semvec/pypi-packages huggingface dataset:
gemshelper 0.0.0
GEMS_friction_surface helper functions
License: MIT
Requires Python: >=2.7

# GEMS Friction Surface pipeline 
Some helper functions and classes for GEMS fricton surface modelling pipeline.

* [Examples (in jupyter notebook)](#Examples-%28run-in-jupyter-notebook%29)
	* [1. Show Wet and Dry season sentinel images for Malawi](#Show-Wet-and-Dry-season-sentinel-images-for-Malawi)
		* [Dry season](#Dry-season)
		* [Wet season](#Wet--season)
	* [2. Load landcover satellite images](#2.-Load-landcover-satellite-images)



# Examples (jupyter notebook)
## 1. Show wet and dry season from [sentinel 2](https://eos.com/sentinel-2/) images for Malawi
```
import ee
import geogems  
# ee.Authenticate() #uncomment for first run to authorise with google 
ee.Initialize()

area= 'Malawi'
lat = -12.8018637
lon = 33.4752805

#time series 
dry_season = ('2019-05-01' , '2019-10-30') 
wet_season = ('2019-11-01', '2020-04-30')

#load the earth engine helper functions
geography_object = geogems.map.ee_helper()
#load the map 
map_malawai=geography_object.show(lat, lon, 6.5)

#get earth engines objects (bounds and sentinet images)
bounds=geography_object.get_country_polygon_by_name(ee, area) #pass in country name
image_dry,image_wet,trueColor_palette= geography_object.\
                                        get_sentinel_dry_wet_season(dry_season,wet_season ,ee,bounds)
```
In the next cell, type the object name to load map. 

```
map_malawai
```
Next, add Sentinel images
```
#Add image
map_malawai.addLayer(image_dry, trueColor_palette, '2019 Dry season true color');
map_malawai.addLayerControl()  
```

Output:
### Dry season 
![dry season malawi](https://github.com/t1nak/geogems/blob/main/pics/sentinel-dry%20season.png?raw=true)


### Wet  season 
![wet season malawi](https://github.com/t1nak/geogems/blob/main/pics/sentinel_wet_season.png?raw=true)

## 2. Load landcover satellite images





## Dependencies 

google earth engine
res-attention 0.0.1
A library for implementing Residual Attention CNN for image classification problems.
License: MIT
Keywords: attention CNN image-classification

# res-attention
---------------

A python library for implementing a Residual Attention Convolutional Neural Network
and training it for image classification problems. This model supports multi-class
classification and is easy to use both for training and testing purposes.

The code is Python 2 and 3 compatible.

# Installation
--------------

Fast install:
-------------

::

        pip install res-attention

For a manual install get this package:
--------------------------------------

.. code:: bash

        $wget https://github.com/garain/res-attention/archive/master.zip
        $unzip master.zip
        $rm master.zip
        $cd res-attention-master

Install the package:
--------------------

::

        python setup.py install    

# Example
---------

.. code:: python

        from models import AttentionResNetCifar10
        

        model = AttentionResNetCifar10(shape=(32,32,3),n_classes=2,n_channels=3)#RGB images
        model = AttentionResNetCifar10(shape=(32,32,1),n_classes=6,n_channels=1)#GrayScale images
        
        model.compile(tf.keras.optimizers.Adam(lr=0.0001), loss='categorical_crossentropy', metrics=['accuracy'])
        model.summary()


# Models supported
------------------
 - AttentionResNetCifar10
 - AttentionResNet92
 - AttentionResNet56