I'm using Python Matplotlib to plot contours. Here's some code I have below as a basis. If you run this, you'll see that the labels are almost at vertical. I'd like to get the labels orientated horizontal, but I have no idea how can achieve this. I've tried with ClabelText, which the documentation suggests, but don't understand how this is supposed to work. I'd appreciate if someone could suggest a way to orientate the labels, either with or without ClabelText.
import itertools as it
import numpy as np
from matplotlib.ticker import FuncFormatter
from matplotlib.contour import ClabelText
import matplotlib.pyplot as plt
from math import pi, log
def getTime(data):
M = data['weight']
Tei = data['temp']
Twasser = 99.8
Teikl = 86.0 ## max allowed temp
k = 0.262 ## estimate was 0.3 W/(m.K),
Crho = 3.18 # (KJ/kgC)
const = pow(Crho, 1.0/3) / (pi*pi*k*pow(4*pi/3,2.0/3))
Tval = const*pow(M,2.0/3)*log(0.76*(Tei-Twasser)/(Teikl-Twasser))
return Tval # coo time in minutes
def contourFmt(val, posn):
mins = int(val // 1)
secs = int(val % 1 *60)
return '{0:d}mm{1:d}ss'.format(mins, secs)
def labeler(val): #is this any use??
print(val)
return
#weights = np.array(range(40, 80, 5))*1.0
#temps = np.array(range(0, 30, 5))*1.0
weights = np.arange(40.0, 80.0, 5.0)
temps = np.arange(0.0, 25.01, 5.0)
X = temps
Y = weights
Z = np.zeros((len(X), len(Y)))
xx = [{'temp':i} for i in X]
yy = [{'weight':i} for i in Y]
plt.figure()
##zz = it.product(xx,yy)
for i, xdicts in enumerate(xx):
for j, ydicts in enumerate(yy):
zd = {}
zd.update(xdicts)
zd.update(ydicts)
zval = getTime(zd)
Z[i,j] = zval
times = np.arange(4.00, 6.50, 0.25)
CS = plt.contour(Y, X, Z, levels=times, colors='b')
lbl = ClabelText(labeler)
lbl.set_rotation('horizontal')
formatter = FuncFormatter(contourFmt)
#plt.clabel(CS, inline=True, fmt=formatter, fontsize=12)
plt.clabel(CS, inline=True, use_clabeltext=True, fmt=formatter, fontsize=12)
plt.grid(True)
plt.clabel(CS, inline=1, fontsize=12)
plt.show()
You can set the rotation of the individual labels after they have been created. The label Text objects are returns by clabel, so you can store them and iterate over them, using .set_rotation(0) to orient them horizontally.
Change the last few lines of your script to:
labels1 = plt.clabel(CS, inline=True, use_clabeltext=True, fmt=formatter, fontsize=12)
labels2 = plt.clabel(CS, inline=1, fontsize=12)
for l in labels1+labels2:
l.set_rotation(0)
Related
I am trying to visualize the temperature field of a dataset, and have attempted to do so by plotting it with matplotlib and cartopy. I have succeeded in creating a general picture, but there is one major flaw that I am trying to figure out how to correct. I want to make the contour interval much smaller (1 kelvin or 0.5 kelvin intervals) to properly visualize the minute details of the dataset. Right now, my figure looks like this:
Potential Temperature w/ inappropriate interval
You can see the general field, but the fine details are completely lost. How can I fix this situation, and see finer details in my temperature field.
Relevant code:
# FOR SINGLE PLOT ONLY
# Get WRF variables
theta_2m = getvar(ds, 'TH2')
wrf_lats, wrf_lons = latlon_coords(theta_2m)
wrf_lons = to_np(wrf_lons)
wrf_lats = to_np(wrf_lats)
# Timestamp
timestamp = to_np(theta_2m.Time).astype('M8[s]').astype('O').isoformat()
time = theta_2m.Time
time_str = str(time.values)
# Get cartopy projection from data set
cart_proj = get_cartopy(theta_2m)
# Plot the relevant data
fig = plt.figure(figsize=(12,6))
ax = plt.axes(projection=cart_proj)
plt.contour(wrf_lons, wrf_lats, theta_2m, colors='black', transform=ccrs.PlateCarree())
plt.contourf(wrf_lons, wrf_lats, theta_2m, transform=ccrs.PlateCarree(), cmap=get_cmap('coolwarm'))
plot_background(ax)
plt.colorbar(ax=ax, shrink=.98)
ax.set_extent([-104.35, -94.45, 36.37, 44.78])
ax.set_title('2m Potential Temperature (K) ' + time_str[:19])
plt.show()
In plt.contour you can set the interval of contour lines with levels parameter:
max_level = 2
min_level = -2
step_level = 0.5
ax.contour(xx, yy, zz, colors = 'black', levels = np.arange(min_level, max_level + step_level, step_level))
Complete Code
import numpy as np
import matplotlib.pyplot as plt
N = 100
x = np.linspace(0, 10, N)
y = np.linspace(0, 10, N)
xx, yy = np.meshgrid(x, y)
zz = np.sin(xx) + np.sin(yy)
max_level = 2
min_level = -2
step_level = 0.5
fig, ax = plt.subplots(figsize = (6, 6))
ax.contour(xx, yy, zz, colors = 'black', levels = np.arange(min_level, max_level + step_level, step_level))
ax.contourf(xx, yy, zz, levels = np.arange(min_level, max_level + step_level, step_level))
plt.show()
step_level = 0.5
step_level = 0.1
I use Windows 10 / 64 / Google chrome
I found a good set-up for animation over Jupyter with the call %matplotlib notebook as here :
import numpy as np
import scipy.stats as st
%matplotlib notebook
import matplotlib.pyplot as plt
import matplotlib.animation as animation
For exemple, this one is working pretty well :
n = 100
X = st.norm(0,1).rvs(200)
number_of_frames = np.size(X)
def update_hist(num, second_argument):
plt.cla()
plt.hist(X[:num], bins = 20)
plt.title("{}".format(num))
plt.legend()
fig = plt.figure()
hist = plt.hist(X)
ani = animation.FuncAnimation(fig, update_hist, number_of_frames, fargs=(X, ), repeat = False )
plt.show()
But, weirdly the code below doesn't work while it's the same structure, it puzzles me :
X = np.linspace(-5,5, 150)
number_of_frames = np.size(X)
N_max = 100
N = np.arange(1,N_max+1)
h = 1/np.sqrt(N)
def update_plot(n, second_argument):
#plt.cla()
plt.plot(X, [f(x) for x in X], c = "y", label = "densité")
plt.plot(X, [fen(sample_sort[:n],h[n],x) for x in X], label = "densité")
plt.title("n = {}".format(n))
fig = plt.figure(6)
plot = plt.plot(X, [f(x) for x in X], c = "y", label = "densité")
ani = animation.FuncAnimation(fig, update_plot, number_of_frames, fargs=(X, ), repeat = False )
plt.show()
Thanks for your help, best regards.
EDIT : You don't have the funciton fen(sample_sort[:n],h[n],x) it is a function from float to float taking a x in argument and returning a flot. The argument sample_sort[:n],h[n] it is just maths things I'm trying to understand some statistics anyway, you can remplace with line with what you want np.cos(N[:n]) for exemple.
EDIT : New code according to the suggestion :
N_max = 100
X = np.linspace(-5,5, N_max )
number_of_frames = np.size(X)
N = np.arange(1,N_max+1)
h = 1/np.sqrt(N)
def update_plot(n):
#plt.cla()
lines.set_data(X, np.array([fen(sample_sort[:n],h[n],x) for x in X]))
ax.set_title("n = {}".format(n))
return lines
fig = plt.figure()
ax = plt.axes(xlim=(-4, 4), ylim=(-0.01, 1))
ax.plot(X, np.array([f(x) for x in X]), 'y-', lw=2, label="d")
lines, = ax.plot([], [], 'b--', lw=3, label="f")
ani = animation.FuncAnimation(fig, update_plot, number_of_frames, repeat = False )
plt.show()
EDIT 2:
I found a code over internet which does exactly what I would like
# Fermi-Dirac Distribution
def fermi(E: float, E_f: float, T: float) -> float:
return 1/(np.exp((E - E_f)/(k_b * T)) + 1)
# Create figure and add axes
fig = plt.figure(figsize=(6, 4))
ax = fig.add_subplot(111)
# Get colors from coolwarm colormap
colors = plt.get_cmap('coolwarm', 10)
# Temperature values
T = np.array([100*i for i in range(1,11)])
# Create variable reference to plot
f_d, = ax.plot([], [], linewidth=2.5)
# Add text annotation and create variable reference
temp = ax.text(1, 1, '', ha='right', va='top', fontsize=24)
# Set axes labels
ax.set_xlabel('Energy (eV)')
ax.set_ylabel('Fraction')
# Animation function
def animate(i):
x = np.linspace(0, 1, 100)
y = fermi(x, 0.5, T[i])
f_d.set_data(x, y)
f_d.set_color(colors(i))
temp.set_text(str(int(T[i])) + ' K')
temp.set_color(colors(i))
# Create animation
ani = animation.FuncAnimation(fig, animate, frames=range(len(T)), interval=500, repeat=False)
# Ensure the entire plot is visible
fig.tight_layout()
# show animation
plt.show()
What I want to draw is a curve at random because the actual state of the function is unknown. The basic structure looks like this, so please modify it based on this.
import numpy as np
import scipy.stats as st
# %matplotlib notebook
import matplotlib.pyplot as plt
import matplotlib.animation as animation
# from IPython.display import HTML
# from matplotlib.animation import PillowWriter
X = np.linspace(-5,5, 100)
number_of_frames = np.size(X)
N_max = 100
N = np.arange(1,N_max+1)
h = 1/np.sqrt(N)
def update_plot(n):
#plt.cla()
lines.set_data(X[:n], h[:n])
lines2.set_data(X[:n], h[:n]*-1)
ax.set_title("n = {}".format(n))
return lines, lines2
fig = plt.figure()
ax = plt.axes(xlim=(-5, 5), ylim=(-1, 1))
lines, = ax.plot([], [], 'y-', lw=2, label="densité")
lines2, = ax.plot([], [], 'b--', lw=3, label="densité2")
ani = animation.FuncAnimation(fig, update_plot, frames=number_of_frames, repeat=False )
plt.show()
# ani.save('lines_ani2.gif', writer='pillow')
# plt.close()
# HTML(ani.to_html5_video())
I'm updating dynamically a plot in a loop:
dat=[0, max(X[:, 0])]
fig = plt.figure()
ax = fig.add_subplot(111)
Ln, = ax.plot(dat)
Ln2, = ax.plot(dat)
plt.ion()
plt.show()
for i in range(1, 40):
ax.set_xlim(int(len(X[:i])*0.8), len(X[:i])) #show last 20% data of X
Ln.set_ydata(X[:i])
Ln.set_xdata(range(len(X[:i])))
Ln2.set_ydata(Y[:i])
Ln2.set_xdata(range(len(Y[:i])))
plt.pause(0.1)
But now I want to update it in a different way: append some values and show them in other colour:
X.append(other_data)
# change colour just to other_data in X
The result should look something like this:
How could I do that?
Have a look at the link I posted. Linesegments can be used to plot colors at a particular location differently. If you want to do it in real-time you can still use line-segments. I leave that up to you.
# adjust from https://stackoverflow.com/questions/38051922/how-to-get-differents-colors-in-a-single-line-in-a-matplotlib-figure
import numpy as np, matplotlib.pyplot as plt
from matplotlib.collections import LineCollection
from matplotlib.colors import ListedColormap, BoundaryNorm
# my func
x = np.linspace(-2 * np.pi, 2 * np.pi, 100)
y = 3000 * np.sin(x)
# select how to color
cmap = ListedColormap(['r','b'])
norm = BoundaryNorm([2000,], cmap.N)
# get segments
xy = np.array([x, y]).T.reshape(-1, 1, 2)
segments = np.hstack([xy[:-1], xy[1:]])
# control which values have which colors
n = y.shape[0]
c = np.array([plt.cm.RdBu(0) if i < n//2 else plt.cm.RdBu(255) for i in range(n)])
# c = plt.cm.Reds(np.arange(0, n))
# make line collection
lc = LineCollection(segments,
colors = c
# norm = norm,
)
# plot
fig, ax = plt.subplots()
ax.add_collection(lc)
ax.autoscale()
ax.axvline(x[n//2], linestyle = 'dashed')
ax.annotate("Half-point", (x[n//2], y[n//2]), xytext = (4, 1000),
arrowprops = dict(headwidth = 30))
fig.show()
I am trying to create a 3D animation scatter plot where each point is plotted as a sphere with radius of r proportional to value M (please see the code below), I guess it should be done by using argument s in ax.scatter, but since this value is unique for each (x,y,z), I don't know how to pass that to graph._offsets3d which accepts (x,y,z) touple. This is the first part of the task, the other part is that the data should appear at their specific time t (please see the code below).
I am currently struggling to change the size of each point according to their corresponding value in M, and color code the point with its corresponding time t, do you know how could I do this?
It would my next task to add a play/pause button to the figure and be able to rotate the the graph?
Does anyone have similar experiences that I could benefit from?
Many thanks!
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.animation as animation
#####Data Generation####
# Space Coordinate
X = np.random.random((100,)) * 255 * 2 - 255
Y = np.random.random((100,)) * 255 * 2 - 255
Z = np.random.random((100,)) * 255 * 2 - 255
# Magnitude of each point
M = np.random.random((100,))*-1+0.5
# Time
t = np.sort(np.random.random((100,))*10)
#ID each point should be color coded. Moreover, each point belongs to a cluster `ID`
ID = np.sort(np.round([np.random.random((100,))*5]))
def update_lines(num):
for i in range (df_IS["EASTING [m]"].size):
dx = X[i]
dy = Y[i]
dz = Z[i]
text.set_text("{:d}: [{:.0f}] Mw[{:.2f}]".format(ID[i], t[i],ID[i])) # for debugging
x.append(dx)
y.append(dy)
z.append(dz)
graph._offsets3d = (x, y, z)
return graph,
fig = plt.figure(figsize=(5, 5))
ax = fig.add_subplot(111, projection="3d")
graph = ax.scatter(X, Y, Z, color='orange') # s argument here
text = fig.text(0, 1, "TEXT", va='top') # for debugging
ax.set_xlim3d(X.min(), X.max())
ax.set_ylim3d(Y.min(), Y.max())
ax.set_zlim3d(Z.min(),Z.max())
# Creating the Animation object
ani = animation.FuncAnimation(fig, update_lines, frames=200, interval=500, blit=False)
plt.show()
In the animation function was looped by the size of the data frame, but rewrote your code partly because the animation argument is linked to the number of frames. Please correct me if I'm wrong. You can also pass in the size with graph.set_sizes(), which you can specify there. Your size variable had a negative value, so I'm recreating it as an integer. I've used a separate library in part because of my working environment.
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.animation as animation
from IPython.display import HTML # Animation on jupyter lab
from matplotlib.animation import PillowWriter # For GIF animation
#####Data Generation####
# Space Coordinate
X = np.random.random((100,)) * 255 * 2 - 255
Y = np.random.random((100,)) * 255 * 2 - 255
Z = np.random.random((100,)) * 255 * 2 - 255
# Magnitude of each point
# M = np.random.random((100,))*-1+0.5
M = np.random.randint(1,70, size=100)
# Time
t = np.sort(np.random.random((100,))*10)
#ID each point should be color coded. Moreover, each point belongs to a cluster `ID`
ID = np.sort(np.round([np.random.random((100,))*5]))
x = []
y = []
z = []
m = []
def update_lines(i):
# for i in range (df_IS["EASTING [m]"].size):
dx = X[i]
dy = Y[i]
dz = Z[i]
dm = M[i]
# text.set_text("{:d}: [{:.0f}] Mw[{:.2f}]".format(ID[i], t[i],ID[i])) # for debugging
x.append(dx)
y.append(dy)
z.append(dz)
m.append(dm)
graph._offsets3d = (x, y, z)
graph.set_sizes(m)
return graph,
fig = plt.figure(figsize=(5, 5))
ax = fig.add_subplot(111, projection="3d")
graph = ax.scatter(X, Y, Z, s=M, color='orange') # s argument here
text = fig.text(0, 1, "TEXT", va='top') # for debugging
ax.set_xlim3d(X.min(), X.max())
ax.set_ylim3d(Y.min(), Y.max())
ax.set_zlim3d(Z.min(), Z.max())
# Creating the Animation object
ani = animation.FuncAnimation(fig, update_lines, frames=100, interval=500, blit=False, repeat=False)
# plt.show()
ani.save('test3Dscatter.gif', writer='pillow')
plt.close()
HTML(ani.to_html5_video())
Edit:
# Time
t = np.sort(np.random.random((100,))*10)
# datapoint for color
cm_name = 'jet'
cm = plt.get_cmap(cm_name, 100)
C = [cm(n) for n in range(cm.N)]
# list for colors add
x = []
y = []
z = []
m = []
c = []
# animation function update
dm = M[i]
dc = C[i] # update
m.append(dm)
c.append(dc) # update
graph._facecolor3d = c # scatter color defined
return graph,
I want to embed subplots canvas inside a cartopy projected map. I wrote this code to show the expected result by using rectangles:
#%%
import numpy as np
import cartopy as cr
import cartopy.crs as ccrs
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
from cartopy.io import shapereader
import geopandas
resolution = '10m'
category = 'cultural'
name = 'admin_0_countries'
shpfilename = shapereader.natural_earth(resolution, category, name)
# read the shapefile using geopandas
df = geopandas.read_file(shpfilename)
# read the country borders
usa = df.loc[df['ADMIN'] == 'United States of America']['geometry'].values[0]
can = df.loc[df['ADMIN'] == 'Canada']['geometry'].values[0]
central_lon, central_lat = -80, 60
extent = [-85, -55, 40, 62]
# ax = plt.axes(projection=ccrs.Orthographic(central_lon, central_lat))
#Golden ratio
phi = 1.618033987
h = 7
w = phi*h
fig = plt.figure(figsize=(w,h))
ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree())
#Set map extent
ax.set_extent(extent)
ax.set_xticks(np.linspace(extent[0],extent[1],11))
ax.set_yticks(np.linspace(extent[2],extent[3],6))
ax.add_geometries(usa, crs=ccrs.PlateCarree(), facecolor='none',
edgecolor='k')
# ax.gridlines()
ax.coastlines(resolution='50m')
nx, ny = 7,6
#Begin firts rectangle
xi = extent[0] + 0.5
yi = extent[2] + 0.5
x, y = xi, yi
#Loop for create the plots grid
for i in range(nx):
for j in range(ny):
#Inner rect height
in_h = 2.8
#Draw the rect
rect = ax.add_patch(mpatches.Rectangle(xy=[x, y], width=phi*in_h, height=in_h,
facecolor='blue',
alpha=0.2,
transform=ccrs.PlateCarree()))
#Get vertex of the drawn rectangle
verts = rect.get_path().vertices
trans = rect.get_patch_transform()
points = trans.transform(verts)
#Refresh rectangle coordinates
x += (points[1,0]-points[0,0]) + 0.2
if j == ny-1:
x = xi
y += (points[2,1]-points[1,1]) + 0.2
# print(points)
fig.tight_layout()
fig.savefig('Figure.pdf',format='pdf',dpi=90)
plt.show()
This routine prints this figure
What I am looking for is a way to embed plots that match every single rectangle in the figure. I tried with fig.add_axes, but I couldn't get that mini-canvas match with the actual rectangles.
Since you want to embed the axes inside the parent axes is recommend using inset_axes, see the documentation here.
I wrote simple code to demonstrate how it works. Clearly there will be some tweaking of the inset_axes positions and sizes necessary for your desired output, but I think my trivial implementation already does decent.
All created axes instances are stored in a list so that they can be accessed later.
import matplotlib.pyplot as plt
import numpy as np
fig, ax = plt.subplots()
axis = []
x = np.linspace(-85, -55)
y = np.linspace(40, 62)
ax.plot(x, y)
offset_l = 0.05
offset_h = 0.12
num_x = 6
num_y = 7
xs = np.linspace(offset_l, 1-offset_h, num_x)
ys = np.linspace(offset_l, 1-offset_h, num_y)
for k in range(num_x):
for j in range(num_y):
ax_ins = ax.inset_axes([xs[k], ys[j], 0.1, 0.1])
ax_ins.axhspan(0, 1, color='tab:blue', alpha=0.2)
axis.append(ax_ins)
Alternatively, you can also specify the inset_axes positions using data coordinates, for this you have to set the kwarg transform in the method to transform=ax.transData, see also my code below.
import matplotlib.pyplot as plt
import numpy as np
#Golden ratio
phi = 1.618033987
h = 7
w = phi*h
fig, ax = plt.subplots(figsize=(w, h))
axis = []
x = np.linspace(-85, -55)
y = np.linspace(40, 62)
ax.plot(x, y)
offset_l = 0.05
offset_h = 0.12
num_x = 6
num_y = 7
fig.tight_layout()
extent = [-85, -55, 40, 62]
xi = extent[0] + 0.5
yi = extent[2] + 0.5
in_h = 2.8
in_w = phi * 2.8
spacing = 0.4
for k in range(num_x):
for j in range(num_y):
ax_ins = ax.inset_axes([xi+k*(in_w + phi*spacing), yi+j*(in_h + spacing),
in_w, in_h], transform=ax.transData)
ax_ins.axhspan(0, 1, color='tab:blue', alpha=0.2)
axis.append(ax_ins)