How can I build a graph using this function? Thanks! :) I tried, but I am getting confused how should I use: https://stackoverflow.com/questions/30553585/graphing-a-parabola-using-matplotlib-in-python#=
def quadratic (a, b, c):
try:
d = b**2-4*a*c
convex_point = -b/(2*a)
if(d == 0):
convex_point = -b/(2*a) #it is the x-interceptor when det is 0
print('the convex point is at',convex_point, 'on the x-axis | the parabola intersect the y-axis at',c, '| the determinant is 0');
elif(d < 0):
print('Determinant is', d,'and if determinant is smaller than zero, there is no real solution');
else:
x_positive = (-b+ math.sqrt(d))/(2*a); # y = 0
x_negative = (-b- math.sqrt(d))/(2*a); # y = 0
print('the convex points is at',convex_point, 'on the x-axis |x_positive',x_positive,' |x_negative',x_negative,'| the parabola intersect the y-axis at',c)
except:
print('try: import math');
def quadratic (a, b, c):
try:
import matplotlib.pyplot as plt
import math
import numpy as np
d = b**2-4*a*c
convex_point = -b/(2*a)
x = np.linspace(-50, 50, 1000);
y = a**2 + b*x + c
fig, ax = plt.subplots();
ax.plot(x, y)
if(d == 0):
convex_point = -b/(2*a) #it is the x-interceptor when det is 0
print('the convex point is at',convex_point, 'on the x-axis | the parabola intersect the y-axis at',c, '| the determinant is 0');
elif(d < 0):
print('Determinant is', d,'and if determinant is smaller than zero, there is no real solution');
else:
x_positive = (-b+ math.sqrt(d))/(2*a); # y = 0
x_negative = (-b- math.sqrt(d))/(2*a); # y = 0
print('the convex points is at',convex_point, 'on the x-axis |x_positive',x_positive,' |x_negative',x_negative,'| the parabola intersect the y-axis at',c);
except:
print('try: import math')
except it does not show the graph :S
def quadratic (a, b, c):
try:
import matplotlib.pyplot as plt
import math
import numpy as np
d = b**2-4*a*c
convex_point = -b/(2*a)
x = np.linspace(-10, 10, 1000);
y = a**2 + b*x + c
fig, ax = plt.subplots();
ax.plot(x, y);
plt.show()
if(d == 0):
convex_point = -b/(2*a) #it is the x-interceptor when det is 0
print('the convex point is at',convex_point, 'on the x-axis | the parabola intersect the y-axis at',c, '| the determinant is 0');
elif(d < 0):
print('Determinant is', d,'and if determinant is smaller than zero, there is no real solution');
else:
x_positive = (-b+ math.sqrt(d))/(2*a); # y = 0
x_negative = (-b- math.sqrt(d))/(2*a); # y = 0
print('the convex points is at',convex_point, 'on the x-axis |x_positive',x_positive,' |x_negative',x_negative,'| the parabola intersect the y-axis at',c);
except:
print('try: import math')
This code work :)
Related
I am interested in creating a form of RandomWalk, using DNA sequence to create the walk (eg T = up, A = down etc). I have created the code, however i am wanting to know if it is possible for each of the 4 base letters to be assigned a colour instead of the final plot graph only being in one colour?
import matplotlib.pyplot as plt
x = y = 0
x_values = [0]
y_values = [0]
dna_seq = ('GGACTTCCCTATGGTGCTAACAAAGAGGCAGACAAA')
for base in dna_seq:
if base == 'T':
y += 1
elif base == 'A':
y -= 1
elif base == 'G':
x += 1
elif base == 'C':
x -= 1
x_values.append(x)
y_values.append(y)
fig, ax = plt.subplots()
ax.plot(x_values, y_values, c='g')
plt.show()
You can use a dictionary to create a list of colors.
Then, use plt.plot to plot the lines, and plt.scatter for coloured dots:
Adapted version of your code:
import matplotlib.pyplot as plt
x = y = 0
x_values = [0]
y_values = [0]
color_lookup = {'A': 'red',
'T':'green',
'G': 'blue',
'C': 'orange'}
dna_seq = ('GGACTTCCCTATGGTGCTAACAAAGAGGCAGACAAA')
colors = ['k'] # initialise starting point with black
for base in dna_seq:
if base == 'T':
y += 1
elif base == 'A':
y -= 1
elif base == 'G':
x += 1
elif base == 'C':
x -= 1
x_values.append(x)
y_values.append(y)
colors.append(color_lookup[base])
fig, ax = plt.subplots()
ax.plot(x_values, y_values, c='k')
ax.scatter(x_values, y_values, c=colors)
plt.show()
A multicolored line based on this example can be used. The idea is to split the line into sequences and then plot the lines using a LineCollection. Each line of the collection can have is own color.
As the random walker uses a few segments more than once, some of the segments have to be shifted a bit.
import matplotlib.pyplot as plt
x = y = 0.
x_values = [0.]
y_values = [0.]
colors = []
dna_seq = ('GGACTTCCCTATGGTGCTAACAAAGAGGCAGACAAA')#
color_lookup = {'A': 'red',
'T':'green',
'G': 'blue',
'C': 'orange'}
for base in dna_seq:
if base == 'T':
y += 1
elif base == 'A':
y -= 1
elif base == 'G':
x += 1
elif base == 'C':
x -= 1
x_values.append(x)
y_values.append(y)
colors.append(color_lookup[base])
import numpy as np
from matplotlib.collections import LineCollection
points = np.array([x_values, y_values]).T.reshape(-1, 1, 2)
segments = np.concatenate([points[:-1], points[1:]], axis=1)
#handle collisions (algorithm could probably be improved :-) )
delta = 0.1
changed=True
while changed:
changed=False
for idx, segment in enumerate(segments):
if idx > 0:
cur_seg = segment.tolist()
if cur_seg in segments[:idx-1].tolist() or [cur_seg[1], cur_seg[0]] in segments[:idx].tolist():
if(cur_seg[0][0] == cur_seg[1][0]):
segment[0][0] += delta
segment[1][0] += delta
else:
segment[0][1] += delta
segment[1][1] += delta
changed=True
fig, ax = plt.subplots()
lc = LineCollection(segments, colors=colors)
lc.set_linewidth(2)
ax.add_collection(lc)
ax.set_aspect('equal')
ax.set_xlim(min(x_values)-.1, max(x_values)+.1)
ax.set_ylim(min(y_values)-.1, max(y_values)+.1)
plt.show()
Background
I am trying to show three variables on a single plot. I have connected the three points using lines of different colours based on some other variables. This is shown here
Problem
What I want to do is to have a different scale on the negative x-axis. This would help me in providing positive x_ticks, different axis label and also clear and uncluttered representation of the lines on left side of the image
Question
How to have a different positive x-axis starting from 0 towards negative direction?
Have xticks based on data plotted in that direction
Have a separate xlabel for this new axis
Additional information
I have checked other questions regarding inclusion of multiple axes e.g. this and this. However, these questions did not serve the purpose.
Code Used
font_size = 20
plt.rcParams.update({'font.size': font_size})
fig = plt.figure()
ax = fig.add_subplot(111)
#read my_data from file or create it
for case in my_data:
#Iterating over my_data
if condition1 == True:
local_linestyle = '-'
local_color = 'r'
local_line_alpha = 0.6
elif condition2 == 1:
local_linestyle = '-'
local_color = 'b'
local_line_alpha = 0.6
else:
local_linestyle = '--'
local_color = 'g'
local_line_alpha = 0.6
datapoint = [case[0], case[1], case[2]]
plt.plot(datapoint[0], 0, color=local_color)
plt.plot(-datapoint[2], 0, color=local_color)
plt.plot(0, datapoint[1], color=local_color)
plt.plot([datapoint[0], 0], [0, datapoint[1]], linestyle=local_linestyle, color=local_color)
plt.plot([-datapoint[2], 0], [0, datapoint[1]], linestyle=local_linestyle, color=local_color)
plt.show()
exit()
You can define a custom scale, where values below zero are scaled differently than those above zero.
import numpy as np
from matplotlib import scale as mscale
from matplotlib import transforms as mtransforms
from matplotlib.ticker import FuncFormatter
class AsymScale(mscale.ScaleBase):
name = 'asym'
def __init__(self, axis, **kwargs):
mscale.ScaleBase.__init__(self)
self.a = kwargs.get("a", 1)
def get_transform(self):
return self.AsymTrans(self.a)
def set_default_locators_and_formatters(self, axis):
# possibly, set a different locator and formatter here.
fmt = lambda x,pos: "{}".format(np.abs(x))
axis.set_major_formatter(FuncFormatter(fmt))
class AsymTrans(mtransforms.Transform):
input_dims = 1
output_dims = 1
is_separable = True
def __init__(self, a):
mtransforms.Transform.__init__(self)
self.a = a
def transform_non_affine(self, x):
return (x >= 0)*x + (x < 0)*x*self.a
def inverted(self):
return AsymScale.InvertedAsymTrans(self.a)
class InvertedAsymTrans(AsymTrans):
def transform_non_affine(self, x):
return (x >= 0)*x + (x < 0)*x/self.a
def inverted(self):
return AsymScale.AsymTrans(self.a)
Using this you would provide a scale parameter a that scales the negative part of the axes.
# Now that the Scale class has been defined, it must be registered so
# that ``matplotlib`` can find it.
mscale.register_scale(AsymScale)
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.plot([-2, 0, 5], [0,1,0])
ax.set_xscale("asym", a=2)
ax.annotate("negative axis", xy=(.25,0), xytext=(0,-30),
xycoords = "axes fraction", textcoords="offset points", ha="center")
ax.annotate("positive axis", xy=(.75,0), xytext=(0,-30),
xycoords = "axes fraction", textcoords="offset points", ha="center")
plt.show()
The question is not very clear about what xticks and labels are desired, so I left that out for now.
Here's how to get what you want. This solution uses two twined axes object to get different scaling to the left and right of the origin, and then hides all the evidence:
import matplotlib.pyplot as plt
import matplotlib as mpl
from numbers import Number
tickkwargs = {m+k:False for k in ('bottom','top','left','right') for m in ('','label')}
p = np.zeros((10, 3, 2))
p[:,0,0] -= np.arange(10)*.1 + .5
p[:,1,1] += np.repeat(np.arange(5), 2)*.1 + .3
p[:,2,0] += np.arange(10)*.5 + 2
fig = plt.figure(figsize=(8,6))
host = fig.add_subplot(111)
par = host.twiny()
host.set_xlim(-6, 6)
par.set_xlim(-1, 1)
for ps in p:
# mask the points with negative x values
ppos = ps[ps[:,0] >= 0].T
host.plot(*ppos)
# mask the points with positive x values
pneg = ps[ps[:,0] <= 0].T
par.plot(*pneg)
# hide all possible ticks/notation text that could be set by the second x axis
par.tick_params(axis="both", **tickkwargs)
par.xaxis.get_offset_text().set_visible(False)
# fix the x tick labels so they're all positive
host.set_xticklabels(np.abs(host.get_xticks()))
fig.show()
Output:
Here's what the set of points p I used in the code above look like when plotted normally:
fig = plt.figure(figsize=(8,6))
ax = fig.gca()
for ps in p:
ax.plot(*ps.T)
fig.show()
Output:
The method of deriving a class of mscale.ScaleBase as shown in other answers may be too complicated for your purpose.
You can pass two scale transform functions to set_xscale or set_yscale, something like the following.
def get_scale(a=1): # a is the scale of your negative axis
def forward(x):
x = (x >= 0) * x + (x < 0) * x * a
return x
def inverse(x):
x = (x >= 0) * x + (x < 0) * x / a
return x
return forward, inverse
fig, ax = plt.subplots()
forward, inverse = get_scale(a=3)
ax.set_xscale('function', functions=(forward, inverse)) # this is for setting x axis
# do plotting
More examples can be found in this doc.
I'm drawing several contour lines over a basemap projection as shown in the following figure:.
There are 3 contours that are not drawn completely (in Oregon, Washington and California) and seems like there is this line that has cut all 3 of them in the same latitude. I'm not sure how to solve this problem.
I added the number of interpolation points, didn't help. changed the ll and ur points to include more area didn't help.
The code is below (not reproducible but might help):
def visualise_bigaus(mus, sigmas, corxys , output_type='pdf', **kwargs):
lllat = 24.396308
lllon = -124.848974
urlat = 49.384358
urlon = -66.885444
fig = plt.figure(figsize=(4, 2.5))
ax = fig.add_subplot(111, axisbg='w', frame_on=False)
m = Basemap(llcrnrlat=lllat,
urcrnrlat=urlat,
llcrnrlon=lllon,
urcrnrlon=urlon,
resolution='i', projection='cyl')
m.drawmapboundary(fill_color = 'white')
#m.drawcoastlines(linewidth=0.2)
m.drawcountries(linewidth=0.2)
m.drawstates(linewidth=0.2, color='lightgray')
#m.fillcontinents(color='white', lake_color='#0000ff', zorder=2)
#m.drawrivers(color='#0000ff')
m.drawlsmask(land_color='gray',ocean_color="#b0c4de", lakes=True)
lllon, lllat = m(lllon, lllat)
urlon, urlat = m(urlon, urlat)
mlon, mlat = m(*(mus[:,1], mus[:,0]))
numcols, numrows = 1000, 1000
X = np.linspace(mlon.min(), urlon, numcols)
Y = np.linspace(lllat, urlat, numrows)
X, Y = np.meshgrid(X, Y)
m.scatter(mlon, mlat, s=0.2, c='red')
shp_info = m.readshapefile('./data/us_states_st99/st99_d00','states',drawbounds=True, zorder=0)
printed_names = []
ax = plt.gca()
ax.xaxis.set_visible(False)
ax.yaxis.set_visible(False)
for spine in ax.spines.itervalues():
spine.set_visible(False)
for k in xrange(mus.shape[0]):
#here x is longitude and y is latitude
#apply softplus to sigmas (to make them positive)
sigmax=np.log(1 + np.exp(sigmas[k][1]))
sigmay=np.log(1 + np.exp(sigmas[k][0]))
mux=mlon[k]
muy=mlat[k]
corxy = corxys[k]
#apply the soft sign
corxy = corxy / (1 + np.abs(corxy))
#now given corxy find sigmaxy
sigmaxy = corxy * sigmax * sigmay
#corxy = 1.0 / (1 + np.abs(sigmaxy))
Z = mlab.bivariate_normal(X, Y, sigmax=sigmax, sigmay=sigmay, mux=mux, muy=muy, sigmaxy=sigmaxy)
#Z = maskoceans(X, Y, Z)
con = m.contour(X, Y, Z, levels=[0.02], linewidths=0.5, colors='darkorange', antialiased=True)
'''
num_levels = len(con.collections)
if num_levels > 1:
for i in range(0, num_levels):
if i != (num_levels-1):
con.collections[i].set_visible(False)
'''
contour_labels = False
if contour_labels:
plt.clabel(con, [con.levels[-1]], inline=True, fontsize=10)
'''
world_shp_info = m.readshapefile('./data/CNTR_2014_10M_SH/Data/CNTR_RG_10M_2014','world',drawbounds=False, zorder=100)
for shapedict,state in zip(m.world_info, m.world):
if shapedict['CNTR_ID'] not in ['CA', 'MX']: continue
poly = MplPolygon(state,facecolor='gray',edgecolor='gray')
ax.add_patch(poly)
'''
if iter:
iter = str(iter).zfill(3)
else:
iter = ''
plt.tight_layout()
plt.savefig('./maps/video/gaus_' + iter + '.' + output_type, frameon=False, dpi=200)
The problem is the meshgrid not covering the complete map. The meshgrid simply doesn't have any points at the positions where you want to draw the gaussian contour line.
An example to reproduce this behaviour is the following, where the meshgrid in x directio starts at -1, such that points lower than that are not drawn.
import matplotlib.pyplot as plt
import matplotlib.mlab as mlab
import numpy as np
fig, ax=plt.subplots()
ax.plot([-2,2],[-2,-2], alpha=0)
X,Y = np.meshgrid(np.linspace(-1,2),np.linspace(-2,2))
Z = mlab.bivariate_normal(X, Y, sigmax=1., sigmay=1., mux=0.1, muy=0.1, sigmaxy=0)
con = ax.contour(X, Y, Z, levels=[Z.max()/3, Z.max()/2., Z.max()*0.8],colors='darkorange')
plt.show()
A similar problem occurs in the code from the question.
While in Y direction, you use the complete map, Y = np.linspace(lllat, urlat, numrows), in X direction you restrict the mesh to start at mlon.min(),
X = np.linspace(mlon.min(), urlon, numcols)
The solution would of course be not to start the mesh in Portland, but somewhere in the ocean, i.e. at the edge of the shown map.
I am trying to go a step further by creating a radar plot like this question states. I using the same source code that the previous question was using, except I'm trying to implement this using pandas dataframe and pivot tables.
import numpy as np
import pandas as pd
from StringIO import StringIO
import matplotlib.pyplot as plt
from matplotlib.projections.polar import PolarAxes
from matplotlib.projections import register_projection
def radar_factory(num_vars, frame='circle'):
"""Create a radar chart with `num_vars` axes."""
# calculate evenly-spaced axis angles
theta = 2 * np.pi * np.linspace(0, 1 - 1. / num_vars, num_vars)
# rotate theta such that the first axis is at the top
theta += np.pi / 2
def draw_poly_frame(self, x0, y0, r):
# TODO: use transforms to convert (x, y) to (r, theta)
verts = [(r * np.cos(t) + x0, r * np.sin(t) + y0) for t in theta]
return plt.Polygon(verts, closed=True, edgecolor='k')
def draw_circle_frame(self, x0, y0, r):
return plt.Circle((x0, y0), r)
frame_dict = {'polygon': draw_poly_frame, 'circle': draw_circle_frame}
if frame not in frame_dict:
raise ValueError, 'unknown value for `frame`: %s' % frame
class RadarAxes(PolarAxes):
"""Class for creating a radar chart (a.k.a. a spider or star chart)
http://en.wikipedia.org/wiki/Radar_chart
"""
name = 'radar'
# use 1 line segment to connect specified points
RESOLUTION = 1
# define draw_frame method
draw_frame = frame_dict[frame]
def fill(self, *args, **kwargs):
"""Override fill so that line is closed by default"""
closed = kwargs.pop('closed', True)
return super(RadarAxes, self).fill(closed=closed, *args, **kwargs)
def plot(self, *args, **kwargs):
"""Override plot so that line is closed by default"""
lines = super(RadarAxes, self).plot(*args, **kwargs)
for line in lines:
self._close_line(line)
def _close_line(self, line):
x, y = line.get_data()
# FIXME: markers at x[0], y[0] get doubled-up
if x[0] != x[-1]:
x = np.concatenate((x, [x[0]]))
y = np.concatenate((y, [y[0]]))
line.set_data(x, y)
def set_varlabels(self, labels):
self.set_thetagrids(theta * 180 / np.pi, labels)
def _gen_axes_patch(self):
x0, y0 = (0.5, 0.5)
r = 0.5
return self.draw_frame(x0, y0, r)
register_projection(RadarAxes)
return theta
def day_radar_plot(df):
fig = plt.figure(figsize=(6,6))
#adjust spacing around the subplots
fig.subplots_adjust(wspace=0.25,hspace=0.20,top=0.85,bottom=0.05)
ldo,rup = 0.1,0.8 #leftdown and right up normalized
ax = fig.add_axes([ldo,ldo,rup,rup],polar=True)
N = len(df['Group1'].unique())
theta = radar_factory(N)
polar_df = pd.DataFrame(df.groupby([df['Group1'],df['Type'],df['Vote']]).size())
polar_df.columns = ['Count']
radii = polar_df['Count'].get_values()
names = polar_df.index.get_values()
#get the number of unique colors needed
num_colors_needed = len(names)
#Create the list of unique colors needed for red and blue shades
Rcolors = []
Gcolors = []
for i in range(num_colors_needed):
ri=1-(float(i)/float(num_colors_needed))
gi=0.
bi=0.
Rcolors.append((ri,gi,bi))
for i in range(num_colors_needed):
ri=0.
gi=1-(float(i)/float(num_colors_needed))
bi=0.
Gcolors.append((ri,gi,bi))
from_x = np.linspace(0,0.95,num_colors_needed)
to_x = from_x + 0.05
i = 0
for d,f,R,G in zip(radii,polar_df.index,Rcolors,Gcolors):
i = i+1
if f[2].lower() == 'no':
ax.plot(theta,d,color=R)
ax.fill(theta,d,facecolor=R,alpha=0.25)
#this is where I think i have the issue
ax.axvspan(from_x[i],to_x[i],color=R)
elif f[2].lower() == 'yes':
ax.plot(theta,d,color=G)
ax.fill(theta,d,facecolor=G,alpha=0.25)
#this is where I think i have the issue
ax.axvspan(from_x[i],to_x[i],color=G)
plt.show()
So, let's say I have this StringIO that has a list of Group1 voting either yes or no and they are from a numbered type..these numbers are arbitrary in labeling but just as an example..
fakefile = StringIO("""\
Group1,Type,Vote
James,7,YES\nRachael,7,YES\nChris,2,YES\nRachael,9,NO
Chris,2,YES\nChris,7,NO\nRachael,9,NO\nJames,2,NO
James,7,NO\nJames,9,YES\nRachael,9,NO
Chris,2,YES\nChris,2,YES\nRachael,7,NO
Rachael,7,YES\nJames,9,YES\nJames,9,NO
Rachael,2,NO\nChris,2,YES\nRachael,7,YES
Rachael,9,NO\nChris,9,NO\nJames,7,NO
James,2,YES\nChris,2,NO\nRachael,9,YES
Rachael,9,YES\nRachael,2,NO\nChris,7,YES
James,7,YES\nChris,9,NO\nRachael,9,NO\n
Chris,9,YES
""")
record = pd.read_csv(fakefile, header=0)
day_radar_plot(record)
The error I get is Value Error: x and y must have same first dimension.
As I indicated in my script, I thought I had a solution for it but apparently I'm going by it the wrong way. Does anyone have any advice or guidance?
Since I'm completely lost in what you are trying to do, I will simply provide a solution on how to draw a radar chart from the given data.
It will answer the question how often have people voted Yes or No.
import pandas as pd
import numpy as np
from StringIO import StringIO
import matplotlib.pyplot as plt
fakefile = StringIO("""\
Group1,Type,Vote
James,7,YES\nRachael,7,YES\nChris,2,YES\nRachael,9,NO
Chris,2,YES\nChris,7,NO\nRachael,9,NO\nJames,2,NO
James,7,NO\nJames,9,YES\nRachael,9,NO
Chris,2,YES\nChris,2,YES\nRachael,7,NO
Rachael,7,YES\nJames,9,YES\nJames,9,NO
Rachael,2,NO\nChris,2,YES\nRachael,7,YES
Rachael,9,NO\nChris,9,NO\nJames,7,NO
James,2,YES\nChris,2,NO\nRachael,9,YES
Rachael,9,YES\nRachael,2,NO\nChris,7,YES
James,7,YES\nChris,9,NO\nRachael,9,NO\n
Chris,9,YES""")
df = pd.read_csv(fakefile, header=0)
df["cnt"] = np.ones(len(df))
pt = pd.pivot_table(df, values='cnt', index=['Group1'],
columns=['Vote'], aggfunc=np.sum)
fig = plt.figure()
ax = fig.add_subplot(111, projection="polar")
theta = np.arange(len(pt))/float(len(pt))*2.*np.pi
l1, = ax.plot(theta, pt["YES"], color="C2", marker="o", label="YES")
l2, = ax.plot(theta, pt["NO"], color="C3", marker="o", label="NO")
def _closeline(line):
x, y = line.get_data()
x = np.concatenate((x, [x[0]]))
y = np.concatenate((y, [y[0]]))
line.set_data(x, y)
[_closeline(l) for l in [l1,l2]]
ax.set_xticks(theta)
ax.set_xticklabels(pt.index)
plt.legend()
plt.title("How often have people votes Yes or No?")
plt.show()
I know the question is not very informative.. but as I do not know the name of his type of plot, I can not be more informative..
[EDIT] I changed the title, and now it is more informative...
You can do something similar with seaborn.swarmplot. I also use seaborn.boxplot (with the whiskers and caps turned off) to plot the mean and range:
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style("whitegrid")
tips = sns.load_dataset("tips")
ax = sns.swarmplot(x="day", y="total_bill", data=tips)
ax = sns.boxplot(x="day", y="total_bill", data=tips,
showcaps=False,boxprops={'facecolor':'None'},
showfliers=False,whiskerprops={'linewidth':0})
plt.show()
If (for whatever reason) you don't want to use seaborn, you can have a go at making them yourself (see e.g. this explanation: https://www.flerlagetwins.com/2020/11/beeswarm.html ).
A simple version is:
#!/usr/bin/env python3
import matplotlib.pyplot as plt
import numpy as np
def simple_beeswarm(y, nbins=None):
"""
Returns x coordinates for the points in ``y``, so that plotting ``x`` and
``y`` results in a bee swarm plot.
"""
y = np.asarray(y)
if nbins is None:
nbins = len(y) // 6
# Get upper bounds of bins
x = np.zeros(len(y))
ylo = np.min(y)
yhi = np.max(y)
dy = (yhi - ylo) / nbins
ybins = np.linspace(ylo + dy, yhi - dy, nbins - 1)
# Divide indices into bins
i = np.arange(len(y))
ibs = [0] * nbins
ybs = [0] * nbins
nmax = 0
for j, ybin in enumerate(ybins):
f = y <= ybin
ibs[j], ybs[j] = i[f], y[f]
nmax = max(nmax, len(ibs[j]))
f = ~f
i, y = i[f], y[f]
ibs[-1], ybs[-1] = i, y
nmax = max(nmax, len(ibs[-1]))
# Assign x indices
dx = 1 / (nmax // 2)
for i, y in zip(ibs, ybs):
if len(i) > 1:
j = len(i) % 2
i = i[np.argsort(y)]
a = i[j::2]
b = i[j+1::2]
x[a] = (0.5 + j / 3 + np.arange(len(b))) * dx
x[b] = (0.5 + j / 3 + np.arange(len(b))) * -dx
return x
fig = plt.figure(figsize=(2, 4))
fig.subplots_adjust(0.2, 0.1, 0.98, 0.99)
ax = fig.add_subplot(1, 1, 1)
y = np.random.gamma(20, 10, 100)
x = simple_beeswarm(y)
ax.plot(x, y, 'o')
fig.savefig('bee.png')