# alexPlot
A simple plotting library for plotting [zfit](https://zfit.readthedocs.io/en/latest/) PDFs and datasets, this package contains functions useful for plotting in 1D. These plotting functions are built with matplotlib functions and make use of zfit.Space and zfit.pdf.SumPDF objects. By default asymmetric errors are applied and pulls are computed with PDF integrals. The libarary can be used with the `only_canvas` option to act like another normal matplotlib plotting function.
```
,ggggggggggg,
,dPYb, dP"""88""""""Y8,,dPYb, I8
IP'`Yb Yb, 88 `8bIP'`Yb I8
I8 8I `" 88 ,8PI8 8I 88888888
I8 8' 88aaaad8P" I8 8' I8
,gggg,gg I8 dP ,ggg, ,gg, ,gg88""""" I8 dP ,ggggg, I8
dP" "Y8I I8dP i8" "8i d8""8b,dP" 88 I8dP dP" "Y8gggI8
i8' ,8I I8P I8, ,8I dP ,88" 88 I8P i8' ,8I ,I8,
,d8, ,d8b,,d8b,_ `YbadP' ,dP ,dP"Y8, 88 ,d8b,_ ,d8, ,d8',d88b,
P"Y8888P"`Y88P'"Y88888P"Y8888" dP" "Y8 88 8P'"Y88P"Y8888P" 8P""Y8
```
# Setting up
To install
```
pip install alexPlot
```
or
```
git clone ssh://git@gitlab.cern.ch:7999/amarshal/alexPlot.git
pip install --no-dependencies -e .
python -c 'import alexPlot'
```
Then
```
import alexPlot
# to ask for help
alexPlot.help()
# to ask for examples
alexPlot.examples()
# to overwrite default options
alexPlot.options.estimate_pulls = False
```
# Plotting data
```
import zfit
import numpy as np
import alexPlot
# plot using numpy array
data = np.random.normal(0,1,1000)
alexPlot.plot_data(data, figure_title='Numpy example')
# plot using a zfit dataset
obs = zfit.Space("x", limits=(-5, 5))
data = zfit.Data.from_numpy(obs=obs, array=data)
alexPlot.plot_data(data, also_plot_hist=True, color='tab:blue', figure_title='zfit example')
```
<img src="./examples/example_numpy.png" width=50% height=50%><img src="./examples/example_zfit.png" width=50% height=50%>
# Plotting pdf
```
# Example with KDE
obs = zfit.Space("x", limits=(-5, 5))
data = np.random.normal(0,1,1000)
data = zfit.Data.from_numpy(obs=obs, array=data)
model_KDE = zfit.pdf.GaussianKDE1DimV1(obs=obs, data=data, bandwidth='silverman')
alexPlot.plot_pdf(model_KDE)
# Example with an exponential plus a Gaussian
obs = zfit.Space("x", limits=(0, 30))
mean = zfit.Parameter("mean", 17,)
sigma = zfit.Parameter("sigma", 2,)
model_Gauss = zfit.pdf.Gauss(mean, sigma, obs)
lam = zfit.Parameter("lam", -0.1)
model_Exp = zfit.pdf.Exponential(lam, obs)
frac = zfit.Parameter("frac", 0.2,)
total_model = zfit.pdf.SumPDF([model_Gauss,model_Exp], obs=obs, fracs=[frac])
alexPlot.plot_pdf(total_model)
```
<img src="./examples/example_KDE.png" width=50% height=50%><img src="./examples/example_exp_plus_Gauss.png" width=50% height=50%>
# Plotting data and pdf
```
# Example with KDE
alexPlot.plot_pdf_data(model_KDE, data, filename='examples/example_KDE_data.png', figure_title='KDE')
# Example with an exponential plus a Gaussian
alexPlot.plot_pdf_data(total_model, data)
```
<img src="./examples/example_KDE_data.png" width=50% height=50%><img src="./examples/example_exp_plus_Gauss_data.png" width=50% height=50%>
# Extra functionality
```
# Add weights
alexPlot.plot_pdf_data(total_model, data_np,
weights=np.abs(np.random.normal(0,1,np.shape(data_np))), stack=True)
# Highlight a signal peak and zoom in
alexPlot.plot_pdf_data(total_model, data_np, dash_signal=True, ymax=50)
```
<img src="./examples/example_exp_plus_Gauss_data_weights.png" width=50% height=50%><img src="./examples/example_exp_plus_Gauss_data_dashed.png" width=50% height=50%>
```
# Add lables
alexPlot.plot_pdf_data(total_model, data,
dash_signal=True, label='Total PDF',
component_labels=['Signal', 'Background'],
xlabel=r'Some dimension (MeV/$c^2$)', units=r'MeV/$c^2$')
# Plot a log yscale
alexPlot.plot_pdf_data(total_model, data, log=True)
```
<img src="./examples/example_exp_plus_Gauss_data_dashed_labels.png" width=50% height=50%><img src="./examples/example_exp_plus_Gauss_data_dashed_labels_log.png" width=50% height=50%>
```
# Plot multiple datasets
data_A = np.random.normal(-1,1,1000)
data_B = np.random.normal(2,1,10000)
alexPlot.plot_data([data_A, data_B], color=['tab:blue','tab:red'], also_plot_hist=True, bins=35)
# Plot multiple datasets normalised
alexPlot.plot_data([data_A, data_B], label=['Dataset A', 'Dataset B'],
density=True, also_plot_hist=True, bins=35)
```
<img src="./examples/example_two.png" width=50% height=50%><img src="./examples/example_two_density.png" width=50% height=50%>
```
# Use custom pyplot commands
alexPlot.plot_pdf_data(total_model, data, log=True,
extra_pyplot_commands=["plt.axvline(x=15,c='k')"])
# Overlay custom pyplot objects
plt.figure(figsize=(13,10))
alexPlot.plot_pdf_data(total_model, data, only_canvas=True, stack=True,
component_colors=['tab:cyan','tab:grey'], color='r', pulls=False)
plt.axhline(y=10,c='r')
plt.savefig("examples/only_canvas.png")
plt.close("all")
```
<img src="./examples/example_exp_plus_Gauss_data_dashed_labels_log_pyplot.png" width=50% height=50%><img src="./examples/only_canvas.png" width=50% height=50%>
```
# Use xlims
alexPlot.plot_pdf_data(total_model, data, stack=True, xmin=10, xmax=22,
component_colors=['tab:cyan','tab:grey'], color='r')
# Plot multiple PDFs at once (note stack only stacks PDFs within same SumPDF)
obs = zfit.Space("x", limits=(-5, 5))
data_np = np.random.normal(0,1,2500)
data = zfit.Data.from_numpy(obs=obs, array=data_np)
model_KDE_A = zfit.pdf.GaussianKDE1DimV1(obs=obs, data=data, bandwidth='silverman')
data = zfit.Data.from_numpy(obs=obs, array=data_np[:1250])
model_KDE_B = zfit.pdf.GaussianKDE1DimV1(obs=obs, data=data, bandwidth='silverman')
yield_A = zfit.Parameter("yield_A", 2500)
model_KDE_A.set_yield(yield_A)
yield_B = zfit.Parameter("yield_B", 1250)
model_KDE_B.set_yield(yield_B)
alexPlot.plot_pdf_data([model_KDE_A, model_KDE_B], data_np, color=["#ffb366",'b'], component_colors=[["#ffb366"],['b']], alpha=[1.,0.25], label=['plot_A', 'plot_B'], stack=True)
```
<img src="./examples/example_exp_plus_Gauss_data_dashed_labels_log_pyplot_lims.png" width=50% height=50%><img src="./examples/PDF_and_data_2_merge_data_yields_labels_stack.png" width=50% height=50%>
```
,ggggggggggg,
,dPYb, dP"""88""""""Y8,,dPYb, I8
IP'`Yb Yb, 88 `8bIP'`Yb I8
I8 8I `" 88 ,8PI8 8I 88888888
I8 8' 88aaaad8P" I8 8' I8
,gggg,gg I8 dP ,ggg, ,gg, ,gg88""""" I8 dP ,ggggg, I8
dP" "Y8I I8dP i8" "8i d8""8b,dP" 88 I8dP dP" "Y8gggI8
i8' ,8I I8P I8, ,8I dP ,88" 88 I8P i8' ,8I ,I8,
,d8, ,d8b,,d8b,_ `YbadP' ,dP ,dP"Y8, 88 ,d8b,_ ,d8, ,d8',d88b,
P"Y8888P"`Y88P'"Y88888P"Y8888" dP" "Y8 88 8P'"Y88P"Y8888P" 8P""Y8
```
test
Raw data
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"description": "# alexPlot\n\nA simple plotting library for plotting [zfit](https://zfit.readthedocs.io/en/latest/) PDFs and datasets, this package contains functions useful for plotting in 1D. These plotting functions are built with matplotlib functions and make use of zfit.Space and zfit.pdf.SumPDF objects. By default asymmetric errors are applied and pulls are computed with PDF integrals. The libarary can be used with the `only_canvas` option to act like another normal matplotlib plotting function. \n\n```\n ,ggggggggggg, \n ,dPYb, dP\"\"\"88\"\"\"\"\"\"Y8,,dPYb, I8 \n IP'`Yb Yb, 88 `8bIP'`Yb I8 \n I8 8I `\" 88 ,8PI8 8I 88888888\n I8 8' 88aaaad8P\" I8 8' I8 \n ,gggg,gg I8 dP ,ggg, ,gg, ,gg88\"\"\"\"\" I8 dP ,ggggg, I8 \n dP\" \"Y8I I8dP i8\" \"8i d8\"\"8b,dP\" 88 I8dP dP\" \"Y8gggI8 \n i8' ,8I I8P I8, ,8I dP ,88\" 88 I8P i8' ,8I ,I8, \n ,d8, ,d8b,,d8b,_ `YbadP' ,dP ,dP\"Y8, 88 ,d8b,_ ,d8, ,d8',d88b, \n P\"Y8888P\"`Y88P'\"Y88888P\"Y8888\" dP\" \"Y8 88 8P'\"Y88P\"Y8888P\" 8P\"\"Y8 \n```\n\n# Setting up\n\nTo install\n```\npip install alexPlot\n```\nor\n```\ngit clone ssh://git@gitlab.cern.ch:7999/amarshal/alexPlot.git\npip install --no-dependencies -e .\npython -c 'import alexPlot'\n```\n\nThen\n```\nimport alexPlot\n\n# to ask for help \nalexPlot.help()\n# to ask for examples\nalexPlot.examples()\n# to overwrite default options\nalexPlot.options.estimate_pulls = False\n```\n\n# Plotting data\n\n```\nimport zfit\nimport numpy as np\nimport alexPlot\n\n# plot using numpy array\ndata = np.random.normal(0,1,1000)\nalexPlot.plot_data(data, figure_title='Numpy example')\n\n# plot using a zfit dataset\nobs = zfit.Space(\"x\", limits=(-5, 5)) \ndata = zfit.Data.from_numpy(obs=obs, array=data)\nalexPlot.plot_data(data, also_plot_hist=True, color='tab:blue', figure_title='zfit example')\n```\n\n<img src=\"./examples/example_numpy.png\" width=50% height=50%><img src=\"./examples/example_zfit.png\" width=50% height=50%>\n\n# Plotting pdf\n\n```\n# Example with KDE\nobs = zfit.Space(\"x\", limits=(-5, 5)) \ndata = np.random.normal(0,1,1000)\ndata = zfit.Data.from_numpy(obs=obs, array=data)\nmodel_KDE = zfit.pdf.GaussianKDE1DimV1(obs=obs, data=data, bandwidth='silverman')\nalexPlot.plot_pdf(model_KDE)\n\n# Example with an exponential plus a Gaussian\nobs = zfit.Space(\"x\", limits=(0, 30))\nmean = zfit.Parameter(\"mean\", 17,)\nsigma = zfit.Parameter(\"sigma\", 2,)\nmodel_Gauss = zfit.pdf.Gauss(mean, sigma, obs)\nlam = zfit.Parameter(\"lam\", -0.1)\nmodel_Exp = zfit.pdf.Exponential(lam, obs)\nfrac = zfit.Parameter(\"frac\", 0.2,)\ntotal_model = zfit.pdf.SumPDF([model_Gauss,model_Exp], obs=obs, fracs=[frac])\nalexPlot.plot_pdf(total_model)\n```\n\n<img src=\"./examples/example_KDE.png\" width=50% height=50%><img src=\"./examples/example_exp_plus_Gauss.png\" width=50% height=50%>\n\n\n# Plotting data and pdf\n\n```\n# Example with KDE\nalexPlot.plot_pdf_data(model_KDE, data, filename='examples/example_KDE_data.png', figure_title='KDE')\n\n# Example with an exponential plus a Gaussian\nalexPlot.plot_pdf_data(total_model, data)\n```\n<img src=\"./examples/example_KDE_data.png\" width=50% height=50%><img src=\"./examples/example_exp_plus_Gauss_data.png\" width=50% height=50%>\n\n# Extra functionality\n\n```\n# Add weights\nalexPlot.plot_pdf_data(total_model, data_np, \n weights=np.abs(np.random.normal(0,1,np.shape(data_np))), stack=True)\n\n# Highlight a signal peak and zoom in\nalexPlot.plot_pdf_data(total_model, data_np, dash_signal=True, ymax=50)\n```\n<img src=\"./examples/example_exp_plus_Gauss_data_weights.png\" width=50% height=50%><img src=\"./examples/example_exp_plus_Gauss_data_dashed.png\" width=50% height=50%>\n\n```\n# Add lables\nalexPlot.plot_pdf_data(total_model, data, \n dash_signal=True, label='Total PDF',\n component_labels=['Signal', 'Background'], \n xlabel=r'Some dimension (MeV/$c^2$)', units=r'MeV/$c^2$')\n\n# Plot a log yscale\nalexPlot.plot_pdf_data(total_model, data, log=True)\n```\n<img src=\"./examples/example_exp_plus_Gauss_data_dashed_labels.png\" width=50% height=50%><img src=\"./examples/example_exp_plus_Gauss_data_dashed_labels_log.png\" width=50% height=50%>\n\n\n```\n# Plot multiple datasets\ndata_A = np.random.normal(-1,1,1000)\ndata_B = np.random.normal(2,1,10000)\nalexPlot.plot_data([data_A, data_B], color=['tab:blue','tab:red'], also_plot_hist=True, bins=35)\n\n# Plot multiple datasets normalised\nalexPlot.plot_data([data_A, data_B], label=['Dataset A', 'Dataset B'], \n density=True, also_plot_hist=True, bins=35)\n```\n<img src=\"./examples/example_two.png\" width=50% height=50%><img src=\"./examples/example_two_density.png\" width=50% height=50%>\n\n```\n# Use custom pyplot commands\nalexPlot.plot_pdf_data(total_model, data, log=True, \n extra_pyplot_commands=[\"plt.axvline(x=15,c='k')\"])\n\n# Overlay custom pyplot objects\nplt.figure(figsize=(13,10))\nalexPlot.plot_pdf_data(total_model, data, only_canvas=True, stack=True, \n component_colors=['tab:cyan','tab:grey'], color='r', pulls=False)\nplt.axhline(y=10,c='r')\nplt.savefig(\"examples/only_canvas.png\")\nplt.close(\"all\")\n```\n<img src=\"./examples/example_exp_plus_Gauss_data_dashed_labels_log_pyplot.png\" width=50% height=50%><img src=\"./examples/only_canvas.png\" width=50% height=50%>\n\n```\n# Use xlims\nalexPlot.plot_pdf_data(total_model, data, stack=True, xmin=10, xmax=22,\n component_colors=['tab:cyan','tab:grey'], color='r')\n\n# Plot multiple PDFs at once (note stack only stacks PDFs within same SumPDF)\nobs = zfit.Space(\"x\", limits=(-5, 5)) \ndata_np = np.random.normal(0,1,2500)\ndata = zfit.Data.from_numpy(obs=obs, array=data_np)\nmodel_KDE_A = zfit.pdf.GaussianKDE1DimV1(obs=obs, data=data, bandwidth='silverman')\ndata = zfit.Data.from_numpy(obs=obs, array=data_np[:1250])\nmodel_KDE_B = zfit.pdf.GaussianKDE1DimV1(obs=obs, data=data, bandwidth='silverman')\nyield_A = zfit.Parameter(\"yield_A\", 2500)\nmodel_KDE_A.set_yield(yield_A)\nyield_B = zfit.Parameter(\"yield_B\", 1250)\nmodel_KDE_B.set_yield(yield_B)\nalexPlot.plot_pdf_data([model_KDE_A, model_KDE_B], data_np, color=[\"#ffb366\",'b'], component_colors=[[\"#ffb366\"],['b']], alpha=[1.,0.25], label=['plot_A', 'plot_B'], stack=True)\n\n```\n<img src=\"./examples/example_exp_plus_Gauss_data_dashed_labels_log_pyplot_lims.png\" width=50% height=50%><img src=\"./examples/PDF_and_data_2_merge_data_yields_labels_stack.png\" width=50% height=50%>\n\n\n```\n ,ggggggggggg, \n ,dPYb, dP\"\"\"88\"\"\"\"\"\"Y8,,dPYb, I8 \n IP'`Yb Yb, 88 `8bIP'`Yb I8 \n I8 8I `\" 88 ,8PI8 8I 88888888\n I8 8' 88aaaad8P\" I8 8' I8 \n ,gggg,gg I8 dP ,ggg, ,gg, ,gg88\"\"\"\"\" I8 dP ,ggggg, I8 \n dP\" \"Y8I I8dP i8\" \"8i d8\"\"8b,dP\" 88 I8dP dP\" \"Y8gggI8 \n i8' ,8I I8P I8, ,8I dP ,88\" 88 I8P i8' ,8I ,I8, \n ,d8, ,d8b,,d8b,_ `YbadP' ,dP ,dP\"Y8, 88 ,d8b,_ ,d8, ,d8',d88b, \n P\"Y8888P\"`Y88P'\"Y88888P\"Y8888\" dP\" \"Y8 88 8P'\"Y88P\"Y8888P\" 8P\"\"Y8 \n```\ntest\n",
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