MATE Floats! Coding Notebook - Day 4¶

Created by Ethan C. Campbell for NCAT/MATE/GO-BGC Marine Technology Summer Program

Thursday, August 24, 2023

In [ ]:

import numpy as np                         # NumPy is an array and math library
import matplotlib.pyplot as plt            # Matplotlib is a visualization (plotting) library
import pandas as pd                        # Pandas lets us work with spreadsheet (.csv) data
from datetime import datetime, timedelta   # Datetime helps us work with dates and times

Part 1: Loading and plotting R/V Rachel Carson CTD casts¶

First, let's download three .csv data files from Google Drive here: https://drive.google.com/drive/folders/1Am6XdlB-APQ3ccOvLeGK8DFPQ2OnPeJD?usp=sharing. Two of the files are CTD casts that were collected from the R/V Rachel Carson off of Carkeek Park near Seattle. *Save these two files to your computer.*

Let's first take a look at the raw files, including their headers.

Next, we can upload the files to this Google Colab notebook. *Click the sidebar folder icon on the left, then use the page-with-arrow icon at the top to select the files and upload them.* NOTE: uploaded files will be deleted from Google Colab when you refresh this notebook!

We will specify each filepath using string variables:

In [ ]:

filepath_0 = '/content/2023051001001_Carkeek.csv'
filepath_1 = '/content/2023051101001_Carkeek.csv'

Now, we can load the files using pandas:

pd.read_csv(FILEPATH, ARGUMENTS...)

This function is very customizable using the many optional ARGUMENTS, which allow it to handle almost any file. You can find documentation about the arguments at this link.

*Let's first take a look at the data file using a simple text editor. Notice the long header. What argument can we use to exclude the header from being loaded?*

Below, we'll load each data file using pd.read_csv() and store each file into a new variable.

We can look at the data using display() (which is a fancy version of print() for DataFrames):

In [ ]:

data_0 = pd.read_csv(filepath_0,comment='#')
data_1 = pd.read_csv(filepath_1,comment='#')

display(data_0)

	Unnamed: 0	altM	CStarTr0	c0mS/cm	density00	depSM	latitude	longitude	flECO-AFL	modError	sbeox0Mg/L	sbeox0ML/L	ph	potemp090C	prDM	sal00	t090C	scan	nbf	flag
0	0	98.53	71.0825	31.662958	1021.7317	2.101	47.71418	-122.40854	2.8127	0	10.6450	7.4488	9.271	10.2155	2.119	28.3385	10.2157	3408	0	0.0
1	1	98.53	71.0825	31.662061	1021.7317	2.005	47.71418	-122.40854	2.8127	0	10.6446	7.4484	9.271	10.2140	2.022	28.3388	10.2143	3409	0	0.0
2	2	98.53	71.0825	31.661464	1021.7323	2.045	47.71418	-122.40854	2.8127	0	10.6443	7.4483	9.271	10.2129	2.062	28.3391	10.2131	3410	0	0.0
3	3	98.53	71.0825	31.660448	1021.7323	2.005	47.71418	-122.40854	2.8713	0	10.6441	7.4481	9.271	10.2117	2.022	28.3390	10.2119	3411	0	0.0
4	4	98.53	71.0825	31.658416	1021.7325	1.981	47.71418	-122.40854	3.1057	0	10.6443	7.4483	9.271	10.2093	1.998	28.3389	10.2095	3412	0	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
8200	8200	11.99	83.1087	31.920640	1024.1134	173.726	47.71316	-122.40812	0.1753	0	7.0198	4.9120	8.788	8.3719	175.266	30.0190	8.3887	11608	0	0.0
8201	8201	11.99	83.1087	31.920640	1024.1135	173.726	47.71316	-122.40812	0.1753	0	7.0201	4.9123	8.788	8.3717	175.266	30.0191	8.3886	11609	0	0.0
8202	8202	11.99	83.1087	31.920820	1024.1141	173.846	47.71316	-122.40812	0.1753	0	7.0204	4.9125	8.788	8.3718	175.387	30.0191	8.3887	11610	0	0.0
8203	8203	11.99	83.1087	31.920579	1024.1129	173.613	47.71316	-122.40812	0.1753	0	7.0205	4.9125	8.783	8.3719	175.152	30.0190	8.3887	11611	0	0.0
8204	8204	11.99	83.1087	31.920340	1024.1135	173.846	47.71316	-122.40812	0.1753	0	7.0209	4.9128	8.788	8.3720	175.387	30.0184	8.3889	11612	0	0.0

8205 rows × 20 columns

The data in a pandas DataFrame is similar to a NumPy 2-D array, except we use column labels to refer to columns and index values to refer to rows.

To retrieve a specific column, we use bracket notation: data_frame[COLUMN_LABEL].

In [ ]:

# For example:
data_0['t090C']

Out[ ]:

0       10.2157
1       10.2143
2       10.2131
3       10.2119
4       10.2095
         ...   
8200     8.3887
8201     8.3886
8202     8.3887
8203     8.3887
8204     8.3889
Name: t090C, Length: 8205, dtype: float64

With these tools, we can make line plots of temperature vs. depth that include both CTD casts.

*Can you try plotting another parameter vs. depth? Note: the file contains salinity, oxygen, fluorescence, and pH data.*

The following line of code flips the y-axis so the surface is at the top: plt.gca().invert_yaxis().

In [ ]:

# Temperature vs. depth profile
plt.plot(data_0['t090C'],data_0['depSM'],label='R/V Carson cast #1 (5/10/23)')
plt.plot(data_1['t090C'],data_1['depSM'],label='R/V Carson cast #2 (5/11/23)')
plt.legend()
plt.gca().invert_yaxis()   # This reverses the y-axis
plt.xlabel('Temperature (°C)')
plt.ylabel('Depth (m)');

# Write your code here:
plt.figure()
plt.plot(data_0['flECO-AFL'],data_0['depSM'],label='R/V Carson cast #1 (5/10/23)')
plt.plot(data_1['flECO-AFL'],data_1['depSM'],label='R/V Carson cast #2 (5/11/23)')
plt.legend()
plt.gca().invert_yaxis()   # This reverses the y-axis
plt.ylabel('Depth (m)');

*How do the casts look similar and how do they look different? What could be some causes of the differences?*

*What do you observe in the other parameter(s) that you plotted?*

Part 2: Loading and plotting seaglider data¶

At this link is a file with a seaglider (or just "glider") profile from the same cruise, one day after the second R/V Carson cast.

If you haven't already, *download* the glider .csv data file from Google Drive here: https://drive.google.com/drive/folders/1Am6XdlB-APQ3ccOvLeGK8DFPQ2OnPeJD?usp=sharing. *Save the file to your computer, then load it into Google Colab.*

Let's load and display the data using pandas:

In [ ]:

filepath_2 = '/content/20230512_glider.csv'

data_2 = pd.read_csv(filepath_2,parse_dates=['time'])

display(data_2)

	sg_data_point	time	pressure	depth	temperature	conductivity	salinity	sigma_theta	density	latitude	longitude	buoyancy	speed	glide_angle	horz_speed	vert_speed	polar_heading
0	0	2023-05-12 19:14:17.464999936	1.076991	1.067980	11.409176	NaN	NaN	NaN	NaN	47.715300	-122.398800	NaN	NaN	NaN	NaN	NaN	1.495747
1	1	2023-05-12 19:14:22.731000064	1.117253	1.107904	11.409607	NaN	NaN	NaN	NaN	47.715302	-122.398799	NaN	NaN	NaN	NaN	NaN	1.628392
2	2	2023-05-12 19:14:29.736000000	1.157514	1.147829	11.411506	NaN	NaN	NaN	NaN	47.715306	-122.398797	NaN	NaN	NaN	NaN	NaN	1.516691
3	3	2023-05-12 19:14:35.012999936	1.157514	1.147829	11.414287	NaN	NaN	NaN	NaN	47.715310	-122.398795	NaN	NaN	NaN	NaN	NaN	1.621411
4	4	2023-05-12 19:14:40.288000000	1.268233	1.257621	11.412624	NaN	NaN	NaN	NaN	47.715314	-122.398794	NaN	NaN	NaN	NaN	NaN	1.574287
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
335	335	2023-05-12 20:06:59.275000064	0.513332	0.509038	11.268633	NaN	NaN	NaN	NaN	47.724696	-122.397495	NaN	NaN	NaN	NaN	NaN	2.405064
336	336	2023-05-12 20:07:04.279000064	0.402614	0.399245	11.278361	NaN	NaN	NaN	NaN	47.724699	-122.397493	NaN	NaN	NaN	NaN	NaN	2.466150
337	337	2023-05-12 20:07:08.697999872	0.422744	0.419208	11.290254	NaN	NaN	NaN	NaN	47.724703	-122.397491	NaN	NaN	NaN	NaN	NaN	2.069960
338	338	2023-05-12 20:07:14.273999872	0.372418	0.369302	11.305961	NaN	NaN	NaN	NaN	47.724706	-122.397490	NaN	NaN	NaN	NaN	NaN	2.134538
339	339	2023-05-12 20:07:19.279000064	0.342222	0.339359	11.332550	NaN	NaN	NaN	NaN	47.724710	-122.397488	NaN	NaN	NaN	NaN	NaN	1.870993

340 rows × 17 columns

*What data parameters do you see, and what do you expect each parameter represents?*

As a first step, let's see whether the glider was sampling the same region as the R/V Rachel Carson. We can plot each of their tracks using latitude and longitude:

In [ ]:

plt.plot(data_0['longitude'],data_0['latitude'],label='R/V Rachel Carson cast #1')
plt.plot(data_1['longitude'],data_1['latitude'],label='R/V Rachel Carson cast #2')
plt.plot(data_2['longitude'],data_2['latitude'],label='Glider')
plt.legend()
plt.xlabel('Longitude (°E)')
plt.ylabel('Latitude (°N)')
plt.title('Ship and glider tracks');

One way to visualize glider data is in time-depth space. In other words, time is on the x-axis and depth is on the y-axis. If we use a scatter plot (plt.scatter()), we can color the points by another quantity, like temperature or buoyancy.

*Try changing the c (color) argument below to plot different quantities in time-depth space. What do you notice?*

In [ ]:

plt.scatter(data_2['time'],data_2['depth'],c=data_2['salinity'])
plt.colorbar(label='Buoyancy (g)')   # This adds the color bar and color label on the right
plt.gca().invert_yaxis()             # This reverses the y-axis
plt.xlabel('Time')
plt.ylabel('Depth (m)');

Of course, we can also plot parameter vs. depth profiles.

*Copy the code from above, where you plotted temperature profiles from the R/V Carson ship CTD casts. Then add the glider temperature data.*

In [ ]:

# Write your code here:
plt.plot(data_0['t090C'],data_0['depSM'],label='R/V Carson cast #1 (5/10/23)')
plt.plot(data_1['t090C'],data_1['depSM'],label='R/V Carson cast #2 (5/11/23)')
plt.plot(data_2['temperature'],data_2['depth'],label='Glider (5/12/23)')
plt.legend()
plt.gca().invert_yaxis()   # This reverses the y-axis
plt.xlabel('Temperature (°C)')
plt.ylabel('Depth (m)');
plt.ylim([25,0])

Out[ ]:

(25.0, 0.0)

We can zoom in to certain depths by changing the y-axis scale using:

plt.ylim([LOWER,UPPER])

*By plt.ylim() to the plot above, what can we observe about the differences between the casts near the surface and near the bottom? Why do these differences exist?*

*If we wanted to calibrate a glider sensor to the R/V Carson's sensor, what depths would we want to use?*

Part 3: Exploring Argo float data¶

Links:

Earth Nullschool map: http://data.scripps.earth/argoviz/
Argovis – get your API key here: https://argovis-keygen.colorado.edu/
Argovis: https://argovis.colorado.edu/
Google Slides for your plots: https://docs.google.com/presentation/d/1MqYfkOHBv9KWO41ZgK0MMnfQ8E9zah3AntKlZkrWrgU/edit?usp=sharing