From 37c28699a75999a846cf567ed9e7ce3fcd8da9a3 Mon Sep 17 00:00:00 2001
From: Joseph Lemaitre <jo.lemaitresamra@gmail.com>
Date: Tue, 9 Apr 2024 14:47:06 +0200
Subject: [PATCH 01/31] starting to gempyorize the things

---
 flepimop/gempyor_pkg/setup.cfg           |  2 +
 flepimop/gempyor_pkg/src/gempyor/loss.py | 51 ++++++++++++++++++++++++
 2 files changed, 53 insertions(+)
 create mode 100644 flepimop/gempyor_pkg/src/gempyor/loss.py

diff --git a/flepimop/gempyor_pkg/setup.cfg b/flepimop/gempyor_pkg/setup.cfg
index 1ad3526b2..338ac7797 100644
--- a/flepimop/gempyor_pkg/setup.cfg
+++ b/flepimop/gempyor_pkg/setup.cfg
@@ -26,6 +26,8 @@ install_requires =
     seaborn
     tqdm
     matplotlib
+    xarray
+    emcee
     click
     confuse
     pyarrow
diff --git a/flepimop/gempyor_pkg/src/gempyor/loss.py b/flepimop/gempyor_pkg/src/gempyor/loss.py
new file mode 100644
index 000000000..be4c7f1af
--- /dev/null
+++ b/flepimop/gempyor_pkg/src/gempyor/loss.py
@@ -0,0 +1,51 @@
+import xarray as xr
+import pandas as pd
+import numpy as np
+import confuse
+
+## https://docs.xarray.dev/en/stable/user-guide/indexing.html#assigning-values-with-indexing
+##
+
+class Statistic:
+    def __init__(self, name, statistic_config: confuse.ConfigView):
+        self.sim_var = statistic_config["sim_var"].as_str()
+        self.data_var = statistic_config["data_var"].as_str()
+        self.name = name
+        self.preprocess = 
+
+
+class LogLoss:
+    def __init__(self, inference_config: confuse.ConfigView, data_dir:str = "."):
+        self.gt = pd.read_csv(f"{data_dir}/"inference_config["gt_data_path"].get())
+        
+
+
+def log_loss(model_df, gt, modinf, statistics):
+    """
+        model_df, gt: dataframe indexed by date
+        TODO: support kwargs for emcee, and this looks very slow
+    """
+    
+    log_loss = xr.DataArray(0, dims=["statistic", "subpop"],  
+                coords={
+                "statistic":statistics.key(),
+                "subpop":modinf.subpop_struct.subpop_names})
+
+    for subpop in modinf.subpop_struct.subpop_names:    
+        # essential to sort by index (date here)
+        gt_s = gt[gt["subpop"]==subpop].sort_index()
+        model_df_s = model_df[model_df["subpop"]==subpop].sort_index()
+
+        # places where data and model overlap
+        first_date = max(gt_s.index.min(), model_df_s.index.min())
+        last_date = min(gt_s.index.max(), model_df_s.index.max())
+
+        gt_s = gt_s.loc[first_date:last_date].drop(["subpop"],axis=1).resample("W-SAT").agg(pd.Series.sum, skipna=False) # if one NA in the interval, skip the itnerval  (see https://stackoverflow.com/questions/54252106/while-resampling-put-nan-in-the-resulting-value-if-there-are-some-nan-values-in)
+        model_df_s = model_df_s.drop(["subpop","time"],axis=1).loc[first_date:last_date].resample("W-SAT").agg(pd.Series.sum, skipna=False) # todo sub subpop here
+        
+        for key, value in statistics.items():
+            assert model_df_s[key].shape == gt_s[value].shape
+
+            log_loss.loc[dict(statistics=key, subpop=subpop)] += np.nansum((model_df_s[key]-gt_s[value])**2)
+
+    return -log_loss,
\ No newline at end of file

From 4551ebb32046addfa0faa016b897eb8a8fbb5f11 Mon Sep 17 00:00:00 2001
From: Joseph Lemaitre <jo.lemaitresamra@gmail.com>
Date: Wed, 10 Apr 2024 13:11:04 +0200
Subject: [PATCH 02/31] seir simulation is a xarray now

---
 flepimop/gempyor_pkg/src/gempyor/outcomes.py | 48 +++++++++++++++++---
 flepimop/gempyor_pkg/src/gempyor/seir.py     | 35 ++++++++++----
 flepimop/gempyor_pkg/tests/seir/test_seir.py | 12 ++---
 3 files changed, 75 insertions(+), 20 deletions(-)

diff --git a/flepimop/gempyor_pkg/src/gempyor/outcomes.py b/flepimop/gempyor_pkg/src/gempyor/outcomes.py
index d29d8f395..dc49577df 100644
--- a/flepimop/gempyor_pkg/src/gempyor/outcomes.py
+++ b/flepimop/gempyor_pkg/src/gempyor/outcomes.py
@@ -1,6 +1,7 @@
 import itertools
 import time, random
 from numba import jit
+import xarray as xr
 import numpy as np
 import pandas as pd
 import tqdm.contrib.concurrent
@@ -9,6 +10,7 @@
 import pandas as pd
 from . import NPI, model_info
 
+
 import logging
 
 logger = logging.getLogger(__name__)
@@ -294,7 +296,7 @@ def read_seir_sim(modinf, sim_id):
     return seir_df
 
 
-def compute_all_multioutcomes(*, modinf, sim_id2write, parameters, loaded_values=None, npi=None, bypass_seir=None):
+def compute_all_multioutcomes(*, modinf, sim_id2write, parameters, loaded_values=None, npi=None, bypass_seir_df:pd.DataFrame=None, bypass_seir_xr: xr.Dataset=None):
     """Compute delay frame based on temporally varying input. We load the seir sim corresponding to sim_id to write"""
     hpar = pd.DataFrame(columns=["subpop", "quantity", "outcome", "value"])
     all_data = {}
@@ -306,10 +308,12 @@ def compute_all_multioutcomes(*, modinf, sim_id2write, parameters, loaded_values
         dates,
         "zeros",
     ).drop("zeros", axis=1)
-    if bypass_seir is None:
+    if bypass_seir_df is None and bypass_seir_xr is None:
         seir_sim = read_seir_sim(modinf, sim_id=sim_id2write)
+    elif bypass_seir_xr is not None:
+        seir_sim = bypass_seir_xr
     else:
-        seir_sim = bypass_seir
+        seir_sim = bypass_seir_df
 
     for new_comp in parameters:
         if "source" in parameters[new_comp]:
@@ -318,7 +322,12 @@ def compute_all_multioutcomes(*, modinf, sim_id2write, parameters, loaded_values
             # 2. compute duration if needed
             source_name = parameters[new_comp]["source"]
             if isinstance(source_name, dict):
-                source_array = get_filtered_incidI(diffI=seir_sim, dates=dates, subpops=modinf.subpop_struct.subpop_names, filters=source_name, outcome_name=new_comp)
+                if isinstance(seir_sim, pd.DataFrame):
+                    source_array = filter_seir_df(diffI=seir_sim, dates=dates, subpops=modinf.subpop_struct.subpop_names, filters=source_name, outcome_name=new_comp)
+                elif isinstance(seir_sim, xr.Dataset):
+                    source_array = filter_seir_xr(diffI=seir_sim, dates=dates, subpops=modinf.subpop_struct.subpop_names, filters=source_name, outcome_name=new_comp)
+                else:
+                    raise ValueError(f"Unknown type for seir simulation provided, got f{type(seir_sim)}")
                 # we don't keep source in this cases
             else:  # already defined outcomes
                 if source_name in all_data:
@@ -478,7 +487,7 @@ def compute_all_multioutcomes(*, modinf, sim_id2write, parameters, loaded_values
     return outcomes, hpar
 
 
-def get_filtered_incidI(diffI, dates, subpops, filters, outcome_name):
+def filter_seir_df(diffI, dates, subpops, filters, outcome_name) -> np.ndarray:
     if list(filters.keys()) == ["incidence"]:
         vtype = "incidence"
     elif list(filters.keys()) == ["prevalence"]:
@@ -490,7 +499,7 @@ def get_filtered_incidI(diffI, dates, subpops, filters, outcome_name):
     # diffI.drop(["mc_value_type"], inplace=True, axis=1)
     filters = filters[vtype]
 
-    incidI_arr = np.zeros((len(dates), len(subpops)), dtype=int)
+    incidI_arr = np.zeros((len(dates), len(subpops)))
     df = diffI
     for mc_type, mc_value in filters.items():
         if isinstance(mc_value, str):
@@ -504,6 +513,33 @@ def get_filtered_incidI(diffI, dates, subpops, filters, outcome_name):
     return incidI_arr
 
 
+def filter_seir_xr(diffI, dates, subpops, filters, outcome_name) -> np.ndarray:
+    # Determine the variable type (prevalence or incidence)
+    if list(filters.keys()) == ["incidence"]:
+        vtype = "incidence"
+    elif list(filters.keys()) == ["prevalence"]:
+        vtype = "prevalence"
+    else:
+        raise ValueError(f"Cannot distinguish the source of outcome {outcome_name}: it is not another previously defined outcome and there is no 'incidence:' or 'prevalence:'.")
+    # Filter the data
+    filters = filters[vtype]
+
+    # Initialize the array to store filtered incidence values
+# Initialize the array to store filtered incidence values
+    incidI_arr = np.zeros((len(dates), len(subpops)))
+
+    diffI_filtered = diffI
+    for mc_type, mc_value in filters.items():
+        # Check if mc_value is a string or list of strings
+        if isinstance(mc_value, str):
+            mc_value = [mc_value]
+        # Filter data along the specified mc_type dimension
+        diffI_filtered = diffI_filtered.where(diffI_filtered[f"mc_{mc_type}"].isin(mc_value), drop=True)
+    # Sum along the compartment dimension
+    incidI_arr += diffI_filtered[vtype].sum(dim='compartment')
+
+    return incidI_arr.to_numpy()
+
 @jit(nopython=True)
 def shift(arr, num, fill_value=0):
     """
diff --git a/flepimop/gempyor_pkg/src/gempyor/seir.py b/flepimop/gempyor_pkg/src/gempyor/seir.py
index 6d688fa78..f6e1d43dd 100644
--- a/flepimop/gempyor_pkg/src/gempyor/seir.py
+++ b/flepimop/gempyor_pkg/src/gempyor/seir.py
@@ -4,6 +4,7 @@
 import pandas as pd
 import scipy
 import tqdm.contrib.concurrent
+import xarray as xr
 
 from . import NPI, model_info, steps_rk4
 from .utils import Timer, aws_disk_diagnosis, read_df
@@ -181,8 +182,30 @@ def steps_SEIR(
             seir_sim = steps_experimental.rk4_aot(**fnct_args)
         else:
             raise ValueError(f"Unknow integration scheme, got {integration_method}")
-    return seir_sim
 
+    # We return an xarray instead of a ndarray now
+    compartment_coords = {}
+    compartment_df = modinf.compartments.get_compartments_explicitDF()
+    # Iterate over columns of the DataFrame and populate the dictionary
+    for column in compartment_df.columns:
+        compartment_coords[column] = ("compartment", compartment_df[column].tolist())
+
+    # comparment is a dimension with coordinate from each of the compartments
+    states = xr.Dataset(
+        data_vars=dict(
+            prevalence=(["date", "compartment", "subpop"], seir_sim[0]),
+            incidence=(["date", "compartment", "subpop"], seir_sim[1]),
+        ),
+        coords=dict(
+            date=pd.date_range(modinf.ti, modinf.tf, freq="D"),
+            **compartment_coords,
+            subpop=modinf.subpop_struct.subpop_names
+        ),
+        attrs=dict(description="Dynamical simulation results", run_id=modinf.in_run_id) # TODO add more information
+    )    
+
+    
+    return states
 
 def build_npi_SEIR(modinf, load_ID, sim_id2load, config, bypass_DF=None, bypass_FN=None):
     with Timer("SEIR.NPI"):
@@ -306,11 +329,7 @@ def states2Df(modinf, states):
     # Tidyup data for  R, to save it:
     #
     # Write output to .snpi.*, .spar.*, and .seir.* files
-
-    (
-        states_prev,
-        states_incid,
-    ) = states  # both are [ndays x ncompartments x nspatial_nodes ]
+    # states is  # both are [ndays x ncompartments x nspatial_nodes ] -> this is important here
 
     # add line of zero to diff, so we get the real cumulative.
     # states_diff = np.zeros((states_cumu.shape[0] + 1, *states_cumu.shape[1:]))
@@ -323,7 +342,7 @@ def states2Df(modinf, states):
     )
     # prevalence data, we use multi.index dataframe, sparring us the array manipulation we use to do
     prev_df = pd.DataFrame(
-        data=states_prev.reshape(modinf.n_days * modinf.compartments.get_ncomp(), modinf.nsubpops),
+        data=states["prevalence"].to_numpy().reshape(modinf.n_days * modinf.compartments.get_ncomp(), modinf.nsubpops),
         index=ts_index,
         columns=modinf.subpop_struct.subpop_names,
     ).reset_index()
@@ -341,7 +360,7 @@ def states2Df(modinf, states):
     )
 
     incid_df = pd.DataFrame(
-        data=states_incid.reshape(modinf.n_days * modinf.compartments.get_ncomp(), modinf.nsubpops),
+        data=states["incidence"].to_numpy().reshape(modinf.n_days * modinf.compartments.get_ncomp(), modinf.nsubpops),
         index=ts_index,
         columns=modinf.subpop_struct.subpop_names,
     ).reset_index()
diff --git a/flepimop/gempyor_pkg/tests/seir/test_seir.py b/flepimop/gempyor_pkg/tests/seir/test_seir.py
index 7e0044920..c410a5294 100644
--- a/flepimop/gempyor_pkg/tests/seir/test_seir.py
+++ b/flepimop/gempyor_pkg/tests/seir/test_seir.py
@@ -112,8 +112,8 @@ def test_constant_population_legacy_integration():
     for it in range(modinf.n_days):
         totalpop = 0
         for i in range(modinf.nsubpops):
-            totalpop += states[0].sum(axis=1)[it, i]
-            assert states[0].sum(axis=1)[it, i] - 1e-3 < origpop[i] < states[0].sum(axis=1)[it, i] + 1e-3
+            totalpop += states["prevalence"].sum(axis=1)[it, i]
+            assert states["prevalence"].sum(axis=1)[it, i] - 1e-3 < origpop[i] < states["prevalence"].sum(axis=1)[it, i] + 1e-3
         assert completepop - 1e-3 < totalpop < completepop + 1e-3
 
 
@@ -177,8 +177,8 @@ def test_constant_population_rk4jit_integration_fail():
         for it in range(modinf.n_days):
             totalpop = 0
             for i in range(modinf.nsubpops):
-                totalpop += states[0].sum(axis=1)[it, i]
-                assert states[0].sum(axis=1)[it, i] - 1e-3 < origpop[i] < states[0].sum(axis=1)[it, i] + 1e-3
+                totalpop += states["prevalence"].sum(axis=1)[it, i]
+                assert states["prevalence"].sum(axis=1)[it, i] - 1e-3 < origpop[i] < states["prevalence"].sum(axis=1)[it, i] + 1e-3
             assert completepop - 1e-3 < totalpop < completepop + 1e-3
 
 
@@ -241,8 +241,8 @@ def test_constant_population_rk4jit_integration():
     for it in range(modinf.n_days):
         totalpop = 0
         for i in range(modinf.nsubpops):
-            totalpop += states[0].sum(axis=1)[it, i]
-            assert states[0].sum(axis=1)[it, i] - 1e-3 < origpop[i] < states[0].sum(axis=1)[it, i] + 1e-3
+            totalpop += states["prevalence"].sum(axis=1)[it, i]
+            assert states["prevalence"].sum(axis=1)[it, i] - 1e-3 < origpop[i] < states["prevalence"].sum(axis=1)[it, i] + 1e-3
         assert completepop - 1e-3 < totalpop < completepop + 1e-3
 
 

From d82efe80d4d77c1796739e622f012dcb5b5bca02 Mon Sep 17 00:00:00 2001
From: Joseph Lemaitre <jo.lemaitresamra@gmail.com>
Date: Wed, 10 Apr 2024 13:24:07 +0200
Subject: [PATCH 03/31] interface for inference

---
 flepimop/gempyor_pkg/src/gempyor/interface.py | 20 +++++++++++++++----
 flepimop/gempyor_pkg/src/gempyor/seir.py      | 11 +++++-----
 2 files changed, 22 insertions(+), 9 deletions(-)

diff --git a/flepimop/gempyor_pkg/src/gempyor/interface.py b/flepimop/gempyor_pkg/src/gempyor/interface.py
index 04c084d84..afc1c3248 100644
--- a/flepimop/gempyor_pkg/src/gempyor/interface.py
+++ b/flepimop/gempyor_pkg/src/gempyor/interface.py
@@ -53,6 +53,7 @@ def __init__(
         out_run_id=None,  # if out_run_id is different from in_run_id, fill this
         out_prefix=None,  # if out_prefix is different from in_prefix, fill this
         spatial_path_prefix="",  # in case the data folder is on another directory
+        autowrite_seir = False
     ):
         self.seir_modifiers_scenario = seir_modifiers_scenario
         self.outcome_modifiers_scenario = outcome_modifiers_scenario
@@ -98,6 +99,7 @@ def __init__(
         )
 
         self.already_built = False  # whether we have already built the costly objects that need just one build
+        self.autowrite_seir = autowrite_seir
 
     def update_prefix(self, new_prefix, new_out_prefix=None):
         self.modinf.in_prefix = new_prefix
@@ -145,6 +147,11 @@ def build_structure(self):
         ) = self.modinf.compartments.get_transition_array()
         self.already_built = True
 
+    def write_last_seir(self):
+        out_df = seir.write_seir(self.debug_sim_id2write, self.modinf, self.debug_states)
+        return out_df
+
+
     # @profile()
     def one_simulation(
         self,
@@ -154,8 +161,13 @@ def one_simulation(
         parallel=False,
     ):
         sim_id2write = int(sim_id2write)
+        self.debug_sim_id2write = sim_id2write
+        self.debug_loadID = load_ID
+        self.debug_sim_id2load = sim_id2load
         if load_ID:
             sim_id2load = int(sim_id2load)
+            self.debug_sim_id2load = sim_id2load
+
 
         with Timer(f">>> GEMPYOR onesim {'(loading file)' if load_ID else '(from config)'}"):
             if not self.already_built:
@@ -250,10 +262,10 @@ def one_simulation(
 
             with Timer("SEIR.postprocess"):
                 if self.modinf.write_csv or self.modinf.write_parquet:
-                    out_df = seir.postprocess_and_write(
-                        sim_id2write, self.modinf, states, p_draw, npi_seir, seeding_data
-                    )
-                    self.debug_out_df = out_df
+                    seir.write_spar_snpi(sim_id2write, self.modinf, p_draw, npi_seir)
+                    if self.autowrite_seir:
+                        out_df = seir.write_seir(sim_id2write, self.modinf, states)
+                        self.debug_out_df = out_df
 
             loaded_values = None
             if load_ID:
diff --git a/flepimop/gempyor_pkg/src/gempyor/seir.py b/flepimop/gempyor_pkg/src/gempyor/seir.py
index f6e1d43dd..14a419917 100644
--- a/flepimop/gempyor_pkg/src/gempyor/seir.py
+++ b/flepimop/gempyor_pkg/src/gempyor/seir.py
@@ -300,7 +300,8 @@ def onerun_SEIR(
 
     with Timer("onerun_SEIR.postprocess"):
         if modinf.write_csv or modinf.write_parquet:
-            out_df = postprocess_and_write(sim_id2write, modinf, states, p_draw, npi, seeding_data)
+            write_spar_snpi(sim_id2write, modinf, p_draw, npi)
+            out_df = write_seir(sim_id2write, modinf, states)
     return out_df
 
 
@@ -378,15 +379,15 @@ def states2Df(modinf, states):
 
     return out_df
 
-
-def postprocess_and_write(sim_id, modinf, states, p_draw, npi, seeding):
-    # aws_disk_diagnosis()
-
+def write_spar_snpi(sim_id, modinf, p_draw, npi):
     # NPIs
     if npi is not None:
         modinf.write_simID(ftype="snpi", sim_id=sim_id, df=npi.getReductionDF())
     # Parameters
     modinf.write_simID(ftype="spar", sim_id=sim_id, df=modinf.parameters.getParameterDF(p_draw=p_draw))
+
+def write_seir(sim_id, modinf, states):
+    # aws_disk_diagnosis()
     out_df = states2Df(modinf, states)
     modinf.write_simID(ftype="seir", sim_id=sim_id, df=out_df)
 

From 19be49a88262ad6ba6e5f2d6750e90e9d16663cd Mon Sep 17 00:00:00 2001
From: Joseph Lemaitre <jo.lemaitresamra@gmail.com>
Date: Wed, 10 Apr 2024 13:27:21 +0200
Subject: [PATCH 04/31] R uses that to write on accept

---
 flepimop/gempyor_pkg/src/gempyor/interface.py | 6 ++++--
 flepimop/main_scripts/inference_slot.R        | 2 ++
 2 files changed, 6 insertions(+), 2 deletions(-)

diff --git a/flepimop/gempyor_pkg/src/gempyor/interface.py b/flepimop/gempyor_pkg/src/gempyor/interface.py
index afc1c3248..58adf5ad5 100644
--- a/flepimop/gempyor_pkg/src/gempyor/interface.py
+++ b/flepimop/gempyor_pkg/src/gempyor/interface.py
@@ -147,8 +147,10 @@ def build_structure(self):
         ) = self.modinf.compartments.get_transition_array()
         self.already_built = True
 
-    def write_last_seir(self):
-        out_df = seir.write_seir(self.debug_sim_id2write, self.modinf, self.debug_states)
+    def write_last_seir(self, sim_id2write=None):
+        if sim_id2write is None:
+            sim_id2write = self.debug_sim_id2write
+        out_df = seir.write_seir(sim_id2write, self.modinf, self.debug_states)
         return out_df
 
 
diff --git a/flepimop/main_scripts/inference_slot.R b/flepimop/main_scripts/inference_slot.R
index 9cc61ad76..d4c868d88 100644
--- a/flepimop/main_scripts/inference_slot.R
+++ b/flepimop/main_scripts/inference_slot.R
@@ -659,6 +659,8 @@ for(seir_modifiers_scenario in seir_modifiers_scenarios) {
 
         if ((opt$this_block == 1) && (last_accepted_index == 0)) {
           print("by default because it's the first iteration of a block 1")
+        } else {
+          gempyor_inference_runner$write_last_seir(sim_id2write=this_index)
         }
 
         # Update the index of the most recent globally accepted parameters

From f63ff13f712227667a6bc2a1f0f0f74b42b331e1 Mon Sep 17 00:00:00 2001
From: Joseph Lemaitre <jo.lemaitresamra@gmail.com>
Date: Wed, 10 Apr 2024 13:31:28 +0200
Subject: [PATCH 05/31] updated the name of the cached storage within interface

---
 flepimop/gempyor_pkg/src/gempyor/interface.py | 38 +++++++++----------
 1 file changed, 19 insertions(+), 19 deletions(-)

diff --git a/flepimop/gempyor_pkg/src/gempyor/interface.py b/flepimop/gempyor_pkg/src/gempyor/interface.py
index 58adf5ad5..6eff3a343 100644
--- a/flepimop/gempyor_pkg/src/gempyor/interface.py
+++ b/flepimop/gempyor_pkg/src/gempyor/interface.py
@@ -149,8 +149,8 @@ def build_structure(self):
 
     def write_last_seir(self, sim_id2write=None):
         if sim_id2write is None:
-            sim_id2write = self.debug_sim_id2write
-        out_df = seir.write_seir(sim_id2write, self.modinf, self.debug_states)
+            sim_id2write = self.lastsim_sim_id2write
+        out_df = seir.write_seir(sim_id2write, self.modinf, self.lastsim_states)
         return out_df
 
 
@@ -163,12 +163,12 @@ def one_simulation(
         parallel=False,
     ):
         sim_id2write = int(sim_id2write)
-        self.debug_sim_id2write = sim_id2write
-        self.debug_loadID = load_ID
-        self.debug_sim_id2load = sim_id2load
+        self.lastsim_sim_id2write = sim_id2write
+        self.lastsim_loadID = load_ID
+        self.lastsim_sim_id2load = sim_id2load
         if load_ID:
             sim_id2load = int(sim_id2load)
-            self.debug_sim_id2load = sim_id2load
+            self.lastsim_sim_id2load = sim_id2load
 
 
         with Timer(f">>> GEMPYOR onesim {'(loading file)' if load_ID else '(from config)'}"):
@@ -217,8 +217,8 @@ def one_simulation(
                         sim_id2load=sim_id2load,
                         config=config,
                     )
-            self.debug_npi_seir = npi_seir
-            self.debug_npi_outcomes = npi_outcomes
+            self.lastsim_npi_seir = npi_seir
+            self.lastsim_npi_outcomes = npi_outcomes
             ### Run every time:
             with Timer("SEIR.parameters"):
                 # Draw or load parameters
@@ -230,9 +230,9 @@ def one_simulation(
                 parsed_parameters = self.modinf.compartments.parse_parameters(
                     parameters, self.modinf.parameters.pnames, self.unique_strings
                 )
-                self.debug_p_draw = p_draw
-                self.debug_parameters = parameters
-                self.debug_parsed_parameters = parsed_parameters
+                self.lastsim_p_draw = p_draw
+                self.lastsim_parameters = parameters
+                self.lastsim_parsed_parameters = parsed_parameters
 
             with Timer("onerun_SEIR.seeding"):
                 if load_ID:
@@ -245,9 +245,9 @@ def one_simulation(
                     seeding_data, seeding_amounts = self.modinf.seeding.get_from_config(
                         sim_id2write, setup=self.modinf
                     )
-                self.debug_seeding_data = seeding_data
-                self.debug_seeding_amounts = seeding_amounts
-                self.debug_initial_conditions = initial_conditions
+                self.lastsim_seeding_data = seeding_data
+                self.lastsim_seeding_amounts = seeding_amounts
+                self.lastsim_initial_conditions = initial_conditions
 
             with Timer("SEIR.compute"):
                 states = seir.steps_SEIR(
@@ -260,19 +260,19 @@ def one_simulation(
                     seeding_data,
                     seeding_amounts,
                 )
-                self.debug_states = states
+                self.lastsim_states = states
 
             with Timer("SEIR.postprocess"):
                 if self.modinf.write_csv or self.modinf.write_parquet:
                     seir.write_spar_snpi(sim_id2write, self.modinf, p_draw, npi_seir)
                     if self.autowrite_seir:
                         out_df = seir.write_seir(sim_id2write, self.modinf, states)
-                        self.debug_out_df = out_df
+                        self.lastsim_out_df = out_df
 
             loaded_values = None
             if load_ID:
                 loaded_values = self.modinf.read_simID(ftype="hpar", sim_id=sim_id2load)
-                self.debug_loaded_values = loaded_values
+                self.lastsim_loaded_values = loaded_values
 
             # Compute outcomes
             with Timer("onerun_delayframe_outcomes.compute"):
@@ -283,8 +283,8 @@ def one_simulation(
                     loaded_values=loaded_values,
                     npi=npi_outcomes,
                 )
-                self.debug_outcomes_df = outcomes_df
-                self.debug_hpar_df = hpar_df
+                self.lastsim_outcomes_df = outcomes_df
+                self.lastsim_hpar_df = hpar_df
 
             with Timer("onerun_delayframe_outcomes.postprocess"):
                 outcomes.postprocess_and_write(

From 5f142ceb34fb94b0739a3be60aedc84684ec872b Mon Sep 17 00:00:00 2001
From: Joseph Lemaitre <jo.lemaitresamra@gmail.com>
Date: Wed, 10 Apr 2024 13:58:49 +0200
Subject: [PATCH 06/31] progressing on stat

---
 flepimop/gempyor_pkg/src/gempyor/loss.py | 24 +++++++++++++++++++++++-
 1 file changed, 23 insertions(+), 1 deletion(-)

diff --git a/flepimop/gempyor_pkg/src/gempyor/loss.py b/flepimop/gempyor_pkg/src/gempyor/loss.py
index be4c7f1af..5f474663c 100644
--- a/flepimop/gempyor_pkg/src/gempyor/loss.py
+++ b/flepimop/gempyor_pkg/src/gempyor/loss.py
@@ -6,12 +6,34 @@
 ## https://docs.xarray.dev/en/stable/user-guide/indexing.html#assigning-values-with-indexing
 ##
 
+
 class Statistic:
     def __init__(self, name, statistic_config: confuse.ConfigView):
         self.sim_var = statistic_config["sim_var"].as_str()
         self.data_var = statistic_config["data_var"].as_str()
         self.name = name
-        self.preprocess = 
+        self.resample_config = statistic_config.get("resample_config", None)
+        self.loss_function = statistic_config.get("loss_function", "rmse")
+
+    def apply_resampling(self, data):
+        if self.resample_config:
+            freq = self.resample_config.get("freq", "W-SAT")
+            agg_func = getattr(pd.Series, self.resample_config.get("agg_func", "sum"))
+            skipna = self.resample_config.get("skipna", False)
+
+            data_resampled = data.resample(freq).agg(agg_func, skipna=skipna)
+            return data_resampled
+        else:
+            return data
+
+    def compute_loss(self, model_data, gt_data):
+        if self.loss_function == "rmse":
+            return np.sqrt(np.mean((model_data - gt_data) ** 2))
+        elif self.loss_function == "poisson_log_loss":
+            epsilon = 1e-10  # to avoid log(0)
+            return -np.mean(model_data - gt_data * np.log(model_data + epsilon))
+        else:
+            raise ValueError("Unsupported loss function")
 
 
 class LogLoss:

From 8c396e1593ea66d3393b712755985279010e2758 Mon Sep 17 00:00:00 2001
From: Joseph Lemaitre <jo.lemaitresamra@gmail.com>
Date: Wed, 10 Apr 2024 13:59:29 +0200
Subject: [PATCH 07/31] compute log of loss

---
 flepimop/gempyor_pkg/src/gempyor/{loss.py => logloss.py} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename flepimop/gempyor_pkg/src/gempyor/{loss.py => logloss.py} (100%)

diff --git a/flepimop/gempyor_pkg/src/gempyor/loss.py b/flepimop/gempyor_pkg/src/gempyor/logloss.py
similarity index 100%
rename from flepimop/gempyor_pkg/src/gempyor/loss.py
rename to flepimop/gempyor_pkg/src/gempyor/logloss.py

From 654a88239f1d095df96d7d2e15894d365e49b925 Mon Sep 17 00:00:00 2001
From: Joseph Lemaitre <jo.lemaitresamra@gmail.com>
Date: Wed, 10 Apr 2024 14:17:34 +0200
Subject: [PATCH 08/31] logloss

---
 flepimop/gempyor_pkg/src/gempyor/logloss.py | 63 ++++++++++++---------
 1 file changed, 37 insertions(+), 26 deletions(-)

diff --git a/flepimop/gempyor_pkg/src/gempyor/logloss.py b/flepimop/gempyor_pkg/src/gempyor/logloss.py
index 5f474663c..8cf822859 100644
--- a/flepimop/gempyor_pkg/src/gempyor/logloss.py
+++ b/flepimop/gempyor_pkg/src/gempyor/logloss.py
@@ -26,9 +26,18 @@ def apply_resampling(self, data):
         else:
             return data
 
-    def compute_loss(self, model_data, gt_data):
+    def compute_logloss(self, model_data, gt_data, skip_resampling=False):
+        if not skip_resampling:
+            model_data = self.apply_resampling(model_data)
+            gt_data = self.apply_resampling(gt_data)
+
+        model_data = model_data[self.sim_var]
+        gt_data = gt_data[self.data_var]
+
+        assert model_data.shape == gt_data.shape
+
         if self.loss_function == "rmse":
-            return np.sqrt(np.mean((model_data - gt_data) ** 2))
+            return -np.sqrt(np.mean((model_data - gt_data) ** 2))
         elif self.loss_function == "poisson_log_loss":
             epsilon = 1e-10  # to avoid log(0)
             return -np.mean(model_data - gt_data * np.log(model_data + epsilon))
@@ -38,36 +47,38 @@ def compute_loss(self, model_data, gt_data):
 
 class LogLoss:
     def __init__(self, inference_config: confuse.ConfigView, data_dir:str = "."):
-        self.gt = pd.read_csv(f"{data_dir}/"inference_config["gt_data_path"].get())
-        
+        self.gt = pd.read_csv(f"{data_dir}/{inference_config['gt_data_path'].get()}")
+        self.statistics = {}
+        for key, value in inference_config["statistics"].items():
+            self.statistics[key] = Statistic(key, value)
 
-
-def log_loss(model_df, gt, modinf, statistics):
-    """
-        model_df, gt: dataframe indexed by date
+    def compute_logloss(self, model_df, modinf):
+        """
+        Compute logloss for all statistics
+        model_df: DataFrame indexed by date
+        modinf: model information
         TODO: support kwargs for emcee, and this looks very slow
-    """
-    
-    log_loss = xr.DataArray(0, dims=["statistic", "subpop"],  
+        """
+        logloss = xr.DataArray(0, dims=["statistic", "subpop"],  
                 coords={
-                "statistic":statistics.key(),
+                "statistic":self.statistics.key(),
                 "subpop":modinf.subpop_struct.subpop_names})
 
-    for subpop in modinf.subpop_struct.subpop_names:    
-        # essential to sort by index (date here)
-        gt_s = gt[gt["subpop"]==subpop].sort_index()
-        model_df_s = model_df[model_df["subpop"]==subpop].sort_index()
+        for subpop in modinf.subpop_struct.subpop_names:
+            # essential to sort by index (date here)
+            gt_s = self.gt[self.gt["subpop"] == subpop].sort_index()
+            model_df_s = model_df[model_df["subpop"] == subpop].sort_index()
+
+            # Places where data and model overlap
+            first_date = max(gt_s.index.min(), model_df_s.index.min())
+            last_date = min(gt_s.index.max(), model_df_s.index.max())
+
+            gt_s = gt_s.loc[first_date:last_date].drop(["subpop"], axis=1)
+            model_df_s = model_df_s.drop(["subpop", "time"], axis=1).loc[first_date:last_date]
 
-        # places where data and model overlap
-        first_date = max(gt_s.index.min(), model_df_s.index.min())
-        last_date = min(gt_s.index.max(), model_df_s.index.max())
+            for key, stat in self.statistics.items():
+                logloss.loc[dict(statistics=key, subpop=subpop)] += stat.compute_logloss(model_df, gt_s)
 
-        gt_s = gt_s.loc[first_date:last_date].drop(["subpop"],axis=1).resample("W-SAT").agg(pd.Series.sum, skipna=False) # if one NA in the interval, skip the itnerval  (see https://stackoverflow.com/questions/54252106/while-resampling-put-nan-in-the-resulting-value-if-there-are-some-nan-values-in)
-        model_df_s = model_df_s.drop(["subpop","time"],axis=1).loc[first_date:last_date].resample("W-SAT").agg(pd.Series.sum, skipna=False) # todo sub subpop here
-        
-        for key, value in statistics.items():
-            assert model_df_s[key].shape == gt_s[value].shape
+        return logloss
 
-            log_loss.loc[dict(statistics=key, subpop=subpop)] += np.nansum((model_df_s[key]-gt_s[value])**2)
 
-    return -log_loss,
\ No newline at end of file

From f4aadbf6061badf1fea67b994ec7a137018f4c91 Mon Sep 17 00:00:00 2001
From: Joseph Lemaitre <jo.lemaitresamra@gmail.com>
Date: Wed, 10 Apr 2024 15:40:58 +0200
Subject: [PATCH 09/31] starting to get an inference structure

---
 flepimop/gempyor_pkg/src/gempyor/inference.py | 75 +++++++++++++++++++
 1 file changed, 75 insertions(+)
 create mode 100644 flepimop/gempyor_pkg/src/gempyor/inference.py

diff --git a/flepimop/gempyor_pkg/src/gempyor/inference.py b/flepimop/gempyor_pkg/src/gempyor/inference.py
new file mode 100644
index 000000000..4f7955978
--- /dev/null
+++ b/flepimop/gempyor_pkg/src/gempyor/inference.py
@@ -0,0 +1,75 @@
+import xarray as xr
+import pandas as pd
+import numpy as np
+import confuse
+
+
+class InferenceParameter:
+    def __init__(self, ptype, pname, subpop, pdist, lb, ub):
+        self.ptype = ptype
+        self.pname = pname
+        self.subpop = subpop
+        self.pdist = pdist
+        self.lb = lb
+        self.ub = ub
+    # TODO: does not support the subpop groups here !!!!!!!
+    def print_summary(self):
+        print(f"{self.ptype}::{self.pname} in [{self.lb}, {self.ub}]"
+              f"   >> affected subpop: {self.subpop}"
+        )
+
+class InferenceParameters:
+    def __init__(self):
+        self.params = []
+
+    def add_param(self, ptype, pname, subpop, pdist, lb, ub):
+        param = InferenceParameter(ptype, pname, subpop, pdist, lb, ub)
+        self.params.append(param)
+
+
+    def 
+
+
+
+    def build_from_config(self, global_config, modinf):
+        for npi in global_config["seir_modifiers"]["modifiers"].get():
+            if global_config["seir_modifiers"]["modifiers"][npi]["perturbation"].exists():
+                c = global_config["seir_modifiers"]["modifiers"][npi]
+                for sp in modinf.subpop_struct.subpop_names:
+                    self.add_param(
+                        ptype="snpi",
+                        pname=npi,
+                        subpop=sp,
+                        pdist=c["value"].as_random_distribution(),
+                        lb=c["value"]["a"].get(),
+                        ub=c["value"]["b"].get()
+                    )
+
+        for npi in global_config["outcome_modifiers"]["modifiers"].get():
+            if global_config["outcome_modifiers"]["modifiers"][npi]["perturbation"].exists():
+                c = global_config["outcome_modifiers"]["modifiers"][npi]
+                for sp in modinf.subpop_struct.subpop_names:
+                    self.add_param(
+                        ptype="hnpi",
+                        pname=npi,
+                        subpop=sp,
+                        pdist=c["value"].as_random_distribution(),
+                        lb=c["value"]["a"].get(),
+                        ub=c["value"]["b"].get()
+                    )
+
+    def print_summary(self):
+        print(f"There are {len(self.params)} parameters in the configuration.")
+        for param in self.params:
+            param.print_summary()
+
+# Create an instance of FittedParams and build it from the config
+fitted_params = InferenceParameters()
+fitted_params.build_from_config(gempyor.config, modinf)
+
+# Print summary
+fitted_params.print_summary()
+
+
+class FittedParameters:
+    def __init__(self, global_config: confuse.ConfigView):

From ba8dfcd728732092dc77357b02c492ae9774138d Mon Sep 17 00:00:00 2001
From: Joseph Lemaitre <jo.lemaitresamra@gmail.com>
Date: Wed, 10 Apr 2024 16:42:28 +0200
Subject: [PATCH 10/31] simpler inferenceParameter structure

---
 flepimop/gempyor_pkg/src/gempyor/inference.py | 71 ++++++-------------
 flepimop/gempyor_pkg/src/gempyor/logloss.py   |  3 +-
 2 files changed, 23 insertions(+), 51 deletions(-)

diff --git a/flepimop/gempyor_pkg/src/gempyor/inference.py b/flepimop/gempyor_pkg/src/gempyor/inference.py
index 4f7955978..a2a0de492 100644
--- a/flepimop/gempyor_pkg/src/gempyor/inference.py
+++ b/flepimop/gempyor_pkg/src/gempyor/inference.py
@@ -3,65 +3,36 @@
 import numpy as np
 import confuse
 
-
-class InferenceParameter:
-    def __init__(self, ptype, pname, subpop, pdist, lb, ub):
-        self.ptype = ptype
-        self.pname = pname
-        self.subpop = subpop
-        self.pdist = pdist
-        self.lb = lb
-        self.ub = ub
-    # TODO: does not support the subpop groups here !!!!!!!
-    def print_summary(self):
-        print(f"{self.ptype}::{self.pname} in [{self.lb}, {self.ub}]"
-              f"   >> affected subpop: {self.subpop}"
-        )
-
 class InferenceParameters:
     def __init__(self):
         self.params = []
 
-    def add_param(self, ptype, pname, subpop, pdist, lb, ub):
-        param = InferenceParameter(ptype, pname, subpop, pdist, lb, ub)
-        self.params.append(param)
-
-
-    def 
-
-
+    def add_modifier(self, pname, ptype, parameter_config):
+        for sp in modinf.subpop_struct.subpop_names:
+            param = {
+                "ptype": ptype,
+                "pname": pname,
+                "subpop": sp,
+                "pdist": parameter_config["value"].as_random_distribution(),
+                "lb": ["value"]["a"].get(),
+                "ub": parameter_config["value"]["b"].get()
+            }
+            self.params.append(param)
+            # TODO: does not support the subpop groups here !!!!!!!
 
     def build_from_config(self, global_config, modinf):
-        for npi in global_config["seir_modifiers"]["modifiers"].get():
-            if global_config["seir_modifiers"]["modifiers"][npi]["perturbation"].exists():
-                c = global_config["seir_modifiers"]["modifiers"][npi]
-                for sp in modinf.subpop_struct.subpop_names:
-                    self.add_param(
-                        ptype="snpi",
-                        pname=npi,
-                        subpop=sp,
-                        pdist=c["value"].as_random_distribution(),
-                        lb=c["value"]["a"].get(),
-                        ub=c["value"]["b"].get()
-                    )
-
-        for npi in global_config["outcome_modifiers"]["modifiers"].get():
-            if global_config["outcome_modifiers"]["modifiers"][npi]["perturbation"].exists():
-                c = global_config["outcome_modifiers"]["modifiers"][npi]
-                for sp in modinf.subpop_struct.subpop_names:
-                    self.add_param(
-                        ptype="hnpi",
-                        pname=npi,
-                        subpop=sp,
-                        pdist=c["value"].as_random_distribution(),
-                        lb=c["value"]["a"].get(),
-                        ub=c["value"]["b"].get()
-                    )
+        for config_part in ["seir_modifiers", "outcome_modifiers"]:
+            if  global_config[config_part].exists():
+                for npi in global_config[config_part]["modifiers"].get():
+                    if global_config[config_part]["modifiers"][npi]["perturbation"].exists():
+                        self.add_modifier(pname=npi, ptype=config_part, parameter_config=global_config[config_part]["modifiers"][npi])
 
     def print_summary(self):
         print(f"There are {len(self.params)} parameters in the configuration.")
         for param in self.params:
-            param.print_summary()
+            print(f"{param['ptype']}::{param['pname']} in [{param['lb']}, {param['ub']}]"
+                f"   >> affected subpop: {param['subpop']}"
+            )
 
 # Create an instance of FittedParams and build it from the config
 fitted_params = InferenceParameters()
@@ -72,4 +43,4 @@ def print_summary(self):
 
 
 class FittedParameters:
-    def __init__(self, global_config: confuse.ConfigView):
+    def __init__(self, global_config: confuse.ConfigView):
\ No newline at end of file
diff --git a/flepimop/gempyor_pkg/src/gempyor/logloss.py b/flepimop/gempyor_pkg/src/gempyor/logloss.py
index 8cf822859..9a1122cfd 100644
--- a/flepimop/gempyor_pkg/src/gempyor/logloss.py
+++ b/flepimop/gempyor_pkg/src/gempyor/logloss.py
@@ -6,7 +6,6 @@
 ## https://docs.xarray.dev/en/stable/user-guide/indexing.html#assigning-values-with-indexing
 ##
 
-
 class Statistic:
     def __init__(self, name, statistic_config: confuse.ConfigView):
         self.sim_var = statistic_config["sim_var"].as_str()
@@ -76,6 +75,8 @@ def compute_logloss(self, model_df, modinf):
             gt_s = gt_s.loc[first_date:last_date].drop(["subpop"], axis=1)
             model_df_s = model_df_s.drop(["subpop", "time"], axis=1).loc[first_date:last_date]
 
+            # TODO: add whole US!! option
+
             for key, stat in self.statistics.items():
                 logloss.loc[dict(statistics=key, subpop=subpop)] += stat.compute_logloss(model_df, gt_s)
 

From 182d25000072dbc7802bf6cd03d0012def6b92e2 Mon Sep 17 00:00:00 2001
From: Joseph Lemaitre <jo.lemaitresamra@gmail.com>
Date: Wed, 10 Apr 2024 17:56:17 +0200
Subject: [PATCH 11/31] class seems to be quite done

---
 flepimop/gempyor_pkg/src/gempyor/inference.py | 151 +++++++++++++++---
 1 file changed, 130 insertions(+), 21 deletions(-)

diff --git a/flepimop/gempyor_pkg/src/gempyor/inference.py b/flepimop/gempyor_pkg/src/gempyor/inference.py
index a2a0de492..624be9dd5 100644
--- a/flepimop/gempyor_pkg/src/gempyor/inference.py
+++ b/flepimop/gempyor_pkg/src/gempyor/inference.py
@@ -2,30 +2,88 @@
 import pandas as pd
 import numpy as np
 import confuse
+from . import NPI
 
 class InferenceParameters:
-    def __init__(self):
+    """
+    A class to manage inference parameters.
+
+    Parameters:
+        global_config (confuse.ConfigView): The global configuration.
+        modinf: An object containing subpopulation structure information.
+    """
+    def __init__(self, global_config, modinf):
         self.params = []
+        self.build_from_config(global_config, modinf)
+
+    def add_modifier(self, pname, ptype, parameter_config, subpops):
+        """
+        Adds a modifier parameter to the parameters list.
+
+        Args:
+            pname (str): The parameter name.
+            ptype (str): The parameter type.
+            parameter_config (confuse.ConfigView): The configuration for the parameter.
+            subpops (list): List of subpopulations affected by the modifier.
+        """
+        # identify spatial group
+        affected_subpops = set(subpops)
+        if parameter_config["subpop"].exists() and parameter_config["subpop"].get() != "all":
+            affected_subpops = {str(n.get()) for n in parameter_config["subpop"]}
+        spatial_groups = NPI.helpers.get_spatial_groups(parameter_config, list(affected_subpops))
+        
+        # ungrouped subpop (all affected subpop by default) have one parameter per subpop
+        if spatial_groups["ungrouped"]:
+            for sp in spatial_groups["ungrouped"]:
+                self.add_single_parameter(
+                    ptype=ptype,
+                    pname= pname,
+                    subpop= sp,
+                    pdist = parameter_config["value"].as_random_distribution(),
+                    lb = parameter_config["value"]["a"].get(),
+                    ub = parameter_config["value"]["b"].get()
+            )
+            
+        # grouped subpop have one parameter per group
+        if self.spatial_groups["grouped"]:
+            for group in self.spatial_groups["grouped"]:
+                self.add_single_parameter(
+                    ptype=ptype,
+                    pname= pname,
+                    subpop=",".join(group),
+                    pdist = parameter_config["value"].as_random_distribution(),
+                    lb = parameter_config["value"]["a"].get(),
+                    ub = parameter_config["value"]["b"].get()
+            )
+
+    def add_single_parameter(self, ptype, pname, subpop, pdist, lb, ub):
+        """
+        Adds a single parameter to the parameters list.
 
-    def add_modifier(self, pname, ptype, parameter_config):
-        for sp in modinf.subpop_struct.subpop_names:
-            param = {
-                "ptype": ptype,
-                "pname": pname,
-                "subpop": sp,
-                "pdist": parameter_config["value"].as_random_distribution(),
-                "lb": ["value"]["a"].get(),
-                "ub": parameter_config["value"]["b"].get()
-            }
-            self.params.append(param)
-            # TODO: does not support the subpop groups here !!!!!!!
+        Args:
+            ptype (str): The parameter type.
+            pname (str): The parameter name.
+            subpop (str): The subpopulation affected by the parameter.
+            pdist: The distribution of the parameter.
+            lb: The lower bound of the parameter.
+            ub: The upper bound of the parameter.
+        """
+        param = {
+            "ptype": ptype,
+            "pname": pname,
+            "subpop": subpop,
+            "pdist": pdist,
+            "lb": lb,
+            "ub": ub
+        }
+        self.params.append(param)
 
     def build_from_config(self, global_config, modinf):
         for config_part in ["seir_modifiers", "outcome_modifiers"]:
             if  global_config[config_part].exists():
                 for npi in global_config[config_part]["modifiers"].get():
                     if global_config[config_part]["modifiers"][npi]["perturbation"].exists():
-                        self.add_modifier(pname=npi, ptype=config_part, parameter_config=global_config[config_part]["modifiers"][npi])
+                        self.add_modifier(pname=npi, ptype=config_part, parameter_config=global_config[config_part]["modifiers"][npi], subpops=modinf.subpop_struct.subpop_names)
 
     def print_summary(self):
         print(f"There are {len(self.params)} parameters in the configuration.")
@@ -34,13 +92,64 @@ def print_summary(self):
                 f"   >> affected subpop: {param['subpop']}"
             )
 
-# Create an instance of FittedParams and build it from the config
-fitted_params = InferenceParameters()
-fitted_params.build_from_config(gempyor.config, modinf)
+    def get_dim(self):
+        return len(self.params)
+    
+    def __len__(self):
+        """
+        so one can use the built-in python len function
+        """
+        return len(self.params)
+    
+    def draw_initial(self, n_draw=1):
+        """
+        Draws initial parameter values.
+
+        Args:
+            n_draw (int): Number of draws, e.g the number of slots or walkers
+
+        Returns:
+            np.ndarray: Array of initial parameter values.
+        """
+        p0 = np.zeros((n_draw, self.get_dim()))
+        for p_idx in range(self.get_dim()):
+            p0[:,p_idx] = self.params["pdist"][p_idx](n_draw)
+
+        return p0
+    
+    # TODO: write a more granular method the return for a single parameter and correct the proposal like we did
+    def check_in_bound(self, proposal):
+        """
+        Checks if the proposal is within parameter bounds.
+
+        Args:
+            proposal: The proposed parameter values.
+
+        Returns:
+            bool: True if the proposal is within bounds, False otherwise.
+        """
+        if (proposal < self.params["lb"]).any() or (proposal > self.params["ub"]).any():
+            return False
+        return True
+    
+    def inject_proposal(self, proposal, hnpi_df=None, snpi_df=None):
+        """
+        Injects the proposal into model inputs, at the right place.
 
-# Print summary
-fitted_params.print_summary()
+        Args:
+            proposal: The proposed parameter values.
+            hnpi_df (pd.DataFrame): DataFrame for hnpi.
+            snpi_df (pd.DataFrame): DataFrame for snpi.
 
+        Returns:
+            pd.DataFrame, pd.DataFrame: Modified hnpi_df and snpi_df.
+        """
+        snpi_df_mod = snpi_df.copy(deep=True)
+        hnpi_df_mod = hnpi_df.copy(deep=True)
 
-class FittedParameters:
-    def __init__(self, global_config: confuse.ConfigView):
\ No newline at end of file
+        for p_idx in range(self.get_dim()):
+            if self.params["ptype"][p_idx] == "snpi":
+                snpi_df_mod.loc[(snpi_df_mod["modifier_name"] == self.params["pname"][p_idx]) & (snpi_df_mod["subpop"] == self.params["subpop"][p_idx]),"value"] = proposal[p_idx]
+            elif self.params["ptype"][p_idx] == "hnpi":
+                hnpi_df_mod.loc[(hnpi_df_mod["modifier_name"] == self.params["pname"][p_idx]) & (hnpi_df_mod["subpop"] == self.params["subpop"][p_idx]),"value"] = proposal[p_idx]
+        return snpi_df_mod, hnpi_df_mod

From e73441bf5913f4ebf4551d5015880f6f22cb9d55 Mon Sep 17 00:00:00 2001
From: Joseph Lemaitre <jo.lemaitresamra@gmail.com>
Date: Thu, 11 Apr 2024 16:04:12 +0200
Subject: [PATCH 12/31] better

---
 flepimop/gempyor_pkg/src/gempyor/inference.py | 155 ------------------
 flepimop/gempyor_pkg/src/gempyor/logloss.py   |  29 +++-
 2 files changed, 25 insertions(+), 159 deletions(-)
 delete mode 100644 flepimop/gempyor_pkg/src/gempyor/inference.py

diff --git a/flepimop/gempyor_pkg/src/gempyor/inference.py b/flepimop/gempyor_pkg/src/gempyor/inference.py
deleted file mode 100644
index 624be9dd5..000000000
--- a/flepimop/gempyor_pkg/src/gempyor/inference.py
+++ /dev/null
@@ -1,155 +0,0 @@
-import xarray as xr
-import pandas as pd
-import numpy as np
-import confuse
-from . import NPI
-
-class InferenceParameters:
-    """
-    A class to manage inference parameters.
-
-    Parameters:
-        global_config (confuse.ConfigView): The global configuration.
-        modinf: An object containing subpopulation structure information.
-    """
-    def __init__(self, global_config, modinf):
-        self.params = []
-        self.build_from_config(global_config, modinf)
-
-    def add_modifier(self, pname, ptype, parameter_config, subpops):
-        """
-        Adds a modifier parameter to the parameters list.
-
-        Args:
-            pname (str): The parameter name.
-            ptype (str): The parameter type.
-            parameter_config (confuse.ConfigView): The configuration for the parameter.
-            subpops (list): List of subpopulations affected by the modifier.
-        """
-        # identify spatial group
-        affected_subpops = set(subpops)
-        if parameter_config["subpop"].exists() and parameter_config["subpop"].get() != "all":
-            affected_subpops = {str(n.get()) for n in parameter_config["subpop"]}
-        spatial_groups = NPI.helpers.get_spatial_groups(parameter_config, list(affected_subpops))
-        
-        # ungrouped subpop (all affected subpop by default) have one parameter per subpop
-        if spatial_groups["ungrouped"]:
-            for sp in spatial_groups["ungrouped"]:
-                self.add_single_parameter(
-                    ptype=ptype,
-                    pname= pname,
-                    subpop= sp,
-                    pdist = parameter_config["value"].as_random_distribution(),
-                    lb = parameter_config["value"]["a"].get(),
-                    ub = parameter_config["value"]["b"].get()
-            )
-            
-        # grouped subpop have one parameter per group
-        if self.spatial_groups["grouped"]:
-            for group in self.spatial_groups["grouped"]:
-                self.add_single_parameter(
-                    ptype=ptype,
-                    pname= pname,
-                    subpop=",".join(group),
-                    pdist = parameter_config["value"].as_random_distribution(),
-                    lb = parameter_config["value"]["a"].get(),
-                    ub = parameter_config["value"]["b"].get()
-            )
-
-    def add_single_parameter(self, ptype, pname, subpop, pdist, lb, ub):
-        """
-        Adds a single parameter to the parameters list.
-
-        Args:
-            ptype (str): The parameter type.
-            pname (str): The parameter name.
-            subpop (str): The subpopulation affected by the parameter.
-            pdist: The distribution of the parameter.
-            lb: The lower bound of the parameter.
-            ub: The upper bound of the parameter.
-        """
-        param = {
-            "ptype": ptype,
-            "pname": pname,
-            "subpop": subpop,
-            "pdist": pdist,
-            "lb": lb,
-            "ub": ub
-        }
-        self.params.append(param)
-
-    def build_from_config(self, global_config, modinf):
-        for config_part in ["seir_modifiers", "outcome_modifiers"]:
-            if  global_config[config_part].exists():
-                for npi in global_config[config_part]["modifiers"].get():
-                    if global_config[config_part]["modifiers"][npi]["perturbation"].exists():
-                        self.add_modifier(pname=npi, ptype=config_part, parameter_config=global_config[config_part]["modifiers"][npi], subpops=modinf.subpop_struct.subpop_names)
-
-    def print_summary(self):
-        print(f"There are {len(self.params)} parameters in the configuration.")
-        for param in self.params:
-            print(f"{param['ptype']}::{param['pname']} in [{param['lb']}, {param['ub']}]"
-                f"   >> affected subpop: {param['subpop']}"
-            )
-
-    def get_dim(self):
-        return len(self.params)
-    
-    def __len__(self):
-        """
-        so one can use the built-in python len function
-        """
-        return len(self.params)
-    
-    def draw_initial(self, n_draw=1):
-        """
-        Draws initial parameter values.
-
-        Args:
-            n_draw (int): Number of draws, e.g the number of slots or walkers
-
-        Returns:
-            np.ndarray: Array of initial parameter values.
-        """
-        p0 = np.zeros((n_draw, self.get_dim()))
-        for p_idx in range(self.get_dim()):
-            p0[:,p_idx] = self.params["pdist"][p_idx](n_draw)
-
-        return p0
-    
-    # TODO: write a more granular method the return for a single parameter and correct the proposal like we did
-    def check_in_bound(self, proposal):
-        """
-        Checks if the proposal is within parameter bounds.
-
-        Args:
-            proposal: The proposed parameter values.
-
-        Returns:
-            bool: True if the proposal is within bounds, False otherwise.
-        """
-        if (proposal < self.params["lb"]).any() or (proposal > self.params["ub"]).any():
-            return False
-        return True
-    
-    def inject_proposal(self, proposal, hnpi_df=None, snpi_df=None):
-        """
-        Injects the proposal into model inputs, at the right place.
-
-        Args:
-            proposal: The proposed parameter values.
-            hnpi_df (pd.DataFrame): DataFrame for hnpi.
-            snpi_df (pd.DataFrame): DataFrame for snpi.
-
-        Returns:
-            pd.DataFrame, pd.DataFrame: Modified hnpi_df and snpi_df.
-        """
-        snpi_df_mod = snpi_df.copy(deep=True)
-        hnpi_df_mod = hnpi_df.copy(deep=True)
-
-        for p_idx in range(self.get_dim()):
-            if self.params["ptype"][p_idx] == "snpi":
-                snpi_df_mod.loc[(snpi_df_mod["modifier_name"] == self.params["pname"][p_idx]) & (snpi_df_mod["subpop"] == self.params["subpop"][p_idx]),"value"] = proposal[p_idx]
-            elif self.params["ptype"][p_idx] == "hnpi":
-                hnpi_df_mod.loc[(hnpi_df_mod["modifier_name"] == self.params["pname"][p_idx]) & (hnpi_df_mod["subpop"] == self.params["subpop"][p_idx]),"value"] = proposal[p_idx]
-        return snpi_df_mod, hnpi_df_mod
diff --git a/flepimop/gempyor_pkg/src/gempyor/logloss.py b/flepimop/gempyor_pkg/src/gempyor/logloss.py
index 9a1122cfd..2a238a41b 100644
--- a/flepimop/gempyor_pkg/src/gempyor/logloss.py
+++ b/flepimop/gempyor_pkg/src/gempyor/logloss.py
@@ -11,12 +11,27 @@ def __init__(self, name, statistic_config: confuse.ConfigView):
         self.sim_var = statistic_config["sim_var"].as_str()
         self.data_var = statistic_config["data_var"].as_str()
         self.name = name
-        self.resample_config = statistic_config.get("resample_config", None)
-        self.loss_function = statistic_config.get("loss_function", "rmse")
+
+        self.resample_config = None
+        if statistic_config["resample"].exists():
+            self.resample_config = statistic_config["resample"].get()
+        
+         self.regularization_config = None
+        if statistic_config["regularization"].exists():
+            self.regularization_config = statistic_config["regularization"].get()
+    
+        self.loss_function = statistic_config["loss"].get()
+
+    def __str__(self) -> str:
+        return f"{self.name}: {self.loss_function} between {self.sim_var} (sim) and {self.data_var} (data)."
+    
+    def __repr__(self) -> str:
+        return f"A Statistic(): {self.__str()}"
 
     def apply_resampling(self, data):
         if self.resample_config:
-            freq = self.resample_config.get("freq", "W-SAT")
+            freq = ""
+            freq = self.resample_config["freq"].get("freq", "W-SAT")
             agg_func = getattr(pd.Series, self.resample_config.get("agg_func", "sum"))
             skipna = self.resample_config.get("skipna", False)
 
@@ -37,20 +52,26 @@ def compute_logloss(self, model_data, gt_data, skip_resampling=False):
 
         if self.loss_function == "rmse":
             return -np.sqrt(np.mean((model_data - gt_data) ** 2))
-        elif self.loss_function == "poisson_log_loss":
+        elif self.loss_function == "poisson":
             epsilon = 1e-10  # to avoid log(0)
             return -np.mean(model_data - gt_data * np.log(model_data + epsilon))
         else:
             raise ValueError("Unsupported loss function")
 
+# TODO: add an autatic test that show that the loss is biggest when gt == modeldata
 
 class LogLoss:
     def __init__(self, inference_config: confuse.ConfigView, data_dir:str = "."):
         self.gt = pd.read_csv(f"{data_dir}/{inference_config['gt_data_path'].get()}")
+        self.gt["date"] = pd.to_datetime(self.gt['date'])
+        self.gt = self.gt.set_index("date")
         self.statistics = {}
         for key, value in inference_config["statistics"].items():
             self.statistics[key] = Statistic(key, value)
 
+    def plot_gt(self, ax):
+        ax.plot(self.gt)
+
     def compute_logloss(self, model_df, modinf):
         """
         Compute logloss for all statistics

From f65c4c2bbce2c0d0ff6d0074217d49ebb7b5ea78 Mon Sep 17 00:00:00 2001
From: Joseph Lemaitre <jo.lemaitresamra@gmail.com>
Date: Thu, 11 Apr 2024 16:04:34 +0200
Subject: [PATCH 13/31] atomic simulation, duplicate

---
 flepimop/gempyor_pkg/src/gempyor/interface.py | 79 +++++++++++++++++++
 1 file changed, 79 insertions(+)

diff --git a/flepimop/gempyor_pkg/src/gempyor/interface.py b/flepimop/gempyor_pkg/src/gempyor/interface.py
index 6eff3a343..964e257c5 100644
--- a/flepimop/gempyor_pkg/src/gempyor/interface.py
+++ b/flepimop/gempyor_pkg/src/gempyor/interface.py
@@ -36,6 +36,8 @@
 # logger.addHandler(handler)
 
 
+
+
 class GempyorSimulator:
     def __init__(
         self,
@@ -429,6 +431,83 @@ def get_reduced_parameters_seir(
             parameters, self.modinf.parameters.pnames, self.unique_strings
         )
         return parsed_parameters
+    
+
+
+# TODO: there is way to many of these functions
+def simulation_atomic(snpi_df_in, hnpi_df_in, modinf, p_draw, unique_strings, transition_array, proportion_array, proportion_info, initial_conditions, seeding_data, seeding_amounts,outcomes_parameters, save=False):
+    
+    # We need to reseed because subprocess inherit of the same random generator state.
+    np.random.seed(int.from_bytes(os.urandom(4), byteorder='little'))
+    random_id = np.random.randint(0,1e8)
+
+
+    npi_seir = seir.build_npi_SEIR(
+        modinf=modinf, load_ID=False, sim_id2load=None, config=config, 
+        bypass_DF=snpi_df_in
+    )
+
+    if modinf.npi_config_outcomes:
+        npi_outcomes = outcomes.build_outcome_modifiers(
+                    modinf=modinf,
+                    load_ID=False,
+                    sim_id2load=None,
+                    config=config,
+                    bypass_DF=hnpi_df_in
+                )
+
+    # reduce them
+    parameters = modinf.parameters.parameters_reduce(p_draw, npi_seir)
+    # Parse them
+    parsed_parameters = modinf.compartments.parse_parameters(
+        parameters, modinf.parameters.pnames, unique_strings
+    )
+
+    # Convert the seeding data dictionnary to a numba dictionnary
+    seeding_data_nbdict = nb.typed.Dict.empty(
+        key_type=nb.types.unicode_type,
+        value_type=nb.types.int64[:])
+
+    for k,v in seeding_data.items():
+        seeding_data_nbdict[k] = np.array(v, dtype=np.int64)
+
+    # Compute the SEIR simulation
+    states = seir.steps_SEIR(
+        modinf,
+        parsed_parameters,
+        transition_array,
+        proportion_array,
+        proportion_info,
+        initial_conditions,
+        seeding_data_nbdict,
+        seeding_amounts,
+    )
+
+    # Compute outcomes
+    outcomes_df, hpar_df = outcomes.compute_all_multioutcomes(
+        modinf=modinf,
+        sim_id2write=0,
+        parameters=outcomes_parameters,
+        loaded_values=None,
+        npi=npi_outcomes,
+        bypass_seir_xr=states
+    )
+    outcomes_df, hpar, hnpi = outcomes.postprocess_and_write(
+        sim_id=0,
+        modinf=modinf,
+        outcomes_df=outcomes_df,
+        hpar=hpar_df,
+        npi=npi_outcomes,
+    )
+    outcomes_df["time"] = outcomes_df["date"] #which one should it be ?
+    
+    
+    if save:
+        modinf.write_simID(ftype="hosp", sim_id=random_id, df=outcomes_df)
+    # needs to be after write... because parquet write discard the index.
+    outcomes_df = outcomes_df.set_index("date")
+
+    return outcomes_df
 
 
 def paramred_parallel(run_spec, snpi_fn):

From 748208f703b9062960121a6d9a92f5b2ae44ab88 Mon Sep 17 00:00:00 2001
From: Joseph Lemaitre <jo.lemaitresamra@gmail.com>
Date: Thu, 11 Apr 2024 16:04:57 +0200
Subject: [PATCH 14/31] entry point for calibration, TODO

---
 flepimop/gempyor_pkg/src/gempyor/calibrate.py | 209 ++++++++++++++++++
 1 file changed, 209 insertions(+)
 create mode 100644 flepimop/gempyor_pkg/src/gempyor/calibrate.py

diff --git a/flepimop/gempyor_pkg/src/gempyor/calibrate.py b/flepimop/gempyor_pkg/src/gempyor/calibrate.py
new file mode 100644
index 000000000..953644c45
--- /dev/null
+++ b/flepimop/gempyor_pkg/src/gempyor/calibrate.py
@@ -0,0 +1,209 @@
+#!/usr/bin/env python
+
+
+import multiprocessing
+import time, os, itertools
+
+import click
+
+from gempyor import seir, outcomes, model_info, file_paths
+from gempyor.utils import config, as_list, profile
+
+# from .profile import profile_options
+
+
+@click.command()
+@click.option(
+    "-c",
+    "--config",
+    "config_file",
+    envvar=["CONFIG_PATH", "CONFIG_PATH"],
+    type=click.Path(exists=True),
+    required=True,
+    help="configuration file for this simulation",
+)
+@click.option(
+    "-s",
+    "--seir_modifiers_scenario",
+    "seir_modifiers_scenarios",
+    envvar="FLEPI_SEIR_SCENARIO",
+    type=str,
+    default=[],
+    multiple=True,
+    help="override the NPI scenario(s) run for this simulation [supports multiple NPI scenarios: `-s Wuhan -s None`]",
+)
+@click.option(
+    "-d",
+    "--outcome_modifiers_scenario",
+    "outcome_modifiers_scenarios",
+    envvar="FLEPI_OUTCOME_SCENARIO",
+    type=str,
+    default=[],
+    multiple=True,
+    help="Scenario of outcomes to run",
+)
+@click.option(
+    "-n",
+    "--nslots",
+    envvar="FLEPI_NUM_SLOTS",
+    type=click.IntRange(min=1),
+    help="override the # of simulation runs in the config file",
+)
+@click.option(
+    "-i",
+    "--first_sim_index",
+    envvar="FIRST_SIM_INDEX",
+    type=click.IntRange(min=1),
+    default=1,
+    show_default=True,
+    help="The index of the first simulation",
+)
+@click.option(
+    "-j",
+    "--jobs",
+    envvar="FLEPI_NJOBS",
+    type=click.IntRange(min=1),
+    default=multiprocessing.cpu_count(),
+    show_default=True,
+    help="the parallelization factor",
+)
+@click.option(
+    "--stochastic/--non-stochastic",
+    "--stochastic/--non-stochastic",
+    "stoch_traj_flag",
+    envvar="FLEPI_STOCHASTIC_RUN",
+    type=bool,
+    default=False,
+    help="Flag determining whether to run stochastic simulations or not",
+)
+@click.option(
+    "--in-id",
+    "--in-id",
+    "in_run_id",
+    envvar="FLEPI_RUN_INDEX",
+    type=str,
+    default=file_paths.run_id(),
+    show_default=True,
+    help="Unique identifier for the run",
+)  # Default does not make sense here
+@click.option(
+    "--out-id",
+    "--out-id",
+    "out_run_id",
+    envvar="FLEPI_RUN_INDEX",
+    type=str,
+    default=None,
+    show_default=True,
+    help="Unique identifier for the run",
+)
+@click.option(
+    "--in-prefix",
+    "--in-prefix",
+    "in_prefix",
+    envvar="FLEPI_PREFIX",
+    type=str,
+    default=None,
+    show_default=True,
+    help="unique identifier for the run",
+)
+@click.option(
+    "--write-csv/--no-write-csv",
+    default=False,
+    show_default=True,
+    help="write CSV output at end of simulation",
+)
+@click.option(
+    "--write-parquet/--no-write-parquet",
+    default=True,
+    show_default=True,
+    help="write parquet file output at end of simulation",
+)
+# @profile_options
+# @profile()
+def simulate(
+    config_file,
+    in_run_id,
+    out_run_id,
+    seir_modifiers_scenarios,
+    outcome_modifiers_scenarios,
+    in_prefix,
+    nslots,
+    jobs,
+    write_csv,
+    write_parquet,
+    first_sim_index,
+    stoch_traj_flag,
+):
+    config.clear()
+    config.read(user=False)
+    config.set_file(config_file)
+
+    # Compute the list of scenarios to run. Since multiple = True, it's always a list.
+    if not seir_modifiers_scenarios:
+        seir_modifiers_scenarios = None
+        if config["seir_modifiers"].exists():
+            if config["seir_modifiers"]["scenarios"].exists():
+                seir_modifiers_scenarios = config["seir_modifiers"]["scenarios"].as_str_seq()
+        # Model Info handles the case of the default scneario
+    if not outcome_modifiers_scenarios:
+        outcome_modifiers_scenarios = None
+        if config["outcomes"].exists() and config["outcome_modifiers"].exists():
+            if config["outcome_modifiers"]["scenarios"].exists():
+                outcome_modifiers_scenarios = config["outcome_modifiers"]["scenarios"].as_str_seq()
+
+    outcome_modifiers_scenarios = as_list(outcome_modifiers_scenarios)
+    seir_modifiers_scenarios = as_list(seir_modifiers_scenarios)
+    print(outcome_modifiers_scenarios, seir_modifiers_scenarios)
+
+    scenarios_combinations = [[s, d] for s in seir_modifiers_scenarios for d in outcome_modifiers_scenarios]
+    print("Combination of modifiers scenarios to be run: ")
+    print(scenarios_combinations)
+    for seir_modifiers_scenario, outcome_modifiers_scenario in scenarios_combinations:
+        print(f"seir_modifier: {seir_modifiers_scenario}, outcomes_modifier:{outcome_modifiers_scenario}")
+
+    if not nslots:
+        nslots = config["nslots"].as_number()
+    print(f"Simulations to be run: {nslots}")
+
+    for seir_modifiers_scenario, outcome_modifiers_scenario in scenarios_combinations:
+        start = time.monotonic()
+        print(f"Running {seir_modifiers_scenario}_{outcome_modifiers_scenario}")
+
+        modinf = model_info.ModelInfo(
+            config=config,
+            nslots=nslots,
+            seir_modifiers_scenario=seir_modifiers_scenario,
+            outcome_modifiers_scenario=outcome_modifiers_scenario,
+            write_csv=write_csv,
+            write_parquet=write_parquet,
+            first_sim_index=first_sim_index,
+            in_run_id=in_run_id,
+            # in_prefix=config["name"].get() + "/",
+            out_run_id=out_run_id,
+            # out_prefix=config["name"].get() + "/" + str(seir_modifiers_scenario) + "/" + out_run_id + "/",
+            stoch_traj_flag=stoch_traj_flag,
+        )
+
+        print(
+            f"""
+    >> Running from config {config_file}
+    >> Starting {modinf.nslots} model runs beginning from {modinf.first_sim_index} on {jobs} processes
+    >> ModelInfo *** {modinf.setup_name} *** from {modinf.ti} to {modinf.tf}
+    >> Running scenario {seir_modifiers_scenario}_{outcome_modifiers_scenario}
+    >> running ***{'STOCHASTIC' if stoch_traj_flag else 'DETERMINISTIC'}*** trajectories
+    """
+        )
+        # (there should be a run function)
+        if config["seir"].exists():
+            seir.run_parallel_SEIR(modinf, config=config, n_jobs=jobs)
+        if config["outcomes"].exists():
+            outcomes.run_parallel_outcomes(sim_id2write=first_sim_index, modinf=modinf, nslots=nslots, n_jobs=jobs)
+        print(
+            f">>> {seir_modifiers_scenario}_{outcome_modifiers_scenario} completed in {time.monotonic() - start:.1f} seconds"
+        )
+
+
+if __name__ == "__main__":
+    simulate()
+
+## @endcond

From a5d2cf1e3b285c8fdd6687cd50506a1871e20a61 Mon Sep 17 00:00:00 2001
From: Joseph Lemaitre <jo.lemaitresamra@gmail.com>
Date: Thu, 11 Apr 2024 16:05:41 +0200
Subject: [PATCH 15/31] Re-adding the parameter manager, after changing name

---
 .../src/gempyor/inference_parameter.py        | 155 ++++++++++++++++++
 1 file changed, 155 insertions(+)
 create mode 100644 flepimop/gempyor_pkg/src/gempyor/inference_parameter.py

diff --git a/flepimop/gempyor_pkg/src/gempyor/inference_parameter.py b/flepimop/gempyor_pkg/src/gempyor/inference_parameter.py
new file mode 100644
index 000000000..9095f67a6
--- /dev/null
+++ b/flepimop/gempyor_pkg/src/gempyor/inference_parameter.py
@@ -0,0 +1,155 @@
+import xarray as xr
+import pandas as pd
+import numpy as np
+import confuse
+from . import NPI
+
+class InferenceParameters:
+    """
+    A class to manage inference parameters.
+
+    Parameters:
+        global_config (confuse.ConfigView): The global configuration.
+        modinf: An object containing subpopulation structure information.
+    """
+    def __init__(self, global_config, modinf):
+        self.params = []
+        self.build_from_config(global_config, modinf)
+
+    def add_modifier(self, pname, ptype, parameter_config, subpops):
+        """
+        Adds a modifier parameter to the parameters list.
+
+        Args:
+            pname (str): The parameter name.
+            ptype (str): The parameter type.
+            parameter_config (confuse.ConfigView): The configuration for the parameter.
+            subpops (list): List of subpopulations affected by the modifier.
+        """
+        # identify spatial group
+        affected_subpops = set(subpops)
+        if parameter_config["subpop"].exists() and parameter_config["subpop"].get() != "all":
+            affected_subpops = {str(n.get()) for n in parameter_config["subpop"]}
+        spatial_groups = NPI.helpers.get_spatial_groups(parameter_config, list(affected_subpops))
+        
+        # ungrouped subpop (all affected subpop by default) have one parameter per subpop
+        if spatial_groups["ungrouped"]:
+            for sp in spatial_groups["ungrouped"]:
+                self.add_single_parameter(
+                    ptype=ptype,
+                    pname= pname,
+                    subpop= sp,
+                    pdist = parameter_config["value"].as_random_distribution(),
+                    lb = parameter_config["value"]["a"].get(),
+                    ub = parameter_config["value"]["b"].get()
+            )
+            
+        # grouped subpop have one parameter per group
+        if spatial_groups["grouped"]:
+            for group in spatial_groups["grouped"]:
+                self.add_single_parameter(
+                    ptype=ptype,
+                    pname= pname,
+                    subpop=",".join(group),
+                    pdist = parameter_config["value"].as_random_distribution(),
+                    lb = parameter_config["value"]["a"].get(),
+                    ub = parameter_config["value"]["b"].get()
+            )
+
+    def add_single_parameter(self, ptype, pname, subpop, pdist, lb, ub):
+        """
+        Adds a single parameter to the parameters list.
+
+        Args:
+            ptype (str): The parameter type.
+            pname (str): The parameter name.
+            subpop (str): The subpopulation affected by the parameter.
+            pdist: The distribution of the parameter.
+            lb: The lower bound of the parameter.
+            ub: The upper bound of the parameter.
+        """
+        param = {
+            "ptype": ptype,
+            "pname": pname,
+            "subpop": subpop,
+            "pdist": pdist,
+            "lb": lb,
+            "ub": ub
+        }
+        self.params.append(param)
+
+    def build_from_config(self, global_config, modinf):
+        for config_part in ["seir_modifiers", "outcome_modifiers"]:
+            if  global_config[config_part].exists():
+                for npi in global_config[config_part]["modifiers"].get():
+                    if global_config[config_part]["modifiers"][npi]["perturbation"].exists():
+                        self.add_modifier(pname=npi, ptype=config_part, parameter_config=global_config[config_part]["modifiers"][npi], subpops=modinf.subpop_struct.subpop_names)
+
+    def print_summary(self):
+        print(f"There are {len(self.params)} parameters in the configuration.")
+        for param in self.params:
+            print(f"{param['ptype']}::{param['pname']} in [{param['lb']}, {param['ub']}]"
+                f"   >> affected subpop: {param['subpop']}"
+            )
+
+    def get_dim(self):
+        return len(self.params)
+    
+    def __len__(self):
+        """
+        so one can use the built-in python len function
+        """
+        return len(self.params)
+    
+    def draw_initial(self, n_draw=1):
+        """
+        Draws initial parameter values.
+
+        Args:
+            n_draw (int): Number of draws, e.g the number of slots or walkers
+
+        Returns:
+            np.ndarray: Array of initial parameter values.
+        """
+        p0 = np.zeros((n_draw, self.get_dim()))
+        for p_idx in range(self.get_dim()):
+            p0[:,p_idx] = self.params["pdist"][p_idx](n_draw)
+
+        return p0
+    
+    # TODO: write a more granular method the return for a single parameter and correct the proposal like we did
+    def check_in_bound(self, proposal):
+        """
+        Checks if the proposal is within parameter bounds.
+
+        Args:
+            proposal: The proposed parameter values.
+
+        Returns:
+            bool: True if the proposal is within bounds, False otherwise.
+        """
+        if (proposal < self.params["lb"]).any() or (proposal > self.params["ub"]).any():
+            return False
+        return True
+    
+    def inject_proposal(self, proposal, hnpi_df=None, snpi_df=None):
+        """
+        Injects the proposal into model inputs, at the right place.
+
+        Args:
+            proposal: The proposed parameter values.
+            hnpi_df (pd.DataFrame): DataFrame for hnpi.
+            snpi_df (pd.DataFrame): DataFrame for snpi.
+
+        Returns:
+            pd.DataFrame, pd.DataFrame: Modified hnpi_df and snpi_df.
+        """
+        snpi_df_mod = snpi_df.copy(deep=True)
+        hnpi_df_mod = hnpi_df.copy(deep=True)
+
+        for p_idx in range(self.get_dim()):
+            if self.params["ptype"][p_idx] == "snpi":
+                snpi_df_mod.loc[(snpi_df_mod["modifier_name"] == self.params["pname"][p_idx]) & (snpi_df_mod["subpop"] == self.params["subpop"][p_idx]),"value"] = proposal[p_idx]
+            elif self.params["ptype"][p_idx] == "hnpi":
+                hnpi_df_mod.loc[(hnpi_df_mod["modifier_name"] == self.params["pname"][p_idx]) & (hnpi_df_mod["subpop"] == self.params["subpop"][p_idx]),"value"] = proposal[p_idx]
+        return snpi_df_mod, hnpi_df_mod

From 77034cab3ab61ba6bde261aa602d617db1e31de8 Mon Sep 17 00:00:00 2001
From: Joseph Lemaitre <jo.lemaitresamra@gmail.com>
Date: Thu, 11 Apr 2024 17:27:25 +0200
Subject: [PATCH 16/31] vectorized inference param

---
 .../src/gempyor/inference_parameter.py        | 61 +++++++++++--------
 flepimop/gempyor_pkg/src/gempyor/logloss.py   |  2 +-
 2 files changed, 38 insertions(+), 25 deletions(-)

diff --git a/flepimop/gempyor_pkg/src/gempyor/inference_parameter.py b/flepimop/gempyor_pkg/src/gempyor/inference_parameter.py
index 9095f67a6..e1ef170bf 100644
--- a/flepimop/gempyor_pkg/src/gempyor/inference_parameter.py
+++ b/flepimop/gempyor_pkg/src/gempyor/inference_parameter.py
@@ -4,16 +4,22 @@
 import confuse
 from . import NPI
 
+# TODO cast uper and lower bound as arrays
 class InferenceParameters:
     """
-    A class to manage inference parameters.
+    A class to manage inference parameters, in a vectorized way
 
     Parameters:
         global_config (confuse.ConfigView): The global configuration.
         modinf: An object containing subpopulation structure information.
     """
     def __init__(self, global_config, modinf):
-        self.params = []
+        self.ptypes = []
+        self.pnames = []
+        self.subpops = []
+        self.pdists = []
+        self.ubs = []
+        self.lbs = []
         self.build_from_config(global_config, modinf)
 
     def add_modifier(self, pname, ptype, parameter_config, subpops):
@@ -68,15 +74,12 @@ def add_single_parameter(self, ptype, pname, subpop, pdist, lb, ub):
             lb: The lower bound of the parameter.
             ub: The upper bound of the parameter.
         """
-        param = {
-            "ptype": ptype,
-            "pname": pname,
-            "subpop": subpop,
-            "pdist": pdist,
-            "lb": lb,
-            "ub": ub
-        }
-        self.params.append(param)
+        self.ptypes.append(ptype)
+        self.pnames.append(pname)
+        self.subpops.append(subpop)
+        self.pdists.append(pdist)
+        self.ubs.append(ub)
+        self.lbs.append(lb)
 
     def build_from_config(self, global_config, modinf):
         for config_part in ["seir_modifiers", "outcome_modifiers"]:
@@ -86,20 +89,20 @@ def build_from_config(self, global_config, modinf):
                         self.add_modifier(pname=npi, ptype=config_part, parameter_config=global_config[config_part]["modifiers"][npi], subpops=modinf.subpop_struct.subpop_names)
 
     def print_summary(self):
-        print(f"There are {len(self.params)} parameters in the configuration.")
-        for param in self.params:
-            print(f"{param['ptype']}::{param['pname']} in [{param['lb']}, {param['ub']}]"
-                f"   >> affected subpop: {param['subpop']}"
+        print(f"There are {len(self.pnames)} parameters in the configuration.")
+        for p_idx in range(self.get_dim()):
+            print(f"{self.ptypes[p_idx]}::{self.pnames[p_idx]} in [{self.lbs[p_idx]}, {self.ubs[p_idx]}]"
+                f"   >> affected subpop: {self.subpops[p_idx]}"
             )
 
     def get_dim(self):
-        return len(self.params)
+        return len(self.pnames)
     
     def __len__(self):
         """
         so one can use the built-in python len function
         """
-        return len(self.params)
+        return len(self.pnames)
     
     def draw_initial(self, n_draw=1):
         """
@@ -113,12 +116,12 @@ def draw_initial(self, n_draw=1):
         """
         p0 = np.zeros((n_draw, self.get_dim()))
         for p_idx in range(self.get_dim()):
-            p0[:,p_idx] = self.params["pdist"][p_idx](n_draw)
+            p0[:,p_idx] = self.pdists[p_idx](n_draw)
 
         return p0
     
     # TODO: write a more granular method the return for a single parameter and correct the proposal like we did
-    def check_in_bound(self, proposal):
+    def check_in_bound(self, proposal) -> bool:
         """
         Checks if the proposal is within parameter bounds.
 
@@ -128,10 +131,20 @@ def check_in_bound(self, proposal):
         Returns:
             bool: True if the proposal is within bounds, False otherwise.
         """
-        if (proposal < self.params["lb"]).any() or (proposal > self.params["ub"]).any():
+        if self.hit_lbs(proposal=proposal).any() or self.hit_ubs(proposal=proposal).any():
             return False
         return True
     
+    def hit_lbs(self, proposal) -> np.ndarray:
+        return np.array((proposal < self.lbs))
+    
+    def hit_ubs(self, proposal) -> np.ndarray:
+        """
+        boolean vector of True if the parameter is bigger than the upper bound and False if not
+        """
+        return np.array((proposal > self.ubs))
+
+    
     def inject_proposal(self, proposal, hnpi_df=None, snpi_df=None):
         """
         Injects the proposal into model inputs, at the right place.
@@ -148,8 +161,8 @@ def inject_proposal(self, proposal, hnpi_df=None, snpi_df=None):
         hnpi_df_mod = hnpi_df.copy(deep=True)
 
         for p_idx in range(self.get_dim()):
-            if self.params["ptype"][p_idx] == "snpi":
-                snpi_df_mod.loc[(snpi_df_mod["modifier_name"] == self.params["pname"][p_idx]) & (snpi_df_mod["subpop"] == self.params["subpop"][p_idx]),"value"] = proposal[p_idx]
-            elif self.params["ptype"][p_idx] == "hnpi":
-                hnpi_df_mod.loc[(hnpi_df_mod["modifier_name"] == self.params["pname"][p_idx]) & (hnpi_df_mod["subpop"] == self.params["subpop"][p_idx]),"value"] = proposal[p_idx]
+            if self.ptypes[p_idx] == "snpi":
+                snpi_df_mod.loc[(snpi_df_mod["modifier_name"] == self.pnames[p_idx]) & (snpi_df_mod["subpop"] == self.subpops[p_idx]),"value"] = proposal[p_idx]
+            elif self.ptypes[p_idx] == "hnpi":
+                hnpi_df_mod.loc[(hnpi_df_mod["modifier_name"] == self.pnames[p_idx]) & (hnpi_df_mod["subpop"] == self.subpops[p_idx]),"value"] = proposal[p_idx]
         return snpi_df_mod, hnpi_df_mod
diff --git a/flepimop/gempyor_pkg/src/gempyor/logloss.py b/flepimop/gempyor_pkg/src/gempyor/logloss.py
index 2a238a41b..6c3a9dcdf 100644
--- a/flepimop/gempyor_pkg/src/gempyor/logloss.py
+++ b/flepimop/gempyor_pkg/src/gempyor/logloss.py
@@ -16,7 +16,7 @@ def __init__(self, name, statistic_config: confuse.ConfigView):
         if statistic_config["resample"].exists():
             self.resample_config = statistic_config["resample"].get()
         
-         self.regularization_config = None
+        self.regularization_config = None
         if statistic_config["regularization"].exists():
             self.regularization_config = statistic_config["regularization"].get()
     

From bc35567f10793263a785612909817b30915a82e9 Mon Sep 17 00:00:00 2001
From: Joseph Lemaitre <jo.lemaitresamra@gmail.com>
Date: Thu, 11 Apr 2024 17:27:46 +0200
Subject: [PATCH 17/31] example and bbenchmark

---
 examples/simple_usa_statelevel/analysis.ipynb |   63 +
 .../data/geodata_2019_statelevel.csv          |   52 +
 .../data/mobility_2011-2015_statelevel.csv    | 2330 +++++++++++++++++
 .../model_input/my_initial_conditions.py      |   17 +
 .../simple_usa_statelevel.yml                 |  100 +
 5 files changed, 2562 insertions(+)
 create mode 100644 examples/simple_usa_statelevel/analysis.ipynb
 create mode 100644 examples/simple_usa_statelevel/model_input/data/geodata_2019_statelevel.csv
 create mode 100644 examples/simple_usa_statelevel/model_input/data/mobility_2011-2015_statelevel.csv
 create mode 100644 examples/simple_usa_statelevel/model_input/my_initial_conditions.py
 create mode 100644 examples/simple_usa_statelevel/simple_usa_statelevel.yml

diff --git a/examples/simple_usa_statelevel/analysis.ipynb b/examples/simple_usa_statelevel/analysis.ipynb
new file mode 100644
index 000000000..86de213a1
--- /dev/null
+++ b/examples/simple_usa_statelevel/analysis.ipynb
@@ -0,0 +1,63 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import gempyor\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "df_l = []\n",
+    "for fn in  gempyor.utils.list_filenames(folder=\"synthetic_dataset/model_output/\", filters=[\"hosp\", \".parquet\"]):\n",
+    "    df_l.append(gempyor.read_df(fn))\n",
+    "fig, axes = plt.subplots(1,1, figsize=(5,3))\n",
+    "ax = axes\n",
+    "for df in df_l:\n",
+    "    ax.plot(df.drop(\"time\",axis=1).set_index(\"date\").groupby(\"date\").sum()[\"incidCase\"], lw=.5)\n",
+    "ax.grid()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.1.undefined"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/simple_usa_statelevel/model_input/data/geodata_2019_statelevel.csv b/examples/simple_usa_statelevel/model_input/data/geodata_2019_statelevel.csv
new file mode 100644
index 000000000..7d053e317
--- /dev/null
+++ b/examples/simple_usa_statelevel/model_input/data/geodata_2019_statelevel.csv
@@ -0,0 +1,52 @@
+USPS,subpop,population
+WY,56000,581024
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+TX,48000,28260856
+CA,06000,39283497
diff --git a/examples/simple_usa_statelevel/model_input/data/mobility_2011-2015_statelevel.csv b/examples/simple_usa_statelevel/model_input/data/mobility_2011-2015_statelevel.csv
new file mode 100644
index 000000000..a2da772ba
--- /dev/null
+++ b/examples/simple_usa_statelevel/model_input/data/mobility_2011-2015_statelevel.csv
@@ -0,0 +1,2330 @@
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+55000,29000,458
+55000,30000,152
+55000,31000,236
+55000,32000,216
+55000,33000,58
+55000,34000,370
+55000,35000,120
+55000,36000,832
+55000,37000,460
+55000,38000,1028
+55000,39000,1152
+55000,40000,264
+55000,41000,96
+55000,42000,454
+55000,44000,20
+55000,45000,374
+55000,46000,312
+55000,47000,378
+55000,48000,1392
+55000,49000,188
+55000,51000,310
+55000,53000,398
+55000,54000,66
+55000,56000,30
+56000,01000,62
+56000,02000,194
+56000,04000,64
+56000,06000,300
+56000,08000,3528
+56000,09000,62
+56000,11000,26
+56000,12000,22
+56000,13000,12
+56000,16000,628
+56000,17000,124
+56000,18000,18
+56000,19000,40
+56000,20000,58
+56000,21000,62
+56000,22000,34
+56000,23000,18
+56000,25000,22
+56000,26000,28
+56000,27000,26
+56000,28000,28
+56000,29000,94
+56000,30000,1674
+56000,31000,1506
+56000,32000,102
+56000,34000,80
+56000,35000,92
+56000,36000,174
+56000,37000,40
+56000,38000,2606
+56000,39000,92
+56000,40000,292
+56000,42000,116
+56000,45000,10
+56000,46000,942
+56000,48000,440
+56000,49000,916
+56000,53000,216
+56000,54000,100
diff --git a/examples/simple_usa_statelevel/model_input/my_initial_conditions.py b/examples/simple_usa_statelevel/model_input/my_initial_conditions.py
new file mode 100644
index 000000000..b49eb2e7a
--- /dev/null
+++ b/examples/simple_usa_statelevel/model_input/my_initial_conditions.py
@@ -0,0 +1,17 @@
+import gempyor.seeding_ic
+import numpy as np
+
+class InitialConditions(gempyor.seeding_ic.InitialConditions):
+
+    def draw(self, sim_id: int, setup) -> np.ndarray:
+        y0 = np.zeros((setup.compartments.compartments.shape[0], setup.nsubpops))
+        S_idx = setup.compartments.get_comp_idx({"infection_stage":"S"})
+        I_idx = setup.compartments.get_comp_idx({"infection_stage":"I"})
+        prop_inf = np.random.uniform(low=0,high=.01, size=setup.nsubpops)
+        y0[S_idx, :] = setup.subpop_pop * (1-prop_inf)
+        y0[I_idx, :] = setup.subpop_pop * prop_inf
+
+        return y0
+    
+    def load(self, sim_id: int, setup) -> np.ndarray:
+        return self.draw(sim_id=sim_id, setup=setup)
diff --git a/examples/simple_usa_statelevel/simple_usa_statelevel.yml b/examples/simple_usa_statelevel/simple_usa_statelevel.yml
new file mode 100644
index 000000000..c070af7f4
--- /dev/null
+++ b/examples/simple_usa_statelevel/simple_usa_statelevel.yml
@@ -0,0 +1,100 @@
+name: USA_influpaint
+setup_name: USA
+start_date: 2023-08-01
+end_date: 2024-07-31
+data_path: model_input
+nslots: 2
+
+subpop_setup:
+  geodata: data/geodata_2019_statelevel.csv
+  mobility: data/mobility_2011-2015_statelevel.csv
+
+initial_conditions:
+  method: plugin
+  plugin_file_path: model_input/my_initial_conditions.py
+
+compartments:
+  infection_stage: ["S", "E", "I", "R"]
+
+seir:
+  integration:
+    method: rk4
+    dt: 2.0
+  parameters:
+    sigma:
+      value: 1 / 4
+    gamma:
+      value: 1 / 5
+    Ro:
+      value: .97
+  transitions:
+    - source: ["S"]
+      destination: ["E"]
+      rate: ["Ro * gamma"]
+      proportional_to: [["S"],["I"]]
+      proportion_exponent: ["1","1"]
+    - source: ["E"]
+      destination: ["I"]
+      rate: ["sigma"]
+      proportional_to: ["E"]
+      proportion_exponent: ["1"]
+    - source: ["I"]
+      destination: ["R"]
+      rate: ["gamma"]
+      proportional_to: ["I"]
+      proportion_exponent: ["1"]
+
+seir_modifiers:
+  scenarios:
+    - Ro_all
+  modifiers:
+    Ro_season:
+      method: SinglePeriodModifier
+      parameter: Ro
+      period_start_date: 2023-11-01
+      period_end_date: 2024-02-01
+      subpop: "all"
+      value: 
+        distribution: uniform
+        low: 1
+        high: 1.4
+    Ro_sunny:
+      method: SinglePeriodModifier
+      parameter: Ro
+      period_start_date: 2024-02-01
+      period_end_date: 2024-04-01
+      subpop: "all"
+      value: 1.3
+    Ro_all:
+      method: StackedModifier
+      modifiers: ["Ro_season","Ro_sunny"]
+
+
+outcomes:
+  method: delayframe
+  outcomes:
+    incidCase: #incidence of detected cases
+      source:
+        incidence:
+          infection_stage: "I"
+      probability:
+        value: 0.5
+      delay:
+        value: 5
+    incidHosp: #incidence of hospitalizations
+      source:
+        incidence:
+          infection_stage: "I"
+      probability:
+        value: 0.05
+      delay:
+        value: 7
+      duration:
+        value: 10
+        name: currHosp # will track number of current hospitalizations (ie prevalence)
+    incidDeath: #incidence of deaths
+      source: incidHosp
+      probability:
+        value: 0.2
+      delay:
+        value: 14

From 2fe63434feeef314744f061acf66439cbcd0279f Mon Sep 17 00:00:00 2001
From: Joseph Lemaitre <jo.lemaitresamra@gmail.com>
Date: Thu, 11 Apr 2024 18:37:03 +0200
Subject: [PATCH 18/31] a much clearner interface and preparing the config

---
 examples/simple_usa_statelevel/analysis.ipynb |  7 +++
 .../simple_usa_statelevel.yml                 | 45 ++++++++++++++
 flepimop/gempyor_pkg/src/gempyor/interface.py |  2 -
 flepimop/gempyor_pkg/src/gempyor/logloss.py   | 62 +++++++++++--------
 4 files changed, 89 insertions(+), 27 deletions(-)

diff --git a/examples/simple_usa_statelevel/analysis.ipynb b/examples/simple_usa_statelevel/analysis.ipynb
index 86de213a1..ba50b9372 100644
--- a/examples/simple_usa_statelevel/analysis.ipynb
+++ b/examples/simple_usa_statelevel/analysis.ipynb
@@ -31,6 +31,13 @@
     "ax.grid()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/examples/simple_usa_statelevel/simple_usa_statelevel.yml b/examples/simple_usa_statelevel/simple_usa_statelevel.yml
index c070af7f4..47f161b0a 100644
--- a/examples/simple_usa_statelevel/simple_usa_statelevel.yml
+++ b/examples/simple_usa_statelevel/simple_usa_statelevel.yml
@@ -98,3 +98,48 @@ outcomes:
         value: 0.2
       delay:
         value: 14
+
+
+inference:
+  iterations_per_slot: 100
+  do_inference: TRUE
+  gt_data_path: data/generated_gt_data.csv
+  statistics:
+    incidCase:
+      name: incidCase
+      sim_var: incidCase
+      data_var: incidCase
+      resample:
+        aggregator: sum
+        freq: W-SAT 
+      scale: sqrt
+      #aggregator: sum
+      #period: "1 weeks" # "W-SAT"
+      #remove_na: TRUE
+      #add_one: TRUE
+      likelihood: 
+        dist: pois
+    incidHosp:
+      name: incidHosp
+      aggregator: sum
+      period: "1 weeks" # "W-SAT"
+      sim_var: incidHosp
+      data_var: incidHosp
+      remove_na: TRUE
+      add_one: TRUE
+      likelihood: 
+        dist: pois
+    incidDeath:
+      name: incidDeath
+      aggregator: sum
+      period: "1 weeks"
+      sim_var: incidDeath
+      data_var: incidDeath
+      remove_na: TRUE
+      add_one: TRUE
+      likelihood: 
+        dist: pois
+      # likelihood: 
+      #   dist: norm_cov
+      #   param: 0.3
+
diff --git a/flepimop/gempyor_pkg/src/gempyor/interface.py b/flepimop/gempyor_pkg/src/gempyor/interface.py
index 964e257c5..534630358 100644
--- a/flepimop/gempyor_pkg/src/gempyor/interface.py
+++ b/flepimop/gempyor_pkg/src/gempyor/interface.py
@@ -36,8 +36,6 @@
 # logger.addHandler(handler)
 
 
-
-
 class GempyorSimulator:
     def __init__(
         self,
diff --git a/flepimop/gempyor_pkg/src/gempyor/logloss.py b/flepimop/gempyor_pkg/src/gempyor/logloss.py
index 6c3a9dcdf..61eef9e33 100644
--- a/flepimop/gempyor_pkg/src/gempyor/logloss.py
+++ b/flepimop/gempyor_pkg/src/gempyor/logloss.py
@@ -12,54 +12,66 @@ def __init__(self, name, statistic_config: confuse.ConfigView):
         self.data_var = statistic_config["data_var"].as_str()
         self.name = name
 
-        self.resample_config = None
+        self.resample = False
         if statistic_config["resample"].exists():
-            self.resample_config = statistic_config["resample"].get()
+            self.resample = True
+            resample_config = statistic_config["resample"]
+            self.resample_freq = ""
+            if resample_config["freq"].exists():
+                self.resample_freq = resample_config["freq"].get()
+            
+            self.resample_aggregator = ""
+            if resample_config["aggregator"].exists():
+                self.resample_aggregator = getattr(pd.Series, resample_config["aggregator"].get())
+            
+            self.resample_skipna = False # TODO
+            if resample_config["aggregator"].exists() and resample_config["skipna"].exists():
+                self.resample_skipna = resample_config["skipna"].get()
         
         self.regularization_config = None
-        if statistic_config["regularization"].exists():
+        if statistic_config["regularize"].exists():
             self.regularization_config = statistic_config["regularization"].get()
+
+        self.scale = False
+        if statistic_config["scale"].exists():
+            self.scale_func = getattr(np, statistic_config["scale"].get())
     
-        self.loss_function = statistic_config["loss"].get()
+        self.loss_function = statistic_config["likelihood"].get()
 
     def __str__(self) -> str:
         return f"{self.name}: {self.loss_function} between {self.sim_var} (sim) and {self.data_var} (data)."
     
     def __repr__(self) -> str:
-        return f"A Statistic(): {self.__str()}"
-
-    def apply_resampling(self, data):
-        if self.resample_config:
-            freq = ""
-            freq = self.resample_config["freq"].get("freq", "W-SAT")
-            agg_func = getattr(pd.Series, self.resample_config.get("agg_func", "sum"))
-            skipna = self.resample_config.get("skipna", False)
+        return f"A Statistic(): {self.__str__()}"
 
-            data_resampled = data.resample(freq).agg(agg_func, skipna=skipna)
-            return data_resampled
+    def apply_resample(self, data):
+        if self.resample:
+            return data.resample(self.resample_freq).agg(self.resample_aggregator, skipna=self.resample_skipna)
+        else:
+            return data
+        
+    def apply_scale(self, data):
+        if self.scale:
+            return self.scale_func(data)
         else:
             return data
 
-    def compute_logloss(self, model_data, gt_data, skip_resampling=False):
-        if not skip_resampling:
-            model_data = self.apply_resampling(model_data)
-            gt_data = self.apply_resampling(gt_data)
-
-        model_data = model_data[self.sim_var]
-        gt_data = gt_data[self.data_var]
+    def compute_logloss(self, model_data, gt_data):
+        model_data = self.apply_scale(self.apply_resampling(model_data[self.sim_var]))
+        gt_data = self.apply_scale(self.apply_resampling(gt_data[self.data_var]))
 
-        assert model_data.shape == gt_data.shape
+        assert model_data.shape == gt_data.shape, f"{self.name} Statistic error: data and groundtruth do not have the same shape"
 
         if self.loss_function == "rmse":
             return -np.sqrt(np.mean((model_data - gt_data) ** 2))
         elif self.loss_function == "poisson":
-            epsilon = 1e-10  # to avoid log(0)
-            return -np.mean(model_data - gt_data * np.log(model_data + epsilon))
+
         else:
             raise ValueError("Unsupported loss function")
 
-# TODO: add an autatic test that show that the loss is biggest when gt == modeldata
 
+
+# TODO: add an autatic test that show that the loss is biggest when gt == modeldata
 class LogLoss:
     def __init__(self, inference_config: confuse.ConfigView, data_dir:str = "."):
         self.gt = pd.read_csv(f"{data_dir}/{inference_config['gt_data_path'].get()}")

From f74769a19ce20ab9cf697e46208346f3baa33ba3 Mon Sep 17 00:00:00 2001
From: Joseph Lemaitre <jo.lemaitresamra@gmail.com>
Date: Thu, 11 Apr 2024 19:19:14 +0200
Subject: [PATCH 19/31] here you go @saraloo

---
 .../simple_usa_statelevel.yml                 |  7 ++--
 flepimop/gempyor_pkg/src/gempyor/logloss.py   | 37 ++++++++++++-------
 2 files changed, 28 insertions(+), 16 deletions(-)

diff --git a/examples/simple_usa_statelevel/simple_usa_statelevel.yml b/examples/simple_usa_statelevel/simple_usa_statelevel.yml
index 47f161b0a..740b55d00 100644
--- a/examples/simple_usa_statelevel/simple_usa_statelevel.yml
+++ b/examples/simple_usa_statelevel/simple_usa_statelevel.yml
@@ -111,14 +111,15 @@ inference:
       data_var: incidCase
       resample:
         aggregator: sum
-        freq: W-SAT 
-      scale: sqrt
+        freq: W-SAT # a pandas resampling string, this is weekly on Saturday, see pandas doc
+        skipna:
+      scale: sqrt   # Any np.[something] function (sqrt, log, )
       #aggregator: sum
       #period: "1 weeks" # "W-SAT"
       #remove_na: TRUE
       #add_one: TRUE
       likelihood: 
-        dist: pois
+        dist: poisson
     incidHosp:
       name: incidHosp
       aggregator: sum
diff --git a/flepimop/gempyor_pkg/src/gempyor/logloss.py b/flepimop/gempyor_pkg/src/gempyor/logloss.py
index 61eef9e33..91fcaa767 100644
--- a/flepimop/gempyor_pkg/src/gempyor/logloss.py
+++ b/flepimop/gempyor_pkg/src/gempyor/logloss.py
@@ -2,6 +2,7 @@
 import pandas as pd
 import numpy as np
 import confuse
+import scipy.stats
 
 ## https://docs.xarray.dev/en/stable/user-guide/indexing.html#assigning-values-with-indexing
 ##
@@ -28,18 +29,21 @@ def __init__(self, name, statistic_config: confuse.ConfigView):
             if resample_config["aggregator"].exists() and resample_config["skipna"].exists():
                 self.resample_skipna = resample_config["skipna"].get()
         
-        self.regularization_config = None
+        self.regularize = False
         if statistic_config["regularize"].exists():
-            self.regularization_config = statistic_config["regularization"].get()
+            raise ValueError("Regularization is not implemented")
+            regularization_config = statistic_config["regularization"].get()
 
         self.scale = False
         if statistic_config["scale"].exists():
             self.scale_func = getattr(np, statistic_config["scale"].get())
     
-        self.loss_function = statistic_config["likelihood"].get()
+        self.dist = statistic_config["likelihood"]["dist"].get()
+        # TODO here get the parameter in a dictionnary
+
 
     def __str__(self) -> str:
-        return f"{self.name}: {self.loss_function} between {self.sim_var} (sim) and {self.data_var} (data)."
+        return f"{self.name}: {self.dist} between {self.sim_var} (sim) and {self.data_var} (data)."
     
     def __repr__(self) -> str:
         return f"A Statistic(): {self.__str__()}"
@@ -55,19 +59,26 @@ def apply_scale(self, data):
             return self.scale_func(data)
         else:
             return data
+        
+    def apply_transforms(self, data):
+        return self.apply_scale(self.apply_resampling(data))
 
     def compute_logloss(self, model_data, gt_data):
-        model_data = self.apply_scale(self.apply_resampling(model_data[self.sim_var]))
-        gt_data = self.apply_scale(self.apply_resampling(gt_data[self.data_var]))
-
-        assert model_data.shape == gt_data.shape, f"{self.name} Statistic error: data and groundtruth do not have the same shape"
-
-        if self.loss_function == "rmse":
-            return -np.sqrt(np.mean((model_data - gt_data) ** 2))
-        elif self.loss_function == "poisson":
+        model_data = self. apply_transforms(model_data[self.sim_var])
+        gt_data = self.apply_transforms(gt_data[self.data_var])
 
+        if not model_data.shape == gt_data.shape:
+            raise ValueError(f"{self.name} Statistic error: data and groundtruth do not have the same shape")
+        
+        if self.dist == "pois":
+            ll = np.log(scipy.stats.poisson.pmf(round(gt_data), model_data))
+        elif self.dist == "norm":
+            ll = np.log(scipy.stats.norm.pdf(gt_data, loc=model_data, scale=param[0]))
+        elif self.dist == "nbinom":
+            ll = np.log(scipy.stats.nbinom.pmf(gt_data, n=param[0], p=model_data))
         else:
-            raise ValueError("Unsupported loss function")
+            raise ValueError("Invalid distribution specified, got {self.dist}")
+        return ll
 
 
 

From 8e1def7a1925f521bdcd1f95d6df6ffc9f09d7f6 Mon Sep 17 00:00:00 2001
From: Joseph Lemaitre <jo.lemaitresamra@gmail.com>
Date: Thu, 11 Apr 2024 19:24:48 +0200
Subject: [PATCH 20/31] new syntax

---
 .../simple_usa_statelevel.yml                  | 18 +++++++++---------
 1 file changed, 9 insertions(+), 9 deletions(-)

diff --git a/examples/simple_usa_statelevel/simple_usa_statelevel.yml b/examples/simple_usa_statelevel/simple_usa_statelevel.yml
index 740b55d00..024b7929e 100644
--- a/examples/simple_usa_statelevel/simple_usa_statelevel.yml
+++ b/examples/simple_usa_statelevel/simple_usa_statelevel.yml
@@ -112,7 +112,7 @@ inference:
       resample:
         aggregator: sum
         freq: W-SAT # a pandas resampling string, this is weekly on Saturday, see pandas doc
-        skipna:
+        skipna: True
       scale: sqrt   # Any np.[something] function (sqrt, log, )
       #aggregator: sum
       #period: "1 weeks" # "W-SAT"
@@ -122,22 +122,22 @@ inference:
         dist: poisson
     incidHosp:
       name: incidHosp
-      aggregator: sum
-      period: "1 weeks" # "W-SAT"
       sim_var: incidHosp
       data_var: incidHosp
-      remove_na: TRUE
-      add_one: TRUE
+      resample:
+        aggregator: sum
+        freq: W-SAT # a pandas resampling string, this is weekly on Saturday, see pandas doc
+        skipna: False
       likelihood: 
         dist: pois
     incidDeath:
       name: incidDeath
-      aggregator: sum
-      period: "1 weeks"
       sim_var: incidDeath
       data_var: incidDeath
-      remove_na: TRUE
-      add_one: TRUE
+      resample:
+        aggregator: sum
+        freq: W-SAT # a pandas resampling string, this is weekly on Saturday, see pandas doc
+        skipna: True
       likelihood: 
         dist: pois
       # likelihood: 

From a16939238c67c42c0ca13c1eb4b5fc2faae8bbe3 Mon Sep 17 00:00:00 2001
From: Joseph Lemaitre <jo.lemaitresamra@gmail.com>
Date: Thu, 11 Apr 2024 19:45:56 +0200
Subject: [PATCH 21/31] example generated data

---
 examples/simple_usa_statelevel/analysis.ipynb |    66 +-
 .../model_input/data/generated_gt_data.csv    | 18667 ++++++++++++++++
 .../model_input/my_initial_conditions.py      |     4 +-
 flepimop/gempyor_pkg/src/gempyor/__init__.py  |     1 +
 4 files changed, 18719 insertions(+), 19 deletions(-)
 create mode 100644 examples/simple_usa_statelevel/model_input/data/generated_gt_data.csv

diff --git a/examples/simple_usa_statelevel/analysis.ipynb b/examples/simple_usa_statelevel/analysis.ipynb
index ba50b9372..fbe306f4f 100644
--- a/examples/simple_usa_statelevel/analysis.ipynb
+++ b/examples/simple_usa_statelevel/analysis.ipynb
@@ -4,12 +4,35 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "import gempyor\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Generate a ground-truth (do once)\n",
+    "There is already a ground-truth data generated on git, but if you want to make yours, first run:\n",
+    "```bash\n",
+    "gempyor-simulate -c simple_usa_statelevel.yml -n 1\n",
+    "```\n",
+    "then execute the two next cells"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 500x300 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -17,18 +40,9 @@
     }
    ],
    "source": [
-    "import gempyor\n",
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "df_l = []\n",
-    "for fn in  gempyor.utils.list_filenames(folder=\"synthetic_dataset/model_output/\", filters=[\"hosp\", \".parquet\"]):\n",
-    "    df_l.append(gempyor.read_df(fn))\n",
-    "fig, axes = plt.subplots(1,1, figsize=(5,3))\n",
-    "ax = axes\n",
-    "for df in df_l:\n",
-    "    ax.plot(df.drop(\"time\",axis=1).set_index(\"date\").groupby(\"date\").sum()[\"incidCase\"], lw=.5)\n",
-    "ax.grid()"
+    "gt=gempyor.read_df(\"model_output/USA_influpaint_Ro_all/20240411_193531/hosp/000000001.20240411_193531.hosp.parquet\")\n",
+    "gt=gt.drop(\"time\", axis=1).set_index(\"date\")\n",
+    "gt[gt[\"subpop\"]==\"56000\"].plot();"
    ]
   },
   {
@@ -36,14 +50,32 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "gempyor.write_df(fname = \"model_input/data/generated_gt_data.csv\", df=gt)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Analysis"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "df_l = []\n",
+    "for fn in  gempyor.utils.list_filenames(folder=\"synthetic_dataset/model_output/\", filters=[\"hosp\", \".parquet\"]):\n",
+    "    df_l.append(gempyor.read_df(fn))\n",
+    "fig, axes = plt.subplots(1,1, figsize=(5,3))\n",
+    "ax = axes\n",
+    "for df in df_l:\n",
+    "    ax.plot(df.drop(\"time\",axis=1).set_index(\"date\").groupby(\"date\").sum()[\"incidCase\"], lw=.5)\n",
+    "ax.grid()"
+   ]
   }
  ],
  "metadata": {
@@ -62,7 +94,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.1.undefined"
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,
diff --git a/examples/simple_usa_statelevel/model_input/data/generated_gt_data.csv b/examples/simple_usa_statelevel/model_input/data/generated_gt_data.csv
new file mode 100644
index 000000000..7cc24ee8a
--- /dev/null
+++ b/examples/simple_usa_statelevel/model_input/data/generated_gt_data.csv
@@ -0,0 +1,18667 @@
+date,subpop,incidCase,incidHosp,currHosp,incidDeath
+2023-08-01,56000,0.0,0.0,0.0,0.0
+2023-08-02,56000,0.0,0.0,0.0,0.0
+2023-08-03,56000,0.0,0.0,0.0,0.0
+2023-08-04,56000,0.0,0.0,0.0,0.0
+2023-08-05,56000,0.0,0.0,0.0,0.0
+2023-08-06,56000,92.88398372605914,0.0,0.0,0.0
+2023-08-07,56000,92.88398372605914,0.0,0.0,0.0
+2023-08-08,56000,195.84940926041025,9.288398372605915,9.288398372605915,0.0
+2023-08-09,56000,195.84940926041025,9.288398372605915,18.57679674521183,0.0
+2023-08-10,56000,236.63712461323638,19.58494092604103,38.16173767125286,0.0
+2023-08-11,56000,236.63712461323638,19.58494092604103,57.74667859729389,0.0
+2023-08-12,56000,251.62969734382241,23.66371246132364,81.41039105861753,0.0
+2023-08-13,56000,251.62969734382241,23.66371246132364,105.07410351994118,0.0
+2023-08-14,56000,255.89551614063149,25.162969734382244,130.23707325432343,0.0
+2023-08-15,56000,255.89551614063149,25.162969734382244,155.40004298870568,0.0
+2023-08-16,56000,255.68158626046628,25.58955161406315,180.98959460276882,0.0
+2023-08-17,56000,255.68158626046628,25.58955161406315,206.57914621683196,0.0
+2023-08-18,56000,253.5807011466193,25.56815862604663,222.85890647027267,0.0
+2023-08-19,56000,253.5807011466193,25.56815862604663,239.1386667237134,0.0
+2023-08-20,56000,250.6709923979212,25.35807011466193,244.9117959123343,0.0
+2023-08-21,56000,250.6709923979212,25.35807011466193,250.6849251009552,0.0
+2023-08-22,56000,247.40244896596528,25.06709923979212,252.08831187942369,1.857679674521183
+2023-08-23,56000,247.40244896596528,25.06709923979212,253.4916986578922,1.857679674521183
+2023-08-24,56000,243.96443010277554,24.74024489659653,253.06897382010646,3.916988185208206
+2023-08-25,56000,243.96443010277554,24.74024489659653,252.64624898232077,3.916988185208206
+2023-08-26,56000,240.43808439771593,24.396443010277554,251.45314037853518,4.7327424922647285
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+2024-07-29,06000,3228.8287086096307,334.47862528511075,3656.806723789894,85.45431734512573
+2024-07-30,06000,3228.8287086096307,334.47862528511075,3592.7194989755517,85.45431734512573
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diff --git a/examples/simple_usa_statelevel/model_input/my_initial_conditions.py b/examples/simple_usa_statelevel/model_input/my_initial_conditions.py
index b49eb2e7a..9ec4fa3f6 100644
--- a/examples/simple_usa_statelevel/model_input/my_initial_conditions.py
+++ b/examples/simple_usa_statelevel/model_input/my_initial_conditions.py
@@ -3,7 +3,7 @@
 
 class InitialConditions(gempyor.seeding_ic.InitialConditions):
 
-    def draw(self, sim_id: int, setup) -> np.ndarray:
+    def get_from_config(self, sim_id: int, setup) -> np.ndarray:
         y0 = np.zeros((setup.compartments.compartments.shape[0], setup.nsubpops))
         S_idx = setup.compartments.get_comp_idx({"infection_stage":"S"})
         I_idx = setup.compartments.get_comp_idx({"infection_stage":"I"})
@@ -13,5 +13,5 @@ def draw(self, sim_id: int, setup) -> np.ndarray:
 
         return y0
     
-    def load(self, sim_id: int, setup) -> np.ndarray:
+    def get_from_file(self, sim_id: int, setup) -> np.ndarray:
         return self.draw(sim_id=sim_id, setup=setup)
diff --git a/flepimop/gempyor_pkg/src/gempyor/__init__.py b/flepimop/gempyor_pkg/src/gempyor/__init__.py
index a47bbcac1..37035edc0 100644
--- a/flepimop/gempyor_pkg/src/gempyor/__init__.py
+++ b/flepimop/gempyor_pkg/src/gempyor/__init__.py
@@ -1,3 +1,4 @@
 ## All functions are in minimal interface.
 
 from .interface import *
+from .utils import *

From f89ad166ef1c9e841688dec6ccda6b2245f4651c Mon Sep 17 00:00:00 2001
From: Joseph Lemaitre <jo.lemaitresamra@gmail.com>
Date: Thu, 11 Apr 2024 19:46:18 +0200
Subject: [PATCH 22/31] benchmark

---
 .../{analysis.ipynb => inference_benchmark.ipynb}                 | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename examples/simple_usa_statelevel/{analysis.ipynb => inference_benchmark.ipynb} (100%)

diff --git a/examples/simple_usa_statelevel/analysis.ipynb b/examples/simple_usa_statelevel/inference_benchmark.ipynb
similarity index 100%
rename from examples/simple_usa_statelevel/analysis.ipynb
rename to examples/simple_usa_statelevel/inference_benchmark.ipynb

From f19b138b9fb11a75e6bd06b05266ef7af1fb42c1 Mon Sep 17 00:00:00 2001
From: Joseph Lemaitre <jo.lemaitresamra@gmail.com>
Date: Thu, 11 Apr 2024 19:47:49 +0200
Subject: [PATCH 23/31] typo

---
 examples/simple_usa_statelevel/simple_usa_statelevel.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/simple_usa_statelevel/simple_usa_statelevel.yml b/examples/simple_usa_statelevel/simple_usa_statelevel.yml
index 024b7929e..3a5800e05 100644
--- a/examples/simple_usa_statelevel/simple_usa_statelevel.yml
+++ b/examples/simple_usa_statelevel/simple_usa_statelevel.yml
@@ -119,7 +119,7 @@ inference:
       #remove_na: TRUE
       #add_one: TRUE
       likelihood: 
-        dist: poisson
+        dist: pois
     incidHosp:
       name: incidHosp
       sim_var: incidHosp

From 15f4249d6cda19ce1eb2b3f46f66fabfcb7ab206 Mon Sep 17 00:00:00 2001
From: Joseph Lemaitre <jo.lemaitresamra@gmail.com>
Date: Thu, 11 Apr 2024 19:59:46 +0200
Subject: [PATCH 24/31] get rid of time in column (only date)

---
 flepimop/gempyor_pkg/src/gempyor/interface.py        | 4 +---
 flepimop/gempyor_pkg/src/gempyor/logloss.py          | 2 +-
 flepimop/gempyor_pkg/src/gempyor/outcomes.py         | 1 -
 flepimop/gempyor_pkg/tests/outcomes/test_outcomes.py | 8 ++++----
 4 files changed, 6 insertions(+), 9 deletions(-)

diff --git a/flepimop/gempyor_pkg/src/gempyor/interface.py b/flepimop/gempyor_pkg/src/gempyor/interface.py
index 534630358..0a8788c8d 100644
--- a/flepimop/gempyor_pkg/src/gempyor/interface.py
+++ b/flepimop/gempyor_pkg/src/gempyor/interface.py
@@ -497,13 +497,11 @@ def simulation_atomic(snpi_df_in, hnpi_df_in, modinf, p_draw, unique_strings, tr
         hpar=hpar_df,
         npi=npi_outcomes,
     )
-    outcomes_df["time"] = outcomes_df["date"] #which one should it be ?
-    
     
     if save:
         modinf.write_simID(ftype="hosp", sim_id=random_id, df=outcomes_df)
     # needs to be after write... because parquet write discard the index.
-    outcomes_df = outcomes_df.set_index("date")
+    outcomes_df = outcomes_df.set_index("date") # after writing
 
     return outcomes_df
 
diff --git a/flepimop/gempyor_pkg/src/gempyor/logloss.py b/flepimop/gempyor_pkg/src/gempyor/logloss.py
index 91fcaa767..3522af5b3 100644
--- a/flepimop/gempyor_pkg/src/gempyor/logloss.py
+++ b/flepimop/gempyor_pkg/src/gempyor/logloss.py
@@ -117,7 +117,7 @@ def compute_logloss(self, model_df, modinf):
             last_date = min(gt_s.index.max(), model_df_s.index.max())
 
             gt_s = gt_s.loc[first_date:last_date].drop(["subpop"], axis=1)
-            model_df_s = model_df_s.drop(["subpop", "time"], axis=1).loc[first_date:last_date]
+            model_df_s = model_df_s.drop(["subpop"], axis=1).loc[first_date:last_date]
 
             # TODO: add whole US!! option
 
diff --git a/flepimop/gempyor_pkg/src/gempyor/outcomes.py b/flepimop/gempyor_pkg/src/gempyor/outcomes.py
index dc49577df..88fd9e459 100644
--- a/flepimop/gempyor_pkg/src/gempyor/outcomes.py
+++ b/flepimop/gempyor_pkg/src/gempyor/outcomes.py
@@ -254,7 +254,6 @@ def read_parameters_from_config(modinf: model_info.ModelInfo):
 
 
 def postprocess_and_write(sim_id, modinf, outcomes_df, hpar, npi):
-    outcomes_df["time"] = outcomes_df["date"]
     modinf.write_simID(ftype="hosp", sim_id=sim_id, df=outcomes_df)
     modinf.write_simID(ftype="hpar", sim_id=sim_id, df=hpar)
 
diff --git a/flepimop/gempyor_pkg/tests/outcomes/test_outcomes.py b/flepimop/gempyor_pkg/tests/outcomes/test_outcomes.py
index 53fa81d05..72aad5db4 100644
--- a/flepimop/gempyor_pkg/tests/outcomes/test_outcomes.py
+++ b/flepimop/gempyor_pkg/tests/outcomes/test_outcomes.py
@@ -43,7 +43,7 @@ def test_outcome():
     outcomes.onerun_delayframe_outcomes(sim_id2write=1, modinf=inference_simulator.modinf, load_ID=False)
 
     hosp = pq.read_table(f"{config_path_prefix}model_output/hosp/000000001.1.hosp.parquet").to_pandas()
-    hosp.set_index("time", drop=True, inplace=True)
+    hosp.set_index("date", drop=True, inplace=True)
     for i, place in enumerate(subpop):
         for dt in hosp.index:
             if dt.date() == date_data:
@@ -230,7 +230,7 @@ def test_outcomes_npi():
     outcomes.onerun_delayframe_outcomes(sim_id2write=1, modinf=inference_simulator.modinf)
 
     hosp = pq.read_table(f"{config_path_prefix}model_output/hosp/000000001.105.hosp.parquet").to_pandas()
-    hosp.set_index("time", drop=True, inplace=True)
+    hosp.set_index("date", drop=True, inplace=True)
     # same as config.yaml (doubled, then NPI halve it)
     for i, place in enumerate(subpop):
         for dt in hosp.index:
@@ -402,7 +402,7 @@ def test_outcomes_npi_custom_pname():
     outcomes.onerun_delayframe_outcomes(sim_id2write=1, modinf=inference_simulator.modinf, load_ID=False, sim_id2load=1)
 
     hosp = pq.read_table(f"{config_path_prefix}model_output/hosp/000000001.105.hosp.parquet").to_pandas()
-    hosp.set_index("time", drop=True, inplace=True)
+    hosp.set_index("date", drop=True, inplace=True)
     # same as config.yaml (doubled, then NPI halve it)
     for i, place in enumerate(subpop):
         for dt in hosp.index:
@@ -592,7 +592,7 @@ def test_outcomes_pcomp():
     outcomes.onerun_delayframe_outcomes(sim_id2write=1, modinf=inference_simulator.modinf, load_ID=False)
 
     hosp_f = pq.read_table(f"{config_path_prefix}model_output/hosp/000000001.111.hosp.parquet").to_pandas()
-    hosp_f.set_index("time", drop=True, inplace=True)
+    hosp_f.set_index("date", drop=True, inplace=True)
     # same as config.yaml (doubled, then NPI halve it)
     for k, p_comp in enumerate(["0dose", "1dose"]):
         hosp = hosp_f

From 85942f6079ef7e866fc0c5392e3c57c9fb5dc51b Mon Sep 17 00:00:00 2001
From: Joseph Lemaitre <jo.lemaitresamra@gmail.com>
Date: Thu, 11 Apr 2024 22:45:24 +0200
Subject: [PATCH 25/31] trying to remove time from the R code

---
 flepimop/R_packages/inference/R/inference_slot_runner_funcs.R | 4 ++--
 flepimop/main_scripts/inference_slot.R                        | 3 +--
 2 files changed, 3 insertions(+), 4 deletions(-)

diff --git a/flepimop/R_packages/inference/R/inference_slot_runner_funcs.R b/flepimop/R_packages/inference/R/inference_slot_runner_funcs.R
index a8d7ddece..9d01bf483 100644
--- a/flepimop/R_packages/inference/R/inference_slot_runner_funcs.R
+++ b/flepimop/R_packages/inference/R/inference_slot_runner_funcs.R
@@ -52,11 +52,11 @@ aggregate_and_calc_loc_likelihoods <- function(
             dplyr::filter(
                 modeled_outcome,
                 !!rlang::sym(obs_subpop) == location,
-                time %in% unique(obs$date[obs$subpop == location])
+                date %in% unique(obs$date[obs$subpop == location])
             ) %>%
             ## Reformat into form the algorithm is looking for
             inference::getStats(
-                "time",
+                "date",
                 "sim_var",
                 stat_list = targets_config,
                 start_date = start_date,
diff --git a/flepimop/main_scripts/inference_slot.R b/flepimop/main_scripts/inference_slot.R
index d4c868d88..5554052ee 100644
--- a/flepimop/main_scripts/inference_slot.R
+++ b/flepimop/main_scripts/inference_slot.R
@@ -588,8 +588,7 @@ for(seir_modifiers_scenario in seir_modifiers_scenarios) {
       # run
       if (config$inference$do_inference){
         sim_hosp <- flepicommon::read_file_of_type(gsub(".*[.]","",this_global_files[['hosp_filename']]))(this_global_files[['hosp_filename']]) %>%
-          dplyr::filter(time >= min(obs$date),time <= max(obs$date))
-
+          dplyr::filter(date >= min(obs$date),date <= max(obs$date))
         lhs <- unique(sim_hosp[[obs_subpop]])
         rhs <- unique(names(data_stats))
         all_locations <- rhs[rhs %in% lhs]

From 7393b9347fb0a61c1a0cb8b05e0ac0ec3f45ebb4 Mon Sep 17 00:00:00 2001
From: Joseph Lemaitre <jo.lemaitresamra@gmail.com>
Date: Tue, 16 Apr 2024 13:07:33 +0200
Subject: [PATCH 26/31] progress

---
 .../simple_usa_statelevel.yml                 |   2 +
 flepimop/gempyor_pkg/src/gempyor/logloss.py   |  85 +----------
 .../gempyor_pkg/src/gempyor/statistics.py     | 139 ++++++++++++++++++
 3 files changed, 147 insertions(+), 79 deletions(-)
 create mode 100644 flepimop/gempyor_pkg/src/gempyor/statistics.py

diff --git a/examples/simple_usa_statelevel/simple_usa_statelevel.yml b/examples/simple_usa_statelevel/simple_usa_statelevel.yml
index 3a5800e05..6488e9dd8 100644
--- a/examples/simple_usa_statelevel/simple_usa_statelevel.yml
+++ b/examples/simple_usa_statelevel/simple_usa_statelevel.yml
@@ -120,6 +120,8 @@ inference:
       #add_one: TRUE
       likelihood: 
         dist: pois
+      regularize:
+        
     incidHosp:
       name: incidHosp
       sim_var: incidHosp
diff --git a/flepimop/gempyor_pkg/src/gempyor/logloss.py b/flepimop/gempyor_pkg/src/gempyor/logloss.py
index 3522af5b3..3d653b8cf 100644
--- a/flepimop/gempyor_pkg/src/gempyor/logloss.py
+++ b/flepimop/gempyor_pkg/src/gempyor/logloss.py
@@ -3,84 +3,10 @@
 import numpy as np
 import confuse
 import scipy.stats
-
-## https://docs.xarray.dev/en/stable/user-guide/indexing.html#assigning-values-with-indexing
-##
-
-class Statistic:
-    def __init__(self, name, statistic_config: confuse.ConfigView):
-        self.sim_var = statistic_config["sim_var"].as_str()
-        self.data_var = statistic_config["data_var"].as_str()
-        self.name = name
-
-        self.resample = False
-        if statistic_config["resample"].exists():
-            self.resample = True
-            resample_config = statistic_config["resample"]
-            self.resample_freq = ""
-            if resample_config["freq"].exists():
-                self.resample_freq = resample_config["freq"].get()
-            
-            self.resample_aggregator = ""
-            if resample_config["aggregator"].exists():
-                self.resample_aggregator = getattr(pd.Series, resample_config["aggregator"].get())
-            
-            self.resample_skipna = False # TODO
-            if resample_config["aggregator"].exists() and resample_config["skipna"].exists():
-                self.resample_skipna = resample_config["skipna"].get()
-        
-        self.regularize = False
-        if statistic_config["regularize"].exists():
-            raise ValueError("Regularization is not implemented")
-            regularization_config = statistic_config["regularization"].get()
-
-        self.scale = False
-        if statistic_config["scale"].exists():
-            self.scale_func = getattr(np, statistic_config["scale"].get())
-    
-        self.dist = statistic_config["likelihood"]["dist"].get()
-        # TODO here get the parameter in a dictionnary
-
-
-    def __str__(self) -> str:
-        return f"{self.name}: {self.dist} between {self.sim_var} (sim) and {self.data_var} (data)."
-    
-    def __repr__(self) -> str:
-        return f"A Statistic(): {self.__str__()}"
-
-    def apply_resample(self, data):
-        if self.resample:
-            return data.resample(self.resample_freq).agg(self.resample_aggregator, skipna=self.resample_skipna)
-        else:
-            return data
-        
-    def apply_scale(self, data):
-        if self.scale:
-            return self.scale_func(data)
-        else:
-            return data
-        
-    def apply_transforms(self, data):
-        return self.apply_scale(self.apply_resampling(data))
-
-    def compute_logloss(self, model_data, gt_data):
-        model_data = self. apply_transforms(model_data[self.sim_var])
-        gt_data = self.apply_transforms(gt_data[self.data_var])
-
-        if not model_data.shape == gt_data.shape:
-            raise ValueError(f"{self.name} Statistic error: data and groundtruth do not have the same shape")
-        
-        if self.dist == "pois":
-            ll = np.log(scipy.stats.poisson.pmf(round(gt_data), model_data))
-        elif self.dist == "norm":
-            ll = np.log(scipy.stats.norm.pdf(gt_data, loc=model_data, scale=param[0]))
-        elif self.dist == "nbinom":
-            ll = np.log(scipy.stats.nbinom.pmf(gt_data, n=param[0], p=model_data))
-        else:
-            raise ValueError("Invalid distribution specified, got {self.dist}")
-        return ll
+from . import statistics
 
 
+## https://docs.xarray.dev/en/stable/user-guide/indexing.html#assigning-values-with-indexing
 
 # TODO: add an autatic test that show that the loss is biggest when gt == modeldata
 class LogLoss:
@@ -90,10 +16,11 @@ def __init__(self, inference_config: confuse.ConfigView, data_dir:str = "."):
         self.gt = self.gt.set_index("date")
         self.statistics = {}
         for key, value in inference_config["statistics"].items():
-            self.statistics[key] = Statistic(key, value)
+            self.statistics[key] = statistics.Statistic(key, value)
+
+    def plot_gt(self, ax, subpop, statistic, **kwargs):
+        self.gt[self.gt["subpop"] == subpop].plot(y=statistic, ax=ax, **kwargs)
 
-    def plot_gt(self, ax):
-        ax.plot(self.gt)
 
     def compute_logloss(self, model_df, modinf):
         """
diff --git a/flepimop/gempyor_pkg/src/gempyor/statistics.py b/flepimop/gempyor_pkg/src/gempyor/statistics.py
new file mode 100644
index 000000000..fc13a36e7
--- /dev/null
+++ b/flepimop/gempyor_pkg/src/gempyor/statistics.py
@@ -0,0 +1,139 @@
+import xarray as xr
+import pandas as pd
+import numpy as np
+import confuse
+import scipy.stats
+
+class Statistic:
+    """
+        A statistic is a function that takes two time series and returns a scalar value.
+        It applies resampling, scaling, and regularization to the data before computing the statistic's log-loss.
+        Configuration:
+        - sim_var: the variable in the simulation data
+        - data_var: the variable in the ground truth data
+        - resample: resample the data before computing the statistic
+            - freq: the frequency to resample the data to
+            - aggregator: the aggregation function to use
+            - skipna: whether to skip NA values
+        - regularize: apply a regularization term to the data before computing the statistic
+    """
+    def __init__(self, name, statistic_config: confuse.ConfigView):
+        self.sim_var = statistic_config["sim_var"].as_str()
+        self.data_var = statistic_config["data_var"].as_str()
+        self.name = name
+
+        self.resample = False
+        if statistic_config["resample"].exists():
+            self.resample = True
+            resample_config = statistic_config["resample"]
+            self.resample_freq = ""
+            if resample_config["freq"].exists():
+                self.resample_freq = resample_config["freq"].get()
+            
+            self.resample_aggregator = ""
+            if resample_config["aggregator"].exists():
+                self.resample_aggregator = getattr(pd.Series, resample_config["aggregator"].get())
+            
+            self.resample_skipna = False # TODO
+            if resample_config["aggregator"].exists() and resample_config["skipna"].exists():
+                self.resample_skipna = resample_config["skipna"].get()
+        
+        self.regularize = False
+        if statistic_config["regularize"].exists():
+            raise ValueError("Regularization is not implemented")
+            regularization_config = statistic_config["regularization"].get()
+
+        self.regularization = None
+        if statistic_config["regularize"].exists():
+            # TODO: support several regularization
+            self.regularization_config = statistic_config["regularize"]
+            self.regularization_name = self.regularization_config["name"].get()
+            self.regularization = getattr(self, f"_{self.regularization_name}_regularize")
+            if self.regularization is None:
+                raise ValueError(f"Unsupported regularization: {self.regularization_name}")
+
+        self.scale = False
+        if statistic_config["scale"].exists():
+            self.scale_func = getattr(np, statistic_config["scale"].get())
+    
+        self.dist = statistic_config["likelihood"]["dist"].get()
+        # TODO here get the parameter in a dictionnary
+
+    def _forecast_regularize(self, data):
+        # scale the data so that the lastest X items are more important
+        last_n = self.regularization_config["last_n"].get()
+        mult = self.regularization_config["mult"].get()
+        # multiply the last n items by mult
+        reg_data = data * np.concatenate([np.ones(data.shape[0]-last_n), np.ones(last_n)*mult])
+        return reg_data
+    
+    def _allsubpop_regularize(self, data):
+        """ add a regularization term that is the sum of all subpopulations
+        """
+
+
+
+    def __str__(self) -> str:
+        return f"{self.name}: {self.dist} between {self.sim_var} (sim) and {self.data_var} (data)."
+    
+    def __repr__(self) -> str:
+        return f"A Statistic(): {self.__str__()}"
+
+    def apply_resample(self, data):
+        if self.resample:
+            return data.resample(self.resample_freq).agg(self.resample_aggregator, skipna=self.resample_skipna)
+        else:
+            return data
+        
+    def apply_scale(self, data):
+        if self.scale:
+            return self.scale_func(data)
+        else:
+            return data
+        
+    def apply_transforms(self, data):
+        return self.apply_scale(self.apply_resampling(data))
+
+    def compute_logloss(self, model_data, gt_data):
+        model_data = self. apply_transforms(model_data[self.sim_var])
+        gt_data = self.apply_transforms(gt_data[self.data_var])
+
+                dist_map = {
+            "pois": scipy.stats.poisson.pmf,
+            "norm": scipy.stats.norm.pdf,
+            "nbinom": scipy.stats.nbinom.pmf,
+        }
+        if self.dist not in dist_map:
+            raise ValueError(f"Invalid distribution specified: {self.dist}")
+        log_likelihood = dist_map[self.dist](round(gt_data), model_data)
+
+        # Apply regularization if defined
+        if self.regularization:
+            log_likelihood -= self.regularization(model_data)
+
+
+            dist_map = {
+            "pois": scipy.stats.poisson.pmf,
+            "norm": lambda x, loc, scale: scipy.stats.norm.pdf(x, loc=loc, scale=self.params.get("scale", scale)),
+            "nbinom": lambda x, n, p: scipy.stats.nbinom.pmf(x, n=self.params.get("n"), p=model_data),
+        }
+        if self.dist not in dist_map:
+            raise ValueError(f"Invalid distribution specified: {self.dist}")
+
+        # Use stored parameters in the distribution function call
+        log_likelihood = dist_map[self.dist](round(gt_data), model_data, **self.params)
+
+
+
+        if not model_data.shape == gt_data.shape:
+            raise ValueError(f"{self.name} Statistic error: data and groundtruth do not have the same shape")
+        
+        if self.dist == "pois":
+            ll = np.log(scipy.stats.poisson.pmf(round(gt_data), model_data))
+        elif self.dist == "norm":
+            ll = np.log(scipy.stats.norm.pdf(gt_data, loc=model_data, scale=param[0]))
+        elif self.dist == "nbinom":
+            ll = np.log(scipy.stats.nbinom.pmf(gt_data, n=param[0], p=model_data))
+        else:
+            raise ValueError("Invalid distribution specified, got {self.dist}")
+        return ll
\ No newline at end of file

From 5cb0a8ddb0e25967c781fd4421ac6bce72892aad Mon Sep 17 00:00:00 2001
From: saraloo <45245630+saraloo@users.noreply.github.com>
Date: Thu, 11 Apr 2024 16:59:27 -0400
Subject: [PATCH 27/31] add other distributions to logloss

---
 flepimop/gempyor_pkg/src/gempyor/logloss.py | 56 ---------------------
 1 file changed, 56 deletions(-)
 delete mode 100644 flepimop/gempyor_pkg/src/gempyor/logloss.py

diff --git a/flepimop/gempyor_pkg/src/gempyor/logloss.py b/flepimop/gempyor_pkg/src/gempyor/logloss.py
deleted file mode 100644
index 3d653b8cf..000000000
--- a/flepimop/gempyor_pkg/src/gempyor/logloss.py
+++ /dev/null
@@ -1,56 +0,0 @@
-import xarray as xr
-import pandas as pd
-import numpy as np
-import confuse
-import scipy.stats
-from . import statistics
-
-
-## https://docs.xarray.dev/en/stable/user-guide/indexing.html#assigning-values-with-indexing
-
-# TODO: add an autatic test that show that the loss is biggest when gt == modeldata
-class LogLoss:
-    def __init__(self, inference_config: confuse.ConfigView, data_dir:str = "."):
-        self.gt = pd.read_csv(f"{data_dir}/{inference_config['gt_data_path'].get()}")
-        self.gt["date"] = pd.to_datetime(self.gt['date'])
-        self.gt = self.gt.set_index("date")
-        self.statistics = {}
-        for key, value in inference_config["statistics"].items():
-            self.statistics[key] = statistics.Statistic(key, value)
-
-    def plot_gt(self, ax, subpop, statistic, **kwargs):
-        self.gt[self.gt["subpop"] == subpop].plot(y=statistic, ax=ax, **kwargs)
-
-
-    def compute_logloss(self, model_df, modinf):
-        """
-        Compute logloss for all statistics
-        model_df: DataFrame indexed by date
-        modinf: model information
-        TODO: support kwargs for emcee, and this looks very slow
-        """
-        logloss = xr.DataArray(0, dims=["statistic", "subpop"],  
-                coords={
-                "statistic":self.statistics.key(),
-                "subpop":modinf.subpop_struct.subpop_names})
-
-        for subpop in modinf.subpop_struct.subpop_names:
-            # essential to sort by index (date here)
-            gt_s = self.gt[self.gt["subpop"] == subpop].sort_index()
-            model_df_s = model_df[model_df["subpop"] == subpop].sort_index()
-
-            # Places where data and model overlap
-            first_date = max(gt_s.index.min(), model_df_s.index.min())
-            last_date = min(gt_s.index.max(), model_df_s.index.max())
-
-            gt_s = gt_s.loc[first_date:last_date].drop(["subpop"], axis=1)
-            model_df_s = model_df_s.drop(["subpop"], axis=1).loc[first_date:last_date]
-
-            # TODO: add whole US!! option
-
-            for key, stat in self.statistics.items():
-                logloss.loc[dict(statistics=key, subpop=subpop)] += stat.compute_logloss(model_df, gt_s)
-
-        return logloss
-
-

From 40989c947c243212f999debbc121509069084679 Mon Sep 17 00:00:00 2001
From: Joseph Lemaitre <jo.lemaitresamra@gmail.com>
Date: Tue, 16 Apr 2024 14:57:49 +0200
Subject: [PATCH 28/31] several regularization

---
 .../inference_benchmark.ipynb                 |  75 ++++++++++
 .../simple_usa_statelevel.yml                 |   6 +-
 .../gempyor_pkg/src/gempyor/statistics.py     | 139 ------------------
 3 files changed, 80 insertions(+), 140 deletions(-)
 delete mode 100644 flepimop/gempyor_pkg/src/gempyor/statistics.py

diff --git a/examples/simple_usa_statelevel/inference_benchmark.ipynb b/examples/simple_usa_statelevel/inference_benchmark.ipynb
index fbe306f4f..74ad46823 100644
--- a/examples/simple_usa_statelevel/inference_benchmark.ipynb
+++ b/examples/simple_usa_statelevel/inference_benchmark.ipynb
@@ -76,6 +76,81 @@
     "    ax.plot(df.drop(\"time\",axis=1).set_index(\"date\").groupby(\"date\").sum()[\"incidCase\"], lw=.5)\n",
     "ax.grid()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import scipy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[0;31mSignature:\u001b[0m \u001b[0mscipy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstats\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpoisson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpmf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mDocstring:\u001b[0m\n",
+      "Probability mass function at k of the given RV.\n",
+      "\n",
+      "Parameters\n",
+      "----------\n",
+      "k : array_like\n",
+      "    Quantiles.\n",
+      "arg1, arg2, arg3,... : array_like\n",
+      "    The shape parameter(s) for the distribution (see docstring of the\n",
+      "    instance object for more information)\n",
+      "loc : array_like, optional\n",
+      "    Location parameter (default=0).\n",
+      "\n",
+      "Returns\n",
+      "-------\n",
+      "pmf : array_like\n",
+      "    Probability mass function evaluated at k\n",
+      "\u001b[0;31mFile:\u001b[0m      ~/anaconda3/lib/python3.11/site-packages/scipy/stats/_distn_infrastructure.py\n",
+      "\u001b[0;31mType:\u001b[0m      method"
+     ]
+    }
+   ],
+   "source": [
+    "scipy.stats.poisson.pmf?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Generate some tests for logprob"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "module 'gempyor.statistics' has no attribute 'plot_poisson_pmf'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[2], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mgempyor\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m statistics\n\u001b[0;32m----> 3\u001b[0m statistics\u001b[38;5;241m.\u001b[39mplot_poisson_pmf(\u001b[38;5;241m10\u001b[39m, \u001b[38;5;241m0.5\u001b[39m, ax)\n",
+      "\u001b[0;31mAttributeError\u001b[0m: module 'gempyor.statistics' has no attribute 'plot_poisson_pmf'"
+     ]
+    }
+   ],
+   "source": [
+    "from gempyor import statistics\n",
+    "\n",
+    "statistics(co)"
+   ]
   }
  ],
  "metadata": {
diff --git a/examples/simple_usa_statelevel/simple_usa_statelevel.yml b/examples/simple_usa_statelevel/simple_usa_statelevel.yml
index 6488e9dd8..81efda823 100644
--- a/examples/simple_usa_statelevel/simple_usa_statelevel.yml
+++ b/examples/simple_usa_statelevel/simple_usa_statelevel.yml
@@ -121,7 +121,11 @@ inference:
       likelihood: 
         dist: pois
       regularize:
-        
+        - name: forecast 
+          last_n: 10
+          mult: 2
+        - name: allsubpop
+          mult: 10
     incidHosp:
       name: incidHosp
       sim_var: incidHosp
diff --git a/flepimop/gempyor_pkg/src/gempyor/statistics.py b/flepimop/gempyor_pkg/src/gempyor/statistics.py
deleted file mode 100644
index fc13a36e7..000000000
--- a/flepimop/gempyor_pkg/src/gempyor/statistics.py
+++ /dev/null
@@ -1,139 +0,0 @@
-import xarray as xr
-import pandas as pd
-import numpy as np
-import confuse
-import scipy.stats
-
-class Statistic:
-    """
-        A statistic is a function that takes two time series and returns a scalar value.
-        It applies resampling, scaling, and regularization to the data before computing the statistic's log-loss.
-        Configuration:
-        - sim_var: the variable in the simulation data
-        - data_var: the variable in the ground truth data
-        - resample: resample the data before computing the statistic
-            - freq: the frequency to resample the data to
-            - aggregator: the aggregation function to use
-            - skipna: whether to skip NA values
-        - regularize: apply a regularization term to the data before computing the statistic
-    """
-    def __init__(self, name, statistic_config: confuse.ConfigView):
-        self.sim_var = statistic_config["sim_var"].as_str()
-        self.data_var = statistic_config["data_var"].as_str()
-        self.name = name
-
-        self.resample = False
-        if statistic_config["resample"].exists():
-            self.resample = True
-            resample_config = statistic_config["resample"]
-            self.resample_freq = ""
-            if resample_config["freq"].exists():
-                self.resample_freq = resample_config["freq"].get()
-            
-            self.resample_aggregator = ""
-            if resample_config["aggregator"].exists():
-                self.resample_aggregator = getattr(pd.Series, resample_config["aggregator"].get())
-            
-            self.resample_skipna = False # TODO
-            if resample_config["aggregator"].exists() and resample_config["skipna"].exists():
-                self.resample_skipna = resample_config["skipna"].get()
-        
-        self.regularize = False
-        if statistic_config["regularize"].exists():
-            raise ValueError("Regularization is not implemented")
-            regularization_config = statistic_config["regularization"].get()
-
-        self.regularization = None
-        if statistic_config["regularize"].exists():
-            # TODO: support several regularization
-            self.regularization_config = statistic_config["regularize"]
-            self.regularization_name = self.regularization_config["name"].get()
-            self.regularization = getattr(self, f"_{self.regularization_name}_regularize")
-            if self.regularization is None:
-                raise ValueError(f"Unsupported regularization: {self.regularization_name}")
-
-        self.scale = False
-        if statistic_config["scale"].exists():
-            self.scale_func = getattr(np, statistic_config["scale"].get())
-    
-        self.dist = statistic_config["likelihood"]["dist"].get()
-        # TODO here get the parameter in a dictionnary
-
-    def _forecast_regularize(self, data):
-        # scale the data so that the lastest X items are more important
-        last_n = self.regularization_config["last_n"].get()
-        mult = self.regularization_config["mult"].get()
-        # multiply the last n items by mult
-        reg_data = data * np.concatenate([np.ones(data.shape[0]-last_n), np.ones(last_n)*mult])
-        return reg_data
-    
-    def _allsubpop_regularize(self, data):
-        """ add a regularization term that is the sum of all subpopulations
-        """
-
-
-
-    def __str__(self) -> str:
-        return f"{self.name}: {self.dist} between {self.sim_var} (sim) and {self.data_var} (data)."
-    
-    def __repr__(self) -> str:
-        return f"A Statistic(): {self.__str__()}"
-
-    def apply_resample(self, data):
-        if self.resample:
-            return data.resample(self.resample_freq).agg(self.resample_aggregator, skipna=self.resample_skipna)
-        else:
-            return data
-        
-    def apply_scale(self, data):
-        if self.scale:
-            return self.scale_func(data)
-        else:
-            return data
-        
-    def apply_transforms(self, data):
-        return self.apply_scale(self.apply_resampling(data))
-
-    def compute_logloss(self, model_data, gt_data):
-        model_data = self. apply_transforms(model_data[self.sim_var])
-        gt_data = self.apply_transforms(gt_data[self.data_var])
-
-                dist_map = {
-            "pois": scipy.stats.poisson.pmf,
-            "norm": scipy.stats.norm.pdf,
-            "nbinom": scipy.stats.nbinom.pmf,
-        }
-        if self.dist not in dist_map:
-            raise ValueError(f"Invalid distribution specified: {self.dist}")
-        log_likelihood = dist_map[self.dist](round(gt_data), model_data)
-
-        # Apply regularization if defined
-        if self.regularization:
-            log_likelihood -= self.regularization(model_data)
-
-
-            dist_map = {
-            "pois": scipy.stats.poisson.pmf,
-            "norm": lambda x, loc, scale: scipy.stats.norm.pdf(x, loc=loc, scale=self.params.get("scale", scale)),
-            "nbinom": lambda x, n, p: scipy.stats.nbinom.pmf(x, n=self.params.get("n"), p=model_data),
-        }
-        if self.dist not in dist_map:
-            raise ValueError(f"Invalid distribution specified: {self.dist}")
-
-        # Use stored parameters in the distribution function call
-        log_likelihood = dist_map[self.dist](round(gt_data), model_data, **self.params)
-
-
-
-        if not model_data.shape == gt_data.shape:
-            raise ValueError(f"{self.name} Statistic error: data and groundtruth do not have the same shape")
-        
-        if self.dist == "pois":
-            ll = np.log(scipy.stats.poisson.pmf(round(gt_data), model_data))
-        elif self.dist == "norm":
-            ll = np.log(scipy.stats.norm.pdf(gt_data, loc=model_data, scale=param[0]))
-        elif self.dist == "nbinom":
-            ll = np.log(scipy.stats.nbinom.pmf(gt_data, n=param[0], p=model_data))
-        else:
-            raise ValueError("Invalid distribution specified, got {self.dist}")
-        return ll
\ No newline at end of file

From 30c256162576543065170c0aa47b504e171f03ac Mon Sep 17 00:00:00 2001
From: Joseph Lemaitre <jo.lemaitresamra@gmail.com>
Date: Tue, 16 Apr 2024 16:11:01 +0200
Subject: [PATCH 29/31] manually undo commit

---
 flepimop/R_packages/inference/R/inference_slot_runner_funcs.R | 4 ++--
 flepimop/main_scripts/inference_slot.R                        | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/flepimop/R_packages/inference/R/inference_slot_runner_funcs.R b/flepimop/R_packages/inference/R/inference_slot_runner_funcs.R
index 9d01bf483..a8d7ddece 100644
--- a/flepimop/R_packages/inference/R/inference_slot_runner_funcs.R
+++ b/flepimop/R_packages/inference/R/inference_slot_runner_funcs.R
@@ -52,11 +52,11 @@ aggregate_and_calc_loc_likelihoods <- function(
             dplyr::filter(
                 modeled_outcome,
                 !!rlang::sym(obs_subpop) == location,
-                date %in% unique(obs$date[obs$subpop == location])
+                time %in% unique(obs$date[obs$subpop == location])
             ) %>%
             ## Reformat into form the algorithm is looking for
             inference::getStats(
-                "date",
+                "time",
                 "sim_var",
                 stat_list = targets_config,
                 start_date = start_date,
diff --git a/flepimop/main_scripts/inference_slot.R b/flepimop/main_scripts/inference_slot.R
index 5554052ee..b164489b8 100644
--- a/flepimop/main_scripts/inference_slot.R
+++ b/flepimop/main_scripts/inference_slot.R
@@ -588,7 +588,7 @@ for(seir_modifiers_scenario in seir_modifiers_scenarios) {
       # run
       if (config$inference$do_inference){
         sim_hosp <- flepicommon::read_file_of_type(gsub(".*[.]","",this_global_files[['hosp_filename']]))(this_global_files[['hosp_filename']]) %>%
-          dplyr::filter(date >= min(obs$date),date <= max(obs$date))
+          dplyr::filter(time >= min(obs$date),time <= max(obs$date))
         lhs <- unique(sim_hosp[[obs_subpop]])
         rhs <- unique(names(data_stats))
         all_locations <- rhs[rhs %in% lhs]

From 7c08809d3312fe50b6d75a720ec2cebeb527a254 Mon Sep 17 00:00:00 2001
From: Joseph Lemaitre <jo.lemaitresamra@gmail.com>
Date: Tue, 16 Apr 2024 16:26:33 +0200
Subject: [PATCH 30/31] change time to date in the R code, for groundtruth and
 python read files

---
 flepimop/R_packages/flepicommon/R/DataUtils.R |   6 +-
 .../flepiconfig/R/create_config_data.R        |   8 +-
 flepimop/R_packages/inference/R/functions.R   |   6 +-
 .../inference/R/inference_slot_runner_funcs.R |   4 +-
 .../inference/R/inference_to_forecast.R       |  20 +--
 .../inference/archive/InferenceTest.R         | 138 +++++++++---------
 .../test-aggregate_and_calc_loc_likelihoods.R |   3 +-
 .../tests/testthat/test-cum_death_forecast.R  |  10 +-
 flepimop/main_scripts/inference_slot.R        |   4 +-
 postprocessing/groundtruth_source.R           |   4 +-
 postprocessing/plot_predictions.R             |   4 +-
 postprocessing/processing_diagnostics.R       |   3 +-
 postprocessing/processing_diagnostics_AWS.R   |   3 +-
 postprocessing/processing_diagnostics_SLURM.R |   3 +-
 postprocessing/sim_processing_source.R        |  12 +-
 15 files changed, 112 insertions(+), 116 deletions(-)

diff --git a/flepimop/R_packages/flepicommon/R/DataUtils.R b/flepimop/R_packages/flepicommon/R/DataUtils.R
index 26cb4f6b9..dc1a1dafe 100755
--- a/flepimop/R_packages/flepicommon/R/DataUtils.R
+++ b/flepimop/R_packages/flepicommon/R/DataUtils.R
@@ -92,7 +92,7 @@ read_file_of_type <- function(extension,...){
     if(extension == 'csv'){
         return(function(x){suppressWarnings(readr::read_csv(x,,col_types = cols(
             .default = col_double(),
-            time=col_date(),
+            darw=col_date(),
             uid=col_character(),
             comp=col_character(),
             subpop=col_character()
@@ -101,8 +101,8 @@ read_file_of_type <- function(extension,...){
     if(extension == 'parquet'){
         return(function(x){
             tmp <- arrow::read_parquet(x)
-            if("POSIXct" %in% class(tmp$time)){
-                tmp$time <- lubridate::as_date(tz="GMT",tmp$time)
+            if("POSIXct" %in% class(tmp$date)){
+                tmp$date <- lubridate::as_date(tz="GMT",tmp$date)
             }
             tmp
         })
diff --git a/flepimop/R_packages/flepiconfig/R/create_config_data.R b/flepimop/R_packages/flepiconfig/R/create_config_data.R
index 187d5cba7..9f8187c7c 100644
--- a/flepimop/R_packages/flepiconfig/R/create_config_data.R
+++ b/flepimop/R_packages/flepiconfig/R/create_config_data.R
@@ -1146,7 +1146,7 @@ daily_mean_reduction <- function(dat,
         ) %>%
         dplyr::select(USPS, subpop, start_date, end_date, mean)
     
-    timeline <- tidyr::crossing(time = seq(from=min(dat$start_date), to=max(dat$end_date), by = 1),
+    timeline <- tidyr::crossing(date = seq(from=min(dat$start_date), to=max(dat$end_date), by = 1),
                                 subpop = unique(dat$subpop))
     
     if(any(stringr::str_detect(dat$subpop, '", "'))){
@@ -1175,12 +1175,12 @@ daily_mean_reduction <- function(dat,
         dplyr::select(subpop, start_date, end_date, mean) %>%
         dplyr::bind_rows(dat %>% dplyr::filter(subpop!="all") %>% dplyr::ungroup() %>% dplyr::select(-USPS)) %>%
         dplyr::left_join(timeline) %>%
-        dplyr::filter(time >= start_date & time <= end_date) %>%
-        dplyr::group_by(subpop, time) %>%
+        dplyr::filter(date >= start_date & date <= end_date) %>%
+        dplyr::group_by(subpop, date) %>%
         dplyr::summarize(mean = prod(1-mean))
     
     if(plot){
-        dat<- ggplot2::ggplot(data= dat, ggplot2::aes(x=time, y=mean))+
+        dat<- ggplot2::ggplot(data= dat, ggplot2::aes(x=date, y=mean))+
             ggplot2::geom_line()+
             ggplot2::facet_wrap(~subpop)+
             ggplot2::theme_bw()+
diff --git a/flepimop/R_packages/inference/R/functions.R b/flepimop/R_packages/inference/R/functions.R
index e3b754d51..80f29c9a6 100644
--- a/flepimop/R_packages/inference/R/functions.R
+++ b/flepimop/R_packages/inference/R/functions.R
@@ -300,9 +300,9 @@ calc_prior_likadj  <- function(params,
 ##'
 compute_cumulative_counts <- function(sim_hosp) {
     res <- sim_hosp %>%
-        gather(var, value, -time, -subpop) %>%
+        gather(var, value, -date, -subpop) %>%
         group_by(subpop, var) %>%
-        arrange(time) %>%
+        arrange(date) %>%
         mutate(cumul = cumsum(value)) %>%
         ungroup() %>%
         pivot_wider(names_from = "var", values_from = c("value", "cumul")) %>%
@@ -324,7 +324,7 @@ compute_cumulative_counts <- function(sim_hosp) {
 ##'
 compute_totals <- function(sim_hosp) {
     sim_hosp %>%
-        group_by(time) %>%
+        group_by(date) %>%
         summarise_if(is.numeric, sum, na.rm = TRUE) %>%
         mutate(subpop = "all") %>%
         select(all_of(colnames(sim_hosp))) %>%
diff --git a/flepimop/R_packages/inference/R/inference_slot_runner_funcs.R b/flepimop/R_packages/inference/R/inference_slot_runner_funcs.R
index a8d7ddece..9d01bf483 100644
--- a/flepimop/R_packages/inference/R/inference_slot_runner_funcs.R
+++ b/flepimop/R_packages/inference/R/inference_slot_runner_funcs.R
@@ -52,11 +52,11 @@ aggregate_and_calc_loc_likelihoods <- function(
             dplyr::filter(
                 modeled_outcome,
                 !!rlang::sym(obs_subpop) == location,
-                time %in% unique(obs$date[obs$subpop == location])
+                date %in% unique(obs$date[obs$subpop == location])
             ) %>%
             ## Reformat into form the algorithm is looking for
             inference::getStats(
-                "time",
+                "date",
                 "sim_var",
                 stat_list = targets_config,
                 start_date = start_date,
diff --git a/flepimop/R_packages/inference/R/inference_to_forecast.R b/flepimop/R_packages/inference/R/inference_to_forecast.R
index da7846ef3..13431f056 100644
--- a/flepimop/R_packages/inference/R/inference_to_forecast.R
+++ b/flepimop/R_packages/inference/R/inference_to_forecast.R
@@ -17,7 +17,7 @@ cum_death_forecast <- function (sim_data,
   require(dplyr)
   
   rc <- sim_data %>%
-    filter(time>start_date)%>%
+    filter(date>start_date)%>%
     inner_join(cum_dat)%>%
     group_by(sim_num, !!sym(loc_column))%>%
     mutate(cum_deaths_corr = cumsum(incidD)+cumDeaths)%>%
@@ -39,7 +39,7 @@ cum_death_forecast <- function (sim_data,
 ##' @param weights if not NA, the weights for the mean
 ##' @param loc_col the name of the  location column
 ##' 
-##' @return a forecast with columns time (end day), quantile steps_ahead and deaths
+##' @return a forecast with columns date (end day), quantile steps_ahead and deaths
 ##' 
 ##' @export
 create_cum_death_forecast <- function(sim_data, 
@@ -51,15 +51,15 @@ create_cum_death_forecast <- function(sim_data,
                                       loc_column="USPS") {
   
   ##Sanity checks
-  if(forecast_date>max(obs_data$time)+1) {stop("forecast date must be within one day after the range of observed times")}
-  if(forecast_date+1<min(sim_data$time)) {stop("no simulation support for first forecast date")}
+  if(forecast_date>max(obs_data$date)+1) {stop("forecast date must be within one day after the range of observed times")}
+  if(forecast_date+1<min(sim_data$date)) {stop("no simulation support for first forecast date")}
   
-  if(max(obs_data$time)==forecast_date){
+  if(max(obs_data$date)==forecast_date){
     ## USA Facts Data updates mid-day so forecasts run after noon will have a forecast date that overlaps with the obs_data
     ##convert data to a cumdeath forecast.
     print(glue::glue("Accumulate deaths through {forecast_date}, typically for USA Facts aggregation after noon."))
     start_deaths <- obs_data%>%
-      filter(time==forecast_date)%>%
+      filter(date==forecast_date)%>%
       select(!!sym(loc_column),cumDeaths)
     
     forecast_sims <- cum_death_forecast(sim_data,
@@ -70,7 +70,7 @@ create_cum_death_forecast <- function(sim_data,
     ## CSSE data updates at midnight so forecasts will not typically have a forecast date one day after the end of the obs_data       
     print(glue::glue("Accumulate deaths through {forecast_date-1}, typically for CSSE aggregation."))
     start_deaths <- obs_data%>%
-      filter(time==forecast_date-1)%>%
+      filter(date==forecast_date-1)%>%
       select(!!sym(loc_column),cumDeaths)
     
     forecast_sims <- cum_death_forecast(sim_data,
@@ -88,7 +88,7 @@ create_cum_death_forecast <- function(sim_data,
   }
   
   rc <- forecast_sims%>%
-    group_by(time, !!sym(loc_column))%>% 
+    group_by(date, !!sym(loc_column))%>% 
     summarize(x=list(enframe(c(quantile(cum_deaths_corr, probs=c(0.01, 0.025,
                                                                  seq(0.05, 0.95, by = 0.05), 0.975, 0.99)),
                                mean=mean(cum_deaths_corr)),
@@ -99,11 +99,11 @@ create_cum_death_forecast <- function(sim_data,
   ##Append on the the other deaths.
   rc<-dplyr::bind_rows(rc,
                        obs_data%>%
-                         select(time, !!sym(loc_column), cumDeaths)%>%
+                         select(date, !!sym(loc_column), cumDeaths)%>%
                          mutate(quantile="data"))
   
   rc<- rc%>%
-    mutate(steps_ahead=as.numeric(time-forecast_date))
+    mutate(steps_ahead=as.numeric(date-forecast_date))
   
   return(rc)
   
diff --git a/flepimop/R_packages/inference/archive/InferenceTest.R b/flepimop/R_packages/inference/archive/InferenceTest.R
index 68c875732..dfa52efc0 100644
--- a/flepimop/R_packages/inference/archive/InferenceTest.R
+++ b/flepimop/R_packages/inference/archive/InferenceTest.R
@@ -1,6 +1,6 @@
 ##'
 ##' Function that does a rapid testing of the inference procedures on a single
-##' time series.
+##' date series.
 ##'
 ##' @param seeding the initial seeding
 ##' @param config the config file with inference info.
@@ -44,8 +44,8 @@ single_loc_inference_test <- function(to_fit,
     # Data to fit
     obs <- to_fit
     
-    # Times based on config
-    sim_times <- seq.Date(as.Date(config$start_date), as.Date(config$end_date), by = "1 days")
+    # dates based on config
+    sim_dates <- seq.Date(as.Date(config$start_date), as.Date(config$end_date), by = "1 days")
     
     # Get unique geonames
     geonames <- unique(obs[[obs_subpop]])
@@ -83,7 +83,7 @@ single_loc_inference_test <- function(to_fit,
         
         seeding_init <- seeding
         for (i in 1:nrow(seeding_init)) {
-            seeding_init$date[i] <- sample(sim_times[1:20], 1)
+            seeding_init$date[i] <- sample(sim_dates[1:20], 1)
             # TODO change amount based on data
             seeding_init$amount[i] <- rpois(1, 10)
         }
@@ -102,7 +102,7 @@ single_loc_inference_test <- function(to_fit,
             write_csv(npi_file, append = file.exists(npi_file))
         
         # Simulate initial hospitalizatoins
-        initial_sim_hosp <- simulate_single_epi(times = sim_times, 
+        initial_sim_hosp <- simulate_single_epi(dates = sim_dates, 
                                                 seeding = initial_seeding,
                                                 R0 = R0, 
                                                 S0 = S0, 
@@ -110,13 +110,13 @@ single_loc_inference_test <- function(to_fit,
                                                 sigma = sigma,
                                                 beta_mults = 1-initial_npis$value) %>% 
             single_hosp_run(config) %>% 
-            dplyr::filter(time %in% obs$date)
+            dplyr::filter(date %in% obs$date)
         
         write_csv(initial_sim_hosp, glue::glue("{epi_dir}sim_slot_{s}_index_0.csv"))
         
         initial_sim_stats <- getStats(
             initial_sim_hosp,
-            "time",
+            "date",
             "sim_var",
             end_date = max(obs$date),
             config$inference$statistics
@@ -156,7 +156,7 @@ single_loc_inference_test <- function(to_fit,
             current_npis <- perturb_expand_npis(initial_npis, config$interventions$settings)
             
             # Simulate  hospitalizatoins
-            sim_hosp <- simulate_single_epi(times = sim_times,
+            sim_hosp <- simulate_single_epi(dates = sim_dates,
                                             seeding = current_seeding,
                                             R0 = R0,
                                             S0 = S0,
@@ -164,11 +164,11 @@ single_loc_inference_test <- function(to_fit,
                                             sigma = sigma,
                                             beta_mults = 1-current_npis$reduction) %>% 
                 single_hosp_run(config) %>% 
-                dplyr::filter(time %in% obs$date)
+                dplyr::filter(date %in% obs$date)
             
             sim_stats <- getStats(
                 sim_hosp,
-                "time",
+                "date",
                 "sim_var",
                 end_date = max(obs$date),
                 config$inference$statistics
@@ -239,7 +239,7 @@ single_loc_inference_test <- function(to_fit,
 
 ##'
 ##' Function that does a rapid testing of the inference procedures on a single
-##' time series.
+##' date series.
 ##'
 ##' @param seeding the initial seeding
 ##' @param config the config file with inference info.
@@ -287,8 +287,8 @@ multi_loc_inference_test <- function(to_fit,
     # Data to fit
     obs <- to_fit
     
-    # Times based on config
-    sim_times <- seq.Date(as.Date(config$start_date), as.Date(config$end_date), by = "1 days")
+    # dates based on config
+    sim_dates <- seq.Date(as.Date(config$start_date), as.Date(config$end_date), by = "1 days")
     
     # Get unique geonames
     geonames <- unique(obs[[obs_subpop]])
@@ -332,7 +332,7 @@ multi_loc_inference_test <- function(to_fit,
         
         seeding_init <- seedings
         for (i in 1:nrow(seeding_init)) {
-            seeding_init$date[i] <- sample(sim_times[1:20], 1)
+            seeding_init$date[i] <- sample(sim_dates[1:20], 1)
             # TODO change amount based on data
             seeding_init$amount[i] <- rpois(1, 10)
         }
@@ -354,7 +354,7 @@ multi_loc_inference_test <- function(to_fit,
             pivot_wider(values_from = "value", names_from = "subpop", id_cols = "date")
         
         # Simulate epi
-        initial_sim_hosp <- simulate_multi_epi(times = sim_times,
+        initial_sim_hosp <- simulate_multi_epi(dates = sim_dates,
                                                seedings = initial_seeding,
                                                R0 = R0,
                                                S0s = S0s,
@@ -364,7 +364,7 @@ multi_loc_inference_test <- function(to_fit,
                                                mob = mob,
                                                beta_mults = 1-as.matrix(npi_mat[,-1])) %>% 
             multi_hosp_run(N, config) %>% 
-            dplyr::filter(time %in% obs$date)
+            dplyr::filter(date %in% obs$date)
         
         write_csv(initial_sim_hosp, glue::glue("{epi_dir}sim_slot_{s}_index_0_multi.csv"))
         
@@ -372,10 +372,10 @@ multi_loc_inference_test <- function(to_fit,
         for(location in all_locations) {
             
             local_sim_hosp <- dplyr::filter(initial_sim_hosp, !!rlang::sym(obs_subpop) == location) %>%
-                dplyr::filter(time %in% unique(obs$date[obs$subpop == location]))
+                dplyr::filter(date %in% unique(obs$date[obs$subpop == location]))
             initial_sim_stats <- inference::getStats(
                 local_sim_hosp,
-                "time",
+                "date",
                 "sim_var",
                 #end_date = max(obs$date[obs[[obs_subpop]] == location]),
                 stat_list = config$inference$statistics
@@ -427,7 +427,7 @@ multi_loc_inference_test <- function(to_fit,
                 pivot_wider(values_from = "reduction", names_from = "subpop", id_cols = "date")
             
             # Simulate  hospitalizatoins
-            sim_hosp <- simulate_multi_epi(times = sim_times,
+            sim_hosp <- simulate_multi_epi(dates = sim_dates,
                                            seedings = current_seeding,
                                            R0 = R0,
                                            S0s = S0s,
@@ -437,16 +437,16 @@ multi_loc_inference_test <- function(to_fit,
                                            mob = mob,
                                            beta_mults = 1-as.matrix(npi_mat[,-1])) %>% 
                 multi_hosp_run(N, config) %>% 
-                dplyr::filter(time %in% obs$date)
+                dplyr::filter(date %in% obs$date)
             
             current_likelihood_data <- list()
             
             for(location in all_locations) {
                 local_sim_hosp <- dplyr::filter(sim_hosp, !!rlang::sym(obs_subpop) == location) %>%
-                    dplyr::filter(time %in% unique(obs$date[obs$subpop == location]))
+                    dplyr::filter(date %in% unique(obs$date[obs$subpop == location]))
                 sim_stats <- inference::getStats(
                     local_sim_hosp,
-                    "time",
+                    "date",
                     "sim_var",
                     #end_date = max(obs$date[obs[[obs_subpop]] == location]),
                     stat_list = config$inference$statistics
@@ -533,11 +533,11 @@ multi_loc_inference_test <- function(to_fit,
 ##'
 ##' TODO: Modify to just call python code
 ##'
-##' @param times the times to run the epidemic on
+##' @param dates the dates to run the epidemic on
 ##' @param seeding the seeding to use
 ##' @param R0 the reproductive number
 ##' @param S0 the initial number susceptible
-##' @param gamma the time between compartments
+##' @param gamma the date between compartments
 ##' @param sigma the incubation period/latent period
 ##' @param beta_mults multipliers to capture the impact of interventions
 ##' @param alpha defaults to 1
@@ -545,13 +545,13 @@ multi_loc_inference_test <- function(to_fit,
 ##' @return the epimic states
 ##'
 ##' @export
-simulate_single_epi <- function(times,
+simulate_single_epi <- function(dates,
                                 seeding,
                                 S0,
                                 R0,
                                 gamma,
                                 sigma,
-                                beta_mults = rep(1, length(times)),
+                                beta_mults = rep(1, length(dates)),
                                 alpha = 1) {
     
     require(tidyverse)
@@ -560,7 +560,7 @@ simulate_single_epi <- function(times,
     beta <- R0 * gamma/3
     
     ##set up return matrix
-    epi <- matrix(0, nrow=length(times), ncol=7)
+    epi <- matrix(0, nrow=length(dates), ncol=7)
     colnames(epi) <- c("S","E","I1","I2","I3","R","incidI")
     
     
@@ -577,12 +577,12 @@ simulate_single_epi <- function(times,
     epi[1,S] <- S0
     
     ##get the indices where seeding occurs
-    seed_times <- which(times%in%seeding$date)
+    seed_dates <- which(dates%in%seeding$date)
     
-    for (i in 1:(length(times)-1)) {
+    for (i in 1:(length(dates)-1)) {
         ##Seed if possible
-        if(i%in% seed_times) {
-            tmp <- seeding$amount[which(i==seed_times)]
+        if(i%in% seed_dates) {
+            tmp <- seeding$amount[which(i==seed_dates)]
             epi[i,S] <- epi[i,S] - tmp
             epi[i,E] <- epi[i,E] + tmp
         }
@@ -607,8 +607,8 @@ simulate_single_epi <- function(times,
     }
     
     epi <- as.data.frame(epi) %>%
-        mutate(time=times)%>%
-        pivot_longer(-time,values_to="N", names_to="comp")
+        mutate(date=dates)%>%
+        pivot_longer(-date,values_to="N", names_to="comp")
     
     return(epi)
 }
@@ -620,22 +620,22 @@ simulate_single_epi <- function(times,
 ##'
 ##' TODO: Modify to just call python code
 ##'
-##' @param times the times to run the epidemic on
+##' @param dates the dates to run the epidemic on
 ##' @param seedings the seeding to use
 ##' @param R0s the reproductive number
 ##' @param S0s the initial number susceptibles
-##' @param gamma the time between compartments
+##' @param gamma the date between compartments
 ##' @param sigma the incubation period/latent period
 ##' @param N the number of nodes
 ##' @param mob mobility matrix N x N
-##' @param pa proportion of time away
+##' @param pa proportion of date away
 ##' @param beta_mults multipliers to capture the impact of interventions
 ##' @param alpha defaults to 1
 ##'
 ##' @return the epimic states
 ##'
 ##' @export
-simulate_multi_epi <- function(times,
+simulate_multi_epi <- function(dates,
                                seedings,
                                S0s,
                                R0,
@@ -644,12 +644,12 @@ simulate_multi_epi <- function(times,
                                N,
                                mob,
                                pa = .5,
-                               beta_mults = matrix(rep(1, N*length(times)), ncol = N),
+                               beta_mults = matrix(rep(1, N*length(dates)), ncol = N),
                                alpha = 1) {
     
     require(tidyverse)
     
-    # proportion time away
+    # proportion date away
     paoverh <- pa/S0s
     oneminusp <- 1-paoverh*rowSums(mob)
     
@@ -657,7 +657,7 @@ simulate_multi_epi <- function(times,
     beta <- R0 * gamma/3
     
     ##set up return matrix
-    epi <- array(0, dim = c(length(times), 7, N))
+    epi <- array(0, dim = c(length(dates), 7, N))
     colnames(epi) <- c("S","E","I1","I2","I3","R","incidI")
     
     ##column indices for convenience
@@ -673,17 +673,17 @@ simulate_multi_epi <- function(times,
     epi[1,S,] <- S0s
     
     ##get the indices where seeding occurs
-    seed_times <- which(times%in%seedings$date)
+    seed_dates <- which(dates%in%seedings$date)
     
-    for (i in 1:(length(times)-1)) {
+    for (i in 1:(length(dates)-1)) {
         
         betaIh <- matrix(beta*beta_mults[i,]*(colSums(epi[i,I1:I3,])^alpha)/S0s, ncol = 1)
         mobbetaIh <- mob %*% betaIh
         
         for (j in 1:N) {
             ##Seed if possible
-            if(i%in% seed_times) {
-                ind <- which(i==seed_times)
+            if(i%in% seed_dates) {
+                ind <- which(i==seed_dates)
                 if (ind == j) {
                     tmp <- seedings$amount[ind]
                     epi[i,S,j] <- epi[i,S,j] - tmp
@@ -714,8 +714,8 @@ simulate_multi_epi <- function(times,
     
     epi <- lapply(1:N, function(x) as.data.frame(epi[,,x]) %>% mutate(subpop = x)) %>%
         bind_rows() %>% 
-        mutate(time=rep(times, N)) %>%
-        pivot_longer(cols = c(-time, -subpop), values_to="N", names_to="comp")
+        mutate(date=rep(dates, N)) %>%
+        pivot_longer(cols = c(-date, -subpop), values_to="N", names_to="comp")
     
     return(epi)
 }
@@ -736,9 +736,9 @@ single_hosp_run <- function(epi, config) {
     p_death <- config$hospitalization$parameters$p_death
     p_death_rate <- config$hospitalization$parameters$p_death_rate
     p_hosp <- p_death/p_death_rate
-    time_hosp_pars <- as_evaled_expression(config$hospitalization$parameters$time_hosp)
-    time_disch_pars <- as_evaled_expression(config$hospitalization$parameters$time_disch)
-    time_hosp_death_pars <- as_evaled_expression(config$hospitalization$parameters$time_hosp_death)
+    date_hosp_pars <- as_evaled_expression(config$hospitalization$parameters$date_hosp)
+    date_disch_pars <- as_evaled_expression(config$hospitalization$parameters$date_disch)
+    date_hosp_death_pars <- as_evaled_expression(config$hospitalization$parameters$date_hosp_death)
     dat_ <- dplyr::filter(epi, comp == "incidI") %>% 
         select(-comp) %>% 
         rename(incidI = N) %>%
@@ -749,9 +749,9 @@ single_hosp_run <- function(epi, config) {
         dat_ <- select(dat_, -subpop)
     }
     
-    dat_H <- hosp_create_delay_frame('incidI',p_hosp,dat_,time_hosp_pars,"H")
-    data_D <- hosp_create_delay_frame('incidH',p_death_rate,dat_H,time_hosp_death_pars,"D")
-    R_delay_ <- round(exp(time_disch_pars[1]))
+    dat_H <- hosp_create_delay_frame('incidI',p_hosp,dat_,date_hosp_pars,"H")
+    data_D <- hosp_create_delay_frame('incidH',p_death_rate,dat_H,date_hosp_death_pars,"D")
+    R_delay_ <- round(exp(date_disch_pars[1]))
     res <- Reduce(function(x, y, ...) merge(x, y, all = TRUE, ...),
                   list(dat_, dat_H, data_D)) %>%
         tidyr::replace_na(
@@ -759,7 +759,7 @@ single_hosp_run <- function(epi, config) {
                  incidH = 0,
                  incidD = 0,
                  hosp_curr = 0)) %>%
-        dplyr::mutate(date_inds = as.integer(time - min(time) + 1)) %>%
+        dplyr::mutate(date_inds = as.integer(date - min(date) + 1)) %>%
         dplyr::arrange(date_inds) %>%
         split(.$uid) %>%
         purrr::map_dfr(function(.x){
@@ -781,14 +781,14 @@ single_hosp_run <- function(epi, config) {
 multi_hosp_run <- function(epi, N, config) {
     map_df(1:N, 
            ~ single_hosp_run(dplyr::filter(epi, subpop == .), config)) %>%
-        dplyr::filter(time >= config$start_date,
-               time <= config$end_date)
+        dplyr::filter(date >= config$start_date,
+               date <= config$end_date)
 }
 
 ##'
 ##' Create NPIs dataframe for a single location for input to SEIR code
 ##'
-##' @param times vector of times to use
+##' @param dates vector of dates to use
 ##' @param config configuration file
 ##'
 ##' @return a dataframe with npi reduction values by date
@@ -797,8 +797,8 @@ multi_hosp_run <- function(epi, N, config) {
 ##' @export
 npis_dataframe <- function(config, random = F, subpop = 1, offset = 0, intervention_multi = 1) {
     
-    times <- seq.Date(as.Date(config$start_date), as.Date(config$end_date), by = "1 days")
-    npis <- tibble(date = times, value = 0, modifier_name = "local_variation", subpop = subpop)
+    dates <- seq.Date(as.Date(config$start_date), as.Date(config$end_date), by = "1 days")
+    npis <- tibble(date = dates, value = 0, modifier_name = "local_variation", subpop = subpop)
     interventions <- config$interventions$settings
     date_changes <- map_chr(interventions[1:2], 
                             ~ifelse(is.null(.$period_start_date),
@@ -809,17 +809,17 @@ npis_dataframe <- function(config, random = F, subpop = 1, offset = 0, intervent
     
     # Apply interventions
     for (d in 1:length(date_changes)) {
-        npis$value[times >= date_changes[d]] <- interventions[[d]]$value$mean * intervention_multi
-        npis$modifier_name[times >= date_changes[d]] <- names(interventions)[d]
+        npis$value[dates >= date_changes[d]] <- interventions[[d]]$value$mean * intervention_multi
+        npis$modifier_name[dates >= date_changes[d]] <- names(interventions)[d]
     }
     
     if(random) {
         # Randomly assign interventions
         for (d in 1:length(date_changes)) {
             if (names(interventions)[d] == "local_variation") {
-                npis$value[times >= date_changes[d]] <- runif(1, -.5, .5)
+                npis$value[dates >= date_changes[d]] <- runif(1, -.5, .5)
             } else {
-                npis$value[times >= date_changes[d]] <- runif(1, 0, 1)
+                npis$value[dates >= date_changes[d]] <- runif(1, 0, 1)
             }
         }
     }
@@ -840,14 +840,14 @@ npis_dataframe <- function(config, random = F, subpop = 1, offset = 0, intervent
 synthetic_data <- function(S0, seeding, config) {
     
     # Simulate single epidemic 
-    times <- seq.Date(as.Date(config$start_date), as.Date(config$end_date), by = "1 days")
+    dates <- seq.Date(as.Date(config$start_date), as.Date(config$end_date), by = "1 days")
     npis <- npis_dataframe(config)
     R0 <- flepicommon::as_evaled_expression(config$seir$parameters$R0s$value)
     gamma <- flepicommon::as_evaled_expression(config$seir$parameters$gamma$value)
     sigma <- flepicommon::as_evaled_expression(config$seir$parameters$sigma)
     
     # Simulate epi
-    epi <- simulate_single_epi(times = times,
+    epi <- simulate_single_epi(dates = dates,
                                seeding = seeding,
                                R0 = R0,
                                S0 = S0,
@@ -858,7 +858,7 @@ synthetic_data <- function(S0, seeding, config) {
     # - - - -
     # Setup fake data
     fake_data <- single_hosp_run(epi, config) %>%
-        rename(date = time) %>% 
+        rename(date = date) %>% 
         dplyr::filter(date >= config$start_date,
                date <= config$end_date)
     
@@ -882,7 +882,7 @@ synthetic_data_multi <- function(S0s, seedings, mob, config, offsets, interventi
     N <- length(S0s)  # number of nodes
     
     # Simulate single epidemic 
-    times <- seq.Date(as.Date(config$start_date), as.Date(config$end_date), by = "1 days")
+    dates <- seq.Date(as.Date(config$start_date), as.Date(config$end_date), by = "1 days")
     npis <- pmap(list(x = 1:N, y = offsets, z = interventions_multi),
                  function(x,y,z) 
                      npis_dataframe(config, 
@@ -899,7 +899,7 @@ synthetic_data_multi <- function(S0s, seedings, mob, config, offsets, interventi
         pivot_wider(values_from = "value", names_from = "subpop", id_cols = "date")
     
     # Simulate epi
-    epi <- simulate_multi_epi(times = times,
+    epi <- simulate_multi_epi(dates = dates,
                               seedings = seedings,
                               R0 = R0,
                               S0s = S0s,
@@ -913,7 +913,7 @@ synthetic_data_multi <- function(S0s, seedings, mob, config, offsets, interventi
     # Setup fake data
     fake_data <- map_df(1:N, 
                         ~ single_hosp_run(dplyr::filter(epi, subpop == .), config)) %>%
-        rename(date = time) %>% 
+        rename(date = date) %>% 
         dplyr::filter(date >= config$start_date,
                date <= config$end_date)
     
@@ -921,7 +921,7 @@ synthetic_data_multi <- function(S0s, seedings, mob, config, offsets, interventi
 }
 
 ##'
-##' Expands the perturb npi into a dataframe with time
+##' Expands the perturb npi into a dataframe with date
 ##'
 ##' @param npis the npis in "long" version (one value by npi, date and place)
 ##' @param intervention_settings intervention_settings from the config file
diff --git a/flepimop/R_packages/inference/tests/testthat/test-aggregate_and_calc_loc_likelihoods.R b/flepimop/R_packages/inference/tests/testthat/test-aggregate_and_calc_loc_likelihoods.R
index a4f2a961e..704e096fe 100644
--- a/flepimop/R_packages/inference/tests/testthat/test-aggregate_and_calc_loc_likelihoods.R
+++ b/flepimop/R_packages/inference/tests/testthat/test-aggregate_and_calc_loc_likelihoods.R
@@ -80,8 +80,7 @@ get_minimal_setup <- function () {
     sim_hosp <- obs %>%
         dplyr::rename(incidD = death_incid, incidC = confirmed_incid) %>%
         dplyr::mutate(incidD = incidD + rpois(length(incidD), incidD))%>%
-        dplyr::mutate(incidC = incidC + rpois(length(incidC), incidC))%>%
-        dplyr::rename(time=date)
+        dplyr::mutate(incidC = incidC + rpois(length(incidC), incidC))
 
     ##the observed node name.
     obs_subpop <- "subpop"
diff --git a/flepimop/R_packages/inference/tests/testthat/test-cum_death_forecast.R b/flepimop/R_packages/inference/tests/testthat/test-cum_death_forecast.R
index ad9b9f87d..496ff8b6e 100644
--- a/flepimop/R_packages/inference/tests/testthat/test-cum_death_forecast.R
+++ b/flepimop/R_packages/inference/tests/testthat/test-cum_death_forecast.R
@@ -3,7 +3,7 @@ context("cu_death_forecast")
 
 test_that("cum_death_forecast gives correct results, for one loc 100 and on 0", {
   sim_data <- dplyr::tibble(incidD=rpois(200, 10),
-                           time=rep(as.Date("2020-01-01")+1:100,2),
+                           date=rep(as.Date("2020-01-01")+1:100,2),
                            sim_num=1,
                            USPS=rep(c("NY","WA"), each=100))
   
@@ -28,7 +28,7 @@ test_that("cum_death_forecast gives correct results, for one loc 100 and on 0",
 
 test_that("Giving a date results in an appropriate forecast only for the future", {
   sim_data <- dplyr::tibble(incidD=rpois(200, 10),
-                            time=rep(as.Date("2020-01-01")+0:99,2),
+                            date=rep(as.Date("2020-01-01")+0:99,2),
                             sim_num=1,
                             USPS=rep(c("NY","WA"), each=100))
   
@@ -43,11 +43,11 @@ test_that("Giving a date results in an appropriate forecast only for the future"
                            "USPS")
   
 
-  expect_that(min(rc$time), equals(as.Date("2020-01-16")))
+  expect_that(min(rc$date), equals(as.Date("2020-01-16")))
   expect_that(nrow(rc),equals(2*85))
   expect_that(max(rc$cum_deaths_corr[rc$USPS=="NY"]),
-             equals(sum(sim_data$incidD[sim_data$time>"2020-01-15" & sim_data$USPS=="NY"])))
+             equals(sum(sim_data$incidD[sim_data$date>"2020-01-15" & sim_data$USPS=="NY"])))
   expect_that(max(rc$cum_deaths_corr[rc$USPS=="WA"]),
-              equals(sum(sim_data$incidD[sim_data$time>"2020-01-15" & sim_data$USPS=="WA"])+100))
+              equals(sum(sim_data$incidD[sim_data$date>"2020-01-15" & sim_data$USPS=="WA"])+100))
   
 })
diff --git a/flepimop/main_scripts/inference_slot.R b/flepimop/main_scripts/inference_slot.R
index b164489b8..5e45d0e19 100644
--- a/flepimop/main_scripts/inference_slot.R
+++ b/flepimop/main_scripts/inference_slot.R
@@ -290,7 +290,7 @@ if (config$inference$do_inference){
   # function to calculate the likelihood when comparing simulation output (sim_hosp) to ground truth data
   likelihood_calculation_fun <- function(sim_hosp){
 
-    sim_hosp <- dplyr::filter(sim_hosp,sim_hosp$time >= min(obs$date),sim_hosp$time <= max(obs$date))
+    sim_hosp <- dplyr::filter(sim_hosp,sim_hosp$date >= min(obs$date),sim_hosp$date <= max(obs$date))
     lhs <- unique(sim_hosp[[obs_subpop]])
     rhs <- unique(names(data_stats))
     all_locations <- rhs[rhs %in% lhs]
@@ -588,7 +588,7 @@ for(seir_modifiers_scenario in seir_modifiers_scenarios) {
       # run
       if (config$inference$do_inference){
         sim_hosp <- flepicommon::read_file_of_type(gsub(".*[.]","",this_global_files[['hosp_filename']]))(this_global_files[['hosp_filename']]) %>%
-          dplyr::filter(time >= min(obs$date),time <= max(obs$date))
+          dplyr::filter(date >= min(obs$date),date <= max(obs$date))
         lhs <- unique(sim_hosp[[obs_subpop]])
         rhs <- unique(names(data_stats))
         all_locations <- rhs[rhs %in% lhs]
diff --git a/postprocessing/groundtruth_source.R b/postprocessing/groundtruth_source.R
index d92f191d5..a1f03c39f 100644
--- a/postprocessing/groundtruth_source.R
+++ b/postprocessing/groundtruth_source.R
@@ -100,12 +100,12 @@ clean_gt_forplots <- function(gt_data){
         pivot_wider(names_from = target, values_from = incid)
 
     gt_long <- gt_long %>%
-        rename(time=date, USPS=source)
+        rename(USPS=source)
     gt_long <- gt_long %>%
         rename(outcome_name = target, outcome = incid)
 
     gt_data <- gt_data %>%
-        rename(time=date, USPS=source)
+        rename(USPS=source)
 
     return(gt_data)
 }
diff --git a/postprocessing/plot_predictions.R b/postprocessing/plot_predictions.R
index 5397966f5..d512a2d93 100644
--- a/postprocessing/plot_predictions.R
+++ b/postprocessing/plot_predictions.R
@@ -39,7 +39,7 @@ state_cw <- fips_us_county %>%
 # GROUND TRUTH ------------------------------------------------------------
 
 gt_data <- gt_data %>% 
-  mutate(time = lubridate::as_date(time)) %>% mutate(date = time)
+  mutate(date = lubridate::as_date(date)) %>%
 colnames(gt_data) <- gsub("incidI", "incidC", colnames(gt_data))
 gt_outcomes <- outcomes_[outcomes_ != "I" & sapply(X = paste0("incid", outcomes_), FUN = function(x=X, y) any(grepl(pattern = x, x = y)), y = colnames(gt_data)) ]
 outcomes_gt_ <- outcomes_[outcomes_ %in% gt_outcomes]
@@ -56,7 +56,7 @@ gt_data_2 <- gt_data_2 %>% mutate(cumH = 0) # incidH is only cumulative from sta
 gt_cl <- NULL
 if (any(outcomes_time_=="weekly")) {
   # Incident
-  gt_data_st_week <- get_weekly_incid(gt_data %>% dplyr::select(time, subpop, USPS, paste0("incid", outcomes_gt_[outcomes_time_gt_=="weekly"])) %>% mutate(sim_num = 0),
+  gt_data_st_week <- get_weekly_incid(gt_data %>% dplyr::select(date, subpop, USPS, paste0("incid", outcomes_gt_[outcomes_time_gt_=="weekly"])) %>% mutate(sim_num = 0),
   # gt_data_st_week <- get_weekly_incid(gt_data %>% dplyr::select(time, subpop, USPS, paste0("incid", outcomes_gt_[outcomes_time_gt_=="weekly"])) %>% mutate(sim_num = 0),
                                       outcomes = outcomes_gt_[outcomes_time_gt_=="weekly"]) 
   
diff --git a/postprocessing/processing_diagnostics.R b/postprocessing/processing_diagnostics.R
index ace858422..74bc68032 100644
--- a/postprocessing/processing_diagnostics.R
+++ b/postprocessing/processing_diagnostics.R
@@ -283,9 +283,8 @@ for(i in 1:length(USPS)){
   
   filter_gt_data <- gt_data %>%
     filter(USPS == state) %>%
-    select(USPS, subpop, time, dplyr::contains("incid") & !dplyr::contains("_")) %>%
+    select(USPS, subpop, date, dplyr::contains("incid") & !dplyr::contains("_")) %>%
     pivot_longer(dplyr::contains('incid'), names_to = "outcome", values_to = "value") %>%
-    rename(date = time) %>%
     mutate(week = lubridate::week(date)) %>%
     group_by(outcome, week) %>%
     mutate(rollmean = zoo::rollmean(x = value, k = 7, fill = NA))
diff --git a/postprocessing/processing_diagnostics_AWS.R b/postprocessing/processing_diagnostics_AWS.R
index fd1853542..5a8af5c27 100644
--- a/postprocessing/processing_diagnostics_AWS.R
+++ b/postprocessing/processing_diagnostics_AWS.R
@@ -282,9 +282,8 @@ for(i in 1:length(USPS)){
 
   filter_gt_data <- gt_data %>%
     filter(USPS == state) %>%
-    select(USPS, subpop, time, dplyr::contains("incid") & !dplyr::contains("_")) %>%
+    select(USPS, subpop, date, dplyr::contains("incid") & !dplyr::contains("_")) %>%
     pivot_longer(dplyr::contains('incid'), names_to = "outcome", values_to = "value") %>%
-    rename(date = time) %>%
     mutate(week = lubridate::week(date)) %>%
     group_by(outcome, week) %>%
     mutate(rollmean = zoo::rollmean(x = value, k = 7, fill = NA))
diff --git a/postprocessing/processing_diagnostics_SLURM.R b/postprocessing/processing_diagnostics_SLURM.R
index 98c7da65e..03e0ebb3e 100644
--- a/postprocessing/processing_diagnostics_SLURM.R
+++ b/postprocessing/processing_diagnostics_SLURM.R
@@ -231,9 +231,8 @@ for(i in 1:length(USPS)){
   
   filter_gt_data <- gt_data %>%
     filter(USPS == state) %>%
-    select(USPS, subpop, time, dplyr::contains("incid") & !dplyr::contains("_")) %>%
+    select(USPS, subpop, date, dplyr::contains("incid") & !dplyr::contains("_")) %>%
     pivot_longer(dplyr::contains('incid'), names_to = "outcome", values_to = "value") %>%
-    rename(date = time) %>%
     mutate(week = lubridate::week(date)) %>%
     group_by(outcome, week) %>%
     mutate(rollmean = zoo::rollmean(x = value, k = 7, fill = NA))
diff --git a/postprocessing/sim_processing_source.R b/postprocessing/sim_processing_source.R
index 001cdec08..9dda42208 100644
--- a/postprocessing/sim_processing_source.R
+++ b/postprocessing/sim_processing_source.R
@@ -35,14 +35,14 @@ combine_and_format_sims <- function(outcome_vars = "incid",
     dirs <- dirs[str_detect(dirs, '/hosp')][1]
     res_subpop_all <- arrow::open_dataset(dirs,
                                           partitioning = c("lik_type", "is_final")) %>%
-        select(time, subpop, starts_with(outcome_vars)) %>%
-        # select(time, subpop, outcome_modifiers_scenario, starts_with(outcome_vars)) %>%
-        filter(time>=forecast_date & time<=end_date) %>%
+        select(date, subpop, starts_with(outcome_vars)) %>%
+        # select(date, subpop, outcome_modifiers_scenario, starts_with(outcome_vars)) %>%
+        filter(date>=forecast_date & date<=end_date) %>%
         collect() %>%
         # filter(stringr::str_detect(outcome_modifiers_scenario, death_filter)) %>%
-        mutate(time=as.Date(time)) %>%
-        # group_by(time, subpop, outcome_modifiers_scenario) %>%
-        group_by(time, subpop) %>%
+        mutate(date=as.Date(date)) %>%
+        # group_by(date, subpop, outcome_modifiers_scenario) %>%
+        group_by(date, subpop) %>%
         dplyr::mutate(sim_num = as.character(seq_along(subpop))) %>%
         ungroup()
     

From 72c1f4a02ba12643db12ec6bb80d85320a8a3f0d Mon Sep 17 00:00:00 2001
From: Joseph Lemaitre <jo.lemaitresamra@gmail.com>
Date: Tue, 16 Apr 2024 16:48:10 +0200
Subject: [PATCH 31/31] fix

---
 flepimop/R_packages/flepicommon/R/DataUtils.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/flepimop/R_packages/flepicommon/R/DataUtils.R b/flepimop/R_packages/flepicommon/R/DataUtils.R
index dc1a1dafe..6aaf80dc3 100755
--- a/flepimop/R_packages/flepicommon/R/DataUtils.R
+++ b/flepimop/R_packages/flepicommon/R/DataUtils.R
@@ -92,7 +92,7 @@ read_file_of_type <- function(extension,...){
     if(extension == 'csv'){
         return(function(x){suppressWarnings(readr::read_csv(x,,col_types = cols(
             .default = col_double(),
-            darw=col_date(),
+            date=col_date(),
             uid=col_character(),
             comp=col_character(),
             subpop=col_character()