Probabilistic Variables¶

In [1]:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from volumetricspy import ProbVar, MonteCarlo

import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns from volumetricspy import ProbVar, MonteCarlo

Constant Values¶

In [2]:

x = ProbVar(name='test1',constant=5.)
x.get_sample()

x = ProbVar(name='test1',constant=5.) x.get_sample()

Out[2]:

5.0

In [3]:

y = ProbVar(
    name = 'test',
    dist = 'lognorm', 
    kw = {'s':0.42, 'scale':500})

sns.histplot(y.get_sample(size=100))

y = ProbVar( name = 'test', dist = 'lognorm', kw = {'s':0.42, 'scale':500}) sns.histplot(y.get_sample(size=100))

Out[3]:

<AxesSubplot:ylabel='Count'>

In [4]:

def ooip(a,h,bo,phi,sw):
    return 7758*a*h*phi*(1-sw)*(1/bo)*(1/1000000)

def ooip(a,h,bo,phi,sw): return 7758*a*h*phi*(1-sw)*(1/bo)*(1/1000000)

In [5]:

a = ProbVar(name = 'area',dist='norm',kw={'loc':1000,'scale':200})
h = ProbVar(name = 'height',dist='uniform',kw={'loc':180,'scale':70})
bo = ProbVar(name = 'bo',constant=1.12)
phi = ProbVar(name = 'phi',dist='norm',kw={'loc':0.18,'scale':0.04})
sw = ProbVar(name = 'sw',dist='norm',kw={'loc':0.4,'scale':0.1})

a = ProbVar(name = 'area',dist='norm',kw={'loc':1000,'scale':200}) h = ProbVar(name = 'height',dist='uniform',kw={'loc':180,'scale':70}) bo = ProbVar(name = 'bo',constant=1.12) phi = ProbVar(name = 'phi',dist='norm',kw={'loc':0.18,'scale':0.04}) sw = ProbVar(name = 'sw',dist='norm',kw={'loc':0.4,'scale':0.1})

In [6]:

OOIP = MonteCarlo(
    name = 'OOIP',
    func = ooip,
    args = [a,h,bo,phi,sw],
)

OOIP = MonteCarlo( name = 'OOIP', func = ooip, args = [a,h,bo,phi,sw], )

In [7]:

ss = OOIP.get_sample(size=250)

sns.histplot(ss)

ss = OOIP.get_sample(size=250) sns.histplot(ss)

Out[7]:

<AxesSubplot:ylabel='Count'>

In [8]:

ooip_df = OOIP.get_sample_df(size=10)
ooip_df

ooip_df = OOIP.get_sample_df(size=10) ooip_df

Out[8]:

	area	height	bo	phi	sw	OOIP
0	925.651164	236.219920	1.12	0.054079	0.387532	50.165557
1	1349.125898	240.997838	1.12	0.188264	0.421510	245.278578
2	921.932967	248.303226	1.12	0.255274	0.367952	255.840605
3	745.707912	198.815503	1.12	0.169090	0.521027	83.172502
4	848.029361	200.931210	1.12	0.153764	0.515933	87.851647
5	1253.576649	202.553816	1.12	0.242674	0.242814	323.183720
6	804.690999	208.950491	1.12	0.183487	0.608677	83.626757
7	1230.468831	236.937588	1.12	0.151678	0.355835	197.313430
8	710.347295	217.855698	1.12	0.192355	0.303205	143.674447
9	1180.073563	247.931515	1.12	0.151773	0.328107	206.665682

In [9]:

sns.histplot(ooip_df['area'])

sns.histplot(ooip_df['area'])

Out[9]:

<AxesSubplot:xlabel='area', ylabel='Count'>

In [10]:

OOIP.get_sample_df(ppf=np.linspace(0.05,0.95,10))

OOIP.get_sample_df(ppf=np.linspace(0.05,0.95,10))

Out[10]:

	area	height	bo	phi	sw	OOIP
0.05	671.029275	183.5	1.12	0.114206	0.235515	74.467510
0.15	792.713322	190.5	1.12	0.138543	0.296357	101.971547
0.25	865.102050	197.5	1.12	0.153020	0.332551	120.874204
0.35	922.935907	204.5	1.12	0.164587	0.361468	137.396387
0.45	974.867731	211.5	1.12	0.174974	0.387434	153.078147
0.55	1025.132269	218.5	1.12	0.185026	0.412566	168.638179
0.65	1077.064093	225.5	1.12	0.195413	0.438532	184.585622
0.75	1134.897950	232.5	1.12	0.206980	0.467449	201.465306
0.85	1207.286678	239.5	1.12	0.221457	0.503643	220.156546
0.95	1328.970725	246.5	1.12	0.245794	0.564485	242.906077

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