I have a dataframe such as below and I am trying to use the example plot code (given below) to generate a similar style line series plot for my dataframe.
df = pd.DataFrame({'x': np.linspace(0, 10, 100),
'run0_Y': np.sin(np.linspace(0, 10, 100)),
'run1_Y': np.cos(np.linspace(0, 10, 100)),
'run2_Y': np.cos(np.linspace(0, 10, 100)),
'run3_Y': np.arctan(np.linspace(0, 10, 100))
})
- I would like to generate a plot like below (see code) but I want to replace the colorbar with my dataframe headings ['run0_Y' ... 'run3_Y'] as legends for each color.
- 'run0_Y' and 'run1_Y' belongs to the same color but differentiated
with solid line
'-k'and dashed line'--k' - I am stuck as to how to plot the line series from my dataframe and associate each dataframe column to its column header in the colorbar as legend.
Example plotting code:
import numpy as np
import matplotlib.pyplot as plt
# Use the spectral colormap for examples
cmap = plt.cm.Spectral
# Generate some fake data
N = 100
nlines = 10
x = np.linspace(-np.pi, np.pi, N)
print('x: \n', x)
y = np.linspace(-np.pi, np.pi, nlines)
print('y: \n', y)
# Use np.newaxis to create [N,1] and [1,Nlines] x and y arrays
# Then broadcasting to generate Z with shape [N,Nlines]
z = np.sin(x[:,np.newaxis] + y[np.newaxis,:]/4)
print('z \n', z)
# Use 0-1 values to generate the colors with the linspace method
line_colors = cmap(np.linspace(0,1,nlines))
# We have to generate our own axis to put the colorbar in
# otherwise it "steals" space from the current axis. Please
# let me know if anyone has found another way around this,
# because the custom axes generation is the only way I've
# figured out.
from matplotlib.gridspec import GridSpec
# fig = plt.figure(figsize = (12,6))
# nrows = 2
# gs = GridSpec(nrows,2,width_ratios=[50,1])
# ax = [plt.subplot(gs[i,0]) for i in range(nrows)]
# cbax1 = plt.subplot(gs[1,1])
# # First, plot lines w/ legend
# a = ax[0]
# a.set_title('Labeling with a legend')
# for i in range(nlines):
# a.plot(x, z[:,i], c=line_colors[i],lw=3,label='{:4.1f}'.format(y[i]))
# leg = a.legend(loc='center left', bbox_to_anchor=(1, 0.5), ncol=2)
# leg.set_title('Y')
# # Next, plot with colorbar
# a = ax[1]
# a.set_title('Labeling with a "continuous" colorbar')
# for i in range(nlines):
# a.plot(x, z[:,i], c=line_colors[i],lw=3,label='{:3.1f}'.format(y[i]))
# # Generate fake ScalarMappable for colorbar
# sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin=y[0],vmax=y[-1]))
# sm.set_array([]) # You have to set a dummy-array for this to work...
# cbar = plt.colorbar(sm, cax=cbax1)
# cbar.set_label('Y')
# cbar.set_ticks(y)
# cbar.set_ticklabels(['{:4.1f}'.format(yi) for yi in y]) # Make 'em nicer-looking
# # Moves colorbar closer to main axis by adjusting width-spacing between subplot axes.
# fig.subplots_adjust(wspace=0.05, hspace=0.4)
# # Set axis limits
# for a in ax:
# a.set_xlim(-np.pi, np.pi)
fig = plt.figure(figsize = (12,6))
nrows = 1
gs = GridSpec(nrows,2,width_ratios=[50,1])
ax = [plt.subplot(gs[i,0]) for i in range(nrows)]
cbax = [plt.subplot(gs[i,1]) for i in range(nrows)]
# We'll use the same fake ScalarMappable and colormap for each example
from matplotlib.colors import ListedColormap
cmap2 = ListedColormap(line_colors)
sm = plt.cm.ScalarMappable(cmap=cmap2,
norm=plt.Normalize(vmin=y[0],vmax=y[-1]))
sm.set_array([])
# # Discrete colorbar with default spacing
# a = ax[0]
# a.set_title('Labeling with a discrete colorbar')
# for i in range(nlines):
# a.plot(x, z[:,i], c=line_colors[i],lw=2,label='{:4.1}'.format(y[i]))
# cbar = plt.colorbar(sm, cax=cbax[0])
# cbar.set_label('Y')
# cbar.set_ticks(y)
# cbar.set_ticklabels(['{:4.1f}'.format(yi) for yi in y]) # Make 'em nicer-looking
# Discrete colorbar with centered ticks
# a = ax[1]
a = ax[0]
a.set_title('Labeling with a discrete colorbar & centered labels')
for i in range(nlines):
a.plot(x, z[:,i], c=line_colors[i],lw=2,label='{:4.1}'.format(y[i]))
# Generate custom bounds so that ticks are centered
dy = y[1]-y[0]
ybounds = np.linspace(y[0]-dy/2., y[-1]+dy/2., nlines+1)
cbar = plt.colorbar(sm, cax=cbax[0], boundaries=ybounds)
cbar.set_label('Y')
cbar.set_ticks(y)
cbar.set_ticklabels(['{:4.1f}'.format(yi) for yi in y]) # Make 'em nicer-looking
# Set axis limits
for a in ax:
a.set_xlim(-np.pi, np.pi)
# Moves colorbar closer to main axis by adjusting width-spacing between subplot axes.
fig.subplots_adjust(wspace=0.05, hspace=0.4)
plt.show()
