Ingredients¶

Libraries¶

import numpy as np, scipy, matplotlib.pyplot as plt, pandas as pd, seaborn as sns
sns.set(style='whitegrid')
import IPython.display as ipd
%run detect_peaks.py
plt.rcParams['figure.figsize'] = [10, 5]
%matplotlib inline

Data¶

data = pd.DataFrame.from_csv('./HackLab_Vol2_Excerpts/HackLab_Vol2_Excerpts/Vol2_P2_Exrpt.csv')
data = data.drop('Time',axis = 1)

data

Preparations¶

f,ax = plt.subplots(5,2,figsize = (32,20), sharex=True, sharey=True,)
ax[0,0].plot(data['p1'],color = 'red')
ax[1,0].plot(data['p2'],color = 'orange')
ax[2,0].plot(data['p3'],color = 'yellow')
ax[3,0].plot(data['p4'],color = 'green')
ax[4,0].plot(data['p5'],color = 'brown')
ax[0,1].plot(data['p6'],color = 'blue')
ax[1,1].plot(data['m1'],color = 'grey')
ax[2,1].plot(data['m2'],color = 'purple')
ax[3,1].plot(data['m3'],color = 'black')
ax[4,1].plot(data['m4'],color = 'black')

[<matplotlib.lines.Line2D at 0x7f90fd832950>]

%run detect_peaks.py

<matplotlib.figure.Figure at 0x7f90fdfa5450>

detect_peaks(data['p1'],show=True)
detect_peaks(data['p2'],show=True)
detect_peaks(data['p3'],show=True)
detect_peaks(data['p4'],show=True)
detect_peaks(data['p5'],show=True)
detect_peaks(data['p6'],show=True)

array([   5,    9,   15,   18,   22,   24,   37,   51,   62,   73,   79,
         85,   88,   91,   94,   97,  103,  112,  119,  122,  141,  160,
        162,  167,  177,  191,  200,  202,  204,  211,  213,  216,  229,
        240,  244,  247,  251,  253,  258,  269,  284,  286,  289,  293,
        307,  320,  323,  330,  334,  337,  350,  366,  369,  372,  374,
        385,  400,  404,  415,  432,  439,  441,  445,  448,  450,  470,
        474,  493,  495,  501,  507,  510,  520,  525,  527,  530,  536,
        539,  542,  547,  552,  555,  564,  566,  573,  577,  580,  590,
        603,  608,  610,  613,  617,  619,  622,  625,  632,  634,  640,
        643,  647,  651,  655,  660,  663,  687,  701,  706,  720,  736,
        742,  748,  752,  762,  771,  784,  786,  802,  819,  824,  830,
        839,  841,  850,  857,  860,  863,  867,  869,  876,  885,  887,
        889,  895,  907,  916,  920,  923,  926,  930,  941,  951,  958,
        962,  970,  973,  976,  981,  992,  995,  998, 1006, 1008, 1019,
       1022, 1027, 1029, 1033, 1039, 1042, 1051, 1061, 1075, 1078, 1081,
       1095, 1106, 1110, 1126, 1134, 1137, 1144, 1148, 1156, 1163, 1171,
       1174, 1179, 1187, 1190, 1199, 1203, 1209, 1211, 1215, 1217, 1224,
       1234, 1240, 1243, 1254, 1263, 1271, 1277, 1287, 1299, 1303, 1309,
       1316, 1322, 1325, 1328, 1331, 1334, 1337, 1346, 1350, 1355, 1361,
       1375, 1390, 1394, 1399])

	p1	p2	p3	p4	p5	p6	m1	m2	m3	m4
Sample
471	1848	1601	1865	1614	2092	1879	1999	2023	1751	1878
472	1855	1587	1866	1605	2084	1880	1994	2014	1763	1880
473	1863	1577	1866	1600	2090	1885	1993	2012	1764	1877
474	1868	1561	1870	1594	2091	1888	1991	2005	1761	1868
475	1874	1556	1874	1587	2086	1894	1993	2060	1750	1863
476	1880	1552	1889	1583	2089	1901	1995	2096	1742	1851
477	1887	1547	1901	1586	2088	1897	1995	2081	1737	1839
478	1886	1543	1915	1578	2085	1896	1995	2089	1720	1854
479	1883	1543	1926	1578	2089	1890	2001	2096	1710	1892
480	1882	1541	1935	1580	2091	1891	1996	2093	1712	1947
481	1881	1537	1941	1580	2086	1888	1990	2077	1699	1960
482	1879	1540	1944	1580	2091	1883	1993	2068	1695	1955
483	1871	1535	1945	1580	2094	1883	2002	2055	1694	1955
484	1865	1533	1939	1583	2092	1882	2011	2044	1686	1991
485	1859	1531	1933	1584	2087	1878	2026	2039	1678	2031
486	1847	1540	1915	1585	2090	1879	2038	2036	1655	2037
487	1838	1571	1904	1581	2085	1877	2045	2019	1638	2014
488	1832	1609	1892	1590	2084	1876	2053	2005	1625	1975
489	1833	1647	1884	1600	2089	1877	2065	1989	1606	1940
490	1830	1686	1875	1604	2082	1876	2065	1968	1596	1916
491	1829	1717	1874	1614	2088	1873	2068	1957	1584	1879
492	1830	1724	1868	1624	2089	1870	2074	1959	1579	1856
493	1827	1734	1872	1632	2080	1872	2078	1961	1578	1848
494	1822	1759	1868	1639	2084	1872	2078	1955	1576	1851
495	1822	1792	1870	1633	2090	1875	2067	1950	1580	1848
496	1830	1828	1869	1627	2081	1874	2052	1950	1579	1859
497	1842	1845	1870	1622	2083	1874	2040	1951	1583	1870
498	1847	1848	1870	1614	2095	1874	2027	1947	1590	1888
499	1844	1845	1870	1603	2081	1870	2015	1939	1587	1931
500	1846	1836	1870	1599	2085	1869	2001	1927	1588	1975
...	...	...	...	...	...	...	...	...	...	...
1842	1715	1650	1902	1657	2103	1888	2013	1859	1822	1949
1843	1711	1663	1906	1670	2110	1893	2014	1849	1788	1918
1844	1716	1681	1915	1674	2107	1901	2014	1845	1772	1889
1845	1718	1694	1925	1683	2111	1906	2007	1837	1754	1880
1846	1726	1712	1927	1681	2111	1912	2007	1835	1753	1881
1847	1733	1721	1938	1676	2098	1912	2007	1836	1753	1874
1848	1744	1724	1941	1675	2105	1906	2005	1836	1742	1856
1849	1758	1734	1947	1672	2126	1904	2009	1829	1720	1839
1850	1764	1734	1954	1669	2100	1903	2009	1823	1704	1838
1851	1772	1722	1960	1664	2118	1896	2012	1824	1705	1851
1852	1786	1706	1954	1662	2118	1894	2013	1825	1698	1843
1853	1797	1694	1947	1660	2098	1894	2013	1821	1692	1840
1854	1806	1666	1933	1654	2112	1888	2010	1821	1678	1835
1855	1817	1637	1919	1645	2128	1888	2012	1817	1670	1827
1856	1830	1616	1909	1640	2121	1884	2017	1823	1668	1845
1857	1843	1602	1902	1636	2123	1878	2030	1825	1665	1862
1858	1858	1597	1893	1626	2117	1875	2048	1826	1672	1870
1859	1862	1591	1887	1616	2102	1874	2069	1827	1675	1879
1860	1866	1596	1887	1605	2115	1875	2081	1821	1681	1888
1861	1868	1595	1884	1595	2127	1876	2085	1816	1687	1893
1862	1868	1595	1878	1599	2103	1875	2091	1810	1688	1890
1863	1862	1598	1877	1596	2117	1874	2092	1799	1683	1894
1864	1862	1600	1873	1592	2121	1870	2103	1793	1691	1886
1865	1859	1600	1871	1588	2098	1871	2105	1783	1696	1875
1866	1856	1596	1872	1588	2103	1865	2098	1773	1700	1867
1867	1843	1598	1877	1595	2102	1865	2083	1762	1700	1860
1868	1827	1599	1878	1596	2089	1862	2068	1750	1709	1851
1869	1815	1600	1890	1604	2083	1865	2059	1728	1694	1845
1870	1805	1599	1898	1612	2091	1866	2048	1724	1684	1836
1871	1793	1601	1908	1621	2084	1865	2040	1717	1668	1844