Innate 0.1.12

docs/source/conf.py

@@ -12,7 +12,7 @@
 release = '06/25/2024'
 
 # The full version, including alpha/beta/rc tags
-release = '0.1.11'
+release = '0.1.12'
 
 # -- Moving documentation building data functions ----------------------------
 

examples/tutorials/3_eqn_regression.py

@@ -5,11 +5,27 @@
 data_file = '../data/emissivity_grids.nc'
 emissivities = innate.DataSet.from_file(data_file)
 
+# Additional approximation techniques can be accessed from the interpolation (interpl) and regression
+# (reg) objects as provided by the input dataset file. For example, the current dataset includes a parametrization for the
+# 2D grids:
+
+emissivities['O3_5007A'].approx.reg.eqn(12250, 122)
+
+# Which produces the same results as above.
+# In 2D datasets it is possible to perform these validations with a visual approach using the plottting function:
+
+emissivities['O3_5007A'].plot.matrix_diagnostic(ax_cfg={'title': '$O^{2+} 500.7nm$'})
+
+# We can compare this plot with another transitions
+
+emissivities['He1_7065A'].plot.matrix_diagnostic(ax_cfg={'title': '$He^{+} 706.5nm$'})
+
+# We can see the second set of emissivities have high discrepancy at high density  low temperature. This means we should
+# review the approximation technique if these conditions are found.
+
+
+# menu. from the programing manner.
 temp, den = 12250, 122
-variables = (temp, den)
-print('Regression Equation', emissivities['O3_5007A'].approx.reg.eqn(variables))
+print('Regression Equation', emissivities['O3_5007A'].approx.reg.eqn(12250, 122))
+
 
-# Compare with the original data
-O3, H1 = pn.Atom('O', 3), pn.RecAtom('H', 1)
-emiss_ratio = O3.getEmissivity(temp, den, wave=5007)/H1.getEmissivity(temp, den, wave=4861)
-print('True value', np.log10(emiss_ratio))

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "innate-stable"
-version = "0.1.11"
+version = "0.1.12"
 readme = "README.rst"
 requires-python = ">=3.10"
 license = {file = "COPYING"}

src/innate/config.toml

@@ -1,5 +1,5 @@
 [metadata]
-version = '0.1.11'
+version = '0.1.12'
 
 [parameter_labels]
 

src/innate/plotting.py

@@ -268,7 +268,7 @@ def matrix_diagnostic(self, output_address=None, num_points=15, technique='rgi',
             in_ax.imshow(grid, aspect=0.05, extent=(x_range.min(), x_range.max(), y_range.min(), y_range.max()))
 
             # Plot discrepancy below
-            idx_interest = percentage_difference < 0.05
+            idx_interest = percentage_difference < 5
             in_ax.scatter(X[idx_interest], Y[idx_interest], c="None", edgecolors='black', linewidths=0.35, label='Error below 1%')
 
             if np.sum(idx_interest == False) > 0: