feat(data): seasonal temp/precip data + distance to water

data.py

@@ -14,7 +14,6 @@ ECOREGIONS = os.path.join(GEODATA, 'ecoregions', 'single-parts.shp')
 ELEVATION = os.path.join(GEODATA, 'srtm', 'topo30-180.tif')
 TEMP = os.path.join(GEODATA, 'air_temp')
 PRECIP = os.path.join(GEODATA, 'precipitation')
-YEAR = 2014
 
 def read_temp_data(year):
     return pd.read_csv(os.path.join(TEMP, 'air_temp.{}'.format(year)), sep='\s+', header=None,
@@ -37,54 +36,83 @@ eco = geopandas.read_file(ECOREGIONS)
 elevation = rasterio.open(ELEVATION)
 elevation_data = elevation.read(1)
 
-temp = read_temp_data(YEAR)
+temp = {}
+precip = {}
+MIN_YEAR = 1900
+MAX_YEAR = 2017
+for year in range(MIN_YEAR, MAX_YEAR + 1):
+    temp[year] = read_temp_data(year)
+    precip[year] = read_precip_data(year)
+    precip[year]['yearly_avg'] = precip[year].mean(axis=1)
 
-precip = read_precip_data(YEAR)
-precip['yearly_avg'] = precip.mean(axis=1)
 
-print('# Elevation')
+world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))[['geometry']].unary_union
-print('bounds: left={} bottom={} top={} right={}'.format(elevation.bounds.left, elevation.bounds.bottom, elevation.bounds.top, elevation.bounds.right))
+boundary = world.boundary
-print('min: {}, max: {}\n'.format(elevation_data.min(), elevation_data.max()))
 
-print('# Temperature ({})'.format(YEAR))
+temp_precip_columns = []
-print('Yearly average min: {}, max: {}\n'.format(temp.yearly_avg.min(), temp.yearly_avg.max()))
 
-print('# Precipitation ({})'.format(YEAR))
+for year in range(MIN_YEAR, MAX_YEAR + 1):
-print('Yearly average min: {}, max: {}\n'.format(precip.yearly_avg.min(), precip.yearly_avg.max()))
+    for s in ['winter', 'spring', 'summer', 'autumn']:
+        temp_precip_columns += ['temp_{}_{}'.format(s, year), 'precip_{}_{}'.format(s, year)]
 
-columns = ['biome_num', 'biome_name', 'elevation', 'temp_yearly_avg', 'precip_yearly_avg']
+columns = ['biome_num', 'biome_name', 'elevation', 'distance_to_water'] + temp_precip_columns
 indices = ['longitude', 'latitude']
 final_data = pd.DataFrame(index=indices, columns=columns)
 
 def get_point_information(longitude, latitude):
-    start_time = time.time()
+    item = {}
     p = Point(longitude, latitude)
-    # print('({},{})'.format(longitude, latitude))
     ecoregion = eco.loc[lambda c: c.geometry.contains(p)]
-    print("er%ss" % (time.time() - start_time))
     if ecoregion.empty:
         return False
-    start_time = time.time()
+
+    item['biome_num'] = ecoregion.BIOME_NUM.iloc[0]
+    item['biome_name'] = ecoregion.BIOME_NAME.iloc[0]
+
     elev = elevation_data[elevation.index(longitude, latitude)]
-    start_time = time.time()
+    item['elevation'] = elev
-    t = np.argmin(np.array((temp.longitude - longitude)**2 + (temp.latitude - latitude)**2))
-    start_time = time.time()
-    p = np.argmin(np.array((precip.longitude - longitude)**2 + (precip.latitude - latitude)**2))
 
-    return {
+    distance_to_sea = p.distance(boundary)
-        'biome_num': ecoregion.BIOME_NUM.iloc[0],
+    item['distance_to_water'] = distance_to_sea
-        'biome_name': ecoregion.BIOME_NAME.iloc[0],
-        'elevation': elev,
-        'temp_yearly_avg': temp.iloc[t, 2:].yearly_avg,
-        'precip_yearly_avg': precip.iloc[p, 2:].yearly_avg
-    }
 
+    t = np.argmin(np.array((temp[MIN_YEAR].longitude - longitude)**2 + (temp[MIN_YEAR].latitude - latitude)**2))
+    p = np.argmin(np.array((precip[MIN_YEAR].longitude - longitude)**2 + (precip[MIN_YEAR].latitude - latitude)**2))
+
+    yearly_temp = {}
+    yearly_precip = {}
+    for year in range(MIN_YEAR, MAX_YEAR + 1):
+        yearly_temp[year] = yt = temp[year].iloc[t, 2:]
+        yearly_precip[year] = yp = precip[year].iloc[p, 2:]
+        winter_temp = [yt.january, yt.february] + ([yearly_temp[year - 1].december] if year > MIN_YEAR else [])
+        winter_precip = [yp.january, yp.february] + ([yearly_precip[year - 1].december] if year > MIN_YEAR else [])
+
+        spring_temp = [yt[month] for month in ['march', 'april', 'may']]
+        spring_precip = [yp[month] for month in ['march', 'april', 'may']]
+
+        summer_temp = [yt[month] for month in ['june', 'july', 'august']]
+        summer_precip = [yp[month] for month in ['june', 'july', 'august']]
+
+        autumn_temp = [yt[month] for month in ['september', 'november', 'october']]
+        autumn_precip = [yp[month] for month in ['september', 'november', 'october']]
+
+        item['temp_winter_{}'.format(year)] = np.mean(winter_temp)
+        item['precip_winter_{}'.format(year)] = np.mean(winter_temp)
+
+        item['temp_spring_{}'.format(year)] = np.mean(spring_temp)
+        item['precip_spring_{}'.format(year)] = np.mean(spring_temp)
+
+        item['temp_summer_{}'.format(year)] = np.mean(summer_temp)
+        item['precip_summer_{}'.format(year)] = np.mean(summer_temp)
+
+        item['temp_autumn_{}'.format(year)] = np.mean(autumn_temp)
+        item['precip_autumn_{}'.format(year)] = np.mean(autumn_temp)
+
+    return item
 
 data_indices = []
-
 data_map = {}
 for col in columns:
-    data_map[col] = {} 
+    data_map[col] = {}
 
 i = 0
 
@@ -103,6 +131,7 @@ for longitude in range(-179, 179):
             data_map[key][(longitude, latitude)] = value
 
         print('+', end='')
+
     print('')
 
 print("--- Calculations: %s seconds ---" % (time.time() - start_time))
@@ -110,7 +139,7 @@ print("--- Calculations: %s seconds ---" % (time.time() - start_time))
 start_time = time.time()
 df = pd.DataFrame(data_map)
 print("--- Generating DataFrame: %s seconds ---" % (time.time() - start_time))
-print(df.head())
+print(df)
 start_time = time.time()
 df.to_pickle('data.p')
 print("--- Pickling DataFrame: %s seconds ---" % (time.time() - start_time))