Fixed bug with memory and core calc. Added noise threshold to grp attrs....

Fixed bug with memory and core calc. Added noise threshold to grp attrs. Always printing progress when writing.

pycroscopy/processing/signal_filter.py

@@ -85,38 +85,23 @@ class SignalFilter(Process):
         self.write_filtered = write_filtered
         self.write_condensed = write_condensed
 
-        self.data = None
-        self.filtered_data = None
-        self.condensed_data = None
-        self.noise_floors = None
-        self.h5_filtered = None
-        self.h5_condensed = None
-        self.h5_noise_floors = None
-
-    def _set_memory_and_cores(self, cores=1, mem=1024):
-        """
-        Checks hardware limitations such as memory, # cpus and sets the recommended datachunk sizes and the
-        number of cores to be used by analysis methods.
-
-        Parameters
-        ----------
-        cores : uint, optional
-            Default - 1
-            How many cores to use for the computation
-        mem : uint, optional
-            Default - 1024
-            The amount a memory in Mb to use in the computation
-        """
-        super(SignalFilter, self)._set_memory_and_cores(cores=cores, mem=mem)
         """
         Remember that the default number of pixels corresponds to only the raw data that can be held in memory
         In the case of signal filtering, the datasets that will occupy space are:
         1. Raw, 2. filtered (real + freq space copies), 3. Condensed (substantially lesser space)
         The actual scaling of memory depends on options:
         """
-        scaling_factor = 1 + 2*self.write_filtered + 0.25*self.write_condensed
+        scaling_factor = 1 + 2 * self.write_filtered + 0.25 * self.write_condensed
         self._max_pos_per_read = int(self._max_pos_per_read / scaling_factor)
 
+        self.data = None
+        self.filtered_data = None
+        self.condensed_data = None
+        self.noise_floors = None
+        self.h5_filtered = None
+        self.h5_condensed = None
+        self.h5_noise_floors = None
+
     def _create_results_datasets(self):
         """
         Creates all the datasets necessary for holding all parameters + data.
@@ -124,12 +109,16 @@ class SignalFilter(Process):
 
         grp_name = self.h5_main.name.split('/')[-1] + '-FFT_Filtering_'
         grp_filt = MicroDataGroup(grp_name, self.h5_main.parent.name)
+
         filter_parms = dict()
         if self.frequency_filters is not None:
             for filter in self.frequency_filters:
                 filter_parms.update(filter.get_parms())
+        if self.noise_threshold is not None:
+            filter_parms['noise_threshold'] = self.noise_threshold
         filter_parms['algorithm'] = 'pycroscopy_SignalFilter'
         filter_parms['last_pixel'] = 0
+
         grp_filt.attrs = filter_parms
 
         if isinstance(self.composite_filter, np.ndarray):
@@ -241,8 +230,7 @@ class SignalFilter(Process):
 
         self.hdf.flush()
 
-        if self.verbose:
-            print('Finished processing upto pixel ' + str(self._end_pos) + ' of ' + str(self.h5_main.shape[0]))
+        print('Finished processing upto pixel ' + str(self._end_pos) + ' of ' + str(self.h5_main.shape[0]))
 
         # Now update the start position
         self._start_pos = self._end_pos