Initial commit. hdf5_lib.py contains functions to read hdf5 file as dataframe. and napp_plotlib contains functions to plot image and spectra. jupyter notebook included to illustrate functions.

napp_plotlib.py

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+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_spectrum(dataframe,filter,):
+
+    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, np.mean(meas['image'],axis=0))
+        ax.set_xlabel('bindingEnergy_eV')
+        ax.set_ylabel('counts Per Second')
+        if len(meas)>1:
+            ax.set_title('\n'+meas['sample'][0]+ '\n' + meas['lastModifiedDatestr'][0])
+        else:
+            ax.set_title(meas['name'][0] + '\n'+meas['sample'][0]+ '\n' + meas['lastModifiedDatestr'][0])
+    ax.legend()
+
+
+