diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 3c7cd86..8413037 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -18,16 +18,10 @@ jobs:
python-version: 3.9,
}
- {
- name: "MacOS_Latest",
- os: macos-latest,
+ name: "MacOS_13",
+ os: macos-13,
python-version: 3.9,
}
- - {
- name: "Windows Latest",
- os: windows-latest,
- python-version: 3.9,
- archiver: "7z a"
- }
steps:
- uses: actions/checkout@master
@@ -64,35 +58,6 @@ jobs:
export PATH=$PATH:/Users/runner/work/KDSource/KDinstall/bin
kdtool -h
kdtool resample -h
- - name: Install and test C API on Windows
- if: startsWith(matrix.config.name, 'Windows')
- run: |
- $KDDIR = "$pwd"
- $INSTALLDIR = "$pwd\..\KDinstall"
- mkdir dependencies
- cd dependencies
- Invoke-Webrequest http://xmlsoft.org/sources/win32/64bit/libxml2-2.9.3-win32-x86_64.7z -Outfile libxml2-2.9.3-win32-x86_64.7z
- Invoke-Webrequest http://xmlsoft.org/sources/win32/64bit/iconv-1.14-win32-x86_64.7z -Outfile iconv-1.14-win32-x86_64.7z
- Invoke-Webrequest http://xmlsoft.org/sources/win32/64bit/zlib-1.2.8-win32-x86_64.7z -Outfile zlib-1.2.8-win32-x86_64.7z
- 7z x libxml2-2.9.3-win32-x86_64.7z -o"libxml2-2.9.3-win32-x86_64" -y
- 7z x iconv-1.14-win32-x86_64.7z -o"iconv-1.14-win32-x86_64" -y
- 7z x zlib-1.2.8-win32-x86_64.7z -o"zlib-1.2.8-win32-x86_64" -y
- $env:PATH = "$KDDIR\dependencies\libxml2-2.9.3-win32-x86_64;" + $env:PATH
- $env:PATH = "$KDDIR\dependencies\iconv-1.14-win32-x86_64;" + $env:PATH
- $env:PATH = "$KDDIR\dependencies\zlib-1.2.8-win32-x86_64;" + $env:PATH
- $env:C_INCLUDE_PATH="$KDDIR\dependencies\iconv-1.14-win32-x86_64\include"
- cd ..
- mkdir build
- cd build
- cmake .. -DCMAKE_INSTALL_PREFIX="$INSTALLDIR" -G "MinGW Makefiles"
- mingw32-make
- mingw32-make test
- mingw32-make install
- cd ..
- echo "$INSTALLDIR\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
- echo "$INSTALLDIR\lib" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
- $env:PATH += ";$INSTALLDIR\bin;$INSTALLDIR\lib"
- kdtool-resample -h
- name: Set up Python ${{ matrix.config.python-version }}
uses: actions/setup-python@v2
with:
@@ -100,4 +65,4 @@ jobs:
- name: Install tox
run: pip install tox tox-gh-actions
- name: Test with tox
- run: tox -r -c python
+ run: tox -r -c python
\ No newline at end of file
diff --git a/docs/examples/Example_surfsource.ipynb b/docs/examples/Example_surfsource.ipynb
new file mode 100644
index 0000000..5956aae
--- /dev/null
+++ b/docs/examples/Example_surfsource.ipynb
@@ -0,0 +1,1418 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "[](https://colab.research.google.com/github/KDSource/KDSource/blob/master/docs/examples/Verification.ipynb)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# SurfSource module example"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "qDgK9MZtOSDg"
+ },
+ "source": [
+ "## Install KDSource"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "This function installs KDSource in a Google Colaboratory instance.\n",
+ "To install locally follow instructions in documentation:\n",
+ "link/to/docs?\n",
+ "Installed KDSource in 0.00 minutes\n"
+ ]
+ }
+ ],
+ "source": [
+ "#\n",
+ "# Executing this cell you will install KDSource\n",
+ "# in this instance of the Google Colaboratory virtual machine.\n",
+ "# The process takes about 1 minutes.\n",
+ "#\n",
+ "\n",
+ "def install_kdsource():\n",
+ " #\n",
+ " # Clone source code from Github, make and install\n",
+ " #\n",
+ " \n",
+ " import os\n",
+ " \n",
+ " if not os.path.isdir('/content'):\n",
+ " print(\"This function installs KDSource in a Google Colaboratory instance.\")\n",
+ " print(\"To install locally follow instructions in documentation:\")\n",
+ " print(\"link/to/docs?\")\n",
+ " return\n",
+ " \n",
+ " %cd -q /content\n",
+ " print(\"Obtaining KDSource source code from Github...\")\n",
+ " !git --no-pager clone --recurse-submodules https://$username:$password@github.com/inti-abbate/KDSource &> /dev/null\n",
+ " %cd -q KDSource\n",
+ " !git --no-pager checkout master &> /dev/null\n",
+ " !mkdir build\n",
+ " %cd -q build\n",
+ " print(\"Running cmake...\")\n",
+ " !cmake .. -DCMAKE_INSTALL_PREFIX=/usr/local/KDSource &> /dev/null\n",
+ " print(\"Running make install...\")\n",
+ " !make install &> /dev/null\n",
+ " print(\"Installing Python API...\")\n",
+ " %cd -q ../python\n",
+ " !pip install . &> /dev/null\n",
+ " \n",
+ " os.environ['PATH'] += \":/usr/local/KDSource/bin\"\n",
+ " \n",
+ " %cd -q /content\n",
+ "\n",
+ "from time import time\n",
+ "t1 = time()\n",
+ "install_kdsource()\n",
+ "t2 = time()\n",
+ "print(\"Installed KDSource in {:.2f} minutes\".format((t2-t1)/60.0))\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "mTYaKwkahq23"
+ },
+ "source": [
+ "## Generate synthetic data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Z-ZBeC61ZtWR"
+ },
+ "source": [
+ "A particle list will be generated with the following joint distribution:\n",
+ "\n",
+ "$$f(u,x,y,z,\\mu,\\phi)=\\left(f_{U,1}(u)f_{X,1}(x)+f_{U,2}(u)f_{X,2}(x)\\right)f_Y(y)\\ f_{\\mu}(\\mu)\\ f_{\\Phi}(\\phi)$$\n",
+ "\n",
+ "Being $u=log(E_0/E)$ the lethargy, $(x,y)$ the 2D position ($z$ is fixed at 0), and $\\mu=cos(\\theta),\\phi$ the polar coordinates, so that $\\hat{\\Omega}=(d_x,d_y,d_z)=(sin(\\theta)cos(\\phi),sin(\\theta)sin(\\phi),cos(\\theta))$ is the direction unit-vector.\n",
+ "\n",
+ "This means that there are two \"clusters\" of particles, each one with a characteristic energy and x distribution, implying that this variables are correlated. The other variables have a separated density distribution.\n",
+ "\n",
+ "The specific distributions for each variable are described as follows: \n",
+ "\n",
+ "* Energy: Normal distribution for lethargy, for each cluster:\n",
+ "\n",
+ " $$f_{U,i}(u)=\\frac{1}{\\sigma_u\\sqrt{2\\pi}}exp\\left(-\\frac{(u-\\mu_{u,i})^2}{2\\sigma_u^2}\\right),\\ i=1,2$$\n",
+ "\n",
+ "* Position: Normal distribution for x, for each cluster. Normal distribution around 0 for y. Fixed z = 0:\n",
+ "\n",
+ " $$f_{x,i}(x)=\\frac{1}{\\sigma_x\\sqrt{2\\pi}}exp\\left(-\\frac{(x-\\mu_{x,i})^2}{2\\sigma_x^2}\\right),\\ i=1,2,\\ f_y(y)=\\frac{1}{\\sigma_y\\sqrt{2\\pi}}exp\\left(-\\frac{y^2}{2\\sigma_y^2}\\right)$$\n",
+ "\n",
+ "* Direction: \"Cosine distribution\", uniform in φ:\n",
+ "\n",
+ " $$f_{\\mu}(\\mu)=2\\mu,\\ \\mu>0,\\ f_{\\phi}(\\phi)=\\frac{1}{2\\pi}$$\n",
+ "\n",
+ "* Weight: Normal distribution around 1.\n",
+ "\n",
+ " $$f(w)=\\frac{1}{\\sigma_w\\sqrt{2\\pi}}exp\\left(-\\frac{(w-1)^2}{2\\sigma_w^2}\\right)$$\n",
+ "\n",
+ " with σw small enough so that w is always greater than 0.\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[ 7.86898673, -4.47678194, 0. ],\n",
+ " [ -1.5534645 , 0.83804229, 0. ],\n",
+ " [ 10.13570675, -20.63651786, 0. ],\n",
+ " ...,\n",
+ " [ -5.31766347, 0.53368685, 0. ],\n",
+ " [ -5.3964052 , -10.14346791, 0. ],\n",
+ " [ 11.36166262, 1.90695862, 0. ]])"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "poss"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[0.11590068],\n",
+ " [0.23534216],\n",
+ " [0.25830334],\n",
+ " ...,\n",
+ " [0.00131621],\n",
+ " [0.00070511],\n",
+ " [0.0021836 ]])"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "Es"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[0.89751165],\n",
+ " [0.32431357],\n",
+ " [0.20807252],\n",
+ " ...,\n",
+ " [0.08887235],\n",
+ " [0.42688587],\n",
+ " [0.20736271]])"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "ts.reshape(1,N).T"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "executionInfo": {
+ "elapsed": 1746,
+ "status": "ok",
+ "timestamp": 1634594735429,
+ "user": {
+ "displayName": "Inti Abbate",
+ "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
+ "userId": "15235311645725350304"
+ },
+ "user_tz": 180
+ },
+ "id": "9sOTsUUNhq25",
+ "outputId": "03b4ca62-aae5-4bf0-ce53-e380f6096946",
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Writing particles into SSV file...\n",
+ "Done. All particles written into samples.ssv\n",
+ "ssv_open_file: Opened file \"samples.ssv\":\n",
+ "MCPL: Attempting to compress file samples.mcpl with gzip\n",
+ "MCPL: Succesfully compressed file into samples.mcpl.gz\n",
+ "Created samples.mcpl.gz\n"
+ ]
+ }
+ ],
+ "source": [
+ "import os\n",
+ "import numpy as np\n",
+ "import kdsource as kds\n",
+ "import mcpl\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "N = int(1E5) # Size of particle list\n",
+ "\n",
+ "pt = \"n\" # Particle type: neutron\n",
+ "\n",
+ "# Energy\n",
+ "E0 = 10.0\n",
+ "sigma_u = 1\n",
+ "mu_u_1 = 5\n",
+ "mu_u_2 = 9\n",
+ "us_1 = np.random.normal(mu_u_1, sigma_u, (int(N/2),1))\n",
+ "us_2 = np.random.normal(mu_u_2, sigma_u, (int(N/2),1))\n",
+ "us = np.concatenate((us_1, us_2), axis=0)\n",
+ "Es = E0 * np.exp(-us)\n",
+ "# Position\n",
+ "sigma_x = sigma_y = 10\n",
+ "mu_x_1 = sigma_x\n",
+ "mu_x_2 = -sigma_x\n",
+ "poss_1 = np.random.normal([mu_x_1,0,0], [sigma_x,sigma_y,0], (int(N/2),3))\n",
+ "poss_2 = np.random.normal([mu_x_2,0,0], [sigma_x,sigma_y,0], (int(N/2),3))\n",
+ "poss = np.concatenate((poss_1, poss_2), axis=0)\n",
+ "# Direction\n",
+ "mus = np.sqrt(np.random.uniform(0,1,N))\n",
+ "phis = np.random.uniform(-np.pi,np.pi,N)\n",
+ "dxs = np.sqrt(1-mus**2) * np.cos(phis)\n",
+ "dys = np.sqrt(1-mus**2) * np.sin(phis)\n",
+ "dzs = mus\n",
+ "dirs = np.stack((dxs,dys,dzs), axis=1)\n",
+ "# Time\n",
+ "ts = np.random.uniform(0, 1, N).reshape(1, N).T\n",
+ "# Stack energies, positions, directions and times\n",
+ "parts = np.concatenate((Es,poss,dirs,ts), axis=1)\n",
+ "np.random.shuffle(parts)\n",
+ "\n",
+ "# Weights\n",
+ "sigma_w = 0.1\n",
+ "ws = np.random.normal(1, sigma_w, N)\n",
+ "\n",
+ "ssvfile = \"samples.ssv\"\n",
+ "kds.savessv(pt, parts, ws, ssvfile)\n",
+ "!ssv2mcpl $ssvfile samples\n",
+ "samples = \"samples.mcpl.gz\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Open the MCPL file with the `SurfaceSourceFile` module"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "It's possible to open a tracks-file generated by a Monte-Carlo code, and then plot the distributions.\n",
+ "\n",
+ "The formats that are compatible at the moment are `.hdf5` (`OpenMC`), `MCPL` and `SSV` (`KDSource` ASCII format)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The important data to open the files are: the path, the source particles simulated in the original simulation, and the source factor (by default, 1.0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Help on class SurfaceSourceFile in module kdsource.surfsource:\n",
+ "\n",
+ "class SurfaceSourceFile(builtins.object)\n",
+ " | SurfaceSourceFile(filepath, S0=1.0, Nparticles=1000000000.0, dA=None, translation=[0, 0, 0], rotation=[0, 0, 0], domain={}, set_domain_first=False, set_rotation_first=False, E0=20, convoluted=False, uvw_reference='w', surface=None, Nmax=1000000000.0, current=None, tpulse=None, skip_cloned=[], pulse_shape='rectangular')\n",
+ " | \n",
+ " | Read a surface source file.\n",
+ " | Possible formats: MCPL, HDF5 (OpenMC), SSV (KDSource)\n",
+ " | \n",
+ " | Parameters\n",
+ " | ----------\n",
+ " | filepath: str\n",
+ " | Path to surface source file.\n",
+ " | S0: float\n",
+ " | Simulation source factor.\n",
+ " | Default: 1.0\n",
+ " | Nparticles: int\n",
+ " | Total source particles simulated.\n",
+ " | Default: 1e9\n",
+ " | dA: float\n",
+ " | Area of the surface [cm$^2$].\n",
+ " | Default: None\n",
+ " | translation: list\n",
+ " | Translation for the position variables [cm].\n",
+ " | Default: [0, 0, 0]\n",
+ " | rotation: list\n",
+ " | Rotation for the position and direction variables [deg].\n",
+ " | Default: [0, 0, 0]\n",
+ " | domain: dict\n",
+ " | Range of the variables: var_min <= 'var' < var_max.\n",
+ " | It must be defined like: {'var':[var_min, var_max]}.\n",
+ " | List of possible variables:\n",
+ " | type [PDG],\n",
+ " | E [MeV], ln(E0/E), lambda [AA],\n",
+ " | x [cm], y [cm], z [cm], R [cm], theta [rad],\n",
+ " | u, v, w, phi [rad], psi [rad],\n",
+ " | t [ms], log(t),\n",
+ " | wgt.\n",
+ " | Default: {}\n",
+ " | set_domain_first: bool\n",
+ " | Define if the variables domains and the normal direction\n",
+ " | must be set before or after the translation and rotation.\n",
+ " | Default: False\n",
+ " | set_rotation_first: bool\n",
+ " | Define if the rotation must be done before or after\n",
+ " | the translation of the coordinates.\n",
+ " | Default: False\n",
+ " | E0: float\n",
+ " | Reference energy [MeV] to calculate the lethargy ln(E0/E).\n",
+ " | Default: 20\n",
+ " | convoluted: bool\n",
+ " | Define if the simulation was performed with a convoluted pulse or not.\n",
+ " | Default: False\n",
+ " | uvw_reference: str\n",
+ " | Versor which is normal to the surface source after the rotation.\n",
+ " | Default: 'w'\n",
+ " | surface: int\n",
+ " | Define the surface id to extract the particles from the tracks file.\n",
+ " | Default: None\n",
+ " | Nmax: int\n",
+ " | Define the maximum number of particles to be readed.\n",
+ " | current: float\n",
+ " | Current, in mA, to normalize the results.\n",
+ " | tpulse: float\n",
+ " | Pulse width, in s, to normalize the results.\n",
+ " | \n",
+ " | Methods defined here:\n",
+ " | \n",
+ " | __init__(self, filepath, S0=1.0, Nparticles=1000000000.0, dA=None, translation=[0, 0, 0], rotation=[0, 0, 0], domain={}, set_domain_first=False, set_rotation_first=False, E0=20, convoluted=False, uvw_reference='w', surface=None, Nmax=1000000000.0, current=None, tpulse=None, skip_cloned=[], pulse_shape='rectangular')\n",
+ " | Initialize self. See help(type(self)) for accurate signature.\n",
+ " | \n",
+ " | __read__(self)\n",
+ " | \n",
+ " | get_distribution(self, vars, bins, scales=['linear'], factor=1.0, filters={}, norm_vars=[], convolution=None, total=False)\n",
+ " | Obtain a N-D distribution for given variables\n",
+ " | \n",
+ " | Parameters\n",
+ " | ----------\n",
+ " | vars: list of str\n",
+ " | Variables to calculate the distribution\n",
+ " | bins: list of int or list of iterable\n",
+ " | Number or array of bins to calculate the distribution.\n",
+ " | If int, then the distributions are going to be calculated between\n",
+ " | min(var), max(var) for int bins.\n",
+ " | If 0, the number of bins will be calculated optimally with\n",
+ " | the knuth_bin_width function from astropy.\n",
+ " | scales: list of str\n",
+ " | Scales of the axis. The options are linear or log.\n",
+ " | Default: ['linear']\n",
+ " | factor: float\n",
+ " | Multiply the distribution by a specific numerical factor.\n",
+ " | Default: 1.0\n",
+ " | filters: dict\n",
+ " | Specify the other coordinates domain for the distribution\n",
+ " | It must be defined like: {'var':[var_min, var_max]}\n",
+ " | Default: {}\n",
+ " | norm_vars: list of str\n",
+ " | List of the variables to calculate the brilliance, i.e. to\n",
+ " | normalize the distribution.\n",
+ " | List of possible variables:\n",
+ " | mAs [mAs], E [MeV], ln(E0/E), lambda [AA],\n",
+ " | x [cm], y [cm], z [cm], R [cm], theta [rad],\n",
+ " | u, v, w, phi [rad], psi [rad],\n",
+ " | t [ms], log(t),\n",
+ " | convolution: dict\n",
+ " | Specify the coordinates to perform a convolution.\n",
+ " | It must be defined like: {'var':[var_convolution]}\n",
+ " | Default: {}\n",
+ " | total: bool\n",
+ " | Define if the integral value is required.\n",
+ " | Default: False\n",
+ " | \n",
+ " | Returns\n",
+ " | -------\n",
+ " | p_df: DataFrame\n",
+ " | Pandas DataFrame with the bins and the distribution.\n",
+ " | bins: list of arrays\n",
+ " | List of the bins where the distributions were calculated.\n",
+ " | p_info: str\n",
+ " | String with the information of the distributions calculated.\n",
+ " | p_total: ufloat\n",
+ " | If total is True, the integral value of the distribution for the\n",
+ " | required normalization variables.\n",
+ " | p_units: str\n",
+ " | If total is True, the units for the integral value of the\n",
+ " | distribution.\n",
+ " | \n",
+ " | get_domain(self, vars, filters={})\n",
+ " | \n",
+ " | get_pandas_dataframe(self)\n",
+ " | Obtain a DataFrame with the particles that were saved in the file\n",
+ " | \n",
+ " | plot_distribution(self, vars, bins, scales=['linear', 'linear'], factor=1.0, filters={}, norm_vars=[], errors=True, convolution=None, zlevels=1, info=False, ylabel=None, zlabel=None, zscale='linear', tolerance=1.0, vmin=None, vmax=None, peak_brilliance=False, **kwargs)\n",
+ " | Plot 1-D or 2-D distribution for given variables\n",
+ " | \n",
+ " | Parameters\n",
+ " | ----------\n",
+ " | vars: list of str\n",
+ " | Variables to calculate the distribution\n",
+ " | bins: list of int or list of iterable\n",
+ " | Number or array of bins to calculate the distribution.\n",
+ " | If int, then the distributions are going to be calculated between\n",
+ " | min(var), max(var) for int bins.\n",
+ " | If 0, the number of bins will be calculated optimally with\n",
+ " | the knuth_bin_width function from astropy.\n",
+ " | scales: list of str\n",
+ " | Scales of the axis. The options are linear or log.\n",
+ " | Default: ['linear', 'linear']\n",
+ " | factor: float\n",
+ " | Multiply the distribution by a specific numerical factor.\n",
+ " | Default: 1.0\n",
+ " | filters: dict\n",
+ " | Specify the other coordinates domain for the distribution\n",
+ " | It must be defined like: {'var':[var_min, var_max]}\n",
+ " | Default: {}\n",
+ " | norm_vars: list of str\n",
+ " | List of the variables to calculate the brilliance, i.e. to\n",
+ " | normalize the distribution.\n",
+ " | errors: bool\n",
+ " | Specify if the error bars should be plotted or not.\n",
+ " | Default: True\n",
+ " | convolution: dict\n",
+ " | Specify the coordinates to perform a convolution.\n",
+ " | It must be defined like: {'var':[var_convolution]}\n",
+ " | Default: {}\n",
+ " | zlevels: int\n",
+ " | Number of countour surfaces to plot with the colormap (if more\n",
+ " | than one variable is required). Deprecated when zscale is 'log'\n",
+ " | Default: 1\n",
+ " | info: bool\n",
+ " | Specify if the information of the plot should be included or not.\n",
+ " | Default: False\n",
+ " | ylabel: str\n",
+ " | Label for the y-axis.\n",
+ " | Default: 'Intensity' (for 1-D plots)\n",
+ " | zlabel: str\n",
+ " | Label for the z-axis (2-D plots).\n",
+ " | Default: 'Intensity'\n",
+ " | zscale: str\n",
+ " | Scale for the colorbar (2-D plots).\n",
+ " | Default: 'linear'\n",
+ " | tolerance: float\n",
+ " | When the realive error is greater or equal than the tolerance,\n",
+ " | the mean values of the distribution are not shown.\n",
+ " | Default: 1.0\n",
+ " | **kwargs\n",
+ " | Extra arguments that will be passed to matplotlib\n",
+ " | (label, color, and so on)\n",
+ " | \n",
+ " | Returns\n",
+ " | -------\n",
+ " | matplotlib 1-D or 2-D plot for the required variables with the proper\n",
+ " | normalization.\n",
+ " | \n",
+ " | save_source_file(self, filepath, **kwargs)\n",
+ " | Save the particles list.\n",
+ " | Possible formats: MCPL, HDF5 (OpenMC), SSV (KDSource)\n",
+ " | \n",
+ " | Parameters\n",
+ " | ----------\n",
+ " | filepath: str\n",
+ " | Path to the surface source file.\n",
+ " | **kwargs:\n",
+ " | Extra arguments for the .h5 format\n",
+ " | \n",
+ " | search_duplicated_particles(self, df, vars)\n",
+ " | \n",
+ " | ----------------------------------------------------------------------\n",
+ " | Data descriptors defined here:\n",
+ " | \n",
+ " | __dict__\n",
+ " | dictionary for instance variables (if defined)\n",
+ " | \n",
+ " | __weakref__\n",
+ " | list of weak references to the object (if defined)\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "help(kds.SurfaceSourceFile)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Number of particles in file samples.mcpl.gz: 100000\n",
+ "100% n, 0% g, 0% e-, 0% e+, 0% p, \n"
+ ]
+ }
+ ],
+ "source": [
+ "ssf = kds.SurfaceSourceFile('samples.mcpl.gz', Nparticles=N, S0=1, E0=E0, )"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The `SurfaceSourceFile` will generate a pandas DataFrame list with all the particles and their variables (position, direction, polarization, energy, time, and weight)\n",
+ "\n",
+ "Also, new coordinates are calculated to then estimate the distributions: lethargy, log(t), polar spatial coordinates, and spherical angular coordinates"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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\n",
+ "