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I am trying to create an animated scatter plot whereby the scatter points plot in order and change color over time, thus the newest scatter points always appear in the same color (in this case, red) while the older scatter points age to different colors using a color map.
The code works except for the newest scatter point in every frame of the animation, which appears as the 'oldest' color in the plot, rather than the newest. How can I get it to appear in the correct color?
My code is this:
import matplotlib.animation as animation
from matplotlib import cm
import matplotlib.pyplot as plt
%matplotlib notebook
brg = cm.get_cmap('hsv',500)
cmapz = brg(range(500))
x = [0]
y = [0]
def update_lines(num):
dx = x[-1]+np.random.random()
x.append(dx)
dy = np.random.random()
y.append(dy)
text.set_text("{:d}: [{:.0f},{:.0f}]".format(num, x[-1], y[-1]))
array = cmapz[:num]
graph.set_offsets(np.c_[x, y])
graph.set_color(array[::-1])
return graph,
fig,ax=plt.subplots(1,1,figsize=(8,5))
ax = plt.axes(xlim=(0,251),ylim=(-1,2))
graph = ax.scatter(x, y,c=cmapz[0])
text = fig.text(0, 1, "TEXT", va='top')
ani = animation.FuncAnimation(fig, update_lines, frames=499, interval=10, blit=False, repeat = False)
plt.show()
I am trying to visulalize a heat distribution across a radial object using plotly.
import numpy as np
import plotly.graph_objects as go
import plotly.express as px
r, theta = np.mgrid[0.1:1:20j, 0:360:2j]
# %%
T = np.linspace(130,100,20)
color = np.ones(r.shape) * T[:,np.newaxis]
fig = go.Figure(go.Barpolar(
r=r.ravel(),
theta=theta.ravel(),
marker_color=color.ravel(),
marker={"showscale": True,
"colorscale": px.colors.sequential.RdBu_r,},
opacity=1))
margin = 10
fig.update_layout(
showlegend=True,
polar=dict(
radialaxis_layer="above traces",
angularaxis_direction="clockwise",
angularaxis_color="white",
angularaxis_showgrid=False,
radialaxis_showgrid=False,
radialaxis_showline=False,
radialaxis_tickvals=[], # Radii ticks
bargap=0, # Ensure cells will touch
hole=0.0, # Center gap
),
)
fig.show()
But now the hover_info shows only the info on radius and theta (spatial), but I would like to actually let it show the color-value (temperature, T).
As a goodie on top, if I could get rid of the white concentric lines between the segments would be amazing. I tried *_showgrid=False but it does not work, maybe it's part of the bar shape?
I use Basemap to plot, which doesn't introduce its own axis type and rather does some extreme math to make its geographic plotting. That let this work:
import matplotlib.pyplot as plt
fig, ax = plt.subplots(); #use instead of fig because it inits an axis too
divider = make_axes_locatable(ax); #prep to add an axis
cax = divider.append_axes('right', size='2.0%', pad=0.15); #make a color bar axis
I preferred this because it kept the new axis within the original axis bounds, which made lining up other subplots (by using an identical divider.append_axes(...) call with them as well) easy and making sure the new colorbar axis didn't clip off the plot easy as well.
But since Basemap is depreciated, I'm trying to move to Cartopy. Unfortunately similar code with Cartopy:
import matplotlib.pyplot as plt
import cartopy as cartopy
fig, ax = plt.subplots(); #use instead of fig because it inits an axis too
ax = plt.axes(projection=cartopy.crs.PlateCarree()); #redefine the axis to be a geographical axis
divider = make_axes_locatable(ax); #prep to add an axis
cax = divider.append_axes('right', size='2.0%', pad=0.15); #make a color bar axis
Gives the error:
KeyError: 'map_projection'
Since under Cartopy the axis ax is changed to a Cartopy axis, the appended axis cax is also a Cartopy axis. If I provide a map_projection=cartopy.crs.PlateCarree() the code runs, but I don't want it to be a Cartopy axis, I want it to be a colorbar (and the Cartopy axis errors if I use it as a colorbar).
How can I append_axes but not inherit the Cartopy axis type?
Additionally, I found not-quite-good-enough work-around using:
import matplotlib.pyplot as plt
import cartopy as cartopy
fig, ax = plt.subplots(); #use instead of fig because it inits an axis too
ax = plt.axes(projection=cartopy.crs.PlateCarree()); #redefine the axis to be a geographical axis
cax = ax.inset_axes((1.02, 0, 0.02, 1)); #make a color bar axis
But that cax is not part of the original axis bounds, which makes it useless for lining up other subplots that use the divider.append_axes() method and the colorbar can clip off the edge of the plot since it is floating in "white space".
I'd like to draw a (vertical) colorbar, which has two different scales (corresponding to two different units for the same quantity) on each side. Think Fahrenheit on one side and Celsius on the other side. Obviously, I'd need to specify the ticks for each side individually.
Any idea how I can do this?
That should get you started:
import matplotlib.pyplot as plt
import numpy as np
# generate random data
x = np.random.randint(0,200,(10,10))
plt.pcolormesh(x)
# create the colorbar
# the aspect of the colorbar is set to 'equal', we have to set it to 'auto',
# otherwise twinx() will do weird stuff.
cbar = plt.colorbar()
pos = cbar.ax.get_position()
cbar.ax.set_aspect('auto')
# create a second axes instance and set the limits you need
ax2 = cbar.ax.twinx()
ax2.set_ylim([-2,1])
# resize the colorbar (otherwise it overlays the plot)
pos.x0 +=0.05
cbar.ax.set_position(pos)
ax2.set_position(pos)
plt.show()
If you create a subplot for the colorbar, you can create a twin axes for that subplot and manipulate it like a normal axes.
import matplotlib.colors as mcolors
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(-1,2.7)
X,Y = np.meshgrid(x,x)
Z = np.exp(-X**2-Y**2)*.9+0.1
fig, (ax, cax) = plt.subplots(ncols=2, gridspec_kw={"width_ratios":[15,1]})
im =ax.imshow(Z, vmin=0.1, vmax=1)
cbar = plt.colorbar(im, cax=cax)
cax2 = cax.twinx()
ticks=np.arange(0.1,1.1,0.1)
iticks=1./np.array([10,3,2,1.5,1])
cbar.set_ticks(ticks)
cbar.set_label("z")
cbar.ax.yaxis.set_label_position("left")
cax2.set_ylim(0.1,1)
cax2.set_yticks(iticks)
cax2.set_yticklabels(1./iticks)
cax2.set_ylabel("1/z")
plt.show()
Note that in newer version of matplotlib, the above answers no long work (as #Ryan Skene pointed out). I'm using v3.3.2. The secondary_yaxis function works for the colorbars in the same way as for regular plot axes and gives one colorbar with two scales: https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.secondary_yaxis.html#matplotlib.axes.Axes.secondary_yaxis
import matplotlib.pyplot as plt
import numpy as np
# generate random data
x = np.random.randint(0,200,(10,10)) #let's assume these are temperatures in Fahrenheit
im = plt.imshow(x)
# create the colorbar
cbar = plt.colorbar(im,pad=0.1) #you may need to adjust this padding for the secondary colorbar label[enter image description here][1]
cbar.set_label('Temperature ($^\circ$F)')
# define functions that relate the two colorbar scales
# e.g., Celcius to Fahrenheit and vice versa
def F_to_C(x):
return (x-32)*5/9
def C_to_F(x):
return (x*9/5)+32
# create a second axes
cbar2 = cbar.ax.secondary_yaxis('left',functions=(F_to_C,C_to_F))
cbar2.set_ylabel('Temperatrue ($\circ$C)')
plt.show()
I am using an inset axis for my colorbar and, for some reason, I found the above to answers no longer worked as of v3.4.2. The twinx took up the entire original subplot.
So I just replicated the inset axis (instead of using twinx) and increased the zorder on the original inset.
axkws = dict(zorder=2)
cax = inset_axes(
ax, width="100%", height="100%", bbox_to_anchor=bbox,
bbox_transform=ax.transAxes, axes_kwargs=axkws
)
cbar = self.fig.colorbar(mpl.cm.ScalarMappable(cmap=cmap), cax=cax)
cbar.ax.yaxis.set_ticks_position('left')
caxx = inset_axes(
ax, width="100%", height="100%",
bbox_to_anchor=bbox, bbox_transform=ax.transAxes
)
caxx.yaxis.set_ticks_position('right')
In matplotlib how to overlay the shapefile (available in folder) as attached below at the top right position outside the plot.
The code referenced by banderkat:
import matplotlib.pyplot as plt
import Image
import numpy as np
im = Image.open('Jbc4j.jpg')
width = im.size[0]
height = im.size[1]
# We need a float array between 0-1, rather than
# a uint8 array between 0-255
im = np.array(im).astype(np.float) / 255
a = np.random.randint(0,100,100)
b = range(100)
fig = plt.figure(1,figsize=(5, 7), dpi=80, facecolor='w')
ax = fig.add_subplot(111)
ax.scatter(a,b)
fig.canvas.draw()
# With newer (1.0) versions of matplotlib, you can
# use the "zorder" kwarg to make the image overlay
# the plot, rather than hide behind it... (e.g. zorder=10)
fig.figimage(im, fig.bbox.xmax - width, fig.bbox.ymax - height, zorder=0)
# (Saving with the same dpi as the screen default to
# avoid displacing the logo image)
fig.savefig('temp.png', dpi=80)
plt.show()
Produces the following result (imaged cropped to save space).
Changing the zorder=1 will place the image on top.
Other helpful references:
How to change background color for scatter plot in matplotlib
How do you change the size of figures drawn with matplotlib?
Python/Matplotlib - Change the relative size of a subplot
In Matplotlib, what does the argument mean in fig.add_subplot(111)?
Customizing Location of Subplot Using GridSpec
You can use basemap toolkit to load and plot shapefile. Here I've plotted shapeFile in a separate axes and aligned it to top-right of other axes plot using 'subplot2grid'.
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
import matplotlib.gridspec as gridspec
def plotShapeFile():
# Lambert Conformal Conic map.
m = Basemap(llcrnrlon=-100.,llcrnrlat=0.,urcrnrlon=-20.,urcrnrlat=57.,
projection='lcc',lat_1=20.,lat_2=40.,lon_0=-60.,
resolution ='l',area_thresh=1000.)
# read shapefile.
shp_info = m.readshapefile('C:/basemap-1.0.6/basemap-1.0.6/examples/huralll020','hurrtracks',drawbounds=False)
# find names of storms that reached Cat 4.
names = []
for shapedict in m.hurrtracks_info:
cat = shapedict['CATEGORY']
name = shapedict['NAME']
if cat in ['H4','H5'] and name not in names:
# only use named storms.
if name != 'NOT NAMED': names.append(name)
# plot tracks of those storms.
for shapedict,shape in zip(m.hurrtracks_info,m.hurrtracks):
name = shapedict['NAME']
cat = shapedict['CATEGORY']
if name in names:
xx,yy = zip(*shape)
# show part of track where storm > Cat 4 as thick red.
if cat in ['H4','H5']:
m.plot(xx,yy,linewidth=1.5,color='r')
elif cat in ['H1','H2','H3']:
m.plot(xx,yy,color='k')
# draw coastlines, meridians and parallels.
m.drawcoastlines()
m.drawcountries()
m.drawmapboundary(fill_color='#99ffff')
m.fillcontinents(color='#cc9966',lake_color='#99ffff')
m.drawparallels(np.arange(10,70,20),labels=[1,1,0,0])
m.drawmeridians(np.arange(-100,0,20),labels=[0,0,0,1])
if __name__ == '__main__':
fig=plt.figure()
plt.subplots_adjust(wspace=0.001, hspace=0.001)
ax1=plt.subplot2grid((5,5), (0,0), colspan=4, rowspan=4)
labels = 'Frogs', 'Hogs', 'Dogs', 'Logs'
fracs = [15,30,45, 10]
explode=(0, 0.05, 0, 0)
p1,t1,at1 = plt.pie(fracs, explode=explode, labels=labels, autopct='%1.1f%%', shadow=True)
plt.title('Raining Hogs and Dogs', bbox={'facecolor':'0.8', 'pad':5})
ax2=plt.subplot2grid((5,5), (0,4), colspan=1, rowspan=1)
#draw shapeFile on the current active axes, i.e. ax2
plotShapeFile()
plt.tight_layout()
plt.show()
Below are links to references I've used:
http://sourceforge.net/projects/matplotlib/files/matplotlib-toolkits/basemap-1.0.6/
http://matplotlib.org/basemap/users/examples.html
Output: