arccoder

class ReadOnlyDict(dict):
    def __setitem__(self, key, value):
        raise RuntimeError("Modification not supported")


rw_dict = {'key': 'original'}
print('Dict: ' + str(rw_dict))
ro_dict = ReadOnlyDict(rw_dict)
print('ReadOnlyDict: ' + str(ro_dict))

arccoder

def cyclic_intersection_pts(pts):
    """
    Sorts 4 points in clockwise direction with the first point been closest to 0,0
    Assumption:
        There are exactly 4 points in the input and
        from a rectangle which is not very distorted
    """
    if pts.shape[0] != 4:
        return None

arccoder

import yfinance as yf

data = yf.download('TSLA', start='2021-02-21', end='2021-02-27', interval='1m')
data = data.iloc[:, 0:1]
data.to_csv('in/tsla-7day-1min.csv')

data = yf.download('TSLA', start='2021-02-01', end='2021-02-27', interval='30m')
data = data.iloc[:, 0:1]
data.to_csv('in/tsla-1month-30min.csv')

arccoder

> python redact.py -h
usage: redact.py [-h] -i INPUT -o OUTPUT -p PATTERNS [-r REPLACE] [-c COLOR]

optional arguments:
  -h, --help            show this help message and exit
  -i INPUT, --input INPUT
                        Path to the document to be redacted
  -o OUTPUT, --output OUTPUT
                        Path to save the redacted document
  -p PATTERNS, --patterns PATTERNS

arccoder

# Load the Pandas libraries with alias 'pd'
import pandas as pd

# Read csv to dataframe
df = pd.read_csv("data.csv")

# Set column types
df['Submission Date'] = pd.to_datetime(df['Submission Date'], format="%Y-%m-%d %H:%M")

print(df)

arccoder

"""
Script contains functions to process the lines in polar coordinate system
returned by the HoughLines function in OpenCV

Line equation from polar to cartesian coordinates
x = rho * cos(theta)
y = rho * sin(theta)

x, y are at a distance of rho from 0,0 at an angle of theta

arccoder

clc; clear all; close all;

% Data Generation
x = 0:10:2000;
y = sigmf(x,[0.1 1000]);
n = rand(1,201)/40 - 0.0125;
f = y;% + n;
fig = figure;
set(fig, 'PaperUnits', 'inches');
set(fig, 'PaperPosition', [0 0 8 2]);

arccoder

import numpy as np


def kmeans(data, numofclasses, options=None):
    """
    Calculates the clusters using k-means algorithm and returns cluster labels and centroids
    :param data: Data to cluster structured as [Number of observations x Data dimensions(variables)]
    :param numofclasses: Number of classes you want to cluster the data into.
    :param options: Optional data in dictionary form to overwrite the defaults
                    max_iterations - int: Maximum number of iterations

arccoder

imageExt = '.png';
imagefiles = dir(['*' imageExt]);

outfilename = 'gifname.gif';
for n = 1:length(imagefiles)
      im = imread(imagefiles(n).name);
      [imind,cm] = rgb2ind(im,256);
      % DelayTime in seconds
      if n == 1
          imwrite(imind,cm,outfilename,'gif','Loopcount',inf,'DelayTime',0.7); 

arccoder

#include "fastcv.h"
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <iostream>

using namespace cv;
using namespace std;

int main()
{