def test_legend(self):
configuration = get_configuration()
configuration['legend']['columns'] = 1
self.run_plotit(configuration)
self.compare_images(
os.path.join(self.output_folder.name, 'histo1.pdf'),
get_golden_file('default_configuration_1column_legend.pdf'))
configuration['legend']['columns'] = 2
self.run_plotit(configuration)
self.compare_images(
os.path.join(self.output_folder.name, 'histo1.pdf'),
get_golden_file('default_configuration_2columns_legend.pdf'))
configuration['legend']['columns'] = 3
self.run_plotit(configuration)
self.compare_images(
os.path.join(self.output_folder.name, 'histo1.pdf'),
get_golden_file('default_configuration_3columns_legend.pdf'))
configuration['legend']['columns'] = 2
configuration['files']['MC_sample1.root']['legend-order'] = 1
configuration['files']['MC_sample2.root']['legend-order'] = 0
self.run_plotit(configuration)
self.compare_images(
os.path.join(self.output_folder.name, 'histo1.pdf'),
get_golden_file(
'default_configuration_2columns_samplesordering_legend.pdf'))
configuration = get_configuration()
configuration['legend']['columns'] = 2
configuration['legend']['position'][2] = 0.75
configuration['legend']['entries'] = [
{
'label': 'Extra MC legend entry',
'type': 'mc',
'order': 1000
},
{
'label': 'Extra signal legend entry',
'type': 'signal',
'order': 1000
},
]
self.run_plotit(configuration)
self.compare_images(
os.path.join(self.output_folder.name, 'histo1.pdf'),
get_golden_file('default_configuration_extra_legend_entries.pdf'))
def convert_code_to_token(code):
query_string = {
'client_id': get_configuration()['appId'],
'redirect_uri': 'http://imbored.davidstrauss.net/bounce/',
'client_secret': get_configuration()['secret'],
'code': code
}
encoded = urllib.urlencode(query_string)
url = "https://graph.facebook.com/oauth/access_token?{0}".format(encoded)
deferred = http_request.run(url)
def cbResponse(response):
log.msg('Parsing response into a token.')
parts = urlparse.parse_qs(response.body)
from pprint import pprint
pprint(parts)
token = parts['access_token'][0]
expires = parts['expires'][0] # Not yet used.
log.msg('Parsed response into token {0}.'.format(token))
return token
def cbErrResponse(error):
log.msg(error)
return None
deferred.addCallback(cbResponse)
deferred.addErrback(cbErrResponse)
return deferred
def test_legend(self):
configuration = get_configuration()
configuration['legend']['columns'] = 1
self.run_plotit(configuration)
self.compare_images(
os.path.join(self.output_folder.name, 'histo1.pdf'),
get_golden_file('default_configuration_1column_legend.pdf')
)
configuration['legend']['columns'] = 2
self.run_plotit(configuration)
self.compare_images(
os.path.join(self.output_folder.name, 'histo1.pdf'),
get_golden_file('default_configuration_2columns_legend.pdf')
)
configuration['legend']['columns'] = 3
self.run_plotit(configuration)
self.compare_images(
os.path.join(self.output_folder.name, 'histo1.pdf'),
get_golden_file('default_configuration_3columns_legend.pdf')
)
configuration['legend']['columns'] = 2
configuration['files']['MC_sample1.root']['legend-order'] = 1
configuration['files']['MC_sample2.root']['legend-order'] = 0
self.run_plotit(configuration)
self.compare_images(
os.path.join(self.output_folder.name, 'histo1.pdf'),
get_golden_file('default_configuration_2columns_samplesordering_legend.pdf')
)
configuration = get_configuration()
configuration['legend']['columns'] = 2
configuration['legend']['position'][2] = 0.75
configuration['legend']['entries'] = [
{'label': 'Extra MC legend entry', 'type': 'mc', 'order': 1000},
{'label': 'Extra signal legend entry', 'type': 'signal', 'order': 1000},
]
self.run_plotit(configuration)
self.compare_images(
os.path.join(self.output_folder.name, 'histo1.pdf'),
get_golden_file('default_configuration_extra_legend_entries.pdf')
)
def single_pv_set(year, key, module_name, inverter_name, orientation):
c = config.get_configuration()
weatherpath = os.path.join(c.paths['weather'], c.pattern['weather'])
weather = pd.read_hdf(weatherpath.format(year=year), 'A' + str(key))
latlon = pd.read_csv(os.path.join(c.paths['geometry'],
c.files['grid_centroid']),
index_col='gid').loc[key]
location = {'latitude': latlon['st_y'], 'longitude': latlon['st_x']}
weather = f.adapt_weather_to_pvlib(weather, location)
sandia_modules = pvlib.pvsystem.retrieve_sam('sandiamod')
sapm_inverters = pvlib.pvsystem.retrieve_sam('sandiainverter')
smodule = {
'module_parameters': sandia_modules[module_name],
'inverter_parameters': sapm_inverters[inverter_name],
'surface_azimuth': orientation['azimuth'],
'surface_tilt': orientation['tilt'],
'albedo': 0.2
}
p_peak = (smodule['module_parameters'].Impo *
smodule['module_parameters'].Vmpo)
mc = f.feedin_pvlib_modelchain(location, smodule, weather)
ac = mc.ac # .clip(0).fillna(0).div(p_peak)
dc = mc.dc.p_mp # .clip(0).fillna(0).div(p_peak)
print('ac:', ac.sum())
print('dc:', dc.sum())
def main():
# Setup and get current configuration
config = get_configuration()
# Print parameters
print_configuration()
#Initialize class - preprocessing
preprocess = Preprocessing(config=config)
# Perform preprocessing
train_input, train_length_input, train_labels, test_input, test_length_input = preprocess.prepare_data(
)
# Initialize class and select mode and model - embeddings
if config.mode != "infer":
if config.emb_model == "glove":
model_emb = GloVeModel(config=config,
dict_vocab=preprocess.dict_vocab)
else:
model_emb = Word2VecModel(config=config,
dict_vocab=preprocess.dict_vocab)
# Fit corpus
model_emb.fit_to_corpus()
# Train embeddings
model_emb.train()
# Train model
RNN_Model(config, preprocess.dict_vocab_reverse, train_input,
train_length_input, train_labels, test_input, test_length_input)
def __init__(self):
database_credentials = configuration.get_configuration('database')
self.conn = mariadb.connect(user=database_credentials["user"],
password=database_credentials["password"],
host=database_credentials["host"],
port=int(database_credentials["port"]),
database=database_credentials["database"])
self.conn.auto_reconnect = True
def get_full_load_hours():
"""pass"""
c = config.get_configuration()
feedinpath = os.path.join(c.paths['feedin'], '{type}', c.pattern['feedin'])
my_idx = pd.MultiIndex(levels=[[], []],
labels=[[], []],
names=['year', 'key'])
df = pd.DataFrame(index=my_idx, columns=['wind'])
years = list()
for vtype in ['solar', 'wind']:
for year in range(1970, 2020):
if os.path.isfile(feedinpath.format(year=year,
type=vtype.lower())):
years.append(year)
years = list(set(years))
# opening one file to get the keys of the weather fields and the columns of
# the solar file (the columns represent the sets).
file = pd.HDFStore(feedinpath.format(year=years[0], type='solar'))
keys = file.keys()
columns = list(file[keys[0]].columns)
for col in columns:
df[col] = ''
file.close()
for key in keys:
df.loc[(0, int(key[2:])), :] = 0
df.loc[(0, 0), :] = 0
for year in years:
df.loc[(year, 0), :] = 0
logging.info("Processing: {0}".format(year))
solar = pd.HDFStore(feedinpath.format(year=year, type='solar'))
wind = pd.HDFStore(feedinpath.format(year=year, type='wind'))
for key in keys:
skey = int(key[2:])
df.loc[(year, skey), 'wind'] = wind[key].sum()
df.loc[(0, skey), 'wind'] += df.loc[(year, skey), 'wind']
df.loc[(year, 0), 'wind'] += df.loc[(year, skey), 'wind']
df.loc[(0, 0), 'wind'] += df.loc[(year, skey), 'wind']
df.loc[(year, skey), columns] = solar[key].sum()
df.loc[(0, skey), columns] += df.loc[(year, skey), columns]
df.loc[(year, 0), columns] += df.loc[(year, skey), columns]
df.loc[(0, 0), columns] += df.loc[(year, skey), columns]
solar.close()
wind.close()
df.loc[(year, 0), :] = (df.loc[(year, 0), :] / len(keys))
for key in keys:
df.loc[(0,
int(key[2:])), :] = df.loc[(0, int(key[2:])), :] / len(years)
df.loc[(0, 0), :] = df.loc[(0, 0), :] / (len(years) * len(keys))
df.sort_index(inplace=True)
df.to_csv(os.path.join(c.paths['analysis'], 'full_load_hours.csv'))
def test_default_ratio(self):
configuration = get_configuration()
configuration['plots']['histo1']['show-ratio'] = True
self.run_plotit(configuration)
self.compare_images(
os.path.join(self.output_folder.name, 'histo1.pdf'),
get_golden_file('default_configuration_ratio.pdf'))
def test_lines(self):
configuration = get_configuration()
configuration['configuration']['line-color'] = "#1685f2"
configuration['configuration']['line-width'] = 3
configuration['configuration']['line-type'] = 4
configuration['plots']['histo1']['vertical-lines'] = [3, 7]
configuration['plots']['histo1']['horizontal-lines'] = [80]
configuration['plots']['histo1']['lines'] = [
[[3, 20], [8, 90]],
[[5.5, float('nan')], [5.5, float('nan')]]
]
self.run_plotit(configuration)
self.compare_images(
os.path.join(self.output_folder.name, 'histo1.pdf'),
get_golden_file('default_configuration_lines.pdf')
)
configuration = get_configuration()
configuration['configuration']['line-color'] = "#1685f2"
configuration['configuration']['line-width'] = 3
configuration['configuration']['line-type'] = 4
configuration['plots']['histo1']['vertical-lines'] = [{
'line-color': '#ae4fea',
'line-width': 10,
'value': 3}, 7]
configuration['plots']['histo1']['horizontal-lines'] = [80]
configuration['plots']['histo1']['lines'] = [
[[3, 20], [8, 90]],
[[5.5, float('nan')], [5.5, float('nan')]]
]
self.run_plotit(configuration)
self.compare_images(
os.path.join(self.output_folder.name, 'histo1.pdf'),
get_golden_file('default_configuration_lines_inline_style.pdf')
)
def parse(inputFile, outputDir):
# getting configuration and BD.
db_handler = Japanese_DB_handler()
#config_data = _parseConf()
config_data = configuration.get_configuration()
if not config_data :
log.error("couldn't find get configuration data")
return
copyfile(inputFile, _generateFileName(config_data.input_files_bk, 'input'))
f = db_handler.base_format
existing_kanjis = db_handler.list(f.vocab, f.vocab.word)
potentialErrors = []
newEntriesList = []
# Parsing input file.
with open(inputFile, 'r') as fin:
for row in csv.reader(fin, delimiter=' '):
# usefull to just get half of the list
# but question are not necessrely before awnser
# we forced japanese as row[0]
word = row[0]
meaning = row[1]
prononciation = row[2] if row[2] else ''
exemple = ''
if word not in existing_kanjis :
newEntriesList.append(['','',word, prononciation, meaning, exemple])
else :
log.error('already exists : '+word)
nb_of_files = len(newEntriesList)//100
if len(newEntriesList)%100 != 0 :
nb_of_files += 1
outputDir += '/'
for nb in range(1, nb_of_files+1, 1):
fileName = _generateFileName(outputDir, "int", str(nb))
with open(fileName, 'w') as fout:
writer = csv.writer(fout, delimiter= ' ')
writer.writerow(['categorie','tag','word','prononciation','meaning','exemple'])
for entry in newEntriesList[100 * (nb - 1) : 100 * nb] :
writer.writerow(entry)
fileName = _generateFileName(outputDir, "int", '_pottentialErrors')
with open(fileName, 'w') as fout:
writer = csv.writer(fout, delimiter= ' ')
for error in potentialErrors:
writer.writerow(error)
log.error(error)
return
def main():
config = cfg.get_configuration()
sw = init(config)
_debug(sw.getGroups())
_debug(sw.getFriends())
grp_id = 19058988 # test group
_debug(sw.getExpenses(group_id=grp_id))
today = dt.date.today()
share_expense_with_group_members(sw, "test_date", 10, grp_id, today)
def test_blinded(self):
configuration = get_configuration()
configuration['plots']['histo1']['blinded-range'] = [3, 5.2]
self.run_plotit(configuration)
self.compare_images(
os.path.join(self.output_folder.name, 'histo1.pdf'),
get_golden_file('default_configuration_blinded_range.pdf'))
configuration = get_configuration()
configuration['plots']['histo1']['blinded-range'] = [3, 5.2]
configuration['plots']['histo1']['log-y'] = True
self.run_plotit(configuration)
self.compare_images(
os.path.join(self.output_folder.name, 'histo1_logy.pdf'),
get_golden_file('default_configuration_blinded_range_logy.pdf'))
def test_default_ratio(self):
configuration = get_configuration()
configuration['plots']['histo1']['show-ratio'] = True
self.run_plotit(configuration)
self.compare_images(
os.path.join(self.output_folder.name, 'histo1.pdf'),
get_golden_file('default_configuration_ratio.pdf')
)
def analyse_pv_orientation(year, key, module_name):
c = config.get_configuration()
weatherpath = os.path.join(c.paths['weather'], c.pattern['weather'])
weather = pd.read_hdf(weatherpath.format(year=year), 'A' + str(key))
latlon = pd.read_csv(os.path.join(c.paths['geometry'],
c.files['grid_centroid']),
index_col='gid').loc[key]
location = {'latitude': latlon['st_y'], 'longitude': latlon['st_x']}
weather = f.adapt_weather_to_pvlib(weather, location)
sandia_modules = pvlib.pvsystem.retrieve_sam('sandiamod')
sapm_inverters = pvlib.pvsystem.retrieve_sam('sandiainverter')
invertername = 'SMA_America__SB5000US_11_208V__CEC_2007_'
azimuth = range(0, 361, 10)
tilt = range(0, 91, 10)
# df_ts = pd.DataFrame()
df_dc = pd.DataFrame()
df_ac = pd.DataFrame()
df_sun = pd.DataFrame()
length = len(azimuth) * len(tilt)
# from matplotlib import pyplot as plt
for az in azimuth:
for tlt in tilt:
name = 'az{0}_tlt{1}'.format(az, tlt)
logging.info("{0}, {1}".format(name, length))
length -= 1
smodule = {
'module_parameters': sandia_modules[module_name],
'inverter_parameters': sapm_inverters[invertername],
'surface_azimuth': az,
'surface_tilt': tlt,
'albedo': 0.2
}
p_peak = (smodule['module_parameters'].Impo *
smodule['module_parameters'].Vmpo)
mc = f.feedin_pvlib_modelchain(location, smodule, weather)
df_dc.loc[az, tlt] = mc.dc.p_mp.clip(0).div(p_peak).sum()
df_ac.loc[az, tlt] = mc.ac.clip(0).div(p_peak).sum()
# print(mc.total_irrad.columns)
# print(mc.total_irrad['poa_global'].fillna(0).div(p_peak).sum())
df_sun.loc[az,
tlt] = mc.total_irrad['poa_global'].div(p_peak).sum()
# df_ts.to_csv(os.path.join(paths['analysis'], 'orientation_feedin.csv'))
df_sun.to_csv(os.path.join(c.paths['analysis'], 'sun.csv'))
df_dc.to_csv(os.path.join(c.paths['analysis'],
'orientation_feedin_dc.csv'))
df_ac.to_csv(os.path.join(c.paths['analysis'],
'orientation_feedin_ac.csv'))
def analyse_optimal_orientation_file():
c = config.get_configuration()
df = pd.read_csv(os.path.join(c.paths['analysis'],
'optimal_orientation_multi.csv'),
index_col=[0, 1, 2],
header=[0, 1])
df.sort_index(axis=0, inplace=True)
df.sort_index(axis=1, inplace=True)
df['avg', 'azimuth'] = df.loc[:, (slice(None), 'azimuth')].sum(1).div(3)
df['avg', 'tilt'] = df.loc[:, (slice(None), 'tilt')].sum(1).div(3)
print(df.index)
print(df['avg'].groupby('year').mean())
def something():
c = config.get_configuration()
cap = pd.read_csv(os.path.join(c.paths['analysis'], 'pv_data.csv'),
header=1,
index_col='year')
print(cap.columns)
cap['inst_mean'] = cap.inst - (cap.inst - cap.inst.shift(1)) / 2
cap['diff'] = cap.inst - cap.inst.shift(1)
cap['VLSt'] = (cap.inst_mean / cap.erzeug) * 1000
cap['factor'] = cap['VLSt'] / cap['mean']
print(cap)
print(cap.sum() / 5)
def __init__(self):
self.log = getLogger()
self.log.msg('Starting up server.')
self.web = None
self.log.msg('Started web server.')
self.aggregator = Aggregator()
self.log.msg('Started Aggregator.')
self.network = None
self.log.msg('Gathering configuration')
self.config = get_configuration()
self.log.msg('Gathered configuration')
self.reactor = None
def test_lines(self):
configuration = get_configuration()
configuration['configuration']['line-color'] = "#1685f2"
configuration['configuration']['line-width'] = 3
configuration['configuration']['line-type'] = 4
configuration['plots']['histo1']['vertical-lines'] = [3, 7]
configuration['plots']['histo1']['horizontal-lines'] = [80]
configuration['plots']['histo1']['lines'] = [[[3, 20], [8, 90]],
[[5.5, float('nan')],
[5.5, float('nan')]]]
self.run_plotit(configuration)
self.compare_images(
os.path.join(self.output_folder.name, 'histo1.pdf'),
get_golden_file('default_configuration_lines.pdf'))
configuration = get_configuration()
configuration['configuration']['line-color'] = "#1685f2"
configuration['configuration']['line-width'] = 3
configuration['configuration']['line-type'] = 4
configuration['plots']['histo1']['vertical-lines'] = [{
'line-color': '#ae4fea',
'line-width': 10,
'value': 3
}, 7]
configuration['plots']['histo1']['horizontal-lines'] = [80]
configuration['plots']['histo1']['lines'] = [[[3, 20], [8, 90]],
[[5.5, float('nan')],
[5.5, float('nan')]]]
self.run_plotit(configuration)
self.compare_images(
os.path.join(self.output_folder.name, 'histo1.pdf'),
get_golden_file('default_configuration_lines_inline_style.pdf'))
def test_blinded(self):
configuration = get_configuration()
configuration['plots']['histo1']['blinded-range'] = [3, 5.2]
self.run_plotit(configuration)
self.compare_images(
os.path.join(self.output_folder.name, 'histo1.pdf'),
get_golden_file('default_configuration_blinded_range.pdf')
)
configuration = get_configuration()
configuration['plots']['histo1']['blinded-range'] = [3, 5.2]
configuration['plots']['histo1']['log-y'] = True
self.run_plotit(configuration)
self.compare_images(
os.path.join(self.output_folder.name, 'histo1_logy.pdf'),
get_golden_file('default_configuration_blinded_range_logy.pdf')
)
def main(_):
assert FLAGS.model_type in _MODEL_LIST, 'Invalid model specified.'
if not tf.gfile.Exists(FLAGS.train_log_dir):
tf.gfile.MakeDirs(FLAGS.train_log_dir)
config = configuration.get_configuration()
config.batch_size = FLAGS.batch_size
training_config = configuration.TrainingConfig()
g = tf.Graph()
with g.as_default():
# If ps_tasks is zero, the local device is used. When using multiple
# (non-local) replicas, the ReplicaDeviceSetter distributes the variables
# across the different devices.
if FLAGS.model_type == 'multi':
vae = convolutional_multi_vae.ConvolutionalMultiVae(
config, mode='train', split_name='train')
elif FLAGS.model_type == 'single' or FLAGS.model_type == 'kronecker':
raise NotImplementedError("%s not implemented" %
(FLAGS.model_type))
vae.build_model()
optimizer = training_config.optimizer(training_config.learning_rate)
tf.losses.add_loss(vae.loss)
total_loss = tf.losses.get_total_loss()
# Set up training.
train_op = slim.learning.create_train_op(total_loss,
optimizer,
check_numerics=False)
saver = vae.setup_saver()
if config.loss_type == 'fwkl':
init_fn = vae.get_forward_kl_init_fn(FLAGS.fwkl_init_dir)
else:
init_fn = None
# Run training.
slim.learning.train(train_op=train_op,
init_fn=init_fn,
logdir=FLAGS.train_log_dir,
graph=g,
number_of_steps=FLAGS.max_number_of_steps,
save_summaries_secs=FLAGS.save_summaries_secs,
save_interval_secs=FLAGS.save_interval_secs,
saver=saver)
def test_eras(self):
configuration = get_configuration()
configuration['files']['MC_sample1.root']['era'] = "1"
configuration['files']['MC_sample1.root']['cross-section'] *= 1.5
configuration['files']['data.root']['era'] = "1"
configuration['files']['MC_sample2.root']['era'] = "2"
configuration['files']['MC_sample2.root']['cross-section'] *= 3.
configuration['configuration']['eras'] = ["1", "2"]
configuration['configuration']['luminosity'] = {"1": 0.67, "2": 0.33}
self.run_plotit(configuration)
self.compare_images(
os.path.join(self.output_folder.name, 'histo1.pdf'),
get_golden_file('default_configuration_eras.pdf'))
def main():
# Setup and get current configuration
config = get_configuration()
# Print parameters
print_configuration()
# Perform preprocessing
preprocess = Preprocessing(config=config)
train_input_encoder, train_input_decoder, \
test_input_encoder, test_input_decoder, = preprocess.prepare_data()
# Initialize model class - train or infer: select mode
Seq2seq(config,
train_input_encoder,
train_input_decoder,
test_input_encoder,
test_input_decoder,
preprocess.dict_vocab_reverse,
mode=None)
def bounce_for_authentication(request):
session = request.getSession()
auth = IFacebookAuth(session)
auth.state = str(uuid.uuid4())
query_string = {
'client_id': get_configuration()['appId'],
'redirect_uri': 'http://imbored.davidstrauss.net/bounce/',
'scope': ','.join(['user_about_me', 'user_birthday', 'email', 'friends_about_me', 'friends_birthday', 'friends_location', 'user_location']),
'state': auth.state
}
encoded = urllib.urlencode(query_string)
uri = 'https://www.facebook.com/dialog/oauth?{0}'.format(encoded)
request.redirect(uri)
def test_multi_stacks(self):
configuration = get_configuration()
# Remove data
del configuration['files']['data.root']
configuration['files']['MC_sample1.root']['stack-index'] = 0
configuration['files']['MC_sample2.root']['stack-index'] = 1
self.run_plotit(configuration)
self.compare_images(
os.path.join(self.output_folder.name, 'histo1.pdf'),
get_golden_file('default_configuration_multi_stacks.pdf'))
configuration['files']['MC_sample1.root']['stack-index'] = 1
configuration['files']['MC_sample2.root']['stack-index'] = 0
self.run_plotit(configuration)
self.compare_images(
os.path.join(self.output_folder.name, 'histo1.pdf'),
get_golden_file(
'default_configuration_multi_stacks_reverse_ordering.pdf'))
configuration['files']['MC_sample1.root']['stack-index'] = 0
configuration['files']['MC_sample2.root']['stack-index'] = 1
configuration['files']['MC_sample1.root']['fill-type'] = 0
configuration['files']['MC_sample1.root']['line-type'] = 1
configuration['files']['MC_sample1.root']['line-width'] = 2
configuration['files']['MC_sample1.root']['legend-style'] = 'l'
configuration['files']['MC_sample2.root']['fill-type'] = 0
configuration['files']['MC_sample2.root']['line-type'] = 1
configuration['files']['MC_sample2.root']['line-width'] = 2
configuration['files']['MC_sample2.root']['legend-style'] = 'l'
self.run_plotit(configuration)
self.compare_images(
os.path.join(self.output_folder.name, 'histo1.pdf'),
get_golden_file(
'default_configuration_multi_stacks_line_type.pdf'))
def analyse_pv_types(year, key, orientation):
c = config.get_configuration()
weatherpath = os.path.join(c.paths['weather'], c.pattern['weather'])
weather = pd.read_hdf(weatherpath.format(year=year), 'A' + str(key))
latlon = pd.read_csv(os.path.join(c.paths['geometry'],
c.files['grid_centroid']),
index_col='gid').loc[key]
location = {'latitude': latlon['st_y'], 'longitude': latlon['st_x']}
weather = f.adapt_weather_to_pvlib(weather, location)
sandia_modules = pvlib.pvsystem.retrieve_sam('sandiamod')
sapm_inverters = pvlib.pvsystem.retrieve_sam('sandiainverter')
invertername = 'ABB__MICRO_0_25_I_OUTD_US_208_208V__CEC_2014_'
for modu in sandia_modules.keys():
if 'BP_Solar' in modu:
print(modu)
exit(0)
df_ts_ac = pd.DataFrame()
df = pd.DataFrame()
length = len(sandia_modules.keys())
for smod in sandia_modules.keys():
name = smod # .replace('__', '_')
logging.info("{0}, {1}".format(name, length))
length -= 1
smodule = {
'module_parameters': sandia_modules[smod],
'inverter_parameters': sapm_inverters[invertername],
'surface_azimuth': orientation['azimuth'],
'surface_tilt': orientation['tilt'],
'albedo': 0.2
}
p_peak = (smodule['module_parameters'].Impo *
smodule['module_parameters'].Vmpo)
mc = f.feedin_pvlib_modelchain(location, smodule, weather)
df_ts_ac[name] = mc.ac.clip(0).fillna(0).div(p_peak)
df.loc[name, 'ac'] = df_ts_ac[name][:8760].sum()
df.loc[name, 'dc_norm'] = mc.dc.p_mp.clip(0).div(p_peak).sum()
df.loc[name, 'dc'] = mc.dc.p_mp.clip(0).sum()
df.to_csv(os.path.join(c.paths['analysis'], 'module_feedin.csv'))
df_ts_ac.to_csv(
os.path.join(c.paths['analysis'], 'module_feedin_ac_ts.csv'))
def main() -> None:
load_dotenv()
config = get_configuration(sys.argv[1:])
logger, error_tracker = _configure_logging(config.log_level)
logger.info("Starting iNaturalist project data extractor.")
logger.info(f"Configuration: {config}")
file_path = _run(config)
if (config.input_file):
merge_bulk_and_api_files(config, file_path)
logger.info("Finished with data extraction.")
if error_tracker.fired:
print(
"There was an error, please carefully review the log details above."
)
sys.exit(1)
def get_maximum_value(filename, pathname=None, icol=None):
if pathname is None:
c = config.get_configuration()
pathname = c.paths['analysis']
if icol is None:
icol = [0]
table = pd.read_csv(os.path.join(pathname, filename), index_col=icol)
idx = None
column = None
if isinstance(table, pd.Series):
max_value = table.max()
idx = table[table == max_value].index[0]
elif isinstance(table, pd.DataFrame):
max_value = table.max().max()
for col in table:
try:
idx = table[col][table[col] == max_value].index[0]
column = col
except IndexError:
pass
print(column, idx)
def analyse_inverter(year, key, module_name, orientation):
c = config.get_configuration()
weatherpath = os.path.join(c.paths['weather'], c.pattern['weather'])
weather = pd.read_hdf(weatherpath.format(year=year), 'A' + str(key))
latlon = pd.read_csv(os.path.join(c.paths['geometry'],
c.files['grid_centroid']),
index_col='gid').loc[key]
location = {'latitude': latlon['st_y'], 'longitude': latlon['st_x']}
weather = f.adapt_weather_to_pvlib(weather, location)
sandia_modules = pvlib.pvsystem.retrieve_sam('sandiamod')
sapm_inverters = pvlib.pvsystem.retrieve_sam('sandiainverter')
inv = pd.DataFrame()
failed = pd.Series()
length = len(sapm_inverters.keys())
for sinv in sapm_inverters.keys():
name = sinv # .replace('__', '_')
logging.info("{0}, {1}".format(name, length))
length -= 1
smodule = {
'module_parameters': sandia_modules[module_name],
'inverter_parameters': sapm_inverters[sinv],
'surface_azimuth': orientation['azimuth'],
'surface_tilt': orientation['tilt'],
'albedo': 0.2
}
p_peak = (smodule['module_parameters'].Impo *
smodule['module_parameters'].Vmpo)
try:
mc = f.feedin_pvlib_modelchain(location, smodule, weather)
inv.loc[name, 'ac'] = mc.ac.clip(0).fillna(0).div(p_peak).sum()
inv.loc[name,
'dc'] = mc.dc.p_mp.clip(0).fillna(0).div(p_peak).sum()
except ValueError:
logging.info("Inverter {0} failed.".format(name))
failed.loc[name] = 'failed'
inv.to_csv(
os.path.join(c.paths['analysis'], 'sapm_inverters_feedin_full2.csv'))
failed.to_csv(
os.path.join(c.paths['analysis'], 'sapm_inverters_failed.csv'))
""" Postfix fabric commands. :copyright: (c) 2014 by Alexander Skiba <*****@*****.**> :licence: MIT :bugreports: [email protected] """ import configuration as config from fabric.api import task, run from os.path import splitext, basename # module configuration ######################################################## MODULE = splitext(basename(__file__))[0] CONFIGURATION = config.get_configuration(MODULE) # tasks ####################################################################### @task def renew_aliases(): """(POSTFIX): Read in the aliases file.""" command = "newaliases" run(command)
self.data = r.data
else:
print(f"Error when requesting {url}")
def find(self, regex):
if not regex:
return None
source = self.data.decode('utf8')
matches = re.findall(regex, source)
return matches
def save(self, folder, filename):
os.makedirs(folder, exist_ok=True)
filename = filename.replace(" ", "_")
filename = filename.replace(":", "-")
with open(os.path.join(folder, filename), 'w', encoding="utf") as f:
f.write(self.data.decode('utf8'))
if __name__ == "__main__":
c = cfg.get_configuration()
s = Scraper()
for i in c.scraper.items:
if not i.enabled:
continue
print(i.name)
s.refresh(i.url)
print(s.find(i.sold_out_regex))
print(s.find(i.price_regex))
s.save(c.notifier.output_path, f"test_{i.name}.html")
# -*- coding: utf-8 -*-
from flask import request, Flask, jsonify
from flask_api import status
from DataAugmentation import DataAugmentation
import configuration
import re
import content_parser
app = Flask(__name__)
config = configuration.get_configuration()
system = DataAugmentation(coreNLPDirectory=config['CORE_NLP_DIR'],
port=config['CORE_NLP_PORT'],
language=config['CORE_NLP_LANG'],
glawiBz2FilePath=config['GLAWI_BZ2'],
glawiPklFilePath=config['GLAWI_PKL'],
lexiconsDirectory=config['LEXICON_DIR'],
spacyModelPath=config['SPACY_MODEL'])
password = config['DATA_AUGMENTATION_PASSWORD']
@app.route("/dafromtext", methods=['POST'])
def data_augmentation_from_text():
if request.method == 'POST':
sentence = request.form['text']
limit = request.form.get('limit')
if sentence == None:
import logging
import constant
import discord
import configuration
from discord.ext import commands
from datetime import datetime
from discord_slash import SlashCommand
DISCORD_COMMANDS_FILES = [
'msqbot.discord_api.commands.commands_meter',
'msqbot.discord_api.commands.commands_movie_time'
]
credentials = configuration.get_configuration('discord')
intents = discord.Intents.all()
bot = commands.Bot(command_prefix=None, intents=intents)
slash = SlashCommand(bot, sync_commands=True, sync_on_cog_reload=True)
def create_log_file():
logfile = 'log_{}.log'.format(str(datetime.now().strftime("%m%d%Y%H%M%S")))
logging.basicConfig(level=logging.DEBUG,
handlers=[logging.FileHandler(constant.LOG_FILE + logfile, 'w', 'utf-8')],
format='%(asctime)s:%(levelname)s:%(name)s: %(message)s')
logger = logging.getLogger('discord')
logger.setLevel(logging.DEBUG)
handler = logging.FileHandler(filename='discord.log', encoding='utf-8', mode='w')
handler.setFormatter(logging.Formatter('%(asctime)s:%(levelname)s:%(name)s: %(message)s'))
logger.addHandler(handler)
def test_systematics(self):
configuration = get_configuration()
configuration['configuration']['luminosity-error'] = 0.4
self.run_plotit(configuration)
self.compare_images(
os.path.join(self.output_folder.name, 'histo1.pdf'),
get_golden_file('default_configuration_lumi_error_0p4.pdf')
)
configuration = get_configuration()
configuration['configuration']['luminosity-error'] = 0.
configuration['systematics'] = []
configuration['systematics'] += [{'syst1': 1.4}]
self.run_plotit(configuration)
self.compare_images(
os.path.join(self.output_folder.name, 'histo1.pdf'),
get_golden_file('default_configuration_one_syst_0p4.pdf')
)
configuration = get_configuration()
configuration['configuration']['luminosity-error'] = 0.
configuration['systematics'] = []
configuration['systematics'] += [{'syst1': 1.2}]
configuration['systematics'] += [{'syst2': {'type': 'ln', 'prior': 1.2}}]
self.run_plotit(configuration)
self.compare_images(
os.path.join(self.output_folder.name, 'histo1.pdf'),
get_golden_file('default_configuration_two_systs_0p28.pdf')
)
configuration = get_configuration()
configuration['configuration']['luminosity-error'] = 0.
configuration['systematics'] = ['alpha', 'beta']
self.run_plotit(configuration)
self.compare_images(
os.path.join(self.output_folder.name, 'histo1.pdf'),
get_golden_file('default_configuration_two_systs_shape.pdf')
)
configuration = get_configuration()
configuration['configuration']['luminosity-error'] = 0.
configuration['systematics'] = ['i-do-not-exist']
self.run_plotit(configuration)
self.compare_images(
os.path.join(self.output_folder.name, 'histo1.pdf'),
get_golden_file('default_configuration_syst_not_found.pdf')
)
def test_systematics(self):
configuration = get_configuration()
configuration['configuration']['luminosity-error'] = 0.4
self.run_plotit(configuration)
self.compare_images(
os.path.join(self.output_folder.name, 'histo1.pdf'),
get_golden_file('default_configuration_lumi_error_0p4.pdf'))
configuration = get_configuration()
configuration['configuration']['luminosity-error'] = 0.
configuration['systematics'] = []
configuration['systematics'] += [{'syst1': 1.4}]
self.run_plotit(configuration)
self.compare_images(
os.path.join(self.output_folder.name, 'histo1.pdf'),
get_golden_file('default_configuration_one_syst_0p4.pdf'))
configuration = get_configuration()
configuration['configuration']['luminosity-error'] = 0.
configuration['systematics'] = []
configuration['systematics'] += [{'syst1': 1.2}]
configuration['systematics'] += [{
'syst2': {
'type': 'ln',
'prior': 1.2
}
}]
self.run_plotit(configuration)
self.compare_images(
os.path.join(self.output_folder.name, 'histo1.pdf'),
get_golden_file('default_configuration_two_systs_0p28.pdf'))
configuration = get_configuration()
configuration['configuration']['luminosity-error'] = 0.
configuration['systematics'] = ['alpha', 'beta']
self.run_plotit(configuration)
self.compare_images(
os.path.join(self.output_folder.name, 'histo1.pdf'),
get_golden_file('default_configuration_two_systs_shape.pdf'))
configuration = get_configuration()
configuration['configuration']['luminosity-error'] = 0.
configuration['systematics'] = ['i-do-not-exist']
self.run_plotit(configuration)
self.compare_images(
os.path.join(self.output_folder.name, 'histo1.pdf'),
get_golden_file('default_configuration_syst_not_found.pdf'))
"--dataset",
dest="dataset",
help="the name of the azure blob",
default=None)
return parser.parse_args()
params = parse_arguments()
if (not os.path.isfile(params.input_file_name)):
print("File: {} does not exist".format(params.input_file_name))
exit()
params.dataset = params.dataset if params.dataset != None else params.input_file_name
# initialize dev configuration environment
config = configuration.get_configuration("dev")
# initlaize the data manager
dm = DataManager(config)
# upoad the file
dm.save_data(params.data_source_type, None, params.input_file_name,
params.dataset)
print("The file: {} was uploaded to the blob: {}".format(
params.input_file_name, params.dataset))
print("The storage account is: {} {}".format(config.storage_account,
config.data_container_name))
for fuel in tpp.index.get_level_values(0).unique():
for year in src.index:
try:
df = tpp.loc[fuel, year]
idx = df.efficiency.notnull()
w_avg = np.average(df[idx].efficiency, weights=df[idx].capacity)
limit = repp.loc[year, (fuel, 'energy')] / w_avg
src.loc[year, (fuel, 'limit')] = limit
except KeyError:
src.loc[year, (fuel, 'limit')] = float('inf')
return src
def prepare_commodity_sources(c):
logging.info("Commodity Sources.")
commodity_sources = initialise_commodity_sources()
commodity_sources = prices_from_bmwi_energiedaten(cfg, commodity_sources)
commodity_sources = emissions_from_znes(cfg, commodity_sources)
commodity_sources = prices_2014_from_znes(cfg, commodity_sources)
commodity_sources = set_limit_by_energy_production(cfg, commodity_sources)
commodity_sources.sort_index(1, inplace=True)
commodity_sources.to_csv(os.path.join(c.paths['commodity'],
c.files['commodity_sources']))
if __name__ == "__main__":
logger.define_logging()
cfg = config.get_configuration()
logging.info("Commodity Sources.")
prepare_commodity_sources(cfg)
import csv
from japanese.Japanese_DB_handler import Japanese_DB_handler
import configuration as configuration
logging.basicConfig()
log = logging.getLogger()
log.setLevel(logging.DEBUG)
output_dir = sys.argv[1]
if not os.path.exists(output_dir):
log.error('directory not found : ' + str(output_dir))
sys.exit(1)
jpDB = Japanese_DB_handler()
f = jpDB.base_format
config_data = configuration.get_configuration()
if not config_data:
log.error("couldn't find get configuration data")
sys.exit(1)
cat_dir = {}
tag_dir = {}
cat_list = jpDB.list_categorie_by_usage()
tag_list = jpDB.list_tag_by_usage()
for cat, _ in cat_list:
if not cat: continue
cat_dir[cat] = [
data[0]
