diff --git a/.gitignore b/.gitignore index 2086218..dc76f14 100644 --- a/.gitignore +++ b/.gitignore @@ -2,6 +2,7 @@ playground shared/BOUT-dev/ plots* +cached_data/* # Ignore log files *.log diff --git a/ReadMe.md b/ReadMe.md index 9325da9..33c5c92 100644 --- a/ReadMe.md +++ b/ReadMe.md @@ -1,5 +1,9 @@ # North Simulation -Numerical model for a magnetically confined plasma in the NORTH tokamak. Note: The bout++ model was developed at DTU Physics by the Plasma Physics and Fusion Energy group. We use [BOUT++](http://boutproject.github.io/) for numerical simulation. +Numerical reduced fluid model of perpendicular dynamics a magnetically confined plasma in the NORTH tokamak. We use [BOUT++](http://boutproject.github.io/) for numerical simulation. Note: The bout++ model is an implementation of the ESEL equations, developed at DTU Physics by the Plasma Physics and Fusion Energy group. + +The results can be seen in the folder [presentation of main results](https://github.com/PPFE-Turbulence/north-simulation/tree/master/presentation%20of%20main%20results). + +To reproduce the main results of the data analysis, open and run the iPython Notebook `main_results.ipynb`. Note: It requires data from a simulation run. See the documentaion below on how to run simulations either on the Niflheim environment or (perhaps easier for a start) on your personal computer. ## Getting started --------------------------------------------------------- @@ -15,7 +19,12 @@ cd ./north-simulation ``` . ./install_bout_env_niflheim.sh north-simulation ``` -3. Navigate to the shared directory and clone and compile the Bout-dev git (make sure the path `~/north-simulation` equals the path to the repo directory): +3. Install python-requirements to the python venv with pip: +``` +. ~/local/venv/north-simulation/bin/activate +pip install -r requirements.txt +``` +4. Navigate to the shared directory and clone and compile the Bout-dev git (make sure the path `~/north-simulation` equals the path to the repo directory): ``` module restore bout cd ~/north-simulation/shared @@ -39,6 +48,52 @@ make ``` ./north_full_ESEL ``` +**NOTE:** The command above starts simulations directly on the node, which is not recommended (but it's useful for verifying that the code will evens start running). Instead you should queue the simulations using slurm: + +### Niflheim: Slurm Commands +I've included the script `./shared/NORTH/sbatch_north_full_ESEL.sh` which takes care of queuing simulations using SLURM. Here is how to do it: +- Before running the sbatch script: Navigate to `shared/NORTH/`. In there, create a directory for output logs: +``` +mkdir slurm_out +``` +- Submit batch job (look at Slurm Batch job script in `shared/NORTH/` for how to write a sbatch file): +``` +sbatch sbatch_north_full_ESEL.sh +``` +- show your queue +``` +squeue -u $USER +``` +- Cancel all your jobs: +``` +scancel -u $USER +``` +- Cancel all pending jobs: +``` +scancel -t PD +``` +- Stop job gracefully (run file with `stopCheck=true` as is done in the slurm sbatch script), then add `BOUT.stop` file: +``` +touch ./data/BOUT.stop +``` + +### Niflheim/VSCode: Trouble-Shooting +**bash: fork: retry: No child processes** + +If VSCode causes a [fork bomb](https://stackoverflow.com/questions/47143093/linux-ssh-bash-fork-retry-no-child-processes) with the error message: `bash: fork: retry: No child processes`, simply open up a terminal and write +``` +kill -9 -1 +``` + +**Python Interpreter Problems** + +If the VSCode Python Extension won't recognize your Python Interpreter. Pressing cmd/ctrl + shift P with the following command names are useful: +- `Python: Create Terminal` +- `Python: Run Python File in Terminal` +- `Python: Select Interpreter` > Enter Interpreter Path (e.g. `~/local/venv/north-simulation/bin/python`). If this doesn't work: +- `Python: Clear Workspace Interpreter Settings` + +Alternatively, try downgrading the Python Extension by going to Extensions > Python (SSH - Installed, not Local - Installed) > click the down arrow next to the uninstall button and choose "Install another version" > 2022.18.2 --------------------------------------------------------- ### Personal Computer: First time setup @@ -126,7 +181,7 @@ conda activate north-simulation pip install -r requirements.txt ``` -Run the file `data_analysis.py` to reproduce the main results. +To reproduce the main results of the data analysis, refer to the iPython Notebook `main_results.ipynb`. ## Usefull commands here are some usefull commands. If you're on your personal computer, I assume you have Docker running, started a Docker container and navigated to the north directory. @@ -143,26 +198,4 @@ here are some usefull commands. If you're on your personal computer, I assume yo - Help on command line options ``` ./north_full_ESEL -h -``` - -### Niflheim: Slurm Commands -- Submit batch job (look at Slurm Batch job script in `shared/NORTH/` for how to write a sbatch file): -``` -sbatch sbatch_north_full_ESEL.sh -``` -- show your queue -``` -squeue -u $USER -``` -- Cancel all your jobs: -``` -scancel -u $USER -``` -- Cancel all pending jobs: -``` -scancel -t PD -``` -- Stop job gracefully (run file with `stopCheck=true` as is done in the slurm sbatch script), then add `BOUT.stop` file: -``` -touch ./data/BOUT.stop ``` \ No newline at end of file diff --git a/Video_script copy.py b/Video_script copy.py deleted file mode 100644 index 4d977b4..0000000 --- a/Video_script copy.py +++ /dev/null @@ -1,66 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Created on Mon Dec 6 10:08:12 2021 - -@author: alec -""" -#%% -import numpy as np -from matplotlib import pyplot as plt -from boutdata import collect - -import matplotlib.cm as cm -import matplotlib.animation as animation -from matplotlib.animation import FuncAnimation -from boututils.datafile import DataFile - - - - -#%% Read data -i = 300 - - -path = './shared/NORTH/data/' -field_list = ['T', 'n', 'phi', 'vort', 'ZMIN','ZMAX', 'dx', 'dy', 'dz'] -par_list = ['t_array'] -fast_list = ['t_array', 'n0', 'phi0', 'n1', 'phi1', 'n2', 'phi2', 'T0', 'T1', 'T2'] - -data, par, fast = {}, {}, {} - -for _field in field_list: - data[_field] = collect(_field, path = path, xguards = False) - -for _par in par_list: - par[_par] = collect(_par, path = path, xguards = False) - -#for _fast in fast_list: -# fast[_fast] = collect(_fast, path = path, prefix = 'BOUT.fast', xguards = False) - -domain = data['n'].shape - - -plt.rcParams["figure.figsize"] = [10.0, 10.0] -plt.rcParams["figure.autolayout"] = True - - -# Create a figure and a set of subplots -fig, ax = plt.subplots() -# Method to change the contour data points -def animate(i): - ax.clear() - ax.contourf(np.transpose(data['vort'][i, :, 0, :]), 100, cmap = 'jet', vmin=-0.5, vmax=0.5) - #ax.contourf(np.transpose(data['n'][i, :, 0, :]), 100, cmap = 'inferno', vmin=0, vmax=1.4) - #ax.contourf(np.transpose(data['T'][i, :, 0, :]), 100, cmap = 'inferno', vmin=0, vmax=1.2) - - - ax.set_xlabel('x') - ax.set_ylabel('y') - - fig.tight_layout() - -# Call animate method -ani = animation.FuncAnimation(fig, animate, frames=i, interval=10, blit=False) - -ani.save('plots/animation_vort.gif', fps = 10) - diff --git a/Video_script.py b/Video_script.py deleted file mode 100644 index 1187769..0000000 --- a/Video_script.py +++ /dev/null @@ -1,130 +0,0 @@ - -#%% -import numpy as np - - -from matplotlib import pyplot as plt -from boutdata import collect -import matplotlib.cm as cm -import matplotlib.animation as animation -from matplotlib.animation import FuncAnimation -from boututils.datafile import DataFile - - - - - - -#%% Read data - -path = './shared/NORTH/data/' -field_list = ['T', 'n', 'phi', 'vort'] -par_list = ['t_array'] -fast_list = ['t_array', 'n0', 'phi0', 'n1', 'phi1', 'n2', 'phi2', 'T0', 'T1', 'T2'] - -data, par, fast = {}, {}, {} - - -dmp_files = DataFile("./shared/NORTH/data/BOUT.dmp.0.nc").list() -fast_files = DataFile("./shared/NORTH/data/BOUT.fast.0.nc").list() - -for _field in field_list: - data[_field] = collect(_field, path = path, xguards = False) - -for _par in par_list: - par[_par] = collect(_par, path = path, xguards = False) - -for _fast in fast_list: - fast[_fast] = collect(_fast, path = path, prefix = 'BOUT.fast', xguards = False) - -domain = data['n'].shape - -#%% Plot data - -probe_pos = [0.80, 0.82, 0.84] -dmp_nr = 80 - -fig, ax = plt.subplots(3) - -plot0 = ax[0].contourf(np.transpose(data['n'][dmp_nr, :, 0, :]), 100, cmap = 'inferno') -for probe in probe_pos: - ax[0].plot([int(probe*domain[1])+2], [int(0.50*domain[3])], 'o') -ax[0].set_xlabel('x') -ax[0].set_ylabel('y') -plt.colorbar(plot0, ax=ax[0]) - -plot1 = ax[1].contourf(np.transpose(data['T'][dmp_nr, :, 0, :]), 100, cmap = 'inferno') -for probe in probe_pos: - ax[1].plot([int(probe*domain[1])+2], [int(0.50*domain[3])], 'o') -ax[1].set_xlabel('x') -ax[1].set_ylabel('y') -plt.colorbar(plot1, ax=ax[1]) - -plot2 = ax[2].contourf(np.transpose(data['vort'][dmp_nr, :, 0, :]), 100, cmap = 'jet') -for probe in probe_pos: - ax[2].plot([int(probe*domain[1])+2], [int(0.50*domain[3])], 'o') -ax[2].set_xlabel('x') -ax[2].set_ylabel('y') -plt.colorbar(plot2, ax=ax[2]) -fig.tight_layout() - - -plt.show(block=False) - -#%% - -fig, ax = plt.subplots(3) - -ax[0].plot(fast['t_array'], fast['T0'][:], label = 'fast output') -ax[0].plot(par['t_array' ], data['T' ][:, int(probe_pos[0]*domain[1])+2, 0, int(0.50*domain[3])], 'o', color = 'k', label = 'dmp output') -ax[0].set_xlabel('t') -ax[0].set_ylabel('n') -ax[0].legend() -ax[0].title.set_text('Probe position = {:.2f}'.format(probe_pos[0])) - -ax[1].plot(fast['t_array'], fast['T1'][:], label = 'fast output') -ax[1].plot(par['t_array' ], data['T' ][:, int(probe_pos[1]*domain[1])+2, 0, int(0.50*domain[3])], 'o', color = 'k', label = 'dmp output') -ax[1].set_xlabel('t') -ax[1].set_ylabel('n') -ax[1].legend() -ax[1].title.set_text('Probe position = {:.2f}'.format(probe_pos[1])) - -ax[2].plot(fast['t_array'], fast['T2'][:], label = 'fast output') -ax[2].plot(par['t_array' ], data['T' ][:, int(probe_pos[2]*domain[1])+2, 0, int(0.50*domain[3])], 'o', color = 'k', label = 'dmp output') -ax[2].set_xlabel('t') -ax[2].set_ylabel('n') -ax[2].legend() -ax[2].title.set_text('Probe position = {:.2f}'.format(probe_pos[2])) - -fig.tight_layout() - -plt.show() - -#%% - -plt.rcParams["figure.figsize"] = [7.0, 7.0] -plt.rcParams["figure.autolayout"] = True - -# Create a figure and a set of subplots -fig, ax = plt.subplots() - -# Method to change the contour data points -def animate(i): - ax.clear() - #ax.contourf(np.transpose(data['vort'][i, :, 0, :]), 100, cmap = 'jet', vmin=-0.6, vmax=0.6) - ax.contourf(np.transpose(data['n'][i, :, 0, :]), 100, cmap = 'inferno', vmin=5.5, vmax=10.5) - #plot_vid = ax.contourf(np.transpose(data['T'][i, :, 0, :]), 100, cmap = 'hot', vmin=0.85, vmax=1.25) - for probe in probe_pos: - ax.plot([int(probe*domain[1])+2], [int(0.50*domain[3])], 'o') - ax.set_xlabel('x') - ax.set_ylabel('y') - fig.tight_layout() - -# Call animate method -ani = animation.FuncAnimation(fig, animate, frames=81, interval=100, blit=False) - -ani.save('plots/animation_ne_2.mp4', - writer = 'ffmpeg', fps = 10) - -plt.show() -# %% diff --git a/analyze_data__student.py b/analyze_data__student.py deleted file mode 100644 index e8dd1e8..0000000 --- a/analyze_data__student.py +++ /dev/null @@ -1,77 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Created on Mon Dec 6 10:08:12 2021 - -@author: alec -""" -#%% -import numpy as np -from matplotlib import pyplot as plt -from boutdata import collect - - - -#%% Read data - -path = './shared/NORTH/data/' -field_list = ['n', 'phi'] -par_list = ['t_array'] -fast_list = ['t_array', 'n0', 'phi0', 'n1', 'phi1', 'n2', 'phi2'] - -data, par, fast = {}, {}, {} - -for _field in field_list: - data[_field] = collect(_field, path = path, xguards = False) - -for _par in par_list: - par[_par] = collect(_par, path = path, xguards = False) - -for _fast in fast_list: - fast[_fast] = collect(_fast, path = path, prefix = 'BOUT.fast', xguards = False) - -domain = data['n'].shape - -#%% Plot data - -probe_pos = [0.80, 0.82, 0.84] - -fig, ax = plt.subplots() - -ax.contourf(np.transpose(data['n'][1, :, 0, :]), 100, cmap = 'inferno') -for probe in probe_pos: - ax.plot([int(probe*domain[1])+2], [int(0.50*domain[3])], 'o') -ax.set_xlabel('x') -ax.set_ylabel('y') - -fig.tight_layout() - - - - -fig, ax = plt.subplots(3) - -ax[0].plot(fast['t_array'], fast['n0'][:], label = 'fast output') -ax[0].plot(par['t_array' ], data['n' ][:, int(probe_pos[0]*domain[1])+2, 0, int(0.50*domain[3])], 'o', color = 'k', label = 'dmp output') -ax[0].set_xlabel('t') -ax[0].set_ylabel('n') -ax[0].legend() -ax[0].title.set_text('Probe position = {:.2f}'.format(probe_pos[0])) - -ax[1].plot(fast['t_array'], fast['n1'][:], label = 'fast output') -ax[1].plot(par['t_array' ], data['n' ][:, int(probe_pos[1]*domain[1])+2, 0, int(0.50*domain[3])], 'o', color = 'k', label = 'dmp output') -ax[1].set_xlabel('t') -ax[1].set_ylabel('n') -ax[1].legend() -ax[1].title.set_text('Probe position = {:.2f}'.format(probe_pos[1])) - -ax[2].plot(fast['t_array'], fast['n2'][:], label = 'fast output') -ax[2].plot(par['t_array' ], data['n' ][:, int(probe_pos[2]*domain[1])+2, 0, int(0.50*domain[3])], 'o', color = 'k', label = 'dmp output') -ax[2].set_xlabel('t') -ax[2].set_ylabel('n') -ax[2].legend() -ax[2].title.set_text('Probe position = {:.2f}'.format(probe_pos[2])) - - -fig.tight_layout() - -plt.show() \ No newline at end of file diff --git a/contours.gif b/contours.gif deleted file mode 100644 index 5e9563a..0000000 Binary files a/contours.gif and /dev/null differ diff --git a/data_analysis.ipynb b/data_analysis.ipynb deleted file mode 100644 index b61fc27..0000000 --- a/data_analysis.ipynb +++ /dev/null @@ -1,1117 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import re\n", - "import os\n", - "job_ids = [re.search('(\\d){7}', dir).group() for dir in os.listdir(\"./shared/NORTH/\") if re.search('data_(\\d){7}', dir)]\n", - "job_ids.sort()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# The last (highest id) job id is the lates job\n", - "job_id = job_ids[-1]" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'5145562'" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "job_id" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/niflheim/s173965/.local/lib/python3.9/site-packages/boutdata/data.py:732: AlwaysWarning: While building x, y, z coordinate arrays, an exception occured: name 'mxg' is not defined\n", - "Evaluating non-scalar options not available\n", - " alwayswarn(\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "from matplotlib import pyplot as plt\n", - "from boutdata import collect\n", - "import matplotlib.cm as cm\n", - "import matplotlib.animation as animation\n", - "from matplotlib.animation import FuncAnimation\n", - "from boutdata.data import BoutData\n", - "\n", - "path = './shared/NORTH/data_' + job_id + '/'\n", - "bdata = BoutData(path)\n", - "outputs = bdata['outputs']\n", - "options =bdata['options']\n", - "\n", - "field_keys = bdata['outputs'].keys()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "from matplotlib import rcParams\n", - "rcParams.update({\n", - " \"font.size\": 16})\n", - "rcParams['axes.titlepad'] = 20" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Read Options" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "def get_option(*keys):\n", - " from numpy import sqrt\n", - " val = options\n", - " try:\n", - " for key in keys:\n", - " val = val[key]\n", - " return eval(str(val))\n", - " except:\n", - " print('Error reading option for keys: ', keys)\n", - " return 0\n", - "\n", - "def get_options(keys_list):\n", - " vals = []\n", - " for keys in keys_list:\n", - " vals.append(get_option(*keys))\n", - " return vals" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.25, 10.0, 2, 1.67e-27, 0.077, 1.6e-19, 2, 0)" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "R, Te0, N_nuclei, mp, B0, e, mxg, myg = get_options([\n", - " ('north', 'R'), ('north', 'Te0'), ('north', 'N_nuclei'),\n", - " ('north', 'mp'), ('north', 'B0'), ('north', 'e'),\n", - " (['mxg']), (['myg'])])\n", - "R, Te0, N_nuclei, mp, B0, e, mxg, myg" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "nx: 80+2*mxg\n", - "ny: 1\n", - "nz: 256\n", - "Lx: north:a/north:rho_s\n", - "rho_s: sqrt(2*e*Te0/(N_nuclei*mp))/oci\n", - "oci: e*B0/(N_nuclei*mp)\n" - ] - } - ], - "source": [ - "print('nx: ', options['mesh']['nx'])\n", - "print('ny: ', options['mesh']['ny'])\n", - "print('nz: ', options['mesh']['nz'])\n", - "print('Lx: ', options['mesh']['Lx'])\n", - "print('rho_s: ', options['north']['rho_s'])\n", - "print('oci: ', options['north']['oci'])" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "nx_all, ny_all, nz_all = get_options([('mesh', 'nx'),('mesh', 'ny'),('mesh', 'nz')])\n", - "oci = get_option('north', 'oci')\n", - "rho_s = get_option('north', 'rho_s')\n", - "Lx = R/rho_s * rho_s\n", - "nx_inner = nx_all - 2*mxg\n", - "ny_inner = ny_all - 2*myg\n", - "nz_inner = nz_all" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "mxsub = 2 mysub = 1 mz = 256\n", - "\n", - "nxpe = 40, nype = 1, npes = 40\n", - "\n", - "Reading from 0: [2-3][0-0] -> [0-1][0-0]\n", - "\n", - "Reading from 1: [2-3][0-0] -> [2-3][0-0]\n", - "\n", - "Reading from 2: [2-3][0-0] -> [4-5][0-0]\n", - "\n", - "Reading from 3: [2-3][0-0] -> [6-7][0-0]\n", - "\n", - "Reading from 4: [2-3][0-0] -> [8-9][0-0]\n", - "\n", - "Reading from 5: [2-3][0-0] -> [10-11][0-0]\n", - "\n", - "Reading from 6: [2-3][0-0] -> [12-13][0-0]\n", - "\n", - "Reading from 7: [2-3][0-0] -> [14-15][0-0]\n", - "\n", - "Reading from 8: [2-3][0-0] -> [16-17][0-0]\n", - "\n", - "Reading from 9: [2-3][0-0] -> [18-19][0-0]\n", - "\n", - "Reading from 10: [2-3][0-0] -> [20-21][0-0]\n", - "\n", - "Reading from 11: [2-3][0-0] -> [22-23][0-0]\n", - "\n", - "Reading from 12: [2-3][0-0] -> [24-25][0-0]\n", - "\n", - "Reading from 13: [2-3][0-0] -> [26-27][0-0]\n", - "\n", - "Reading from 14: [2-3][0-0] -> [28-29][0-0]\n", - "\n", - "Reading from 15: [2-3][0-0] -> [30-31][0-0]\n", - "\n", - "Reading from 16: [2-3][0-0] -> [32-33][0-0]\n", - "\n", - "Reading from 17: [2-3][0-0] -> [34-35][0-0]\n", - "\n", - "Reading from 18: [2-3][0-0] -> [36-37][0-0]\n", - "\n", - "Reading from 19: [2-3][0-0] -> [38-39][0-0]\n", - "\n", - "Reading from 20: [2-3][0-0] -> [40-41][0-0]\n", - "\n", - "Reading from 21: [2-3][0-0] -> [42-43][0-0]\n", - "\n", - "Reading from 22: [2-3][0-0] -> [44-45][0-0]\n", - "\n", - "Reading from 23: [2-3][0-0] -> [46-47][0-0]\n", - "\n", - "Reading from 24: [2-3][0-0] -> [48-49][0-0]\n", - "\n", - "Reading from 25: [2-3][0-0] -> [50-51][0-0]\n", - "\n", - "Reading from 26: [2-3][0-0] -> [52-53][0-0]\n", - "\n", - "Reading from 27: [2-3][0-0] -> [54-55][0-0]\n", - "\n", - "Reading from 28: [2-3][0-0] -> [56-57][0-0]\n", - "\n", - "Reading from 29: [2-3][0-0] -> [58-59][0-0]\n", - "\n", - "Reading from 30: [2-3][0-0] -> [60-61][0-0]\n", - "\n", - "Reading from 31: [2-3][0-0] -> [62-63][0-0]\n", - "\n", - "Reading from 32: [2-3][0-0] -> [64-65][0-0]\n", - "\n", - "Reading from 33: [2-3][0-0] -> [66-67][0-0]\n", - "\n", - "Reading from 34: [2-3][0-0] -> [68-69][0-0]\n", - "\n", - "Reading from 35: [2-3][0-0] -> [70-71][0-0]\n", - "\n", - "Reading from 36: [2-3][0-0] -> [72-73][0-0]\n", - "\n", - "Reading from 37: [2-3][0-0] -> [74-75][0-0]\n", - "\n", - "Reading from 38: [2-3][0-0] -> [76-77][0-0]\n", - "\n", - "Reading from 39: [2-3][0-0] -> [78-79][0-0]\n", - "\n", - "\n", - "mxsub = 2 mysub = 1 mz = 256\n", - "\n", - "nxpe = 40, nype = 1, npes = 40\n", - "\n", - "Reading from 0: [2-3][0-0] -> [0-1][0-0]\n", - "\n", - "Reading from 1: [2-3][0-0] -> [2-3][0-0]\n", - "\n", - "Reading from 2: [2-3][0-0] -> [4-5][0-0]\n", - "\n", - "Reading from 3: [2-3][0-0] -> [6-7][0-0]\n", - "\n", - "Reading from 4: [2-3][0-0] -> [8-9][0-0]\n", - "\n", - "Reading from 5: [2-3][0-0] -> [10-11][0-0]\n", - "\n", - "Reading from 6: [2-3][0-0] -> [12-13][0-0]\n", - "\n", - "Reading from 7: [2-3][0-0] -> [14-15][0-0]\n", - "\n", - "Reading from 8: [2-3][0-0] -> [16-17][0-0]\n", - "\n", - "Reading from 9: [2-3][0-0] -> [18-19][0-0]\n", - "\n", - "Reading from 10: [2-3][0-0] -> [20-21][0-0]\n", - "\n", - "Reading from 11: [2-3][0-0] -> [22-23][0-0]\n", - "\n", - "Reading from 12: [2-3][0-0] -> [24-25][0-0]\n", - "\n", - "Reading from 13: [2-3][0-0] -> [26-27][0-0]\n", - "\n", - "Reading from 14: [2-3][0-0] -> [28-29][0-0]\n", - "\n", - "Reading from 15: [2-3][0-0] -> [30-31][0-0]\n", - "\n", - "Reading from 16: [2-3][0-0] -> [32-33][0-0]\n", - "\n", - "Reading from 17: [2-3][0-0] -> [34-35][0-0]\n", - "\n", - "Reading from 18: [2-3][0-0] -> [36-37][0-0]\n", - "\n", - "Reading from 19: [2-3][0-0] -> [38-39][0-0]\n", - "\n", - "Reading from 20: [2-3][0-0] -> [40-41][0-0]\n", - "\n", - "Reading from 21: [2-3][0-0] -> [42-43][0-0]\n", - "\n", - "Reading from 22: [2-3][0-0] -> [44-45][0-0]\n", - "\n", - "Reading from 23: [2-3][0-0] -> [46-47][0-0]\n", - "\n", - "Reading from 24: [2-3][0-0] -> [48-49][0-0]\n", - "\n", - "Reading from 25: [2-3][0-0] -> [50-51][0-0]\n", - "\n", - "Reading from 26: [2-3][0-0] -> [52-53][0-0]\n", - "\n", - "Reading from 27: [2-3][0-0] -> [54-55][0-0]\n", - "\n", - "Reading from 28: [2-3][0-0] -> [56-57][0-0]\n", - "\n", - "Reading from 29: [2-3][0-0] -> [58-59][0-0]\n", - "\n", - "Reading from 30: [2-3][0-0] -> [60-61][0-0]\n", - "\n", - "Reading from 31: [2-3][0-0] -> [62-63][0-0]\n", - "\n", - "Reading from 32: [2-3][0-0] -> [64-65][0-0]\n", - "\n", - "Reading from 33: [2-3][0-0] -> [66-67][0-0]\n", - "\n", - "Reading from 34: [2-3][0-0] -> [68-69][0-0]\n", - "\n", - "Reading from 35: [2-3][0-0] -> [70-71][0-0]\n", - "\n", - "Reading from 36: [2-3][0-0] -> [72-73][0-0]\n", - "\n", - "Reading from 37: [2-3][0-0] -> [74-75][0-0]\n", - "\n", - "Reading from 38: [2-3][0-0] -> [76-77][0-0]\n", - "\n", - "Reading from 39: [2-3][0-0] -> [78-79][0-0]\n", - "\n", - "\n", - "mxsub = 2 mysub = 1 mz = 256\n", - "\n", - "nxpe = 40, nype = 1, npes = 40\n", - "\n", - "Reading from 0: [2-3][0-0] -> [0-1][0-0]\n", - "\n", - "Reading from 1: [2-3][0-0] -> [2-3][0-0]\n", - "\n", - "Reading from 2: [2-3][0-0] -> [4-5][0-0]\n", - "\n", - "Reading from 3: [2-3][0-0] -> [6-7][0-0]\n", - "\n", - "Reading from 4: [2-3][0-0] -> [8-9][0-0]\n", - "\n", - "Reading from 5: [2-3][0-0] -> [10-11][0-0]\n", - "\n", - "Reading from 6: [2-3][0-0] -> [12-13][0-0]\n", - "\n", - "Reading from 7: [2-3][0-0] -> [14-15][0-0]\n", - "\n", - "Reading from 8: [2-3][0-0] -> [16-17][0-0]\n", - "\n", - "Reading from 9: [2-3][0-0] -> [18-19][0-0]\n", - "\n", - "Reading from 10: [2-3][0-0] -> [20-21][0-0]\n", - "\n", - "Reading from 11: [2-3][0-0] -> [22-23][0-0]\n", - "\n", - "Reading from 12: [2-3][0-0] -> [24-25][0-0]\n", - "\n", - "Reading from 13: [2-3][0-0] -> [26-27][0-0]\n", - "\n", - "Reading from 14: [2-3][0-0] -> [28-29][0-0]\n", - "\n", - "Reading from 15: [2-3][0-0] -> [30-31][0-0]\n", - "\n", - "Reading from 16: [2-3][0-0] -> [32-33][0-0]\n", - "\n", - "Reading from 17: [2-3][0-0] -> [34-35][0-0]\n", - "\n", - "Reading from 18: [2-3][0-0] -> [36-37][0-0]\n", - "\n", - "Reading from 19: [2-3][0-0] -> [38-39][0-0]\n", - "\n", - "Reading from 20: [2-3][0-0] -> [40-41][0-0]\n", - "\n", - "Reading from 21: [2-3][0-0] -> [42-43][0-0]\n", - "\n", - "Reading from 22: [2-3][0-0] -> [44-45][0-0]\n", - "\n", - "Reading from 23: [2-3][0-0] -> [46-47][0-0]\n", - "\n", - "Reading from 24: [2-3][0-0] -> [48-49][0-0]\n", - "\n", - "Reading from 25: [2-3][0-0] -> [50-51][0-0]\n", - "\n", - "Reading from 26: [2-3][0-0] -> [52-53][0-0]\n", - "\n", - "Reading from 27: [2-3][0-0] -> [54-55][0-0]\n", - "\n", - "Reading from 28: [2-3][0-0] -> [56-57][0-0]\n", - "\n", - "Reading from 29: [2-3][0-0] -> [58-59][0-0]\n", - "\n", - "Reading from 30: [2-3][0-0] -> [60-61][0-0]\n", - "\n", - "Reading from 31: [2-3][0-0] -> [62-63][0-0]\n", - "\n", - "Reading from 32: [2-3][0-0] -> [64-65][0-0]\n", - "\n", - "Reading from 33: [2-3][0-0] -> [66-67][0-0]\n", - "\n", - "Reading from 34: [2-3][0-0] -> [68-69][0-0]\n", - "\n", - "Reading from 35: [2-3][0-0] -> [70-71][0-0]\n", - "\n", - "Reading from 36: [2-3][0-0] -> [72-73][0-0]\n", - "\n", - "Reading from 37: [2-3][0-0] -> [74-75][0-0]\n", - "\n", - "Reading from 38: [2-3][0-0] -> [76-77][0-0]\n", - "\n", - "Reading from 39: [2-3][0-0] -> [78-79][0-0]\n", - "\n", - "\n", - "mxsub = 2 mysub = 1 mz = 256\n", - "\n", - "nxpe = 40, nype = 1, npes = 40\n", - "\n", - "Reading from 0: [2-3][0-0] -> [0-1][0-0]\n", - "\n", - "Reading from 1: [2-3][0-0] -> [2-3][0-0]\n", - "\n", - "Reading from 2: [2-3][0-0] -> [4-5][0-0]\n", - "\n", - "Reading from 3: [2-3][0-0] -> [6-7][0-0]\n", - "\n", - "Reading from 4: [2-3][0-0] -> [8-9][0-0]\n", - "\n", - "Reading from 5: [2-3][0-0] -> [10-11][0-0]\n", - "\n", - "Reading from 6: [2-3][0-0] -> [12-13][0-0]\n", - "\n", - "Reading from 7: [2-3][0-0] -> [14-15][0-0]\n", - "\n", - "Reading from 8: [2-3][0-0] -> [16-17][0-0]\n", - "\n", - "Reading from 9: [2-3][0-0] -> [18-19][0-0]\n", - "\n", - "Reading from 10: [2-3][0-0] -> [20-21][0-0]\n", - "\n", - "Reading from 11: [2-3][0-0] -> [22-23][0-0]\n", - "\n", - "Reading from 12: [2-3][0-0] -> [24-25][0-0]\n", - "\n", - "Reading from 13: [2-3][0-0] -> [26-27][0-0]\n", - "\n", - "Reading from 14: [2-3][0-0] -> [28-29][0-0]\n", - "\n", - "Reading from 15: [2-3][0-0] -> [30-31][0-0]\n", - "\n", - "Reading from 16: [2-3][0-0] -> [32-33][0-0]\n", - "\n", - "Reading from 17: [2-3][0-0] -> [34-35][0-0]\n", - "\n", - "Reading from 18: [2-3][0-0] -> [36-37][0-0]\n", - "\n", - "Reading from 19: [2-3][0-0] -> [38-39][0-0]\n", - "\n", - "Reading from 20: [2-3][0-0] -> [40-41][0-0]\n", - "\n", - "Reading from 21: [2-3][0-0] -> [42-43][0-0]\n", - "\n", - "Reading from 22: [2-3][0-0] -> [44-45][0-0]\n", - "\n", - "Reading from 23: [2-3][0-0] -> [46-47][0-0]\n", - "\n", - "Reading from 24: [2-3][0-0] -> [48-49][0-0]\n", - "\n", - "Reading from 25: [2-3][0-0] -> [50-51][0-0]\n", - "\n", - "Reading from 26: [2-3][0-0] -> [52-53][0-0]\n", - "\n", - "Reading from 27: [2-3][0-0] -> [54-55][0-0]\n", - "\n", - "Reading from 28: [2-3][0-0] -> [56-57][0-0]\n", - "\n", - "Reading from 29: [2-3][0-0] -> [58-59][0-0]\n", - "\n", - "Reading from 30: [2-3][0-0] -> [60-61][0-0]\n", - "\n", - "Reading from 31: [2-3][0-0] -> [62-63][0-0]\n", - "\n", - "Reading from 32: [2-3][0-0] -> [64-65][0-0]\n", - "\n", - "Reading from 33: [2-3][0-0] -> [66-67][0-0]\n", - "\n", - "Reading from 34: [2-3][0-0] -> [68-69][0-0]\n", - "\n", - "Reading from 35: [2-3][0-0] -> [70-71][0-0]\n", - "\n", - "Reading from 36: [2-3][0-0] -> [72-73][0-0]\n", - "\n", - "Reading from 37: [2-3][0-0] -> [74-75][0-0]\n", - "\n", - "Reading from 38: [2-3][0-0] -> [76-77][0-0]\n", - "\n", - "Reading from 39: [2-3][0-0] -> [78-79][0-0]\n", - "\n", - "\n", - "mxsub = 2 mysub = 1 mz = 256\n", - "\n", - "nxpe = 40, nype = 1, npes = 40\n", - "\n", - "Reading from 0: [2-3][0-0] -> [0-1][0-0]\n", - "\n", - "Reading from 1: [2-3][0-0] -> [2-3][0-0]\n", - "\n", - "Reading from 2: [2-3][0-0] -> [4-5][0-0]\n", - "\n", - "Reading from 3: [2-3][0-0] -> [6-7][0-0]\n", - "\n", - "Reading from 4: [2-3][0-0] -> [8-9][0-0]\n", - "\n", - "Reading from 5: [2-3][0-0] -> [10-11][0-0]\n", - "\n", - "Reading from 6: [2-3][0-0] -> [12-13][0-0]\n", - "\n", - "Reading from 7: [2-3][0-0] -> [14-15][0-0]\n", - "\n", - "Reading from 8: [2-3][0-0] -> [16-17][0-0]\n", - "\n", - "Reading from 9: [2-3][0-0] -> [18-19][0-0]\n", - "\n", - "Reading from 10: [2-3][0-0] -> [20-21][0-0]\n", - "\n", - "Reading from 11: [2-3][0-0] -> [22-23][0-0]\n", - "\n", - "Reading from 12: [2-3][0-0] -> [24-25][0-0]\n", - "\n", - "Reading from 13: [2-3][0-0] -> [26-27][0-0]\n", - "\n", - "Reading from 14: [2-3][0-0] -> [28-29][0-0]\n", - "\n", - "Reading from 15: [2-3][0-0] -> [30-31][0-0]\n", - "\n", - "Reading from 16: [2-3][0-0] -> [32-33][0-0]\n", - "\n", - "Reading from 17: [2-3][0-0] -> [34-35][0-0]\n", - "\n", - "Reading from 18: [2-3][0-0] -> [36-37][0-0]\n", - "\n", - "Reading from 19: [2-3][0-0] -> [38-39][0-0]\n", - "\n", - "Reading from 20: [2-3][0-0] -> [40-41][0-0]\n", - "\n", - "Reading from 21: [2-3][0-0] -> [42-43][0-0]\n", - "\n", - "Reading from 22: [2-3][0-0] -> [44-45][0-0]\n", - "\n", - "Reading from 23: [2-3][0-0] -> [46-47][0-0]\n", - "\n", - "Reading from 24: [2-3][0-0] -> [48-49][0-0]\n", - "\n", - "Reading from 25: [2-3][0-0] -> [50-51][0-0]\n", - "\n", - "Reading from 26: [2-3][0-0] -> [52-53][0-0]\n", - "\n", - "Reading from 27: [2-3][0-0] -> [54-55][0-0]\n", - "\n", - "Reading from 28: [2-3][0-0] -> [56-57][0-0]\n", - "\n", - "Reading from 29: [2-3][0-0] -> [58-59][0-0]\n", - "\n", - "Reading from 30: [2-3][0-0] -> [60-61][0-0]\n", - "\n", - "Reading from 31: [2-3][0-0] -> [62-63][0-0]\n", - "\n", - "Reading from 32: [2-3][0-0] -> [64-65][0-0]\n", - "\n", - "Reading from 33: [2-3][0-0] -> [66-67][0-0]\n", - "\n", - "Reading from 34: [2-3][0-0] -> [68-69][0-0]\n", - "\n", - "Reading from 35: [2-3][0-0] -> [70-71][0-0]\n", - "\n", - "Reading from 36: [2-3][0-0] -> [72-73][0-0]\n", - "\n", - "Reading from 37: [2-3][0-0] -> [74-75][0-0]\n", - "\n", - "Reading from 38: [2-3][0-0] -> [76-77][0-0]\n", - "\n", - "Reading from 39: [2-3][0-0] -> [78-79][0-0]\n", - "\n", - "\n", - "mxsub = 2 mysub = 1 mz = 256\n", - "\n", - "nxpe = 40, nype = 1, npes = 40\n", - "\n", - "Reading from 0: [2-3][0-0] -> [0-1][0-0]\n", - "\n", - "Reading from 1: [2-3][0-0] -> [2-3][0-0]\n", - "\n", - "Reading from 2: [2-3][0-0] -> [4-5][0-0]\n", - "\n", - "Reading from 3: [2-3][0-0] -> [6-7][0-0]\n", - "\n", - "Reading from 4: [2-3][0-0] -> [8-9][0-0]\n", - "\n", - "Reading from 5: [2-3][0-0] -> [10-11][0-0]\n", - "\n", - "Reading from 6: [2-3][0-0] -> [12-13][0-0]\n", - "\n", - "Reading from 7: [2-3][0-0] -> [14-15][0-0]\n", - "\n", - "Reading from 8: [2-3][0-0] -> [16-17][0-0]\n", - "\n", - "Reading from 9: [2-3][0-0] -> [18-19][0-0]\n", - "\n", - "Reading from 10: [2-3][0-0] -> [20-21][0-0]\n", - "\n", - "Reading from 11: [2-3][0-0] -> [22-23][0-0]\n", - "\n", - "Reading from 12: [2-3][0-0] -> [24-25][0-0]\n", - "\n", - "Reading from 13: [2-3][0-0] -> [26-27][0-0]\n", - "\n", - "Reading from 14: [2-3][0-0] -> [28-29][0-0]\n", - "\n", - "Reading from 15: [2-3][0-0] -> [30-31][0-0]\n", - "\n", - "Reading from 16: [2-3][0-0] -> [32-33][0-0]\n", - "\n", - "Reading from 17: [2-3][0-0] -> [34-35][0-0]\n", - "\n", - "Reading from 18: [2-3][0-0] -> [36-37][0-0]\n", - "\n", - "Reading from 19: [2-3][0-0] -> [38-39][0-0]\n", - "\n", - "Reading from 20: [2-3][0-0] -> [40-41][0-0]\n", - "\n", - "Reading from 21: [2-3][0-0] -> [42-43][0-0]\n", - "\n", - "Reading from 22: [2-3][0-0] -> [44-45][0-0]\n", - "\n", - "Reading from 23: [2-3][0-0] -> [46-47][0-0]\n", - "\n", - "Reading from 24: [2-3][0-0] -> [48-49][0-0]\n", - "\n", - "Reading from 25: [2-3][0-0] -> [50-51][0-0]\n", - "\n", - "Reading from 26: [2-3][0-0] -> [52-53][0-0]\n", - "\n", - "Reading from 27: [2-3][0-0] -> [54-55][0-0]\n", - "\n", - "Reading from 28: [2-3][0-0] -> [56-57][0-0]\n", - "\n", - "Reading from 29: [2-3][0-0] -> [58-59][0-0]\n", - "\n", - "Reading from 30: [2-3][0-0] -> [60-61][0-0]\n", - "\n", - "Reading from 31: [2-3][0-0] -> [62-63][0-0]\n", - "\n", - "Reading from 32: [2-3][0-0] -> [64-65][0-0]\n", - "\n", - "Reading from 33: [2-3][0-0] -> [66-67][0-0]\n", - "\n", - "Reading from 34: [2-3][0-0] -> [68-69][0-0]\n", - "\n", - "Reading from 35: [2-3][0-0] -> [70-71][0-0]\n", - "\n", - "Reading from 36: [2-3][0-0] -> [72-73][0-0]\n", - "\n", - "Reading from 37: [2-3][0-0] -> [74-75][0-0]\n", - "\n", - "Reading from 38: [2-3][0-0] -> [76-77][0-0]\n", - "\n", - "Reading from 39: [2-3][0-0] -> [78-79][0-0]\n", - "\n", - "\n", - "mxsub = 2 mysub = 1 mz = 256\n", - "\n", - "nxpe = 40, nype = 1, npes = 40\n", - "\n", - "Reading from 0: [2-3][0-0] -> [0-1][0-0]\n", - "\n", - "Reading from 1: [2-3][0-0] -> [2-3][0-0]\n", - "\n", - "Reading from 2: [2-3][0-0] -> [4-5][0-0]\n", - "\n", - "Reading from 3: [2-3][0-0] -> [6-7][0-0]\n", - "\n", - "Reading from 4: [2-3][0-0] -> [8-9][0-0]\n", - "\n", - "Reading from 5: [2-3][0-0] -> [10-11][0-0]\n", - "\n", - "Reading from 6: [2-3][0-0] -> [12-13][0-0]\n", - "\n", - "Reading from 7: [2-3][0-0] -> [14-15][0-0]\n", - "\n", - "Reading from 8: [2-3][0-0] -> [16-17][0-0]\n", - "\n", - "Reading from 9: [2-3][0-0] -> [18-19][0-0]\n", - "\n", - "Reading from 10: [2-3][0-0] -> [20-21][0-0]\n", - "\n", - "Reading from 11: [2-3][0-0] -> [22-23][0-0]\n", - "\n", - "Reading from 12: [2-3][0-0] -> [24-25][0-0]\n", - "\n", - "Reading from 13: [2-3][0-0] -> [26-27][0-0]\n", - "\n", - "Reading from 14: [2-3][0-0] -> [28-29][0-0]\n", - "\n", - "Reading from 15: [2-3][0-0] -> [30-31][0-0]\n", - "\n", - "Reading from 16: [2-3][0-0] -> [32-33][0-0]\n", - "\n", - "Reading from 17: [2-3][0-0] -> [34-35][0-0]\n", - "\n", - "Reading from 18: [2-3][0-0] -> [36-37][0-0]\n", - "\n", - "Reading from 19: [2-3][0-0] -> [38-39][0-0]\n", - "\n", - "Reading from 20: [2-3][0-0] -> [40-41][0-0]\n", - "\n", - "Reading from 21: [2-3][0-0] -> [42-43][0-0]\n", - "\n", - "Reading from 22: [2-3][0-0] -> [44-45][0-0]\n", - "\n", - "Reading from 23: [2-3][0-0] -> [46-47][0-0]\n", - "\n", - "Reading from 24: [2-3][0-0] -> [48-49][0-0]\n", - "\n", - "Reading from 25: [2-3][0-0] -> [50-51][0-0]\n", - "\n", - "Reading from 26: [2-3][0-0] -> [52-53][0-0]\n", - "\n", - "Reading from 27: [2-3][0-0] -> [54-55][0-0]\n", - "\n", - "Reading from 28: [2-3][0-0] -> [56-57][0-0]\n", - "\n", - "Reading from 29: [2-3][0-0] -> [58-59][0-0]\n", - "\n", - "Reading from 30: [2-3][0-0] -> [60-61][0-0]\n", - "\n", - "Reading from 31: [2-3][0-0] -> [62-63][0-0]\n", - "\n", - "Reading from 32: [2-3][0-0] -> [64-65][0-0]\n", - "\n", - "Reading from 33: [2-3][0-0] -> [66-67][0-0]\n", - "\n", - "Reading from 34: [2-3][0-0] -> [68-69][0-0]\n", - "\n", - "Reading from 35: [2-3][0-0] -> [70-71][0-0]\n", - "\n", - "Reading from 36: [2-3][0-0] -> [72-73][0-0]\n", - "\n", - "Reading from 37: [2-3][0-0] -> [74-75][0-0]\n", - "\n", - "Reading from 38: [2-3][0-0] -> [76-77][0-0]\n", - "\n", - "Reading from 39: [2-3][0-0] -> [78-79][0-0]\n", - "\n", - "\n", - "mxsub = 2 mysub = 1 mz = 256\n", - "\n", - "nxpe = 40, nype = 1, npes = 40\n", - "\n" - ] - } - ], - "source": [ - "#%% Read data\n", - "\n", - "field_list = ['T', 'n', 'phi', 'vort','source_n', 'source_T','wall_shadow']\n", - "par_list = ['t_array']\n", - "# fast_list = ['t_array', 'n0', 'phi0', 'n1', 'phi1', 'n2', 'phi2', 'T0', 'T1', 'T2']\n", - "#%%\n", - "data, par, fast = {}, {}, {}\n", - "\n", - "for _field in field_list:\n", - " data[_field] = collect(_field, path = path, xguards = False)\n", - "\n", - "for _par in par_list:\n", - " par[_par] = collect(_par, path = path, xguards = False)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "dmp_nr = 4\n", - "\n", - "thetas = np.linspace(0, 2 * np.pi, nz_inner)\n", - "rhos = np.linspace(1/(2*nx_inner), Lx+1/(2*nx_inner), nx_inner)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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ShXvzcFE2gEqqyQdERE/Z2d55LYBvxVlSzMB4Odnc3LSzvfOG4XD0gNHQYE8vEZjmDSUTkGxA5PN0MxwENPnCml5HQEs9Px2sVFA1ASkbF9U0oPr9E6fbWbtqa2vrxsu8UHMhtBBw2traekEc85NPBiPS1jE1DKZp3dI0ULIFkgkS0wKoTGVtCnCp56ODlQqqJiFlclE2Yd4sgOr1T5xuZ/Wnfd//h7My1IXAoPDMdjYadcnh5DjOV3fWOz/f6w/0ld8zgKkpt1QFJRuXpAJJB6MyaHin8FGFCLXnENA4d74mUKluSg35TJAqy0epLsomzJsdUAO/01n5EyL68uVg4UunSwonIrpTp9N5a68/8OssAa6Cadowbla3VOWSpgWSCUQ+z6fuKaBA+7ohwvR1W9K+5aCycVMqpJpyUVWJ8noh3mCt2+2+m4juxsw9LLJiBsbL3rrGRETr3e7Ge3q943XjMuAW5QJNgWkWt1QXSiqQdDAqA5HHzS18G1Kkfa2AAi2sqkBlclMypEzhng5SVS6qbh7KHlAORsPwi7e2tv6aiB58NksMzrYuCZyIyNna2vrLk5PRl8wbTLOGcU1BqQpIOkCUQcjH7IAKEFmDToVVGah0bkqGVFVOSgaYzkXNBVC69xyECAPvgdvb2y8H8LzKA5ZqVJcETtvb2y8NxuGDgxolA02AaRa3VBbC2UKpCkg6UJRByJ1hUokIsbFtK2hJUbgMqjI3ZQMpHaCAvItSw7xGAGWogeofD6jrrF3d6XT+vt/vv7b8olwqMbBMiM+u9fX1Z7iu97z+8cC6Z+60wWTrlmQolYVuMpSqgKQDRhmEWlNMkT3m2NimDloqrORQUHZUJjclOyk5J1UV6tUJ89REeVOAyqb87ay+kog+slzV5fR0qnBKE+CvNi5GsGBgsoESYOeUZCiVAUkHjTIA+Q3P3z+uqI0udVUGNyW2N+Gi6uShbEoNbAHV7w/anU73L4noDgu3Lh4zYBpRcYZ1anAiItrc3Lru6LC/sihgsg3jbNxSXSiVAUmFURWA3NrzptkDrcxlyaCqclNqTqoMUjaAAuzzUIV3PyWggnG4vbOz81oA32hsfKnGdGpw2tjY+KEoiu9WuiCBJLvVdpsB02lByQQkGxiZAOQ7U0zoWNrvVA4uo7NKt5sgJZ7XQUoN9UxlB2VhXpWDKltCHbAD1GgcoBX4T3Qc52GLtPQ5xwCPz19n4qlM00tEd45j/ul+/0T/eoprmqbA8rTB5HMrA5OX/ku2+9mX02MXHrvw4WZgcuFkYGqRk4HJJye7uUSFm+/ob7p9q24rrmPdnnxefnq+4ia/F/EexXsuXIvs+kyuW3Yt0c6usXzdbT4n+XMtfuYt6bn834vNqs2qxMrCnfXOW4hovXYDZ0RV69al+3wLEX2MiD5KRK+Xtr+NiA6I6P8q+xMR/Wy6zt3HieiaqvOYu3NKwrnN644Oj63CuUUCU5VbmsYpyS5JdkiyM1LdkNE1zWMabPXnQ/pBjpizcw44zt6L1k1pnJT4+MtCvWnDvGkc1LTh3Xgcbi1UeMcAwmYmUrBZt46I7gbgRwF8JTPvE9EXSU28BMAagOcqTT8Lyfzi92TmWDlGq7nDaWNj44fjiO+qDedKlm8SmgVMdRPfNm4pO15yStm2mlAyAUmFkQlC7gy+1wUhsokE5NeITU8k0uanFEiVhXq6XJQuzKsLqNxZNwCo0ThAO2w90XGch8dx/A7tC51d2axb950Afl0slsnM/yGeYOa/JKKHa9r9bgBXiWJW+RiT5gonIrpLp9N5Ub8/dGyGp1QlwOs6pty+U4DJ1i1NCyUTkFQY6SDkN2CafBcIlB/cMmi5LiESUx2VgKoMUkICUqqLUssOdCUHdQFlM9SlDqAIhKOjY6/T6bw57b27tKsJM8CB9R9EE+vW3R0AiOhdAFwAL2Tmt1W87pcBeBoRPQXAFwBcw8yfKDtgbnAiImeWcG7eoVxVfsnGLel63+pAyQQkGUY6CM3imAptAUUYOfLzBlhpHZUeUqJ2SvTuqS6qrOzgUgGqSlJ493sAnmx94KVXE+vWeQDuBuDhSJZ/+lsiug8zH5Qc0wYwZOYr02XJXw3gq6teZC7a3Nz8/yLLcM6mZ65MTYCpLIyzcUu63rcqKNkASQVRmWPyKqBlmvVYdVAFYOVApDkB8bwRUsr+6mNDLuq0AZU7JYvwbjwOEAT+1zuO87VxHJ+J6VUs9G+oXrfuswDey8wBgE8T0U1IYPUPMOuzAP44vf9mAK+pOpG5wImIdjqdzv/s94f6KvAK1XFNpwWmOiGcLZR0QCpzTSb4eJaX2EtPWZc79ZCHlwysHKwcsU3jqGpAqspFmQAF5BPlswIqd/pThHe93onX7XbeSERfckkXSAgb6xyxWbfuLQCeAeA1RHQBSZj3qYp23wLgEQA+DeBhAG6qOpG5wGlne+elvd7x2mmEc/nnpgdTWX6pyi1NCyWdQ5K3yTDSAajKKZkkrrDqpDxXAZfOPWm3KSengZTo3Svko2RgWQAqOf92Y4CqnX9SRCBwjN21tfXnAni5ccczIst1664D8HVE9DEAEYAfYeaLAEBEfwfgngA6RPRZAM9h5usA/DyA1xHR8wH0AVxddS6Nr1tHRLfvdLo39/uDdgFOc8wznQUwqQAqA5JngFTSZrOfWcT5z0mGloCVvE04KuGcio85bZcRxJP7yXPJTmOOEaX0CpAYjpCS/8X8UiHCbGZOkSiXF2EQYZ6AkTyJndgmD3VRe/BkB6WGdzKgCnBSDBKD0ems9Pr9/hcx8xCnrAfdweV3/8Cq1b7tHzm+fNet29nZffXBwVERTDNq3mA6DbdkglIVkGQY+Qa3VAdYKox8MAIJPq472UeEezlXpYR4xcdS+6UuarKbKczLnBWVV5SbHNS0JQaybNxTFHFnc3PzfwD4CeOOS9VSo3Aiov+00d14WBwzmnZN+efswSRrHmCqG8LJkFKhJB6bYKQCyHemc1B++o0PJIjIQBL7ZMBy9KASj7P8lDWkJm9KF+YZe/NqAip7bw3kn3LSJMcHgxG5Lv0QEf0iMx/qD5yTGOCwuQkIF0WNwml7e/t1+/tHLeMEcgbNkgDXqayOaRYwNRXC6aCkA1J+Wx5EJqfklzioQHFMritCrvS8VWg5k+0qqHTuSQspCVARc3KtNAnzMUvTtdQE1OSa5AHVZP7JJjk+Hkdr29s7LwHwXVhqZjUGJyJ66ObG5n20YJqhdKBuAvw0wWTjlmyhpAJJhlEOUhr42DooX8o+y65JtCng5bqMiCmFktgGaZvktHQhnvZ+3kUFMafXLR/mTQMoucxAqApQJs0S3o3HAVot71uJ6CeZ+ZbSF2pSTODgkq9V0rgaeUfpdCivOzzs6+dpyu0733BO3aYrFwDswGQbxtmGcHWglD02uKeqHJPvTJJIQayZ5UBxTcl7nsDIJ0bAlIEKgAQi8Zi1kMrKEhypDEFxUeYwLw+o7D1IgAJgrINSZ9AEoN02j/COQBicjFZ2dnb+N4An6g9aylaNwMl13ccz853qLO2kk204Z5sA160ZJ/fMzQIm24S37JbknJKfu68HktE9SeBRn9PJdYvlN5NQLpEAmAwjASsVVBnEKiBVuG/KRcX5QcW6RDmAXJK8rFBzmgS57RQrVe4pimMEQfh1RHRXZr7ZuGODYiZw2OCwgQXRzHAiItrY2HjV0dGxft253L7Vk8cJlYVzOul65oTKhqRUgUmXX6rKLdm4JdkpmaA02WcCozJIyXIp/1zE+T/eIpSidD+CjyKsVFDBmcBrAiYFUmp4p+SiAk5DOy2gJolyUWqg9uLVBVR2bWqGd3XdU7930t7Z2X01gK8pfZGlStWEc/oqgnOhrmtqMpybtmeuDpiqwrhp3JIMJROQ9IDKg8crgdTkeuT3CTP4JNsFvASYXDfKuasgdvLJbwlSYpvspOBQLrzTw6rKQUk9eUoOCkDlUJeyBHn2slOEd1XuiQEEQfBgIrrNqeSeGIiXOaeidnZ2Xrq3d1Q711SmuuGcus02AQ7MBiYbt5Qcx1q35OvckxNrADUBiwwik3NyDdsjKfek5qQEvMLYgevGiNjJOSg4sQQuztyRzknBSVxUctzERelgFcWT0NgEKACFJDlIPxavSk2Fdzlpc0/D9vb29k9iuaTU1JoJTkR0p42NzXvN0zVVqSrPVNYzNyuY6vTEmdySDZQEkHLuyQJSqtT9stAt3R7FDnwnzmClA1XmqEoghVxvHk8ABeRdlC4PZchB6XrxVEABaDy8k1XHPYVRjCAInklEP3gpqsbPg2aC087O7s8cHBxVkqXMNc2SBNeFc0Jq7xxQTIAn+00gKk9zogNTE2Gc6pZUKJmA5OoARUUoeZrkt6owSksk1JAuBZPrxKWgQgmkknalx44EKKAIpjipjXLlMM8SUABq9eBNE97N4p7iGGurq6vPAvCK6gZmENMyrJNFRKudTuepNtXgsmZNgk8bzul65kx1TDZgmibpbXJLKpRMQMqFdxKETGGcSfL+UezAE2PbInfilAygSlyRHlK50A6QIDSpj5LDPFOiXD5WBygA1j141ddifu7p5GRIGxtrP4V5w+mcamo4tdvtZ8QxVmfJNU2TBBcyhXO2PXNlYMpe1xJMNiUCMpjkZLcAkwqlQmiXwkgGi+fkXVIdSIn8k2gjjN0MRB6iUlAJN+WSU4BUljiXgFPIRWkAleSeYAQUgNx4PLXEAEAuvMuu0SV2T0TONhE9iJmvr25gOjETOFoOX8m0urL6woPDfnG+plN2TWUqS4ADyI3p0tUxyZPB2YLJVCKghnE6t6RCSQWSDKMcpDShnG5bKP0Bi+ezEE+AyYnyjxVQyc8BKDgpOHHBRcm5qCpAJe2jAChRSQ5AW2Ig559sKsiTa1xMjtuojns6POz5u7sXfgbA46xfYCkAU8KJiO65ubn1xYvumpLn7BPgQBFMcvJbB6aqMC73WAGTzi2pUJJhAeShUze0U/eJYicHKfm+AJDqqNzILUBKhHsJSMpdVBWgRA4KKPbiiey5rgcP0Oefcu+/JDkuNLN7KogwGg0fRkSduc01fk5zTk71LkXt7Oz+RO+oXyRPw66pbhIcKA/ngGICXO2Zy4V1FmDynXxvnJpfqgKT68RY8UKsuGEGJteJ0fYCeE563w/guRFWWmN4bgTPjdD2A7hOjFYrQKsVwPWi7NZqjUtvk/3SY9PXUF+n7QfZebS9ZD/PTe6vuGHO7XlOjLYbTrbn3nNyPXIdA+k1k6+nfK1186SLdfTEmnlCHrv5z1j+7JX18NS/I/G4bP07neS/59yPsDqONJlOZWV1dfXbKhtdEFWtW0dEzyKiLxDRDent6nT7/Yno3eladh8moqdJxzySiD6Q7v9OIrpr1XnUxi0RuevrnSfEXL52bxOuqUxlY+dswjlTzxwwqfxWvyQ6MCXH25UJ6MAkuyXVKQkno4Z3riceS87Js58h1k3DoSidZsNtRYhiN3suCiduSby+CP88J0KY7osoDekcB27s5EI94aLkMK+ug1LzT2LiOqCYfwLy4V3yXuolx7XXqgH3NBiMaGuz86OY00yZDCCOpvIZBdmsW5fqjcz8fcq2EwDfxsyfIKLbArieiK5LFz74DQBPYuaPE9H3APhxJGvZGTWNF3yI5/mrVOPDnrdrmiacA8oT4PLslKJcoApMujBuso8dmFQomYCkA5S8vUwylAAgit0csASsXERwwzgL/8LIzUEqA1iEogefE6DU/BOgD+/U3rtke3lyvLHck66sgPkKIrqCmb9g/QKXRjbr1mnFzDdJ9z9HRP8B4AoAB0gYupE+vQngc1Xt1YbTzs7uc/f2DtzC1Cg1BviaNK1rklU3nAPyeSbZKQlI2YIp1zunqV+yBZMKJROQZBC5nv2yRvK+UegZwRSFLuBN3BSAAqQmDSFJgjtx5qKC2IFLDoaRNxdAAebyAgDa4kxbNZ17Go+Clu/7TwTwW7UPrlK9nFMT69YBwDcS0dcgWajg+cwsHwMiejCAFoBPppuuBvBnRDQAcATgoVUnWhtOQTB+QnlAN31Ip5PJNdkkwQG7cK64wq4ZTJNj5wsmASWjY0oB47j5JLctpKLQS49PvnRx5MD1wgxWMqjU+7nXS91TlkCP3IKLWkGIiB24xBiK5y0AFVhEqrrwDjAnx5t2T7Yajsa0u7t5DeYBp3pqYt26PwHwBmYeEdFzAfwOgK8VTxLRbQD8LoBvFyv8Ang+gMcz83uJ6EcA/BIqFjmoBSciuuvm5tZ6nfnBbUO6qh667LkSd1XmmsrCuWRbPs8kV37n9nP0YNLll5LzrQaTSETLUAJQBJQCJBlEKqQAwPHzoJJ/XWUoJW0lwHLccQ5UCKWcVIjMSblhnMtF5VQS5skFmyZAhenf16TuCUb3pAvvgNNzT7ahHQCMRqO7EVGbeapuP7O4uZwTLNatEyutpHoVgF8QD4hoA8CfAvgxZn5Puu0KAPdj5vemu70RQNUKwfXg1O12rzrun5T20p2GaxKq45oA+3BOlhrO2YLJVMNkAyYdlFQgiccygHSAkiWAJBRHDpz00sWBDKYJqFzPKUBKhHsoM2kGQK2Ij8QAqACU/CjMEN4B5uQ4cGncE4HgkNtGskrudY023qwq161TZlt4IoCPp9tbSBbMfC0z/6F0yD6ATSK6e5qXerQ4pky14OS67nPCKC4OVzFoXq6pbq7JVb4l04RzdcEkj42bFkwqlFQgyTBSXRIZxtjJlcSOP3FTAkxim3gMFCGV5aEcN8lPSS7KjWKMwvT6GwDlkj5JHoCSfJ4m/2TbF6kmx4Hm3NOsOur1nQsXLlyDBYaT5bp11xDRE5H8Nexh0uv2LUjmsNolIrHtWcx8AxF9J4A/IqIYCayeXXUu1uvWEdH2xsbG53pHg5Xik3rnVDb7QBWcqma4rBo/pxuioqtpMoVzap5pxa3OMdUBk+vEWGmNK8GkQkkFlAwhFVBVksM8AS2xTYQJceAhjpwsRyXuR6GbuajJ/06WLI9iB6PQT+5zkhwXSfJR5CWP00LNiAknkXhM6f9JCDiMkmlWAk4qyCf/c7YuXsSMgONsLbwAUW4NPHX9OwEned07UZQpD2kR7inObdOvdWdc504K7RiM9fWV3vFxf5MbXDDyfrtr/Lavv7vVvrf93Q+dv3XrPM/7hjCIipalwZCuTjW4LJNrAlDqmnyyc4Bla8XVqfpWk986MLVWRhmUgMQdqVASQMqFdKpz8vQhnjydq+OHE0Cl9103Akeu5KpCOD7gBHHmpLJ4LpQeasK8tpd+qRUHFcZxocwgmytdyj9FUTqoWgnvylyUOjmdUJV7Khtz15QIBN/zVwHcD8ANc3mRcyRrOG1ubv7AxYuHxbF0Bk0T0tVVWa5JVzqghnHJuUwfzs0CJs+NSsFUBSUZRjKIqMI9yZebAw+ul3xhOXQmsEr/d4AcpESIF0dOep6pi0rDuzFaVoDynDhXBxUgqQ2T809RRIlrTcM7uVnT2DtgMrRFyCb3VCWb0K50Kl9Jh0c9b2tr+9kArqncuYYaTIgvjKzgRERup9P5T/M+GSHTEIOyavDkfrlrAuyS4MVj7MEkT3NSWS6ghHI6MAkoZXkmEc6lQKLscdFPkK8smx0oQyu8KFuMkfwJrGRQ5SDlqu4p76JarTEix8V4nHfQ2Zi+FFCReGzowYtYBlPSe6dLkpeVGlTlnnLnZxhzp0uM29Q8lfXaMQMcx9+ChuF0HmXrnO7heb5fpypc1rQhXen+U/bQqbJxTYVjmgCTlweT3x6XgsnxQyOQchDyzKkMkmugQsody4GbwUqAykFYgFQRTMUwr4VxAVBykjw3AFlJkAcQYIK2904GknlgcL7nLjnrSd0TUJ4Yn3doF3O80WRJATNlOcHzJNt39KDBYFiMvyzzTbJmDel0M1wC9XroqlyTTe+c8fzqFFhKyW8bMJEfFoEkwYj8GjnWdF8OUkh5IRASyI8yhyVDKjkmPafIzVyUE8QIRi2ogHLjKBeL5eYvl1aCCdOJ7HThndx7Z6p9qirUVKvGgfmFdrZyXc8HcB8A76/a93KWFZyuuHDFU79w6x5slxkvyzdVqSqkkyWHdEBzrkm7r0U4N1Xlt4CSAiZ3ZVRwS+RFOSjlYOQRROU+tao/Jx6n7XoAQjFHCYMDAiEqQIq8OEumi+hKuCYfSs4jDfHkHJRcrOlSrM0/yeFdEFEGqmndE5CvGk/eoj4x3kRoZ5t36vePPd/zH4yG4JQ4p8t0srlxMH5w1ZAVk04zpAPyY+iy/Q11Tdq2Da4p9xo1wFRo35Rn0jgmrVuSoeRpYOTb/TCQDyBIv0gtCVbgCaS8EBRGUq5q4qJUQAEe/LaUzwnTITfp0/LcU5HrFPJPUeSk4VyMKHKza17lnqzrn5TEeJVmDe3K8k5hGOGKCztPwZxmKTgvqvxLTpPhW3WGrJjUZEhXlQhvGRxTVV1Tcf9iEjx7zgJMqmtKroMmnCsBk4ASkIZuMpRkGHnKx9nSXOOx9IUT+4fhBFYpqISjYgCUIiDLSXnJuYYnKwVAJb2Lac+YCO08wJPCumy1l9DJ7ovygihys5yTjXvS9dyJyepMZQXJc5cytCOMxqP7NdBQJjHlzXmSzc/s3T3P96F+eJaFl3VUJ6RTpYZ0gH4MXZV0yzhNzq9YaJlstwPTBEpRAUoA8jkmBUwTKFEeSgIwEohYhVTuQiXPUSg5iJafQMvzElClcGHEmZOCx1kSPW0I3tpQKuSchHgiB9UCstDOdSafj8g/ifAumR44ziXHbd2TLuekrnlXeL4itLNR2QKcVSIQmLlDRC0um+P3MpcNSR401CXDL5HqhnRAsXzAZtaBybH5uZmA4ng5nYxgSgf1agst5eS3BkxZ+OZ7BShlQGrZhcgs9hsnX8wcwmVICSeFvItKd4SDsNCT50rlBq6YdiV1UWIK4DBOpwWWeu9EctzGPQm0yosiRBVF17ahXd2xdtPkndKk+JcD+EDlzpepKuF04cKFp9x66751MlxWk/kmm146oDyks5Uu16QO6E32q5cAN/bO6cI5HZgUKOmAxJ7md0QG1jj/JRNQ4lY7ec7zJq5KgpRwUTpAeWvDXIg3Ce28JJSV8k8ivBPuKYzcBEgl7ilbFt0BItEfoMk5uUTGxHhToV0dlS1+cNw/8XzffwgagNNlmxAPgvAh0ybDZVXlm0xToUzTSwfUC+nK6prKXFO2T00wqWPl5CLLLAFuApPslFLoZEBSXBO7mmu6mmyjKPlSZlCCBCogDymgElCOH2ZJckdyTtmULHGUC+9E751Ijpvck1z3JFeNh7mck31iXMhUkFmlqfNOSlI8CENcuLDzZCTT1y6lUSmciMjpdDrbZclw2/om6xNS8k2y5hXSFV5HGdybtGN2Tdk5acCkSjeYV/yfC+eSi1EKJhVKMoy4VRyfLYuxAhqnq2Sv+qAoyEAlQ0q+PGWAcuQyg9Q5xVEru++GLiK4cL0I0TiZ9ldMUqe6J9FzJ9c9iarxID0jj/RDWqpCO6C8IHOeBZh5Ecbj0f0baYrpskyI39bzfLcsGS5rlmR4E9INV6l1vAIkAMYeuuyYkrIBAAXXBOQH86rhHACpXIByYJLDOPb8ApRkIMWt1cr3m7taote7lc3XBoI9oMiLszKDZIYDLwcp14tK3ZPouUum9o0RYOKgAGRgsg3toqgIKV1Bpkk2eadZ6p2S5cq5U3kil7GqaHKbRiYHN6jpfBNQXXipSp0XPGtb6a0z9dABqBXOqYAC5PFyiWsqJMBzb7AIJgElGUjsVTtaGafZK40TQAETMNkAKgkB8wlyJ5sPauKeRHJcdU9Zz50TlybGTaGd2muXhPTlBZnA/PNOZXIcxyGidWY+nqUdBs5lzqnqW3ybIAimetd1Z720lU2+CbCfs6l4nD4Rnj1WZh4oLAluMTeTWjqgJsEBFMK5XCjXaidQSsEUt1YRt1bBXmtya61V36T9szZaK1nbudcTYaXnAb6XnJ9HWUFoAlYpRHWjwuyduTov6RoCyIM+hVXOsdJk/qzss1dWyLEdyJ0dw82mJOrKdVwHwG0u6UloVLVunbTfNxIRE9GVyvY7ElGfiH64bpuySp2T67q3G5wMqImEuNCsyXCdTPmmOjKFdNm5KGUDplxTleRKcPEYkAfxToosS8GkuCXhlLi1lr0We/V+ICgcI26twgFA4yHY9bMwr8xBweNCeBcHSZJc555E7knuuYuUcC5Mn7MJ7dThLNPknWRV5Z3qJMXLeuzimF0kcLrZqjGDmCk3dnEW2a5bR0RdAD8A4L3FVvBLAN5at01Vpe9oc3PzfnEDE/aZ1qZTNW0yPHl+ug/HNqSbPCfPD54Hku2MlgAKPXRJg4prAirBVHBKXnty89eNt6TtdgYw2UllDip1UWUOKjl/lirY89DVuaeJg8o7UnF95WstPiPd55btR+pz+UVSVenSAabUwTw1Go0cALc99RcuV7ZuXVogKtatU/XTAF4MYChvJKInA/g0gI9O0WZOpd9o13XvVtXApZIp32SrsuEquceaL5B63+SadOGc6pp0PXTJ/5PeOZ0EmADkoARAAdBK4Sbvw/76BGap6xKAEtICKpUI74Bk7B95UTa8Ra54ByRQOZOeTPnaqaEdMIGQcLWyu5U/P5vly324xjSATurCGnXHgBakpG+HoxGtrKzcZbZGE4npkatuSNetk27fpTSlW7fudrm3QfRAAHdg5j9VtncA/DcAP1W3TZ1Kw7ooim9fFtI1NWylTHV/0arqm0xLPgH6GqfsuJKQLttfs3KKTibXRD6DWm6la9KBCUAOSGWSn6dwCPbXQcFx1g6NTxJAAVmIV5AMzvEY5CfJcQTJe+KwBccPEUVuNkwnCpEuOZWGaUpZgRzaASj02omCTFNJgU/1652AfFJc+3xJmDfrDAWdTuc+U5zyLJpp3ToicpCEbc/SPP1CAL/MzH2qOWxMp1KiBEGwO/MrNCjbZDhgrm/SySbfNNnX4KQ8NTFu55oAVLumacDkW/RSB32wt2IFKNGLV8g/hSGo5SS5pyB5L2JwMIIExEjzT27k5MoKorGb1TmJ6ynui7yTnHOSJeedctfdMN7OVqfbY0cg0JfN2goz6dcPnE5V69Z1kQy7eUcKoC8BcG26GstDAHwTEf0CgC0AMRENAVxf0aZW5XaHudXEbAQ6zWyRG5Ju8YKyfJOQ3EuXD/fsXROgJMJFXZOQXGipKAvlKqDkeN3CsXHYy+0rY1kFFIAsSZ6VGYSamQ2Eewopmb0gBZJIjMdRAlThoIDkWkWQwBRPwKQmeOVq8UD5m5w2KQ5MXynelMIwXLTeutJ165j5EMAF8ZiI3gHgh5n5/QC+Wtr+QgB9Zn4ZEXllbZpkTNIQkctA8ZthUfbURBnBafTUmQb6Tu7b5Zuy7Qb3pLomAIWiS0Cal0ktuFRck+yYgCKYHK+b3XQqPCcg5a3kk+VpklyVMfckvxdNYlxOjsvV86YwWc07qapKildJ17Fy2grCcONSn4MsZg4BiHXrPg7gTWLdutQdNdZm1XFln84Fz9NVAjUrtWeubLlxoLk/qKpk+DT5puy+0jMnpCaIjYlwoNQ1CSXJ7CKYJq9nzj1xNMz2jcNecnwa5gGJgwImpQm58E4Oe1o+kA0UptLQTlwb4aIm4V06ZNiJJtOppNXisnyHc8WYVfId0laKL4rSqGTmGT8YaKyUAACY+c8A/Jmy7ScM+z7csP2FVW1WqewdrREayGpZyrTkOJAvI9A/X++DMRVf1pEp36S6J51rAqREOJAVMuYS4alMrkk4nOS4PJjIXSkFk7pPBjTFQRnrpFRwyg5KKspUJV8D9TrlQE/6HJ9ONj12OlWVE1T9SDYlqi6EvmxVZkO8ptNNs6xR15RMPXWybJLh2TYl3yRL554KiXBNSJepYm4muddNBlN27q4+KR5G/ew+uSuZi5Id1OQ1JucQA3DlnrtWe+KafA8YF3M3VXknAMakuKyyHrsq+eTkJp7z2C1Mn1JXTc2K2YSvY6YsX3eeVPZV9cCz48m2AHNW2dQ4qXmJbLtT709EHbKikyg6FDIlwpMGlZAO+nmZ5OrvTFIC3AZM4jn5eZ2DkvNP+XNYyc9+YJF3KpNu5gYh00R+00iudTptVczcQdREv/s5VDmcmhy30qB0lhxA6YBf23FXpvCu6osihymyY6pKhKshHYD85HFq+YAa0qHYI1cGJnU/sW8BUFWSXV1JsSiQD+fka6OCyWb4j07qGLuzpBRLM4d2ok6s6naWVBrWcSOmc7FVlmuqzHe4xV992TGpeaYcpKSFMOXZB9R8k0mmQksZTE5JuUYsTf/huR2EUT8L8QAk7kk5hsLT63KXywlMtU7nQb1ebwXACoCZZiY4jypD6Rn7DUqkS4Auwq9pZUgH1Mo3qSJ3xRpMVc+b3FNhWIsuKS4NZQGQ5dcAxTUpYa8O9KrUHxJdjVrTKuuoaVAzkVcUYVoOXzkzKvt4w3kVYJ5lmdaiKxNJXzxtLx1QmW+ah2RA2YaCZ0F1p04BqnuE56VutzsEcHJJXnzBVRbWhdxMZ8JlKV1PXU7SMuKmfFNdhVH/XEHGpKqeuqi5PPrclRayz9R1yAAiPlv5JBtVOKelmpQc4mTbDPmm05Ccd5LLCxZdYjWWRtssGfg7bzE3MC/ROVTZtyHA0jkVlEzCb/9DpybFgXwyfBGVjb3TyBkPTvFMLgvN/B1jUKGa/jyo7B2NGGeP6LYDPht9zWkL4GYYHURhfhqTrJctVVyyCKT6vHBNuTaCopMq7a0L9TVNHOr/xKIw/7tok6xVHVNwCuFbyPOdoYDyU7kvJakMTl+IFnlgUg0FZ+FdVNQK2UoOz3SAiuNRaTgnuyYKh6DgGBSOshkKZFGoCYXC5GKzZup5sYadjc6jE1DFYGRrvy9VkPEvgJmDdDTxwsm0tE/A5h8hU5JU/TWWH8tJRt2X5VJ1zYpBuUJlYZiAkQolIA+mKtdUkLxy8Lg8XxMHE5ckA0p1nKYiwSpQhQv+42OaQxwAXNedPdnH57MIs/RsiTA+T4WYTTmosnFMIlwRX0L5i9mEKFTcUFAEjE1yWwemOOxl7cmuKff642G2WrAqHqe/AGlXChveuy2gpnFPps94zDEixNrloS6lPM///KU+h0VV6afvef7+rC8gpjCdt8YlrklWqNktiNVcxuSyhBVfEPFlmnndsAr3QeG4EFrJeSfhnmRA6SAlb+domAeTpl0gmXSOwrE2GU6GXFN2XimgVEjL18vmF118JkHqbOv02KmOetZBvwAaGfSbpLLx6SZaCWLH6naWVAEn93OFjTz9B1v2gc6SeIxq5hR1YYDNH7sc5s3DIue+6OMRKApA46EeCnJop4RhcogmYKTCSt4nA1PQz8Ckc035cw3yoV2QrvYbpK5Jgg+n4W8ZoOQQOXedK+p35B8bXehu6iDRpQYCaZ6q01mSHBgMB5WTrp22qtaYI6LnEdFHiOgGInonEd0r3X5nIhqk228golek29eI6E+J6B+J6KNE9PM251H6ycdxfPO8qwnUPwLdH4Vcg2Jaa6yudA4qfx4TWOWclCbPJL5kIkSxDul0lByP9InmVImLmXyJdO4JyLsiWep2GUxZmxL4ZNeUhXRqvklAVUmGyz114ppEoZfrqcsBKi53U8Lhqk7XpCAu/9tt6m9pWrVbLfT7/U/M2g5z4vBtblWS1ph7HIB7AXiGgI+k1zPzfZj5/gB+AcmCB0KfZOb7p7fnSdv/FzPfE8ADAHwlET2u6lxKz/bo6OjDi16IOUsOodA1XeiqLndKtglxnjFxLtyT3JVP4SiDCIXDDC5x2NNCSgclFUwiz5S1r+mhyzQe5ZyeKd9kkwyXr2PuvgiZDa429wPC6nOLnyttr7RjAMXo5NKqco05Zj6SHq6jwsEw8wkz/3V6fwzgA0gWOShV1eorn1lb6/DgZHTqjAoxsp5HfBYFMeC68mOC65b/YcuFmGGUrCASxS5cRIhCN13+yIPj5vNtHDogP3EWlA585XGc9CbL1eHj0WSFk1Z+dgIBKDG3EwXHycop4XDyF+J3SnvwJm9WckuSA8s7s6Jryjm7MDSGdHK+KbsfOTmnmeXsDInwQAGUDlSqCw5qgOlSLm7gJoMyb5m1nZrDVy4Q0fulx69k5ldKj3VrzD1EbYSIvhfACwC0AHyt9NRdiOiDAI4A/Dgz/51y3BaAJwB4adWJVnUl3dLy/XiAkfann3msnUgr5lHlIgdRPJp5BZaQIuPyUEHME18YJ5OhmvxLxARfgn/yWNx34KU5rSB24KZj5sLYzQYBR7EzAVMryoEpjhw4PrLZIKWTB+SBv6kLIRSHsSRQSN6OmBVAuBr22nlAeSt2pQCibRlKUp5JDedyEq4pTeLzOE5DOiqEdBy5uZBOSJdvimIn+5JVJW9FAaYaGc86rm6uy0Ip+do4imI0AKeammndOiFm/nUAv05EVwH4cQDfjuS93JGZLxLRgwC8hYjuLZxWugrLGwD8KjN/quo1KuEEQoQZp3SYRQFG2dzOAY0La9cV9udkKn7d1CmRmKU/BjzlHamOKQFR6o7S9dMAAasobc/JpvrIwJQ6KCAPJtEWB142ZQoHBN1apBSGufXhxGBgGg/F6YO9VnnYlco075MKpWRbHky5/W1ckyakkx0TkOac0pAuCifTeOjyTSqoqnrqqsoITKoaV1eWHJ+lNzqK4xiJw5hJDQ9fqVq3TtXvA/gNAGDmEZDQnZmvJ6JPArg7AOHUXgngE8z8KzYnUgWnfwvDcLEKQ1IFFBRmxBxzbL0kecgAlJBO/NELFyU7KPHh+9Ic154TIYxc7VxEceTA9VLHJFxU4MH1xsnKJGJ8XchgxPlCYVEtrgnvZEAJcWuttGfNpFziWwMmbThX4Zo4dMGhgzjwCr10akgnJGBUlm/SJcNteurkMoIAUa0yArUHOaoaEiTtX7XaL4NBhMECDvotXbcOAIjobswsEvlfD+AT6fYrAOwxc0REXwrgbgA+lT73MwA2AVxteyKlcGLmYHNzq8fg1aq5nWIO57YkOZD8upXNuRMhrr0KC1AM6YC8iwpiB3BQGtrZ5J04cgE/zOedEOVDu0Ca6wlpeFcGqNZq6Xg3EfKVSYZS8tgSTBrXxKFbSITHkZNCadJLNwGUkwvpxPUN4rxj0uWbqpLhZT11AQWFnrp5lRGUVYe32ysfb+p1mpqpgZlDIhJrzLkAXi3WrQPwfma+FsD3EdGjkEwOsI8kpAOArwHwIiIKkPx+Po+Z94jo9gB+DMA/AvhAOmX6y5j5VWXnUkmTVqv1QaD/mNzEmBxZLa4pFMXjbKGDmIPCKiwRB3DJR8gjeNTOHpsUIsytXxcgmmni+iAG4BTzTqgR2tXJOxVCO6TuSUyfouafFEAByHJQgHnkKI1P9IsiKPtk96VCSxswZa4pdHK5JuGatGFd7CY3TUgXsVMZ0iXbJuevuqfTSIY3UYDpOoTj4/7/mbmhOahq3Tpm/gHDcX8E4I802z+LKWbWrYTT/v7em33fe0x4GkPAFdn02JmS4hGztBIksqQ4KvJOcPKLa9YJ7dS8k3ANct5JDu0AgBCl+SVNeIfJJ5rLQSGFVPrdkv1i3U9Jdl5y8tsWTBxI4VwKo+ymuKYodHOuSfwvHJS4vqaQTt4WxvlkeBNDk+aaDFfU7Xaig8P9d53aC55BVcIpiqL3dbsb4eFBv/GYzbbHzpQUN+WdgPKVWIRE3kl1TQFTmo+aPbQDJnknObQDvNxc4gUHFeRzUHKSHCi6KCH1XVfBSnZKACbj5izBJIdzZbkmAIVEeBi5OdeUXWsUQ7pkWzG8C7iYbwpizvJN8pg6Xb7p1CaZU3rqojgKANzQSNN8PmdxsAHOR6MoDEz7zlJO0LSmzTsB9qEdkMwjHrEDROl9KbRDCESOHNrFcNyxtqRAdk/wOAeoZHvRRbHuvnz5lUjFHQ/BLUOPnVQiUIBSdr8CTEERTGWuSVfbpOaddCHdxEVNzl9X31RVfGma0UKVSIaL/JMuGT5tT10ymJ4HzLycO7xElXBi5vHm5lbfJinehMryTnJSvE7eKfuDrRHaAfleO0huSXyJXHeyfJGb/nwL91QV2jkIIV9+SnNYIsRL7leHecnFCLJFEXTrN9IgyBdy6mYVSIekyGPmrMAk9c7lwrkggZLooVNzTcI1idomkQgv66UrC+nq1DfNIxlep6cOaDYZzqDGEuKLJKtQrd1u3QD0H12V0yrrsbNNiquaNu8k6p0QA75jPm85tEPMhWpxOMgVZCbt2SXGI0fqtQtiACGcyM1CLRVQgJSD8llfZpDtByAMs4JNHajy+1cM4gX0UAJKwRQPWhmURMFlwTnF+lwToHdNAlQ2vXRlJQRl06ScRjLc1FPnEHB8fPwnU53AZSQrOB0cHLzZ97xHh7KPrtljZ1LTeacIMcZstzy59nzSfJOcGE9yTnHmnuokxkWvXS6sA6oBFVKyInBJmJdsS8SabVaSgQRMpm6R3RJgBybJOWnDuRLXBOgT4XLhpVwVbgrpTCUEIUWFkC4/oPwUk+Eb3ejwcP+dTbXHsB8MfZZkBacgCN67tbUTHh70Tnd5EI2mCe10Q1nk0E7876ohngwq8fqSe5okyqMssTtxAnJiPEmAC/cETMI7QA8oYOKiECROihED43FuxZbJvpIkR1Wm/BQteSgBUNySk+uVk0M5XZ5JDucApPeTXjm5hy65VsIpOYVEeAKlyf1i6UBzIV22T9P5JiUZztxcMvw8yxY2N4ZhcGpJcdt6p9wxJaGdbiiLTolr4jTEQ2E4S133FDku4CHnnpJLmIR3VAAUoHNRQHUuCmF+8DCNA/O85OrEdlL4BshV3yjml0rAFA5bEpgm4ZxIggswyT10ulyTbriKmgjXlQ6YeukK+xlCuqp8kymkq1sZzowTZm5sLS7m4owa50FWcGLm8e7u7r/2Mbj7LEnxWfNOTYV2kcY1JQ5I2U984Ab3BABu6p5Ezx1Q7p6AMAvvkPbcyT/8lS4KaU1UGCWrtxicVHIiFvMVyUACtFACUB9MFeGcqGsy5ZpEAlwdrqJLhFf10l2KkM5cGc5otfy3Nv6C51DWYdrJyeBl7Zb/q+Ox9AevyTtNM4xllhkKbEI7tSDTNzipUEqMC/dUlntKzj3fc1fmnkRINwnvkDyGUo8UiPXuvKSaXMxmEADk5yEFIMtJlYJKUQYjIA8kmKEEwBpM43FLck7FcE5UgwvXJCfAdbkmuaeubiiXvc0pQrqmtbnZjS5evPi/m2wzmTLlMnVOADAcDv5oc3PrJeNxqKWIKbSbVrYlBbljlNDOpiCzbKYCITn3JOqecu5JqXsC8u5J1D6lZ4lg1IIbOemS5alTMroooPAxCUilSXORkwIwcVRlkuyHDKTkcR5KyX1DfklKfucck1Q2EMUOhuOWNpwDkiS4XNekyzUl++mnR5ET4TaFl3VDuibzTQxGGIYjAO+2b+DylTWcmPlzm5tbhwz+ojqhXVneaV6hncgzqAWZusS4L70VtaxAdk9Ase5JVI2X1j2FceKcUkiJdyNCvMn/gAopUbDpIASHLf1y5iInJUCVXIAKpW9aA6TkcR5KSM/FFkzjcavgmHThnJwEj3gSxtm4pjrDVdSBvtOEdE3kmwDG6urKB/v93kIuubZoqhV/OQ69gQg/kOu3nmEQcH57vdDO1Gtnck9liXF9Dko/W4FwT1l4J7myyCmvGs9xSAJTHLUkFwXYQEqEexy6k2EwqaOqkrzgZW4hAiV8E/8LKAGoDSY1z2QK59RqcLWHrizXNE0ifJ4hnSnftL6+xhcvXvyVmRrXvR4u41ICoYODg9/e2tr+nsODfmkXWlPTp6ihna4gsyoxXtc95fdFwT3ZJMdV9wRIzkkBlAwmcT+Wck46SImclAoqIA+b3Ng9zdJV8rpyak4JQG6MnAolAJU5pjByMQr9HJjk3rmIJ2CSgTRJhqdXiafLNdkmwk8jpAMAx6ERM19n38DlrboE+VAYBkMG+7rQbpqSgjqhnZAc2gmp7gmMXGLcVFZg6rmrCu/k5HghvFOqnoFJeFcEVHL2Ovekg1SUliBIFyMDFYBc6Gda1DK3Koq8AIEEJdUpifva/FLoasGkJsCD2MEw9DIwyTVNIpybhHfldU11XJNNIrxMTZUQeJ77b8xsMbl7XZ3P4Su1yqiZmVdWVv4GqBHwa2T69SmbabBsyaicXS/8UsaFBTcnf9DTvY/JL/vklz8LUdIvXiGciZ3cl3g8bmUh0HjYzr70wSjp+QpGrQwM4XByX4RZ0bCdPQ5PVrL70aCV3Th0wKGT2xZJVd2544btfLtpL5y4H4xa2TnZgmkU+jkwRZJjkq+dDCZdOGeqayqT6poCGmtnIBB/VzaFlzYyhXS+72E0Gr1iqkZPWRbr1r2AiD5GRB8mor8kojtJz92RiN5ORB9P97mzcuyvEpFVjVft2OvWW2992UZ38/G93skEbA2VFMhSJ6AT0iXGJ8+ZywpMuaeAubRq3OSeSnvvUjc1Cvws/huOW/DcCF7swHVcuF40cVHJyaf1UBGiEOl0K152Xzgrx5dclijgTB0VgJyrMq2bJy9VpVu6SXVK8v1i1bddKKdLgGf/S2AyFVw25Zom2+snwmcJ6dbWVsaHhwdvsm+gxktxfraGWSStW/doJCuv/AMRXcvMH5N2+yCAK5n5hIi+G8nadU9Ln3stgJ9l5j8nog6kKhkiuhLAtu25TEOPd8QcjUyzFNiGdqaCzFkS40LiF1MkxtXck1z3ZBfmmQHlupzrvUMshXNOPrTLKQVTNHbhOhEipGBKHwMogEmfl4L0PDJg2UheR063EIF4LKCUXJ9yt6Qmv2UwjSKvCCZNnkmXBBefjQom7aXVuKbsvmYGgmkS4fWrwuMjZv6M9QtcOmXr1gEAEYl16zI4iTXoUr0HwDPTfe8FwGPmP0/3yxxSCr2XIJmP/Ck2J1IbTsw8unDhwt/3+4NHzjJ1r41M0/eWlRWU5Z50dU/qdCq65HhZceYETJP8kzzxlxuZr0k0dpKePUhOCshDK3VTMphkaAHIYCWUW4JKkeqmcgtdhkVAqVCS7zcBJlOeSU2Cm8I6W9dUp3yg6ZCu5XsA8PKpGrV5XdQqwmxk3TpJzwEgKt7vDuCAiP4YwF0A/AWA/87MEYDvA3AtM99ClsPJpoq7Ll68+GObm1sPOzo8Lj2+TminS4zbSrgn27on03QqxmEtpt48pThTV14whAd1qjfxBfZcafWWEAX3JENLDflkMEXpdzCbeTOqLoaV15BLjsk7JhsoAWgETGV5JptwTpata8r2L3FNTYR0DEZ7pTU8Ojr6NfsG5qpG1q0DACJ6JoArATws3eQB+GokS45/BsAbATyLiN4K4JsBPLxO+9Mmhd4Xx/EBgy80FdrJ0oV2Nu4p29dQ96QbcyeXFohhLUFWjKn+X51/gqgWl74zAlBh5CJynfxinFJFuedGWjdVgFYKi3x+Sg8lx41z7qhwrTULXco5JfFYQEmcqw2UdMlvHZjKygZ0YCq8B001eIgwA9Os5QOqdCFdxVi6jzDzrZUNTylGcQqZGWS1bl26+sqPAXhYul4dkLisG6SQ8C0AHgrg8wDuCuDm1DWtEdHNzHzXshOZCk7MzN1u95fabf/nxqPmxtpNU1aQ7WfhnsqS43J450rf5SKoLBLkxJP9Uw3hJZPUpd8fGUzifi03BRRgBSB7LrsumggvUmqecmvIaQClgxKARsGklg2USU2CyzJVg5e5Jp2mdk2KNja60cWLF3906gZOXzbr1j0AwP8G8Fhm/g/l2C0iuoKZv4BkmfL3M/OfAvgS6fh+FZiA6Z0T+v3+Kzqd7k+MRsHKorgnm6pxOTmuC+/U3jsBqrAGoNQEeU4OEIUOXFLAFLvwnMjKTUXj1DmlsAKQA5atTJBSZ60UUBLbZCgBaAxM04ZzwjVVhXMm11SnfCAuARqAQkgXx1EPwF+bD2hAXOw8mLopu3XrXgKgA+APUif0GWZ+YrqY5g8D+EtKnrgewG9Oey5Tw4mZ9y9cuPDOfv/kUVMsSVVLdeZ1AvRV47rkuAjvdL13av5JJMqrxt8hRpJzkhLkMqAy+EiQyiBk4aaicVromcIKQA5YQqLHr3At4yK8ZEip0+fqnFK2XQOl5HrOD0y58zaEc5Pn7euaZNm6pqqQrt324Tj0UmZWf6IWWhbr1j2q5Ng/B3DfivY7Nucx0xiTixcvvmBzc+v6o8PjCTmk0E64JzW0m6aswLbnTueeTMlxtfdOzT/pEuRezkkZACVyPAqgwtiBp+absmMm19XkprJJ7dz8MugysLI2LBYZlcscckuBKxXuJiiJfarcEoCZwCSrLJyb3De7JvO1aN41+b47ODo6+pXKhpfSaiY4MfNHtre3P8vgu5zGyiyqpiktKAvv1PxTWYLco2pARUxw03IDl9IVgw2QCpA8b3JTAlTyMTKsgDxgbCUfk4NVLIVxSvgmtk/jlgBYg0nINpyzTYKfhmsiAO1W++097h0YG2lIDSfEF0Yzj87t9Xo/1O2u/2G/N5ipYnwa92RsS0mOhwgBQiG8k3vv1PyTmiA3ASo5L7tePMROBiEZUkGcgskCVAAyWAHInJWQ7J7M17rYeyfDKNuWuiQA1lAS16MsjEterxpMpjyTbThnO4Zu2rqmnJSK8I3Nzvji3sX/NnvDl69mhlMURdcy8xGDt5oaDJw8X141bhveVfXeqeUFIv9kCygR6lkDCshBKopSAFmCCkA+pJOABUwgYytdaCeABCDLKYn7KpSS+0W3NNmu75EzgSk7LwVM2Xal2FLunbNJgk/rmuqUDzAYjkOfZOZ/0u7QsBj1xx6eBc0MJ2aO1tbW/3uns/Ybx/3BhE4zuifjPrqZMWvUPslDW3TlBT45UwEKQHUvXuSmZQYTSCXHSftpr0seVECSTAeQA5aQ5+qT4ep+k+uXh5F4TflxFZSSffWFlbohKSYw6XrmABSqwE29czbhXO69z8M1bawH+/v7z5q94ctbjSz1NBicvKrb7f7MrEWZxeft3JNJuvBOzT+ZEuR1AVWVgwoiSuYilyAVAKl7Eo/1bgpAzlEBE6cUpC/gy8lwaUqUMgVKeFcGJHm7KYSTH8uFlWp+CbADk5CaAFfzTNn5W4ZzutKB5lxTDNd1PszM7ys9iQbFNee5OitqBE7MHHmed/XGRuePekcn0tSK1T13quq6p7rhnZp/ahJQyfMoACoAwXeS42VIJftLPXuAEVQAtLCaXLfqvJNpELIMqTIgicfmhQj0bin5nPJTn1TlmIBiz5wpzyRPhzJrOFd8rl4PXbe7Pt7f3/+v5QctZaPGFsmMouhaIvwbg+/Y1BzjyfPlyfHcOUyRf9L14E0LKM8x1EEBeYBhAikBrizkA4ygSrblYQUgA1bWtgFCqkJpP21oJwEp/7gcSuK+KYwDZgPTtHkmWaZwbpYeOsch+L73V6eVaxJa5pwqxMxMRFdtbGy+o3d0Is3/OlvdU/J8/fCuTv2T2oNnCygAhtwTMkBFEdI8EyGKaDK0RZb4wlqAKtkvH8pFytg5AS9VMoBkBTlImYEEwBpKgDmMAyZgymA0A5hsx87NEs5ppbim9bXV4d7e3tXlBy1lq0aXF2fmd+3s7HycEd+H6k2yWdCs4Z1Jav5pFkBpCzUBDaxSF5XeF5DKYJTWQ2XHQg8qkaPK3r8MMfk9Wl57eZoNHaCS7fkiSnmbWrcEQOuWAHN+KTneHkzZeSlgMuWZmlq0oMo1+Z6HVst/IzMXBsmehpY5Jwvt7+9ftbGxef3R0XErC++mcE+qbMK7afJPjQMKMOehkjeS7iOFe4AVqABkM17KwBLyK2qcTOGeDkZA3iXJz6lOCShCKTm+GMYl9/MzDOh65ZL3VgSTrtCyTp5pliR4ToVpUbyTvb295xd3XGpaNQ4nZr5xd3f3XQ7RI3IjDxooLZhH/qkOoBI5eQhlJ0clzgkQa12GoKQuKg33EvBIx0ntyqDKEumpZGBl77lmhbi6nFAgQarMJcmPy0I4cR/Q55ey+3MEkyxbMJlkck0rKy14nvdrzLxv1VDDSnJO5y/p1DicAGBvb+87ut2Nf+r1TtqzlhaUhXdV+adZASUkACVXkrtEubF4yRvIJ8ojpLMaqDCTw73kjQNABqosNwXFNcmuCgmwAOSgVSUZQNl1NDgnFUjyNpNTAoq5peQ5cxgHzA9MdXvnkucrSgcU1+R5Tu/g4OBFpY0uVVtzgRMz/8vOzu5rV1faVw+HY21hZhPhnSxdeCdrGkDJZQYAikNdgPJEefp8EUr5x8JNpW8QAHKOygSryXufflyjDkbJ9mL7skuSHxdBlIdS0kbeLSXP5YekALODSZYpnMvtM0MSHAC63bWYOX4BM5+UH7hUXc0FTgCwv7/3/d1u98k85CuqSgvmEd7pEuSzAEoe6iIk56EAFMM8sc0IJUhQEtuKoAKKsAKgTYbbSgWaDkZAfkCpDZTEYxlKSfv6MA5AofIbmB5Ms+SZVFUO7nUA13U/dHBw9FulDc1ZrJm94Txoti61EjHzqNfrPa7bXQtYjpFkS2z40HW/XsVfOP0fX9UfqviDFn/g6tp3k27q9Mshvizpl0f8wo85RpDe1HBFrIkXMGchjnATUQwMo8m2ME5uwyjN20jbRtFk8v+ICcMoSUSL2zBKtsn7lN3E/rp25P105zSMkm2DqPh+io+l6xCzcl3yYdysYJJVB0w61Q3n1tbaJwcH+49nPl9ksFi37muI6ANEFBLRN0nbH0FEN0i3IRE9OX2OiOhnieimdE27a6rOY27OCQCY+fqdnd3fnkd4p6ppBwWgOIdeRR4qOfF0ByeZuC6bWRNSDkrZb/KmkZ6fNG9UlF9gIeesxHs3D6WrlDrVhjyjovycySXltxXdki63BOTDOABWdUw6ME3TM5c8N2s4tx5HUfD9zPz58gPnL8bEmc4qy3XrPgPgWQB+OHceyZJR90/b2QFwM4C3p08/C8nc5Pdk5piIvqjqXOYKJyAL757EQ/6ieYZ3yfPNAApArTxUXoZkeXbS+ZxUEEnLUWlAlZwrckBSgTWNdNO66mAE5GtodInubD9NCJfsUy+MSx7XB5OsOmBSZRfOOR/s9U5eU9rQ2ZTNunX/nD5XVrvyTQDeKuXivhvAVWJWUGXuca3mDidmHhHR47vd7rt7vYFfVvukyqb3rmlAAaidh7JxUcU3J9FFA6qcy1KeS849keqiqmSalEwd/qADkrxddUmAHZQAvVsCZgeTzVzgOjDVDefW11ZODg72v2FRwjlG/nOoUNPr1pn0dAC/JD3+MgBPI6KnAPgCgGuY+RNlDcwdToAI73Zevbra/q7hYPbwbp6AAlCaKM+/sfxDnYtKTrC4KScNqArHGY4NpGhDtyBotl/J365aXWx2TWaXlNtWASVgejDpcorydhVMVQnwOsWWALDRXY+iOPzeRQjnplRj69aZRES3AXAfJIskCLUBDJn5SiJ6KoBXI1njzqhTgRMA7O/vX9PtbjyFoYR3FsWZ0wAqt28NQAEozUNVuSigBFJAuZsCcqCqck+F92l+yigdtKqABNhBKTmn6dxSckw9MKmqkwCXVRrOec4Hj/aPf6e04VNWUoRZPjqghqzWravQtwB4M3PuA/gsgD9O778ZQGVIfGpwYuYxET222914T6930qoqzrRZ767OAGEToADkCjUBWOeh8ief/GeuLJdKDwCjm4qYs7nMk/3y10kF1izSjcdSK42rgJQcYw8lwN4tJfvYlQsk72f6BLhlOHe8v7844dycVLlunYWeAUBdq+8tAB4B4NNIVgi+qaqRU4MTADDzB7e3d17c7az9WK9/4syafwJmB5S8XXwRbPJQyX5KVTlQCPUAnZMCtG5K81QBVhAOaraMuGm4Q2G1kwogAeVQAqrdUnJcdX4JqO6VAxpKgBfmaVoLg/H4Kmb+99LGLoGYufBDOENblevWEdFXIHE/2wCeQEQ/xcz3BgAiujMS5/U3StM/D+B1RPR8AH0AlbM30Gn/CBARbW/vvH0wGD5qNFTchxTeCUCp7kkHKN3wFjXEkyElj8OTK8lz26VpfkUuSky5AiCbF8qT+C7mJxfzQyXHSqvxStSRl0T3KW+FVBiJBT9VqfvZqix5qnZJm4AEFHNKQDmUgHphXPL65UNSpgFTnQQ4AKx3VmPfd1+yt7dXqPlZBHWdK/iBK0+12vdvB6+8ft45p6Z0qs4JyOZ9+oaNjY2bxqPgjqbBwbMkyJP96iXJC9sNYR6AXKgnnJI21JOel3NSgMlNCSlxm6mHDWwEV5nKamJ068TJ0rmk5FzqQSk5vl4Yp25vCkw5KWBqt334vvu3+/v7C7ukOCP/WZwXnTqcgKy84CGdTuemfn/YNSXITxNQALR5KADGZDkAY6gHwBju1QMVUFbIH0XFsK9MVV3OZTACqoEE6KGUbK/nloD5gUmWKQEOMFpt71/39/cfd87zTAupSwInAGDmzxPRIzqd7rv6/YF29gJZ8wIUgMZclBWkgFJQ6RLp1bLPkFf16uhe2wQkYHYoJfuePphs8kydzkrv6Ojoocw8LDS41Nx1yeAEJPVP3W73u7vdtd/s9U7cqgS5LaAAWNdBAeVhHlDtogA7SCXPmUFlUrWzslcV+HThQRmQgGooJe2aQzjAvkzg9MC0Ou73e49i5s8VGlwwMbjwGZ0HXVI4AUCv13vNzs7uV3XWV7/j+HhYWqAJ2AEKqFeoCZjDvMJziosC7CAFVINKHRqTtO2W5hOmcVpV+QndH7otkJLnilBK2p2fW0r2aQZM3e5aBPD3n+byTksVdcnhBAD7+3vfubm59UDPd+8fyiXPpwAoANZhnnhOdlFANaSA+qCaXIPipuyYCnCVqeqXVncuasLf5JKA5qAEnB6YAGB9bZU9z3vd3t7FV+KsiPSf11nXQsApHaX8sI3uxseOo+HtYrk3aQ6AAtCYiwKqIQWgFqgm+xmABb3LqquqP+gyGE320bskoD6U1OeA6WuYpgFTe8WH33Kv39/fe7a20aVOVQsBJwBg5iMiul+32/3ocX/4xVUlBsD0gEr2tQ/zALOLAuwgBdiBKmnHS/fTAwto9pdSWwKRqgpIgNklJcebHVEdtwTMF0ytto92u/Xhg4ODr2LmM2VDGFz6GZ5VLQycAICZLxLRfbvd7kf7vcEFVJQYAM0DCigP8wBMBSkAlaAC9LASMtZSNSTT66rnCJS7pKQtOyjpnp82jEv2rQ8mv+VhZcX/+OHhwUOYqyZ2Wuq0tFBwApJ5Xojofp1O98Z+/2Q7t/5dA4ACij15QHWYB8wOKUAPKqAcVkImeDQpHYiA4vkC5UACZocSMH8weZ6D1ZXWJw8PD7/irJYMMPhU/jZOWwsHJwBg5s+lId4N/d5gp0kHBUznogB9qAdUQwrQgwqoDytVMrxsVdWm7nyyY3XT484IJWA2t5Tsr6/8LnVMvovV1fYnD48OH8DMx8bGl7okWkg4AQAz/ysR3afb7X7ouD+8UJaDAlB7ql8ToICiiwLMoR5ghpS8nw5UgN5VAWY4yOP7ADvQlMn0Oln7ppV0NdubgBJwOmBqtX2srPj/eHh4eOVZBxMjnvnvYBG1sHACMgd17263++GT49EXm3rxkn3NQ10A/YBhXZiXHFOcG6oq1AOKkDLup3yxTa4KyAMLqIbJLDKBCNDDCNADp0koJfvWC+OAql65Ftpt/yNpjmmgbXypS66FhhOQ5aDuvdHd+NBgOL5doQ4K0I7FAzB3FwWUQwrQu6nC/gZYAeXAkKVCTJVtO6ZzkmUDJNN+TUApOcYivwQUV0xZbXOr7X/g4ODgK5fJ78XWwsMJyHrx7rW1ufWOIIzuf3w8oKrBwkD9PBSgd1GAPaSAajdVtj9gBoNXAqC68Kl6LVmmmSZ1QDLtXwdKyf4zgklxS6Ly2/e9N+/t7T2DWTclwdkUg+fqqC+V5rZuXdNi5qODw4Mr223/1d3uWsRq6bSyHp74Q1WnxYh5VLoMUBSPDdXGgabOZqT90kUcaPMv8k23v3zTKcSo8Zv2GpSci817sLlGZW6pLIxT80vWYOqsjonwwxcvXvzm8wSmechi3bo2Eb0xff696QRzICKfiH6HiD6Srk33o9IxzyeijxLRjUT0BiJaqTqPM+GchNJlZa7udrt/3+msvrwwm0FJHgqwD/MAfaiXHGd2UoCdmwL0oV/u9Q1fXrWdaWVqX5XJHZW1Ubb6yTQhXHJc/TAu3YBOZ+W4f9x/bBzH7yx9kTOqZOBvMxGq5bp1zwGwz8x3JaKnA3gxgKcB+GYAbWa+DxGtAfgYEb0BQADgGgD3YuYBEb0JyfS/v112LmcKTkK9Xu/VRPShTqfzVyfHo41CTx5gHeYB+mQ5YA71kmOLkEqOKYZ8gD2ohHTAUttpWmUgsnl9E5SmDd+SYy2T3oC2uLLd9m/p9Y4eysyfKX2hpYQq161LH78wvf+HAF5GRIRkJOg6EXkAVgGMARyl9z0Aq0QUAFgDUDnbw5mEE5BMt0JE99jY2HjPeBzeaTgcoywPBWAmFwVMB6nkODOoAL0bKgOFCVw2sgGQrCoYTuOSkuOqu76n7Y1jMDrra+z57nsPDvYfddZLBaqUFGFaf65NrFuX7ZPOOX4IYBcJqJ4E4BYkAHo+M+8BABH9LyQrBQ8AvJ2Z344KnVk4AdmEdffY3t7+E9/zHplbNAEwhnlAfRcFTAep5DgzqAA9AMrCt7qAsZWtKysDEnCKUAJMie/Q97yX7+3v/eByBsuC5rlu3YMBRABui2Txg78jor8AsI8EWncBcADgD4jomcz8e2WNnWk4AdmUv4/Z2tr+qW537b8Vlp3ShHlAuYtKts0GqaSNclAlbdQP32bJO00TFlbBCCgHUtKGXZHgtG4p3YhOZ2UQReGzj44Of9/qBc+FuMlw32bdOrHPZ9MQbhPARSRLSL0tXa/uP4joXQCuRBLufZqZvwAARPTHAP4LgPMNJyBZNAHATxDRm7vdjbeFYXTFYDAylhskx5SvkVcHUsB0oErasINV7pg55p1sQCRUBaSkvYahBOjDuM5a7LrOTYeHB49Z5pdm0j+get26awF8O4B3A/gmAH+VLlzyGQBfC+B3iWgdwEMB/AqSnNND0yT5AMAjAbwfFToXcBJi5g8S0R22t7d/1fOcZ/d6A38WFwXYQQood1NJO/kvsi2sZNmAy1Z1ICRkA6OkbfuhFLNAKd2ITmd16Dj044eHB790OYZxjLixUN9m3ToAv4UEQDcD2EMCMCDp5XsNEX0UyYDY1zDzhwGAiP4QwAcAhAA+CKByMr9TX7futERED+x2u28Nw7joorKdXOWYCVhMqw1XQUqWCVT6dpspEWhStjASagpKQC239InULf1LrZM9R/LcNd5YuYfVvvsnNyzXrbvUYuYPTFyU+x3aJdA1oR7QjJMCqsO+fLtFEJwWsOpCSKgOjCavNRuU0o2pW8L/PDw8+MXL0S3JYm4057QwOrdwAgBO/tKfR0S/2e123xoG8YXBUJOLAuYKKaD4RbZxVdNCY56aBkhAM1BKKr3XYselmw8PDx/DzP881cksdSZ0ZoavzCJmvr7X691hZbX9qm53dUTE0A5/UUMHZRiMboVYMaSibEiMTmKYRtlwjUst9Rzrnqfu2qjXUb7GyQbN5wCG57vodFZOyMGPHh4e3nMJpvOvc5tzMomI7rSzs/OaMIy+8uio3yITn5V8VHJs3u2Y8lLJc80kr+vkraZVk3A0Qbo00Q0YnZLjENbWVoatlv8He3t7PyiK+paayHVWeLV1h+odARyPbl7mnBZVaeL0a4noy7e3t18XRfG9jo6OPW0+CjCGewCMIV/y3ORLOguoFtVVybIBEmAPJSDpgdroro893//rvb2LVzPzZ2c+0aXOlC47OAkx840A7kdEX7m5ufl6jnG7o96xWwdSQD4vBVSDKtmnuZKASyVbIAF1ocTY2FgPXdf58P7+/n9l5n+c6UQvAzF4IfOTs+qyhZMQM7+LiO7suu4Tut21VzHThX7/pFh6IH+ZZgAVcDZhVZZTswYSUAWliIj+5fDw4Cpmfu9UJ7rUudFlDycgqzC/lohus7a29pxOZ/XFRM5Gr3fsaOujNG4qaccMKsAeVsm+lwZYton9JoAEJOHb5mY3Yub/6PWOns3M113upQG1xXwmwv+6uuwS4jYiIjdxUt1fYsbtDw97PkDQgio7qJhAn7SnT2qXJdRtZQuxur2J+WP1c7MZYQRUAInhOoT1zurI87wb9/b2fhDAu5ZQmk4Otdj3LljtOw5vOTMJ8SWcKkRE99nd3f1fo9H4a4IgXBmNgnJIAaWgStos74FrAlrTygQioAJGQCmQgHQO77UVdhwMWq3WH+3t7f34chzc7CLy2HM3rfYNo70zA6dlWFchZv4IgMcQ0Xan03luu+2/gMjZPjzseUY3pX5JS8K/bBcJWGWAkFUHYrZtyqqEEWAFJIeA7kZnTITPDwaDnzw5Gb1xuerJUlVaOqeaSmf8+4rd3d2fGw1H/8Vx3PZRr+9Uhn25RsqdVfE151frZAWg3AHlMAKQFrgytjY3oiAMBisr7WsvXrz4Imb+p+nOcqkyEbnsOB2rfeP46Mw4pyWcZhARtQE87MKFC983HA4f7nn+yuFhz2eGPaiA2rA6NVmAKNsVDM9zsb6+Oo7juO/73rV7e3uvBPA+5hoNLVVbSzgtVarUUd13a2vrWXHMzyBgazgct0djixxVacNzBteU3BDDf9ZW2+x57shx3X8Pw/BV/X7vDcz8ySZPcalyETlMlh0jzMMlnC53EdFtV1ZWv3FtbfV7xuPgTp7n+cf9Ey8IQ9QKARdEIlRbabewsroahOE4aLdXPra/v/fLcRy/lZn3L/U5Xq5qGk5E9FgAL0Uyn9OrmPnnlefbAF4L4EFIZsB8mhjrmC4H9Rwk0/Vew8zX2bSpPY8lnOYvSpJG9/Z9/yFbm1tPGY3H9+c47rqe5x8fn3hBsFjAykC00sbKSnschkHout6+73vvvfXWW98M4HoANy3DtcVQk3BKl4a6CdLSUACeIS8NRUTfA+C+zPy8dGmopzDz04joXgDegGQu8dsC+AsAd08PK21Tp2Vv3Skonbrlg+ntFUCyACGAe7uu++ALF3aeMh6PHxDH8YbneS4AdzwOnMHJgJKfjgm0ZgXYZDaG5H+HCGtrq+z5fgRwFIZB5Lnevud777711lvfMhyeCBDFM73wUvNVc78TsywN9SQAv58u8/7pdKbMB6f7VbVZ0BJOl0jpJPA3pLdXAhmwbgvgNgBu4/utO25sdO/rOM5doyi6XRCE22BugeC5rkcEEqQiEMDMlIKM01XEOHmtmMMoYiIERDTyPP+i6zqfjaLopoODgw/1j3ufQ7Kczy0AblmC6LLWLEtD3Q7Ae5Rjb5fer2qzoCWcFkgpsP4lvRmVQuwCgDYAH8nn6KX3YyTzNMu3AZIlgZZh2LkUX8cI7ErEgZWKdesWRks4nUGlELvlUp/HUoshZn5sg83NsjRU2bFVbRZ0WcyEudRSS1krWxoq7ch5OpKloGSJpaEAaWmodPvTiaidLi11NwDvs2yzoKVzWmqppTLNsjRUut+bkCS6QwDfK1IJujarzmVZSrDUUkstpJZh3VJLLbWQWsJpqaWWWkgt4bTUUkstpJZwWmqppRZSSzgttdRSC6klnJZaaqmF1BJOSy211EJqCaelllpqIfX/AAXlXxkH/LPtAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(subplot_kw={'projection': 'polar'})\n", - "\n", - "plot0 = ax.contourf(thetas, rhos, data['n'][dmp_nr, :, :, :].squeeze(), 100, cmap = 'inferno')\n", - "ax.set_rticks([])\n", - "ax.set_xticks([])\n", - "plt.colorbar(plot0, ax=ax)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# fig, ax = plt.subplots(subplot_kw={'projection': 'polar'})\n", - "\n", - "# plot0 = ax.contourf(thetas, rhos, data['n_source'][:, :, :].squeeze(), 100, cmap = 'inferno')\n", - "# plt.colorbar(plot0, ax=ax)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "vmax = data['n'][data['n'].shape[0]-1, :, :, :].max()" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "def plot_cyl_data(data, cbarlabel):\n", - " fig = plt.figure(figsize=(5,5))\n", - "\n", - " # the carthesian axis:\n", - " rect = [0, 0, 1, 1]\n", - " ax_carthesian = fig.add_axes(rect,zorder=2)\n", - " ax_carthesian.patch.set_alpha(0)\n", - " ax_polar = fig.add_axes(rect, polar=True, frameon=False,zorder=1)\n", - "\n", - " # the polar axis:\n", - " contour_set = ax_polar.contourf(thetas, rhos, data, 100, cmap = 'inferno', vmin=0, vmax=vmax)\n", - " ax_polar.grid(False)\n", - " ax_polar.set_xticks([])\n", - " ax_polar.set_rticks([])\n", - " # im_ratio = data.shape[1]/data.shape[0]\n", - " # # plt.colorbar(label=cbarlabel,fraction=0.046*im_ratio, pad=0.04)\n", - " # plt.colorbar(contour_set, ax=ax_polar, label=cbarlabel, fraction=0.046*im_ratio, pad=0.04)\n", - " # plt.colorbar(contour_set, ax=ax_carthesian, label=cbarlabel, fraction=0.046*im_ratio, pad=0.04, alpha=0)\n", - "\n", - " ticks = np.round(np.linspace(0,Lx,11),3)\n", - " ax_carthesian.set_xlim(0,Lx)\n", - " ax_carthesian.set_ylim(0,Lx)\n", - " ax_carthesian.set_xticks(ticks)\n", - " ax_carthesian.set_yticks(ticks)\n", - " ax_carthesian.set_xticklabels(ticks,rotation = 90)\n", - " ax_carthesian.set_xlabel(r'$x$ [m]')\n", - " ax_carthesian.set_ylabel(r'$y$ [m]')\n", - " ax_carthesian.grid()\n", - "\n", - "plot_cyl_data(data['n'][dmp_nr, :, :, :].squeeze(), cbarlabel=r'$n \\,\\, [\\mathrm{m^{-3}}]$')" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(subplot_kw={'projection': 'polar'})\n", - "\n", - "plot0 = ax.contourf(thetas, rhos, data['T'][dmp_nr, :, :, :].squeeze(), 100, cmap = 'inferno', vmin=0,vmax=data['T'].max())\n", - "ax.set_rticks([])\n", - "ax.set_xticks([])\n", - "plt.colorbar(plot0, ax=ax)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "# Method to change the contour data points\n", - "def plot_field(field, i, cmap='inferno'):\n", - " vmin = data[field].min()\n", - " vmax = data[field].max()\n", - " # ax.clear()\n", - " contour_set = ax.contourf(thetas, rhos, data[field][i, :, :, :].squeeze(), 100, cmap = cmap, vmin=vmin, vmax=vmax)\n", - " ax.set_rticks([])\n", - " ax.set_xticks([])\n", - " fig.tight_layout()\n", - " return contour_set\n", - "\n", - "def animate_n(i):\n", - " plot_field('n',i)\n", - "def animate_T(i):\n", - " plot_field('T',i)\n", - "def animate_vort(i):\n", - " plot_field('vort',i, cmap='jet')" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "MovieWriter ffmpeg unavailable; using Pillow instead.\n" - ] - } - ], - "source": [ - "# Call animate method\n", - "fig, ax = plt.subplots(subplot_kw={'projection': 'polar'})\n", - "\n", - "plt.colorbar(plot_field('n',0), ax=ax)\n", - "ani = animation.FuncAnimation(fig, animate_n, frames=data['n'].shape[0], interval=100, blit=False)\n", - "ani.save('plots/' + job_id + '_animation_n.gif', fps = 4)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "MovieWriter ffmpeg unavailable; using Pillow instead.\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Call animate method\n", - "fig, ax = plt.subplots(subplot_kw={'projection': 'polar'})\n", - "\n", - "plt.colorbar(plot_field('T',-1), ax=ax)\n", - "ani = animation.FuncAnimation(fig, animate_T, frames=data['T'].shape[0], interval=100, blit=False)\n", - "ani.save('plots/' + job_id + 'animation_T.gif', fps = 4)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "MovieWriter ffmpeg unavailable; using Pillow instead.\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Call animate method\n", - "fig, ax = plt.subplots(subplot_kw={'projection': 'polar'})\n", - "\n", - "plt.colorbar(plot_field('vort',-1), ax=ax)\n", - "ani = animation.FuncAnimation(fig, animate_vort, frames=data['vort'].shape[0], interval=100, blit=False)\n", - "ani.save('plots/' + job_id + 'animation_vort.gif', fps = 4)\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.9.6 ('north-simulation': venv)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.6" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "224ae1986ea58ff94007dbe32974f2e348ab1cb1b1c3796c4332e053e2c649e7" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/data_analysis_animation.ipynb b/data_analysis_animation.ipynb deleted file mode 100644 index 82f5e0d..0000000 --- a/data_analysis_animation.ipynb +++ /dev/null @@ -1,1009 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from matplotlib import rcParams\n", - "# rcParams.update({\n", - "# \"font.size\": 16})\n", - "# rcParams['axes.titlepad'] = 20" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load in Data" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "job_id 5151993\n" - ] - } - ], - "source": [ - "from boutdata import collect\n", - "from boutdata.data import BoutData\n", - "import re\n", - "import os\n", - "\n", - "job_ids = [re.search('(\\d){7}', dir).group() for dir in os.listdir(\"./shared/NORTH/\") if re.search('data_(\\d){7}', dir)]\n", - "job_ids.sort()\n", - "job_id = job_ids[-2]\n", - "#job_id = '5145562'\n", - "print('job_id', job_id)\n", - "path = './shared/NORTH/data_' + job_id + '/'" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/ppfe/s173956/local/venv/north-simulation/lib/python3.9/site-packages/boutdata/data.py:732: AlwaysWarning: While building x, y, z coordinate arrays, an exception occured: name 'mxg' is not defined\n", - "Evaluating non-scalar options not available\n", - " alwayswarn(\n" - ] - } - ], - "source": [ - "bdata = BoutData(path)\n", - "outputs = bdata['outputs']\n", - "field_keys = outputs.keys()\n", - "options = bdata['options']" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "def get_option(*keys):\n", - " from numpy import sqrt\n", - " val = options\n", - " try:\n", - " for key in keys:\n", - " val = val[key]\n", - " return eval(str(val))\n", - " except:\n", - " print('Error reading option for keys: ', keys)\n", - " return 0\n", - "\n", - "def get_options(keys_list):\n", - " vals = []\n", - " for keys in keys_list:\n", - " vals.append(get_option(*keys))\n", - " return vals" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.25, 10.0, 2, 1.67e-27, 0.077, 1.6e-19, 2, 0)" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "R, Te0, N_nuclei, mp, B0, e, mxg, myg = get_options([\n", - " ('north', 'R'), ('north', 'Te0'), ('north', 'N_nuclei'),\n", - " ('north', 'mp'), ('north', 'B0'), ('north', 'e'),\n", - " (['mxg']), (['myg'])])\n", - "R, Te0, N_nuclei, mp, B0, e, mxg, myg" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "nx: 80+2*mxg\n", - "ny: 1\n", - "nz: 256\n", - "Lx: north:a/north:rho_s\n", - "rho_s: sqrt(2*e*Te0/(N_nuclei*mp))/oci\n", - "oci: e*B0/(N_nuclei*mp)\n" - ] - } - ], - "source": [ - "print('nx: ', options['mesh']['nx'])\n", - "print('ny: ', options['mesh']['ny'])\n", - "print('nz: ', options['mesh']['nz'])\n", - "print('Lx: ', options['mesh']['Lx'])\n", - "print('rho_s: ', options['north']['rho_s'])\n", - "print('oci: ', options['north']['oci'])" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "nx_all, ny_all, nz_all = get_options([('mesh', 'nx'),('mesh', 'ny'),('mesh', 'nz')])\n", - "oci = get_option('north', 'oci')\n", - "rho_s = get_option('north', 'rho_s')\n", - "Lx = R/rho_s * rho_s\n", - "nx_inner = nx_all - 2*mxg\n", - "ny_inner = ny_all - 2*myg\n", - "nz_inner = nz_all" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "mxsub = 2 mysub = 1 mz = 256\n", - "\n", - "nxpe = 40, nype = 1, npes = 40\n", - "\n", - "Reading from 0: [2-3][0-0] -> [0-1][0-0]\n", - "\n", - "Reading from 1: [2-3][0-0] -> [2-3][0-0]\n", - "\n", - "Reading from 2: [2-3][0-0] -> [4-5][0-0]\n", - "\n", - "Reading from 3: [2-3][0-0] -> [6-7][0-0]\n", - "\n", - "Reading from 4: [2-3][0-0] -> [8-9][0-0]\n", - "\n", - "Reading from 5: [2-3][0-0] -> [10-11][0-0]\n", - "\n", - "Reading from 6: [2-3][0-0] -> [12-13][0-0]\n", - "\n", - "Reading from 7: [2-3][0-0] -> [14-15][0-0]\n", - "\n", - "Reading from 8: [2-3][0-0] -> [16-17][0-0]\n", - "\n", - "Reading from 9: [2-3][0-0] -> [18-19][0-0]\n", - "\n", - "Reading from 10: [2-3][0-0] -> [20-21][0-0]\n", - "\n", - "Reading from 11: [2-3][0-0] -> [22-23][0-0]\n", - "\n", - "Reading from 12: [2-3][0-0] -> [24-25][0-0]\n", - "\n", - "Reading from 13: [2-3][0-0] -> [26-27][0-0]\n", - "\n", - "Reading from 14: [2-3][0-0] -> [28-29][0-0]\n", - "\n", - "Reading from 15: [2-3][0-0] -> [30-31][0-0]\n", - "\n", - "Reading from 16: [2-3][0-0] -> [32-33][0-0]\n", - "\n", - "Reading from 17: [2-3][0-0] -> [34-35][0-0]\n", - "\n", - "Reading from 18: [2-3][0-0] -> [36-37][0-0]\n", - "\n", - "Reading from 19: [2-3][0-0] -> [38-39][0-0]\n", - "\n", - "Reading from 20: [2-3][0-0] -> [40-41][0-0]\n", - "\n", - "Reading from 21: [2-3][0-0] -> [42-43][0-0]\n", - "\n", - "Reading from 22: [2-3][0-0] -> [44-45][0-0]\n", - "\n", - "Reading from 23: [2-3][0-0] -> [46-47][0-0]\n", - "\n", - "Reading from 24: [2-3][0-0] -> [48-49][0-0]\n", - "\n", - "Reading from 25: [2-3][0-0] -> [50-51][0-0]\n", - "\n", - "Reading from 26: [2-3][0-0] -> [52-53][0-0]\n", - "\n", - "Reading from 27: [2-3][0-0] -> [54-55][0-0]\n", - "\n", - "Reading from 28: [2-3][0-0] -> [56-57][0-0]\n", - "\n", - "Reading from 29: [2-3][0-0] -> [58-59][0-0]\n", - "\n", - "Reading from 30: [2-3][0-0] -> [60-61][0-0]\n", - "\n", - "Reading from 31: [2-3][0-0] -> [62-63][0-0]\n", - "\n", - "Reading from 32: [2-3][0-0] -> [64-65][0-0]\n", - "\n", - "Reading from 33: [2-3][0-0] -> [66-67][0-0]\n", - "\n", - "Reading from 34: [2-3][0-0] -> [68-69][0-0]\n", - "\n", - "Reading from 35: [2-3][0-0] -> [70-71][0-0]\n", - "\n", - "Reading from 36: [2-3][0-0] -> [72-73][0-0]\n", - "\n", - "Reading from 37: [2-3][0-0] -> [74-75][0-0]\n", - "\n", - "Reading from 38: [2-3][0-0] -> [76-77][0-0]\n", - "\n", - "Reading from 39: [2-3][0-0] -> [78-79][0-0]\n", - "\n", - "\n", - "mxsub = 2 mysub = 1 mz = 256\n", - "\n", - "nxpe = 40, nype = 1, npes = 40\n", - "\n", - "Reading from 0: [2-3][0-0] -> [0-1][0-0]\n", - "\n", - "Reading from 1: [2-3][0-0] -> [2-3][0-0]\n", - "\n", - "Reading from 2: [2-3][0-0] -> [4-5][0-0]\n", - "\n", - "Reading from 3: [2-3][0-0] -> [6-7][0-0]\n", - "\n", - "Reading from 4: [2-3][0-0] -> [8-9][0-0]\n", - "\n", - "Reading from 5: [2-3][0-0] -> [10-11][0-0]\n", - "\n", - "Reading from 6: [2-3][0-0] -> [12-13][0-0]\n", - "\n", - "Reading from 7: [2-3][0-0] -> [14-15][0-0]\n", - "\n", - "Reading from 8: [2-3][0-0] -> [16-17][0-0]\n", - "\n", - "Reading from 9: [2-3][0-0] -> [18-19][0-0]\n", - "\n", - "Reading from 10: [2-3][0-0] -> [20-21][0-0]\n", - "\n", - "Reading from 11: [2-3][0-0] -> [22-23][0-0]\n", - "\n", - "Reading from 12: [2-3][0-0] -> [24-25][0-0]\n", - "\n", - "Reading from 13: [2-3][0-0] -> [26-27][0-0]\n", - "\n", - "Reading from 14: [2-3][0-0] -> [28-29][0-0]\n", - "\n", - "Reading from 15: [2-3][0-0] -> [30-31][0-0]\n", - "\n", - "Reading from 16: [2-3][0-0] -> [32-33][0-0]\n", - "\n", - "Reading from 17: [2-3][0-0] -> [34-35][0-0]\n", - "\n", - "Reading from 18: [2-3][0-0] -> [36-37][0-0]\n", - "\n", - "Reading from 19: [2-3][0-0] -> [38-39][0-0]\n", - "\n", - "Reading from 20: [2-3][0-0] -> [40-41][0-0]\n", - "\n", - "Reading from 21: [2-3][0-0] -> [42-43][0-0]\n", - "\n", - "Reading from 22: [2-3][0-0] -> [44-45][0-0]\n", - "\n", - "Reading from 23: [2-3][0-0] -> [46-47][0-0]\n", - "\n", - "Reading from 24: [2-3][0-0] -> [48-49][0-0]\n", - "\n", - "Reading from 25: [2-3][0-0] -> [50-51][0-0]\n", - "\n", - "Reading from 26: [2-3][0-0] -> [52-53][0-0]\n", - "\n", - "Reading from 27: [2-3][0-0] -> [54-55][0-0]\n", - "\n", - "Reading from 28: [2-3][0-0] -> [56-57][0-0]\n", - "\n", - "Reading from 29: [2-3][0-0] -> [58-59][0-0]\n", - "\n", - "Reading from 30: [2-3][0-0] -> [60-61][0-0]\n", - "\n", - "Reading from 31: [2-3][0-0] -> [62-63][0-0]\n", - "\n", - "Reading from 32: [2-3][0-0] -> [64-65][0-0]\n", - "\n", - "Reading from 33: [2-3][0-0] -> [66-67][0-0]\n", - "\n", - "Reading from 34: [2-3][0-0] -> [68-69][0-0]\n", - "\n", - "Reading from 35: [2-3][0-0] -> [70-71][0-0]\n", - "\n", - "Reading from 36: [2-3][0-0] -> [72-73][0-0]\n", - "\n", - "Reading from 37: [2-3][0-0] -> [74-75][0-0]\n", - "\n", - "Reading from 38: [2-3][0-0] -> [76-77][0-0]\n", - "\n", - "Reading from 39: [2-3][0-0] -> [78-79][0-0]\n", - "\n", - "\n", - "mxsub = 2 mysub = 1 mz = 256\n", - "\n", - "nxpe = 40, nype = 1, npes = 40\n", - "\n", - "Reading from 0: [2-3][0-0] -> [0-1][0-0]\n", - "\n", - "Reading from 1: [2-3][0-0] -> [2-3][0-0]\n", - "\n", - "Reading from 2: [2-3][0-0] -> [4-5][0-0]\n", - "\n", - "Reading from 3: [2-3][0-0] -> [6-7][0-0]\n", - "\n", - "Reading from 4: [2-3][0-0] -> [8-9][0-0]\n", - "\n", - "Reading from 5: [2-3][0-0] -> [10-11][0-0]\n", - "\n", - "Reading from 6: [2-3][0-0] -> [12-13][0-0]\n", - "\n", - "Reading from 7: [2-3][0-0] -> [14-15][0-0]\n", - "\n", - "Reading from 8: [2-3][0-0] -> [16-17][0-0]\n", - "\n", - "Reading from 9: [2-3][0-0] -> [18-19][0-0]\n", - "\n", - "Reading from 10: [2-3][0-0] -> [20-21][0-0]\n", - "\n", - "Reading from 11: [2-3][0-0] -> [22-23][0-0]\n", - "\n", - "Reading from 12: [2-3][0-0] -> [24-25][0-0]\n", - "\n", - "Reading from 13: [2-3][0-0] -> [26-27][0-0]\n", - "\n", - "Reading from 14: [2-3][0-0] -> [28-29][0-0]\n", - "\n", - "Reading from 15: [2-3][0-0] -> [30-31][0-0]\n", - "\n", - "Reading from 16: [2-3][0-0] -> [32-33][0-0]\n", - "\n", - "Reading from 17: [2-3][0-0] -> [34-35][0-0]\n", - "\n", - "Reading from 18: [2-3][0-0] -> [36-37][0-0]\n", - "\n", - "Reading from 19: [2-3][0-0] -> [38-39][0-0]\n", - "\n", - "Reading from 20: [2-3][0-0] -> [40-41][0-0]\n", - "\n", - "Reading from 21: [2-3][0-0] -> [42-43][0-0]\n", - "\n", - "Reading from 22: [2-3][0-0] -> [44-45][0-0]\n", - "\n", - "Reading from 23: [2-3][0-0] -> [46-47][0-0]\n", - "\n", - "Reading from 24: [2-3][0-0] -> [48-49][0-0]\n", - "\n", - "Reading from 25: [2-3][0-0] -> [50-51][0-0]\n", - "\n", - "Reading from 26: [2-3][0-0] -> [52-53][0-0]\n", - "\n", - "Reading from 27: [2-3][0-0] -> [54-55][0-0]\n", - "\n", - "Reading from 28: [2-3][0-0] -> [56-57][0-0]\n", - "\n", - "Reading from 29: [2-3][0-0] -> [58-59][0-0]\n", - "\n", - "Reading from 30: [2-3][0-0] -> [60-61][0-0]\n", - "\n", - "Reading from 31: [2-3][0-0] -> [62-63][0-0]\n", - "\n", - "Reading from 32: [2-3][0-0] -> [64-65][0-0]\n", - "\n", - "Reading from 33: [2-3][0-0] -> [66-67][0-0]\n", - "\n", - "Reading from 34: [2-3][0-0] -> [68-69][0-0]\n", - "\n", - "Reading from 35: [2-3][0-0] -> [70-71][0-0]\n", - "\n", - "Reading from 36: [2-3][0-0] -> [72-73][0-0]\n", - "\n", - "Reading from 37: [2-3][0-0] -> [74-75][0-0]\n", - "\n", - "Reading from 38: [2-3][0-0] -> [76-77][0-0]\n", - "\n", - "Reading from 39: [2-3][0-0] -> [78-79][0-0]\n", - "\n", - "\n", - "mxsub = 2 mysub = 1 mz = 256\n", - "\n", - "nxpe = 40, nype = 1, npes = 40\n", - "\n", - "Reading from 0: [2-3][0-0] -> [0-1][0-0]\n", - "\n", - "Reading from 1: [2-3][0-0] -> [2-3][0-0]\n", - "\n", - "Reading from 2: [2-3][0-0] -> [4-5][0-0]\n", - "\n", - "Reading from 3: [2-3][0-0] -> [6-7][0-0]\n", - "\n", - "Reading from 4: [2-3][0-0] -> [8-9][0-0]\n", - "\n", - "Reading from 5: [2-3][0-0] -> [10-11][0-0]\n", - "\n", - "Reading from 6: [2-3][0-0] -> [12-13][0-0]\n", - "\n", - "Reading from 7: [2-3][0-0] -> [14-15][0-0]\n", - "\n", - "Reading from 8: [2-3][0-0] -> [16-17][0-0]\n", - "\n", - "Reading from 9: [2-3][0-0] -> [18-19][0-0]\n", - "\n", - "Reading from 10: [2-3][0-0] -> [20-21][0-0]\n", - "\n", - "Reading from 11: [2-3][0-0] -> [22-23][0-0]\n", - "\n", - "Reading from 12: [2-3][0-0] -> [24-25][0-0]\n", - "\n", - "Reading from 13: [2-3][0-0] -> [26-27][0-0]\n", - "\n", - "Reading from 14: [2-3][0-0] -> [28-29][0-0]\n", - "\n", - "Reading from 15: [2-3][0-0] -> [30-31][0-0]\n", - "\n", - "Reading from 16: [2-3][0-0] -> [32-33][0-0]\n", - "\n", - "Reading from 17: [2-3][0-0] -> [34-35][0-0]\n", - "\n", - "Reading from 18: [2-3][0-0] -> [36-37][0-0]\n", - "\n", - "Reading from 19: [2-3][0-0] -> [38-39][0-0]\n", - "\n", - "Reading from 20: [2-3][0-0] -> [40-41][0-0]\n", - "\n", - "Reading from 21: [2-3][0-0] -> [42-43][0-0]\n", - "\n", - "Reading from 22: [2-3][0-0] -> [44-45][0-0]\n", - "\n", - "Reading from 23: [2-3][0-0] -> [46-47][0-0]\n", - "\n", - "Reading from 24: [2-3][0-0] -> [48-49][0-0]\n", - "\n", - "Reading from 25: [2-3][0-0] -> [50-51][0-0]\n", - "\n", - "Reading from 26: [2-3][0-0] -> [52-53][0-0]\n", - "\n", - "Reading from 27: [2-3][0-0] -> [54-55][0-0]\n", - "\n", - "Reading from 28: [2-3][0-0] -> [56-57][0-0]\n", - "\n", - "Reading from 29: [2-3][0-0] -> [58-59][0-0]\n", - "\n", - "Reading from 30: [2-3][0-0] -> [60-61][0-0]\n", - "\n", - "Reading from 31: [2-3][0-0] -> [62-63][0-0]\n", - "\n", - "Reading from 32: [2-3][0-0] -> [64-65][0-0]\n", - "\n", - "Reading from 33: [2-3][0-0] -> [66-67][0-0]\n", - "\n", - "Reading from 34: [2-3][0-0] -> [68-69][0-0]\n", - "\n", - "Reading from 35: [2-3][0-0] -> [70-71][0-0]\n", - "\n", - "Reading from 36: [2-3][0-0] -> [72-73][0-0]\n", - "\n", - "Reading from 37: [2-3][0-0] -> [74-75][0-0]\n", - "\n", - "Reading from 38: [2-3][0-0] -> [76-77][0-0]\n", - "\n", - "Reading from 39: [2-3][0-0] -> [78-79][0-0]\n", - "\n", - "\n", - "mxsub = 2 mysub = 1 mz = 256\n", - "\n", - "nxpe = 40, nype = 1, npes = 40\n", - "\n", - "Reading from 0: [2-3][0-0] -> [0-1][0-0]\n", - "\n", - "Reading from 1: [2-3][0-0] -> [2-3][0-0]\n", - "\n", - "Reading from 2: [2-3][0-0] -> [4-5][0-0]\n", - "\n", - "Reading from 3: [2-3][0-0] -> [6-7][0-0]\n", - "\n", - "Reading from 4: [2-3][0-0] -> [8-9][0-0]\n", - "\n", - "Reading from 5: [2-3][0-0] -> [10-11][0-0]\n", - "\n", - "Reading from 6: [2-3][0-0] -> [12-13][0-0]\n", - "\n", - "Reading from 7: [2-3][0-0] -> [14-15][0-0]\n", - "\n", - "Reading from 8: [2-3][0-0] -> [16-17][0-0]\n", - "\n", - "Reading from 9: [2-3][0-0] -> [18-19][0-0]\n", - "\n", - "Reading from 10: [2-3][0-0] -> [20-21][0-0]\n", - "\n", - "Reading from 11: [2-3][0-0] -> [22-23][0-0]\n", - "\n", - "Reading from 12: [2-3][0-0] -> [24-25][0-0]\n", - "\n", - "Reading from 13: [2-3][0-0] -> [26-27][0-0]\n", - "\n", - "Reading from 14: [2-3][0-0] -> [28-29][0-0]\n", - "\n", - "Reading from 15: [2-3][0-0] -> [30-31][0-0]\n", - "\n", - "Reading from 16: [2-3][0-0] -> [32-33][0-0]\n", - "\n", - "Reading from 17: [2-3][0-0] -> [34-35][0-0]\n", - "\n", - "Reading from 18: [2-3][0-0] -> [36-37][0-0]\n", - "\n", - "Reading from 19: [2-3][0-0] -> [38-39][0-0]\n", - "\n", - "Reading from 20: [2-3][0-0] -> [40-41][0-0]\n", - "\n", - "Reading from 21: [2-3][0-0] -> [42-43][0-0]\n", - "\n", - "Reading from 22: [2-3][0-0] -> [44-45][0-0]\n", - "\n", - "Reading from 23: [2-3][0-0] -> [46-47][0-0]\n", - "\n", - "Reading from 24: [2-3][0-0] -> [48-49][0-0]\n", - "\n", - "Reading from 25: [2-3][0-0] -> [50-51][0-0]\n", - "\n", - "Reading from 26: [2-3][0-0] -> [52-53][0-0]\n", - "\n", - "Reading from 27: [2-3][0-0] -> [54-55][0-0]\n", - "\n", - "Reading from 28: [2-3][0-0] -> [56-57][0-0]\n", - "\n", - "Reading from 29: [2-3][0-0] -> [58-59][0-0]\n", - "\n", - "Reading from 30: [2-3][0-0] -> [60-61][0-0]\n", - "\n", - "Reading from 31: [2-3][0-0] -> [62-63][0-0]\n", - "\n", - "Reading from 32: [2-3][0-0] -> [64-65][0-0]\n", - "\n", - "Reading from 33: [2-3][0-0] -> [66-67][0-0]\n", - "\n", - "Reading from 34: [2-3][0-0] -> [68-69][0-0]\n", - "\n", - "Reading from 35: [2-3][0-0] -> [70-71][0-0]\n", - "\n", - "Reading from 36: [2-3][0-0] -> [72-73][0-0]\n", - "\n", - "Reading from 37: [2-3][0-0] -> [74-75][0-0]\n", - "\n", - "Reading from 38: [2-3][0-0] -> [76-77][0-0]\n", - "\n", - "Reading from 39: [2-3][0-0] -> [78-79][0-0]\n", - "\n", - "\n", - "mxsub = 2 mysub = 1 mz = 256\n", - "\n", - "nxpe = 40, nype = 1, npes = 40\n", - "\n", - "Reading from 0: [2-3][0-0] -> [0-1][0-0]\n", - "\n", - "Reading from 1: [2-3][0-0] -> [2-3][0-0]\n", - "\n", - "Reading from 2: [2-3][0-0] -> [4-5][0-0]\n", - "\n", - "Reading from 3: [2-3][0-0] -> [6-7][0-0]\n", - "\n", - "Reading from 4: [2-3][0-0] -> [8-9][0-0]\n", - "\n", - "Reading from 5: [2-3][0-0] -> [10-11][0-0]\n", - "\n", - "Reading from 6: [2-3][0-0] -> [12-13][0-0]\n", - "\n", - "Reading from 7: [2-3][0-0] -> [14-15][0-0]\n", - "\n", - "Reading from 8: [2-3][0-0] -> [16-17][0-0]\n", - "\n", - "Reading from 9: [2-3][0-0] -> [18-19][0-0]\n", - "\n", - "Reading from 10: [2-3][0-0] -> [20-21][0-0]\n", - "\n", - "Reading from 11: [2-3][0-0] -> [22-23][0-0]\n", - "\n", - "Reading from 12: [2-3][0-0] -> [24-25][0-0]\n", - "\n", - "Reading from 13: [2-3][0-0] -> [26-27][0-0]\n", - "\n", - "Reading from 14: [2-3][0-0] -> [28-29][0-0]\n", - "\n", - "Reading from 15: [2-3][0-0] -> [30-31][0-0]\n", - "\n", - "Reading from 16: [2-3][0-0] -> [32-33][0-0]\n", - "\n", - "Reading from 17: [2-3][0-0] -> [34-35][0-0]\n", - "\n", - "Reading from 18: [2-3][0-0] -> [36-37][0-0]\n", - "\n", - "Reading from 19: [2-3][0-0] -> [38-39][0-0]\n", - "\n", - "Reading from 20: [2-3][0-0] -> [40-41][0-0]\n", - "\n", - "Reading from 21: [2-3][0-0] -> [42-43][0-0]\n", - "\n", - "Reading from 22: [2-3][0-0] -> [44-45][0-0]\n", - "\n", - "Reading from 23: [2-3][0-0] -> [46-47][0-0]\n", - "\n", - "Reading from 24: [2-3][0-0] -> [48-49][0-0]\n", - "\n", - "Reading from 25: [2-3][0-0] -> [50-51][0-0]\n", - "\n", - "Reading from 26: [2-3][0-0] -> [52-53][0-0]\n", - "\n", - "Reading from 27: [2-3][0-0] -> [54-55][0-0]\n", - "\n", - "Reading from 28: [2-3][0-0] -> [56-57][0-0]\n", - "\n", - "Reading from 29: [2-3][0-0] -> [58-59][0-0]\n", - "\n", - "Reading from 30: [2-3][0-0] -> [60-61][0-0]\n", - "\n", - "Reading from 31: [2-3][0-0] -> [62-63][0-0]\n", - "\n", - "Reading from 32: [2-3][0-0] -> [64-65][0-0]\n", - "\n", - "Reading from 33: [2-3][0-0] -> [66-67][0-0]\n", - "\n", - "Reading from 34: [2-3][0-0] -> [68-69][0-0]\n", - "\n", - "Reading from 35: [2-3][0-0] -> [70-71][0-0]\n", - "\n", - "Reading from 36: [2-3][0-0] -> [72-73][0-0]\n", - "\n", - "Reading from 37: [2-3][0-0] -> [74-75][0-0]\n", - "\n", - "Reading from 38: [2-3][0-0] -> [76-77][0-0]\n", - "\n", - "Reading from 39: [2-3][0-0] -> [78-79][0-0]\n", - "\n", - "\n", - "mxsub = 2 mysub = 1 mz = 256\n", - "\n", - "nxpe = 40, nype = 1, npes = 40\n", - "\n" - ] - } - ], - "source": [ - "#%% Read data\n", - "\n", - "field_list = ['T', 'n', 'phi', 'vort', 'source_T','wall_shadow']\n", - "par_list = ['t_array']\n", - "# fast_list = ['t_array', 'n0', 'phi0', 'n1', 'phi1', 'n2', 'phi2', 'T0', 'T1', 'T2']\n", - "#%%\n", - "data, par, fast = {}, {}, {}\n", - "\n", - "for _field in field_list:\n", - " data[_field] = collect(_field, path = path, xguards = False)\n", - "\n", - "for _par in par_list:\n", - " par[_par] = collect(_par, path = path, xguards = False)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "thetas = np.linspace(0, 2 * np.pi, nz_inner)\n", - "rhos = np.linspace(Lx/(2*nx_inner), Lx+Lx/(2*nx_inner), nx_inner)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "times = par['t_array']/oci" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot Time Frames" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "from matplotlib.gridspec import GridSpec\n", - "\n", - "def cbar_ticks(vmin, vmax, n_ticks=4, label_rounding=2):\n", - " ticks = np.linspace(vmin, vmax, n_ticks, endpoint=True)\n", - " labels = np.round(ticks,3)\n", - " return ticks, labels\n", - "\n", - "def plot_time_frames(start_frame=0, end_frame=None):\n", - " fields = ['n', 'T', 'vort', 'phi']\n", - " cmaps = ['inferno', 'inferno', 'jet', 'jet']\n", - " titles = [r'$n$', r'$T$', r'$\\Omega$', r'$\\phi$']\n", - " N_frames = 6\n", - " cm = 1/2.54 # 1 cm = 1/2.54 inch\n", - " nrows = 5*N_frames + 1 # 5 (merged rows) for each frame plot. 1 row for colobar\n", - " n_cols = 1 + len(fields)*6 # 1 column for each timestamp text. 3 (merged columns) for each field plot.\n", - " fig = plt.figure(constrained_layout=True, figsize=(16*cm, 20*cm))\n", - " gs = GridSpec(nrows, n_cols, figure=fig)\n", - " def get_row_range(i_frame):\n", - " return slice(5*i_frame,5*(i_frame+1))\n", - " def get_col_range(i_field):\n", - " return slice(1+6*i_field,1+6*(i_field+1))\n", - " if end_frame is None:\n", - " end_frame = data[fields[0]].shape[0]\n", - " frames = np.linspace(start_frame, end_frame - 1, N_frames, dtype=int)\n", - " # frames = np.arange(start_frame, end_frame, int(np.ceil(end_frame/N_frames)))\n", - "\n", - " for i_frame, frame in enumerate(frames):\n", - " row_range = get_row_range(i_frame)\n", - " ax = fig.add_subplot(gs[row_range, 0])\n", - " ax.text(0.5, 0.5, r'$t = ' + str(round(times[frame]*(10**6),1)) + '\\, \\mathrm{\\mu \\, s}$', va=\"center\", ha=\"center\")\n", - " plt.axis('off')\n", - "\n", - " for i_field, field in enumerate(fields):\n", - " field_data = data[field].squeeze()\n", - " vmin = field_data.min()\n", - " vmax = field_data.max()\n", - " col_range = get_col_range(i_field)\n", - " for i_frame, frame in enumerate(frames):\n", - " row_range = get_row_range(i_frame)\n", - " ax = fig.add_subplot(gs[row_range, col_range], polar=True)\n", - " if i_frame == 0:\n", - " ax.set_title(titles[i_field])\n", - " cont = ax.contourf(thetas, rhos, field_data[frame, :, :], 10, cmap = cmaps[i_field], vmin=vmin, vmax=vmax)\n", - " ax.set_rticks([])\n", - " ax.set_xticks([])\n", - "\n", - " cax = fig.add_subplot(gs[-1, col_range])\n", - " latest = field_data[frames[-1], :, :]\n", - " c_ticks, c_labels = cbar_ticks(latest.min(), latest.max())\n", - " cbar = fig.colorbar(cont, cax=cax, ticks=c_ticks, orientation='horizontal')\n", - " cax.set_title(titles[i_field] + ' [norm. units]')\n", - " cbar.ax.set_xticklabels(c_labels, rotation=45)\n", - " return fig\n", - "\n", - "fig = plot_time_frames()" - ] - }, - { - "cell_type": "code", - "execution_count": 96, - "metadata": {}, - "outputs": [], - "source": [ - "# fig.savefig('./plots/' + job_id + '_time_frames.pdf', format=\"pdf\", bbox_inches=\"tight\")" - ] - }, - { - "cell_type": "code", - "execution_count": 97, - "metadata": {}, - "outputs": [], - "source": [ - "fig.savefig('./plots/' + job_id + '_time_frames.png', dpi=600)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## gif Animations" - ] - }, - { - "cell_type": "code", - "execution_count": 98, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.animation as animation\n", - "\n", - "def animate_data(field, cmap='inferno', vmin = None, vmax = None):\n", - " field_data = data[field].squeeze()\n", - " if vmin is None:\n", - " vmin = field_data.min()\n", - " if vmax is None:\n", - " vmax = field_data.max()\n", - " fig, ax = plt.subplots(subplot_kw={'projection': 'polar'})\n", - " ax.set_rticks([])\n", - " ax.set_xticks([])\n", - " cont = ax.contourf(thetas, rhos, field_data[0, :, :], 100, cmap = cmap, vmin=vmin, vmax=vmax) # first image on screen\n", - " fig.colorbar(cont, ax=ax)\n", - " fig.tight_layout()\n", - "\n", - " # animation function\n", - " def animate(i):\n", - " for c in ax.collections:\n", - " c.remove() # removes only the contours, leaves the rest intact\n", - " cont = ax.contourf(thetas, rhos, field_data[i, :, :], 100, cmap = cmap, vmin=vmin, vmax=vmax)\n", - " return cont.collections\n", - " \n", - " anim = animation.FuncAnimation(fig, animate, frames=field_data.shape[0], interval=100, blit=True)\n", - " anim.save('./plots/' + job_id + '_animation_' + field +'.gif', fps=4)\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 99, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "MovieWriter ffmpeg unavailable; using Pillow instead.\n" - ] - }, - { - "data": { - "image/png": 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", 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", 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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "animate_data('phi', cmap='jet')" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.9.6 ('north-simulation': venv)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.6" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "283665b67cdf6e633021a77ac207d327212d30d88d5d66cf50c6b3784b87797d" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/data_analysis_energy.ipynb b/data_analysis_energy.ipynb deleted file mode 100644 index 86dfe80..0000000 --- a/data_analysis_energy.ipynb +++ /dev/null @@ -1,798 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from matplotlib import rcParams\n", - "rcParams.update({\n", - " \"font.size\": 16})\n", - "rcParams['axes.titlepad'] = 20" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load in Data" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "job_id 5151993\n" - ] - } - ], - "source": [ - "from boutdata import collect\n", - "from boutdata.data import BoutData\n", - "import re\n", - "import os\n", - "\n", - "job_ids = [re.search('(\\d){7}', dir).group() for dir in os.listdir(\"./shared/NORTH/\") if re.search('data_(\\d){7}', dir)]\n", - "job_ids.sort()\n", - "# job_id = job_ids[-1]\n", - "job_id = '5151993'\n", - "print('job_id', job_id)\n", - "path = './shared/NORTH/data_' + job_id + '/'" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/ppfe/s173956/local/venv/north-simulation/lib/python3.9/site-packages/boutdata/data.py:732: AlwaysWarning: While building x, y, z coordinate arrays, an exception occured: name 'mxg' is not defined\n", - "Evaluating non-scalar options not available\n", - " alwayswarn(\n" - ] - } - ], - "source": [ - "bdata = BoutData(path)\n", - "outputs = bdata['outputs']\n", - "field_keys = outputs.keys()\n", - "options = bdata['options']" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "def get_option(*keys):\n", - " from numpy import sqrt\n", - " val = options\n", - " try:\n", - " for key in keys:\n", - " val = val[key]\n", - " return eval(str(val))\n", - " except:\n", - " print('Error reading option for keys: ', keys)\n", - " return 0\n", - "\n", - "def get_options(keys_list):\n", - " vals = []\n", - " for keys in keys_list:\n", - " vals.append(get_option(*keys))\n", - " return vals" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(1e+16, 0.125, 0.25, 10.0, 2, 1.67e-27, 0.077, 1.6e-19, 2, 0)" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "n0, a, R, Te0, N_nuclei, mp, B0, e, mxg, myg = get_options([\n", - " ('north', 'n0'), ('north', 'a'), ('north', 'R'), ('north', 'Te0'), ('north', 'N_nuclei'),\n", - " ('north', 'mp'), ('north', 'B0'), ('north', 'e'),\n", - " (['mxg']), (['myg'])])\n", - "n0, a, R, Te0, N_nuclei, mp, B0, e, mxg, myg" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "nx: 80+2*mxg\n", - "ny: 1\n", - "nz: 256\n", - "Lx: north:a/north:rho_s\n", - "rho_s: sqrt(2*e*Te0/(N_nuclei*mp))/oci\n", - "oci: e*B0/(N_nuclei*mp)\n" - ] - } - ], - "source": [ - "print('nx: ', options['mesh']['nx'])\n", - "print('ny: ', options['mesh']['ny'])\n", - "print('nz: ', options['mesh']['nz'])\n", - "print('Lx: ', options['mesh']['Lx'])\n", - "print('rho_s: ', options['north']['rho_s'])\n", - "print('oci: ', options['north']['oci'])" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "nx_all, ny_all, nz_all = get_options([('mesh', 'nx'),('mesh', 'ny'),('mesh', 'nz')])\n", - "oci = get_option('north', 'oci')\n", - "rho_s = get_option('north', 'rho_s')\n", - "Lx = a/rho_s * rho_s\n", - "nx_inner = nx_all - 2*mxg\n", - "ny_inner = ny_all - 2*myg\n", - "nz_inner = nz_all" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "mxsub = 2 mysub = 1 mz = 256\n", - "\n", - "nxpe = 40, nype = 1, npes = 40\n", - "\n", - "Reading from 0: [2-3][0-0] -> [0-1][0-0]\n", - "\n", - "Reading from 1: [2-3][0-0] -> [2-3][0-0]\n", - "\n", - "Reading from 2: [2-3][0-0] -> [4-5][0-0]\n", - "\n", - "Reading from 3: [2-3][0-0] -> [6-7][0-0]\n", - "\n", - "Reading from 4: [2-3][0-0] -> [8-9][0-0]\n", - "\n", - "Reading from 5: [2-3][0-0] -> [10-11][0-0]\n", - "\n", - "Reading from 6: [2-3][0-0] -> [12-13][0-0]\n", - "\n", - "Reading from 7: [2-3][0-0] -> [14-15][0-0]\n", - "\n", - "Reading from 8: [2-3][0-0] -> [16-17][0-0]\n", - "\n", - "Reading from 9: [2-3][0-0] -> [18-19][0-0]\n", - "\n", - "Reading from 10: [2-3][0-0] -> [20-21][0-0]\n", - "\n", - "Reading from 11: [2-3][0-0] -> [22-23][0-0]\n", - "\n", - "Reading from 12: [2-3][0-0] -> [24-25][0-0]\n", - "\n", - "Reading from 13: [2-3][0-0] -> [26-27][0-0]\n", - "\n", - "Reading from 14: [2-3][0-0] -> [28-29][0-0]\n", - "\n", - "Reading from 15: [2-3][0-0] -> [30-31][0-0]\n", - "\n", - "Reading from 16: [2-3][0-0] -> [32-33][0-0]\n", - "\n", - "Reading from 17: [2-3][0-0] -> [34-35][0-0]\n", - "\n", - "Reading from 18: [2-3][0-0] -> [36-37][0-0]\n", - "\n", - "Reading from 19: [2-3][0-0] -> [38-39][0-0]\n", - "\n", - "Reading from 20: [2-3][0-0] -> [40-41][0-0]\n", - "\n", - "Reading from 21: [2-3][0-0] -> [42-43][0-0]\n", - "\n", - "Reading from 22: [2-3][0-0] -> [44-45][0-0]\n", - "\n", - "Reading from 23: [2-3][0-0] -> [46-47][0-0]\n", - "\n", - "Reading from 24: [2-3][0-0] -> [48-49][0-0]\n", - "\n", - "Reading from 25: [2-3][0-0] -> [50-51][0-0]\n", - "\n", - "Reading from 26: [2-3][0-0] -> [52-53][0-0]\n", - "\n", - "Reading from 27: [2-3][0-0] -> [54-55][0-0]\n", - "\n", - "Reading from 28: [2-3][0-0] -> [56-57][0-0]\n", - "\n", - "Reading from 29: [2-3][0-0] -> [58-59][0-0]\n", - "\n", - "Reading from 30: [2-3][0-0] -> [60-61][0-0]\n", - "\n", - "Reading from 31: [2-3][0-0] -> [62-63][0-0]\n", - "\n", - "Reading from 32: [2-3][0-0] -> [64-65][0-0]\n", - "\n", - "Reading from 33: [2-3][0-0] -> [66-67][0-0]\n", - "\n", - "Reading from 34: [2-3][0-0] -> [68-69][0-0]\n", - "\n", - "Reading from 35: [2-3][0-0] -> [70-71][0-0]\n", - "\n", - "Reading from 36: [2-3][0-0] -> [72-73][0-0]\n", - "\n", - "Reading from 37: [2-3][0-0] -> [74-75][0-0]\n", - "\n", - "Reading from 38: [2-3][0-0] -> [76-77][0-0]\n", - "\n", - "Reading from 39: [2-3][0-0] -> [78-79][0-0]\n", - "\n", - "\n", - "mxsub = 2 mysub = 1 mz = 256\n", - "\n", - "nxpe = 40, nype = 1, npes = 40\n", - "\n", - "Reading from 0: [2-3][0-0] -> [0-1][0-0]\n", - "\n", - "Reading from 1: [2-3][0-0] -> [2-3][0-0]\n", - "\n", - "Reading from 2: [2-3][0-0] -> [4-5][0-0]\n", - "\n", - "Reading from 3: [2-3][0-0] -> [6-7][0-0]\n", - "\n", - "Reading from 4: [2-3][0-0] -> [8-9][0-0]\n", - "\n", - "Reading from 5: [2-3][0-0] -> [10-11][0-0]\n", - "\n", - "Reading from 6: [2-3][0-0] -> [12-13][0-0]\n", - "\n", - "Reading from 7: [2-3][0-0] -> [14-15][0-0]\n", - "\n", - "Reading from 8: [2-3][0-0] -> [16-17][0-0]\n", - "\n", - "Reading from 9: [2-3][0-0] -> [18-19][0-0]\n", - "\n", - "Reading from 10: [2-3][0-0] -> [20-21][0-0]\n", - "\n", - "Reading from 11: [2-3][0-0] -> [22-23][0-0]\n", - "\n", - "Reading from 12: [2-3][0-0] -> [24-25][0-0]\n", - "\n", - "Reading from 13: [2-3][0-0] -> [26-27][0-0]\n", - "\n", - "Reading from 14: [2-3][0-0] -> [28-29][0-0]\n", - "\n", - "Reading from 15: [2-3][0-0] -> [30-31][0-0]\n", - "\n", - "Reading from 16: [2-3][0-0] -> [32-33][0-0]\n", - "\n", - "Reading from 17: [2-3][0-0] -> [34-35][0-0]\n", - "\n", - "Reading from 18: [2-3][0-0] -> [36-37][0-0]\n", - "\n", - "Reading from 19: [2-3][0-0] -> [38-39][0-0]\n", - "\n", - "Reading from 20: [2-3][0-0] -> [40-41][0-0]\n", - "\n", - "Reading from 21: [2-3][0-0] -> [42-43][0-0]\n", - "\n", - "Reading from 22: [2-3][0-0] -> [44-45][0-0]\n", - "\n", - "Reading from 23: [2-3][0-0] -> [46-47][0-0]\n", - "\n", - "Reading from 24: [2-3][0-0] -> [48-49][0-0]\n", - "\n", - "Reading from 25: [2-3][0-0] -> [50-51][0-0]\n", - "\n", - "Reading from 26: [2-3][0-0] -> [52-53][0-0]\n", - "\n", - "Reading from 27: [2-3][0-0] -> [54-55][0-0]\n", - "\n", - "Reading from 28: [2-3][0-0] -> [56-57][0-0]\n", - "\n", - "Reading from 29: [2-3][0-0] -> [58-59][0-0]\n", - "\n", - "Reading from 30: [2-3][0-0] -> [60-61][0-0]\n", - "\n", - "Reading from 31: [2-3][0-0] -> [62-63][0-0]\n", - "\n", - "Reading from 32: [2-3][0-0] -> [64-65][0-0]\n", - "\n", - "Reading from 33: [2-3][0-0] -> [66-67][0-0]\n", - "\n", - "Reading from 34: [2-3][0-0] -> [68-69][0-0]\n", - "\n", - "Reading from 35: [2-3][0-0] -> [70-71][0-0]\n", - "\n", - "Reading from 36: [2-3][0-0] -> [72-73][0-0]\n", - "\n", - "Reading from 37: [2-3][0-0] -> [74-75][0-0]\n", - "\n", - "Reading from 38: [2-3][0-0] -> [76-77][0-0]\n", - "\n", - "Reading from 39: [2-3][0-0] -> [78-79][0-0]\n", - "\n", - "\n", - "mxsub = 2 mysub = 1 mz = 256\n", - "\n", - "nxpe = 40, nype = 1, npes = 40\n", - "\n", - "Reading from 0: [2-3][0-0] -> [0-1][0-0]\n", - "\n", - "Reading from 1: [2-3][0-0] -> [2-3][0-0]\n", - "\n", - "Reading from 2: [2-3][0-0] -> [4-5][0-0]\n", - "\n", - "Reading from 3: [2-3][0-0] -> [6-7][0-0]\n", - "\n", - "Reading from 4: [2-3][0-0] -> [8-9][0-0]\n", - "\n", - "Reading from 5: [2-3][0-0] -> [10-11][0-0]\n", - "\n", - "Reading from 6: [2-3][0-0] -> [12-13][0-0]\n", - "\n", - "Reading from 7: [2-3][0-0] -> [14-15][0-0]\n", - "\n", - "Reading from 8: [2-3][0-0] -> [16-17][0-0]\n", - "\n", - "Reading from 9: [2-3][0-0] -> [18-19][0-0]\n", - "\n", - "Reading from 10: [2-3][0-0] -> [20-21][0-0]\n", - "\n", - "Reading from 11: [2-3][0-0] -> [22-23][0-0]\n", - "\n", - "Reading from 12: [2-3][0-0] -> [24-25][0-0]\n", - "\n", - "Reading from 13: [2-3][0-0] -> [26-27][0-0]\n", - "\n", - "Reading from 14: [2-3][0-0] -> [28-29][0-0]\n", - "\n", - "Reading from 15: [2-3][0-0] -> [30-31][0-0]\n", - "\n", - "Reading from 16: [2-3][0-0] -> [32-33][0-0]\n", - "\n", - "Reading from 17: [2-3][0-0] -> [34-35][0-0]\n", - "\n", - "Reading from 18: [2-3][0-0] -> [36-37][0-0]\n", - "\n", - "Reading from 19: [2-3][0-0] -> [38-39][0-0]\n", - "\n", - "Reading from 20: [2-3][0-0] -> [40-41][0-0]\n", - "\n", - "Reading from 21: [2-3][0-0] -> [42-43][0-0]\n", - "\n", - "Reading from 22: [2-3][0-0] -> [44-45][0-0]\n", - "\n", - "Reading from 23: [2-3][0-0] -> [46-47][0-0]\n", - "\n", - "Reading from 24: [2-3][0-0] -> [48-49][0-0]\n", - "\n", - "Reading from 25: [2-3][0-0] -> [50-51][0-0]\n", - "\n", - "Reading from 26: [2-3][0-0] -> [52-53][0-0]\n", - "\n", - "Reading from 27: [2-3][0-0] -> [54-55][0-0]\n", - "\n", - "Reading from 28: [2-3][0-0] -> [56-57][0-0]\n", - "\n", - "Reading from 29: [2-3][0-0] -> [58-59][0-0]\n", - "\n", - "Reading from 30: [2-3][0-0] -> [60-61][0-0]\n", - "\n", - "Reading from 31: [2-3][0-0] -> [62-63][0-0]\n", - "\n", - "Reading from 32: [2-3][0-0] -> [64-65][0-0]\n", - "\n", - "Reading from 33: [2-3][0-0] -> [66-67][0-0]\n", - "\n", - "Reading from 34: [2-3][0-0] -> [68-69][0-0]\n", - "\n", - "Reading from 35: [2-3][0-0] -> [70-71][0-0]\n", - "\n", - "Reading from 36: [2-3][0-0] -> [72-73][0-0]\n", - "\n", - "Reading from 37: [2-3][0-0] -> [74-75][0-0]\n", - "\n", - "Reading from 38: [2-3][0-0] -> [76-77][0-0]\n", - "\n", - "Reading from 39: [2-3][0-0] -> [78-79][0-0]\n", - "\n", - "\n", - "mxsub = 2 mysub = 1 mz = 256\n", - "\n", - "nxpe = 40, nype = 1, npes = 40\n", - "\n", - "Reading from 0: [2-3][0-0] -> [0-1][0-0]\n", - "\n", - "Reading from 1: [2-3][0-0] -> [2-3][0-0]\n", - "\n", - "Reading from 2: [2-3][0-0] -> [4-5][0-0]\n", - "\n", - "Reading from 3: [2-3][0-0] -> [6-7][0-0]\n", - "\n", - "Reading from 4: [2-3][0-0] -> [8-9][0-0]\n", - "\n", - "Reading from 5: [2-3][0-0] -> [10-11][0-0]\n", - "\n", - "Reading from 6: [2-3][0-0] -> [12-13][0-0]\n", - "\n", - "Reading from 7: [2-3][0-0] -> [14-15][0-0]\n", - "\n", - "Reading from 8: [2-3][0-0] -> [16-17][0-0]\n", - "\n", - "Reading from 9: [2-3][0-0] -> [18-19][0-0]\n", - "\n", - "Reading from 10: [2-3][0-0] -> [20-21][0-0]\n", - "\n", - "Reading from 11: [2-3][0-0] -> [22-23][0-0]\n", - "\n", - "Reading from 12: [2-3][0-0] -> [24-25][0-0]\n", - "\n", - "Reading from 13: [2-3][0-0] -> [26-27][0-0]\n", - "\n", - "Reading from 14: [2-3][0-0] -> [28-29][0-0]\n", - "\n", - "Reading from 15: [2-3][0-0] -> [30-31][0-0]\n", - "\n", - "Reading from 16: [2-3][0-0] -> [32-33][0-0]\n", - "\n", - "Reading from 17: [2-3][0-0] -> [34-35][0-0]\n", - "\n", - "Reading from 18: [2-3][0-0] -> [36-37][0-0]\n", - "\n", - "Reading from 19: [2-3][0-0] -> [38-39][0-0]\n", - "\n", - "Reading from 20: [2-3][0-0] -> [40-41][0-0]\n", - "\n", - "Reading from 21: [2-3][0-0] -> [42-43][0-0]\n", - "\n", - "Reading from 22: [2-3][0-0] -> [44-45][0-0]\n", - "\n", - "Reading from 23: [2-3][0-0] -> [46-47][0-0]\n", - "\n", - "Reading from 24: [2-3][0-0] -> [48-49][0-0]\n", - "\n", - "Reading from 25: [2-3][0-0] -> [50-51][0-0]\n", - "\n", - "Reading from 26: [2-3][0-0] -> [52-53][0-0]\n", - "\n", - "Reading from 27: [2-3][0-0] -> [54-55][0-0]\n", - "\n", - "Reading from 28: [2-3][0-0] -> [56-57][0-0]\n", - "\n", - "Reading from 29: [2-3][0-0] -> [58-59][0-0]\n", - "\n", - "Reading from 30: [2-3][0-0] -> [60-61][0-0]\n", - "\n", - "Reading from 31: [2-3][0-0] -> [62-63][0-0]\n", - "\n", - "Reading from 32: [2-3][0-0] -> [64-65][0-0]\n", - "\n", - "Reading from 33: [2-3][0-0] -> [66-67][0-0]\n", - "\n", - "Reading from 34: [2-3][0-0] -> [68-69][0-0]\n", - "\n", - "Reading from 35: [2-3][0-0] -> [70-71][0-0]\n", - "\n", - "Reading from 36: [2-3][0-0] -> [72-73][0-0]\n", - "\n", - "Reading from 37: [2-3][0-0] -> [74-75][0-0]\n", - "\n", - "Reading from 38: [2-3][0-0] -> [76-77][0-0]\n", - "\n", - "Reading from 39: [2-3][0-0] -> [78-79][0-0]\n", - "\n", - "\n" - ] - }, - { - "ename": "ValueError", - "evalue": "Variable 'source_n' not found", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m/home/ppfe/s173956/north-simulation/data_analysis_energy.ipynb Cell 9'\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 7\u001b[0m data, par, fast \u001b[39m=\u001b[39m {}, {}, {}\n\u001b[1;32m 9\u001b[0m \u001b[39mfor\u001b[39;00m _field \u001b[39min\u001b[39;00m field_list:\n\u001b[0;32m---> 10\u001b[0m data[_field] \u001b[39m=\u001b[39m collect(_field, path \u001b[39m=\u001b[39;49m path, xguards \u001b[39m=\u001b[39;49m \u001b[39mFalse\u001b[39;49;00m)\n\u001b[1;32m 12\u001b[0m \u001b[39mfor\u001b[39;00m _par \u001b[39min\u001b[39;00m par_list:\n\u001b[1;32m 13\u001b[0m par[_par] \u001b[39m=\u001b[39m collect(_par, path \u001b[39m=\u001b[39m path, xguards \u001b[39m=\u001b[39m \u001b[39mFalse\u001b[39;00m)\n", - "File \u001b[0;32m~/local/venv/north-simulation/lib/python3.9/site-packages/boutdata/collect.py:237\u001b[0m, in \u001b[0;36mcollect\u001b[0;34m(varname, xind, yind, zind, tind, path, yguards, xguards, info, prefix, strict, tind_auto, datafile_cache)\u001b[0m\n\u001b[1;32m 235\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mVariable \u001b[39m\u001b[39m'\u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m not found\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39mformat(varname))\n\u001b[1;32m 236\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 237\u001b[0m varname \u001b[39m=\u001b[39m findVar(varname, f\u001b[39m.\u001b[39;49mlist())\n\u001b[1;32m 239\u001b[0m dimensions \u001b[39m=\u001b[39m f\u001b[39m.\u001b[39mdimensions(varname)\n\u001b[1;32m 241\u001b[0m var_attributes \u001b[39m=\u001b[39m f\u001b[39m.\u001b[39mattributes(varname)\n", - "File \u001b[0;32m~/local/venv/north-simulation/lib/python3.9/site-packages/boutdata/collect.py:57\u001b[0m, in \u001b[0;36mfindVar\u001b[0;34m(varname, varlist)\u001b[0m\n\u001b[1;32m 51\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39mlen\u001b[39m(v) \u001b[39m>\u001b[39m \u001b[39m1\u001b[39m:\n\u001b[1;32m 52\u001b[0m \u001b[39mprint\u001b[39m(\n\u001b[1;32m 53\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mVariable \u001b[39m\u001b[39m'\u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m not found, and is ambiguous. Could be one of: \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39mformat(\n\u001b[1;32m 54\u001b[0m varname, v\n\u001b[1;32m 55\u001b[0m )\n\u001b[1;32m 56\u001b[0m )\n\u001b[0;32m---> 57\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mVariable \u001b[39m\u001b[39m'\u001b[39m\u001b[39m\"\u001b[39m \u001b[39m+\u001b[39m varname \u001b[39m+\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m'\u001b[39m\u001b[39m not found\u001b[39m\u001b[39m\"\u001b[39m)\n", - "\u001b[0;31mValueError\u001b[0m: Variable 'source_n' not found" - ] - } - ], - "source": [ - "#%% Read data\n", - "\n", - "field_list = ['T', 'n', 'phi', 'vort', 'source_T','wall_shadow']\n", - "par_list = ['t_array']\n", - "# fast_list = ['t_array', 'n0', 'phi0', 'n1', 'phi1', 'n2', 'phi2', 'T0', 'T1', 'T2']\n", - "#%%\n", - "data, par, fast = {}, {}, {}\n", - "\n", - "for _field in field_list:\n", - " data[_field] = collect(_field, path = path, xguards = False)\n", - "\n", - "for _par in par_list:\n", - " par[_par] = collect(_par, path = path, xguards = False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "thetas = np.linspace(0, 2 * np.pi, nz_inner)\n", - "rhos = np.linspace(Lx/(2*nx_inner), Lx+Lx/(2*nx_inner), nx_inner)\n", - "t = par['t_array']/oci" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "dmp_nr = 20\n", - "\n", - "fig, ax = plt.subplots(subplot_kw={'projection': 'polar'})\n", - "ax.set_rticks([])\n", - "ax.set_xticks([])\n", - "cont = ax.contourf(thetas, rhos, data['n'].squeeze()[dmp_nr, :, :], 100) # first image on screen\n", - "fig.colorbar(cont, ax=ax)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(subplot_kw={'projection': 'polar'})\n", - "ax.set_rticks([])\n", - "ax.set_xticks([])\n", - "cont = ax.contourf(thetas, rhos, data['T'].squeeze()[dmp_nr, :, :], 100) # first image on screen\n", - "fig.colorbar(cont, ax=ax)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "((101, 80, 256), (101, 80, 256), (101, 80, 256))" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mesh_t, mesh_rhos, mesh_thetas = np.meshgrid(t, rhos, thetas, indexing='ij')\n", - "mesh_t.shape, mesh_rhos.shape, mesh_thetas.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Jfun = lambda t, r, theta: np.abs(r*(R + r*np.cos(theta)))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.contour(thetas, rhos, Jfun(mesh_t, mesh_rhos, mesh_thetas)[0, :, :], 40, cmap='jet')\n", - "plt.colorbar()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "test of Jacobian. Volume of torus is: 0.0780701129383045 m^3\n" - ] - } - ], - "source": [ - "volume = 2*np.pi*np.trapz(np.trapz(Jfun(mesh_t, mesh_rhos, mesh_thetas)[0,:,:], rhos, axis=0), thetas, axis=0)\n", - "print('test of Jacobian. Volume of torus is: ' + str(volume) + ' m^3')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def energy():\n", - " n = data['n'].squeeze()*n0\n", - " T = data['T'].squeeze()*Te0\n", - " J = Jfun(mesh_t, mesh_rhos, mesh_thetas)\n", - " return 2*np.pi*np.trapz(np.trapz(J*((3/2)*n*T), rhos, axis=1), thetas, axis=1)\n", - "\n", - "E = energy()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "rcParams.update({\n", - " \"font.size\": 16})\n", - "rcParams['axes.titlepad'] = 20" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(6,5))\n", - "plt.plot(t*(10**6), E, 'k-')\n", - "plt.xlabel(r'$t$ $\\mathrm{\\mu s}$')\n", - "plt.ylabel(r'$E$ $\\mathrm{[eV]}$')\n", - "plt.grid()\n", - "plt.savefig('plots/' + job_id + 'energy.pdf')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.9.6 ('north-simulation': venv)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.6" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "283665b67cdf6e633021a77ac207d327212d30d88d5d66cf50c6b3784b87797d" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Profiles.py b/deprecated data analysis/Profiles.py similarity index 100% rename from Profiles.py rename to deprecated data analysis/Profiles.py diff --git a/Profiles_polar.py b/deprecated data analysis/Profiles_polar.py similarity index 100% rename from Profiles_polar.py rename to deprecated data analysis/Profiles_polar.py diff --git a/animations.py b/deprecated data analysis/animations.py similarity index 100% rename from animations.py rename to deprecated data analysis/animations.py diff --git a/deprecated data analysis/data_analysis.ipynb b/deprecated data analysis/data_analysis.ipynb new file mode 100644 index 0000000..421a544 --- /dev/null +++ b/deprecated data analysis/data_analysis.ipynb @@ -0,0 +1,1105 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import re\n", + "import os\n", + "job_ids = [re.search('(\\d){7}', dir).group() for dir in os.listdir(\"./shared/NORTH/\") if re.search('data_(\\d){7}', dir)]\n", + "job_ids.sort()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# The last (highest id) job id is the lates job\n", + "job_id = job_ids[-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'5680179'" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "job_id" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/niflheim/s173965/local/venv/north-simulation/lib64/python3.6/site-packages/boutdata/data.py:769: AlwaysWarning: While building x, y, z coordinate arrays, an exception occured: name 'mxg' is not defined\n", + "Evaluating non-scalar options not available\n", + " + \"\\nEvaluating non-scalar options not available\"\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "from matplotlib import pyplot as plt\n", + "from boutdata import collect\n", + "import matplotlib.cm as cm\n", + "import matplotlib.animation as animation\n", + "from matplotlib.animation import FuncAnimation\n", + "from boutdata.data import BoutData\n", + "\n", + "path = './shared/NORTH/data_' + job_id + '/'\n", + "bdata = BoutData(path)\n", + "outputs = bdata['outputs']\n", + "options =bdata['options']\n", + "\n", + "field_keys = bdata['outputs'].keys()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from matplotlib import rcParams\n", + "rcParams.update({\n", + " \"font.size\": 16})\n", + "rcParams['axes.titlepad'] = 20" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Read Options" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def get_option(*keys):\n", + " from numpy import sqrt, pi\n", + " from ast import parse, walk, Name\n", + " val = options\n", + " group = keys[0]\n", + " for key in keys:\n", + " val = val[key]\n", + " try:\n", + " return eval(str(val))\n", + " except:\n", + " # Parse undefined variables from the Bout.inp file:\n", + " val_str = str(val).replace(':', '____') # Replace group indicator ':' with '____' to make string parsable.\n", + " for node in walk(parse(val_str)):\n", + " if type(node) is Name:\n", + " missing_keys = node.id.split('____')\n", + " var = missing_keys[-1]\n", + " if var not in locals():\n", + " try:\n", + " locals()[var] = get_option(*missing_keys)\n", + " except:\n", + " missing_keys.insert(0, group)\n", + " try:\n", + " locals()[var] = get_option(*missing_keys)\n", + " except:\n", + " print('Error reading option for keys: ', keys)\n", + " return 0\n", + " return eval(str(val))\n", + "\n", + "def get_options(keys_list):\n", + " vals = []\n", + " for keys in keys_list:\n", + " vals.append(get_option(*keys))\n", + " return vals" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "oci, rho_s, R, mxg, myg, nx_all, ny_all, nz_all = get_options([\n", + " ('north', 'oci'), ('north', 'rho_s'), ('north', 'R'),\n", + " ('mxg',), ('myg',),\n", + " ('mesh', 'nx'),('mesh', 'ny'),('mesh', 'nz')])\n", + "Lx = R/rho_s * rho_s\n", + "nx_inner = nx_all - 2*mxg\n", + "ny_inner = ny_all - 2*myg\n", + "nz_inner = nz_all" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mxsub = 2 mysub = 1 mz = 128\n", + "\n", + "nxpe = 24, nype = 1, npes = 24\n", + "\n", + "Reading from 0: [2-3][0-0] -> [0-1][0-0]\n", + "\n", + "Reading from 1: [2-3][0-0] -> [2-3][0-0]\n", + "\n", + "Reading from 2: [2-3][0-0] -> [4-5][0-0]\n", + "\n", + "Reading from 3: [2-3][0-0] -> [6-7][0-0]\n", + "\n", + "Reading from 4: [2-3][0-0] -> [8-9][0-0]\n", + "\n", + "Reading from 5: [2-3][0-0] -> [10-11][0-0]\n", + "\n", + "Reading from 6: [2-3][0-0] -> [12-13][0-0]\n", + "\n", + "Reading from 7: [2-3][0-0] -> [14-15][0-0]\n", + "\n", + "Reading from 8: [2-3][0-0] -> [16-17][0-0]\n", + "\n", + "Reading from 9: [2-3][0-0] -> [18-19][0-0]\n", + "\n", + "Reading from 10: [2-3][0-0] -> [20-21][0-0]\n", + "\n", + "Reading from 11: [2-3][0-0] -> [22-23][0-0]\n", + "\n", + "Reading from 12: [2-3][0-0] -> [24-25][0-0]\n", + "\n", + "Reading from 13: [2-3][0-0] -> [26-27][0-0]\n", + "\n", + "Reading from 14: [2-3][0-0] -> [28-29][0-0]\n", + "\n", + "Reading from 15: [2-3][0-0] -> [30-31][0-0]\n", + "\n", + "Reading from 16: [2-3][0-0] -> [32-33][0-0]\n", + "\n", + "Reading from 17: [2-3][0-0] -> [34-35][0-0]\n", + "\n", + "Reading from 18: [2-3][0-0] -> [36-37][0-0]\n", + "\n", + "Reading from 19: [2-3][0-0] -> [38-39][0-0]\n", + "\n", + "Reading from 20: [2-3][0-0] -> [40-41][0-0]\n", + "\n", + "Reading from 21: [2-3][0-0] -> [42-43][0-0]\n", + "\n", + "Reading from 22: [2-3][0-0] -> [44-45][0-0]\n", + "\n", + "Reading from 23: [2-3][0-0] -> [46-47][0-0]\n", + "\n", + "\n", + "mxsub = 2 mysub = 1 mz = 128\n", + "\n", + "nxpe = 24, nype = 1, npes = 24\n", + "\n", + "Reading from 0: [2-3][0-0] -> [0-1][0-0]\n", + "\n", + "Reading from 1: [2-3][0-0] -> [2-3][0-0]\n", + "\n", + "Reading from 2: [2-3][0-0] -> [4-5][0-0]\n", + "\n", + "Reading from 3: [2-3][0-0] -> [6-7][0-0]\n", + "\n", + "Reading from 4: [2-3][0-0] -> [8-9][0-0]\n", + "\n", + "Reading from 5: [2-3][0-0] -> [10-11][0-0]\n", + "\n", + "Reading from 6: [2-3][0-0] -> [12-13][0-0]\n", + "\n", + "Reading from 7: [2-3][0-0] -> [14-15][0-0]\n", + "\n", + "Reading from 8: [2-3][0-0] -> [16-17][0-0]\n", + "\n", + "Reading from 9: [2-3][0-0] -> [18-19][0-0]\n", + "\n", + "Reading from 10: [2-3][0-0] -> [20-21][0-0]\n", + "\n", + "Reading from 11: [2-3][0-0] -> [22-23][0-0]\n", + "\n", + "Reading from 12: [2-3][0-0] -> [24-25][0-0]\n", + "\n", + "Reading from 13: [2-3][0-0] -> [26-27][0-0]\n", + "\n", + "Reading from 14: [2-3][0-0] -> [28-29][0-0]\n", + "\n", + "Reading from 15: [2-3][0-0] -> [30-31][0-0]\n", + "\n", + "Reading from 16: [2-3][0-0] -> [32-33][0-0]\n", + "\n", + "Reading from 17: [2-3][0-0] -> [34-35][0-0]\n", + "\n", + "Reading from 18: [2-3][0-0] -> [36-37][0-0]\n", + "\n", + "Reading from 19: [2-3][0-0] -> [38-39][0-0]\n", + "\n", + "Reading from 20: [2-3][0-0] -> [40-41][0-0]\n", + "\n", + "Reading from 21: [2-3][0-0] -> [42-43][0-0]\n", + "\n", + "Reading from 22: [2-3][0-0] -> [44-45][0-0]\n", + "\n", + "Reading from 23: [2-3][0-0] -> [46-47][0-0]\n", + "\n", + "\n", + "mxsub = 2 mysub = 1 mz = 128\n", + "\n", + "nxpe = 24, nype = 1, npes = 24\n", + "\n", + "Reading from 0: [2-3][0-0] -> [0-1][0-0]\n", + "\n", + "Reading from 1: [2-3][0-0] -> [2-3][0-0]\n", + "\n", + "Reading from 2: [2-3][0-0] -> [4-5][0-0]\n", + "\n", + "Reading from 3: [2-3][0-0] -> [6-7][0-0]\n", + "\n", + "Reading from 4: [2-3][0-0] -> [8-9][0-0]\n", + "\n", + "Reading from 5: [2-3][0-0] -> [10-11][0-0]\n", + "\n", + "Reading from 6: [2-3][0-0] -> [12-13][0-0]\n", + "\n", + "Reading from 7: [2-3][0-0] -> [14-15][0-0]\n", + "\n", + "Reading from 8: [2-3][0-0] -> [16-17][0-0]\n", + "\n", + "Reading from 9: [2-3][0-0] -> [18-19][0-0]\n", + "\n", + "Reading from 10: [2-3][0-0] -> [20-21][0-0]\n", + "\n", + "Reading from 11: [2-3][0-0] -> [22-23][0-0]\n", + "\n", + "Reading from 12: [2-3][0-0] -> [24-25][0-0]\n", + "\n", + "Reading from 13: [2-3][0-0] -> [26-27][0-0]\n", + "\n", + "Reading from 14: [2-3][0-0] -> [28-29][0-0]\n", + "\n", + "Reading from 15: [2-3][0-0] -> [30-31][0-0]\n", + "\n", + "Reading from 16: [2-3][0-0] -> [32-33][0-0]\n", + "\n", + "Reading from 17: [2-3][0-0] -> [34-35][0-0]\n", + "\n", + "Reading from 18: [2-3][0-0] -> [36-37][0-0]\n", + "\n", + "Reading from 19: [2-3][0-0] -> [38-39][0-0]\n", + "\n", + "Reading from 20: [2-3][0-0] -> [40-41][0-0]\n", + "\n", + "Reading from 21: [2-3][0-0] -> [42-43][0-0]\n", + "\n", + "Reading from 22: [2-3][0-0] -> [44-45][0-0]\n", + "\n", + "Reading from 23: [2-3][0-0] -> [46-47][0-0]\n", + "\n", + "\n", + "mxsub = 2 mysub = 1 mz = 128\n", + "\n", + "nxpe = 24, nype = 1, npes = 24\n", + "\n", + "Reading from 0: [2-3][0-0] -> [0-1][0-0]\n", + "\n", + "Reading from 1: [2-3][0-0] -> [2-3][0-0]\n", + "\n", + "Reading from 2: [2-3][0-0] -> [4-5][0-0]\n", + "\n", + "Reading from 3: [2-3][0-0] -> [6-7][0-0]\n", + "\n", + "Reading from 4: [2-3][0-0] -> [8-9][0-0]\n", + "\n", + "Reading from 5: [2-3][0-0] -> [10-11][0-0]\n", + "\n", + "Reading from 6: [2-3][0-0] -> [12-13][0-0]\n", + "\n", + "Reading from 7: [2-3][0-0] -> [14-15][0-0]\n", + "\n", + "Reading from 8: [2-3][0-0] -> [16-17][0-0]\n", + "\n", + "Reading from 9: [2-3][0-0] -> [18-19][0-0]\n", + "\n", + "Reading from 10: [2-3][0-0] -> [20-21][0-0]\n", + "\n", + "Reading from 11: [2-3][0-0] -> [22-23][0-0]\n", + "\n", + "Reading from 12: [2-3][0-0] -> [24-25][0-0]\n", + "\n", + "Reading from 13: [2-3][0-0] -> [26-27][0-0]\n", + "\n", + "Reading from 14: [2-3][0-0] -> [28-29][0-0]\n", + "\n", + "Reading from 15: [2-3][0-0] -> [30-31][0-0]\n", + "\n", + "Reading from 16: [2-3][0-0] -> [32-33][0-0]\n", + "\n", + "Reading from 17: [2-3][0-0] -> [34-35][0-0]\n", + "\n", + "Reading from 18: [2-3][0-0] -> [36-37][0-0]\n", + "\n", + "Reading from 19: [2-3][0-0] -> [38-39][0-0]\n", + "\n", + "Reading from 20: [2-3][0-0] -> [40-41][0-0]\n", + "\n", + "Reading from 21: [2-3][0-0] -> [42-43][0-0]\n", + "\n", + "Reading from 22: [2-3][0-0] -> [44-45][0-0]\n", + "\n", + "Reading from 23: [2-3][0-0] -> [46-47][0-0]\n", + "\n", + "\n", + "mxsub = 2 mysub = 1 mz = 128\n", + "\n", + "nxpe = 24, nype = 1, npes = 24\n", + "\n", + "Reading from 0: [2-3][0-0] -> [0-1][0-0]\n", + "\n", + "Reading from 1: [2-3][0-0] -> [2-3][0-0]\n", + "\n", + "Reading from 2: [2-3][0-0] -> [4-5][0-0]\n", + "\n", + "Reading from 3: [2-3][0-0] -> [6-7][0-0]\n", + "\n", + "Reading from 4: [2-3][0-0] -> [8-9][0-0]\n", + "\n", + "Reading from 5: [2-3][0-0] -> [10-11][0-0]\n", + "\n", + "Reading from 6: [2-3][0-0] -> [12-13][0-0]\n", + "\n", + "Reading from 7: [2-3][0-0] -> [14-15][0-0]\n", + "\n", + "Reading from 8: [2-3][0-0] -> [16-17][0-0]\n", + "\n", + "Reading from 9: [2-3][0-0] -> [18-19][0-0]\n", + "\n", + "Reading from 10: [2-3][0-0] -> [20-21][0-0]\n", + "\n", + "Reading from 11: [2-3][0-0] -> [22-23][0-0]\n", + "\n", + "Reading from 12: [2-3][0-0] -> [24-25][0-0]\n", + "\n", + "Reading from 13: [2-3][0-0] -> [26-27][0-0]\n", + "\n", + "Reading from 14: [2-3][0-0] -> [28-29][0-0]\n", + "\n", + "Reading from 15: [2-3][0-0] -> [30-31][0-0]\n", + "\n", + "Reading from 16: [2-3][0-0] -> [32-33][0-0]\n", + "\n", + "Reading from 17: [2-3][0-0] -> [34-35][0-0]\n", + "\n", + "Reading from 18: [2-3][0-0] -> [36-37][0-0]\n", + "\n", + "Reading from 19: [2-3][0-0] -> [38-39][0-0]\n", + "\n", + "Reading from 20: [2-3][0-0] -> [40-41][0-0]\n", + "\n", + "Reading from 21: [2-3][0-0] -> [42-43][0-0]\n", + "\n", + "Reading from 22: [2-3][0-0] -> [44-45][0-0]\n", + "\n", + "Reading from 23: [2-3][0-0] -> [46-47][0-0]\n", + "\n", + "\n", + "mxsub = 2 mysub = 1 mz = 128\n", + "\n", + "nxpe = 24, nype = 1, npes = 24\n", + "\n", + "Reading from 0: [2-3][0-0] -> [0-1][0-0]\n", + "\n", + "Reading from 1: [2-3][0-0] -> [2-3][0-0]\n", + "\n", + "Reading from 2: [2-3][0-0] -> [4-5][0-0]\n", + "\n", + "Reading from 3: [2-3][0-0] -> [6-7][0-0]\n", + "\n", + "Reading from 4: [2-3][0-0] -> [8-9][0-0]\n", + "\n", + "Reading from 5: [2-3][0-0] -> [10-11][0-0]\n", + "\n", + "Reading from 6: [2-3][0-0] -> [12-13][0-0]\n", + "\n", + "Reading from 7: [2-3][0-0] -> [14-15][0-0]\n", + "\n", + "Reading from 8: [2-3][0-0] -> [16-17][0-0]\n", + "\n", + "Reading from 9: [2-3][0-0] -> [18-19][0-0]\n", + "\n", + "Reading from 10: [2-3][0-0] -> [20-21][0-0]\n", + "\n", + "Reading from 11: [2-3][0-0] -> [22-23][0-0]\n", + "\n", + "Reading from 12: [2-3][0-0] -> [24-25][0-0]\n", + "\n", + "Reading from 13: [2-3][0-0] -> [26-27][0-0]\n", + "\n", + "Reading from 14: [2-3][0-0] -> [28-29][0-0]\n", + "\n", + "Reading from 15: [2-3][0-0] -> [30-31][0-0]\n", + "\n", + "Reading from 16: [2-3][0-0] -> [32-33][0-0]\n", + "\n", + "Reading from 17: [2-3][0-0] -> [34-35][0-0]\n", + "\n", + "Reading from 18: [2-3][0-0] -> [36-37][0-0]\n", + "\n", + "Reading from 19: [2-3][0-0] -> [38-39][0-0]\n", + "\n", + "Reading from 20: [2-3][0-0] -> [40-41][0-0]\n", + "\n", + "Reading from 21: [2-3][0-0] -> [42-43][0-0]\n", + "\n", + "Reading from 22: [2-3][0-0] -> [44-45][0-0]\n", + "\n", + "Reading from 23: [2-3][0-0] -> [46-47][0-0]\n", + "\n", + "\n", + "mxsub = 2 mysub = 1 mz = 128\n", + "\n", + "nxpe = 24, nype = 1, npes = 24\n", + "\n", + "Reading from 0: [2-3][0-0] -> [0-1][0-0]\n", + "\n", + "Reading from 1: [2-3][0-0] -> [2-3][0-0]\n", + "\n", + "Reading from 2: [2-3][0-0] -> [4-5][0-0]\n", + "\n", + "Reading from 3: [2-3][0-0] -> [6-7][0-0]\n", + "\n", + "Reading from 4: [2-3][0-0] -> [8-9][0-0]\n", + "\n", + "Reading from 5: [2-3][0-0] -> [10-11][0-0]\n", + "\n", + "Reading from 6: [2-3][0-0] -> [12-13][0-0]\n", + "\n", + "Reading from 7: [2-3][0-0] -> [14-15][0-0]\n", + "\n", + "Reading from 8: [2-3][0-0] -> [16-17][0-0]\n", + "\n", + "Reading from 9: [2-3][0-0] -> [18-19][0-0]\n", + "\n", + "Reading from 10: [2-3][0-0] -> [20-21][0-0]\n", + "\n", + "Reading from 11: [2-3][0-0] -> [22-23][0-0]\n", + "\n", + "Reading from 12: [2-3][0-0] -> [24-25][0-0]\n", + "\n", + "Reading from 13: [2-3][0-0] -> [26-27][0-0]\n", + "\n", + "Reading from 14: [2-3][0-0] -> [28-29][0-0]\n", + "\n", + "Reading from 15: [2-3][0-0] -> [30-31][0-0]\n", + "\n", + "Reading from 16: [2-3][0-0] -> [32-33][0-0]\n", + "\n", + "Reading from 17: [2-3][0-0] -> [34-35][0-0]\n", + "\n", + "Reading from 18: [2-3][0-0] -> [36-37][0-0]\n", + "\n", + "Reading from 19: [2-3][0-0] -> [38-39][0-0]\n", + "\n", + "Reading from 20: [2-3][0-0] -> [40-41][0-0]\n", + "\n", + "Reading from 21: [2-3][0-0] -> [42-43][0-0]\n", + "\n", + "Reading from 22: [2-3][0-0] -> [44-45][0-0]\n", + "\n", + "Reading from 23: [2-3][0-0] -> [46-47][0-0]\n", + "\n", + "\n", + "mxsub = 2 mysub = 1 mz = 128\n", + "\n", + "nxpe = 24, nype = 1, npes = 24\n", + "\n" + ] + } + ], + "source": [ + "#%% Read data\n", + "\n", + "field_list = ['B', 'T', 'n', 'phi', 'vort', 'source_T','wall_shadow']\n", + "par_list = ['t_array']\n", + "# fast_list = ['t_array', 'n0', 'phi0', 'n1', 'phi1', 'n2', 'phi2', 'T0', 'T1', 'T2']\n", + "#%%\n", + "data, par, fast = {}, {}, {}\n", + "\n", + "for _field in field_list:\n", + " data[_field] = collect(_field, path = path, xguards = False)\n", + "\n", + "for _par in par_list:\n", + " par[_par] = collect(_par, path = path, xguards = False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Magnetic Field Plot (Sanity Check)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "thetas = np.linspace(0, 2 * np.pi, nz_inner)\n", + "rhos = np.linspace(1/(2*nx_inner), Lx+1/(2*nx_inner), nx_inner)\n", + "\n", + "fig, ax = plt.subplots(subplot_kw={'projection': 'polar'})\n", + "\n", + "plot0 = ax.contourf(thetas, rhos, data['B'].squeeze(), 100, cmap = 'inferno', vmin=0, vmax=data['B'].max())\n", + "ax.set_rticks([])\n", + "ax.set_xticks([])\n", + "plt.colorbar(plot0, ax=ax)\n", + "plt.title(r'$B/B_0$')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "N_theta = len(thetas)\n", + "X_data = np.linspace(-1,1,data['B'].shape[0]*2)\n", + "B_data = np.hstack((np.flip(data['B'][:,0,int(N_theta/2)]), data['B'][:,0,0]))\n", + "plt.plot(X_data, B_data)\n", + "plt.xlabel(r'$(R - R_0)/a$')\n", + "plt.ylabel(r'$B/B_0$')\n", + "plt.grid()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(59, 48, 1, 128)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['phi'].shape" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "48" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(rhos)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "phi_of_rho = data['phi'][25,:,:,32].flatten()\n", + "plt.plot(rhos, phi_of_rho)\n", + "plt.grid()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "N_theta = len(thetas)\n", + "X_data = np.linspace(-1,1,data['B'].shape[0]*2)\n", + "B_data = np.hstack((np.flip(data['B'][:,0,int(N_theta/2)]), data['B'][:,0,0]))\n", + "plt.plot(X_data, B_data)\n", + "plt.xlabel(r'$(R - R_0)/a$')\n", + "plt.ylabel(r'$B/B_0$')\n", + "plt.grid()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Density Buildup" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [], + "source": [ + "rcParams.update({\n", + " \"font.size\": 16})\n", + "rcParams['axes.titlepad'] = 20" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "t = par['t_array']/oci\n", + "n_mean = np.mean(np.mean(data['n'].squeeze(),axis=1), axis=1)\n", + "plt.plot(t*(10**6),n_mean, 'k-')\n", + "plt.xlabel(r'$t$ $\\mathrm{\\mu s}$')\n", + "plt.ylabel(r'$n$ [norm. unit]')\n", + "plt.grid()\n", + "plt.savefig('plots/' + job_id + 'density_vs_time.pdf')\n", + "plt.savefig('plots/' + job_id + 'density_vs_time.png')" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "dmp_nr = 4\n", + "\n", + "thetas = np.linspace(0, 2 * np.pi, nz_inner)\n", + "rhos = np.linspace(1/(2*nx_inner), Lx+1/(2*nx_inner), nx_inner)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(subplot_kw={'projection': 'polar'})\n", + "\n", + "plot0 = ax.contourf(thetas, rhos, data['n'][dmp_nr, :, :, :].squeeze(), 100, cmap = 'inferno')\n", + "ax.set_rticks([])\n", + "ax.set_xticks([])\n", + "plt.colorbar(plot0, ax=ax)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "# fig, ax = plt.subplots(subplot_kw={'projection': 'polar'})\n", + "\n", + "# plot0 = ax.contourf(thetas, rhos, data['n_source'][:, :, :].squeeze(), 100, cmap = 'inferno')\n", + "# plt.colorbar(plot0, ax=ax)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "vmax = data['n'][data['n'].shape[0]-1, :, :, :].max()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "# def plot_cyl_data(data, cbarlabel):\n", + "# fig = plt.figure(figsize=(5,5))\n", + "\n", + "# # the carthesian axis:\n", + "# rect = [0, 0, 1, 1]\n", + "# ax_carthesian = fig.add_axes(rect,zorder=2)\n", + "# ax_carthesian.patch.set_alpha(0)\n", + "# ax_polar = fig.add_axes(rect, polar=True, frameon=False,zorder=1)\n", + "\n", + "# # the polar axis:\n", + "# contour_set = ax_polar.contourf(thetas, rhos, data, 100, cmap = 'inferno', vmin=0, vmax=vmax)\n", + "# ax_polar.grid(False)\n", + "# ax_polar.set_xticks([])\n", + "# ax_polar.set_rticks([])\n", + "# # im_ratio = data.shape[1]/data.shape[0]\n", + "# # # plt.colorbar(label=cbarlabel,fraction=0.046*im_ratio, pad=0.04)\n", + "# # plt.colorbar(contour_set, ax=ax_polar, label=cbarlabel, fraction=0.046*im_ratio, pad=0.04)\n", + "# # plt.colorbar(contour_set, ax=ax_carthesian, label=cbarlabel, fraction=0.046*im_ratio, pad=0.04, alpha=0)\n", + "\n", + "# ticks = np.round(np.linspace(0,Lx,11),3)\n", + "# ax_carthesian.set_xlim(0,Lx)\n", + "# ax_carthesian.set_ylim(0,Lx)\n", + "# ax_carthesian.set_xticks(ticks)\n", + "# ax_carthesian.set_yticks(ticks)\n", + "# ax_carthesian.set_xticklabels(ticks,rotation = 90)\n", + "# ax_carthesian.set_xlabel(r'$x$ [m]')\n", + "# ax_carthesian.set_ylabel(r'$y$ [m]')\n", + "# ax_carthesian.grid()\n", + "\n", + "# plot_cyl_data(data['n'][dmp_nr, :, :, :].squeeze(), cbarlabel=r'$n \\,\\, [\\mathrm{m^{-3}}]$')" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(subplot_kw={'projection': 'polar'})\n", + "\n", + "plot0 = ax.contourf(thetas, rhos, data['T'][dmp_nr, :, :, :].squeeze(), 100, cmap = 'inferno', vmin=0,vmax=data['T'].max())\n", + "ax.set_rticks([])\n", + "ax.set_xticks([])\n", + "plt.colorbar(plot0, ax=ax)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "# Method to change the contour data points\n", + "def plot_field(field, i, cmap='inferno'):\n", + " vmin = data[field].min()\n", + " vmax = data[field].max()\n", + " # ax.clear()\n", + " contour_set = ax.contourf(thetas, rhos, data[field][i, :, :, :].squeeze(), 100, cmap = cmap, vmin=vmin, vmax=vmax)\n", + " ax.set_rticks([])\n", + " ax.set_xticks([])\n", + " fig.tight_layout()\n", + " return contour_set\n", + "\n", + "def animate_n(i):\n", + " plot_field('n',i)\n", + "def animate_T(i):\n", + " plot_field('T',i)\n", + "def animate_vort(i):\n", + " plot_field('vort',i, cmap='jet')" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "MovieWriter ffmpeg unavailable; using Pillow instead.\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Call animate method\n", + "fig, ax = plt.subplots(subplot_kw={'projection': 'polar'})\n", + "\n", + "plt.colorbar(plot_field('n',0), ax=ax)\n", + "ani = animation.FuncAnimation(fig, animate_n, frames=data['n'].shape[0], interval=100, blit=False)\n", + "ani.save('plots/' + job_id + '_animation_n.gif', fps = 4)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "MovieWriter ffmpeg unavailable; using Pillow instead.\n" + ] + }, + { + "data": { + "image/png": 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6/yhtGsti9ICe1/RIPMWpTFwlLQP7L12wlpohWQCjIhrPJ5AcITAf1mu7xMzRnjENrdcbmh6JpzgVXmm9W7zVGifFnzqMCkgOrxqd1IqGbxW3XucSqfyjbBetcROEk1s6pumRVImkfYOqrA9L2iTpYkn7Fdz3wGDfhyRtlnSFpMwsXJKOl2SS7k147srgufhjdZHxVJLPNShdceB4ukVPdDIrdAmMPc+iiAJYLG+r1GO0vHsPIttH8rsGeV9Tc77Gkmk3keu1tHhNk+Er3Ldkqs125MDtAf2rJfUSqpx2nkA3LseVyD4Jl4vxbOAKSYeb2eaMffcEvo3Lw3oq8BjwZ8G+R5rZjxP22QM4D7gvY1hrg+NFubPI66koWXYvcL634Ru/BeQUJAx9r/GwrMXd42IaOYbArJ/SsRPd4ts7wKxSJ04gss0L7PBO8BzgvQ0OxGFZ595EnAIcBBxiZrcBSFqLKwR4KnBuxr7vxCW1/rWwSoCky3FVXv8KVx47zn8HbgDWA7+ZctxHzOza8i+lgntHFzIy2MkLa5ZFNhrXOrZfZBHB4iM+oTUeypW8HUhxN7SBUlZrFTlpS7oLmncR9AB7jwoHPXeK44BrQ2EFCGpfXQO8Nmffo4Bbo+VXAkv3auDVbg5oEUlH4+pr/dGMxj5GBVeb7vcJsIuyKIhD6zXlmkkS2PD3RIHOzF0wdvDYvi387KpO9t0ZgQ2t1971DQ6iKg4DbkzYvo7FKq5p9IEnErZvBXYiUhVW0nLgAuBvokKewgsC/+82SWslnZzTfshMxVXSPmALXljzSbJEhxNbCauzso6xSJmPs3krtrBI1VVFYcJJr/rpAYPnS9qx6ZGUZJWk6yKPt8eeXwk8mLDfRiBvEcVPgGcGvlcA5C6QIyPHDjkNV3AwL2/ut4DVOIv69Tj3xKcknZmzHzBzn6vu8hECafTGVmZFxXS4LUH0FieyXPv4pFXc9zqLia3W0ITYFXgvmvW/hqFZ3Afs0dAgJmFDhTW0PoErHvi/Jf0JbkLrDODA4PkBgKSDg+2vM7PHsw5oZu+PbfqqpK8AZ0g6z8wezdp/ZuaLW+ZqPS+s+STGrkYWESStziqeX6BMbGuGVVzx59hK10OUAqLevHtgsPuc1dx6kGQLNc2iHWJm/wmcgCvHfRvwM+DFwEeDJuuDn/8TF5FwraQ9goiBFbgwtz0k7ZQzxi8COwK5CzpmeG+om7yvtQiLvtCkyam0aAFInsxKzu2avj16nNbT9C16qwU2tF576/Nadoh1OL9rnEOBm/J2NrN/An4paH+wmb0I2BW4x8zujhzrd3FiHT7eBDw9+L1oiZ3c25aZuAUkPQ1QG/x4rSPnAg0npKJhWIrEBxvpoS5ZroHxNsFNRUJI18hY2+IaaFpYQ1rtIhDQ30nScjPb1sAAZs0lwEckHRRYokg6ADgaOL3IAcxdAD8O9n068EbgbyJNjsdZnlFOx1m8fwCMLSaIcQKwBcgtgT4jn6v3tZYh26pcGPvb6I/EukZ9r8mUiWNNb1uVaLTeJRCntQI7vOY+Cbyt5s6r4JPAH+N8m2firMOzgHuA88NGkvYHbgfWmNmaYNtyXNzqVcAmnAX8Ppw1/D/CfZNiViW9DdhqZldGtr0UJ7oX4xYN7I5b2HAccHrWgoaQqcVV0g7Acm+1FiCetzXqU1VvRFhHxDQQWPf7+IRU6iKDlO1px2kNbbFao7TJqh+hB/RPYg7E1cw2SzoW5ye9EPfNcRmwOjZ5JNzyz6joGPBM4M24Sb57gc8Af21mSSFaeawPjr8GWAVsw63WerOZfbHIATTtKjqpdxlwrF/qOo4IQ3tCAV2GWIbUo6cdgp/L6Gn5UFyTrNpQII0+ZkHZDOtHtg9iv/cJrdHodsdg6BoY2Tba4eKvTViubRTXkJzrpRn3QB+wI83sB3X2utDbyXbd8Rn5DQM2bVl3fYXRAq1jKnPT5Wu1Y73VOk5cQBIzWRGNbU0W1tTjK6/9bGJeq6hfVS29nMeU5PrQm/hi6AH6XgMdezKY9mx7kfe1liF/Zr+X8C8eHZAYCzsvUQMTW61FxXMGIttKy9okaWV+O09dTHuWfc8L62QM41gj4VW9nI8jPtmVfuyc5bAlLORuMMnrmZElm0D91uswLGss85OnOSY+uyTt6hcNlCcpf8DoRNZowhZgzHodO2bqCq8JXQNNWWYT9TutQE64f+vcAwIGewWllTwtYIozs3eZXzQwHVG/aS8tAUtaIpcwPraAFVrGl5vc12w+49kLzqwsz2oEtl6G7rmjGh6IJ2CKUKzBkT5CIIfoxadR6zJpCWwWPXr0GTgrV8kxrlmhV2G/hXMNLDnKxAYXo/7Y1x4wuIYaZ5izzrelzkQfgssj4CeyyqKYn29UYNMzYsXLwBTpZ+oSMK2yypKoQj8m8MO27n0yFVgf76mBCc/Q3g+8sKYzevubPYsvFoYTWWkZsYr1WT45S5m2lfsQWydSJcgYe72+16HB8xs1dupJYUJxHezqxbU4YwUHi7gBilqpBRPSZ1WHrSMka7YiU/V4uxxN0QN0SdOj8ExwFkl6SvDbrMcy58Rv8WPLX4Pne+oNhbU3Et+6aOEOt5XIkpU2Hk8as3MP1B85YNK0s5ieqZngA9BH/YU5GemB/kHIVdrzscmvoqu5Fq3a2cyGdy7hytR08TwfugZe1PBAljyTnD0neKt1cuKRAkkLB8TCMPa1qHsgqZ+xbYWO1XZBafH4WmO9CtB3auzQk0CpM9XV7GljZqDu4UR2MUKgF5ngmuaYpdu1oTJsqyezWizmqQiwGZdwqgdJ+0q6KCgKuEnSxZL2K7jvfpI+J+luSVsk3SLpbEm7RNo8TdKHghpeD0m6X9Jlkn4t4XiflWQJj/OKjKfsB/AyH4JVkLFb6vwwn7RcrtVRILZzBqn2lpQ7IeP9qi/u1V2jkvY3s7tq6HAmSNoZV4JlKy53qgFnA1dIOjwrh2ogoP8OLAf+Argb+BXgr3CpCN8YNH1R8Pv/Aq7FlXj5b8CVko4zs6/FDn0/LodrlELVH0qKq/7VC2s2iUISux0fyeE6thR2sW0Y3N9Tj4EBgp5Bfyyf68JIMHc8sbZLQVgmSD69bbOF+Zpi9gsMqkeA/g14VtMjKcEpwEHAIWHJa0lrcVVXTwXOzdj3aJyIvsLMvhlsuyJIZvNuSTub2WPAt4Fnmdn2cEdJl+KSar8XiIvrE0kJtotQ+J4nSC/ocwmUJC0MK+7/dH7W8WiA4slaisXIJvpd2+AamCda4eYQYM+stg9zOYYLPgpwHHBtKKwAZnYHcA3w2px9VwQ/N8W2P0Rknb6ZPRQV1mDbduCHuPpbM6PM1fR87xIoS3Z0wLBVyUmr0eoF0054eUEtxmzep/pcJAIMSU+qqcNZcBhwY8L2dbjCgln8O87CPUfSoZJ2Daoa/CnwiRyXwgpcpdikrGJ7SdogaXvgwz1NBYPLS7gFfHrByRmNcV3cOo04Vu2PjXY26kdszjXQoS+CVpSFEb2efhu4qOGBhKySdF3k7wvM7ILI3ytJLqG9keSS20PM7HFJxwD/hBPjkE/h6nJl8QFgH1zxwSg/BK4Pjrcj8DpcddhnAn+Yc8xSPtcVXlwnJ8kNMPp3eoxrGRFNrrE1W7/r0mU270mdE1uDgf0j7cmwtKGqMi8ukol/BPYC3oKb0DoSeD+wHXhnyn5vxhUiPMvMro4+Z2bnxZp/XdKjwGpJ55jZrVljKiSuizkivbhmUeSWLzl/QBjTOnoNDKw/fL4n6JfIQBROamUVNKwjS1an62VNS+PWq4BBh8x9HiTZQk2zaKOcDLwMONjMbg+2fUvSw8AFkj5hZjdEd5D0GuCzwKfN7C8LjvGLwGrgCJwbIpWib/x+XlhLkCIYw+WqBd/2uNhWN+FU/rhLKrxqhC5plfuMOrQUdh3O7xrnUOCmnH2fBzwYEdaQ7wc/nxPdKOk3gC8DX8FFIpQl91uz6Jvul9JVgLQwwWRWSvLsgomzJ2aeLcwGqHdiqzPhWJcAR0k6KNwg6QBcmFVeMpr7gCdLOji2/VeDnz+NHPPFwFdxZbtPtHJJaU/ACWtupd1CpbWl3jbQsm59a9fP8IIZClEwYaVl9LSCnpbT0zIWeju4h5azoGWIBRa0PPGYA+u7ctn0GUTKavdt+0ipbWBYbttSymqbhb7bxeezS24vtg12GBlbEb/hbN0CbTn/SrpNUq6xevyuA2Bwopl9YdZHXujtaDvvsH/h9o8+fktmae1gIcANwBbgTJyInQXsBhxuZo8G7fYHbgfWmNmaYNsBwFqcyH4Q53M9Areg4BbgSDMbuFzUfAcXsvU24PHoGMKY1qCPC4EvAbcBO+AmtN4GnG9miT7cKEUntJZ5t8CEKL/qQFJ8axm/Z7x93t+p+5b0uy7NBQXQvck+fQ6YubjOGjPbHIRPfRQnbMJZl6tDYQ0QbpKuF9n3TklH4Wb+zwZWAfcAFwAfjFinR+H8uk8GrkgYRih0j+CiFE4D9sZ94DcDfwJ8rMjrybVcg8mswVQVYZYIo5ZrGH61DLEMBRZrb/hz+dBy7bF86F+Nr9AKJ7UGbEu1XMO2oeUa/g2MPU9wtPhzw+1J26KUtF7n03KFUuKacY1V/+VkQB8zm7l1tNDb0XZasW/h9pu33pZpuc4bRc7WfbputarAv1n0Mb4taYVUpKJrSn7WKPFJrcXjFE89OE6ZfSYXtNn7FbtkLUZo1F/duUmtuaHIG97ZyawywjkrkR2nPef0WB6DjOoEhY/Z8S/eyWnP55qPAOITPZ6KKXCG6Mtds1ynEcqpRTYtDCu1RPbs4ruzIgaKhXFFfMOdEg9PNj55dhMUuYI6NZk1K0uqzHHy2uatzkq79Z+GpBIwo8ymOoHrqzvnR9uoMSTrwpo68gQUuMK6Mxs86xN19scre8sdtSRnI8CFXANj+EUGU9O837UtS2CXDJlXTZAthi5YrlVdzFULbJlVW1kLDorsX9g1MEEKwvrEtE2TWl1ynXTHSJoX8s6OvZeysM6y7/G8ruVXZ03KmFsiVriw9ERy6vLe9p8rSxP/uTRB3lX1tFpGMQV1XNDl+wje1inyraaPpYCFOmHy61m4BqqLuvBMi6Rdmx7DUqKAuPoLBdLiWGPbIhbdqFDNWGBT/KZJz43tW6FroD7a5BooQaMWv6ADxtI8kXPl6DX1DGMy6raQpu0vrcTLJCSV5C7S9+LfU7oGPF3k6bM9nAV5L4o9lhp5V9RJtYxiApq69Sy3qis7p0AdJOePjW+bIL618SxZbblYu/Kl5C3Xusk7Mxa8W2C21CWyiQliNCqiWe6FqGtgZNusabwUylJC5zQ9gqVE3tXSSmX1EyaLRONfs0rFDH/PcQ8kHSfVmvXWa9eYaXVTTzY54uqtisJEcriObl60EKNJW6odSsFy3Lmlt5Os17bRMYFt7AtJ0AEfhqR9JV0k6WFJmyRdLGm/Avt9QJKlPB6Pte1Jep+kOyU9LukGSb+fcMwrU463ushrKZBHsGnrZJTOWK0zEKVpJwHCWlnghDQ8XnS7+3shko4w5VjD/duYyzQclyeHVl88knYGLge24uZ7DJeb9QpJh2eVx8ZVef1GbNsuwbZ4FYOzgHcDZ+Cqux4PfFnSq83s67G2axkvA3NnkdeTKq5Fa3N7xmln+FKywA7HOqylNy7G2QdtughfSLcFtp7E4234nDI5BTgIOMTMbgOQtBZXCPBU4Ny0Hc3sXuDe6DZJb8Fp3Oci2/bCCeuHzewjweYrgvIwHwbi4vpIWJ2gLFln46q2fdG11motUJCwsB8zhXiZl8GE1mPc/xpdcOC+T3tjftnu0JRF3YX3q6XXzijHAdeGwgpgZncA1wCvneB4JwE/By6NbHsFsAL4fKzt54HnSTpwgn4SyTordphVJ0ubai88I/t2vghpAuuEuIaogZlavm1zWXhKcBhwY8L2dbgKsIWRtC/wcuALZrY91sdWXF2seB8k9POCwP+7TdJaSScXHUOWzzW5Yl5DtNZqHSF/VVbZ7FaDHF9onaS6ClrjGgjptougKxjk+upjrJJ0XeTvC8zsgsjfK4EHE/bbiKt5VYYTcSfB52LbVwIP2Xh9q42R50O+has9dguwB/BW4FOSnmZmZ+cNIEtcfdGsIpSY/c1yBRiDkeejIjbL1S1JxxqZ3KIXWMNtnLgqQzh2L7ItYkONNbTeCvw/M1s76QHM7P2xTV+V9BXgDEnnxYomjpF15nlx7Rh5Ihx/3mxAUsn2xdCxEq6BxmNe0xjQ7S+J2dLyWloPkmyhplm0iUg6Eng241Zr2MceQeHVeB+waMGm8UVgR+B5eePohLh2wyXgUDTpScnzeFILNS6Q4a1adHu0GmyaqDZK5W6FqkW2zZo1QpujgNbhfKJxDgVuKnGck4BtwD+k9LED8IyEPijRT+4J2wlx7TJhXasyQjsLN0CasE5LZrav1lqvUQY0bs02+z7t1mTnOVwCHCXpoHCDpAOAoxmPVU0kSPB/PPB/zez+hCbfwAnvCbHtJwI3BtEJWZwAbAF+lDeWLAFtz0zKJKSdwLO0kEb6mC7UKiQqrOFkVlJEgNlgZPuigI62nZ2w9lw0T95xppncMqtZeJJeyzQWaCf81Jl+wob5JPDHON/mmTjr8CzgHuD8sJGk/YHbgTVmtiZ2jFfjbvGTXAKY2S8knQu8T9IjwH8AbwSOxYWChX28FDgduBi3aGB3nEV8HHB6zoIGIFtct2c8117yLs7w+WlFNrOfclbqwNKLFIbiWCbGNRTSWVqsUXIXGLQueqAMcx9p0Nrr2sw2SzoW+ChwIe7r/DJgdWzySDj3RtIHdRLOb/q1jK7OwH3J/CnwVOAnwBvMLLrP+uD4a4BVOGt3LfBmM/tikdej8YiE4AnpENDNTbtoSvlby1o90wjAWF+RhQJaFsSM9pCW09Oy4LGcnpazMPy9h1hA9BLFdWD9IBfm+AKC0HKNCmnc1zqpuC4eK+wn3H8wtFxHxTXh+JO+t61wLUwjrgXf64T3p/oVWtsxs5m9wb3eCtth2VMLt3982z3X1xgt0DhZZ1Frv+ESmeSinPRCThDWxedm4x4oE986ErY1M2FdZKLX0AqR9HiaY37EdVIqFYHsuNYoA+sXFtQkwYwK4jTCWpRCZWwmeW9b4U5ovd/U0wGyxPWJ2kYxLdMKZJn9C7ZNz5uaTVRkoy6BOFGXwHDbFL7V8QmxlGPNqFKsxzPvZF0pD3Qgi87sLt4ix0lsky822SuzClqrKZNZYwsDUqzWtDpG5eobZb3WjOeWlMC2dTKsA9fynJF6JphZ45Zr7YsHpHQhyBGIRQEtXk57kDg5VB6zcSt25PnYUtq0onEl14mX88UuKYH1eAotFDA6kq5sdkw80TU7qyV0CXSLnDjPzoRotdX6nBZ14c2fG3LOopaLauPWUPbbVybfeFqilri/NRqCVcQlUNQqHrVa8xYKTCE+WXcH4fOeCrDhf556yLtK/IdRgGkrD4QWaqZfNGPxwLSLBMq6A6JklenO3jFBZL2wVow2NT2CpUSeW2AA1s7y2o1fiJML6oABCzmLMwpPdGUI4yxTFS4y4yWejX+ODdCYa2TwrlkfsZpzbD7IU4jv1jKKeUBBar4hxTJjLU5q9afysU4c2zqp1Rp5XRNbr62ky2PPwsAt6fTURF5p7b+tZxhdI11YwmWveaSJWlRkk+JbZ8k07oB85lWkOo0X1xrJuwJ+5t2u1VHkliq+tDVp8UAd5PmV21rx1jPCz5oewFIi74rw33RjzGbyKkqShRrdlmRhhpNcaUKbW5Ug1WotItxhgpq896JrgtvceGsqq/1wxZ1MjaR9JV0UFAXcJOliSfsV3NdSHr8ca7enpL+V9J+Stki6Q9LfS3pKrN1nU453XpHx5E1orW+l5drYJEhKUpYUkZmlNVeklHZRi7Yqd0ByKsJO5DhdEiQU5WsVknYGLsdVZz0JJz5nA1dIOrxIDlXgs0RyvwbcEulDuMTbzwLeD/wYV4VgDXCEpBfH3qf7ieR5DShkdGaKq5k9Pl5qxpNMiRyuNhgT5NBS7alX2tc6WSyrZ5SuWdllaLWmRjkFOAg4xMxuA5C0FrgVOBU4t8Axfmpm12Y8/0zgJcCpkcqzV0oaAB/Hie5PIu2fyDleKgXOKNGhD6dCZpNKMM54dqyKqr5WJayFUizOs3B1BXXh9uE44NpQWAGCsivXAK+dUR8rgp/xmN+Hgp8zO1mLHKi/tMU1HmIVIzMkKZ1BQt7UJCYRxapiDwulGUylzQLb5rHNAgNXQqXtHAbcmLB9HYsFBPN4p6Stkh6TdHlQriV+rG8BfyHpCEm7BtVi34+ru/XjWPu9JG2QtF3SLZJOU8GllwXOKntLkQO1l17OI2+/cbJEdJLKxWmrskb+tslzDdTtDkh/f9ooYjWPqRG3pwH28QY6jrNK0nWRx9tjz68kuYT2RpJLbsf5PPDfgN8E3g7sCVwu6WVhg8Cf+ru4W/8fAI8A3wP+E/j92PF+CPw58AacVX0V8CHGfbqJFKnwen13LdciF84sLq78IHqzfqbwGoOhKI25ChIqDbQK9cgtXOhJpaZIgTKlqQsftuT5uKHKMi9mI4bg1ZK+irOEzwaOiTz3SeAo4B24Ca3nAH8FXCTpNRa8KDM7L9bF1yU9CqyWdI6Z3Zo1niLKcls3xbUai2TEKssQy6JJW+IVBMrEvhY5sZtaKNAN67VNY6kKd+2a2baGB1KEB0m2UNMs2kzM7BHgX4FfCbdJehXwJuAtZna+mX3LzM4H3oKzaF+Tc9iwOGHul0Tu2eVUvGuTWjUIa4nnQiyhsGBm+6y8AfES2hVZjoXdHKXcIW0QtTaMoS46MZkFzh96WML2Q5nO8o6K1/OCnz+Itfl+8PM5ExwzkaJn2BKf1MpiugQu0KYQqfxrsMykXXbbJsWtyr7bpmOdmcwCF396lKSDwg2SDgCODp4rhaQnAa9mUTgB7gt+Hhlr/qvBz5/mHPYE3JsaF+fx/ovEFUt6M/S+UPcFkVqJIDf2dvbjHBOKoaXWG3leQQIXaWGxvPawzHZ02wJSj95wv1E3Qii4YRltWKybFV+dZZE24d/x4xSjWErDcddF5O/ctsX6rI6qz+GM15NyrVXrc+0D9gIz++Gsj9zTClu+bFXh9k9sX59ZWlvSLsANwBbgTJyInQXsBhxuZo8G7fYHbgfWmNmaYNu7gUOAK3DLfPcHwm2/YWZXB+2ehPOzKjj2zcCzgb/E1Q081MweDfq4EPgScBuwA/A64G3A+Wb2zrzXW2RCCzo1qVWDsFbApNZr21O+Ja/ailLnCq72uQJqmsxaV3EnM8HMNks6FvgoTtgEXAasDoU1QMACox/oT3Di9zpgd1wc6zXAyWY2tFzNbJOko4APAO8FnoZbcfUvwAci/TyCi1I4Ddgbd5LeDPwJ8LEir6eo5doD+sW1eDZMZrnO7gJKFdWY1Rq2VWR7EcsVGLFekxhE/LTAmOWaVX2gvGBParlG9p0w10FW/7OhLmEtZ7lWK64G9DGzSpZZztpynTcKnXE2vLq6Yr1Oh0oUGQzbl6VIzOpYpdcik2A1WIGZr3eqOmJVCWD7LNb66Mxk1txR4qzT3E5qhWJaSFQTrNYijFiWMZHMSsrSyrjWCSj+BZS3uKMMszxWEcr7W6vFYDF0yFMzJc48e3nd4lrlLVNhMR3ZabILNa34ICyKZ1xgU+tltdbHmh//W87Cn0YY6xbVyaje3zoA7M+qO76NlWrPeiw1yjhRvxsso6OVNbVKMNEE1YhgTJZPwK3SSl9ckC6odYRqpZ/8Um9mFnT+BFec6PubNzHmWWS4eOAXDQ9kyVL4jDSz7fNQ93x6Yc2jhNhGJqkSn88QtKbdBbnZr6byvaYxSY6IpYoBvUdzm3kqo+RZaW9oX5B0cWYjrCm3vCliUjyBdfHJq/YsOpiMJVcSpvYoAdcDDH41t5mnMsqe5Zd2dVKrCmGdRiSSxNNsMHwUad96snIvzJ3AtunzCd13xNPneWqk1BnuEiG0IM9A1TOv6lV0W+sYneDKtkLbnGawUGLsJSWwbcEA9dte1mXemcScu7VOca3+9ilGqhg0LwRpE0HTW7XNWV1zL7CNuQTstyruxJPDBEuuBr8FunPmI2kDJYR1ooUDgQiGEQNmAzcTT3+4Ymukfa5V26Zb0SixJa05+V7LRxC0jTaNfegS+HYtPXXc/18lpRXCzO6KfIDzwxQW6yTVB+JEhdToj/7dYn/rrCzPubRgG7srl3Ukf+tcM+EZre1dEtfiq67izCIY3jG2eCCxtEt/Yh/r5FQl3MV9r8Mm8yiwMWpK1HJaxZ14CjDh2WwvbtzvOiuroGEfa55VOh6iVSzR9qyZhXVeVGC7JbJtu6sYANaV/K1jSNpX0kWSHpa0SdLFkvab4DinSzJJie4RSb8k6TOS7gsKGt4h6UOxNlcGx4g/VhcZw6Rprq6fl9VaZWnqwu+mT3LydIKd98M2NpElzAYPVdxRJUjaGbgc2AqchHtBZwNXSDrczDYXPM5BuHywiavTggTc1wB34FII/hw4ADg4ofla4NTYtjuLjGMicTUzkxY2gT1pPsW1REaskhZdfFIr3JZ0nLIWankxamBJa4lihmkFGz1pDAB7VdOjmIJTgIOAQ8zsNgBJa4FbcQJ3bsHjfBz4Ai5RdpLGfQJXceDlEd/0VSnHesTMri3Y7whTmGGDg9t3SwRJY0q9OBOFcZq4zNHn80RhtGJArJpAiyexypHwnpX8QpooyU7GMdIe5Un5jJoLvwK4tOKOquQ44NpQWAHM7A6clfnaIgdwVVN4IfC+lOefAbwC+LuqJ/0mPlvN7H6Xa6Ae32ulfteKKSOUSSu0Jk9+nUXD4j2hD7eISM5GOLuGAdpm3Y6NOgxXCjvOOlyRwkwkPRlXxeC9ZrYxpdnRwc8tkv4t8Lc+KOl/S9ozof0LAv/vNklrJZ1c5IXA1LM29tuNX6Q1MesLNLwGyt7yRutmRf9OOnadtLcYYXHKfcZtslpdL2DPqKGjKllJcgntjSSX3I7zN8AtwGcz2jw9+PmZoO3v4KIrXgVcqlH/3LeA1TiL+vU498SnJJ1ZYCxT1225fL4mtkrerpb1tzJw/skUH2vaPnNLCf+rJ4thesF76u+31Oe3StJ1kb8vMLMLZjESSS8F3gq8MGfZb3jhXWlmfxT8frmkh3HFCF8B/F8AM3t/bN+vSvoKcIak82J1vVI7mghX/qX3YKOugTmgiIB23yWQcqpVmMOhOtpmtQ4Ae0UNHU3LBjM7IvKIC+uDJFuoaRZtlPOBTwP3StpD0h4443Eh+HuHoN0Dwc9/i+3/zeDnC3L6+SKwI/C8nHazuF8bHBh8uNMfahISv6RmKx5V+eyigjkP2dvLTvgt7thFgW0Lw4msuFh0kXU4v2ucQ4GbcvZ9DvAOnAiHj6OBo4Lfw1LYeZVwi150uYI39VltZg/Pc32tNhH3t448V/icaKlgt0Bgp6pQ26ivtXfnnGTAugQ4KohTBYYxqUcHz2Xx8oTHDbgJspcDFwXtrgXuw93+R3ll8PMHOf2cAGwBfpTTrlhp7dyDSM8E3UJC8pEqGCu5nVhqu0CylaKJsAsnyI6UfwniWMN9o23j27JKv0Bk8itBXJO3ZbkQJhfX2VWfzSrk15z4d09cXelsYNeiAfazRFpmyxZ2L9x+e39jZmltSbvgBHELbhGAAWcBuwGHhz5OSfsDtwNrzGxNxvGuBJaZ2TGx7SfhJr3OBy7GLR74IPBD4FgXx6+XAqcHz98J7I5b2HAccLqZnZP3eqed0ALAzG51QtHQxJZZgsAOqHKWepLFAzNZQho75vi2ZiNxpl5ZFb5HrZzoapOwul5c3tZB7cJaBWa2WdKxuHCqC3FichmwOjZ5JJwlN9EFZWafkzTARQn8F1w0wueB90XuANYHx18DrAK24VZrvdnMClXUnYnlCiDpJaBr2my9JlqgKYUH8/ZLFsp0yzW6T6I1m2K9xq1WSA7DKhYLO51gFS5ZM631OjxQvQKbP+42ievQav0lM/tZxZ0lMmvLdd6YmSllZt8BaltUkDCA/CYt8zeOrsjqx57rJwpl217D5BQ49Wr0w3ZLWF0voO1NCasnnxmfvfa8RiMHxminECX5TN3v/URRzc2c1dLXmU9BgW18sqtt728YX2pPaXoknnRmetaa2Tpqsl67EvM6rfCVKbtdpUugDJWErtVU06zcjo1GCGw2s4dq6MwzIRWcsXZAY9Zri6JREm/pSwhl0vbuWqhZlDgFG7Fi2+gOGACDVlit0YrFeY+lxszPVDO7u66412Inc45AjXzo9Z0AY5UJSmTFmr8TteRpOEOR7d4X1gDo/cTMtjQ9Ek82FZkBtre3Xicj7Vs+tfLrrGbmu0jlVmwbrVYDBs+toTPPlFRydprZA9B7ojPWa0MUHUf9uQemp7zftf5Jq+z3tW3CCm5MOtfMttfUoWcKKjyjBys7Y73WeJs97S19UmxrNrN5bfW4IiY4HRuPJKiL0Gq1dzc9Ek8xKjsz3XK83k/bar1O3644WSJYplpBapuWWa3TUVNhyE5arbx8TnIILAkqPpMbzJiVcw5mT2zVS1LZ7aSsWOWtx3a4P9pG94TV3AjMrqypQ88MqFRcgxo1v1aHwFZpvc7aR5s2YVUmzWCdfuP6oxOautUvLqz1ES5ztQMbHIRnAio/i83saqCZlIRlrddSh673dj09FjatD2+1JpH+mZcT1nrdAb17zeyumjr0zIiZZMXKx54Ettkls6kua5Zh4wldxsjIlmWDKSdI0o8dlngZ/l2m1EuDiwnmKaZ2Vu9b3e4AGOxXU4clsdZE3rSRWu6/zOwx4JhG3AMTW6/VC1ox6zd9HN5qpbCvvJt+1j5gByylSSxJ+0q6KKi4uknSxZJyv1wk7S/pq5LukrRF0gZJV0n63Zz9Tpdkkr6d8NydwXPxx+8VeS01Wa5gZtdIvT7YQu05X8fyvc7Gei1ifZr1M5Nhpx1jcotxdsI6L1Zr94QVFt0B/btr7LRRJO0MXA5sxSWmNuBs4ApJh+ckBN8V2IBLsn0v8CTgFOBfJf2+mV2c0N9BQftfZBz3UuADsW0/KfJ6ahNXRzvdA/Fb9kWBHRXhsXY5xx0fV/L+k+RJrTr8qhPCOvUY2ziBBe13B1TGKcBBwCFmdhuApLW4ktanAuem7RgkjTo5uk3SvwJ34BJij4kr8HHgC8AhpGvhBjO7ttzLcNQ6LVuneyCh8+yny050lOp6tBDhRMfw7oCJ6N4E1tAdsP9ScgcEHAdcGworgJndAVwDvLbswYKVbA8DYyvaJL0ZeCHwvolHm0PtMS9mdg01RA9MHZqVNjs/dexpeYGtW1ibt1oL9D9VPa+2CisEdz53mNk9NXbaFg7DFRSMsw5XATYXST1JyyQ9VdL7gWcBfx9r82RcKZn3mtnGnEO+RtJjkrZKuraovxUaCyi03bo8uZVPQhxrPAH2TONZZ7fEtXlhLcBcCysGg2fU2GmdrJJ0XeTx9tjzK3FlsONsBJ5csI//jqt3tR54D3C8mV0Wa/M3wC24IoVZ/AvwLlyl2BOAx4GvSDqxyEBq9rk6zGyLq7nV/46ruVXjBFfO5NaIXzRlcqtYWNW47zU+uRUKQKIftoBAz5JZi2plITpzK6xhnlbm2R2woYYaWucBXwKeCrwV+AdJrzezrwEEVV3fCrww7302s3dF/5b0FVxp7g/hChpm0ljWCzP7LvQ2Vm3BJl4gY+9pRtjV8GLODs0qPCmVUhcr/ojvU5U7oAprdTphLe+qKdZ3F4RVH1yi7oCQB0m2UNMs2jHM7F4zu87MvmZmb8CJ4UciTc4HPg3cK2kPSXvgjMyF4O8dMo7dB74M7CPpaXljaTil0GAVMOiO/7WswJZdVVWm7SD1+MWO20YXwJIW1kfMBmfW2HEbWYfzu8Y5FLhpwmNeBxwc+fs5wDtwYh0+jgaOCn5/Z8Hj5p4kjbgFQszMJO0EbM2MPZ1FX/HwrDH3AEwSnjXWz5iLILl9KJp5JbWTmU5Uq2Ryq3VyYZ1VHKs7Vt135AYwgEHxGtWtYqbn0yXARyQdZGb/CSDpAJz4nV72YHIX4jHA7ZHNL09oeh7OP/ku4LaE58PjLQPeCNxtZvfl9d+ouAKY2ROS9gL7Re3xr4kCG22fFteaIcKkCSxkiWxxJgzlqsFK9cJaluHdx85z7GctwyeBPwa+KulM3DfPWcA9uNt5wK3GwgnmGjNbE2z7AM59cA1wH87nejJwJPDmcN+kzGKSHgKWRZ+T9CZc+NfXg/73Bv4IF771piIvpnFxBTCz+yU9H/o3dGeCq6zAjh+7PCVDuOpMAt4qYc06ZluEdTiB9VQz21pz563EzDZLOhYXJnUhTgguA1ab2aORpsIJRfRi+g9gNXA8sDtOYG8AXhqEf5blDmAvXGTBSmAzzsXwSjO7tMgB1KYvTGnhG2CvcO9ZdQKbuHprzIIdFcER8RwRzV5ym+Fh08S0jMgWF64m/KheWMsSLhTghWb2/2rufGZIC9br7Vy4/WDw6PU1RAu0hlbVyDDrvxLYVvVKo9lFEIy2K5fgehB7FH1unKbKF5fJPztO2i37IFNYs/vMcAO0SlgHQO+aLgurJ59WiavDdsQFUlfbS0UCO9aOojPzxcW03HFnz/SimiGsOf1mHzdpp3RRrV9YIRjndrP+MQ107qmRVvhco5jZQNJuYI82kuClrA8WCid5CYWwaB7XxDG3qBxNeWoUVWiRtRoycN1jqbGUnaN14XztoYWWK0FxQ54eJLCotq9pLViIxcFmuwnc4cvdxjd12z/sfypLFWq3VqGlwmqA7WrtCzD2VEDrLNcQM1vvkuT27646gqC4BQv5cbBh21ErFlKWubb0OpvN8tWaQ6wgVVTdMZsU1gHAbkFmOM8SoJWWa0iwFPCg9liwkGmZjkzGjFtr01uA1ZC3/LY8OZZqA9ZqC4R1j1g4kWfOabW4AmE+x0PaKrBuvzQ3Qdg2WWTrFtokEZ3dGHIm5AqKanYkQBeiAaIMx7ynmT3c4EA8DdBat0AUM7tF0qHQv6kdLgLIvfUfmewK28NYpqwWWrLlyBn/VDkBihy/jaIKEWHdq0DOUM8c0nrLNcTMfgw8qy4LNjEXbOJEVwErNseS7R4FrdSqLFVosbUKkbGvMrP7Gx6MpyE6I64AZnYrNflgYTo3QXGR7YLQDig03gKCCtWKaouE9clm9kDDg/E0SKfEFYY+2P3aKbAlRDZVaAsKWWWUHENJK3ViUYWWRgJECcOteJKZPdTsWDxN0wmfaxwzu0fS06H/M/f9UO13RHjhjmXUghRfLBQKxYoKUurCgpZZtiVDx/J9ygWO13pRHSZhsSCO1YdbebpnuYaY2XpgV3flNWzFlrRkEwUnagEWtAZrYYLxFItEKGipttoFABFh3Q62fGkJq6VGoEwalSJpX0kXSXpY0iZJF7t499z9jpB0gaSbg4KCd0v6gqQDY+2eJelvJa2V9Kik9ZIucVn5xo55pSRLeKwu8lo6abmGBCnKloEeh8HyqrNpQUo0gRtMSm7Y/CiB5JyxYcOCAjvpktoZCfhMCynmFpJsg6iCE9Y+0PuxWb9QdVJPOpJ2Bi4HtgIn4d7gs4ErJB0erNxM43hcFYP/iato8EvAXwDXSfrlSPmc38YlzP4cLk3hHsB7gWslHWNm18eOuxY4NbbtzkKvp00pB6fBpSscvKLOfLCJIusGk7FXtghmCm2LKBdCNt2t/2KfbTpXh2kDf9XMvt/wYBpB6llGyakxzB7PTDko6U+Bc4FDzOy2YNuBwK24MtjnZuz7lHhkRpBU+w7gbDN7f7BtFfBANDm5pN1xgvkvZvbWyPYrcUm0J0qy040ruQBBusJfdid8PbfTqbemGbezeRNF1Qb6T8Zk4yk6IZb1XoX9t8UFEDIgENZ9lqqwVsRxwLWhsMJwAvsaXFWAVJJC3szsLuB+nBUbbtsQr/oQLPC4JdpuFsyNuAKY2Q3AXot+sHouyMlEFkrlbJ2RT2uSYxejZJRDJ0U1tFbNcKVZftrwgOaNw4AbE7avwxUpLIWk5+CqCfw4p91K4Lkp7V4Q+H+3BX7ak4v232mfaxJByZgdgC1gvTr8sMO+k6IK3KAWf091GcQFqfz3Xr0W7gR9FXRBtUtQQ8IvbD0OA1/zajJWSbou8vcFZnZB5O+0EtobSS65nUpQTPATOMv10znN/w4nEufFtn8L+ALOqt0DeCvwKUlPM7Oz88Ywd+IKYGZPAAvSwgPQX1l3Xa7USS83OPcz0y8L6eLVxM3GFKLdaUENGU5cnW/Wf0fTo+kwG2os8/L3wEuAV5lZkmADIOl9uAKGJ0fdEQChnzbCVyV9BThD0nl5iXjmyi0Qx6y/J/CSRT9sfRewRf6lDK7QrfE4WQsOyi4+mOWxAqKvq+AkVXuFNRTVPsCzvLBWzoMkW6hpFm0ikj4MvB34r2b2zYx27wD+GjjTzD5T8PBfBHYEnpfXcC4t1yhm9l0X4mGPgC3U6SYYjiHNXTBsEBOXXKu2CDW5CCa4O26vmEYZWquPgu1h5Wuge8qzDud3jXMocFORA0g6AzgNeJeZXZjR7i3Ax4D/YWYfnGCsuSfxXFuuIWa2xWywDOyYJqzY4TjyrNlhQ0t+NM0UYyr82htnzFrdzQtrFkXvfgp92V8CHCXpoHCDpAOAo4PnMpH0J7i42DPM7O8z2r0O+F/Ap8zs3UUGFuEEYAvwo9zxLDW/vLNitQloxIodG88s+5/W4p3hudB+EU0iaq0OvLWag4tzXV64vdkTeXGuuwA34MTrTNwHchawG3B46OMM4ldvB9aY2Zpg2/HAPwCXAn8VO/QmM7spaPdrwDdxVvK7GFX9rWFFXkkvBU4HLsbFwO6OW9hwHHC6mZ2T93rn3i0QJ1ieuEzSMdC/elFgmxHZuAhNJbYNflF2U0xDRkL3nm3W/0mz41maBCsujwU+ClyIuygvA1bHJo+Em6WO3nm/Mtj+yuAR5SrgZcHvxwI7AC/Exc9GuQs4IPh9fXD8NcAqYBtutdabzeyLRV7PkrNco7TNik1jptbtlHRbRJMYWqubYbC7t1aLM2vLdd5YEj7XNMzsscAX+2uLfrb2iYfl/Kuzz/lhxLf6bLP+rl5YPbNkSYtriJldDawA/bTJCa9JyRPfSf7NLyOi+hmgZ2beDeCZOUvO55qGmW0D9gmc6huhv6Jpf6xnlhiLvtXeT8CeazbY3vCgPHOMF9cYQVqzHSTtCfZzGCx4ke0yI6K6BdjTrL+l2TF5lgLeLZCCmT3g/LHs76bhu+Uq8ISC2gdsO7DSrL+zmXlh9dSCF9cczOxus0EP7LkwqK3qgWdSRkR1AOxjNlietb7c46kCL64FMbN1gcgevXjxemu2PYyIqgHPNhss+LSAFZO2mrCNKwxrxotrSczsO2YDgT0LrO9Ftmmis/+2DXia2cBHAHgax4vrhJjZrYFPdnfggdE4WS+01RK1UvuALgd2NBusMLP7Gh2axxPgxXVKzGyTWX8VsAD2m4t+WW/Nzp64P9WOMDOZ9X/DzLY2PDiPZwQvrjPCzAZmdpnzy7IXsGnRsvJCOzlxK5XvA7sF/tR4pU6PpzV4ca0AM7vfrL870AP7FRcK5IW2GNG41O0EgroJ7NmBlfqreRngPZ424MW1QsxxXRAKJLD9gVtGhcP7aMet0wFgXwCeEgjq7n6Cqo34JdVZeHGtERcz2z/ERRvwJLDXL/pol5pVGxdUG4C9EjcxJbPBiWa2odEhempH0r6SLgoqrm6SdLGk/Qru+9eSvinpAUkm6W0ZbU+RdLOkrZJ+EpR8ibf5bHCc+OO8IuPxy18bwsweAf4J6ElaAHsxcKUrRWOMLrft8tJbi/yM/i6AJ8COBNaaDZbKt4onBZcClMuBrbjE1IarLHCFpMODpelZvAv4IfA1XKXWtH5OAc4HPgT8O/AbwMckycw+Hmt+Py5BdpT1RV6PF9cWEKS6+zbB5yGpB3Yw2ItAF3ZHcDOFdDvYHwDXA/d6MfUkcApwEHBIWIlV0lrgVuBU4Nyc/Xc3s4Gkg0kR16Dk9geBC83sjGDzFZKeDpwl6VNBEqeQJ8zs2klejHcLtJAg8uAWM/ui2WBZ4EZYcAsXBm9yixdCv23ouw3dCuHDGBW5qUcVeYR99COPcBwD3ASe/R7YvkAvuM1fbmb/bGb32FLO0O7J4jjg2miJazO7A1cx4LV5O5tZkUJdLwaeAnw+tv1CYE/gmMKjzcGLa0cIBPdWM/tSKLhukoydwA4EewkMXg/27UB8bVEA4yJc5hHdd4A7rm0H+zTYa91tve0LrHCTT0Mh/aqZ3euF1FOCw4AbE7avw1WAnVUfJPSzLvgZ72cvSRskbZd0i6TTnBsvH+8W6Dhm9jiugNqdwaZ/SmonaTnYKmA57nNfFvl9gUUVDR/bcL6vB3yGfk8Kl8L2VSXa7yjpusjfF5jZBZG/VwJJCXY2Ak+eZIAJrAx+xvvZGHsenP/2epzw7gi8DuenfSbwh3kdeXFdIgR+pEKOeI+nCGYWLwQ4V5jZebFNX5f0KLBa0jlmdmvW/t4t4PF42sKDJFuoaRbtpH2Q0E9osW4km7Dya26hRS+uHo+nLaxj0Sca5VDgphn2QUI/oa+1aD+5cwleXD0eT1u4BDhK0kHhBkkHAEcHz82C7wIbgBNi20/EWa3X5Ox/Ak5Yf5DXkfe5ejyetvBJ4I+Br0o6EydiZwH34IL+AZC0P3A7sMbM1kS2/zouzOqpwaYjAh8pZnZR8HObpL/ALRr4KW4RwbHAfwXeZWZPRPq4EPgScBuwA25C623A+WZ2e96LkY+U8Xg8bSFY6vpR4Ldwq08uA1ab2Z2RNgcAdwB/ZWYfiGy/Evj1pOMGYYvRfk4F/hzYH7gb+KiZfSzy/Epc6fUXAHvj4hBvDrZ9rEhMrRdXj8fjqQDvc/V4PJ4K8OLq8Xg8FeDF1ePxeCrAi6vH4/FUgBdXj8fjqQAvrh6Px1MBXlw9Ho+nAry4ejweTwX8/y7lHJnxXCqiAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Call animate method\n", + "fig, ax = plt.subplots(subplot_kw={'projection': 'polar'})\n", + "\n", + "plt.colorbar(plot_field('T',-1), ax=ax)\n", + "ani = animation.FuncAnimation(fig, animate_T, frames=data['T'].shape[0], interval=100, blit=False)\n", + "ani.save('plots/' + job_id + 'animation_T.gif', fps = 4)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "MovieWriter ffmpeg unavailable; using Pillow instead.\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Call animate method\n", + "fig, ax = plt.subplots(subplot_kw={'projection': 'polar'})\n", + "\n", + "plt.colorbar(plot_field('vort',-1), ax=ax)\n", + "ani = animation.FuncAnimation(fig, animate_vort, frames=data['vort'].shape[0], interval=100, blit=False)\n", + "ani.save('plots/' + job_id + 'animation_vort.gif', fps = 4)\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.6.6 ('north-simulation': venv)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "58f89f0882dc77e77c57dba94bed5153ff3c368884663369d545e0daa46a4df0" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/data_analysis.py b/deprecated data analysis/data_analysis.py similarity index 100% rename from data_analysis.py rename to deprecated data analysis/data_analysis.py diff --git a/data_analysis_Asbjorn.ipynb b/deprecated data analysis/data_analysis_Asbjorn.ipynb similarity index 100% rename from data_analysis_Asbjorn.ipynb rename to deprecated data analysis/data_analysis_Asbjorn.ipynb diff --git a/deprecated data analysis/data_analysis_animation.ipynb b/deprecated data analysis/data_analysis_animation.ipynb new file mode 100644 index 0000000..8f76331 --- /dev/null +++ b/deprecated data analysis/data_analysis_animation.ipynb @@ -0,0 +1,953 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib import rcParams\n", + "import matplotlib as mpl\n", + "from matplotlib.colors import ListedColormap, LinearSegmentedColormap\n", + "# rcParams.update({\n", + "# \"font.size\": 16})\n", + "# rcParams['axes.titlepad'] = 20" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load in Data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "job_id 5699565\n" + ] + } + ], + "source": [ + "from boutdata import collect\n", + "from boutdata.data import BoutData\n", + "import re\n", + "import os\n", + "\n", + "job_ids = [re.search('(\\d){7}', dir).group() for dir in os.listdir(\"./shared/NORTH/\") if re.search('data_(\\d){7}', dir)]\n", + "job_ids.sort()\n", + "job_id = job_ids[-1]\n", + "# job_id = '5688618'\n", + "print('job_id', job_id)\n", + "path = './shared/NORTH/data_' + job_id + '/'" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/niflheim/s173965/local/venv/north-simulation/lib64/python3.6/site-packages/boutdata/data.py:769: AlwaysWarning: While building x, y, z coordinate arrays, an exception occured: name 'mxg' is not defined\n", + "Evaluating non-scalar options not available\n", + " + \"\\nEvaluating non-scalar options not available\"\n" + ] + } + ], + "source": [ + "bdata = BoutData(path)\n", + "outputs = bdata['outputs']\n", + "field_keys = outputs.keys()\n", + "options = bdata['options']" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def get_option(*keys):\n", + " from numpy import sqrt, pi\n", + " from ast import parse, walk, Name\n", + " val = options\n", + " group = keys[0]\n", + " for key in keys:\n", + " val = val[key]\n", + " try:\n", + " return eval(str(val))\n", + " except:\n", + " # Parse undefined variables from the Bout.inp file:\n", + " val_str = str(val).replace(':', '____') # Replace group indicator ':' with '____' to make string parsable.\n", + " for node in walk(parse(val_str)):\n", + " if type(node) is Name:\n", + " missing_keys = node.id.split('____')\n", + " var = missing_keys[-1]\n", + " if var not in locals():\n", + " try:\n", + " locals()[var] = get_option(*missing_keys)\n", + " except:\n", + " missing_keys.insert(0, group)\n", + " try:\n", + " locals()[var] = get_option(*missing_keys)\n", + " except:\n", + " print('Error reading option for keys: ', keys)\n", + " return 0\n", + " return eval(str(val))\n", + "\n", + "def get_options(keys_list):\n", + " vals = []\n", + " for keys in keys_list:\n", + " vals.append(get_option(*keys))\n", + " return vals" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "oci, rho_s, R, n0, n_n, E_ion, mxg, myg, nx_all, ny_all, nz_all = get_options([\n", + " ('north', 'oci'), ('north', 'rho_s'), ('north', 'R'), ('north', 'n0'), ('north', 'n_n'), ('north', 'E_ion'),\n", + " ('mxg',), ('myg',),\n", + " ('mesh', 'nx'),('mesh', 'ny'),('mesh', 'nz')])\n", + "Lx = R/rho_s * rho_s\n", + "nx_inner = nx_all - 2*mxg\n", + "ny_inner = ny_all - 2*myg\n", + "nz_inner = nz_all" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "nx: 48+2*mxg\n", + "ny: 1\n", + "nz: 128\n", + "Lx: north:a/north:rho_s\n", + "rho_s: c_s/oci\n", + "oci: e*B0/(mi)\n" + ] + } + ], + "source": [ + "print('nx: ', options['mesh']['nx'])\n", + "print('ny: ', options['mesh']['ny'])\n", + "print('nz: ', options['mesh']['nz'])\n", + "print('Lx: ', options['mesh']['Lx'])\n", + "print('rho_s: ', options['north']['rho_s'])\n", + "print('oci: ', options['north']['oci'])" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mxsub = 2 mysub = 1 mz = 128\n", + "\n", + "nxpe = 24, nype = 1, npes = 24\n", + "\n", + "Reading from 0: [2-3][0-0] -> [0-1][0-0]\n", + "\n", + "Reading from 1: [2-3][0-0] -> [2-3][0-0]\n", + "\n", + "Reading from 2: [2-3][0-0] -> [4-5][0-0]\n", + "\n", + "Reading from 3: [2-3][0-0] -> [6-7][0-0]\n", + "\n", + "Reading from 4: [2-3][0-0] -> [8-9][0-0]\n", + "\n", + "Reading from 5: [2-3][0-0] -> [10-11][0-0]\n", + "\n", + "Reading from 6: [2-3][0-0] -> [12-13][0-0]\n", + "\n", + "Reading from 7: [2-3][0-0] -> [14-15][0-0]\n", + "\n", + "Reading from 8: [2-3][0-0] -> [16-17][0-0]\n", + "\n", + "Reading from 9: [2-3][0-0] -> [18-19][0-0]\n", + "\n", + "Reading from 10: [2-3][0-0] -> [20-21][0-0]\n", + "\n", + "Reading from 11: [2-3][0-0] -> [22-23][0-0]\n", + "\n", + "Reading from 12: [2-3][0-0] -> [24-25][0-0]\n", + "\n", + "Reading from 13: [2-3][0-0] -> [26-27][0-0]\n", + "\n", + "Reading from 14: [2-3][0-0] -> [28-29][0-0]\n", + "\n", + "Reading from 15: [2-3][0-0] -> [30-31][0-0]\n", + "\n", + "Reading from 16: [2-3][0-0] -> [32-33][0-0]\n", + "\n", + "Reading from 17: [2-3][0-0] -> [34-35][0-0]\n", + "\n", + "Reading from 18: [2-3][0-0] -> [36-37][0-0]\n", + "\n", + "Reading from 19: [2-3][0-0] -> [38-39][0-0]\n", + "\n", + "Reading from 20: [2-3][0-0] -> [40-41][0-0]\n", + "\n", + "Reading from 21: [2-3][0-0] -> [42-43][0-0]\n", + "\n", + "Reading from 22: [2-3][0-0] -> [44-45][0-0]\n", + "\n", + "Reading from 23: [2-3][0-0] -> [46-47][0-0]\n", + "\n", + "\n", + "mxsub = 2 mysub = 1 mz = 128\n", + "\n", + "nxpe = 24, nype = 1, npes = 24\n", + "\n", + "Reading from 0: [2-3][0-0] -> [0-1][0-0]\n", + "\n", + "Reading from 1: [2-3][0-0] -> [2-3][0-0]\n", + "\n", + "Reading from 2: [2-3][0-0] -> [4-5][0-0]\n", + "\n", + "Reading from 3: [2-3][0-0] -> [6-7][0-0]\n", + "\n", + "Reading from 4: [2-3][0-0] -> [8-9][0-0]\n", + "\n", + "Reading from 5: [2-3][0-0] -> [10-11][0-0]\n", + "\n", + "Reading from 6: [2-3][0-0] -> [12-13][0-0]\n", + "\n", + "Reading from 7: [2-3][0-0] -> [14-15][0-0]\n", + "\n", + "Reading from 8: [2-3][0-0] -> [16-17][0-0]\n", + "\n", + "Reading from 9: [2-3][0-0] -> [18-19][0-0]\n", + "\n", + "Reading from 10: [2-3][0-0] -> [20-21][0-0]\n", + "\n", + "Reading from 11: [2-3][0-0] -> [22-23][0-0]\n", + "\n", + "Reading from 12: [2-3][0-0] -> [24-25][0-0]\n", + "\n", + "Reading from 13: [2-3][0-0] -> [26-27][0-0]\n", + "\n", + "Reading from 14: [2-3][0-0] -> [28-29][0-0]\n", + "\n", + "Reading from 15: [2-3][0-0] -> [30-31][0-0]\n", + "\n", + "Reading from 16: [2-3][0-0] -> [32-33][0-0]\n", + "\n", + "Reading from 17: [2-3][0-0] -> [34-35][0-0]\n", + "\n", + "Reading from 18: [2-3][0-0] -> [36-37][0-0]\n", + "\n", + "Reading from 19: [2-3][0-0] -> [38-39][0-0]\n", + "\n", + "Reading from 20: [2-3][0-0] -> [40-41][0-0]\n", + "\n", + "Reading from 21: [2-3][0-0] -> [42-43][0-0]\n", + "\n", + "Reading from 22: [2-3][0-0] -> [44-45][0-0]\n", + "\n", + "Reading from 23: [2-3][0-0] -> [46-47][0-0]\n", + "\n", + "\n", + "mxsub = 2 mysub = 1 mz = 128\n", + "\n", + "nxpe = 24, nype = 1, npes = 24\n", + "\n", + "Reading from 0: [2-3][0-0] -> [0-1][0-0]\n", + "\n", + "Reading from 1: [2-3][0-0] -> [2-3][0-0]\n", + "\n", + "Reading from 2: [2-3][0-0] -> [4-5][0-0]\n", + "\n", + "Reading from 3: [2-3][0-0] -> [6-7][0-0]\n", + "\n", + "Reading from 4: [2-3][0-0] -> [8-9][0-0]\n", + "\n", + "Reading from 5: [2-3][0-0] -> [10-11][0-0]\n", + "\n", + "Reading from 6: [2-3][0-0] -> [12-13][0-0]\n", + "\n", + "Reading from 7: [2-3][0-0] -> [14-15][0-0]\n", + "\n", + "Reading from 8: [2-3][0-0] -> [16-17][0-0]\n", + "\n", + "Reading from 9: [2-3][0-0] -> [18-19][0-0]\n", + "\n", + "Reading from 10: [2-3][0-0] -> [20-21][0-0]\n", + "\n", + "Reading from 11: [2-3][0-0] -> [22-23][0-0]\n", + "\n", + "Reading from 12: [2-3][0-0] -> [24-25][0-0]\n", + "\n", + "Reading from 13: [2-3][0-0] -> [26-27][0-0]\n", + "\n", + "Reading from 14: [2-3][0-0] -> [28-29][0-0]\n", + "\n", + "Reading from 15: [2-3][0-0] -> [30-31][0-0]\n", + "\n", + "Reading from 16: [2-3][0-0] -> [32-33][0-0]\n", + "\n", + "Reading from 17: [2-3][0-0] -> [34-35][0-0]\n", + "\n", + "Reading from 18: [2-3][0-0] -> [36-37][0-0]\n", + "\n", + "Reading from 19: [2-3][0-0] -> [38-39][0-0]\n", + "\n", + "Reading from 20: [2-3][0-0] -> [40-41][0-0]\n", + "\n", + "Reading from 21: [2-3][0-0] -> [42-43][0-0]\n", + "\n", + "Reading from 22: [2-3][0-0] -> [44-45][0-0]\n", + "\n", + "Reading from 23: [2-3][0-0] -> [46-47][0-0]\n", + "\n", + "\n", + "mxsub = 2 mysub = 1 mz = 128\n", + "\n", + "nxpe = 24, nype = 1, npes = 24\n", + "\n", + "Reading from 0: [2-3][0-0] -> [0-1][0-0]\n", + "\n", + "Reading from 1: [2-3][0-0] -> [2-3][0-0]\n", + "\n", + "Reading from 2: [2-3][0-0] -> [4-5][0-0]\n", + "\n", + "Reading from 3: [2-3][0-0] -> [6-7][0-0]\n", + "\n", + "Reading from 4: [2-3][0-0] -> [8-9][0-0]\n", + "\n", + "Reading from 5: [2-3][0-0] -> [10-11][0-0]\n", + "\n", + "Reading from 6: [2-3][0-0] -> [12-13][0-0]\n", + "\n", + "Reading from 7: [2-3][0-0] -> [14-15][0-0]\n", + "\n", + "Reading from 8: [2-3][0-0] -> [16-17][0-0]\n", + "\n", + "Reading from 9: [2-3][0-0] -> [18-19][0-0]\n", + "\n", + "Reading from 10: [2-3][0-0] -> [20-21][0-0]\n", + "\n", + "Reading from 11: [2-3][0-0] -> [22-23][0-0]\n", + "\n", + "Reading from 12: [2-3][0-0] -> [24-25][0-0]\n", + "\n", + "Reading from 13: [2-3][0-0] -> [26-27][0-0]\n", + "\n", + "Reading from 14: [2-3][0-0] -> [28-29][0-0]\n", + "\n", + "Reading from 15: [2-3][0-0] -> [30-31][0-0]\n", + "\n", + "Reading from 16: [2-3][0-0] -> [32-33][0-0]\n", + "\n", + "Reading from 17: [2-3][0-0] -> [34-35][0-0]\n", + "\n", + "Reading from 18: [2-3][0-0] -> [36-37][0-0]\n", + "\n", + "Reading from 19: [2-3][0-0] -> [38-39][0-0]\n", + "\n", + "Reading from 20: [2-3][0-0] -> [40-41][0-0]\n", + "\n", + "Reading from 21: [2-3][0-0] -> [42-43][0-0]\n", + "\n", + "Reading from 22: [2-3][0-0] -> [44-45][0-0]\n", + "\n", + "Reading from 23: [2-3][0-0] -> [46-47][0-0]\n", + "\n", + "\n", + "mxsub = 2 mysub = 1 mz = 128\n", + "\n", + "nxpe = 24, nype = 1, npes = 24\n", + "\n", + "Reading from 0: [2-3][0-0] -> [0-1][0-0]\n", + "\n", + "Reading from 1: [2-3][0-0] -> [2-3][0-0]\n", + "\n", + "Reading from 2: [2-3][0-0] -> [4-5][0-0]\n", + "\n", + "Reading from 3: [2-3][0-0] -> [6-7][0-0]\n", + "\n", + "Reading from 4: [2-3][0-0] -> [8-9][0-0]\n", + "\n", + "Reading from 5: [2-3][0-0] -> [10-11][0-0]\n", + "\n", + "Reading from 6: [2-3][0-0] -> [12-13][0-0]\n", + "\n", + "Reading from 7: [2-3][0-0] -> [14-15][0-0]\n", + "\n", + "Reading from 8: [2-3][0-0] -> [16-17][0-0]\n", + "\n", + "Reading from 9: [2-3][0-0] -> [18-19][0-0]\n", + "\n", + "Reading from 10: [2-3][0-0] -> [20-21][0-0]\n", + "\n", + "Reading from 11: [2-3][0-0] -> [22-23][0-0]\n", + "\n", + "Reading from 12: [2-3][0-0] -> [24-25][0-0]\n", + "\n", + "Reading from 13: [2-3][0-0] -> [26-27][0-0]\n", + "\n", + "Reading from 14: [2-3][0-0] -> [28-29][0-0]\n", + "\n", + "Reading from 15: [2-3][0-0] -> [30-31][0-0]\n", + "\n", + "Reading from 16: [2-3][0-0] -> [32-33][0-0]\n", + "\n", + "Reading from 17: [2-3][0-0] -> [34-35][0-0]\n", + "\n", + "Reading from 18: [2-3][0-0] -> [36-37][0-0]\n", + "\n", + "Reading from 19: [2-3][0-0] -> [38-39][0-0]\n", + "\n", + "Reading from 20: [2-3][0-0] -> [40-41][0-0]\n", + "\n", + "Reading from 21: [2-3][0-0] -> [42-43][0-0]\n", + "\n", + "Reading from 22: [2-3][0-0] -> [44-45][0-0]\n", + "\n", + "Reading from 23: [2-3][0-0] -> [46-47][0-0]\n", + "\n", + "\n", + "mxsub = 2 mysub = 1 mz = 128\n", + "\n", + "nxpe = 24, nype = 1, npes = 24\n", + "\n", + "Reading from 0: [2-3][0-0] -> [0-1][0-0]\n", + "\n", + "Reading from 1: [2-3][0-0] -> [2-3][0-0]\n", + "\n", + "Reading from 2: [2-3][0-0] -> [4-5][0-0]\n", + "\n", + "Reading from 3: [2-3][0-0] -> [6-7][0-0]\n", + "\n", + "Reading from 4: [2-3][0-0] -> [8-9][0-0]\n", + "\n", + "Reading from 5: [2-3][0-0] -> [10-11][0-0]\n", + "\n", + "Reading from 6: [2-3][0-0] -> [12-13][0-0]\n", + "\n", + "Reading from 7: [2-3][0-0] -> [14-15][0-0]\n", + "\n", + "Reading from 8: [2-3][0-0] -> [16-17][0-0]\n", + "\n", + "Reading from 9: [2-3][0-0] -> [18-19][0-0]\n", + "\n", + "Reading from 10: [2-3][0-0] -> [20-21][0-0]\n", + "\n", + "Reading from 11: [2-3][0-0] -> [22-23][0-0]\n", + "\n", + "Reading from 12: [2-3][0-0] -> [24-25][0-0]\n", + "\n", + "Reading from 13: [2-3][0-0] -> [26-27][0-0]\n", + "\n", + "Reading from 14: [2-3][0-0] -> [28-29][0-0]\n", + "\n", + "Reading from 15: [2-3][0-0] -> [30-31][0-0]\n", + "\n", + "Reading from 16: [2-3][0-0] -> [32-33][0-0]\n", + "\n", + "Reading from 17: [2-3][0-0] -> [34-35][0-0]\n", + "\n", + "Reading from 18: [2-3][0-0] -> [36-37][0-0]\n", + "\n", + "Reading from 19: [2-3][0-0] -> [38-39][0-0]\n", + "\n", + "Reading from 20: [2-3][0-0] -> [40-41][0-0]\n", + "\n", + "Reading from 21: [2-3][0-0] -> [42-43][0-0]\n", + "\n", + "Reading from 22: [2-3][0-0] -> [44-45][0-0]\n", + "\n", + "Reading from 23: [2-3][0-0] -> [46-47][0-0]\n", + "\n", + "\n", + "mxsub = 2 mysub = 1 mz = 128\n", + "\n", + "nxpe = 24, nype = 1, npes = 24\n", + "\n", + "Reading from 0: [2-3][0-0] -> [0-1][0-0]\n", + "\n", + "Reading from 1: [2-3][0-0] -> [2-3][0-0]\n", + "\n", + "Reading from 2: [2-3][0-0] -> [4-5][0-0]\n", + "\n", + "Reading from 3: [2-3][0-0] -> [6-7][0-0]\n", + "\n", + "Reading from 4: [2-3][0-0] -> [8-9][0-0]\n", + "\n", + "Reading from 5: [2-3][0-0] -> [10-11][0-0]\n", + "\n", + "Reading from 6: [2-3][0-0] -> [12-13][0-0]\n", + "\n", + "Reading from 7: [2-3][0-0] -> [14-15][0-0]\n", + "\n", + "Reading from 8: [2-3][0-0] -> [16-17][0-0]\n", + "\n", + "Reading from 9: [2-3][0-0] -> [18-19][0-0]\n", + "\n", + "Reading from 10: [2-3][0-0] -> [20-21][0-0]\n", + "\n", + "Reading from 11: [2-3][0-0] -> [22-23][0-0]\n", + "\n", + "Reading from 12: [2-3][0-0] -> [24-25][0-0]\n", + "\n", + "Reading from 13: [2-3][0-0] -> [26-27][0-0]\n", + "\n", + "Reading from 14: [2-3][0-0] -> [28-29][0-0]\n", + "\n", + "Reading from 15: [2-3][0-0] -> [30-31][0-0]\n", + "\n", + "Reading from 16: [2-3][0-0] -> [32-33][0-0]\n", + "\n", + "Reading from 17: [2-3][0-0] -> [34-35][0-0]\n", + "\n", + "Reading from 18: [2-3][0-0] -> [36-37][0-0]\n", + "\n", + "Reading from 19: [2-3][0-0] -> [38-39][0-0]\n", + "\n", + "Reading from 20: [2-3][0-0] -> [40-41][0-0]\n", + "\n", + "Reading from 21: [2-3][0-0] -> [42-43][0-0]\n", + "\n", + "Reading from 22: [2-3][0-0] -> [44-45][0-0]\n", + "\n", + "Reading from 23: [2-3][0-0] -> [46-47][0-0]\n", + "\n", + "\n", + "mxsub = 2 mysub = 1 mz = 128\n", + "\n", + "nxpe = 24, nype = 1, npes = 24\n", + "\n" + ] + } + ], + "source": [ + "#%% Read data\n", + "\n", + "field_list = ['B', 'T', 'n', 'phi', 'vort', 'source_T', 'wall_shadow']\n", + "par_list = ['t_array']\n", + "# fast_list = ['t_array', 'n0', 'phi0', 'n1', 'phi1', 'n2', 'phi2', 'T0', 'T1', 'T2']\n", + "#%%\n", + "data, par, fast = {}, {}, {}\n", + "\n", + "for _field in field_list:\n", + " data[_field] = collect(_field, path = path, xguards = False)\n", + "\n", + "for _par in par_list:\n", + " par[_par] = collect(_par, path = path, xguards = False)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "data['p'] = data['n'] * data['T']\n", + "\n", + "def k_ionization(T):\n", + " return 2.0e-13*np.sqrt(T/E_ion)/(6.0 + T/E_ion)*np.exp(-E_ion/T)*n0/(oci)\n", + "data['S_n'] = n_n*data['n']*k_ionization(data['T'])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "thetas = np.linspace(0, 2 * np.pi, nz_inner)\n", + "rhos = np.linspace(Lx/(2*nx_inner), Lx+Lx/(2*nx_inner), nx_inner)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot Time Frames" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from matplotlib.gridspec import GridSpec\n", + "\n", + "def cbar_ticks(vmin, vmax, n_ticks=4, label_rounding=2):\n", + " ticks = np.linspace(vmin, vmax, n_ticks, endpoint=True)\n", + " labels = np.round(ticks,3)\n", + " return ticks, labels\n", + "\n", + "def plot_time_frames(start_frame=0, end_frame=None):\n", + " fields = ['n', 'T', 'vort', 'phi']\n", + " cmaps = ['inferno', 'inferno', 'jet', 'jet']\n", + " titles = [r'$n$', r'$T$', r'$\\Omega$', r'$\\phi$']\n", + " N_frames = 6\n", + " cm = 1/2.54 # 1 cm = 1/2.54 inch\n", + " nrows = 5*N_frames + 1 # 5 (merged rows) for each frame plot. 1 row for colobar\n", + " n_cols = 1 + len(fields)*6 # 1 column for each timestamp text. 3 (merged columns) for each field plot.\n", + " fig = plt.figure(constrained_layout=True, figsize=(16*cm, 20*cm))\n", + " gs = GridSpec(nrows, n_cols, figure=fig)\n", + " def get_row_range(i_frame):\n", + " return slice(5*i_frame,5*(i_frame+1))\n", + " def get_col_range(i_field):\n", + " return slice(1+6*i_field,1+6*(i_field+1))\n", + " if end_frame is None:\n", + " end_frame = data[fields[0]].shape[0]\n", + " frames = np.linspace(start_frame, end_frame - 1, N_frames, dtype=int)\n", + " # frames = np.arange(start_frame, end_frame, int(np.ceil(end_frame/N_frames)))\n", + "\n", + " for i_frame, frame in enumerate(frames):\n", + " row_range = get_row_range(i_frame)\n", + " ax = fig.add_subplot(gs[row_range, 0])\n", + " ax.text(0.5, 0.5, r'$t = ' + str(round(times[frame]*(10**6),1)) + '\\, \\mathrm{\\mu \\, s}$', va=\"center\", ha=\"center\")\n", + " plt.axis('off')\n", + "\n", + " for i_field, field in enumerate(fields):\n", + " field_data = data[field].squeeze()\n", + " vmin = field_data.min()\n", + " vmax = field_data.max()\n", + " col_range = get_col_range(i_field)\n", + " for i_frame, frame in enumerate(frames):\n", + " row_range = get_row_range(i_frame)\n", + " ax = fig.add_subplot(gs[row_range, col_range], polar=True)\n", + " if i_frame == 0:\n", + " ax.set_title(titles[i_field])\n", + " cont = ax.contourf(thetas, rhos, field_data[frame, :, :], 10, cmap = cmaps[i_field], vmin=vmin, vmax=vmax)\n", + " ax.set_rticks([])\n", + " ax.set_xticks([])\n", + "\n", + " cax = fig.add_subplot(gs[-1, col_range])\n", + " latest = field_data[frames[-1], :, :]\n", + " c_ticks, c_labels = cbar_ticks(latest.min(), latest.max())\n", + " cbar = fig.colorbar(cont, cax=cax, ticks=c_ticks, orientation='horizontal')\n", + " cax.set_title(titles[i_field] + ' [norm. units]')\n", + " cbar.ax.set_xticklabels(c_labels, rotation=45)\n", + " return fig\n", + "\n", + "fig = plot_time_frames()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## gif Animations" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.animation as animation\n", + "\n", + "def get_cmap(cmap, min_p=0.0, N_cmap=256):\n", + " cmap = plt.get_cmap(cmap)\n", + " return ListedColormap(cmap(np.linspace(0,1,int(N_cmap/(1-min_p))))[-N_cmap:,:])\n", + "\n", + "def get_cbar_mappable(cmap, vmin, vmax):\n", + " norm = mpl.colors.Normalize(vmin=vmin, vmax=vmax)\n", + " return mpl.cm.ScalarMappable(norm=norm, cmap=get_cmap(cmap))\n", + "\n", + "def get_extend(field_data, vmin, vmax):\n", + " if field_data.min() < vmin and vmax >= field_data.max():\n", + " return 'min'\n", + " elif field_data.min() <= vmin and vmax < field_data.max():\n", + " return 'max'\n", + " elif field_data.min() < vmin and vmax < field_data.max():\n", + " return 'both'\n", + " else:\n", + " return 'neither'\n", + "\n", + "def plot_first_frame(field, cmap='inferno', vmin = None, vmax = None, frame=0):\n", + " field_data = data[field].squeeze()\n", + " if vmin is None:\n", + " vmin = field_data.min()\n", + " if vmax is None:\n", + " vmax = field_data.max()\n", + " fig, ax = plt.subplots(subplot_kw={'projection': 'polar'})\n", + " ax.set_rticks([])\n", + " ax.set_xticks([])\n", + " ax.contourf(thetas, rhos, field_data[frame, :, :], 100, cmap = cmap, vmin=vmin, vmax=vmax) # first image on screen\n", + " fig.colorbar(get_cbar_mappable(cmap, vmin, vmax), orientation='vertical', label=r'$' + field + '$ [norm. u.]', extend=get_extend(field_data, vmin, vmax), ax=ax)\n", + " fig.tight_layout()\n", + " return fig, ax, field_data, vmin, vmax\n", + "\n", + "def animate_data(field, cmap='inferno', vmin = None, vmax = None):\n", + " fig, ax, field_data, vmin, vmax = plot_first_frame(field, cmap, vmin, vmax)\n", + "\n", + " # animation function\n", + " def animate(i):\n", + " for c in ax.collections:\n", + " c.remove() # removes only the contours, leaves the rest intact\n", + " cont = ax.contourf(thetas, rhos, field_data[i, :, :], 100, cmap = cmap, vmin=vmin, vmax=vmax)\n", + " return cont.collections\n", + " \n", + " anim = animation.FuncAnimation(fig, animate, frames=field_data.shape[0], interval=100, blit=True)\n", + " anim.save('./plots/' + job_id + '_animation_' + field +'.gif', fps=4)\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "MovieWriter ffmpeg unavailable; using Pillow instead.\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# SET VMIN AND VMAX to symmetrise around 0\n", + "vmax = np.max([data['vort'].max(), np.abs(data['vort'].min())])\n", + "vmin = -vmax\n", + "\n", + "animate_data('vort', cmap='RdBu_r', vmin=vmin, vmax=vmax)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "MovieWriter ffmpeg unavailable; using Pillow instead.\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "vmax = np.max([data['phi'].max(), np.abs(data['phi'].min())])\n", + "vmin = -vmax\n", + "\n", + "animate_data('phi', cmap='RdBu_r', vmin=vmin, vmax=vmax)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.6.8 ('north-simulation': venv)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "58f89f0882dc77e77c57dba94bed5153ff3c368884663369d545e0daa46a4df0" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/deprecated data analysis/data_analysis_energy.ipynb b/deprecated data analysis/data_analysis_energy.ipynb new file mode 100644 index 0000000..d4eb9f3 --- /dev/null +++ b/deprecated data analysis/data_analysis_energy.ipynb @@ -0,0 +1,761 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib import rcParams\n", + "rcParams.update({\n", + " \"font.size\": 16})\n", + "rcParams['axes.titlepad'] = 20" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load in Data" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "job_id 5699565\n" + ] + } + ], + "source": [ + "from boutdata import collect\n", + "from boutdata.data import BoutData\n", + "import re\n", + "import os\n", + "\n", + "job_ids = [re.search('(\\d){7}', dir).group() for dir in os.listdir(\"./shared/NORTH/\") if re.search('data_(\\d){7}', dir)]\n", + "job_ids.sort()\n", + "job_id = job_ids[-1]\n", + "# job_id = '5672085'\n", + "print('job_id', job_id)\n", + "path = './shared/NORTH/data_' + job_id + '/'" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/niflheim/s173965/local/venv/north-simulation/lib64/python3.6/site-packages/boutdata/data.py:769: AlwaysWarning: While building x, y, z coordinate arrays, an exception occured: name 'mxg' is not defined\n", + "Evaluating non-scalar options not available\n", + " + \"\\nEvaluating non-scalar options not available\"\n" + ] + } + ], + "source": [ + "bdata = BoutData(path)\n", + "outputs = bdata['outputs']\n", + "field_keys = outputs.keys()\n", + "options = bdata['options']" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "def get_option(*keys):\n", + " from numpy import sqrt, pi\n", + " from ast import parse, walk, Name\n", + " val = options\n", + " group = keys[0]\n", + " for key in keys:\n", + " val = val[key]\n", + " try:\n", + " return eval(str(val))\n", + " except:\n", + " # Parse undefined variables from the Bout.inp file:\n", + " val_str = str(val).replace(':', '____') # Replace group indicator ':' with '____' to make string parsable.\n", + " for node in walk(parse(val_str)):\n", + " if type(node) is Name:\n", + " missing_keys = node.id.split('____')\n", + " var = missing_keys[-1]\n", + " if var not in locals():\n", + " try:\n", + " locals()[var] = get_option(*missing_keys)\n", + " except:\n", + " missing_keys.insert(0, group)\n", + " try:\n", + " locals()[var] = get_option(*missing_keys)\n", + " except:\n", + " print('Error reading option for keys: ', keys)\n", + " return 0\n", + " return eval(str(val))\n", + "\n", + "def get_options(keys_list):\n", + " vals = []\n", + " for keys in keys_list:\n", + " vals.append(get_option(*keys))\n", + " return vals" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "oci, rho_s, R, a, n0, n_n, E_ion, mxg, myg, nx_all, ny_all, nz_all = get_options([\n", + " ('north', 'oci'), ('north', 'rho_s'), ('north', 'R'), ('north', 'a'), ('north', 'n0'), ('north', 'n_n'), ('north', 'E_ion'),\n", + " ('mxg',), ('myg',),\n", + " ('mesh', 'nx'),('mesh', 'ny'),('mesh', 'nz')])\n", + "Lx = a/rho_s * rho_s\n", + "nx_inner = nx_all - 2*mxg\n", + "ny_inner = ny_all - 2*myg\n", + "nz_inner = nz_all" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "nx: 48+2*mxg\n", + "ny: 1\n", + "nz: 128\n", + "Lx: north:a/north:rho_s\n", + "rho_s: c_s/oci\n", + "oci: e*B0/(mi)\n" + ] + } + ], + "source": [ + "print('nx: ', options['mesh']['nx'])\n", + "print('ny: ', options['mesh']['ny'])\n", + "print('nz: ', options['mesh']['nz'])\n", + "print('Lx: ', options['mesh']['Lx'])\n", + "print('rho_s: ', options['north']['rho_s'])\n", + "print('oci: ', options['north']['oci'])" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mxsub = 2 mysub = 1 mz = 128\n", + "\n", + "nxpe = 24, nype = 1, npes = 24\n", + "\n", + "Reading from 0: [2-3][0-0] -> [0-1][0-0]\n", + "\n", + "Reading from 1: [2-3][0-0] -> [2-3][0-0]\n", + "\n", + "Reading from 2: [2-3][0-0] -> [4-5][0-0]\n", + "\n", + "Reading from 3: [2-3][0-0] -> [6-7][0-0]\n", + "\n", + "Reading from 4: [2-3][0-0] -> [8-9][0-0]\n", + "\n", + "Reading from 5: [2-3][0-0] -> [10-11][0-0]\n", + "\n", + "Reading from 6: [2-3][0-0] -> [12-13][0-0]\n", + "\n", + "Reading from 7: [2-3][0-0] -> [14-15][0-0]\n", + "\n", + "Reading from 8: [2-3][0-0] -> [16-17][0-0]\n", + "\n", + "Reading from 9: [2-3][0-0] -> [18-19][0-0]\n", + "\n", + "Reading from 10: [2-3][0-0] -> [20-21][0-0]\n", + "\n", + "Reading from 11: [2-3][0-0] -> [22-23][0-0]\n", + "\n", + "Reading from 12: [2-3][0-0] -> [24-25][0-0]\n", + "\n", + "Reading from 13: [2-3][0-0] -> [26-27][0-0]\n", + "\n", + "Reading from 14: [2-3][0-0] -> [28-29][0-0]\n", + "\n", + "Reading from 15: [2-3][0-0] -> [30-31][0-0]\n", + "\n", + "Reading from 16: [2-3][0-0] -> [32-33][0-0]\n", + "\n", + "Reading from 17: [2-3][0-0] -> [34-35][0-0]\n", + "\n", + "Reading from 18: [2-3][0-0] -> [36-37][0-0]\n", + "\n", + "Reading from 19: [2-3][0-0] -> [38-39][0-0]\n", + "\n", + "Reading from 20: [2-3][0-0] -> [40-41][0-0]\n", + "\n", + "Reading from 21: [2-3][0-0] -> [42-43][0-0]\n", + "\n", + "Reading from 22: [2-3][0-0] -> [44-45][0-0]\n", + "\n", + "Reading from 23: [2-3][0-0] -> [46-47][0-0]\n", + "\n", + "\n", + "mxsub = 2 mysub = 1 mz = 128\n", + "\n", + "nxpe = 24, nype = 1, npes = 24\n", + "\n", + "Reading from 0: [2-3][0-0] -> [0-1][0-0]\n", + "\n", + "Reading from 1: [2-3][0-0] -> [2-3][0-0]\n", + "\n", + "Reading from 2: [2-3][0-0] -> [4-5][0-0]\n", + "\n", + "Reading from 3: [2-3][0-0] -> [6-7][0-0]\n", + "\n", + "Reading from 4: [2-3][0-0] -> [8-9][0-0]\n", + "\n", + "Reading from 5: [2-3][0-0] -> [10-11][0-0]\n", + "\n", + "Reading from 6: [2-3][0-0] -> [12-13][0-0]\n", + "\n", + "Reading from 7: [2-3][0-0] -> [14-15][0-0]\n", + "\n", + "Reading from 8: [2-3][0-0] -> [16-17][0-0]\n", + "\n", + "Reading from 9: [2-3][0-0] -> [18-19][0-0]\n", + "\n", + "Reading from 10: [2-3][0-0] -> [20-21][0-0]\n", + "\n", + "Reading from 11: [2-3][0-0] -> [22-23][0-0]\n", + "\n", + "Reading from 12: [2-3][0-0] -> [24-25][0-0]\n", + "\n", + "Reading from 13: [2-3][0-0] -> [26-27][0-0]\n", + "\n", + "Reading from 14: [2-3][0-0] -> [28-29][0-0]\n", + "\n", + "Reading from 15: [2-3][0-0] -> [30-31][0-0]\n", + "\n", + "Reading from 16: [2-3][0-0] -> [32-33][0-0]\n", + "\n", + "Reading from 17: [2-3][0-0] -> [34-35][0-0]\n", + "\n", + "Reading from 18: [2-3][0-0] -> [36-37][0-0]\n", + "\n", + "Reading from 19: [2-3][0-0] -> [38-39][0-0]\n", + "\n", + "Reading from 20: [2-3][0-0] -> [40-41][0-0]\n", + "\n", + "Reading from 21: [2-3][0-0] -> [42-43][0-0]\n", + "\n", + "Reading from 22: [2-3][0-0] -> [44-45][0-0]\n", + "\n", + "Reading from 23: [2-3][0-0] -> [46-47][0-0]\n", + "\n", + "\n", + "mxsub = 2 mysub = 1 mz = 128\n", + "\n", + "nxpe = 24, nype = 1, npes = 24\n", + "\n", + "Reading from 0: [2-3][0-0] -> [0-1][0-0]\n", + "\n", + "Reading from 1: [2-3][0-0] -> [2-3][0-0]\n", + "\n", + "Reading from 2: [2-3][0-0] -> [4-5][0-0]\n", + "\n", + "Reading from 3: [2-3][0-0] -> [6-7][0-0]\n", + "\n", + "Reading from 4: [2-3][0-0] -> [8-9][0-0]\n", + "\n", + "Reading from 5: [2-3][0-0] -> [10-11][0-0]\n", + "\n", + "Reading from 6: [2-3][0-0] -> [12-13][0-0]\n", + "\n", + "Reading from 7: [2-3][0-0] -> [14-15][0-0]\n", + "\n", + "Reading from 8: [2-3][0-0] -> [16-17][0-0]\n", + "\n", + "Reading from 9: [2-3][0-0] -> [18-19][0-0]\n", + "\n", + "Reading from 10: [2-3][0-0] -> [20-21][0-0]\n", + "\n", + "Reading from 11: [2-3][0-0] -> [22-23][0-0]\n", + "\n", + "Reading from 12: [2-3][0-0] -> [24-25][0-0]\n", + "\n", + "Reading from 13: [2-3][0-0] -> [26-27][0-0]\n", + "\n", + "Reading from 14: [2-3][0-0] -> [28-29][0-0]\n", + "\n", + "Reading from 15: [2-3][0-0] -> [30-31][0-0]\n", + "\n", + "Reading from 16: [2-3][0-0] -> [32-33][0-0]\n", + "\n", + "Reading from 17: [2-3][0-0] -> [34-35][0-0]\n", + "\n", + "Reading from 18: [2-3][0-0] -> [36-37][0-0]\n", + "\n", + "Reading from 19: [2-3][0-0] -> [38-39][0-0]\n", + "\n", + "Reading from 20: [2-3][0-0] -> [40-41][0-0]\n", + "\n", + "Reading from 21: [2-3][0-0] -> [42-43][0-0]\n", + "\n", + "Reading from 22: [2-3][0-0] -> [44-45][0-0]\n", + "\n", + "Reading from 23: [2-3][0-0] -> [46-47][0-0]\n", + "\n", + "\n", + "mxsub = 2 mysub = 1 mz = 128\n", + "\n", + "nxpe = 24, nype = 1, npes = 24\n", + "\n", + "Reading from 0: [2-3][0-0] -> [0-1][0-0]\n", + "\n", + "Reading from 1: [2-3][0-0] -> [2-3][0-0]\n", + "\n", + "Reading from 2: [2-3][0-0] -> [4-5][0-0]\n", + "\n", + "Reading from 3: [2-3][0-0] -> [6-7][0-0]\n", + "\n", + "Reading from 4: [2-3][0-0] -> [8-9][0-0]\n", + "\n", + "Reading from 5: [2-3][0-0] -> [10-11][0-0]\n", + "\n", + "Reading from 6: [2-3][0-0] -> [12-13][0-0]\n", + "\n", + "Reading from 7: [2-3][0-0] -> [14-15][0-0]\n", + "\n", + "Reading from 8: [2-3][0-0] -> [16-17][0-0]\n", + "\n", + "Reading from 9: [2-3][0-0] -> [18-19][0-0]\n", + "\n", + "Reading from 10: [2-3][0-0] -> [20-21][0-0]\n", + "\n", + "Reading from 11: [2-3][0-0] -> [22-23][0-0]\n", + "\n", + "Reading from 12: [2-3][0-0] -> [24-25][0-0]\n", + "\n", + "Reading from 13: [2-3][0-0] -> [26-27][0-0]\n", + "\n", + "Reading from 14: [2-3][0-0] -> [28-29][0-0]\n", + "\n", + "Reading from 15: [2-3][0-0] -> [30-31][0-0]\n", + "\n", + "Reading from 16: [2-3][0-0] -> [32-33][0-0]\n", + "\n", + "Reading from 17: [2-3][0-0] -> [34-35][0-0]\n", + "\n", + "Reading from 18: [2-3][0-0] -> [36-37][0-0]\n", + "\n", + "Reading from 19: [2-3][0-0] -> [38-39][0-0]\n", + "\n", + "Reading from 20: [2-3][0-0] -> [40-41][0-0]\n", + "\n", + "Reading from 21: [2-3][0-0] -> [42-43][0-0]\n", + "\n", + "Reading from 22: [2-3][0-0] -> [44-45][0-0]\n", + "\n", + "Reading from 23: [2-3][0-0] -> [46-47][0-0]\n", + "\n", + "\n", + "mxsub = 2 mysub = 1 mz = 128\n", + "\n", + "nxpe = 24, nype = 1, npes = 24\n", + "\n", + "Reading from 0: [2-3][0-0] -> [0-1][0-0]\n", + "\n", + "Reading from 1: [2-3][0-0] -> [2-3][0-0]\n", + "\n", + "Reading from 2: [2-3][0-0] -> [4-5][0-0]\n", + "\n", + "Reading from 3: [2-3][0-0] -> [6-7][0-0]\n", + "\n", + "Reading from 4: [2-3][0-0] -> [8-9][0-0]\n", + "\n", + "Reading from 5: [2-3][0-0] -> [10-11][0-0]\n", + "\n", + "Reading from 6: [2-3][0-0] -> [12-13][0-0]\n", + "\n", + "Reading from 7: [2-3][0-0] -> [14-15][0-0]\n", + "\n", + "Reading from 8: [2-3][0-0] -> [16-17][0-0]\n", + "\n", + "Reading from 9: [2-3][0-0] -> [18-19][0-0]\n", + "\n", + "Reading from 10: [2-3][0-0] -> [20-21][0-0]\n", + "\n", + "Reading from 11: [2-3][0-0] -> [22-23][0-0]\n", + "\n", + "Reading from 12: [2-3][0-0] -> [24-25][0-0]\n", + "\n", + "Reading from 13: [2-3][0-0] -> [26-27][0-0]\n", + "\n", + "Reading from 14: [2-3][0-0] -> [28-29][0-0]\n", + "\n", + "Reading from 15: [2-3][0-0] -> [30-31][0-0]\n", + "\n", + "Reading from 16: [2-3][0-0] -> [32-33][0-0]\n", + "\n", + "Reading from 17: [2-3][0-0] -> [34-35][0-0]\n", + "\n", + "Reading from 18: [2-3][0-0] -> [36-37][0-0]\n", + "\n", + "Reading from 19: [2-3][0-0] -> [38-39][0-0]\n", + "\n", + "Reading from 20: [2-3][0-0] -> [40-41][0-0]\n", + "\n", + "Reading from 21: [2-3][0-0] -> [42-43][0-0]\n", + "\n", + "Reading from 22: [2-3][0-0] -> [44-45][0-0]\n", + "\n", + "Reading from 23: [2-3][0-0] -> [46-47][0-0]\n", + "\n", + "\n", + "mxsub = 2 mysub = 1 mz = 128\n", + "\n", + "nxpe = 24, nype = 1, npes = 24\n", + "\n", + "Reading from 0: [2-3][0-0] -> [0-1][0-0]\n", + "\n", + "Reading from 1: [2-3][0-0] -> [2-3][0-0]\n", + "\n", + "Reading from 2: [2-3][0-0] -> [4-5][0-0]\n", + "\n", + "Reading from 3: [2-3][0-0] -> [6-7][0-0]\n", + "\n", + "Reading from 4: [2-3][0-0] -> [8-9][0-0]\n", + "\n", + "Reading from 5: [2-3][0-0] -> [10-11][0-0]\n", + "\n", + "Reading from 6: [2-3][0-0] -> [12-13][0-0]\n", + "\n", + "Reading from 7: [2-3][0-0] -> [14-15][0-0]\n", + "\n", + "Reading from 8: [2-3][0-0] -> [16-17][0-0]\n", + "\n", + "Reading from 9: [2-3][0-0] -> [18-19][0-0]\n", + "\n", + "Reading from 10: [2-3][0-0] -> [20-21][0-0]\n", + "\n", + "Reading from 11: [2-3][0-0] -> [22-23][0-0]\n", + "\n", + "Reading from 12: [2-3][0-0] -> [24-25][0-0]\n", + "\n", + "Reading from 13: [2-3][0-0] -> [26-27][0-0]\n", + "\n", + "Reading from 14: [2-3][0-0] -> [28-29][0-0]\n", + "\n", + "Reading from 15: [2-3][0-0] -> [30-31][0-0]\n", + "\n", + "Reading from 16: [2-3][0-0] -> [32-33][0-0]\n", + "\n", + "Reading from 17: [2-3][0-0] -> [34-35][0-0]\n", + "\n", + "Reading from 18: [2-3][0-0] -> [36-37][0-0]\n", + "\n", + "Reading from 19: [2-3][0-0] -> [38-39][0-0]\n", + "\n", + "Reading from 20: [2-3][0-0] -> [40-41][0-0]\n", + "\n", + "Reading from 21: [2-3][0-0] -> [42-43][0-0]\n", + "\n", + "Reading from 22: [2-3][0-0] -> [44-45][0-0]\n", + "\n", + "Reading from 23: [2-3][0-0] -> [46-47][0-0]\n", + "\n", + "\n", + "mxsub = 2 mysub = 1 mz = 128\n", + "\n", + "nxpe = 24, nype = 1, npes = 24\n", + "\n" + ] + } + ], + "source": [ + "#%% Read data\n", + "\n", + "field_list = ['T', 'n', 'phi', 'vort', 'source_T','wall_shadow']\n", + "par_list = ['t_array']\n", + "# fast_list = ['t_array', 'n0', 'phi0', 'n1', 'phi1', 'n2', 'phi2', 'T0', 'T1', 'T2']\n", + "#%%\n", + "data, par, fast = {}, {}, {}\n", + "\n", + "for _field in field_list:\n", + " data[_field] = collect(_field, path = path, xguards = False)\n", + "\n", + "for _par in par_list:\n", + " par[_par] = collect(_par, path = path, xguards = False)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "thetas = np.linspace(0, 2 * np.pi, nz_inner)\n", + "rhos = np.linspace(Lx/(2*nx_inner), Lx+Lx/(2*nx_inner), nx_inner)\n", + "t = par['t_array']/oci" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "dmp_nr = 20\n", + "\n", + "fig, ax = plt.subplots(subplot_kw={'projection': 'polar'})\n", + "ax.set_rticks([])\n", + "ax.set_xticks([])\n", + "cont = ax.contourf(thetas, rhos, data['n'].squeeze()[dmp_nr, :, :], 100) # first image on screen\n", + "fig.colorbar(cont, ax=ax)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(subplot_kw={'projection': 'polar'})\n", + "ax.set_rticks([])\n", + "ax.set_xticks([])\n", + "cont = ax.contourf(thetas, rhos, data['T'].squeeze()[dmp_nr, :, :], 100) # first image on screen\n", + "fig.colorbar(cont, ax=ax)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((40, 48, 128), (40, 48, 128), (40, 48, 128))" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mesh_t, mesh_rhos, mesh_thetas = np.meshgrid(t, rhos, thetas, indexing='ij')\n", + "mesh_t.shape, mesh_rhos.shape, mesh_thetas.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "Jfun = lambda t, r, theta: np.abs(r*(R + r*np.cos(theta)))" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.contour(thetas, rhos, Jfun(mesh_t, mesh_rhos, mesh_thetas)[0, :, :], 40, cmap='jet')\n", + "plt.colorbar()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "test of Jacobian. Volume of torus is: 0.07871266530816706 m^3\n" + ] + } + ], + "source": [ + "volume = 2*np.pi*np.trapz(np.trapz(Jfun(mesh_t, mesh_rhos, mesh_thetas)[0,:,:], rhos, axis=0), thetas, axis=0)\n", + "print('test of Jacobian. Volume of torus is: ' + str(volume) + ' m^3')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def energy():\n", + " n = data['n'].squeeze()*n0\n", + " T = data['T'].squeeze()*Te0\n", + " J = Jfun(mesh_t, mesh_rhos, mesh_thetas)\n", + " return 2*np.pi*np.trapz(np.trapz(J*((3/2)*n*T), rhos, axis=1), thetas, axis=1)\n", + "\n", + "E = energy()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "rcParams.update({\n", + " \"font.size\": 16})\n", + "rcParams['axes.titlepad'] = 20" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(6,5))\n", + "plt.plot(t*(10**6), E, 'k-')\n", + "plt.xlabel(r'$t$ $\\mathrm{\\mu s}$')\n", + "plt.ylabel(r'$E$ $\\mathrm{[eV]}$')\n", + "plt.grid()\n", + "plt.savefig('plots/' + job_id + 'energy.pdf')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.6.8 ('north-simulation': venv)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "58f89f0882dc77e77c57dba94bed5153ff3c368884663369d545e0daa46a4df0" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/deprecated data analysis/parameter_scan.ipynb b/deprecated data analysis/parameter_scan.ipynb new file mode 100644 index 0000000..4234c4d --- /dev/null +++ b/deprecated data analysis/parameter_scan.ipynb @@ -0,0 +1,782 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "import re\n", + "import os\n", + "job_ids = [re.search('(\\d){7}', dir).group() for dir in os.listdir(\"./shared/NORTH/\") if re.search('data_(\\d){7}', dir)]\n", + "job_ids.sort()\n", + "# The last (highest id) job id is the lates job\n", + "job_id = job_ids[-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/niflheim/s173965/venv/lib/python3.8/site-packages/boutdata/data.py:732: AlwaysWarning: While building x, y, z coordinate arrays, an exception occured: name 'mxg' is not defined\n", + "Evaluating non-scalar options not available\n", + " alwayswarn(\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "from matplotlib import pyplot as plt\n", + "from boutdata import collect\n", + "import matplotlib.cm as cm\n", + "import matplotlib.animation as animation\n", + "from matplotlib.animation import FuncAnimation\n", + "from boutdata.data import BoutData\n", + "\n", + "path = './shared/NORTH/data_' + job_id + '/'\n", + "bdata = BoutData(path)\n", + "outputs = bdata['outputs']\n", + "options =bdata['options']\n", + "\n", + "field_keys = bdata['outputs'].keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "def get_CLI_option(job_id, key):\n", + " for i, line in enumerate(open('./shared/NORTH/slurm_out/north_full_' + job_id + '_ESEL.log', 'r')):\n", + " match = re.search('Command line options for this run.*' + key + '\\s*\\=\\s*(\\S*)', line)\n", + " if match:\n", + " try:\n", + " return eval(match.group(1))\n", + " except:\n", + " return match.group(1)\n", + "\n", + "def get_option(*keys):\n", + " cli_option = get_CLI_option(job_id, keys[-1])\n", + " if cli_option == None:\n", + " from numpy import sqrt, pi\n", + " from ast import parse, walk, Name\n", + " val = options\n", + " group = keys[0]\n", + " for key in keys:\n", + " val = val[key]\n", + " try:\n", + " return eval(str(val))\n", + " except:\n", + " # Parse undefined variables from the Bout.inp file:\n", + " val_str = str(val).replace(':', '____') # Replace group indicator ':' with '____' to make string parsable.\n", + " for node in walk(parse(val_str)):\n", + " if type(node) is Name:\n", + " missing_keys = node.id.split('____')\n", + " var = missing_keys[-1]\n", + " if var not in locals():\n", + " try:\n", + " locals()[var] = get_option(*missing_keys)\n", + " except:\n", + " missing_keys.insert(0, group)\n", + " try:\n", + " locals()[var] = get_option(*missing_keys)\n", + " except:\n", + " print('Error reading option for keys: ', keys)\n", + " return 0\n", + " return eval(str(val))\n", + " else:\n", + " return cli_option\n", + "\n", + "def get_options(keys_list):\n", + " vals = []\n", + " for keys in keys_list:\n", + " vals.append(get_option(*keys))\n", + " return vals" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "oci, rho_s, R, n0, Te0, mxg, myg, nx_all, ny_all, nz_all = get_options([\n", + " ('north', 'oci'), ('north', 'rho_s'), ('north', 'R'),\n", + " ('north', 'n0'), ('north', 'Te0'),\n", + " ('mxg',), ('myg',),\n", + " ('mesh', 'nx'),('mesh', 'ny'),('mesh', 'nz')])\n", + "Lx = R/rho_s * rho_s\n", + "nx_inner = nx_all - 2*mxg\n", + "ny_inner = ny_all - 2*myg\n", + "nz_inner = nz_all" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Strength of vorticity Sink**" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "vorticity sink strength: 10\n" + ] + } + ], + "source": [ + "try:\n", + " vort_sink_strength = get_option('north', 'vort_sink_strength')\n", + "except:\n", + " if job_id == '5347831' or job_id == '5352599':\n", + " vort_sink_strength = 0.1\n", + " else:\n", + " print('unknown vorticity sink strength')\n", + "print('vorticity sink strength: ' + str(vort_sink_strength))" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mxsub = 2 mysub = 1 mz = 128\n", + "\n", + "nxpe = 24, nype = 1, npes = 24\n", + "\n", + "Reading from 0: [2-3][0-0] -> [0-1][0-0]\n", + "\n", + "Reading from 1: [2-3][0-0] -> [2-3][0-0]\n", + "\n", + "Reading from 2: [2-3][0-0] -> [4-5][0-0]\n", + "\n", + "Reading from 3: [2-3][0-0] -> [6-7][0-0]\n", + "\n", + "Reading from 4: [2-3][0-0] -> [8-9][0-0]\n", + "\n", + "Reading from 5: [2-3][0-0] -> [10-11][0-0]\n", + "\n", + "Reading from 6: [2-3][0-0] -> [12-13][0-0]\n", + "\n", + "Reading from 7: [2-3][0-0] -> [14-15][0-0]\n", + "\n", + "Reading from 8: [2-3][0-0] -> [16-17][0-0]\n", + "\n", + "Reading from 9: [2-3][0-0] -> [18-19][0-0]\n", + "\n", + "Reading from 10: [2-3][0-0] -> [20-21][0-0]\n", + "\n", + "Reading from 11: [2-3][0-0] -> [22-23][0-0]\n", + "\n", + "Reading from 12: [2-3][0-0] -> [24-25][0-0]\n", + "\n", + "Reading from 13: [2-3][0-0] -> [26-27][0-0]\n", + "\n", + "Reading from 14: [2-3][0-0] -> [28-29][0-0]\n", + "\n", + "Reading from 15: [2-3][0-0] -> [30-31][0-0]\n", + "\n", + "Reading from 16: [2-3][0-0] -> [32-33][0-0]\n", + "\n", + "Reading from 17: [2-3][0-0] -> [34-35][0-0]\n", + "\n", + "Reading from 18: [2-3][0-0] -> [36-37][0-0]\n", + "\n", + "Reading from 19: [2-3][0-0] -> [38-39][0-0]\n", + "\n", + "Reading from 20: [2-3][0-0] -> [40-41][0-0]\n", + "\n", + "Reading from 21: [2-3][0-0] -> [42-43][0-0]\n", + "\n", + "Reading from 22: [2-3][0-0] -> [44-45][0-0]\n", + "\n", + "Reading from 23: [2-3][0-0] -> [46-47][0-0]\n", + "\n", + "\n", + "mxsub = 2 mysub = 1 mz = 128\n", + "\n", + "nxpe = 24, nype = 1, npes = 24\n", + "\n", + "Reading from 0: [2-3][0-0] -> [0-1][0-0]\n", + "\n", + "Reading from 1: [2-3][0-0] -> [2-3][0-0]\n", + "\n", + "Reading from 2: [2-3][0-0] -> [4-5][0-0]\n", + "\n", + "Reading from 3: [2-3][0-0] -> [6-7][0-0]\n", + "\n", + "Reading from 4: [2-3][0-0] -> [8-9][0-0]\n", + "\n", + "Reading from 5: [2-3][0-0] -> [10-11][0-0]\n", + "\n", + "Reading from 6: [2-3][0-0] -> [12-13][0-0]\n", + "\n", + "Reading from 7: [2-3][0-0] -> [14-15][0-0]\n", + "\n", + "Reading from 8: [2-3][0-0] -> [16-17][0-0]\n", + "\n", + "Reading from 9: [2-3][0-0] -> [18-19][0-0]\n", + "\n", + "Reading from 10: [2-3][0-0] -> [20-21][0-0]\n", + "\n", + "Reading from 11: [2-3][0-0] -> [22-23][0-0]\n", + "\n", + "Reading from 12: [2-3][0-0] -> [24-25][0-0]\n", + "\n", + "Reading from 13: [2-3][0-0] -> [26-27][0-0]\n", + "\n", + "Reading from 14: [2-3][0-0] -> [28-29][0-0]\n", + "\n", + "Reading from 15: [2-3][0-0] -> [30-31][0-0]\n", + "\n", + "Reading from 16: [2-3][0-0] -> [32-33][0-0]\n", + "\n", + "Reading from 17: [2-3][0-0] -> [34-35][0-0]\n", + "\n", + "Reading from 18: [2-3][0-0] -> [36-37][0-0]\n", + "\n", + "Reading from 19: [2-3][0-0] -> [38-39][0-0]\n", + "\n", + "Reading from 20: [2-3][0-0] -> [40-41][0-0]\n", + "\n", + "Reading from 21: [2-3][0-0] -> [42-43][0-0]\n", + "\n", + "Reading from 22: [2-3][0-0] -> [44-45][0-0]\n", + "\n", + "Reading from 23: [2-3][0-0] -> [46-47][0-0]\n", + "\n", + "\n", + "mxsub = 2 mysub = 1 mz = 128\n", + "\n", + "nxpe = 24, nype = 1, npes = 24\n", + "\n", + "Reading from 0: [2-3][0-0] -> [0-1][0-0]\n", + "\n", + "Reading from 1: [2-3][0-0] -> [2-3][0-0]\n", + "\n", + "Reading from 2: [2-3][0-0] -> [4-5][0-0]\n", + "\n", + "Reading from 3: [2-3][0-0] -> [6-7][0-0]\n", + "\n", + "Reading from 4: [2-3][0-0] -> [8-9][0-0]\n", + "\n", + "Reading from 5: [2-3][0-0] -> [10-11][0-0]\n", + "\n", + "Reading from 6: [2-3][0-0] -> [12-13][0-0]\n", + "\n", + "Reading from 7: [2-3][0-0] -> [14-15][0-0]\n", + "\n", + "Reading from 8: [2-3][0-0] -> [16-17][0-0]\n", + "\n", + "Reading from 9: [2-3][0-0] -> [18-19][0-0]\n", + "\n", + "Reading from 10: [2-3][0-0] -> [20-21][0-0]\n", + "\n", + "Reading from 11: [2-3][0-0] -> [22-23][0-0]\n", + "\n", + "Reading from 12: [2-3][0-0] -> [24-25][0-0]\n", + "\n", + "Reading from 13: [2-3][0-0] -> [26-27][0-0]\n", + "\n", + "Reading from 14: [2-3][0-0] -> [28-29][0-0]\n", + "\n", + "Reading from 15: [2-3][0-0] -> [30-31][0-0]\n", + "\n", + "Reading from 16: [2-3][0-0] -> [32-33][0-0]\n", + "\n", + "Reading from 17: [2-3][0-0] -> [34-35][0-0]\n", + "\n", + "Reading from 18: [2-3][0-0] -> [36-37][0-0]\n", + "\n", + "Reading from 19: [2-3][0-0] -> [38-39][0-0]\n", + "\n", + "Reading from 20: [2-3][0-0] -> [40-41][0-0]\n", + "\n", + "Reading from 21: [2-3][0-0] -> [42-43][0-0]\n", + "\n", + "Reading from 22: [2-3][0-0] -> [44-45][0-0]\n", + "\n", + "Reading from 23: [2-3][0-0] -> [46-47][0-0]\n", + "\n", + "\n", + "mxsub = 2 mysub = 1 mz = 128\n", + "\n", + "nxpe = 24, nype = 1, npes = 24\n", + "\n", + "Reading from 0: [2-3][0-0] -> [0-1][0-0]\n", + "\n", + "Reading from 1: [2-3][0-0] -> [2-3][0-0]\n", + "\n", + "Reading from 2: [2-3][0-0] -> [4-5][0-0]\n", + "\n", + "Reading from 3: [2-3][0-0] -> [6-7][0-0]\n", + "\n", + "Reading from 4: [2-3][0-0] -> [8-9][0-0]\n", + "\n", + "Reading from 5: [2-3][0-0] -> [10-11][0-0]\n", + "\n", + "Reading from 6: [2-3][0-0] -> [12-13][0-0]\n", + "\n", + "Reading from 7: [2-3][0-0] -> [14-15][0-0]\n", + "\n", + "Reading from 8: [2-3][0-0] -> [16-17][0-0]\n", + "\n", + "Reading from 9: [2-3][0-0] -> [18-19][0-0]\n", + "\n", + "Reading from 10: [2-3][0-0] -> [20-21][0-0]\n", + "\n", + "Reading from 11: [2-3][0-0] -> [22-23][0-0]\n", + "\n", + "Reading from 12: [2-3][0-0] -> [24-25][0-0]\n", + "\n", + "Reading from 13: [2-3][0-0] -> [26-27][0-0]\n", + "\n", + "Reading from 14: [2-3][0-0] -> [28-29][0-0]\n", + "\n", + "Reading from 15: [2-3][0-0] -> [30-31][0-0]\n", + "\n", + "Reading from 16: [2-3][0-0] -> [32-33][0-0]\n", + "\n", + "Reading from 17: [2-3][0-0] -> [34-35][0-0]\n", + "\n", + "Reading from 18: [2-3][0-0] -> [36-37][0-0]\n", + "\n", + "Reading from 19: [2-3][0-0] -> [38-39][0-0]\n", + "\n", + "Reading from 20: [2-3][0-0] -> [40-41][0-0]\n", + "\n", + "Reading from 21: [2-3][0-0] -> [42-43][0-0]\n", + "\n", + "Reading from 22: [2-3][0-0] -> [44-45][0-0]\n", + "\n", + "Reading from 23: [2-3][0-0] -> [46-47][0-0]\n", + "\n", + "\n", + "mxsub = 2 mysub = 1 mz = 128\n", + "\n", + "nxpe = 24, nype = 1, npes = 24\n", + "\n", + "Reading from 0: [2-3][0-0] -> [0-1][0-0]\n", + "\n", + "Reading from 1: [2-3][0-0] -> [2-3][0-0]\n", + "\n", + "Reading from 2: [2-3][0-0] -> [4-5][0-0]\n", + "\n", + "Reading from 3: [2-3][0-0] -> [6-7][0-0]\n", + "\n", + "Reading from 4: [2-3][0-0] -> [8-9][0-0]\n", + "\n", + "Reading from 5: [2-3][0-0] -> [10-11][0-0]\n", + "\n", + "Reading from 6: [2-3][0-0] -> [12-13][0-0]\n", + "\n", + "Reading from 7: [2-3][0-0] -> [14-15][0-0]\n", + "\n", + "Reading from 8: [2-3][0-0] -> [16-17][0-0]\n", + "\n", + "Reading from 9: [2-3][0-0] -> [18-19][0-0]\n", + "\n", + "Reading from 10: [2-3][0-0] -> [20-21][0-0]\n", + "\n", + "Reading from 11: [2-3][0-0] -> [22-23][0-0]\n", + "\n", + "Reading from 12: [2-3][0-0] -> [24-25][0-0]\n", + "\n", + "Reading from 13: [2-3][0-0] -> [26-27][0-0]\n", + "\n", + "Reading from 14: [2-3][0-0] -> [28-29][0-0]\n", + "\n", + "Reading from 15: [2-3][0-0] -> [30-31][0-0]\n", + "\n", + "Reading from 16: [2-3][0-0] -> [32-33][0-0]\n", + "\n", + "Reading from 17: [2-3][0-0] -> [34-35][0-0]\n", + "\n", + "Reading from 18: [2-3][0-0] -> [36-37][0-0]\n", + "\n", + "Reading from 19: [2-3][0-0] -> [38-39][0-0]\n", + "\n", + "Reading from 20: [2-3][0-0] -> [40-41][0-0]\n", + "\n", + "Reading from 21: [2-3][0-0] -> [42-43][0-0]\n", + "\n", + "Reading from 22: [2-3][0-0] -> [44-45][0-0]\n", + "\n", + "Reading from 23: [2-3][0-0] -> [46-47][0-0]\n", + "\n", + "\n", + "mxsub = 2 mysub = 1 mz = 128\n", + "\n", + "nxpe = 24, nype = 1, npes = 24\n", + "\n", + "Reading from 0: [2-3][0-0] -> [0-1][0-0]\n", + "\n", + "Reading from 1: [2-3][0-0] -> [2-3][0-0]\n", + "\n", + "Reading from 2: [2-3][0-0] -> [4-5][0-0]\n", + "\n", + "Reading from 3: [2-3][0-0] -> [6-7][0-0]\n", + "\n", + "Reading from 4: [2-3][0-0] -> [8-9][0-0]\n", + "\n", + "Reading from 5: [2-3][0-0] -> [10-11][0-0]\n", + "\n", + "Reading from 6: [2-3][0-0] -> [12-13][0-0]\n", + "\n", + "Reading from 7: [2-3][0-0] -> [14-15][0-0]\n", + "\n", + "Reading from 8: [2-3][0-0] -> [16-17][0-0]\n", + "\n", + "Reading from 9: [2-3][0-0] -> [18-19][0-0]\n", + "\n", + "Reading from 10: [2-3][0-0] -> [20-21][0-0]\n", + "\n", + "Reading from 11: [2-3][0-0] -> [22-23][0-0]\n", + "\n", + "Reading from 12: [2-3][0-0] -> [24-25][0-0]\n", + "\n", + "Reading from 13: [2-3][0-0] -> [26-27][0-0]\n", + "\n", + "Reading from 14: [2-3][0-0] -> [28-29][0-0]\n", + "\n", + "Reading from 15: [2-3][0-0] -> [30-31][0-0]\n", + "\n", + "Reading from 16: [2-3][0-0] -> [32-33][0-0]\n", + "\n", + "Reading from 17: [2-3][0-0] -> [34-35][0-0]\n", + "\n", + "Reading from 18: [2-3][0-0] -> [36-37][0-0]\n", + "\n", + "Reading from 19: [2-3][0-0] -> [38-39][0-0]\n", + "\n", + "Reading from 20: [2-3][0-0] -> [40-41][0-0]\n", + "\n", + "Reading from 21: [2-3][0-0] -> [42-43][0-0]\n", + "\n", + "Reading from 22: [2-3][0-0] -> [44-45][0-0]\n", + "\n", + "Reading from 23: [2-3][0-0] -> [46-47][0-0]\n", + "\n", + "\n", + "mxsub = 2 mysub = 1 mz = 128\n", + "\n", + "nxpe = 24, nype = 1, npes = 24\n", + "\n" + ] + } + ], + "source": [ + "#%% Read data\n", + "\n", + "field_list = ['T', 'n', 'phi', 'vort', 'source_T','wall_shadow']\n", + "par_list = ['t_array']\n", + "# fast_list = ['t_array', 'n0', 'phi0', 'n1', 'phi1', 'n2', 'phi2', 'T0', 'T1', 'T2']\n", + "#%%\n", + "data, par, fast = {}, {}, {}\n", + "\n", + "for _field in field_list:\n", + " data[_field] = collect(_field, path = path, xguards = False)\n", + "\n", + "for _par in par_list:\n", + " par[_par] = collect(_par, path = path, xguards = False)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "thetas = np.linspace(0, 2 * np.pi, nz_inner)\n", + "rhos = np.linspace(Lx/(2*nx_inner), Lx+Lx/(2*nx_inner), nx_inner)\n", + "t = par['t_array']/oci" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Mean Density" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "n_mean = np.mean(np.mean(data['n'].squeeze(),axis=1), axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(3,2))\n", + "plt.plot(t*(10**6),n_mean, 'k-')\n", + "plt.xlabel(r'$t$ $\\mathrm{\\mu s}$')\n", + "plt.ylabel(r'$\\left$ [norm. unit]')\n", + "plt.grid()\n", + "plt.title('With vorticity sink strength: ' + str(vort_sink_strength), pad=20)\n", + "plt.savefig('plots/' + job_id + 'density_vs_time.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Contained Energy" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "mesh_t, mesh_rhos, mesh_thetas = np.meshgrid(t, rhos, thetas, indexing='ij')\n", + "\n", + "Jfun = lambda t, r, theta: np.abs(r*(R + r*np.cos(theta)))\n", + "def energy():\n", + " n = data['n'].squeeze()*n0\n", + " T = data['T'].squeeze()*Te0\n", + " J = Jfun(mesh_t, mesh_rhos, mesh_thetas)\n", + " return 2*np.pi*np.trapz(np.trapz(J*((3/2)*n*T), rhos, axis=1), thetas, axis=1)\n", + "\n", + "E = energy()" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(3,2))\n", + "plt.plot(t*(10**6), E, 'k-')\n", + "plt.xlabel(r'$t$ $\\mathrm{\\mu s}$')\n", + "plt.ylabel(r'$E$ $\\mathrm{[eV]}$')\n", + "plt.grid()\n", + "plt.title('With vorticity sink strength: ' + str(vort_sink_strength), pad=20)\n", + "plt.savefig('plots/' + job_id + '_energy.png')" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "# Method to change the contour data points\n", + "def plot_field(field, i, cmap='inferno'):\n", + " vmin = data[field].min()\n", + " vmax = data[field].max()\n", + " # ax.clear()\n", + " contour_set = ax.contourf(thetas, rhos, data[field][i, :, :, :].squeeze(), 100, cmap = cmap, vmin=vmin, vmax=vmax)\n", + " ax.set_rticks([])\n", + " ax.set_xticks([])\n", + " fig.tight_layout()\n", + " return contour_set\n", + "\n", + "def animate_n(i):\n", + " plot_field('n',i)\n", + "def animate_T(i):\n", + " plot_field('T',i)\n", + "def animate_vort(i):\n", + " plot_field('vort',i, cmap='jet')" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.animation as animation\n", + "\n", + "def animate_data(field, cmap='inferno', vmin = None, vmax = None):\n", + " field_data = data[field].squeeze()\n", + " if vmin is None:\n", + " vmin = field_data.min()\n", + " if vmax is None:\n", + " vmax = field_data.max()\n", + " fig, ax = plt.subplots(subplot_kw={'projection': 'polar'})\n", + " fig.suptitle('With vorticity sink strength: ' + str(vort_sink_strength))\n", + " \n", + " ax.set_rticks([])\n", + " ax.set_xticks([])\n", + " cont = ax.contourf(thetas, rhos, field_data[0, :, :], 100, cmap = cmap, vmin=vmin, vmax=vmax) # first image on screen\n", + " fig.colorbar(cont, ax=ax)\n", + " fig.tight_layout()\n", + "\n", + " # animation function\n", + " def animate(i):\n", + " for c in ax.collections:\n", + " c.remove() # removes only the contours, leaves the rest intact\n", + " cont = ax.contourf(thetas, rhos, field_data[i, :, :], 100, cmap = cmap, vmin=vmin, vmax=vmax)\n", + " return cont.collections\n", + " \n", + " anim = animation.FuncAnimation(fig, animate, frames=field_data.shape[0], interval=100, blit=True)\n", + " anim.save('./plots/' + job_id + '_animation_' + field +'.gif', fps=4)\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "MovieWriter ffmpeg unavailable; using Pillow instead.\n" + ] + }, + { + "data": { + "image/png": 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sg6jXmek1Ozs7lxKRuAfeklHqM5OIbtNur3/v6Ei76/GxVsDBW/+PN47HGm3bUn+VlcUc/FLl4dB48ubm5pVE1MjbJsHiKO1ZSUSntdvr3+73tVvpet7WeBEsrkmIJrKl/UorCxHh+Fij4dC418bG5tVEtJq3TYLFUMqzkYhOb7Xa3+r3tZ1ivoX44kokTT3ito+6f0F+DIcGhkPjZzY2Nr9ORM287RFkT+nORCI6s9Vqf6Pf1zaLaX48cfQT1CChFSJbXkYjA4OBftb6+sa3REWu6lOqs5CIzmq12l/t97X1shXGnhnMCvFUw7aLLrIibFA0VJUxGOh3skR2I297BNlRmjOPiO7carW/0u9r7eKKazSP01tYJY/HdBt3u3ixXCG0RULTgONj/Q7r6xtfJ6JW3vYIsqEUZxwR3b7Val9TbM81ibiGeZjeQhvUfzjBQi5YHLoODIfG6RsbG18VU2urSeHPLiI6td1e//rRkb5ZNnF1MyuuXtvImH2faYusGy/RDXoI0kJVGcOh8VMbG5tXEdFK3vYI0qXQZwsR3ardXv/G0ZG2XVxTow1q+YmrLajjh5XzKlHNJbRZi2wchBinyWjEGI2Mu25ubn2JiOp52yNIj8KeAUTUWF/fuOroSDuFuahmJhdXt6B6TSYgSC6PNli0ijnVVghvFMwULv2eW1vbHy7gjBlBQgp5pBMRbW1tf1RV+Y6VFVeHoLpzYJ0DWmPx9fBmk2cXFAEhum4GA500zTh/Y2PzorxtEaRDIY/qzc2tl2ma8fDBoJBTtJDMi5wWV/N5yKQCt9BWTmTdLLfYWtNqJcPgP6rVaj+Xtz2C+SnckdxoNB6t68ZFvV4R7p01HxPx8xbX8XaYLfDi9nDH23kM9FVLZG2W07u1RLa2srL6d0R0Rt72COajUEcuEZ1Vrzc+UOxbvMQNDfiL6+Ty3zs84OzTW2T9QhGTtuUXWpvlEVvzFjTaWru9foWY7VVuCnO0EtFmu93+Ur+vrVZHXO3XPuLqDAE4Mgns7caPOUTWbVs1qL7YEskYDIxTNje3/oGKVeBYEINCfHFEJG9sbP7TYMA7Zcx1DR3U8hFXc5lDVB0DWjNim4LIVsujtamu0Oo6oKp8j+3tnbfkbYsgGYU4Mre3ty/RNJxTzLKDwLyDWuZ/b3EdL3OK5FT8NT2RdfdfLbGtpld7dKSSqupPb7fbz8jbFkF8cj8aNzY2LlRV46lHR2pR4wKRCYq7Ah6ea0AM1hZUp4h6iew00UV2yqbKCW51hNYu2A1IbySie+dtjyAeuR6FRHQuM15b7Fu9JIu7OtePxdItrlNpWPLsw8NTdYqse53b3qgVu9w2hz3KQzWElojQ72uNVqv9eSI6JW97BNHJ7egjopV2e/0TvZ5WL7u4erXxFUSHuJr9mGI63nbKg5WnvVmHkE734S+ydr9pEkWEiyXE5RdaIgmDgdHe2tq+LG9bBNHJ7ajb3t55k6riRBUGSP1CA1PpViR5i6tDUO2/qTaYFmpz8GvWc/bLkZ3e/2I/6+KJb7mPNcMgaJrxs81m85fytkUQjVyOOCK6r6pqvzgcFnZUC2mEBuznQeLq/JvsefLnFNlJf7JvPDasbkEeQutFfqJbbm+211MlSZLfLEIF5WDhR5oVGvh4r6fWqh4a8O9jIq52G7+HU2SjiLnThqD3UhShdbJYwS2n0BIRBgOjubW1/dG8bRGEs/AjbHt75y2ahp2indwTotsVNKEA8PZeneLqV1HLnablFll3qMAdj43znvxmkBWBxQhusd5zFAyDoOt8n2az+bS8bREEs9Cji4jup6raBcUt4hJMmPc63o68BcEtrnafEs0+7HW2mIaJrL3fcbuQot2+7zFg2m7eZCe25fNmu92RJEnym4joVnnbIvBnYUeVFRr4WFlDA27CvFd7G68Br/F6OMV0OkXLKbRRbAmehGCTTEh883RzFOHshLYcEBGOj3URKig4Czuiih8aCCau9zqTQeAODVjiOpWmNXVJPBHaMC/Wz8aJl5uO0AaRlwin79WWx5tllqDrfO7aWvOX87ZF4M1CjqRyhAaSeK/TaVmz23l5tdPiCsx6Y5KzOEyAyE716REqsPu2t/F+z4sTk6yFN32hLT7d7kiSZfmvRKigmGR+FBHRatlDA1FP2iixV6++JZKnHoC/yAbtzzd1K1Bkgbzn8actuukJbfG92UmoYOtjedsimCXzo2dzc+uPVLU6oYEo3mtQ7HU6NCCNBdUri0Dy8YCjhgr8RDa4YpmEvAUXSCe7IV2hLS5mqAD3kiRJ3AWhYGR65BDRlqZpzy7rhII4BOa9ujIHgNn8VQmzoucUWXeoIMyOIJE1l0ctC+kW3HzEdx6xTUdoiy2y3e5QbrfX3ylumFgsMj1qtrd3/mI4NApcQDsYf+/Vexu/vFfnemfs1RZPW1wlyDMPt8jaBHmxYSIb3ZsNI0h8sxPhpGI7v9AWV2SJJDBLt15ZWfnFvG0RTMjsiCGi01RVfbKmZbWHNEj69qdjmmEnutt7BTBz+R81qT7Mi/VfPhuqcLaZX2z9yFaAFy+0xRXZXm8o1Wq11xFRLW9bBCaZHS3b2ztvPTrSGlX2XoO292rnjL2OlzleO2sSjOOwMb1Y9369ljlt9sxyyExsvUhHdJN4tVUTWSIJhiFvttvrz8nbFoFJJkcKEZ2tadpDDaPI4jq/9xo2uOUOD0xt6xBPs1en6E4LreQWZZ9sBXt7v1CBc9lMmxCxXeytfOYT3DhCm9ybLabIHh2pxMx/RERredsiyOgo2drafk+3Oypwndd4hA1ghbd3x2Fd4YKxmE5mcjmXj7dzebFBxWDCRNbP7iDB8StIkz3JBDeu0Cazq1gQETQNrc3NrZflbYsggyOEiO6r63z34t68EEgr7zVse3fu6/TlvT3AJU2ts71XGbXx6yRebJTlgT8cEWPCZj/+1cCyEeF4YhtVaJN5s8UT2eHQgKZpv01E23nbsuykenQQEW1ubr632x0oafZbHILDA1Ov4R7Umh7Qcm/rrgkLYCyy0/24BsdieLGe+44pPkmnpmYnvtkIbXwbigMRYTg0Vre3d/4yb1uWnVSPDFmWzzcMumOVvNdYgyYBkwvGe6fpQSr3/5m7Glji686VtfNive2YTsVyvw/P2WZzjMb7PSL3k4roRvdql0FkNQ0YjdQnEdFpeduyzKR2VBCR1Gy23tHtDousrqmSJP7qfu4bb3WJr7Od5OrTt0ZBiMgGCW0aM+/mEeD5BDdcaKO8xzKLLBHh6EhtbG/vvD1vW5aZNAX2UYB0SlmnxIbjHR4IzFkNiL86twkb5HJ6sVH2a9rp791OvQ7oJ+tShHGEN5ngRhPaMBvjUZzjn1mCqmr/VXix+ZHa0bCxsXlxtzssztHlSTbhAc/Up4BR+slz2fO58/Xs/bqCi8CEebFetoV5lIsoO+hlTxTBjUaw0Ia9r7KKrFkIRm1sbW2LjIKcSOVIIKJzmHHH6nqv4Uzfptt7gCso/jq1vfXnFuApoXWJix0mmF5mC3G4yNrbRRGTrMsO+tk1/8SI5N5s/IG9YpwLuk5QVfUCIlrN25ZlJJWjYGfnxJ93u4OCT89L94APq0sQZZk7BOCVSeDOMHCHCdxibtvmvN23s51bZOcRWs/9LkB8w8Q2mHBvNmzfZYKIYBjS6tra2rPytmUZmftoIaLt4XD4MOZyHXhugsMD3vHXJP17Vc3yw0ts7T68cmK9vFhPW2a8X/9wRlqCkpX4zie0/raG7TMaxTgnjo9VkmXlpaLS1uKZ+wjY2Nh8garyyjJ+d1FGwv3bylP/AUBiafwYL7O8VC8P1x0mmN63vxfrZVvUSQdZkIboetkYvfatv01B+4tG/iJLJEGSlC0AD8nblmVjrm+fiEjX9QtHI07LnozI9yD3uowHQuKwLqGd7i84TOCVvRBVZKN4b1kLrtOWNKplRRNafzvmJ3+RPTw8lnd2TrwybzuWjXm/+YfIcq29jN6rjd8J6Hd7GHvdTN4rTwTQLRDOAS9nmGCqvSNMEGpzgMja7ylusZSshTeJ4MYT2mBv1q//8iBhNBrdjYhO5G3JMjHXEbKzc+LPDg+PSz+xIPhECU/BinO5HdcOd7jA+d896WCqHSSH1xo8m8tPeOaJkWYtvHHE1kto/clKZHO+ijKnz66sr2/8Tq6GLBmJv3UiOjEaje6e94ETTrLR8NT2HpDYP46tss+sKi/BdYirc7upO9QG5bwG1iXwL9ad1meShfAmqTEQ7s167yes32DyPVdUlcHMzxGDXYsj8Tfebq9fOBoZSzO4FZZBQJidquokaEBqajvXYBZZAuzOizW39S8oY/c1sc9HPCOKrLkuu5zXNEQ3in3RvdnqhQuICJKkrAN4QN62LAtzeLB4TvEHt4qJe9KATZQUq9kJCFHSsvxDBV59RElzWsQkg3kEN04WwGJFNl8htga7/iRXI5aIRN82EZ0hSUoJBrfiXTbmgR0eCBJct41e6VozIYMIAjn12jPWHH066qIFN3KbiN6s/3vN4r3kecxJGAwG96Fil7yrDIm+6Var/dR+f1hP25gyEclznBHGeGEC+zmAcZggqH+vEoZjkXZ5sc5lfv1Ntot/LmY9oyuO2M7vzYYPdHr1VUSICIpSbwC4f962LAOJjgZFUX5d06oZHsgqvujEL/91trCL18BXcBx2alu/QauEIptGgewsRDcNoV28yOYnxIeHR/LOzonfys2AJSL2t0xEW4Zh3Go5Cruk/x6jxFnD2kSJw7rzaIEAQYgospPt070NTFqiG9WrXYTIFhsZo9HoMXlbsQzEPjokSXqUpqGRhTHpUuwD33377SnP1TGLy51N4IfzhohTt6eJ4MWa282KbFShzSKcl9bU2aD+/dpNtkkuskX2YokIsqysEdFZuRiwRMT+hre2tp93fDwq+uhWaZgJH7Dk+Xy8zBFeCPOGw7zYMJH1ss93XxnfZXaeabNhfQa1mWfgq8jx2F5vUG+327+Ytx1VJ9YRQES14XB4TtG9w6gU7QQI8lD9SJovOmkfTWTjjdxne1fZeafMevXn1WayPsL048ShgnyOQV0HZFl5Ri47XyLifrsPkqTa0kwuSJOwabB+2NkDzlvGeA10zcwQ8wgTRI3/Bl0+J/1Rykpw4wptUD9B28/anGaoYPEQSTDHUmgnb1uqTKxvf2fnxIXd7lExj5gSY0+XNZ9Pp9fPbOuIzc7eOcHjduK+WQH+Xqx7/cy6OYR20n+6ghvVqw2yPQ2RTUY+p5Sqcl1RlMfmsvMlIfI3S0Q0HA5/HjEKRudHdgdsGqPGfqlYXoIap6+ZdSFe7Dwia7ZJr4BLmoIbty5BWNs4xYDK5MUOBiptbGz+bt52VJk43/w5ilJfXcbwQFqXtF6DTmFIoJlJBl5VteLgt+8kImvvP20RmVdso3qzfm39tk0ajw3/fPIQYQnD4fBsIlrqSUNZEvlbXV9ff0q3e1Tw+24VH7+iK9PbeAtWoLfqyosNu7PseN1MelbysoVpVcea3X9ysY1b/MXZzmtb255piumhhmGma9UbAM7L25aqEvnIqNcbjzOM5fNekxKn8LXpodLUMvdz9zJPIcRsfVj31Nnxcp9QgV/fXtsFUTSxTeLNziOyZfFiDw/7Uru9/vCF73hJiPyNDgaDOyNBjLBMpDkrh1mPv3+fr8MOE4xfBwx0hfUduXhLgMgmyUXNQnDjCm2cmgTONtVGQqPReHTeVlSVSEcPEW1KktxYxvirG2Yj1f6STBaI2keY+AZ5sWHt5xGetMU2idD6rosgsvN4scWDMBgMzsjbiqoS9Qi4F5Fckvjr/DmRaRLVw3QSmKblGuDyGuhyp2vN3FUh4M4FM8tCRHZeEUlTbOOWV5xrXwntLVqYwCzCLa8Q0cZCd7wkRPo22+31hx4e9svwc1x4/C7rx/FYD1GNejK7JxxE6WemBGJMkbXbpFkVK60c2/Dt/Dz/6KGCKnixRJIC4F5521FFIn37Zoym+AdKViSJp4YxfTNDmvo/tZ1jnfPOs+b/KANpft7qdLGZpP1M95nNvbsS95GxyMaNawf1nSeHh0dyu91+SN52VJFI3/RwODyj6gNceeB5FwNHJa2oRLnflh0miFo3Nc5+vNpnIbaJ2ka8/Y3ffpMxrxe7aAEmNBoronxhBoR+k0S0QSSJ+gMZ4SemfkLrvMPB1GvXVxnlJotBXuy8IuvspyhCG7w+msim5cUWCwmDweDMvK2oIlGO1ntZMRpBhgRNk5VAgcIyVTPWIw5ri2IcLxYIFtkyCm1SkU22Xfy827ywBroaRLSety1VI/RbbrVaD+52j8r881wa3AVfnMvs5eZ/b6/Vi7ATOSwWG5zSNN+9uuYlidAmEdnQzzCCF1v0MIGVJfRfFrrTJSD0W1xZWX0sswgPuGGklw8bNMFgZhl7e6fp7TN+/DexDSkKbbz9JvhhKGF2QBy63b7carUfnLcdVSP0KDEHuKp1MOXFVGJ/DFH1wi8MMNWXQ0jcYYKptilcOicVWbv/tPJpo+8z3UkU3vsuT5iAmbCyIga60ibwGyZzZEvM4JqTmfoAjkv8MEGdVNMixzL/gS6/OKwXcbzYqCJbBKGNvr94IhvFi01buBeHBFVVT8vbiqoR9o2vS5Jc5KMiVeaZBhsWMgi6vTaAGRGlmBMOgi7t3XmzUbzYpCJr7ydPoU1LZNPZ7zynz+JOPSKCYRjNhe1wSQj7Bm8rSVKJBDZfU6MIdLSBKZr6P90+3kBXWLpW/PhlDPFKQWgTt40RMog3dXg+Lzasvzxh5jqJy9VUCft2TzWMUtzCIFfmnekV5rkmudOB2Zd/zDepF2tuF8/DzHMgbP5pt+kKYJHDBLIsSwDaedtRJUIFdjAYFveIWAJswXWGEPymzBJkh2frN7vLvw4CEF1kzW3jZxwkJWuRTcvrTD9MsDgkSZEAnJq3HVUi8Jtvt9tnjUbqomwRzEFQhkKUWV1JKZM3G95/skLj84YJioJhsAwhsKkSeOSsrKz+DEQNglgwooUL7DvJOqto0VSJE+9yhZPtZ786z4EpD+H1GuxK6sVO2hTbmy1SrDMei7N7OBxJAG67sB0uAYHfHjOfXpbLm2wIH7RKowB3WHEX8hHbKG3TIqrILtKbjd0mxVBB/DCBcx/uHOZinGPD4QjNZlPUJEiRwG9W07RbCw92gp2KlfZdDYIIKmE4ee3tuTrjsHaYIKkX67cfLxblzS7Sk00SJpimGCIaDGF1de2ueVtRJQK/dV3XW4syZJnwraA1NZkgfJqst7DKM8vT9JAkSJl5s0nIIiab2m3aSxeHlQDgznlbUSVCQgSoVTUtLm4tgSyKbvsRVlnLiygClYYXO7EjfW92USKblhdblEv79CBomnabvK2oEr5HCBG1JEkq209waQmeVODvzYbdFDFsBtl4u8i3RHH2Hd2bjco8g19xiFNlbJnQdV3kwaZI0FG2IUlSNd3XEuIlwF74pWvNFoeZ9mID9x0iNkUQ2SIk8JPPlUDYtkXBulpdzl+WjAj6lmtigCtbvFKxwl4D812aBuXEBhXkrqLIFukSv0C2iJM+RYK+VXEXgxDSrAkbBb/sAa8asXGKcUe/11bw/a2yyJfNE6/3GjcOO91H0vdens9MME2gwIrCD9VAwvTglpcX6xbZwDzPFAa/olDWUEHJEed8iggP1kUaOa5x+3B7plHjrdN9JBwZn5lWOx1H9BPaeUS2SCJYoEvzoiAENkXCBFZ82BUlyIt1ZxT4Ce0iRtqFF7tYRqORuHJNkaAjUWZemB0psdiYaJb4ebFxp8bGvbOBPX1VIslTaGfa+ohsml5s1iIbNxwSFIeNk0lQRIbDQS1vG6pE0BGgid+x+chyECxpjViBIIh2e/2YuXyuVVEJElgVgPig5yRqda1lpIgTEASCNAn0YIW+AosIOxgF+5yr8qMgYrGJKNbBWHJCBFZ82G6SXPYbPnUMiiKsi87nFRSaYhyUFSFQYKseiclLWAya7DepyEZt5/ce/US/Kt5rXMo4IJURFT/rF0vQUTUCqi6x6RFWbcuIIVxckGPccOXzCk93KSjGwRcCEZ1PRN8johuI6Pc91jeI6H3W+iuJ6HTHuous5d8jokdG7TMJQQJ7Ute1UnzYi8IpolkX3fYTWaf3G7/P2bbOZU7v1S2uvn0usIyjIFus5IFR3naEQWbu3BsAPArAOQCeSkTnuDZ7JoA9Zj4TwGsBvNJqew6ACwDcFcD5AN5IRHLEPmPjK7DMrMGMwwoi4hQrI8Tb81ufPGTgvz+39+wVHggLDQjvdRlg1Gq1Tt5WWJwgoqsdj99wrPtZADcw878x8wjApQCe4Gr/BAB/Yz3/AICHWxMongDgUmYeMvP3Adxg9Relz9iETYcdMTPExI50MKw/r4Qj02Od/ZyTCG7UOKp/fDZaaEB4r1WDIcvyD/O2wuIkM5/rs+52AP7D8fqHAO7rtw0za0R0AGDHWv5lV9vbWc/D+oxNYGS/VqvtlSQkkyvOcEGc0IH7cj9MTN3rg73W6INbWQxshXnwwGLvbRaG8NABgKHr+g15W1ElAgVWluUfLaPAZn3iG1NxTyNUaN2v3WLgF5dl136C+hj3lYL3GkVc41Lk7IbsxXkx4k8EHBzsf2MhO5uPmwDc3vH6NGuZ5zZEpADYALAb0DZKn7EJFFjDMK4vn8AWyxMx2EiWOwueEtaomQVxxS1oYCvL0ECRvNc0mbqaKdixGMbq6grrul6UEEEQXwHwU0R0JyKqwxy0usy1zWUAftV6/iQAn7OmAF8G4AIry+BOAH4KwFUR+4xNYAy22z38liiolQ5hHqQBhpzBZ21MCWgawhiWjlYcUclKxKv641CvK3q/j5vztiMMK6b6OwAuh3mLm7cz83eI6OUArmbmywC8DcC7iOgGAB2Ygglru/cDuBbmIP5vs3VQe/U5r60UVNeBiP57s7nxweNjvWQqO38lpelpltN3Wh3fCcBxixXzNtXy+LlENfOWgFSDbD0UNCCjhhoaUFhBDXUobLZRWIYEAnncRsYAgy2PViMdDAMa6TBgQIcGgwzruQoDBhi69d9caou5LbBeqVlRvNe0xDWJQCUJEcTZT5C36X7f7n6nPk8PD3bS3tUucp7xYgR9Y6Mx2tvbPZOZ/yN86+gQySxJa7HaGEbvmoBBrtIQpkQ312q1av5c58D4hLNFzZJBP9xhguC+EwhQinHNLD3XPOOv84ZD/NpHF9fFoWmaAeAnedtRJUIFlqjauTjRDuz0Dn4/sbAHqmxP1Sv+OjsY5jVxYOK9eu8/wuh+TO81jrgu6vI6Le91uWDNygEVpESYwP7Y+lUTpIxTLKOc4E7BjZquFRR/jTNrK61810WFBhZJVcTZChWqedtRNQIFlpmHQLU92KyYjsXpngNMhitFyxZO9vBeF0VcwSha3DVrgt5HNhkEixJwhqIoewva2dIQOhokSVJXFDifJs7JMz19dhJ7dTOb68qe4joesLL/Twl0cK0BL+bxXrMU13nIanBrPoru6RqQZeVbeVtRNUIFtlarfaX4B4eb4tpruARyah15D3oZYN917n6DcA+y+a0fv85RXIvovcalTANc9bqCTufkh/O2o2qECmyns/vhel3crsMmTa+GYVcn8J+55Sm44wExL094doDLt+B3QvErsrgu0lsugjCmxdpaQzUM4+q87agaoQJrGMbVa2sNEfz2YMYTYd0VEoh/qWqLZ5IUramBq5giFdV7rZK4xg0PLD7+ujhUVdUAfDdvO6pGlIz861R1tHRlC9P2hNjhWdqTAWycouqF02O1t4kTEnA+n0fAiiyu8feVrQhOC3TSfS0qpY0hy9I+i/JoqRMqsMysybJ8KAa6ggmqqGW4vJvxRANrBpYbg4ypBxAgvglPQrdNTuY5z/IS1zR/EKN4r0kFuojxV8BAvV4X4YEMiDSn1Pzwi3AgZEOWB3lUsTKsKQZRwgJxRDWN+gNR91kWcU37+y57eKBel7G7e/JDedtRRSIJbKfT+UitVraBrnwP9DDRMMaX7N5e7PS2jkEvZ1pWSIpWFJuieK9hfTMbifNci+S5mv3Fi71G7aPIiAGu7IgksLquXdVsioGuNJiaXQU/sZwMcE0mH0x7t16Dac4CL05mswrSvJzOL96aTNTnf+9+xV28ibOtd7usscZYxABXBkQtO3WtpqlLN9A1TcAIckTv0b5cD7of17RwhocLvDIIoto001dM7zUvrzX5vuNPqsiypkERwgnmAJd8aN2DT5AykQSWmTVJksRAV0zsk9O/8IruOdAVXjs22okZFn8NneWVgbimQV6ea5R+0skeWCRigCtLIhdOrdfrXy3HAeOkuPa6xcuuS+AMCUyndvGMGMfLs/Wu+zpeHyNuGH9QKT2vNbsi2vG91ziDW/GyBxZ33NZqMjqdzkcWtsMlI7LAdjq7715drQsXdk4mwqlPLYuL3324vPYXZ/m4f792CcQ1DeYR1iShgSz7KRJra/WRrmtfzNuOqhJZYHVd/4SiYJilMXniXQN1fk/CfYND9zqv2gTuWVyhNz2cykhIUvl/uk0a4loUrzWpKIZ5nOHea3GvnmyYGbquHjHz9XnbUlUiCywz70mSdEtV70cUB/eU2ChEGbn3Kl84ziYgY0qQnW2c/wGvGG5weCAKccU1DeYV1rQmA0Rr4/+eixoeAHTU642PL3CHS0f0m1cB0DTtrYpSsttzFRS/FK0or238MgiA4AGu6TSjcO81quCUxWud7CdZaKAK3isArK839d3dk2/O244qE0tge73ue5vNRsluKZHtwe5X8GV66uz0nQWcdWEnIjntoc5OlY2fSZBV8ezZ/RRDWE1b5hPXOANbYX15bV+E1CzADA9o2nAI4Iq8bakysQSWmf9V17W+SNdKhu/9uKKmXVmXvVEHuKbaxhSGyTYhglMQYTVtSV9cAytupe69LlJ8Dayurn5N5L9mSyyBBYBarfZRVKAYchbMeivBl+n2DQrdOOOqXnFXr23N57Ox4agTEJKEBuYtLZimsC5CXOf1XovE2lqDd3dPXpy3HVUntsB2Ortv3thoVlJhw0/SdOKCnoWyyRiHCcw9hYcE3FNkx8sjeaYhkxCCap/OERJIM5c17kBWmuKaxHstzuAWIEnGkJkvX+hOl5DYAgvgSlUdDsoVJsh+9Nw33zTCiekWK980KRjjbIKgAS73fuzwQFTvNUxc45KmtzqxI56wpn0nCrvfCdE/w7xhNqAoyk3M3M3blqoTW2CZWV9dXf1qWUZKi4w7D9b2Yp3rwkIEQfiKr3PQLUNxzWLmVVpeq7nO2755QwOe+yrQ+VKrSRgOh2/K245lIIkHi93dk69tNhtlcmFzZSyijhN13lu6OPtNCz8xjBMSyMpbTZLXmiQ+mkZoIH7mwGLFd3W1Njo66r9/oTtdUhIJLDN/msgYpG1MtqQtRvEnG4wtcZz4XgNdbjFx3q/LDg+4JxbYA1zu+Ks7PBDkvXoRV1jTJOlkgbCQwLziWtaBLcBMz2I2Dpn5xrxtWQaSCmyvVqtdX8a512EkO6GjXGbOxkynRVQfhwmc64PThLyzENy1DryIEhqIIq5F8VYn9iQbvEsurvMMbIW3TxtFAYjoHQvd6RKTSGABoNPZvajdXi32z3VKTJ9sEbw+W0wD45nTsVYnXjO5kuS+uvcVbxQ9XKjSzgaYJ04ZZSAra3EtuvcKAKuryvDgYP+1eduxLCQWWACfMgy1tyzZBFEJyoUdn6gzIQF95tLfPZMLwNQ2YTVkAf/aA1PhhZjimtZsqzRE1bQnmrAmFVd/olwBFMt7ZdahKMr1zHzzQne8xCQWWGbWazXlbxQlTXOqid9J6zfQFZTzOu5zLMqTClqGPU3XS9Q97qbg9WPgn3mQXFjTFNSJPdFSr6JcRQS2jxB3Tdb34r3ddnvV6HR2f3/hO15i5vFgsbe396rV1VrlShjGSgPyGIwKwvYqjXFMdhIqMF9PhNCdphUnTSpu5aw0hTULQTVt0WMJa5DX6uW5Vjk0wMwwDLUPQEwuWCBzCSwz/1CWpX8r12BXuid89L1OD2iZ/yfi6gwPeN/9dVp4k9jp570GiWvkfWQgqKYN0UV10iae1xp0eZ9EXIvovcoyo1arvYvLdbKWnrkEFgD29/de0G6vFP8nfE7CBrqiiFGUY9tLZJ3iar92hgfs9Cy/8ECQrZ5iHsFrzcpLNfcfX1TNdsF2F0NcFw8zY3W1Ntzb67wib1uWjbkjqMx8ObN+wMxbRNWpFcswQAl/f8YnHgFg87/dl3kCStZ/GQZ0a40EAwYkxz4967zGCEOYtrjEOYK4+r6vjMRjXqcqyo9B1HZJ07Gik4cAG6jXa19j5h/lsPOlZm4PlpkNZn7lyopconSCYngZzkkBzvoC7hsbJonDsqsPIFhc48Ysk+D0TJN6qZO+wmeMBdk9r7iWJTQAAOvrq9ru7snn5rLzJWdugQWAXq/7BlnmkhWAiY/fnPQoA13MjroDARXx3aUK/db5hQe8vNcZWzzE1Xu7+RL+0xDS2X6jDboFfQ9eg1nZi2s+mLYatzDzlXnbsoykIrDM3Gs0Gn8nSWUS2OQnqV/7OHFYp8iNhdIx2OUlrml5r2E2xxHWNL1SP/vi1DdI6rWa67MU13wEuNms8/HxkUjNyolUBBYAdndPvrjZrI2q7sXGwa9egd+Eg8l6fUpodahT4YOwwS1735Pn3qGBIE8u8D1lIKTT+4hfMCZMWMNCAlUUV/Nc1Huj0ejSXAwQpCewzPx9RVE+Vy4vNh28hGZK1HhWAJ35sFOCiWkxnPZ0gwXQYGtU3yMty+5vbI8rTOEvTtkJqts7TSqqSYQ1eAJBsquTotFs1hjA/2ZmNW9blpXUBBYAOp3dZ62tKSWKxcYPE/ila8WNw5rbeeW7Tm/jVRM2zHu12zntdYpr0Pub2JqOoPqJ6DyCFc3T9o8rT7aZT1yL7b0aAPT9Xq/7ulwMEABIWWCZ+aZarfa+Wq066VpxiZsP6xRKw+WtetUb8J1e6/JenbZ4iav3bKZkSf1ZiOjUPhyeahRhjeK1VllcAaDdbhij0fBCcVPDfElVYAGg09l9Xr1OR8vjxQa0Y2Mch3WHCUxhne3H9k69Hra4BmUOmH0YoeI6bWf0gilZCOjMvmIIqts2v/4m2/oJa3XE1coc+MloNHpfbkYIAGQgsMy8pyjK6xoNOe2uC4rDK0oYJnAKptmj1yX/9PTamX4t79VzSq6PuAYJ6yKE1LYn6aywOHmwcbxWu2+/PsPJN17bbjf0bvfwaVweL6eypC6wALC/v/cyReFueb7fdE+IqGGCoAwDA/rUY2qdy3v1Cg1EEVcvu9MWVD9vPGmObdwJBsHeubfXOp+45guzDknCDYZhfC5vWwQZCSwzHwP4Q2sUsxJECRP4i5Z3mACwY6fTt3txi4/zddjAljt7wd3eS3DmGb1PW0A99xdR+Gc+t0BhjZcpEP295O69qvv7e7+YqxGCMZkILGDO7iIy9sqT3jKPncmyCWzhdYusl9Day519Or1Xv+yEIMGJ6glmIZq++4wZ5/WyKzgcEC8kYO8jGnkf6zoURb6Gmb+asyECi8wElpm14XB4YbvdyPuoS404g10z6VAOLzbKPmxBdQuu+X8SGnDv0zcdyx1CCBhpX4SQOu1IY2JBeAaEv7AGhQTKIq7MjGazPup0dn85V0MEU2QmsAAwGg3fDxg/XiYv1muwazb3VPf1YsdtfDxHr3oDdmjALa7OsIC93G/2VhaX82mncMXzVm2y9lrtfeSLWe9VuZyZb8jbFsGETAWWmbnX6/5Su13XyjPgFcw8XmxYv5NQgXfBbae4OkMDfnFX87X/BIO4KVBZiGbgvn086mj5usHCWiVxZWasrMiDTmf3N/O2RTBNpgILALquf0FR5M+WZ/JBdl6se7DL6cXa200EVJ96AI6Yq0/clV3i6yWufsK6yDxXL8JivdEnQfgLq9lPtOyDaOQvrgDQatUNSZJeKm5mWDwWcsvCTmf3Ka1W+0ZV1deJMtf0FDAQ9NvDmC7GzWzA7315rWMYgFWIGwwYBEgMEJm5w37303J6pc6psF7iaq9zt3Xbtkji5bjGnaYbEtuOGPtOa3+LQ4ckKTccHBy8Om9LBLMsRO2Y+eD4+OiCKoUK/An3Yr1bOTzcoBhsCuKaxDuNk5oVN/sgeblDA1E81qqKKzNjbU0ZHBzsP0JMKigmC3MnNU37hKLIn6lKqCCtjAKnQI5F1iUyzmVxxHU2dhk+Yp5FapZfzdhkBWXCRdXcZ/QUr/j7LgbNZs2QJOkPmfkHedsi8IYW+cNHROutVvs/+v2yhAqAsN8gZ6hg+j1J1jIZBGm8zn5uL7fbEeSp9pJrv+47wrrF1Vw+u8ze3k2YsGRV6zUZcUILEQbuEolkcYTVREe73fjewcH+TxfdeyWSWZLWYrUxjN41zHxuRiYtjIWqHDMfHh8fP6WqoYLpk9t/FN854DV+bQmm05t1/k0NPkUU16C0rIkt2d6RIBkGonqqNlHDHlUQV0do4JFFF9dlZ+FupKapn1QU+dPLFCqYFlKn+HmIrENo3aP5zvXT/XqLq9uG6e2LKKTxLsHjZDskD3UUS1wBoNWqGUT0ByI0UHxyuU7vdHYvqNfpsCoTEPxPXP8Br/Fzl8ia63XPx/Q2znitv7g607oWJ6p+wplMSJ3ETSGbT1iLeHzqkGVc3+0evjZvSwTh5CKwVqjgycscKnDO8JrKHghL5ncIpru/mf5d+/YnqiBmJ5xBJMnLnW9wrojCOgkN7O+L0EBZyG2kSdPUyxVF/lS9XpbBrnRDBfZrp8iOl3nsy0swg8TV3i646EkxvbR5JjvMn/VQvM/DxgoNvJiZb8zbFkE0clW3Tmf3yfU6/Tj/WGBU0gsVzA56uWZfwZh6jNvz9GCYt2j73SW1eIKa1uyxdIS1WJ+Nk1qNoCj0pW738OK8bRFEZyEzufxg5j4R3a/Van+n39eb5UjdMhB1ltf0LC6zHbMOkJmuxWyYs7kweU0kBV7Sh8ZbPYV18Swivj5vfu6E4gqriY5Go/ajvb09ERooGbkKLAAw8w8kSXpUq9X+bK+n14jKkl0QjSgiSySNhTlOqlGwuEbopzSDjBPSE1Wg+MJqfkfNptI7PDy8LzMf5W2PIB6FcBkNw/hHWZZe3GrVjHL8QM8Tj/UPF4TNnJoOF7jaRBDXvAq5zEOaM8omFDscYMPMaLWUUb/fewQz/zBvewTxKYTAAsDe3t6razX5I6urShkUFqkMernzYL3Sq2bisNPx19l46+x+yySoQPgPzXyU43MwxbWmA3ieYRhX5G2PIBmFEVjAzI+t1ehfJKkkGptYZJ0DXK4cWQ9B9Fzm6bWGi3rRSLvugT/l8Fpt1tZqXKtJ7z88PHhj3rYIklMogWVm9eBg/7zVVWm/DOJgkp7Iem3rNyMriteaN2EFZLITUyflElYAkCSGouDaTqfzK3nbIpiP3Ae53DDzLhE9sNVqX93vGyvlGPSKnlkAOAe+7BPfkTkQ8nbDsgSym49fJsr7/ph1rK4quwcH+w9kZi1vewTzUSgP1oaZv6Pr2tNaLaVEM73mH/gyl0ct7Rc9JLB4rzEvyuetOjFvXKgMut3uA5h5P297BPNTSIEFgKOjow8qinxxeTILgLgn96zIRm0fLSRQfUEFijqBIi5WxoCqaeqTmfl7edsjSIfCCiwA7O11Xlirye8sl8j64zkFdkYY3dNYvR5B7f33VS3KL6o2lrhqzMZvHB8ffzRvewTpUWiBZWbudHafoSjSB5rNWkkmscSfTptkQGo5vdZqeKtObHEl4t/tdg8vydseQboUWmABU2T39joX1GrSx6ouspFL8C2N1+rvuVcBS1x1WaYXHxyIdKwqUniBBcYi+99qNekza2u1MigsoohskNBGyYV191d+qi2oTuyJBIoiv3xvb+/P87ZHkA2lEFgAYGZjb6/zqHqdPre2plTCkwXChTHMsy1vSCA4tlxlbHGVZfo/nc7uy/O2R5AdpRFYAGBmfW9v7xG1mvT3VQkXAMm9z+IKa7yBumXCjrnKMr1kb6/z0rztEWRLqQQWGHuyj6vXpQ82m2XJLogmslEFc/7boGT9EHgxGdDCC/b2On+Wtz2C7CmdwAJ2dkHnybWa9O7ypHDNJ57JJwoI4SsCdp4rwBceHOz/Zd72CBZDKQUWGA98Pb1Wk9/YatX0KokskMbsKyGqRcGs6SoPmfWnHx4e/HXe9ggWR+FqEcTBCsI+u9VqX9Vsyn/d72uN4t8VIbhuQXr7EBQDA82mfNjrHT6Mma/J2xrBYim6GkWi1+u+q9c7vH+zKR8SVcuTLVbfgqgwMxoNCa1W7Qe9XvcuQlyXk0oILAAw89d6ve5Zzaby/UZDQvFDBlkIoRDXImClYRkrK8qnDw8P7sLMP87bJkE+VEZgAYCZf3J4ePDTq6vK5eUY/EpTEIW4FgFHGtYr9vY6j2TmYd42CfKjUgILAMw87HQ6j1IU6WWtlqIWofB0MGkMRhX9PS4HzDqaTel4ODx+wt5e56UlSdQWZEipB7n8sA7slzcajSubzcaH+31tlUjO26wQkg5+CXEtArIMrKzUdrvdw/OY+fq87REUg8p5sE6Gw+HlvV73Hq1W7aRcdH0FEE8sRRpWEWBmrK4q3Gwq3+52D88U4ipwUmmBBQBm/pdu9/CMVqt+RTnyZaOIphDWIsBsoNVS1JUV+e37+/v3FHchELipZIjADTMfAjiv2Wz+UrMpv3kwMJqGUeR7fQkBLTLMjJUVGYoinex2Dx7DzFflbZOgmFTeg3XS7/ff0+t179RuN64sR5aBoGjYXuvqau2Sbvfw9kJcBUEshQfrhJn/E8D9ms3m05pN+U3F92YFRUB4rYIkLJUH66Tf77+71+veud1uXCW8WUEQzAbabUVdXa39jfBaBXFYOg/WCTPfAuC+a2vNX2425b86PtabzEv7myNw4fBadw8PDx7DzFfmbZOgXAg1AXB01H9Xr9e98/p64yvCmxUAzlir8s5u9/A0Ia6CJCy1B+uEmW8hovuurKw8tdmsvYFZ3jg6UolIxGeXCTMc0DCI+KaDg/3/IYRVMA/Cg3XAzHx8fPzeXq97a1k2Lmo2pV69TiUoHCOYF2bG2prCrZZy8vi496SDg/07CnEVzIsQWA+YeXRwcPDKXq976sqKcnGzKR3LMguhrSBmnFXiZlM6ZFZ/u9s9vM1oNPqwqCMgSAMhsAEwc29vr/O8Xq97h3Z75dJWSx4CuhDaCsDMqNUIzabUr9XoT3q97q17vd5fMbOet22C6iBisBFg5pMAnkpEd9je3rlE0/QHdLvDevELyAjcMDNkmbGyogzqdeWdnU7nhcx8kLddgmoiBDYGzHwjgIcR0V23trbfYxh818PDgSKEtvgwM4gMtNv1oaIol3c6u78pCmELskYIbAKY+TsA7klE993e3nm9pml36/fVhmEQRNZBsbBv3aIodFyvr1ze6ez+HjP/a952CZYDIbBzYI0y34eIbr+9vfPy4XD4ZGZ59fhYE+ldOWPmsTYMZq0rSdLFh4cHrxWhAMGiITFgkx5EtNZstn5dkqSXSJKyeXh4pACy8GoXBDNDkgy0WisjItzU7R6+QNf1y8TAVb4QySxJa7HaGEbvGmY+NyOTFoYQ2Ayw3Nef3dk58YrhcPgAXafGYKBR8W8pXk4sb5WZ1X693vhAp7P7Cma+IW+7BCZCYAWZQUTrzWbzmbKsvEiS5O1eb1DTdUCI7XwwG2g0ZNTr8lCWpRsPDw9erOv63zGzmrdtgmmEwAoWAhGdvb6+8TRJkp5uGLyjqkZjMFAJkEQYIQTzODXQbK4woA/r9dq/9/v91w+Hgw8x88152yfwRwisYOEQ0Ylarf649fX13x0Oh2dJUq3R7R5JImY7wUyt0rG+3tRUdThcXV298uTJ//wLAJ9h5qO87RNEQwisIFeIqAHgwSdOnPI7g8HgIYpSX+l2j2qGsVyerXksMhSFsLZWHzHrXVlWPrS313krgKu5+PdgF3ggBFZQGKwBsrtvbm79GoBf0DRtR1HqynCo1gaDEVXFw7XFFDDQaq2yJGGk65par9d/OBwOLun3++9j5n/P2UxBCgiBFRQWS3DPAHCvnZ0Tj9N1/UGapp1QlHptNNJqx8dDK4Zb3EGzIDE1DOMz+/t7lwO4RsRSq4kQWEGpsET3TgDuvbNz4nGGYTxIVdVTFKWmEEmyqmry8fGQDIMB0PiRlec7EVD7El/CysqKoSiSbhi6oevaqNFo/FDX9U/t7+99GqaYimmqS4IQWEHpsUT3NgBOBXBbSZJuu7m5eTdZVs42DOM0VVVPMHODGTVFkYlIkjWNZcMwYBgGmJkMg2EYOnTdABFBkiTrQZAkiYkIsiyxLJNuGIah65oBQJVl+VhRlFskSfrBaDS89uDg4DoANwP4EYCbmLmT40cjyJlFCywR/TmAxwEYAfhXAL/GzPs+28oAroZ5nD7WWvYwAK8GUAdwDYBnMrNGRBsA3g3gDjBnwb6amd8RaIsQ2OXCmu1wAqYQnwqgAfNgUQDUHP8NAJrjoVr/j2AK580A9kXdVEEYRHR1XLFM0sbR9hEAPmeJ4isBgJlf5LPt8wGcC2CdmR9rnR8/APBwZr6eiF4O4AfM/DYiejGADWZ+ERGdAuB7AG7DzCM/W0QtgiXDGom/xXp8I2dzBMvBSSK6OmabFVebtzDzW6I0ZOZPOV5+GcCTvLYjotMAPAbAKwA831q8A2DEzNdbrz8N4CIAb4MZA2tbV4stAB2YTocvQmAFAkGmMPP5Oe7+GQDe57PuYgAvBNB2LDsJQCGic5n5apjifHtr3esBXAbzCq4N4ClhqYPFHXoWCAQCH4joM0T0bY/HExzb/AFMD/M9Hu0fC+AWZr7GudwKeV0A4LVEdBWALgC7WNAjAXwdwG0B3BPA64loPchO4cEKBILSwcw/F7SeiJ4O4LEwY6le4wQPAPB4Ino0gBUA60T0bmZ+GjNfAeBBVj+PAHCW1ebXAPyZ1d8NRPR9AGcDuMrPDuHBCgSCSkFE58O89H+835RqZr6ImU9j5tNheqyfY+anWe1vZf1vAHgRgDdZzW4E8HBr3a0B3AXAvwXZIgRWIBBUjdfDjJF+moi+TkRvAgAiui0R/X2E9v+LiK4D8E0AH2Xmz1nL/xjAeUT0LQCfBfAi6359vog0LYFAIMgI4cEKBAJBRgiBFQgEgoz4/4AfMhha4phDAAAAAElFTkSuQmCC", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "animate_data('T', vmin=0, vmax=5)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "animate_data('vort', cmap='jet')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "animate_data('phi', cmap='jet')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "venv" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.6" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "224ae1986ea58ff94007dbe32974f2e348ab1cb1b1c3796c4332e053e2c649e7" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/deprecated data analysis/parameter_scan.py b/deprecated data analysis/parameter_scan.py new file mode 100644 index 0000000..56c5e50 --- /dev/null +++ b/deprecated data analysis/parameter_scan.py @@ -0,0 +1,187 @@ +# mq submit parameter_scan.py -R 16:xeon16:1h +# %% +import re +import os +import numpy as np +from matplotlib import pyplot as plt +from boutdata import collect +import matplotlib.cm as cm +import matplotlib.animation as animation +from matplotlib.animation import FuncAnimation +from boutdata.data import BoutData + +def read_job(job_id): + print(job_id) + path = './shared/NORTH/data_' + job_id + '/' + bdata = BoutData(path) + # outputs = bdata['outputs'] + options =bdata['options'] + + # field_keys = bdata['outputs'].keys() + + # %% + def get_CLI_option(job_id, key): + for i, line in enumerate(open('./shared/NORTH/slurm_out/north_full_' + job_id + '_ESEL.log', 'r')): + match = re.search('Command line options for this run.*' + key + '\s*\=\s*(\S*)', line) + if match: + try: + return eval(match.group(1)) + except: + return match.group(1) + + def get_option(*keys): + cli_option = get_CLI_option(job_id, keys[-1]) + if cli_option == None: + from numpy import sqrt, pi + from ast import parse, walk, Name + val = options + group = keys[0] + for key in keys: + val = val[key] + try: + return eval(str(val)) + except: + # Parse undefined variables from the Bout.inp file: + val_str = str(val).replace(':', '____') # Replace group indicator ':' with '____' to make string parsable. + for node in walk(parse(val_str)): + if type(node) is Name: + missing_keys = node.id.split('____') + var = missing_keys[-1] + if var not in locals(): + try: + locals()[var] = get_option(*missing_keys) + except: + missing_keys.insert(0, group) + try: + locals()[var] = get_option(*missing_keys) + except: + print('Error reading option for keys: ', keys) + return 0 + return eval(str(val)) + else: + return cli_option + + def get_options(keys_list): + vals = [] + for keys in keys_list: + vals.append(get_option(*keys)) + return vals + + # %% + oci, rho_s, R, n0, Te0, mxg, myg, nx_all, ny_all, nz_all = get_options([ + ('north', 'oci'), ('north', 'rho_s'), ('north', 'R'), + ('north', 'n0'), ('north', 'Te0'), + ('mxg',), ('myg',), + ('mesh', 'nx'),('mesh', 'ny'),('mesh', 'nz')]) + Lx = R/rho_s * rho_s + nx_inner = nx_all - 2*mxg + ny_inner = ny_all - 2*myg + nz_inner = nz_all + + # %% + try: + vort_sink_strength = get_option('north', 'vort_sink_strength') + except: + if job_id == '5347831' or job_id == '5352599': + vort_sink_strength = 0.1 + else: + print('unknown vorticity sink strength') + print('vorticity sink strength: ' + str(vort_sink_strength)) + + #%% Read data + + field_list = ['T', 'n', 'phi', 'vort', 'source_T','wall_shadow'] + par_list = ['t_array'] + # fast_list = ['t_array', 'n0', 'phi0', 'n1', 'phi1', 'n2', 'phi2', 'T0', 'T1', 'T2'] + #%% + data, par, fast = {}, {}, {} + + for _field in field_list: + data[_field] = collect(_field, path = path, xguards = False) + + for _par in par_list: + par[_par] = collect(_par, path = path, xguards = False) + + # %% + thetas = np.linspace(0, 2 * np.pi, nz_inner) + rhos = np.linspace(Lx/(2*nx_inner), Lx+Lx/(2*nx_inner), nx_inner) + t = par['t_array']/oci + + # # %% Mean Density + # n_mean = np.mean(np.mean(data['n'].squeeze(),axis=1), axis=1) + + # plt.figure(figsize=(6,4)) + # plt.plot(t*(10**6),n_mean, 'k-') + # plt.xlabel(r'$t$ $\mathrm{\mu s}$') + # plt.ylabel(r'$\left$ [norm. unit]') + # plt.grid() + # plt.title('With vorticity sink strength: ' + str(vort_sink_strength), pad=20) + # # plt.show() + # plt.savefig('plots/' + job_id + '_density_vs_time.png') + # plt.savefig('plots/' + job_id + '_density_vs_time.pdf') + + # # %% Contained Energy + # mesh_t, mesh_rhos, mesh_thetas = np.meshgrid(t, rhos, thetas, indexing='ij') + + # Jfun = lambda t, r, theta: np.abs(r*(R + r*np.cos(theta))) + # def energy(): + # n = data['n'].squeeze()*n0 + # T = data['T'].squeeze()*Te0 + # J = Jfun(mesh_t, mesh_rhos, mesh_thetas) + # return 2*np.pi*np.trapz(np.trapz(J*((3/2)*n*T), rhos, axis=1), thetas, axis=1) + + # E = energy() + + # plt.figure(figsize=(6,4)) + # plt.plot(t*(10**6), E, 'k-') + # plt.xlabel(r'$t$ $\mathrm{\mu s}$') + # plt.ylabel(r'$E$ $\mathrm{[eV]}$') + # plt.grid() + # plt.title('With vorticity sink strength: ' + str(vort_sink_strength), pad=20) + # # plt.show() + # plt.savefig('plots/' + job_id + '_energy.png') + # plt.savefig('plots/' + job_id + '_energy.pdf') + + # %% Animation + import matplotlib.animation as animation + + def animate_data(field, cmap='inferno', vmin = None, vmax = None): + field_data = data[field].squeeze() + if vmin is None: + vmin = field_data.min() + if vmax is None: + vmax = field_data.max() + fig, ax = plt.subplots(subplot_kw={'projection': 'polar'}) + fig.suptitle('With vorticity sink strength: ' + str(vort_sink_strength)) + + ax.set_rticks([]) + ax.set_xticks([]) + cont = ax.contourf(thetas, rhos, field_data[0, :, :], 100, cmap = cmap, vmin=vmin, vmax=vmax) # first image on screen + fig.colorbar(cont, ax=ax) + fig.tight_layout() + + # animation function + def animate(i): + for c in ax.collections: + c.remove() # removes only the contours, leaves the rest intact + cont = ax.contourf(thetas, rhos, field_data[i, :, :], 100, cmap = cmap, vmin=vmin, vmax=vmax) + return cont.collections + + anim = animation.FuncAnimation(fig, animate, frames=field_data.shape[0], interval=100, blit=True) + anim.save('./plots/' + job_id + '_animation_' + field +'.gif', fps=4) + # plt.show() + + animate_data('n') + animate_data('T', vmin=0, vmax=5) + animate_data('vort', cmap='jet') + animate_data('phi', cmap='jet') + +if __name__ == '__main__': + job_ids = [re.search('(\d){7}', dir).group() for dir in os.listdir("./shared/NORTH/") if re.search('data_(\d){7}', dir)] + job_ids.sort() + n_jobs = 6 + # The last (highest id) job id is the lates job + job_ids = job_ids[-n_jobs:] + # job_ids = ['5352599', '5355427'] + for job_id in job_ids: + read_job(job_id) \ No newline at end of file diff --git a/install_bout_env_niflheim.sh b/install_bout_env_niflheim.sh index 30099ea..e161dad 100755 --- a/install_bout_env_niflheim.sh +++ b/install_bout_env_niflheim.sh @@ -19,9 +19,9 @@ MOD_FILES_DIR=$MOD_DIR/modules/all MOD_DOWN_DIR=$MOD_DIR/downloads MOD_INSTALL_DIR=$MOD_DIR/software -echo "purging modules and loading tool-chains: foss, Python" +echo "purging modules and loading tool-chains: foss/2021b, Python" module purge -module load foss Python +module load foss/2021b Python echo "copying module files to $MOD_FILES_DIR" mkdir -p $MOD_FILES_DIR @@ -103,17 +103,19 @@ $PIP completion --bash >> bin/activate # Purge and reload all required modules module purge module use $MOD_FILES_DIR -module load foss HDF5/1.12.1 netCDF/2022 Python +module load foss/2021b HDF5/1.12.1 netCDF/2022 Python module save bout -# # Install Bout-Dev -# echo "####################################" -# echo "Installing Bout-Dev" -# cd ~/north-simulation/shared +# Install Bout-Dev +echo "####################################" +echo "Installing Bout-Dev" +cd ~/north-simulation/shared # git clone https://github.com/boutproject/BOUT-dev.git -# cd BOUT-dev -# ./configure -# make +cd BOUT-dev +# git checkout 2b2223b51cb4c1f2013950a4586619f8a917d32c # This commit has previously worked +# git pull +./configure +make echo "####################################" echo "Succesfully installed all requirements" diff --git a/main_results.ipynb b/main_results.ipynb new file mode 100644 index 0000000..c154fa8 --- /dev/null +++ b/main_results.ipynb @@ -0,0 +1,1499 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Main Results\n", + "## Numerical Simulation of Perpendicular Dynamics in the NORTH Tokamak\n", + "This Interactive Python Notebook presents the main results of the fluid simulation of North." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib import rcParams\n", + "import matplotlib as mpl\n", + "from matplotlib.colors import ListedColormap, LinearSegmentedColormap" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load in Data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "job_id 5703369\n" + ] + } + ], + "source": [ + "from boutdata import collect\n", + "from boutdata.data import BoutData\n", + "from boututils.datafile import DataFile\n", + "import re\n", + "import os\n", + "\n", + "job_ids = [re.search('(\\d){7}', dir).group() for dir in os.listdir(\"./shared/NORTH/\") if re.search('data_(\\d){7}', dir)]\n", + "job_ids.sort()\n", + "# job_id = job_ids[-1]\n", + "job_id = '5703369'\n", + "# job_id = '5703257' # Unstable run, but interesting!\n", + "# job_id = '5703126' # Best run without vorticity source, but with both global and wall vort sink.\n", + "# job_id = '5698085' # Best run without vorticity sinks\n", + "print('job_id', job_id)\n", + "path = './shared/NORTH/data_' + job_id + '/'" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/niflheim/s173965/local/venv/north-simulation/lib64/python3.6/site-packages/boutdata/data.py:769: AlwaysWarning: While building x, y, z coordinate arrays, an exception occured: name 'mxg' is not defined\n", + "Evaluating non-scalar options not available\n", + " + \"\\nEvaluating non-scalar options not available\"\n" + ] + } + ], + "source": [ + "bdata = BoutData(path)\n", + "outputs = bdata['outputs']\n", + "field_keys = outputs.keys()\n", + "options = bdata['options']" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def get_option(*keys):\n", + " from numpy import sqrt, pi\n", + " from ast import parse, walk, Name\n", + " val = options\n", + " group = keys[0]\n", + " for key in keys:\n", + " val = val[key]\n", + " try:\n", + " return eval(str(val))\n", + " except:\n", + " # Parse undefined variables from the Bout.inp file:\n", + " val_str = str(val).replace(':', '____') # Replace group indicator ':' with '____' to make string parsable.\n", + " for node in walk(parse(val_str)):\n", + " if type(node) is Name:\n", + " missing_keys = node.id.split('____')\n", + " var = missing_keys[-1]\n", + " if var not in locals():\n", + " try:\n", + " locals()[var] = get_option(*missing_keys)\n", + " except:\n", + " missing_keys.insert(0, group)\n", + " try:\n", + " locals()[var] = get_option(*missing_keys)\n", + " except:\n", + " print('Error reading option for keys: ', keys)\n", + " return 0\n", + " return eval(str(val))\n", + "\n", + "def get_options(keys_list):\n", + " vals = []\n", + " for keys in keys_list:\n", + " vals.append(get_option(*keys))\n", + " return vals" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "oci, rho_s, R, a, n0, Te0, e, n_n, E_ion, mxg, myg, nx_all, ny_all, nz_all = get_options([\n", + " ('north', 'oci'), ('north', 'rho_s'), ('north', 'R'), ('north', 'a'), ('north', 'n0'), ('north', 'Te0'), ('north', 'e'), ('north', 'n_n'), ('north', 'E_ion'),\n", + " ('mxg',), ('myg',),\n", + " ('mesh', 'nx'),('mesh', 'ny'),('mesh', 'nz')])\n", + "Lx = a/rho_s * rho_s\n", + "nx_inner = nx_all - 2*mxg\n", + "ny_inner = ny_all - 2*myg\n", + "nz_inner = nz_all" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# %% Read data. \n", + "cache_data = './cached_data/data_' + job_id + '.npz'\n", + "\n", + "if os.path.exists(cache_data):\n", + " data_tot = np.load(cache_data, allow_pickle=True)['data_tot'].item()\n", + "else:\n", + " # Uncomment this cell to load data in and cache it anew.\n", + " key_list = ['t_array', 'B', 'T', 'n', 'phi', 'vort', 'source_T', 'wall_shadow']\n", + "\n", + " data_tot = {}\n", + " for key in key_list:\n", + " data_tot[key] = collect(key, path = path, xguards = False, info=False)\n", + "\n", + " # Calculate Pressure and Ionization Source\n", + " data_tot['p'] = data_tot['n'] * data_tot['T']\n", + "\n", + " def k_ionization(T):\n", + " return 2.0e-13*np.sqrt(T/E_ion)/(6.0 + T/E_ion)*np.exp(-E_ion/T)*n0/(oci)\n", + " data_tot['S_n'] = n_n*data_tot['n']*k_ionization(data_tot['T'])\n", + "\n", + " # Cach data\n", + " np.savez(cache_data, data_tot=data_tot)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "times_tot = data_tot['t_array']/oci\n", + "\n", + "# Set maximum time for analysis (s)\n", + "# (in case the simulation time is unnecessary long)\n", + "time_end = np.min([times_tot[-1], 3300*10**(-6)]) \n", + "\n", + "# Uncomment line below to use all simulated data points\n", + "# time_end = times_tot[-1]\n", + "\n", + "# Save seperate variables for transient, ´\n", + "# steady state and all time behaviour\n", + "# so that we can analyze them individually.\n", + "\n", + "# Time in seconds of shift from transient\n", + "# to steady state behaviour. Set by\n", + "# looking at the animation of n.\n", + "time_trans = 1300*10**(-6)\n", + "\n", + "times = times_tot[times_tot <= time_end]\n", + "times_trans = times_tot[times_tot <= time_trans]\n", + "times_ss = times_tot[np.all([time_trans < times_tot, times_tot <= time_end], axis=0)]\n", + "\n", + "data, data_trans, data_ss = {}, {}, {}\n", + "for key in data_tot.keys():\n", + " if len(data_tot[key].shape) > 0 and data_tot[key].shape[0] == data_tot['t_array'].shape[0]:\n", + " i_data = np.where(times_tot <= time_end)\n", + " i_trans = np.where(times_tot <= time_trans)\n", + " i_ss = np.where(np.all([time_trans < times_tot, times_tot <= time_end], axis=0))\n", + " data[key] = np.take(data_tot[key], i_data, axis=0).squeeze()\n", + " data_trans[key] = np.take(data_tot[key], i_trans, axis=0).squeeze()\n", + " data_ss[key] = np.take(data_tot[key], i_ss, axis=0).squeeze()\n", + " else:\n", + " data[key] = data_tot[key]\n", + " data_trans[key] = data_tot[key]\n", + " data_ss[key] = data_tot[key]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Save simulation coordinates for plotting\n", + "# theta-angles and\n", + "# rho (toroidal (inner) radius coordinate) and\n", + "# times\n", + "thetas = np.linspace(0, 2 * np.pi, nz_inner)\n", + "rhos = np.linspace(Lx/(2*nx_inner), Lx+Lx/(2*nx_inner), nx_inner)\n", + "times = data['t_array']/oci" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plots of the Fields" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Time Frames Plots" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": {}, + "outputs": [], + "source": [ + "rcParams.update(mpl.rcParamsDefault)\n", + "rcParams.update({\n", + " \"text.usetex\": True,\n", + " \"font.family\": \"serif\",\n", + " \"font.sans-serif\": [\"Computer Modern Roman\"]})\n", + "rcParams['axes.titlepad'] = 20\n", + "\n", + "from matplotlib.gridspec import GridSpec\n", + "\n", + "def cbar_ticks(vmin, vmax, n_ticks=4, label_rounding=3):\n", + " ticks = np.linspace(vmin, vmax, n_ticks, endpoint=True)\n", + " labels = np.round(ticks,label_rounding)\n", + " return ticks, labels\n", + "\n", + "def plot_time_frames(data=data, start_frame=0, end_frame=None, resolution=2):\n", + " fields = ['n', 'T', 'vort', 'phi']\n", + " cmaps = ['inferno', 'inferno', 'jet', 'jet']\n", + " titles = [r'$n$', r'$T$', r'$\\Omega$', r'$\\phi$']\n", + " N_frames = 6\n", + " cm = 1/2.54 # 1 cm = 1/2.54 inch\n", + " nrows = 5*N_frames + 1 # 5 (merged rows) for each frame plot. 1 row for colobar\n", + " n_cols = 1 + len(fields)*6 # 1 column for each timestamp text. 3 (merged columns) for each field plot.\n", + " fig = plt.figure(constrained_layout=True, figsize=(16*cm, 20*cm))\n", + " gs = GridSpec(nrows, n_cols, figure=fig)\n", + " def get_row_range(i_frame):\n", + " return slice(5*i_frame,5*(i_frame+1))\n", + " def get_col_range(i_field):\n", + " return slice(1+6*i_field,1+6*(i_field+1))\n", + " if end_frame is None:\n", + " end_frame = data[fields[0]].shape[0]\n", + " frames = np.linspace(start_frame, end_frame - 1, N_frames, dtype=int)\n", + " # frames = np.arange(start_frame, end_frame, int(np.ceil(end_frame/N_frames)))\n", + "\n", + " for i_frame, frame in enumerate(frames):\n", + " row_range = get_row_range(i_frame)\n", + " ax = fig.add_subplot(gs[row_range, 0])\n", + " ax.text(0.5, 0.5, r'$t = ' + str(round(times[frame]*(10**6),1)) + '\\, \\mathrm{\\mu \\, s}$', va=\"center\", ha=\"center\")\n", + " plt.axis('off')\n", + "\n", + " for i_field, field in enumerate(fields):\n", + " field_data = data[field].squeeze()\n", + " vmin = field_data.min()\n", + " vmax = field_data.max()\n", + " col_range = get_col_range(i_field)\n", + " for i_frame, frame in enumerate(frames):\n", + " row_range = get_row_range(i_frame)\n", + " ax = fig.add_subplot(gs[row_range, col_range], polar=True)\n", + " if i_frame == 0:\n", + " ax.set_title(titles[i_field])\n", + " cont = ax.contourf(thetas, rhos, field_data[frame, :, :], resolution, cmap = cmaps[i_field], vmin=vmin, vmax=vmax)\n", + " ax.set_rticks([])\n", + " ax.set_xticks([])\n", + "\n", + " cax = fig.add_subplot(gs[-1, col_range])\n", + " latest = field_data[frames[-1], :, :]\n", + " c_ticks, c_labels = cbar_ticks(latest.min(), latest.max())\n", + " cbar = fig.colorbar(cont, cax=cax, ticks=c_ticks, orientation='horizontal')\n", + " cax.set_title(titles[i_field] + ' [norm. units]')\n", + " cbar.ax.set_xticklabels(c_labels, rotation=45)\n", + " return fig\n", + "\n", + "# fig = plot_time_frames(resolution=2)\n", + "# plt.show()\n", + "\n", + "# # # Uncomment this section to save plot time frames\n", + "# # # fig.savefig('./plots/' + job_id + '_time_frames.pdf', format=\"pdf\", bbox_inches=\"tight\")\n", + "# # fig.savefig('./plots/' + job_id + '_time_frames.png', dpi=600)" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "metadata": {}, + "outputs": [], + "source": [ + "# fig = plot_time_frames(end_frame=20, resolution=30)\n", + "# plt.show()\n", + "\n", + "# # Uncomment this section to save plot time frames\n", + "# # fig.savefig('./plots/' + job_id + '_time_frames.pdf', format=\"pdf\", bbox_inches=\"tight\")\n", + "# fig.savefig('./plots/' + job_id + '_time_frames_trans.png', dpi=600)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Gif Animation Helper Functions" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.animation as animation\n", + "\n", + "def get_cmap(cmap, min_p=0.0, N_cmap=256):\n", + " cmap = plt.get_cmap(cmap)\n", + " return ListedColormap(cmap(np.linspace(0,1,int(N_cmap/(1-min_p))))[-N_cmap:,:])\n", + "\n", + "def get_cbar_mappable(cmap, vmin, vmax):\n", + " norm = mpl.colors.Normalize(vmin=vmin, vmax=vmax)\n", + " return mpl.cm.ScalarMappable(norm=norm, cmap=get_cmap(cmap))\n", + "\n", + "def get_extend(field_data, vmin, vmax):\n", + " if field_data.min() < vmin and vmax >= field_data.max():\n", + " return 'min'\n", + " elif field_data.min() <= vmin and vmax < field_data.max():\n", + " return 'max'\n", + " elif field_data.min() < vmin and vmax < field_data.max():\n", + " return 'both'\n", + " else:\n", + " return 'neither'\n", + "\n", + "def plot_frame(field, frame, cmap, vmin = None, vmax = None, clabel = None, title=None, ax=None):\n", + " field_data = data[field].squeeze()\n", + " if vmin is None:\n", + " vmin = field_data.min()\n", + " if vmax is None:\n", + " vmax = field_data.max()\n", + " if clabel is None:\n", + " clabel = r'$' + field + '$ [norm. u.]'\n", + " fig, ax = plt.subplots(subplot_kw={'projection': 'polar'})\n", + " if title is not None:\n", + " ax.set_title(title)\n", + " ax.set_rticks([])\n", + " ax.set_xticks([])\n", + " ax.contourf(thetas, rhos, field_data[frame, :, :], 100, cmap = cmap, vmin=vmin, vmax=vmax) # first image on screen\n", + " txt = ax.text(0.5, -0.15, r'$t = ' + str(round(times[frame]*(10**6),0)) + '\\, \\mathrm{\\mu \\, s}$', va=\"center\", ha=\"center\", transform=ax.transAxes)\n", + " # ax.subtitle(r'$t = ' + 0 + '\\, \\mathrm{\\mu s}$')\n", + " fig.colorbar(get_cbar_mappable(cmap, vmin, vmax), orientation='vertical', label=clabel, extend=get_extend(field_data, vmin, vmax), ax=ax)\n", + " fig.tight_layout()\n", + " return fig, ax, txt, field_data, vmin, vmax\n", + "\n", + "def animate_data(field, cmap='inferno', vmin = None, vmax = None, clabel=None, title=None):\n", + " fig, ax, txt, field_data, vmin, vmax = plot_frame(field, frame=0, cmap=cmap, vmin=vmin, vmax=vmax, clabel=clabel, title=title)\n", + "\n", + " # animation function\n", + " def animate(i):\n", + " for c in ax.collections:\n", + " c.remove() # removes only the contours, leaves the rest intact\n", + " cont = ax.contourf(thetas, rhos, field_data[i, :, :], 100, cmap = cmap, vmin=vmin, vmax=vmax)\n", + " txt.set_text(r'$t = ' + str(round(times[i]*(10**6))) + '\\, \\mathrm{\\mu \\, s}$')\n", + " return cont.collections\n", + " \n", + " anim = animation.FuncAnimation(fig, animate, frames=field_data.shape[0], interval=100, blit=True)\n", + " anim.save('./plots/' + job_id + '_animation_' + field +'.gif', fps=4, dpi=300)\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "rcParams.update({\n", + " \"text.usetex\": True,\n", + " \"font.family\": \"serif\",\n", + " \"font.sans-serif\": [\"Computer Modern Roman\"],\n", + " \"font.size\": 16})\n", + "rcParams['axes.titlepad'] = 20" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot last frame of all fields" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot last frame of all fields\n", + "\n", + "# Density\n", + "fig, ax, _, _, _, _ = plot_frame('n', frame=-1, cmap='inferno', vmin=0, vmax=np.quantile(data['n'], 0.99), clabel=r'$n$ [norm. u.]', title=r'$n$')\n", + "fig.savefig('./plots/' + job_id + '_n_last_frame.png', dpi=300)\n", + "\n", + "# Temperature\n", + "fig, ax, _, _, _, _ = plot_frame('T', frame=-1, cmap='inferno', vmin=0, vmax=np.quantile(data['T'], 0.995), clabel=r'$T$ [norm. u.]', title=r'$T$')\n", + "fig.savefig('./plots/' + job_id + '_T_last_frame.png', dpi=300)\n", + "\n", + "# Vorticity\n", + "# Set vmin and vmax to symmetrise around 0\n", + "vmax = np.max([data['vort'].max(), np.abs(data['vort'].min())])\n", + "vmin = -vmax\n", + "\n", + "fig, ax, _, _, _, _ = plot_frame('vort', frame=-1, cmap='RdBu_r', vmin=vmin, vmax=vmax, clabel=r'$\\Omega$ [norm. u.]', title=r'$\\Omega$')\n", + "fig.savefig('./plots/' + job_id + '_vort_last_frame.png', dpi=300)\n", + "\n", + "# Electric Potential\n", + "# Set vmin and vmax to symmetrise around 0\n", + "vmax = np.max([data['phi'].max(), np.abs(data['phi'].min())])\n", + "vmin = -vmax\n", + "\n", + "fig, ax, _, _, _, _ = plot_frame('phi', frame=-1, cmap='RdBu_r', vmin=vmin, vmax=vmax, clabel=r'$\\phi$ [norm. u.]', title=r'$\\phi$')\n", + "fig.savefig('./plots/' + job_id + '_phi_last_frame.png', dpi=300)\n", + "\n", + "# Pressure\n", + "fig, ax, _, _, _, _ = plot_frame('p', frame=-1, cmap='inferno', vmin=0, vmax=np.quantile(data['p'], 0.99), title=r'$p$')\n", + "fig.savefig('./plots/' + job_id + '_p_last_frame.png', dpi=300)\n", + "\n", + "# Ionization source\n", + "fig, ax, _, _, _, _ = plot_frame('S_n', frame=-1, cmap='inferno', vmin=0, vmax=np.quantile(data['S_n'], 1.0), clabel=r'$(\\partial_t n)^{\\mathrm{iz}}$ [norm. u.]', title=r'$(\\partial_t n)^{\\mathrm{iz}}$')\n", + "fig.savefig('./plots/' + job_id + '_S_n_last_frame.png', dpi=300)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Make Animations" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "MovieWriter ffmpeg unavailable; using Pillow instead.\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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s4oZvfwfXXn3VmV1de/5IRG/cX0ekiAAjt1oSqV+AhoiGTZ52/Np3vfeC0W8597zY9lEB3+6q48dieqpOnRWjA4wuNtOjgUybZLkALlzEZhqkBYxlGolcJP/vJuAiFDcSkq4eD/zRqLiYjU7qIQZARLj62s+hrdR25m9/tXgJEc1h5mrim+ovIoJRzC2aJOpz0BDRsInHTFr93os+MvoNb5mLuNiaDJkgSNiPxYhg796KnRgwMljKmuBF0TTQU3UwrGTBNAhFy8BQ728BmJJp1IElDChAuphLXIA5aY2bpNK5V3GwSWLV+EeJ8MlPX0WlUunUX9zys38Q0an7G2wIuUWTVH0KGiLqmHjspCfnfeQT4856y9sj2yaJxwjIqK6SGsDVAUbAJWr4us2zXIaWLBQtAyXLREfBhElAe8H0wVIIwEafUNeMkuRuhAGmrkRoglvSuUjNwAaoAefSyz9BzHzKrT+/5U6vrsr+E7PJYzSJ1WegIaLSMZOnPP6eCz58iIBM2Je+Ech0V+06K0Z1kXqqTgAwYTGZommgzTLQXjQxtGShvegCpmQaaC+YKFnu6FBHwUDBMGAQaevGaN9bRGa2bqQoTEkslzjI6Kr3JVEUbKTsHK0IwGUfX0Dd3d2v/d2vf/UHL2azf8DGG3XKFa8+AQ0RmcdNn7XyLe989+FxMZk4yIigr3Cj9lbsUCtGBgzgWjHy/nI1GJMsWqb/wAjIDC8V0FEw0WYZ6CiYGOZBxyBCyTL8jFtdmc3A+/IZQj5s1Oxm06rPgUmruNrEzUhYNbIahc2Vn76Kenp6zrrzjj/9H4Bzm7+71oty0CRWn4BmwlETb37V6XMOf8d7okeXsoBMmBUTHH2y64K/bV6QFwCGlayAJTO06FoxI0oW2kz3b8t0H+qi98UzED4xUc1DcUD+3+IYUa2R4Ivpt4v82GKlg0yUAZPkWdK5TEndKPf6hM9cex1t3rRp7hFHHPGJ9evXfznRiX0pIph5MDiReh00Bx9y6IemzZh17kWXXVF3LCyBLAvI6KwYGTBybEZ+OIa11YK/wpJRIdNmufOECoYLGnE+afpTr8Um1RLhDJIS5KgGHhEfYZGBrP9sowAUZcGokIlym5KAQzcSFWfVuPdBuOFb3zHe9qazF7a3ty/bt2/f32Mv1ociAEYTy9oMJvUqaCzLOnnS1OO+ft0N3zOTJIZGJuJlBBldbKa9aAbcJhH4LZkubAqmEbBkiqabzCYgQ3AfSF2hcHdHzYIBaoCxUAOPzZ5VJD4LR5oeQPqC6mndId1/Qdx6UlHS1bNJY9UAQKlUwg9+8jN6x9ve/FcimsDM65PfQS+LkFs0CdVrUxCI6NDDDj/irq/cdHOxo2NIbPvAUiaawG+WkFHjObKKkgsl1FFwh7NNQy6/ULNiBGRMg2ARQMww7ErdZhFgkQuo9oKBokmwDHcrmu5WMGqbKSwmAooGefVk3I0IdZv+/yH8eFwAuNFF6cT/R9II00EHHYQbvvVdY8rUqY8T0dCGLtoboujpB3n8pqZeAQ0RtR9+xJFLr/nqt4cedEiCuUtqkSovT0YeXcoSMra3+dd3uC4wDLjJd7rvjq5+S+D9O1XAses2qvaAqj0B8AjAyMAxvf0FbxqA6UHHJKmOjASd2ueeDD7i/Pp94e3l9x0m3aBZUtjMnDULF3/k0o5p045bSkT9bk4eIILBRuSWy1XLXSciovETjrrz/Is+csiMk05JPGwqIOMm3dUyfuUh7DSQcfcHIbOv280PE8W/7KqDMlBnwUQpMis2ZJSInFpeGhuWCx54s4EBkOHGhWxyH1aC94A6DNMkf8Kj4eWzmAhOeIxbt6p27xGxGNXbUyCadhnetC4UALzjne/CE088PsW0zB8CeG+qk3tDBJiFHCZJ1PJP6fAjjrz25FNPe8Vb3vXfidrXxWW8OjKByZFKnoxudAkIZviqw9zlqgNmBjP7Fo0AjprYJ5SmIDkAsPwgG3pfnpyqv/mWjlMFMftWi2zhyG6ZqIans3ICFfGItJtO6rlA85BRpcOeuH9VV13zWYwZO/a/Dz74kHckvkAviUAwTCNyy+WqpZ8EEU0aOqLzUx+76rpE15GXPfGnFvi1Y2quU8VxIiHTU+cG1Vwm2V0CXCvGsRmOzQEXyreIQmI3srLKLvMtHRU2ElwCMSDS1/qVh9V14NEprE0SgCSFTJKSEv77Fe/BNPGd799Iw4YP+ykRjUp0od6SARhFM3LL5aploCEia8Ixx95z1fU3GIVi9EoFQNC8j4rLqNMKwiCjm6/kuxee9SIgI+Di2K6FI/pVAaMum+Led7B/+VGyHQYTue4R4Fs1/usoSbABoIdNiHUD1D+wQD144iCkA4j6CURBJgpuKnKipn6MGjUan7rqmuLxJ5xwV3iPfSECGUbklstVyz6JY6dM+9rrznzjqElTj49tq12hQHKZwuIyQjpLBkBdbEZuq0JG/tdW+ilaRmyKv/ygyIl3AEJhkwg4CMZ6ZNgA9cPoKnDk/Uktj7C2SSETBa+khbLUa7zxTW/G+AlHTR930EHvStVBC0VEMItW5JbLVUtAQ0STDdP68Acu+Xhgf1Twsa6IuOIyiUmSYdMKZOliM/Lf6soPHPJrKpeBANyEPN0a2nIgVlg1Ajb+0K4KGwU4deDxjsuB44B1ooGNmrejAsdvG7OpCuun7jNL4KLJSooccV8Lv/JVdI7o/AkRjU5+lRYqH3VKrMw/CddlmvTPTy/8htHR3hbbXjciUoOLE+kyxdWPCVxHcpv8fVXHh4z417QMmJbhz9SWi1lF3X/Y1XWw0QEnoJDAsZD8MMuwkY/J925AD4skShr4zSJlJM7aGTtmDD519TWF4084YUnzV8tCueuUVJl/EsdMmfb1097wplHHTIlfpjZ0UTe7VrxKWDO6uIxQHGSiRAb5G+Caw24ZCBNFs1bUSpR/EHLvSbwP71/JihFWjTgegE0YcBTwxLlX6lQHfz/VtxEyEA2euOPNQEZnMaWdJvrmN78ZEyceffy4cQf9v5SnZi4iglEsRG65XGUKGiKaXCi2XfyBiy/XHhcFxMWmSg4AizKcwppJGpdJI1PKlzEtA4U2E+1eMav2oulPpuyIqDnCEmR0UmETBhzd5l8jYe6RGviV94fFU+LAEta/7jpq2ziXrFFd/5WvYtToUT/rcxeK3CVlorZcrjKNVk0/6ZR/XXrVdbAKyUkuWzPua8dfc1q2ZgDUuUxpIOO7Fpbhu09udTQDhkkwLQNWwUTRMjCsVMCwNne2tqigpx9xYpiorZfkAO6iawb5y5UQ9PVaxD0lhQgQhFbU+/T7p2Bb+WFPGpRNO6rULFCSJPaNHDUKl13+cfq/W275AwD9gl+9ISIYGQR8iWgBgLUARgIAM98Y074TwDwAo5i5fnZyP1RmyCWiU0eOGXvAlONnJD5HLuwkWzMAIq0ZoDFLpqhYMABQaDN9yAwtWegommj3tqEly1/BQC67KeAIoM59UiV2Bywb2dVKsanXSZSXksLiSGOFtAIyafT2uedg584dJxHREb12UUVuFndzMRoiWghgLTMv9gAzgYjmRrSfDWA2gAkAOrN5J61XJqAhIjp26nF/+NAn/if1uao1A6AuNgPUWzNRKkrWhztqZAZem5YBIoJVMP1/BWRGdBQxakjRj810FEytNePer+cGhbhP6mRCFRjqFiZdm0CpichPI/1oUCN99SZkANdlufLT/2PMmDlzca9eWBZRFqNO85hZfg+3Apgf1piZl3jtdzR3872rTEAzvLPzHZOOmzHs8PFHJT5HLVNZcURguDbSJNSoNSN/+cUokr9qgbe1izhM0cRQyWUSsRnxUKnD2v7qlv6Ca7XRJzmPRjdzWWepuH1EA0h3Tho1CptGEvsAae5WyBampO/v9NlzMHTosBlEdFyiE7IWoak8GiLSmf874FosA0pNg4aIrDEHHnzzBy/9hO9OJJU80gTU/6oLtwkIz5kJU1EBQ9EyA7AZKlfN8yDTXqi5TCXL9GMzQbepdg+q+xQnljZZaVynuL7iFJURnCZrGIgOMidhWhxwYs8nwtWf+Qwdf8IJv22im6au36TrNBLANmWf+npAqOlI1oRjjr38lafNKY458KDE5+iKbse5TUJpYzMCLKKPNsvw+2jz3Kqi5RYZ7+wo1JZQMSn0IRPWV8Fw71PU9rUZgWAwvGFxOfgr1JhNEq1GLJ1GrJwoN6l3HShg1okn4WUvO/xIIno1M9/TqxdPFgweTUQPSq9vlIK9neFdUycz72juBtOJiN4GYHyDp69l5l+HHWwKNETUPv7oY687/6JLm+nGV0VjGoRZMSL7V7VcZBVNA2Xb8WHj75diNu1FN19mmLeEiqgLrLNm6u7XcdBmehm+DO1Tpi5VItRMTKNR16lZxd1zb0NG6KprPoO1a9f8mojGspr23WIlsFq2MvOskGM74I00SVJf96bmALgCjf1XfglAa0Azbcashaee8V/WsBEjtMcrNtfNERLWjCMl61WkB0eNzwjp1mBKojDYiIdmWFsNMGKUSTfSFCV1mNvf74Sv5thXsEijpDBM0qqRoXVtPwLaUhdHH3MMZs6cNXrN00+/CcDvGu48pdy5Tk0l5W1DvVXTCQC9bc14WsHMuxo5kYhWRB1vOEZDRIU9u3dfdM5/fyDxOfUBYGk6gCY+k5WKprs2k4gptBc8K0aBTMkyfZcpbKTJfR/hcRqRmCda6EaZkigsMJymj7RKm2AXF2MJ6yvJNeJgpMaPPvbxT2Di0UffFHvTWarJuU7MvAL1o0cjAfTJFAtmbvjzizu3YdAMGz7iHae8bo5lldoj26VZBK2ifLnCClBFqc2bp6TbhrVZGNZmuX97gWABGRGXiVtNUrwd+V7VYW4hFTZyH82CJEsIpc3cTRLEbXameFIJ4Bxx5JE49LDDRhPRCQ13llqZzHX6pZI3MwfAIv8KROOj8mr6QkT0diL6S5pzGnadDj3iyO+9/T311ozDXLdkqwobdXRKF5sB4AdxxRdRBHKFO6SqLaIEp85tqls/23OZdLGZiu3UD3HbDFHJUXafgFpmsLx0rHr9MEXVZkki8XEnCfRm6R6l6U93XjMxLJOAiz50Ma1fu+7rAF7X0E2kFBnNZwYz83wiWuAl4o0HsEbJq5kLFz6LAX9IfLa3X2QVL/Gso97SCnE/SdXQp0REM08987+GjTs0vtB4VpJHjoDoILBoL0v+4srlH3zIJHgqtbCRAsK2A0hxZj9Go67o2EiMIuycSPdDExtKeq5QqwGTZR+vPfU1aCu1vdoLCm9p+oYSKIsZ2sx8fcyx66XXK+A+6KHntFrMvA5AKjerIdC86vQzFr/9Pe/3X1ccJ/UC9roYTMEgiMUHCoYB27HrACMPT8tqC4wquX+rcFH/lSEjrJna/XFoMNhmeEEZ91xHGnJiBmxvuTR5XSP5jpN8UqJ97EoDMVZAHGzC1JuAyUpEhPPPf69pGsY1AD7cCxcEWfHVIweiiGh4msBxahwT0bBNG54/YvrJrwptE5W4lySpT0BLdnHkUSPVRYqCjFy4Kq0lU3HY3/x9isvm1zi262ekq9nBQk6CLa6tTmkspWZzYZLGVqLKh7Zi2aN3nHsu9nR1vY+SrFDYtAgwjOhtPxcRDddtABam6Sf1J3HUMZM+8rqz31o3BV61UHRACYOM7I6I0R4BABkUKmzEJqSDjNyHfFy2wJJYY2qgOnhMLobuWTX+1ASxPzzLV1bSaQZhwNGdlyZAnCTImwYwWbVLquHDh+Okk05qB3Bqdr2GiAhUKEZu+7OIaATcUbHtANYBWC+9TrUqRWrXqdBWuvrMt79TnyOjuFBx1oscJDYJqCh/u315iXmSCyWAEYjZREBGPS7uUR5lSvJlF+5UxXYA0wBsBwWvUJVbUB1+lrBwocQyto3k0SQJCutcrEbWUALih6qTqJn5VFkN27/3fe/Hyief/CaA1s6BIsRWQ9yfxcw7iehGZr5Q3k9E09P2lcqiIaLxww8YWRw15kAAIVMJmsh/KUijPbJbo7o8qlvUKGTCVPUmdqqb+/7UXCAOfA421yrvidfuv9HD0IlncodYO+qnnjbgHPZpZG3BxPWRhU488UTs3bt3EhGVsulRLxocpTzr6t0w80MAjkzTSapPYtK04y98xWlnBPY1Apu44/LSswUjOOtaBY6a/BUFGSEBmbQB7KgF5CqOE7DghAsFILSiYBRYYsGkAU52KY6uegswan/NiojwutNOt9DqYW4iwCpGb/u5mHlnyKFUUyVSuU7FUvuHT1FAA4RMNdCMRKW1dmquk/tv2FK15WrwWBhkosCi5p6oD7Y6IgXUu0+AB14T/lQE5tqa12FL0wavEz1crd5LUjcp7ehTXJ+tCOTKfTfrRp199tl4cNkDVwK4I5ObCtEAsVq08mI0v9QcGo9W5dEQUefkE2Z2jD3oYO1xYdnIwGnEjXIDwx5cbMeL3IfDJg4yaRX2BRcPatV26qYnyLO5a+0ZcAimUbNskoyD6GBUv5Z2OGzknJ2kEErDjCxne0e5d83C5uRTTsG2bdteTkTUsomWZAwIqyVMXoxmFIAvIjhVYq2XS5NYiUFTau84e9arT4vMLwH0wIlqJ8c8dF8ut6xvEDZANEh0WcBp3aRGJKy4il17/yJuDASnKKQZfBXwCUzYbDA/JonCwJClRSS3CU1GbAI2lmXhhOnTC6tXr54ON8EtexFA5sANBnu6gpn/2mwniUFzwkmnfPmU088EEIRDGHTSzHHSye3X8OMiQTcKSAKcuC97VKKhbI2JNmkebtmFEqEd2RBK8hurwqhudrh0P2lGmhoZkcpyOoPuvCSwSQvWN7zhbFq1cuWHASSf+ZtKNCByZaKUBWSAhMFgIips2bRh3IRJU+uO6ZLakirsHDWvRg4MF8za5EcRKI4rrB1lzYgC6OqmttHdu7+apu34o091QWElQJyiwgWY64GUJM6jU9hDqu7WwSLuAW92YmTYdeXrN2K9zZ4zB1XbPqeJ24qWlxkcteVyldSieeWUmSfHJluGgkMuhalpEzapsmAQKg67MRHvoS4YhmuJmORNaqxBRAVCb7hLYRJ5NQWT6lyfKNjopnDJAWWg3rJppZJAJrNrRVg2jWjEiBEYM2bMECI6lJmfz6xjWQPcoslKiT6lqdNnfuTlr53T8EWirB4ZMnHelmzZAKhZN9Jwtbz1lWTg1eXYxFgkwupRYRR2Wpa1adJAIwsrptl7CDtf3s488yxj+PDhb8ro9oIiI7doEirR01gpV14zKcV6TZF9ieVupYXikkiM9AgzWoWJCp0slSp7WHry5SqCsqJW6wy0azIxRusGSftipxuENGj1RMpmYj2qXn7SiZgyZcp7mr0nrcRAxQCe65SVYl0nIqIjJx47fPgB0fk5aaChqq66Xoq+BAQCwdsMYRNmGemC4PKInAg0i/o8YaNxKmzqhrKVUatGPKZGArl9BRn5Olm4UdOOOw4vvvjihAxuqU7kzXXKFa8kMZoJhxwxXtuuEbjEmfpJ+1SHPnXAaVYyZMSDF76YXK1WjUhgVGEjjgHhMEwbfxHdpBlOBvqukHirFPb+Ozo60NbWNpSIisxczvaqNKDnOmWpJLbdzIlTT6j7X0wKhCRzeMJcKTWmoz7kumc1i/iM2kcaA6l+LpT7nhzmwGhUxWZ/U9XoyBJQ+w9NG28JvO5jaybr6x133HEFAPVDphloEMx1ykSxn8S0GSf+v4lT00+CjZscGBWnCQscR801UpUmKBwVRJYfuqiC5aoC64pLVpZuRnvWsJGls3jkx7eVuTdZKIvrzpo1y+ro6Dgpg9sJiggwC9FbLgAJQFMul18xcUo9aNSSlkkUFwSOysdJAxlVKkiSjk6F/bKr8Zn6FRzqg8AqbOrrJicHi3CtVIio1kxD0wUGmk8F4IQTTsCECRPemn3P5E5DiNoGoBopTh75SRAR9fR0j+gcObq5O4tRHGDCINOqZUeA+gcuiTWjtc5CYAPoirRHv6FGU2eSWjNZukzErN0aUewEz5jjxx1/PLq7u09o6OJRIoANK3IboEpdnDzu6Rl/yOH6QDCQ3qpJ0z4KMEDvQiaN5KV9a/uSwyapklozve0yxQGlVbCJ0pAhQ1AoFIYRUca+DLn0j9oGoJh5Xdo1oOKe/ElHHn1sZIMweDRrgqeJh2Qp3X03ei9ZwEZcWnxndSNSSVymRnNm0qhRiCRVM7CZOHGiiZTFmhJpkObRENFpadpH2nZWoXDIAWMOjP3fLZiG1m3Q1XapDQHLkxZF7ol+lEln2WRZ+lG+10YlT3AMDHWHzHaPWzlCB5Sw2Iz8t/w+QmdhJ3SZWiViBjf4a99ofs0hhxxiAjgIwOqGLqwREw1k98gXEb0N9YWu5gM4MWkfkZ/SxGMnv3zU2HGJOgqDDZAdcIAgdJI8IDoYJX2wdJZM0vW4ddIVCIuSas0A4S5T/bnNxWV0bfuLZNgkvceDDz7YBKAvptSMBmjAV4iIvg+30NVa5VBnmn4iQVNq75g0cuyBiTvTwUNWoFgTB88RqthOaNYtEO3GhFk+SRXVt3pP6n3L10kbuxLJfAJCsjUju0w6yASsGuW4+tb7KzgaUdr3Mu6ggzBmzJiMM4QHhUVzFzP/St1JRG9P00nkp2RYhUmiEHkahT1olQhrJAw8NUXV642HUBpFWS1RgNEdl/uSrZkwt8l3j4zkkNG5S0kgMxCHssM0btw4HHXUUSdn3vEAt2gitDxN40jQ7NqxvW3oAdkNbUf90hcQnW1sWkZoTKagyQJPOz0iiRWizURO4F6p7pIMGdmaSQKZrEaXWlH+gYkSBYQbjc80KmLGwePGob29PdvsYKLBMAVhhRejWQFgm7T/CgAXJe0kEjSO4xQKxaJ2zWmhZmIW9X1JRb41AT91bDIOTE3fTwpXqnZO/X6d9WJQ0NJRISMHfpMOYSd1lRLFthpcGyopbHpL4l7GjRuHPXv2RM8MbkA88C2a2QAWAWC4dYMJbnyGkQVoiMg4Zupx5uiO8NmpWU5gVJUkaFrppZhD3L1EjR4ZIb/ecp86yLTCipHPbaWExaIDTm9bM0KdnZ3Yt29fR7a90oAewvbUycx1b5KIvpSmkyiLZvSoUaPRofNLfNWONTI3p/m6wq3/5QwDReA+0owk6YatpdElAZlGAKMeC1438S1mpr6CipAMOiICUcbmBxGQQTCYiBbAHdUZCQDMfGOW7ZtUWGH3L6bpJOpTGj58xHC0WUm/LJQaNsWE3/5mC0C1SlnlFEYBphm4+McbfN4bdZ/6q2Jr0TbUaXNfAiJaCGAZMy8Wr4lornjdbPsMxER0BDOvV/ZfAOArSTuJAo1VKhRQSvUtpVSrJSZOvDIbS87LYgZ02tq8ST8u+f3IgJGtl2bgkvZ+Bpp0bptpmiAig5mz+enKJmFvHjPLy87eCmAhwucSpW3frC4EMN1jtMilGQVgOjIDTbGAYW31rlMUIKIe7brzpKcgDiSNVVtz+8/aIEr6GxYFA/X9COslqxyYrADTl1aNgEVWLphlWYD7nc+uAFYT90ZEuvq4O+AGYJtun5HGA/gkggvIEYAFaTqJBE2bCZScHv3RkGE9HeHDvig6eMQtcB+lRlDUTLnIph5AM2itZPEwt8p66W3YqNZIVsDJHjTUrOs0EsEhY2heN9M+C13AzA+pO4nopTSdRIHGNJ0qjC6pP/VDVWDDplU/eU8Fj9SHOFuGkz+ErQOZoQcZEP8lDAVKgqcz6eoMaeQPWdsVwLHBRhuq/WdUuE69BZs0M791/+dR5997770AMALA3gZvr04JXKfRRPSg9PpGKXjbGXYSEXUy8w5ld9r2WehIb1Xhh+WdOvhEKepTqjrlfTD3bg/sDHywyn80m0p3yn9C3X+KBx2/Fw24VJHuPzbiV0VcM7RFgoSrqNoCSXx0cqr1Oyt28FjVu45hurVMUliBvSX52vtbkJicKk45+WS+b+nSHVn1yURJrKytzDwr5NgO1E9WjMr1Sds+C70TwBfUnUQ0nJl3Je0kEjSV7n1wdruWWaDau/RwklV7DMmOBosa9NeBRIYTVcL7qjUKQYh3j7qvgfa6musnEUXdQ92FpWiRAh+yy+61HQMwbJB3//L7ZtKvpdSX8OlLqbPAw6wZAfNypewA0FC/QTE3+9lvQ72V0ul2rbVO0rbPQreifkIlAMxDRsHgql2pAFX3aedqBbCCv+1UKILLdvAsjYVAlt4moAoiH2z5Iat7vEJ+SeLg5V8XCayRNP53nGXkKJ+TOvBBBmBUXfeTHcAxwIYFcuxa3wp0hGT49BZ0+tvQd5Js5Gq1CgB2XLs0aubTZuYVRLRD2T0SwJIs2mekOQC+RERrUQsIE4DTkRVoypWyCxh/TyUAjcCxQK9BsHDPvsibiFwbJwW4YFggW39POqj4j0mE+Rtp/SjXjpXOhZLOZxZgkYBDhv9lFnfJhhV4sHTQ6Q3gZA2bRqYuxJ0ju63lcqWS2dA2XMhk8Dn/UsmDmQM35R8AQETjAcyQjke2b4FmAbge9UHnzjSdRD0d27ft3A2uBgP06us4hS0LGgmsMJCI9hK4Ei3gFWFthEILiAQXoMIrvF2kPEgwvIfCsMAogrx97j2UQ4ED6Edlsl7HeiDIse3MrZlmP2Jmnk9EC4hoNtyh5DVK8t1cuDBZnLB91rqCmf+q7vQsnMSKBM32XXuAJPOZIuZ7CDCpwAm1hgDfXUsi7t7r9Z+iHKwMOcXaSmtdBc5Ncw+AbwWxYYGqFTcBDC5IVOsmKXB627rpby5UmBzHQbWa8lcygbL4ZJn5+phj12v29YoEZIhoOFzr5kFm3pXZqBMz85QJh/cAaAs9O8WEsrSWUFql6T9q8fVE4Ao7psZhVNWBynVFfUvGsEB2tQYbwwIcDyi2BBzPHYhyqXLrJug2vfTSNrSVSjsyvQAPjkC8V2VvHtwYzQgiWszM56bpI7rwlWn27KtU29rbWrC+cAYzv7kShEvSdZC53B3fJgJcOlCRVUjkAqqjdyziXk7Vh4sPGwEgtgAyAu4UvOMujNyAsf8aeuumVQ/F/mDVbNy0EQXL2phln4zss877m4jo4wBuY+YLpX1vJ6KPM3MmwWCMGjF838at24ePPyR9lb0spIIkTs6+roavlcTtEZAIgMqz6rQuohI8ByQrpFD0LSCuQgsb0V61bnxcqNZNH8Kmv0nNXdq0aRMq1cqarK/TXyf8Zqh1aoyGmX9FRBek6SQSNFXG6s079hw44fDDIjvJ2i2KAkxkbEeogfvRvoeQuJIKD9lK4XJ3qEtJVsGNP1kFcKUcPE9j2fijYfI+ARvTckexDM/acaqhrlRvxG2atWpaXSxr46bNeOGFDY9m2SczwP2oyFeLFPYGM5uCgF17uh7b+NL2V8d1QlYxM9iEQSYUMDHX5WZctHK3dqF2rpbrICTkWireNQ0DXC37Vo4PExU2nuvDkgUkLBMSsRkNbADJlRKw0bhS+3vcRrZOGp0tvWnTJt65c+f6jG7JVysXMuwnmqBmARPREQBOAvDrpJ1E/q89uurpZRtf3JaoXF+WsFGlhUzItVKBJep+BRw0/ZFh1M61igFLJ2CpOE4dbBJJWCoAwFyDDeS4DFyYePGcONgArXelWhGrUV0g+f0kPQcANm7cSAA2ZHlv7vD2gCfNjQDuJiKGm0sjpjycnqaTuJ+HDS+8mMpCSqYQGOismaSQiQRMIwCMcKXYcWqWjsa6Ud0iff/JrRoAQaC4N+HDBkC9G9UPgsTNSjtHrEFt2LAByBg0QDbD2/1ZzLwTwCwiOh3ADABrdcuvxCkONI89+tT6xCMwWVs1TUMmawtLgkoANuI+NMHfhqVYNdp4jQeb0JhNH8CmEatGF5/JEjJwbKxdt94B8Ex2nboaBMFgAH4+jR8UDqm6F6q4RJgNz25+qRwW8ErlDsRItWaSQIYdRw+ZajkxZLhS0W6NqNGkwcg+pQdOjESp++HYtWNO1Z9H5bdx7Lpz1IfbNPQTNvujoiCkO7Z9+w6YprEzy+kHAMBgODHbQBERDZc3uMutJFYkaJiZR40Ytuf5LfXuUysho5UGMkna6a/XHFDi5LtNnsUT9Vklzf2BDAb5YZKeHRlEjcAG2P/KP8TKsfHQI4+go6NjWeZ9s2vRRG37u4joAiJyAGwHsA7AeriJe/PS9BOb2kumefeKVeuC+5qBTIJgbXAiZ7110owV0xRcZDdRuE3ePj+uEgEZ3+LRWTMJFyKLsmpqf3v7Q37Ak8Cmt4CTpdtUd573mTz08MNYev8DP22o0wgxxBB3+DYANIGZDWY2mXkUM4/0ll/5cppOYkHzrxWP/uzh1S5oyCrGQiYthGRrhquVesjIbaNcpQxFhWi3Ji1kyCpoIaOzZhK7XzqrRnWhRJ+KVRPYh/AclkaA0x8toocefoSZOdUSrkk1CFynu0L2p1puJclkpeUPrHqmJ0tXSUiFTEASPLKIxSQRFQrhkBHQ0ECGCkUXGoYBGIYP5DrASECqQclsallV3a9/EhdKPTcqYS4pcPoyCBxmzQDAkytX2QBWN9RxhESZiKhtAIi9vBlV2WUGe3ph/YbNVWZua3pZHAkWoZBRAKNVE3CRQcKVSqz1EuYuBawYjQVTOz/CglEAEzgvbWKaV7sGcqEsMQrlHVNHogBEjkapUkHSl6U9k+TTAMDOnTthGMYeZs60RAQAP0YzwNXy5VYAuAHhqUcftXHDi9uOOmTsqEZu1FUKyNQBpkWJgKkho1gxAHwLRux3z4t2jyIBA8RCRs4WFvOd6sqmKm3SwAZAohUHsp5y0Apr5qFH/oOhQ4e0xG1icEuXhe4navlyK74KJv3z3keePOqcOa9K03dNYQl6AjJhVkyLS0uEKiboq1oxKmDiLBe/7wTxmFQp9zqrJkohsAHirZv9RUvvvx9L73/gh63oe5BkBmey3EqigjIPr3z6e7f/+8FEn2hcwp6wZlTIBOIwUuwlLM+lJUPUVjExZMgqwigNcWFhFUDtQ6RYjRncRH9ezCYQu5FlWJms5exfTw0Mq/EaIDRmA0THbZq6r6TWjGPXtrC+IqwZALjjL3fZzPznhm40Rszu+vFR2/4kIqoL8IYVuFL3E9H3ovpO+q1esfTxp7uqVXuoZYX/Umoho3GZdJAJvFbgofZbV63Pax/rCqkKmxgpZ/yKoK7iKvmAkYO6/v01VmlPVRprxrdIFKsm1oUK60e87gXLJg4Wic9TtGnzZnR3d+9g5pYtspbFssv9SBOI6IMNnEdw4zahSvRNZmZn2rFH33vfoytf/+rpUxq4jxjIaAATZRnJxyJHwxoYKYuzYkgZPfJdjzALpdWSpyqklC5eE9gvXme4NG2ry0HIuuMvd+LFrVu/3ar+s6gZ3M90ARpfJ+qXUQcTf0MfW/XUN/9470OzXz19itbdirNmAoqADIcFhBWRVHBKC5sUkKkrBREDmTrAtAAoTS0en9CqCSgGNkDz1k0ilymhNaOVcu7tt/8JGzZsuKXxDqPFzKgMoGEnbwLlzlb0nWbh4LuX3Lfc0QEldVwGQZCEQcYpV7WbaCPa1V1fgQwZRuQWOC8hZMgqgIrtfq1f3dZKBXJlxL40ozYx8Zqw/og5s2VRWgmZ7q7dWLNu3T5mfqrxTuNlc/SWy1Vi0DDzPofMVaue3QCulgObVklyYKrlAGQEPGSgaLtWgOPfoyYwrCtcFZCAi5LFGwYZKpZ8KyYOKL0FHe16UVK2MKAJDEtKCxsguQuUGEzNQEaju/9xDxwnfTmDNHKDwU7klstVGosGL23f/v3b/9VYSkKdNSMPaUt/C4A4lWrkJrdV+xDSWivqJtpKI0FRkIFhga1SrdSmbtO9/7RL7WrhEf/AJrJqNHOhGoVNFEQij2VcBkLt+493/BmrVq9u5cJq/vB21JbLVSrQbNq67bbb712R6gJJC4zLlokACQDY5UrdpraJraqnulLKMLMMmFjISIBh0/K34AWyGaWJg03AfUpj1ejaqNdLCBugBhx1C22fpcuk6du2bdx73/0VAPdl1nGIctcpmVKBhpk3Vxze9vTzmxq7mjpJUnF1ZAtFhooqGTZRLhaAepcoZAmUqOFrFTI6uMQtnZtZ1muT52hdqLAyLSlgk1SZQ0aTB7Tkb3/HsGHD7mnJtANJzIyq7URuuVylAg0ArFr/3Ed+9Me7G7uaZiRIdXlkSwWoBX3lDaiHDTuOG+epVOqAJsNFQEXeACgTIpXRJQUybvtsk+uiVPdwNmHVBBQXr9Gc1wwwk0AmC5j94Mc/5WXLl3+s6Y5ixBjYFg0RnUZEJyj7jiSit4VMtAxVatDs2LXntjvuXcGVaoY+tiIBEdUlUkecUlk20E0NMGoboB9dKrYHIaMCJgFsQh8ep6rfkvQRBpuoazbiQknnycfTACHqXnTtEvetsWY2bd6MNevW7WLmRxLfYINiABXHidz2R3mAceCWiFhBRDYRXQ4AzLwOwN0AUq2RlRo0zFw+eOyYf/7hngcTtU9cQS5E6rC2OuIku1cBqybsPhS4AAIw+iHsOsggQWA30ShLxMMUApwo2Oj69dtrXKM4Fyqp9REaKPaOJYGMrl2jls1Pf/4L7Ny589qGTk4rBhyHI7f9TUR0JNyVD+YDOArATADnAng5ET1FRK9j5h0AUgUiG7L971q64gPVSvnpt73u5MTn6JaMFVMGtLk5yq+BXdF/8YyiFYCNUbTcT6BaBlvF4KehwEW9P3/WtQcZJIGM+BXWWQlhSvyL7bWTs3TDpg5IS7Jos4WjJlzK7aVkvrprhUzWTAWFrOI+GmumWq3i57feVn3u+RdubKzTdHItmv0PJjGaB2Cml7wn9BCAxQDEcrgXAFiSptPUFg0AMPOass2bH306oqh8RP6KXHIhiXSQsStV2FJ8xi5XavEa2arRDXvrAsTyyJOUJ+O+l4SWTKsU9WuflQuV1GVrNHirmRwZBZnIzzpkIugfbv8TRo8e/Q9m3tPYTaYTg2Fz9LYf6kEFMgEx86+Y+SZmfn2aThsCDQD8++HHz/v2bXckahtwn1IARo67hGUJ62AjXCh/eoNkSWlrx2jmLqnB3z6DjFBC2IS1d9tFuFAR7bSwSQOcFgV9VX33ph/gn//6d6rKb02Je7/CHhEtIKK5RDSPiGILhBNRp3fOwoSXaAkdGwYNgLtXrF7X8+L2XeEtFKumkTWPVGtGHX3SwkaMQlUqtWkKlbKfrayrHyPHZXwLxtSPLMU+JH1Yx0VnpSQNxkYNeceWcQgURw/Zj/SB5Kj7lvt56OFHYNvOC16wslfkMFCuOpFblvJgsZaZFzPzjXBnW8+NaD8bwGwAEwB0JrxMo5MqI9UwaJiZt+3ee9lXb/ldzBU8f1+xaoT7RIWCG4g1DBhFvdWgm24gXospCzJsKl37fBdKrSvsx4PqCoqbQZdJgkWoNZPwgak7v1HrKAsXKs6qSQubwP2lrB2TVhG1cz5//Vfw7/uWhj50rVIvWzTzmHmx9PpWuEFbrZh5idd+R4prHEVEx+sOeKNRX/SWYEnlQzflDzy/acuNf37g0YUf3rx12GEHjk50ji4oLMsouLdU7e4J7JctmLpzihbs7jKcchUFlGAULTiVKoyi5cZqrKK7BC0AaivV7iUCMg1bM35DCjz87JXcrN20lTwo3IwSBoZDg8nKTG/xHtLWytFKBpk6m7zufYR/r++9byle3PrSc8y8NPFNZSBmztxqCRMRzdDs3gHXYslSXwKwloh+AUBMA5gJ4B1wA8AXMPMuIko1vaMZ1wnMbK97YeN7PvfDxdENFavGDb7WWzVxEpARgWA5IFyzZrrhlKuodveg2tUNp9wN3tcFLnfX7lsGneIu1b3HhNZM6FSEKMuol+M+iV0oIDZzOHL4Ou44O/V9plhEUu6THRtXX/cFLH1g2ZmJO8hIvbwKwkgAagGvzAt6eUPXswCcBHeY+0a4dYNnM/O5HmTejnRWUnMWDQB095R/v2L1+hefXPf8mElHHgqgFnANDFsbBuA4oEIxdP6TPAkybNa1HLNxNPkyRtFCpasbBZRgGwaMggUyyqCqa0lF5fVkYc3IsPFdEsm68YuAa4auYy2cJPk7HtgCFoq2bX1NmrpzVMsG0Foeia28hDCJspZIAeCf/nIX9u7bdx8zP5HsJrITM5LAZDQRyUlnN3rxlbTqDDtARJ0eIDIRM6+FCxvdtUYAWMLMqWI5TYOGmZmI3nr1Tbfec9sXLqfgyowKcCTYAO4vAkn5Llwt+3Eau1xxXSLN0LYKGPW1ep7vQpW7wVbBnWpQLQC+66SZYoDgFz444TBhbMbri+xqzbIJA453H2mU2H2RgKHNwwlzoZRz3YuGA0d/kwktlaT9Sf3ato3rFn7FWfHwI+emOzkbMRK5TluZWfvQptQO1AdqWxK4jVLU0HeUMrHdmfnfMyYfvfb+x1ZPOPmEqXXHA8AJg41ol3DlA9ZMWHOgt3CqXd0oeHDh7i7XqqpWgEoZ1NZe37lSQyYxZHQPvlPVAwcAmMOvEyMtZNKOdslWTRrYiHMD1zb0+zOUGtD+xW2/Boh+z8zPteyiEWIGqk24R96IURwktzHzfLhuUqdyrNO9j+ysmVYpsyDBQ08+9Zarb7r10b98ewrCFpojqxiATfBOglaNWSmg2t0Ds2AF82kqFR8ytrTfLFpg2wnAhgwDFXS7I1rdLsAMy3PdDAOEjsAthLlMiRR2nrRf/krGQSe1Gh1STwMbIPx9JgVMXD8hUiHc09ODr37z29XHn1z5wVQdZSj2hrcbP58Xw8u4TdB2BRHtUHaPRMoM3b5SU8FgWcz82O6e6rLb/xU9B8p3rQyjloUrAsPycdTHaexAnZrgF88uV2GXqy5sKhU3UOyNRNndZVS69vkFt7i7ywVdteINySqB3bTWjBF0t0LLeooJmYYVqGXDZkQRrTiAhLSJis9EWk7yELJdrR8qj5n8Wd9fNdU5YSsz1Bq4D/b3f/AjWIXCTcycan2hrNXLmcG/VPJm5gDwR3+IaHxUXk1fKtNhj+WPrXzz1Ytuef7V0ycbI4YOCW0nWza+C1Utu8PQlYprgRRqc5jE8LUqtaSEkFm0fMtGxGuEC0WG4Vs1TncXDKvgjjwZFmCTPwwdNqepTgpk3DdYz++6Qt/qcX3v9ZZPK6QGhtVC5R5s6uDVVOKd5I5FxGd0kFm7fj1+ePMtXStXrb688RtoXk4vDm8DADPP97J8Z8MdCVqj5NXMhQsfMS9pBtzh77ne6wVwA7npqtdloExBw8wbpxx1xGVXfPPH3/j+pz7s75czgsXQsg8b/06KoGoZ8CZaGkUHZqXgZvt6QDELlnakyS7XD9EK2FS6avuEC0VWF1hArlqpBYWBusBp4veue2jkCYjqaK5ZDLobIdeM+02ULY7IUSYlzlIH0xjYyNeKK/KVSBrLpQ7GGsg4joOLPnI5r1y1eg4z72v+RppTK6YZRImZr485dr30egXcXJjQc3pLmSdyPLHmmW8N62i//C/3rXjZGafU5xgJ6HC14sNGDgzDcQAFJmah3qIR1owKGfm1gI1TtkCG4fdhFC1QoQBnXxdgGJ5Vsw/k8YZRb3FEKQAZ6eEMPJCmpcnY9dpK7ooOQPVzjRLCJUaJYAOEAqfZ68fdW+C+PH3/f3+EjZs3/5yZW16mM05uHs3+WXOmt5X5t8Qb7j75yu/c/PzJ044JdaFEhrAMG666bhMKBVC17ObAeO6TLq9GQIWVXxUVNupQd8CFsgpga68LO+9Xn+yqCz3xECaxbCTIBKrwyZ9NWH/yqJS7w2tnB8+D9wAmdesSSAsbIBFwgvccrVAgxQ1rS5BZs249Fv3wx12rVj/VexMnI9SbmcH7uzILBsti5o1dPZWLF3zjx5ETKdVjIjAMrxCVUbRgFgswilboPCgBGbtsB8Bjl204FTdAbHf3wK7UMoftcgV2d9nNGO52s4ZF9jA5VYDZfYDiApiapVTqIENG/WYWg5sIEBdK7iaCxIU29+E2C/652iVckpYVTVoKwv0QNefHr4UdpjggaQPw0j24LtNlvHLV6tn9wWUCasPbUVsuVy3LgV/33Ibvjz1gxBV3/GvZ4We96sTQdjVXquZCwYvVqFaNUbRgVAqw7eA8KNmCEX+bRTOwn0zDj9fY3nyqOhcKqFXWs0rJXQTPmglAxvulbmiZFcl9El9V35UTzx47jc2fChle1s5hipqLFAUbjeUTPK4PAkfFxL676Cbs3tP1m96ezxSlZoe3B5NaBhrhQl3xzR8/d8rxk6zOYUOlq3qWjB8YVirtWUU3qc+q+EFhkU8jAsLuqJQMmPr/cBk2Vbhv1im7UxQA1Fwo1J5fAwDah7g5PZY3AZMoeSDUg0xtSFuK2cRAx3VjzFphKslNYnhWgThORuOwAdIBB0iXEaxxtVKvEiFB7umnn8JNP/nZnpWrVp8Xf/HeE6M1NWcGolriOgkx86Ztu7ve975rvg5HFzQLW/rEKrjL1RYK/lB3lPukk112UN5TCbhRbDuo7N3n5dV0o9rdg8ruvXA8t4l7uuF0d9XcqGrNlaorLqW4L36ynwIZ4eY4ZgFM5G9Vhr+JfY7p1ihmq80/13e3DGnSZpTFkDZeE+Iexk6I1E2MTKIEQ9q1lRuq6Nq9C+fPv9hZuWr1aczcHX5S70tYNL1Vj2Z/VktBAwCbtrz4s9XPb/7fq7/3U30DURfG8mZwi2FnJVZDhgFTAKdQgFm0YBaDD5xdsQMb4LpSAjaVrm6w7QTiNdXuHjexb/d2OHt318GGy/tA1dr3uy7WoIw0+RKQ8SAiz+gt2xxYkqNs1475MDKsGmwkxQ5hN6q0wPFvqAnohPXn3Q8z4wMXX2Zv2rzlEmZels0FshU7HLnlctUy10nW6rXr5//K4ZlTjzpy+jvPeI3mLgpAtRKM11iFQKymMKQd7DgwS0W3LITnPpEmh0ZIwAaouVFGQfMw+VnJXXAAkOfSGQCofQi4CpCmIJamowAg2GtrO+yv8RNmatsATIPcsm3e37oruUHq8AJQgXYJFJqEF5HTEuoCamaEB64RZ81IkAGAL3z1G/z4kytvfe75F74b/g76TuytgpArXr0CGmZ2iOjUaxb9/OmJLzt47MxpkzR3EoSNPBfKAMCOA6vU5k4p8ILC2OfGdFyI6H9RVdiI/Bsyjbr8GqD2gbBVgIMabOBU3biNLtEsYo6UgIwATGgGsNcWBsEUE70NCxQWdJUtCAkCqVZjkNpHZv0mjeNEKSVkfvfHP+E3v799/eqnnv7v5BfpbTGcfDXKRGq56yTEzLvXPvv8Ke//7A2VTVtD6vXIMRvFhTKKJRgFC2ap6G4FC1Z70XefzKIBs+DlsdgMlpYJtCu2DyI5XiPmQ1W6ul33qVL1C2Xp3Cjt0iqq2yRZMzrIhBVIkgFkGuSuXy0P8zrVmjXDTs2lke5JX2ic6zeNxPnaPhpxq9ScoigpkHn08Sdw9eev3/Po40/MaPWytk1pAK7r1Cr1GmgAgJnXPvPCprPO+cQXKj3lkHIQ/hIoXrxGCgybpSLMYgGFISWYpSKMgvu3VQrJsVGB48Vr3L+r/nQGMc1BrsonJl9yTze4WgFXym5CYdiDJQeBdfeCGnCilkwV1owLERtkl7WQAaAHXwKoxLVJC5wwxbpMcmzH6/fFrS/hvRdeUl25+qlZ/b38AQNwbI7ccrnqVdAAwJ69+/66aev2a+Zf9y2Hwx4ECTbUVgKVhoCKJRjtQ2ANKYEMA4UhJRSGlECmOypllSzfqiGzPrqhWjUAfKtGyClX/VUUnH1d3rItFdeakWd6M0c+cKo1E/Z1E7cp4jFFk2AaBMOuREPG23wgREEjpuSme8MNAEcnARLZyktSVsLrr6enB+/+wIX8/IaNb2XmVeE33E+UWzSJ1eugAYD1zz3/pWWPrb7t41//X5ZhE1wpQYKN4kIVhrT7LlSho913odzNC+ya5G+yBGTUmd9q0XNRUkJeG0pYNWmlYs8kbzNcsFgGBSBD1Z4aZCo9gF0B2WV39EsHGASBEgaXuONhwIqK+9RNJg1zmWRrRgOZSqWC93zwImzfueu67dt3/DH0gv1K0SNO+ahTTb0SDFblJfO9ixkdHQXrjZ+75H3+sUBNYavgP6RyUp0fsPWWWgHgFcNyh6HNshMIAtddX/kCOOUqzJKyRK5hgCsVULFUVyiLnCpgE2BUwWS4c5XkdauZYRoE2/b+lUaSAtfw9hVNLyZT8fJ27LJvxQjgAKjBBdBbKHFuTUStl/oEvVr9YXHtpBMoQ12mkAC2bdt434WXYMtL27778H8evTrRRfqBmAE7DwYnUp+ABvBh85Zf3Y0/l9qKcz49r5b06c/mrpR92HC1DKN9iA8bs1qGQEPP9j0w/dncXSgOLaC8B5GwEUPdZLr5OTqJtcF9VSuApvQn2VWpjk3NbfCNKQk2gGfJeMcsAsizYsAOqNJd7za5H1hDdYsDUs/RzC8KTkEIwkY9T2fNhE0oDYMMV8qY95GPY9uOnbfeu/SBD2M/U261JFOfgQbwh73P+s3fCkuZedan550Ho9jmH/etG8+FkmFjtLuzwq1KFTjAbV8GUDoA6N7uwsYuG7AV60bEaaySu/YT2ouwK/UWTajEcLOI0xgWANP92yy6kAAAw/KtGZMAU3Lh/FGlao+XeRwCGBku8sPZgPsG1E9i1eXMaIt+RXbaOGSccg8u+OgnsP655xffc+/Sdya/aP8QM8POs38TqU9BAwDMbBPRyUTGP/b1lF/5+Y/ND9QcFrChUgfQHbRsDAD+o6PARsxnAtyhbzXPRvwSse0AhdrIU+w0B8OsKyIFx/bdJ3LcEhPk2CDDdHOA5KJWjg2qVIND1rY7mkWV7hpchIUhQyXtrGll6D24BrkEHV3pCUUqRPzpEep1wvpRcn0qlQre++HL8MLGzT/599L73xv9RvqvQgc0cgXU56ABfNi8pqPUds/lC7/ziq9e8eFQ2FC14JbgjIGNq+ipMW5gWJpeYBhwClVUutwqBGQVQcVS8CTH9jOF/TiNYblBW8ONY5A/ZOsFptHjgwWA21YaQSIxbO5U3aCzdx0AoWtghSkQUFfBJE9ylJMjQztT3KYIyEQWdlcg09PTg3fPvwRbtm777r3373/ukhAzYFdz0CRRvwAN4LtRrzJN8453f+JzZ9z0uQXoaA8uXyvcKKM0JAgbq4gC4C+nC7ilINyh7zIqXT1186Jk2WU3ViNX4BPJe3Kcxr1Wwb2Xcq0iH7yHjFlk8vbUXSMAFtktEnBx7BpU0rpGcqlUBUx14Ikr4aCRDJFgHZzkkBGxphe3voR3z7sY3eXy9ffev+yK1DfTz5THaJKp34AGcAPEAM488bgpV7/+A5d/9hdf+wwOHTfGPx4Jm2EHgCy34AwZBsq793pu0G6Y3uqVcsIeKSNAYmY34AaXnSG1X2GjUgE5jjsHynFAxZLrygEgqwqqoDabWxc8VaAiLA2nUgaqlYBLwyHrWumWDCal3IYvBTxRq3PW3aof4HXfRx1kkloymmkR/3n8Sbz/4st405atb928ZcvvEt9UfxXnoEmqfgUaoWX/efxayzTvPvvCK/7x/c9cbpxywhT/mFxf2CgNAVcrdUPf/mqXBaVWcKGqrTUs9qllP912FRSGVGF6sSG73O2CpmuXm0xoiSVjCtoHWlgYAiZcrdRgkqLerAogNzgeEnOpVtLBRgVFAsiEDnWHzLv67e134Nrrv97z2JOrjt8vkvESiMH58HZC9UvQAEDVtv9FREd+6NqvP3Hpf88dcv5bgmu4U6EYKCJuGIZb/9ebrlAyDFQ8q6bS1e3/a3e7bo0MHSF1rSgA/nwos1REYUjZq4tTcl0qsR6VgA1Q90DLrkya0aK6Mqdyv4bhg6e2CmiwiJgKmySSLbLEkAkExesh4zgOvvC1b+E3t9+x7rEnV83o79MKUim3aBKr34IGAJj5WSI6cNEv//jQo6vXTPzSxy6EZZl+lTkfNlbBDRLDfSCdfV0wrCKKVhFW2a2iJ6wbHlJCpasbZBrgkBEmp1IJzINyylUfOEbRglXq9otxqQvf1eXeWFJ5Ut1+SXIBdr+EpyidIVsljgN4bd2SGtGukc6a8YEkF/BSIKNb3aEOMmr2sWTFdHXtxQcu+RjWPfv8bx59/Mlz+vUEyQaVWzTJ1K9BAwDM3EVExxQK1s1v/tCV7/7RF6/E2DGj/S9+mCulWjfCKrG7yy5wPICIuIxOdsUFjFmw/PZGxS0pKtwzMgy3gHqhBpKwIXIZJGR5FogAk1V0pz2Idt6CevLoUKp4i86aEZ9ZCGTkIezQeIysiPIUT69Zg/M/dJmza0/X5StXP3VDspvev8ScTzNIqn4PGsAPEr/nyMMO+cMZH/z4Lz514XvonLNOdw9KwPGtG8sdAhfWjVkswWjv9kEj/hWJeuJvVXLGsICLmPIgAGQULVS7ewDPJSPDALrgwad2vlF0YUWKJcKVigsbsVJnxvLBJEMm4BJFWDHSeWExGRUwTrWC7/zgJ7jpp7/Ys3L1U6f118p4WSmfOJlM+wVohNY998IviWjJj37954d+9Ze/v+xbV1+OMSM73YOGCWprd/Nc4Fo37n4D3NPtTsi0iiCry4eLWI9buEZOuepbFWGS51fJUq0Yu1yBXa7ALBbq2unWqEoivQvkuWzCSlFjRUmsGN2UgpSAgWPj6XXr8aGPfwovbH7x5qeeXjO/v9X4zVzMcEJGCXMFtV+BBgCYeRuAww8ZN/bc2e/96C1XXXS+Ofes02oNBHC8oXCjNATsTV9gqwAuloBq2bdw2HFQ9dZ6kiEiwCNL5xIJy0YnFTKREsAQEIqybtS4kA4yad0k9+LevnDI6ADjOA6++8ObcePN/7dn5eqn5vSnJVFaKQaDG1jjajBqvwON0AubttxKRHd985bf3POrO/8x+ZtXXVazboAAcFApg6oFHzhwHN+lYseBUexyQeSBxl14rhYMBurLSOigI1sqAjKGtIaUaFMXOA6BTN1IVsaAAcKnEqiQ0QEGANasfwYXXn4l7+7a+5uVq596d39Z3K1XxOzmQuWK1X4LGsC3bqaMGD7s7a867+Iff3r+eUPPe+PrYZry3BsFOOhwaxMXS+4qB5Wyb+Ww48CslsGVSgA8QupwuCo1JiMUOjKVAWBSuUjefiAZYMLgAgDd3T343o9uxk9/+euex55c9br+sBZ2r4tziyapaKBMCiOikTOnTf4RO/Z/XXPx+403nHoyjEIwxwRAcA6Rl5UrrJxAcp0HGr/wlRjuTuiTa4e7Y8Ditk9hvQAQK2u6f4cEeeuOSTGiJFMSpIfJtm3csvg3uOF7P3CGDhv6o6XLll8yqKwYSYWRL+PRpy+IbLNp8SXLmXlWVtckogUA1gIYCQDMfGNE204A87yXJwK4K6p9K7VfWzSyPOvmzUT0ss997ye//NqPfvHy6y69AK84cbqS/FZwh4w9K4cQhA4AHzzwjnG14iYGSpCJCxrXBXylmEsSsATaJYULEJ5w595U7X7iAKP8UjMzbr9zCT7/tW/jgM7Ovz+2ctXbvc988IrRqxYNES0EsIyZF4vXRDRXvNboSma+Qjp/DRFFwqlVGjCgEWLmZwGcTERTPn3DTX/rHDZkzOcv/xAmTzzSb1N70OuhA9TAAyAIHyAwbSBuZrU25yWQS5MAKoDWLQJC4KI9nhAwuoeGHdz7wHJc/aWvolAorlz+yKOvZ+bnwjsZTGI4ves6zZPBAeBWAAsB1IHGs2bGK7sXAbgCQA6arMTMjwMYS0SvfGHz1tunHT1hxJUfOh9HHX5YoJ0MHQA18HiV9Egt16BMI0gyrUBbjkFn1QABEISCBYiHCxAPmJg1o1Y88hiu+/q38NL2Hc/d98DyM5j5Sf0Jg1Pci8PbRDRDs3sHgNkRp80movHMvFZqr8KnVzRgQSPEzP8mogNWrn3mv55Ys/7msSM7Oy85/x04/RUnujVv1MXRRBDXz3r1LIxiyYWKKOXpPaQR61bqpTzwdRCS1/NWLBagHix1bYEgYGRF/fp6cKlWq/jtHXfiuz/8KZjxwr/vXzZ3sAxXpxYz2O41i2YkANVVDXVdvTllByi75wBYku1tJdOABw3gZxb/AcABRHTclu27vv/JL3/3pPef80bzvDedgREjOutP0hXy1k0tSFq7N6ReS13ZTKXMROgERvXcMLgIqZBR1sreuHkLbr71V/jlb//otJXa//DgQw9fyszrozsd5Epm0Ywmogel1zc2GCPpDDtARJ1xk1U9V2o2gNMbuHbTGhSgkcXM/wHwCiIa/Z2e8uXfu+U3l8yaduyQD77jjXjFrBmByn6Jauemqa8rpKlZo83AjQKL31cMYDg8aG3bNu78+z34wc9+weuefX7nCxs3/8+27dt/wsx7ojvN5SrR8PbWjEaddsAbaZKkvo7STQDOYeYVGdxLag060Agx81YAVxLRp554as0rV61//ps7du4+9vWvOrH9jae/Bq85aQbEyHQocHRFrqKuGVbDJWqFyziQRYBEp31du/HXe+7DH+68G0uXP+yMHTN66T/+vfQiD8C5UogRP/oYJSKaC+DcmGbbmHk+XDepUznWCfhuUtR1FgBYxMx94jYBgxg0Qp5b9S8AM4iobdWatafeff9DF9uVyuuPO/ao4htnv4bOOvUVOGDE8Ni1jwKH4tZAiisG3oil5Eld82nDps24fcnf8ce7/oYXNm22O0eMuPMf/176eQBLn1j1VJ5x1qiaDAZ7w9JhQ9Nq2xVEtEPZPRIxMRcPZisEZIhodl8AZ9CDRhYz9wC4E8CdREQr16w7buW6Zz90/aKb3zmqc/iws177Sjp5+jQcP2Uyhg7pECe5/yrAEVm1sZXoQoASuYRtjLbv2ImHHn0C9z24Anfc/U9mxp6ecnnRI48/uYiZn26441xBca8Pb/9SyZuZA3fIGgBAROMBzJDybGbDg5EXoxkJYAb6ICA8YDKDWy0iOrijvf3sV544/aKt27Yfa9tOYeoxE8yZUyfRjGmTMX3KsTX4RLhUSVd79BVj2WzbvgMrHvkPVjz6BJb/5wmsWrOuWigUdrW3ty9b+uCKHzPzX5h5e7qL5koiY8hYbpt6TmSb7ge+24rM4BXwhqnlwLJ3bA4zz/HAovt/X8zM0TfdAuWgaVBEVAAwuaO9/eXTp01+94svbT/aNI3hk8Yf3n7wgWMwbuxoHHzgGIwbMwYHHTgGBx84BiM7RwSCzbJ0AHIcBy++tA0bt2zFxs1bsHHLi9i0eSs2bN6CDZu2YP3zLzjtpdLeYcOGLr/7nvtuZOblAJ5iThm4ydWQjCFjuDj57ZFteh5clClo9lflrlODYuYKgEe87UYAICLrydVPHw7gIAAHH3Tg2ImTj55waluhMGXbjp1Du/bubTfIMMlwcWNaJkzDdEzTNGzHRrVqw/byMhyHna7unp2lUtuOglXY0FPueerZ51/4T1fX3mcBbASwAcBzOVT6UHk9msTKLZo+ErmsEevfmQCq3uZw/p+yX4iI/gxgdEyzrcx8ZkybAa8cNLly5Wq5GqspmStXrlwplIMmV65cLVcOmly5crVcOWhy5crVcuWgyZUrV8uVgyZXrlwtVw6aXLlytVw5aHLlytVy5aDJlStXy5WDJleuXC1XDppcuXK1XDlocuXK1XLloMmVK1fLlYMmV65cLVcOmly5crVcOWhy5crVcuWgyZUrV8uVgyZXrlwtV16cvEXylruYB3cp0wkAwMxXKG0WeH+eCGAZM1+vHJ8nvezUHF8AYC28pVEbXNM5V67Wi5kH3QZgeS9cY6F6TQDzpNeLNMcXSK8XKMdnKMcXApgb9jrf8q0/bYPOdfJW79vWC5eaq1gka+GuLCisnR1K+0UA5kuvA2sys7s4+4nSrnlcW7EQAO5Szs+Vq99o0IEG7sN+V29ch4OuzHgAy7y/RwJY4C1hCqWN0DYiuk288KB1q/f3DM31tgGY3fRd58rVAg0a0BDRDC+mMQ/AKCk+0hIx81r52t6+66VjM+U2cAEor4k8H8AMItru3es2yYIZiXqrbId3rU55JxGNJ6K7lH3L1Xbe/gVENJuI5hLRwhCg5cqVWoMmGMzMK4hoLYArWQnKqiKiRVHHJV3BzDsi+ukE8A4A5wC4QL0fpd1sADOl42u9+zgXbvzlegACNJ2aywnwjETQLZsL120T1xoPYLx6357FtJaZl3ivQ99XrlxpNWhA42k2gAfjGjFzJrEO72G+EcCNnhWxiPUjQ7cBOF2xghYBuI2ZryeiuQBuIqLx7C7QvgPeSJMk8Vq1dObAjf8IzUbQchJaC2AREY0EsEQAJ1euLDRoXCdPc+A+1C2XxjVZhOADL9othGsZyRaOcLWWeP8uhmvtzPWabEO9VdPptd2h7FfBMgderEeWd6353vE1Ye5VrlyNaDBaNAsB14VQYiS+mnWdvJGtu4jogBjXai6AuwRkiGiG9/dIAGvktp4rtdj7e4XGtRkJxVLxgLVWuYfZAK5Q3z8RzfZgI1ynhXDjWYHcnVy5GtFgA81I74HthDvCowVNBq7TgwCuVx7wOajFWASMRgJY4t3PSLjxmBXMvISIAvfgtZHv90YimisFiFUXCXCh4t+Dd81O7zMIxG7gBp4huUy3Ih/FypWRiJn7+h56Td6v9DLAd0daea0ZqD3ogcxgDxrbNact9mIwImg7H5Jlo8Z3pMzg8QB2aI4vh+tmLfLuY5vX511wgSZbNCLnR8R4xrOSiZwrV6MaVKAZbCIiZmbq6/vIlWuwBYMHjTw3KR85ytUvlINm4Go8emmELVeuOOWuU65cuVqu3KLJlStXy5WDJleuXC1XDppcuXK1XDlocuXK1XLloMmVK1fLlYMmV65cLdf/B4Km7dTQiv0QAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Set vmin and vmax to symmetrise around 0\n", + "vmax = np.max([data['vort'].max(), np.abs(data['vort'].min())])\n", + "vmin = -vmax\n", + "\n", + "animate_data('vort', cmap='RdBu_r', vmin=vmin, vmax=vmax, clabel=r'$\\Omega$ [norm. u.]', title=r'$\\Omega$')" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "MovieWriter ffmpeg unavailable; using Pillow instead.\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Set vmin and vmax to symmetrise around 0\n", + "vmax = np.max([data['phi'].max(), np.abs(data['phi'].min())])\n", + "vmin = -vmax\n", + "\n", + "animate_data('phi', cmap='RdBu_r', vmin=vmin, vmax=vmax, clabel=r'$\\phi$ [norm. u.]', title=r'$\\phi$')" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "MovieWriter ffmpeg unavailable; using Pillow instead.\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "animate_data('p', vmin=0, vmax=np.quantile(data['p'], 0.99), clabel=r'$p$ [norm. u.]', title=r'$p$')" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "MovieWriter ffmpeg unavailable; using Pillow instead.\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "animate_data('S_n', vmax=np.quantile(data['S_n'], 1.0), clabel=r'$(\\partial_t n)^{\\mathrm{iz}}$ [norm. u.]', title=r'$(\\partial_t n)^{\\mathrm{iz}}$')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Time Series of Volume Integrals" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [], + "source": [ + "rcParams.update({\n", + " \"text.usetex\": True,\n", + " \"font.family\": \"serif\",\n", + " \"font.sans-serif\": [\"Computer Modern Roman\"],\n", + " \"font.size\": 16})\n", + "rcParams['axes.titlepad'] = 20" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "test of Jacobian. Volume of torus is: 0.0787 m^3\n" + ] + } + ], + "source": [ + "# Define Jacobian\n", + "mesh_t, mesh_rhos, mesh_thetas = np.meshgrid(times, rhos, thetas, indexing='ij')\n", + "mesh_t.shape, mesh_rhos.shape, mesh_thetas.shape\n", + "\n", + "Jfun = lambda t, r, theta: np.abs(r*(R + r*np.cos(theta)))\n", + "\n", + "# plt.contour(thetas, rhos, Jfun(mesh_t, mesh_rhos, mesh_thetas)[0, :, :], 40, cmap='jet')\n", + "# plt.colorbar()\n", + "\n", + "# Calculate Torus Volume:\n", + "V = 2*np.pi*np.trapz(np.trapz(Jfun(mesh_t, mesh_rhos, mesh_thetas)[0,:,:], rhos, axis=0), thetas, axis=0)\n", + "print('test of Jacobian. Volume of torus is: ' + str(round(V,4)) + ' m^3')" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [], + "source": [ + "def int_dV(field_data):\n", + " '''Calculate volume integral of field_data'''\n", + " J = Jfun(mesh_t, mesh_rhos, mesh_thetas)\n", + " return 2*np.pi*np.trapz(np.trapz(J*field_data, rhos, axis=1), thetas, axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "# Calculate energy\n", + "def energy():\n", + " n = data['n'].squeeze()*n0\n", + " T = data['T'].squeeze()*Te0\n", + " return int_dV((3/2)*n*T)\n", + "\n", + "E = energy()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Density" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "N_e = int_dV(data['n'].squeeze()*n0)\n", + "plt.figure(figsize=(4,3))\n", + "plt.plot(times*(10**6), N_e, 'k-')\n", + "plt.xlabel(r'$t$ $[\\mathrm{\\mu s}]$')\n", + "plt.ylabel(r'$N_e = \\int n \\, dV$')\n", + "plt.grid()\n", + "plt.tight_layout()\n", + "plt.savefig('./plots/' + job_id + '_N_e.png', dpi=300)\n", + "plt.savefig('./plots/' + job_id + '_N_e.pdf')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Temperature" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "T_mean = int_dV(data['T'].squeeze()*Te0) / V\n", + "plt.figure(figsize=(4,3))\n", + "plt.plot(times*(10**6), T_mean, 'k-')\n", + "plt.xlabel(r'$t$ $[\\mathrm{\\mu s}]$')\n", + "plt.ylabel(r'$\\left< T_e \\right> = \\frac{1}{V} \\int T_e \\, dV$ [eV]')\n", + "plt.grid()\n", + "plt.tight_layout()\n", + "plt.savefig('./plots/' + job_id + '_T_mean.png', dpi=300)\n", + "plt.savefig('./plots/' + job_id + '_T_mean.pdf')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Energy" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Calculate energy\n", + "def energy():\n", + " n = data['n'].squeeze()*n0\n", + " T = data['T'].squeeze()*Te0\n", + " return int_dV((3/2)*n*T)\n", + "\n", + "E = energy()\n", + "\n", + "plt.figure(figsize=(4,3))\n", + "plt.plot(times*(10**6), E, 'k-')\n", + "plt.xlabel(r'$t$ $[\\mathrm{\\mu s}]$')\n", + "plt.ylabel(r'$E = \\int \\frac{3}{2} \\, n \\, T_e \\, dV$ $\\mathrm{[eV]}$')\n", + "plt.grid()\n", + "plt.tight_layout()\n", + "plt.savefig('./plots/' + job_id + '_energy.png', dpi=300)\n", + "plt.savefig('./plots/' + job_id + '_energy.pdf')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Estimated confinement time = 0.759 ms\n" + ] + } + ], + "source": [ + "P_ext = 1/e # External heating source in eV/s\n", + "\n", + "# Energy confinement estimate\n", + "tau_E = float(np.mean(E[times*10**6 >= 2000])/P_ext)\n", + "\n", + "print('Estimated confinement time = ' + str(round(tau_E * 10**3, 3)) + ' ms')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Time Averaged Profiles" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Time Averaged Domain Plots" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_time_averaged_field(data, field, cmap, vmin = None, vmax = None, clabel = None, title=None, ax=None):\n", + " field_data_avg = np.mean(data[field].squeeze(), axis=0)\n", + "\n", + " if vmin is None:\n", + " vmin = field_data_avg.min()\n", + " if vmax is None:\n", + " vmax = field_data_avg.max()\n", + " if clabel is None:\n", + " clabel = r'$' + field + '$ [norm. u.]'\n", + " fig, ax = plt.subplots(subplot_kw={'projection': 'polar'})\n", + " if title is not None:\n", + " ax.set_title(title)\n", + " ax.set_rticks([])\n", + " ax.set_xticks([])\n", + " ax.contourf(thetas, rhos, field_data_avg, 100, cmap = cmap, vmin=vmin, vmax=vmax) # first image on screen\n", + " # ax.subtitle(r'$t = ' + 0 + '\\, \\mathrm{\\mu s}$')\n", + " fig.colorbar(get_cbar_mappable(cmap, vmin, vmax), orientation='vertical', label=clabel, extend=get_extend(field_data_avg, vmin, vmax), ax=ax)\n", + " fig.tight_layout()\n", + " return fig, ax" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot time averaged field profiles\n", + "\n", + "# Density\n", + "fig, ax = plot_time_averaged_field(data_ss, 'n', cmap='inferno', vmin=0, vmax=np.quantile(data_ss['n'], 0.99), clabel=r'$n$ [norm. u.]', title=r'$\\langle n \\rangle_t$')\n", + "fig.savefig('./plots/' + job_id + '_n_time_avg.png', dpi=300)\n", + "\n", + "# Temperature\n", + "fig, ax = plot_time_averaged_field(data_ss, 'T', cmap='inferno', vmin=0, vmax=np.quantile(data_ss['T'], 0.995), clabel=r'$T$ [norm. u.]', title=r'$\\langle T\\rangle_t$')\n", + "fig.savefig('./plots/' + job_id + '_T_time_avg.png', dpi=300)\n", + "\n", + "# Vorticity\n", + "# Set vmin and vmax to symmetrise around 0\n", + "vmax = np.max([data_ss['vort'].max(), np.abs(data_ss['vort'].min())])\n", + "vmin = -vmax\n", + "\n", + "fig, ax = plot_time_averaged_field(data_ss, 'vort', cmap='RdBu_r', vmin=vmin, vmax=vmax, clabel=r'$\\Omega$ [norm. u.]', title=r'$\\langle\\Omega\\rangle_t$')\n", + "fig.savefig('./plots/' + job_id + '_vort_time_avg.png', dpi=300)\n", + "\n", + "# Electric Potential\n", + "# Set vmin and vmax to symmetrise around 0\n", + "vmax = np.max([data_ss['phi'].max(), np.abs(data_ss['phi'].min())])\n", + "vmin = -vmax\n", + "\n", + "fig, ax = plot_time_averaged_field(data_ss, 'phi', cmap='RdBu_r', vmin=vmin, vmax=vmax, clabel=r'$\\phi$ [norm. u.]', title=r'$\\langle\\phi\\rangle_t$')\n", + "fig.savefig('./plots/' + job_id + '_phi_time_avg.png', dpi=300)\n", + "\n", + "# Pressure\n", + "fig, ax = plot_time_averaged_field(data_ss, 'p', cmap='inferno', vmin=0, vmax=np.quantile(data_ss['p'], 0.99), title=r'$\\langle p \\rangle_t$')\n", + "fig.savefig('./plots/' + job_id + '_p_time_avg.png', dpi=300)\n", + "\n", + "# Ionization source\n", + "fig, ax = plot_time_averaged_field(data_ss, 'S_n', cmap='inferno', vmin=0, vmax=np.quantile(data_ss['S_n'], 1.0), clabel=r'$(\\partial_t n)^{\\mathrm{iz}}$ [norm. u.]', title=r'$\\left\\langle(\\partial_t n)^{\\mathrm{iz}} \\right\\rangle_t$')\n", + "fig.savefig('./plots/' + job_id + '_S_n_time_avg.png', dpi=300)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Radial Profiles" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "rcParams.update({\n", + " \"text.usetex\": True,\n", + " \"font.family\": \"serif\",\n", + " \"font.sans-serif\": [\"Computer Modern Roman\"],\n", + " \"font.size\": 16})\n", + "rcParams['axes.titlepad'] = 20" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "def get_radial_profile(field, theta=0):\n", + " N_theta = len(thetas)\n", + " field_data = np.mean(data[field], axis=0).squeeze()\n", + " field_data_inv = np.flip(field_data[:,(theta + int(N_theta/2)) % N_theta])\n", + " # x = np.hstack([-np.flip(rhos), rhos])\n", + " x = np.linspace(-1,1,field_data.shape[0]*2)\n", + " radial_field = np.hstack((field_data_inv, field_data[:,theta]))\n", + "\n", + " return x, radial_field\n", + "\n", + "def plot_radial_profile(field, ylabel, theta=0):\n", + " x, radial_field = get_radial_profile(field, theta=theta)\n", + " plt.figure(figsize=(4,3))\n", + " plt.plot(x, radial_field, 'k-')\n", + " plt.xlabel(r'$(R - R_0)/a$')\n", + " plt.ylabel(ylabel)\n", + " plt.grid()\n", + " plt.tight_layout()\n", + " plt.savefig('./plots/' + job_id + '_radial_' + field + '.png', dpi=300)\n", + " plt.savefig('./plots/' + job_id + '_radial_' + field + '.pdf')\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_radial_profile('T', r'$\\left< T \\right>_t$ [norm. u.]')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fast Probes" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [], + "source": [ + "rcParams.update({\n", + " \"text.usetex\": True,\n", + " \"font.family\": \"serif\",\n", + " \"font.sans-serif\": [\"Computer Modern Roman\"],\n", + " \"font.size\": 16})\n", + "rcParams['axes.titlepad'] = 20" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [], + "source": [ + "cache_fast = './cached_data/fast_' + job_id + '.npz'\n", + "\n", + "if os.path.exists(cache_fast):\n", + " fast = np.load(cache_fast, allow_pickle=True)['fast'].item()\n", + "else:\n", + " fast = {}\n", + " for file in [f for f in os.listdir(path) if 'BOUT.fast' in f]:\n", + " dir = path + file\n", + " fast_data = DataFile(dir)\n", + " for key in fast_data.keys():\n", + " for search_pattern in ['t_array', 'n\\d', 'T\\d', 'phi\\d']:\n", + " match = re.search(search_pattern, key)\n", + " if match:\n", + " fast[match.string] = fast_data[match.string]\n", + "\n", + " # Cach fast data\n", + " np.savez(cache_fast, fast=fast)\n", + "\n", + "times_fast = fast['t_array']/oci" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [], + "source": [ + "N_probes = len([k for k in fast.keys() if re.search('n(\\d.*)', k)])\n", + "n_fast = []\n", + "for i in range(N_probes):\n", + " n_fast.append(fast['n' + str(i)])\n", + "\n", + "probe_pos_bout_norm = np.array([0.05, 0.14, 0.23, 0.32, 0.41, 0.50, 0.59, 0.68, 0.77, 0.86, 0.95])\n", + "probe_pos = probe_pos_bout_norm*a" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "N_plots = int(np.ceil(N_probes/2)) # number of plots\n", + "probe_range = range(0, N_probes, 2) # Range (indices) of probes to plot\n", + "\n", + "fig, ax = plt.subplots(N_plots, 1, figsize=(7, 7), sharex=True)\n", + "t_max = times_fast[-1]\n", + "for i_plot, i_probe in enumerate(probe_range):\n", + " n_probe = n_fast[i_probe]\n", + " ax[i_plot].plot(times_fast[times_fast < t_max]*10**6, n_probe[times_fast < t_max], 'k', label=r'$n(r=' + str(round(probe_pos[i_probe]*100)) + ' \\, \\mathrm{cm})$', linewidth=1.0)\n", + " # ax[i_plot].set_title(r'$n(r=' + str(round(probe_pos[i_probe]*100)) + ' \\, \\mathrm{cm})$', x=-0.1, y=0.5)\n", + " # ax[i_plot].legend(bbox_to_anchor=(-0.05,0.5), loc='center right', ncol=1)\n", + " ax[i_plot].annotate(r'$n(r=' + str(round(probe_pos[i_probe]*100)) + ' \\, \\mathrm{cm})$', (-5000,0.5), annotation_clip=False)\n", + " ax[i_plot].grid()\n", + " ax[i_plot].set_ylim(0,1.2)\n", + " ax[i_plot].set_yticks([0,1])\n", + "ax[-1].set_xlabel('time [µs]')\n", + "ax[0].set_title('[norm. u.]', x=-0.1, y=0.8)\n", + "fig.tight_layout()\n", + "plt.show()\n", + "fig.savefig('./plots/' + job_id + '_n_probes_all_times.pdf')\n", + "fig.savefig('./plots/' + job_id + '_n_probes_all_times.png', dpi=300)" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "N_plots = int(np.ceil(N_probes/2)) # number of plots\n", + "probe_range = range(0, N_probes, 2) # Range (indices) of probes to plot\n", + "\n", + "fig, ax = plt.subplots(N_plots, 1, figsize=(7, 7), sharex=True)\n", + "t_max = time_trans/7\n", + "for i_plot, i_probe in enumerate(probe_range):\n", + " n_probe = n_fast[i_probe]\n", + " ax[i_plot].plot(times_fast[times_fast < t_max]*10**6, n_probe[times_fast < t_max], 'k', label=r'$n(r=' + str(round(probe_pos[i_probe]*100)) + ' \\, \\mathrm{cm})$')\n", + " # ax[i_plot].set_title(r'$n(r=' + str(round(probe_pos[i_probe]*100)) + ' \\, \\mathrm{cm})$', x=-0.1, y=0.5)\n", + " # ax[i_plot].legend(bbox_to_anchor=(-0.05,0.5), loc='center right', ncol=1)\n", + " ax[i_plot].annotate(r'$n(r=' + str(round(probe_pos[i_probe]*100)) + ' \\, \\mathrm{cm})$', (-75,0.82), annotation_clip=False)\n", + " ax[i_plot].grid()\n", + " ax[i_plot].set_ylim(0,2.2)\n", + " ax[i_plot].set_yticks([0,1])\n", + "ax[-1].set_xlabel('time [µs]')\n", + "ax[0].set_title('[norm. u.]', x=-0.1, y=0.8)\n", + "fig.tight_layout()\n", + "plt.show()\n", + "fig.savefig('./plots/' + job_id + '_n_probes_transient.pdf')\n", + "fig.savefig('./plots/' + job_id + '_n_probes_transient.png', dpi=300)" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [], + "source": [ + "t_min = time_trans\n", + "t_max = time_end\n", + "t_filter = np.all([t_min <= times_fast, times_fast < t_max], axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "N_plots = int(np.ceil(N_probes/2)) # number of plots\n", + "probe_range = range(0, N_probes, 2) # Range (indices) of probes to plot\n", + "\n", + "fig, ax = plt.subplots(N_plots, 1, figsize=(7, 7), sharex=True)\n", + "\n", + "for i_plot, i_probe in enumerate(probe_range):\n", + " n_probe = n_fast[i_probe]\n", + " ax[i_plot].plot(times_fast[t_filter]*10**6, n_probe[t_filter], 'k', label=r'$n(r=' + str(round(probe_pos[i_probe]*100)) + ' \\, \\mathrm{cm})$')\n", + " # ax[i_plot].set_title(r'$n(r=' + str(round(probe_pos[i_probe]*100)) + ' \\, \\mathrm{cm})$', x=-0.1, y=0.5)\n", + " # ax[i_plot].legend(bbox_to_anchor=(-0.05,0.5), loc='center right', ncol=1)\n", + " ax[i_plot].annotate(r'$n(r=' + str(round(probe_pos[i_probe]*100)) + ' \\, \\mathrm{cm})$', (t_min*10**6 - 900,0.4), annotation_clip=False)\n", + " ax[i_plot].grid()\n", + " ax[i_plot].set_ylim(0,1.2)\n", + " ax[i_plot].set_yticks([0, 0.5, 1])\n", + "ax[-1].set_xlabel('time [µs]')\n", + "ax[0].set_title('[norm. u.]', x=-0.1, y=0.8)\n", + "fig.tight_layout()\n", + "plt.show()\n", + "fig.savefig('./plots/' + job_id + '_n_probes_ss.pdf')\n", + "fig.savefig('./plots/' + job_id + '_n_probes_ss.png', dpi=300)" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sampling Frequency is 0.743 MHz\n" + ] + } + ], + "source": [ + "# Sample signal with constant sample frequency\n", + "from scipy import interpolate\n", + "\n", + "dt = np.max(np.diff(times_fast))\n", + "fs = 1/dt\n", + "print('Sampling Frequency is ' + str(np.round(fs*10**(-6),3)) + ' MHz')\n", + "t_sampled = np.arange(t_min, t_max, dt)\n", + "i_probe = 5\n", + "\n", + "n_interp = interpolate.interp1d(times_fast, n_fast[i_probe])\n", + "n_sampled = n_interp(t_sampled)\n", + "n_sampled_stand = n_sampled - np.mean(n_sampled)" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(5.5,4))\n", + "plt.plot(times_fast*10**6, n_fast[i_probe], 'k', label=r'$n(r=' + str(round(probe_pos[i_probe]*100,2)) + ' \\, \\mathrm{cm})$', linewidth=1.0)\n", + "plt.grid()\n", + "plt.legend()\n", + "plt.xlabel('time [µs]')\n", + "plt.ylabel(r'$n$ [norm. u.]')\n", + "plt.tight_layout()\n", + "plt.savefig('./plots/' + job_id + '_n' + str(i_probe) + '_all_time.pdf')\n", + "plt.savefig('./plots/' + job_id + '_n' + str(i_probe) + '_all_time.png', dpi=300)" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot steady state signal\n", + "n_ss_mean = round(np.mean(n_sampled),2)\n", + "plt.figure(figsize=(5.5,4))\n", + "plt.plot(t_sampled*10**6, n_sampled,'-k', label='$n(r = ' + str(round(probe_pos[i_probe]*100,2)) + ' \\, \\mathrm{cm})$')\n", + "plt.legend()\n", + "plt.xlabel('time [µs]')\n", + "plt.ylabel(r'$n$ [norm. u.]')\n", + "plt.yticks([0.5, n_ss_mean, 0.6, 0.7])\n", + "plt.grid()\n", + "plt.tight_layout()\n", + "plt.savefig('./plots/' + job_id + '_n' + str(i_probe) + '_ss.pdf')\n", + "plt.savefig('./plots/' + job_id + '_n' + str(i_probe) + '_ss.png', dpi=300)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from scipy.signal import find_peaks\n", + "\n", + "f = np.fft.fft(n_sampled_stand)\n", + "freq = np.fft.fftfreq(t_sampled.shape[-1], d=dt) \n", + "F = np.abs(f[:int(len(f)/2)])\n", + "Freq = freq[:int(len(f)/2)]\n", + "# Freq = freq[:int(N_n0/2)]\n", + "\n", + "peaks, _ = find_peaks(F, prominence=0.5, width=1.0)\n", + "big_peaks = peaks[np.argsort(-F[peaks])[:2]]\n", + "f_fft = freq[big_peaks]\n", + "\n", + "plt.figure(figsize=(5,4))\n", + "plt.plot(Freq*10**(-3), F, '-k', linewidth=1.0)\n", + "plt.grid()\n", + "plt.xlim(0,40)\n", + "plt.xlabel('$f_n \\,\\, \\mathrm{[kHz]}$')\n", + "plt.ylim(0,int(np.max(F)+50))\n", + "plt.title(r'Fourier Spectrum of probe at $r = ' + str(round(probe_pos[i_probe]*100,2)) + '$ cm')\n", + "\n", + "for big_peak in big_peaks:\n", + " p_x = Freq[big_peak]*10**(-3)\n", + " p_y = F[big_peak]\n", + " plt.plot(p_x, p_y, \"kx\", markersize=8)\n", + " plt.annotate(str(np.round(p_x,1)) + ' kHz', xy=(p_x + 1, p_y))\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig('./plots/' + job_id + '_n' + str(i_probe) + '_fft.pdf')\n", + "plt.savefig('./plots/' + job_id + '_n' + str(i_probe) + '_fft.png', dpi=300)" + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0.08070826 -0.27796542]\n" + ] + } + ], + "source": [ + "# Make fit with first harmonic frequency\n", + "from scipy.optimize import curve_fit\n", + "\n", + "f0 = Freq[big_peaks[0]]\n", + "\n", + "def func(tt, A, phi):\n", + " return np.abs(A) * np.sin(2 * np.pi * f0 * tt - phi)\n", + "\n", + "p, pcov = curve_fit(func, t_sampled, n_sampled_stand)\n", + "print(p)" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot fit\n", + "n_ss_mean = round(np.mean(n_sampled),2)\n", + "plt.figure(figsize=(5.5,4))\n", + "plt.plot(t_sampled*10**6, n_sampled,'-.x', label='$n(r = ' + str(round(probe_pos[i_probe]*100,2)) + ' \\, \\mathrm{cm})$',linewidth=1.0, color = 'gray', markersize=1.2, markerfacecolor='darkblue',markeredgecolor='black')\n", + "plt.plot(t_sampled*10**6, func(t_sampled, p[0], p[1]) + np.mean(n_sampled), '-',\n", + " label=r'$' + str(n_ss_mean) + ' + ' + str(round(np.abs(p[0]), 2)) + ' \\cdot \\sin(2 \\pi (' + str(round(f0*10**(-3))) + ' \\\\times 10^{3}) \\, t)$',\n", + " linewidth=1.2, color = 'royalblue', markersize=1, markerfacecolor='darkblue', markeredgecolor='k')\n", + "plt.legend()\n", + "plt.xlabel('time [µs]')\n", + "plt.ylabel(r'$n$ [norm. u.]')\n", + "plt.yticks([0.5, n_ss_mean, 0.6, 0.7])\n", + "plt.grid()\n", + "plt.tight_layout()\n", + "plt.savefig('./plots/' + job_id + '_n' + str(i_probe) + '_ss_with_fit.pdf')\n", + "plt.savefig('./plots/' + job_id + '_n' + str(i_probe) + '_ss_with_fit.png', dpi=300)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.6.8 ('north-simulation': venv)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "58f89f0882dc77e77c57dba94bed5153ff3c368884663369d545e0daa46a4df0" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/presentation of main results/00-most-relevant/S_n_last_frame.png b/presentation of main 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--git a/presentation of main results/ReadMe.md b/presentation of main results/ReadMe.md new file mode 100644 index 0000000..639f5f9 --- /dev/null +++ b/presentation of main results/ReadMe.md @@ -0,0 +1,6 @@ +# Presentation of Main Results + +On this page we present the main results. +- The document `North Simulation - Prelimenary Results.pdf` goes through the derivation of the physical model behind the simulation, implementation details and presents the prelimineary results. +- In the folder `00-most-relevant` you will find the most important plots: That is animations of the fields and images of the last frames of the animation. +- In the folder `01-all-other-plots` you will find all other plots from the data analysis. \ No newline at end of file diff --git a/shared/NORTH/data/BOUT.inp b/shared/NORTH/data/BOUT.inp index 518f37e..d64267a 100755 --- a/shared/NORTH/data/BOUT.inp +++ b/shared/NORTH/data/BOUT.inp @@ -5,8 +5,8 @@ # y - The height of the cylinder (z) # z - The azimuthal coordinate (theta) -timestep = 5 # Output timestep -nout = 40 # Number of output steps +timestep = 50 # Output timestep +nout = 1000 # Number of output steps myg = 0 # No need for Y communications mxg = 2 # x-guard cells @@ -14,16 +14,16 @@ mxg = 2 # x-guard cells monitor_timestep = true [mesh] -nx = 80+2*mxg # internal grid points plus guard cells along x-axis +nx = 48+2*mxg # internal grid points plus guard cells along x-axis ny = 1 # y is along magnetic field, set to 1 -nz = 256 # internal grid points along z-axis +nz = 128 # internal grid points along z-axis # Number of processors. If running on e.g. xeon40, # we must have NXPE * NYPE = 40 # Also, we must satisfy the constraints: # (nx - 2*mxg)/NXPE = integer, and # ny/NYPE = integer -NXPE = 40 # Number of processors in x-direction +NXPE = 24 #Number of processors in x-direction NYPE = 1 # Number of processors in y-direction Lx = north:a/north:rho_s # simulation length along x-axis @@ -36,7 +36,7 @@ ZMIN = 0.0 ZMAX = 1.0 # So we will get: dz = 2*pi(ZMAX - ZMIN)/(nz-1) # The metrics -xl = (x * Lx) # x (r)-coordinate in range [0,Lx] +xl = (x * Lx) # x (r)-coordinate in range (0,Lx) xl_non_zero = max(xl,Lx/(2*nx)) # make sure xl is >0 when defining J and g22, g33 sqrt_g_22 = (north:R/north:rho_s + xl_non_zero*cos(z)) sqrt_g_33 = xl_non_zero @@ -77,49 +77,72 @@ upwind = W3 # v_x d/dx (f) flux = U1 # d/dx (v_x f) [laplace] -flags = 0 # Flags for Laplacian inversion +# flags = 0 # Flags for Laplacian inversion +include_yguards = true +inner_boundary_flags = 3 +outer_boundary_flags = 0 [solver] type = pvode [north] -R = 0.250 # m, major radius -a = 0.125 # m, minor radius -n0 = 1e16 # m^-3, plasma density -Te0 = 10.0 # eV, electron temperature -B0 = 0.077 # T, magnetic field on axis -R_res = 0.219 # m ECR radial position +R = 0.250 # (m), major radius +a = 0.125 # (m), minor radius +n0 = 1e16 # (m^-3), plasma density +Te0 = 10.0 # (eV), electron temperature +B0 = 0.077 # (T), magnetic field on axis +R_res = 0.219 # (m) ECR radial position E_ion = 24.59/Te0 # absolute value of the Bohm normalized ionization energy -n_n = 1.2e19 # Bohm normalized neutral number density [m^-3] +n_n_SI = 1.2e19 +n_n = n_n_SI/n0 # Bohm normalized neutral number density -e = 1.60e-19 # C -mp = 1.67e-27 # kg -N_nuclei = 2 # Atom number (number of nuclei) +e = 1.60e-19 # (C) +me = 9.1094e-31 # Electron mass (kg) +mp = 1.67e-27 # Proton mass (kg) +atomic_mass_number = 4 # Atomic mass number (for He) +mi = atomic_mass_number*mp # Ion nucleus mass (kg) -oci = e*B0/(N_nuclei*mp) # ion gyrofrequency -rho_s = sqrt(2*e*Te0/(N_nuclei*mp))/oci # cold-ion hybrid thermal gyro-radius -c_s = sqrt(Te0/(N_nuclei*mp)) # Cold-ion sound speed +oci = e*B0/(mi) # ion gyrofrequency (s^-1) +c_s = sqrt(Te0*e/(mi)) # Cold-ion sound speed (m/s) +rho_s = c_s/oci # cold-ion hybrid thermal gyro-radius (m) +rho_Le_SI = sqrt(me * (Te0*e)/(e^2 * B0^2)) # Larmor radius (m) +rho_Le = rho_Le_SI/rho_s # Normalised Larmor radius kappa = rho_s^2/(R*a) # Effective gravity. -Dvort = 50*2.2e-3 # Vorticity diffusion -Dn = 100*7.6e-5 # Density diffusion -DT = 100*7.6e-5 # Temperature diffusion +eps0 = 8.854187815e-12 # Vacuum Permitivity (kg^-1 * m^-3 * s^2 * C^2) +lambda_D = sqrt((eps0 * Te0 * e) / (n0 * e^2)) # Debye Length (m) + +Dvort = 5e-3 # Vorticity diffusion +Dn = 5e-3 # Density diffusion (deprecated) + +diff_scale_ei = 1 # We unfortunately need to increase scale of diffusion to avoid numerical instability. +diff_scale_ea = 500 +ln_lambda = log(12*pi*n0*lambda_D^3) +k_ei_SI = 2^(1/2) * e^4 * ln_lambda / (12*pi^(3/2) * eps0^2 * me^(1/2) * (Te0 * e)^(3/2)) # ion-electrons collision rate coefficient (m^3 s^-1) +k_ei = diff_scale_ei * k_ei_SI * n0/oci +sigma_ea = 6e-21 # Neutral electron cross section. +nu_ea_SI = (8 * sqrt(2) / (3 * sqrt(pi))) * n_n_SI * sqrt(Te0*e/me) * sigma_ea # electron-neutrals collision frequency (s^-1) +nu_ea = diff_scale_ea * nu_ea_SI/oci + +DT = 5e-3 # Temperature diffusion tau_source = 3.66e2 # Source characteristic time -tau_sink_vort = 2.07e2 # Global sink characteristic time -tau_wall_n = 40.0 # Density wall shadow characteristic time -tau_wall_Te = 40.0 # Temperature wall shadow characteristic time -tau_wall_vort = 40.0 # Vorticity wall shadow characteristic time -tau_common_sink = 1e3 #sink experiment +tau_wall_scale = 10 +tau_wall_n = tau_wall_scale*(pi*north:R/north:c_s)*north:oci # Density wall shadow characteristic time +tau_wall_Te = tau_wall_scale*(pi*north:R/north:c_s)*north:oci # Temperature wall shadow characteristic time +tau_wall_vort = tau_wall_scale*(pi*north:R/north:c_s)*north:oci # Vorticity wall shadow characteristic time + +n_bck = 0.01 +T_bck = 1.133986420/north:Te0 #tau_wall_n = 14.1 # Density wall shadow characteristic time #tau_wall_Te = 7.1 # Temperature wall shadow characteristic time -#tau_wall_vort = 14.1 # Vorticity wall shadow characteristic time - +recombination = true +ionization = true bracket = 2 # 0 = std, 1 = simple, 2 = arakawa @@ -139,7 +162,7 @@ sigma_x = 0.005/north:a # ============================================================================= [n] scale = 1.0 -function = 1.0 +function = north:n_bck #function = 1. + 0.2*mixmode(2*pi*x) * mixmode(z) # function = 1 + 0.5*sin(6*2*pi*x)*sin(12*z) # function = exp(-(x-(north:a + 7.1e-3)/north:R)^2/(all:sigma_z^2))*exp(-(z/(2*pi)-0.5)^2/(2*all:sigma_z^2)) @@ -152,13 +175,14 @@ function = 1.0 # Boundaries bndry_xin = none # Manual boundary found in code -bndry_xout = neumann_o2(0.0) # Set to neumann +#bndry_xout = neumann_o2(0.0) # Set to neumann +bndry_xout = dirichlet(north:n_bck) # ============================================================================= # ============================================================================= [T] scale = 1.0 -function = 1.0 +function = north:T_bck #scale = 1 #function = 1/(2*pi*all:sigma_x*all:sigma_z)*exp(-(north:R/north:a + x*cos(z)-north:R_res/north:a)^2/(2*all:sigma_x^2))*exp(-(x*sin(z))^2/(2*all:sigma_z^2)) @@ -169,7 +193,8 @@ function = 1.0 # Boundaries bndry_xin = none # Manual boundary found in code -bndry_xout = neumann_o2(0.0) # Set to neumann +#bndry_xout = neumann_o2(0.0) # Set to neumann +bndry_xout = dirichlet(north:T_bck) # Set to neumann # ============================================================================= # ============================================================================= @@ -180,6 +205,11 @@ function = 0.0 # Boundaries bndry_xin = none # Manual boundary found in code bndry_xout = neumann_o2(0.0) # Set to neumann +# ============================================================================= +[B] +scale = 1.0 +function = (north:R/north:a)/(north:R/north:a + x*cos(z)) + # ============================================================================= [source_T] @@ -195,7 +225,12 @@ scale = 1 # function = exp((x-0.97)/0.03)/2.71828**2 # function = -1 + exp((x-0.5)^20/(1e-6) + (z-0.5/(2*pi))^20/(1e-6)) # function = -1 + exp((x-0.5)^20/(1e-6))*exp((z/(2*pi)-0.5)^20/(1e-6)) -function = 0.5 + 0.5*tanh((x-(north:a/north:a - north:rho_s/north:a))/(0.025)) + +# function = 0.5 + 0.5*tanh((x-(north:a/north:a - north:rho_s/north:a))/(0.025)) + +width = north:rho_s + +function = exp((x-1)/(width/north:a)) [bracket_prefactor] scale = 1 diff --git a/shared/NORTH/north.cxx b/shared/NORTH/north.cxx deleted file mode 100755 index 2e04489..0000000 --- a/shared/NORTH/north.cxx +++ /dev/null @@ -1,179 +0,0 @@ - -#include -#include -#include -#include -#include -#include "BoutFastOutput/fast_output.hxx" - -class NORTH : public PhysicsModel { -public: - NORTH(); - -private: - Field3D n, vort; // Evolving density and vorticity - Field3D phi; // Electrostatic potential - Field3D source_n; // Density source - - // Model parameters - BoutReal kappa; // Effective gravity - BoutReal Dvort, Dn; // Diffusion - BoutReal tau_source, tau_sink; - - // Method to use: BRACKET_ARAKAWA, BRACKET_STD or BRACKET_SIMPLE - BRACKET_METHOD bm; // Bracket method for advection terms - - class Laplacian* phiSolver; // Laplacian solver for vort -> phi - - int fields(), interchange(), diffusive(), source(), sink(); - - // Create object from FastOutput class - FastOutput fast_output; - -protected: - int init(bool UNUSED(restart)); - int rhs(BoutReal UNUSED(t)); -}; - -NORTH::NORTH(){} - -int NORTH::init(bool UNUSED(restart)){ - auto& options = Options::root()["north"]; - kappa = options["kappa"].withDefault(1e-3); - Dvort = options["Dvort"].withDefault(1e-2); - Dn = options["Dn"].withDefault(1e-2); - tau_source = options["tau_source"].withDefault(1); - tau_sink = options["tau_sink"].withDefault(1); - - initial_profile("source_n", source_n); - - SOLVE_FOR(n, vort); - SAVE_REPEAT(phi); - SAVE_ONCE(source_n); - - phiSolver = Laplacian::create(); - phi = 0.; // Starting phi - - // Choose method to use for Poisson bracket advection terms - switch(options["bracket"].withDefault(0)) { - case 0: { - bm = BRACKET_STD; - output << "\tBrackets: default differencing\n"; - break; - } - case 1: { - bm = BRACKET_SIMPLE; - output << "\tBrackets: simplified operator\n"; - break; - } - case 2: { - bm = BRACKET_ARAKAWA; - output << "\tBrackets: Arakawa scheme\n"; - break; - } - case 3: { - bm = BRACKET_CTU; - output << "\tBrackets: Corner Transport Upwind method\n"; - break; - } - default: - output << "ERROR: Invalid choice of bracket method. Must be 0 - 3\n"; - return 1; - } - -// Write fast output -if (fast_output.enabled) { - // Add monitor if necessary - if (fast_output.enable_monitor) { - solver->addMonitor(&fast_output); - } - - // Add points from the input file - int i = 0; - BoutReal xpos, ypos, zpos; - int ix, iy, iz; - Options* fast_output_options = Options::getRoot()->getSection("fast_output"); - while (true) { - // Add more points if explicitly set in input file - fast_output_options->get("xpos"+std::to_string(i), xpos, -1.); - fast_output_options->get("ypos"+std::to_string(i), ypos, -1.); - fast_output_options->get("zpos"+std::to_string(i), zpos, -1.); - if (xpos<0. || ypos<0. || zpos<0.) { - output.write("\tAdded %i fast_output points\n", i); - break; - } - ix = int(xpos*mesh->GlobalNx); - iy = int(ypos*mesh->GlobalNy); - iz = int(zpos*mesh->GlobalNz); - - // Add fields to be monitored - fast_output.add("n"+std::to_string(i), n, ix, iy, iz); - fast_output.add("phi"+std::to_string(i), phi, ix, iy, iz); - i++; - } -} - - return 0; - } - -int NORTH::rhs(BoutReal t) { - fields(); - interchange(); - diffusive(); - source(); - sink(); - if (fast_output.enabled){ - fast_output.monitor_method(t); // Store fast output in BOUT.fast. - } - return 0; -} - -int NORTH::fields() { - ddt(n) = 0; - ddt(vort) = 0; - - return 0; - } - - int NORTH::interchange() { - // Solve for potential - phi = phiSolver->solve(vort, phi); - - // Communicate variables - mesh->communicate(n, vort, phi); - - // Convective transport - ddt(n) += -bracket(phi, n, bm) + kappa*(DDZ(n)-n*DDZ(phi)); - ddt(vort) += -bracket(phi, vort, bm) + kappa/n*DDZ(n); - - return 0; - } - - int NORTH::diffusive() { - // Communicate variables - mesh->communicate(n, vort); - - // Diffusive transport - ddt(n) += Dn*Delp2(n); - ddt(vort) += Dvort*Delp2(vort); - - return 0; - } - - int NORTH::source() { - // Source term - ddt(n) += source_n/tau_source; - - return 0; - } - - int NORTH::sink() { - // Sink terms - ddt(n) += -n/tau_sink; - ddt(vort) += -vort/tau_sink; - - return 0; - } - -// Define a main() function -BOUTMAIN(NORTH); diff --git a/shared/NORTH/north_asbjorn b/shared/NORTH/north_asbjorn deleted file mode 100644 index d320f30..0000000 Binary files a/shared/NORTH/north_asbjorn and /dev/null differ diff --git a/shared/NORTH/north_asbjorn.cxx b/shared/NORTH/north_asbjorn.cxx deleted file mode 100644 index ad16854..0000000 --- a/shared/NORTH/north_asbjorn.cxx +++ /dev/null @@ -1,187 +0,0 @@ -#include -#include -#include -#include -#include -#include "BoutFastOutput/fast_output.hxx" - -class NORTH : public PhysicsModel { -public: - NORTH(); - -private: - Field3D n, vort; // Evolving density and vorticity - Field3D phi; // Electrostatic potential - Field3D source_n; // Density source - - // Model parameters - BoutReal kappa; // Effective gravity - BoutReal Dvort, Dn; // Diffusion - BoutReal tau_source, tau_sink; - - // Method to use: BRACKET_ARAKAWA, BRACKET_STD or BRACKET_SIMPLE - BRACKET_METHOD bm; // Bracket method for advection terms - - class Laplacian* phiSolver; // Laplacian solver for vort -> phi - - int fields(), interchange(), diffusive(), source(), sink(), curvature(); - - // Create object from FastOutput class - FastOutput fast_output; - -protected: - int init(bool UNUSED(restart)); - int rhs(BoutReal UNUSED(t)); -}; - -NORTH::NORTH(){} - -int NORTH::init(bool UNUSED(restart)){ - auto& options = Options::root()["north"]; - kappa = options["kappa"].withDefault(1e-3); - Dvort = options["Dvort"].withDefault(1e-2); - Dn = options["Dn"].withDefault(1e-2); - tau_source = options["tau_source"].withDefault(1); - tau_sink = options["tau_sink"].withDefault(1); - - initial_profile("source_n", source_n); - - SOLVE_FOR(n, vort); - SAVE_REPEAT(phi); - SAVE_ONCE(source_n); - - phiSolver = Laplacian::create(); - phi = 0.; // Starting phi - - // Choose method to use for Poisson bracket advection terms - switch(options["bracket"].withDefault(0)) { - case 0: { - bm = BRACKET_STD; - output << "\tBrackets: default differencing\n"; - break; - } - case 1: { - bm = BRACKET_SIMPLE; - output << "\tBrackets: simplified operator\n"; - break; - } - case 2: { - bm = BRACKET_ARAKAWA; - output << "\tBrackets: Arakawa scheme\n"; - break; - } - case 3: { - bm = BRACKET_CTU; - output << "\tBrackets: Corner Transport Upwind method\n"; - break; - } - default: - output << "ERROR: Invalid choice of bracket method. Must be 0 - 3\n"; - return 1; - } - -// Write fast output -if (fast_output.enabled) { - // Add monitor if necessary - if (fast_output.enable_monitor) { - solver->addMonitor(&fast_output); - } - - // Add points from the input file - int i = 0; - BoutReal xpos, ypos, zpos; - int ix, iy, iz; - Options* fast_output_options = Options::getRoot()->getSection("fast_output"); - while (true) { - // Add more points if explicitly set in input file - fast_output_options->get("xpos"+std::to_string(i), xpos, -1.); - fast_output_options->get("ypos"+std::to_string(i), ypos, -1.); - fast_output_options->get("zpos"+std::to_string(i), zpos, -1.); - if (xpos<0. || ypos<0. || zpos<0.) { - output.write("\tAdded %i fast_output points\n", i); - break; - } - ix = int(xpos*mesh->GlobalNx); - iy = int(ypos*mesh->GlobalNy); - iz = int(zpos*mesh->GlobalNz); - - // Add fields to be monitored - fast_output.add("n"+std::to_string(i), n, ix, iy, iz); - fast_output.add("phi"+std::to_string(i), phi, ix, iy, iz); - i++; - } -} - - return 0; - } - -int NORTH::rhs(BoutReal t) { - fields(); - interchange(); - diffusive(); - source(); - sink(); - curvature(); // curvature operator C - if (fast_output.enabled){ - fast_output.monitor_method(t); // Store fast output in BOUT.fast. - } - return 0; -} - -int NORTH::fields() { - ddt(n) = 0; - ddt(vort) = 0; - - return 0; - } - - int NORTH::interchange() { - // Solve for potential - phi = phiSolver->solve(vort, phi); - - // Communicate variables - mesh->communicate(n, vort, phi); - - // Convective transport - ddt(n) += -bracket(phi, n, bm) ; - ddt(vort) += -bracket(phi, vort, bm) ; - - return 0; - } - - int NORTH::diffusive() { - // Communicate variables - mesh->communicate(n, vort); - - // Diffusive transport - ddt(n) += Dn*Delp2(n); - ddt(vort) += Dvort*Delp2(vort); - - return 0; - } - - int NORTH::source() { - // Source term - ddt(n) += source_n/tau_source; - - return 0; - } - - int NORTH::sink() { - // Sink terms - ddt(n) += -n/tau_sink; - ddt(vort) += -vort/tau_sink; - - return 0; - } - - int NORTH::curvature() { - // curvature terms - ddt(n) += kappa*(DDZ(n)-n*DDZ(phi)); - ddt(vort) += kappa/n*DDZ(n); - return 0; - } - - -// Define a main() function -BOUTMAIN(NORTH); diff --git a/shared/NORTH/north_full_ESEL.cxx b/shared/NORTH/north_full_ESEL.cxx index 6e19355..e2c928b 100644 --- a/shared/NORTH/north_full_ESEL.cxx +++ b/shared/NORTH/north_full_ESEL.cxx @@ -13,8 +13,10 @@ class NORTH : public PhysicsModel { private: Field3D n, vort, T; // Evolving density, vorticity and electron temperature - Field3D phi; // Electrostatic potential + Field3D phi, B; // Electrostatic potential and B-field Field3D source_T, wall_shadow, bracket_prefactor, cos_z_over_x, sin_z_field; // Density source, Temperature source + Field3D S_n, S_T, S_w; // Volume source/sink terms + Field3D n_bck, T_bck; // Model parameters BoutReal kappa; // Effective gravity @@ -23,9 +25,12 @@ class NORTH : public PhysicsModel { BoutReal rho_s; // Ion larmor radius BoutReal oci; // Ion cyclotron frequency + BoutReal n0; // Reference density - BoutReal Dvort, Dn, DT; // Diffusion - BoutReal tau_source, tau_sink_vort, tau_wall_n, tau_wall_T, tau_wall_vort, tau_common_sink; // Characteristic times + bool recombination, ionization; + + BoutReal Dvort, Dn, nu_ea, k_ei, rho_Le, DT; // Diffusion + BoutReal tau_source, tau_wall_n, tau_wall_T, tau_wall_vort; // Characteristic times ToroidalBCs toroidalBCs; // Class containing methods which sets the ghost points at singularity (r=0) @@ -37,6 +42,7 @@ class NORTH : public PhysicsModel { int fields(), interchange(), diffusive(), source(), sink(), curvature(); Field3D C(const Field3D &f); Field3D k_ionization(const Field3D &f); + Field3D k_recombination(const Field3D &f); // Create object from FastOutput class FastOutput fast_output; @@ -56,18 +62,32 @@ int NORTH::init(bool UNUSED(restart)) { E_ion = options["E_ion"].withDefault(2.0); rho_s = options["rho_s"].withDefault(1.0e3); oci = options["oci"].withDefault(2.0); + n0 = options["n0"].withDefault(1.0e16); Dvort = options["Dvort"].withDefault(1.0e-2); - Dn = options["Dn"].withDefault(1.0e-2); + Dn = options["Dn"].withDefault(1.0e-2); // Deprecated + k_ei = options["k_ei"].withDefault(1.05e-6); + nu_ea = options["nu_ea"].withDefault(0.1103); + rho_Le = options["rho_Le"].withDefault(85.6); DT = options["DT"].withDefault(1.0e-2); + std::cout << "\n************* This run is with ***************\n"; + std::cout << "\n No vort source. Old Diffusion Coefficients. Global vorticity sink, and weak vort wall sink. \n"; + std::cout << Dn; + // std::cout << nu_ea * pow(rho_Le, 2); + tau_source = options["tau_source"].withDefault(1.0); - tau_sink_vort = options["tau_sink_vort"].withDefault(1.0); tau_wall_n = options["tau_wall_n"].withDefault(1.0); tau_wall_T = options["tau_wall_T"].withDefault(1.0); tau_wall_vort = options["tau_wall_vort"].withDefault(1.0); - tau_common_sink = options["tau_common_sink"].withDefault(1.0); - + + recombination = options["recombination"].withDefault(true); + ionization = options["ionization"].withDefault(true); + + n_bck = options["n_bck"].withDefault(1.0e-2); + T_bck = options["T_bck"].withDefault(1.0e-2); + + initial_profile("B", B); initial_profile("source_T", source_T); initial_profile("wall_shadow", wall_shadow); @@ -77,7 +97,7 @@ int NORTH::init(bool UNUSED(restart)) { SOLVE_FOR(T, vort, n); SAVE_REPEAT(phi); - SAVE_ONCE(source_T, wall_shadow, bracket_prefactor, cos_z_over_x, sin_z_field); + SAVE_ONCE(B, source_T, wall_shadow, bracket_prefactor, cos_z_over_x, sin_z_field); phiSolver = Laplacian::create(); phi = 0.; // Starting phi @@ -152,12 +172,13 @@ int NORTH::rhs(BoutReal t) { toroidalBCs.applyCenterBC(vort); toroidalBCs.applyCenterBC(phi); - fields(); - interchange(); - diffusive(); + fields(); + interchange(); + diffusive(); source(); - sink(); + sink(); curvature(); + if (fast_output.enabled){ fast_output.monitor_method(t); // Store fast output in BOUT.fast. } @@ -185,21 +206,50 @@ int NORTH::interchange() { return 0; } +int NORTH::curvature() { + // curvature terms + mesh->communicate(n, vort, T, phi); + ddt(n) += (C(n*T)-n*C(phi)); + ddt(T) += -2/3*T*C(phi) + 7/3*T*C(T) + 2/3*pow(T,2)/n*C(n); + ddt(vort) += C(n*T); + return 0; +} + int NORTH::diffusive() { // Communicate variables mesh->communicate(n, vort, T); // Diffusive transport - ddt(n) += Dn*Delp2(n); + // ddt(n) += k_ei * pow(rho_Le, 2) * Div(n * Grad_perp(n)); // Diffusion from ion-electron collisions (approximation 1) + // ddt(n) += k_ei * pow(rho_Le, 2) * n * Delp2(n); // Diffusion from ion-electron collisions (approximation 2) + // ddt(n) += nu_ea * pow(rho_Le, 2) * Delp2(n); // Diffusion from electron-atom collisions + ddt(n) += Dn*Delp2(n); // Deprecated ddt(T) += DT*Delp2(T); ddt(vort) += Dvort*Delp2(vort); return 0; } int NORTH::source() { - // Source term - ddt(n) += n_n*n*k_ionization(T); - ddt(T) += source_T/(tau_source*n); + // Volumen source term + S_n = 0; + S_T = 0; + + if (ionization){ + S_n += n_n*n*k_ionization(T); + S_T -= (E_ion+T)*n_n*k_ionization(T); + } + + if(recombination){ + S_n -= n*n*k_recombination(T); + } + + // S_w = - n_n * k_ionization(T) * ((n/B) * vort + Grad_perp(phi) * Grad(n/B)); + + ddt(n) += S_n; + ddt(T) += S_T; + // ddt(vort) += S_w; + + ddt(T) += source_T/(tau_source*n); // Heating source return 0; } @@ -207,34 +257,31 @@ int NORTH::source() { int NORTH::sink() { // Sink terms mesh->communicate(n, vort, T); - ddt(n) += -n*wall_shadow/tau_wall_n; - ddt(T) += -T*wall_shadow/tau_wall_T - (E_ion+T)/n*k_ionization(T); - ddt(vort) += -vort*wall_shadow/tau_wall_vort -vort/tau_sink_vort; - return 0; -} - - + ddt(n) -= (n-n_bck)*wall_shadow/tau_wall_n; + ddt(T) -= (T-T_bck)*wall_shadow/tau_wall_T; + ddt(vort) -= vort*wall_shadow/tau_wall_vort; + + // Artifical global vorticity sink + ddt(vort) -= vort/tau_wall_vort; -int NORTH::curvature() { - // curvature terms - mesh->communicate(n, vort, T, phi); - ddt(n) += (C(n*T)-n*C(phi)); - ddt(T) += -2/3*T*C(phi) + 7/3*T*C(T) + 2/3*pow(T,2)/n*C(n); - ddt(vort) += C(n*T); return 0; } Field3D NORTH::C(const Field3D &f) { - return 1.0*kappa*(sin_z_field*DDX(f) + cos_z_over_x*DDZ(f)); + return 2.0*kappa*(sin_z_field*DDX(f) + cos_z_over_x*DDZ(f)); } Field3D NORTH::k_ionization(const Field3D &f) { //return 2.0e-13*pow(f/E_ion, 0.5)/(6.0 + f/E_ion)*exp(-E_ion/f)/(pow(rho_s,2)*c_s); - return 2.0e-13*pow(f/E_ion, 0.5)/(6.0 + f/E_ion)*exp(-E_ion/f)/(oci); + return 2.0e-13*pow(f/E_ion, 0.5)/(6.0 + f/E_ion)*exp(-E_ion/f)*n0/(oci); //return 0*f; } +Field3D NORTH::k_recombination(const Field3D &f) { + return 7.0e-20*pow(E_ion/f, 0.5)*n0/(oci); +} + /* Field3D NORTH::max_n(const Field3D &f) { if (f>0.05){ diff --git a/shared/NORTH/notes.md b/shared/NORTH/notes.md new file mode 100644 index 0000000..1087c3a --- /dev/null +++ b/shared/NORTH/notes.md @@ -0,0 +1,29 @@ +## Runs +This is an overview of which job ids corresponds to which simulation runs (i.e. which parameters/setup has been used). The note is only usefull for my specific use on Niflheim. + +**5703369**: Best run with global and wall vorticity sink. This run gives the stable steady-state behaviour, and it is this that is the data source of the data analysis produced in main results. + +**5703257**: Best run with wall vorticity sink, but no global vorticity sink. This run does not give steady-state (at least not immediately, but it gives an interesting behaviour). + +**5698085**: Best run without any vorticity sinks, but with vorticity source from ionization. This run does not give steady-state - probably not at any time. + +### DEPRECATED RUNS + +**Without vorticity** +5672316: Without vorticity term (first run) +5673946: Without vorticity term (first run) + +**With vorticity** +5672245: With vorticity term (first run) +5674003: With vorticity term (second run) +5680179: With vorticity term (third run) + + +- 5686920: No interchange and diffusion terms. applyCenterBC after rhs. (Old Diffusion coefficients, but not used?). +- 5686950: Michael Løiten's BC implementation. applyCenterBC before rhs. Old Diffusion coefficients. With interchange terms. +- 5686964: No diffusion. Stopped after 1 step (~ 12 minutes) +- 5686987: i-e_diff. Stopped before 1 step (~1 minute) +- 5686977: Pedersen diff (neutrals/electrons). Stopped after 1 step (~13 minutes). + +- 5688618: 4 tasks, old diffusion coefficients. ML's BCs (second run - 3 hours, total 4 hours) +- 5688668: 4 tasks, new diffusion coefficients. ML's BCs (first run - 3 hours). \ No newline at end of file diff --git a/shared/NORTH/sbatch_north_full_ESEL.sh b/shared/NORTH/sbatch_north_full_ESEL.sh index e2a4fa4..7ba2cb3 100644 --- a/shared/NORTH/sbatch_north_full_ESEL.sh +++ b/shared/NORTH/sbatch_north_full_ESEL.sh @@ -1,39 +1,55 @@ #!/bin/bash +#SBATCH --job-name=no_vort_source_but_vort_wall_sink_and_global_vort_sink #SBATCH --mail-type=NONE -#SBATCH --partition=xeon40 +#SBATCH --partition=xeon24 #SBATCH -N 1 # Number of nodes -#SBATCH -n 40 # Total number of tasks -#SBATCH --time=0-02:10:00 -#SBATCH --output=./slurm_out/north_full_%j_ESEL.log -#SBATCH --error=./slurm_out/north_full_%j_ESEL_err.log +#SBATCH -n 24 # Total number of tasks +#SBATCH --time=0-2:10:00 +#SBATCH --output=./slurm_out/north_%j_%x_ESEL.log +#SBATCH --error=./slurm_out/north_%j_%x_ESEL_err.log # Set input and output directory IN_DIR=$PWD/data +# NOTE: Change IN_DIR if using restart and append=true +# IN_DIR=$PWD/data_5703126 + OUT_DIR=$PWD/data_$SLURM_JOB_ID +SCRATCH_DIR=/scratch/$USER/$SLURM_JOB_ID # Load in modules +module purge module use ~/local/modules/modules/all module restore bout +# # Reconfigure Bout-Dev +# cd ../BOUT-dev/ +# ./configure +# make +# cd ../NORTH/ + # Copy all necessary files to scratch folder -cp $IN_DIR /scratch/$USER/data -r -cp ./BoutFastOutput /scratch/$USER/BoutFastOutput -r -cp ./dependencies /scratch/$USER/dependencies -r -cp north_full_ESEL.cxx /scratch/$USER -cp makefile /scratch/$USER -cd /scratch/$USER +mkdir -p $SCRATCH_DIR +cp $IN_DIR $SCRATCH_DIR/data -r +cp ./BoutFastOutput $SCRATCH_DIR/BoutFastOutput -r +cp ./dependencies $SCRATCH_DIR/dependencies -r +cp north_full_ESEL.cxx $SCRATCH_DIR +cp makefile $SCRATCH_DIR +cd $SCRATCH_DIR # Compile and run make -mpirun -n $SLURM_NTASKS ./north_full_ESEL stopCheck=true append=false wall_limit = 2 + +# mpirun -n $SLURM_NTASKS ./north_full_ESEL stopCheck=true append=false wall_limit = 3 # NOTE: Change IN_DIR if using restart and append=true -#mpirun -n $SLURM_NTASKS ./north_full_ESEL stopCheck=true restart append=true wall_limit = 2 +# mpirun -n $SLURM_NTASKS ./north_full_ESEL stopCheck=true restart append=true wall_limit = 24 +# mpirun -n $SLURM_NTASKS ./north_full_ESEL stopCheck=true restart append=true wall_limit=6 north:Dn=5e-3 north:DT=5e-3 north:Dvort=5e-3 north:tau_wall_scale=10 + # NOTE: Use this to set flags for input parameters while running -# mpirun -n $SLURM_NTASKS ./north_full_ESEL stopCheck=true append=false wall_limit = 1 laplace:flags=0 +mpirun -n $SLURM_NTASKS ./north_full_ESEL stopCheck=true append=false wall_limit=2 north:Dn=5e-3 north:DT=5e-3 north:Dvort=5e-3 north:tau_wall_scale=10 # When done: Move output files back to output directory mkdir -p $OUT_DIR -mv /scratch/$USER/data/* $OUT_DIR +mv $SCRATCH_DIR/data/* $OUT_DIR # Clear cache -rm -rf /scratch/$USER/* \ No newline at end of file +rm -rf $SCRATCH_DIR/* \ No newline at end of file diff --git a/test2.ipynb b/test2.ipynb deleted file mode 100644 index 2646dba..0000000 --- a/test2.ipynb +++ /dev/null @@ -1,71 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "MovieWriter ffmpeg unavailable; using Pillow instead.\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.animation as animation\n", - "from celluloid import Camera\n", - "\n", - "X, Y = np.meshgrid(np.linspace(0, 10, 100), np.linspace(0, 10, 100))\n", - "fig, ax = plt.subplots()\n", - "camera = Camera(fig)\n", - "for i in np.linspace(0, 2 * np.pi, 30, endpoint=False):\n", - " ax.contourf(np.sin(X + i) + np.sin(Y - i))\n", - " camera.snap()\n", - "anim = camera.animate()\n", - "\n", - "anim.save('contours.gif')" - ] - } - ], - "metadata": { - "interpreter": { - "hash": "ee0b440b2bafa8d228fbafc2eb4759e29756aa421afaba341345d8e84d5ef515" - }, - "kernelspec": { - "display_name": "Python 3.9.6 64-bit", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.6" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -}