def compute_sketches():
count = 0
height_maps = []
sketch_maps = []
for filename in Path('./data_downsampled_blurred').glob('**/*.tif'):
file_path = str(filename)
file_id = file_path.split('/')
detailed_data = gr.from_file('./data/' + file_id[-1])
data = gr.from_file(str(filename))
if data.mean() < 5:
continue
ridges, peaks = compute_ridges(filename)
rivers, basins = compute_rivers(filename)
height_map = np.array(detailed_data.raster, dtype=np.float32)
height_map = np.expand_dims(height_map, axis=-1)
height_map = (height_map - np.amin(height_map)) / \
(np.amax(height_map) - np.amin(height_map))
height_map = height_map * 2 - 1
sketch_map = np.stack((ridges, rivers, peaks, basins), axis=2)
sketch_map = np.squeeze(sketch_map, axis=-1)
print(sketch_map.shape)
height_maps.append(height_map)
sketch_maps.append(sketch_map)
training_output = np.array(height_maps, dtype=np.float32)
training_input = np.array(sketch_maps, dtype=np.float32)
np.savez('training_data.npz', x=training_input, y=training_output)
def read_images(self):
self.im_red = gr.from_file(self.path_red)
self.im_green = gr.from_file(self.path_green)
self.im_blue = gr.from_file(self.path_blue)
self.im_nir = gr.from_file(self.path_nir)
self.im_rededge = gr.from_file(self.path_rededge)
self.load_List_P()
def load_tile(basedirs, region, skip_truth=False):
d, city, id = region.split('.')
BASEDIR = [x for x in basedirs if city in x][0]
fname = '{}/MUL-PanSharpen/MUL-PanSharpen_{}_img{}.tif'.format(BASEDIR, city, id)
data1 = georasters.from_file(fname).raster
data1 = data1.filled(0)
data1 = numpy.transpose(data1, (2, 1, 0))
fname = '{}/PAN/PAN_{}_img{}.tif'.format(BASEDIR, city, id)
data2 = georasters.from_file(fname).raster
data2 = data2.filled(0)
data2 = numpy.transpose(data2, (1, 0))
input_im = numpy.zeros((1300, 1300, 9), dtype='uint8')
for i in xrange(8):
input_im[:, :, i] = (data1[:, :, i] / 8).astype('uint8')
input_im[:, :, 8] = (data2 / 8).astype('uint8')
if not skip_truth:
fname = '{}/{}.png'.format(TARGETS, region)
if not os.path.isfile(fname):
return None
output_im = scipy.ndimage.imread(fname)
if len(output_im.shape) == 3:
output_im = 255 - output_im[:, :, 0:1]
output_im = (output_im > 1).astype('uint8') * 255
else:
output_im = numpy.expand_dims(output_im, axis=2)
else:
output_im = numpy.zeros((1300 / OUTPUT_SCALE, 1300 / OUTPUT_SCALE, OUTPUT_CHANNELS), dtype='uint8')
return input_im, numpy.swapaxes(output_im, 0, 1)
def test_raster_diff(self):
height = gr.from_file(
os.path.expanduser(
"~/Downloads/datasets/elevation/one_deg_height.tif")).raster
stddev = gr.from_file(
os.path.expanduser(
"~/Downloads/datasets/elevation/one_deg_stddev.tif.tif")
).raster
out = height - stddev
print(np.ma.min(out), np.ma.max(out), np.ma.average(out),
np.ma.sum(out))
plt.imshow(out)
plt.show()
def test_get_dims(self):
DATA = "~/Downloads/datasets/elevation/viewfinder_dem3/15-J.tif" # from http://www.viewfinderpanoramas.org/dem3.html
DATA = os.path.expanduser(DATA)
data = gr.from_file(DATA)
print(data.geot)
NDV, xsize, ysize, GeoT, Projection, DataType = gr.get_geo_info(DATA)
print(NDV, xsize, ysize, GeoT, DataType)
def aggregate(input_rst, output_rst, scale):
"""
Downsample (upscale) a raster by a given factor and replace no_data value with 0.
Args:
input_rst: path to the input raster in a format supported by georaster
output_rst: path to the scaled output raster in a format supported by georaster
scale: The scale (integer) by which the raster in upsampeld.
Returns:
Save the output raster to disk.
# https://github.com/pasquierjb/GIS_RS_utils/blob/master/aggregate_results.py
"""
import georasters as gr
input_gr = gr.from_file(input_rst)
# No data values are replaced with 0 to prevent summing them in each block.
print(
len(
input_gr.raster.data.astype(np.float32) == np.float32(
input_gr.nodata_value)))
input_gr.raster.data[input_gr.raster.data.astype(np.float32) == np.float32(
input_gr.nodata_value)] = 0
input_gr.nodata_value = 0
output_gr = input_gr.aggregate(block_size=(scale, scale))
output_gr.to_tiff(output_rst.replace(".tif", ""))
def test_main():
import georasters as gr
raster = os.path.join(DATA, 'pre1500.tif')
A = gr.from_file(raster)
assert A.count() == 2277587
assert A.min() == 0
assert A.projection.ExportToProj4() == '+proj=longlat +datum=WGS84 +no_defs '
def extract_patches_from_raster():
count = 0
for raster_file in Path('./world_map').glob('**/*.tif'):
data = gr.from_file(str(raster_file))
raster_blocks = view_as_blocks(data.raster, (225, 225))
for i in range(raster_blocks.shape[0]):
for j in range(raster_blocks.shape[1]):
raster_data = raster_blocks[i, j]
src = cv2.pyrDown(raster_data,
dstsize=(raster_data.shape[1] // 2,
raster_data.shape[0] // 2))
data_out_downsampled = gr.GeoRaster(
src,
data.geot,
nodata_value=data.nodata_value,
projection=data.projection,
datatype=data.datatype,
)
data_out_downsampled.to_tiff(
'./data_downsampled_blurred/data_q' + str(count) + str(i) +
str(j))
data_out = gr.GeoRaster(
raster_data,
data.geot,
nodata_value=data.nodata_value,
projection=data.projection,
datatype=data.datatype,
)
data_out.to_tiff('./data/data_q' + str(count) + str(i) +
str(j))
count += 1
def test_main():
import georasters as gr
raster = os.path.join(DATA, 'pre1500.tif')
A = gr.from_file(raster)
assert A.count() == 2277587
assert A.min() == 0
assert A.projection.ExportToProj4(
) == '+proj=longlat +datum=WGS84 +no_defs '
def test_br(self):
DATA = "~/Downloads/datasets/elevation/viewfinder_dem3/15-J.tif" # from http://www.viewfinderpanoramas.org/dem3.html
DATA = os.path.expanduser(DATA)
data = gr.from_file(DATA)
aggregated = br_wrapper(data, 1, 1)
print(aggregated)
plt.imshow(aggregated.raster)
plt.show()
def get(self, date):
try:
data = gr.from_file("./dados/Daily/prec4kmclim_" + str(date)[0:2] +
"_" + str(date)[2:4] + "_1998.tif")
df = data.to_pandas().head(100)
return jsonify(df.to_dict())
except:
return jsonify({'info': 'data inválida para o merge daily'})
def get(self, date):
try:
data = gr.from_file("./dados/Yearly/prec4km_masked_02_01_" +
str(date) + ".tif")
df = data.to_pandas().head(100)
return jsonify(df.to_dict())
except:
return jsonify({'info': 'ano inválido para merge yearly'})
def test_extract():
import georasters as gr
raster = os.path.join(DATA, 'pre1500.tif')
data = gr.from_file(raster)
(xmin,xsize,x,ymax,y,ysize)=data.geot
(x,y)=(xmin+2507*xsize, ymax+1425*ysize)
assert data.raster[gr.map_pixel(x,y,data.x_cell_size,data.y_cell_size,data.xmin,data.ymax)]==data.extract(x,y).max()
assert data.raster[gr.map_pixel(x,y,data.x_cell_size,data.y_cell_size,data.xmin,data.ymax)]==data.map_pixel(x,y)
def rast_to_df(p):
y = p.split("/")[-1].split(".")[0][-4:]
rast = gr.from_file(p)
# centroid of each cell
df2 = rast.to_pandas()
df2['x'] = df2.x + rast.x_cell_size/2
df2['y'] = df2.y + rast.y_cell_size/2
df2['year'] = int(y)
return df2
def get(self, date):
try:
mes = str(date)
if int(mes) < 10: mes = "0" + mes
data = gr.from_file("./dados/Monthly/prec4kmclim_masked_02_" +
mes + "_1998.tif")
df = data.to_pandas().head(100)
return jsonify(df.to_dict())
except:
return jsonify({'info': 'mês inválido para o merge monthly'})
def test_aggregate(self):
DATA = "~/Downloads/datasets/elevation/viewfinder_dem3/15-J.tif" # from http://www.viewfinderpanoramas.org/dem3.html
DATA = os.path.expanduser(DATA)
x_ranges = list(gen_ranges(0, 60, 1))
y_ranges = list(gen_ranges(45, 0, 1))
data = gr.from_file(DATA)
aggregated = aggregate_grid(data, x_ranges, y_ranges)
print(aggregated)
plt.imshow(aggregated)
plt.show()
def read_tiff2df(f,bound = (-180,180,-90,90)):
"""
读tiff文件为dataframe,
bound: 边界框
"""
minx,maxx,miny,maxy = bound[0], bound[1], bound[2], bound[3]
data = gr.from_file(f)
df = data.to_pandas()
if bound == (-180,180,-90,90):
return df
else:
return df[(df['x']>=minx)&(df['x']<=maxx)&(df['y']>=miny)&(df['y']<=maxy)]
def test_extract():
import georasters as gr
raster = os.path.join(DATA, 'pre1500.tif')
data = gr.from_file(raster)
(xmin, xsize, x, ymax, y, ysize) = data.geot
(x, y) = (xmin + 2507 * xsize, ymax + 1425 * ysize)
assert data.raster[gr.map_pixel(x, y, data.x_cell_size, data.y_cell_size,
data.xmin,
data.ymax)] == data.extract(x, y).max()
assert data.raster[gr.map_pixel(x, y, data.x_cell_size, data.y_cell_size,
data.xmin,
data.ymax)] == data.map_pixel(x, y)
def fileToDataframe(file, columnName):
if '.xyz' in file:
df = pandas.DataFrame(
pandas.read_csv(file,
delim_whitespace=True,
encoding="utf-8-sig",
dtype=numpy.float64))
df = df.interpolate()
else:
df = gr.from_file(file).to_pandas()
df = df[["x", "y", "value"]].copy()
df.columns = ['Lat', 'Long', columnName]
return df
def load_tile(basedirs, region, skip_truth=False):
d, city, id = region.split('.')
BASEDIR = [x for x in basedirs if city in x][0]
fname = '{}/MUL-PanSharpen/MUL-PanSharpen_{}_img{}.tif'.format(
BASEDIR, city, id)
data1 = georasters.from_file(fname).raster
data1 = data1.filled(0)
data1 = numpy.transpose(data1, (2, 1, 0))
fname = '{}/PAN/PAN_{}_img{}.tif'.format(BASEDIR, city, id)
data2 = georasters.from_file(fname).raster
data2 = data2.filled(0)
data2 = numpy.transpose(data2, (1, 0))
input_im = numpy.zeros((1300, 1300, 9), dtype='uint8')
for i in xrange(8):
input_im[:, :, i] = (data1[:, :, i] / 8).astype('uint8')
input_im[:, :, 8] = (data2 / 8).astype('uint8')
if not skip_truth:
fname = '{}/{}.png'.format(TARGETS, region)
if not os.path.isfile(fname):
return None
output_im = scipy.ndimage.imread(fname)
if len(output_im.shape) == 3:
output_im = 255 - output_im[:, :, 0:1]
output_im = (output_im > 1).astype('uint8') * 255
else:
output_im = numpy.expand_dims(output_im, axis=2)
else:
output_im = numpy.zeros(
(1300 / OUTPUT_SCALE, 1300 / OUTPUT_SCALE, OUTPUT_CHANNELS),
dtype='uint8')
return input_im, numpy.swapaxes(output_im, 0, 1)
def main():
# DATA = "../data/relief_san_andres.tif"
DATA = "~/Downloads/datasets/elevation/viewfinder_dem3/15-J.tif" # from http://www.viewfinderpanoramas.org/dem3.html
DATA = os.path.expanduser(DATA)
data = gr.from_file(DATA)
(xmin, xsize, x, ymax, y, ysize) = data.geot
print(data.geot)
NDV, xsize, ysize, GeoT, Projection, DataType = gr.get_geo_info(DATA)
print(NDV, xsize, ysize, GeoT, DataType)
print(Projection)
# ok, when looking again it looks like the max and min are the edges + half the difference.
# I might just try indexing for regions that are a multiple of the dimensions into the raster.
# find top coords of grid cell in map for lat/long
dx = 1.0 # in degrees
dy = 1.0 # in degrees
nw_corner = (round_to_nearest(xmin, dx), round_to_nearest(ymax, dy))
print(nw_corner)
se_corner = (round_to_nearest(nw_corner[0]+dx, dx), round_to_nearest(nw_corner[1]-dy, dy))
print(se_corner)
# data.plot()
# plt.show()
# get the array indexes for the map
print(type(data.raster))
print(data.raster.shape)
print(data.raster)
print(GeoT)
x_indexes = int(dx / GeoT[1])
y_indexes = int(dy / -GeoT[5])
print(x_indexes, y_indexes)
# determine the desired final raster size.
# wait, i want to figure out how to divide this up to give each chunk its own list of data to take stats on
# so I need to determine the next chunk. Or just iterate through the whole damn image and append the values to
# a dict for that chunk
# yeah let's do that, it's easy.
# lol never mind just get the map pixels for the corners and iterate over them
# col, row = gr.map_pixel(x,y,GeoT[1],GeoT[-1], GeoT[0],GeoT[3])
# col, row = gr.map_pixel()
print(data.map_pixel_location(13, 13))
row, col = data.map_pixel_location(13,13)
print(row, col)
def get_full_pop_raster(path='.'):
url = "https://www.dropbox.com/s/l9iphmawfjzt4lf/brazil_pop.tif.tar.xz?dl=1"
fn = os.path.join(path, 'brazil_pop.tif.tar.xz')
wget.download(url=url, out=path)
fn = os.path.join(path, 'brazil_pop.tif.tar.xz')
with lzma.open('brazil_pop.tif.tar.xz') as f:
with tarfile.open(fileobj=f) as tar:
tar.extractall()
# with open('brazil_pop.tif', 'wb') as brr:
# brr.write(tar.extractall(path=path))
os.unlink('brazil_pop.tif.tar.xz')
raster = gr.from_file('brazil_pop.tif.tif')
os.unlink('brazil_pop.tif.tif')
return raster
def add_elevation(df, file, indexes):
try:
table = gr.from_file(file)
for index in indexes:
try:
row = df.loc[index]
val = table.map_pixel(row['lon'], row['lat'])
df.loc[index, 'elevation'] = float(val)
except:
df.loc[index, 'elevation'] = -9999
except:
for index in indexes:
df.loc[index, 'elevation'] = -9999
return df
def get(self):
try:
dataByYear = []
for i in range(21):
dataByYear.append(
gr.from_file("./dados/Yearly/prec4km_masked_02_01_" +
str(1998 + i) + ".tif").to_pandas().head(100))
df = {}
for i in range(21):
soma = 0
for value in dataByYear[i]["value"]:
soma = soma + float(value)
df[str(1998 + i)] = str(soma / 100)
return jsonify(df)
except:
return jsonify({'info': 'no data in data series'})
def test_load_chelsea(self):
DATA = "~/Downloads/datasets/chelsea/CHELSA_prec_01_V1.2_land.tif" # from http://chelsa-climate.org/downloads/
data = gr.from_file(
os.path.expanduser(
"~/Downloads/datasets/chelsea/CHELSA_prec_01_V1.2_land.tif"))
# aggregated = aggregate_grid(data, x_ranges, y_ranges)
print(data.raster.shape)
data.raster.fill
one_deg = br_wrapper(data, 1, 1)
"""
What's happening is that the mask is being removed.
I need to aggregate the mask to the same 1 degree grid, then decide how to use it.
I can dither it, or I can use a hard cutoff.
I might end up just using my own aggregation function for this because of how the y min and maxes work here.
"""
plt.imshow(one_deg.raster)
plt.show()
def test_union():
import georasters as gr
raster = os.path.join(DATA, 'pre1500.tif')
data = gr.from_file(raster)
(xmin, xsize, x, ymax, y, ysize) = data.geot
data1 = gr.GeoRaster(data.raster[:data.shape[0] / 2, :],
data.geot,
nodata_value=data.nodata_value,
projection=data.projection,
datatype=data.datatype)
data2 = gr.GeoRaster(
data.raster[data.shape[0] / 2:, :],
(xmin, xsize, x, ymax + ysize * data.shape[0] / 2, y, ysize),
nodata_value=data.nodata_value,
projection=data.projection,
datatype=data.datatype)
'''
import matplotlib.pyplot as plt
plt.figure()
data1.plot()
plt.savefig(os.path.join(DATA,'data1.png'))
plt.figure()
data2.plot()
plt.savefig(os.path.join(DATA,'data2.png'))
from rasterstats import zonal_stats
import geopandas as gp
import pandas as pd
# Import shapefiles
pathshp = os.path.join(DATA, 'COL.shp')
dfcol=gp.GeoDataFrame.from_file(pathshp)
pathshp = os.path.join(DATA, 'TUR.shp')
dftur=gp.GeoDataFrame.from_file(pathshp)
# Joint geopandas df
df=dfcol.append(dftur)
df.reset_index(drop=True,inplace=True)
stats = zonal_stats(df, raster, copy_properties=True, all_touched=True, raster_out=True, opt_georaster=True)
dfcol=pd.merge(dfcol,pd.DataFrame(data=stats),
'''
assert (data1.union(data2).raster == data.raster).sum() == data.count()
def get(self):
try:
dataByMonth = []
for mes in range(12):
mes = str(mes + 1)
if int(mes) < 10: mes = "0" + mes
dataByMonth.append(
gr.from_file("./dados/Monthly/prec4kmclim_masked_02_" +
mes + "_1998.tif").to_pandas().head(100))
df = {}
for i in range(12):
soma = 0
for value in dataByMonth[i]["value"]:
soma = soma + float(value)
df[str(i + 1)] = str(soma / 100)
return jsonify(df)
except:
return jsonify({'info': 'no data in data series'})
def main():
# ET = ((18 * MWT) - (10 * MCM))/(MWM - MCM + 8)
# MWT = Mean temperature of warmest month of year
# MCT = Mean Temperature of coldest month of year
# load each chelsea mean temperature file
# reduce to the min and max of these files
mct = np.full((20880, 43200), 2000, dtype=np.int16)
mwt = np.full((20880, 43200), -5000, dtype=np.int16)
x_ranges = list(gen_ranges(-180, 180, 1))
y_ranges = list(gen_ranges(90, -90, 1))
temps = []
for file in tqdm(
glob.glob(
os.path.expanduser(
"~/Downloads/datasets/chelsea/CHELSA_temp10_*.tif"))):
temp01 = gr.from_file(os.path.expanduser(file))
# temps.append(temp01)
# temp_ag = aggregate_grid(temp01, x_ranges, y_ranges)
mwt = np.ma.maximum(mwt, temp01.raster)
mct = np.ma.minimum(mct, temp01.raster)
print("saving mwt and mct")
np.save("./data/mean_warmest_temperature.npy", mwt.data)
np.save("./data/mean_coldest_temperature.npy", mct.data)
exit()
# ET = ((18 * MWT) - (10 * MCM))/(MWM - MCM + 8)
# do a bunch of jank to keep memory usage down
temp01 = None
lower = mwt - mct
lower += 80
mwt *= 18
mct *= 10
mwt -= mct
mct = None
effective_temperature = mwt / lower
np.save("./data/effective_temperature_large.npy",
effective_temperature.data)
np.save("./data/effective_temperature_large_mask.npy",
effective_temperature.mask)
def main():
files = glob.glob(os.path.expanduser("~/Downloads/datasets/elevation/viewfinder_dem3/*.tif"))
# files = files[:2]
agg_first = True
how = np.ma.std
# how = np.ma.mean
# fake a no data value
ndv = -1000
print("loading files")
rasters = []
for filename in tqdm(files):
raster = gr.from_file(filename)
if agg_first:
raster.ndv = ndv
# standard deviation goes to a double
if how == np.ma.std:
raster.datatype = "Float64"
# print(raster.shape)
if raster.shape[0] % 2 == 1:
raster = clip_gr(raster)
aggregated = br_wrapper(raster, 1, 1, how)
rasters.append(aggregated)
else:
rasters.append(raster)
# exit()
print("starting merging")
merged = gr.merge(rasters)
print(merged.shape)
print("starting aggregation")
if not agg_first:
one_deg = br_wrapper(merged, 1, 1, how)
one_deg = clip_gr(one_deg)
else:
one_deg = merged
print("Saving")
one_deg.to_tiff(os.path.expanduser("~/Downloads/datasets/elevation/one_deg_stddev.tif"))
def process_pop_data(pop_file, country):
"""Takes a tif file for an individual country and converts it to a csv with the country name appended to every row"""
start_time = time.time()
pop_data = gr.from_file(pop_file)
pop_df = pop_data.to_pandas()
print("DataFrame created!")
pop_df['value'] = pop_df['value'].apply(lambda x: round(x, 3))
print("Total population of", country, ":",
'{:,}'.format(pop_df['value'].sum())) #Sanity Check
pop_df['lat'] = pop_df['y']
pop_df['long'] = pop_df['x']
print("Lat/Long Created!")
pop_df['country'] = country
print("Converting to CSV..")
pop_df.to_csv(country + '_pop_data.csv', index_label='key')
del pop_df #clear memory
end_time = time.time()
time_taken = round((end_time - start_time) / 60, 1)
print("Done!")
print("Time taken:", time_taken, "minutes")
def aggregate(input_rst, output_rst, scale):
"""
Downsample (upscale) a raster by a given factor and replace no_data value with 0.
Args:
input_rst: path to the input raster in a format supported by georaster
output_rst: path to the scaled output raster in a format supported by georaster
scale: The scale (integer) by which the raster in upsampeld.
Returns:
Save the output raster to disk.
"""
import georasters as gr
input_gr = gr.from_file(input_rst)
# No data values are replaced with 0 to prevent summing them in each block.
input_gr.raster.data[input_gr.raster.data == input_gr.nodata_value] = 0
input_gr.nodata_value = 0
output_gr = input_gr.aggregate(block_size=(scale, scale))
output_gr.to_tiff(output_rst.replace(".tif", ""))
def add_slope_aspect_curvature(df, file, indexes):
for attr in ['slope_percentage', 'aspect', 'profile_curvature']:
table = None
try:
table = rd.TerrainAttribute(rd.LoadGDAL(file, no_data=-9999),
attrib=attr)
rd.SaveGDAL("./temp.tif", table)
table = None
table = gr.from_file("./temp.tif")
for index in indexes:
try:
row = df.loc[index]
val = table.map_pixel(row['lon'], row['lat'])
df.loc[index, attr] = float(val)
except:
df.loc[index, attr] = np.nan
os.remove("./temp.tif")
except:
for index in indexes:
df.loc[index, attr] = np.nan
return df
def aggregate(self, scale):
"""
Downsample (upscale) a raster by a given factor and replace no_data value with 0.
Args:
scale: The scale (integer) by which the raster in upsampeld.
Returns:
Save the output raster to disk.
# https://github.com/pasquierjb/GIS_RS_utils/blob/master/aggregate_results.py
"""
import georasters as gr
input_gr = gr.from_file(self.path_to_raster)
# No data values are replaced with 0 to prevent summing them in each block.
input_gr.raster.data[input_gr.raster.data.astype(np.float32) == np.float32(input_gr.nodata_value)] = 0
input_gr.nodata_value = 0
output_gr = input_gr.aggregate(block_size=(scale, scale))
output_gr.to_tiff(self.path_agg_raster.replace(".tif", ""))
return BaseLayer(self.path_agg_raster, self.lon, self.lat)
def test_union():
import georasters as gr
raster = os.path.join(DATA, 'pre1500.tif')
data = gr.from_file(raster)
(xmin,xsize,x,ymax,y,ysize)=data.geot
data1 = gr.GeoRaster(data.raster[:data.shape[0]/2,:], data.geot,
nodata_value=data.nodata_value, projection=data.projection, datatype=data.datatype)
data2 = gr.GeoRaster(data.raster[data.shape[0]/2:,:], (xmin,xsize,x,ymax+ysize*data.shape[0]/2,y,ysize),
nodata_value=data.nodata_value, projection=data.projection, datatype=data.datatype)
'''
import matplotlib.pyplot as plt
plt.figure()
data1.plot()
plt.savefig(os.path.join(DATA,'data1.png'))
plt.figure()
data2.plot()
plt.savefig(os.path.join(DATA,'data2.png'))
from rasterstats import zonal_stats
import geopandas as gp
import pandas as pd
# Import shapefiles
pathshp = os.path.join(DATA, 'COL.shp')
dfcol=gp.GeoDataFrame.from_file(pathshp)
pathshp = os.path.join(DATA, 'TUR.shp')
dftur=gp.GeoDataFrame.from_file(pathshp)
# Joint geopandas df
df=dfcol.append(dftur)
df.reset_index(drop=True,inplace=True)
stats = zonal_stats(df, raster, copy_properties=True, all_touched=True, raster_out=True, opt_georaster=True)
dfcol=pd.merge(dfcol,pd.DataFrame(data=stats),
'''
assert (data1.union(data2).raster==data.raster).sum()==data.count()
def test_stats4():
import georasters as gr
raster = os.path.join(DATA, 'pre1500.tif')
data = gr.from_file(raster)
assert data.max() == data.raster.max()
def test_stats6():
import georasters as gr
raster = os.path.join(DATA, 'pre1500.tif')
data = gr.from_file(raster)
assert data.median() == np.ma.median(data.raster)
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see <http://www.gnu.org/licenses/>. ''' from __future__ import division import georasters as gr import geopandas as gp import os # Path to data pathtestdata = gr.__path__[0]+'/../tests/data/' # Load raster data data = gr.from_file(pathtestdata+'pre1500.tif') # Load country geometries col = gp.read_file(pathtestdata+'COL.shp') df = data.clip(col, keep=True) print(df) # Select clipped raster colraster = df.GeoRaster[0] colraster.plot(cmap='Reds') # Compute Global autocorrelation stats colraster.pysal_G() colraster.pysal_Gamma() colraster.pysal_Geary() colraster.pysal_Join_Counts() colraster.pysal_Moran()
Merge all the geotiff files into one big image:
$ mkdir nh_riks_WGS84_geotiff
$ gdal_merge.py -o nh_riks_Sweref_99_TM_geotiff/out.tif nh_riks_Sweref_99_TM_geotiff/*
Now warp the images from SWEREF99TM to WGS84
$ gdalwarp -t_srs "EPSG:4326" nh_riks_Sweref_99_TM_geotiffout.tif nh_riks_WGS84_geotiff/out.tif
And lastly produce the elevation file from that data:
`python build_imagedata.py`
"""
import georasters as gr
import numpy as np
DATA_IN = "nh_riks_WGS84_geotiff/out.tif"
# DATA_IN = "nh_riks_WGS84_geotiff/nh_61_3.tif"
OUTFILE = "elevation_data.npz"
data = gr.from_file(DATA_IN).raster
# Data from lantmateriet has 10 decimals, none of them are significant
data = data.round(decimals=0)
# MaskedArrays can't be save to disk, convert to ndarray
data = data.filled(101)
# We don't care about data larger than 255 meters, which will convert to white
data = data.clip(0, 101)
np.savez_compressed(OUTFILE, data)