB2izabGAoKCgpaQHurku4G8H9mtgKJci1EmqtnIikNr0PFcrJoeWKI1NkrmgMmNi5sgYpaBKsbEEpFDVu/nBLURlXSkuizwRdVYiCxUfUoz3mDslKerH7ZrMOA2BM6mKNHw0Ap+RwmZeGmo9lEG2lTq6LHwsNEn4lVUVsJ9dnq1aOxAw21s0ojrKIy1SdDNz+1TwpZIVVrxv4psj4FKbRWlsxShKp9g+E/JmyR5hWtHgmmuu/vTCatkptKDPTN31nEICNtVu8x0nThyHPinPEiopwXW81EGuNiuogMM0NUuFSYK5tBXzI+y0RlynyWPimjA9trpaxbaP+q+N0j40zrTbBIBwYNalKHKXGcChYcX+rk1LDFSUdNpEih4h4fWhff4/Ax8bS6q2VV+dBoY1WSu38PwPfMbCZSrqTtkJQBXwdwWngrDYshJwYz2wzJrepkpAUMnwewhZktRLLAn+vuYxKcXlBQUPCCYyOmuGOB+D6P6hs9nMTwYySR4wMA7mFGPjPbGsAhAD5hZpflltvccOhCxXP70maBKK9J8VW3GKytlg5IkMu/SOlASW6DqYpWbxqJpSqWycY6dFECci4NqKFYoIyVnmayoMQpsb1Y6hJsbIWoxxnVkYuGzqXiByqX9VnC9KaTzetwa0grSpHJE9WPPmwoO0gtpkeDaJ+QHAqjW3lDvc5aBZVyqLevvfNhx5irdhE9gJ2sfvuvoh0h90yqQe9Pm62Fef9NbHMZV8V7aUVkhdUeZhV1yFC4WSVVoVzcrQOXhgnVLKuo2zhWFoDoiOy3vf1V2SHRin9SFTUCYYS4cx2PyeL+lln8sNEMqtDeMcbmvrrCHB9KJTs+i0oJusbEaNHeqqR1xnATw+HuPkjYCoPGdwF8N1LCFhQUFLy40MaqpLHAkBMDJwUz+wqAz7k705bCzM5w9zNyE8eGxfOo/FaC/imjouJT/fJvSxslKbqbPFB9zXsyZTn2fRRtC+ptQSo4IB48HUyweLIcSPoqkdvkk0pYeb1MhtQnhNWT9KsTCFesUFuJJiXNpeNpUHJlorzQ3y6SQja2evX8eWxPq4osTn65xBCQdsozdcYzz5bL8fGUnVDw0OekLWWBSCXTVAnOB/yBlA1EWx+sV6K2Xx38o2JTX1cVrYjI7oPkMA4aka/Hh/S1m0gRuQSubGrtG3oyzZS1KDrYEBfIgdp5lGRq0SDRZ2o6nBr5ue6UbLO8jlbsVbGVPukJqWapfBkoBWlEDt8xtQ/tTpuGflX4LNqWY0VH29QraSzQivH5SAB7m9mn3Z3d9DoAZ6y3Wo0aK1GpZcJyqZ3PD44GI8VvHe/62lCS1bHIz4O+qNxfW8OF/6j+hqoYtZ9NYvK2e6WQldXgxuguTWTWF1v144xZKvdJ0xeRCdmHes/4QVUX15k5VRKD05bIQ/Xwjmopv6OpNgAejwlB26g/tmoXjS+h1p/fEZ2jcmvosMpq05ymPqJLMwfwY9cvT99H9d8fMgeKCyuvp14MjLtUq3icqjkWqQ3S6rH+ORamqpvJv4wfqsJSlsM+Wyz1p7pT/R6WyoRAsJF1kR8+uqb+iEqqyq8/ttpqJGK1hQtiBlkp72ynTgjEWNDRTUCVZGb/lSl+GilVx/cy+xpoJYnekwBeDeB4M/tCZAccMhF4QUFBwUYPqpJG+ntxYzyS0ZVG6JchyeGnmNlnhzuxFYnBInruGDM7A4nbbTX8KRsKmnY7tt3C62cFo1XxoC9tthIqmou4V1ZKTY6qkrg6prqD9geT6lMZmvRcGfIxND9qqFBUrJaKOyhajzT/ylBJ3SmHxT2UEI2v7wJQkUQd/yoFkSurcLNz6Mo6tJGogtFIuWXBOrt1uUwON8nh1RsqmMMlsR71cb+sish8p4sBlt2osU9sf5VyyKqVFC8RVtrDg/XhKSrWumTLTCGfSSzbe8TzrZAeIKvWyLUQbufK2GNVVAHHK2v1KHho372ZD6oiiKZLI9s/TsqoL+ySVqRbq+oYKX6qKMBnEqlk50jmuEAegENFq8LLaFV7ok+0PyfGe9Ktg7mlZRRGwCYgMSBp7w6NNRhgZl8CcA3SIj13D3diKxJD44119zMAfBz1SICCgoKCFx8GWvh7caMXdT7RDWD7iEUbdsHrESUGdz+96f8rUPc824gwDhUvDFa9Uvgup0GVGIJyKQFWbz/GWClJIfvWc0hy1ZOuweZ0sXraLVX3yzTIXRrMRvqnqbhpP5FkdfNi6deFMorDm7VDDdJP1euuV1MSqM6ZtDeqrYLnbC1uhj1koJqF/lRKNSoa8eFFH8+W7X23XPBzaav2oTDazhTaOS6Ytno88hR9JqrcNRBLJYrd4p+ZUtjNJIGaM3NZ2IIOVs0+lziVhZcG/iV2yZqufcGNdcmXcPPs+XZVtFeUaavxDVYfhpwfBcumaDqQnPSgNgiKwnOldWbQ2JLJUXGkHDcpjrtKvjExWKapN0d8LTpU7A6ooEu5WR04OPbUyF57ptFi0/BK+iSAO2OhH0MyCfxbBCtfN9yJwwW4LcXgJeoQN3B3H3Yx6YKCgoKNFo6290py96+Y2ZUA9kP6bn/Q3UmL3zvcucO5qzZIl5n90t1HzA++4bEGFRu9NG1yC6go4YuscUpmVBfK05/KlKkUQXktl325ZoxgHZQ5zuGRyhPp2nmHlNEdVBPrRdCYLvsYzHdAKs0qqBBBb1p1lMl5Wun7wyR76mvUqLbqpO8N28ceKh3QxURDushAB9e/1pgDTftQ9YOq7SnxKCnmc/ZLmUoUvI7erpvPpJScHsaa1noHDiaxBTUSN4pPLBmvDppwldYGXpk5jM+nCQ4pF6tNi05HtXQaIh3P3bHpQKDyiq69EyEB5KjfJHHdejSO09yJ9I6T6y2Nl0vjC9nmfVLWH1vtT74ZNbNCZ65wFHjxq4pawQoAv0dSFuxiZru4+40jndRqrqSW1wotKCgo2OixCRifzexUAH+HxOPuBHAAgJsBHDrSuW2WXXUZKoYdYf+bCU8hbc5ERCk5Ut7OsaOnkHUrURqXOY7xEONEYmgkUdMEZQviLluLe0on9bvKzZkyQ+MdoguV1ocqv0PY4tZB0VQ9S9amUoIKN2SlSiZJ1tQnfTpZp9LX2nKmRC7ojWkmLq2KeL1MvIkGbI0P6UFNRuwvTWrOqujboPEQuWzPXFqzW9k1u0K9zBaH99VkTWdCu4O8Xq8IknaNHMbGFMNPZzS62glUoiM4fFQyohyj9hO1eS0N4WyiOlXRWqgGM9ZnmtgTWIk95OSnQ2JQdy9mQBHjwcR4YXYXCZaObLlchvpeUYLS12XcWHkltb+N4e+Q8tzd4u6HmNksAB9p5cThbAxvkH97mv6Hu1+KgoKCghcr2lxiQMqGvcLMYGZbuPv9ZrbbyKcNLzEcI79vaPrfUaN4GwumAPjr+B2cUaOE2STKAoMO6xhRXS4ZmZILshfV15NVq62CBFoZ6XQyr9wi6J3ixdKgXn8qZfS/0SU0gz79iaTxztyDy0Pqo/MO86VMyRiZttpXLoqtDoae0J9PVNGCVFAd0HcLf5POt2AwJN6hM2wph4p9ZWVcR7Sj0z+TtrtLpVlXFaBoT1ApQp9zQaaM1zlSxUdCHf1ew5uotEc9vCy8tEPwqpPlteFyrHqP8CjbSxb+eSAGnHrr5NKy0L6icQB6DoWuiVK4J7tEDWsUM3SYUUpaqcvK0hognxGm0fiYnBueXZ0y+I4NW8RjUhc+i2app+Ckr/FYOCVtCsZnAPPNrAfA/wK41sz+iLqAPSSGMz6/bYwqV1BQULBxYRNQJbn76+PnGWb2YyQf/qtaOXc4VdJLAfS5+03x/z+iCiu+yN0fHOrcDYelqOhXPJrSj46mLdDwpFkmhE917tTbqgqWU65emjYK1WNzIZlaSm9KLXpyI8xQowhI0TQjGumpZnMKvq/UkTGN+1dFHTwlw5L0amprySVvI1NVyYMrZ9aCJPjSab1Iw6epkpucUFJB19KPB9gBancIkWeKOP/kUnHzt0oEqtumYKWSHX8vlvtNpheRel9RfOzUZAD0vtKkgnH3rl2roqURv6JiJln641VRT8QEZBfvkTIOLZWM1BbEJtxTyjpzIa7sRzVu8ORparWg0UWek0KENjafTysTHaDDgw5gCwcfVmsidRRbJ7S/Kglm9hKkJlsaf3ti6Ez7DQynSvoUgG/I/38N4FwkDclHUE8FOlSl+qMyAwBWu/s+mWMOBvBZpDGwyN0PivIepLWw9kSa39/u7jePdM+CgoKCEbFpeCV9FMBbkbgPKaFjHb2SdmtaLPo5d//PuOFPhjgnh0PcfVFuR3z8vwhgnrs/amZTZffZAK5y9+PMrBN1n5chMAkVqw6e2CVxsdvHSKi5OKSNChE6XsholMVs1rQPqJi0RuFyv6YQ6uWCJ/tKIS8+RfS3XfT112hoUrn3SFks3bqXZMQ8IraaWjo8lXYWyYh1VpW5MjPqckXwaLSN+qSToe4oC7b08uJKwxtmL1lVCHs070TFuPeoiraAmnAAACAASURBVPaOu7xTbCkRuzlNxLm5MWK1bygRKLtWRSuDmyV2ufGcSppfHk46szQamsx4ofjSzI0Q8B5Jxd2woYhkNDUa55/FysNsrsLkKRGdJBHSS+M1l6KGQJazfQGVLN0vZZ1xnd2/WZXtz5gLDSigPWRPIZv7xu9ukQA7I7d3p4yQE+L59hfrQPTd7rqOE4eFhIl8OyRPfSfVo2zU2ARUSUgre81w95UjHtmE4SaG8U3/6xdqMsYGJwG41N0fBQB3fxIAzGwSUvj2W6N8JerKl4KCgoLRYxOQGJCym/cgZcheKww3MSw1s13d/bdAY+U2hC/ss8Ocp3AA15iZA/iyu5/btH9XAOMil8dEAGfHmg87I/Ho/zGzlyMFJ/yduw9aBdzMTkOs/LLDDtujohusojC050Knq4uXbF3bAKgzS6pFVVyhJUDJMGdKvU5unQMutzle89qTWHapnppc73wpex+vImURId0ptXlF2CWUWgUh75EySgr6vMowKSmogEWJQlkDTRq1eACKEX1Sxsc7WhToXVy0R89enSkLDyV9mVlBIaLTQgF9uIg+9JnXOms/8VCVMihR5PpwlnqUsfGU+bJvD1D5kXfXqG/GO8iqBH2RnvRouQnvIRWcGFLLgWIzYvIbbSK1WnGM5hyt1IZ2dzTIbD2AF9UhSlehuToe2Xca3BBBDX3iL/WWEL/0xaL54uNVEVMzqYQ3Zrl42t8r6eMAfmlm90AUA+7+uqFPSRhuYjgdwBVmdhYqY8XeAD6IFDjRCl7p7k+EiuhaM7u/KRx787jmYUgLNt9sZrdE+V4A3u3uPzezswG8H8C/Nt8gJptzAWCfffYsEdoFBQUjY9NQJX0NwL8jcbe1mgaHc1e9KoLa3odKqX0PgDe4+z1Dndd0jSdi+6SZXYaUzEknhvlIBudlAJaZ2Y1I3OsnAOa7O7XklyBNDCNgMVJbAJXEILpOslhRHC8Mnb9GW6pfP4mS7ieBU90WOZN664xv2gdUyznWLsgWmSGFPTxLNfxsdg2fDY6/RFKb8jJKd4NV6+hgcKrw9xrDJAnOOaeo6Eb23S9lO/Oly62K1qWF7OI/zxyopqmI9u6TIjpsSaMPBNvVPiQpVaasA5jPpEybfawZWcn1lwlz72aDqSsQb3iAelotaNoCFbuWVveHBhU1JD+p4E/W1OupV9Y+VimIrap6YAoAt0kZrT2LReqaXFvfNUA7yDIZDd0chxpNEZ+ZJbJ8K6+t/jGZxSg4jNRclknSuvbYNFRJi9w9t4rbiBg2JUZMALlopBERqV03c/el8fs1AM5sOux7AD4fq8J1In0FP+PuC8zsMTPbzd1/gyRRjInNqaCgoADApjAx3GFmH0dKiK+qpNG7q5rZuQA+5+6DVvqJD/0JAJ53928MOjlhWwCXmRnvc1FIIe+Iyp3j7veZ2VUAfoVEdM4TaeTdAL4RHkkPAWgh4G4LVP7/ITm4SAyktuJPPSXKJkh4r/JZkiJlZrnxRBajLu4k7BrwMTM8kKapuMEb1kKuybzU+TvnwXNHvaJARRNVqc6lEORy1OCr8KKZmXgZtTvk8kSRcWsbNUQKDVmlALBc/KC6SLVVSU+ersNzdb0CQEV3RRneEaduJVQ5F+qpnmLsemWivI2ybzanRoI3Aru1/emdNVO4zBzKG/qc5PgiGdnL0nZP8TLjA8iN5zRdAajGntqE1JTF59P2yOW7pQBW82kki1d5n8p+VcnsFQNMV07k4BuQ9qBAodHV9EqSBFBTYqCtFClC+27U2DRUScyIfYCUrbO76hcB/KuZzUaSvBcivRszkYbE+ajHOdTg7g+h/haw/Jym/z+FFDPRfNydqCenLigoKBgbbAIpMdz9kNGeO5yN4U4AbzSzLZE+0NsBWA7gvlDvbIQYQJ3fAjCJ1OwICSqzqLOepWyYenj1jyFx05XNKB2oNMHb1NYuaNRFfpNqq2J8BwaZq42BtZQcPKTN4wYX1QwBmZxQhJYplfhebFUKykXcUghR+8rMYGM96pjDVdHUq2cPPpNaACj1aYOEZtyEOr4qJA8NewyvJE1dxAxUegfVs2t1CLJubVayb2Xpb4vB0KERP/T5V9LckAr0leuLrT5njJanRWJgf8ognBjJDpZdJmWx1ZdTBbbhJF0FbU5qGzsqBLvx2vEc2JrildLbMmnVCfF7stgdloVopy8WQ7I1SCYqsb8Ye3Qp8HXCGKmSzGweUtxVB5LW4xNN+7dAsojtjfR0J7h7f+z7AIBTojbvcferzWw8kmy2BdKAuYQraZrZN5C+x6sA3Argr919zGWfVpb2fBbA9WN944KCgoINhjFSJZlZB4AvIIWVzgdwm5ld7u5qEz0FwB/dfRczOxHJU+gEM9sdwIlIOuLtAVxnZrsi2QMOdfdnzWwcgJvM7AfufguSloYU6yIApwL40ro/SR1tth7DlqgiTMl3xN5NtiM0cGUwPpUqVYdJUiFBvfXcRwFqyjU/KgOaVdXf8FPRwvtiW3Mc780c+OHYqq9MyDRdf1MVzY5xopQvKjNTFMxPRJlaLH4svynUKKPms6u3CyUj1V1zf4/Sa5q8NFlPb4g1PbrGxCmx1ZMpQfVXRTMjLOa1IhqF90zveVXR01Gx46qimvTAZ1IC+USmjFLE0VLG6xyqpiBGDqvi/jUh3eyhIhTX85YIb0oRsyVf1OywUs2WnggJZZp83I66YvBt9W40a+kz8VUYKWcRn7MRFQ1xnHtV5kBtJGONJINud0jCs6UnJkQjqpAc7khzRCKbFQEbuhr6WmPsvJL2A/BgqM5hZt9CCkzRieFYAGfE70uQHG4syr/l7s8DeNjMHgSwX6T+oVvluPhzAHD3Rty9md2KIRQS64o2mxgKCgoKWkDrE8M2Zqa88NymQN1e1DVy81Gf2mrHuPtqM3saSZvZC+CWpnN7gYYkcgeSXvUL4rqP2D8OwF9giJiyCEQ+Nq7nSFzncne/L3d8M0acGMxsz1bjFjY8dM3nWBdZPWAIibakpKDkWnW0ZFQaoEmSpkyaVvIVmePUpNEIHBXm1chJU3Pr0ZynBMeGrgNNv5K/rIo44PXRSf+EGpJn6201poHaf1Uh8zJqX+HgUGmJ9ol35ZKnaAMfwyupZYc+DZqFhTp6aZf+eAJVCVAqkQouyLglaZ/8YfDuRtuomYDtoUyaMs1MMVJNp8+/DoaGTUljG/gs2sLfja3akWI9aaX1/BTJfXk7/d5poDLHqzYXx3e/lLH66k1HU4CarXrYEGqgo3eWNtI0Sj8ZLzNtYf7UzywrKE00nr+bzIlrhdZVSYtyyT8FNsTVWzlmyHPdfQDAnMgnd1nmO/xFADe6+6C8dWb2LwDeBOBbSHYIIEkW3zSzbzXbQHJoRWI4J1xGv4rkcrou3bGeoUt7xodEByi/euL2R2Pa1vLS6cvE74w+NE/XyYKUQsN6qDFR98aGMVAP5JdZ/QyXxOeqJ7e8TL+Ucb+oI26OWqs1OG58g3ysnmjaAvWPAauodOlPYqvNyvdLDbX8uDwudejNGSt/FJ+rQ4+SQqrPdAbhl7W/KuqLsh/Jp5DPJ2kyeDWtn/Yn+1GfieeoBzHzHuZWLa2tNslB0ydlbGTNYtjJhtA+Zg9sWRVx6Vc1ylLfI53DevXLYap05PdWxyOfWX0hqNxRFeMTTfsA4CR2vM4WOQq5P11YJYV8N9OPC3thI/bJuZnopcfH6gs0Nl5J81HPBP5SDPaQ5jHzI2ZrK6RuGPFcd18SKYPmIVrXzE5HfVWyZpwCYI9mo7SZfRopUnTEiSGXjb0Gdz8QKcX2dAC3m9lFZnbECKcVFBQUbLygKmmkv5FxG4CZZrZTEOgTkQLKFJejEumPA/Ajd/coP9HMtjCznZAcAG81sykhKcDMugAcjnCKM7NTkaI/3uTuQ01ta1DPCEdshxanw5ZsDO7+gJl9CImf/BeAV4Tx5IMb19rPnahMb+HyOP7WajcJqMyjA0urMwlljlQ5PJYpU6LEMZRbRERVNSTQutxJg8LpNN0QRzTcjg+QWehmia7XGNt+OSxjaGYaBGXSGnjC8pHURpSwlDWT9tQSnpGWqqjVOEmNz5RVNBFb0OXl4sZJL0/lRWFJXiHDn7dQ9qyKK/adsuZcegm2vvY7I4f03Ibk1y9lOR/QTl5RZVQqbSTojYNPByk7T+zzrJeODhVMeah+MTj01MWB41sFO56rmbgbJ6uYmXs5eFLNfZcNInfmOcqZFzZtAfRyWOR0gK1ijLySwmbwLiQ7fweA8939XjM7E8Dt7n45gK8AuDCMy08hTR6I4y5GkotWA3inuw+Y2XYAvhZ2hs0AXCxLIJyDNJRvjuDhS929OaPE3wP4oZk9gGr07ID0UXxXK8/Vio3hZUhRx38K4FoAx7j7L8xseyQP8o1oYigoKChoEWMUxxCeQlc2lX1Yfq8AcPwQ554F4Kymsl+hilpuPr6VEIOrwu11PySmbAhX2rBdjIhWJIbPA/hvJOlgudz8iZAiNiIsR8U8w7ilVI50WfTPZJFq6lUdLNW62pprMsdRAlCDNInPLClrCAV6Mqm5Uloyr6laMz6AGiaDcfUIw3wgpCT1q43rKWFlvdSlUR1Eayy4qVqabIUMVI/vi60asxvtoKaDw2PbLw/fRwatvD6cCbpEVtk5OvLa4StNKUhvq0yaXZGTGFSaYk9oG1Kv/1q94YrMgRwYWok9Y3R17S2FNMoKk6bpU8+lMlcG3Kyo9CNiq1Lpl5KEmipy6nruzyWJVMliXBTO1YtQqlHRgoaJt4tktDL6tlOca/cIx5EesTuwIzSIkYNwXSWGNs6VFGqmW0Y8cAi0MjEcBWA5Zxoz2wzAeHd/zt0vHO2NCwoKCjYYNo1cSaNGKxPDdUi8jn6gE5DyPs8d8owNhs1RsWmuiCO7Se/EV5GOMqoLz+lgVddMFqmX5hjrkzISdtUgN9SsWkjmogx/KvmrWgXYXUpFKSGJF8u+oYT9mVCqlU33l9upyj+3/KUyad5ZbSm0QWi2RZJItRI0CrXhqFdWhXZDztBCLtkqUkRHXH2mPGcYQcaLEWdViDcauKgOW6yCCnE5V10+s7YH04Wo59bhMc7GK8ulZ7uyVN6wV+0rZNDSxxZc/ynh+hQ9tYLxzNuKK7SuIEtBWaUIVivnop1LIKhjhW20RKSWRgoUtTFwLaYHpO9mahhe4PGQFPSDTe9eFckYhbmuTvRtnitpXdDKxDA+0mIASCkyzKyF9ZcLCgoKNlK0uSppXdHKxLDMzPZiDm8z2xtJmb8RogMVhepPG1USM1exZOHqzMQxKDgDKkPOLWVJLqdkmKng1Eu9kfVCaRvJkxoo9o9Ra8rlyO92kTLKPF+uih7PKF/jOacIhb8zDlPi1Df4zJqTCKUptTGQ2/6JlFECq2X5IGFUwwMfRSWoRuoE9a+hJJgJWFR9NlmkkOuJIR48pEtjyilsdu3jnFRIqBRECUy/Meza6XoTmlC0jxtGF31OPvsPpSz6fR+xb7KCGigZvzXpoY6emlAWoMuKOsStatoHVGGUKlXlbDONRlSxmw0yRw9ky0l+lF4u1vTTqqyPv6WFWcHvYfTYBFRJZrYUg4PtgGSMdncfcpXUViaGvwfwHTPj92E7pLUYCgoKCl68aH+J4TNI5vsLkSaDkwFMdPdPjnRiK65Pt0Xejd3i4vevjzSvY4PNUbGv8CFRikMPB2WsQXeV3K0ZvLt2Ck0U6t0xLVOWSx/BQM5pOlc/kDmQLdz5rBSSv6obCJNBixQxJXz9lTpGNo2FIkzwasoWVfdO64ZKPCS8NZf0QHPWMKDeHr05f3x2l6mfEKPX3yhlHKrCJvkEKtLUJI+ElSEpaB/3yW8OEV3UhsKFCpzsY312Din9xrBdp2veDZ6stL3h9qNyCV141FMpOk8j4xu5OAYf1i2K3vFyTm6hIY713GJG+py5hajYnuo3N6c/fqgUxxdBTWPd7E9N78JxrcEZmahv8SocNTYNVdKR7q55m75kZj8HsO4TQ2BfpHdpc6TgNrj7BcOfUlBQULCRYhNQJQEYMLOTkXImOVL+pLGJYzCzC5F8A+6Uizqqpdg3IjyPir9EWKxSfQ4EIWiLo0z1qUpOVjVtgYq4KSHsj+3OmeOU6DVYq3YPPTjUK6khMSibpOeR6qSvia2kaSbFV3120MQpIh5cvWZw/ZQh046gz04VslZ/cdM+oGLcNSUmGbKm/WJ65t9JHAP10106PHNR31Gm9hqyUnl2XlljNG6T3+z7XHyCMuRcEj16/eRsEbX2Z8drw/GRarEqfD59vUKayok0YjfhyplKqPWZ2Y9afw4HlQ5YVRW++OxqiqNgUnt2/qN9TANFTWKgzUhjVeKdXfLbqojJqlQczQXYrC02gRXcAJyEtIDQ2UhP/NMoGxGtSAz7ANg9cnsUFBQUtAfaXJUUq8QdO9JxObQyMdyDpCH9/Whu8MJiAqo417CP7y1MdH6wE2EuVHer3lU9rHnotEyZMmmuVaJ67FyuoT7+UJpFfaxS7u7XxQ9VSrO7lGXF8z4jebsaSm45LCq2WFgSF4RVVqnWCz5fLn/S0sxxumIIpRAliRNJhjWRKqUIFQS6Xh0/dHiGtLRc/KG6gknvKU7zZOnSbCSY2ofKkBl8/QMp47hQd/wVTfuAqm9V0mr8VspNaq4N3GjY3Guoi/fEGFZPHz6n0P+OcPCZI/79udy86lX1dNM+oCLnh0sZSb8G5/PaR6iky4Z9m5Sxj/Ulm8wU8vq2xV16RIJaGF9vfQ0yS/OuNTYBVVKkxfgSgG3dfc9Ib/Q6d//YSOeOmF0VyU/w12Z2tZldzr91rHNBQUHBhsPYZVfdmPHfAD6AmAIjB9OJrZzYisRwxqirtUHQ/EjChdjRwponBvvYU5iXRvCSbebsCYqMM0zDqye37kgWNQZD7xTl63Qw0HSWwSwnSab88cEDVccd1FeZMgNz1cagVWAMgi4dlctiRCKbc0RRgjyFEozSEUo3mkS2IXvcNfjALqGiz/xP2qqzPt2qpNvZ5up5oz5QtzcdB+T155SS1M0jl0Oosb9PClnHg6SsIcJoK7GH1NIVV3/o6cGH6YIR0ej9UpSLWtdIED6ztgf7WKVfymkaitAILteHp/iZy65aY/pNWZABVP0tucBWhBudijRjlbbzxf/hHwkT3P3WyMJKrB7qYEUr7qo3mNmOAGa6+3UR9TwW5p+CgoKCDYNNQJUEYJGZzUAEuZnZcWjRJNCKV9JfIWU72RrJO6kXKSf4YcOdt2GwHBXriGyji4UfkfaITnRlsKyh1JYkQ6qHJ/HVoFPmyJ0vZTlO1JhRVYxgbIHeZCZ1zMom6cOvsQ08SWjb48EslRHdNfi2jcPlt55yZ6YGZJYqebDtlJGTJNZWC8ktfEDbgr6kXXw+yRjbYJESEcx202XF2BzixUJWrM+mUiGlAyXfrKJKUyT42oa5VVm7Ob6USbMhcmuFTn2lFPKVlOdcMjD4ehx8IlQt/sOgIo0rbkgSms+GzaRdwt+q1idh1zZiewxIx3e8tukEoJIeaol0KP1I5z0a76qaHTgIxbQ0sC5ZVYlNwyvpnQDOBTDLzB4H8DBSkNuIaEWV9E6kvN4/BxqL9kwdZUULCgoKNg60sSopsmDv4+6Hm1k3gM3cfYjEP4PRysTwvLuvpJ4q1izdSF1XV6Li7BFROVm40JtiJHy2KuoMF40nhIWoFxHJUM4hRLX/dLhQhkaCpKu1NVTpWkh1ci38lNRXr0h3E6VjQSMfzYQ06zAIpqoskHXpkzLVP5O4qXMTbQeaA4nX1DZi29SkdTasiiBkk5NeJoVkyzOkLOwqLnp20ldV+meCL3JJXfWZcqvNUYpSD6Sc9xUFHs3c2hgMKkYwF7H2SUNyVbVv9PuAfLXo1aMBCLy2PMiasGOovUATvJIg1zzFMBhsJ3U2otR1nJTNoq1IBwONEPpy5Fy3PAa9iWWqM+SXH8lxzGumUsn2g8vWGm2uSnL3NbGy3MXuvmzEE5rQilfSDWb2QQBdsdbzdwD839reqKCgoGCjwabhlXStmf2zmU03s63518qJrUgM7wdwCpJa9q+RlrA7b/R1XZ/QOAa6gQhH/kWwlIxrkEoJSurIuDoyxyph4RjKRTkrMemc0rQTqLJsZhXySq/JVdXHPZ5zkqyFTMFC7xGuJkod+mKbW7IXyC6322gHdQTiAmOqGiY77VHJiA2nN2k8s1o6mCNJ/NmX3Zi2SmVI8XUxBNJd6dCloXTvk8NyWUL1O8A+09gHEneVuvi7xrwPyhzIes/VgA39TWw5uDI8THXi7GPpnNzCcVqv3GptvLTejl8OFQTYnLVVFNiPKh3wt7ov8YXSl4Pi9MpfDy7TAcd3VSufe5C1xaaRK+ntsX2nlDnqn7ssWvFKWoPkD/vfo6paQUFBwcaGNlYlmdnx7v4dAIe5+0MjnpBBK15JDyNjU3D3EWcdM+tH4gsDAFa7+z6ZYw5G0vqPA7DI3Q+SfR1IfPBxdz+6+dzBWIYqW2PwppXiD06GqcJUlKmNQYkN1xhQTw96pajfPiNCc/mClJ1mF3ioLXNGkPfJymwNCqqRBavrh+uNFUF91Snm/sxh6jhCsqaCDImvOobkZNPpuUSwFE0OlbJGhfQ5KSkI5e4OrrpYJAv+VG8usk3pML7/qrpW5FZw4+BW0j8hU8bjulWxT5quDcNB9YCMx5dyzWe1WsSzd4qMemVQW+12PrMcllt1TocCh0huvQaViGlX0WZtCLq5iH1tOI5lZeM0ZqmkzlXplkh78CXSwcWHks/bAn1B1wXt65X0ASSV/yWoWzNbRqu5kojxAI5H/lswFA5x90W5HWbWA+CLAOa5+6MZb6e/A3AfmnKxDY3xqPQy8aHpFAfSnUNs7ZdTYhqaLVqLbhkwHJf6MvG7pnnCKEHr95kfDXVXbcwqmditmkjeuLMu+0ioQB9Xf0xSp/Hl1NXcY2KYIrqFxfHVUPO2fkg4IeSeKbe8ZY0p8CuqK5PyRqoqyBpgeUedVeKZH5OJgR+QjG5H04fzOXQtWs3JNj1Txi5RVRI/jjpRNsiBfjDZIPrmcOKYmWMB0seLFw+uDL+dqkbjx7i/Kpod+78u41cDLyc2bYFKHapjgNVXjV8ffxwphWwIZRO5fOzsn06tTbgk62THD/7iTJlMKtMmNO0bDdpblbTYzH4MYKdclgp3f13mnBpaUSUtbir6rJndBODDLVdzaJwE4FJ3fzTu9SR3mNlLAfwpgLMA/OMY3KugoKAgoY1VSUjfzb2QFuj5z9FcoBVVkooimyHxoJyXWw4O4BozcwBfdvdzm/bvCmCcmV0f1zxb1nn4LID3rcW9kJgmrXIRDLZAqBfpsGYCC/fGAWFZyjZzBj3G3ChJ5BjLqZJUOKD42qsyF4mUntwfjqN9ejJZtS5uEsxLRZpLmioKVAxUKr0qKjiUsZLeoMqQuzNlFABqQYK5dSTJ8LThqMuYJRLDslsHn5tJp90QUTTALTpPm5IerKrx0CrkqsVnUZllz8xxjefcV8p4QC4/wHLRk3TFAcuFuubWy+SQ1ohKjlvpPMqM2vTaXBQ4NLiPbrY5t9zasqxsUKWJVBFptj022L6S4+TxGMuTpI+vijJ9FylMXT24Moul2dTxYZ3QphKDu68EcIuZzXX3hSOekEErqiSdcVYjCa9vzB86CK909ydCRXStmd3v7jc23X9vpCjqLgA3m9ktSBPGk+5+R9gghoSZnYYUmY0ddliLOaSgoGDTRXurkgAAo50UgNZUSYeMdMww5z4R2yfN7DKkCGqdGOYjGZyXAVhmZjciadr3AvA6MzsKiX9NMrOvu/ubM/c4FynsG/vs0+MVBelLmxUSS08bX4bWd8icsllm0XjVvVMC0LgqMrN+KaPJQMnklBybJAVSfSvzeNd076Rjcufl0ZwjLOJC28YyGSp8L5SBKeunUVadKilNKXGkzaUWFMPn7JMyHpDzjXwyY/1XsYQr66j4/5nYaltGBfXR+chqctHL0OyjtlGeo2YftsPueiDrr3p22hO0MXN5Q3qiIXKumFdKGaU99eiNtrxOitisKhHosKDor7YgPpNWgc2ubTiRJyn3okihjcmT75ZQSY6Fa8VZmg3cL+degUFYGC+bGsIfGHzY2qO9VUnrjFZUScPq993900Oc1wjDjt+vAXBm02HfA/D5iKbuRPrifSZcrT4Q1zkYwD/nJoWCgoKCUaN9vZLWGa16Je0LgNbtY5BYfy4fm2JbAJdFKo3NAVzk7leZ2TsAwN3Pcff7zOwqAL9C6qbz3P2eIa84IlahkRJjICQFlQ5IWFTACoazWGiIMmQmYFDyR1Vof+bS6rU4s2lf7TrKMOkuowwmu9pL/FZtHM9RDw3SeWXSUenuTK+p45AGu/XFVhkam1Ofk5ilVJTPp1SUgo5KAmSgXVLZ/uC+avygEjzTd/hxVbQgXvbcSpCqe9dBxmfRfuejqBTUkBT0Odl3Os5YL3VpoqSgA4nUviNznPZntMNi0cdPjodZKsdRItImP0J+cyRps3KMqsmrhwNcfQF3zhzIspxhTZ+9P7ba6BRl9DnDJqb2BD6LSrVjktq5jVVJZvY5DJO2yN3fM9I1WpkYtgGwFxMwmdkZAL7j7qcOd1IEVrw8U35O0/+fAvCpYa5zPYDrW6hnQUFBQWtob1UScwG8Esmj+tvx//Goe64MiVYmhh1QV8+uRJOvzMaDbjRoacfeUXZxtfuR4P/KWIMFKlvU+BoSoH4pI+lTh4pcVosss6Fri7IsMmnVXXeTjmlK5ngWE76+JGqhggUrod46oavNBbWplKBsk3pddV3nMymTnp0LbuAzacNSya2suYsVF1+fvrCr/EwsGTmvJD6MVJrvuqryWRVtjlyQmnrm5OIQGyfp0qQMUumTMjJujVtjn6j4SFWG8ePjuQAAIABJREFUVpYSnQbJLBxcv58vqe0CUMVUaDfot49dkVtxtEf7hFKQuiVRrFSDGRm3unvl7FsU31RaZWeIOuexePH00ekFpwJ2zStsXdCmEoO7fw0AzOytSHFkq+L/cwBc08o1WkmidyGAW83sDDM7HclEesEI5xQUFBRsvBjDJHpmNs/MfmNmD5rZ+zP7tzCzb8f+n5tZn+z7QJT/xsyOjLLx/6+9b4+yuyzPfV4mGRKGwJSApA6whmAAwUu4GEFFuXgotrYopQWtyqG2rrZ4vLT22PacY7VLltpaFZc3KODBKyJCpVZBBBX1gFxVJCCwcIoJIjeHhJAYMrznj9/77P389rx7z57JTmZm53vWmrX3fL/bd9u/73mvn5ndaGY/NrM7zOw9cv7+cY974p6Drc8TPBN1d4Fd0ZKRrR268Uo628y+gaafzJnufluna2YPIjHgo/EpkaarQ2JQP+nQcSoRHZPvnBuqWqW+U9kkj2ejVPPvH0wK6XGjbk4NaqmuSox41s3S41OpIRmaKpOjIUrgmSJCGaS2iRLDmJRRdVybXew8FS1YVTVgkEU+V8NjYwpuEDltKI4rbWFltIwimzBRkm+NL6b7v5J1tZFwPLX7OTxDKlqcHJ9LkhP1Yk4anQzsIx0TSog6xD+bfN7maKfEtqfB0LyNTi2dt+wTHeMhFmocTLbLEiUF1R2wjioZce7pjlVsu7pQxXBvFmmP3n5ZDIrOW+3CrcFED4zPkbbn46jMOWsA3GRmV7i7mrneCODX7v4sMzsdwAcAnGZmh6Dag/lQVL39LTM7EFX2++Pd/QkzWwjg+2b2DXe/Ia79sLtfHBLAGwF8sk313g/gtoiCBqo8D+/upl3dSAxAJcmtc/dzAKwxs/27vK6goKBgzqGHAsMqAPe6+30RWHYxmhSCOBnARfH9UgAnWOWVczKAi939N+7+c1Ra3VVegdsYLow/j2uORzOE9SIAr2rbRvdPo6Isl6PaKftoqpmmQjfuqv+IyjPpIACfjkp+DnXl9xzBk2jaVlg9sTGQiuiG7LGfvKr3lXyTZSq5GG45pt9Vv8uJNaSsmTrpbCOTA5THkiNpZf+r5RPAvuEvrhlR6EIuHh/jT0+uMymNMjR1ElmfHCdbq0lBlBSUNXOvU42bf8aBmFyLuKMqkW9I9hWhVKAptoOJ3iQDxmBv2c6nwa41d1/meq9DMsihUD07mbTWle2rbVca/2yeYrMdSkHqchO0f5MwaY6T1pkOQ2oPY/V0FqnEMJIlQWI+J5X2CB1PDom+LXkfNVJxaipfDsXzw9Im2gkkyqghB+v2TK3nAzPMCpegR96qI6hbT9agRfbXc9x9i5k9juoHPgLghpZrR4CGJHILqjfGx939h2a2J4Bxd9/Sen4HrEJT2+Poci+dbiSGVwP4A8TUiKC1EmJcUFAwb0GnpKn+AOxpZjfL35tabmVtbt/NOW2vdfcJd1+JSlG3ysye0+Wzmg81ez+qRKSr4+8tZva+ducruvFK2uzuHvmOGLg2R7EYzR1CqOTcMvk0JaTBYpSRZOxEzRJkzcrgqHtXqjBKqq1UlGXaiw0Gp1xvz/hUTnjA5PPWBzXTNo1hEkhpsuypSoDV1kKaoenFG1XV/DhMuKPtPIlERjXC9N3SaRfj87iIPBTfVApiMIWG9catlayPxqcS22wHSLVBNBxtNMUoG60Sw5FJ2TBzAqlYGLT5YZFb6Lmj9gQaeTSdV8zHMTmNY6fzjb2qTyUD1vEaUOp3UHICy3TqHRptelSil/lVJxDbokNMiUGDXybaX6rjxGqp1HpUfL5QRfqFyYnTxDTCGB7JtgsQrEFdqN4Hk6ccz1kTwby7o5oRU17r7uORS+4kVOmJhs1sQUgN2bMUvwtgZeypAzO7CNVmqX/f4RoA3UkMl5jZuVGhP0dlQiqb9hQUFMxb9NDGcBOAFeEtNIjKmNya6voKAGfE91MBXOvuHuWnh9fS/qhcAG40s71iSwKY2WJUNOyuuObbaG69fQaq7BGdoO4Tu7c9qwXdeCV9MPZ6XoeKX7zL3a/u9gHbFxvRDK9kYpknmoe5DOqO96EUU9aszIZqYPWKIEPNSEzNx5rKbfXzJqvWixuUV6kXt+/U/RgiY6wmzSGdVNrMBsiyz3ppVcjpb5IyJcNUCesPhLeubdjCE1R3vTbqOKJz8T/jU7TI43dXn5KMs2FH2DS57EFpALtQx4u/AvHbaujZtcoHy/fGjdSLiEp8ZdLsY00UNRxPXCtbq47EiRoUku2jxfZJA9iVaoqgkKQhBFnWX1qjBrShGqVN8Vc3CKGoq21fFz8Q5aIcWzUKUCrQyv54ctl90T4Vajnc+lgKTmpDoBC3QcSlIYoWtbTF00Ov4tvCZvBmVEqFAQAXuvsdZvZPAG529ysAXADgs2Z2L6qf4Olx7R1mdgmqpm8BcJa7T5jZbwO4KOwMOwG4xN2ZSeqdAC42s/eiYv8XdKje+9D0SjIAL0UX0gIwxcIQFbvK3V8OYI4uBgUFBQXTR6/i29z966inPYS7v0u+b0IVdZxdezaqPWe07Cdoum+0nn8fKoNyN/X6YqihXoBqYXinuz/Y+aoKHReGWL2eNLPd3f3xTufODeyEJi/k1p4iHlBdrwxnvHb2JFBlqpNoUcsn0GTi6mDSSBejbPG+1oNoMq/nyj4EA5QK1EcjPK7GpE0UkDSkOaSItcL4sg3vyarV70mrynNVGCHrW6JeJ3T3UZ1vGp5KcUm8qiiqKftjJXQvzpB+lISTaKvEQ2L7cFKmMrX+OpZQYlDFKuulIhaHQkWUzTGptGO/k7xyWEnxqpoIRbvaAdgNWcSyjg27VyU8DveQTOZF2d6kCg6u/gDYZpVq2WEax8Brdeyinb8Qlx/eRgUQeq8kpohaV/L4So2p6EEgg2OHyaHH6T8A4EVmBne/bKqLujE+bwJwu5ldDZEGu0nEVFBQUDAX0d+pkiqY2YUAnodKH8110FHFNHRENwvDf6KpHJ7jMDSbFNtMK7tjhKYyp/AJWCok/JlCS8k8lcVkTJrOHRpA2vA8Uh9x0r8sPemAuoucEJ+ahD/2sR6XylKFr77kUTE1payrH6pVJcueCtR12sQyul2p7poOVMdL4Tj57QlyIsdG3Jduj0ASlXjY/8k2aypoUTo7RsrG4lOZNMfwJVI2qG5JNDioVxXFKB0ndo4yVs4v9SanQKTJmShNiaN3w16TUFf1B+es2HfyaViufUSFvIqAWi+Onbb9gJikm8UCwPonNp7aZKFkIV78D0dbWvcDBuovYv6G9PfC2bNMf58cmxcmJ/5t8pBpoE9TJSmOcvdDpj5tMtouDGa2n7vf322kXEFBQcF8QR9n3VZcb2aHtKTn6AqdJIZ/RzgImNlX3P0PZ1q77YfdUe2DDTQUwgOSYYaeJuo9E+xjk+h+lbCQZap+l9xKb0P2vUw9nkllVVfLG+73Uikkq74Qk6FePTFcK8VH6sfBzRKKuYcwOZI/3QY62zZAI2XJaJerxMP2KQ9pfN+zWTZMqqeO+5Hx1iUanWKNpqolgxbx5uGY2joOrL+GhFByUy+yfckwdRw0DDqLT2CZ6S62KgIEuIezxr5yyt2alInOnwxa7SEUUFTQpVCwXCccqbaKQbxIRSg1YQ63nAcAd22YXHZPct4/t9wDaEym9SLxZGmi2Ca9dJSFKpUwq6vWn1KQihvZfuLTxI6gSkKVMuN6M3sQ1S/NALi7P2+qCzstDBplt7ztWQUFBQXzDDuIxHAhgNej8nielq2908Lgbb7PYWxE0+9/rPpQlkg3HHVtCdqwSHS1DwuLIUNV/W7mldT4nm2BpjrfBtvR9JPM76SJ/mkQ+UqzaDzY6aVyGnXzOsuvnlzEdmTOQqo2Vh/y5ZRC1BOFFEFvzjZtFq+qwVPii06x6HjdySvLwUNvJBFfOGQqGFEW0SHmOKnEkG5ene3HrNKexckbRLoZSnbYoJCutJ/flQ2Hrn+zSBG0HtW2gY5PNW00xkxF2azfaDtYJ2VqaGJ9tMMo6ai0xHS0qoCgVCjtXB2vGnXm4vionMv21Yh+QxyVMtYrC5zJsvRuJXYAr6T7I45i2ui0MDzfzNahkhwWx3egKY7s1v7SgoKCgrmLHUSVdJeZfQFV4rwGRdgqd1V378nWqtsXjmZupOCTSmfIdlQBSjoj7HRQKBzJpGYsIoFW9kovjFTnppSQdVgm+vgG7Vc+z4T1wsK5NKshgIxPlO/c91iJFS/VjTSoflZpqJYdkf+oEYKeKOrZQiY6qNp+tknLInJbGSv7Jglf3iB2Hx7OCLLWn9KPEsyGPl4HcUy+MxJYzSFHxU9D3Wt2SpL+MLpH98ViqLIESzwYUpKOMIXLbFtyFTxfxjmsb7J9kzJScpWG1M5BiUPFQl6j4VkqQhKcX9ImjoU6lGXxFWzTCh13SiAaJ0Kqmb2x1bg0mhyfJnYQVdJiVAvCiVLWM3fVeQTHpGQBiyXXwm6xaKqhMHQUd8nLWyV2Tnqdv/xdqSja0FLom5U3UjszN6mZ0GA2ql004d9Y9TEo9X866q8/HH6Xl1UWUMT6afWWtXxqlWv3VsMwk+epGyQvGheXx2F+l4XhnvgpamfS51TVS/Fi0v5lVcakbDQ+9T3Nd+KyzIqu+g1tNPMu6mLnsYrsJ/yIabQ11JP6IH2ZRps2yTzjXVSLyeqoxonVrrmmjrYcBOpG59YyHXjVSfE+Ov+5eU6W4kRVSWyfzD0e1kWYo63tbCwSo5iMF8l3Dl4WiKcBbll6kRmg3xcGdz9zptd2u1FPQUFBQd9gGmm35y3M7EAzu8bMfhr/P8/M/nc31/aZxLAQTdpH71qJ1985GLeyrQjhV+lbdWj0nNMEYGTfyjiW75sUkiqpAbCRK1rl+bH43FXKSGklwG19sFg1dFI8kEqTfOtprL/GCZGpKlkc1I5gVx4nZZl1tJGITSk5kxeK7oH3+1LyDEmnPREMX22oC1s+26EhyGijWD9NFa7tZEeo1stC1/Go0GY2RaUbVT8Ru0yua7azp+YqIEiMl6o7MPtck99xWr9Byvgm07ZrlCLrqhIDK6ljwvuIb/PmEN9UGMkyyLP+mh2l0b0qkXEiquRJ3aZ2EtW+KhbqxJ4hdhBV0r+hCgM8F6hyMIXN4b1TXdhnC0NBQUFBd9gBvJJ2cfcbqx1BG0g2qJmMPlsYtqCpAGYw0qHNw0MRZaSMKejOUtFr/ky3xIxPZXU8rGrPBjL3u9qJ7HLdFpbuqurCSgh9yiyw0dwNouOmLKJ72oy3fAJN+58SuZoRgpRQGSjZpLZpkDKUSAzjiSKYieSUTtI4KlSUzVTTBh+r1aMTgHqhLqceXnMtsK6aOS/bH1J1B+OrJ5fRHqK6dzZTJYfH6qcDTbKvjJvdqmSYx5eqzYLZ99Vpgrp5peu8WDtJB5wSgPqNMpmzTHAm99NdVNmFWn9KoS8S0Wht9Fdtv0le/KKkTMWqx5My/la1P3okMcx3VVEXeMTMDkCEG5jZqQB+2c2FfbYwFBQUFEyNHUSVdBaA8wAcbGZrAfwcwOu6ubDPFobfoOnoFykx1olnFpmGsiyyK2FjewijJZFSfTc5vDq2NAqVSdPG8AJh0hNBiwb2kROzbVdCitBs56yEUp1gVEPiRvC9kJFH5TSyU60zu6GWUE7ZJLNaqL6bTHuxUMwN4dM5JCnCh6Pe/0/8PSnEqSjDYCqpwy6JKYX6eCXD9IapqQSy/Uo5DroZUOuGigCwOBmn78l5kl2lAT5cxoSjqGYMEl+VCQ9q+QRED699zkZroGSWX5w3UhEk8+D5onynCCDSFCUd9Uxh/dUxqjFVRKJ8KhPtOGd0TOg6rD+DseQh7MzHkrKtRL8vDLF3w8tjO+ad3L3r0MA+WxgKCgoKpkY/78dgZn/dphwA4O4fmuoefbYw7IymNvea6mM3UfpvCPqkmeSi6HbRxWZpI9TGQFJUSyWRKSzpOXKIsP5lpIQflxNZZ7ULxdBoUBZnsjLXYFc/kllOJqdkkb5NqnongZuQZwyoSJEFVjUCCoRvjbJNuqFmQCUodqJWItqyVupAVp15IKl3GJltLa8wmaW2g7YNtStoVBaxk4wT7QjKTjkx1CtprPp4ULgYbSOaMJ3dpnVNdvbEpuiHpSq5MeGfDijbqWIJ66UTWO1ptCeo3SG+r5VK8PIsjlJvR/nwu3Itc97VxngMk8Gfpc4ttkk7hDYofYt3tQdZZzjqmV76DJTXDkLll8a0GL8P4LpubtBnC0NBQUFBd+hXicHd3wMAZvZNAIdThWRm7wbw5W7u0WcLwy5o7rZybnwKx6SrhDKqYFxDQoWUlVKFq+pR6uZrzkZZDmiyImVoDYnhxVKYeZBFHICmZCBT0hkdUokSNHqTZNuRquSTmlyUgZJAqw0i28X9UdJcuTujnLOUJCpqLawfUqjzElm4hk+wCrXu5Y1U3CD11XZkucazRHhaxkroBAkWrySXh7WL2K06JpR41FlnWeYJdlV8akwC7QlZPIa6cym75hzV+sf8GhHx4Luqz6+fVus2ToVa+nn2u4pLFC20Tewwlch4rXp9sc7fQk+xgxif90NdMNqMLhOKbNOFwczGUE2BCQBb3P3I5JxjAXwE1bR4xN1fZmb7AvgMqnn4NIDz3P2cbVnXgoKCHQc7iLvqZwHcaGaXo2ryq1Ht0TAltofEcJy7P5IdMLNhAJ8AcJK7329mz4hDWwD8jbvfamZLANxiZldPvRPRIjSpFOMExpqHl8c2kiubRWTFy5V2CpXYK9jXsuTwwHOkcDQ+lf4x0nZIlduxWU1ty8uwh9S472urj+F/axZlyt+o2EKhrC8L8Wa9UG46NKl0MEwx42gp1Mzf1FQeq3IGd+1RKqquJYHxUGuqLj/zmgnpYWWy+coS6Q4KdL+j5zGCXaUDelIdmZynTFhdgZhie72IZ2yy5kVKopwn4u2iEtuKkJLU7rMyynRTm5W8SL2NWG9l0rWMgAH2g0o0fNNpsMHq5LteE/cZl76hoKuOReyOJSp1RbeN6JhwXyOtA38w2d6kP0vO05/BAy2fAFb3IO32jiAxuPvZZvYNNBM4nOnut3W6hphtVdJrAVzm7vcDgLs/FJ+/RARiuPt6M7sTlSJo2lvUFRQUFGToVxuDwt1vRT3HblfY1guDA/immTmAc939vJbjBwJYaGbfQUVQznH3z+gJZjaKKotKlpUGZvYmAG8CgP32G0YVzwE0lO9rJY6B3imq/wwqPSEeH0os6Wav+8Q3slGrdwrZ39ekjEzvGPXWYZdr2u3hlk8AiA1ilA2TEUoF1yYpkh8OSUFTNPE0tZWcQhquQRrKxt4en5cJtzqF0o0qjEnxRHnNuuquHTR+yDS9PSQyZdx05lJyyqfdJWxy3+jrIWXcjJHQtE2sloRZaAot7BWSgtpzOB+UXVNC/GqziExap9Tl8cZR3ftglOlwcuwOziKVtYw3UnGPFElFWTXKEHpNmIIelT5k/dXbiN/1dmz6IvEeWxbfF6kkwE2ktP6c1ppLnDdUG1S2oRLbKRN3vEcSQx97JW01tvXC8GJ3fyBURFeb2V3uru5SC1DpVk5AlTv8ejO7wd3vBgAz2xXVFmZvc/d1rTcHgFhszgOAI4/cZ57sNFdQUDCb6Oc4hl5gmy4M7v5AfD4UBpBVqPvRrkFlcN4AYIOZXYeKnN9tZgtRLQqf72bHoQo7o0kLg54obaPyVL02wnVE2ZGyut2TsobGXd2SqBNW9tqAZo6hN5JSPZpgkuykKr7wuyjURoJqq+RAday2if7z2h0NyqTO9Zp+lYKC6us95JBNIo8sjjaNyW4vpPufk2vJyIWxsj5K4HdJymil2VcZJuudKcPVg4fXKEXUrmZ9lNFSRNTI5+jYcY0ZiYnxmFgyWVcVNijxLM92clIbAvtc7UjUw6tAyTZrJ7F9WmfR4a+Ndo6IKHZfsH61Io3yfCmr5T4KLOJAab+yb7LsAtqm+1qOAc0OU7tDzPlHRUpQ6XJr0O82hq3BNtuPwcyGwnCMCMk+EfXQIKASyo8xswVmtguq19KdVoXoXQDgzm6i9AoKCgqmgx1hP4atwbaUGPYGcHmEYS8A8AV3v9LM/gIA3P1T7n6nmV0J4CeouMb57v5TM3sJgNcDuN3MGKf8D+7+9cmPaQVjAsI9ZVA831eHqJBEfi4RNjYmx0lKR2QJ3ZDJoBQAlNVR97pBKP4Q6bd68lCE0XUzOOZ60dsnW3syE6YKQRRaslxDtcnOxo0iB0UOFTPowbNIFPIcltFVzbJrY4c61T/TQ0nEr2WTi1I0mqxMlIEC+gwycvXV581VSMtC2RWUwFQCDPNVbYe/uFaJ72h8qsVvw6QvaAq3Omc4z5Si0xylbVejAEEhTgMopGK8pUaZU9BVUxWnhVaLU29E6TrvrbYZSlraSayr9j/H6dtSxp+q/j431esJ1B3FZoodwStpa7DNFoZI4PT8pPxTLf//C4B/aSn7PoBaEvGCgoKCXqLYGNpjtt1VtyFCb/+ocGlSIHWoIdsUP+lRNXPH7HlUZtFS6miPl/PIOhN/fAwJk25wH9knohFrIfT00eCbSvspeMgzBoJlHSnNpNyhRL9RPdVxk0Iq465tbhCfi5WqRr1NldtRoTHZxzrbZznJREo2/5SUUcWs1WoMmerjD09OpGihjScFVikh082rtwvtDcqG496DIrENR2drkxjhoayocVznDMcxsztoiDfrr87aK5IyemRJiMl60dezmSMinq2JiqlgRFPWC9QmwAHQmBdKHtrQhpQsZcxuq5IA66X2CWbf1XGK544IvX+yB2/0p1G8kjqhjxeGgoKCgvYoEkN79NnCsBHAHfE9aNNSocCMbNX9ABKPlQmZMWRPtYSOwZSGxeNmgKaD18h51K2OKj2lwvijUkbqLjmTsrz2ZLQSc8E4AD0t2xmM5G9Mrh2lzndMTtQbUfG8RPNNsSdE7PKohOq9ea12HPXFIp09XD9UQ5bdc5lsK9aogXpuvTq5EdusOm6VZChtJbanmodMVPZWkeJYR02Cy0v1cbxkr+9KYRZEzrmnkmdmT0hCRxpSkgzNEhFlbg3p4ldSxm64CZOxWfr1YEowWn8+T73zOO6vkDJ2jir12T71iKNgKu3dHB2ndoUehDH01MZgZicBOAeVrHO+u7+/5fjOqFL8HIFKBj3N3cfi2N8DeGNU5y3uflWnlEBmthLAp1CN8hYAf+XuN6LH2GZeSQUFBQVzFb3ySjKzAVQ59F+Bapl7jZkd0nLaGwH82t2fBeDDAD4Q1x4C4HRU+tmTAHwi7seUQM8GcBSAs+Se/wzgPe6+EsC74v+eo88kBkXQlI2iJKZeVj0+kjBPJY4kZKrGHs72FabuW11R/pJfTpNC5qTUhE2kYWPNIuqBVdggExT9+XODoa1PTtM4BtodXqjsjnRR2anSKMYCKHWfuLv6HBDKTX18tveCsvmQRhaJx8pTUUmVGCh8qafVIdTDq+2A0o2mouKvWZk+81ctE1vJJqHanANZfiKdDNHXh0v9mY9KrTC8zRKRvvR7A6yC2hMo4GbnK9g56oHEB2scg0gPHGYVljhvsjRGB2shfzO6Cx6fnW1+rnkK2CbtJFZCcypx7GS+DUYf7SEiWa9sAz2SGFYBuDecbWBmF6NK1KbWn5MBvDu+XwrgY+GSfzKAi939NwB+bmb3Aljl7tejfUogRzOfwO6o92DP0GcLw28B+MP4HoL8YvlljEecnE54qhlkliyViTkUL7ZFmRuq/qCZvO1ZUraUvwTJodDwURyVMv6it0wuUtDI9/3Jh5ao+oDXiqF2OX+c+lagIVSNh5oEcIgXHSGFSWaSFbEKXC2dmKk6uNjJy305p7UuPvGyPUTHiYF3qp95fssxoO4mSTwj2rFZSIIm9+PYqoqR9dLV6Zktx9B84S9Rf1u+1DXIjvPsDVLG4+rswJ+8jj+dHVSllG1WQyagxlup/3ISGXneUvaxOh1wLLT/eU9NHElk+d0fS8p0sSaZ0GwxfIa2PVRrAzKuI/xZZbnau8Q0Ip/3NDNRYOK8ltQ+I6iPzBrUZ2TtHHffYmaPo+rxEQA3tFxbiyVMUgK9DcBVZvZBVBof/cX2DH22MBQUFBRMjWnkSnok2y5AkLnVt6bmaXdOx2vbpAT6SwBvd/evmNkfowoEfvnk22wd+mxheBzNXU1CPbNBYuK4ruvWmGReone5T76TlOwtKoXRRt5tuQ+Di9Sd8tGgy0uzHWJ0W/i4+YTooci4lM1/KT5FYiC5Wi0si4R1XBgaSfqochkyZFWmqv7pyGDYQ99sllECGBHR4/6J+oOBJkPW+wXv2ixMj818QFQFjZQSIozvRYb/3+R+5Fqq7iGrr0XQjNcfBtRdlqln0/Fkf4p0c1/Mm+VimfvR+smXPpf9ea0UUhWmG85wfFQ9RolTJSPqe5Rx8xpt09qWY0B9bKNN49LXTIqngt3SWt6UAKUHtVJTAlGJgfNL60VBTTtpj5ZjQFOiTOI875M5sylLFjgD9MgraQ3qstU+mKze4TlrzGwBKrn9sU7XdkgJdAaAt8b3LwM4vzfNqKMYnwsKCnY40Ctpqr8ucBOAFWa2v5kNojImX9FyzhWoXugAcCqAa93do/x0M9vZzPZHtdTeOEVKoAfQDDs5HvWg9Z6hzySGAQC7xvegLkM/aB5eFk5vx8klkd1aDWjLhfXvHSH7Q2poJllWvSzdVFVXTiaruYqHmDDvCTkxqJyyJy7ZqvcmxLaxKOpwuJ5H5p5FiKkbIbfvSDb+AQAMk+KJgWU47CDj4kCYuNE26qBlITgNyi9uWbC/ZSppRXfsdbCUZbptCtBZmga1xns8UH/pajvIUjaQ84nUsvz5LccArKQkoGyYrF9Z8yvjMzP0Z9tz6v3Yh2rOzIzT1L2r/UTtYNEnw6p4iGutyE7aAAAT/klEQVQWqe2pkVc+uY/Wi/NbpRL+JlTrzjZrn7NvMqlbjdlRF/1NNub1pzFj9GoHt7AZvBmVqmIAwIXufoeZ/ROAm939ClQv+c+GcfkxVIsH4rxLUI3sFgBnufvEFCmB/hzAOSF5bEJsOdBr9NnCUFBQUNAdehXHEC/sr7eUvUu+bwLwR22uPRvA2S1lbVMCxbEjsmO9RJ8tDAvQpCyh0N4ozJaKVGVbVP8Ly9osib2GeK4q3SgAqP8Ada/qiXIsaVjmYnSLfA8pR10PWW3dbYcUR1kW9cqqMydzVLZOxqdVoZ+Dco4hEYM8RBgTb6k7omKq7yaT1XqxrzVXIJ+ndaAFUPXGPK6ePlQI7z3FeUMtx4Ame03MOgCajFYdrnhPZf2j8akeQWyzzg8yY/UXoVSgfURPG2Xm5IfKrjm2ei3NUSqBcH6MSpkyd/aNSoi8fiApe66UcZ7pvTm/9H7sG70fpdlsjLXfMmswn6G/q58l500TZT+GzuizhaGgoKBgapQd3DqjzxaGBWimnAgPswFRRDKmTIPQ6Dcuet5B1VmTcWksI809GrPA61UaWRfsejcVLUi9hIWvCxFEWeyDLZ9Ak+LoeaNJ/TYl5zFm4aXJtUPPQwrjpkKPNMsOjfo/KZSWHiPqi8Ey1fVnUVRZArndkmOHxaeyWN5vSE5cFxRTmT7rlcU4AE2JJ9ti8yVSxjFWfTelgjEpY1+rEpuSjjJ8epfp2FFg0xwbnAOZ4KlSYZYLRaWWLGiPxzNPJO3/3ZPjtLmodMNna/+zzdom9kfmYaRjzLF7nZRxvNS7cJooEkNn9NnCUFBQUNAdyn4M7dFnC4OjycQj2njwRDke/vjKmKhL1nD9LKJSWRgFANXfZpuWkNm8QDZJ5GxUJzOyNX0G2Zp6KZPiKLujPKweK2SWhyfnqZ59lF80/PQE+b5m8vEHQwrKttNURs7jyhLZX1p/sk2VJujBMyZltL/oMzhOE0KbsxwPZNKXS9l/yHeOp3p2keUqSyebV5ZL6XNUyjgWGkuReeHQ7pNJWupNRKh/P++T9aVKNNkczewvOv9b76f3zCKu9Q3Le49JWZa0gW3ReZTFZlB6v0rKtH0zRK+8kvoVfbYwFBQUFEyNsoNbZ/TZwrAQTTeYYL7+zuZhsnTVy5LhK7NSKrEhKSO7Un9wsvndpCzz6SbjVeZIRqVsjIxKffnp+SSMb3Mk2RtUP//R+MxsIKo3JrteqtNA8yqHt9TGJGZBpSr2kUoHFDaSbRprbPLU+FSWSBaurJ/eK9dLGRn5bqIAH4i6aoTxHi2fQF2aYt4eZc1sp3o8sWu0DymNaOwA26SSBZ+tEg/7QyVFSipZCnO1lvKtpv3GMVFPH1Wk857qwca263xk9LKOMe0hOqc4b4eTMu1LSj8qrVJS0LdzliuJ/ToqZVuRI0lRbAzt0WcLQ0FBQcHUKF5JndFnC8NGNB3Bg34rGyNLURZItqMMR5nlWHyOJvdR/S5ZkeaSIRvSfRMpeWSbr6i0QfakSWwYESpUZzApa7BSZaxJSmOM0B1GH6IR2ZFjebEwctoYtA8zX36m/FIdMpneQFKWebEos6U0oh48rPbtItGwzzMPI5UUdV7QBKT9xbFV2w1128r6+bw/ljK2T+uqbSHYdu3LHyXnZfdgXVV/z7qoV8+i5LiydLZJbRrZNrXsI+0P5hzVfqO3kdrasvlBwV7tGBwnzUzAtqj9pwfGgeKV1Bl9tjAUFBQUdIdiY2iPPl4YIjxyNwmVfepXk0/jRjjqOCQeQ8wDPyE+0wPUwSoD5ffjkjLVSZMVZbNSpY2x+FRGRd2r6lhpl1DWyePK+DIWPhH0b+DApDJAg5JPCCMng/uknEZ2fq+U8RKNAyDb1P5gH2V7LygzZ/u03xgVroyV/atMOsvlpEISr1cbz1hLXYA8qpr1Vi8z9pGyZvaHRm5ncSka6U5wPPUZjP7Vfkui+Gu0mNLiqUmZ9lcWpc15oz8hjrtKe7RFaF/Ss0ilcrZTJRU+V+vMduq8re1YMDMUr6TO6OOFoaCgoCBH8UrqjD5bGCYwmZoJHaNUkO0jKaxnIMkRP5Cxb7UT0MtFWRbvqQyf31UHTm8XtVnwWrWQkflq/anH1vqR1R0jZbolY+v9hiUKe0JEFOqERZqq+dITlKZU4qHu+mtSRnuIsk56dulWKBwy7SPmE1J2mvn6UyrRscnGQRjtprhmkUa8s7+USVPSUSkii3gnC9fnkeUqTc285LLcUWyTMm4+T8ed49kuWy6v0TlKaL1YHxEU00yqiZdco97aH2ynZk3lNSpBsX36DEqNKo1mEeAzQLExtEefLQwFBQUFU+NpFK+kTuizhWERgEPjO5sm+zGcEhvZaxZTshn1HFLmS0amLIyMRvXn1IWq9wTjGFTfShammTx5reajIfNS1pn5jfM8ZYZktqqjZzyE6tb3W1V9br6xWTaookVQ8qfubhZl0bMrkjLSMc3VQx/4USl7ZlLGX6zGGmRtpzSi9hWyZpUYKARp/USPsIht0jcFx0L7NYt85nmq9+YYKzPnnFLWzzmgNoZMl79XyzEAODo+dS6zHSp9qY2K/a8eWRwnjdJmDEjWX+qVxPZpf2S/J/ahSgKsi+5pQkld+4jSi2bp7RGKxNAefbYwFBQUFEyNYmPojD5bGJ5G0w+fdEcMBmSRyjDJcDTHe5Y3SX2wsx2pyJA0cyt10XrvzJefzFgllcxlImPmWY59UiGVNui9UstmGVlTB1VZn2z2kDFH9YZhvbVfeVyZO5+tjJW+8Fr/0fhU3TurpX1EhqxeOLy32gZ4XMc1iyMZTY5n2WE1+pd9rXYpSg9ZPim112R1eDo5jxKgSgesl9pFsliJLBeRsnTOkSwPk44nx077kH2sz63ttR3gOKr0yHaqVEvpUaU0XqtvcZUaZ4jildQZfbYwFBQUFHSHIjG0R58tDLofA20Lh8rx66oPpQpkd8p61POFUP/yTF9Pz6JMV6vKTF6jUgSfPSZlwcw0poLkbpmyrCwLKPd1VsbH47W9gpO9ISbk55LpfOmnrrpm1j+zc2QR5cdLGa/V2IaBGJSNQpvZl1p/5k1S/TPrl0gb41I2rKyT0mCW80fHk9KKsnSyeJUe6UGjbx5KRmrH4DzUvmQdVNqj59m+yXkq7LEvdbczlWSy/E+so85lSjLZrnRaRklXbSSUnFUaoqSiHkjsX/VUolSi+yyMxWePDQIl8rkzZmVhMLMxVNN0AsAWdz8yOedYAB9BNY0fcfeXtZ5TUFBQMBOUXEmdMZsSw3Hu/kh2wMyGAXwCwEnufr+ZPaO7W24EcEd8T6g0o5xVOqAXkTIcZXoHTL4NvtfyCKDJhpWpkuGpBMLZqOzp3uS8YJMDUtfhqMMmsROkHjWUXtSrh6z0NPE6uivEIGXrylTJMCeS48rcySZV18/+VOmMEoUy5N9Jnvv8kBR+LGXUiys7JdvUjKtkp2pLifxDNdW0MuTo44mvNosGyNJ1TNif6mVG9q2mGfaXsnWOt7aJfaOSQJbNN9szgX2ujJv9q3aRJNi/JjGwPsrSObZq02BbVCr8enyqVEVPscxspQPAfs1sG9ke0pmEvZUoEkN7zFVV0msBXObu9wOAuz/U3WWL0Iysuig+ZUYtbPkEmpNRX466SPAHkbnV6UTP0gPwBZFtVKLPiJfBZll8norv+h7ke0HfoSv4w9cfcfaC4I0uE53YKdH4W+TNr/XnC0sfyHbqS+qelmNA81enCd34ElPjLe+jwX23J+dpNnCC9dIXNV/oqtrhGGtqcl3AQzU0oIsFv+sYcyqpKo/P1pc7X3a6WPN5+sLnd325c65oXdmXOm8pY+sY0/1Vr9UFMlt02Dea3j0jPrslZVSp6bWcZ9omzil15ea11ybP0IWBbdK3eA/SbhevpM4wd9/+DzX7OYBfoxqfc939vJbjVCEdiup1c467f6bNvd4E4E3x73OQx+bON+yJ2kbL8xb90g6gf9rSL+0AgIPcfcnUp02GmV2JpkGyEx5x95Nm8oz5jNlaGJ7p7g+EiuhqAP/D3a+T4x9DxYlOALAYlbLg99z97vSGzetuzuwV8w2lHXMP/dKWfmkH0F9tmWvYaepTeg93fyA+H0K1E++qllPWALjS3TeEHeI65B7SBQUFBQU9xnZfGMxsyMyW8DuAEzFZ/fNVAMeY2QIz2wVVqNid27emBQUFBTsmZsP4vDeAy82Mz/+Cu19pZn8BAO7+KXe/M3SAP0Fldjrf3buxHZw39SnzAqUdcw/90pZ+aQfQX22ZU5gVG0NBQUFBwdzFrNgYCgoKCgrmLsrCUFBQUFBQQ18sDGZ2kpn9zMzuNbO/m+36dAsz29fMvm1md5rZHWb21ijfw8yuNrN74vO3Zruu3cLMBszsNjP7Wvy/v5n9MNryJTMbnOoesw0zGzazS83srhibo+frmJjZ22Nu/dTMvmhmi+bDmJjZhWb2kJn9VMrSMbAKH43f/0/M7PD2dy7oBvN+YTCzAQAfR5U67hAArzGzQzpfNWewBcDfuPuzARwF4Kyo+98BuMbdVwC4Jv6fL3gr6h5kHwDw4WjLrwG8cVZqNT2cg8pd+mBUbtJ3Yh6OiZmNAHgLgCPd/Tmo4pxPx/wYk/8LoDWwrN0YvAJVLPUKVMGun9xOdexbzPuFAVUMxL3ufp+7bwZwMYCTZ7lOXcHdf+nut8b39aheQCOo6s+cHhcBeNXs1HB6MLN9APwegPPjf0OVS/XSOGXOt8XMdgPwUgAXAIC7b3b3cczTMUHl+bfYzBagSubxS8yDMYmA19Y8x+3G4GQAn/EKNwAYNrPf3j417U/0w8IwgnrmoTWob7Q4L2BmowAOQ5UdZm93/yVQLR4AukwiOOv4CID/iWZmm6UAxt2deb3nw9gsR5WN59OhEjs/4m3m3Zi4+1oAHwRwP6oF4XEAt2D+jQnRbgz64h0wl9APC4MlZfPKB9fMdgXwFQBvc/d1s12fmcDMXgngIXe/RYuTU+f62CxAlUf1k+5+GKrUc3NebZQhdPAnA9gfVVq7ITR361DM9TGZCvNxns1p9MPCsAb13Jb7IM9nOidhZgtRLQqfd/fLovhXFIXjs8vssrOKFwP4g9hr42JU6oqPoBLrGUg5H8ZmDYA17s68npeiWijm45i8HMDP3f1hd38KwGWoNticb2NCtBuDef0OmIvoh4XhJgArwtNiEJVx7YpZrlNXCB38BQDudPcPyaErAJwR389AlSJkTsPd/97d93H3UVRjcK27/wmAbwM4NU6b821x9wcB/MLMmL/9BFQ7SMy7MUGlQjrKzHaJuca2zKsxEbQbgysAvCG8k44C8DhVTgUzQ19EPpvZ76JipwMALnT3s2e5Sl3BzF6CKvv97Wjq5f8BlZ3hEgD7ofpx/5G7ZxuOzknE7nvvcPdXmtlyVBLEHgBuA/A6d/9Np+tnG2a2EpUBfRDVTgpnoiJR825MzOw9AE5D5QF3G4A/Q6V/n9NjYmZfBHAsqtTYvwLwjwD+HckYxKL3MVReTE8CONPdb87uW9Ad+mJhKCgoKCjoHfpBlVRQUFBQ0EOUhaGgoKCgoIayMBQUFBQU1FAWhoKCgoKCGsrCUFBQUFBQQ1kYCqaNSBPRs0SFZnYss7H2Gmb2bjN7x1beY7GZfTcSNk732jeb2Zlb8/yCgu2NsjAUTBvu/mfuvnq267EtIBHBij8FcJm7T8zglheiynBaUDBvUBaGghRmNhr7EVwUOe4vNbNd4th3zOzIDtc+YWb/ama3mtk1ZrZX63Vmtmekz2i99mVm9qP4u83MlkT535rZTVGX90xR9/9l1f4c3wJwkJSnzzez/25mXzaz/wDwzeSWf4KIsg3p5jvW3K/h8xFgBTN7v5mtjjp+EADc/UkAY2a2qlOdCwrmEsrCUNAJBwE4z92fB2AdgL/q8rohALe6++EAvosqarVbvAPAWe6+EsAxADaa2Ymocu2vArASwBFm9tLsYjM7AlVKjsMAnALgBV0+92gAZ7j78S33GwSw3N3HpPgwAG9Dtf/HcgAvNrM9ALwawKHRX++V82+OthQUzAuUhaGgE37h7j+I758D8JIur3sawJdmcB0A/ADAh8zsLQCGIz30ifF3G4BbARyMaqHIcAyAy939ychU223erKvbpLjYE8B4S9mN7r7G3Z8G8CMAo6gWzk0AzjezU1ClZiAeQpXdtKBgXqAsDAWd0JovZab5U3jdFjTn3KL0RPf3o8rnsxjADWZ2MKq0yu9z95Xx9yx3v2Aa9SY6PX9Dm2s2JudqXqEJAAtiAVuFKlPuqwBcKecsivsUFMwLlIWhoBP2M7Oj4/trAHy/y+t2QjN752vlujEAR8T3U5HAzA5w99vd/QOoVDAHA7gKwJ/GvhUwsxEza7dRznUAXh2eREsA/L4cm/L5rXD3XwMYMLN0IZN67wpgd3f/Oio100o5fCCAn6YXFhTMQWQeGAUFxJ0AzjCzcwHcg+730t0A4FAzuwXVrmGnRfkHAVxiZq8HcG2ba99mZsehYuKrAXzD3X9jZs8GcH3YeZ8A8DokeyK4+61m9iVUKp7/QpW9lujm+Rm+iUod9q0O5ywB8NVYQAzA2+XYiwF0NJgXFMwllOyqBSliq9GvxSby0732CXffteeVmiWY2WEA/trdX789ry0omC0UVVJBwRRw99sAfHsmAW6ojNf/p8dVKijYpigSQ8GMYWY/BLBzS/Hr3f327fDspQCuSQ6d4O6PbuvnFxT0M8rCUFBQUFBQQ1ElFRQUFBTUUBaGgoKCgoIaysJQUFBQUFBDWRgKCgoKCmooC0NBQUFBQQ3/Hy+fuMnPISH2AAAAAElFTkSuQmCC\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#for comparison to original data:\n", - "show_num(621)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/qdev_wrappers/fitting/Fitting from saved data.ipynb b/qdev_wrappers/fitting/Fitting from saved data.ipynb new file mode 100644 index 00000000..1e13b13b --- /dev/null +++ b/qdev_wrappers/fitting/Fitting from saved data.ipynb @@ -0,0 +1,930 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/anaconda3/envs/qcodes/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", + " from ._conv import register_converters as _register_converters\n" + ] + } + ], + "source": [ + "import qcodes as qc\n", + "from qdev_wrappers.fitting.least_squares_models import DecayingRabis, DecayT1\n", + "from qdev_wrappers.fitting.fitter import Fitter\n", + "from qdev_wrappers.fitting.plot_analysis_by_id import plot_analysis_by_id\n", + "from qcodes.dataset.plotting import plot_by_id" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from qcodes.dataset.data_export import get_data_by_id\n", + "from qcodes.dataset.measurements import Measurement" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fitting by run id from saved dataset: 1D data\n", + "\n", + "The Fitter takes a model, and optionally also a guess and an r2 limit for rejecting \"fits\" that fit the data very poorly. By default, the guess will be the found using the guess function of the model, and the r2 limit will be None.\n", + "\n", + "The model includes information about the paramters to be fitted, their labels and units, a default initial guess for the parameters, and the specifics about the type of fit that the model uses. Information specific to the method of fitting is handeled by the model, not the fitter. \n", + "\n", + "Let's say we want to fit the following data, with run id 1, using a least squares fit:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "([], [None])" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_by_id(1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Perform fit and see result\n", + "\n", + "We will use the model *'DecayT1'* from the 'least squares' fitting models. Least squares fitting uses the scipy curve_fit function, and returns, in addition to fitted parameter values, a variance matrix." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "model = DecayT1()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can check the parameters and the function that will be used for fitting:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'a': {'label': '$a$', 'unit': ''},\n", + " 'T': {'label': '$T$', 'unit': 's'},\n", + " 'c': {'label': '$c$', 'unit': ''}}" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'a*np.exp(-x/T)+c'" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.func_np " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We initialize the fitter using the default guess method and no r2_limit:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "fitter = Fitter(model)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To fit to an existing dataset, we run fitter.fit_by_id. This requires arguments:\n", + "\n", + "run_id: id of the dataset to be fitted to\n", + "data_name: the name of the dependent variable you want to fit for.\n", + "\n", + "It also needs at least one keyword argument telling it which part of the function is a variable, and what data to assign to that variable. In this instance we are using the function *a * np.exp(-x/T)+c*, and we want it to recognize *x* as a variable, and assign it the data array for 'pulse_readout_delay'.\n", + "\n", + "If you are uncertain of the names of the variables in the data, they can be read in the data dictionary, or you can enter some random string and it will throw an error that gives you a list of all the data names in the data dictionary.\n", + "\n", + "Running fit_by_id will return a 'fit' object." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "inserting {'model': {'method': 'Least squares', 'function': {'func_str': '$f(x) = a \\\\exp(-x/T) \\\\sin(\\\\omega x +\\\\phi) + c$', 'func_np': 'a*np.exp(-x/T)*np.sin(w*x+p)+c'}, 'parameters': {'a': {'label': '$a$', 'unit': ''}, 'T': {'label': '$T$', 'unit': 's'}, 'w': {'label': '$\\\\omega$', 'unit': 'Hz'}, 'p': {'label': '$\\\\phi$', 'unit': ''}, 'c': {'label': '$c$', 'unit': ''}}, 'r2_limit': 0.5}, 'inferred_from': {'run_id': 1, 'exp_id': 1, 'dept_var': 'data'}}\n", + "\n", + " Analysis saved with run id 224\n" + ] + } + ], + "source": [ + "fit = fitter.fit_by_id(1, 'data', x='pulse_readout_delay')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "By default *fit_by_id* will save the fit results. If you don't want it to do that, set *save_fit=False*." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "fit = fitter.fit_by_id(1, 'data', x='pulse_readout_delay', save_fit=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can get the results as a dictionary, and/or plot them. The fit summary box next to the plot can be with or without information about the standard deviation on each parameter. Setting *save_plot=True* will save a pdf and png of the plot." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'param_values': {'a': 0.00020686194829005843,\n", + " 'T': 1.7792117433091448e-07,\n", + " 'c': 0.0016790061428729915},\n", + " 'variance': {'a': 1.1218893660499864e-10,\n", + " 'T': 3.483289026855526e-16,\n", + " 'c': 1.6822488724253306e-11},\n", + " 'start_values': {'a': 0.00017795331091539001,\n", + " 'T': 2e-07,\n", + " 'c': 0.001690665726187212}}" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fit.get_result()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "([], None)" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fit.plot()\n", + "fit.plot(show_variance=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Save analysis, and plot saved analysis from the experiment database\n", + "\n", + "If you didn't allow fit_by_id to save the analysis automatically, you can save the fit result to our experiment database afterwards. This will create an 'analysis' run, with its own run id." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "User schema at /Users/Lillian/qcodesrc_schema.json not found.User settings won't be validated\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "inserting {'model': {'method': 'Least squares', 'function': {'func_str': '$f(x) = a \\\\exp(-x/T) + c$', 'func_np': 'a*np.exp(-x/T)+c'}, 'parameters': {'a': {'label': '$a$', 'unit': ''}, 'T': {'label': '$T$', 'unit': 's'}, 'c': {'label': '$c$', 'unit': ''}}}, 'inferred_from': {'run_id': 1, 'exp_id': 1, 'dept_var': 'data'}}\n", + "\n", + " Analysis saved with run id 219\n" + ] + }, + { + "data": { + "text/plain": [ + "analysis #219@/Users/Lillian/experiments.db\n", + "-------------------------------------------\n", + "a - numeric\n", + "a_variance - numeric\n", + "a_start_values - numeric\n", + "T - numeric\n", + "T_variance - numeric\n", + "T_start_values - numeric\n", + "c - numeric\n", + "c_variance - numeric\n", + "c_start_values - numeric" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fit.save()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also plot the saved analysis from the dataset later. It is not currently possible to show the variance on the plot from the saved analysis. \n", + "\n", + "By default, the plot is not saved by running *plot_analysis_by_id*, but setting *save_plot=True* will save the plot." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_analysis_by_id(219)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "## Fitting by run id from saved dataset: 2D data\n", + "\n", + "Lets say we now want to fit the following 2D rabi measurement. We will use the model *DecayingRabis()*. Initially, lets not set any criteria for the r2 limit. " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "([],\n", + " [])" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_by_id(6)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Perform fit and see result\n", + "\n", + "The fit is performed in the same way as it was for a 1D dataset. We can retrieve the fit results at a particular setpoint using *get_result*." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "User schema at /Users/Lillian/qcodesrc_schema.json not found.User settings won't be validated\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "inserting {'model': {'method': 'Least squares', 'function': {'func_str': '$f(x) = a \\\\exp(-x/T) \\\\sin(\\\\omega x +\\\\phi) + c$', 'func_np': 'a*np.exp(-x/T)*np.sin(w*x+p)+c'}, 'parameters': {'a': {'label': '$a$', 'unit': ''}, 'T': {'label': '$T$', 'unit': 's'}, 'w': {'label': '$\\\\omega$', 'unit': 'Hz'}, 'p': {'label': '$\\\\phi$', 'unit': ''}, 'c': {'label': '$c$', 'unit': ''}}}, 'inferred_from': {'run_id': 6, 'exp_id': 1, 'dept_var': 'data'}}\n", + "\n", + " Analysis saved with run id 220\n" + ] + } + ], + "source": [ + "fitter = Fitter(DecayingRabis())\n", + "fit2 = fitter.fit_by_id(6, 'data', x='pulse_duration')" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'param_values': {'a': 0.0005647538800030159,\n", + " 'T': 3.505714222730291e-08,\n", + " 'w': 1273021.6184877206,\n", + " 'p': 3.040156533555888,\n", + " 'c': 0.01005772038592006},\n", + " 'variance': {'a': 0.6130620896564186,\n", + " 'T': 2.0594564679445174e-14,\n", + " 'w': 3.1751885490918917e+18,\n", + " 'p': 20215.13264502227,\n", + " 'c': 2.0463646074653032e-10},\n", + " 'start_values': {'a': 0.00014999418615408051,\n", + " 'T': 1.00502512562814e-07,\n", + " 'w': 31258846.903218467,\n", + " 'p': 0,\n", + " 'c': 0.010063517863704075},\n", + " 'setpoints': {'frequency': 5500000000}}" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fit2.get_result(frequency=5.5e9)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The full dictionary of all fit results can also be accessed as follows (but is very long):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fit2.fit_results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As before, we can plot the results by running *fit.plot()*. This will return a heatmap of the full fit, and a plot of each parameter vs. the setpoint. \n", + "\n", + "Least squares fitting includes variance on the parameters, and I'd like to see it, so I've set *show_variance=True*. \n", + "\n", + "Since I haven't asked it to, this will NOT save the plot." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "([,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ],\n", + " )" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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1aalIzHnhNFBuMmotMtq1SmY301jn8XoJXY1sdzXy3DLeW8vIVMvmuVpvsevh8fVEemgtw3pdp6sdqlnN5HGZ7+3JupG7SW+Hw+FwrIiqrNu7W5sF12E4HA7HGtnoL+muN67DcDgcjjVgJr3PrFvomVVah8PhWCfcpLfD4XA4SrMWX1ibGddhOBwOxxpwb3pvcvppyO2DXWzxO/ikzCRNAHoaMul3aHp9AOaSGq20Tk0iACb9Ngke0/FYntak3wEglJiZpElPQwDGvS4Tfi/3ijuTNKhJxKTfBowscTZp5OqJSb9DKDGttA5AO63maWT7raSWSz2n/Pk8DTASwiwNgFZap51W87KMe10iDZix4X1J8/IDTCdjJOoxbvNrev28/Nl+lkZWniwNIK/HrPxZPc4ltdyeYvmLtgBMBfO5LVk6414XwNRjUs3DZ+lkX8KsHottkZV/sbZoJTVCSVbVFln5i7ZnbZHVQbEtxv3eMddR2bbIyp+VZ7QeV7Jl9JoevY6ya9GUtV3qOhpqC3stmrpOVt0WTa9PTyt5WxSvo2IaAON+j5oMVtUWxXqM1D+ua3q9WN4j7unHadVhOBwOx8nCOB90HYbD4XA4VkARIucaxOFwOBwrobp+i65tFlyH4XA4HGtC3It7DofD4VgZ5cx7wjizSutwOBzrSIJXalsJEXmXiBwUkVsKx6ZE5HoRud3+37pE3M+IyIyIfGIdi7YoJ/QJQ0TuAlpAAsSq+qiR878DvKhgywOBHao6vVLcxQglZk94lLsGO3h47W5CSQDYG23lgvAQRxIjm+2kVR5euzvfPxBv4ZLKvlySeCDewp7gKADTqQnzkOq9+f6ReIyLKgcBI+GbSRpss1LOmbRBKAkXVw7k+3NJjQvCw0P7mXwQzMs/Z4cmv1Zap5cGXGjTn0ka9LTCjoI8MZSEHf6cDV8jwcvDt9IakQa5/BGMRHHKn8/PVyTh4sr+kfDtvA4TPCasfe20yrjf5WxvOt8fqM/OwOTfTPskeLm0tJ0aKS3AhNelnVbzL8zucIZeGub7ocTURNgdDACoeRG9NMxlj3vCo/TSMC9HzTPtk9VdFr4mJv5kpT0Uvun1hySUWfgs/pQ/PzRp2fD6uew0q+dI/bzusv3sZa1MYuxbL7WZp9zd4Wy+H6mfn98RzA3te6R5vU34vTx8Zuse72i+74vikbLN1m0WP3vTOJOSFt88DomoBK1Fw0/6Hab8+aHwmTS2iE9ibW8xyqTfGbrOAJrSz+0ZZak0iv/zfCVitzd7THhgqA4yJvxeLjEupgEsmc7xosh6LqB0NfA24B8Lx14DfE5VrxCR19j9Vy8S901AA3j5ehmzFCdjSOpJqnp4sROq+iZMYRGRy4DfVNXpMnEdDofjVKKQv+tx3Gmp3igiF4wcfhbwRPv5PcDnWaTDUNXPicgTR4+fCDbSHMYLgQ+eaiMcDoejHLKa9TC2i8hNhf2rVPWqFeLsUtV9AKq6T0R2rsXK9eREdxgKXCciCrxjqQoSkQbwdOB/rSHu5cDlADv2hIsFcTgcjnVHWdWb3ofLDKtvdE50h/E4Vd1re8brReQ2Vb1xkXCXAV8aGY4qFdd2JFcBXPyQ+vEveeZwOBwlOcEr7h0QkbPs08VZwMETmVkZTqhKSlX32v8HgWuAxywR9AWMDEetIq7D4XCcdFSFVL1S2xr5GPBi+/nFwEfXxfDj4IR1GCLSFJHx7DPw08Ati4TbAjyBQmWUjetwOBynCjPp7ZfaVkJEPgh8GbhERO4TkV8GrgCeKiK3A0+1+4jIo0Tk7wtxvwB8GHiyjfu09S+t4UQOSe0CrhGRLJ8PqOpnROQVAKp6pQ33HOA6VW2vFHelDLd4ytPrA7q1O2ilEXt8I5+8tHKYEOXi0Ej9GvVZ6lIFjAwv0nnmdUDHmtCrHMbHjG5dJB3GatM0POOF06NDyjyd1Eg553Wenmr+aHqhtGiIz5iY8KF0gA5dNXl3tE07TYls+JA5aiKMe2b+pS4KdIjUyDu7epiWxvSsfO+CwIRviLkIx7wIzwomATppm47GdNTYnyCEKOOeCd+QHlVZmOvp6wzzOqCdmvgRgo/SMHXPuBdSlRDP/raItEdXI1rWvsgOAtZs+Ib41G36oQSkDOirkTe20ohOoa5ClKbn5XVl7FoI39GIVprkdZXZ1RBz2Ta8Ch6DQl1FdDShVyh7TZRxG74uobUptWWPcpuKddX0TFnHpDJSVxEdmwdAT01dZWUfk8DaZOKnpHTSAfPWvuw6qVlZ7fB1EtgyZNdJPHKdGLsaeVmqtv5N2ub6TfLrJLNrbKiuiteJsWu0rrLrqiHhMWVf3XUSD10no3WVXSdZuVPSvD1M+TVvD2CR64RVXife0HWyPqzfmt6q+sIlTj15kbA3Ab9S2P/JdTGiBCesw1DVO4CHLXL8ypH9qzEa5BXjOhwOx0bBTHo71yAOh8PhKIFzb+5wOByOFVnnN703Ba7DcDgcjjWSuicMh8PhcKyEKkSp6zAcDofDsQJmSMp1GJuWmJQjaZtWmnIoqTFjF4XvaQWPlAnPyGin/A6TXiuXEKYorTRlxno6nSksbu9LSlMGTFoPp1u8OJcGAgxUaaVBIa+ABC/3fDvpdRn3Ypo2SnZ59eyFdiit0E6r+UIsoSSMe30mrAfWhqf4BXlhRz0OJRXaaiSGiXrWRiP1HPcSGqKEWX6qJk5smrqThkNvpzYFGp7k9mX5zFonr3sTpZ1SkCwGhEIevuElhGguz2ylCT0rtW1r5pytasvmU5OYpvUImwq00pRpKyXtaUxPAwaatUtoPdqa8DVJSFA6VhYZJTEDPHrWAVyv0GYmv5SaxHSsx1VPElKFgW2FngZEWsulkb6khJLmUtAKfTzpk9ryDPCI1CNSc53k8mCbX4WEULq5JDsLk9kXqT9U96GkVIjs5/4x8SINGGDrQoUkkTxMKD1CSfN9HyWx9hlb/ZHx9YRQBrmtPorPwqRtpB6tY5YcjXKPzz7ZNbVwg+zhMWN/YSeJkGoCtq5DSfABz37HfJQIoW1t2q9CQkpq29ITBYSKLa8nmscBaCVCatslIRmZbA7w8fKygZEwH7IS4jTukyCFOOu3rOoJftN7w3FadRgOh8NxsnCyWofD4XCUxA1JORwOh6Mkbk1vh8PhcKyIUUmt33zIZsB1GA6Hw7EG3It7DofD4SjNZhuSsusLPQ7YA3QxXsBvUtV02YiW06rDSNTIY1tpwFxa40gyDsB03CTSgNDKM6eCNjv8OXb6ZjH5hpcCC5LFRD06VqI5kzToaZjLCyf9Djv8OSatJ9wJGVCThHEr2R0kTWaSOnvTrWZfA3zSwmL3bSa9Lg3PyAnHPSN1nEmMLPdIMsZdgx25vNGXlHGvx6TftuF7NCXKpaYDfGaSOvcnkwC00jqR+ni5l88+k347ty/7n6U/l9a4P57MZcSRBviS0vD6efxx6TFu900Yj4PJGACduEo7rebpeaTUbNmaXp+mDHKJsSdKTwOOJE0TV6v00jCX0fqihBLTzPK20uIs7XZaoachHTW29tJwSF6Zxa2ReSONiLSSy2Z7GuZbXlbS/LqoeRE1iejZr4UvqZG22vyzuMWJzlDivLw1iQitrBTMW8AD9Qv5+UQa4OXS1oW6CkmoSIKXSUEXiVssaxY3y69ir89M5pmoR4SfrzmdlSELF0pMSJJLkI0sd8H7ahY3KfyCztonyz/7TmRkeWZ1u1hcn0zyPBw3Sn0rCw5sWpLHy+zN4noovqS5ramVzC7sD09EZ3G9gmx5PdhMKikReRLwGmAK+AZmMaYa8GzgIhH5Z+CvVXVuuXROqw7D4XA4TiabSCX1DOBlqnrP6AkRCYCfxay58ZHlEnEdhsPhcKwBVSHePB3GX6nqgcVOqGoMXFsmkU1TWofD4dhopCqltg3AzSJyvYi81K5yuiZch+FwOBxrIJvD2CQdxtnAXwE/CXxfRK4VkeeLSH01ibgOw+FwONbIZukwVDVR1c+q6kuAc4F3Yya87xSR95dNx3UYDofDsQay9zA2Q4dRRFUHwHeBW4E54EFl455Wk94RHvuTOoeSCe4abOfe/jYADvTGmY+r1iMmTIRddlVbnFc9AsDZ4VEmvU6eTkerzCQNAO4bTHGgP0Errubna37EjoqR5J5VmWVXOJvLXjO5YyutAXA4GudI1GQuNvtx6lP1YyZDI8vdFrbYFczl8WsS4fspM1Z6ejge5/vR7jx+Pw0IJaUZGOnp1qDD9rCVy3bHvS4JHi0r0703muJbnXOZT6p5fIC6b+ScW4IuW4MF2W0m88zi7422cjRuMp/Y/JOABKHqGXnlmN9nS9AZit+zXn9bSZ1WWsvjtpIa/TTIv0CeKA1vwFiwIPmteVEuad6rPr00zOuyk1TpppUhyWVVIhp+5tl3QMPr51JRMNLbLL1OWqGXhsQFCXAoSV6Wht830tiC5DMqSFs7SZV+Ggx5dC3Gr3kRVW84fqqSS0U7acVIa+3bwQnegszUS6h6C3LpUGI80byuIg2Md910wfNt8UaUxQ+l6C02zaW4qXqmLLZt0oKE1tSlqYtifK9Qj1n8rOyR+svG96wn3SyNLH4m+43S4Jh3GDyU0MvKnwx57zX1ldWFn6c3Gt/URTyUdy6XL8SH21gPNtN7GCJyHvB84IVAE/gn4FmqemvZNE6rDsPhcDhOFqoQb5IFlETk/2LmMT4MXK6qN60lHddhOBwOxxrZaMNNy/B7wI2qelxvL7oOw+FwONbAZvIlpao3AIjIDwG/DlxA4f6vqs8sk47rMBwOh2ON6CbpMApcC/wD8HGglP+oIqU7DBFpAj1VTVYM7HA4HGcAm2nS29JT1b9da+QlOwwR8YAXAC8CHg30gaqIHAI+BVylqrevNWOHw+HYzKhuqjmMjLeKyOuB6zD3dABU9etlIi/3hPFvwL9iJktuydzfisgU8CTgChG5RlXft1qLReQuoIVZMT5W1UeNnP8dTEeV2fhAYIeqTi+XbqI+R5IxDsXjRgoaVwAYFKSc2X47rnLUN9LVptcnlJhxMfLOHf4cDTF1Oel32B5uYd/AeIOdHjRpxVXubG8H4O7ONsbDHlOhkcXurMyxK5hlT3gUgAsrB433W13wfnsg2sLBaAKA/+zs5Jbk7Ny+ipfQDPpss+ltD1s8vHkP456R4VYkHpLNHknGOBo3ub27C4BWXKOfBLmEuOrFTIQ9dlZaAGwN2kz6ndwjrEdKpD6t1KSX1d3RyMiKu0mFSL3ca2jdi5gIekwFxr4tfodxv0fNepYF6Kmp907q009DOknFphVaWa1RlmSyxzBdkHLWWJCG1iRi3OsxrqZdWl6NVlpjNm7k6XW0wZGomZe16sWM+aZsWwJTzi1WcuyLkqjQ14LsN6nRSY19R6LmkDO5qh9TlSiX/U4F88ZDbCYdJbVyVyu7TSscjZt5eTPpq5d7XE1o+AO2BKYtG14/lzFnktCo4Bm3lVToFOTQRRlpJikelQQX00sRBlbS2x+RxfbTYMirayAJiXh4vm0TFqS5AL5nvMMWved2kgUvxb00pGPbPStr1YtzmWxNYsb8Xp4e/oL33iy9Xhoyb+XjyTEeZ5MFz76S0CikldVvUfJr7DNpZZ5w87pb/UjMEgjJJlFJFXgI8AvAT7EwJKV2f0WW6zCeoqrR6EF70/4I8BERCVdn6xBPUtXDi51Q1TcBbwIQkcuA31yps3A4HI6TzSacw3gOcKF9eW/VLNk9Zp2FiPyDiDy8eE5E3lAMc4J5IfDBk5CPw+FwlGY9fUmJyLtE5KCI3FI4NmUdBt5u/29dB7NvBibXGrnM89TTgKtF5BcLx0pJsJZBgetE5GsicvlSgUSkATydZXy0i8jlInKTiNw0Ox0fp1kOh8NREjXzGGW2ElyNudcVeQ3wOVW9GPic3T9edgG3ichnReRj2VY2chmV1EHgicD7ReTHgFfCcUsDHqeqe+1ygdeLyG2qeuMi4S4DvrTccJSqXgVcBXDxQxrru6SWw+FwLMN6qaTLfBXXAAAgAElEQVRU9UYRuWDk8LMw916A9wCfB159nFm9/ngil+kwxC7bd5kdiroBWLM/dQBV3Wv/HxSRa4DHAIt1GC/ADUc5HI4NiJ74Se9dqroPQFX32R/Ya0JERA03rBRmuXTKlDZ/XFHVNwB/DtxV0s7FjGqKyHj2GfhpzELko+G2AE8APrrWvBwOh+NEsoohqe3Z0LndlhyKP0H8m4j8unVAmCMiFRH5KRF5D/DilRJZ8QlDVV8/sv8J4BOrtbbALuAaEcny/4CqfkZEXmHTv9KGew5wnaq2yybsS8qk16Hl1fFQulbeON2vM91tMIgWilsJY6bqRm65pzHHDzUOc14l8147nXtfbXp9Jv127g32bm879/Sm2NcxstiZfp1evC1PtxbETFR77KwZb7bn1I5ydvUou4MZAHYHM+wOZmhXjVzyUDzBvmiSvT0zD3WoP8Y985MMEmNrqkLVjxmvGKno9to8u6otzqqY9Kb8eXbXZsxy7hhJ63Tc5EBkHgIPDca4uz3Ft6OzAOjHJt3QN9LQsXDAtmo7lwVvD+c5pzLNRdUDtk6VXhoya733TsdNjkRN7u5MATAfVRmk/pAsuBFYT7iVLlNhm6027bMqM9S8KJeQ9jRkPqkxGxtJ7x2DHbSiWi5LHSQm3cAz6r9GMGA86DMRmrbZFrYZ93tUvQXtRaR+LpM9OJhgLq7nnn57ScggXZCmeqLU/Iim9XY7EfYY8zs0PLOfeWzN5JqzSYP7BjXa1nPxfFKhl4TEI2lWrJS07kc0g34u880kuZmcuJNWmY7H7OcK7bhK13qT7SfB0PKfniiBpNStrXU/GhoYzqSkWTsZWWkl907ctZ+H0kQJPHMdVL2Yuh8tyG69CC+p5Z5/EyvLzdLrp0G+gfFGOyQh9hIqVuac1WXVi/P0Mtlv5rk3SztLL1afRGVIgh3auIGXDHkJNnWqQ7amBfl0lBrbs3Zaz5ftVqGSOjz6+kAJDojIWfbp4izM9MBaeTrwUuCD1j3IDOau4WPeyXizqn5zpUSWe3GvBSz2eCKAqurEWqxW1TuAhy1y/MqR/asxE0EOh8Ox4TBPDydUVvsxzK/+K+z/NY+2qGoPeDvwdvs6xHagq6ozq0lnyQ5DVcezzyLyDVX90bUa63A4HKcj6/Wmt4h8EDPBvV1E7sNMTl8BfEhEfhm4B/j59cjLvg6xby1xy/qScuojh8PhGOH4nIUX09EXLnHqyeuTw/rgvNU6HA7HGlCEdPO5BjkulpvDeG5hd3JkH1X9lxNmlcPhcGwCNuPQi4icD1ysqv8qInUgUNVWmbjLPWFcVvh8w8i+Aq7DcDgcZy4nftJ73RGRlwGXA1PARcA5wJWUHPpabtL7Jeth4MmkKikXhx12+h3ODo5yftX4NvxBfRf3dKc42DMSxtagSpz4dCIjv9zfHSdWL5dzdtIK51dM3N3BHBcEM1wYGjHBw6v3cmSswf2Rcetyz2A79/a2cqhvNAJzgyrdKOSe2MhkD/ea3FXdxs6q6cDPrh5lTziTy2zPDY/i1+8kGjeSv5m0zsF4gn02/X2DLRzujzEX1aytE+zvTnCrtxswUtCpSpvd1VkAdgVznFuZ5kG1+wHj8TXSgLb1JHokHuNwPMEB6y13etBkNqpzpG88vn5PdxF4KQ0r35wMu+yotNgeGvsfXL+P8bEuIUaOmSL0tMKc9Qw6nYxxODJ1MRM1ONCf4O6OkR0biWxCxUo5J4Iek2GH7aGRIF9UO8i436UmwzLZzJPuTNJgNm5w1Hqrva+3lW4S5jJJD6Xqx9StzHYi6HJubTr3Vpt5h828lWaeZlvW9tmkQSupcWRgrpNuGhKl/oJXXUmpejHNwMhk99SOMu71CjLcOPeIm6XfSSu0UpP+fGw843aT0NaHl9seWJno9sq8tXXAuN+jYb0Kh5LkstGsXnppSCc1Et9OWqGTVHIpeWJXg8tkrnV/wFSlndta8yIaXj/3vJv9L3p87aRVeumCJ95+GgxJiENJqYfG827Vi6l5US5xrklMtVDXxiZvOH3riTfDE82lslViQi8lKNiXeautWnlyZnOxTrL//TTES9XWrU+oKYl3AlwHbb5HjF/DvCj97wCqevtqXghcbkjqHOACVf2i3f8tYMye/oCq/mDNJjscDsdpwGZ7wgD6qjqw78EhIgGr6PaWm7F5E8NeDV8OtG3if7R6Ox0Oh+P0QYE0lVLbBuIGEXktUBeRpwIfxizXWorl5jAusW91Z3RU9a8BROQLazLV4XA4ThcU2HxPGK8Bfhn4NuYh4FPA35eNvFyHURvZL06KbMPhcDjOcNbrPYyThV059Z3AO+3qqees5HCwyHJDUi0ReUAho2kAEbkUmF+jvQ6Hw3H6oCW3DYKIfF5EJmxn8U3g3SLyN2XjL9dhvB74hIi8WEQeYrdfwvg3OS6f6g6Hw7H5EVTLbRuILXa5iucC71bVRwJPKRt5OVntZ+zLer8L/IY9fAvwXFU9xh35RiDEY6c/xrjXx5N55qycseFPkqrQGhgZ35FWk34vRBOrFPCVu6sxd4yZkbbzxrdxYMx4e31gfS8XhQfZExgPqZNByi5tsds3D1k7gznG/D25J81BMsVsr06ra/JKEtMnh6GRAI7X+uxqtrigaTzjXlg/xAWVw+yxMtsLghkuCGZoV/cCcCgZ467BDu7s7wDg3u5W9nfGOdg2grVBtM3IVa332UYlYqrW4azGnClLbZpzKtPstrLgiyoHubS6n8h6AZ1JG+yPJrlvYLzP3tfbyoHeOPd2jd7he9FO4tRDrDwz88a7vWo90NZmOasyy67QyHovru7nIbV7TdnVo6NVjliPrIfjcfYNtnDQSpDvmN9GK9pDlBSkmn5CM7SS3kqX7ZU2OypG0rs9bPHDtQM0rdQUYKA+rcTIbqeTJoejcQ5bWez++Qnm4wo966FXVRDR3JvuWDhgS9hlqmLKMhW0+aHqIRp1k35FEhK83PttK6kzHTeZiYys9/7uJPNxlZ71LBynnvVWa9qi5seMhT0mrfR0a2AkxJlUtuZFJLYdMhlrJvE9Gje4pzdFOzZ595KQQaGeAi+1noGtp92gx0TQZWvtqLkOvIGR4lpZa4JHP12QEB+OxphPtuWed0c9+YLxPFzzTV3V/QFjwYAtBdlvUTabSWb7VoZ7OB5jPqnmMt9uEjJIgyHfS4GXENjvTd2PTB7Ws28m080kzT664Ek3DZlNG3Rtu3SSkG7Ba3DRI29WjmrBc27m8Xdd2EBPDyUJrOfb5wG/v+rIy520HcMvLhfG4XA4zkgUdGMpoMrwx8BngS+q6n+IyIXA7WUjLzkkJSJXichDljjXFJGXisiLVm2uw+FwnDZIyW1joKofVtWHquqv2v07VPXnysZf7gnj7cAf2E7jFuAQRjl1MTABvAt4/5otdzgcjs3OJhuSEpEdwMuACyjc/1X1pWXiLzeH8U3geSIyBjwKOAvoAreq6veOw2aHw+E4PdhkHQZmEaYvAP8KJCuEPYYyS7TOA59ftVkOh8NxOrM5X9xrqOqr1xr5zHLm7nA4HOuIWaZ15W0D8QkRecZaI59WCyjNpB7Xtpu00u0ciLZwV8/IZO+en2Lf/ASzc0YOGbcDvK6PJAu/DnqBsq9hJIeHmmP8YNzE/ebYOZzdmGGP9Qa7PWxZD7BGwtdKa+wfTHJ4YLy9zg5qtPsV+l0jL0y6ATLwGFg1RdsbY19lK9+u7wGg3hywtdnhrIaRju6uzbGtMs+Yb2S8PkpPF5qp4sXUfOMVFSCKffrdEB0Ye2ZjYR/buMU3571KQliPGW+Y9KbqHbbX2my3UtKJoJvLDQGmKm08SXNp6GFRjnYbdKxMeLoXcH/sQaYOEQVfCapWsliLGKsZaeRUrcNUtZPLSuv+gKoXc3bNSHy3hF1mozozfeuNtl9nplNnf9940o0HPmm88JtGPMUL01yi3Kz3Ga/02VI1ZZsIe4yHPZpWmtms94nUz6Wjc1GNmX4tlyTfNagQRT5xZOpOUyO7FZulHybUKhGNipGWjld7TFT6uSffuh/lnmsBukmFTlxh3kph93XG6Qy20RtY2W3sG5m1LtSdH1hPuIHJqx7avCp9xoJBLmudrHTwRYmsdLSXhHTikGlbd+3BTrpRwCAyeSWpN6TgEU/xvZRKaNspjKmHg1xi3AgiGsEArzDGEqtX8JI8TjcO6Q7Mdd2PA+LEI7ELCGWS5cw7bhgkpkyBya8WRDSCiIqVf2dy2kFq7J2PqwwSn05s0h8kAb04IIqtVDbxhhYrElECf6HuKn5CxeZV9c13JMsrTn06VHK5bbyeix5tPpXUK4HXisgAyNxCq6pOlIm8YochIg/eqO9dOBwOx6lENtbTw4qo6vjxxC/zhHGliFSAqzFuzWeOJ0OHw+E4Ldhgbj/KIiLPBB5vdz8/4mR2WVZ8NlPVnwBeBJwL3CQiH7BucR0Oh+MMRszwYpltgyAiV2CGpb5rt1faY6UoNYdhV2V6HXAT8LfAj4pZgeO1bm1vh8NxxrL5njCeATzceq1FRN4DfAPj9nxFVnzCEJGHisibgVuBnwIuU9UH2s9vXqvVDofDselJS24bi+LCeFtWE7HME8bbMP7TX6uq3eygqu61Tx0Oh8Nx5rE538P4c+AbIvJvGJ8ljwd+r2zkMh3GM4CuqiYAIuIBNVXtqOp7l4soIncBLcwbhbGqPmqRME8E3gKEwGFVfULZuKMMNODeaBvzSY2Dg3EO9IxS7Ei3SatdI+6Y4vptH7/j4UULcdNASAam8eNIOGq9zCapxyD1c6+bc9U6Y34vlx920gpzUY1eYuSAUeIbCaCVA0rfw+t7eJG9sBTU90g75vx8J6TbqTDbNPLIQ80mO+pttlY6ADSDPr4ofesRNVXjEdX3Ult/ai7c2Hre7XlILLl6Q72AQVjhcM1IS482muxtbGGyYfr+yWqXyWqXppWKepISq0+QeVwNYuphtCBv9Hwj1+yb+hFbrgRT/lZYo1U1EuMDtQmqtYhx6/11S63LRKWfS0UzaWUmvRzTPmnBHXSaCmnsowP7IBwJiUKmhu75dY5UUryqsbVSjXOpLUAzHFAL4jyfQFImKv1c+ul7SscL6dlrIOoFpIMgr8skrTCQGrNWorw/VLwwoWIlxLVqRLMyoGG969b8eMhb7XilT+glVHzrVXUQ0huExFYCnQ4C4rbJq69mkZlM0ouf4ldSgorNqxJRD2NqVgZbDWxeVjrqVXvUAp9eaNqhGwX0ByGRlQynfZ9B4tG1MtBZAE8RK031AiWoxFQC2+5hRDWMCW1ZAi+l6sf4VRO+GsYM4oC+vS4GUUAU+URWBt1NqiM3U0V8xbMy4iBMqFjpbVae0E/y71XFN3lHgU0/9nNvvYMoIEk8Bn1T1jSRYQmxKOKZ/AD8IM1lvibtVb/gvCSbUCX1QRH5PPBoe+jVqrq/bPwyHca/YvylZ4smNYDrgB8vmceTVPXwYidEZBLjs+rpqnqPiOwsG9fhcDhOOZusw7A8FvgJjPU+cE3ZiGXeYKlZ9yBA7iqksVoLl+C/A/+iqvfYtA+uU7oOh8PhGEFE3g68ArOm9y3Ay0Xk78rGL/OE0RaRR6jq122Gj8Q4ISyDAteJWX3nHap61cj5BwChfUQaB96qqv9YMi7WnsuBywG27hldhtzhcDhOHJttSAp4AvDgbB1vq5L6dtnIZTqMVwEfFpG9dv8s4Pkl03+cnRzfCVwvIrep6o0j+T8SeDJQB74sIl9R1e+XiAuA7UiuAjjvwRObr/kcDsfmRNmMrkG+B5wH3G33zwW+VTZyGW+1/yEilwKXYGbVb1PVaIVoWdy99v9BEbkGeAxQvOnfh5nobmOeZG4EHgZ8v0Rch8PhOLVsvp+o24BbReSrdv/RmB/qHwNQ1WcuF7ms88FHs7Dgxo+KCIWho0URkSbgqWrLfv5pzPKART4KvE1EAqAC/Bjw5pJxHQ6H45SynkNSIvJKzOJGArxTVd8ycn4rZuG6i4Ae8NI1+Pn7w+OxsYzzwfdiDPwmCwtuKLBshwHsAq4xL4QTYPxQfUZEXgGgqleq6q0i8hnMI1EK/L2q3mLXmT0m7oq2ooSSUPUiQi8d8rypSu6dVmJBEhZ+HQioBxrYA9U0l05O1HrW46qRudY949GzYxegnx40OdAbZ9+8kfAebTWI5ivIvJEAegNBYslXaUwrSlpLoWm9ho4N2DrWYWfD6Aqmqm3G/EEua+2nAbNRncM9I1Wd7jSY7dQZtE3+dHwkEryC510NldTKH6UeU2tETFhvtVvrHSYrvVzaGnoJ/STgiPW2O9OvMdOrM2+90w46IWnfR6y0VRJbFitZTJsJfi2mUjPladb7TFjvsWPhgJof52WJU+ON9FDX5DXfr9LpVRj0rGR44EPkDT3mq69IaKWfTSNpbdSMjLVRGdAMB7mM1RMlVaFnJcgz/Trd+ZBOz9RVNAhIBh5YybTaF6pyKWuQ4tdjwsqCbLYeRrmUteIneKK5t9Mo8eknAYes99v+wEhLk8z7beINv7TlgfgpfrggA65WMpms8baayVgzMu+0gzigGwXMdsw8XRz7JLGH2nbPZKXimXYR33jCzTz7Vhr93KsrgO+leX2BkY/HiZdLV+d7VWbbdeNdF9BR6apnvPp6Vpbr+yn1WpRLVwMvzfMASFVIUo/E5hfFRn7et9fZXFJDUy9vE5PJQnl8P8XzFjzhVisLdSXCUFlUIVFTHjCebuPEY2C9BrfWcxhpnToMEXkwprN4DDAAPiMin1TV4nrbrwW+qarPsaM+f4cZzl8NN2Fek0hF5AHApcCny44alXnCeBTwoGySpCyqegdmeGn0+JUj+28C3lQmrsPhcGwo1u8J44HAV1S1AyAiNwDPAf6yEOZBmBfvUNXbROQCEdmlqgdWkc+NwE/ap5XPYTqQ52P8Ba5IGVntLcDuVRjkcDgcpz2i5Tdgu4jcVNguH0nuFuDxIrJNRBqYF6bPHQlzM/BcABF5DHA+cM6qzTad0nOB/09VnwP8SNnIZZ4wtgPftZMk+WoxK02OOBwOx2lP+eGtw8t5q7DD838BXI95SfpmIB4JdgXwVhH5JkYK+41FwqyEiMhjMU8Uv2yP+WUjl+kw3rBKgxwOh+OMYD0nvVX1H4B/ABCRP8OoSIvn54CX2PMC3Gm31fAqjO+oa1T1O3a++N/KRi4jq71BRM4HLlbVf7WPS6V7JIfD4ThtWV+V1E77GsF5mCGjx46cnwQ6qjoAfgW40XYipVHVG4AbrPo0my/+jbLxy7g3fxnwz8A77KGzgWtXY6TD4XCcdqxuDqMMHxGR7wIfB35NVY+KyCsyZSlmYvw7InIb8DOYhZBWhYg81uZxq91/mHUXUooyQ1K/hpF6/TvkiymNOgncEFQl5uLKfu6OtnO/bmUuMpK9mXadaL6Cbz3ESiykAaQNK/kbSwi29Nk9aaStF0xMc2HD+Dw8qzJDwxsQqYl7OB7n3t5W7m5PAbCvNcHcXJ3EesKVvoekgoYm7WQiJmxETI4ZWe7O5jxnNeaYCtsAjPl9UoT5xMglD/XH+MH8do50jPR0tlNj0A3RKJOCipE0WqlpuGPAWL3P1rpJf6raZUulS73girefBrRiWxeDOve0Jpnvmf3+ICCO/AVZphgvn5kcc+tUm7Fqn/HQTF+NhQMqXpzLJWP16CUh85GRrs4Pqsz2jOfdA3PjxIlPmsmZrUwysNLOWiVm23ib2lZjayOIjHTV/mxLEQaJn8tku3FIPwrox2Z/er7B4XQsL6eIEvjpkFfSiWqPHU3TroEsyDwz2wdWGgtGuhqnC3LMKPaJYp95MXXleSm+t+AhNvATakHEmPWO640tSEgB4tQjSn1iK1VNUiFKfTK9oRa8uQ4Sn0SFgRhbQj/B99JcOlqtxoxX8+BGBqseSbrgVTkpevq1/3PPvJIiI8PtHpqfD4ME9YW61X+nNSOD1Vx2K6TIkM0imreVCEMyWhGTdrEtQz/J7ar4CaoyJIXNwmW2ZenCsWkXy5aVt1h2XxfayZwbLvsdrBPrOyT1k4scu7Lw+cvAxceZzVuApwHZi3o3i8jjl4+yQJkOo6+qA/tOBPYlu833fqPD4XCsM7LxFkdaEVW9V4Z/PZT2915GVnuDiLwWqNu1vD+MeWRyOBwOx+biXhH5cUBFpCIiv40dnipDmQ7jNcAhjIzr5cCnALfSnsPhcGjJbePwCsw0w9kYFdbD7X4pyqikUswSre9co4EOh8Nx+rG6Ce0NgV2Qbuit7kwxVYYyvqTuZJE+UlUvLJuJw+FwnJZsog5DRM7GLE/xLTsvvRPzXsYvAXvKpFHWl1RGDfh5YGp1pjocDsdpyCbpMETkVcDvAz8AqiLyVuBvME5kH1k2nTJDUkdGDr1FRL7IcbrJPRGMeSk/XuvwiOqdPLp2F98ZM53m1ycv4NbZXdw3twWAdqdKmni5p82x+oBd4y0uGjdS2ksaB/jhqlkXfaffIpSEtvVOuz/YQigJqS5M/9SCmM6YWZBeVaiEMeNWarmrMc+e+gznVacB2B7MMeH38rjttMqheJxeGubHAklphMYjaziewDhUfOs9t9JnqtpmR8VIRbeH84z5PWpipKkpQjutcjQ2T5lHowbdJKSXmPRTFcYqCx5la37MWNhnPDD7k2GXcb9H1cpyfZRI/dy++aTKXFyjbWW6g8RIUWvWvkYjyj3h1vyIMX9A3bfeZf0BoSwIMrJ0u9a2blKhm4TEtm7j1KcRRIxZSW/FS6h6cZ5e1YsJJRn2iIpHPw3y+P00WNjX4Sm7QIxsteIZ20NJrJdj6x3XekBN7VRflHpE6hNbD7KRekPXARjvv5kkNPCSIft8+z8pyD+z+Flamaw0C5PFMRLYlFAy24YlwqPpJcjQfpZuRpZebhuZVHVY9lMsX5bm6PE8zUJcTzRPcymSgk2LpTuaXsZS6S6W3lLctOzZcgibSiV1OXCJqk7bFwN/ADxeVb+ymkTKDEk9orDrYZ44xleTicPhcJx2bK45jJ6qTgOo6j0i8v3VdhZQbkjqrwufY+Au4HmrzcjhcDhOOzZPh3GOiPxtYX9ncV9VS7kHKTMk9aQ1GOdwOBynP5unw/idkf2vrSWRMkNSv7XceVX9m7Vk7HA4HJudzTIkparvWY90yqqkHo31PQJchlm16d71MMDhcDg2LZukw1gvyi6g9AhVbQGIyBuAD6vqr5xIwxwOh2NDo5tKJbUulOkwzsMsSp4xAC44IdYcJx2Fmwc+Pa0xkzSYjhdeYGwEEY2KkXv2BiFxNySdN9LQdq/B3f2t3JOcB5jVRDJVXhpCWlXSqr0y6gl+PaZWM2mN1/o0K312NVuAkakG3oJ0NE597mpv47a5XSavqEJ3ENKLjJS0HwUkkU9ivdGSiPEcW/zlYr28AuApXpDiBcYeP0gJ/DT3ABv4Kb4ovjd8JRc9eSYquUfW1Hpnzb2GpkKaFrySWk+fQ77KRPG8Bc+h4mkuaPS8Ba+onqg5P/LcXvR4OuqxVAteR3Uk7DG/5uypYvoyuj/0mWMY9WI6atvQuWOjL2bO8LF1GrMYtWW1nG52FFmtTetqwyZ7whCRqUwttRbKdBjvBb4qItdgquc5mJc9HA6H44xms8xhFPh3u8Tru4FPq47+ZFqeFZ0PquobMcsCHgVmgJeo6p+txVKHw+E4rdh8zgcfAFwF/ALwAxH5MxF5QNnIZbzVAjSAOVV9K3CfiPzQ6u10OByO04iyncUG6jDUcL2qvhCzzOuLMSNIN4jIY1eIXkpW+3qMUuoSzGNMCLwPeNxxWe5wOBybGGHzDUmJyDbgf2CeMA4Av45RwD4cs9bRsg8DZeYwngP8KPB1AFXdKyLONYjD4Tjj2WwdBvBlzLz0s1X1vsLxm0TkyiXi5JQZkhrYiRGF1flOdzgcjtOaTTYkBbxOVf+k2FmIyM8DqOpfrBS5zBPGh0TkHcCkiLwMeCnHuZiSiNwFtDBrycaq+qhFwjwRs2B5CBxW1SeslO50PMYHjzwWT1L6acBsVDfH+w0Od5rMzZv9wWyVYNanMmMXpJ+F6qxSmzHS1HAuJmgZD6leuw+9PsRWKqsKngeBrbpahbQxQbtpvNnONAOiMZ9ozKQdNYW4DnHDBE9qkFSVtGKvokBRv+DFTMVou2O7hnoieAPB75t9bwB+D6zDVvw++H3N971I8QeKJFb2mg7/ClIB9QXrcBX1haQiWIeupKEYKbF1npsGZtPsvA94MOqsNMsiKWjT0xQkMRuAF1t7ivvJQnhJ1Xy2iY3+elMBZCFv9QT1ivt28xcPjywcY+HQcH5Fh3K6cCyry+L50f2hiihSVAYX8l/08yLnlkp20WwKAWX0ZrWYs7yyN7MR5WquZJUSYZZLdqn8V2HnYmkcp/q3PBurMyjDa4APjRz7Pcxw1IqU8SX1V3Yt7znMPMYfqur1q7VyEZ5kV386BhGZBN4OPN16Vty5Dvk5HA7H+rGJvNWKyM8AzwDOHnFCOIFxKluKZTsMEfGBz6rqU4D16CTK8t+Bf1HVewBU9eBJzNvhcDjKsUk6DGAvZhmQZzLseLAF/GbZRJbtMFQ1EZGOiGxR1dk1mblE0sB1Yl65fIeqXjVy/gFAKCKfx6y98VZVXfRlQRG5HLM4CGO7G+toosPhcCzPZnENoqo3AzeLyPtVtfQTxShl5jB6wLdF5HqgXTCglP/0JXicVVvtBK4XkdtU9cYRux4JPBmoA18Wka+o6vdHE7KdzVUAOx+0bfP09w6HY9OziYakPqSqzwO+IYv4RlHVh5ZJp0yH8Um7rRuqutf+P2hdjjwG4wE34z7MRHcbaIvIjcDDgGM6DIfD4TglbDwF1HK80v7/2eNJZMkOQ0TOU9V71suPeiHdJuCpast+/mngj0eCfRR4m4gEQAX4MeDN62mHw+FwHDebpMNQ1X324yd9QXQAABafSURBVHOBD6nq/WtJZ7knjGuBRwCIyEdU9efWksEi7AKuEeM6NAA+oKqfEZFXAKjqlap6q4h8BvgWkAJ/r6q3rJSwJ0rVj6lKRNVbGKYbpAHzQZWWt9C6EguelaKGHaU6l1I5ag4ERzrIrPE+q3Mt0m4XTRY80EoQ4tVr5vP4GJ420cBoN72a0XSmvvUOG0JShdSobhcktaEZ/FR/QU6b2SWRkdIC+D3B70HQNcGCLgRdJeip3Vf8Xoo3MOl5cWoktZlPMRHUNxtAUvFIKx5k9gXWxkp2niFZrfp2K0hTYUEam8lkxVa3F0PmrFdi8CLwYh06l0l+vRhQPUb2m7mVzSSyWV3iWYmvJwu2eeQS4UzumyeX2ZqNM6t58ai4X5T5SpptC5LkRWW2IxQlpkNSWQ9UZKjupBjeppmbWpTEju4vIvk91rZiRR5r5zEMyX1lSflvMWxm+6KeeZeTwi4hlV7TDXcl+e5JktRuxje9Maqo60RkGvgn4J9V9UDZyMt1GMVqv3CNxh2Dqt6BGV4aPX7lyP6bgDetV74Oh8Ox3mQ/LjYLqvpHwB+JyEOB5wM3iMh9Vgm7Isu96b3S60gOh8Nx5rLOzgdF5JUicouIfEdEXrXI+S0i8nERudmGeclxWH/w/7V398F2VeUdx7+/c869uQkJREyCaBV8SQVhSkCLYCoG7fjCwKQ4dsSxrWMtVAuD2GqLznRqfemgttZ2aGWi0OgMYK0RZFARVORFixAwCeFtZIRaGiSGgOHm7d5z9tM/1trn7HNyXvY997zce+7zmTlz98taa6+9z07W2Xuv/SzgV8DTQO733NpdYZwkaQ/hSmNxnCbOm5kd3m1NnXNuFPTqlpSkE4HzCR2ApoCbJH3bzH6eSXYh8KCZnSNpJfBI7CY71aTIVtv5AOHKYiXwDeB8M3swb/6WDYZZGmDBOedcU72793I8cJeZ7QOQdBsh8OtnG7a2TOEB8FJgNzN4Szs6BrjEzLZ0U8k83Wqdc841MYMrjBWSNmfmNzS8sLwd+HQMP76fEMYjmx7gckIo8h2EF5rfaWYzenXQzC4FiO/ATWSW/zJPfm8wnHOuW/kbjF3NgqxWiwk9Qz9DCME0CWzl0KuHtwBbgDcCLye89HyHme0hJ0nnAJ8HXkh4jnEM8BBwQp78I9VgTE4v4s5fvYzERMVUHRw+MWEGixeFW30TR01jq0QS1+8z2JtJbyzCLDa+trJukHkzwnyaNtvNETBLwKbqB6YPgeHr56vdDNNywgIbM2wMksVhefmIZlFH1TBfoNp/wTJpaOiG2URYb627OOTpDtnY/bMuXbafaTVV27p17HrZKX0refI1flfNtt8xf2/Sd/rP6NDy8tU79/qm2+i+rBmV12X5s84zw/J7GRrEzK4ErgSQ9A+EF5iz3gtcFoebeFTSY8BxwN0z2MyngNOA75vZyZLOBN6VN3PeIVqdc85lpO9h5PnkKi9G5Zb0EsILdtc2JPklIVwSko4iRA//xQyrPW1mTwMFSQUzu5Uw2l4uI3WF4ZxzA2U9vYzZFJ9hTAMXmtkz2ReagU8CGyXdT2iv/qbVEBFtPCtpKSEU09WSdtKr8ObOOeda6+Wb3mb2+ibLrshM7yCEUpqN9YSH6h8C3g0cwaGhmVryW1LOOdeNHr+410+SXiFprZntNbPEzMoxTuAWYHnecrzBcM65LtXij7X/zAFfIAyW1GhfXJeL35JyzrkuzZHGII9jzWxb40Iz2yzp2LyFjGSDUUhvLMa/6XOphGz32Np00thFNqPa1bZJ+lqaQ9O3nG+xvPGy9dBy2q9vNer9IfvUZh/bpWnc53ZpW4YLbXVp3nJ5zn3KW16bMlMd70l3XJ8jVOpMblH0Le3MQ7p2db9+DtyOgT5FlTV6/dC7nybarFuctxC/JeWcc13qZbfaPrtH0vmNCyW9j/oxvtsaySsM55wbiLnRGORxCWEcondTayBeQxig7ty8hXiD4ZxzXZhPAyjFQZJeF9/sPjEu/raZ/XAm5XiD4Zxz3TCbjwMo3Qrc2m1+bzCcc65b86u9mDVvMJxzrkvz5ZZUr4xUgzFRnOaVz9sJ1HcDTbvT1i2zQmZadekOTdu6S22zvHnztJpv7ObaWHaePK3StUrbstss7XsONqtbnnrl2W6nbc90WzNN16hTXTtvd1bZm5Q3u/rMlW10a+j/Vxswz25JzdZINRjOOTdQC6u98AbDOee65beknHPO5TLfeknNljcYzjnXjTkSiXaQvMFwzrkuhBf3FlaL4Q2Gc851a/5Eq+2JvjYYkh4nxGCvAGUze03D+nXAt4DH4qJvmtkn8uRtpmwFdh9cAuTpVpuvG2yrLq7turV27CZbV379PjR2U22Xt1n+TtFym3ezbV9Gq2Vw6BV503QtfoQ1307ztK3L7qI78Ey30SFfx66nuX6E9qCMXGnyd5Od0Y/nvGm77KY7VyPl+hVG753ZYdzZO8zs7C7zOufccPgzDOecc/nMv1hSs9Xv8TAMuFnSvZIuaJHmdElbJX1X0gkzzIukCyRtlrR56tn9vay7c861Z5bvMyL6fYWx1sx2SFoF3CLpYTO7PbP+PuAYM5uUdBZwPbA6Z14AzGwDsAFg+XGrRuebcc7NbTavhmjtib5eYZjZjvh3J3AdcGrD+j1mNhmnvwOMSVqRJ69zzg3dArvC6FuDIekwScvSaeDNwPaGNC+QpDh9aqzP03nyOufc0FnOz4jo5y2powhDAqbbucbMbpL0fgAzuwJ4B/ABSWVgP3CemZmkpnk7bbCcFHjmYBjPvJDph5d2JW3s+prOVxKRICpJIaaDJE5XEpEkhVoZiUgSYZXavCWCpDavSm2eCihR7dI1EarULmVVCetUieuNhvXUrVcSxwnOzieZS+MEChWr5W9Yr8RCXsvMJ7UQB0ovsyvZeaulr1joSphJHwaSqR7YWldDM0ion4/LQt6GX2CZdXXrsw8WG3+xZafTdJY0X59N02p9s1+DbcP1dnFPYr784tQci1Rb6N3v2//pUTnq5vufx/rWYJjZL4CTmiy/IjN9OXB53rzOOTdnGP7innPOuc6E+Yt7zjnncvIGwznnXC4LrMHo94t7zjk3mtJnGHk+OUj6oKTtkh6QdEmT9R+RtCV+tkuqSDqyNzuTjzcYzjnXJSVJrk/HcqQTgfMJ75udBJwtaXU2jZl9zszWmNka4KPAbWa2uw+71dJI3ZIaL1Z40WF7KFuBA+USe6fHAZiqlEhMjBVDX9SJ0jRLx6YYL1SqeaeSIpMx/YHyGNOVIhC6506Uplk8Ng3AktI0BYyyhbZ2X3mM/eUxpsq1Q1kqVlhcqqUfL1SqUWgPlEvsK48xVQnpK0mBYiFhvFiupp8olatllZMC+8pjHIzp03ql+7K4NM1EsUypEE7KxMSBSokDsT7pvhfj+kXFMhPFMuMxf0HGVKXIgVj+/vIYlaRQ7ZY8VqgwXgp5AEqFhHJSYCoJ9dg/PcZ0Uqx2Ox4rVhiLx3VJabpar1CXsJ10X8yEZNX0E6Uy44VKddvpdg7GfZmO2ywWrG5fsvs+lRSZisfoYKVU3UaafryY1H3v6fFKj236vQKUlLAo1ik9VompmuZAuVStU7p+rFCpO1bpfkA4xxq3ke77eLFSt++N+1Kx0LW7FPsvjzWkhxCtOU2ffifp+qKS6jay+55+j+WkQCVTrwJW3Ua6b+k20u+ybt+xeB5Xqscum76cFJiuFOuiMad1StOnxze7L2nX9wRRiH27x4oVSoWkOp+uT49zup10fbGQUCok1ToB8AZ6oKcv5R0P3GVm+wAk3QacC3y2Rfp3Adf2auN5+RWGc851I76DlPNN7xVpzLv4aYyPtx04Q9LzJS0BzgJe3Gyzcf1bgU193LumRuoKwznnBir/exi72o3pY2YPSfoMcAswCWwFyi2SnwP8eNC3o8CvMJxzrmsyy/XJw8yuNLNTzOwMYDfw8xZJz2MIt6PArzCcc657PexWK2mVme2U9BLg7cDpTdIcQXgC80c92/AMeIPhnHPdMINKT2ODbJL0fGAauNDMnmmIvQfhQfjNZra3lxvOyxsM55zrVg+vMMzs9U2WXdEwvxHY2LONztBINRjjhQrHLHn6kOVJzkc1lZwD1CczHMg+sZk9KqrQfZTQmdatlq83j7OSWdS9mbzfSTd6tc+dtzN3or72+vsZpLl0HKsW2JveI9VgOOfcwBiHhssfcd5gOOdcV6x+7JUFwBsM55zrhtHrh95znjcYzjnXLX+G4ZxzLhdvMJxzznXW0+CD88JINRiJif2VcSYr4ywtTjFZCdFnFxenmUpKjBdCaJappERJlWq3yoKSui6W2fl0uhSjdqblTCXh0I0XyuyvjLG0OAVQ3fae8gQAy8f2sac8wfKxfQA8O72E5WP72D11GAArFk2y6+BSViyaBKhO7zq4tLp+58FlHD2xB4AnDxzO0RN7ePLA4QDV6bm2Pp1etei5un1pt69Hju+tHp/ssXp2ekn1WD47vYTDSwfqjnX6PafTi4vTLb+rPOdBQWn029rydL7ZuVGoRmYtdtxeer5k65udbjyPWq3fU57g8NKBuvMsz7E7cmxv2/Wd8ne7Pq1rp++u0/53+z1n15dUi9g7KwbkCF0+SkaqwXDOuYHyKwznnHOd9Tw0yJznDYZzznXDwPw9DOecc7n4m97OOedy8WcYzjnnOjJbcL2kZENoISU9DjwHVIBy49CFktYB3wIei4u+aWaf6FTuMScus49tOoUKBRITlUyU2sQK1Siw2enqfKZbbYLq5zNRMtMys8uy0XAbo6um6VpFRm2MTNsqIme7yKp5IpDOJurroKK6Nt/2HIxQ6nIpaO7++v7qa6+6t92QqXkcUVxhpx92Tq6033tu46y3NxcM8wrjTDPb1Wb9HWZ29sBq45xzM2JYpUfvdMwTfkvKOee6sQDDmw/rXoMBN0u6V9IFLdKcLmmrpO9KOqFVQZIukLRZ0ubJZ6b7U1vnnGvGknyfETGsK4y1ZrZD0irgFkkPm9ntmfX3AceY2aSks4DrgdXNCjKzDcAGCM8w+l1x55yD8KvX/Aqj/8xsR/y7E7gOOLVh/R4zm4zT3wHGJK0YeEWdc64VswV3hTHwBkPSYZKWpdPAm4HtDWleIElx+tRYz0MH63bOuSGySiXXZ1QMvFutpJcRriog3BK7xsw+Len9AGZ2haSLgA8AZWA/8Jdm9pMcZT8HPNKfms87K4B2vdAWCj8ONX4sal5pZstmU4CkmwjHNI9dZvbW2WxvLhjKexj9ImnzKPR17gU/FoEfhxo/FjV+LLozvDeynHPOzSveYDjnnMtl1BqMDcOuwBzixyLw41Djx6LGj0UXRuoZhnPOuf4ZtSsM55xzfeINhnPOuVxGpsGQ9FZJj0h6VNKlw67PIEl6XNL9krZI2hyXHSnpFkk/j3+fN+x69oOkqyTtlLQ9s6zpviv413iObJN0yvBq3nstjsXHJf1fPDe2xFA76bqPxmPxiKS3DKfWvSfpxZJulfSQpAckfTAuX5DnRS+NRIMhqQj8G/A24FXAuyS9ari1GrgzzWxNpm/5pcAPzGw18IM4P4o2Ao0vRLXa97cRYpKtBi4AvjigOg7KRg49FgD/HM+NNTHUDvHfx3nACTHPv8d/R6OgDPyVmR0PnAZcGPd3oZ4XPTMSDQYhFtWjZvYLM5sCvgasH3Kdhm098JU4/RXgD4ZYl76JQSt3Nyxute/rga9acBewXNLRg6lp/7U4Fq2sB75mZgfN7DHgURpius1XZvakmd0Xp58DHgJexAI9L3ppVBqMFwH/m5l/Ii5bKJqFiz/KzJ6E8A8IWDW02g1eq31fqOfJRfFWy1WZW5ML4lhIOhY4Gfgpfl7M2qg0GM3G8VxI/YXXmtkphEvrCyWdMewKzVEL8Tz5IvByYA3wJPBPcfnIHwtJS4FNwCVmtqdd0ibLRupY9MqoNBhPAC/OzP8WsGNIdRm4FuHin0ovq+PfncOr4cC12vcFd56Y2VNmVjGzBPgStdtOI30sJI0RGourzeybcbGfF7M0Kg3GPcBqSS+VNE54mHfDkOs0EG3Cxd8AvCcmew/wreHUcCha7fsNwJ/EXjGnAb9Jb1GMqoZ78edSG0rgBuA8SYskvZTwwPfuQdevH+LQCFcCD5nZ5zOr/LyYpZEY09vMyjEk+veAInCVmT0w5GoNylHAdXH4kDRc/E2S7gG+Lul9wC+BPxxiHftG0rXAOmCFpCeAvwMuo/m+fwc4i/CAdx/w3oFXuI9aHIt1ktYQbrE8Dvw5gJk9IOnrwIOEXkUXmtmoDNywFvhj4H5JW+Kyj7FAz4te8tAgzjnnchmVW1LOOef6zBsM55xzuXiD4ZxzLhdvMJxzzuXiDYZzzrlcvMFwHUmqxEin2yX9l6QlHdJvlPSOWW5znaTfSPpZjKZ6u6SzZ1Nmk218rGH+Jz0s+wud3riXdJSkGyVtlfSgpDQw4EpJN/WqLs71ijcYLo/9MdLpicAU8P4BbfcOMzvZzF4JXAxcLulNeTPniL5a12CY2eu6qGOz7R4JnBaDAbbzCeAWMzvJzNJoqpjZr4EnJa3tRX2c6xVvMNxM3QG8QtKxDeMufFjSxxsTS7os/nreJukf47KVkjZJuid+Ov7HaGZbCP/BXhTLqLuKkTQZ/66LYyFcA9wfl10fAzM+kAZnlHQZsDheOV3dUIYkfS5eUd0v6Z2Zsn8k6RuSHpZ0dXyruNE7gOoVgsJ4JX8v6b5Y3nFx1dGEsBTpPm7LlHE98O5Ox8W5QRqJN73dYEgqEQIc5rpdEn9pnwscZ2YmaXlc9S+EMRrulPQSwhv6x+co8j7gIznSnQqcGMN2A/ypme2WtBi4R9ImM7tU0kVmtqZJ/rcTgvWdBKyIedKrhZMJY0jsAH5MeKv4zob8a4FvNCzbZWanSPoL4MPAnxHGcPnPGKXg+8B/pHHBgM3Ap3Lsq3MD41cYLo/FMcTCZkJIhStz5tsDHAC+LOnthLALAL9PuL20hRDH5/A0HlYHzX7NN3N3prEAuFjSVuAuQpC51R3y/x5wbQza9xRwG/C7mbKfiMH8tgDHNsl/NPDrhmVpALx70zxm9j3gZYSggMcBP5O0MqbbCbywQz2dGyi/wnB57G/8JS6pTP0PjonGTDHG16nAmwgBIS8C3hjznW5m+2dYj5MJg+FAiH9UiHURMJ5JtzdTz3WEBup0M9sn6UfN6tqgXcN0MDNdofm/of1NtpHmq8tjZruBa4BrJN0InEGIsjoRy3FuzvArDNetp4BVkp4vaRFwSA8mhfEIjojDgl5CuM0DcDPxWURM1+y2UGNZvwP8LeE2DoRAeq+O0+uBsRZZjwCeiY3FcYQhO1PTCmGwG90OvFNSMf7iP4OZRXJ9CHhFp0SS3pj2OItXWC8nXMEB/Da1yLLOzQneYLiumNk04SH0T4EbgYebJFsG3ChpG+G2zofi8ouB18QH4Q/SutfV69NutYSG4mIz+0Fc9yXgDZLuBl5L5qqiwU1AKdbhk4TbUqkNwLb0oXfGdcA2YCvwQ+CvzexXLcpv5tuEqLGdvBrYHOv238CXzeyeuO7MWI5zc4ZHq3WuDyTdCZxtZs92mf92YL2ZPdPbmjnXPW8wnOsDSa8lPPvZ1jHxoXlXEobdvb73NXOue95gOOecy8WfYTjnnMvFGwznnHO5eIPhnHMuF28wnHPO5eINhnPOuVz+H0cJt5r00ffVAAAAAElFTkSuQmCC\n", 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\n", 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MBj4FAhEZLyL/EpG1IvKmiPxnqutQ31RPUX4RIdn3IyjKL7IWgTEZ6PiJx3fcD0mI8tJy/yrTS6lOLwH+tQjagatUdRpwFHCFiExPZQXqmvZNL+EpLii2MQJjMtDowaM77k8ZMSXjuoXAPas4heMD4FMgUNXNqrrSvb8bWAuMTWUdukov4bGuIWMyU1VNFQDnTj+XN+veZG3dWp9rFL+axuC0CDqISCkwG3i50/OXi8gKEVlRV5f4o/Ou0kt4ivOdDKSqmvByjTHJU1XrBIIbTrqB7FA2i1ct9rlG8WmPtLO1aWswxgg8IjIIeAi4UlV3RS9T1dtUdY6qzikuTvxVerpKQe0pyi9iT/seGtsaE16uMSZ5qmqrOKDwAA4cdiBnTDmDO1ffSVu4ze9qxay+qR5Fg9E1BCAiOThB4B5V/Vsqyw5Hwmxr3rbfMQKwcwmMyTRratZQVlIGwIJZC6hprOGJ957wuVax8+NkMvBv1pAAfwbWqur/S3X5W5u3ouh+xwjA0kwYk0n2tO/h3a3vMnPkTAA+PfnTjBo0ittfv93nmsXOSy8RlK6hY4CLgRNFZJV7Oz1VhXeXXsJjaSaMyTxr69YS1jAzS5xAkB3K5pKyS3jsncfY0rDF59rFpiPhXBBaBKr6gqqKqpap6iz3tixV5XeXXsJjaSaMyTzeQLHXIgC4bPZlhDXM3Wvu9qtacfG6hgIzRuCn7tJLeKxryJjMU1VTRW5WLpNHTO54bmrRVI4efzS3v357RswCrGmsITcrlyG5Q1JabjADQQ9dQ0Nyh5ATyrGuIWMySFVtFdOKp5Ed2juF2oJZC1hbv5aXN73czSvTR21jLSWDSnCGUVMnmIHA3cF31zUkIk6aCesaMiZjVNVW7dUt5Dnv4PPIz8nPiEHjmsaalHcLQT8PBOVLyilfUr7P8/VN9QzNHUpOVk63ry0uKLYWgTEZYlvzNj7e/XGXgWBw7mDOnX4uf3njLzS2pve5QX7kGYJ+Hgi6s7/0Eh5LM2FM5vBSS3gzhjpbMHsBu1t389Dah1JZrbj5kV4CghoI9pNewmMtAmMyR1czhqIdN+E4Dhp+UFp3D6mqLwnnIKCBoL6pvscWQVGejREYkymqaqoYNnAYYwaP6XK5iHDZrMt49sNnWb9tfYprF5vte7bTHmlP+clkENBAUNdUR1Fe1wPFnuKCYna27MyoPCXGBFVVbRUzS2bud7bNJYdcQkhCLFm1JHUVi4Nf6SUggIFAVZ2uoRjGCMDOJTAm3UU0whu1b3TbLeQZN2Qcp046lSWrlxCOhFNUu9h1nFVsLYLk29Wyi7ZIW49jBB1nF9s4gTFp7cMdH7K7dXdHsrn9WTB7ARt3beSf7/8zBTWLj5dnyMYIUqCn9BIey0BqTGboaaA42plTzmR43nBuX5V+g8bWNZRCPaWX8FjXkDGZwZs6OmPkjB7Xzc3O5Uszv8Qj6x5ha9PWZFctLrWNtYQkxIj8ESkvO3iBoIf0Eh7LQGpMZqiqraK0sJTBuYNjWn/B7AW0hlu5t+reJNcsPjWNNRTnFxOS1O+WgxcIYmwRDM8b7qxvXUPGpLXuUkt055BRh3Do6EPTrnuoprHGl4FiCGAgiHWMIDuUzfC84dY1ZEwaa2lv4e36t+MKBOAkolu1ZRWvb349STWLn1/pJSCAgaCusY6B2QMpyCnocV3vIvbGmPS0rn7dXhejidUXZ36RnFAOX3/861RWVyapdvHx66xiCGIgaHLSS8SS5rUov8gCgTFpLJ4ZQ9Hern+btkgbL216ifl3zk+LYOBXniEIYCCob6rvsVvIU1xQbGMExqSxqpoqckI5TBkxJa7XVWyoQHAOBlvDrVRsqEhC7WLX0NpAU1uTjRGkSiyZRz2WgdSY9OZdjGZ/KeW7Ul5azoCsAQBkhbIoLy1PQu1i551VbF1DKRJL5lGPFwgiGklyrYwxvRHvjCHP3PFzWfrFpYAzcDx3/NxEVy0ufp5MBkEMBE2xB4Ki/CLCGmbHnh1JrpUxJl7bm7ezcdfGXgUCgJMnnczEoRPZ0eL/79tLL2FdQymwp30PDa0NcY0RgJ1dbEw6eqP2DaD7i9HEoqykrOPMZD91JJyzFkHyeTv0eMYIwE4qMyYdralZA8Q/YyhaWUkZ6+rX0dLekqhq9YrXNRTrvinRAhUIYk0v4bE0E8akr6raKgoHFjJuyLheb2PmyJmENcza+rUJrFn8ahprGDZwWMcAdqoFKxDEmF7C05GK2loExqQdb6A4lnOCuuOlrva7e8jP9BIQsEAQa3oJj2UgNSY9qWpMF6PpyeQRk8nNyu3oZvKLn2cVQ8ACQbxdQ3k5eRTkFFjXkDFp5qOdH7GrZVefBorBySk2vXg6a2pTGwjKl5RTvqS847GfeYYgaIGgqY6QhBiWNyzm11iaCWPST29TS3QlHWYO+ZleAgIWCOqb6hmRNyKufN/FBXZ2sTHpJp6L0fSkrKSMzQ2bfRsLbA23smPPDusaSpV40kt4ivMt35Ax6aaqtooJQycwdODQPm/La1V4rYxU8/Oi9Z64A4GIFIhIVjIqk2zxpJfwFBdYKmpj0k1vU0t0xe+ZQ36nl4AYAoGIhETkQhF5XERqgXXAZhF5U0RuEpHJ8RYqIreLSK2IvNGbSvdWb1oERXlFadEi6Dy4ZExQtYZbWVe/LmGBoGRQCSMLRvo2c8jvhHMQW4vgX8Ak4NvAKFUdr6ojgeOAl4AbRORLcZa7BDgtztf0WX1TPUV5sU0d9RQXFNPc3kxTW1OSamWMicfb9W/THmnv84yhaDNHzkz5zCGP33mGALJjWOckVW3r/KSqbgMeAh4SkbhywKrqcyJSGs9r+iocCbO1aWuvxgjA6VaaWDgxGVUzxsQhkTOGPGUlZdy64lbCkTBZodT2fGdE15AXBETkaRE5PXqZiNwWvU4iicjlIrJCRFbU1fW9a2Zb8zYUjXuMoOPsYhsnMCYtVNVUkR3K5lNFn0rYNstKymhub2b99vUJ22asahprKMgpoGBAz5fPTZZ4BosPAK4Rke9HPTcnwfXpoKq3qeocVZ1TXNz3REzxppfwWAZSY9LLmto1TC2amtC8PF7rwo9xAr/PKob4AsEOYD5QIiJLRaTv87ZSKN70Eh7LQGpMeqmqqeqY6ZMo04unE5KQLzOH/M4zBPEFAlHVdlX9Os7YwAuAv2EsDvGml/BYBlJj0seOPTuo3lWd0PEBcNLJTB4+2ZcBY7/TS0B8geBW746qLgEuBZ7qTaEich9QCXxKRDaKyFd6s5149LZraGjuULJD2dYiMCYNdFyMJsGBAJxxgqB2DfU4a0hEfguoe/83nRY39KZQVf1ib17XF17X0Ii8EXG9TkQoyi+yMQJj0oDXdZPIqaOespIyHnjrARpaGxg0YFDCt9+VcCRMXVNdRrQIVgCvubfPRt33bhmhrrGOIblDyM3Ojfu1xfl2drEx6aCqtoqhuUMZP2R8wrfttTK8VkcqbG3eSkQjvo8R9NgiUNU7vPsicmX040wSz0XrO7MMpMakh6raKmaMnNGni9F0xxuAXlOzhqPGHZXw7XclHc4qhvhzDWlSapECvUkv4SkusMRzxvhNVamqSVyOoc4mFk5k8IDBKZ05lA4nk0GAso/WN9XHPXXUU5xvqaiN8dvGXRvZ2bIzKeMDACEJMWPkjJTOHEqH9BIQW9K53SKyS0R2AWXefe/5FNQxIXqTedRTnF/M9j3baQsn/ATqjGAJ70w6SEZqic68mUOqqen86EhBne4tAlUdrKpD3Ft21P3BqjokFZXsK1Xt8xgBOAM7xhh/JPJiNN0pKyljx54dbNq9KWllRKtpqCEnlEPhwMKUlNedQHQN7W7dTWu4tfddQ71IM1FZXcn1z19PZXVlr8o0xuztmQ+eYUjuENbVr0taGalONVHTWMPIgpFJGfyORyznEXwOGKeqv3cfvwx4h9bXqOoDSaxfQng78F4PFseZZqKyupLjFh9HRCMMzB7I05c8zdzxc3tVtjFB9fHuj3l548u8tPElnnr/KVZtWQXA/DvnJ+035Y0/rKlZw+mTT+9h7b5Lh/QSEFsa6m8BF0Q9zgUOBwqAxUDaB4LeppfwRGcg9frKK75c0e36971xH2ENA85FNCo2VFggMKYbldWVPPX+U5QUlNDQ2sDLm5yd/8ZdGwHICeU4R80Iiib1N1U4sJAJQyek7LKV6XBWMcQWCAaoanXU4xdUdSuwVUT8y5sah96ml/B05BuKsUXgTQkDyAplUV5a3qtyjemPdrXsYvWW1azasoon1z/JsneXoVEz00sLSzl2wrEcOfZIjhx7JLNHz+b1za8z/875tIZbGZA1IKm/qZkjZ6aua6ihhoOLD05JWfsTSyAYFv1AVb8R9bDv+aFToLeZRz1eWopYxghaw608s+EZjp1wLK9vfp3DxhxmrQETSMs/Ws7Sd5YyIn8Ee9r3sGrLKlZtWbVXzv/8nPyOIBCSENcecy0/nf/TfbY1d/xcnr7kaSo2VFBeWp7U31RZSRlPrn+yI+gki6o6XUM+zxiC2ALByyLyb6r6p+gnReSrwCvJqVZi9bVrKCcrh2EDh8V0dvGyd5dR31TPtcdcS8WGCn718q/Y0rCFUYNG9apsYzJFY2sjKz5ewfLq5Tz+7uO8WP3iXssPGn4Qs0fP5rJZlzFr1Cxmj57Nhu0bOOmukzp2umdMOaPb7c8dPzclB1VlJWW0R9pZV78u4emuo4U1TGu4NWO6hv4LeERELgRWus8dhjNW8PlkVSyR6prqyM3K7VMiqVjTTCxetZhRg0Zx6kGnMmn4JG6uvJk7V9/Jt475Vq/LNiZdVFZXUrGhgnkT5zF68GiWVy+ncmMllRsrWb1ldcfYWHTrOyQhvnv8d/lh+Q/32d6YwWNSdqQfq+iZQ8kMBK3hVsD/k8kgtlxDtcDRInIi4HVmPa6qzyS1ZgnkpZfoyxSt4oKezy6uaajh8Xce57/n/jfZoWymFk3luAnHsWjlIq4++mrfp4gZ0xf/WP8Pzrj3DFojrXs9P2jAII4YewTXHnstc8fN5ahxR/HO1nf26tM/bdJp3W43VUf6sZoyYgoDsgYkfZygIxBkQteQiIg6ngG63Pl76yS8dgnSl/QSnuL8Yt7f/j7D84Z3u849VfcQ1jBfnvXljucWHrqQSx+5lOc/ep7jJx7fpzoYk2oNrQ0sfXsp9795P4+981jHEb8gnDnlTH50wo+YMXLGPhd8n5ufuj79RMvJymF68fSkzxzyMhWkQ9dQLCeU/UtE/kNEJkQ/KSIDROREEbkD5yI1aasv6SU8PaWiVlUWr1rMEWOPYHrx9I7nz5l+DkNyh7Bo5aI+lW9MqjS3NfPQWw9x3gPnMfKmkVz4twt59eNXOXva2eRm5ZIlWQzMHsi1x17LIaMO2ScIeOaOn8u3j/t2RgUBTypmDnktq4zoGgJOAxYA94nIATjXLs7DCSJPAb9U1VXJq2Lf1TXVMWn4pD5tw7s4zZThU7rs4lm5eSVv1L7BHz7zh72ez8/J56KZF7F41WJ+8+nfJP1U8ljOczAmmqqy7N1l3P/m/dQ01LB843IaWhsYWTCSBbMXcP7B53PMhGMISahjjCDTjvLjVVZSxl1r7mJr01ZG5Md3MatYtYXbEKTPvRWJEMsYwR7gFuAWEckBioBmVd2R7MolSl1jHUV5fewaKiimPdJOWMNky74f2+JVi8nNyuWCGRfss2zhoQv5w4o/cG/VvXz98K/3qR5mXy989AJPvPcEnznoMxw94Wi/q5NWvB338ROPp7SwlPe2vffJbbvz9+36t2lub+54zWenfJZvHvlN5pXOIzu09/96uvXnJ4s3SFxVW5W0cxa8tDedP2M/xFUDVW0DNiepLkkR0Qi7W3f3+mQyj9e11Bpu3eeLa2lv4d6qezlr2lldHvEfOvpQZo+azaKViywQJNgtr97CFcuuAOBnz/+Mg4YdxCGjDmHy8MkcNPwgJo9w/o4eNJqXNr7Ub45mo4/Mjxp3FDtbdrJp1yY27tpt5LkPAAAV6UlEQVTIpt3O35Ufr2Tpu0uJaGSf1+eEcjhg2AEcNPwg8rLzeGnjSyhKlmRx1LijmH/gfB/eVfqInjmUrEDQFm5Li24hiDMQZCJvQKavYwRe860t3AY5ey979O1H2b5nO5fNuqzb1y88dCFXLLuClZtXcujoQ/tUF+NMAPjWP77F4lWLO54ThKxQFm/UvsGjbz9KW+STtOG5Wbm0hltRlOxQNlfNvYpTJzlTfMcNGUdIPhku87P7I7rsw8ceTn1TPVsatlDTUENNYw1bGrbw+ubXeeCtBwhrGEEYkDWAlnDLPtsqyCnoCALe4O43jvgGBw0/iPFDx3cc0FRWV6bsrN1MMWrQKIryi5I6TtAaabVAkCrezqDPLQL39dE7F8/iVYsZN2Qc8w/o/ijqwpkXctVTV7Fo5SJu+cwtfapLkKkqd66+k6ueuoqdLTu5eObFPLj2wY6d2OLPLWbu+Lm0R9qp3lnNu9ve5b1t73HPmntYvnE5AO2Rdn7+4s/5+Ys/B5wgceCwA5k0fBL52fk8vO5hwpEwOVk53HXWXZx04EkMHTh0r2DhiTVoVFZX8q8N/2LOmDkcUHgAtY211DXVOX8bnb9r69fy9AdPd3kEHy07lN0xe0dR5oyZw1lTz2LckHGMHTKWcUPGMXrQaFZuXrnXDv7aY6/tso6pPGs3U4gIZSVlSZ051BpuTYupoxBHIBCRXwPTcC5XuRq4N90HieGTubqJmD4K7HNxmo93f8yT65/k2mOu7Xb2BDjJrM6dfi73VN3DzafcTH5Ofp/qE0Tr6tfxtce/RsWGCo4efzR/POOPzBg5g69Vf22fnVh2KJsDhh3AAcMO4JRJpzB71Oy9dop3f+FuCgcWdvSXr9++nve2vcfaurUdwb4l3MJ5D54HOCdFDRs4jOF5wztu4UiYZz54hrCGCUmIeaXzKMgpoLm9mea25o6/O/bsYHPD/ntUh+QOIVuy9zqCP6H0BM6efjYlBSWMGjSKkkEllBSUUFVTtdfZuDedfFOfd/BB6fuPx8yRM/nTyj8RjoT3+9vurbZwW1pMHYX4WgRrgcdwOkamA3eLyK2q+ruk1CxB2iPtQN+7hrprEdy1+i4iGtnr3IHuLDx0IXetuYsH3nyAS2el9YzbtLKnfQ8/e/5n3PDCDRQMKOC2M27jK4d+peMIPZadWHc7xRMPOHGv9ZZ/tJz5d83vGAu65phrGJ43nK1NW9nWvI1te7axrXkbdU11rN+2nnZ1/r/CGqaqpoqxQ8aSl51HXk4ehQMLycvJ471t77GlYQuKIgjnTD+Hr8z+CsUFxYwsGElRfhEDswfu00XzkxN/0uX7OnrC0baDT4GykjKa2pp4f/v7TB4xOaHbDkfChDWceS0CVb016uEyEfkd8CqQ1oHAaxH0tWsoPyefvOy8vVoEqsqS1Us4ZvwxMf2jHDfhOCYPn8yi1xdZIIhBZXUli1Yu4sn1T7Jp9ya+VPYlfnHKL3p9FBXLTvHoCUfzzCXPxNzdE73j/vsFf+9y/c7r/ddR/2VH8BkgeuZQogOBd0CZsWMEIvLvwEHAYCDtr1ncFmnraNb3VXFBMS3tnwzKvbzpZdbVr2PRmbGdLCYiLDx0Idf88xrW1a9jatHUPtepv2kNt/Lqple5c/WdLHp9ERGNIAi/Pu3XfPPIb6akDrHuZGPdcdsOPjNNL56OIKypWcMXpn0hodv2DlAzsWvIsww4GfgCcH1iq5N4beE2hucNT0gfX3F+MR9s/6Dj8eLXF5Ofk895B58X8zYuOeQSrnvmOv688s/cdMpNfa5TOoln4NRbb/bo2byy6RUqNlTw7IfPUlldudecdnD65xtbG5Nd/V6JJ2jYDj6z5OfkM3nE5KTMHEqnPEMQ32DxX4Hvq+pa4M8ishh4HWfcIG21Rdr6PD7gKcov4p2t7wDOafh/efMvnD3tbAbnDo55G6MGjeLMKWdyx+o7+On8nyY133kqLX17KWfdfxZhDZMlWZwy6RRKC0vJz8mnIKfA+TuggM27N3Nz5c3OWZUiZEkWbRHnDMuykjIuP+xy5k2cx8DsgZz917NtSqPxVVlJWcclMhPJ62LOxK6hu4H7xcmv8BowCNj/PLc00BZu6/P4gKe4oLjjC3x43cPsatm133MHurPw0IU8vO5hlr69lLOnn52QuvmpYkMFFz50YceUxrCGqayuZMXHK2hsa6SpranL16kqR487mquPuZrjJhzHsLy9u+9sSqPx28yRM3norYdoaG3oUxr7zrw8QxnXNaSqjwKPikgZMAsn19CyZFUsUdoibQnL5VGcX9zxBS5ZtYTSwlLmlc6LezunTjqVsYPHsuj1RRkdCCIa4cYXb+S6Z65j3OBxtGs7beE2BmQNYNlFyzp23hGNsKd9D42tjTz/0fNc9LeLOta78eQb99u3bgHA+KmspAxFebP2TY4cd2TCttsWbutI3pcO4h4jUNU1QGou6JkAbeHEdQ0V5xcT0QhNbU089+FzfG/e97o8yagnWaEsFsxewE+e+wkf7fyICUMn9PyiNLOteRuXPnIpj73zGOcdfB6LzlzEG7VvdHkEH5IQ+Tn55Ofk84VpX4h5Vo4xfoueOZTIQJDsy2DGq1+fWayqCR8jANi4ayOKcukhvZ8C6gWCxa8v5vvl309I/eLRlyylKz5ewbkPnMumXZv47ad/yxWHX4GI2MCp6XdKC0spyClI+IBxa7iVnKycnldMkfgPZzNIx8lkCRwjANjSsIXy0nIOGHZAr7dVWljKSQeexO2rbiccCSekfsmmqvzh1T9wzO3HEI6Eef6y5/nGEd+wK6+ZfiskIUoLS3nsnceorK5M2HbbIm3WIgAQkdOAXwNZwCJVvSHRZXj9+YkcIwAnv0tvBok7W3joQs5/8HwWPrqQyw+7PK2PksORMBc/fDH3VN3DaQedxt1n3Z20PO3GpIvK6krW1a8jrGFOuOMEfnnqLykrKSMrlEVIQmSJ8zckIapqq1i5eSWnTz6dkw48ab/bbQ23Upib3GuTxMOXQCAiWcDvcc5H2Ai8KiKPqupbiSxnR7NzyYS6xp4vOh8LL6AIwrjB4/q8PW8O8ZLVS7hzzZ0cO+FYDi4+eJ/cMqMGjWJ783Z2t+6msrpyvwFjZ8tOdu7ZmbD1AGoaa1i/bT0vVL/Aj0/4Md857ju9GhsxJtNUbKjomA3XEm7h68t6TiP/y5d+SUFOAaWFpYwfOp7xQ5zbuCHjGD90PGMGj6E90k5TW1NMv79U8KtFcATwnqq+DyAifwE+ByQsEFRWV7J++3oArvnnNRwx9og+f+AbdmwAnBbBGfedwdOXPN2nbS6vXk6IEBEiRDTCuvp1vFn7Jlubt3b7mqNvP7rjKKSziEY6/mkTsV7ndXOzcpl/wHwLAiYwykvLycvOoyXcQk4ohxtPvpGpRVMJR8JENNLx+3jwrQe5r+o+IkQ6zokpGVRC9c5qVm5eSW1j7T7b3tGyg/l3zu/zfiQR/AoEY4HqqMcbgb2G5EXkcuBygAkT4p9VU7GhAkUBZ6ygYkNFnz/slZtXdtxvDbf2eZvlpeXkZud2zCB45PxHmDt+Lm3hNmobazvyz9/++u08tPYhwGmNHDvhWI4ev++VuJZXL+fZD59N2Hqd103U52hMpog1PUhJQQl/W/u3jt/yL075xV7r7mnfw6Zdm6jeVc0tr97CA289ACRmP5IIfgWCrkYXda8HqrcBtwHMmTNHu1h/v8pLywlJiIhGEnZmaqK32d0/WU5WDmOHjGXskLEADBs4jIfXPUxEIwzMHsj186/vNrnZsYuPTdh6nde1M3xNEPUlu61nYPZAJg2fxKThk8jNyuWhtQ+l1W9KVOPex/a9UJG5wA9U9VT38bcBVLXL3EVz5szRFStWxF3O7D/OZueendzzhXsSFnGTsc1Elpvo9eJd1xjTs1T9pkTkNVWd09N6frUIXgUmi8gBwCbgAuDCRBcyNHcoQ3OHJvSDTsY2E1luoteLd11jTM/S7TflSyBQ1XYR+QbwJM700dtV9U0/6hJUvTmRzBjTP/l2HoGqLiMDchUZY0x/Z/MAjTEm4CwQGGNMwFkgMMaYgLNAYIwxAWeBwBhjAs4CgTHGBJwFAmOMCTgLBMYYE3AWCIwxJuAsEBhjTMBZIDDGmICzQGCMMQHnW9I5kxyWVdQYEy9rERhjTMBZIDDGmICzQGCMMQFngcAYYwLOAoExxgScBQJjjAk4CwTGGBNwdh5BnGyevjGmv7EWgTHGBJwFAmOMCTgLBMYYE3A2RmB6ZOMixvRv1iIwxpiAs0BgjDEBZ4HAGGMCzgKBMcYEnAUCY4wJOAsExhgTcCkPBCIyVUQqRaRFRP4n1eUbY4zZmx/nEWwDvgl83oeyjTHGdJLyFoGq1qrqq0Bbqss2xhizr7QdIxCRy0VkhYisqKur87s6xhjTb6VtIFDV21R1jqrOKS4u9rs6xhjTb6UkEIjIFSKyyr2NSUWZxhhjYpOSwWJV/T3w+1SU1V9Z4jdjTLKkfNaQiIwCVgBDgIiIXAlMV9Vdqa6LMcYYHwKBqm4BxqW6XGOMMV1L28FiY4wxqWGBwBhjAs6uUGaMMSmWbpM/rEVgjDEBZ4HAGGMCzgKBMcYEnAUCY4wJOAsExhgTcBYIjDEm4CwQGGNMwFkgMMaYgLNAYIwxAWeBwBhjAs4CgTHGBJwFAmOMCTgLBMYYE3AWCIwxJuAsEBhjTMBZIDDGmICzQGCMMQFngcAYYwLOAoExxgScBQJjjAk4CwTGGBNwFgiMMSbgLBAYY0zAZftdgWSq+HKF31Uwxpi0Zy0CY4wJOAsExhgTcBYIjDEm4CwQGGNMwFkgMMaYgEt5IBCRi0RkjXtbLiKHpLoOxhhjPuHH9NEPgHmqul1EPg3cBhzpQz2MMcbgQyBQ1eVRD18CxqW6DsYYYz7h9xjBV4AnulogIpeLyAoRWVFXV5fiahljTHCIqvpTsMgJwC3Asaq6tYd164APe1lUEVDfy9emG3sv6am/vJf+8j7A3otnoqoW97RSSgKBiFwB/Jv78HScN/Yw8GlVfSfJZa9Q1TnJLCNV7L2kp/7yXvrL+wB7L/FKSdeQqv5eVWep6iyccYm/ARcnOwgYY4zpmR+zhr4HjABuERGA9v4SuY0xJhP5MWtoIbAwhUXelsKyks3eS3rqL++lv7wPsPcSF98Gi40xxqQHv6ePGmOM8ZkFAmOMCbh+c4UyEdkA7AbCdDEALSLlwN9xUlwA/E1Vf5TKOu5PX+rf02v9FEvd3Pf2KyAHqFfVeamsY3f6UvdM/k5E5GrgIvdhNjANKFbVbamsZ3f6Uv8M/16GAncDE3De182qujghhatqv7gBG4Ci/SwvBx7zu57JqH9Pr03z91UIvAVMcB+P9LvOiah7Jn8nndY9E3jG7zonqv6Z/L0A3wF+7t4vBrYBAxJRtnUNGb9diNO6+QhAVWt9rk88MrnusfoicJ/fleiDTK9/NAUGizPvfhBOIGhPxIb7UyBQ4CkReU1ELu9mnbkislpEnhCRg1NZuRj0pf6xvNYvPdVtCjBMRCrcdS5Jcf32py91z+TvBAARyQdOAx5KWc1i05f6Z/L38jucbq6PgSrgP1U1kpiS06BJlKBm1Rj370hgNXB8p+VDgEHu/dOBd/2uc6Lq39Nr0/x9/Q4nC20BTuqRd4Epfte7r3XP5O8kar3zgaV+1zeR9c/k7wU4B/glIMBBOOOFQxJRdr9pEajqx+7fWpw8Rkd0Wr5LVRvc+8uAHBEpSnlFu9GX+vf0Wj/FULeNwP+paqOq1gPPAWlxsaK+1D3DvxPPBaRht0pf6p/h38tlOF2Rqqrv4QSCqYkou18EAhEpEJHB3n3gFOCNTuuMcvvWEJEjcN77frOepkpf6h/La/0SY93+DhwnItluU/5IYG1qa7qvvtS9H3wn3gyVeTjvMW30pf794Hv5CJjvrlMCfAp4PxHl95fpoyXAw+5+Mhu4V1X/T0T+HUBVb8VpVn1NRNqBZuACddtbaaDX9Xf/IfZ5rR9vogs9vi9VXSsi/wesASLAIlVNhx9nr+suIgd29Vpf3sW+YvlfAzgLeEpVG/2pZrf6Uv+M/q0APwaWiEgVTvfQNW5LtM8sxYQxxgRcv+gaMsYY03sWCIwxJuAsEBhjTMBZIDDGmICzQGCMMQFngcCkNREJi8iqqFup33VKJBGZLSKLoh6fJiKviMg69/3eLyIT3GVLROScTq9v2M+2B4jIcyLSX6aJmySxfxCT7ppVdVZ3C0UkW1UTknjLJ98BfgIgIjOA3wKfVdW17nOfBUpxTiaKi6q2isjTOKkW7klUhU3/Yy0Ck3FE5Msi8oCILAWecp+7WkReFZE1IvLDqHWvE5G3ReSfInKfiPyP+3yFiMxx7xeJkwseEckSkZuitvVV9/ly9zUPukfr90Sd6X24iCwXJyHgKyIyWESeF5FZUfV4UUTKOr2PwUCZqq52n7oG+JkXBABU9VFVfS6Gz+RHUa2mTSLi5al/hE9y8xvTJWsRmHSXJyKr3PsfqOpZ7v25ODvRbSJyCjAZJzeLAI+KyPFAI06+mdk4/+srgdd6KO8rwE5VPVxEcoEXReQpd9ls4GCc7I8vAseIyCvA/cD5qvqqiAzBOfN7EfBl4EoRmQLkquqaTmXNYe80AgcDN/dQv5tE5Ludn1TV7wHfc1MrPI+TEA93+4f3sE0TcBYITLrrrmvoH/rJFbNOcW+vu48H4QSGwcDDqtoEICKPxlDeKUBZVF/8UHdbrcArqrrR3dYqnC6bncBmVX0VnOSA7vIHgP8V52pZC4AlXZQ1GqjrqhIiMgJ4GsgHblNVL0BcraoPRq3XEHVfcLqAfqmqr7n1CYtIq4gMVtXdMbx/E0AWCEymis4hI8D1qvrH6BVE5EqcHO9daeeTrtGBnbb1H6r6ZKdtlQMtUU+FcX4/0lUZqtokIv8APgech3P031lzp7LfBA4FVqvqVmCW25U1qJv30NkPgI267+ULc4E9MW7DBJCNEZj+4ElggYgMAhCRsSIyEict9Fkikuf2x58Z9ZoNwGHu/XM6betrIpLjbmuKmw2yO+uAMSJyuLv+4KhZOouA3wCvatfX+12Lk1fecyNwnYhMi3oufz9ldxCRM4CTgW92en4EUKeqbbFsxwSTtQhMxlPVp9ydZ6U7ftsAfElVV4rI/cAq4EOcvnPPzcBfReRi4Jmo5xfhdPmsdLta6oDP76fsVhE5H/itiOThHOWfBDSo6msisgvo8gLjqrpORIZ63TaqWiUi/wnc6QaurTizhb4fw8dwFTAGeMX9DB51xw1OAJbF8HoTYJZ91ASGiPwAZwfd04BsosobA1QAU7WbSwqKyH8Bu1V1UVfLE1CHvwHfVtW3k7F90z9Y15AxSSDO9YtfBq7rLgi4/sDeYw+JrMMA4BELAqYn1iIwxpiAsxaBMcYEnAUCY4wJOAsExhgTcBYIjDEm4CwQGGNMwP1/WrL0nnaF5ocAAAAASUVORK5CYII=\n", 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fit2.plot(show_variance=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Unsurprisingly, the fit is fine around the center of the scan, but not very good otherwise. To remove fits that differ greatly from the data, we can set an r2_limit for the fitter, and try again. This time, I will ask it to save the plot." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/Lillian/qdev-wrappers/qdev_wrappers/fitting/fitter.py:127: UserWarning: Unsuccessful fit - r2 for fit is below limit 0.5\n", + " warnings.warn(f'Unsuccessful fit - r2 for fit is below limit {self.r2_limit}')\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "inserting {'model': {'method': 'Least squares', 'function': {'func_str': '$f(x) = a \\\\exp(-x/T) \\\\sin(\\\\omega x +\\\\phi) + c$', 'func_np': 'a*np.exp(-x/T)*np.sin(w*x+p)+c'}, 'parameters': {'a': {'label': '$a$', 'unit': ''}, 'T': {'label': '$T$', 'unit': 's'}, 'w': {'label': '$\\\\omega$', 'unit': 'Hz'}, 'p': {'label': '$\\\\phi$', 'unit': ''}, 'c': {'label': '$c$', 'unit': ''}}, 'r2_limit': 0.5}, 'inferred_from': {'run_id': 6, 'exp_id': 1, 'dept_var': 'data'}}\n", + "\n", + " Analysis saved with run id 225\n" + ] + } + ], + "source": [ + "fitter = Fitter(DecayingRabis(), r2_limit=0.5)\n", + "fit3 = fitter.fit_by_id(6, 'data', x='pulse_duration')" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "([,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ],\n", + " )" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fit3.plot(show_variance=True, save_plot=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we try to retrieve the fit result at 5.5 GHz that we looked at before, we can see it has now been rejected due to the r2 limit:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'param_values': None, 'setpoints': {'frequency': 5500000000}}" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fit3.get_result(frequency=5.5e9)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Save analysis, and plot saved analysis from the experiment database\n", + "\n", + "If we hadn't allowed it to save the result when running fit_by_id, we might want to save the fit later. Again we can run *fit.save()* in that case. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fit3.save()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For 2D data, plotting options are a bit different, depending on whether you plot the results of the fit object (above), or plot from a saved analysis. To plot from a saved dataset, we use the same function we did above for the 1D dataset: *plot_analysis_by_id*. As for the 1D fits, with *plot_analysis_by_id* you can choose to set *save_plot=True* to save the plots.\n", + "\n", + "Plotting from a saved fit will not return a heatmap, but can plot cuts of the data (by specifying the setpoint for the cut). Plotting from a saved dataset also can't currently retrieve the variance information from the database, so it does not have the option to make plots including variance.\n", + "\n", + "Just running *plot_analysis_by_id(run_id)* for the analysis is similar to the plot above, but without the heatmap and without variance. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[,\n", + " ,\n", + " ,\n", + " ,\n", + " ]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_analysis_by_id(186, save_plot=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Specifying the setpoint you want to see the fit at plots a cut with the fit and data at that point." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_analysis_by_id(186, frequency=5.65e9, save_plot=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/qdev_wrappers/fitting/Fitting while measuring.ipynb b/qdev_wrappers/fitting/Fitting while measuring.ipynb new file mode 100644 index 00000000..427468e0 --- /dev/null +++ b/qdev_wrappers/fitting/Fitting while measuring.ipynb @@ -0,0 +1,579 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/anaconda3/envs/qcodes/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", + " from ._conv import register_converters as _register_converters\n" + ] + } + ], + "source": [ + "import qcodes as qc\n", + "from qdev_wrappers.fitting.least_squares_models import DecayingRabis, DecayT1\n", + "from qdev_wrappers.fitting.fitter import Fitter\n", + "from qdev_wrappers.fitting.plot_analysis_by_id import plot_analysis_by_id\n", + "from qcodes.dataset.plotting import plot_by_id" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from qcodes.dataset.data_export import get_data_by_id\n", + "from qcodes.dataset.measurements import Measurement" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Initialize fitter\n", + "\n", + "The Fitter takes a model, and optionally also a guess and an r2 limit for rejecting \"fits\" that fit the data very poorly. By default, the guess will be the found using the guess function of the model, and the r2 limit will be None.\n", + "\n", + "The model includes information about the paramters to be fitted, their labels and units, a default initial guess for the parameters, and the specifics about the type of fit that the model uses. Information specific to the method of fitting is handeled by the model, not the fitter. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Perform fit and see result\n", + "\n", + "We will use the model *DecayingRabis()* from the 'least squares' fitting models. Least squares fitting uses the scipy curve_fit function, and returns, in addition to fitted parameter values, a variance matrix." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "model = DecayingRabis()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can check the parameters and the function that will be used for fitting:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'a': {'label': '$a$', 'unit': ''},\n", + " 'T': {'label': '$T$', 'unit': 's'},\n", + " 'w': {'label': '$\\\\omega$', 'unit': 'Hz'},\n", + " 'p': {'label': '$\\\\phi$', 'unit': ''},\n", + " 'c': {'label': '$c$', 'unit': ''}}" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'a*np.exp(-x/T)*np.sin(w*x+p)+c'" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.func_np " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We initialize the fitter using the default guess method, and give it an r2_limit of 0.5 to reject especially poor fits (see other example notebook for examples with and without r2 limits):" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "fitter = Fitter(model, r2_limit=0.5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fitting while measuring\n", + "\n", + "Since we aren't fitting from saved data, we will not use *fitter.fit_by_id*. Instead, we will use *fitter.fit*, which takes numpy arrays, and fits to them. The first argument is the outputted array. Other arguments must be keyword arguments, telling the fitter which function variable to assign the data array to. For example, it would accept:\n", + "\n", + "fitter.fit( np.array( [ 1, 2, 3, 4 ] ) , x=np.array( [ 1, 2, 3, 4 ] ) )\n", + "\n", + "This can then be incorportated into a measurement, to fit the data during the measurement:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "# data I will be using\n", + "frequency = get_data_by_id(6)[0][0]\n", + "pulse_duration = get_data_by_id(6)[0][1]\n", + "cavity_response = get_data_by_id(6)[0][2]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Setup\n", + "\n", + "Setup of the measurement is as normal. The process similar for setting up the fit 'measurement'. The setpoints that the parameters depend on (in this case, frequency) and fit parameters need to be registered to the fit 'measurement'." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "# create a measurement for the normal measurement, and register measurement parameters\n", + "meas = Measurement()\n", + "\n", + "meas.register_custom_parameter(name='frequency', label='Frequency', unit='Hz')\n", + "meas.register_custom_parameter(name='pulse_duration', label='Pulse Duration', unit='s')\n", + "meas.register_custom_parameter(name='cavity_response', label='Cavity Response', unit='V', \n", + " setpoints=('pulse_duration', 'frequency'))\n", + "\n", + "# create measurement to store the fit, and register setpoints and fit parameters\n", + "analysis = Measurement()\n", + "analysis.name = 'analysis'\n", + "\n", + "analysis.register_custom_parameter(name='frequency', label='Frequency', unit='Hz')\n", + "\n", + "for param_name, param_info in model.parameters.items():\n", + " analysis.register_custom_parameter(name=param_name, label=param_info['label'], unit=param_info['unit'], \n", + " setpoints=('frequency', ))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The measurement then proceeds as normal. The fit 'measurement' is nested inside the normal measurement. In order to identify what is being fitted, and how, later, some metadata needs to be added to the datasaver for the fit measurement.\n", + "\n", + "After recording each sweep on the measurement, the arrays that were saved for that sweep are passed to fitter.fit, returning paramter values and a param_dict. \n", + "\n", + "The param_dict contains additional information about the fit that we aren't worrying about now - for example, the variance of each parameter. You could choose to register other things (f.x. variance) as parameters and save them as well. In this instance, we are doing the simplest possible thing and just saving the parameter values - we aren't saving any information about initial guesses or variance.\n", + "\n", + "*param_values* will be **None** if the fit was rejected (by the r2 test), and nothing will be saved at that frequency. Otherwise, the parameter results at the frequency are saved. Then it goes on to the next frequency." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting experimental run with id: 230\n", + "Starting experimental run with id: 231\n", + "inserting {'inferred_from': {'run_id': 230, 'dept_var': 'cavity_response'}}\n", + "inserting {'model': {'method': 'Least squares', 'function': {'func_str': '$f(x) = a \\\\exp(-x/T) \\\\sin(\\\\omega x +\\\\phi) + c$', 'func_np': 'a*np.exp(-x/T)*np.sin(w*x+p)+c'}, 'parameters': {'a': {'label': '$a$', 'unit': ''}, 'T': {'label': '$T$', 'unit': 's'}, 'w': {'label': '$\\\\omega$', 'unit': 'Hz'}, 'p': {'label': '$\\\\phi$', 'unit': ''}, 'c': {'label': '$c$', 'unit': ''}}, 'r2_limit': 0.5}}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/Lillian/qdev-wrappers/qdev_wrappers/fitting/fitter.py:127: UserWarning: Unsuccessful fit - r2 for fit is below limit 0.5\n", + " warnings.warn(f'Unsuccessful fit - r2 for fit is below limit {self.r2_limit}')\n" + ] + } + ], + "source": [ + "with meas.run() as datasaver:\n", + " run_id = datasaver.run_id\n", + " \n", + " with analysis.run() as fitsaver: \n", + " analysis_id = fitsaver.run_id\n", + " \n", + " # add metadata to fit, for identifying model and dataset for fit later\n", + " inferred_from = {'run_id': run_id, 'dept_var': 'cavity_response'}\n", + " metadata = model.summary\n", + " if fitter.r2_limit is not None:\n", + " metadata['r2_limit'] = fitter.r2_limit\n", + " fitsaver.dataset.add_metadata('inferred_from', inferred_from)\n", + " fitsaver.dataset.add_metadata('model', metadata)\n", + " \n", + " # measurement as normal, plus fitting\n", + " freqs = np.unique(frequency['data'])\n", + "\n", + " for freq in freqs:\n", + " # get data from the 1d sweep at freq\n", + " indices = [np.argwhere(frequency['data'] == freq).flatten()]\n", + " pulse_dur = pulse_duration['data'][indices]\n", + " cav_response = cavity_response['data'][indices]\n", + " # save sweep result\n", + " datasaver.add_result(('frequency', freq),\n", + " ('pulse_duration', pulse_dur),\n", + " ('cavity_response', cav_response))\n", + " \n", + " # fit to the 1d sweep\n", + " param_values, param_dict = fitter.fit(cav_response, x=pulse_dur)\n", + " #save fit result if successful fit was found\n", + " if param_values is not None:\n", + " sweep_result = [('frequency', freq)]\n", + " for param_name in model.parameters.keys(): \n", + " sweep_result.append((param_name, param_values[param_name]))\n", + " fitsaver.add_result(*sweep_result)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Comparison to taking a full dataset, then measuring\n", + "\n", + "Fitting after each sweep and saving the measurement and fit simultaneously gives you the same result for both the measurement and the fit as taking the full dataset and then fitting afterwards.\n", + "\n", + "The original, full dataset is run 6. For comparison, the dataset saved while fitting after each 1D sweep (run 231):" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "([],\n", + " [])" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#full dataset\n", + "plot_by_id(6)\n", + "#data saved while fitting\n", + "plot_by_id(230)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also compare the results of the fit taken and saved in between sweeps with the results of a fit for the completed dataset:" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/Lillian/qdev-wrappers/qdev_wrappers/fitting/fitter.py:127: UserWarning: Unsuccessful fit - r2 for fit is below limit 0.5\n", + " warnings.warn(f'Unsuccessful fit - r2 for fit is below limit {self.r2_limit}')\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "inserting {'model': {'method': 'Least squares', 'function': {'func_str': '$f(x) = a \\\\exp(-x/T) \\\\sin(\\\\omega x +\\\\phi) + c$', 'func_np': 'a*np.exp(-x/T)*np.sin(w*x+p)+c'}, 'parameters': {'a': {'label': '$a$', 'unit': ''}, 'T': {'label': '$T$', 'unit': 's'}, 'w': {'label': '$\\\\omega$', 'unit': 'Hz'}, 'p': {'label': '$\\\\phi$', 'unit': ''}, 'c': {'label': '$c$', 'unit': ''}}, 'r2_limit': 0.5}, 'inferred_from': {'run_id': 6, 'exp_id': 1, 'dept_var': 'data'}}\n", + "\n", + " Analysis saved with run id 232\n" + ] + } + ], + "source": [ + "fit = fitter.fit_by_id(6, 'data', x='pulse_duration')" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[,\n", + " ,\n", + " ,\n", + " ,\n", + " ]" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# fitted 'while measuring'\n", + "plot_analysis_by_id(231)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[,\n", + " ,\n", + " ,\n", + " ,\n", + " ]" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# fitted to completed dataset\n", + "plot_analysis_by_id(232)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# fitted 'while measuring'\n", + "plot_analysis_by_id(231, frequency=5.65e9)\n", + "# fitted to completed dataset\n", + "plot_analysis_by_id(232, frequency=5.65e9)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/qdev_wrappers/fitting/RoughPlotting.py b/qdev_wrappers/fitting/RoughPlotting.py deleted file mode 100644 index 6870d9e0..00000000 --- a/qdev_wrappers/fitting/RoughPlotting.py +++ /dev/null @@ -1,105 +0,0 @@ -import numpy as np -import matplotlib.pyplot as plt - - -def plot_fit1d(data, fit1d, fitclass, plottype='scatter'): - - plot = plt.figure() - axes = plot.add_axes([0.1, 0.1, 0.8, 0.8]) - - # get data, label and units for the two axes, and set appropriate axis labels - xname = fit1d['inferred_from']['inputs'][fitclass.fun_vars[0]] - xdata = data[xname]['data'] - xlabel = data[xname]['label'] - xunit = data[xname]['unit'] - axes.set_xlabel('{} [{}]'.format(xlabel, xunit)) - - yname = fit1d['inferred_from']['output'][fitclass.fun_output[0]] - ydata = data[yname]['data'] - ylabel = data[yname]['label'] - yunit = data[yname]['unit'] - axes.set_ylabel('{} [{}]'.format(ylabel, yunit)) - - # set plot title - axes.set_title('{} vs. {}'.format(ylabel, xlabel)) - - # make list of fitted parameter values in the order the fitclass method 'fun' takes them - parameters = list(fitclass.p_labels) - for index, parameter in enumerate(parameters): - parameters[index] = fit1d['parameters'][parameter]['value'] - - # add the fit to the plot, using the fitted parameter list to find points on the fit - x = np.linspace(xdata.min(), xdata.max(), len(xdata) * 10) - axes.plot(x, fitclass.fun(x, *parameters), color='C0') - - # plot the data - if plottype == 'line': - axes.plot(xdata, ydata, color='C0') - elif plottype == 'scatter': - axes.plot(xdata, ydata, marker='.', - markersize=5, linestyle='', color='C0') - else: - raise ValueError('Unknown plottype. Please choose point or line plot.') - - -def plot_fit2d_slice(data, fits2d, fitclass, setvalue, plottype='scatter'): - """ Takes a 2D dataset and fit, and plots a 1D cross-section of the data at the setpoint specified by - 'setvalue', along with the fit at that setpoint. """ - - plot = plt.figure() - axes = plot.add_axes([0.1, 0.1, 0.8, 0.8]) - - # get setpoint name and data - setpoint = fits2d['inferred_from']['setpoints'][0] - setdata = data[setpoint]['data'] - - # confirm tha the setvalue tha the cross-section should be taken at is in fact one of the setpoints from the data - if setvalue not in setdata: - print('{}: {}'.format(setname, np.unique(setdata))) - raise RuntimeError( - 'Setvalue not in set points for this fit. Choose from setvalues above.') - - # get data, label and units for the two axes, and set appropriate axis labels - xname = fits2d['inferred_from']['inputs'][fitclass.fun_vars[0]] - xdata = data[xname]['data'] - xlabel = data[xname]['label'] - xunit = data[xname]['unit'] - axes.set_xlabel('{} [{}]'.format(xlabel, xunit)) - - yname = fits2d['inferred_from']['output'][fitclass.fun_output[0]] - ydata = data[yname]['data'] - ylabel = data[yname]['label'] - yunit = data[yname]['unit'] - axes.set_ylabel('{} [{}]'.format(ylabel, yunit)) - - # set plot title - axes.set_title('{} vs. {} at {}={}'.format( - ylabel, xlabel, setpoint, setvalue)) - - # select, from the full data for the two axes, only the data that corresponds to the chosen setpoint - x_dat = [] - y_dat = [] - for xpoint, ypoint, setpoint in zip(xdata, ydata, setdata): - if setpoint == setvalue: - x_dat.append(xpoint) - y_dat.append(ypoint) - xdata = np.array(x_dat) - ydata = np.array(y_dat) - - # make list of fitted parameter values in the order the fitclass method 'fun' takes them - parameters = list(fitclass.p_labels) - for index, parameter in enumerate(parameters): - parameters[index] = fits2d[setvalue]['parameters'][parameter]['value'] - - # add the fit to the plot, using the fitted parameter list to find points on the fit - x = np.linspace(xdata.min(), xdata.max(), len(xdata) * 10) - axes.plot(x, fitclass.fun(x, *parameters), color='C0') - - # plot the data - if plottype == 'line': - axes.plot(xdata, ydata, color='C0') - elif plottype == 'scatter': - axes.plot(xdata, ydata, marker='.', - markersize=5, linestyle='', color='C0') - else: - raise ValueError('Unknown plottype. Please choose point or line plot.') diff --git a/qdev_wrappers/fitting/analysis.py b/qdev_wrappers/fitting/analysis.py new file mode 100644 index 00000000..04425993 --- /dev/null +++ b/qdev_wrappers/fitting/analysis.py @@ -0,0 +1,372 @@ +import numpy as np +import os +import matplotlib.pyplot as plt +from qcodes import config +from qcodes.dataset.data_export import get_data_by_id, load_by_id +from qcodes.dataset.plotting import _rescale_ticks_and_units, plot_on_a_plain_grid +from qcodes import ParamSpec, new_experiment, new_data_set +from qcodes.dataset.database import initialise_database + + +class Analysis: + + def __init__(self, run_id, dept_var: str, model, **function_variables): + + self.experiment_info = {'exp_name': load_by_id(run_id).exp_name, + 'exp_id': load_by_id(run_id).exp_id, + 'run_id': run_id, + 'sample_name': load_by_id(run_id).sample_name} + + # retrieve data based on run id and dependent variable, organize + all_data = self._select_relevant_data(run_id, dept_var, **function_variables) + self.function_vars = all_data[0] + self.dept_var = all_data[1] + self.setpoints = all_data[2] + self.all_data = all_data[3] + self.model = model + + self.metadata = {} + self.fit_results = {} + + def _select_relevant_data(self, run_id, dept_var, **function_variables): + """ + This function goes through the all the data retrieved by + get_data_by_id(run_id), and returns the data that contains + the dependent variable, or throws an error if no matching + data is found. + + Calling get_data_by_id(run_id) returns data as a list of lists + of dictionaries: + + [ + # each element in this list refers + # to one dependent (aka measured) parameter + [ + # each element in this list is a dictionary referring + # to one independent (aka setpoint) parameter + # that the dependent parameter depends on; + # a dictionary with the data and metadata of the dependent + # parameter is in the *last* element in this list + + ] + ] + """ + # Find the data corresponding to the dependent variable + index = None + for idx, measured_data in enumerate(get_data_by_id(run_id)): + if measured_data[-1]['name'] == dept_var: + index = idx + if index is None: + raise RuntimeError(f"No data found with dependent variable {dept_var}. " + f"Dataset contains variables: {load_by_id(run_id).parameters}") + all_data = get_data_by_id(run_id)[index] + + # organize into dependent, independent and setpoints + if not function_variables: + raise RuntimeError("Please specify at least one variable! " + f"Dataset contains variables: {load_by_id(run_id).parameters}") + for var, name in function_variables.items(): + if name not in load_by_id(run_id).parameters.split(','): + raise RuntimeError(f"The variable {name} was not found in the data! " + f"Dataset contains variables: {load_by_id(run_id).parameters}") + function_variables.update({var: data for data in all_data if data['name'] == name}) + + dependent_variable = [data for data in all_data if data['name'] == dept_var][0] + setpoints = {data['name']: data for data in all_data if + data['name'] != dept_var and + data not in function_variables.values()} + + return [function_variables, dependent_variable, setpoints, all_data] + + def get_result(self, **setpoint_values): + """ + Args: + dictionary of setpoint values. + ie. {'frequency:1e9, 'power':-10} + Returns: + dict for fit where these conditions are satisfied + """ + + if len(self.setpoints) == 0: + return self.fit_results[0] + elif setpoint_values is None or len(setpoint_values) != len(self.setpoints): + raise RuntimeError('Please specify a value for each setpoint') + else: + return next(res for res in self.fit_results if + all(res['setpoints'][setpoint_name] == value for + setpoint_name, value in setpoint_values.items())) + + def _plot_1d(self, xdata, ydata, parameter_values, param_variance, rescale_axes=True): + + # plot data, and fit if successful + fig, ax = plt.subplots(1, 1) + ax.plot(xdata['data'], ydata['data'], marker='.', markersize=5, linestyle='', color='C0') + if parameter_values is None: + pass + else: + x = np.linspace(xdata['data'].min(), xdata['data'].max(), len(xdata['data']) * 10) + param_args = [value for value in parameter_values.values()] + ax.plot(x, self.model.evaluate(x, *param_args), color='C1') + + # set axes labels and title + ax.set_xlabel(f"{xdata['label']} ({xdata['unit']})") + ax.set_ylabel(f"{ydata['label']} ({ydata['unit']})") + + if rescale_axes: + data_lst = [xdata, ydata] + _rescale_ticks_and_units(ax, data_lst) + + # add fit result summary box + if parameter_values is None: + textstr = '{} \n Unsuccessful fit'.format(self.model.func_str) + else: + p_label_list = [self.model.func_str] + for parameter in parameter_values: + value = parameter_values[parameter] + unit = self.metadata['parameters'][parameter]['unit'] + if param_variance is not None: + standard_dev = np.sqrt(param_variance[parameter]) + p_label_list.append(r'{} = {:.3g} ± {:.3g} {}'.format(parameter, value, standard_dev, unit)) + else: + p_label_list.append(r'{} = {:.3g} {}'.format(parameter, value, unit)) + textstr = '\n'.join(p_label_list) + + ax.text(1.05, 0.7, textstr, transform=ax.transAxes, fontsize=14, + verticalalignment='top', bbox={'ec': 'k', 'fc': 'w'}) + + return ax + + def plot(self, rescale_axes: bool = True, show_variance=False, save_plot=False): + """ + setpoint : None or a dict of form {name: value, name2: value, ...} + """ + + if len(self.function_vars) > 1: + raise NotImplementedError("Sorry, you have specified multiple function variables, and plotting only " + "works for 1D functions right now") + else: + indept_var = [data_dict for data_dict in self.function_vars.values()][0] + axes = [] + colorbar = None + title = "Run #{} fitted, Experiment {} ({})".format(self.experiment_info['run_id'], + self.experiment_info['exp_id'], + self.experiment_info['sample_name']) + if len(self.setpoints) == 0: # 1D PLOTTING + + parameter_values = self.fit_results[0]['param_values'] + if show_variance: + param_variance = self.fit_results[0]['variance'] + else: + param_variance = None + ax = self._plot_1d(indept_var, self.dept_var, parameter_values, param_variance, rescale_axes) + ax.set_title(title) + axes.append(ax) + + elif len(self.setpoints) == 1: # 2D PLOTTING: 2D heat-map + params plots + + # get setpoint info for plot labels + setpoint_name = [key for key in self.setpoints][0] + setpoint_unit = self.setpoints[setpoint_name]['unit'] + setpoint_label = self.setpoints[setpoint_name]['label'] + + # organize all data for plotting + x, y, z = [], [], [] + params = {'param_setpoints': []} + for param_name in [name for name in self.metadata['parameters'].keys()]: + params[param_name] = [] + if show_variance: + params[param_name + '_variance'] = [] + + for result in self.fit_results.copy(): + + setpoint_value = result['setpoints'][setpoint_name] + data_length = len(indept_var['data']) + x.append(list(indept_var['data'])) + y.append([setpoint_value] * data_length) + if result['param_values'] is not None: + param_args = [value for value in result['param_values'].values()] + z.append(list(self.model.evaluate(indept_var['data'], *param_args))) + params['param_setpoints'].append(setpoint_value) + for parameter in result['param_values']: + params[parameter].append(result['param_values'][parameter]) + if show_variance: + params[parameter + '_variance'].append(result['variance'][parameter]) + else: + z.append([None]*len(indept_var['data'])) + + xpoints = np.array(x).flatten() + ypoints = np.array(y).flatten() + zpoints = np.array(z).flatten() + for item in params: + params[item] = np.array(params[item]) + + # Make 2D heatmap plot + fig, ax = plt.subplots(1, 1) + ax, colorbar = plot_on_a_plain_grid(xpoints, ypoints, zpoints, ax, colorbar) + + ax.set_xlabel(f"{indept_var['label']} ({indept_var['unit']})") + ax.set_ylabel(f"{setpoint_label} ({setpoint_unit})") + ax.set_title(title) + colorbar.set_label(f"{self.dept_var['label']} ({self.dept_var['unit']})") + + if rescale_axes: + data_lst = [indept_var, self.setpoints[setpoint_name], self.dept_var] + _rescale_ticks_and_units(ax, data_lst, colorbar) + + axes.append(ax) + + # Make parameter vs setpoint plots + order = params['param_setpoints'].argsort() + xdata = self.setpoints[setpoint_name] + xpoints = params['param_setpoints'][order] + + for param in self.model.parameters.keys(): + fig, ax = plt.subplots(1, 1) + ydata = self.model.parameters[param].copy() + ydata['data'] = params[param][order] + if show_variance: + param_variance = params[param + '_variance'][order] + param_standard_dev = np.array([np.sqrt(variance) for variance in param_variance]) + ax.errorbar(xpoints, ydata['data'], param_standard_dev, None, 'g.-') + else: + ax.plot(xpoints, ydata['data'], 'g.-') + ax.set_title(title) + ax.set_xlabel(f"{xdata['label']} ({xdata['unit']})") + ax.set_ylabel(f"{ydata['label']} ({ydata['unit']})") + if rescale_axes: + data_lst = [xdata, ydata] + _rescale_ticks_and_units(ax, data_lst) + axes.append(ax) + + elif len(self.setpoints) > 1: + raise NotImplementedError(f'Sorry, the fit has {len(self.setpoints)} setpoints ({self.setpoints}), ' + f'and this plotting function can currently deal with 1. Try saving the fit ' + f'and plotting from the saved fit result.') + if save_plot is True: + self._save_image(axes) + return axes, colorbar + + def save(self, save_plots=False): + # Todo: save dill (model only? model and fitter?) + + # Save plots to folder + if save_plots: + axes, colorbars = self.plot() + self._save_image(axes) + + # Save fit data to database + paramspecs = [] + + setpoint_paramspecs = {} + for setpoint, setpoint_dict in self.setpoints.items(): + paramspec = ParamSpec(setpoint, 'numeric', + label=setpoint_dict['label'], + unit=setpoint_dict['unit']) + setpoint_paramspecs[setpoint] = paramspec + paramspecs.append(paramspec) + + for param, param_dict in self.model.parameters.items(): + paramspec = ParamSpec( + param, 'numeric', + label=param_dict['label'], + unit=param_dict['unit'], + depends_on=list( + setpoint_paramspecs.values()) or None) + paramspecs.append(paramspec) + + # if the fit returned other info than the parameter values, fx. variance, create paramspecs for them + for param_info_name, metadata in self.model.data_saving_info.items(): + if 'unit' in metadata.keys(): + unit = metadata['unit'] + else: + unit = param_dict['unit'] + paramspec = ParamSpec( + "_".join([param, param_info_name]), + metadata['type'], + label=param_dict['label'] + metadata['label'], + unit=unit) + paramspecs.append(paramspec) + # Todo: What if, in some model, there is extra fit data that isn't associated with a particular parameter? + # Currently it won't be saved, but maybe there should be an option to add it to metadata? + + if self.experiment_info['exp_id'] is None: + initialise_database() + exp = new_experiment(self.experiment_info['sample_name'], + sample_name=self.experiment_info['sample_name']) + self.experiment_info['exp_id'] = exp.exp_id + all_metadata = {'model': self.metadata, + 'inferred_from': {'run_id': self.experiment_info['run_id'], + 'exp_id': self.experiment_info['exp_id'], + 'dept_var': self.dept_var['name']}} + dataset = new_data_set('analysis', + specs=paramspecs, + metadata=all_metadata) + + results = [] + for r in self.fit_results: + if r['param_values'] is not None: + result = {} + for param_name in self.model.parameters: + result.update({param_name: r['param_values'][param_name]}) + # add any additional info about parameter, fx. variance + for param_info_name in self.model.data_saving_info.keys(): + result.update({"_".join([param_name, param_info_name]): r[param_info_name][param_name]}) + if 'setpoints' in r.keys(): + for setpoint_name in r['setpoints'].keys(): + result[setpoint_name] = r['setpoints'][setpoint_name] + results.append(result) + dataset.add_results(results) + dataset.mark_complete() + print(f"\n Analysis saved with run id {dataset.run_id}") + return dataset + + def _save_image(self, axes): + """ + Save the plots as pdf and png + """ + plt.ioff() + + mainfolder = config.user.mainfolder + experiment_name = self.experiment_info['exp_name'] + sample_name = self.experiment_info['sample_name'] + meas_run_id = self.experiment_info['run_id'] + + storage_dir = os.path.join(mainfolder, experiment_name, sample_name) + os.makedirs(storage_dir, exist_ok=True) + + png_dir = os.path.join(storage_dir, 'png') + pdf_dir = os.path.join(storage_dir, 'pdf') + + os.makedirs(png_dir, exist_ok=True) + os.makedirs(pdf_dir, exist_ok=True) + + save_pdf = True + save_png = True + + for i, ax in enumerate(axes): + if save_pdf: + start_name = f'{meas_run_id}_fitted_{i}.pdf' + file_name = self._make_file_name(pdf_dir, start_name) + full_path = os.path.join(pdf_dir, file_name) + ax.figure.savefig(full_path, dpi=500) + if save_png: + start_name = f'{meas_run_id}_fitted_{i}.png' + file_name = self._make_file_name(png_dir, start_name) + full_path = os.path.join(png_dir, file_name) + ax.figure.savefig(full_path, dpi=500) + plt.ion() + return axes + + def _make_file_name(self, directory, initial_name): + full_path = os.path.join(directory, initial_name) + name = initial_name.split('.')[0] + file_format = initial_name.split('.')[1] + new_name = name + i = 1 + while os.path.isfile(full_path): + new_name = f'{name}_{i}' + file_path = '.'.join([new_name, file_format]) + full_path = os.path.join(directory, file_path) + i += 1 + final_name = '.'.join([new_name, file_format]) + return final_name diff --git a/qdev_wrappers/fitting/data_extraction.py b/qdev_wrappers/fitting/data_extraction.py deleted file mode 100644 index 71a347a0..00000000 --- a/qdev_wrappers/fitting/data_extraction.py +++ /dev/null @@ -1,279 +0,0 @@ -import qcodes as qc -import numpy as np -from os.path import expanduser -from qcodes.dataset.data_export import get_data_by_id -from qcodes.dataset.sqlite_base import connect -from qdev_wrappers.show_num import check_experiment_is_initialized -from qdev_wrappers.transmon.file_helpers import get_data_num - - -def load_data_dict(run_id, datatype='SQL'): - """ - Args: - run_id (int) - datatype (str, default 'SQL'): 'SQL' or 'qcodes_legacy' - """ - if datatype == 'qcodes_legacy': - converter = Legacy_Converter() - - elif datatype == 'SQL': - converter = SQL_Converter() - - else: - raise NotImplementedError( - 'datatype {} is not currently supported'.format(datatype)) - - return converter.convert(run_id) - - -class DataConverter: - """ - Base class for converters that convert data into a python dictionary. - """ - def find_data(self, run_id): - """ - Args: - run_id (int) - Returns: - data - """ - raise NotImplementedError( - 'find_data not implemented in the base class.') - - def make_data_dictionary(self, data): - """ - Args: - data as returned by find_data - Returns: - data (dict): keys are variables from the data, values are - dictionaries in the format - {'label': str, 'unit': str, 'data': np.array } - """ - raise NotImplementedError( - 'make_data_dictionary not implemented in the base class.') - - def find_experiment(self, run_id): - """ - Args: - run_id - Returns: - corresponding experiment id - """ - raise NotImplementedError( - 'find_experiment not implemented in the base class.') - - def find_dependencies(self, run_id): # TODO: why do we need this? - """ - Args: - run_id (int) - data (dict) - Returns: - dependencies (dict): eg if A and B were measured, and they both - depend on C and D - dependencies = {A: [C, D], B: [C, D]} - """ - raise NotImplementedError( - 'find_dependencies not implemented in the base class.') - - def find_variables(self, data): - """ - Args: - data (dict) - Returns: - list of all variables contained in the data dict - """ - raise NotImplementedError( - 'find_variables not implemented in the base class.') - - def convert(self, run_id): - """ - Args: - run_id (int) - Returns: - data (dict): containing all the data from the specified - run as numpy arrays, plus relevant metadata - (experiment id, run id, dependencies, names, labels, and units) - """ - data = self.find_data(run_id) - data_dict = self.make_data_dictionary(data) - - data_dict['exp_id'] = self.find_experiment(run_id) - data_dict['run_id'] = run_id - data_dict['sample_name'] = self.find_sample_name(run_id) -# data_dict['dependencies'] = self.find_dependencies(run_id) - data_dict['variables'] = self.find_variables(data) - - return data_dict - - -class SQL_Converter(DataConverter): - - def find_data(self, run_id): - """ - Uses the database location specified in qc.config to - get the data using get_data_by_id function. - """ - data = get_data_by_id(run_id) - if len(data) == 0: - raise RuntimeError('No data found for run id {}'.format(run_id)) - return data - - def find_dependencies(self, run_id): - file = expanduser(qc.config['core']['db_location']) - - # connect to SQL database - conn = connect(file) - cur = conn.cursor() - names = {} - dependencies = {} - - # extract names from layouts table - for row in cur.execute( - 'SELECT layout_id, parameter FROM layouts WHERE run_id is ?', str(run_id)): - names[row[0]] = row[1] - - # extract dependencies from dependencies table - for row in cur.execute( - 'SELECT dependent, independent FROM dependencies'): - if row[0] in names.keys(): - dependencies[names[row[0]]] = [] - - for row in cur.execute( - 'SELECT dependent, independent FROM dependencies'): - if row[0] in names.keys(): - dependencies[names[row[0]]].append(names[row[1]]) - - conn.close() - - return dependencies - - def find_variables(self, data): - all_variables = [] - - for data_subset in data: - for variable in data_subset: - if variable['name'] not in all_variables: - all_variables.append(variable['name']) - - return all_variables - - def find_experiment(self, run_id): - file = expanduser(qc.config['core']['db_location']) - conn = connect(file) - cur = conn.cursor() - - execute = cur.execute( - 'SELECT exp_id FROM runs WHERE run_id is {}'.format(run_id)) - exp_id = execute.fetchone()[0] - - return exp_id - - def find_sample_name(self, run_id): - return None # TODO - - def make_data_dictionary(self, data): - data_dict = {} - for data_subset in data: - for variable in data_subset: - name = variable['name'] - if name in data_dict.keys(): - if not np.array_equal(variable['data'], - data_dict[name]['data']): - print(name) - raise RuntimeError( - 'Variables with identical names contain ' - 'non-identical data arrays!') - - data_dict[name] = variable - - return data_dict - - -class Legacy_Converter(DataConverter): - - def find_data(self, run_id): - str_id = '{0:03d}'.format(run_id) - check_experiment_is_initialized() # TODO: what does this do? - path = qc.DataSet.location_provider.fmt.format(counter=str_id) - data = qc.load_data(path) - return data - - def find_dependencies(self, run_id): - data = self.find_data(run_id) - dep_vars = [key for key in data.arrays.keys() - if "_set" not in key[-4:]] - indep_vars = [key for key in data.arrays.keys() if "_set" in key[-4:]] - dependencies = {} - for variable in dep_vars: - dependencies[variable] = indep_vars - return dependencies - - def find_variables(self, data): - all_variables = [variable for variable in data.arrays.keys()] - return all_variables - - def find_experiment(self, run_id): - return None # TODO: this will never work for legacy datasets right? - - def resize_data(self, data1, data2): - scan_size = len(data1[0]) - new_data2 = [] - for setvalue in data2: - array = np.array([setvalue for datapoint in range(scan_size)]) - new_data2.append(array) - return np.array(new_data2) - - def make_data_dictionary(self, data): - data_dict = {} - all_variables = self.find_variables(data) - for variable in all_variables: - qc_data = getattr(data, variable) - name = getattr(qc_data, 'name', 'No Name') - label = getattr(qc_data, 'label', '') - unit = getattr(qc_data, 'unit', '') - np_data = qc_data.ndarray - - data_dict[variable] = {'name': variable, - 'label': label, - 'var_name': name, - 'unit': unit, - 'data': np_data} - - '''checks if the data set used set points - if so, it reformats the data - arrays so that it looks the same as it would look if it were imported - from SQL. All data is stored as arrays of the same length, rather than - set_points being stored as single numbers - (i.e. for setpoints = [1, 2, 3] , data = [[a, b, c], [d, e, f], [g, h, i]] - becomes setpoints = [[1, 1, 1], [2, 2, 2], [3, 3, 3]], data = data)''' - dependencies = self.find_dependencies(get_data_num(data)) - - dep_vars = [key for key in dependencies.keys()] - indep_vars = dependencies[dep_vars[0]] - - if len(indep_vars) == 2: - data1 = data_dict[dep_vars[0]]['data'] - data2 = data_dict[indep_vars[0]]['data'] - data3 = data_dict[indep_vars[1]]['data'] - - if type(data2[0]) != np.ndarray: - data_dict[indep_vars[0]]['data'] = self.resize_data( - data1, data2) - if type(data3[0]) != np.ndarray: - data_dict[indep_vars[1]]['data'] = self.resize_data( - data1, data3) - - '''reformatting nested arrays as a single 1d array, i.e. - setpoints = [[1, 1, 1], [2, 2, 2], [3, 3, 3]], data = [[a, b, c], [d, e, f], [g, h, i]] - becomes setpoints [1, 1, 1, 2, 2, 2, 3, 3, 3], data = [a, b, c, d, e, f, g, h, i]''' - - for variable in all_variables: - data1 = data_dict[variable]['data'] - - new_data = [] - for array in data1: - for datapoint in array: - new_data.append(datapoint) - - data_dict[variable]['data'] = np.array(new_data) - - return data_dict diff --git a/qdev_wrappers/fitting/fit_saving.py b/qdev_wrappers/fitting/fit_saving.py deleted file mode 100644 index eb45bd2a..00000000 --- a/qdev_wrappers/fitting/fit_saving.py +++ /dev/null @@ -1,85 +0,0 @@ -from qcodes import ParamSpec, new_data_set, new_experiment -from qdev_wrappers.fitting.fitter import Fitter -from qcodes.dataset.database import initialise_database -from qcodes.dataset.data_set import load_by_id - - -def save_fit_result(fitter: Fitter): - paramspecs = [] - setpoint_paramspecs = {} - for setpoint, setpoint_dict in fitter.setpoints.items(): - paramspec = ParamSpec(setpoint, 'numeric', - label=setpoint_dict['label'], - unit=setpoint_dict['unit']) - setpoint_paramspecs[setpoint] = paramspec - paramspecs.append(paramspec) - indept_var_paramspec = ParamSpec( - fitter.indept_var['name'], - 'numeric', - label=fitter.indept_var['label'], - unit=fitter.indept_var['unit'], - depends_on=list(setpoint_paramspecs.values()) or None) - paramspecs.append(indept_var_paramspec) - dept_var_paramspec = ParamSpec( - fitter.dept_var['name'], - 'numeric', - label=fitter.dept_var['label'], - unit=fitter.dept_var['unit'], - depends_on=[indept_var_paramspec]+list(setpoint_paramspecs.values())) - paramspecs.append(dept_var_paramspec) - for param, param_dict in fitter.fit_parameters.items(): - paramspec = ParamSpec( - param, 'numeric', - label=param_dict['label'], - unit=param_dict['unit'], - depends_on=list(setpoint_paramspecs.values()) or None) # TODO: should also have inferred from, depends on? - paramspec_initial_val = ParamSpec( - param + '_initial_val', 'numeric', - label=param_dict['label'] + ' Initial Value', - unit=param_dict['unit']) - paramspec_variance = ParamSpec( - param + '_variance', 'numeric', - label=param_dict['label'] + ' Variance') # TODO: should also have inferred from, depends on? - paramspecs.extend( - [paramspec, paramspec_initial_val, paramspec_variance]) - estimate_paramspec = ParamSpec( - fitter.dept_var['name'] + '_estimate', - 'numeric', - label = fitter.dept_var['label'] + ' Fit Estimate', - unit = fitter.dept_var['unit'], - depends_on=[indept_var_paramspec]+list(setpoint_paramspecs.values())) - paramspecs.append(estimate_paramspec) - if fitter.experiment_info['exp_id'] is None: - initialise_database() - exp = new_experiment(fitter.experiment_info['sample_name'], - sample_name=fitter.experiment_info['sample_name']) - fitter.experiment_info['exp_id'] = exp.exp_id - metadata = {'estimator': fitter.estimator, - 'dept_var': fitter.dept_var['name'], - 'indept_var': fitter.indept_var['name'], - 'inferred_from': fitter.experiment_info['run_id']} - dataset = new_data_set('analysis', -# exp_id=fitter.experiment_info['exp_id'], - specs=paramspecs) -# metadata=metadata) - results = [] - for r in fitter.fit_results: - result = {} - for setpoint_name in r['setpoint_names']: - result[setpoint_name] = r['setpoint_values'][setpoint_name] - for param_name in r['param_names']: - if r['param_values'] is not None: - result.update( - {param_name: r['param_values'][param_name], - param_name + '_initial_val': r['param_start_values'][param_name], - param_name + '_variance': r['param_variance'][param_name]}) - for j in range(len(r['indept_var_values'])): - result = result.copy() - result.update( - {r['indept_var_name']: r['indept_var_values'][j], - r['dept_var_name']: r['dept_var_values'][j], - r['dept_var_name'] + '_estimate': r['estimate_values'][j]}) - results.append(result) - dataset.add_results(results) - dataset.mark_complete() - return dataset diff --git a/qdev_wrappers/fitting/fitter.py b/qdev_wrappers/fitting/fitter.py index c947c8bd..77ab6e6b 100644 --- a/qdev_wrappers/fitting/fitter.py +++ b/qdev_wrappers/fitting/fitter.py @@ -1,438 +1,146 @@ import qcodes as qc import numpy as np -from scipy.optimize import curve_fit from itertools import product -from qdev_wrappers.fitting.least_squares_fit import LeastSquaresFit -from typing import Dict - -import matplotlib.pyplot as plt -from qcodes.dataset.data_set import load_by_id -from qcodes.dataset.data_export import get_data_by_id, flatten_1D_data_for_plot -from qcodes.dataset.data_export import (datatype_from_setpoints_1d, - datatype_from_setpoints_2d, reshape_2D_data) -from qcodes.dataset.plotting import _rescale_ticks_and_units, plot_on_a_plain_grid - import warnings -warnings.simplefilter("always") - +# warnings.simplefilter("always") +from qdev_wrappers.fitting.analysis import Analysis class Fitter: """ - Class which performs fit for data based on data, fitclass and names of - Args: - data (dict) - fitclass (currently limited to LeastSquaresFit) - indept_var (str): name of parameter in data dict to be used as - independent variable in fit procedure. eg 'pulse_readout_delay' - dept_var (str): name of parameter in data dict which represents - dependent variable for fitting. eg 'cavity_magnitude_result' + 'model' + 'guess' : Optional, and unnecessary if the model contains a suitable default guess. + 'r2_limit' : Optional, used to reject very bad fits. Useful but questionable. """ - def __init__(self, run_id, fitclass: LeastSquaresFit, - indept_var: str, dept_var: str, r2_limit = 0.6): - self.fitclass = fitclass - self.experiment_info = {'exp_id': load_by_id(run_id).exp_name, - 'run_id': run_id, - 'sample_name': load_by_id(run_id).sample_name} - self.all_data = self._select_relevant_data(run_id, indept_var, dept_var) - self.indept_var = [ data for data in self.all_data if data['name'] == indept_var ][0] - self.dept_var = [ data for data in self.all_data if data['name'] == dept_var ][0] - self.setpoints = {data['name']: data for data in self.all_data if - (data['name'] != dept_var) and (data['name'] != indept_var)} - self.fit_parameters = { - fitclass.param_names[i]: - {'name': fitclass.param_names[i], - 'label': fitclass.param_labels[i], - 'unit': fitclass.param_units[i]} for - i in range(len(fitclass.param_names))} - self.estimator = {'method': 'LSF', - 'type': fitclass.name, - 'fit_function_str': fitclass.fun_str, - 'r2_limit': r2_limit} # TODO: add dill here - self.fit_results = self._do_fitting_procedure() + def __init__(self, model, guess=None, r2_limit=None): + self.model = model + if guess is not None: + self.model.guess = guess - def _select_relevant_data(self, run_id, indept_var, dept_var): - """ - This function goes through the all the data retrieved by - get_data_by_id(run_id), and returns the data that contains - the dependent variable, or throws an error if no matching - data is found. + self.r2_limit = r2_limit - Calling get_data_by_id(run_id) returns data as a list of lists - of dictionaries: + def fit_by_id(self, run_id, dept_var: str, save_fit=True, **other_function_variables): - [ - # each element in this list refers - # to one dependent (aka measured) parameter - [ - # each element in this list is a dictionary referring - # to one independent (aka setpoint) parameter - # that the dependent parameter depends on; - # a dictionary with the data and metadata of the dependent - # parameter is in the *last* element in this list + analysis = Analysis(run_id, dept_var, self.model, **other_function_variables) + analysis.metadata = self.model.summary + if self.r2_limit is not None: + analysis.metadata['r2_limit'] = self.r2_limit - ] - ] - """ + fit = self._do_fitting_procedure(analysis) + if save_fit: + fit.save() - index = None - for idx, measured_data in enumerate(get_data_by_id(run_id)): - if measured_data[-1]['name'] == dept_var: - index = idx - if index == None: - raise RuntimeError(f"No data found with dependent variable {dept_var}") - all_data = get_data_by_id(run_id)[index] - if indept_var not in [data['name'] for data in all_data]: - raise RuntimeError(f"Specified independent variable not found in data") - return all_data + return fit - def _do_fitting_procedure(self): + def _do_fitting_procedure(self, analysis): """ Populates fit_results list with one dictionary per combination of setpoints. - eg - for self.setpoints = {'frequency': + eg setpoints = {'frequency': {'label': '', 'unit': 'Hz', 'data': [1e9, 2e9, 3e9]}, - 'power': + 'power': {'label': '', 'unit': 'dBm', 'data': [-10, -30]}} will make 6 dictionaries of the form { - 'setpoint_names': ['frequency', 'power'], - 'setpoint_labels': {'frequency': '' , 'power': ''}, - 'setpoint_units': {'frequency': 'Hz' , 'power': 'dBm'}, 'setpoint_values': {'frequency': 1e9 , 'power': -10}, - 'param_names': ['a', 'b'], - 'param_labels': {'a': 'T1', 'b': 'b'}, - 'param_units': {'a': 's', 'b': ''}, - 'param_start_values': {'a': 3, 'b': 10}, + 'start_values': {'a': 3, 'b': 10}, 'param_values': {'a': 2.5, 'b': 11}, - 'param_variance': {'a': 0.1, 'b': 2}, - 'indept_var_name': 'pulse_readout_delay', - 'indept_var_label': 'Pulse Readout Delay', - 'indept_var_unit': 's', - 'indept_var_values': [0.1, 0.2, 0.3, 0.4], - 'dept_var_name': 'cavity_magnitude_response', - 'dept_var_label': 'Cavity Response', - 'dept_var_unit': '', - 'dept_var_values': [1.15, 1.26, 1.34, 1.23], - 'estimate_values' [1.1, 1.2, 1.3, 1.4] + 'variance': {'a': 0.1, 'b': 2}, } """ fit_results = [] + setpoints = analysis.setpoints + full_function_variable_arrays = {var: data_dict['data'] for var, data_dict in analysis.function_vars.items()} + full_output_data_array = analysis.dept_var['data'] + + # input_data_array = indept_var['data'] + # if setpoints then perform fit for all - if len(self.setpoints) > 0: -# raise RuntimeError('many setpoints is currently broken, apologies') + if len(setpoints) > 0: # find all possible combinations of setpoint values setpoint_combinations = product( - *[set(v['data']) for v in self.setpoints.values()]) - setpoint_names = list(self.setpoints.keys()) - setpoint_labels = [v['label'] for v in self.setpoints.values()] - setpoint_units = [v['unit'] for v in self.setpoints.values()] - data = {data['name'] : data for data in self.all_data} + *[set(v['data']) for v in setpoints.values()]) + setpoint_names = list(setpoints.keys()) for setpoint_combination in setpoint_combinations: # find indices where where setpoint combination is satisfied indices = set(np.argwhere( - data[setpoint_names[0]]['data'] == setpoint_combination[0]).flatten()) + setpoints[setpoint_names[0]]['data'] == setpoint_combination[0]).flatten()) for i in range(1, len(setpoint_names)): new_indices = np.argwhere( - data[setpoint_names[0]]['data'] == setpoint_combination[0]).flatten() + setpoints[setpoint_names[0]]['data'] == setpoint_combination[0]).flatten() indices = indices.intesection(new_indices) indices = list(indices) - input_data_array = self.indept_var['data'][indices] - output_data_array = self.dept_var['data'][indices] - param_dict = self._perform_fit(input_data_array, - output_data_array) + output_data_array = full_output_data_array[indices] + function_variable_arrays = {} + for var, data in full_function_variable_arrays.items(): + function_variable_arrays[var] = data[indices] + param_values, param_dict = self.fit(output_data_array, **function_variable_arrays) result = param_dict.copy() - result.update( - {'setpoint_names': setpoint_names, - 'setpoint_labels': dict(zip(setpoint_names, - setpoint_labels)), - 'setpoint_units': dict(zip(setpoint_names, - setpoint_units)), - 'setpoint_values': dict(zip(setpoint_names, - setpoint_combination)), - 'indept_var_values': input_data_array, - 'dept_var_values': output_data_array, - 'indept_var_name': self.indept_var['name'], - 'dept_var_name': self.dept_var['name'], - 'indept_var_label': self.indept_var['label'], - 'dept_var_label': self.dept_var['label'], - 'indept_var_unit': self.indept_var['unit'], - 'dept_var_unit': self.dept_var['unit'], - 'estimate_values': - self._find_estimate(input_data_array, - param_dict['param_values'])}) + result.update({'setpoints': dict(zip(setpoint_names, setpoint_combination))}) fit_results.append(result) # except Exception: # TODO: what kind of exception # print('no data for setpoint combination ', dict( # zip(self.setpoints.keys(), setpoint_combination))) else: - input_data_array = self.indept_var['data'] - output_data_array = self.dept_var['data'] - param_dict = self._perform_fit(input_data_array, - output_data_array) - result = param_dict.copy() - result.update( - {'setpoint_names': [], - 'indept_var_values': input_data_array, - 'dept_var_values': output_data_array, - 'indept_var_name': self.indept_var['name'], - 'dept_var_name': self.dept_var['name'], - 'indept_var_label': self.indept_var['label'], - 'dept_var_label': self.dept_var['label'], - 'indept_var_unit': self.indept_var['unit'], - 'dept_var_unit': self.dept_var['unit'], - 'estimate_values': - self._find_estimate(input_data_array, - param_dict['param_values'])}) - fit_results.append(result) - return fit_results + function_variable_arrays = full_function_variable_arrays + output_data_array = full_output_data_array + param_values, param_dict = self.fit(output_data_array, **function_variable_arrays) + fit_results.append(param_dict) - def get_result(**setpoint_values): - """ - Args: - kwargs for each setpoints - eg 'frequency=1e9, power=-10' - Returns: - dict for fit where these conditions are satisfied - """ - if len(setpoint_values) != len(self.setpoints): - raise RuntimeError('Must specify a value for each setpoint') - elif len(self.setpoints) == 0: - return self.fit_results[0] - else: - return next(res for res in self.fit_results if - all(res[setpoint] == value for - setpoint, value in setpoint_values.items())) - - def get_r2(self, estimate, data): - """ - Finds residual and total sum of squares, calculates the R^2 value - """ - ss_res = np.sum((data - estimate) ** 2) - ss_tot = np.sum((data - np.mean(data)) ** 2) - r2 = 1 - (ss_res / ss_tot) - return r2 + analysis.fit_results = fit_results + return analysis - def _perform_fit(self, input_data_array, output_data_array): + def fit(self, output_data_array, **function_variable_arrays): - # make fit params dictionary - params_dict = {'param_names': [], 'param_labels': {}, - 'param_units': {}, 'param_start_values': {}, - 'param_variance': {}, 'param_values': {}} - for i, param_name in enumerate(self.fitclass.param_names): - params_dict['param_names'].append(param_name) - params_dict['param_labels'][param_name] = self.fitclass.param_labels[i] - params_dict['param_units'][param_name] = self.fitclass.param_units[i] + param_values = {parameter: None for parameter in self.model.parameters.keys()} + fit_info = None - # find start parameters, run curve_fit function to perform fit - p_guess = self.fitclass.guess(input_data_array, output_data_array) try: - popt, pcov = curve_fit(self.fitclass.fun, - input_data_array, - output_data_array, - p0=p_guess) + # Do fit, using fit_procedure from model + popt, fit_info = self.model.fit_procedure(output_data_array, **function_variable_arrays) + # Store parameter values + for i, param_name in enumerate(self.model.parameters.keys()): + param_values[param_name] = popt[i] except RuntimeError: - params_dict['param_values'] = None - params_dict['param_variance'] = None - warnings.warn('Unsuccessful fit - curve_fit did not find optimal parameters') - - return params_dict + warnings.warn('Unsuccessful fit - did not find optimal parameters') + param_values = None - # add guess and fit results to dict - for i, param_name in enumerate(self.fitclass.param_names): - params_dict['param_start_values'][param_name] = p_guess[i] - params_dict['param_values'][param_name] = popt[i] - params_dict['param_variance'][param_name] = pcov[i, i] + params_dict = {'param_values': param_values, + 'fit_info': fit_info} # r2 test to approve fit - est_data = self._find_estimate(input_data_array, params_dict['param_values']) - r2 = self.get_r2(est_data, output_data_array) - if self.estimator['r2_limit'] == None: - pass - elif r2 < self.estimator['r2_limit']: - params_dict['param_values'] = None - params_dict['param_variance'] = None - warnings.warn('Unsuccessful fit - r2 for fit is below limit {}' - .format(self.estimator['r2_limit'])) - - return params_dict - - def _find_estimate(self, input_data_array, params_values_dict): - if params_values_dict is None: - return [None] * len(input_data_array) - else: - return self.fitclass.func(input_data_array, **params_values_dict) - - def _plot_1d(self, ax, xdata, ydata, parameter_values, parameter_variance, rescale_axes): - - # plot data, and fit if successful - ax.plot(xdata, ydata, marker='.', markersize=5, linestyle='', color='C0') - if parameter_values is None: - pass - else: - x = np.linspace(xdata.min(), xdata.max(), len(xdata) * 10) - ax.plot(x, self.fitclass.func(x, **parameter_values), color='C1') - - # set axes labels and title - ax.set_xlabel(f"{self.indept_var['label']} ({self.indept_var['unit']})") - ax.set_ylabel(f"{self.dept_var['label']} ({self.dept_var['unit']})") - - if rescale_axes: - data_lst = [self.indept_var, self.dept_var] - _rescale_ticks_and_units(ax, data_lst) - - # add fit result summary box - if parameter_values is None: - textstr = '{} \n Unsuccessful fit'.format(self.fitclass.fun_str) - else: - p_label_list = [self.fitclass.fun_str] - for parameter in parameter_values: - value = parameter_values[parameter] - unit = self.fit_results[0]['param_units'][parameter] - standard_dev = np.sqrt(parameter_variance[parameter]) - p_label_list.append(r'{} = {:.3g} +/- {:.3g} {}' - .format(parameter, value, standard_dev, unit)) - textstr = '\n'.join(p_label_list) - - ax.text(1.05, 0.7, textstr, transform=ax.transAxes, fontsize=14, - verticalalignment='top', bbox={'ec': 'k', 'fc': 'w'}) - - def plot(self, setpoint=None, rescale_axes: bool=True): - """ - setpoint : None or a dict of form {name: value, name2: value, ...} - """ - # make figure, axes, title - fig, ax = plt.subplots(1, 1) - axes = [ax] - colorbar = None - title = "Run #{} fitted, Experiment {} ({})".format(self.experiment_info['run_id'], - self.experiment_info['exp_id'], self.experiment_info['sample_name']) - - - if len(self.setpoints) == 0: # 1D PLOTTING - - ax.set_title(title) - - parameter_values = self.fit_results[0]['param_values'] - parameter_variance = self.fit_results[0]['param_variance'] - xpoints = self.indept_var['data'] - ypoints = self.dept_var['data'] - - self._plot_1d(ax, xpoints, ypoints, parameter_values, parameter_variance, rescale_axes) - - - elif len(self.setpoints) >= 1 : # 2D PLOTTING - - # make copy of fit_results, remove all results not at specified setpoints - plot_results = self.fit_results.copy() - - if setpoint is not None: - setpoint_names = [key for key in setpoint.keys()] - for name in setpoint_names: - for result in self.fit_results: - if result['setpoint_values'][name] != setpoint[name]: - plot_results.remove(result) - - # if only 1 fit is left, plot the 1D fit of this cut - if len(plot_results) == 1: - - title += '\n' + str(setpoint) - ax.set_title(title) - - parameter_values = plot_results[0]['param_values'] - parameter_variance = plot_results[0]['param_variance'] - xpoints = plot_results[0]['indept_var_values'] - ypoints = plot_results[0]['dept_var_values'] - - self._plot_1d(ax, xpoints, ypoints, parameter_values, parameter_variance, rescale_axes) - - # if there are many fits left in plot_results, make 2D plot + params plots - elif len(plot_results) > 1: - - # confirm that there is at most 1 variable setpoint - if setpoint is None: - unspecified_setpoints = [name for name in self.setpoints] - else: - unspecified_setpoints = [name for name in self.setpoints if name not in setpoint] - if len(unspecified_setpoints) > 1: - raise NotImplementedError('Function has too many ({}) unspecified setpoints.' - .format(len(setpoint_list))) - - # get setpoint info for plot labels - setpoint_name = unspecified_setpoints[0] - setpoint_unit = self.setpoints[setpoint_name]['unit'] - setpoint_label = self.setpoints[setpoint_name]['label'] - - # organize all data for plotting - x, y, z = [], [], [] - params = {'param_setpoints' : []} - for param_name in self.fit_parameters: - params[param_name] = [] - params[param_name + '_variance'] = [] - - for result in plot_results: - setpoint_value = result['setpoint_values'][setpoint_name] - data_length = len(result['indept_var_values']) - x.append(list(result['indept_var_values'])) - y.append([setpoint_value] * data_length) - z.append(list(result['estimate_values'])) - - if result['param_values'] is not None: - params['param_setpoints'].append(setpoint_value) - for parameter in result['param_values']: - params[parameter].append( result['param_values'][parameter] ) - params[parameter + '_variance'].append( result['param_variance'][parameter] ) - - xpoints = np.array(x).flatten() - ypoints = np.array(y).flatten() - zpoints = np.array(z).flatten() - for item in params: params[item] = np.array(params[item]) - - # Make 2D heatmap plot - ax, colorbar = plot_on_a_plain_grid(xpoints, ypoints, zpoints, ax, colorbar) - - ax.set_xlabel(f"{self.indept_var['label']} ({self.indept_var['unit']})") - ax.set_ylabel(f"{setpoint_label} ({setpoint_unit})") - ax.set_title(title) - colorbar.set_label(f"{self.dept_var['label']} ({self.dept_var['unit']})") - - if rescale_axes: - data_lst = [self.indept_var, self.setpoints[setpoint_name], self.dept_var] - _rescale_ticks_and_units(ax, data_lst, colorbar) - - # Select data for parameter vs setpoint plots - order = params['param_setpoints'].argsort() - xdata = self.setpoints[setpoint_name] - xpoints = params['param_setpoints'][order] - - for param in self.fit_parameters: - ydata = self.fit_parameters[param] - ydata['data'] = params[param][order] - param_variance = params[param + '_variance'][order] - param_standard_dev = np.array( [np.sqrt(variance) for variance in param_variance] ) - - #Make parameter vs setpoint plot - fig, ax = plt.subplots(1, 1) - ax.errorbar(xpoints, ydata['data'], param_standard_dev, None, 'g.-') - ax.set_title(title) - ax.set_xlabel(f"{xdata['label']} ({xdata['unit']})") - ax.set_ylabel(f"{ydata['label']} ({ydata['unit']})") - if rescale_axes: - data_lst = [xdata, ydata] - _rescale_ticks_and_units(ax, data_lst) - axes.append(ax) - - return axes, colorbar - - - - + if self.r2_limit is not None and param_values is not None: + est_data = self._find_estimate(params_dict['param_values'], **function_variable_arrays) + r2 = self._get_r2(est_data, output_data_array) + if r2 < self.r2_limit: + params_dict['param_values'] = None + params_dict['fit_info'] = None + param_values = None + warnings.warn(f'Unsuccessful fit - r2 for fit is below limit {self.r2_limit}') + + # Organize fit info (fx. variance, inital guess, etc). Organization is defined in the model + params_dict = self.model.organize_fit_info(params_dict) + + return param_values, params_dict + + def _find_estimate(self, param_values_dict, **function_variable_arrays): + variable_args = [value for value in function_variable_arrays.values()] + param_args = [value for value in param_values_dict.values()] + return self.model.evaluate(*variable_args, *param_args) + + def _get_r2(self, estimate, data): + """ + Finds residual and total sum of squares, calculates the R^2 value + """ + ss_res = np.sum((data - estimate) ** 2) + ss_tot = np.sum((data - np.mean(data)) ** 2) + r2 = 1 - (ss_res / ss_tot) + return r2 diff --git a/qdev_wrappers/fitting/guess.py b/qdev_wrappers/fitting/guess.py index 1bc87dd1..f904c4f9 100644 --- a/qdev_wrappers/fitting/guess.py +++ b/qdev_wrappers/fitting/guess.py @@ -7,12 +7,10 @@ def __init__(self): pass - class ExpDecayGuess(Guess): - """Guess for f(x) = a * e^(-x/b) + c""" - def make_guess(self, x, y): + def make_guess(self, y, x): length = len(y) val_init = y[0:round(length / 20)].mean() @@ -32,7 +30,7 @@ def make_guess(self, x, y): class ExpDecaySinGuess(Guess): """Guess for f(x) = a * e^(-x/b) sin(wx+p) + c""" - def make_guess(self, x, y): + def make_guess(self, y, x): a = y.max() - y.min() @@ -56,7 +54,7 @@ class PowerDecayGuess(Guess): """Guess for f(x) = a * b^x + c""" - def make_guess(self, x, y): + def make_guess(self, y, x): length = len(y) val_init = y[0:round(length / 20)].mean() @@ -76,9 +74,9 @@ def make_guess(self, x, y): class RabiT1Guess(Guess): - """Guess for f(x) = e^(-x/b) cos^2(wx/2)""" + """Guess for f(x) = e^(-x/b) cos^2(wx/2 + p)""" - def make_guess(self, x, y): + def make_guess(self, y, x): # guess b as point half way point in data b = x[round(len(x) / 2)] @@ -90,3 +88,24 @@ def make_guess(self, x, y): w = freqs[idx] * 2 return [b, w] + + +class CosineGuess(Guess): + + """Guess for f(x) = a * cos(wx + p) + b""" + + def make_guess(self, y, x): + + b = y.mean() + + a = y.max() - y.min() + + # Get initial guess for frequency from a fourier transform + yhat = fftpack.rfft(y - y.mean()) + idx = (yhat ** 2).argmax() + freqs = fftpack.rfftfreq(len(x), d=(x[1] - x[0]) / (2 * np.pi)) + w = freqs[idx] + + p = 0 + + return [a, w, p, b] diff --git a/qdev_wrappers/fitting/least_squares_fit.py b/qdev_wrappers/fitting/least_squares_fit.py deleted file mode 100644 index ed0461fa..00000000 --- a/qdev_wrappers/fitting/least_squares_fit.py +++ /dev/null @@ -1,147 +0,0 @@ -import numpy as np -import scipy.fftpack as fftpack -from typing import List -from qcodes import Instrument -from scipy.optimize import curve_fit -from collections import OrderedDict - - -class LeastSquaresFit(Instrument): - """ - Base class for fit functions to be used with curve_fit. Specifies - a function for fitting, function for guessing initial parameters for fit - and attributes specifying inputs and output of fit function as - well as and names, labels and units of fit parameters. - - Only one input and one output is currently allowed. - - Order of parameters in model_parameters ordered dictionary must match function guess - output and order of appearance in func - """ - model_parameters = OrderedDict({}) - def __init__(self, name, **kwargs): - super().__init__(name, **kwargs) - for param_name, param_kwargs in self.model_parameters.items(): - self.add_parameter(param_name, set_cmd=False, **kwargs) - del self.parameters['IDN'] - - def func(self, *args): - """ - The mathematical function to fit to - """ - raise NotImplementedError('This is not implemented in the base class.') - - def guess(self, *args): - """ - Function for determining an initial guess for the fit parameters from - given values or based on perform guess function - """ - raise NotImplementedError - - def perform_fit(self, input_data_array, output_data_array): - - # find start parameters, run curve_fit function to perform fit - p_guess = self.guess(input_data_array, output_data_array) - popt, pcov = curve_fit(self.func, - input_data_array, - output_data_array, - p0=p_guess) - - # update guess and fit results in fit parameters - for i, param in enumerate(self.model_parameters): - self.parameters[param]._save_val(popt[i]) - self.parameters[param].start_value = p_guess[i] - self.parameters[param].variance = pcov[i, i] - - -class ExpDecay(LeastSquaresFit): - - model_parameters = OrderedDict({'a': {'label':'$a$', 'unit':''}, - 'T': {'label':'$T$', 'unit':'s'}, - 'c': {'label':'$c$', 'unit':''} }) - def __init__(self, name, guess=None): - super().__init__(name) - self.guess_params = guess - self.fun_str=r'$f(x) = a \exp(-x/T) + c$' - self.fun_np='a*np.exp(-x/T)+c' - - def func(self, x, a, T, c): - return eval(self.fun_np) - - def guess(self, x, y): - if self.guess_params is not None: - return self.guess_params - length = len(y) - val_init = y[0:round(length / 20)].mean() - val_fin = y[-round(length / 20):].mean() - a = val_init - val_fin - c = val_fin - # guess T1 as point where data has fallen to 1/e of init value - idx = (np.abs(y - a / np.e - c)).argmin() - T = x[idx] - return [a, T, c] - - -class ExpDecaySin(LeastSquaresFit): - - model_parameters = OrderedDict({'a': {'label': '$a$', 'unit': ''}, - 'T': {'label': '$T$', 'unit': 's'}, - 'w': {'label': r'$\omega$', 'unit': 'Hz'}, - 'p': {'label': r'$\phi$', 'unit': ''}, - 'c': {'label': '$c$', 'unit': ''}}) - def __init__(self, name, guess=None): - super().__init__(name) - self.guess_params = guess - self.fun_str = r'$f(x) = a \sin(\omega x +\phi)\exp(-x/T) + c$' - self.fun_np = 'a*np.exp(-x/T)*np.sin(w*x+p)+c' - - def func(self, x, a, T, w, p, c): - return eval(self.fun_np) - - def guess(self, x, y): - if self.guess_params is not None: - return self.guess_params - a = y.max() - y.min() - c = y.mean() - # guess T2 as point half way point in data - T = x[round(len(x) / 2)] - # Get initial guess for frequency from a fourier transform - yhat = fftpack.rfft(y - y.mean()) - idx = (yhat**2).argmax() - freqs = fftpack.rfftfreq(len(x), d=(x[1] - x[0]) / (2 * np.pi)) - w = freqs[idx] - p = 0 - return [a, T, w, p, c] - - -class PowerDecay(LeastSquaresFit): - - model_parameters = OrderedDict({'a': {'label': '$a$', 'unit': 'V'}, - 'p': {'label': '$p$', 'unit': ''}, - 'b': {'label': '$c$', 'unit': 'V'}}) - def __init__(self, name, guess=None): - super().__init__(name) - self.guess_params = guess - self.fun_str = r'$f(x) = A p^x + B$' - self.fun_np = 'a * p**x + b' - - def func(self, x, a, p, b): - return eval(self.fun_np) - - def guess(self, x, y): - if self.guess_params is not None: - return self.guess_params - - length = len(y) - val_init = y[0:round(length / 20)].mean() - val_fin = y[-round(length / 20):].mean() - a = val_init - val_fin - b = val_fin - - # guess T as point where data has fallen to 1/e of init value - idx = (np.abs(y - a / np.e - b)).argmin() - T = x[idx] - #guess p as e^(-1/T): - p = np.e**(-1/T) - - return [a, p, b] \ No newline at end of file diff --git a/qdev_wrappers/fitting/least_squares_fitter.py b/qdev_wrappers/fitting/least_squares_fitter.py deleted file mode 100644 index c1c77cb9..00000000 --- a/qdev_wrappers/fitting/least_squares_fitter.py +++ /dev/null @@ -1,27 +0,0 @@ -from qcodes.instrument.base import Instrument -from scipy.optimize import curve_fit - -class LeastSquaresFitter(Instrument): - - def __init__(self, name, model, guess): - super().__init__(name) - self.model = model - self.guess = guess - - for param_name, param_kwargs in self.model.model_parameters.items(): - self.add_parameter(param_name, **param_kwargs) #set_cmd=False, - - def update(self, input_data_array, output_data_array): - - # find start parameters, run curve_fit function to perform fit - p_guess = self.guess.make_guess(input_data_array, output_data_array) - popt, pcov = curve_fit(self.model.func, - input_data_array, - output_data_array, - p0=p_guess) - - # update guess and fit results in fitter parameters - for i, param in enumerate(self.model.modelparam_names): - self.parameters[param]._save_val(popt[i]) - self.parameters[param].start_value = p_guess[i] - self.parameters[param].variance = pcov[i, i] diff --git a/qdev_wrappers/fitting/least_squares_models.py b/qdev_wrappers/fitting/least_squares_models.py index 72f2ab08..ff1f88f2 100644 --- a/qdev_wrappers/fitting/least_squares_models.py +++ b/qdev_wrappers/fitting/least_squares_models.py @@ -1,85 +1,153 @@ -from collections import OrderedDict import numpy as np +from models import Model +import guess as g +from scipy.optimize import curve_fit -class LeastSquaresModel(object): - def __init__(self, model_parameters, model_function): - self.modelparam_names = [key for key in model_parameters] - self.model_parameters = model_parameters - self.func_str = model_function['str'] - self.func_np = model_function['np'] +class LeastSquaresModel(Model): + def __init__(self, model_parameters, model_vars, model_function, guess): + super().__init__(model_parameters, model_vars, model_function, guess) + self.summary = {'method': 'Least squares', + 'function': {'func_str': self.func_str, 'func_np': self.func_np}, + 'parameters': self.parameters.copy()} # TODO: add dill here? - #arg_list = ", ".join(self.modelparam_names) - #self.func = eval(f"lambda {arg_list}: {self.func_np}") + """"data_saving_info" contains information for saving the additional information returned + by the model, beyond the parameter values. In the case of least squares fitting, this is + variance and initial guess values. If these results don't share a unit with their parameter, + the dictionary should also include 'units'.""" + self.data_saving_info = {'variance': {'type': 'numeric', 'label': ' variance'}, + 'start_values': {'type': 'numeric', 'label': ' initial value'}} - def func(self, *args): + def evaluate(self, *args): + """Takes an array, x, and a list of parameter values, in the order they are given + in model parameters. Evaluates the output of the model function at each point in x, + and returns an array of the output""" + arg_list = self.variables + [key for key in self.parameters.keys()] + arg_string = ", ".join(arg_list) + func = eval(f"lambda {arg_string}: {self.func_np}") + return func(*args) - """The mathematical function to fit to""" + def fit_procedure(self, output_data_array, **function_variable_arrays): + """Performs a fit based on the model. Returns : + an array of parameter values, ordered like the model_parameters dictionary AND + a fit_info list with the covariance matrix and the initial parameter guesses + """ - raise NotImplementedError('This is not implemented in the base class.') + if len(function_variable_arrays) > 1: + raise NotImplementedError("Least squares model guess and fit procedure are not set up for 2D fits yet") + else: + input_data_array = [array for array in function_variable_arrays.values()][0] + p_guess = self.get_initial_guess(output_data_array, **function_variable_arrays) + popt, pcov = curve_fit(self.evaluate, input_data_array, output_data_array, p0=p_guess) + return popt, [pcov, p_guess] + def organize_fit_info(self, params_dict): + """Takes the params_dict as produced by the fit procedure: -class DecayT1(LeastSquaresModel): + {'param_values': {'a' : 1, 'b', 2} , 'param_info': fit_info } - def __init__(self): - model_parameters = OrderedDict({'a': {'label': '$a$', 'unit': '' }, - 'T': {'label': '$T$', 'unit': 's'}, - 'c': {'label': '$c$', 'unit': '' } }) + In the case of Least Mean Squares fit models, fit_info is + [pcov, p_guess] (the covariance matrix and initial guess). Organizes + the additional fit info, and returns an updated params_dict.""" - model_function = {'str': r'$f(x) = a \exp(-x/T) + c$', - 'np': 'a*np.exp(-x/T)+c'} + if params_dict['fit_info'] is not None: + params_dict['variance'] = {} + params_dict['start_values'] = {} - super().__init__(model_parameters, model_function) + pcov = params_dict['fit_info'][0] + p_guess = params_dict['fit_info'][1] - def func(self, x, a, T, c): - return eval(self.func_np) + for i, param_name in enumerate(self.parameters.keys()): + params_dict['variance'][param_name] = pcov[i, i] + params_dict['start_values'][param_name] = p_guess[i] + del params_dict['fit_info'] + return params_dict -class DecayingRabis(LeastSquaresModel): + +class Benchmarking(LeastSquaresModel): def __init__(self): - model_parameters = OrderedDict({'a': {'label': '$a$', 'unit': '' }, - 'T': {'label': '$T$', 'unit': 's' }, - 'w': {'label': r'$\omega$', 'unit': 'Hz'}, - 'p': {'label': r'$\phi$', 'unit': '' }, - 'c': {'label': '$c$', 'unit': '' } }) + guess = g.PowerDecayGuess() + model_parameters = {'a': {'label': '$a$', 'unit': 'V'}, + 'p': {'label': '$p$', 'unit': ''}, + 'b': {'label': '$b$', 'unit': 'V'}} + model_vars = ['x'] + model_function = {'str': r'$f(x) = A p^x + B$', + 'np': 'a * p**x + b'} - model_function = {'str': r'$f(x) = a \exp(-x/T) \sin(\omega x +\phi) + c$', - 'np': 'a*np.exp(-x/T)*np.sin(w*x+p)+c'} + super().__init__(model_parameters, model_vars, model_function, guess) - super().__init__(model_parameters, model_function) - def func(self, x, a, T, w, p, c): - return eval(self.func_np) +class CosineModel(LeastSquaresModel): + def __init__(self): + guess = g.CosineGuess() + model_parameters = {'a': {'label': '$a$', 'unit': ''}, + 'w': {'label': r'$\omega$', 'unit': 'Hz'}, + 'p': {'label': r'$\phi$', 'unit': ''}, + 'b': {'label': '$c$', 'unit': ''}} + model_vars = ['x'] + model_function = {'str': r'$f(x) = a\cos(\omega x + \phi)+b$', + 'np': 'a * np.cos(w * x + p) + b'} -class Benchmarking(LeastSquaresModel): + super().__init__(model_parameters, model_vars, model_function, guess) + + +class CosineModelSwapped(LeastSquaresModel): def __init__(self): - model_parameters = OrderedDict({'a': {'label': '$a$', 'unit': 'V'}, - 'p': {'label': '$p$', 'unit': '' }, - 'b': {'label': '$b$', 'unit': 'V'} }) - model_function = {'str': r'$f(x) = A p^x + B$', - 'np': 'a * p**x + b'} + guess = g.CosineGuess() + model_parameters = {'a': {'label': '$a$', 'unit': ''}, + 'p': {'label': r'$\phi$', 'unit': ''}, + 'w': {'label': r'$\omega$', 'unit': 'Hz'}, + 'b': {'label': '$c$', 'unit': ''}} + model_vars = ['x'] + model_function = {'str': r'$f(x) = a\cos(\omega x + \phi)+b$', + 'np': 'a * np.cos(w * x + p) + b'} - super().__init__(model_parameters, model_function) + super().__init__(model_parameters, model_vars, model_function, guess) - def func(self, x, a, p, b): - return eval(self.func_np) +class DecayT1(LeastSquaresModel): -class RabiT1(LeastSquaresModel): + def __init__(self): + guess = g.ExpDecayGuess() + model_parameters = {'a': {'label': '$a$', 'unit': ''}, + 'T': {'label': '$T$', 'unit': 's'}, + 'c': {'label': '$c$', 'unit': ''}} + model_vars = ['x'] + model_function = {'str': r'$f(x) = a \exp(-x/T) + c$', + 'np': 'a*np.exp(-x/T)+c'} + + super().__init__(model_parameters, model_vars, model_function, guess) + + +class DecayingRabis(LeastSquaresModel): def __init__(self): - model_parameters = OrderedDict({'T': {'label': '$T_2$', 'unit': 's' }, - 'w': {'label': '$w_{rabi}$', 'unit': 'Hz' } }) - model_function = {'str': r'$f(x) = e^(-x/T_1) \cos^2(\omega x / 2) + (1 - e^(-x/T_1)) / 2$', - 'np': 'np.exp(-x/T) * np.cos(w * x / 2) ** 2 + (1 - np.exp(-x/T)) / 2'} + guess = g.ExpDecaySinGuess() + model_parameters = {'a': {'label': '$a$', 'unit': ''}, + 'T': {'label': '$T$', 'unit': 's'}, + 'w': {'label': r'$\omega$', 'unit': 'Hz'}, + 'p': {'label': r'$\phi$', 'unit': ''}, + 'c': {'label': '$c$', 'unit': ''}} + model_vars = ['x'] + model_function = {'str': r'$f(x) = a \exp(-x/T) \sin(\omega x +\phi) + c$', + 'np': 'a*np.exp(-x/T)*np.sin(w*x+p)+c'} - super().__init__(model_parameters, model_function) + super().__init__(model_parameters, model_vars, model_function, guess) - def func(self, x, T, w): - return eval(self.func_np) +class RabiT1(LeastSquaresModel): + + def __init__(self): + guess = g.RabiT1Guess() + model_parameters = {'T': {'label': '$T_2$', 'unit': 's'}, + 'w': {'label': '$w_{rabi}$', 'unit': 'Hz'}} + model_vars = ['x'] + model_function = {'str': r'$f(x) = e^(-x/T_1) \cos^2(\omega x / 2) + (1 - e^(-x/T_1)) / 2$', + 'np': 'np.exp(-x/T) * np.cos(w * x / 2 ) ** 2 + (1 - np.exp(-x/T)) / 2'} + super().__init__(model_parameters, model_vars, model_function, guess) diff --git a/qdev_wrappers/fitting/models.py b/qdev_wrappers/fitting/models.py new file mode 100644 index 00000000..7153701b --- /dev/null +++ b/qdev_wrappers/fitting/models.py @@ -0,0 +1,94 @@ +import numpy as np + + +class Model(object): + def __init__(self, model_parameters, model_vars, model_function, guess): + self.parameters = model_parameters + self.variables = model_vars + self.func_str = model_function['str'] + self.func_np = model_function['np'] + self.default_guess = guess # defined by model, should not be changed + self.guess = guess # to be updated if a different guess than the default is given + + self.summary = {'method': '', + 'function': [self.func_str, self.func_np]} # TODO: add dill here + + """Any results the model returns for the parameters beyond the parameter values themselves + should be included in "data_saving_info". See least_squares_models for example""" + self.data_saving_info = {} + + def evaluate(self, *args): + """Used for plotting and for producing estimates from data. Takes values + for function variables followed by parameter values. Returns a function + output from evaluating the model function at the provided values.""" + raise NotImplementedError('This is not implemented in the base class.') + + def get_initial_guess(self, output_array, **function_variable_arrays): + """ Takes an output array - i.e. data array - as the first argument. Also takes + keyword arguments for the variables in the function to be fitted. Returns a list of + initial values for the parameters in the model. + If a user-defined list for initial parameter guesses was given to the fitter, it + returns that list. Otherwise, it returns a guess, as defined by the model's 'guess', + based on the arrays given.""" + if self.guess != self.default_guess: + initial_guess = self.guess + else: + initial_guess = self.guess.make_guess(output_array, **function_variable_arrays) + return initial_guess + + def fit_procedure(self, output_data_array, **function_variable_arrays): + """ Performs the fit, returns parameter values and any additional information + the fitting function returned, in the format: + + [list of parameter values], [fit info] + + where fit info is any additional information returned by the model (fx. variance). + If there is no additional information beyond the returned parameters, the list + should be left empty. """ + raise NotImplementedError('This is not implemented in the base class.') + + def organize_fit_info(self, parameter_dict): + """Organize the additional information the model provides and incorporate in + into the parameter dictionary. Default: ignore all fit information beyond + parameter values""" + del parameter_dict['fit_info'] + return parameter_dict + + +class SimpleMinimum(Model): + def __init__(self): + guess = None + model_parameters = {'location': {'label': 'location of minimum', 'unit': ''}, + 'value': {'label': 'minimum value', 'unit': ''}} + model_vars = ['x'] + model_function = {'str': 'find minimum (simple)', + 'np': None} + + super().__init__(model_parameters, model_vars, model_function, guess) + self.summary = {'method': 'using np.argmin to find minimum', + 'function': [self.func_str, self.func_np]} + + def evaluate(self, *args): + raise NotImplementedError("This model only returns the minimum, not " + "parameters that fit a function.") + + def fit_procedure(self, output_array, **function_variable_arrays): + """No additional info beyond parameter values, so the returned fit_info + list is empty""" + if len(function_variable_arrays) > 1: + raise RuntimeError("Simple minimum can only find the minimum with respect to one variable. " + "You seem to have provided multiple variables.") + input_array = [data_array for data_array in function_variable_arrays.values()][0] + + min_index = np.argmin(output_array) + + min_point = input_array[min_index] + min_val = output_array[min_index] + + popt = np.array([min_point, min_val]) + + return popt, [] + + def update_unit(self, parameter, unit): + self.parameters[parameter]['unit'] = unit + print('{} unit updated to {}'.format(parameter, unit)) diff --git a/qdev_wrappers/fitting/plot_analysis_by_id.py b/qdev_wrappers/fitting/plot_analysis_by_id.py new file mode 100644 index 00000000..8bf20281 --- /dev/null +++ b/qdev_wrappers/fitting/plot_analysis_by_id.py @@ -0,0 +1,263 @@ +import qcodes as qc +import numpy as np +import matplotlib.pyplot as plt +import os +from os.path import expanduser +from qcodes import config +from qcodes.dataset.data_export import get_data_by_id, load_by_id +from qcodes.dataset.plotting import _rescale_ticks_and_units, plot_by_id +from qcodes.dataset.sqlite_base import connect, transaction + + +def plot_analysis_by_id(analysis_run_id, save_plot=False, **setpoints): + """Takes run_id of fit results, finds corresponding dataset. + If dataset is 1d: plots fit result on top of dataset + If dataset is 2d: plots fit result on top of data at setpoint given. + If dataset is 2d and no setpoints are given: just plot coefficients vs setpoints (run plot_by_id) + + setpoint should be a dictionary, fx. {'frequency' : 5.4e9}""" + + data, data_run_id = _retrieve_basis_dataset(analysis_run_id) + analysis_info = load_by_id(analysis_run_id) + model_info = eval(analysis_info.metadata['model']) + + # check if this analysis run has a numpy function saved for plotting (in a locate-able format) + # ToDo: save dill of model, use this instead of model metadata from fit + if 'function' not in model_info.keys(): + raise RuntimeError("That model did not save function information for plotting, so plotting doesn't work.") + elif type(model_info['function']) is not dict: + raise RuntimeError("Function information for that model was not saved as a dictionary, so the numpy function " + "for plotting can't be retrieved.") + elif 'func_np' not in model_info['function'].keys(): + raise RuntimeError("That model did not save a numpy function for plotting, so plotting doesn't work.") + + # If data is for a 1D plot, retrieve fit parameters and x- and y-data + if len(data) == 2: + + param_dict = _retrieve_parameter_dict(analysis_run_id, 2) + + xdata = data[0] + ydata = data[1] + + axes = _plot_1d(analysis_run_id, data_run_id, xdata, ydata, param_dict) + + elif len(data) == 3: + + # If data is for a 2D plot and setpoints are specified, find xdata, ydata and param_dict to plot a 1d cut + if setpoints: + param_dict = _retrieve_parameter_dict(analysis_run_id, 3, **setpoints) + # Get data at the specified setpoints + plot_data = data.copy() + setpoint_data = {} + for i, item in enumerate(plot_data): + if item['name'] in setpoints.keys(): + setpoint_data[item['name']] = item + del(plot_data[i]) + if len(plot_data) > 2: + raise NotImplementedError("Plotting cuts at setpoints only implemented for 1D fits." + f"The data at the specified setpoints is {len(plot_data)-1}D") + + setpoint_indices = [] + for name, value in setpoints.items(): + setpoint_indices.append(np.where(setpoint_data[name]['data'] == value)[0]) + data_indices = setpoint_indices[0] + for array in setpoint_indices: + data_indices = np.array([i for i in array if i in data_indices]) + + for variable in plot_data: + variable['data'] = variable['data'][data_indices] + + xdata = plot_data[0] + ydata = plot_data[1] + + axes = _plot_1d(analysis_run_id, data_run_id, xdata, ydata, param_dict, **setpoints) + + # If no setpoints are specified, plot parameters as a function of setpoints + else: + axes = plot_by_id(analysis_run_id)[0] + + else: + raise NotImplementedError(f'sorry, {len(data)-1} is too many dimensions for now') + # ToDo: it could still attempt plot_by_id for 2 setpoints, that should work fine + if save_plot is True: + _save_plot(axes, analysis_run_id) + + return axes + + +def _retrieve_basis_dataset(analysis_run_id): + inferred_from = eval(load_by_id(analysis_run_id).metadata['inferred_from']) + data_run_id = inferred_from['run_id'] + dept_var = inferred_from['dept_var'] + + # Find the data corresponding to the dependent variable + index = None + for idx, measured_data in enumerate(get_data_by_id(data_run_id)): + if measured_data[-1]['name'] == dept_var: + index = idx + if index is None: + raise RuntimeError(f"No data found with dependent variable {dept_var}") + data = get_data_by_id(data_run_id)[index] + + return data, data_run_id + + +def _retrieve_parameter_dict(analysis_run_id, data_length, **setpoints): + + analysis_info = load_by_id(analysis_run_id) + model_info = eval(analysis_info.metadata['model']) + + if data_length == 2: + # get fit parameters from database + file = expanduser(qc.config['core']['db_location']) + conn = connect(file) + sql = f"SELECT * FROM '{analysis_info.table_name}'" + + c = transaction(conn, sql) + fit_result = c.fetchall()[0] + + param_dict = {} + for name, result in zip(fit_result.keys(), tuple(fit_result)): + if name in [name for name in model_info['parameters'].keys()]: + param_dict[name] = result + + elif data_length == 3: + if setpoints: + param_dict = {} + fit_res = {} + + # Organize data from fit, store in fit_data + all_fit_results = get_data_by_id(analysis_run_id) + for param in all_fit_results: + for param_info in param: + fit_res[param_info['name']] = param_info['data'] + + # indices is a list of arrays, with one array per setpoint. + # Each array specifies the indices in the fit data where the setpoint value matches the one specified + indices = [] + for name, val in setpoints.items(): + if val not in fit_res[name]: + raise RuntimeError(f"The specified {name} setpoint, {val}, was not found in the fit data") + i = np.where(fit_res[name] == val)[0] + indices.append(i) + + # Starting with the first array, remove any elements that aren't present in subsequent arrays + index = indices[0] + for index_list in indices: + index = [i for i in index if i in index_list] + + # Confirm that there is only one, and only one, index (and therefore one fit) left + if len(index) == 0: + raise RuntimeError("No fit found with the specified combination of setpoints") + elif len(index) > 1: + raise RuntimeError(f"That combination of setpoints has {len(index)} fits. Please be more specific.") + + # Make param dict with parameters from the results + i = index[0] + for param, param_values in fit_res.items(): + if param not in setpoints.keys(): + param_dict[param] = param_values[i] + + return param_dict + + +def _plot_1d(analysis_run_id, data_run_id, xdata, ydata, param_dict, **setpoints): + + analysis_info = load_by_id(analysis_run_id) + model_info = eval(analysis_info.metadata['model']) + title = f"Analysis #{analysis_run_id} (measurement #{data_run_id}) \n " \ + f"{analysis_info.exp_name} ({analysis_info.sample_name})" + + # plot for 1D fit or 1D cut of fit + fig, ax = plt.subplots(1, 1) + ax.plot(xdata['data'], ydata['data'], marker='.', markersize=5, linestyle='', color='C0') + + # plot fit if successful, define textbox string for fit + if param_dict is None: + textstr = '{} \n Unsuccessful fit'.format(model_info['function']['func_str']) + else: + param_arg_list = ", ".join([name for name in model_info['parameters'].keys()]) + func = eval(f"lambda x,{param_arg_list}: {model_info['function']['func_np']}") + + x = np.linspace(xdata['data'].min(), xdata['data'].max(), len(xdata['data']) * 10) + ax.plot(x, func(x=x, **param_dict), color='C1') + + p_label_list = [model_info['function']['func_str']] + for parameter in param_dict: + value = param_dict[parameter] + unit = model_info['parameters'][parameter]['unit'] + label = model_info['parameters'][parameter]['label'] + p_label_list.append(r'{} = {:.3g} {}' + .format(label, value, unit)) + for setpoint, value in setpoints.items(): + p_label_list.append(f'{setpoint}: {value}') + title += f'\n {setpoint}: {value}' + textstr = '\n'.join(p_label_list) + + # set axes labels, textbox and title + ax.set_xlabel(f"{xdata['label']} ({xdata['unit']})") + ax.set_ylabel(f"{ydata['label']} ({ydata['unit']})") + + data_lst = [xdata, ydata] + _rescale_ticks_and_units(ax, data_lst) + + ax.text(1.05, 0.7, textstr, transform=ax.transAxes, fontsize=14, + verticalalignment='top', bbox={'ec': 'k', 'fc': 'w'}) + + ax.set_title(title) + + return [ax] + + +def _save_plot(axes, analysis_id): + """ + Save the plots + """ + plt.ioff() + + mainfolder = config.user.mainfolder + experiment_name = load_by_id(analysis_id).exp_name + sample_name = load_by_id(analysis_id).sample_name + + storage_dir = os.path.join(mainfolder, experiment_name, sample_name) + os.makedirs(storage_dir, exist_ok=True) + + png_dir = os.path.join(storage_dir, 'png') + pdf_dir = os.path.join(storage_dir, 'pdf') + + os.makedirs(png_dir, exist_ok=True) + os.makedirs(pdf_dir, exist_ok=True) + + save_pdf = True + save_png = True + + + for i, ax in enumerate(axes): + if save_pdf: + start_name = f'{analysis_id}_{i}.pdf' + file_name = _make_file_name(pdf_dir, start_name) + full_path = os.path.join(pdf_dir, file_name) + ax.figure.savefig(full_path, dpi=500) + if save_png: + start_name = f'{analysis_id}_{i}.png' + file_name = _make_file_name(png_dir, start_name) + full_path = os.path.join(png_dir, file_name) + ax.figure.savefig(full_path, dpi=500) + + plt.ion() + return axes + + +def _make_file_name(directory, initial_name): + full_path = os.path.join(directory, initial_name) + name = initial_name.split('.')[0] + file_format = initial_name.split('.')[1] + new_name = name + i = 1 + while os.path.isfile(full_path): + new_name = f'{name}_{i}' + file_path = '.'.join([new_name, file_format]) + full_path = os.path.join(directory, file_path) + i += 1 + final_name = '.'.join([new_name, file_format]) + return final_name diff --git a/qdev_wrappers/fitting/rough_plot.py b/qdev_wrappers/fitting/rough_plot.py deleted file mode 100644 index 2f6d1fd1..00000000 --- a/qdev_wrappers/fitting/rough_plot.py +++ /dev/null @@ -1,49 +0,0 @@ -# take least_squares_fitter or baysian_analyzer, and data -#output plot of both -import matplotlib.pyplot as plt -import numpy as np - - -def plot_1d(xdata, ydata, instrument, rescale_axes=True): - - fig, ax = plt.subplots(1, 1) - - params = instrument.model.modelparam_names - parameter_values = [instrument.get(param) for param in params] - #parameter_variance = [instrument.param.variance] - - title = "Run #{} fitted, Experiment {} ({}) \n {} results".format('No run_id', #self.experiment_info['run_id'], - 'No exp_id',#self.experiment_info['exp_id'], - 'No sample',#self.experiment_info['sample_name'], - instrument.name) - ax.set_title(title) - - # plot data - ax.plot(xdata['data'], ydata['data'], marker='.', markersize=5, linestyle='', color='C0') - - # plot model - x = np.linspace(xdata['data'].min(), xdata['data'].max(), len(xdata['data']) * 10) - ax.plot(x, instrument.model.func(x, *parameter_values), color='C1') - - # set axes labels and title - ax.set_xlabel(f"{xdata['label']} ({xdata['unit']})") - ax.set_ylabel(f"{ydata['label']} ({ydata['unit']})") - if rescale_axes: - pass - #data_lst = [xdata, ydata] - #_rescale_ticks_and_units(ax, data_lst) - - # add fit result summary box - p_label_list = [instrument.model.func_str] - for param in params: - value = instrument.get(param) - unit = instrument.model.model_parameters[param]['unit'] - p_label_list.append(r'{} = {:.3g} {}' - .format(param, value, unit)) - #standard_dev = np.sqrt(parameter_variance[parameter]) - #p_label_list.append(r'{} = {:.3g} +/- {:.3g} {}' - # .format(parameter, value, standard_dev, unit)) - textstr = '\n'.join(p_label_list) - - ax.text(1.05, 0.7, textstr, transform=ax.transAxes, fontsize=14, - verticalalignment='top', bbox={'ec': 'k', 'fc': 'w'}) \ No newline at end of file From 29020fee2f3ecb9fa4fa18cd096d628f3ad1b929 Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Tue, 12 Feb 2019 10:17:53 +0100 Subject: [PATCH 097/116] Change fit saving to stop cutting off text box displaying fit parameters --- .../fitting/Fitting from saved data.ipynb | 171 +++++++++--------- .../fitting/Fitting while measuring.ipynb | 117 ++++++------ qdev_wrappers/fitting/analysis.py | 14 +- qdev_wrappers/fitting/least_squares_models.py | 27 +++ qdev_wrappers/fitting/models.py | 95 ++++++++++ qdev_wrappers/fitting/plot_analysis_by_id.py | 8 +- 6 files changed, 272 insertions(+), 160 deletions(-) diff --git a/qdev_wrappers/fitting/Fitting from saved data.ipynb b/qdev_wrappers/fitting/Fitting from saved data.ipynb index 1e13b13b..ffd254a5 100644 --- a/qdev_wrappers/fitting/Fitting from saved data.ipynb +++ b/qdev_wrappers/fitting/Fitting from saved data.ipynb @@ -54,7 +54,7 @@ { "data": { "text/plain": [ - "([], [None])" + "([], [None])" ] }, "execution_count": 3, @@ -65,7 +65,7 @@ "data": { "image/png": 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -177,16 +177,16 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "inserting {'model': {'method': 'Least squares', 'function': {'func_str': '$f(x) = a \\\\exp(-x/T) \\\\sin(\\\\omega x +\\\\phi) + c$', 'func_np': 'a*np.exp(-x/T)*np.sin(w*x+p)+c'}, 'parameters': {'a': {'label': '$a$', 'unit': ''}, 'T': {'label': '$T$', 'unit': 's'}, 'w': {'label': '$\\\\omega$', 'unit': 'Hz'}, 'p': {'label': '$\\\\phi$', 'unit': ''}, 'c': {'label': '$c$', 'unit': ''}}, 'r2_limit': 0.5}, 'inferred_from': {'run_id': 1, 'exp_id': 1, 'dept_var': 'data'}}\n", + "inserting {'model': {'method': 'Least squares', 'function': {'func_str': '$f(x) = a \\\\exp(-x/T) + c$', 'func_np': 'a*np.exp(-x/T)+c'}, 'parameters': {'a': {'label': '$a$', 'unit': ''}, 'T': {'label': '$T$', 'unit': 's'}, 'c': {'label': '$c$', 'unit': ''}}}, 'inferred_from': {'run_id': 1, 'exp_id': 1, 'dept_var': 'data'}}\n", "\n", - " Analysis saved with run id 224\n" + " Analysis saved with run id 246\n" ] } ], @@ -253,7 +253,7 @@ { "data": { "text/plain": [ - "([], None)" + "([], None)" ] }, "execution_count": 11, @@ -264,7 +264,7 @@ "data": { "image/png": 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\n", 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -283,7 +283,7 @@ ], "source": [ "fit.plot()\n", - "fit.plot(show_variance=True)" + "fit.plot(show_variance=True, save_plot=True)" ] }, { @@ -352,24 +352,24 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 14, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -377,7 +377,7 @@ } ], "source": [ - "plot_analysis_by_id(219)" + "plot_analysis_by_id(233, save_plot=True)" ] }, { @@ -388,7 +388,7 @@ "\n", "## Fitting by run id from saved dataset: 2D data\n", "\n", - "Lets say we now want to fit the following 2D rabi measurement. We will use the model *DecayingRabis()*. Initially, lets not set any criteria for the r2 limit. " + "Lets say we now want to fit the following 2D rabi measurement." ] }, { @@ -399,8 +399,8 @@ { "data": { "text/plain": [ - "([],\n", - " [])" + "([],\n", + " [])" ] }, "execution_count": 15, @@ -411,7 +411,7 @@ "data": { "image/png": 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -428,6 +428,8 @@ "source": [ "### Perform fit and see result\n", "\n", + "We will use the model *DecayingRabis()*. Initially, lets not set any criteria for the r2 limit. \n", + "\n", "The fit is performed in the same way as it was for a 1D dataset. We can retrieve the fit results at a particular setpoint using *get_result*." ] }, @@ -438,20 +440,13 @@ "scrolled": false }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "User schema at /Users/Lillian/qcodesrc_schema.json not found.User settings won't be validated\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ "inserting {'model': {'method': 'Least squares', 'function': {'func_str': '$f(x) = a \\\\exp(-x/T) \\\\sin(\\\\omega x +\\\\phi) + c$', 'func_np': 'a*np.exp(-x/T)*np.sin(w*x+p)+c'}, 'parameters': {'a': {'label': '$a$', 'unit': ''}, 'T': {'label': '$T$', 'unit': 's'}, 'w': {'label': '$\\\\omega$', 'unit': 'Hz'}, 'p': {'label': '$\\\\phi$', 'unit': ''}, 'c': {'label': '$c$', 'unit': ''}}}, 'inferred_from': {'run_id': 6, 'exp_id': 1, 'dept_var': 'data'}}\n", "\n", - " Analysis saved with run id 220\n" + " Analysis saved with run id 234\n" ] } ], @@ -519,35 +514,35 @@ "\n", "Least squares fitting includes variance on the parameters, and I'd like to see it, so I've set *show_variance=True*. \n", "\n", - "Since I haven't asked it to, this will NOT save the plot." + "Since I haven't asked it to, this will not save the plots." ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "([,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ],\n", - " )" + "([,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ],\n", + " )" ] }, - "execution_count": 17, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -612,12 +607,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Unsurprisingly, the fit is fine around the center of the scan, but not very good otherwise. To remove fits that differ greatly from the data, we can set an r2_limit for the fitter, and try again. This time, I will ask it to save the plot." + "Unsurprisingly, the fit is fine around the center of the scan, but not very good otherwise. To remove fits that differ greatly from the data, we can set an r2_limit for the fitter, and try again. This time, I will ask it to save the plot. If any of the fits are rejected by the r2 test, you will see a warning, to let you know that this has happened, at what the fitter r2 limit currently is." ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -634,7 +629,7 @@ "text": [ "inserting {'model': {'method': 'Least squares', 'function': {'func_str': '$f(x) = a \\\\exp(-x/T) \\\\sin(\\\\omega x +\\\\phi) + c$', 'func_np': 'a*np.exp(-x/T)*np.sin(w*x+p)+c'}, 'parameters': {'a': {'label': '$a$', 'unit': ''}, 'T': {'label': '$T$', 'unit': 's'}, 'w': {'label': '$\\\\omega$', 'unit': 'Hz'}, 'p': {'label': '$\\\\phi$', 'unit': ''}, 'c': {'label': '$c$', 'unit': ''}}, 'r2_limit': 0.5}, 'inferred_from': {'run_id': 6, 'exp_id': 1, 'dept_var': 'data'}}\n", "\n", - " Analysis saved with run id 225\n" + " Analysis saved with run id 235\n" ] } ], @@ -645,22 +640,22 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "([,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ],\n", - " )" + "([,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ],\n", + " )" ] }, - "execution_count": 26, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, @@ -668,7 +663,7 @@ "data": { "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -738,7 +733,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -747,7 +742,7 @@ "{'param_values': None, 'setpoints': {'frequency': 5500000000}}" ] }, - "execution_count": 23, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -782,33 +777,33 @@ "\n", "Plotting from a saved fit will not return a heatmap, but can plot cuts of the data (by specifying the setpoint for the cut). Plotting from a saved dataset also can't currently retrieve the variance information from the database, so it does not have the option to make plots including variance.\n", "\n", - "Just running *plot_analysis_by_id(run_id)* for the analysis is similar to the plot above, but without the heatmap and without variance. " + "Just running *plot_analysis_by_id(run_id)* for the analysis is similar to the plot above from plotting the 'fit' object, but without the heatmap and without variance. " ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[,\n", - " ,\n", - " ,\n", - " ,\n", - " ]" + "[,\n", + " ,\n", + " ,\n", + " ,\n", + " ]" ] }, - "execution_count": 3, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", + "image/png": 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\n", + "image/png": 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\n", 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\n", 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -856,7 +851,7 @@ } ], "source": [ - "plot_analysis_by_id(186, save_plot=True)" + "plot_analysis_by_id(235, save_plot=True)" ] }, { diff --git a/qdev_wrappers/fitting/Fitting while measuring.ipynb b/qdev_wrappers/fitting/Fitting while measuring.ipynb index 427468e0..bcac1f15 100644 --- a/qdev_wrappers/fitting/Fitting while measuring.ipynb +++ b/qdev_wrappers/fitting/Fitting while measuring.ipynb @@ -2,18 +2,9 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/anaconda3/envs/qcodes/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", - " from ._conv import register_converters as _register_converters\n" - ] - } - ], + "outputs": [], "source": [ "import qcodes as qc\n", "from qdev_wrappers.fitting.least_squares_models import DecayingRabis, DecayT1\n", @@ -24,7 +15,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -55,7 +46,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -71,7 +62,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -84,7 +75,7 @@ " 'c': {'label': '$c$', 'unit': ''}}" ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -95,7 +86,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -104,7 +95,7 @@ "'a*np.exp(-x/T)*np.sin(w*x+p)+c'" ] }, - "execution_count": 6, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -122,7 +113,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -144,7 +135,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -165,7 +156,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -203,16 +194,16 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Starting experimental run with id: 230\n", - "Starting experimental run with id: 231\n", - "inserting {'inferred_from': {'run_id': 230, 'dept_var': 'cavity_response'}}\n", + "Starting experimental run with id: 236\n", + "Starting experimental run with id: 237\n", + "inserting {'inferred_from': {'run_id': 236, 'dept_var': 'cavity_response'}}\n", "inserting {'model': {'method': 'Least squares', 'function': {'func_str': '$f(x) = a \\\\exp(-x/T) \\\\sin(\\\\omega x +\\\\phi) + c$', 'func_np': 'a*np.exp(-x/T)*np.sin(w*x+p)+c'}, 'parameters': {'a': {'label': '$a$', 'unit': ''}, 'T': {'label': '$T$', 'unit': 's'}, 'w': {'label': '$\\\\omega$', 'unit': 'Hz'}, 'p': {'label': '$\\\\phi$', 'unit': ''}, 'c': {'label': '$c$', 'unit': ''}}, 'r2_limit': 0.5}}\n" ] }, @@ -276,17 +267,17 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "([],\n", - " [])" + "([],\n", + " [])" ] }, - "execution_count": 35, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, @@ -294,7 +285,7 @@ "data": { "image/png": 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\n", 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -327,7 +318,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 13, "metadata": { "scrolled": true }, @@ -346,7 +337,7 @@ "text": [ "inserting {'model': {'method': 'Least squares', 'function': {'func_str': '$f(x) = a \\\\exp(-x/T) \\\\sin(\\\\omega x +\\\\phi) + c$', 'func_np': 'a*np.exp(-x/T)*np.sin(w*x+p)+c'}, 'parameters': {'a': {'label': '$a$', 'unit': ''}, 'T': {'label': '$T$', 'unit': 's'}, 'w': {'label': '$\\\\omega$', 'unit': 'Hz'}, 'p': {'label': '$\\\\phi$', 'unit': ''}, 'c': {'label': '$c$', 'unit': ''}}, 'r2_limit': 0.5}, 'inferred_from': {'run_id': 6, 'exp_id': 1, 'dept_var': 'data'}}\n", "\n", - " Analysis saved with run id 232\n" + " Analysis saved with run id 238\n" ] } ], @@ -356,20 +347,20 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[,\n", - " ,\n", - " ,\n", - " ,\n", - " ]" + "[,\n", + " ,\n", + " ,\n", + " ,\n", + " ]" ] }, - "execution_count": 43, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, @@ -377,7 +368,7 @@ "data": { "image/png": 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\n", 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\n", 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\n", 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -431,20 +422,20 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[,\n", - " ,\n", - " ,\n", - " ,\n", - " ]" + "[,\n", + " ,\n", + " ,\n", + " ,\n", + " ]" ] }, - "execution_count": 44, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, @@ -452,7 +443,7 @@ "data": { "image/png": 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\n", 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\n", 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -506,24 +497,24 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 46, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, diff --git a/qdev_wrappers/fitting/analysis.py b/qdev_wrappers/fitting/analysis.py index 04425993..4aadb429 100644 --- a/qdev_wrappers/fitting/analysis.py +++ b/qdev_wrappers/fitting/analysis.py @@ -80,9 +80,8 @@ def _select_relevant_data(self, run_id, dept_var, **function_variables): def get_result(self, **setpoint_values): """ - Args: - dictionary of setpoint values. - ie. {'frequency:1e9, 'power':-10} + Keyword arguments: + setpoint values, i.e. frequency=1e9, power=-10 Returns: dict for fit where these conditions are satisfied """ @@ -99,7 +98,11 @@ def get_result(self, **setpoint_values): def _plot_1d(self, xdata, ydata, parameter_values, param_variance, rescale_axes=True): # plot data, and fit if successful - fig, ax = plt.subplots(1, 1) + plt.figure(figsize=(10, 4)) + ax = plt.subplot(111) + box = ax.get_position() + ax.set_position([box.x0, box.y0, box.width * 0.7, box.height]) + ax.plot(xdata['data'], ydata['data'], marker='.', markersize=5, linestyle='', color='C0') if parameter_values is None: pass @@ -137,9 +140,6 @@ def _plot_1d(self, xdata, ydata, parameter_values, param_variance, rescale_axes= return ax def plot(self, rescale_axes: bool = True, show_variance=False, save_plot=False): - """ - setpoint : None or a dict of form {name: value, name2: value, ...} - """ if len(self.function_vars) > 1: raise NotImplementedError("Sorry, you have specified multiple function variables, and plotting only " diff --git a/qdev_wrappers/fitting/least_squares_models.py b/qdev_wrappers/fitting/least_squares_models.py index ff1f88f2..da162f4e 100644 --- a/qdev_wrappers/fitting/least_squares_models.py +++ b/qdev_wrappers/fitting/least_squares_models.py @@ -151,3 +151,30 @@ def __init__(self): 'np': 'np.exp(-x/T) * np.cos(w * x / 2 ) ** 2 + (1 - np.exp(-x/T)) / 2'} super().__init__(model_parameters, model_vars, model_function, guess) + +class RabiT1(LeastSquaresModel): + + def __init__(self): + guess = g.RabiT1Guess() + model_parameters = {'T': {'label': '$T_2$', 'unit': 's'}, + 'w': {'label': '$w_{rabi}$', 'unit': 'Hz'}} + model_vars = ['x'] + model_function = {'str': r'$f(x) = e^(-x/T_1) \cos^2(\omega x / 2) + (1 - e^(-x/T_1)) / 2$', + 'np': 'np.exp(-x/T) * np.cos(w * x / 2 ) ** 2 + (1 - np.exp(-x/T)) / 2'} + + super().__init__(model_parameters, model_vars, model_function, guess) + + + +class LauritsMobility1(LeastSquaresModel): + + def __init__(self): + guess = None + model_parameters = {'vt': {'label': '$V_th$', 'unit': 'V'}, + 'a': {'label': r'$\alpha$', 'unit': ''}, + 'r': {'label': '$R_s$', 'unit': ''}} + model_vars = ['vg'] + model_function = {'str': r'$f(V_g) = 1/ (R + (\alpha /(V_g - V_th))$', + 'np': '1/(r + a/(vg-vt))'} + + super().__init__(model_parameters, model_vars, model_function, guess) \ No newline at end of file diff --git a/qdev_wrappers/fitting/models.py b/qdev_wrappers/fitting/models.py index 7153701b..34f1a338 100644 --- a/qdev_wrappers/fitting/models.py +++ b/qdev_wrappers/fitting/models.py @@ -1,4 +1,5 @@ import numpy as np +from scipy.optimize import curve_fit class Model(object): @@ -92,3 +93,97 @@ def fit_procedure(self, output_array, **function_variable_arrays): def update_unit(self, parameter, unit): self.parameters[parameter]['unit'] = unit print('{} unit updated to {}'.format(parameter, unit)) + + +class LauritsMobility(Model): + + def __init__(self): + guess = None + model_parameters = {'vt': {'label': '$V_th$', 'unit': 'V'}, + 'a': {'label': r'$\alpha$', 'unit': ''}, + 'r': {'label': '$R_s$', 'unit': ''}, + 'vt1': {'label': '$V_th$', 'unit': 'V'}, + 'a2': {'label': r'$\alpha$', 'unit': ''}, + 'r2': {'label': '$R_s$', 'unit': ''}} + model_vars = ['vg'] + model_function = {'str': r'$f(V_g) = 1/ (R + (\alpha /(V_g - V_th))$', 'np': '1/(r + a/(vg-vt))'} + + super().__init__(model_parameters, model_vars, model_function, guess) + + def evaluate(self, *args): + """Takes an array, x, and a list of parameter values, in the order they are given + in model parameters. Evaluates the output of the model function at each point in x, + and returns an array of the output""" + arg_list = self.variables + [key for key in self.parameters.keys()] + arg_string = ", ".join(arg_list) + func = eval(f"lambda {arg_string}: {self.func_np}") + return func(*args) + + def fit_procedure(self, output_data_array, **function_variable_arrays): + """Performs a fit based on the model. Returns : + an array of parameter values, ordered like the model_parameters dictionary AND + a fit_info list with the covariance matrix and the initial parameter guesses + """ + + p_guess = self.get_initial_guess(output_data_array, **function_variable_arrays) + vt_guess = p_guess[0] + + gate_voltages = [array for array in function_variable_arrays.values()][0] + single_scan_length = int(len(gate_voltages) / 2) + + # get arrays for curve 1 + gate_sweep_1 = gate_voltages[:single_scan_length] + conductance_1 = output_data_array[:single_scan_length] + above_vt = np.where(gate_sweep_1 > vt_guess) + Vg_1 = gate_sweep_1[above_vt] + G_1 = conductance_1[above_vt] + + # get arrays for curve 2 + gate_sweep_2 = gate_voltages[single_scan_length:] + conductance_2 = output_data_array[single_scan_length:] + above_vt = np.where(gate_sweep_1 > vt_guess) + Vg_2 = gate_sweep_2[above_vt] + G_2 = conductance_2[above_vt] + + # find diff + diff = np.abs(G_2 - G_1) + + # fit to both curves + func = eval(f"lambda vg, vt, a, r: {self.func_np}") + popt1, pcov1 = curve_fit(func, Vg_1, G_1, p0=p_guess) + popt2, pcov2 = curve_fit(func, Vg_2, G_2, p0=p_guess) + + # combine fit info + popt = np.concatenate([popt1, popt2]) + + pcov = [pcov1, pcov2] + + return popt, [pcov, p_guess, diff] + + +def organize_fit_info(self, params_dict): + """Takes the params_dict as produced by the fit procedure: + + {'param_values': {'a' : 1, 'b', 2} , 'param_info': fit_info } + + In the case of Least Mean Squares fit models, fit_info is + [pcov, p_guess] (the covariance matrix and initial guess). Organizes + the additional fit info, and returns an updated params_dict.""" + + if params_dict['fit_info'] is not None: + params_dict['variance'] = {} + params_dict['start_values'] = {} + + pcov = params_dict['fit_info'][0] + p_guess = params_dict['fit_info'][1] + diff = params_dict['fit_info'][2] + + for i, param_name in enumerate(self.parameters.keys()): + params_dict['variance'][param_name] = pcov[i, i] + params_dict['start_values'][param_name] = p_guess[i] + + params_dict['difference'] = diff + + del params_dict['fit_info'] + return params_dict + diff --git a/qdev_wrappers/fitting/plot_analysis_by_id.py b/qdev_wrappers/fitting/plot_analysis_by_id.py index 8bf20281..11259bd4 100644 --- a/qdev_wrappers/fitting/plot_analysis_by_id.py +++ b/qdev_wrappers/fitting/plot_analysis_by_id.py @@ -169,7 +169,11 @@ def _plot_1d(analysis_run_id, data_run_id, xdata, ydata, param_dict, **setpoints f"{analysis_info.exp_name} ({analysis_info.sample_name})" # plot for 1D fit or 1D cut of fit - fig, ax = plt.subplots(1, 1) + plt.figure(figsize=(10, 4)) + ax = plt.subplot(111) + box = ax.get_position() + ax.set_position([box.x0, box.y0, box.width*0.7, box.height]) + ax.plot(xdata['data'], ydata['data'], marker='.', markersize=5, linestyle='', color='C0') # plot fit if successful, define textbox string for fit @@ -201,7 +205,7 @@ def _plot_1d(analysis_run_id, data_run_id, xdata, ydata, param_dict, **setpoints data_lst = [xdata, ydata] _rescale_ticks_and_units(ax, data_lst) - ax.text(1.05, 0.7, textstr, transform=ax.transAxes, fontsize=14, + ax.text(1.05, 0.75, textstr, transform=ax.transAxes, fontsize=14, verticalalignment='top', bbox={'ec': 'k', 'fc': 'w'}) ax.set_title(title) From 7a4a5018f980af598da099b1b9391bdd9ec132be Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Tue, 12 Feb 2019 10:22:48 +0100 Subject: [PATCH 098/116] Didn't mean to add those --- qdev_wrappers/fitting/analysis.py | 2 +- qdev_wrappers/fitting/least_squares_models.py | 37 -------- qdev_wrappers/fitting/models.py | 95 ------------------- 3 files changed, 1 insertion(+), 133 deletions(-) diff --git a/qdev_wrappers/fitting/analysis.py b/qdev_wrappers/fitting/analysis.py index 4aadb429..c8139e7d 100644 --- a/qdev_wrappers/fitting/analysis.py +++ b/qdev_wrappers/fitting/analysis.py @@ -102,7 +102,7 @@ def _plot_1d(self, xdata, ydata, parameter_values, param_variance, rescale_axes= ax = plt.subplot(111) box = ax.get_position() ax.set_position([box.x0, box.y0, box.width * 0.7, box.height]) - + ax.plot(xdata['data'], ydata['data'], marker='.', markersize=5, linestyle='', color='C0') if parameter_values is None: pass diff --git a/qdev_wrappers/fitting/least_squares_models.py b/qdev_wrappers/fitting/least_squares_models.py index da162f4e..335781b0 100644 --- a/qdev_wrappers/fitting/least_squares_models.py +++ b/qdev_wrappers/fitting/least_squares_models.py @@ -140,41 +140,4 @@ def __init__(self): super().__init__(model_parameters, model_vars, model_function, guess) -class RabiT1(LeastSquaresModel): - def __init__(self): - guess = g.RabiT1Guess() - model_parameters = {'T': {'label': '$T_2$', 'unit': 's'}, - 'w': {'label': '$w_{rabi}$', 'unit': 'Hz'}} - model_vars = ['x'] - model_function = {'str': r'$f(x) = e^(-x/T_1) \cos^2(\omega x / 2) + (1 - e^(-x/T_1)) / 2$', - 'np': 'np.exp(-x/T) * np.cos(w * x / 2 ) ** 2 + (1 - np.exp(-x/T)) / 2'} - - super().__init__(model_parameters, model_vars, model_function, guess) - -class RabiT1(LeastSquaresModel): - - def __init__(self): - guess = g.RabiT1Guess() - model_parameters = {'T': {'label': '$T_2$', 'unit': 's'}, - 'w': {'label': '$w_{rabi}$', 'unit': 'Hz'}} - model_vars = ['x'] - model_function = {'str': r'$f(x) = e^(-x/T_1) \cos^2(\omega x / 2) + (1 - e^(-x/T_1)) / 2$', - 'np': 'np.exp(-x/T) * np.cos(w * x / 2 ) ** 2 + (1 - np.exp(-x/T)) / 2'} - - super().__init__(model_parameters, model_vars, model_function, guess) - - - -class LauritsMobility1(LeastSquaresModel): - - def __init__(self): - guess = None - model_parameters = {'vt': {'label': '$V_th$', 'unit': 'V'}, - 'a': {'label': r'$\alpha$', 'unit': ''}, - 'r': {'label': '$R_s$', 'unit': ''}} - model_vars = ['vg'] - model_function = {'str': r'$f(V_g) = 1/ (R + (\alpha /(V_g - V_th))$', - 'np': '1/(r + a/(vg-vt))'} - - super().__init__(model_parameters, model_vars, model_function, guess) \ No newline at end of file diff --git a/qdev_wrappers/fitting/models.py b/qdev_wrappers/fitting/models.py index 34f1a338..c408367c 100644 --- a/qdev_wrappers/fitting/models.py +++ b/qdev_wrappers/fitting/models.py @@ -1,6 +1,4 @@ import numpy as np -from scipy.optimize import curve_fit - class Model(object): def __init__(self, model_parameters, model_vars, model_function, guess): @@ -94,96 +92,3 @@ def update_unit(self, parameter, unit): self.parameters[parameter]['unit'] = unit print('{} unit updated to {}'.format(parameter, unit)) - -class LauritsMobility(Model): - - def __init__(self): - guess = None - model_parameters = {'vt': {'label': '$V_th$', 'unit': 'V'}, - 'a': {'label': r'$\alpha$', 'unit': ''}, - 'r': {'label': '$R_s$', 'unit': ''}, - 'vt1': {'label': '$V_th$', 'unit': 'V'}, - 'a2': {'label': r'$\alpha$', 'unit': ''}, - 'r2': {'label': '$R_s$', 'unit': ''}} - model_vars = ['vg'] - model_function = {'str': r'$f(V_g) = 1/ (R + (\alpha /(V_g - V_th))$', 'np': '1/(r + a/(vg-vt))'} - - super().__init__(model_parameters, model_vars, model_function, guess) - - def evaluate(self, *args): - """Takes an array, x, and a list of parameter values, in the order they are given - in model parameters. Evaluates the output of the model function at each point in x, - and returns an array of the output""" - arg_list = self.variables + [key for key in self.parameters.keys()] - arg_string = ", ".join(arg_list) - func = eval(f"lambda {arg_string}: {self.func_np}") - return func(*args) - - def fit_procedure(self, output_data_array, **function_variable_arrays): - """Performs a fit based on the model. Returns : - an array of parameter values, ordered like the model_parameters dictionary AND - a fit_info list with the covariance matrix and the initial parameter guesses - """ - - p_guess = self.get_initial_guess(output_data_array, **function_variable_arrays) - vt_guess = p_guess[0] - - gate_voltages = [array for array in function_variable_arrays.values()][0] - single_scan_length = int(len(gate_voltages) / 2) - - # get arrays for curve 1 - gate_sweep_1 = gate_voltages[:single_scan_length] - conductance_1 = output_data_array[:single_scan_length] - above_vt = np.where(gate_sweep_1 > vt_guess) - Vg_1 = gate_sweep_1[above_vt] - G_1 = conductance_1[above_vt] - - # get arrays for curve 2 - gate_sweep_2 = gate_voltages[single_scan_length:] - conductance_2 = output_data_array[single_scan_length:] - above_vt = np.where(gate_sweep_1 > vt_guess) - Vg_2 = gate_sweep_2[above_vt] - G_2 = conductance_2[above_vt] - - # find diff - diff = np.abs(G_2 - G_1) - - # fit to both curves - func = eval(f"lambda vg, vt, a, r: {self.func_np}") - popt1, pcov1 = curve_fit(func, Vg_1, G_1, p0=p_guess) - popt2, pcov2 = curve_fit(func, Vg_2, G_2, p0=p_guess) - - # combine fit info - popt = np.concatenate([popt1, popt2]) - - pcov = [pcov1, pcov2] - - return popt, [pcov, p_guess, diff] - - -def organize_fit_info(self, params_dict): - """Takes the params_dict as produced by the fit procedure: - - {'param_values': {'a' : 1, 'b', 2} , 'param_info': fit_info } - - In the case of Least Mean Squares fit models, fit_info is - [pcov, p_guess] (the covariance matrix and initial guess). Organizes - the additional fit info, and returns an updated params_dict.""" - - if params_dict['fit_info'] is not None: - params_dict['variance'] = {} - params_dict['start_values'] = {} - - pcov = params_dict['fit_info'][0] - p_guess = params_dict['fit_info'][1] - diff = params_dict['fit_info'][2] - - for i, param_name in enumerate(self.parameters.keys()): - params_dict['variance'][param_name] = pcov[i, i] - params_dict['start_values'][param_name] = p_guess[i] - - params_dict['difference'] = diff - - del params_dict['fit_info'] - return params_dict - From 08cfb219a43bdac0f14b5ca37e8e00c18c5d8352 Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Tue, 12 Feb 2019 10:33:58 +0100 Subject: [PATCH 099/116] Change analysis dataset name from analysis to results --- qdev_wrappers/fitting/analysis.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/qdev_wrappers/fitting/analysis.py b/qdev_wrappers/fitting/analysis.py index c8139e7d..ab774e7b 100644 --- a/qdev_wrappers/fitting/analysis.py +++ b/qdev_wrappers/fitting/analysis.py @@ -298,7 +298,7 @@ def save(self, save_plots=False): 'inferred_from': {'run_id': self.experiment_info['run_id'], 'exp_id': self.experiment_info['exp_id'], 'dept_var': self.dept_var['name']}} - dataset = new_data_set('analysis', + dataset = new_data_set('results', specs=paramspecs, metadata=all_metadata) From 8d594da63175f50f6c09f57124965a1c806ca1cc Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Tue, 19 Feb 2019 08:43:56 +0100 Subject: [PATCH 100/116] Add parameters dictionary to minimum model for plotting --- qdev_wrappers/fitting/models.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/qdev_wrappers/fitting/models.py b/qdev_wrappers/fitting/models.py index c408367c..969b8692 100644 --- a/qdev_wrappers/fitting/models.py +++ b/qdev_wrappers/fitting/models.py @@ -10,7 +10,8 @@ def __init__(self, model_parameters, model_vars, model_function, guess): self.guess = guess # to be updated if a different guess than the default is given self.summary = {'method': '', - 'function': [self.func_str, self.func_np]} # TODO: add dill here + 'function': [self.func_str, self.func_np], + 'parameters': self.parameters.copy()} # TODO: add dill here """Any results the model returns for the parameters beyond the parameter values themselves should be included in "data_saving_info". See least_squares_models for example""" @@ -65,7 +66,8 @@ def __init__(self): super().__init__(model_parameters, model_vars, model_function, guess) self.summary = {'method': 'using np.argmin to find minimum', - 'function': [self.func_str, self.func_np]} + 'function': [self.func_str, self.func_np], + 'parameters': self.parameters.copy()} def evaluate(self, *args): raise NotImplementedError("This model only returns the minimum, not " From 42aa83d77a92ca76f8ff1199f88153ff7b5e772b Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Tue, 19 Feb 2019 08:46:08 +0100 Subject: [PATCH 101/116] Move error message affecting only 1d plotting to 1d plotting function --- qdev_wrappers/fitting/plot_analysis_by_id.py | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/qdev_wrappers/fitting/plot_analysis_by_id.py b/qdev_wrappers/fitting/plot_analysis_by_id.py index 11259bd4..6dabfc30 100644 --- a/qdev_wrappers/fitting/plot_analysis_by_id.py +++ b/qdev_wrappers/fitting/plot_analysis_by_id.py @@ -25,11 +25,6 @@ def plot_analysis_by_id(analysis_run_id, save_plot=False, **setpoints): # ToDo: save dill of model, use this instead of model metadata from fit if 'function' not in model_info.keys(): raise RuntimeError("That model did not save function information for plotting, so plotting doesn't work.") - elif type(model_info['function']) is not dict: - raise RuntimeError("Function information for that model was not saved as a dictionary, so the numpy function " - "for plotting can't be retrieved.") - elif 'func_np' not in model_info['function'].keys(): - raise RuntimeError("That model did not save a numpy function for plotting, so plotting doesn't work.") # If data is for a 1D plot, retrieve fit parameters and x- and y-data if len(data) == 2: @@ -163,6 +158,12 @@ def _retrieve_parameter_dict(analysis_run_id, data_length, **setpoints): def _plot_1d(analysis_run_id, data_run_id, xdata, ydata, param_dict, **setpoints): + if type(model_info['function']) is not dict: + raise RuntimeError("Function information for that model was not saved as a dictionary, so the numpy function " + "for plotting can't be retrieved.") + elif 'func_np' not in model_info['function'].keys(): + raise RuntimeError("That model did not save a numpy function for plotting, so plotting doesn't work.") + analysis_info = load_by_id(analysis_run_id) model_info = eval(analysis_info.metadata['model']) title = f"Analysis #{analysis_run_id} (measurement #{data_run_id}) \n " \ From 432cc2900a51f7e587008755b804babe9589a947 Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Wed, 27 Feb 2019 12:52:02 +0100 Subject: [PATCH 102/116] Fix typo --- qdev_wrappers/fitting/fitter.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/qdev_wrappers/fitting/fitter.py b/qdev_wrappers/fitting/fitter.py index 77ab6e6b..8aad6c52 100644 --- a/qdev_wrappers/fitting/fitter.py +++ b/qdev_wrappers/fitting/fitter.py @@ -76,7 +76,7 @@ def _do_fitting_procedure(self, analysis): for i in range(1, len(setpoint_names)): new_indices = np.argwhere( setpoints[setpoint_names[0]]['data'] == setpoint_combination[0]).flatten() - indices = indices.intesection(new_indices) + indices = indices.intersection(new_indices) indices = list(indices) output_data_array = full_output_data_array[indices] function_variable_arrays = {} From db55a9234f91c71a839fa69874f09c4525bdc803 Mon Sep 17 00:00:00 2001 From: Lillian Austin Date: Sat, 16 Mar 2019 00:52:12 +0100 Subject: [PATCH 103/116] Fix import statements --- qdev_wrappers/fitting/least_squares_models.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/qdev_wrappers/fitting/least_squares_models.py b/qdev_wrappers/fitting/least_squares_models.py index 335781b0..912d3f5d 100644 --- a/qdev_wrappers/fitting/least_squares_models.py +++ b/qdev_wrappers/fitting/least_squares_models.py @@ -1,6 +1,6 @@ import numpy as np -from models import Model -import guess as g +from qdev_wrappers.fitting.models import Model +import qdev_wrappers.fitting.guess as g from scipy.optimize import curve_fit From 57d4db7b02a01d46a6bae718d27da2f7c5810fde Mon Sep 17 00:00:00 2001 From: nataliejpg Date: Mon, 8 Apr 2019 16:29:22 +0200 Subject: [PATCH 104/116] WIP reorganising fitter --- Untitled.ipynb | 102 ----- examples/examples.db | Bin 0 -> 16625664 bytes examples/examples2.db | Bin 0 -> 8916992 bytes examples/fitting/Fit_by_id.ipynb | 126 ++++++ .../fitting/Fitting from saved data.ipynb | 2 +- .../fitting/Fitting while measuring.ipynb | 0 examples/fitting/qcodesrc.json | 77 ++++ examples/fitting/qcodesrc_schema.json | 312 +++++++++++++++ qdev_wrappers/fitting/analysis.py | 372 ------------------ qdev_wrappers/fitting/fit_by_id.py | 107 +++++ qdev_wrappers/fitting/fitter.py | 338 +++++++++------- qdev_wrappers/fitting/guess.py | 127 +++--- qdev_wrappers/fitting/helpers.py | 71 ++++ qdev_wrappers/fitting/least_squares_models.py | 143 ------- qdev_wrappers/fitting/models.py | 96 ----- ...by_id.py => plot_analysis_by_id_legacy.py} | 0 qdev_wrappers/fitting/plot_fit_by_id.py | 36 ++ qdev_wrappers/fitting/plotting.py | 216 ++++++++++ 18 files changed, 1200 insertions(+), 925 deletions(-) delete mode 100644 Untitled.ipynb create mode 100644 examples/examples.db create mode 100755 examples/examples2.db create mode 100644 examples/fitting/Fit_by_id.ipynb rename {qdev_wrappers => examples}/fitting/Fitting from saved data.ipynb (99%) rename {qdev_wrappers => examples}/fitting/Fitting while measuring.ipynb (100%) create mode 100644 examples/fitting/qcodesrc.json create mode 100644 examples/fitting/qcodesrc_schema.json delete mode 100644 qdev_wrappers/fitting/analysis.py create mode 100644 qdev_wrappers/fitting/fit_by_id.py create mode 100644 qdev_wrappers/fitting/helpers.py delete mode 100644 qdev_wrappers/fitting/least_squares_models.py delete mode 100644 qdev_wrappers/fitting/models.py rename qdev_wrappers/fitting/{plot_analysis_by_id.py => plot_analysis_by_id_legacy.py} (100%) create mode 100644 qdev_wrappers/fitting/plot_fit_by_id.py create mode 100644 qdev_wrappers/fitting/plotting.py diff --git a/Untitled.ipynb b/Untitled.ipynb deleted file mode 100644 index eed06f6c..00000000 --- a/Untitled.ipynb +++ /dev/null @@ -1,102 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "User schema at /Users/Lillian/qcodesrc_schema.json not found.User settings won't be validated\n" - ] - } - ], - "source": [ - "from qdev_wrappers.fitting.least_squares_fitter import LeastSquaresFitter\n", - "from qdev_wrappers.fitting.least_squares_models import DecayT1\n", - "from qdev_wrappers.fitting.guess import ExpDecayGuess" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "fitter = LeastSquaresFitter('test', DecayT1(), ExpDecayGuess())" - ] - }, - { - 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