NS154-Fermentation

import numpy as np import pandas as pd import scipy.stats as sts from sklearn.linear_model import LinearRegression from sklearn.metrics import mean_squared_error from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split import matplotlib.pyplot as plt

data = pd.read_csv('/home/jovyan/work/Daily experimental records - Data.csv') print(data.columns) data_sub = data[['pH 2 (day 1)', 'Solvent volume 2 (ml) (day 1)', 'Average Temperature (degree C)', 'Liquid amount (ml) (day 4)', 'Success']] data_sub.columns = ['pH', 'solvent volume', 'temp', 'liquid volume', 'success']

Index(['Sample', 'pH 1 (day 1)', 'pH 2 (day 1)',
       'Solvent volume 1 (ml) (day 1)', 'Solvent volume 2 (ml) (day 1)',
       'Cooker (day 1)', 'Contained (day 1)',
       'Temperature 1 (degree C) (day 1)', 'Temperature 2 (degree C) (day 2)',
       'Temperature 3 (degree C) (day 4)', 'Average Temperature (degree C)',
       'Container (day 1)', 'CO2 level (day 4)', 'Smell (day 4)',
       'Taste (day 4)', 'Texture (day 4)', 'Color (day 4)',
       'Liquid characteristics', 'Liquid amount (ml) (day 4)', 'Success',
       'Note'],
      dtype='object')

data_sub

temp_1 = data_sub['liquid volume'][data_sub['temp'] == 60] temp_2 = data_sub['liquid volume'][data_sub['temp'] != 60] pH_1 = data_sub['liquid volume'][data_sub['pH'] == 9.5] pH_2 = data_sub['liquid volume'][data_sub['pH'] == 7] pH_3 = data_sub['liquid volume'][data_sub['pH'] == 4]

plt.bar(['Temp = 60C', 'Temp = 40C'], [temp_1.mean(), temp_2.mean()], yerr = [temp_1.std(), temp_2.std()], capsize=3) plt.title('Averaged liquid volume at 2 temperature levels') plt.xlabel('Conditions') plt.ylabel('Liquid volume (ml)')

plt.bar(['pH = 9.5', 'pH = 7', 'pH = 4'], [pH_1.mean(), pH_2.mean(), pH_3.mean()], yerr = [pH_1.std(), pH_2.std(), pH_3.std()], capsize=3) plt.title('Averaged liquid volume at 3 pH conditions') plt.xlabel('Conditions') plt.ylabel('Liquid volume (ml)')

data_sub['pH scaled'] = StandardScaler().fit_transform((data_sub['pH']).to_numpy().reshape(-1,1))

/shared-libs/python3.7/py-core/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  """Entry point for launching an IPython kernel.

data_sub['solvent volume scaled'] = StandardScaler().fit_transform((data_sub['solvent volume']).to_numpy().reshape(-1,1))

/shared-libs/python3.7/py-core/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  """Entry point for launching an IPython kernel.

data_sub['temp scaled'] = StandardScaler().fit_transform((data_sub['temp']).to_numpy().reshape(-1,1))

/shared-libs/python3.7/py-core/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  """Entry point for launching an IPython kernel.

X_train, X_test, y_train, y_test = train_test_split(data_sub[['pH scaled', 'temp scaled']], data_sub['liquid volume'], test_size = 0.3, random_state=123)

regr = LinearRegression() regr.fit(X_train, y_train)

pred = regr.predict(X_test) pred

# R2 score regr.score(X_test, y_test)

# coefficients regr.coef_

def cohens_d (x,y): n1 = x.shape[0] n2 = y.shape[0] sd = (((n1 - 1) * x.var() + (n2 - 1) * y.var())/(n1 + n2 - 2))**0.5 return ((x.mean()-y.mean())/sd)

sts.ttest_ind(temp_1, temp_2, equal_var=False)

cohens_d(temp_1, temp_2)

sts.ttest_ind(pH_1, pH_2, equal_var=False)

cohens_d(pH_1, pH_2)

sts.ttest_ind(pH_2, pH_3, equal_var=False)

cohens_d(pH_2, pH_3)

sts.ttest_ind(pH_3, pH_1, equal_var=True)

cohens_d(pH_3, pH_1)

data2 = pd.read_csv('/home/jovyan/work/Daily experimental records - Round 2.csv')

data2

# different groups basic = data2['CO2 (ml)'][data2['pH'] == 9.5] neutral = data2['CO2 (ml)'][data2['pH'] == 7] acidic = data2['CO2 (ml)'][data2['pH'] == 4]

plt.bar(['pH = 9.5', 'pH = 7', 'pH = 4'], [basic.mean(), neutral.mean(), acidic.mean()], yerr = [basic.std(), neutral.std(), acidic.std()], capsize=3) plt.title('Averaged CO2 volume at 3 pH conditions') plt.xlabel('Conditions') plt.ylabel('CO2 volume (ml)')

X_train_2, X_test_2, y_train_2, y_test_2 = train_test_split(data2['pH'].to_numpy().reshape(-1,1), data2['CO2 (ml)'], random_state=123, test_size=0.3)

regr2 = LinearRegression() regr2.fit(X_train_2, y_train_2)

regr2.score(X_test_2, y_test_2)

cohens_d(basic, neutral)

cohens_d(neutral, acidic)

cohens_d(basic, acidic)

sts.ttest_ind(basic, neutral, equal_var=False)

sts.ttest_ind(neutral, acidic, equal_var=False)

sts.ttest_ind(basic, acidic, equal_var=False)