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README.md

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- [Motivation](#motivation)
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- [Features](#features)
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- [Installation](#installation)
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- [Getting started with examples](#getting-started-with-examples)
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- [Read the documentation](#read-the-documentation)
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- [Directory Structure](#directory-structure)
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- [Data folder description](#data-folder-description)
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- [Contributing](#contributing)
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- [License](#license)
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- Node degree
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- Polar plots of street orientation
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## Installation
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First you need to certify that you have [Python 3.9](https://www.python.org/downloads/release/python-380/) installed.
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Then you can install the package using pip:
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```bash
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```bash
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pip install git+https://github.com/Gui-FernandesBR/Last-Mile-Routing-Analyzer/lmr_analyzer.git
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```
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```
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The package will automatically install the dependencies listed at [requirements.txt]() file.
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The lmr_analyzer package is organized as follows:
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__init__.py - Main module
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amz_serializer.py - Amazon S3 data serializer
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analysis.py - Analysis module, to analyze a set of routes
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distance_matrix.py - Distance Matrix module
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geometry.py - Handle with spatial information from shapefiles
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package.py - Store package information
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route.py - Store route information
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stop.py - Store stop information
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utils.py - Utilities module to be used on other modules
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vehicle.py - Store vehicle information
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`__init__.py` - Main module
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amz_serializer.py - Amazon S3 data serializer
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analysis.py - Analysis module, to analyze a set of routes
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distance_matrix.py - Distance Matrix module
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geometry.py - Handle with spatial information from shapefiles
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package.py - Store package information
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route.py - Store route information
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stop.py - Store stop information
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utils.py - Utilities module to be used on other modules
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vehicle.py - Store vehicle information
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The architecture of the package is represented by the following diagram:
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Due to the large amount of information, the data folder can be a bit confusing, so here is a brief description of what each folder contains:
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* data/driving_distances:
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* A set of csv files containing the driving distances between some pairs of points in the dataset. The files are named as follows: `driving_distances_{dataset_name}.csv`. The dataset name is the same as the name of the folder containing the dataset.
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* data/shapefiles:
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* `los_angeles_majors`: Shapefile containing the major neighborhoods of Los Angeles.
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* `los_angeles_minors`: Shapefile containing the minor neighborhoods of Los Angeles.
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* `guarulhos_osm` : geospatial data describing boundaries of all neighborhoods of the Guarulhos city. source: _OpenStreetMap_
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* `urban_regions_Sao_Paulo`: geospatial data describing land use over state of Sao Paulo. source: unknown
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```bash
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data/
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├── driving_distances/ - Driving distances to be used with Amazon dataset
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│ ├── austin.csv
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│ ├── boston.csv
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│ ├── ...
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│ └── seattle.csv
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├── results/ - Results from the examples
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│ ├── dataframes/ - all .csv or .txt files
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│ ├── graph_shapefile/ - gespatial data files
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│ ├── graphs/ - Report of the Graphs generated with the examples
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│ ├── pickles/ - Objects saved as pickle files
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│ └── polar_plots/ - Polar plots generated with the examples
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└── shapefiles/ - geospatial data files
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├── austin/ - Neighborhoods of Austin county
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├── los_angeles_minors/ - Neighborhoods of Los Angeles county
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├── guarulhos_osm/ - Neighborhoods of Guarulhos city
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├── ...
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└── urban_regions_Sao_Paulo/ - Urban regions of Sao Paulo state
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```
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## Contributing
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