I apologise if this has already been asked, I've searched long and hard on this site and couldn't find anything that worked. I'm using Julia, specifically the Juno IDE, and I am trying to use PyPlot to create my graphs. I wanted to set the y axis height when plotting, but leave the x axis variable. Here is the code I have been using to generate my plots
fig = figure()
ax = fig[:add_axes]
BEFE250 = (plot(s1, s2, lw=1.0, "-", color="b"))
ylabel("u(x,t)", size=20)
xlabel("t", size=20)
gcf()
which gives me
However, I need space in the top left corner as I am going to layer another picture on top in latex. So I need to set the y-axis height to between -3 and 3. However, if I set the axes height in PyPlot
fig = figure()
ax = fig[:add_axes]([0.1, 0.1, -3.0, 3.0])
BEFE250 = (plot(s1, s2, lw=1.0, "-", color="b"))
ylabel("u(x,t)", size=20)
xlabel("t", size=20)
gcf()
then it switches the orientation of the x-axis. If I set the axis height after running the plot, PyPlot puts the picture in a box in a legend off to the side of the main picture, and the main picture is empty? If someone could help me out it would be greatly appreciated.
Thanks for your help.
EDIT: Using xlim=(-10.,10.) and ylim=(-2.,12.) doesn't work either. PyPlot still adapts the axes to the data.
Try xlim(-10, 10) and ylim(-2, 12) after the plot command:
plot(s1, s2, lw=1.0, "-", color="b")
ylim(-3, 3)
Just try this, without the add_axes.
You probably also want LaTeX labels -- just add an L before the string, which gives a special LaTeX string from the LaTeXString package. You can either just add the L, or add $ inside too:
ylabel(L"u(x,t)", size=20)
ylabel(L"$u(x,t)$", size=20)
[The $ are necessary in certain circumstances that I forget.]
I'm not sure how good the PyPlot support is in Juno.
You might want to try this in IJulia.
By the way, is there a reason you want to layer on a separate figure in LaTeX? That might not be the best way to do it.
Related
I've been trying to plot a shapefile over a basemap. My issue here is the placement of the legend. I wanted it to be placed outside (next to) the map.
Specifically, I am plotting the "Ecoregion" column of the shapefile which basically labels each polygon with a colour (I figured this was better than actually putting the names on each polygon). I've tried the following code and receive an error:
pip install geopandas
pip install contextily
import geopandas as gpd
import contextily as ctx
data = gpd.read_file("icemap.shp")
plt.rcParams.update({'font.size': 14})
ax = data.plot(
figsize=(12, 10),
column="Ecoregion",
cmap="tab10",
)
map = Basemap(
llcrnrlon=-50,
llcrnrlat=30,
urcrnrlon=70.0,
urcrnrlat=85.0,
resolution="i",
lat_0=39.5,
lon_0=1,
)
ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left')
map.fillcontinents(color="lightgreen")
map.drawcoastlines()
map.drawparallels(np.arange(10,90,20),labels=[1,1,1,1])
map.drawmeridians(np.arange(-180,180,30),labels=[1,1,0,1])
plt.title("Map", fontsize=16)
The error is -
WARNING:matplotlib.legend:No handles with labels found to put in legend.
So I tried adding
legend-True
in the "ax" parentheses and removed the "ax.legend(...)", but then the legend appears on top of the map as the picture below.
Does "handle" refer to the column that is plotted? If so, I'm confused as to why I get this error. Or do I need to add another line of code?
I'd be grateful to receive some help in this.
(Attached file link: https://drive.google.com/file/d/1OfOAstBbbxiqSybpl_CQf-o47YgpbY7D/view?usp=sharing)
I would also consider Cartopy, since Basemap has been end-of-life for a long time. The resolution of your vector is also well beyond what's plotted on screen, so you could really increase performance by simplifying it a little.
But you can pass the legend keywords along when plotting the Geodataframe.
ax = data.plot(
figsize=(10, 8),
column="Ecoregion",
cmap="tab10",
legend=True,
legend_kwds=dict(bbox_to_anchor=(1.05, 1), loc='upper left'),
)
I am creating a series of boxplots in order to compare different cancer types with each other (based on 5 categories). For plotting I use seaborn/matplotlib. It works fine for most of the cancer types (see image right) however in some the x axis collapses slightly (see image left) or strongly (see image middle)
https://i.imgur.com/dxLR4B4.png
Looking into the code how seaborn plots a box/violin plot https://github.com/mwaskom/seaborn/blob/36964d7ffba3683de2117d25f224f8ebef015298/seaborn/categorical.py (line 961)
violin_data = remove_na(group_data[hue_mask])
I realized that this happens when there are too many nans
Is there any possibility to prevent this collapsing by code only
I do not want to modify my dataframe (replace the nans by zero)
Below you find my code:
boxp_df=pd.read_csv(pf_in,sep="\t",skip_blank_lines=False)
fig, ax = plt.subplots(figsize=(10, 10))
sns.violinplot(data=boxp_df, ax=ax)
plt.xticks(rotation=-45)
plt.ylabel("label")
plt.tight_layout()
plt.savefig(pf_out)
The output is a per cancer type differently sized plot
(depending on if there is any category completely nan)
I am expecting each plot to be in the same width.
Update
trying to use the order parameter as suggested leads to the following output:
https://i.imgur.com/uSm13Qw.png
Maybe this toy example helps ?
|Cat1|Cat2|Cat3|Cat4|Cat5
|3.93| |0.52| |6.01
|3.34| |0.89| |2.89
|3.39| |1.96| |4.63
|1.59| |3.66| |3.75
|2.73| |0.39| |2.87
|0.08| |1.25| |-0.27
Update
Apparently, the problem is not the data but the length of the title
https://github.com/matplotlib/matplotlib/issues/4413
Therefore I would close the question
#Diziet should I delete it or does my issue might help other ones?
Sorry for not including the line below in the code example:
ax.set_title("VERY LONG TITLE", fontsize=20)
It's hard to be sure without data to test it with, but I think you can pass the names of your categories/cancers to the order= parameter. This forces seaborn to use/display those, even if they are empty.
for instance:
tips = sns.load_dataset("tips")
ax = sns.violinplot(x="day", y="total_bill", data=tips, order=['Thur','Fri','Sat','Freedom Day','Sun','Durin\'s Day'])
I have a set of 7x4 plots arranged in a grid using subplot. I now want to add diagonal axes on top of these.
I know you can superpose axes on top of previously made subplots by setting the background to 'none':
ax = fig.add_subplot(111)
ax.set_axis_bgcolor('none')
But I can't find a rotated axis thing. Currently I'm trying to use a top view 3D axes, but I'm far from a usable solution there.
I'm willing to accept drawing the axis+ticks by hand, if this is the only way possible.
EDIT: using the floating_axis module, I was able to draw rotated (and sheared) axes, but unable to edit the ticks, which is very necessary for what I need. The following snippet demonstrates adding a floating_axis to an existing figure fig. Any manipulation of the axes' ticks fails.
from matplotlib.transforms import Affine2D
import mpl_toolkits.axisartist.floating_axes as floating_axes
trafo = Affine2D().skew_deg(-10,-10).rotate_deg(45)
grid_helper = floating_axes.GridHelperCurveLinear(trafo, extremes=(0, 4, 0, 4))
artistax = floating_axes.FloatingSubplot(fig, 111, grid_helper=grid_helper)
artistax.set_axis_bgcolor('none')
artistax.axis["top"].set_visible(False)
artistax.axis["right"].set_visible(False)
fig.add_subplot(artistax)
I am making log-log plots for different data sets and need to include the best fit line equation. I know where in the plot I should place the equation, but since the data sets have very different values, I'd like to use relative coordinates in the annotation. (Otherwise, the annotation would move for every data set.)
I am aware of the annotate() function of matplotlib, and I know that I can use textcoords='axes fraction' to enable relative coordinates. When I plot my data on the regular scale, it works. But then I change at least one of the scales to log and the annotation disappears. I get no error message.
Here's my code:
plt.clf()
samplevalues = [100,1000,5000,10^4]
ax = plt.subplot(111)
ax.plot(samplevalues,samplevalues,'o',color='black')
ax.annotate('hi',(0.5,0.5), textcoords='axes fraction')
ax.set_xscale('log')
ax.set_yscale('log')
plt.show()
If I comment out ax.set_xcale('log') and ax.set_ycale('log'), the annotation appears right in the middle of the plot (where it should be). Otherwise, it doesn't appear.
Thanks in advance for your help!
It may really be a bug as pointed out by #tcaswell in the comment but a workaround is to use text() in axis coords:
plt.clf()
samplevalues = [100,1000,5000,10^4]
ax = plt.subplot(111)
ax.loglog(samplevalues,samplevalues,'o',color='black')
ax.text(0.5, 0.5,'hi',transform=ax.transAxes)
plt.show()
Another approach is to use figtext() but that is more cumbersome to use if there are already several plots (panels).
By the way, in the code above, I plotted the data using log-log scale directly. That is, instead of:
ax.plot(samplevalues,samplevalues,'o',color='black')
ax.set_xscale('log')
ax.set_yscale('log')
I did:
ax.loglog(samplevalues,samplevalues,'o',color='black')
I am using the following example Example to create two polar contour subplots. When I create as the pdf there is a lot of white space which I want to remove by changing figsize.
I know how to change figsize usually but I am having difficulty seeing where to put it in this code example. Any guidance or hint would be greatly appreciated.
Many thanks!
import numpy as np
import matplotlib.pyplot as plt
#-- Generate Data -----------------------------------------
# Using linspace so that the endpoint of 360 is included...
azimuths = np.radians(np.linspace(0, 360, 20))
zeniths = np.arange(0, 70, 10)
r, theta = np.meshgrid(zeniths, azimuths)
values = np.random.random((azimuths.size, zeniths.size))
#-- Plot... ------------------------------------------------
fig, ax = plt.subplots(subplot_kw=dict(projection='polar'))
ax.contourf(theta, r, values)
plt.show()
Another way to do this would be to use the figsize kwarg in your call to plt.subplots.
fig, ax = plt.subplots(figsize=(6,6), subplot_kw=dict(projection='polar')).
Those values are in inches, by the way.
You can easily just put plt.figsize(x,y) at the beginning of the code, and it will work. plt.figsize changes the size of all future plots, not just the current plot.
However, I think your problem is not what you think it is. There tends to be quite a bit of whitespace in generated PDFs unless you change options around. I usually use
plt.savefig( 'name.pdf', bbox_inches='tight', pad_inches=0 )
This gives as little whitespace as possible. bbox_inches='tight' tries to make the bounding box as small as possible, while pad_inches sets how many inches of whitespace there should be padding it. In my case I have no extra padding at all, as I add padding in whatever I'm using the figure for.