Merge pull request #259 from pycroscopy/plotly-support

Plotly support

Author: gduscher

.gitignore

@@ -298,3 +298,5 @@ pyTEMlib/eds_tools/get_bote_salvat.py
 .virtual_documents/notebooks/2_Image_Tools.ipynb
 .virtual_documents/notebooks/3_Spectroscopy_Tools.ipynb
 pyTEMlib/diffraction_tools/kinematic copy.py
+example_data/01-EELS Acquire_STO.hf5
+example_data/01-EELS Acquire_STO-1.hf5

notebooks/Diffraction/D3_Diffraction_Kikuchi_HOLZ-Copy1.ipynb

@@ -244,7 +244,7 @@ def calculate_holz(dif):
 
     g_allowed = dif['allowed']['g']
      #  g_allowed[:,0] is 2 Bragg angles theta
-    d_theta = (g_allowed[:,0]/2-np.arcsin(g_allowed[:,2]/g_allowed[:,3]))
+    d_theta = g_allowed[:,0]/2-np.arcsin(g_allowed[:,2]/g_allowed[:,3])
 
     # Calculate nearest point of HOLZ and Kikuchi lines
     g_closest = dif['allowed']['g'].copy()

notebooks/Imaging/I4_Structure_Analysis.ipynb

@@ -173,9 +173,14 @@ def detect_peaks(dataset, minimum_number_of_peaks=30, prominence=10):
 
     minor_peaks, _  = scipy.signal.find_peaks(new_spectrum, prominence=prominence)
 
-    while len(minor_peaks) > minimum_number_of_peaks:
-        prominence+=10
-        minor_peaks, _  = scipy.signal.find_peaks(new_spectrum, prominence=prominence)
+    if len(minor_peaks) < minimum_number_of_peaks:
+        while len(minor_peaks) < minimum_number_of_peaks:
+            prominence/=10
+            minor_peaks, _  = scipy.signal.find_peaks(new_spectrum, prominence=prominence)
+    else:
+        while len(minor_peaks) > minimum_number_of_peaks:
+            prominence+=10
+            minor_peaks, _  = scipy.signal.find_peaks(new_spectrum, prominence=prominence)
     return np.array(minor_peaks)+start
 
 def peaks_element_correlation(spectrum, minor_peaks):
@@ -468,6 +473,7 @@ def fit_model(spectrum, use_detector_efficiency=False):
     def residuals(pp, yy):
         """ residuals for fit"""
         model = np.zeros(len(yy))
+        pp = np.abs(pp)
         for i in range(len(pp)-4):
             model += peaks[i]*pp[i]
         if use_detector_efficiency:
@@ -481,8 +487,8 @@ def residuals(pp, yy):
     y = np.array(spectrum)  # .compute()
     [p, _] = scipy.optimize.leastsq(residuals, pin, args=(y,), maxfev=10000)
 
-    update_fit_values(spectrum.metadata['EDS'], peaks, p)
-    return np.array(peaks), np.array(p)
+    update_fit_values(spectrum.metadata['EDS'], peaks, np,abs(p))
+    return np.array(peaks), np.abs(p)
 
 
 def update_fit_values(out_tags, peaks, p):

notebooks/Spectroscopy/EDS-SI.ipynb

@@ -115,13 +115,16 @@ def find_associated_edges(dataset: sidpy.Dataset) -> None:
         if key.isdigit():
             onsets.append(edge['onset'])
             core_loss[key]['associated_peaks'] = {}
-        peaks = dataset.metadata['peak_fit'].get('peaks', [])
-        for key, peak in enumerate(peaks):
-            distances  = (onsets-peak[0]) * -1
-            distances[distances < -0.3] = 1e6
-            if np.min(distances) < 50:
-                index = np.argmin(distances)
-                core_loss[str(index)]['associated_peaks'][key] = peak
+            
+    peaks = dataset.metadata['peak_fit'].get('peak_out_list', [])
+    onsets = np.array(onsets)
+    print(peaks)
+    for key, peak in enumerate(peaks):
+        distances  = (onsets-float(peak[0])) * -1
+        distances[distances < -0.3] = 1e6
+        if np.min(distances) < 50:
+            index = np.argmin(distances)
+            core_loss[str(index)]['associated_peaks'][key] = peak
 
 
 def find_white_lines(dataset: sidpy.Dataset) -> Union[None, dict]:

notebooks/Spectroscopy/EDS.ipynb

@@ -58,7 +58,8 @@ def model_smooth(x: np.ndarray,
 # ################################################################
 
 @jit
-def gauss(x, p):
+def gauss(x: np.ndarray, 
+          p: list[float]) -> np.ndarray:
     """Gaussian Function
 
         p[0]==mean, p[1]= amplitude p[2]==fwhm
@@ -79,19 +80,21 @@ def gmm(x, p):
     for i in range(number_of_peaks):
         index = i*3
         p[index + 1] = p[index + 1]
-        # print(p[index + 1])
         p[index + 2] = abs(p[index + 2])
         y = y + gauss(x, p[index:index+3])
     return y
 
 @jit
-def residuals3(pp, xx, yy):
+def residuals3(pp: list[float],
+               xx: np.ndarray,
+               yy: np.ndarray) -> np.ndarray:
     """Residuals for Gaussian Mixture Model"""
     err = yy - gmm(xx, pp)
     return err
 
 
-def find_maxima(y, number_of_peaks):
+def find_maxima(y: np.ndarray, 
+                number_of_peaks: int) -> np.ndarray:
     """ find the first most prominent peaks
 
     peaks are then sorted by energy
@@ -117,7 +120,8 @@ def find_maxima(y, number_of_peaks):
     return peaks[peak_indices]
 
 
-def find_peaks(dataset, energy_scale):  #, fit_start, fit_end, sensitivity=2):
+def find_peaks(dataset: sidpy.Dataset| np.ndarray,
+               energy_scale: np.ndarray) -> list[list[float]]:
     """find peaks in spectrum"""
 
     peaks, _ = scipy.signal.find_peaks(np.abs(dataset)+1, width=5)

pyTEMlib/diffraction_tools/kinematic.py

@@ -1,5 +1,5 @@
 """
 version
 """
-__version__ = '0.2026.1.3'
+__version__ = '0.2026.3.0'
 __time__ = '2026-01-28 20:58:26'