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

	pHfloat64 4 - 9.5	solvent volumeint64 220 - 320	tempfloat64 33.333 - 60	liquid volumefloat64 0 - 37.5	successint64 0 - 1
0	9.5	220	60	0	0
1	9.5	220	60	0	0
2	7	270	60	0	0
3	7	270	60	0	0
4	4	320	60	0	0
Expand rows 5 - 6
7	9.5	220	33.33333333	25	1
8	7	270	33.33333333	37.5	1
9	7	270	33.33333333	32.5	1
10	4	320	33.33333333	30	0
11	4	320	33.33333333	25	1
12 rows × 5 columns

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

	Sampleint64 1 - 12	pHfloat64 4 - 9.5	CO2 (ml)int64 0 - 205
0	1	9.5	180
1	2	9.5	190
2	3	9.5	195
3	4	9.5	200
4	5	7	200
Expand rows 5 - 6
7	8	7	190
8	9	4	0
9	10	4	0
10	11	4	50
11	12	4	50
12 rows × 3 columns

# 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)