So I'm relatively new to coding and have recently taken the monstrous task of building a few climate models for my MSc thesis. Using this code I have adapted it and it now shows no error messages except now it doesn't show any figure as an output. Any solutions?
I input
%matplotlib notebook at the top of the code, and also put plt.show(); at the bottom of the script (as per some recommendations through some similar queries)... but still doesn't work. Prior to this it was showing <Figure Ssize 432x288 with 0 Axes> which i presumed may be the problem but i can't figure out why there are 0 axes?
Any recommendations/solutions?
Thanks!
As requested - my code:
import iris.quickplot as qplt
import iris.analysis.cartography
import matplotlib.dates as mdates
def main():
Current45 = '....X.nc'
Current45 = iris.load_cube(Current45)
lats = iris.coords.DimCoord(Current45.coords()[1].points[:,0], \
standard_name='latitude', units='degrees')
lons = Current45.coords()[2].points[0]
for i in range(len(lons)):
if lons[i]>100.:
lons[i] = lons[i]-360.
lons = iris.coords.DimCoord(lons, \
standard_name='longitude', units='degrees')
Current45.remove_coord('latitude')
Current45.remove_coord('longitude')
Current45.add_dim_coord(lats, 1)
Current45.add_dim_coord(lons, 2)
Current45.convert_units('Celsius')
Colombia = iris.Constraint(longitude=lambda v: -74.73 <= v <= -76.20, \
latitude=lambda v: 5.30 <= v <= 4.43)
Current45 = Current45.extract(Colombia)
iriscc.add_day_of_year(Current45, 'time')
Current45.coord('latitude').guess_bounds()
Current45.coord('longitude').guess_bounds()
Current45_grid_areas = iris.analysis.cartography.area_weights(Current45)
Current45 = Current45.collapsed(['latitude', 'longitude'],
iris.analysis.MEAN,
weights=Current45_grid_areas)
Histogram = Current45.data
#frq, bins, patches = plt.hist(Histogram, bins=np.arange(20,37,2))
frq, bins, patches = plt.hist(Histogram, bins=np.arange(16,45,2), color='blue')
print (frq)
thresh = 32
plt.axvline(x=thresh, color='green', linestyle='dashed', linewidth=2)
plt.xlabel("Daily Max Temperature / Celsius")
plt.ylabel("Number of days")
fig = plt.gcf()
plt.show();
My code with blank figure at the bottom
In the code, you are never calling the main function, so the figure you are showing is empty.
You should call main() at some point in your code before the plt.gcf() or plt.show.
Edit
In more detail:
You are writing your main() function in this snippet of code, and then, without indent, you are calling pyplot to get the current figure, where pyplot just gives you en empty figure back (the gcf()-call is not necessary anyways in your code) and plt.show() shows no an empty figure.
You can or cannot move the plt.show() into you main() function, but at one point you must definitely call that function otherwise none of it is executed.
Edit 2:
# function definition
def main():
...
# function call
main()
# show figure
plt.show()
Related
I am writing a Python tool that needs several figures open at the same time, each one with its own widgets (sliders, for the most part). I don't need any interactions across the figures here. Each figure is independent of the other ones, with its own plot and its own sliders affecting only itself.
I can get Matplotlib sliders working fine on a single figure, but I can't get them to work on multiple figures concurrently. Only the sliders of the LAST figure to open are working. The other ones are unresponsive.
I recreated my problem with the simple code below, starting from the example in the Matplotlib.Slider doc. If I run it as-is, only the sliders for the second figure (amplitude) works. The other doesn't. If I invert the two function calls at the bottom, it's the other way around.
I've had no luck googling solutions or pointers. Any help would be much appreciated.
I'm on Python 3.9.12, btw. I can upload a requirements file if someone tries and cannot reproduce the issue. Thank you!
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.widgets import Slider
# The parametrized function to be plotted
def f(time, amplitude, frequency):
return amplitude * np.sin(2 * np.pi * frequency * time)
# Define initial parameters
init_amplitude = 5
init_frequency = 3
t = np.linspace(0, 1, 1000)
def create_first_fig():
# Create the figure and the line that we will manipulate
fig1, ax1 = plt.subplots()
line1, = ax1.plot(t, f(t, init_amplitude, init_frequency), lw=2, color='b')
ax1.title.set_text('First plot - interactive frequency')
ax1.set_xlabel('Time [s]')
# adjust the main plot to make room for the sliders
fig1.subplots_adjust(left=0.25, bottom=0.25)
# Make a horizontal slider to control the frequency.
axfreq = fig1.add_axes([0.25, 0.1, 0.65, 0.03])
freq_slider = Slider(
ax=axfreq,
label='Frequency [Hz]',
valmin=0,
valmax=30,
valinit=init_frequency,
)
# register the update function with each slider
freq_slider.on_changed(lambda val: update_first_fig(val, fig1, line1))
plt.draw()
plt.pause(0.1)
return fig1
# The function to be called anytime a slider's value changes
def update_first_fig(val, fig, line):
line.set_ydata(f(t, init_amplitude, val))
fig.canvas.draw_idle()
plt.pause(0.1)
def create_second_fig():
# Create the figure and the line that we will manipulate
fig2, ax2 = plt.subplots()
line2, = ax2.plot(t, f(t, init_amplitude, init_frequency), lw=2, color='r')
ax2.title.set_text('Second plot - interactive amplitude')
ax2.set_xlabel('Time [s]')
# adjust the main plot to make room for the sliders
fig2.subplots_adjust(left=0.25, bottom=0.25)
# Make a vertically oriented slider to control the amplitude
axamp = fig2.add_axes([0.1, 0.25, 0.0225, 0.63])
amp_slider = Slider(
ax=axamp,
label="Amplitude",
valmin=0,
valmax=10,
valinit=init_amplitude,
orientation="vertical",
)
# register the update function with each slider
amp_slider.on_changed(lambda val: update_second_fig(val, fig2, line2))
plt.draw()
plt.pause(0.1)
return fig2
# The function to be called anytime a slider's value changes
def update_second_fig(val, fig, line):
line.set_ydata(f(t, val, init_frequency))
fig.canvas.draw_idle()
plt.pause(0.1)
figure1 = create_first_fig()
figure2 = create_second_fig()
plt.show()
I would expect the slider in both figures to work the way it does when I only open the corresponding figure. So far it's only the slider in the figure that's created last that works.
Edit in case someone else looks at this: see Yulia V's answer below. It works perfectly, including in my initial application. The site doesn't let me upvote it because I am too new on here, but it's a perfect solution to my problem. Thanks Yulia V!
You need to save the references to sliders as variables to make it work. No idea why, but this is how matplotlib works.
Specifically, in your functions, you need to have
return freq_slider, fig1
...
return amp_slider, fig2
instead of
return fig1
...
return fig2
and in the main script,
freq_slider, figure1 = create_first_fig()
amp_slider, figure2 = create_second_fig()
instead of
figure1 = create_first_fig()
figure2 = create_second_fig()
Just to illustrate my comment below #Yulia V's answer, it works too if we store the sliders as an attribute of the figure instead of returning them:
def create_first_fig():
...
fig1._slider = freq_slider
...
return fig1
def create_first_fig():
...
fig2._slider = amp_slider
...
return fig2
...
figure1 = create_first_fig()
figure2 = create_second_fig()
For a ML project I'm currently on, I need to verify if the trained data are good or not.
Let's say that I'm "splitting" the sky into several altitude grids (let's take 3 values for the moment) and for a given region (let's say, Europe).
One grid could be a signal reception strength (RSSI), another one the signal quality (RSRQ)
Each cell of the grid is therefor a rectangle and it has a mean value of each measurement (i.e. RSSI or RSRQ) performed in that area.
I have hundreds of millions of data
In the code below, I know how to draw a coloured mesh with xarray for each altitude: I just use xr.plot.pcolormesh(lat,lon, the_data_set); that's fine
But this will only give me a "flat" figure like this:
RSSI value at 3 different altitudes
I need to draw all the pcolormesh() of a dataset for each altitude in such way that:
1: I can have the map at the bottom
2: Each pcolormesh() is stacked and "displayed" at its altitude
3: I need to add a 3d scatter plot for testing my trained data
4: Need to be interactive as I have to zoom in areas
For 2 and 3 above, I managed to do something using plt and cartopy :
enter image description here
But plt/cartopy combination is not as interactive as plotly.
But plotly doesn't have the pcolormesh functionality
And still ... I don't know in anycase, how to "stack" the pcolormesh results that I did get above.
I've been digging Internet for few days but I didn't find something that could satisfy all my criteria.
What I did to get my pcolormesh:
import numpy as np
import xarray as xr
import cartopy.crs as ccrs
import matplotlib.pyplot as plt
class super_data():
def __init__(self, lon_bound,lat_bound,alt_bound,x_points,y_points,z_points):
self.lon_bound = lon_bound
self.lat_bound = lat_bound
self.alt_bound = alt_bound
self.x_points = x_points
self.y_points = y_points
self.z_points = z_points
self.lon, self.lat, self.alt = np.meshgrid(np.linspace(self.lon_bound[0], self.lon_bound[1], self.x_points),
np.linspace(self.lat_bound[0], self.lat_bound[1], self.y_points),
np.linspace(self.alt_bound[0], self.alt_bound[1], self.z_points))
self.this_xr = xr.Dataset(
coords={'lat': (('latitude', 'longitude','altitude'), self.lat),
'lon': (('latitude', 'longitude','altitude'), self.lon),
'alt': (('latitude', 'longitude','altitude'), self.alt)})
def add_data_array(self,ds_name,ds_min,ds_max):
def create_temp_data(ds_min,ds_max):
data = np.random.randint(ds_min,ds_max,size=self.y_points * self.x_points)
return data
temp_data = []
# Create "z_points" number of layers in the z axis
for i in range(self.z_points):
temp_data.append(create_temp_data(ds_min,ds_max))
data = np.concatenate(temp_data)
data = data.reshape(self.z_points,self.x_points, self.y_points)
self.this_xr[ds_name] = (("altitude","longitude","latitude"),data)
def plot(self,dataset, extent=None, plot_center=False):
# I want t
if np.sqrt(self.z_points) == np.floor(np.sqrt(self.z_points)):
side_size = int(np.sqrt(self.z_points))
else:
side_size = int(np.floor(np.sqrt(self.z_points) + 1))
fig = plt.figure()
i_ax=1
for i in range(side_size):
for j in range(side_size):
if i_ax < self.z_points+1:
this_dataset = self.this_xr[dataset].sel(altitude=i_ax-1)
# Initialize figure with subplots
ax = fig.add_subplot(side_size, side_size, i_ax, projection=ccrs.PlateCarree())
i_ax += 1
ax.coastlines()
this_dataset.plot.pcolormesh('lon', 'lat', ax=ax, infer_intervals=True, alpha=0.5)
else:
break
plt.tight_layout()
plt.show()
if __name__ == "__main__":
# Wanted coverage :
lons = [-15, 30]
lats = [35, 65]
alts = [1000, 5000]
xarr = super_data(lons,lats,alts,10,8,3)
# Add some fake data
xarr.add_data_array("RSSI",-120,-60)
xarr.add_data_array("pressure",700,1013)
xarr.plot("RSSI",0)
Thanks for you help
As a beginner in Python3, I’ve been following the Python Crash Course book. The following code prints the graph but does not ask for an input as intended. I have tried placing plt.show() after the if statement but the program ends up loading for a long time, eventually failing to display the graph. Is there a way to fix this?
Code:
import matplotlib.pyplot as plt
from random_walk import RandomWalk
while True:
rw = RandomWalk()
rw.fill_walk()
plt.style.use('classic')
fig, ax = plt.subplots()
point_numbers = range(rw.num_points)
ax.scatter(rw.x_values, rw.y_values,c=point_numbers, cmap=plt.cm.Blues, edgecolors='none', s=15)
plt.show()
The program does not print the following for input
keep_running = input("Make another walk? (y/n):")
if keep_running == 'n':
break
The following is saved on a separate file
from random import choice
class RandomWalk:
def __init__(self, num_points=5000):
"""Initialize attributes of a walk"""
self.num_points = num_points
self.x_values = [0]
self.y_values = [0]
def fill_walk(self):
""" calculating all the points in the walk"""
# Keep taking steps until the walk reaches the desired length.
while len(self.x_values) < self.num_points:
# Decide which direction to go and how far to go in that direction.
x_direction = choice([1,-1])
x_distance = choice([0,1,2,3,4])
x_step = x_direction*x_distance
y_direction = choice([1,-1])
y_distance = choice([0,1,2,3,4])
y_step = y_direction*y_distance
# Reject moves that go nowhere
if x_step == 0 and y_step == 0:
continue
# Calculate the new position
x = self.x_values[-1] + x_step
y = self.y_values[-1] + y_step
self.x_values.append(x)
self.y_values.append(y)
For years, I've been struggling to get efficient live plotting in matplotlib, and to this day I remain unsatisfied.
I want a redraw_figure function that updates the figure "live" (as the code runs), and will display the latest plots if I stop at a breakpoint.
Here is some demo code:
import time
from matplotlib import pyplot as plt
import numpy as np
def live_update_demo():
plt.subplot(2, 1, 1)
h1 = plt.imshow(np.random.randn(30, 30))
redraw_figure()
plt.subplot(2, 1, 2)
h2, = plt.plot(np.random.randn(50))
redraw_figure()
t_start = time.time()
for i in xrange(1000):
h1.set_data(np.random.randn(30, 30))
redraw_figure()
h2.set_ydata(np.random.randn(50))
redraw_figure()
print 'Mean Frame Rate: %.3gFPS' % ((i+1) / (time.time() - t_start))
def redraw_figure():
plt.draw()
plt.pause(0.00001)
live_update_demo()
Plots should update live when the code is run, and we should see the latest data when stopping at any breakpoint after redraw_figure(). The question is how to best implement redraw_figure()
In the implementation above (plt.draw(); plt.pause(0.00001)), it works, but is very slow (~3.7FPS)
I can implement it as:
def redraw_figure():
plt.gcf().canvas.flush_events()
plt.show(block=False)
And it runs faster (~11FPS), but plots are not up-to date when you stop at breakpoints (eg if I put a breakpoint on the t_start = ... line, the second plot does not appear).
Strangely enough, what does actually work is calling the show twice:
def redraw_figure():
plt.gcf().canvas.flush_events()
plt.show(block=False)
plt.show(block=False)
Which gives ~11FPS and does keep plots up-to-data if your break on any line.
Now I've heard it said that the "block" keyword is deprecated. And calling the same function twice seems like a weird, probably-non-portable hack anyway.
So what can I put in this function that will plot at a reasonable frame rate, isn't a giant kludge, and preferably will work across backends and systems?
Some notes:
I'm on OSX, and using TkAgg backend, but solutions on any backend/system are welcome
Interactive mode "On" will not work, because it does not update live. It just updates when in the Python console when the interpreter waits for user input.
A blog suggested the implementation:
def redraw_figure():
fig = plt.gcf()
fig.canvas.draw()
fig.canvas.flush_events()
But at least on my system, that does not redraw the plots at all.
So, if anybody has an answer, you would directly make me and thousands of others very happy. Their happiness would probably trickle through to their friends and relatives, and their friends and relatives, and so on, so that you could potentially improve the lives of billions.
Conclusions
ImportanceOfBeingErnest shows how you can use blit for faster plotting, but it's not as simple as putting something different in the redraw_figure function (you need to keep track of what things to redraw).
First of all, the code that is posted in the question runs with 7 fps on my machine, with QT4Agg as backend.
Now, as has been suggested in many posts, like here or here, using blit might be an option. Although this article mentions that blit causes strong memory leakage, I could not observe that.
I have modified your code a bit and compared the frame rate with and without the use of blit. The code below gives
28 fps when run without blit
175 fps with blit
Code:
import time
from matplotlib import pyplot as plt
import numpy as np
def live_update_demo(blit = False):
x = np.linspace(0,50., num=100)
X,Y = np.meshgrid(x,x)
fig = plt.figure()
ax1 = fig.add_subplot(2, 1, 1)
ax2 = fig.add_subplot(2, 1, 2)
img = ax1.imshow(X, vmin=-1, vmax=1, interpolation="None", cmap="RdBu")
line, = ax2.plot([], lw=3)
text = ax2.text(0.8,0.5, "")
ax2.set_xlim(x.min(), x.max())
ax2.set_ylim([-1.1, 1.1])
fig.canvas.draw() # note that the first draw comes before setting data
if blit:
# cache the background
axbackground = fig.canvas.copy_from_bbox(ax1.bbox)
ax2background = fig.canvas.copy_from_bbox(ax2.bbox)
plt.show(block=False)
t_start = time.time()
k=0.
for i in np.arange(1000):
img.set_data(np.sin(X/3.+k)*np.cos(Y/3.+k))
line.set_data(x, np.sin(x/3.+k))
tx = 'Mean Frame Rate:\n {fps:.3f}FPS'.format(fps= ((i+1) / (time.time() - t_start)) )
text.set_text(tx)
#print tx
k+=0.11
if blit:
# restore background
fig.canvas.restore_region(axbackground)
fig.canvas.restore_region(ax2background)
# redraw just the points
ax1.draw_artist(img)
ax2.draw_artist(line)
ax2.draw_artist(text)
# fill in the axes rectangle
fig.canvas.blit(ax1.bbox)
fig.canvas.blit(ax2.bbox)
# in this post http://bastibe.de/2013-05-30-speeding-up-matplotlib.html
# it is mentionned that blit causes strong memory leakage.
# however, I did not observe that.
else:
# redraw everything
fig.canvas.draw()
fig.canvas.flush_events()
#alternatively you could use
#plt.pause(0.000000000001)
# however plt.pause calls canvas.draw(), as can be read here:
#http://bastibe.de/2013-05-30-speeding-up-matplotlib.html
live_update_demo(True) # 175 fps
#live_update_demo(False) # 28 fps
Update:
For faster plotting, one may consider using pyqtgraph.
As the pyqtgraph documentation puts it: "For plotting, pyqtgraph is not nearly as complete/mature as matplotlib, but runs much faster."
I ported the above example to pyqtgraph. And although it looks kind of ugly, it runs with 250 fps on my machine.
Summing that up,
matplotlib (without blitting): 28 fps
matplotlib (with blitting): 175 fps
pyqtgraph : 250 fps
pyqtgraph code:
import sys
import time
from pyqtgraph.Qt import QtCore, QtGui
import numpy as np
import pyqtgraph as pg
class App(QtGui.QMainWindow):
def __init__(self, parent=None):
super(App, self).__init__(parent)
#### Create Gui Elements ###########
self.mainbox = QtGui.QWidget()
self.setCentralWidget(self.mainbox)
self.mainbox.setLayout(QtGui.QVBoxLayout())
self.canvas = pg.GraphicsLayoutWidget()
self.mainbox.layout().addWidget(self.canvas)
self.label = QtGui.QLabel()
self.mainbox.layout().addWidget(self.label)
self.view = self.canvas.addViewBox()
self.view.setAspectLocked(True)
self.view.setRange(QtCore.QRectF(0,0, 100, 100))
# image plot
self.img = pg.ImageItem(border='w')
self.view.addItem(self.img)
self.canvas.nextRow()
# line plot
self.otherplot = self.canvas.addPlot()
self.h2 = self.otherplot.plot(pen='y')
#### Set Data #####################
self.x = np.linspace(0,50., num=100)
self.X,self.Y = np.meshgrid(self.x,self.x)
self.counter = 0
self.fps = 0.
self.lastupdate = time.time()
#### Start #####################
self._update()
def _update(self):
self.data = np.sin(self.X/3.+self.counter/9.)*np.cos(self.Y/3.+self.counter/9.)
self.ydata = np.sin(self.x/3.+ self.counter/9.)
self.img.setImage(self.data)
self.h2.setData(self.ydata)
now = time.time()
dt = (now-self.lastupdate)
if dt <= 0:
dt = 0.000000000001
fps2 = 1.0 / dt
self.lastupdate = now
self.fps = self.fps * 0.9 + fps2 * 0.1
tx = 'Mean Frame Rate: {fps:.3f} FPS'.format(fps=self.fps )
self.label.setText(tx)
QtCore.QTimer.singleShot(1, self._update)
self.counter += 1
if __name__ == '__main__':
app = QtGui.QApplication(sys.argv)
thisapp = App()
thisapp.show()
sys.exit(app.exec_())
Here's one way to do live plotting: get the plot as an image array then draw the image to a multithreaded screen.
Example using a pyformulas screen (~30 FPS):
import pyformulas as pf
import matplotlib.pyplot as plt
import numpy as np
import time
fig = plt.figure()
screen = pf.screen(title='Plot')
start = time.time()
for i in range(10000):
t = time.time() - start
x = np.linspace(t-3, t, 100)
y = np.sin(2*np.pi*x) + np.sin(3*np.pi*x)
plt.xlim(t-3,t)
plt.ylim(-3,3)
plt.plot(x, y, c='black')
# If we haven't already shown or saved the plot, then we need to draw the figure first...
fig.canvas.draw()
image = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
image = image.reshape(fig.canvas.get_width_height()[::-1] + (3,))
screen.update(image)
#screen.close()
Disclaimer: I'm the maintainer of pyformulas
I am writing an image processing module in Python using matplotlib.pyplot and numpy backend.
The images will largely be in tiff format, so the code below uses tifffile to convert a 3D image file to a 4D array in numpy. The below code aims to move through the z-plane of the 3D image, one image at a time, using z and x as hotkeys. My problem is quite interesting and I can't figure it out: the time between event and action (pressing x and displaying z+1 image) gets twice as long with each event.
I timed it, results below:
1st z-press: 0.124 s
2nd z-prss: 0.250 s
3rd z-press: 0.4875 s
It is a bonafide linear increase, but I can't find where in my code the bug could be.
import matplotlib.pyplot as plt
import numpy as np
import tifffile as tiff
class Image:
def __init__ (self, fname):
self.fname = fname
self.fig = plt.figure()
self.z = 0
self.ax = self.fig.add_subplot(111)
self.npimg = tiff.imread(self.fname)
self.plotimg()
self.connect()
def plotimg(self):
plt.imshow(self.npimg[self.z][0])
plt.show()
def connect(self):
self.cidkeypress = self.fig.canvas.mpl_connect('key_press_event',self.keypress)
def disconnect(self):
self.fig.canvas.mpl_disconnect(self.cidkeypress)
def keypress(self, event):
if event.key == 'x':
self.z += 1
self.plotimg()
elif event.key == 'z':
self.z -= 1
self.plotimg()
Since you didn't provide an example file, I can't test your code. But I think the problem is, that you call plt.imshow() repeatedly. Each time a new AxesImage object is added to the Figure. This is why the timings increase linearly.
So the solution is to only have one AxesImage object and only update the data. Adapt __init__ and plotimg as follows:
def __init__ (self, fname):
self.fname = fname
self.fig = plt.figure()
self.z = 0
self.ax = self.fig.add_subplot(111)
self.npimg = tiff.imread(self.fname)
self.pltim = plt.imshow(self.npimg[self.z][0])
self.connect()
plt.show()
def plotimg(self):
self.pltim.set_data(self.npimg[self.z][0])
plt.draw()
Beware: Untested code!
EDIT:
Difference between plt.imshow and plt.show:
Despite their similar name, they do very different things: plt.imshow is a plotting function, which draws an image to the current axes (similar to plt.plot, plt.scatter, etc.) plt.show however, dispays the figure on the screen and enters the backends mainloop (i.e. blocks your code). This answer explains it in more detail