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forest-puller-1.4.1
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مشاهده بیشترتوضیحات
A package for retrieving data concerning forests on the European continent.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | forest-puller-1.4.1 |
| نام | forest-puller |
| نسخه کتابخانه | 1.4.1 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Lucas Sinclair |
| ایمیل نویسنده | lucas.sinclair@me.com |
| آدرس صفحه اصلی | http://github.com/xapple/forest_puller/ |
| آدرس اینترنتی | https://pypi.org/project/forest-puller/ |
| مجوز | MIT |
[](https://badge.fury.io/py/forest-puller)
[](https://github.com/xapple/forest_puller/commits/master)
[](https://github.com/xapple/forest_puller/blob/master/LICENSE)
<p align="center">
<img src="docs/title_art/title.jpeg?raw=true">
</p>
`forest_puller` is a python package for retrieving data concerning forests of European countries. This includes the amount of forested areas, the forest inventory (standing stock), the forest growth rates as well as the forest loss dynamics (disturbances).
This software is accompanied by a scientific publication that is available in preprint here: https://www.preprints.org/manuscript/202011.0661/v1
There are several forest public data sources accessible online that provide these types of information in various forms and granularity. This package automates the process of scrapping these websites and parsing the resulting CSV tables or excel files.
Once `forest_puller` is installed you can easily access forest data through standard python pandas data frames.
<p align="center">
<img height="300" src="forest_puller/reports/template/logo.png?raw=true">
</p>
## Scope and sources
Currently `forest_puller` provides data for the following 27 EU member states (past and present):
* Austria, Belgium, Bulgaria, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Netherlands, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, United Kingdom
Currently `forest_puller` caches and provides programmatic access to the forest-relevant data from these data sources:
* IPCC (https://tinyurl.com/y474yu9e)
* SOEF (https://dbsoef.foresteurope.org/)
* FAOSTAT (http://www.fao.org/faostat/en/)
* HPFFRE (https://doi.org/10.5061/dryad.4t880qh)
* FRA (http://www.fao.org/forest-resources-assessment/en/)
What other data source you would like to see added here? Contact the authors by opening an issue in the [the tracker](https://github.com/xapple/forest_puller/issues).
## Prerequisites
Since `forest_puller` is written in python, it is compatible with all operating systems: Linux, macOS and Windows. The only prerequisite is `python3` (which is often installed by default) along with the `pip3` package manager.
To check if you have `python3` installed, type the following on your terminal:
$ python3 -V
If you do not have `python3` installed, please refer to the section [obtaining python3](docs/markdown/installing_tips.md#obtaining-python3).
To check if you have `pip3` installed, type the following on your terminal:
$ pip3 -V
If you do not have `pip3` installed, please refer to the section [obtaining pip3](docs/markdown/installing_tips.md#obtaining-pip3).
## Installing
Simply type the following on your terminal:
$ pip3 install --user forest_puller
Or if you want to install it for all users of the system:
$ sudo pip3 install forest_puller
## Usage
For instance, to retrieve the net carbon dioxide emission of Austria in 2017 that were due to *coniferous* forest land from the IPCC official data source, you can use the following python code:
```python
# Import #
from forest_puller.ipcc.country import countries
# Get the country #
austria = countries['AT']
# Get the 2017 indexed dataframe #
at_2017 = austria.years[2017].indexed
# Print some data #
print(at_2017.loc['remaining_forest', 'Coniferous']['net_co2'])
```
904282.4970403439
To see what other information is available, you can of course display the column titles and row indexes of the data frame at hand:
```python
print(at_2017.columns)
# Index(['area', 'area_mineral', 'area_organic', 'biomass_gains_ratio',
# 'biomass_losses_ratio', 'biomass_net_change_ratio', 'net_dead_ratio',
# 'net_litter_ratio', 'net_mineral_soil_ratio', 'net_organic_soil_ratio',
# 'biomass_gains', 'biomass_losses', 'biomass_net_change', 'net_dead',
# 'net_litter', 'net_mineral_soils', 'net_organic_soils', 'net_co2'],
# dtype='object', name='category')
print(at_2017.index)
# MultiIndex(levels=[['cropland_to_forest', 'grassland_to_forest',
# 'land_to_forest', 'other_land_to_forest', 'remaining_forest',
# 'settlements_to_forest', 'total_forest', 'wetlands_to_forest'],
# ['', 'Coniferous', 'Deciduous', 'Forest not in yield', 'Total']])
```
To examine what countries and what years are available:
```python
print(list(c.iso2_code for c in countries.values()))
# ['AT', 'BE', 'BG', 'HR', 'CZ', 'DK', 'EE', 'FI', 'FR', 'DE', 'GR',
# 'HU', 'IE', 'IT', 'LV', 'LT', 'LU', 'NL', 'PL', 'PT', 'RO', 'SK', 'SI',
# 'ES', 'SE', 'GB', 'ZZ']
print(list(y for y in austria.years))
# [1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000,
# 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012,
# 2013, 2014, 2015, 2016, 2017]
```
To get a large data frame with all years and all countries inside:
```python
from forest_puller.ipcc.concat import df
print(df)
```
To get a data frame that compares forest area between several sources:
```python
from forest_puller.viz.area_comp import area_comp_data
print(area_comp_data.df)
```
To get a data frame that compares gains (increments) and losses (harvest) between sources:
```python
from forest_puller.viz.converted_to_tons import converted_tons_data
print(converted_tons_data.df)
```
## Cache
When you import `forest_puller`, we will check the `$FOREST_PULLER_CACHE` environment variable to see where to download and store the cached data. If this variable is not set, we will default to `~/.forest_puller` and clone a repository there.
## Data sources
### IPCC
To access the same forest data directly from the IPCC website without the use of `forest_puller`, you would have to first select your country of interest from the CRF country table in a browser at [this address](https://tinyurl.com/y474yu9e).
Then you would have to manually download the `zip` file for that specific country through another page.
Next, you would have to uncompress the `zip` file and locate the `xls` file that concerns the year you are interested in.
Finally you would have to scroll to the right sheet in your spreadsheet software and find the pertinent cell.
This operation would have to be repeated for every country, and every year you are interested in.
With `forest_puller` you can easily display any information you want for all countries at the same time:
```python
from forest_puller.ipcc.country import countries
category, key = ['total_forest', 'biomass_net_change']
biomass_net_change = {
k: c.last_year.indexed.loc[category, ''][key]
for k,c in countries.items()
}
import pprint
pprint.pprint(biomass_net_change)
```
{'AT': 1367857.0940855271,
'BE': 374245.08695361385,
'BG': 2192942.031982918,
'CZ': 387870.89395249996,
'DE': 12317598.87352293,
'DK': -216454.31026543948,
'EE': 320710.2459538891,
'ES': 8917649.261547482,
'FI': 6603815.0,
'FR': 15051831.9827214,
'GB': 2892518.0859005335,
'GR': 583205.0978272819,
'HR': 1477791.7578513895,
'HU': 1259385.5890665338,
'IE': 1069648.7636722159,
'IT': 5752883.095908434,
'LT': 2146933.309581986,
'LU': 101929.37461705346,
'LV': 1244965.2120000012,
'NL': 499021.93968,
'PL': 9353198.2907701,
'PT': 1536917.4736652463,
'RO': 5561343.4405591395,
'SE': 10185839.738999998,
'SI': 35391.09710503432,
'SK': 1184611.3471376207}
### Forest Europe (SOEF)
This data is provided by the "Ministerial Conference on the Protection of Forests in Europe" and is accessible at: https://dbsoef.foresteurope.org/
Three tables are provided for every country:
* Table 1.1a: Forest area
* Table 1.3a1: Age class distribution (area of even-aged stands)
* Table 3.1: Increment and fellings
It is accessed in a similar way to other data sources:
```python
from forest_puller.soef.country import countries
country = countries['AT']
print(country.forest_area.indexed)
print(country.age_dist.indexed)
print(country.fellings.indexed)
```
There is also a large data frame containing all countries concatenated together:
```python
from forest_puller.soef.concat import tables
print(tables['forest_area'])
print(tables['age_dist'])
print(tables['fellings'])
```
### Faostat (forestry)
This data is acquired by picking the "All Data Normalized" option from the "Bulk download" sidebar at this address: http://www.fao.org/faostat/en/#data/FO
It is accessed in a similar way to other data sources:
```python
from forest_puller.faostat.forestry.country import countries
country = countries['AT']
print(country.df)
```
There is also a large data frame containing all countries concatenated together:
```python
from forest_puller.faostat.forestry.concat import df
print(df)
```
### Faostat (land)
This data is acquired by picking the "All Data Normalized" option from the "Bulk download" sidebar at this address: http://www.fao.org/faostat/en/#data/GF
It is accessed in a similar way to other data sources:
```python
from forest_puller.faostat.land.country import countries
country = countries['AT']
print(country.df)
```
There is also a large data frame containing all countries concatenated together:
```python
from forest_puller.faostat.land.concat import df
print(df)
```
### HPFFRE
Diabolo was a project run by a consortium of 33 partners from 25 countries. Experts in the fields of policy analysis, forest inventory, forest modelling. 7 work packages.
Link: http://diabolo-project.eu/
One of the outcomes of the Diabolo project is the following publication:
Vauhkonen et al. 2019 - [Harmonised projections of future forest resources in Europe](https://doi.org/10.1007/s13595-019-0863-6)
Abbreviated "hpffre". The authors used EFDM (mainly) to project forest area, growing stock, fellings and above ground carbon for European countries. There are several scenario outcomes.
The dataset is available at: https://doi.org/10.5061/dryad.4t880qh
It is accessed in a similar way to other data sources:
```python
from forest_puller.hpffre.country import countries
country = countries['AT']
print(country.df)
```
There is also a large data frame containing all countries concatenated together:
```python
from forest_puller.hpffre.concat import df
print(df)
```
### FRA
FRA stands for "Forest Resource Assessment".
The data is acquired by picking the "CSV download" option from the page located at: http://countrystat.org/home.aspx?c=FOR&tr=3
The home page is at: http://www.fao.org/forest-resources-assessment/en/
Five datasets are provided in the package:
* "Forest characteristics (1 000 ha) by FRA categories"
* "Extent of forest and other wooded land (1 000 ha)"
* "Forest establishment total (ha/yr) by FRA categories"
* "Carbon stock (Million metric tonnes) by Forest/Other wooded land"
* "Biomass stock (Million metric tonnes) by Forest/Other wooded land"
FRA is an effort of the Food and Agriculture Organization of the United Nations (FAO)".
It is accessed in a similar way to other data sources:
```python
from forest_puller.fra.country import countries
country = countries['AT']
print(country.df)
```
There is also a large data frame containing all countries concatenated together:
```python
from forest_puller.fra.concat import df
print(df)
```
## Visualizations
The `forest_puller` package can also generate several plots that enable the user to compare and visualize the data.
For instance here is are a series of graphs comparing the total reported forest area between data sources as seen in the `forest_puller.viz.area` submodule:
Another type of graph that can be produced is the comparison of gains and losses across several data-sources and across countries. This code is found in the `forest_puller.viz.increments` submodule and shows the five largest countries in terms of forest area.
With data from the SOEF source, we can also plot a breakdown of the growing stock volume genus composition of many countries across time. This code is found in the `forest_puller.viz.genus_barstack` submodule.
## Auto-generated report
All the visualization produced by the package are assembled inside an auto-generated PDF report. You can download a cached version of this report here:
<p align="center">
<table>
<tr>
<td>
<a href="https://xapple.github.io/forest_puller/reports/comparison.pdf">
<img src="docs/images/pdf_icon.png" width="120em">
</a>
</td>
</tr>
<tr>
<td>
<a href="https://xapple.github.io/forest_puller/reports/comparison.pdf">
<p align="center">Comparison report</p>
</a>
</td>
</tr>
</table>
</p>
## Reporting issues
If you encounter an error when using the `forest_puller` package, please open an issue on [the tracker](https://github.com/xapple/forest_puller/issues) and we will get in contact with you.
## Extra documentation
More documentation is available at:
<http://xapple.github.io/forest_puller/forest_puller>
This documentation is simply generated from the source code with:
$ pdoc --html --output-dir docs --force forest_puller
نحوه نصب
نصب پکیج whl forest-puller-1.4.1:
pip install forest-puller-1.4.1.whl
نصب پکیج tar.gz forest-puller-1.4.1:
pip install forest-puller-1.4.1.tar.gz