jpeg_csv_converterANDstats

from collections import Counter
import seaborn as sns
import statistics as stats

# Plotting cell
from matplotlib import pyplot as plt

# font
plt.rcParams.update({'font.size': 8})

# reset the default figsize value
plt.rcParams["figure.figsize"] = plt.rcParamsDefault["figure.figsize"]

# 144 is good for a high-resolution display. Try 100 if it's too big
plt.rcParams["figure.dpi"] = (120)

def printInfo(dv):
  print(f"Length: {len(dv)}")
  print(f"Minimum: {min(dv)}")
  print(f"Maximum: {max(dv)}")
  print(f"Mean: {stats.mean(dv)}")
  print(f"16%,50%,84% Percentile values: {stats.quantile(dv,0.16)},{stats.quantile(dv,0.50)},{stats.quantile(dv,0.84)}")
  print(f"Median: {stats.median(dv)}")
  print(f"Std Deviation: {stats.standard_deviation(dv)}")
  print(f"Interquartile Range: {stats.interquartile_range(dv)}")

import csv
gamma = []
with open('gamma.csv') as csvfile:
  rdr = csv.reader(csvfile)
  for row in rdr:
    gamma.append(float(row[0]))

printInfo(gamma)

sns.histplot(x=gamma)
plt.axvline(x=stats.mean(gamma),color='red')
plt.axvline(x=stats.median(gamma),color='blue')

plt.axvline(x=stats.quantile(gamma,0.16),color='black')
plt.axvline(x=stats.quantile(gamma,0.84),color='black')
plt.axvline(x=stats.median(gamma),color='blue')

plt.title(f"Gamma distribution with mean (red) and median (blue)")
plt.show()

import random
def centralLimit(somedata):
  # select 100 values using random.sample() function 
  sample_100 = random.sample(somedata, 100)
  stats.mean(sample_100)

  #  use for loop to simulate this process 10,000 times
  # store each mean into an array called means_size_100
  means_size_100 = []
  sums_size_100 = []
  for _ in range(10000):
      sample_size_100 = random.sample(somedata, 100)
      means_size_100.append(stats.mean(sample_size_100))
      sums_size_100.append(sum(sample_size_100))
  return sample_100,means_size_100,sums_size_100

sample_100,means_size_100,sums_size_100 = centralLimit(gamma)

# plot histogram of 100 samples
plt.hist(sample_100)
plt.xlabel('Mean')
plt.ylabel('Frequency')

plt.title('Frequency of Mean');

# plot histogram of 100 samples tested 10K time
plt.hist(means_size_100)
plt.xlabel('Mean')
plt.ylabel('Frequency')
plt.title('Frequency of Mean');

# plot histogram of 100 samples tested 10K time
plt.hist(sums_size_100)
plt.xlabel('Sum')
plt.ylabel('Frequency')
plt.title('Frequency of Sum');

printInfo(means_size_100)

printInfo(sums_size_100)

# open the image as an array
from PIL import Image
from numpy import asarray
# load the image
#insert and jpeg below; function is designed to work on images and then convert them to one dimensional csv files
#but image was inserted as anselAdams_blackSun.csv is a 1-d csv file
image = Image.open('anselAdams_blackSun.csv')
# convert image to numpy array
data = asarray(image)
print(type(data))
# summarize shape
print(data.shape)

flat = data.flatten()

np.savetxt('anselAdams_blackSun.csv',data)

# create Pillow image
image2 = Image.fromarray(data)
print(type(image2))

# summarize image details
print(image2.mode)
print(image2.size)
image2

#image2.save()?
#csv.save(image2)?