h5analysis 0.0.1

Library to analyse, plot, and export data stored in HDF5 files.

CLS H5Analysis

This is a library to analyse, plot, and export HDF5 data. The package is meant to provide a framework to load data into jupyter and enable data interaction.

Installation

Install the package from PyPi with the pip package manager. This is the recommended way to obtain a copy for your local machine and will install all required dependencies.

    $ pip install h5analysis

You will also need Jupyter Notebook together with python 3 on your local machine.

In case that certain widgets aren't rendered properly, make sure to enable the appropriate jupyter extensions

    $ jupyter nbextension enable --py widgetsnbextension

Running

Launch your local jupyter installation with

    $ jupyter notebook

Examples

Load the required module

Before you start, you will need to import the required h5analysis package, and enable bokeh plotting.

## Setup necessarry inputs
from h5analysis.LoadData import *
from bokeh.io import show, output_notebook
output_notebook(hide_banner=True)

All data loaders require a proper configuration variable which is beamline/data format specific. An example configuration for the CLS REIXS beamline may look like this:

config = h5Config()

config.key("SCAN_{scan:03d}",'scan')
config.sca_folder('Data')

config.sca('Mono Energy','Data/beam')
config.sca('Mesh Current','Data/i0')
config.sca('Sample Current','Data/tey')
config.sca('TEY','Data/tey','Data/i0')
config.sca("MCP Energy", 'Data/mcpMCA_scale')
config.sca("SDD Energy", 'Data/sddMCA_scale')
config.sca("XEOL Energy", 'Data/xeolMCA_scale')

config.mca('SDD','Data/sddMCA','Data/sddMCA_scale',None)
config.mca('MCP','Data/mcpMCA','Data/mcpMCA_scale',None)
config.mca('XEOL','Data/xeolMCA','Data/xeolMCA_scale',None)

config.stack('mcpSTACK','Data/mcp_a_img','Data/mcpMCA_scale',None,None)

1d plots

sca = Load1d()
sca.load(config,'FileName.h5','x_stream','y_stream',1,2,3,4)  # Loads multiple scans individually
sca.add(config,'FileName.h5','x_stream','y_stream',1,2,3,4)  # Adds multiple scans
sca.subtract(config,'FileName.h5','x_stream','y_stream',1,2,3,4,norm=False) # Subtracts scans from the first scan
sca.xlim(lower_lim,upper_lim) # Sets the horizontal axis plot region
sca.ylim(lower_lim,upper_lim) # Sets the vertical axis plot region
sca.plot_legend("pos string as per bokeh") # Determines a specific legend position
sca.vline(position) # Draws a vertical line
sca.hline(position) # Draws a horizontal line
sca.label(pos_x,pos_y,'Text') # Adds a label to the plot
sca.plot() # Plots the defined object
sca.exporter() # Exports the data by calling an exporter widget

1. Create "Loader" object

2. Enter the file name of the scan to analyse ('FileName.h5')

3. Options for x_stream quantities include:

• All quantities contained in the sca folder(s) specified in the config
• All SCA specified in the config

4. Options for y_stream quantities include:

• All quantities contained in the sca folder(s) specified in the config
• All SCA specified in the config
• All MCA specified in the config with applied ROI
• All STACK specified in the config with two applied ROIs

5. List all scans to analyse (comma-separated)

6. Set optional flags. Options include:

• norm (Normalizes to [0,1])
• xcoffset (Defines a constant shift in the x-stream)
• xoffset (Takes a list of tuples and defines a polynomial fit of the x-stream)
• ycoffset (Defines a constant shift in the y-stream)
• yoffset (Takes a list of tuples and defines a polynomial fit of the y-stream) e.g. offset = [(100,102),(110,112),(120,121)]
• grid_x (Takes a list with three arguments to apply 1d interpolation gridding) e.g. grid_x = [Start Energy, Stop Energy, Delta]
• savgol (Takes a list with two or three arguments to apply data smoothing and derivatives) e.g. savgol = [Window length, Polynomial order, deriavtive] as specified in the scipy Savitzky-Golay filter
• binsize (int, allows to perform data binning to improve Signal-to-Noise)
• legend_items (dict={scan_number:"name"}, overwrites generic legend names; works for the load method)
• legend_item (str, overwrites generic legend name in the add/subtract method)

2d Images

Note: Can only load one scan at a time!

General loader for MCA detector data

load2d = Load2d()
load2d.load(config,'Filename.h5','x_stream','detector',1)
load2d.plot()
load2d.exporter()

1. Create "Loader" object

2. Enter the file name of the scan to analyse ('FileName.h5')

3. Options for x_stream quantities include:

• All quantities contained in the sca folder(s) specified in the config
• All SCA specified in the config

4. Options for detector quantities include:

• All MCA specified in the config
• All STACK specified in the config with applied ROI

5. Select scan to analyse (comma-separated)

6. Set optional flags. Options include:

• norm (Normalizes to [0,1])
• xcoffset (Defines a constant shift in the x-stream)
• xoffset (Takes a list of tuples and defines a polynomial fit of the x-stream)
• ycoffset (Defines a constant shift in the y-stream)
• yoffset (Takes a list of tuples and defines a polynomial fit of the y-stream) e.g. offset = [(100,102),(110,112),(120,121)]
• grid_x (Takes a list with three arguments to apply 1d interpolation gridding) e.g. grid_x = [Start Energy, Stop Energy, Delta]
• norm_by (Normalizes to specified stream)

2d histogram

mesh = LoadMesh()
mesh.load(config,'Filename.h5','x_stream','y_stream','z_stream',24)
mesh.plot()
mesh.exporter()

1. Create "Loader" object

2. Enter the file name of the scan to analyse ('FileName.h5')

3. Options for x_stream quantities include:

• All quantities contained in the sca folder(s) specified in the config
• All SCA specified in the config
• All MCA specified in the config with ROI specified
• All STACK specified in the config with two ROIs specified

4. Options for y_stream quantities include:

• All quantities contained in the sca folder(s) specified in the config
• All SCA specified in the config
• All MCA specified in the config with ROI specified
• All STACK specified in the config with two ROIs specified

5. Options for z_stream quantities include:

• All quantities contained in the sca folder(s) specified in the config
• All SCA specified in the config
• All MCA specified in the config with ROI specified
• All STACK specified in the config with two ROIs specified

6. Specify scan to analyse

7. Set optional flags. Options include:

• norm (Normalizes to [0,1])
• xcoffset (Defines a constant shift in the x-stream)
• xoffset (Takes a list of tuples and defines a polynomial fit of the x-stream)
• ycoffset (Defines a constant shift in the y-stream)
• yoffset (Takes a list of tuples and defines a polynomial fit of the y-stream) e.g. offset = [(100,102),(110,112),(120,121)]

3d Images

Note: Can only load one scan at a time!

load3d = Load2d()
load3d.load(config,'Filename.h5','stack',1)
load3d.plot()
load3d.export()

1. Create "Loader" object

2. Enter the file name of the scan to analyse ('FileName.h5')

3. Options for stack quantities include:

• All STACK specified in the config

4. Select scan to analyse

5. Set optional flags. Options include:

• norm (Normalizes to [0,1])
• xcoffset (Defines a constant shift in the x-stream)
• xoffset (Takes a list of tuples and defines a polynomial fit of the x-stream)
• ycoffset (Defines a constant shift in the y-stream)
• yoffset (Takes a list of tuples and defines a polynomial fit of the y-stream) e.g. offset = [(100,102),(110,112),(120,121)]
• grid_x (Takes a list with three arguments to apply 1d interpolation gridding) e.g. grid_x = [Start Energy, Stop Energy, Delta]
• norm_by (Normalizes to specified stream)

Meta Data

bl = LoadBeamline()
bl.load(config,'Filename.h5','path to variable')
bl.plot()

1. Create "Loader" object

2. Enter the file name of the scan to analyse ('FileName.h5')

3. Options for path to variable quantities include:

• All directory paths within the specified h5 file

Spreadsheet

df = getSpreadsheet(config,'Filename.h5',columns=None)

1. Create "Loader" object

2. Enter the file name of the scan to analyse ('FileName.h5')

3. Options for columns quantities include:

• Custom dictionary with column headers and quantities, see example below:

columns = dict()

columns['Command'] = 'command'
columns['Sample Stage (ssh)'] = 'Endstation/Motors/ssh'
columns['Sample Stage (ssv)'] = 'Endstation/Motors/ssv'
columns['Sample Stage (ssd)'] = 'Endstation/Motors/ssd'
columns['Spectrometer (XES dist)'] = 'Endstation/Motors/spd'
columns['Spectrometer (XES angl)'] = 'Endstation/Motors/spa'
columns['Flux 4-Jaw (mm)'] = 'Beamline/Apertures/4-Jaw_2/horz_gap'
columns['Mono Grating'] = '/Beamline/Monochromator/grating'
columns['Mono Mirror'] = '/Beamline/Monochromator/mirror'
columns['Polarization'] = 'Beamline/Source/EPU/Polarization'
#columns['Comment'] = 'command'
columns['Status'] = 'status'