I am trying to create a scatter plot of measurements where the x labels are WIFI channels. By default matplotlib is spacing the labels in proportion to their numerical value. However, I would like them to be spaced uniformly over the scatter plot. Is that possible?
This is basically what my plot code currently looks like:
- where chanPoints is a list of frequencies and measurements is a list of measurements.
plt.scatter(chanPoints,measurements)
plt.xlabel('Frequency (MHz)')
plt.ylabel('EVM (dB)')
plt.xticks(Tchan,rotation = 90)
plt.title('EVM for 5G Channels by Site')
plt.show()
Numpy
You may use numpy to create an array which maps the unique items within chanPoints to numbers 0,1,2.... You can then give each of those numbers the corresponding label.
import matplotlib.pyplot as plt
import numpy as np
chanPoints = [4980, 4920,4920,5500,4980,5500,4980, 5500, 4920]
measurements = [5,6,4,3,5,8,4,6,3]
unique, index = np.unique(chanPoints, return_inverse=True)
plt.scatter(index, measurements)
plt.xlabel('Frequency (MHz)')
plt.ylabel('EVM (dB)')
plt.xticks(range(len(unique)), unique)
plt.title('EVM for 5G Channels by Site')
plt.show()
Seaborn
If you're happy to use seaborn, this can save a lot of manual work. Seaborn is specialized for plotting categorical data. The chanPoints would be interpreted as categories on the x axis, and have the same spacing between them, if you were e.g. using a swarmplot. If several points would then overlap, they are plotted next to each other, which may be an advantage as it allows to see the number of measurement for that channel.
import matplotlib.pyplot as plt
import seaborn.apionly as sns
chanPoints = [4980, 4920,4920,5500,4980,5500,4980, 5500, 4920]
measurements = [5,6,4,3,5,8,4,6,3]
sns.swarmplot(chanPoints, measurements)
plt.xlabel('Frequency (MHz)')
plt.ylabel('EVM (dB)')
plt.title('EVM for 5G Channels by Site')
plt.show()
Replace chanPoints with an index.
index = numpy.searchsorted(Tchan, chanPoints)
plt.scatter(index, measurements)
Then build your xticks with the corresponding lables.
ticks = range(len(Tchan))
plt.xticks(ticks, labels=Tchan, rotation = 90)
Related
I am using the matplotlib.pylot module to generate thousands of figures that all deal with a value called "Total Vertical Depth(TVD)". The data that these values come from are all negative numbers but the industry standard is to display them as positive (I.E. distance from zero / absolute value). My y axis is used to display the numbers and of course uses the actual value (negative) to label the axis ticks. I do not want to change the values, but am wondering how to access the text elements and just remove the negative symbols from each value(shown in red circles on the image).
Several iterations of code after diving into the matplotlib documentation has gotten me to the following code, but I am still getting an error.
locs, labels = plt.yticks()
newLabels = []
for lbl in labels:
newLabels.append((lbl[0], lbl[1], str(float(str(lbl[2])) * -1)))
plt.yticks(locs, newLabels)
It appears that some of the strings in the "labels" list are empty and therefore the cast isn't working correctly, but I don't understand how it has any empty values if the yticks() method is retrieving the current tick configuration.
#SiHA points out that if we change the data then the order of labels on the y-axis will be reversed. So we can use a ticker formatter to just change the labels without changing the data as shown in the example below:
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import numpy as np
#ticker.FuncFormatter
def major_formatter(x, pos):
label = str(-x) if x < 0 else str(x)
return label
y = np.linspace(-3000,-1000,2001)
fig, ax = plt.subplots()
ax.plot(y)
ax.yaxis.set_major_formatter(major_formatter)
plt.show()
This gives me the following plot, notice the order of y-axis labels.
Edit:
based on the Amit's great answer, here's the solution if you want to edit the data instead of the tick formatter:
import matplotlib.pyplot as plt
import numpy as np
y = np.linspace(-3000,-1000,2001)
fig, ax = plt.subplots()
ax.plot(-y) # invert y-values of the data
ax.invert_yaxis() # invert the axis so that larger values are displayed at the bottom
plt.show()
In the following I use scatter and an own ListedColormap to plot some coloured data points. In addition the corresponding colorbar is also plotted.
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap, ListedColormap, BoundaryNorm
from numpy import arange
fig, ax = plt.subplots()
my_cm = ListedColormap(['#a71b1b','#94258f','#ea99e6','#ec9510','#ece43b','#a3f8ff','#2586df','#035e0d'])
bounds=range(8)
norm = BoundaryNorm(bounds, my_cm.N)
data = [1,2,1,3,0,5,3,4]
ret = ax.scatter(range(my_cm.N), [1]*my_cm.N, c=data, edgecolors='face', cmap=my_cm, s=50)
cbar = fig.colorbar(ret, ax=ax, boundaries=arange(-0.5,8,1), ticks=bounds, norm=norm)
cbar.ax.tick_params(axis='both', which='both',length=0)
If my data is not covering each value of the boundary interval, the colorbar does not show all colours (like in the added figure). If data would be set to range(8), I get a dot of each colour and the colorbar also shows all colours.
How can I force the colorbar to show all defined colours even if data does not contain all boundary values?
You need to manually set vminand vmax in your call to ax.scatter:
ret = ax.scatter(range(my_cm.N), [1]*my_cm.N, c=data, edgecolors='face', cmap=my_cm, s=50, vmin=0, vmax=7)
resulting in
If my data is not covering each value of the boundary interval, the colorbar does not show all colours (like in the added figure).
If either vminor vmax are `None the color limits are set via the method
autoscale_None, and the minimum and maximum of your data are therefore used.
So using your code it is actually not necessary for showing all colors in the colorbar that every value of the boundary interval is covered, only the minimum and maximum need to be included.
Using e.g. data = [0,0,0,0,0,0,0,7] results in the following:
When looking for something else, I found another solution to that problem: colorbar-for-matplotlib-plot-surface-command.
In that case, I do not need to set vmin and vmax and it is also working in cases if the arrays/lists of points to plot are empty. Instead a ScalarMappable is defined and provided to colorbar instead of the scatterinstance.
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap, ListedColormap, BoundaryNorm
import matplotlib.cm as cm
from numpy import arange
fig, ax = plt.subplots()
my_cm = ListedColormap(['#a71b1b','#94258f','#ea99e6','#ec9510','#ece43b','#a3f8ff','#2586df','#035e0d'])
bounds=range(8)
norm = BoundaryNorm(bounds, my_cm.N)
mappable = cm.ScalarMappable(cmap=my_cm)
mappable.set_array(bounds)
data = [] # also x and y can be []
ax.scatter(x=range(my_cm.N), y=[1]*my_cm.N, c=data, edgecolors='face', cmap=my_cm, s=50)
cbar = fig.colorbar(mappable, ax=ax, boundaries=arange(-0.5,8,1), ticks=bounds, norm=norm)
cbar.ax.tick_params(axis='both', which='both',length=0)
I have created a map of precipitation levels in a region based on precipitation data from NetCDF files. I would like to add a custom scale such that if precipitation is less than 800mm it would be one colour, 800-1000mm another, etc. Similar to the map found here: http://www.metmalawi.com/climate/climate.php
At the moment I am using a gradient scale but it isn't showing the detail I need. This is the code for the plot at the moment (where 'Average' is my data that I have already formatted).
#load color palette
colourA = mpl_cm.get_cmap('BuPu')
#plot map with physical features
ax = plt.axes(projection=cartopy.crs.PlateCarree())
ax.add_feature(cartopy.feature.COASTLINE)
ax.add_feature(cartopy.feature.BORDERS)
ax.add_feature(cartopy.feature.LAKES, alpha=0.5)
ax.add_feature(cartopy.feature.RIVERS)
#set map boundary
ax.set_extent([32.5, 36., -9, -17])
#set axis tick marks
ax.set_xticks([33, 34, 35])
ax.set_yticks([-10, -12, -14, -16])
lon_formatter = LongitudeFormatter(zero_direction_label=True)
lat_formatter = LatitudeFormatter()
ax.xaxis.set_major_formatter(lon_formatter)
ax.yaxis.set_major_formatter(lat_formatter)
#plot data and set colour range
plot = iplt.contourf(Average, cmap=colourA, levels=np.arange(0,15500,500), extend='both')
#add colour bar index and a label
plt.colorbar(plot, label='mm per year')
#give map a title
plt.title('Pr 1990-2008 - Average_ERAINT ', fontsize=10)
#save the image of the graph and include full legend
plt.savefig('ERAINT_Average_Pr_MAP_Annual', bbox_inches='tight')
plt.show()
Anyone know how I can do this?
Thank you!
This is a matplotlib question disguised as an Iris question as the issue has appeared via Iris plotting routines, but to answer this we need only a couple of matplotlib commands. As such, I'm basing this answer on this matplotlib gallery example. These are levels (containing values for the upper bound of each contour) and colors (specifying the colours to shade each contour). It's best if there are the same number of levels and colours.
To demonstrate this, I put the following example together. Given that there's no sample data provided, I made my own trigonometric data. The levels are based on the trigonometric data values, so do not reflect the levels required in the question, but could be changed to the original levels. The colours used are the hex values of the levels specified by image in the link in the question.
The code:
import matplotlib.pyplot as plt
import numpy as np
x = np.arange(-25, 25)
y = np.arange(-20, 20)
x2d, y2d = np.meshgrid(x, y)
vals = (3 * np.cos(x2d)) + (2 * np.sin(y2d))
colours = ['#bf8046', '#df9f24', '#e0de30', '#c1de2d', '#1ebf82',
'#23de27', '#1dbe20', '#11807f', '#24607f', '#22427e']
levels = range(-5, 6)
plt.contourf(vals, levels=levels, colors=colours)
plt.colorbar()
plt.show()
The produced image:
Colours could also be selected from a colormap (one way of doing this is shown in this StackOverflow answer). There are also other ways, including in the matplotlib gallery example linked above. Given, though, that the sample map linked in the question had specific colours I chose to use those colours directly.
I was using the iris data from sci-kit-learn to obtain following data frame:
df = pd.DataFrame(data= np.c_[iris['data'], iris['target']],
columns= iris['feature_names'] + ['target'])
Plotting the scatter_matrix and using matshow to plot the correlation matrix give me the graphs scatter_matrix plot and
matshow(df.corr()), respectively.
My question is the following. Is there a way to stack these graphs? In other words, plot the scatter_matrix over the matshow(df.corr()) ?
Thanks in advance.
I suppose what you really want is to colorize the background of the respective axes in the color that would appear in a matshow plot of the correlation matrix.
To this end we can find out the color by supplying the normalized (to 0..1) correlation matrix to a matplotlib colormap and change the axes background color using ax.set_facecolor.
import seaborn.apionly as sns
import pandas as pd
import matplotlib.pyplot as plt
# taking the iris from seaborn (should be same as scikit)
df = sns.load_dataset("iris")
axes =pd.scatter_matrix(df)
corr = df.corr().values
corr_norm = (corr-corr.min())/(corr.max()-corr.min())
for i, ax in enumerate(axes.flatten()):
c = plt.cm.viridis(corr_norm.flatten()[i])
ax.set_facecolor(c)
plt.show()
I'm trying to animate several text objects in matplotlib at the same time. I have a 3-D numpy array (arrow_data) of data which stores the index of a unicode arrow which I need to plot (text for all unicode arrows are stored in a list arrows). The first and second dimension of this np.array indicate the location on a grid where this arrow needs to be plotted and the 3rd dimension is the 'time' dimension over which I need to update the plot (ie the arrows change through time over the 3d dimension of the array).
Below is the code I have for animating these arrows through 'time' using a loop, but I don't see how I can make a collection of text objects, as I can with other matplotlib functions like scatter, and then collectively (and efficiently) update the set_text property of each of them. The loop over the 'time' variable is fine but I'd prefer to not use a loop over the grid of text objects if possible.
Any thoughts on how to do this without the double nested loop?
Thanks.
from matplotlib import pyplot as plt
import numpy as np
import time
import matplotlib
matplotlib.interactive(True)
arrows = [u'\u2190', u'\u2196', u'\u2191', u'\u2197', u'\u2192', u'\u2198', u'\u2193', u'\u2199']
rest_time = 0.25
steps = 20
fig, ax = plt.subplots(1, 1)
ax.plot([0,4],[0,4], c = 'white')
objs = [[[] for i in range(4)] for j in range(4)]
for i in range(4):
for j in range(4):
objs[i][j] = ax.text(i+0.5,j+0.5, arrows[i], ha = 'center', va = 'center')
fig.canvas.draw()
arrow_data = np.random.randint(0,len(arrows), (4,4,steps))
for t in range(arrow_data.shape[2]):
for i in range(4):
for j in range(4):
objs[i][j].set_text(arrows[arrow_data[i,j,t]])
fig.canvas.draw()
time.sleep(rest_time)