When attempting to plot wind barbs using matplotlib on a cartopy map, I get a QhullError. I've never seen this error before and my code hasn't changed since I last used it. I've also made sure the packages are up to date and the grib2 file being used is valid by printing the xarray variables. Below is the code:
file = xr.open_dataset('/Users/victoralvarez/prog2/grib/&var_UGRD=on&var_VGRD=on.grb',
engine='cfgrib')
# Mask the barbs where winds < 50.
masknum = int(input('BARB THRESHOLD: '))
# Extract the lon/lat.
x = file.variables['longitude'].values
y = file.variables['latitude'].values
# Extract the desired data.
u_wind = file.variables['u'].values * units('m/s')
v_wind = file.variables['v'].values * units('m/s')
# Calculate the wind speed.
wndspd = mpcalc.wind_speed(u_wind, v_wind).to('kt')
wnds_f = wndspd.astype(float)
mask = np.ma.masked_less_equal(wnds_f, masknum).mask
u_wind[mask] = np.nan
v_wind[mask] = np.nan
fig = plt.figure(1, figsize=(15,15))
ax = plt.axes(projection=ccrs.LambertConformal(central_longitude=-100,
central_latitude=35,
standard_parallels=(30, 60)))
ax.set_extent([-121, -75, 25, 50], ccrs.PlateCarree())
ax.add_feature(cfeature.OCEAN.with_scale('50m'), facecolor='#626262',
edgecolor='black',
zorder=0,
linewidth=.5)
ax.add_feature(cfeature.LAND.with_scale('50m'), edgecolor='black',
facecolor='#626262',
zorder=1)
ax.add_feature(cfeature.STATES.with_scale('50m'), linewidth=.5,
edgecolor='black',
zorder=5)
b1 = ax.barbs(x, y, u_wind.to('kt').m, v_wind.to('kt').m,
color='black', length=4.5, regrid_shape=20, pivot='middle',
linewidth=1.5, zorder=103, transform=ccrs.PlateCarree())
b2 = ax.barbs(x, y, u_wind.to('kt').m, v_wind.to('kt').m,
color='white', length=4.5, regrid_shape=10, pivot='middle',
linewidth=0.5, zorder=104, transform=ccrs.PlateCarree())
plt.savefig('img.png', dpi=300, bbox_inches='tight')
When running the script through terminal, the below errors show:
Traceback (most recent call last):
File "winds_barb.py", line 63, in <module>
linewidth=0.5, zorder=104, transform=ccrs.PlateCarree())
File "/anaconda3/lib/python3.7/site-packages/cartopy/mpl/geoaxes.py", line 1826, in barbs
target_extent=target_extent)
File "/anaconda3/lib/python3.7/site-packages/cartopy/vector_transform.py", line 146, in vector_scalar_to_grid
return _interpolate_to_grid(nx, ny, x, y, u, v, *scalars, **kwargs)
File "/anaconda3/lib/python3.7/site-packages/cartopy/vector_transform.py", line 68, in _interpolate_to_grid
method='linear'),)
File "/anaconda3/lib/python3.7/site-packages/scipy/interpolate/ndgriddata.py", line 222, in griddata
rescale=rescale)
File "interpnd.pyx", line 248, in scipy.interpolate.interpnd.LinearNDInterpolator.__init__
File "qhull.pyx", line 1828, in scipy.spatial.qhull.Delaunay.__init__
File "qhull.pyx", line 354, in scipy.spatial.qhull._Qhull.__init__
scipy.spatial.qhull.QhullError: QH6019 qhull input error: can not scale last coordinate. Input is cocircular
or cospherical. Use option 'Qz' to add a point at infinity.
While executing: | qhull d Qz Qt Qbb Q12 Qc
Options selected for Qhull 2015.2.r 2016/01/18:
run-id 1133843321 delaunay Qz-infinity-point Qtriangulate Qbbound-last
Q12-no-wide-dup Qcoplanar-keep _pre-merge _zero-centrum Qinterior-keep
Pgood
This error is most likely caused by your input latitude coordinate containing the pole. The following code will reproduce the error you are seeing:
import cartopy.crs as ccrs
import matplotlib.pyplot as plt
import numpy as np
lons = np.array([-110, -100, -90])
lats = np.array([-90, 30, 40, 50])
u = np.ones([len(lats), len(lons)]) * 10
v = np.ones_like(u) * 10
p = ccrs.LambertConformal(
central_longitude=-100,
central_latitude=35,
standard_parallels=(30, 60),
)
ax = plt.axes(projection=p)
ax.coastlines()
ax.set_extent([-121, -75, 25, 50], crs=ccrs.PlateCarree())
ax.barbs(lons, lats, u, v, regrid_shape=3, transform=ccrs.PlateCarree())
plt.show()
But when the pole is removed there is no error:
lats = np.array([30, 40, 50])
Since your example is not runnable I cannot suggest the exact fix for your case. However, you can likely exclude such points from your input data prior to plotting to avoid this problem and still use your desired projection.
It appears the solution (sorta) to this was to simply use a different projection than the LambertConformal projection I was using originally. Not exactly sure what was wrong so this is only a circumvention to the original problem.
Related
Using the python seaborn package I was trying to plot the nested bar graphs with three different y-axes as shown in the below figure:
And the code that I have used is :
import matplotlib.pyplot as plt
from matplotlib import rc
import numpy as np
import seaborn as sns
#plt.style.use(['science'])
rc('font', **{'family': 'serif', 'serif': ['Computer Modern']})
rc('text', usetex=True)
HV = [388, 438]
YS = [1070, 1200]
UTS = [1150, 1400]
Z = [15, 12.5]
x = [1, 2]
fig, ax1 = plt.subplots(figsize=(5, 5.5))
colors=sns.color_palette("rocket",4)
ax1 = sns.barplot(x[0],YS[0],color="blue")
ax1 = sns.barplot(x[0],color="blue")
ax1 = sns.barplot(x[1],YS[1],color="blue")
ax1 = sns.barplot(x[1],UTS[1],color="blue")
ax2 = ax1.twinx()
ax2 = sns.barplot(x[0], HV[0],color="green")
ax2 = sns.barplot(x[1], HV[1],color="green")
ax3 = ax1.twinx()
ax3 = sns.barplot(x[0],Z[0],color="red")
ax3 = sns.barplot(x[1],Z[1],color="red")
#ax3.spines['right'].set_position(('outward',60))
ax3.spines['right'].set_position(('axes',1.15))
ax1.set_ylabel("First",color="blue")
ax2.set_ylabel("Second",color="green")
ax3.set_ylabel("Third",color="red")
ax1.tick_params(axis='y',colors="blue")
ax2.tick_params(axis='y',colors="green")
ax3.tick_params(axis='y',colors="red")
ax2.spines['right'].set_color("green")
ax3.spines['right'].set_color("red")
ax3.spines['left'].set_color("blue")
plt.show()
And I'm getting the following error:
Traceback (most recent call last):
File "/usr/lib/python3/dist-packages/seaborn/utils.py", line 531, in categorical_order
order = values.cat.categories
AttributeError: 'int' object has no attribute 'cat'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/lib/python3/dist-packages/seaborn/utils.py", line 534, in categorical_order
order = values.unique()
AttributeError: 'int' object has no attribute 'unique'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/sspenkulinti/these/thesis_E185_fatigue/test_matrix/E185_properties_AB_HT.py", line 21, in <module>
ax1 = sns.barplot(x[0],YS[0],color="blue")
File "/usr/lib/python3/dist-packages/seaborn/categorical.py", line 3147, in barplot
plotter = _BarPlotter(x, y, hue, data, order, hue_order,
File "/usr/lib/python3/dist-packages/seaborn/categorical.py", line 1614, in __init__
self.establish_variables(x, y, hue, data, orient,
File "/usr/lib/python3/dist-packages/seaborn/categorical.py", line 200, in establish_variables
group_names = categorical_order(groups, order)
File "/usr/lib/python3/dist-packages/seaborn/utils.py", line 536, in categorical_order
order = pd.unique(values)
File "/usr/lib/python3/dist-packages/pandas/core/algorithms.py", line 395, in unique
values = _ensure_arraylike(values)
File "/usr/lib/python3/dist-packages/pandas/core/algorithms.py", line 204, in _ensure_arraylike
inferred = lib.infer_dtype(values, skipna=False)
File "pandas/_libs/lib.pyx", line 1251, in pandas._libs.lib.infer_dtype
TypeError: 'int' object is not iterable
The error is because you can't call sns.barplot with a single number as first parameter. The x-values need to be a list.
To get want you want using seaborn, the data needs to be presented as if it comes from a dataframe. hue_order is needed to preserve enough space for each of the bars, even when nothing is plotted there.
import matplotlib.pyplot as plt
from matplotlib import rc
import numpy as np
import seaborn as sns
HV = [388, 438]
YS = [1070, 1200]
UTS = [1150, 1400]
Z = [15, 12.5]
x = ["As built", "200ºC-850ºC"]
names = ['YS', 'UTS', 'HV', 'Z']
fig, ax1 = plt.subplots(figsize=(9, 5.5))
colors = sns.color_palette("tab10", len(names))
sns.barplot(x=x + x, y=YS + UTS, hue=[names[0]] * len(x) + [names[1]] * len(x),
hue_order=names, palette=colors, alpha=0.7, ax=ax1)
# ax1 will already contain the full legend, the third handle needs to
# be updated to show the hatching
ax1.legend_.legendHandles[2].set_hatch('///')
ax2 = ax1.twinx()
sns.barplot(x=x, y=HV, hue=[names[2]] * len(x), hue_order=names, palette=colors, hatch='//', alpha=0.7, ax=ax2)
ax2.legend_.remove() # seaborn automatically creates a new legend
ax3 = ax1.twinx()
sns.barplot(x=x, y=Z, hue=[names[3]] * len(x), hue_order=names, palette=colors, alpha=0.7, ax=ax3)
ax3.legend_.remove()
ax3.spines['right'].set_position(('axes', 1.15))
ax1.set_ylabel("First", color=colors[0])
ax2.set_ylabel("Second", color=colors[2])
ax3.set_ylabel("Third", color=colors[3])
ax1.tick_params(axis='y', colors=colors[0])
ax2.tick_params(axis='y', colors=colors[2])
ax3.tick_params(axis='y', colors=colors[3])
ax2.spines['right'].set_color(colors[2])
ax3.spines['right'].set_color(colors[3])
plt.tight_layout()
plt.show()
I have this plot in which I can adapt the curve as I want. My problem is I need to draw on an image. I don´t know how to put both together.
1
import matplotlib.pyplot as plt
from matplotlib.patches import Polygon
#theta = np.arange(0, 2*np.pi, 0.1)
#r = 1.5
#xs = r*np.cos(theta)
#ys = r*np.sin(theta)
xs = (921, 951, 993, 1035, 1065, 1045, 993, 945)
ys = (1181, 1230, 1243, 1230, 1181, 1130, 1130, 1130)
poly = Polygon(list(zip(xs, ys)), animated=True)
fig, ax = plt.subplots()
ax.add_patch(poly)
p = PolygonInteractor(ax, poly, visible=False)
ax.set_title('Click and drag a point to move it')
ax.set_xlim((800, 1300))
ax.set_ylim((1000, 1300))
plt.show()
Try call ax.imshow before draw the polygon? Like this:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Polygon
from scipy import misc
xs = (21, 51, 93, 135, 100, 90, 21, 10)
ys = (111, 130, 143, 230, 501, 530, 530, 513)
poly = Polygon(list(zip(xs, ys)), color='r')
fig, ax = plt.subplots()
ax.imshow(misc.face(), origin='lower')
ax.add_patch(poly)
# ax.set_xlim([0,2000])
# ax.set_ylim([0,2000])
fig.show()
BTW, your xlim and ylim is also not proper. Your image is in the range of y=0~700, but your polygon is y=1000~1300. You at least need to ax.set_ylim([0,1400]) for your image and polygon shown together.
I have the following data and labels I am transforming through PCA.
The labels are only 0 or 1.
from mpl_toolkits.mplot3d import Axes3D
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
import seaborn as sns
import numpy as np
fields = ["Occupancy", "Temperature", "Humidity", "Light", "CO2", "HumidityRatio", "NSM", "WeekStatus"]
df = pd.read_csv('datatraining-updated.csv', skipinitialspace=True, usecols=fields, sep=',')
#Get the output from pandas as a numpy matrix
final_data=df.values
#Data
X = final_data[:,1:8]
#Labels
y = final_data[:,0]
#Normalize features
X_scaled = StandardScaler().fit_transform(X)
#Apply PCA on them
pca = PCA(n_components=7).fit(X_scaled)
#Transform them with PCA
X_reduced = pca.transform(X_scaled)
Then, I just want to show, in a 3D graph, the 3 PCA features with highest variance, I can find them as follows
#Show variable importance
importance = pca.explained_variance_ratio_
print('Explained variation per principal component:
{}'.format(importance))
After that, I want to plot only the top-3 highest variance features. So, I previously select them in the code below
X_reduced=X_reduced[:, [0, 4, 5]]
Ok, here is my problem: I can plot them without the legend. When I try to plot them using the following code
# Create plot
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax = fig.gca(projection='3d')
colors = ("red", "gray")
for data, color, group in zip(X_reduced, colors, y):
dim1,dim2,dim3=data
ax.scatter(dim1, dim2, dim3, c=color, edgecolors='none',
label=group)
plt.title('Matplot 3d scatter plot')
plt.legend(y)
plt.show()
I get the following error:
plot_data-3d-pca.py:56: UserWarning: Requested projection is different from current axis projection, creating new axis with requested projection.
ax = fig.gca(projection='3d')
plot_data-3d-pca.py:56: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance. In a future version, a new instance will always be created and returned. Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.
ax = fig.gca(projection='3d')
Traceback (most recent call last):
File "/home/unica-server/.local/lib/python3.6/site-packages/matplotlib/backends/backend_gtk3.py", line 307, in idle_draw
self.draw()
File "/home/unica-server/.local/lib/python3.6/site-packages/matplotlib/backends/backend_gtk3agg.py", line 76, in draw
self._render_figure(allocation.width, allocation.height)
File "/home/unica-server/.local/lib/python3.6/site-packages/matplotlib/backends/backend_gtk3agg.py", line 20, in _render_figure
backend_agg.FigureCanvasAgg.draw(self)
File "/home/unica-server/.local/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py", line 388, in draw
self.figure.draw(self.renderer)
File "/home/unica-server/.local/lib/python3.6/site-packages/matplotlib/artist.py", line 38, in draw_wrapper
return draw(artist, renderer, *args, **kwargs)
File "/home/unica-server/.local/lib/python3.6/site-packages/matplotlib/figure.py", line 1709, in draw
renderer, self, artists, self.suppressComposite)
File "/home/unica-server/.local/lib/python3.6/site-packages/matplotlib/image.py", line 135, in _draw_list_compositing_images
a.draw(renderer)
File "/home/unica-server/.local/lib/python3.6/site-packages/matplotlib/artist.py", line 38, in draw_wrapper
return draw(artist, renderer, *args, **kwargs)
File "/home/unica-server/.local/lib/python3.6/site-packages/mpl_toolkits/mplot3d/axes3d.py", line 292, in draw
reverse=True)):
File "/home/unica-server/.local/lib/python3.6/site-packages/mpl_toolkits/mplot3d/axes3d.py", line 291, in <lambda>
key=lambda col: col.do_3d_projection(renderer),
File "/home/unica-server/.local/lib/python3.6/site-packages/mpl_toolkits/mplot3d/art3d.py", line 545, in do_3d_projection
ecs = (_zalpha(self._edgecolor3d, vzs) if self._depthshade else
File "/home/unica-server/.local/lib/python3.6/site-packages/mpl_toolkits/mplot3d/art3d.py", line 847, in _zalpha
rgba = np.broadcast_to(mcolors.to_rgba_array(colors), (len(zs), 4))
File "<__array_function__ internals>", line 6, in broadcast_to
File "/home/unica-server/.local/lib/python3.6/site-packages/numpy/lib/stride_tricks.py", line 182, in broadcast_to
return _broadcast_to(array, shape, subok=subok, readonly=True)
File "/home/unica-server/.local/lib/python3.6/site-packages/numpy/lib/stride_tricks.py", line 127, in _broadcast_to
op_flags=['readonly'], itershape=shape, order='C')
ValueError: operands could not be broadcast together with remapped shapes [original->remapped]: (0,4) and requested shape (1,4)
My y's shape is (8143,) and my X_reduced's shape is (8143,3)
What is my mistake?
EDIT: The data I am using can be found here
The first warning Requested projection is different from current axis projection
is because you are trying to change the projection of an axis after its creation with ax = fig.gca(projection='3d') but you cannot. Set the projection at creation instead.
To fix the second error, replace edgecolors='none' by edgecolors=None.
The following corrected code works for me.
# Create plot
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1, projection='3d') # set projection at creation of axis
# ax = fig.gca(projection='3d') # you cannot change the projection after creation
colors = ("red", "gray")
for data, color, group in zip(X_reduced, colors, y):
dim1,dim2,dim3=data
# replace 'none' by None
ax.scatter(dim1, dim2, dim3, c=color, edgecolors=None, label=group)
plt.title('Matplot 3d scatter plot')
plt.legend(y)
plt.show()
EDIT : Above is my answer to what I understood of the original question. Below is a looped version of mad's own answer.
class_values = [0, 1]
labels = ['Empty', 'Full']
n_class = len(class_values)
# allocate lists
index_class = [None] * n_class
X_reduced_class = [None] * n_class
for i, class_i in enumerate(class_values) :
# get where are the 0s and 1s labels
index_class[i] = np.where(np.isin(y, class_i))
# get reduced PCA for each label
X_reduced_class[i] = X_reduced[index_class[i]]
colors = ['blue', 'red']
# To getter a better understanding of interaction of the dimensions
# plot the first three PCA dimensions
fig = plt.figure(1, figsize=(8, 6))
ax = Axes3D(fig, elev=-150, azim=110)
ids_plot = [0, 4, 5]
for i in range(n_class) :
# get the three interesting columns
data = X_reduced_class[i][:, ids_plot]
ax.scatter(data[:,0], data[:,1], data[:,2], c=colors[i], edgecolor='k', s=40, label=labels[i])
ax.set_title("Data Visualization with 3 highest variance dimensions with PCA")
ax.set_xlabel("1st eigenvector")
ax.w_xaxis.set_ticklabels([])
ax.set_ylabel("2nd eigenvector")
ax.w_yaxis.set_ticklabels([])
ax.set_zlabel("3rd eigenvector")
ax.w_zaxis.set_ticklabels([])
ax.legend()
plt.show()
I solved the error in a different way.
I did not know that, for each label, I had to do a different scatterplot. Thanks to this post I found the answer.
My solution was first to separate the labels and data from one class, and then do the same for the other class. Finally, I plot them separately with different scatterplots. So, firstly I identify the different labels (I have only two labels, 0 or 1) and their data (their corresponding Xs).
#Get where are the 0s and 1s labels
index_class1 = np.where(np.isin(y, 0))
index_class2 = np.where(np.isin(y, 1))
#Get reduced PCA for each label
X_reducedclass1=X_reduced[index_class1][:]
X_reducedclass2=X_reduced[index_class2][:]
Then, I will plot each PCA reduced vectors from each class in different scatterplots
colors = ['blue', 'red']
# To getter a better understanding of interaction of the dimensions
# plot the first three PCA dimensions
fig = plt.figure(1, figsize=(8, 6))
ax = Axes3D(fig, elev=-150, azim=110)
scatter1=ax.scatter(X_reducedclass1[:, 0], X_reducedclass1[:, 4], X_reducedclass1[:, 5], c=colors[0], cmap=plt.cm.Set1, edgecolor='k', s=40)
scatter2=ax.scatter(X_reducedclass2[:, 0], X_reducedclass2[:, 4], X_reducedclass2[:, 5], c=colors[1], cmap=plt.cm.Set1, edgecolor='k', s=40)
ax.set_title("Data Visualization with 3 highest variance dimensions with PCA")
ax.set_xlabel("1st eigenvector")
ax.w_xaxis.set_ticklabels([])
ax.set_ylabel("2nd eigenvector")
ax.w_yaxis.set_ticklabels([])
ax.set_zlabel("3rd eigenvector")
ax.w_zaxis.set_ticklabels([])
#ax.legend(np.unique(y))
ax.legend([scatter1, scatter2], ['Empty', 'Full'], loc="upper right")
plt.show()
Which gives me this beautiful image
Of course, such a code can be simplified with a for loop too (altough I have no idea how to do that).
After defining ax1=fig1.add_subplot(111) and plotting 8 data series with associated label values, I used the following line of code to add a legend.
ax1.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))
I have used this method many times before without a problem, but on this occasion it produces an error saying IndexError: tuple index out of range
Traceback (most recent call last):
File "interface_tension_adhesion_plotter.py", line 45, in <module>
ax1.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))
File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/matplotlib/axes/_axes.py", line 564, in legend
self.legend_ = mlegend.Legend(self, handles, labels, **kwargs)
File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/matplotlib/legend.py", line 386, in __init__
self._init_legend_box(handles, labels, markerfirst)
File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/matplotlib/legend.py", line 655, in _init_legend_box
fontsize, handlebox))
File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/matplotlib/legend_handler.py", line 119, in legend_artist
fontsize, handlebox.get_transform())
File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/matplotlib/legend_handler.py", line 476, in create_artists
self.update_prop(coll, barlinecols[0], legend)
IndexError: tuple index out of range
I have no idea why this is happening and would really appreciate suggestions.
1. If data is intact and arrays are not empty, this code works perfectly.
fig = plt.gcf()
ax=fig.add_subplot(111)
for i in range(8):
x = np.arange(10)
y = i + random.rand(10)
yerr = .1*y
l = .1*i
ax.errorbar(x,y,yerr=yerr,label="adhsion={:02.1f}".format(l))
ax.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))
2. I had same error when I applied a filter to my data and got empty arrays. This could be reproduced as follows:
fig = plt.gcf()
ax=fig.add_subplot(111)
for i in range(8):
x = np.arange(10)
y = i + random.rand(10)
yerr = .1*y
l = .1*i
if i == 7:
ind = np.isnan(y)
y = y[ind]
x = x[ind]
yerr = yerr[ind]
ax.errorbar(x,y,yerr=yerr,label="adhsion={:02.1f}".format(l))
ax.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))
This code gives identical Traceback as in the question. Empty array for errors results in wrong handles for errorbars.
Workaround mentioned by #crevell:
handles, labels = ax.get_legend_handles_labels()
handles = [h[0] for h in handles]
ax.legend(handles, labels,loc='center left', bbox_to_anchor=(1.0, 0.5))
It works, but legend appears without errorbar lines.
So one should check the data supplied to the matplotlib errorbar function.
I can't seem to find the answer anywhere! I found a discussion here, but trying this I get a TypeError: 'NoneType' object is not iterable:
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> x, y = np.meshgrid(np.arange(10),np.arange(10))
>>> z = x + y
>>> cs = plt.contourf(x,y,z,levels=[2,3])
>>> cs.collections[0].set_label('test')
>>> plt.legend()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/matplotlib/pyplot.py", line 2791, in legend
ret = gca().legend(*args, **kwargs)
File "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/matplotlib/axes.py", line 4475, in legend
self.legend_ = mlegend.Legend(self, handles, labels, **kwargs)
File "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/matplotlib/legend.py", line 365, in __init__
self._init_legend_box(handles, labels)
File "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/matplotlib/legend.py", line 627, in _init_legend_box
handlebox)
File "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/matplotlib/legend_handler.py", line 110, in __call__
handlebox.get_transform())
File "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/matplotlib/legend_handler.py", line 352, in create_artists
width, height, fontsize)
File "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/matplotlib/legend_handler.py", line 307, in get_sizes
size_max = max(orig_handle.get_sizes())*legend.markerscale**2
TypeError: 'NoneType' object is not iterable
EDIT: I'm looking for something like this:
You could also do it directly with the lines of the contour, without using proxy artists.
import matplotlib
import numpy as np
import matplotlib.cm as cm
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
matplotlib.rcParams['xtick.direction'] = 'out'
matplotlib.rcParams['ytick.direction'] = 'out'
delta = 0.025
x = np.arange(-3.0, 3.0, delta)
y = np.arange(-2.0, 2.0, delta)
X, Y = np.meshgrid(x, y)
Z1 = mlab.bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)
Z2 = mlab.bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
# difference of Gaussians
Z = 10.0 * (Z2 - Z1)
# Create a simple contour plot with labels using default colors. The
# inline argument to clabel will control whether the labels are draw
# over the line segments of the contour, removing the lines beneath
# the label
plt.figure()
CS = plt.contour(X, Y, Z)
plt.clabel(CS, inline=1, fontsize=10)
plt.title('Simplest default with labels')
labels = ['line1', 'line2','line3','line4',
'line5', 'line6']
for i in range(len(labels)):
CS.collections[i].set_label(labels[i])
plt.legend(loc='upper left')
Will produce:
However, you might also want to look into annotations for your own need. In my opinion it will give you a more fine grained control on where and what you write on the image, here is the same example with some annotation:
### better with annotation, more flexible
plt.figure(2)
CS = plt.contour(X, Y, Z)
plt.clabel(CS, inline=1, fontsize=10)
plt.title('Simplest default with labels')
plt.annotate('some text here',(1.4,1.6))
plt.annotate('some text there',(-2,-1.5))
You can create proxy artists to make the legend:
import numpy as np
import matplotlib.pyplot as plt
x, y = np.meshgrid(np.arange(10),np.arange(10))
z = np.sqrt(x**2 + y**2)
cs = plt.contourf(x,y,z,levels=[2,3,4,6])
proxy = [plt.Rectangle((0,0),1,1,fc = pc.get_facecolor()[0])
for pc in cs.collections]
plt.legend(proxy, ["range(2-3)", "range(3-4)", "range(4-6)"])
plt.show()
Adding to this answer to make it less manual:
import numpy as np
import matplotlib.pyplot as plt
x, y = np.meshgrid(np.arange(10),np.arange(10))
z = np.sqrt(x**2 + y**2)
levels=[2,3,4,6]
cs = plt.contourf(x,y,z,levels=levels)
proxy = [plt.Rectangle((0,0),1,1,fc = pc.get_facecolor()[0])
for pc in cs.collections]
plt.legend(proxy, [f"{lower:2.1f} - {upper:2.1f}" for lower, upper in zip(levels[:-1], levels[1:])])
plt.show()
I had a similar question but needed to go a bit beyond HYRY's answer. To make a package user friendly I wanted ax.legend() to work without requiring users to pass any handles, which can be achieved by passing the label on to the proxy
proxy = plt.Rectangle((0, 0), 1, 1, fc='red', label='some label')
and then adding the proxy to the axis' patches:
ax.patches += [proxy]
(do ax = plt.gca() to get the current axis)
This is described in more detail in this answer.