Synch with remote repo
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@ -1,69 +1,69 @@
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import sys
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import os
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root_dir = os.path.abspath(os.curdir)
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sys.path.append(root_dir)
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import h5py
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import yaml
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import numpy as np
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import pandas as pd
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from plotly.subplots import make_subplots
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import plotly.graph_objects as go
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import plotly.express as px
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#import plotly.io as pio
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from src.hdf5_ops import get_parent_child_relationships
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def display_group_hierarchy_on_a_treemap(filename: str):
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"""
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filename (str): hdf5 file's filename"""
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with h5py.File(filename,'r') as file:
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nodes, parents, values = get_parent_child_relationships(file)
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metadata_list = []
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metadata_dict={}
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for key in file.attrs.keys():
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#if 'metadata' in key:
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if isinstance(file.attrs[key], str): # Check if the attribute is a string
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metadata_key = key[key.find('_') + 1:]
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metadata_value = file.attrs[key]
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metadata_dict[metadata_key] = metadata_value
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metadata_list.append(f'{metadata_key}: {metadata_value}')
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#metadata_dict[key[key.find('_')+1::]]= file.attrs[key]
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#metadata_list.append(key[key.find('_')+1::]+':'+file.attrs[key])
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metadata = '<br>'.join(['<br>'] + metadata_list)
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customdata_series = pd.Series(nodes)
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customdata_series[0] = metadata
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fig = make_subplots(1, 1, specs=[[{"type": "domain"}]],)
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fig.add_trace(go.Treemap(
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labels=nodes, #formating_df['formated_names'][nodes],
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parents=parents,#formating_df['formated_names'][parents],
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values=values,
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branchvalues='remainder',
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customdata= customdata_series,
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#marker=dict(
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# colors=df_all_trees['color'],
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# colorscale='RdBu',
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# cmid=average_score),
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#hovertemplate='<b>%{label} </b> <br> Number of files: %{value}<br> Success rate: %{color:.2f}',
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hovertemplate='<b>%{label} </b> <br> Count: %{value} <br> Path: %{customdata}',
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name='',
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root_color="lightgrey"
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))
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fig.update_layout(width = 800, height= 600, margin = dict(t=50, l=25, r=25, b=25))
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fig.show()
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file_name, file_ext = os.path.splitext(filename)
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fig.write_html(file_name + ".html")
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#pio.write_image(fig,file_name + ".png",width=800,height=600,format='png')
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#
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import sys
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import os
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root_dir = os.path.abspath(os.curdir)
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sys.path.append(root_dir)
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import h5py
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import yaml
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import numpy as np
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import pandas as pd
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from plotly.subplots import make_subplots
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import plotly.graph_objects as go
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import plotly.express as px
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#import plotly.io as pio
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from src.hdf5_ops import get_parent_child_relationships
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def display_group_hierarchy_on_a_treemap(filename: str):
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"""
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filename (str): hdf5 file's filename"""
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with h5py.File(filename,'r') as file:
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nodes, parents, values = get_parent_child_relationships(file)
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metadata_list = []
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metadata_dict={}
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for key in file.attrs.keys():
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#if 'metadata' in key:
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if isinstance(file.attrs[key], str): # Check if the attribute is a string
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metadata_key = key[key.find('_') + 1:]
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metadata_value = file.attrs[key]
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metadata_dict[metadata_key] = metadata_value
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metadata_list.append(f'{metadata_key}: {metadata_value}')
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#metadata_dict[key[key.find('_')+1::]]= file.attrs[key]
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#metadata_list.append(key[key.find('_')+1::]+':'+file.attrs[key])
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metadata = '<br>'.join(['<br>'] + metadata_list)
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customdata_series = pd.Series(nodes)
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customdata_series[0] = metadata
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fig = make_subplots(1, 1, specs=[[{"type": "domain"}]],)
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fig.add_trace(go.Treemap(
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labels=nodes, #formating_df['formated_names'][nodes],
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parents=parents,#formating_df['formated_names'][parents],
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values=values,
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branchvalues='remainder',
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customdata= customdata_series,
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#marker=dict(
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# colors=df_all_trees['color'],
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# colorscale='RdBu',
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# cmid=average_score),
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#hovertemplate='<b>%{label} </b> <br> Number of files: %{value}<br> Success rate: %{color:.2f}',
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hovertemplate='<b>%{label} </b> <br> Count: %{value} <br> Path: %{customdata}',
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name='',
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root_color="lightgrey"
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))
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fig.update_layout(width = 800, height= 600, margin = dict(t=50, l=25, r=25, b=25))
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fig.show()
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file_name, file_ext = os.path.splitext(filename)
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fig.write_html(file_name + ".html")
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#pio.write_image(fig,file_name + ".png",width=800,height=600,format='png')
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#
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@ -1,54 +1,54 @@
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import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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def plot_image(dataframe,filter):
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for meas_idx in dataframe.loc[filter,:].index:
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meas = dataframe.loc[meas_idx,:] # pandas Series
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fig = plt.figure()
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ax = plt.gca()
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rows, cols = meas['image'].shape
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scientaEkin_eV = meas['scientaEkin_eV'].flatten()
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x_min, x_max = np.min(scientaEkin_eV), np.max(scientaEkin_eV)
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y_min, y_max = 0, rows
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ax.imshow(meas['image'],extent = [x_min,x_max,y_min,y_max])
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ax.set_xlabel('scientaEkin_eV')
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ax.set_ylabel('Replicates')
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ax.set_title(meas['name'][0] + '\n' + meas['sample'][0]+ '\n' + meas['lastModifiedDatestr'][0])
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def plot_spectra(dataframe,filter):
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""" plot_spectra plots XPS spectra associated to 'dataframe' after row reduced by 'filter'.
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When more than one row are specified by the 'filter' input, indivial spectrum are superimposed
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on the same plot.
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Parameters:
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dataframe (pandas.DataFrame): table with heterogenous entries obtained by read_hdf5_as_dataframe.py.
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filter (binaray array): binary indexing array with same number of entries as rows in dataframe.
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"""
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fig = plt.figure()
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ax = plt.gca()
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for meas_idx in dataframe.loc[filter,:].index:
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meas = dataframe.loc[meas_idx,:] # pandas Series
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rows, cols = meas['image'].shape
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bindingEnergy_eV = meas['bindingEnergy_eV'].flatten()
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spectrum_countsPerSecond = meas['spectrum_countsPerSecond'].flatten()
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x_min, x_max = np.min(bindingEnergy_eV), np.max(bindingEnergy_eV)
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y_min, y_max = 0, rows
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#for i in range(cols):
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#ax.plot(bindingEnergy_eV, spectrum_countsPerSecond,label = meas['name'][0])
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ax.plot(bindingEnergy_eV, spectrum_countsPerSecond,label = meas['name'])
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ax.set_xlabel('bindingEnergy_eV')
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ax.set_ylabel('counts Per Second')
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ax.set_title('\n'+meas['sample']+ '\n' + 'PE spectra')
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#ax.set_title('\n'+meas['sample'][0]+ '\n' + 'PE spectra')
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#ax.set_title(meas['name'][0] + '\n'+meas['sample'][0]+ '\n' + meas['lastModifiedDatestr'][0])
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ax.legend()
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import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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def plot_image(dataframe,filter):
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for meas_idx in dataframe.loc[filter,:].index:
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meas = dataframe.loc[meas_idx,:] # pandas Series
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fig = plt.figure()
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ax = plt.gca()
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rows, cols = meas['image'].shape
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scientaEkin_eV = meas['scientaEkin_eV'].flatten()
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x_min, x_max = np.min(scientaEkin_eV), np.max(scientaEkin_eV)
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y_min, y_max = 0, rows
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ax.imshow(meas['image'],extent = [x_min,x_max,y_min,y_max])
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ax.set_xlabel('scientaEkin_eV')
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ax.set_ylabel('Replicates')
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ax.set_title(meas['name'][0] + '\n' + meas['sample'][0]+ '\n' + meas['lastModifiedDatestr'][0])
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def plot_spectra(dataframe,filter):
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""" plot_spectra plots XPS spectra associated to 'dataframe' after row reduced by 'filter'.
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When more than one row are specified by the 'filter' input, indivial spectrum are superimposed
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on the same plot.
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Parameters:
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dataframe (pandas.DataFrame): table with heterogenous entries obtained by read_hdf5_as_dataframe.py.
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filter (binaray array): binary indexing array with same number of entries as rows in dataframe.
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"""
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fig = plt.figure()
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ax = plt.gca()
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for meas_idx in dataframe.loc[filter,:].index:
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meas = dataframe.loc[meas_idx,:] # pandas Series
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rows, cols = meas['image'].shape
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bindingEnergy_eV = meas['bindingEnergy_eV'].flatten()
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spectrum_countsPerSecond = meas['spectrum_countsPerSecond'].flatten()
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x_min, x_max = np.min(bindingEnergy_eV), np.max(bindingEnergy_eV)
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y_min, y_max = 0, rows
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#for i in range(cols):
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#ax.plot(bindingEnergy_eV, spectrum_countsPerSecond,label = meas['name'][0])
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ax.plot(bindingEnergy_eV, spectrum_countsPerSecond,label = meas['name'])
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ax.set_xlabel('bindingEnergy_eV')
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ax.set_ylabel('counts Per Second')
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ax.set_title('\n'+meas['sample']+ '\n' + 'PE spectra')
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#ax.set_title('\n'+meas['sample'][0]+ '\n' + 'PE spectra')
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#ax.set_title(meas['name'][0] + '\n'+meas['sample'][0]+ '\n' + meas['lastModifiedDatestr'][0])
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ax.legend()
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