def command_line_arguments(year, month, day, plot_day, plot_month):
if day!='None':
limit = monthrange(int(year), int(month))[1]
if int(day) > limit:
print("Invalid Date for given month and year. Exiting...\n")
exit()
val = get_day(year, month, day)
if plot_day:
try:
plot_points = get_day(year, month, day, rtr=True); val=False
pl = plotting.Plotting(year=year, month=month, day=day)
pl.normal_plot(plot_points)
return "plotted"
except IndexError:
pass
elif day == 'None':
if month and year:
print("\n YEAR: {} | MONTH: {} | \n".format(year, month))
get_data.main(year, month)
if plot_month:
try:
monthly_average = get_month(year, month)
plot = plotting.Plotting(year=year, month=month)
plot.normal_plot(monthly_average, day=False)
except IndexError:
raise("Index Error. Can't Plot Monthy Average Values.")
def extract(self):
get_data.main(self.images_root_dir, self.image_path_label_csv_path)
if config_retrieval["model_kind"] == 'delf':
self.delf.extract_feature_from_imagelabelcsv(
self.image_path_label_csv_path, self.feature_npy_path)
if config_retrieval['model_kind'] == 'ArcFace':
pass
def get_day(year, month, day, rtr=False):
response = get_data.get_value_from_database(year, month, day)
if response:
if rtr:
return json.loads(response[0][1])
print("\n YEAR: {} | MONTH: {} | DAY: {}".format(year, month, day))
print(response[0][1])
return 1
else:
get_data.main(year,month,display=False)
get_day(year, month, day)
def main(BUILD_STATE, IMAGES_PER_LABEL, TRAIN_MODEL):
labels = [0, 1]
get_data.main(BUILD_STATE, IMAGES_PER_LABEL, labels)
datapath = 'data'
dataset, plot_set = preprocess.compileData(data_path=datapath,
make_new=BUILD_STATE)
if BUILD_STATE:
preprocess.example_plot(plot_set)
convmodel = netmodel.buildModel(print_summary=False)
if TRAIN_MODEL:
netmodel.train(convmodel)
def data(run):
"""The basic interface depths are further processed to
pull out maxima / minima using peakdetect. Parallax
correction is applied here, as well as conversion of the
data units from pixels in to tank relative numbers (units
of fluid depth, lock length).
At this point some 'sanity' images are written out with
some of the measured data superimposed.
The data extracted here are saved to another file.
"""
get_data.main(run)
def cla():
val = True
try:
year = sys.argv[1]
month = sys.argv[2]
except IndexError:
display_error()
exit()
try:
if int(year) > 2013 or int(year) < 1950:
print("Year beyond the range. Exiting...\n")
display_error()
exit()
if int(month) < 1 or int(month) > 12:
print("Invlaid Month. Exiting...\n")
display_error()
exit()
except ValueError:
print("Invalid Numerical Value")
exit()
try:
day = sys.argv[3]
if int(day) > 31 or int(day) < 1:
print("Invalid Dates. Exiting...\n")
display_error()
exit()
limit = monthrange(int(year), int(month))[1]
if int(day) > limit:
print("Invalid Date for given month and year. Exiting...\n")
display_error()
exit()
try:
if not sys.argv[4]:
val = get_day(year, month, day)
except:
pass
except IndexError:
pass
try:
if sys.argv[4]:
plot_points = get_day(year, month, day, rtr=True)
val = False
pl = plotting.Plotting(year=year, month=month, day=day)
pl.normal_plot(plot_points)
return "plotted"
except IndexError:
pass
if val:
get_data.main(year, month)
def main():
# get all possible flights and user params from get_data script
params, df_outbound, df_inbound = get_data.main()
# print()
# get common destinations for outbound and inbound trips
# print('OUTBOUND FLIGHTS-------')
# print('Date: ', params['date_outbound'])
# print('Getting common destinations and calculating prices...')
df_common_dest_outbound = get_common_dest(df_outbound)
# print()
# print('Saving flights to sorted_common_dest.txt')
save_df_to_json(df_common_dest_outbound, 'Data/sorted_common_dest.json')
# print()
# print_top_flights(params, df_common_dest_outbound)
# if params['date_inbound'] != None:
# print()
# print('INBOUND FLIGHTS-------')
# print('Date: ', params['date_inbound'])
# print('Getting common destinations and calculating prices...')
#df_common_dest_inbound = get_common_dest(df_inbound)
# have to figure out how to combine inbound and outbound
#
print()
print('Done!')
async def main():
while True:
with open(get_data.OUT_FILE) as f:
old = f.read()
get_data.main()
print(
"Fetched for updates, time:", strftime("%d %b %Y %H:%M:%S +0000", gmtime())
)
with open(get_data.OUT_FILE) as f:
new = f.read()
if new != old:
print("Pushing new data")
os.system("git pull")
os.system(f"git add {get_data.OUT_FILE}")
os.system('git commit -m "Update COVID-19 Data"')
os.system("git push")
await asyncio.sleep(30 * 60) # 30 min
def get_all():
substitutions = get_data.main()
all_substitutions = []
for teacher in substitutions.keys():
teacher_subs = [{
**create_sub(sub), "substitute_teacher": teacher
} for sub in substitutions[teacher]]
all_substitutions += sorted(teacher_subs,
key=lambda sub: sub["lesson_id"])
return jsonify({"data": all_substitutions})
def stocks():
stock_data = get_data.main()
stock_data_df = pd.DataFrame(stock_data)
stock_data_df.drop(index = 0, inplace = True)
stock_data_df['date'] = stock_data_df['date'].astype(str)
stock_data_df=stock_data_df.rename(columns = {'date':'date_local'})
stock_data_lod = stock_data_df.to_dict('records')
# return render_template('index2.html', stock_data = stock_data)
return jsonify(stock_data_lod)
def retail():
stock_data, covid_data = get_data.main()
topic = 'covid-19 retail industry'
article_count = 5
articles = get_data.get_articles(topic, article_count)
return render_template('retail.html',
covid_data=covid_data,
stock_data=stock_data,
articles=articles)
def delivery():
stock_data, covid_data = get_data.main()
topic = 'covid-19 delivery industry'
article_count = 5
articles = get_data.get_articles(topic, article_count)
return render_template('delivery.html',
covid_data=covid_data,
stock_data=stock_data,
articles=articles)
def index():
stock_data, covid_data = get_data.main()
topic = 'covid-19'
article_count = 5
articles = get_data.get_articles(topic, article_count)
return render_template('index.html',
covid_data=covid_data,
stock_data=stock_data,
articles=articles)
def med_services():
stock_data, covid_data = get_data.main()
topic = 'covid-19 medical services'
article_count = 5
articles = get_data.get_articles(topic, article_count)
return render_template('med_services.html',
covid_data=covid_data,
stock_data=stock_data,
articles=articles)
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import get_data
data = get_data.main()
data = data[data['Country/Region'] == 'US']
data = data[data['Province/State'] == 'Minnesota']
data_to_plot = data.get(['Province/State', 'date', 'n_cases'])
data_to_plot.plot()
plt.savefig('us_cases_by_state.pdf')
#! /usr/bin/env python """ This script should reproduce our best submission on Kaggle, using an already trained CNN architecture for twitter sentiment analysis. Our model is (at least) 1.6 GB, so we didn't attach it. Instead, you can download it using the `get_data.py` script (which this script calls) For the training, see the file `train_CNN.py`, and for evaluation, `eval_CNN.py` Our code is an adaptation from the open source (Apache license) software at https://github.com/dennybritz/cnn-text-classification-tf Authors: András Ecker, Valentin Vasiliu, Ciprian I. Tomoiagă """ # Data preparation: # ================================================== import get_data get_data.main() # Model evaluation: # ================================================== import eval_CNN
def __iter__(self):
pre_frame = -self.__frame_gap - 1
if self.__length == 0:
loop = lambda x: True
else:
loop = lambda x: x < self.__length
self.__reader.set(cv2.CAP_PROP_POS_FRAMES, self.__now_frame)
self.__now_frame
while loop(self.__now_frame):
ret, im = self.__reader.read()
if self.__now_frame - pre_frame > self.__frame_gap:
pre_frame = self.__now_frame
yield self.__now_frame, np.flip(im, 2)
self.__frame_count -= 1
if self.__frame_count == 0:
break
self.__now_frame += 1
if __name__ == "__main__":
import get_data
import sys
pics = main(get_data.main(sys.argv[1], sys.argv[2]))
for pic in pics:
cv2.imshow('a', pic)
k = cv2.waitKey(30) & 0xff
if k == 29:
break
cv2.destroyAllWindows()
def index():
stock_data = get_data.main()
return render_template('index.html', stock_data = stock_data)
def main():
args = parser.parse_args()
if args.cfg_file is not None:
cfg_from_file(args.cfg_file)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
# select device
torch.cuda.set_device(args.gpu_id)
print('[Info] use gpu: {}'.format(torch.cuda.current_device()))
# get parameters
sys.path.insert(0, args.model_dir)
from params import params
assert len(params) > 1
last_cfg = params[0][-1]
last_cfg()
get_data.main()
dataset = VQADataset('test', params[0][1])
itoa = dataset.codebook['itoa']
vote_buff = [{} for i in range(len(dataset))]
conf_buff = np.zeros((len(dataset), len(itoa)))
sm_conf_buff = np.zeros((len(dataset), len(itoa)))
l2_conf_buff = np.zeros((len(dataset), len(itoa)))
que_ids = dataset.que_id
for fpath, mgrp, mname, acc, cfg_func, in params:
# data
if cfg_func != last_cfg:
cfg_func()
get_data.main()
last_cfg = cfg_func
dataset = VQADataset('test', mgrp)
itoa = dataset.codebook['itoa']
dataset.reload_obj(mgrp)
dataloader = torch.utils.data.DataLoader(
dataset, batch_size=args.bs, shuffle=False,
num_workers=2, pin_memory=True)
# model
model_group = import_module('models.' + mgrp)
model = getattr(model_group, mname)
num_words=dataset.num_words,
num_ans=dataset.num_ans,
emb_size=get_emb_size())
cp_file = os.path.join(args.model_dir, fpath)
checkpoint = torch.load(cp_file, map_location=lambda s, l: s.cuda(0))
model.load_state_dict(checkpoint['state_dict'])
model.cuda()
model.eval()
# predict
bar = progressbar.ProgressBar()
start = 0
# sample: (que_id, img, que, [obj])
for sample in bar(dataloader):
sample_var = [Variable(d).cuda() for d in list(sample)[1:]]
score = model(*sample_var)
sm_score = torch.nn.functional.softmax(score)
l2_score = torch.nn.functional.normalize(score)
bs = score.size(0)
conf_buff[start:start+bs] += score.data.cpu().numpy()
sm_conf_buff[start:start+bs] += sm_score.data.cpu().numpy()
l2_conf_buff[start:start+bs] += l2_score.data.cpu().numpy()
_, ans_ids = torch.max(score.data, dim=1)
for i, ans_id in enumerate(ans_ids):
ans = itoa[ans_id]
ans_score = acc + vote_buff[start + i].get(ans, 0)
vote_buff[start + i][ans] = ans_score
start += bs
# -*- coding: utf-8 -*- import get_data as get_data get_data.main(product="", cve_id="", file_name="opensource_product_list5.csv")
def f_data(run):
"""Fast version of data.
"""
get_data.main(run, 22)
def get_by_teacher(name):
teachers = get_data.main()
subs = teachers.get(name)
substitutions_of_teacher = [create_sub(sub) for sub in subs]
return jsonify({"data": substitutions_of_teacher})
parser.add_argument('-w','--nwalkers', default=8, help='(int) Number of walkers in parameter space', type=int)
args = parser.parse_args()
N_CPU = args.ncpu
MCMC_STEPS = args.chainlength
N_WALKERS = args.nwalkers
#%%
"""
Get data from measurements
Choose relevant transects.
Slice by date
"""
fn_weather_data = Path('data/weather_station_historic_data.xlsx')
dfs_by_transects = get_data.main(fn_weather_data)
# Choose transects
relevant_transects = ['P002', 'P012', 'P016', 'P018']
dfs_relevant_transects = {x: dfs_by_transects[x] for x in relevant_transects}
dfs_sliced_relevant_transects = {}
# Slice by julian day
jday_bounds = {'P002':[775, 817], # 660: 22/10/2019; 830: 9/4/2020
'P012':[740, 771],
'P015':[707, 724],
'P016':[707, 731],
'P018':[815, 827]
}
for key, df in dfs_relevant_transects.items():
import pandas as pd
import get_data
import time
from oanda_api import buy_sell, accsum
buy_sell.mkt_position('AUD_USD', '-1')
current_status = 0 # 1 = in long position, 0 = in sort position
print('This is where the fun begins')
while True:
instrument = "AUD_USD"
df = get_data.main()
account_value = accsum.account_value()
units = round((0.04 * float(account_value)) / df['Close'].iloc[-1])
if df['psarbull'].iloc[-1] != "NaN":
if current_status == 0:
current_status = 1
print('Buy Signal')
buy_sell.close_all('short', 'AUD_USD')
time.sleep(5)
buy_sell.mkt_position('AUD_USD', str(units))
elif df['psarbear'].iloc[-1] != "NaN":
if current_status == 1:
current_status = 0
print('Sell Signal')
buy_sell.close_all('long', 'AUD_USD')
time.sleep(5)
buy_sell.mkt_position('AUD_USD', str(-units))
print(current_status)
import get_data url = "https://www.betexplorer.com/handball/sweden/she-women/" x, y = get_data.main(url) print(x, '\n\n', y)
def stock_page():
stock_data = get_data.main()
return render_template('stock-page.html')
def get_teachers():
teachers = list(get_data.main().keys())
filtered_teachers = filter_dismissed_lessons(teachers)
return jsonify({"data": filtered_teachers})
