pyidw 0.2.20
IDW interpolation library for python
License: MIT


# All in one IDW package for python
> **This is an example map created using the pyidw library.**
> ![idw interpolated map using pyidw](https://github.com/yahyatamim/pyidw/blob/master/images/output_map.png?raw=true)

## Features
 1. Simple IDW Interpolation.
 2. IDW with external raster (eg, elevation raster) covariable.
 3. Accuracy Score.
 4. Built-in raster visualisation with coordinate and colour bar.

## Why pyidw ?

Inverse distance weighted interpolation is one of the simplest geospatial interpolation methods available in GIS. Although it is easy to produce an idw raster using conventional desktop GIS software (eg. ArcGIS, QGIS). It was never straightforward to create such a beautiful map image using python. This is why I have created the pyidw library where you can create beautiful idw maps of your desired location using your favourite programming language 🐍

pyidw package also incorporates a clever technique to use additional raster data as a covariable using polynomial regression. For example, if you are working with temperature data, it is widely known that temperature is inversely proportional to elevation, the higher the elevation, the lower the temperature is. With pyidw, you can easily add elevation data with traditional idw calculation to obtain a different result.

---

## Installation
**pyidw** library can be installed using a simple `pip install pyidw` command. However, if you are facing trouble installing **pyidw** on your windows machine, please try the commands below on the windows command line. 

    pip install wheel
    pip install pipwin
    pipwin refresh
    pipwin install numpy
    pipwin install pandas
    pipwin install shapely
    pipwin install gdal
    pipwin install fiona
    pipwin install pyproj
    pipwin install six
    pipwin install rtree
    pipwin install geopandas
    pipwin install rasterio
    pip install pyidw
    pipwin refresh

---
## Example
If you are convinced enough to give **pyidw** a try, here is a simple tutorial for you. You should first download the **[pyidw_example.zip](https://github.com/yahyatamim/pyidw/blob/master/example/pyidw_example.zip?raw=true)** file. This zip file contains four files, 
- ***pyidw_tutorial.ipynb***
- ***Bangladesh_Temperature.shp***
- ***Bangladesh_Border.shp***
- ***Bangladesh_Elevation.tif***

The ***pyidw_tutorial.ipynb*** file is a jupyter notebook file of this example tutorial, which you could try to run and then modify with your own data. The ***Bangladesh_Temperature.shp***  file is an ESRI point shapefile that contains maximum and minimum temperature values for 34 weather stations all over Bangladesh. Its attribute table looks something like this.

|Station\_Name|Station\_ID|Latitude|Longitude|Max\_Temp|Min\_Temp|
|-------------|-----------|--------|---------|---------|---------|
|BARISAL      |BGM00041950|22.75   |90.37    |36.75    |9.60     |
|BHOLA        |41951099999|22.68   |90.65    |35.62    |10.19    |
|BOGRA        |BGM00041883|24.85   |89.37    |38.62    |8.29     |
|CHANDPUR     |41941099999|23.27   |90.70    |35.87    |11.28    |
|CHITTAGONG   |BGM00041978|22.25   |91.81    |36.92    |11.24    |
|CHUADANGA    |41926099999|23.65   |88.82    |37.84    |8.59     |
|COMILLA      |41933099999|23.43   |91.18    |35.41    |10.35    |
|COXS BAZAR   |BGM00041992|21.45   |91.96    |37.11    |11.51    |

For those who are not familiar with shapefile, every shapefile consists of seven different files with the same name but seven different file extensions. Namely **.cpg .dbf .prj .sbn .sbx .shp** and **.shx**. If any of these files are missing then the shapefile system won't work properly. Note that **Max_Temp** and **Min_Temp** column in ***Bangladesh_Temperature.shp***  files attribute table, we will use one of these columns later when creating IDW interpolated maps.

The ***Bangladesh_Border.shp*** is an ESRI polygon shapefile that covers all the areas of the country Bangladesh. We will use this shapefile to define the calculation extent for IDW interpolation. And finally, the ***Bangladesh_Elevation.tif*** file which is a raster file containing elevation information in meter, We don't need this file for standard IDW interpolation but with regression_idw, we will use this file as an external covariable. All the files and their spatial dimension is shown below.

![Images of input files with their spatial dimensions.](https://github.com/yahyatamim/pyidw/raw/master/images/point_extent_elevation.png)

---
### idw_interpolation()
Now the fun part begins. Write these few lines of code from below in any python interpreter while you are on **pyidw_example** directory.
```python
from pyidw import idw

idw.idw_interpolation(
    input_point_shapefile="Bangladesh_Temperature.shp",
    extent_shapefile="Bangladesh_Border.shp",
    column_name="Max_Temp",
    power=2,
    search_radious=4,
    output_resolution=250,
)
```
It will take a few seconds to complete, then a map image like below will be shown. And a new file will be created namely ***Bangladesh_Temperature_idw.tif***, this is the saved raster file of the interpolated map. This file is named after input_point_shapefile name with ***\_idw.tif*** suffix. **idw_interpolation()** function take six parameters. 

- The first parameter ***input_point_shapefile=*** take an ESRI point shapefile which should contain the particular data value we are interested to create an interpolation map. Also, there shouldn't be any value outside of our given extent_shapefile area. 
- The second parameter ***extent_shapefile=*** take an ESRI polygon shapefile, this shapefile is used for defining the calculation and mapping boundary. The coordinate system of extent_shapefile should be the same as input_point_shapefile.
- The third parameter ***column_name=*** take the column name of a particular field as a string. This is the value upon which the IDW map will be created.
- The fourth parameter ***power=*** is an optional parameter with a default value of 2, this is the power parameter from [idw equation](https://en.wikipedia.org/wiki/Inverse_distance_weighting#:~:text=A%20general%20form,the%20power%20parameter.).
- The fifth parameter ***search_radious=*** is also an optional parameter with a default value of 4, it determines how many nearest points will be used for idw calculation.
- The sixth parameter ***output_resolution=*** is also optional with default value of 250. This parameter defines the maximum height or width (which one is higher) of the resulting ***\_idw.tif*** file in pixel.
>   ![Standard idw interpolated map](https://github.com/yahyatamim/pyidw/raw/master/images/standard_idw_interpolated_map.png)

> Output map  from **idw_interpolation()** function. 
---
### accuracy_standard_idw()
If you are interested in accuracy assessment of your interpolation then you could use **accuracy_standard_idw()** function from **pyidw** which take 6 parameters same as **idw_interpolation()** function. But instead of creating a idw interpolated map, the **accuracy_standard_idw()** function return tow python list. The first one contains actual data values from the input shapefile and the second list contains the interpolated values for those data points using [LeaveOneOut](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.LeaveOneOut.html#sklearn.model_selection.LeaveOneOut) cross-validation method. Then you could compare them to obtain your desired accuracy score. An example code for **accuracy_standard_idw()** function is given below.
 
```python
from pyidw import idw
from sklearn.metrics import mean_squared_error

original_value, interpolated_value = idw.accuracy_standard_idw(
    input_point_shapefile="Bangladesh_Temperature.shp",
    extent_shapefile="Bangladesh_Border.shp",
    column_name="Max_Temp",
    power=2,
    search_radious=6,
    output_resolution=250,
)

print("RMSE:", mean_squared_error(original_value, interpolated_value, squared=False))
```
> Output: `RMSE: 1.401379`
---
## show_map()
We have also implemented a raster visualization function named **show_map()**. This function incorporates easy map visualization with a built-in colour bar and coordinate tick marks. It takes 4 parameters.

 - ***input_raster=***  take raster file name as argument. 
 - ***colormap=*** is an optional parameter which take [matplotlib colormaps](https://matplotlib.org/stable/tutorials/colors/colormaps.html)  parameter. By changing this, you can easily alter the looks of your map image. 
 - ***image_size=*** is also an optional parameter with a default value set to 1.5 which you can change to make your resulting image larger or smaller.
 - The last parameter ***return_figure=*** is for those people who wish to alter the resulting image to their liking. by default it is set to false and **show_map()** function won't return anything other than showing the map on the screen. If set to true, then **show_map()** function will return figure, axes and color_bar to the user. We will see a detailed example of this in the next section.

Here is an example code of **show_map()** function.
```python
from pyidw import idw

show_map(
    input_raster="Bangladesh_Temperature_idw.tif",
    colormap="nipy_spectral_r",
    image_size=1.5,
    return_figure=False,
)
```
> ![Show_map() function example image](https://github.com/yahyatamim/pyidw/raw/master/images/show_map_example.png)

Below is an example code of setting ***return_figure=*** to **True** and adding some extra elements to the map 🗺 image.
```python
from pyidw import idw
from matplotlib import pyplot as plt

fig, ax, cbar = idw.show_map(
    input_raster="Bangladesh_Temperature_idw.tif",
    colormap="CMRmap",
    image_size=1.5,
    return_figure=True)

ax.set_title("Maximum temperature map")
ax.set_xlabel("Longitude")
ax.set_ylabel("Latitude")
cbar.set_label("maximum annual temperature")
plt.show()
```
> ![show_map() example with axis label](https://github.com/yahyatamim/pyidw/raw/master/images/show_map_example_with_axis_label.png)
---
## regression_idw_interpolation()
This function is quite different than regular IDW interpolation as it incorporates external raster covariable, polynomial regression and r_squared value. This is an experimental method and we don't recommend using it as it doesn't always produce reliable output and accuracy score are also lower than regular idw interpolation. It take same parameters as ***idw_interpolation()*** function only with 2 extra parameters namely ***input_raster_file=*** which is the raster covariable.  input_raster file should be larger than ***extent_shapefile***. The other parameter is ***polynomial_degre=*** with a default value set to 1 which would define the [polynomial regression equation](https://numpy.org/doc/stable/reference/generated/numpy.polyfit.html).  An example code for ***regression_idw_interpolation()*** is given below.
```python
from pyidw import idw

idw.regression_idw_interpolation(
    input_point_shapefile="Bangladesh_Temperature.shp",
    input_raster_file="Bangladesh_Elevation.tif",
    extent_shapefile="Bangladesh_Border.shp",
    column_name="Min_Temp",
    power=2,
    polynomial_degree=1,
    search_radious=5,
    output_resolution=250,
)
```
> ![Polynomial regression idw interpolation](https://github.com/yahyatamim/pyidw/raw/master/images/regression_idw.png)
---
## accuracy_regression_idw()
This function is similar to **accuracy_standard_idw()**  function. An example code is given below.

```python
from pyidw import idw
from sklearn.metrics import mean_squared_error

original_value, interpolated_value = idw.accuracy_regression_idw(
    input_point_shapefile="Bangladesh_Temperature.shp",
    input_raster_file="Bangladesh_Elevation.tif",
    extent_shapefile="Bangladesh_Border.shp",
    column_name="Min_Temp",
    power=2,
    polynomial_degree=1,
    search_radious=5,
    output_resolution=250,
)

print("RMSE:", mean_squared_error(original_value, interpolated_value, squared=False))
```
> Output: `RMSE: 1.086343`
---
If you have any questions, problems or suggestion, feel free to contact me at: yahyatamim0@gmail.com