From a17dd4ab200c3687a324569888d7c438b67476d7 Mon Sep 17 00:00:00 2001
From: Arnau Quera-Bofarull <arnauq@protonmail.com>
Date: Wed, 19 Jul 2023 13:12:17 +0100
Subject: [PATCH 01/24] cleaned gitignore

---
 .gitignore | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/.gitignore b/.gitignore
index a973b4d..5ae677c 100644
--- a/.gitignore
+++ b/.gitignore
@@ -138,3 +138,7 @@ example_scripts/**/*
 !example_scripts/*
 !example_scripts/**/*.py
 configs/tests.yaml
+build_pip.sh
+.DS_Store
+**/*pkl
+**/*yaml

From 4302129fdea0d1694935bd3467f09d4ca5916922 Mon Sep 17 00:00:00 2001
From: Arnau Quera-Bofarull <arnauq@protonmail.com>
Date: Wed, 19 Jul 2023 13:59:46 +0100
Subject: [PATCH 02/24] added function to group people by geography

---
 .gitignore                       |  1 +
 grad_june/demographics.py        | 14 ++++++++++++++
 grad_june/infection.py           |  4 +++-
 test/conftest.py                 |  2 ++
 test/unit/test_demographics.py   | 13 +++++++++++++
 test/unit/test_infection_seed.py |  6 ++++++
 test/unit/test_model.py          |  3 +--
 test/unit/test_utils.py          |  2 +-
 8 files changed, 41 insertions(+), 4 deletions(-)
 create mode 100644 grad_june/demographics.py
 create mode 100644 test/unit/test_demographics.py

diff --git a/.gitignore b/.gitignore
index 5ae677c..b852e64 100644
--- a/.gitignore
+++ b/.gitignore
@@ -142,3 +142,4 @@ build_pip.sh
 .DS_Store
 **/*pkl
 **/*yaml
+.vscode/settings.json
diff --git a/grad_june/demographics.py b/grad_june/demographics.py
new file mode 100644
index 0000000..8b7d845
--- /dev/null
+++ b/grad_june/demographics.py
@@ -0,0 +1,14 @@
+import torch
+
+def get_people_per_area(agent_ids: torch.Tensor, area_ids: torch.Tensor):
+    """Gets people ids in each area.
+    
+    **Arguments:**
+
+    - `agent_ids`: Ids of all agents.
+    - `area_ids`: Area ids of all agents.
+    """
+    people_per_area = {}
+    for area_id in torch.unique(area_ids):
+        people_per_area[area_id] = agent_ids[area_ids == area_id]
+    return people_per_area
\ No newline at end of file
diff --git a/grad_june/infection.py b/grad_june/infection.py
index 45204d9..2a1347f 100644
--- a/grad_june/infection.py
+++ b/grad_june/infection.py
@@ -31,7 +31,6 @@ def infect_people(data, timer, new_infected):
 def infect_fraction_of_people(
     data, timer, symptoms_updater, fraction, device
 ):
-    n_infections = data["agent"].susceptibility.shape[0]
     n_agents = data["agent"].id.shape[0]
     probs = fraction * torch.ones(n_agents, device=device)
     sampler = IsInfectedSampler()
@@ -61,3 +60,6 @@ def infect_people_at_indices(data, indices, device="cpu"):
         current_stage, device=device
     )
     return data
+
+def infect_people_at_districts():
+    pass
\ No newline at end of file
diff --git a/test/conftest.py b/test/conftest.py
index 8041003..5a4ee7c 100644
--- a/test/conftest.py
+++ b/test/conftest.py
@@ -40,6 +40,8 @@ def make_agent_data(sampler):
     data["agent"].id = torch.arange(0, n_agents)
     data["agent"].age = torch.randint(0, 100, (n_agents,))
     data["agent"].sex = torch.randint(0, 2, (n_agents,))
+    data["agent"].area_id = torch.randint(0, 10, (n_agents,))
+    data["agent"].district_id = torch.randint(0, 3, (n_agents,))
     inf_params = {}
     inf_params_values = sampler(n_agents)
     inf_params["max_infectiousness"] = inf_params_values[0]
diff --git a/test/unit/test_demographics.py b/test/unit/test_demographics.py
new file mode 100644
index 0000000..5a31c4a
--- /dev/null
+++ b/test/unit/test_demographics.py
@@ -0,0 +1,13 @@
+from grad_june.demographics import get_people_per_area
+
+class TestGetPeoplePerArea:
+    def test__get_people_per_area(self, data):
+        agent_ids = data["agent"].id
+        area_ids = data["agent"].area_id
+        people_per_area = get_people_per_area(agent_ids, area_ids)
+        assert len(people_per_area) == 10
+        assert sum([len(people_per_area[k]) for k in people_per_area]) == 100
+        district_ids = data["agent"].district_id
+        people_per_district = get_people_per_area(agent_ids, district_ids)
+        assert len(people_per_district) == 3
+        assert sum([len(people_per_district[k]) for k in people_per_district]) == 100
\ No newline at end of file
diff --git a/test/unit/test_infection_seed.py b/test/unit/test_infection_seed.py
index 16a690a..0dfeb12 100644
--- a/test/unit/test_infection_seed.py
+++ b/test/unit/test_infection_seed.py
@@ -3,6 +3,7 @@
 from grad_june.infection import (
     infect_people_at_indices,
     infect_fraction_of_people,
+    infect_people_at_districts
 )
 from grad_june.timer import Timer
 from grad_june.symptoms import SymptomsUpdater
@@ -44,3 +45,8 @@ def test__differentiable_seed(self, data):
                 assert data["agent"]["is_infected"][i] == 0
                 assert data["agent"]["infection_time"][i] == 0.0
                 assert data["agent"]["symptoms"]["next_stage"][i] == 1.0
+
+    def test__seeding_by_district(self, data):
+        cases_per_district = {0: 10, 1: 20, 2: 30}
+
+
diff --git a/test/unit/test_model.py b/test/unit/test_model.py
index a310ffc..8e48e41 100644
--- a/test/unit/test_model.py
+++ b/test/unit/test_model.py
@@ -158,8 +158,7 @@ def test__likelihood_gradient(self, model, data2):
         )
         log_likelihood.backward()
         parameters = [
-            model.infection_networks[name].log_beta
-            for name in ["company", "school"]
+            model.infection_networks[name].log_beta for name in ["company", "school"]
         ]
         grads = np.array([p.grad.cpu() for p in parameters])
         assert len(grads) == 2
diff --git a/test/unit/test_utils.py b/test/unit/test_utils.py
index 1b35270..0c59d5a 100644
--- a/test/unit/test_utils.py
+++ b/test/unit/test_utils.py
@@ -78,4 +78,4 @@ def test(self):
         #    assert data["agent"]["susceptibility"][i] == 1.0
         #    assert data["agent"]["is_infected"][i] == 0
         #    assert data["agent"]["infection_time"][i] == 0.0
-        #    assert data["agent"]["symptoms"]["next_stage"][i] == 1.0
+        #    assert data["agent"]["symptoms"]["next_stage"][i] == 1.0
\ No newline at end of file

From 10f0d97f1bd765b437bea9384861cf657a63befb Mon Sep 17 00:00:00 2001
From: Arnau Quera-Bofarull <arnauq@protonmail.com>
Date: Wed, 19 Jul 2023 14:33:42 +0100
Subject: [PATCH 03/24] infections per district work

---
 grad_june/demographics.py        |  2 +-
 grad_june/infection.py           | 22 ++++++++++++++++++++--
 test/unit/test_infection_seed.py | 18 ++++++++++++++++--
 3 files changed, 37 insertions(+), 5 deletions(-)

diff --git a/grad_june/demographics.py b/grad_june/demographics.py
index 8b7d845..39608b6 100644
--- a/grad_june/demographics.py
+++ b/grad_june/demographics.py
@@ -10,5 +10,5 @@ def get_people_per_area(agent_ids: torch.Tensor, area_ids: torch.Tensor):
     """
     people_per_area = {}
     for area_id in torch.unique(area_ids):
-        people_per_area[area_id] = agent_ids[area_ids == area_id]
+        people_per_area[area_id.item()] = agent_ids[area_ids == area_id]
     return people_per_area
\ No newline at end of file
diff --git a/grad_june/infection.py b/grad_june/infection.py
index 2a1347f..b177c89 100644
--- a/grad_june/infection.py
+++ b/grad_june/infection.py
@@ -1,4 +1,7 @@
 import torch
+import torch_geometric
+
+from grad_june.demographics import get_people_per_area
 
 class IsInfectedSampler(torch.nn.Module):
     def forward(self, not_infected_probs):
@@ -61,5 +64,20 @@ def infect_people_at_indices(data, indices, device="cpu"):
     )
     return data
 
-def infect_people_at_districts():
-    pass
\ No newline at end of file
+def infect_people_at_districts(data: torch_geometric.data.HeteroData, cases_per_district: dict):
+    """
+    Infects people at districts. The number of cases per district is given in the `cases_per_district` dictionary.
+
+    **Arguments:**
+
+    - `data`: The data object containing the agent data.
+    - `cases_per_district`: A dictionary mapping district ids to the number of cases in that district.
+    """
+    people_per_district = get_people_per_area(data["agent"].id, data["agent"].district_id)
+    ids_to_infect = []
+    for district in people_per_district:
+        n_to_infect = cases_per_district.get(district, 0)
+        random_idcs = torch.randperm(len(people_per_district[district]))[:n_to_infect]
+        agent_ids = people_per_district[district][random_idcs]
+        ids_to_infect.extend(list(agent_ids.cpu().numpy()))
+    infect_people_at_indices(data, ids_to_infect)
\ No newline at end of file
diff --git a/test/unit/test_infection_seed.py b/test/unit/test_infection_seed.py
index 0dfeb12..a1a6eae 100644
--- a/test/unit/test_infection_seed.py
+++ b/test/unit/test_infection_seed.py
@@ -1,4 +1,5 @@
 import numpy as np
+import torch
 
 from grad_june.infection import (
     infect_people_at_indices,
@@ -7,6 +8,7 @@
 )
 from grad_june.timer import Timer
 from grad_june.symptoms import SymptomsUpdater
+from grad_june.demographics import get_people_per_area
 
 
 class TestInfectionSeed:
@@ -30,8 +32,8 @@ def test__differentiable_seed(self, data):
         infect_fraction_of_people(
             data=data, timer=timer, symptoms_updater=su, fraction=0.2, device="cpu"
         )
-        assert np.isclose(
-            data["agent"].is_infected.sum(), 0.2 * data["agent"].id.shape[0], rtol=1e-1
+        assert torch.isclose(
+            data["agent"].is_infected.sum(), torch.tensor(0.2 * data["agent"].id.shape[0]), rtol=2e-1
         )
         for i in range(len(data["agent"].id)):
             if data["agent"].is_infected[i]:
@@ -48,5 +50,17 @@ def test__differentiable_seed(self, data):
 
     def test__seeding_by_district(self, data):
         cases_per_district = {0: 10, 1: 20, 2: 30}
+        infect_people_at_districts(data, cases_per_district)
+        assert data["agent"].is_infected.sum() == 60
+        people_per_district = get_people_per_area(data["agent"].id, data["agent"].district_id)
+        for i, district in enumerate(people_per_district):
+            infected = 0
+            for person_id in people_per_district[district]:
+                if data["agent"].is_infected[person_id]:
+                    infected += 1
+            assert infected == cases_per_district[i]
+
+
+
 
 

From 5a7bf4e1aafe3c2c6f8ba003894199da1d1cab30 Mon Sep 17 00:00:00 2001
From: Arnau Quera-Bofarull <arnauq@protonmail.com>
Date: Wed, 19 Jul 2023 14:59:20 +0100
Subject: [PATCH 04/24] moved functions from runner to demographics

---
 grad_june/demographics.py | 50 ++++++++++++++++++++++++++++++++++
 grad_june/runner.py       | 57 +++++----------------------------------
 2 files changed, 56 insertions(+), 51 deletions(-)

diff --git a/grad_june/demographics.py b/grad_june/demographics.py
index 39608b6..76dffaf 100644
--- a/grad_june/demographics.py
+++ b/grad_june/demographics.py
@@ -1,4 +1,54 @@
 import torch
+import torch_geometric
+from torch_geometric.data import HeteroData
+
+def store_differentiable_deaths(data: HeteroData, dead_idx: int):
+    """
+    Returns differentiable deaths. The results are stored
+    in data["results"]
+    """
+    symptoms = data["agent"].symptoms
+    #dead_idx = self.model.symptoms_updater.stages_ids[-1]
+    deaths = (
+        (symptoms["current_stage"] == dead_idx)
+        * symptoms["current_stage"]
+        / dead_idx
+    )
+    if data["results"]["deaths_per_timestep"] is not None:
+        data["results"]["deaths_per_timestep"] = torch.hstack(
+            (data["results"]["deaths_per_timestep"], deaths.sum())
+        )
+    else:
+        data["results"]["deaths_per_timestep"] = deaths.sum()
+
+def get_cases_by_age(data: HeteroData, age_bins: torch.Tensor):
+    device = age_bins.device
+    ret = torch.zeros(age_bins.shape[0] - 1, device=device)
+    for i in range(1, age_bins.shape[0]):
+        mask1 = data["agent"].age < age_bins[i]
+        mask2 = data["agent"].age > age_bins[i - 1]
+        mask = mask1 * mask2
+        ret[i - 1] = (data["agent"].is_infected * mask).sum()
+    return ret
+
+def get_people_by_age(ages: torch.Tensor, age_bins: torch.Tensor):
+    ret = {}
+    for i in range(1, age_bins.shape[0]):
+        mask1 = ages < age_bins[i]
+        mask2 = ages > age_bins[i - 1]
+        mask = mask1 * mask2
+        ret[int(age_bins[i].item())] = mask.sum()
+    return ret
+
+def get_cases_by_ethnicity(data: HeteroData, ethnicities):
+    device = ethnicities.device
+    ret = torch.zeros(len(ethnicities), device=device)
+    for i, ethnicity in enumerate(ethnicities):
+        mask = torch.tensor(
+            data["agent"].ethnicity == ethnicity, device=device
+        )
+        ret[i] = (mask * data["agent"].is_infected).sum()
+    return ret
 
 def get_people_per_area(agent_ids: torch.Tensor, area_ids: torch.Tensor):
     """Gets people ids in each area.
diff --git a/grad_june/runner.py b/grad_june/runner.py
index 3f84b8a..87d9f44 100644
--- a/grad_june/runner.py
+++ b/grad_june/runner.py
@@ -10,6 +10,7 @@
 from grad_june import GradJune, Timer, TransmissionSampler
 from grad_june.utils import read_path
 from grad_june.infection import infect_fraction_of_people
+from grad_june.demographics import get_people_by_age, get_cases_by_age, store_differentiable_deaths
 
 
 class Runner(torch.nn.Module):
@@ -33,7 +34,7 @@ def __init__(
         self.age_bins = torch.tensor(age_bins, device=self.device)
         self.ethnicities = np.sort(np.unique(data["agent"].ethnicity))
         self.n_agents = data["agent"].id.shape[0]
-        self.population_by_age = self.get_people_by_age()
+        self.population_by_age = get_people_by_age(data["agent"].age, self.age_bins)
         self.save_path = Path(save_path)
         self.input_parameters = parameters
         self.restore_initial_data()
@@ -156,8 +157,8 @@ def forward(self):
         self.restore_initial_data()
         self.set_initial_cases()
         cases_per_timestep = data["agent"].is_infected.sum()
-        cases_by_age = self.get_cases_by_age(data)
-        self.store_differentiable_deaths(data)
+        cases_by_age = get_cases_by_age(data, self.age_bins)
+        store_differentiable_deaths(data, self.model.symptoms_updater.stages_ids[-1])
         dates = [timer.date]
         i = 0
         while timer.date < timer.final_date:
@@ -166,8 +167,8 @@ def forward(self):
             data = model(data, timer)
             cases = data["agent"].is_infected.sum()
             cases_per_timestep = torch.hstack((cases_per_timestep, cases))
-            self.store_differentiable_deaths(data)
-            cases_age = self.get_cases_by_age(data)
+            store_differentiable_deaths(data, self.model.symptoms_updater.stages_ids[-1])
+            cases_age = get_cases_by_age(data, self.age_bins)
             cases_by_age = torch.vstack((cases_by_age, cases_age))
             dates.append(timer.date)
         results = {
@@ -194,49 +195,3 @@ def save_results(self, results, is_infected):
         df = pd.DataFrame()
         df["is_infected"] = is_infected
         df.to_csv(self.save_path / "results_is_infected.csv")
-
-    def store_differentiable_deaths(self, data):
-        """
-        Returns differentiable deaths. The results are stored
-        in data["results"]
-        """
-        symptoms = data["agent"].symptoms
-        dead_idx = self.model.symptoms_updater.stages_ids[-1]
-        deaths = (
-            (symptoms["current_stage"] == dead_idx)
-            * symptoms["current_stage"]
-            / dead_idx
-        )
-        if data["results"]["deaths_per_timestep"] is not None:
-            data["results"]["deaths_per_timestep"] = torch.hstack(
-                (data["results"]["deaths_per_timestep"], deaths.sum())
-            )
-        else:
-            data["results"]["deaths_per_timestep"] = deaths.sum()
-
-    def get_cases_by_age(self, data):
-        ret = torch.zeros(self.age_bins.shape[0] - 1, device=self.device)
-        for i in range(1, self.age_bins.shape[0]):
-            mask1 = data["agent"].age < self.age_bins[i]
-            mask2 = data["agent"].age > self.age_bins[i - 1]
-            mask = mask1 * mask2
-            ret[i - 1] = (data["agent"].is_infected * mask).sum()
-        return ret
-
-    def get_people_by_age(self):
-        ret = {}
-        for i in range(1, self.age_bins.shape[0]):
-            mask1 = self.data["agent"].age < self.age_bins[i]
-            mask2 = self.data["agent"].age > self.age_bins[i - 1]
-            mask = mask1 * mask2
-            ret[int(self.age_bins[i].item())] = mask.sum()
-        return ret
-
-    def get_cases_by_ethnicity(self, data):
-        ret = torch.zeros(len(self.ethnicities), device=self.device)
-        for i, ethnicity in enumerate(self.ethnicities):
-            mask = torch.tensor(
-                self.data["agent"].ethnicity == ethnicity, device=self.device
-            )
-            ret[i] = (mask * data["agent"].is_infected).sum()
-        return ret

From a5b4e7951d87a31acd428adec43440e119d0dc33 Mon Sep 17 00:00:00 2001
From: Arnau Quera-Bofarull <arnauq@protonmail.com>
Date: Wed, 19 Jul 2023 16:36:28 +0100
Subject: [PATCH 05/24] reworked infection seed

---
 grad_june/demographics.py        |  3 +-
 grad_june/infection.py           | 69 +++++------------------
 grad_june/infection_seed.py      | 95 ++++++++++++++++++++++++++++++++
 grad_june/model.py               | 32 +++--------
 grad_june/runner.py              | 26 ++++-----
 grad_june/utils.py               |  1 -
 test/conftest.py                 |  2 +-
 test/unit/test_infection_seed.py | 40 +++++++++-----
 test/unit/test_runner.py         |  8 ---
 9 files changed, 155 insertions(+), 121 deletions(-)
 create mode 100644 grad_june/infection_seed.py

diff --git a/grad_june/demographics.py b/grad_june/demographics.py
index 76dffaf..940871c 100644
--- a/grad_june/demographics.py
+++ b/grad_june/demographics.py
@@ -1,5 +1,4 @@
 import torch
-import torch_geometric
 from torch_geometric.data import HeteroData
 
 def store_differentiable_deaths(data: HeteroData, dead_idx: int):
@@ -61,4 +60,4 @@ def get_people_per_area(agent_ids: torch.Tensor, area_ids: torch.Tensor):
     people_per_area = {}
     for area_id in torch.unique(area_ids):
         people_per_area[area_id.item()] = agent_ids[area_ids == area_id]
-    return people_per_area
\ No newline at end of file
+    return people_per_area
diff --git a/grad_june/infection.py b/grad_june/infection.py
index b177c89..c772f99 100644
--- a/grad_june/infection.py
+++ b/grad_june/infection.py
@@ -1,8 +1,9 @@
 import torch
-import torch_geometric
+from torch_geometric.data import HeteroData
 
 from grad_june.demographics import get_people_per_area
 
+
 class IsInfectedSampler(torch.nn.Module):
     def forward(self, not_infected_probs):
         """
@@ -20,64 +21,20 @@ def forward(self, not_infected_probs):
         is_infected = 1.0 - infection[0, :]
         return is_infected
 
+def infect_people(data: HeteroData, time: int, new_infected: torch.Tensor):
+    """
+    Sets the `new_infected` individuals to infected at time `time`.
 
-def infect_people(data, timer, new_infected):
+    **Arguments:**
+
+    - `data`: the graph data
+    - `time`: the time step at which the infection happens
+    - `new_infected`: a tensor of size [N] where N is the number of agents.
+    """
     data["agent"].susceptibility = torch.clamp(
         data["agent"].susceptibility - new_infected, min=0.0
     )
     data["agent"].is_infected = data["agent"].is_infected + new_infected
     data["agent"].infection_time = data["agent"].infection_time + new_infected * (
-        timer.now - data["agent"].infection_time
-    )
-
-
-def infect_fraction_of_people(
-    data, timer, symptoms_updater, fraction, device
-):
-    n_agents = data["agent"].id.shape[0]
-    probs = fraction * torch.ones(n_agents, device=device)
-    sampler = IsInfectedSampler()
-    new_infected = sampler(
-        1.0 - probs
-    )  # sampler takes not inf probs
-    infect_people(data, timer, new_infected)
-    return new_infected
-
-
-def infect_people_at_indices(data, indices, device="cpu"):
-    susc = data["agent"]["susceptibility"].cpu().numpy()
-    is_inf = data["agent"]["is_infected"].cpu().numpy()
-    inf_t = data["agent"]["infection_time"].cpu().numpy()
-    next_stage = data["agent"]["symptoms"]["next_stage"].cpu().numpy()
-    current_stage = data["agent"]["symptoms"]["current_stage"].cpu().numpy()
-    susc[indices] = 0.0
-    is_inf[indices] = 1.0
-    inf_t[indices] = 0.0
-    next_stage[indices] = 2
-    current_stage[indices] = 1
-    data["agent"]["susceptibility"] = torch.tensor(susc, device=device)
-    data["agent"]["is_infected"] = torch.tensor(is_inf, device=device)
-    data["agent"]["infection_time"] = torch.tensor(inf_t, device=device)
-    data["agent"]["symptoms"]["next_stage"] = torch.tensor(next_stage, device=device)
-    data["agent"]["symptoms"]["current_stage"] = torch.tensor(
-        current_stage, device=device
-    )
-    return data
-
-def infect_people_at_districts(data: torch_geometric.data.HeteroData, cases_per_district: dict):
-    """
-    Infects people at districts. The number of cases per district is given in the `cases_per_district` dictionary.
-
-    **Arguments:**
-
-    - `data`: The data object containing the agent data.
-    - `cases_per_district`: A dictionary mapping district ids to the number of cases in that district.
-    """
-    people_per_district = get_people_per_area(data["agent"].id, data["agent"].district_id)
-    ids_to_infect = []
-    for district in people_per_district:
-        n_to_infect = cases_per_district.get(district, 0)
-        random_idcs = torch.randperm(len(people_per_district[district]))[:n_to_infect]
-        agent_ids = people_per_district[district][random_idcs]
-        ids_to_infect.extend(list(agent_ids.cpu().numpy()))
-    infect_people_at_indices(data, ids_to_infect)
\ No newline at end of file
+        time - data["agent"].infection_time
+    )
\ No newline at end of file
diff --git a/grad_june/infection_seed.py b/grad_june/infection_seed.py
new file mode 100644
index 0000000..4e839b9
--- /dev/null
+++ b/grad_june/infection_seed.py
@@ -0,0 +1,95 @@
+import torch
+from torch_geometric.data import HeteroData
+
+from grad_june.demographics import get_people_per_area
+from grad_june.infection import IsInfectedSampler
+from grad_june.timer import Timer
+
+def infect_people(data: HeteroData, time: int, new_infected: torch.Tensor):
+    data["agent"].susceptibility = torch.clamp(
+        data["agent"].susceptibility - new_infected, min=0.0
+    )
+    data["agent"].is_infected = data["agent"].is_infected + new_infected
+    data["agent"].infection_time = data["agent"].infection_time + new_infected * (
+        time - data["agent"].infection_time
+    )
+
+def infect_people_at_indices(data, indices, device="cpu"):
+    susc = data["agent"]["susceptibility"].cpu().numpy()
+    is_inf = data["agent"]["is_infected"].cpu().numpy()
+    inf_t = data["agent"]["infection_time"].cpu().numpy()
+    next_stage = data["agent"]["symptoms"]["next_stage"].cpu().numpy()
+    current_stage = data["agent"]["symptoms"]["current_stage"].cpu().numpy()
+    susc[indices] = 0.0
+    is_inf[indices] = 1.0
+    inf_t[indices] = 0.0
+    next_stage[indices] = 2
+    current_stage[indices] = 1
+    data["agent"]["susceptibility"] = torch.tensor(susc, device=device)
+    data["agent"]["is_infected"] = torch.tensor(is_inf, device=device)
+    data["agent"]["infection_time"] = torch.tensor(inf_t, device=device)
+    data["agent"]["symptoms"]["next_stage"] = torch.tensor(next_stage, device=device)
+    data["agent"]["symptoms"]["current_stage"] = torch.tensor(
+        current_stage, device=device
+    )
+    return data
+
+
+class InfectionSeedByDistrict(torch.nn.Module):
+    def __init__(self, cases_per_district: dict, device: str):
+        """
+        Seeds infections at districts. The number of cases per district is given in the `cases_per_district` dictionary.
+
+        **Arguments:**
+
+        - `cases_per_district`: a dictionary mapping district ids to a list of cases per time step
+        - `device`: the device to use for the tensor operations
+        """
+        super().__init__()
+        self.cases_per_district = cases_per_district
+        self.device = device
+
+    def forward(
+        self, data: HeteroData, time_step: int
+    ):
+        people_per_district = get_people_per_area(
+            data["agent"].id, data["agent"].district_id
+        )
+        ids_to_infect = []
+        for district in people_per_district:
+            if district in self.cases_per_district:
+                n_to_infect = self.cases_per_district[district][time_step]
+            else:
+                n_to_infect = 0
+            people = people_per_district[district]
+            infected_status = data["agent"].is_infected[people]
+            susceptible_people = people[~infected_status.bool()]
+            random_idcs = torch.randperm(len(susceptible_people))[:n_to_infect]
+            agent_ids = susceptible_people[random_idcs]
+            ids_to_infect.extend(list(agent_ids.cpu().numpy()))
+        infect_people_at_indices(data, ids_to_infect)
+
+class InfectionSeedByFraction(torch.nn.Module):
+    def __init__(self, fraction: float, device: str):
+        """
+        Infects a fraction of the population at random
+
+        **Arguments:**
+
+        - `fraction`: the fraction of the population to infect
+        - `device`: the device to use for the tensor operations
+        """
+        super().__init__()
+        self.fraction = fraction
+        self.device = device
+
+    def forward(
+        self, data: HeteroData, time_step: int
+    ):
+        if time_step > 0:
+            return
+        n_agents = data["agent"].id.shape[0]
+        probs = self.fraction * torch.ones(n_agents, device=self.device)
+        sampler = IsInfectedSampler()
+        new_infected = sampler(1.0 - probs)  # sampler takes not inf probs
+        infect_people(data, time_step, new_infected)
\ No newline at end of file
diff --git a/grad_june/model.py b/grad_june/model.py
index 181a455..054ed4f 100644
--- a/grad_june/model.py
+++ b/grad_june/model.py
@@ -1,6 +1,5 @@
 import torch
 import yaml
-from torch.utils.checkpoint import checkpoint
 
 from grad_june import (
     TransmissionUpdater,
@@ -8,8 +7,9 @@
     SymptomsUpdater,
     InfectionNetworks,
 )
+from grad_june.infection_seed import InfectionSeedByFraction
+from grad_june.infection import infect_people
 from grad_june.policies import Policies
-from grad_june.cuda_utils import get_fraction_gpu_used
 from grad_june.paths import default_config_path
 
 
@@ -31,6 +31,7 @@ def __init__(
         symptoms_updater=None,
         policies=None,
         infection_networks=None,
+        infection_seed=None,
         device="cpu",
     ):
         super().__init__()
@@ -44,6 +45,7 @@ def __init__(
         self.policies = policies
         if infection_networks is None:
             infection_networks = InfectionNetworks.from_file()
+        self.infection_seed = infection_seed
         self.infection_networks = infection_networks
 
         # Initializes transmission updater, is_infected_sampler, and device.
@@ -87,28 +89,6 @@ def from_parameters(cls, params):
             device=params["system"]["device"],
         )
 
-    def infect_people(self, data, timer, new_infected):
-        """
-        This method infects people based on the given parameters.
-
-        Args:
-            data: A dictionary containing simulation data.
-            timer: An integer representing the current simulation time.
-            new_infected: A tensor representing which agents will be infected.
-
-        Returns:
-            None.
-        """
-        # Updates agent susceptibility, infection status, and infection time based on new_infected tensor.
-        data["agent"].susceptibility = torch.maximum(
-            torch.tensor(0.0, device=self.device),
-            data["agent"].susceptibility - new_infected,
-        )
-        data["agent"].is_infected = data["agent"].is_infected + new_infected
-        data["agent"].infection_time = data["agent"].infection_time + new_infected * (
-            timer.now - data["agent"].infection_time
-        )
-
     def forward(self, data, timer):
         """
         This function represents a forward pass through the epidemiological simulation model.
@@ -120,6 +100,8 @@ def forward(self, data, timer):
         Returns:
             A A PyTorch geometric data object containing updated simulation data.
         """
+        if self.infection_seed is not None:
+            self.infection_seed(data, timer.now)
 
         # Updates agent transmission based on current transmission updater values.
         data["agent"].transmission = self.transmission_updater(data=data, timer=timer)
@@ -135,7 +117,7 @@ def forward(self, data, timer):
         new_infected = self.is_infected_sampler(not_infected_probs)
 
         # Infects agents who were sampled as new_infected.
-        self.infect_people(data, timer, new_infected)
+        infect_people(data, timer.now, new_infected)
 
         # Updates agents' symptoms based on their infection status.
         self.symptoms_updater(data=data, timer=timer, new_infected=new_infected)
diff --git a/grad_june/runner.py b/grad_june/runner.py
index 87d9f44..e772bf7 100644
--- a/grad_june/runner.py
+++ b/grad_june/runner.py
@@ -9,7 +9,6 @@
 from grad_june.paths import default_config_path
 from grad_june import GradJune, Timer, TransmissionSampler
 from grad_june.utils import read_path
-from grad_june.infection import infect_fraction_of_people
 from grad_june.demographics import get_people_by_age, get_cases_by_age, store_differentiable_deaths
 
 
@@ -136,18 +135,17 @@ def restore_initial_data(self):
         self.data["results"] = {}
         self.data["results"]["deaths_per_timestep"] = None
 
-    def set_initial_cases(self):
-        fraction_initial_cases = 10.0**self.log_fraction_initial_cases
-        new_infected = infect_fraction_of_people(
-            data=self.data,
-            timer=self.timer,
-            symptoms_updater=self.model.symptoms_updater,
-            device=self.device,
-            fraction=fraction_initial_cases,
-        )
-        self.model.symptoms_updater(
-            data=self.data, timer=self.timer, new_infected=new_infected
-        )
+    #def set_initial_cases(self):
+    #    fraction_initial_cases = 10.0**self.log_fraction_initial_cases
+    #    new_infected = infect_fraction_of_people(
+    #        data=self.data,
+    #        timer=self.timer,
+    #        device=self.device,
+    #        fraction=fraction_initial_cases,
+    #    )
+    #    self.model.symptoms_updater(
+    #        data=self.data, timer=self.timer, new_infected=new_infected
+    #    )
 
     def forward(self):
         timer = self.timer
@@ -155,7 +153,7 @@ def forward(self):
         data = self.data
         timer.reset()
         self.restore_initial_data()
-        self.set_initial_cases()
+        #self.set_initial_cases()
         cases_per_timestep = data["agent"].is_infected.sum()
         cases_by_age = get_cases_by_age(data, self.age_bins)
         store_differentiable_deaths(data, self.model.symptoms_updater.stages_ids[-1])
diff --git a/grad_june/utils.py b/grad_june/utils.py
index a7917be..fa36142 100644
--- a/grad_june/utils.py
+++ b/grad_june/utils.py
@@ -11,7 +11,6 @@
 import torch_geometric.transforms as T
 
 from grad_june.paths import grad_june_path
-from grad_june.infection import infect_people_at_indices
 
 
 def read_path(path_str):
diff --git a/test/conftest.py b/test/conftest.py
index 5a4ee7c..b986e39 100644
--- a/test/conftest.py
+++ b/test/conftest.py
@@ -7,7 +7,7 @@
 from torch_geometric.data import HeteroData
 
 from grad_june.transmission import TransmissionSampler
-from grad_june.infection import infect_people_at_indices
+from grad_june.infection_seed import infect_people_at_indices
 from grad_june.timer import Timer
 
 
diff --git a/test/unit/test_infection_seed.py b/test/unit/test_infection_seed.py
index a1a6eae..dd542ee 100644
--- a/test/unit/test_infection_seed.py
+++ b/test/unit/test_infection_seed.py
@@ -1,10 +1,10 @@
 import numpy as np
 import torch
 
-from grad_june.infection import (
+from grad_june.infection_seed import (
     infect_people_at_indices,
-    infect_fraction_of_people,
-    infect_people_at_districts
+    InfectionSeedByDistrict,
+    InfectionSeedByFraction
 )
 from grad_june.timer import Timer
 from grad_june.symptoms import SymptomsUpdater
@@ -29,9 +29,8 @@ def test__simple_seed(self, data):
     def test__differentiable_seed(self, data):
         su = SymptomsUpdater.from_file()
         timer = Timer.from_file()
-        infect_fraction_of_people(
-            data=data, timer=timer, symptoms_updater=su, fraction=0.2, device="cpu"
-        )
+        seed = InfectionSeedByFraction(0.2, device="cpu")
+        seed(data, 0)
         assert torch.isclose(
             data["agent"].is_infected.sum(), torch.tensor(0.2 * data["agent"].id.shape[0]), rtol=2e-1
         )
@@ -49,18 +48,31 @@ def test__differentiable_seed(self, data):
                 assert data["agent"]["symptoms"]["next_stage"][i] == 1.0
 
     def test__seeding_by_district(self, data):
-        cases_per_district = {0: 10, 1: 20, 2: 30}
-        infect_people_at_districts(data, cases_per_district)
-        assert data["agent"].is_infected.sum() == 60
+        cases_per_district = {0: [3, 5, 2], 1: [7, 2, 4], 2: [2, 3, 4]}
+        seed = InfectionSeedByDistrict(cases_per_district, device="cpu")
+        seed(data, 0)
+        assert data["agent"].is_infected.sum() == 12
         people_per_district = get_people_per_area(data["agent"].id, data["agent"].district_id)
         for i, district in enumerate(people_per_district):
             infected = 0
             for person_id in people_per_district[district]:
                 if data["agent"].is_infected[person_id]:
                     infected += 1
-            assert infected == cases_per_district[i]
-
-
-
-
+            assert infected == cases_per_district[i][0]
+        seed(data, 1)
+        assert data["agent"].is_infected.sum() == 12 + 10
+        for i, district in enumerate(people_per_district):
+            infected = 0
+            for person_id in people_per_district[district]:
+                if data["agent"].is_infected[person_id]:
+                    infected += 1
+            assert infected == sum(cases_per_district[i][:2])
+        seed(data, 2)
+        assert data["agent"].is_infected.sum() == 12 + 10 + 10
+        for i, district in enumerate(people_per_district):
+            infected = 0
+            for person_id in people_per_district[district]:
+                if data["agent"].is_infected[person_id]:
+                    infected += 1
+            assert infected == sum(cases_per_district[i])
 
diff --git a/test/unit/test_runner.py b/test/unit/test_runner.py
index 72b6cf9..9e3e6e2 100644
--- a/test/unit/test_runner.py
+++ b/test/unit/test_runner.py
@@ -34,14 +34,6 @@ def test__get_data(self, runner):
         assert inf_params["rate"].shape == n_agents
         assert inf_params["shift"].shape == n_agents
 
-    def test__seed(self, runner):
-        runner.set_initial_cases()
-        assert np.isclose(
-            runner.data["agent"].is_infected.sum().item(),
-            0.10 * runner.n_agents,
-            rtol=3e-1,
-        )
-
     def test__restore_data(self, runner):
         n_agents = runner.data["agent"].id.shape
         runner.data["agent"].transmission = torch.rand(n_agents)

From e289544b46ae2acc12784ceb19005b8ebcea640b Mon Sep 17 00:00:00 2001
From: Arnau Quera-Bofarull <arnauq@protonmail.com>
Date: Wed, 19 Jul 2023 19:48:43 +0100
Subject: [PATCH 06/24] changed so seed uses log

---
 grad_june/infection_seed.py      | 9 +++++----
 test/unit/test_infection_seed.py | 2 +-
 2 files changed, 6 insertions(+), 5 deletions(-)

diff --git a/grad_june/infection_seed.py b/grad_june/infection_seed.py
index 4e839b9..a39cb96 100644
--- a/grad_june/infection_seed.py
+++ b/grad_june/infection_seed.py
@@ -70,17 +70,17 @@ def forward(
         infect_people_at_indices(data, ids_to_infect)
 
 class InfectionSeedByFraction(torch.nn.Module):
-    def __init__(self, fraction: float, device: str):
+    def __init__(self, log_fraction: float, device: str):
         """
         Infects a fraction of the population at random
 
         **Arguments:**
 
-        - `fraction`: the fraction of the population to infect
+        - `log_fraction`: the fraction of the population to infect in log10
         - `device`: the device to use for the tensor operations
         """
         super().__init__()
-        self.fraction = fraction
+        self.log_fraction = log_fraction
         self.device = device
 
     def forward(
@@ -89,7 +89,8 @@ def forward(
         if time_step > 0:
             return
         n_agents = data["agent"].id.shape[0]
-        probs = self.fraction * torch.ones(n_agents, device=self.device)
+        fraction = 10 ** self.log_fraction
+        probs = fraction * torch.ones(n_agents, device=self.device)
         sampler = IsInfectedSampler()
         new_infected = sampler(1.0 - probs)  # sampler takes not inf probs
         infect_people(data, time_step, new_infected)
\ No newline at end of file
diff --git a/test/unit/test_infection_seed.py b/test/unit/test_infection_seed.py
index dd542ee..de9555c 100644
--- a/test/unit/test_infection_seed.py
+++ b/test/unit/test_infection_seed.py
@@ -29,7 +29,7 @@ def test__simple_seed(self, data):
     def test__differentiable_seed(self, data):
         su = SymptomsUpdater.from_file()
         timer = Timer.from_file()
-        seed = InfectionSeedByFraction(0.2, device="cpu")
+        seed = InfectionSeedByFraction(np.log10(0.2), device="cpu")
         seed(data, 0)
         assert torch.isclose(
             data["agent"].is_infected.sum(), torch.tensor(0.2 * data["agent"].id.shape[0]), rtol=2e-1

From 119d8963b9f3627e3d3650d15aab4bb6b1f237b7 Mon Sep 17 00:00:00 2001
From: Arnau Quera-Bofarull <arnauq@protonmail.com>
Date: Wed, 19 Jul 2023 20:12:04 +0100
Subject: [PATCH 07/24] updated notebook with new seed

---
 grad_june/configs/default.yaml |  26 +++---
 grad_june/infection_seed.py    |   7 +-
 grad_june/model.py             |   6 +-
 grad_june/runner.py            |  22 +----
 notebooks/quickstart.ipynb     | 157 ++++++++++++++++-----------------
 5 files changed, 105 insertions(+), 113 deletions(-)

diff --git a/grad_june/configs/default.yaml b/grad_june/configs/default.yaml
index dc943b5..8bd8ba8 100644
--- a/grad_june/configs/default.yaml
+++ b/grad_june/configs/default.yaml
@@ -43,31 +43,33 @@ timer:
       - household
 
 infection_seed:
-  log_fraction_initial_cases: -1
+  type: InfectionSeedByFraction
+  params:
+    log_fraction: -1.5
 
 networks:
   household:
-    log_beta: -0.4
+    log_beta: 0.4
   company:
-    log_beta: -0.3
+    log_beta: 0.3
   school:
-    log_beta: -0.3
+    log_beta: 0.3
   pub:
-    log_beta: -1.2
+    log_beta: 0.2
   gym:
-    log_beta: -1.2
+    log_beta: 0.2
   grocery:
-    log_beta: -1.2
+    log_beta: 0.2
   visit:
-    log_beta: -1.2
+    log_beta: 0.2
   cinema:
-    log_beta: -1.2
+    log_beta: 0.2
   university:
-    log_beta: -0.5
+    log_beta: 0.5
   care_visit:
-    log_beta: -0.4
+    log_beta: 0.4
   care_home:
-    log_beta: -0.4
+    log_beta: 0.4
 
 policies:
   interaction:
diff --git a/grad_june/infection_seed.py b/grad_june/infection_seed.py
index a39cb96..9e0b97d 100644
--- a/grad_june/infection_seed.py
+++ b/grad_june/infection_seed.py
@@ -93,4 +93,9 @@ def forward(
         probs = fraction * torch.ones(n_agents, device=self.device)
         sampler = IsInfectedSampler()
         new_infected = sampler(1.0 - probs)  # sampler takes not inf probs
-        infect_people(data, time_step, new_infected)
\ No newline at end of file
+        infect_people(data, time_step, new_infected)
+
+def get_seed_from_parameters(params: dict, device: str):
+    seed_type = params["infection_seed"]["type"]
+    params = params["infection_seed"]["params"]
+    return eval(seed_type)(**params, device=device)
\ No newline at end of file
diff --git a/grad_june/model.py b/grad_june/model.py
index 054ed4f..b841f33 100644
--- a/grad_june/model.py
+++ b/grad_june/model.py
@@ -7,10 +7,10 @@
     SymptomsUpdater,
     InfectionNetworks,
 )
-from grad_june.infection_seed import InfectionSeedByFraction
 from grad_june.infection import infect_people
 from grad_june.policies import Policies
 from grad_june.paths import default_config_path
+from grad_june.infection_seed import get_seed_from_parameters
 
 
 class GradJune(torch.nn.Module):
@@ -82,10 +82,14 @@ def from_parameters(cls, params):
         symptoms_updater = SymptomsUpdater.from_parameters(params)
         policies = Policies.from_parameters(params)
         infection_networks = InfectionNetworks.from_parameters(params)
+        infection_seed = get_seed_from_parameters(
+            params, device=params["system"]["device"]
+        )
         return cls(
             symptoms_updater=symptoms_updater,
             policies=policies,
             infection_networks=infection_networks,
+            infection_seed=infection_seed,
             device=params["system"]["device"],
         )
 
diff --git a/grad_june/runner.py b/grad_june/runner.py
index e772bf7..578e788 100644
--- a/grad_june/runner.py
+++ b/grad_june/runner.py
@@ -18,7 +18,6 @@ def __init__(
         model,
         data,
         timer,
-        log_fraction_initial_cases,
         save_path,
         parameters,
         age_bins=(0, 18, 65, 100),
@@ -28,7 +27,6 @@ def __init__(
         self.data = data
         self.data_backup = self.backup_infection_data(data)
         self.timer = timer
-        self.log_fraction_initial_cases = log_fraction_initial_cases
         self.device = model.device
         self.age_bins = torch.tensor(age_bins, device=self.device)
         self.ethnicities = np.sort(np.unique(data["agent"].ethnicity))
@@ -54,9 +52,6 @@ def from_parameters(cls, params):
             model=model,
             data=data,
             timer=timer,
-            log_fraction_initial_cases=params["infection_seed"][
-                "log_fraction_initial_cases"
-            ],
             save_path=params["save_path"],
             parameters=params,
             age_bins = age_bins_to_save
@@ -135,18 +130,6 @@ def restore_initial_data(self):
         self.data["results"] = {}
         self.data["results"]["deaths_per_timestep"] = None
 
-    #def set_initial_cases(self):
-    #    fraction_initial_cases = 10.0**self.log_fraction_initial_cases
-    #    new_infected = infect_fraction_of_people(
-    #        data=self.data,
-    #        timer=self.timer,
-    #        device=self.device,
-    #        fraction=fraction_initial_cases,
-    #    )
-    #    self.model.symptoms_updater(
-    #        data=self.data, timer=self.timer, new_infected=new_infected
-    #    )
-
     def forward(self):
         timer = self.timer
         model = self.model
@@ -154,6 +137,7 @@ def forward(self):
         timer.reset()
         self.restore_initial_data()
         #self.set_initial_cases()
+        model(data, timer)
         cases_per_timestep = data["agent"].is_infected.sum()
         cases_by_age = get_cases_by_age(data, self.age_bins)
         store_differentiable_deaths(data, self.model.symptoms_updater.stages_ids[-1])
@@ -162,7 +146,7 @@ def forward(self):
         while timer.date < timer.final_date:
             i += 1
             next(timer)
-            data = model(data, timer)
+            model(data, timer)
             cases = data["agent"].is_infected.sum()
             cases_per_timestep = torch.hstack((cases_per_timestep, cases))
             store_differentiable_deaths(data, self.model.symptoms_updater.stages_ids[-1])
@@ -179,7 +163,7 @@ def forward(self):
         }
         for (i, key) in enumerate(self.age_bins[1:]):
             results[f"cases_by_age_{key:02d}"] = cases_by_age[:, i]
-        return results, data["agent"].is_infected
+        return results
 
     def save_results(self, results, is_infected):
         self.save_path.mkdir(exist_ok=True, parents=True)
diff --git a/notebooks/quickstart.ipynb b/notebooks/quickstart.ipynb
index 4417846..75950b4 100644
--- a/notebooks/quickstart.ipynb
+++ b/notebooks/quickstart.ipynb
@@ -3,18 +3,20 @@
   {
    "cell_type": "code",
    "execution_count": 1,
+   "id": "6ad8ebf5-bc81-4022-838b-b53d75434c68",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
    "id": "b84696bd-4bb6-4077-9bfb-db3b9b739925",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/arnull/miniconda3/envs/torch/lib/python3.10/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-      "  from .autonotebook import tqdm as notebook_tqdm\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import torch\n",
     "import yaml\n",
@@ -23,24 +25,26 @@
     "import torch_geometric\n",
     "from torch_geometric.data import HeteroData\n",
     "\n",
-    "from torch_june.transmission import TransmissionSampler\n",
-    "from torch_june.infection_networks import HouseholdNetwork, SchoolNetwork, InfectionNetworks\n",
-    "from torch_june.symptoms import SymptomsSampler, SymptomsUpdater\n",
-    "from torch_june.policies import Policies\n",
-    "from torch_june.model import TorchJune\n",
-    "from torch_june.timer import Timer\n",
-    "\n",
-    "from torch_june.runner import Runner"
+    "from grad_june.transmission import TransmissionSampler\n",
+    "from grad_june.infection_networks import HouseholdNetwork, SchoolNetwork, InfectionNetworks\n",
+    "from grad_june.symptoms import SymptomsSampler, SymptomsUpdater\n",
+    "from grad_june.policies import Policies\n",
+    "from grad_june.model import GradJune\n",
+    "from grad_june.infection_seed import InfectionSeedByFraction\n",
+    "from grad_june.timer import Timer\n",
+    "from grad_june.runner import Runner\n",
+    "from grad_june.paths import default_config_path"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "e5b84506-3a0e-4644-bfdb-2ef27f5f944e",
    "metadata": {},
    "outputs": [],
    "source": [
-    "device = \"cpu\" # could be cuda:0 for gpu, etc."
+    "device = \"cpu\" # could be cuda:0 for gpu, etc.\n",
+    "june_config_path = default_config_path # change to custom config"
    ]
   },
   {
@@ -62,7 +66,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "6e3caf08-7dc6-4bb4-8c15-37ac19a8cd31",
    "metadata": {},
    "outputs": [],
@@ -74,6 +78,7 @@
     "data[\"agent\"].id = torch.arange(0, n_agents)\n",
     "data[\"agent\"].age = torch.randint(0, 100, (n_agents,))\n",
     "data[\"agent\"].sex = torch.randint(0, 2, (n_agents,))\n",
+    "data[\"agent\"].area_id = torch.randint(0, 10, (n_agents,))\n",
     "\n",
     "\n",
     "# Now we initialize some venues (1 household and 1 school)\n",
@@ -123,7 +128,7 @@
    "id": "ea031c6a-eae4-4513-81a5-8664a42e949d",
    "metadata": {},
    "source": [
-    "TorchJune provides functionality to convert a world from the original \n",
+    "GradABM-JUNE provides functionality to convert a world from the original \n",
     "June world, created with a script like this: \n",
     "https://github.com/IDAS-Durham/JUNE/blob/master/example_scripts/create_world.py\n",
     "The created world (in h5 format) can then be converted into a TorchJune world by using the script\n",
@@ -152,14 +157,14 @@
    "metadata": {},
    "source": [
     "We now define the \"active\" infection networks. That is, the world can have schools in it, but we may not want to simulate the contacts in there,\n",
-    "so we can choose the active venues. These networks are specified as classes and they be easily extended to include more venues (see `torch_june/infection_networks/base.py`.\n",
+    "so we can choose the active venues. These networks are specified as classes and they be easily extended to include more venues (see `grad_june/infection_networks/base.py`.\n",
     "\n",
     "We also need to define a contact intensity (beta parameters) for each location. These are typically calibrated to data."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "id": "e5cdf42f-4221-4d5a-b23e-c250c40cf404",
    "metadata": {},
    "outputs": [],
@@ -188,12 +193,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "72345490-cfd1-4149-8c5b-03c725fc17d0",
    "metadata": {},
    "outputs": [],
    "source": [
-    "symptoms_sampler = SymptomsSampler.from_file(\"../configs/default.yaml\") # you may need to change the path accordingly\n",
+    "symptoms_sampler = SymptomsSampler.from_file(june_config_path) # you may need to change the path accordingly\n",
     "symptoms_updater = SymptomsUpdater(symptoms_sampler) # this updates the symptoms according to the sampler"
    ]
   },
@@ -215,65 +220,69 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "e30249ac-d51a-4cd5-858e-6a769961ca91",
    "metadata": {},
    "outputs": [],
    "source": [
-    "policies = Policies.from_file(\"../configs/default.yaml\")"
+    "policies = Policies.from_file(june_config_path)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "b8649745-b938-422d-9609-a79fae6cb8be",
+   "id": "693a77a7-b909-4a19-ae1d-e0a9bd33cded",
    "metadata": {},
    "source": [
-    "## 2.4 Build the model"
+    "## 2.4 Infection Seed"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "c8bfec7b-6d66-4b47-aa95-c64619f186d6",
+   "cell_type": "markdown",
+   "id": "00a5ac7a-8771-448b-8c0c-f3e1da2f0e93",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "model = TorchJune(infection_networks=infection_networks, symptoms_updater = symptoms_updater, policies=policies, device=device)"
+    "Here we specify the initial number of infections. We will start by infecting 5% of the population."
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "b63283d4-d0c4-4162-aa6a-b76532a7583d",
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a53c0514-868e-4b45-add1-6452508b225b",
    "metadata": {},
+   "outputs": [],
    "source": [
-    "A forward pass of this model then runs the interactions of one time-step. The time is controlled by the Timer class."
+    "log_fraction = np.log10(0.05)\n",
+    "infection_seed = InfectionSeedByFraction(log_fraction, device=device)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "10d96425-3151-4023-8870-019cea128e6c",
+   "id": "b8649745-b938-422d-9609-a79fae6cb8be",
    "metadata": {},
    "source": [
-    "## 2.5 Initialisation, infection seed"
+    "## 2.5 Build the model"
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "34ee9fc5-209d-4100-8829-a79384fe4489",
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "c8bfec7b-6d66-4b47-aa95-c64619f186d6",
    "metadata": {},
+   "outputs": [],
    "source": [
-    "We infected a few agents"
+    "model = GradJune(infection_networks=infection_networks,\n",
+    "                 symptoms_updater = symptoms_updater,\n",
+    "                 policies=policies,\n",
+    "                 device=device,\n",
+    "                infection_seed=infection_seed)"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "80d42c53-66b5-40a8-8415-8c153d13fdd4",
+   "cell_type": "markdown",
+   "id": "b63283d4-d0c4-4162-aa6a-b76532a7583d",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "from torch_june.infection_seed import infect_people_at_indices # this infects agents at selected indices\n",
-    "data = infect_people_at_indices(data, [0,1,2], device=device)"
+    "A forward pass of this model then runs the interactions of one time-step. The time is controlled by the Timer class."
    ]
   },
   {
@@ -286,7 +295,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "id": "630c3bb6-3458-4673-b2ad-e92aabc30905",
    "metadata": {},
    "outputs": [],
@@ -310,7 +319,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "id": "aad8971c-a581-4a63-802a-cf16e28266f1",
    "metadata": {},
    "outputs": [],
@@ -325,30 +334,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "id": "00569374-b97d-4b49-be0c-16b49109d018",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x14fe6fd30>]"
+       "[<matplotlib.lines.Line2D at 0x2aa118970>]"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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ZqfDwcN17771KT09v1JExAM7v3c0HdaS8WgmOYE0ZlHjxFwCAh2tSCZk/f74kadSoUWc9vnDhQk2fPl2S9Mc//lF+fn664YYbVF1drfHjx+uFF15wS1jAV9XUufTimjxJ0t2juioogFEQAG3fJZ0npCVwnhDgu97ZWKAH/75d0WF2ffrgaAUH+puOBABnafXzhABoeXX1Ls1bkytJumtkKgUEgNeghAAe7sPthdpfclKR7YM0Na2z6TgA4DaUEMCDuVyWnl99ehRk5ogUhQQ166h6APBIlBDAg63YVaTcIxUKDw7Q7enJpuMAgFtRQgAPZVmW/rzq9CjIHcNTFBYcaDgRALgXJQTwUKt2H9GXhU61D/LXHcO7mI4DAG5HCQE8kGVZeu7rUZDb0rsoIiTIcCIAcD9KCOCB1uUe07aCUgUH+mnWVSkXfwEAtEGUEMAD/Xnl6VGQqUOTFRXKBR4BeCdKCOBhdhc5tWHfcQX5++muq1NNxwGAFkMJATzM8h2nLwx5dc9oxYYHG04DAC2HEgJ4mBU7T5eQCX3jDCcBgJZFCQE8yN6jFcopLleAn01je8eajgMALYoSAniQ5V+PglzZLUqOEE5OBsC7UUIAD3JmKmZiP6ZiAHg/SgjgIQqOn9SOQ2Xys0nj+jAVA8D7UUIAD/HRrtOjIENTItWRc4MA8AGUEMBDLG+Yiok3nAQAWgclBPAAxc4qbd5/QpI0nkNzAfgISgjgAc5MxQzqHKE4BycoA+AbKCGABzhzllSmYgD4EkoIYFhJRbWy80skSRM4NBeAD6GEAIZ9/EWxXJbUNyFcSZEhpuMAQKuhhACGLecEZQB8FCUEMKjsVK3+lXdMkjSB/UEA+BhKCGDQqt3Fqq231D0mVN1iQk3HAYBWRQkBDPrmqBimYgD4HkoIYEhldZ0yvzoqiakYAL6JEgIYsibnqKrrXEruGKLe8WGm4wBAq6OEAIYs31ko6fS5QWw2m+E0AND6KCGAAVW19Vq9+4gkzpIKwHdRQgADPt1zTJU19UpwBGtgosN0HAAwghICGHBmKmY8UzEAfBglBGhlNXUuffJFsSSmYgD4NkoI0MrW7y2Rs6pOUaFBGpzcwXQcADCGEgK0shVfT8WM6xsnfz+mYgD4LkoI0IrqXZb+uevMVAxnSQXg2yghQCvakH9cJZU1crQL1LDUjqbjAIBRlBCgFZ2Zivlen1gF+vPXD4Bv41sQaCUul6UVu7hgHQCcQQkBWsmWglIVO6sVag/QiO5RpuMAgHGUEKCVfPT1KMg1vWJkD/A3nAYAzKOEAK3AsqyGs6QyFQMAp1FCgFaw67BTBcdPKTjQT1f3jDYdBwA8AiUEaAUrdp6eihnVI0YhQQGG0wCAZ6CEAK3gzFTMBKZiAKABJQRoYXuKy5V3tFKB/jZd0zvGdBwA8BiUEKCFLf96KmZEtyiFBwcaTgMAnoMSArSwMyVkYr94w0kAwLNQQoAWtL+kUl8WOuXvZ9P3+sSajgMAHoUSArSgM6Mgw1Ij1aF9kOE0AOBZKCFACzpTQiYwFQMA30EJAVrI4dJT2lZQKptNGt+XqRgA+DZKCNBCzpyg7IrkDooJCzacBgA8DyUEaCErmIoBgAuihAAt4Eh5lTbuPy6Js6QCwPlQQoAW8M9dxbIsaWCiQ50i2pmOAwAeiRICtICPdp2eihnPKAgAnBclBHCz0pM1Wp9XIomzpALAhVBCADf7+Iti1bks9YoLU0pUe9NxAMBjUUIAN/vmqBimYgDgQighgBuVV9Xq0z3HJDEVAwAXQwkB3GjV7iOqqXcpNaq9esSGmo4DAB6NEgK40b9PxdhsNsNpAMCzUUIANzlVU681OUclMRUDAI1BCQHcJPOrIzpVW6/EDu3Ur1O46TgA4PEoIYCbLD8zFdOXqRgAaAxKCOAG1XX1WvXlEUnSxP4cmgsAjUEJAdzgs9xjKq+uU2y4XZcndTAdBwDahCaXkLVr12rSpElKSEiQzWbT+++/f9bz06dPl81mO+s2YcIEd+UFPNKZo2LG942Tnx9TMQDQGE0uIZWVlRo4cKDmzZt33mUmTJigwsLChtubb755SSEBT1ZX79LHXxRL4iypANAUAU19wcSJEzVx4sQLLmO32xUXx5cxfEN2/nGdOFmryPZBGtol0nQcAGgzWmSfkDVr1igmJkY9e/bUPffco5KSkvMuW11dLafTedYNaEuW7yyUJH2vd6wC/NnNCgAay+3fmBMmTNDixYu1cuVK/f73v1dmZqYmTpyo+vr6cy4/d+5cORyOhltSUpK7IwEtxuWy9NGur6diOCoGAJqkydMxF3PTTTc1/Ll///4aMGCAunbtqjVr1mjMmDHfWf6RRx7RAw880HDf6XRSRNBmbD5wQkfLqxUWHKDhXaNMxwGANqXFx45TU1MVFRWl3Nzccz5vt9sVHh5+1g1oK5bvOH1UzNjesQoKYCoGAJqixb81Dx48qJKSEsXHcy0NeBfLsvTRrm8uWAcAaJomT8dUVFScNaqRn5+vrVu3KjIyUpGRkXryySd1ww03KC4uTnl5eXrwwQfVrVs3jR8/3q3BAdO2HyzTodJTCgny19U9ok3HAYA2p8klZNOmTRo9enTD/TP7c0ybNk3z58/X9u3b9eqrr6q0tFQJCQkaN26cfvvb38put7svNeABzlwrZnTPGAUH+htOAwBtT5NLyKhRo2RZ1nmf/+ijjy4pENAWWJalFV8fmstUDAA0D3vSAc2wu6hc+0pOKijAT6N7xZiOAwBtEiUEaIYzUzEju0cr1O72I90BwCdQQoBm+OjrEjKRqRgAaDZKCNBEe49WKKe4XAF+No3tHWs6DgC0WZQQoInOTMVc2S1KjpBAw2kAoO2ihABNtIKpGABwC0oI0AQFx09qx6Ey+dmkcX2YigGAS0EJAZrgzGnah6ZEqmMoJ+ADgEtBCQGa4Mz+IBP6MhUDAJeKEgI0UrGzSpv3n5AkTejHBRkB4FJRQoBGOjMVc3nnCMU5gg2nAYC2jxICNNLyHRwVAwDuRAkBGqGkolrZ+SWSpIlMxQCAW1BCgEb4+ItiuSypb0K4kiJDTMcBAK9ACQEaYcUupmIAwN0oIcBFlJ2q1We5xyRxVAwAuBMlBLiIVbuLVVtvqXtMqLrFhJqOAwBegxICXARHxQBAy6CEABdQWV2nzK+OSmIqBgDcjRICXMCanKOqrnMpuWOIeseHmY4DAF6FEgJcwPKdhZKkCf3iZLPZDKcBAO9CCQHOo6q2Xqt3H5HECcoAoCVQQoDz+HTPMVXW1CvBEayBiQ7TcQDA61BCgPM4MxUznqkYAGgRlBDgHGrqXPrki2JJ0oS+HJoLAC2BEgJ8S73L0oN/2yZnVZ2iw+y6okuk6UgA4JUoIcC/cbksPfz37Xp/62EF+Nk09/r+8vdjKgYAWgIlBPiaZVl6dNlOvbv5oPxs0p9uulxj+8SajgUAXosSAuh0AfnNh1/ojewDstmkZ358ma4dwGG5ANCSKCHweZZl6akVu7Xws32SpN9PGaDJl3cyGwoAfAAlBD7vjx9/pb9k7pUk/W5yP/14SJLhRADgGygh8GnPr9qj51blSpIen9RHtw5LNpwIAHwHJQQ+669r8/Q///xKkvTIxF66Y3iK4UQA4FsoIfBJiz7L13//325J0s+/10N3Xd3VcCIA8D2UEPicJdkH9MQHX0iS5ozupnvHdDecCAB8EyUEPuXdTQX61dIdkqQ7R6bq5+N6GE4EAL6LEgKfsWzrIT349+2SpOlXdtEjE3txYToAMIgSAp/wfzsK9cA722RZ0tS0znp8Uh8KCAAYRgmB1/v4i2L99M0tqndZ+tHgRP3uun4UEADwAJQQeLU1OUeU8cbnqnNZuu6yBD11wwD5cUE6APAIlBB4rc9yj+mu1zarpt6l7/eP0//70UCuiAsAHoQSAq+UvbdEM1/dqOo6l8b2jtWfbrpcAf5s7gDgSfhWhtfZvP+EZizaqKpal67uEa15t1yuQAoIAHgcvpnhVbYfLNX0VzaosqZew7t11F9uGyx7gL/pWACAc6CEwGvsOlym217eoPLqOg3tEqmXbr9CwYEUEADwVJQQeIWconLd9vIGlZ2q1aDOEXrljiEKCQowHQsAcAGUELR5eUcrdMuCbB2vrNGARIcWzRiqUDsFBAA8HSUEbdr+kkpNfSlLxyqq1Ts+XItnDFV4cKDpWACARqCEoM06eOKkpr6UrWJntXrEhur1mUMVERJkOhYAoJEoIWiTCstO6eaXsnSo9JRSo9rr9Vlp6hhqNx0LANAElBC0OUecVZr6UrYKjp9ScscQLZk9TDFhwaZjAQCaiBKCNuVYRbWmLshW/rFKdYpopyWzhynOQQEBgLaIEoI240RljW5dkK3cIxWKCw/Wm7OHqVNEO9OxAADNRAlBm1B2qla3vZKt3UXlig6za8nsNHXuGGI6FgDgElBC4PHKq2o17ZUN2nnIqY7tg7RkVppSo0NNxwIAXCJKCDxaZXWdZizaqK0FpYoICdTrs9LUPTbMdCwAgBtQQuCxqmrrNevVTdq474TCggP02ow09Y4PNx0LAOAmlBB4pKraes1evEnr95aofZC/Xp0xVP0THaZjAQDciBICj1NT51LGG5/r0z3H1C7QXwvvGKpBnTuYjgUAcDNKCDxKbb1LP31zi1buPiJ7gJ9ennaFhqZEmo4FAGgBlBB4jHqXpQfe2aYVu4oU5O+nv95+ha7sFmU6FgCghVBC4BFcLku//Ns2fbDtsAL8bHrhlkG6uke06VgAgBZECYFxLpel/3x/h977/JD8/Wx6furlGtsn1nQsAEALo4TAKMuy9MQHu/TmhgL52aQ/3niZJvSLNx0LANAKKCEwxrIs/dc/vtTi9ftls0l/+OFA/cfABNOxAACthBICIyzL0h8+ytGCdfmSpP++vr9uGJxoOBUAoDVRQmDEcytz9cKaPEnSb67rq5uHdjacCADQ2ighaHXz1+Tpj598JUn69bW9dXt6F7OBAABGUELQql5el6/fr9gtSfrl+J6adVWq4UQAAFOaXELWrl2rSZMmKSEhQTabTe+///5Zz1uWpccee0zx8fFq166dxo4dqz179rgrL9qw17L267cffiFJ+tmY7soY3c1wIgCASU0uIZWVlRo4cKDmzZt3zueffvppPffcc3rxxReVnZ2t9u3ba/z48aqqqrrksGi73t54QI++v1OSdM+orrpvbHfDiQAApgU09QUTJ07UxIkTz/mcZVl69tln9etf/1rXXXedJGnx4sWKjY3V+++/r5tuuunS0qJNeu/zg3r4vR2SpJkjUvTg+J6y2WyGUwEATGtyCbmQ/Px8FRUVaezYsQ2PORwOpaWlaf369ecsIdXV1aqurm6473Q63RkJl6jsVK1eW79PJZU1zXp9Va1Lb288IMuSbhuWrF9f25sCAgCQ5OYSUlRUJEmKjT37lNuxsbENz33b3Llz9eSTT7ozBtzEWVWr21/O1raDZZf8XjcNSdKT/9GXAgIAaODWEtIcjzzyiB544IGG+06nU0lJSQYTQZIqqut0x8KN2nawTB1CAnXz0M5qbn9I7theNwxKlJ8fBQQA8A23lpC4uDhJUnFxseLjv7n+R3FxsS677LJzvsZut8tut7szBi7RqZp6zVy0UZv3n1B4cIBem5mmfp0cpmMBALyMW88TkpKSori4OK1cubLhMafTqezsbKWnp7vzo9BCqmrrNXvxJmXnH1eonQICAGg5TR4JqaioUG5ubsP9/Px8bd26VZGRkercubPuu+8+/e53v1P37t2VkpKiRx99VAkJCZo8ebI7c6MFVNfV657XN2td7jGFBPnr1RlDNDApwnQsAICXanIJ2bRpk0aPHt1w/8z+HNOmTdOiRYv04IMPqrKyUnfeeadKS0s1YsQIrVixQsHBwe5LDberrXdpzpItWp1zVMGBfnpl+hANTo40HQsA4MVslmVZpkP8O6fTKYfDobKyMoWHh5uO4xPq6l362Vtb9Y8dhQoK8NMr04ZoRPco07EAAG1Ic36/uXaMj6t3Wfrl37brHzsKFehv019uHUwBAQC0CkqID3O5LD3y3nYt3XJIAX42PT91kEb3ijEdCwDgIyghPsqyLD32vzv1zqaD8rNJf7rpco3vG2c6FgDAh1BCfJBlWfrth1/q9awDstmkZ358ma4dEH/xFwIA4EaUEB9jWZZ+vyJHr3yWL0n6/ZQBmnx5J8OpAAC+iBLiY579ZI9ezMyTJP12cj/9eAinyAcAmEEJ8SHzVufqTyv3SJIe+0Ef3TYs2XAiAIAvo4T4iAWf7tUfPsqRJD08sZdmjEgxnAgA4OuMX0UXF1dSUa2X1+XrVG19s15fdqpW731+SJL0wPd66O6ru7ozHgAAzUIJaQP+6x9f6r0thy75fTJGd9W913RzQyIAAC4dJcTD7S+p1LJthyVJM4anqF1Q82bQeseH69r+8bLZbO6MBwBAs1FCPNz8NXmqd1m6uke0HpvUx3QcAADchh1TPdih0lP6++cHJUk/HcM0CgDAu1BCPNhfMvNUW28pPbWjBidHmo4DAIBbUUI81BFnld7aWCBJ7EwKAPBKlBAP9dKne1VT59Lg5A5K79rRdBwAANyOEuKBSiqq9XrWAUnSnGu6cUQLAMArUUI80CufnT4xWf9ODo3qEW06DgAALYIS4mHKTtbq1X/tl8QoCADAu1FCPMyif+1TRXWdesaG6Xu9Y03HAQCgxVBCPEhFdZ1e+Sxf0ulRED8/RkEAAN6LEuJBXlu/X2WnapUa3V7f7x9vOg4AAC2KEuIhTtXUa8GneyVJGaO6yZ9REACAl6OEeIglGw6opLJGSZHt9B+XJZiOAwBAi6OEeICq2nr9dW2eJOkno7op0J//LQAA78evnQf42+aDKnZWK94RrCmDOpmOAwBAq6CEGFZb79L8NadHQe4amSp7gL/hRAAAtA5KiGFLtxzSodJTigq166ahnU3HAQCg1VBCDKqrd+mF1bmSpDtHpig4kFEQAIDvoIQY9I8dhdpXclIdQgJ1S1qy6TgAALQqSoghLpel51edHgWZOSJF7e0BhhMBANC6KCGGfLSrSHuOVCgsOEC3X9nFdBwAAFodJcQAy7L0569HQe64sovCgwMNJwIAoPVRQgxYtfuIvih0qn2Qv+4YnmI6DgAARlBCWtm/j4Lcmp6sDu2DDCcCAMAMSkgrW5d7TFsLSmUP8NOsEamm4wAAYAwlpJWdGQW5eWhnRYfZDacBAMAcSkgryt5bog35xxXk76e7rmYUBADg2yghrej5r8+O+sMrEhXvaGc4DQAAZlFCWsmWAyf06Z5j8vez6Z6ru5qOAwCAcZSQVvDvR8Rcf3knJUWGGE4EAIB5lJBW8OdVuVq1+4j8bNJPRjEKAgCARAlpcS9m5umZj7+SJP3q+72VGh1qOBEAAJ6BEtKCXl6Xr6eW75Yk/XJ8T826iiNiAAA4gxLSQl7L2q/ffviFJOmnY7orY3Q3w4kAAPAslJAW8M7GAj36/k5J0l1Xp+r+sd0NJwIAwPNQQtxs6ZaDeui97ZKkO4Z30cMTeslmsxlOBQCA56GEuNGH2w/r5+9sk2VJtw7rrMd+0IcCAgDAeVBC3OSjXUX62Vtb5bKkG69I0m/+ox8FBACAC6CEuMHq3Uc0Z8nnqndZmnJ5J/33lP7y86OAAABwIZSQS/TpnqO66/XNqq23dO2AeD39wwHyp4AAAHBRlJBLkLW3RLMXb1JNnUvj+sTq2RsvU4A/qxQAgMbgF7OZNu07rhmLNqqq1qXRPaP156mXK5ACAgBAo/Gr2QxbC0o1feFGnayp11XdozT/1sGyB/ibjgUAQJtCCWminYfKdPvL2aqortOw1Ej99bYrFBxIAQEAoKkoIU2wu8ip217OlrOqToOTO+jlaUPULogCAgBAc1BCGmnv0QrduiBbJ07WamCiQwvvGKL29gDTsQAAaLMoIY302LJdOlZRo74J4Vo8I03hwYGmIwEA0KZRQhrh8wMntC73mAL8bHrx1sFyhFBAAAC4VJSQRnh+Va4kacqgTkqKDDGcBgAA70AJuYidh8q0avcR+dmke0Z1Mx0HAACvQQm5iDOjIJMGJiglqr3hNAAAeA9KyAV8VVyuFbuKJElzRjMKAgCAO1FCLmDe6tOjIBP7xal7bJjhNAAAeBdKyHnkH6vUB9sOS5LmXMMoCAAA7kYJOY8XVufKZUljesWob4LDdBwAALwOJeQcCo6f1NIthyQxCgIAQEuhhJzDi5l5qnNZuqp7lC7v3MF0HAAAvBIl5FuKyqr07qaDkjgiBgCAluT2EvLEE0/IZrOddevVq5e7P6bF/GVtnmrqXRraJVJpqR1NxwEAwGu1yGVg+/btq08++eSbDwloG1ebPVperTc3HJAk3TuGURAAAFpSi7SDgIAAxcXFtcRbt6iX1+WrqtalgUkRGtEtynQcAAC8WovsE7Jnzx4lJCQoNTVVt9xyiw4cOHDeZaurq+V0Os+6mXCiskavrd8nSbp3dDfZbDYjOQAA8BVuLyFpaWlatGiRVqxYofnz5ys/P19XXXWVysvLz7n83Llz5XA4Gm5JSUnujtQoC/+1T5U19eodH64xvWOMZAAAwJfYLMuyWvIDSktLlZycrGeeeUYzZ878zvPV1dWqrq5uuO90OpWUlKSysjKFh4e3ZLRvPrOqVsOfWqXyqjq9cMsgfb9/fKt8LgAA3sLpdMrhcDTp97vF9xiNiIhQjx49lJube87n7Xa77HZ7S8e4oNfW71d5VZ26xYRqQt+2ty8LAABtUYufJ6SiokJ5eXmKj/fM0YWTNXVa8OleSafPC+Lnx74gAAC0BreXkF/84hfKzMzUvn379K9//UvXX3+9/P39dfPNN7v7o9zijawDOnGyVskdQ/SDAZ5ZlAAA8EZun445ePCgbr75ZpWUlCg6OlojRoxQVlaWoqOj3f1Rl6yqtl5//XoUJGNUNwX4cwJZAABai9tLyFtvveXut2wx72wq0NHyanWKaKfJl3cyHQcAAJ/is//0r6lz6cU1eZKku69OVVCAz64KAACM8Nlf3vc+P6jDZVWKCbPrR1eYOTcJAAC+zCdLSF29Sy98PQpy58hUBQf6G04EAIDv8ckS8r/bDuvA8ZPq2D5IU9M6m44DAIBP8rkSUu+yNG/16ROnzbwqRSFBbeMKvwAAeBufKyHLdxYq72ilHO0CdduwZNNxAADwWT5VQlwuS8+vOj0KcsfwLgoLDjScCAAA3+VTJeSTL4u1u6hcofYATb+yi+k4AAD4NJ8pIZZl6fmv9wW5LT1ZESFBhhMBAODbfKaErN1zTNsPlik40E+zRqSYjgMAgM/zmUNDhnTpoF9f21vVdS51DLWbjgMAgM/zmRISEhSgWVelmo4BAAC+5jPTMQAAwLNQQgAAgBGUEAAAYAQlBAAAGEEJAQAARlBCAACAEZQQAABgBCUEAAAYQQkBAABGUEIAAIARlBAAAGAEJQQAABhBCQEAAEZ43FV0LcuSJDmdTsNJAABAY5353T7zO94YHldCysvLJUlJSUmGkwAAgKYqLy+Xw+Fo1LI2qymVpRW4XC4dPnxYYWFhstlsbn1vp9OppKQkFRQUKDw83K3v7c1Yb03HOmse1lvzsN6ah/XWdBdaZ5Zlqby8XAkJCfLza9zeHh43EuLn56fExMQW/Yzw8HA2uGZgvTUd66x5WG/Nw3prHtZb051vnTV2BOQMdkwFAABGUEIAAIARPlVC7Ha7Hn/8cdntdtNR2hTWW9OxzpqH9dY8rLfmYb01nbvXmcftmAoAAHyDT42EAAAAz0EJAQAARlBCAACAEZQQAABghM+UkHnz5qlLly4KDg5WWlqaNmzYYDqSR3viiSdks9nOuvXq1ct0LI+zdu1aTZo0SQkJCbLZbHr//ffPet6yLD322GOKj49Xu3btNHbsWO3Zs8dMWA9ysfU2ffr072x/EyZMMBPWQ8ydO1dDhgxRWFiYYmJiNHnyZOXk5Jy1TFVVlTIyMtSxY0eFhobqhhtuUHFxsaHEnqEx623UqFHf2d7uvvtuQ4k9w/z58zVgwICGk5Klp6dr+fLlDc+7a1vziRLy9ttv64EHHtDjjz+uzz//XAMHDtT48eN15MgR09E8Wt++fVVYWNhwW7dunelIHqeyslIDBw7UvHnzzvn8008/reeee04vvviisrOz1b59e40fP15VVVWtnNSzXGy9SdKECRPO2v7efPPNVkzoeTIzM5WRkaGsrCx9/PHHqq2t1bhx41RZWdmwzP33368PPvhA7777rjIzM3X48GFNmTLFYGrzGrPeJGn27NlnbW9PP/20ocSeITExUU899ZQ2b96sTZs26ZprrtF1112nXbt2SXLjtmb5gKFDh1oZGRkN9+vr662EhARr7ty5BlN5tscff9waOHCg6RhtiiRr6dKlDfddLpcVFxdn/eEPf2h4rLS01LLb7dabb75pIKFn+vZ6syzLmjZtmnXdddcZydNWHDlyxJJkZWZmWpZ1etsKDAy03n333YZlvvzyS0uStX79elMxPc6315tlWdbVV19t/exnPzMXqo3o0KGDtWDBArdua14/ElJTU6PNmzdr7NixDY/5+flp7NixWr9+vcFknm/Pnj1KSEhQamqqbrnlFh04cMB0pDYlPz9fRUVFZ217DodDaWlpbHuNsGbNGsXExKhnz5665557VFJSYjqSRykrK5MkRUZGSpI2b96s2tras7a3Xr16qXPnzmxv/+bb6+2MN954Q1FRUerXr58eeeQRnTx50kQ8j1RfX6+33npLlZWVSk9Pd+u25nEXsHO3Y8eOqb6+XrGxsWc9Hhsbq927dxtK5fnS0tK0aNEi9ezZU4WFhXryySd11VVXaefOnQoLCzMdr00oKiqSpHNue2eew7lNmDBBU6ZMUUpKivLy8vSrX/1KEydO1Pr16+Xv7286nnEul0v33Xefhg8frn79+kk6vb0FBQUpIiLirGXZ3r5xrvUmSVOnTlVycrISEhK0fft2PfTQQ8rJydF7771nMK15O3bsUHp6uqqqqhQaGqqlS5eqT58+2rp1q9u2Na8vIWieiRMnNvx5wIABSktLU3Jyst555x3NnDnTYDL4gptuuqnhz/3799eAAQPUtWtXrVmzRmPGjDGYzDNkZGRo586d7KfVROdbb3feeWfDn/v376/4+HiNGTNGeXl56tq1a2vH9Bg9e/bU1q1bVVZWpr/97W+aNm2aMjMz3foZXj8dExUVJX9//+/stVtcXKy4uDhDqdqeiIgI9ejRQ7m5uaajtBlnti+2vUuXmpqqqKgotj9Jc+bM0YcffqjVq1crMTGx4fG4uDjV1NSotLT0rOXZ3k4733o7l7S0NEny+e0tKChI3bp10+DBgzV37lwNHDhQf/rTn9y6rXl9CQkKCtLgwYO1cuXKhsdcLpdWrlyp9PR0g8naloqKCuXl5Sk+Pt50lDYjJSVFcXFxZ217TqdT2dnZbHtNdPDgQZWUlPj09mdZlubMmaOlS5dq1apVSklJOev5wYMHKzAw8KztLScnRwcOHPDp7e1i6+1ctm7dKkk+vb2di8vlUnV1tXu3NffuO+uZ3nrrLctut1uLFi2yvvjiC+vOO++0IiIirKKiItPRPNbPf/5za82aNVZ+fr712WefWWPHjrWioqKsI0eOmI7mUcrLy60tW7ZYW7ZssSRZzzzzjLVlyxZr//79lmVZ1lNPPWVFRERYy5Yts7Zv325dd911VkpKinXq1CnDyc260HorLy+3fvGLX1jr16+38vPzrU8++cQaNGiQ1b17d6uqqsp0dGPuuecey+FwWGvWrLEKCwsbbidPnmxY5u6777Y6d+5srVq1ytq0aZOVnp5upaenG0xt3sXWW25urvWb3/zG2rRpk5Wfn28tW7bMSk1NtUaOHGk4uVkPP/ywlZmZaeXn51vbt2+3Hn74Yctms1n//Oc/Lcty37bmEyXEsizrz3/+s9W5c2crKCjIGjp0qJWVlWU6kke78cYbrfj4eCsoKMjq1KmTdeONN1q5ubmmY3mc1atXW5K+c5s2bZplWacP03300Uet2NhYy263W2PGjLFycnLMhvYAF1pvJ0+etMaNG2dFR0dbgYGBVnJysjV79myf/0fDudaXJGvhwoUNy5w6dcr6yU9+YnXo0MEKCQmxrr/+equwsNBcaA9wsfV24MABa+TIkVZkZKRlt9utbt26Wb/85S+tsrIys8ENmzFjhpWcnGwFBQVZ0dHR1pgxYxoKiGW5b1uzWZZlNXNkBgAAoNm8fp8QAADgmSghAADACEoIAAAwghICAACMoIQAAAAjKCEAAMAISggAADCCEgIAAIyghAAAACMoIQAAwAhKCAAAMIISAgAAjPj/gSpls1ZOJu4AAAAASUVORK5CYII=",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -377,40 +384,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "id": "53626e9e-fd9c-483b-b816-3d9f74a6ff20",
    "metadata": {},
    "outputs": [],
    "source": [
-    "runner = Runner.from_file(\"../configs/default.yaml\")"
+    "runner = Runner.from_file(june_config_path)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "id": "0b72fc76-54a0-4e41-8167-757fb7fe36e0",
    "metadata": {},
    "outputs": [],
    "source": [
-    "results, _ = runner()"
+    "results = runner()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "id": "d2419ba8-91b9-4fd3-b687-732420a4cc90",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x216 with 3 Axes>"
+       "<Figure size 1000x300 with 3 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -422,21 +427,13 @@
     "\n",
     "fig.autofmt_xdate()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d7569ab4-4947-4bcb-a8c9-c00d058e9b82",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "torch",
+   "display_name": "torch2",
    "language": "python",
-   "name": "torch"
+   "name": "torch2"
   },
   "language_info": {
    "codemirror_mode": {
@@ -448,7 +445,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.4"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,

From b32667cd972990ae452f8bd97ff6d099951b9bbd Mon Sep 17 00:00:00 2001
From: Arnau Quera-Bofarull <arnauq@protonmail.com>
Date: Wed, 19 Jul 2023 20:20:19 +0100
Subject: [PATCH 08/24] updated how seed params are passed

---
 grad_june/infection_seed.py | 21 ++++++++++++++++++++-
 grad_june/runner.py         |  7 ++-----
 test/unit/test_runner.py    |  9 ++++-----
 3 files changed, 26 insertions(+), 11 deletions(-)

diff --git a/grad_june/infection_seed.py b/grad_june/infection_seed.py
index 9e0b97d..f8daa4b 100644
--- a/grad_june/infection_seed.py
+++ b/grad_june/infection_seed.py
@@ -1,4 +1,5 @@
 import torch
+import pandas as pd
 from torch_geometric.data import HeteroData
 
 from grad_june.demographics import get_people_per_area
@@ -49,6 +50,20 @@ def __init__(self, cases_per_district: dict, device: str):
         self.cases_per_district = cases_per_district
         self.device = device
 
+    def _read_cases_df(self, cases_per_district_path):
+        ret = {}
+        df = pd.read_csv(cases_per_district_path)
+        df["date"] = pd.to_datetime(df["date"])
+        df = df.sort_values("date")
+        df = df.set_index("district_id")
+        for district_id in df.index.unique():
+            ret[district_id] = df.loc[district_id]["cases"].values
+        return ret
+
+    def from_params(self, params: dict, device: str):
+        cases_per_district = self._read_cases_df(params["cases_per_district_path"])
+        return self.__init__(cases_per_district, device)
+
     def forward(
         self, data: HeteroData, time_step: int
     ):
@@ -83,6 +98,10 @@ def __init__(self, log_fraction: float, device: str):
         self.log_fraction = log_fraction
         self.device = device
 
+    @classmethod
+    def from_params(cls, params: dict, device: str):
+        return cls(**params, device=device)
+
     def forward(
         self, data: HeteroData, time_step: int
     ):
@@ -98,4 +117,4 @@ def forward(
 def get_seed_from_parameters(params: dict, device: str):
     seed_type = params["infection_seed"]["type"]
     params = params["infection_seed"]["params"]
-    return eval(seed_type)(**params, device=device)
\ No newline at end of file
+    return eval(seed_type).from_params(params, device=device)
\ No newline at end of file
diff --git a/grad_june/runner.py b/grad_june/runner.py
index 578e788..968ac22 100644
--- a/grad_june/runner.py
+++ b/grad_june/runner.py
@@ -165,7 +165,7 @@ def forward(self):
             results[f"cases_by_age_{key:02d}"] = cases_by_age[:, i]
         return results
 
-    def save_results(self, results, is_infected):
+    def save_results(self, results):
         self.save_path.mkdir(exist_ok=True, parents=True)
         df = pd.DataFrame(index=results["dates"])
         df.index.name = "date"
@@ -173,7 +173,4 @@ def save_results(self, results, is_infected):
             if key in ("dates"):
                 continue
             df[key] = results[key].detach().cpu().numpy()
-        df.to_csv(self.save_path / "results.csv")
-        df = pd.DataFrame()
-        df["is_infected"] = is_infected
-        df.to_csv(self.save_path / "results_is_infected.csv")
+        df.to_csv(self.save_path / "results.csv")
\ No newline at end of file
diff --git a/test/unit/test_runner.py b/test/unit/test_runner.py
index 9e3e6e2..1201a33 100644
--- a/test/unit/test_runner.py
+++ b/test/unit/test_runner.py
@@ -51,7 +51,7 @@ def test__restore_data(self, runner):
         assert runner.data["agent"].symptoms["time_to_next_stage"].sum().item() == 0
 
     def test__run_model(self, runner):
-        results, is_infected = runner()
+        results = runner()
         n_timesteps = 16
         assert len(results["dates"]) == n_timesteps
         assert len(results["cases_per_timestep"]) == n_timesteps
@@ -59,12 +59,11 @@ def test__run_model(self, runner):
         assert len(results["cases_by_age_18"]) == n_timesteps
         assert len(results["cases_by_age_65"]) == n_timesteps
         assert len(results["cases_by_age_100"]) == n_timesteps
-        assert len(is_infected) == runner.n_agents
 
     def test__save_results(self, runner):
         with torch.no_grad():
-            results, is_infected = runner()
-        runner.save_results(results, is_infected)
+            results = runner()
+        runner.save_results(results)
         loaded_results = pd.read_csv("./example/results.csv", index_col=0)
         for key in results:
             if key in ("dates"):
@@ -72,7 +71,7 @@ def test__save_results(self, runner):
             assert np.allclose(loaded_results[key], results[key].numpy())
 
     def test__deaths_gradient(self, runner):
-        results, is_infected = runner()
+        results = runner()
         assert results["cases_per_timestep"].requires_grad
         data = runner.data
         data_results = data["results"]

From 5b013fdbd7c158260d6c0bd0c6b5c67c082e1b1d Mon Sep 17 00:00:00 2001
From: Arnau Quera-Bofarull <arnauq@protonmail.com>
Date: Thu, 20 Jul 2023 15:34:00 +0100
Subject: [PATCH 09/24] correct district seeding

---
 grad_june/configs/default.yaml   |  2 ++
 grad_june/infection_seed.py      | 46 +++++++++++++++++++++-----------
 grad_june/runner.py              | 46 +++++++++++++++++++++++---------
 grad_june/utils.py               | 11 ++++++++
 test/unit/test_infection_seed.py |  2 +-
 5 files changed, 78 insertions(+), 29 deletions(-)

diff --git a/grad_june/configs/default.yaml b/grad_june/configs/default.yaml
index 8bd8ba8..6dcf37a 100644
--- a/grad_june/configs/default.yaml
+++ b/grad_june/configs/default.yaml
@@ -8,6 +8,8 @@ data_path: '@grad_june/test/data/data.pkl'
 
 save_path: ./example
 age_bins_to_save: [0, 18, 65, 100]
+store_differentiable_deaths: true
+store_cases_by_age: true
 
 timer:
   total_days: 15
diff --git a/grad_june/infection_seed.py b/grad_june/infection_seed.py
index f8daa4b..9ab8d18 100644
--- a/grad_june/infection_seed.py
+++ b/grad_june/infection_seed.py
@@ -5,6 +5,8 @@
 from grad_june.demographics import get_people_per_area
 from grad_june.infection import IsInfectedSampler
 from grad_june.timer import Timer
+from grad_june.utils import detach_numpy
+
 
 def infect_people(data: HeteroData, time: int, new_infected: torch.Tensor):
     data["agent"].susceptibility = torch.clamp(
@@ -15,6 +17,7 @@ def infect_people(data: HeteroData, time: int, new_infected: torch.Tensor):
         time - data["agent"].infection_time
     )
 
+
 def infect_people_at_indices(data, indices, device="cpu"):
     susc = data["agent"]["susceptibility"].cpu().numpy()
     is_inf = data["agent"]["is_infected"].cpu().numpy()
@@ -37,7 +40,7 @@ def infect_people_at_indices(data, indices, device="cpu"):
 
 
 class InfectionSeedByDistrict(torch.nn.Module):
-    def __init__(self, cases_per_district: dict, device: str):
+    def __init__(self, cases_per_district: dict, n_days_to_seed: int, device: str):
         """
         Seeds infections at districts. The number of cases per district is given in the `cases_per_district` dictionary.
 
@@ -48,8 +51,10 @@ def __init__(self, cases_per_district: dict, device: str):
         """
         super().__init__()
         self.cases_per_district = cases_per_district
+        self.n_days_to_seed = n_days_to_seed
         self.device = device
 
+    @classmethod
     def _read_cases_df(self, cases_per_district_path):
         ret = {}
         df = pd.read_csv(cases_per_district_path)
@@ -60,17 +65,23 @@ def _read_cases_df(self, cases_per_district_path):
             ret[district_id] = df.loc[district_id]["cases"].values
         return ret
 
-    def from_params(self, params: dict, device: str):
-        cases_per_district = self._read_cases_df(params["cases_per_district_path"])
-        return self.__init__(cases_per_district, device)
-
-    def forward(
-        self, data: HeteroData, time_step: int
-    ):
+    @classmethod
+    def from_params(cls, params: dict, device: str):
+        cases_per_district = cls._read_cases_df(params["cases_per_district_path"])
+        n_days_to_seed = params["n_days_to_seed"]
+        return cls(cases_per_district, n_days_to_seed, device)
+
+    def forward(self, data: HeteroData, time_step: float):
+        # check if time_step is int
+        if int(time_step) != time_step:
+            raise ValueError("time_step must be an integer for district seeding.")
+        if time_step > self.n_days_to_seed - 1:
+            return
+        time_step = int(time_step)
         people_per_district = get_people_per_area(
             data["agent"].id, data["agent"].district_id
         )
-        ids_to_infect = []
+        ids_to_infect = torch.tensor([], dtype=torch.long, device=self.device)
         for district in people_per_district:
             if district in self.cases_per_district:
                 n_to_infect = self.cases_per_district[district][time_step]
@@ -81,8 +92,12 @@ def forward(
             susceptible_people = people[~infected_status.bool()]
             random_idcs = torch.randperm(len(susceptible_people))[:n_to_infect]
             agent_ids = susceptible_people[random_idcs]
-            ids_to_infect.extend(list(agent_ids.cpu().numpy()))
-        infect_people_at_indices(data, ids_to_infect)
+            ids_to_infect = torch.cat((ids_to_infect, agent_ids))
+            # ids_to_infect.extend(list(detach_numpy(agent_ids.cpu())))
+        new_infected = torch.zeros(len(data["agent"].id), device=self.device)
+        new_infected[ids_to_infect] = 1.0
+        infect_people(data, time_step, new_infected)
+
 
 class InfectionSeedByFraction(torch.nn.Module):
     def __init__(self, log_fraction: float, device: str):
@@ -102,19 +117,18 @@ def __init__(self, log_fraction: float, device: str):
     def from_params(cls, params: dict, device: str):
         return cls(**params, device=device)
 
-    def forward(
-        self, data: HeteroData, time_step: int
-    ):
+    def forward(self, data: HeteroData, time_step: int):
         if time_step > 0:
             return
         n_agents = data["agent"].id.shape[0]
-        fraction = 10 ** self.log_fraction
+        fraction = 10**self.log_fraction
         probs = fraction * torch.ones(n_agents, device=self.device)
         sampler = IsInfectedSampler()
         new_infected = sampler(1.0 - probs)  # sampler takes not inf probs
         infect_people(data, time_step, new_infected)
 
+
 def get_seed_from_parameters(params: dict, device: str):
     seed_type = params["infection_seed"]["type"]
     params = params["infection_seed"]["params"]
-    return eval(seed_type).from_params(params, device=device)
\ No newline at end of file
+    return eval(seed_type).from_params(params, device=device)
diff --git a/grad_june/runner.py b/grad_june/runner.py
index 968ac22..50b5ca9 100644
--- a/grad_june/runner.py
+++ b/grad_june/runner.py
@@ -9,7 +9,11 @@
 from grad_june.paths import default_config_path
 from grad_june import GradJune, Timer, TransmissionSampler
 from grad_june.utils import read_path
-from grad_june.demographics import get_people_by_age, get_cases_by_age, store_differentiable_deaths
+from grad_june.demographics import (
+    get_people_by_age,
+    get_cases_by_age,
+    store_differentiable_deaths,
+)
 
 
 class Runner(torch.nn.Module):
@@ -21,6 +25,8 @@ def __init__(
         save_path,
         parameters,
         age_bins=(0, 18, 65, 100),
+        store_cases_by_age=False,
+        store_differentiable_deaths=False,
     ):
         super().__init__()
         self.model = model
@@ -35,6 +41,8 @@ def __init__(
         self.save_path = Path(save_path)
         self.input_parameters = parameters
         self.restore_initial_data()
+        self.store_cases_by_age = store_cases_by_age
+        self.store_differentiable_deaths = store_differentiable_deaths
 
     @classmethod
     def from_file(cls, fpath=default_config_path):
@@ -48,13 +56,17 @@ def from_parameters(cls, params):
         data = cls.get_data(params)
         timer = Timer.from_parameters(params)
         age_bins_to_save = params.get("age_bins_to_save", (0, 18, 65, 100))
+        store_differentiable_deaths = params.get("store_differentiable_deaths", False)
+        store_cases_by_age = params.get("store_cases_by_age", False)
         return cls(
             model=model,
             data=data,
             timer=timer,
             save_path=params["save_path"],
             parameters=params,
-            age_bins = age_bins_to_save
+            age_bins=age_bins_to_save,
+            store_cases_by_age=store_cases_by_age,
+            store_differentiable_deaths=store_differentiable_deaths,
         )
 
     @staticmethod
@@ -136,11 +148,15 @@ def forward(self):
         data = self.data
         timer.reset()
         self.restore_initial_data()
-        #self.set_initial_cases()
+        # self.set_initial_cases()
         model(data, timer)
         cases_per_timestep = data["agent"].is_infected.sum()
-        cases_by_age = get_cases_by_age(data, self.age_bins)
-        store_differentiable_deaths(data, self.model.symptoms_updater.stages_ids[-1])
+        if self.store_cases_by_age:
+            cases_by_age = get_cases_by_age(data, self.age_bins)
+        if self.store_differentiable_deaths:
+            store_differentiable_deaths(
+                data, self.model.symptoms_updater.stages_ids[-1]
+            )
         dates = [timer.date]
         i = 0
         while timer.date < timer.final_date:
@@ -149,9 +165,13 @@ def forward(self):
             model(data, timer)
             cases = data["agent"].is_infected.sum()
             cases_per_timestep = torch.hstack((cases_per_timestep, cases))
-            store_differentiable_deaths(data, self.model.symptoms_updater.stages_ids[-1])
-            cases_age = get_cases_by_age(data, self.age_bins)
-            cases_by_age = torch.vstack((cases_by_age, cases_age))
+            if self.store_cases_by_age:
+                cases_age = get_cases_by_age(data, self.age_bins)
+                cases_by_age = torch.vstack((cases_by_age, cases_age))
+            if self.store_differentiable_deaths:
+                store_differentiable_deaths(
+                    data, self.model.symptoms_updater.stages_ids[-1]
+                )
             dates.append(timer.date)
         results = {
             "dates": dates,
@@ -159,10 +179,12 @@ def forward(self):
             "daily_cases_per_timestep": torch.diff(
                 cases_per_timestep, prepend=torch.tensor([0.0], device=self.device)
             ),
-            "deaths_per_timestep": data.results["deaths_per_timestep"],
         }
-        for (i, key) in enumerate(self.age_bins[1:]):
-            results[f"cases_by_age_{key:02d}"] = cases_by_age[:, i]
+        if self.store_cases_by_age:
+            for i, key in enumerate(self.age_bins[1:]):
+                results[f"cases_by_age_{key:02d}"] = cases_by_age[:, i]
+        if self.store_differentiable_deaths:
+            results["deaths_per_timestep"] = data["results"]["deaths_per_timestep"]
         return results
 
     def save_results(self, results):
@@ -173,4 +195,4 @@ def save_results(self, results):
             if key in ("dates"):
                 continue
             df[key] = results[key].detach().cpu().numpy()
-        df.to_csv(self.save_path / "results.csv")
\ No newline at end of file
+        df.to_csv(self.save_path / "results.csv")
diff --git a/grad_june/utils.py b/grad_june/utils.py
index fa36142..f7086a7 100644
--- a/grad_june/utils.py
+++ b/grad_june/utils.py
@@ -13,6 +13,7 @@
 from grad_june.paths import grad_june_path
 
 
+
 def read_path(path_str):
     path = Path(path_str)
     if path.parts[0] == "@grad_june":
@@ -130,3 +131,13 @@ def create_simple_connected_graph(n_agents):
     )
     data = T.ToUndirected()(data)
     return data
+
+def detach_numpy(tensor):
+    """
+    Converts a tensor to numpy when using forward mode.
+    """
+    tensor = tensor.detach().cpu()
+    if torch._C._functorch.is_gradtrackingtensor(tensor):
+        tensor = torch._C._functorch.get_unwrapped(tensor)
+        return np.array(tensor.storage().tolist()).reshape(tensor.shape)
+    return tensor.numpy()
\ No newline at end of file
diff --git a/test/unit/test_infection_seed.py b/test/unit/test_infection_seed.py
index de9555c..757619e 100644
--- a/test/unit/test_infection_seed.py
+++ b/test/unit/test_infection_seed.py
@@ -49,7 +49,7 @@ def test__differentiable_seed(self, data):
 
     def test__seeding_by_district(self, data):
         cases_per_district = {0: [3, 5, 2], 1: [7, 2, 4], 2: [2, 3, 4]}
-        seed = InfectionSeedByDistrict(cases_per_district, device="cpu")
+        seed = InfectionSeedByDistrict(cases_per_district, device="cpu", n_days_to_seed=3)
         seed(data, 0)
         assert data["agent"].is_infected.sum() == 12
         people_per_district = get_people_per_area(data["agent"].id, data["agent"].district_id)

From fa319be98f0c8a8361f402f3aacc4153ca81bd9e Mon Sep 17 00:00:00 2001
From: arnauq <arnau.quera@gmail.com>
Date: Tue, 25 Jul 2023 17:19:35 +0100
Subject: [PATCH 10/24] allow none symptoms

---
 grad_june/model.py    |  5 ++---
 grad_june/symptoms.py | 12 ++----------
 2 files changed, 4 insertions(+), 13 deletions(-)

diff --git a/grad_june/model.py b/grad_june/model.py
index b841f33..b5cfa44 100644
--- a/grad_june/model.py
+++ b/grad_june/model.py
@@ -37,8 +37,6 @@ def __init__(
         super().__init__()
 
         # Initializes symptoms updater, policies, and infection networks.
-        if symptoms_updater is None:
-            symptoms_updater = SymptomsUpdater.from_file()
         self.symptoms_updater = symptoms_updater
         if policies is None:
             policies = Policies.from_file()
@@ -124,7 +122,8 @@ def forward(self, data, timer):
         infect_people(data, timer.now, new_infected)
 
         # Updates agents' symptoms based on their infection status.
-        self.symptoms_updater(data=data, timer=timer, new_infected=new_infected)
+        if self.symptoms_updater is not None:
+            self.symptoms_updater(data=data, timer=timer, new_infected=new_infected)
 
         # Returns updated simulation data.
         return data
diff --git a/grad_june/symptoms.py b/grad_june/symptoms.py
index af396a2..c886b09 100644
--- a/grad_june/symptoms.py
+++ b/grad_june/symptoms.py
@@ -143,20 +143,10 @@ class SymptomsUpdater(torch.nn.Module):
         from_parameters(cls, params): Class method for creating a SymptomsUpdater instance from a dictionary of parameters.
         forward(self, data, timer, new_infected): Method for updating symptoms of agents based on current symptoms and other factors.
         stages_ids(self): Property for getting the IDs of all possible symptom stages.
-
-    Raises:
-        TypeError: If symptoms_sampler is not an instance of SymptomsSampler.
-        KeyError: If data does not contain the "agent" key.
-        KeyError: If symptoms does not contain the "current_stage", "next_stage", or "time_to_next_stage" keys.
-
-    Returns:
-        A SymptomsUpdater instance.
     """
 
     def __init__(self, symptoms_sampler):
         super().__init__()
-        if not isinstance(symptoms_sampler, SymptomsSampler):
-            raise TypeError("symptoms_sampler must be an instance of SymptomsSampler.")
         self.symptoms_sampler = symptoms_sampler
 
     @classmethod
@@ -198,6 +188,8 @@ def from_parameters(cls, params):
         Returns:
             A SymptomsUpdater instance.
         """
+        if "symptoms" not in params:
+            return None
         ss = SymptomsSampler.from_parameters(params)
         return cls(symptoms_sampler=ss)
 

From 590de9e10f7035a2c34e5ba6cda84c1c0c73fc72 Mon Sep 17 00:00:00 2001
From: Arnau Quera-Bofarull <arnauq@protonmail.com>
Date: Thu, 27 Jul 2023 16:58:12 +0100
Subject: [PATCH 11/24] new way of computing people per group

---
 grad_june/infection_networks/base.py            | 13 +++++--------
 grad_june/infection_networks/leisure_network.py | 14 ++++++++++++--
 test/unit/test_model.py                         |  4 +++-
 3 files changed, 20 insertions(+), 11 deletions(-)

diff --git a/grad_june/infection_networks/base.py b/grad_june/infection_networks/base.py
index 1adee22..74000f8 100644
--- a/grad_june/infection_networks/base.py
+++ b/grad_june/infection_networks/base.py
@@ -10,7 +10,7 @@
 
 class InfectionNetwork(MessagePassing):
     def __init__(self, log_beta, device="cpu"):
-        super().__init__( aggr="add", node_dim=-1)
+        super().__init__(aggr="add", node_dim=-1)
         self.device = device
         if type(log_beta) != torch.nn.Parameter:
             self.log_beta = torch.tensor(float(log_beta))
@@ -41,8 +41,8 @@ def _get_beta(self, policies, timer, data):
         beta = beta * torch.ones(len(data[self.name]["id"]), device=self.device)
         return beta
 
-    def _get_people_per_group(self, data):
-        return data[self.name]["people"]
+    def _get_people_per_group(self, data, timer):
+        return data[self.name].people
 
     def _get_transmissions(self, data, policies, timer):
         if policies.quarantine_policies:
@@ -60,7 +60,7 @@ def _get_susceptibilities(self, data, policies, timer):
 
     def forward(self, data, timer, policies):
         beta = self._get_beta(policies=policies, timer=timer, data=data)
-        people_per_group = self._get_people_per_group(data)
+        people_per_group = self._get_people_per_group(data, timer)
         p_contact = torch.maximum(
             torch.minimum(
                 1.0 / (people_per_group - 1), torch.tensor(1.0, device=self.device)
@@ -134,7 +134,7 @@ def forward(
             network = self.networks[activity]
             trans_susc += network(data=data, timer=timer, policies=policies)
         trans_susc = torch.clamp(
-            trans_susc, min=1e-6, max = 100
+            trans_susc, min=1e-6, max=100
         )  # this is necessary to avoid gradient nans
         not_infected_probs = torch.exp(-trans_susc * delta_time)
         not_infected_probs = torch.clamp(not_infected_probs, min=0.0, max=1.0)
@@ -148,8 +148,6 @@ def _get_transmissions(self, data, policies, timer):
     def _get_susceptibilities(self, data, policies, timer):
         return data["agent"].susceptibility
 
-    pass
-
 
 class CareHomeNetwork(InfectionNetwork):
     pass
@@ -165,4 +163,3 @@ class CompanyNetwork(InfectionNetwork):
 
 class UniversityNetwork(InfectionNetwork):
     pass
-
diff --git a/grad_june/infection_networks/leisure_network.py b/grad_june/infection_networks/leisure_network.py
index 3f75d5e..4ee4f16 100644
--- a/grad_june/infection_networks/leisure_network.py
+++ b/grad_june/infection_networks/leisure_network.py
@@ -55,8 +55,18 @@ def _get_beta(self, policies, timer, data):
         beta = beta * torch.ones(len(data["leisure"]["id"]), device=self.device)
         return beta
 
-    def _get_people_per_group(self, data):
-        return data["leisure"]["people"]
+    def _get_people_per_group(self, data, timer):
+        if self.weekday_probabilities is None:
+            self.initialize_leisure_probabilities(data)
+        if timer.day_type == "weekday":
+            leisure_mask = self.weekday_probabilities
+        else:
+            leisure_mask = self.weekend_probabilities
+        aux = torch.ones(len(data["leisure"]["id"]), device=self.device)
+        prob_leisure = leisure_mask
+        edge_index = self._get_edge_index(data)
+        people_per_group = self.propagate(edge_index, x=prob_leisure, y=aux)
+        return people_per_group
 
     def _get_transmissions(self, data, policies, timer):
         if self.weekday_probabilities is None:
diff --git a/test/unit/test_model.py b/test/unit/test_model.py
index 8e48e41..36ffa87 100644
--- a/test/unit/test_model.py
+++ b/test/unit/test_model.py
@@ -4,6 +4,7 @@
 import torch_geometric.transforms as T
 
 from grad_june import GradJune, Timer
+from grad_june.symptoms import SymptomsUpdater
 from grad_june.infection_networks import (
     CompanyNetwork,
     SchoolNetwork,
@@ -19,7 +20,8 @@ def make_model(self):
         hn = HouseholdNetwork(log_beta=torch.nn.Parameter(torch.tensor(0.5)))
         sn = SchoolNetwork(log_beta=torch.nn.Parameter(torch.tensor(0.5)))
         networks = InfectionNetworks(household=hn, company=cn, school=sn)
-        model = GradJune(infection_networks=networks)
+        symptoms_updater = SymptomsUpdater.from_file()
+        model = GradJune(infection_networks=networks, symptoms_updater=symptoms_updater)
         return model
 
     def test__run_model(self, model, inf_data, timer):

From a68b55ce256dc9a9c2123a27053fbbe95c314cfc Mon Sep 17 00:00:00 2001
From: Arnau Quera-Bofarull <arnauq@protonmail.com>
Date: Tue, 8 Aug 2023 15:25:04 +0100
Subject: [PATCH 12/24] added beta factor per venue

---
 grad_june/infection_networks/base.py          |   8 ++++++--
 grad_june/june_world_loader/network_loader.py |   8 ++++++++
 test/data/data.pkl                            | Bin 220765 -> 270559 bytes
 test/data/june_world.h5                       | Bin 1630724 -> 936684 bytes
 test/unit/infection_networks/test_base.py     |   9 +++++++++
 5 files changed, 23 insertions(+), 2 deletions(-)

diff --git a/grad_june/infection_networks/base.py b/grad_june/infection_networks/base.py
index 74000f8..828c2f4 100644
--- a/grad_june/infection_networks/base.py
+++ b/grad_june/infection_networks/base.py
@@ -33,13 +33,17 @@ def _get_edge_index(self, data):
     def _get_reverse_edge_index(self, data):
         return data["rev_attends_" + self.name].edge_index
 
+    def _get_beta_factor(self, data):
+        return data[self.name].beta_factor
+
     def _get_beta(self, policies, timer, data):
+        beta_factor = self._get_beta_factor(data)
         interaction_policies = policies.interaction_policies
         beta = 10.0**self.log_beta
         if interaction_policies:
             beta = interaction_policies.apply(beta=beta, name=self.name, timer=timer)
-        beta = beta * torch.ones(len(data[self.name]["id"]), device=self.device)
-        return beta
+        beta = beta * beta_factor
+        return beta 
 
     def _get_people_per_group(self, data, timer):
         return data[self.name].people
diff --git a/grad_june/june_world_loader/network_loader.py b/grad_june/june_world_loader/network_loader.py
index 1b2ba57..c438c02 100644
--- a/grad_june/june_world_loader/network_loader.py
+++ b/grad_june/june_world_loader/network_loader.py
@@ -27,6 +27,12 @@ def _get_group_ids(self):
             group_ids = f[self.plural]["id"][:]
         return group_ids
 
+    def _get_group_super_areas(self):
+        with h5py.File(self.june_world_path, "r") as f:
+            super_area_names = f["geography"]["super_area_name"][:]
+            group_super_area_ids = f[self.plural]["super_area"][:]
+            return super_area_names[group_super_area_ids]
+
     def load_network(self, data):
         people_per_group = self._get_people_per_group()
         adjlist_i = []
@@ -36,6 +42,8 @@ def load_network(self, data):
                 adjlist_i.append(person)
                 adjlist_j.append(group_id)
         data[self.spec].id = self._get_group_ids()
+        data[self.spec].super_area = self._get_group_super_areas()
+        data[self.spec].beta_factor = torch.ones(len(data[self.spec].id))
         data[self.spec].people = torch.tensor(
             [len(people_per_group[i]) for i in data[self.spec].id]
         )
diff --git a/test/data/data.pkl b/test/data/data.pkl
index 687b3c18da027c424437e1dbdea3941e1eaf2912..a2b4556b8cfccbe5d2e4ca15916a135be4638e75 100644
GIT binary patch
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diff --git a/test/data/june_world.h5 b/test/data/june_world.h5
index 66be48e82f9ca7b3eeeffa83958a29d05e0bc518..996c16da31884315e3ba27879c2f98de90a228f9 100644
GIT binary patch
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diff --git a/test/unit/infection_networks/test_base.py b/test/unit/infection_networks/test_base.py
index 7c5a54c..092c08a 100644
--- a/test/unit/infection_networks/test_base.py
+++ b/test/unit/infection_networks/test_base.py
@@ -27,6 +27,7 @@ def make_small_data(self):
 
         data["school"].id = torch.arange(2)
         data["school"].people = torch.tensor([2, 2])
+        data["school"].beta_factor = torch.ones(2)
 
         edges_1 = torch.arange(6)
         edges_2 = torch.tensor([0, 0, 0, 1, 1, 1])
@@ -43,6 +44,14 @@ def test__infection_passing(self, networks, small_data, school_timer):
         expected = np.exp(-np.array([1.2, 2.4, 3.6, 1.5, 2.1, 3]))
         assert np.allclose(infection_probabilities.detach().numpy(), expected)
 
+    def test__beta_factors(self, networks, small_data, school_timer):
+        small_data["school"].beta_factor = torch.tensor([0.5, 1.0])
+        infection_probabilities = networks(
+            data=small_data, timer=school_timer, policies=Policies()
+        )
+        expected = np.exp(-np.array([0.6, 1.2, 1.8, 1.5, 2.1, 3]))
+        assert np.allclose(infection_probabilities.detach().numpy(), expected)
+
 
 # def test__people_only_active_once(self, timer, inf_data):
 #     data = inf_data

From 6b906ca2d448e3a555bfdfa089a1513c41cb11f3 Mon Sep 17 00:00:00 2001
From: Arnau Quera-Bofarull <arnauq@protonmail.com>
Date: Tue, 8 Aug 2023 15:32:11 +0100
Subject: [PATCH 13/24] added super_area for leisure

---
 grad_june/june_world_loader/leisure_loader.py |   7 +++++++
 grad_june/june_world_loader/network_loader.py |   2 +-
 test/data/data.pkl                            | Bin 270559 -> 424675 bytes
 test/unit/test_june_world_loader.py           |   1 +
 4 files changed, 9 insertions(+), 1 deletion(-)

diff --git a/grad_june/june_world_loader/leisure_loader.py b/grad_june/june_world_loader/leisure_loader.py
index 4e910a8..162a9e0 100644
--- a/grad_june/june_world_loader/leisure_loader.py
+++ b/grad_june/june_world_loader/leisure_loader.py
@@ -35,6 +35,11 @@ def _get_people_per_super_area(self):
                 ret[super_area_id] = list(people_in_super_area)
         return ret
 
+    def _get_super_area_names(self):
+        with h5py.File(self.june_world_path, "r") as f:
+            super_area_names = f["geography"]["super_area_name"][:].astype("U")
+            return super_area_names
+
     def _generate_ball_tree(self):
         ball_tree = BallTree(self._super_area_coordinates, metric="haversine")
         return ball_tree
@@ -68,6 +73,8 @@ def load_network(self, data):
             ret = torch.hstack((ret, edges))
         data["agent", "attends_leisure", "leisure"].edge_index = ret
         data["leisure"].id = torch.tensor(list(close_people_per_super_area.keys()))
+        data["leisure"].super_area = self._get_super_area_names()
+        data["leisure"].beta_factor = torch.ones(len(data["leisure"].id))
         data["leisure"].people = torch.tensor(
             [len(close_people_per_super_area[sa]) for sa in close_people_per_super_area]
         )
diff --git a/grad_june/june_world_loader/network_loader.py b/grad_june/june_world_loader/network_loader.py
index c438c02..71409d0 100644
--- a/grad_june/june_world_loader/network_loader.py
+++ b/grad_june/june_world_loader/network_loader.py
@@ -29,7 +29,7 @@ def _get_group_ids(self):
 
     def _get_group_super_areas(self):
         with h5py.File(self.june_world_path, "r") as f:
-            super_area_names = f["geography"]["super_area_name"][:]
+            super_area_names = f["geography"]["super_area_name"][:].astype("U")
             group_super_area_ids = f[self.plural]["super_area"][:]
             return super_area_names[group_super_area_ids]
 
diff --git a/test/data/data.pkl b/test/data/data.pkl
index a2b4556b8cfccbe5d2e4ca15916a135be4638e75..d738c5da4733fac1d4299dfbaaf0dbc9e5b75176 100644
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diff --git a/test/unit/test_june_world_loader.py b/test/unit/test_june_world_loader.py
index 9c3b88c..4340b98 100644
--- a/test/unit/test_june_world_loader.py
+++ b/test/unit/test_june_world_loader.py
@@ -126,6 +126,7 @@ def test__load_leisure_network(self, leisure_loader):
         assert len(data["leisure"]["id"]) == 3
         assert data["leisure"]["people"][0] == 769
         assert data["leisure"]["people"][2] == 769
+        assert (data["leisure"]["super_area"] == ['E02000978', 'E02003270', 'E02003353']).all()
 
 
 class TestLoadGraph:

From e2f59fa897e62a00a3725d128581b99a12704584 Mon Sep 17 00:00:00 2001
From: Arnau Quera-Bofarull <arnauq@protonmail.com>
Date: Tue, 8 Aug 2023 16:52:08 +0100
Subject: [PATCH 14/24] fixed and working

---
 grad_june/infection_networks/leisure_network.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/grad_june/infection_networks/leisure_network.py b/grad_june/infection_networks/leisure_network.py
index 4ee4f16..7918fe8 100644
--- a/grad_june/infection_networks/leisure_network.py
+++ b/grad_june/infection_networks/leisure_network.py
@@ -47,12 +47,16 @@ def _get_edge_index(self, data):
     def _get_reverse_edge_index(self, data):
         return data["rev_attends_leisure"].edge_index
 
+    def _get_beta_factor(self, data):
+        return data["leisure"].beta_factor
+
     def _get_beta(self, policies, timer, data):
+        beta_factor = self._get_beta_factor(data)
         interaction_policies = policies.interaction_policies
         beta = 10.0**self.log_beta
         if interaction_policies:
             beta = interaction_policies.apply(beta=beta, name=self.name, timer=timer)
-        beta = beta * torch.ones(len(data["leisure"]["id"]), device=self.device)
+        beta = beta * beta_factor
         return beta
 
     def _get_people_per_group(self, data, timer):

From 0f0062e486348827a0ed7b2403556ff92c953e27 Mon Sep 17 00:00:00 2001
From: arnauq <arnau.quera@gmail.com>
Date: Sat, 12 Aug 2023 10:04:56 +0100
Subject: [PATCH 15/24] seed difference

---
 grad_june/infection_seed.py | 4 +++-
 grad_june/model.py          | 6 ++++--
 2 files changed, 7 insertions(+), 3 deletions(-)

diff --git a/grad_june/infection_seed.py b/grad_june/infection_seed.py
index 9ab8d18..244f1a9 100644
--- a/grad_june/infection_seed.py
+++ b/grad_june/infection_seed.py
@@ -89,11 +89,13 @@ def forward(self, data: HeteroData, time_step: float):
                 n_to_infect = 0
             people = people_per_district[district]
             infected_status = data["agent"].is_infected[people]
+            infected_time = data["agent"].infection_time[people]
+            n_people_infected_in_this_timestep = len(infected_status[infected_time == 0.])
+            n_to_infect = max(0, n_to_infect - n_people_infected_in_this_timestep)
             susceptible_people = people[~infected_status.bool()]
             random_idcs = torch.randperm(len(susceptible_people))[:n_to_infect]
             agent_ids = susceptible_people[random_idcs]
             ids_to_infect = torch.cat((ids_to_infect, agent_ids))
-            # ids_to_infect.extend(list(detach_numpy(agent_ids.cpu())))
         new_infected = torch.zeros(len(data["agent"].id), device=self.device)
         new_infected[ids_to_infect] = 1.0
         infect_people(data, time_step, new_infected)
diff --git a/grad_june/model.py b/grad_june/model.py
index b5cfa44..7c03c42 100644
--- a/grad_june/model.py
+++ b/grad_june/model.py
@@ -102,8 +102,6 @@ def forward(self, data, timer):
         Returns:
             A A PyTorch geometric data object containing updated simulation data.
         """
-        if self.infection_seed is not None:
-            self.infection_seed(data, timer.now)
 
         # Updates agent transmission based on current transmission updater values.
         data["agent"].transmission = self.transmission_updater(data=data, timer=timer)
@@ -125,5 +123,9 @@ def forward(self, data, timer):
         if self.symptoms_updater is not None:
             self.symptoms_updater(data=data, timer=timer, new_infected=new_infected)
 
+        # we seed at the end to avoid the seeded cases generating new ones
+        if self.infection_seed is not None:
+            self.infection_seed(data, timer.now)
+
         # Returns updated simulation data.
         return data

From 0cf2bd1206319e63ddd45e88d800e17650927bca Mon Sep 17 00:00:00 2001
From: Arnau Quera-Bofarull <arnauq@protonmail.com>
Date: Sat, 12 Aug 2023 10:14:58 +0100
Subject: [PATCH 16/24] fixed seed

---
 grad_june/infection_seed.py         |  2 +-
 grad_june/paths.py                  |  2 ++
 grad_june/runner.py                 |  1 -
 test/unit/test_june_world_loader.py | 48 ++++++++++++++++++++---------
 4 files changed, 36 insertions(+), 17 deletions(-)

diff --git a/grad_june/infection_seed.py b/grad_june/infection_seed.py
index 244f1a9..e8b067c 100644
--- a/grad_june/infection_seed.py
+++ b/grad_june/infection_seed.py
@@ -90,7 +90,7 @@ def forward(self, data: HeteroData, time_step: float):
             people = people_per_district[district]
             infected_status = data["agent"].is_infected[people]
             infected_time = data["agent"].infection_time[people]
-            n_people_infected_in_this_timestep = len(infected_status[infected_time == 0.])
+            n_people_infected_in_this_timestep = sum((infected_status == 1.0) & (infected_time == time_step))
             n_to_infect = max(0, n_to_infect - n_people_infected_in_this_timestep)
             susceptible_people = people[~infected_status.bool()]
             random_idcs = torch.randperm(len(susceptible_people))[:n_to_infect]
diff --git a/grad_june/paths.py b/grad_june/paths.py
index f5c6c2f..32d2d6a 100644
--- a/grad_june/paths.py
+++ b/grad_june/paths.py
@@ -6,3 +6,5 @@
 default_config_path = (
     Path(os.path.abspath(__file__)).parent / "configs/default.yaml"
 )
+
+test_path = grad_june_path / "test"
diff --git a/grad_june/runner.py b/grad_june/runner.py
index 50b5ca9..efbddb7 100644
--- a/grad_june/runner.py
+++ b/grad_june/runner.py
@@ -148,7 +148,6 @@ def forward(self):
         data = self.data
         timer.reset()
         self.restore_initial_data()
-        # self.set_initial_cases()
         model(data, timer)
         cases_per_timestep = data["agent"].is_infected.sum()
         if self.store_cases_by_age:
diff --git a/test/unit/test_june_world_loader.py b/test/unit/test_june_world_loader.py
index 4340b98..1e93b4d 100644
--- a/test/unit/test_june_world_loader.py
+++ b/test/unit/test_june_world_loader.py
@@ -1,4 +1,4 @@
-import torch
+import h5py
 import pytest
 from pytest import fixture
 from torch_geometric.data import HeteroData
@@ -11,6 +11,12 @@
 from grad_june.june_world_loader.school_loader import SchoolNetworkLoader
 from grad_june.june_world_loader.university_loader import UniversityNetworkLoader
 from grad_june.june_world_loader.leisure_loader import LeisureNetworkLoader
+from grad_june.paths import test_path
+
+
+@fixture(name="june_world")
+def get_june_world():
+    return h5py.File(test_path / "data/june_world.h5")
 
 
 class TestLoadAgentData:
@@ -18,26 +24,37 @@ class TestLoadAgentData:
     def make_agent_loader(self, june_world_path):
         return AgentDataLoader(june_world_path)
 
-    def test__agent_properties(self, agent_data_loader):
+    def test__agent_properties(self, agent_data_loader, june_world):
         data = HeteroData()
         agent_data_loader.load_agent_data(data)
-        assert len(data["agent"]["id"]) == 769
-        assert len(data["agent"]["area"]) == 769
-        assert len(data["agent"]["age"]) == 769
-        assert len(data["agent"]["sex"]) == 769
-        assert data["agent"]["age"][14] == 6
-        assert data["agent"]["sex"][14] == 1
-        assert data["agent"]["age"][22] == 8
-        assert data["agent"]["sex"][22] == 0
-        assert data["agent"]["area"][14] == "E00023664"
-        assert data["agent"]["area"][300] == "E00079478"
+        sex_d = {b"m": 0, b"f": 1}
+        assert len(data["agent"]["id"]) == len(june_world["population"]["id"])
+        assert len(data["agent"]["area"]) == len(june_world["population"]["area"])
+        assert len(data["agent"]["age"]) == len(june_world["population"]["id"])
+        assert len(data["agent"]["sex"]) == len(june_world["population"]["id"])
+        for agent_id in june_world["population"]["id"]:
+            assert data["agent"]["id"][agent_id] == agent_id
+            assert (
+                data["agent"]["age"][agent_id]
+                == june_world["population"]["age"][agent_id]
+            )
+            assert (
+                data["agent"]["sex"][agent_id]
+                == sex_d[june_world["population"]["sex"][agent_id]]
+            )
+            assert (
+                data["agent"]["area"][agent_id]
+                == june_world["geography"]["area_name"][
+                    june_world["population"]["area"][agent_id]
+                ].decode()
+            )
 
 
 class TestNetworks:
     @pytest.mark.parametrize(
         "loader_class, ids, expected",
         [
-            (HouseholdNetworkLoader, (0, 50), ([272], [220, 248])),
+            (HouseholdNetworkLoader, (0, 50), ([293], [254, 258])),
             (
                 CompanyNetworkLoader,
                 (0, 11),
@@ -57,7 +74,6 @@ class TestNetworks:
     )
     def test__get_people_per_group(self, june_world_path, loader_class, ids, expected):
         loader = loader_class(june_world_path)
-        print(loader)
         ret = loader._get_people_per_group()
         for id, exp in zip(ids, expected):
             assert set(exp).issubset(set(ret[id]))
@@ -126,7 +142,9 @@ def test__load_leisure_network(self, leisure_loader):
         assert len(data["leisure"]["id"]) == 3
         assert data["leisure"]["people"][0] == 769
         assert data["leisure"]["people"][2] == 769
-        assert (data["leisure"]["super_area"] == ['E02000978', 'E02003270', 'E02003353']).all()
+        assert (
+            data["leisure"]["super_area"] == ["E02000978", "E02003270", "E02003353"]
+        ).all()
 
 
 class TestLoadGraph:

From 8925c2525db5fbd07dc972ea3dd6625e8107a3eb Mon Sep 17 00:00:00 2001
From: arnauq <arnau.quera@gmail.com>
Date: Sat, 12 Aug 2023 10:55:48 +0100
Subject: [PATCH 17/24] faster sum?

---
 grad_june/infection_seed.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/grad_june/infection_seed.py b/grad_june/infection_seed.py
index e8b067c..d8477d8 100644
--- a/grad_june/infection_seed.py
+++ b/grad_june/infection_seed.py
@@ -1,5 +1,6 @@
 import torch
 import pandas as pd
+from time import time
 from torch_geometric.data import HeteroData
 
 from grad_june.demographics import get_people_per_area
@@ -90,7 +91,7 @@ def forward(self, data: HeteroData, time_step: float):
             people = people_per_district[district]
             infected_status = data["agent"].is_infected[people]
             infected_time = data["agent"].infection_time[people]
-            n_people_infected_in_this_timestep = sum((infected_status == 1.0) & (infected_time == time_step))
+            n_people_infected_in_this_timestep = int((infected_status * (infected_time == time_step)).sum())
             n_to_infect = max(0, n_to_infect - n_people_infected_in_this_timestep)
             susceptible_people = people[~infected_status.bool()]
             random_idcs = torch.randperm(len(susceptible_people))[:n_to_infect]
@@ -98,7 +99,7 @@ def forward(self, data: HeteroData, time_step: float):
             ids_to_infect = torch.cat((ids_to_infect, agent_ids))
         new_infected = torch.zeros(len(data["agent"].id), device=self.device)
         new_infected[ids_to_infect] = 1.0
-        infect_people(data, time_step, new_infected)
+        infect_people(data, time_step, new_infeced)
 
 
 class InfectionSeedByFraction(torch.nn.Module):

From 67d157af75c01ea7b486e3f4a7201c1f906600dd Mon Sep 17 00:00:00 2001
From: Arnau Quera-Bofarull <arnauq@protonmail.com>
Date: Sat, 12 Aug 2023 11:02:18 +0100
Subject: [PATCH 18/24] seed can remove infections too

---
 grad_june/infection_seed.py | 36 +++++++++++++++++++++++++++++-------
 1 file changed, 29 insertions(+), 7 deletions(-)

diff --git a/grad_june/infection_seed.py b/grad_june/infection_seed.py
index d8477d8..fa8bd3a 100644
--- a/grad_june/infection_seed.py
+++ b/grad_june/infection_seed.py
@@ -19,6 +19,16 @@ def infect_people(data: HeteroData, time: int, new_infected: torch.Tensor):
     )
 
 
+def recover_people(data: HeteroData, time: int, new_recovered: torch.Tensor):
+    data["agent"].susceptibility = torch.clamp(
+        data["agent"].susceptibility + new_recovered, max=1.0
+    )
+    data["agent"].is_infected = data["agent"].is_infected - new_recovered
+    data["agent"].infection_time = (
+        data["agent"].infection_time - new_recovered * data["agent"].infection_time
+    )
+
+
 def infect_people_at_indices(data, indices, device="cpu"):
     susc = data["agent"]["susceptibility"].cpu().numpy()
     is_inf = data["agent"]["is_infected"].cpu().numpy()
@@ -83,6 +93,7 @@ def forward(self, data: HeteroData, time_step: float):
             data["agent"].id, data["agent"].district_id
         )
         ids_to_infect = torch.tensor([], dtype=torch.long, device=self.device)
+        ids_to_recover = torch.tensor([], dtype=torch.long, device=self.device)
         for district in people_per_district:
             if district in self.cases_per_district:
                 n_to_infect = self.cases_per_district[district][time_step]
@@ -91,15 +102,26 @@ def forward(self, data: HeteroData, time_step: float):
             people = people_per_district[district]
             infected_status = data["agent"].is_infected[people]
             infected_time = data["agent"].infection_time[people]
-            n_people_infected_in_this_timestep = int((infected_status * (infected_time == time_step)).sum())
-            n_to_infect = max(0, n_to_infect - n_people_infected_in_this_timestep)
-            susceptible_people = people[~infected_status.bool()]
-            random_idcs = torch.randperm(len(susceptible_people))[:n_to_infect]
-            agent_ids = susceptible_people[random_idcs]
-            ids_to_infect = torch.cat((ids_to_infect, agent_ids))
+            n_people_infected_in_this_timestep = int(
+                (infected_status * (infected_time == time_step)).sum()
+            )
+            n_to_infect = n_to_infect - n_people_infected_in_this_timestep
+            if n_to_infect > 0:
+                susceptible_people = people[~infected_status.bool()]
+                random_idcs = torch.randperm(len(susceptible_people))[:n_to_infect]
+                agent_ids = susceptible_people[random_idcs]
+                ids_to_infect = torch.cat((ids_to_infect, agent_ids))
+            elif n_to_infect < 0:
+                infected_people = people[infected_status.bool()]
+                random_idcs = torch.randperm(len(infected_people))[:abs(n_to_infect)]
+                agent_ids = infected_people[random_idcs]
+                ids_to_recover = torch.cat((ids_to_recover, agent_ids))
         new_infected = torch.zeros(len(data["agent"].id), device=self.device)
         new_infected[ids_to_infect] = 1.0
-        infect_people(data, time_step, new_infeced)
+        infect_people(data, time_step, new_infected)
+        new_recovered = torch.zeros(len(data["agent"].id), device=self.device)
+        new_recovered[ids_to_recover] = 1.0
+        recover_people(data, time_step, new_recovered)
 
 
 class InfectionSeedByFraction(torch.nn.Module):

From 0d1c9aa2ac5138e7435271811c798d904ac7e80b Mon Sep 17 00:00:00 2001
From: arnauq <arnau.quera@gmail.com>
Date: Sat, 12 Aug 2023 16:27:30 +0100
Subject: [PATCH 19/24] clearing memory

---
 grad_june/policies/policies.py |  5 +++-
 grad_june/runner.py            | 54 +++++++++++++++++-----------------
 2 files changed, 31 insertions(+), 28 deletions(-)

diff --git a/grad_june/policies/policies.py b/grad_june/policies/policies.py
index e10e9a8..4435060 100644
--- a/grad_june/policies/policies.py
+++ b/grad_june/policies/policies.py
@@ -42,6 +42,9 @@ def __init__(self, policies: Policy):
     def __getitem__(self, idx):
         return self.policies[idx]
 
+    def __len__(self):
+        return len(self.policies)
+
 
 class Policies(torch.nn.Module):
     def __init__(
@@ -120,7 +123,7 @@ def _get_policies_by_type(cls, policies, type):
         return [policy for policy in policies if policy.spec == type]
 
     def apply(self, data, timer):
-        if self.quarantine_policies:
+        if len(self.quarantine_policies) > 0:
             self.quarantine_policies.apply(
                 timer=timer, symptom_stages=data["agent"]["symptoms"]["current_stage"]
             )
diff --git a/grad_june/runner.py b/grad_june/runner.py
index efbddb7..33da2d1 100644
--- a/grad_june/runner.py
+++ b/grad_june/runner.py
@@ -88,13 +88,13 @@ def get_data(params):
         data["agent"].susceptibility = torch.ones(n_agents, device=device)
         data["agent"].is_infected = torch.zeros(n_agents, device=device)
         data["agent"].infection_time = torch.zeros(n_agents, device=device)
-        symptoms = {}
-        symptoms["current_stage"] = torch.ones(
-            n_agents, dtype=torch.long, device=device
-        )
-        symptoms["next_stage"] = torch.ones(n_agents, dtype=torch.long, device=device)
-        symptoms["time_to_next_stage"] = torch.zeros(n_agents, device=device)
-        data["agent"].symptoms = symptoms
+        #symptoms = {}
+        #symptoms["current_stage"] = torch.ones(
+        #    n_agents, dtype=torch.long, device=device
+        #)
+        #symptoms["next_stage"] = torch.ones(n_agents, dtype=torch.long, device=device)
+        #symptoms["time_to_next_stage"] = torch.zeros(n_agents, device=device)
+        #data["agent"].symptoms = symptoms
         return data
 
     def backup_infection_data(self, data):
@@ -103,17 +103,17 @@ def backup_infection_data(self, data):
         ret["is_infected"] = data["agent"].is_infected.detach().clone()
         ret["infection_time"] = data["agent"].infection_time.detach().clone()
         ret["transmission"] = data["agent"].transmission.detach().clone()
-        symptoms = {}
-        symptoms["current_stage"] = (
-            data["agent"]["symptoms"]["current_stage"].detach().clone()
-        )
-        symptoms["next_stage"] = (
-            data["agent"]["symptoms"]["next_stage"].detach().clone()
-        )
-        symptoms["time_to_next_stage"] = (
-            data["agent"]["symptoms"]["time_to_next_stage"].detach().clone()
-        )
-        ret["symptoms"] = symptoms
+        #symptoms = {}
+        #symptoms["current_stage"] = (
+        #    data["agent"]["symptoms"]["current_stage"].detach().clone()
+        #)
+        #symptoms["next_stage"] = (
+        #    data["agent"]["symptoms"]["next_stage"].detach().clone()
+        #)
+        #symptoms["time_to_next_stage"] = (
+        #    data["agent"]["symptoms"]["time_to_next_stage"].detach().clone()
+        #)
+        #ret["symptoms"] = symptoms
         return ret
 
     def restore_initial_data(self):
@@ -129,15 +129,15 @@ def restore_initial_data(self):
         self.data["agent"].infection_time = (
             self.data_backup["infection_time"].detach().clone()
         )
-        self.data["agent"].symptoms["current_stage"] = (
-            self.data_backup["symptoms"]["current_stage"].detach().clone()
-        )
-        self.data["agent"].symptoms["next_stage"] = (
-            self.data_backup["symptoms"]["next_stage"].detach().clone()
-        )
-        self.data["agent"].symptoms["time_to_next_stage"] = (
-            self.data_backup["symptoms"]["time_to_next_stage"].detach().clone()
-        )
+        #self.data["agent"].symptoms["current_stage"] = (
+        #    self.data_backup["symptoms"]["current_stage"].detach().clone()
+        #)
+        #self.data["agent"].symptoms["next_stage"] = (
+        #    self.data_backup["symptoms"]["next_stage"].detach().clone()
+        #)
+        #self.data["agent"].symptoms["time_to_next_stage"] = (
+        #    self.data_backup["symptoms"]["time_to_next_stage"].detach().clone()
+        #)
         # reset results
         self.data["results"] = {}
         self.data["results"]["deaths_per_timestep"] = None

From 9af4125d090c9e2287a4a06a5f8bbf642838388b Mon Sep 17 00:00:00 2001
From: Arnau Quera-Bofarull <arnauq@protonmail.com>
Date: Sat, 12 Aug 2023 16:52:54 +0100
Subject: [PATCH 20/24] read beta factors from region data

---
 grad_june/infection_networks/base.py            | 2 +-
 grad_june/infection_networks/leisure_network.py | 2 +-
 grad_june/june_world_loader/leisure_loader.py   | 7 ++++++-
 grad_june/june_world_loader/network_loader.py   | 8 +++++++-
 4 files changed, 15 insertions(+), 4 deletions(-)

diff --git a/grad_june/infection_networks/base.py b/grad_june/infection_networks/base.py
index 828c2f4..029b7c0 100644
--- a/grad_june/infection_networks/base.py
+++ b/grad_june/infection_networks/base.py
@@ -34,7 +34,7 @@ def _get_reverse_edge_index(self, data):
         return data["rev_attends_" + self.name].edge_index
 
     def _get_beta_factor(self, data):
-        return data[self.name].beta_factor
+        return data["region"].beta_factor[data[self.name].region]
 
     def _get_beta(self, policies, timer, data):
         beta_factor = self._get_beta_factor(data)
diff --git a/grad_june/infection_networks/leisure_network.py b/grad_june/infection_networks/leisure_network.py
index 7918fe8..5a64f14 100644
--- a/grad_june/infection_networks/leisure_network.py
+++ b/grad_june/infection_networks/leisure_network.py
@@ -48,7 +48,7 @@ def _get_reverse_edge_index(self, data):
         return data["rev_attends_leisure"].edge_index
 
     def _get_beta_factor(self, data):
-        return data["leisure"].beta_factor
+        return data["region"].beta_factor[data["leisure"].region]
 
     def _get_beta(self, policies, timer, data):
         beta_factor = self._get_beta_factor(data)
diff --git a/grad_june/june_world_loader/leisure_loader.py b/grad_june/june_world_loader/leisure_loader.py
index 162a9e0..4edcb90 100644
--- a/grad_june/june_world_loader/leisure_loader.py
+++ b/grad_june/june_world_loader/leisure_loader.py
@@ -40,6 +40,11 @@ def _get_super_area_names(self):
             super_area_names = f["geography"]["super_area_name"][:].astype("U")
             return super_area_names
 
+    def _get_group_regions(self):
+        with h5py.File(self.june_world_path, "r") as f:
+            super_area_regions = f["geography"]["super_area_region"][:]
+            return super_area_regions
+
     def _generate_ball_tree(self):
         ball_tree = BallTree(self._super_area_coordinates, metric="haversine")
         return ball_tree
@@ -74,7 +79,7 @@ def load_network(self, data):
         data["agent", "attends_leisure", "leisure"].edge_index = ret
         data["leisure"].id = torch.tensor(list(close_people_per_super_area.keys()))
         data["leisure"].super_area = self._get_super_area_names()
-        data["leisure"].beta_factor = torch.ones(len(data["leisure"].id))
+        data["leisure"].region = self._get_group_regions()
         data["leisure"].people = torch.tensor(
             [len(close_people_per_super_area[sa]) for sa in close_people_per_super_area]
         )
diff --git a/grad_june/june_world_loader/network_loader.py b/grad_june/june_world_loader/network_loader.py
index 71409d0..1812ec0 100644
--- a/grad_june/june_world_loader/network_loader.py
+++ b/grad_june/june_world_loader/network_loader.py
@@ -33,6 +33,12 @@ def _get_group_super_areas(self):
             group_super_area_ids = f[self.plural]["super_area"][:]
             return super_area_names[group_super_area_ids]
 
+    def _get_group_regions(self):
+        with h5py.File(self.june_world_path, "r") as f:
+            super_area_regions = f["geography"]["super_area_region"][:]
+            group_super_area_ids = f[self.plural]["super_area"][:]
+            return super_area_regions[group_super_area_ids]
+
     def load_network(self, data):
         people_per_group = self._get_people_per_group()
         adjlist_i = []
@@ -43,7 +49,7 @@ def load_network(self, data):
                 adjlist_j.append(group_id)
         data[self.spec].id = self._get_group_ids()
         data[self.spec].super_area = self._get_group_super_areas()
-        data[self.spec].beta_factor = torch.ones(len(data[self.spec].id))
+        data[self.spec].region = self._get_group_regions()
         data[self.spec].people = torch.tensor(
             [len(people_per_group[i]) for i in data[self.spec].id]
         )

From b0e8e60bea57a3ce767bb39408a26d6f93cb4669 Mon Sep 17 00:00:00 2001
From: Arnau Quera-Bofarull <arnauq@protonmail.com>
Date: Sat, 12 Aug 2023 19:00:27 +0100
Subject: [PATCH 21/24] clean

---
 grad_june/june_world_loader/leisure_loader.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/grad_june/june_world_loader/leisure_loader.py b/grad_june/june_world_loader/leisure_loader.py
index 4edcb90..4985e8a 100644
--- a/grad_june/june_world_loader/leisure_loader.py
+++ b/grad_june/june_world_loader/leisure_loader.py
@@ -1,4 +1,3 @@
-from collections import defaultdict
 import numpy as np
 import h5py
 import torch

From 53ddc2684917b1184c7fcc3d494686f1f2019052 Mon Sep 17 00:00:00 2001
From: Arnau Quera-Bofarull <arnauq@protonmail.com>
Date: Mon, 14 Aug 2023 16:50:13 +0100
Subject: [PATCH 22/24] fixed bug in infection profile

---
 grad_june/configs/default.yaml |  2 +-
 grad_june/model.py             |  2 +-
 grad_june/transmission.py      | 18 +++++++++---------
 3 files changed, 11 insertions(+), 11 deletions(-)

diff --git a/grad_june/configs/default.yaml b/grad_june/configs/default.yaml
index 6dcf37a..58c59a2 100644
--- a/grad_june/configs/default.yaml
+++ b/grad_june/configs/default.yaml
@@ -116,7 +116,7 @@ transmission:
     scale: 0.03
   shift:
     dist: Normal
-    loc: -2.12
+    loc: 2.12
     scale: 0.1
 
 symptoms:
diff --git a/grad_june/model.py b/grad_june/model.py
index 7c03c42..acdac6b 100644
--- a/grad_june/model.py
+++ b/grad_june/model.py
@@ -104,7 +104,7 @@ def forward(self, data, timer):
         """
 
         # Updates agent transmission based on current transmission updater values.
-        data["agent"].transmission = self.transmission_updater(data=data, timer=timer)
+        data["agent"].transmission = self.transmission_updater(data=data, time=timer.now)
 
         # Calculates probability of not being infected for each agent based on current policies.
         not_infected_probs = self.infection_networks(
diff --git a/grad_june/transmission.py b/grad_june/transmission.py
index 1e55bec..66f3da8 100644
--- a/grad_june/transmission.py
+++ b/grad_june/transmission.py
@@ -36,16 +36,16 @@ def from_parameters(cls, params):
 
 
 class TransmissionUpdater(torch.nn.Module):
-    def forward(self, data, timer):
+    def forward(self, data, time):
         shape = data["agent"]["infection_parameters"]["shape"]
         shift = data["agent"]["infection_parameters"]["shift"]
         rate = data["agent"]["infection_parameters"]["rate"]
         max_infectiousness = data["agent"]["infection_parameters"]["max_infectiousness"]
-        time_from_infection = timer.now - data["agent"].infection_time
-        sign = (torch.sign(time_from_infection - shift + 1e-10) + 1) / 2
-        aux = torch.exp(-torch.lgamma(shape)) * torch.pow(
-            (time_from_infection - shift) * rate, shape - 1.0
-        )
-        aux2 = torch.exp((shift - time_from_infection) * rate) * rate
-        ret = max_infectiousness * sign * aux * aux2 * data["agent"].is_infected
-        return ret
+        time_from_infection = time - data["agent"].infection_time
+        ret = (rate / torch.exp(torch.lgamma(shape)))
+        ret *= torch.pow(rate * (time_from_infection - shift), shape -1.0)
+        ret *= torch.exp(-rate * (time_from_infection - shift))
+        ret *= data["agent"].is_infected * max_infectiousness
+        return torch.where(time_from_infection < shift, torch.zeros_like(ret), ret)
+
+

From febc1538bb89598223966a8592af8ade6f40c2ae Mon Sep 17 00:00:00 2001
From: Arnau Quera-Bofarull <arnauq@protonmail.com>
Date: Mon, 14 Aug 2023 16:54:40 +0100
Subject: [PATCH 23/24] assertion to pick old config files

---
 grad_june/transmission.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/grad_june/transmission.py b/grad_june/transmission.py
index 66f3da8..a117664 100644
--- a/grad_june/transmission.py
+++ b/grad_june/transmission.py
@@ -30,6 +30,7 @@ def from_parameters(cls, params):
         ret = {}
         tparams = params["transmission"]
         device = params["system"]["device"]
+        assert tparams["shift"]["loc"] > 0
         for key in tparams:
             ret[key] = parse_distribution(tparams[key], device=device)
         return cls(**ret)

From 1311acb1c0ddd4bc35d7dca5bfc5c4663a7493b3 Mon Sep 17 00:00:00 2001
From: Arnau Quera-Bofarull <arnauq@protonmail.com>
Date: Wed, 11 Oct 2023 16:03:01 +0100
Subject: [PATCH 24/24] default beta_factor to 1.0

---
 grad_june/infection_networks/base.py            | 2 ++
 grad_june/infection_networks/leisure_network.py | 2 ++
 2 files changed, 4 insertions(+)

diff --git a/grad_june/infection_networks/base.py b/grad_june/infection_networks/base.py
index 029b7c0..db0a212 100644
--- a/grad_june/infection_networks/base.py
+++ b/grad_june/infection_networks/base.py
@@ -34,6 +34,8 @@ def _get_reverse_edge_index(self, data):
         return data["rev_attends_" + self.name].edge_index
 
     def _get_beta_factor(self, data):
+        if not hasattr(data["region"], "beta_factor"):
+            return 1.0
         return data["region"].beta_factor[data[self.name].region]
 
     def _get_beta(self, policies, timer, data):
diff --git a/grad_june/infection_networks/leisure_network.py b/grad_june/infection_networks/leisure_network.py
index 5a64f14..8e20313 100644
--- a/grad_june/infection_networks/leisure_network.py
+++ b/grad_june/infection_networks/leisure_network.py
@@ -48,6 +48,8 @@ def _get_reverse_edge_index(self, data):
         return data["rev_attends_leisure"].edge_index
 
     def _get_beta_factor(self, data):
+        if not hasattr(data["region"], "beta_factor"):
+            return 1.0
         return data["region"].beta_factor[data["leisure"].region]
 
     def _get_beta(self, policies, timer, data):