def calculate(process_name, tasks, results):
print('[%s] evaluation routine starts' % process_name)
while True:
new_value = tasks.get()
if new_value == "None":
print('[%s] evaluation routine quits' % process_name)
# Indicate finished
results.put(-1)
break
else:
# Initialise icestupa object
location = new_value
icestupa = Icestupa(location)
# Derive all the input parameters
icestupa.derive_parameters()
# icestupa.read_input()
# Generate results
icestupa.melt_freeze()
# Summarise and save model results
icestupa.save()
# Read Output
# icestupa.read_output()
# Create figures for web interface
icestupa.summary_figures()
# Compute result and mimic a long-running task
compute = icestupa.df.iceV.max()
# Output which process received the value
print('[%s] received value: %s' % (process_name, new_value))
print('[%s] calculated max ice volume: %.1f' %
(process_name, compute))
# Add result to the queue
results.put(compute)
return
# level=logging.WARNING,
level=logging.INFO,
logger=logger,
)
answers = dict(
# location="Schwarzsee 2019",
location="Guttannen 2021",
# location="Gangles 2021",
run="yes",
# run="no",
)
# Initialise icestupa object
# icestupa = Icestupa(answers["location"], params='best')
icestupa = Icestupa(answers["location"])
if answers["run"] == "yes":
# Derive all the input parameters
icestupa.derive_parameters()
# Generate results
icestupa.melt_freeze(test=True)
# icestupa.melt_freeze()
# Summarise and save model results
icestupa.save()
# Create figures for web interface
icestupa.summary_figures()
else:
CB91_Blue = "#2CBDFE"
CB91_Green = "#47DBCD"
CB91_Pink = "#F3A0F2"
CB91_Purple = "#9D2EC5"
CB91_Violet = "#661D98"
CB91_Amber = "#F5B14C"
# index = pd.date_range(start ='1-1-2022',
# end ='1-1-2024', freq ='D', name= "When")
# df_out = pd.DataFrame(columns=locations,index=index)
fig, ax = plt.subplots(len(locations), 1, sharex='col')
for i, location in enumerate(locations):
SITE, FOLDER = config(location)
icestupa = Icestupa(location)
icestupa.read_output()
icestupa.self_attributes()
variance = []
mean = []
evaluations = []
data = un.Data()
# filename1 = FOLDER['sim']+ "SE_full.h5"
filename1 = FOLDER['sim'] + "full.h5"
filename2 = FOLDER['sim'] + "fountain.h5"
# filename1 = FOLDER['sim']+ "efficiency.h5"
# print(data)
if location == 'schwarzsee19':
sys.path.append(
os.path.dirname(
os.path.dirname(os.path.dirname(os.path.realpath(__file__)))))
from src.utils.settings import config
from src.models.methods.metadata import get_parameter_metadata
from src.models.icestupaClass import Icestupa
if __name__ == "__main__":
locations = ["gangles21", "guttannen21"]
# locations = ['guttannen21', 'gangles21']
# locations = ['guttannen21']
for ctr, location in enumerate(locations):
SITE, FOLDER = config(location)
icestupa = Icestupa(location)
icestupa.read_output()
print(icestupa.df.loc[icestupa.df.Discharge > 0, 'Discharge'].count())
print(icestupa.df.loc[icestupa.df.fountain_froze >= SITE['D_F'] * 60,
'fountain_runoff'].count())
icestupa.df.loc[icestupa.df.Qfreeze == 0, 'Qfreeze'] = np.nan
icestupa.df.loc[icestupa.df.Qmelt == 0, 'Qmelt'] = np.nan
icestupa.df.loc[icestupa.df.Discharge == 0, 'fountain_froze'] = np.nan
icestupa.df['melted'] /= 60
icestupa.df['fountain_froze'] /= 60
print(icestupa.df.fountain_froze.max())
icestupa.df = icestupa.df.rename(
{
# params = ['IE', 'A_I', 'Z', 'SA_corr', 'DX']
# params = ['DX', 'SA_corr']
params = ['Z', 'SA_corr', 'DX']
kind = ['volume', 'area']
sns.set(style="darkgrid")
fig, ax = plt.subplots(nrows=len(kind),
ncols=len(params),
sharey="row",
figsize=(18, 8))
for obj in kind:
# Creating an empty Dataframe with column names only
dfx = pd.DataFrame(columns=params)
for ctr, location in enumerate(locations):
icestupa = Icestupa(location)
SITE, FOLDER = config(location)
icestupa.read_output()
file_path = 'cv-' + obj + '-'
file_path += '-'.join('{}'.format(key) for key in params)
df = pd.read_csv(FOLDER['sim'] + file_path)
df = df.set_index('rmse').sort_index().reset_index()
df['params'] = df['params'].apply(literal_eval)
num_selected = int(0.1 * df.shape[0])
num_total = df.shape[0]
print()
print("\tObjective %s Site %s" % (obj, location))
print("\tSelected %s out of %s" % (num_selected, num_total))
sys.path.append(
os.path.dirname(
os.path.dirname(os.path.dirname(os.path.realpath(__file__)))))
from src.utils.settings import config
from src.models.methods.metadata import get_parameter_metadata
from src.models.icestupaClass import Icestupa
if __name__ == "__main__":
# locations = ["gangles21", "guttannen21"]
locations = ["gangles21"]
fig, ax = plt.subplots()
for ctr, location in enumerate(locations):
SITE, FOLDER = config(location)
icestupa = Icestupa(location)
icestupa.read_output()
icestupa.df = icestupa.df[:icestupa.last_hour]
df = icestupa.df[[
"Qs",
"Ql",
"SW",
"LW",
"Qf",
"Qg",
"SA",
"T_a",
"v_a",
"RH",
"Discharge",
from src.utils.settings import config
if __name__ == "__main__":
# Main logger
logger = logging.getLogger(__name__)
coloredlogs.install(
fmt="%(funcName)s %(levelname)s %(message)s",
level=logging.WARNING,
# level=logging.INFO,
logger=logger,
)
location = 'schwarzsee19'
# Initialise icestupa object
icestupa = Icestupa(location)
# Derive all the input parameters
icestupa.derive_parameters()
with cProfile.Profile() as pr:
# Generate results
icestupa.melt_freeze()
# # Summarise and save model results
# icestupa.summary()
# # Create figures for web interface
# icestupa.summary_figures()
stats = pstats.Stats(pr)
stats.sort_stats(pstats.SortKey.TIME)
stats.print_stats("get_temp")
]
zip_iterator = zip(names, names_label)
param_dictionary = dict(zip_iterator)
evaluations = []
percent_change = []
efficiency_change = []
site = []
param = []
result = []
freeze_rate = []
melt_rate = []
fig, ax = plt.subplots()
for location in locations:
SITE, FOLDER = config(location)
icestupa = Icestupa(location)
icestupa.read_output()
icestupa.self_attributes()
feature_name = "efficiency"
icestupa.se = (icestupa.M_water +
icestupa.M_ice) / icestupa.M_input * 100
for j in range(0, icestupa.df.shape[0]):
if icestupa.df.loc[j, "fountain_froze"] != 0:
freeze_rate.append([
get_parameter_metadata(location)["shortname"],
j,
icestupa.df.loc[j, "fountain_froze"] / 60,
])
if icestupa.df.loc[j, "melted"] != 0:
location = st.sidebar.radio(
"built at",
# ( "Guttannen 2021","Gangles 2021", "Diavolezza 2021","Guttannen 2020", "Schwarzsee 2019"),
# ("Guttannen 2021", "Gangles 2021", "Guttannen 2020", "Schwarzsee 2019"),
# ("Guttannen 2021", "Gangles 2021", "Guttannen 2020", "Schwarzsee 2019"),
("Gangles 2021", "Guttannen 2021", "Guttannen 2020"),
# ("Guttannen 2021", "Guttannen 2020", "Schwarzsee 2019"),
)
# location = "Gangles 2021"
# trigger = "Manual"
SITE, FOLDER = config(location)
icestupa = Icestupa(location)
icestupa.read_output()
icestupa.self_attributes()
df_in = icestupa.df
(
input_cols,
input_vars,
output_cols,
output_vars,
derived_cols,
derived_vars,
) = vars(df_in)
# df_in = df_in[df_in.columns.drop(list(df_in.filter(regex="Unnamed")))]
# df_in = df_in.set_index("When")
fmt="%(funcName)s %(levelname)s %(message)s",
level=logging.ERROR,
logger=logger,
)
answers = dict(
# location="Schwarzsee 2019",
location="Guttannen 2021",
# location="Gangles 2021",
trigger="Manual",
)
# Get settings for given location and trigger
SITE, FOUNTAIN, FOLDER, *args = config(answers["location"])
# Initialise icestupa object
icestupa = Icestupa(answers["location"])
cmap = plt.cm.rainbow # define the colormap
norm = mpl.colors.Normalize(vmin=-100, vmax=0)
sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
sm.set_array([])
filename = FOLDER["sim"] + "/DX_sim.csv"
filename2 = FOLDER["sim"] + "/DX_sim.h5"
figures = FOLDER["sim"] + "/DX_sim.pdf"
filename = FOLDER["sim"] + "/Tune_sim.csv"
figures = FOLDER["sim"] + "/Tune_sim.pdf"
df = pd.read_csv(filename, sep=",")
logger.info(df)
fmt="%(funcName)s %(levelname)s %(message)s",
# level=logging.WARNING,
level=logging.INFO,
logger=logger,
)
# locations = ['gangles21', 'guttannen21', 'guttannen20']
locations = ['gangles21', 'guttannen21']
fig, ax = plt.subplots()
custom_colors = sns.color_palette("Set1", len(locations))
for i, location in enumerate(locations):
SITE, FOLDER = config(location)
icestupa = Icestupa(location)
icestupa.self_attributes()
icestupa.read_output()
# icestupa.df = icestupa.df[1:-1]
icestupa.df = icestupa.df[1:icestupa.last_hour - 1]
df_c = pd.read_csv(
FOLDER["raw"] + location + "_drone.csv",
sep=",",
header=0,
parse_dates=["When"],
)
# a_pred = []
# a_true = df_c.Area.values