I am trying to plot 3d-coordinates from an array as a surface with plot_surface. My array contain x,y,z-data (each the same size). I have successfully managed to plot the data via the plot_trisurf function. This gives me the following plot:
plot_trisurf function
To add a fourth dimension in color I would like to plot the same surface with plot_surface. I have managed to get nearly the result I want with the following code:
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
points = np.loadtxt(...)
x,y,z = points[:,0],points[:,1],points[:,2]
X, Y = np.meshgrid(x,y)
Z = np.outer(z.T, np.ones(z.size))
fig = plt.figure()
ax = fig.gca(projection='3d')
ax.plot_surface(X,Y,Z,ls='None')
ax.set_xlabel('x - Axis in mm')
ax.set_ylabel('y - Axis in mm')
ax.set_zlabel('z - Axis in mm')
ax.set_xlim([-np.max((np.abs(points))), np.max((np.abs(points)))])
ax.set_ylim([-np.max((np.abs(points))), np.max((np.abs(points)))])
ax.set_zlim([-np.max((np.abs(points))), np.max((np.abs(points)))])
which gave me the following plot:
plot_surface-function, view 1
plot_surface-function, view 2
How do I manage to eliminate the connection between the base contact points on the ground?
Related
Surprisingly nobody took the pain to make an example in the bokeh gallery for 2D histogram plotting
histogram2d of numpy gives the raw material, but would be nice to have an example as it happens for matplotlib
Any idea for a short way to make one?
Following up a proposed answer let me attach a case in which hexbin does not the job because exagons are not a good fit for the job. Also check out matplotlib result.
Of course I am not saying bokeh cannot do this, but it seem not straightfoward. Would be enough to change the hexbin plot into a square bin plot, but quad(left, right, top, bottom, **kwargs) seems not to do this, nor hexbin to have an option to change "tile" shapes.
You can make something close with relatively few lines of code (comapring with this example from the matplotib gallery). Note bokeh has some examples for hex binning in the gallery here and here. Adapting those and the example provided in the numpy docs you can get the below:
import numpy as np
from bokeh.plotting import figure, show
from bokeh.layouts import row
# normal distribution center at x=0 and y=5
x = np.random.randn(100000)
y = np.random.randn(100000) + 5
H, xe, ye = np.histogram2d(x, y, bins=100)
# produce an image of the 2d histogram
p = figure(x_range=(min(xe), max(xe)), y_range=(min(ye), max(ye)), title='Image')
p.image(image=[H], x=xe[0], y=ye[0], dw=xe[-1] - xe[0], dh=ye[-1] - ye[0], palette="Spectral11")
# produce hexbin plot
p2 = figure(title="Hexbin", match_aspect=True)
p.grid.visible = False
r, bins = p2.hexbin(x, y, size=0.1, hover_color="pink", hover_alpha=0.8, palette='Spectral11')
show(row(p, p2))
A colleague and I have been trying to set custom legend labels, but so far have failed. Code and details below - any ideas much appreciated!
Notebook: toy example uploaded here
Goal: change default rate values used in the legend to corresponding percentage values
Problem: cannot figure out how to access the legend object or pass legend_kwds to geopandas.GeoDataFrame.plot()
Data: KCMO metro area counties
Excerpts from toy example
Step 1: read data
# imports
import geopandas as gpd
import matplotlib.pyplot as plt
%matplotlib inline
# read data
gdf = gpd.read_file('kcmo_counties.geojson')
Option 1 - get legend from ax as suggested here:
ax = gdf.plot('val', legend=True)
leg = ax.get_legend()
print('legend object type: ' + str(type(leg))) # <class NoneType>
plt.show()
Option 2: pass legend_kwds dictionary - I assume I'm doing something wrong here (and clearly don't fully understand the underlying details), but the _doc_ from Geopandas's plotting.py - for which GeoDataFrame.plot() is simply a wrapper - does not appear to come through...
# create number of tick marks in legend and set location to display them
import numpy as np
numpoints = 5
leg_ticks = np.linspace(-1,1,numpoints)
# create labels based on number of tickmarks
leg_min = gdf['val'].min()
leg_max = gdf['val'].max()
leg_tick_labels = [str(round(x*100,1))+'%' for x in np.linspace(leg_min,leg_max,numpoints)]
leg_kwds_dict = {'numpoints': numpoints, 'labels': leg_tick_labels}
# error "Unknown property legend_kwds" when attempting it:
f, ax = plt.subplots(1, figsize=(6,6))
gdf.plot('val', legend=True, ax=ax, legend_kwds=leg_kwds_dict)
UPDATE
Just came across this conversation on adding in legend_kwds - and this other bug? which clearly states legend_kwds was not in most recent release of GeoPandas (v0.3.0). Presumably, that means we'll need to compile from the GitHub master source rather than installing with pip/conda...
I've just come across this issue myself. After following your link to the Geopandas source code, it appears that the colourbar is added as a second axis to the figure. so you have to do something like this to access the colourbar labels (assuming you have plotted a chloropleth with legend=True):
# Get colourbar from second axis
colourbar = ax.get_figure().get_axes()[1]
Having done this, you can manipulate the labels like this:
# Get numerical values of yticks, assuming a linear range between vmin and vmax:
yticks = np.interp(colourbar.get_yticks(), [0,1], [vmin, vmax])
# Apply some function f to each tick, where f can be your percentage conversion
colourbar.set_yticklabels(['{0:.2f}%'.format(ytick*100) for ytick in yticks])
This can be done by passing key-value pairs to dictionary argument legend_kwds:
gdf.plot(column='col1', cmap='Blues', alpha=0.5, legend=True, legend_kwds={'label': 'FOO', 'shrink': 0.5}, ax=ax)
I am trying to add a permanent label on nodes for a networkx graph using spring_layout and bokeh library. I would like for this labels to be re-positioned as the graph scales or refreshed like what string layout does, re-positioning the nodes as the graph scales or refreshed.
I tried to create the graph, and layout, then got pos from the string_layout. However, as I call pos=nx.spring_layout(G), it will generated a set of positions for the nodes in graph G, which I can get coordinates of to put into the LabelSet. However, I have to call graph = from_networkx(G, spring_layout, scale=2, center=(0,0)) to draw the network graph. This will create a new set of position for the node. Therefore, the positions of the nodes and the labels will not be the same.
How to fix this issues?
Thanks for asking this question. Working through it, I've realized that it is currently more work than it should be. I'd very strongly encourage you to open a GitHub issue so that we can discuss what improvements can best make this kind of thing easier for users.
Here is a complete example:
import networkx as nx
from bokeh.io import output_file, show
from bokeh.models import CustomJSTransform, LabelSet
from bokeh.models.graphs import from_networkx
from bokeh.plotting import figure
G=nx.karate_club_graph()
p = figure(x_range=(-3,3), y_range=(-3,3))
p.grid.grid_line_color = None
r = from_networkx(G, nx.spring_layout, scale=3, center=(0,0))
r.node_renderer.glyph.size=15
r.edge_renderer.glyph.line_alpha=0.2
p.renderers.append(r)
So far this is all fairly normal Bokeh graph layout code. Here is the additional part you need to add permanent labels for each node:
from bokeh.transform import transform
# add the labels to the node renderer data source
source = r.node_renderer.data_source
source.data['names'] = [str(x*10) for x in source.data['index']]
# create a transform that can extract the actual x,y positions
code = """
var result = new Float64Array(xs.length)
for (var i = 0; i < xs.length; i++) {
result[i] = provider.graph_layout[xs[i]][%s]
}
return result
"""
xcoord = CustomJSTransform(v_func=code % "0", args=dict(provider=r.layout_provider))
ycoord = CustomJSTransform(v_func=code % "1", args=dict(provider=r.layout_provider))
# Use the transforms to supply coords to a LabelSet
labels = LabelSet(x=transform('index', xcoord),
y=transform('index', ycoord),
text='names', text_font_size="12px",
x_offset=5, y_offset=5,
source=source, render_mode='canvas')
p.add_layout(labels)
show(p)
Basically, since Bokeh (potentially) computes layouts in the browser, the actual node locations are only available via the "layout provider" which is currently a bit tedious to access. As I said, please open a GitHub issue to suggest making this better for users. There are probably some very quick and easy things we can do to make this much simpler for users.
The code above results in:
similar solution as #bigreddot.
#Libraries for this solution
from bokeh.plotting import figure ColumnDataSource
from bokeh.models import LabelSet
#Remove randomness
import numpy as np
np.random.seed(1337)
#Load positions
pos = nx.spring_layout(G)
#Dict to df
labels_df = pd.DataFrame.from_dict(pos).T
#Reset index + column names
labels_df = labels_df.reset_index()
labels_df.columns = ["names", "x", "y"]
graph_renderer = from_networkx(G, pos, center=(0,0))
.
.
.
plot.renderers.append(graph_renderer)
#Set labels
labels = LabelSet(x='x', y='y', text='names', source=ColumnDataSource(labels_df))
#Add labels
plot.add_layout(labels)
Fixed node positions
From the networkx.spring_layout() documentation: you can add a list of nodes with a fixed position as a parameter.
import networkx as nx
import matplotlib.pyplot as plt
g = nx.Graph()
g.add_edges_from([(0,1),(1,2),(0,2),(1,3)])
pos = nx.spring_layout(g)
nx.draw(g,pos)
plt.show()
Then you can plot the nodes at a fixed position:
pos = nx.spring_layout(g, pos=pos, fixed=[0,1,2,3])
nx.draw(g,pos)
plt.show()
I'm trying to do 4 plots using for loop.But I'm not sure how to do it.how can I display the plots one by one orderly?or save the figure as png?
Here is my code:
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from astropy.io import fits
import pyregion
import glob
# read in the image
xray_name = glob.glob("*.fits")
for filename in xray_name:
f_xray = fits.open(filename)
#name = file_name[:-len('.fits')]
try:
from astropy.wcs import WCS
from astropy.visualization.wcsaxes import WCSAxes
wcs = WCS(f_xray[0].header)
fig = plt.figure()
ax = plt.subplot(projection=wcs)
fig.add_axes(ax)
except ImportError:
ax = plt.subplot(111)
ax.imshow(f_xray[0].data, cmap="summer", vmin=0., vmax=0.00038, origin="lower")
reg_name=glob.glob("*.reg")
for i in reg_name:
r =pyregion.open(i).as_imagecoord(header=f_xray[0].header)
from pyregion.mpl_helper import properties_func_default
# Use custom function for patch attribute
def fixed_color(shape, saved_attrs):
attr_list, attr_dict = saved_attrs
attr_dict["color"] = "red"
kwargs = properties_func_default(shape, (attr_list, attr_dict))
return kwargs
# select region shape with tag=="Group 1"
r1 = pyregion.ShapeList([rr for rr in r if rr.attr[1].get("tag") == "Group 1"])
patch_list1, artist_list1 = r1.get_mpl_patches_texts(fixed_color)
r2 = pyregion.ShapeList([rr for rr in r if rr.attr[1].get("tag") != "Group 1"])
patch_list2, artist_list2 = r2.get_mpl_patches_texts()
for p in patch_list1 + patch_list2:
ax.add_patch(p)
#for t in artist_list1 + artist_list2:
# ax.add_artist(t)
plt.show()
the aim of the code is to plot a region on fits file image,if there is a way to change the color of the background image to white and the brighter (centeral region) as it is would be okay.Thanks
You are using colormap "summer" with provided limits. It is not clear to me what you want to achieve since the picture you posted looks more or less digital black and white pixelwise.
In matplotlib there are built in colormaps, and all of those have a reversed twin.
'summer' has a reversed twin with 'summer_r'
This can be picked up in the mpl docs at multiple spots, like colormap example, or SO answers like this.
Hope that is what you are looking for. For the future, when posting code like this, try to remove all non relevant portions as well as at minimum provide a description of the data format/type. Best is to also include a small sample of the data and it's structure. A piece of code only works together with a set of data, so only sharing one is only half the problem formulation.
How do I add grid at a specific location on the y axis in a matplotlib plot?
Yes. It's very simple. Use the set_[x|y]ticks methods of axes object and toggle the grid as normal:
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.set_yticks([0.2, 0.6, 0.8], minor=False)
ax.set_yticks([0.3, 0.55, 0.7], minor=True)
ax.yaxis.grid(True, which='major')
ax.yaxis.grid(True, which='minor')
plt.show()
If you only want to put in a line or two you can use
ax.axhline(y, linestyle='--', color='k') # horizontal lines
ax.axvline(x, linestyle='--', color='k') # vertical lines
with line style and color (or all the rest of line/artist properties) set to what ever you want
To improve the answer of #tacaswell here's an example using the concept of axhline and tweaking it to look similar to a line grid. In this exapmle it's used a starting default grid only on the x-axis, but it's possible to add a grid also on the y-axis (or only on this axis) by simpy add ax.xaxis.grid(True) to the code.
First one simply start drawing a line at the desired position:
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.xaxis.grid(True)
ynew = 0.3
ax.axhline(ynew)
plt.show()
obtaining the following result
that is not very similar to a line grid.
By changing color and line width like below:
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.xaxis.grid(True)
ynew = 0.3
ax.axhline(ynew, color='gray', linewidth=0.5)
plt.show()
we obtain this, that now is in practice equal to a line grid.
If then we want also to add a tick and related label on the y-axis, in the position where the new line is:
import matplotlib.pyplot as plt
import numpy as np
fig, ax = plt.subplots()
ax.xaxis.grid(True)
ynew = 0.3
ax.axhline(ynew, color='gray', linewidth=0.5)
yt = ax.get_yticks()
yt=np.append(yt,ynew)
ax.set_yticks(yt)
ax.set_yticklabels(yt)
plt.show()
that leads to:
Oh no! Some approximation occurred and the label at 0.6 not represents exactly the number 0.6. Don't worry, we can fix that simply by rounding the label array like follow:
import matplotlib.pyplot as plt
import numpy as np
fig, ax = plt.subplots()
ax.xaxis.grid(True)
ynew = 0.3
ax.axhline(ynew, color='gray', linewidth=0.5)
yt = ax.get_yticks()
yt=np.append(yt,ynew)
ax.set_yticks(yt)
ax.set_yticklabels(np.round(yt,1))
plt.show()
and TA-DAAA :)