def invdistnn(in_path,
out_path,
field_name,
output_bounds=None,
param=None):
"""
最邻近反距离加权插值 将shp文件插值为TIF
:param in_path: 输入的shp文件
:param out_path: 输出的tif文件
:param field_name: 被插值的属性字段
:param output_bounds: 输出的tif四角范围 (xmin, ymin, xmax, ymax)
:param param: 插值参数，字符串，不传参以默认值进行
[power：加权系数（默认为3.0）
smoothing：平滑参数（默认为0.0）
radius：搜索圆的半径，不应为零（默认值为1.0）
max_points：要使用的最大数据点数。搜索的点数不要超过此数字。 加权时，发现的点将从最远到最远的距离排名（默认值为12）
min_points：要使用的最小数据点数。如果发现的点数量较少，则网格节点被认为是空的，并将被NODATA标记填充（默认值为0）
nodata：NODATA标记以填充空白点（默认为0.0）]
:return:
"""
# TODO 是否为密集点时比较试用？
# TODO radius设置太小会导致数据镂空
if param:
if len(param) != 6 or not all(isinstance(x, str) for x in param):
print('input param format error.')
return
else:
param_str = 'invdistnn'
param_str += ':power=' + param[0]
param_str += ':smoothing=' + param[1]
param_str += ':radius=' + param[2]
param_str += ':max_points=' + param[3]
param_str += ':min_points=' + param[4]
param_str += ':nodata=' + param[5]
else:
param = 'invdistnn:power=3.0:smoothing=0.0:radius=1.0:max_points=0:min_points=0:nodata=0.0'
if output_bounds:
options = gdal.GridOptions(algorithm=param,
format="GTiff",
outputType=gdal.GDT_Float32,
zfield=field_name,
outputBounds=output_bounds)
else:
options = gdal.GridOptions(algorithm=param,
format="GTiff",
outputType=gdal.GDT_Float32,
zfield=field_name)
gdal.Grid(destName=out_path, srcDS=in_path, options=options)
def invdist(in_path, out_path, field_name, output_bounds=None, param=None):
"""
反距离加权插值 将shp文件插值为TIF
:param in_path: 输入shp文件
:param out_path: 输出tif文件
:param field_name: 被插值的属性字段
:param output_bounds: 输出tif四角范围 (xmin, ymin, xmax, ymax) 默认为点shp四角坐标
:param param: 插值参数，字符串，不传参以默认值进行
[ power：加权系数（默认为3.0）,
smoothing：平滑参数（默认为0.0）,
radius1：搜索椭圆的第一个半径（如果旋转角度为0，则为X轴），将此参数设置为零以使用整个点数组（默认值为0.0）,
radius2：搜索椭圆的第二个半径（如果旋转角度为0，则为Y轴），将此参数设置为零以使用整个点数组（默认值为0.0）,
angle：搜索椭圆的旋转角度，以度为单位（逆时针，默认为0.0）,
max_points：要使用的最大数据点数。搜索的点数不要超过此数字。仅在设置搜索椭圆（两个半径都不为零）时使用。零表示应使用所有找到的点（默认值为0）,
min_points：要使用的最小数据点数。如果发现的点数量较少，则网格节点被认为是空的，并将被NODATA标记填充。仅在设置搜索椭圆（两个半径都不为零）时使用（默认值为0）,
nodata：NODATA标记以填充空白点（默认为0.0）]
:return:
"""
if param:
if len(param) != 8 or not all(isinstance(x, str) for x in param):
print('input param format error.')
return
else:
param_str = 'invdist'
param_str += ':power=' + param[0]
param_str += ':smoothing=' + param[1]
param_str += ':radius1=' + param[2]
param_str += ':radius2=' + param[3]
param_str += ':angle=' + param[4]
param_str += ':max_points=' + param[5]
param_str += ':min_points=' + param[6]
param_str += ':nodata=' + param[7]
else:
param_str = 'invdist:power=3.0:smoothing=0.0:radius1=0.0:radius2=0.0:angle=0.0:max_points=0:min_points=0:nodata=0.0'
if output_bounds:
options = gdal.GridOptions(algorithm=param_str,
format="GTiff",
outputType=gdal.GDT_Float32,
zfield=field_name,
outputBounds=output_bounds)
else:
options = gdal.GridOptions(algorithm=param_str,
format="GTiff",
outputType=gdal.GDT_Float32,
zfield=field_name)
gdal.Grid(destName=out_path, srcDS=in_path, options=options)
def grid_option(factor_name='aqi', time_interval='d', start_time=None):
'''
构造插值选项
factor--- 插值因子名称
time_interval--- 时间尺度, 取值 y/m
start_time--- 累计插值数据开始时间, 格式为 yyyymmdd / yyyymmddHH
current_task_name--- 累计数据插值当前时间, 格式为 yyyymmdd / yyyymmddHH
'''
max_val = get_factor_max_val(factor_name, time_interval)
return gdal.GridOptions(
options=[],
format='GTiff',
outputType=gdal.GDT_Float32,
width=1091,
height=800,
creationOptions=None,
outputSRS='EPSG:3857',
noData=None,
algorithm=grid_algorithm,
layers=None,
SQLStatement=None,
where='{factorName} >= 0 and {factorName} <= {maxVal}'.format(
factorName=factor_name, maxVal=max_val),
spatFilter=None,
zfield=factor_name,
z_increase=None,
z_multiply=None,
callback=None,
callback_data=None)
def average(in_path, out_path, field_name, output_bounds=None, param=None):
"""
移动平均法 将shp文件插值为TIF
:param in_path: 输入的shp文件
:param out_path: 输出的tif文件
:param field_name: 被插值的属性字段
:param output_bounds: 输出的tif四角范围 (xmin, ymin, xmax, ymax)
:param param: 插值参数，字符串，不传参以默认值进行
[ radius1：搜索椭圆的第一个半径（如果旋转角度为0，则为X轴）。将此参数设置为零以使用整个点数组（默认值为0.0）
radius2：搜索椭圆的第二个半径（如果旋转角度为0，则为Y轴）。将此参数设置为零以使用整个点数组（默认值为0.0）
angle：搜索椭圆的旋转角度，以度为单位（逆时针，默认为0.0）
min_points：要使用的最小数据点数。 如果发现的点数量较少，则网格节点被认为是空的，并将被NODATA标记填充（默认值为0）
nodata：NODATA标记以填充空白点（默认为0.0）]
:return:
"""
# TODO 有问题？？
if param:
if len(param) != 5 or not all(isinstance(x, str) for x in param):
print('input param format error.')
return
else:
param_str = 'average'
param_str += ':radius1=' + param[0]
param_str += ':radius2=' + param[1]
param_str += ':angle=' + param[2]
param_str += ':min_points=' + param[3]
param_str += ':nodata=' + param[4]
else:
param = 'average:radius1=1:radius2=1:angle=0.0:min_points=0:nodata=0.0'
if output_bounds:
options = gdal.GridOptions(algorithm=param,
format="GTiff",
outputType=gdal.GDT_Float32,
zfield=field_name,
outputBounds=output_bounds)
else:
options = gdal.GridOptions(algorithm=param,
format="GTiff",
outputType=gdal.GDT_Float32,
zfield=field_name)
gdal.Grid(destName=out_path, srcDS=in_path, options=options)
def interpolate():
print(power_option)
print(smoothing_option)
#power_option = str(power_option)
#smoothing_option = str(smoothing_option)
algorithm_option = 'invdist:power='+str(power_option)+':smoothing='+str(smoothing_option)
gridOptions = gdal.GridOptions(format='Gtiff', algorithm=algorithm_option, zfield="nitr_ran")
out = gdal.Grid(outputTest, wellNitrate, options=gridOptions)
out = None
del out
def nearest(in_path, out_path, field_name, output_bounds=None, param=None):
"""
最邻近法 将shp文件插值为TIF
:param in_path: 输入的shp文件
:param out_path: 输出的tif文件
:param field_name: 被插值的属性字段
:param output_bounds: 输出的tif四角范围 (xmin, ymin, xmax, ymax)
:param param: 插值参数，字符串，不传参以默认值进行
[radius1：搜索椭圆的第一个半径（如果旋转角度为0，则为X轴）。将此参数设置为零以使用整个点数组（默认值为0.0）
radius2：搜索椭圆的第二个半径（如果旋转角度为0，则为Y轴）。将此参数设置为零以使用整个点数组（默认值为0.0）
angle：搜索椭圆的旋转角度，以度为单位（逆时针，默认为0.0）
nodata：NODATA标记以填充空白点（默认为0.0）]
:return:
"""
if param:
if len(param) != 4 or not all(isinstance(x, str) for x in param):
print('input param format error.')
return
else:
param_str = 'nearest'
param_str += ':radius1=' + param[0]
param_str += ':radius2=' + param[1]
param_str += ':angle=' + param[2]
param_str += ':nodata=' + param[3]
else:
param = 'nearest:radius1=0.0:radius2=0.0:angle=0.0:nodata=0.0'
if output_bounds:
options = gdal.GridOptions(algorithm=param,
format="GTiff",
outputType=gdal.GDT_Float32,
zfield=field_name,
outputBounds=output_bounds)
else:
options = gdal.GridOptions(algorithm=param,
format="GTiff",
outputType=gdal.GDT_Float32,
zfield=field_name)
gdal.Grid(destName=out_path, srcDS=in_path, options=options)
def linear(in_path, out_path, field_name, output_bounds=None, param=None):
"""
线性法 将shp文件插值为TIF
:param in_path: 输入的shp文件
:param out_path: 输出的tif文件
:param field_name: 被插值的属性字段
:param output_bounds: 输出的tif四角范围 (xmin, ymin, xmax, ymax)
:param param: 插值参数，字符串，不传参以默认值进行
[radius:如果要插入的点不适合Delaunay三角剖分的三角形，请使用该最大距离搜索最近的邻居，否则使用nodata
如果设置为-1，则搜索距离是无限的
如果设置为0，则将始终使用nodata值。默认值为-1
nodata：NODATA标记以填充空白点（默认为0.0）]
:return:
"""
# TODO 外扩性不太好
if param:
if len(param) != 2 or not all(isinstance(x, str) for x in param):
print('input param format error.')
return
else:
param_str = 'linear'
param_str += ':radius=' + param[0]
param_str += ':nodata=' + param[1]
else:
param = 'linear:radius=-1:nodata=0.0'
if output_bounds:
options = gdal.GridOptions(algorithm=param,
format="GTiff",
outputType=gdal.GDT_Float32,
zfield=field_name,
outputBounds=output_bounds)
else:
options = gdal.GridOptions(algorithm=param,
format="GTiff",
outputType=gdal.GDT_Float32,
zfield=field_name)
gdal.Grid(destName=out_path, srcDS=in_path, options=options)
def gdal_grid(path):
grid_opt = gdal.GridOptions(
format='GTiff',
outputType=gdal.GDT_Int16,
algorithm=
'invdist:power=3.0:smothing=0.0:radius1=0.0:radius2=0.0:angle=0.0:max_points=0:min_points=0:nodata=0.0',
layers='bugs',
zfield='value',
)
# Use .tiff and not .tif
try:
output = gdal.Grid('/tmp/output.tiff', path, options=grid_opt)
status = True
return status
except:
status = False
return status
def grid_var(var_array, coordinates, grid_kwargs):
"""
A function for gridding a variable array in gdal
:param var_array: a column array of the variable to grid
:param coordinates: utm coordinate array
:param grid_kwargs: keyword argument dictionary for gridding using gdal_grid
:return:
:param gridded: A gridded array with shape (height, width)
:param geotransform: the gdal geotransform for the gridded array
"""
if len(var_array.shape) == 1:
var_array = var_array.reshape([-1, 1])
if grid_kwargs['log_grid']:
var_array = np.log10(var_array)
temp_a = np.concatenate((coordinates, var_array), axis=1)
# Output the points into a temporary csv
temp_dir = r'C:\temp\tempfiles'
# temp_dir = tempfile.gettempdir() # getting permissions error
if not os.path.exists(temp_dir):
os.mkdir(temp_dir)
os.chdir(temp_dir)
temp_file = 'pts2grid_temp.csv'
with open(temp_file, 'w') as f:
f.write('x,y,z\n')
# Write the array
np.savetxt(f, temp_a, delimiter=',')
# Write vrt
vrt_file = misc_utils.write_vrt_from_csv(temp_file, 'x', 'y', 'z')
# Now grid using gdal_grid
gridopt = gdal.GridOptions(
format=grid_kwargs['format'],
outputType=gdal.GDT_Float32,
algorithm=grid_kwargs['gdal_algorithm'],
width=grid_kwargs['width'],
height=grid_kwargs['height'],
outputSRS=grid_kwargs['outputSRS'], # "EPSG:28352"
outputBounds=grid_kwargs['outputBounds'],
zfield='z',
layers=vrt_file.split('\\')[-1].split('.')[0])
# Getting errors frmo tempdir
outfile = 'temp_grid.tif'
print("Gridding ", outfile)
out_ds = gdal.Grid(destName=outfile, srcDS=vrt_file, options=gridopt)
print("Finished gridding ", outfile)
geotransform = out_ds.GetGeoTransform()
gridded = out_ds.ReadAsArray()
# Currently the nulls are very annoyingly retunr zeros
null = np.float(grid_kwargs['gdal_algorithm'].split('nodata=')[-1])
gridded[gridded == null] = np.nan
if grid_kwargs['log_grid']:
gridded = 10**gridded
# Remove trash
temp_a = None
out_ds = None
gc.collect()
# Delete the temporary files
for file in [temp_file, vrt_file, outfile]:
try:
os.remove(file)
except PermissionError:
print('Permission error. Unable to delete ', file)
return gridded, geotransform
def processFile(job_data, file_data):
try:
#Set output filename
tileName = file_data.fileName + ".tif"
tile = os.path.join(dem_directory, tileName).replace("\\","/")
csv_data = []
#Open source file and create a array representing csv data
with open(file_data.filePath, 'r') as dmr_file:
for l in dmr_file:
if l != "":
xyz_line = l.strip().split(job_data.sourceDel)
csv_data.append("{xc},{yc},{h}\n".format(xc=xyz_line[0],yc=xyz_line[1],h=xyz_line[2]))
#Check if last row is empty, and remove it
if csv_data[len(csv_data) - 1] == "":
csv_data.pop()
#Check if last row ends with new line, trim row
lastrow = csv_data[len(csv_data) - 1]
if lastrow.endswith('\n'):
lastrow = lastrow.rstrip()
csv_data[len(csv_data) - 1] = lastrow
csv_file = os.path.join(dem_directory, file_data.fileName + ".csv").replace("\\","/")
if os.path.isfile(csv_file):
os.remove(csv_file)
#Write csv data to file
with open(csv_file, 'w') as csf:
csf.writelines(csv_data)
#Create array representing lines in a vrt file
vrtData = []
vrtData.append("<OGRVRTDataSource>\n")
vrtData.append(' <OGRVRTLayer name="{name}">\n'.format(name=file_data.fileName))
vrtData.append(' <SrcDataSource>{csvfile}</SrcDataSource>\n'.format(csvfile=csv_file))
vrtData.append(' <GeometryType>wkbPoint25D</GeometryType>\n')
vrtData.append(' <LayerSRS>{sepsg}</LayerSRS>\n'.format(sepsg=job_data.sourceEpsg))
vrtData.append(' <GeometryField encoding="PointFromColumns" x="field_1" y="field_2" z="field_3"/>\n')
vrtData.append(' </OGRVRTLayer>\n')
vrtData.append('</OGRVRTDataSource>')
vrt_file = os.path.join(dem_directory, file_data.fileName + ".vrt").replace("\\","/")
if os.path.isfile(vrt_file):
os.remove(vrt_file)
#Write vrt to file
with open(vrt_file, 'w') as vrf:
vrf.writelines(vrtData)
sleep(0.01)
vrts = gdal.OpenEx(vrt_file, 0)
#Visit https://gdal.org/python/osgeo.gdal-module.html#GridOptions to see other options, like width and height for a higher resolution
option = gdal.GridOptions(format='GTiff',width=job_data.tileHeight,height=job_data.tileWidth,outputSRS=job_data.sourceEpsg,algorithm='invdist:power={pow}:smoothing={smo}'.format(pow=job_data.powerVal,smo=job_data.smoothingVal),zfield='field_3')
#Interapolate TIN to a grid
ds = gdal.Grid(tile,vrts,options=option)
ds = None
del ds
vrts = None
#Remove csv and vrt file
if cleanup:
os.remove(csv_file)
os.remove(vrt_file)
sleep(0.05)
return file_data
except Exception as e:
QgsMessageLog.logMessage('Error while converting {file}, Error: {er}'.format(file=file_data.fileName, er=str(e)),CATEGORY, Qgis.Info)
return None
def gridByTxt(inputPath, outputPath, prjFile, cellSize, extend=None):
"""
txt文件插值为栅格 TODO 统一输入输出文件规范
:param inputPath: str 输入txt文件路径
:param outputPath: str 输出栅格文件路径
:param cellSize: float 输出栅格分辨率，单位：度
:param extend: tuple 输出栅格范围 (ulx, uly, lrx, lry)
:return:
"""
# shp生成csv
tempPath = BaseUtil.file_path_info(inputPath)[0]
baseName = BaseUtil.file_path_info(inputPath)[1]
# csvName = baseName + ".csv"
# csvPath = os.path.join(tempPath, csvName)
# ShapeUtil.pointToCsv(inputPath, csvPath, zField)
# 生成vrt
vrtName = baseName + ".vrt"
vrtPath = os.path.join(tempPath, vrtName)
dom = XmlUtil.createDom()
roodNode = XmlUtil.createRootNode(dom, "OGRVRTDataSource")
OGRVRTLayerNode = XmlUtil.appendNode(dom,
roodNode,
"OGRVRTLayer",
attrInfo={"name": baseName})
XmlUtil.appendNode(dom,
OGRVRTLayerNode,
"SrcDataSource",
text=inputPath)
XmlUtil.appendNode(dom,
OGRVRTLayerNode,
"GeometryType",
text="wkbPoint")
XmlUtil.appendNode(dom,
OGRVRTLayerNode,
"GeometryField",
attrInfo={
"encoding": "PointFromColumns",
"x": "field_1",
"y": "field_2",
"z": "field_3"
})
XmlUtil.saveDom(dom, vrtPath)
# 计算输出范围
if extend:
xMin = extend[0]
xMax = extend[2]
yMin = extend[3]
yMax = extend[1]
outputBounds = extend
width = int((xMax - xMin) / cellSize)
height = int((yMax - yMin) / cellSize)
else:
csvData = pd.read_csv(inputPath, header=None, sep=" ")
xMin = csvData[0].min()
xMax = csvData[0].max()
yMin = csvData[1].min()
yMax = csvData[1].max()
outputBounds = (xMin, yMax, xMax, yMin)
width = int((xMax - xMin) / cellSize)
height = int((yMax - yMin) / cellSize)
# 插值算法参数
algo = 'invdist:power=2.0:smoothing=0.0:radius1=0.0:radius2=0.0:angle=0.0:max_points=0:min_points=0:nodata=0.0'
options = gdal.GridOptions(outputType=gdal.GDT_Float32,
outputBounds=outputBounds,
layers=baseName,
format='GTiff',
algorithm=algo,
width=width,
height=height,
outputSRS=prjFile)
gdal.Grid(outputPath, vrtPath, options=options)
print(outputDirectory)
print(outputTest)
wellNitrate = str(wellsShapefile)
print(wellNitrate)
#creating interpolation for well nitrate data
power_option = input("enter a k value > 0: ")
smoothing_option = input("enter smoothing variable >= 0")
algorithm_option = 'invdist:power='+power_option+':smoothing='+smoothing_option
print(algorithm_option)
gridOptions = gdal.GridOptions(format='Gtiff', algorithm=algorithm_option, zfield="nitr_ran")
out = gdal.Grid(outputTest, wellNitrate, options=gridOptions)
print('interpolation complete')
print('reprojecting raster')
print('input file:', outputTest)
print('output file:', rasterWarpOut)
out = None
del out
warpOptions = gdal.WarpOptions(srcSRS='EPSG:4269', dstSRS='EPSG:4269')
print('warp options input')
warp_out = gdal.Warp(rasterWarpOut, outputTest, options=warpOptions)
print('raster reprojected')
print('changing resolution')