In [1]:
import sys
sys.version
sys.version_info
Out[1]:
sys.version_info(major=3, minor=4, micro=3, releaselevel='final', serial=0)
In [2]:
import numpy as np
np.__version__
Out[2]:
'1.13.1'
In [3]:
import requests
requests.__version__
Out[3]:
'2.18.3'
In [4]:
import pandas as pd
pd.__version__
Out[4]:
'0.20.3'
In [5]:
import scipy
scipy.__version__
Out[5]:
'0.19.1'
pip install git+http://github.com/paradigm4/scidb-py.git@devel
In [6]:
import scidbpy
scidbpy.__version__
Out[6]:
'16.9'
In [7]:
from scidbpy import connect
conectarse al servidor de Base de datos
In [8]:
sdb = connect('http://localhost:8080')
In [9]:
import urllib.request # urllib2 in python2 the lib that handles the url stuff
target_url = "https://www.physionet.org/physiobank/database/mimic2wdb/matched/RECORDS-waveforms"
data = urllib.request.urlopen(target_url) # it's a file like object and works just like a file
In [10]:
lines = data.readlines();
line = str(lines[0])
Quitarle caracteres especiales
In [11]:
carpeta,onda = line.replace('b\'','').replace('\'','').replace('\\n','').split("/")
onda
Out[11]:
's00001-2896-10-09-01-56'
In [12]:
import wfdb
In [13]:
sig, fields = wfdb.srdsamp(onda,pbdir='mimic3wdb/matched/'+carpeta) #, sampfrom=11000
In [14]:
print(sig)
print("signame: " + str(fields['signame']))
print("units: " + str(fields['units']))
print("fs: " + str(fields['fs']))
print("comments: " + str(fields['comments']))
print("fields: " + str(fields))
[[ nan nan nan nan]
[ nan nan nan nan]
[ nan nan nan nan]
...,
[ nan nan nan nan]
[ nan nan nan nan]
[ nan nan nan nan]]
signame: ['II', 'V', 'MCL1', 'ABP']
units: ['mV', 'mV', 'mV', 'mmHg']
fs: 125
comments: ['<age>: 60 <sex>: F']
fields: {'signame': ['II', 'V', 'MCL1', 'ABP'], 'units': ['mV', 'mV', 'mV', 'mmHg'], 'fs': 125, 'comments': ['<age>: 60 <sex>: F']}
Busca la ubicacion de la señal tipo II
In [15]:
signalII = None
try:
signalII = fields['signame'].index("II")
except ValueError:
print("List does not contain value")
if(signalII!=None):
print("List contain value")
List contain value
Normaliza la señal y le quita los valores en null
In [16]:
array = wfdb.processing.normalize(x=sig[:, signalII], lb=-2, ub=2)
arrayNun = array[~numpy.isnan(array)]
arrayNun = numpy.trim_zeros(arrayNun)
arrayNun
Out[16]:
array([], dtype=float64)
Cambiar los guiones "-" por raya al piso "_" porque por algun motivo SciDB tiene problemas con estos caracteres Si el arreglo sin valores nulos no queda vacio lo sube al SciDB
In [17]:
ondaName = onda.replace("-", "_")
if arrayNun.size>0 :
sdb.input(upload_data=array).store(ondaName,gc=False)
# sdb.iquery("store(input(<x:int64>[i], '{fn}', 0, '{fmt}'), "+ondaName+")", upload_data=array)
lista de los arreglos que actualmente se encuentran en SciDB
In [13]:
dir(sdb.arrays)
Out[13]:
['s00001_2896_10_10_00_31',
's00020_2567_03_30_17_47',
's00033_2559_01_25_12_35',
's00033_2559_01_26_13_11',
's00052_3036_03_14_02_21',
's00052_3036_03_14_12_15',
's00076_3103_08_06_18_45',
's00079_2756_08_14_01_25',
's00079_2756_08_14_12_28',
's00123_2772_11_02_03_01',
's00123_2772_11_03_12_58',
's00124_3297_08_03_21_51',
's00124_3297_08_04_15_23',
's00124_3297_08_12_01_20',
's00135_2909_04_11_10_04',
's00138_2817_11_01_13_39',
's00151_2509_04_15_12_12',
's00151_2509_04_16_14_43',
's00177_3299_12_03_12_35',
's00194_2675_05_03_14_34',
's00208_2955_05_18_00_06',
's00214_3084_11_28_16_23',
's00214_3084_12_02_22_02',
's00217_2669_08_23_12_28',
's00262_2554_11_12_18_06',
's00263_2931_11_13_23_52',
's00263_2931_11_15_19_57',
's00263_2931_11_16_11_58',
's00279_3271_08_20_21_22',
's00283_2948_10_24_13_16',
's00308_2915_08_04_12_57',
's00308_2915_08_28_04_22',
's00317_3234_10_03_15_24',
's00318_3031_08_09_07_15',
's00328_3144_10_20_14_50',
's00368_2568_06_23_19_56',
's00377_3016_03_04_15_47',
's00379_3192_07_24_12_20',
's00402_3438_01_09_16_11',
's00402_3438_01_12_12_03',
's00408_3350_09_28_22_27',
's00408_3350_09_29_18_23',
's00408_3350_09_30_19_10',
's00409_2994_09_17_14_20',
's00416_2509_05_22_15_14',
's00416_2509_05_23_00_20',
's00427_2977_04_30_13_51',
's00439_3242_12_28_20_29',
's00439_3242_12_29_13_54',
's00462_2830_04_26_15_50',
's00491_2546_04_19_14_08',
's00491_2546_04_20_13_09',
's00515_3148_05_26_11_35',
's00515_3148_06_10_10_44',
's00521_2866_08_28_22_58',
's00565_3474_10_22_14_14',
's00586_3425_11_10_05_11',
's00586_3425_11_10_17_13',
's00593_2907_06_03_22_02',
's00600_3152_10_20_17_06',
's00608_2683_04_16_08_12',
's00608_2683_04_17_19_50',
's00618_3063_12_18_19_00',
's00618_3063_12_21_17_09',
's00625_2706_03_31_03_12',
's00625_2706_04_02_00_49',
's00625_2706_04_08_20_10',
's00631_3289_01_31_13_30',
's00638_2668_11_25_12_59',
's00639_2970_10_11_11_35',
's00639_2970_10_11_13_18',
's00639_2970_10_11_17_48',
's00652_2965_06_24_13_10',
's00666_3040_03_07_20_46',
's00666_3040_03_08_10_17',
's00672_2521_05_13_10_52',
's00703_2568_07_07_15_45',
's00708_2868_08_02_12_02',
's00708_2868_08_02_12_43',
's00719_3416_08_25_23_53',
's00719_3416_08_27_06_11',
's00743_2989_01_24_15_13',
's00743_2989_01_29_14_03',
's00787_2535_06_18_04_17',
's00787_2535_06_18_08_02',
's00793_3469_04_20_10_43',
's00798_2680_03_11_19_45',
's00801_2861_07_08_13_56',
's00834_3173_09_25_05_17',
's00834_3173_10_04_18_07',
's00843_2781_09_16_17_57',
's00843_2781_09_17_14_50',
's00843_2781_09_19_14_49',
's00849_2591_09_20_15_05',
's00870_3269_01_25_05_48',
's00871_2502_04_28_03_20',
's00871_2502_04_30_10_12',
's00875_3015_07_14_12_21',
's00875_3015_07_16_02_55',
's00875_3015_07_26_17_24',
's00886_3429_04_19_10_27',
's00891_3467_09_21_03_10',
's00894_2746_08_20_22_25',
's00894_2746_08_23_02_00',
's00901_3079_01_21_18_13',
's00946_2684_03_25_08_08',
's00948_2600_12_12_13_11',
's00948_2600_12_12_14_54',
's00952_3410_04_15_12_26',
's00952_3410_11_27_17_16',
's00974_2967_04_13_13_42',
's00985_2636_07_20_17_12',
's01002_3079_10_03_21_16',
's01004_2531_07_20_08_50',
's01012_2816_05_10_23_01',
's01021_3445_12_23_15_58',
's01028_3459_03_23_15_41',
's01028_3459_03_25_20_48',
's01029_3415_07_03_20_22',
's01033_2725_02_06_14_26',
's01042_3028_11_04_18_56',
's01044_2733_05_28_17_28',
's01046_2639_09_09_17_06',
's01049_3449_09_09_15_33',
's01072_3305_09_28_11_55',
's01075_2821_03_17_17_27',
's01075_2821_03_19_13_50',
's01075_2821_03_26_10_37',
's01083_3076_08_22_21_51',
's01083_3076_08_23_11_01',
's01092_3089_08_16_13_01',
's01123_3042_03_29_04_19',
's01123_3042_03_29_11_33',
's01144_3060_09_04_16_53',
's01158_3162_04_26_09_33',
's01158_3162_04_28_17_28',
's01160_3368_12_09_19_56',
's01170_3160_08_18_15_31',
's01182_2688_03_25_23_14',
's01182_2688_03_28_15_04',
's01192_3171_11_04_06_50',
's01192_3171_11_04_16_25',
's01202_2957_02_07_15_18',
's01207_2750_08_31_20_12',
's01217_2546_02_19_10_40',
's01222_2773_07_18_13_19',
's01222_2773_07_19_05_13',
's01222_2773_07_21_21_30',
's01222_2773_07_29_04_18',
's01226_2564_04_30_16_12',
's01241_3195_12_18_05_38',
's01241_3195_12_18_15_38',
's01244_2621_10_04_05_00',
's01244_2621_10_04_08_22',
's01279_2726_09_14_13_38',
's01280_3391_10_19_23_53',
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's01338_3149_10_28_13_04',
's01357_2847_04_19_19_56',
's01364_2626_11_06_16_20',
's01378_3117_06_03_22_51',
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's01398_2612_07_15_21_25',
's01408_2713_08_25_00_05',
's01409_3441_08_06_12_20',
's01414_3118_03_21_16_02',
's01418_2947_11_14_12_10',
's01430_3068_02_22_10_26',
's01449_2835_10_05_15_08',
's01474_2669_11_17_14_09',
's01501_3477_12_09_15_02',
's01501_3477_12_13_19_05',
's01502_2719_07_27_20_26',
's01528_3147_01_09_21_58',
's01531_2944_04_14_18_13',
's01531_2944_04_16_00_52',
's01531_2944_04_16_18_28',
's01531_2944_04_18_00_48',
's01557_2646_01_13_18_14',
's01569_3469_03_25_19_28',
's01569_3469_03_26_16_03',
's01569_3469_03_26_21_12',
's01604_2883_05_31_14_30',
's01604_2883_06_04_16_57',
's01604_2883_06_07_14_17',
's01606_2794_10_04_21_53',
's01649_2695_10_16_00_19',
's01650_3106_09_07_12_58',
's01693_2718_07_23_14_27',
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's01791_2924_04_29_11_33',
's01795_2503_06_28_21_47',
's01795_2503_07_01_02_34',
's01795_2503_07_03_17_43',
's01802_2678_03_10_08_31',
's01802_2678_03_11_17_32',
's01818_3222_07_27_00_03',
's01818_3222_07_27_21_30',
's01824_3288_09_21_12_42',
's01840_3454_10_24_18_46',
's01854_3250_07_09_02_59',
's01855_3408_03_17_09_02',
's01855_3408_03_17_10_41',
's01868_2749_08_26_21_09',
's01868_2749_08_28_12_43',
's01885_2547_07_21_12_59',
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's01892_2785_07_18_00_52',
's01898_3425_03_31_02_14',
's01898_3425_04_01_17_56',
's01900_2506_08_20_17_57',
's01908_2508_08_02_22_15',
's01908_2508_08_04_08_50',
's01924_3174_09_05_14_43',
's01931_3027_08_15_13_58',
's01931_3027_11_22_23_21',
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's01941_3293_08_20_12_33',
's01944_2567_05_23_22_46',
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's01950_3274_06_27_15_04',
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's01950_3274_07_01_18_59',
's01950_3274_07_02_00_42',
's01973_3287_03_12_14_08',
's01973_3287_03_15_11_41',
's01973_3287_03_15_21_55',
's01978_2790_09_24_06_15',
's01978_2790_09_24_13_51',
's01978_2790_09_26_14_58',
's01978_2790_10_05_11_22',
's01978_2790_10_12_08_27',
's01979_2930_03_03_14_33',
's01995_3123_12_07_19_24',
's01995_3123_12_10_15_38',
's02029_3437_04_26_17_40',
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's02052_3494_11_09_10_32',
's02063_3061_11_07_18_17',
's02066_2538_09_09_12_51',
's02104_2542_04_20_17_33',
's02104_2542_04_23_16_41',
's02148_3239_09_16_14_34',
's02148_3239_09_19_16_59',
's02148_3239_09_23_11_25',
's02148_3239_09_26_09_54',
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's02157_2906_11_27_15_50',
's02172_2970_09_21_18_17',
's02172_2970_09_25_04_06',
's02187_2770_06_05_00_53',
's02187_2770_06_05_16_10',
's02213_2650_12_19_14_03',
's02224_2758_10_05_20_48',
's02228_3347_07_15_15_58',
's02229_3115_05_25_03_34',
's02237_2755_06_19_17_00',
's02237_2755_06_25_05_20',
's02246_2945_09_05_00_59',
's02251_3453_07_11_17_15',
's02261_2674_11_21_09_32',
's02264_2918_04_13_17_57',
's02264_2918_04_14_04_48',
's02274_3154_03_25_22_46',
's02280_2980_12_26_18_07',
's02280_2981_02_01_12_33',
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's02280_2981_02_13_18_33',
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's02700_3028_12_08_23_12',
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Obtiene el arreglo s00515_3148_06_10_10_44 de SciDB
In [20]:
myArray = sdb.iquery('scan(s00600_3152_10_19_18_13)', fetch=True, as_dataframe=True)
Imprime el contenido del arreglo
In [21]:
import matplotlib.pyplot as plt
simpleArray = myArray["x"];
simpleArray.plot()
plt.show()
In [22]:
simpleArray
Out[22]:
0 -0.001793
1 -0.001793
2 -0.001793
3 -0.001793
4 -0.001793
5 -0.001793
6 -0.001793
7 -0.001793
8 -0.001793
9 -0.001793
10 -0.001793
11 -0.001793
12 -0.001793
13 -0.001793
14 -0.001793
15 -0.001793
16 -0.001793
17 -0.001793
18 -0.001793
19 -0.001793
20 -0.001793
21 -0.001793
22 -0.001793
23 -0.001793
24 -0.001793
25 -0.001793
26 -0.001793
27 -0.001793
28 -0.001793
29 -0.001793
...
5865202 -0.000253
5865203 -0.000288
5865204 -0.000267
5865205 -0.000267
5865206 -0.000232
5865207 -0.000267
5865208 -0.000267
5865209 -0.000232
5865210 -0.000253
5865211 -0.000302
5865212 -0.000267
5865213 -0.000267
5865214 -0.000267
5865215 -0.000288
5865216 -0.000267
5865217 -0.000232
5865218 -0.000267
5865219 -0.000253
5865220 -0.000267
5865221 -0.000253
5865222 -0.000267
5865223 -0.000267
5865224 -0.000267
5865225 -0.000232
5865226 -0.000211
5865227 -0.000176
5865228 -0.000105
5865229 -0.000091
5865230 -0.000035
5865231 0.000056
Name: x, Length: 5865232, dtype: float64
Lee el contenido del archivo donde se encuentran las ondas ya leidas
In [14]:
file = open("readedWaves.txt","r")
arrays = dir(sdb.arrays)
filelines = file.readlines()
arrays.extend([w.replace('\n','') for w in filelines])
#type(arrays)
arrays
Out[14]:
['s00001_2896_10_10_00_31',
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...]
In [ ]:
Content source: davidgutierrez/HeartRatePatterns
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