Jared Burgess jaredjburgess

## progress_bar.py
import progressbar

pbar = progressbar.ProgressBar(maxval=len(object))
pbar.start()

for i in range(len(object)):
    pbar.update(i)

pbar.finish()

## pretty_print.py
from pprint
import pprintpprint(example_json)

## pg_create_fkey.sql
alter table table_name add constraint table_name_column_name_fkey foreign key (column_name) references other_table_name (other_column_name) on delete cascade;

## pg_dump_table.sql
pg_dump -Fc -v -U user_name -d db_name -t table_name > /path/to/file.bak

## pg_array_overlap.sql
where array_column && array['string1', 'string2']::text[]

## pg_drop_col.sql
alter table table_name drop column_name;

## write_json.py
open(path_to_file, 'w').write(json.dumps(data))

## read_json.py
data = json.loads(open(path_to_file, 'r').read())

## hist_plot.py
import numpy as np
import matplotlib.pyplot as plt
counts = [10, 8, 6, 14]
weights = np.ones_like(counts) / float(len(counts))
plt.figure(figsize=(10, 5))
plt.hist(counts, bins=range(1, max(counts)+2), align='left', weights=weights)
plt.xticks(range(1, max(counts)+1))
plt.show()

## tsne_vis.py
# Visualise given word embeddings
# words is a list of words
# data is the vector representation of each word

# Train the algorithm
from sklearn.manifold import TSNE
vis_algo = TSNE(random_state=0, verbose=10, init='pca', n_iter=200)
vis = vis_algo.fit_transform(data)

# Plot the resulting visualisation
	import progressbar

	pbar = progressbar.ProgressBar(maxval=len(object))
	pbar.start()

	for i in range(len(object)):
	pbar.update(i)

	pbar.finish()
	import numpy as np
	import matplotlib.pyplot as plt
	counts = [10, 8, 6, 14]
	weights = np.ones_like(counts) / float(len(counts))
	plt.figure(figsize=(10, 5))
	plt.hist(counts, bins=range(1, max(counts)+2), align='left', weights=weights)
	plt.xticks(range(1, max(counts)+1))
	plt.show()
	# Visualise given word embeddings
	# words is a list of words
	# data is the vector representation of each word

	# Train the algorithm
	from sklearn.manifold import TSNE
	vis_algo = TSNE(random_state=0, verbose=10, init='pca', n_iter=200)
	vis = vis_algo.fit_transform(data)

	# Plot the resulting visualisation