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docs: typos in example notebooks corrected.
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2 changed files with 6 additions and 19 deletions
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@ -11,7 +11,7 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Import pyerrors, as well as autograd wrapped numpy and matplotlib."
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"Import numpy, matplotlib and pyerrors."
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]
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},
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{
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@ -91,7 +91,7 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"`pyerrors` overloads all basic math operations, the user can work with these `Obs` as if they were real numbers. The proper resampling is performed in the background via automatic differentiation."
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"`pyerrors` overloads all basic math operations, the user can work with these `Obs` as if they were real numbers. The proper error propagation is performed in the background via automatic differentiation."
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]
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},
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{
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@ -179,7 +179,7 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"This figure shows the windowsize dependence of the integrated autocorrelation time. The red vertical line signalizes the window chosen by the automatic windowing procedure with $S=2.0$.\n",
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"This figure shows the windowsize dependence of the integrated autocorrelation time. The vertical line signalizes the window chosen by the automatic windowing procedure with $S=2.0$.\n",
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"Choosing a larger windowsize would not significantly alter $\\tau_\\text{int}$, so everything seems to be correct here."
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]
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},
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@ -283,7 +283,7 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"We can now redo the error analysis and alter the value of S or attach a tail to the autocorrelation function to take into account long range autocorrelations"
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"We can now redo the error analysis and alter the value of S or attach a tail to the autocorrelation function via the parameter `tau_exp` to take into account long range autocorrelations"
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]
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},
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{
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@ -242,7 +242,7 @@
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"id": "4a9d13b2",
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"metadata": {},
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"source": [
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"We can also use the priodicity of the lattice in order to obtain the cosh effective mass"
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"We can also use the periodicity of the lattice in order to obtain the cosh effective mass"
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]
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},
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{
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@ -387,8 +387,7 @@
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"source": [
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"## Missing Values \n",
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"\n",
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"Apart from the build-in functions, there is another reason, why one should use a **Corr** instead of a list of **Obs**. \n",
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"Missing values are handled for you. \n",
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"Apart from the build-in functions, another benefit of using ``Corr`` objects is that they can handle missing values. \n",
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"We will create a second correlator with missing values. "
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]
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},
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@ -459,18 +458,6 @@
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"The important thing is that, whatever you do, correlators keep their length **T**. So there will never be confusion about how you count timeslices. You can also take a plateau or perform a fit, even though some values might be missing."
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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": 18,
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"id": "f3c4609c",
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"metadata": {},
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"outputs": [],
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"source": [
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"assert first_derivative.T == my_correlator.T == len(first_derivative.content) == len(my_correlator.content)\n",
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"assert first_derivative.content[0] is None\n",
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"assert first_derivative.content[-1] is None"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7fcbcac4",
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