bump msfc-ccd to 0.6.0

Author: roytsmart

esis/_caching.py

@@ -5,7 +5,7 @@
     "memory",
 ]
 
-_path_cache = pathlib.Path(__file__).parent.parent / ".cache"
+_path_cache = pathlib.Path.home() / ".esis/cache"
 
 memory = joblib.Memory(location=_path_cache, mmap_mode="r", verbose=0)
 """A representation of the cache which stores intermediate results."""

esis/data/__init__.py

@@ -26,10 +26,12 @@
  * Cosmic ray removal
 """
 
+from . import abc
 from ._level_0 import Level_0
 from ._level_1 import Level_1
 
 __all__ = [
+    "abc",
     "Level_0",
     "Level_1",
 ]

esis/data/_level_0/_level_0.py

@@ -12,6 +12,7 @@
 import named_arrays as na
 import msfc_ccd
 import esis
+from .. import abc
 
 __all__ = [
     "Level_0",
@@ -21,6 +22,7 @@
 @dataclasses.dataclass(eq=False, repr=False)
 class Level_0(
     msfc_ccd.SensorData,
+    abc.AbstractChannelData,
 ):
     """
     Representation of ESIS Level-0 images, the raw data gathered by the instrument.
@@ -33,12 +35,6 @@ class Level_0(
     timeline: None | esis.nsroc.Timeline = None
     """The sequence of NSROC events associated with these images."""
 
-    axis_time: str = dataclasses.field(default="time", kw_only=True)
-    """The name of the logical axis corresponding to changing time."""
-
-    axis_channel: str = dataclasses.field(default="channel", kw_only=True)
-    """The name of the logical axis corresponding to the different channels."""
-
     @classmethod
     def from_fits(
         cls,
@@ -193,164 +189,3 @@ def dark(self) -> Self:
     def dark_subtracted(self):
         """Subtract the master :attr:`dark` from each image in the sequence."""
         return self - self.dark.outputs
-
-    def animate(
-        self,
-        ax: None | matplotlib.axes.Axes | na.AbstractArray = None,
-        cmap: None | str | matplotlib.colors.Colormap = None,
-        norm: None | str | matplotlib.colors.Normalize = None,
-        vmin: None | na.ArrayLike = None,
-        vmax: None | na.ArrayLike = None,
-        cbar_fraction: float = 0.1,
-    ) -> matplotlib.animation.FuncAnimation:
-        """
-        Create an animation using the frames in this dataset.
-
-        Parameters
-        ----------
-        ax
-            The :class:`~matplotlib.axes.Axes` instance(s) to use.
-            If :obj:`None`, a new set of axes will be created.
-        cmap
-            The colormap used to map scalar data to colors.
-        norm
-            The normalization method used to scale data into the range [0, 1] before
-            mapping to colors.
-        vmin
-            The minimum value of the data range.
-            If `norm` is :obj:`None`, this parameter will be ignored.
-        vmax
-            The maximum value of the data range.
-            If `norm` is :obj:`None`, this parameter will be ignored.
-        cbar_fraction
-            The fraction of the space to use for the colorbar axes.
-        """
-        axis_time = self.axis_time
-        axis_channel = self.axis_channel
-
-        if ax is None:
-            figwidth = plt.rcParams["figure.figsize"][0]
-            figwidth_eff = figwidth * (1 - 2 * cbar_fraction)
-            shape = self.shape
-            num_channel = shape[axis_channel]
-            num_x = shape[self.axis_x]
-            num_y = shape[self.axis_y]
-            figheight = num_channel * figwidth_eff * num_y / num_x
-            fig, ax = na.plt.subplots(
-                axis_rows=axis_channel,
-                nrows=num_channel,
-                sharex=True,
-                sharey=True,
-                constrained_layout=True,
-                figsize=(figwidth, figheight),
-                origin="upper",
-            )
-            na.plt.set_xlabel("detector $x$ (pix)", ax=ax[{axis_channel: ~0}])
-            na.plt.set_ylabel("detector $y$ (pix)", ax=ax)
-
-        data = self.outputs
-        unit = na.unit(data)
-
-        if norm is None:
-            if vmin is None:
-                vmin = data.percentile(1).ndarray
-            if vmax is None:
-                vmax = data.percentile(99).ndarray
-
-            if unit is not None:
-                vmin = vmin.to(unit).value
-                vmax = vmax.to(unit).value
-
-            norm = plt.Normalize(
-                vmin=vmin,
-                vmax=vmax,
-            )
-
-        colorizer = plt.Colorizer(
-            cmap=cmap,
-            norm=norm,
-        )
-
-        pixel = self.inputs.pixel
-
-        ani = na.plt.pcolormovie(
-            self.inputs.time.mean(axis_channel),
-            pixel.x,
-            pixel.y,
-            C=data,
-            axis_time=axis_time,
-            ax=ax,
-            kwargs_pcolormesh=dict(
-                colorizer=colorizer,
-            ),
-        )
-        na.plt.text(
-            x=0.5,
-            y=1.01,
-            s=self.channel,
-            transform=na.plt.transAxes(ax),
-            ax=ax,
-            ha="center",
-            va="bottom",
-        )
-        na.plt.set_aspect("equal", ax=ax)
-
-        plt.colorbar(
-            mappable=plt.cm.ScalarMappable(colorizer=colorizer),
-            ax=ax.ndarray,
-            label=f"signal ({unit:latex_inline})",
-            fraction=cbar_fraction,
-        )
-
-        return ani
-
-    def to_jshtml(
-        self,
-        ax: None | matplotlib.axes.Axes | na.AbstractArray = None,
-        cmap: None | str | matplotlib.colors.Colormap = None,
-        norm: None | str | matplotlib.colors.Normalize = None,
-        vmin: None | na.ArrayLike = None,
-        vmax: None | na.ArrayLike = None,
-        cbar_fraction: float = 0.1,
-    ) -> IPython.display.HTML:
-        """
-        Create a Javascript animation ready to be displayed in a Jupyter notebook.
-
-        Converts the output of :meth:`animate` to Javascript using
-        :meth:`matplotlib.animation.Animation.to_jshtml`,
-        and then wraps the html string in :class:`IPython.display.HTML`.
-
-        Parameters
-        ----------
-        ax
-            The :class:`~matplotlib.axes.Axes` instance(s) to use.
-            If :obj:`None`, a new set of axes will be created.
-        cmap
-            The colormap used to map scalar data to colors.
-        norm
-            The normalization method used to scale data into the range [0, 1] before
-            mapping to colors.
-        vmin
-            The minimum value of the data range.
-            If `norm` is :obj:`None`, this parameter will be ignored.
-        vmax
-            The maximum value of the data range.
-            If `norm` is :obj:`None`, this parameter will be ignored.
-        cbar_fraction
-            The fraction of the space to use for the colorbar axes.
-        """
-        ani = self.animate(
-            ax=ax,
-            cmap=cmap,
-            norm=norm,
-            vmin=vmin,
-            vmax=vmax,
-            cbar_fraction=cbar_fraction,
-        )
-
-        result = ani.to_jshtml()
-        result = IPython.display.HTML(result)
-
-        plt.close(ani._fig)
-
-        return result

esis/data/_level_0/_level_0_test.py

@@ -6,6 +6,7 @@
 import named_arrays as na
 from msfc_ccd._images._tests.test_sensor_images import AbstractTestAbstractSensorData
 import esis
+from ..abc._channel_data_test import AbstractTestAbstractChannelData
 
 
 @pytest.mark.parametrize(
@@ -16,6 +17,7 @@
 )
 class TestLevel_0(
     AbstractTestAbstractSensorData,
+    AbstractTestAbstractChannelData,
 ):
     def test_timeline(self, a: esis.data.Level_0):
         result = a.timeline
@@ -63,13 +65,3 @@ def test_dark_subtracted(self, a: esis.data.Level_0):
         result = a[index].dark_subtracted
         assert isinstance(result, type(a))
         assert np.all(result.darks.outputs.mean() < 1 * u.DN)
-
-    def test_to_jshtml(self, a: esis.data.Level_0):
-        index = {
-            a.axis_time: slice(0, 1),
-            a.axis_x: slice(0, 100),
-            a.axis_y: slice(0, 100),
-        }
-        a = a[index]
-        result = a.to_jshtml()
-        assert isinstance(result, IPython.display.HTML)

esis/data/_level_1/_level_1.py

@@ -1,6 +1,7 @@
 from typing import Self
 import dataclasses
-import named_arrays as na
+import esis
+from .. import abc
 from .. import Level_0
 
 __all__ = [
@@ -10,32 +11,23 @@
 
 @dataclasses.dataclass
 class Level_1(
-    na.FunctionArray[
-        na.TemporalSpectralPositionalVectorArray,
-        na.ScalarArray,
-    ],
+    abc.AbstractChannelData,
 ):
     """
     ESIS images represented in terms of photoelectrons collected by the sensor.
 
     This class is intended to be created from an instance of :class:`Level_0`
     using the :meth:from_level_0` method.
     """
-
-    axis_time: str = dataclasses.field(default="time", kw_only=True)
-    """The name of the logical axis corresponding to changing time."""
-
-    axis_channel: str = dataclasses.field(default="channel", kw_only=True)
-    """The name of the logical axis corresponding to the different channels."""
-
-    axis_x: str = dataclasses.field(default="detector_x", kw_only=True)
-    """The name of the horizontal axis of the sensor."""
-
-    axis_y: str = dataclasses.field(default="detector_y", kw_only=True)
-    """The name of the vertical axis of the sensor."""
+    instrument: None | esis.optics.Instrument = None
+    """A model of the optical system associated with these images."""
 
     @classmethod
-    def from_level_0(cls, a: Level_0) -> Self:
+    def from_level_0(
+        cls,
+        a: Level_0,
+        instrument: None | esis.optics.Instrument = None,
+    ) -> Self:
         """
         Create a new instance of this class from an instance of :class:`Level_0`.
 
@@ -55,6 +47,8 @@ def from_level_0(cls, a: Level_0) -> Self:
         ----------
         a
             An instance of :class:`Level_0` to convert.
+        instrument
+            A model of the ESIS instrument to associate with these observations.
         """
         taps = a.taps
         taps = taps.unbiased
@@ -68,6 +62,7 @@ def from_level_0(cls, a: Level_0) -> Self:
         return cls(
             inputs=a.inputs,
             outputs=a.outputs,
+            instrument=instrument,
             axis_time=a.axis_time,
             axis_channel=a.axis_channel,
             axis_x=a.axis_x,

esis/data/_level_1/_level_1_test.py

@@ -1,5 +1,5 @@
 import pytest
-from msfc_ccd._images._tests.test_sensor_images import AbstractTestAbstractSensorData
+from msfc_ccd._images._tests.test_images import AbstractTestAbstractImageData
 import esis
 
 
@@ -10,6 +10,6 @@
     ],
 )
 class TestLevel_1(
-    AbstractTestAbstractSensorData,
+    AbstractTestAbstractImageData,
 ):
     pass

esis/data/abc/__init__.py

@@ -0,0 +1,5 @@
+from ._channel_data import AbstractChannelData
+
+__all__ = [
+    "AbstractChannelData",
+]