.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/02-mass_distribution.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_02-mass_distribution.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_02-mass_distribution.py:


Mass distribution
=================

This tutorial shows how to compute and fit mass distributions from a movie once
a contrast-to-mass calibration is available:

1. Download an example movie.
2. Set a contrast-to-mass calibration.
3. Run detection + PSF fitting to obtain a table of event masses.
4. Fit peaks in the mass histogram and visualize the result.

.. GENERATED FROM PYTHON SOURCE LINES 15-18

Imports
-------
We first import necessary packages and modules.

.. GENERATED FROM PYTHON SOURCE LINES 18-24

.. code-block:: Python


    from pprint import pprint

    import matplotlib.pyplot as plt
    import numpy as np








.. GENERATED FROM PYTHON SOURCE LINES 25-30

Download example data
---------------------

This tutorial uses a short example movie from Zenodo. You can replace this
block with a direct file path once you have a local movie.

.. GENERATED FROM PYTHON SOURCE LINES 30-47

.. code-block:: Python

    from zenodozipdownloader import ZenodoZipDownloader

    import massphoto as mp

    files = ZenodoZipDownloader(
        doi="10.5281/zenodo.15800729",
        download_dir="example_data",
    ).download("Figure 2.zip/006_dynamin_6.mp")
    if not files:
        raise RuntimeError(
            "Failed to download example data. Please download it manually from https://zenodo.org/record/15800729 "
            "or set path_movie to your own movie."
        )
    path_movie = files[0]

    print(f"Using movie: {path_movie}")





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Using movie: /home/runner/work/massphoto/massphoto/docs/source/_examples/example_data/006_dynamin_6.mp




.. GENERATED FROM PYTHON SOURCE LINES 48-54

Analyse with default parameters
-------------------------------

Set mass calibration parameters. See the mass calibration example for how
to obtain these from a calibration movie:
:ref:`sphx_glr_auto_examples_01-mass_calibration.py`.

.. GENERATED FROM PYTHON SOURCE LINES 54-66

.. code-block:: Python


    # Disable progress bars for cleaner output in this tutorial
    mp.defaults.set("progress_bar", False)

    # Set contrast-to-mass calibration parameters (intercept, slope). Adjust to match your calibration.
    mp.defaults.set("mass_calibration", (-5.2, 3.1e4))

    # Run detection and fitting. Keep the ratiometric frames for inspecting events.
    # Sequential execution limits peak memory while retaining those frames.
    fitres, movie_r = mp.fit(path_movie, return_ratiometric=True, scheduler="synchronous")
    fitres[["frame", "x", "y", "contrasts", "masses_kDa"]].head().round({"x": 2, "y": 2, "contrasts": 5, "masses_kDa": 1})






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>frame</th>
          <th>x</th>
          <th>y</th>
          <th>contrasts</th>
          <th>masses_kDa</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>20</td>
          <td>86.09</td>
          <td>6.87</td>
          <td>0.00343</td>
          <td>101.2</td>
        </tr>
        <tr>
          <th>1</th>
          <td>20</td>
          <td>165.06</td>
          <td>72.34</td>
          <td>0.00604</td>
          <td>182.1</td>
        </tr>
        <tr>
          <th>2</th>
          <td>20</td>
          <td>205.38</td>
          <td>74.17</td>
          <td>0.00648</td>
          <td>195.8</td>
        </tr>
        <tr>
          <th>3</th>
          <td>20</td>
          <td>124.95</td>
          <td>80.87</td>
          <td>0.00580</td>
          <td>174.5</td>
        </tr>
        <tr>
          <th>4</th>
          <td>20</td>
          <td>189.78</td>
          <td>93.18</td>
          <td>0.01202</td>
          <td>367.4</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 67-68

Fit mass distribution

.. GENERATED FROM PYTHON SOURCE LINES 68-71

.. code-block:: Python

    histfit = mp.distfit.fit(fitres["masses_kDa"], bins=np.arange(-100, 700, 2.5), include_background=True)
    histfit.peaks[["mu", "sigma", "A"]].round(2)






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>mu</th>
          <th>sigma</th>
          <th>A</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>89.37</td>
          <td>7.15</td>
          <td>4199.50</td>
        </tr>
        <tr>
          <th>1</th>
          <td>182.20</td>
          <td>7.82</td>
          <td>5248.13</td>
        </tr>
        <tr>
          <th>2</th>
          <td>370.17</td>
          <td>9.50</td>
          <td>3235.14</td>
        </tr>
        <tr>
          <th>3</th>
          <td>557.33</td>
          <td>10.71</td>
          <td>290.48</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 72-73

Plot results

.. GENERATED FROM PYTHON SOURCE LINES 73-81

.. code-block:: Python

    plt.figure(figsize=(6, 3))
    plt.bar(histfit.bin_centers, histfit.counts, width=histfit.bin_widths, edgecolor="none", label="Data")
    plt.plot(histfit.model_x, histfit.model_y, color="black", lw=1, label="Fit")
    plt.xlabel("Mass (kDa)")
    plt.ylabel("Counts")
    plt.legend()
    plt.tight_layout()




.. image-sg:: /auto_examples/images/sphx_glr_02-mass_distribution_001.png
   :alt: 02 mass distribution
   :srcset: /auto_examples/images/sphx_glr_02-mass_distribution_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 82-85

Analyse with custom parameters
------------------------------


.. GENERATED FROM PYTHON SOURCE LINES 85-137

.. code-block:: Python


    # Read the movie
    movie, metadata = mp.read_movie(path_movie, convert_metadata=True)


    # Inspect the metadata
    print("Metadata:")
    pprint(metadata)

    # Check default thresholds for detection
    print("Default detection thresholds:", mp.defaults.get("event_correlation_step.thresholds"))

    # We could adjust these if we want to be more or less sensitive, for all following analyses. For example:
    # mp.defaults.set("event_correlation_step.thresholds", [0.5, 0.4, 0.001])

    # Check default averaging window
    print("Default navg:", mp.defaults.get("navg"))

    # We could adjust the default like this:
    # mp.defaults.set("navg", 5)


    ##
    # We can also specify detection arguments here, which will only apply to this fit call.
    fitres = mp.fit(
        movie,
        movie_correct_drift=True,
        ratiometric_args={
            "navg": 5,
        },
        detection_args={
            "thresholds": [
                0.5,  # temporal threshold
                0.4,  # spatial threshold
                0.001,  # contrast threshold
            ],
            # navg for the temporal filter (longer than navg for the ratiometric filter is often beneficial)
            "correlation_step_args": {"navg": 10},
        },
    )

    # We decreased the integration time and lowered the thresholds, which leads to more events being detected, but also more noise.
    # To remove some of the noise, we can filter based on fit quality or mass uncertainty.
    filter_str = "masses_kDa_se < 10 & r2_fit > 0.4"
    histfit = mp.distfit.fit(
        fitres.query(filter_str),
        bins=np.arange(-100, 700, 2.5),
        initial_peaks=(90, 180, 360, 540),
        include_background=False,
    )
    histfit.peaks[["mu", "sigma", "A"]].round(2)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Metadata:
    {'camera': 'GS3_U3_23S6M_C',
     'exposuretime': 7.646799087524414,
     'exposuretime_effective': 15.293598175048828,
     'exposuretime_unit': 'ms',
     'field_of_view': (11.7528, 16.4964),
     'field_of_view_unit': 'um',
     'framebinning': 2,
     'framerate': 128.20632934570312,
     'framerate_effective': 64.10316467285156,
     'framerate_unit': 'Hz',
     'image_shape': (166, 233),
     'instrument': 'Refeyn TwoMP',
     'pixelbinning': 6,
     'pixelsize': 0.0118,
     'pixelsize_effective': 0.0708,
     'pixelsize_unit': 'um'}
    Default detection thresholds: (0.6, 0.5, 0.001)
    Default navg: 10


.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>mu</th>
          <th>sigma</th>
          <th>A</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>89.72</td>
          <td>8.04</td>
          <td>4527.06</td>
        </tr>
        <tr>
          <th>1</th>
          <td>182.05</td>
          <td>8.22</td>
          <td>5284.42</td>
        </tr>
        <tr>
          <th>2</th>
          <td>369.90</td>
          <td>9.73</td>
          <td>3245.49</td>
        </tr>
        <tr>
          <th>3</th>
          <td>557.76</td>
          <td>12.82</td>
          <td>372.53</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 295-296

Compare the unfiltered distribution, selected events, and fitted peaks.

.. GENERATED FROM PYTHON SOURCE LINES 296-306

.. code-block:: Python


    plt.figure(figsize=(6, 3))
    plt.hist(fitres["masses_kDa"], bins=histfit.bin_edges, edgecolor="none", facecolor="orange", label="Data (unfiltered)")
    plt.bar(histfit.bin_centers, histfit.counts, width=histfit.bin_widths, edgecolor="none", label="Data")
    plt.plot(histfit.model_x, histfit.model_y, color="black", lw=1, label="Fit")
    plt.xlabel("Mass (kDa)")
    plt.ylabel("Counts")
    plt.legend()
    plt.tight_layout()




.. image-sg:: /auto_examples/images/sphx_glr_02-mass_distribution_002.png
   :alt: 02 mass distribution
   :srcset: /auto_examples/images/sphx_glr_02-mass_distribution_002.png
   :class: sphx-glr-single-img






.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (1 minutes 3.736 seconds)


.. _sphx_glr_download_auto_examples_02-mass_distribution.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: 02-mass_distribution.ipynb <02-mass_distribution.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: 02-mass_distribution.py <02-mass_distribution.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: 02-mass_distribution.zip <02-mass_distribution.zip>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_