mreddata

Version 0.3.80:

To install: pip install mreddata. (PyPi page here: https://pypi.org/project/mreddata/)

Run with --no-load to only load the hdf5 file info, without collected the tables. Useful for large files/lots of histograms.
--files <select files here> to manually select which hdf5 files; default is all files ending with .hdf5 in the current directory.

Note: The current version requires the following Hdf5 file attributes to be set: "gfu" and "nIons"

>>> from mreddata import options, plot_options, Hdf5Data
>>> options.files
['250nmAl_250nmAl_10keV000_1.hdf5', '250nmAl_250nmAl_50keV000_19.hdf5', '250nmAl_250nmAu_10keV000_1.hdf5', '250nmAl_250nmAu_50keV000_19.hdf5', '250nmAl_250nmCo_10keV000_1.hdf5', '250nmAl_250nmCo_50keV000_4.hdf5', '250nmAl_250nmCu_10keV000_1.hdf5', '250nmAl_250nmCu_50keV000_19.hdf5', '250nmAl_250nmRu_10keV000_1.hdf5', '250nmAl_250nmRu_50keV000_19.hdf5', '250nmAl_250nmW_10keV000_1.hdf5', '250nmAl_250nmW_50keV000_19.hdf5', '250nmAu_250nmAl_10keV000_1.hdf5', '250nmAu_250nmAl_50keV000_19.hdf5', '250nmCo_250nmAl_10keV000_1.hdf5', '250nmCo_250nmAl_50keV000_19.hdf5', '250nmCo_250nmCu_50keV000_19.hdf5', '250nmCu_250nmAl_10keV000_1.hdf5', '250nmCu_250nmAl_50keV000_19.hdf5', '250nmCu_250nmCo_10keV000_2.hdf5', '250nmCu_250nmCo_50keV000_19.hdf5', '250nmCu_250nmCu_10keV000_3.hdf5', '250nmCu_250nmCu_50keV000_19.hdf5', '250nmRu_250nmAl_10keV000_1.hdf5', '250nmRu_250nmAl_50keV000_19.hdf5', '250nmW_250nmAl_10keV000_1.hdf5', '250nmW_250nmAl_50keV000_19.hdf5']

data = Hdf5Data() creates the histogram object list. (The Histogram class is accessible by from mreddata.datatools import Histogram)

Calling the Hdf5Data object displays the filesystem tree with histograms currenlty selected in data.histograms organized according to their parent file. This is useful for selecting/fitlering.

from mreddata import options, plot_options, Hdf5Data
data = Hdf5Data()
>>> data
---------------------------------------
tungsten001_9.hdf5
  |
  ├── n7_sd
  ├── n6_sd
  ├── n3_sd
  ├── n10_sd
  ├── n9_sd
  ├── n8_sd
  ├── n5_sd
  ├── n4_sd
  ├── n2_sd
  ├── n1_sd
---------------------------------------
tungsten000_9.hdf5
  |
  ├── n7_sd
  ├── n6_sd
  ├── n3_sd
  ├── n10_sd
  ├── n9_sd
  ├── n8_sd
  . . . 

data.selectHistograms() accepts string arguments that filter based on the full path of the histogram (i.e. the filname AND the name of the histogram)

data.dropHistograms() is similar, but drops histograms from the data.histograms list that match the strings passed.

data.resetHistograms() restores the histogram list to its original state, removing all filters.

data.combineHistograms(newHistName, histograms=[]) automatically combines the histograms in data.histograms, appending the new histogram object to data.customHistograms with the name passed. Alternatively, you can manually pass a list of histogram objects to the function.

data.attributes() will display the file attributes for all files with histograms currently loaded in data.histograms. You can also pass strings to return only specific attributes (e.g.: data.attributes("gfu", "nIons"))

data.plot() will (by default) plot the 'reverse integrated' data for all histograms in the current histogram object list (data.histograms). Standard pandas/matplotlib plot options can be passed as keyword arguments directly, or by using plot_options (from mreddata import plot_options).

The histogram objects themselves have attributes which handle their plotting behavior. For example:

histogram = data.histograms[0]
histogram.label = 'Label text'  # defaults to the histogram.fullpath
histogram.color = 'red'
histogram.dashes = [1,4] 		#(None, None) is default

The pandas DataFrame object is accesible via histogram.df

Columns are calculated by default when loading files (unless --no-load selected) to include the following columns: ['x', 'y_raw', 'n', 'w', 'y_norm', 'y_int', 'y_diff']. y_norm is normalized using the gfu and nIons file attributes, and y_int and y_diff correspond to the integrated and differential values, respectively. Any of these columns can be substituted in place of the default y_int when plotting using the standard pandas format for data selection: data.plot(y='y_diff')