[c2]: Add module c2TopRunDF #52

qltysh · 2026-01-28T17:39:02Z

Found 2 issues:

1. Incorrect formatting, autoformat by running qlty fmt. [black:fmt]

2. Incorrect formatting, autoformat by running qlty fmt. [ruff:fmt]

qltysh · 2026-01-28T17:03:51Z

Deeply nested control flow (level = 4) [qlty:nested-control-flow]

qltysh · 2026-01-28T17:03:51Z

Function with high complexity (count = 40): c2TopRunDFMain [qlty:function-complexity]

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@@ Expand Up / @@ -23,6 +23,7 @@ jobs: @@
         - uses: actions/checkout@v4
           with:
             fetch-depth: 0
+            submodules: recursive
         - name: Set up Python
           uses: actions/setup-python@v5
           with:
@@ Expand Down @@

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+    [submodule "debrisframe/c2TopRunDF/pyTopRunDFRepo"]
+    	path = debrisframe/c2TopRunDF/pyTopRunDFRepo
+    	url = https://github.com/schidli/pyTopRunDF.git

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+    [GENERAL]
+    name = Scenario1
+    # The user needs to declare a starting point of the simulation in X (easting) and Y (northing) coordinates.
+    # Those coordinates must lay within the applied digital terrain model and have to be defined in the same projection.
+    # Starting point can be a distinct change within the longitudinal flow-profile
+    # (significant change in slope gradient at fan apex) or obstacles forcing the debris flow to deposit.
+    # pyTopRunDF reacts sensitively to the starting point, which is why the program changes the starting point after each
+    # single flow path and randomly sets a new one in a buffer around the initial starting cell (default maximum buffer = 3 cells).
+    # However, the user might need to accomplish maybe several simulations to achieve plausible results.
+    xKoord = 660926
+    yKoord = 151744
+    energyHeight = 0.1
+    # The volume must correspond to the unit of length measurement used for the projection of the digital terrain input model.
+    # In the example the volume is given in m 3 .
+    volume = 4000
+    # The mobility coefficient k B is a dimensionless parameter
+    coefficient = 28

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+    """
+    @author: Christian Scheidl
+    (modified by Paula Spannring)
+    """
+    import rasterio
+    import numpy as np
+    import logging
+    import mmap
+    from scipy.ndimage import convolve
+    import matplotlib as mpl
+    import debrisframe.c2TopRunDF.pyTopRunDFRepo.RandomSingleFlow as randomsfp
+    from debrisframe.c2TopRunDF.pyTopRunDFRepo.PlotResult import HillshadePlotter
+    import avaframe.in1Data.getInput as gI
+    import avaframe.in3Utils.initialiseDirs as iD
+    # To get a reproduceable result, set the seed:
+    # np.random.seed(42)
+    # create local logger under avaframe namespace to use its logging configuration
+    log = logging.getLogger("avaframe.debrisframe.c2TopRunDF")
+    # Set global font size for plots
+    mpl.rcParams['font.size'] = 8  # Set font size to 12
+    mpl.rcParams['axes.titlesize'] = 12  # Set title font size
+    mpl.rcParams['axes.labelsize'] = 8  # Set axis label font size
+    mpl.rcParams['xtick.labelsize'] = 8  # Set x-axis tick font size
+    mpl.rcParams['ytick.labelsize'] = 8  # Set y-axis tick font size
+    def c2TopRunDFMain(cfgMain, cfgDebris):
+        # 3) try to replace some functions (read in data,...)
+        # 4) try to allow computing several scenarios in one run (only for one DEM -> difference to original!!!)
+        avaDir = cfgMain["MAIN"]["avalancheDir"]
+        output_dir, dem_file = initializeSimulation(avaDir)
+        # get input data
+        eventName = cfgDebris["GENERAL"]["name"]
+        xKoord = cfgDebris["GENERAL"].getfloat("xKoord")
+        yKoord = cfgDebris["GENERAL"].getfloat("yKoord")
+        volume = cfgDebris["GENERAL"].getfloat("volume")
+        coefficient = cfgDebris["GENERAL"].getfloat("coefficient")
+        artificial_height = cfgDebris["GENERAL"]["energyHeight"]
+        if artificial_height == "elevation":
+            artificial_raster_height = rasterio.open(output_dir / "elevation.asc")
+        else:
+            artificial_height = parse_decimal(str(artificial_height))
+        # Open the DEM file
+        # Preprocess the DEM file if necessary
+        processed_dem_file = preprocess_raster(dem_file)
+        dataset = rasterio.open(processed_dem_file)
+        band = dataset.read(1)
+        gridsize = dataset.res[0]
+        # Initialize variables
+        simarea = volume ** (2 / 3) * coefficient
+        perimeter = simarea / gridsize ** 2
+        row, col = dataset.index(xKoord, yKoord)
+        band2 = np.copy(band)
+        band3 = np.copy(band)
+        band3.fill(0)
+        area = 0
+        mcsmax = 500
+        # Flowpath simulation
+        for x in range(0, 100000):
+            if area >= perimeter:
+                break
+            else:
+                # In order to avoid implausible deposition heights due to an identical starting point, each starting point
+                # of a single flow run is determined randomly within a certain radius.
+                random_radius = (
+# Define the radius for random starting points to be defined; Default: 3 gridsizes.
+                )
+                row = np.random.randint(max(0, row - random_radius), min(dataset.height, row + random_radius))
+                col = np.random.randint(max(0, col - random_radius), min(dataset.width, col + random_radius))
+                position = [row, col]
+                band2.fill(0)
+                mcs = 0
+                while (
+                        mcs < mcsmax
+                        and position[0] <= dataset.height - 1
+                        and position[1] <= dataset.width - 1
+                ):
+                    if position[0] > 0 and position[1] > 0:
+                        if area >= perimeter:
+                            break
+                        else:
+                            # Adjust energy height dynamically to avoid unplausible depo-heights at the start cell.
+                            # The denominator in the exponent of the decay_factor (default: 100) scales the "range" of the
+                            # decay. A larger denominator results in slower decay, meaning the decay factor remains
+                            # significant over longer distances. A smaller denominator causes faster decay, meaning
+                            # the decay factor approaches zero more quickly.
+                            distance = np.sqrt((position[0] - row) ** 2 + (position[1] - col) ** 2)
+                            decay_factor = np.exp(-distance / 100)  # Example decay factor with denominantor=100
+                            if isinstance(artificial_height, float):
+                                temp_height = artificial_height * gridsize * decay_factor
+                            else:
+                                temp_height = (
+                                        artificial_raster_height.read(1)[position[0], position[1]]
+                                        * gridsize * decay_factor
+                                )
+                            obj1 = randomsfp.MonteCarloSingleFlowPath(
+                                dataset, band2, position, temp_height
+                            )
+                            position = obj1.NextStartCell()
+                            band2[position[0], position[1]] = True
+                            band3[position[0], position[1]] += 1
+                            if band3[position[0], position[1]] == 1:
+                                area += 1
+                    else:
+                        mcs += 1
+                        position = [row, col]
+                        band2.fill(0)
+        band3[0, 0] = 0
+        max_val = np.amax(band3)
+        band3 = band3 / max_val
+        meanh = volume / perimeter
+        band4 = band3 * meanh
+        dummy = np.sum(band3)
+        diff = volume / (dummy * gridsize ** 2)
+        meannew = meanh * diff
+        band4 = band3 * meannew
+        #############################################################################################
+        # Several strategies for distributing the input volume plausibly across the storage area:
+        #############################################################################################
+        # --A-- # Diffusion algorithm:
+        # A diffusion algorithm is a method used to smooth values in a grid or matrix
+        # and distribute them more evenly. It simulates the physical process of diffusion,
+        # in which material or energy moves from areas of high concentration to areas of low
+        # concentration.
+        kernel = np.array([[0.05, 0.1, 0.05],
+                           [0.1, 0.4, 0.1],
+                           [0.05, 0.1, 0.05]])
+        band4 = convolve(band4, kernel, mode='constant', cval=0.0)
+        #############################################################################################
+        # --B-- # Apply Gaussian smoothing to reduce sharp peaks
+        # from scipy.ndimage import gaussian_filter
+        # band4 = gaussian_filter(band4, sigma=2)
+        #############################################################################################
+        # --C-- # Ablagerungshöhe über mittlere Ablagerungshöhe normiert:
+        # band4 = band4 / np.max(band4) * meanh
+        # Adjust deposition values to match input volume
+        total_deposited_volume = np.sum(band4) * gridsize ** 2
+        volume_difference = volume - total_deposited_volume
+        if abs(volume_difference) > 1e-6:
+            adjustment_factor = volume / total_deposited_volume
+            band4 *= adjustment_factor
+            log.info(f"Adjusted deposition values by factor: {adjustment_factor}")
+        else:
+            log.info("Deposition volume matches input volume.")
+        # Save the output raster
+        out_meta = dataset.meta.copy()
+        out_meta.update({"driver": "AAIGrid", "dtype": "float32"})
+        output_raster_path = output_dir / "depo.asc"
+        with rasterio.open(output_raster_path, "w", **out_meta) as dest:
+            dest.write(band4, 1)
+        # Clean up the temporary file if preprocessing was done
+        if processed_dem_file != dem_file:
+            processed_dem_file.unlink()  # Deletes the temporary file
+        fin = "finished"
+        if fin is None:
+            fin = "terminated"
+        log.info(f"Simulation {fin}")
+        # Create an instance of the HillshadePlotter class
+        plotter = HillshadePlotter()
+        # Generate the plot
+        plotter.plot(output_raster_path, dem_file, eventName, output_dir)
+    def initializeSimulation(avaDir):
+        demFile = gI.getDEMPath(avaDir)
+        _, outputDir = iD.initialiseRunDirs(avaDir, modName="c2TopRunDF", cleanRemeshedRasters=False)
+        return outputDir, demFile
+    # Funktion zum Testen ob unterschiedliche Dezimaltrennzeichen in den Rasterdaten vorliegen
+    def needs_preprocessing(file_path):
+        """Check if the file contains commas as decimal separators."""
+        with open(file_path, "r", encoding="utf-8") as f:
+            with mmap.mmap(f.fileno(), length=0, access=mmap.ACCESS_READ) as mm:
+                return b',' in mm
+    def preprocess_raster(file_path):
+        """Preprocess raster file to replace commas with periods in numeric values."""
+        if not needs_preprocessing(file_path):
+            return file_path  # Return the original file if no preprocessing is needed
+        temp_file = file_path.with_suffix(".asc")  # Create a temporary file
+        with open(file_path, "r", encoding="utf-8") as f_in:
+            # Map the file into memory
+            with mmap.mmap(f_in.fileno(), length=0, access=mmap.ACCESS_READ) as mm:
+                # Read the entire file content
+                content = mm.read().decode("utf-8")
+                # Replace commas with periods
+                updated_content = content.replace(",", ".")
+                # Ensure no extra newlines are introduced
+                updated_content = "\n".join(line.strip() for line in updated_content.splitlines())
+        # Write the updated content to a temporary file
+        with open(temp_file, "w", encoding="utf-8") as f_out:
+            f_out.write(updated_content)
+        return temp_file
+    # Funktion zur Adaptierung unterschiedlicher Dezimaltrennzeichen für Eingabewerte
+    def parse_decimal(input_string):
+        # Prüfen, ob ein Komma als Dezimaltrennzeichen verwendet wird
+        if ',' in input_string and '.' not in input_string:
+            input_string = input_string.replace(',', '.')
+        try:
+            return float(input_string)
+        except ValueError:
+            raise ValueError("Invalid input. Please enter a number with a valid decimal separator.")

[c2]: Add module c2TopRunDF #52

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