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| <html> | ||
| <head> | ||
| <meta http-equiv="refresh" content="0;URL='http://soft-matter.github.io/trackpy/v0.6.2/'" /> | ||
| <meta http-equiv="refresh" content="0;URL='http://soft-matter.github.io/trackpy/v0.6.3/'" /> | ||
| </head> | ||
| <html> |
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| # Sphinx build info version 1 | ||
| # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. | ||
| config: f98e6d6cb993554fd2d4a370bc00abaa | ||
| tags: 645f666f9bcd5a90fca523b33c5a78b7 |
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| .. _api_ref: | ||
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| API reference | ||
| ============= | ||
| The core functionality of trackpy is grouped into three separate steps: | ||
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| 1. Locating features in an image | ||
| 2. Refining feature coordinates to obtain subpixel precision | ||
| 3. Identifying features through time, linking them into trajectories. | ||
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| Convenience functions for feature finding, refinement, and linking are readily available: | ||
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| .. autosummary:: | ||
| :toctree: generated/ | ||
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| trackpy.locate | ||
| trackpy.batch | ||
| trackpy.link | ||
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| For more control on your tracking "pipeline", the following core functions are provided: | ||
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| Feature finding | ||
| --------------- | ||
| .. autosummary:: | ||
| :toctree: generated/ | ||
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| trackpy.grey_dilation | ||
| trackpy.find_link | ||
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| Coordinate refinement | ||
| --------------------- | ||
| .. autosummary:: | ||
| :toctree: generated/ | ||
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| trackpy.refine_com | ||
| trackpy.refine_leastsq | ||
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| Linking | ||
| ------- | ||
| .. autosummary:: | ||
| :toctree: generated/ | ||
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| trackpy.link | ||
| trackpy.link_iter | ||
| trackpy.link_df_iter | ||
| trackpy.link_partial | ||
| trackpy.reconnect_traj_patch | ||
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| :func:`~trackpy.linking.link` and :func:`~trackpy.linking.link_df_iter` run | ||
| the same underlying code. :func:`~trackpy.linking.link` operates on a single | ||
| DataFrame containing data for an entire movie. | ||
| :func:`~trackpy.linking.link_df_iter` streams through larger data sets, | ||
| in the form of one DataFrame for each video frame. | ||
| :func:`~trackpy.linking.link_iter` streams through a series of numpy | ||
| ndarrays. | ||
| :func:`~trackpy.linking.link_partial` can patch a region of trajectories in | ||
| an already linked dataset. | ||
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| See the tutorial on large data sets for more. | ||
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| Static Analysis | ||
| --------------- | ||
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| .. autosummary:: | ||
| :toctree: generated/ | ||
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| trackpy.static.proximity | ||
| trackpy.static.pair_correlation_2d | ||
| trackpy.static.pair_correlation_3d | ||
| trackpy.static.cluster | ||
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| Motion Analysis | ||
| --------------- | ||
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| .. autosummary:: | ||
| :toctree: generated/ | ||
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| trackpy.motion.msd | ||
| trackpy.motion.imsd | ||
| trackpy.motion.emsd | ||
| trackpy.motion.compute_drift | ||
| trackpy.motion.subtract_drift | ||
| trackpy.motion.vanhove | ||
| trackpy.motion.relate_frames | ||
| trackpy.motion.velocity_corr | ||
| trackpy.motion.direction_corr | ||
| trackpy.motion.is_typical | ||
| trackpy.motion.diagonal_size | ||
| trackpy.motion.theta_entropy | ||
| trackpy.motion.min_rolling_theta_entropy | ||
| trackpy.filtering.filter_stubs | ||
| trackpy.filtering.filter_clusters | ||
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| Prediction Framework | ||
| -------------------- | ||
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| Trackpy extends the Crocker--Grier algoritm using a prediction framework, described in the prediction tutorial. | ||
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| .. autosummary:: | ||
| :toctree: generated/ | ||
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| trackpy.predict.NullPredict | ||
| trackpy.predict.ChannelPredict | ||
| trackpy.predict.DriftPredict | ||
| trackpy.predict.NearestVelocityPredict | ||
| trackpy.predict.predictor | ||
| trackpy.predict.instrumented | ||
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| Plotting Tools | ||
| -------------- | ||
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| Trackpy includes functions for plotting the data in ways that are commonly useful. If you don't find what you need here, you can plot the data any way you like using matplotlib, seaborn, or any other plotting library. | ||
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| .. autosummary:: | ||
| :toctree: generated/ | ||
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| trackpy.annotate | ||
| trackpy.scatter | ||
| trackpy.plot_traj | ||
| trackpy.annotate3d | ||
| trackpy.scatter3d | ||
| trackpy.plot_traj3d | ||
| trackpy.plot_displacements | ||
| trackpy.subpx_bias | ||
| trackpy.plot_density_profile | ||
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| These two are almost too simple to justify their existence -- just a convenient shorthand for a common plotting task. | ||
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| .. autosummary:: | ||
| :toctree: generated/ | ||
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| trackpy.mass_ecc | ||
| trackpy.mass_size | ||
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| Image Conversion | ||
| ---------------- | ||
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| By default, :func:`~trackpy.feature.locate` applies a bandpass and a percentile-based | ||
| threshold to the image(s) before finding features. You can turn off this functionality | ||
| using ``preprocess=False, percentile=0``.) In many cases, the default bandpass, which | ||
| guesses good length scales from the ``diameter`` parameter, "just works." But if you want | ||
| to executre these steps manually, you can. | ||
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| .. autosummary:: | ||
| :toctree: generated/ | ||
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| trackpy.find.percentile_threshold | ||
| trackpy.preprocessing.bandpass | ||
| trackpy.preprocessing.lowpass | ||
| trackpy.preprocessing.scale_to_gamut | ||
| trackpy.preprocessing.invert_image | ||
| trackpy.preprocessing.convert_to_int | ||
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| Framewise Data Storage & Retrieval Interface | ||
| -------------------------------------------- | ||
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| Trackpy implements a generic interface that could be used to store and | ||
| retrieve particle tracking data in any file format. We hope that it can | ||
| make it easier for researchers who use different file formats to exchange data. Any in-house format could be accessed using the same simple interface in trackpy. | ||
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| At present, the interface is implemented only for HDF5 files. There are | ||
| several different implementations, each with different performance | ||
| optimizations. :class:`~trackpy.framewise_data.PandasHDFStoreBig` is a good general-purpose choice. | ||
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| .. autosummary:: | ||
| :toctree: generated/ | ||
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| trackpy.PandasHDFStore | ||
| trackpy.PandasHDFStoreBig | ||
| trackpy.PandasHDFStoreSingleNode | ||
| trackpy.FramewiseData | ||
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| That last class cannot be used directly; it is meant to be subclassed | ||
| to support other formats. See *Writing Your Own Interface* in the streaming tutorial for | ||
| more. | ||
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| Logging | ||
| ------- | ||
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| Trackpy issues log messages. This functionality is mainly used to report the | ||
| progress of lengthy jobs, but it may be used in the future to report details of | ||
| feature-finding and linking for debugging purposes. | ||
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| When trackpy is imported, it automatically calls `handle_logging()`, which sets | ||
| the logging level and attaches a logging handler that plays nicely with | ||
| IPython notebooks. You can override this by calling `ignore_logging()` and | ||
| configuring the logger however you like. | ||
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| .. autosummary:: | ||
| :toctree: generated/ | ||
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| trackpy.quiet | ||
| trackpy.handle_logging | ||
| trackpy.ignore_logging | ||
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| Utility functions | ||
| ----------------- | ||
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| .. autosummary:: | ||
| :toctree: generated/ | ||
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| trackpy.minmass_v03_change | ||
| trackpy.minmass_v04_change | ||
| trackpy.utils.fit_powerlaw | ||
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| Diagnostic functions | ||
| -------------------- | ||
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| .. autosummary:: | ||
| :toctree: generated/ | ||
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| trackpy.diag.performance_report | ||
| trackpy.diag.dependencies | ||
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| Low-Level API (Advanced) | ||
| ------------------------ | ||
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| Switching Between Numba and Pure Python | ||
| ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ | ||
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| Trackpy implements the most intensive (read: slowest) parts of the core feature-finding and linking algorithm in pure Python (with numpy) and also in `numba <http://numba.pydata.org/>`_, which accelerates Python code. Numba can offer a major performance boost, but it is still relatively new, and it can be challenging to use. If numba is available, trackpy will use the numba implementation by default; otherwise, it will use pure Python. The following functions allow sophisticated users to manually switch between numba and pure-Python modes. This may be used, for example, to measure the performance of these two implementations on your data. | ||
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| .. autosummary:: | ||
| :toctree: generated/ | ||
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| trackpy.enable_numba | ||
| trackpy.disable_numba | ||
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| Low-Level Linking API | ||
| ^^^^^^^^^^^^^^^^^^^^^ | ||
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| All of the linking functions in trackpy provide the same level of control over the linking algorithm itself. For almost all users, the functions above will be sufficient. But :func:`~trackpy.linking.link_df` and :func:`~trackpy.linking.link_df_iter` above do assume that the data is stored in a pandas DataFrame. For users who want to use some other iterable data structure, the functions below provide direct access to the linking code. | ||
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| .. autosummary:: | ||
| :toctree: generated/ | ||
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| trackpy.link_iter | ||
| trackpy.link | ||
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| And the following classes can be subclassed to implement a customized linking procedure. | ||
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| .. autosummary:: | ||
| :toctree: generated/ | ||
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| trackpy.SubnetOversizeException | ||
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| Masks | ||
| ^^^^^ | ||
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| These functions may also be useful for rolling your own algorithms: | ||
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| .. autosummary:: | ||
| :toctree: generated/ | ||
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| trackpy.masks.binary_mask | ||
| trackpy.masks.r_squared_mask | ||
| trackpy.masks.x_squared_masks | ||
| trackpy.masks.cosmask | ||
| trackpy.masks.sinmask | ||
| trackpy.masks.theta_mask | ||
| trackpy.masks.gaussian_kernel | ||
| trackpy.masks.mask_image | ||
| trackpy.masks.slice_image | ||
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| Full API reference | ||
| ------------------ | ||
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| A full overview of all modules and functions can be found below: | ||
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| .. autosummary:: | ||
| :toctree: generated/ | ||
| :recursive: | ||
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| trackpy | ||
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| .. | ||
| Note: we excluded trackpy.tests in conf.py (autosummary_mock_imports) |
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trackpy/v0.6.3/_sources/generated/trackpy.FramewiseData.close.rst.txt
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| trackpy.FramewiseData.close | ||
| =========================== | ||
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| .. automethod:: FramewiseData.close |
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trackpy/v0.6.3/_sources/generated/trackpy.FramewiseData.dump.rst.txt
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| trackpy.FramewiseData.dump | ||
| ========================== | ||
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| .. automethod:: FramewiseData.dump |
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trackpy/v0.6.3/_sources/generated/trackpy.FramewiseData.frames.rst.txt
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| trackpy.FramewiseData.frames | ||
| ============================ | ||
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| .. autoproperty:: FramewiseData.frames |
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trackpy/v0.6.3/_sources/generated/trackpy.FramewiseData.get.rst.txt
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| trackpy.FramewiseData.get | ||
| ========================= | ||
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| .. automethod:: FramewiseData.get |
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trackpy/v0.6.3/_sources/generated/trackpy.FramewiseData.max_frame.rst.txt
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| trackpy.FramewiseData.max\_frame | ||
| ================================ | ||
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trackpy/v0.6.3/_sources/generated/trackpy.FramewiseData.put.rst.txt
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| trackpy.FramewiseData.put | ||
| ========================= | ||
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trackpy/v0.6.3/_sources/generated/trackpy.FramewiseData.rst.txt
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| trackpy.FramewiseData | ||
| ===================== | ||
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| .. autoclass:: FramewiseData | ||
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| .. HACK -- the point here is that we don't want this to appear in the output, but the autosummary should still generate the pages. | ||
| .. autosummary:: | ||
| :toctree: | ||
| FramewiseData.close | ||
| FramewiseData.dump | ||
| FramewiseData.get | ||
| FramewiseData.put | ||
| .. HACK -- the point here is that we don't want this to appear in the output, but the autosummary should still generate the pages. | ||
| .. autosummary:: | ||
| :toctree: | ||
| FramewiseData.frames | ||
| FramewiseData.max_frame | ||
| FramewiseData.t_column | ||
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trackpy/v0.6.3/_sources/generated/trackpy.FramewiseData.t_column.rst.txt
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| trackpy.FramewiseData.t\_column | ||
| =============================== | ||
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trackpy/v0.6.3/_sources/generated/trackpy.PandasHDFStore.close.rst.txt
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| trackpy.PandasHDFStore.close | ||
| ============================ | ||
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| .. automethod:: PandasHDFStore.close |
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