def plot_maps_data(self, coastline_lon=None, coastline_lat=None, maptype=0, background_img=None):
'''
Generator of a large set of images.
Arguments :
* coastline_lon * 1-D array
* coastline_lat * 1-D array
* map_type * integer, flag used to choose a plot method in mapplot module
= 0, the default, to call mapplot.mapplot()
= 1, to call mapplot.mapplot_onlycolor()
= 2, to call mapplot.mapplot_nocolor()
'''
fig = None
ax = None
for f in self.__netcdffileslist:
longdate , shortdate = get_date_string(f)
for p in self.__plotlist:
try:
de = DataExtractor(self._mask, filename=f, varname=p.varname)
except NotFoundError as e:
msg="File: %s\n%s" % (f, e)
warn_user(msg)
continue
for i,l in enumerate(p.layerlist):
outfile = "%s/ave.%s.%s.%s" % (self.__outputdir,shortdate, p.varname, l)
mapdata = MapBuilder.get_layer_average(de, l)
try:
clim = p.climlist[i]
if clim is None:
raise ValueError
except ValueError:
if p.clim is None:
raise ValueError("No clim defined for %s %s" % (p.varname, repr(l)))
else:
clim = p.clim
if maptype == 0:
fig, ax = mapplot({'varname':p.varname, 'clim':clim, 'layer':l, 'data':mapdata, 'date':longdate}, fig=fig, ax=ax, mask=self._mask, ncolors=24, coastline_lon=coastline_lon, coastline_lat=coastline_lat)
fig.savefig(outfile + ".png")
if maptype == 1:
dateobj=datetime.datetime.strptime(shortdate,'%Y%m%d')
mapdict={'varname':p.varname, 'longname':p.longvarname(), 'clim':clim, 'layer':l, 'data':mapdata, 'date':dateobj,'units':p.units()}
fig, ax = mapplot_medeaf(mapdict, fig=fig, ax=ax, mask=self._mask, ncolors=24,background_img=background_img)
fig.savefig(outfile + ".png",dpi=86)
pl.close(fig)
if maptype == 2:
fig, ax = mapplot_nocolor({'varname':p.varname, 'clim':clim, 'layer':l, 'data':mapdata, 'date':longdate}, fig=fig, ax=ax, mask=self._mask, ncolors=24, coastline_lon=coastline_lon, coastline_lat=coastline_lat)
fig.canvas.print_figure(outfile + ".svg")
return
fig = None
ax = None
def extract_from_runs(run, varname, subbasin, coast=CoastEnum.open_sea, stat=StatEnum.mean):
"""
Extracts a time series based on a list of file paths.
Args:
- *run*: name of the run
- *varname*: name of the variable to extract
- *subbasin*: an element from SubBasinEnum.
- *coast* (optional): an element from CoastEnum (default:
CoastEnum.open_sea).
- *stat* (optional): an element from StatEnum (default: StatEnum.mean).
Returns: statistics and dates
"""
MYLIST = list()
Data_dict = {}
for dep in DEPTH_LIST:
Data_dict[dep] = list()
run_path = args.indir + run + args.pathtxt
print(run_path)
file_list = sorted(glob(run_path + "/*nc"))
label_list = list()
for f in file_list:
# Get date string from file name
_, ds = get_date_string(path.basename(f))
# Create datetime object from date string
dt = datetime.strptime(ds, "%Y%m%d")
# Append the date to label_list
label_list.append(dt)
# Open it with netCDF4
dset = netCDF4.Dataset(f)
# Append the variable value to data_list
for idep, dep in enumerate(DEPTH_LIST):
depth_index = TheMask.getDepthIndex(dep)
value = dset[varname][subbasin, coast, depth_index, stat].copy()
Data_dict[dep].append(value)
# Close the file
dset.close()
print(value)
LIST = [i for i in range(len(DEPTH_LIST) + 1)]
LIST[0] = label_list
for idep, dep in enumerate(DEPTH_LIST):
LIST[idep + 1] = Data_dict[dep]
return LIST
def plot_from_files(file_list, varname, subbasin, coast=CoastEnum.open_sea, stat=StatEnum.mean, depth_index=0, fig=None, ax=None):
"""
Plots a time series based on a list of file paths.
Args:
- *file_list*: a list of path strings for the files in the time series.
- *varname*: name of the variable to plot.
- *subbasin*: an element from SubBasinEnum.
- *coast* (optional): an element from CoastEnum (default:
CoastEnum.open_sea).
- *stat* (optional): an element from StatEnum (default: StatEnum.mean).
- *depth_index* (optional): the depth index z (default: 0).
- *fig* (optional): an instance of matplotlib figure. A new one will be
created if it is set to None (default: None).
- *ax* (optional): an instance of matplotlib axes. A new one will be
created if it is set to None (default: None).
Returns: a matplotlib figure and axes object.
"""
if (fig is None) or (ax is None):
fig , ax = plt.subplots()
plot_list = list()
label_list = list()
#For each file in file_list
for f in file_list:
#Get date string from file name
_, ds = get_date_string(path.basename(f))
#Create datetime object from date string
dt = datetime.strptime(ds,'%Y%m%d')
#Append the date to label_list
label_list.append(dt)
#Open it with netCDF4
dset = netCDF4.Dataset(f)
#Append the variable value to plot_list
plot_list.append(dset[varname][subbasin, coast, depth_index, stat])
#Close the file
dset.close()
#Plot data
ax.plot(label_list, plot_list)
return fig,ax
def plot_Hovmoeller_diagram(file_list, varname, subbasin, coast=CoastEnum.open_sea, stat=StatEnum.mean, depths=72, fig=None, ax=None):
"""
Plots a time series Hovmoeller diagram.
Args:
- *file_list*: a list of path strings for the files in the time series.
- *varname*: name of the variable to plot.
- *subbasin*: an element from SubBasinEnum.
- *coast* (optional): an element from CoastEnum (default:
CoastEnum.open_sea).
- *stat* (optional): an element from StatEnum (default: StatEnum.mean).
- *depths* (optional): integer OR list OR Numpy array of depth values
(default: 72). If you pass a single integer it will be interpreted
as the lenght of the depths array if you pass a list or Numpy array
it will be used to set the labels and its lenght will be assumed as
the lenght of the depths array.
- *fig* (optional): an instance of matplotlib figure. A new one will be
created if it is set to None (default: None).
- *ax* (optional): an instance of matplotlib axes. A new one will be
created if it is set to None (default: None).
Returns: a matplotlib Figure, Axes and QuadMesh object tuple.
"""
if (fig is None) or (ax is None):
fig , ax = plt.subplots()
dlabels = None
if isinstance(depths, (int, long)):
pass
elif isinstance(depths, (list, tuple)):
dlabels = np.array(depths)
depths = len(dlabels)
elif isinstance(depths, np.ndarray):
#If 1D array
if len(depths.shape) == 1:
dlabels = np.copy(depths)
depths = dlabels.shape[0]
else:
raise ValueError("Invalid depths argument")
else:
raise ValueError("Invalid depths argument")
plotmat = np.zeros([depths, len(file_list)])
xlabel_list = list()
#For each file
for i,f in enumerate(file_list):
#Get date string from file name
_, ds = get_date_string(path.basename(f))
#Create datetime object from date string
dt = datetime.strptime(ds,'%Y%m%d')
#Append the date to xlabel_list
xlabel_list.append(dt)
#Open it with netCDF4
dset = netCDF4.Dataset(f)
#Copy the data in the plot matrix
plotmat[:,i] = dset[varname][subbasin, coast, 0:depths, stat]
#Close the file
dset.close()
#Create the meshgrid
xlabel_list = mpldates.date2num(xlabel_list)
xs,ys = np.meshgrid(xlabel_list, dlabels)
#Plot the matrix
quadmesh = ax.pcolormesh(xs, ys, plotmat,shading='flat')# default is 'flat'
#Inform matplotlib that the x axis is made by dates
ax.xaxis_date()
return fig, ax, quadmesh
