<jupyter_start><jupyter_text>Welcome to BunmixThis App implements Bayesian unmixing of remanence acquisition curves (video tutorials are not available yet).<jupyter_code>import bunmix as bx<jupyter_output>/anaconda/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  from ._conv import register_converters as _register_converters<jupyter_text>(1) Paste in your data (Video tutorial: Local or Cloud)You should paste in acquisition data with applied fields in mT (maximum of 500 rows).<jupyter_code>X = {}
X = bx.input.enter_data(X)<jupyter_output><empty_output><jupyter_text>(2) Import and preprocess your data (Video tutorial: Local or Cloud)You can set your sample parameters, select your input and output data units and limit the field range for analysis.<jupyter_code>im = bx.buttons.LoadedButton(description="Import Data", value=X)
im.on_click(bx.buttons.data)
display(im)
X = im.value<jupyter_output><empty_output><jupyter_text>(3) Set the unmixing model parameters (Video tutorial: Local or Cloud)Select the number of mixture components and parameters for the NUTS sampler.<jupyter_code>mp = bx.buttons.LoadedButton(description="Model Parameters", value=X)
mp.on_click(bx.buttons.params)
display(mp)
X = mp.value<jupyter_output><empty_output><jupyter_text>(4) Run the unmixing model (Video tutorial: Local or Cloud)This may take some time and the rotating cogs show the model is progressing. A message will appear when the model is finished.<jupyter_code>md = bx.buttons.LoadedButton(description="Estimate Model", value=X)
md.on_click(bx.buttons.model)
display(md)
X = md.value<jupyter_output><empty_output><jupyter_text>(5) Plot the unmixing model (Video tutorial: Local or Cloud)This may take a little time as the model uncertainties are calculated.<jupyter_code>pt = bx.buttons.LoadedButton(description="Plot Model", value=X)
pt.on_click(bx.buttons.plot)
display(pt)
X = pt.value<jupyter_output><empty_output>
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