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Difference in plotting with different matplotlib versions
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This is my first time using Matplotlib. I have a series of latitude and longitude co-ordinates in two lists, and I want to represent these in a meaningful manner. I would not like to use Basemap for several reasons.
lat = ['35.905333', '35.896389', '35.901281', '35.860491', '35.807607', '35.832267', '35.882414', '35.983794', '35.974463', '35.930951']
long = ['14.471970', '14.477780', '14.518173', '14.572245', '14.535320', '14.455894', '14.373217', '14.336096', '14.351006', '14.401137']
I am trying to represent these in a meaningful manner using Matplotlib.
import numpy as np
import matplotlib.pyplot as plt
fig = plt.figure()
plt.scatter(lat, long)
plt.show()
However my figure is as follows:
I am unable to set the axis in order to obtain a meaningful representation of these coordinates. How can this be done? What am I doing wrong?
I am looking for something like this:
import numpy as np
import matplotlib.pyplot as plt
N = 50
x = np.random.rand(N)
y = np.random.rand(N)
plt.scatter(x, y)
plt.show()
I get the expected outcome.
I have also tried to plot on a cartesian coordinate system.
EDIT:
As per the comment below:
import numpy as np
import matplotlib.pyplot as plt
fig = plt.figure()
plt.scatter(lat, long)
plt.axis('square')
plt.show()
As mentioned in the comment, change the type to float:
import numpy as np
import matplotlib.pyplot as plt
lat = np.array(['35.905333', '35.896389', '35.901281', '35.860491', '35.807607',
'35.832267', '35.882414', '35.983794', '35.974463', '35.930951'], dtype=float)
long = np.array(['14.471970', '14.477780', '14.518173', '14.572245', '14.535320',
'14.455894', '14.373217', '14.336096', '14.351006', '14.401137'], dtype=float)
fig, ax = plt.subplots(figsize=(10, 6))
ax.scatter(lat, long)
# ax.axis('equal')
plt.show()
Related
I have some lists that each of which has a different shape and I would like to plot all of them together in one polar scatter plot. I also tried to use iter tools but I could not find the solution.
import numpy as np
import matplotlib.pyplot as plt
a1=[1,2,3,4,5,6]
a2=[2,3,5,6]
a3=[1,2,3]
a4=[1,2,3,4,4,56,7,8]
ax1 = plt.subplot(111,polar= True)
for i in range (0,3):
theta = 4 * np.pi * np.random.rand(len(a[i]))
ax1.set_ylim(0,0.1)
ax1.set_rlabel_position(180)
for i in range (0,3):
ax1.scatter(theta,a[i], cmap='hsv', alpha=0.5)
Be carefull i modified your lists for a better visual exmaple!
I hope I understood your question correctly...
import numpy as np
import matplotlib.pyplot as plt
a1=[1,2,3,4,5,6]
a2=[2,3,5,6]
a3=[1,2,3]
a4=[1,2,3,4,4,7,7,8]
ax1 = plt.subplot(111,polar= True)
for onelist in [a1,a2,a3,a4]:
theta_list = np.linspace(0,2*np.pi,len(onelist))
ax1.plot(theta_list,onelist,marker="x")
plt.show()
I am trying to plot vehicle position (coordinates - x,y) against time(1s,2s,3s...). I tried with matplotlib but could not succeed. I am new in python. Could anyone help me please.
My code:
import matplotlib.pyplot as plt
import numpy as np
coordinate = [[524.447876,1399.091919], [525.1377563,1399.95105], [525.7932739,1400.767578], [526.4627686,1401.601563],
[527.2360229,1402.564575], [527.8989258,1403.390381], [528.5689697,1404.224854]]
timestamp =[0,0.05,0.1,0.15,0.2,0.25,0.3]
plt.plot(coordinate,timestamp)
Plot comes like: But this is wrong one. I did wrong.
Plot supposed to become, in particular, timestamp (1s) the vehicle position is (x,y). So there should be one line just like vehicle trajectory.
Thanks.
I believe this is the output you're looking for:
import matplotlib.pyplot as plt
import numpy as np
coordinate = [[524.447876,1399.091919],
[525.1377563,1399.95105],
[525.7932739,1400.767578],
[526.4627686,1401.601563],
[527.2360229,1402.564575],
[527.8989258,1403.390381],
[528.5689697,1404.224854]]
v1 = [y[1] for y in coordinate]
v2 = [y[0] for y in coordinate]
x = [0,0.05,0.1,0.15,0.2,0.25,0.3]
plt.plot(x,v1)
plt.plot(x,v2,'--')
plt.ylim(0,1500)
plt.show()
Does something simple like this meet your needs:
import matplotlib.pyplot as plt
coordinates = [
(524.447876,1399.091919),
(525.1377563,1399.95105),
(525.7932739,1400.767578),
(526.4627686,1401.601563),
(527.2360229,1402.564575),
(527.8989258,1403.390381),
(528.5689697,1404.224854),
]
timestamp = [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3]
x, y = zip(*coordinates)
ax = plt.axes(projection="3d")
ax.plot(x, y, timestamp);
plt.show()
Matplotlib will let you rotate the image with the mouse to view it from various angles.
Hi I think the problem over here is that you are using a two-dimensional list, so matplotlib plots the coordinates and not the timestamp.
Code:
import matplotlib.pyplot as plt
import numpy as np
coordinate = np.array([[524.447876,1399.091919], [525.1377563,1399.95105], [525.7932739,1400.767578], [526.4627686,1401.601563], [527.2360229,1402.564575], [527.8989258,1403.390381], [528.5689697,1404.224854]])
timestamp =np.array([0,0.05,0.1,0.15,0.2,0.25,0.3])
plt.plot(coordinate)
Output:
You have to convert it into a single dimension list like this:
coordinate_new = np.array([524.447876,525.1377563,1399.95105, 525.7932739,1400.767578, 526.4627686,1401.601563])
timestamp =np.array([0,0.05,0.1,0.15,0.2,0.25,0.3])
plt.plot(coordinate_new, timestamp)
Then the output will be:
Hope I could help!!
If you want to plot it in 3-d, here is what you can do:
import matplotlib.pyplot as plt
#importing matplotlib
fig = plt.figure() #adding figure
ax_3d = plt.axes(projection="3d") #addign 3-d axes
coordinate_x = [524.447876, 525.137756, 525.7932739, 526.4627686, 527.2360229, 527.8989258, 528.5689697]
coordinate_y = [1399.091919, 1399.95105,1400.767578,1401.601563,1402.564575,1403.390381,1404.224854]
timestamp =[0,0.05,0.1,0.15,0.2,0.25,0.3]
# defining the variables
ax.plot(coordinate_x, coordinate_y, timestamp)
#plotting them
Output:
All the Best!
I want to replicate plots from this paper: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5000555/pdf/nihms774453.pdf I'm particularly interested in plot on page 16, right panel. I tried to do this in matplotlib but it seems to me that there is no way to access lines in linecollection.
I don't know how to change the color of the each line, according to the value at every index. I'd like to eventually get something like here: https://matplotlib.org/3.1.1/gallery/lines_bars_and_markers/multicolored_line.html but for every line, according to the data.
this is what I tried:
the data in numpy array: https://pastebin.com/B1wJu9Nd
import pandas as pd, numpy as np
import matplotlib.pyplot as plt
from matplotlib.collections import LineCollection
from matplotlib import colors as mcolors
%matplotlib inline
base_range = np.arange(qq.index.max()+1)
fig, ax = plt.subplots(figsize=(12,8))
ax.set_xlim(qq.index.min(), qq.index.max())
# ax.set_ylim(qq.columns[0], qq.columns[-1])
ax.set_ylim(-5, len(qq.columns) +5)
line_segments = LineCollection([np.column_stack([base_range, [y]*len(qq.index)]) for y in range(len(qq.columns))],
cmap='viridis',
linewidths=(5),
linestyles='solid',
)
line_segments.set_array(base_range)
ax.add_collection(line_segments)
axcb = fig.colorbar(line_segments)
plt.show()
my result:
what I want to achieve:
I have pandas series of complex numbers, which I would like to plot. Currently, I am looping through each point and assigning it a color. I would prefer to generate the plot without the need to loop over each point... Using Series.plot() would be preferable. Converting series to numpy is ok though.
Here is an example of what I currently have:
import pandas as pd
import numpy as np
from matplotlib import pyplot
s = pd.Series((1+np.random.randn(500)*0.05)*np.exp(1j*np.linspace(-np.pi, np.pi, 500)))
cmap = pyplot.cm.viridis
for i, val in enumerate(s):
pyplot.plot(np.real(val), np.imag(val), 'o', ms=10, color=cmap(i/(len(s)-1)))
pyplot.show()
You can use pyplot.scatter, which allows coloring of points based on a value.
pyplot.scatter(np.real(s), np.imag(s), s=50, c=np.arange(len(s)), cmap='viridis')
Here, we set c to an increasing sequence to get the same result as in the question.
You can simply plot the real and imaginary part of the series without a loop.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
s = pd.Series((1+np.random.randn(500)*0.05)*np.exp(1j*np.linspace(-np.pi, np.pi, 500)))
plt.plot(s.values.real,s.values.imag, marker="o", ls="")
plt.show()
However, you need to use a scatter plot if you want to have different colors:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
s = pd.Series((1+np.random.randn(500)*0.05)*np.exp(1j*np.linspace(-np.pi, np.pi, 500)))
plt.scatter(s.values.real,s.values.imag, c = range(len(s)), cmap=plt.cm.viridis)
plt.show()
Python (and matplotlib) newbie here coming over from R, so I hope this question is not too idiotic. I'm trying to make a loglog plot on a natural log scale. But after some googling I cannot somehow figure out how to force pyplot to use a base e scale on the axes. The code I have currently:
import matplotlib.pyplot as pyplot
import math
e = math.exp(1)
pyplot.loglog(range(1,len(degrees)+1),degrees,'o',basex=e,basey=e)
Where degrees is a vector of counts at each value of range(1,len(degrees)+1). For some reason when I run this code, pyplot keeps giving me a plot with powers of 2 on the axes. I feel like this ought to be easy, but I'm stumped...
Any advice is greatly appreciated!
When plotting using plt.loglog you can pass the keyword arguments basex and basey as shown below.
From numpy you can get the e constant with numpy.e (or np.e if you import numpy as np)
import numpy as np
import matplotlib.pyplot as plt
# Generate some data.
x = np.linspace(0, 2, 1000)
y = x**np.e
plt.loglog(x,y, basex=np.e, basey=np.e)
plt.show()
Edit
Additionally if you want pretty looking ticks you can use matplotlib.ticker to choose the format of your ticks, an example of which is given below.
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.ticker as mtick
x = np.linspace(1, 4, 1000)
y = x**3
fig, ax = plt.subplots()
ax.loglog(x,y, basex=np.e, basey=np.e)
def ticks(y, pos):
return r'$e^{:.0f}$'.format(np.log(y))
ax.xaxis.set_major_formatter(mtick.FuncFormatter(ticks))
ax.yaxis.set_major_formatter(mtick.FuncFormatter(ticks))
plt.show()
It can also works for semilogx and semilogy to show them in e and also change their name.
import matplotlib.ticker as mtick
fig, ax = plt.subplots()
def ticks(y, pos):
return r'$e^{:.0f}$'.format(np.log(y))
plt.semilogy(Time_Series, California_Pervalence ,'gray', basey=np.e )
ax.yaxis.set_major_formatter(mtick.FuncFormatter(ticks))
plt.show()
Take a look at the image.