bakinam/fcast repository overview

⁠FloodCast Docker Images

⁠Description

This docker image is used to create a jupyter notebook that has the geospatial libraries installed.

The image is used for the floodCast project --phase III (http://floodcast.info/wordpress/⁠)

We appreciate any contributions to this Dockerfile.

Docker image is built on

1. Ubuntu latest as the base image
2. Jupyter notebook is the entry point

⁠Installed Packages:

These are the required packages for the fcast project

matplotlib⁠ -- Used for interactive graphing, scientific publishing, user interface development and web application servers targeting multiple user interfaces and hardcopy output formats,

gcsfs⁠ -- Used for pythonic file-system interface to Google Cloud Storage ,

xarray⁠ -- Used for allows working with labelled multi-dimensional arrays,

numpy⁠ -- Used for scientific computing,

pandas⁠ -- Used for high-performance, easy-to-use data structures and data analysis tools,

scipy⁠ -- Used for expanding the set of scientific computing libraries,

boto3⁠ -- Used to interact with AWS services, such as EC2 and S3,

requests⁠ -- Used to make HTTP requets in python,

beautifulsoup4⁠ -- Used to interact with HTML and XML files,

fiona⁠ -- Used for reading and writing geographic vector data,

shapely⁠ -- Used for manipulation and analysis of planar geometric objects,

lxml⁠ -- Used to allow easy handling of XML and HTML files,

toolz⁠-- Toolz provides a set of utility functions for iterators, functions, and dictionaries,

dask⁠-- library for parallel computing,

dask distributed⁠ -- Dask.distributed is a centrally managed, distributed, dynamic task scheduler ,

h5netcdf⁠ -- A Python interface for the netCDF4 file-format that reads and writes HDF5 files API directly via h5py, without relying on the Unidata netCDF library ,

rasterio⁠-- To access geospatial raster data ,

pyproj⁠-- Cartographic projections and coordinate transformations library ,

s3fs⁠-- Builds on boto3 to provide a convenient Python filesystem interface for S3 ,

xlrd⁠--Extract data from Excel spreadsheets,

rtree⁠-- Python wrapper of libspatialindex that provides a number of advanced spatial indexing features,

geopandas⁠--It combines the capabilities of pandas and shapely, providing geospatial operations in pandas and a high-level interface to multiple geometries to shapely ,

libspatialindex⁠-- An extensible framework that will support robust spatial indexing methods

osgeo⁠ -- This Python package and extensions are a number of tools for programming and manipulating the GDAL Geospatial Data Abstraction Library. Actually, it is two libraries – GDAL for manipulating geospatial raster data and OGR for manipulating geospatial vector data

⁠How to use/test

To run the Docker image using this command from your Linux terminal: Docker run command will pull the required docker image then run it.

docker run -p 8887:8888 bakinam/fcast:0.1

To open the Jupyternotebook you need to copy the provided URL from running the previous command and map the port in the URL from 8888 to 8887 to avoid having a token.

We want to test that all the libraries installed in this image is working as they should be.

On a new notebook run:

import matplotlib 
import gcsfs
import xarray
import numpy
import pandas
import scipy
import boto3
import requests
import fiona
import shapely
import lxml  
import toolz  
import dask  
import h5netcdf 
import rasterio    
import pyproj  
import s3fs  
import xlrd
import bs4 
import dask.distributed 
import rtree
import geopandas 
from osgeo import  *
import gdal
import ogr
import osr
import gdalconst

⁠Building Images

In case you want to build your own Docker image you can copy the Dockerfile to a directory in your Linux system and use this command:

Docker build -t NameOfImage:Tag .

⁠Releasing