02 multiRaster listDict

Uncomment the following line to install geoca, tqdm, and pymannkendall if needed.

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# !pip install geoca tqdm pymannkendall

# !pip install geoca tqdm pymannkendall

Import modules: rasterconvert, tqdm, and pymannkendall.

Use rasterconvert to convert the raster data format, tqdm to create a progress bar when running the program, and pymannkendall to perform MK trend tests.

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from geoca import rasterconvert
from tqdm import tqdm
import pymannkendall as mk

from geoca import rasterconvert from tqdm import tqdm import pymannkendall as mk

rasterconvert.multiRaster_listDict(folder_path, index_list):

• Reads multiple raster data (.tif) from a folder and stores it in a dictionary based on specific index values.

The example data is the precipitation raster data of a region from 1961-2022, read the data of each year by multiRaster_listDict function, and input the raster into the data dictionary in the form of list.

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index_list = list(range(1961, 2022 + 1))
data_dict = rasterconvert.multiRaster_listDict("../Pre_Year_NE", index_list)

index_list = list(range(1961, 2022 + 1)) data_dict = rasterconvert.multiRaster_listDict("../Pre_Year_NE", index_list)

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# Output the list of 2011 data in the result dictionary line by line
# Example of the list of 2011 data: [[None, 1, 2],[4, 5, 3],[7, 6, None]]
for row in data_dict[2011]:
    print(row)

# Output the list of 2011 data in the result dictionary line by line # Example of the list of 2011 data: [[None, 1, 2],[4, 5, 3],[7, 6, None]] for row in data_dict[2011]: print(row)

rasterconvert.reorganize_multiRaster_listDict(data_dict, index_list):

• Reorganizes data from a list dictionary representing multiple raster data on the basis of the multiRaster_listDict function.

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data_index_list, data_value_dict = rasterconvert.reorganize_multiRaster_listDict(data_dict, index_list)

data_index_list, data_value_dict = rasterconvert.reorganize_multiRaster_listDict(data_dict, index_list)

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# Output data_index_list list line by line
# The list is a two-dimensional indexed list with the same number of rows and columns as the original raster data, and the list element values are tuples of horizontal and vertical coordinates
# Example of the data_index_list list: [[(1, 1), (1, 2), (1, 3)],[(2, 1), (2, 2), (2, 3)],[(3, 1), (3, 2), (3, 3)]]
for row in data_index_list:
    print(row)

# Output data_index_list list line by line # The list is a two-dimensional indexed list with the same number of rows and columns as the original raster data, and the list element values are tuples of horizontal and vertical coordinates # Example of the data_index_list list: [[(1, 1), (1, 2), (1, 3)],[(2, 1), (2, 2), (2, 3)],[(3, 1), (3, 2), (3, 3)]] for row in data_index_list: print(row)

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# Outputs the first key-value pair in the data_value_dict dictionary that does not contain None in the value
# The dictionary with the key being the coordinate tuple (index list element value) and the value being a list of all raster image element values corresponding to the coordinates (in index_list order)
# Example of output: (2, 3): [3, 2, 6, 7, 9]
for index, data_list in data_value_dict.items():
    if None in data_list:
        continue
    print(f"{index}: {data_list}")
    break

# Outputs the first key-value pair in the data_value_dict dictionary that does not contain None in the value # The dictionary with the key being the coordinate tuple (index list element value) and the value being a list of all raster image element values corresponding to the coordinates (in index_list order) # Example of output: (2, 3): [3, 2, 6, 7, 9] for index, data_list in data_value_dict.items(): if None in data_list: continue print(f"{index}: {data_list}") break

Once the conversion is complete, the corresponding multiple raster pixel values can be overlaid and analyzed based on these data. For example, the pymannkendall module is used to perform an MK trend test on the time-series meteorological raster data to obtain the Slope value of each image element:

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# Create an empty list template based on the number of rows and columns of any list in the data_dict dictionary
key1 = list(data_dict.keys())[0]
list1 = data_dict[key1]
rows, cols = len(list1), len(list1[0])
slope_list = [[None for _ in range(cols)] for _ in range(rows)]

# Iterate through the data_value_dict dictionary, perform an MK trend test on the lists in the dictionary,
# and add Slope values to the corresponding positions in the list templates in sequence
for index, data_list in tqdm(data_value_dict.items()):
    if None in data_list:
        continue
    i, j = index
    mkTest = mk.original_test(data_list)
    slope_list[i][j] = mkTest.slope

# Create an empty list template based on the number of rows and columns of any list in the data_dict dictionary key1 = list(data_dict.keys())[0] list1 = data_dict[key1] rows, cols = len(list1), len(list1[0]) slope_list = [[None for _ in range(cols)] for _ in range(rows)] # Iterate through the data_value_dict dictionary, perform an MK trend test on the lists in the dictionary, # and add Slope values to the corresponding positions in the list templates in sequence for index, data_list in tqdm(data_value_dict.items()): if None in data_list: continue i, j = index mkTest = mk.original_test(data_list) slope_list[i][j] = mkTest.slope

rasterconvert.process_raster_data(input_raster_path, output_raster_path, new_data, nodata_value):

• Create a raster template based on the input raster, process the raster data by replacing pixel values with the new data.

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rasterconvert.process_raster_data("../Pre_Year_NE/Pre_2011.tif", "../MKData/mkSlope.tif", slope_list, None)

rasterconvert.process_raster_data("../Pre_Year_NE/Pre_2011.tif", "../MKData/mkSlope.tif", slope_list, None)