We Keep Coding

store values and continue running function

I am using astropy to detect sources in an image. I am trying to write a function that will detect my sources, with an option of storing their coordinates in an array, and another option to plot the sources. This is my code:
def SourceDetect(file, SIG, FWHM, THRESH, store = False, PlotStars = False):
image = pf.open(file)
im = image[0].data
mean, median, std = sigma_clipped_stats(im, sigma=SIG)
daofind = DAOStarFinder(fwhm = FWHM, threshold = THRESH * std)
sources = daofind(im - median)
positions = np.transpose((sources['xcentroid'], sources['ycentroid']))
if(store):
return positions
if(PlotStars):
apertures = CircularAperture(positions, r = 6.)
norm = ImageNormalize(stretch = SqrtStretch())
plt.imshow(im, cmap = 'Greys', origin = 'lower', norm = norm,
interpolation = 'nearest')
apertures.plot(color = 'blue', lw = 1.5, alpha = 1)
for i in range(0, len(sources)):
plt.text(sources[i]['xcentroid'], sources[i]['ycentroid'], i, color='black');
However when I run this code with both store and plot set to True only the store part of the function runs and I can't get it to plot unless I make store False. Is there a way to write this code where I will be able to have my coordinates stored and plotted?

Simple change order - first PlotStars, next store
if PlotStars:
# ... code ...
if store:
return positions
and this will first display plot and later it exits function with value.
But if you want first get value and later plot then you should run it two times - first only with store=True and later only with PlotStars=True

How to Create a Boxplot / Group Boxplot from [Min ,Q1 ,Q2 ,Q3 ,Max] in Python? [duplicate]

From what I can see, boxplot() method expects a sequence of raw values (numbers) as input, from which it then computes percentiles to draw the boxplot(s).
I would like to have a method by which I could pass in the percentiles and get the corresponding boxplot.
For example:
Assume that I have run several benchmarks and for each benchmark I've measured latencies ( floating point values ). Now additionally, I have precomputed the percentiles for these values.
Hence for each benchmark, I have the 25th, 50th, 75th percentile along with the min and max.
Now given these data, I would like to draw the box plots for the benchmarks.

As of 2020, there is a better method than the one in the accepted answer.
The matplotlib.axes.Axes class provides a bxp method, which can be used to draw the boxes and whiskers based on the percentile values. Raw data is only needed for the outliers, and that is optional.
Example:
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
boxes = [
{
'label' : "Male height",
'whislo': 162.6, # Bottom whisker position
'q1' : 170.2, # First quartile (25th percentile)
'med' : 175.7, # Median (50th percentile)
'q3' : 180.4, # Third quartile (75th percentile)
'whishi': 187.8, # Top whisker position
'fliers': [] # Outliers
}
]
ax.bxp(boxes, showfliers=False)
ax.set_ylabel("cm")
plt.savefig("boxplot.png")
plt.close()
This produces the following image:

To draw the box plot using just the percentile values and the outliers ( if any ) I made a customized_box_plot function that basically modifies attributes in a basic box plot ( generated from a tiny sample data ) to make it fit according to your percentile values.
The customized_box_plot function
def customized_box_plot(percentiles, axes, redraw = True, *args, **kwargs):
"""
Generates a customized boxplot based on the given percentile values
"""
box_plot = axes.boxplot([[-9, -4, 2, 4, 9],]*n_box, *args, **kwargs)
# Creates len(percentiles) no of box plots
min_y, max_y = float('inf'), -float('inf')
for box_no, (q1_start,
q2_start,
q3_start,
q4_start,
q4_end,
fliers_xy) in enumerate(percentiles):
# Lower cap
box_plot['caps'][2*box_no].set_ydata([q1_start, q1_start])
# xdata is determined by the width of the box plot
# Lower whiskers
box_plot['whiskers'][2*box_no].set_ydata([q1_start, q2_start])
# Higher cap
box_plot['caps'][2*box_no + 1].set_ydata([q4_end, q4_end])
# Higher whiskers
box_plot['whiskers'][2*box_no + 1].set_ydata([q4_start, q4_end])
# Box
box_plot['boxes'][box_no].set_ydata([q2_start,
q2_start,
q4_start,
q4_start,
q2_start])
# Median
box_plot['medians'][box_no].set_ydata([q3_start, q3_start])
# Outliers
if fliers_xy is not None and len(fliers_xy[0]) != 0:
# If outliers exist
box_plot['fliers'][box_no].set(xdata = fliers_xy[0],
ydata = fliers_xy[1])
min_y = min(q1_start, min_y, fliers_xy[1].min())
max_y = max(q4_end, max_y, fliers_xy[1].max())
else:
min_y = min(q1_start, min_y)
max_y = max(q4_end, max_y)
# The y axis is rescaled to fit the new box plot completely with 10%
# of the maximum value at both ends
axes.set_ylim([min_y*1.1, max_y*1.1])
# If redraw is set to true, the canvas is updated.
if redraw:
ax.figure.canvas.draw()
return box_plot
USAGE
Using inverse logic ( code at the very end ) I extracted the percentile values from this example
>>> percentiles
(-1.0597368367634488, 0.3977683984966961, 1.0298955252405229, 1.6693981537742526, 3.4951447843464449)
(-0.90494930553559483, 0.36916539612108634, 1.0303658700697103, 1.6874542731392828, 3.4951447843464449)
(0.13744105279440233, 1.3300645202649739, 2.6131540656339483, 4.8763411136047647, 9.5751914834437937)
(0.22786243898199182, 1.4120860286080519, 2.637650402506837, 4.9067126578493259, 9.4660357513550899)
(0.0064696168078617741, 0.30586770128093388, 0.70774153557312702, 1.5241965711101928, 3.3092932063051976)
(0.007009744579241136, 0.28627373934008982, 0.66039691869500572, 1.4772725266672091, 3.221716765477217)
(-2.2621660374110544, 5.1901313713883352, 7.7178532139979357, 11.277744848353247, 20.155971739152388)
(-2.2621660374110544, 5.1884411864079532, 7.3357079047721054, 10.792299385806913, 18.842012119715388)
(2.5417888074435702, 5.885996170695587, 7.7271286220368598, 8.9207423361593179, 10.846938621419374)
(2.5971767318505856, 5.753551925927133, 7.6569980004033464, 8.8161056254143233, 10.846938621419374)
Note that to keep this short I haven't shown the outliers vectors which will be the 6th element of each of the percentile array.
Also note that all usual additional kwargs / args can be used since they are simply passed to the boxplot method inside it :
>>> fig, ax = plt.subplots()
>>> b = customized_box_plot(percentiles, ax, redraw=True, notch=0, sym='+', vert=1, whis=1.5)
>>> plt.show()
EXPLANATION
The boxplot method returns a dictionary mapping the components of the boxplot to the individual matplotlib.lines.Line2D instances that were created.
Quoting from the matplotlib.pyplot.boxplot documentation :
That dictionary has the following keys (assuming vertical boxplots):
boxes: the main body of the boxplot showing the quartiles and the median’s confidence intervals if enabled.
medians: horizonal lines at the median of each box.
whiskers: the vertical lines extending to the most extreme, n-outlier data points. caps: the horizontal lines at the ends of the whiskers.
fliers: points representing data that extend beyond the whiskers (outliers).
means: points or lines representing the means.
For example observe the boxplot of a tiny sample data of [-9, -4, 2, 4, 9]
>>> b = ax.boxplot([[-9, -4, 2, 4, 9],])
>>> b
{'boxes': [<matplotlib.lines.Line2D at 0x7fe1f5b21350>],
'caps': [<matplotlib.lines.Line2D at 0x7fe1f54d4e50>,
<matplotlib.lines.Line2D at 0x7fe1f54d0e50>],
'fliers': [<matplotlib.lines.Line2D at 0x7fe1f5b317d0>],
'means': [],
'medians': [<matplotlib.lines.Line2D at 0x7fe1f63549d0>],
'whiskers': [<matplotlib.lines.Line2D at 0x7fe1f5b22e10>,
<matplotlib.lines.Line2D at 0x7fe20c54a510>]}
>>> plt.show()
The matplotlib.lines.Line2D objects have two methods that I'll be using in my function extensively. set_xdata ( or set_ydata ) and get_xdata ( or get_ydata ).
Using these methods we can alter the position of the constituent lines of the base box plot to conform to your percentile values ( which is what the customized_box_plot function does ). After altering the constituent lines' position, you can redraw the canvas using figure.canvas.draw()
Summarizing the mappings from percentile to the coordinates of the various Line2D objects.
The Y Coordinates :
The max ( q4_end - end of 4th quartile ) corresponds to the top most cap Line2D object.
The min ( q1_start - start of the 1st quartile ) corresponds to the lowermost most cap Line2D object.
The median corresponds to the ( q3_start ) median Line2D object.
The 2 whiskers lie between the ends of the boxes and extreme caps ( q1_start and q2_start - lower whisker; q4_start and q4_end - upper whisker )
The box is actually an interesting n shaped line bounded by a cap at the lower portion. The extremes of the n shaped line correspond to the q2_start and the q4_start.
The X Coordinates :
The Central x coordinates ( for multiple box plots are usually 1, 2, 3... )
The library automatically calculates the bounding x coordinates based on the width specified.
INVERSE FUNCTION TO RETRIEVE THE PERCENTILES FROM THE boxplot DICT:
def get_percentiles_from_box_plots(bp):
percentiles = []
for i in range(len(bp['boxes'])):
percentiles.append((bp['caps'][2*i].get_ydata()[0],
bp['boxes'][i].get_ydata()[0],
bp['medians'][i].get_ydata()[0],
bp['boxes'][i].get_ydata()[2],
bp['caps'][2*i + 1].get_ydata()[0],
(bp['fliers'][i].get_xdata(),
bp['fliers'][i].get_ydata())))
return percentiles
NOTE:
The reason why I did not make a completely custom boxplot method is because, there are many features offered by the inbuilt box plot that cannot be fully reproduced.
Also excuse me if I may have unnecessarily explained something that may have been too obvious.

Here is an updated version of this useful routine. Setting the vertices directly appears to work for both filled boxes (patchArtist=True) and unfilled ones.
def customized_box_plot(percentiles, axes, redraw = True, *args, **kwargs):
"""
Generates a customized boxplot based on the given percentile values
"""
n_box = len(percentiles)
box_plot = axes.boxplot([[-9, -4, 2, 4, 9],]*n_box, *args, **kwargs)
# Creates len(percentiles) no of box plots
min_y, max_y = float('inf'), -float('inf')
for box_no, pdata in enumerate(percentiles):
if len(pdata) == 6:
(q1_start, q2_start, q3_start, q4_start, q4_end, fliers_xy) = pdata
elif len(pdata) == 5:
(q1_start, q2_start, q3_start, q4_start, q4_end) = pdata
fliers_xy = None
else:
raise ValueError("Percentile arrays for customized_box_plot must have either 5 or 6 values")
# Lower cap
box_plot['caps'][2*box_no].set_ydata([q1_start, q1_start])
# xdata is determined by the width of the box plot
# Lower whiskers
box_plot['whiskers'][2*box_no].set_ydata([q1_start, q2_start])
# Higher cap
box_plot['caps'][2*box_no + 1].set_ydata([q4_end, q4_end])
# Higher whiskers
box_plot['whiskers'][2*box_no + 1].set_ydata([q4_start, q4_end])
# Box
path = box_plot['boxes'][box_no].get_path()
path.vertices[0][1] = q2_start
path.vertices[1][1] = q2_start
path.vertices[2][1] = q4_start
path.vertices[3][1] = q4_start
path.vertices[4][1] = q2_start
# Median
box_plot['medians'][box_no].set_ydata([q3_start, q3_start])
# Outliers
if fliers_xy is not None and len(fliers_xy[0]) != 0:
# If outliers exist
box_plot['fliers'][box_no].set(xdata = fliers_xy[0],
ydata = fliers_xy[1])
min_y = min(q1_start, min_y, fliers_xy[1].min())
max_y = max(q4_end, max_y, fliers_xy[1].max())
else:
min_y = min(q1_start, min_y)
max_y = max(q4_end, max_y)
# The y axis is rescaled to fit the new box plot completely with 10%
# of the maximum value at both ends
axes.set_ylim([min_y*1.1, max_y*1.1])
# If redraw is set to true, the canvas is updated.
if redraw:
ax.figure.canvas.draw()
return box_plot

Here is a bottom-up approach where the box_plot is build up using matplotlib's vline, Rectangle, and normal plot functions
def boxplot(df, ax=None, box_width=0.2, whisker_size=20, mean_size=10, median_size = 10 , line_width=1.5, xoffset=0,
color=0):
"""Plots a boxplot from existing percentiles.
Parameters
----------
df: pandas DataFrame
ax: pandas AxesSubplot
if to plot on en existing axes
box_width: float
whisker_size: float
size of the bar at the end of each whisker
mean_size: float
size of the mean symbol
color: int or rgb(list)
If int particular color of property cycler is taken. Example of rgb: [1,0,0] (red)
Returns
-------
f, a, boxes, vlines, whisker_tips, mean, median
"""
if type(color) == int:
color = plt.rcParams['axes.prop_cycle'].by_key()['color'][color]
if ax:
a = ax
f = a.get_figure()
else:
f, a = plt.subplots()
boxes = []
vlines = []
xn = []
for row in df.iterrows():
x = row[0] + xoffset
xn.append(x)
# box
y = row[1][25]
height = row[1][75] - row[1][25]
box = plt.Rectangle((x - box_width / 2, y), box_width, height)
a.add_patch(box)
boxes.append(box)
# whiskers
y = (row[1][95] + row[1][5]) / 2
vl = a.vlines(x, row[1][5], row[1][95])
vlines.append(vl)
for b in boxes:
b.set_linewidth(line_width)
b.set_facecolor([1, 1, 1, 1])
b.set_edgecolor(color)
b.set_zorder(2)
for vl in vlines:
vl.set_color(color)
vl.set_linewidth(line_width)
vl.set_zorder(1)
whisker_tips = []
if whisker_size:
g, = a.plot(xn, df[5], ls='')
whisker_tips.append(g)
g, = a.plot(xn, df[95], ls='')
whisker_tips.append(g)
for wt in whisker_tips:
wt.set_markeredgewidth(line_width)
wt.set_color(color)
wt.set_markersize(whisker_size)
wt.set_marker('_')
mean = None
if mean_size:
g, = a.plot(xn, df['mean'], ls='')
g.set_marker('o')
g.set_markersize(mean_size)
g.set_zorder(20)
g.set_markerfacecolor('None')
g.set_markeredgewidth(line_width)
g.set_markeredgecolor(color)
mean = g
median = None
if median_size:
g, = a.plot(xn, df['median'], ls='')
g.set_marker('_')
g.set_markersize(median_size)
g.set_zorder(20)
g.set_markeredgewidth(line_width)
g.set_markeredgecolor(color)
median = g
a.set_ylim(np.nanmin(df), np.nanmax(df))
return f, a, boxes, vlines, whisker_tips, mean, median
This is how it looks in action:
import numpy as np
import pandas as pd
import matplotlib.pylab as plt
nopts = 12
df = pd.DataFrame()
df['mean'] = np.random.random(nopts) + 7
df['median'] = np.random.random(nopts) + 7
df[5] = np.random.random(nopts) + 4
df[25] = np.random.random(nopts) + 6
df[75] = np.random.random(nopts) + 8
df[95] = np.random.random(nopts) + 10
out = boxplot(df)

Python : Multiple Line\any form of graph by defining Matching column values with large datasets

Trying to Plot this Raw JSON data into graph, doesn't want to have a individual graph for each entity/LDEV value ( will be difficult to compare). For Example : I wanted to create a graph with multiple line plot for LDEV_NUMBER - 00:42:26 , with X-axis value as Record time and Y- Axis value as "READ_IO_COUNT", Similarly, next line graph will represent next LDEV and so on.
import io
import pandas as pd
from pathlib import Path
import matplotlib.pyplot as plt
data = b'RECORD_TIME,LDEV_NUMBER,READ_IO_COUNT,WRITE_IO_COUNT,READ_MBYTES,WRITE_MBYTES,READ_RESPONSE_RATE,WRITE_RESPONSE_RATE,TOTAL_RESPONSE_RATE\r\ntime_t,string(16),ulong,ulong,ulong,ulong,float,float,float\r\n2020-03-16 00:00:42,"00:42:26",217,0,1,0,9.200517E+01,0.000000E+00,9.200517E+01\r\n2020-03-16 00:01:02,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:02:02,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:03:02,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:04:01,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:05:02,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:06:02,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:07:02,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:08:01,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:09:01,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:10:02,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:11:01,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:12:02,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:13:01,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:14:02,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:15:02,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:16:01,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:17:02,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:18:02,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:19:01,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:20:01,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:21:01,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:22:01,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:23:01,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:24:01,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:25:02,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:26:02,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:27:02,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:28:02,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:29:01,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:30:01,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:31:02,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:32:02,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:33:02,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:34:01,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:35:01,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:36:02,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:37:02,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:38:02,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:39:01,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:40:02,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:41:01,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:42:01,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:43:02,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:44:02,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:45:02,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:46:02,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:47:02,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:48:02,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:49:01,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:50:01,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:51:02,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:52:02,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:53:02,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:54:02,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:55:01,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:56:02,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:57:01,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:58:02,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:59:02,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:00:33,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:01:01,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:02:01,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:03:01,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:04:02,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:05:01,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:06:02,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:07:01,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:08:02,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:09:02,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:10:01,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:11:01,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:12:01,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:13:02,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:14:01,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:15:01,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:16:02,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:17:02,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:18:02,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:19:02,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:20:01,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:21:01,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:22:01,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:23:02,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:24:02,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:25:02,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:26:02,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:27:02,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:28:02,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:29:01,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:30:02,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:31:01,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:32:02,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:33:02,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:34:02,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:35:01,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:36:02,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:37:01,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:38:01,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:39:01,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:40:02,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:41:02,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:42:01,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:43:01,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:44:01,"00:42:26",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:55:02,"00:42:28",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:56:02,"00:42:28",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:57:01,"00:42:28",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:58:01,"00:42:28",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:59:02,"00:42:28",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 02:00:33,"00:42:28",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:17:02,"00:42:2B",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:18:02,"00:42:2B",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:19:01,"00:42:2B",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:20:01,"00:42:2B",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:21:01,"00:42:2B",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:22:01,"00:42:2B",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:23:01,"00:42:2B",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:24:01,"00:42:2B",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:25:02,"00:42:2B",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:26:02,"00:42:2B",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:27:02,"00:42:2B",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:28:02,"00:42:2B",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:29:01,"00:42:2B",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:30:01,"00:42:2B",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:31:02,"00:42:2B",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:32:02,"00:42:2B",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:33:02,"00:42:2B",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:34:01,"00:42:2B",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:35:01,"00:42:2B",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:36:02,"00:42:2B",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:37:02,"00:42:2B",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:38:02,"00:42:2B",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:39:01,"00:42:2B",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:40:02,"00:42:2B",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:41:01,"00:42:2B",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:42:01,"00:42:2B",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:43:02,"00:42:2B",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:44:02,"00:42:2B",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:45:02,"00:42:2B",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:46:02,"00:42:2B",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:47:02,"00:42:2B",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:48:02,"00:42:2B",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:49:01,"00:42:2B",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:50:01,"00:42:2B",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:51:02,"00:42:2B",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:52:02,"00:42:2B",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:53:02,"00:42:2B",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:54:02,"00:42:2B",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:55:01,"00:42:2B",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:56:02,"00:42:2B",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:57:01,"00:42:2B",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:58:02,"00:42:2B",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 00:59:02,"00:42:2B",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:00:33,"00:42:2B",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:01:01,"00:42:2B",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:02:01,"00:42:2B",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:03:01,"00:42:2B",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:04:02,"00:42:2B",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:05:01,"00:42:2B",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:06:02,"00:42:2B",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:07:01,"00:42:2B",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:08:02,"00:42:2B",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:09:02,"00:42:2B",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:10:01,"00:42:2B",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:11:01,"00:42:2B",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:12:01,"00:42:2B",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:13:02,"00:42:2B",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:14:01,"00:42:2B",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:15:01,"00:42:2B",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:16:02,"00:42:2B",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:17:02,"00:42:2B",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:18:02,"00:42:2B",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 01:59:02,"00:42:2B",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n2020-03-16 02:00:33,"00:42:2B",0,0,0,0,0.000000E+00,0.000000E+00,0.000000E+00\r\n\r\n'
df = pd.read_csv(io.StringIO(data.decode('ascii')))
df1=df.drop(df.index[0])
Lun_name=["00:42:2A","00:42:2B","00:42:27","00:42:26","00:42:28","00:42:29"]
for i in range(len(Lun_name)):
plt.plot(df.loc[df['LDEV_NUMBER'] =='%s'%Lun_name[i]]['RECORD_TIME'], df.loc[df['LDEV_NUMBER'] == '%s'%Lun_name[i]]['READ_IO_COUNT'])
plt.show()
Axes values are disoriented when i try to plot this using above code.
[READ IOPS]

with help of my colleague , i resolved it.Issue here is Y- Axis plotting data value are (for Ex: TOTAL_MBYTES ) with datatype as object , after converting it to integer, the axes looks fine . We did a slight modifications to the code as below
for i in range(len(portname)):
# plt.plot(df.loc[df['LDEV_NUMBER'] =='%s'%Lun_name[i]]['RECORD_TIME'], df.loc[df['LDEV_NUMBER'] == '%s'%Lun_name[i]]['READ_IO_COUNT'])
X = list(df1.loc[df1['PORT_NAME'] == '%s' % portname[i]]['DATETIME'])
Y = list(df1.loc[df1['PORT_NAME'] == '%s' % portname[i]]['TOTAL_MBYTES'])
ax = plt.axes()
ax.xaxis.set_minor_locator(dates.HourLocator(interval=4)) # every 4 hours
ax.xaxis.set_minor_formatter(dates.DateFormatter('%H:%M')) # hours and minutes
ax.xaxis.set_major_locator(dates.DayLocator(interval=1)) # every day
ax.xaxis.set_major_formatter(dates.DateFormatter('\n%d-%m-%Y'))
for i in range(len(Y)):
Y[i] = int(Y[i])
plt.plot(X, Y)
plt.ylabel('Processor throughput')
plt.xlabel('Time')
plt.legend(portname)
plt.savefig('port_throughput.png')

How to do a second interpolation in python

I did my first interpolation with numpy.polyfit() and numpy.polyval() for 50 longitude values for a full satellite orbit.
Now, I just want to look at a window of 0-4.5 degrees longitude and do a second interpolation so that I have 6,000 points for longitude in the window.
I need to use the equation/curve from the first interpolation to create the second one because there is only one point in the window range. I'm not sure how to do the second interpolation.
Inputs:
lon = [-109.73105744378498, -104.28690174554579, -99.2435132929552, -94.48533149079628, -89.91054414962821, -85.42671400689177, -80.94616150449806, -76.38135021210172, -71.6402674905218, -66.62178379632216, -61.21120467960157, -55.27684029674759, -48.66970878028004, -41.23083703244677, -32.813881865289346, -23.332386757370532, -12.832819226213942, -1.5659455609661785, 10.008077792630402, 21.33116444634303, 31.92601575632583, 41.51883213364072, 50.04498630545507, 57.58103957109249, 64.26993028992476, 70.2708323505337, 75.73441871754586, 80.7944079829813, 85.56734813043659, 90.1558676264546, 94.65309120129724, 99.14730128118617, 103.72658922048785, 108.48349841714494, 113.51966824008079, 118.95024882101737, 124.9072309203375, 131.5395221402974, 139.00523971191907, 147.44847902856114, 156.95146022590976, 167.46163867248032, 178.72228750873975, -169.72898181991064, -158.44642409799974, -147.8993300787564, -138.35373014113995, -129.86955508919888, -122.36868103811106, -115.70852432245486]
myOrbitJ2000Time = [ 20027712., 20027713., 20027714., 20027715., 20027716.,
20027717., 20027718., 20027719., 20027720., 20027721.,
20027722., 20027723., 20027724., 20027725., 20027726.,
20027727., 20027728., 20027729., 20027730., 20027731.,
20027732., 20027733., 20027734., 20027735., 20027736.,
20027737., 20027738., 20027739., 20027740., 20027741.,
20027742., 20027743., 20027744., 20027745., 20027746.,
20027747., 20027748., 20027749., 20027750., 20027751.,
20027752., 20027753., 20027754., 20027755., 20027756.,
20027757., 20027758., 20027759., 20027760., 20027761.]
Code:
deg = 30 #polynomial degree for fit
fittime = myOrbitJ2000Time - myOrbitJ2000Time[0]
'Longitude Interpolation'
fitLon = np.polyfit(fittime, lon, deg) #gets fit coefficients
polyval_lon = np.polyval(fitLon,fittime) #interp.s to get actual values
'Get Longitude values for a window of 0-4.5 deg Longitude'
lonwindow =[]
for i in range(len(polyval_lon)):
if 0 < polyval_lon[i] < 4.5: # get lon vals in window
lonwindow.append(polyval_lon[i]) #append lon vals
lonwindow = np.array(lonwindow)

First, generate the polynomial fit coefficients using the old time (x-axis) values, and interpolated longitude (y-axis) values.
import numpy as np
import matplotlib.pyplot as plt
poly_deg = 3 #degree of the polynomial fit
polynomial_fit_coeff = np.polyfit(original_times, interp_lon, poly_deg)
Next, use np.linspace() to generate arbitrary time values based on the number of desire points in the window.
start = 0
stop = 4
num_points = 6000
arbitrary_time = np.linspace(start, stop, num_points)
Finally, use the fit coefficients and the arbitrary time to get the actual interpolated longitude (y-axis) values and plot.
lon_intrp_2 = np.polyval(polynomial_fit_coeff, arbitrary_time)
plt.plot(arbitrary_time, lon_intrp_2, 'r') #interpolated window as a red curve
plt.plot(myOrbitJ2000Time, lon, '.') #original data plotted as points

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()