kylemcdonald

shawwn

def lerp(a, b, t):
  return (b - a) * t + a

def bias(i, x, beta):
  return 1 - jnp.asarray(beta, x.dtype) ** (i + 1)

@optimizer
def adam(step_size, b1=0.9, b2=0.999, eps=1e-8) -> OptimizerResult:
  """Construct optimizer triple for Adam.

Olshansk

function video_to_gif {
  local input_video_path="$1"
  local output_gif_path="$2"
  local fps="${3:-10}"
  local scale="${4:-1080}"
  local loop="${5:-0}"

  ffmpeg -i "${input_video_path}" -vf "setpts=PTS/1,fps=${fps},scale=${scale}:-2:flags=lanczos,split[s0][s1];[s0]palettegen[p];[s1][p]paletteuse" -loop $loop "${output_gif_path}"
}

jakevdp

PaoloLeonard

"""
Custom table expectation which checks whether the row count is greater than the row count of other tables.

There are different ways to compare the row counts:
    * With absolute values, if one row count value of the other tables is greater than the current then the validation
    fails,
    * With mean values, if the mean of value of the other tables row count is greater than the current row count then
    the validation fails.
"""
from copy import deepcopy

rvanbruggen

//colruyt datascience homework assignment in Neo4j

// from: https://github.com/MarkiesFredje/data-engineering-exercise/blob/master/data_engineer_exercise.ipynb
// json file download location: https://ecgplacesmw.colruytgroup.com/ecgplacesmw/v3/nl/places/filter/clp-places


//import into neo4j using apoc
//create indexes and constraint
create index on :Address(streetName);
create index on :City(name);

twiecki

import dask
import dask.array as da
import dask.dataframe as dd
import sparse

@dask.delayed(pure=True)
def corr_on_chunked(chunk1, chunk2, corr_thresh=0.9):
    return sparse.COO.from_numpy((np.dot(chunk1, chunk2.T) > corr_thresh))
        
def chunked_corr_sparse_dask(data, chunksize=5000, corr_thresh=0.9):

vdalv

Convert PascalVOC Annotations to YOLO

This script reads PascalVOC xml files, and converts them to YOLO txt files.

Note: This script was written and tested on Ubuntu. YMMV on other OS's.

Disclaimer: This code is a modified version of Joseph Redmon's voc_label.py

Instructions:

1. Place the convert_voc_to_yolo.py file into your data folder.

gdsaxton

jayspeidell

# Info on how to get your api key (kaggle.json) here: https://github.com/Kaggle/kaggle-api#api-credentials
!pip install kaggle
api_token = {"username":"USERNAME","key":"API_KEY"}
import json
import zipfile
import os
with open('/content/.kaggle/kaggle.json', 'w') as file:
    json.dump(api_token, file)
!chmod 600 /content/.kaggle/kaggle.json
!kaggle config path -p /content