Assignment 3 - Numpy and Matplotlib¶

1 Creating and Manipulating Arrays¶

First import numpy and matplotlib

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1.1. Create two 2D arrays (xx and yy) representing coordinates x, y on the cartesian plan¶

Both should cover the range (-2, 2) and have 100 points in each direction

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1.2. Visualize each 2D array using pcolormesh¶

Use the correct coordiantes for the x and y axes. Provide axis labels for all of your plots in this assignment.

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1.3 From your cartesian coordinates, create polar coordinates $r$ and $\varphi$¶

Refer to the wikipedia page for the conversion formula. You will need to use numpy's arctan2 function. Read its documentation.

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1.4. Visualize $r$ and $\varphi$ as functions of $x$ and $y$¶

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1.5 Define the function $f = \cos^2(4r) + \sin^2(4\varphi)$ and Plot it as a function of $x$ and $y$¶

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1.6 Plot the mean of f with respect to the x axis¶

as a function of y

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1.7 Plot the mean of f with respect to the y axis¶

as a function of x

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Part II: Making plots with Matplotlib for real data¶

In this problem, we will plot some daily weather data from a NOAA station in Millbrook, NY.

The cell below uses pandas to load the data and populate a bunch of numpy arrays (t_daily_min, t_daily_max, etc.)

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import pandas as pd

df = pd.read_csv('Millbrook_NY_daily_weather.csv', parse_dates=['LST_DATE'])
df = df.set_index('LST_DATE')

#########################################################
#### BELOW ARE THE VARIABLES YOU SHOULD USE IN THE PLOTS!
#### (numpy arrays) 
#### NO PANDAS ALLOWED!
#########################################################

t_daily_min = df.T_DAILY_MIN.values
t_daily_max = df.T_DAILY_MAX.values
t_daily_mean = df.T_DAILY_MEAN.values
p_daily_calc = df.P_DAILY_CALC.values
soil_moisture_5 = df.SOIL_MOISTURE_5_DAILY.values
soil_moisture_10 = df.SOIL_MOISTURE_10_DAILY.values
soil_moisture_20 = df.SOIL_MOISTURE_20_DAILY.values
soil_moisture_50 = df.SOIL_MOISTURE_50_DAILY.values
soil_moisture_100 = df.SOIL_MOISTURE_100_DAILY.values
date = df.index.values

import pandas as pd df = pd.read_csv('Millbrook_NY_daily_weather.csv', parse_dates=['LST_DATE']) df = df.set_index('LST_DATE') ######################################################### #### BELOW ARE THE VARIABLES YOU SHOULD USE IN THE PLOTS! #### (numpy arrays) #### NO PANDAS ALLOWED! ######################################################### t_daily_min = df.T_DAILY_MIN.values t_daily_max = df.T_DAILY_MAX.values t_daily_mean = df.T_DAILY_MEAN.values p_daily_calc = df.P_DAILY_CALC.values soil_moisture_5 = df.SOIL_MOISTURE_5_DAILY.values soil_moisture_10 = df.SOIL_MOISTURE_10_DAILY.values soil_moisture_20 = df.SOIL_MOISTURE_20_DAILY.values soil_moisture_50 = df.SOIL_MOISTURE_50_DAILY.values soil_moisture_100 = df.SOIL_MOISTURE_100_DAILY.values date = df.index.values

2.1 Use numpy to calculate mean temperature, precipitation and soil moisture at different layers.¶

Write a loop to make the code short and efficient.

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2.2 Use the numpy arrays to try to re-create the plot you see below¶

Hint: Try fill_between to plot range values

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