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analysis.py - v 1.0.8.003
changelog: - added p_value function
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#number of easter eggs: 2
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#number of easter eggs: 2
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#setup:
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#setup:
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__version__ = "1.0.8.002"
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__version__ = "1.0.8.003"
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#changelog should be viewed using print(analysis.__changelog__)
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#changelog should be viewed using print(analysis.__changelog__)
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__changelog__ = """changelog:
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__changelog__ = """changelog:
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1.0.8.003:
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- added p_value function
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1.0.8.002:
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1.0.8.002:
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- updated __all__ correctly to contain changes made in v 1.0.8.000 and v 1.0.8.001
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- updated __all__ correctly to contain changes made in v 1.0.8.000 and v 1.0.8.001
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1.0.8.001:
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1.0.8.001:
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@ -142,6 +144,7 @@ import pandas
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import random
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import random
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import scipy
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import scipy
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from scipy.optimize import curve_fit
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from scipy.optimize import curve_fit
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from scipy import stats
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from sklearn import *
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from sklearn import *
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#import statistics <-- statistics.py functions have been integrated into analysis.py as of v 1.0.3.002
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#import statistics <-- statistics.py functions have been integrated into analysis.py as of v 1.0.3.002
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import time
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import time
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@ -872,6 +875,9 @@ def select_best_regression(eqs, rmss, r2s, overfit, selector):
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return b_eq, b_rms, b_r2, b_overfit
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return b_eq, b_rms, b_r2, b_overfit
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def p_value(x, y): #takes 2 1d arrays
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return stats.ttest_ind(x, y)[1]
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def basic_analysis(data): #assumes that rows are the independent variable and columns are the dependant. also assumes that time flows from lowest column to highest column.
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def basic_analysis(data): #assumes that rows are the independent variable and columns are the dependant. also assumes that time flows from lowest column to highest column.
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