Mustererkennung in Funkmessdaten

Aufgabe 1: Laden der Datenbank in Jupyter Notebook


In [17]:
# imports
import pandas as pd
import matplotlib.pyplot as plt

In [18]:
hdf = pd.HDFStore('../../data/raw/TestMessungen_NEU.hdf')
print(hdf.keys)


<bound method HDFStore.keys of <class 'pandas.io.pytables.HDFStore'>
File path: ../../data/raw/TestMessungen_NEU.hdf
/x1/t1/trx_1_2            frame        (shape->[227,12009])
/x1/t1/trx_1_4            frame        (shape->[229,12009])
/x1/t1/trx_2_3            frame        (shape->[226,12009])
/x1/t1/trx_3_1            frame        (shape->[233,12009])
/x1/t1/trx_3_4            frame        (shape->[226,12009])
/x1/t1/trx_4_2            frame        (shape->[230,12009])
/x1/t2/trx_1_2            frame        (shape->[223,12009])
/x1/t2/trx_1_4            frame        (shape->[220,12009])
/x1/t2/trx_2_3            frame        (shape->[218,12009])
/x1/t2/trx_3_1            frame        (shape->[221,12009])
/x1/t2/trx_3_4            frame        (shape->[218,12009])
/x1/t2/trx_4_2            frame        (shape->[221,12009])
/x1/t3/trx_1_2            frame        (shape->[246,12009])
/x1/t3/trx_1_4            frame        (shape->[250,12009])
/x1/t3/trx_2_3            frame        (shape->[246,12009])
/x1/t3/trx_3_1            frame        (shape->[244,12009])
/x1/t3/trx_3_4            frame        (shape->[252,12009])
/x1/t3/trx_4_2            frame        (shape->[247,12009])
/x2/t1/trx_1_2            frame        (shape->[195,12009])
/x2/t1/trx_1_4            frame        (shape->[194,12009])
/x2/t1/trx_2_3            frame        (shape->[201,12009])
/x2/t1/trx_3_1            frame        (shape->[200,12009])
/x2/t1/trx_3_4            frame        (shape->[198,12009])
/x2/t1/trx_4_2            frame        (shape->[199,12009])
/x2/t2/trx_1_2            frame        (shape->[155,12009])
/x2/t2/trx_1_4            frame        (shape->[168,12009])
/x2/t2/trx_2_3            frame        (shape->[157,12009])
/x2/t2/trx_3_1            frame        (shape->[165,12009])
/x2/t2/trx_3_4            frame        (shape->[164,12009])
/x2/t2/trx_4_2            frame        (shape->[160,12009])
/x2/t3/trx_1_2            frame        (shape->[152,12009])
/x2/t3/trx_1_4            frame        (shape->[150,12009])
/x2/t3/trx_2_3            frame        (shape->[150,12009])
/x2/t3/trx_3_1            frame        (shape->[153,12009])
/x2/t3/trx_3_4            frame        (shape->[144,12009])
/x2/t3/trx_4_2            frame        (shape->[145,12009])
/x3/t1/trx_1_2            frame        (shape->[208,12009])
/x3/t1/trx_1_4            frame        (shape->[211,12009])
/x3/t1/trx_2_3            frame        (shape->[208,12009])
/x3/t1/trx_3_1            frame        (shape->[210,12009])
/x3/t1/trx_3_4            frame        (shape->[213,12009])
/x3/t1/trx_4_2            frame        (shape->[212,12009])
/x3/t2/trx_1_2            frame        (shape->[243,12009])
/x3/t2/trx_1_4            frame        (shape->[245,12009])
/x3/t2/trx_2_3            frame        (shape->[251,12009])
/x3/t2/trx_3_1            frame        (shape->[247,12009])
/x3/t2/trx_3_4            frame        (shape->[249,12009])
/x3/t2/trx_4_2            frame        (shape->[242,12009])
/x3/t3/trx_1_2            frame        (shape->[260,12009])
/x3/t3/trx_1_4            frame        (shape->[253,12009])
/x3/t3/trx_2_3            frame        (shape->[257,12009])
/x3/t3/trx_3_1            frame        (shape->[256,12009])
/x3/t3/trx_3_4            frame        (shape->[261,12009])
/x3/t3/trx_4_2            frame        (shape->[255,12009])
/x4/t1/trx_1_2            frame        (shape->[121,12009])
/x4/t1/trx_1_4            frame        (shape->[129,12009])
/x4/t1/trx_2_3            frame        (shape->[126,12009])
/x4/t1/trx_3_1            frame        (shape->[130,12009])
/x4/t1/trx_3_4            frame        (shape->[143,12009])
/x4/t1/trx_4_2            frame        (shape->[132,12009])
/x4/t2/trx_1_2            frame        (shape->[173,12009])
/x4/t2/trx_1_4            frame        (shape->[177,12009])
/x4/t2/trx_2_3            frame        (shape->[171,12009])
/x4/t2/trx_3_1            frame        (shape->[181,12009])
/x4/t2/trx_3_4            frame        (shape->[180,12009])
/x4/t2/trx_4_2            frame        (shape->[179,12009])
/x4/t3/trx_1_2            frame        (shape->[168,12009])
/x4/t3/trx_1_4            frame        (shape->[169,12009])
/x4/t3/trx_2_3            frame        (shape->[162,12009])
/x4/t3/trx_3_1            frame        (shape->[169,12009])
/x4/t3/trx_3_4            frame        (shape->[168,12009])
/x4/t3/trx_4_2            frame        (shape->[169,12009])>

Aufgabe 2: Inspektion eines einzelnen Dataframes


In [19]:
df_x1_t1_trx_1_4 = hdf.get('/x1/t1/trx_1_4')
print(df_x1_t1_trx_1_4.shape)


(229, 12009)

In [ ]: