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kracekumar / collect_tweet.py
Last active May 21, 2020
A simple twiiter program to collect a tweet on a query using tweepy.
View collect_tweet.py
import tweepy
import json
from sqlite_utils import Database
def authenticate():
# twitter API credentials
cred = json.load(open('.cred.json', 'r'))
auth = tweepy.OAuthHandler(consumer_key=cred['api_key'], consumer_secret=cred['api_secret_key'])
auth.set_access_token(key=cred['access_token'], secret=cred['access_token_secret'])
api = tweepy.API(auth)
View response.json
{
"cropHintsAnnotation": {
"cropHints": [
{
"boundingPoly": {
"vertices": [
{
"x": 89
},
{
View custom_predict_mnist_cnn.py
import torch
import torch.nn.functional as F
import torchvision
import torchvision.models as models
import mlflow
import mlflow.pytorch
import numpy as np
class Model(mlflow.pyfunc.PythonModel):
@kracekumar
kracekumar / Analysis.ipynb
Last active May 25, 2019
2019 General and Bye-Elections votes analysis
View Analysis.ipynb
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View lmdb.ipynb
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View psql.sql
annotation=> explain (analyze, timing off) select label, value from data limit 10000;
QUERY PLAN
------------------------------------------------------------------------------------------------
Limit (cost=0.00..322.22 rows=10000 width=8) (actual rows=10000 loops=1)
-> Seq Scan on data (cost=0.00..33860.40 rows=1050840 width=8) (actual rows=10000 loops=1)
Total runtime: 5.994 ms
(3 rows)
Time: 6.801 ms
@kracekumar
kracekumar / fizzbuzz.py
Created Jan 9, 2018
tf.data Experiments
View fizzbuzz.py
# Fizz Buzz in Tensorflow!
# see http://joelgrus.com/2016/05/23/fizz-buzz-in-tensorflow/
import time
import sys
import numpy as np
import tensorflow as tf
from utils import binary_encode, fizz_buzz_encode, fizz_buzz
#from models import gen, gen_all
View strace.txt
open("/tmp/empty.json", O_RDONLY|O_CLOEXEC) = 12
fstat(12, {st_mode=S_IFREG|0640, st_size=3, ...}) = 0
ioctl(12, TCGETS, 0x3fffed9c8200) = -1 ENOTTY (Inappropriate ioctl for device)
lseek(12, 0, SEEK_CUR) = 0
ioctl(12, TCGETS, 0x3fffed9c8170) = -1 ENOTTY (Inappropriate ioctl for device)
lseek(12, 0, SEEK_CUR) = 0
lseek(12, 0, SEEK_CUR) = 0
fstat(12, {st_mode=S_IFREG|0640, st_size=3, ...}) = 0
read(12, "{}\n", 4) = 3
View read.py
def read_data(files, total_records):
records = []
while len(records) < total_records:
# Rotate the files queue
files.rotate(1)
with open(files[0]) as handle:
data = json.load(handle)
for record in data:
records.append(record)
If len(records) >= total_records:
View sqlalchemy_row_to_json.py
from sqlalchemy import func, select, Column, ForeignKey, Integer, String, create_engine
from sqlalchemy.orm import sessionmaker, relationship
from sqlalchemy.sql.expression import literal_column
from sqlalchemy.ext.declarative import declarative_base
Base = declarative_base()
engine = create_engine('postgresql+psycopg2://postgres:password@localhost/test', echo=True)