Cropreporter adding pam

docs/analyze_yii.md

@@ -8,7 +8,8 @@ The photosynthesis subpackage is dependent on a PSII_Data instance file structur
 **returns** YII DataArray, YII histograms
 
 - **Parameters:**
-    - ps_da - Photosynthesis xarray DataArray (either ojip_dark or ojip_light). Can either have a pair of frames F0,Fm or pair(s) of Fp,Fmp.
+    - ps_da - Photosynthesis xarray DataArray (either `ojip_dark`, `ojip_light`, `pam_dark`, or `pam_light`).
+    Can either have a pair of frames F0, Fm or pair(s) of Fp, Fmp.
     - labeled_mask - Labeled mask of objects (32-bit).
     - n_labels - Total number expected individual objects (default = 1).
     - auto_fm - Automatically calculate the frame with maximum fluorescence per label, otherwise use a fixed frame for all labels (default = False).

docs/photosynthesis_read_cropreporter.md

@@ -14,9 +14,9 @@ with labeled frames.
 - **Context:**
     - Reads in binary image files to be processed and does so using the metadata contained within a corresponding .INF
       file.
-    - Measurements from dark-adapted plant state are stored in the attribute `ojip_dark`. Frames F0 and Fm are
+    - Measurements from dark-adapted plant state are stored in the attribute `ojip_dark` or `pam_dark`, depending on the measurement protocol. Frames F0 and Fm are
       labeled according to the metadata in .INF. The default measurement label is 't0'.
-    - Measurements from light-adapted plant state are stored in the attribute `ojip_light`. Frames Fp and Fmp are
+    - Measurements from light-adapted plant state are stored in the attribute `ojip_light` or `pam_light`, depending on the measurement protocol. Frames Fp and Fmp are
       labeled according to the metadata in .INF. The default measurement label is 't1'.
     - Measurements from chlorophyll fluorescence are stored in the attribute `chlorophyll` and include a dark frame
       (Fdark) and chlorophyll fluorescence frame (Chl).

plantcv/plantcv/analyze/yii.py

@@ -1,5 +1,4 @@
 """Fluorescence Analysis (Fv/Fm parameter)."""
-
 import os
 import numpy as np
 import pandas as pd
@@ -14,7 +13,7 @@ def yii(ps_da, labeled_mask, n_labels=1, auto_fm=False, measurement_labels=None,
     Calculate and analyze PSII efficiency estimates from fluorescence image data.
 
     Inputs:
-    ps_da               = Photosynthesis xarray DataArray (either ojip_dark or ojip_light)
+    ps_da               = Photosynthesis xarray DataArray (either ojip_dark, ojip_light, pam_dark, or pam_light)
     labeled_mask        = Labeled mask of objects (32-bit).
     n_labels            = Total number expected individual objects (default = 1).
     auto_fm             = Automatically calculate the frame with maximum fluorescence per label, otherwise
@@ -47,7 +46,7 @@ def yii(ps_da, labeled_mask, n_labels=1, auto_fm=False, measurement_labels=None,
     var = ps_da.name.lower()
 
     # Validate that var is a supported type
-    if var not in ['ojip_dark', 'ojip_light']:
+    if var not in ['ojip_dark', 'ojip_light', 'pam_dark', 'pam_light']:
         fatal_error(f"Unsupported DataArray type: {var}")
 
     # Make an zeroed array of the same shape as the input DataArray
@@ -78,12 +77,12 @@ def yii(ps_da, labeled_mask, n_labels=1, auto_fm=False, measurement_labels=None,
         yii_masked = ps_da.astype('float').where(submask > 0, other=np.nan)
 
         # Dark-adapted datasets (Fv/Fm)
-        if var == 'ojip_dark':
+        if var in ['ojip_dark', 'pam_dark']:
             # Calculate Fv/Fm
             yii_lbl = (yii_masked.sel(frame_label='Fm') - yii_masked.sel(frame_label='F0')) / yii_masked.sel(frame_label='Fm')
 
         # Light-adapted datasets (Fq'/Fm')
-        if var == 'ojip_light':
+        if var in ['ojip_light', 'pam_light']:
             # Calculate Fq'/Fm'
             yii_lbl = yii_masked.groupby('measurement', squeeze=False).map(_calc_yii)
 

plantcv/plantcv/photosynthesis/read_cropreporter.py

@@ -1,5 +1,4 @@
-# Read in fluorescence images from a .DAT file
-
+"""Read in fluorescence images from a .DAT file."""
 import os
 import numpy as np
 import xarray as xr
@@ -46,6 +45,12 @@ def read_cropreporter(filename):
     # Light-adapted measurements
     _process_psl_data(ps=ps, metadata=metadata_dict)
 
+    # Dark-adapted PAM measurements
+    _process_pmd_data(ps=ps, metadata=metadata_dict)
+
+    # Light-adapted PAM Pmeasurements
+    _process_pml_data(ps=ps, metadata=metadata_dict)
+
     # Chlorophyll fluorescence data
     _process_chl_data(ps=ps, metadata=metadata_dict)
 
@@ -153,6 +158,74 @@ def _process_psl_data(ps, metadata):
                col_wrap=int(np.ceil(ps.ojip_light.frame_label.size / 4)))
 
 
+def _process_pmd_data(ps, metadata):
+    """
+    Create an xarray DataArray for a PMD dataset.
+
+    Inputs:
+        ps       = PSII_data instance
+        metadata = INF file metadata dictionary
+
+    :param ps: plantcv.plantcv.classes.PSII_data
+    :param metadata: dict
+    """
+    bin_filepath = _dat_filepath(dataset="PMD", datapath=ps.datapath, filename=ps.filename)
+    if os.path.exists(bin_filepath):
+        img_cube, frame_labels, frame_nums = _read_dat_file(dataset="PMD", filename=bin_filepath,
+                                                            height=int(metadata["ImageRows"]),
+                                                            width=int(metadata["ImageCols"]))
+        frame_labels = ["Fdark", "F0", "Fm", "Fs"]
+        pmd = xr.DataArray(
+            data=img_cube[..., None],
+            dims=('x', 'y', 'frame_label', 'measurement'),
+            coords={'frame_label': frame_labels,
+                    'frame_num': ('frame_label', frame_nums),
+                    'measurement': ['t0']},
+            name='pam_dark'
+        )
+        pmd.attrs["long_name"] = "pam dark-adapted measurements"
+        ps.add_data(pmd)
+
+        _debug(visual=ps.pam_dark.squeeze('measurement', drop=True),
+               filename=os.path.join(params.debug_outdir, f"{str(params.device)}_PMD-frames.png"),
+               col='frame_label',
+               col_wrap=int(np.ceil(ps.pam_dark.frame_label.size / 4)))
+
+
+def _process_pml_data(ps, metadata):
+    """
+    Create an xarray DataArray for a PML dataset.
+
+    Inputs:
+        ps       = PSII_data instance
+        metadata = INF file metadata dictionary
+
+    :param ps: plantcv.plantcv.classes.PSII_data
+    :param metadata: dict
+    """
+    bin_filepath = _dat_filepath(dataset="PML", datapath=ps.datapath, filename=ps.filename)
+    if os.path.exists(bin_filepath):
+        img_cube, frame_labels, frame_nums = _read_dat_file(dataset="PML", filename=bin_filepath,
+                                                            height=int(metadata["ImageRows"]),
+                                                            width=int(metadata["ImageCols"]))
+        frame_labels = ["Flight", "Fp", "Fmp", "Fs"]
+        pml = xr.DataArray(
+            data=img_cube[..., None],
+            dims=('x', 'y', 'frame_label', 'measurement'),
+            coords={'frame_label': frame_labels,
+                    'frame_num': ('frame_label', frame_nums),
+                    'measurement': ['t0']},
+            name='pam_light'
+        )
+        pml.attrs["long_name"] = "pam light-adapted measurements"
+        ps.add_data(pml)
+
+        _debug(visual=ps.pam_light.squeeze('measurement', drop=True),
+               filename=os.path.join(params.debug_outdir, f"{str(params.device)}_PML-frames.png"),
+               col='frame_label',
+               col_wrap=int(np.ceil(ps.pam_light.frame_label.size / 4)))
+
+
 def _process_chl_data(ps, metadata):
     """
     Create an xarray DataArray for a CHL dataset.

tests/plantcv/photosynthesis/conftest.py

@@ -17,7 +17,7 @@ def __init__(self):
         self.datadir_v653 = os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", "..", "testdata","cropreporter_v653")
         # CropReporter data file
         self.cropreporter = os.path.join(self.datadir_v441, "PSII_HDR_020321_WT_TOP_1.INF")
-        self.cropreporter_v653_ojip = os.path.join(self.datadir_v653, "HDR_OJIP_dark_light.INF")
+        self.cropreporter_v653 = os.path.join(self.datadir_v653, "HDR_dark_light.INF")
         # Mask image
         self.ps_mask = os.path.join(self.datadir_v441, "PSII_HDR_020321_WT_TOP_1_mask.png")
 

tests/plantcv/photosynthesis/test_read_cropreporter.py

@@ -1,3 +1,4 @@
+"""Tests for plantcv.photosynthesis.read_cropreporter."""
 import os
 import shutil
 from plantcv.plantcv import PSII_data
@@ -10,16 +11,16 @@ def test_read_cropreporter(photosynthesis_test_data, tmpdir):
     assert isinstance(ps, PSII_data) and ps.ojip_dark.shape == (966, 1296, 21, 1)
 
     # Check with different naming conventioned of phenovation
-    ps = read_cropreporter(filename=photosynthesis_test_data.cropreporter_v653_ojip)
+    ps = read_cropreporter(filename=photosynthesis_test_data.cropreporter_v653)
     assert isinstance(ps, PSII_data) and ps.ojip_dark.shape == (1500, 2048, 7, 1)
     # check labels
     true_labels = ['Fdark', 'F0', 'PSD2', 'PSD3', 'Fm', 'PSD5', 'PSD6']
     assert all([a == b for a, b in zip(ps.ojip_dark.coords['frame_label'].to_dict()['data'], true_labels)])
 
     # Create dataset with only 3 frames
     cache_dir = os.path.join(tmpdir, "sub")
-    shutil.copytree(os.path.dirname(photosynthesis_test_data.cropreporter_v653_ojip), cache_dir)
-    inffilename = os.path.join(cache_dir, photosynthesis_test_data.cropreporter_v653_ojip.split(os.sep)[-1])
+    shutil.copytree(os.path.dirname(photosynthesis_test_data.cropreporter_v653), cache_dir)
+    inffilename = os.path.join(cache_dir, photosynthesis_test_data.cropreporter_v653.split(os.sep)[-1])
 
     # Modify the inf file
     metadata_dict = {}
@@ -41,6 +42,10 @@ def test_read_cropreporter(photosynthesis_test_data, tmpdir):
     assert all([a == b for a, b in zip(ps.ojip_dark.coords['frame_label'].to_dict()['data'], true_dark_labels)])
     true_light_labels = ['Flight', 'Fp', 'Fmp', 'PSL3', 'PSL4', 'PSL5', 'PSL6']
     assert all([a == b for a, b in zip(ps.ojip_light.coords['frame_label'].to_dict()['data'], true_light_labels)])
+    true_pam_dark_labels = ["Fdark", "F0", "Fm", "Fs"]
+    assert all([a == b for a, b in zip(ps.pam_dark.coords['frame_label'].to_dict()['data'], true_pam_dark_labels)])
+    true_pam_light_labels = ["Flight", "Fp", "Fmp", "Fs"]
+    assert all([a == b for a, b in zip(ps.pam_light.coords['frame_label'].to_dict()['data'], true_pam_light_labels)])
 
 
 def test_read_cropreporter_spc_only(photosynthesis_test_data, tmpdir):