human
I am working on the file "Antoine_ISNARDY_el_nino.ipynb".
The first task is:
These two plots support the previous claim: temperatures in the east zone of EN are way more correlated to the temperature of the whole zone than the ones in the west zone.
Given the bunch of time series we had at our disposal, I first looked at a way of summarizing it through correlations. This leads to my first approach, below.

<a id='paag'></a>
First approach: Pacific As A Graph (PaaG)

The idea of this approach mainly relies on ideas found at http://www.geosci-model-dev-discuss.net/gmd-2015-273/gmd-2015-273.pdf
In a nutshell, it aims at:
Studying correlations in a grid of points, transformed into a graph.
Feeding regression techniques with variables depending on correlations (trees in my case)



First step was then to transform the data into a graph of points.
As suggested in the starting kit, I limited the grid to Pacific points, with El Nino points at the center. The graph looks like the picture below (it is not perfectly symetric in order to match coordinate system of X_ds)
Can you write Python program for it?

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What is a the Python program you requested?
assistant
#Here is the Python program you requested.

class Physical_point:
    """
    Point defined by its latitute and longitude
    """
    def __init__(self, lat, lon):
        self.lat = lat
        self.lon = lon
        
en=[-5, 5, 190, 240] # El Nino zone

en_points = [Physical_point(5,192.5),
             Physical_point(5, 242.5),
             Physical_point(0, 212.5),
             Physical_point(-5, 192.5),
             Physical_point(-5, 242.5)]
pacific_points = [Physical_point(60, 212.5),
                  Physical_point(50, 162.5),
                  Physical_point(50, 262.5),
                  Physical_point(40, 212.5),
                  Physical_point(30, 162.5),
                  Physical_point(30, 262.5),
                  Physical_point(20, 212.5),
                  Physical_point(10, 262.5),
                  Physical_point(10, 162.5),
                  Physical_point(-10, 262.5),
                  Physical_point(-10, 162.5),
                  Physical_point(-20, 212.5),
                  Physical_point(-30, 262.5),
                  Physical_point(-30, 162.5),
                  Physical_point(-40, 212.5),
                  Physical_point(-50, 162.5),
                  Physical_point(-50, 262.5),
                  Physical_point(-60, 212.5),
                  Physical_point(0, 162.5),
                  Physical_point(0, 262.5)]