update readme

Author: cjber

.gitignore

@@ -6,6 +6,8 @@ covid_access
 data_story.Rmd
 debug.log
 
+reports/
+
 __pycache__
 
 .mypy_cache

README.md

@@ -39,6 +39,8 @@ This is a computationally intense calculation, with the total road network used
 
 This calculation was made possible through the GPU accelerated Python library `cugraph`, part of the [NVIDIA RAPIDS ecosystem](https://rapids.ai), allowing the computation to be highly parallel, taking minutes, rather than days.
 
+## Methodology
+
 ## Project layout
 
 ```bash
@@ -84,6 +86,34 @@ ahah
     to determine buffer suitability when converted to a graph
 - All processed data written to respective files
 
+#### 3. Combine into index `ahah/create_index.py`
+
+- Combine both processed secure and open data
+- Intermediate variables calculated
+  - All variables ranked
+  - Exponential default calculated for all ranked variables
+  - Percentiles calculated from ranked variables
+- Domains Scores calculated
+  - Domain scores calculated from mean of each domains input variables
+  - Domain scores ranked
+  - Domain percentiles calculated
+  - Exponential transformation calculated for each domain
+- AHAH index calculated from mean of domain exponential transformations
+  - Ranked AHAH index calculated
+  - AHAH percentiles calculated
+
+Exponential default:
+
+$$
+(x-0.5)/len(y)
+$$
+
+Exponential transformation:
+
+$$
+X=-23 ln(1-R(1-exp(-100/23)))
+$$
+
 #### 3. Routing `ahah/routing.py`
 
 > The routing stage of this project primarily makes use of the RAPIDS

ahah/create_index.py

@@ -2,8 +2,6 @@
 import pandas as pd
 from scipy.stats import norm
 
-from ahah.common.utils import Config, combine_lsoa
-
 
 def exp_trans(x, df):
     return -23 * np.log(1 - (x / len(df)) * (1 - np.exp(-100 / 23)))