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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,46 @@ | ||
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| import requests | ||
| import json | ||
| import sys | ||
| import argparse | ||
| from datetime import date | ||
|
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| def open_issue(token): | ||
| url = "https://api.github.com/repos/qutip/qutip-jax/issues" | ||
| data = json.dumps({ | ||
| "title": f"Automated tests failed on {date.today()}", | ||
| "labels": ["bug"], | ||
| "body": "Scheduled test failed!" | ||
| }) | ||
|
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| headers = { | ||
| "Accept": "application/vnd.github.v3+json", | ||
| "Authorization" : f"token {token}", | ||
| } | ||
|
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| post_request = requests.post(url=url, data=data, headers=headers) | ||
|
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| if post_request.status_code == 201: | ||
| print("Success") | ||
|
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| else: | ||
| print( | ||
| "Fail:", | ||
| post_request.status_code, | ||
| post_request.reason, | ||
| post_request.content | ||
| ) | ||
|
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||
|
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| def main(): | ||
| parser = argparse.ArgumentParser( | ||
| description="""Open an issue on failed tests.""" | ||
| ) | ||
| parser.add_argument("token") | ||
| args = parser.parse_args() | ||
| print(args.token) | ||
| open_issue(args.token) | ||
|
|
||
|
|
||
| if __name__ == "__main__": | ||
| sys.exit(main()) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,68 @@ | ||
| --- | ||
| jupyter: | ||
| jupytext: | ||
| text_representation: | ||
| extension: .md | ||
| format_name: markdown | ||
| format_version: '1.3' | ||
| jupytext_version: 1.13.8 | ||
| kernelspec: | ||
| display_name: Python 3 (ipykernel) | ||
| language: python | ||
| name: python3 | ||
| --- | ||
|
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| # Using qutip.distributions | ||
|
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| Author: Mathis Beaudoin (2025) | ||
|
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| ### Introduction | ||
|
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| This notebook shows how to use probability distributions inside QuTiP. We begin by importing the necessary packages. | ||
|
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| ```python | ||
| from qutip import fock, about | ||
| from qutip.distributions import HarmonicOscillatorWaveFunction | ||
| from qutip.distributions import HarmonicOscillatorProbabilityFunction | ||
| import matplotlib.pyplot as plt | ||
| ``` | ||
|
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| ### Harmonic Oscillator Wave Function | ||
|
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| Here, we display the spatial distribution of the wave function for the harmonic oscillator (n=0 to n=7) with the `HarmonicOscillatorWaveFunction()` class. | ||
|
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| Optionally, define a range of values for each coordinate with the parameter called `extent`. Also, define a number of data points inside the given range with the optional parameter called `steps`. From this information, the distribution is generated and can be visualized with the `.visualize()` method. | ||
|
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| ```python | ||
| M = 8 | ||
| N = 20 | ||
|
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| fig, ax = plt.subplots(M, 1, figsize=(10, 12), sharex=True) | ||
|
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| for n in range(M): | ||
| psi = fock(N, n) | ||
| wf = HarmonicOscillatorWaveFunction(psi, 1.0, extent=[-10, 10]) | ||
| wf.visualize(fig=fig, ax=ax[M-n-1], show_ylabel=False, show_xlabel=(n == 0)) | ||
| ``` | ||
|
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| ### Harmonic Oscillator Probability Function | ||
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| The class `HarmonicOscillatorProbabilityFunction()` is the squared magnitude of the data that would normally be in `HarmonicOscillatorWaveFunction()`. We use the same example as before. | ||
|
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| ```python | ||
| M = 8 | ||
| N = 20 | ||
|
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| fig, ax = plt.subplots(M, 1, figsize=(10, 12), sharex=True) | ||
|
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| for n in range(M): | ||
| psi = fock(N, n) | ||
| wf = HarmonicOscillatorProbabilityFunction(psi, 1.0, extent=[-10, 10]) | ||
| wf.visualize(fig=fig, ax=ax[M-n-1], show_ylabel=False, show_xlabel=(n == 0)) | ||
| ``` | ||
|
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| ### About | ||
|
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| ```python | ||
| about() | ||
| ``` |