the-winter

SlayterDev

import praw
import time

seen = []

def main():
	user_agent = ("User stalker 1.0")
	r = praw.Reddit(user_agent=user_agent)
	r.login('username', 'password')

wassname

# from https://gist.github.com/wassname/00b94dc7eb7220c136685fce782be3a4
from io import StringIO
import pandas as pd
import numpy as np
from sklearn import metrics
import sklearn

def parse_classification_report(classification_report):
    """Parse a sklearn classification report to a dict."""
    return pd.read_fwf(

wassname

3Dflex.py

A script for calculating 3D flexure in python. Converted from GARRY QUINLAN's fortran program 3dflex.f.

This programme calculates the flexure for 3D elastic sheet where the sheet can have one of its 4 edges as a free edge. It uses a finite difference approach to the 3D flexure problem and lets Te, restoring force and ;loading vary in a random fashion. There are many comments written in the program to guide the user through.

INPUT:

wassname

def isfinite(x):
    """
    Quick pytorch test that there are no nan's or infs.
    
    note: torch now has torch.isnan
    url: https://gist.github.com/wassname/df8bc03e60f81ff081e1895aabe1f519
    """
    not_inf = ((x + 1) != x)
    not_nan = (x == x)
    return not_inf & not_nan

asross

"""
Examples:

with figure_grid(5, 3) as grid:
  grid.next()
  # plot something
  grid.next()
  # plot something
  # ...etc
  

Kaixhin

Probability notation

Note: Great refresher/glossary on probability/statistics and related topics here

Notation	Definition
X	Random variable
P(X)	Probability distribution over random variable X
X ~ P(X)	Random variable X follows (~) the probability distribution P(X) *

wassname

class AdamStepLR(torch.optim.Adam):
    """Combine Adam and lr_scheduler.StepLR so we can use it as a normal optimiser"""
    def __init__(self, params, lr=0.001, betas=(0.9, 0.999), eps=1e-08, weight_decay=0, step_size=50000, gamma=0.5):
        super().__init__(params, lr, betas, eps, weight_decay)
        self.scheduler = torch.optim.lr_scheduler.StepLR(self, step_size, gamma)
        
    def step(self):
        self.scheduler.step()
        return super().step()

wassname

import torch.utils.data


class NumpyDataset(torch.utils.data.Dataset):
    """Dataset wrapping arrays.

    Each sample will be retrieved by indexing array along the first dimension.

    Arguments:
        *arrays (numpy.array): arrays that have the same size of the first dimension.

fvisin

class RunningStats:
    """Computes running mean and standard deviation
    Adapted from:
        *
        <http://stackoverflow.com/questions/1174984/how-to-efficiently-\
calculate-a-running-standard-deviation>
        * <http://mathcentral.uregina.ca/QQ/database/QQ.09.02/carlos1.html>
    """

    def __init__(self):