jbants

from flask import Flask, send_from_directory


app = Flask(__name__)


@app.route('/base/<path:filename>')
def base_static(filename):
    return send_from_directory(app.root_path + '/../static/', filename)

jbants

#!/bin/sh -x
############################################################################
#
# MODULE:       r.hillshade.multi
#
# AUTHOR(S):    Emmanuel Sambale esambale@yahoo.com emmanuel.sambale@gmail.com
#               with comments and improvement from the GRASS user mailing list.
#
# PURPOSE:      Creates a multidirectional, oblique-weighted, shaded-relief image
#               from an input DEM image.  Original ARC 6.0.1 GRID procedure was 

jbants

# ----------------------------------------------------------------------
# Remote Sensing Image Classification Workflow for Landsat data with soft
# voting on a SVM and Gradient Boosting classifier. Outlier in the 
# training data are flagged through an Isolation Forest algorithm. 
# Feature Selection is done by a Recursive Feature Elimination method.
# The results are classification and classification probability raster 
# images in TIF format.
#
# Written by Dimo Dimov, MapTailor, 2017
# ----------------------------------------------------------------------

jbants

# For Windows users# Note: <> denotes changes to be made

#Create a conda environment
conda create --name <environment-name> python=<version:2.7/3.5>

#To create a requirements.txt file:
conda list #Gives you list of packages used for the environment

conda list -e > requirements.txt #Save all the info about packages to your folder

jbants

Project Title

One Paragraph of project description goes here

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

Prerequisites

jbants

Overview

When you're working on multiple coding projects, you might want a couple different version of Python and/or modules installed. That way you can keep each project in its own sandbox instead of trying to juggle multiple projects (each with different dependencies) on your system's version of Python. This intermediate guide covers one way to handle multiple Python versions and Python environments on your own (i.e., without a package manager like conda). See the Using the workflow section to view the end result.

Use cases

1. Working on 2+ projects that each have their own dependencies; e.g., a Python 2.7 project and a Python 3.6 project, or developing a module that needs to work across multiple versions of Python. It's not reasonable to uninstall/reinstall modules every time you want to switch environments.
2. If you want to execute code on the cloud, you can set up a Python environment that mirrors the relevant