Example Applications
********************

Read a raw measurement file, fit a curve, save and plot the results
===================================================================

.. doctest:: generates-my-results

    >>> import matplotlib.pyplot as plt
    >>> import numpy as np
    >>> from SDF.data_model import Workspace, ArrayDataset1D
    >>> from SDF.file_io import write_sdf, load_from_sdf

    >>> # 0. create some data (usually read from measurement file)
    >>> n_points = 20
    >>> a, b = 3, 1.5
    >>> x = np.linspace(0, 10, n_points)
    >>> y = a*x + b + np.random.normal(size=n_points)
    >>> ws = Workspace(name="top-level workspace", owner="Albertus")
    >>> ws.datasets.add(ArrayDataset1D(name="x", data=x, unit="mm"))
    >>> ws.datasets.add(ArrayDataset1D(name="y", data=y, unit="nmol"))
    >>> ws.instruments["Supermeter 2000"] = {"nickname": "Sir cannot-fail",
    ...                                      "settings": {"good day": True, "mood": 10}}
    >>> write_sdf(ws, "my-results.sdf")

    >>> # 1. read data, fit curve
    >>> ws = load_from_sdf("my-results.sdf")
    >>> fit_a, fit_b = np.polyfit(x, y, deg=1)

    >>> # 2. add fit results to workspace
    >>> ws.parameters["fit results"] = {"a": (fit_a, "nmol"),
    ...                                 "b": (fit_b, "nmol/mm"),
    ...                                 "method": {"name": "np.polyfit", "degree": 1}}
    >>> write_sdf(ws, "my-results.sdf")

    >>> # 3. plot
    >>> x = ws.datasets["x"].data
    >>> x_unit = ws.datasets["x"].unit
    >>> y = ws.datasets["y"].data
    >>> y_unit = ws.datasets["y"].unit
    >>> fit_a = float(ws.parameters["fit results"]["a"].value)
    >>> fit_b = float(ws.parameters["fit results"]["b"].value)
    >>> def plot(x, y, y_fit, x_unit, y_unit):
    ...     plt.plot(x, y, "xk", label="measurement")
    ...     plt.plot(x, y_fit, "--r", label="fit")
    ...     plt.xlabel(f"$x$ /{x_unit}")
    ...     plt.ylabel(f"$y$ /{y_unit}")
    ...     plt.legend()
    ...     plt.title("Results")
    ...     plt.tight_layout()
    >>> plot(x, y, fit_a*x + fit_b, x_unit, y_unit)
    >>> plt.savefig("my-results.png")

The resulting file :code:`my-results.sdf` will look like this:

.. code-block:: xml
    :caption: my-results.sdf

    <?xml version="1.0" encoding="utf-8"?>
    <!DOCTYPE workspace>
    <workspace>
       <name>
          top-level workspace
       </name>
       <owner>
          Albertus
       </owner>
       <par name="fit results">
          <par name="a" value="2.9781712899700308" unit="nmol"/>
          <par name="b" value="1.5456773747402146" unit="nmol/mm"/>
          <par name="method">
             <par name="degree" value="1"/>
             <par name="name" value="np.polyfit"/>
          </par>
       </par>
       <instrument>
          <name>
             Supermeter 2000
          </name>
          <par name="nickname" value="Sir cannot-fail"/>
          <par name="settings">
             <par name="good day" value="True"/>
             <par name="mood" value="10"/>
          </par>
       </instrument>
       <dataset type="sc">
          <name>
             x
          </name>
          <unit value="mm"/>
          <data rows="20" cols="1" type="float">
             0.0 0.5263157894736842 1.0526315789473684 1.5789473684210527 2.1052631578947367 2.631578947368421
             3.1578947368421053 3.6842105263157894 4.2105263157894735 4.7368421052631575 5.263157894736842 5.789473684210526
             6.315789473684211 6.842105263157895 7.368421052631579 7.894736842105263 8.421052631578947 8.947368421052632
             9.473684210526315 10.0
          </data>
       </dataset>
       <dataset type="sc">
          <name>
             y
          </name>
          <unit value="nmol"/>
          <data rows="20" cols="1" type="float">
             1.0194935272080436 3.313324719409985 4.016299212544986 7.627843194351737 6.31005069389586 10.487992770591923
             11.123057149457448 11.80545784430888 14.494353262577148 14.824419224980307 18.545437261147292 18.63781216938566
             20.146171321403752 22.963561113724747 23.964314703838614 25.80381228448588 24.440242187106243
             27.620432980775806 29.404287266310554 32.18231360430237
          </data>
       </dataset>
    </workspace>

The plot will look like this:

.. figure:: /img/sdf-data-model-example-plot-curve-fit.png
   :alt: example curve fit plot

   my-results.png

Generate a nested Workspace with multiple Datasets
==================================================

.. doctest:: generates-my-results

    >>> from datetime import datetime
    >>> from PIL import Image
    >>> import numpy as np
    >>> from SDF.data_model import Workspace, ImageDataset, ArrayDataset1D
    >>> from SDF.file_io import write_sdf

    >>> sdf = Workspace(name="my experiment", owner="me", date=datetime.now())
    >>> sdf.comment = "comment on the experiment"
    >>> sdf.samples["my first sample"] = "sample description"
    >>> sdf.samples["my second sample"] = "sample description"

    >>> measurement_1 = Workspace("my first measurement")
    >>> sdf.workspaces.add(measurement_1)
    >>> measurement_1.comment = "comment on my first measurement:\n" \
    ...                         "  - it consists of two images\n" \
    ...                         "  - they are random, but beautiful!"
    >>> measurement_1.instruments["my microscope"] = dict(cost=(9001, "EUR"), color="black")
    >>> image_1 = Image.fromarray(np.random.randint(0, 255, (10, 10), dtype=np.uint8))  # random 10x10 image
    >>> image_2 = Image.fromarray(np.random.randint(0, 255, (10, 10), dtype=np.uint8))
    >>> measurement_1.datasets.add(ImageDataset("image 1", image_1))
    >>> measurement_1.datasets["image 1"].parameters["zoom-level"] = 30
    >>> measurement_1.datasets["image 1"].parameters["wavelength"] = 509, "nm"
    >>> measurement_1.datasets.add(ImageDataset("image 2", image_2))

    >>> measurement_2 = Workspace("my second measurement")
    >>> sdf.workspaces.add(measurement_2)
    >>> measurement_2.comment = "comment on my first measurement"
    >>> x = np.arange(100)
    >>> y = np.random.randint(0, 10, 100)
    >>> measurement_2.datasets.add(ArrayDataset1D("x", x))
    >>> measurement_2.datasets.add(ArrayDataset1D("y", y))

    >>> write_sdf(sdf, "my-results.sdf")

The resulting file :code:`my-results.sdf` would look like this:

.. code-block:: xml
    :caption: my-results.sdf

    <?xml version="1.0" encoding="utf-8"?>
    <!DOCTYPE workspace>
    <workspace>
       <name>
          my experiment
       </name>
       <owner>
          me
       </owner>
       <date>
          2021.03.03 11:01:28
       </date>
       <comment>
          comment on the experiment
       </comment>
       <sample>
          <name>
             my first sample
          </name>
          <comment>
             sample description
          </comment>
       </sample>
       <sample>
          <name>
             my second sample
          </name>
          <comment>
             sample description
          </comment>
       </sample>
       <workspace>
          <name>
             my first measurement
          </name>
          <comment>
             comment on my first measurement:
               - it consists of two images
               - they are random, but beautiful!
          </comment>
          <instrument>
             <name>
                my microscope
             </name>
             <par name="color" value="black"/>
             <par name="cost" value="9001" unit="EUR"/>
          </instrument>
          <dataset type="img">
             <name>
                image 1
             </name>
             <par name="zoom-level" value="30"/>
             <par name="wavelength" value="509" unit="nm"/>
             <data encoding="base64" type="image/png">
                iVBORw0KGgoAAAANSUhEUgAAAAoAAAAKCAAAAACoWZBhAAAAeUlEQVR4nAFuAJH/AZAWa0VE4fDv
                DEMAWrhy8Ap+NRE0VwIP2W6xVnz36/tkBECg6Br6DZSfxtwArc1sCMhI+QTlQASDQCtSFPwonKkl
                ABlDAnNLCJI3owsBn1+a/M2JOlv88QBxoT/oB/x8FtApAak8PoLosFYFybBPszA9DBBXFAAAAABJ
                RU5ErkJggg==
             </data>
          </dataset>
          <dataset type="img">
             <name>
                image 2
             </name>
             <data encoding="base64" type="image/png">
                iVBORw0KGgoAAAANSUhEUgAAAAoAAAAKCAAAAACoWZBhAAAAeUlEQVR4nAFuAJH/AV2nPj/syu3s
                q1EAGGJHzgLvp7gliQJd+s1UeOgI0TtGALgZ+RDJLsZBD6cC0WE59UEZBdHxFwAhlFXietBCH3tj
                AFEbsOITtH4B3qcAzD/+lO8Iz5QyfAGIISIjCDwTyvp2AFHbtb0Fkcn8INWzmDFI/vtkfAAAAABJ
                RU5ErkJggg==
             </data>
          </dataset>
       </workspace>
       <workspace>
          <name>
             my second measurement
          </name>
          <comment>
             comment on my first measurement
          </comment>
          <dataset type="sc">
             <name>
                x
             </name>
             <data rows="100" cols="1" type="int">
                0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38
                39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74
                75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99
             </data>
          </dataset>
          <dataset type="sc">
             <name>
                y
             </name>
             <data rows="100" cols="1" type="int">
                0 1 4 6 7 3 4 0 8 5 9 7 7 6 2 8 1 4 9 5 8 9 5 6 8 4 5 8 1 9 8 9 1 5 1 3 7 2 0 9 0 2 0 6 6 2 0 3 6 4 0 1 2 2
                5 6 4 6 9 9 2 0 6 3 4 1 6 2 8 7 7 6 5 5 6 4 0 6 7 5 8 7 8 4 2 0 3 0 4 5 2 7 5 7 4 5 8 2 2 0
             </data>
          </dataset>
       </workspace>
    </workspace>

.. testcleanup:: generates-my-results

    import os
    if os.path.isfile("my-results.sdf"):
        os.remove(os.path.abspath("my-results.sdf"))
    if os.path.isfile("my-results.png"):
        os.remove("my-results.png")