Merge pull request #676 from SasView/fix-plotly-plotting

Update examples and plotters for bumps webview

Author: pkienzle

example/fit.py example/fit2d.pyexample/fit.py renamed to example/fit2d.py

@@ -1,6 +1,7 @@
 #!/usr/bin/env python
 
 import sys
+from pathlib import Path
 
 from bumps.names import FitProblem
 
@@ -9,11 +10,12 @@
 from sasmodels.data import load_data, set_beam_stop, set_top
 
 """ IMPORT THE DATA USED """
-radial_data = load_data('DEC07267.DAT')
+path = Path(__file__).resolve().parent
+radial_data = load_data(str(path / 'DEC07267.DAT'))
 set_beam_stop(radial_data, 0.00669, outer=0.025)
 set_top(radial_data, -.0185)
 
-tan_data = load_data('DEC07266.DAT')
+tan_data = load_data(str(path / 'DEC07266.DAT'))
 set_beam_stop(tan_data, 0.00669, outer=0.025)
 set_top(tan_data, -.0185)
 #sas.set_half(tan_data, 'right')
@@ -188,7 +190,15 @@
 else:
     problem = FitProblem(M)
 
+M.problem = problem
+
 if __name__ == "__main__":
     import matplotlib.pyplot as plt
-    problem.plot()
+
+    problem.summarize()
+    # Plot using plotly
+    fig = M._plot(backend="plotly")
+    fig.show(renderer='browser')
+    # Plot using matplotlib
+    M._plot(backend="matplotlib")
     plt.show()

example/polynomial.py example/models/polynomial.pyexample/polynomial.py renamed to example/models/polynomial.py

@@ -28,6 +28,8 @@
 
 from bumps.names import FitProblem
 
+from sasdata import data_path
+
 from sasmodels.bumps_model import Experiment, Model
 from sasmodels.core import load_model
 from sasmodels.data import load_data
@@ -36,7 +38,7 @@
 ## Load the data
 #data = load_data('DEC07267.DAT')
 #set_beam_stop(data, 0.003, outer=0.025)
-data = load_data('latex_smeared.xml', index=0)
+data = load_data(str(data_path / '1d_data' / 'latex_smeared.xml'), index=0)
 
 ## Define the model
 kernel = load_model("ellipsoid")
@@ -78,5 +80,5 @@
 if __name__ == "__main__":
     #M.theory()
     M.plot()
-    import pylab
-    pylab.show()
+    import matplotlib.pyplot as plt
+    plt.show()

example/multiscatfit.py

@@ -1,12 +1,14 @@
 import numpy as np
 from bumps.names import FitProblem
 
+from sasdata import data_path
+
 from sasmodels.bumps_model import Experiment, Model
 from sasmodels.core import load_model
 from sasmodels.data import load_data
 
 # Spherical particle data, not ellipsoids
-sans, usans = load_data('latex_smeared.xml', index='all')
+sans, usans = load_data(str(data_path / '1d_data' / 'latex_smeared.xml'), index='all')
 usans.qmin, usans.qmax = np.min(usans.x), np.max(usans.x)
 usans.mask = (usans.x < 0.0)
 usans.oriented = True

example/oriented_usans.py

@@ -1,45 +1,54 @@
 """
 This is a data file used to load in sesans data and fit it using the bumps engine
+
+Usage:
+
+    bumps sesans_sphere_2micron.py
 """
-import sesansfit
-from bumps.names import Parameter
+from bumps.names import FitProblem, Parameter
 
-# Enter the model name to use
-model_name = "sphere"
+from sasdata import data_path
 
-# DO NOT MODIFY THIS LINE
-model = sesansfit.get_bumps_model(model_name)
+from sasmodels.bumps_model import Experiment, Model
+from sasmodels.core import load_model
+from sasmodels.data import load_data
 
-# Enter any custom parameters
-# name = Parameter(initial_value, name='name')
-phi = Parameter(0.0855, name='phi')
-# Add the parameters to this list that should be displayed in the fitting window
-custom_params = {"phi" : phi}
+# Enter the model name and the datafile path
+model_name = "sphere"
+data_file = data_path / "sesans_data" / "sphere2micron.ses"
 
-# SESANS data file name
-sesans_file = "spheres2micron.ses"
+# Custom parameters for use in expressions
+# name = Parameter(initial_value, name='name')
+phi = Parameter(0.0855, name='phi')  # scale = phi*(1-phi)
 
-# Initial parameter values (if other than defaults)
-# "model_parameter_name" : value
-initial_vals = {
+# Initial parameter values and expressions (if other than defaults)
+# "model_parameter_name" : value or expression
+pars = {
+    "scale": phi*(1-phi),
     "sld" : 1.41,
     "radius" : 10000,
     "sld_solvent" : 2.70,
 }
 
-# Ranges for parameters if other than default
-# "model_parameter_name" : [min, max]
-param_range = {
-    "phi" : [0.001, 0.5],
-    "radius" : [100, 100000]
-}
+# DO NOT MODIFY THIS LINE
+model = Model(load_model(model_name), **pars)
 
-# Constraints
+# Bounds constraints
 # model.param_name = f(other params)
-# EXAMPLE: model.scale = model.radius*model.radius*(1 - phi) - where radius
-# and scale are model functions and phi is a custom parameter
-model.scale = phi*(1-phi)
+model.radius.range(100, 100000)
+phi.range(0.001, 0.5)
+
 
 # Send to the fitting engine
-# DO NOT MODIFY THIS LINE
-problem = sesansfit.sesans_fit(sesans_file, model, initial_vals, custom_params, param_range)
+# DO NOT MODIFY THESE LINES
+data = load_data(str(data_file))
+M = Experiment(data=data, model=model)
+problem = FitProblem([M])
+
+if __name__ == "__main__":
+    import matplotlib.pyplot as plt
+
+    print(f"==== {model_name} model for {data_file.name} has χ² = {problem.chisq_str()} ====")
+    print(problem.summarize())
+    problem.plot()
+    plt.show()

example/sesans_sphere_2micron.py

@@ -1,48 +1,23 @@
-import numpy as np
+from pathlib import Path
+
 from bumps.names import FitProblem, Parameter
 
 from sasmodels import bumps_model, core
+from sasmodels.data import load_data
 
-if True: # fix when data loader exists
-#    from sas.dataloader.readers\
-    from sas.dataloader.loader import Loader
-    loader = Loader()
-    filename = 'sphere.ses'
-    data = loader.load(filename)
-    if data is None:
-        raise OSError("Could not load file %r"%(filename,))
-    data.x /= 10
-#    print data
-#    data = load_sesans('mydatfile.pz')
-#    sans_data = load_sans('mysansfile.xml')
-
-else:
-    SElength = np.linspace(0, 2400, 61) # [A]
-    data = np.ones_like(SElength)
-    err_data = np.ones_like(SElength)*0.03
-
-    class Sample:
-        zacceptance = 0.1 # [A^-1]
-        thickness = 0.2 # [cm]
-
-    class SESANSData1D:
-        #q_zmax = 0.23 # [A^-1]
-        lam = 0.2 # [nm]
-        x = SElength
-        y = data
-        dy = err_data
-        sample = Sample()
-    data = SESANSData1D()
+path = Path(__file__).resolve().parent
+data = load_data(str(path / 'sphere.ses'))
 
 radius = 1000
 data.Rmax = 3*radius # [A]
 
 ##  Sphere parameters
 
-kernel = core.load_model("sphere", dtype='single')
+kernel = core.load_model("sphere")
 phi = Parameter(0.1, name="phi")
-model = bumps_model.Model(kernel,
-    scale=phi*(1-phi), sld=7.0, solvent_sld=1.0, radius=radius,
+model = bumps_model.Model(
+    kernel,
+    scale=phi*(1-phi), sld=7.0, sld_solvent=1.0, radius=radius,
     )
 phi.range(0.001,0.5)
 #model.radius.pmp(40)
@@ -74,3 +49,9 @@ class SESANSData1D:
 else:
     M_sesans = bumps_model.Experiment(data=data, model=model)
     problem = FitProblem(M_sesans)
+
+
+if __name__ == "__main__":
+    import matplotlib.pyplot as plt
+    problem.plot()
+    plt.show()

example/sesansfit_CodeCampIII.py

@@ -1,13 +1,14 @@
-import numpy as np
 from bumps.names import FitProblem, FreeVariables
 
+from sasdata import data_path
+
 from sasmodels.bumps_model import Experiment, Model
 from sasmodels.core import load_model
 from sasmodels.data import load_data
 
 # latex data, same sample usans and sans
 # particles radius ~2300, uniform dispersity
-datasets = load_data('latex_smeared.xml', index='all')
+datasets = load_data(str(data_path / '1d_data' / 'latex_smeared.xml'), index='all')
 #[print(data) for data in datasets]
 
 # A single sphere model to share between the datasets.  We will use
@@ -20,14 +21,14 @@
 model = Model(kernel, **pars)
 
 # radius and polydispersity (if any) are shared
-model.radius.range(0, np.inf)
+model.radius.range(0, 3000.)
 #model.radius_pd.range(0, 1)
 
 # Contrast and dilution are the same for both measurements, but are not
 # separable with a single measurement (i.e., I(q) ~ F(q) contrast^2 Vf),
 # so fit one of scale, sld or solvent sld.  With absolute scaling from
 # data reduction, can use the same parameter for both datasets.
-model.scale.range(0, np.inf)
+model.scale.range(0, 1.0)
 #model.sld.range(-inf, inf)
 #model.sld_solvent.range(-inf, inf)
 
@@ -37,7 +38,7 @@
     names=[data.run[0] for data in datasets],
     background=model.background,
     )
-free.background.range(-np.inf, np.inf)
+free.background.range(-1, 1)
 
 # Note: can access the parameters for the individual models using
 # free.background[0] and free.background[1], setting constraints or
@@ -61,3 +62,8 @@
 M = [Experiment(data=data, model=model, name=data.run[0]) for data in datasets]
 
 problem = FitProblem(M, freevars=free)
+
+if __name__ == "__main__":
+    import matplotlib.pyplot as plt
+    problem.plot()
+    plt.show()

example/simul_fit.py

@@ -240,6 +240,7 @@ def parameters(self):
         """
         Return a dictionary of parameters
         """
+        # TODO: tie the background and scale to the data rather than the model
         pars = self.model.parameters()
         if self.extra_pars is not None:
             pars.update(self.extra_pars)
@@ -264,7 +265,9 @@ def residuals(self):
         """
         Return theory minus data normalized by uncertainty.
         """
-        #if np.any(self.err ==0): print("zeros in err")
+        # if np.any(self.err ==0): print("zeros in err")
+        # from bumps.fitproblem import fitness_show_parameters
+        # if any(np.isnan(self.theory())): (print("nan in theory for:"),fitness_show_parameters(self))
         return (self.theory() - self.Iq) / self.dIq
 
     def nllf(self):
@@ -281,15 +284,24 @@ def nllf(self):
     #def __call__(self):
     #    return 2 * self.nllf() / self.dof
 
-    def plot(self, view=None):
+    def _plot(self, view=None, backend='matplotlib'):
         # type: (str) -> None
         """
         Plot the data and residuals.
         """
         data, theory, resid = self._data, self.theory(), self.residuals()
         # TODO: hack to display oriented usans 2-D pattern
-        Iq_calc = self.Iq_calc if isinstance(self.Iq_calc, tuple) else None
-        plot_theory(data, theory, resid, view, Iq_calc=Iq_calc)
+        #Iq_calc = self.Iq_calc if isinstance(self.Iq_calc, tuple) else None
+        label = f"I(q) {self.model.sasmodel.info.name}"
+        Iq_calc = self.Iq_calc
+        fig = plot_theory(data, theory, resid, view, Iq_calc=Iq_calc, label=label, backend=backend)
+        return fig
+
+    def plot(self, view=None):
+        return self._plot(view=view, backend='matplotlib')
+
+    def plotly(self, view=None):
+        return self._plot(view=view, backend='plotly')
 
     def simulate_data(self, noise=None):
         # type: (float) -> None