I cannot add create 3x3 subplots with common axes and labels using the following code:
fig = plt.figure(figsize=(24, 12))
ax = fig.add_subplot(111) # The big subplot
fig.text(0.5, 0.04, 'Time (minutes)', ha='center')
fig.text(0.04, 0.5, 'Distance travelled (pixels)', va='center',
rotation='vertical')
# Turn off axis lines and ticks of the big subplot
ax.spines['top'].set_color('none')
ax.spines['bottom'].set_color('none')
ax.spines['left'].set_color('none')
ax.spines['right'].set_color('none')
ax.tick_params(labelcolor='w', top='off', bottom='off',
left='off', right='off')
ax1 = fig.add_subplot(331)
ax1.errorbar(Time, MD11, MD11err, capsize=3, ls='none',
color='red', elinewidth=0.5, capthick=1)
ax1.scatter(Time, S1, s=20, color = 'grey')
ax1.plot(Time, MD11, color = 'red', label="First 1ug/g
injection")
ax1.plot(Time, S1, color = 'grey', label="Saline")
ax1.spines['right'].set_visible(False)
ax1.spines['top'].set_visible(False)
ax1.title ('MDMA 1 ug/g ')
ax1.ylabel('First Injection', va='center', rotation='vertical')
ax2 = fig.add_subplot(332)
ax2.errorbar(Time, MD31, MD31err, capsize=5, ls='none',
color='red', elinewidth=1, capthick=2)
ax2.errorbar(Time, S4, S4err, capsize=5, ls='none',
color='gray', elinewidth=1, capthick=2)
ax2.scatter(Time, MD31,s=20, color = 'red')
ax2.scatter(Time, S4,s=20, color = 'grey')
ax2.plot(Time, MD31, color = 'red', label="First 3ug/g
injection")
ax2.plot(Time, S4, color = 'grey', label="Saline")
and so on till ax9. The above code just created the 331 subplot and not the other ones. I have defined all the variables properly. Please help.
Related
please see the result graph image below.
I wish to remove only one major grid line at y-axis value of 10 (Blue horizontal line), and keep all other grid lines.
Is there a way to do that?
plt.rcParams['font.family'] = 'Arial'
fig, ax = plt.subplots(figsize=(14.78, 9.84))
plt.xlim(0, 105)
plt.ylim(0, 10)
ax.xaxis.set_minor_locator(AutoMinorLocator(2))
ax.yaxis.set_minor_locator(AutoMinorLocator(2))
ax.spines['bottom'].set_linewidth(1.5)
ax.spines['left'].set_linewidth(1.5)
ax.spines['top'].set_linewidth(0)
ax.spines['right'].set_linewidth(0)
# Grid setting
plt.grid(True, color='#0100FF', which="major", ls="-")
plt.grid(True, color='#0BC904', which="minor", ls="-")
plt.xlabel("Indicator Amplitude, %FSH", fontsize=28, labelpad=15)
plt.ylabel("Function Generator Output, V", fontsize=28, labelpad=15)
# Axis setting
plt.tick_params(which="major", labelsize=22, length=10, pad=10, width=1.5)
plt.tick_params(which="minor", length=8, width=1.5)
# Plot scatter & line
plt.plot(FSH_axis, x_value[2:], color='black', marker='^', linewidth=1.5, markersize=8, label="40 dB")
plt.plot(FSH_axis, y_value[2:], color='red', marker='o', linewidth=1.5, markersize=8, label="60 dB")
plt.plot(FSH_axis, z_value[2:], color='blue', marker='v', linewidth=1.5, markersize=8, label="80 dB")
plt.legend(loc=(1 / 16, 58 / 90), ncol=1, fontsize=20, frameon=True, framealpha=1, edgecolor="black")
plt.show()
We can catch all gridlines with get_ygridlines(), then access individual gridlines as Line2D objects to modify them:
from matplotlib import pyplot as plt
from matplotlib.ticker import AutoMinorLocator
plt.rcParams['font.family'] = 'Arial'
fig, ax = plt.subplots(figsize=(14.78, 9.84))
plt.xlim(0, 105)
plt.ylim(0, 10)
ax.xaxis.set_minor_locator(AutoMinorLocator(2))
ax.yaxis.set_minor_locator(AutoMinorLocator(2))
ax.spines['bottom'].set_linewidth(1.5)
ax.spines['left'].set_linewidth(1.5)
ax.spines['top'].set_linewidth(0)
ax.spines['right'].set_linewidth(0)
# Grid setting
plt.grid(True, color='#0100FF', which="major", ls="-")
plt.grid(True, color='#0BC904', which="minor", ls="-")
#this part is added
#set the last horizontal gridline invisible
ygridlines = ax.get_ygridlines()
gridline_of_interest = ygridlines[-1]
gridline_of_interest.set_visible(False)
plt.xlabel("Indicator Amplitude, %FSH", fontsize=28, labelpad=15)
plt.ylabel("Function Generator Output, V", fontsize=28, labelpad=15)
# Axis setting
plt.tick_params(which="major", labelsize=22, length=10, pad=10, width=1.5)
plt.tick_params(which="minor", length=8, width=1.5)
# Plot scatter & line
FSH_axis = [10, 40, 100]
plt.plot(FSH_axis, [1, 3, 2], color='black', marker='^', linewidth=1.5, markersize=8, label="40 dB")
plt.plot(FSH_axis, [2, 2, 3], color='red', marker='o', linewidth=1.5, markersize=8, label="60 dB")
plt.plot(FSH_axis, [2, 1, 1], color='blue', marker='v', linewidth=1.5, markersize=8, label="80 dB")
plt.legend(loc=(1 / 16, 58 / 90), ncol=1, fontsize=20, frameon=True, framealpha=1, edgecolor="black")
plt.show()
Sample output:
Of course, the corresponding get_xgridlines() also exists.
I have tried a number of different things to fix my chart, from zorder on the plots to plt.rcParams.
I feel that this is such a simple problem but I just dont know where I have gone wrong. As you can see the bottom annotation in cyan blue is unreadable and mashed with the y label.
Ideally, the annotation sits over the y label to a point where text inside annotation is readable.
If possible just for the annotation to sit on top and still overlay the y label..something like this
Any help on this would be greatly appreciated.
ax = df.plot(x=df.columns[0], y=df.columns[1], legend=False, zorder=0, linewidth=1)
y1 =df.loc[:, df.columns[2]].tail(1)
y2= df.loc[:, df.columns[1]].tail(1)
colors = plt.rcParams["axes.prop_cycle"].by_key()["color"]
print(colors)
for var in (y1, y2):
plt.annotate('%0.2f' % var.max(), xy=(1, var.max()), zorder=1, xytext=(8, 0),
xycoords=('axes fraction', 'data'),
textcoords='offset points',
bbox=dict(boxstyle="round", fc=colors[0], ec=colors[0],))
ax2 = ax.twinx()
df.plot(x=df.columns[0], y=df.columns[2], ax=ax2, legend=False, color='#fa8174', zorder=0,linewidth=1)
ax.figure.legend(prop=subtitle_font)
ax.grid(True, color="white",alpha=0.2)
pack = [df.columns[1], df.columns[2], freq[0]]
plt.text(0.01, 0.95,'{0} v {1} - ({2})'.format(df.columns[1], df.columns[2], freq[0]),
horizontalalignment='left',
verticalalignment='center',
transform = ax.transAxes,
zorder=10,
fontproperties=subtitle_font)
ax.text(0.01,0.02,"Sources: FRED, Quandl, #Paul92s",
color="white",fontsize=10,
horizontalalignment='left',
transform = ax.transAxes,
verticalalignment='center',
zorder=20,
fontproperties=subtitle_font)
ax.xaxis.set_major_locator(matplotlib.dates.YearLocator())
ax.xaxis.set_minor_locator(matplotlib.dates.MonthLocator((4,7,10)))
ax.xaxis.set_major_formatter(matplotlib.dates.DateFormatter("%Y"))
ax.xaxis.set_minor_formatter(ticker.NullFormatter()) # matplotlib.dates.DateFormatter("%m")
plt.setp(ax.get_xticklabels(), rotation=0, ha="center", zorder=-1)
plt.setp(ax2.get_yticklabels(), rotation=0, zorder=-1)
plt.setp(ax.get_yticklabels(), rotation=0, zorder=-1)
plt.gcf().set_size_inches(14,7)
ax.set_xlabel('Data as of; {0}'.format(df['Date'].max().strftime("%B %d, %Y")), fontproperties=subtitle_font)
y1 =df.loc[:, df.columns[2]].tail(1)
y2= df.loc[:, df.columns[1]].tail(1)
for var in (y1, y2):
plt.annotate('%0.2f' % var.max(), xy=(1, var.max()), zorder=1,xytext=(8, 0),
xycoords=('axes fraction', 'data'),
textcoords='offset points',
bbox=dict(boxstyle="round", fc="#fa8174", ec="#fa8174"))
plt.title('{0}'.format("FRED Velocity of M2 Money Stock v Trade Weighted U.S. Dollar Index: Broad"),fontproperties=heading_font)
ax.texts.append(ax.texts.pop())
ax.set_facecolor('#181818')
ax.figure.set_facecolor('#181818')
plt.rcParams['axes.axisbelow'] = True
I don't figure out why zorder doesn't work, but you can directly set the label style of tick labels:
import matplotlib.pyplot as plt
import numpy as np
from numpy.random import rand
import matplotlib.patches as mpatches
fig, ax = plt.subplots(1, 1)
ax.plot(rand(100), '^', color='r')
for label in ax.get_xticklabels():
label.set_bbox(dict(facecolor='orange'))
ax1 = ax.twinx()
ax1.plot(rand(100), 'o', color='b')
index_to_add_bbox = [2, 4]
ax1_labels = ax1.get_yticklabels()
for i in index_to_add_bbox:
ax1_labels[i].set_bbox(dict(boxstyle='Circle', facecolor='orange'))
plt.show()
I create two scatterplots with matplotlib in python with this code, the data for the code is here:
import matplotlib.pyplot as plt
from matplotlib.colors import Normalize
fig = plt.figure(figsize=(20,12))
ax1 = fig.add_subplot(111)
ax3 = ax1.twinx()
norm = Normalize(vmin=0.95*min(arr), vmax=1.05*max(arr))
ax1.scatter(x, y1, s=20, c=arr, cmap='Blues_r', norm=norm, marker='x', label='bla1')
ax3.scatter(x, y2, s=(20*(1.1-arr))**3.5, c=arr, cmap='Reds_r', norm=norm, marker='^', label='bla1')
The created fig. looks like this:
So, the dot size (in ax3) and the dot colour (in ax1 and ax3) are taken from arrays containing floats with all kinds of values in the range [0,1]. My question: How do I create a legend that displays the corresponding y-values for, let's say 5 different dot sizes and 5 different colour nuances?
I would like the legend to look like in the figure below (source here), but with the colour bar and size bar put into a single legend, if possible. Thanks for suggestions and code!
# using your data in dataframe df
# create s2
df['s2'] = (20*(1.1-df.arr))**3.5
fig = plt.figure(figsize=(20,12))
ax1 = fig.add_subplot(111)
ax3 = ax1.twinx()
norm = Normalize(vmin=0.95*min(df.arr), vmax=1.05*max(df.arr))
p1 = ax1.scatter(df.x, df.y1, s=20, c=df.arr, cmap='Blues_r', norm=norm, marker='x')
fig.colorbar(p1, label='arr')
p2 = ax3.scatter(df.x, df.y2, s=df.s2, c=df.arr, cmap='Reds_r', norm=norm, marker='^')
fig.colorbar(p2, label='arr')
# create the size legend for red
for x in [15, 80, 150]:
plt.scatter([], [], c='r', alpha=1, s=x, label=str(x), marker='^')
plt.legend(loc='upper center', bbox_to_anchor=(1.23, 1), ncol=1, fancybox=True, shadow=True, title='s2')
plt.show()
There's no legend for p1 because the size is static.
I think this would be better as two separate plots
I used Customizing Plot Legends: Legend for Size of Points
Separate
fig, (ax1, ax2) = plt.subplots(nrows=2, figsize=(20, 10))
norm = Normalize(vmin=0.95*min(df.arr), vmax=1.05*max(df.arr))
p1 = ax1.scatter(df.x, df.y1, s=20, c=df.arr, cmap='Blues_r', norm=norm, marker='x')
fig.colorbar(p1, ax=ax1, label='arr')
p2 = ax2.scatter(df.x, df.y2, s=df.s2, c=df.arr, cmap='Reds_r', norm=norm, marker='^')
fig.colorbar(p2, ax=ax2, label='arr')
# create the size legend for red
for x in [15, 80, 150]:
plt.scatter([], [], c='r', alpha=1, s=x, label=str(x), marker='^')
plt.legend(loc='upper center', bbox_to_anchor=(1.2, 1), ncol=1, fancybox=True, shadow=True, title='s2')
plt.show()
I'm building a visualization with Python.
There I'd like to visualize fuel stops and the fuel costs of my car. Furthermore, car washes and their costs should be visualized as well as repairs. The fuel costs and laundry costs should have a higher bar depending on the costs. I created the visualization below to describe the concepts.
How to create such a visualization with matplotlib?
This is the visualization being built:
Yes, this kind of visualization is perfectly possible with matplotlib. To store the data, numpy arrays are usually very handy.
Here is some code to get you started:
import matplotlib.pyplot as plt
import numpy as np
refuel_km = np.array([0, 505.4, 1070, 1690])
refuel_cost = np.array([40.1, 50, 63, 55])
carwash_km = np.array([302.0, 605.4, 901, 1331, 1788.2])
carwash_cost = np.array([35.0, 40.0, 35.0, 35.0, 35.0])
repair_km = np.array([788.0, 1605.4])
repair_cost = np.array([135.0, 74.5])
fig, ax = plt.subplots(figsize=(12,3))
plt.scatter(refuel_km, np.full_like(refuel_km, 0), marker='o', s=100, color='lime', edgecolors='black', zorder=3, label='refuel')
plt.bar(refuel_km, refuel_cost, bottom=15, color='lime', ec='black', width=20, label='refuel cost')
plt.scatter(carwash_km, np.full_like(carwash_km, 0), marker='d', s=100, color='tomato', edgecolors='black', zorder=3, label='car wash')
plt.bar(carwash_km, -carwash_cost, bottom=-15, color='tomato', ec='black', width=20, label='car wash cost')
plt.scatter(repair_km, np.full_like(repair_km, 0), marker='^', s=100, color='lightblue', edgecolors='black', zorder=3, label='car repair')
#plt.bar(repair_km, -repair_cost, bottom=-15, color='lightblue', ec='black', width=20)
ax.spines['bottom'].set_position('zero')
ax.spines['top'].set_color('none')
ax.spines['right'].set_color('none')
ax.spines['left'].set_color('none')
ax.tick_params(axis='x', length=20)
ax.set_yticks([]) # turn off the yticks
_, xmax = ax.get_xlim()
ymin, ymax = ax.get_ylim()
ax.set_xlim(-15, xmax)
ax.set_ylim(ymin, ymax+25) # make room for the legend
ax.text(xmax, -5, "km", ha='right', va='top', size=14)
plt.legend(ncol=5, loc='upper left')
plt.tight_layout()
plt.show()
How do we draw an average line (horizontal) for a histogram in using matplotlib?
Right now, I'm able to draw the histogram without any issues.
Here is the code I'm using:
## necessary variables
ind = np.arange(N) # the x locations for the groups
width = 0.2 # the width of the bars
plt.tick_params(axis='both', which='major', labelsize=30)
plt.tick_params(axis='both', which='minor', labelsize=30)
ax2 = ax.twinx()
## the bars
rects1 = ax.bar(ind, PAAE1, width,
color='0.2',
error_kw=dict(elinewidth=2,ecolor='red'),
label='PAAE1')
rects2 = ax.bar(ind+width, PAAE2, width,
color='0.3',
error_kw=dict(elinewidth=2,ecolor='black'),
label='PAAE2')
rects3 = ax2.bar(ind+width+width, AAE1, width,
color='0.4',
error_kw=dict(elinewidth=2,ecolor='red'),
label='AAE1')
rects4 = ax2.bar(ind+3*width, AAE2, width,
color='0.5',
error_kw=dict(elinewidth=2,ecolor='black'),
label='AAE3')
maxi = max(dataset[2])
maxi1 = max(dataset[4])
f_max = max(maxi, maxi1)
lns = [rects1,rects2,rects3,rects4]
labs = [l.get_label() for l in lns]
ax.legend(lns, labs, loc='upper center', ncol=4)
# axes and labels
ax.set_xlim(-width,len(ind)+width)
ax.set_ylim(0, 100)
ax.set_ylabel('PAAE', fontsize=25)
ax2.set_ylim(0, f_max+500)
ax2.set_ylabel('AAE (mW)', fontsize=25)
xTickMarks = dataset[0]
ax.set_xticks(ind+width)
xtickNames = ax.set_xticklabels(xTickMarks)
plt.setp(xtickNames, rotation=90, fontsize=25)
I want to plot the average line for PAAE 1, 2 and AAE 1, 2.
What should I be using to plot the average line?
If you'd like a vertical line to denote the mean use axvline(x_value). This will place a vertical line that always spans the full (or specified fraction of) y-axis. There's also axhline for horizontal lines.
In other works, you might have something like this:
ax.axvline(data1.mean(), color='blue', linewidth=2)
ax.axvline(data2.mean(), color='green', linewidth=2)
As a more complete, but unnecessarily complex example (most of this is nicely annotating the means with curved arrows):
import numpy as np
import matplotlib.pyplot as plt
data1 = np.random.normal(0, 1, 1000)
data2 = np.random.normal(-2, 1.5, 1000)
fig, ax = plt.subplots()
bins = np.linspace(-10, 5, 50)
ax.hist(data1, bins=bins, color='blue', label='Dataset 1',
alpha=0.5, histtype='stepfilled')
ax.hist(data2, bins=bins, color='green', label='Dataset 2',
alpha=0.5, histtype='stepfilled')
ax.axvline(data1.mean(), color='blue', linewidth=2)
ax.axvline(data2.mean(), color='green', linewidth=2)
# Add arrows annotating the means:
for dat, xoff in zip([data1, data2], [15, -15]):
x0 = dat.mean()
align = 'left' if xoff > 0 else 'right'
ax.annotate('Mean: {:0.2f}'.format(x0), xy=(x0, 1), xytext=(xoff, 15),
xycoords=('data', 'axes fraction'), textcoords='offset points',
horizontalalignment=align, verticalalignment='center',
arrowprops=dict(arrowstyle='-|>', fc='black', shrinkA=0, shrinkB=0,
connectionstyle='angle,angleA=0,angleB=90,rad=10'),
)
ax.legend(loc='upper left')
ax.margins(0.05)
plt.show()