12 task 2b primary locations work

.gitignore

@@ -160,3 +160,6 @@ Thumbs.db
 
 # Misc
 .copier-answers.yml
+
+# Ignore data path
+data/

.pre-commit-config.yaml

@@ -1,3 +1,6 @@
+# Exclude notebooks from pre-commit
+exclude: \.ipynb$
+
 ci:
   autoupdate_commit_msg: "chore: update pre-commit hooks"
   autofix_commit_msg: "style: pre-commit fixes"

BACKGROUND.md

@@ -17,19 +17,19 @@ Since the SPC currently uses 2011 OA11CD and MSOA11CD codes, 2011 boundaries wil
 
 ### Adding activity patterns to synthetic population
 
-#### NTS data 
+#### NTS data
 - We are currently using the entire NTS sample, but this could include trips with unrepresentative distances (e.g. commuting distance in London is not the same as liverpool). See https://github.com/Urban-Analytics-Technology-Platform/acbm/issues/16
 
-#### Household level matching 
+#### Household level matching
 - We use categorical matching at the household level (level 1) and then propensity score matching (PSM) at the individual level (level 2)
 - We need to implement PSM from the beginning to ensure that each individual in the SPC is matched to at least one sample from the NTS. See https://github.com/Urban-Analytics-Technology-Platform/acbm/issues/13
 - Matching variables are decided using trial and error (see [2_match_households_and_individuals](https://github.com/Urban-Analytics-Technology-Platform/acbm/blob/d2f9e747c3d55148316661b13b1650fac4a5a4ad/notebooks/2_match_households_and_individuals.ipynb). Using PSM would allow us to use all variables
 - For each SPC household, we randomly select one of the matched NTS households
 - Rest of the assumptions are outlined in the [wiki page](https://github.com/Urban-Analytics-Technology-Platform/acbm/wiki/Adding-activity-patterns-to-synthetic-population)
 
-#### Individual level matching 
+#### Individual level matching
 - Done based on age_group and sex only. PSM without replacement
-  
+
 ### Assigning activities to geographic locations
 
 #### Mode and trip purpose mapping 
@@ -51,11 +51,11 @@ Since the SPC currently uses 2011 OA11CD and MSOA11CD codes, 2011 boundaries wil
 - For education POIs, I've done the following:   
 
 > "kindergarden": ["education_kg", "work"],
-> 
+>
 > "school": ["education_school", "work"],
-> 
+>
 > "university": ["education_university", "work"],
-> 
+>
 > "college": ["education_college", "work"],
 
 ##### Selecting feasible zones for each activity
@@ -68,27 +68,27 @@ Since the SPC currently uses 2011 OA11CD and MSOA11CD codes, 2011 boundaries wil
 - If an individual in the NTS has an "education" activity, I map their age to an education type. See the age_group_mapping dictionary in 3_locations_primary:
 
 > age_group_mapping = {
-> 
+>
 > 1: "education_kg",   # "0-4"
-> 
+>
 > 2: "education_school", # "5-10"
-> 
+>
 > 3: "education_school", # "11-16"
-> 
+>
 > 4: "education_university", # "17-20"
-> 
+>
 > 5: "education_university", # "21-29"
-> 
+>
 > 6: "education_university", # "30-39"
-> 
+>
 > 7: "education_university", # "40-49"
-> 
+>
 > 8: "education_university", # "50-59"
-> 
+>
 > 9: "education_university" # "60+"
 > }
 
-    
+
 - When selecting a location for an education activity in [select_zone](https://github.com/Urban-Analytics-Technology-Platform/acbm/blob/c548fa7a6398dd0afde1398f7799e418b6068cd6/src/acbm/assigning.py#L578), we try to select a zone that has a POI that matches the persons age group. If we can't we choose any other feasible zone with an education POI
 - This logic should be moved upstream to the [get_possible_zone](https://github.com/Urban-Analytics-Technology-Platform/acbm/blob/c548fa7a6398dd0afde1398f7799e418b6068cd6/src/acbm/assigning.py#L201). For each activity, we should always ensure that our list of feasible zones has a zone with our specific POI category. This should be added in the [filter_by_activity](https://github.com/Urban-Analytics-Technology-Platform/acbm/blob/c548fa7a6398dd0afde1398f7799e418b6068cd6/src/acbm/assigning.py#L374) logic. The filter_by_activity logic currently looks at activity purpose from the NTS (e.g. "education"). We need to add the extra level of detail from age_group_mapping, and then filter based on that instead
 - We select a zone from the feasible zones probabilistically based on total floor area of the POIs that match the relevant activity. See [select_zone](https://github.com/Urban-Analytics-Technology-Platform/acbm/blob/c548fa7a6398dd0afde1398f7799e418b6068cd6/src/acbm/assigning.py#L578)
@@ -103,4 +103,3 @@ Since the SPC currently uses 2011 OA11CD and MSOA11CD codes, 2011 boundaries wil
 - (**DONE** [here](https://github.com/Urban-Analytics-Technology-Platform/acbm/commit/6acecb928ea2b9bf26952eb45b86f2918a6dccdf)) migrate logic for age_group_mapping from `select_zone()` to `get_possible_zones()`
 - edit `get_possible_zones()` to ensure it never returns an empty list of zones. See above for how to do this
     - 14/05/2024: I created another function `fill_missing_zones()`. see [this commit](https://github.com/Urban-Analytics-Technology-Platform/acbm/commit/10ae82b3923cdc51474d3721df80e332ea74ba03#diff-48d91584494e303c162dd8c5b8881de35f33976f2f688cd5a56db01b7ff1f233)
-

notebooks/1_prep_synthpop.ipynb

@@ -53,7 +53,7 @@
    "source": [
     "# Pick a region with SPC output saved\n",
     "path = \"../data/external/spc_output/raw/\"\n",
-    "region = \"west-yorkshire\""
+    "region = \"leeds\""
    ]
   },
   {
@@ -195,7 +195,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.8"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,