In [ ]:import file_processor as fp #contains simple routines for sorting files and making directories import processing_tools as pt #bulk of the processing import int_plot as ip #allows for interactive plots
As it is necessary to often plot many files, a script is included that will go through a directory, make a folder for each file (which ends in .h5, but you may specify other endings by the optional parameter 'file_ending' ) This plots all the defaults, both in pandas and bokeh and then moves them in said directory.
In :directory = './example' fp.plot_defaults(directory, file_ending='.h5')
In :list_of_files = fp.directory_list('./example') #returns a list of files that #can be sorted in your preferred method fp.sort_nicely(list_of_files) # sorts in a nice way, but you ought to check #in this case the files are too different to work, so I provide similar files list_of_files = ['./example/noise_10kSI_MASP.h5', './example/noise_10kSI_MASP.h5', './example/noise_10kSI_MASP.h5'] from bokeh.plotting import show from bokeh.io import output_notebook #To view on a notebook such as this output_notebook() # allows Bokeh to output to the notebook int_plot = ip.interactive_plot(list_of_files,'z_pos','CoM_y',num_slices=100, undulator_period=0.0275,k_fact=1) #what to plot show(int_plot)
In :import matplotlib.pyplot as plt test = pt.ProcessedData('./example/example.h5',undulator_period=0.00275,num_slices=100) panda_data = test.StatsFrame() ax = panda_data.plot(x='z_pos',y='CoM_y') panda_data.plot(ax=ax, x='z_pos',y='std_y',c='b') #first option allows shared axes, one can even mix different runs #by plotting another dataset on the same axis plt.show()
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