Annotating ranges of data in matplotlib - matplotlib

How can I annotate a range of my data? E.g., say the data from x = 5 to x = 10 is larger than some cut-off, how could I indicate that on the graph. If I was annotating by hand, I would just draw a large bracket above the range and write my annotation above the bracket.
The closest I've seen is using arrowstyle='<->' and connectionstyle='bar', to make two arrows pointing to the edges of your data with a line connecting their tails. But that doesn't quite do the right thing; the text that you enter for the annotation will end up under one of the arrows, rather than above the bar.
Here is my attempt, along with it's results:
annotate(' ', xy=(1,.5), xycoords='data',
xytext=(190, .5), textcoords='data',
arrowprops=dict(arrowstyle="<->",
connectionstyle="bar",
ec="k",
shrinkA=5, shrinkB=5,
)
)
Another problem with my attempted solution is that the squared shape of the annotating bracket does not really make it clear that I am highlighting a range (unlike, e.g., a curly brace). But I suppose that's just being nitpicky at this point.

As mentioned in this answer, you can construct curly brackets with sigmoidal functions. Below is a function that adds curly brackets just above the x-axis. The curly brackets it produces should look the same regardless of the axes limits, as long as the figure width and height don't vary.
import numpy as np
import matplotlib.pyplot as plt
def draw_brace(ax, xspan, text):
"""Draws an annotated brace on the axes."""
xmin, xmax = xspan
xspan = xmax - xmin
ax_xmin, ax_xmax = ax.get_xlim()
xax_span = ax_xmax - ax_xmin
ymin, ymax = ax.get_ylim()
yspan = ymax - ymin
resolution = int(xspan/xax_span*100)*2+1 # guaranteed uneven
beta = 300./xax_span # the higher this is, the smaller the radius
x = np.linspace(xmin, xmax, resolution)
x_half = x[:resolution//2+1]
y_half_brace = (1/(1.+np.exp(-beta*(x_half-x_half[0])))
+ 1/(1.+np.exp(-beta*(x_half-x_half[-1]))))
y = np.concatenate((y_half_brace, y_half_brace[-2::-1]))
y = ymin + (.05*y - .01)*yspan # adjust vertical position
ax.autoscale(False)
ax.plot(x, y, color='black', lw=1)
ax.text((xmax+xmin)/2., ymin+.07*yspan, text, ha='center', va='bottom')
ax = plt.gca()
ax.plot(range(10))
draw_brace(ax, (0, 8), 'large brace')
draw_brace(ax, (8, 9), 'small brace')
Output:

I modified Joooeey's answer to allow to change the vertical position of braces:
def draw_brace(ax, xspan, yy, text):
"""Draws an annotated brace on the axes."""
xmin, xmax = xspan
xspan = xmax - xmin
ax_xmin, ax_xmax = ax.get_xlim()
xax_span = ax_xmax - ax_xmin
ymin, ymax = ax.get_ylim()
yspan = ymax - ymin
resolution = int(xspan/xax_span*100)*2+1 # guaranteed uneven
beta = 300./xax_span # the higher this is, the smaller the radius
x = np.linspace(xmin, xmax, resolution)
x_half = x[:int(resolution/2)+1]
y_half_brace = (1/(1.+np.exp(-beta*(x_half-x_half[0])))
+ 1/(1.+np.exp(-beta*(x_half-x_half[-1]))))
y = np.concatenate((y_half_brace, y_half_brace[-2::-1]))
y = yy + (.05*y - .01)*yspan # adjust vertical position
ax.autoscale(False)
ax.plot(x, y, color='black', lw=1)
ax.text((xmax+xmin)/2., yy+.07*yspan, text, ha='center', va='bottom')
ax = plt.gca()
ax.plot(range(10))
draw_brace(ax, (0, 8), -0.5, 'large brace')
draw_brace(ax, (8, 9), 3, 'small brace')
Output:
Also note that in Joooeey's answer, line
x_half = x[:resolution/2+1]
should be
x_half = x[:int(resolution/2)+1]
Otherwise, the number that the script tries to use as index here is a float.
Finally, note that right now the brace will not show up if you move it out of bounds. You need to add parameter clip_on=False, like this:
ax.plot(x, y, color='black', lw=1, clip_on=False)

You can just wrap it all up in a function:
def add_range_annotation(ax, start, end, txt_str, y_height=.5, txt_kwargs=None, arrow_kwargs=None):
"""
Adds horizontal arrow annotation with text in the middle
Parameters
----------
ax : matplotlib.Axes
The axes to draw to
start : float
start of line
end : float
end of line
txt_str : string
The text to add
y_height : float
The height of the line
txt_kwargs : dict or None
Extra kwargs to pass to the text
arrow_kwargs : dict or None
Extra kwargs to pass to the annotate
Returns
-------
tuple
(annotation, text)
"""
if txt_kwargs is None:
txt_kwargs = {}
if arrow_kwargs is None:
# default to your arrowprops
arrow_kwargs = {'arrowprops':dict(arrowstyle="<->",
connectionstyle="bar",
ec="k",
shrinkA=5, shrinkB=5,
)}
trans = ax.get_xaxis_transform()
ann = ax.annotate('', xy=(start, y_height),
xytext=(end, y_height),
transform=trans,
**arrow_kwargs)
txt = ax.text((start + end) / 2,
y_height + .05,
txt_str,
**txt_kwargs)
if plt.isinteractive():
plt.draw()
return ann, txt
Alternately,
start, end = .6, .8
ax.axvspan(start, end, alpha=.2, color='r')
trans = ax.get_xaxis_transform()
ax.text((start + end) / 2, .5, 'test', transform=trans)

Here is a minor modification to guzey and jooeey's answer to plot the flower braces outside the axes.
def draw_brace(ax, xspan, yy, text):
"""Draws an annotated brace outside the axes."""
xmin, xmax = xspan
xspan = xmax - xmin
ax_xmin, ax_xmax = ax.get_xlim()
xax_span = ax_xmax - ax_xmin
ymin, ymax = ax.get_ylim()
yspan = ymax - ymin
resolution = int(xspan/xax_span*100)*2+1 # guaranteed uneven
beta = 300./xax_span # the higher this is, the smaller the radius
x = np.linspace(xmin, xmax, resolution)
x_half = x[:int(resolution/2)+1]
y_half_brace = (1/(1.+np.exp(-beta*(x_half-x_half[0])))
+ 1/(1.+np.exp(-beta*(x_half-x_half[-1]))))
y = np.concatenate((y_half_brace, y_half_brace[-2::-1]))
y = yy + (.05*y - .01)*yspan # adjust vertical position
ax.autoscale(False)
ax.plot(x, -y, color='black', lw=1, clip_on=False)
ax.text((xmax+xmin)/2., -yy-.17*yspan, text, ha='center', va='bottom')
# Sample code
fmax = 1
fstart = -100
fend = 0
frise = 50
ffall = 20
def S(x):
if x<=0:
return 0
elif x>=1:
return 1
else:
return 1/(1+np.exp((1/(x-1))+(1/x)))
x = np.linspace(700,1000,500)
lam = [fmax*(S((i-880)/60)-S(((i-1000)/25)+1)) for i in x]
fig = plt.figure(1)
ax = fig.add_subplot(111)
plt.plot(x,lam)
plt.xlim([850,1000])
ax.set_aspect(50,adjustable='box')
plt.ylabel('$\lambda$')
plt.xlabel('$x$')
ax.xaxis.set_label_coords(0.5, -0.35)
draw_brace(ax, (900,950),0.2, 'rise')
draw_brace(ax, (980,1000),0.2, 'fall')
plt.text(822,0.95,'$(\lambda_{\mathrm{max}})$')
Sample output

a minor modification of the draw_brace of #Joooeey and #guezy to have also the brace upside down
+argument upsidedown
def draw_brace(ax, xspan, yy, text, upsidedown=False):
"""Draws an annotated brace on the axes."""
# shamelessly copied from https://stackoverflow.com/questions/18386210/annotating-ranges-of-data-in-matplotlib
xmin, xmax = xspan
xspan = xmax - xmin
ax_xmin, ax_xmax = ax.get_xlim()
xax_span = ax_xmax - ax_xmin
ymin, ymax = ax.get_ylim()
yspan = ymax - ymin
resolution = int(xspan/xax_span*100)*2+1 # guaranteed uneven
beta = 300./xax_span # the higher this is, the smaller the radius
x = np.linspace(xmin, xmax, resolution)
x_half = x[:int(resolution/2)+1]
y_half_brace = (1/(1.+np.exp(-beta*(x_half-x_half[0])))
+ 1/(1.+np.exp(-beta*(x_half-x_half[-1]))))
if upsidedown:
y = np.concatenate((y_half_brace[-2::-1], y_half_brace))
else:
y = np.concatenate((y_half_brace, y_half_brace[-2::-1]))
y = yy + (.05*y - .01)*yspan # adjust vertical position
ax.autoscale(False)
line = ax.plot(x, y, color='black', lw=1)
if upsidedown:
text = ax.text((xmax+xmin)/2., yy+-.07*yspan, text, ha='center', va='bottom',fontsize=7)
else:
text = ax.text((xmax+xmin)/2., yy+.07*yspan, text, ha='center', va='bottom',fontsize=7)
return line, text

I updated the previous answers to have some of the features I wanted, like an option for a vertical brace, that I wanted to place in multi-plot figures. One still has to futz with the beta_scale parameter sometimes depending on the scale of the data that one is applying this to.
def rotate_point(x, y, angle_rad):
cos,sin = np.cos(angle_rad),np.sin(angle_rad)
return cos*x-sin*y,sin*x+cos*y
def draw_brace(ax, span, position, text, text_pos, brace_scale=1.0, beta_scale=300., rotate=False, rotate_text=False):
'''
all positions and sizes are in axes units
span: size of the curl
position: placement of the tip of the curl
text: label to place somewhere
text_pos: position for the label
beta_scale: scaling for the curl, higher makes a smaller radius
rotate: true rotates to place the curl vertically
rotate_text: true rotates the text vertically
'''
# get the total width to help scale the figure
ax_xmin, ax_xmax = ax.get_xlim()
xax_span = ax_xmax - ax_xmin
resolution = int(span/xax_span*100)*2+1 # guaranteed uneven
beta = beta_scale/xax_span # the higher this is, the smaller the radius
# center the shape at (0, 0)
x = np.linspace(-span/2., span/2., resolution)
# calculate the shape
x_half = x[:int(resolution/2)+1]
y_half_brace = (1/(1.+np.exp(-beta*(x_half-x_half[0])))
+ 1/(1.+np.exp(-beta*(x_half-x_half[-1]))))
y = np.concatenate((y_half_brace, y_half_brace[-2::-1]))
# put the tip of the curl at (0, 0)
max_y = np.max(y)
min_y = np.min(y)
y /= (max_y-min_y)
y *= brace_scale
y -= max_y
# rotate the trace before shifting
if rotate:
x,y = rotate_point(x, y, np.pi/2)
# shift to the user's spot
x += position[0]
y += position[1]
ax.autoscale(False)
ax.plot(x, y, color='black', lw=1, clip_on=False)
# put the text
ax.text(text_pos[0], text_pos[1], text, ha='center', va='bottom', rotation=90 if rotate_text else 0)

Related

How to create a scalebar using cartopy and matplotlib?

in respect to the previous examples in stackoverflow, I searched for other alternatives in order to create a scalebar.
In my research, I verified that the Basemap class from mpl_toolkits.basemap see here. It has the "drawmapscale" method. This method has the option barstyle = 'fancy' for a more interesting scalebar drawing.
Therefore, I attempted to convert the "drawmapscale" from the Basemap into a cartopy version.
Nevertheless, the results were not positive, and I got error messages from the figure. I believe that the error is in the Transform of the data.
Here is the script:
import numpy as np
import matplotlib.pyplot as plt
import pyproj
import cartopy.crs as ccrs
from matplotlib import is_interactive
from cartopy.crs import (WGS84_SEMIMAJOR_AXIS, WGS84_SEMIMINOR_AXIS)
from pyproj import Transformer
class Scalebar():
def __init__(self,
ax,
suppress_ticks=True,
geographical_crs = 4326,
planar_crs = 5880,
fix_aspect=True,
anchor='C',
celestial=False,
round=False,
noticks=False,
metric_ccrs=ccrs.TransverseMercator()):
self.ax = ax
self.fix_aspect = fix_aspect
# setting metric ccrs for reference in the plotting
self.metric_ccrs = metric_ccrs
self.anchor = anchor
# geographic or celestial coords?
self.celestial = celestial
# map projection.
self.projection = ax.projection
self.geographical_crs = geographical_crs
self.planar_crs = planar_crs
self._initialized_axes = set()
self.round = round
# map boundary not yet drawn.
self._mapboundarydrawn = False
self.rmajor = np.float(ax.projection.globe.semimajor_axis or WGS84_SEMIMAJOR_AXIS)
self.rminor = np.float(ax.projection.globe.semiminor_axis or WGS84_SEMIMINOR_AXIS)
# set instance variables defining map region.
self.xmin = self.projection.boundary.bounds[0]
self.xmax = self.projection.boundary.bounds[2]
self.ymin = self.projection.boundary.bounds[1]
self.ymax = self.projection.boundary.bounds[3]
self._width = self.xmax - self.xmin
self._height = self.ymax - self.ymin
self.noticks = noticks
def __call__(self,x,y,
inverse=False
):
"""
Calling the class instance with the arguments lon, lat will
convert lon/lat (in degrees) to x/y map projection
coordinates (in meters).
If optional keyword ``inverse`` is True (default is False),
the inverse transformation from x/y to lon/lat is performed.
Input arguments:
lon, lat can be either scalar floats, sequences, or numpy arrays.
"""
if not inverse:
transformer = Transformer.from_crs("epsg:{0}".format(self.geographical_crs),
"epsg:{0}".format(self.planar_crs))
else:
transformer = Transformer.from_crs("epsg:{0}".format(self.planar_crs),
"epsg:{0}".format(self.geographical_crs))
return transformer.transform(x, y)
def drawmapscale(self,
lon,
lat,
length,
lon0=None,
lat0=None,
barstyle='simple',\
units='km',
fontsize=9,
yoffset=None,
labelstyle='simple',\
fontcolor='k',
fillcolor1='w',
fillcolor2='k',\
format='%d',
zorder=None,
linecolor=None,
linewidth=None):
"""
Draw a map scale at ``lon,lat`` of length ``length``
representing distance in the map
projection coordinates at ``lon0,lat0``.
.. tabularcolumns:: |l|L|
============== ====================================================
Keywords Description
============== ====================================================
units the units of the length argument (Default km).
barstyle ``simple`` or ``fancy`` (roughly corresponding
to the styles provided by Generic Mapping Tools).
Default ``simple``.
fontsize for map scale annotations, default 9.
fontcolor for map scale annotations, default black.
labelstyle ``simple`` (default) or ``fancy``. For
``fancy`` the map scale factor (ratio betwee
the actual distance and map projection distance
at lon0,lat0) and the value of lon0,lat0 are also
displayed on the top of the scale bar. For
``simple``, just the units are display on top
and the distance below the scale bar.
If equal to False, plot an empty label.
format a string formatter to format numeric values
yoffset yoffset controls how tall the scale bar is,
and how far the annotations are offset from the
scale bar. Default is 0.02 times the height of
the map (0.02*(self.ymax-self.ymin)).
fillcolor1(2) colors of the alternating filled regions
(default white and black). Only relevant for
'fancy' barstyle.
zorder sets the zorder for the map scale.
linecolor sets the color of the scale, by default, fontcolor
is used
linewidth linewidth for scale and ticks
============== ====================================================
Extra keyword ``ax`` can be used to override the default axis instance.
"""
# get current axes instance (if none specified).
ax = self.ax
# convert length to meters
lenlab = length
if units == 'km':
length = length*1000
elif units == 'mi':
length = length*1609.344
elif units == 'nmi':
length = length*1852
elif units == 'ft':
length = length*0.3048
elif units != 'm':
msg = "units must be 'm' (meters), 'km' (kilometers), "\
"'mi' (miles), 'nmi' (nautical miles), or 'ft' (feet)"
raise KeyError(msg)
# Setting the center coordinates of the axes:
xmin, xmax, ymin, ymax = self.ax.get_extent()
if lon0 == None:
lon0 = np.mean([xmin, xmax])
if lat0 == None:
lat0 = np.mean([ymin, ymax])
# reference point and center of scale.
x0,y0 = self(lon0,lat0)
print('\n\n Central coords prior to transform')
print('lon0,lat0: ', [lon0,lat0])
print('\n\n central coordinates after transform')
print('x0,y0: ', [x0,y0])
xc,yc = self(lon,lat)
print('\n\n positional coordinates prior to transform')
print('lon, lat: ', [lon,lat])
print('\n\n central coordinates after transform')
print('xc,yc: ', [xc,yc])
print('-'*20, '\n')
# make sure lon_0 between -180 and 180
lon_0 = ((lon0+360) % 360) - 360
if lat0>0:
if lon>0:
lonlatstr = u'%g\N{DEGREE SIGN}N, %g\N{DEGREE SIGN}E' % (lat0,lon_0)
elif lon<0:
lonlatstr = u'%g\N{DEGREE SIGN}N, %g\N{DEGREE SIGN}W' % (lat0,lon_0)
else:
lonlatstr = u'%g\N{DEGREE SIGN}, %g\N{DEGREE SIGN}W' % (lat0,lon_0)
else:
if lon>0:
lonlatstr = u'%g\N{DEGREE SIGN}S, %g\N{DEGREE SIGN}E' % (lat0,lon_0)
elif lon<0:
lonlatstr = u'%g\N{DEGREE SIGN}S, %g\N{DEGREE SIGN}W' % (lat0,lon_0)
else:
lonlatstr = u'%g\N{DEGREE SIGN}S, %g\N{DEGREE SIGN}' % (lat0,lon_0)
# left edge of scale
lon1,lat1 = self(x0-length/2,y0, inverse=True)
x1,y1 = self(lon1,lat1)
# right edge of scale
lon4,lat4 = self(x0+length/2,y0, inverse=True)
x4,y4 = self(lon4,lat4)
x1 = x1-x0+xc
y1 = y1-y0+yc
print('\n\n positional coordinates prior to transform')
print('lon1,lat1: ', [lon1,lat1])
print('\n\n positional coordinates prior to transform')
print('x1, y1: ', [x1,y1])
print()
print('\n\n central coordinates after transform')
print('lon4,lat4: ', [lon4,lat4])
print('-'*20, '\n')
print('\n\n central coordinates after transform')
print('x4,y4: ', [x4,y4])
print('-'*20, '\n')
x4 = x4-x0+xc
y4 = y4-y0+yc
if x1 > 1.e20 or x4 > 1.e20 or y1 > 1.e20 or y4 > 1.e20:
raise ValueError("scale bar positioned outside projection limb")
# scale factor for true distance
gc = pyproj.Geod(a=self.rmajor,b=self.rminor)
az12,az21,dist = gc.inv(lon1,lat1,lon4,lat4)
scalefact = dist/length
# label to put on top of scale bar.
if labelstyle=='simple':
labelstr = units
elif labelstyle == 'fancy':
labelstr = units+" (scale factor %4.2f at %s)"%(scalefact,lonlatstr)
elif labelstyle == False:
labelstr = ''
else:
raise KeyError("labelstyle must be 'simple' or 'fancy'")
# default y offset is 2 percent of map height.
if yoffset is None:
yoffset = 0.02*(self.ymax-self.ymin)
rets = [] # will hold all plot objects generated.
# set linecolor
if linecolor is None:
linecolor = fontcolor
# 'fancy' style
if barstyle == 'fancy':
#we need 5 sets of x coordinates (in map units)
#quarter scale
lon2,lat2 = self(x0-length/4,y0,inverse=True)
x2,y2 = self(lon2,lat2)
x2 = x2-x0+xc; y2 = y2-y0+yc
#three quarter scale
lon3,lat3 = self(x0+length/4,y0,inverse=True)
x3,y3 = self(lon3,lat3)
x3 = x3-x0+xc; y3 = y3-y0+yc
#plot top line
ytop = yc+yoffset/2
ybottom = yc-yoffset/2
ytick = ybottom - yoffset/2
ytext = ytick - yoffset/2
lontext , lattext = self(lon0,ytext, inverse=True)
#lon_top, lat_top = self(lon4,ytop,inverse=True)
#lon_top, lat_bottom = self(lon4,ybottom,inverse=True)
transform = self.metric_ccrs # this crs projection is meant to be for metric data
rets.append(self.plot([x1,x4],
[ytop,ytop],
transform=transform,
color=linecolor,
linewidth=linewidth)[0])
#plot bottom line
rets.append(self.plot([x1,x4],
[ybottom,ybottom],
transform=transform,
color=linecolor,
linewidth=linewidth)[0])
#plot left edge
rets.append(self.plot([x1,x1],
[ybottom,ytop],
transform=transform,
color=linecolor,
linewidth=linewidth)[0])
#plot right edge
rets.append(self.plot([x4,x4],
[ybottom,ytop],
transform=transform,
color=linecolor,
linewidth=linewidth)[0])
#make a filled black box from left edge to 1/4 way across
rets.append(ax.fill([x1,x2,x2,x1,x1],
[ytop,ytop,ybottom,ybottom,ytop],
transform=transform,
ec=fontcolor,
fc=fillcolor1)[0])
#make a filled white box from 1/4 way across to 1/2 way across
rets.append(ax.fill([x2,x0,x0,x2,x2],
[ytop,ytop,ybottom,ybottom,ytop],
transform=transform,
ec=fontcolor,
fc=fillcolor2)[0])
#make a filled white box from 1/2 way across to 3/4 way across
rets.append(ax.fill([x0,x3,x3,x0,x0],
[ytop,ytop,ybottom,ybottom,ytop],
transform=transform,
ec=fontcolor,
fc=fillcolor1)[0])
#make a filled white box from 3/4 way across to end
rets.append(ax.fill([x3,x4,x4,x3,x3],
[ytop,ytop,ybottom,ybottom,ytop],
transform=transform,
ec=fontcolor,
fc=fillcolor2)[0])
#plot 3 tick marks at left edge, center, and right edge
rets.append(self.plot([x1,x1],
[ytick,ybottom],
color=linecolor,
transform=transform,
linewidth=linewidth)[0])
rets.append(self.plot([x0,x0],
[ytick,ybottom],
transform=transform,
color=linecolor,
linewidth=linewidth)[0])
rets.append(self.plot([x4,x4],
[ytick,ybottom],
transform=transform,
color=linecolor,
linewidth=linewidth)[0])
#label 3 tick marks
rets.append(ax.text(x1,lattext,format % (0),\
horizontalalignment='center',\
verticalalignment='top',\
fontsize=fontsize,color=fontcolor))
rets.append(ax.text(x0,lattext,format % (0.5*lenlab),\
horizontalalignment='center',\
verticalalignment='top',\
fontsize=fontsize,color=fontcolor))
rets.append(ax.text(x4,lattext,format % (lenlab),\
horizontalalignment='center',\
verticalalignment='top',\
fontsize=fontsize,color=fontcolor))
#put units, scale factor on top
rets.append(ax.text(x0,ytop+yoffset/2,labelstr,\
horizontalalignment='center',\
verticalalignment='bottom',\
fontsize=fontsize,color=fontcolor))
# 'simple' style
elif barstyle == 'simple':
rets.append(self.plot([x1,x4],[yc,yc],color=linecolor, linewidth=linewidth)[0])
rets.append(self.plot([x1,x1],[yc-yoffset,yc+yoffset],color=linecolor, linewidth=linewidth)[0])
rets.append(self.plot([x4,x4],[yc-yoffset,yc+yoffset],color=linecolor, linewidth=linewidth)[0])
rets.append(ax.text(xc,yc-yoffset,format % lenlab,\
verticalalignment='top',horizontalalignment='center',\
fontsize=fontsize,color=fontcolor))
#put units, scale factor on top
rets.append(ax.text(xc,yc+yoffset,labelstr,\
horizontalalignment='center',\
verticalalignment='bottom',\
fontsize=fontsize,color=fontcolor))
else:
raise KeyError("barstyle must be 'simple' or 'fancy'")
if zorder is not None:
for ret in rets:
try:
ret.set_zorder(zorder)
except:
pass
return rets
def plot(self, *args, **kwargs):
"""
Draw lines and/or markers on the map
(see matplotlib.pyplot.plot documentation).
If ``latlon`` keyword is set to True, x,y are intrepreted as
longitude and latitude in degrees. Data and longitudes are
automatically shifted to match map projection region for cylindrical
and pseudocylindrical projections, and x,y are transformed to map
projection coordinates. If ``latlon`` is False (default), x and y
are assumed to be map projection coordinates.
Extra keyword ``ax`` can be used to override the default axis instance.
Other \**kwargs passed on to matplotlib.pyplot.plot.
"""
ax = self.ax
self._save_use_hold(ax, kwargs)
try:
ret = ax.plot(*args,
**kwargs)
finally:
self._restore_hold(ax)
# set axes limits to fit map region.
self.set_axes_limits(ax=ax)
# clip to map limbs
ret,c = self._cliplimb(ax,ret)
return ret
def _save_use_hold(self, ax, kwargs):
h = kwargs.pop('hold', None)
if hasattr(ax, '_hold'):
self._tmp_hold = ax._hold
if h is not None:
ax._hold = h
def _restore_hold(self, ax):
if hasattr(ax, '_hold'):
ax._hold = self._tmp_hold
def set_axes_limits(self,ax=None):
"""
Final step in Basemap method wrappers of Axes plotting methods:
Set axis limits, fix aspect ratio for map domain using current
or specified axes instance. This is done only once per axes
instance.
In interactive mode, this method always calls draw_if_interactive
before returning.
"""
# get current axes instance (if none specified).
ax = ax or self._check_ax()
# If we have already set the axes limits, and if the user
# has not defeated this by turning autoscaling back on,
# then all we need to do is plot if interactive.
if (hash(ax) in self._initialized_axes
and not ax.get_autoscalex_on()
and not ax.get_autoscaley_on()):
if is_interactive():
import matplotlib.pyplot as plt
plt.draw_if_interactive()
return
self._initialized_axes.add(hash(ax))
# Take control of axis scaling:
ax.set_autoscale_on(False)
# update data limits for map domain.
corners = ((self.xmin, self.ymin), (self.xmax, self.ymax))
ax.update_datalim(corners)
ax.set_xlim((self.xmin, self.xmax))
ax.set_ylim((self.ymin, self.ymax))
# if map boundary not yet drawn for elliptical maps, draw it with default values.
# make sure aspect ratio of map preserved.
# plot is re-centered in bounding rectangle.
# (anchor instance var determines where plot is placed)
if self.fix_aspect:
ax.set_aspect('equal',anchor=self.anchor)
else:
ax.set_aspect('auto',anchor=self.anchor)
# make sure axis ticks are turned off.
if self.noticks:
ax.set_xticks([])
ax.set_yticks([])
# force draw if in interactive mode.
if is_interactive():
import matplotlib.pyplot as plt
plt.draw_if_interactive()
def _cliplimb(self,ax,coll):
if not self._mapboundarydrawn:
return coll, None
c = self._mapboundarydrawn
if c not in ax.patches:
p = ax.add_patch(c)
#p.set_clip_on(False)
try:
coll.set_clip_path(c)
except:
for item in coll:
item.set_clip_path(c)
return coll,c
# now the test
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
import geopandas as gpd
def get_standard_gdf():
""" basic function for getting some geographical data in geopandas GeoDataFrame python's instance:
An example data can be downloaded from Brazilian IBGE:
ref: ftp://geoftp.ibge.gov.br/organizacao_do_territorio/malhas_territoriais/malhas_municipais/municipio_2017/Brasil/BR/br_municipios.zip
"""
gdf_path = r'C:\my_file_path\Shapefile.shp'
return gpd.read_file(gdf_path)
def format_ax(ax, projection, xlim, ylim):
ax.set_xlim(xlim)
ax.set_ylim(ylim)
ax.set_global()
ax.coastlines()
def main():
fig = plt.figure(figsize=(8, 10))
# Label axes of a Plate Carree projection with a central longitude of 180:
#for enum, proj in enumerate(['Mercator, PlateCarree']):
gdf = get_standard_gdf()
xmin, ymin, xmax, ymax = gdf.total_bounds
xlim = [xmin, xmax]
ylim = [ymin, ymax]
lon_c = np.mean(xlim)
lat_c = np.mean(ylim)
projection = ccrs.PlateCarree(central_longitude=0)
ax1 = fig.add_subplot(3, 1, 1,
projection=projection,
xlim=[xmin, xmax],
ylim=[ymin, ymax])
gdf.plot(ax=ax1, transform=projection)
format_ax(ax1, projection, xlim, ylim)
Grider = ax1.gridlines(draw_labels=True)
Grider.xformatter = LONGITUDE_FORMATTER
Grider.yformatter = LATITUDE_FORMATTER
Grider.xlabels_top = False
Grider.ylabels_right = False
# Label axes of a Mercator projection without degree symbols in the labels
# and formatting labels to include 1 decimal place:
ax2 = fig.add_subplot(3, 1, 2,
projection=ccrs.Mercator(),
xlim=[xmin, xmax],
ylim=[ymin, ymax])
gdf.plot(ax=ax2, transform=projection)
format_ax(ax2, projection, xlim, ylim)
Grider = ax2.gridlines(draw_labels=True)
Grider.xformatter = LONGITUDE_FORMATTER
Grider.yformatter = LATITUDE_FORMATTER
Grider.xlabels_top = False
Grider.ylabels_right = False
ax3 = fig.add_subplot(3, 1, 3,
projection=ccrs.Robinson(central_longitude=lon_c,
#central_latitude=lat_c
),
xlim=[xmin, xmax],
ylim=[ymin, ymax])
gdf.plot(ax=ax3, transform=projection)
format_ax(ax3, projection, xlim, ylim)
ax3.set_xticks([-180, -120, -60, 0, 60, 120, 180])
ax3.set_yticks([-78.5, -60, -25.5, 25.5, 60, 80])
ax3.xaxis.set_major_formatter(LONGITUDE_FORMATTER)
ax3.yaxis.set_major_formatter(LATITUDE_FORMATTER)
plt.draw()
return fig, fig.get_axes()
if __name__ == '__main__':
length = 1000
fig, axes = main()
gdf = get_standard_gdf()
xmin, ymin, xmax, ymax = gdf.total_bounds
xoff = 0.3 * (xmax - xmin)
yoff = 0.2 * (ymax - ymin)
for ax in axes:
if hasattr(ax, 'projection'):
x0, x1, y0, y1 = np.ravel(ax.get_extent())
Scaler = Scalebar(ax=ax,
metric_ccrs=ccrs.Geodetic())
Scaler.drawmapscale(lon = xmin+xoff,
lat = ymin + yoff,
length=length,
units = 'km',
barstyle='fancy',
yoffset=0.2 * (ymax - ymin)
)
fig.suptitle('Using Cartopy')
fig.show()
When the above code is run, the scalebar is misplaced in the geoaxes. The scalebar xticks are misplaced, and its yaxis height proportion is also wrong.
Here is an example: the geopandas is plotted in blue. Note that the scalebar is only visible in the second and third geoaxes.
I found a solution for the current problem.
For sake of brevety, the code is presented in here.
Feel free to check it out. The algorithm still requires some adjustment in order to support other cartopy projections.
Meanwhile, it can be applied to PlateCarree projection.

How can I have a colormap legend for different circle labels

I am drawing different circles with Matplotlib. Each circle has a label, and each label has a colour. What can I do to have a colourmap legend for these different labels?
I have tried a lot of solutions online, including the most naive one by just adding plt.colorbar(), which I will get the error
RuntimeError('No mappable was found to use for colorbar')
Here is my complete code. It's a little bit long. Please note that the key part just starts from if labels is None:. I just include everything for completeness.
def plot_gaussian_circles(loc_list, scale_list, save_path=None, sigma_coe=3, num_to_plot=300, labels=None):
mu_x_max = -float('inf')
mu_y_max = -float('inf')
mu_x_min = float('inf')
mu_y_min = float('inf')
color_idx = 0
rvs = []
lim_loc_list = loc_list[:num_to_plot]
lim_scale_list = scale_list[:num_to_plot]
for a_mu_, a_sigma_ in zip(lim_loc_list, lim_scale_list):
a_mu = a_mu_.squeeze()
a_sigma_ = a_sigma_.squeeze()
if not type(a_sigma_) is np.ndarray:
a_sigma_ = a_sigma_.numpy()
radius = sigma_coe * np.max(a_sigma_)
a_mu_x = a_mu[0]
a_mu_y = a_mu[1]
if (a_mu_x + radius) >= mu_x_max:
mu_x_max = a_mu_x + radius
if (a_mu_x - radius) <= mu_x_min:
mu_x_min = a_mu_x - radius
if (a_mu_y + radius) >= mu_y_max:
mu_y_max = a_mu_y + radius
if (a_mu_y - radius) <= mu_y_min:
mu_y_min = a_mu_y - radius
if labels is None:
rv = plt.Circle(a_mu, radius, fill=False, clip_on=False)
else:
colors = cm.rainbow(np.linspace(0, 1, len(set(labels))))
rv = plt.Circle(a_mu, radius, color=colors[labels[color_idx]], fill=False, clip_on=False)
rvs.append(rv)
color_idx = (color_idx + 1)
fig, ax = plt.subplots()
ax.set_xlabel('X axis')
ax.set_ylabel('Y axis')
axes = plt.gca()
axes.set_xlim([mu_x_min - 1, mu_x_max + 1])
axes.set_ylim([mu_y_min - 1, mu_y_max + 1])
for rv in rvs:
ax.add_artist(rv)
if not(labels is None):
# plt.legend(colors, list(range(len(set(labels)))))
plt.colorbar()
if save_path is None:
plt.plot()
plt.show()
# plt.savefig('plotcircles_test.png')
else:
plt.savefig(save_path, dpi=200)
The image here is currently what I am getting, while I wish to have a legend of the colormap.
I found doing this will have the colour for the circles. Thanks ImportanceOfBeingErnest's comment for suggesting PatchCollection.
p = PatchCollection(rvs, cmap=cm.jet, alpha=0.4)
p.set_array(labels)
ax.add_collection(p)
fig.colorbar(p, ax=ax)

AttributeError: 'Axes' object has no attribute 'get_proj' in matplotlib

I am making the representation of a polynom function.
I have an error in a matplotlib code and cannot understand where it is coming from. any advice is welcome.
I tried already Gtk3agg but nothing changed.
Below is the failure code.
For any reason 'get_proj' dont work here for creating labels.
And: when I use ax.get_proj() instead,
a) all labels appear bottom left
b) not all labels appear at bottom left (all points are identified by the cursor bot the labels are not written at the bottom left).
The final project will be (few things still to be done):
- on button -> labelling with coordinate appear at each cursor movement (temporary)
- click right button, the labels will be persistent till button clear is clicked
- off button -> no labelling appear
My feeling: the 3x button creation is messing anything up.
# -*- coding: utf-8 -*-
import matplotlib as mpl
from mpl_toolkits.mplot3d.proj3d import proj_transform
import matplotlib.pyplot as plt
from matplotlib.widgets import Button
import numpy as np
mpl.use('tkagg')
def distance(point, event):
plt.sca(ax) # <------------------ introduce this one !!!!!!!!!!!!!!!!!!!!!!!!!!!
x2, y2, _ = proj_transform(point[0], point[1], point[2], plt.gca().get_proj())
x3, y3 = ax.transData.transform((x2, y2))
return np.sqrt ((x3 - event.x)**2 + (y3 - event.y)**2)
def calcClosestDatapoint(X, event):
distances = [distance(X[i, 0:3], event) for i in range(Sol)]
return np.argmin(distances)
#
def annotatePlot(X, index):
global last_mark, generated_labels
if activated_labelling:
x2, y2, _ = proj_transform(X[index, 0], X[index, 1], X[index, 2], ax.get_proj())
last_mark = plt.annotate(generated_labels[index],
xy = (x2, y2), xytext = (-20, 20), textcoords = 'offset points', ha = 'right', va = 'bottom',
bbox = dict(boxstyle = 'round,pad=0.5', fc = 'yellow', alpha = 0.5),
arrowprops = dict(arrowstyle = '->', connectionstyle = 'arc3,rad=0'))
fig.canvas.draw()
#
def onMouseMotion(event):
global Coord
if activated_labelling:
closestIndex = calcClosestDatapoint(Coord, event)
last_mark.remove()
annotatePlot(Coord, closestIndex)
def show_on(event):
global activated_labelling, last_mark,pid,mid
if activated_labelling == False:
activated_labelling = True
x2, y2, _ = proj_transform(Coord[0,0], Coord[0,1], Coord[0,2], ax.get_proj())
last_mark = plt.annotate("3D measurement on " + generated_labels[0],
xy = (x2, y2), xytext = (-20, 20), textcoords = 'offset points', ha = 'right', va = 'bottom',
bbox = dict(boxstyle = 'round,pad=0.5', fc = 'yellow', alpha = 0.5),
arrowprops = dict(arrowstyle = '->', connectionstyle = 'arc3,rad=0'))
mid = fig.canvas.mpl_connect('motion_notify_event', onMouseMotion)
#
def show_off(event):
global activated_labelling
'''
deactivate the persistent XYZ position labels at the grafic
'''
if activated_labelling:
activated_labelling = False
last_mark.remove()
fig.canvas.draw()
fig.canvas.mpl_disconnect(mid)
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
#ax = fig.gca(projection='3d')
activated_labelling = False
Wide = 100
Minimum = -50
ScanLimit = 3 # searching between o and 3; 4 and 5 are no solutions
Search = 45
Coord=[]
values=[]
generated_labels = []
#
XMin = 0
XMax = 0
YMin = 0
YMax = 0
ZMin = 0
ZMax = 0
# count the solutions found in the scan area defined above
Sol=0
for i in range(Wide+1):
for j in range(Wide+1):
for k in range(Wide+1):
########################################################################
########################################################################
####
#### THIS IS THE POLYNOM TO BE REPRESENTED
####
param_dens = ((i+Minimum)**3)+((j+Minimum)**3)+((k+Minimum)**3) -Search
if abs(param_dens) <= abs(ScanLimit):
Coord.append([i+Minimum,j+Minimum,k+Minimum])
if ScanLimit !=0:
values.append([abs(param_dens)])
labelling = "value {}\nin X:{} Y:{} Z:{}".format(Search+param_dens,i+Minimum,j+Minimum,k+Minimum)
generated_labels.append(labelling)
print(labelling+"\n")
# increase the number indicating the solutions found
Sol +=1
# for centering the window
if XMin > i+Minimum:
XMin = i+Minimum
if YMin > j+Minimum:
YMin = j+Minimum
if ZMin > k+Minimum:
ZMin = k+Minimum
if XMax < i+Minimum:
XMax = i+Minimum
if YMax < j+Minimum:
YMax = j+Minimum
if ZMax < k+Minimum:
ZMax = k+Minimum
print('######################################################')
print('## statistics / move this to a parallel search engine?')
print('## search ')
print("## total solution %d for searching center %d" % (Sol,Search))
print("## from %d to %d" % (Search-ScanLimit,Search+ScanLimit))
print("## from %d to %d" % (Minimum,Wide+Minimum))
print('##')
print('#######################################################')
#
values = np.array(values, dtype='int64')
Coord = np.array(Coord, dtype='int64')
#
if ScanLimit !=0:
cmap = plt.cm.jet # define the colormap
# extract all colors from the .jet map
cmaplist = [cmap(i) for i in range(cmap.N)]
# force the first color entry to be black
cmaplist[0] = (0, 0, 0, 1.0)
# create the new map
cmap = mpl.colors.LinearSegmentedColormap.from_list('Custom cmap', cmaplist, cmap.N)
# define the bins and normalize
bounds = np.linspace(0, ScanLimit, ScanLimit+1)
norm = mpl.colors.BoundaryNorm(bounds, cmap.N)
# create a second axes for the colorbar
ax2 = fig.add_axes([0.95, 0.1, 0.03, 0.8])
cb = mpl.colorbar.ColorbarBase(ax2, cmap=cmap, norm=norm,
spacing='proportional', ticks=bounds, boundaries=bounds, format='%1i')
#
ax.set_xlim3d(XMin-5, XMax+5)
ax.set_ylim3d(YMin-5, YMax+5)
ax.set_zlim3d(ZMin-5, ZMax+5)
#
ax.set_xlabel('X X')
ax.set_ylabel('Y Y')
ax.set_zlabel('Z Z')
ax.set_aspect(aspect=1)
# extract the scatterplot drawing in a separate function so we ca re-use the code
def draw_scatterplot():
if ScanLimit !=0:
ax.scatter3D(Coord[:,0], Coord[:,1], Coord[:,2], s=20, c=values[:,0], cmap=cmap, norm=norm)
else:
ax.scatter3D(Coord[:,0], Coord[:,1], Coord[:,2], s=20, c='green')
# draw the initial scatterplot
draw_scatterplot()
# create the "on" button, and place it somewhere on the screen
ax_on = plt.axes([0.0, 0.0, 0.1, 0.05])
button_on = Button(ax_on, 'on')
#
ax_off = plt.axes([0.12, 0.0, 0.1, 0.05])
button_off = Button(ax_off, 'off')
#
#ax_off = plt.axes([0.24, 0.0, 0.1, 0.05])
#button_off = Button(ax_off, 'off')
# link the event handler function to the click event on the button
button_on.on_clicked(show_on)
button_off.on_clicked(show_off)
#fig.colorbar(img)
plt.show()
Traceback (most recent call last):
File "C:\Program Files\Anaconda3\lib\site-packages\matplotlib\cbook\__init__.py", line 388, in process
proxy(*args, **kwargs)
File "C:\Program Files\Anaconda3\lib\site-packages\matplotlib\cbook\__init__.py", line 228, in __call__
return mtd(*args, **kwargs)
File "C:/Users/../Desktop/heat.py", line 137, in onClick
closestIndex,LowestDistance = calcClosestDatapoint(Coord, event)
File "C:/Users/../Desktop/heat.py", line 50, in calcClosestDatapoint
distances = [distance(X[i, 0:3], event) for i in range(Sol)]
File "C:/Users/../Desktop/heat.py", line 50, in <listcomp>
distances = [distance(X[i, 0:3], event) for i in range(Sol)]
File "C:/Users/../Desktop/heat.py", line 35, in distance
x2, y2, _ = proj_transform(point[0], point[1], point[2], plt.gca().get_proj())
AttributeError: 'Axes' object has no attribute 'get_proj'

contour lines from the edge of a map don't show up on basemap

I'm drawing several contour lines over a basemap projection as shown in the following figure:.
There are 3 contours that are not drawn completely (in Oregon, Washington and California) and seems like there is this line that has cut all 3 of them in the same latitude. I'm not sure how to solve this problem.
I added the number of interpolation points, didn't help. changed the ll and ur points to include more area didn't help.
The code is below (not reproducible but might help):
def visualise_bigaus(mus, sigmas, corxys , output_type='pdf', **kwargs):
lllat = 24.396308
lllon = -124.848974
urlat = 49.384358
urlon = -66.885444
fig = plt.figure(figsize=(4, 2.5))
ax = fig.add_subplot(111, axisbg='w', frame_on=False)
m = Basemap(llcrnrlat=lllat,
urcrnrlat=urlat,
llcrnrlon=lllon,
urcrnrlon=urlon,
resolution='i', projection='cyl')
m.drawmapboundary(fill_color = 'white')
#m.drawcoastlines(linewidth=0.2)
m.drawcountries(linewidth=0.2)
m.drawstates(linewidth=0.2, color='lightgray')
#m.fillcontinents(color='white', lake_color='#0000ff', zorder=2)
#m.drawrivers(color='#0000ff')
m.drawlsmask(land_color='gray',ocean_color="#b0c4de", lakes=True)
lllon, lllat = m(lllon, lllat)
urlon, urlat = m(urlon, urlat)
mlon, mlat = m(*(mus[:,1], mus[:,0]))
numcols, numrows = 1000, 1000
X = np.linspace(mlon.min(), urlon, numcols)
Y = np.linspace(lllat, urlat, numrows)
X, Y = np.meshgrid(X, Y)
m.scatter(mlon, mlat, s=0.2, c='red')
shp_info = m.readshapefile('./data/us_states_st99/st99_d00','states',drawbounds=True, zorder=0)
printed_names = []
ax = plt.gca()
ax.xaxis.set_visible(False)
ax.yaxis.set_visible(False)
for spine in ax.spines.itervalues():
spine.set_visible(False)
for k in xrange(mus.shape[0]):
#here x is longitude and y is latitude
#apply softplus to sigmas (to make them positive)
sigmax=np.log(1 + np.exp(sigmas[k][1]))
sigmay=np.log(1 + np.exp(sigmas[k][0]))
mux=mlon[k]
muy=mlat[k]
corxy = corxys[k]
#apply the soft sign
corxy = corxy / (1 + np.abs(corxy))
#now given corxy find sigmaxy
sigmaxy = corxy * sigmax * sigmay
#corxy = 1.0 / (1 + np.abs(sigmaxy))
Z = mlab.bivariate_normal(X, Y, sigmax=sigmax, sigmay=sigmay, mux=mux, muy=muy, sigmaxy=sigmaxy)
#Z = maskoceans(X, Y, Z)
con = m.contour(X, Y, Z, levels=[0.02], linewidths=0.5, colors='darkorange', antialiased=True)
'''
num_levels = len(con.collections)
if num_levels > 1:
for i in range(0, num_levels):
if i != (num_levels-1):
con.collections[i].set_visible(False)
'''
contour_labels = False
if contour_labels:
plt.clabel(con, [con.levels[-1]], inline=True, fontsize=10)
'''
world_shp_info = m.readshapefile('./data/CNTR_2014_10M_SH/Data/CNTR_RG_10M_2014','world',drawbounds=False, zorder=100)
for shapedict,state in zip(m.world_info, m.world):
if shapedict['CNTR_ID'] not in ['CA', 'MX']: continue
poly = MplPolygon(state,facecolor='gray',edgecolor='gray')
ax.add_patch(poly)
'''
if iter:
iter = str(iter).zfill(3)
else:
iter = ''
plt.tight_layout()
plt.savefig('./maps/video/gaus_' + iter + '.' + output_type, frameon=False, dpi=200)
The problem is the meshgrid not covering the complete map. The meshgrid simply doesn't have any points at the positions where you want to draw the gaussian contour line.
An example to reproduce this behaviour is the following, where the meshgrid in x directio starts at -1, such that points lower than that are not drawn.
import matplotlib.pyplot as plt
import matplotlib.mlab as mlab
import numpy as np
fig, ax=plt.subplots()
ax.plot([-2,2],[-2,-2], alpha=0)
X,Y = np.meshgrid(np.linspace(-1,2),np.linspace(-2,2))
Z = mlab.bivariate_normal(X, Y, sigmax=1., sigmay=1., mux=0.1, muy=0.1, sigmaxy=0)
con = ax.contour(X, Y, Z, levels=[Z.max()/3, Z.max()/2., Z.max()*0.8],colors='darkorange')
plt.show()
A similar problem occurs in the code from the question.
While in Y direction, you use the complete map, Y = np.linspace(lllat, urlat, numrows), in X direction you restrict the mesh to start at mlon.min(),
X = np.linspace(mlon.min(), urlon, numcols)
The solution would of course be not to start the mesh in Portland, but somewhere in the ocean, i.e. at the edge of the shown map.

How to make matplotlib Density Map

Let's say I have two lists, x and y of same length. This length is not fixed, but always nonzero. They are x and y values of non-negative points.
I want to create a a color density map much like http://www.mathworks.com/matlabcentral/fx_files/31726/1/datadensitymap.jpg .
Here's my attempt borrowing from a few places I've found on the internet:
density = stats.gaussian_kde([x,y])
color = density([x,y])
x1 = np.array(x)
y1 = np.array(y)
xmin = x1.min()
xmax = x1.max()
ymin = y1.min()
ymax = y1.max()
xscale = (xmax-xmin)/100
yscale = (ymax-ymin)/100
X, Y = np.mgrid[xmin:xmax:xscale, ymin:ymax:yscale]
positions = np.vstack([X.ravel(), Y.ravel()])
Z = np.reshape(density(positions).T, X.shape)
cmap = plt.get_cmap("hot")
plt.imshow(np.rot90(Z), cmap=cmap, extent=[xmin, xmax, ymin, ymax])
plt.scatter(x, y, c=color, cmap=cmap)
When I run this code the plot doesn't render; the title and labels are mashed together.
When I take out the call to imshow, the scatterplot shows perfectly with the density colors on the points showing correctly.
As it turns out, the plot was rendering, but because x had values much larger than y the rendered graph had no height. Setting aspect="auto" fixed it:
plt.imshow(np.rot90(Z), cmap=cmap, extent=[xmin, xmax, ymin, ymax], aspect="auto")