I'm having trouble giving colorbars to a grid of line plots in Matplotlib.
I have a grid of plots, which each shows 64 lines. The lines depict the penalty value vs time when optimizing the same system under 64 different values of a certain hyperparameter h.
Since there are so many lines, instead of using a standard legend, I'd like to use a colorbar, and color the lines by the value of h. In other words, I'd like something that looks like this:
The above was done by adding a new axis to hold the colorbar, by calling figure.add_axes([0.95, 0.2, 0.02, 0.6]), passing in the axis position explicitly as parameters to that method. The colorbar was then created as in the example code here, by instantiating a ColorbarBase(). That's fine for single plots, but I'd like to make a grid of plots like the one above.
To do this, I tried doubling the number of subplots, and using every other subplot axis for the colorbar. Unfortunately, this led to the colorbars having the same size/shape as the plots:
Is there a way to shrink just the colorbar subplots in a grid of subplots like the 1x2 grid above?
Ideally, it'd be great if the colorbar just shared the same axis as the line plot it describes. I saw that the colorbar.colorbar() function has an ax parameter:
ax
parent axes object from which space for a new colorbar axes will be stolen.
That sounds great, except that colorbar.colorbar() requires you to pass in a imshow image, or a ContourSet, but my plot is neither an image nor a contour plot. Can I achieve the same (axis-sharing) effect using ColorbarBase?
It turns out you can have different-shaped subplots, so long as all the plots in a given row have the same height, and all the plots in a given column have the same width.
You can do this using gridspec.GridSpec, as described in this answer.
So I set the columns with line plots to be 20x wider than the columns with color bars. The code looks like:
grid_spec = gridspec.GridSpec(num_rows,
num_columns * 2,
width_ratios=[20, 1] * num_columns)
colormap_type = cm.cool
for (x_vec_list,
y_vec_list,
color_hyperparam_vec,
plot_index) in izip(x_vec_lists,
y_vec_lists,
color_hyperparam_vecs,
range(len(x_vecs))):
line_axis = plt.subplot(grid_spec[grid_index * 2])
colorbar_axis = plt.subplot(grid_spec[grid_index * 2 + 1])
colormap_normalizer = mpl.colors.Normalize(vmin=color_hyperparam_vec.min(),
vmax=color_hyperparam_vec.max())
scalar_to_color_map = mpl.cm.ScalarMappable(norm=colormap_normalizer,
cmap=colormap_type)
colorbar.ColorbarBase(colorbar_axis,
cmap=colormap_type,
norm=colormap_normalizer)
for (line_index,
x_vec,
y_vec) in zip(range(len(x_vec_list)),
x_vec_list,
y_vec_list):
hyperparam = color_hyperparam_vec[line_index]
line_color = scalar_to_color_map.to_rgba(hyperparam)
line_axis.plot(x_vec, y_vec, color=line_color, alpha=0.5)
For num_rows=1 and num_columns=1, this looks like:
Related
I am trying to create two images side by side: one satellite image alone, and next to it, the same satellite image with outlines of agricultural fields. My raster data "raster_clip" is loaded into rioxarray (original satellite image from NAIP, converted from .sid to .tif), and my vector data "ag_clip" is in geopandas. My code is as follows:
fig, (ax1, ax2) = plt.subplots(ncols = 2, figsize=(14,8))
raster_clip.plot.imshow(ax=ax1)
raster_clip.plot.imshow(ax=ax2)
ag_clip.boundary.plot(ax=ax1, color="yellow")
I can't seem to figure out how to get the y axes in each plot to be the same. When the vector data is excluded, then the two plots end up the same shape and size.
I have tried the following:
Setting sharey=True in the subplots method. Doesn't affect shape of resulting images, just removes the tic labels on the second image.
Setting "aspect='equal'" in the imshow method, leads to an error, which doesn't make sense because the 'aspect' kwarg is listed in the documentation for xarray.plot.imshow.
plt.imshow's 'aspect' kwarg is not available in xarray
Removing the "figsize" variable, doesn't affect the ratio of the two plots.
not entirely related to your question but i've used cartopy before for overlaying a GeoDataFrame to a DataArray
plt.figure(figsize=(16, 8))
ax = plt.subplot(projection=ccrs.PlateCarree())
ds.plot(ax=ax)
gdf.plot(ax=ax)
I have a DataFrame of Heart Disease patients, which has over 300 values. What I have done initially is filter the patients aging over 50. Now I am trying to plot that DF, but running on Google, I found this piece of code that helped me plotting it.
But I am not able to understand the concept of ax = ax here:
fig, ax = plt.subplots()
over_50.plot(x="age",
y="chol",
c="target",
kind="scatter",
---------> ax=ax); <---------
I want to learn the concept behind this little piece of code here. What is it doing at its core?
In this case (a single axes plot) you can do without this parameter.
But there are more complex cases, when you create subplots with
a number of axes objects (a grid).
In this case ax (the second result from plt.subplots()) is an array
of axes objects.
Then, creating each plot, you should specify in which axes this plot
is to be created.
See e.g. https://matplotlib.org/3.1.0/gallery/subplots_axes_and_figures/subplots_demo.html
and find title Stacking subplots in one direction.
It contains such example:
fig, axs = plt.subplots(2)
fig.suptitle('Vertically stacked subplots')
axs[0].plot(x, y)
axs[1].plot(x, -y)
Here:
there is created a figure composed of 2 columns,
in the first axes there is created one line plot, and in the second - another plot.
Alternative form of how to specify axes object in which particular plot
is to be created is just ax parameter, like in our code,
where you can pass one of axes objects from the current figure.
I am plotting a relplot with Seaborn, but getting the legend (and an empty axis plot) printed under the main plot.
Here is how it looks like (in 2 photos, as my screen isn't that big):
Here is the code I used:
fig, axes = plt.subplots(1, 1, figsize=(12, 5))
clean_df['tax_class_at_sale'] = clean_df['tax_class_at_sale'].apply(str)
sns.relplot(x="sale_price_millions", y='gross_sqft_thousands', hue="neighborhood", data=clean_df, ax=axes)
fig.suptitle('Sale Price by Neighborhood', position=(.5,1.05), fontsize=20)
fig.tight_layout()
fig.show()
Does someone has an idea how to fix that, so that the legend (maybe much smaller, but it's not a problem) is printed next to the plot, and the empty axis disappears?
Here is my dataset form (in 2 screenshot, to capture all columns. "sale_price_millions" is the target column)
Since you failed to provide a Minimal, Complete, and Verifiable example, no one can give you a final working answer because we can't reproduce your figure. Nevertheless, you can try specifying the location for placing the legend as following and see if it works as you want
sns.relplot(x="sale_price_millions", y='gross_sqft_thousands', hue="neighborhood", data=clean_df, ax=axes)
plt.legend(loc=(1.05, 0.5))
I am trying to visualize some data and have built a scatter plot with this code -
sns.regplot(y="Calls", x="clientid", data=Drop)
This is the output -
I don't want it to consider the x-axis. I just want to see how the data lie w.r.t y-axis. Is there a way to do that?
As #iayork suggested, you can see the distribution of your points with a striplot or a swarmplot (you could also combine them with a violinplot). If you need to move the points closer to the y-axis, you can simply adjust the size of the figure so that the width is small compared to the height (here i'm doing 2 subplots on a 4x5 in figure, which means that each plot is roughly 2x5 in).
fig, (ax1,ax2) = plt.subplots(1,2, figsize=(4,5))
sns.stripplot(d, orient='vert', ax=ax1)
sns.swarmplot(d, orient='vert', ax=ax2)
plt.tight_layout()
However, I'm going to suggest that maybe you want to use distplot instead. This function is specifically created to show the distribution of you data. Here i'm plotting the KDE of the data, as well as the "rugplot", which shows the position of the points along the y-axis:
fig = plt.figure()
sns.distplot(d, kde=True, vertical=True, rug=True, hist=False, kde_kws=dict(shade=True), rug_kws=dict(lw=2, color='orange'))
I'm drawing the bloxplot shown below using python and matplotlib. Is there any way I can reduce the distance between the two boxplots on the X axis?
This is the code that I'm using to get the figure above:
import matplotlib.pyplot as plt
from matplotlib import rcParams
rcParams['ytick.direction'] = 'out'
rcParams['xtick.direction'] = 'out'
fig = plt.figure()
xlabels = ["CG", "EG"]
ax = fig.add_subplot(111)
ax.boxplot([values_cg, values_eg])
ax.set_xticks(np.arange(len(xlabels))+1)
ax.set_xticklabels(xlabels, rotation=45, ha='right')
fig.subplots_adjust(bottom=0.3)
ylabels = yticks = np.linspace(0, 20, 5)
ax.set_yticks(yticks)
ax.set_yticklabels(ylabels)
ax.tick_params(axis='x', pad=10)
ax.tick_params(axis='y', pad=10)
plt.savefig(os.path.join(output_dir, "output.pdf"))
And this is an example closer to what I'd like to get visually (although I wouldn't mind if the boxplots were even a bit closer to each other):
You can either change the aspect ratio of plot or use the widths kwarg (doc) as such:
ax.boxplot([values_cg, values_eg], widths=1)
to make the boxes wider.
Try changing the aspect ratio using
ax.set_aspect(1.5) # or some other float
The larger then number, the narrower (and taller) the plot should be:
a circle will be stretched such that the height is num times the width. aspect=1 is the same as aspect=’equal’.
http://matplotlib.org/api/axes_api.html#matplotlib.axes.Axes.set_aspect
When your code writes:
ax.set_xticks(np.arange(len(xlabels))+1)
You're putting the first box plot on 0 and the second one on 1 (event though you change the tick labels afterwards), just like in the second, "wanted" example you gave they are set on 1,2,3.
So i think an alternative solution would be to play with the xticks position and the xlim of the plot.
for example using
ax.set_xlim(-1.5,2.5)
would place them closer.
positions : array-like, optional
Sets the positions of the boxes. The ticks and limits are automatically set to match the positions. Defaults to range(1, N+1) where N is the number of boxes to be drawn.
https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.boxplot.html
This should do the job!
As #Stevie mentioned, you can use the positions kwarg (doc) to manually set the x-coordinates of the boxes:
ax.boxplot([values_cg, values_eg], positions=[1, 1.3])