.. _sphx_glr_generated_examples_general:

General Examples
----------------



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    <div class="sphx-glr-thumbcontainer" tooltip="Most iterative baselines in pybaselines that allow specifying the maximum number of iterations (`max_iter`) and minimum tolerance (`tol`) will output a tol_history item in the parameter dictionary, which is a numpy array of the measured tolerance value at each iteration. The tol_history parameter can be helpful for determining appropriate max_iter or tol values.">

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  .. image:: /generated/examples/general/images/thumb/sphx_glr_plot_algorithm_convergence_thumb.png
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  :ref:`sphx_glr_generated_examples_general_plot_algorithm_convergence.py`

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      <div class="sphx-glr-thumbnail-title">Algorithm convergence</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="There are cases where data needs to be filtered/masked before processing, for example a faulty detector can result in problematic regions in measurements. In these cases, baseline correction on the raw data could lead to severely incorrect fits. One such use of masking in literature is presented by Temmink, et al. for removing downward spikes in mid-infrared data collected from the James Webb Space Telescope before performing baseline correction. This example will detail  how to handle working with masked data for the various types of baseline correction algorithms in pybaselines.">

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  .. image:: /generated/examples/general/images/thumb/sphx_glr_plot_masked_data_thumb.png
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  :ref:`sphx_glr_generated_examples_general_plot_masked_data.py`

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      <div class="sphx-glr-thumbnail-title">Working with Masked or Missing Data</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="Many baseline correction algorithms were created without considering noise in the experimental data, which can lead to an underestimation of the baseline. This example will show how to reduce this issue by simply smoothing the data before performing baseline correction.">

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  .. image:: /generated/examples/general/images/thumb/sphx_glr_plot_noisy_data_thumb.png
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  :ref:`sphx_glr_generated_examples_general_plot_noisy_data.py`

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      <div class="sphx-glr-thumbnail-title">Fitting noisy data</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="Several baseline algorithms, notably ../../../algorithms/smooth and ../../../algorithms/morphological algorithms, pad the input data in order to reduce edge effects from calculations. Padding is performed by pad_edges, which is a thin wrapper around numpy.pad that also allows linear extrapolation.">

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  .. image:: /generated/examples/general/images/thumb/sphx_glr_plot_padding_thumb.png
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  :ref:`sphx_glr_generated_examples_general_plot_padding.py`

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      <div class="sphx-glr-thumbnail-title">Padding data</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="Since version 0.7.0 of pybaselines, a list/tuple of two separate values can be specified for extrapolate_window, giving more control over the padding.">

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  .. image:: /generated/examples/general/images/thumb/sphx_glr_plot_padding_extrapolate_thumb.png
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  :ref:`sphx_glr_generated_examples_general_plot_padding_extrapolate.py`

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      <div class="sphx-glr-thumbnail-title">Padding with 'extrapolate' mode</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="When fitting multiple datasets that all share the same independant variable, pybaselines allows saving time by reusing the same Baseline object to allow only performing some of the computationally heavy setup only once. For example, polynomial methods &lt;../../../algorithms/polynomial&gt; will only compute the Vandermonde matrix, and potentially its pseudoinverse, once. Likewise, spline methods &lt;../../../algorithms/spline&gt; will only have to compute the spline basis matrix once. Note that this only applies if the same non-data parameters (eg. poly_order, lam, etc.) are used for each fit.">

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  .. image:: /generated/examples/general/images/thumb/sphx_glr_plot_reuse_Baseline_thumb.png
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  :ref:`sphx_glr_generated_examples_general_plot_reuse_Baseline.py`

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      <div class="sphx-glr-thumbnail-title">Fitting Multiple Datasets</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="Some types of characterization data, such as FTIR and XPS, are typically expressed with their x-values in decending order. Rather than having to ensure users rearrange their data into ascending order before using pybaselines, the Baseline object handles this internally and returns values in the same order as the input x-values. This is especially important if other parameters from the selected baseline method are desired such as weights, since those are likewise returned in the same order as the input x-values.">

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  .. image:: /generated/examples/general/images/thumb/sphx_glr_plot_sorted_data_thumb.png
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  :ref:`sphx_glr_generated_examples_general_plot_sorted_data.py`

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      <div class="sphx-glr-thumbnail-title">Sorting Data</div>
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    </div>


.. toctree::
   :hidden:

   /generated/examples/general/plot_algorithm_convergence
   /generated/examples/general/plot_masked_data
   /generated/examples/general/plot_noisy_data
   /generated/examples/general/plot_padding
   /generated/examples/general/plot_padding_extrapolate
   /generated/examples/general/plot_reuse_Baseline
   /generated/examples/general/plot_sorted_data