diff --git a/docs/source/examples/output/jupyters/rssa.ipynb b/docs/source/examples/output/jupyters/rssa.ipynb index 68da2c7..4a0d68f 100644 --- a/docs/source/examples/output/jupyters/rssa.ipynb +++ b/docs/source/examples/output/jupyters/rssa.ipynb @@ -41,13 +41,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "RSSA file small_cyl.w was recorded using the following surfaces:\n", + "RSSA file was recorded using the following surfaces:\n", " Surface ID: 1, type: 1\n", "The total number of tracks recorded is 72083.\n", "Neutrons: 72083 photons: 0, The simulation that produced this RSSA run 100000 histories.\n", @@ -66,7 +66,7 @@ "from f4enix.output.rssa import RSSA, PlotParameters, SpectraPlotParameters\n", "\n", "# The class is initialized by providing the path to the file\n", - "my_rssa = RSSA(\"small_cyl.w\")\n", + "my_rssa = RSSA.read_from_file(\"small_cyl.w\")\n", "my_rssa # printing shows relevant information" ] }, @@ -136,14 +136,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "D:\\WORK\\Softare_development\\F4Enix\\src\\f4enix\\output\\rssa\\rssa_plotting.py:99: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n", + "D:\\WORK\\Softare_development\\F4Enix\\src\\f4enix\\output\\rssa\\rssa_plotting.py:110: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n", " fig.show()\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 3, @@ -152,7 +152,7 @@ }, { "data": { - "image/png": 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", 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", 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", 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", 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", + "image/png": 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"text/plain": [ "
" ] @@ -309,7 +309,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 6, @@ -402,12 +402,107 @@ "plt.show()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Managing very large files\n", + "RSSA files can become very large (100+ Gb). These files may not fit in the memory of the computer.\n", + "For that reason, the `scan_tracks` methods has been developed. It returns a LazyFrame instead of a DataFrame.\n", + "This allows the reading of the file in batches but also the use of polars expressions to filter the data before loading it in memory.\n", + "This way we can load only the data we are interested in and not the whole file." + ] + }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "import polars as pl\n", + "\n", + "from f4enix.output.rssa.rssa_reader import parse_header, scan_tracks\n", + "\n", + "file_parameters = parse_header(\"small_cyl.w\")\n", + "# Modify the default batch size in this function depending on your memory capabilities\n", + "lazy_tracks = scan_tracks(\"small_cyl.w\", default_batch_size=10_000_000)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "shape: (5, 11)\n", + "┌─────┬─────┬──────────┬───────────┬───┬────────────┬───────────┬───────────┬─────┐\n", + "│ a ┆ b ┆ wgt ┆ erg ┆ … ┆ z ┆ u ┆ v ┆ c │\n", + "│ --- ┆ --- ┆ --- ┆ --- ┆ ┆ --- ┆ --- ┆ --- ┆ --- │\n", + "│ i64 ┆ i64 ┆ f64 ┆ f64 ┆ ┆ f64 ┆ f64 ┆ f64 ┆ i64 │\n", + "╞═════╪═════╪══════════╪═══════════╪═══╪════════════╪═══════════╪═══════════╪═════╡\n", + "│ 2 ┆ 8 ┆ 0.979868 ┆ 13.550991 ┆ … ┆ 5.162862 ┆ 0.573595 ┆ -0.782844 ┆ 0 │\n", + "│ -3 ┆ 8 ┆ 1.0 ┆ 14.0 ┆ … ┆ 25.294793 ┆ -0.618427 ┆ -0.449477 ┆ 0 │\n", + "│ 4 ┆ 8 ┆ 0.979868 ┆ 13.596932 ┆ … ┆ -19.163922 ┆ 0.727551 ┆ -0.092989 ┆ 0 │\n", + "│ -9 ┆ 8 ┆ 1.0 ┆ 14.0 ┆ … ┆ -9.707136 ┆ -0.236817 ┆ 0.921489 ┆ 0 │\n", + "│ -11 ┆ 8 ┆ 1.0 ┆ 14.0 ┆ … ┆ -5.822491 ┆ -0.66983 ┆ -0.717654 ┆ 0 │\n", + "└─────┴─────┴──────────┴───────────┴───┴────────────┴───────────┴───────────┴─────┘\n" + ] + } + ], + "source": [ + "# We can read only the first N tracks of the file immediately before deciding on more\n", + "# advanced filters\n", + "first_5_tracks = lazy_tracks.head(5).collect()\n", + "print(first_5_tracks)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "shape: (4_490, 7)\n", + "┌─────┬──────────┬───────────┬───────────┬────────────┬────────────┬─────┐\n", + "│ b ┆ wgt ┆ erg ┆ x ┆ y ┆ z ┆ c │\n", + "│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n", + "│ i64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ i64 │\n", + "╞═════╪══════════╪═══════════╪═══════════╪════════════╪════════════╪═════╡\n", + "│ 8 ┆ 0.979868 ┆ 13.550991 ┆ 20.858324 ┆ -21.562243 ┆ 5.162862 ┆ 0 │\n", + "│ 8 ┆ 0.979868 ┆ 13.596932 ┆ 29.855251 ┆ -2.943469 ┆ -19.163922 ┆ 0 │\n", + "│ 8 ┆ 0.914503 ┆ 4.688492 ┆ 22.458954 ┆ -19.88958 ┆ 3.333919 ┆ 0 │\n", + "│ 8 ┆ 0.966763 ┆ 0.570175 ┆ 22.557919 ┆ -19.777267 ┆ -2.555544 ┆ 0 │\n", + "│ 8 ┆ 0.960412 ┆ 7.01652 ┆ 29.460265 ┆ 5.665051 ┆ -3.958737 ┆ 0 │\n", + "│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │\n", + "│ 8 ┆ 0.979868 ┆ 13.949697 ┆ 29.728642 ┆ 4.025897 ┆ -4.338646 ┆ 0 │\n", + "│ 8 ┆ 0.979868 ┆ 13.58059 ┆ 27.518898 ┆ -11.94614 ┆ -6.107839 ┆ 0 │\n", + "│ 8 ┆ 0.979868 ┆ 1.675429 ┆ 24.225364 ┆ -17.695528 ┆ -15.579247 ┆ 0 │\n", + "│ 8 ┆ 0.979868 ┆ 11.392901 ┆ 28.96016 ┆ -7.830015 ┆ -7.329891 ┆ 0 │\n", + "│ 8 ┆ 0.941852 ┆ 13.263379 ┆ 24.570253 ┆ 17.213444 ┆ 18.129407 ┆ 0 │\n", + "└─────┴──────────┴───────────┴───────────┴────────────┴────────────┴─────┘\n" + ] + } + ], + "source": [ + "# We can build an RSSA object taking all the tracks that fulfill a filtering expression\n", + "filtered_tracks = (\n", + " lazy_tracks.drop([\"u\", \"v\", \"a\", \"tme\"])\n", + " .filter(pl.col(\"wgt\") < 1.0)\n", + " .filter(pl.col(\"x\") > 0)\n", + ").collect()\n", + "\n", + "custom_rssa = RSSA(file_parameters, filtered_tracks)\n", + "# custom_rssa.save_to_files(\"path_to_folder\")\n", + "# loaded_rssa = RSSA.load_from_saved_files(\"path_to_folder\")\n", + "\n", + "print(custom_rssa.tracks)" + ] } ], "metadata": { diff --git a/src/f4enix/output/rssa/rssa.py b/src/f4enix/output/rssa/rssa.py index b6415e7..b2a303d 100644 --- a/src/f4enix/output/rssa/rssa.py +++ b/src/f4enix/output/rssa/rssa.py @@ -1,79 +1,95 @@ """This module is related to the parsing of D1S-UNED meshinfo files.""" + +from dataclasses import dataclass from pathlib import Path import polars as pl -from f4enix.output.rssa.rssa_helpers import NEUTRON_INDICATOR +from f4enix.output.rssa.rssa_helpers import NEUTRON_INDICATOR, FileParameters from f4enix.output.rssa.rssa_plotting import RSSAPlot, RSSASpectraPlot from f4enix.output.rssa.rssa_reader import parse_header, parse_tracks +@dataclass class RSSA: - def __init__(self, path: Path | str): - """Representation of a RSSA file. + """Representation of a RSSA file. + + Attributes + ---------- + parameters: FileParameters + Parameters extracted from the RSSA file header. + + np1 # Number of histories of the simulation, given as a negative number + nrss # Number of tracks recorded + nrcd # Number of values recorded for each particle, it should be 11 + njsw # Number of surfaces in JASW + niss # Number of different histories that reached the SSW surfaces + niwr # Number of cells in RSSA file + mipts # Source particle type + kjaq # Flag for macrobodies surfaces + surfaces + tracks: pl.DataFrame + DataFrame containing the tracks recorded in the RSSA file. + + Each row of the table has 11 values + 0 a, # History number of the particle, negative if uncollided + 1 b, # Packed variable, the sign is the sign of the third direction cosine + # starts with 8 = neutron, 16 = photon + 2 wgt, + 3 erg, + 4 tme, + 5 x, + 6 y, + 7 z, + 8 u, # Particle direction cosine with X-axis + 9 v, # Particle direction cosine with Y-axis, to calculate w (Z-axis) use + # the sign from b + 10 c # Surface id + + Examples + -------- + >>> from f4enix.output.rssa import RSSA + ... my_rssa = RSSA.read_from_file('small_cyl.w') + ... print(my_rssa) + RSSA file small_cyl.w was recorded using the following surfaces: + Surface ID: 1, type: 1 + The total number of tracks recorded is 72083. + Neutrons: 72083 photons: 0. + The simulation that produced this RSSA run 100000 histories. + The amount of independent histories that reached the RSSA surfaces was 70797. + """ + + parameters: FileParameters + tracks: pl.DataFrame + + @staticmethod + def read_from_file(path: Path | str) -> "RSSA": + """Loads the RSSA file from the given path as generated by MCNP.""" + parameters = parse_header(path) + tracks = parse_tracks(path) + return RSSA(parameters, tracks) + + def save_to_files(self, output_dir: Path | str) -> None: + """Saves the RSSA as two files: a JSON file with the file parameters and a + parquet file with the tracks DataFrame.""" + directory_path = Path(output_dir) + directory_path.mkdir(parents=True, exist_ok=True) + self.parameters.save_to_json(directory_path / "parameters.json") + self.tracks.write_parquet(directory_path / "tracks.parquet") + + @staticmethod + def load_from_saved_files(directory: Path | str) -> "RSSA": + """Loads the RSSA from the JSON and parquet files generated by `save_to_files()` Parameters ---------- - path: Path - Path to the RSSA file. - - Attributes - ---------- - path: Path - Path to the RSSA file. - parameters: _FileParameters - Parameters extracted from the RSSA file header. - - np1 # Number of histories of the simulation, given as a negative number - nrss # Number of tracks recorded - nrcd # Number of values recorded for each particle, it should be 11 - njsw # Number of surfaces in JASW - niss # Number of different histories that reached the SSW surfaces - niwr # Number of cells in RSSA file - mipts # Source particle type - kjaq # Flag for macrobodies surfaces - surfaces - tracks: pl.DataFrame - DataFrame containing the tracks recorded in the RSSA file. - - Each row of the table has 11 values - 0 a, # History number of the particle, negative if uncollided - 1 b, # Packed variable, the sign is the sign of the third direction cosine - # starts with 8 = neutron, 16 = photon - 2 wgt, - 3 erg, - 4 tme, - 5 x, - 6 y, - 7 z, - 8 u, # Particle direction cosine with X-axis - 9 v, # Particle direction cosine with Y-axis, to calculate w (Z-axis) use - # the sign from b - 10 c # Surface id - - Examples - -------- - >>> from f4enix.output.rssa import RSSA - ... my_rssa = RSSA('small_cyl.w') - ... print(my_rssa) - RSSA file small_cyl.w was recorded using the following surfaces: - Surface ID: 1, type: 1 - The total number of tracks recorded is 72083. - Neutrons: 72083 photons: 0. - The simulation that produced this RSSA run 100000 histories. - The amount of independent histories that reached the RSSA surfaces was 70797. + directory: Path | str + Directory where the `parameters.json` and `tracks.parquet` files are located """ - self.path = Path(path) - with open(path, "rb") as infile: - self.parameters = parse_header(infile) - self.tracks = parse_tracks(infile) - - # Modify the value of "b" for fast filtering of neutrons and photons - self.tracks = self.tracks.with_columns( - (pl.col("b").abs() / (10 ** pl.col("b").abs().log10().floor())) - .cast(int) - .alias("b") - ) + directory_path = Path(directory) + parameters = FileParameters.load_from_json(directory_path / "parameters.json") + tracks = pl.read_parquet(directory_path / "tracks.parquet") + return RSSA(parameters, tracks) def __repr__(self) -> str: return self.get_summary() @@ -83,8 +99,7 @@ def __str__(self) -> str: def get_summary(self) -> str: """Returns a summary of the RSSA file.""" - summary = f"RSSA file {self.path.name} was recorded using the following" - summary += " surfaces:\n" + summary = "RSSA file was recorded using the following surfaces:\n" for surface in self.parameters.surfaces: summary += f" Surface ID: {surface.id}, type: {surface.type}\n" diff --git a/src/f4enix/output/rssa/rssa_helpers.py b/src/f4enix/output/rssa/rssa_helpers.py index e789a3e..554c844 100644 --- a/src/f4enix/output/rssa/rssa_helpers.py +++ b/src/f4enix/output/rssa/rssa_helpers.py @@ -1,4 +1,6 @@ +import json from dataclasses import dataclass +from pathlib import Path import numpy as np @@ -31,3 +33,49 @@ class FileParameters: mipts: int # Source particle type kjaq: int # Flag for macrobodies surfaces surfaces: list[SurfaceParameters] # List with surface ids that appear in the file + + def save_to_json(self, path: Path) -> None: + """Saves the file parameters to a JSON file.""" + + # The attributes should have Python basic types not numpy types for + # serialization + data = { + "np1": int(self.np1), + "nrss": int(self.nrss), + "nrcd": int(self.nrcd), + "njsw": int(self.njsw), + "niss": int(self.niss), + "niwr": int(self.niwr), + "mipts": int(self.mipts), + "kjaq": int(self.kjaq), + "surfaces": [ + { + "id": int(surface.id), + "info": int(surface.info), + "type": int(surface.type), + "num_parameters": int(surface.num_parameters), + "parameters": [int(param) for param in surface.parameters], + } + for surface in self.surfaces + ], + } + + with open(path, "w") as outfile: + json.dump(data, outfile, indent=4) + + @staticmethod + def load_from_json(path: Path) -> "FileParameters": + """Loads the file parameters from a JSON file.""" + with open(path) as infile: + data = json.load(infile) + data["surfaces"] = [ + SurfaceParameters( + id=surface["id"], + info=surface["info"], + type=surface["type"], + num_parameters=surface["num_parameters"], + parameters=surface["parameters"], + ) + for surface in data["surfaces"] + ] + return FileParameters(**data) diff --git a/src/f4enix/output/rssa/rssa_reader.py b/src/f4enix/output/rssa/rssa_reader.py index a1af5a7..38ad38e 100644 --- a/src/f4enix/output/rssa/rssa_reader.py +++ b/src/f4enix/output/rssa/rssa_reader.py @@ -1,7 +1,10 @@ +from collections.abc import Iterator +from pathlib import Path from typing import BinaryIO import numpy as np import polars as pl +from polars.io.plugins import register_io_source from f4enix.output.rssa.rssa_helpers import ( BYTE, @@ -13,8 +16,113 @@ SurfaceParameters, ) +BYTES_PER_TRACK = 96 + +SCHEMA = pl.Schema( + { + "a": int, + "b": int, + "wgt": float, + "erg": float, + "tme": float, + "x": float, + "y": float, + "z": float, + "u": float, + "v": float, + "c": int, + }, +) + -def parse_header(infile: BinaryIO) -> FileParameters: +def parse_header(path: Path | str) -> FileParameters: + """Reads the header of the RSSA file and returns the file parameters as a + FileParameters object.""" + with open(path, "rb") as infile: + return _parse_header_binary(infile) + + +def parse_tracks(path: Path | str) -> pl.DataFrame: + """Reads the tracks recorded in the RSSA file and returns them as a polars + DataFrame.""" + with open(path, "rb") as infile: + # Skip the header + _ = _parse_header_binary(infile) + return _parse_tracks_binary(infile) + + +def scan_tracks( + path_to_file: Path | str, default_batch_size: int = 100_000_000 +) -> pl.LazyFrame: + """Scans the RSSA to return a polars LazyFrame instead of a DataFrame. + + Parameters + ---------- + path_to_file: Path | str + The RSSA file to read. + default_batch_size: int + The default batch size to use when reading the file in chunks. This is the + number of tracks to read at once. The optimal value depends on the size of the + file and the available memory, but a good starting point is around 100 million + tracks (~8 Gb). + + Examples + -------- + >>> from f4enix.output.rssa import RSSA + >>> from f4enix.output.rssa.rssa_reader import parse_header, scan_tracks + >>> parameters = parse_header('small_cyl.w') + >>> lazy_tracks = scan_tracks('small_cyl.w') + >>> # We will only read the first 20 tracks of the file + >>> tracks = lazy_tracks.head(20).collect() + >>> rssa = RSSA(parameters, tracks) + """ + + def source_generator( + with_columns: list[str] | None, + predicate: pl.Expr | None, + n_rows: int | None, + batch_size: int | None, + ) -> Iterator[pl.DataFrame]: + """ + Generator function that creates the source. + This function will be registered as IO source. + """ + if batch_size is None: + batch_size = default_batch_size + + # Initialize the reader + with open(path_to_file, "rb") as reader: + # Skip the header + _ = _parse_header_binary(reader) + + while n_rows is None or n_rows > 0: + if n_rows is not None: + batch_size = min(batch_size, n_rows) + + number_of_bytes_to_read = ( + batch_size * BYTES_PER_TRACK + ) # each particle record is 96 bytes long + + df = _parse_tracks_binary(reader, number_of_bytes_to_read) + + if df.shape[0] == 0: + break + + if n_rows is not None: + n_rows -= df.shape[0] + + # Apply the column selection and predicate filtering if provided + if with_columns is not None: + df = df.select(with_columns) + if predicate is not None: + df = df.filter(predicate) + + yield df + + return register_io_source(io_source=source_generator, schema=SCHEMA) + + +def _parse_header_binary(infile: BinaryIO) -> FileParameters: first_record = _read_fortran_record(infile) # The first line of the file with information like the code version, date and title formatted_record_id = first_record.tobytes().decode("UTF-8") @@ -89,9 +197,11 @@ def parse_header(infile: BinaryIO) -> FileParameters: ) -def parse_tracks(file: BinaryIO) -> pl.DataFrame: +def _parse_tracks_binary( + file: BinaryIO, number_of_bytes_to_read: int = -1 +) -> pl.DataFrame: # Read the whole remaining of the file at once, store all the bytes as a 1D np array - data = np.fromfile(file, BYTE) + data = np.fromfile(file, BYTE, number_of_bytes_to_read) # Reshape the array so each index holds the information of a single particle # we can do this because we know that the particle records have always the same @@ -109,22 +219,16 @@ def parse_tracks(file: BinaryIO) -> pl.DataFrame: # all the data is already converted from bytes to floats data = data.reshape(-1, 11) - return pl.DataFrame( - data, - schema={ - "a": int, - "b": int, - "wgt": float, - "erg": float, - "tme": float, - "x": float, - "y": float, - "z": float, - "u": float, - "v": float, - "c": int, - }, + # Build the DataFrame + df = pl.DataFrame(data, schema=SCHEMA) + + # Modify the value of "b" for fast filtering of neutrons and photons + df = df.with_columns( + (pl.col("b").abs() / (10 ** pl.col("b").abs().log10().floor())) + .cast(int) + .alias("b") ) + return df def _read_fortran_record(infile: BinaryIO): diff --git a/tests/rssa_test.py b/tests/test_rssa.py similarity index 87% rename from tests/rssa_test.py rename to tests/test_rssa.py index d86515e..bbdf763 100644 --- a/tests/rssa_test.py +++ b/tests/test_rssa.py @@ -6,6 +6,7 @@ import tests.resources.rssa as res from f4enix.core.egroups import GROUP_STRUCTURES from f4enix.output.rssa import RSSA, PlotParameters, SpectraPlotParameters +from f4enix.output.rssa.rssa_reader import parse_header, scan_tracks RESOURCES = files(res) @@ -13,7 +14,7 @@ @pytest.fixture def rssa(): path = Path(RESOURCES.joinpath("small_cyl.w")) # type: ignore - return RSSA(path) + return RSSA.read_from_file(path) def test_read_rssa_parameters(rssa): @@ -60,6 +61,28 @@ def test_properties(rssa): assert True +def test_save_and_load_parameters(rssa, tmp_path): + rssa.save_to_files(tmp_path) + saved_rssa = RSSA.load_from_saved_files(tmp_path) + assert saved_rssa.parameters == rssa.parameters + assert saved_rssa.tracks.equals(rssa.tracks) + + +def test_scan_tracks_file(rssa): + path = Path(RESOURCES.joinpath("small_cyl.w")) # type: ignore + file_parameters = parse_header(path) + scanned_tracks = scan_tracks(path) + scanned_tracks = ( + scanned_tracks.head(3) + .with_columns() # We can filter by columns + .filter() # We can apply any predicate to the LazyFrame + ) + small_rssa = RSSA(file_parameters, scanned_tracks.collect()) + + assert small_rssa.tracks.shape == (3, 11) + assert rssa.tracks.head(3).equals(small_rssa.tracks) + + def test_plot_cyl(rssa, tmp_path): ( rssa.plot_cyl()