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docs: removed hint about not working examples from README
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2 changed files with 18 additions and 19 deletions
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@ -3,7 +3,7 @@
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`pyerrors` is a python package for error computation and propagation of Markov chain Monte Carlo data.
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- **Documentation:** https://fjosw.github.io/pyerrors/pyerrors.html
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- **Examples**: https://github.com/fjosw/pyerrors/tree/develop/examples (Do not work properly at the moment)
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- **Examples**: https://github.com/fjosw/pyerrors/tree/develop/examples
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- **Contributing:** https://github.com/fjosw/pyerrors/blob/develop/CONTRIBUTING.md
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- **Bug reports:** https://github.com/fjosw/pyerrors/issues
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@ -51,7 +51,7 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The standard matrix product can be performed with @"
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"The standard matrix product can be performed with `@`"
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]
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},
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{
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@ -106,7 +106,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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@ -131,7 +131,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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@ -151,12 +151,12 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"For a vector of `Obs`, we again use np.asarray to end up with the correct object"
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"For a vector of `Obs`, we again use `np.asarray` to end up with the correct object"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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@ -185,7 +185,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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@ -212,7 +212,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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@ -233,13 +233,12 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Matrix to matrix operations\n",
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"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."
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"The cholesky decomposition can be obtained as follows"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"execution_count": 10,
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"metadata": {},
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"outputs": [
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{
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"execution_count": 11,
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"metadata": {},
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"outputs": [
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{
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@ -293,7 +292,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"execution_count": 12,
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"metadata": {},
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"outputs": [
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{
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@ -301,7 +300,7 @@
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"output_type": "stream",
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"text": [
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"[[Obs[0.494(12)] Obs[0.0]]\n",
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" [Obs[0.280(39)] Obs[1.150(38)]]]\n",
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" [Obs[0.280(40)] Obs[1.150(39)]]]\n",
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"Check:\n",
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"[[Obs[1.0] Obs[0.0]]\n",
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" [Obs[0.0] Obs[1.0]]]\n"
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@ -328,7 +327,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"execution_count": 13,
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"metadata": {},
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"outputs": [
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{
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@ -336,10 +335,10 @@
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"output_type": "stream",
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"text": [
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"Eigenvalues:\n",
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"[Obs[0.705(56)] Obs[4.39(20)]]\n",
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"[Obs[0.705(57)] Obs[4.39(19)]]\n",
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"Eigenvectors:\n",
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"[[Obs[-0.283(25)] Obs[-0.9592(74)]]\n",
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" [Obs[-0.9592(74)] Obs[0.283(25)]]]\n"
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"[[Obs[-0.283(26)] Obs[-0.9592(75)]]\n",
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" [Obs[-0.9592(75)] Obs[0.283(26)]]]\n"
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]
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}
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],
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@ -364,7 +363,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"execution_count": 14,
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"metadata": {},
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"outputs": [
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{
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