Updated site contents

Author: NokuzaSezo97

docs/About.md

@@ -33,7 +33,7 @@ PyAEZ has been developed to be used within the tropical region, hence some of th
 
 PyAEZ package consists of six main AEZ modules and one additional utility module stated as below:
 
-* Module 1: Climate regime - calculation of agro-climatic indicators for evaluation of climatic suitability of crops.
+* **Module 1: Climate regime** - calculation of agro-climatic indicators for evaluation of climatic suitability of crops.
 
 * **Module 2: Crop simulation** - simulate an optimal crop cycle for the highest attainable yield.
 * **Module 3: Climate constraints** - application of agro-climatic constraints to the calculated yield of a particular crop.

docs/Acknowledgements.md

@@ -1,7 +1,7 @@
 # Acknowledgements
 
-The free and open source package Python Agro-Ecological Zoning (PyAEZ) was developed to address country-specific spatial information needs on future agricultural production. The finalization of this publication was led by Dr. Manzul Kumar Hazarika,  Dr. Kittiphon Boonma and Swun Wunna Htet are the main authors for this publication with technical advice and contribution from Prof. Rajendra P. Shrestha.
+The free and open source package Python Agro-Ecological Zoning (PyAEZ) was developed to address country-specific spatial information needs on future agricultural production. The finalization of this web-based PyAEZ documentation was done by Swun Wunna Htet, Senior Research Associate of GIC-AIT.
 
-The authors acknowledge the contribution from the Asian Institute of Technology - Geoinformatics Center and the Food and Agriculture Organization of the United Nations (FAO) through the Geospatial Unit and the Regional Office for Asia and the Pacific (FAO-RAP). The PyAEZ development received financial support under a regional initiative on “Capacity building for Agro-Ecological Zone (AEZ) mapping and modelling to project climate suitability of crops and land uses” in collaboration with the FAO-RAP and the “Strengthening agro-climatic monitoring and information systems to improve adaptation to climate change and food security in Lao PDR (GCP /LAO/021/LDF)” project. The code development received contribution from Lakmal Nawarathnage, Thaileng Thol, Gianluca Franceschini, Shraddha Sharma, Dr. Kavinda Gunasekara, Dr. Kittiphon Boonma, Swun Wunna Htet and Dwijendra Das.
+The authors acknowledge the contribution from the Asian Institute of Technology - Geoinformatics Center (GIC-AIT) and the Food and Agriculture Organization of the United Nations (FAO) through the Geospatial Unit and the Regional Office for Asia and the Pacific (FAO-RAP). The PyAEZ development received financial support under a regional initiative on “Capacity building for Agro-Ecological Zone (AEZ) mapping and modelling to project climate suitability of crops and land uses” in collaboration with the FAO-RAP and the “Strengthening agro-climatic monitoring and information systems to improve adaptation to climate change and food security in Lao PDR (GCP /LAO/021/LDF)” project. The code development received contribution from Lakmal Nawarathnage, Thaileng Thol, Gianluca Franceschini, Shraddha Sharma, Dr. Kavinda Gunasekara, Dr. Kittiphon Boonma, Swun Wunna Htet and Dwijendra Das.
 
-This achievement would have not been made possible without the continuous technical contribution and advice from Günther Fischer from the International Institute for Applied Systems Analysis (IIASA) and Freddy Nachtergaele. The authors are also grateful for the financial and management support from FAO, especially Beau Damon, Monica Petri, Federica Chiozza, Joyce Ahimbisibwe, Dario Spiller, Rutendo Mukaratirwa and Matieu Henry.
+This achievement would have not been made possible without the continuous technical contribution and advice from Günther Fischer from the International Institute for Applied Systems Analysis (IIASA) and Freddy Nachtergaele. The authors are also grateful for the financial, management and technical support from FAO, especially Beau Damon, Monica Petri, Federica Chiozza, Joyce Ahimbisibwe, Dario Spiller, Rutendo Mukaratirwa, Filippo Sarvia and Matieu Henry.

docs/Data_Prep.md

@@ -12,23 +12,23 @@ PyAEZ package requires the following additional open-source Python packages to b
 4. Pandas: allows PyAEZ to read MS Excel sheets with user-defined parameters.
 5. Numba: aware optimizing compiler used to speed up some computationally heavy routines within PyAEZ.
 
-!!! note "Additional Information"
+!!! info "Additional Information"
 
-    Numpy: https://numpy.org/install/
+    Numpy: [click-here](https://numpy.org/install/)
 
-    GDAL : https://anaconda.org/conda-forge/gdal
+    GDAL : [click-here](https://anaconda.org/conda-forge/gdal)
 
-    SciPy : https://scipy.org/install
+    SciPy : [click-here](https://scipy.org/install)
 
-    Pandas : https://pandas.pydata.org/docs/getting_started/install.html
+    Pandas : [click-here](https://pandas.pydata.org/docs/getting_started/install.html)
 
-    Numba : https://numba.readthedocs.io/en/stable/user/installing.html
+    Numba : [click-here](https://numba.readthedocs.io/en/stable/user/installing.html)
 
-    Copernicus Climate Data Stores : https://cds.climate.copernicus.eu/
+    Copernicus Climate Data Stores : [click-here](https://cds.climate.copernicus.eu/)
 
-    ECMWF : https://www.ecmwf.int/en/forecasts/datasets
+    ECMWF : [click-here](https://www.ecmwf.int/en/forecasts/datasets)
 
-    Google Earth Engine : https://developers.google.com/earth-engine/datasets
+    Google Earth Engine : [click-here](https://developers.google.com/earth-engine/datasets)
 
 ___
 
@@ -56,7 +56,13 @@ PyAEZ requires to provide all mandatory crop parameters to be prepared by users'
 3. Crop-specific thermal characteristics
 4. Land utilization type characteristics
 
-While most of the parameterizations can be referred to GAEZv4 Appendix (Source: https://s3.eu-west-1.amazonaws.com/data.gaezdev.aws.fao.org/documentation/GAEZ4_Appendices.xlsx), some requires additional references apart from GAEZ context. The crop parameters can also be user-defined, or experimental, i.e., some parameters can be estimated from laboratory experiments, as FAO scientists initiated in the early 1900's.
+While most of the parameterizations can be referred to [GAEZv4 Appendix](https://s3.eu-west-1.amazonaws.com/data.gaezdev.aws.fao.org/documentation/GAEZ4_Appendices.xlsx), some requires additional references apart from GAEZ context. The crop parameters can also be user-defined, or experimental, i.e., some parameters can be estimated from laboratory experiments, as FAO scientists initiated in the early 1900's.
+
+!!! info
+
+    Additional crop information can be referred to the following resources:
+
+    1. [ECOCROP](https://gaez.fao.org/pages/ecocrop) is a crop database designed to collect and provide information on plant characteristics and crop environmental requirements for more than 200 plant species. It provides sutability of a crop for a specified enfironment. Several information includes category, life form, growth habit, life span and environmental description (minimum and maximum temperature, annual precipitation, soil pH, etc.,).
 
 An extensive list of crop paramters to prepare as an excel sheet are provided as below:
 

docs/M1.md

@@ -11,7 +11,7 @@ These following functions are required to set up first before AEZ project initia
 
 ### Initialization of M1 Object Class Creation
 
-PyAEZ codes utilizes ‘Object-Oriented Programming’ style, meaning that each module has its own Classes containing separate attributes and functions. Therefore, it is essential that the necessary object-classes are initiated at the beginning of each module. For Module 1, the Class that we need is called ‘ClimateRegime’, and is imported and initiated as
+PyAEZ codes utilizes ‘Object-Oriented Programming’ style, meaning that each module has its own Classes containing separate attributes and functions. Therefore, it is essential that the necessary object-classes are initiated at the beginning of each module. For Module 1, the Class that we need is called `ClimateRegime`, and is imported and initiated.
 
 ```py title="M1 Object Class Creation" linenums="1"
 from pyaez import ClimateRegime 

docs/M2.md

@@ -180,6 +180,7 @@ aez.ImportLGPandLGPT(lgp, lgpt5, lgpt10)
 **Output**
 
 |Arguments|Description|Unit|Data Format|
+|---|---|---|---|
 |None|---|---|---|
 
 ___
@@ -188,7 +189,7 @@ ___
 
 ### Setting the Study Area Inputs
 
-Users can provide the study area mask of to omit unnecessary pixels calculations.
+Users can provide the study area mask to omit unnecessary pixels calculations.
 
 ```py title="Setting Study Area" linenums="1"
 # Study Area Mask
@@ -205,6 +206,7 @@ aez.setStudyAreaMask(admin_mask, no_data_value=0)
 **Output**
 
 |Arguments|Description|Unit|Data Format|
+|---|---|---|---|
 |None|---|---|---|
 
 ___
@@ -232,6 +234,7 @@ aez.setThermalClimateScreening(tclimate, no_t_climate)
 **Output**
 
 |Arguments|Description|Data Format|
+|---|---|---|
 |None|---|---|
 
 ### Permafrost Screening
@@ -252,11 +255,12 @@ aez.setPermafrostScreening(permafrost_class)
 **Output**
 
 |Arguments|Description|Data Format|
+|---|---|---|
 |None|---|---|
 
 ### TSUM Screening
 
-The matching of the individual crop LUT heat unit requirements with the prevailing temperature sum is the purpose of temperature summation screening. TSUM is evaluated from base temperature of 0 ℃ for each individual cycle length duration. In version 2.1.0, the new algorithm for TSUM screening is introduced which is implemented with different inputs. 
+The matching of the individual crop LUT heat unit requirements with the prevailing temperature sum is the purpose of temperature summation screening. TSUM is evaluated from base temperature of 0 ℃ for each individual cycle length duration. Starting from version 2.1.0, the new algorithm for TSUM screening is introduced which is implemented with different inputs. 
 
 TSUM screening works to evaluate three conditions, each deciding the TSUM suitability termed as: “Optimum”, “Sub-optimum" and "Not-suitable”. Optimum condition requires no reduction factor to the calculated yield, while the rest of two conditions calculates the TSUM related reduction factor. Each condition has upper and lower boundaries (See Figure 6); defined as threshold points for the users to provide as below:
 
@@ -271,7 +275,7 @@ Any TSUM values within the optimum threshold range (green line) are considered a
 
 !!! note "Additional Information"
 
-    TSUM setting is done in the excel sheet from [data preparation](Data_Prep.md#crop-parameter-preparation). TSUM screening requires providing all six thresholds to activate. If one of the thresholds is missing, TSUM screening will not be activate. If users do not want to apply TSUM screening, provide **nan** value to all six variables. 
+    TSUM setting is done in the excel sheet from [data preparation](Data_Prep.md#crop-parameter-preparation). TSUM screening requires providing all six thresholds to activate. If one of the thresholds is missing, TSUM screening will not be activated. If users do not want to apply TSUM screening, provide **nan** value to all six variables. 
 
 **Figure: Implementation of TSUM Screening**
 ![Alt text](assets\TSUM Screening.png)
@@ -321,7 +325,9 @@ aez.setupCropSpecificRule(file_path, crop_name)
 **Output**
 
 |Arguments|Description|Data Format|
+|---|---|---|
 |None|---|---|
+
 ___
 
 ## Crop Simulation
@@ -335,7 +341,7 @@ The general schematic of the crop simulation is implemented as shown in figure b
 
 A newly implemented crop simulation routine for sugarcane is introduced as below:
 
-**Procedure of Calculation of Module 2 Outputs (Irrigated and Rainfed) for Sugarcane**
+**Figure: Procedure of Calculation of Module 2 Outputs (Irrigated and Rainfed) for Sugarcane**
 ![Alt text](assets\M2_sugarcane.png)
 
 ### Irrigated Crop Simulation
@@ -359,6 +365,7 @@ aez.simulateIrrigatedCropCycle(start_doy =1, end_doy = 365, step_doy = 1, leap_y
 **Output**
 
 |Arguments|Description|Data Format|
+|---|---|---|
 |None|---|---|
 
 ___
@@ -384,6 +391,7 @@ aez.simulateRainfedCropCycle(start_doy =1, end_doy = 365, step_doy = 1, leap_yea
 **Output**
 
 |Arguments|Description|Data Format|
+|---|---|---|
 |None|---|---|
 
 ___

docs/M5.md

@@ -126,7 +126,7 @@ __
 
 Once all the mandatory settings are done, users can run this function which executes the terrain-related yield reduction factors.
 
-```py title="Import climate and slope" linenums="s1"
+```py title="Import climate and slope" linenums="1"
 yld_out = terrain.applyTerrainConstraints(yield_in, irr_or_rain)
 ```
 
@@ -149,7 +149,7 @@ ___
 
 This function returns the terrain suitability map (fc5) after applying the terrain constraint function. Based on the setting from `applyTerrainConstraints`, the fc5 map can be representative for either rainfed or irrigated. Fc5 values ranges from 0 (Not Suitable) to 1 (Very Suitable).
 
-```py title="Get Fc5 map" linenums="s1"
+```py title="Get Fc5 map" linenums="1"
 fc5_map = terrain.getTerrainReductionFactor()
 ```
 
@@ -171,7 +171,7 @@ ___
 
 This function extracts the Fournier Index (FI) map after the execution of `calculateFI` function.
 
-```py title="Get FI" linenums="s1"
+```py title="Get FI" linenums="1"
 fi = terrain.getFI()
 ```
 
@@ -185,6 +185,6 @@ fi = terrain.getFI()
 
 |Arguments| Description|Unit|Data Type|
 |---|---|---|---|
-|None|-|-|-|
+|fi|Fournier Index|-|2D-NumPy Array|
 
 ___

mkdocs.yml

@@ -1,10 +1,15 @@
-site_name: PyAEZ v2.4 Documentation
+site_name: PyAEZ v2.3 Documentation
 site_dir: site
 use_directory_urls: true
 repo_url: https://github.com/gicait/PyAEZ
 repo_name: gicait/PyAEZ
 plugins:
   - search
+  - git-revision-date-localized:
+      type: datetime
+      fallback_to_build_date: false
+      enable_creation_date: false
+      enabled: true
 nav:
   - Home: index.md
   - Acknowledgements: Acknowledgements.md
@@ -78,6 +83,7 @@ markdown_extensions:
       anchor_linenums: true
       line_spans: __span
       pygments_lang_class: true
+      use_pygments: true
   - pymdownx.inlinehilite
   - pymdownx.snippets
   - pymdownx.superfences: