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analysis.py v 1.1.0.002

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ltcptgeneral 2019-09-13 13:59:13 -05:00
parent b2ac70a148
commit 3fa6af8594
2 changed files with 14 additions and 27 deletions
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data analysis/analysis/analysis.py
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@ -7,10 +7,14 @@
# number of easter eggs: 2 # number of easter eggs: 2
# setup: # setup:
__version__ = "1.1.0.001" __version__ = "1.1.0.002"
# changelog should be viewed using print(analysis.__changelog__) # changelog should be viewed using print(analysis.__changelog__)
__changelog__ = """changelog: __changelog__ = """changelog:
1.1.0.002:
- snapped (removed) majority of uneeded imports
- forced object mode (bad) on all jit
- TODO: stop numba complaining about not being able to compile in nopython mode
1.1.0.001: 1.1.0.001:
- removed from sklearn import * to resolve uneeded wildcard imports - removed from sklearn import * to resolve uneeded wildcard imports
1.1.0.000: 1.1.0.000:
@ -132,28 +136,11 @@ __all__ = [
# imports (now in alphabetical order! v 1.0.3.006): # imports (now in alphabetical order! v 1.0.3.006):
from bisect import bisect_left, bisect_right
import collections
import csv import csv
from decimal import Decimal
import functools
from fractions import Fraction
from itertools import groupby
import math
import matplotlib
import numba import numba
from numba import jit from numba import jit
import numbers
import numpy as np import numpy as np
import pandas
import random
import scipy
from scipy.optimize import curve_fit
from scipy import stats
from sklearn import preprocessing from sklearn import preprocessing
# import statistics <-- statistics.py functions have been integrated into analysis.py as of v 1.0.3.002
import time
import torch
class error(ValueError): class error(ValueError):
pass pass
@ -172,7 +159,7 @@ def _init_device(setting, arg): # initiates computation device for ANNs
else: else:
raise error("specified device does not exist") raise error("specified device does not exist")
@jit @jit(forceobj=True)
def load_csv(filepath): def load_csv(filepath):
with open(filepath, newline='') as csvfile: with open(filepath, newline='') as csvfile:
file_array = np.array(list(csv.reader(csvfile))) file_array = np.array(list(csv.reader(csvfile)))
@ -180,7 +167,7 @@ def load_csv(filepath):
return file_array return file_array
# data=array, mode = ['1d':1d_basic_stats, 'column':c_basic_stats, 'row':r_basic_stats], arg for mode 1 or mode 2 for column or row # data=array, mode = ['1d':1d_basic_stats, 'column':c_basic_stats, 'row':r_basic_stats], arg for mode 1 or mode 2 for column or row
@jit @jit(forceobj=True)
def basic_stats(data): def basic_stats(data):
data_t = np.array(data).astype(float) data_t = np.array(data).astype(float)
@ -193,13 +180,13 @@ def basic_stats(data):
return _mean, _median, _stdev, _variance return _mean, _median, _stdev, _variance
# returns z score with inputs of point, mean and standard deviation of spread # returns z score with inputs of point, mean and standard deviation of spread
@jit @jit(forceobj=True)
def z_score(point, mean, stdev): def z_score(point, mean, stdev):
score = (point - mean) / stdev score = (point - mean) / stdev
return score return score
# expects 2d array, normalizes across all axes # expects 2d array, normalizes across all axes
@jit @jit(forceobj=True)
def z_normalize(array, *args): def z_normalize(array, *args):
array = np.array(array) array = np.array(array)
@ -210,7 +197,7 @@ def z_normalize(array, *args):
return array return array
@jit @jit(forceobj=True)
# expects 2d array of [x,y] # expects 2d array of [x,y]
def histo_analysis(hist_data): def histo_analysis(hist_data):
@ -228,22 +215,22 @@ def histo_analysis(hist_data):
return mean_derivative, stdev_derivative return mean_derivative, stdev_derivative
@jit @jit(forceobj=True)
def mean(data): def mean(data):
return np.mean(data) return np.mean(data)
@jit @jit(forceobj=True)
def median(data): def median(data):
return np.median(data) return np.median(data)
@jit @jit(forceobj=True)
def stdev(data): def stdev(data):
return np.std(data) return np.std(data)
@jit @jit(forceobj=True)
def variance(data): def variance(data):
return np.var(data) return np.var(data)