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https://github.com/ibaris/rasterpy

:floppy_disk: Basic routines to read and write raster files with python
https://github.com/ibaris/rasterpy

conversion raster reader remote-sensing save writer

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:floppy_disk: Basic routines to read and write raster files with python

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README 

        


  


  RadOptics




Basic Routines to Read and Write Raster Files with Python





  

    Gitter

  

  

  

      

  






  Description •

  Installation •

  Example •

    Doumentation •

  Author •

  Acknowledgments






  

  

    Documentation Status






# Description

Rasterpy contains basic routines to read and write raster files with python. 



### Key Features

With Rasterpy one can read the following raster formats:

* **'tif'**: All `.tiff` formats like GEOtiff etc.

* **'.hdr'**: All ENVI formats like `.bin` and `.hdr` formats. This is usefull if you want to read data from third-party software like Polsarpro. 



Moreover, these additional functions are integrated:

* **Conversion**: Convert the files between a BRDF, BRF and BSC in linear or dB unit.

* **Stacking**: Basic stacking routines.



# Installation

There are currently different methods to install `rasterpy`.



### Using pip

The ` rasterpy ` package is provided on pip. You can install it with::



    pip install rasterpy

    

### Standard Python

You can also download the source code package from this repository or from pip. Unpack the file you obtained into some directory (it can be a temporary directory) and then run::



    python setup.py install

  

### Test installation success

Independent how you installed ` rasterpy `, you should test that it was sucessfull by the following tests::



    python -c "from rasterpy import Raster"



If you don't get an error message, the module import was sucessfull.



# Example

## Raster Calculation

Here is an example of some basic features that rasterpy provides. Three bands are read from an image and averaged to produce something like a panchromatic band. This new band is then written to a new single band TIFF.



At first import rasterpy:

```python

import rasterpy as rpy

import numpy as np

```

After that we define a path where our test tif file is located:

```python

path = ".\\rasterpy\\tests\data"

```



Then we open the grid and read it to an multidimensional array:

```python

grid = rpy.Raster('RGB.byte.tif', path)

grid.to_array()

```



By default the loaded grid is flatten. The reason is as following: With a flatten 2 dimensional array the calculations based on the array are much easier:

```python

>>> print(grid.array)

[[0. 0. 0. ... 0. 0. 0.]

 [0. 0. 0. ... 0. 0. 0.]

 [0. 0. 0. ... 0. 0. 0.]]

```



We can reshape the array to their original shapes with:

```python

>>> grid.reshape()

>>> print(grid.array)

[[[0. 0. 0. ... 0. 0. 0.]

  [0. 0. 0. ... 0. 0. 0.]

  [0. 0. 0. ... 0. 0. 0.]

  ...

  [0. 0. 0. ... 0. 0. 0.]

  [0. 0. 0. ... 0. 0. 0.]

  [0. 0. 0. ... 0. 0. 0.]]

 [[0. 0. 0. ... 0. 0. 0.]

  [0. 0. 0. ... 0. 0. 0.]

  [0. 0. 0. ... 0. 0. 0.]

  ...

  [0. 0. 0. ... 0. 0. 0.]

  [0. 0. 0. ... 0. 0. 0.]

  [0. 0. 0. ... 0. 0. 0.]]

 [[0. 0. 0. ... 0. 0. 0.]

  [0. 0. 0. ... 0. 0. 0.]

  [0. 0. 0. ... 0. 0. 0.]

  ...

  [0. 0. 0. ... 0. 0. 0.]

  [0. 0. 0. ... 0. 0. 0.]

  [0. 0. 0. ... 0. 0. 0.]]]

```

And we can also flatten it again with:

```python

>>> grid.flatten()

>>> print(grid.array)

[[0. 0. 0. ... 0. 0. 0.]

 [0. 0. 0. ... 0. 0. 0.]

 [0. 0. 0. ... 0. 0. 0.]]

```



Now average each pixels of the RGB bands:

```python

pan = np.mean(grid.array, axis=0)

```



After that we can write the reshaped pan array as a tiff:

```python

grid.write(data=pan.reshape(grid.rows, grid.cols), filename='RGB_total.tif', path=path)

```



The result is as following:




RGB_total.tif



## Raster Conversion

Three bands are read from an image. Then we do some raster conversion on it. This new bands are then written to a new multi band TIFF.



At first import rasterpy.

```python

import rasterpy as rpy

import numpy as np

```



After that we define a path where our test tif file is located.

```python

path = ".\\rasterpy\\tests\data"

```



Then we open the grid and read it to an multidimensional array:

```python

grid = rpy.Raster('RGB.BRF.tif', path)

```



The quantification factor is a factor which scales reflectance value between 0 and 1. For sentinel 2 the factor is 10000

```python

grid.to_array(flatten=False, quantification_factor=10000)

```



By default the loaded grid is flatten. The reason is as following: With a flatten 2 dimensional array the calculations based on the array are much easier. But in our case this is not necessary:

```python

>>> print(grid.array)

[[[0.1049 0.0979 0.1047 ... 0.0469 0.0551 0.0553]

  [0.1008 0.0974 0.0984 ... 0.0449 0.0497 0.0574]

  [0.1039 0.0957 0.1002 ... 0.0477 0.0546 0.0653]

  ...

  [0.1226 0.1151 0.1363 ... 0.0641 0.0599 0.0643]

  [0.1136 0.1299 0.144  ... 0.0654 0.0625 0.0651]

  [0.1069 0.1034 0.1356 ... 0.0616 0.0546 0.0629]]

 [[0.1025 0.0968 0.1027 ... 0.0435 0.0567 0.0585]

  [0.1008 0.0943 0.0996 ... 0.0466 0.0531 0.0625]

  [0.0969 0.0929 0.0987 ... 0.0523 0.0604 0.0692]

  ...

  [0.1223 0.112  0.1367 ... 0.0683 0.0587 0.0621]

  [0.112  0.1282 0.1409 ... 0.0658 0.0591 0.0635]

  [0.106  0.1018 0.1394 ... 0.0616 0.0551 0.0588]]

 [[0.1146 0.1088 0.114  ... 0.0484 0.0644 0.0703]

  [0.1112 0.1076 0.1148 ... 0.048  0.0599 0.0773]

  [0.1125 0.1064 0.1119 ... 0.0564 0.0747 0.0886]

  ...

  [0.1381 0.1259 0.1473 ... 0.0873 0.0746 0.0746]

  [0.1268 0.1404 0.1624 ... 0.0782 0.0702 0.0758]

  [0.1207 0.1194 0.1543 ... 0.0752 0.0617 0.0678]]]

```



Now we convert the whole file from a Bidirectional Reflectance Factor (BRF) into a Bidirectional Reflectance Distribution Function (BRDF):

```python

grid.convert(system='BRF', to='BRDF', output_unit='dB')

```



```python

>>> print(grid.array)

[[[-14.763744 -15.063672 -14.772033 ... -18.259771 -17.559982 -17.544247]

  [-14.936894 -15.085909 -15.041548 ... -18.449036 -18.007935 -17.38238 ]

  [-14.805344 -15.16238  -14.962821 ... -18.186316 -17.599571 -16.822367]

  ...

  [-14.086595 -14.360746 -13.626541 ... -16.90292  -17.197231 -16.889389]

  [-14.417715 -13.835407 -13.387875 ... -16.815722 -17.0127   -16.83569 ]

  [-14.681722 -14.826293 -13.648902 ... -17.075691 -17.599571 -16.984993]]

 [[-14.86426  -15.112745 -14.855795 ... -18.586605 -17.435669 -17.29994 ]

  [-14.936894 -15.226382 -14.988905 ... -18.28764  -17.720554 -17.0127  ]

  [-15.108261 -15.291342 -15.028326 ... -17.786482 -17.161129 -16.570438]

  ...

  [-14.097234 -14.479319 -13.613813 ... -16.62729  -17.285118 -17.040583]

  [-14.479319 -13.892618 -13.482389 ... -16.78924  -17.255625 -16.943762]

  [-14.718439 -14.89402  -13.528872 ... -17.075691 -17.559982 -17.277725]]

 [[-14.379653 -14.605209 -14.402451 ... -18.123045 -16.88264  -16.501945]

  [-14.510451 -14.653377 -14.372081 ... -18.159086 -17.197231 -16.089705]

  [-14.459973 -14.702083 -14.483198 ... -17.458708 -16.238293 -15.497161]

  ...

  [-13.569563 -13.971242 -13.289471 ... -15.561357 -16.24411  -16.24411 ]

  [-13.940307 -13.497828 -12.865639 ... -16.03943  -16.508127 -16.174807]

  [-14.154426 -14.201455 -13.08784  ... -16.20932  -17.068647 -16.6592  ]]]



```



After that we can write the converted data as a multi band tiff:

```python

grid.write(data=grid.array, filename='RGB.BRDF.tif', path=path)

```



We can also convert the arrays from a BRF or BRDF into Backscatter coefficients (BSC). For this we need the inclination (iza) and viewing (vza) angles. These information is in the first two bands of the dataset. Thus, we specify it with parameter `band=(1, 2)`:

```python

angle = rpy.Raster('RGB.BRF.Angle.tif')

angle.to_array(band=(1, 2), flatten=False)

```



Note, that the arrays were converted from a BRF into a BRDF (dB) in the previous step:

```python

grid.convert(system='BRDF', to='BSC', system_unit='dB', output_unit='dB', iza=angle.array[0], vza=angle.array[1])

```



```python

>>> print(grid.array)

[[[-4.96271712 -5.26264553 -4.97100609 ... -7.62126904 -6.92148086

   -6.9057452 ]

  [-5.13586758 -5.28488271 -5.24052164 ... -7.81053428 -7.36943237

   -6.74387795]

  [-5.00431669 -5.36135311 -5.16179448 ... -7.5478137  -6.96106918

   -6.18386485]

  ...

  [-3.90826298 -4.18241472 -3.44820966 ... -6.10610002 -6.40041111

   -6.09256871]

  [-4.23938353 -3.65707628 -3.20954311 ... -6.0189023  -6.21587891

   -6.03886975]

  [-4.50339076 -4.64796155 -3.47057097 ... -6.27887148 -6.80275109

   -6.18817325]]

 [[-5.06323355 -5.31171929 -5.05476833 ... -7.9481029  -6.79716676

   -6.66143884]

  [-5.13586758 -5.42535533 -5.18787861 ... -7.64913781 -7.08205228

   -6.374197  ]

  [-5.3072346  -5.49031489 -5.22729991 ... -7.14797961 -6.52262663

   -5.93193652]

  ...

  [-3.91890241 -4.3009875  -3.43548169 ... -5.83047042 -6.48829808

   -6.24376328]

  [-4.3009875  -3.7142869  -3.30405791 ... -5.99241989 -6.45880451

   -6.14694236]

  [-4.54010816 -4.71568912 -3.3505403  ... -6.27887148 -6.76316277

   -6.48090502]]

 [[-4.57862624 -4.80418309 -4.60142373 ... -7.4845433  -6.24413949

   -5.86344365]

  [-4.7094249  -4.8523499  -4.57105403 ... -7.52058505 -6.5587292

   -5.45120237]

  [-4.65894679 -4.90105644 -4.68217143 ... -6.82020658 -5.59979082

   -4.85865945]

  ...

  [-3.39123113 -3.79291025 -3.1111394  ... -4.76453681 -5.44729009

   -5.44729009]

  [-3.76197545 -3.31949574 -2.68730743 ... -5.24261142 -5.71130764

   -5.37798651]

  [-3.97609467 -4.02312353 -2.90950882 ... -5.41249989 -6.27182743

   -5.86238026]]]

```



After the conversion we can wrtie the array to a raster file:

```python

grid.write(data=grid.array, filename='RGB.BSC.tif', path=path)

```



The result is:







# Documentation

You can find the full documentation here.



# Built With

* Python 2.7 (But it works with Python 3.5 as well)

* Requirements: numpy, gdal



# Authors

* **Ismail Baris** - *Initial work* - ([email protected])



## Acknowledgments

*  John Truckenbrodt and Felix Cremer 



---



> ResearchGate [@Ismail_Baris](https://www.researchgate.net/profile/Ismail_Baris)  · 

> GitHub [@ibaris](https://github.com/ibaris)  · 

> Instagram [@ism.baris](https://www.instagram.com/ism.baris/)