def main():
try:
validate_config(Config)
except ConfigurationError as e:
sys.stdout.write(str(e))
sys.exit(1)
setup_logging()
parser = create_parser()
args = parser.parse_args()
if args.command == 'init':
initialize_dialogflow_intents()
elif args.command == 'run':
if args.platform == 'telegram':
run_telegram_bot()
elif args.platform == 'vk':
run_vk_bot()
else:
sys.stdout.write('Unknown command. Please refer for help.')
sys.exit(1)
else:
sys.stdout.write('Unknown command. Please refer for help.')
sys.exit(1)
def create_app(name=None):
setup_logging()
app = Flask(name or __name__)
setup_routes(app)
app.debug = False
init_db()
init_data()
schedule_job()
return app
def create_app(name=None):
setup_logging()
app = Flask(name or 'App',
template_folder=os.path.join(SOURCE_ROOT, 'templates'))
app.config.root_path = os.path.dirname(os.path.abspath(__file__))
app.config.from_pyfile('settings.py')
load_api(app)
load_moudle(app)
#CORS(apps)
return app
def main():
parser = ArgumentParser(description="Train and evaluate resnet model.")
parser.add_argument("--params",
help="Path to file to save params",
default="./data/cdiscount/model_params.torch")
parser.add_argument("--train",
action='store_true',
help="Train model? Otherwise, will test")
args = parser.parse_args()
setup_logging()
if args.train:
train(args.params)
else:
accuracy = test(args.params)
print("Accuracy: {:.2f}%".format(accuracy * 100))
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# import os,sys,inspect
# currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
# parentdir = os.path.dirname(currentdir)
# sys.path.insert(0,parentdir)
# Setup Logging
from settings import setup_logging
from logging import getLogger
setup_logging()
logger = getLogger('dataset')
from analyzer import TweetPreprocessor, TweetTokenizer
from feature_extraction import make_text_extractor, Bunch
from csv import reader
from collections import namedtuple
import numpy as np
import scipy as sp
import csv
import yaml
class SMS(namedtuple('SMS', ['sms_id_str', 'user_id_str', 'label', 'text'])):
def _asdict(self):
'Return a new OrderedDict which maps field names to their values'
return dict(zip(self._fields, self))
"""
FUNCTIONS TO PLOT OUT LIGHT CURVES/APERTURE OF STAR
"""
from utils import get_sub_kwargs, clip_array, build_arr_n_names, format_arr, is_n_bools
from aperture import run_photometry, improve_aperture, get_aperture_center, \
calculate_better_aperture, model_background, make_background_mask
from settings import setup_logging, mpl_setup
logger = setup_logging()
import os
import csv
import numpy as np
from matplotlib import pyplot as plt
from matplotlib import gridspec as gs
from matplotlib.backends.backend_pdf import PdfPages
def plot_data(target, count=0):
"""
creates plot of light curve and aperture for one target
assumes already have right data before from target
:return: matplotlib figure generated
"""
fig = plt.figure(figsize=(11, 8))
gs.GridSpec(3, 3)
plt.subplot2grid((3, 3), (1, 2))
plt.title(target.kic, fontsize=20)
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Setup Logging
from settings import setup_logging
from logging import getLogger
setup_logging()
logger = getLogger('eval')
logger.info('importing packages...')
# Import standard packages
from time import time, sleep
from pprint import pprint, pformat
# Import 3rd-party packages
import numpy as np
import scipy as sp
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix
from sklearn.metrics.metrics import _check_clf_targets
from sklearn.utils.multiclass import unique_labels
from sklearn.cross_validation import KFold, StratifiedKFold, cross_val_score
from sklearn.learning_curve import learning_curve
from sklearn.linear_model import LogisticRegression, SGDClassifier
from sklearn.naive_bayes import MultinomialNB
from sklearn.svm import SVC, LinearSVC
from sklearn.neighbors import KNeighborsClassifier
def main():
settings.setup_logging()
scrape_toysrus()
scrape_well()
import settings
from utils import get_nth_col
from parse import table_api
from mast import mast_api
logger = settings.setup_logging()
import kplr
import numpy as np
import ast
class api(object):
"""
api to get necessary parameters from combined databases
"""
def __init__(self):
self.sources = ["q1-17", "mast", "mast_table", "periodic", "nonperiodic", \
"lc_img", "lc_new", "lc_old"]
self.updated_dir = settings.filename_stellar_params
self.nonperiodic_dir = settings.filename_nonperiods
self.periodic_dir = settings.filename_periods
self.mast_table_dir = settings.filename_mast_table
self.gaia_dir = settings.filename_gaia_table
self.kic10_dir = settings.filename_kic10_table
self.lc_img_dir = settings.filename_lc_img
self.lc_new_dir = settings.filename_lc_new
self.lc_old_dir = settings.filename_lc_old
self.lc_obs_dir = settings.filename_lc_obs
self.lc_var_dir = settings.filename_lc_var
def get_params(self, kics, params, **neighbour_filters):