Added esis.flights.f1.data.level_0() function to load the 2019 Level-0 dataset.

.github/workflows/tests.yml

@@ -17,7 +17,7 @@ jobs:
         python-version: ["3.11"]
     name: ${{ matrix.os }}, Python ${{ matrix.python-version }} tests
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v4
         with:
           lfs: true
       - name: Pull LFS objects
@@ -36,10 +36,10 @@ jobs:
           pip install pytest pytest-cov
           pytest --cov=. --cov-report=xml --cov-report=html
       - name: Upload coverage to Codecov
-        uses: codecov/codecov-action@v3
+        uses: codecov/codecov-action@v5
         with:
           token: ${{ secrets.CODECOV_TOKEN }}
-          file: coverage.xml
+          files: coverage.xml
           flags: unittests
           env_vars: OS,PYTHON
           name: codecov-umbrella

README.md

@@ -1,6 +1,6 @@
 # ESIS
 
-![tests](https://github.com/Kankelborg-Group/ESIS/workflows/tests/badge.svg)
+[![tests](https://github.com/Kankelborg-Group/ESIS/actions/workflows/tests.yml/badge.svg)](https://github.com/Kankelborg-Group/ESIS/actions/workflows/tests.yml)
 [![codecov](https://codecov.io/gh/Kankelborg-Group/ESIS/graph/badge.svg?token=CALT5W6YG3)](https://codecov.io/gh/Kankelborg-Group/ESIS)
 [![Black](https://github.com/Kankelborg-Group/ESIS/actions/workflows/black.yml/badge.svg)](https://github.com/Kankelborg-Group/ESIS/actions/workflows/black.yml)
 [![Ruff](https://github.com/Kankelborg-Group/ESIS/actions/workflows/ruff.yml/badge.svg)](https://github.com/Kankelborg-Group/ESIS/actions/workflows/ruff.yml)

esis/__init__.py

@@ -4,10 +4,12 @@
 
 from . import optics
 from . import nsroc
+from . import data
 from . import flights
 
 __all__ = [
     "optics",
     "nsroc",
+    "data",
     "flights",
 ]

esis/data/__init__.py

@@ -0,0 +1,18 @@
+"""
+Represent and process ESIS observations into spatial-spectral cubes.
+
+Description of the ESIS Data Levels
+===================================
+
+Level 0
+-------
+
+* The raw data gathered by the ESIS instrument, saved as FITS files.
+
+"""
+
+from ._level_0 import Level_0
+
+__all__ = [
+    "Level_0",
+]

esis/data/_level_0/__init__.py

@@ -0,0 +1,5 @@
+from ._level_0 import Level_0
+
+__all__ = [
+    "Level_0",
+]

esis/data/_level_0/_level_0.py

@@ -0,0 +1,71 @@
+import numpy as np
+from typing_extensions import Self
+import dataclasses
+import pathlib
+import numpy.typing as npt
+import named_arrays as na
+import msfc_ccd
+import esis
+
+__all__ = [
+    "Level_0",
+]
+
+
+@dataclasses.dataclass(eq=False, repr=False)
+class Level_0(
+    msfc_ccd.SensorData,
+):
+    """
+    Representation of ESIS Level-0 images, the raw images gathered by the
+    Data Acquisition and Control System (DACS).
+    """
+
+    timeline: None | esis.nsroc.Timeline = None
+    """
+    The sequence of NSROC events associated with these images.
+    """
+
+    @classmethod
+    def from_fits(
+        cls,
+        path: str | pathlib.Path | na.AbstractScalarArray,
+        sensor: msfc_ccd.abc.AbstractSensor,
+        axis_x: str = "detector_x",
+        axis_y: str = "detector_y",
+        timeline: None | esis.nsroc.Timeline = None,
+    ) -> Self:
+
+        self = super().from_fits(
+            path=path,
+            sensor=sensor,
+            axis_x=axis_x,
+            axis_y=axis_y,
+        )
+
+        self.timeline = timeline
+
+        self.inputs
+
+        return self
+
+    @property
+    def channel(self) -> na.ScalarArray[npt.NDArray[str]]:
+        """
+        The name of each ESIS channel in a human-readable format.
+        """
+
+        sn = self.inputs.serial_number
+        where_1 = sn == "6"
+        where_2 = sn == "7"
+        where_3 = sn == "9"
+        where_4 = sn == "1"
+
+        result = np.empty_like(sn, dtype=object)
+
+        result[where_1] = "Channel 1"
+        result[where_2] = "Channel 2"
+        result[where_3] = "Channel 3"
+        result[where_4] = "Channel 4"
+
+        return result

esis/data/_level_0/_level_0_test.py

@@ -0,0 +1,18 @@
+import pytest
+from msfc_ccd._images._tests.test_sensor_images import AbstractTestAbstractSensorData
+import esis
+
+
+@pytest.mark.parametrize(
+    argnames="a",
+    argvalues=[
+        esis.flights.f1.data.level_0(),
+    ],
+)
+class TestLevel_0(
+    AbstractTestAbstractSensorData,
+):
+    def test_timeline(self, a: esis.data.Level_0):
+        result = a.timeline
+        if result is not None:
+            assert isinstance(result, esis.nsroc.Timeline)

esis/flights/f1/data/__init__.py

@@ -3,7 +3,9 @@
 """
 
 from ._fits import path_fits
+from ._level_0 import level_0
 
 __all__ = [
     "path_fits",
+    "level_0",
 ]

esis/flights/f1/data/_level_0/__init__.py

@@ -0,0 +1,5 @@
+from ._level_0 import level_0
+
+__all__ = [
+    "level_0",
+]

esis/flights/f1/data/_level_0/_level_0.py

@@ -0,0 +1,158 @@
+import msfc_ccd
+import esis
+from ... import nsroc
+from .. import path_fits
+
+__all__ = [
+    "level_0",
+]
+
+
+def level_0(
+    axis_time: str = "time",
+    axis_channel: str = "channel",
+    axis_x: str = "detector_x",
+    axis_y: str = "detector_y",
+) -> esis.data.Level_0:
+    """
+    All the raw images captured by ESIS during the 2019 flight.
+
+    Parameters
+    ----------
+    axis_time
+        The name of the logical axis representing time.
+    axis_channel
+        The name of the logical axis representing the different ESIS channels.
+    axis_x
+        The name of the logical axis representing the detector's long axis.
+    axis_y
+        The name of the logical axis representing the detector's short axis.
+
+    Examples
+    --------
+
+    Load the Level-0 dataset into a :class:`esis.data.Level_0` instance.
+
+    .. jupyter-execute::
+
+        import IPython.display
+        import matplotlib.pyplot as plt
+        import astropy.visualization
+        import named_arrays as na
+        import esis
+
+        # Define the names of the logical axes
+        # to use for constructing the Level-0 dataset
+        axis_time = "time"
+        axis_channel = "channel"
+
+        # Load the Level-0 dataset into memory
+        level_0 = esis.flights.f1.data.level_0(
+            axis_time=axis_time,
+            axis_channel=axis_channel,
+        )
+
+    Make a movie of three frames of the Level-0 dataset.
+
+    .. jupyter-execute::
+
+        # Define a slice of three frames near apogee
+        index = {axis_time: slice(20, 23)}
+
+        # Create a figure
+        fig, axs = na.plt.subplots(
+            axis_rows="rows",
+            axis_cols="cols",
+            nrows=level_0.shape[axis_channel] // 2,
+            ncols=2,
+            sharex=True,
+            sharey=True,
+            constrained_layout=True,
+            figsize=(10, 5),
+        )
+
+        # Reorganize the axes into a flat array
+        ax = axs.combine_axes(("rows", "cols"), axis_channel)
+        ax = ax[{axis_channel: slice(None, None, -1)}]
+
+        # Define the colormap
+        colorizer = plt.Colorizer(
+            norm=plt.Normalize(
+                vmin=level_0.outputs.percentile(1).ndarray,
+                vmax=level_0.outputs.percentile(99).ndarray,
+            ),
+        )
+
+        # Animate the Level-0 dataset frames
+        ani = na.plt.pcolormovie(
+            level_0.inputs.time[index].mean(axis_channel),
+            level_0.inputs.pixel.x,
+            level_0.inputs.pixel.y,
+            C=level_0.outputs[index],
+            axis_time=axis_time,
+            ax=ax,
+            kwargs_pcolormesh=dict(
+                colorizer=colorizer,
+            ),
+        )
+
+        # Create labels for each axis
+        na.plt.text(
+            x=0.5,
+            y=1.01,
+            s=level_0.channel,
+            transform=na.plt.transAxes(ax),
+            ax=ax,
+            ha="center",
+            va="bottom",
+        )
+        na.plt.set_aspect("equal", ax=ax)
+        na.plt.set_xlabel("detector $x$ (pix)", ax=axs[dict(rows=0)])
+        na.plt.set_ylabel("detector $y$ (pix)", ax=axs[dict(cols=0)])
+
+        # Plot the colorbar using the colormap
+        plt.colorbar(
+            mappable=plt.cm.ScalarMappable(colorizer=colorizer),
+            ax=ax.ndarray,
+            label="signal (DN)"
+        )
+
+        # Render the movie as a javascript animation
+        plt.close(fig)
+        IPython.display.HTML(ani.to_jshtml())
+
+    Plot the FPGA temperatures over the flight.
+
+    .. jupyter-execute::
+
+        # Convert the time array from ISO to a Python :class:`datetime.datetime`
+        # instance
+        time = level_0.inputs.time
+        time = time.replace(ndarray=time.ndarray.datetime)
+
+        # Plot the result as a line plot
+        with astropy.visualization.quantity_support():
+            fig, ax = plt.subplots()
+            na.plt.plot(
+                time,
+                level_0.inputs.temperature_fpga,
+                axis=axis_time,
+                ax=ax,
+                label=level_0.channel,
+            )
+            ax.set_ylabel(f"FPGA temperature ({ax.get_ylabel()})")
+            ax.legend()
+    """
+
+    path = path_fits(
+        axis_time=axis_time,
+        axis_channel=axis_channel,
+    )
+
+    return esis.data.Level_0.from_fits(
+        path=path,
+        sensor=msfc_ccd.TeledyneCCD230(),
+        axis_x=axis_x,
+        axis_y=axis_y,
+        timeline=nsroc.timeline(),
+    )

esis/flights/f1/data/_level_0/_level_0_test.py

@@ -0,0 +1,7 @@
+import esis
+
+def test_level_0():
+
+    lvl0 = esis.flights.f1.data.level_0()
+
+    assert isinstance(lvl0, esis.data.Level_0)

pyproject.toml

@@ -17,8 +17,9 @@ dependencies = [
     "numpy",
     "matplotlib",
     "astropy",
-    "optika==0.11.0",
-    "aastex==0.3.1",
+    "optika>=0.12.0",
+    "msfc-ccd>=0.2.0",
+    "aastex>=0.3.1",
 ]
 dynamic = ["version"]
 
@@ -49,6 +50,7 @@ packages = ["esis"]
 testpaths = [
     "esis/optics/_tests",
     "esis/flights",
+    "esis/data",
     "esis/science/papers/instrument/_tests",
 ]