Release Pylake v0.10.1

changelog.md

@@ -1,25 +1,27 @@
 # Changelog
 
-## v0.10.1 | t.b.d.
+## v0.10.1 | 2021-10-27
 
 #### New features
 
-* Add support for axial force detection (i.e. force detection along the Z axis). The high-frequency data can be accessed with `f.force1z` and `f.force2z` while the downsampled low-frequency channels can be accessed with `f.downsampled_force1z` and `f.downsampled_force2z`. The calibrations can be accessed with `f.force1z.calibration` and `f.force2z.calibration`. The Z component is *not* factored in the calculation of the total force `f.downsampled_force1` and `f.downsampled_force2`.
+* Added support for axial force detection (i.e. force detection along the Z axis). The high-frequency data can be accessed with `f.force1z` and `f.force2z` while the downsampled low-frequency channels can be accessed with `f.downsampled_force1z` and `f.downsampled_force2z`. The calibrations can be accessed with `f.force1z.calibration` and `f.force2z.calibration`. The Z component is *not* factored in the calculation of the total force `f.downsampled_force1` and `f.downsampled_force2`.
 * Added `KymoLineGroup.fit_binding_times()` to allow for dwelltime analysis of bound states found using kymotracker. See [kymotracking](https://lumicks-pylake.readthedocs.io/en/latest/tutorial/kymotracking.html) for more information.
 * Allow using data acquired with a fast force sensor by passing `fast_sensor=True` when creating a `PassiveCalibrationModel`.
 * Allow using hydrodynamically correct power spectrum when performing force calibration. See [force calibration](https://lumicks-pylake.readthedocs.io/en/latest/tutorial/force_calibration.html) for more information.
+* Added ability to crop `CorrelatedStacks`. See [Correlated stacks](https://lumicks-pylake.readthedocs.io/en/latest/tutorial/correlatedstacks.html#correlated-stacks) for more information.
 
 #### Bug fixes
 
 * Show an error message when user attempts to refine lines before tracking or loading them so the kymotracker widget does not become unresponsive.
 * Force calibration models now throw an error when a bead diameter of less than `10^-2` microns is used (rather than produce `NaN` results).
 * Fixed bug that prevented export of `CorrelatedStack` if the alignment matrices were missing from the metadata.
 * Fixed bug where the exported metadata fields were erroneously exported as `"Applied channel X alignment"` for `CorrelatedStack` where the alignment was not actually applied.
+* Fixed error in optimal point determination for MSD estimator. Note that this bug led to an error being thrown when the number of points in the trace was exactly 4. Now it results in a more informative error message (`You need at least 5 time points to estimate the number of points to include in the fit.`).
 
 #### Improvements
 
-* Switch to trust region reflective algorithm for fitting thermal calibration spectrum. This results in fewer optimization failures.
-* Make sure that `f_diode` stays below the Nyquist frequency during fitting.
+* Switched to trust region reflective algorithm for fitting thermal calibration spectrum. This results in fewer optimization failures.
+* Pylake now ensures that `f_diode` stays below the Nyquist frequency during fitting.
 * Implemented a bias correction for the thermal calibration. Note that this typically leads to a small correction unless you use a very low number of points per block.
 
 #### Deprecations

docs/api.rst

@@ -27,7 +27,6 @@ Force calibration
 
     calculate_power_spectrum
     fit_power_spectrum
-    CalibrationSettings
     PassiveCalibrationModel
     force_calibration.power_spectrum.PowerSpectrum
     force_calibration.power_spectrum_calibration.CalibrationResults
@@ -85,8 +84,7 @@ Notebook widgets
     :toctree: _api
 
     FdRangeSelector
-    nb_widgets.fd_selector.SliceRangeSelector
-    nb_widgets.fd_selector.FdRangeSelectorWidget
+    FdDistanceRangeSelector
 
 Population Dynamics
 -------------------

docs/requirements.txt

@@ -9,3 +9,4 @@ scipy
 sphinx
 sphinx_rtd_theme
 sphinxcontrib-bibtex==0.4.2
+docutils<0.18

docs/tutorial/kymotracking.rst

@@ -52,8 +52,8 @@ If we zoom in a bit, we can see that our traces in this image are about 0.3 micr
 .. image:: kymo_zoom.png
 
 The peaks have an intensity of about 3-7 photon counts, whereas the background fluctuates around 0-2. Let's set our
-`pixel_threshold` to 4. We also see that sometimes, a particle momentarily disappears. To still connect these in a
-single trace, we want to allow for some gaps in the connection step. Let's use a `window` size of 9 in this test.
+`pixel_threshold` to 3. We also see that sometimes, a particle momentarily disappears. To still connect these in a
+single trace, we want to allow for some gaps in the connection step. Let's use a `window` size of 6 in this test.
 Considering that we only want to run the kymotracker on part of the image, we also pass the rect argument, which defines a rectangle over which to track peaks.
 In this case, we track particles between 0 and 350 seconds, and 7 and 22 micron.
 Running the algorithm is easy using the function :func:`~lumicks.pylake.track_greedy`::
@@ -337,9 +337,9 @@ With pure diffusive motion (a complete absence of drift) in an isotropic medium,
 
 .. math::
 
-    \rho[n] = 2 D t + offset
+    \rho[n] = 2 D n \Delta t + offset
 
-where :math:`D` is the diffusion constant in :math:`um^2/s`, :math:`t` is time, and the offset is determined by the localization accuracy:
+where :math:`D` is the diffusion constant in :math:`um^2/s`, :math:`\Delta t` is the time step, :math:`n` is the step index and the offset is determined by the localization accuracy:
 
 .. math::
 
@@ -479,7 +479,7 @@ Here, a random dataset with the same size as the original is sampled (with repla
 is then fit using the MLE method, just as for the original dataset. The fit results in a new estimate for the model parameters.
 This process is repeated many times, and the distribution of the resulting parameters can be analyzed to estimate certain statistics about the them::
 
-    dwell_2.run_bootstrap(iterations=1000)
+    dwell_2.calculate_bootstrap(iterations=1000)
     dwell_2.bootstrap.plot(alpha=0.05)
 
 .. image:: kymo_bind_bootstrap_2.png

docs/tutorial/nbwidgets.rst

@@ -24,14 +24,13 @@ regions using a widget. Let's load the file and run the widget::
 
 You can use the left mouse button to select time ranges (by clicking the left and then the right boundary of the region
 you wish to select). The right mouse button can be used to remove previous selections. We can access the selected
-timestamps of the ranges we selected by invoking
-:attr:`~lumicks.pylake.nb_widgets.fd_selector.SliceRangeSelector.ranges`::
+timestamps of the ranges we selected by invoking `selector.ranges`::
 
     >>> selector.ranges
     [array([1572279165841737600, 1572279191523516800], dtype=int64),
     array([1572279201850211200, 1572279224153072000], dtype=int64)]
 
-And the actual slices from :attr:`~lumicks.pylake.nb_widgets.fd_selector.SliceRangeSelector.slices`. If we want to
+And the actual slices from `selector.slices`. If we want to
 plot all of our selections in separate plots for instance, we can do the following::
 
     for data_slice in selector.slices:

lumicks/pylake/__about__.py

@@ -1,5 +1,5 @@
 __title__ = "lumicks.pylake"
-__version__ = "0.10.0"
+__version__ = "0.10.1"
 __summary__ = "Bluelake data analysis tools"
 __url__ = "https://github.com/lumicks/pylake"