def masknetcdf(netcdf_filename, shp_filename, cl_field=None):
if cl_field == None:
# If no field is provided, assume first part of file name is the field to use
head, tail = os.path.split(netcdf_filename)
cl_field = tail.split(".")[0]
# Open dataset
data_source = gdal.Open(netcdf_filename)
# Number of columns of each layer
cols = data_source.RasterXSize
#Number of rows of each layer
rows = data_source.RasterYSize
# Number of bands of each layer
nbands = data_source.RasterCount
# Year of the netcdf file
year = int(netcdf_filename.split('.')[-2])
#========================================
# Importing data by bands as a dictionary
#========================================
datach = {}
# First day of the year as reference
yearc = str(year) + "-01-01"
# Converting year in a datatime object
fi_date = datetime.datetime.strptime(yearc, "%Y-%m-%d").date()
# For loop of the layers that contains the netcdf file
for i in range(1, nbands + 1):
index_date = fi_date + datetime.timedelta(days=i - 1)
in_band = data_source.GetRasterBand(i)
# Converting default nan in np.na
in_band_wnan = in_band.ReadAsArray()
in_band_wnan[in_band_wnan == -9.9692100e+36] = np.nan
datach[index_date.strftime("%Y-%m-%d")] = in_band_wnan
#====================================
# Parameters of transformation
#====================================
x = 0.5 * cols - rows
delta_Lon = 360 / cols
delta_Lat = 180 / (rows + x)
if x == 0:
LonUpper_Left = 0
LatUpper_Left = 90
else:
LonUpper_Left = -delta_Lon / 2
LatUpper_Left = delta_Lat * (rows / 2)
gt = [LonUpper_Left, delta_Lon, 0, LatUpper_Left, 0, -delta_Lat]
# Inverse affine transformation
inv_gt = gdal.InvGeoTransform(gt)
inv_gt
#=====================================================================
# Importing shapefile and transforming coordinates to rows and columns
#=====================================================================
# Calling driver
driver = ogr.GetDriverByName('ESRI Shapefile')
# Open dataset
datasource1 = driver.Open(shp_filename, 1)
# Get layer
layer = datasource1.GetLayer()
# Get number of features
fc = layer.GetFeatureCount()
# Creating lists for rows, columns and time index
xt_p = []
yt_p = []
dayc = []
for fea in layer:
# Time
a = fea.GetField("timestamp")[0:10]
dayc.append(a)
# Coord
pt = fea.geometry()
# Transforming coord
xt, yt = gdal.ApplyGeoTransform(inv_gt, pt.GetX(), pt.GetY())
xt_p.append(xt)
yt_p.append(yt)
#=================================
# Extracting values by time
#=================================
# Converting list of days to array
array_indexday = np.array(dayc)
# Creating a vector of nan values
value = np.zeros(fc)
value[value == 0] = np.nan
# For loop to extract values by time
for j in np.unique(dayc):
# Condition about the year of interest
year_d = datetime.datetime.strptime(j, "%Y-%m-%d").year
if year_d == year:
# Match day of the features with the day of the band array
index = np.where(array_indexday == j)[0].tolist()
in_band = datach[j]
# Extracting values of the layer that correspond to certain time
for k in range(0, len(index)):
data = in_band[int(yt_p[index[k]]), int(xt_p[index[k]])]
value[index[k]] = data
else:
continue
#============================
# Loading values to shapefile
#============================
# Calling the shapefile again but in edition version (1)
driver = ogr.GetDriverByName('ESRI Shapefile')
datasource2 = driver.Open(shp_filename, 1)
layer2 = datasource2.GetLayer()
# Reset index
datasource2.SyncToDisk()
# List of the fields of the shapefile
schema = []
ldefn = layer2.GetLayerDefn()
for n in range(ldefn.GetFieldCount()):
fdefn = ldefn.GetFieldDefn(n)
schema.append(fdefn.name)
# So, if the field exists this function updates the field, otherwise, it will create
# A new field with the specified name
if np.any(np.array(schema) == cl_field):
i = 0
# For loop quering the existency of the value for certain period
for fe in layer2:
year_2 = datetime.datetime.strptime(dayc[i], "%Y-%m-%d").year
if year_2 == year:
fe.SetField(cl_field, value[i])
layer2.SetFeature(fe)
i = i + 1
else:
# Creating the the new field
new_field = ogr.FieldDefn(cl_field, ogr.OFTReal)
layer2.CreateField(new_field)
i = 0
for fe in layer2:
fe.SetField(cl_field, value[i])
layer2.SetFeature(fe)
i = i + 1
# Destroy data source shapefile
datasource1.Destroy()
datasource2.Destroy()
# Close source netcdf
data_source = None
del value
print(f"Done! Year: {year}; Field {cl_field}")
def pixelToMap(pX, pY, geoTransform):
return gdal.ApplyGeoTransform(geoTransform, pX + 0.5, pY + 0.5)
def mapToPixel(mX, mY, geoTransform):
(pX, pY) = gdal.ApplyGeoTransform(gdal.InvGeoTransform(geoTransform), mX,
mY)
return (int(pX), int(pY))
def geo_to_corner(point_coor, raster_geo):
# 计算逆放射变换系数
raster_inv_geo = gdal.InvGeoTransform(raster_geo)
off_ulx, off_uly = map(round, gdal.ApplyGeoTransform(raster_inv_geo, point_coor[1], point_coor[0]))
return off_ulx, off_uly
def world_to_pix(self, x, y, proj=None):
"""
Take world coordinates and return pixel coordinates
*Arguments*:
- x = the world x-coord.
- y = the world y-coord.
- proj = the coordinate system of the input coordinates. Default (None) uses the same system as this image. Otherwise
an osr.SpatialReference can be passed (HyImage.project), or an EPSG string (e.g. get_projection_EPSG(...)).
*Returns*:
- the pixel coordinates based on the affine transform stored in self.affine.
"""
try:
from osgeo import osr
import osgeo.gdal as gdal
from osgeo import ogr
except:
assert False, "Error - GDAL must be installed to work with spatial projections in hylite."
# parse project
if proj is None:
proj = self.projection
elif isinstance(proj, str) or isinstance(proj, int):
epsg = proj
if isinstance(epsg, str):
try:
epsg = int(str.split(':')[1])
except:
assert False, "Error - %s is an invalid EPSG code." % proj
proj = osr.SpatialReference()
proj.ImportFromEPSG(epsg)
# check we have all the required info
assert isinstance(proj, osr.SpatialReference
), "Error - invalid spatial reference %s" % proj
assert (not self.affine is None) and (
not self.projection is None
), "Error - project information is undefined."
# project to this images CS (if different)
if not proj.IsSameGeogCS(self.projection):
P = ogr.Geometry(ogr.wkbPoint)
if proj.EPSGTreatsAsNorthingEasting():
P.AddPoint(x, y)
else:
P.AddPoint(y, x)
P.AssignSpatialReference(
proj) # tell the point what coordinates it's in
P.AddPoint(x, y)
P.TransformTo(
self.projection) # reproject it to the out spatial reference
x, y = P.GetX(), P.GetY()
if self.projection.EPSGTreatsAsLatLong(
): # do we need to transpose?
x, y = y, x # we want lon,lat not lat,lon
inv = gdal.InvGeoTransform(self.affine)
assert not inv is None, "Error - could not invert affine transform?"
#apply
return gdal.ApplyGeoTransform(inv, x, y)
# print(in_files)
# 新建一幅大图
driver = gdal.GetDriverByName('GTiff')
columns = small_image_width * width_num
rows = small_image_height * height_num
read_information = gdal.Open(in_files[0]) # 读取一张获取信息
information_band = read_information.GetRasterBand(1)
# print(information_band.DataType)
# print(gdal.GDT_Byte, gdal.GDT_UInt16) # 8位的图和16位的图 1 2
out_tif = driver.Create(output_path, columns, rows, 3, information_band.DataType)
information_geotrans = read_information.GetGeoTransform() # 图像的坐标和分辨率信息等
information_proj = read_information.GetProjection() # 图像的投影信息
out_tif.SetProjection(information_proj) # 给大图设置投影信息
out_tif.SetGeoTransform(information_geotrans) #
# 定义仿射逆变换
inv_geotrans = gdal.InvGeoTransform(information_geotrans)
for i, small_sat in enumerate(in_files):
in_ds = gdal.Open(small_sat)
in_gt = in_ds.GetGeoTransform()
offset = gdal.ApplyGeoTransform(inv_geotrans, in_gt[0], in_gt[3])
x, y = map(int, offset) # x，y是开始写入时左上角像元行列号
data = in_ds.ReadAsArray()
# print('data.shape:', data.shape) # (3, 512, 512)
for k in range(3): # 将三个波段依次写入大图对应位置
out_tif.GetRasterBand(k + 1).WriteArray(data[k], x, y)
# out_tif.WriteArray(data, x, y)
print(f'第{i+1}张图完成')
del in_ds
