[reproclasses] new FileStimulusData class

fishbook/reproclasses.py

@@ -653,9 +653,109 @@ class FIData:
         return boltzmann_fit
 
 
+class FileStimulusData:
+
+    def __init__(self, dataset: Dataset):
+        """
+        Constructor.
+
+        :param dataset: The dataset entity for which the filestimulus repro data should be loaded.
+        """
+        self.__spike_data = []
+        self.__contrasts = []
+        self.__stimuli = []
+        self.__dataset = dataset
+        self.__repros = None
+        self.__cell = dataset.cells[0]  # Beware: Assumption that there is only a single cell
+        self._get_data()
+
+    def _get_data(self):
+        if not self.__dataset:
+            return
+        self.__repros = RePro.find("FileStimulus", cell_id=self.__cell.id)
+        for r in self.__repros:
+            sd, c, stims = self.__read_spike_data_from_nix(r) if self.__dataset.has_nix else self.__read_spike_data_from_directory(r)
+            if sd is not None and len(sd) > 1:
+                self.__spike_data.extend(sd)
+                self.__contrasts.extend(c)
+                self.__stimuli.extend(stims)
+            else:
+                continue
+
+    def __do_read_spike_data_from_nix(self, mt: nix.pycore.MultiTag, stimulus: Stimulus, repro: RePro):
+        spikes = None
+        contrast = 0.0
+        stim_file = ""
+
+        r_settings = repro.settings.split("\n")
+        s_settings = stimulus.settings.split("\n")
+        delay = 0.0
+        for s in r_settings:
+            if "delay:" in s:
+                delay = float(s.split(":")[-1])
+                break
+        start_time = stimulus.start_time - delay
+        end_time = stimulus.start_time + mt.extents[stimulus.index]
+        contrast = 0.0 # this is a quick fix!!!
+        embed()
+        for s in s_settings:
+            if "Contrast:" in s and "PreContrast" not in s and "\t\t" not in s and "+-" not in s:
+                contrast = float(s.split(":")[-1])
+                break
+
+        return spikes, contrast, stim_file
+
+
+
+
+        local_eod = eod_da[start_index_eod:end_index_eod]
+        spikes = self.__all_spikes[(self.__all_spikes >= start_time) & (self.__all_spikes < end_time)] - start_time - delay
+        time = np.asarray(eod_da.dimensions[0].axis(end_index_eod - start_index_eod)) - delay
+        return spikes, local_eod, time, contrast
+
+        return spikes, contrast, stim_file
+
+    def __read_spike_data_from_nix(self, repro: RePro):
+        spikes = []
+        contrasts = []
+        stim_files = []
+        stimuli = Stimulus.find(cell_id=repro.cell_id, repro_id=repro.id)
+        if len(stimuli) == 0:
+            return spikes, contrasts, stim_files
+        data_source = os.path.join(self.__dataset.data_source, self.__dataset.id + ".nix")
+        if not os.path.exists(data_source):
+            print("Data not found! Trying from directory")
+            return self.__read_spike_data_from_directory(repro)
+        f = nix.File.open(data_source, nix.FileMode.ReadOnly)
+        b = f.blocks[0]
+        self.__all_spikes = b.data_arrays["Spikes-1"][:]
+        mt = None
+        for i in tqdm(range(len(stimuli)), desc="Loading data"):
+            s = stimuli[i]
+            if not mt or mt.id != s.multi_tag_id:
+                mt = b.multi_tags[s.multi_tag_id]
+            sp, c, stim = self.__do_read_spike_data_from_nix(mt, s, repro)
+            spikes.append(sp)
+            contrasts.append(c)
+            stim_files.append(stim)
+        f.close()
+        return spikes, contrasts, stim_files
+
+    def __read_spike_data_from_directory(self, repro: RePro):
+        print("not yet my friend!")
+        spikes = []
+        contrast = 0.0
+        stim = None
+
+        return spikes, contrast, stim
+
+    def read_stimulus(self, index=0):
+        pass
+
+
 if __name__ == "__main__":
     # dataset = Dataset(dataset_id='2011-06-14-ag')
-    # dataset = Dataset(dataset_id="2018-01-19-ac-invivo-1")
-    dataset = Dataset(dataset_id='2013-04-18-ac')
-    fi_curve = FIData(dataset)
+    dataset = Dataset(dataset_id="2018-09-13-ac-invivo-1")
+    # dataset = Dataset(dataset_id='2013-04-18-ac')
+    fi_curve = FileStimulusData(dataset)
     embed()