Adding gen_modules and auto_examples to documentation

docs/auto_examples/example_example.ipynb

+{
+  "nbformat_minor": 0,
+  "metadata": {
+    "kernelspec": {
+      "language": "python",
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "version": "3.5.2",
+      "mimetype": "text/x-python",
+      "file_extension": ".py",
+      "name": "python",
+      "pygments_lexer": "ipython3",
+      "nbconvert_exporter": "python",
+      "codemirror_mode": {
+        "version": 3,
+        "name": "ipython"
+      }
+    }
+  },
+  "nbformat": 4,
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "%matplotlib inline"
+      ],
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\nAn Example on writing examples\n==============================\n\nThe example docstring will be printed as text along with the code of the example.\nIt should contain the desciption of the example code.\nOnly examples that begine with plot_* will be run when generating the docs.\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Code source: Chris Smith\n# Liscense: MIT\nprint('I am an example')"
+      ],
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
+}
\ No newline at end of file

docs/auto_examples/example_example.py

+
+"""
+An Example on writing examples
+==============================
+
+The example docstring will be printed as text along with the code of the example.
+It should contain the desciption of the example code.
+Only examples that begine with plot_* will be run when generating the docs.
+
+"""
+
+# Code source: Chris Smith
+# Liscense: MIT
+print('I am an example')

docs/auto_examples/example_example.rst

+
+
+.. _sphx_glr_auto_examples_example_example.py:
+
+
+An Example on writing examples
+==============================
+
+The example docstring will be printed as text along with the code of the example.
+It should contain the desciption of the example code.
+Only examples that begine with plot_* will be run when generating the docs.
+
+
+
+
+.. code-block:: python
+
+
+    # Code source: Chris Smith
+    # Liscense: MIT
+    print('I am an example')
+
+**Total running time of the script:** ( 0 minutes  0.000 seconds)
+
+
+
+.. container:: sphx-glr-footer
+
+
+  .. container:: sphx-glr-download
+
+     :download:`Download Python source code: example_example.py <example_example.py>`
+
+
+
+  .. container:: sphx-glr-download
+
+     :download:`Download Jupyter notebook: example_example.ipynb <example_example.ipynb>`
+
+.. rst-class:: sphx-glr-signature
+
+    `Generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_

docs/auto_examples/index.rst

+:orphan:
+
+==================
+Pycroscopy Gallery
+==================
+
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="The example docstring will be printed as text along with the code of the example. It should con...">
+
+.. only:: html
+
+    .. figure:: /auto_examples/images/thumb/sphx_glr_example_example_thumb.png
+
+        :ref:`sphx_glr_auto_examples_example_example.py`
+
+.. raw:: html
+
+    </div>
+
+
+.. toctree::
+   :hidden:
+
+   /auto_examples/example_example
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="">
+
+.. only:: html
+
+    .. figure:: /auto_examples/images/thumb/sphx_glr_microdata_example_thumb.png
+
+        :ref:`sphx_glr_auto_examples_microdata_example.py`
+
+.. raw:: html
+
+    </div>
+
+
+.. toctree::
+   :hidden:
+
+   /auto_examples/microdata_example
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="Conventionally, the h5py package is used to create, read, write, and modify h5 files.">
+
+.. only:: html
+
+    .. figure:: /auto_examples/images/thumb/sphx_glr_load_dataset_example_thumb.png
+
+        :ref:`sphx_glr_auto_examples_load_dataset_example.py`
+
+.. raw:: html
+
+    </div>
+
+
+.. toctree::
+   :hidden:
+
+   /auto_examples/load_dataset_example
+.. raw:: html
+
+    <div style='clear:both'></div>
+
+
+
+.. container:: sphx-glr-footer
+
+
+  .. container:: sphx-glr-download
+
+    :download:`Download all examples in Python source code: auto_examples_python.zip <//home/challtdow/workspace/pycroscopy/docs/auto_examples/auto_examples_python.zip>`
+
+
+
+  .. container:: sphx-glr-download
+
+    :download:`Download all examples in Jupyter notebooks: auto_examples_jupyter.zip <//home/challtdow/workspace/pycroscopy/docs/auto_examples/auto_examples_jupyter.zip>`
+
+.. rst-class:: sphx-glr-signature
+
+    `Generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_

docs/auto_examples/load_dataset_example.ipynb

+{
+  "nbformat_minor": 0,
+  "metadata": {
+    "kernelspec": {
+      "language": "python",
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "version": "3.5.2",
+      "mimetype": "text/x-python",
+      "file_extension": ".py",
+      "name": "python",
+      "pygments_lexer": "ipython3",
+      "nbconvert_exporter": "python",
+      "codemirror_mode": {
+        "version": 3,
+        "name": "ipython"
+      }
+    }
+  },
+  "nbformat": 4,
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "%matplotlib inline"
+      ],
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Load Dataset\n\n\nConventionally, the h5py package is used to create, read, write, and modify h5 files.\n\nPycroscopy uses h5py to read hdf5 files and the ioHDF5 subpackage (a light wrapper around h5py) within pycroscopy\nto create / write back to the file. Please see the example on writing hdf5 files for more information on creating and\nwriting to h5 files using pycroscopy.\n\nIn the event that modification / addition of data to the existing file is of interest,\nit is recommended that the file be opened using ioHDF5. The same h5py handles can be obtained easily from ioHDF5.\nNote that ioHDF5 always reads the files in the 'r+' mode that allows modification of the file.\n\nIn this example, we will be loading the Raw_Data dataset from the hdf5 file.\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Code source: pycroscopy\n# Liscense: MIT\n\nfrom __future__ import division, print_function, absolute_import, unicode_literals\nimport h5py\ntry:\n    # This package is not part of anaconda and may need to be installed.\n    import wget\nexcept ImportError:\n    import pip\n    pip.main(['install', 'wget'])\n    import wget\n\nfrom os import remove\nimport pycroscopy as px\n\n# Downloading the file from the pycroscopy Github project\nurl = 'https://raw.githubusercontent.com/pycroscopy/pycroscopy/master/data/BELine_0004.h5'\nh5_path = 'temp.h5'\n_ = wget.download(url, h5_path)\n\n# h5_path = px.io_utils.uiGetFile(caption='Select .h5 file', filter='HDF5 file (*.h5)')\n\n# Read the file using using h5py:\nh5_file1 = h5py.File(h5_path, 'r')\n\n# Look at the contents of the file:\npx.hdf_utils.print_tree(h5_file1)\n\n# Access the \"Raw_Data\" dataset from its absolute path\nh5_raw1 = h5_file1['Measurement_000/Channel_000/Raw_Data']\nprint('h5_raw1: ', h5_raw1)\n\n# We can get to the same dataset through relative paths:\n\n# Access the Measurement_000 group first\nh5_meas_grp = h5_file1['Measurement_000']\nprint('h5_meas_grp:', h5_meas_grp)\n\n# Now we can access the \"Channel_000\" group via the h5_meas_grp object\nh5_chan_grp = h5_meas_grp['Channel_000']\n\n# And finally, the same raw dataset can be accessed as:\nh5_raw1_alias_1 = h5_chan_grp['Raw_Data']\nprint('h5_raw1_alias_1:', h5_raw1_alias_1)\n\n# Another way to get this dataset is via functions written in pycroscopy:\nh5_dsets = px.hdf_utils.getDataSet(h5_file1, 'Raw_Data')\nprint('h5_dsets:', h5_dsets)\n\n# In this case, there is only a single Raw_Data, so we an access it simply as:\nh5_raw1_alias_2 = h5_dsets[0]\nprint('h5_raw1_alias_2:', h5_raw1_alias_2)\n\n# Let's just check to see if these are indeed aliases of the same dataset:\nprint('All aliases of the same dataset?', h5_raw1 == h5_raw1_alias_1 and h5_raw1 == h5_raw1_alias_2)\n\n# Let's close this file\nh5_file1.close()\n\n# Load the dataset with pycroscopy\nhdf = px.ioHDF5(h5_path)\n\n# Getting the same h5py handle to the file:\nh5_file2 = hdf.file\n\nh5_raw2 = h5_file2['Measurement_000/Channel_000/Raw_Data']\nprint('h5_raw2:', h5_raw2)\n\nh5_file2.close()\n\n# Delete the temporarily downloaded h5 file:\nremove(h5_path)"
+      ],
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
+}
\ No newline at end of file

docs/auto_examples/load_dataset_example.py

+
+"""
+============
+Load Dataset
+============
+
+Conventionally, the h5py package is used to create, read, write, and modify h5 files.
+
+Pycroscopy uses h5py to read hdf5 files and the ioHDF5 subpackage (a light wrapper around h5py) within pycroscopy
+to create / write back to the file. Please see the example on writing hdf5 files for more information on creating and
+writing to h5 files using pycroscopy.
+
+In the event that modification / addition of data to the existing file is of interest,
+it is recommended that the file be opened using ioHDF5. The same h5py handles can be obtained easily from ioHDF5.
+Note that ioHDF5 always reads the files in the 'r+' mode that allows modification of the file.
+
+In this example, we will be loading the Raw_Data dataset from the hdf5 file.
+
+"""
+
+# Code source: pycroscopy
+# Liscense: MIT
+
+from __future__ import division, print_function, absolute_import, unicode_literals
+import h5py
+try:
+    # This package is not part of anaconda and may need to be installed.
+    import wget
+except ImportError:
+    import pip
+    pip.main(['install', 'wget'])
+    import wget
+
+from os import remove
+import pycroscopy as px
+
+# Downloading the file from the pycroscopy Github project
+url = 'https://raw.githubusercontent.com/pycroscopy/pycroscopy/master/data/BELine_0004.h5'
+h5_path = 'temp.h5'
+_ = wget.download(url, h5_path)
+
+# h5_path = px.io_utils.uiGetFile(caption='Select .h5 file', filter='HDF5 file (*.h5)')
+
+# Read the file using using h5py:
+h5_file1 = h5py.File(h5_path, 'r')
+
+# Look at the contents of the file:
+px.hdf_utils.print_tree(h5_file1)
+
+# Access the "Raw_Data" dataset from its absolute path
+h5_raw1 = h5_file1['Measurement_000/Channel_000/Raw_Data']
+print('h5_raw1: ', h5_raw1)
+
+# We can get to the same dataset through relative paths:
+
+# Access the Measurement_000 group first
+h5_meas_grp = h5_file1['Measurement_000']
+print('h5_meas_grp:', h5_meas_grp)
+
+# Now we can access the "Channel_000" group via the h5_meas_grp object
+h5_chan_grp = h5_meas_grp['Channel_000']
+
+# And finally, the same raw dataset can be accessed as:
+h5_raw1_alias_1 = h5_chan_grp['Raw_Data']
+print('h5_raw1_alias_1:', h5_raw1_alias_1)
+
+# Another way to get this dataset is via functions written in pycroscopy:
+h5_dsets = px.hdf_utils.getDataSet(h5_file1, 'Raw_Data')
+print('h5_dsets:', h5_dsets)
+
+# In this case, there is only a single Raw_Data, so we an access it simply as:
+h5_raw1_alias_2 = h5_dsets[0]
+print('h5_raw1_alias_2:', h5_raw1_alias_2)
+
+# Let's just check to see if these are indeed aliases of the same dataset:
+print('All aliases of the same dataset?', h5_raw1 == h5_raw1_alias_1 and h5_raw1 == h5_raw1_alias_2)
+
+# Let's close this file
+h5_file1.close()
+
+# Load the dataset with pycroscopy
+hdf = px.ioHDF5(h5_path)
+
+# Getting the same h5py handle to the file:
+h5_file2 = hdf.file
+
+h5_raw2 = h5_file2['Measurement_000/Channel_000/Raw_Data']
+print('h5_raw2:', h5_raw2)
+
+h5_file2.close()
+
+# Delete the temporarily downloaded h5 file:
+remove(h5_path)

docs/auto_examples/load_dataset_example.rst

+
+
+.. _sphx_glr_auto_examples_load_dataset_example.py:
+
+
+============
+Load Dataset
+============
+
+Conventionally, the h5py package is used to create, read, write, and modify h5 files.
+
+Pycroscopy uses h5py to read hdf5 files and the ioHDF5 subpackage (a light wrapper around h5py) within pycroscopy
+to create / write back to the file. Please see the example on writing hdf5 files for more information on creating and
+writing to h5 files using pycroscopy.
+
+In the event that modification / addition of data to the existing file is of interest,
+it is recommended that the file be opened using ioHDF5. The same h5py handles can be obtained easily from ioHDF5.
+Note that ioHDF5 always reads the files in the 'r+' mode that allows modification of the file.
+
+In this example, we will be loading the Raw_Data dataset from the hdf5 file.
+
+
+
+
+.. code-block:: python
+
+
+    # Code source: pycroscopy
+    # Liscense: MIT
+
+    from __future__ import division, print_function, absolute_import, unicode_literals
+    import h5py
+    try:
+        # This package is not part of anaconda and may need to be installed.
+        import wget
+    except ImportError:
+        import pip
+        pip.main(['install', 'wget'])
+        import wget
+
+    from os import remove
+    import pycroscopy as px
+
+    # Downloading the file from the pycroscopy Github project
+    url = 'https://raw.githubusercontent.com/pycroscopy/pycroscopy/master/data/BELine_0004.h5'
+    h5_path = 'temp.h5'
+    _ = wget.download(url, h5_path)
+
+    # h5_path = px.io_utils.uiGetFile(caption='Select .h5 file', filter='HDF5 file (*.h5)')
+
+    # Read the file using using h5py:
+    h5_file1 = h5py.File(h5_path, 'r')
+
+    # Look at the contents of the file:
+    px.hdf_utils.print_tree(h5_file1)
+
+    # Access the "Raw_Data" dataset from its absolute path
+    h5_raw1 = h5_file1['Measurement_000/Channel_000/Raw_Data']
+    print('h5_raw1: ', h5_raw1)
+
+    # We can get to the same dataset through relative paths:
+
+    # Access the Measurement_000 group first
+    h5_meas_grp = h5_file1['Measurement_000']
+    print('h5_meas_grp:', h5_meas_grp)
+
+    # Now we can access the "Channel_000" group via the h5_meas_grp object
+    h5_chan_grp = h5_meas_grp['Channel_000']
+
+    # And finally, the same raw dataset can be accessed as:
+    h5_raw1_alias_1 = h5_chan_grp['Raw_Data']
+    print('h5_raw1_alias_1:', h5_raw1_alias_1)
+
+    # Another way to get this dataset is via functions written in pycroscopy:
+    h5_dsets = px.hdf_utils.getDataSet(h5_file1, 'Raw_Data')
+    print('h5_dsets:', h5_dsets)
+
+    # In this case, there is only a single Raw_Data, so we an access it simply as:
+    h5_raw1_alias_2 = h5_dsets[0]
+    print('h5_raw1_alias_2:', h5_raw1_alias_2)
+
+    # Let's just check to see if these are indeed aliases of the same dataset:
+    print('All aliases of the same dataset?', h5_raw1 == h5_raw1_alias_1 and h5_raw1 == h5_raw1_alias_2)
+
+    # Let's close this file
+    h5_file1.close()
+
+    # Load the dataset with pycroscopy
+    hdf = px.ioHDF5(h5_path)
+
+    # Getting the same h5py handle to the file:
+    h5_file2 = hdf.file
+
+    h5_raw2 = h5_file2['Measurement_000/Channel_000/Raw_Data']
+    print('h5_raw2:', h5_raw2)
+
+    h5_file2.close()
+
+    # Delete the temporarily downloaded h5 file:
+    remove(h5_path)
+
+**Total running time of the script:** ( 0 minutes  0.000 seconds)
+
+
+
+.. container:: sphx-glr-footer
+
+
+  .. container:: sphx-glr-download
+
+     :download:`Download Python source code: load_dataset_example.py <load_dataset_example.py>`
+
+
+
+  .. container:: sphx-glr-download
+
+     :download:`Download Jupyter notebook: load_dataset_example.ipynb <load_dataset_example.ipynb>`
+
+.. rst-class:: sphx-glr-signature
+
+    `Generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_

docs/auto_examples/microdata_example.ipynb

+{
+  "nbformat_minor": 0,
+  "metadata": {
+    "kernelspec": {
+      "language": "python",
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "version": "3.5.2",
+      "mimetype": "text/x-python",
+      "file_extension": ".py",
+      "name": "python",
+      "pygments_lexer": "ipython3",
+      "nbconvert_exporter": "python",
+      "codemirror_mode": {
+        "version": 3,
+        "name": "ipython"
+      }
+    }
+  },
+  "nbformat": 4,
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "%matplotlib inline"
+      ],
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Writing to hdf5 using the Microdata objects\n\n\n\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Code source: Chris Smith -- cq6@ornl.gov\n# Liscense: MIT\n\nimport numpy as np\nimport pycroscopy as px"
+      ],
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Create some MicroDatasets and MicroDataGroups that will be written to the file.\nWith h5py, groups and datasets must be created from the top down,\nbut the Microdata objects allow us to build them in any order and link them later.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# First create some data\ndata1 = np.random.rand(5, 7)"
+      ],
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now use the array to build the dataset.  This dataset will live\ndirectly under the root of the file.  The MicroDataset class also implements the\ncompression and chunking parameters from h5py.Dataset.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "ds_main = px.MicroDataset('Main_Data', data=data1, parent='/')"
+      ],
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can also create an empty dataset and write the values in later\nWith this method, it is neccessary to specify the dtype and maxshape kwarg parameters.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "ds_empty = px.MicroDataset('Empty_Data', data=[], dtype=np.float32, maxshape=[7, 5, 3])"
+      ],
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can also create groups and add other MicroData objects as children.\nIf the group's parent is not given, it will be set to root.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "data_group = px.MicroDataGroup('Data_Group', parent='/')\n\nroot_group = px.MicroDataGroup('/')\n\n# After creating the group, we then add an existing object as its child.\ndata_group.addChildren([ds_empty])\nroot_group.addChildren([ds_main, data_group])"
+      ],