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Openpyxl minor gridlines

I am working on a Python application where I am collecting data from a device, and attempting to plot it in an excel file by using the Openpyxl library. I am successfully able to do everything including plotting the data, and formatting the scatter plot that I made, but I am having some trouble in adding minor gridlines to the plot.
I feel like this is definitely possible because in the API, I can see under the openpyxl.chart.axis module, there is a “minorGridlines” attribute, but it is not a boolean input (ON/OFF), rather it takes a Chartlines class. I tried going a bit down the rabbit-hole of seeing how I would do this, but I am wondering what the most straightforward way of adding the minor-gridlines would be? Do you have to construct chart lines manually, or is there a simple way of doing this?
I would really appreciate any help!

I think I answered my own question, but I will post it here if anybody else needs this (as I don’t see any other answers to this question on the forum).
Example Code (see lines 4, 38):
# Imports for script
from openpyxl import Workbook # For plotting things in excel
from openpyxl.chart import ScatterChart, Reference, Series
from openpyxl.chart.axis import ChartLines
from math import log10
# Variables for script
fileName = 'testFile.xlsx'
dataPoints = 100
# Generating a workbook to test with
wb = Workbook()
ws = wb.active # Fill data into the first sheet
ws_name = ws.title
# We will just generate a logarithmic plot, and scale the axis logarithmically (will look linear)
x_data = []
y_data = []
for i in range(dataPoints):
x_data.append(i + 1)
y_data.append(log10(i + 1))
# Go back through the data, and place the data into the sheet
ws['A1'] = 'x_data'
ws['B1'] = 'y_data'
for i in range(dataPoints):
ws['A%d' % (i + 2)] = x_data[i]
ws['B%d' % (i + 2)] = y_data[i]
# Generate a reference to the cells that we can plot
x_axis = Reference(ws, range_string='%s!A2:A%d' % (ws_name, dataPoints + 1))
y_axis = Reference(ws, range_string='%s!B2:B%d' % (ws_name, dataPoints + 1))
function = Series(xvalues=x_axis, values=y_axis)
# Actually create the scatter plot, and append all of the plots to it
ScatterPlot = ScatterChart()
ScatterPlot.x_axis.minorGridlines = ChartLines()
ScatterPlot.x_axis.scaling.logBase = 10
ScatterPlot.series.append(function)
ScatterPlot.x_axis.title = 'X_Data'
ScatterPlot.y_axis.title = 'Y_Data'
ScatterPlot.title = 'Openpyxl Plotting Test'
ws.add_chart(ScatterPlot, 'D2')
# Save the file at the end to output it
wb.save(fileName)
Background on solution:
I looked at how the code for Openpyxl generates the Major axis gridlines, which seems to follow a similar convention as the Minor axis gridlines, and I found that in the ‘NumericAxis’ class, they generated the major gridlines with the following line (labeled ‘##### This Line #####’ which is originally copied from the ‘openpyxl->chart->axis’ file):
class NumericAxis(_BaseAxis):
tagname = "valAx"
axId = _BaseAxis.axId
scaling = _BaseAxis.scaling
delete = _BaseAxis.delete
axPos = _BaseAxis.axPos
majorGridlines = _BaseAxis.majorGridlines
minorGridlines = _BaseAxis.minorGridlines
title = _BaseAxis.title
numFmt = _BaseAxis.numFmt
majorTickMark = _BaseAxis.majorTickMark
minorTickMark = _BaseAxis.minorTickMark
tickLblPos = _BaseAxis.tickLblPos
spPr = _BaseAxis.spPr
txPr = _BaseAxis.txPr
crossAx = _BaseAxis.crossAx
crosses = _BaseAxis.crosses
crossesAt = _BaseAxis.crossesAt
crossBetween = NestedNoneSet(values=(['between', 'midCat']))
majorUnit = NestedFloat(allow_none=True)
minorUnit = NestedFloat(allow_none=True)
dispUnits = Typed(expected_type=DisplayUnitsLabelList, allow_none=True)
extLst = Typed(expected_type=ExtensionList, allow_none=True)
__elements__ = _BaseAxis.__elements__ + ('crossBetween', 'majorUnit',
'minorUnit', 'dispUnits',)
def __init__(self,
crossBetween=None,
majorUnit=None,
minorUnit=None,
dispUnits=None,
extLst=None,
**kw
):
self.crossBetween = crossBetween
self.majorUnit = majorUnit
self.minorUnit = minorUnit
self.dispUnits = dispUnits
kw.setdefault('majorGridlines', ChartLines()) ######## THIS Line #######
kw.setdefault('axId', 100)
kw.setdefault('crossAx', 10)
super(NumericAxis, self).__init__(**kw)
#classmethod
def from_tree(cls, node):
"""
Special case value axes with no gridlines
"""
self = super(NumericAxis, cls).from_tree(node)
gridlines = node.find("{%s}majorGridlines" % CHART_NS)
if gridlines is None:
self.majorGridlines = None
return self
I took a stab, and after importing the ‘Chartlines’  class like so:
from openpyxl.chart.axis import ChartLines
 
I was able to add minor gridlines to the x-axis like so:
ScatterPlot.x_axis.minorGridlines = ChartLines()
As far as formatting the minor gridlines, I’m at a bit of a loss, and personally have no need, but this at least is a good start.

Change the scale of the graph image

I try to generate a graph and save an image of the graph in python. Although the "plotting" of the values seems ok and I can get my picture, the scale of the graph is badly shifted.
If you compare the correct graph from tutorial example with my bad graph generated from different dataset, the curves are cut at the bottom to early: Y-axis should start just above the highest values and I should also see the curves for the highest X-values (in my case around 10^3).
But honestly, I think that problem is the scale of the y-axis, but actually do not know what parameteres should I change to fix it. I tried to play with some numbers (see below script), but without any good results.
This is the code for calculation and generation of the graph image:
import numpy as np
hic_data = load_hic_data_from_reads('/home/besy/Hi-C/MOREX/TCC35_parsedV2/TCC35_V2_interaction_filtered.tsv', resolution=100000)
min_diff = 1
max_diff = 500
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(12, 12))
for cnum, c in enumerate(hic_data.chromosomes):
if c in ['ChrUn']:
continue
dist_intr = []
for diff in xrange(min_diff, min((max_diff, 1 + hic_data.chromosomes[c]))):
beg, end = hic_data.section_pos[c]
dist_intr.append([])
for i in xrange(beg, end - diff):
dist_intr[-1].append(hic_data[i, i + diff])
mean_intrp = []
for d in dist_intr:
if len(d):
mean_intrp.append(float(np.nansum(d)) / len(d))
else:
mean_intrp.append(0.0)
xp, yp = range(min_diff, max_diff), mean_intrp
x = []
y = []
for k in xrange(len(xp)):
if yp[k]:
x.append(xp[k])
y.append(yp[k])
l = plt.plot(x, y, '-', label=c, alpha=0.8)
plt.hlines(mean_intrp[2], 3, 5.25 + np.exp(cnum / 4.3), color=l[0].get_color(),
linestyle='--', alpha=0.5)
plt.text(5.25 + np.exp(cnum / 4.3), mean_intrp[2], c, color=l[0].get_color())
plt.plot(3, mean_intrp[2], '+', color=l[0].get_color())
plt.xscale('log')
plt.yscale('log')
plt.ylabel('number of interactions')
plt.xlabel('Distance between bins (in 100 kb bins)')
plt.grid()
plt.ylim(2, 250)
_ = plt.xlim(1, 110)
fig.savefig('/home/besy/Hi-C/MOREX/TCC35_V2_results/filtered/TCC35_V2_decay.png', dpi=fig.dpi)
I think that problem is in scale I need y-axis to start from 10^-1 (0.1), in order to change this I tried this:
min_diff = 0.1
.
.
.
dist_intr = []
for diff in xrange(min_diff, min((max_diff, 0.1 + hic_data.chromosomes[c]))):
.
.
.
plt.ylim((0.1, 20))
But this values return: "integer argument expected, got float"
I also tried to play with:
max_diff, plt.ylim and plt.xlim parameters little bit, but nothing changed to much.
I would like to ask you what parameter/s and how I need change to generate image of the correctly focused graph. Thank you in advance.

Multiple route mapping to different matplotlib graphs in flask app

I have this "flask app" with two links, each mapping to different matplotlib visualizations, for example: localhost:5000/line_chart and localhost:5000/bar_chart.
When I start the server, and click the a route (any of them), I see what I expect.
localhost:5000/bar_chart
When I go back and view the other link, both graphs break.
localhost:5000/line_chart
localhost:5000/bar_chart
I can reproduce this every time by closing the server then running the "run.py" script again. Seems to be an overwriting conflict with the in-memory buffer. Has anyone had this issue before?
app/views.py
import matplotlib
matplotlib.use('Agg') # this allows PNG plotting
import matplotlib.pyplot as plt
import base64
from flask import render_template
from app import app
from io import BytesIO
#app.route('/')
#app.route('/index')
def index():
res = ''
navigation = [['Line Chart','line_chart'],['Bar Chart','bar_chart']]
res = res + '<h1>Matplotlib Chart Examples</h1>'
res = res + '<ul>'
for item in navigation:
name = item[0]
link = item[1]
res = res + '<li>'+ name +'</li>'
res = res +'</ul>'
return res
#app.route('/bar_chart')
def bar_chart():
movies = ["Annie Hall", "Ben-Hur", "Casablanca", "Gandhi", "West Side Story"]
num_oscars = [5, 11, 3, 8, 10]
# bars are by default width 0.8, so we'll add 0.1 to the left coordinates
# so that each bar is centered
xs = [i + 0.1 for i, _ in enumerate(movies)]
# plot bars with left x-coordinates [xs], heights [num_oscars]
plt.bar(xs, num_oscars)
plt.ylabel("# of Academy Awards")
plt.title("My Favorite Movies")
# label x-axis with movie names at bar centers
plt.xticks([i + 0.5 for i, _ in enumerate(movies)], movies)
return compute(plt)
#app.route('/line_chart')
def line_chart():
years = [1950, 1960, 1970, 1980, 1990, 2000, 2010]
gdp = [300.2, 543.3, 1075.9, 2862.5, 5979.6, 10289.7, 14958.3]
# create a line chart, years on x-axis, gdp on y-axis
plt.plot(years, gdp, color='green', marker='o', linestyle='solid')
# add a title
plt.title("Nominal GDP")
# add a label to the y-axis
plt.ylabel("Billions of $")
return compute(plt)
def compute(plt):
# run plt.plot, plt.title, etc.
figfile = BytesIO()
plt.savefig(figfile, format='png')
figfile.seek(0) # rewind to beginning of file
#figfile.getvalue() extracts string (stream of bytes)
figdata_png = base64.b64encode(figfile.getvalue())
return render_template('index.html',
title='matplotlib chart',
results=figdata_png)
Thank you for your time.

I guess you need two figures, test this code and tell what happened:
#app.route('/bar_chart')
def bar_chart():
movies = ["Annie Hall", "Ben-Hur", "Casablanca", "Gandhi", "West Side Story"]
num_oscars = [5, 11, 3, 8, 10]
# bars are by default width 0.8, so we'll add 0.1 to the left coordinates
# so that each bar is centered
xs = [i + 0.1 for i, _ in enumerate(movies)]
# plot bars with left x-coordinates [xs], heights [num_oscars]
plt.figure(1)
plt.bar(xs, num_oscars)
plt.ylabel("# of Academy Awards")
plt.title("My Favorite Movies")
# label x-axis with movie names at bar centers
plt.xticks([i + 0.5 for i, _ in enumerate(movies)], movies)
return compute(plt, 1)
#app.route('/line_chart')
def line_chart():
years = [1950, 1960, 1970, 1980, 1990, 2000, 2010]
gdp = [300.2, 543.3, 1075.9, 2862.5, 5979.6, 10289.7, 14958.3]
# create a line chart, years on x-axis, gdp on y-axis
plt.figure(2)
plt.plot(years, gdp, color='green', marker='o', linestyle='solid')
# add a title
plt.title("Nominal GDP")
# add a label to the y-axis
plt.ylabel("Billions of $")
return compute(plt,2)
def compute(plt, fignum):
# run plt.plot, plt.title, etc.
plt.figure(fignum)
figfile = BytesIO()
plt.savefig(figfile, format='png')
figfile.seek(0) # rewind to beginning of file
#figfile.getvalue() extracts string (stream of bytes)
figdata_png = base64.b64encode(figfile.getvalue())
return render_template('index.html',
title='matplotlib chart',
results=figdata_png)

In my case, that solution didn't work. It seems that there is a race condition when trying to access plot. I first tried to use a lock from a library, but that didn't work, so instead I sort of engineered out a lock. In my case, I wanted to create n images using the same function on the same view, so I started by creating a list in the following way:
queue = [False for i in range(n)]
Then, my flask app look something like this:
#app.route('/vis/<j>')
def vis(j):
global queue
# We check that it's image's #j turn, as if it was single threaded
j = int(j)
if j == 0:
for i in range(len(queue)):
queue[i] = False
else:
while not queue[j-1]:
# If it's not, we sleep for a short time (from time import sleep)
sleep(0.5)
# This is not important, it's how I was plotting some random figures
# (from random import seed) (from datetime import datetime)
seed(datetime.now())
n = 10
p1 = [randint(0, 10) for _ in range(n)]
p2 = [randint(0, 10) for _ in range(n)]
t = [i for i in range(n)]
fig = plt.figure(j)
plt.clf()
plt.plot(t, p1, color='blue')
plt.plot(t, p2, color='orange')
plt.xlabel('Time')
plt.ylabel('Value')
# Save the plot
img = BytesIO()
fig.savefig(img, dpi=128)
img.seek(0)
# We finished using everything related to plot, so we free the "lock"
queue[j] = True
# Return the object as a file that can be accessed
return send_file(img, mimetype='image/png')
Finally, when wanting to display this in my flask app, all I had to do was using this <img src="/vis/1"> in my html file.
Edit: I forgot one of the most important part! For some reason, this would still create some unrelated thread issue. I looked it up and that's when I came with the full solution. The threading issue was solved by adding at the beginning of the file:
import matplotlib
import matplotlib.pyplot as plt
matplotlib.use('Agg')
For some reason, using that Agg backend solved the second threading I was having. I don't really have a good explanation for that, but it does work, so it's enough for me.
Alternatively, what also worked was running the app disabling threads by adding:
if __name__ == '__main__':
app.run(threading=False, debug=True)
I don't know however, at the moment, whether this works in production, so I preferred the other solution. :)
I hope this helps if you had the same issue!

Matplotlib lines do not join smoothly, Python

I am using matplotlib to draw the outline of a cylindrical body, however the lines do not want to join up smoothly, as seen in the range x[40,60].
It is really subtle in this image I know, but it is unfortunately not acceptable for my purposes. I hope it is visible for you to see.
Using more data points does not seem to make a difference.
Is there a way to get curved lines to join up more smoothly in matplotlib?
Original code:
import numpy as np
import matplotlib.pylab as plt
length = 100.
a = 40
b = 20
n = 2.
alpha = np.radians(25.)
d = 18.
x_nose = np.linspace(0,a,1000)
r_nose = (0.5*d*(1 - ((x_nose-a)/a)**2)**(1/n))
x_mid = np.linspace(x_nose[-1],a+b,2)
r_mid = np.array([r_nose[-1],r_nose[-1]])
x_tail = np.linspace(x_mid[-1],length,1000)
l_tail = length-a-b
r_tail = (0.5*d - ((3*d)/(2*l_tail**2) - np.tan(alpha)/l_tail)*(x_tail-a-b)**2 + (d/l_tail**3 - np.tan(alpha)/l_tail**2)*(x_tail-a-b)**3)
fig = plt.figure()
plt.plot(x_nose,r_nose,'k',linewidth=2,antialiased=True)
plt.plot(x_mid,r_mid,'k',linewidth=2,antialiased=True)
plt.plot(x_tail,r_tail,'k',linewidth=2,antialiased=True)
plt.axis('equal')
plt.show()
You can see the effect more easily when zoomed in:

I'm not sure why this is happening, but you may be able to mitigate by constructing a single x and r array with the full line to draw.
x = np.append(x_nose, x_mid)
x = np.append(x, x_tail )
r = np.append(r_nose, r_mid)
r = np.append(r, r_tail )
plt.plot(x,r,'k',linewidth=2,antialiased=True)
This obviously prevents you altering line styles of individual elements, but it looks like you don't want to do that. This works for me:

Adding a single label to the legend for a series of different data points plotted inside a designated bin in Python using matplotlib.pyplot.plot()

I have a script for plotting astronomical data of redmapping clusters using a csv file. I could get the data points in it and want to plot them using different colors depending on their redshift values: I am binning the dataset into 3 bins (0.1-0.2, 0.2-0.25, 0.25,0.31) based on the redshift.
The problem arises with my code after I distinguish to what bin the datapoint belongs: I want to have 3 labels in the legend corresponding to red, green and blue data points, but this is not happening and I don't know why. I am using plot() instead of scatter() as I also had to do the best fit from the data in the same figure. So everything needs to be in 1 figure.
import numpy as np
import matplotlib.pyplot as py
import csv
z = open("Sheet4CSV.csv","rU")
data = csv.reader(z)
x = []
y = []
ylow = []
yupp = []
xlow = []
xupp = []
redshift = []
for r in data:
x.append(float(r[2]))
y.append(float(r[5]))
xlow.append(float(r[3]))
xupp.append(float(r[4]))
ylow.append(float(r[6]))
yupp.append(float(r[7]))
redshift.append(float(r[1]))
from operator import sub
xerr_l = map(sub,x,xlow)
xerr_u = map(sub,xupp,x)
yerr_l = map(sub,y,ylow)
yerr_u = map(sub,yupp,y)
py.xlabel("$Original\ Tx\ XCS\ pipeline\ Tx\ keV$")
py.ylabel("$Iterative\ Tx\ pipeline\ keV$")
py.xlim(0,12)
py.ylim(0,12)
py.title("Redmapper Clusters comparison of Tx pipelines")
ax1 = py.subplot(111)
##Problem starts here after the previous line##
for p in redshift:
for i in xrange(84):
p=redshift[i]
if 0.1<=p<0.2:
ax1.plot(x[i],y[i],color="b", marker='.', linestyle = " ")#, label = "$z < 0.2$")
exit
if 0.2<=p<0.25:
ax1.plot(x[i],y[i],color="g", marker='.', linestyle = " ")#, label="$0.2 \leq z < 0.25$")
exit
if 0.25<=p<=0.3:
ax1.plot(x[i],y[i],color="r", marker='.', linestyle = " ")#, label="$z \geq 0.25$")
exit
##There seems nothing wrong after this point##
py.errorbar(x,y,yerr=[yerr_l,yerr_u],xerr=[xerr_l,xerr_u], fmt= " ",ecolor='magenta', label="Error bars")
cof = np.polyfit(x,y,1)
p = np.poly1d(cof)
l = np.linspace(0,12,100)
py.plot(l,p(l),"black",label="Best fit")
py.plot([0,15],[0,15],"black", linestyle="dotted", linewidth=2.0, label="line $y=x$")
py.grid()
box = ax1.get_position()
ax1.set_position([box.x1,box.y1,box.width, box.height])
py.legend(loc='center left',bbox_to_anchor=(1,0.5))
py.show()
In the 1st 'for' loop, I have indexed every value 'p' in the list 'redshift' so that bins can be created using 'if' statement. But if I add the labels that are hashed out against each py.plot() inside the 'if' statements, each data point 'i' that gets plotted in the figure as an intersection of (x[i],y[i]) takes the label and my entire legend attains in total 87 labels (including the 3 mentioned in the code at other places)!!!!!!
I essentially need 1 label for each bin...
Please tell me what needs to done after the bins are created and py.plot() commands used...Thanks in advance :-)
Sorry I cannot post my image here due to low reputation!
The data 'appended' for x, y and redshift lists from the csv file are as follows:
x=[5.031,10.599,10.589,8.548,9.089,8.675,3.588,1.244,3.023,8.632,8.953,7.603,7.513,2.917,7.344,7.106,3.889,7.287,3.367,6.839,2.801,2.316,1.328,6.31,6.19,6.329,6.025,5.629,6.123,5.892,5.438,4.398,4.542,4.624,4.501,4.504,5.033,5.068,4.197,2.854,4.784,2.158,4.054,3.124,3.961,4.42,3.853,3.658,1.858,4.537,2.072,3.573,3.041,5.837,3.652,3.209,2.742,2.732,1.312,3.635,2.69,3.32,2.488,2.996,2.269,1.701,3.935,2.015,0.798,2.212,1.672,1.925,3.21,1.979,1.794,2.624,2.027,3.66,1.073,1.007,1.57,0.854,0.619,0.547]
y=[5.255,10.897,11.045,9.125,9.387,17.719,4.025,1.389,4.152,8.703,9.051,8.02,7.774,3.139,7.543,7.224,4.155,7.416,3.905,6.868,2.909,2.658,1.651,6.454,6.252,6.541,6.152,5.647,6.285,6.079,5.489,4.541,4.634,8.851,4.554,4.555,5.559,5.144,5.311,5.839,5.364,3.18,4.352,3.379,4.059,4.575,3.914,5.736,2.304,4.68,3.187,3.756,3.419,9.118,4.595,3.346,3.603,6.313,1.816,4.34,2.732,4.978,2.719,3.761,2.623,2.1,4.956,2.316,4.231,2.831,1.954,2.248,6.573,2.276,2.627,3.85,3.545,25.405,3.996,1.347,1.679,1.435,0.759,0.677]
redshift = [0.12,0.25,0.23,0.23,0.27,0.26,0.12,0.27,0.17,0.18,0.17,0.3,0.23,0.1,0.23,0.29,0.29,0.12,0.13,0.26,0.11,0.24,0.13,0.21,0.17,0.2,0.3,0.29,0.23,0.27,0.25,0.21,0.11,0.15,0.1,0.26,0.23,0.12,0.23,0.26,0.2,0.17,0.22,0.26,0.25,0.12,0.19,0.24,0.18,0.15,0.27,0.14,0.14,0.29,0.29,0.26,0.15,0.29,0.24,0.24,0.23,0.26,0.29,0.22,0.13,0.18,0.24,0.14,0.24,0.24,0.17,0.26,0.29,0.11,0.14,0.26,0.28,0.26,0.28,0.27,0.23,0.26,0.23,0.19]

Working with numerical data like this, you should really consider using a numerical library, like numpy.
The problem in your code arises from processing each record (a coordinate (x,y) and the corresponding value redshift) one at a time. You are calling plot for each point, thereby creating legends for each of those 84 datapoints. You should consider your "bins" as groups of data that belong to the same dataset and process them as such. You could use "logical masks" to distinguish between your "bins", as shown below.
It's also not clear why you call exit after each plotting action.
import numpy as np
import matplotlib.pyplot as plt
x = np.array([5.031,10.599,10.589,8.548,9.089,8.675,3.588,1.244,3.023,8.632,8.953,7.603,7.513,2.917,7.344,7.106,3.889,7.287,3.367,6.839,2.801,2.316,1.328,6.31,6.19,6.329,6.025,5.629,6.123,5.892,5.438,4.398,4.542,4.624,4.501,4.504,5.033,5.068,4.197,2.854,4.784,2.158,4.054,3.124,3.961,4.42,3.853,3.658,1.858,4.537,2.072,3.573,3.041,5.837,3.652,3.209,2.742,2.732,1.312,3.635,2.69,3.32,2.488,2.996,2.269,1.701,3.935,2.015,0.798,2.212,1.672,1.925,3.21,1.979,1.794,2.624,2.027,3.66,1.073,1.007,1.57,0.854,0.619,0.547])
y = np.array([5.255,10.897,11.045,9.125,9.387,17.719,4.025,1.389,4.152,8.703,9.051,8.02,7.774,3.139,7.543,7.224,4.155,7.416,3.905,6.868,2.909,2.658,1.651,6.454,6.252,6.541,6.152,5.647,6.285,6.079,5.489,4.541,4.634,8.851,4.554,4.555,5.559,5.144,5.311,5.839,5.364,3.18,4.352,3.379,4.059,4.575,3.914,5.736,2.304,4.68,3.187,3.756,3.419,9.118,4.595,3.346,3.603,6.313,1.816,4.34,2.732,4.978,2.719,3.761,2.623,2.1,4.956,2.316,4.231,2.831,1.954,2.248,6.573,2.276,2.627,3.85,3.545,25.405,3.996,1.347,1.679,1.435,0.759,0.677])
redshift = np.array([0.12,0.25,0.23,0.23,0.27,0.26,0.12,0.27,0.17,0.18,0.17,0.3,0.23,0.1,0.23,0.29,0.29,0.12,0.13,0.26,0.11,0.24,0.13,0.21,0.17,0.2,0.3,0.29,0.23,0.27,0.25,0.21,0.11,0.15,0.1,0.26,0.23,0.12,0.23,0.26,0.2,0.17,0.22,0.26,0.25,0.12,0.19,0.24,0.18,0.15,0.27,0.14,0.14,0.29,0.29,0.26,0.15,0.29,0.24,0.24,0.23,0.26,0.29,0.22,0.13,0.18,0.24,0.14,0.24,0.24,0.17,0.26,0.29,0.11,0.14,0.26,0.28,0.26,0.28,0.27,0.23,0.26,0.23,0.19])
bin3 = 0.25 <= redshift
bin2 = np.logical_and(0.2 <= redshift, redshift < 0.25)
bin1 = np.logical_and(0.1 <= redshift, redshift < 0.2)
plt.ion()
labels = ("$z < 0.2$", "$0.2 \leq z < 0.25$", "$z \geq 0.25$")
colors = ('r', 'g', 'b')
for bin, label, co in zip( (bin1, bin2, bin3), labels, colors):
plt.plot(x[bin], y[bin], color=co, ls='none', marker='o', label=label)
plt.legend()
plt.show()