Extracting datasets to do specialised plots

Description

giwaxs_toolbox also has functions to extract the 1d or 2d datasets from i07 processed files which can then be used in personalised plots

first create a data loader using the following syntax:

from giwaxs_toolbox.processing import data_loader
loader =  data_loader(datafolder='path/to/your/data')

Once you have a data loader for your data folder you can extract a series of datasets using the syntax:

filelist=['file1','file2','file3']
results= loader.loadfiles(filelist)

This will then give you a list of your datasets, each of which will either be of the type result1d or result2d. These result objects have the following attributes:

result1d

result1d.data  # the one dimensional profile data
result1d.x_axis # the axis data for the x-axis

result2d

result2d.data   # the two dimensional image data
result2d.x_axis # the axis data for the x-axis
result2d.y_axis # the axis data for the y-axis

Load in 1d datasets

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from giwaxs_toolbox.processing import data_loader
from giwaxs_toolbox.plotting import reset_plots
import matplotlib.pyplot as plt
from pprint import pprint
import numpy as np
import os

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folder1="/dls/science/groups/das/ExampleData/i07/fast_rsm_example_data/tests_versioned/v2.4.1_i07_2026-04-14"
filelist= [file for file in os.listdir(folder1) if file.endswith('.hdf5')]

#create your data loader for your data directory path
loader=data_loader(datafolder=folder1)

#choose which datasets you want to load

example_ivqfiles=['IvsQ_432196_2026-04-14_14h19m06s.hdf5',
                  'IvsQ_610009_2026-04-14_14h22m41s.hdf5']

# load the files into a results object
ivqresults=loader.loadfiles(example_ivqfiles,index1vals= np.arange(0,100,10))

You can then use the results objects in your plotting routines, as shown below using the attributes ‘.x_axis’ and ‘.data’

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#set interactive ipympl
%matplotlib ipympl

#create your figure
fig,axs=plt.subplots(figsize=(10,4))

#use the result objects from the results list to plot onto the graph
for num,res in enumerate(ivqresults):
    axs.semilogy(res.x_axis,res.data,label=example_ivqfiles[num])

axs.axvline(2,ls='--')
axs.axvline(6,ls='--',color='red')
plt.legend()
plt.show

This will give the example plot

image2.png

loading in 2d datasets

You can do the same process for 2d datasets as well, below is an example using the result2d objects along with extra plotting shapes for customised plots

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import matplotlib.pyplot as plt
from matplotlib.patches import Annulus, Wedge


#create your data loader for your data directory path, note if the 2d datasets are in the same folder as the 1d datasets the loader from the previous section can be reused
loader=data_loader(datafolder=folder1)


#choose which datasets you want to load
exitmap_filenames=['exitmap_432196_2026-04-14_14h19m03s.hdf5','exitmap_610009_2026-04-14_14h22m49s.hdf5']

# load the files into a results object
exitmapresults=loader.loadfiles(exitmap_filenames)

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fig,axs=plt.subplots(1,2,figsize=(10,4))

axlist=axs.flatten()
for i,res in enumerate(exitmapresults):
    maplimits=[res.x_axis.min(), res.x_axis.max(), res.y_axis.min(),res.y_axis.max()]
    map2d=res.data
    axlist[i].imshow(map2d,vmax=map2d.mean()+2*map2d.std(),extent=maplimits,alpha=0.95)
    #axlist[i].set_aspect('equal')
    axlist[i].set_xlabel('exit_para (deg)')
    axlist[i].set_ylabel('exit_perp (deg)')

selection_ring=Annulus((0,0), 15, 1.25,color='red',alpha=0.55)
selection_wedge = Wedge((0.0),40,40,60,color='red',alpha=0.5)
axlist[0].add_patch(selection_ring)
axlist[1].add_patch(selection_wedge)
plt.tight_layout()
plt.show()

Similar to the previous example this will output a specialised plot using the loaded data result objects

image4.png

Using supplementary data

If you have supplementary data saved in your output file (e.g. adc1, adc2 values) this will be available in the loaded result object to plot

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from giwaxs_toolbox.processing import data_loader
from giwaxs_toolbox.plotting import reset_plots, plot_1d_profile
import matplotlib.pyplot as plt
from pprint import pprint
import numpy as np
import os

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#folder1="/dls/science/groups/das/ExampleData/i07/fast_rsm_example_data/tests_versioned/v2.4.1_i07_2026-04-14"
folder1="/dls/science/groups/das/ExampleData/i07/fast_rsm_example_data/tests_local/local_i07_2026-07-10"

#create your data loader for your data directory path
loader1=data_loader(datafolder=folder1)

#choose which datasets you want to load
example_ivqfiles=['IvsQ_652795_2026-07-10_14h59m28s.hdf5']

#you can select the number of the dataset you want to load from the stack of datasets, if not speficied the loader will load the first dataset in the stack by default.
selection_num = 500

# load the files into a results object
ivqresult1=loader1.loadfiles([example_ivqfiles[0]], index1vals=[selection_num])

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fig,ax = plt.subplots(2,1)
for i,res in enumerate([ivqresult1[0]]):
    plot_1d_profile(
            res.data,
            res.x_axis,
            example_ivqfiles[i],
            fig,
            ax[0],
            logscale=False,
            axlabels=[res.data_name, res.x_axis_name],
            label = 'adc1: ' +str(round(ivqresult1[0].supplementary_data['adc1'][selection_num],4))
        )

ax[0].legend()

#here you can plot supplementary data if it is available, for example the adc1 value
ax[1].plot(ivqresult1[0].supplementary_data['adc1'][:])
ax[1].axvline(selection_num, ls='--')
plt.tight_layout()

Similar to the previous example this will output a specialised plot using the loaded data result objects, as well as a second plot showing the supplementary data (in this case the adc1 value) for the selected dataset.

image5.png 
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