Develop Reference

Mean spectra over multiple pixels from datacube

I have a fits datacube with galactic longitude, latitude and velocity in the 3 axis. To extract the spectrum from the datacube at a particular pixel value of longitude and latitude, I use the function
cube[:, 1935, 1407].quicklook()
plt.show()
and the image is extracted with the function
cube.to_pvextractor()
plt.show()
A sample spectrum
and a zoomed image is attached here.
The bright spots are the detections. How do I use several pixels and average the spectra to get a mean spectrum so that I reduce the noise and analyze the peak? I have been trying to code this but I don't know how to proceed as I am new to python. Can anybody please give a hint?

You can use spectrapepper for this:
import spectrapepper as spep
import matplotlib.pyplot as plt
# load sample data from library
x, y = spep.load_spectras()
# calculate the average spectra of the set
avg = spep.avg(y)
# plot the result compared to the data set
for i in y:
plt.plot(x, i, lw=0.5)
plt.plot(x, avg, c='red', lw=1)
plt.show()
The library has also other tools for data set analysis. Check example 6 included in the docs for more options of similar nature.

Density plot using seaborn

I'm trying to make a density plot of the hourly demand:
data
The 'hr' means different hours, 'cnt' means demand.
I know how to make a density plot such as:
sns.kdeplot(bike['hr'])
However, this only works when the demand for different hours is unknown. Thus I can count each hour as its demand. Now I know the demand count of each hour, how I can make a density plot of such data?

A density plot aims to show an estimate of a distribution. To make a graph showing the density of hourly demand, we would really expect to see many iid samples of demand, with time-stamps, i.e. one row per sample. Then a density plot would make sense.
But in the type of data here, where the demand ('cnt') is sampled regularly and aggregated over that sample period (the hour), a density plot is not directly meaningful. But a bar graph as a histogram does make sense, using the hours as the bins.
Below I show how to use pandas functions to produce such a plot -- really simple. For reference I also show how we might produce a density plot, through a sort of reconstruction of "original" samples.
df = pd.read_csv("../data/hour.csv") # load dataset, inc cols hr, cnt, no NaNs
# using the bar plotter built in to pandas objects
fig, ax = plt.subplots(1,2)
df.groupby('hr').agg({'cnt':sum}).plot.bar(ax=ax[0])
# reconstructed samples - has df.cnt.sum() rows, each one containing an hour of a rental.
samples = np.hstack([ np.repeat(h, df.cnt.iloc[i]) for i, h in enumerate(df.hr)])
# plot a density estimate
sns.kdeplot(samples, bw=0.5, lw=3, c="r", ax=ax[1])
# to make a useful comparison with a density estimate, we need to have our bar areas
# sum up to 1, so we use groupby.apply to divide by the total of all counts.
tot = float(df.cnt.sum())
df.groupby('hr').apply(lambda x: x['cnt'].sum()/tot).plot.bar(ax=ax[1], color='C0')
Demand for bikes seems to be low during the night... But it is also apparent that they are probably used for commuting, with peaks at hours 8am and 5-6pm.

Draw a curve from the scatter plot in matplotlib in Python?

My Question:
How can i draw a curve though this data, thus describing an equation for this plot..
I generated this scatter plot by following code, but I am not able to figure out how to generate an equation for this data and draw the corresponding curve on this plot simultaneously. Please Help.!
def draw(data,xlabel,ylabel):
print('length of data : ',len(data))
x,y = [],[]
for i in data:
x.append((i[1]))
y.append((i[0]))
plt.scatter(x, y,marker=r'o',color='b')
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.show()
Basically I want something like this:

You have to perform a curve fitting procedure, which is called a regression problem in mathematics. In your case it seems that data is more or less exponential, but you can fit arbitrary function through scipy.optimize.curve_fit
http://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.curve_fit.html

Plotting histogram with matplotlib

i try to plot data in a histogram or bar in python. The data size (array size) is between 0-10000. The data itself (each entry of the array) depends on the input and has a range between 0 and e+20 (mostly the data is in th same range). So i want to do a hist plot with matplotlib. I want to plot how often a data is in some intervall (to illustrate the mean and deviation). Sometimes it works like this:
hist1.
But sometimes there is a problem with the intevall size like this:
hist2.
In this plot i need more bars at point 0-100 etc.
Can anyone help me with this?
The plots are just made with:
from numpy.linalg import *
import matplotlib.pyplot as plt
plt.hist(numbers,bins=100)
plt.show()

By default, hist produces a plot with an x range that covers the full range of your data.
If you have one outsider at very high x in comparison with the other values, then you will see this image with a 'compressed' figure.
I you want to have always the same view you can fix the limits with xlim.
Alternatively, if you want to see your distribution always centered and as nicer as possible, you can calculate the mean and the standard deviation of your data and fix the x range accordingly (p.e. for mean +/- 5 stdev)

Number density contours in Python

I'm trying to reproduce this plot in python with little luck:
It's a simple number density contour currently done in SuperMongo. I'd like to drop it in favor of Python but the closest I can get is:
which is by using hexbin(). How could I go about getting the python plot to resemble the SuperMongo one? I don't have enough rep to post images, sorry for the links. Thanks for your time!

Example simple contour plot from a fellow SuperMongo => python sufferer:
import numpy as np
from matplotlib.colors import LogNorm
from matplotlib import pyplot as plt
plt.interactive(True)
fig=plt.figure(1)
plt.clf()
# generate input data; you already have that
x1 = np.random.normal(0,10,100000)
y1 = np.random.normal(0,7,100000)/10.
x2 = np.random.normal(-15,7,100000)
y2 = np.random.normal(-10,10,100000)/10.
x=np.concatenate([x1,x2])
y=np.concatenate([y1,y2])
# calculate the 2D density of the data given
counts,xbins,ybins=np.histogram2d(x,y,bins=100,normed=LogNorm())
# make the contour plot
plt.contour(counts.transpose(),extent=[xbins.min(),xbins.max(),
ybins.min(),ybins.max()],linewidths=3,colors='black',
linestyles='solid')
plt.show()
produces a nice contour plot.
The contour function offers a lot of fancy adjustments, for example let's set the levels by hand:
plt.clf()
mylevels=[1.e-4, 1.e-3, 1.e-2]
plt.contour(counts.transpose(),mylevels,extent=[xbins.min(),xbins.max(),
ybins.min(),ybins.max()],linewidths=3,colors='black',
linestyles='solid')
plt.show()
producing this plot:
And finally, in SM one can do contour plots on linear and log scales, so I spent a little time trying to figure out how to do this in matplotlib. Here is an example when the y points need to be plotted on the log scale and the x points still on the linear scale:
plt.clf()
# this is our new data which ought to be plotted on the log scale
ynew=10**y
# but the binning needs to be done in linear space
counts,xbins,ybins=np.histogram2d(x,y,bins=100,normed=LogNorm())
mylevels=[1.e-4,1.e-3,1.e-2]
# and the plotting needs to be done in the data (i.e., exponential) space
plt.contour(xbins[:-1],10**ybins[:-1],counts.transpose(),mylevels,
extent=[xbins.min(),xbins.max(),ybins.min(),ybins.max()],
linewidths=3,colors='black',linestyles='solid')
plt.yscale('log')
plt.show()
This produces a plot which looks very similar to the linear one, but with a nice vertical log axis, which is what was intended:

Have you checked out matplotlib's contour plot?

Unfortunately I couldn't view yours images. Do you mean something like this? It was done by MathGL -- GPL plotting library, which have Python interface too. And you can use arbitrary data arrays as input (including numpy's one).

You can use numpy.histogram2d to get a number density distribution of your array.
Try this example:
http://micropore.wordpress.com/2011/10/01/2d-density-plot-or-2d-histogram/