Changes during analysis to accomodate more data

types

Author: wl-stepp

NNio.py

@@ -2,6 +2,8 @@
 Input/Ouput module for data generated using the network_watchdog approach for adaptive temporal
 sampling on the iSIM
 '''
+# There are mixed naming styles here unfortunately
+# pylint: disable=C0103
 
 import contextlib
 import glob
@@ -37,7 +39,6 @@ def loadiSIMmetadata(folder):
                 new_name = file.split("Timing_")[0] + "Timing_z" + file.split("Timing_")[1]
                 os.rename(file, new_name)
 
-
     delay = []
     fileList = sorted(glob.glob(folder + '/iSIMmetadata*.txt'))
     for name in fileList:
@@ -54,13 +55,15 @@ def loadiSIMmetadata(folder):
         except ValueError:
             # This means that delay was > 240 and an empty frame was added
             delay[-1] = delay[-1] + 240
+
     return delay
 
 
 def loadElapsedTime(folder, progress=None, app=None):
     """ get the Elapsed time for all .tif files in folder or from a stack """
 
     elapsed = []
+    step = None
     # Check for folder or stack mode
     # if not re.match(r'*.tif*', folder) is None:
     #     # get microManager elapsed times if available
@@ -72,9 +75,11 @@ def loadElapsedTime(folder, progress=None, app=None):
     if os.path.isfile(folder + '/img_channel001_position000_time000000000_z000.tif'):
         fileList = sorted(glob.glob(folder + '/img_channel001*'))
         numFrames = len(fileList)
+        step = 1
     else:
-        fileList = glob.glob(folder + '/img_*[0-9].tif')
+        fileList = sorted(glob.glob(folder + '/img_*[0-9].tif'))
         numFrames = int(len(fileList)/2)
+        step = 2
 
     if progress is not None:
         progress.setRange(0, numFrames*2)
@@ -99,7 +104,7 @@ def loadElapsedTime(folder, progress=None, app=None):
             progress.setValue(i)
         i = i + 1
 
-    return elapsed
+    return elapsed[0::step]
 
 
 def loadNNData(folder):
@@ -468,13 +473,13 @@ def calculateNNforStack(file, model=None, nnPath=None, img_range=None):
     """ calculate neural network output for all frames in a stack and write to new stack """
     # dataOrder = 1  # 0 for drp/foci first, 1 for mito/structure first
     if model is None:
-        from tensorflow import keras
+        from tensorflow import keras  # pylint: disable=C0415
         modelPath = '//lebnas1.epfl.ch/microsc125/Watchdog/GUI/model_Dora.h5'
         modelPath = '//lebnas1.epfl.ch/microsc125/Watchdog/Model/paramSweep5/f32_c07_b08.h5'
         model = keras.models.load_model(modelPath, compile=True)
     elif isinstance(model, str):
         if os.path.isfile(model):
-            from tensorflow import keras
+            from tensorflow import keras  # pylint: disable=C0415
             model = keras.models.load_model(model, compile=True)
         else:
             raise FileNotFoundError
@@ -489,7 +494,8 @@ def calculateNNforStack(file, model=None, nnPath=None, img_range=None):
             mdInfo = tif.ome_metadata.encode(encoding='UTF-8', errors='strict')
             # extract only the planes that was written to
             mdInfoDict = xmltodict.parse(mdInfo)
-            mdInfoDict['OME']['Image']['Pixels']['Plane'] = mdInfoDict['OME']['Image']['Pixels']['Plane'][0::2]
+            intermediateInfo = mdInfoDict['OME']['Image']['Pixels']['Plane'][0::2]
+            mdInfoDict['OME']['Image']['Pixels']['Plane'] = intermediateInfo
 
             dataOrder = int(mdInfoDict['OME']['Image']['Description']['@dataOrder'])
             mdInfo = xmltodict.unparse(mdInfoDict).encode(encoding='UTF-8', errors='strict')
@@ -526,14 +532,15 @@ def calculateNNforStack(file, model=None, nnPath=None, img_range=None):
 
 
 def calculateNNforFolder(folder, model=None):
+    """ Recalculate all the neural network frames for a folder with peak/structure frames """
     if model is None:
-        from tensorflow import keras
+        from tensorflow import keras  # pylint: disable=C0415
         modelPath = '//lebnas1.epfl.ch/microsc125/Watchdog/GUI/model_Dora.h5'
         # modelPath = '//lebnas1.epfl.ch/microsc125/Watchdog/Model/paramSweep5/f32_c07_b08.h5'
         model = keras.models.load_model(modelPath, compile=True)
     elif isinstance(model, str):
         if os.path.isfile(model):
-            from tensorflow import keras
+            from tensorflow import keras  # pylint: disable=C0415
             model = keras.models.load_model(model, compile=True)
         else:
             raise FileNotFoundError
@@ -546,7 +553,7 @@ def calculateNNforFolder(folder, model=None):
         filelist = sorted(glob.glob(folder + '/img_*.tif'))
         re_odd = re.compile(r'.*time\d*[13579]_.*')
         bact_filelist = [file for file in filelist if re_odd.match(file)]
-        re_even = re.compile(".*time\d*[02468]_.*")
+        re_even = re.compile(r'.*time\d*[02468]_.*')
         ftsz_filelist = [file for file in filelist if re_even.match(file)]
 
     for index, bact_file in tqdm(enumerate(bact_filelist)):
@@ -707,13 +714,14 @@ def on_select(*_):
                                    minspanx=5, minspany=5, spancoords='pixels', rectprops=rectprops,
                                    interactive=True, state_modifier_keys={'square': 'shift'})
     plt.show()
-    rectProp = rectHandle._rect_bbox
+    # There might be different properties there that could give the same information
+    # https://matplotlib.org/stable/api/widgets_api.html#matplotlib.widgets.RectangleSelector
+    rectProp = rectHandle._rect_bbox  # pylint: disable=W0212
     rectProp = tuple(int(x) for x in rectProp)
     print(rectProp)
     return rectProp
 
 
-
 def main():
     """ Main method calculating a nn stack for a set of old Mito/drp stacks """
 
@@ -735,7 +743,6 @@ def main():
     # makePrepImages(folder, model)
     # loadiSIMmetadata(folder)
 
-
     # allFiles = glob.glob('//lebnas1.epfl.ch/microsc125/iSIMstorage/Users/Willi/'
     #                      '180420_DRP_mito_Dora/**/*MMStack*lzw.ome.tif', recursive=True)
     # mainPath = 'W:/iSIMstorage/Users/Willi/160622_caulobacter/160622_CB15N_WT/SIM images'

QtImageViewerSeries.py

@@ -10,7 +10,8 @@
 import pyqtgraph as pg
 from PyQt5 import QtGui
 from PyQt5.QtCore import QRectF, QTimer, pyqtSignal
-from PyQt5.QtWidgets import QApplication, QGridLayout, QInputDialog, QWidget
+from PyQt5.QtWidgets import (QApplication, QFileDialog, QGridLayout,
+                             QInputDialog, QWidget)
 from skimage import io
 
 from SmartMicro.QtImageViewerMerge import QtImageViewerMerge
@@ -20,7 +21,7 @@ def main():
     """ Method to test the Viewer in a QBoxLayout with 1 and 2 channels"""
     app = QApplication(sys.argv)
     viewer = QtImageViewerSeries()
-    fname = ('//lebnas1.epfl.ch/microsc125/Watchdog/Model/Drp1.h5')
+    fname = ('//lebnas1.epfl.ch/microsc125/Watchdog/Model/Mito.h5')
     # fname = ('W:/Watchdog/Model/Proc.h5')
     # fname = ('C:/Users/stepp/Documents/02_Raw/SmartMito/__short.tif')
     viewer.loadSeries(fname)
@@ -55,12 +56,22 @@ def __init__(self):
         self.timer.setInterval(20)
 
         self.series = None
+        self.lastKey = None
 
     def newSlider(self, event):
         """ ??? """
         print(self.region.getRegion())
         print(event)
 
+    def keyPressEvent(self, keyEvent: QtGui.QKeyEvent) -> None:
+        """ Save the current frame that is displayed if Ctrl+Save is pressed """
+        if self.lastKey == 16777249 and keyEvent.key() == 83:
+            fname, *_ = QFileDialog.getSaveFileName(QWidget(), 'Save file')
+            frame = int(self.slider.position)
+            io.imsave(fname, self.series[frame, :, :])
+        self.lastKey = keyEvent.key()
+        return super().keyPressEvent(keyEvent)
+
     def onTimer(self, i=None):
         """Set the current frame if timer is running. Also accesible programmatically to set a
         certain frame by setting the i input
@@ -71,14 +82,14 @@ def onTimer(self, i=None):
             i = int(np.round(i))
         print(i)
         if self.series.ndim == 4:
-            num_channels = self.series.shape[3]
+            numChannels = self.series.shape[3]
         else:
-            num_channels = 1
+            numChannels = 1
 
-        if num_channels == 1:
+        if numChannels == 1:
             self.viewer.setImage(self.series[i, :, :], 0)
         else:
-            for channel in range(num_channels):
+            for channel in range(numChannels):
                 self.viewer.setImage(self.series[i, :, :, channel-1], channel-1)
 
     def loadSeries(self, filePath):
@@ -112,7 +123,6 @@ def loadSeries(self, filePath):
         self.slider.setSliderRange([-1, self.series.shape[0]-1])
         self.viewer.resetZoom()
 
-
     def startTimer(self):
         """ start Timer when slider is pressed """
         self.timer.start()
@@ -304,6 +314,5 @@ def mouseReleaseEvent(self, _):
             self.buttonReleased.emit()
 
 
-
 if __name__ == '__main__':
     main()