I'm plotting a bar graphic and horizontal spans with this code:
fig = plt.figure('Graphic', figsize=(20,15), dpi=400)
ax1 = fig.add_axes([0.1, 0.1, 0.85, 0.75])
data.plot('DATE',["PP"],kind='bar',color='black', fontsize = 15.0,ax=ax1,alpha=1)
data.plot('DATE',['PP'],kind='line',marker='*',style=['--'],linewidth=1,color='gray', ms=5,ax=ax1)
ax1.axhspan(0, 1, facecolor='lightyellow', alpha=1)
ax1.axhspan(1, 1.5, facecolor='yellow', alpha=1)
ax1.axhspan(1.5, 2, facecolor='lime', alpha=1)
ax1.axhspan(2, 3.5, facecolor='green', alpha=1)
ax1.axhspan(0, -1, facecolor='bisque', alpha=1)
ax1.axhspan(-1, -1.5, facecolor='orange', alpha=1)
ax1.axhspan(-1.5, -2, facecolor='pink', alpha=1)
ax1.axhspan(-2, -3.5, facecolor='red', alpha=1)
The issue is that spans are hiding the Bar graphic. I would like to be able to visualize the spans with the bar graphs. Both with alpha=1. I don't want to reduce the alpha values.
Is this possible?
Thanks in advance.
I am displaying the image with axhspans with alpha=1 covering the bar charts.
I noticed two things that needed to change.
When you use pandas line and bar plots with X-axis being dates, there was/is a bug. Refer to this link. The workaround used here is what was there. Using matplotlib plot instead of pandas helped resolve this.
Refer to zorder. You can specify the order of the various components (line plot, bar, spans) to tell it what will come on top of what. Higher the zorder, the higher the plot will be. I have used 1 for the spans zorder, 2 for the bar plot zorder and 2 for line plot.
Updated code is below. See if this helps.
fig = plt.figure('Graphic', figsize=(20,15), dpi=400)
ax1 = fig.add_axes([0.1, 0.1, 0.85, 0.75])
data.plot('DATE',["PP"],kind='bar',color='black', fontsize = 15.0,ax=ax1,alpha=1, zorder=2) ## Added zorder
# Changed to matplotlib, increased linewidth to 3 so you can see it and zorder=3
ax1.plot(data[['PP']], marker='*',ls='--',linewidth=3,color='gray', ms=5, zorder=3)
## All zorder = 0
ax1.axhspan(0, 1, facecolor='lightyellow', alpha=1, zorder=1)
ax1.axhspan(1, 1.5, facecolor='yellow', alpha=1, zorder=1)
ax1.axhspan(1.5, 2, facecolor='lime', alpha=1, zorder=1)
ax1.axhspan(2, 3.5, facecolor='green', alpha=1, zorder=1)
ax1.axhspan(0, -1, facecolor='bisque', alpha=1, zorder=1)
ax1.axhspan(-1, -1.5, facecolor='orange', alpha=1, zorder=1)
ax1.axhspan(-1.5, -2, facecolor='pink', alpha=1, zorder=1)
ax1.axhspan(-2, -3.5, facecolor='red', alpha=1, zorder=1)
Plot
The order of display in the pandas plot is not adjustable, so I guess we have to deal with it in matplotlib. ax is set up with a line chart and horizontal fill, and a bar chart is added as a second axis. Then I get the order of the line chart, add 1 to the value of the line chart, and set the display order to the bar chart. Since no data was provided, stock price data was used as a sample.
import yfinance as yf
import pandas as pd
data = yf.download("AAPL", start="2022-06-01", end="2022-09-01")
data.index = pd.to_datetime(data.index)
import matplotlib.pyplot as plt
fig = plt.figure('Graphic', figsize=(10,7.5), dpi=100)
ax1 = fig.add_axes([0.1, 0.1, 0.85, 0.75])
ax1.plot(data.index, data['Close'], marker='*', linestyle='--', linewidth=1, color='gray', ms=5)
ax1.axhspan(170, 180, facecolor='lightyellow', alpha=1)
ax1.axhspan(160, 170, facecolor='yellow', alpha=1)
ax1.axhspan(150, 160, facecolor='lime', alpha=1)
ax1.axhspan(145, 150, facecolor='green', alpha=1)
ax1.axhspan(140, 145, facecolor='bisque', alpha=1)
ax1.axhspan(135, 140, facecolor='orange', alpha=1)
ax1.axhspan(130, 135, facecolor='pink', alpha=1)
ax1.axhspan(120, 130, facecolor='red', alpha=1)
ax2 = ax1.twinx()
ax2.bar(x=data.index, height=data['Volume'], color='black')
ax2.set_zorder(ax1.get_zorder()+1)
ax2.set_frame_on(False)
ax1.set_ylim(120, 180)
plt.show()
Related
I have a plot with both a colorbar and a legend. I want to place the legend outside of the plot to the right of the colorbar. To accomplish this, I use bbox_to_anchor argument, but this causes the legend to get cut off:
import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import norm
_, ax = plt.subplots()
extent = np.r_[0, 1, 0, 1]
space = np.linspace(0, 1)
probs = np.array([[norm.cdf(x + y) for x in space] for y in space])
colormap = ax.imshow(probs, aspect="auto", origin="lower", extent=extent, alpha=0.5)
colorbar = plt.colorbar(colormap, ax=ax)
colorbar.set_label(f"Probability")
ax.scatter(
[0.2, 0.4, 0.6], [0.8, 0.6, 0.4], color="r", label="Labeled Points",
)
plt.legend(loc="center left", bbox_to_anchor=(1.3, 0.5))
plt.title
plt.show()
Plot with legend cut off
To fix the legend, I insert a call to plt.tight_layout() before plt.show(), but this causes the aspect ratio to get distorted:
Plot with distorted aspect ratio
How can I show the entire legend and preserve the aspect ratio of the axes?
You can manage the ratio between axis height and width with matplotlib.axes.Axes.set_aspect. Since you want them to be equal:
ax.set_aspect(1)
Then you can use matplotlib.pyplot.tight_layout to fit the legend within the figure.
If you want to adjust margins too, you can use matplotlib.pyplot.subplots_adjust.
Complete Code
import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import norm
_, ax = plt.subplots()
extent = np.r_[0, 1, 0, 1]
space = np.linspace(0, 1)
probs = np.array([[norm.cdf(x + y) for x in space] for y in space])
colormap = ax.imshow(probs, aspect="auto", origin="lower", extent=extent, alpha=0.5)
colorbar = plt.colorbar(colormap, ax=ax)
colorbar.set_label(f"Probability")
ax.scatter([0.2, 0.4, 0.6], [0.8, 0.6, 0.4], color="r", label="Labeled Points",)
plt.legend(loc="center left", bbox_to_anchor=(1.3, 0.5))
ax.set_aspect(1)
plt.tight_layout()
plt.subplots_adjust(left = 0.1)
plt.show()
My x-axis minor gridlines are not showing, this is my code
ax = plt.gca()
ax.minorticks_on()
plt.semilogx(data_x1,data_y1,"red")
plt.semilogx(data_x2,data_y2,"blue")
ax.grid(b=True, which='major',axis="both", color='k', linestyle='-', linewidth=0.5)
ax.grid(b=True, which='minor',axis="both", color='k', linestyle='-', linewidth=0.2)
plt.xlabel("frequency(Hz)")
plt.ylabel("Iramp(dB)")
plt.show()
enter image description here
Either I'm not sure of what you want, or your code is actually working correctly. The minor grid lines are those between the powers of 10. I made a little example to show a comparison of your plot with the minor grid lines on and off.
import numpy as np
import matplotlib.pyplot as plt
data_x1 = np.linspace(0,2,10)
data_x2 = np.linspace(0,4,10)
data_y1 = np.random.rand(10)
data_y2 = np.random.rand(10)
fig, axall =plt.subplots(1,2, figsize=(10,5))
# your code with some changes
ax = axall[0]
ax.minorticks_on()
ax.semilogx(data_x1,data_y1,"red")
ax.semilogx(data_x2,data_y2,"blue")
ax.grid(b=True, which='major',axis="both", color='k', linestyle='-', linewidth=0.5)
ax.grid(b=True, which='minor',axis="both", color='k', linestyle='-', linewidth=0.2)
ax.set_xlabel("frequency(Hz)")
ax.set_ylabel("Iramp(dB)")
# code to make the plot on the right.
ax = axall[1]
ax.minorticks_on()
ax.semilogx(data_x1,data_y1,"red")
ax.semilogx(data_x2,data_y2,"blue")
ax.grid(b=True, which='major',axis="both", color='k', linestyle='-', linewidth=0.5)
# ax.grid(b=True, which='minor',axis="both", color='k', linestyle='-', linewidth=0.2)
ax.set_xlabel("frequency(Hz)")
ax.set_ylabel("Iramp(dB)")
plt.show()
Note how I commented out your minor grid lines.
I would like to have a step-wise area plot in matplotib with pandas. I adjusted the code for a step-wise line plot but I get an error message. Here is the current code:
import pandas as pd
from matplotlib import pyplot as plt
%matplotlib inline
columns = ['Conventional control', 'Optimized control']
power_values = [[0.73,1.28],
[0.21, 0.21],
[0.18, 0.18],
[0.16, 1.00],
[0.57, 0.76],
[1.63, 1.62],
[3.28, 2.77],
[3.92, 0.47],
[3.29, 0.51],
[2.01, 3.64],
[1.72, 4.45],
[2.2, 0.59],
[2.33, 4.34],
[2.01, 2.05],
[1.39, 1.68],
[2.06, 0.55],
[3.07, 0.61],
[4.07, 0.61],
[3.66, 0.59],
[2.67, 0.59] ,
[1.54, 1.65],
[1.37, 1.55],
[1.36, 0.95],
[1.1, 1.70],
[0,0]]
wind_data = pd.DataFrame(power_values, index=range(0, 25), columns=columns)
fig = plt.figure(linewidth=1, figsize=(9, 5))
ax = wind_data.plot.area(ax=plt.gca(), color =["saddlebrown", "limegreen"], stacked=False, drawstyle="steps-post" )
ax.set_facecolor("white")
ax.set_xlabel("Time of day", fontsize = 14, labelpad=8)
ax.set_ylabel("Electrical power in kW", fontsize = 14,labelpad=8)
ax.set_xlim(0, 24)
ax.set_ylim(0, 5)
plt.xticks(wind_data.index, labels=[f'{h:02d}:00' for h in wind_data.index], rotation=90)
plt.grid(axis='y', alpha=.4)
plt.tight_layout()
hours = list(range(25)) # [0, 1, 2, ... 22, 23, 24]
labels = [f'{h:02d}:00' for h in hours] # ["00:00", "01:00", ... "23:00", "24:00"]
ax.tick_params(axis='both', which='major', labelsize=14)
ax.legend(loc='center left', bbox_to_anchor=(0.15, 1.07), fontsize = 14, ncol=3)
plt.savefig('CS_Cost_PerTimeslot.png', edgecolor='black', dpi=400, bbox_inches='tight')
plt.show()
If I do not use the argument "drawstyle="steps-post" I get just an normal area plot. But I would like to have a step-wise area plot. When using this attribut (as with the line plot) I get the error message:"
AttributeError: 'PolyCollection' object has no property 'drawstyle'
". I'd be very happy if someone could help me on that. Maybe there is also another way how to tell matpoltlib not to linearly interpolate the lines between the data points.
I think the simplest way to solve your problem is to use the pyplot fill_between command directly. That way you get superb control over all the plotting elements you might want. Slightly less user friendly than the DataFrame.plot api, but still good.
Replace the line
ax = wind_data.plot.area(ax=plt.gca(), color =["saddlebrown", "limegreen"], stacked=False,drawstyle="steps-post")
with
ax=plt.gca()
for column,color in zip(wind_data.columns,['saddlebrown','limegreen']):
ax.fill_between(
x=wind_data.index,
y1=wind_data[column],
y2=0,
label=column,
color=color,
alpha=.5,
step='post',
linewidth=2,
)
and you're good.
I am trying to inset a graph inside the parent graph following the accepted answer here.
However, the plotting framework I am using is quite different:
1) First of all, the Legend is located outside the graph, for which I have followed the second answer posted here, i.e. via shrinking the parent graph:
plt.figure()
# Shrink current axis by 20%
box = ax1.get_position()
ax1.set_position([box.x0, box.y0, box.width * 0.8, box.height])
2) For the labeling, I am using the ax1.legend((p1, p2, p3), ("label for p1", "label for p2", "label for p3")) procedure.
If I ran the code with plt.show() and sys.exit() commented, the entire graph is produced:
However when implementing the inset answer, and trying to inset to the area of the pink and blue data:
(1) Replace plt.figure() by fig, ax1 = plt.subplots(),
(2) Using a ax2 for the inset, since a ax1 has already been used for placing the legend outside the graph,
(3) Uncommentplt.show() and sys.exit(),
no data is plotted both in the parent and inset graphs:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.font_manager import FontProperties
import sys
x_1 = np.array([ 2.56480648, 2.56635664, 2.57565757, 2.59425943, 2.61906191, 2.64463946,
2.66711671, 2.69966997, 2.72292229, 2.75392539, 2.79422942, 2.81360636,
2.84460946, 2.88026303, 2.91746675, 2.94846985, 2.99187419, 3.01822682,
3.06085609, 3.08565857, 3.12286229, 3.18331833, 3.24067407, 3.25772577,
3.36158616, 3.35616062, 3.43056806, 3.45847085, 3.61574815, 3.65387259,
3.89764927, 9.1 ])
x_2 = np.array([ 5.77982798, 5.77827783, 5.79067907, 5.81083108, 5.82788279, 5.85113511,
5.87438744, 5.89763976, 5.92011701, 5.94414441, 5.96584658, 5.98987399,
6.01467647, 6.03637864, 6.06118112, 6.08443344, 6.11543654, 6.13248825,
6.16194119, 6.18751875, 6.21077108, 6.23479848, 6.26192619, 6.29137914,
6.32083208, 6.35028503, 6.38128813, 6.41306631, 6.45569557, 6.47894789,
6.50530053, 6.55645565, 6.5959846 , 6.62853785, 6.67349235, 6.71612161,
6.76417642, 6.80293029, 6.86726173, 6.91841684, 6.96182118, 7.04165417,
7.10908591, 7.23774877, 7.30208021, 9.40021502])
y_1 = np.array([ 0., 10., 30.1, 50.2, 70.3, 90.4, 110.51, 130.61, 150.71,
170.81, 190.91, 211.01, 231.11, 251.21, 271.31, 291.41, 311.52, 331.62,
351.72, 371.82, 391.92, 412.02, 432.12, 452.22, 472.32, 492.42, 512.53,
532.63, 552.73, 572.83, 592.93, 592.93])
y_2 = np.array([ 0., 10., 30.1, 50.2, 70.3, 90.4, 110.51, 130.61, 150.71,
170.81, 190.91, 211.01, 231.11, 251.21, 271.31, 291.41, 311.52, 331.62,
351.72, 371.82, 391.92, 412.02, 432.12, 452.22, 472.32, 492.42, 512.53,
532.63, 552.73, 572.83, 592.93, 613.03, 633.13, 653.23, 673.33, 693.43,
713.54, 733.64, 753.74, 773.84, 793.94, 814.04, 834.14, 854.24, 874.34,
894.44])
x_3 = np.array([ 273.15, 323.15, 373.15, 423.15, 473.15])
x_4 = np.array([ 295.16725084, 378.53216084, 476.23703528, 490.56204235])
y_3 = np.array([ 1.4709975, 1.42196425, 1.372931 , 1.32389775, 1.2748645 ])
y_4 = np.array([ 1.43766266, 1.46267139, 1.51159861, 1.52367087])
### Plotting:
plt.figure()
#fig, ax1 = plt.subplots()
p1 = plt.scatter(x_1, y_1, color='red', marker='o', s=40)
p3 = plt.scatter(x_2, y_2, marker="o", color='black', facecolors='none', s=40)
p3c = plt.scatter(y_3, x_3, color='magenta', marker="o", facecolors='none', s=40)
p4 = plt.scatter(y_4, x_4, color='blue', marker='o', s=40)
fontP = FontProperties()
fontP.set_size('12')
ax1 = plt.subplot(111)
# Shrink current axis by 20% in order to allow the legend to be outside the plot:
box = ax1.get_position()
ax1.set_position([box.x0, box.y0, box.width * 0.8, box.height])
ax1.legend((\
p1,\
p3,\
p3c,\
p4\
),\
(\
"1",\
"2",\
"3",\
"4"\
),\
prop=fontP, loc='center left', bbox_to_anchor=(1, 0.5))# loc=4)
extraticks=[273.15+25]
plt.yticks(list(plt.yticks()[0]) + extraticks)
plt.gca().set_xlim(right=8)
plt.gca().set_ylim(bottom=-41.72, top=1040)
plt.grid()
plt.show()
sys.exit()
left, bottom, width, height = [0.25, 0.6, 0.2, 0.2]
ax2 = fig.add_axes([left, bottom, width, height])
p3 = ax2.scatter(x_2, y_2, color='black', marker="o", facecolors='none', s=40)
p3c = ax2.scatter(x_3, y_3, color='magenta', marker="o", facecolors='none', s=40)
ax2.set_xlim(right=2.26)
plt.gca().set_ylim(bottom=200, top=1040)
plt.grid()
plt.savefig('plot.pdf', bbox_inches='tight')
plt.show()
I think there are simply some useless commands all over the place which make the code produce 2 figures instead of one and several subplots instead of one. Also the limits of the inset seem to be off. Removing all of this would give you the following plot, which might be what you're after.
### Plotting:
fig, ax1 = plt.subplots()
p1 = plt.scatter(x_1, y_1, color='red', marker='o', s=40)
p3 = plt.scatter(x_2, y_2, marker="o", color='black', facecolors='none', s=40)
p3c = plt.scatter(y_3, x_3, color='magenta', marker="o", facecolors='none', s=40)
p4 = plt.scatter(y_4, x_4, color='blue', marker='o', s=40)
fontP = FontProperties()
fontP.set_size('12')
box = ax1.get_position()
ax1.set_position([box.x0, box.y0, box.width * 0.8, box.height])
ax1.legend((p1,p3,p3c,p4),("1","2","3","4"),
prop=fontP, loc='center left', bbox_to_anchor=(1, 0.5))
extraticks=[273.15+25]
plt.yticks(list(plt.yticks()[0]) + extraticks)
plt.gca().set_xlim(right=8)
plt.gca().set_ylim(bottom=-41.72, top=1040)
plt.grid()
left, bottom, width, height = [0.25, 0.6, 0.2, 0.2]
ax2 = fig.add_axes([left, bottom, width, height])
p3 = ax2.scatter(x_2, y_2, color='black', marker="o", facecolors='none', s=40)
p3c = ax2.scatter(x_3, y_3, color='magenta', marker="o", facecolors='none', s=40)
plt.grid()
plt.savefig('plot.pdf', bbox_inches='tight')
plt.show()
Is there any simpler way to have this line (with two annotations) using matplotlib?
The idea is just draw a line showing a interval ([0,T]) and some points with annotations. This code is too big for such a small thing.
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111)
plt.ylim(-.3,.3)
plt.xlim(0, 1)
xmin, xmax = ax.get_xlim()
# removing the default axis on all sides:
for side in ['bottom','right','top','left']:
ax.spines[side].set_visible(False)
# removing the axis ticks
plt.xticks([])
plt.yticks([])
ax.xaxis.set_ticks_position('none')
ax.yaxis.set_ticks_position('none')
# draw x and y axis
ax.arrow(xmin, 0, xmax-xmin, 0, fc='k', ec='k',
length_includes_head= True, clip_on = False)
esp=0.05
ax.text(0.5, esp, r'$\Delta t$', ha='center')
ax.text(0.45, 0, r'$|$', ha='center')
ax.text(0.45, -esp, r'$t_i$', ha='center')
ax.text(0.55, 0, r'$|$', ha='center')
ax.text(0.55, -esp, r'$t_{i+1}$', ha='center')
ax.text(0, 0, r'$|$', ha='center')
ax.text(0, -esp, r'$0$', ha='center')
ax.text(1, 0, r'$|$', ha='center')
ax.text(1, -esp, r'$T$', ha='center')
plt.show()
One could use the xaxis as the line to draw the intervals. That would allow to use its ticks and ticklabels for the annotations.
import matplotlib.pyplot as plt
fig,ax = plt.subplots()
plt.subplots_adjust(bottom=0.5, top=0.7)
for side in ['right','top','left']:
ax.spines[side].set_visible(False)
ax.set_yticks([])
ticks=[0,.45,.55,1]
ticklabels=[0,r'$t_i$', r'$t_{i+1}$',r'$T$']
ax.set_xticks(ticks)
ax.set_xticklabels(ticklabels)
ax.tick_params(direction='inout', length=10,pad=7)
ax.text(0.5, 0.2, r'$\Delta t$', ha='center')
plt.show()
I found similar solution using the x-axis but it is a little more compact
plt.ylim(0,.3)
plt.xticks([0, 0.45, 0.55, 1],
('$0$', '$t_i$', '$t_{i+1}$', '$T$'),
size=20,
verticalalignment='top')
plt.tick_params(axis='x',
direction='inout',
length=20,
pad=15,
bottom=True,
top=False)
Another approach is drawing the ticks as scatterplot and annotating each, i.e.
for x, label in [(0, '$0$'), (0.45, '$t_i$'), (0.55, '$t_{i+1}$'), (1, '$T$')]:
plt.scatter(x, 0, s=50, marker='|')
plt.annotate(label, [x,-0.05], size=20)