Olshansk

Verifying my Blockstack ID is secured with the address 1PwrzAjKF65Wme7JsddXGsqjTKeuQ42K3e https://explorer.blockstack.org/address/1PwrzAjKF65Wme7JsddXGsqjTKeuQ42K3e

Olshansk

from datetime import timedelta
from apscheduler.schedulers.blocking import BlockingScheduler

import requests, json, datetime, numpy, requests_cache
import matplotlib.dates as mdate
import matplotlib.pyplot as plot
import numpy as np

ALL_HISTORICAL_DATA_ENDPOINT = 'https://blockchain.info/charts/market-price?timespan=all&format=json'
NUM_UNIQUE_ADDRESSES = 'https://blockchain.info/charts/n-unique-addresses?timespan=all&format=json'

Olshansk

from pstats import func_std_string, f8
from dataclasses import dataclass
from typing import Dict

@dataclass(unsafe_hash=True)
class FunctionProfile:
    ncalls: int
    tottime: float
    percall_tottime: float
    cumtime: float

Olshansk

import cProfile, pstats
import time
from random import randint

START_TIME = int(time.time())

timestamped_stats_profiles = []

def sleep1():
    time.sleep(0.1)

Olshansk

from collections import Counter
import itertools

TIME_SLICE = 10 # Aggregate logs every 10 seconds

def time_to_bucket(time):
    return (time-START_TIME) // TIME_SLICE

def bucket_to_time(bucket):
    return bucket * TIME_SLICE + START_TIME

Olshansk

import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.ticker import FormatStrFormatter

WIDTH = 0.4

ind = np.arange(len(time_sliced_counters))

x_axis = tuple(time for (time, c) in time_sliced_counters)

Olshansk

import cProfile, pstats
import time
from random import randint

def sleep1():
    time.sleep(0.1)

def sleep2():
    time.sleep(0.2)
    

Olshansk

# Data Generation

NUM_STUDENTS = 30
MEAN = 80
STD = 20

# https://stackoverflow.com/questions/36894191/how-to-get-a-normal-distribution-within-a-range-in-numpy
# Need to cap the values of the distributions to [0,100]
def get_truncated_normal(mean, sd, size, low, upp):
    return truncnorm((low - mean) / sd, (upp - mean) / sd, loc=mean, scale=sd).rvs(size)

Olshansk

NUM_BINS = 10
bins = np.linspace(0, 100, NUM_BINS + 1)

# Note: we set include_lowest to true to make sure that we include zeroes
bucket_GT = pd.cut(grades_GT, bins=bins, include_lowest=True, right=True)
bucket_P = pd.cut(grades_P, bins=bins, include_lowest=True, right=True)

# Output of the cut function
pd.DataFrame({'grades': grades_GT, 'bucket': bucket_GT}).head()

Olshansk

# Generate a pandas dataframe where the index represents the student number
df_GT = pd.DataFrame({'bucket': bucket_GT}).reset_index()
display(df_GT.head())

df_P = pd.DataFrame({'bucket': cut_P}).reset_index()
display(df_P.head())

# Merged the actual predicted grades 
merged_df = pd.merge(df_GT, df_P, on=['index'], suffixes=('_grouth_truth', '_predicted'))
display(merged_df.head())