# define date range
date_range_off = pd.date_range('2001-10-01', '2016-12-31', freq='MS')
date_range = date_range_off[3:]
# define products and their properties
products = [
{'path': 'MODIS/MOD14A2/prepared', 'col': 'fire_pixels', 'stat': 'sum',
'compute_prev': False, 'date_range': date_range},
{'path': 'MODIS/MOD13A3/prepared', 'col': 'evi', 'stat': 'mean',
'compute_prev': True, 'date_range': date_range_off},
{'path': 'TRMM/3B43/prepared', 'col': 'ppt', 'stat': 'mean',
'compute_prev': True, 'date_range': date_range_off},
]
for prod in products:
data = create_data_array(prod['path'], prod['date_range'])
nd = get_nodata_value(prod['path'])
for i, month in enumerate(date_range):
# calculate stat for current month
stat = get_data_statistic(data, nd, month, month, prod['stat'])
df.loc[i, prod['col']] = stat
# compute previous 3 month period
if prod['compute_prev']:
start = month - pd.DateOffset(months=3)
end = month - pd.DateOffset(months=1)
stat = get_data_statistic(data, nd, start, end, prod['stat'])
df.loc[i, f'{prod["col"]}_prev'] = stat
# set index as date and change data types
df.index = date_range
import seaborn as sns
from code.functions import beautify_ax, create_data_array, get_nodata_value, \
init_sns
from code.variables import edge_color, face_color, hue_one
if __name__ == '__main__':
# change directory
os.chdir('../../data/tif/MODIS')
fire_path = 'MOD14A2/prepared'
dtnf_path = 'derived/DTNF'
# create fire array and get NoData value
date_range = pd.date_range('2002', '2017', freq='MS', closed='left')
fire_data = create_data_array(fire_path, date_range)
fire_nd = get_nodata_value(fire_path)
fire_mask = (fire_data != fire_nd) & (fire_data != 0)
# create fire pixels mask grouped by year
grouped_fire_mask = fire_mask.groupby(fire_data.t.dt.year).any(dim='t').values
# create DataArray for forest proximity and fire
years = date_range.year.unique().astype('str')
arr = create_data_array(dtnf_path, years).values
nd = 32767
mask = (arr != nd)
# get distance values for fire pixels and create bins for the histogram
values = arr[grouped_fire_mask & mask]
bins = np.arange(values.min(), values.max()) - 0.5 # one pixel bins
import seaborn as sns
from code.functions import beautify_ax, create_data_array, get_nodata_value, \
init_sns
from code.variables import edge_color, evi_scaling_factor, face_color, \
hue_one, hue_two
if __name__ == '__main__':
# change directory and define product paths
os.chdir('../../data/tif')
fire_path = 'MODIS/MOD14A2/prepared'
paths = ['TRMM/3B43/prepared', 'MODIS/MOD13A3/prepared']
# create fire array and get NoData value
date_range = pd.date_range('2002', '2017', freq='MS', closed='left')
fire_arr = create_data_array(fire_path, date_range).values
fire_nd = get_nodata_value(fire_path)
fire_mask = (fire_arr != fire_nd) & (fire_arr != 0)
# initialize seaborn environment and create figure and axes
init_sns()
fig, axs = plt.subplots(ncols=2, nrows=1)
hue_colors = [hue_one, hue_two]
labels = ['Precipitation (mm/month)', 'Enhanced Vegetation Index']
for i, path in enumerate(paths):
arr = create_data_array(path, date_range, offset=3).values
nd = get_nodata_value(path)
mask = (arr != nd)
# get all values (excluding NoData) and masked values (for fire-pixels)
if __name__ == '__main__':
# change directory
os.chdir('../../data/tif/MODIS')
# define product paths
fire_path = 'MOD14A2/prepared'
lc_path = 'MCD12Q1/prepared'
dtnf_path = 'derived/DTNF'
# define year and date ranges
year = '2009'
years = pd.date_range('2002', '2016', freq='AS').year.astype('str')
months = pd.date_range('2002', '2017', freq='MS', closed='left')
# create DataArrays
fire_data = create_data_array(fire_path, months)
lc_data = create_data_array(lc_path, years)
dtnf_data = create_data_array(dtnf_path, years)
# create empty DataFrame
cols = ['year', 'is_fire_pixel', 'lc_code', 'forest_distance']
df = pd.DataFrame(columns=cols)
for year in years:
# extract fire values for the given year
fire_arr = fire_data.loc[year].values
fire_nd = get_nodata_value(fire_path)
fire_mask = (fire_arr == fire_nd)
fire_arr = np.ma.array(fire_arr, mask=fire_mask).sum(axis=0)
fire_arr = np.ma.where(fire_arr > 0, 1, fire_arr).filled(fire_nd)
os.chdir('../../data/tif/MODIS')
# define folders for fire and landcover products
fire_folder = 'MOD14A2/prepared'
lc_folder = 'MCD12Q1/prepared'
# define NoData values
fire_nd = get_nodata_value(fire_folder)
lc_nd = get_nodata_value(lc_folder)
# define date ranges to create the data arrays
months = pd.date_range('2002', '2017', freq='MS', closed='left')
years = pd.date_range('2002', '2016', freq='AS').year.astype('str')
# create data arrays
fire_data = create_data_array(fire_folder, months)
lc_data = create_data_array(lc_folder, years)
# create empty DataFrame
df = pd.DataFrame(columns=list(landcovers.values()))
for i, month in enumerate(months):
# filter DataArrays by month
fire_arr = fire_data.loc[month].values
lc_arr = lc_data.loc[str(month.year)].values
# define masks
fire_mask = (fire_arr != 0) & (fire_arr != fire_nd)
# compute number of fire pixels for each type of landcover
fire_pixels_per_cover = []
# =============================================================================
import os
import numpy as np
import pandas as pd
from code.functions import create_data_array, get_nodata_value
from code.variables import landcovers
if __name__ == '__main__':
# change directory
os.chdir('../../data/tif/MODIS/MCD12Q1/prepared')
# create landcover DataArray
years = pd.date_range('2002', '2016', freq='AS').year.astype('str')
data = create_data_array('.', years)
nd = get_nodata_value('.')
# create empty DataFrame
cols = ['year', 'code', 'pixels', 'proportion']
df = pd.DataFrame(columns=cols)
for i, year in enumerate(years):
# filter DataArray by year and get pixel count by landcover
arr = data.loc[year].values
mask = (arr != 0) & (arr != nd)
values, counts = np.unique(arr[mask], return_counts=True)
# create year's DataFrame
year_df = pd.DataFrame(columns=cols)
year_df['code'] = values