Update 4.x

docs/analyze_spectral.md

@@ -14,7 +14,8 @@ the values out as observations to get saved out. Can also print out a histogram
     - label         - Optional label parameter, modifies the variable name of observations recorded. (default `label="default"`)
 - **Example use:**
     - Below 
-- **Output data stored:** Data ('max_reflectance', 'min_reflectance', 'median_reflectance', 'spectral_std', 'spectral_frequencies', 'global_mean_reflectance', 'global_median_reflectance', 'global_spectral_std') automatically gets stored to the 
+- **Output data stored:** Data ('global_mean_reflectance', 'global_median_reflectance', 'global_spectral_std', 'wavelength_means', 'max_reflectance', 
+    'min_reflectance', 'spectral_std', 'spectral_frequencies') automatically gets stored to the 
     [`Outputs` class](outputs.md) when this function is ran. 
     These data can always get accessed during a workflow (example below). For more detail about data output see [Summary of Output Observations](output_measurements.md#summary-of-output-observations)
 

plantcv/plantcv/hyperspectral/analyze_spectral.py

@@ -39,7 +39,7 @@ def analyze_spectral(array, mask, histplot=None, label="default"):
     wavelength_data = array_data[np.where(mask > 0)]
 
     # Calculate mean reflectance across wavelengths
-    wavelength_freq = wavelength_data.mean(axis=0)
+    wavelength_means = wavelength_data.mean(axis=0)
     max_per_band = wavelength_data.max(axis=0)
     min_per_band = wavelength_data.min(axis=0)
     std_per_band = wavelength_data.std(axis=0)
@@ -51,14 +51,14 @@ def analyze_spectral(array, mask, histplot=None, label="default"):
     # Create lists with wavelengths in float format rather than as strings
     # and make a list of the frequencies since they are in an array
     new_wavelengths = []
-    new_freq = []
+    band_averages = []
     new_std_per_band = []
     new_max_per_band = []
     new_min_per_band = []
 
     for i, wavelength in enumerate(array.wavelength_dict):
         new_wavelengths.append(wavelength)
-        new_freq.append((wavelength_freq[i]).astype(float))
+        band_averages.append((wavelength_means[i]).astype(float))
         new_std_per_band.append(std_per_band[i].astype(float))
         new_max_per_band.append(max_per_band[i].astype(float))
         new_min_per_band.append(min_per_band[i].astype(float))
@@ -83,9 +83,9 @@ def analyze_spectral(array, mask, histplot=None, label="default"):
                             trait='pixel-wise standard deviation per band',
                             method='plantcv.plantcv.hyperspectral.analyze_spectral', scale='None', datatype=float,
                             value=float(std_reflectance), label='reflectance')
-    outputs.add_observation(sample=label, variable='global_spectral_std', trait='pixel-wise standard deviation ',
-                            method='plantcv.plantcv.hyperspectral.analyze_spectral', scale='None', datatype=float,
-                            value=float(std_reflectance), label='reflectance')
+    outputs.add_observation(sample=label, variable='wavelength_means', trait='mean reflectance per band',
+                            method='plantcv.plantcv.hyperspectral.analyze_spectral', scale='reflectance', datatype=list,
+                            value=band_averages, label=wavelength_labels)
     outputs.add_observation(sample=label, variable='max_reflectance', trait='maximum reflectance per band',
                             method='plantcv.plantcv.hyperspectral.analyze_spectral', scale='reflectance', datatype=list,
                             value=new_max_per_band, label=wavelength_labels)
@@ -95,12 +95,9 @@ def analyze_spectral(array, mask, histplot=None, label="default"):
     outputs.add_observation(sample=label, variable='spectral_std', trait='pixel-wise standard deviation per band',
                             method='plantcv.plantcv.hyperspectral.analyze_spectral', scale='None', datatype=list,
                             value=new_std_per_band, label=wavelength_labels)
-    outputs.add_observation(sample=label, variable='spectral_frequencies', trait='spectral frequencies',
-                            method='plantcv.plantcv.hyperspectral.analyze_spectral', scale='frequency', datatype=list,
-                            value=new_freq, label=wavelength_labels)
 
     dataset = pd.DataFrame({'Wavelength (' + array.wavelength_units + ')': new_wavelengths,
-                            'Reflectance': wavelength_freq})
+                            'Reflectance': wavelength_means})
     mean_spectra = (ggplot(data=dataset,
                     mapping=aes(x='Wavelength (' + array.wavelength_units + ')', y='Reflectance'))
                     + geom_line(color='purple')

tests/plantcv/hyperspectral/test_analyze_spectral.py

@@ -10,4 +10,4 @@ def test_analyze_spectral(hyperspectral_test_data):
     mask = cv2.imread(hyperspectral_test_data.hsi_mask_file, -1)
     _ = analyze_spectral(array=hyperspectral_test_data.load_hsi(hyperspectral_test_data.hsi_file), mask=mask,
                          histplot=True, label="prefix")
-    assert len(outputs.observations['prefix']['spectral_frequencies']['value']) == 978
+    assert len(outputs.observations['prefix']['wavelength_means']['value']) == 978