Refactor LossViewer to use matplotlib (#1899)

* use updated syntax for QtAgg backend of matplotlib

* start add features to `MplCanvas` to replace QtCharts features in `LossViewer` (untested)

* remove QtCharts imports and replace with MplCanvas

* remove QtCharts imports and replace with MplCanvas

* start using MplCanvas in LossViwer instead of QtCharts (untested)

* use updated syntax

* Uncomment all commented out QtChart

* Add debug code

* Refactor monitor to use LossViewer._init_series method

* Add monitor only debug code

* Add methods for setting up axes and legend

* Add the matplotlib canvas to the widget

* Resize axis with data (no log support yet)

* Try using PathCollection for "batch"

* Get "batch" plotting with ax.scatter (no log support yet)

* Add log support

* Add a _resize_axis method

* Modify init_series to work for ax.plot as well

* Use matplotlib to plot epoch_loss line

* Add method _add_data_to_scatter

* Add _add_data_to_plot method

* Add docstring to _resize_axes

* Add matplotlib plot for val_loss

* Add matplotlib scatter for val_loss_best

* Avoid errors with setting log scale before any positive values

* Add x and y axes labels

* Set title (removing html tags)

* Add legend

* Adjust positioning of plot

* Lint

* Leave MplCanvas unchanged

* Removed unused training_monitor.LossViewer

* Resize fonts

* Move legend outside of plot

* Add debug code for montitor aesthetics

* Use latex formatting to bold parts of title

* Make axes aesthetic

* Add midpoint grid lines

* Set initial limits on x and y axes to be 0+

* Ensure x axis minimum is always resized to 0+

* Adjust plot to account for plateau patience title

* Add debug code for plateau patience title line

* Lint

* Set thicker line width

* Remove unused import

* Set log axis on initialization

* Make tick labels smaller

* Move plot down a smidge

* Move ylabel left a bit

* Lint

* Add class LossPlot

* Refactor LossViewer to use LossPlot

* Remove QtCharts code

* Remove debug codes

* Allocate space for figure items based on item's size

* Refactor LossPlot to use underscores for internal methods

* Ensure y_min, y_max not equal
Otherwise we get an unnecessary teminal message:
UserWarning: Attempting to set identical bottom == top == 3.0 results in singular transformations; automatically expanding.
  self.axes.set_ylim(y_min, y_max)

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Co-authored-by: roomrys <[email protected]>
Co-authored-by: roomrys <[email protected]>

sleap/gui/app.py

@@ -44,7 +44,6 @@
 frame and instances listed in data view table.
 """
 
-
 import os
 import platform
 import random