Analyze etr

docs/analyze_etr.md

@@ -0,0 +1,30 @@
+## Analyze Electron Transport Rate
+
+Estimate mean and median electron transport rate (ETR).
+Calculates `(Fq’/Fm’) * AlphaL * PSI/PSIIratio * Actinic_light`. This requires [calculating alphaL](analyze_alphaL.md) and [calculating Fq'/Fm'](analyze_yii.md) before ETR can be calculated.
+
+**plantcv.analyze.etr**(*actinic_light, psi_psii_ratio=0.5*)
+
+**returns** None
+
+- **Parameters:**
+    - actinic_light - Light intensity in PAR
+	- psi_psii_ratio - Light absorption ratio between photosynthesis 1 and 2. Defaults to 0.5.
+
+- **Context:**
+    - Used to calculate ETR after YII and AlphaL are calculated. This requires APH frames and the frames input to [`analyze.yii`](analyze_yii.md).
+
+- **Example use:**
+    - Below
+
+- **Output data stored:** Data (mean_etr, median_etr) are stored to the [`Outputs` class](outputs.md) when this function is run.
+
+```python
+from plantcv import plantcv as pcv
+
+# calculate ETR
+pcv.analyze.etr(actinic_light=10)
+# check results
+pcv.outputs.observations["plant_1"]["mean_etr"]["values"]
+
+```

docs/updating.md

@@ -384,6 +384,11 @@ pages for more details on the input and output variable types.
 * pre v4.2.1: NA
 * post v4.2.1: dist_chart = **plantcv.analyze.distribution**(*labeled_mask, n_labels=1, direction="down", bin_size=100, hist_range="absolute", label=None*)
 
+#### plantcv.analyze.etr
+
+* pre v5.0: NA
+* post v5.0: _ = **plantcv.analyze.etr**(*actinic_light, psi_psii_ratio=0.5*)
+
 #### plantcv.analyze.grayscale
 
 * pre v4.0: (see plantcv.analyze_nir_intensity)

mkdocs.yml

@@ -42,6 +42,7 @@ nav:
         - 'Analyze Spectral Index': analyze_spectral_index.md
         - 'Analyze YII': analyze_yii.md
         - 'Analyze NPQ': analyze_npq.md
+        - 'Analyze ETR': analyze_etr.md
       - 'Annotation Tools':
           - 'Points': Points.md
       - 'Apply Mask': apply_mask.md

plantcv/plantcv/analyze/__init__.py

@@ -9,8 +9,9 @@
 from plantcv.plantcv.analyze.yii import yii
 from plantcv.plantcv.analyze.npq import npq
 from plantcv.plantcv.analyze.alphaL import alphaL
+from plantcv.plantcv.analyze.etr import etr
 from plantcv.plantcv.analyze.distribution import distribution
 from plantcv.plantcv.analyze.texture import texture
 
 __all__ = ["color", "bound_horizontal", "bound_vertical", "grayscale", "size", "thermal", "spectral_reflectance",
-           "spectral_index", "yii", "npq", "alphaL", "distribution", "texture"]
+           "spectral_index", "yii", "npq", "alphaL", "etr", "distribution", "texture"]

plantcv/plantcv/analyze/alphaL.py

@@ -160,4 +160,4 @@ def _analyze_alphaL(img, mask, label, min_bin, max_bin, red, farred):
                             value=hist_df['proportion of pixels (%)'].values.tolist(),
                             label=np.around(hist_df["counts"].values.tolist(), decimals=2).tolist())
 
-    return np.where(mask > 0, alphaL_mat, img)
+    return img + alphaL_mat

plantcv/plantcv/analyze/etr.py

@@ -0,0 +1,50 @@
+"""Analyze electron transport rate"""
+
+import re
+from plantcv.plantcv._globals import outputs
+from plantcv.plantcv.fatal_error import fatal_error
+
+
+def etr(actinic_light, psi_psii_ratio=0.5):
+    """Calculate electron transport rate from yii and alphaL outputs and add to outputs.
+
+    Parameters
+    ----------
+    actinic_light = int or float,
+        Light intensity in PAR
+    psi_psii_ratio = float,
+        PSI/PSII ratio. This should generally be left as 0.5
+
+    Returns
+    -------
+    None, etr values are added to outputs
+    """
+    obs = outputs.observations
+    for label, _ in obs.items():
+        labs = [label2 for label2, _ in obs[label].items() if re.search("yii_mean.*[fqfm|t1]$", label2)]
+        labs2 = [label2 for label2, _ in obs[label].items() if re.search("yii_median.*[fqfm|t1]$", label2)]
+        aph_labs = [label3 for label3, _ in obs[label].items() if re.search("alphaL", label3)]
+        if len(labs) < 1:
+            fatal_error("YII mean data must be present in outputs," +
+                        "run plantcv.plantcv.analyze.yii before plantcv.plantcv.analyze.etr.")
+        yii_mean_val = obs[label][labs[0]]["value"]
+        yii_median_val = obs[label][labs2[0]]["value"]
+        if not bool(aph_labs):
+            fatal_error("AlphaL mean data must be present in outputs," +
+                        "run plantcv.plantcv.analyze.alphaL before plantcv.plantcv.analyze.etr.")
+        alphaL_mean_val = obs[label]["alphaL_mean"]["value"]
+        alphaL_median_val = obs[label]["alphaL_median"]["value"]
+        # calculate ETR
+        etr_mean_val = yii_mean_val * alphaL_mean_val * psi_psii_ratio * actinic_light
+        etr_median_val = yii_median_val * alphaL_median_val * psi_psii_ratio * actinic_light
+        # store outputs
+        outputs.add_observation(sample=label,
+                                variable='mean_etr', trait='mean electron transport rate',
+                                method='plantcv.plantcv.analyze.etr',
+                                scale='none', datatype=float,
+                                value=etr_mean_val, label='none')
+        outputs.add_observation(sample=label,
+                                variable='median_etr', trait='median electron transport rate',
+                                method='plantcv.plantcv.analyze.etr',
+                                scale='none', datatype=float,
+                                value=etr_median_val, label='none')

tests/plantcv/analyze/test_etr.py

@@ -0,0 +1,72 @@
+import pytest
+from plantcv.plantcv._globals import outputs
+from plantcv.plantcv.analyze.etr import etr
+
+
+def test_etr():
+    """Test for PlantCV."""
+    outputs.clear()
+    outputs.add_observation(sample="label1",
+                                variable = 'yii_mean_t1',
+                                trait='dummy yii',
+                                method='dummy.method',
+                                scale='none', datatype=float,
+                                value=10, label='none')
+    outputs.add_observation(sample="label1",
+                                variable = 'alphaL_mean',
+                                trait='dummy alpha',
+                                method='dummy.alpha.method',
+                                scale='none', datatype=float,
+                                value=5, label='none')
+    outputs.add_observation(sample="label1",
+                                variable = 'yii_median_t1',
+                                trait='dummy yii',
+                                method='dummy.method',
+                                scale='none', datatype=float,
+                                value=10, label='none')
+    outputs.add_observation(sample="label1",
+                                variable = 'alphaL_median',
+                                trait='dummy alpha',
+                                method='dummy.alpha.method',
+                                scale='none', datatype=float,
+                                value=5, label='none')
+    etr(10)
+    assert outputs.observations["label1"]["mean_etr"]["value"] == 250
+
+
+def test_etr_no_yii():
+    """Test for PlantCV."""
+    outputs.clear()
+    outputs.add_observation(sample="label1",
+                                variable = 'alphaL_mean',
+                                trait='dummy alpha',
+                                method='dummy.alpha.method',
+                                scale='none', datatype=float,
+                                value=5, label='none')
+    outputs.add_observation(sample="label1",
+                                variable = 'alphaL_median',
+                                trait='dummy alpha',
+                                method='dummy.alpha.method',
+                                scale='none', datatype=float,
+                                value=5, label='none')
+    with pytest.raises(RuntimeError):
+        etr(10)
+
+
+def test_etr_no_alphaL():
+    """Test for PlantCV."""
+    outputs.clear()
+    outputs.add_observation(sample="label1",
+                                variable = 'yii_mean_t1',
+                                trait='dummy yii',
+                                method='dummy.method',
+                                scale='none', datatype=float,
+                                value=10, label='none')
+    outputs.add_observation(sample="label1",
+                                variable = 'yii_median_t1',
+                                trait='dummy yii',
+                                method='dummy.method',
+                                scale='none', datatype=float,
+                                value=10, label='none')
+    with pytest.raises(RuntimeError):
+        etr(10)