From e0ce5838ac183ea087b1414b1f59c8bf3005db3d Mon Sep 17 00:00:00 2001 From: Zain Sohail Date: Mon, 6 Nov 2023 15:44:46 +0100 Subject: [PATCH] Build docs with github actions (#235) * deployment with tutorials * trigger this branch * modified lock file * fix index file and action * copy config files * fix paths again * add badge, add cache in comments (not yet implemented) --- .github/workflows/autodoc_tutorials.yml | 63 -- .github/workflows/build_deploy_docs.yml | 97 ++ README.md | 2 +- docs/conf.py | 17 +- docs/index.rst | 6 +- docs/tutorial/1_binning_fake_data.rst | 302 ------ .../1_binning_fake_data_13_0.png | Bin 115637 -> 0 bytes .../1_binning_fake_data_8_0.png | Bin 269652 -> 0 bytes ...e_for_example_time-resolved_ARPES_data.rst | 896 ------------------ ..._example_time-resolved_ARPES_data_10_3.png | Bin 36803 -> 0 bytes ..._example_time-resolved_ARPES_data_12_3.png | Bin 34024 -> 0 bytes ..._example_time-resolved_ARPES_data_18_2.png | Bin 29787 -> 0 bytes ..._example_time-resolved_ARPES_data_18_4.png | Bin 26611 -> 0 bytes ..._example_time-resolved_ARPES_data_22_2.png | Bin 34885 -> 0 bytes ..._example_time-resolved_ARPES_data_29_2.png | Bin 106355 -> 0 bytes ..._example_time-resolved_ARPES_data_38_2.png | Bin 97201 -> 0 bytes ..._example_time-resolved_ARPES_data_40_5.png | Bin 27308 -> 0 bytes ..._example_time-resolved_ARPES_data_52_1.png | Bin 145494 -> 0 bytes ...r_example_time-resolved_ARPES_data_5_1.png | Bin 30089 -> 0 bytes ...etadata_collection_and_export_to_NeXus.rst | 399 -------- ...ata_collection_and_export_to_NeXus_7_3.png | Bin 47062 -> 0 bytes ...ata_collection_and_export_to_NeXus_8_3.png | Bin 40455 -> 0 bytes ...ata_collection_and_export_to_NeXus_8_5.png | Bin 27235 -> 0 bytes poetry.lock | 397 ++++---- pyproject.toml | 3 +- 25 files changed, 327 insertions(+), 1855 deletions(-) delete mode 100644 .github/workflows/autodoc_tutorials.yml create mode 100644 .github/workflows/build_deploy_docs.yml delete mode 100644 docs/tutorial/1_binning_fake_data.rst delete mode 100644 docs/tutorial/1_binning_fake_data_files/1_binning_fake_data_13_0.png delete mode 100644 docs/tutorial/1_binning_fake_data_files/1_binning_fake_data_8_0.png delete mode 100644 docs/tutorial/2_conversion_pipeline_for_example_time-resolved_ARPES_data.rst delete mode 100644 docs/tutorial/2_conversion_pipeline_for_example_time-resolved_ARPES_data_files/2_conversion_pipeline_for_example_time-resolved_ARPES_data_10_3.png delete mode 100644 docs/tutorial/2_conversion_pipeline_for_example_time-resolved_ARPES_data_files/2_conversion_pipeline_for_example_time-resolved_ARPES_data_12_3.png delete mode 100644 docs/tutorial/2_conversion_pipeline_for_example_time-resolved_ARPES_data_files/2_conversion_pipeline_for_example_time-resolved_ARPES_data_18_2.png delete mode 100644 docs/tutorial/2_conversion_pipeline_for_example_time-resolved_ARPES_data_files/2_conversion_pipeline_for_example_time-resolved_ARPES_data_18_4.png delete mode 100644 docs/tutorial/2_conversion_pipeline_for_example_time-resolved_ARPES_data_files/2_conversion_pipeline_for_example_time-resolved_ARPES_data_22_2.png delete mode 100644 docs/tutorial/2_conversion_pipeline_for_example_time-resolved_ARPES_data_files/2_conversion_pipeline_for_example_time-resolved_ARPES_data_29_2.png delete mode 100644 docs/tutorial/2_conversion_pipeline_for_example_time-resolved_ARPES_data_files/2_conversion_pipeline_for_example_time-resolved_ARPES_data_38_2.png delete mode 100644 docs/tutorial/2_conversion_pipeline_for_example_time-resolved_ARPES_data_files/2_conversion_pipeline_for_example_time-resolved_ARPES_data_40_5.png delete mode 100644 docs/tutorial/2_conversion_pipeline_for_example_time-resolved_ARPES_data_files/2_conversion_pipeline_for_example_time-resolved_ARPES_data_52_1.png delete mode 100644 docs/tutorial/2_conversion_pipeline_for_example_time-resolved_ARPES_data_files/2_conversion_pipeline_for_example_time-resolved_ARPES_data_5_1.png delete mode 100644 docs/tutorial/3_metadata_collection_and_export_to_NeXus.rst delete mode 100644 docs/tutorial/3_metadata_collection_and_export_to_NeXus_files/3_metadata_collection_and_export_to_NeXus_7_3.png delete mode 100644 docs/tutorial/3_metadata_collection_and_export_to_NeXus_files/3_metadata_collection_and_export_to_NeXus_8_3.png delete mode 100644 docs/tutorial/3_metadata_collection_and_export_to_NeXus_files/3_metadata_collection_and_export_to_NeXus_8_5.png diff --git a/.github/workflows/autodoc_tutorials.yml b/.github/workflows/autodoc_tutorials.yml deleted file mode 100644 index aade7b32..00000000 --- a/.github/workflows/autodoc_tutorials.yml +++ /dev/null @@ -1,63 +0,0 @@ -name: autogenerate docs from tutorials -on: - # Triggers the workflow on push but only for the main branch - push: - branches: [ auto-doc-generation ] - paths: ['**.ipynb'] - -jobs: - docs-from-tutorials: - runs-on: ubuntu-latest - steps: - - name: Maximize build space - uses: easimon/maximize-build-space@master - with: - root-reserve-mb: 512 - swap-size-mb: 1024 - remove-dotnet: 'true' - remove-codeql: 'true' - remove-android: 'true' - remove-docker-images: 'true' - - # Check out repo and set up Python - - name: Check out the repository - uses: actions/checkout@v4 - with: - lfs: true - - # Use cached python and dependencies, install poetry - - name: "Setup Python, Poetry and Dependencies" - uses: packetcoders/action-setup-cache-python-poetry@main - with: - python-version: 3.8 - poetry-version: 1.2.2 - - - name: Install jupyter - run: poetry install -E notebook - - - name: Install pandoc - run: | - sudo wget https://github.com/jgm/pandoc/releases/download/3.1.8/pandoc-3.1.8-1-amd64.deb - sudo dpkg -i pandoc-3.1.8-1-amd64.deb - - # Execute and convert notebooks - - name: execute notebooks - run: | - cd $GITHUB_WORKSPACE/tutorial - curl -L --output ./WSe2.zip https://zenodo.org/record/6369728/files/WSe2.zip - unzip -d . -o ./WSe2.zip - find $GITHUB_WORKSPACE/tutorial -type f -name "*.ipynb" -exec poetry run jupyter-nbconvert --execute --to notebook --inplace {} \; - - - name: create docs/tutorial directory - run: mkdir -p $GITHUB_WORKSPACE/docs/tutorial - - - name: convert notebooks to rst and save to docs - run: find $GITHUB_WORKSPACE/tutorial -type f -name "*.ipynb" -exec poetry run jupyter-nbconvert --to rst --output-dir $GITHUB_WORKSPACE/docs/tutorial {} \; - # find tutorial -type f -name "*.ipynb" -exec poetry run jupyter-nbconvert --to rst --output-dir docs/tutorial {} \; - - - name: Commit changes - uses: EndBug/add-and-commit@v9 - with: - default_author: github_actions - message: 'Updating tutorial rsts for docs' - add: 'docs/tutorial/' diff --git a/.github/workflows/build_deploy_docs.yml b/.github/workflows/build_deploy_docs.yml new file mode 100644 index 00000000..487e4ae0 --- /dev/null +++ b/.github/workflows/build_deploy_docs.yml @@ -0,0 +1,97 @@ +name: build and deploy docs to pages +on: + # Triggers the workflow on push but only for the main branch + push: + branches: [ auto-doc-generation ] + # Allows you to run this workflow manually from the Actions tab + workflow_dispatch: + + # Sets permissions of the GITHUB_TOKEN to allow deployment to GitHub Pages +permissions: + contents: read + pages: write + id-token: write + +# Allow only one concurrent deployment, skipping runs queued between the run in-progress and latest queued. +# However, do NOT cancel in-progress runs as we want to allow these production deployments to complete. +concurrency: + group: "pages" + cancel-in-progress: false + +jobs: + build: + runs-on: ubuntu-latest + steps: + - name: Maximize build space + uses: easimon/maximize-build-space@master + with: + root-reserve-mb: 512 + swap-size-mb: 1024 + remove-dotnet: 'true' + remove-codeql: 'true' + remove-android: 'true' + remove-docker-images: 'true' + + # Check out repo and set up Python + - name: Check out the repository + uses: actions/checkout@v4 + with: + lfs: true + + # Use cached python and dependencies, install poetry + - name: "Setup Python, Poetry and Dependencies" + uses: packetcoders/action-setup-cache-python-poetry@main + with: + python-version: 3.8 + poetry-version: 1.2.2 + + - name: Install docs and notebook dependencies + run: poetry install -E notebook -E docs + + - name: Install pandoc + run: | + sudo wget https://github.com/jgm/pandoc/releases/download/3.1.8/pandoc-3.1.8-1-amd64.deb + sudo dpkg -i pandoc-3.1.8-1-amd64.deb + + - name: copy tutorial files to docs + run: | + cp -r $GITHUB_WORKSPACE/tutorial $GITHUB_WORKSPACE/docs/ + cp -r $GITHUB_WORKSPACE/sed/config $GITHUB_WORKSPACE/docs/sed + + # To be included later + # - name: Cache docs build + # id: cache-docs + # uses: actions/cache@v3 + # with: + # path: $GITHUB_WORKSPACE/_build + # key: ${{ runner.os }}-docs + + - name: download WSe2 data + # if: steps.cache-primes.outputs.cache-hit != 'true' + run: | + cd $GITHUB_WORKSPACE/docs/tutorial + curl -L --output ./WSe2.zip https://zenodo.org/record/6369728/files/WSe2.zip + unzip -o ./WSe2.zip -d . + + - name: build Sphinx docs + run: poetry run sphinx-build -b html $GITHUB_WORKSPACE/docs $GITHUB_WORKSPACE/_build + + - name: Setup Pages + uses: actions/configure-pages@v3 + + - name: Upload artifact + uses: actions/upload-pages-artifact@v2 + with: + path: '_build' + + # Deployment job + deploy: + environment: + name: github-pages + url: ${{ steps.deployment.outputs.page_url }} + runs-on: ubuntu-latest + needs: build + steps: + - name: Deploy to GitHub Pages + id: deployment + uses: actions/deploy-pages@v2 diff --git a/README.md b/README.md index 27a88b21..5ea5c0f6 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,5 @@ # sed -[![Documentation Status](https://readthedocs.org/projects/sed/badge/?version=latest)](https://sed.readthedocs.io/en/latest/?badge=latest) +[![Documentation Status](https://github.com/OpenCOMPES/sed/actions/workflows/build_deploy_docs.yml/badge.svg)](https://opencompes.github.io/sed/) ![](https://github.com/OpenCOMPES/sed/actions/workflows/linting.yml/badge.svg?branch=main) ![](https://github.com/OpenCOMPES/sed/actions/workflows/testing_multiversion.yml/badge.svg?branch=main) ![](https://img.shields.io/pypi/pyversions/sedprocessor) diff --git a/docs/conf.py b/docs/conf.py index fa9e0dc6..e1bc1bd0 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -10,12 +10,14 @@ # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. # +import tomlkit import os import sys sys.path.insert(0, os.path.abspath('..')) -import tomlkit # -- Project information ----------------------------------------------------- + + def _get_project_meta(): with open('../pyproject.toml') as pyproject: file_contents = pyproject.read() @@ -25,7 +27,7 @@ def _get_project_meta(): pkg_meta = _get_project_meta() project = str(pkg_meta['name']) -copyright = '2022, OpenCOMPES team' +copyright = '2022, OpenCOMPES team' author = 'OpenCOMPES team' # The short X.Y version @@ -38,9 +40,11 @@ def _get_project_meta(): # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. -extensions = ['sphinx_rtd_theme','sphinx.ext.autodoc','sphinx.ext.napoleon', -'sphinx.ext.todo','sphinx.ext.coverage','sphinx.ext.autosummary', -'sphinx.ext.coverage','sphinx_autodoc_typehints'] +extensions = ['sphinx_rtd_theme', 'sphinx.ext.autodoc', 'sphinx.ext.napoleon', + 'sphinx.ext.todo', 'sphinx.ext.coverage', 'sphinx.ext.autosummary', + 'sphinx.ext.coverage', 'sphinx_autodoc_typehints', "bokeh.sphinxext.bokeh_autodoc", + "bokeh.sphinxext.bokeh_plot", 'nbsphinx'] + autoclass_content = 'class' autodoc_member_order = 'bysource' @@ -61,7 +65,6 @@ def _get_project_meta(): } - # Set `typing.TYPE_CHECKING` to `True`: # https://pypi.org/project/sphinx-autodoc-typehints/ napoleon_use_param = True @@ -90,4 +93,4 @@ def _get_project_meta(): # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". -html_static_path = ['_static'] \ No newline at end of file +html_static_path = ['_static'] diff --git a/docs/index.rst b/docs/index.rst index 396e7c27..6d8755b6 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -21,9 +21,9 @@ Single-Event DataFrame (SED) documentation :numbered: :caption: Examples - tutorial/1_binning_fake_data - tutorial/2_conversion_pipeline_for_example_time-resolved_ARPES_data - tutorial/3_metadata_collection_and_export_to_NeXus + tutorial/1_binning_fake_data.ipynb + tutorial/2_conversion_pipeline_for_example_time-resolved_ARPES_data.ipynb + tutorial/3_metadata_collection_and_export_to_NeXus.ipynb .. toctree:: :maxdepth: 2 diff --git a/docs/tutorial/1_binning_fake_data.rst b/docs/tutorial/1_binning_fake_data.rst deleted file mode 100644 index 1fadd8a2..00000000 --- a/docs/tutorial/1_binning_fake_data.rst +++ /dev/null @@ -1,302 +0,0 @@ -Binning demonstration on locally generated fake data -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -In this example, we generate a table with random data simulating a -single event dataset. We showcase the binning method, first on a simple -single table using the bin_partition method and then in the distributed -mehthod bin_dataframe, using daks dataframes. The first method is never -really called directly, as it is simply the function called by the -bin_dataframe on each partition of the dask dataframe. - -.. code:: ipython3 - - import sys - - import dask - import numpy as np - import pandas as pd - import dask.dataframe - - import matplotlib.pyplot as plt - - sys.path.append("../") - from sed.binning import bin_partition, bin_dataframe - -Generate Fake Data ------------------- - -.. code:: ipython3 - - n_pts = 100000 - cols = ["posx", "posy", "energy"] - df = pd.DataFrame(np.random.randn(n_pts, len(cols)), columns=cols) - df - - - - -.. raw:: html - -
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
posxposyenergy
0-0.892244-1.4597830.311661
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20.597229-0.429607-0.370872
3-0.481183-0.139158-0.314332
41.105455-0.1892310.412213
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999980.345514-0.203688-0.598167
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-

100000 rows × 3 columns

-
- - - -Define the binning range ------------------------- - -.. code:: ipython3 - - binAxes = ["posx", "posy", "energy"] - nBins = [120, 120, 120] - binRanges = [(-2, 2), (-2, 2), (-2, 2)] - coords = {ax: np.linspace(r[0], r[1], n) for ax, r, n in zip(binAxes, binRanges, nBins)} - -Compute the binning along the pandas dataframe ----------------------------------------------- - -.. code:: ipython3 - - %%time - res = bin_partition( - part=df, - bins=nBins, - axes=binAxes, - ranges=binRanges, - hist_mode="numba", - ) - - -.. parsed-literal:: - - CPU times: user 1.32 s, sys: 31.5 ms, total: 1.35 s - Wall time: 1.39 s - - -.. code:: ipython3 - - fig, axs = plt.subplots(1, 3, figsize=(8, 2.5), constrained_layout=True) - for i in range(3): - axs[i].imshow(res.sum(i)) - - - -.. image:: 1_binning_fake_data_files/1_binning_fake_data_8_0.png - - -Transform to dask dataframe ---------------------------- - -.. code:: ipython3 - - ddf = dask.dataframe.from_pandas(df, npartitions=50) - ddf - - - - -.. raw:: html - -
Dask DataFrame Structure:
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npartitions=50
0float64float64float64
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Dask Name: from_pandas, 1 graph layer
- - - -compute distributed binning on the partitioned dask dataframe -------------------------------------------------------------- - -In this example, the small dataset does not give significant improvement -over the pandas implementation, at least using this number of -partitions. A single partition would be faster (you can try…) but we use -multiple for demonstration purpouses. - -.. code:: ipython3 - - %%time - res = bin_dataframe( - df=ddf, - bins=nBins, - axes=binAxes, - ranges=binRanges, - hist_mode="numba", - ) - - - -.. parsed-literal:: - - 0%| | 0/50 [00:00>`cM^QmW69htH1A!1w&{2UszwJHvfCph$SzXun z4j)~iX3mx%RWnydTL)KL8*>_WOJ^4w2YWtFK~6q)8f#ZqM;8$;F1!E#08R&IE3R<~ z>3v`j3`Ye$7Z3=~?B5NcCeg`sdx;;_oNlVPI8;V-RS#>Cky@2bzAPCNMCzgPP> zZn3qkRiL<4OTrd8P-`>XU>%j9pqL~Po^(d1fN0Ghj#~4u+I|c3uV{pIxL=|$M#_di zlxs#rcX)T+&VTk@hv&Cc9npo;1fl=W-4-L(^7hz zkqKn*|G&FFRkCi2*Z*g<78d%nivN9#48EWxa$@rT8GY;9SJOJe{}~5(wvv?JxsG<0 zh&||3)6jrC!=KMQ^582Zxze-Q#>qY^w4r(uvbUS$9m6IyU<=UnZl{tKll!d z=oi3eC?k7r?Z(hRke%xL(8M7tnu*E9#jmk(abfFWz&9q|o5&sP>guxeubw}8xtQHG z^68;%d1&u4EjPFGAYoK3nnoRXt85&T{W!X_EWV(DkA_P>#~VE!RU*(Cca8-Ag#9-I zPH9Dk{q+;)&|mX}P7R|Z))7LWyOfB@OW{AwkG|r$!5x}scFM={#+^?4?oCfv)h4hQ zf)7WPFMRz|WA&ag(O+i}EFn{J)CTs2ScHCu14E|puJ|b%a 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- - - -.. code:: ipython3 - - # The time elapsed in the scan - sp.loader.get_elapsed_time() - - - - -.. parsed-literal:: - - 2588.4949999999994 - - - -.. code:: ipython3 - - # Apply jittering to X, Y, t, ADC columns. - # Columns are defined in the config, or can be provided as list. - sp.add_jitter() - -.. code:: ipython3 - - # Inspect data in dataframe Columns: - # axes = ['X', 'Y', 't', 'ADC'] - # bins = [100, 100, 100, 100] - # ranges = [(0, 1800), (0, 1800), (130000, 140000), (0, 9000)] - # sp.viewEventHistogram(dfpid=1, axes=axes, bins=bins, ranges=ranges) - sp.view_event_histogram(dfpid=2) - - - - - -.. raw:: html - - -
- - - - - -Distortion correction and Momentum Calibration workflow -------------------------------------------------------- - -Distortion correction -~~~~~~~~~~~~~~~~~~~~~ - -1. step: -^^^^^^^^ - -Bin and load part of the dataframe in detector coordinates, and choose -energy plane where high-symmetry points can well be identified. Either -use the interactive tool, or pre-select the range: - -.. code:: ipython3 - - #sp.bin_and_load_momentum_calibration(df_partitions=20, plane=170) - sp.bin_and_load_momentum_calibration(df_partitions=100, plane=33, width=10, apply=True) - - - -.. parsed-literal:: - - 0%| | 0/100 [00:00 -
- Figure -
- - - - - -2. Step: -^^^^^^^^ - -Next, we select a number of features corresponding to the rotational -symmetry of the material, plus the center. These can either be -auto-detected (for well-isolated points), or provided as a list (these -can be read-off the graph in the cell above). These are then symmetrized -according to the rotational symmetry, and a spline-warping correction -for the x/y coordinates is calculated, which corrects for any geometric -distortions from the perfect n-fold rotational symmetry. - -.. code:: ipython3 - - #features = np.array([[203.2, 341.96], [299.16, 345.32], [350.25, 243.70], [304.38, 149.88], [199.52, 152.48], [154.28, 242.27], [248.29, 248.62]]) - #sp.define_features(features=features, rotation_symmetry=6, include_center=True, apply=True) - # Manual selection: Use a GUI tool to select peaks: - #sp.define_features(rotation_symmetry=6, include_center=True) - #sp.generate_splinewarp(rotation_symmetry=6, include_center=True, fwhm=10, sigma=12, sigma_radius=4) - # Autodetect: Uses the DAOStarFinder routine to locate maxima. - # Parameters are: - # fwhm: Full-width at half maximum of peaks. - # sigma: Number of standard deviations above the mean value of the image peaks must have. - # sigma_radius: number of standard deviations around a peak that peaks are fitted - sp.define_features(rotation_symmetry=6, auto_detect=True, include_center=True, fwhm=10, sigma=12, sigma_radius=4, apply=True) - - - -.. parsed-literal:: - - interactive(children=(Dropdown(description='Point:', options=(0, 1, 2, 3, 4, 5, 6), value=0), Output()), _dom_… - - - -.. parsed-literal:: - - interactive(children=(FloatText(value=153.4099341149909, description='point_x'), FloatText(value=243.056397194… - - - -.. parsed-literal:: - - Button(description='apply', style=ButtonStyle()) - - - -.. raw:: html - - -
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- - - -3. Step: -^^^^^^^^ - -Generate nonlinear correction using splinewarp algorithm. If no -landmarks have been defined in previous step, default parameters from -the config are used - -.. code:: ipython3 - - # Option whether a central point shall be fixed in the determiantion fo the correction - sp.generate_splinewarp(include_center=True) - - -.. parsed-literal:: - - Calulated thin spline correction based on the following landmarks: - pouter: [[153.40993411 243.05639719] - [199.53872932 152.7795494 ] - [203.00074516 342.99054667] - [299.87814564 346.19500548] - [305.62593678 150.19077869] - [350.95039536 244.78353201]] - pcent: (249.22957768646916, 249.24792658340425) - Original slice with reference features - - - - - -.. raw:: html - - -
- - - - - -.. parsed-literal:: - - Corrected slice with target features - - - - - -.. raw:: html - - -
- - - - - -.. parsed-literal:: - - Original slice with target features - - - - - -.. raw:: html - - -
- - - - - -Optional (Step 3a): -^^^^^^^^^^^^^^^^^^^ - -Save distortion correction parameters to configuration file in current -data folder: - -.. code:: ipython3 - - # Save generated distortion correction parameters for later reuse - sp.save_splinewarp() - -4. Step: -^^^^^^^^ - -To adjust scaling, position and orientation of the corrected momentum -space image, you can apply further affine transformations to the -distortion correction field. Here, first a postential scaling is -applied, next a translation, and finally a rotation around the center of -the image (defined via the config). One can either use an interactive -tool, or provide the adjusted values and apply them directly. - -.. code:: ipython3 - - #sp.pose_adjustment(xtrans=14, ytrans=18, angle=2) - sp.pose_adjustment(xtrans=8, ytrans=7, angle=-4, apply=True) - - - -.. parsed-literal:: - - interactive(children=(FloatSlider(value=1.0, description='scale', max=1.2, min=0.8, step=0.01), FloatSlider(va… - - - -.. parsed-literal:: - - Button(description='apply', style=ButtonStyle()) - - - -.. raw:: html - - -
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- - - -5. Step: -^^^^^^^^ - -Finally, the momentum correction is applied to the dataframe, and -corresponding meta data are stored - -.. code:: ipython3 - - sp.apply_momentum_correction() - - -.. parsed-literal:: - - Adding corrected X/Y columns to dataframe: - Calculating inverse deformation field, this might take a moment... - Dask DataFrame Structure: - X Y t ADC Xm Ym - npartitions=100 - float64 float64 float64 float64 float64 float64 - ... ... ... ... ... ... - ... ... ... ... ... ... ... - ... ... ... ... ... ... - ... ... ... ... ... ... - Dask Name: apply_dfield, 206 graph layers - - -Momentum calibration workflow -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -1. Step: -^^^^^^^^ - -First, the momentum scaling needs to be calibtrated. Either, one can -provide the coordinates of one point outside the center, and provide its -distane to the Brillouin zone center (which is assumed to be located in -the center of the image), one can specify two points on the image and -their distance (where the 2nd point marks the BZ center),or one can -provide absolute k-coordinates of two distinct momentum points. - -If no points are provided, an interactive tool is created. Here, left -mouse click selectes the off-center point (brillouin_zone_cetnered=True) -or toggle-selects the off-center and center point. - -.. code:: ipython3 - - k_distance = 4/3*np.pi/3.28 - #sp.calibrate_momentum_axes(k_distance = k_distance) - point_a = [308, 345] - sp.calibrate_momentum_axes(point_a=point_a, k_distance = k_distance, apply=True) - #point_b = [247, 249] - #sp.calibrate_momentum_axes(point_a=point_a, point_b = point_b, k_coord_a = [.5, 1.1], k_coord_b = [1.3, 0], equiscale=False - - - -.. parsed-literal:: - - interactive(children=(IntText(value=308, description='point_a_x'), IntText(value=345, description='point_a_y')… - - - -.. parsed-literal:: - - Button(description='apply', style=ButtonStyle()) - - - -.. raw:: html - - -
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- - - -Optional (Step 1a): -''''''''''''''''''' - -Save momentum calibration parameters to configuration file in current -data folder: - -.. code:: ipython3 - - # Save generated momentum calibration parameters for later reuse - sp.save_momentum_calibration() - -2. Step: -^^^^^^^^ - -Now, the distortion correction and momentum calibration needs to be -applied to the dataframe. - -.. code:: ipython3 - - sp.apply_momentum_calibration() - - -.. parsed-literal:: - - Adding kx/ky columns to dataframe: - Dask DataFrame Structure: - X Y t ADC Xm Ym kx ky - npartitions=100 - float64 float64 float64 float64 float64 float64 float64 float64 - ... ... ... ... ... ... ... ... - ... ... ... ... ... ... ... ... ... - ... ... ... ... ... ... ... ... - ... ... ... ... ... ... ... ... - Dask Name: assign, 216 graph layers - - -Energy Correction (optional) ----------------------------- - -The purpose of the energy correction is to correct for any -momentum-dependent distortion of the energy axis, e.g. from geometric -effects in the flight tube, or from space charge - -1st step: -^^^^^^^^^ - -Here, one can select the functional form to be used, and adjust its -parameters. The binned data used for the momentum calibration is plotted -around the Fermi energy (defined by tof_fermi), and the correction -function is plotted ontop. Possible correction functions are: “sperical” -(parameter: diameter), “Lorentzian” (parameter: gamma), “Gaussian” -(parameter: sigma), and “Lorentzian_asymmetric” (parameters: gamma, -amplitude2, gamma2). - -One can either use an interactive alignment tool, or provide parameters -directly. - -.. code:: ipython3 - - #sp.adjust_energy_correction(amplitude=2.5, center=(730, 730), gamma=920, tof_fermi = 66200) - sp.adjust_energy_correction(amplitude=2.5, center=(730, 730), gamma=920, tof_fermi = 66200, apply=True) - - - -.. parsed-literal:: - - interactive(children=(FloatSlider(value=2.5, description='amplitude', max=10.0), FloatSlider(value=730.0, desc… - - - -.. parsed-literal:: - - Button(description='apply', style=ButtonStyle()) - - - -.. raw:: html - - -
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- - - -Optional (Step 1a): -''''''''''''''''''' - -Save energy correction parameters to configuration file in current data -folder: - -.. code:: ipython3 - - # Save generated energy correction parameters for later reuse - sp.save_energy_correction() - -2. Step -^^^^^^^ - -After adjustment, the energy correction is directly applied to the TOF -axis. - -.. code:: ipython3 - - sp.apply_energy_correction() - - -.. parsed-literal:: - - Applying energy correction to dataframe... - Dask DataFrame Structure: - X Y t ADC Xm Ym kx ky tm - npartitions=100 - float64 float64 float64 float64 float64 float64 float64 float64 float64 - ... ... ... ... ... ... ... ... ... - ... ... ... ... ... ... ... ... ... ... - ... ... ... ... ... ... ... ... ... - ... ... ... ... ... ... ... ... ... - Dask Name: assign, 230 graph layers - - -3. Energy calibration ---------------------- - -For calibrating the energy axis, a set of data taken at different bias -voltages around the value where the measurement was taken is required. - -1. Step: -^^^^^^^^ - -In a first step, the data are loaded, binned along the TOF dimension, -and normalized. The used bias voltages can be either provided, or read -from attributes in the source files if present. - -.. code:: ipython3 - - # Load energy calibration EDCs - energycalfolder = data_path + "/energycal_2019_01_08/" - scans = np.arange(1,12) - voltages = np.arange(12,23,1) - files = [energycalfolder + r'Scan' + str(num).zfill(3) + '_' + str(num+11) + '.h5' for num in scans] - sp.load_bias_series(data_files=files, normalize=True, biases=voltages, ranges=[(64000, 75000)]) - - - -.. parsed-literal:: - - 0%| | 0/11 [00:00 - - - - - -2. Step: -^^^^^^^^ - -Next, the same peak or feature needs to be selected in each curve. For -this, one needs to define “ranges” for each curve, within which the peak -of interest is located. One can either provide these ranges manually, or -provide one range for a “reference” curve, and infer the ranges for the -other curves using a dynamic time warping algorithm. - -.. code:: ipython3 - - # Option 1 = specify the ranges containing a common feature (e.g an equivalent peak) for all bias scans - # rg = [(129031.03103103103, 129621.62162162163), (129541.54154154155, 130142.14214214214), (130062.06206206206, 130662.66266266267), (130612.61261261262, 131213.21321321322), (131203.20320320321, 131803.8038038038), (131793.7937937938, 132384.38438438438), (132434.43443443443, 133045.04504504506), (133105.10510510512, 133715.71571571572), (133805.8058058058, 134436.43643643643), (134546.54654654654, 135197.1971971972)] - # sp.find_bias_peaks(ranges=rg, infer_others=False) - # Option 2 = specify the range for one curve and infer the others - # This will open an interactive tool to select the correct ranges for the curves. - # IMPORTANT: Don't choose the range too narrow about a peak, and choose a refid - # somewhere in the middle or towards larger biases! - rg = (66100, 67000) - sp.find_bias_peaks(ranges=rg, ref_id=5, infer_others=True, apply=True) - - - -.. parsed-literal:: - - interactive(children=(IntSlider(value=5, description='refid', max=10), IntRangeSlider(value=(66100, 67000), de… - - - -.. parsed-literal:: - - Button(description='apply', style=ButtonStyle()) - - - -.. raw:: html - - -
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- Figure -
- -
- - - -3. Step: -^^^^^^^^ - -Next, the detected peak positions and bias voltages are used to -determine the calibration function. This can be either done by fitting -the functional form d\ :sup:`2/(t-t0)`\ 2 via lmfit (“lmfit”), or using -a polynomial approxiamtion (“lstsq” or “lsqr”). Here, one can also -define a reference id, and a reference energy. Those define the absolute -energy position of the feature used for calibration in the “reference” -trace, at the bias voltage where the final measurement has been -performed. The energy scale can be either “kientic” (decreasing energy -with increasing TOF), or “binding” (increasing energy with increasing -TOF). - -After calculating the calibration, all traces corrected with the -calibration are plotted ontop of each other, the calibration function -together with the extracted features is plotted. - -.. code:: ipython3 - - # use the refid of the bias that the measurement was taken at - # Eref can be used to set the absolute energy (kinetic energy, E-EF) of the feature used for energy calibration (if known) - refid=4 - Eref=-0.5 - # the lmfit method uses a fit of (d/(t-t0))**2 to determine the energy calibration - sp.calibrate_energy_axis(ref_energy=Eref, ref_id=refid, energy_scale="kinetic", method="lmfit") - - -.. parsed-literal:: - - [[Fit Statistics]] - # fitting method = leastsq - # function evals = 130 - # data points = 11 - # variables = 3 - chi-square = 0.00212334 - reduced chi-square = 2.6542e-04 - Akaike info crit = -88.0792774 - Bayesian info crit = -86.8855916 - [[Variables]] - d: 1.09544520 +/- 0.03592430 (3.28%) (init = 1) - t0: 7.6073e-07 +/- 7.4247e-09 (0.98%) (init = 1e-06) - E0: -46.6134126 +/- 0.64226365 (1.38%) (init = -22) - [[Correlations]] (unreported correlations are < 0.100) - C(d, t0) = -0.9997 - C(d, E0) = -0.9988 - C(t0, E0) = +0.9974 - Quality of Calibration: - - - - - -.. raw:: html - - -
- - - - - -.. parsed-literal:: - - E/TOF relationship: - - - -.. raw:: html - - -
-
- Figure -
- -
- - - -Optional (Step 3a): -''''''''''''''''''' - -Save energy calibration parameters to configuration file in current data -folder: - -.. code:: ipython3 - - # Save generated energy calibration parameters for later reuse - sp.save_energy_calibration() - -4. Step: -^^^^^^^^ - -Finally, the the energy axis is added to the dataframe. - -.. code:: ipython3 - - sp.append_energy_axis() - - -.. parsed-literal:: - - Adding energy column to dataframe: - Dask DataFrame Structure: - X Y t ADC Xm Ym kx ky tm energy - npartitions=100 - float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 - ... ... ... ... ... ... ... ... ... ... - ... ... ... ... ... ... ... ... ... ... ... - ... ... ... ... ... ... ... ... ... ... - ... ... ... ... ... ... ... ... ... ... - Dask Name: assign, 240 graph layers - - -4. Delay calibration: ---------------------- - -The delay axis is calculated from the ADC input column based on the -provided delay range. ALternatively, the delay scan range can also be -extracted from attributes inside a source file, if present. - -.. code:: ipython3 - - #from pathlib import Path - #datafile = "file.h5" - #print(datafile) - #sp.calibrate_delay_axis(datafile=datafile) - delay_range = (-500, 1500) - sp.calibrate_delay_axis(delay_range=delay_range, preview=True) - - -.. parsed-literal:: - - Adding delay column to dataframe: - X Y t ADC Xm \ - 0 0.212945 0.212945 0.212945 0.212945 -12.966236 - 1 364.881143 1001.881143 70100.881143 6316.881143 354.714506 - 2 761.446814 818.446814 75615.446814 6316.446814 790.350068 - 3 692.455863 971.455863 66455.455863 6317.455863 712.673503 - 4 670.565557 711.565557 73025.565557 6316.565557 695.169961 - 5 299.434403 1164.434403 68459.434403 6316.434403 282.149004 - 6 570.680891 664.680891 73902.680891 6315.680891 588.507368 - 7 822.115300 545.115300 72632.115300 6318.115300 846.594377 - 8 817.568558 415.568558 72421.568558 6316.568558 836.477911 - 9 1005.829423 666.829423 72801.829423 6316.829423 1037.903575 - - Ym kx ky tm energy delay - 0 87.166832 -2.417453 -2.106699 -47.985445 -25.209509 -660.265673 - 1 1030.009193 -1.276388 0.819329 70083.868621 -9.300472 1471.943002 - 2 838.353388 0.075568 0.224542 75614.555583 -16.703309 1471.796393 - 3 983.456676 -0.165494 0.674858 66449.755902 -0.820206 1472.137000 - 4 739.476304 -0.219815 -0.082314 73025.171634 -13.802182 1471.836475 - 5 1183.226206 -1.501589 1.294825 68432.920826 -5.958803 1471.792204 - 6 699.539735 -0.550833 -0.206254 73899.753463 -14.873129 1471.537854 - 7 585.834730 0.250118 -0.559127 72627.964178 -13.280445 1472.359595 - 8 465.231161 0.218722 -0.933409 72411.898419 -12.986664 1471.837488 - 9 707.647623 0.843829 -0.181091 72794.353709 -13.501836 1471.925543 - - -5. Visualization of calibrated histograms ------------------------------------------ - -With all calibrated axes present in the dataframe, we can visualize the -corresponding histograms, and determine the respective binning ranges - -.. code:: ipython3 - - axes = ['kx', 'ky', 'energy', 'delay'] - ranges = [[-3, 3], [-3, 3], [-6, 2], [-600, 1600]] - sp.view_event_histogram(dfpid=1, axes=axes, ranges=ranges) - - - - - -.. raw:: html - - -
- - - - - -Define the binning ranges and compute calibrated data volume ------------------------------------------------------------- - -.. code:: ipython3 - - axes = ['kx', 'ky', 'energy', 'delay'] - bins = [100, 100, 200, 50] - ranges = [[-2, 2], [-2, 2], [-4, 2], [-600, 1600]] - res = sp.compute(bins=bins, axes=axes, ranges=ranges) - - - -.. parsed-literal:: - - 0%| | 0/100 [00:00 - - - - -.. raw:: html - - -
-
- Figure -
- -
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These data and the corresponding (machine and -processing) metadata are then stored to a NeXus file following the -NXmpes NeXus standard -(https://fairmat-experimental.github.io/nexus-fairmat-proposal/9636feecb79bb32b828b1a9804269573256d7696/classes/contributed_definitions/NXmpes.html#nxmpes) -using the ‘dataconverter’ of the pynxtools package -(https://github.com/FAIRmat-NFDI/pynxtools). - -.. code:: ipython3 - - %load_ext autoreload - %autoreload 2 - import numpy as np - import matplotlib.pyplot as plt - import os - - import sed - - %matplotlib widget - -Load Data ---------- - -.. code:: ipython3 - - data_path = '.' # Put in Path to a storage of at least 20 Gbyte free space. - if not os.path.exists(data_path + "/WSe2.zip"): - os.system(f"curl -L --output {data_path}/WSe2.zip https://zenodo.org/record/6369728/files/WSe2.zip") - if not os.path.isdir(data_path + "/Scan049_1") or not os.path.isdir(data_path + "/energycal_2019_01_08/"): - os.system(f"unzip -d {data_path} -o {data_path}/WSe2.zip") - -.. code:: ipython3 - - metadata = {} - # manual Meta data. These should ideally come from an Electronic Lab Notebook. - #General - metadata['experiment_summary'] = 'WSe2 XUV NIR pump probe data.' - metadata['entry_title'] = 'Valence Band Dynamics - 800 nm linear s-polarized pump, 0.6 mJ/cm2 absorbed fluence' - metadata['experiment_title'] = 'Valence band dynamics of 2H-WSe2' - - #User - # Fill general parameters of NXuser - # TODO: discuss how to deal with multiple users? - metadata['user0'] = {} - metadata['user0']['name'] = 'Julian Maklar' - metadata['user0']['role'] = 'Principal Investigator' - metadata['user0']['affiliation'] = 'Fritz Haber Institute of the Max Planck Society' - metadata['user0']['address'] = 'Faradayweg 4-6, 14195 Berlin' - metadata['user0']['email'] = 'maklar@fhi-berlin.mpg.de' - - #NXinstrument - metadata['instrument'] = {} - #analyzer - metadata['instrument']['analyzer']={} - metadata['instrument']['analyzer']['slow_axes'] = "delay" # the scanned axes - metadata['instrument']['analyzer']['spatial_resolution'] = 10. - metadata['instrument']['analyzer']['energy_resolution'] = 110. - metadata['instrument']['analyzer']['momentum_resolution'] = 0.08 - metadata['instrument']['analyzer']['working_distance'] = 4. - metadata['instrument']['analyzer']['lens_mode'] = "6kV_kmodem4.0_30VTOF.sav" - - #probe beam - metadata['instrument']['beam']={} - metadata['instrument']['beam']['probe']={} - metadata['instrument']['beam']['probe']['incident_energy'] = 21.7 - metadata['instrument']['beam']['probe']['incident_energy_spread'] = 0.11 - metadata['instrument']['beam']['probe']['pulse_duration'] = 20. - metadata['instrument']['beam']['probe']['frequency'] = 500. - metadata['instrument']['beam']['probe']['incident_polarization'] = [1, 1, 0, 0] # p pol Stokes vector - metadata['instrument']['beam']['probe']['extent'] = [80., 80.] - #pump beam - metadata['instrument']['beam']['pump']={} - metadata['instrument']['beam']['pump']['incident_energy'] = 1.55 - metadata['instrument']['beam']['pump']['incident_energy_spread'] = 0.08 - metadata['instrument']['beam']['pump']['pulse_duration'] = 35. - metadata['instrument']['beam']['pump']['frequency'] = 500. - metadata['instrument']['beam']['pump']['incident_polarization'] = [1, -1, 0, 0] # s pol Stokes vector - metadata['instrument']['beam']['pump']['incident_wavelength'] = 800. - metadata['instrument']['beam']['pump']['average_power'] = 300. - metadata['instrument']['beam']['pump']['pulse_energy'] = metadata['instrument']['beam']['pump']['average_power']/metadata['instrument']['beam']['pump']['frequency']#µJ - metadata['instrument']['beam']['pump']['extent'] = [230., 265.] - metadata['instrument']['beam']['pump']['fluence'] = 0.15 - - #sample - metadata['sample']={} - metadata['sample']['preparation_date'] = '2019-01-13T10:00:00+00:00' - metadata['sample']['preparation_description'] = 'Cleaved' - metadata['sample']['sample_history'] = 'Cleaved' - metadata['sample']['chemical_formula'] = 'WSe2' - metadata['sample']['description'] = 'Sample' - metadata['sample']['name'] = 'WSe2 Single Crystal' - - metadata['file'] = {} - metadata['file']["trARPES:Carving:TEMP_RBV"] = 300. - metadata['file']["trARPES:XGS600:PressureAC:P_RD"] = 5.e-11 - metadata['file']["KTOF:Lens:Extr:I"] = -0.12877 - metadata['file']["KTOF:Lens:UDLD:V"] = 399.99905 - metadata['file']["KTOF:Lens:Sample:V"] = 17.19976 - metadata['file']["KTOF:Apertures:m1.RBV"] = 3.729931 - metadata['file']["KTOF:Apertures:m2.RBV"] = -5.200078 - metadata['file']["KTOF:Apertures:m3.RBV"] = -11.000425 - - # Sample motor positions - metadata['file']['trARPES:Carving:TRX.RBV'] = 7.1900000000000004 - metadata['file']['trARPES:Carving:TRY.RBV'] = -6.1700200225439552 - metadata['file']['trARPES:Carving:TRZ.RBV'] = 33.4501953125 - metadata['file']['trARPES:Carving:THT.RBV'] = 423.30500940561586 - metadata['file']['trARPES:Carving:PHI.RBV'] = 0.99931647456264949 - metadata['file']['trARPES:Carving:OMG.RBV'] = 11.002500171914066 - -.. code:: ipython3 - - # The Scan directory - fdir = data_path + '/Scan049_1' - # create sed processor using the config file, and collect the meta data from the files: - sp = sed.SedProcessor(folder=fdir, config="../sed/config/mpes_example_config.yaml", metadata=metadata, collect_metadata=True) - - -.. parsed-literal:: - - Configuration loaded from: [/home/runner/work/sed/sed/sed/config/mpes_example_config.yaml] - Folder config loaded from: [/home/runner/work/sed/sed/tutorial/sed_config.yaml] - Default config loaded from: [/home/runner/work/sed/sed/sed/config/default.yaml] - Gathering metadata from different locations - Collecting time stamps... - Collecting file metadata... - Collecting data from the EPICS archive... - - -.. code:: ipython3 - - # Apply jittering to X, Y, t, ADC columns. - sp.add_jitter() - -.. code:: ipython3 - - # Calculate machine-coordinate data for pose adjustment - sp.bin_and_load_momentum_calibration(df_partitions=10, plane=33, width=10, apply=True) - - - -.. parsed-literal:: - - 0%| | 0/10 [00:00 -

- Figure -
- - - - - -.. code:: ipython3 - - # Adjust pose alignment, using stored distortion correction - sp.pose_adjustment(xtrans=8, ytrans=7, angle=-4, apply=True, use_correction=True) - - -.. parsed-literal:: - - Calulated thin spline correction based on the following landmarks: - pouter: [[203.2 341.96] - [299.16 345.32] - [350.25 243.7 ] - [304.38 149.88] - [199.52 152.48] - [154.28 242.27]] - pcent: (248.29, 248.62) - - - -.. parsed-literal:: - - interactive(children=(FloatSlider(value=1.0, description='scale', max=1.2, min=0.8, step=0.01), FloatSlider(va… - - - -.. parsed-literal:: - - Button(description='apply', style=ButtonStyle()) - - - -.. raw:: html - - -
-
- Figure -
- -
- - - - -.. parsed-literal:: - - Output() - - - -.. raw:: html - - -
-
- Figure -
- -
- - - -.. code:: ipython3 - - # Apply stored momentum correction - sp.apply_momentum_correction() - - -.. parsed-literal:: - - Adding corrected X/Y columns to dataframe: - Calculating inverse deformation field, this might take a moment... - Dask DataFrame Structure: - X Y t ADC Xm Ym - npartitions=100 - float64 float64 float64 float64 float64 float64 - ... ... ... ... ... ... - ... ... ... ... ... ... ... - ... ... ... ... ... ... - ... ... ... ... ... ... - Dask Name: apply_dfield, 206 graph layers - - -.. code:: ipython3 - - # Apply stored config momentum calibration - sp.apply_momentum_calibration() - - -.. parsed-literal:: - - Adding kx/ky columns to dataframe: - Dask DataFrame Structure: - X Y t ADC Xm Ym kx ky - npartitions=100 - float64 float64 float64 float64 float64 float64 float64 float64 - ... ... ... ... ... ... ... ... - ... ... ... ... ... ... ... ... ... - ... ... ... ... ... ... ... ... - ... ... ... ... ... ... ... ... - Dask Name: assign, 216 graph layers - - -.. code:: ipython3 - - # Apply stored config energy correction - sp.apply_energy_correction() - - -.. parsed-literal:: - - Applying energy correction to dataframe... - Dask DataFrame Structure: - X Y t ADC Xm Ym kx ky tm - npartitions=100 - float64 float64 float64 float64 float64 float64 float64 float64 float64 - ... ... ... ... ... ... ... ... ... - ... ... ... ... ... ... ... ... ... ... - ... ... ... ... ... ... ... ... ... - ... ... ... ... ... ... ... ... ... - Dask Name: assign, 230 graph layers - - -.. code:: ipython3 - - # Apply stored config energy calibration - sp.append_energy_axis() - - -.. parsed-literal:: - - Adding energy column to dataframe: - Dask DataFrame Structure: - X Y t ADC Xm Ym kx ky tm energy - npartitions=100 - float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 - ... ... ... ... ... ... ... ... ... ... - ... ... ... ... ... ... ... ... ... ... ... - ... ... ... ... ... ... ... ... ... ... - ... ... ... ... ... ... ... ... ... ... - Dask Name: assign, 240 graph layers - - -.. code:: ipython3 - - # Apply delay calibration - delay_range = (-500, 1500) - sp.calibrate_delay_axis(delay_range=delay_range, preview=True) - - -.. parsed-literal:: - - Adding delay column to dataframe: - X Y t ADC Xm \ - 0 0.304069 0.304069 0.304069 0.304069 -23.287495 - 1 364.575699 1001.575699 70100.575699 6316.575699 352.397573 - 2 761.340241 818.340241 75615.340241 6316.340241 790.823530 - 3 692.158208 971.158208 66455.158208 6317.158208 713.107004 - 4 671.361639 712.361639 73026.361639 6317.361639 695.964200 - 5 298.818915 1163.818915 68458.818915 6315.818915 279.973019 - 6 571.053798 665.053798 73903.053798 6316.053798 587.429179 - 7 822.155640 545.155640 72632.155640 6318.155640 845.225397 - 8 818.261624 416.261624 72422.261624 6317.261624 834.627119 - 9 1006.253903 667.253903 72802.253903 6317.253903 1036.080260 - - Ym kx ky tm energy delay - 0 96.990568 -2.450894 -2.078351 -47.888991 -25.060130 -660.234913 - 1 1032.180586 -1.287266 0.818260 70083.559408 -9.287393 1471.839898 - 2 837.298641 0.070692 0.214642 75614.451566 -16.576959 1471.760419 - 3 982.064607 -0.170023 0.663033 66449.469058 -0.845837 1472.036526 - 4 740.392674 -0.223120 -0.085510 73025.980144 -13.731258 1472.105195 - 5 1184.982772 -1.511590 1.291542 68432.305535 -5.971073 1471.584444 - 6 701.331526 -0.559291 -0.206496 73900.142328 -14.783116 1471.663729 - 7 585.869445 0.239194 -0.564122 72628.005210 -13.216942 1472.373212 - 8 466.420982 0.206368 -0.934096 72412.616768 -12.928625 1472.071434 - 9 706.827017 0.830338 -0.189474 72794.762764 -13.435300 1472.068828 - - -Compute final data volume -------------------------- - -.. code:: ipython3 - - axes = ['kx', 'ky', 'energy', 'delay'] - bins = [100, 100, 200, 50] - ranges = [[-2, 2], [-2, 2], [-4, 2], [-600, 1600]] - res = sp.compute(bins=bins, axes=axes, ranges=ranges) - - - -.. parsed-literal:: - - 0%| | 0/100 [00:00 - - - diff --git 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{file = "PyCifRW-4.4.5-cp311-cp311-win_amd64.whl", hash = "sha256:b3768955cd235dfc936565d3425d7ee60c0e23d6154d73ca3960af4c10bd67da"}, - {file = "PyCifRW-4.4.5-cp39-cp39-manylinux_2_5_x86_64.whl", hash = "sha256:4b6f793603dfa731b9aba4bf9e379d42e843b57e77829a3fce3fb8bc9a563c1a"}, - {file = "PyCifRW-4.4.5.tar.gz", hash = "sha256:a8fd092cbefe6ddd4b7e667d05682e5c8c2e64ba63e7ef8b3ce4c07f2ff62827"}, + {file = "PyCifRW-4.4.6-cp311-cp311-manylinux_2_5_x86_64.whl", hash = "sha256:a89844ed5811700f995d1913a248d29d5745078ffd0f957b7e0574d74a48d0df"}, + {file = "PyCifRW-4.4.6.tar.gz", hash = "sha256:02bf5975e70ab71540bff62fbef3e8354ac707a0f0ab914a152047962891ef15"}, ] +[package.dependencies] +numpy = "*" +ply = "*" + [[package]] name = "pycodestyle" version = "2.11.1" @@ -5274,23 +5305,23 @@ test = ["cython", "filelock", "html5lib", "pytest (>=4.6)"] [[package]] name = "sphinx-autodoc-typehints" -version = "1.24.0" +version = "1.24.1" description = "Type hints (PEP 484) support for the Sphinx autodoc extension" optional = true python-versions = ">=3.8" files = [ - {file = "sphinx_autodoc_typehints-1.24.0-py3-none-any.whl", hash = "sha256:6a73c0c61a9144ce2ed5ef2bed99d615254e5005c1cc32002017d72d69fb70e6"}, - {file = "sphinx_autodoc_typehints-1.24.0.tar.gz", hash = "sha256:94e440066941bb237704bb880785e2d05e8ae5406c88674feefbb938ad0dc6af"}, + {file = "sphinx_autodoc_typehints-1.24.1-py3-none-any.whl", hash = "sha256:4cc16c5545f2bf896ca52a854babefe3d8baeaaa033d13a7f179ac1d9feb02d5"}, + {file = "sphinx_autodoc_typehints-1.24.1.tar.gz", hash = "sha256:06683a2b76c3c7b1931b75e40e0211866fbb50ba4c4e802d0901d9b4e849add2"}, ] [package.dependencies] -furo = {version = ">=2023.5.20", optional = true, markers = "extra == \"docs\""} -sphinx = ">=7.0.1" +furo = {version = ">=2023.7.26", optional = true, markers = "extra == \"docs\""} +sphinx = ">=7.1.2" [package.extras] -docs = ["furo (>=2023.5.20)", "sphinx (>=7.0.1)"] +docs = ["furo (>=2023.7.26)", "sphinx (>=7.1.2)"] numpy = ["nptyping (>=2.5)"] -testing = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "diff-cover (>=7.5)", "pytest (>=7.3.1)", "pytest-cov (>=4.1)", "sphobjinv (>=2.3.1)", "typing-extensions (>=4.6.3)"] +testing = ["covdefaults (>=2.3)", "coverage (>=7.3)", "diff-cover (>=7.7)", "pytest (>=7.4)", "pytest-cov (>=4.1)", "sphobjinv (>=2.3.1)", "typing-extensions (>=4.7.1)"] [[package]] name = "sphinx-basic-ng" @@ -5584,13 +5615,13 @@ files = [ [[package]] name = "tomlkit" -version = "0.12.1" +version = "0.12.2" description = "Style preserving TOML library" optional = false python-versions = ">=3.7" files = [ - {file = "tomlkit-0.12.1-py3-none-any.whl", hash = "sha256:712cbd236609acc6a3e2e97253dfc52d4c2082982a88f61b640ecf0817eab899"}, - {file = "tomlkit-0.12.1.tar.gz", hash = "sha256:38e1ff8edb991273ec9f6181244a6a391ac30e9f5098e7535640ea6be97a7c86"}, + {file = "tomlkit-0.12.2-py3-none-any.whl", hash = "sha256:eeea7ac7563faeab0a1ed8fe12c2e5a51c61f933f2502f7e9db0241a65163ad0"}, + {file = "tomlkit-0.12.2.tar.gz", hash = "sha256:df32fab589a81f0d7dc525a4267b6d7a64ee99619cbd1eeb0fae32c1dd426977"}, ] [[package]] @@ -5888,13 +5919,13 @@ zstd = ["zstandard (>=0.18.0)"] [[package]] name = "wcwidth" -version = "0.2.8" +version = "0.2.9" description = "Measures the displayed width of unicode strings in a terminal" optional = false python-versions = "*" files = [ - {file = "wcwidth-0.2.8-py2.py3-none-any.whl", hash = "sha256:77f719e01648ed600dfa5402c347481c0992263b81a027344f3e1ba25493a704"}, - {file = "wcwidth-0.2.8.tar.gz", hash = "sha256:8705c569999ffbb4f6a87c6d1b80f324bd6db952f5eb0b95bc07517f4c1813d4"}, + {file = "wcwidth-0.2.9-py2.py3-none-any.whl", hash = "sha256:9a929bd8380f6cd9571a968a9c8f4353ca58d7cd812a4822bba831f8d685b223"}, + {file = "wcwidth-0.2.9.tar.gz", hash = "sha256:a675d1a4a2d24ef67096a04b85b02deeecd8e226f57b5e3a72dbb9ed99d27da8"}, ] [[package]] @@ -6020,10 +6051,10 @@ docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.link testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"] [extras] -docs = ["sphinx", "sphinx-autodoc-typehints", "sphinx-rtd-theme", "tomlkit"] +docs = ["nbsphinx", "sphinx", "sphinx-autodoc-typehints", "sphinx-rtd-theme", "tomlkit"] notebook = ["ipykernel", "jupyter", "jupyterlab-h5web"] [metadata] lock-version = "2.0" python-versions = ">=3.8,<3.11" -content-hash = "1269eba7417fa4ce82173aa10912f814e367a6d3c452edb00cc3058c40a42fa4" +content-hash = "10a18aa12d733655eb7581612b98c5c0ed22191ddb3bfe7f0a4f8e66b0e5e6e4" diff --git a/pyproject.toml b/pyproject.toml index 98a1b0f0..d9bda125 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -38,11 +38,12 @@ sphinx = {version = ">4.4.0", extras = ["docs"], optional = true} sphinx-rtd-theme = {version = ">1.0.0", extras = ["docs"], optional = true} tomlkit = {version = ">0.10.0", extras = ["docs"], optional = true} sphinx-autodoc-typehints = {version = ">1.17.0", extras = ["docs"], optional = true} +nbsphinx = {version = "^0.9.3", extras = ["docs"], optional = true} [tool.poetry.extras] notebook = ["jupyter", "ipykernel", "jupyterlab-h5web"] -docs = ["Sphinx", "sphinx-rtd-theme", "tomlkit", "sphinx-autodoc-typehints"] +docs = ["Sphinx", "sphinx-rtd-theme", "tomlkit", "sphinx-autodoc-typehints", "nbsphinx"] [tool.poetry.group.dev.dependencies] pytest = "^7.0.1"