$2-$D Histograms

A$)$ Download and load "stars.txt$"$ in your program. Make a $2$D Histogram of Magnitude of

the Stars vs Temperature of the Stars. Choose a $5050$ bins size.

B$)$ Change the color scale to LogNorm instead and add the colorbar to the plot and

increase the figure size to $(12,8)$

from matplotlib. colors import LogNorm

from pylab import colorbar, figure

C$)$ hist$2$D also returns the histogram information to you in tuple form, but everything is in

$2$D now!

$h2=$ hist$2D(stars[$:$,0],$stars$[$:$,1])$

$pr{\displaystyle \underset{\phantom{}}{\overset{\phantom{}}{}}}(type(h2))$

$h2[0]$ is the NxM array of the values of the histogram.

$h2[1]$ is the array of $x$ bin edges

$h2[2]$ is the array of $y$ bin edges

The bin edges have $1$ more elements than the number of bins as usual, but we can use

BinCenter to fix that. Use BinCenter to save the arrays of the Temperature Bin Center

and Magnitude Bin Center.

D$)h2[0]$ is a $2$D array so that's harder to deal with. Write a program that loops over the

$y-$axis $($Magnitude$)$ of h$2[0]$ and compute the WeightedAvg for each row of $x-$values

$($Temperature$).$ Save these values to an array.

Plot Magnitude vs WeightedAvg Temperature you computed and overlay it with the

Histogram.

E$)$ Noticed that your WeightedAvg goes through the top of the Histogram most in the

bulk but does NOT near the top or bottom. Explain why briefly.

stars.txt $=7828\mathrm{.}831919\mathrm{.}67$

$2828\mathrm{.}838318\mathrm{.}78$

$2929\mathrm{.}918919\mathrm{.}17$

$1103\mathrm{.}104714\mathrm{.}19$

$1929\mathrm{.}037117\mathrm{.}29$

$7028\mathrm{.}015912\mathrm{.}08$

$9614\mathrm{.}193616\mathrm{.}92$