def main():
# Get the name of the config file
config_file = leverage_efficiency.base.get_config_filename(sys.argv)
# Extract the data from source data folder into common format
import extract
extract.main(config_file)
# Update data with most recent values (optional)
#import update # This doesn't connect to the rest of the pipeline yet
#update.main(config_file)
# Calculate derived quantities like returns for input into calculations
import transform
transform.main(config_file)
# Perform leverage efficiency calculations
import analysis
analysis.main(config_file)
# Create figures
import plots
plots.main(config_file)
# Create exact figures used in the paper
import paper_plots
paper_plots.main(config_file)
# Create figures used in the EE lecture notes
import lecture_plots
lecture_plots.main(config_file)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--crawl')
args = parser.parse_args()
if args.crawl == 'yes':
c = raw_input("This will crawl the websites and will take very long time. Do you want to continue? ")
if c == 'yes':
crawl.crawl_trains()
extract.main()
transform.main()
os.system('bash export.sh')
def test_main():
urls = [
(
"https://factfinder.census.gov/bkmk/table/1.0/en/"
"ACS/13_5YR/B07010/0100000US|0400000US01|0500000US01001",
"https://data.census.gov/cedsci/table?g="
"0100000US_0400000US01_0500000US01001&tid=ACSDT5Y2013.B07010&y=2013",
),
(
"https://factfinder.census.gov/bkmk/table/1.0/en/DEC/10_113/H1",
"https://data.census.gov/cedsci/table?tid=DECENNIALCD1132010.H1&y=2010",
),
(
"http://factfinder.census.gov/bkmk/table/1.0/en/DEC/10_SF1/H10",
"https://data.census.gov/cedsci/table?tid=DECENNIALSF12010.H10&y=2010",
),
(
"https://factfinder.census.gov/bkmk/table/1.0/en/NES/2016/00A1",
"https://data.census.gov/cedsci/table?tid=NONEMP2016.NS1600NONEMP&y=2016",
),
(
"https://factfinder.census.gov/bkmk/table/1.0/en/SBO/2012/00CSA01",
"https://data.census.gov/cedsci/table?tid=SBOCS2012.SB1200CSA01&y=2012",
),
(
"http://factfinder.census.gov/bkmk/cf/1.0/en/place/Chicago city, Illinois"
"/POPULATION/DECENNIAL_CNT",
"https://data.census.gov/cedsci/profile?q=Chicago+city%2C+Illinois",
),
]
for old, new in urls:
assert transform.main(old) == new
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--crawl')
args = parser.parse_args()
if args.crawl == 'yes':
c = raw_input(
"This will crawl the websites and will take very long time. Do you want to continue? "
)
if c == 'yes':
crawl.crawl_trains()
extract.main()
transform.main()
os.system('bash export.sh')
def go_to_images():
filename = request.args.get("filename")
filepath = "/static/uploads/" + filename
# get id by removing ".png" from the filename
file_id = filename.split(".")
db_id = file_id[0]
# get image from db based on id
image = Image.query.get(db_id)
# check if the image already has directions associated with it
if not image.directions:
# if not, get the ws_rows value and send it to the transform functions
directions = transform.main(image.ws_rows)
# save the directions as a string to the database entry for the image
image.directions = str(directions)
model.session.commit()
else:
#otherwise, retrieve the directions from the database and change it back to a list for displaying
directions = eval(image.directions)
return render_template("directions.html",
filepath=filepath,
directions=directions)
def predict():
if request.method == 'POST':
# Get the file from post request
f = request.files['image']
# Save the file to ./uploads
# basepath = os.path.dirname(__file__)
file_name = secure_filename(f.filename)
file_path = os.path.join("imgs", file_name)
f.save(file_path)
questions = 20
# imgPath =f"./imgs/{filename}"
im = cv2.imread(file_path)
resp = main(im, file_path, questions)
return json.dumps(resp)
def go_to_images():
filename = request.args.get("filename")
filepath = "/static/uploads/" + filename
# get id by removing ".png" from the filename
file_id = filename.split(".")
db_id = file_id[0]
# get image from db based on id
image = Image.query.get(db_id)
# check if the image already has directions associated with it
if not image.directions:
# if not, get the ws_rows value and send it to the transform functions
directions = transform.main(image.ws_rows)
# save the directions as a string to the database entry for the image
image.directions = str(directions)
model.session.commit()
else:
#otherwise, retrieve the directions from the database and change it back to a list for displaying
directions = eval(image.directions)
return render_template("directions.html", filepath=filepath, directions=directions)
import anpocs43, anpocs44, transform
if __name__ == "__main__":
anpocs43.main()
anpocs44.main()
transform.main()
#!/usr/bin/env python3
from subprocess import check_output, call, STDOUT
import os
import json
import transform
DOMAIN = [(2, 128), (2, 128), (2, 128)]
def codegen(filename, i, j, k):
with open(filename, "w") as fp:
row = dict(i=int(i), j=int(j), k=int(k))
json.dump(row, fp)
def run(filename):
with open(filename) as fp:
params = json.load(fp)
cmd = "clang++ -std=c++11 -O3 -DTILE0={i} -DTILE1={j} -DTILE2={k} matmul.cpp -o matmul"
os.system(cmd.format(**params))
p = check_output(["./matmul", "1024"])
elapsed = float(p.decode("utf-8").split("nodes/s")[0])
print(params, elapsed)
return elapsed
if __name__ == '__main__':
transform.main(DOMAIN, codegen, run)