Add regression test for example tide prediction

pytides/observations.py

@@ -0,0 +1,71 @@
+import abc
+import csv
+import datetime
+
+from itertools import tee
+
+import pytz
+import numpy
+
+
+class Observations(object):
+    __metaclass__ = abc.ABCMeta
+
+    @staticmethod
+    def from_csv(filename):
+        return CsvObservations(filename)
+
+    @staticmethod
+    def separate_datetimes_heights(observations):
+        """
+        Return a pair of generators: datetimes and heights from a single
+        generator of (datetime, height) tuples.
+        """
+        x, y = tee(observations, 2)  # copy the generator, can't rewind it.
+        return (tup[0] for tup in x), (tup[1] for tup in y)
+
+    @abc.abstractmethod
+    def to_numpy_arrays(self):
+        """
+        Return (datetimes, heights) where both are numpy arrays.
+        """
+        pass
+
+    @abc.abstractmethod
+    def __iter__(self):
+        """
+        Yield observations values as (datetime, height) tuples.
+
+        This must iterate from the start of the observations every time it's
+        called.
+        """
+        pass
+
+
+class CsvObservations(Observations):
+    def __init__(self, filename):
+        self.filename = filename
+
+    def __iter__(self):
+        return self.observations()
+
+    def to_numpy_arrays(self):
+        datetimes, heights = Observations.separate_datetimes_heights(
+            self.observations())
+        return (
+            numpy.array(list(datetimes)),
+            numpy.array(list(heights))
+        )
+
+    def observations(self):
+        with open(self.filename, 'r') as f:
+            reader = csv.DictReader(
+                line for line in f if not line.startswith('#'))
+            for row in reader:
+                yield (CsvObservations.parse_datetime(row['datetime']),
+                       float(row['height']))
+
+    @staticmethod
+    def parse_datetime(string):
+        return datetime.datetime.strptime(
+            string, '%Y-%m-%dT%H:%M:%S+00:00').replace(tzinfo=pytz.UTC)