docs: removed hint about not working examples from README

This commit is contained in:
Fabian Joswig 2022-01-06 12:16:36 +01:00
parent 615411337c
commit b00fab4838
2 changed files with 18 additions and 19 deletions

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`pyerrors` is a python package for error computation and propagation of Markov chain Monte Carlo data. `pyerrors` is a python package for error computation and propagation of Markov chain Monte Carlo data.
- **Documentation:** https://fjosw.github.io/pyerrors/pyerrors.html - **Documentation:** https://fjosw.github.io/pyerrors/pyerrors.html
- **Examples**: https://github.com/fjosw/pyerrors/tree/develop/examples (Do not work properly at the moment) - **Examples**: https://github.com/fjosw/pyerrors/tree/develop/examples
- **Contributing:** https://github.com/fjosw/pyerrors/blob/develop/CONTRIBUTING.md - **Contributing:** https://github.com/fjosw/pyerrors/blob/develop/CONTRIBUTING.md
- **Bug reports:** https://github.com/fjosw/pyerrors/issues - **Bug reports:** https://github.com/fjosw/pyerrors/issues

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@ -51,7 +51,7 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"The standard matrix product can be performed with @" "The standard matrix product can be performed with `@`"
] ]
}, },
{ {
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}, },
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"cell_type": "code", "cell_type": "code",
"execution_count": 16, "execution_count": 5,
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{ {
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}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 5, "execution_count": 6,
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{ {
@ -151,12 +151,12 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"For a vector of `Obs`, we again use np.asarray to end up with the correct object" "For a vector of `Obs`, we again use `np.asarray` to end up with the correct object"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 6, "execution_count": 7,
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"outputs": [ "outputs": [
{ {
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}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 7, "execution_count": 8,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
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}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 8, "execution_count": 9,
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"outputs": [ "outputs": [
{ {
@ -233,13 +233,12 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"## Matrix to matrix operations\n", "The cholesky decomposition can be obtained as follows"
"For matrix operations with a matrix as return value we can use another wrapper `mat_mat_op`. Take as an example the cholesky decompostion. __Here we need to use the autograd wrapped version of numpy__ (imported as anp) to use automatic differentiation."
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 9, "execution_count": 10,
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{ {
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}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 10, "execution_count": 11,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -293,7 +292,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 11, "execution_count": 12,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
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"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"[[Obs[0.494(12)] Obs[0.0]]\n", "[[Obs[0.494(12)] Obs[0.0]]\n",
" [Obs[0.280(39)] Obs[1.150(38)]]]\n", " [Obs[0.280(40)] Obs[1.150(39)]]]\n",
"Check:\n", "Check:\n",
"[[Obs[1.0] Obs[0.0]]\n", "[[Obs[1.0] Obs[0.0]]\n",
" [Obs[0.0] Obs[1.0]]]\n" " [Obs[0.0] Obs[1.0]]]\n"
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}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 12, "execution_count": 13,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
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"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"Eigenvalues:\n", "Eigenvalues:\n",
"[Obs[0.705(56)] Obs[4.39(20)]]\n", "[Obs[0.705(57)] Obs[4.39(19)]]\n",
"Eigenvectors:\n", "Eigenvectors:\n",
"[[Obs[-0.283(25)] Obs[-0.9592(74)]]\n", "[[Obs[-0.283(26)] Obs[-0.9592(75)]]\n",
" [Obs[-0.9592(74)] Obs[0.283(25)]]]\n" " [Obs[-0.9592(75)] Obs[0.283(26)]]]\n"
] ]
} }
], ],
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}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 13, "execution_count": 14,
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{ {