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import numpy as np
import scipy.stats as st
import pandas as pd
import sys
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sys.path.append('/Users/chrismorrow/repos/sci_analysis')
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import sci_analysis as a
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np.random.seed(987654321)
input_array = st.norm.rvs(size=200)
a.analyze(input_array)
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np.random.seed(987654321)
input_array = st.norm.rvs(size=200)
a.analyze(input_array, cdf=True, fit=True)
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np.random.seed(987654321)
input_array = st.weibull_min.rvs(1.7, size=500)
a.analyze(input_array, cdf=True, fit=True, distribution='weibull_min')
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source_path = "/Users/chrismorrow/Dropbox/Data/"
df = pd.read_csv(source_path + "/BodyFat.csv")
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df.corr()
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df['BMI'] = (df['WEIGHT'] / df['HEIGHT'] ** 2) * 703
df['Age_Range'] = pd.cut(df['AGE'].values, [20, 26, 33, 39, 45, 51, 57, 63, 69, 75, 81])
df['Class'] = pd.cut(df['BMI'].values, [0, 15, 16, 18.5, 25, 30, 35, 40, 200],
labels=['Very Severly Underweight', 'Severely Underweight', 'Underweight', 'Normal',
'Overweight', 'Moderately Obese', 'Severely Obese', 'Very Severely Obese'])
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a.analyze(df['ABDOMEN'], df['BODYFAT'], xname='Abdomen', yname='Bodyfat')
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a.analyze(df['ABDOMEN'], df['BODYFAT'], xname='Abdomen', yname='Bodyfat', boxplot_borders=True)
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a.analyze(df['ABDOMEN'], df['BODYFAT'], xname='Abdomen', yname='Bodyfat', boxplot_borders=True, fit=False,
contours=True)
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a.analyze(df['BODYFAT'], name='Bodyfat', fit=True)
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a.analyze(df['AGE'], name='Age')
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df.groupby('Age_Range').mean()['BODYFAT']
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df2 = df[df['HEIGHT'] > 60]
height = {name: group['HEIGHT'].values for name, group in df2[df2['Age_Range'] != '(75, 81]'].groupby('Age_Range')}
a.analyze(height)
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df2 = df[df['HEIGHT'] > 60]
groups = list()
height = list()
for name, group in df2[df2['Age_Range'] != '(75, 81]'].groupby('Age_Range'):
groups.append(name)
height.append(group['HEIGHT'].values)
a.analyze(height, groups=groups, categories='Age Group', name='Height', title='Height by Age Group')
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