I would like to plot select data from a dictionary of the following format:
dictdata = {key_A: [(1,2),(1,3)]; key_B: [(3,2),(2,3)]; key_C: [(4,2),(1,4)]}
I am using the following function to extract data corresponding to a specific key and then separate the x and y values into two lists which can be plotted.
def plot_dictdata(ax1, key):
data = list()
data.append(dictdata[key])
for list_of_points in data:
for point in list_of_points:
x = point[0]
y = point[1]
ax1.scatter(x,y)
I'd like to be able to call this function multiple times (see code below) and have all relevant sets of data appear on the same graph. However, the final plot only shows the last set of data. How can I graph all sets of data on the same graph without clearing the previous set of data?
fig, ax1 = plt.subplots()
plot_dictdata(ax1, "key_A")
plot_dictdata(ax1, "key_B")
plot_dictdata(ax1, "key_C")
plt.show()
I have only just started using matplotlib, and wasn't able to figure out a solution using the following examples discussing related problems. Thank you in advance.
how to add a plot on top of another plot in matplotlib?
How to draw multiple line graph by using matplotlib in Python
Plotting a continuous stream of data with MatPlotLib
It could be that the problem is at a different point than you think it to be. The reason you only get the last point plotted is that in each loop step x and y are getting reassigned, such that at the end of the loop, each of them contain a single value.
As a solution you might want to use a list to append the values to, like
import matplotlib.pyplot as plt
dictdata = {"key_A": [(1,2),(1,3)], "key_B": [(3,2),(2,3)], "key_C": [(4,2),(1,4)]}
def plot_dictdata(ax1, key):
data = list()
data.append(dictdata[key])
x=[];y=[]
for list_of_points in data:
for point in list_of_points:
x.append(point[0])
y.append(point[1])
ax1.scatter(x,y)
fig, ax1 = plt.subplots()
plot_dictdata(ax1, "key_A")
plot_dictdata(ax1, "key_B")
plot_dictdata(ax1, "key_C")
plt.show()
resulting in
It would be worth noting that the plot_dictdata function could be simplified a lot, giving the same result as the above:
def plot_dictdata(ax1, key):
x,y = zip(*dictdata[key])
ax1.scatter(x,y)
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()
How to get many figures without closing first one in matplotlib one by one.
My code is as follows:
import matplotlib.pyplot as plt
x = [1,2,3,4,5,6,7,8,9,10]
y = [1,4,9,16,25,36,49,64,81,100]
z=[1,3,4,5,19,13,17,12,15,10]
v=[1,2,4,5,7,8,90,3,2,2]
def func(x,y):
plt.plot(x, y)# I want the first graph to stay and get second one without closing first
plt.show()
return
func(x,y)
myinput=int(input())
if myinput==1:
func(x,z)
else:
func(x,v)
#something like this i want
#answering myself after spending lot of time
x = [1,2,3,4,5,6,7,8,9,10]
y = [1,4,9,16,25,36,49,64,81,100]
z=[1,3,4,5,19,13,17,12,15,10]
v=[1,2,4,5,7,8,90,3,2,2]
def func(x,y,index):
fig=plt.figure(index)
fig1=fig.add_subplot(1,1,1)
fig1.plot(x, y)
plt.show(block=False)
return
my_input=1
while(my_input!=-1):
my_input=int(input())
func(x,y,my_input)
As a shortened version of #noman's answer:
plt.show(block=False)
For example, I used it like this to generate two plots from one script, without needing to close the first plot:
# first plot
fig = plt.figure()
plt.scatter(.....whatever....)
plt.show(block=False)
# second plot
fig = plt.figure()
plt.line(.....whatever....)
plt.show()
I am new to matplotlib and I was playing with this library to plot data from a csv file. Without using the animation function the graph looks correct, but When I tried to use the animation, the graph connected the first and the last point. I looked stuff up, but I can't figure out how to solve this. Does anyone know how to solve this issue? Below is my code. Thanks in advance!
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import csv
x = []
y = []
fig = plt.figure()
ax1 = fig.add_subplot(1,1,1)
def animate(i):
with open("example.txt", "r") as csvfile:
plots = csv.reader(csvfile, delimiter=',')
for row in plots:
x.append(int(row[0]))
y.append(int(row[1]))
ax1.clear()
ax1.plot(x,y)
ani = animation.FuncAnimation(fig, animate, interval=1000)
plt.show()
You append all the same points over and over again to the lists to plot. So say the csv file contains numbers 1,2,3 what you are doing is reading them in, appending them to the list, plotting them, then reading them in again and appending them etc.
So x contains in
Step 1 : 1,2,3
Step 2 : 1,2,3,1,2,3
Step 3 : 1,2,3,1,2,3,1,2,3
Hence from step 2 on there will be a connection between 3 and 1.
I don't know what the purpose of this animation is since animating all the same points is quite useless. So there is no straight forward solution, apart from not animating at all.
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.