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RRV

Paciente

  • Nombre: Ramón Reyes Vera
  • Edad: 79 años
  • Telefono: 55491959 (casa)

Antecedentes

Miastenia grave

  • Detección: 1990
  • Tratamiento:
    • Un mes mestinon (lo hacia sentirse más mal)
    • Desde entonces a la fecha: potasio (dos veces por semana en la noche)

Hipertensión

  • Detección: 2003 (posiblemente la padece desde antes)

    • Sep 2007: Ecocardiograma modo M, bidimensional y Doppler de onda pulsada, continua y Doppler codificado en color
      • Ventrículo izquierdo: Hipertrofia concéntrica
      • Aorta: el Doppler color revela insuficiencia leve
      • Hipertrofia concéntrica y disfunción diastólica del ventrículo izquierdo por alteraciones en la relajación
      • Función sistólica ventricular conservada con FEVI 84% por el método de Teicholz
      • Insuficiencia tricuspídea moderada, con datos hipertensión arterial pulmonar leve, presión sistólica de 40 mmHg
      • Insuficiencia leve de las válvulas mitral y aórtica
    • Oct 2008: Prueba de esfuerzo
      • Sugestiva de insuficiencia coronaria significativa
      • Respuesta cronotrópica adecuada
      • Respuesta presora hipertensiva, con franco componente hipereactor
    • Abr 2010: ISSSTE, medicina nuclear
      • Pared inferior: Reversibilidad leve
      • IDX
        • Isquemia leve de la región inferior que abarca sus tres tercios.
        • FEVI 68%
        • VI de tamaño normal
    • Ago 2013: ISSSTE, medicación. Soplo sistólico mistral
    • Mayo 2 2017: Consulta particular, Dr. Victor Garduño (Cardiología, "20 Nov")
      • 24 horas monitor de presión y Holter
        • Pulso acelerado en promedio
        • Varias arritmias
        • En reposo aumenta la presión arterial
        • La insuficiencia coronaria ha aumentado
    • Julio 11 2017: Medico familiar del ISSSTE
    • Agosto 14 y 21: Consulta particular, Dr. Alexanderson
  • Tratamiento:

    • 2007: Norvas + Co-Aprovel + Tenormin
    • 2007: Micardis Plus + Nexus + Tenormin + Zyloprim
    • 4 Dic 2007: Micardis Plus --> Exforge
    • 18 Dic 2007: suspendió Nexus
    • Feb 2008: Exforge + Higroton + Zyloprim
    • Mayo 2008: añadió Norvas
    • Agosto 2008: Tenormin + Exforge + Higroton + Zyloprim
    • De 2010 a 2013: amlodipono + metoprolol
    • De 2013 a Marzo 2017: amlodipino + losartan
    • Abril 2017: nifedipino + losartan
    • Mayo 2 2017: amlodipino + losartan + lipitor
      • Se espera poder medicarle aspirina o algún anti-coagulante para poder regular el pulso
      • Se debe considerar cateterismo
    • Agosto 21 2017: Norvas + Cozaor
    • Diciembre 2017:
    • Febrero 21 2018: Verapamilo, prabioquin, Olmesartan

Purpura PTI

Bronquitis + infeccion vias urinarias ...

  • Detección: Al ingresar a urgencias (27 Marzo)
    • Durante el internamiento (27 Marzo - 14 Abril) además presentó:
      • Niveles bajos (45) de oxigenación en la sangre
      • Niveles bajos (1.2) de potasio en la sangre
    • De alta en su casa a los 5 días de terminar el tratamiento de antibiótico
      • 2 de mayo temperaturas de hasta 38.5
      • 6 de mayo temperaturas de hasta 39.8
    • Fiebre
      • 2 de junio temperaturas de hasta 39.7. El hematólogo diagnostica infección en las vias urinarias en base a los análisis de orina.
    • Tomografía 16 Agosto 2017 Interpretación
    • Urocultivo 24 Agosto 2017
    • EGO 12 de enero 2018
    • EGO 22 de enero 2018
    • Fiebre
    • Radiografia Urgencias ISSSTE 9 Febrero 2018
    • EGO 12 de febrero 2018
    • Urocultivo 16 de febrero 2018
    • EGO 27 de febrero 2018
    • Consulta 28 de febrero 2018 en neumonia Dario Fernandez ISSSTE informe
  • Tratamiento: (además de: nifedipino, losartan, prednisona)
    • Antibióticos intravenosos varios
    • potasio intravenoso y sales
    • Omeprasol intravenoso
    • Nebulizaciones.
    • Aspirómetro.
    • 4 de mayo en urgencias ISSSTE medicaron antibióticos.
    • 6 de mayo medico internista cambio a ceftriaxona.
    • 2 de junio antibiotico oral para infección en vias urinarias.
    • 28 de agosto antibiotico inyectado para infección en vias urinarias.
    • 22-26 enero internamiento para antibiotico intravenoso para infeccion de vias urinarias.
    • 9-11 febrero nebulizaciones
    • 12-19 febrero antibiotico inyectado y tomado para pulmonia, medicaron en urgencias ISSSTE.
    • 28 de febrero: oxigeno, ...

Lecturas recientes (6-30 de mayo) de la presión arterial pulso y temperatura (desde 2 de mayo)

In [1]:
import matplotlib.pyplot as plt
import datetime
import numpy as np

x2p = np.array([
 datetime.datetime(2017, 7, 1, 5, 20),datetime.datetime(2017, 7, 1, 16, 9),datetime.datetime(2017, 7, 1, 17, 29),datetime.datetime(2017, 7, 1, 19, 29),datetime.datetime(2017, 7, 1, 21, 29),
 datetime.datetime(2017, 7, 2, 2, 20),datetime.datetime(2017, 7, 2, 7, 29),datetime.datetime(2017, 7, 2, 14, 20),datetime.datetime(2017, 7, 2, 19, 29),datetime.datetime(2017, 7, 2, 21, 29),datetime.datetime(2017, 7, 2, 22, 29),
 datetime.datetime(2017, 7, 3, 2, 29),datetime.datetime(2017, 7, 3, 7, 29),datetime.datetime(2017, 7, 3, 16, 20),datetime.datetime(2017, 7, 3, 20, 20),
 datetime.datetime(2017, 7, 4, 7, 19),datetime.datetime(2017, 7, 4, 16, 20),datetime.datetime(2017, 7, 4, 20, 20),
 datetime.datetime(2017, 7, 5, 1, 20),datetime.datetime(2017, 7, 5, 7, 29),
 datetime.datetime(2017, 7, 6, 2, 19),datetime.datetime(2017, 7, 6, 6, 20),datetime.datetime(2017, 7, 6, 8, 20),
 datetime.datetime(2017, 7, 7, 3, 59),datetime.datetime(2017, 7, 7, 7, 20),datetime.datetime(2017, 7, 7, 20, 20),
 datetime.datetime(2017, 7, 8, 1, 29),datetime.datetime(2017, 7, 8, 6, 29),datetime.datetime(2017, 7, 8, 17, 20),datetime.datetime(2017, 7, 8, 21, 20),
 datetime.datetime(2017, 7, 9, 7, 20),datetime.datetime(2017, 7, 9, 20, 29),
 datetime.datetime(2017, 7, 10, 3, 19),datetime.datetime(2017, 7, 10, 7, 20),datetime.datetime(2017, 7, 10, 20, 20),
 datetime.datetime(2017, 7, 11, 3, 29),datetime.datetime(2017, 7, 11, 21, 20),
 datetime.datetime(2017, 7, 12, 4, 29),datetime.datetime(2017, 7, 12, 8, 29),datetime.datetime(2017, 7, 12, 13, 20),datetime.datetime(2017, 7, 12, 21, 20),
 datetime.datetime(2017, 7, 13, 1, 19),datetime.datetime(2017, 7, 13, 7, 20),datetime.datetime(2017, 7, 13, 20, 20),
 datetime.datetime(2017, 7, 14, 2, 19),datetime.datetime(2017, 7, 14, 7, 20),datetime.datetime(2017, 7, 14, 21, 20),
 datetime.datetime(2017, 7, 15, 2, 19),datetime.datetime(2017, 7, 15, 7, 20),datetime.datetime(2017, 7, 15, 21, 20),
 datetime.datetime(2017, 7, 16, 2, 19),datetime.datetime(2017, 7, 16, 7, 20),datetime.datetime(2017, 7, 16, 21, 20),
 datetime.datetime(2017, 7, 17, 2, 29),datetime.datetime(2017, 7, 17, 11, 29),datetime.datetime(2017, 7, 17, 14, 20),datetime.datetime(2017, 7, 17, 20, 20),
 datetime.datetime(2017, 7, 18, 2, 19),datetime.datetime(2017, 7, 18, 7, 20),datetime.datetime(2017, 7, 18, 21, 20),
 datetime.datetime(2017, 7, 19, 3, 19),datetime.datetime(2017, 7, 19, 14, 20),datetime.datetime(2017, 7, 19, 21, 20),
 datetime.datetime(2017, 7, 20, 3, 19),datetime.datetime(2017, 7, 20, 7, 20),datetime.datetime(2017, 7, 20, 20, 20),
 datetime.datetime(2017, 7, 21, 2, 29),datetime.datetime(2017, 7, 21, 7, 29),datetime.datetime(2017, 7, 21, 20, 20),datetime.datetime(2017, 7, 21, 22, 20),
 datetime.datetime(2017, 7, 22, 2, 29),datetime.datetime(2017, 7, 22, 6, 29),datetime.datetime(2017, 7, 22, 16, 20),datetime.datetime(2017, 7, 22, 20, 20),
 datetime.datetime(2017, 7, 23, 2, 20),datetime.datetime(2017, 7, 23, 21, 29),
 datetime.datetime(2017, 7, 24, 2, 19),datetime.datetime(2017, 7, 24, 7, 20),datetime.datetime(2017, 7, 24, 22, 20),
 datetime.datetime(2017, 7, 25, 2, 19),datetime.datetime(2017, 7, 25, 15, 20),datetime.datetime(2017, 7, 25, 21, 20),
 datetime.datetime(2017, 7, 26, 2, 19),datetime.datetime(2017, 7, 26, 7, 20),datetime.datetime(2017, 7, 26, 14, 20),
 datetime.datetime(2017, 7, 27, 2, 20),datetime.datetime(2017, 7, 27, 7, 9),datetime.datetime(2017, 7, 27, 17, 29),datetime.datetime(2017, 7, 27, 20, 29),datetime.datetime(2017, 7, 27, 22, 29),
 datetime.datetime(2017, 7, 28, 2, 19),datetime.datetime(2017, 7, 28, 7, 20),datetime.datetime(2017, 7, 28, 21, 20),
 datetime.datetime(2017, 7, 29, 7, 19),datetime.datetime(2017, 7, 29, 16, 20),datetime.datetime(2017, 7, 29, 21, 20),
 datetime.datetime(2017, 7, 30, 2, 29),datetime.datetime(2017, 7, 30, 9, 29),
    datetime.datetime(2017,8,6,2,00),datetime.datetime(2017,8,6,7,00),datetime.datetime(2017,8,6,20,00),datetime.datetime(2017,8,7,3,00),datetime.datetime(2017,8,7,7,00),datetime.datetime(2017,8,7,23,00),datetime.datetime(2017,8,8,2,00),datetime.datetime(2017,8,8,7,00),datetime.datetime(2017,8,8,21,00),datetime.datetime(2017,8,9,4,00),datetime.datetime(2017,8,9,9,30),datetime.datetime(2017,8,9,17,00),datetime.datetime(2017,8,9,21,30),datetime.datetime(2017,8,10,2,30),datetime.datetime(2017,8,10,7,00),datetime.datetime(2017,8,10,22,00),datetime.datetime(2017,8,11,2,00),datetime.datetime(2017,8,11,17,00),datetime.datetime(2017,8,12,2,30),datetime.datetime(2017,8,12,12,30),datetime.datetime(2017,8,12,22,00),datetime.datetime(2017,8,14,12,00),datetime.datetime(2017,8,14,23,59),datetime.datetime(2017,8,19,7,30),datetime.datetime(2017,8,19,19,00),datetime.datetime(2017,8,20,21,30),datetime.datetime(2017,8,21,8,30),datetime.datetime(2017,8,21,23,00),datetime.datetime(2017,8,22,8,00),datetime.datetime(2017,8,22,10,00),datetime.datetime(2017,8,22,21,30),datetime.datetime(2017,8,23,3,00),datetime.datetime(2017,8,23,7,00),datetime.datetime(2017,8,23,21,00),datetime.datetime(2017,8,24,1,30),datetime.datetime(2017,8,24,7,30),datetime.datetime(2017,8,25,2,00),datetime.datetime(2017,8,25,9,00),datetime.datetime(2017,8,25,20,00),datetime.datetime(2017,8,26,2,00),datetime.datetime(2017,8,26,8,00),datetime.datetime(2017,8,26,20,00),datetime.datetime(2017,8,27,8,30),datetime.datetime(2017,8,27,18,00),datetime.datetime(2017,8,27,21,30),datetime.datetime(2017,8,28,1,30),datetime.datetime(2017,8,28,8,30),datetime.datetime(2017,8,28,14,00),datetime.datetime(2017,8,28,20,30),datetime.datetime(2017,8,29,3,00),datetime.datetime(2017,8,29,7,30),datetime.datetime(2017,8,29,14,30),datetime.datetime(2017,8,29,21,00),datetime.datetime(2017,8,29,22,00),datetime.datetime(2017,8,30,1,00),datetime.datetime(2017,8,30,3,00),datetime.datetime(2017,8,30,10,00),datetime.datetime(2017,8,30,19,00),datetime.datetime(2017,8,30,20,00),datetime.datetime(2017,8,30,23,59),datetime.datetime(2017,9,1,4,00),datetime.datetime(2017,9,1,8,00),datetime.datetime(2017,9,1,12,00),datetime.datetime(2017,9,1,16,00),datetime.datetime(2017,9,1,19,30),datetime.datetime(2017,9,1,19,45),datetime.datetime(2017,9,2,3,00),datetime.datetime(2017,9,2,7,00),datetime.datetime(2017,9,2,10,00),datetime.datetime(2017,9,2,16,00),datetime.datetime(2017,9,2,17,30),datetime.datetime(2017,9,2,20,30),datetime.datetime(2017,9,3,7,00),datetime.datetime(2017,9,3,16,30),datetime.datetime(2017,9,3,22,00),datetime.datetime(2017,9,4,8,00),datetime.datetime(2017,9,4,17,00),datetime.datetime(2017,9,4,20,00)
    ])

x3p = np.array([
        datetime.datetime(2017,8,19,19,00),datetime.datetime(2017,8,20,21,30),datetime.datetime(2017,8,21,8,30),datetime.datetime(2017,8,21,23,00),datetime.datetime(2017,8,22,8,00),datetime.datetime(2017,8,23,7,00),datetime.datetime(2017,8,23,21,00),datetime.datetime(2017,8,24,1,30),datetime.datetime(2017,8,24,7,30),datetime.datetime(2017,8,25,2,00),datetime.datetime(2017,8,25,9,00),datetime.datetime(2017,8,25,20,00),datetime.datetime(2017,8,26,2,00),datetime.datetime(2017,8,26,8,00),datetime.datetime(2017,8,26,20,00),datetime.datetime(2017,8,27,8,30),datetime.datetime(2017,8,27,18,00),datetime.datetime(2017,8,27,21,30),datetime.datetime(2017,8,28,1,30),datetime.datetime(2017,8,28,8,30),datetime.datetime(2017,8,28,20,30),datetime.datetime(2017,8,30,10,00),datetime.datetime(2017,8,30,19,00),datetime.datetime(2017,9,1,8,00),datetime.datetime(2017,9,1,12,00),datetime.datetime(2017,9,2,7,00),datetime.datetime(2017,9,2,10,00),datetime.datetime(2017,9,2,17,30),datetime.datetime(2017,9,2,20,30),datetime.datetime(2017,9,3,16,30),datetime.datetime(2017,9,4,8,00),datetime.datetime(2017,9,4,17,00),datetime.datetime(2017,9,4,20,00)
        ])

si2 = np.array([167,143,129,134,156,167,157,140,144,142,150,170,163,128,157,154,128,151,159,166,163,161,119,151,155,154,169,154,121,160,
               164,133,159,153,152,157,158,159,154,146,159,152,142,136,174,157,136,169,147,156,176,151,138,156,128,136,152,
               163,132,140,158,132,136,163,164,134,147,159,112,122,162,153,129,144,175,131,172,151,131,169,132,154,152,149,117,
               154,149,132,152,144,163,150,146,159,136,163,181,149,
165,154,133,168,143,145,145,147,167,153,138,144,169,156,145,153,157,157,163,116,137,142,150,142,135,147,140,137,170,131,154,152,145,143,141,152,155,146,139,144,154,137,179,142,161,143,163,142,113,151,162,167,182,177,172,166,138,128,160,167,158,168,141,133,159,187,159,174,152,138,135,173,167,128,178,162,173,183
               ])
di2 = np.array([92,105,70,78,85,89,91,81,81,82,86,90,88,70,82,87,72,87,88,85,89,84,65,79,85,85,87,90,71,83,
               92,81,84,85,82,81,85,89,85,82,87,79,88,75,101,88,75,89,77,85,91,85,78,94,72,76,80,
               91,85,74,65,79,76,91,84,74,81,89,65,68,92,89,73,79,97,71,98,84,72,88,69,83,85,80,67,
               88,90,74,83,77,94,90,83,92,71,86,98,85,
94,95,73,94,88,82,82,88,90,93,87,78,91,90,88,87,98,92,86,76,75,80,82,79,85,84,81,82,89,79,85,81,85,86,77,85,86,83,84,79,86,85,88,74,86,80,86,88,65,91,91,107,103,94,98,92,78,85,90,100,84,85,82,77,92,93,80,87,85,82,81,105,83,83,105,92,95,111
               ])
pp2 = np.array([68,80,72,62,70,66,67,75,78,74,67,67,68,66,65,62,67,61,70,61,67,65,90,68,60,63,68,65,65,60,65,79,68,60,66,65,70,67,66,74,84,73,72,74,75,68,70,66,67,68,67,67,74,73,78,76,70,
               68,72,74,84,86,65,67,67,67,61,65,74,71,69,65,76,66,67,68,67,59,73,67,69,62,62,61,82,65,65,74,66,69,64,66,69,63,76,70,67,63,
72,67,66,74,67,76,0,81,78,77,74,80,80,70,68,82,79,81,74,0,71,73,78,60,70,71,73,83,64,67,67,61,70,91,68,60,60,65,82,65,60,66,63,75,73,58,57,74,97,74,62,76,78,69,68,62,72,85,80,0,63,56,64,75,71,70,57,62,77,65,73,74,60,86,75,64,76,76
               ])

oxi = np.array([
     89,83,88,86,86,87,87,84,87,89,91,88,87,85,91,90,87,84,86,87,86,88,87,90,88,87,90,89,83,86,85,87,86   
    ])

fig, ax = plt.subplots()
ax.text(datetime.datetime(2017, 7, 31, 12, 0), 153, '150', color='red')
ax.text(datetime.datetime(2017, 7, 31, 2, 0), 92, '90', color='blue')
plt.plot(x2p,si2,'ro',x2p,di2,'b^')
plt.axhline(y=150, color='r', linestyle='-')
plt.axhline(y=90, color='b', linestyle='-')
plt.title('Presion')
plt.ylabel('mmHg')
plt.axis(xmin=datetime.datetime(2017, 7, 1, 0, 0),xmax=datetime.datetime(2017, 9, 5, 0, 0),ymin=40,ymax=200)
plt.show()

fig, ax = plt.subplots()
plt.plot(x2p,pp2,'ko')
plt.axhline(y=90, color='k', linestyle='-')
plt.title('Pulso')
plt.ylabel('ppm')
plt.axis(xmin=datetime.datetime(2017, 7, 1, 0, 0),xmax=datetime.datetime(2017, 9, 5, 0, 0),ymin=60,ymax=100)
plt.show()
Out[1]:
Out[1]:

Dudas

  • Seguimiento en hematología?
  • Se mencionó un padecimiento raro/deficiencia hormonal relacionada con el potasio bajo e hipertension (?)

Datos exactos de los signos vitales

In [2]:
ant = datetime.datetime(2017, 7, 1, 0, 0)

for i in range(0, 175, 1):
    if ant.date() < x2p[i].date():
        print "\n",x2p[i].date()
        print "Hora\tPresión\tPulso"
        ant = x2p[i]
    print str(x2p[i].hour)+"\t",str(si2[i])+"/"+str(di2[i]),"\t",pp2[i]
5	167/92 	68
16	143/105 	80
17	129/70 	72
19	134/78 	62
21	156/85 	70

2017-07-02
Hora	Presión	Pulso
2	167/89 	66
7	157/91 	67
14	140/81 	75
19	144/81 	78
21	142/82 	74
22	150/86 	67

2017-07-03
Hora	Presión	Pulso
2	170/90 	67
7	163/88 	68
16	128/70 	66
20	157/82 	65

2017-07-04
Hora	Presión	Pulso
7	154/87 	62
16	128/72 	67
20	151/87 	61

2017-07-05
Hora	Presión	Pulso
1	159/88 	70
7	166/85 	61

2017-07-06
Hora	Presión	Pulso
2	163/89 	67
6	161/84 	65
8	119/65 	90

2017-07-07
Hora	Presión	Pulso
3	151/79 	68
7	155/85 	60
20	154/85 	63

2017-07-08
Hora	Presión	Pulso
1	169/87 	68
6	154/90 	65
17	121/71 	65
21	160/83 	60

2017-07-09
Hora	Presión	Pulso
7	164/92 	65
20	133/81 	79

2017-07-10
Hora	Presión	Pulso
3	159/84 	68
7	153/85 	60
20	152/82 	66

2017-07-11
Hora	Presión	Pulso
3	157/81 	65
21	158/85 	70

2017-07-12
Hora	Presión	Pulso
4	159/89 	67
8	154/85 	66
13	146/82 	74
21	159/87 	84

2017-07-13
Hora	Presión	Pulso
1	152/79 	73
7	142/88 	72
20	136/75 	74

2017-07-14
Hora	Presión	Pulso
2	174/101 	75
7	157/88 	68
21	136/75 	70

2017-07-15
Hora	Presión	Pulso
2	169/89 	66
7	147/77 	67
21	156/85 	68

2017-07-16
Hora	Presión	Pulso
2	176/91 	67
7	151/85 	67
21	138/78 	74

2017-07-17
Hora	Presión	Pulso
2	156/94 	73
11	128/72 	78
14	136/76 	76
20	152/80 	70

2017-07-18
Hora	Presión	Pulso
2	163/91 	68
7	132/85 	72
21	140/74 	74

2017-07-19
Hora	Presión	Pulso
3	158/65 	84
14	132/79 	86
21	136/76 	65

2017-07-20
Hora	Presión	Pulso
3	163/91 	67
7	164/84 	67
20	134/74 	67

2017-07-21
Hora	Presión	Pulso
2	147/81 	61
7	159/89 	65
20	112/65 	74
22	122/68 	71

2017-07-22
Hora	Presión	Pulso
2	162/92 	69
6	153/89 	65
16	129/73 	76
20	144/79 	66

2017-07-23
Hora	Presión	Pulso
2	175/97 	67
21	131/71 	68

2017-07-24
Hora	Presión	Pulso
2	172/98 	67
7	151/84 	59
22	131/72 	73

2017-07-25
Hora	Presión	Pulso
2	169/88 	67
15	132/69 	69
21	154/83 	62

2017-07-26
Hora	Presión	Pulso
2	152/85 	62
7	149/80 	61
14	117/67 	82

2017-07-27
Hora	Presión	Pulso
2	154/88 	65
7	149/90 	65
17	132/74 	74
20	152/83 	66
22	144/77 	69

2017-07-28
Hora	Presión	Pulso
2	163/94 	64
7	150/90 	66
21	146/83 	69

2017-07-29
Hora	Presión	Pulso
7	159/92 	63
16	136/71 	76
21	163/86 	70

2017-07-30
Hora	Presión	Pulso
2	181/98 	67
9	149/85 	63

2017-08-06
Hora	Presión	Pulso
2	165/94 	72
7	154/95 	67
20	133/73 	66

2017-08-07
Hora	Presión	Pulso
3	168/94 	74
7	143/88 	67
23	145/82 	76

2017-08-08
Hora	Presión	Pulso
2	145/82 	0
7	147/88 	81
21	167/90 	78

2017-08-09
Hora	Presión	Pulso
4	153/93 	77
9	138/87 	74
17	144/78 	80
21	169/91 	80

2017-08-10
Hora	Presión	Pulso
2	156/90 	70
7	145/88 	68
22	153/87 	82

2017-08-11
Hora	Presión	Pulso
2	157/98 	79
17	157/92 	81

2017-08-12
Hora	Presión	Pulso
2	163/86 	74
12	116/76 	0
22	137/75 	71

2017-08-14
Hora	Presión	Pulso
12	142/80 	73
23	150/82 	78

2017-08-19
Hora	Presión	Pulso
7	142/79 	60
19	135/85 	70

2017-08-20
Hora	Presión	Pulso
21	147/84 	71

2017-08-21
Hora	Presión	Pulso
8	140/81 	73
23	137/82 	83

2017-08-22
Hora	Presión	Pulso
8	170/89 	64
10	131/79 	67
21	154/85 	67

2017-08-23
Hora	Presión	Pulso
3	152/81 	61
7	145/85 	70
21	143/86 	91

2017-08-24
Hora	Presión	Pulso
1	141/77 	68
7	152/85 	60

2017-08-25
Hora	Presión	Pulso
2	155/86 	60
9	146/83 	65
20	139/84 	82

2017-08-26
Hora	Presión	Pulso
2	144/79 	65
8	154/86 	60
20	137/85 	66

2017-08-27
Hora	Presión	Pulso
8	179/88 	63
18	142/74 	75
21	161/86 	73

2017-08-28
Hora	Presión	Pulso
1	143/80 	58
8	163/86 	57
14	142/88 	74
20	113/65 	97

2017-08-29
Hora	Presión	Pulso
3	151/91 	74
7	162/91 	62
14	167/107 	76
21	182/103 	78
22	177/94 	69

2017-08-30
Hora	Presión	Pulso
1	172/98 	68
3	166/92 	62
10	138/78 	72
19	128/85 	85
20	160/90 	80
23	167/100 	0

2017-09-01
Hora	Presión	Pulso
4	158/84 	63
8	168/85 	56
12	141/82 	64
16	133/77 	75
19	159/92 	71
19	187/93 	70

2017-09-02
Hora	Presión	Pulso
3	159/80 	57
7	174/87 	62
10	152/85 	77
16	138/82 	65
17	135/81 	73
20	173/105 	74

2017-09-03
Hora	Presión	Pulso
7	167/83 	60
16	128/83 	86
22	178/105 	75

2017-09-04
Hora	Presión	Pulso
8	162/92 	64
17	173/95 	76
In [1]:
import matplotlib.pyplot as plt
import datetime
import numpy as np

x = np.array([ datetime.datetime(2017, 4, 4, 11, 0),datetime.datetime(2017, 4, 5, 8, 20),
  datetime.datetime(2017, 4, 5, 12, 0),datetime.datetime(2017, 4, 5, 15, 0),datetime.datetime(2017, 4, 5, 20, 0),datetime.datetime(2017, 4, 5, 22, 0),datetime.datetime(2017, 4, 5, 23, 30),
  datetime.datetime(2017, 4, 6, 8, 40),datetime.datetime(2017, 4, 6, 11, 30),datetime.datetime(2017, 4, 6, 14, 30),datetime.datetime(2017, 4, 6, 19, 30),datetime.datetime(2017, 4, 6, 22, 40),
  datetime.datetime(2017, 4, 7, 6, 0),datetime.datetime(2017, 4, 7, 8, 52),datetime.datetime(2017, 4, 7, 12, 10),datetime.datetime(2017, 4, 7, 16, 0),datetime.datetime(2017, 4, 7, 23, 00),
  datetime.datetime(2017, 4, 8, 8, 0),datetime.datetime(2017, 4, 8, 9, 10),datetime.datetime(2017, 4, 8, 16, 0),datetime.datetime(2017, 4, 8, 23, 0),
  datetime.datetime(2017, 4, 9, 6, 50),datetime.datetime(2017, 4, 9, 9, 10),datetime.datetime(2017, 4, 9, 13, 0),datetime.datetime(2017, 4, 9, 17, 25),datetime.datetime(2017, 4, 9, 20, 44),
  datetime.datetime(2017, 4, 10, 0, 0),datetime.datetime(2017, 4, 10, 9, 45),datetime.datetime(2017, 4, 10, 16, 0),datetime.datetime(2017, 4, 10, 19, 0),datetime.datetime(2017, 4, 10, 22, 00),
  datetime.datetime(2017, 4, 11, 5, 45),datetime.datetime(2017, 4, 11, 9, 25),datetime.datetime(2017, 4, 11, 15, 45),datetime.datetime(2017, 4, 11, 19, 35),datetime.datetime(2017, 4, 11, 22, 20),
  datetime.datetime(2017, 4, 12, 5, 45),datetime.datetime(2017, 4, 12, 9, 25),datetime.datetime(2017, 4, 12, 13, 30),datetime.datetime(2017, 4, 12, 19, 0),datetime.datetime(2017, 4, 12, 22, 30),
  datetime.datetime(2017, 4, 13, 9, 0),datetime.datetime(2017, 4, 13, 16, 0),datetime.datetime(2017, 4, 13, 20, 45),datetime.datetime(2017, 4, 13, 22, 10),
  datetime.datetime(2017, 4, 14, 5, 30),datetime.datetime(2017, 4, 14, 9, 0),datetime.datetime(2017, 4, 14, 12, 0),
  datetime.datetime(2017, 4, 21, 7, 0),datetime.datetime(2017, 4, 21, 20, 0),
  datetime.datetime(2017, 4, 22, 7, 0),datetime.datetime(2017, 4, 22, 14, 0),datetime.datetime(2017, 4, 22, 22, 0),
  datetime.datetime(2017, 4, 23, 7, 0),datetime.datetime(2017, 4, 23, 10, 0),datetime.datetime(2017, 4, 23, 11, 0),datetime.datetime(2017, 4, 23, 14, 0),datetime.datetime(2017, 4, 23, 20, 0),
  datetime.datetime(2017, 4, 24, 20, 0),datetime.datetime(2017, 4, 24, 22, 0),
  datetime.datetime(2017, 4, 25, 7, 0),datetime.datetime(2017, 4, 25, 20, 0),
  datetime.datetime(2017, 4, 26, 7, 0),datetime.datetime(2017, 4, 26, 8, 30),datetime.datetime(2017, 4, 26, 20, 0),
  datetime.datetime(2017, 4, 27, 7, 0),datetime.datetime(2017, 4, 27, 8, 30),datetime.datetime(2017, 4, 27, 20, 0),datetime.datetime(2017, 4, 27, 21, 0),
  datetime.datetime(2017, 4, 28, 7, 0),datetime.datetime(2017, 4, 28, 8, 30),datetime.datetime(2017, 4, 28, 10, 0),datetime.datetime(2017, 4, 28, 15, 0),datetime.datetime(2017, 4, 28, 16, 30),datetime.datetime(2017, 4, 28, 18, 0),datetime.datetime(2017, 4, 28, 21, 40),
  datetime.datetime(2017, 4, 29, 7, 0),datetime.datetime(2017, 4, 29, 14, 0),datetime.datetime(2017, 4, 29, 17, 0),
    datetime.datetime(2017, 4, 30, 10, 0)])

sis = np.array([
        137,134,150,168,160,156,110,112,107,108,126,100,114,126,102,120,108,109,107,150,107,122,120,118,128,120,114,110,122,
        129,125,133,125,124,127,135,138,130,142,130,125,130,128,160,156,122,160,131,
        134,161,162,134,157,157,130,126,141,153,156,165,146,149,163,150,162,153,151,157,163,168,170,170,129,130,132,141,128,134,126,124])
dis = np.array([
        70,75,70,75,75,82,55,58,59,65,72,60,71,78,64,60,62,59,63,80,60,64,67,67,57,62,66,60,71,73,73,78,60,74,74,65,69,80,80,70,67,80,73,83,82,79,80,80,
        71,92,85,76,81,77,72,71,73,91,86,80,80,91,85,80,94,85,86,100,90,88,93,90,80,78,76,78,73,86,70,72])


fig, ax = plt.subplots()
ax.text(datetime.datetime(2017, 5, 1, 12, 0), 143, '140', color='red')
ax.text(datetime.datetime(2017, 5, 1, 20, 0), 92, '90', color='blue')
plt.plot(x,sis,'ro',x,dis,'b^')
plt.axhline(y=140, color='r', linestyle='-')
plt.axhline(y=90, color='b', linestyle='-')
plt.ylabel('mmHg')
plt.axis(xmin=datetime.datetime(2017, 4, 20, 0, 0),xmax=datetime.datetime(2017, 5, 2, 10, 0),ymin=40,ymax=200)
plt.show()
In [2]:
xp = np.array([
  datetime.datetime(2017, 4, 4, 8, 20),datetime.datetime(2017, 4, 4, 22, 30),datetime.datetime(2017, 4, 5, 8, 40),datetime.datetime(2017, 4, 5, 11, 30),datetime.datetime(2017, 4, 5, 14, 30),datetime.datetime(2017, 4, 5, 19, 30),datetime.datetime(2017, 4, 7, 16, 30),datetime.datetime(2017, 4, 7, 23, 0),datetime.datetime(2017, 4, 12, 5, 45),datetime.datetime(2017, 4, 13, 22, 10),
  datetime.datetime(2017, 4, 29, 7, 0),
  datetime.datetime(2017, 4, 28, 10, 0),datetime.datetime(2017, 4, 28, 15, 0),datetime.datetime(2017, 4, 28, 18, 0),datetime.datetime(2017, 4, 28, 21, 40),
  datetime.datetime(2017, 4, 29, 7, 0),datetime.datetime(2017, 4, 30, 10, 0),
  datetime.datetime(2017, 5, 2, 15, 0),datetime.datetime(2017, 5, 2, 20, 0),datetime.datetime(2017, 5, 2, 23, 0)])

temp = np.array([36.9,36.5,36,36.2,36,36,36.8,36.6,36.9,36.4,36.8,37.5,37.4,37,36.5,37.9,37,38,38,38])

fig, ax = plt.subplots()
plt.plot(xp,temp,'k.')   
plt.axhline(y=36, color='k', linestyle='-')
plt.ylabel('grados C')
plt.axis(xmin=datetime.datetime(2017, 4, 27, 0, 0),xmax=datetime.datetime(2017, 5, 5, 10, 0),ymin=34,ymax=40)
plt.show()
In [1]:
xp = np.array([
 datetime.datetime(2017, 5, 2, 21, 20),datetime.datetime(2017, 5, 2, 23, 59),datetime.datetime(2017, 5, 3, 4, 20),datetime.datetime(2017, 5, 3, 6, 59),
 datetime.datetime(2017, 5, 5, 21, 20),datetime.datetime(2017, 5, 5, 23, 59),datetime.datetime(2017, 5, 6, 2, 20),datetime.datetime(2017, 5, 6, 6, 59),
 datetime.datetime(2017, 5, 6, 21, 20),datetime.datetime(2017, 5, 6, 23, 59),
 datetime.datetime(2017, 5, 7, 8, 20),datetime.datetime(2017, 5, 7, 12, 20),datetime.datetime(2017, 5, 7, 21, 20),datetime.datetime(2017, 5, 8, 3, 20),datetime.datetime(2017, 5, 8, 4, 20),
 datetime.datetime(2017, 5, 8, 8, 0),datetime.datetime(2017, 5, 8, 13, 0),datetime.datetime(2017, 5, 8, 21, 20),
 datetime.datetime(2017, 5, 9, 3, 20),datetime.datetime(2017, 5, 9, 6, 20),datetime.datetime(2017, 5, 9, 8, 20),datetime.datetime(2017, 5, 9, 13, 20),datetime.datetime(2017, 5, 9, 15, 20),datetime.datetime(2017, 5, 9, 21, 20),
 datetime.datetime(2017, 5, 10, 7, 20),datetime.datetime(2017, 5, 10, 17, 20),datetime.datetime(2017, 5, 10, 22, 20),
 datetime.datetime(2017, 5, 11, 2, 20),datetime.datetime(2017, 5, 11, 7, 20),datetime.datetime(2017, 5, 11, 14, 20),datetime.datetime(2017, 5, 11, 19, 20),datetime.datetime(2017, 5, 11, 21, 20),
 datetime.datetime(2017, 5, 12, 2, 20),datetime.datetime(2017, 5, 12, 5, 20),datetime.datetime(2017, 5, 12, 8, 20),datetime.datetime(2017, 5, 12, 15, 20),datetime.datetime(2017, 5, 12, 19, 20),datetime.datetime(2017, 5, 12, 21, 20),
 datetime.datetime(2017, 5, 13, 4, 20),datetime.datetime(2017, 5, 13, 7, 20),datetime.datetime(2017, 5, 13, 18, 20),datetime.datetime(2017, 5, 13, 20, 20),datetime.datetime(2017, 5, 13, 23, 20),
 datetime.datetime(2017, 5, 14, 8, 20),datetime.datetime(2017, 5, 14, 13, 20),datetime.datetime(2017, 5, 14, 19, 0),
 datetime.datetime(2017, 5, 15, 2, 20),datetime.datetime(2017, 5, 15, 7, 20),datetime.datetime(2017, 5, 15, 19, 20),
 datetime.datetime(2017, 5, 16, 2, 20),datetime.datetime(2017, 5, 16, 7, 20),datetime.datetime(2017, 5, 16, 19, 20),
 datetime.datetime(2017, 5, 17, 2, 20),datetime.datetime(2017, 5, 17, 7, 20),datetime.datetime(2017, 5, 17, 19, 20)
])

sis = np.array([120,135,140,135,120,135,140,135,119,135,130,120,122,147,154,144,129,127,148,148,142,141,118,133,147,122,
               129,149,151,126,134,136,144,138,134,123,117,142,141,147,122,116,126,129,119,121,142,132,131,141,136,
               109,142,137,118])
dis = np.array([80,80,80,80,80,80,80,80,71,73,71,76,66,80,79,77,74,65,83,86,83,78,70,76,83,73,78,83,82,69,82,74,83,84,
                84,70,69,81,82,85,66,65,72,76,70,73,86,78,73,80,79,64,80,81,74])
tem = np.array([38.2,38.5,38.5,37.3,38.5,39,39.8,37.5,36.8,36.5,37.2,37,36.9,37.2,37.2,37.5,37.6,37,37.2,37.7,37.8,
                37.1,37.3,36.8,36.8,37.3,36.8,36.6,36.5,36.6,37.5,36.2,37,37,37,37.3,37,36.5,37.1,37,37.3,37.1,37.1,
                37,36.8,37.1,37.2,37,37,36.8,37.1,37.2,36.8,37.2,37])
pul = np.array([72,76,69,78,68,72,77,74,72,71,70,86,69,72,67,75,70,74,74,84,83,71,70,68,70,79,80,77,70,73,75,63,75,75,
                76,71,69,68,68,74,78,71,71,68,65,77,74,70,67,70,69,79,68,76,71])
oxi = np.array([80,80,80,80,80,80,80,80,86,85,88,90,71,88,88,87,87,88,74,86,82,88,87,85,87,88,86,86,86,89,90,90,85,94,
                90,85,94,90,87,86,89,90,84,88,87,86,88,87,83,75,84,82,84,85,91])

fig, ax = plt.subplots()
ax.text(datetime.datetime(2017, 5, 18, 12, 0), 153, '150', color='red')
ax.text(datetime.datetime(2017, 5, 18, 12, 0), 92, '90', color='blue')
plt.plot(xp,sis,'ro',xp,dis,'b^')
plt.axhline(y=150, color='r', linestyle='-')
plt.axhline(y=90, color='b', linestyle='-')
plt.title('Presion')
plt.ylabel('mmHg')
plt.axis(xmin=datetime.datetime(2017, 5, 6, 10, 0),xmax=datetime.datetime(2017, 5, 18, 10, 0),ymin=40,ymax=200)
plt.show()

fig, ax = plt.subplots()
plt.plot(xp,pul,'ko')
plt.axhline(y=90, color='k', linestyle='-')
plt.title('Pulso')
plt.ylabel('ppm')
plt.axis(xmin=datetime.datetime(2017, 5, 6, 10, 0),xmax=datetime.datetime(2017, 5, 18, 10, 0),ymin=60,ymax=100)
plt.show()

fig, ax = plt.subplots()
plt.plot(xp,tem,'ko')
plt.axhline(y=37, color='k', linestyle='-')
plt.title('Temperatura')
plt.ylabel('grados C')
plt.axis(xmin=datetime.datetime(2017, 5, 2, 0, 0),xmax=datetime.datetime(2017, 5, 18, 10, 0),ymin=36,ymax=40)
plt.show()

fig, ax = plt.subplots()
plt.plot(xp,oxi,'ko')
plt.axhline(y=90, color='k', linestyle='-')
plt.title('Oxigenacion')
plt.ylabel('U/mm3')
plt.axis(xmin=datetime.datetime(2017, 5, 6, 10, 0),xmax=datetime.datetime(2017, 5, 18, 10, 0),ymin=60,ymax=100)
plt.show()
In [2]:
xxp = np.array([
 datetime.datetime(2017, 5, 18, 15, 20),datetime.datetime(2017, 5, 18, 20, 59),
 datetime.datetime(2017, 5, 19, 07, 20),datetime.datetime(2017, 5, 19, 21, 59),
 datetime.datetime(2017, 5, 20, 04, 20),datetime.datetime(2017, 5, 20, 8, 59),datetime.datetime(2017, 5, 20, 20, 20),
 datetime.datetime(2017, 5, 21, 04, 59),datetime.datetime(2017, 5, 21, 07, 20),datetime.datetime(2017, 5, 21, 18, 59),
 datetime.datetime(2017, 5, 22, 04, 20),datetime.datetime(2017, 5, 22, 07, 59),datetime.datetime(2017, 5, 22, 12, 20),datetime.datetime(2017, 5, 22, 19, 59),
 datetime.datetime(2017, 5, 23, 03, 20),datetime.datetime(2017, 5, 23, 8, 59),datetime.datetime(2017, 5, 23, 13, 20),datetime.datetime(2017, 5, 23, 20, 59),
 datetime.datetime(2017, 5, 24, 05, 20),datetime.datetime(2017, 5, 24, 10, 59),datetime.datetime(2017, 5, 24, 20, 20),datetime.datetime(2017, 5, 24, 22, 59),
 datetime.datetime(2017, 5, 25, 03, 20),datetime.datetime(2017, 5, 25, 07, 59),datetime.datetime(2017, 5, 25, 20, 20),
 datetime.datetime(2017, 5, 26, 04, 20),datetime.datetime(2017, 5, 26, 07, 59),
 datetime.datetime(2017, 5, 27, 04, 20),datetime.datetime(2017, 5, 27, 07, 59),datetime.datetime(2017, 5, 27, 16, 20),datetime.datetime(2017, 5, 27, 20, 59),
 datetime.datetime(2017, 5, 28, 02, 59),datetime.datetime(2017, 5, 28, 8, 20),datetime.datetime(2017, 5, 28, 20, 59),
 datetime.datetime(2017, 5, 29, 04, 20),datetime.datetime(2017, 5, 29, 07, 59),datetime.datetime(2017, 5, 29, 17, 20),datetime.datetime(2017, 5, 29, 19, 59),
 datetime.datetime(2017, 5, 30, 03, 20),datetime.datetime(2017, 5, 30, 07, 59),datetime.datetime(2017, 5, 30, 13, 20)])

si = np.array([116,128,130,122,140,138,129,145,128,130,141,149,111,127,160,143,116,133,157,105,130,131,148,132,129,145,148,144,152,130,122,148,150,132,
               148,136,105,132,144,141,107])
di = np.array([71,72,77,62,81,82,75,81,77,67,82,81,64,71,84,81,66,71,86,63,76,71,81,78,76,84,75,83,87,75,73,86,79,88,81,75,63,81,76,81,66])
pu = np.array([68,63,67,89,67,64,71,71,66,78,64,71,69,66,67,68,73,67,67,95,70,66,65,65,64,68,67,68,68,67,72,67,68,72,62,62,63,67,61,63,70])
te = np.array([37.3,37.3,37,36.5,36.8,37,37.2,37,37.3,37.1,37,37,37.2,37.4,36.8,37.2,37.2,37,37,36.8,37,37.2,36.8,36.6,36.9,36.5,36.8,37.7,37,37.3,37,36.5,
              37,37,36.9,37,36.7,36.8,36.6,36.5,36.8])
ox = np.array([85,80,80,82,84,88,83,84,85,81,82,82,88,86,82,79,84,84,84,82,87,80,82,83,91,87,89,89,85,84,90,83,85,88,82,85,83,91,81,80,84])

fig, ax = plt.subplots()
ax.text(datetime.datetime(2017, 5, 31, 12, 0), 153, '150', color='red')
ax.text(datetime.datetime(2017, 5, 31, 12, 0), 92, '90', color='blue')
plt.plot(xxp,si,'ro',xxp,di,'b^')
plt.axhline(y=150, color='r', linestyle='-')
plt.axhline(y=90, color='b', linestyle='-')
plt.title('Presion')
plt.ylabel('mmHg')
plt.axis(xmin=datetime.datetime(2017, 5, 17, 20, 0),xmax=datetime.datetime(2017, 5, 31, 10, 0),ymin=40,ymax=200)
plt.show()

fig, ax = plt.subplots()
plt.plot(xxp,pu,'ko')
plt.axhline(y=90, color='k', linestyle='-')
plt.title('Pulso')
plt.ylabel('ppm')
plt.axis(xmin=datetime.datetime(2017, 5, 17, 20, 0),xmax=datetime.datetime(2017, 5, 31, 10, 0),ymin=60,ymax=100)
plt.show()

fig, ax = plt.subplots()
plt.plot(xxp,te,'ko')
plt.axhline(y=37, color='k', linestyle='-')
plt.title('Temperatura')
plt.ylabel('grados C')
plt.axis(xmin=datetime.datetime(2017, 5, 17, 20, 0),xmax=datetime.datetime(2017, 5, 31, 10, 0),ymin=36,ymax=40)
plt.show()

fig, ax = plt.subplots()
plt.plot(xxp,ox,'ko')
plt.axhline(y=90, color='k', linestyle='-')
plt.title('Oxigenacion')
plt.ylabel('U/mm3')
plt.axis(xmin=datetime.datetime(2017, 5, 17, 20, 0),xmax=datetime.datetime(2017, 5, 21, 10, 0),ymin=60,ymax=100)
plt.show()
In [3]:
x3p = np.array([
 datetime.datetime(2017, 5, 31, 7, 20),datetime.datetime(2017, 5, 31, 18, 29),datetime.datetime(2017, 5, 31, 20, 29),
 datetime.datetime(2017, 6, 1, 2, 20),datetime.datetime(2017, 6, 1, 7, 59),datetime.datetime(2017, 6, 1, 15, 20),datetime.datetime(2017, 6, 1, 19, 59),
 datetime.datetime(2017, 6, 2, 1, 29),datetime.datetime(2017, 6, 2, 4, 29),
 datetime.datetime(2017, 6, 3, 14, 19),datetime.datetime(2017, 6, 3, 18, 20),datetime.datetime(2017, 6, 3, 21, 20),
 datetime.datetime(2017, 6, 4, 2, 20),datetime.datetime(2017, 6, 4, 7, 59),datetime.datetime(2017, 6, 4, 13, 20),datetime.datetime(2017, 6, 4, 20, 20),
 datetime.datetime(2017, 6, 5, 02, 19),datetime.datetime(2017, 6, 5, 07, 20),
 datetime.datetime(2017, 6, 6, 07, 59),datetime.datetime(2017, 6, 6, 20, 20),
 datetime.datetime(2017, 6, 7, 07, 20),
 datetime.datetime(2017, 6, 8, 20, 20),
 datetime.datetime(2017, 6, 9, 07, 20),
 datetime.datetime(2017, 6, 10, 03, 29),datetime.datetime(2017, 6, 10, 16, 20),
 datetime.datetime(2017, 6, 11, 02, 59),datetime.datetime(2017, 6, 11, 07, 20),datetime.datetime(2017, 6, 11, 20, 20),
 datetime.datetime(2017, 6, 12, 04, 59),datetime.datetime(2017, 6, 12, 07, 20),datetime.datetime(2017, 6, 12, 20, 20),
 datetime.datetime(2017, 6, 13, 04, 59),datetime.datetime(2017, 6, 13, 07, 20),datetime.datetime(2017, 6, 13, 13, 20),datetime.datetime(2017, 6, 13, 20, 20),
 datetime.datetime(2017, 6, 14, 02, 59),datetime.datetime(2017, 6, 14, 07, 20),datetime.datetime(2017, 6, 14, 21, 02),datetime.datetime(2017, 6, 14, 21, 40),
 datetime.datetime(2017, 6, 15, 2, 29),datetime.datetime(2017, 6, 15, 8, 20),datetime.datetime(2017, 6, 15, 14, 20),datetime.datetime(2017, 6, 15, 20, 20),
 datetime.datetime(2017, 6, 16, 07, 29),datetime.datetime(2017, 6, 16, 20, 20),
 datetime.datetime(2017, 6, 17, 03, 29),datetime.datetime(2017, 6, 17, 07, 20),datetime.datetime(2017, 6, 17, 18, 20),datetime.datetime(2017, 6, 17, 20, 20),
 datetime.datetime(2017, 6, 18, 4, 59),datetime.datetime(2017, 6, 18, 8, 20),datetime.datetime(2017, 6, 18, 16, 20),
 datetime.datetime(2017, 6, 19, 20, 50),
 datetime.datetime(2017, 6, 20, 3, 29),datetime.datetime(2017, 6, 20, 8, 20),
 datetime.datetime(2017, 6, 21, 01, 59),datetime.datetime(2017, 6, 21, 04, 20),datetime.datetime(2017, 6, 21, 07, 20),
 datetime.datetime(2017, 6, 21, 04, 20),datetime.datetime(2017, 6, 21, 04, 20),datetime.datetime(2017, 6, 21, 04, 59)])

sii = np.array([144,110,119,150,147,125,147,140,134,127,120,143,146,127,125,134,146,138,120,135,140,140,136,156,118,156,149,137,156,148,113,151,156,119,139,
                146,141,118,115,149,141,123,134,146,159,153,127,138,150,145,100,136,142,143,141,135,151,143,143,134,127])
dii = np.array([80,65,73,80,80,69,82,76,76,70,68,77,80,75,73,86,80,74,80,79,83,71,76,82,64,84,80,83,75,85,71,87,87,70,79,86,81,69,84,81,69,76,80,81,84,
                80,74,83,83,81,58,78,83,84,78,77,80,76,76,71,67])
ppu = np.array([62,90,89,71,78,70,76,80,83,65,61,68,71,70,72,77,69,65,75,62,68,64,65,68,82,66,65,67,64,65,88,69,64,73,65,63,65,81,67,69,67,63,70,72,67,
                63,74,68,62,69,75,69,65,69,61,65,82,87,83,81,82])
oox = np.array([89,80,86,83,82,85,81,60,78,82,87,85,87,87,85,88,82,80,89,87,87,86,87,83,85,85,85,86,90,84,87,78,87,85,88,82,84,84,80,79,86,80,83,79,87,
                81,85,82,84,80,83,85,84,90,80,84,77,75,72,77,63])

tmpr = np.array([36.4,36.6,0,36.2,37.3,36.8,37.2,38.9,37,36,36.4,36.5,36.4,36.9,36.5,36.7,36.4,37.1,36.8,36.8,36.2,0,36.8,0,0,36.1,36.7,0,36.3,0,0,0,0,0,36.2,36.4,0,0,0,0,0,0,0,0,0,0,0,0,36,0,0,36,0,0,0,37.4,38,38,38.5,0,0])

    
fig, ax = plt.subplots()
ax.text(datetime.datetime(2017, 6, 21, 12, 0), 153, '150', color='red')
ax.text(datetime.datetime(2017, 6, 21, 2, 0), 92, '90', color='blue')
plt.plot(x3p,sii,'ro',x3p,dii,'b^')
plt.axhline(y=150, color='r', linestyle='-')
plt.axhline(y=90, color='b', linestyle='-')
plt.title('Presion')
plt.ylabel('mmHg')
plt.axis(xmin=datetime.datetime(2017, 5, 31, 5, 0),xmax=datetime.datetime(2017, 6, 21, 10, 0),ymin=40,ymax=200)
plt.show()

fig, ax = plt.subplots()
plt.plot(x3p,ppu,'ko')
plt.axhline(y=90, color='k', linestyle='-')
plt.title('Pulso')
plt.ylabel('ppm')
plt.axis(xmin=datetime.datetime(2017, 5, 31, 5, 0),xmax=datetime.datetime(2017, 6, 21, 10, 0),ymin=60,ymax=100)
plt.show()

fig, ax = plt.subplots()
plt.plot(x3p,tmpr,'ko')
plt.axhline(y=37, color='k', linestyle='-')
plt.title('Temperatura')
plt.ylabel('grados C')
plt.axis(xmin=datetime.datetime(2017, 5, 31, 5, 0),xmax=datetime.datetime(2017, 6, 21, 10, 0),ymin=36,ymax=40)
plt.show()

fig, ax = plt.subplots()
plt.plot(x3p,oox,'ko')
plt.axhline(y=90, color='k', linestyle='-')
plt.title('Oxigenacion')
plt.ylabel('U/mm3')
plt.axis(xmin=datetime.datetime(2017, 5, 31, 5, 0),xmax=datetime.datetime(2017, 6, 21, 10, 0),ymin=60,ymax=100)
plt.show()
;