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

Related

I can't use coordinate values in list for hough line function

I want to use the Hough line function. When I look at my list, yes, I found the coordinate values of the edges, but when I want to plot it in a for loop, I get an error. Here is the error message: TypeError: 'numpy.ndarray' object is not callable
import cv2
import numpy as np
img = cv2.imread("a.png")
img = cv2.resize(img,(640,480))
# cap = cv2.VideoCapture(0)
while True:
# ret, frame = cap.read()
# frame = cv2.flip(frame,1)
# print(frame.shape) # 480,640
ycrbc = cv2.cvtColor(img, cv2.COLOR_BGR2YCrCb)
minYCrCb = np.array([0,140,90],np.uint8)
maxYCrCb = np.array([230,170,120],np.uint8)
imgeYCrCb = cv2.cvtColor(img,cv2.COLOR_BGR2YCR_CB)
skinRegionYCrCb = cv2.inRange(imgeYCrCb,minYCrCb,maxYCrCb)
skinYCrCb = cv2.bitwise_and(img, img, mask = skinRegionYCrCb)
median_ycrcb = cv2.medianBlur(skinYCrCb, 3)
_, esik = cv2.threshold(median_ycrcb, 20, 255, cv2.THRESH_BINARY)
median_binary = cv2.medianBlur(esik, 7)
gray = cv2.cvtColor(median_binary, cv2.COLOR_BGR2GRAY)
contours, hierarchy = cv2.findContours(gray, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
areas = [cv2.contourArea(c) for c in contours]
max_index = np.argmax(areas)
x,y,w,h = cv2.boundingRect(contours[max_index])
gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
mask = np.zeros_like(gray_img)
mask[y:h, x:w] = 255
masked_image = cv2.bitwise_and(gray_img, mask)
edges = cv2.Canny(gray,75,250)
lines = cv2.HoughLinesP(edges, 1, np.pi/180, 50)
print(lines)
for line in lines:
x1, y1, x2, y2 = line[0]
cv2.line(img(x1, y1), (x2, y2), (0,255,0), 2)
# height, width = h, w
# region_of_interest_vertices = [(width/4, 11*height/12), (width/4, 13*height/16), (3*width/4, 13*height/16), (3*width/4, 11*height/12)]
cv2.imshow("ycrbc",ycrbc)
cv2.imshow("skinYCrCb",median_ycrcb)
cv2.imshow("binary goruntu", esik)
cv2.imshow("median_binary", median_binary)
cv2.imshow("Kesilmis Goruntu",masked_image)
cv2.imshow("Edges",edges)
if cv2.waitKey(5) & 0xFF == ord("q"):
break
# cap.release()
cv2.destroyAllWindows()
I want to draw the edges, then I will try to produce results from the output I get

How to override mpl_toolkits.mplot3d.Axes3D.draw() method?

I'm doing a small project which requires to resolve a bug in matplotlib in order to fix zorders of some ax.patches and ax.collections. More exactly, ax.patches are symbols rotatable in space and ax.collections are sides of ax.voxels (so text must be placed on them). I know so far, that a bug is hidden in draw method of mpl_toolkits.mplot3d.Axes3D: zorder are recalculated each time I move my diagram in an undesired way. So I decided to change definition of draw method in these lines:
for i, col in enumerate(
sorted(self.collections,
key=lambda col: col.do_3d_projection(renderer),
reverse=True)):
#col.zorder = zorder_offset + i #comment this line
col.zorder = col.stable_zorder + i #add this extra line
for i, patch in enumerate(
sorted(self.patches,
key=lambda patch: patch.do_3d_projection(renderer),
reverse=True)):
#patch.zorder = zorder_offset + i #comment this line
patch.zorder = patch.stable_zorder + i #add this extra line
It's assumed that every object of ax.collection and ax.patch has a stable_attribute which is assigned manually in my project. So every time I run my project, I must be sure that mpl_toolkits.mplot3d.Axes3D.draw method is changed manually (outside my project). How to avoid this change and override this method in any way inside my project?
This is MWE of my project:
import matplotlib.pyplot as plt
import numpy as np
#from mpl_toolkits.mplot3d import Axes3D
import mpl_toolkits.mplot3d.art3d as art3d
from matplotlib.text import TextPath
from matplotlib.transforms import Affine2D
from matplotlib.patches import PathPatch
class VisualArray:
def __init__(self, arr, fig=None, ax=None):
if len(arr.shape) == 1:
arr = arr[None,None,:]
elif len(arr.shape) == 2:
arr = arr[None,:,:]
elif len(arr.shape) > 3:
raise NotImplementedError('More than 3 dimensions is not supported')
self.arr = arr
if fig is None:
self.fig = plt.figure()
else:
self.fig = fig
if ax is None:
self.ax = self.fig.gca(projection='3d')
else:
self.ax = ax
self.ax.azim, self.ax.elev = -120, 30
self.colors = None
def text3d(self, xyz, s, zdir="z", zorder=1, size=None, angle=0, usetex=False, **kwargs):
d = {'-x': np.array([[-1.0, 0.0, 0], [0.0, 1.0, 0.0], [0, 0.0, -1]]),
'-y': np.array([[0.0, 1.0, 0], [-1.0, 0.0, 0.0], [0, 0.0, 1]]),
'-z': np.array([[1.0, 0.0, 0], [0.0, -1.0, 0.0], [0, 0.0, -1]])}
x, y, z = xyz
if "y" in zdir:
x, y, z = x, z, y
elif "x" in zdir:
x, y, z = y, z, x
elif "z" in zdir:
x, y, z = x, y, z
text_path = TextPath((-0.5, -0.5), s, size=size, usetex=usetex)
aff = Affine2D()
trans = aff.rotate(angle)
# apply additional rotation of text_paths if side is dark
if '-' in zdir:
trans._mtx = np.dot(d[zdir], trans._mtx)
trans = trans.translate(x, y)
p = PathPatch(trans.transform_path(text_path), **kwargs)
self.ax.add_patch(p)
art3d.pathpatch_2d_to_3d(p, z=z, zdir=zdir)
p.stable_zorder = zorder
return p
def on_rotation(self, event):
vrot_idx = [self.ax.elev > 0, True].index(True)
v_zorders = 10000 * np.array([(1, -1), (-1, 1)])[vrot_idx]
for side, zorder in zip((self.side1, self.side4), v_zorders):
for patch in side:
patch.stable_zorder = zorder
hrot_idx = [self.ax.azim < -90, self.ax.azim < 0, self.ax.azim < 90, True].index(True)
h_zorders = 10000 * np.array([(1, 1, -1, -1), (-1, 1, 1, -1),
(-1, -1, 1, 1), (1, -1, -1, 1)])[hrot_idx]
sides = (self.side3, self.side2, self.side6, self.side5)
for side, zorder in zip(sides, h_zorders):
for patch in side:
patch.stable_zorder = zorder
def voxelize(self):
shape = self.arr.shape[::-1]
x, y, z = np.indices(shape)
arr = (x < shape[0]) & (y < shape[1]) & (z < shape[2])
self.ax.voxels(arr, facecolors=self.colors, edgecolor='k')
for col in self.ax.collections:
col.stable_zorder = col.zorder
def labelize(self):
self.fig.canvas.mpl_connect('motion_notify_event', self.on_rotation)
s = self.arr.shape
self.side1, self.side2, self.side3, self.side4, self.side5, self.side6 = [], [], [], [], [], []
# labelling surfaces of side1 and side4
surf = np.indices((s[2], s[1])).T[::-1].reshape(-1, 2) + 0.5
surf_pos1 = np.insert(surf, 2, self.arr.shape[0], axis=1)
surf_pos2 = np.insert(surf, 2, 0, axis=1)
labels1 = (self.arr[0]).flatten()
labels2 = (self.arr[-1]).flatten()
for xyz, label in zip(surf_pos1, [f'${n}$' for n in labels1]):
t = self.text3d(xyz, label, zdir="z", zorder=10000, size=1, usetex=True, ec="none", fc="k")
self.side1.append(t)
for xyz, label in zip(surf_pos2, [f'${n}$' for n in labels2]):
t = self.text3d(xyz, label, zdir="-z", zorder=-10000, size=1, usetex=True, ec="none", fc="k")
self.side4.append(t)
# labelling surfaces of side2 and side5
surf = np.indices((s[2], s[0])).T[::-1].reshape(-1, 2) + 0.5
surf_pos1 = np.insert(surf, 1, 0, axis=1)
surf = np.indices((s[0], s[2])).T[::-1].reshape(-1, 2) + 0.5
surf_pos2 = np.insert(surf, 1, self.arr.shape[1], axis=1)
labels1 = (self.arr[:, -1]).flatten()
labels2 = (self.arr[::-1, 0].T[::-1]).flatten()
for xyz, label in zip(surf_pos1, [f'${n}$' for n in labels1]):
t = self.text3d(xyz, label, zdir="y", zorder=10000, size=1, usetex=True, ec="none", fc="k")
self.side2.append(t)
for xyz, label in zip(surf_pos2, [f'${n}$' for n in labels2]):
t = self.text3d(xyz, label, zdir="-y", zorder=-10000, size=1, usetex=True, ec="none", fc="k")
self.side5.append(t)
# labelling surfaces of side3 and side6
surf = np.indices((s[1], s[0])).T[::-1].reshape(-1, 2) + 0.5
surf_pos1 = np.insert(surf, 0, self.arr.shape[2], axis=1)
surf_pos2 = np.insert(surf, 0, 0, axis=1)
labels1 = (self.arr[:, ::-1, -1]).flatten()
labels2 = (self.arr[:, ::-1, 0]).flatten()
for xyz, label in zip(surf_pos1, [f'${n}$' for n in labels1]):
t = self.text3d(xyz, label, zdir="x", zorder=-10000, size=1, usetex=True, ec="none", fc="k")
self.side6.append(t)
for xyz, label in zip(surf_pos2, [f'${n}$' for n in labels2]):
t = self.text3d(xyz, label, zdir="-x", zorder=10000, size=1, usetex=True, ec="none", fc="k")
self.side3.append(t)
def vizualize(self):
self.voxelize()
self.labelize()
plt.axis('off')
arr = np.arange(60).reshape((2,6,5))
va = VisualArray(arr)
va.vizualize()
plt.show()
This is an output I get after external change of ...\mpl_toolkits\mplot3d\axes3d.py file:
This is an output (an unwanted one) I get if no change is done:
What you want to achieve is called Monkey Patching.
It has its downsides and has to be used with some care (there is plenty of information available under this keyword). But one option could look something like this:
from matplotlib import artist
from mpl_toolkits.mplot3d import Axes3D
# Create a new draw function
#artist.allow_rasterization
def draw(self, renderer):
# Your version
# ...
# Add Axes3D explicitly to super() calls
super(Axes3D, self).draw(renderer)
# Overwrite the old draw function
Axes3D.draw = draw
# The rest of your code
# ...
Caveats here are to import artist for the decorator and the explicit call super(Axes3D, self).method() instead of just using super().method().
Depending on your use case and to stay compatible with the rest of your code you could also save the original draw function and use the custom only temporarily:
def draw_custom():
...
draw_org = Axes3D.draw
Axes3D.draw = draw_custom
# Do custom stuff
Axes3D.draw = draw_org
# Do normal stuff

Rotating labels on second axis

I am adding a second x-axis to my plot like this:
ax2 = ax.twiny()
offset = 0, -25
new_axisline = ax2.get_grid_helper().new_fixed_axis
ax2.axis["bottom"] = new_axisline(loc="bottom", axes=ax2, offset=offset)
ax2.axis["top"].set_visible(False)
ax2.set_xticks(xticks)
ax2.xaxis.set_major_formatter(ticker.NullFormatter())
ax2.xaxis.set_minor_locator(ticker.FixedLocator(xticks))
ax2.xaxis.set_minor_formatter(ticker.FixedFormatter(xticks_labels))
the problem is I don't know how I can rotate the labels from there.
Also: If I add ticks to my first axis:
plt.xticks(xticks1, xticks1_labels, rotation='vertical')
the rotation argument gets ignored and I don't understand why either.
I have tried
ax2.set_xticklabels(ax2.xaxis.get_minorticklabels(), rotation=45)
but it also has no effect.
Any help would be appreciated.
You can take a look at the complete plotting logic below:
def event_plot(event_list, labels=None, figsize=(16, 9), padding=0.85, grid=False, title=None, colors=None):
fig = plt.figure(figsize=figsize)
ax = SubplotHost(fig, 111)
# ax = fig.add_subplot(111)
fig.add_subplot(ax)
ax.grid(grid)
if title is not None:
ax.set_title(title)
max_end = 0
for i, events in enumerate(event_list):
for event in events:
start = event[0]
end = event[1]
max_end = max(max_end, end)
y = (i, i + padding)
c = 'red' if colors is None else colors[i]
plt.fill_between([start, end], y[0], y2=y[1], color=c, alpha=0.35, linewidth=0.0)
plt.legend(['Recording data available for channel'], loc='upper center')
if labels is not None:
labels_ids = np.asarray(range(len(labels))) + 1
labels_y = labels_ids - 0.5 - (1 - padding) / 2.
plt.yticks(labels_y, labels)
for y in labels_y:
plt.axhline(y, alpha=0.125, color='k', linestyle='--')
return ax, fig
def plot_case_windows(all_records, case_windows, filename_title, filename=None):
channel_event_list = list()
labels = list()
for group_name, group in all_records.channel_groups.items():
[(labels.append(x[0]), channel_event_list.append(x[1])) for x in group.items()]
recording_time = all_records.end - all_records.start
title = 'File: {:s}, recording time: {:d} sec'.format(os.path.basename(filename_title), int(recording_time))
ax1, fig = event_plot(channel_event_list, title=title, labels=labels)
xticksmax = 0
xticksmin = float('Inf')
xticks1 = list()
xticks1_labels = list()
xticks2 = list()
xticks2_labels = list()
for case_win in case_windows:
xticks1.append(int(case_win.start + (case_win.end - case_win.start)/2.))
xticks2.append(case_win.start)
xticksmax = max(xticksmax, case_win.end)
xticksmin = min(xticksmin, case_win.start)
xticks1_labels.append(case_win.name)
xticks2_labels.append(str(case_win.start) + ' s')
plt.axvline(x=case_win.start, color='k', linestyle='--')
plt.axvline(x=case_win.end, color='k', linestyle='--')
xticks2 = (np.asarray(xticks2) - xticksmin) / (xticksmax - xticksmin)
plt.xlim([xticksmin, xticksmax])
ax1.set_xticks(xticks1)
ax1.xaxis.set_major_formatter(ticker.NullFormatter())
ax1.xaxis.set_minor_locator(ticker.FixedLocator(xticks1))
ax1.xaxis.set_minor_formatter(ticker.FixedFormatter(xticks1_labels))
ax2 = ax1.twiny()
offset = 0, -20
new_axisline = ax2.get_grid_helper().new_fixed_axis
ax2.axis["bottom"] = new_axisline(loc="bottom", axes=ax2, offset=offset)
ax2.axis["top"].set_visible(False)
ax2.set_xticks(xticks2)
ax2.xaxis.set_major_formatter(ticker.NullFormatter())
ax2.xaxis.set_minor_locator(ticker.FixedLocator(xticks2))
ax2.xaxis.set_minor_formatter(ticker.FixedFormatter(xticks2_labels))
plt.setp(ax2.xaxis.get_minorticklabels(), rotation=45)
# ax2.set_xticklabels(ax1.xaxis.get_minorticklabels(), rotation=45)
#plt.show()
plt.tight_layout()
if filename:
plt.savefig(filename)

Matplotlib multiple animate multiple lines

I have been researching on how to animate multiple lines for a flight path. The object it that I read multiple GPS files time sync them them animate each path with respect to time. I found how to animate one line using append in the animate functions. Now I need to add a second and third for as many files are imported.
I know the problem is somewhere in how I perform the set_data with the lines. I ahve seen multiple example but I do not understand what structure is required to set up multiple lines. Yes I am a newbie.
fig = plt.figure()
ax1 = plt.axes(xlim=(-108, -104), ylim=(31,34))
line, = ax1.plot([], [], lw=2)
plt.xlabel('Longitude')
plt.ylabel('Latitude')
plotlays, plotcols = [2], ["black","red"]
lines = []
for index in range(2):
lobj = ax1.plot([],[],lw=2,color=plotcols[index])[0]
lines.append(lobj)
def init():
for line in lines:
line.set_data([],[])
return lines
x1,y1 = [],[]
x2,y2 = [],[]
frame_num = len(gps_data[0])
# animation function. This is called sequentially
def animate(i):
x = gps_data[0][i,3]
y = gps_data[0][i,2]
x1.append(x)
y1.append(y)
x = gps_data[1][i,3]
y = gps_data[1][i,2]
x2.append(x)
y2.append(y)
#X = np.array(x1, x2)
#Y = np.array(y1, y2)
#for index in range(0,1):
for lnum,line in enumerate(lines):
line.set_data([x1,y1, x2,y2])
return lines,
# call the animator. blit=True means only re-draw the parts that have changed.
anim = animation.FuncAnimation(fig, animate, init_func=init,
frames=frame_num, interval=1, blit=True)
plt.show()
The Matplotlib documentation for the line2d artist explains how set_data works. It "ACCEPTS: 2D array (rows are x, y) or two 1D arrays." It also works with lists. You've given it a four element list instead. You need to set the x and y data of each line separately. I've included an example with fake data below.
import matplotlib.pyplot as plt
from matplotlib import animation
from numpy import random
fig = plt.figure()
ax1 = plt.axes(xlim=(-108, -104), ylim=(31,34))
line, = ax1.plot([], [], lw=2)
plt.xlabel('Longitude')
plt.ylabel('Latitude')
plotlays, plotcols = [2], ["black","red"]
lines = []
for index in range(2):
lobj = ax1.plot([],[],lw=2,color=plotcols[index])[0]
lines.append(lobj)
def init():
for line in lines:
line.set_data([],[])
return lines
x1,y1 = [],[]
x2,y2 = [],[]
# fake data
frame_num = 100
gps_data = [-104 - (4 * random.rand(2, frame_num)), 31 + (3 * random.rand(2, frame_num))]
def animate(i):
x = gps_data[0][0, i]
y = gps_data[1][0, i]
x1.append(x)
y1.append(y)
x = gps_data[0][1,i]
y = gps_data[1][1,i]
x2.append(x)
y2.append(y)
xlist = [x1, x2]
ylist = [y1, y2]
#for index in range(0,1):
for lnum,line in enumerate(lines):
line.set_data(xlist[lnum], ylist[lnum]) # set data for each line separately.
return lines
# call the animator. blit=True means only re-draw the parts that have changed.
anim = animation.FuncAnimation(fig, animate, init_func=init,
frames=frame_num, interval=10, blit=True)
plt.show()
fig, ax1 = plt.subplots(figsize=(20,10))
plt.xticks()
ax1.set_xlabel("time")
ax1.set_ylabel("amp")
ax1.grid(True)
ox = get_data_o("/tmp/bf_ox.npy")
oy = get_data_o("/tmp/bf_oy.npy")
graphic_data = []
graphic_data.append(ax1.plot(ox, oy,"b-")[0])
sx = get_data_o("/tmp/bf_sx.npy")
i = 1
for p in sp.procs:
c = get_c(i)
sy = get_data_o("/tmp/bf_" + p + ".npy")
graphic_data.append(ax1.plot(sx, sy,c + "-")[0])
i = i + 1
def animate(i):
print ("frame")
ox = get_data_o("/tmp/bf_ox.npy")
oy = get_data_o("/tmp/bf_oy.npy")
i = 0
graphic_data[i].set_xdata(ox)
graphic_data[i].set_ydata(oy)
i = 1
sx = get_data_o("/tmp/bf_sx.npy")
for p in sp.procs:
sy = get_data_o("/tmp/bf_" + p + ".npy")
graphic_data[i].set_xdata(sx)
graphic_data[i].set_ydata(sy)
i = i + 1
return graphic_data
ani = animation.FuncAnimation(fig, animate, interval=2000, blit=True)
plt.show()

Annotating ranges of data in 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)