use directory dependencies

Add sample data file

create demo analysis file

remame color analysis classes

Update submodules

add example analysis

change default measurement notes and shortname

Updated submodule submodules/colour-science

add distributed pytest

ruff autofixes

some ruff fixes

update colour-version

quality fixes

update license and package properties

format toml

update license, contact info, and more

update specio

.gitignore

@@ -838,4 +838,5 @@ FodyWeavers.xsd
 ### VisualStudio Patch ###
 # Additional files built by Visual Studio
 
-# End of https://www.toptal.com/developers/gitignore/api/windows,linux,macos,visualstudiocode,visualstudio,python,emacs,vim,pycharm
+# End of
+# https://www.toptal.com/developers/gitignore/api/windows,linux,macos,visualstudiocode,visualstudio,python,emacs,vim,pycharm

LICENSE

@@ -0,0 +1,11 @@
+Copyright 2013 Tucker Downs
+
+Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
+
+3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS “AS IS” AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE

README.md

@@ -1,3 +1,3 @@
-# Color Workbench
+# Colour Workbench
 
 This is a useless README file. Thank you.

colour_workbench/ETC_reports/__init__.py

@@ -1,2 +1,13 @@
-from .analysis import FundamentalData, ReflectanceData, analyse_measurements_from_file
-from .pdf import generate_report_page
+from analysis import (
+    ColourPrecisionAnalysis,
+    ReflectanceData,
+    analyse_measurements_from_file,
+)
+from pdf import generate_report_page
+
+__all__ = [
+    "generate_report_page",
+    "ReflectanceData",
+    "analyse_measurements_from_file",
+    "ColourPrecisionAnalysis",
+]

colour_workbench/ETC_reports/analysis.py

@@ -9,8 +9,10 @@
 
 import numpy as np
 import numpy.typing as npt
-import xxhash
-from colour.colorimetry.spectrum import MultiSpectralDistributions, SpectralDistribution
+from colour.colorimetry.spectrum import (
+    MultiSpectralDistributions,
+    SpectralDistribution,
+)
 from colour.colorimetry.tristimulus_values import sd_to_XYZ
 from colour.difference.delta_e import delta_E_CIE2000
 from colour.models.cie_lab import XYZ_to_Lab
@@ -21,8 +23,11 @@
 from colour.models.rgb.transfer_functions import st_2084 as pq
 from colour.plotting.common import XYZ_to_plotting_colourspace
 from colour.temperature.ohno2013 import XYZ_to_CCT_Ohno2013
-from matplotlib import pyplot as plt
-from specio.fileio import MeasurementList, MeasurementList_Notes, load_measurements
+from specio.fileio import (
+    MeasurementList,
+    MeasurementList_Notes,
+    load_measurements,
+)
 
 
 @dataclass
@@ -41,7 +46,7 @@ def glossiness_ratio(self) -> float:
         return self.reflectance_45_45 / self.reflectance_45_0
 
 
-class FundamentalData:
+class ColourPrecisionAnalysis:
     @property
     def _snr_mask(self):
         snr = 10 * np.log10(
@@ -58,13 +63,19 @@ def _analysis_mask(self):
         t = np.all(
             (
                 ~np.any(
-                    np.isinf([m.spd.values for m in self._data.measurements]), axis=1
+                    np.isinf([m.spd.values for m in self._data.measurements]),
+                    axis=1,
                 ),
                 ~np.any(
-                    np.isnan([m.spd.values for m in self._data.measurements]), axis=1
+                    np.isnan([m.spd.values for m in self._data.measurements]),
+                    axis=1,
+                ),
+                ~np.any(
+                    np.isnan([m.XYZ for m in self._data.measurements]), axis=1
+                ),
+                ~np.any(
+                    np.isinf([m.XYZ for m in self._data.measurements]), axis=1
                 ),
-                ~np.any(np.isnan([m.XYZ for m in self._data.measurements]), axis=1),
-                ~np.any(np.isinf([m.XYZ for m in self._data.measurements]), axis=1),
             ),
             axis=0,
         )
@@ -84,14 +95,18 @@ def black(self) -> dict:
         tmp["measurements"] = measurements = self._data.measurements[mask]
         spd_shape = measurements[0].spd.shape
 
-        tmp["values"] = np.transpose(np.array([m.spd.values for m in measurements]))
+        tmp["values"] = np.transpose(
+            np.array([m.spd.values for m in measurements])
+        )
         tmp["spectral_deviation"] = np.std(tmp["values"], axis=1)
 
         tmp["spd"] = np.mean(tmp["values"], axis=1)
         tmp["spd"] = savgol_filter(tmp["spd"], 5, 2, mode="nearest")
         tmp["spd"] = SpectralDistribution(tmp["spd"], domain=spd_shape)
 
-        tmp["XYZ"] = sd_to_XYZ(SpectralDistribution(tmp["spd"], spd_shape), k=683)
+        tmp["XYZ"] = sd_to_XYZ(
+            SpectralDistribution(tmp["spd"], spd_shape), k=683
+        )
         tmp["power"] = np.sum(tmp["spd"].values)
         return self._black
 
@@ -101,12 +116,19 @@ def primary_matrix(self) -> npt.NDArray:
             return self._pm
 
         color_masks = []
-        color_masks.append(np.all(self._data.test_colors[:, (1, 2)] == 0, axis=1))
-        color_masks.append(np.all(self._data.test_colors[:, (0, 2)] == 0, axis=1))
-        color_masks.append(np.all(self._data.test_colors[:, (0, 1)] == 0, axis=1))
+        color_masks.append(
+            np.all(self._data.test_colors[:, (1, 2)] == 0, axis=1)
+        )
+        color_masks.append(
+            np.all(self._data.test_colors[:, (0, 2)] == 0, axis=1)
+        )
+        color_masks.append(
+            np.all(self._data.test_colors[:, (0, 1)] == 0, axis=1)
+        )
         color_masks.append(
             np.all(
-                self._data.test_colors[:, (1, 2)].T == self._data.test_colors[:, (0)],
+                self._data.test_colors[:, (0)]
+                == self._data.test_colors[:, (1, 2)].T,
                 axis=0,
             )
         )
@@ -115,15 +137,17 @@ def primary_matrix(self) -> npt.NDArray:
 
         xy = np.zeros((4, 2))
         for idx, m in enumerate(color_masks):
-            color_measurements = self._data.measurements[m & self._analysis_mask]
+            color_measurements = self._data.measurements[
+                m & self._analysis_mask
+            ]
             color_XYZ = [t.XYZ for t in color_measurements] - self.black["XYZ"]
             xys = XYZ_to_xy(color_XYZ)
 
             try:
                 # Find mean chromaticity without being influenced by outliers
                 cov = EllipticEnvelope().fit(xys)
                 xy[idx, :] = cov.location_
-            except ValueError as e:
+            except ValueError:
                 # Covariance fit failed, probably because the data is well
                 # clustered, traditional mean can be used instead.
                 xy[idx, :] = np.mean(xys, axis=0)
@@ -139,7 +163,8 @@ def grey(self):
 
         grey = self._grey = {}
         grey_mask = np.all(
-            self._data.test_colors[:, (1, 2)].T == self._data.test_colors[:, (0)],
+            self._data.test_colors[:, (0)]
+            == self._data.test_colors[:, (1, 2)].T,
             axis=0,
         )
         grey_mask = grey_mask & self._analysis_mask
@@ -182,10 +207,13 @@ def white(self):
 
         white["xyz"] = self.primary_matrix.dot([1, 1, 1])
 
-        single_color_idx = np.all(self._data.test_colors == [1023, 1023, 1023], axis=1)
+        single_color_idx = np.all(
+            self._data.test_colors == [1023, 1023, 1023], axis=1
+        )
         single_color_measurements = self._data.measurements[single_color_idx]
         white["peak"] = np.mean(
-            [m.XYZ - self.black["XYZ"] for m in single_color_measurements], axis=0
+            [m.XYZ - self.black["XYZ"] for m in single_color_measurements],
+            axis=0,
         )
         white["nits_quantized"] = pq.eotf_ST2084(
             np.round(pq.eotf_inverse_ST2084(white["peak"][1]) * 1023) / 1023
@@ -206,7 +234,9 @@ def test_colors_linear(self):
         if hasattr(self, "_test_colors_linear"):
             return self._test_colors_linear
 
-        tmp = self._test_colors_linear = pq.eotf_ST2084(self.test_colors.T / 1023)
+        tmp = self._test_colors_linear = pq.eotf_ST2084(
+            self.test_colors.T / 1023
+        )
         clipping_mask = tmp > self.white["nits_quantized"]
         tmp[clipping_mask] = self.white["nits_quantized"]
         return self._test_colors_linear
@@ -247,14 +277,17 @@ def error(self):
             return self._err
         norm = partial(np.linalg.norm, axis=1)
         err = {}
-        err["XYZ"] = norm(self.measured_colors["XYZ"] - self.expected_colors["XYZ"])
+        err["XYZ"] = norm(
+            self.measured_colors["XYZ"] - self.expected_colors["XYZ"]
+        )
 
         err["ICtCp"] = 720 * norm(
             (self.measured_colors["ICtCp"] - self.expected_colors["ICtCp"])
             * (1, 0.5, 1)
         )
         err["dI"] = 720 * norm(
-            (self.measured_colors["ICtCp"] - self.expected_colors["ICtCp"]) * (1, 0, 0)
+            (self.measured_colors["ICtCp"] - self.expected_colors["ICtCp"])
+            * (1, 0, 0)
         )
         err["dChromatic"] = 720 * norm(
             (self.measured_colors["ICtCp"] - self.expected_colors["ICtCp"])
@@ -272,14 +305,19 @@ def error(self):
     def metadata(self) -> MeasurementList_Notes:
         return self._data.metadata
 
+    @metadata.setter
+    def metadata(self, new_data: MeasurementList_Notes):
+        self._data.metadata = new_data
+
     @property
     def shortname(self) -> str:
-        if self._shortname is None:
-            if self.metadata.notes is None:
-                spds = MultiSpectralDistributions([m.spd for m in self.measurements])
-                return xxhash.xxh32_hexdigest(np.ascontiguousarray(spds.values).data)
-            return self.metadata.notes
-        return self._shortname
+        if self._shortname is not None:
+            return self._shortname
+
+        if self.metadata.notes is None or self.metadata.notes == "":
+            return self._data.shortname
+
+        return self.metadata.notes
 
     @shortname.setter
     def shortname(self, name: str | None):
@@ -317,12 +355,5 @@ def __init__(self, measurements: MeasurementList):
 def analyse_measurements_from_file(file: str):
     measurements = load_measurements(file)
 
-    fundamentalData = FundamentalData(measurements)
+    fundamentalData = ColourPrecisionAnalysis(measurements)
     return fundamentalData
-    pass
-
-
-if __name__ == "__main__":
-    file = "/Users/tucker/Dev/ETC_Jan/data/measurements/F_BP2v2_Brompton.csmf"
-    data = analyse_measurements_from_file(file)
-    print(data)