world-ecoregion/biomes/data.py

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Python
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import geopandas
import rasterio
import pandas as pd
import numpy as np
import time
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from matplotlib import pyplot
from shapely.geometry import Point
from constants import *
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def read_temp_data(year):
return pd.read_csv(os.path.join(TEMP, 'air_temp.{}'.format(year)), sep='\s+', header=None,
names=['longitude', 'latitude', 'january',
'february', 'march', 'april',
'may', 'june', 'july', 'august',
'september', 'november', 'october',
'december', 'yearly_avg'])
def read_precip_data(year):
return pd.read_csv(os.path.join(PRECIP, 'precip.{}'.format(year)), sep='\s+', header=None,
names=['longitude', 'latitude', 'january',
'february', 'march', 'april',
'may', 'june', 'july', 'august',
'september', 'november', 'october',
'december', 'yearly_avg'])
eco = geopandas.read_file(ECOREGIONS)
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elevation = rasterio.open(ELEVATION)
elevation_data = elevation.read(1)
temp = {}
precip = {}
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)
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world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))[['geometry']].unary_union
boundary = world.boundary
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temp_precip_columns = []
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for year in range(MIN_YEAR, MAX_YEAR + 1):
for s in SEASONS:
temp_precip_columns += ['temp_{}_{}'.format(s, year), 'precip_{}_{}'.format(s, year)]
columns = ['longitude', 'latitude', 'biome_num', 'biome_name', 'elevation', 'distance_to_water'] + temp_precip_columns
final_data = pd.DataFrame(columns=columns)
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def get_point_information(longitude, latitude):
item = {}
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p = Point(longitude, latitude)
ecoregion = eco.loc[lambda c: c.geometry.contains(p)]
if ecoregion.empty:
return False
item['longitude'] = longitude
item['latitude'] = latitude
item['biome_num'] = ecoregion.BIOME_NUM.iloc[0]
item['biome_name'] = ecoregion.BIOME_NAME.iloc[0]
elev = elevation_data[elevation.index(longitude, latitude)]
item['elevation'] = elev
distance_to_sea = p.distance(boundary)
item['distance_to_water'] = distance_to_sea
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 SPRING_MONTHS]
spring_precip = [yp[month] for month in SPRING_MONTHS]
summer_temp = [yt[month] for month in SUMMER_MONTHS]
summer_precip = [yp[month] for month in SUMMER_MONTHS]
autumn_temp = [yt[month] for month in AUTUMN_MONTHS]
autumn_precip = [yp[month] for month in AUTUMN_MONTHS]
item['temp_winter_{}'.format(year)] = np.mean(winter_temp)
item['precip_winter_{}'.format(year)] = np.mean(winter_precip)
item['temp_spring_{}'.format(year)] = np.mean(spring_temp)
item['precip_spring_{}'.format(year)] = np.mean(spring_precip)
item['temp_summer_{}'.format(year)] = np.mean(summer_temp)
item['precip_summer_{}'.format(year)] = np.mean(summer_precip)
item['temp_autumn_{}'.format(year)] = np.mean(autumn_temp)
item['precip_autumn_{}'.format(year)] = np.mean(autumn_precip)
return item
data = {}
for col in columns:
data[col] = []
# i = 0
start_time = time.time()
for longitude in range(-179, 179):
print('-', end='')
for latitude in range(-89, 89):
# generate data and save to file
d = get_point_information(longitude, latitude)
if d == False:
print('.', end='')
continue
for key, value in d.items():
data[key].append(value)
print('+', end='')
print('')
print("--- Calculations: %s seconds ---" % (time.time() - start_time))
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start_time = time.time()
df = pd.DataFrame(data)
print("--- Generating DataFrame: %s seconds ---" % (time.time() - start_time))
print(df)
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start_time = time.time()
df.to_pickle('data.p')
print("--- Pickling DataFrame: %s seconds ---" % (time.time() - start_time))