asalix 0.1.3

A comprehensive collection of mathematical tools and utilities designed to support Lean Six Sigma practitioners in their process improvement journey.

ASALIX is a Python package that collects mathematical tools and utilities designed to support Lean Six Sigma practitioners in their process improvement journey. This repository aims to provide a robust set of tools for data analysis and statistical modeling.
Whether you are a Lean Six Sigma professional, a data analyst, or someone interested in process improvement, ASALIX offers a wide range of functions and modules to aid you in your Lean Six Sigma projects.

1. Installation

To use the ASALIX library, you'll need to have Python installed on your system. You can install ASALIX using pip:

pip install asalix

2. Examples

2.1 Extract dataset from Pandas DataFrame

import pandas as pd
import numpy as np
from asalix import __init__ as ax

dataset = ax.extract_dataset(pd.DataFrame({"normal_dataset": np.random.normal(10, 2, 1000),
                                           "not_normal_dataset": list(range(0, 1000))}),
                             data_column_name="normal_dataset")

2.1 Calculate mean value and standard deviation

import pandas as pd
import numpy as np
import asalix as ax

# Extract the dataset from a Pandas Dataframe that contains normal and not normal data
dataset = ax.extract_dataset(pd.DataFrame({"normal_dataset": np.random.normal(10, 2, 1000),
                                           "not_normal_dataset": list(range(0, 1000))}),
                             data_column_name="normal_dataset")

# Print the calculated mean values on screen
print("\nMean value")
print("\u03BC:   ", ax.calculate_mean_value(dataset))  # Population mean value
print("xbar:", ax.calculate_mean_value(dataset))  # Sample mean value

# Print the calculated standard deviation on screen
print("\nStandard deviation")
print("\u03C3:", ax.calculate_standard_deviation(dataset, population=True))  # Population standard deviation
print("s:", ax.calculate_standard_deviation(dataset, population=False))  # Sample standard deviation

2.2 Perform normality test

import pandas as pd
import numpy as np
import asalix as ax

# Extract the dataset from a Pandas Dataframe that contains normal and not normal data
dataset = ax.extract_dataset(pd.DataFrame({"normal_dataset": np.random.normal(10, 2, 1000),
                                           "not_normal_dataset": list(range(0, 1000))}),
                             data_column_name="normal_dataset")

# Print the p-value of different normality test on screen
print("\nNormality test (P-value)")
print("Basic:              ", ax.normality_test(dataset))
print("Anderson-Darling:   ", ax.normality_test(dataset, test="anderson_darling"))
print("Kolmogorov-Smirnov: ", ax.normality_test(dataset, test="kolmogorov_smirnov"))
print("Shapiro-Wilk:       ", ax.normality_test(dataset, test="shapiro_wilk"))

2.3 Fit dataset with normal distribution

import pandas as pd
import numpy as np
import asalix as ax

# Extract the dataset from a Pandas Dataframe that contains normal and not normal data
dataset = ax.extract_dataset(pd.DataFrame({"normal_dataset": np.random.normal(10, 2, 1000),
                                           "not_normal_dataset": list(range(0, 1000))}),
                             data_column_name="normal_dataset")

# Fit dataset with a normal distribution
res = ax.normal_distribution_fit(dataset)
print("\nNormal fit")
print("p-value:", res.p_value)  # p-value
print("A:      ", res.normal_coefficient)  # Coefficient
print("\u03BC:      ", res.mean_value)  # Mean value
print("\u03C3:      ", res.standard_deviation)  # Standard deviation

2.4 Plot data using histogram

import pandas as pd
import numpy as np
import asalix as ax

# Extract the dataset from a Pandas Dataframe that contains normal and not normal data
dataset = ax.extract_dataset(pd.DataFrame({"normal_dataset": np.random.normal(10, 2, 1000),
                                           "not_normal_dataset": list(range(0, 1000))}),
                             data_column_name="normal_dataset")

# Create the histogram with a normal distribution fitted curve and plot it
ax.create_histogram(dataset, normal_distribution_fitting=True, plot=True, density=False)

2.5 Plot data using boxplot and calculate quartiles

import pandas as pd
import numpy as np
import asalix as ax

# Extract the dataset from a Pandas Dataframe that contains normal and not normal data
dataset = ax.extract_dataset(pd.DataFrame({"normal_dataset": np.random.normal(10, 2, 1000),
                                           "not_normal_dataset": list(range(0, 1000))}),
                             data_column_name="normal_dataset")

# Print the quartile values of the dataset on screen
quartiles = ax.create_quartiles(dataset, plot=True, fig_number=1)
print("\nQuartiles")
print("Minimum: ", quartiles.minimum)
print("1st:     ", quartiles.first)
print("Median:  ", quartiles.median)
print("3rd:     ", quartiles.third)
print("Maximum: ", quartiles.maximum)

2.6 Calculate confidence interval

import pandas as pd
import numpy as np
import asalix as ax

# Extract the dataset from a Pandas Dataframe that contains normal and not normal data
dataset = ax.extract_dataset(pd.DataFrame({"normal_dataset": np.random.normal(100, 20, 20),
                                           "not_normal_dataset": list(range(1, 21))}),
                             data_column_name="normal_dataset")

# Print the confidence interval on screen
print("\n95% confidence internval")
print("\u03C3 known:  ", ax.calculate_confidence_interval(dataset, 0.95, population=True))
print("\u03C3 unknown:", ax.calculate_confidence_interval(dataset, 0.95, population=False))

2.7 Check dataset using Nelson rules

import pandas as pd
import numpy as np
import asalix as ax

# Extract the dataset from a Pandas Dataframe that contains normal and not normal data
dataset = ax.extract_dataset(pd.DataFrame({"normal_dataset": np.random.normal(100, 20, 150),
                                           "not_normal_dataset": list(range(1, 151))}),
                             data_column_name="normal_dataset")

# Print the confidence interval on screen
nelson_rules = ax.check_dataset_using_nelson_rules(dataset)
print("\nNelson rules")
print("Rule 1: ", nelson_rules.rule1)
print("Rule 2: ", nelson_rules.rule2)
print("Rule 3: ", nelson_rules.rule3)
print("Rule 4: ", nelson_rules.rule4)
print("Rule 5: ", nelson_rules.rule5)
print("Rule 6: ", nelson_rules.rule6)
print("Rule 7: ", nelson_rules.rule7)

2.8 Plot control charts

import pandas as pd
import numpy as np
import asalix as ax

# Extract the dataset with time dependent data from a Pandas Dataframe
number_of_points = 150
number_of_timestamp = 10

data_matrix = np.random.rand(number_of_timestamp, number_of_points)
time_matrix = np.ones_like(data_matrix)

for i in range(0, number_of_timestamp):
    time_matrix[i, :] = time_matrix[i, :] * (i + 1)

time_dependent_dataset = ax.extract_time_dependent_dataset(pd.DataFrame({"time": time_matrix.ravel(),
                                                                         "data": data_matrix.ravel()}),
                                                           "data",
                                                           "time")

# Create the control charts and plot it
xbar_chart_limit, range_chart_limit = ax.create_control_charts(time_dependent_dataset,
                                                               'XbarR',
                                                               plot=True)

# Print on screen the contro limits
print("\nXbar chart")
print("LCL:  ", xbar_chart_limit.lcl)
print("UCL:  ", xbar_chart_limit.ucl)
print("CL:   ", xbar_chart_limit.cl)

print("\nRange chart")
print("LCL:  ", range_chart_limit.lcl)
print("UCL:  ", range_chart_limit.ucl)
print("CL:   ", range_chart_limit.cl)

3. For developers

To upload a new version of ASALIX on PyPi:

pip install --upgrade .
python example.py
python setup.py check
python setup.py sdist
twine upload dist/*