add haversine

src/ecofocipy/math/haversine.py

@@ -0,0 +1,43 @@
+#!/usr/bin/env python
+# Haversine formula example in Python
+# Author: Wayne Dyck
+
+import math
+import numpy as np
+
+def distance(origin, destination):
+    lat1, lon1 = origin
+    lat2, lon2 = destination
+    radius = 6371 # km
+
+    dlat = math.radians(lat2-lat1)
+    dlon = math.radians(lon2-lon1)
+    a = math.sin(dlat/2) * math.sin(dlat/2) + math.cos(math.radians(lat1)) \
+        * math.cos(math.radians(lat2)) * math.sin(dlon/2) * math.sin(dlon/2)
+    c = 2 * math.atan2(math.sqrt(a), math.sqrt(1-a))
+    d = radius * c
+
+    return d
+
+def nearest_point(origin, latpoints, lonpoints, grid='1d'):
+
+    if grid == '1d':
+        dist = np.zeros((np.shape(latpoints)[0], np.shape(lonpoints)[0]))
+
+        for i, lat in enumerate(latpoints):
+            for j, lon in enumerate(lonpoints):
+                dist[i,j] = distance(origin,[lat,lon])
+
+        lati, loni = np.where(dist == dist.min())
+        return (dist.min(), latpoints[lati[0]], lonpoints[loni[0]], lati[0], loni[0] )
+
+    elif grid == '2d':
+        dist = np.zeros_like(latpoints)
+
+        for i, latrow in enumerate(latpoints):
+            for j, lonrow in enumerate(latrow):
+                dist[i,j] = distance(origin,[latpoints[i,j],lonpoints[i,j]])
+
+        lati, loni = np.where(dist == dist.min())
+
+        return (dist.min(), latpoints[lati[0]][loni[0]], lonpoints[lati[0]][loni[0]], lati[0], loni[0] )

tools/find_closest_ctd.py

@@ -29,7 +29,6 @@
 import mysql.connector
 
 # User defined
-import io_utils.ConfigParserLocal as ConfigParserLocal
 import calc.haversine as sphered
 
 __author__ = "Shaun Bell"