Updates for v4.0

emva1288/process/data.py

@@ -54,8 +54,10 @@ def __init__(self,
         self.data = {}
         self.data['temporal'] = self._get_temporal(data['temporal'])
         self.data['spatial'] = self._get_spatial(data['spatial'])
+        self.data['darkcurrent'] =self._get_temporal(data['darkcurrent'], True)
+        print()
 
-    def _get_temporal(self, data):
+    def _get_temporal(self, data, dark_only=False):
         """Fill the temporal dict, with the stuff that we need.
         Compute the averages and variances from the sums (sum and pvar)
 
@@ -84,8 +86,10 @@ def _get_temporal(self, data):
                time and photon count,
                *'u_ydark'*: the array of the mean digital dark value
                for each exposure time
-               and *'s2_ydark'*: the
-               array of the digital dark value variance for each exposure time.
+               *'s2_ydark'*: the
+               array of the digital dark value variance for each exposure time
+               and *'diff_u_y'*: the difference of the mean values of the images
+               for each exposure time.
 
         Raises
         ------
@@ -106,6 +110,7 @@ def _get_temporal(self, data):
         s2_y = []
         u_ydark = []
         s2_ydark = []
+        diff_u_y = []
 
         for t in exposures:
             # photons is a list of photon counts
@@ -115,7 +120,7 @@ def _get_temporal(self, data):
             if 0.0 not in photons:
                 raise ValueError('Every exposure point must have a 0.0 photon')
 
-            if len(photons) < 2:
+            if len(photons) < 2 and not dark_only:
                 raise ValueError('There must be at least one bright photon')
 
             # get data for dark image
@@ -129,13 +134,15 @@ def _get_temporal(self, data):
                 d = self._get_temporal_data(data[t][p])
                 u_y.append(d['mean'])
                 s2_y.append(d['var'] - d['dmean'])
+                diff_u_y.append(data[t][p]['dmean'])    # we need to use the raw difference here
 
         # Append all data to temporal dict
         temporal['u_p'] = np.asarray(u_p)
         temporal['u_y'] = np.asarray(u_y)
         temporal['s2_y'] = np.asarray(s2_y)
         temporal['u_ydark'] = np.asarray(u_ydark)
         temporal['s2_ydark'] = np.asarray(s2_ydark)
+        temporal['diff_u_y'] = np.asarray(diff_u_y)
 
         # In case we have only one exposure, we need arrays with the
         # same length as the up

emva1288/process/loader.py

@@ -53,7 +53,7 @@ def __init__(self,
         fload_kwargs : dict, optional
                        The kwargs dictionary for the fload function.
         """
-        self.data = {'temporal': {}, 'spatial': {}}
+        self.data = {'temporal': {}, 'spatial': {}, 'darkcurrent': {}}
         self._fload = fload
         self._fload_args = fload_args
         self._fload_kwargs = fload_kwargs
@@ -72,7 +72,7 @@ def _load_data(self, images):
         the images and fills the temporal and spatial dicts
         """
         # iterate over the kind
-        for kind in ('temporal', 'spatial'):
+        for kind in ('temporal', 'spatial', 'darkcurrent'):
             exposures = set(images[kind].keys())
 
             # iterate over the exposure times for each kind
@@ -85,9 +85,10 @@ def _load_data(self, images):
                     # image with a 0 photon count.
                     raise ValueError('Every exposure must have a 0.0 photons'
                                      ' for dark information')
-                if len(photon_counts) < 2:
+                if len(photon_counts) < 2 and kind != "darkcurrent":
                     # Every exposure time should have at least one dark image
                     # and one bright image.
+                    # NOTE: We can allow only dark values for dark current measurement
                     raise ValueError('All exposure must have at least 2 points'
                                      ' one dark and one bright')
 

emva1288/process/parser.py

@@ -49,6 +49,7 @@ def __init__(self, filename, path=None, loglevel=logging.INFO):
         self.format = {}  # bits, witdth, height
         self.version = None
         self.images = {'temporal': {},
+                       'darkcurrent': {},
                        'spatial': {}}
 
         logging.basicConfig()
@@ -89,28 +90,31 @@ def _get_images_filenames(self):
 
         return fnames
 
-    def _get_kind(self, fnames):
+    def _get_kind(self, fnames, img_type=""):
         """
         Guess what kind of data based on the number of images
         Temporal = 2 images for each measurement point
         Spatial = >2 images for each measurement point
         """
         L = len(fnames)
         if L == 2:
-            kind = 'temporal'
+            if img_type == "dc":
+                kind = 'darkcurrent'
+            else:
+                kind = 'temporal'
         else:
             kind = 'spatial'
 
         return kind
 
-    def _add_pcount(self, exposure, photons, fnames):
-        """Add images to a given exposure/phton
+    def _add_pcount(self, exposure, photons, fnames, img_type=""):
+        """Add images to a given exposure/photon
 
         For a given exposure and photon count
         add the appropiate image filenames to the self.images dict
         """
         # is it temporal or spatial data
-        kind = self._get_kind(fnames)
+        kind = self._get_kind(fnames, img_type)
         # create the exposure time dictionary for this exposure time
         # if it is not already existing
         self.images[kind].setdefault(exposure, {})
@@ -145,7 +149,7 @@ def _fill_info(self):
             # b exposureTime(ns) numberPhotons  (bright image)
             # i relativePathToTheImage
             # d exposureTime(ns)                (dark image)
-
+            
             if l[0] == 'v':
                 # if line starts with 'v', this is the version
                 self.version = l[1]
@@ -189,7 +193,7 @@ def _fill_info(self):
 
                 continue
 
-            if l[0] == 'd':
+            if l[0] in ['d', 'dc']:
                 # For lines that starts with d, there is always 2 elements
                 # d + exposureTime (dark images)
                 if len(l) != 2:  # pragma: no cover
@@ -201,7 +205,7 @@ def _fill_info(self):
                 # For this exposure, get all the corresponding images
                 fnames = self._get_images_filenames()
                 # Add the images path to the images dict.
-                self._add_pcount(exposure, float(0.0), fnames)
+                self._add_pcount(exposure, float(0.0), fnames, l[0])
 
                 continue
 

emva1288/process/plotting.py

@@ -194,6 +194,93 @@ def plot(self, test):
         self.set_legend(ax)
 
 
+class PlotUyDarkCurrent(Emva1288Plot):
+    name = 'Dark Current From Mean'
+    xlabel = 'exposure time [ns]'
+    ylabel = r'$\mu_{y.dark}$ [DN]'
+
+    def plot(self, test):
+        ax = self.ax
+
+        if len(test.darkcurrent['texp']) > 2:
+            data = test.darkcurrent
+        else:
+            data = test.temporal
+
+        if test.dark_current_fit_mean() is not np.nan:
+            X = data['texp']
+
+            ax.plot(X,
+                    data['u_ydark'],
+                    marker='o',
+                    markersize=5,
+                    label='Data',
+                    gid='%d:data' % test.id)
+            ax.plot(X,
+                    test.dark_current_fit_mean()['fit_slope'] *
+                    X + test.dark_current_fit_mean()['fit_offset'],
+                    linestyle='--',
+                    label='Fit',
+                    gid='%d:fit' % test.id)
+            ax.ticklabel_format(axis='x', style='sci', scilimits=(1, 4))
+            self.set_legend(ax)
+
+class PlotVaryDarkCurrent(Emva1288Plot):
+    name = 'Dark Current From Variance'
+    xlabel = 'exposure time [ns]'
+    ylabel = r'$\sigma^2_{y.dark}$ [DN$^2$]'
+
+
+    def plot(self, test):
+        ax = self.ax
+
+        if len(test.darkcurrent['texp']) > 2:
+            data = test.darkcurrent
+        else:
+            data = test.temporal
+
+        if test.dark_current_fit_var() is not np.nan:
+            X = data['texp']
+
+            ax.plot(X,
+                    data['s2_ydark'],
+                    marker='o',
+                    markersize=5,
+                    label='Data',
+                    gid='%d:data' % test.id)
+            ax.plot(X,
+                    test.dark_current_fit_var()['fit_slope'] *
+                    X + test.dark_current_fit_var()['fit_offset'],
+                    linestyle='--',
+                    label='Fit',
+                    gid='%d:fit' % test.id)
+            ax.ticklabel_format(axis='x', style='sci', scilimits=(1, 4))
+            self.set_legend(ax)
+
+
+class PlotStabilityCheck(Emva1288Plot):
+    ''' Create the stability check plot '''
+    name = 'Stability Check'
+    xlabel = r'gray value [DN]'
+    ylabel = r'difference/std gray value [DN]'
+
+    def plot(self, test):
+        ax = self.ax
+
+        X = test.temporal['u_y'] - test.temporal['u_ydark']
+        Y1 = np.sqrt(test.temporal['s2_y'])
+        Y2 = test.temporal['diff_u_y']
+        ax.plot(X, Y1,
+                label='Std',
+                gid='%d:std' % test.id)
+        ax.plot(X, Y2,
+                label='Difference',
+                marker='.',
+                ls=' ',
+                gid='%d:difference' % test.id)
+        self.set_legend(ax)
+
+
 class PlotPTC(Emva1288Plot):
     '''Create Photon Transfer plot'''
 
@@ -792,10 +879,13 @@ def rearrange(self):
 
 EVMA1288plots = [PlotPTC,
                  PlotSNR,
+                 PlotStabilityCheck,
                  PlotSensitivity,
                  PlotUyDark,
                  PlotLinearity,
                  PlotDeviationLinearity,
+                 PlotUyDarkCurrent,
+                 PlotVaryDarkCurrent,
                  PlotHorizontalSpectrogramPRNU,
                  PlotHorizontalSpectrogramDSNU,
                  PlotVerticalSpectrogramPRNU,
@@ -812,10 +902,13 @@ def rearrange(self):
 
     - :class:`~emva1288.process.plotting.PlotPTC`
     - :class:`~emva1288.process.plotting.PlotSNR`
+    - :class:`~emva1288.process.plotting.PlotStabilityCheck`
     - :class:`~emva1288.process.plotting.PlotSensitivity`
     - :class:`~emva1288.process.plotting.PlotUyDark`
     - :class:`~emva1288.process.plotting.PlotLinearity`
     - :class:`~emva1288.process.plotting.PlotDeviationLinearity`
+    - :Class:`~emva1288.process.plotting.PlotUyDarkCurrent`
+    - :Class:`~emva1288.process.plotting.PlotVaryDarkCurrent`
     - :class:`~emva1288.process.plotting.PlotHorizontalSpectrogramPRNU`
     - :class:`~emva1288.process.plotting.PlotHorizontalSpectrogramDSNU`
     - :class:`~emva1288.process.plotting.PlotVerticalSpectrogramPRNU`