baseline class should be able to read old data

baseline_analysis.py

@@ -1,16 +1,79 @@
 import datajoint as dj
+import os
+import numpy as np
 from database import Dataset, Repro
 schema = dj.schema("fish_book", locals())
 
 
-def get_baseline_data(cell_id=None, restriction={}):
-    dsets = []
-    if dataset_name:
-        dsets = (Dataset & ("dataset_id like'%s'" % dataset_name) & restriction)
-    else:
-        dsets = (Dataset & restriction)
+class BaselineData(object):
 
-    if len(dsets) == 0:
-        return None
-    for d in dsets:
-        d
+    def __init__(self, dataset:Dataset):
+        self.__data = []
+        self.__dataset = dataset
+        self._get_data()
+
+
+    def _get_data(self):
+        if not self.__dataset:
+            self.__data = []
+        self.__data = []
+
+        repros = (Repro & self.__dataset & "repro_name like 'BaselineActivity%'")
+        for r in repros:
+            self.__data.append(self.__read_data(r))
+
+    def __read_data(self, r:Repro):
+        if self.__dataset["has_nix"]:
+            return self.__read_data_from_nix(r)
+        else:
+            return self.__read_data_from_directory(r)
+
+    @property
+    def dataset(self):
+        return self.__dataset
+
+    @property
+    def data(self, index:int=0):
+        return self.__data[0] if len(self.__data) >= index else None
+
+    @property
+    def size(self):
+        return len(self.__data)
+
+    def __str__(self):
+        str = "Baseline data of %s " % self.__dataset
+
+    def __read_data_from_nix(self, r)->np.ndarray:
+        # FIXME!
+        return np.asarray([])
+
+    def __read_data_from_directory(self, r)->np.ndarray:
+        data = []
+        data_source = os.path.join(self.__dataset["data_source"], "basespikes1.dat")
+        if os.path.exists(data_source):
+            found_run = False
+            with open(data_source, 'r') as f:
+                l = f.readline()
+                while l:
+                    if "index" in l:
+                        index = int(l.strip("#").strip().split(":")[-1])
+                        found_run = index == r["run"]
+                    if l.startswith("#Key") and found_run:
+                        data = self.__do_read(f)
+                        break
+                    l = f.readline()
+
+        return data
+
+    def __do_read(self, f)->np.ndarray:
+        data = []
+        f.readline()
+        f.readline()
+        l = f.readline()
+        while l and "#" not in l and len(l.strip()) > 0:
+            data.append(float(l.strip()))
+            l = f.readline()
+        return np.asarray(data)
+
+if __name__ == "__main__":
+    print("Test")

database.py

@@ -6,7 +6,6 @@ import glob
 import util as ut
 import uuid
 import yaml
-import pyrelacs
 
 from IPython import embed
 
@@ -170,14 +169,14 @@ class CellDatasetMap(dj.Manual):
 @schema
 class Repro(dj.Manual):
     definition = """
-    repro_id : varchar(512)
-    run : smallint
+    repro_id : varchar(512)     # The name that was given to the RePro run by relacs
+    run : smallint              # A counter counting the runs of the ReProp in this dataset
     -> Dataset # this is wrong! should be cell!?! In our case it is the same...
     ----
-    repro_name : varchar(512)
-    settings : varchar(3000)
-    start : float
-    duration : float
+    repro_name : varchar(512)   # The original name of the RePro itself, not any given name by user or relacs
+    settings : varchar(3000)    # Yaml formatted string containing the repro settings (tag.metadata in case of a nix file)
+    start : float               # The start time of the repro
+    duration : float            # The duration of the repro
     """
 
     @staticmethod
@@ -518,7 +517,7 @@ if __name__ == "__main__":
     data_dir = "/data/apteronotus"
     # data_dir = "../high_freq_chirps/data"
     # drop_tables()
-
-    datasets = glob.glob(os.path.join(data_dir, '/data/eigenmannia/201*'))
+    datasets = glob.glob("/Users/jan/zwischenlager/2012-*")
+    # datasets = glob.glob(os.path.join(data_dir, '/data/eigenmannia/201*'))
     populate(datasets, update=False)