{
    "cells": [
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# CXI File Browser\n",
                "This notebook provides a simple example to show you how to use the CXI file browser."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 1,
            "metadata": {},
            "outputs": [],
            "source": [
                "import cdiutils\n",
                "\n",
                "cdiutils.plot.update_plot_params()"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Set up the file path\n",
                "First, provide the path to the CXI file you want to explore."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "# Load the data\n",
                "# Replace with the actual path to your data file\n",
                "path = \"path/to/your/data.cxi\""
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Basic usage\n",
                "Create an explorer instance and print a summary of the file contents.\n",
                "\n",
                "The `CXIExplorer` provides four main methods for exploring CXI files:\n",
                "\n",
                "1. `summarise()` - Provides an overview of the file including file size, number of groups and datasets\n",
                "2. `tree(max_depth=None, show_attributes=False)` - Displays the hierarchical structure of the file in a tree format\n",
                "3. `search(pattern)` - Finds datasets, groups, or attributes matching a specific pattern\n",
                "4. `show(path)` - Visualises a specific dataset, with automatic plotting for array data\n",
                "\n",
                "Additionally, the `explore()` method launches an interactive widget-based browser."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 3,
            "id": "2bcd5068",
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "CXI File Summary: S706_postprocessed_data.cxi\n",
                        "File size: 142.77 MB\n",
                        "Groups: 45\n",
                        "Datasets: 198\n",
                        "Total data size: 142.65 MB\n",
                        "\n",
                        "Entries: 1\n",
                        "\n",
                        "entry_1:\n",
                        "  Default: data_1\n",
                        "  amplitude: 1\n",
                        "  data: 12\n",
                        "  detector: 1\n",
                        "  displacement: 2\n",
                        "  dspacing: 1\n",
                        "  geometry: 1\n",
                        "  het: 3\n",
                        "  image: 12\n",
                        "  lattice: 1\n",
                        "  numpy: 1\n",
                        "  parameters: 1\n",
                        "  phase: 1\n",
                        "  process: 1\n",
                        "  result: 5\n",
                        "  sample: 1\n",
                        "  source: 1\n",
                        "  support: 1\n",
                        "  surface: 1\n"
                    ]
                }
            ],
            "source": [
                "explorer = cdiutils.io.CXIExplorer(path)\n",
                "\n",
                "explorer.summarise()"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## View hierarchical structure\n",
                "Display the file's hierarchical structure as a tree, limiting depth for clarity."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 4,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "CXI File: S706_postprocessed_data.cxi\n",
                        "└── /\n",
                        "    ├── creator (scalar) object\n",
                        "    ├── cxi_version (scalar) int64\n",
                        "    ├── entry_1\n",
                        "    │   ├── amplitude -> /entry_1/image_1\n",
                        "    │   ├── data_1\n",
                        "    │   │   └── 1 more entrie(s)...\n",
                        "    │   ├── data_10\n",
                        "    │   │   └── 1 more entrie(s)...\n",
                        "    │   ├── data_11\n",
                        "    │   │   └── 1 more entrie(s)...\n",
                        "    │   ├── data_12\n",
                        "    │   │   └── 1 more entrie(s)...\n",
                        "    │   ├── data_2\n",
                        "    │   │   └── 1 more entrie(s)...\n",
                        "    │   ├── data_3\n",
                        "    │   │   └── 1 more entrie(s)...\n",
                        "    │   ├── data_4\n",
                        "    │   │   └── 1 more entrie(s)...\n",
                        "    │   ├── data_5\n",
                        "    │   │   └── 1 more entrie(s)...\n",
                        "    │   ├── data_6\n",
                        "    │   │   └── 1 more entrie(s)...\n",
                        "    │   ├── data_7\n",
                        "    │   │   └── 1 more entrie(s)...\n",
                        "    │   ├── data_8\n",
                        "    │   │   └── 1 more entrie(s)...\n",
                        "    │   ├── data_9\n",
                        "    │   │   └── 1 more entrie(s)...\n",
                        "    │   ├── detector_1\n",
                        "    │   │   └── 7 more entrie(s)...\n",
                        "    │   ├── displacement -> /entry_1/image_5\n",
                        "    │   ├── displacement_gradient -> /entry_1/image_6\n",
                        "    │   ├── dspacing -> /entry_1/image_11\n",
                        "    │   ├── geometry_1\n",
                        "    │   │   └── 8 more entrie(s)...\n",
                        "    │   ├── het_strain -> /entry_1/image_7\n",
                        "    │   ├── het_strain_from_dspacing -> /entry_1/image_9\n",
                        "    │   ├── het_strain_with_ramp -> /entry_1/image_8\n",
                        "    │   ├── image_1\n",
                        "    │   │   └── 5 more entrie(s)...\n",
                        "    │   ├── image_10\n",
                        "    │   │   └── 5 more entrie(s)...\n",
                        "    │   ├── image_11\n",
                        "    │   │   └── 5 more entrie(s)...\n",
                        "    │   ├── image_12\n",
                        "    │   │   └── 5 more entrie(s)...\n",
                        "    │   ├── image_2\n",
                        "    │   │   └── 5 more entrie(s)...\n",
                        "    │   ├── image_3\n",
                        "    │   │   └── 5 more entrie(s)...\n",
                        "    │   ├── image_4\n",
                        "    │   │   └── 5 more entrie(s)...\n",
                        "    │   ├── image_5\n",
                        "    │   │   └── 5 more entrie(s)...\n",
                        "    │   ├── image_6\n",
                        "    │   │   └── 5 more entrie(s)...\n",
                        "    │   ├── image_7\n",
                        "    │   │   └── 5 more entrie(s)...\n",
                        "    │   ├── image_8\n",
                        "    │   │   └── 5 more entrie(s)...\n",
                        "    │   ├── image_9\n",
                        "    │   │   └── 5 more entrie(s)...\n",
                        "    │   ├── lattice_parameter -> /entry_1/image_12\n",
                        "    │   ├── numpy_het_strain -> /entry_1/image_10\n",
                        "    │   ├── parameters_1\n",
                        "    │   │   └── 35 more entrie(s)...\n",
                        "    │   ├── phase -> /entry_1/image_4\n",
                        "    │   ├── process_1\n",
                        "    │   │   └── 5 more entrie(s)...\n",
                        "    │   ├── result_1\n",
                        "    │   │   └── 5 more entrie(s)...\n",
                        "    │   ├── result_2\n",
                        "    │   │   └── 4 more entrie(s)...\n",
                        "    │   ├── result_3\n",
                        "    │   │   └── 3 more entrie(s)...\n",
                        "    │   ├── result_4\n",
                        "    │   │   └── 5 more entrie(s)...\n",
                        "    │   ├── result_5\n",
                        "    │   │   └── 5 more entrie(s)...\n",
                        "    │   ├── sample_1\n",
                        "    │   │   └── 3 more entrie(s)...\n",
                        "    │   ├── source_1\n",
                        "    │   │   └── 2 more entrie(s)...\n",
                        "    │   ├── support -> /entry_1/image_2\n",
                        "    │   └── surface -> /entry_1/image_3\n",
                        "    ├── file_path (scalar) object\n",
                        "    ├── time (scalar) |S19\n",
                        "    └── version (scalar) object\n"
                    ]
                }
            ],
            "source": [
                "explorer.tree(max_depth=2)"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Search functionality\n",
                "Search for specific path using keywords.\n",
                "\n",
                "Ex: you know that the dataset you are looking for contains the word \"strain\" in its name."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 7,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "Found 16 matches for 'strain':\n",
                        "- Soft Link: entry_1/het_strain (soft link name match (→ /entry_1/image_7))\n",
                        "- Soft Link: entry_1/het_strain_from_dspacing (soft link name match (→ /entry_1/image_9))\n",
                        "- Soft Link: entry_1/het_strain_with_ramp (soft link name match (→ /entry_1/image_8))\n",
                        "- Soft Link: entry_1/numpy_het_strain (soft link name match (→ /entry_1/image_10))\n",
                        "- Group: entry_1/result_5/strain_averages (name match)\n",
                        "- Dataset: entry_1/result_5/strain_averages/bulk (name match)\n",
                        "- Dataset: entry_1/result_5/strain_averages/bulk_density (name match)\n",
                        "- Dataset: entry_1/result_5/strain_averages/overall (name match)\n",
                        "- Dataset: entry_1/result_5/strain_averages/surface (name match)\n",
                        "- Dataset: entry_1/result_5/strain_averages/surface_density (name match)\n",
                        "- Group: entry_1/result_5/strain_fwhms (name match)\n",
                        "- Dataset: entry_1/result_5/strain_fwhms/bulk (name match)\n",
                        "- Dataset: entry_1/result_5/strain_fwhms/bulk_density (name match)\n",
                        "- Dataset: entry_1/result_5/strain_fwhms/overall (name match)\n",
                        "- Dataset: entry_1/result_5/strain_fwhms/surface (name match)\n",
                        "- Dataset: entry_1/result_5/strain_fwhms/surface_density (name match)\n"
                    ]
                }
            ],
            "source": [
                "explorer.search(\"strain\")"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Display specific datasets\n",
                "View the contents of a specific dataset by its path."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 8,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "Path: entry_1/dspacing\n",
                        "Type: Soft Link → /entry_1/image_11\n",
                        "Target Content:\n",
                        "\n",
                        "\tPath: /entry_1/image_11\n",
                        "\tTitle: dspacing\n",
                        "\tType: Group with 5 items.\n",
                        "\tContent:\n",
                        "\n",
                        "\t\tPath: /entry_1/image_11/data\n",
                        "\t\tType: Dataset\n",
                        "\t\tShape: (110, 110, 110)\n",
                        "\t\tDtype: float64\n",
                        "\t\tData summary: min=2.049, max=2.051, mean=2.050\n"
                    ]
                },
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 1200x400 with 6 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "\n",
                        "\t\tPath: /entry_1/image_11/data_space\n",
                        "\t\tType: Dataset\n",
                        "\t\tValue: Direct space\n",
                        "\n",
                        "\t\tPath: /entry_1/image_11/image_size\n",
                        "\t\tType: Dataset\n",
                        "\t\tShape: (3,)\n",
                        "\t\tDtype: int64\n",
                        "\t\tValues: [110 110 110]\n",
                        "\n",
                        "\t\tPath: /entry_1/image_11/process_1\n",
                        "\t\tType: Soft Link → /entry_1/process_1\n",
                        "\n",
                        "\t\tPath: /entry_1/image_11/title\n",
                        "\t\tType: Dataset\n",
                        "\t\tValue: dspacing\n"
                    ]
                }
            ],
            "source": [
                "explorer.show(\"entry_1/dspacing\")"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Interactive exploration\n",
                "Launch an interactive browser to navigate through the file."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 9,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "application/vnd.jupyter.widget-view+json": {
                            "model_id": "f4dd9d39d0c340cc90b2e7ec1c2c1006",
                            "version_major": 2,
                            "version_minor": 0
                        },
                        "text/plain": [
                            "VBox(children=(Dropdown(description='Path:', layout=Layout(width='80%'), options=('creator', 'cxi_version', 'e…"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "explorer.explore()"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Closing the explorer\n",
                "Always close the explorer when finished to release file resources. When you delete the explorer (`del explorer`), the file will also be closed automatically."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 10,
            "metadata": {},
            "outputs": [],
            "source": [
                "explorer.close()"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Using context manager\n",
                "A cleaner approach is to use the context manager, which automatically closes the file when done. Note that using the context manager prevents using the interactive `explore()` method after exiting the context."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 11,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "File summary:\n",
                        "CXI File Summary: S706_postprocessed_data.cxi\n",
                        "File size: 142.77 MB\n",
                        "Groups: 45\n",
                        "Datasets: 198\n",
                        "Total data size: 142.65 MB\n",
                        "\n",
                        "Entries: 1\n",
                        "\n",
                        "entry_1:\n",
                        "  Default: data_1\n",
                        "  amplitude: 1\n",
                        "  data: 12\n",
                        "  detector: 1\n",
                        "  displacement: 2\n",
                        "  dspacing: 1\n",
                        "  geometry: 1\n",
                        "  het: 3\n",
                        "  image: 12\n",
                        "  lattice: 1\n",
                        "  numpy: 1\n",
                        "  parameters: 1\n",
                        "  phase: 1\n",
                        "  process: 1\n",
                        "  result: 5\n",
                        "  sample: 1\n",
                        "  source: 1\n",
                        "  support: 1\n",
                        "  surface: 1\n",
                        "\n",
                        "Tree:\n",
                        "CXI File: S706_postprocessed_data.cxi\n",
                        "└── /\n",
                        "    ├── creator (scalar) object\n",
                        "    ├── cxi_version (scalar) int64\n",
                        "    ├── entry_1\n",
                        "    │   └── 47 more entrie(s)...\n",
                        "    ├── file_path (scalar) object\n",
                        "    ├── time (scalar) |S19\n",
                        "    └── version (scalar) object\n",
                        "\n",
                        "Search specific key word:\n",
                        "Found 16 matches for 'strain':\n",
                        "- Soft Link: entry_1/het_strain (soft link name match (→ /entry_1/image_7))\n",
                        "- Soft Link: entry_1/het_strain_from_dspacing (soft link name match (→ /entry_1/image_9))\n",
                        "- Soft Link: entry_1/het_strain_with_ramp (soft link name match (→ /entry_1/image_8))\n",
                        "- Soft Link: entry_1/numpy_het_strain (soft link name match (→ /entry_1/image_10))\n",
                        "- Group: entry_1/result_5/strain_averages (name match)\n",
                        "- Dataset: entry_1/result_5/strain_averages/bulk (name match)\n",
                        "- Dataset: entry_1/result_5/strain_averages/bulk_density (name match)\n",
                        "- Dataset: entry_1/result_5/strain_averages/overall (name match)\n",
                        "- Dataset: entry_1/result_5/strain_averages/surface (name match)\n",
                        "- Dataset: entry_1/result_5/strain_averages/surface_density (name match)\n",
                        "- Group: entry_1/result_5/strain_fwhms (name match)\n",
                        "- Dataset: entry_1/result_5/strain_fwhms/bulk (name match)\n",
                        "- Dataset: entry_1/result_5/strain_fwhms/bulk_density (name match)\n",
                        "- Dataset: entry_1/result_5/strain_fwhms/overall (name match)\n",
                        "- Dataset: entry_1/result_5/strain_fwhms/surface (name match)\n",
                        "- Dataset: entry_1/result_5/strain_fwhms/surface_density (name match)\n",
                        "\n",
                        "Show specific dataset:\n",
                        "Path: entry_1/dspacing\n",
                        "Type: Soft Link → /entry_1/image_11\n",
                        "Target Content:\n",
                        "\n",
                        "\tPath: /entry_1/image_11\n",
                        "\tTitle: dspacing\n",
                        "\tType: Group with 5 items.\n",
                        "\tContent:\n",
                        "\n",
                        "\t\tPath: /entry_1/image_11/data\n",
                        "\t\tType: Dataset\n",
                        "\t\tShape: (110, 110, 110)\n",
                        "\t\tDtype: float64\n",
                        "\t\tData summary: min=2.049, max=2.051, mean=2.050\n"
                    ]
                },
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 1200x400 with 6 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "\n",
                        "\t\tPath: /entry_1/image_11/data_space\n",
                        "\t\tType: Dataset\n",
                        "\t\tValue: Direct space\n",
                        "\n",
                        "\t\tPath: /entry_1/image_11/image_size\n",
                        "\t\tType: Dataset\n",
                        "\t\tShape: (3,)\n",
                        "\t\tDtype: int64\n",
                        "\t\tValues: [110 110 110]\n",
                        "\n",
                        "\t\tPath: /entry_1/image_11/process_1\n",
                        "\t\tType: Soft Link → /entry_1/process_1\n",
                        "\n",
                        "\t\tPath: /entry_1/image_11/title\n",
                        "\t\tType: Dataset\n",
                        "\t\tValue: dspacing\n"
                    ]
                }
            ],
            "source": [
                "with cdiutils.io.CXIExplorer(path) as explorer:\n",
                "    # Print a summary of the file\n",
                "    print(\"File summary:\")\n",
                "    explorer.summarise()\n",
                "\n",
                "    # Print the hierarchical tree structure\n",
                "    print(\"\\nTree:\")\n",
                "    explorer.tree(max_depth=1)\n",
                "\n",
                "    # Search for specific datasets\n",
                "    print(\"\\nSearch specific key word:\")\n",
                "    explorer.search(\"strain\")\n",
                "\n",
                "    # Show a specific dataset\n",
                "    print(\"\\nShow specific dataset:\")\n",
                "    explorer.show(\"entry_1/dspacing\")"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Direct use of CXIFile class\n",
                "The `CXIExplorer` can also be accessed directly from the `CXIFile` class using `get_explorer()`, which returns a ready-to-use `CXIExplorer` instance."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 12,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "Summary:\n",
                        "CXI File Summary: S706_postprocessed_data.cxi\n",
                        "File size: 142.77 MB\n",
                        "Groups: 45\n",
                        "Datasets: 198\n",
                        "Total data size: 142.65 MB\n",
                        "\n",
                        "Entries: 1\n",
                        "\n",
                        "entry_1:\n",
                        "  Default: data_1\n",
                        "  amplitude: 1\n",
                        "  data: 12\n",
                        "  detector: 1\n",
                        "  displacement: 2\n",
                        "  dspacing: 1\n",
                        "  geometry: 1\n",
                        "  het: 3\n",
                        "  image: 12\n",
                        "  lattice: 1\n",
                        "  numpy: 1\n",
                        "  parameters: 1\n",
                        "  phase: 1\n",
                        "  process: 1\n",
                        "  result: 5\n",
                        "  sample: 1\n",
                        "  source: 1\n",
                        "  support: 1\n",
                        "  surface: 1\n",
                        "\n",
                        "Tree:\n",
                        "CXI File: S706_postprocessed_data.cxi\n",
                        "└── /\n",
                        "    ├── creator (scalar) object\n",
                        "    ├── cxi_version (scalar) int64\n",
                        "    ├── entry_1\n",
                        "    │   └── 47 more entrie(s)...\n",
                        "    ├── file_path (scalar) object\n",
                        "    ├── time (scalar) |S19\n",
                        "    └── version (scalar) object\n"
                    ]
                }
            ],
            "source": [
                "cxi_file = cdiutils.CXIFile(path)\n",
                "\n",
                "# Quick interactive exploration\n",
                "print(\"Summary:\")\n",
                "cxi_file.get_explorer().summarise()\n",
                "\n",
                "# or:\n",
                "explorer = cxi_file.get_explorer()\n",
                "print(\"\\nTree:\")\n",
                "explorer.tree(max_depth=1)\n",
                "\n",
                "# When finished\n",
                "cxi_file.close()"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "c3209367",
            "metadata": {},
            "source": [
                "## Load data from a CXI file\n",
                "There are several ways to open a CXI file. The most common way is to use the `CXIFile` class, which provides a simple interface for reading and writing CXI files.\n",
                "- You can load data from a CXI file using the classic approach:\n",
                "<!-- ```python\n",
                "cxi_file = CXIFile(file_path)\n",
                "cxi_file.open()\n",
                "data = cxi_file[\"entry_1/amplitude\"]\n",
                "cxi.close()  # Don't forget to close the file!\n",
                "```\n",
                "- Or you can use the context manager, i.e. the `with` statement to automatically close the file when done:\n",
                "```python\n",
                "with CXIFile(file_path) as cxi_file:\n",
                "    data = cxi_file[\"entry_1/amplitude\"]\n",
                "```\n",
                "- `cdiutils` provides a convenient function to load data from CXI files. You can specify the path to the CXI file and a dataset name to load. If the dataset name is not the exact full \"key path\", say `\"voxel_size\"` instead of `\"entry_1/result_1/voxel_size\"`, the function will find it for you anyway. Note that you can provide as much as keys as you want, and the function will return a dictionary with the keys as the dataset names and the values as the data loaded from the CXI file. -->\n",
                "\n"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "efa661ab",
            "metadata": {},
            "outputs": [],
            "source": [
                "cxi_file = cdiutils.CXIFile(path)\n",
                "cxi_file.open()\n",
                "data = cxi_file[\"entry_1/amplitude\"]\n",
                "cxi_file.close()  # Don't forget to close the file!"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "31959a1c",
            "metadata": {},
            "source": [
                "- Or you can use the context manager, i.e. the `with` statement to automatically close the file when done:"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "20a5315d",
            "metadata": {},
            "outputs": [],
            "source": [
                "with cdiutils.CXIFile(path) as cxi_file:\n",
                "    data = cxi_file[\"entry_1/amplitude\"]"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "e0a41fba",
            "metadata": {},
            "source": [
                "- Finally, you can use the `cdiutils.io.load_cxi` function to conveniently load data from CXI files. The `cdiutils` library provides a convenient function to load data from CXI files. It requires the path to the CXI file and a dataset name to load. If the dataset name is not the exact full \"key path\", say `\"voxel_size\"` instead of `\"entry_1/result_1/voxel_size\"`, the function will find it for you anyway. Note that you can provide as much as keys as you want, and the function will return a dictionary with the keys as the dataset names and the values as the data loaded from the CXI file."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "voxel_size = cdiutils.io.load_cxi(path, \"voxel_size\")  # simple value\n",
                "\n",
                "data = cdiutils.io.load_cxi(path, \"amplitude\", \"het_strain\")  # dictionary\n",
                "\n",
                "print(data.keys())"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "be736180",
            "metadata": {},
            "source": [
                "## Credits\n",
                "This notebook was created by Clément Atlan, ESRF, 2025. It is part of the `cdiutils` package, which provides tools for BCDI data analysis and visualisation.\n",
                "If you have used this notebook or the `cdiutils` package in your research, please consider citing the package https://github.com/clatlan/cdiutils/\n",
                "You'll find the citation information in the `cdiutils` package documentation.\n",
                "\n",
                "```bibtex\n",
                "@software{Atlan_Cdiutils_A_python,\n",
                "author = {Atlan, Clement},\n",
                "doi = {10.5281/zenodo.7656853},\n",
                "license = {MIT},\n",
                "title = {{Cdiutils: A python package for Bragg Coherent Diffraction Imaging processing, analysis and visualisation workflows}},\n",
                "url = {https://github.com/clatlan/cdiutils},\n",
                "version = {0.2.0}\n",
                "}\n",
                "```"
            ]
        }
    ],
    "metadata": {
        "kernelspec": {
            "display_name": "Python 3",
            "language": "python",
            "name": "python3"
        },
        "language_info": {
            "codemirror_mode": {
                "name": "ipython",
                "version": 3
            },
            "file_extension": ".py",
            "mimetype": "text/x-python",
            "name": "python",
            "nbconvert_exporter": "python",
            "pygments_lexer": "ipython3",
            "version": "3.12.9"
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    },
    "nbformat": 4,
    "nbformat_minor": 5
}