f56935c748
:Release Notes: shit results - shit interpolation :D :Detailed Notes: - :Testing Performed: Mostly similar to the visible results :QA Notes: - :Issues Addressed: -
74 lines
2.4 KiB
Python
74 lines
2.4 KiB
Python
import pandas as pd
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import numpy as np
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from interpolationConfiguration import InterpolConfig
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from scipy.stats import norm
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from scipy.optimize import fsolve
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import numpy as np
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import logging
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import matplotlib.pyplot as plt
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logging.basicConfig(level=logging.INFO,
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format='%(asctime)s - %(levelname)s - %(message)s')
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class Interpol:
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"""
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Huhu, interpol
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Interpolation of the uncompleted distribution
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"""
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def __init__(self, config='shit.yml'):
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self.config = InterpolConfig(config)
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self.available_data = pd.read_csv(self.config.available_data).iloc[:,1]
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pass
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def save(self, res: np.array):
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array = [[1] * len(res),
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res]
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df = pd.DataFrame(array).transpose()
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df.to_csv(self.config.out_file, header=False, index=False)
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# to interpolate data we first must find "true mean"
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# for this we would take min value from csv, max value from image
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# and take as granted that we have len(data)/total_points already there
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def interpolate_part_of_data():
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pass
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def normal_dist(self):
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mu = self.maximum_likelihood()
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res = np.random.normal(loc=mu, scale=8, size=150)
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_ = plt.figure(figsize=(10, 6), dpi=300)
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plt.hist(res)
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plt.title('Interpolated results')
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plt.show()
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res = res[res >= self.config.available_limit]
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res = np.append(res, self.available_data, axis=0)
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plt.hist(res)
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plt.title('Combined results')
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plt.show()
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self.save(res)
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def maximum_likelihood(self):
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def equation(mu, A, B, dAC, n_known, total):
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prob_AB = norm.cdf((B-mu)/dAC) - norm.cdf((A-mu)/dAC)
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return prob_AB - n_known/total
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# Your values
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A = self.available_data.min() # left boundary
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B = float(self.config.available_limit) # right boundary of known data
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C = np.max(self.config.image_data) # right boundary of distribution
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dAC = self.config.std_source.iloc[:,1].std() # standard deviation
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logging.info(f"dAC={dAC}")
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n_known = len(self.available_data)
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total = n_known * 3
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logging.info(f"A={A}; B={B}; C={C}")
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# Solve for mu
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mu_estimate = fsolve(equation, x0=B, args=(A, B, dAC, n_known, total))[0]
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logging.info(f"Estimated mean: {mu_estimate:.3f}")
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return mu_estimate
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a = Interpol()
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a.normal_dist() |