def onselect(self, verts):
path = Path(verts)
self.ind = np.nonzero(path.contains_points(self.xys))[0]
self.fc[:, -1] = self.alpha_other
self.fc[self.ind, -1] = 1
self.collection.set_facecolors(self.fc)
self.canvas.draw_idle()
def points_to_label(self, points):
"""Get label of region that contains all the points, or return None if points are not in any region.
"""
for label, paths in self.paths.items():
for path in paths:
if np.sum(path.contains_points(points)) == 4:
return label
return None
def get_iso3(self, coordinates):
points = self._map_(*coordinates)
# don't ask questions
points = (points, )
result = None
for iso, path in self._paths:
if path.contains_points(points):
result = iso
break
return result
def get_country_code(self, coordinates):
points = self.map_(*coordinates)
# don't ask questions
points = (points, )
result = None
for iso, path in self._cache_path:
if path.contains_points(points):
result = iso
break
return result
def find_stars_in_contour(self, contour, survey):
"""
From the photometry catalog we
determine which stars are inside
the contour of the cloud.
"""
verts = np.array(contour, float)
path = Path(verts)
if (survey == "UKIDSS") or (survey == "VISTA"):
points = np.column_stack((self.catalog['L'], self.catalog['B']))
yo = path.contains_points(points)
try:
self.catalog["CloudMask"] = yo
except ValueError:
self.catalog['CloudMask'] = self.catalog['CloudMask'] + yo
def find_stars_in_contour(self,contour,survey):
"""
From the photometry catalog we
determine which stars are inside
the contour of the cloud.
"""
verts = np.array(contour,float)
path = Path(verts)
if (survey == "UKIDSS") or (survey == "VISTA"):
points = np.column_stack((self.catalog['L'],self.catalog['B']))
yo = path.contains_points(points)
try:
self.catalog["CloudMask"] = yo
except ValueError:
self.catalog['CloudMask'] = self.catalog['CloudMask'] + yo
def __init__(self, polygon, thickness, bbox = None, d=None, material=None, folder=None):
Iso_FROM_FILE.__init__(self, d=d, material=material, folder=folder)
polygon= np.array(polygon)
p_min = polygon.min(0)
p_max = polygon.max(0)
poly_centre = (p_min+p_max)/2
poly_size = p_max-p_min
if bbox is None:
bbox_corner = p_min
bbox_size = poly_size
elif len(bbox) == 2:
bbox_corner = poly_centre - np.array(bbox)/2
bbox_size = np.array(bbox)
elif len(bbox) == 4:
bbox_corner = np.array(bbox[:2])
bbox_size = np.array(bbox[-2:])
else:
raise ValueError('Argument bbox must have length 2 or 4')
px_size = np.ceil((bbox_size / self.d)).astype(int)
scale = min(px_size / bbox_size)
# map the vertices into dipole space
self.verts = (polygon - bbox_corner)*scale
path = mpl.path.Path(self.verts)
x, y = np.meshgrid(np.arange(px_size[0]),np.arange(px_size[1]))
x, y = x.flatten(), y.flatten()
xy = np.vstack((x,y)).T
grid = path.contains_points(xy)
grid = grid.reshape(px_size).astype(int)
self.grid = np.tile(grid, (round(thickness/self.d), 1, 1))
self.origin = np.array(tuple(poly_centre)+(thickness/2.,))
def run(self):
"""Code à exécuter pendant l'exécution du thread."""
if self.basin == 'ALB_W':
y = [37, 37, 34, 34]
x = [-6, -4.5, -4.5, -6]
elif self.basin == 'ALB_C':
y = [37, 34, 34, 37]
x = [-4.5, -4.5, -3, -3]
elif self.basin == 'ALB_E':
y = [34, 37, 37, 34]
x = [-2.5, -2.5, -1, -1]
elif self.basin == 'ALG_W':
y = [35, 38, 39, 35]
x = [-0.6, -0.6, 1, 1]
# if self.basin == 'ALL_MED':
# y = [37,35,35,30.5,30.5,38,46,45,44];
# x = [-5.5,-5.5,10,10,36.5,36.5,13.5,10,3];
# elif self.basin == 'W':
# y = [37, 35, 35, 36.4, 37.8, 38, 38.5, 39.5, 45, 44]
# x = [-5.5, -5.5, 10, 10, 12.8, 15, 16.5, 16.5, 10, 3]
# elif self.basin == 'E':
# y = [36.4, 37.8, 38, 38.5, 39.5, 45, 46, 38, 30.5, 30.5]
# x = [10, 12.8, 15, 16.5, 16.5, 10, 13.5, 36.5, 36.5, 10]
# elif self.basin == 'GOL':
# y = [43.44829732432269, 42.97458858683228, 43.0604306783546, 42.96837802751135, 41.96306909843022,
# 41.43113753799402, 41.16267859938984, 40.91067143551019, 39.12845260900497, 39.938244, 38.756497,
# 39.247095, 46.4938123, 44.4124705]
# x = [11.68790444824612, 10.82516607380484, 9.82460045736472, 9.409248985801327, 9.294900137395576,
# 9.198952404436941, 9.339783959671131, 9.097279040223231, 8.925644550468935, 4.159323, -0.162487,
# -1.17747, 5.2446879, 10.1001797]
# elif self.basin == 'ALB':
# y = [37.252960, 35.093681, 35, 37]
# x = [-2.159892, -0.690137, -5.5, -5.5]
# elif self.basin == 'ALG':
# y = [35.093681, 37.252960, 38.756497, 39.938244, 39.1284526, 36.93272338, 35.930524]
# x = [-0.690137, -2.159892, -0.162487, 4.159323, 8.92564455, 9.90934876, 3.994854]
# elif self.basin == 'TYR':
# y = [39.12845260900497, 37.88508389278273, 38.03532811253486, 38.19020427258793, 38.206530069008,
# 38.49522011750975, 39.25645461354518, 40.44930918149475, 43.44829732432269, 42.97458858683228,
# 43.0604306783546, 42.96837802751135, 41.96306909843022, 41.43113753799402, 41.16267859938984,
# 40.91067143551019]
# x = [8.925644550468935, 13.3111744311976, 15.29159159227882, 15.48719160249115, 15.74199259491107,
# 16.21860737848962, 16.43223864162354, 16.01437911440215, 11.68790444824612, 10.82516607380484,
# 9.824600457364721, 9.409248985801327, 9.294900137395576, 9.198952404436941, 9.339783959671131,
# 9.097279040223231]
# elif self.basin == 'CMED':
# y = [39.12845260900497, 37.88508389278273, 37.001816, 31.924008, 33.259229, 36.93272338]
# x = [8.925644550468935, 13.3111744311976, 15.084801, 15.081913, 7.954713, 9.90934876]
# elif self.basin == 'ION':
# y = [37.88508389278273, 38.03532811253486, 38.19020427258793, 38.206530069008, 38.49522011750975,
# 39.25645461354518, 40.44930918149475, 40.857014, 40.138887, 39.74605, 38.155586, 37.503355,
# 29.658354, 30.941890, 31.924008, 37.001816]
# x = [13.3111744311976, 15.29159159227882, 15.48719160249115, 15.74199259491107, 16.21860737848962,
# 16.43223864162354, 16.01437911440215, 16.854632, 18.343058, 20.052826, 23.556638, 21.936077,
# 20.090485, 15.297679, 15.081913, 15.084801]
# elif self.basin == 'ADR':
# y = [45.123088, 46.248509, 42.990612, 41.876774, 39.746050, 40.138887, 40.857014, 41.497860]
# x = [10.040714, 14.099647, 17.690915, 20.079333, 20.052826, 18.343058, 16.854632, 15.316464]
# elif self.basin == 'AEG':
# y = [39.746050, 38.155586, 37.503355, 35.335479, 35.045582, 35.478996, 36.113327, 40.879240, 42.000219,
# 41.876774]
# x = [20.052826, 23.556638, 21.936077, 23.620988, 26.217209, 27.159167, 28.006273, 30.504893, 23.493337,
# 20.079333]
# elif self.basin == 'LEV':
# y = [37.503355, 35.335479, 35.045582, 35.478996, 36.113327, 40.879240, 37.180585, 30.329515, 29.658354]
# x = [21.936077, 23.620988, 26.217209, 27.159167, 28.006273, 30.504893, 37.441401, 35.238352, 20.090485]
else:
print 'Les coordonnées pour : ', self.basin, ' ne sont pas définies ici'
print 'Basin :', self.basin
print ' '
# Selecting the data
vertices = np.array([np.hstack(
(x, 0)), np.hstack((y, 0))], float).transpose()
path = Path(vertices, codes=None, closed=True)
indices = path.contains_points(np.array([
np.reshape(lon_t, (np.size(lon_t))),
np.reshape(lat_t, (np.size(lat_t)))
]).T,
transform=None,
radius=0.0)
indices_re = np.reshape(indices,
np.shape(lon_t)) # this our mask for the zone
vol_levels = read_csv('../vol_lev_' + self.basin + '.csv')
j = 0 # compteur
for nutrient in nut_list:
for vert_lev in vert_lev_list:
vars()[nutrient + '_ALL_STOCK_' + self.basin + '_' +
vert_lev] = np.zeros([len(list_len)])
vars()[nutrient + '_SUMM_' + self.basin] = np.zeros(
[len(list_len)])
for o, fn in enumerate(list_len):
our_file = list_len[o]
# capturer la date
date_file = our_file[0:8]
f = Dataset(file_path + list_len[o], 'r') # open netcdf file
for nutrient in nut_list:
# Extracting data from files in file_list
extracted_var = f.variables[nutrient][:]
extracted_nut = extracted_var # pourquoi cette étape est-elle utile???
for vert_lev in vert_lev_list:
if vert_lev in ('SW'):
#lev_range = range(30, 43)
lev_range = range(31, 43)
#vars()[nutrient + '_' + self.basin + '_' + vert_lev + '_' + date_file] = np.zeros([14])
vars()[nutrient + '_' + self.basin + '_' + vert_lev +
'_' + date_file] = np.zeros([13])
elif vert_lev in ('IW'):
lev_range = range(23, 30)
vars()[nutrient + '_' + self.basin + '_' + vert_lev +
'_' + date_file] = np.zeros([8])
elif vert_lev in ('DW'):
lev_range = range(0, 23)
vars()[nutrient + '_' + self.basin + '_' + vert_lev +
'_' + date_file] = np.zeros([23])
else:
print 'Error vertical levels'
for z in lev_range:
curr_ind = len(lev_range) - (
(lev_range[len(lev_range) - 1] + 1) - z)
nut = extracted_nut[0, z, :, :]
# On applique notre masque au nouveau jeu de données
new_nut = np.ma.masked_where(np.ma.getmask(new_bath),
nut)
vars()[nutrient + '_' + self.basin + '_' + vert_lev +
'_' + date_file][curr_ind] = np.nansum(
np.ma.masked_array(
new_nut, indices_re
== False)) * vol_levels.vol_m3[z]
# tous les niveaux faits
# sum le tout = stock sur tous les niveaux à date en question
vars()[nutrient + '_ALL_STOCK_' + self.basin + '_' +
vert_lev][j] = np.nansum(
vars()[nutrient + '_' + self.basin + '_' +
vert_lev + '_' + date_file])
# vars()[nutrient + '_SUMM_' + self.basin][j] = (vars()[nutrient + '_ALL_STOCK_' + self.basin + '_SW'][j] + vars()[nutrient + '_ALL_STOCK_' + self.basin + '_IW'][j]
# + vars()[nutrient + '_ALL_STOCK_' + self.basin + '_DW'][j])
vars()[nutrient + '_SUMM_' +
self.basin][j] = vars()[nutrient + '_ALL_STOCK_' +
self.basin + '_SW'][j]
extracted_var = [] # TEST ALEX 01/11 18h15
j += 1
f.close() # closing netcdf file
print 'SECOND LOOP TO WRITE CSV FILES'
for vert_lev in vert_lev_list:
for nutrient in nut_list:
print 'Creating variable:', nutrient + '_ALL_STOCK_' + self.basin + '_' + vert_lev
if nutrient in ('nitrate', 'ammonium', 'modn', 'smopn',
'lmopn', 'nanon', 'dian', 'synen', 'silice',
'smopsi', 'lmopsi', 'diasi', 'phosphate'):
vars()[nutrient + '_STOCK_' + self.basin + '_' +
vert_lev] = DataFrame(dates, columns=['date'])
vars()[nutrient + '_STOCK_' + self.basin + '_' +
vert_lev]['var'] = vars()[nutrient + '_ALL_STOCK_' +
self.basin + '_' +
vert_lev]
vars(
)[nutrient + '_STOCK_' + self.basin + '_' +
vert_lev].to_csv(
path_or_buf=
'/tmpdir/alex/PYTHON/FIGS/BestAjustRivE_BoostW_BOUCLE/'
+ nutrient + '_STOCK_' + self.basin + '_' +
vert_lev + '.csv')
elif nutrient in ('zoonanoc', 'zoomicroc', 'zoomesoc'):
vars()[nutrient + '_N_ALL_STOCK_' + self.basin + '_' +
vert_lev] = vars()[nutrient + '_ALL_STOCK_' +
self.basin + '_' +
vert_lev] * 0.18
vars()[nutrient + '_N_STOCK_' + self.basin + '_' +
vert_lev] = DataFrame(dates, columns=['date'])
vars()[nutrient + '_N_STOCK_' + self.basin + '_' +
vert_lev]['var'] = vars()[
nutrient + '_N_ALL_STOCK_' + self.basin + '_' +
vert_lev] # final results seem ok
vars(
)[nutrient + '_N_STOCK_' +
self.basin + '_' + vert_lev].to_csv(
path_or_buf=
'/tmpdir/alex/PYTHON/FIGS/BestAjustRivE_BoostW_BOUCLE/'
+ nutrient + '_N_STOCK_' + self.basin + '_' +
vert_lev + '.csv') # nécessite pd.DataFrame
elif nutrient in ('bactc'):
vars()[nutrient + '_N_ALL_STOCK_' + self.basin + '_' +
vert_lev] = vars()[nutrient + '_ALL_STOCK_' +
self.basin + '_' +
vert_lev] * 0.232
vars()['bactn_STOCK_' + self.basin + '_' +
vert_lev] = DataFrame(dates, columns=['date'])
vars()['bactn_STOCK_' + self.basin + '_' +
vert_lev]['var'] = vars()[
nutrient + '_N_ALL_STOCK_' + self.basin + '_' +
vert_lev] # final results seem ok
vars(
)['bactn_STOCK_' + self.basin + '_' + vert_lev].to_csv(
path_or_buf=
'/tmpdir/alex/PYTHON/FIGS/BestAjustRivE_BoostW_BOUCLE/bactn_STOCK_'
+ self.basin + '_' + vert_lev +
'.csv') # nécessite pd.DataFrame
else:
print 'Error nutrient'
vars()['allzoo_ALL_STOCK_' + self.basin + '_' + vert_lev] = (
vars()['zoonanoc_N_ALL_STOCK_' + self.basin + '_' + vert_lev] +
vars()['zoomicroc_N_ALL_STOCK_' + self.basin + '_' + vert_lev]
+
vars()['zoomesoc_N_ALL_STOCK_' + self.basin + '_' + vert_lev])
vars()['allphy_ALL_STOCK_' + self.basin + '_' + vert_lev] = (
vars()['synen_ALL_STOCK_' + self.basin + '_' + vert_lev] +
vars()['nanon_ALL_STOCK_' + self.basin + '_' + vert_lev] +
vars()['dian_ALL_STOCK_' + self.basin + '_' + vert_lev])
vars()['allzoo_STOCK_' + self.basin + '_' + vert_lev] = DataFrame(
dates, columns=['date'])
vars()['allzoo_STOCK_' + self.basin + '_' +
vert_lev]['var'] = vars()['allzoo_ALL_STOCK_' + self.basin +
'_' +
vert_lev] # final results seem ok
vars()['allzoo_STOCK_' + self.basin + '_' + vert_lev].to_csv(
path_or_buf=
'/tmpdir/alex/PYTHON/FIGS/BestAjustRivE_BoostW_BOUCLE/allzoo_STOCK_'
+ self.basin + '_' + vert_lev +
'.csv') # nécessite pd.DataFrame
vars()['allphy_STOCK_' + self.basin + '_' + vert_lev] = DataFrame(
dates, columns=['date'])
vars()['allphy_STOCK_' + self.basin + '_' +
vert_lev]['var'] = vars()['allphy_ALL_STOCK_' + self.basin +
'_' +
vert_lev] # final results seem ok
vars()['allphy_STOCK_' + self.basin + '_' + vert_lev].to_csv(
path_or_buf=
'/tmpdir/alex/PYTHON/FIGS/BestAjustRivE_BoostW_BOUCLE/allphy_STOCK_'
+ self.basin + '_' + vert_lev +
'.csv') # nécessite pd.DataFrame
vars()['DIN_ALL_STOCK_' + self.basin + '_' + vert_lev] = (
vars()['nitrate_ALL_STOCK_' + self.basin + '_' + vert_lev] +
vars()['ammonium_ALL_STOCK_' + self.basin + '_' + vert_lev])
vars()['DIN_STOCK_' + self.basin + '_' + vert_lev] = DataFrame(
dates, columns=['date'])
vars()['DIN_STOCK_' + self.basin + '_' +
vert_lev]['var'] = vars()['DIN_ALL_STOCK_' + self.basin +
'_' +
vert_lev] # final results seem ok
vars()['DIN_STOCK_' + self.basin + '_' + vert_lev].to_csv(
path_or_buf=
'/tmpdir/alex/PYTHON/FIGS/BestAjustRivE_BoostW_BOUCLE/DIN_STOCK_'
+ self.basin + '_' + vert_lev +
'.csv') # nécessite pd.DataFrame
vars()['DIN_DIP_ALL_STOCK_' + self.basin + '_' + vert_lev] = (
vars()['DIN_ALL_STOCK_' + self.basin + '_' + vert_lev] /
vars()['phosphate_ALL_STOCK_' + self.basin + '_' + vert_lev])
vars()['DIN_DIP_STOCK_' + self.basin + '_' + vert_lev] = DataFrame(
dates, columns=['date'])
vars()['DIN_DIP_STOCK_' + self.basin + '_' +
vert_lev]['var'] = vars()['DIN_DIP_ALL_STOCK_' +
self.basin + '_' +
vert_lev] # final results seem ok
vars()['DIN_DIP_STOCK_' + self.basin + '_' + vert_lev].to_csv(
path_or_buf=
'/tmpdir/alex/PYTHON/FIGS/BestAjustRivE_BoostW_BOUCLE/DIN_DIP_STOCK_'
+ self.basin + '_' + vert_lev +
'.csv') # nécessite pd.DataFrame
vars()['allzoo_SUMM_' + self.basin] = vars()['allzoo_ALL_STOCK_' +
self.basin + '_SW']
# vars()['allzoo_SUMM_' + self.basin] = (vars()['allzoo_ALL_STOCK_' + self.basin + '_SW'] + vars()['allzoo_ALL_STOCK_' + self.basin + '_IW']
# + vars()['allzoo_ALL_STOCK_' + self.basin + '_DW'])
vars()['allphy_SUMM_' + self.basin] = vars()['allphy_ALL_STOCK_' +
self.basin + '_SW']
# vars()['allphy_SUMM_' + self.basin] = (vars()['allphy_ALL_STOCK_' + self.basin + '_SW'] + vars()['allphy_ALL_STOCK_' + self.basin + '_IW']
# + vars()['allphy_ALL_STOCK_' + self.basin + '_DW'])
vars()['bactn_SUMM_' + self.basin] = vars()['bactc_N_ALL_STOCK_' +
self.basin + '_SW']
# vars()['bactn_SUMM_' + self.basin] = (vars()['bactc_N_ALL_STOCK_' + self.basin + '_SW'] + vars()['bactc_N_ALL_STOCK_' + self.basin + '_IW']
# + vars()['bactc_N_ALL_STOCK_' + self.basin + '_DW'])
vars()['synen_SUMM_' + self.basin] = vars()['synen_ALL_STOCK_' +
self.basin + '_SW']
vars()['nanon_SUMM_' + self.basin] = vars()['nanon_ALL_STOCK_' +
self.basin + '_SW']
vars()['dian_SUMM_' + self.basin] = vars()['dian_ALL_STOCK_' +
self.basin + '_SW']
# vars()['synen_SUMM_' + self.basin] = (vars()['synen_ALL_STOCK_' + self.basin + '_SW'] + vars()['synen_ALL_STOCK_' + self.basin + '_IW']
# + vars()['synen_ALL_STOCK_' + self.basin + '_DW'])
# vars()['nanon_SUMM_' + self.basin] = (vars()['nanon_ALL_STOCK_' + self.basin + '_SW'] + vars()['nanon_ALL_STOCK_' + self.basin + '_IW']
# + vars()['nanon_ALL_STOCK_' + self.basin + '_DW'])
# vars()['dian_SUMM_' + self.basin] = (vars()['dian_ALL_STOCK_' + self.basin + '_SW'] + vars()['dian_ALL_STOCK_' + self.basin + '_IW']
# + vars()['dian_ALL_STOCK_' + self.basin + '_DW'])
vars()['diasi_SUMM_' + self.basin] = vars()['diasi_ALL_STOCK_' +
self.basin + '_SW']
# vars()['diasi_SUMM_' + self.basin] = (vars()['diasi_ALL_STOCK_' + self.basin + '_SW'] + vars()['diasi_ALL_STOCK_' + self.basin + '_IW']
# + vars()['diasi_ALL_STOCK_' + self.basin + '_DW'])
vars()['zoonanon_SUMM_' +
self.basin] = vars()['zoonanoc_N_ALL_STOCK_' + self.basin +
'_SW']
vars()['zoomicron_SUMM_' +
self.basin] = vars()['zoomicroc_N_ALL_STOCK_' + self.basin +
'_SW']
vars()['zoomeson_SUMM_' +
self.basin] = vars()['zoomesoc_N_ALL_STOCK_' + self.basin +
'_SW']
# vars()['zoonanon_SUMM_' + self.basin] = (vars()['zoonanoc_N_ALL_STOCK_' + self.basin + '_SW'] + vars()['zoonanoc_N_ALL_STOCK_' + self.basin + '_IW']
# + vars()['zoonanoc_N_ALL_STOCK_' + self.basin + '_DW'])
# vars()['zoomicron_SUMM_' + self.basin] = (vars()['zoomicroc_N_ALL_STOCK_' + self.basin + '_SW'] + vars()['zoomicroc_N_ALL_STOCK_' + self.basin + '_IW']
# + vars()['zoomicroc_N_ALL_STOCK_' + self.basin + '_DW'])
# vars()['zoomeson_SUMM_' + self.basin] = (vars()['zoomesoc_N_ALL_STOCK_' + self.basin + '_SW'] + vars()['zoomesoc_N_ALL_STOCK_' + self.basin + '_IW']
# + vars()['zoomesoc_N_ALL_STOCK_' + self.basin + '_DW'])
vars()['DIN_SUMM_' + self.basin] = vars()['DIN_ALL_STOCK_' +
self.basin + '_SW']
vars()['DIN_DIP_SUMM_' + self.basin] = vars()['DIN_DIP_ALL_STOCK_' +
self.basin + '_SW']
# vars()['DIN_SUMM_' + self.basin] = (vars()['DIN_ALL_STOCK_' + self.basin + '_SW'] + vars()['DIN_ALL_STOCK_' + self.basin + '_IW']
# + vars()['DIN_ALL_STOCK_' + self.basin + '_DW'])
# vars()['DIN_DIP_SUMM_' + self.basin] = ((vars()['DIN_DIP_ALL_STOCK_' + self.basin + '_SW']*13 + vars()['DIN_DIP_ALL_STOCK_' + self.basin + '_IW']*7
# + vars()['DIN_DIP_ALL_STOCK_' + self.basin + '_DW']*23)/43) # Moyenne par nombre de niveaux
# attention ici DIN_DIP_SUMM est en fait une moyenne sur la colonne d'eau et pas une SUMM (notation conservee pour simplifier le code)
for nutrient in ('nitrate', 'ammonium', 'modn', 'smopn', 'lmopn',
'allphy', 'allzoo', 'bactn', 'synen', 'nanon', 'dian',
'zoonanon', 'zoomicron', 'zoomeson', 'silice',
'smopsi', 'lmopsi', 'diasi', 'phosphate', 'DIN',
'DIN_DIP'):
vars()[nutrient + '_SUM_' + self.basin] = DataFrame(
dates, columns=['date'])
vars()[nutrient + '_SUM_' +
self.basin]['var'] = vars()[nutrient + '_SUMM_' +
self.basin]
vars()[nutrient + '_SUM_' + self.basin].to_csv(
path_or_buf=
'/tmpdir/alex/PYTHON/FIGS/BestAjustRivE_BoostW_BOUCLE/' +
nutrient + '_SUM_' + self.basin + '.csv')
"""Fin du code à exécuter."""
def run(self):
"""Code à exécuter pendant l'exécution du thread."""
if self.basin == 'ALB':
y = [37.252960, 35.093681, 35, 37]
x = [-2.159892, -0.690137, -5.5, -5.5]
elif self.basin == 'ALG':
y = [
35.093681, 37.252960, 38.756497, 39.938244, 39.1284526,
36.93272338, 35.930524
]
x = [
-0.690137, -2.159892, -0.162487, 4.159323, 8.92564455,
9.90934876, 3.994854
]
elif self.basin == 'GIB':
y = [35.5, 36.5, 36.5, 35.5]
x = [-5.5, -5.5, -5, -5]
else:
print 'Les coordonnées pour : ', self.basin, ' ne sont pas définies ici'
print 'Basin :', self.basin
print ' '
# Selecting the data
vertices = np.array([np.hstack(
(x, 0)), np.hstack((y, 0))], float).transpose()
path = Path(vertices, codes=None, closed=True)
indices = path.contains_points(np.array([
np.reshape(lon_t, (np.size(lon_t))),
np.reshape(lat_t, (np.size(lat_t)))
]).T,
transform=None,
radius=0.0)
indices_re = np.reshape(indices,
np.shape(lon_t)) # this our mask for the zone
j = 0 # compteur
for nutrient in nut_list:
for vert_lev in vert_lev_list:
vars()[nutrient + '_ALL_STOCK_' + self.basin + '_' +
vert_lev] = np.zeros([43, len(list_len)])
for o, fn in enumerate(list_len):
our_file = list_len[o]
print 'o:', o
print 'our_file:', our_file
# capturer la date
date_file = our_file[0:8]
f = Dataset(file_path + list_len[o], 'r') # open netcdf file
for nutrient in nut_list:
print 'nutrient:', nutrient
# Extracting data from files in file_list
extracted_var = f.variables[nutrient][0, :, :, :]
print 'shape extracted_var:', np.shape(extracted_var)
lev_range = range(0, 43)
for z in lev_range:
print 'z:', z
temp_arr = extracted_var[z, :, :]
temp_arr2 = np.ma.masked_where(np.ma.getmask(new_bath),
temp_arr)
temp_arr3 = np.ma.masked_where(indices_re == False,
temp_arr2)
print 'indexing:', nutrient + '_ALL_STOCK_' + self.basin + '_' + vert_lev, z, o
vars()[nutrient + '_ALL_STOCK_' + self.basin + '_' +
vert_lev][z, o] = np.mean(temp_arr3)
print 'np mean:', np.mean(temp_arr3)
#print 'np nanmean:', np.nanmean(temp_arr3)
print 'before changing file'
j += 1
f.close() # closing netcdf file
print 'SECOND LOOP TO WRITE CSV FILES'
for vert_lev in vert_lev_list:
for nutrient in nut_list:
print 'Creating variable:', nutrient + '_OUTPUT_' + self.basin + '_' + vert_lev
if nutrient in ('nitrate', 'ammonium', 'modn', 'smopn',
'lmopn', 'phosphate', 'modp', 'smopp', 'lmopp',
'sal'):
print nutrient
print nutrient + '_ALL_STOCK_' + self.basin + '_' + vert_lev
print 'shape Dataframe:', np.shape(
vars()[nutrient + '_ALL_STOCK_' + self.basin + '_' +
vert_lev])
vars()[nutrient + '_OUTPUT_' + self.basin + '_' +
vert_lev] = DataFrame(
vars()[nutrient + '_ALL_STOCK_' + self.basin +
'_' + vert_lev])
vars(
)[nutrient + '_OUTPUT_' + self.basin + '_' +
vert_lev].to_csv(
path_or_buf=
'/tmpdir/alex/PYTHON/FIGS/BestAjustRivE_BoostW_BOUCLE/'
+ nutrient + '_1DMEAN_' + self.basin + '_' +
vert_lev + '.csv')
else:
print 'Error nutrient'
"""Fin du code à exécuter."""
def run(self):
"""Code à exécuter pendant l'exécution du thread."""
if self.basin == 'ALB_W':
y = [37, 37, 34, 34]
x = [-6, -4.5, -4.5, -6]
elif self.basin == 'ALB_C':
y = [37, 34, 34, 37]
x = [-4.5, -4.5, -3, -3]
elif self.basin == 'ALB_E':
y = [34, 37, 37, 34]
x = [-2.5, -2.5, -1, -1]
elif self.basin == 'ALG_W':
y = [35, 38, 39, 35]
x = [-0.6, -0.6, 1, 1]
# if self.basin == 'NWE':
# y = [40,40,45,45];
# x = [0,9,9,0];
# elif self.basin == 'SWE':
# y = [40,40,35,35];
# x = [1.5,9,9,1.5];
# elif self.basin == 'ION':
# y = [30,38,38,40,40,30];
# x = [15,15,16,16,22.5,22.5];
# elif self.basin == 'LEV':
# y = [30,35,35,39,39,30];
# x = [22.5,22.5,28,28,37,37];
else:
print 'Les coordonnées pour : ', self.basin, ' ne sont pas définies ici'
print 'Basin :', self.basin
print ' '
# Selecting the data
vertices = np.array([np.hstack(
(x, 0)), np.hstack((y, 0))], float).transpose()
path = Path(vertices, codes=None, closed=True)
indices = path.contains_points(np.array([
np.reshape(lon_t, (np.size(lon_t))),
np.reshape(lat_t, (np.size(lat_t)))
]).T,
transform=None,
radius=0.0)
indices_re = np.reshape(indices,
np.shape(lon_t)) # this our mask for the zone
for nutrient in nut_list:
for basin in basin_list:
vars()['all_mean_' + nutrient + '_' + self.basin] = np.empty(
[len(list_len)])
#print list_len
for o, fn in enumerate(list_len):
our_file = list_len[o]
# capturer la date
date_file = our_file[0:8]
f = Dataset(file_path + list_len[o], 'r') # open netcdf file
for nutrient in nut_list:
# Extracting data from files in file_list
extracted_var = f.variables[nutrient][:]
extracted_nut = extracted_var # pourquoi cette étape est-elle utile???
# Modif Alex 20/05/2018 for 2D variables
if nutrient == 'chl_tot':
nut = extracted_nut[0, 42, :, :]
else:
nut = extracted_nut[0, :, :]
# On applique notre masque au nouveau jeu de données
new_nut = np.ma.masked_where(np.ma.getmask(new_bath), nut)
vars()['all_mean_' + nutrient + '_' +
self.basin][o] = np.nanmean(
np.ma.masked_array(new_nut, indices_re == False))
extracted_var = [] # TEST ALEX 01/11 18h15
new_nut = []
nut = []
extracted_nut = []
f.close() # closing netcdf file
print 'SECOND LOOP TO WRITE CSV FILES'
# for vert_lev in vert_lev_list:
for nutrient in nut_list:
print 'Creating variable:', 'all_mean_' + nutrient + '_' + self.basin
vars()[nutrient + '_2D_' + self.basin] = DataFrame(
dates, columns=['date'])
vars()[nutrient + '_2D_' +
self.basin]['var'] = vars()['all_mean_' + nutrient + '_' +
self.basin]
vars()[nutrient + '_2D_' + self.basin].to_csv(
path_or_buf=
'/tmpdir/alex/PYTHON/FIGS/BestAjustRivE_BoostW_BOUCLE/' +
nutrient + '_2D_' + self.basin + '.csv')
"""Fin du code à exécuter."""
