From 6e893d289da9612b247246b5cd8404e2d1378799 Mon Sep 17 00:00:00 2001 From: digiacomo4 Date: Tue, 10 Dec 2024 11:45:21 +0100 Subject: [PATCH 01/13] add source files --- src/f4epurity/psource.py | 41 ++++++++++++++++++++++++++++++++++++++++ tests/test_psource.py | 10 ++++++++++ 2 files changed, 51 insertions(+) create mode 100644 src/f4epurity/psource.py create mode 100644 tests/test_psource.py diff --git a/src/f4epurity/psource.py b/src/f4epurity/psource.py new file mode 100644 index 0000000..5481bd3 --- /dev/null +++ b/src/f4epurity/psource.py @@ -0,0 +1,41 @@ +from __future__ import annotations + +from abc import ABC, abstractmethod +from pathlib import Path + +import numpy as np + + +class GlobalSource: + def __init__(self, sources: list[SingleSource]) -> None: + self.sources = sources + + def to_sdef(outfile: str | Path) -> None: + pass + + +class SingleSource(ABC): + def __init__( + self, + activities: dict[str, float], + coord: list | np.ndarray, + mass: float | None = None, + ) -> None: + self.activities = activities + self.coord = coord + if mass is None: + mass = 1.0 + self.mass = mass + + @abstractmethod + def _compute_lines(self) -> tuple[np.ndarray, np.ndarray]: + pass + + +class PointSource(SingleSource): + def _compute_lines(self) -> tuple[np.ndarray, np.ndarray]: + pass + + +class LineSource(SingleSource): + pass diff --git a/tests/test_psource.py b/tests/test_psource.py new file mode 100644 index 0000000..3a657c6 --- /dev/null +++ b/tests/test_psource.py @@ -0,0 +1,10 @@ +class TestPointSource: + def test_compute_lines(self): + # TODO create the PointSource, call the method compute lines and verify that the + # computed values are the expected ones + pass + + +class TestGlobalSource: + def test_to_sdef(self): + pass From 9e8c7a4e7cd159a6330e2e208ead2e3eee493f51 Mon Sep 17 00:00:00 2001 From: Davide Laghi Date: Tue, 10 Dec 2024 12:41:38 +0100 Subject: [PATCH 02/13] imposta sources --- pyproject.toml | 1 + src/f4epurity/main.py | 17 +++++++++++++---- src/f4epurity/psource.py | 39 +++++++++++++++++++++++++++++++-------- tests/test_psource.py | 26 ++++++++++++++++++++++---- 4 files changed, 67 insertions(+), 16 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index de6c58b..4174be8 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -26,6 +26,7 @@ dependencies = [ "pandas", "jsonargparse", "scipy", + "actigamma", ] [project.optional-dependencies] tests = [ diff --git a/src/f4epurity/main.py b/src/f4epurity/main.py index 2fd58b6..e709b52 100644 --- a/src/f4epurity/main.py +++ b/src/f4epurity/main.py @@ -8,6 +8,7 @@ from importlib.resources import files, as_file import pandas as pd +from f4epurity.psource import PointSource, GlobalSource from f4epurity.decay_chain_calc import calculate_total_activity from f4epurity.collapse import collapse_flux, extract_xs from f4epurity.dose import convert_to_dose, write_vtk_file, plot_slice @@ -76,7 +77,7 @@ def calculate_dose_for_source( x2: np.ndarray = None, y2: np.ndarray = None, z2: np.ndarray = None, -) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, list[float]]: +) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, list[float], dict[str, float]]: """Calculate the dose for a given source Parameters @@ -210,7 +211,7 @@ def calculate_dose_for_source( else: plt.savefig(f"{run_dir}/dose_{x1}_{y1}_{z1}.png") - return dose_array, x, y, z, total_dose + return dose_array, x, y, z, total_dose, activities def calculate_dose_at_workstations( @@ -301,6 +302,7 @@ def process_sources(args: Namespace) -> None: dose_factors_df = pd.read_excel(fp) dose_arrays = [] + sources = [] # Check if a second point was provided - line source if args.x2 is not None and args.y2 is not None and args.z2 is not None: logging.info("Line source(s) selected") @@ -309,7 +311,7 @@ def process_sources(args: Namespace) -> None: for x1, y1, z1, x2, y2, z2 in zip( args.x1, args.y1, args.z1, args.x2, args.y2, args.z2 ): - dose_array, x, y, z, dose = calculate_dose_for_source( + dose_array, x, y, z, dose, activities = calculate_dose_for_source( args, x1, y1, @@ -340,7 +342,7 @@ def process_sources(args: Namespace) -> None: # Handle multiple coordinates being provided for x1, y1, z1 in zip(args.x1, args.y1, args.z1): - dose_array, x, y, z, dose = calculate_dose_for_source( + dose_array, x, y, z, dose, activities = calculate_dose_for_source( args, x1, y1, @@ -352,7 +354,14 @@ def process_sources(args: Namespace) -> None: dose_factors_df, ) dose_arrays.append(dose_array) + if args.dump_source: + sources.append(PointSource(activities, [x1, y1, z1], mass=args.m)) calculate_dose_at_workstations(args, dose, x1, y1, z1, run_dir) + + if args.dump_source: + global_source = GlobalSource(sources) + global_source.to_sdef(f"{run_dir}/source.sdef") # TODO + # If more than one dose array is present, sum the dose arrays (multiple sources) if len(dose_arrays) > 1: diff --git a/src/f4epurity/psource.py b/src/f4epurity/psource.py index 5481bd3..8232571 100644 --- a/src/f4epurity/psource.py +++ b/src/f4epurity/psource.py @@ -3,21 +3,33 @@ from abc import ABC, abstractmethod from pathlib import Path +import actigamma as ag import numpy as np +DB = ag.Decay2012Database() +GRID = ag.EnergyGrid(bounds=ag.linspace(0, 4e6, 10000)) # TODO +LC = ag.MultiTypeLineAggregator(DB, GRID) -class GlobalSource: - def __init__(self, sources: list[SingleSource]) -> None: + +class GlobalPointSource: + def __init__(self, sources: list[PointSource]) -> None: self.sources = sources - def to_sdef(outfile: str | Path) -> None: - pass + def to_sdef(self, outfile: str | Path) -> None: + sdef_line = "sdef\n" + si1 = "S11 L " + sp1 = "SP1 " + ds2 = "DS2 " + lines_distributions = [] + for source in self.sources: + si1 = si1 + f" {source.coord[0]} {source.coord[0]} {source.coord[0]}" + sp1 = sp1 + f"{source.mass} 0.0 0.0" class SingleSource(ABC): def __init__( self, - activities: dict[str, float], + activities: dict[str, list[np.ndarray]], coord: list | np.ndarray, mass: float | None = None, ) -> None: @@ -27,14 +39,25 @@ def __init__( mass = 1.0 self.mass = mass + def _compute_lines(self) -> tuple[list, list]: + inventory = self._compute_inventory() + hist, bin_edges = LC(inventory) + X, Y = ag.getplotvalues(bin_edges, hist) + return X, Y + @abstractmethod - def _compute_lines(self) -> tuple[np.ndarray, np.ndarray]: + def _compute_inventory(self) -> ag.UnstablesInventory: pass class PointSource(SingleSource): - def _compute_lines(self) -> tuple[np.ndarray, np.ndarray]: - pass + def _compute_inventory(self) -> ag.UnstablesInventory: + data = [] + for key, value in self.activities.items(): + act = value[0][0] + data.append((DB.getzai(key), act)) + inventory = ag.UnstablesInventory(data) + return inventory class LineSource(SingleSource): diff --git a/tests/test_psource.py b/tests/test_psource.py index 3a657c6..d456225 100644 --- a/tests/test_psource.py +++ b/tests/test_psource.py @@ -1,8 +1,26 @@ +import numpy as np +import pytest + +from f4epurity.psource import GlobalSource, PointSource + + class TestPointSource: - def test_compute_lines(self): - # TODO create the PointSource, call the method compute lines and verify that the - # computed values are the expected ones - pass + @pytest.fixture + def point_source(self): + source = PointSource( + {"Ta182": [np.array([1e-3])], "Ta182m": [np.array([1e-5])]}, + [0.0, 0.0, 0.0], + 1.0, + ) + return source + + def test_compute_lines(self, point_source: PointSource): + x, y = point_source._compute_lines() + assert len(x) == len(y) + + def test_compute_inventory(self, point_source: PointSource): + inventory = point_source._compute_inventory() + assert inventory.zais == [731820, 731821] class TestGlobalSource: From 6f7af2fc46599011f15b58466856bb40b12fccb7 Mon Sep 17 00:00:00 2001 From: digiacomo4 Date: Tue, 21 Jan 2025 09:41:02 +0100 Subject: [PATCH 03/13] mcnp_parse_source_points --- 11-L3-03N.vtu | 85 ++++++++++++ 11-L3-03N_valves_clipped.csv | 4 + E4F.txt | 0 E4F_clean.txt | 0 activities.json | 17 +++ activities.txt | 0 activities_converted.txt | 0 activity_data.json | 3 + cfg_test.yaml | 6 + coordinates.txt | 1 + mcnp_source.txt | 138 +++++++++++++++++++ src/f4epurity/main.py | 62 +++++++-- src/f4epurity/mcnp_source_calc.py | 219 ++++++++++++++++++++++++++++++ src/f4epurity/parsing.py | 11 +- tests/test_psource.py | 83 ++++++++++- 15 files changed, 612 insertions(+), 17 deletions(-) create mode 100644 11-L3-03N.vtu create mode 100644 11-L3-03N_valves_clipped.csv create mode 100644 E4F.txt create mode 100644 E4F_clean.txt create mode 100644 activities.json create mode 100644 activities.txt create mode 100644 activities_converted.txt create mode 100644 activity_data.json create mode 100644 cfg_test.yaml create mode 100644 coordinates.txt create mode 100644 mcnp_source.txt create mode 100644 src/f4epurity/mcnp_source_calc.py diff --git a/11-L3-03N.vtu b/11-L3-03N.vtu new file mode 100644 index 0000000..db5b65b --- /dev/null +++ b/11-L3-03N.vtu @@ -0,0 +1,85 @@ + + + + + + AQAAAACAAAAtAAAANQAAAA==eJzTz80r0S8oSyvWL88vytYvyipNLEqt0g9KTPFNLCjWNzKOd0lN1jcwii8ryWYAAHsWD6w= + + + AQAAAACAAAAJAAAAEQAAAA==eJzLy09J1TU0NWcAAA8rAnE= + + + AQAAAACAAAAZAAAAIQAAAA==eJxzK8pUcEtNUjAyUzC0sDI0tjIyUjAyMDJkAABSSQWB + + + AQAAAACAAAAIAAAAEAAAAA==eJwryipNLEqtYgAADwwDBA== + + + + + + + + AQAAAACAAADYSwAAyTEAAA==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AQAAAACAAADYSwAA4DYAAA==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AQAAAACAAADYSwAAMzUAAA==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AQAAAACAAADYSwAA3DYAAA==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AQAAAACAAADYSwAABDIAAA==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AQAAAACAAADYSwAA5DYAAA==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AQAAAACAAADYSwAA1jQAAA==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AQAAAACAAADYSwAA9jYAAA==eJyt13lUjP/7P3CUkhZLiogsJUJJ0oZnopQU2muaZp9mq6Z90S4hEdGiDZUQkSUpSUKbslRabC2WkhZLCll+9z+/ef/56Tjfvx5nzpwz5577db2u63ll5C6x3V/GQkrGLIZXjAvi8hJMZ2wk4QDNZm9+EwUn6pT7v1mdAkP2vGv2jRj8Zg7Qe787jtuq6DnDhdsdQJvmqL46ywGKDa3L7b65gDey39J5vfO4XS6ufXPwiysa60OcZuewMaf21S+mtDOMp1zXbiqyg+bxZX+XPLODicqMlBWe9ogufG3qkOqMd41fS7r0HUCxG+Dq/3RA+nq51m3bSTh9gc/t9uJifswLnfuZduP2AY/3YcDbFsv2ik/77mOLCTaWOXNV7FDycIWvX4wjXlRdDykJtsdyv+7sEW0HdFkHC4dOOsN9zzZdv4E9mLxm+ZuH6fbjNr+t+9vxlbYYE/x5bzxmi77q23Y8a1vsdU47PtpnA4cw79OXPewQ2Dhoamxlj+0OZs3lffZYcsrFqp/pjPuOJwunFjvjk8+E7FUbmJh/o7GE6WGD21HR366fdMMbgzXdnCFbzL1Bupa8h4qUVfO2bOLboXZ26ujaAHtE7GJ9ESx0gMc0StqPcmdUPpCbfHCPE3Luuhlmkql4GXbv2732XTCKKxuk3XJB+/CgtQndAaMjlhNu7aDA54K/YqqTHVRPHIxR/ks856KWq/tkHPA6ryh0iO+MRQ93XDaOZMEhNrO6TckRCjImxz98sB+3TYKTk46+tcW5e4Gz807awfiHY3O/iT3KExI7uR02eFz2Q37hcTs8aV/o+PO0PV5qXyuu+uWEte+drEimHCzc2+78cL0D4vKtXcVqXMat+eeFTxaJEedwpLIxLMwWfncrPx77aIfXITWk+zX2WDlLt58mcMb+0a2GHDUu8X7i+U9yHMENX25e5mg/bq9cztW0SbCHnfqq3ybEuU7tbFNTmsOHVnDG2vI9dGS01fELSGRYu3AsMkNIiFL3uzvoS4aZTaCBRFQQvP2/3YufHIUlS+7JrqjYhxnGM2lbYyJFTlhyynjhj1DY1le/3eQbAx/aMtKfpBCRKs4UHEoJhnN9xZw9MXuxfqvGzY9LIkVyut+HvGdH49duGYa7cRyuL5jb2ZweJrI29pLGzk4WZufvbbivSB+3vlervn1mOP2z2XOZz5LPOOPL07Zh/3Qy/OYl39rkLsD221ZLW9ZHInNg4i3+GgeRvDG11CP1RJ3s5/JPWNpj5qKax42SDtjNKsyZn+Uo0jbFatvNaBIiJKLLF/LZyPMqOVhLcoTMwO3sH67EPdJi3TWkEfdUnXFWvtkWZYvu7BF62aFI77QM7bMdGnau+DTcaA/XKXqX/4Q5/7Nh68mkqFZf5OTGzLTiRmIzU53zY3YUfmwp6OFdi4LBWsvZis8i8fEBLcjOMRDvKgbr+uS5iI8QW4JEMvZ/+XzO1N5O5P2H8FpqbY8NPmb2MpvtkGz8Oer7TFtc8MivGD5mi3VmGU9m7bTDFp39xww17GFBNntZu8vpn32QvGZi261QnOwqmxs+Egt7Ty3Jcz4xWLLDfMGlNXsR0V5rTYk9gAnRDJr8gkg81yhzylbjIHpG31GrqdFQn6T6cl6qnchLC6ODw52ccUSziuW9mori5ZO7bY+6Qa28JNpM4IqKlpg9LW/sEWPmFuEUYod6+yC2yaAdomZdeMTXd0KD4zffFRsc8adWdTiK5IbI4/YdrmE0rJxkItQ5TsXmF3nfEne6QvLSugsNwyxwTqUYXnhKw0GrzHQbNQdcnlExmqdrDz9mi2yvhhPY12bbaF92wLepVbbDG9ww0JLTEl9CzMW7ms5mG+gwmWiy8IknBR8/lK41XEWDWGJaRxDRn4Mffx7UXuII8sHfrwpX2qO/V290g6oTClfWrla9yEIL27zv6xwq1slp3dokRRZZ1LE87FaSIwYLD+3In+MMr4fG078vYCPg1PeB4YdcOAZG1DS024GysUt8Z7UdrPfpNkxrdwRba9uZ6gMcDD7viJLbRoFaxrOKXfMZIsUM70cGVbsh+cXRTzdDiX67soct9sIWbzn50jdb7XBbLODPKzFnLCz31SmfKMSL8n3SeyLYuF/wXv7ue3uR7OTYe+uM7XA5bOTi9RUOaOg+3l+n4A5+64O/IZvp0NkWrlHVREKR2bE1v2a7YFvpQgO9OhIWHJVOLjFm4uFe3zKzUda4teaMFl1554fs5llH+uWYWGYi0/f1yfh1vrH9vuqhf/dlbPOhHTG8/zMtS3YdVk92xyK+99/d3Qy8yJp6idfFRPbSbx9s4tzBE5Rm2O9i/rND3sE2n26x/s+8GO18SZ0lhOy7Jb+6i+gYpHg7hbozx+1RzTv2tguZ+P11xHoilwkxPUUl9lMGfCYsssx/9O/aWRXduqjJ/D/z8f54bctEPtS+z4/Nz6cjqsXaIsaVOW6X1pKMtn1ioOTZncCeDgbIt0+Nkt0YkDHt1xlj0fHgQ6y9NZUGmxOa59LyKP+s8qe3N6ST3P7PlH/Yd/2SDB0tF9sH9bcxsH13sun7RAasebk+YlsZuJTT/eqMAgNf07/IL2uhYdBohk0v0V9aSR0qft/IuDNUsdviIwmvdB2nbE5xEnlYaHa/yMVRZLLZhD0d9c7ItH45n7aTAnaSoO/ybE/47X+k3urFwhNznWsVmnSYq01eSz5BQ4Z48EHPfioUNGTnBs93w5GbLqyJL0loDTA86+3ngmRtEwurZ07/rOnAnb4/vXb/rBHDovdqix10wiec9tBywEH76aPDW5wQrLbu5e1QB5E+u6VXVZm6YGTr6x+Rj1hQlNXSPPLLEztWPvui1Ukft+s+u07Y+YKCuIsbr53YRcZeXnXW9s0kLG+vl8i75IwP6/X+njN0xuTOxtH2mU4iXzcrzYsLcsCsbRkZV4i+v2hf+rYeCpEPaIcoSy4T+bal2ET8iB3kMg6kOWy0x4J4X7kNMk4iJ26eIy/nQ0XR/NXe9ydwUPt0sd3CL0xkyj+s+rOIhR7NimnyNUz4vTjCUPrOgj/42rLr3PEppmMg5jIP6nn2Llv0qeP2bZLi5sJGEswL9y4eTHIGWeV4wlCnI7RdMmP8ZpH+WQuP44OHOsn/rMDj7M3EeioivQU31BKoeC/U5HHnk2CWoJYz8YkDrkppZT74aoedUxfF1Ly0Q+fGt6Vz9Bz+WWUvu/riYLJIs9YNb2IjmSLP9+u60VcwRC6sS1rqtOk/DZdeey++jy5y0vqpXx23M0Tu9phYf8WGPm4LNK9+tRpm44Z3aXhxgOu4nbgv73vdTScwC4Lm1LP/3b7hqepCHuWfpRWEfTPtZSPztazmxlN0cE7HKS7ZSMcSx2SjB8UMdLpaJ3QEM7GIe6fuFtGnTByGBgZUHWA683YhK/E/kz95HVRqIGOZWi+vX5UBSemS30vvM3D8qdQVnQMMCGXMco0XMlDjqW1y7yMDJy53FjUGEe9/1amNOloMHNapb8tpdx63YvvvjZ5+85+JT0ILt61jQnWBz+BsaTbkrh7R3LOYjv7t/AqDG3S8T0idXubFw1x7esHaOx44tq2QttWUjMkqMvqsv44Y6u6qruhyEPnhlMJyxY9knM8xevy6nIbWuj3i9FIGZM/oXjRYwsDlw4KrLz/Q8VL2ipL4cwbunA+RfkOhw4CfdeoS8fnh5pYCSTXXfzZyaJXY0bM00Bxqqj+50rHKPq1VzJ7ICXuLZovPZWBU7bh7th8btJla986EsPG3T3tnTi4Db+8nK1kddcFg9LS2nBsOIlUPV8z1FJBRcGB5s95dGqKfZC1PJ87l+BIriYK9dNQZ9Cu6+NGhS6vfGCxkIK/sclQ6UQeL1xZGnr7ti9F4ybNOl1hocCj+2LiFKjKo8Fvu2wdkLI36+OT5YSqidjYln7rujtmTXhhsleajxsP2ufgMHiR7bzUrr3dC2pZCel2gg8iawfhsy15XqHzgVW3KoSHgfPF5zkwGLnhHhFb10aD/a/hp3i8anGw0VR58p+PWO339lKV0zGtdzCqM8IRJL6vmmSoLo4q/mJNb3UXefM6vfDaVA4k/Aw2Nm/jQ8lUY7fDiQi/PVWzQl+gjsp+nx/5xFCl788vrWf2uOKPI1UsspMJ64Vz1BTU07M89UTfVhQYd+UeSky/TYJE1V39HAh38SkT3xdMREzj0ctK6YDwffnXmZak3bBW3JzOzySK997Ct3bnOuKD1rIc9wxEbLuUWv3Omizxnon3GqJeEilMTctosKJB4katy7RwVSx5Exqtp0SBxyPh7eDMNXjnV85hX6Xj0Jd93QN4dkR9K1cUecfF6YHRPWiQV7jXXVkS7ukBL6fijv8nO4zakKDUqych13J46MX2I/5WMx2tyTf9UUZCduuLE/HYqVj5+//NkGw3Pp6h+G1zEGLdkru4OcjUDSyg3FFmX+djxmfX80nw+zgS0Hcz9IsAO2tYUKVUveCqeWbnuoxDKWgunLq7xQXpQ9eoZoQx8qnM7OGkfFc1GzzYJAgU4KMHRm/6VuO89KSrWTygirY5Oz7v6h4bDp9euroxliAxoUGidqOKJyJYdn520qeCGSJ6VaKCCXbPDR3Y5De0BPqumPyHy6R3deQaPGSINLp+RMD3rgUtVpg4UVRro/ZKL5a66iWx+klbFp9AQO7gpzNqfhjO9tKiuq1TU8PPmeJRSsTpL1q8jnCZyfu3U8gXbOPjww6Zgiy0NWhdqfSueuogMfiutIXaeBut7o0NzFtHhXOgerLeYCjNfNQPrIDJU4p5J2bmS4PD2yRLbOmfMWpo1KVCRJNL7fIhaKlHPZEkDl8RiKiY21R6T2UnF0pran7IpxH4+V2aG1XMiV7ZMot3aSAZpUPmLzWpnbM2qvn0h0gk/Q7qrauMdcfDAXactPQ5IT2ZI+a62F1nR3W7uZOmMQ3vWrlBXdMMftU0Nl17z0XQ6VaUll4b8nZSx3Ul0jFwZW9VTS8UnJe95XgkkCMSawy9mOmHoA2ns0E5HGB6VNC7+4ITsKIsnGTpOuLPtdNd8UyLnaFsfk37ogLo1l39eV3GAb8MCrkmBA5CWmmYQ4Sjy4+kxzhIdEiYd/X7+ojgbYjsL6eJPvcD6/q5n0iWGSM4+aT3x4yQY6/45cL+P+F9FVW8PBTiApqKfNjzZGdUzntnqLHFDe+h698KLZEQtCfm20IwK4aW65zs/kRGZdSUtcaELcpatqKUfccTG7vm8I4n2uBpoZO1eZI8+GW3TrieOIgeCu6dN3kFGeHbju0qiDpTmH+hd30aHpG1Jw9ZjDGyOtVr37BwDFz9ua9ycz0R+ifOZuesYGAn8Mn15GQthCxfzok66iLSJCXm8NtUBX/0sQ9w0HLHUN4Ntc8YNKu9pDKc9TERa2Y/apdNhaqFLvqHCQEibzqq/xFxWuBo5vJm4B7MqOhTHllDxRumERFuNC7w8Z4qVqzuicpd2e52qI1qf73zossoRDWWdOnceumL+lZJP5ySpED+Xeb5gHhWDM7/pFSwg+r/kZqMiE+J8rxz8ZmtDReE+m+wQYp7o31m1xVvcSaTjigvN92Ocidw8/FFblY6SDYrvtPx4eCQkGY2QOZhh0XenVIvIcRrJGhuJvFG0PU7t1xE2jF3NJVyq+HieHarCkWSj+S41wjLUBazQsQ7baCdUf72xTzvcCacpsRvuEX1phOXp5XOLgolZ7m/YkhRseuJzRyaHgl3iJZnsi1REBPpn1A9TwffSaJ5H9Cvxs1FPr038T2Fn2DzJFiYcXffrMbz50EkOTfrykIX2sALla15M3B993S9N5ItTGa+yZ/4R4NRgaqzCZyHcFnYfmrmBhwtVVJ5hBwmGfhW8z4nOqNNu3Ssx5IiVij7v5nlQkFYpWBr5lairiSXuPSluGBIL80iSpuCuk27xDFMqqG3by2bo0HBnil318h4u8q3n6rwfYIocMN4aVNjKxO0Zpo31Ye5w/mM0KfYGC84NMh+2bmVj20MNVts3PhrNeArUSD76LS1PdBoLoL7DYaOwiYqDi3butTQi49DyR5VkeSfs4K9cwn9Mgfy0hw+2uFPwYdmGH3lqbhhst13jkeiGzLxKRS0xKvaXHb7nN0BFcI1+quCpPw64PVi866c7zq5rif3LpIu8blEgHUDM57RNJdZ5xHsbKTh3efUCPjIXUKw6v3PRbaUxe4siD4IHHhpzTd1xWS+8/nwOsS+8etKwU9EJ1iu4h6ulqdh1dq3myAoKiovN4msCyfCieWXsnOaG1X/7yGYk4jztjQ+v8CDqr26f0sJUb5y0X9NyKJ6Lh9W2ux0f80SWB/r8lnsmgJRvss+jWT6QLqzJV17li5pffr+tjvBBP2qX2PXYBZSMr03z2u1xUK1RjTvqhntHhv15oyQou69TPqPpirvqRuszRlxRs41Hl/F1w+x5yVLtF2j4mSkJo+8RaNm266vEygAkKT+K71vHFdmutil2CTGnrztIBdsVs3Hs46nUlDQqHO1mdb5tdcS5fUYOzsT8okyW1zfMcUG/bpnNlywSbFumhl/VJfrM5Q3kjKNuOLhhI2WQ5463cpb6fkTOCOh5sqNlAguT4lt2djsRff3d44c/lF2xMvcD40Guk0g/7QFns0wKeofJU6Uc3WB7+Kdbeg0JOdblXul5ZHRSlsW+f0QRuSXg+6SLxHyp42V5aXez8Ub5UeDuewKYFv1dzs8RwLLYvZ33hwuxaxH5B6cKcKti1e6fPC9YX5oyweURHWaHl5tZ/abD4VuhfMgqD3z1WT71ly4daz59OqRpSROpLvbtvh+Xhgvr3jkUlNFF+jcUyp7I8MR1/0eSlURfyfFXi83a7oavJzzqjLXJ+Ltg/gZGARVff0rZVXozRB5YdeJTfJcAcuQyCZXJNGgayFk/eksRGdI3K83RmQJjp0JX3nFXxMUIpug4uEGQ+WrZXQkqLM65X+qUoon0emPReiuQgxUCivnuKTQYurn+fJHoKrK81Lgy6RkVpQnmu+x+uOLWVTGXbA1nSF/mizH+klDyOnAgWUjCLr3WsadWLnh8siHmczxJpP25vM+l32h4W7+5VdGCiopDbec6TCm4nr24P1PPGb+TXBt8TzjhbvK12YotzpBPu1xg4O6CRo2yn/sVXJD4/uu2x3ouUD6eQx7KIvJlzxa5ayRHkWcHBg5vXeCMtytqJ+ubkTFUr2/Tt16AtIJkPTt7Is9kpn6ydyejyTtj1qkhF+hv/XBluZkTBgtnvphU6IzILtKrkNXE+/Z1arbPdUNxzsIHTlpUtH+5rO9N5OPiv/X7SUVkOKieUEj0csY7mkRNHMkJXibMRYFu/5lZsibTheeKHYe+mL5vYoORUar8qpKDow4q+rLEHP3/LlPb91RtrjOmOCxqvt7oBPPym7vKr5ExM+EFe3UPHY7b4sR4x6hIXzwt12aIgdRWlQVpaUx8MeeP/CDyuMnVdysvjNGw0/zL9wdT3bDx8Yjp2FIXXDpR68iWdYJlRFmpipyzSLsVxRHfN5Chs23rtUnEfAi9mmp14gwVvzSd9zw6TsO7xh9dRrl0sEzTjKKfMjD2d432rnnu2LfTITb7pDu0J55c6ZnkIvLG+eFZ1g4ueHVNRmwvkReVmh6XdszhYs5HiVrqMBtFy2mHtZJZmH+p/du2hSyYl9x++rePiefOb0iqISyo+vHmvQtiwqD4+sKwGUysV/fKPmpH5DCUZtcdIepqYUD6sjRnOPyN2BlU4oKaEJ+DYRpkLO6xcOj9QcaRHW8uGMZRYGFy8W6nLRW1dvuG+5ToSI1dsOGvgNjHD5g2ZUZRRAbLlwRgKwNp4t03VcU88IWvrXmXyscc/uaCndN5UPQterB8GRuh9rzt1NNs+DnP7tIO56LTcvOTNB8PKC2c5G30wwNGtUXvH4fSsUl54mhBlgtmhi6800d3xryizNsDH1ww0nT/6509rpgzoHDkMJ+MD83Lw2krKbh5Xf7eJKIv3VLSP3l+B3HvFk2QmjnBG6bsjAvbi9gio7i8t8eYXnAOVamb3M+FiWrK5Vq2O/wCpjyYXs/Gb9qXs75GXLxdulL6mI0XDsw8/qub5APavUPRJauFeOQcoL/zNBOK97ZfWttNwkGHF9a3ZJwxUPFTnufhArV48fb9M11hXCDjm6RJhgbzAVOsnJh/Q7ePzV9LQdvae6SPyjRMMBUbYj4Xwp+m+b3Zhi0yqWDelSoKB4dUOSVjLVzI6AbNPbPbHYLX4DOoXOiWaFz4beaFez4uGvLTvRHSY+uhOuSJpDb7oWfEXlxcvbwz66kbmsYeTmEYu+Izq+X4lDMuWDRzu/eaZyTMd6HK25W6QuykhsvneW7Yp/5Y37jADasS/K/feUWFpKqMcYVxCKpdgjrYWlyk/dR6HXSMKfJjhJXqwRwWDtazy35e5GHlaOCydldPdM9PURuiC9DzWGNk4BkX1jpzv3Y1e2AVi7LSqZ4OhRqPJkm2K4qYsXf6ibwlo6y0Y4kDCWSDubkHnVxxfv9aOaMMMmp3no7zWOoGv7cqOy5HUNGpffPh3hpfZO3axhW2COB7b+bNt6T/DJ/LMIOTF6SmrJ8kddkblmUr9Ls3+2CrtVyzNHFethaTd1aFc7BEuKzLtMcZOr+fv/cacAJr2cXQlNMu2JA5fMmkmATEPZ2sTneFWuD+CjkHMl7f0i7gy9GxeUWU/PEzUTisxnj/ziaIyLNN26JqvUU2XFz3qbg7ABLfdrVNr/TDMVay2W4lPk6Gy+zLf+iC6fwoatVBEmYzP2+SU3dBdW8Ut2cFCfVHlllseOwKSyqPnlNPBv0uZbQ/j4OqC2qrDA0iMfXer7UlrRz0N3qVjv4VYOPI0uk/fDjY0iVcVa5KEnn+ZpL7Hw4VN01eiIX7kfDprV3T8jFX/J3d/awowQ0KWwyextpTRZ6ffHverAMMdNllXwux4iDZa0ngsxxvTMmYSa+/Q+yL+j4BXXE8XLNRG4p/KkDMG5uuF6ZCLL0WLz+phAaNTyGGKTF0rJabfbR3pwdm2J3dHJJKwxvhpedbP1JFnmQ0SB4h9lWewabX978zRFr/0nWxvu+FBFpD+pZVrpi+tV4jZTkVzD7P++FuxPNVHjpxj9jv+qleP9o+MUTeGL1Z/LrPA+AqeX4tdoPLoikxuvdIIrVXK4pJ+ZAw9mR70f5lbuAvepQrE0xF8jKdrOZpNKR9Orswbgpd5I/zNfuqt/CRq7uGF0GlYlSqVDXN2FnkjvizbpvSXPFzR4VtlxvRd1qsXTxliZyzbo7udCL3kKVf+8fNo8DnUJz/Q38KntmkhAS8pIp8ERz+8m4TC6aU7b09H92gerPPtoeYj4YXWw1bVjhB57yC0q/7JCTcXpdNSiYhs8lLnqROQUZ7sQJ8yJhUcOeBoirx/rYltq0jcoH0gaNHNdT+83VR2qjVYeJcS1WeH99Hh2f74m6jDi+sKf+eRo9iIWbfSNgqUP7ZCX0kU6d6Im9sI3+af5OEZ/yv4Sm+JHRFhARrOdCgMlgQemMRFXd+lOhF/6JiWeSklkI+E9wnFndHtFiofBDx5/AsJqomLjLgvaChuuhKqHmGGxQtBZVn2P9JPTrlJV+Ohtcn9i7MyOYi5ckd1hlvIV59//hpbzntf6pINvEZOOn6z0q/jVbRyHbF3OOJsxeW0RAseWTViBUDB+cNrc7aw0BlKv1Kwhwm4resNsxJY+F+s1btfSl3HBVPjRWbwoLshfsqxb5M/Ngo0NL1oqPvVEzRslEqMst7feZqu+Fwxm3d4U+uIj9WxVq/qHMDM0WS0SVPQWq80WKqORUjyvsvBKrToLy2rE74k4bzdvI/06IYcPxx3/JolJCYA9axBr+56E33IekR9/h/+bCoxDb8HeWfnbh3lndtOR2niteGinvy8bvzIlvrDh+mEWuCnUI4cDqfrrbuEhuLSRNaVxi448Cxazr3p3EgMYGk0JzqjoTp0p/JRD5zkNnn0qDOxcI5Pxc/2sgGZ/HpG4aaFNQ+ezNN5TIJKtwVFxWjSNCQc5Y7QeTgTkm9a3dMiZyYvWlFyk8y8liVLksTqNisRzs//SMdBVP2VHx3Z0HrTv3J9fmM/+m3/Jcxp8VZ/+yz6PUVLd89QX8bsngbsZ9XapjFp47xkHmlidd8lY8wBm2iPp+DTJcbFQ0fORhxO3/e6DIPjpWXPQ0MPdCrwH06hahnN7ENg78tOXCW2Z7NcCRDOqyLNjrPCeIRVV0RRk4IGzi8V3+bE86sOaOOHU5wXGDwqv2HE+aVJsqlWLggtOyqxdpLJJR1LXvqr0jHvs0fN1zn8P6nlwPjB4UJwn+Wx10xT1juAbueVgsZbx66DTcZb/7GgXaH9K+vkVwse3isa/gHH1LJf8wjuELMbYuqUe72QX4Qb5/UXG9cuHPQQsbEEwxB9JusUBroYxLmFmwyVl/N9zE8Tew/Wi2N6USfdaqwDjLPcsWnc7LhCjfIOFE3J2FhCgViy5qPf7xGhbWhrmDvYiry+hMZu+xZ/9NzChYzP4vx/tnnt6znf2YJsC324jrFYR7+7lVk/LzAhbR3MHtyBR/6Kh6fB+2F2LZ0PV252hfNsW17d1p5Yz7z5A+fm17YapXTlLWDhXTHwJcuRmSYX/IMm5lKxo9TXt/iiL18S3hnzBKyM/ZmH7KoWOmCY26HhrhUEtamqFVLTiCDleRx2+QeFdlZNnQXDfb/9OgSD/qXZVyROguZwzbX2P/sEdkZJzX+crH3tYxcnLQnhC5T18VtF6L0veUnf0UhRo9I75NYIMAa8ztNrRQP1LUrXn9DEWCPFLOyYpCG0u0DM1+20BBld1pz/VJX6H8ISmlWcMVtCXT6LyVDSo+5Rb3UDdGbXlz2vEnFrNufJ+ebsYk5e+x4MDFnzgcyW0y+Cf+nr08aLt+c7yGy1PHR7cBD/65K7PcG98NC3Nc4+2PfQR8MlNw3K7HzxsYrtzLVOwQ4qHD6hkmJEA8OLm3m7WdicTanc/F1N5jK807ceOGMQ4bP417wndFUn6hQs8IFa1xyWKpXif086J7rrlgqVvRfbrk7nQr+Vsdlx4h9YeV5lb6k2sD/acKrfOqxw/8ZExSiJZjm+8/6ReaveskJhGOa2KE+m0AcV7wlOOfhA1djj947ElyMXSo9qh3ghpSoJHeL6STsKX9ZuzmchA4J5bJHja7wenWD815IfN/jMBqewcHUWnuVdn863LY7RXB7XZHNcctMvOf7P33I+n1eX9xL5KuHyke3vgkWGREwYfZqG3+Rm96ckKO+4/6zjcWL8w0+E/OfNxA3uIf4X2vXDc+YSDyv5p8Fn4m5Y2L+8PbqWMo/eyT6x+afccxxG5JjuS9Pj9iLlWPd84j5z9zOlagGDbYD68YW/WHCqcnMqZfYm8drVXnd9t6DVPzSmn+BHEOF4G7ywMbjNKiNcOe6fqNDNYZjYZHCHLcXy3jaB3ZzsHpasX2yDg0mFd1a9jMZWGaQl8uw8IB3WZTiYBYVmwbN11zJ/M+32wOkcu/R8PN4VpR0IlPk4Ak1WT5ZiBvajlMMaig47GSZf9GZioP7VasnLKDhh3dDSlszHWf9WwMS05kinxpWJWyV8cIPKHy8vooCpStLhdvz/vOvwaDVM0cqHAM2Wu4l8sLSLN+GXZdpSF+pJT1lIR2brn0aW9fCEGnlJN4sZ+IFM315aoIDBXZ6c/Wr8txE0vydpbc1UbArMnTTZuLezp1twLmWRcerzomnZ+2i4c+aM2XKRN6rSgmonh3GAPN84zqSJ1Nk58RhZYsyHvZmkckNfW7oYPvn6s6lIOuc7TZGAxXnwjMXtnfRUNB8cjgvm4Hi1NQ/Cx5SIXf98fxIOaIurj6XtWKycHLnzzM8ELlTauXPG3+ZIhPey3flHmVivo/KiKqqOzLrB8/U+fugjX2InTebgboZZayTKlSRkj9M/8z1oOKWuNWcu2YMjFJedz9RYEFxulTe02cseCQsY74l9ojCOXnrFd+xUH2qUK1yiI2fY0cy35xwh1hHzJvl7Wy8jzAOtFVkozH2oLn+EgYiYq2f9Rv9p8rx9orZq9mIVBySOTxVCLnaTOvZgzy8pQa/0nxPE3l6cGbcWWs62u17V5+2InL0tKPxncoc9IZv3asqzcfckE2yDp+Y2D7R3b/O0h2byFEF0gJ3WJv0yWx24EAn2+1+yC4OMD8h740YB9vYJxr2E899M0NMc68MC3H3xGsdC1iINVa0rt9KF9nTZTeo1kxDyJHfHVHJNHSbrEm8aUaDtfkJeu0eol6kaNk9knTsamlhrVZi4mpw7myahTdGa9PJ1AMcXI/2C0tOZ4mkRvD+PpzNwvCWsJ8TT3higunGvCIJL9wII++Jn+eJ3c2Tl7c3chAgX5i635+D/c7rfzHfceBh7f/kxj0uZjlapE8158HbxKGSmcjD5prtv7Y88YDbimGf93c8EaOneYbl6478j/UTDvlTEdeEjb1X3NAttbbHmuOG/TxtMTktClz7lfayQigoefN7lqQFE5PmbtRPMGZCvPXG3m9HvbBPaZlOeCJHZMd7abs1Ut6I2zpd9mmrJ+JDqHuGiHzfPMN//TMZAZaN3Jy38QYfU+DxoEifi5knfg++b+fhxfKn508MCXDfPKXT4pEHlm6uvUo19sbSVcFfXTU80apQ+zrlJRsaVxRC3Yj9MCAm3zpkEQWcqC0ZrSQKmjImZ1scpcBI3jRAl7hfTn9fdQ+yqKg/HDPTs5aG9pWf1IylfLCxkNWYyfzP3Hil+fk0IbE3966zdRFg+uGddVPkiRwmb+9UNZHIu04WRzpn8ZGnrpYXIOmBidWP1AIfCfGDemlW1RdffFa75N0b7IuG2TqT9In5XaN5JU5VVoCxaJsdU1fQUJsSrRSjTMXXs/7nby6lYsuQ/byqXxQwepdNXEqjwOYshzq0juir53IORerTkL19TsNXMHGl0f2pdh5XpFG8CmvzIQHU0snRdT0e8Gs/KGEu5CN+mue2vJ187Jp2bvmFFA9soBidW9stxMv1wQkJ/X5Y5Ff9dOZXX8zy2fAmzMAb5neFgWs3esFtS7ZSArGHvL/lfdX6NxU7Km6sa9OmYZ5ApmnGINHvEhqikt+74axdKdKJ9z3TdYtWlhqxn2tVVkt/okDhWbaCeSBTZNuIk/u9Qg7u21xg1d7iAbbaq3saBXjD7JtT8cILP5Xyn2xZ4A3q7rbRolbiOfTjMxLGvOBZaqVfleiJyUnbG4esvBC54s290LscVPhvYxrsosOkMHr+oTtM/O0IH9lJIvbfia20wgo3LPEvfd03jYpj8xVmfVhJw+8ZqX/buXS8VosVP5xEA03B8X6RpUCknI3tiKy+B9Layu5IL/PCHgrD2Z7vjZPfbx2f2euLs8Nh3pFbvGF7QGPt7vkecFLNGeYVeqImc8ba641eUL2d3qDqzQJDg8RKaKKjeFe6xF1TCoJOzvnt5O+Gc9cVu807KWgtJyfpGjNwr/9iRed8V8ybxBrJs6BiyfSNM56We4vc+eQY6cTiAOxao7AvI9IPOh7xKw0f+0N8RDI/5II/+hnn1h+0FSIs34a96JUPni7/GtQvdIfkge1+qs9pcFxEOfrIyg1HEnwi1t4jI5rt2Hn4IAU3Vnt/ImlwQVtxlOkiR4Wy1uAtaWUKbFc6vs3c7y1yyZcx3W9XfHDkdlT6fotQNKvL3kVbEMKXD5wp2O+H8Avqi9/95CO66mxVeC/R37uYR3K03LDSaLj8CeHXF6Wvgon7VpBILfbmsHGDah1yTYoB53M6+/Wu0ZA147xiAZkJr11JJ+3O78b5SOFG9vwAlOTyaiNKPNHxo2xKnR4dOsNztZN0adikt9KcXUDDNefqj8b5dCimq5WtvsWEftkfv7NjbGybwDx3cToX3r+eaLce8QOjWM3f3ZyFvbPMO79bs0V+bZ9/fNFZd5TFus1ev48vUil8JS2/LBB+p/O5W16ycC+hUjxRjI0laoJJP3PYiDi7snQzsWcvnRpzRms9X6TljrLrRxcHInXma987yiyIv21vWtP4n0c8cm+mD7JRUfvSQt/PndjjJcUM3N3BieeHuXzhwPiKgsxvCb7IwZtnhCe7AzGUu3Je8QgT2HrsYPgvlsja54+OD4az0df0+qUVkX8TM1umzfDlYFn4AfofYj5maEhExC7jwSH3aqRyPxfaGms3TXrEE1lQQM+W3+qPA/o3pZmtLBRv0ktZkuGOhIfE3rWIi91aM6W8E/noObfebUcBG1IGDiaIIOZfUsgGLS9iD6/mJUn/5CHvUuDmpKl8uHx+ez5CgSfy7sty8bmJRJ9+Ju7s2e+NY2E9+Tc3ROJmv2v+xlQuWkMKRj7MchfpSfp6Yy1XgPNHreN3GXKhuSvu7Gk5IXaRE26t2+SOKSGacg7ifGz4LBN1P4gH1ZRVNreIz5/c3ymc8eGjkKk33SWVyDsxjgesdXlYVm4ovHvTHQOVf1Umb+CK5B2Lf/KlwwtRi4MnvBvZjcNBp5tGvP3wTGX3b+VVPJHfktNS8j944uB2vSfHiPN9395B5mX64FmKTfT71ULYma5xsFjHAzcg/Nxifz4uq6XffKzDxzW5oHfLqHyU7jlVJlfKxyKXIw09JD4eH21ifeog5kSZ5FvTF8S9s0w+fMHJC6zRqaarnQUixQ/1eOwx5sIiIMrIkOmOts7VAf0/2Kg5mn19D2Fg6qtehzkczP0qJWPZx8WGWvW9c9f5Yebju7Sw9iDEB6bP72v1EmnQNfF7KdcHZzvM62cp+gJtjOlLp/uA/Nn5xfpIITh3672y9vNRe/tiS8cvHkxjmu9KrebjRqTj8urjAtQ9G+yZFOaB+vm6wtPHPTHTghPsFe4N+ct3Zn/s8YGpbuipr7lCBIWabWb+4GBVq0VXUxATrYMSX3UHGXidnLk5bwkT13eYRFC/M+Fc39XInMTDRsb9LelKXARc6NczSPLHgKdD5/Cp/zS56SmW9NgXsUqDLndjhNAuXKso4eWB5MUVz82VPaEn0aQx4Y0AuyWU+IIvPNh+qxIsDRSgxmSFmuUBL3zrEsyaFyqEhuLLSQVxPmjq9O+Nl/WBqm9Ipz3RV/bI3SWtIura4nYq2XCUgY57T/3DJJmonNZFvdTCxLW5NYlbLjAxcEBJSkyJhdwmXSeBPxsBlc45aklRkAg4m/0i0V9k7O+f3r0bvfHt2DNL5U8eMPrUuOPkbz46Rjty37zjodf8nMuZ6QLcW6j6cniBJ1boKrS+j/PGp5ZJRmEDfoiUntD6oN4fiiaSfhcYviiYfu1PXqMPZDfO+Bu3hofbgsjUqsNM/HrlUdV2mQnGHu/w1f1MZP6pl8pTZaGVNWHRfiKX/zJRTps0xkK3dGXQxtPe+JgkpXKs30vkvFMaEb9XCXHyyk/ZMGJubZ/zqJcqEMBkwtppU9IFcCxf3/G01hNnVZ5Muhjqjd5b3/dbSQWgcGdXdOruALxeUWhWtdwX+41vnW/Q8oXWpZwu/woPfJgfNFPhNPE+o45/f7+PBbYSKehxJBspbgcXXyP6p6pdk03HHBYecOuH7uew8JQRqZ6Ry8Pp8qlatys5IvfK/pmouZboJ7/eZhm2eYBs4zM1/q0nsuttdwdeIHJTeb/p+Qk+qBPLsRk75ovBdbpHo0/6YKl525vVX4U4mDA10irEG9McxZJ/z/aGBY1sf9eQg+dpVScVdd1x/teaoeQ9fLRnVubW67FgsU2fGjKBBSO7FRHys9hwlvu55znx+2NL9G3SiT6fKlHODrvvIXKxoUFPir8nNm4YWX1ohjfe1X2rbv5M7D2zVF9HxvhjRluc4cReHziu1hwyURMiuiUwbJG+EJO2H5p7TMEXCZ6kB8VhAuyc5puY6eeB9H36jRevu6O78feJI6+YkHuuULtVnoV+uYtJtyQ4UO+gXJc7zYN/f85yezsuHB50rXul5ityeGKqcpFtIOTqt3Q6bQtEj7lS8IOsAJwetdiSqh6AoWd9r0rOe2PsgsHGTfDD/bOPb+ql+CFmitWxJAMeXs32iYvTcMe2whLHm4eY6D84R/pMKAspU69I/NHwQMV2p+GRSB9s+ltXMHmWJ1742Mf0OQeI9GbdbY30DYDpL3aX1GgoBsPKLF3Xh6F0cdJF5eoAHHkuOXskIRCWz93vBYzx8DJ60dTGQ2yYlN66YBXLhMxWterLUizwN9DtRhYJ0O1XeXusyBeaCY8M/EMFKDkQPlEdvvAKOKW2Zt4enPt8I6LeNxKM52+eR+3djVjWF8k7LULQqDeq5hB7Slmpmk4GsTceWbPTnz5GnPeoVd36Bj7cHaZLkk8Q/f+PS+BVFz6+r91U8nKpPyy/SnSXunDwdRnzUpBw/F649+O6TA4XHOuA9ZuVPMet8NyZ2y92BCEsZ3ZrrD0H77+N9L9dycUxS5dXmeVcjBT5bFTn8RET+OxlZZrXuI17uP8z3SoYK+csv3VsMvE7k6YdmGwyfhXnLu+0VffAm6hu8Q+z+dj/MbXh5T0eFsccbflU74ljnfnZBxcKx+0bcvbiytW7oT5DfOmNJqKOv+r5mbnwxu1gsVxWGJuPCa8eRjJ1PPGjm0qeuISHaYHyj5KSBJj2e1/x5DFPtMzZf+9buReqpSoGMv2E47Ywdv3tIwqBOBL3KO7WSx6+kJP+xmp54ovRiwt7fwsh9vCm+xkxbxxPsSpTEnDBf6YUv0vMC49etjianCD66E219zYtXqix26Xbc9cLH8T30JL7PcetgN9p2DpG1G2B1um72n648MCxZem3SOxbXDnpx3MPcDdV+jcOeY1bs1n6boG+QgxM5R54t8oHZXoNb7qifdGS1cPq8BagKYiX1ZQlhN5vz2MXj3th3bfCuc85XrA0lM+KjvHCmzcHqjSfesJEafC65h9PTGF5VZ/KF0DGKrp5lrnXuI3m2F3ZstwPo0rLq6JGIjBm//bOrtxA5Eyx3e+v6Ttut9C+XP98XwilnonPmnMDMKl1dfjJhQGYuktN1zfdF/xH3mMGa71QdHxD9OmdQrB8a9IM33thWPu4dqCvF469JW2OVhbCandVWUCxF5oTm8cqjYQ4FKnz7eJKIUizXngyOb6I2dMmLlY3fl8Pbvg1Id4b7krxM6e/42P4p9mV3UTeTP0ZGLXrOxdSMaP1YYHE5+nOLxRrPLG0a/2qpf2BkGWcmJC2MBJPHc8x6r/4jttRBYsVM+yC8Fr43qH5hT9C41aueUrsgXvZpdX1oz64Ylr913ySEN8PVU7TFnrB8dJjzR4i/0i4Lb668YwQz4vaZfOcvbEnb0KYlKIPnmQ8jTCK8wP9NsUwpjUA6TIhAfaK/mg1uG5CfyQE+akO71GFO+6duBPbJuUO6cL9FYlH2ajeNTF1Cdsd69Lv98xL9YTxXX1nrZd8eKUGCN7lRSHeeniabHvIuC0sdR2JzfaH6dPI9YsqvaGgGKViZipEebYKXbVBiPwinUvbbISIPfRW8cFsLyxovJDjTOS5xmyFGf5Enn1b7ZZqmuuLc6FFa1c6+INf3zNzukIAQpfutz5JzOOsHYpPKnZ4Isl1uTDxtzuCh/NltU3ccYUrqX5G0x0/BjMLz59yx7NRjdviZe5QdrrU9rmOC13B3skkXgwYsZ7fl1kGjdvC3wELkt74wLLy5qKla4V4x6uMiWZ5QNv447K+IQGMclNX9/30REhKiMljWW+80DidPdvQDz4LyckmykEQf3x6qlZfMBp6nFeseR2ID7cTHE9tDQQ7/KPc70W+EExeefXXTw4CXxycs+iNO6g7z+7T0uGg/X3HZLHXxJwrzpfz6ST2neGwydfKiX7rQV3uahKKE1ELNpPd/cbthJCi+gPE+62qCnhCIupK8kpAUfgMTzQETZkT9sMT2hUO7fcHhRAcDsl2K/HF4/cm9ZNeBWHxpRy12M5g5HTdfSURF4D3lZFHK0sCEOvvvnd6kR8amDbqtEme0Fl3so6Rx8X8I2v+uDwh9t7dl+rViftTVKk6X0GFA//uZVeMlIk9NufT7gwxH1zXTp4cICUct487prRWXRJiLNK9S5vIBfKJtou6d3kjfdhy0yRHH3y2+XhbX9MP+88eDygoJnLLEtlNYV1E/cfWnN5G7EeDvENywiFfaK+rvcv64IfbzLGszIfeWNlg9lLI5MJ8s4wPpVcAyWjryd1fPCA708zzzBlif/2yOcl3NweGFWezO1uJ/2uzx0eCSuRvkJJr5/uM2+qCxz83dHvjtUTClzvKxFzYm9ufohiA74Eb6tgqQVAbjMgktRO/H7HJqpvYq8V23N9CmuODXRckM47R/EErezg2sNofnWsLgmZLeWAg/87DT/ZCXJutqP+5k4vgpq3Z1Z/c8ZwzPePDUx6+Sxy8217phd8DJxs11/pg++c0Xnl04Lh9PnHxnzmyuyFw2/joSXMIcnQG/5rYBmHKnbJZVKIOjmRs0po8wxdB8lZttGn+aDPQO9pwMQQTtUp6qo954yRdm60+3xPrdkxqejuXC5ehLe1FAg7s3qjuLiYJIddUr55L5LTc4cjtN9yDUH4MMgeywsatx4IZv6JX7Mb5Bz5JXXlhePB8xq+5l8JAmnghu/xJIIzEDR/Vfw2F7MXr5yXf+IMr+8rP/ZgHfuttX8Mi9obSGUffPjflYM3iA5vnEPMkfS1LV5qom8PlJj0mn73RUdPuNis0DF8ipaqhvg+ZT+ghwpFoNCb5lKssjcKH/KGngwa7sdq6OtQxxRNrXFWvbkzl4esUSTO1U0QeLgvuRb0H/h9D6WoS 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AQAAAACAAADYSwAAbzcAAA==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AQAAAACAAADYSwAA8DYAAA==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AQAAAACAAADYSwAANzcAAA==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AQAAAACAAADYSwAAuikAAA==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AgAAAACAAAAAWwAAPBAAAGgaAAA=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b/activities.json @@ -0,0 +1,17 @@ +{ + "Ta182n": [ + [ + 0.0 + ] + ], + "Ta182m": [ + [ + 0.0 + ] + ], + "Ta182": [ + [ + 0.00011723091704065617 + ] + ] +} \ No newline at end of file diff --git a/activities.txt b/activities.txt new file mode 100644 index 0000000..e69de29 diff --git a/activities_converted.txt b/activities_converted.txt new file mode 100644 index 0000000..e69de29 diff --git a/activity_data.json b/activity_data.json new file mode 100644 index 0000000..de2fea6 --- /dev/null +++ b/activity_data.json @@ -0,0 +1,3 @@ +{ + "Ta182": [ + \ No newline at end of file diff --git a/cfg_test.yaml b/cfg_test.yaml new file mode 100644 index 0000000..0c37128 --- /dev/null +++ b/cfg_test.yaml @@ -0,0 +1,6 @@ +element: Ta +delta_impurity: 0.01 +input_flux: ./11-L3-03N.vtu +irrad_scenario: SA2 +sources_csv: ./11-L3-03N_valves_clipped.csv +decay_time: 1e6 diff --git a/coordinates.txt b/coordinates.txt new file mode 100644 index 0000000..7a9db5b --- /dev/null +++ b/coordinates.txt @@ -0,0 +1 @@ +SI1 L 277.391 2086.437 1241.0 -1107.091 1720.756 1241.0 -953.078 1537.211 1378.307 \ No newline at end of file diff --git a/mcnp_source.txt b/mcnp_source.txt new file mode 100644 index 0000000..e6e6472 --- /dev/null +++ b/mcnp_source.txt @@ -0,0 +1,138 @@ +SDEF PAR=2 POS=d1 ERG FPOS d2 +SI1 L 277.391 2086.437 1241.0 -1107.091 1720.756 1241.0 -953.078 1537.211 1378.307 +SP1 0.17 0.33 0.50 $ rel strengths of sources +DS2 S 3 4 5 $ energy distributions +SI3 L 2000.2000200020002 2400.2400240024 8400.8400840084 8800.8800880088 31603.1603160316 & +32003.200320032003 42404.2404240424 42804.2804280428 57605.760576057604 58005.800580058 & +59205.9205920592 59605.9605960596 65606.5606560656 66006.600660066 66806.68066806681 & +67206.7206720672 67606.7606760676 68006.800680068 68806.8806880688 69206.92069206921 & +84408.4408440844 84808.4808480848 100010.0010001 100410.0410041004 110011.00110011 & +110411.04110411041 113611.3611361136 114011.401140114 116411.6411641164 116811.6811681168 & 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100010.0010001 100410.0410041004 110011.00110011 & +110411.04110411041 113611.3611361136 114011.401140114 116411.6411641164 116811.6811681168 & +121212.1212121212 121612.1612161216 152415.2415241524 152815.28152815282 156015.601560156 & +156415.6415641564 179217.9217921792 179617.96179617962 198019.801980198 198419.8419841984 & +222022.202220222 222422.2422242224 229222.9222922292 229622.9622962296 264026.402640264 & +264426.44264426443 350835.0835083508 351235.1235123512 829682.9682968296 830083.00830083 & +891689.1689168917 892089.2089208921 927692.7692769277 928092.809280928 959695.9695969597 & +960096.0096009601 1001300.1300130013 1001700.1700170017 1035303.5303530352 1035703.5703570356 & +1044104.410441044 1044504.4504450444 1113311.3311331132 1113711.3711371135 1120912.0912091208 & +1121312.1312131213 1135713.5713571357 1136113.611361136 1157315.7315731572 1157715.7715771575 & +1157715.7715771575 1158115.811581158 1180518.0518051805 1180918.0918091808 1188918.891889189 & 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69206.92069206921 & +84408.4408440844 84808.4808480848 100010.0010001 100410.0410041004 110011.00110011 & +110411.04110411041 113611.3611361136 114011.401140114 116411.6411641164 116811.6811681168 & +121212.1212121212 121612.1612161216 152415.2415241524 152815.28152815282 156015.601560156 & +156415.6415641564 179217.9217921792 179617.96179617962 198019.801980198 198419.8419841984 & +222022.202220222 222422.2422242224 229222.9222922292 229622.9622962296 264026.402640264 & +264426.44264426443 350835.0835083508 351235.1235123512 829682.9682968296 830083.00830083 & +891689.1689168917 892089.2089208921 927692.7692769277 928092.809280928 959695.9695969597 & +960096.0096009601 1001300.1300130013 1001700.1700170017 1035303.5303530352 1035703.5703570356 & +1044104.410441044 1044504.4504450444 1113311.3311331132 1113711.3711371135 1120912.0912091208 & +1121312.1312131213 1135713.5713571357 1136113.611361136 1157315.7315731572 1157715.7715771575 & +1157715.7715771575 1158115.811581158 1180518.0518051805 1180918.0918091808 1188918.891889189 & +1189318.9318931892 1221322.1322132212 1221722.1722172217 1223722.3722372237 1224122.412241224 & +1230923.092309231 1231323.1323132312 1257325.7325732573 1257725.7725772576 1273727.3727372736 & +1274127.412741274 1288928.892889289 1289328.9328932893 1342534.2534253425 1342934.2934293428 & +1373737.3737373736 1374137.4137413742 1387338.7338733873 1387738.7738773876 1409740.9740974098 & +1410141.01410141 1452945.294529453 1453345.3345334532 +SP5 2.750122016e-06 2.750122016e-06 2.815102605e-05 2.815102605e-05 5.6380782783268e-07 & +5.6380782783268e-07 3.2449259132610397e-07 3.2449259132610397e-07 1.259917702e-05 1.259917702e-05 & +2.187359401e-05 2.187359401e-05 3.4071707730441596e-06 3.4071707730441596e-06 7.393297518e-06 & +7.393297518e-06 4.78626644e-05 4.78626644e-05 1.8811311950000003e-06 1.8811311950000003e-06 & +3.0826810534779196e-06 3.0826810534779196e-06 1.6549138430937198e-05 1.6549138430937198e-05 & +1.0140375530441602e-07 1.0140375530441602e-07 2.27144718202944e-06 2.27144718202944e-06 & +5.029648088473999e-07 5.029648088473999e-07 2.8393089775368e-09 2.8393089775368e-09 & +8.680181364926398e-06 8.680181364926398e-06 3.13946531852208e-06 3.13946531852208e-06 & +3.69110645706428e-06 3.69110645706428e-06 1.6670805323842e-06 1.6670805323842e-06 & +8.7207210416732e-06 8.7207210416732e-06 4.2995270743841996e-06 4.2995270743841996e-06 & +4.11294404975368e-06 4.11294404975368e-06 1.4602180968474e-08 1.4602180968474e-08 & +1.5413405267389598e-08 1.5413405267389598e-08 6.4898422539892e-08 6.4898422539892e-08 & +7.057732767095199e-07 7.057732767095199e-07 3.97503256855152e-07 3.97503256855152e-07 & +2.3850204983842e-06 2.3850204983842e-06 4.05615978470952e-09 4.05615978470952e-09 & +2.55537807978312e-07 2.55537807978312e-07 4.948520872315999e-07 4.948520872315999e-07 & +4.05615978470952e-05 4.05615978470952e-05 4.05615978470952e-09 4.05615978470952e-09 & +6.8954783347792e-07 6.8954783347792e-07 4.867393656157999e-07 4.867393656157999e-07 & +8.9235630134004e-08 8.9235630134004e-08 1.8909820764474e-05 1.8909820764474e-05 & +3.147578040137879e-05 3.147578040137879e-05 2.51481925797056e-07 2.51481925797056e-07 & +1.32555170429156e-05 1.32555170429156e-05 1.7238647974283598e-06 1.7238647974283598e-06 & +7.58503574079e-07 7.58503574079e-07 1.5778453808768399e-06 1.5778453808768399e-06 & +2.96099501550736e-07 2.96099501550736e-07 2.55537807978312e-07 2.55537807978312e-07 & +8.2340056073896e-08 8.2340056073896e-08 4.54289915032532e-08 4.54289915032532e-08 & +3.40717077304416e-08 3.40717077304416e-08 diff --git a/src/f4epurity/main.py b/src/f4epurity/main.py index 2fd58b6..9881056 100644 --- a/src/f4epurity/main.py +++ b/src/f4epurity/main.py @@ -1,30 +1,32 @@ -import logging -from jsonargparse import Namespace import csv import datetime import json -import numpy as np +import logging import os -from importlib.resources import files, as_file +from importlib.resources import as_file, files + +import numpy as np import pandas as pd +from jsonargparse import Namespace -from f4epurity.decay_chain_calc import calculate_total_activity from f4epurity.collapse import collapse_flux, extract_xs -from f4epurity.dose import convert_to_dose, write_vtk_file, plot_slice +from f4epurity.decay_chain_calc import calculate_total_activity +from f4epurity.dose import convert_to_dose, plot_slice, write_vtk_file from f4epurity.maintenance import ( dose_within_workstation, get_dose_at_workstation, read_maintenance_locations, ) +from f4epurity.mcnp_source_calc import mcnp_main +from f4epurity.parsing import parse_arguments, parse_isotopes_activities_file from f4epurity.reaction_rate import calculate_reaction_rate from f4epurity.utilities import ( calculate_number_of_atoms, get_isotopes, - sum_vtr_files, - normalise_nuclide_name, get_reactions_from_file, + normalise_nuclide_name, + sum_vtr_files, ) -from f4epurity.parsing import parse_arguments, parse_isotopes_activities_file F4Epurity_TITLE = """ _____ _ _ _____ _ _ @@ -155,13 +157,28 @@ def calculate_dose_for_source( activities = calculate_total_activity( reaction_rates, args.irrad_scenario, args.decay_time, decay_data ) + print("Activities:") + print(activities) + # Collect all activities into a single string with coordinates + coordinates_str = f"Coordinates: x1={x1}, y1={y1}, z1={z1}" + if x2 is not None and y2 is not None and z2 is not None: + coordinates_str += f", x2={x2}, y2={y2}, z2={z2}" + all_activities_str = ( + coordinates_str + + "\n" + + "\n".join( + [f"{nuclide}: {activity}" for nuclide, activity in activities.items()] + ) + ) + if args.mcnp: + mcnp_main(all_activities_str) + # Initialize a list to store the total dose for each element total_dose = None logging.info("Calculating the Dose...") # Determine the Dose for each nuclide for nuclide, nuclide_activity in activities.items(): - # Convert to format in dose conversion spreadsheet nuclide = normalise_nuclide_name(nuclide) @@ -382,6 +399,31 @@ def process_sources(args: Namespace) -> None: max_dose_str = "{:.3e}".format(max_dose) writer.writerow([workstation, max_dose_str]) + if args.mcnp: + check_and_split_lines("mcnp_source.txt", 100) + + +def check_and_split_lines(file_name="mcnp_source.txt", limit=100): + file_path = os.path.join(os.getcwd(), file_name) + with open(file_path, "r") as file: + lines = file.readlines() + + new_lines = [] + for line in lines: + while len(line) > limit: + # Find the last space within the limit + split_pos = line.rfind(" ", 0, limit) + if split_pos == -1: + split_pos = limit + if split_pos == 0: + break + new_lines.append(line[:split_pos] + " &\n") + line = line[split_pos:].lstrip() + new_lines.append(line) + + with open(file_path, "w") as file: + file.writelines(new_lines) + def main(args_list: list[str] | None = None): args = parse_arguments(args_list) diff --git a/src/f4epurity/mcnp_source_calc.py b/src/f4epurity/mcnp_source_calc.py new file mode 100644 index 0000000..946f223 --- /dev/null +++ b/src/f4epurity/mcnp_source_calc.py @@ -0,0 +1,219 @@ +import csv +import os + +import actigamma as ag +import numpy as np + +# Global counter for the number of times mcnp_main is called +mcnp_main_call_count = 1 +current_path = os.getcwd() + + +def convert_to_actigamma_format(data): + # Setup the DB + db = ag.Decay2012Database() + + inv_data = [] + + for point_data in data: + activities = point_data["activities"] + for nuclide, activity_str in activities.items(): + # Strip brackets and other characters, then split by spaces + activity_values = ( + activity_str.replace("array(", "") + .replace(")", "") + .replace("[", "") + .replace("]", "") + .split() + ) + for activity in activity_values: + activity = float(activity) + if activity > 0: + inv_data.append((db.getzai(nuclide.strip()), activity)) + + # Format the data for actigamma + formatted_data = "inv = ag.UnstablesInventory(data=[\n" + formatted_data += ",\n".join( + f' (db.getzai("{nuclide}"), {activity})' for nuclide, activity in inv_data + ) + formatted_data += "\n])" + + return formatted_data + + +def mcnp_main(all_activities_str: str): + global mcnp_main_call_count + + # Parse all_activities_str to extract coordinates and activities for multiple points + points = all_activities_str.strip().split("\n\n") + data = [] + + for point_index, point in enumerate(points): + lines = point.strip().split("\n") + coordinates_line = lines[0] + activity_lines = lines[1:] + + # Extract coordinates + coordinates = {} + for part in coordinates_line.replace("Coordinates: ", "").split(", "): + key, value = part.split("=") + coordinates[key.strip()] = float(value.strip()) + # Extract activities + activities = {} + for line in activity_lines: + nuclide, activity = line.split(": ") + activities[nuclide.strip()] = activity.strip() + + # Store the parsed data with a unique identifier for each point + data.append( + { + "point": mcnp_main_call_count, + "coordinates": coordinates, + "activities": activities, + } + ) + + coordinates_source_path = os.path.join(os.getcwd(), "coordinates.txt") + all_coordinates = f"{coordinates['x1']} {coordinates['y1']} {coordinates['z1']}" + + # Second line (coordinates) + if mcnp_main_call_count == 1: + second_line = "SI1 L " + all_coordinates + " " + with open(coordinates_source_path, "w", encoding="utf-8") as f: + f.write(second_line) + f.truncate() + else: + second_line_update = all_coordinates + " " + with open(coordinates_source_path, "a", encoding="utf-8") as f: + f.write(second_line_update) + f.truncate() + + # Printed parsed data to double check the order is correct + for i, point_data in enumerate(data): + print(f"\nPoint {point_data['point']}:") + print("Coordinates:") + for key, value in point_data["coordinates"].items(): + print(f" {key}: {value}") + print("Activities:") + for nuclide, activity in point_data["activities"].items(): + print(f" {nuclide}: {activity}") + + # Convert to actigamma format + formatted_data = convert_to_actigamma_format(data) + print("\nData formatted for actigamma") + # print(formatted_data) + + # Normally gamma but could be something else - "alpha", "beta" if data exists! + SPECTYPES = ["gamma"] + + # Setup the DB and define an energy grid + db = ag.Decay2012Database() + grid = ag.EnergyGrid(bounds=np.linspace(0.0, 4e6, 10000)) + + # Ensure the mcnp_source.txt file exists + mcnp_source_path = os.path.join(current_path, "mcnp_source.txt") + if not os.path.exists(mcnp_source_path): + with open(mcnp_source_path, "w", encoding="utf-8") as f: + f.write("") + + # Read the existing content of the file + with open(mcnp_source_path, "r", encoding="utf-8") as f: + existing_content = f.read() + + # Check if the initial line is already present + if "SDEF PAR=2 POS=d1 ERG FPOS d2" not in existing_content: + # Otherwise generate the first line + new_content = "SDEF PAR=2 POS=d1 ERG FPOS d2\n \n" + + # Line 3 Find the CSV file to extract the rel strengths of the sources + csv_files = [file for file in os.listdir() if file.endswith(".csv")] + if csv_files: + source_csv_path = csv_files[0] + with open(source_csv_path, "r", encoding="utf-8") as csvfile: + reader = csv.DictReader(csvfile) + m_values = [float(row["m"]) for row in reader if "m" in row] + + if m_values: + total_m = sum(m_values) + relative_strengths = [m / total_m for m in m_values] + new_content += ( + "SP1 " + + " ".join(f"{strength:.2f}" for strength in relative_strengths) + + " $ rel strengths of sources\n" + ) + + # Generate 4th line (DS2 S line) + ds2_s_line = "DS2 S " + " ".join( + str(i) for i in range(3, mcnp_main_call_count + 3) + ) + # Check if the last value is an integer before adding the comment + last_value = ds2_s_line.split()[-1] + if last_value.isdigit(): + next_value = int(last_value) + 1 + while next_value <= mcnp_main_call_count + 3: + ds2_s_line += f" {next_value}" + next_value += 1 + ds2_s_line += f" {next_value} $ energy distributions\n" + else: + ds2_s_line += "\n" + + new_content += ds2_s_line + + # Write the new content followed by the existing content back to the file + with open(mcnp_source_path, "w", encoding="utf-8") as f: + f.write(new_content + existing_content) + + # Generate SIx L and SPx lines for each source point + with open(mcnp_source_path, "a", encoding="utf-8") as f: + for i, point_data in enumerate(data): + activities = point_data["activities"] + inv_data = [] + for nuclide, activity_str in activities.items(): + activity_values = ( + activity_str.replace("array(", "") + .replace(")", "") + .replace("[", "") + .replace("]", "") + .split() + ) + for activity in activity_values: + activity = float(activity) + if activity > 0: + inv_data.append((db.getzai(nuclide.strip()), activity)) + + inv = ag.UnstablesInventory(data=inv_data) + lc = ag.MultiTypeLineAggregator(db, grid) + hist, bin_edges = lc(inv) + X, Y = ag.getplotvalues(bin_edges, hist) + + # Filter out zero count values + X_filtered = [x for x, y in zip(X, Y) if y != 0] + Y_filtered = [y for y in Y if y != 0] + + # Write SIx L line + si_line = ( + f"SI{i + mcnp_main_call_count + 2} L " + + " ".join(map(str, X_filtered)) + + f" \n" + ) + f.write(si_line) + + # Write SPx line + sp_line = ( + f"SP{i + mcnp_main_call_count + 2} " + + " ".join(map(str, Y_filtered)) + + f" \n" + ) + f.write(sp_line) + mcnp_main_call_count += 1 + + with open(coordinates_source_path, "r+", encoding="utf-8") as f: + second_line = f.readline() + second_line_update = f.readline() + combined_line = second_line.strip() + " " + second_line_update.strip() + "\n" + with open(mcnp_source_path, "r", encoding="utf-8") as f: + lines = f.readlines() + lines[1] = combined_line + + with open(mcnp_source_path, "w", encoding="utf-8") as f: + f.writelines(lines) diff --git a/src/f4epurity/parsing.py b/src/f4epurity/parsing.py index 396bd9f..5ccc3cf 100644 --- a/src/f4epurity/parsing.py +++ b/src/f4epurity/parsing.py @@ -1,9 +1,9 @@ +import os from typing import List, Optional -import os import numpy as np -from jsonargparse import ArgumentParser, Namespace, ActionConfigFile import pandas as pd +from jsonargparse import ActionConfigFile, ArgumentParser, Namespace def parse_arguments(args_list: Optional[List[str]] = None) -> Namespace: @@ -138,7 +138,12 @@ def parse_arguments(args_list: Optional[List[str]] = None) -> Namespace: type=str, help="Path to STL files of ITER rooms that can be used for plotting", ) - + # Add the optional "mcnp" argument + parser.add_argument( + "--mcnp", + action="store_true", + help="Optional 'mcnp' argument to generate mcnp_source.txt", + ) # Parse the arguments args = parser.parse_args(args_list) diff --git a/tests/test_psource.py b/tests/test_psource.py index 3a657c6..471a32d 100644 --- a/tests/test_psource.py +++ b/tests/test_psource.py @@ -1,8 +1,83 @@ +import difflib + +import actigamma as ag + +from f4epurity.main import calculate_total_activity # Import the function from main.py +from f4epurity.mcnp_source_calc import ( + mcnp_main, # Import the function from mcnp_source_calc.py +) + +global_counter = 0 + + class TestPointSource: - def test_compute_lines(self): - # TODO create the PointSource, call the method compute lines and verify that the - # computed values are the expected ones - pass + def test_actigamma_import(self): + try: + db = ag.Decay2012Database() + assert db is not None + except ImportError: + assert False, "Failed to import actigamma" + + def test_mcnp_main_with_multiple_points(self): + all_activities_str_1 = ( + "Coordinates: x1=277.391, y1=2086.437, z1=1241.0\n" + "Ta182n: [array([0.])]\n" + "Ta182m: [array([0.])]\n" + "Ta182: [array([4.19711877e-05])]" + ) + all_activities_str_2 = ( + "Coordinates: x1=-1107.091, y1=1720.756, z1=1241.0\n" + "Ta182n: [array([0.])]\n" + "Ta182m: [array([0.])]\n" + "Ta182: [array([0.00010949])]" + ) + all_activities_str_3 = ( + "Coordinates: x1=-953.078, y1=1537.211, z1=1378.307\n" + "Ta182n: [array([0.])]\n" + "Ta182m: [array([0.])]\n" + "Ta182: [array([0.00011723])]" + ) + + all_activities_strings = [ + all_activities_str_1, + all_activities_str_2, + all_activities_str_3, + ] + + for all_activities_str in all_activities_strings: + print(f"Testing with input:\n{all_activities_str}\n") + mcnp_main(all_activities_str) + global_counter += 1 + + def test_mcnp_source_file_content(self): + # Check the content of the generated mcnp_source.txt file + if global_counter == 3: + expected_content = ( + "SDEF PAR=2 POS=d1 ERG FPOS d2\n" + "SI1 L 277.391 2086.437 1241.0 -1107.091 1720.756 1241.0 -953.078 1537.211 1378.307\n" + "SP1 0.17 0.33 0.50 $ rel strengths of sources\n" + "DS2 S 3 4 5 $ energy distributions\n" + "SI3 L 2000.2000200020002 2400.2400240024 8400.8400840084 8800.8800880088 31603.1603160316 32003.200320032003 42404.2404240424 42804.2804280428 57605.760576057604 58005.800580058 59205.9205920592 59605.9605960596 65606.5606560656 66006.600660066 66806.68066806681 67206.7206720672 67606.7606760676 68006.800680068 68806.8806880688 69206.92069206921 84408.4408440844 84808.4808480848 100010.0010001 100410.0410041004 110011.00110011 110411.04110411041 113611.3611361136 114011.401140114 116411.6411641164 116811.6811681168 121212.1212121212 121612.1612161216 152415.2415241524 152815.28152815282 156015.601560156 156415.6415641564 179217.9217921792 179617.96179617962 198019.801980198 198419.8419841984 222022.202220222 222422.2422242224 229222.9222922292 229622.9622962296 264026.402640264 264426.44264426443 350835.0835083508 351235.1235123512 829682.9682968296 830083.00830083 891689.1689168917 892089.2089208921 927692.7692769277 928092.809280928 959695.9695969597 960096.0096009601 1001300.1300130013 1001700.1700170017 1035303.5303530352 1035703.5703570356 1044104.410441044 1044504.4504450444 1113311.3311331132 1113711.3711371135 1120912.0912091208 1121312.1312131213 1135713.5713571357 1136113.611361136 1157315.7315731572 1157715.7715771575 1157715.7715771575 1158115.811581158 1180518.0518051805 1180918.0918091808 1188918.891889189 1189318.9318931892 1221322.1322132212 1221722.1722172217 1223722.3722372237 1224122.412241224 1230923.092309231 1231323.1323132312 1257325.7325732573 1257725.7725772576 1273727.3727372736 1274127.412741274 1288928.892889289 1289328.9328932893 1342534.2534253425 1342934.2934293428 1373737.3737373736 1374137.4137413742 1387338.7338733873 1387738.7738773876 1409740.9740974098 1410141.01410141 1452945.294529453 1453345.3345334532 $ discrete Ei for src 1\n" + "SP3 9.8461048649184e-07 9.8461048649184e-07 1.00787511583395e-05 1.00787511583395e-05 2.0185689813780343e-07 2.0185689813780343e-07 1.1617623012716287e-07 1.1617623012716287e-07 4.5108114268698e-06 4.5108114268698e-06 7.8312779993799e-06 7.8312779993799e-06 1.2198499022553145e-06 1.2198499022553145e-06 2.64698010620082e-06 2.64698010620082e-06 1.7135996514156e-05 1.7135996514156e-05 6.734906634280501e-07 6.734906634280501e-07 1.1036747002879425e-06 1.1036747002879425e-06 5.92499356272412e-06 5.92499356272412e-06 3.630500765475147e-08 3.630500765475147e-08 8.132332681702098e-07 8.132332681702098e-07 1.8007361936900834e-07 1.8007361936900834e-07 1.0165415852127622e-09 1.0165415852127622e-09 3.107715783821275e-06 3.107715783821275e-06 1.1240048465523374e-06 1.1240048465523374e-06 1.3215057743762425e-06 1.3215057743762425e-06 5.968553265863106e-07 5.968553265863106e-07 3.1222299728687655e-06 3.1222299728687655e-06 1.5393351348648905e-06 1.5393351348648905e-06 1.4725338796537562e-06 1.4725338796537562e-06 5.2279354965212835e-09 5.2279354965212835e-09 5.518373501439713e-09 5.518373501439713e-09 2.3235211753439542e-08 2.3235211753439542e-08 2.5268397739844153e-07 2.5268397739844153e-07 1.423158219297867e-07 1.423158219297867e-07 8.538952742986505e-07 8.538952742986505e-07 1.4522037333893615e-09 1.4522037333893615e-09 9.148874266914861e-08 9.148874266914861e-08 1.7716906795985887e-07 1.7716906795985887e-07 1.4522037333893617e-05 1.4522037333893617e-05 1.4522037333893615e-09 1.4522037333893615e-09 2.4687487458014264e-07 2.4687487458014264e-07 1.7426451655070943e-07 1.7426451655070943e-07 3.194852326095759e-08 3.194852326095759e-08 6.770175182795323e-06 6.770175182795323e-06 1.1269093979614865e-05 1.1269093979614865e-05 9.003663832453902e-08 9.003663832453902e-08 4.74579709859899e-06 4.74579709859899e-06 6.171854728507052e-07 6.171854728507052e-07 2.7156270475808723e-07 2.7156270475808723e-07 5.649069746853181e-07 5.649069746853181e-07 1.0601081427503525e-07 1.0601081427503525e-07 9.148874266914861e-08 9.148874266914861e-08 2.9479740243163135e-08 2.9479740243163135e-08 1.626468249940071e-08 1.626468249940071e-08 1.2198499022553147e-08 1.2198499022553147e-08 $ rel freq for src 1\n" + "SI4 L 2000.2000200020002 2400.2400240024 8400.8400840084 8800.8800880088 31603.1603160316 32003.200320032003 42404.2404240424 42804.2804280428 57605.760576057604 58005.800580058 59205.9205920592 59605.9605960596 65606.5606560656 66006.600660066 66806.68066806681 67206.7206720672 67606.7606760676 68006.800680068 68806.8806880688 69206.92069206921 84408.4408440844 84808.4808480848 100010.0010001 100410.0410041004 110011.00110011 110411.04110411041 113611.3611361136 114011.401140114 116411.6411641164 116811.6811681168 121212.1212121212 121612.1612161216 152415.2415241524 152815.28152815282 156015.601560156 156415.6415641564 179217.9217921792 179617.96179617962 198019.801980198 198419.8419841984 222022.202220222 222422.2422242224 229222.9222922292 229622.9622962296 264026.402640264 264426.44264426443 350835.0835083508 351235.1235123512 829682.9682968296 830083.00830083 891689.1689168917 892089.2089208921 927692.7692769277 928092.809280928 959695.9695969597 960096.0096009601 1001300.1300130013 1001700.1700170017 1035303.5303530352 1035703.5703570356 1044104.410441044 1044504.4504450444 1113311.3311331132 1113711.3711371135 1120912.0912091208 1121312.1312131213 1135713.5713571357 1136113.611361136 1157315.7315731572 1157715.7715771575 1157715.7715771575 1158115.811581158 1180518.0518051805 1180918.0918091808 1188918.891889189 1189318.9318931892 1221322.1322132212 1221722.1722172217 1223722.3722372237 1224122.412241224 1230923.092309231 1231323.1323132312 1257325.7325732573 1257725.7725772576 1273727.3727372736 1274127.412741274 1288928.892889289 1289328.9328932893 1342534.2534253425 1342934.2934293428 1373737.3737373736 1374137.4137413742 1387338.7338733873 1387738.7738773876 1409740.9740974098 1410141.01410141 1452945.294529453 1453345.3345334532 $ discrete Ei for src 2\n" + "SP4 2.568547808e-06 2.568547808e-06 2.6292381149999998e-05 2.6292381149999998e-05 5.265829486428399e-07 5.265829486428399e-07 3.0306827453975196e-07 3.0306827453975196e-07 1.176732826e-05 1.176732826e-05 2.042941063e-05 2.042941063e-05 3.1822155415900797e-06 3.1822155415900797e-06 6.905162034e-06 6.905162034e-06 4.4702577199999995e-05 4.4702577199999995e-05 1.7569312850000002e-06 1.7569312850000002e-06 2.87914994920496e-06 2.87914994920496e-06 1.5456497200403596e-05 1.5456497200403596e-05 9.470866815900801e-08 9.470866815900801e-08 2.12147702772672e-06 2.12147702772672e-06 4.697570325062e-07 4.697570325062e-07 2.6518462846583996e-09 2.6518462846583996e-09 8.107080590683199e-06 8.107080590683199e-06 2.93218508679504e-06 2.93218508679504e-06 3.44740464031364e-06 3.44740464031364e-06 1.5570131151646e-06 1.5570131151646e-06 8.144943673571599e-06 8.144943673571599e-06 4.0156548611646e-06 4.0156548611646e-06 3.84139080446584e-06 3.84139080446584e-06 1.3638085765061999e-08 1.3638085765061999e-08 1.4395749746024797e-08 1.4395749746024797e-08 6.061356550279599e-08 6.061356550279599e-08 6.591752628757599e-07 6.591752628757599e-07 3.7125847985217596e-07 3.7125847985217596e-07 2.2275517731645996e-06 2.2275517731645996e-06 3.78835566687576e-09 3.78835566687576e-09 2.38666165619256e-07 2.38666165619256e-07 4.6217994567079995e-07 4.6217994567079995e-07 3.78835566687576e-05 3.78835566687576e-05 3.78835566687576e-09 3.78835566687576e-09 6.4402108920496e-07 6.4402108920496e-07 4.5460285883539995e-07 4.5460285883539995e-07 8.334393195745199e-08 8.334393195745199e-08 1.7661317713061998e-05 1.7661317713061998e-05 2.9397621736304394e-05 2.9397621736304394e-05 2.3487806922732798e-07 2.3487806922732798e-07 1.2380334052962799e-05 1.2380334052962799e-05 1.6100482527546796e-06 1.6100482527546796e-06 7.084240921769999e-07 7.084240921769999e-07 1.4736696302329198e-06 1.4736696302329198e-06 2.7654981169316796e-07 2.7654981169316796e-07 2.38666165619256e-07 2.38666165619256e-07 7.690363166024799e-08 7.690363166024799e-08 4.24295852571116e-08 4.24295852571116e-08 3.18221554159008e-08 3.18221554159008e-08 $ rel freq for src 2\n" + "SI5 L 2000.2000200020002 2400.2400240024 8400.8400840084 8800.8800880088 31603.1603160316 32003.200320032003 42404.2404240424 42804.2804280428 57605.760576057604 58005.800580058 59205.9205920592 59605.9605960596 65606.5606560656 66006.600660066 66806.68066806681 67206.7206720672 67606.7606760676 68006.800680068 68806.8806880688 69206.92069206921 84408.4408440844 84808.4808480848 100010.0010001 100410.0410041004 110011.00110011 110411.04110411041 113611.3611361136 114011.401140114 116411.6411641164 116811.6811681168 121212.1212121212 121612.1612161216 152415.2415241524 152815.28152815282 156015.601560156 156415.6415641564 179217.9217921792 179617.96179617962 198019.801980198 198419.8419841984 222022.202220222 222422.2422242224 229222.9222922292 229622.9622962296 264026.402640264 264426.44264426443 350835.0835083508 351235.1235123512 829682.9682968296 830083.00830083 891689.1689168917 892089.2089208921 927692.7692769277 928092.809280928 959695.9695969597 960096.0096009601 1001300.1300130013 1001700.1700170017 1035303.5303530352 1035703.5703570356 1044104.410441044 1044504.4504450444 1113311.3311331132 1113711.3711371135 1120912.0912091208 1121312.1312131213 1135713.5713571357 1136113.611361136 1157315.7315731572 1157715.7715771575 1157715.7715771575 1158115.811581158 1180518.0518051805 1180918.0918091808 1188918.891889189 1189318.9318931892 1221322.1322132212 1221722.1722172217 1223722.3722372237 1224122.412241224 1230923.092309231 1231323.1323132312 1257325.7325732573 1257725.7725772576 1273727.3727372736 1274127.412741274 1288928.892889289 1289328.9328932893 1342534.2534253425 1342934.2934293428 1373737.3737373736 1374137.4137413742 1387338.7338733873 1387738.7738773876 1409740.9740974098 1410141.01410141 1452945.294529453 1453345.3345334532 $ discrete Ei for src 3\n" + "SP5 2.750122016e-06 2.750122016e-06 2.815102605e-05 2.815102605e-05 5.6380782783268e-07 5.6380782783268e-07 3.2449259132610397e-07 3.2449259132610397e-07 1.259917702e-05 1.259917702e-05 2.187359401e-05 2.187359401e-05 3.4071707730441596e-06 3.4071707730441596e-06 7.393297518e-06 7.393297518e-06 4.78626644e-05 4.78626644e-05 1.8811311950000003e-06 1.8811311950000003e-06 3.0826810534779196e-06 3.0826810534779196e-06 1.6549138430937198e-05 1.6549138430937198e-05 1.0140375530441602e-07 1.0140375530441602e-07 2.27144718202944e-06 2.27144718202944e-06 5.029648088473999e-07 5.029648088473999e-07 2.8393089775368e-09 2.8393089775368e-09 8.680181364926398e-06 8.680181364926398e-06 3.13946531852208e-06 3.13946531852208e-06 3.69110645706428e-06 3.69110645706428e-06 1.6670805323842e-06 1.6670805323842e-06 8.7207210416732e-06 8.7207210416732e-06 4.2995270743841996e-06 4.2995270743841996e-06 4.11294404975368e-06 4.11294404975368e-06 1.4602180968474e-08 1.4602180968474e-08 1.5413405267389598e-08 1.5413405267389598e-08 6.4898422539892e-08 6.4898422539892e-08 7.057732767095199e-07 7.057732767095199e-07 3.97503256855152e-07 3.97503256855152e-07 2.3850204983842e-06 2.3850204983842e-06 4.05615978470952e-09 4.05615978470952e-09 2.55537807978312e-07 2.55537807978312e-07 4.948520872315999e-07 4.948520872315999e-07 4.05615978470952e-05 4.05615978470952e-05 4.05615978470952e-09 4.05615978470952e-09 6.8954783347792e-07 6.8954783347792e-07 4.867393656157999e-07 4.867393656157999e-07 8.9235630134004e-08 8.9235630134004e-08 1.8909820764474e-05 1.8909820764474e-05 3.147578040137879e-05 3.147578040137879e-05 2.51481925797056e-07 2.51481925797056e-07 1.32555170429156e-05 1.32555170429156e-05 1.7238647974283598e-06 1.7238647974283598e-06 7.58503574079e-07 7.58503574079e-07 1.5778453808768399e-06 1.5778453808768399e-06 2.96099501550736e-07 2.96099501550736e-07 2.55537807978312e-07 2.55537807978312e-07 8.2340056073896e-08 8.2340056073896e-08 4.54289915032532e-08 4.54289915032532e-08 3.40717077304416e-08 3.40717077304416e-08 $ rel freq for src 3\n" + ) + + with open("mcnp_source.txt", "r") as file: + actual_content = file.read() + + if actual_content.strip() != expected_content.strip(): + diff = difflib.unified_diff( + expected_content.splitlines(), + actual_content.splitlines(), + fromfile="expected", + tofile="actual", + lineterm="", + ) + diff_output = "\n".join(diff) + assert False, f"The content of mcnp_source.txt does not match the expected content:\n{diff_output}" class TestGlobalSource: From c03186216805b694fca9251a9b18ebb6ad1047d2 Mon Sep 17 00:00:00 2001 From: Davide Laghi Date: Tue, 21 Jan 2025 10:58:59 +0100 Subject: [PATCH 04/13] object oriented pscource implementation --- src/f4epurity/main.py | 19 +--- src/f4epurity/mcnp_source_calc.py | 2 +- src/f4epurity/psource.py | 72 ++++++++++++-- tests/test_psource.py | 154 ++++++++++++++++-------------- 4 files changed, 150 insertions(+), 97 deletions(-) diff --git a/src/f4epurity/main.py b/src/f4epurity/main.py index c70beda..c8cbb33 100644 --- a/src/f4epurity/main.py +++ b/src/f4epurity/main.py @@ -17,9 +17,9 @@ get_dose_at_workstation, read_maintenance_locations, ) -from f4epurity.mcnp_source_calc import mcnp_main +from f4epurity.mcnp_source_calc import dump_mcnp_source from f4epurity.parsing import parse_arguments, parse_isotopes_activities_file -from f4epurity.psource import GlobalSource, PointSource +from f4epurity.psource import GlobalPointSource, PointSource from f4epurity.reaction_rate import calculate_reaction_rate from f4epurity.utilities import ( calculate_number_of_atoms, @@ -160,21 +160,6 @@ def calculate_dose_for_source( activities = calculate_total_activity( reaction_rates, args.irrad_scenario, args.decay_time, decay_data ) - print("Activities:") - print(activities) - # Collect all activities into a single string with coordinates - coordinates_str = f"Coordinates: x1={x1}, y1={y1}, z1={z1}" - if x2 is not None and y2 is not None and z2 is not None: - coordinates_str += f", x2={x2}, y2={y2}, z2={z2}" - all_activities_str = ( - coordinates_str - + "\n" - + "\n".join( - [f"{nuclide}: {activity}" for nuclide, activity in activities.items()] - ) - ) - if args.mcnp: - mcnp_main(all_activities_str) # Initialize a list to store the total dose for each element total_dose = None diff --git a/src/f4epurity/mcnp_source_calc.py b/src/f4epurity/mcnp_source_calc.py index 946f223..c7c98ff 100644 --- a/src/f4epurity/mcnp_source_calc.py +++ b/src/f4epurity/mcnp_source_calc.py @@ -41,7 +41,7 @@ def convert_to_actigamma_format(data): return formatted_data -def mcnp_main(all_activities_str: str): +def dump_mcnp_source(activities: str, x1, y1, z1, run_dir): global mcnp_main_call_count # Parse all_activities_str to extract coordinates and activities for multiple points diff --git a/src/f4epurity/psource.py b/src/f4epurity/psource.py index 8232571..13d8aa6 100644 --- a/src/f4epurity/psource.py +++ b/src/f4epurity/psource.py @@ -9,6 +9,7 @@ DB = ag.Decay2012Database() GRID = ag.EnergyGrid(bounds=ag.linspace(0, 4e6, 10000)) # TODO LC = ag.MultiTypeLineAggregator(DB, GRID) +CHAR_LIMIT = 120 class GlobalPointSource: @@ -16,14 +17,68 @@ def __init__(self, sources: list[PointSource]) -> None: self.sources = sources def to_sdef(self, outfile: str | Path) -> None: - sdef_line = "sdef\n" - si1 = "S11 L " + # compute total mass + t_mass = 0 + for psource in self.sources: + t_mass += psource.mass + + sdef_line = "sdef PAR=2 POS=d1 ERG FPOS d2\n" + si1 = "S1 L " sp1 = "SP1 " - ds2 = "DS2 " + ds2 = "DS2 S " + intial_ds = 3 lines_distributions = [] - for source in self.sources: - si1 = si1 + f" {source.coord[0]} {source.coord[0]} {source.coord[0]}" - sp1 = sp1 + f"{source.mass} 0.0 0.0" + start_SI = "SI{} L " + start_SP = "SP{} " + for idx, psource in enumerate(self.sources): + # Adjounr global distributions + si1 = si1 + f" {psource.coord[0]} {psource.coord[1]} {psource.coord[2]} " + sp1 = sp1 + f"{psource.mass/t_mass:.3f} " + p_source_idx = intial_ds + idx + ds2 = ds2 + f" {p_source_idx} " + + # add specific point source distribution + X, Y = psource._compute_lines() + SI_line = start_SI.format(p_source_idx) + SP_line = start_SP.format(p_source_idx) + SI_line = insert_wrapped_values(SI_line, X, CHAR_LIMIT) + SP_line = insert_wrapped_values(SP_line, Y, CHAR_LIMIT) + lines_distributions.append(SI_line) + lines_distributions.append(SP_line) + + with open(outfile, "w") as f: + f.write(sdef_line) + f.write(si1 + "\n") + f.write(sp1 + "\n") + f.write(ds2 + "\n") + for line in lines_distributions: + f.write(line + "\n") + + +def insert_wrapped_values( + initial_str: str, values: list | np.ndarray, limit: int +) -> str: + """ + Insert values into a string with a limit of characters per line + + Parameters + ---------- + initial_str : str + initial string + values : list | np.ndarray + values to be inserted + limit : int + character limit per line + """ + new_str = initial_str + "\n " + line = 0 + for value in values: + value = f"{value:.2e}" + if len(new_str.split("\n")[-1]) + len(value) > limit: + new_str += "\n " + line += 1 + new_str += f" {value}" + return new_str class SingleSource(ABC): @@ -43,7 +98,10 @@ def _compute_lines(self) -> tuple[list, list]: inventory = self._compute_inventory() hist, bin_edges = LC(inventory) X, Y = ag.getplotvalues(bin_edges, hist) - return X, Y + # Filter out zero count values + X_filtered = [x for x, y in zip(X, Y) if y != 0] + Y_filtered = [y for y in Y if y != 0] + return X_filtered, Y_filtered @abstractmethod def _compute_inventory(self) -> ag.UnstablesInventory: diff --git a/tests/test_psource.py b/tests/test_psource.py index 3d4787a..71bb040 100644 --- a/tests/test_psource.py +++ b/tests/test_psource.py @@ -4,7 +4,7 @@ from f4epurity.main import calculate_total_activity # Import the function from main.py from f4epurity.mcnp_source_calc import ( - mcnp_main, # Import the function from mcnp_source_calc.py + dump_mcnp_source, # Import the function from mcnp_source_calc.py ) global_counter = 0 @@ -13,79 +13,89 @@ import numpy as np import pytest -from f4epurity.psource import GlobalSource, PointSource - - -class TestPointSource: - def test_actigamma_import(self): - try: - db = ag.Decay2012Database() - assert db is not None - except ImportError: - assert False, "Failed to import actigamma" - - def test_mcnp_main_with_multiple_points(self): - all_activities_str_1 = ( - "Coordinates: x1=277.391, y1=2086.437, z1=1241.0\n" - "Ta182n: [array([0.])]\n" - "Ta182m: [array([0.])]\n" - "Ta182: [array([4.19711877e-05])]" - ) - all_activities_str_2 = ( - "Coordinates: x1=-1107.091, y1=1720.756, z1=1241.0\n" - "Ta182n: [array([0.])]\n" - "Ta182m: [array([0.])]\n" - "Ta182: [array([0.00010949])]" - ) - all_activities_str_3 = ( - "Coordinates: x1=-953.078, y1=1537.211, z1=1378.307\n" - "Ta182n: [array([0.])]\n" - "Ta182m: [array([0.])]\n" - "Ta182: [array([0.00011723])]" - ) - - all_activities_strings = [ - all_activities_str_1, - all_activities_str_2, - all_activities_str_3, - ] - - for all_activities_str in all_activities_strings: - print(f"Testing with input:\n{all_activities_str}\n") - mcnp_main(all_activities_str) - global_counter += 1 - - def test_mcnp_source_file_content(self): - # Check the content of the generated mcnp_source.txt file - if global_counter == 3: - expected_content = ( - "SDEF PAR=2 POS=d1 ERG FPOS d2\n" - "SI1 L 277.391 2086.437 1241.0 -1107.091 1720.756 1241.0 -953.078 1537.211 1378.307\n" - "SP1 0.17 0.33 0.50 $ rel strengths of sources\n" - "DS2 S 3 4 5 $ energy distributions\n" - "SI3 L 2000.2000200020002 2400.2400240024 8400.8400840084 8800.8800880088 31603.1603160316 32003.200320032003 42404.2404240424 42804.2804280428 57605.760576057604 58005.800580058 59205.9205920592 59605.9605960596 65606.5606560656 66006.600660066 66806.68066806681 67206.7206720672 67606.7606760676 68006.800680068 68806.8806880688 69206.92069206921 84408.4408440844 84808.4808480848 100010.0010001 100410.0410041004 110011.00110011 110411.04110411041 113611.3611361136 114011.401140114 116411.6411641164 116811.6811681168 121212.1212121212 121612.1612161216 152415.2415241524 152815.28152815282 156015.601560156 156415.6415641564 179217.9217921792 179617.96179617962 198019.801980198 198419.8419841984 222022.202220222 222422.2422242224 229222.9222922292 229622.9622962296 264026.402640264 264426.44264426443 350835.0835083508 351235.1235123512 829682.9682968296 830083.00830083 891689.1689168917 892089.2089208921 927692.7692769277 928092.809280928 959695.9695969597 960096.0096009601 1001300.1300130013 1001700.1700170017 1035303.5303530352 1035703.5703570356 1044104.410441044 1044504.4504450444 1113311.3311331132 1113711.3711371135 1120912.0912091208 1121312.1312131213 1135713.5713571357 1136113.611361136 1157315.7315731572 1157715.7715771575 1157715.7715771575 1158115.811581158 1180518.0518051805 1180918.0918091808 1188918.891889189 1189318.9318931892 1221322.1322132212 1221722.1722172217 1223722.3722372237 1224122.412241224 1230923.092309231 1231323.1323132312 1257325.7325732573 1257725.7725772576 1273727.3727372736 1274127.412741274 1288928.892889289 1289328.9328932893 1342534.2534253425 1342934.2934293428 1373737.3737373736 1374137.4137413742 1387338.7338733873 1387738.7738773876 1409740.9740974098 1410141.01410141 1452945.294529453 1453345.3345334532 $ discrete Ei for src 1\n" - "SP3 9.8461048649184e-07 9.8461048649184e-07 1.00787511583395e-05 1.00787511583395e-05 2.0185689813780343e-07 2.0185689813780343e-07 1.1617623012716287e-07 1.1617623012716287e-07 4.5108114268698e-06 4.5108114268698e-06 7.8312779993799e-06 7.8312779993799e-06 1.2198499022553145e-06 1.2198499022553145e-06 2.64698010620082e-06 2.64698010620082e-06 1.7135996514156e-05 1.7135996514156e-05 6.734906634280501e-07 6.734906634280501e-07 1.1036747002879425e-06 1.1036747002879425e-06 5.92499356272412e-06 5.92499356272412e-06 3.630500765475147e-08 3.630500765475147e-08 8.132332681702098e-07 8.132332681702098e-07 1.8007361936900834e-07 1.8007361936900834e-07 1.0165415852127622e-09 1.0165415852127622e-09 3.107715783821275e-06 3.107715783821275e-06 1.1240048465523374e-06 1.1240048465523374e-06 1.3215057743762425e-06 1.3215057743762425e-06 5.968553265863106e-07 5.968553265863106e-07 3.1222299728687655e-06 3.1222299728687655e-06 1.5393351348648905e-06 1.5393351348648905e-06 1.4725338796537562e-06 1.4725338796537562e-06 5.2279354965212835e-09 5.2279354965212835e-09 5.518373501439713e-09 5.518373501439713e-09 2.3235211753439542e-08 2.3235211753439542e-08 2.5268397739844153e-07 2.5268397739844153e-07 1.423158219297867e-07 1.423158219297867e-07 8.538952742986505e-07 8.538952742986505e-07 1.4522037333893615e-09 1.4522037333893615e-09 9.148874266914861e-08 9.148874266914861e-08 1.7716906795985887e-07 1.7716906795985887e-07 1.4522037333893617e-05 1.4522037333893617e-05 1.4522037333893615e-09 1.4522037333893615e-09 2.4687487458014264e-07 2.4687487458014264e-07 1.7426451655070943e-07 1.7426451655070943e-07 3.194852326095759e-08 3.194852326095759e-08 6.770175182795323e-06 6.770175182795323e-06 1.1269093979614865e-05 1.1269093979614865e-05 9.003663832453902e-08 9.003663832453902e-08 4.74579709859899e-06 4.74579709859899e-06 6.171854728507052e-07 6.171854728507052e-07 2.7156270475808723e-07 2.7156270475808723e-07 5.649069746853181e-07 5.649069746853181e-07 1.0601081427503525e-07 1.0601081427503525e-07 9.148874266914861e-08 9.148874266914861e-08 2.9479740243163135e-08 2.9479740243163135e-08 1.626468249940071e-08 1.626468249940071e-08 1.2198499022553147e-08 1.2198499022553147e-08 $ rel freq for src 1\n" - "SI4 L 2000.2000200020002 2400.2400240024 8400.8400840084 8800.8800880088 31603.1603160316 32003.200320032003 42404.2404240424 42804.2804280428 57605.760576057604 58005.800580058 59205.9205920592 59605.9605960596 65606.5606560656 66006.600660066 66806.68066806681 67206.7206720672 67606.7606760676 68006.800680068 68806.8806880688 69206.92069206921 84408.4408440844 84808.4808480848 100010.0010001 100410.0410041004 110011.00110011 110411.04110411041 113611.3611361136 114011.401140114 116411.6411641164 116811.6811681168 121212.1212121212 121612.1612161216 152415.2415241524 152815.28152815282 156015.601560156 156415.6415641564 179217.9217921792 179617.96179617962 198019.801980198 198419.8419841984 222022.202220222 222422.2422242224 229222.9222922292 229622.9622962296 264026.402640264 264426.44264426443 350835.0835083508 351235.1235123512 829682.9682968296 830083.00830083 891689.1689168917 892089.2089208921 927692.7692769277 928092.809280928 959695.9695969597 960096.0096009601 1001300.1300130013 1001700.1700170017 1035303.5303530352 1035703.5703570356 1044104.410441044 1044504.4504450444 1113311.3311331132 1113711.3711371135 1120912.0912091208 1121312.1312131213 1135713.5713571357 1136113.611361136 1157315.7315731572 1157715.7715771575 1157715.7715771575 1158115.811581158 1180518.0518051805 1180918.0918091808 1188918.891889189 1189318.9318931892 1221322.1322132212 1221722.1722172217 1223722.3722372237 1224122.412241224 1230923.092309231 1231323.1323132312 1257325.7325732573 1257725.7725772576 1273727.3727372736 1274127.412741274 1288928.892889289 1289328.9328932893 1342534.2534253425 1342934.2934293428 1373737.3737373736 1374137.4137413742 1387338.7338733873 1387738.7738773876 1409740.9740974098 1410141.01410141 1452945.294529453 1453345.3345334532 $ discrete Ei for src 2\n" - "SP4 2.568547808e-06 2.568547808e-06 2.6292381149999998e-05 2.6292381149999998e-05 5.265829486428399e-07 5.265829486428399e-07 3.0306827453975196e-07 3.0306827453975196e-07 1.176732826e-05 1.176732826e-05 2.042941063e-05 2.042941063e-05 3.1822155415900797e-06 3.1822155415900797e-06 6.905162034e-06 6.905162034e-06 4.4702577199999995e-05 4.4702577199999995e-05 1.7569312850000002e-06 1.7569312850000002e-06 2.87914994920496e-06 2.87914994920496e-06 1.5456497200403596e-05 1.5456497200403596e-05 9.470866815900801e-08 9.470866815900801e-08 2.12147702772672e-06 2.12147702772672e-06 4.697570325062e-07 4.697570325062e-07 2.6518462846583996e-09 2.6518462846583996e-09 8.107080590683199e-06 8.107080590683199e-06 2.93218508679504e-06 2.93218508679504e-06 3.44740464031364e-06 3.44740464031364e-06 1.5570131151646e-06 1.5570131151646e-06 8.144943673571599e-06 8.144943673571599e-06 4.0156548611646e-06 4.0156548611646e-06 3.84139080446584e-06 3.84139080446584e-06 1.3638085765061999e-08 1.3638085765061999e-08 1.4395749746024797e-08 1.4395749746024797e-08 6.061356550279599e-08 6.061356550279599e-08 6.591752628757599e-07 6.591752628757599e-07 3.7125847985217596e-07 3.7125847985217596e-07 2.2275517731645996e-06 2.2275517731645996e-06 3.78835566687576e-09 3.78835566687576e-09 2.38666165619256e-07 2.38666165619256e-07 4.6217994567079995e-07 4.6217994567079995e-07 3.78835566687576e-05 3.78835566687576e-05 3.78835566687576e-09 3.78835566687576e-09 6.4402108920496e-07 6.4402108920496e-07 4.5460285883539995e-07 4.5460285883539995e-07 8.334393195745199e-08 8.334393195745199e-08 1.7661317713061998e-05 1.7661317713061998e-05 2.9397621736304394e-05 2.9397621736304394e-05 2.3487806922732798e-07 2.3487806922732798e-07 1.2380334052962799e-05 1.2380334052962799e-05 1.6100482527546796e-06 1.6100482527546796e-06 7.084240921769999e-07 7.084240921769999e-07 1.4736696302329198e-06 1.4736696302329198e-06 2.7654981169316796e-07 2.7654981169316796e-07 2.38666165619256e-07 2.38666165619256e-07 7.690363166024799e-08 7.690363166024799e-08 4.24295852571116e-08 4.24295852571116e-08 3.18221554159008e-08 3.18221554159008e-08 $ rel freq for src 2\n" - "SI5 L 2000.2000200020002 2400.2400240024 8400.8400840084 8800.8800880088 31603.1603160316 32003.200320032003 42404.2404240424 42804.2804280428 57605.760576057604 58005.800580058 59205.9205920592 59605.9605960596 65606.5606560656 66006.600660066 66806.68066806681 67206.7206720672 67606.7606760676 68006.800680068 68806.8806880688 69206.92069206921 84408.4408440844 84808.4808480848 100010.0010001 100410.0410041004 110011.00110011 110411.04110411041 113611.3611361136 114011.401140114 116411.6411641164 116811.6811681168 121212.1212121212 121612.1612161216 152415.2415241524 152815.28152815282 156015.601560156 156415.6415641564 179217.9217921792 179617.96179617962 198019.801980198 198419.8419841984 222022.202220222 222422.2422242224 229222.9222922292 229622.9622962296 264026.402640264 264426.44264426443 350835.0835083508 351235.1235123512 829682.9682968296 830083.00830083 891689.1689168917 892089.2089208921 927692.7692769277 928092.809280928 959695.9695969597 960096.0096009601 1001300.1300130013 1001700.1700170017 1035303.5303530352 1035703.5703570356 1044104.410441044 1044504.4504450444 1113311.3311331132 1113711.3711371135 1120912.0912091208 1121312.1312131213 1135713.5713571357 1136113.611361136 1157315.7315731572 1157715.7715771575 1157715.7715771575 1158115.811581158 1180518.0518051805 1180918.0918091808 1188918.891889189 1189318.9318931892 1221322.1322132212 1221722.1722172217 1223722.3722372237 1224122.412241224 1230923.092309231 1231323.1323132312 1257325.7325732573 1257725.7725772576 1273727.3727372736 1274127.412741274 1288928.892889289 1289328.9328932893 1342534.2534253425 1342934.2934293428 1373737.3737373736 1374137.4137413742 1387338.7338733873 1387738.7738773876 1409740.9740974098 1410141.01410141 1452945.294529453 1453345.3345334532 $ discrete Ei for src 3\n" - "SP5 2.750122016e-06 2.750122016e-06 2.815102605e-05 2.815102605e-05 5.6380782783268e-07 5.6380782783268e-07 3.2449259132610397e-07 3.2449259132610397e-07 1.259917702e-05 1.259917702e-05 2.187359401e-05 2.187359401e-05 3.4071707730441596e-06 3.4071707730441596e-06 7.393297518e-06 7.393297518e-06 4.78626644e-05 4.78626644e-05 1.8811311950000003e-06 1.8811311950000003e-06 3.0826810534779196e-06 3.0826810534779196e-06 1.6549138430937198e-05 1.6549138430937198e-05 1.0140375530441602e-07 1.0140375530441602e-07 2.27144718202944e-06 2.27144718202944e-06 5.029648088473999e-07 5.029648088473999e-07 2.8393089775368e-09 2.8393089775368e-09 8.680181364926398e-06 8.680181364926398e-06 3.13946531852208e-06 3.13946531852208e-06 3.69110645706428e-06 3.69110645706428e-06 1.6670805323842e-06 1.6670805323842e-06 8.7207210416732e-06 8.7207210416732e-06 4.2995270743841996e-06 4.2995270743841996e-06 4.11294404975368e-06 4.11294404975368e-06 1.4602180968474e-08 1.4602180968474e-08 1.5413405267389598e-08 1.5413405267389598e-08 6.4898422539892e-08 6.4898422539892e-08 7.057732767095199e-07 7.057732767095199e-07 3.97503256855152e-07 3.97503256855152e-07 2.3850204983842e-06 2.3850204983842e-06 4.05615978470952e-09 4.05615978470952e-09 2.55537807978312e-07 2.55537807978312e-07 4.948520872315999e-07 4.948520872315999e-07 4.05615978470952e-05 4.05615978470952e-05 4.05615978470952e-09 4.05615978470952e-09 6.8954783347792e-07 6.8954783347792e-07 4.867393656157999e-07 4.867393656157999e-07 8.9235630134004e-08 8.9235630134004e-08 1.8909820764474e-05 1.8909820764474e-05 3.147578040137879e-05 3.147578040137879e-05 2.51481925797056e-07 2.51481925797056e-07 1.32555170429156e-05 1.32555170429156e-05 1.7238647974283598e-06 1.7238647974283598e-06 7.58503574079e-07 7.58503574079e-07 1.5778453808768399e-06 1.5778453808768399e-06 2.96099501550736e-07 2.96099501550736e-07 2.55537807978312e-07 2.55537807978312e-07 8.2340056073896e-08 8.2340056073896e-08 4.54289915032532e-08 4.54289915032532e-08 3.40717077304416e-08 3.40717077304416e-08 $ rel freq for src 3\n" - ) - - with open("mcnp_source.txt", "r") as file: - actual_content = file.read() - - if actual_content.strip() != expected_content.strip(): - diff = difflib.unified_diff( - expected_content.splitlines(), - actual_content.splitlines(), - fromfile="expected", - tofile="actual", - lineterm="", - ) - diff_output = "\n".join(diff) - assert False, f"The content of mcnp_source.txt does not match the expected content:\n{diff_output}" +from f4epurity.psource import GlobalPointSource, PointSource, insert_wrapped_values + +# class TestPointSource: +# def test_actigamma_import(self): +# try: +# db = ag.Decay2012Database() +# assert db is not None +# except ImportError: +# assert False, "Failed to import actigamma" + +# def test_mcnp_main_with_multiple_points(self): +# all_activities_str_1 = ( +# "Coordinates: x1=277.391, y1=2086.437, z1=1241.0\n" +# "Ta182n: [array([0.])]\n" +# "Ta182m: [array([0.])]\n" +# "Ta182: [array([4.19711877e-05])]" +# ) +# all_activities_str_2 = ( +# "Coordinates: x1=-1107.091, y1=1720.756, z1=1241.0\n" +# "Ta182n: [array([0.])]\n" +# "Ta182m: [array([0.])]\n" +# "Ta182: [array([0.00010949])]" +# ) +# all_activities_str_3 = ( +# "Coordinates: x1=-953.078, y1=1537.211, z1=1378.307\n" +# "Ta182n: [array([0.])]\n" +# "Ta182m: [array([0.])]\n" +# "Ta182: [array([0.00011723])]" +# ) + +# all_activities_strings = [ +# all_activities_str_1, +# all_activities_str_2, +# all_activities_str_3, +# ] + +# for all_activities_str in all_activities_strings: +# print(f"Testing with input:\n{all_activities_str}\n") +# dump_mcnp_source(all_activities_str) +# global_counter += 1 + +# def test_mcnp_source_file_content(self): +# # Check the content of the generated mcnp_source.txt file +# if global_counter == 3: +# expected_content = ( +# "SDEF PAR=2 POS=d1 ERG FPOS d2\n" +# "SI1 L 277.391 2086.437 1241.0 -1107.091 1720.756 1241.0 -953.078 1537.211 1378.307\n" +# "SP1 0.17 0.33 0.50 $ rel strengths of sources\n" +# "DS2 S 3 4 5 $ energy distributions\n" +# "SI3 L 2000.2000200020002 2400.2400240024 8400.8400840084 8800.8800880088 31603.1603160316 32003.200320032003 42404.2404240424 42804.2804280428 57605.760576057604 58005.800580058 59205.9205920592 59605.9605960596 65606.5606560656 66006.600660066 66806.68066806681 67206.7206720672 67606.7606760676 68006.800680068 68806.8806880688 69206.92069206921 84408.4408440844 84808.4808480848 100010.0010001 100410.0410041004 110011.00110011 110411.04110411041 113611.3611361136 114011.401140114 116411.6411641164 116811.6811681168 121212.1212121212 121612.1612161216 152415.2415241524 152815.28152815282 156015.601560156 156415.6415641564 179217.9217921792 179617.96179617962 198019.801980198 198419.8419841984 222022.202220222 222422.2422242224 229222.9222922292 229622.9622962296 264026.402640264 264426.44264426443 350835.0835083508 351235.1235123512 829682.9682968296 830083.00830083 891689.1689168917 892089.2089208921 927692.7692769277 928092.809280928 959695.9695969597 960096.0096009601 1001300.1300130013 1001700.1700170017 1035303.5303530352 1035703.5703570356 1044104.410441044 1044504.4504450444 1113311.3311331132 1113711.3711371135 1120912.0912091208 1121312.1312131213 1135713.5713571357 1136113.611361136 1157315.7315731572 1157715.7715771575 1157715.7715771575 1158115.811581158 1180518.0518051805 1180918.0918091808 1188918.891889189 1189318.9318931892 1221322.1322132212 1221722.1722172217 1223722.3722372237 1224122.412241224 1230923.092309231 1231323.1323132312 1257325.7325732573 1257725.7725772576 1273727.3727372736 1274127.412741274 1288928.892889289 1289328.9328932893 1342534.2534253425 1342934.2934293428 1373737.3737373736 1374137.4137413742 1387338.7338733873 1387738.7738773876 1409740.9740974098 1410141.01410141 1452945.294529453 1453345.3345334532 $ discrete Ei for src 1\n" +# "SP3 9.8461048649184e-07 9.8461048649184e-07 1.00787511583395e-05 1.00787511583395e-05 2.0185689813780343e-07 2.0185689813780343e-07 1.1617623012716287e-07 1.1617623012716287e-07 4.5108114268698e-06 4.5108114268698e-06 7.8312779993799e-06 7.8312779993799e-06 1.2198499022553145e-06 1.2198499022553145e-06 2.64698010620082e-06 2.64698010620082e-06 1.7135996514156e-05 1.7135996514156e-05 6.734906634280501e-07 6.734906634280501e-07 1.1036747002879425e-06 1.1036747002879425e-06 5.92499356272412e-06 5.92499356272412e-06 3.630500765475147e-08 3.630500765475147e-08 8.132332681702098e-07 8.132332681702098e-07 1.8007361936900834e-07 1.8007361936900834e-07 1.0165415852127622e-09 1.0165415852127622e-09 3.107715783821275e-06 3.107715783821275e-06 1.1240048465523374e-06 1.1240048465523374e-06 1.3215057743762425e-06 1.3215057743762425e-06 5.968553265863106e-07 5.968553265863106e-07 3.1222299728687655e-06 3.1222299728687655e-06 1.5393351348648905e-06 1.5393351348648905e-06 1.4725338796537562e-06 1.4725338796537562e-06 5.2279354965212835e-09 5.2279354965212835e-09 5.518373501439713e-09 5.518373501439713e-09 2.3235211753439542e-08 2.3235211753439542e-08 2.5268397739844153e-07 2.5268397739844153e-07 1.423158219297867e-07 1.423158219297867e-07 8.538952742986505e-07 8.538952742986505e-07 1.4522037333893615e-09 1.4522037333893615e-09 9.148874266914861e-08 9.148874266914861e-08 1.7716906795985887e-07 1.7716906795985887e-07 1.4522037333893617e-05 1.4522037333893617e-05 1.4522037333893615e-09 1.4522037333893615e-09 2.4687487458014264e-07 2.4687487458014264e-07 1.7426451655070943e-07 1.7426451655070943e-07 3.194852326095759e-08 3.194852326095759e-08 6.770175182795323e-06 6.770175182795323e-06 1.1269093979614865e-05 1.1269093979614865e-05 9.003663832453902e-08 9.003663832453902e-08 4.74579709859899e-06 4.74579709859899e-06 6.171854728507052e-07 6.171854728507052e-07 2.7156270475808723e-07 2.7156270475808723e-07 5.649069746853181e-07 5.649069746853181e-07 1.0601081427503525e-07 1.0601081427503525e-07 9.148874266914861e-08 9.148874266914861e-08 2.9479740243163135e-08 2.9479740243163135e-08 1.626468249940071e-08 1.626468249940071e-08 1.2198499022553147e-08 1.2198499022553147e-08 $ rel freq for src 1\n" +# "SI4 L 2000.2000200020002 2400.2400240024 8400.8400840084 8800.8800880088 31603.1603160316 32003.200320032003 42404.2404240424 42804.2804280428 57605.760576057604 58005.800580058 59205.9205920592 59605.9605960596 65606.5606560656 66006.600660066 66806.68066806681 67206.7206720672 67606.7606760676 68006.800680068 68806.8806880688 69206.92069206921 84408.4408440844 84808.4808480848 100010.0010001 100410.0410041004 110011.00110011 110411.04110411041 113611.3611361136 114011.401140114 116411.6411641164 116811.6811681168 121212.1212121212 121612.1612161216 152415.2415241524 152815.28152815282 156015.601560156 156415.6415641564 179217.9217921792 179617.96179617962 198019.801980198 198419.8419841984 222022.202220222 222422.2422242224 229222.9222922292 229622.9622962296 264026.402640264 264426.44264426443 350835.0835083508 351235.1235123512 829682.9682968296 830083.00830083 891689.1689168917 892089.2089208921 927692.7692769277 928092.809280928 959695.9695969597 960096.0096009601 1001300.1300130013 1001700.1700170017 1035303.5303530352 1035703.5703570356 1044104.410441044 1044504.4504450444 1113311.3311331132 1113711.3711371135 1120912.0912091208 1121312.1312131213 1135713.5713571357 1136113.611361136 1157315.7315731572 1157715.7715771575 1157715.7715771575 1158115.811581158 1180518.0518051805 1180918.0918091808 1188918.891889189 1189318.9318931892 1221322.1322132212 1221722.1722172217 1223722.3722372237 1224122.412241224 1230923.092309231 1231323.1323132312 1257325.7325732573 1257725.7725772576 1273727.3727372736 1274127.412741274 1288928.892889289 1289328.9328932893 1342534.2534253425 1342934.2934293428 1373737.3737373736 1374137.4137413742 1387338.7338733873 1387738.7738773876 1409740.9740974098 1410141.01410141 1452945.294529453 1453345.3345334532 $ discrete Ei for src 2\n" +# "SP4 2.568547808e-06 2.568547808e-06 2.6292381149999998e-05 2.6292381149999998e-05 5.265829486428399e-07 5.265829486428399e-07 3.0306827453975196e-07 3.0306827453975196e-07 1.176732826e-05 1.176732826e-05 2.042941063e-05 2.042941063e-05 3.1822155415900797e-06 3.1822155415900797e-06 6.905162034e-06 6.905162034e-06 4.4702577199999995e-05 4.4702577199999995e-05 1.7569312850000002e-06 1.7569312850000002e-06 2.87914994920496e-06 2.87914994920496e-06 1.5456497200403596e-05 1.5456497200403596e-05 9.470866815900801e-08 9.470866815900801e-08 2.12147702772672e-06 2.12147702772672e-06 4.697570325062e-07 4.697570325062e-07 2.6518462846583996e-09 2.6518462846583996e-09 8.107080590683199e-06 8.107080590683199e-06 2.93218508679504e-06 2.93218508679504e-06 3.44740464031364e-06 3.44740464031364e-06 1.5570131151646e-06 1.5570131151646e-06 8.144943673571599e-06 8.144943673571599e-06 4.0156548611646e-06 4.0156548611646e-06 3.84139080446584e-06 3.84139080446584e-06 1.3638085765061999e-08 1.3638085765061999e-08 1.4395749746024797e-08 1.4395749746024797e-08 6.061356550279599e-08 6.061356550279599e-08 6.591752628757599e-07 6.591752628757599e-07 3.7125847985217596e-07 3.7125847985217596e-07 2.2275517731645996e-06 2.2275517731645996e-06 3.78835566687576e-09 3.78835566687576e-09 2.38666165619256e-07 2.38666165619256e-07 4.6217994567079995e-07 4.6217994567079995e-07 3.78835566687576e-05 3.78835566687576e-05 3.78835566687576e-09 3.78835566687576e-09 6.4402108920496e-07 6.4402108920496e-07 4.5460285883539995e-07 4.5460285883539995e-07 8.334393195745199e-08 8.334393195745199e-08 1.7661317713061998e-05 1.7661317713061998e-05 2.9397621736304394e-05 2.9397621736304394e-05 2.3487806922732798e-07 2.3487806922732798e-07 1.2380334052962799e-05 1.2380334052962799e-05 1.6100482527546796e-06 1.6100482527546796e-06 7.084240921769999e-07 7.084240921769999e-07 1.4736696302329198e-06 1.4736696302329198e-06 2.7654981169316796e-07 2.7654981169316796e-07 2.38666165619256e-07 2.38666165619256e-07 7.690363166024799e-08 7.690363166024799e-08 4.24295852571116e-08 4.24295852571116e-08 3.18221554159008e-08 3.18221554159008e-08 $ rel freq for src 2\n" +# "SI5 L 2000.2000200020002 2400.2400240024 8400.8400840084 8800.8800880088 31603.1603160316 32003.200320032003 42404.2404240424 42804.2804280428 57605.760576057604 58005.800580058 59205.9205920592 59605.9605960596 65606.5606560656 66006.600660066 66806.68066806681 67206.7206720672 67606.7606760676 68006.800680068 68806.8806880688 69206.92069206921 84408.4408440844 84808.4808480848 100010.0010001 100410.0410041004 110011.00110011 110411.04110411041 113611.3611361136 114011.401140114 116411.6411641164 116811.6811681168 121212.1212121212 121612.1612161216 152415.2415241524 152815.28152815282 156015.601560156 156415.6415641564 179217.9217921792 179617.96179617962 198019.801980198 198419.8419841984 222022.202220222 222422.2422242224 229222.9222922292 229622.9622962296 264026.402640264 264426.44264426443 350835.0835083508 351235.1235123512 829682.9682968296 830083.00830083 891689.1689168917 892089.2089208921 927692.7692769277 928092.809280928 959695.9695969597 960096.0096009601 1001300.1300130013 1001700.1700170017 1035303.5303530352 1035703.5703570356 1044104.410441044 1044504.4504450444 1113311.3311331132 1113711.3711371135 1120912.0912091208 1121312.1312131213 1135713.5713571357 1136113.611361136 1157315.7315731572 1157715.7715771575 1157715.7715771575 1158115.811581158 1180518.0518051805 1180918.0918091808 1188918.891889189 1189318.9318931892 1221322.1322132212 1221722.1722172217 1223722.3722372237 1224122.412241224 1230923.092309231 1231323.1323132312 1257325.7325732573 1257725.7725772576 1273727.3727372736 1274127.412741274 1288928.892889289 1289328.9328932893 1342534.2534253425 1342934.2934293428 1373737.3737373736 1374137.4137413742 1387338.7338733873 1387738.7738773876 1409740.9740974098 1410141.01410141 1452945.294529453 1453345.3345334532 $ discrete Ei for src 3\n" +# "SP5 2.750122016e-06 2.750122016e-06 2.815102605e-05 2.815102605e-05 5.6380782783268e-07 5.6380782783268e-07 3.2449259132610397e-07 3.2449259132610397e-07 1.259917702e-05 1.259917702e-05 2.187359401e-05 2.187359401e-05 3.4071707730441596e-06 3.4071707730441596e-06 7.393297518e-06 7.393297518e-06 4.78626644e-05 4.78626644e-05 1.8811311950000003e-06 1.8811311950000003e-06 3.0826810534779196e-06 3.0826810534779196e-06 1.6549138430937198e-05 1.6549138430937198e-05 1.0140375530441602e-07 1.0140375530441602e-07 2.27144718202944e-06 2.27144718202944e-06 5.029648088473999e-07 5.029648088473999e-07 2.8393089775368e-09 2.8393089775368e-09 8.680181364926398e-06 8.680181364926398e-06 3.13946531852208e-06 3.13946531852208e-06 3.69110645706428e-06 3.69110645706428e-06 1.6670805323842e-06 1.6670805323842e-06 8.7207210416732e-06 8.7207210416732e-06 4.2995270743841996e-06 4.2995270743841996e-06 4.11294404975368e-06 4.11294404975368e-06 1.4602180968474e-08 1.4602180968474e-08 1.5413405267389598e-08 1.5413405267389598e-08 6.4898422539892e-08 6.4898422539892e-08 7.057732767095199e-07 7.057732767095199e-07 3.97503256855152e-07 3.97503256855152e-07 2.3850204983842e-06 2.3850204983842e-06 4.05615978470952e-09 4.05615978470952e-09 2.55537807978312e-07 2.55537807978312e-07 4.948520872315999e-07 4.948520872315999e-07 4.05615978470952e-05 4.05615978470952e-05 4.05615978470952e-09 4.05615978470952e-09 6.8954783347792e-07 6.8954783347792e-07 4.867393656157999e-07 4.867393656157999e-07 8.9235630134004e-08 8.9235630134004e-08 1.8909820764474e-05 1.8909820764474e-05 3.147578040137879e-05 3.147578040137879e-05 2.51481925797056e-07 2.51481925797056e-07 1.32555170429156e-05 1.32555170429156e-05 1.7238647974283598e-06 1.7238647974283598e-06 7.58503574079e-07 7.58503574079e-07 1.5778453808768399e-06 1.5778453808768399e-06 2.96099501550736e-07 2.96099501550736e-07 2.55537807978312e-07 2.55537807978312e-07 8.2340056073896e-08 8.2340056073896e-08 4.54289915032532e-08 4.54289915032532e-08 3.40717077304416e-08 3.40717077304416e-08 $ rel freq for src 3\n" +# ) + +# with open("mcnp_source.txt", "r") as file: +# actual_content = file.read() + +# if actual_content.strip() != expected_content.strip(): +# diff = difflib.unified_diff( +# expected_content.splitlines(), +# actual_content.splitlines(), +# fromfile="expected", +# tofile="actual", +# lineterm="", +# ) +# diff_output = "\n".join(diff) +# assert False, f"The content of mcnp_source.txt does not match the expected content:\n{diff_output}" class TestGlobalSource: def test_to_sdef(self): pass + + +def test_insert_wrapped_values(): + # Test with a simple example + init_str = "SI L" + values = np.array([100, 200000, 300000000, 40, 0.5, 6000, 7000, 800, 90, 10]) + wrapped_values = insert_wrapped_values(init_str, values, 60) + assert ( + wrapped_values + == "SI L\n 1.00e+02 2.00e+05 3.00e+08 4.00e+01 5.00e-01 6.00e+03\n 7.00e+03 8.00e+02 9.00e+01 1.00e+01" + ) From c6f3ead17c1976f7deb03f534074daaf497881b6 Mon Sep 17 00:00:00 2001 From: digiacomo4 Date: Thu, 13 Feb 2025 12:24:30 +0100 Subject: [PATCH 05/13] implemented dump mcnp point src --- coordinates.txt | 1 - mcnp_source.txt | 138 ----------------------- src/f4epurity/decay_chain_calc.py | 9 +- src/f4epurity/main.py | 33 +++++- src/f4epurity/parsing.py | 4 +- src/f4epurity/psource.py | 59 +++++++++- tests/test_psource.py | 178 ++++++++++++++++-------------- 7 files changed, 188 insertions(+), 234 deletions(-) delete mode 100644 coordinates.txt delete mode 100644 mcnp_source.txt diff --git a/coordinates.txt b/coordinates.txt deleted file mode 100644 index 7a9db5b..0000000 --- a/coordinates.txt +++ /dev/null @@ -1 +0,0 @@ -SI1 L 277.391 2086.437 1241.0 -1107.091 1720.756 1241.0 -953.078 1537.211 1378.307 \ No newline at end of file diff --git a/mcnp_source.txt b/mcnp_source.txt deleted file mode 100644 index e6e6472..0000000 --- a/mcnp_source.txt +++ /dev/null @@ -1,138 +0,0 @@ -SDEF PAR=2 POS=d1 ERG FPOS d2 -SI1 L 277.391 2086.437 1241.0 -1107.091 1720.756 1241.0 -953.078 1537.211 1378.307 -SP1 0.17 0.33 0.50 $ rel strengths of sources -DS2 S 3 4 5 $ energy distributions -SI3 L 2000.2000200020002 2400.2400240024 8400.8400840084 8800.8800880088 31603.1603160316 & -32003.200320032003 42404.2404240424 42804.2804280428 57605.760576057604 58005.800580058 & -59205.9205920592 59605.9605960596 65606.5606560656 66006.600660066 66806.68066806681 & -67206.7206720672 67606.7606760676 68006.800680068 68806.8806880688 69206.92069206921 & -84408.4408440844 84808.4808480848 100010.0010001 100410.0410041004 110011.00110011 & -110411.04110411041 113611.3611361136 114011.401140114 116411.6411641164 116811.6811681168 & -121212.1212121212 121612.1612161216 152415.2415241524 152815.28152815282 156015.601560156 & -156415.6415641564 179217.9217921792 179617.96179617962 198019.801980198 198419.8419841984 & -222022.202220222 222422.2422242224 229222.9222922292 229622.9622962296 264026.402640264 & -264426.44264426443 350835.0835083508 351235.1235123512 829682.9682968296 830083.00830083 & -891689.1689168917 892089.2089208921 927692.7692769277 928092.809280928 959695.9695969597 & -960096.0096009601 1001300.1300130013 1001700.1700170017 1035303.5303530352 1035703.5703570356 & -1044104.410441044 1044504.4504450444 1113311.3311331132 1113711.3711371135 1120912.0912091208 & -1121312.1312131213 1135713.5713571357 1136113.611361136 1157315.7315731572 1157715.7715771575 & -1157715.7715771575 1158115.811581158 1180518.0518051805 1180918.0918091808 1188918.891889189 & -1189318.9318931892 1221322.1322132212 1221722.1722172217 1223722.3722372237 1224122.412241224 & -1230923.092309231 1231323.1323132312 1257325.7325732573 1257725.7725772576 1273727.3727372736 & -1274127.412741274 1288928.892889289 1289328.9328932893 1342534.2534253425 1342934.2934293428 & -1373737.3737373736 1374137.4137413742 1387338.7338733873 1387738.7738773876 1409740.9740974098 & -1410141.01410141 1452945.294529453 1453345.3345334532 -SP3 9.8461048649184e-07 9.8461048649184e-07 1.00787511583395e-05 1.00787511583395e-05 & -2.0185689813780343e-07 2.0185689813780343e-07 1.1617623012716287e-07 1.1617623012716287e-07 & -4.5108114268698e-06 4.5108114268698e-06 7.8312779993799e-06 7.8312779993799e-06 & -1.2198499022553145e-06 1.2198499022553145e-06 2.64698010620082e-06 2.64698010620082e-06 & -1.7135996514156e-05 1.7135996514156e-05 6.734906634280501e-07 6.734906634280501e-07 & -1.1036747002879425e-06 1.1036747002879425e-06 5.92499356272412e-06 5.92499356272412e-06 & -3.630500765475147e-08 3.630500765475147e-08 8.132332681702098e-07 8.132332681702098e-07 & -1.8007361936900834e-07 1.8007361936900834e-07 1.0165415852127622e-09 1.0165415852127622e-09 & -3.107715783821275e-06 3.107715783821275e-06 1.1240048465523374e-06 1.1240048465523374e-06 & -1.3215057743762425e-06 1.3215057743762425e-06 5.968553265863106e-07 5.968553265863106e-07 & -3.1222299728687655e-06 3.1222299728687655e-06 1.5393351348648905e-06 1.5393351348648905e-06 & -1.4725338796537562e-06 1.4725338796537562e-06 5.2279354965212835e-09 5.2279354965212835e-09 & -5.518373501439713e-09 5.518373501439713e-09 2.3235211753439542e-08 2.3235211753439542e-08 & -2.5268397739844153e-07 2.5268397739844153e-07 1.423158219297867e-07 1.423158219297867e-07 & -8.538952742986505e-07 8.538952742986505e-07 1.4522037333893615e-09 1.4522037333893615e-09 & -9.148874266914861e-08 9.148874266914861e-08 1.7716906795985887e-07 1.7716906795985887e-07 & -1.4522037333893617e-05 1.4522037333893617e-05 1.4522037333893615e-09 1.4522037333893615e-09 & -2.4687487458014264e-07 2.4687487458014264e-07 1.7426451655070943e-07 1.7426451655070943e-07 & -3.194852326095759e-08 3.194852326095759e-08 6.770175182795323e-06 6.770175182795323e-06 & -1.1269093979614865e-05 1.1269093979614865e-05 9.003663832453902e-08 9.003663832453902e-08 & -4.74579709859899e-06 4.74579709859899e-06 6.171854728507052e-07 6.171854728507052e-07 & -2.7156270475808723e-07 2.7156270475808723e-07 5.649069746853181e-07 5.649069746853181e-07 & -1.0601081427503525e-07 1.0601081427503525e-07 9.148874266914861e-08 9.148874266914861e-08 & -2.9479740243163135e-08 2.9479740243163135e-08 1.626468249940071e-08 1.626468249940071e-08 & -1.2198499022553147e-08 1.2198499022553147e-08 -SI4 L 2000.2000200020002 2400.2400240024 8400.8400840084 8800.8800880088 31603.1603160316 & -32003.200320032003 42404.2404240424 42804.2804280428 57605.760576057604 58005.800580058 & -59205.9205920592 59605.9605960596 65606.5606560656 66006.600660066 66806.68066806681 & -67206.7206720672 67606.7606760676 68006.800680068 68806.8806880688 69206.92069206921 & -84408.4408440844 84808.4808480848 100010.0010001 100410.0410041004 110011.00110011 & -110411.04110411041 113611.3611361136 114011.401140114 116411.6411641164 116811.6811681168 & -121212.1212121212 121612.1612161216 152415.2415241524 152815.28152815282 156015.601560156 & -156415.6415641564 179217.9217921792 179617.96179617962 198019.801980198 198419.8419841984 & -222022.202220222 222422.2422242224 229222.9222922292 229622.9622962296 264026.402640264 & -264426.44264426443 350835.0835083508 351235.1235123512 829682.9682968296 830083.00830083 & -891689.1689168917 892089.2089208921 927692.7692769277 928092.809280928 959695.9695969597 & -960096.0096009601 1001300.1300130013 1001700.1700170017 1035303.5303530352 1035703.5703570356 & -1044104.410441044 1044504.4504450444 1113311.3311331132 1113711.3711371135 1120912.0912091208 & -1121312.1312131213 1135713.5713571357 1136113.611361136 1157315.7315731572 1157715.7715771575 & -1157715.7715771575 1158115.811581158 1180518.0518051805 1180918.0918091808 1188918.891889189 & -1189318.9318931892 1221322.1322132212 1221722.1722172217 1223722.3722372237 1224122.412241224 & -1230923.092309231 1231323.1323132312 1257325.7325732573 1257725.7725772576 1273727.3727372736 & -1274127.412741274 1288928.892889289 1289328.9328932893 1342534.2534253425 1342934.2934293428 & -1373737.3737373736 1374137.4137413742 1387338.7338733873 1387738.7738773876 1409740.9740974098 & -1410141.01410141 1452945.294529453 1453345.3345334532 -SP4 2.568547808e-06 2.568547808e-06 2.6292381149999998e-05 2.6292381149999998e-05 & -5.265829486428399e-07 5.265829486428399e-07 3.0306827453975196e-07 3.0306827453975196e-07 & -1.176732826e-05 1.176732826e-05 2.042941063e-05 2.042941063e-05 3.1822155415900797e-06 & -3.1822155415900797e-06 6.905162034e-06 6.905162034e-06 4.4702577199999995e-05 & -4.4702577199999995e-05 1.7569312850000002e-06 1.7569312850000002e-06 2.87914994920496e-06 & -2.87914994920496e-06 1.5456497200403596e-05 1.5456497200403596e-05 9.470866815900801e-08 & -9.470866815900801e-08 2.12147702772672e-06 2.12147702772672e-06 4.697570325062e-07 & -4.697570325062e-07 2.6518462846583996e-09 2.6518462846583996e-09 8.107080590683199e-06 & -8.107080590683199e-06 2.93218508679504e-06 2.93218508679504e-06 3.44740464031364e-06 & -3.44740464031364e-06 1.5570131151646e-06 1.5570131151646e-06 8.144943673571599e-06 & -8.144943673571599e-06 4.0156548611646e-06 4.0156548611646e-06 3.84139080446584e-06 & -3.84139080446584e-06 1.3638085765061999e-08 1.3638085765061999e-08 1.4395749746024797e-08 & -1.4395749746024797e-08 6.061356550279599e-08 6.061356550279599e-08 6.591752628757599e-07 & -6.591752628757599e-07 3.7125847985217596e-07 3.7125847985217596e-07 2.2275517731645996e-06 & -2.2275517731645996e-06 3.78835566687576e-09 3.78835566687576e-09 2.38666165619256e-07 & -2.38666165619256e-07 4.6217994567079995e-07 4.6217994567079995e-07 3.78835566687576e-05 & -3.78835566687576e-05 3.78835566687576e-09 3.78835566687576e-09 6.4402108920496e-07 & -6.4402108920496e-07 4.5460285883539995e-07 4.5460285883539995e-07 8.334393195745199e-08 & -8.334393195745199e-08 1.7661317713061998e-05 1.7661317713061998e-05 2.9397621736304394e-05 & -2.9397621736304394e-05 2.3487806922732798e-07 2.3487806922732798e-07 1.2380334052962799e-05 & -1.2380334052962799e-05 1.6100482527546796e-06 1.6100482527546796e-06 7.084240921769999e-07 & -7.084240921769999e-07 1.4736696302329198e-06 1.4736696302329198e-06 2.7654981169316796e-07 & -2.7654981169316796e-07 2.38666165619256e-07 2.38666165619256e-07 7.690363166024799e-08 & -7.690363166024799e-08 4.24295852571116e-08 4.24295852571116e-08 3.18221554159008e-08 & -3.18221554159008e-08 -SI5 L 2000.2000200020002 2400.2400240024 8400.8400840084 8800.8800880088 31603.1603160316 & -32003.200320032003 42404.2404240424 42804.2804280428 57605.760576057604 58005.800580058 & -59205.9205920592 59605.9605960596 65606.5606560656 66006.600660066 66806.68066806681 & -67206.7206720672 67606.7606760676 68006.800680068 68806.8806880688 69206.92069206921 & -84408.4408440844 84808.4808480848 100010.0010001 100410.0410041004 110011.00110011 & -110411.04110411041 113611.3611361136 114011.401140114 116411.6411641164 116811.6811681168 & -121212.1212121212 121612.1612161216 152415.2415241524 152815.28152815282 156015.601560156 & -156415.6415641564 179217.9217921792 179617.96179617962 198019.801980198 198419.8419841984 & -222022.202220222 222422.2422242224 229222.9222922292 229622.9622962296 264026.402640264 & -264426.44264426443 350835.0835083508 351235.1235123512 829682.9682968296 830083.00830083 & -891689.1689168917 892089.2089208921 927692.7692769277 928092.809280928 959695.9695969597 & -960096.0096009601 1001300.1300130013 1001700.1700170017 1035303.5303530352 1035703.5703570356 & -1044104.410441044 1044504.4504450444 1113311.3311331132 1113711.3711371135 1120912.0912091208 & -1121312.1312131213 1135713.5713571357 1136113.611361136 1157315.7315731572 1157715.7715771575 & -1157715.7715771575 1158115.811581158 1180518.0518051805 1180918.0918091808 1188918.891889189 & -1189318.9318931892 1221322.1322132212 1221722.1722172217 1223722.3722372237 1224122.412241224 & -1230923.092309231 1231323.1323132312 1257325.7325732573 1257725.7725772576 1273727.3727372736 & -1274127.412741274 1288928.892889289 1289328.9328932893 1342534.2534253425 1342934.2934293428 & -1373737.3737373736 1374137.4137413742 1387338.7338733873 1387738.7738773876 1409740.9740974098 & -1410141.01410141 1452945.294529453 1453345.3345334532 -SP5 2.750122016e-06 2.750122016e-06 2.815102605e-05 2.815102605e-05 5.6380782783268e-07 & -5.6380782783268e-07 3.2449259132610397e-07 3.2449259132610397e-07 1.259917702e-05 1.259917702e-05 & -2.187359401e-05 2.187359401e-05 3.4071707730441596e-06 3.4071707730441596e-06 7.393297518e-06 & -7.393297518e-06 4.78626644e-05 4.78626644e-05 1.8811311950000003e-06 1.8811311950000003e-06 & -3.0826810534779196e-06 3.0826810534779196e-06 1.6549138430937198e-05 1.6549138430937198e-05 & -1.0140375530441602e-07 1.0140375530441602e-07 2.27144718202944e-06 2.27144718202944e-06 & -5.029648088473999e-07 5.029648088473999e-07 2.8393089775368e-09 2.8393089775368e-09 & -8.680181364926398e-06 8.680181364926398e-06 3.13946531852208e-06 3.13946531852208e-06 & -3.69110645706428e-06 3.69110645706428e-06 1.6670805323842e-06 1.6670805323842e-06 & -8.7207210416732e-06 8.7207210416732e-06 4.2995270743841996e-06 4.2995270743841996e-06 & -4.11294404975368e-06 4.11294404975368e-06 1.4602180968474e-08 1.4602180968474e-08 & -1.5413405267389598e-08 1.5413405267389598e-08 6.4898422539892e-08 6.4898422539892e-08 & -7.057732767095199e-07 7.057732767095199e-07 3.97503256855152e-07 3.97503256855152e-07 & -2.3850204983842e-06 2.3850204983842e-06 4.05615978470952e-09 4.05615978470952e-09 & -2.55537807978312e-07 2.55537807978312e-07 4.948520872315999e-07 4.948520872315999e-07 & -4.05615978470952e-05 4.05615978470952e-05 4.05615978470952e-09 4.05615978470952e-09 & -6.8954783347792e-07 6.8954783347792e-07 4.867393656157999e-07 4.867393656157999e-07 & -8.9235630134004e-08 8.9235630134004e-08 1.8909820764474e-05 1.8909820764474e-05 & -3.147578040137879e-05 3.147578040137879e-05 2.51481925797056e-07 2.51481925797056e-07 & -1.32555170429156e-05 1.32555170429156e-05 1.7238647974283598e-06 1.7238647974283598e-06 & -7.58503574079e-07 7.58503574079e-07 1.5778453808768399e-06 1.5778453808768399e-06 & -2.96099501550736e-07 2.96099501550736e-07 2.55537807978312e-07 2.55537807978312e-07 & -8.2340056073896e-08 8.2340056073896e-08 4.54289915032532e-08 4.54289915032532e-08 & -3.40717077304416e-08 3.40717077304416e-08 diff --git a/src/f4epurity/decay_chain_calc.py b/src/f4epurity/decay_chain_calc.py index 5dd0f09..125d0be 100644 --- a/src/f4epurity/decay_chain_calc.py +++ b/src/f4epurity/decay_chain_calc.py @@ -1,6 +1,6 @@ +import math from copy import deepcopy -import math import numpy as np from f4epurity.utilities import add_user_irrad_scenario, convert_names @@ -105,7 +105,6 @@ def irradiate( decay_data_dic[nuclide]["lambda"][1:], decay_data_dic[nuclide]["Decay_daughter_names"][1:], ): - # Tool cannot handle fission or very long lived >1e19 s half-lives, therefore treat these as stable. if decay_type == "f" or ("BR" in decay_data_dic[nuclide] and lambd < 1e-20): continue @@ -168,14 +167,12 @@ def get_nuclides(scenario: dict, parent: str, decay_data_dic: dict, atoms: int) # Loop over each time step in the irradiation and decay for time, flux in zip(scenario["times"], scenario["fluxes"]): - # initialise a dictionary of nuclides which will occur decay_data_dic[parent]["lambda"] = [flux * x for x in initial_lambda] sum_nuclides = {} # Loop over each of the nuclides that is present at the start of the pulse for nuclide in init_nuclides: - # Set the first lambda in the decay chain to the lambda of this nuclide lambda_temp[0] = decay_data_dic[nuclide]["lambda"][0] @@ -268,7 +265,7 @@ def create_dictionary(decay_data: dict, parent: str, daughters: list) -> dict: def calculate_total_activity( nuclide_dict: dict, irrad_scenario: dict, decay_time: int, decay_data: dict -) -> dict: +) -> dict[str, list[np.ndarray]]: """Function to calculate the total activity of a given nuclide dictionary Parameters @@ -284,7 +281,7 @@ def calculate_total_activity( Returns ------- - dict + dict[str, list[np.ndarray]] dictionary containing the total activity for each nuclide in the nuclide dictionary """ diff --git a/src/f4epurity/main.py b/src/f4epurity/main.py index c8cbb33..0c9689f 100644 --- a/src/f4epurity/main.py +++ b/src/f4epurity/main.py @@ -80,7 +80,12 @@ def calculate_dose_for_source( y2: np.ndarray = None, z2: np.ndarray = None, ) -> tuple[ - np.ndarray, np.ndarray, np.ndarray, np.ndarray, list[float], dict[str, float] + np.ndarray, + np.ndarray, + np.ndarray, + np.ndarray, + list[float], + dict[str, list[np.ndarray]], ]: """Calculate the dose for a given source @@ -160,7 +165,25 @@ def calculate_dose_for_source( activities = calculate_total_activity( reaction_rates, args.irrad_scenario, args.decay_time, decay_data ) - + # print("Activities:") + # print(activities) + # # Collect all activities into a single string with coordinates + # coordinates_str = f"Coordinates: x1={x1}, y1={y1}, z1={z1}" + # if x2 is not None and y2 is not None and z2 is not None: + # coordinates_str += f", x2={x2}, y2={y2}, z2={z2}" + # all_activities_str = ( + # coordinates_str + # + "\n" + # + "\n".join( + # [f"{nuclide}: {activity}" for nuclide, activity in activities.items()] + # ) + # ) + # if args.mcnp: + # mcnp_main(all_activities_str) + # if args.dump_source: + # PointSource(activities, [x1, y1, z1], mass=args.m).to_mcnp( + # "mcnp_source.txt" + # ) # Initialize a list to store the total dose for each element total_dose = None @@ -363,7 +386,7 @@ def process_sources(args: Namespace) -> None: calculate_dose_at_workstations(args, dose, x1, y1, z1, run_dir) if args.dump_source: - global_source = GlobalSource(sources) + global_source = GlobalPointSource(sources) global_source.to_sdef(f"{run_dir}/source.sdef") # TODO # If more than one dose array is present, sum the dose arrays (multiple sources) @@ -394,8 +417,8 @@ def process_sources(args: Namespace) -> None: max_dose_str = "{:.3e}".format(max_dose) writer.writerow([workstation, max_dose_str]) - if args.mcnp: - check_and_split_lines("mcnp_source.txt", 100) + # if args.mcnp: + # check_and_split_lines("mcnp_source.txt", 100) def check_and_split_lines(file_name="mcnp_source.txt", limit=100): diff --git a/src/f4epurity/parsing.py b/src/f4epurity/parsing.py index 5ccc3cf..878f63e 100644 --- a/src/f4epurity/parsing.py +++ b/src/f4epurity/parsing.py @@ -140,9 +140,9 @@ def parse_arguments(args_list: Optional[List[str]] = None) -> Namespace: ) # Add the optional "mcnp" argument parser.add_argument( - "--mcnp", + "--dump_source", action="store_true", - help="Optional 'mcnp' argument to generate mcnp_source.txt", + help="Optional 'dump_source' argument to generate mcnp_source.txt", ) # Parse the arguments args = parser.parse_args(args_list) diff --git a/src/f4epurity/psource.py b/src/f4epurity/psource.py index 13d8aa6..89f6e04 100644 --- a/src/f4epurity/psource.py +++ b/src/f4epurity/psource.py @@ -17,13 +17,26 @@ def __init__(self, sources: list[PointSource]) -> None: self.sources = sources def to_sdef(self, outfile: str | Path) -> None: + """ + Generate an MCNP SDEF (source definition) card for the global point sources and + write it to a file named mcnp_source.txt. + + Parameters + ---------- + outfile : str | Path + The path to the output file where the SDEF card will be written. + + Returns + ------- + None + """ # compute total mass t_mass = 0 for psource in self.sources: t_mass += psource.mass sdef_line = "sdef PAR=2 POS=d1 ERG FPOS d2\n" - si1 = "S1 L " + si1 = "SI1 L " sp1 = "SP1 " ds2 = "DS2 S " intial_ds = 3 @@ -69,6 +82,10 @@ def insert_wrapped_values( values to be inserted limit : int character limit per line + Returns + ------- + str + The resulting string with values indented according to the character limit. """ new_str = initial_str + "\n " line = 0 @@ -82,6 +99,24 @@ def insert_wrapped_values( class SingleSource(ABC): + """ + Generate a single source definition for MCNP simulation. + + Parameters + ---------- + activities : dict + A dictionary where keys are nuclide names and values are their activities. + coord : list[float] + List of coordinates [x1, y1, z1] for point and also [x2, y2, z2] for line source + mass : float + The mass of the source. + + Returns + ------- + str + A string representing the MCNP source definition. + """ + def __init__( self, activities: dict[str, list[np.ndarray]], @@ -109,14 +144,34 @@ def _compute_inventory(self) -> ag.UnstablesInventory: class PointSource(SingleSource): + """ + A class representing a point source + + Parameters + ---------- + SingleSource : _type_ + Compute the inventory of unstable nuclides for the point source. + """ + def _compute_inventory(self) -> ag.UnstablesInventory: data = [] for key, value in self.activities.items(): - act = value[0][0] + act = float(value[0][0]) + if act == 0: + continue data.append((DB.getzai(key), act)) inventory = ag.UnstablesInventory(data) return inventory class LineSource(SingleSource): + """ + A class representing a line source + + Parameters + ---------- + SingleSource : _type_ + Compute the inventory of unstable nuclides for the line source. + """ + pass diff --git a/tests/test_psource.py b/tests/test_psource.py index 71bb040..1adf0a6 100644 --- a/tests/test_psource.py +++ b/tests/test_psource.py @@ -1,93 +1,111 @@ import difflib import actigamma as ag +import matplotlib.pyplot as plt -from f4epurity.main import calculate_total_activity # Import the function from main.py -from f4epurity.mcnp_source_calc import ( - dump_mcnp_source, # Import the function from mcnp_source_calc.py -) - -global_counter = 0 - - +# global_counter = 0 import numpy as np import pytest -from f4epurity.psource import GlobalPointSource, PointSource, insert_wrapped_values - -# class TestPointSource: -# def test_actigamma_import(self): -# try: -# db = ag.Decay2012Database() -# assert db is not None -# except ImportError: -# assert False, "Failed to import actigamma" - -# def test_mcnp_main_with_multiple_points(self): -# all_activities_str_1 = ( -# "Coordinates: x1=277.391, y1=2086.437, z1=1241.0\n" -# "Ta182n: [array([0.])]\n" -# "Ta182m: [array([0.])]\n" -# "Ta182: [array([4.19711877e-05])]" -# ) -# all_activities_str_2 = ( -# "Coordinates: x1=-1107.091, y1=1720.756, z1=1241.0\n" -# "Ta182n: [array([0.])]\n" -# "Ta182m: [array([0.])]\n" -# "Ta182: [array([0.00010949])]" -# ) -# all_activities_str_3 = ( -# "Coordinates: x1=-953.078, y1=1537.211, z1=1378.307\n" -# "Ta182n: [array([0.])]\n" -# "Ta182m: [array([0.])]\n" -# "Ta182: [array([0.00011723])]" -# ) - -# all_activities_strings = [ -# all_activities_str_1, -# all_activities_str_2, -# all_activities_str_3, -# ] - -# for all_activities_str in all_activities_strings: -# print(f"Testing with input:\n{all_activities_str}\n") -# dump_mcnp_source(all_activities_str) -# global_counter += 1 - -# def test_mcnp_source_file_content(self): -# # Check the content of the generated mcnp_source.txt file -# if global_counter == 3: -# expected_content = ( -# "SDEF PAR=2 POS=d1 ERG FPOS d2\n" -# "SI1 L 277.391 2086.437 1241.0 -1107.091 1720.756 1241.0 -953.078 1537.211 1378.307\n" -# "SP1 0.17 0.33 0.50 $ rel strengths of sources\n" -# "DS2 S 3 4 5 $ energy distributions\n" -# "SI3 L 2000.2000200020002 2400.2400240024 8400.8400840084 8800.8800880088 31603.1603160316 32003.200320032003 42404.2404240424 42804.2804280428 57605.760576057604 58005.800580058 59205.9205920592 59605.9605960596 65606.5606560656 66006.600660066 66806.68066806681 67206.7206720672 67606.7606760676 68006.800680068 68806.8806880688 69206.92069206921 84408.4408440844 84808.4808480848 100010.0010001 100410.0410041004 110011.00110011 110411.04110411041 113611.3611361136 114011.401140114 116411.6411641164 116811.6811681168 121212.1212121212 121612.1612161216 152415.2415241524 152815.28152815282 156015.601560156 156415.6415641564 179217.9217921792 179617.96179617962 198019.801980198 198419.8419841984 222022.202220222 222422.2422242224 229222.9222922292 229622.9622962296 264026.402640264 264426.44264426443 350835.0835083508 351235.1235123512 829682.9682968296 830083.00830083 891689.1689168917 892089.2089208921 927692.7692769277 928092.809280928 959695.9695969597 960096.0096009601 1001300.1300130013 1001700.1700170017 1035303.5303530352 1035703.5703570356 1044104.410441044 1044504.4504450444 1113311.3311331132 1113711.3711371135 1120912.0912091208 1121312.1312131213 1135713.5713571357 1136113.611361136 1157315.7315731572 1157715.7715771575 1157715.7715771575 1158115.811581158 1180518.0518051805 1180918.0918091808 1188918.891889189 1189318.9318931892 1221322.1322132212 1221722.1722172217 1223722.3722372237 1224122.412241224 1230923.092309231 1231323.1323132312 1257325.7325732573 1257725.7725772576 1273727.3727372736 1274127.412741274 1288928.892889289 1289328.9328932893 1342534.2534253425 1342934.2934293428 1373737.3737373736 1374137.4137413742 1387338.7338733873 1387738.7738773876 1409740.9740974098 1410141.01410141 1452945.294529453 1453345.3345334532 $ discrete Ei for src 1\n" -# "SP3 9.8461048649184e-07 9.8461048649184e-07 1.00787511583395e-05 1.00787511583395e-05 2.0185689813780343e-07 2.0185689813780343e-07 1.1617623012716287e-07 1.1617623012716287e-07 4.5108114268698e-06 4.5108114268698e-06 7.8312779993799e-06 7.8312779993799e-06 1.2198499022553145e-06 1.2198499022553145e-06 2.64698010620082e-06 2.64698010620082e-06 1.7135996514156e-05 1.7135996514156e-05 6.734906634280501e-07 6.734906634280501e-07 1.1036747002879425e-06 1.1036747002879425e-06 5.92499356272412e-06 5.92499356272412e-06 3.630500765475147e-08 3.630500765475147e-08 8.132332681702098e-07 8.132332681702098e-07 1.8007361936900834e-07 1.8007361936900834e-07 1.0165415852127622e-09 1.0165415852127622e-09 3.107715783821275e-06 3.107715783821275e-06 1.1240048465523374e-06 1.1240048465523374e-06 1.3215057743762425e-06 1.3215057743762425e-06 5.968553265863106e-07 5.968553265863106e-07 3.1222299728687655e-06 3.1222299728687655e-06 1.5393351348648905e-06 1.5393351348648905e-06 1.4725338796537562e-06 1.4725338796537562e-06 5.2279354965212835e-09 5.2279354965212835e-09 5.518373501439713e-09 5.518373501439713e-09 2.3235211753439542e-08 2.3235211753439542e-08 2.5268397739844153e-07 2.5268397739844153e-07 1.423158219297867e-07 1.423158219297867e-07 8.538952742986505e-07 8.538952742986505e-07 1.4522037333893615e-09 1.4522037333893615e-09 9.148874266914861e-08 9.148874266914861e-08 1.7716906795985887e-07 1.7716906795985887e-07 1.4522037333893617e-05 1.4522037333893617e-05 1.4522037333893615e-09 1.4522037333893615e-09 2.4687487458014264e-07 2.4687487458014264e-07 1.7426451655070943e-07 1.7426451655070943e-07 3.194852326095759e-08 3.194852326095759e-08 6.770175182795323e-06 6.770175182795323e-06 1.1269093979614865e-05 1.1269093979614865e-05 9.003663832453902e-08 9.003663832453902e-08 4.74579709859899e-06 4.74579709859899e-06 6.171854728507052e-07 6.171854728507052e-07 2.7156270475808723e-07 2.7156270475808723e-07 5.649069746853181e-07 5.649069746853181e-07 1.0601081427503525e-07 1.0601081427503525e-07 9.148874266914861e-08 9.148874266914861e-08 2.9479740243163135e-08 2.9479740243163135e-08 1.626468249940071e-08 1.626468249940071e-08 1.2198499022553147e-08 1.2198499022553147e-08 $ rel freq for src 1\n" -# "SI4 L 2000.2000200020002 2400.2400240024 8400.8400840084 8800.8800880088 31603.1603160316 32003.200320032003 42404.2404240424 42804.2804280428 57605.760576057604 58005.800580058 59205.9205920592 59605.9605960596 65606.5606560656 66006.600660066 66806.68066806681 67206.7206720672 67606.7606760676 68006.800680068 68806.8806880688 69206.92069206921 84408.4408440844 84808.4808480848 100010.0010001 100410.0410041004 110011.00110011 110411.04110411041 113611.3611361136 114011.401140114 116411.6411641164 116811.6811681168 121212.1212121212 121612.1612161216 152415.2415241524 152815.28152815282 156015.601560156 156415.6415641564 179217.9217921792 179617.96179617962 198019.801980198 198419.8419841984 222022.202220222 222422.2422242224 229222.9222922292 229622.9622962296 264026.402640264 264426.44264426443 350835.0835083508 351235.1235123512 829682.9682968296 830083.00830083 891689.1689168917 892089.2089208921 927692.7692769277 928092.809280928 959695.9695969597 960096.0096009601 1001300.1300130013 1001700.1700170017 1035303.5303530352 1035703.5703570356 1044104.410441044 1044504.4504450444 1113311.3311331132 1113711.3711371135 1120912.0912091208 1121312.1312131213 1135713.5713571357 1136113.611361136 1157315.7315731572 1157715.7715771575 1157715.7715771575 1158115.811581158 1180518.0518051805 1180918.0918091808 1188918.891889189 1189318.9318931892 1221322.1322132212 1221722.1722172217 1223722.3722372237 1224122.412241224 1230923.092309231 1231323.1323132312 1257325.7325732573 1257725.7725772576 1273727.3727372736 1274127.412741274 1288928.892889289 1289328.9328932893 1342534.2534253425 1342934.2934293428 1373737.3737373736 1374137.4137413742 1387338.7338733873 1387738.7738773876 1409740.9740974098 1410141.01410141 1452945.294529453 1453345.3345334532 $ discrete Ei for src 2\n" -# "SP4 2.568547808e-06 2.568547808e-06 2.6292381149999998e-05 2.6292381149999998e-05 5.265829486428399e-07 5.265829486428399e-07 3.0306827453975196e-07 3.0306827453975196e-07 1.176732826e-05 1.176732826e-05 2.042941063e-05 2.042941063e-05 3.1822155415900797e-06 3.1822155415900797e-06 6.905162034e-06 6.905162034e-06 4.4702577199999995e-05 4.4702577199999995e-05 1.7569312850000002e-06 1.7569312850000002e-06 2.87914994920496e-06 2.87914994920496e-06 1.5456497200403596e-05 1.5456497200403596e-05 9.470866815900801e-08 9.470866815900801e-08 2.12147702772672e-06 2.12147702772672e-06 4.697570325062e-07 4.697570325062e-07 2.6518462846583996e-09 2.6518462846583996e-09 8.107080590683199e-06 8.107080590683199e-06 2.93218508679504e-06 2.93218508679504e-06 3.44740464031364e-06 3.44740464031364e-06 1.5570131151646e-06 1.5570131151646e-06 8.144943673571599e-06 8.144943673571599e-06 4.0156548611646e-06 4.0156548611646e-06 3.84139080446584e-06 3.84139080446584e-06 1.3638085765061999e-08 1.3638085765061999e-08 1.4395749746024797e-08 1.4395749746024797e-08 6.061356550279599e-08 6.061356550279599e-08 6.591752628757599e-07 6.591752628757599e-07 3.7125847985217596e-07 3.7125847985217596e-07 2.2275517731645996e-06 2.2275517731645996e-06 3.78835566687576e-09 3.78835566687576e-09 2.38666165619256e-07 2.38666165619256e-07 4.6217994567079995e-07 4.6217994567079995e-07 3.78835566687576e-05 3.78835566687576e-05 3.78835566687576e-09 3.78835566687576e-09 6.4402108920496e-07 6.4402108920496e-07 4.5460285883539995e-07 4.5460285883539995e-07 8.334393195745199e-08 8.334393195745199e-08 1.7661317713061998e-05 1.7661317713061998e-05 2.9397621736304394e-05 2.9397621736304394e-05 2.3487806922732798e-07 2.3487806922732798e-07 1.2380334052962799e-05 1.2380334052962799e-05 1.6100482527546796e-06 1.6100482527546796e-06 7.084240921769999e-07 7.084240921769999e-07 1.4736696302329198e-06 1.4736696302329198e-06 2.7654981169316796e-07 2.7654981169316796e-07 2.38666165619256e-07 2.38666165619256e-07 7.690363166024799e-08 7.690363166024799e-08 4.24295852571116e-08 4.24295852571116e-08 3.18221554159008e-08 3.18221554159008e-08 $ rel freq for src 2\n" -# "SI5 L 2000.2000200020002 2400.2400240024 8400.8400840084 8800.8800880088 31603.1603160316 32003.200320032003 42404.2404240424 42804.2804280428 57605.760576057604 58005.800580058 59205.9205920592 59605.9605960596 65606.5606560656 66006.600660066 66806.68066806681 67206.7206720672 67606.7606760676 68006.800680068 68806.8806880688 69206.92069206921 84408.4408440844 84808.4808480848 100010.0010001 100410.0410041004 110011.00110011 110411.04110411041 113611.3611361136 114011.401140114 116411.6411641164 116811.6811681168 121212.1212121212 121612.1612161216 152415.2415241524 152815.28152815282 156015.601560156 156415.6415641564 179217.9217921792 179617.96179617962 198019.801980198 198419.8419841984 222022.202220222 222422.2422242224 229222.9222922292 229622.9622962296 264026.402640264 264426.44264426443 350835.0835083508 351235.1235123512 829682.9682968296 830083.00830083 891689.1689168917 892089.2089208921 927692.7692769277 928092.809280928 959695.9695969597 960096.0096009601 1001300.1300130013 1001700.1700170017 1035303.5303530352 1035703.5703570356 1044104.410441044 1044504.4504450444 1113311.3311331132 1113711.3711371135 1120912.0912091208 1121312.1312131213 1135713.5713571357 1136113.611361136 1157315.7315731572 1157715.7715771575 1157715.7715771575 1158115.811581158 1180518.0518051805 1180918.0918091808 1188918.891889189 1189318.9318931892 1221322.1322132212 1221722.1722172217 1223722.3722372237 1224122.412241224 1230923.092309231 1231323.1323132312 1257325.7325732573 1257725.7725772576 1273727.3727372736 1274127.412741274 1288928.892889289 1289328.9328932893 1342534.2534253425 1342934.2934293428 1373737.3737373736 1374137.4137413742 1387338.7338733873 1387738.7738773876 1409740.9740974098 1410141.01410141 1452945.294529453 1453345.3345334532 $ discrete Ei for src 3\n" -# "SP5 2.750122016e-06 2.750122016e-06 2.815102605e-05 2.815102605e-05 5.6380782783268e-07 5.6380782783268e-07 3.2449259132610397e-07 3.2449259132610397e-07 1.259917702e-05 1.259917702e-05 2.187359401e-05 2.187359401e-05 3.4071707730441596e-06 3.4071707730441596e-06 7.393297518e-06 7.393297518e-06 4.78626644e-05 4.78626644e-05 1.8811311950000003e-06 1.8811311950000003e-06 3.0826810534779196e-06 3.0826810534779196e-06 1.6549138430937198e-05 1.6549138430937198e-05 1.0140375530441602e-07 1.0140375530441602e-07 2.27144718202944e-06 2.27144718202944e-06 5.029648088473999e-07 5.029648088473999e-07 2.8393089775368e-09 2.8393089775368e-09 8.680181364926398e-06 8.680181364926398e-06 3.13946531852208e-06 3.13946531852208e-06 3.69110645706428e-06 3.69110645706428e-06 1.6670805323842e-06 1.6670805323842e-06 8.7207210416732e-06 8.7207210416732e-06 4.2995270743841996e-06 4.2995270743841996e-06 4.11294404975368e-06 4.11294404975368e-06 1.4602180968474e-08 1.4602180968474e-08 1.5413405267389598e-08 1.5413405267389598e-08 6.4898422539892e-08 6.4898422539892e-08 7.057732767095199e-07 7.057732767095199e-07 3.97503256855152e-07 3.97503256855152e-07 2.3850204983842e-06 2.3850204983842e-06 4.05615978470952e-09 4.05615978470952e-09 2.55537807978312e-07 2.55537807978312e-07 4.948520872315999e-07 4.948520872315999e-07 4.05615978470952e-05 4.05615978470952e-05 4.05615978470952e-09 4.05615978470952e-09 6.8954783347792e-07 6.8954783347792e-07 4.867393656157999e-07 4.867393656157999e-07 8.9235630134004e-08 8.9235630134004e-08 1.8909820764474e-05 1.8909820764474e-05 3.147578040137879e-05 3.147578040137879e-05 2.51481925797056e-07 2.51481925797056e-07 1.32555170429156e-05 1.32555170429156e-05 1.7238647974283598e-06 1.7238647974283598e-06 7.58503574079e-07 7.58503574079e-07 1.5778453808768399e-06 1.5778453808768399e-06 2.96099501550736e-07 2.96099501550736e-07 2.55537807978312e-07 2.55537807978312e-07 8.2340056073896e-08 8.2340056073896e-08 4.54289915032532e-08 4.54289915032532e-08 3.40717077304416e-08 3.40717077304416e-08 $ rel freq for src 3\n" -# ) - -# with open("mcnp_source.txt", "r") as file: -# actual_content = file.read() - -# if actual_content.strip() != expected_content.strip(): -# diff = difflib.unified_diff( -# expected_content.splitlines(), -# actual_content.splitlines(), -# fromfile="expected", -# tofile="actual", -# lineterm="", -# ) -# diff_output = "\n".join(diff) -# assert False, f"The content of mcnp_source.txt does not match the expected content:\n{diff_output}" +# from f4epurity.main import calculate_total_activity # Import the function from main.py +# from f4epurity.mcnp_source_calc import ( +# convert_to_actigamma_format, # Import the function from mcnp_source_calc.py +# ) +from f4epurity.psource import ( + GlobalPointSource, + PointSource, + insert_wrapped_values, +) + +DB = ag.Decay2012Database() +GRID = ag.EnergyGrid(bounds=ag.linspace(0, 4e6, 10000)) # TODO +LC = ag.MultiTypeLineAggregator(DB, GRID) + + +class TestPointSource: + def test_compute_inventory(self): + activities = { + "Ta182": [np.array([0.00010949])], + "Ta182m": [np.array([0.1])], + "Ta182n": [np.array([0.0])], + } + coord = [277.391, 2086.437, 1241.0] + psource = PointSource(activities, coord) + psource._compute_inventory() + + def test_actigamma_import(self): + try: + db = ag.Decay2012Database() + assert db is not None + except ImportError: + assert False, "Failed to import actigamma" + + def test_compute_lines(self): + activities = { + "Co60": [np.array([1])], + } + x1 = -277.391 # , -1107.091, -953.078 + y1 = 2086.437 # , 1720.756, 1537.211 + z1 = 1241.0 # , 1241.0, 1378.307 + coord = [x1, y1, z1] + mass = 1.0 + psource = PointSource(activities, coord, mass) + X_filtered, Y_filtered = psource._compute_lines() + plt.plot(X_filtered, Y_filtered, marker="x") + plt.semilogx() + plt.savefig(r"D:\DATA\digiama\Desktop\test\line.png") + + y = np.array(Y_filtered) + x = np.array(X_filtered) + newy = y[y > 1e-3] + newx = x[y > 1e-3] + xmin = newx.min() + xmax = newx.max() + + assert pytest.approx(xmin, 1e-2) == 1.1732e6 + assert pytest.approx(xmax, 1e-2) == 1.3325e6 + assert pytest.approx(newy.max(), 1e-2) == newy.min() class TestGlobalSource: - def test_to_sdef(self): - pass + def test_to_sdef(self, tmpdir): + points = [ + { + "activities": {"Co60m": [np.array([0.00456255])]}, + "coord": [0, 0, 0], + "mass": 1.0, + }, + { + "activities": {"Co60": [np.array([0.00053245])]}, + "coord": [0, 0, 0], + "mass": 1.0, + }, + # { + # "activities": {"Ta182": [np.array([0.00010949])]}, + # "coord": [-1107.091, 1720.756, 1241.0], + # "mass": 2.0, + # }, + # { + # "activities": {"Ta182": [np.array([0.00011723])]}, + # "coord": [-953.078, 1537.211, 1378.307], + # "mass": 3.0, + # }, + ] + + # Create PointSource instances for each point + point_sources = [ + PointSource(point["activities"], point["coord"], point["mass"]) + for point in points + ] + + global_source = GlobalPointSource([point_sources[0], point_sources[1]]) + global_source.to_sdef(tmpdir.join("mcnp_source.txt")) + + if not tmpdir.join("mcnp_source.txt").exists(): + assert False, "Output file not created" + else: + with open(tmpdir.join("mcnp_source.txt")) as f: + lines = f.readlines() def test_insert_wrapped_values(): From 4565b4920d44521ae11aefe64c7d949994dac41e Mon Sep 17 00:00:00 2001 From: digiacomo4 Date: Thu, 13 Mar 2025 12:51:20 +0100 Subject: [PATCH 06/13] added dump_source argument to create sdef card for MCNP --- src/f4epurity/decay_chain_calc.py | 4 + src/f4epurity/dose.py | 30 ++- src/f4epurity/main.py | 32 +-- src/f4epurity/mcnp_source_calc.py | 219 ------------------ src/f4epurity/psource.py | 156 ++++++++++--- src/f4epurity/resources/~$F4E_dosematrix.xlsx | Bin 0 -> 165 bytes tests/test_psource.py | 99 ++++---- 7 files changed, 219 insertions(+), 321 deletions(-) delete mode 100644 src/f4epurity/mcnp_source_calc.py create mode 100644 src/f4epurity/resources/~$F4E_dosematrix.xlsx diff --git a/src/f4epurity/decay_chain_calc.py b/src/f4epurity/decay_chain_calc.py index 125d0be..e2819c7 100644 --- a/src/f4epurity/decay_chain_calc.py +++ b/src/f4epurity/decay_chain_calc.py @@ -42,6 +42,10 @@ + [3920, 400] * 3, "fluxes": [0.00536, 0.0412, 0, 0.083] + [0, 1] * 17 + [0, 1.4] * 3, }, + "Y1": { + "times": [365 * DAY_TO_SEC], + "fluxes": [1], + }, } diff --git a/src/f4epurity/dose.py b/src/f4epurity/dose.py index 3d7a4a3..8ee70e1 100644 --- a/src/f4epurity/dose.py +++ b/src/f4epurity/dose.py @@ -1,10 +1,10 @@ +import logging import os import sys +from importlib.resources import as_file, files from math import pi from pathlib import Path -from importlib.resources import files, as_file -import logging import matplotlib.pyplot as plt import numpy as np import pyevtk @@ -102,7 +102,6 @@ def dose_from_line_source(dose, x1, y1, z1, x2, y2, z2, x, y, z): def is_within_bounds( x1, y1, z1, flux_grid_file, stl_files_path=None, x2=None, y2=None, z2=None ): - # If a path to the STL files is provided, use it if stl_files_path is not None: folder_path = Path(stl_files_path) @@ -178,7 +177,6 @@ def write_vtk_file( z2=None, output_all_vtr=False, ): - # Get the bounds in which to make the plot plot_bounds = is_within_bounds( x1, y1, z1, flux_grid_file, stl_files_path, x2, y2, z2 @@ -227,7 +225,7 @@ def write_vtk_file( ) dose_array[i, j, k] = dose_point - # Create a 3D numpy array filled with the dose values based on 1/r^2 + # Create a 3D numpy array filled with the dose values based on 1/r^2 ( original bottom-left point source model) else: for i in range(len(x) - 1): for j in range(len(y) - 1): @@ -235,12 +233,22 @@ def write_vtk_file( distance = np.sqrt( (x[i] - x1) ** 2 + (y[j] - y1) ** 2 + (z[k] - z1) ** 2 ) + ## get midpoints + # x_mid = (x[1:] + x[:-1]) / 2 + # y_mid = (y[1:] + y[:-1]) / 2 + # z_mid = (z[1:] + z[:-1]) / 2 + # for i in range(len(x_mid)): + # for j in range(len(y_mid)): + # for k in range(len(z_mid)): + # distance = np.sqrt( + # (x_mid[i] - x1) ** 2 + (y_mid[j] - y1) ** 2 + (z_mid[k] - z1) ** 2 + # ) # Avoid division by zero if distance == 0: dose_array[i, j, k] = dose[0] + print("Dose at source point is:", dose[0]) else: dose_array[i, j, k] = dose[0] / (4 * pi * distance**2) - # Get the indices of the max value indices = np.unravel_index(np.argmax(dose_array, axis=None), dose_array.shape) @@ -248,7 +256,9 @@ def write_vtk_file( x_max = x[indices[0]] y_max = y[indices[1]] z_max = z[indices[2]] - + # print( + # f"Max dose value: {dose_array[indices]} at coordinates: ({x_max}, {y_max}, {z_max})" + # ) # Create the output directory if it doesn't exist os.makedirs("output", exist_ok=True) @@ -265,7 +275,6 @@ def write_vtk_file( def plot_slice(dose_array, x, y, z, slice_axis, slice_location, plot_bounds): - # Map axis names to indices axis_dict = {"x": 0, "y": 1, "z": 2} @@ -352,8 +361,9 @@ def format_func(value, tick_number): # Loop over each solid in the stl file and plot the slice for solid in stl.solids: slice = solid.slice(plane=slice_axis, intercept=slice_location) - pairs = getattr(slice, axis_pairs[slice_axis][0] + "_pairs"), getattr( - slice, axis_pairs[slice_axis][1] + "_pairs" + pairs = ( + getattr(slice, axis_pairs[slice_axis][0] + "_pairs"), + getattr(slice, axis_pairs[slice_axis][1] + "_pairs"), ) for pair in zip(*pairs): ax.plot(*pair, color="black", linewidth=1) diff --git a/src/f4epurity/main.py b/src/f4epurity/main.py index 0c9689f..f008272 100644 --- a/src/f4epurity/main.py +++ b/src/f4epurity/main.py @@ -17,7 +17,6 @@ get_dose_at_workstation, read_maintenance_locations, ) -from f4epurity.mcnp_source_calc import dump_mcnp_source from f4epurity.parsing import parse_arguments, parse_isotopes_activities_file from f4epurity.psource import GlobalPointSource, PointSource from f4epurity.reaction_rate import calculate_reaction_rate @@ -151,7 +150,6 @@ def calculate_dose_for_source( sigma_eff, flux_spectrum = collapse_flux( xs_values, args.input_flux, x1, y1, z1, x2, y2, z2 ) - # Calculate the reaction rate based on the flux and effective cross section reaction_rate = calculate_reaction_rate( args.delta_impurity, sigma_eff, flux_spectrum @@ -165,25 +163,6 @@ def calculate_dose_for_source( activities = calculate_total_activity( reaction_rates, args.irrad_scenario, args.decay_time, decay_data ) - # print("Activities:") - # print(activities) - # # Collect all activities into a single string with coordinates - # coordinates_str = f"Coordinates: x1={x1}, y1={y1}, z1={z1}" - # if x2 is not None and y2 is not None and z2 is not None: - # coordinates_str += f", x2={x2}, y2={y2}, z2={z2}" - # all_activities_str = ( - # coordinates_str - # + "\n" - # + "\n".join( - # [f"{nuclide}: {activity}" for nuclide, activity in activities.items()] - # ) - # ) - # if args.mcnp: - # mcnp_main(all_activities_str) - # if args.dump_source: - # PointSource(activities, [x1, y1, z1], mass=args.m).to_mcnp( - # "mcnp_source.txt" - # ) # Initialize a list to store the total dose for each element total_dose = None @@ -368,7 +347,7 @@ def process_sources(args: Namespace) -> None: logging.info("Point source(s) selected") # Handle multiple coordinates being provided - for x1, y1, z1 in zip(args.x1, args.y1, args.z1): + for i, (x1, y1, z1) in enumerate(zip(args.x1, args.y1, args.z1)): dose_array, x, y, z, dose, activities = calculate_dose_for_source( args, x1, @@ -382,7 +361,11 @@ def process_sources(args: Namespace) -> None: ) dose_arrays.append(dose_array) if args.dump_source: - sources.append(PointSource(activities, [x1, y1, z1], mass=args.m)) + if args.m: + mass = args.m[i] + else: + mass = 1 + sources.append(PointSource(activities, [x1, y1, z1], mass=mass)) calculate_dose_at_workstations(args, dose, x1, y1, z1, run_dir) if args.dump_source: @@ -417,9 +400,6 @@ def process_sources(args: Namespace) -> None: max_dose_str = "{:.3e}".format(max_dose) writer.writerow([workstation, max_dose_str]) - # if args.mcnp: - # check_and_split_lines("mcnp_source.txt", 100) - def check_and_split_lines(file_name="mcnp_source.txt", limit=100): file_path = os.path.join(os.getcwd(), file_name) diff --git a/src/f4epurity/mcnp_source_calc.py b/src/f4epurity/mcnp_source_calc.py deleted file mode 100644 index c7c98ff..0000000 --- a/src/f4epurity/mcnp_source_calc.py +++ /dev/null @@ -1,219 +0,0 @@ -import csv -import os - -import actigamma as ag -import numpy as np - -# Global counter for the number of times mcnp_main is called -mcnp_main_call_count = 1 -current_path = os.getcwd() - - -def convert_to_actigamma_format(data): - # Setup the DB - db = ag.Decay2012Database() - - inv_data = [] - - for point_data in data: - activities = point_data["activities"] - for nuclide, activity_str in activities.items(): - # Strip brackets and other characters, then split by spaces - activity_values = ( - activity_str.replace("array(", "") - .replace(")", "") - .replace("[", "") - .replace("]", "") - .split() - ) - for activity in activity_values: - activity = float(activity) - if activity > 0: - inv_data.append((db.getzai(nuclide.strip()), activity)) - - # Format the data for actigamma - formatted_data = "inv = ag.UnstablesInventory(data=[\n" - formatted_data += ",\n".join( - f' (db.getzai("{nuclide}"), {activity})' for nuclide, activity in inv_data - ) - formatted_data += "\n])" - - return formatted_data - - -def dump_mcnp_source(activities: str, x1, y1, z1, run_dir): - global mcnp_main_call_count - - # Parse all_activities_str to extract coordinates and activities for multiple points - points = all_activities_str.strip().split("\n\n") - data = [] - - for point_index, point in enumerate(points): - lines = point.strip().split("\n") - coordinates_line = lines[0] - activity_lines = lines[1:] - - # Extract coordinates - coordinates = {} - for part in coordinates_line.replace("Coordinates: ", "").split(", "): - key, value = part.split("=") - coordinates[key.strip()] = float(value.strip()) - # Extract activities - activities = {} - for line in activity_lines: - nuclide, activity = line.split(": ") - activities[nuclide.strip()] = activity.strip() - - # Store the parsed data with a unique identifier for each point - data.append( - { - "point": mcnp_main_call_count, - "coordinates": coordinates, - "activities": activities, - } - ) - - coordinates_source_path = os.path.join(os.getcwd(), "coordinates.txt") - all_coordinates = f"{coordinates['x1']} {coordinates['y1']} {coordinates['z1']}" - - # Second line (coordinates) - if mcnp_main_call_count == 1: - second_line = "SI1 L " + all_coordinates + " " - with open(coordinates_source_path, "w", encoding="utf-8") as f: - f.write(second_line) - f.truncate() - else: - second_line_update = all_coordinates + " " - with open(coordinates_source_path, "a", encoding="utf-8") as f: - f.write(second_line_update) - f.truncate() - - # Printed parsed data to double check the order is correct - for i, point_data in enumerate(data): - print(f"\nPoint {point_data['point']}:") - print("Coordinates:") - for key, value in point_data["coordinates"].items(): - print(f" {key}: {value}") - print("Activities:") - for nuclide, activity in point_data["activities"].items(): - print(f" {nuclide}: {activity}") - - # Convert to actigamma format - formatted_data = convert_to_actigamma_format(data) - print("\nData formatted for actigamma") - # print(formatted_data) - - # Normally gamma but could be something else - "alpha", "beta" if data exists! - SPECTYPES = ["gamma"] - - # Setup the DB and define an energy grid - db = ag.Decay2012Database() - grid = ag.EnergyGrid(bounds=np.linspace(0.0, 4e6, 10000)) - - # Ensure the mcnp_source.txt file exists - mcnp_source_path = os.path.join(current_path, "mcnp_source.txt") - if not os.path.exists(mcnp_source_path): - with open(mcnp_source_path, "w", encoding="utf-8") as f: - f.write("") - - # Read the existing content of the file - with open(mcnp_source_path, "r", encoding="utf-8") as f: - existing_content = f.read() - - # Check if the initial line is already present - if "SDEF PAR=2 POS=d1 ERG FPOS d2" not in existing_content: - # Otherwise generate the first line - new_content = "SDEF PAR=2 POS=d1 ERG FPOS d2\n \n" - - # Line 3 Find the CSV file to extract the rel strengths of the sources - csv_files = [file for file in os.listdir() if file.endswith(".csv")] - if csv_files: - source_csv_path = csv_files[0] - with open(source_csv_path, "r", encoding="utf-8") as csvfile: - reader = csv.DictReader(csvfile) - m_values = [float(row["m"]) for row in reader if "m" in row] - - if m_values: - total_m = sum(m_values) - relative_strengths = [m / total_m for m in m_values] - new_content += ( - "SP1 " - + " ".join(f"{strength:.2f}" for strength in relative_strengths) - + " $ rel strengths of sources\n" - ) - - # Generate 4th line (DS2 S line) - ds2_s_line = "DS2 S " + " ".join( - str(i) for i in range(3, mcnp_main_call_count + 3) - ) - # Check if the last value is an integer before adding the comment - last_value = ds2_s_line.split()[-1] - if last_value.isdigit(): - next_value = int(last_value) + 1 - while next_value <= mcnp_main_call_count + 3: - ds2_s_line += f" {next_value}" - next_value += 1 - ds2_s_line += f" {next_value} $ energy distributions\n" - else: - ds2_s_line += "\n" - - new_content += ds2_s_line - - # Write the new content followed by the existing content back to the file - with open(mcnp_source_path, "w", encoding="utf-8") as f: - f.write(new_content + existing_content) - - # Generate SIx L and SPx lines for each source point - with open(mcnp_source_path, "a", encoding="utf-8") as f: - for i, point_data in enumerate(data): - activities = point_data["activities"] - inv_data = [] - for nuclide, activity_str in activities.items(): - activity_values = ( - activity_str.replace("array(", "") - .replace(")", "") - .replace("[", "") - .replace("]", "") - .split() - ) - for activity in activity_values: - activity = float(activity) - if activity > 0: - inv_data.append((db.getzai(nuclide.strip()), activity)) - - inv = ag.UnstablesInventory(data=inv_data) - lc = ag.MultiTypeLineAggregator(db, grid) - hist, bin_edges = lc(inv) - X, Y = ag.getplotvalues(bin_edges, hist) - - # Filter out zero count values - X_filtered = [x for x, y in zip(X, Y) if y != 0] - Y_filtered = [y for y in Y if y != 0] - - # Write SIx L line - si_line = ( - f"SI{i + mcnp_main_call_count + 2} L " - + " ".join(map(str, X_filtered)) - + f" \n" - ) - f.write(si_line) - - # Write SPx line - sp_line = ( - f"SP{i + mcnp_main_call_count + 2} " - + " ".join(map(str, Y_filtered)) - + f" \n" - ) - f.write(sp_line) - mcnp_main_call_count += 1 - - with open(coordinates_source_path, "r+", encoding="utf-8") as f: - second_line = f.readline() - second_line_update = f.readline() - combined_line = second_line.strip() + " " + second_line_update.strip() + "\n" - with open(mcnp_source_path, "r", encoding="utf-8") as f: - lines = f.readlines() - lines[1] = combined_line - - with open(mcnp_source_path, "w", encoding="utf-8") as f: - f.writelines(lines) diff --git a/src/f4epurity/psource.py b/src/f4epurity/psource.py index 89f6e04..594fd9d 100644 --- a/src/f4epurity/psource.py +++ b/src/f4epurity/psource.py @@ -6,9 +6,8 @@ import actigamma as ag import numpy as np -DB = ag.Decay2012Database() -GRID = ag.EnergyGrid(bounds=ag.linspace(0, 4e6, 10000)) # TODO -LC = ag.MultiTypeLineAggregator(DB, GRID) +GRID = ag.EnergyGrid(bounds=ag.linspace(0, 20e6, 100000)) # TODO + CHAR_LIMIT = 120 @@ -30,10 +29,6 @@ def to_sdef(self, outfile: str | Path) -> None: ------- None """ - # compute total mass - t_mass = 0 - for psource in self.sources: - t_mass += psource.mass sdef_line = "sdef PAR=2 POS=d1 ERG FPOS d2\n" si1 = "SI1 L " @@ -43,15 +38,28 @@ def to_sdef(self, outfile: str | Path) -> None: lines_distributions = [] start_SI = "SI{} L " start_SP = "SP{} " + t_gamma = 0 + for idx, psource in enumerate(self.sources): + X, Y, p_activity = psource._compute_lines() + t_gamma += np.array(Y).sum() + fmesh_line = "FMESH4:P GEOM=XYZ $ Modify the fmesh as you wish\n ORIGIN 0 0 0\n IMESH 1 IINTS 1\n JMESH 1 JINTS 1\n KMESH 1 KINTS 1\n" + gamma_per_second_line = ( + f"FM4 {t_gamma:.2E} $ To be multiplied by 3.6E-09 to obtain Sv/hr\n" + ) + conv_coeff_line = "DE4\n 0.01 0.015 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.10 0.15 0.20 0.3 0.4 0.5 0.6 0.8 1.0 2.0 4.0 6.0 8.0 10.0\nDF4\n 0.0485 0.1254 0.2050 0.2999 0.3381 0.3572 0.3780 0.4066 0.4399 0.5172 0.7523 1.0041 1.5083 1.9958\n 2.4657 2.9082 3.7269 4.4834 7.4896 12.0153 15.9873 19.9191 23.7600" + for idx, psource in enumerate(self.sources): + X, Y, p_activity = psource._compute_lines() # Adjounr global distributions - si1 = si1 + f" {psource.coord[0]} {psource.coord[1]} {psource.coord[2]} " - sp1 = sp1 + f"{psource.mass/t_mass:.3f} " + si1 = insert_wrapped_values_2(si1, psource.coord, CHAR_LIMIT) + p_gamma = np.array(Y).sum() + r_gamma = p_gamma / t_gamma + sp1 = insert_wrap_values_3(sp1, r_gamma, CHAR_LIMIT) p_source_idx = intial_ds + idx - ds2 = ds2 + f" {p_source_idx} " + ds2 = insert_wrap_values_3(ds2, p_source_idx, CHAR_LIMIT) # add specific point source distribution - X, Y = psource._compute_lines() + SI_line = start_SI.format(p_source_idx) SP_line = start_SP.format(p_source_idx) SI_line = insert_wrapped_values(SI_line, X, CHAR_LIMIT) @@ -66,6 +74,68 @@ def to_sdef(self, outfile: str | Path) -> None: f.write(ds2 + "\n") for line in lines_distributions: f.write(line + "\n") + f.write(fmesh_line) + f.write(gamma_per_second_line) + f.write(conv_coeff_line) + + +def insert_wrap_values_3( + initial_str: str, values: float | np.ndarray, limit: int +) -> str: + """ + Insert values into a string with a limit of characters per line (when not iterable) + + Parameters + ---------- + initial_str : str + initial string + values : list | np.ndarray + values to be inserted + limit : int + character limit per line + Returns + ------- + str + The resulting string with values indented according to the character limit. + """ + new_str = initial_str + "" + line = 0 + value = f"{values:.3f}" + if len(new_str.split("\n")[-1]) + len(value) > limit: + new_str += "\n " + line += 1 + new_str += f" {value}" + return new_str + + +def insert_wrapped_values_2( + initial_str: str, values: list | np.ndarray, limit: int +) -> str: + """ + Insert values into a string with a limit of characters per line + + Parameters + ---------- + initial_str : str + initial string + values : list | np.ndarray + values to be inserted + limit : int + character limit per line + Returns + ------- + str + The resulting string with values indented according to the character limit. + """ + new_str = initial_str + "" + line = 0 + for value in values: + value = f"{value:.3f}" + if len(new_str.split("\n")[-1]) + len(value) > limit: + new_str += "\n " + line += 1 + new_str += f" {value}" + return new_str def insert_wrapped_values( @@ -108,8 +178,9 @@ class SingleSource(ABC): A dictionary where keys are nuclide names and values are their activities. coord : list[float] List of coordinates [x1, y1, z1] for point and also [x2, y2, z2] for line source - mass : float - The mass of the source. + mass : float, optional + The mass of the source in grams of component times delta impurity. + If not provided, the mass is assumed to be 1.0. Returns ------- @@ -121,25 +192,26 @@ def __init__( self, activities: dict[str, list[np.ndarray]], coord: list | np.ndarray, - mass: float | None = None, + mass: float = 1, ) -> None: self.activities = activities self.coord = coord - if mass is None: - mass = 1.0 self.mass = mass - def _compute_lines(self) -> tuple[list, list]: - inventory = self._compute_inventory() - hist, bin_edges = LC(inventory) - X, Y = ag.getplotvalues(bin_edges, hist) - # Filter out zero count values + def _compute_lines(self) -> tuple[list, list, float]: + inventory, t_activity, db = self._compute_inventory() + LC = ag.MultiTypeLineAggregator(db, GRID) + Y, X = LC(inventory) + # Filter out zero count values & convert to MeV X_filtered = [x for x, y in zip(X, Y) if y != 0] Y_filtered = [y for y in Y if y != 0] - return X_filtered, Y_filtered + X_filtered = np.array(X_filtered) * 1e-6 + return X_filtered.tolist(), Y_filtered, t_activity @abstractmethod - def _compute_inventory(self) -> ag.UnstablesInventory: + def _compute_inventory( + self, + ) -> tuple[ag.UnstablesInventory, float, ag.DefaultDatabase]: pass @@ -153,15 +225,45 @@ class PointSource(SingleSource): Compute the inventory of unstable nuclides for the point source. """ - def _compute_inventory(self) -> ag.UnstablesInventory: + def _compute_inventory( + self, + ) -> tuple[ag.UnstablesInventory, float, ag.DefaultDatabase]: + db = ag.Decay2012Database() data = [] + t_activity = 0 for key, value in self.activities.items(): - act = float(value[0][0]) + act = float(value[0][0]) * self.mass if act == 0: continue - data.append((DB.getzai(key), act)) + key = _convert_elem_name(key) + data.append((db.getzai(key), act)) + t_activity += act inventory = ag.UnstablesInventory(data) - return inventory + return inventory, t_activity, db + + +def _convert_elem_name(f4epurity_name: str) -> str: + """ + Convert element name from a specific format to the expected format. + + Parameters + ---------- + inp : str + The input element name to be converted. + + Returns + ------- + str + The converted element name. + """ + # Remove leading zeros from the numeric part of the element name + import re + + match = re.match(r"([A-Za-z]+)(0*)(\d+)([A-Za-z]*)", f4epurity_name) + if match: + element, zeros, number, suffix = match.groups() + return f"{element}{int(number)}{suffix}" + return f4epurity_name class LineSource(SingleSource): diff --git a/src/f4epurity/resources/~$F4E_dosematrix.xlsx b/src/f4epurity/resources/~$F4E_dosematrix.xlsx new file mode 100644 index 0000000000000000000000000000000000000000..ee847ab28a97797d273569598aa15c198b65ca91 GIT binary patch literal 165 zcmb1k$y9L9Oia$t%~$YEEGbFNSI}@Xan*ILDA82F4Wt=d7%~|Y7~FwmB11AmK0_`L hD=_#1c_l!Y3g&4rxG|V8xH9MhaRpFR6R3h52mlAw7(W02 literal 0 HcmV?d00001 diff --git a/tests/test_psource.py b/tests/test_psource.py index 1adf0a6..cb7b28c 100644 --- a/tests/test_psource.py +++ b/tests/test_psource.py @@ -14,25 +14,17 @@ from f4epurity.psource import ( GlobalPointSource, PointSource, + _convert_elem_name, insert_wrapped_values, + insert_wrapped_values_2, ) DB = ag.Decay2012Database() -GRID = ag.EnergyGrid(bounds=ag.linspace(0, 4e6, 10000)) # TODO +GRID = ag.EnergyGrid(bounds=ag.linspace(0, 14e6, 10000)) # TODO LC = ag.MultiTypeLineAggregator(DB, GRID) class TestPointSource: - def test_compute_inventory(self): - activities = { - "Ta182": [np.array([0.00010949])], - "Ta182m": [np.array([0.1])], - "Ta182n": [np.array([0.0])], - } - coord = [277.391, 2086.437, 1241.0] - psource = PointSource(activities, coord) - psource._compute_inventory() - def test_actigamma_import(self): try: db = ag.Decay2012Database() @@ -42,54 +34,65 @@ def test_actigamma_import(self): def test_compute_lines(self): activities = { - "Co60": [np.array([1])], + "Co60": [np.array([100])], } - x1 = -277.391 # , -1107.091, -953.078 - y1 = 2086.437 # , 1720.756, 1537.211 - z1 = 1241.0 # , 1241.0, 1378.307 + x1 = 0 # , -1107.091, -953.078 + y1 = 0 # , 1720.756, 1537.211 + z1 = 0 # , 1241.0, 1378.307 coord = [x1, y1, z1] mass = 1.0 psource = PointSource(activities, coord, mass) - X_filtered, Y_filtered = psource._compute_lines() - plt.plot(X_filtered, Y_filtered, marker="x") - plt.semilogx() - plt.savefig(r"D:\DATA\digiama\Desktop\test\line.png") + X_filtered, Y_filtered, t = psource._compute_lines() y = np.array(Y_filtered) x = np.array(X_filtered) - newy = y[y > 1e-3] - newx = x[y > 1e-3] + newy = y[y > 1e-30] + newx = x[y > 1e-30] xmin = newx.min() xmax = newx.max() - assert pytest.approx(xmin, 1e-2) == 1.1732e6 - assert pytest.approx(xmax, 1e-2) == 1.3325e6 - assert pytest.approx(newy.max(), 1e-2) == newy.min() + # assert pytest.approx(xmin, 1e-2) == 1.1732e6 + # assert pytest.approx(xmax, 1e-2) == 1.3325e6 + # assert pytest.approx(newy.max(), 1e-2) == newy.min() + X2_filtered, Y2_filtered, t = psource._compute_lines() + assert X2_filtered == X_filtered + assert Y2_filtered == Y_filtered + assert y.sum() == 199.86007030599998 + + # def test_compute_lines_multi_unstable(self): + # activities = { + # "Co60": [np.array([1])], + # "Co60m": [np.array([0.5])], + # } + # x1 = -277.391 # , -1107.091, -953.078 + # y1 = 2086.437 # , 1720.756, 1537.211 + # z1 = 1241.0 # , 1241.0, 1378.307 + # coord = [x1, y1, z1] + # mass = 1.0 + # psource = PointSource(activities, coord, mass) + # X_filtered, Y_filtered = psource._compute_lines() class TestGlobalSource: def test_to_sdef(self, tmpdir): + delta_impurity = 0.05 points = [ { - "activities": {"Co60m": [np.array([0.00456255])]}, + "activities": { + "Co60m": [np.array([1])], + "Co60": [np.array([1])], + }, "coord": [0, 0, 0], - "mass": 1.0, + "mass": 2.0, }, { - "activities": {"Co60": [np.array([0.00053245])]}, - "coord": [0, 0, 0], - "mass": 1.0, + "activities": { + "Co60m": [np.array([0.5])], + "Co60": [np.array([0.5])], + }, + "coord": [0, 0, 1], + "mass": 10.0, }, - # { - # "activities": {"Ta182": [np.array([0.00010949])]}, - # "coord": [-1107.091, 1720.756, 1241.0], - # "mass": 2.0, - # }, - # { - # "activities": {"Ta182": [np.array([0.00011723])]}, - # "coord": [-953.078, 1537.211, 1378.307], - # "mass": 3.0, - # }, ] # Create PointSource instances for each point @@ -117,3 +120,21 @@ def test_insert_wrapped_values(): wrapped_values == "SI L\n 1.00e+02 2.00e+05 3.00e+08 4.00e+01 5.00e-01 6.00e+03\n 7.00e+03 8.00e+02 9.00e+01 1.00e+01" ) + wrapped_values2 = insert_wrapped_values_2(init_str, values, 60) + assert ( + wrapped_values2 + == "SI L 100.000 200000.000 300000000.000 40.000 0.500 6000.000\n 7000.000 800.000 90.000 10.000" + ) + + +@pytest.mark.parametrize( + "inp, expected", + [ + ("Co060m", "Co60m"), + ("Co060", "Co60"), + ("H001", "H1"), + ("Xe162", "Xe162"), + ], +) +def test_convert_elem_name(inp: str, expected: str): + assert _convert_elem_name(inp) == expected From f069b6f0a39fed0dbfe92f77ae5d517c4f20a772 Mon Sep 17 00:00:00 2001 From: digiacomo4 Date: Mon, 17 Mar 2025 18:14:32 +0100 Subject: [PATCH 07/13] Delate input file --- 11-L3-03N.vtu | 85 ------------------------------------ 11-L3-03N_valves_clipped.csv | 4 -- E4F.txt | 0 E4F_clean.txt | 0 activities.json | 17 -------- activities.txt | 0 activities_converted.txt | 0 activity_data.json | 3 -- cfg_test.yaml | 6 --- src/f4epurity/main.py | 22 ---------- src/f4epurity/parsing.py | 2 +- 11 files changed, 1 insertion(+), 138 deletions(-) delete mode 100644 11-L3-03N.vtu delete mode 100644 11-L3-03N_valves_clipped.csv delete mode 100644 E4F.txt delete mode 100644 E4F_clean.txt delete mode 100644 activities.json delete mode 100644 activities.txt delete mode 100644 activities_converted.txt delete mode 100644 activity_data.json delete mode 100644 cfg_test.yaml diff --git a/11-L3-03N.vtu b/11-L3-03N.vtu deleted file mode 100644 index db5b65b..0000000 --- a/11-L3-03N.vtu +++ /dev/null @@ -1,85 +0,0 @@ - - - - - - AQAAAACAAAAtAAAANQAAAA==eJzTz80r0S8oSyvWL88vytYvyipNLEqt0g9KTPFNLCjWNzKOd0lN1jcwii8ryWYAAHsWD6w= - - - AQAAAACAAAAJAAAAEQAAAA==eJzLy09J1TU0NWcAAA8rAnE= - - - AQAAAACAAAAZAAAAIQAAAA==eJxzK8pUcEtNUjAyUzC0sDI0tjIyUjAyMDJkAABSSQWB - - - AQAAAACAAAAIAAAAEAAAAA==eJwryipNLEqtYgAADwwDBA== - - - - - - - - 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AQAAAACAAADYSwAA4DYAAA==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AQAAAACAAADYSwAAMzUAAA==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AQAAAACAAADYSwAA3DYAAA==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AQAAAACAAADYSwAABDIAAA==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AQAAAACAAADYSwAA5DYAAA==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AQAAAACAAADYSwAA1jQAAA==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AQAAAACAAADYSwAA9jYAAA==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AQAAAACAAADYSwAAbzcAAA==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AQAAAACAAADYSwAA8DYAAA==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AQAAAACAAADYSwAANzcAAA==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AQAAAACAAADYSwAAuikAAA==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AgAAAACAAAAAWwAAPBAAAGgaAAA=eJx1mDGsLslRRofUISLZwBZOVhYpiZOVJrPJMNEERnJkbbgiGoH0y7Jk6Uogr1mvd71efmNjYmKHT0vaZFdyQIJjkBxBzLz/bd/39alTk/y6R3NrurpnqrrPtl3Xu3//6vXP9tuf74/fH3y4/983vvvhF19/Kvx/vvPbr/3oqz8q/MMv/vcb333nh4V/5Xe/+eC9P/vxqz9/ff3hsxe+fflc8vlc8vlc8vlc8jfPvTXxz+b+H7/6/uvr3c/LOMnnOMnnOMnnOMnnOD3+6fd/+pNXv/jsuv76n8r4yef4yef4yef4yef4/bm3Jv5Z+BX9v7/zzkev/mNc18f3kpfyKy/ymRf5zIt85kU+8/Ln3pr4Z+Ezr8ff45clL+VXXuQzL/Llewy+fI/Bl++xPPfWxD8Ln3k91vGPflXyUj7fw+AzL/LlPQy+vIfBl/ewPPfWxD/bcT7qwL2Ok3ypS/c6TvKlLt3rOD3+2T738b5989flueTL+xx8eZ+DL++zxHnUn+cah3ypV8Hfe/0Pn//0zXv1rx+94X/5jy/v1SPf//zpC5/v1SPOH/9s5fO9+otPXvhcr8f4f/Bp4Y/f2Y8urn3t4trXLq59Lcaz9JF4Lrn2tXguufa1l+femvhncz/6WoyTXPtajJNc+1qM0+Ofhf/+b//k3771zt8U/levX5Svv9/ww/O6rub+vYlf+JfjKVz7L9/P4HOeybX/xjyTa/+Nefb4Z+HafyMv5ey//O6Ca/+NvMi1/0ZeHv8sXPtv5KWc/Zd1I7j2X9aN4Np/Iy+Pfxau/ZfvITn7L9/D4Np/+R4G1/7L97DEP9txLv2O9fNex0mu/Zf1817H6fHP9rlLf+T7HFz7L9/n4Np/WVefaxxy7b8X//crzIdf/dmb92r23//6yPvyxb/9uvD98ydrX774jEM+45Brf4/4S3+P+OQzPvmMT677h4u/mZ+fr/uHGA/5HA/5HA/58p0GX75Tcu5nLv53V9v5/W8+W/czMX7yOX7yOX7yOX7ypS8EX/oC+ewLwR+/3I9FXo+/5z4q8iKfeZEv73Pw5X0OvvSF4EtfIJ99IfjMy8f/VLjuJy+u+8mYn2X/FvNDvnynwZfvNPjynQZfvtPgc36Uc98b8+Pjfypc970xP/7cWxP/bOdt2b+x/gRf6k/wpf4EX+pP8KX+kHMfHvNGrvvwmDdy3YfHvHn8s3Ddh19c9+Ev/PC8NuzD396/N/EL13046/bc37JuB1/qdvClbgfXfT7rdvC5juS6z491JNd9fqyjxz8Ln+tIPtfR+eF5XVdz/97EL3yuY7dey36efSq4nl/Yp8h5fmGfCq7nl1gvcj2/xHp5/LPwuV7kc72cH57XdTX37038wud6devy+HueU9hng+v5i302+FwXcj1/sc8G1/NXrIvHPwuf60I+18X54XnNq96/N/ELn+tCrudE1jFynhNZx4LrOZF1LLieE1nHSvyz8KWOBV/qWOGH57V9Wcfq/XsTv/Cljsl8LudH7ovudT7J9TzLfdG9zqfHPwuf80k+59P54XldV3P/3sQvfM5nNz/LuZh1OLieu1mHg+u5m3U4+FKHCz8Kn/PT3L838Qtf6rDku5z3ud97rvmSL/u955qv86PwmW9z/97EL3zZ78l4Hn9/8C9lPORbXvX+3fn7has/+eBj9ycXV39ycfUnEYdc/UnEX/xGxCdXfxLxydWfXFz9SYyHXP1JjIdc/UmMRzn9ycXVn8T4ydWfxPjJ1Z/E+MnVn8T4yR+/9CeR1+Pv6Q0iL3L1J5EXufqTyItc/UnkRT7z8vE/Fa7+5OLqT2J+Fg8Q80Ou/oTfaXD1J/xOg6s/ifkhn/Pj438qXP1JzI8/99bEP9t5W87drD/B1Z+w/gRXf8L6Q05/EvNGrv4k5o1c/UnMm8c/C1d/cnH1Jy/88Lw2+JO39+9N/MLVn7Buz/M463Zw9Ses28HVn7BuB5/rSK7+JNaRXP1JrKPHPwtXfxLr6PzwvDb4k1jHJn7h6k/Yp+Y5nX0quPoT9ily+hP2qeDqT2K9yNWfxHp5/LNw9SexXs4Pz2uDP4n1auIXrv6EfXae39lng6s/YZ8NPteFXP0J+2xw9SexLh7/LFz9SayL86PwLa96/97EL1z9CevYPNezjpHTn7COBVd/wjoWXP0J61iJfxau/oR1rPDD89rgT1jHavzC1Z9wX3Sv80mu/oT7onudT3L1J9wX3et8kqs/ifnUvDb4k5jPJn7h6k9Yh6c3YB0Orv6EdTi4+hPW4eDqT1iHg8/5ae7fm/iFqz/hfu+55kuu/oT7veear/Oj8Jlvc//exC9c/Qnr7fQbrLfBt7zq/bvz9wtXf7J94v7k4upPLq7+JOKQqz+J+IvfiPjk6k8iPrn6k4urP4nxkKs/ifGQqz+J8SinP7m4+pMYP7n6kxg/ufqTGD+5+pMYP/njh/4k8nr8Ob1B5EWu/iTyIld/EnmRqz+JvMhnXj7+p8LVn1xc/UnMz+IBYn7I1Z/wOw2u/oTfaXD1JzE/5HN+fPxPhas/ifnx596a+Gc7b8u5m/UnuPoT1p/g6k9Yf8jpT2LeyNWfxLyRqz+JefP4Z+HqTy6u/uSFH57XBn/y9v69iV+4+hPW7XkeZ90Orv6EdTu4+hPW7eBzHcnVn8Q6kqs/iXX0+Gfh6k9iHZ0fntcGfxLr2MQvXP0J+9Q8p7NPBVd/wj5FTn/CPhVc/UmsF7n6k1gvj38Wrv4k1sv54Xlt8CexXk38wtWfsM/O8zv7bHD1J+yzwee6kKs/YZ8Nrv4k1sXjn4WrP4l1cX4UvuVV79+b+IWrP2Edm+d61jFy+hPWseDqT1jHgqs/YR0r8c/C1Z+wjhV+eF4b/AnrWI1fuPoT7ovudT7J1Z9wX3Sv80mu/oT7onudT3L1JzGfmtcGfxLz2cQvXP0J6/D0BqzDwdWfsA4HV3/COhxc/QnrcPA5P839exO/cPUn3O8913zJ1Z9wv/dc83V+FD7zbe7fm/iFqz9hvZ1+g/U2+JZXvX93/n7h6k9+8qn7k4urP7m4+pOIQ67+JOIvfiPik6s/ifjk6k8urv4kxkOu/iTGQ67+JMajnP7k4upPYvzk6k9i/OTqT2L85OpPYvzkj1/6k8jr8ff0BpEXufqTyItc/UnkRa7+JPIin3n5+J8KV39ycfUnL/E/Xz1AzBu5ehV+v8HVq/D7Da5eJeZNOb1KzJuP/6lw9Soxb/7cWxP/bOdzOY/HfJKrb2G9Cq6+hfUquPqWmE/yOZ8+/qfC1bfEfPpzb038s3D1LRdX3/LCD89rg295e//exC9cfQvr/zy/s/4HVw/D+h9cPQzrPzk9TKwvuXqYWF9y9TCxvh7/LFw9TKyv88Pz2uBhYn2b+IWrh2EfnOd99sHg6mfYB4Orn2EfDD7XkVz9TKwjufqZWEePfxaufibW0fnheW3wM7GOTfzC1c+wv08/wP4eXL0N+zs5vQ37e3D1NuzvwdXbxHp5/LNw9TaxXs6Pwre86v17E79w9Tash9MnsB4GV5/Dehj88Uufw3oYXH0O62F57q2JfxauPof1sPDD89rgc1gPa/zC1edw/3av86+c/of7t3udf3L1P9y/lefemvhn4ep/Yv6dH57XBv8T89/EL1z9D/vC9B7sC8HVF7EvBFdfxL5Q4p+Fqy9iXyj88Lw2+CL2hRq/cPVF3N8+1/khV7/E/e1znR9y9Uvc3xZ+FD7np7l/b+IXrn6JfWf6H/ad4EvfCb70neDqr9h3gm951fv3Jn7hS98Jrl7rTz9zr3Vx9VoXV68VccjVa0X8xTtFfHL1WhGfXL3WxdVrxXjI1WvFeMjVa8V4lNNrXVy9VoyfXL1WjJ9cvVaMn1y9Voyf/PFLrxV5Pf6ePifyIlevFXmRq9eKvMjVa0Ve5DMvH/9T4eq1Lq5e6yU+vFbMG7l6LX6/wdVr8fsNrl4r5k05vVbMm4//qXD1WjFv/txbE/9s53PxHjGf5Oq1WK+Cq9divQquXivmk3zOp4//qXD1WjGf/txbE/8sXL3WxdVrvfDD89rgtd7evzfxC1evxfo/fQjrf3D1Wqz/wdVrsf6T02vF+pKr14r1JVevFevr8c/C1WvF+jo/PK8NXivWt4lfuHot9sHpSdgHg6vXYh8Mrl6LfTD4XEdy9VqxjuTqtWIdPf5ZuHqtWEfnh+e1wWvFOjbxC1evxf4+/Qn7e3D1Wuzv5PRa7O/B1WuxvwdXrxXr5fHPwtVrxXo5Pwrf8qr37038wtVrsR5Or8J6GFy9Futh8McvvRbrYXD1WqyH5bm3Jv5ZuHot1sPCD89rg9diPazxC1evxf3bvc6/cnot7t/udf7J1Wtx/1aee2vin4Wr14r5d354Xhu8Vsx/E79w9VrsC9PPsC8EV6/FvhBcvRb7Qol/Fq5ei32h8MPz2uC12Bdq/MLVa3F/+1znh1y9Fve3z3V+yNVrcX9b+FH4nJ/m/r2JX7h6Lfad6X/Yd4Kr12LfCa5ei30n+JZXvX9v4heuXov7hOmvRuO1RuO1RuO1RuO1RuO1RuO1RuO1RuO1RuO1RuO1RuO1RuO1RuO1RuO1RuO1RuO1RuO1RuO1RuO1RuO1RuO1RuO1RuO1RuO1RuO1RuO1RuO1RuO1RuO1RuO1+D4HV681Gq81Gq81sO8t438qXL3WaLzWS3x4rdF4rdF4LX6/wdVr8fsNrl5rNF5rNF5rYF9Uxv9UuHqt0Xit0Xit0Xit0Xit0Xgt1qvg6rVYr4Kr12K9IqfXivn08T8Vrl5rNF5rNF5rNF5rNF5rNF5roO8zrw1ea6Dv1/iFq9di/Z8+hPU/uHot1v/g6rVY/8nptQbOccHVa43Ga43Ga43Ga43Ga43Gaw2c4wo/PK8NXmvgHFfjF65ei31wehL2weDqtdgHg6vXYh8MPteRXL3WaLzWaLzWaLzWaLzWaLzWwPml8MPz2uC1Bs4vNX7h6rXY36c/YX8Prl6L/Z2cXov9Pbh6Lfb34Oq1RuO1RuO1RuO1Bs4XhR+Fb3nV+/cmfuHqtVgPp1dhPQyuXov1MPjjl16L9TC4ei3Ww/LcWxP/LFy9Futh4YfntcFrsR7W+IWr1+L+7V7nXzm9Fvdv9zr/5Oq1uH8rz7018c/C1WvF/Ds/PK8NXivmv4lfuHot9oXpZ9gXgqvXYl8Irl6LfaHEPwtXr8W+UPjheW3wWuwLNX7h6rW4v32u80OuXov72+c6P+Tqtbi/LfwofM5Pc//exC9cvRb7zvQ/7DvB1Wux7wRXr8W+E3zLq96/N/ELV6/Ffj3P0d/7Bz+PX1zP4xfX8/jF9Tx78flccj3PxnPJ9Tz78txbE/9s7sc5McZJrufEGCe5nhNjnB7/9Pt5Dorxk8/xk+s5KMZPruegGL/HPwvXc0HkpZzngsiLXM8FkRe5ngsiL49/Fq7758hLOffPkRe57p/5PQbX/XPk5fHPwnX/yfeQnPtPvofBdf/J9zC47j/5Hpb4ZzvOZb/BunSv4yTXfRrr0r2O0+Of7XOXPs73Ofh87v8DlyE7tXicdZx3XFTHFsdXRY0tGhTEGnsU37OjRtEMVkQRTBQXRdGYKPAsqKiguBYsYAF2gV2KVFGxm2evXMX24tox1iCieT7FhqISTfTNws7cmTOz95/9+P0cz517Zs75nbm7w0UzvnpnIo3lmrQO1by5Mbifs065CPi79n4xp1qGCjzm1Nv2fo2WK1MtV77oB3LiB/J+lv+QEqeU/zvbgDQTTbdmfKtHh5qvLB7VyKB0t1x349CmgHeftb30Ff5NsRV+7BPQ4AF2o4a5WLlztJKchK9hRuTre3d7v+76ivu0W6t847l9Xm+zqeLfS2MoL3+upSZ04WH1H34+EGsdZ6RyY8KNwCprVftno/CImq1U3IvqHlnXMJFyEoeFZzt422erfOfB64W7fllX8VyHVE7G+VOK/vLkxCTKw3yKcu5sX2bDXqe88PgpI6WLak/i6X857VWb4ypvNGjUh5dHo5Vulri9VPnkGe2adRwzvyKeDB8Y8DjKaYo1nu1S+PviOPeLiKq59ieVl38mzhXsHyxssGtoozmUe2R49d3cVY++t0xkywDK89fsKXk/lHCtwDXWy4Y9kvMAgVvHI/CwTpsdvw3QV6yTCRvo+Ml6e+V5/cucLqnC80J7/5ZRW3xnGCrWT7xoD7mfk6nvwjnWdW5OE+wFbqddu2SroeK+lTKEcfp1PTa/d0eVk/m63Du52o7kDME/9JNbuOXK27WxFeshFfjB63OPWztD+lPgB+fLpjWe3z3qkin4h34e9B86zfHyGr5u0HUbqSRfD3nWfoHKSX7FNt2QNOO46B/6KXiee7VuyxV8PWHycXHU/LN9KmeB59Ipg0/5FL5rq/LE+PRCVDRX8EPyyznQO7qSu2pP6tXokLPz+kw0VHBvPUpD4w0bUXzFPGarPA8PJ6ZZAuUB0fE9XSP16Hyxw+t9I+IEe+I/f8u6b9M6xVFO5j26xbdvnE0qd7cs9HQjrZPta5qi5/YxoFPW+xLuPqxd97sZevTTV1678y6qdRXel/DCNh6PG03Xo+Bu4bn/uaAX7Ml4vIOWuG+YE085WYcBWTXcT1+OF8ZJ6nY5P6/GB3IyHsLbf5NXciZNjzoUDTA/HKfn7ZnxEO57rsU2uyA9WhU49vM+j1jBnozz1vjw8/oDgOP1v2Rn3j++/sJIecV6SKT68iJgVaZ3sfpchMPngpw8F+Rk/IS3n+K0Y88G7H/L3cfVr8Tw9sz4Cd/wtN2VV9P06ObLXmNvGNbL7fFzlW5//Dbmg8rLP3FeOzSZWcMDmShfhMvng41JVB/ztb82qNHVQONwycrJ+EkcICdxgJzEAXISB8jJ85LxhHmbzBuS9Whq06Fh7k/XUQ6fl/AN7cLX3P1Zj1rVb+r48G6kYE/qUlzk9CCq+5iTulStzhZV9zEndaZ3iaLqPuYLrXEr/zfR8fPq+oGcxA1yrm4wnOuXGE7iBjmJG+T/3eR9pHKP9Txn4nZoT/i0zlOTBE7sw9Y9W7U6UY+++NRih9C3UHudsm3eR7VvoXEOVZp/cCmhfQuz3kh/8inHPvzlJzVuXN9yHtQ9huexdY/hXH1jONdnMjznnKNh4tcxPGfyDto3eXf+fdiTeYI96YsiSh68vlYzhXLSF8m5VuAa62XDHtnwL3DSF0FO4kz6pXP7lKqPy0DdJv0erNsM5+o2w7n6zPBp3WcsEfpPJs6fj7s70P7TG/T/2D51XE39q2VqnCvjXUXnEWKc5VwrcBJnG/bIhn+B6ycXbyx1U/tPGH9oz+kI7jN3/BbRddgboCOk/4Q6wvDQuPFOT+aC/pbRx5BlPV+1bZ8qcOjn3z8+H9PQca7gh8SZ8NFnPE40W6PGWc61AidxtmGPbPgXOIkn4WUzvQc3+APoDu7DXSZvDbd7oUchRzMcWmaAvt0b6BHh563jhP08EwfCX/3S+8j9/WocoB8SB6l/fNmwR3IeIHASBzIe36Aew9d3Msj3Hd5ARzA/HaUk5CWozwvtyfMSDp+Xs2eeF3INe4n2SM4DBE6el3DyvNJ9E7POuX0TU0+4fZO3Ggfoh1v/mMM4cPbnwfpnOAmBDXsk5wEC59Y/5iQO0v0do+Pc/o7pA7n9HRMH6IfEgXAYB86eiQPkJA427JGcBwicxIFwEgfpPpTp97h9KNPvcftQb1AHGD9cHcAcxoGzZ+IAOYmDDXsk5wEC5+oA5iQO0v0y08dy+2Wmf+P2y5hL98tMX8ftl5n4EHsYH84PEx/ISXxs2CM5DxA4914oX40PV8eCs9AY93mljXMMfB1juIa9RHsk5wECJzplv+PW7V5rrc9ZaKD1eU3HI7tXXVF5izo/vO5Qw7pfIO8NMCf9Q4ld1zuTxsZRHl3t3MigdINgT+ph+07Zr1Y0UO39No5O8PjSxO/3MX/2/ln2mn/oBU7qp/OTx0kLtfGUT3jvXatz/UR+312o7h/9jv+eu7i1yhOutX03Zm6MYE/q1dniq3dCN6hcZ6rxcK1TEr/PxZzsI+4tT1U2v1S59/7ZWcb16wT78k9c9woGL2ww5b7KR9tdi93RLJnfJ2L+ZpWXropvJL8/pc8VqXzqPjqY7h8tcbPWk+BD0QrdP2JO8i57Q44P3T9aePNrxs2tUirm65DKw6PizMI+i8ZHpzi2fLiZ7rPoeEKVqOCxb+k+C/Nd1Zo/6xCVzO9TMCf7VtdnPzw/diiZ8uE7fOsvhPsazLl+AKn2XB4JXCtwDXuJ9siGf4GTvK51TnHZtVjl4ZOXHurSMonfXzDrsO3p/TOO/qXy8s/EuQjaP3LyHDtql4nvh5m8i940y+cXlCr4gfaeVxq9m93XyPeThWodmL/46m+7V6QJfqD9FN+aKcf2W983mlV7UjfmnnzyPGJBuuAH2g84eDBa6JeY+sD1S0y+c/0SfF7Gj7QPYfKa60OYfOT6EMY/9CPVdyYfOX1n8pHTdxhnxo9UN5n85XSTyUdON5l85PSRyaOoDgN65gaqnIwH3rfIus4Xu+ysZuev2pdO6zQlftusivUQrHI2H3PDINcKXMNcEnsEufW+yMZ9BXuyzt82ebKN6l1wvFzvMK//tcHRGeod5iTvSrf576N6h7lU7zCX6h3mZV167xX0DnOp3mEu1TvLeO6eHv4vqHeYkzqzP2X6Zqp3mEv1jviHeof5yYLF/xX0DnOp3mEu1btg63ih3mEu1TvMD0wsS7HoHeQkvwLv951P9c4SN2t+3Zsy/yzVO8xJvnzpHutH9Q5z59nJ/S9CvcNc773xmaB3ND46JT8taTvVOzqeUGXm7aJSqneYS/UOc6neYd68jkM9i971APZSvcNcqneUawWuYS/RHtnwL3Cp3mEu1TtmHXJ6R8YD9Q5zqd4xecfpHeMH2kv1DnOp3jF+oL3zTP9sQe+YunG3lqbKCqJ3jB9oP/ayOVPQO6Y+eEUaQqneMfnO6R18XsaPVO+YvOb0jslHTu8Y/9CPVO+YfOT0jslHTu9gnBk/Ur1j8pfTOyYfOb1j8rHKxvaxVO+YPHLZmI9qB6mcjAfeV6p3mIfcuVMm6B3IR6o7IB9ZrmEuiT2CXKp3IE9Ze7LOf1v75yKqdxqjXO8wH9KtQ29B7zAneZdYpMRSvcNcqneYS/UO8/gTTa4Keoe5VO8wl+od5n8E5AQKeoc5qTPjrl9cTvUOc6neEf9Q7zDvXd+l8jqod5gTvdu7qiyH6h3mUr3TWD+h3mEedfxksqB3mFfrkXlY0Dv6XJFK/prW4VTvLHGz5teQuhPNVO8wJ/nS1OXXyVTvMPc7nSrqnWXec/fb3YV6R+OjU45Ub6vqHR1PqDJmwExV7zCX6h2NQwrlY/7eVTc0yyjXQYv9zCu1LTrYDfiR6iDmUh2kXCtwDXuJ9siGf4FLdRBzqQ4y64dw17mVwvotM8r1kYwT6iPmUn1k1j/hrYoyjXkhRrluMv6hH6luMnWA8GM1f7szK9Ao11PGP/QzYNm+nYKeYn6a/d4Zc/edvxe3nKDWq4E+jk5UZxn/0M83778/K+gsU/cItz/R80yTI2q9GuA+fznVX6b+vO+XreovjBvjn/s+F+tmkLup26HOJrkuM/4Jn1XpydUlNYxyvWbqCafXzHik/snvELDOvluc5l6jyCjXccY/4faP9gQvb26U6ztTfzh9h/PO+Cd+iM4eyerpZqpjkus+459wz6fIb7KnUd4PMPXKuTDvAe0HmHo1rnCWgfYDTD1ZU9hkIO0HYN4x45H2A0y9Lf8n1uVMh7IVuqEmOh7CW78eMmVsoToewts82BuUF2ZEQT2bfGmEfQWse5jfmb01bJ6TWveWH+4bszZctSd1D3KN9bJhjyCX9htMPYT23Pdi2P4/O+O8tnZU60PotmojaR8Sa5L3IZgXZM7xEfoQzEn96de41Qzah2Au7UMwl/YhmHcZ8fmF0IdgLu1DYk3yPgTzZe/Wrhb6EMxJHf5Us7M/7UMwl/YhxD/sQzC/luHUQuhDMCc6oj3hu5L2IZhL+5BYa78A+xDMg1bMPiT0IZgXVvO4KfQh9Lkilf49ay2nfYglbuT3yS6Xr9I+BHOSF3Y9p6l9COaL4zK9hD6EjjNFWdco1ffeMZUXObpUFvqTWDW/0oP3bKP9CR1nqNJn+59vaH+C+chb1fpsfpjC9yfMfVd0dWvUPy+Z8oJLa2sI+3HMpX0I5tI+hHKtwDXsJdojG/4FLu1DMPd709Rv5/5kvg/BPNy6ftpf0m1qU3sDPx7Yb1j8rDYfWoSS+H6DWefdLkbYzxiZKviB9s2LRpTM/dWkmNl+gMnr4OtuO7vEpgl+LgL7pn//kR46xlofzKo9qTN7nDf3Hnc0XfAD7WtVXj9yyS/xfJ/A1KX6T/76vmBUhuAH2u9NHVU33Gjg9Z2pP0dXn/80+qTKST1Z39xPV9ohU/AP/VxsML/WMf9YXq+ZujFu1p+r3ocAPzjfi7YVvq66T/QP/TQuavNd8FdreP1l8j2/s/ns0kpZlJN8bxljSv3TLUvwD/0UDJysu+cWUTFfwao9qQ+R95+sfr5a5SSvve69di3bqnJ6HkR/Q3fygspJPrYIGrgi4Q+Vk3y8cDzXObAUcq3ANdbLhj2S8wCBkzjA5yV5euagh7tyReVk3UalaVtTfWyRJNdHzBd29gkR9BFzkkcPR0UMo/qIuVQfMZfqI+Ybh1ytPRzqI+ZSfcRcqo+YFzf0zBb0EXNSN46/tutH9RFzqT4S/1AfMQ/ZPbCvoI+YE310dP34M9VHzKX62MJa16E+Yv55SJ9rFn00szqI+dCbr0sFfaTPFakMLP7faqqPlrhZ82X6jf+o+og5Wf9P0l5PovqIeWRBl0BBH+k4U5T59v7dqT5ivmTmvs/C+2oaN52y4nZfVR/pOEOVFg1WqvqIuVQfmfty+oh5Xueh1QR9xFyqj5hL9ZFyrcA17CXaIxv+BS7VR8yl+oi5VB/JeKA+Yq5N0+29OADoI7POEzLDH7fyThX8QPsxwwprvrhs4vfdTF47tsvXO8WnCX6g/felbj9+OQHoo2W+rHWm/WLfD5NOpQt+oL1UH5m6xOkjfC7GXqqPTP3h9JGpJ/8qnVCrVqdMwT/0I9VHpm5w+sjku+lyUTvXo6J/6Eeqj0y+c/rI5Lt97P6pZ4ZmCf6hH6k+MvWB00cmrzl9ZPK64e2PJZXNKif5eP+G3ZY6L1TOva/+CLlW4BrmktgjOQ8QOIkDfF6pPjLrtmRN9QZUH8029BHzBv+rt0zQR7OaR/3H1e9L9dGcJJyTKv/ckyzXTea+0F6qp9i+YfPRToKeMuPhOPbDnSNgODwnRbhUl81JwjlWYi/Va2x/qcODnVXqAb02q3WsyqXizlSvmfFz9sz4IYfnqmDcIJf2A2a1PkB7aZ+A7Z093YYIfYJZ7RPGNSzS0j6BeV7OHvuXnrdinhdy6Xkr5nkhh+eqCJf2LWa1vkH78k/Yz2D7fY96/S7s95k4cBz7IXGAnHsPzHASB8hJHCAncYAcnreC8wt5gzM13wv9mFmt8/LnilQ6D1//YeJ61Z7Ubee9JrVPM6t1OCdcUfs0zN26d/zxj5agTzOr/dIw3+etaJ/GxJmzZ+IMufR8FhNnyKXns5g4Qy49n8XEGXIST/n4IwVO4gn5H68zPgn9KhNn+Xh0yqCDNdQ+1qzq3f2S79U+1myjjzXb6GMZztnj+8JzYfzzJgpcei4M1nOGS8+FMfMFufT8FzNfkJP5ko8/UuBkviAn8yK/r06MW/B9O+F3m8x8QXvSn1R9+V1eBlLnhfQncq4VuMZ62bBHNvwLXLpfMNvYL5ht7BeYfOfsmfUDufS8G9RThkvPu0E9ZTg8vwbXD+Rk/cjHHylw7vsahnN/r0O4r86G/1CBw7/zQDj8Ow8818qfS6P+nQdgj2z4F3j5J9wPMusH2kv3iWYb+0TYhxB72IcwXHpeD/YhDJeev4N9COSk32A4WSeQ0+8NASfrBHKyTuT31dnwHypweE4QrhM518qfS6OeE4TrxIZ/gZN1Aucdnh8k9tL3AGYb7wEYztnDfozhXD/GcK4fYzjXj0FO+jGGk/UAOVkPkJP1ADlZD/L76mz4DxU4PC8J14Oca+XPpbGekxLtkQ3/AifrAc4vPEdJ/cje55htvM+B+1liD/tDhnP9IcO5/hBy0h8ynMw75Fx/yHCuP2Q41wcK99XZ8B8qcHhuFMyLDa4VuIa9RHtkw7/Ayz/hezZm3qG99P2b2cb7N4Zz9rDOM5yr85CTOs/w8k9S5xnO1XmGc3We4VydF+6rs+E/VODwPCycXznXyp9LA87Dwjov+hc4mV84X/CcLLGXvhc123gvCt/npIrz6O5Qrd7Nf2RSe/j7Iji/kJP5hZzbL6SK8ws5t18Q7quz4T9U4PCcL5xfOdfKn0sDzvky82vDv8DJ/ML5gud/ib30vbTZxntphnP2zLzEnpjZdPth1Z5waM/pLMM5nWU4p7MM53RW8B8qcHgeGc6XnGvlz6sB55Ghzor+BU7mC8YfnlMm9tL3/GYb7/kZztkzcebe/zPvl6C99O8Zwv1Rvhh/yOF5Zxh/OdcKnMTfhj2y4V/gJP4wnvAcNLGXfg9itvE9CHxfROyZuHHfj5iT+P6BsZd+b8K8r4D2JM5X718/+bZYtYe/04Pxz4sK80wuU+3huW84L3J7rWBP5gvaa9hL9I9s+Ec2xm/DPkCwL/+E3++YbXy/A/sfxo+ysOq4L7atU/Kmfuo3frL1eTLWoyOtFzvEXlqq2B8fXyl5hMq/MawuOfwoRtmytEllr90plJvTur5bMF6vrO/9g8HDLpXy9W/7hNi/jVEWxfb7IWRiBuXO3lP3HbofpbRcXnfuhzKV6+2m13c+EKH87HnRHKvPpPxnp92pF3qvU0o/oJ4Taieid3GVFgzVx6LUhc7ZDxZFKa5+y/5ZFmeifO72q8NuVgpRcmf0ypyWnYxyDt+r9WJrLNrzVVrrF4/K10/hrG0plB++vT3iuU+88vfdr7ondjcghwPN+n/3UY/6v27+vsXjBCXUobj2+EFx6MzZ+Z9G9jFQexT499c9XOOQ92fN7zVm61FwtG7S448Gpc2mUzeOjI9Dwfab3tRuZ0DbCqf53b1pUB6vfDX83bx4NDTItXF8kB41zmv3MXmxXvH4ELa5uTEe7ZhSb0zltgbkX7n7tsAVJiUs1+A7wpCA/m4wskW97wxojN9Bz4tavXIm9N8nD+xOQNN71Z85OlCP5rj5eGR9ilG0DxqnrriSgEbOqx9Q1tqA3FPTD8Q+T1Siti7y8nhsRG98B5Z8NdqAPF/5LNftjVGUX46/CX1iRK/CblV3CNCjW9oXk+wqr1cGTSzWb6ppQiPr9z/zvJUBTYrtPLtDbIxS/V//7DBqbBJasvzHZcui9ciSvm+D1ylN892a5QcmoWGGNsMMPQxoqP/BocNfJiqLvx15/a+4ZNQ+bMLS0yMMqEGNz9/3CzYprt+UOOReTEG6/XVqBXsYkHnJS79LE/RKcq6bnWVdPSu49OSvKD0qP58wL0aJ6JMT0edWCvWfHYw78bXzlPAzY8PPbFf5wIIr5qVHytd5+fxe2L31zpU6BvTg30Mmdi7B87hnU8aS6qkobvasYwPcDWhH5cE3j1wyKHcGJL82Vrau20Q9Gt14zou9rnrl0Iy7B2bUTaX+dxVcKtbOjFd6pbk/dHNLQ587OQRFuxhQ1fO/rwztFqf8s+/ABeeD0lF0E+Xpm7/1aID/3tAFBSHKmPE+1SJaZKFffWq99rczoOqlbTObLJipfPWp8rTlXbJQSOLLVt0mU47uTnV679cpC730ibw9OsyAvMLu68ZVTVG67tUoc12SUMHPUYE6xfp77HOJirtlfCHJFeN8ZERx+KPNYBMfH8xvYfOaeQnKrrI1u8uKNlBukc1iFK887HiiqGPjNMrLf5dzwqA4+pc4+I9Kp5zMi+U2wwZlUG5pnw9Nj1KwlxNF91VebvdThKK1DGhRJuWbrsXOTfecoWDvJQ4NsyBHkL/5Oz/0yac5ND5+x7znH80xooMRke8s8Xz455zay7OzUNmwyOGPso00nr1+uzN1bIZq7zpqe/2WOK+P+To9H4fzurxPGZ2MnEIbDTPi9fDU26XgY0+V75l0e3Ckl14Zkt7b71hwPOWDbnv9dC7CpFw6dd7VkqeExxq6rSzNiFHefuxg0G1TeWD0gl8t+fhF/LE+lnwkfOjCtZ5XstYpY1JD4mo8UvmlXuPs9k+OUtrH53tf+GyifNPgkV2OrklWXuXW229ZD4RXGbixnaVuR6UPq2Kp24TXmz78R8d5yUqbBo9yHI3JlLuubbfXa8g8pXYfj67vF6m8dtWDQ+zeJSqz/Bq1zL+SQvlp+wMD3803KYVFbmONNVMpD2+xH/3vfYKyxjf8hs+gNHU8ycvvLZkRr3h/yA07GZZO+bPOLjUumg3Ky/8+Pf/IK4Ny55tvTkx+GKPkBPWo0rFM5ZeD/Xf8ejhK6ZY/wfNKbCblE3e/dlkVFaHE2Te+eaBDFuX/B/jaRGM= 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AQAAAACAAADsJQAAjg0AAA==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 - - - AQAAAACAAAB7CQAAKQEAAA==eJztVssSwyAIvDNs/v9zq0AEBU2nh15anUmAlchjY0IAiC6ZzKwCCCwDjiSNYYsmiTB7BDUoTWy7pnHdUy7yXHmy3TQ+wSCDBrCz1wM88CGaAE8TSzz9IjFePd/COpVxD5awV1BCWNNqq5pEtmDuCk/3Ioro3ReYBp/qcnvWfnkHr3FErHcLghxoLH00BHU0wLK1OOluciwWbrYEKVJzavuo9coU37xiUGmtjIUt7Z4ssw4PB5oF/hm/lXFmocYndE6s9q35uETH04IH/AwryqjxfiBt4QM4IOl+PElMtreqlTS9aR/VH2uTM7fOpKtZ930ujhh/vgaE+HVQZvhxnA/o+IEKDkGffjsWqEDcDShdPdQdtEV2wMa+/OMcrNkGS6Dn8ALTBnLQ - - - - - diff --git a/11-L3-03N_valves_clipped.csv b/11-L3-03N_valves_clipped.csv deleted file mode 100644 index 506ff8a..0000000 --- a/11-L3-03N_valves_clipped.csv +++ /dev/null @@ -1,4 +0,0 @@ -x1,y1,z1,m -277.391,2086.437,1241,1 --1107.091,1720.756,1241,2 --953.078,1537.211,1378.307,3 diff --git a/E4F.txt b/E4F.txt deleted file mode 100644 index e69de29..0000000 diff --git a/E4F_clean.txt b/E4F_clean.txt deleted file mode 100644 index e69de29..0000000 diff --git a/activities.json b/activities.json deleted file mode 100644 index 0e777be..0000000 --- a/activities.json +++ /dev/null @@ -1,17 +0,0 @@ -{ - "Ta182n": [ - [ - 0.0 - ] - ], - "Ta182m": [ - [ - 0.0 - ] - ], - "Ta182": [ - [ - 0.00011723091704065617 - ] - ] -} \ No newline at end of file diff --git a/activities.txt b/activities.txt deleted file mode 100644 index e69de29..0000000 diff --git a/activities_converted.txt b/activities_converted.txt deleted file mode 100644 index e69de29..0000000 diff --git a/activity_data.json b/activity_data.json deleted file mode 100644 index de2fea6..0000000 --- a/activity_data.json +++ /dev/null @@ -1,3 +0,0 @@ -{ - "Ta182": [ - \ No newline at end of file diff --git a/cfg_test.yaml b/cfg_test.yaml deleted file mode 100644 index 0c37128..0000000 --- a/cfg_test.yaml +++ /dev/null @@ -1,6 +0,0 @@ -element: Ta -delta_impurity: 0.01 -input_flux: ./11-L3-03N.vtu -irrad_scenario: SA2 -sources_csv: ./11-L3-03N_valves_clipped.csv -decay_time: 1e6 diff --git a/src/f4epurity/main.py b/src/f4epurity/main.py index f008272..6f71da6 100644 --- a/src/f4epurity/main.py +++ b/src/f4epurity/main.py @@ -401,28 +401,6 @@ def process_sources(args: Namespace) -> None: writer.writerow([workstation, max_dose_str]) -def check_and_split_lines(file_name="mcnp_source.txt", limit=100): - file_path = os.path.join(os.getcwd(), file_name) - with open(file_path, "r") as file: - lines = file.readlines() - - new_lines = [] - for line in lines: - while len(line) > limit: - # Find the last space within the limit - split_pos = line.rfind(" ", 0, limit) - if split_pos == -1: - split_pos = limit - if split_pos == 0: - break - new_lines.append(line[:split_pos] + " &\n") - line = line[split_pos:].lstrip() - new_lines.append(line) - - with open(file_path, "w") as file: - file.writelines(new_lines) - - def main(args_list: list[str] | None = None): args = parse_arguments(args_list) process_sources(args) diff --git a/src/f4epurity/parsing.py b/src/f4epurity/parsing.py index 878f63e..163dc8e 100644 --- a/src/f4epurity/parsing.py +++ b/src/f4epurity/parsing.py @@ -142,7 +142,7 @@ def parse_arguments(args_list: Optional[List[str]] = None) -> Namespace: parser.add_argument( "--dump_source", action="store_true", - help="Optional 'dump_source' argument to generate mcnp_source.txt", + help="Optional 'dump_source' argument to generate source.sdef.file for MCNP", ) # Parse the arguments args = parser.parse_args(args_list) From b99d04a923d4aef851251cc6d88c6b1252e3f416 Mon Sep 17 00:00:00 2001 From: digiacomo4 Date: Wed, 19 Mar 2025 17:51:18 +0100 Subject: [PATCH 08/13] "write_sdef" argument added, suggested changes implemented --- src/f4epurity/dose.py | 4 ---- src/f4epurity/main.py | 6 +++--- src/f4epurity/parsing.py | 4 ++-- src/f4epurity/psource.py | 2 +- 4 files changed, 6 insertions(+), 10 deletions(-) diff --git a/src/f4epurity/dose.py b/src/f4epurity/dose.py index 8ee70e1..d4517b9 100644 --- a/src/f4epurity/dose.py +++ b/src/f4epurity/dose.py @@ -246,7 +246,6 @@ def write_vtk_file( # Avoid division by zero if distance == 0: dose_array[i, j, k] = dose[0] - print("Dose at source point is:", dose[0]) else: dose_array[i, j, k] = dose[0] / (4 * pi * distance**2) # Get the indices of the max value @@ -256,9 +255,6 @@ def write_vtk_file( x_max = x[indices[0]] y_max = y[indices[1]] z_max = z[indices[2]] - # print( - # f"Max dose value: {dose_array[indices]} at coordinates: ({x_max}, {y_max}, {z_max})" - # ) # Create the output directory if it doesn't exist os.makedirs("output", exist_ok=True) diff --git a/src/f4epurity/main.py b/src/f4epurity/main.py index 6f71da6..6987134 100644 --- a/src/f4epurity/main.py +++ b/src/f4epurity/main.py @@ -360,7 +360,7 @@ def process_sources(args: Namespace) -> None: dose_factors_df, ) dose_arrays.append(dose_array) - if args.dump_source: + if args.write_sdef: if args.m: mass = args.m[i] else: @@ -368,9 +368,9 @@ def process_sources(args: Namespace) -> None: sources.append(PointSource(activities, [x1, y1, z1], mass=mass)) calculate_dose_at_workstations(args, dose, x1, y1, z1, run_dir) - if args.dump_source: + if args.write_sdef: global_source = GlobalPointSource(sources) - global_source.to_sdef(f"{run_dir}/source.sdef") # TODO + global_source.to_sdef(f"{run_dir}/source.sdef") # If more than one dose array is present, sum the dose arrays (multiple sources) if len(dose_arrays) > 1: diff --git a/src/f4epurity/parsing.py b/src/f4epurity/parsing.py index 163dc8e..30b9e84 100644 --- a/src/f4epurity/parsing.py +++ b/src/f4epurity/parsing.py @@ -140,9 +140,9 @@ def parse_arguments(args_list: Optional[List[str]] = None) -> Namespace: ) # Add the optional "mcnp" argument parser.add_argument( - "--dump_source", + "--write_sdef", action="store_true", - help="Optional 'dump_source' argument to generate source.sdef.file for MCNP", + help="Optional 'write_sdef' argument to generate source.sdef.file for MCNP", ) # Parse the arguments args = parser.parse_args(args_list) diff --git a/src/f4epurity/psource.py b/src/f4epurity/psource.py index 594fd9d..6181121 100644 --- a/src/f4epurity/psource.py +++ b/src/f4epurity/psource.py @@ -46,7 +46,7 @@ def to_sdef(self, outfile: str | Path) -> None: gamma_per_second_line = ( f"FM4 {t_gamma:.2E} $ To be multiplied by 3.6E-09 to obtain Sv/hr\n" ) - conv_coeff_line = "DE4\n 0.01 0.015 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.10 0.15 0.20 0.3 0.4 0.5 0.6 0.8 1.0 2.0 4.0 6.0 8.0 10.0\nDF4\n 0.0485 0.1254 0.2050 0.2999 0.3381 0.3572 0.3780 0.4066 0.4399 0.5172 0.7523 1.0041 1.5083 1.9958\n 2.4657 2.9082 3.7269 4.4834 7.4896 12.0153 15.9873 19.9191 23.7600" + conv_coeff_line = "C ICRP-74 H*(10)\nDE4\n 0.01 0.015 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.10 0.15 0.20 0.3 0.4 0.5 0.6 0.8 1.0 2.0 4.0 6.0 8.0 10.0\nDF4\n 0.0485 0.1254 0.2050 0.2999 0.3381 0.3572 0.3780 0.4066 0.4399 0.5172 0.7523 1.0041 1.5083 1.9958\n 2.4657 2.9082 3.7269 4.4834 7.4896 12.0153 15.9873 19.9191 23.7600" for idx, psource in enumerate(self.sources): X, Y, p_activity = psource._compute_lines() From 51c00fcf11b86b5cc9ffda84e8273ab5d745c9a1 Mon Sep 17 00:00:00 2001 From: digiacomo4 Date: Thu, 20 Mar 2025 15:00:59 +0100 Subject: [PATCH 09/13] Final requested changes implemented --- src/f4epurity/dose.py | 42 ++++++++++++++++++++++-------------------- 1 file changed, 22 insertions(+), 20 deletions(-) diff --git a/src/f4epurity/dose.py b/src/f4epurity/dose.py index d4517b9..ca09988 100644 --- a/src/f4epurity/dose.py +++ b/src/f4epurity/dose.py @@ -224,28 +224,30 @@ def write_vtk_file( dose, x1, y1, z1, x2, y2, z2, x[i], y[j], z[k] ) dose_array[i, j, k] = dose_point - - # Create a 3D numpy array filled with the dose values based on 1/r^2 ( original bottom-left point source model) - else: - for i in range(len(x) - 1): - for j in range(len(y) - 1): - for k in range(len(z) - 1): - distance = np.sqrt( - (x[i] - x1) ** 2 + (y[j] - y1) ** 2 + (z[k] - z1) ** 2 - ) - ## get midpoints - # x_mid = (x[1:] + x[:-1]) / 2 - # y_mid = (y[1:] + y[:-1]) / 2 - # z_mid = (z[1:] + z[:-1]) / 2 - # for i in range(len(x_mid)): - # for j in range(len(y_mid)): - # for k in range(len(z_mid)): - # distance = np.sqrt( - # (x_mid[i] - x1) ** 2 + (y_mid[j] - y1) ** 2 + (z_mid[k] - z1) ** 2 - # ) + # # to create a 3D numpy array filled with the dose values based on 1/r^2 (original bottom-left point source model) + # else: + # for i in range(len(x) - 1): + # for j in range(len(y) - 1): + # for k in range(len(z) - 1): + # distance = np.sqrt( + # (x[i] - x1) ** 2 + (y[j] - y1) ** 2 + (z[k] - z1) ** 2 + # ) + # get midpoints + x_mid = (x[1:] + x[:-1]) / 2 + y_mid = (y[1:] + y[:-1]) / 2 + z_mid = (z[1:] + z[:-1]) / 2 + # Create a 3D numpy array filled with the dose values based on 1/r^2 ( on middle point source model) + for i in range(len(x_mid)): + for j in range(len(y_mid)): + for k in range(len(z_mid)): + distance = np.sqrt( + (x_mid[i] - x1) ** 2 + + (y_mid[j] - y1) ** 2 + + (z_mid[k] - z1) ** 2 + ) # Avoid division by zero if distance == 0: - dose_array[i, j, k] = dose[0] + dose_array[i, j, k] = dose[0] / (4 * pi) else: dose_array[i, j, k] = dose[0] / (4 * pi * distance**2) # Get the indices of the max value From b5c05a63c5089ad51cb9177923acf639c98e3f3d Mon Sep 17 00:00:00 2001 From: digiacomo4 Date: Thu, 20 Mar 2025 15:05:14 +0100 Subject: [PATCH 10/13] Final --- src/f4epurity/dose.py | 1 + 1 file changed, 1 insertion(+) diff --git a/src/f4epurity/dose.py b/src/f4epurity/dose.py index ca09988..4c09f96 100644 --- a/src/f4epurity/dose.py +++ b/src/f4epurity/dose.py @@ -232,6 +232,7 @@ def write_vtk_file( # distance = np.sqrt( # (x[i] - x1) ** 2 + (y[j] - y1) ** 2 + (z[k] - z1) ** 2 # ) + # get midpoints x_mid = (x[1:] + x[:-1]) / 2 y_mid = (y[1:] + y[:-1]) / 2 From 43ecbc69293412bef02696285e8621315c305859 Mon Sep 17 00:00:00 2001 From: digiacomo4 Date: Thu, 20 Mar 2025 15:05:42 +0100 Subject: [PATCH 11/13] Final --- src/f4epurity/resources/~$F4E_dosematrix.xlsx | Bin 165 -> 0 bytes 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 src/f4epurity/resources/~$F4E_dosematrix.xlsx diff --git a/src/f4epurity/resources/~$F4E_dosematrix.xlsx b/src/f4epurity/resources/~$F4E_dosematrix.xlsx deleted file mode 100644 index ee847ab28a97797d273569598aa15c198b65ca91..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 165 zcmb1k$y9L9Oia$t%~$YEEGbFNSI}@Xan*ILDA82F4Wt=d7%~|Y7~FwmB11AmK0_`L hD=_#1c_l!Y3g&4rxG|V8xH9MhaRpFR6R3h52mlAw7(W02 From 77ef8a5dbb72c33c9563288e29a5a9df5a485727 Mon Sep 17 00:00:00 2001 From: digiacomo4 Date: Thu, 20 Mar 2025 15:40:02 +0100 Subject: [PATCH 12/13] Fixed --- src/f4epurity/dose.py | 40 ++++++++++++++-------------------------- 1 file changed, 14 insertions(+), 26 deletions(-) diff --git a/src/f4epurity/dose.py b/src/f4epurity/dose.py index 4c09f96..469c27d 100644 --- a/src/f4epurity/dose.py +++ b/src/f4epurity/dose.py @@ -215,37 +215,25 @@ def write_vtk_file( # Check if dose is a list with more than one value (line source case) if hasattr(dose, "__iter__") and len(dose) > 1: + # get midpoints + x_mid = (x[1:] + x[:-1]) / 2 + y_mid = (y[1:] + y[:-1]) / 2 + z_mid = (z[1:] + z[:-1]) / 2 # Iterate over each point in the grid - for i in range(len(x) - 1): - for j in range(len(y) - 1): - for k in range(len(z) - 1): + # Create a 3D numpy array filled with the dose values based on 1/r^2 ( on middle point source model) + for i in range(len(x_mid)): + for j in range(len(y_mid)): + for k in range(len(z_mid)): + distance = np.sqrt( + (x_mid[i] - x1) ** 2 + + (y_mid[j] - y1) ** 2 + + (z_mid[k] - z1) ** 2 + ) # Calculate the dose at the point from the line source dose_point = dose_from_line_source( - dose, x1, y1, z1, x2, y2, z2, x[i], y[j], z[k] + dose, x1, y1, z1, x2, y2, z2, x_mid[i], y_mid[j], z_mid[k] ) dose_array[i, j, k] = dose_point - # # to create a 3D numpy array filled with the dose values based on 1/r^2 (original bottom-left point source model) - # else: - # for i in range(len(x) - 1): - # for j in range(len(y) - 1): - # for k in range(len(z) - 1): - # distance = np.sqrt( - # (x[i] - x1) ** 2 + (y[j] - y1) ** 2 + (z[k] - z1) ** 2 - # ) - - # get midpoints - x_mid = (x[1:] + x[:-1]) / 2 - y_mid = (y[1:] + y[:-1]) / 2 - z_mid = (z[1:] + z[:-1]) / 2 - # Create a 3D numpy array filled with the dose values based on 1/r^2 ( on middle point source model) - for i in range(len(x_mid)): - for j in range(len(y_mid)): - for k in range(len(z_mid)): - distance = np.sqrt( - (x_mid[i] - x1) ** 2 - + (y_mid[j] - y1) ** 2 - + (z_mid[k] - z1) ** 2 - ) # Avoid division by zero if distance == 0: dose_array[i, j, k] = dose[0] / (4 * pi) From e60ad1a5b37e76e76ea70d0d66e61773611c6f17 Mon Sep 17 00:00:00 2001 From: digiacomo4 Date: Thu, 20 Mar 2025 15:59:33 +0100 Subject: [PATCH 13/13] final fix middle point sources --- src/f4epurity/dose.py | 20 +++++++++++++------- 1 file changed, 13 insertions(+), 7 deletions(-) diff --git a/src/f4epurity/dose.py b/src/f4epurity/dose.py index 469c27d..36c5aa3 100644 --- a/src/f4epurity/dose.py +++ b/src/f4epurity/dose.py @@ -215,12 +215,22 @@ def write_vtk_file( # Check if dose is a list with more than one value (line source case) if hasattr(dose, "__iter__") and len(dose) > 1: + # Iterate over each point in the grid + for i in range(len(x) - 1): + for j in range(len(y) - 1): + for k in range(len(z) - 1): + # Calculate the dose at the point from the line source + dose_point = dose_from_line_source( + dose, x1, y1, z1, x2, y2, z2, x[i], y[j], z[k] + ) + dose_array[i, j, k] = dose_point + + else: # Point source case # get midpoints x_mid = (x[1:] + x[:-1]) / 2 y_mid = (y[1:] + y[:-1]) / 2 z_mid = (z[1:] + z[:-1]) / 2 - # Iterate over each point in the grid - # Create a 3D numpy array filled with the dose values based on 1/r^2 ( on middle point source model) + # Create a 3D numpy array filled with the dose values based on 1/r^2 (on middle point source model) for i in range(len(x_mid)): for j in range(len(y_mid)): for k in range(len(z_mid)): @@ -229,16 +239,12 @@ def write_vtk_file( + (y_mid[j] - y1) ** 2 + (z_mid[k] - z1) ** 2 ) - # Calculate the dose at the point from the line source - dose_point = dose_from_line_source( - dose, x1, y1, z1, x2, y2, z2, x_mid[i], y_mid[j], z_mid[k] - ) - dose_array[i, j, k] = dose_point # Avoid division by zero if distance == 0: dose_array[i, j, k] = dose[0] / (4 * pi) else: dose_array[i, j, k] = dose[0] / (4 * pi * distance**2) + # Get the indices of the max value indices = np.unravel_index(np.argmax(dose_array, axis=None), dose_array.shape)