I've a fig with two subplots, I want them to have the same y-scale.
fig, ax = plt.subplots(1, 2,figsize=(10,6))
ax[0].set_title('Over Global region, {} points'. Format(len(df)))
ax[0].plot(df.groupby('type')['thickness'].mean(),label='Cloud mean thickness',color='red')
ax[0].set_xlabel('Cloud Type',fontsize=13)
ax[0].set_ylabel('(in km)',fontsize=15)
#Put xticks with labels at the bottom of the plot
ax[0].set_xticks(cloudlabel,cloudtype_dict.values(),rotation=50)
lon_left, lon_right, lat_bot, lat_top = 60, 100, 0, 40
dfcopy = df.copy()
dfcopy_aoi = dfcopy[(dfcopy['lon']>=lon_left) & (dfcopy['lon']<=lon_right) & (dfcopy['lat']>=lat_bot) & (dfcopy['lat']<=lat_top)]
ax[1].set_title('Over Indian subcontinent region, {} points'.format(len(dfcopy_aoi)))
ax[1].plot(dfcopy_aoi.groupby('type')['thickness'].mean(),label='Cloud mean thickness',color='red')
ax[1].set_xlabel('Cloud Type',fontsize=13)
ax[1].set_ylabel('(in km)',fontsize=15)
ax[1].set_xticks(cloudlabel,cloudtype_dict.values(),rotation=50);
#Shared y-axis
y_lim=[0,0]
y_lim = [min(ax[0].get_ylim()[0], ax[1].get_ylim()[0]), max(ax[0].get_ylim()[1], ax[1].get_ylim()[1])]
for i in range(2):
ax[i].set_ylim(y_lim)
ax[0].legend(loc='best');
ax[1].legend(loc='best');
Probelm
I'm not getting the optimal legend placement for ax[0] even though I'm putting the legend argument at the end of everything.
Below is the image I am getting with bad legend loc for the first axis.
Related
I'm trying to plot multiple things in a figure with subplots.
When I try generating a legend for the figure, it is only showing labels for the data in the very last subplot, instead of for the entire figure.
fig, axs = plt.subplots(3, 2, figsize=(16,18))
first_pops = {"M71 (Masseron)":[1., "purple"], \
"6273 (Yong)":[0.1, "yellowgreen"], \
"2419 (Carretta)":[0.0, "olive"], \
"6218 (Carretta)":[0.25, "darkcyan"],
}
elements = ["O I", "Na I", "Mg I", "Si I", "Ti I", "Ca I"]
element_labels = ["[O/Fe]", "[Na/Fe]", "[Mg/Fe]", "[Si/Fe]", "[Ti/Fe]", "[Ca/Fe]"]
for k, ax in enumerate(axs.ravel()):
element = elements[k]
element_label = element_labels[k]
for GC in first_pops.keys():
try:
plot_background_stars(glob_dict[GC]["[Fe/H]"],\
glob_dict[GC][element_label],\
label = GC, ax=ax, color = first_pops[GC][1],\
s = 140, marker="*", facecolors='none', linewidth=2)
except KeyError:
print(GC+" doesn't have "+element+" measured")
ax.set_xlabel("[Fe/H]")
ax.set_ylabel(element_label)
handles, labels = fig.gca().get_legend_handles_labels()
by_label = dict(zip(labels, handles))
fig.legend(by_label.values(), by_label.keys())
I've tried replacing "fig" with "plt" and vice versa.
I'm using the following to plot each data set:
def plot_background_stars(x, y, ax=None, **plt_kwargs):
if ax is None:
ax = plt.gca()
ax.scatter(x, y, **plt_kwargs)
return(ax)
The resulting legend has just two of the plotted colors, instead of four. Like maybe it's only pulling the labels from the last subplot? But why would it do that?
I was making a plot of f(x,y,z) and wanted this to be displayed in a 2D-plane. To avoid cluttering my legend i decided to have different linestyles for y, different colors for z and place the two in two separate legends. I couldn't find out how to do this even after a lot of digging, so I'm posting the solution i came up with here :) If anyone has more elegant solutions I'm all ears :)
Basically the solution was to make three plots, set two of them to have size (0,0) and place those two where i wanted the legends. It feels like an ugly way to do it, but it gave a nice plot and i didn't find any other way :) The resulting plot looks like this:
def plot_alt(style = 'log'):
cmap = cm.get_cmap('inferno')
color_scale = 1.2 #Variable to get colors from a certain part of the colormap
#Making grids for delta T and average concentration
D_T_axis = -np.logspace(np.log10(400), np.log10(1), 7)
C_bar_list = np.linspace(5,10,4)
ST_list = np.logspace(-3,-1,100)
# f(x,y,z)
DC_func = lambda C_bar, ST, DT: 2*C_bar * (1 - np.exp(ST*DT))/(1 + np.exp(ST*DT))
#Some different linestyles
styles = ['-', '--', '-.', ':']
fig, ax = plt.subplots(1,3, figsize = (10,5))
plt.sca(ax[0])
for i, C_bar in enumerate(C_bar_list): #See plot_c_rel_av_DT() for 'enumerate'
for j, DT in enumerate(D_T_axis):
plt.plot(ST_list, DC_func(C_bar, ST_list, DT), color = cmap(np.log10(-DT)/(color_scale*np.log10(-D_T_axis[0]))),
linestyle = styles[i])
# Generating separate legends by plotting lines in the two other subplots
# Basically: to get two separate legends i make two plots, place them where i want the legends
# and set their size to zero, then display their legends.
plt.sca(ax[1]) #Set current axes to ax[1]
for i, C_bar in enumerate(C_bar_list):
# Plotting the different linestyles
plt.plot(C_bar_list, linestyle = styles[i], color = 'black', label = str(round(C_bar, 2)))
plt.sca(ax[2])
for DT in D_T_axis:
#plotting the different colors
plt.plot(D_T_axis, color = cmap(np.log10(-DT)/(color_scale*np.log10(-D_T_axis[0]))), label = str(int(-DT)))
#Placing legend
#This is where i move and scale the three plots to make one plot and two legends
box0 = ax[0].get_position() #box0 is an object that contains the position and dimentions of the ax[0] subplot
box2 = ax[2].get_position()
ax[0].set_position([box0.x0, box0.y0, box2.x0 + 0.4*box2.width, box0.height])
box0 = ax[0].get_position()
ax[1].set_position([box0.x0 + box0.width, box0.y0 + box0.height + 0.015, 0,0])
ax[1].set_axis_off()
ax[2].set_position([box0.x0 + box0.width ,box0.y0 + box0.height - 0.25, 0,0])
ax[2].set_axis_off()
#Displaying plot
plt.sca(ax[0])
plt.xscale('log')
plt.xlim(0.001, 0.1)
plt.ylim(0, 5)
plt.xlabel(r'$S_T$')
plt.ylabel(r'$\Delta C$')
ax[1].legend(title = r'$\langle c \rangle$ [mol/L]',
bbox_to_anchor = (1,1), loc = 'upper left')
ax[2].legend(title = r'$-\Delta T$ [K]', bbox_to_anchor = (1,1), loc = 'upper left')
#Suptitle is the title of the figure. You can also have titles for the individual subplots
plt.suptitle('Steady state concentration gradient as a function of Soret-coefficient\n'
'for different temperature gradients and total concentrations')
A quite basic question about ticks' labels for x and y-axis. According to this code
fig, axes = plt.subplots(6,12, figsize=(50, 24), constrained_layout=True, sharex=True , sharey=True)
fig.subplots_adjust(hspace = .5, wspace=.5)
custom_xlim = (-1, 1)
custom_ylim = (-0.2,0.2)
for i in range(72):
x_data = ctheta[i]
y_data = phi[i]
y_err = err_phi[i]
ax = fig.add_subplot(6, 12, i+1)
ax.plot(x_data_new, bspl(x_data_new))
ax.axis('off')
ax.errorbar(x_data,y_data, yerr=y_err, fmt="o")
ax.set_xlim(custom_xlim)
ax.set_ylim(custom_ylim)
I get the following output:
With y labels for plots on the first column and x labels for theone along the last line, although I call them off.
Any idea?
As #BigBen wrote in their comment, your issue is caused by you adding axes to your figure twice, once via fig, axes = plt.subplots() and then once again within your loop via fig.add_subplot(). As a result, the first set of axes is still visible even after you applied .axis('off') to the second set.
Instead of the latter, you could change your loop to:
for i in range(6):
for j in range(12):
ax = axes[i,j] # these are the axes created via plt.subplots(6,12,...)
ax.axis('off')
# … your other code here
I have generated a plot that shows a topographic profile with points along the profile that represent dated points. However, these dated points also have symmetric uncertainty values/error bars (that typically vary for each point).
In this example, I treat non-dated locations as 'np.nan'. I would like to add uncertainty values to the y2 axis (Mean Age) with defined uncertainty values as y2err.
Everytime I use the ax2.errorbar( ... ) line, my graph is squeezed and distorted.
import numpy as np
import matplotlib.pyplot as plt
fig, ax1 = plt.subplots()
#Longitude = x; Elevation = y
x = (-110.75696,-110.75668,-110.75640,-110.75612,-110.75584,-110.75556,-110.75528)
y = (877,879,878,873,871,872,872)
ax1.plot(x, y)
ax1.set_xlabel('Longitude')
# Make the y-axis label, ticks and tick labels match the line color.
ax1.set_ylabel('Elevation', color='b')
ax1.tick_params('y', colors='b')
ax2 = ax1.twinx()
# Mean Age, np.nan = 0.0
y2 = (np.nan,20,np.nan,np.nan,np.nan,np.nan,np.nan)
y2err = (np.nan,5,np.nan,np.nan,np.nan,np.nan,np.nan)
ax2.scatter(x, y2, color='r')
#add error bars to scatter plot points
# (??????) ax2.errorbar(x, y, y2, y2err, capsize = 0, color='black')
ax2.set_ylim(10,30)
ax2.set_ylabel('Mean Age', color='r')
ax2.tick_params('y', colors='r')
fig.tight_layout()
plt.show()
If I do not apply the ax2.errorbar... line my plot looks like the first image, which is what I want but with the points showing uncertainty values (+/- equal on both side of point in the y-axis direction).
Plot of Elevation vs Age without error bars
When I use the ax2.errorbar line it looks like the second image:
Plot when using ax2.errorbar line
Thanks!
I have a trellis-like plot I am trying to produce in matplotlib. Here is a sketch of what I'm going for:
One thing I am having trouble with is getting a shared row label for each row. I.e. in my plot, I have four rows for four different sets of experiments, so I want row labels "1 source node, 2 source nodes, 4 source nodes and 8 source nodes".
Note that I am not referring to the y axis label, which is being used to label the dependent variable. The dependent variable is the same in all subplots, but the row labels I am after are to describe the four categories of experiments conducted, one for each row.
At the moment, I'm generating the plot with:
fig, axes = plt.subplots(4, 5, sharey=True)
While I've found plenty of information on sharing the y-axis label, I haven't found anything on adding a single shared row label.
As far as I know there is no ytitle or something. You can use text to show some text. The x and y are in data-coordinates. ha and va are horizontal and vertical alignment, respectively.
import numpy
import matplotlib
import matplotlib.pyplot as plt
n_rows = 4
n_cols = 5
fig, axes = plt.subplots(n_rows, n_cols, sharey = True)
axes[0][0].set_ylim(0,10)
for i in range(n_cols):
axes[0][i].text(x = 0.5, y = 12, s = "column label", ha = "center")
axes[n_rows-1][i].set_xlabel("xlabel")
for i in range(n_rows):
axes[i][0].text(x = -0.8, y = 5, s = "row label", rotation = 90, va = "center")
axes[i][0].set_ylabel("ylabel")
plt.show()
You could give titles to subplots on the top row like Robbert suggested
fig, axes = plt.subplots(4,3)
for i, ax in enumerate(axes[0,:]):
ax.set_title('col%i'%i)