boscacci

from icevision.models.inference import *
import json
import numpy as np

[pred.add_component(FilepathRecordComponent()) for pred in preds]
[preds[_].set_filepath(img_files[_]) for _ in range(len(preds))]

for p in preds:
    process_bbox_predictions(p, 
                             PIL.Image.open(Path(p.pred.filepath)),

boscacci

def twosample_tstat_welch(experimental, control):
    """
    Runs Welch's unequal variances t-test
    Accepts 2 pandas series or numpy arrays
    Returns a t-stat
    """
    xbar_1 = experimental.mean()
    xbar_2 = control.mean()
    var_1 = experimental.var()
    var_2 = control.var()

boscacci

import re

def extract_scene_headings(film_script_txtfile_path="scripts/no_country.txt", mode=1):
    # A couple options on regex patterns, depending on script format. Might need tweaks per script
    film_scene_heading_regexp_1 = "(?<=INT. |EXT. ).*(?=,)"
    film_scene_heading_regexp_2 = "(?<=INT. |EXT. ).*(?= -)"
    
    if mode == 1:
        regexp = film_scene_heading_regexp_1
    elif mode == 2:

boscacci

import pandas as pd
from sqlalchemy import create_engine

# Your connection string format may vary by SQL flavor;
con = f'postgresql://{username}:{password}@{host}:5432/{database}'
eng = create_engine(con)

# Open csv file as stream and write to SQL, appending as you go:
for chunk in pd.read_csv('filename.csv', chunksize = 1000):
    chunk.to_sql(name = 'giant_table',