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rasterconvert module

The RasterConverter module provides a set of functions for efficient data conversion and manipulation of raster datasets.

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.

Parameters:

Name	Type	Description	Default
folder_path	str	The path to the folder containing raster data (.tif).	required
index_list	list	A list of indices corresponding to the raster files.	required

Returns:

Type	Description
dict	A dictionary with a specific index value as the key and a list of stored raster pixel values as the value.

Source code in geoca/rasterconvert.py

def 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.

    Args:
        folder_path (str): The path to the folder containing raster data (.tif).
        index_list (list): A list of indices corresponding to the raster files.

    Returns:
        dict: A dictionary with a specific index value as the key and a list of stored raster pixel values as the value.
    """
    # Create a list of .tif files in the specified folder
    tif_list = list()
    for file in os.listdir(folder_path):
        if file[-4:] == ".tif":
            tif_list.append(file)

    # Check that the raster file list is the same length as the index list
    if len(tif_list) != len(index_list):
        raise Exception("The length of the raster file list does not match the index list..")

    print("Data reading...")
    # Create a data dictionary with keys as index values for the corresponding positions in the index list and values as the data list generated by the raster file conversion.
    data_dict = dict()
    for i in tqdm(range(len(tif_list))):
        index, tif = index_list[i], tif_list[i]
        tif_path = os.path.join(folder_path, tif)
        data_dict[index] = raster_list(tif_path)
    print("Data reading is complete!")

    return data_dict

process_raster_data(input_raster_path, output_raster_path, new_data, nodata_value)

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

Parameters:

Name	Type	Description	Default
input_raster_path	str	Path to the original raster file.	required
output_raster_path	str	Path to save the output raster file.	required
new_data	list	New data in the form of a 2D list.	required
nodata_value	num	NoData value in new_data.	required

Returns:

Type	Description
	None

Source code in geoca/rasterconvert.py

def process_raster_data(input_raster_path, output_raster_path, new_data, nodata_value):
    """
    Create a single-band raster template based on the input raster, process the raster data by replacing pixel values with the new data.

    Args:
        input_raster_path (str): Path to the original raster file.
        output_raster_path (str): Path to save the output raster file.
        new_data (list): New data in the form of a 2D list.
        nodata_value (num): NoData value in new_data.

    Returns:
        None
    """
    # Open the original raster dataset
    input_raster = rasterio.open(input_raster_path)

    # Set the NoData parameter of the output raster
    nodata_raster = input_raster.nodata
    # If nodata_value is not None, set the nodata_raster to the value of nodata_value
    if nodata_value is not None:
        nodata_raster = nodata_value

    # Create a new raster from the original raster dataset
    output_raster = rasterio.open(output_raster_path, "w", driver="GTiff",
                                  height=input_raster.height, width=input_raster.width, count=1,
                                  dtype=input_raster.dtypes[0], crs=input_raster.crs, transform=input_raster.transform,
                                  nodata=nodata_raster)

    # Replace pixel values with new data，and set the NoData value
    masked_array = new_data == nodata_value
    output_raster.write(np.array(new_data), 1, masked=masked_array)

    print(f"Processed raster data saved to {output_raster_path}.")

    # Close datasets
    input_raster.close()
    output_raster.close()

raster_list(file_path)

Read raster data from a file, convert NoData values to None, and convert the data into a Python list.

Parameters:

Name	Type	Description	Default
file_path	str	Path to the raster file.	required

Returns:

Type	Description
list	Raster data as a Python list with NoData values converted to None.

Source code in geoca/rasterconvert.py

def raster_list(file_path):
    """
    Read raster data from a file, convert NoData values to None, and convert the data into a Python list.

    Args:
        file_path (str): Path to the raster file.

    Returns:
        list: Raster data as a Python list with NoData values converted to None.
    """
    # Open the raster file
    dataset = rasterio.open(file_path)

    # Check if the dataset was opened successfully
    if dataset is None:
        raise Exception("Failed to open the raster file.")

    # Get the raster band
    band = dataset.read(1)

    # Get the NoData value
    nodata_value = dataset.nodata

    # Get the number of rows and columns of raster data
    rows, cols = band.shape

    # Convert raster data to matrix
    raster_matrix = band.reshape(rows, cols)

    # Converts data types to object types to support storing None
    raster_matrix = raster_matrix.astype(object)

    # Convert NoData values to None
    raster_matrix[raster_matrix == nodata_value] = None

    # Convert NumPy array to a Python list
    raster_list = [[raster_matrix[row][col] for col in range(cols)] for row in range(rows)]

    return raster_list

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.

Parameters:

Name	Type	Description	Default
data_dict	dict	Resulting dictionary file generated by multiRaster_listDict function.	required
index_list	list	A list of indexes corresponding to the list of keys of the dictionary file in a defined order, the same as the index_list parameter of the multiRaster_listDict function.	required

Returns:

Type

Description

tuple

The first parameter 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. The second parameter is a data 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).

Source code in geoca/rasterconvert.py

def 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.

    Args:
        data_dict (dict): Resulting dictionary file generated by multiRaster_listDict function.
        index_list (list): A list of indexes corresponding to the list of keys of the dictionary file in a defined order,
                           the same as the index_list parameter of the multiRaster_listDict function.

    Returns:
        tuple: The first parameter 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. The second parameter
               is a data 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).
    """
    key1 = list(data_dict.keys())[0]
    list1 = data_dict[key1]
    rows, cols = len(list1), len(list1[0])

    data_index_list, data_value_dict = list(), dict()
    print("Data conversion...")
    for i in tqdm(range(rows)):
        inner_list = list()
        for j in range(cols):
            row, col = i + 1, j + 1
            inner_list.append((row, col))

            value_list = list()
            for index in index_list:
                data_list = data_dict[index]
                value_list.append(data_list[i][j])
            data_value_dict[(row, col)] = value_list

        data_index_list.append(inner_list)
    print("Data conversion is complete!")

    return data_index_list, data_value_dict