PlotBasics
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The Three Ways to plot
There are three ways to go about plotting in matplotlib.
1. You can use the pylab environment
2. You can use the matplotlib.pyplot environment, with plotting commands and functions.
3. You can define plot objects, and then use the pyplot methods on those objects.
The last way gives you most control, but the other two are somewhat easier. The examples below uses the second way.
Supernova Cosmology Example
- We will import the pyplot and numpy packages
import numpy as np
import matplotlib.pyplot as plt
Reading Data from the Web
We are going to download data from the internet so we will import the urllib package
import urllib
Begin by reading the Union2 SN cosmology data from LBL, because they are fun.
SN_list = []
z_array = np.array([])
mod_array = np.array([])
moderr_array = np.array([])
f = urllib.urlopen('http://supernova.lbl.gov/Union/figures/SCPUnion2_mu_vs_z.txt')
for line in f:
if line[0] == '#': continue # Ignore anything that starts with a #
SN, z, mod, moderr = line.split()
SN_list.append(SN)
z_array = np.append(z_array,np.float64(z))
mod_array = np.append(mod_array,np.float64(mod))
moderr_array = np.append(moderr_array,np.float64(moderr))
f.close()
Simple Plots
Now to the plotting. First we close whatever windows we might have:
plt.close()
Now let us plot some points:
plt.plot(z_array, mod_array)
Notice it is a mess; by default it connects the lines. We will close it and start over.
plt.close()
We clearly need a title and axes labels
plt.title("Union2 SN Cosmology Data")
plt.xlabel('z', fontsize=20)
But we want a Greek Letter, so we can put some LaTeX syle code with the r command:
plt.ylabel(r'$\mu=m-M$', fontsize=20)
Now we add a format string to the plot command:
plt.plot(z_array, mod_array,'ro')
Now this looks better, we have red circles. The ro string, is what told it to give us red circles. A b. would give us blue dots, or ys would give yellow squares.
Now we start a new plot:
plt.close()
Plotting with Error Bars
Now let's plot with the error bars
plt.errorbar(z_array, mod_array, yerr=moderr_array, fmt='.')
And putting labels and colors we can do:
plt.xlabel(r'$z$', fontsize=20)
plt.ylabel(r'$\mu=m-M$', fontsize=20)
plt.title("Union2 SN Cosmology Data")
plt.errorbar(z_array, mod_array, yerr=moderr_array, fmt='.', capsize=0,
elinewidth=1.0, ecolor=(0.6,0.0,1.0), color='green' )
Notice that colors can be specified with a fmt or a color keyword. With the color keyword they can be given via an RGB tuple, an RGB hex code, a name, or a single number between '0.0' and '1.0' for gray scale.
plt.close()
Plotting Histograms
Now there are a lot of points, so let's figure our what our distribution is in z:
plt.hist(z_array, 25)
# And slap a label on it
plt.xlabel(r'$z$', fontsize=20)
plt.close()
Plotting With Asymmetrical Error Bars
Now, let's find the "real" distance from the distance modulus. To do this we will define a function and a constant.
def distance_Mpc(m,z):
return 0.00001 * (10**(m/5)) / (1.0 + z)
c = 299792.458 # km/s
So the distance can be found with:
d_array = distance_Mpc(mod_array,z_array)
Now we will calculate the error bars in the distance, both ways:
d_error_plus = ( distance_Mpc((mod_array+moderr_array),z_array) - d_array )
d_error_minus = ( d_array - distance_Mpc((mod_array-moderr_array),z_array) )
And plot the graph with asymetrical horizontal error bars, and labels Notice the different color formats that can be used.
plt.errorbar(z_array, d_array, yerr=(d_error_minus,d_error_plus), fmt='s',
capsize=5, elinewidth=1.0, color=(0.4,0.0,1.0), ecolor='aqua', barsabove=True)
plt.xlabel('z', fontsize=15, color='0.0')
plt.ylabel('Distance (Mpc)', fontsize=15, color='g')
plt.title("Union2 SN Cosmology Data", color=(0.4,0.0,1.0))
Plotting With a Second Axis
Now we will make a plot with both Mpc and Gly on the same plot.
First we read the existing plot limits into an array
axes1_range = np.array( plt.axis() )
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We want to use the same x-axis so we use the function.
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plt.twinx() # This swaps the Y axis
plt.ylabel('Distance in Billions of Light Years', fontsize=15, color='purple')
Now we will make the second axes the right scale.
axes2_range = axes1_range.copy() # Don't forget, we need to make a copy of it.
axes2_range[2:4] = 3.26*axes1_range[2:4]/1000.0 # set second y-axis to Gly
plt.axis(axes2_range)
plt.close()
Plotting With a Multiple Axes and Annotation
Now imagine we want to make a plot for the public, so we need to find the distance in light years. Also, we want to plot cz.
plt.errorbar(3.26*d_array/1000.0, c*z_array,
xerr=(3.26*d_error_minus/1000.0, 3.26*d_error_plus/1000.0),
fmt='.', capsize=0, elinewidth=1.0, color=(0.4,0.0,1.0), ecolor='aqua',
barsabove=False, zorder=2)
Now we make room for each axis
plt.subplots_adjust(right=0.875, top=0.8)
And plot the labels.
plt.ylabel('cz (km/s)', fontsize=15, color='aqua')
plt.xlabel('Distance (Billions of Light Years)', fontsize=15, color='aqua')
Now, you want to make the plot also include z and Mpc, so we need to convert our axes:
axes1_range = np.array( plt.axis() ) # get the existing axes and convert to an array
print(axes1_range)
axes2_range = axes1_range.copy() # Don't forget, we need to make a copy of it.
axes2_range[0:2] = 1000*axes1_range[0:2]/3.26 # set second x-axis to Mpc
axes2_range[2:4] = axes1_range[2:4]/c # set second y-axis to z
print(axes2_range)
Now we switch to the second axis
plt.twinx() # This swaps the Y axis
plt.ylabel('z', fontsize=15, color='r') # I am not sure why this has to be before plt.twiny()
plt.twiny() # This swaps the X axis
plt.xlabel('Distance (Mpc)', fontsize=15, color='purple')
plt.title("Union2 SN Cosmology Data", color=(0.4,0.0,1.0), x=0.5, y=1.15)
plt.axis(axes2_range)
And we will put Hubble's law on it just for fun. Using the zorder keyword, we can make sure the line is plotted on top on the points.
H = 70.8 # km/s/Mpc
z_limits = np.array([0.0,1.0]) # Plot is relative to the new axes
d_limits = z_limits*c/H
plt.plot(d_limits,z_limits, 'y-', zorder=1)
Notice that the second plot rescaled the axes, which is a problem for this plot, because both x axes and both y axes represent the same physical quantity. So we reissue the axis function:
plt.axis(axes2_range) # this needs to be after the plot command
Now we will put an arrow on the graph and a label.
plt.annotate("Hubble's law", xy=(d_limits[1],z_limits[1]), color='y', zorder=3,
xytext=(1.2*d_limits[1],1.2*z_limits[1]), arrowprops=dict(facecolor='y', shrink=0.05))
Saving as a PDF
We can save a file as a PDF or most any other common format this way:
plt.savefig('Union2_plot.pdf', format="pdf", transparent=True, bbox_inches='tight')
It is important to note that some file formats are raster graphics and others are scalable vector graphics.
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Magnetic Needle Example
Here we show another plotting example, where we calculate the magnetic field surrounding a thin magnetic needle.
First we import matplolib and numpy:
import matplotlib
import numpy as np
import matplotlib.pyplot as plt
Next we define the grid size, length or needle, and the size of circular masks around the poles.
N = 20 # Size of NxN array.
R = 0.25 # Size of the two circular masks.
l = 1.0 # Length of the rod.
l2 = l/2.0 # Saves on typing l/2.0 all the time.
Now we will define min and max in the cylindrically radial coordinate (s), and the axial coordinate (z).
# Define s and z
s_delt = 1.0/(N/2)
s_max = 1*(1.0 + 0.5*s_delt)
s_min = -1*(1.0 - 0.5*s_delt)
# Square Axes
z_delt = 1.0/(N/2)
z_min = -1*(1.0 - 0.5*z_delt)
z_max = 1*(1.0 + 0.5*z_delt)
Now we will find the 1-D space. Notice that it would have been slightly easier to use linspace here instead.
s_space_1D = np.arange(s_min,s_max,s_delt)
z_space_1D = np.arange(z_min,z_max,z_delt)
Now we define a 2-D grid of s and z values:
s, z = np.meshgrid(s_space_1D,z_space_1D)
And of course we find the magnetic field:
# Find each term in turn
Bs = 1.0/np.sqrt(s**2 + (z - l2)**2) - 1.0/np.sqrt(s**2 + (z + l2)**2)
Bs = Bs + (z + l2)**2 / (s**2 + (z + l2)**2)**1.5 - (z - l2)**2 / (s**2 + (z - l2)**2)**1.5
Bs = (1.0/(l*s))*Bs
Bz = (z + l2) / (s**2 + (z + l2)**2)**1.5 - (z - l2) / (s**2 + (z - l2)**2)**1.5
Bz = (-1.0/l)*Bz
Now we make and apply the mask to cover both poles:
mask = np.logical_or(np.sqrt(s**2 + (z-l2)**2) < R,np.sqrt(s**2 + (z+l2)**2) < R)
Bs = np.ma.masked_array(Bs, mask)
Bz = np.ma.masked_array(Bz, mask)
Now we can make an arrow plot of the magnetic field:
plt.close()
plt.box(on='on')
plt.axis('scaled')
plt.axis((-1.1, 1.1, -1.1, 1.1))
plt.title('Magnetic Field Surrounding a Thin Magnetic Needle')
plt.xlabel(r'$\bf s$', fontsize=20)
plt.ylabel(r'$\bf z$', fontsize=20)
plt.quiver(s,z,Bs,Bz,pivot='middle')
plt.savefig("Magnetic_field_of_thin_magnet.pdf", format="pdf", transparent=True,
bbox_inches='tight')
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Vector Potential Example
Here we show another plotting example, where we calculate the vector potential surrounding a square 2D magnet. We will also find the magnetic field, and plot a contour plot with a vector plot overlaid.
In this example, the vector potential has only a z component, so we will plot it as a filled contour plot, with the location of the magnet and the magnetic field vectors overlaid.
First we need to import the packages:
import numpy as np
import matplotlib.pyplot as plt
Now, we define the magnet dimensions and sizes of arrays
L = 1.0 # Length of Magnet
W = 1.0 # Width of Magnet
Nplt = 16 # Size of output magnetic field quiver plot
skip = 5 # Plot every skip point in each direction
smooth = 2 #Median of points for finding derivatives for added robustness
N = skip*(Nplt + 2) # Make NxN array for the total calculation
n = 30 # Size of nxn array that bounds the magnet
Next we will define the X and Y ranges
# Define Maximum and minimum area of the magnet
X_max = W/2.0
X_min = -1*W/2.0
Y_max = L/2.0
Y_min = -1*L/2.0
# Define the X and Y 1 D arrays
X = np.linspace(X_min,X_max,num=n)
Y = np.linspace(Y_min,Y_max,num=n)
# And corresponing 2D arrays
XX, YY = np.meshgrid(X,Y)
# Define Maximum and minimum area of the calculation
scale =( N + 4*smooth )/(N.__float__() - 1.0)
x_max = 1.0*scale
x_min = -1.0*scale
y_max = 1.0*scale
y_min = -1.0*scale
# Define the x and y 2D arrays for whole area
x, y = np.meshgrid(np.linspace(x_min,x_max,num=N),np.linspace(y_min,y_max,num=N))
# Define dx, dy, dX, dY, dR
dx = x[N/2+1,N/2+1] - x[N/2,N/2]
dy = y[N/2+1,N/2+1] - y[N/2,N/2]
dX = X[n/2+1] - X[n/2]
dY = Y[n/2+1] - Y[n/2]
dR = np.sqrt(dX**2 + dY**2)
Now we define the magnetization array as a constant vector in the y-direction.
My = 1.0 * (np.abs(YY) >= L/2.0)*(np.abs(YY) >= W/2.0)
Mx = 0.0 * My
M = np.sqrt(Mx**2 + My**2)
Now, we find the differential vector potential (dAz) as a function of the location of the magnetic dipole element (X,Y) and the position of interest (x,y).
First we initialize some variables:
dAz = np.zeros((n,n,N,N)) # initialize dA as an n x n x N x N array
rR2 = np.zeros((N,N)) # initialize N x N matrixe
Az = np.zeros((N,N))
pi = 3.14159265359
- We need to make sure that we are not integrating at distances smaller than the
- gridsize, otherwise the vector potential may depend on how the two grids line up.
dR2 = dR**2 # The X Y diagonal gridsize spacing squared rR2max = (x_max-x_min+X_max-X_min)**2 + (y_max-y_min+Y_max-Y_min)**2
i = 0 while i < n: # The X Loop
j = 0
while j < n: # The Y Loop
rR2 = (x-X[i])**2 + (y-Y[j])**2
rR2 = rR2.clip(2*dR2, 10*rR2max) # Clip at two grid spacing, and large distances
dAz[j,i,:,:] = (0.5/pi) * ( Mx[j,i]*(y-Y[j]) - My[j,i]*(x - X[i]) ) / rR2
j += 1
i += 1
- End Looping
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- Numerically integrate
Az = np.trapz(np.trapz(dAz, Y, axis=0), X, axis=0)
- Now we find the curl of Az in order to find Bx, By
Bx = np.zeros((N,N)) By = np.zeros((N,N)) Dx = np.zeros(2*smooth-2) # Initiate the Dx and Dy arrays Dy = np.zeros(2*smooth-2) i = 2*smooth while i < N-2*smooth: # The x Loop
j = 2*smooth
while j < N-2*smooth: # The y Loop
k = 2
while k < 2*smooth: # This loop is to add robustness by calculating multiple derivatives
Dx[k-2] = (Az[j,i+k+1] - Az[j,i-k])/(x[j,i+k+1] - x[j,i-k])
Dy[k-2] = (Az[j+k+1,i] - Az[j-k,i])/(y[j+k+1,i] - y[j-k,i])
k += 1
Bx[j,i] = np.median(Dy)
By[j,i] = -1*np.median(Dx)
j += 1
i += 1
- End Looping
- Make smaller arrays to plot the magnetic field
step = skip start = (N-skip*Nplt)/2 + 2 stop = (N+skip*(Nplt-1))/2 + 1 BpltX = Bx[start:stop:step,start:stop:step] BpltY = By[start:stop:step,start:stop:step] xplt = x[start:stop:step,start:stop:step] yplt = y[start:stop:step,start:stop:step]
- The arrow scale factor -- notice the bigger it is the smaller the arrow
scale = np.max(np.sqrt(BpltX**2 + BpltY**2)) * Nplt * 1.01
- Make the Plot
TITLE ="A 2D Square Magnet" print(TITLE) plt.close() plt.box(on='on') plt.axis('scaled') plt.axis((-1.1, 1.1, -1.1, 1.1)) plt.spectral() plt.xlabel(r'$\bf x$', fontsize=20) plt.ylabel(r'$\bf y$', fontsize=20) plt.title(TITLE)
box1x = (-1*L/2.0,L/2.0,L/2.0,-1*L/2.0) box1y = (-1*W/2.0,-1*W/2.0,W/2.0,W/2.0)
plt.fill(box1x,box1y, facecolor='silver', edgecolor='None', alpha=0.5 , zorder=1)
temp = raw_input('hit return to show next plot')
plt.contourf(x,y,Az,20, alpha=1.0, zorder=0)
- plt.pcolor(x,y,Az, alpha=1.0, zorder=0)
plt.colorbar(orientation='vertical') plt.figtext(0.92, 0.35, 'The Vector Potential', rotation=-90, weight='bold')
- filename = TITLE.replace(' ','_') + "plot_1" + ".pdf"
- plt.savefig(filename, format="pdf", transparent=True, \
- bbox_inches='tight')
- print("Saved file as: " + filename)
temp = raw_input('hit return to show next plot')
plt.quiver(xplt,yplt,BpltX,BpltY,pivot='middle',units='height', scale=scale,
zorder=2, color='white')
filename = TITLE.replace(' ','_') + "plot_3" + ".pdf" plt.savefig(filename, format="pdf", transparent=True, \
bbox_inches='tight')
print("Saved file as: " + filename)
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