{ "cells": [ { "cell_type": "markdown", "id": "252023b5", "metadata": {}, "source": [ "

Women in WACREN Workshop

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Day 2; Lecture ON ```Introduction to Python for Climate Data Analysis```

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\"Learning to code is useful no matter what your career ambitions are.”—Arianna Huffington

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\n", "\n", " \n", " \n", " \n", " \n", "
\n", " \"Miss\n", " \n", "

\n", " Miss Mercy Mawunyo Apaw
\n", " E-mail: apawmercy@gmail.com / apawmercymawunyo@gmail.com\n", "

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\n" ] }, { "cell_type": "markdown", "id": "6fd8c4e8", "metadata": {}, "source": [ "

Why Python?

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Python (programming language) Python is an interpreted, high-level, general-purpose programming language. Created by Guido van Rossum and first released in 1991, Python's design philosophy emphasizes code readability with its notable use of significant whitespace.\n", "Since most modern OS are written in C, compilers/interpreters for modern high-level languages are also written in C. Python is not an exception - its most popular/\"traditional\" implementation is called CPython and is written in C.\n", "As python is object-oriented, it has its own garbage collector whereas in C user has to manage memory on his own. C has compiled language. The complete source code is converted into a machine language which is easier for a computer to understand. Python on other hand is interpreted.

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Python can be integrated with other languages such as C, C++, and Java. This means that a programmer can take advantage of work already done in another language while using Python. It also means that he or she can leverage the strengths of other languages, such as the extra speed that C or C++ can offer, while still enjoying the ease of development that's a hallmark of Python programming. (M. Dawson, 2003, Python Programming for the Absolute Beginner)\n", "

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The advantages of Python programming spans, but is not limited to the following:

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HELPFUL RESOURCES FOR PYTHON HELP AND SELF-STUDY

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This is just a little information on the above subject matter


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“I came in with near zero programming knowledge and halfway in, I’m quite confident of what I can achieve.”—Kelechi,

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Let's Get Started!!! :)

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Outline

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" ] }, { "cell_type": "code", "execution_count": null, "id": "30a4b270", "metadata": {}, "outputs": [], "source": [ "#!pip install pandas \n", "#!pip install numpy \n", "#!pip install matplotlib\n", "#!pip install xarray\n", "#!pip install netCDF4 h5netcdf\n", "#!pip install cartopy" ] }, { "cell_type": "markdown", "id": "4effb76d", "metadata": {}, "source": [ "# Numerical Python (NumPy)\n", "\n", "NumPy is a fundamental library for numerical computing in Python. It provides a wide range of mathematical functions to operate on data structures efficiently. The N-dimensional Arrays(ndarray): is the core feacture of NumPy and can hold elements of the same data type, store and manipulate large datasets. \n", "\n", "Array can be: \n", "* One-dimensional — Lists orsequence of elements of the same type.\n", "* Two-dimensional — Like a matrix or a table with rows and columns. \n", "* Multi-dimensional — Have more than two dimensions.\n", "\n", "\n", "\n", " Why NumPy:\n", "\n", "* Performance:NumPy arrays are faster and more efficient than Python lists for numerical computations, especially when working with large datasets.\n", "* Functionality:Provides a wide range of mathematical, statistical, and linear algebra functions.\n", "* Interoperability: Works seamlessly with other scientific libraries in Python, like pandas, SciPy, and matplotlib.\n", "* Simplicity: Makes complex mathematical and scientific calculations simpler to implement and understand." ] }, { "cell_type": "markdown", "id": "565537be", "metadata": {}, "source": [ "# NumPy" ] }, { "cell_type": "code", "execution_count": 1, "id": "fea9edc4", "metadata": {}, "outputs": [], "source": [ "import numpy as np # importing numpy library" ] }, { "cell_type": "code", "execution_count": 2, "id": "98f4b026", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "NumPy Array Example:\n" ] }, { "data": { "text/plain": [ "array([282.55669753, 280.13340897, 282.35365855, 281.5945037 ,\n", " 280.28445094, 280.88671303, 281.65401184, 281.98823586,\n", " 281.93720382, 278.59430107])" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print(\"NumPy Array Example:\")\n", "array = 278 + 5 * np.random.rand(10) # creates an array of temperature values\n", "array" ] }, { "cell_type": "code", "execution_count": 3, "id": "c7db0bb2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(10,)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "array.shape #Shape of array" ] }, { "cell_type": "code", "execution_count": 4, "id": "361dfcb4", "metadata": {}, "outputs": [], "source": [ "da = array + 10\n", "sum_array = np.sum(array) #Sum the data array\n", "mean_array = np.mean(array) # Calculate the mean\n", "maxi_array = np.max(array) # Maximum value in the data array\n" ] }, { "cell_type": "markdown", "id": "649bfe1c", "metadata": {}, "source": [ "# Array indexing and slicing" ] }, { "cell_type": "code", "execution_count": 5, "id": "7434450d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "282.55669752758206" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "array[0] # selects First data \n", " " ] }, { "cell_type": "code", "execution_count": 6, "id": "37871985", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([282.55669753, 280.13340897])" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "array[0:2] # first two data" ] }, { "cell_type": "code", "execution_count": 7, "id": "1d8de097", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([282.55669753, 280.13340897, 282.35365855, 281.5945037 ,\n", " 280.28445094, 280.88671303, 281.65401184, 281.98823586,\n", " 281.93720382, 278.59430107])" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "array[:] # display all the data" ] }, { "cell_type": "code", "execution_count": 8, "id": "aa2b50c3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[1, 2, 3],\n", " [4, 5, 6],\n", " [7, 8, 9]])" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "matrix = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) # Creates a 2D array (matrix)\n", "matrix" ] }, { "cell_type": "code", "execution_count": 9, "id": "9b2caa2d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(3, 3)" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "matrix.shape # gives the shape of the Matrix" ] }, { "cell_type": "code", "execution_count": 10, "id": "79d72b30", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[1, 4, 7],\n", " [2, 5, 8],\n", " [3, 6, 9]])" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.transpose(matrix) # Gives transpose of matrix" ] }, { "cell_type": "code", "execution_count": 11, "id": "67728bb6", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[1, 4, 7],\n", " [2, 5, 8],\n", " [3, 6, 9]])" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "matrix.T # Gives transpose of matrix" ] }, { "cell_type": "code", "execution_count": 12, "id": "69edcadd", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(2, 3)" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "da = np.array([[1, -2, 3], [4, 5, -6]]) # creates an array with 2 rows and 3 columns\n", "da.shape" ] }, { "cell_type": "code", "execution_count": 13, "id": "27ed81e3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 1],\n", " [-2],\n", " [ 3],\n", " [ 4],\n", " [ 5],\n", " [-6]])" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "da.reshape(6,1) # reshapes da into 6 rows and a column" ] }, { "cell_type": "code", "execution_count": 14, "id": "eb32e5e0", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 1, -2, 3, 4, 5, -6])" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reshaped = da.reshape((6,)) # Reshapes array from 2D to 1D:\n", "reshaped\n" ] }, { "cell_type": "code", "execution_count": 15, "id": "2650c698", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "5" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.max(reshaped) # Print the maximum value in an array" ] }, { "cell_type": "code", "execution_count": 16, "id": "a63505ed", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "4" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.argmax(reshaped) #Print the position of the maximum value in an array (Remember python array indexing starts from 0" ] }, { "cell_type": "code", "execution_count": 17, "id": "831a9d74", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[-6, -2, 1, 3, 4, 5]])" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.sort([reshaped]) #Arrange the array from least to greatest value." ] }, { "cell_type": "code", "execution_count": 18, "id": "e85c8b24", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([1, 2, 3, 4, 5, 6])" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.abs(reshaped) # Prints out the absolute value of the given variable" ] }, { "cell_type": "markdown", "id": "ffcdd667", "metadata": {}, "source": [ "\n", "# Pandas\n", "Pandas is a library in Python used primarily for data manipulation and analysis particularly tabular data.\n", "A two-dimensional labeled data structure with columns of potentially different types in Pandas is called DataFrame which similar to a table in an Excel spreadsheet. Each column in a DataFrame is a Series.\n", "\n", "Pandas can read and write data from a wide variety of formats including:\n", "* CSV\n", "* Excel\n", "\n", " Why Pandas:\n", " It is useful for \n", "- Calculating statistics of the data, and making inferrence such as\n", " - What's the average, median, max, or min of each column?\n", " - Does column A correlate with column B?\n", " - What does the distribution of data in column C look like?, etc.\n", "- Cleaning the data (eg. removing missing values and filtering rows or columns by certain criteria)\n", "- Visualizing the data with help from Matplotlib or Seaborn. \n", "- Storing the cleaned and transformed data into other file.\n", "\n", "\n", "\n", " isna() / notna():Used to detect missing values. Returns a DataFrame of boolean values indicating where data is missing.\n", " \n", " \n", " fillna():Used to fill missing data with a specific value or method (like forward fill, backward fill\n", " \n", " \n", " dropna():Used to remove missing data by dropping rows or columns with NaN values.\n", "\n" ] }, { "cell_type": "markdown", "id": "933d5180", "metadata": {}, "source": [ "### How to Install Pandas\n", "\n", "Being a pure python package `pandas` can be installed either with `pip` or with `conda`. In general you would install by opening up your terminal / command line and install using either:\n", " \n", "```bash\n", "$> conda install pandas\n", "```\n", "\n", "or\n", "\n", "```bash\n", "$> pip install pandas\n", "```\n", "\n", "Precede the commands with (!) if you are attempting to install via the Jupyter cell or Spyder console (`!pip install pandas`)." ] }, { "cell_type": "code", "execution_count": 19, "id": "234f0bf7", "metadata": {}, "outputs": [], "source": [ "import pandas as pd # importing pandas library" ] }, { "cell_type": "code", "execution_count": 20, "id": "ae2ae5f5", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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YearMonthTemp
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" ], "text/plain": [ " Year Month Temp\n", "722 2020.0 3.0 34.52\n", "723 2020.0 4.0 34.88\n", "724 2020.0 5.0 33.17\n", "725 2020.0 6.0 29.95\n", "726 2020.0 7.0 26.98\n", "727 2020.0 8.0 27.51\n", "728 2020.0 9.0 26.88\n", "729 2020.0 10.0 28.90\n", "730 2020.0 11.0 32.66\n", "731 2020.0 12.0 33.34" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.tail(10) # will show the last 20 rows of the dataframe" ] }, { "cell_type": "code", "execution_count": 23, "id": "278daccd", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['Year', 'Month', 'Temp'], dtype='object')" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.columns # will provides the column headers" ] }, { "cell_type": "code", "execution_count": 24, "id": "0519541f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 732 entries, 0 to 731\n", "Data columns (total 3 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 Year 731 non-null float64\n", " 1 Month 731 non-null float64\n", " 2 Temp 730 non-null float64\n", "dtypes: float64(3)\n", "memory usage: 17.3 KB\n" ] } ], "source": [ "data.info() # will provide the dataset information" ] }, { "cell_type": "code", "execution_count": 25, "id": "81ca8e84", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " Year Month Temp\n", "3 1960.0 4.0 32.31\n", "4 1960.0 5.0 31.80\n", "11 1960.0 12.0 31.24\n", "12 1961.0 1.0 32.51\n", "15 1961.0 4.0 31.24\n", ".. ... ... ...\n", "707 2018.0 12.0 32.83\n", "712 2019.0 5.0 32.17\n", "718 2019.0 11.0 30.55\n", "719 2019.0 12.0 32.66\n", "730 2020.0 11.0 32.66\n", "\n", "[233 rows x 3 columns]" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data[(data['Temp']>30) & (data['Temp']<33) ] # Dataseries values ranging from greater than 30 to less than 33" ] }, { "cell_type": "code", "execution_count": 29, "id": "2a166f8f", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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YearMonthTemp
01960.01.033.17
11960.02.035.09
21960.03.034.79
31960.04.032.31
41960.05.031.80
............
7232020.04.034.88
7242020.05.033.17
7252020.06.029.95
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460 rows × 3 columns

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" ], "text/plain": [ " Year Month Temp\n", "0 1960.0 1.0 33.17\n", "1 1960.0 2.0 35.09\n", "2 1960.0 3.0 34.79\n", "3 1960.0 4.0 32.31\n", "4 1960.0 5.0 31.80\n", ".. ... ... ...\n", "723 2020.0 4.0 34.88\n", "724 2020.0 5.0 33.17\n", "725 2020.0 6.0 29.95\n", "730 2020.0 11.0 32.66\n", "731 2020.0 12.0 33.34\n", "\n", "[460 rows x 3 columns]" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data[(data['Temp'] > 29) | (data['Temp'] < 20)] # will display dataseries values either exceeding 29 or below 20" ] }, { "cell_type": "code", "execution_count": 3, "id": "2a125b2a", "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'data' is not defined", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "Input \u001b[1;32mIn [3]\u001b[0m, in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mdata\u001b[49m\u001b[38;5;241m.\u001b[39misna()\n", "\u001b[1;31mNameError\u001b[0m: name 'data' is not defined" ] } ], "source": [ "data.isna() # creates a DataFrame of the same shape as data \n", " # but with boolean values (True or False) to indicate whether there is NaN or not" ] }, { "cell_type": "code", "execution_count": 1, "id": "cfec46bd", "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'data' is not defined", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "Input \u001b[1;32mIn [1]\u001b[0m, in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mdata\u001b[49m\u001b[38;5;241m.\u001b[39misnull()\u001b[38;5;241m.\u001b[39msum()\n", "\u001b[1;31mNameError\u001b[0m: name 'data' is not defined" ] } ], "source": [ "data.isnull().sum() #Gives a Series where the index is the column names,\n", " #and the number of missing entries (NaNs) in each column" ] }, { "cell_type": "code", "execution_count": 2, "id": "b37f74b8", "metadata": { "scrolled": true }, "outputs": [ { "ename": "NameError", "evalue": "name 'data' is not defined", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "Input \u001b[1;32mIn [2]\u001b[0m, in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m data_newframe \u001b[38;5;241m=\u001b[39m \u001b[43mdata\u001b[49m\u001b[38;5;241m.\u001b[39mdropna(axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m) \u001b[38;5;66;03m# Dropping rows with NaN from dataset\"\u001b[39;00m\n\u001b[0;32m 2\u001b[0m data_newframe\u001b[38;5;241m.\u001b[39mshape\n", "\u001b[1;31mNameError\u001b[0m: name 'data' is not defined" ] } ], "source": [ "data_newframe = data.dropna(axis=0) # Dropping rows with NaN from dataset\"\n", "data_newframe.shape # This shows a data_newframe array of 728 rows and 3 columns" ] }, { "cell_type": "code", "execution_count": 33, "id": "d8dd2f1e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(732, 3)" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.shape # This shows a data array of 732 rows and 3 columns" ] }, { "cell_type": "code", "execution_count": 34, "id": "28305266", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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YearMonthTemp
01960.01.033.17
11960.02.035.09
21960.03.034.79
31960.04.032.31
41960.05.031.80
............
7272020.08.027.51
7282020.09.026.88
7292020.010.028.90
7302020.011.032.66
7312020.012.033.34
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732 rows × 3 columns

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" ], "text/plain": [ " Year Month Temp\n", "0 1960.0 1.0 33.17\n", "1 1960.0 2.0 35.09\n", "2 1960.0 3.0 34.79\n", "3 1960.0 4.0 32.31\n", "4 1960.0 5.0 31.80\n", ".. ... ... ...\n", "727 2020.0 8.0 27.51\n", "728 2020.0 9.0 26.88\n", "729 2020.0 10.0 28.90\n", "730 2020.0 11.0 32.66\n", "731 2020.0 12.0 33.34\n", "\n", "[732 rows x 3 columns]" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_ffill = data.ffill() # Using Forward fill to solve null problem\n", "data_ffill" ] }, { "cell_type": "code", "execution_count": 35, "id": "7a8d73c1", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Year 0\n", "Month 0\n", "Temp 0\n", "dtype: int64" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_ffill.isnull().sum()" ] }, { "cell_type": "markdown", "id": "d57c9bcf", "metadata": {}, "source": [ "# What is a NetCDF file?\n", "NetCDF (network Common Data Form) is a file format for storing multidimensional scientific data (variables) containing a combination of:\n", "\n", "* Spatial information — Location on the surface of the Earth.\n", "* Time information — At what time of the day and year, the measurements were taken.\n", "* Scientific values — Like Temperature, Rainfall, etc. which we discussed before.\n", "\n", "The above features contribute to the huge size of the dataset and we want it to be scalable and appendable (as new data is generated every day). Simply put, this is what the NetCDF data format does. It holds spatial information in the form of latitudes and longitudes, time, and also scientific measurements in an easy-to-read manner. Network Common Data Form Or NetCDF (in short) is a data format and also a set of software libraries created to aid the scientific community and more particularly the Geo Sciences. The primary source of information about netCDF data is the Unidata community.

\n", "\n", "\n", "Data in netCDF format is:\n", "\n", "* Self-Describing: A netCDF file includes information about the data it contains.\n", "* Portable: A netCDF file can be accessed by computers with different ways of storing integers, characters, and floating-point numbers.\n", "* Scalable: Small subsets of large datasets in various formats may be accessed efficiently through netCDF interfaces, even from remote servers.\n", "* Appendable: Data may be appended to a properly structured netCDF file without copying the dataset or redefining its structure.\n", "* Sharable: One writer and multiple readers may simultaneously access the same netCDF file.\n", "* Archivable: Access to all earlier forms of netCDF data will be supported by current and future versions of the software.

\n", " \n" ] }, { "cell_type": "markdown", "id": "72ab3283", "metadata": {}, "source": [ "# Importing Xarray package" ] }, { "cell_type": "code", "execution_count": 4, "id": "04b96b7f", "metadata": {}, "outputs": [], "source": [ "import xarray as xr # importing the Xarray library\n", "from warnings import filterwarnings # imports the filterwarnings function from Python's built-in warnings module\n", "filterwarnings('ignore') # tells Python to ignore all warnings." ] }, { "cell_type": "markdown", "id": "6e8cc8ac", "metadata": {}, "source": [ "# Open and Close NetCDF File(s)\n", "
\n", "You can read a single .nc file using the open_dataset method and open_mfdataset if you are dealing with multiple .nc files." ] }, { "cell_type": "code", "execution_count": 5, "id": "29b97299", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.Dataset>\n",
       "Dimensions:  (lon: 10, lat: 14, time: 1440)\n",
       "Coordinates:\n",
       "  * lon      (lon) float32 -3.25 -2.75 -2.25 -1.75 ... -0.25 0.25 0.75 1.25\n",
       "  * lat      (lat) float32 4.75 5.25 5.75 6.25 6.75 ... 9.75 10.25 10.75 11.25\n",
       "  * time     (time) datetime64[ns] 1901-01-16 1901-02-15 ... 2020-12-16\n",
       "Data variables:\n",
       "    pre      (time, lat, lon) float32 ...\n",
       "    stn      (time, lat, lon) float64 ...\n",
       "Attributes:\n",
       "    Conventions:  CF-1.4\n",
       "    title:        CRU TS4.05 Precipitation\n",
       "    institution:  Data held at British Atmospheric Data Centre, RAL, UK.\n",
       "    source:       Run ID = 2103051243. Data generated from:pre.2103041709.dtb\n",
       "    history:      Fri  5 Mar 14:41:58 GMT 2021 : User harry : Program makegri...\n",
       "    references:   Information on the data is available at http://badc.nerc.ac...\n",
       "    comment:      Access to these data is available to any registered CEDA user.\n",
       "    contact:      support@ceda.ac.uk
" ], "text/plain": [ "\n", "Dimensions: (lon: 10, lat: 14, time: 1440)\n", "Coordinates:\n", " * lon (lon) float32 -3.25 -2.75 -2.25 -1.75 ... -0.25 0.25 0.75 1.25\n", " * lat (lat) float32 4.75 5.25 5.75 6.25 6.75 ... 9.75 10.25 10.75 11.25\n", " * time (time) datetime64[ns] 1901-01-16 1901-02-15 ... 2020-12-16\n", "Data variables:\n", " pre (time, lat, lon) float32 ...\n", " stn (time, lat, lon) float64 ...\n", "Attributes:\n", " Conventions: CF-1.4\n", " title: CRU TS4.05 Precipitation\n", " institution: Data held at British Atmospheric Data Centre, RAL, UK.\n", " source: Run ID = 2103051243. Data generated from:pre.2103041709.dtb\n", " history: Fri 5 Mar 14:41:58 GMT 2021 : User harry : Program makegri...\n", " references: Information on the data is available at http://badc.nerc.ac...\n", " comment: Access to these data is available to any registered CEDA user.\n", " contact: support@ceda.ac.uk" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds = xr.open_dataset('gh_cru_data.nc') # opens the the dataset\n", "ds" ] }, { "cell_type": "code", "execution_count": 6, "id": "e56e6dee", "metadata": {}, "outputs": [], "source": [ "file_paths = ['gh_cru_data.nc', 'MV.nc']\n", "\n", "dss = xr.open_mfdataset(file_paths, combine='by_coords') # Open multiple .nc files and combine them by coordinate" ] }, { "cell_type": "markdown", "id": "7494c1fa", "metadata": {}, "source": [ "# Variable Selection" ] }, { "cell_type": "code", "execution_count": 7, "id": "6f19c28f", "metadata": {}, "outputs": [], "source": [ "ds_Data = xr.open_dataset('gh_cru_data.nc')" ] }, { "cell_type": "code", "execution_count": 8, "id": "aa502fb7", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.DataArray 'pre' (time: 1440, lat: 14, lon: 10)>\n",
       "[201600 values with dtype=float32]\n",
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       "  * lat      (lat) float32 4.75 5.25 5.75 6.25 6.75 ... 9.75 10.25 10.75 11.25\n",
       "  * time     (time) datetime64[ns] 1901-01-16 1901-02-15 ... 2020-12-16\n",
       "Attributes:\n",
       "    long_name:                   precipitation\n",
       "    units:                       mm/month\n",
       "    correlation_decay_distance:  450.0
" ], "text/plain": [ "\n", "[201600 values with dtype=float32]\n", "Coordinates:\n", " * lon (lon) float32 -3.25 -2.75 -2.25 -1.75 ... -0.25 0.25 0.75 1.25\n", " * lat (lat) float32 4.75 5.25 5.75 6.25 6.75 ... 9.75 10.25 10.75 11.25\n", " * time (time) datetime64[ns] 1901-01-16 1901-02-15 ... 2020-12-16\n", "Attributes:\n", " long_name: precipitation\n", " units: mm/month\n", " correlation_decay_distance: 450.0" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds_Data.pre # Option 1 for selecting the data variable 'pre'" ] }, { "cell_type": "code", "execution_count": 9, "id": "3dafbb54", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.DataArray 'pre' (time: 1440, lat: 14, lon: 10)>\n",
       "[201600 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * lon      (lon) float32 -3.25 -2.75 -2.25 -1.75 ... -0.25 0.25 0.75 1.25\n",
       "  * lat      (lat) float32 4.75 5.25 5.75 6.25 6.75 ... 9.75 10.25 10.75 11.25\n",
       "  * time     (time) datetime64[ns] 1901-01-16 1901-02-15 ... 2020-12-16\n",
       "Attributes:\n",
       "    long_name:                   precipitation\n",
       "    units:                       mm/month\n",
       "    correlation_decay_distance:  450.0
" ], "text/plain": [ "\n", "[201600 values with dtype=float32]\n", "Coordinates:\n", " * lon (lon) float32 -3.25 -2.75 -2.25 -1.75 ... -0.25 0.25 0.75 1.25\n", " * lat (lat) float32 4.75 5.25 5.75 6.25 6.75 ... 9.75 10.25 10.75 11.25\n", " * time (time) datetime64[ns] 1901-01-16 1901-02-15 ... 2020-12-16\n", "Attributes:\n", " long_name: precipitation\n", " units: mm/month\n", " correlation_decay_distance: 450.0" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds_Data['pre'] # Option 2 for selecting the data variable 'pre'" ] }, { "cell_type": "markdown", "id": "0cd94c17", "metadata": {}, "source": [ "# Datasets (ds) & DataArrays (da)\n", "\n", "\n", "***DataArray***\n", "- A multi-dimensional array with labeled or named dimensions. DataArray objects add metadata such as dimension names, coordinates, and attributes (defined below) to underlying “unlabeled” data structures such as numpy and Dask arrays. If its optional name property is set, it is a named DataArray.

\n", "\n", "***Dataset***\n", "- A dict-like collection of DataArray objects with aligned dimensions. Thus, most operations that can be performed on the dimensions of a single DataArray can be performed on a dataset. Datasets have data variables (see Variable below), dimensions, coordinates, and attributes." ] }, { "cell_type": "code", "execution_count": 10, "id": "25850b7a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "xarray.core.dataset.Dataset" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(ds_Data) # used to check and return the type of the object" ] }, { "cell_type": "code", "execution_count": 11, "id": "cab365db", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "xarray.core.dataarray.DataArray" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(ds_Data.pre)" ] }, { "cell_type": "markdown", "id": "6a7bd9d6", "metadata": {}, "source": [ "# Check Dataset Properties" ] }, { "cell_type": "markdown", "id": "977ea325", "metadata": {}, "source": [ "***Check Data Variables***\n", "\n", "```Data Variables: ``` data_vars" ] }, { "cell_type": "code", "execution_count": 12, "id": "69c973d8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Data variables:\n", " pre (time, lat, lon) float32 ...\n", " stn (time, lat, lon) float64 ..." ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds_Data.data_vars # used to check the data variables in the dataset " ] }, { "cell_type": "markdown", "id": "d3d3b06c", "metadata": {}, "source": [ "***Check Shape and Size of Data Variables***" ] }, { "cell_type": "code", "execution_count": 13, "id": "87b921bd", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1440, 14, 10)" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds_Data.pre.shape # Provides the shape of the data variable \"pre\"" ] }, { "cell_type": "code", "execution_count": 14, "id": "3784cfdc", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "201600" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds_Data.pre.size # Provides the shape of the data variable 'pre'" ] }, { "cell_type": "markdown", "id": "7a43e01e", "metadata": {}, "source": [ "***Co-ordinates & Dimensions***" ] }, { "cell_type": "code", "execution_count": 15, "id": "bc635831", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Coordinates:\n", " * lon (lon) float32 -3.25 -2.75 -2.25 -1.75 ... -0.25 0.25 0.75 1.25\n", " * lat (lat) float32 4.75 5.25 5.75 6.25 6.75 ... 9.75 10.25 10.75 11.25\n", " * time (time) datetime64[ns] 1901-01-16 1901-02-15 ... 2020-12-16" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds_Data.coords # Provides the coordinate of the dimensions" ] }, { "cell_type": "code", "execution_count": 16, "id": "211c93a7", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Frozen({'lon': 10, 'lat': 14, 'time': 1440})" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds_Data.dims" ] }, { "cell_type": "markdown", "id": "93c18c60", "metadata": {}, "source": [ "***Data Attributes***" ] }, { "cell_type": "code", "execution_count": 17, "id": "15022b8e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'Conventions': 'CF-1.4',\n", " 'title': 'CRU TS4.05 Precipitation',\n", " 'institution': 'Data held at British Atmospheric Data Centre, RAL, UK.',\n", " 'source': 'Run ID = 2103051243. Data generated from:pre.2103041709.dtb',\n", " 'history': 'Fri 5 Mar 14:41:58 GMT 2021 : User harry : Program makegridsauto.for called by update.for',\n", " 'references': 'Information on the data is available at http://badc.nerc.ac.uk/data/cru/',\n", " 'comment': 'Access to these data is available to any registered CEDA user.',\n", " 'contact': 'support@ceda.ac.uk'}" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds_Data.attrs # provides arbitrary information associated with the DataArray or Dataset" ] }, { "cell_type": "code", "execution_count": 18, "id": "1e097d2e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'CF-1.4'" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds_Data.attrs['Conventions'] # will provide the information attached to Conventions" ] }, { "cell_type": "markdown", "id": "37521ca6", "metadata": {}, "source": [ "***Checking Attributes of A DataArray***" ] }, { "cell_type": "code", "execution_count": 19, "id": "423dc899", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'long_name': 'precipitation',\n", " 'units': 'mm/month',\n", " 'correlation_decay_distance': 450.0}" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds_Data.pre.attrs" ] }, { "cell_type": "code", "execution_count": 20, "id": "7b71c722", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'mm/month'" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds_Data.pre.attrs['units']" ] }, { "cell_type": "markdown", "id": "9fa5bb62", "metadata": {}, "source": [ "# Selection / Subsetting Data\n", "\n", "***Selections or slicing can be performed along any dimension.***" ] }, { "cell_type": "code", "execution_count": 21, "id": "842648f6", "metadata": {}, "outputs": [], "source": [ "da = ds_Data.pre " ] }, { "cell_type": "markdown", "id": "c2145ea3", "metadata": {}, "source": [ "***Selection along a single dimension***" ] }, { "cell_type": "code", "execution_count": 22, "id": "eff980f1", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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lon (lon) float32 -3.25 -2.75 -2.25 -1.75 ... -0.25 0.25 0.75 1.25\n", " * lat (lat) float32 4.75 5.25 5.75 6.25 6.75 ... 9.75 10.25 10.75 11.25" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "da.resample(time='QS-Dec').mean('time') # resamples data to quaterly starting from the month 'December' then calculates the mean for each quater" ] }, { "cell_type": "code", "execution_count": 36, "id": "8252d7f2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "da.resample(time='QS-Dec').mean('time')[0].plot() " ] }, { "cell_type": "markdown", "id": "d7d701d6", "metadata": {}, "source": [ "# Simple Visualizations\n", "\n", "***1D - line plots***
\n", "***2D - Spatial / contour plots***
\n", "***3D ++ - Histogram***
\n", "\n" ] }, { "cell_type": "code", "execution_count": 37, "id": "88c053ff", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ds_Data.pre.sel(lon=5, lat=12, method='nearest').plot() # shows a plot of a 1D dataset" ] }, { "cell_type": "code", "execution_count": 38, "id": "ef8b70af", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ds_Data.pre.sel(time='2020-01').plot() " ] }, { "cell_type": "code", "execution_count": 40, "id": "cecdc299", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([553., 214., 312., 259., 90., 91., 31., 5., 3., 2.]),\n", " array([ 0. , 46.730003, 93.46001 , 140.19 , 186.92001 ,\n", " 233.65001 , 280.38 , 327.11002 , 373.84003 , 420.57 ,\n", " 467.30002 ], dtype=float32),\n", " )" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ds_Data.pre.sel(time=slice('2020-01','2020-12')).plot() # shows a plot of a 3D dataset" ] }, { "cell_type": "markdown", "id": "97676da4", "metadata": {}, "source": [ "\n", "# Cartopy\n", "Cartopy is a Python package designed for geospatial data processing and visualization.\n", "It provides tools to create maps and perform various geospatial analyses, such as plotting data on different map projections, adding coastlines, borders, rivers, and more.\n", "\n", "Cartopy handles and plots geospatial data in various formats including: NetCDF, GeoJSON, and Shapefiles.\n", "* NetCDF\n", "* Shapefiles: consists of a set of files with various extensions like '.shp', '.shx', '.dbf' that store geometric location and attribute information of geographic features.\n", "* GeoJSON: GeoJSON is a format for encoding a variety of geographic data structures using JavaScript Object Notation (JSON).\n" ] }, { "cell_type": "markdown", "id": "46dc1d44", "metadata": {}, "source": [ "# Import Cartopy " ] }, { "cell_type": "code", "execution_count": 41, "id": "d1863ac5", "metadata": {}, "outputs": [], "source": [ "from cartopy import crs, feature \n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "id": "7c0f0560", "metadata": {}, "source": [ "# Sample Geographical Map" ] }, { "cell_type": "code", "execution_count": 42, "id": "d72cc2e4", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(12, 8))\n", "ax = fig.add_subplot(projection = crs.PlateCarree() )\n", "\n", "ax.coastlines()\n", "ax.add_feature(feature.BORDERS, linestyle='dashed')\n", "ax.add_feature(feature.OCEAN)\n", "\n", "ax.set_extent( [-25, 55, -37.5, 35] )" ] }, { "cell_type": "code", "execution_count": 43, "id": "d980ab6b", "metadata": {}, "outputs": [], "source": [ "import xarray as xr # importing xarray library" ] }, { "cell_type": "code", "execution_count": 44, "id": "52c4b214", "metadata": {}, "outputs": [], "source": [ "ds_Ghana = xr.open_dataset('Ghana_cru_data.nc') # opening dataset \n", "da_RR = ds_Ghana.pre # selecting data variable \"pre\" from the dataset" ] }, { "cell_type": "code", "execution_count": 45, "id": "eba0b6d3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "da_RR.sel(time='1991-01').plot() # ploting the selected time '1991-01'" ] }, { "cell_type": "code", "execution_count": 46, "id": "6b45216f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(10,8))\n", "ax1 = fig.add_subplot(1,1,1, projection=crs.PlateCarree())\n", "\n", "ax1.coastlines()\n", "ax1.add_feature(feature.BORDERS)\n", "\n", "da_RR.sel(time='1991-01').plot( ax = ax1 )\n" ] }, { "cell_type": "code", "execution_count": 47, "id": "ecfe5982", "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(10,8))\n", "ax1 = fig.add_subplot(1,1,1, projection=crs.PlateCarree())\n", "\n", "ax1.coastlines()\n", "ax1.add_feature(feature.BORDERS)\n", "\n", "da_RR.groupby('time.year').sum('time').mean('year').plot(ax = ax1, vmax=2000)\n", "\n", "ax.set_extent( [-3.5, 4.5, -1.5, 11.5 ] )" ] }, { "cell_type": "code", "execution_count": 48, "id": "d494ff0b", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "import cartopy.crs as crs\n", "import cartopy.feature as feature\n", "\n", "# Assuming da_RR is your data array with the precipitation data\n", "fig = plt.figure(figsize=(10, 8))\n", "ax1 = fig.add_subplot(1, 1, 1, projection=crs.PlateCarree())\n", "\n", "# Add coastlines and borders\n", "ax1.coastlines()\n", "ax1.add_feature(feature.BORDERS)\n", "\n", "# Plot the data, adjusting vmax as needed\n", "da_RR.groupby('time.year').sum('time').mean('year').plot(ax=ax1, vmax=2000)\n", "\n", "# Set the extent to focus on Ghana\n", "ax1.set_extent([-3.5, 4.5, 1.5, 11.5], crs=crs.PlateCarree())\n", "\n", "# Show the plot\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 49, "id": "1678fb51", "metadata": {}, "outputs": [], "source": [ "Jan = da_RR.groupby('time.month').mean('time').sel(month=1)\n", "Apr = da_RR.groupby('time.month').mean('time').sel(month=4)\n", "Jun = da_RR.groupby('time.month').mean('time').sel(month=6)\n", "Dec = da_RR.groupby('time.month').mean('time').sel(month=12)" ] }, { "cell_type": "code", "execution_count": 50, "id": "9877b3e5", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(ncols=2, nrows=2, figsize= (12,8), subplot_kw={'projection':crs.PlateCarree()})\n", "plt.subplots_adjust(hspace=0.5, right=0.8)\n", "\n", "ax1 = axes[0,0]\n", "Jan.plot(ax = ax1 , vmax=400, add_colorbar=False)\n", "ax1.coastlines()\n", "ax1.add_feature(feature.BORDERS)\n", "\n", "ax2 = axes[0,1]\n", "Apr.plot( ax = ax2, vmax=400, add_colorbar=False)\n", "ax2.coastlines()\n", "ax2.add_feature(feature.BORDERS)\n", "\n", "\n", "ax3 = axes[1,0]\n", "Jun.plot( ax = ax3, vmax=400, add_colorbar=False)\n", "ax3.coastlines()\n", "ax3.add_feature(feature.BORDERS)\n", "\n", "ax4 = axes[1,1]\n", "cb = Dec.plot( ax = ax4, vmax=400, add_colorbar=False)\n", "ax4.coastlines()\n", "ax4.add_feature(feature.BORDERS)\n", "\n", "\n", "caxes = fig.add_axes([0.85, 0.2, 0.02, 0.615])\n", "fig.colorbar(cb, cax=caxes, orientation='vertical', label='Rainfall [mm/month]', extend='max')\n" ] }, { "cell_type": "markdown", "id": "3fa05a0d", "metadata": {}, "source": [ "# THANK YOU\n", "\n", "# QUESTIONS?" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 5 }