Export a specio.Spectrum to a pandas.DataFrameΒΆ
This example illustrates how to export a spectrum into a dataframe which can be useful for some later processing.
Out:
400.199219 402.729828 405.259583 \
20170410EDL_L01_cell01_c1.spc 173.333344 187.333328 188.333328
20170410EDL_L01_cell01_c2.spc 191.111115 203.222229 201.777771
20170410EDL_L01_cell01_c3.spc 195.777771 214.666687 206.333328
20170410EDL_L01_cell01_n1.spc 231.555557 240.888885 250.111099
20170410EDL_L01_cell01_n2.spc 206.444458 222.555557 220.888885
407.788513 410.316620 412.843903 \
20170410EDL_L01_cell01_c1.spc 177.666672 187.222229 182.444443
20170410EDL_L01_cell01_c2.spc 193.666672 204.111115 198.777771
20170410EDL_L01_cell01_c3.spc 207.000000 204.777771 209.333328
20170410EDL_L01_cell01_n1.spc 234.444443 252.000000 243.000000
20170410EDL_L01_cell01_n2.spc 226.888885 221.333328 221.000000
415.370331 417.895966 420.420746 \
20170410EDL_L01_cell01_c1.spc 178.777786 191.444443 185.111115
20170410EDL_L01_cell01_c2.spc 197.444458 199.666672 199.777771
20170410EDL_L01_cell01_c3.spc 213.555557 211.666672 210.444443
20170410EDL_L01_cell01_n1.spc 245.777771 235.777771 241.444443
20170410EDL_L01_cell01_n2.spc 229.000000 225.666672 229.555557
422.943268 ... 3784.909668 \
20170410EDL_L01_cell01_c1.spc 186.888885 ... 105.707924
20170410EDL_L01_cell01_c2.spc 202.555542 ... 103.928505
20170410EDL_L01_cell01_c3.spc 213.333328 ... 101.811989
20170410EDL_L01_cell01_n1.spc 238.333328 ... 104.658981
20170410EDL_L01_cell01_n2.spc 227.444427 ... 112.821648
3786.437256 3787.964111 3789.490967 \
20170410EDL_L01_cell01_c1.spc 104.930267 105.930382 109.041618
20170410EDL_L01_cell01_c2.spc 101.259399 109.590294 103.810089
20170410EDL_L01_cell01_c3.spc 102.580002 100.570229 101.671547
20170410EDL_L01_cell01_n1.spc 107.662598 108.332893 106.225388
20170410EDL_L01_cell01_n2.spc 112.157310 111.270737 111.828621
3791.018066 3792.543945 3794.069336 \
20170410EDL_L01_cell01_c1.spc 105.041733 108.819626 105.041962
20170410EDL_L01_cell01_c2.spc 103.252106 106.138565 104.913902
20170410EDL_L01_cell01_c3.spc 98.772881 102.652000 101.197777
20170410EDL_L01_cell01_n1.spc 110.006783 102.343719 109.014008
20170410EDL_L01_cell01_n2.spc 109.942055 111.055489 113.391159
3795.594482 3797.119141 3798.643555
20170410EDL_L01_cell01_c1.spc 107.153191 106.042191 109.708969
20170410EDL_L01_cell01_c2.spc 108.467026 101.909042 105.128845
20170410EDL_L01_cell01_c3.spc 100.965775 101.178215 101.501770
20170410EDL_L01_cell01_n1.spc 102.462067 104.576797 99.691521
20170410EDL_L01_cell01_n2.spc 109.393486 110.173584 110.509224
[5 rows x 1732 columns]
# Authors: Guillaume Lemaitre <guillaume.lemaire@inria.fr>
# License: BSD3
import matplotlib.pyplot as plt
from specio.datasets import load_csv_path
from specio import specread
# read the spectrum
spectra = specread(load_csv_path())
# convert to a dataframe
df_spectra = spectra.to_dataframe()
# print the head of dataframe
print(df_spectra.head())
# plot the five first spectra
df_spectra.head().T.plot()
# or on different plots
df_spectra.head().T.plot(subplots=True)
plt.show()
Total running time of the script: ( 0 minutes 3.133 seconds)