diff --git a/experiment/meteo_france_SCM_study/abstract_study.py b/experiment/meteo_france_SCM_study/abstract_study.py
index 6da9988178d75e275ab0cf05a02e085b87b24816..56b61fc83959a4d62b48226db48fc857ee832c6f 100644
--- a/experiment/meteo_france_SCM_study/abstract_study.py
+++ b/experiment/meteo_france_SCM_study/abstract_study.py
@@ -13,7 +13,7 @@ from PIL import ImageDraw
from netCDF4 import Dataset
from experiment.meteo_france_SCM_study.abstract_variable import AbstractVariable
-from experiment.meteo_france_SCM_study.altitude import ALTITUDES, ZS_INT_23, ZS_INT_MASK
+from experiment.meteo_france_SCM_study.scm_constants import ALTITUDES, ZS_INT_23, ZS_INT_MASK, LONGITUDES, LATITUDES
from experiment.meteo_france_SCM_study.visualization.utils import get_km_formatter
from extreme_estimator.extreme_models.margin_model.margin_function.abstract_margin_function import \
AbstractMarginFunction
@@ -173,12 +173,14 @@ class AbstractStudy(object):
@property
def df_massifs_longitude_and_latitude(self) -> pd.DataFrame:
# DataFrame object that represents the massif coordinates in degrees extracted from the SCM data
- any_ordered_dict = list(self.year_to_dataset_ordered_dict.values())[0]
- longitude = np.array(any_ordered_dict.variables['longitude'])[self.altitude_mask]
- latitude = np.array(any_ordered_dict.variables['latitude'])[self.altitude_mask]
+ # any_ordered_dict = list(self.year_to_dataset_ordered_dict.values())[0]
+ # longitude = np.array(any_ordered_dict.variables['longitude'])
+ # latitude = np.array(any_ordered_dict.variables['latitude'])
+ longitude = np.array(LONGITUDES)
+ latitude = np.array(LATITUDES)
index = self.altitude_to_massif_names[self.altitude]
columns = [AbstractSpatialCoordinates.COORDINATE_X, AbstractSpatialCoordinates.COORDINATE_Y]
- data = dict(zip(columns, [longitude, latitude]))
+ data = dict(zip(columns, [longitude[self.altitude_mask], latitude[self.altitude_mask]]))
return pd.DataFrame(data=data, index=index, columns=columns)
def load_df_centroid(self) -> pd.DataFrame:
diff --git a/experiment/meteo_france_SCM_study/altitude.py b/experiment/meteo_france_SCM_study/altitude.py
deleted file mode 100644
index 3b78a5babd478dff13554ac2d861d162767f1c4e..0000000000000000000000000000000000000000
--- a/experiment/meteo_france_SCM_study/altitude.py
+++ /dev/null
@@ -1,44 +0,0 @@
-
-"""
-ZS was extracted from a netcdf file
-"""
-import numpy as np
-
-ZS = """[ 300. 600. 900. 1200. 1500. 1800. 2100. 2400. 2700. 3000. 3300. 300.
-600. 900. 1200. 1500. 1800. 2100. 2400. 2700. 3000. 300. 600. 900.
-1200. 1500. 1800. 2100. 2400. 2700. 3000. 3300. 3600. 3900. 4200. 4500.
-4800. 0. 300. 600. 900. 1200. 1500. 1800. 2100. 2400. 300. 600.
-900. 1200. 1500. 1800. 2100. 2400. 2700. 3000. 600. 900. 1200. 1500.
-1800. 2100. 2400. 2700. 3000. 3300. 3600. 3900. 0. 300. 600. 900.
-1200. 1500. 1800. 2100. 0. 300. 600. 900. 1200. 1500. 1800. 2100.
-2400. 2700. 3000. 0. 300. 600. 900. 1200. 1500. 1800. 2100. 2400.
-2700. 3000. 3300. 300. 600. 900. 1200. 1500. 1800. 2100. 2400. 2700.
-3000. 3300. 3600. 3900. 900. 1200. 1500. 1800. 2100. 2400. 2700. 3000.
-3300. 3600. 3900. 600. 900. 1200. 1500. 1800. 2100. 2400. 2700. 3000.
-3300. 3600. 900. 1200. 1500. 1800. 2100. 2400. 2700. 3000. 3300. 0.
-300. 600. 900. 1200. 1500. 1800. 2100. 2400. 0. 300. 600. 900.
-1200. 1500. 1800. 2100. 2400. 2700. 3000. 3300. 3600. 3900. 4200. 900.
-1200. 1500. 1800. 2100. 2400. 2700. 3000. 3300. 3600. 3900. 4200. 900.
-1200. 1500. 1800. 2100. 2400. 2700. 3000. 3300. 300. 600. 900. 1200.
-1500. 1800. 2100. 2400. 2700. 3000. 600. 900. 1200. 1500. 1800. 2100.
-2400. 2700. 3000. 3300. 3600. 600. 900. 1200. 1500. 1800. 2100. 2400.
-2700. 3000. 3300. 600. 900. 1200. 1500. 1800. 2100. 2400. 2700. 3000.
-3300. 3600. 0. 300. 600. 900. 1200. 1500. 1800. 2100. 2400. 2700.
-3000. 3300. 300. 600. 900. 1200. 1500. 1800. 2100. 2400. 2700. 3000.
-3300. 300. 600. 900. 1200. 1500. 1800. 2100. 2400. 2700. 3000.]"""
-
-ZS_INT = [int(float(e)) for e in ZS[1:-1].split()]
-ALTITUDES = sorted(set(ZS_INT))
-
-
-# Create a ZS_INT with only the 23 first massifs
-ZS_INT_23 = ZS_INT[:-10].copy()
-
-# Create a ZS_INT with np.nan all altitudes corresponding to the 24th massif
-ZS_INT_MASK = np.array(ZS_INT)
-ZS_INT_MASK[-10:] = np.nan
-
-
-
-
-
diff --git a/experiment/meteo_france_SCM_study/massif.py b/experiment/meteo_france_SCM_study/massif.py
deleted file mode 100644
index 92456e2cc03729b2c34fe5fd9ff4d12c12d5f80d..0000000000000000000000000000000000000000
--- a/experiment/meteo_france_SCM_study/massif.py
+++ /dev/null
@@ -1,42 +0,0 @@
-from utils import first
-
-MASSIF_NAMES_1800 = ['Chablais', 'Aravis', 'Mont-Blanc', 'Bauges', 'Beaufortain',
- 'Haute-Tarentaise', 'Chartreuse', 'Belledonne', 'Maurienne', 'Vanoise',
- 'Haute-Maurienne', 'Grandes-Rousses', 'Thabor', 'Vercors', 'Oisans',
- 'Pelvoux', 'Queyras', 'Devoluy', 'Champsaur', 'Parpaillon', 'Ubaye',
- 'Alpes-Azur', 'Mercantour']
-# Some massif like Chartreuse do not have massif whose altitude is higher or equal to 2400
-MASSIF_NAMES_2400 = ['Chablais', 'Aravis', 'Mont-Blanc', 'Beaufortain', 'Haute-Tarentaise',
- 'Belledonne', 'Maurienne', 'Vanoise', 'Haute-Maurienne',
- 'Grandes-Rousses', 'Thabor', 'Vercors', 'Oisans', 'Pelvoux', 'Queyras',
- 'Devoluy', 'Champsaur', 'Parpaillon', 'Ubaye', 'Alpes-Azur',
- 'Mercantour']
-
-
-class Massif(object):
-
- def __init__(self, name: str, id: int, lat: float, lon: float) -> None:
- self.lon = lon
- self.lat = lat
- self.id = id
- self.name = name
-
- @classmethod
- def from_str(cls, s: str):
- name, id, lat, lon = s.split(',')
- return cls(name.strip(), int(id), float(lat), float(lon))
-
-
-def safran_massif_names_from_datasets(datasets, altitude):
- # Massifs names are extracted from SAFRAN dataset
- reference_massif_list = MASSIF_NAMES_1800 if altitude == 1800 else MASSIF_NAMES_2400
- if hasattr(datasets[0], 'massifsList'):
- # Assert the all the datasets have the same indexing for the massif
- assert len(set([dataset.massifsList for dataset in datasets])) == 1
- # List of the name of the massif used by all the SAFRAN datasets
- massifs = [Massif.from_str(massif_str) for massif_str in first(datasets).massifsList.split('/')]
- # IMPORTANT: Sort the massif names
- massifs = sorted(massifs, key=lambda massif: massif.id)
- safran_names = [massif.name for massif in massifs]
- assert reference_massif_list == safran_names, '{} \n{}'.format(reference_massif_list, safran_names)
- return reference_massif_list
diff --git a/experiment/meteo_france_SCM_study/scm_constants.py b/experiment/meteo_france_SCM_study/scm_constants.py
new file mode 100644
index 0000000000000000000000000000000000000000..4701e5317a400466ebc3e9f5f13f4ac0af97ab7d
--- /dev/null
+++ b/experiment/meteo_france_SCM_study/scm_constants.py
@@ -0,0 +1,48 @@
+
+"""
+ZS was extracted from a netcdf file
+"""
+import numpy as np
+
+ZS = """[ 300. 600. 900. 1200. 1500. 1800. 2100. 2400. 2700. 3000. 3300. 300.
+600. 900. 1200. 1500. 1800. 2100. 2400. 2700. 3000. 300. 600. 900.
+1200. 1500. 1800. 2100. 2400. 2700. 3000. 3300. 3600. 3900. 4200. 4500.
+4800. 0. 300. 600. 900. 1200. 1500. 1800. 2100. 2400. 300. 600.
+900. 1200. 1500. 1800. 2100. 2400. 2700. 3000. 600. 900. 1200. 1500.
+1800. 2100. 2400. 2700. 3000. 3300. 3600. 3900. 0. 300. 600. 900.
+1200. 1500. 1800. 2100. 0. 300. 600. 900. 1200. 1500. 1800. 2100.
+2400. 2700. 3000. 0. 300. 600. 900. 1200. 1500. 1800. 2100. 2400.
+2700. 3000. 3300. 300. 600. 900. 1200. 1500. 1800. 2100. 2400. 2700.
+3000. 3300. 3600. 3900. 900. 1200. 1500. 1800. 2100. 2400. 2700. 3000.
+3300. 3600. 3900. 600. 900. 1200. 1500. 1800. 2100. 2400. 2700. 3000.
+3300. 3600. 900. 1200. 1500. 1800. 2100. 2400. 2700. 3000. 3300. 0.
+300. 600. 900. 1200. 1500. 1800. 2100. 2400. 0. 300. 600. 900.
+1200. 1500. 1800. 2100. 2400. 2700. 3000. 3300. 3600. 3900. 4200. 900.
+1200. 1500. 1800. 2100. 2400. 2700. 3000. 3300. 3600. 3900. 4200. 900.
+1200. 1500. 1800. 2100. 2400. 2700. 3000. 3300. 300. 600. 900. 1200.
+1500. 1800. 2100. 2400. 2700. 3000. 600. 900. 1200. 1500. 1800. 2100.
+2400. 2700. 3000. 3300. 3600. 600. 900. 1200. 1500. 1800. 2100. 2400.
+2700. 3000. 3300. 600. 900. 1200. 1500. 1800. 2100. 2400. 2700. 3000.
+3300. 3600. 0. 300. 600. 900. 1200. 1500. 1800. 2100. 2400. 2700.
+3000. 3300. 300. 600. 900. 1200. 1500. 1800. 2100. 2400. 2700. 3000.
+3300. 300. 600. 900. 1200. 1500. 1800. 2100. 2400. 2700. 3000.]"""
+
+ZS_INT = [int(float(e)) for e in ZS[1:-1].split()]
+ALTITUDES = sorted(set(ZS_INT))
+
+
+# Create a ZS_INT with only the 23 first massifs
+ZS_INT_23 = ZS_INT[:-10].copy()
+
+# Create a ZS_INT with np.nan all altitudes corresponding to the 24th massif
+ZS_INT_MASK = np.array(ZS_INT)
+ZS_INT_MASK[-10:] = np.nan
+
+# Longitudes and Latitudes in degrees
+LONGITUDES = [6.64493, 6.64493, 6.64493, 6.64493, 6.64493, 6.64493, 6.64493, 6.64493, 6.64493, 6.64493, 6.64493, 6.39738, 6.39738, 6.39738, 6.39738, 6.39738, 6.39738, 6.39738, 6.39738, 6.39738, 6.39738, 6.82392, 6.82392, 6.82392, 6.82392, 6.82392, 6.82392, 6.82392, 6.82392, 6.82392, 6.82392, 6.82392, 6.82392, 6.82392, 6.82392, 6.82392, 6.82392, 6.10178, 6.10178, 6.10178, 6.10178, 6.10178, 6.10178, 6.10178, 6.10178, 6.10178, 6.57668, 6.57668, 6.57668, 6.57668, 6.57668, 6.57668, 6.57668, 6.57668, 6.57668, 6.57668, 6.90053, 6.90053, 6.90053, 6.90053, 6.90053, 6.90053, 6.90053, 6.90053, 6.90053, 6.90053, 6.90053, 6.90053, 5.80795, 5.80795, 5.80795, 5.80795, 5.80795, 5.80795, 5.80795, 5.80795, 6.00201, 6.00201, 6.00201, 6.00201, 6.00201, 6.00201, 6.00201, 6.00201, 6.00201, 6.00201, 6.00201, 6.35451, 6.35451, 6.35451, 6.35451, 6.35451, 6.35451, 6.35451, 6.35451, 6.35451, 6.35451, 6.35451, 6.35451, 6.61786, 6.61786, 6.61786, 6.61786, 6.61786, 6.61786, 6.61786, 6.61786, 6.61786, 6.61786, 6.61786, 6.61786, 6.61786, 6.91492, 6.91492, 6.91492, 6.91492, 6.91492, 6.91492, 6.91492, 6.91492, 6.91492, 6.91492, 6.91492, 6.21836, 6.21836, 6.21836, 6.21836, 6.21836, 6.21836, 6.21836, 6.21836, 6.21836, 6.21836, 6.21836, 6.59154, 6.59154, 6.59154, 6.59154, 6.59154, 6.59154, 6.59154, 6.59154, 6.59154, 5.4932, 5.4932, 5.4932, 5.4932, 5.4932, 5.4932, 5.4932, 5.4932, 5.4932, 5.99951, 5.99951, 5.99951, 5.99951, 5.99951, 5.99951, 5.99951, 5.99951, 5.99951, 5.99951, 5.99951, 5.99951, 5.99951, 5.99951, 5.99951, 6.45769, 6.45769, 6.45769, 6.45769, 6.45769, 6.45769, 6.45769, 6.45769, 6.45769, 6.45769, 6.45769, 6.45769, 6.79352, 6.79352, 6.79352, 6.79352, 6.79352, 6.79352, 6.79352, 6.79352, 6.79352, 5.8499, 5.8499, 5.8499, 5.8499, 5.8499, 5.8499, 5.8499, 5.8499, 5.8499, 5.8499, 6.23469, 6.23469, 6.23469, 6.23469, 6.23469, 6.23469, 6.23469, 6.23469, 6.23469, 6.23469, 6.23469, 6.50065, 6.50065, 6.50065, 6.50065, 6.50065, 6.50065, 6.50065, 6.50065, 6.50065, 6.50065, 6.67076, 6.67076, 6.67076, 6.67076, 6.67076, 6.67076, 6.67076, 6.67076, 6.67076, 6.67076, 6.67076, 6.79647, 6.79647, 6.79647, 6.79647, 6.79647, 6.79647, 6.79647, 6.79647, 6.79647, 6.79647, 6.79647, 6.79647, 7.31586, 7.31586, 7.31586, 7.31586, 7.31586, 7.31586, 7.31586, 7.31586, 7.31586, 7.31586, 7.31586, 7.3025, 7.3025, 7.3025, 7.3025, 7.3025, 7.3025, 7.3025, 7.3025, 7.3025, 7.3025]
+LATITUDES = [46.17685, 46.17685, 46.17685, 46.17685, 46.17685, 46.17685, 46.17685, 46.17685, 46.17685, 46.17685, 46.17685, 45.89494, 45.89494, 45.89494, 45.89494, 45.89494, 45.89494, 45.89494, 45.89494, 45.89494, 45.89494, 45.89794, 45.89794, 45.89794, 45.89794, 45.89794, 45.89794, 45.89794, 45.89794, 45.89794, 45.89794, 45.89794, 45.89794, 45.89794, 45.89794, 45.89794, 45.89794, 45.65578, 45.65578, 45.65578, 45.65578, 45.65578, 45.65578, 45.65578, 45.65578, 45.65578, 45.65756, 45.65756, 45.65756, 45.65756, 45.65756, 45.65756, 45.65756, 45.65756, 45.65756, 45.65756, 45.54313, 45.54313, 45.54313, 45.54313, 45.54313, 45.54313, 45.54313, 45.54313, 45.54313, 45.54313, 45.54313, 45.54313, 45.37753, 45.37753, 45.37753, 45.37753, 45.37753, 45.37753, 45.37753, 45.37753, 45.27395, 45.27395, 45.27395, 45.27395, 45.27395, 45.27395, 45.27395, 45.27395, 45.27395, 45.27395, 45.27395, 45.32783, 45.32783, 45.32783, 45.32783, 45.32783, 45.32783, 45.32783, 45.32783, 45.32783, 45.32783, 45.32783, 45.32783, 45.411, 45.411, 45.411, 45.411, 45.411, 45.411, 45.411, 45.411, 45.411, 45.411, 45.411, 45.411, 45.411, 45.26072, 45.26072, 45.26072, 45.26072, 45.26072, 45.26072, 45.26072, 45.26072, 45.26072, 45.26072, 45.26072, 45.11517, 45.11517, 45.11517, 45.11517, 45.11517, 45.11517, 45.11517, 45.11517, 45.11517, 45.11517, 45.11517, 45.01923, 45.01923, 45.01923, 45.01923, 45.01923, 45.01923, 45.01923, 45.01923, 45.01923, 45.00409, 45.00409, 45.00409, 45.00409, 45.00409, 45.00409, 45.00409, 45.00409, 45.00409, 44.94609, 44.94609, 44.94609, 44.94609, 44.94609, 44.94609, 44.94609, 44.94609, 44.94609, 44.94609, 44.94609, 44.94609, 44.94609, 44.94609, 44.94609, 44.83699, 44.83699, 44.83699, 44.83699, 44.83699, 44.83699, 44.83699, 44.83699, 44.83699, 44.83699, 44.83699, 44.83699, 44.77139, 44.77139, 44.77139, 44.77139, 44.77139, 44.77139, 44.77139, 44.77139, 44.77139, 44.69552, 44.69552, 44.69552, 44.69552, 44.69552, 44.69552, 44.69552, 44.69552, 44.69552, 44.69552, 44.70565, 44.70565, 44.70565, 44.70565, 44.70565, 44.70565, 44.70565, 44.70565, 44.70565, 44.70565, 44.70565, 44.57217, 44.57217, 44.57217, 44.57217, 44.57217, 44.57217, 44.57217, 44.57217, 44.57217, 44.57217, 44.44757, 44.44757, 44.44757, 44.44757, 44.44757, 44.44757, 44.44757, 44.44757, 44.44757, 44.44757, 44.44757, 44.12458, 44.12458, 44.12458, 44.12458, 44.12458, 44.12458, 44.12458, 44.12458, 44.12458, 44.12458, 44.12458, 44.12458, 44.12649, 44.12649, 44.12649, 44.12649, 44.12649, 44.12649, 44.12649, 44.12649, 44.12649, 44.12649, 44.12649, 46.39, 46.39, 46.39, 46.39, 46.39, 46.39, 46.39, 46.39, 46.39, 46.39]
+
+
+
+
+
diff --git a/experiment/meteo_france_SCM_study/visualization/studies_visualization/studies.py b/experiment/meteo_france_SCM_study/visualization/studies_visualization/studies.py
index 7a64c33e0b390b9df33f015b8627149b1e121d56..350f90d6f2043a86bf57df6a0483ca1a476f45cc 100644
--- a/experiment/meteo_france_SCM_study/visualization/studies_visualization/studies.py
+++ b/experiment/meteo_france_SCM_study/visualization/studies_visualization/studies.py
@@ -2,7 +2,7 @@ from collections import OrderedDict
from typing import Dict
from experiment.meteo_france_SCM_study.abstract_study import AbstractStudy
-from experiment.meteo_france_SCM_study.altitude import ALTITUDES
+from experiment.meteo_france_SCM_study.scm_constants import ALTITUDES
class Studies(object):
diff --git a/experiment/meteo_france_SCM_study/visualization/study_visualization/main_study_visualizer.py b/experiment/meteo_france_SCM_study/visualization/study_visualization/main_study_visualizer.py
index 57128a0c42464e25f940f00960a0f8630afda259..7432c1d15c1d30c1be9500d2188cd826359a8254 100644
--- a/experiment/meteo_france_SCM_study/visualization/study_visualization/main_study_visualizer.py
+++ b/experiment/meteo_france_SCM_study/visualization/study_visualization/main_study_visualizer.py
@@ -1,3 +1,4 @@
+import time
from typing import Generator, List
from experiment.meteo_france_SCM_study.abstract_study import AbstractStudy
@@ -37,7 +38,7 @@ def study_iterator(study_class, only_first_one=False, both_altitude=False, verbo
for alti in altis:
if verbose:
- print('alti: {}, nb_day: {}'.format(alti, nb_day), end='')
+ print('alti: {}, nb_day: {} '.format(alti, nb_day), end='')
study = study_class(altitude=alti, nb_consecutive_days=nb_day) if is_safran_study \
else study_class(altitude=alti)
massifs = study.altitude_to_massif_names[alti]
@@ -119,20 +120,29 @@ def trend_analysis():
save_to_file = True
only_first_one = False
short_altitudes = [300, 1200, 2100, 3000][:1]
- full_altitude_with_at_least_2_stations = [0, 300, 600, 900, 1200, 1500, 1800, 2100, 2400, 2700, 3000, 3300, 3600, 3900, 4200][-5:]
- altitudes = full_altitude_with_at_least_2_stations
+ full_altitude_with_at_least_2_stations = [0, 300, 600, 900, 1200, 1500, 1800, 2100, 2400, 2700, 3000, 3300, 3600, 3900, 4200][:]
+ durand_altitude = [1800]
+ altitudes = durand_altitude
normalization_class = [None, BetweenMinusOneAndOneNormalization, BetweenZeroAndOneNormalization][-1]
- study_classes = [CrocusSwe, CrocusDepth, SafranSnowfall, SafranRainfall, SafranTemperature][:1]
+ study_classes = [CrocusSwe, CrocusDepth, SafranSnowfall, SafranRainfall, SafranTemperature][2:3]
for study in study_iterator_global(study_classes, only_first_one=only_first_one, altitudes=altitudes):
study_visualizer = StudyVisualizer(study, save_to_file=save_to_file,
transformation_class=normalization_class)
study_visualizer.visualize_temporal_trend_relevance(complete_analysis=False, multiprocessing=True)
-if __name__ == '__main__':
+def main_run():
# annual_mean_vizu_compare_durand_study(safran=True, take_mean_value=True, altitude=2100)
# normal_visualization(temporal_non_stationarity=True)
trend_analysis()
+
+
# max_stable_process_vizu_compare_gaume_study(altitude=1800, nb_days=1)
# extended_visualization()
# complete_analysis()
+
+if __name__ == '__main__':
+ start = time.time()
+ main_run()
+ duration = time.time() - start
+ print('Full run took {}s'.format(round(duration, 1)))
diff --git a/experiment/meteo_france_SCM_study/visualization/study_visualization/study_visualizer.py b/experiment/meteo_france_SCM_study/visualization/study_visualization/study_visualizer.py
index 8e5062e518ef3b03d3c1581035b00c83a70c14b0..7c4bce2429a486c1ae08400624fd913097dacf2f 100644
--- a/experiment/meteo_france_SCM_study/visualization/study_visualization/study_visualizer.py
+++ b/experiment/meteo_france_SCM_study/visualization/study_visualization/study_visualizer.py
@@ -134,14 +134,7 @@ class StudyVisualizer(object):
self._observations.convert_to_spatio_temporal_index(self.coordinates)
if self.normalization_under_one_observations:
self._observations.normalize()
- # Write a summary of observations
- df = self._observations.df_maxima_gev
- print('Observations summary:', ' ', end='')
- print('Mean value:', df.mean().mean(), ' ', end='')
- print('Min value:', df.min().min(), ' ', end='')
- print('Max value:', df.max().max(), ' ', end='')
- print('# of zero values:', df.size - np.count_nonzero(df.values), '\n')
-
+ self._observations.print_summary()
return self._observations
# Graph for each massif / or groups of massifs
@@ -177,11 +170,11 @@ class StudyVisualizer(object):
trend_tests = [ConditionalIndedendenceLocationTrendTest(dataset=self.dataset, verbose=verbose,
multiprocessing=multiprocessing)]
- # max_stable_models = load_test_max_stable_models(default_covariance_function=self.default_covariance_function)
- # for max_stable_model in [max_stable_models[1], max_stable_models[-2]]:
- # trend_tests.append(MaxStableLocationTrendTest(dataset=self.dataset,
- # max_stable_model=max_stable_model, verbose=verbose,
- # multiprocessing=multiprocessing))
+ max_stable_models = load_test_max_stable_models(default_covariance_function=self.default_covariance_function)
+ for max_stable_model in [max_stable_models[1], max_stable_models[-2]]:
+ trend_tests.append(MaxStableLocationTrendTest(dataset=self.dataset,
+ max_stable_model=max_stable_model, verbose=verbose,
+ multiprocessing=multiprocessing))
nb_trend_tests = len(trend_tests)
fig, axes = plt.subplots(1, nb_trend_tests, figsize=self.figsize)
diff --git a/spatio_temporal_dataset/spatio_temporal_observations/abstract_spatio_temporal_observations.py b/spatio_temporal_dataset/spatio_temporal_observations/abstract_spatio_temporal_observations.py
index 7ff8ecb67d5889d166ec4ad134c00876f0ba6ed8..c55d3dfa8ffd1e112ef139c2315f335230239afc 100644
--- a/spatio_temporal_dataset/spatio_temporal_observations/abstract_spatio_temporal_observations.py
+++ b/spatio_temporal_dataset/spatio_temporal_observations/abstract_spatio_temporal_observations.py
@@ -112,7 +112,8 @@ class AbstractSpatioTemporalObservations(object):
print('Mean value:', df.mean().mean(), ' ', end='')
print('Min value:', df.min().min(), ' ', end='')
print('Max value:', df.max().max(), ' ', end='')
- print('# of zero values:', df.size - np.count_nonzero(df.values), '\n')
+ percentage = round(100 * (df.size - np.count_nonzero(df.values)) / df.size, 1)
+ print('Percentage of zero values {} out of {} observations'.format(percentage, df.size), '\n')
@_df_maxima.setter
def _df_maxima(self, value):