added 30 year normals

README.md

@@ -11,6 +11,7 @@ The data in mongodb has the following fields:
 * ```year``` - Year of the data
 * ```min_temps``` - Minimum daily temperature as an array. One element is one day
 * ```max_temps``` - Maximum daily temperature as an array. One element is one day
+* ```normal``` - Indicates whether the data is a 30 year normal or not
 
 #### API Endpoints
 * ```POST /api/:product/:year```

to_mongo.py

@@ -4,6 +4,18 @@ import tqdm
 import datetime
 from pymongo import MongoClient
 
+def is_leap_year(year):
+    if (year % 4) == 0:
+        if (year % 100) == 0:
+            if (year % 400) == 0:
+                return True
+            else:
+                return False
+        else:
+            return True
+    else:
+        return False 
+
 client = MongoClient('0.0.0.0', 27017)
 
 db = client["gdd_database"]
@@ -21,12 +33,12 @@ coords = xr.open_dataset("coords.nc")
 lat = coords.latitude.data
 lon = coords.longitude.data
 
-years = list(range(1981, 1982 + 1))
+years = list(range(1981, 2020 + 1))
 
 for year in years:
     soy = np.datetime64("%s-01-01" % year)
     print (year)
-    data = xr.open_dataset("data/temps_2019.nc")
+    data = xr.open_dataset("data/temps_%s.nc" % year)
     x = np.where(~np.isnan(np.nanmean(data.tmin.data, axis=0)))
     x = [(a, b) for a, b in zip(x[0], x[1])]
 
@@ -66,8 +78,87 @@ for year in years:
         t["min_temps"] = list([float(a) for a in tmin_])
         t["max_temps"] = list([float(a) for a in tmax_])
         t["_id"] = _id
+        t["normal"] = False
      
         locs.append(t)
         
         count += 1
 
+
+### 30 YEAR NORMALS ###
+### Covers from 1981-2010 ###
+
+single_year_min = np.zeros((365, 621, 1405))
+single_year_max = np.zeros((365, 621, 1405))
+
+single_year_min[:] = np.nan
+single_year_max[:] = np.nan
+
+for year in range(1981, 2010+1):
+    print (year)
+    data = xr.open_dataset("data/temps_%s.nc" % year)
+
+    tmins = data.tmin.data
+    tmaxs = data.tmax.data  
+
+    if is_leap_year(year): # extra day in leap year screws everything up
+
+        tmin_1 = tmins[:59]
+        tmin_2 = tmins[60:]
+
+        tmax_1 = tmaxs[:59]
+        tmin_2 = tmaxs[60:]
+
+        tmins = np.concatenate([tmin_1, tmin_2], axis=0)
+        tmaxs = np.concatenate([tmin_1, tmin_2], axis=0)
+
+    single_year_max += tmaxs/30
+    single_year_min += tmins/30
+
+
+x = np.where(~np.isnan(np.nanmean(single_year_max, axis=0)))
+x = [(a, b) for a, b in zip(x[0], x[1])]
+
+lat = lat[::-1]
+
+tmins = single_year_min
+tmaxs = single_year_max
+
+locs = []
+
+count = 0
+for i in tqdm.tqdm(x):
+    if len(locs) % 100 == 0 and len(locs) != 0:
+        new_result = gdd.insert_many(locs)
+        locs = []
+    
+    tmin_ = tmins[:, i[0], i[1]]
+    tmax_ = tmaxs[:, i[0], i[1]]
+    
+    lat_ = lat[i[0]]
+    lon_ = lon[i[1]]
+
+    a = i
+
+    t = {}
+
+    _id = str(year) + "_"
+
+    _id += str(a[0]) + "_" + str(a[1]) + "_" + "normal"
+    
+    t["location"] = {"type": "Point", "coordinates": [float(lon_), float(lat_)]}
+    t["prism_lat"] = int(a[0])
+    t["prism_lon"] = int(a[1])
+    
+    t["last_date"] = 0#datetime.datetime.strptime(str(soy + np.timedelta64(len(tmin_) - 1, "D")) , "%Y-%m-%d")
+    t["year"] = 0
+    t["min_temps"] = list([float(a) for a in tmin_])
+    t["max_temps"] = list([float(a) for a in tmax_])
+    t["_id"] = _id
+
+    t["normal"] = True
+
+    
+    locs.append(t)
+    
+    count += 1
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