quadtree csv reader (#186)

* quadtree csv reader
* included depth columns in reader
* reader name change
* cartesian_grid.bounds added
* check for zero area

csep/core/regions.py

@@ -517,6 +517,9 @@ def __init__(self, polygons, dh, name='cartesian2d', mask=None):
         # index values of polygons array into the 2d cartesian grid, based on the midpoint.
         self.xs = xs
         self.ys = ys
+        # Bounds [origin, top_right]
+        orgs = self.origins()
+        self.bounds = numpy.column_stack((orgs, orgs + dh))
 
     def __eq__(self, other):
         return self.to_dict() == other.to_dict()
@@ -772,13 +775,16 @@ def geographical_area_from_bounds(lon1, lat1, lon2, lat2):
     Returns:
         Area of cell in Km2
     """
-    earth_radius_km = 6371.
-    R2 = earth_radius_km ** 2
-    rad_per_deg = numpy.pi / 180.0e0
+    if lon1 == lon2 or lat1 == lat2:
+        return 0
+    else:
+        earth_radius_km = 6371.
+        R2 = earth_radius_km ** 2
+        rad_per_deg = numpy.pi / 180.0e0
 
-    strip_area_steradian = 2 * numpy.pi * (1.0e0 - numpy.cos((90.0e0 - lat1) * rad_per_deg)) \
+        strip_area_steradian = 2 * numpy.pi * (1.0e0 - numpy.cos((90.0e0 - lat1) * rad_per_deg)) \
                            - 2 * numpy.pi * (1.0e0 - numpy.cos((90.0e0 - lat2) * rad_per_deg))
-    area_km2 = strip_area_steradian * R2 / (360.0 / (lon2 - lon1))
+        area_km2 = strip_area_steradian * R2 / (360.0 / (lon2 - lon1))
     return area_km2
 
 def quadtree_grid_bounds(quadk):

csep/utils/readers.py

@@ -719,7 +719,7 @@ def _parse_datetime_to_zmap(date, time):
         out['second'] = dt.second
         return out
 
-def load_quadtree_forecast(ascii_fname):
+def quadtree_ascii_loader(ascii_fname):
     """ Load quadtree forecasted stored as ascii text file
 
         Note: This function is adapted form csep.forecasts.load_ascii
@@ -756,4 +756,33 @@ def load_quadtree_forecast(ascii_fname):
     # reshape rates into correct 2d format
     rates = data[:, -1].reshape(n_poly, n_mag_bins)
 
+    return rates, region, mws
+
+
+def quadtree_csv_loader(csv_fname):
+    """ Load quadtree forecasted stored as csv file
+
+        The format expects forecast as a comma separated file, in which first column corresponds to quadtree grid cell (quadkey).
+        The second and thrid columns indicate depth range.
+        The corresponding enteries in the respective row are forecast rates corresponding to the magnitude bins.
+        The first line of forecast is a header, and its format is listed here:
+            'Quadkey', depth_min, depth_max, Mag_0, Mag_1, Mag_2, Mag_3 , ....
+             Quadkey is a string. Rest of the values are floats.
+        For the purposes of defining region objects quadkey is used.
+
+        We assume that the starting value of magnitude bins are provided in the header.
+        Args:
+            csv_fname: file name of csep forecast in csv format
+        Returns:
+            rates, region, mws (numpy.ndarray, QuadtreeRegion2D, numpy.ndarray): rates, region, and magnitude bins needed
+                                                                                 to define QuadTree forecasts
+     """
+
+    data = numpy.genfromtxt(csv_fname, dtype='str', delimiter=',')
+    quadkeys = data[1:, 0]
+    mws = data[0, 3:]
+    rates = data[1:, 3:]
+    rates = rates.astype(float)
+    region = QuadtreeGrid2D.from_quadkeys(quadkeys, magnitudes=mws)
+
     return rates, region, mws