Data analysis with client tools: python¶

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OCN-463

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Syllabus

Course Description
Purpose of the Class
Organization
Credit and Level
Evaluation
Adherence to Student Conduct Code
Student Learning Outcomes
Student Learning: Title IX, Sexual Discrimination or Harassment

Logistics

Class specifics
Class schedule
Class environment (machines)
Class wiki

Markup language

Markup language (pmwiki)
Hypertext Markup Language (HTML)

Operating system

UNIX/Linux

Data tools

Shell scripting: Generic Mapping Tools (GMT)
Data analysis with client tools: python
Data analysis with client tools: Matlab
Data analysis with client tools: QGIS

Introduction to python
Arrays and program flow
More overview
Readng data
Pandas
Now try dates
matplotlib
Tide Gauges
Working with HOT data
Working with HOT data (cont’d)
Spectrum
Spectrum (cont’d)
ADCP time series
SVP global drifting buoys
Pacific variability and ENSO
Sea surface temperatures (SST)
ENSO (cont’d)
Pacific Variability- ENSO
Making maps
Making Maps (cont’d)
Data and maps
1. Plotting data on maps: Understanding the transform and projection keywords
2. GIS maps: geopandas (and matplotlib)
3. GIS maps: geojson and plotly
4. GIS maps: cartopy and shapely
# Satellite data (part 1)
1A. Read local data using netCDF4.Dataset
1B. Read local data with xarray
# Satellite data (part 2)
1A. Plot map of sea level from AVISO at particular time
1B. Make a time-series at a point
1C. Compare to tide gauge data
2A. Make lat/lon contour plots of SST and ice concentrations
2B. Data at a point: computing the annual cycle
3A. Compute Ekman flow
3B. Compute Ekman transport
5A. Downlading data from Python
5B. Importing NetCDF4 data in Python
5C. Working with the extracted data
5D. Comparison of chlorophyll data from different sensors
5E. Extract data within a shapefile using ERDDAP
5F. Example of loading and working which an ESRI shapefile instead of .csv file
5G. Extract data along a turtle track
On your own!
Plot #2
# Satellite data (part 3)
1A. Plot map of sea level from AVISO at particular time
1B. Make a time-series at a point
1C. Compare to tide gauge data
2A. Make lat/lon contour plots of SST and ice concentrations
2B. Data at a point: computing the annual cycle
# Satellite data (part 4)
3A. Compute Ekman flow
3B. Compute Ekman transport
5A. Downlading data from Python
5B. Importing NetCDF4 data in Python
5C. Working with the extracted data
5D. Comparison of chlorophyll data from different sensors
5E. Extract data within a shapefile using ERDDAP
5F. Example of loading and working which an ESRI shapefile instead of .csv file
5G. Extract data along a turtle track
On your own!
Plot #2
# Model output (part 1)
1. Wave forecast: compare to point
2. Investigating model output: future warming
But first… introduction to functions
Climate model output
High Frequency doppler Radars
A note about time
# Model output (part 2)
3. Investigating model output: sea level
4. Analysis of numerical model output: ENSO variability