.. _sphx_glr_auto_examples_plot_missing_data.py: ============================= Using the missing_inds function to label interpolated values ============================= If you have data with missing values, Hypertools will try to interpolate them using PPCA. To visualize how well its doing, you can use the missing_inds function and then highlight the values that were interpolated. Here, we generated some synthetic data, removed some values, and then plotted the original data, data with missing values and highlighted the missing datapoints with stars. .. image:: /auto_examples/images/sphx_glr_plot_missing_data_001.png :align: center .. code-block:: python # Code source: Andrew Heusser # License: MIT # import from scipy.linalg import toeplitz import numpy as np from copy import copy import hypertools as hyp # simulate data K = 10 - toeplitz(np.arange(10)) data1 = np.cumsum(np.random.multivariate_normal(np.zeros(10), K, 250), axis=0) data2 = copy(data1) # randomly remove 5% of the data missing = .01 inds = [(i,j) for i in range(data1.shape[0]) for j in range(data1.shape[1])] missing_data = [inds[i] for i in np.random.choice(int(len(inds)), int(len(inds)*missing))] for i,j in missing_data: data2[i,j]=np.nan # reduce the data data1_r,data2_r = hyp.reduce([data1, data2], ndims=3) # pull out missing inds missing_inds = hyp.tools.missing_inds(data2) missing_data = data2_r[missing_inds, :] # plot hyp.plot([data1_r, data2_r, missing_data], ['-', '--', '*'], legend=['Full', 'Missing', 'Missing Points']) **Total running time of the script:** ( 0 minutes 0.151 seconds) .. only :: html .. container:: sphx-glr-footer .. container:: sphx-glr-download :download:`Download Python source code: plot_missing_data.py ` .. container:: sphx-glr-download :download:`Download Jupyter notebook: plot_missing_data.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_