Commit cdaf7b7a authored by Stefan Mauerberger's avatar Stefan Mauerberger
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Merge branch 'master' into FieldTools

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# CORBASS
### CORrelation Based Archeomagnetic SnapShot model
# License
GNU General Public License, Version 3, 29 June 2007
Copyright (C) 2019 Helmholtz Centre Potsdam GFZ, German Research Centre for Geosciences, Potsdam, Germany
CORBASS is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
CORBASS is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <https://www.gnu.org/licenses/>.
# Citation
> Schanner, Maximilian Arthus and Mauerberger, Stefan (2019)
> CORBASS: CORrelation Based Archeomagnetic SnapShot model. V. 1.0.
> GFZ Data Services. http://doi.org/10.5880/GFZ.2.3.2019.008
[![DOI](https://img.shields.io/badge/DOI-10.5880%2FGFZ.2.3.2019.008-blue.svg)](http://doi.org/10.5880/GFZ.2.3.2019.008)
# Documentation
The `CORBASS` model is described in TODO: #12. In a way this
repository can be seen as supplementary material to this publication. Below this
......@@ -13,8 +38,8 @@ graph LR;
ParameterFile-->Exploration-->Integration-->Evaluation;
```
`CORBASS` uses parameter files, which include among other things a link to
datasets, in a format similar to the [GEOMAGIA](http://geomagia.gfz-potsdam.de/)
`CORBASS` uses parameter files, which include among other things a link to
datasets, in a format similar to the [GEOMAGIA](http://geomagia.gfz-potsdam.de/)
output. Thus a first step is to create such a file for your data. You can find
an [example](Example_Parfile.py)
in the [*examples* section].
......@@ -22,7 +47,7 @@ in the [*examples* section].
Once you have a parameter file, the recommended way to run `CORBASS` is to use
```console
(CORBASS)$ python run.py <path/to/parfile.py>
(CORBASS)$ python run.py <path/to/parfile.py>
```
This way the `CORBASS` posterior model coefficients, the NEZ- and DIF-field models
......@@ -30,40 +55,33 @@ and down component and intensity at the CMB are calculated and provided as `.txt
files. The output location is specified in the parameter file.
Under the hood, `CORBASS` first explores the model parameter space by using the
`exploration` module. This way the region of interest, i.e. where the
`exploration` module. This way the region of interest, i.e. where the
probability mass is concentrated, is found and can be accessed by the
`integration` module. The output can then be used to calculate further results,
using the `evaluation` module.
If you want to use the individual models, you can find Jupyter notebooks for
each model in the [*examples* section].
If you want to use the individual modules, you can find Jupyter notebooks for
each module in the [*examples* section].
[*examples* section]: (examples)
Cite as
> Schanner, M. and Mauerberger, S. (2019)
> CORBASS: CORrelation Based Archeomagnetic SnapShot model. V. 1.0.
> GFZ Data Services. http://doi.org/XXX
TODO: #10
# Installation
0. Clone the repository
0. Clone the repository
```console
$ git clone https://gitext.gfz-potsdam.de/arthus/corbass.git
```
In the following `<corbass>` refers to the path you cloned the `CORBASS` repository into.
1. Download and install [Miniconda](https://conda.io/miniconda.html) for Python 3.
By default, the installation directory `<conda>` is `~/miniconda3/`.
By default, the installation directory `<conda>` is `~/miniconda3/`.
If you let conda modify your `bash.rc`, `<conda>/bin/conda` may be replaced by
`conda`.
2. Build and install [FieldTools]
1. Install `conda-build` and `numpy` (to base)
1. Install `conda-build` (to base)
```console
$ <conda>/bin/conda install conda-build numpy
$ <conda>/bin/conda install conda-build
```
2. Navigate to `<corbass>` and build [FieldTools]
......@@ -81,7 +99,7 @@ TODO: #10
$ source <conda>/bin/activate CORBASS
```
Careful with tcshell, you have to use activate.csh.
Careful with tcshell, you have to use activate.csh.
When you are done, deactivate the environment by
```console
(CORBASS)$ conda deactivate`.
......@@ -100,35 +118,16 @@ TODO: #10
[FieldTools]: https://gitup.uni-potsdam.de/matusche/fieldtools
# License
GNU General Public License, Version 3, 29 June 2007
Copyright (C) 2019 Maximilian Schanner, GFZ Potsdam
Copyright (C) 2019 Stefan Mauerberger, University of Potsdam
CORBASS is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
CORBASS is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <https://www.gnu.org/licenses/>.
# Contact
* [Maximilian Schanner](mailto:arthus@gfz-potsdam.de)
Helmholtz Centre Potsdam German Research Centre for Geoscienes GFZ
Section 2.3: Geomagnetism
Telegrafenberg
* [Maximilian Schanner](mailto:arthus@gfz-potsdam.de)
Helmholtz Centre Potsdam German Research Centre for Geoscienes GFZ
Section 2.3: Geomagnetism
Telegrafenberg
14473 Potsdam, Germany
* [Stefan Mauerberger](mailto:mauerber@uni-potsdam.de)
Institut of mathematics
University of Potsdam
Campus Golm, Haus 9
Karl-Liebknecht-Str. 24-25
* [Stefan Mauerberger](mailto:mauerber@uni-potsdam.de)
Institut of mathematics
University of Potsdam
Campus Golm, Haus 9
Karl-Liebknecht-Str. 24-25
14476 Potsdam OT Golm, Germany
"""
Copyright (C) 2019 Maximilian Schanner, GFZ Potsdam
Copyright (C) 2019 Stefan Mauerberger, University of Potsdam
Copyright (C) 2019 Helmholtz Centre Potsdam GFZ,
German Research Centre for Geosciences, Potsdam, Germany
Cite as:
Schanner, M. and Mauerberger, S. (2019)
Schanner, Maximilian Arthus and Mauerberger, Stefan (2019)
CORBASS: CORrelation Based Archeomagnetic SnapShot model. V. 1.0.
GFZ Data Services. http://doi.org/XXX TODO XXX
GFZ Data Services. http://doi.org/10.5880/GFZ.2.3.2019.008
This file is part of CORBASS.
......
......@@ -5,13 +5,13 @@
proxys of these) for quantities of interest, such as the power spectrum,
the dipole intensity, the dipole location etc.
Copyright (C) 2019 Maximilian Schanner, GFZ Potsdam
Copyright (C) 2019 Stefan Mauerberger, University of Potsdam
Copyright (C) 2019 Helmholtz Centre Potsdam GFZ,
German Research Centre for Geosciences, Potsdam, Germany
Cite as:
Schanner, M. and Mauerberger, S. (2019)
Schanner, Maximilian Arthus and Mauerberger, Stefan (2019)
CORBASS: CORrelation Based Archeomagnetic SnapShot model. V. 1.0.
GFZ Data Services. http://doi.org/XXX TODO XXX
GFZ Data Services. http://doi.org/10.5880/GFZ.2.3.2019.008
This file is part of CORBASS.
......
......@@ -2,13 +2,13 @@
This module is part of the CORBASS algorithm. It provides a routine for the
first step, the exploration of the parameter space.
Copyright (C) 2019 Maximilian Schanner, GFZ Potsdam
Copyright (C) 2019 Stefan Mauerberger, University of Potsdam
Copyright (C) 2019 Helmholtz Centre Potsdam GFZ,
German Research Centre for Geosciences, Potsdam, Germany
Cite as:
Schanner, M. and Mauerberger, S. (2019)
Schanner, Maximilian Arthus and Mauerberger, Stefan (2019)
CORBASS: CORrelation Based Archeomagnetic SnapShot model. V. 1.0.
GFZ Data Services. http://doi.org/XXX TODO XXX
GFZ Data Services. http://doi.org/10.5880/GFZ.2.3.2019.008
This file is part of CORBASS.
......
......@@ -5,13 +5,13 @@
assisting routine for finding a region of high probability mass, given
output from the previous exploration step.
Copyright (C) 2019 Maximilian Schanner, GFZ Potsdam
Copyright (C) 2019 Stefan Mauerberger, University of Potsdam
Copyright (C) 2019 Helmholtz Centre Potsdam GFZ,
German Research Centre for Geosciences, Potsdam, Germany
Cite as:
Schanner, M. and Mauerberger, S. (2019)
Schanner, Maximilian Arthus and Mauerberger, Stefan (2019)
CORBASS: CORrelation Based Archeomagnetic SnapShot model. V. 1.0.
GFZ Data Services. http://doi.org/XXX TODO XXX
GFZ Data Services. http://doi.org/10.5880/GFZ.2.3.2019.008
This file is part of CORBASS.
......@@ -352,9 +352,9 @@ def integrate(name, data, N, bounds, r_ref=3200.,
mu_f_cmb += res['zf_mean'][1] * weight
var_z_cmb += res['zf_var'][0] * weight
var_z_cmb += res['zf_var'][0]**2 * weight
var_z_cmb += res['zf_mean'][0]**2 * weight
var_f_cmb += res['zf_var'][1] * weight
var_f_cmb += res['zf_var'][1]**2 * weight
var_f_cmb += res['zf_mean'][1]**2 * weight
pbar.update(pbar.value + 1)
# close progressbarr
......
......@@ -2,13 +2,13 @@
This module is part of the CORBASS algorithm. It can be seen as the core
module, where the actual inversion code is defined.
Copyright (C) 2019 Maximilian Schanner, GFZ Potsdam
Copyright (C) 2019 Stefan Mauerberger, University of Potsdam
Copyright (C) 2019 Helmholtz Centre Potsdam GFZ,
German Research Centre for Geosciences, Potsdam, Germany
Cite as:
Schanner, M. and Mauerberger, S. (2019)
Schanner, Maximilian Arthus and Mauerberger, Stefan (2019)
CORBASS: CORrelation Based Archeomagnetic SnapShot model. V. 1.0.
GFZ Data Services. http://doi.org/XXX TODO XXX
GFZ Data Services. http://doi.org/10.5880/GFZ.2.3.2019.008
This file is part of CORBASS.
......@@ -1019,11 +1019,6 @@ class Inversion:
epsilons = np.dot(jac, jac.T)
"""
lap_appr = np.linalg.inv(np.linalg.multi_dot((jac.T,
np.linalg.inv(errs),
jac)))
"""
trans_grad[3*it:3*it+3, 3*it:3*it+3] = jac
epsilon[3*it:3*it+3, 3*it:3*it+3] = errs
rho[3*it:3*it+3, 3*it:3*it+3] = epsilons
......@@ -1159,7 +1154,7 @@ if __name__ == '__main__':
ax[0, 0].set_title('Z @ CMB')
ax[1, 0].tripcolor(lat, lon, var_z,
cmap=plt.cm.plasma)
cmap=plt.cm.binary)
ax[1, 0].coastlines(zorder=4)
ax[0, 1].tripcolor(lat, lon, f)
......@@ -1167,7 +1162,7 @@ if __name__ == '__main__':
ax[0, 1].set_title('F @ CMB')
ax[1, 1].tripcolor(lat, lon, var_f,
cmap=plt.cm.plasma)
cmap=plt.cm.binary)
ax[1, 1].coastlines(zorder=4)
plt.show()
......@@ -3,13 +3,13 @@
that are used throughut the algorithm, e.g. a routine to load a parameter
file or scaling routines for coefficicients and covariances.
Copyright (C) 2019 Maximilian Schanner, GFZ Potsdam
Copyright (C) 2019 Stefan Mauerberger, University of Potsdam
Copyright (C) 2019 Helmholtz Centre Potsdam GFZ,
German Research Centre for Geosciences, Potsdam, Germany
Cite as:
Schanner, M. and Mauerberger, S. (2019)
Schanner, Maximilian Arthus and Mauerberger, Stefan (2019)
CORBASS: CORrelation Based Archeomagnetic SnapShot model. V. 1.0.
GFZ Data Services. http://doi.org/XXX TODO XXX
GFZ Data Services. http://doi.org/10.5880/GFZ.2.3.2019.008
This file is part of CORBASS.
......
......@@ -53,13 +53,6 @@
"scrolled": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" 0% (1 of 15625) | | Elapsed Time: 0:00:00 ETA: 0:27:50"
]
},
{
"name": "stdout",
"output_type": "stream",
......@@ -71,8 +64,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
"100% (15625 of 15625) |##################| Elapsed Time: 0:38:49 Time: 0:38:49\n",
"N/A% (0 of 15625) | | Elapsed Time: 0:00:00 ETA: --:--:--"
"100% (15625 of 15625) |##################| Elapsed Time: 0:43:00 Time: 0:43:00\n"
]
},
{
......@@ -86,7 +78,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
"100% (15625 of 15625) |##################| Elapsed Time: 0:52:59 Time: 0:52:59\n"
"100% (15625 of 15625) |##################| Elapsed Time: 0:58:53 Time: 0:58:53\n"
]
}
],
......@@ -165,16 +157,6 @@
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, 'Residual term [nT]')"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
......@@ -217,24 +199,25 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"For illustration purposes, we show the marginal distribution for the error scaling and the residual, together with the Gaussian from the empirical mean and variance."
"For illustration purposes, we show the marginal distribution for the error scaling and the residual (blue), together with the Gaussian from the empirical mean and variance (orange). The black lines show the interval which will be integrated in the next step of `CORBASS`."
]
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 9,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'plt' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-1-044c32537748>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mscipy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstats\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnorm\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merrs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmarginal\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mress\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merrs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnorm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpdf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merrs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmoments\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscale\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmoments\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'plt' is not defined"
]
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
......@@ -242,13 +225,17 @@
"fig, ax = plt.subplots(1, 2)\n",
"\n",
"ax[0].plot(errs[0], marginal.sum(axis=1)*ress[1])\n",
"ax[0].plot(errs[0], norm.pdf(errs[0], loc=moments[1, 0], scale=np.sqrt(moments[1, 1])))\n",
"ax[0].plot(errs[0], norm.pdf(errs[0], loc=moments[1, 0], scale=np.sqrt(moments[1, 1])), ls='--')\n",
"ax[0].axvline(integ_bounds[1, 0], color='black')\n",
"ax[0].axvline(integ_bounds[1, 1], color='black')\n",
"ax[0].set_xlabel('Error level scaling factor')\n",
"ax[0].set_ylabel('[Arb.]')\n",
"ax[0].set_yticks([])\n",
"\n",
"ax[1].plot(ress[0], marginal.sum(axis=0)*errs[1])\n",
"ax[1].plot(ress[0], norm.pdf(ress[0], loc=moments[2, 0], scale=np.sqrt(moments[2, 1])), ls='--')\n",
"ax[1].axvline(integ_bounds[2, 0], color='black')\n",
"ax[1].axvline(integ_bounds[2, 1], color='black')\n",
"ax[1].set_xlabel('Residual term [nT]')\n",
"ax[1].set_ylabel('[Arb.]')\n",
"ax[1].set_yticks([])"
......@@ -266,16 +253,6 @@
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, 'Residual term [nT]')"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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0slZbSSdIGpOWRwPvA54AVgHzU7X5wB1peRUwV9JISacBk4EN6VLYXkkzJAmYl2tjZmYDQNFBi5eRnVV8MSJeTGcUf1WjzQRguaShZImnMyLulLQe6JR0OfA0MAcgIrZI6gQeJ5vm/orUkwtgIW92DV6Nb76bmQ0oKjpcI90vmZwGMJ4AHBMRO5oaXQkdGhvn6IL+DsOs/7VC1+Dw80wGirWx8qGImFZvu6K9uT5H1n13cSoaDnyr3o2ZmVl7KnqZ6w+AM4GHASJiV1dXYTMb4Br4qz/8gAmrU9Hz3/1pEGIASDq6eSGZmVmrKZpMOlNvrjGS/gRYC3y9eWGZmVkrKTpr8BclXQi8DLwNuDoi1jQ1MjMzaxlF75mQkscaeGOalA9HxK1Ni8zMzFpGrYGHHZIWS7pR0kXK/DnwE7KxJ2ZmZjXPTG4BXgDWA58gG6g4ApgVEZuaHJuZmbWIWsnk9Ij4DQBJXweeA06OiL1Nj8zMzFpGrd5cB7oW0tQmO5xIzMysu1pnJr8p6eW0LGB0+iyy5191NDU6MzNrCbWetDi0rwIxM7PWVf8McGZmZt04mZiZWWlOJmZmVpqTiZmZleZkYmZmpTmZmJlZaU4mZmZWmpOJmZmV5mRiZmalOZmYmVlpTiZmZlaak4mZmZXmZGJmZqU5mZiZWWlOJmZmVpqTiZmZleZkYmZmpTmZmJlZaU1LJpJOknSPpK2Stki6MpWPlbRG0pPp/fhcm8WStkvaJuniXPnZkjan726QpGbFbWZm9WvmmclB4L9GxH8CZgBXSJoCLALWRcRkYF36TPpuLjAVmAkskdT1DPqbgAXA5PSa2cS4zcysTk1LJhGxOyIeTst7ga3ARGAWsDxVWw7MTsuzgBURsS8idgDbgemSJgAdEbE+IgK4OdfGzMwGgD65ZyLpVOBM4EFgfETshizhACemahOBZ3LNdqayiWm5e3ml7SyQtFHSxgPs681dMDOzKpqeTCQdA/wT8BcR8XK1qhXKokr5kYURSyNiWkRMG87I+oM1M7OGNDWZSBpOlkhujYjbUvGz6dIV6X1PKt8JnJRrPgnYlconVSg3M7MBopm9uQR8A9gaEdfnvloFzE/L84E7cuVzJY2UdBrZjfYN6VLYXkkz0jrn5dqYmdkAMKyJ6z4X+CiwWdKmVPbXwLVAp6TLgaeBOQARsUVSJ/A4WU+wKyLiUGq3EFgGjAZWp5eZmQ0QyjpItZ8OjY1zdEF/h2Fm1lLWxsqHImJave08At7MzEpzMjEzs9KcTMzMrDQnEzMzK83JxMzMSnMyMTOz0pxMzMysNCcTMzMrzcnEzMxKczIxM7PSnEzMzKw0JxMzMyvNycTMzEpzMjEzs9KcTMzMrDQnEzMzK83JxMzMSnMyMTOz0pxMzMysNCcTMzMrzcnEzMxKczIxM7PSnEzMzKw0JxMzMyvNycTMzEpzMjEzs9KcTMzMrDQnEzMzK83JxMzMSnMyMTOz0pxMzMystKYlE0n/KGmPpMdyZWMlrZH0ZHo/PvfdYknbJW2TdHGu/GxJm9N3N0hSs2I2M7PGNPPMZBkws1vZImBdREwG1qXPSJoCzAWmpjZLJA1NbW4CFgCT06v7Os3MrJ81LZlExL8Az3crngUsT8vLgdm58hURsS8idgDbgemSJgAdEbE+IgK4OdfGzMwGiGF9vL3xEbEbICJ2SzoxlU8EHsjV25nKDqTl7uUVSVpAdhYD8MraWLmttwLvQ+OA5/o7iD4wGPZzMOwjeD/bzdsaadTXyaQnle6DRJXyiiJiKbC0t4LqD5I2RsS0/o6j2QbDfg6GfQTvZ7uRtLGRdn3dm+vZdOmK9L4nle8ETsrVmwTsSuWTKpSbmdkA0tfJZBUwPy3PB+7Ilc+VNFLSaWQ32jekS2J7Jc1Ivbjm5dqYmdkA0bTLXJL+N3AeME7STuBzwLVAp6TLgaeBOQARsUVSJ/A4cBC4IiIOpVUtJOsZNhpYnV7trKUv09VhMOznYNhH8H62m4b2U1knKTMzs8Z5BLyZmZXmZGJmZqU5mfQTSTPT1DHbJS2q8P15kl6StCm9ru6POMuoNKVOt++VpsjZLulRSWf1dYxlFdjHlj+OAJJOknSPpK2Stki6skKddjieRfazpY+ppFGSNkj6UdrHv61Qp/5jGRF+9fELGAr8K3A6MAL4ETClW53zgDv7O9aS+/ke4CzgsR6+fz9ZhwoBM4AH+zvmJuxjyx/HtB8TgLPS8rHAjyv8m22H41lkP1v6mKbjc0xaHg48CMwoeyx9ZtI/pgPbI+InEbEfWEE2pUxbicpT6uTNAm6OzAPAmK5xSK2iwD62hYjYHREPp+W9wFaOnI2iHY5nkf1saen4vJI+Dk+v7j2x6j6WTib9YyLwTO5zT9PEvCudiq6WNLVvQutTRX8Ora6tjqOkU4Ezyf6izWur41llP6HFj6mkoZI2kQ0cXxMRpY/lQJlOZbApMk3Mw8ApEfGKpPcD3yMbzNlO6poup0W11XGUdAzwT8BfRMTL3b+u0KQlj2eN/Wz5YxrZOL4zJI0Bbpf0jojI3/er+1j6zKR/9DR9zBsi4uWuU9GIuAsYLmlc34XYJ2r+HFpdOx1HScPJfsHeGhG3VajSFsez1n620zGNiBeBezny0R51H0snk/7xQ2CypNMkjSB7lsuqfAVJv9b1IDBJ08mO1S/6PNLmWgXMSz1HZgAvRZpVul20y3FM+/ANYGtEXN9DtZY/nkX2s9WPqaQT0hkJkkYD7wOe6Fat7mPpy1z9ICIOSvpz4PtkPbv+MbIpZT6Zvv8q8EFgoaSDwGvA3EjdLFqFKk+pMxze2Me7yHqNbAd+CXy8fyJtXIF9bPnjmJwLfBTYnK61A/w1cDK0z/Gk2H62+jGdACxX9gDCIUBnRNzZ7fdP3cfS06mYmVlpvsxlZmalOZmYmVlpTiZmZlaak4mZmZXmZGJmZqU5mVi/k3QoNwPrJlWYRbkXt3WepDsH+jpz6/6YpBvT8iclzeul9f5OmjF2UxprUE/b2ZKm9EYc1j48zsQGgtci4oxqFSQNjTcf5YykYRFxsNaKi9ZrBan/f2/5MPDFiPhmA21nA3eSPWa7kHY6DlaZz0xswJL0lKSrJd0PzJF0r6QvSLoPuFLSKZLWpectrJN0cmq3TNL1ku4Brquy/qOVPY/kh5IekTQrlT+Yn7wvbffsnupXWf9UZc+N2JRinJzK56XPP5J0Syr7/bTdRyStlTS+wvqukfSZXEzXpfX/WNLvpPKjJHWm9X8nrXNat/V8ArgMuFrSrZKOST+/hyVtzu9X91gl/TZwCfD3ab/eKukMSQ+kerdLOj4X4xvHq9rPytpAf8+t75dfwCFgU+71oVT+FPDZXL17gSW5z/8HmJ+W/xj4XlpeRvaX89AK2zqP9CwK4AvAR9LyGLJnVxwN/CXwt6l8AvDjGvXfWGe3bX0F+HBaHgGMBqYC24BxqXxsej+eNwcRfwL4Ulr+GHBjWr4G+EzuZ9FV5/3A2rT8GeBrafkdwEFgWoXYlgEfTMvDgI60PI5s1LOqxPpG2/T5UeB30/L/AL5c6Xj51d4vX+aygaDaZa7vVPn8LuDStHwL8L9y3303cpfFenARcEnXX/vAKLJpMzqBNWRTo1wGfLdG/Z6sB/6bpEnAbRHxpKT3Aisj4jmAiOh6Fsok4DvKnhkxAthRI3aArkkIHwJOTcvvBv4hrfsxSY8WWI+AL0h6D3CYbKrx8UBPsb7ZUDoOGBMR96Wi5bz584Ijj5+1KScTG+herfE5Lz83ULV6XQT8YURsO+IL6ReS3gl8CPjTavUrXZICiIhvS3oQ+ADw/XR5SVSeyvsrwPURsUrSeWRnIbXsS++HePP/cqWpw2v5MHACcHZEHJD0FFmi7CnWehQ5DtYGfM/EWtn/J5txGbJfiPfX2f77wKekN2aAPTP33Qrgs8BxEbG5QP0jSDod+ElE3EA2C+s7gXXAZZL+Q6ozNlU/DvhZWp5f537k3U92NkXqcfUbBdocB+xJieR84JRU3lOse8keaUtEvAS80HXPhmySxPuwQcfJxAaC0d26Bl9bsN2ngY+nSzkfpf6bvH9HNsPvo5IeS5+7rCRLVJ0F61fyIeAxZbPPvp3sMahbgM8D90n6EdA1zfk1wHcl/V/guTr3I28JcEL6mVxFdj/jpRptbgWmSdpIlpSfAKgS6wrgr1JngbeSJb+/T9s8g+y+iQ0ynjXYrI0om1Z8eES8nn7RrwN+PSL293No1uZ8z8SsvRwF3KPsaYECFjqRWF/wmYmZmZXmeyZmZlaak4mZmZXmZGJmZqU5mZiZWWlOJmZmVtq/AzDvHHS3250/AAAAAElFTkSuQmCC\n",
......
......@@ -123,16 +123,6 @@
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, 'Residual term [nT]')"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
......
......@@ -52,7 +52,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
"/home/arthus/Documents/CORBASS/examples/../corbass/evaluation.py:215: RuntimeWarning: covariance is not positive-semidefinite.\n",
"<corbass>/examples/../corbass/evaluation.py:215: RuntimeWarning: covariance is not positive-semidefinite.\n",
" for it in par_samps]\n"
]
}
......@@ -74,16 +74,6 @@
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x7ff341919e80>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
......@@ -121,25 +111,6 @@
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.axis.XTick at 0x7ff3418b92b0>,\n",
" <matplotlib.axis.XTick at 0x7ff3418aaba8>,\n",
" <matplotlib.axis.XTick at 0x7ff3418d2c50>,\n",
" <matplotlib.axis.XTick at 0x7ff341857710>,\n",
" <matplotlib.axis.XTick at 0x7ff341857be0>,\n",
" <matplotlib.axis.XTick at 0x7ff341863198>,\n",
" <matplotlib.axis.XTick at 0x7ff341863710>,\n",
" <matplotlib.axis.XTick at 0x7ff341857a58>,\n",
" <matplotlib.axis.XTick at 0x7ff341863cc0>,\n",
" <matplotlib.axis.XTick at 0x7ff34186a278>]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
......@@ -178,16 +149,6 @@
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7ff341794a58>]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
......@@ -222,16 +183,6 @@
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.PolyCollection at 0x7ff384859f28>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
......
......@@ -18,7 +18,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
"/home/arthus/Documents/CORBASS\n"
"<corbass>/CORBASS\n"
]
}
],
......@@ -39,53 +39,31 @@
"metadata": {},
"outputs": [
{
"name": "stderr",
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"N/A% (0 of 15625) | | Elapsed Time: 0:00:00 ETA: --:--:--"
"Exploring 1450, this may take a while...\n",
"100% (15625 of 15625) |##################| Elapsed Time: 0:22:41 Time: 0:22:41\n",
"Integrating 1450, this may take a while...