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This page provides example Python scripts for:
- Processing plug-ins:
Python Script - Image to Image [Scripting]
1) Take an image (with axes) and return an image (with axes)
A brief YouTube overview of how to use Python scripting from within DAWN’s processing perspective can be found below
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Building on this tutorial, this page outlines example Python scripts for:
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Image to Image
Image with axes input, returning an image with axes
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import numpy as np def run(xaxis, yaxis, data, **kwargs): #generate random noise the same shape as the data noise = np.random.rand(*data.shape) #add it to the output dictionary script_outputs = {"data":noise} #also return the x-axis script_outputs["xaxis"] = xaxis #and the y-axis script_outputs["yaxis"] = yaxis return script_outputs |
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Image input, returning an image of random noise of the same dimensions, overwriting axes,
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data and titles
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# Example script for use with the 'Python Script - Image to Image [Scripting]' processing plug-in.
#
# Last updated 2016-11-02 |
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#
# Copyright (c) 2016 Diamond Light Source Ltd.
#
#
# The DAWN Python PyDev actor will execute this script, invoking a 'run' method in this file.
# This actor will pass the data and any associated variables from the dataset, which we will
# catch as a dictionary.
#
# The dictionary keys passed for the XY to XY plugin are always:
#
# 'current_slice' = The current slice of the data, typically [0, Frame Number, X Length]
# 'data' = The data, as a 2D array
# 'data_dimensions' = The dimensionality of the data
# 'data_title' = The title of the data
# 'dataset_name' = By default, the NeXus path to the dataset, or a given title of the dataset
# 'file_path' = The path to the file
# 'total' = Number of frames passed
# 'xaxis' = The x axis values, i.e. the x axis scale
# 'xaxis_title' = The title of the x-axis values/scale
# 'yaxis' = The y axis values, i.e. the y axis scale
# 'yaxis_title' = The title of the y-axis values/scale
#
# with potentially one or more of the following, depending on the dataset passed:
#
# 'auxiliary' = Any auxiliary data associated with the dataset
# 'error' = Any error values for the data, as a 2D array
# 'mask' = A mask, indicating if there are any values not to evaluate, as a 2D boolean array
#
# The PyDev actor will expect, at least, all of the first set of keys to be returned.
# However, it will accept any of the second set of keys being inserted or removed to/from
# the returned dictionary.
#
# An error will be thrown back to the processing perspective GUI if the script does not execute.
#
# Below is a simple example where random data, of the same size to the input is returned
# complete with all the potential additional keys added as well.
# Always handy to have numpy
import numpy
# The method the the PyDev actor will call, we'll catch any and all arguements as a dictionary
def run(**kwargs):
# Extract out the data
data = kwargs['data']
# and find it's length
x, y = data.shape
# Generate some random data the same length as the input
# N.B. The output data length can be different to the input but must be 2D
randomData = numpy.random.randn(x, y)
# Generate some error values for the plot
randomError = numpy.abs(randomData) * 10
# Generate some data values as well
randomData = numpy.abs(randomData) * 100
# Input the error values into the input dictionary
kwargs['data'] = randomData
# Input the data values into the input dictionary
kwargs['error'] = randomError
# Delineate some axes so that the axis titles plot
# (No data in the xaxis/yaxis variables means no titles!)
kwargs['xaxis'] = numpy.arange(0, x)
kwargs['yaxis'] = numpy.arange(0, y)
# Give new titles for everything
kwargs['data_title'] = "Random Data"
kwargs['xaxis_title'] = "New X-Axis Title"
kwargs['yaxis_title'] = "New Y-Axis Title"
# Return the dictionary to DAWN
return kwargs |
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Image to XY
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1) Take an image (with axes) and return XY data (with axes) converting data to noise
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Image with axes input, returning XY data with axes, converting the data to noise
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import numpy as np def run(xaxis, yaxis, data, **kwargs): #generate random noise the same shape as the data noise = np.random.rand(data.shape[1]) #add it to the output dictionary script_outputs = {"data":noise} #also return the x-axis script_outputs["xaxis"] = xaxis return script_outputs |
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Image input, returning XY data of random noise of the same length as the image y-axis, overwriting axes, data and titles
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# Example script for use with the 'Python Script - Image to XY [Scripting]' processing plug-in.
#
# Last updated 2016-11- |
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02 # # Copyright (c) 2016 Diamond Light Source Ltd. # # # The DAWN Python PyDev actor will execute this script, invoking a 'run' method in this file. # This actor will pass the data and any associated variables from the dataset, which we will # catch as a dictionary. # # The dictionary keys passed for the XY to XY plugin are always: # # 'current_slice' = The current slice of the data, typically [0, Frame Number, X Length] # 'data' = The data, as a 2D array # 'data_dimensions' = The dimensionality of the data # 'data_title' = The title of the data # 'dataset_name' = By default, the NeXus path to the dataset, or a given title of the dataset # 'file_path' = The path to the file # 'total' = Number of frames passed # 'xaxis' = The x axis values, i.e. the x axis scale # 'xaxis_title' = The title of the x-axis values/scale # 'yaxis' = The y axis values, i.e. the y axis scale # 'yaxis_title' = The title of the y-axis values/scale # # with potentially one or more of the following, depending on the dataset passed: # # 'auxiliary' = Any auxiliary data associated with the dataset # 'error' = Any error values for the data, as a 2D array # 'mask' = A mask, indicating if there are any values not to evaluate, as a 2D boolean array # # The PyDev actor will expect, at least, all of the first set of keys to be returned. # However, it will accept any of the second set of keys being inserted or removed to/from # the returned dictionary. # # For scripts made for this plug-in the returned data should be reduced from a 2D to a 1D array, # furthermore removing the yaxis title, whilst not essential, will not result in an error # # An error will be thrown back to the processing perspective GUI if the script does not execute. # # Below is a simple example where random data, of the same size to the input is returned # complete with all the potential additional keys added as well. # Always handy to have numpy import numpy # The method the the PyDev actor will call, we'll catch any and all arguements as a dictionary def run(**kwargs): # Extract out the data data = kwargs['data'] # and find it's length x, y = data.shape # Generate some random data the same length as the input # N.B. The output data length can be different to the input but must be 1D randomData = numpy.random.randn(x) # Generate some error values for the plot randomError = numpy.abs(randomData) * 10 # Generate some data values as well randomData = numpy.abs(randomData) * 100 # Input the error values into the input dictionary kwargs['data'] = randomData # Input the data values into the input dictionary kwargs['error'] = randomError # Delineate some axes so that the axis titles plot # (No data in the xaxis/yaxis variables means no titles!) kwargs['xaxis'] = numpy.arange(0, x) # Give new titles for everything kwargs['data_title'] = "Random Data" kwargs['xaxis_title'] = "New X-Axis Title" # Return the dictionary to DAWN return kwargs |
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XY to
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Quick links:
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XY
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XY data input,
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returning XY data, converting the data to noise
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import numpy as np
def run(xaxis, data, **kwargs):
#generate random noise the same shape as the data
noise = np.random.rand(*data.shape)
#add it to the output dictionary
script_outputs = {"data":noise}
#also return the x-axis
script_outputs["xaxis"] = xaxis
return script_outputs |
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XY data
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input, returning XY data of random noise of the same length, overwriting axes, data and titles
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# Example script for use with the 'Python Script - XY to XY [Scripting]' processing plug-in.
#
# Last updated 2016-11-02 |
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#
# Copyright (c) 2016 Diamond Light Source Ltd.
#
#
# The DAWN Python PyDev actor will execute this script, invoking a 'run' method in this file.
# This actor will pass the data and any associated variables from the dataset, which we will
# catch as a dictionary.
#
# The dictionary keys passed for the XY to XY plugin are always:
#
# 'current_slice' = The current slice of the data, typically [0, Frame Number, X Length]
# 'data' = The data, as a 1D array
# 'data_dimensions' = The dimensionality of the data
# 'data_title' = The title of the data
# 'dataset_name' = By default, the NeXus path to the dataset, or a given title of the dataset
# 'file_path' = The path to the file
# 'total' = Number of frames passed
# 'xaxis' = The x axis values, i.e. the x axis scale
# 'xaxis_title' = The title of the y-axis values/scale
#
# with potentially one or more of the following, depending on the dataset passed:
#
# 'auxiliary' = Any auxiliary data associated with the dataset
# 'error' = Any error values for the data, as a 1D array
# 'mask' = A mask, indicating if there are any values not to evaluate, as a 1D boolean array
# 'yaxis' = The y axis values, i.e. the y axis scale
#
# The PyDev actor will expect, at least, all of the first set of keys to be returned.
# However, it will accept any of the second set of keys being inserted or removed to/from
# the returned dictionary.
#
# An error will be thrown back to the processing perspective GUI if the script does not execute.
#
# Below is a simple example where random data, of the same size to the input is returned
# complete with all the potential additional keys added as well.
# Always handy to have numpy
import numpy
# The method the the PyDev actor will call, we'll catch any and all arguements as a dictionary
def run(**kwargs):
# Extract out the data
data = kwargs['data']
# and find it's length
x, = data.shape
# Generate some random data the same length as the input
# N.B. The output data length can be different to the input but must be 1D
randomData = numpy.random.randn(x)
# Generate some error values for the plot
randomError = numpy.abs(randomData) * 10
# Generate some data values as well
randomData = numpy.abs(randomData) * 100
# Input the error values into the input dictionary
kwargs['data'] = randomData
# Input the data values into the input dictionary
kwargs['error'] = randomError
# Delineate some axes so that the axis titles plot
# (No data in the xaxis/yaxis variables means no titles!)
kwargs['xaxis'] = numpy.arange(0, x)
# Give new titles for everything
kwargs['data_title'] = "Random Data"
kwargs['xaxis_title'] = "New X-Axis Title"
# Return the dictionary to DAWN
return |
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3) Take
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XY small angle scattering
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data input, returning XY small angle scattering data transformed into a Guinier plot
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# Example script for use with the 'Python Script - XY to XY [Scripting]' processing plug-in.
#
# Last updated 2016-11-02 - For DAWN 2.3
#
# Copyright (c) 2016 Diamond Light Source Ltd.
#
#
# The DAWN Python PyDev actor will execute this script, invoking a 'run' method in this file.
# This actor will pass the data and any associated variables from the dataset, which we will
# catch as a dictionary.
#
# Below is a simple example where reduced SAXS data is taken in and converted to display a Guinier plot
# Always handy to have numpy
import numpy as np
# The method the the PyDev actor will call, we'll catch any and all arguements as a dictionary
def run(**kwargs):
# Extract out the data and xaxis from the dictionary
data = kwargs['data']
xaxis = kwargs['xaxis']
# Do some 'error' handling
if 'error' in kwargs:
del kwargs['error']
# Do the required mathematics on the data
guinier = np.log(data)
guinier_x = np.power(xaxis,2)
# Set the plot titles and data values
kwargs['data_title'] = 'ln(I)'
kwargs['data'] = guinier
kwargs['xaxis_title'] = 'q^2'
kwargs['xaxis'] = guinier_x
return kwargs |
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XY small angle scattering
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data input, returning XY small angle scattering data transformed into a Kratky plot
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# Example script for use with the 'Python Script - XY to XY [Scripting]' processing plug-in. # # Last updated 2016-11-02 - For DAWN 2.3 # # Copyright (c) 2016 Diamond Light Source Ltd. # # # The DAWN Python PyDev actor will execute this script, invoking a 'run' method in this file. # This actor will pass the data and any associated variables from the dataset, which we will # catch as a dictionary. # # Below is a simple example where reduced SAXS data is taken in and converted to display a Kratky plot # Always handy to have numpy import numpy as np # The method the the PyDev actor will call, we'll catch any and all arguements as a dictionary def run(**kwargs): # Extract out the data and xaxis from the dictionary data = kwargs['data'] xaxis = kwargs['xaxis'] # Do some 'error' handling if 'error' in kwargs: del kwargs['error'] # Do the required mathematics on the data kratky = np.power(xaxis,2)*data # Set the plot titles and data values kwargs['data_title'] = 'I*q^2' kwargs['data'] = kratky kwargs['xaxis_title'] = 'q' return kwargs |
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XY small angle scattering
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data input, returning XY small angle scattering data transformed into a Porod plot
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# Example script for use with the 'Python Script - XY to XY [Scripting]' processing plug-in. # # Last updated 2016-11-02 |
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#
# Copyright (c) 2016 Diamond Light Source Ltd.
#
#
# The DAWN Python PyDev actor will execute this script, invoking a 'run' method in this file.
# This actor will pass the data and any associated variables from the dataset, which we will
# catch as a dictionary.
#
# Below is a simple example where reduced SAXS data is taken in and converted to display a Porod plot
# Always handy to have numpy
import numpy as np
# The method the the PyDev actor will call, we'll catch any and all arguements as a dictionary
def run(**kwargs):
# Extract out the data and xaxis from the dictionary
data = kwargs['data']
xaxis = kwargs['xaxis']
# Do some 'error' handling
if 'error' in kwargs:
del kwargs['error']
# Do the required mathematics on the data
porod = np.power(xaxis, 4) * data
porod_x = np.power(xaxis, 4)
# Set the plot titles and data values
kwargs['data_title'] = 'I*q^4'
kwargs['data'] = porod
kwargs['xaxis_title'] = 'q^4'
kwargs['xaxis'] = porod_x
# Return the data
return kwargs |
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