I have a contour plot on a basemap and i wish to adjust the range of the colour bar so that they fit to the visible data. The default setting makes the colour range to fit to all data, i.e. also those which are not plotted. Is there a setting for this?
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap, addcyclic, shiftgrid
myllcrnrlat=35
myurcrnrlat=65
myllcrnrlon=-45
myurcrnrlon=45
m = Basemap(projection='cyl', llcrnrlat=myllcrnrlat, urcrnrlat=myurcrnrlat,\
llcrnrlon=myllcrnrlon, urcrnrlon=myurcrnrlon, resolution='c')
lonsin=np.asarray(range(0,360,10))
latsin=np.asarray(range(-90,90,10))
valin=np.random.rand(len(latsin), len(lonsin))
valin[0,0]=5 #this is a value outside my basemap area and higher than all inside.
valin_cyclic, lons_cyclic = addcyclic(valin, lonsin)
valin_cyclic, lons_cyclic = shiftgrid(180., valin_cyclic, lons_cyclic, start=False)
lon2d, lat2d = np.meshgrid(lons_cyclic, latsin)
x, y = m(lon2d, lat2d)
cs = m.pcolormesh(x, y, valin_cyclic,cmap=plt.get_cmap('autumn_r'))
cbar = plt.colorbar(cs)
plt.show()
Of couse I can use vmin, vmax by doing something like the following, but it seems rather long, so maybe there is a special setting?
lonsin_inbasemap=np.asarray([a for a in lonsin if myllcrnrlon <= a if a <= myurcrnrlon])
latsin_inbasemap=np.asarray([a for a in latsin if myllcrnrlat <= a if a <= myurcrnrlat])
valin_inbasemap_tmp = np.transpose(np.asarray([valin[:,a] for a in range(len(lonsin)) if lonsin[a] in lonsin_inbasemap]))
valin_inbasemap = np.asarray([valin_inbasemap_tmp[a,:] for a in range(len(latsin)) if latsin[a] in latsin_inbasemap])
del(valin_inbasemap_tmp)
vmax=np.amax(valin_inbasemap)
cs = m.pcolormesh(x, y, valin_cyclic,vmax=vmax, cmap=plt.get_cmap('autumn_r'))
cbar = plt.colorbar(cs)
plt.show()
If you want to mask some data that below some value.
For example, the minus data you would not want to show:
You can use ``
valin = np.ma.masked_less(valin_cyclic,0)
cmap1 = plt.cm,get_cmap("autumn_r")
cmap1.set_bad("w")
p =plt.pcolor((x, y,conc,cmap=cmap1,alpha =1,zorder =2)
Related
Am trying to find hist()'s figsize and layout parameter for sns.pairplot().
I have a pairplot that gives me nice scatterplots between the X's and y. However, it is oriented horizontally and there is no equivalent layout parameter to make them vertical to my knowledge. 4 plots per row would be great.
This is my current sns.pairplot():
sns.pairplot(X_train,
x_vars = X_train.select_dtypes(exclude=['object']).columns,
y_vars = ["SalePrice"])
This is what I would like it to look like: Source
num_mask = train_df.dtypes != object
num_cols = train_df.loc[:, num_mask[num_mask == True].keys()]
num_cols.hist(figsize = (30,15), layout = (4,10))
plt.show()
What you want to achieve isn't currently supported by sns.pairplot, but you can use one of the other figure-level functions (sns.displot, sns.catplot, ...). sns.lmplot creates a grid of scatter plots. For this to work, the dataframe needs to be in "long form".
Here is a simple example. sns.lmplot has parameters to leave out the regression line (fit_reg=False), to set the height of the individual subplots (height=...), to set its aspect ratio (aspect=..., where the subplot width will be height times aspect ratio), and many more. If all y ranges are similar, you can use the default sharey=True.
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
# create some test data with different y-ranges
np.random.seed(20230209)
X_train = pd.DataFrame({"".join(np.random.choice([*'uvwxyz'], np.random.randint(3, 8))):
np.random.randn(100).cumsum() + np.random.randint(100, 1000) for _ in range(10)})
X_train['SalePrice'] = np.random.randint(10000, 100000, 100)
# convert the dataframe to long form
# 'SalePrice' will get excluded automatically via `melt`
compare_columns = X_train.select_dtypes(exclude=['object']).columns
long_df = X_train.melt(id_vars='SalePrice', value_vars=compare_columns)
# create a grid of scatter plots
g = sns.lmplot(data=long_df, x='SalePrice', y='value', col='variable', col_wrap=4, sharey=False)
g.set(ylabel='')
plt.show()
Here is another example, with histograms of the mpg dataset:
import matplotlib.pyplot as plt
import seaborn as sns
mpg = sns.load_dataset('mpg')
compare_columns = mpg.select_dtypes(exclude=['object']).columns
mpg_long = mpg.melt(value_vars=compare_columns)
g = sns.displot(data=mpg_long, kde=True, x='value', common_bins=False, col='variable', col_wrap=4, color='crimson',
facet_kws={'sharex': False, 'sharey': False})
g.set(xlabel='')
plt.show()
I am plotting some contours with tricontourf. I want the colormap to be scaled in log values and tick labels and colours bounds to be in log base 2. Here's my code:
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.tri as tri
import matplotlib.ticker as ticker
import matplotlib.colors as colors
section = 'T7'
data = np.loadtxt( section + '_values.dat')
x = data[:,0]
y = data[:,1]
z = data[:,2]
triang = tri.Triangulation(x,y)
fig1, ax1 = plt.subplots()
ax1.set_aspect('equal')
bounds = [2.**-1,2.**1,2**3,2**5,2**7,2**9]
norm = colors.LogNorm()
formatter = ticker.LogFormatter(2)
tcf = ax1.tricontourf(triang, z, levels = bounds, cmap='hot_r', norm = norm )
fig1.colorbar(tcf, format=formatter)
plt.show()
And here's the result:
What are thos ugly minor ticks and how do I get rid of them?
Using Matplotlib 3.3.0 an Mac OS
You could use cb.ax.minorticks_off() to turn off the minor tick and cb.ax.minorticks_on() to turn it on.
cb = fig1.colorbar(tcf, format=formatter)
cb.ax.minorticks_off()
matplotlib.pyplot.colorbar returns a Colorbar object which extends ColorbarBase.
You can find that two functions in the document of class matplotlib.colorbar.ColorbarBase.
I want to increase the grey area around the plot, but keeping the plot the same size. I've already tried changing the figure size, which ends up stretching the plot.
The axes inside the figure is positionned relative to the figure. Per default you have e.g. a fraction of 0.125 of figure width as space at the left. This means that resizing the figure, scales the axes as well.
You may calculate how much the spacings need to change such that if the figure is rescaled, the axes size remains constant. The new spacings then need to be set using fig.subplots_adjust.
import matplotlib.pyplot as plt
def set_figsize(figw,figh, fig=None):
if not fig: fig=plt.gcf()
w, h = fig.get_size_inches()
l = fig.subplotpars.left
r = fig.subplotpars.right
t = fig.subplotpars.top
b = fig.subplotpars.bottom
hor = 1.-w/float(figw)*(r-l)
ver = 1.-h/float(figh)*(t-b)
fig.subplots_adjust(left=hor/2., right=1.-hor/2., top=1.-ver/2., bottom=ver/2.)
fig, ax=plt.subplots()
ax.plot([1,3,2])
set_figsize(9,7)
plt.show()
You may then also use this function to update the subplot params when the figure window is resized.
import matplotlib.pyplot as plt
class Resizer():
def __init__(self,fig=None):
if not fig: fig=plt.gcf()
self.fig=fig
self.w, self.h = self.fig.get_size_inches()
self.l = self.fig.subplotpars.left
self.r = self.fig.subplotpars.right
self.t = self.fig.subplotpars.top
self.b = self.fig.subplotpars.bottom
def set_figsize(self, figw,figh):
hor = 1.-self.w/float(figw)*(self.r-self.l)
ver = 1.-self.h/float(figh)*(self.t-self.b)
self.fig.subplots_adjust(left=hor/2., right=1.-hor/2., top=1.-ver/2., bottom=ver/2.)
def resize(self, event):
figw = event.width/self.fig.dpi
figh = event.height/self.fig.dpi
self.set_figsize( figw,figh)
fig, ax=plt.subplots()
ax.plot([1,3,2])
r = Resizer()
cid = fig.canvas.mpl_connect("resize_event", r.resize)
plt.show()
In the window of a matplotlib figure, there's a button called 'Configure subplots' (see below picture, screenshot on Windows 10 with matplotlib version 1.5.2). Try to change the parameters 'left' and 'right'. You can also change these parameters with plt.subplots_adjust(left=..., bottom=..., right=..., top=..., wspace=..., hspace=...).
I'm trying to get a colorbar for the following minimal example of my code.
g1 = gridspec.GridSpec(1, 1)
f, ((ax0)) = plt.subplots(1, 1)
ax0 = subplot(g1[0])
cmap = matplotlib.cm.get_cmap('viridis')
for i in linspace(0,1,11):
x = [-1,0,1]
y = [i,i,i]
rgba = cmap(i)
im = ax0.plot(x,y,color=rgba)
f.colorbar(im)
I also tried f.colorbar(cmap)
Probably pretty obvious, but I get errors such as
'ListedColormap' object has no attribute 'autoscale_None'
In reality, the value defining i is more complex, but I think this should do the trick. My data is plotted with plot and not with imshow (for which I know how to make the colormap).
The answers so far seem overly complicated. fig.colorbar() expects a ScalarMappable as its first argument. Often ScalarMappables are produced by imshow or contourplots and are readily avaible.
In this case you would need to define your custom ScalarMappable to provide to the colorbar.
import matplotlib.pyplot as plt
import numpy as np
fig, ax = plt.subplots()
cmap = plt.cm.get_cmap('viridis')
for i in np.linspace(0,1,11):
x = [-1,0,1]
y = [i,i,i]
rgba = cmap(i)
im = ax.plot(x,y,color=rgba)
sm = plt.cm.ScalarMappable(cmap=cmap)
sm.set_array([])
fig.colorbar(sm)
plt.show()
You should pass an Image or ContourSet when you call colorbar on a Figure.
You can make an image of the data points by calling plt.imshow with the data. You can start with this:
data = []
for i in np.linspace(0,1,11):
x = [-1,0,1]
y = [i,i,i]
rgba = cmap(i)
ax0.plot(x,y,color=rgba)
data.append([x, y])
image = plt.imshow(data)
figure.colorbar(image)
plt.show()
Reference:
https://matplotlib.org/api/figure_api.html#matplotlib.figure.Figure.colorbar
Oluwafemi Sule's solution almost works, but it plots the matrix into the same figure as the lines. Here a solution that opens a second figure, does the imshow call on that second figure, uses the result to draw the colorbar in the first figure, and then closes the second figure before calling plt.show():
import matplotlib
from matplotlib import pyplot as plt
from matplotlib import gridspec
import numpy as np
cmap = matplotlib.cm.get_cmap('viridis')
g1 = gridspec.GridSpec(1, 1)
f0, ((ax0)) = plt.subplots(1, 1)
f1, ((ax1)) = plt.subplots(1, 1)
for i in np.linspace(0,1,11):
x = [-1,0,1]
y = [i,i,i]
rgba = cmap(i)
ax0.plot(x,y,color=rgba)
data = np.linspace(0,1,100).reshape((10,10))
image = ax1.imshow(data)
f0.colorbar(image)
plt.close(f1)
plt.show()
The result looks like this:
I want to create a plot using matplotlib. I basically draw many lines which overlap. I want to set alpha transparency on these lines such that if lines overlap the alpha values add up. My intention is to show the density of the overlapping lines with a more solid color, and lines that don't should be shown very lightly, this is the code I have but it is not giving the desired efect:
import numpy as np
import matplotlib.pyplot as plt
dt = 0.00008
nstep = 3000
paths = 100
X = np.zeros((nstep,paths))
Y = np.zeros((nstep,paths))
gv1 = np.sqrt(dt)*np.random.randn(nstep,paths)
gv2 = np.sqrt(dt)*np.random.randn(nstep,paths)
for i in range(nstep-1):
X[i+1] = X[i] + gv1[i]
Y[i+1] = Y[i] + gv2[i]
plt.plot(X,Y,lw=1,alpha=0.05)
plt.show()