def draw_map(intensity, city, save_file_path=None):
# 切り出し
intensity_city, lats_city, lons_city = crop_latlon(
intensity, city, inifile.getint('area', 'd'))
flat_lats_city = np.ravel(lats_city)
flat_lons_city = np.ravel(lons_city)
# 色指定
# black指定 -> 海岸線白，背景黒 white指定 -> 海岸線黒，背景白
base_color = inifile.get('image', 'base_color')
coastline_color = 'white' if base_color == 'black' else 'black'
zero_color = 0 if base_color == 'black' else 1
# 色の変更
cm = eval('plt.cm.' + inifile.get('image', 'color_map'))
cm_list = cm(np.arange(cm.N))
cm_list[0, :3] = zero_color # カラースケール0番目の値の色を変更
mycmap = ListedColormap(cm_list)
# 描画矩形座標を指定
m = Basemap(llcrnrlat=lats_city.min(), urcrnrlat=lats_city.max(), \
llcrnrlon=lons_city.min(), urcrnrlon=lons_city.max(), \
resolution=inifile.get('image', 'coastline_quality'))
# 海岸線
if inifile.getboolean('image', 'draw_coastline'):
m.drawcoastlines(color=coastline_color)
# 描画
m.contourf(x=flat_lons_city, y=flat_lats_city, data=intensity_city, \
levels=list(range(0,255)), latlon=True, tri=True, cmap=mycmap)
# 枠を消す
plt.gca().spines['right'].set_visible(False)
plt.gca().spines['top'].set_visible(False)
plt.gca().spines['left'].set_visible(False)
plt.gca().spines['bottom'].set_visible(False)
# カラーバー
if inifile.getboolean('image', 'draw_colorbar'):
plt.colorbar()
# save
plt.savefig(save_file_path, transparent=True, \
bbox_inches = 'tight', pad_inches = 0, facecolor=base_color) # 余白を消す
plt.clf()
def plot_cappi(self, field, cappi_height, plot_km=False,
mask_procedure=None, mask_tuple=None,
cminmax=(0., 60.), clevs=25, vmin=15., vmax=60.,
cmap='gist_ncar', discrete_cmap_levels=None,
title=" ", title_size=20,
color_bar=True, clabel='dBZ', cb_pad="5%",
cb_loc='right', cb_tick_int=2, ax=None, fig=None,
save_kmz=False, kmz_filepath=None, kmz_filename=None):
"""
Produce a CAPPI (constant altitude plan position indicator) plot
using the Tail Doppler Radar data.
Parameters
----------
field : str
3-D variable (e.g. Reflectivity [dBZ]) to use in plot.
cappi_height : float
Height in meters at which to plot the horizontal field.
plot_km : boolean
True to convert meters to kilometers for cappi_height. False
retains meters information.
mask_procedure : str
String indicating how to apply mask via numpy, possibilities are:
'less', 'less_equal', 'greater', 'greater_equal',
'equal', 'inside', 'outside'.
mask_tuple : (str, float[, float])
Tuple containing the field name and value(s) below which to mask
field prior to plotting, for example to mask all data where.
cminmax : tuple
(min,max) values for controur levels.
clevs : int
Number of contour levels.
vmin : float
Minimum contour value to display.
vmax : float
Maximum contour value to display.
cmap : str
Matplotlib color map to use.
discrete_cmap_levels : array
A list of levels to be used for display. If chosen discrete
color will be used in the colorbar instead of a linear luminance
mapping.
title : str
Plot title.
title_size : int
Font size of title to display.
color_bar : boolean
True to add colorbar, False does not.
clabel : str
Label for colorbar (e.g. units 'dBZ').
cb_pad : str
Pad to move colorbar, in the form "5%", pos is to
right for righthand location.
cb_loc : str
Location of colorbar, default is 'right', also available:
'bottom', 'top', 'left'.
cb_tick_int : int
Interval to use for colorbar tick labels,
higher number "thins" labels.
ax : Matplotlib axis instance
Axis to plot.
None will use the current axis.
fig : Matplotlib figure instance
Figure on which to add the plot.
None will use the current figure.
Notes
-----
Defaults are established during DYNAMO project analysis.
"""
# parse parameters
ax, fig = common._parse_ax_fig(ax, fig)
# Grab the variable dictionary of interest to plot
Var = self.fields[field]
# Return masked or unmasked variable
Var, Data = self._get_variable_dict_data(field)
if mask_procedure is not None:
Data = common.get_masked_data(Data, mask_procedure, mask_tuple)
# Create contour level array
clevels = np.linspace(cminmax[0], cminmax[1], clevs)
# Find the closest vertical point
Zind = common.find_nearest_indices(
self.height['data'][:], cappi_height)
if plot_km:
levelht = self.height['data'][Zind] / 1000.
else:
levelht = self.height['data'][Zind]
print("Closest level: %4.1f" % levelht)
# Convert lats/lons to 2D grid
Lon2D, Lat2D = np.meshgrid(self.longitude['data'][:],
self.latitude['data'][:])
# Convert lats/lons to map projection coordinates
x, y = self.basemap(Lon2D, Lat2D)
# Get the colormap and calculate data spaced by number of levels
norm = None
if discrete_cmap_levels is not None:
cm = plt.get_cmap(cmap)
try:
levpos = np.rint(np.squeeze(
[np.linspace(0, 255,
len(discrete_cmap_levels))])).astype(int)
# Convert levels to colormap values
cmap, norm = from_levels_and_colors(
discrete_cmap_levels, cm(levpos), extend='max')
except:
print("Keyword error: 'discrete_cmap_levels' must "
"be a list of float or integer")
# Plot the data
cs = self.basemap.pcolormesh(x, y, Data[Zind, :, :],
vmin=vmin, vmax=vmax,
norm=norm, cmap=cmap)
#NG Set a couple of keyword defaults for KMZ option
show_legend, legend_label = False, ' '
# Add Colorbar
if color_bar:
cbStr = "%s at %4.1f %s" % (Var['long_name'],
self.height['data'][Zind],
self.height['units'])
cb = self.basemap.colorbar(
cs, location=cb_loc, pad=cb_pad) # ,ticks=clevels)
cb.set_label(cbStr)
# Set the number of ticks in the colorbar based upon
# number of contours and the tick interval selected
tick_locator = ticker.MaxNLocator(nbins=int(clevs / cb_tick_int))
cb.locator = tick_locator
cb.update_ticks()
show_legend = True
legend_label = cbStr
# Save the KMZ file if requested
if save_kmz:
lonrange = (np.min(self.longitude['data'][:]),
np.max(self.longitude['data'][:]))
latrange = (np.min(self.latitude['data'][:]),
np.max(self.latitude['data'][:]))
times = None
if kmz_filepath is None:
kmz_filepath = os.getcwd()
if kmz_filename is None:
kmz_filename = ('awot_' + self.radar['platform'] + '_' +
self.radar['flight_number'] + '_altitude' +
'.kmz')
util.write_kmz.write_kmz(fig, ax, cs, lonrange, latrange, times,
file_path=kmz_filepath,
file_name=kmz_filename,
show_legend=show_legend,
legend_label=legend_label)
# Add title
ax.set_title(title, fontsize=title_size)
return
def plot_lf(self, field=None, mask_procedure=None, mask_tuple=None,
cminmax=(0., 60.), clevs=25, vmin=15., vmax=60.,
cmap='gist_ncar', discrete_cmap_levels=None,
title=" ", title_size=20,
color_bar=True, clabel='dBZ',
cb_pad="5%", cb_loc='right', cb_tick_int=2,
ax=None, fig=None):
"""
Produce a CAPPI (constant altitude plan position indicator) plot
using the Tail Doppler Radar data.
Parameters
----------
field : str
3-D variable (e.g. Reflectivity [dBZ]) to use in plot
mask_procedure : str
String indicating how to apply mask via numpy, possibilities are:
'less', 'less_equal', 'greater', 'greater_equal',
'equal', 'inside', 'outside'.
mask_tuple : (str, float[, float])
Tuple containing the field name and value(s) below which to mask
field prior to plotting, for example to mask all data where.
cminmax : tuple
(min,max) values for controur levels
clevs : int
Number of contour levels.
vmin : float
Minimum contour value to display.
vmax : float
Maximum contour value to display.
cmap : str
Matplotlib color map to use.
discrete_cmap_levels : array
A list of levels to be used for display. If chosen discrete
color will be used in the colorbar instead of a linear luminance
mapping.
title : str
Plot title.
title_size : int
Font size of title to display.
clabel : str
Label for colorbar (e.g. units 'dBZ').
color_bar : boolean
True to add colorbar, False does not.
cb_pad : str
Pad to move colorbar, in the form "5%", pos is to right
for righthand location.
cb_loc : str
Location of colorbar, default is 'right', also available:
'bottom', 'top', 'left'.
cb_tick_int : int
Interval to use for colorbar tick labels,
higher number "thins" labels.
ax : Matplotlib axis instance
Axis to plot. None will use the current axis.
fig : Matplotlib figure instance
Figure on which to add the plot. None will use the current figure.
"""
# parse parameters
ax, fig = common._parse_ax_fig(ax, fig)
# Grab the variable dictionary of interest to plot
if field is None:
field = 'reflectivity'
# Return masked or unmasked variable
Var, Data = self._get_variable_dict_data(field)
if mask_procedure is not None:
Data = common.get_masked_data(Data, mask_procedure, mask_tuple)
# Create contour level array
clevels = np.linspace(cminmax[0], cminmax[1], clevs)
# Convert lats/lons to 2D grid
Lon2D, Lat2D = np.meshgrid(self.longitude['data'][
:], self.latitude['data'][:])
# Convert lats/lons to map projection coordinates
x, y = self.basemap(Lon2D, Lat2D)
# Get the colormap and calculate data spaced by number of levels
norm = None
if discrete_cmap_levels is not None:
cm = plt.get_cmap(cmap)
try:
levpos = np.rint(np.squeeze(
[np.linspace(0, 255,
len(discrete_cmap_levels))])).astype(int)
# Convert levels to colormap values
cmap, norm = from_levels_and_colors(
discrete_cmap_levels, cm(levpos), extend='max')
except:
print("Keyword error: 'discrete_cmap_levels' must "
"be a list of float or integer")
p = self.basemap.pcolormesh(x, y, Data, vmin=vmin, vmax=vmax,
norm=norm, cmap=cmap)
# Add Colorbar
if color_bar:
cbStr = Var['long_name'] + ' ' + self.height['units']
cb = self.basemap.colorbar(
p, location=cb_loc, pad=cb_pad) # ,ticks=clevels)
cb.set_label(cbStr)
# Set the number of ticks in the colorbar based upon number of
# contours
tick_locator = ticker.MaxNLocator(nbins=int(clevs / cb_tick_int))
cb.locator = tick_locator
cb.update_ticks()
# Add title
ax.set_title(title, fontsize=title_size)
def plot_cappi(self,
field,
cappi_height,
plot_km=False,
mask_procedure=None,
mask_tuple=None,
cminmax=(0., 60.),
clevs=25,
vmin=15.,
vmax=60.,
cmap='gist_ncar',
discrete_cmap_levels=None,
title=" ",
title_size=20,
color_bar=True,
clabel='dBZ',
cb_pad="5%",
cb_loc='right',
cb_tick_int=2,
ax=None,
fig=None,
save_kmz=False,
kmz_filepath=None,
kmz_filename=None):
"""
Produce a CAPPI (constant altitude plan position indicator) plot
using the Tail Doppler Radar data.
Parameters
----------
field : str
3-D variable (e.g. Reflectivity [dBZ]) to use in plot.
cappi_height : float
Height in meters at which to plot the horizontal field.
plot_km : boolean
True to convert meters to kilometers for cappi_height. False
retains meters information.
mask_procedure : str
String indicating how to apply mask via numpy, possibilities are:
'less', 'less_equal', 'greater', 'greater_equal',
'equal', 'inside', 'outside'.
mask_tuple : (str, float[, float])
Tuple containing the field name and value(s) below which to mask
field prior to plotting, for example to mask all data where.
cminmax : tuple
(min,max) values for controur levels.
clevs : int
Number of contour levels.
vmin : float
Minimum contour value to display.
vmax : float
Maximum contour value to display.
cmap : str
Matplotlib color map to use.
discrete_cmap_levels : array
A list of levels to be used for display. If chosen discrete
color will be used in the colorbar instead of a linear luminance
mapping.
title : str
Plot title.
title_size : int
Font size of title to display.
color_bar : boolean
True to add colorbar, False does not.
clabel : str
Label for colorbar (e.g. units 'dBZ').
cb_pad : str
Pad to move colorbar, in the form "5%", pos is to
right for righthand location.
cb_loc : str
Location of colorbar, default is 'right', also available:
'bottom', 'top', 'left'.
cb_tick_int : int
Interval to use for colorbar tick labels,
higher number "thins" labels.
ax : Matplotlib axis instance
Axis to plot.
None will use the current axis.
fig : Matplotlib figure instance
Figure on which to add the plot.
None will use the current figure.
Notes
-----
Defaults are established during DYNAMO project analysis.
"""
# parse parameters
ax, fig = common._parse_ax_fig(ax, fig)
# Grab the variable dictionary of interest to plot
Var = self.fields[field]
# Return masked or unmasked variable
Var, Data = self._get_variable_dict_data(field)
if mask_procedure is not None:
Data = common.get_masked_data(Data, mask_procedure, mask_tuple)
# Create contour level array
clevels = np.linspace(cminmax[0], cminmax[1], clevs)
# Find the closest vertical point
Zind = common.find_nearest_indices(self.height['data'][:],
cappi_height)
if plot_km:
levelht = self.height['data'][Zind] / 1000.
else:
levelht = self.height['data'][Zind]
print("Closest level: %4.1f" % levelht)
# Convert lats/lons to 2D grid
Lon2D, Lat2D = np.meshgrid(self.longitude['data'][:],
self.latitude['data'][:])
# Convert lats/lons to map projection coordinates
x, y = self.basemap(Lon2D, Lat2D)
# Get the colormap and calculate data spaced by number of levels
norm = None
if discrete_cmap_levels is not None:
cm = plt.get_cmap(cmap)
try:
levpos = np.rint(
np.squeeze(
[np.linspace(0, 255,
len(discrete_cmap_levels))])).astype(int)
# Convert levels to colormap values
cmap, norm = from_levels_and_colors(discrete_cmap_levels,
cm(levpos),
extend='max')
except:
print("Keyword error: 'discrete_cmap_levels' must "
"be a list of float or integer")
# Plot the data
cs = self.basemap.pcolormesh(x,
y,
Data[Zind, :, :],
vmin=vmin,
vmax=vmax,
norm=norm,
cmap=cmap)
#NG Set a couple of keyword defaults for KMZ option
show_legend, legend_label = False, ' '
# Add Colorbar
if color_bar:
cbStr = "%s at %4.1f %s" % (Var['long_name'],
self.height['data'][Zind],
self.height['units'])
cb = self.basemap.colorbar(cs, location=cb_loc,
pad=cb_pad) # ,ticks=clevels)
cb.set_label(cbStr)
# Set the number of ticks in the colorbar based upon
# number of contours and the tick interval selected
tick_locator = ticker.MaxNLocator(nbins=int(clevs / cb_tick_int))
cb.locator = tick_locator
cb.update_ticks()
show_legend = True
legend_label = cbStr
# Save the KMZ file if requested
if save_kmz:
lonrange = (np.min(self.longitude['data'][:]),
np.max(self.longitude['data'][:]))
latrange = (np.min(self.latitude['data'][:]),
np.max(self.latitude['data'][:]))
times = None
if kmz_filepath is None:
kmz_filepath = os.getcwd()
if kmz_filename is None:
kmz_filename = ('awot_' + self.radar['platform'] + '_' +
self.radar['flight_number'] + '_altitude' +
'.kmz')
util.write_kmz.write_kmz(fig,
ax,
cs,
lonrange,
latrange,
times,
file_path=kmz_filepath,
file_name=kmz_filename,
show_legend=show_legend,
legend_label=legend_label)
# Add title
ax.set_title(title, fontsize=title_size)
return
def plot_lf(self,
field=None,
mask_procedure=None,
mask_tuple=None,
cminmax=(0., 60.),
clevs=25,
vmin=15.,
vmax=60.,
cmap='gist_ncar',
discrete_cmap_levels=None,
title=" ",
title_size=20,
color_bar=True,
clabel='dBZ',
cb_pad="5%",
cb_loc='right',
cb_tick_int=2,
ax=None,
fig=None):
"""
Produce a CAPPI (constant altitude plan position indicator) plot
using the Tail Doppler Radar data.
Parameters
----------
field : str
3-D variable (e.g. Reflectivity [dBZ]) to use in plot
mask_procedure : str
String indicating how to apply mask via numpy, possibilities are:
'less', 'less_equal', 'greater', 'greater_equal',
'equal', 'inside', 'outside'.
mask_tuple : (str, float[, float])
Tuple containing the field name and value(s) below which to mask
field prior to plotting, for example to mask all data where.
cminmax : tuple
(min,max) values for controur levels
clevs : int
Number of contour levels.
vmin : float
Minimum contour value to display.
vmax : float
Maximum contour value to display.
cmap : str
Matplotlib color map to use.
discrete_cmap_levels : array
A list of levels to be used for display. If chosen discrete
color will be used in the colorbar instead of a linear luminance
mapping.
title : str
Plot title.
title_size : int
Font size of title to display.
clabel : str
Label for colorbar (e.g. units 'dBZ').
color_bar : boolean
True to add colorbar, False does not.
cb_pad : str
Pad to move colorbar, in the form "5%", pos is to right
for righthand location.
cb_loc : str
Location of colorbar, default is 'right', also available:
'bottom', 'top', 'left'.
cb_tick_int : int
Interval to use for colorbar tick labels,
higher number "thins" labels.
ax : Matplotlib axis instance
Axis to plot. None will use the current axis.
fig : Matplotlib figure instance
Figure on which to add the plot. None will use the current figure.
"""
# parse parameters
ax, fig = common._parse_ax_fig(ax, fig)
# Grab the variable dictionary of interest to plot
if field is None:
field = 'reflectivity'
# Return masked or unmasked variable
Var, Data = self._get_variable_dict_data(field)
if mask_procedure is not None:
Data = common.get_masked_data(Data, mask_procedure, mask_tuple)
# Create contour level array
clevels = np.linspace(cminmax[0], cminmax[1], clevs)
# Convert lats/lons to 2D grid
Lon2D, Lat2D = np.meshgrid(self.longitude['data'][:],
self.latitude['data'][:])
# Convert lats/lons to map projection coordinates
x, y = self.basemap(Lon2D, Lat2D)
# Get the colormap and calculate data spaced by number of levels
norm = None
if discrete_cmap_levels is not None:
cm = plt.get_cmap(cmap)
try:
levpos = np.rint(
np.squeeze(
[np.linspace(0, 255,
len(discrete_cmap_levels))])).astype(int)
# Convert levels to colormap values
cmap, norm = from_levels_and_colors(discrete_cmap_levels,
cm(levpos),
extend='max')
except:
print("Keyword error: 'discrete_cmap_levels' must "
"be a list of float or integer")
p = self.basemap.pcolormesh(x,
y,
Data,
vmin=vmin,
vmax=vmax,
norm=norm,
cmap=cmap)
# Add Colorbar
if color_bar:
cbStr = Var['long_name'] + ' ' + self.height['units']
cb = self.basemap.colorbar(p, location=cb_loc,
pad=cb_pad) # ,ticks=clevels)
cb.set_label(cbStr)
# Set the number of ticks in the colorbar based upon number of
# contours
tick_locator = ticker.MaxNLocator(nbins=int(clevs / cb_tick_int))
cb.locator = tick_locator
cb.update_ticks()
# Add title
ax.set_title(title, fontsize=title_size)
dens = [x.replace(',', '') for x in dens]
dens = [float(x) for x in dens]
color = [x / max(dens) for x in dens]
for a, b in enumerate(stateDens):
stateDens[a].append(color[a])
for shape_dict in m.states_info:
states_names.append(shape_dict['NAME'])
# get Texas and draw the filled polygon
for state in states_names:
stateD = [x for x in stateDens if x[0] == state]
if stateD:
stateD = stateD[0]
seg = m.states[states_names.index(state)]
poly = Polygon(seg, facecolor=cm(stateD[-1]), zorder=0)
ax.add_patch(poly)
lats = [i[0] for i in NPL]
lons = [i[1] for i in NPL]
m.scatter(lons,
lats,
latlon=True,
zorder=2,
s=1,
color="black",
picker=True)
print(ind)
#print(lats, lons)
def onclick(event):