Synch with remote repo

2025-02-03 10:31:48 +01:00
parent a3ccff4079
commit 32bba4239a
102 changed files with 19584 additions and 19584 deletions
@@ -1,54 +1,54 @@
-import pandas as pd
-import numpy as np
-import matplotlib.pyplot as plt
-
-def plot_image(dataframe,filter):
-
-    for meas_idx in dataframe.loc[filter,:].index:
-        meas = dataframe.loc[meas_idx,:] # pandas Series    
-        fig = plt.figure()
-        ax = plt.gca()
-        rows, cols = meas['image'].shape
-        scientaEkin_eV = meas['scientaEkin_eV'].flatten()
-        x_min, x_max = np.min(scientaEkin_eV), np.max(scientaEkin_eV)
-        y_min, y_max = 0, rows
-        ax.imshow(meas['image'],extent = [x_min,x_max,y_min,y_max])
-        ax.set_xlabel('scientaEkin_eV')
-        ax.set_ylabel('Replicates')
-        ax.set_title(meas['name'][0] + '\n' + meas['sample'][0]+ '\n' + meas['lastModifiedDatestr'][0])
-
-def plot_spectra(dataframe,filter):
-
-    """ plot_spectra plots XPS spectra associated to 'dataframe' after row reduced by 'filter'. 
-    When more than one row are specified by the 'filter' input, indivial spectrum are superimposed
-    on the same plot.
-
-    Parameters:
-    dataframe (pandas.DataFrame): table with heterogenous entries obtained by read_hdf5_as_dataframe.py.
-    filter (binaray array): binary indexing array with same number of entries as rows in dataframe.
-
-    """
-    fig = plt.figure()
-    ax = plt.gca()
-
-    for meas_idx in dataframe.loc[filter,:].index:
-        meas = dataframe.loc[meas_idx,:] # pandas Series   
-
-        rows, cols = meas['image'].shape
-        bindingEnergy_eV = meas['bindingEnergy_eV'].flatten()
-        spectrum_countsPerSecond = meas['spectrum_countsPerSecond'].flatten()
-        x_min, x_max = np.min(bindingEnergy_eV), np.max(bindingEnergy_eV)
-        y_min, y_max = 0, rows
-        #for i in range(cols):
-        #ax.plot(bindingEnergy_eV, spectrum_countsPerSecond,label = meas['name'][0])
-        ax.plot(bindingEnergy_eV, spectrum_countsPerSecond,label = meas['name'])
-
-    ax.set_xlabel('bindingEnergy_eV')
-    ax.set_ylabel('counts Per Second')
-    ax.set_title('\n'+meas['sample']+ '\n' + 'PE spectra')
-    #ax.set_title('\n'+meas['sample'][0]+ '\n' + 'PE spectra')
-    #ax.set_title(meas['name'][0] + '\n'+meas['sample'][0]+ '\n' + meas['lastModifiedDatestr'][0])
-    ax.legend()
-
-
-
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+
+def plot_image(dataframe,filter):
+
+    for meas_idx in dataframe.loc[filter,:].index:
+        meas = dataframe.loc[meas_idx,:] # pandas Series    
+        fig = plt.figure()
+        ax = plt.gca()
+        rows, cols = meas['image'].shape
+        scientaEkin_eV = meas['scientaEkin_eV'].flatten()
+        x_min, x_max = np.min(scientaEkin_eV), np.max(scientaEkin_eV)
+        y_min, y_max = 0, rows
+        ax.imshow(meas['image'],extent = [x_min,x_max,y_min,y_max])
+        ax.set_xlabel('scientaEkin_eV')
+        ax.set_ylabel('Replicates')
+        ax.set_title(meas['name'][0] + '\n' + meas['sample'][0]+ '\n' + meas['lastModifiedDatestr'][0])
+
+def plot_spectra(dataframe,filter):
+
+    """ plot_spectra plots XPS spectra associated to 'dataframe' after row reduced by 'filter'. 
+    When more than one row are specified by the 'filter' input, indivial spectrum are superimposed
+    on the same plot.
+
+    Parameters:
+    dataframe (pandas.DataFrame): table with heterogenous entries obtained by read_hdf5_as_dataframe.py.
+    filter (binaray array): binary indexing array with same number of entries as rows in dataframe.
+
+    """
+    fig = plt.figure()
+    ax = plt.gca()
+
+    for meas_idx in dataframe.loc[filter,:].index:
+        meas = dataframe.loc[meas_idx,:] # pandas Series   
+
+        rows, cols = meas['image'].shape
+        bindingEnergy_eV = meas['bindingEnergy_eV'].flatten()
+        spectrum_countsPerSecond = meas['spectrum_countsPerSecond'].flatten()
+        x_min, x_max = np.min(bindingEnergy_eV), np.max(bindingEnergy_eV)
+        y_min, y_max = 0, rows
+        #for i in range(cols):
+        #ax.plot(bindingEnergy_eV, spectrum_countsPerSecond,label = meas['name'][0])
+        ax.plot(bindingEnergy_eV, spectrum_countsPerSecond,label = meas['name'])
+
+    ax.set_xlabel('bindingEnergy_eV')
+    ax.set_ylabel('counts Per Second')
+    ax.set_title('\n'+meas['sample']+ '\n' + 'PE spectra')
+    #ax.set_title('\n'+meas['sample'][0]+ '\n' + 'PE spectra')
+    #ax.set_title(meas['name'][0] + '\n'+meas['sample'][0]+ '\n' + meas['lastModifiedDatestr'][0])
+    ax.legend()
+
+
+