def zoommap(vals, title, label):
fig = plt.figure(figsize=(12,8))
plt.subplots_adjust(left=0.05,right=0.95,top=0.90,bottom=0.05,
wspace=0.15,hspace=0.05)
ax = plt.subplot(211)
m = Basemap(projection = 'mill', llcrnrlat = -45, llcrnrlon = -160,
urcrnrlat= 82, urcrnrlon = 170, resolution = 'c')
m.drawcoastlines(linewidth = 0.5)
plt.subplots_adjust(left=0.05,right=0.95,top=0.90,bottom=0.05,
wspace=0.15,hspace=0.05)
for row in vals:
x,y = m(vals.lon.values, vals.lat.values)
m.scatter(x,y, s = 20, color = vals.color.values)
ax.text(0.03,0.95, label, size = 12, horizontalalignment='center',
verticalalignment='center', transform=ax.transAxes)
plt.title(title, fontsize = 12)
#Zoom Europe
axins_1 = zoomed_inset_axes(ax, 2, loc=2, bbox_to_anchor=(0.42, 0.48),
bbox_transform=ax.figure.transFigure)
axins_1.scatter(x, y, s = 20, c = vals.color.values)
m.drawcoastlines(linewidth = 0.5)
x2,y2 = m(-12,35)
x3,y3 = m(40,65)
axins_1.set_xlim(x2,x3)
axins_1.set_ylim(y2,y3)
axes = mark_inset(ax, axins_1, loc1=1, loc2=2, linewidth=1)
#Zoom Australia
axins_2 = zoomed_inset_axes(ax, 2.2, loc=3, bbox_to_anchor=(0.61, 0.255),
bbox_transform=ax.figure.transFigure)
axins_2.scatter(x, y, s = 20, c = vals.color.values)
m.drawcoastlines(linewidth = 0.5)
x2,y2 = m(110,-43)
x3,y3 = m(155,-10)
axins_2.set_xlim(x2,x3)
axins_2.set_ylim(y2,y3)
axes = mark_inset(ax, axins_2, loc1=1, loc2=2,linewidth=1)
#Zoom US
axins_3 = zoomed_inset_axes(ax, 1.6, loc=3, bbox_to_anchor=(0.21, 0.25),
bbox_transform=ax.figure.transFigure)
axins_3.scatter(x, y, s = 20, c = vals.color.values)
m.drawcoastlines(linewidth = 0.5)
x2,y2 = m(-130,22)
x3,y3 = m(-60,63)
axins_3.set_xlim(x2,x3)
axins_3.set_ylim(y2,y3)
axes = mark_inset(ax, axins_3, loc1=1, loc2=2, linewidth=1)
return(fig, axes)
figure()
d = NormalDistr(1, 1) * NormalDistr(1, 1)
demo_distr(d)
#show()
figure()
d = NormalDistr(2, 1) * NormalDistr(2, 1)
demo_distr(d)
#show()
figure()
d = NormalDistr(3, 1) * NormalDistr(3, 1)
d.plot()
d.hist()
ax = gca()
axins = zoomed_inset_axes(ax, 6, loc=1)
d.plot(xmin=-1.5, xmax=1.5)
axins.set_xlim(-1.5, 1.5)
xticks(rotation="vertical")
axins.set_ylim(0, 0.01)
mark_inset(ax, axins, loc1=2, loc2=4, fc="none", ec="0.5")
#show()
figure()
d = NormalDistr(4, 1) * NormalDistr(4, 1)
d.plot()
d.hist()
ax = gca()
axins = zoomed_inset_axes(ax, 12000, loc=1)
d.plot(xmin=-.001, xmax=.001)
axins.set_xlim(-.001, .001)
return z, (-3, 4, -4, 3) fig = plt.figure(1, [5, 4]) ax = fig.add_subplot(111) # prepare the demo image Z, extent = get_demo_image() Z2 = np.zeros([150, 150], dtype="d") ny, nx = Z.shape Z2[30:30 + ny, 30:30 + nx] = Z # extent = [-3, 4, -4, 3] ax.imshow(Z2, extent=extent, interpolation="nearest", origin="lower") axins = zoomed_inset_axes(ax, 6, loc=1) # zoom = 6 axins.imshow(Z2, extent=extent, interpolation="nearest", origin="lower") # sub region of the original image x1, x2, y1, y2 = -1.5, -0.9, -2.5, -1.9 axins.set_xlim(x1, x2) axins.set_ylim(y1, y2) plt.xticks(visible=False) plt.yticks(visible=False) # draw a bbox of the region of the inset axes in the parent axes and # connecting lines between the bbox and the inset axes area mark_inset(ax, axins, loc1=2, loc2=4, fc="none", ec="0.5") plt.draw()
grid = AxesGrid(fig, 111, nrows_ncols=(3, 2),
axes_pad=0.0,
share_all=True,
)
# loop on each map
for i, fname in enumerate(fname_ext):
data = np.flipud(pyfits.fitsopen(fname)[0].data.T)
if i == 0:
data /= 8.
data = data ** .25
data[np.isnan(data)] = 0
extent = [0., 192., 0., 192.]
im = grid[i].imshow(data, extent=extent, interpolation="nearest")
grid[i].text(10, 170, letters[i], fontsize=20, color="white")
data_zoom = data[135:155, 80:100,]
axins = zoomed_inset_axes(grid[i], 2, loc=3) # zoom = 6
extent = [80., 100., 192. - 155., 192. - 135, ]
im2 = axins.imshow(data_zoom, extent=extent, interpolation="nearest")
im2.set_clim([data.min(), data.max()])
plt.xticks(visible=False)
plt.yticks(visible=False)
im.set_clim([0., .3])
im2.set_clim([0., .15])
mark_inset(grid[i], axins, loc1=2, loc2=4, fc="none", ec="0.5")
fontsize=20
posx = 10
posy = 170
grid[-1].text(posx, posy, "(a): PL", fontsize=fontsize, color="black")
posy -= 25
grid[-1].text(posx, posy, "(b): PLI", fontsize=fontsize, color="black")
for j, c in enumerate(r):
axis.text(i + subextent[0] + 0.5, j + subextent[2] + 0.5, c,
weight='demibold',
horizontalalignment='center',
verticalalignment='center',
color=_determine_ideal_value(c, ma=array.max()))
if __name__ == '__main__':
tux = skimage.io.imread('./tux_icon-33px.png')
fig, ax = plt.subplots(figsize=(5, 5))
extent0 = [0, 33, 0, 36]
ax.imshow(tux, extent=extent0, interpolation='nearest')
axins = zoomed_inset_axes(ax, 3.4, loc=3)
extent1 = [18, 23, 18, 23]
overlay_numbers(axins, tux, extent0, extent1, interpolation='none')
axins.set_xlim(*extent1[:2])
axins.set_ylim(*extent1[2:])
plt.xticks(visible=False)
plt.yticks(visible=False)
plt.gca().xaxis.set_major_locator(plt.NullLocator())
plt.gca().yaxis.set_major_locator(plt.NullLocator())
mark_inset(ax, axins, loc1=2, loc2=4, fc="none", ec="0.5")
ax.axis('off')
fig.tight_layout()
fig.savefig('./tux_inset.png', dpi=150)
# plt.show()
out_fname = fname + '.png'
print('displaying ' + fname)
title_str = fname.split(os.sep)[-1]
t = np.flipud(pyfits.fitsopen(fname_ext)[0].data.T)
fig = plt.figure(1, [5,4])
ax = fig.add_subplot(111)
#imshow(t , interpolation="nearest")
#imshow((t - t.min())) ** .25, interpolation="nearest")
tt = t ** .25
tt[np.isnan(tt)] = 0
extent = [0., 192., 0., 192.]
ax.imshow(tt, extent=extent, interpolation="nearest")
tzoom = tt[135:155, 80:100,]
axins = zoomed_inset_axes(ax, 2, loc=3) # zoom = 6
extent = [80., 100., 192. - 155., 192. - 135, ]
im = axins.imshow(tzoom, extent=extent, interpolation="nearest")
im.set_clim([tt.min(), tt.max()])
plt.xticks(visible=False)
plt.yticks(visible=False)
#x1, x2, y1, y2 = 80., 100., 135., 155.,
#axins.set_xlim(x1, x2)
#axins.set_ylim(y1, y2)
mark_inset(ax, axins, loc1=2, loc2=4, fc="none", ec="0.5")
#plt.title(title_str)
#plt.colorbar()
#plt.xlabel('Right Ascension')
#plt.ylabel('Declination')
plt.show()
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid.inset_locator import zoomed_inset_axes
from mpl_toolkits.axes_grid.inset_locator import mark_inset
import numpy as np
def get_demo_image():
from matplotlib.cbook import get_sample_data
import numpy as np
f = get_sample_data("axes_grid/bivariate_normal.npy", asfileobj=False)
z = np.load(f)
return z, (-3,4,-4,3)
fig = plt.figure(1, [5,4])
ax = fig.add_subplot(111)
Z, extent = get_demo_image()
Z2 = np.zeros([150, 150], dtype="d")
ny, nx = Z.shape
Z2[30:30+ny, 30:30+nx] = Z
ax.imshow(Z2, extent=extent, interpolation="nearest",
origin="lower")
axins = zoomed_inset_axes(ax, 6, loc=1) # zoom = 6
axins.imshow(Z2, extent=extent, interpolation="nearest",
origin="lower")
x1, x2, y1, y2 = -1.5, -0.9, -2.5, -1.9
axins.set_xlim(x1, x2)
axins.set_ylim(y1, y2)
plt.xticks(visible=False)
plt.yticks(visible=False)
mark_inset(ax, axins, loc1=2, loc2=4, fc="none", ec="0.5")
plt.draw()
plt.show()
def zoommap(vals, title, label):
fig = plt.figure(figsize=(12, 8))
plt.subplots_adjust(left=0.05,
right=0.95,
top=0.90,
bottom=0.05,
wspace=0.15,
hspace=0.05)
ax = plt.subplot(211)
m = Basemap(projection='mill',
llcrnrlat=-45,
llcrnrlon=-160,
urcrnrlat=82,
urcrnrlon=170,
resolution='c')
x, y = m(lons, lats)
m.drawcoastlines(linewidth=0.5)
plt.subplots_adjust(left=0.05,
right=0.95,
top=0.90,
bottom=0.05,
wspace=0.15,
hspace=0.05)
cmap = matplotlib.colors.ListedColormap(
['#d73027', '#fc8d59', '#fee090', '#e0f3f8', '#91bfdb', '#4575b4'])
bounds = [0, 0.3, 0.5, 0.7, 0.8, 0.9, 1.0]
norm = matplotlib.colors.BoundaryNorm(bounds, cmap.N)
m.scatter(x, y, s=40, c=vals, cmap=cmap, norm=norm)
plt.colorbar(orientation="vertical",
boundaries=bounds,
spacing='proportional',
ticks=bounds)
ax.text(0.03,
0.95,
label,
size=12,
horizontalalignment='center',
verticalalignment='center',
transform=ax.transAxes)
plt.title(title, fontsize=14)
#Zoom Europe
axins_1 = zoomed_inset_axes(ax,
2,
loc=2,
bbox_to_anchor=(0.396, 0.48),
bbox_transform=ax.figure.transFigure)
axins_1.scatter(x, y, s=20, c=vals, cmap=cmap, norm=norm)
m.drawcoastlines(linewidth=0.5)
x2, y2 = m(-12, 35)
x3, y3 = m(40, 65)
axins_1.set_xlim(x2, x3)
axins_1.set_ylim(y2, y3)
axes = mark_inset(ax, axins_1, loc1=1, loc2=2, linewidth=1)
#Zoom Australia
axins_2 = zoomed_inset_axes(ax,
2.2,
loc=3,
bbox_to_anchor=(0.59, 0.255),
bbox_transform=ax.figure.transFigure)
axins_2.scatter(x, y, s=20, c=vals, cmap=cmap, norm=norm)
m.drawcoastlines(linewidth=0.5)
x2, y2 = m(110, -43)
x3, y3 = m(155, -10)
axins_2.set_xlim(x2, x3)
axins_2.set_ylim(y2, y3)
axes = mark_inset(ax, axins_2, loc1=1, loc2=2, linewidth=1)
#Zoom US
axins_3 = zoomed_inset_axes(ax,
1.6,
loc=3,
bbox_to_anchor=(0.19, 0.25),
bbox_transform=ax.figure.transFigure)
axins_3.scatter(x, y, s=20, c=vals, cmap=cmap, norm=norm)
m.drawcoastlines(linewidth=0.5)
x2, y2 = m(-130, 22)
x3, y3 = m(-60, 63)
axins_3.set_xlim(x2, x3)
axins_3.set_ylim(y2, y3)
axes = mark_inset(ax, axins_3, loc1=1, loc2=2, linewidth=1)
return (fig, axes)
j + subextent[2] + 0.5,
c,
weight='demibold',
horizontalalignment='center',
verticalalignment='center',
color=_determine_ideal_value(c, ma=array.max()))
if __name__ == '__main__':
tux = skimage.io.imread('./tux_icon-33px.png')
fig, ax = plt.subplots(figsize=(5, 5))
extent0 = [0, 33, 0, 36]
ax.imshow(tux, extent=extent0, interpolation='nearest')
axins = zoomed_inset_axes(ax, 3.4, loc=3)
extent1 = [18, 23, 18, 23]
overlay_numbers(axins, tux, extent0, extent1, interpolation='none')
axins.set_xlim(*extent1[:2])
axins.set_ylim(*extent1[2:])
plt.xticks(visible=False)
plt.yticks(visible=False)
plt.gca().xaxis.set_major_locator(plt.NullLocator())
plt.gca().yaxis.set_major_locator(plt.NullLocator())
mark_inset(ax, axins, loc1=2, loc2=4, fc="none", ec="0.5")
ax.axis('off')
fig.tight_layout()
fig.savefig('./tux_inset.png', dpi=150)
# plt.show()
nrows_ncols=(3, 2),
axes_pad=0.0,
share_all=True,
)
# loop on each map
for i, fname in enumerate(fname_ext):
data = np.flipud(pyfits.fitsopen(fname)[0].data.T)
if i == 0:
data /= 8.
data = data**.25
data[np.isnan(data)] = 0
extent = [0., 192., 0., 192.]
im = grid[i].imshow(data, extent=extent, interpolation="nearest")
grid[i].text(10, 170, letters[i], fontsize=20, color="white")
data_zoom = data[135:155, 80:100, ]
axins = zoomed_inset_axes(grid[i], 2, loc=3) # zoom = 6
extent = [
80.,
100.,
192. - 155.,
192. - 135,
]
im2 = axins.imshow(data_zoom, extent=extent, interpolation="nearest")
im2.set_clim([data.min(), data.max()])
plt.xticks(visible=False)
plt.yticks(visible=False)
im.set_clim([0., .3])
im2.set_clim([0., .3])
mark_inset(grid[i], axins, loc1=2, loc2=4, fc="none", ec="0.5")
grid[0].set_xticks(())
fig = plt.figure(1, [5.5, 3])
# first subplot
ax = fig.add_subplot(1,2,1)
ax.set_aspect(1.)
axins = inset_axes(ax,
width="30%", # width = 30% of parent_bbox
height=1., # height : 1 inch
loc=3)
plt.xticks(visible=False)
plt.yticks(visible=False)
# second subplot
ax = fig.add_subplot(1,2,2)
ax.set_aspect(1.)
axins = zoomed_inset_axes(ax, 0.5, loc=1) # zoom = 0.5
plt.xticks(visible=False)
plt.yticks(visible=False)
add_sizebar(ax, 0.5)
add_sizebar(axins, 0.5)
plt.draw()
plt.show()
pl.yticks(fontsize=15) pl.ylim(0, 1) pl.xlim(0, 90) pl.grid() # inset plot # akuederle.com/matplotlib-zoomed-up-inset # 'upper right' : 1, # 'upper left' : 2, # 'lower left' : 3, # 'lower right' : 4 fig, ax = plt.subplots() # create a new figure with a default 111 subplot ax.plot(overview_data_x, overview_data_y) # zoom-factor: 2.5, location: upper-left axins = zoomed_inset_axes(ax, 2.5, loc=2) locate the position of inset # left down width hight fraction of main axix ip = InsetPosition(ax, [0.1, 0.05, 0.4, 0.3]) axins.set_axes_locator(ip) axins.plot(overview_data_x, overview_data_y) x1, x2, y1, y2 = 47, 60, 3.7, 4.6 # specify the limits axins.set_xlim(x1, x2) # apply the x-limits axins.set_ylim(y1, y2) # apply the y-limits plt.yticks(visible=False) plt.xticks(visible=False) mark_inset(ax, axins, loc1=2, loc2=4, fc="none", ec="0.5") # Error bar:
fig = plt.figure(1, [5.5, 3])
# first subplot
ax = fig.add_subplot(1, 2, 1)
ax.set_aspect(1.)
axins = inset_axes(
ax,
width="30%", # width = 30% of parent_bbox
height=1., # height : 1 inch
loc=3)
plt.xticks(visible=False)
plt.yticks(visible=False)
# second subplot
ax = fig.add_subplot(1, 2, 2)
ax.set_aspect(1.)
axins = zoomed_inset_axes(ax, 0.5, loc=1) # zoom = 0.5
plt.xticks(visible=False)
plt.yticks(visible=False)
add_sizebar(ax, 0.5)
add_sizebar(axins, 0.5)
plt.draw()
plt.show()
# formatting the plot
ax.xaxis.set_minor_locator(pylab.MultipleLocator(5))
ax.yaxis.set_minor_locator(pylab.MultipleLocator(0.1))
ax.grid(b=True,which="minor",axis='x')
ax.grid(b=True,which="minor",axis='y')
ax.set_xticks(range(1850,2030,10))
ax.set_xlim(1843,2021)
ax.set_ylim(-1.03,0.89)
ax.set_xlabel("Year")
legend = ax.legend(loc="upper left",fontsize=14)
frame = legend.get_frame()
frame.set_facecolor('1.0')
ax.set_ylabel("Anomaly")
# adding an inset axis to view the downturn at the end better
inset_axes = zoomed_inset_axes(ax, 3, loc=4)
inset_axes.scatter(df2.Date,df2.Anomaly,s=15,marker='o',facecolor="1.0",lw=0.5,edgecolor="0.0")
inset_axes.plot(df2.Date[s12:-e12],yr1LP2,'-y',label='Annual LP')
inset_axes.plot(df2.Date[s152:-e152],yr15LP2,'-g',label='>15 yr LP')
inset_axes.plot(df2.Date[s752:-e752],yr75LP2,'-b',label='>30 yr LP')
inset_axes.plot(df2.Date,yr15SG2,'--r',label='S-G 15 yr')
x1, x2, y1, y2 = 2000, 2015, 0.3, 0.6
inset_axes.set_xlim(x1, x2)
inset_axes.set_ylim(y1, y2)
inset_axes.set_xticks([])
inset_axes.set_yticks([])
inset_axes.set_axis_bgcolor("1.0")
ax.set_title("HadCrut4 Monthly Anomaly Smoothing by CTRM and Savitsky-Golay")
mark_inset(ax, inset_axes, loc1=1, loc2=2, fc="none", ec="0.0");
pylab.show()
figure()
d = NormalDistr(1,1) * NormalDistr(1,1)
demo_distr(d)
#show()
figure()
d = NormalDistr(2,1) * NormalDistr(2,1)
demo_distr(d)
#show()
figure()
d = NormalDistr(3,1) * NormalDistr(3,1)
d.plot()
d.hist()
ax=gca()
axins = zoomed_inset_axes(ax, 6, loc=1)
d.plot(xmin=-1.5, xmax=1.5)
axins.set_xlim(-1.5, 1.5)
xticks(rotation="vertical")
axins.set_ylim(0, 0.01)
mark_inset(ax, axins, loc1=2, loc2=4, fc="none", ec="0.5")
#show()
figure()
d = NormalDistr(4,1) * NormalDistr(4,1)
d.plot()
d.hist()
ax=gca()
axins = zoomed_inset_axes(ax, 12000, loc=1)
d.plot(xmin=-.001, xmax=.001)
axins.set_xlim(-.001, .001)
z = np.load(f)
# z is a numpy array of 15x15
return z, (-3,4,-4,3)
fig = plt.figure(1, [5,4])
ax = fig.add_subplot(111)
Z, extent = get_demo_image()
ax.set(aspect=1,
xlim=(-15, 15),
ylim=(-20, 5))
axins = zoomed_inset_axes(ax, 2, loc=2) # zoom = 6
im = axins.imshow(Z, extent=extent, interpolation="nearest",
origin="lower")
plt.xticks(visible=False)
plt.yticks(visible=False)
# colorbar
cax = inset_axes(axins,
width="5%", # width = 10% of parent_bbox width
height="100%", # height : 50%
loc=3,
bbox_to_anchor=(1.05, 0., 1, 1),
bbox_transform=axins.transAxes,
borderpad=0,
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid.inset_locator import inset_axes, zoomed_inset_axes
fig, ax1 = plt.subplots()
axins = inset_axes(ax1,
1,
1,
loc=2,
bbox_to_anchor=(0.2, 0.55),
bbox_transform=ax1.figure.transFigure)
axins_zoomed = zoomed_inset_axes(ax1,
3.5,
loc=2,
bbox_to_anchor=(0.2, 0.55),
bbox_transform=ax1.figure.transFigure)
data_gelf = range(10)
ax1.plot(data_gelf, color='red')
axins.set_xlim(3, 4)
axins.set_ylim(3, 4)
axins.plot(data_gelf, color='red')
axins_zoomed.set_xlim(3, 4)
axins_zoomed.set_ylim(3, 4)
axins_zoomed.plot(data_gelf, color='red')
plt.show()
