Repository: IN-CORE/pyincore (language: python)

Issue:
Main to develop



Discussion / hints:

Fix patch:
diff --git a/.gitignore b/.gitignore
index d1085d15d..8c4c6e9e8 100644
--- a/.gitignore
+++ b/.gitignore
@@ -47,6 +47,8 @@ original/
 
 # possible output file
 *.csv
+!pyincore/analyses/*/*.csv
+!/tests/pyincore/analyses/*/*.csv
 # a pair of negation pattern; everything else except below will be ignored by git
 *.json
 !tests/data/*/*.json
diff --git a/CHANGELOG.md b/CHANGELOG.md
index c8772b487..7d49a917d 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -6,6 +6,21 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/)
 and this project adheres to [Semantic Versioning](http://semver.org/).
 
 
+## [1.18.0] - 2024-04-03
+
+### Added
+- Add CoreCGEML module [#468](https://github.com/IN-CORE/pyincore/issues/468)
+- Add ML enabled SLC CGE [#508](https://github.com/IN-CORE/pyincore/issues/508)
+- More mathematical functions for the DFR3 expression evaluation [#531](https://github.com/IN-CORE/pyincore/issues/531)
+
+### Changed
+- Need to use left join so the building number stays the same [#514](https://github.com/IN-CORE/pyincore/issues/541)
+- Building Functionality column renaming [#510](https://github.com/IN-CORE/pyincore/issues/510)
+- Update the post-processing step for generating population dislocation map layer [#526](https://github.com/IN-CORE/pyincore/issues/526)
+
+### Fixed
+- Fixed JWT token validation [#529](https://github.com/IN-CORE/pyincore/issues/529)
+
 ## [1.17.0] - 2024-02-22
 
 ### Added
@@ -16,6 +31,7 @@ and this project adheres to [Semantic Versioning](http://semver.org/).
 
 ### Changed
 - Retrofitted Building Damage [#469](https://github.com/IN-CORE/pyincore/issues/469) 
+- Optimize building damage performance [#513](https://github.com/IN-CORE/pyincore/issues/513)
 
 
 ## [1.16.0] - 2024-02-07
diff --git a/docs/source/conf.py b/docs/source/conf.py
index cf9be1b0c..ac7025546 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -33,9 +33,9 @@
 author = ''
 
 # The short X.Y version
-version = '1.17'
+version = '1.18'
 # The full version, including alpha/beta/rc tags
-release = '1.17.0'
+release = '1.18.0'
 
 # -- General configuration ---------------------------------------------------
 
diff --git a/pyincore/analyses/buildingdamage/buildingdamage.py b/pyincore/analyses/buildingdamage/buildingdamage.py
index b6a8b863c..e776e71c7 100755
--- a/pyincore/analyses/buildingdamage/buildingdamage.py
+++ b/pyincore/analyses/buildingdamage/buildingdamage.py
@@ -165,36 +165,35 @@ def building_damage_analysis_bulk_input(self, buildings, hazards, hazard_types,
 
             values_payload = []
             values_payload_liq = []  # for liquefaction, if used
-            unmapped_buildings = []
-            mapped_buildings = []
-            for b in buildings:
+
+            # Pre-filter buildings that are in fragility_sets to reduce the number of iterations
+            mapped_buildings = [b for b in buildings if b["id"] in fragility_sets]
+            unmapped_buildings = [b for b in buildings if b["id"] not in fragility_sets]
+
+            for b in mapped_buildings:
                 bldg_id = b["id"]
-                if bldg_id in fragility_sets:
-                    location = GeoUtil.get_location(b)
-                    loc = str(location.y) + "," + str(location.x)
-                    demands, units, adjusted_to_original = \
-                        AnalysisUtil.get_hazard_demand_types_units(b,
-                                                                   fragility_sets[bldg_id],
-                                                                   hazard_type,
-                                                                   allowed_demand_types)
-                    adjust_demand_types_mapping.update(adjusted_to_original)
-                    value = {
-                        "demands": demands,
-                        "units": units,
+                location = GeoUtil.get_location(b)
+                loc = str(location.y) + "," + str(location.x)
+                demands, units, adjusted_to_original = \
+                    AnalysisUtil.get_hazard_demand_types_units(b,
+                                                               fragility_sets[bldg_id],
+                                                               hazard_type,
+                                                               allowed_demand_types)
+                adjust_demand_types_mapping.update(adjusted_to_original)
+                value = {
+                    "demands": demands,
+                    "units": units,
+                    "loc": loc
+                }
+                values_payload.append(value)
+
+                if use_liquefaction and geology_dataset_id is not None:
+                    value_liq = {
+                        "demands": [""],
+                        "units": [""],
                         "loc": loc
                     }
-                    values_payload.append(value)
-                    mapped_buildings.append(b)
-
-                    if use_liquefaction and geology_dataset_id is not None:
-                        value_liq = {
-                            "demands": [""],
-                            "units": [""],
-                            "loc": loc
-                        }
-                        values_payload_liq.append(value_liq)
-                else:
-                    unmapped_buildings.append(b)
+                    values_payload_liq.append(value_liq)
 
             hazard_vals = hazard.read_hazard_values(values_payload, self.hazardsvc)
 
@@ -202,9 +201,10 @@ def building_damage_analysis_bulk_input(self, buildings, hazards, hazard_types,
             # worst code I have ever written
             # e.g. 1.04 Sec Sa --> 1.04 SA --> 1.0 SA
             for payload, response in zip(values_payload, hazard_vals):
-                for i in range(len(payload["demands"])):
-                    adjust_demand_types_mapping[response["demands"][i]] = adjust_demand_types_mapping[payload[
-                        "demands"][i]]
+                adjust_demand_types_mapping.update({
+                    response_demand: adjust_demand_types_mapping[payload_demand]
+                    for payload_demand, response_demand in zip(payload["demands"], response["demands"])
+                })
 
             # record hazard value for each hazard type for later calcu
             multihazard_vals[hazard_type] = hazard_vals
diff --git a/pyincore/analyses/buildingfunctionality/buildingfunctionality.py b/pyincore/analyses/buildingfunctionality/buildingfunctionality.py
index 2ec438ce1..cede29948 100644
--- a/pyincore/analyses/buildingfunctionality/buildingfunctionality.py
+++ b/pyincore/analyses/buildingfunctionality/buildingfunctionality.py
@@ -121,8 +121,8 @@ def run(self):
             functionality_probabilities.append([building_guid, probability])
             functionality_samples.append([building_guid, sample])
 
-        fp_results = pd.DataFrame(functionality_probabilities, columns=['building_guid', 'probability'])
-        fs_results = pd.DataFrame(functionality_samples, columns=['building_guid', 'samples'])
+        fp_results = pd.DataFrame(functionality_probabilities, columns=['guid', 'probability'])
+        fs_results = pd.DataFrame(functionality_samples, columns=['guid', 'failure'])
 
         self.set_result_csv_data("functionality_probability",
                                  fp_results,
diff --git a/pyincore/analyses/core_cge_ml/__init__.py b/pyincore/analyses/core_cge_ml/__init__.py
new file mode 100644
index 000000000..3c5fba791
--- /dev/null
+++ b/pyincore/analyses/core_cge_ml/__init__.py
@@ -0,0 +1,7 @@
+# Copyright (c) 2024 University of Illinois and others. All rights reserved.
+#
+# This program and the accompanying materials are made available under the
+# terms of the Mozilla Public License v2.0 which accompanies this distribution,
+# and is available at https://www.mozilla.org/en-US/MPL/2.0/
+
+from pyincore.analyses.core_cge_ml.corecgeml import CoreCGEML
\ No newline at end of file
diff --git a/pyincore/analyses/core_cge_ml/corecgeml.py b/pyincore/analyses/core_cge_ml/corecgeml.py
new file mode 100644
index 000000000..487f8dc3f
--- /dev/null
+++ b/pyincore/analyses/core_cge_ml/corecgeml.py
@@ -0,0 +1,267 @@
+# Copyright (c) 2024 University of Illinois and others. All rights reserved.
+#
+# This program and the accompanying materials are made available under the
+# terms of the Mozilla Public License v2.0 which accompanies this distribution,
+# and is available at https://www.mozilla.org/en-US/MPL/2.0/
+
+from typing import List, Dict, Optional
+from collections import defaultdict
+import numpy as np
+import pandas as pd
+
+from pyincore import BaseAnalysis
+
+
+class CoreCGEML(BaseAnalysis):
+    """Core CGE ML"""
+
+    def __init__(
+        self,
+        incore_client,
+        sectors: Dict[str, List[str]],
+        labor_groups: Optional[List[str]] = None,
+    ):
+        super(CoreCGEML, self).__init__(incore_client)
+
+        self.factors = [
+            "domestic_supply",
+            "gross_income",
+            "household_count",
+            "factor_demand",
+        ]
+        self.sectors = sectors
+        self.labor_groups = ["L1", "L2", "L3"] if labor_groups is None else labor_groups
+
+    def construct_output(self, predictions: dict) -> Dict[str, Dict[str, list]]:
+        """construct_output will construct the output in the format required by the pyincore.
+        The output will be a tuple of 5 elements. Each element will be a dictionary according to the following format:
+        - Domestic Supply:
+            Sectors: list of domestic supply sectors
+            DS0: domestic supply values before disaster
+            DSL: domestic supply values after disaster
+        - Gross Income:
+            Household Group: list of houshold sectors
+            Y0: Household income before disaster
+            YL: Household income after disaster
+        - Household Count:
+            Household Group: list of houshold sectors
+            HH0: Household count before disaster
+            HHL: Household count after disaster
+        - Pre-Disaster Factor Demand:
+            Labor Group: list of labor groups
+            sector1:
+            sector2:
+            .
+            .
+            .
+            sectorN:
+        - Post-Disaster Factor Demand:
+            Labor Group: list of labor groups
+            sector1:
+            sector2:
+            .
+            .
+            .
+            sectorN:
+
+        Parameters
+        ----------
+        predictions : dict
+            This is a dictionary with keys as factors and values as a dictionary with keys "before" and "after".
+
+        Returns
+        -------
+        Dict[str, Dict[str, list]]
+            A dictionary of 5 dictionaries.
+        """
+        constructed_outputs = {}
+        if predictions.get("ds", None) is not None:
+            constructed_outputs["ds"] = {
+                "Sectors": self.sectors["ds"],
+                "DS0": predictions["ds"]["before"],
+                "DSL": predictions["ds"]["after"],
+            }
+        if predictions.get("dy", None) is not None:
+            constructed_outputs["dy"] = {
+                "Household Group": self.sectors["dy"],
+                "Y0": predictions["dy"]["before"],
+                "YL": predictions["dy"]["after"],
+            }
+        if predictions.get("migt", None) is not None:
+            constructed_outputs["migt"] = {
+                "Household Group": self.sectors["migt"],
+                "HH0": predictions["migt"]["before"],
+                "HHL": predictions["migt"]["after"],
+            }
+        if predictions.get("dffd", None) is not None:
+            prefd = {
+                "Labor Group": self.labor_groups,
+            }
+            postfd = {
+                "Labor Group": self.labor_groups,
+            }
+
+            temp_prefd: Dict[str, Dict[str, float]] = defaultdict(dict)
+            temp_postfd: Dict[str, Dict[str, float]] = defaultdict(dict)
+            for i, fd_sector in enumerate(self.sectors["dffd"]):
+                splits = fd_sector.split("_")
+                if len(splits) > 2:
+                    sector = "_".join(splits[:-1])
+                    grp = splits[-1]
+
+                temp_prefd[sector][grp] = predictions["dffd"]["before"][i]
+                temp_postfd[sector][grp] = predictions["dffd"]["after"][i]
+
+            for sector in temp_prefd.keys():
+                prefd_l = []
+                postfd_l = []
+                for grp in self.labor_groups:
+
+                    if temp_prefd[sector].get(grp, None) is None:
+                        prefd_l.append(-1)
+                    else:
+                        prefd_l.append(temp_prefd[sector][grp])
+
+                    if temp_postfd[sector].get(grp, None) is None:
+                        postfd_l.append(-1)
+                    else:
+                        postfd_l.append(temp_postfd[sector][grp])
+
+                prefd[sector] = prefd_l
+                postfd[sector] = postfd_l
+
+            constructed_outputs["prefd"] = prefd
+            constructed_outputs["postfd"] = postfd
+
+        return constructed_outputs
+
+    def run_core_cge_ml(
+        self,
+        base_cap: np.ndarray,
+        capital_shocks: np.ndarray,
+        model_coeffs: Dict[str, np.ndarray],
+        base_cap_factors: List[np.ndarray],
+    ) -> None:
+        """run_core_cge_ml will use the model coefficients to predict the change in capital stock for each sector.
+        The predicted change will then be added to base_cap_factors to get the final capital stock for each sector 
+        after a disaster.
+        The model requires capital stock loss in dollar amount, hence the base_cap will be used to 
+        calculate the loss in dollar amount.
+        The capital_shocks is the percentage of capital stock that remains and hence to get the loss we 
+        use 1 - capital_shocks.
+
+        Some variables for parameters:
+        - n: number of factors
+        - k_i: number of sectors (including subsectors) for factor i.
+        - K: number of sectors (including subsectors) for input to model.
+        - l_i: number of coefficients for a model for factor i.
+
+        The length of model_coeffs == length of base_cap_factors == n.
+
+        #TODO: Add detail about the length of base_cap being equal to K and l_i. Meaning K == l_i for i = 1, 2, ..., n.
+
+        Parameters
+        ----------
+        base_cap : (1 X K) np.ndarray
+           This is the base capital for each sector in dollar amount in Millions. This is a (1, K) array with K elements.
+            The shape should match the shape of capital_shocks.
+        capital_shocks : (1 X K) np.ndarray
+            This is the capital shock for each sector in percentage. This is a (1, K) array with K elements.
+        model_coeffs : Dict[str, np.ndarray]
+            This is a dictionary of 2D arrays with shape [n, (k_i, l_i)]. 
+            Each entry in the dictionary corresponds to a factor and each factor has k_i number of models.
+            It is assumed that the intercept term is included in the model coefficients and is at the 0th column.
+        base_cap_factors : List[np.ndarray]
+            This is a list of 1D array with each entry corresponding to a factor representing its base capital by sectors.
+            This is the base capital for each sector for a given factor.
+            The list would look like [(m_1, 1), (m_2, 1), ..., (m_n, 1)].
+        """
+
+        # check if the shape of base_cap and capital_shocks match
+        if base_cap.shape != capital_shocks.shape:
+            raise ValueError(
+                "The shape of base_cap and capital_shocks do not match. Base Cap shape {}, Capital Shocks shape {}".format(
+                    base_cap.shape, capital_shocks.shape
+                )
+            )
+
+        # Convert capital_shocks to capital percent loss and then to capital loss in dollar amount
+        capital_loss = (1 - capital_shocks) * base_cap
+
+        # add a bias term to the capital loss with value 1 resulting in a shape of (1, 1+K)
+        capital_loss = np.hstack((np.ones((1, 1)), capital_loss))
+
+        assert len(model_coeffs) == len(
+            base_cap_factors
+        ), "The length of model_coeffs and base_cap_factors do not match. required length {}, observed length {}".format(
+            len(model_coeffs), len(base_cap_factors)
+        )
+
+        predictions = {}
+        for factor_base_cap_before, (factor, factor_model_coeff) in zip(
+            base_cap_factors, model_coeffs.items()
+        ):
+            # multiply the capital loss with the model coefficients (k_i x 1) = (1, 1+K) . (k_i, l_i)
+            factor_capital_loss: np.ndarray = capital_loss.dot(factor_model_coeff.T).T
+
+            assert (
+                factor_capital_loss.shape == factor_base_cap_before.shape
+            ), "Number of sectors in models and base_cap_factors do not match. required shape {}, observed shape {}".format(
+                factor_capital_loss.shape, factor_base_cap_before.shape
+            )
+            # add the predicted change in capital stock to the base_cap_factors
+            factor_base_cap_after: np.ndarray = (
+                factor_base_cap_before + factor_capital_loss
+            )
+
+            predictions[factor] = {
+                "before": np.squeeze(factor_base_cap_before).tolist(),
+                "after": np.squeeze(factor_base_cap_after).tolist(),
+            }
+
+        constructed_outputs = self.construct_output(predictions)
+
+        file_prefix = (
+            self.get_parameter("result_name")
+            if self.get_parameter("result_name") is not None
+            else ""
+        )
+        file_prefix += "_"
+
+        if constructed_outputs.get("ds", None) is not None:
+            self.set_result_csv_data(
+                "domestic-supply",
+                pd.DataFrame(constructed_outputs["ds"]),
+                name=f"{file_prefix}domestic-supply",
+                source="dataframe",
+            )
+        if constructed_outputs.get("dy", None) is not None:
+            self.set_result_csv_data(
+                "gross-income",
+                pd.DataFrame(constructed_outputs["dy"]),
+                name=f"{file_prefix}gross-income",
+                source="dataframe",
+            )
+        if constructed_outputs.get("migt", None) is not None:
+            self.set_result_csv_data(
+                "household-count",
+                pd.DataFrame(constructed_outputs["migt"]),
+                name=f"{file_prefix}household-count",
+                source="dataframe",
+            )
+        if constructed_outputs.get("prefd", None) is not None:
+            self.set_result_csv_data(
+                "pre-disaster-factor-demand",
+                pd.DataFrame(constructed_outputs["prefd"]),
+                name=f"{file_prefix}pre-disaster-factor-demand",
+                source="dataframe",
+            )
+        if constructed_outputs.get("postfd", None) is not None:
+            self.set_result_csv_data(
+                "post-disaster-factor-demand",
+                pd.DataFrame(constructed_outputs["postfd"]),
+                name=f"{file_prefix}post-disaster-factor-demand",
+                source="dataframe",
+            )
+
+        return True
diff --git a/pyincore/analyses/mlenabledcgeslc/DDS_coefficients.csv b/pyincore/analyses/mlenabledcgeslc/DDS_coefficients.csv
new file mode 100644
index 000000000..37950f8f1
--- /dev/null
+++ b/pyincore/analyses/mlenabledcgeslc/DDS_coefficients.csv
@@ -0,0 +1,79 @@
+Testbed,Hazard,Model_Type,Model_Name,(Intercept),ART_ACC_R1,ART_ACC_R2,ART_ACC_R3,ART_ACC_R4,ART_ACC_R5,ART_ACC_R6,ART_ACC_R7,COMMER_R1,COMMER_R2,COMMER_R3,COMMER_R4,COMMER_R5,COMMER_R6,COMMER_R7,CONS_R1,CONS_R2,CONS_R3,CONS_R4,CONS_R5,CONS_R6,CONS_R7,EDU_R1,EDU_R2,EDU_R3,EDU_R4,EDU_R5,EDU_R6,EDU_R7,HS1_R1,HS1_R2,HS1_R3,HS1_R4,HS1_R5,HS1_R6,HS1_R7,HS2_R1,HS2_R2,HS2_R3,HS2_R4,HS2_R5,HS2_R6,HS2_R7,HEALTH_R1,HEALTH_R2,HEALTH_R3,HEALTH_R4,HEALTH_R5,HEALTH_R6,HEALTH_R7,MANU_R1,MANU_R2,MANU_R3,MANU_R4,MANU_R5,MANU_R6,MANU_R7,RELIG_R1,RELIG_R2,RELIG_R3,RELIG_R4,RELIG_R5,RELIG_R6,RELIG_R7,UTIL_R1,UTIL_R2,UTIL_R3,UTIL_R4,UTIL_R5,UTIL_R6,UTIL_R7,AG_MI_R1,AG_MI_R2,AG_MI_R6,AG_MI_R7
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+SLC,earthquake,sectorRegion_model,DDS_ ART_ACC_R1,0.628657105312516,-0.248652006868071,0.0,0.0,0.0,0.0,0.0,-0.0028497068529578,-0.0003076180981205,-9.14155281333016e-06,-2.05766319659795e-05,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0006956860963851,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,-0.0035680374082481,-0.0010010539988471,-0.0001572769451488,-0.0001628204889186,-0.0001179136137465,-6.76191815564304e-05,-0.0001076108728057,-0.0001763678953369,-0.000482062128356,-0.0001309246063528,-0.0001522782381492,-7.72713966245355e-05,-0.0001465428346204,0.0,-6.52098733421209e-06,0.0050910248641325,0.0,0.0,0.0,0.0,0.0,5.67922006587713e-05,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0002792907414687,0.0,0.0,0.0,0.0,0.0,-0.0074503171432256,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
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diff --git a/pyincore/analyses/mlenabledcgeslc/DFFD_coefficients.csv b/pyincore/analyses/mlenabledcgeslc/DFFD_coefficients.csv
new file mode 100644
index 000000000..772de63ec
--- /dev/null
+++ b/pyincore/analyses/mlenabledcgeslc/DFFD_coefficients.csv
@@ -0,0 +1,229 @@
+Testbed,Hazard,Model_Type,Model_Name,(Intercept),ART_ACC_R1,ART_ACC_R2,ART_ACC_R3,ART_ACC_R4,ART_ACC_R5,ART_ACC_R6,ART_ACC_R7,COMMER_R1,COMMER_R2,COMMER_R3,COMMER_R4,COMMER_R5,COMMER_R6,COMMER_R7,CONS_R1,CONS_R2,CONS_R3,CONS_R4,CONS_R5,CONS_R6,CONS_R7,EDU_R1,EDU_R2,EDU_R3,EDU_R4,EDU_R5,EDU_R6,EDU_R7,HS1_R1,HS1_R2,HS1_R3,HS1_R4,HS1_R5,HS1_R6,HS1_R7,HS2_R1,HS2_R2,HS2_R3,HS2_R4,HS2_R5,HS2_R6,HS2_R7,HEALTH_R1,HEALTH_R2,HEALTH_R3,HEALTH_R4,HEALTH_R5,HEALTH_R6,HEALTH_R7,MANU_R1,MANU_R2,MANU_R3,MANU_R4,MANU_R5,MANU_R6,MANU_R7,RELIG_R1,RELIG_R2,RELIG_R3,RELIG_R4,RELIG_R5,RELIG_R6,RELIG_R7,UTIL_R1,UTIL_R2,UTIL_R3,UTIL_R4,UTIL_R5,UTIL_R6,UTIL_R7,AG_MI_R1,AG_MI_R2,AG_MI_R6,AG_MI_R7
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diff --git a/pyincore/analyses/mlenabledcgeslc/DS_base_val.csv b/pyincore/analyses/mlenabledcgeslc/DS_base_val.csv
new file mode 100644
index 000000000..3ac94d4e1
--- /dev/null
+++ b/pyincore/analyses/mlenabledcgeslc/DS_base_val.csv
@@ -0,0 +1,78 @@
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diff --git a/pyincore/analyses/mlenabledcgeslc/DY_coefficients.csv b/pyincore/analyses/mlenabledcgeslc/DY_coefficients.csv
new file mode 100644
index 000000000..8a06758fd
--- /dev/null
+++ b/pyincore/analyses/mlenabledcgeslc/DY_coefficients.csv
@@ -0,0 +1,30 @@
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diff --git a/pyincore/analyses/mlenabledcgeslc/FD_base_val.csv b/pyincore/analyses/mlenabledcgeslc/FD_base_val.csv
new file mode 100644
index 000000000..499b45164
--- /dev/null
+++ b/pyincore/analyses/mlenabledcgeslc/FD_base_val.csv
@@ -0,0 +1,228 @@
+,labor,value
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diff --git a/pyincore/analyses/mlenabledcgeslc/GI_base_val.csv b/pyincore/analyses/mlenabledcgeslc/GI_base_val.csv
new file mode 100644
index 000000000..1e9108fd5
--- /dev/null
+++ b/pyincore/analyses/mlenabledcgeslc/GI_base_val.csv
@@ -0,0 +1,29 @@
+,household,value
+0,HH1_R1,68.36900987934743
+1,HH2_R1,270.00932123731434
+2,HH3_R1,497.3788280786873
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diff --git a/pyincore/analyses/mlenabledcgeslc/HH_base_val.csv b/pyincore/analyses/mlenabledcgeslc/HH_base_val.csv
new file mode 100644
index 000000000..b3f8967a8
--- /dev/null
+++ b/pyincore/analyses/mlenabledcgeslc/HH_base_val.csv
@@ -0,0 +1,29 @@
+,household,value
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diff --git a/pyincore/analyses/mlenabledcgeslc/MIGT_coefficients.csv b/pyincore/analyses/mlenabledcgeslc/MIGT_coefficients.csv
new file mode 100644
index 000000000..c016c4413
--- /dev/null
+++ b/pyincore/analyses/mlenabledcgeslc/MIGT_coefficients.csv
@@ -0,0 +1,30 @@
+Testbed,Hazard,Model_Type,Model_Name,(Intercept),ART_ACC_R1,ART_ACC_R2,ART_ACC_R3,ART_ACC_R4,ART_ACC_R5,ART_ACC_R6,ART_ACC_R7,COMMER_R1,COMMER_R2,COMMER_R3,COMMER_R4,COMMER_R5,COMMER_R6,COMMER_R7,CONS_R1,CONS_R2,CONS_R3,CONS_R4,CONS_R5,CONS_R6,CONS_R7,EDU_R1,EDU_R2,EDU_R3,EDU_R4,EDU_R5,EDU_R6,EDU_R7,HS1_R1,HS1_R2,HS1_R3,HS1_R4,HS1_R5,HS1_R6,HS1_R7,HS2_R1,HS2_R2,HS2_R3,HS2_R4,HS2_R5,HS2_R6,HS2_R7,HEALTH_R1,HEALTH_R2,HEALTH_R3,HEALTH_R4,HEALTH_R5,HEALTH_R6,HEALTH_R7,MANU_R1,MANU_R2,MANU_R3,MANU_R4,MANU_R5,MANU_R6,MANU_R7,RELIG_R1,RELIG_R2,RELIG_R3,RELIG_R4,RELIG_R5,RELIG_R6,RELIG_R7,UTIL_R1,UTIL_R2,UTIL_R3,UTIL_R4,UTIL_R5,UTIL_R6,UTIL_R7,AG_MI_R1,AG_MI_R2,AG_MI_R6,AG_MI_R7
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+SLC,earthquake,sectorRegion_model,MIGT_ HH1_R1,-0.187793234310407,-0.0016619440972415,-7.33411605261053e-05,0.0,0.0,0.0,0.0,0.0,-0.0019281003348138,-6.97889403000531e-05,-0.0001269176697893,-4.77514166654119e-05,-3.06601020361791e-05,-2.82578875798041e-05,0.0,0.0,0,0.0,0.0,0.0,0.0,0.0,0,-3.48373637652814e-05,0.0,0.0,0.0,0.0,0.0,-0.0082098315602191,9.68761416517593e-06,0.0,4.77731231975305e-05,2.46133197917183e-05,4.49384680416987e-05,0.0001020339601128,-0.0080516018216699,0.0,0.0,3.40433290237416e-05,1.45961730167707e-05,0.0,0.0,-0.0002410951175814,0.0,0.0,0.0,0.0,0.0,0.0,-0.0003820290789693,0.0,0.0,0,0.0,0.0,0.0,-0.0016460188550304,0.0,0.0,0.0,0.0,0.0,0.0,-0.001391838272694,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+SLC,earthquake,sectorRegion_model,MIGT_ HH2_R1,0.465148481404032,-0.0070720384418788,-0.0001026758339893,0.0,0.0,0.0,0.0,0.0,-0.0093391810941963,-0.0003074389428417,-0.0005588765136789,-0.000107399483599,-0.0001038034536621,0.0,0.0,0.0,0,0.0,0.0,0.0,0.0,0.0,0,-4.7686365291376e-05,0.0,0.0,0.0,0.0,0.0,-0.0641455721477715,0.0002540910146308,8.06503871676172e-05,0.0003836020022075,0.0002069547149632,0.0003471241213176,0.000886713617249,-0.0627051140405231,9.29273936197183e-05,0.0,0.0001499361222967,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,-0.0012711369556523,0.0,0.0,0,0.0,0.0,0.0,-0.0063414563507786,0.0,0.0,0.0,0.0,0.0,0.0,-0.0030580566431457,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+SLC,earthquake,sectorRegion_model,MIGT_ HH3_R1,0.100692750251518,-0.0333523671931993,-0.0007838158066841,0.0,0.0,0.0,0.0,0.0,-0.0339839357162596,-0.001159091062227,-0.0013535801273465,-0.001128648788539,-0.0007928532889407,-0.0009492485473249,-0.00180314963748,0.0,0,0.0,0.0,0.0,0.0,0.0,0,-0.0008188149448544,0.0,0.0,0.0,0.0,0.0,-0.112420719959261,0.0011908170488222,0.0008011339582609,0.0013400852976033,0.0008182140777924,0.0013719454904471,0.0040478273323208,-0.110787679196338,0.0009293630510881,0.0005863028114863,0.0010580337473832,0.0004165447101563,0.0004438214419367,0.0015072082880417,-0.0068934005770569,0.0,0.0,-0.0008171302603536,-0.0001581245067198,-0.0005013840334843,0.0,-0.0092001435432291,0.0,0.0,0,-0.000313895514884,0.0,0.0,-0.0304125028351077,0.0,0.0,0.0,0.0,0.0,-0.0081354434814796,-0.0136093259017107,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
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+SLC,earthquake,sectorRegion_model,MIGT_ HH4_R5,0.4193146165683,0.0,-0.0003038449256774,0.0,0.0,-0.0146738394959127,0.0,0.0,0.0,-0.0026170923550616,-0.0017540574012075,-0.0007986611655741,-0.0210183135476649,-2.28981226881173e-06,0.0,0.0,0,0.0,0.0,0.0,0.0,0.0,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0045803658513801,0.0032118892968788,0.0014608456645125,0.0034980145196971,-0.165898591396297,0.0037831309325478,0.0131909592486806,0.0,0.0018501448429188,0.0,0.001884932959522,-0.16315036515377,0.0,0.0,0.0,0.0,0.0,0.0,-0.0094819086458772,0.0,0.0,0.0,0.0,0.0,0,-0.004566786055398,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+SLC,earthquake,sectorRegion_model,MIGT_ HH1_R6,-0.187112004859232,0.0,0.0,0.0,0.0,0.0,-0.0010848802622892,0.0,5.20381149428334e-07,0.0,-9.78307543885384e-06,0.0,0.0,-0.0021763437414246,0.0,0.0,0,0.0,0.0,0.0,0.0,0.0,0,0.0,0.0,0.0,0.0,0.0,0.0,-5.59243411427528e-05,-9.74772259761352e-05,-7.3121974084232e-05,-8.84857432499077e-05,-6.96555510397443e-05,-0.0089214027038583,-0.0003764962297452,0.0,-9.73388868385563e-05,-2.94586959948e-05,-5.23493889594981e-05,0.0,-0.0088398777141812,-0.000113297330031,0.0,0.0,0.0,0.0,0.0,-0.0018244243489031,0.0,0.0,0.0,0.0,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,-0.0020543268197591,0.0,0.0,0.0,0.0,0.0
+SLC,earthquake,sectorRegion_model,MIGT_ HH2_R6,0.0423896640347969,0.0,-5.91714327594499e-05,0.0,0.0,0.0,0.0,0.0,6.23512805063204e-06,-0.0001826011756693,-0.0003365655067954,-0.0001004200710018,-0.0002730931933722,-0.0053998554964622,0.0,0.0,0,0.0,0.0,0.0,0.0,0.0,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0008645433516885,0.0001750314054778,0.0001801299401754,0.0002263961042797,7.21739740891956e-06,-0.0328708195813128,0.0002308504878051,2.82680592103454e-05,5.8437182798912e-05,8.16835449917485e-05,7.87357543138041e-05,0.0,-0.0323935722863662,0.0,0.0,0.0,0.0,0.0,0.0,-0.0028109013503908,0.0,0.0,0.0,0.0,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+SLC,earthquake,sectorRegion_model,MIGT_ HH3_R6,-1.33345884971887,0.0,-0.0003740121544017,0.0,0.0,0.0,0.0,0.0,-0.0011346474151471,-0.001011262542371,-0.0012373674941015,-0.0008288097138655,-0.0009931634577535,-0.0225697999378102,0.0,0.0,0,0.0,0.0,0.0,0.0,0.0,0,-0.0002608558204457,0.0,0.0,0.0,0.0,0.0,0.0022497824450868,0.0012791557595603,0.0010858117922871,0.0013667375701181,0.0005094547031313,-0.0825785995723471,0.0029324267329756,0.0,0.0008329193202104,0.0005167551378305,0.0006900496690963,0.0,-0.0804037019306093,0.0,0.0,0.0,0.0,-0.0002351662158727,0.0,-0.0124926883637315,0.0,0.0,0.0,0.0,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+SLC,earthquake,sectorRegion_model,MIGT_ HH4_R6,-2.97476744367756,0.0,-0.0004199026015241,0.0,0.0,0.0,0.0,0.0,-0.0017432219197592,-0.0022472183689362,-0.0021879691788396,-0.0012070814499895,-0.002695968491171,-0.0303547458092377,0.0,0.0,0,0.0,0.0,0.0,0.0,0.0,0,-0.0009080774808908,0.0,0.0,0.0,0.0,0.0,0.0047037715757984,0.0037879215923505,0.0017292372205751,0.0040517563417966,0.001886801922984,-0.240787261229982,0.0127820885975394,0.0,0.0028214557585418,0.0008785109175938,0.0029788720623872,0.000885318468417,-0.236910300782853,0.0034334753486644,0.0,0.0,0.0,-0.0008352657544332,0.0,-0.014163942648932,0.0,0.0,0.0,0.0,0,-0.0019674094697072,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+SLC,earthquake,sectorRegion_model,MIGT_ HH1_R7,-0.151555036878537,0.0,-2.34794457571882e-06,0.0,0.0,0.0,0.0,0.0,1.58370005435141e-05,-1.20761744526269e-05,-2.88136392228462e-05,0.0,-8.58585535802263e-06,-4.67040230374116e-07,-0.000283247933995,0.0,0,0.0,0.0,0.0,0.0,0.0,0,0.0,0.0,0.0,0.0,0.0,0.0,8.78134006022735e-05,1.57670886614132e-05,1.97441363924653e-05,2.4785301238693e-05,8.4418693745114e-06,9.10618779825444e-06,-0.0014902823600288,0.0,8.78628032359097e-06,0.0,2.08373612969427e-06,0.0,0.0,-0.0012957300820957,0.0,0.0,0.0,0.0,0.0,0.0,-5.26999663714823e-05,0.0,0.0,0.0,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+SLC,earthquake,sectorRegion_model,MIGT_ HH2_R7,0.393468835396561,0.0,-0.0001545991749821,0.0,0.0,0.0,0.0,0.0,0.0,-0.0002287877806664,-0.0004797763026328,-5.50435522111939e-05,-0.0002732101297018,-0.0001246594783043,-0.0091943979053052,0.0,0,0.0,0.0,0.0,0.0,0.0,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0012938212724083,0.0004158361988126,0.0003875986189319,0.0005090663754705,0.0001997962166577,0.0002810115038342,-0.0609986954102239,0.0002865775251311,0.0003044868240992,0.0001770633156825,0.0002607883557915,0.0,0.0,-0.0595851689777894,0.0,0.0,0.0,0.0,0.0,0.0,-0.0029075267159597,0.0,0.0,0.0,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+SLC,earthquake,sectorRegion_model,MIGT_ HH3_R7,-0.0251567658236738,-0.00049601848151,-0.0004744640441426,0.0,0.0,0.0,0.0,-0.0019452133002414,-0.0005317004359404,-0.0005978349188776,-0.0008158934638148,-0.0004929762435147,-0.0006030108126657,-0.0006515295510052,-0.021419058541126,0.0,0,0.0,0.0,0.0,0.0,0.0,0,-0.0004094917040544,0.0,0.0,0.0,0.0,0.0,0.0019536057146488,0.0010599758664647,0.0009031274438388,0.0011258868931706,0.0005778907645587,0.0008382610612094,-0.0684787158481283,0.0010032377681629,0.0009470428230441,0.0007193597969334,0.0008985300320522,0.0003046801563908,0.0004630744103764,-0.066979401255509,0.0,-2.14421443242964e-05,0.0,-0.0004866259337311,-0.0002387337100583,-0.0009513682641018,-0.0116582767233226,-0.0003207375059647,0.0,0.0,0,-0.0003853623073465,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+SLC,earthquake,sectorRegion_model,MIGT_ HH4_R7,-42.3611296931534,-0.009417292314701,-0.0024511921465049,0.0,0.0,0.0,-0.0110923067203903,-0.155612762034721,-0.0109608070606787,-0.006206064638891,-0.0056242634422349,-0.0088186930717186,-0.0051109427138085,-0.0099009022146417,-0.118152903489619,0.0876274037678235,0,-0.0098296133621306,0.0,-1.00016117683496,-0.0588466151117679,0.628605412585162,0,-0.007364261967017,0.0,0.0,0.0,-0.0505267183115559,0.519934001656624,0.012499838588256,0.0115303826136568,0.0083865966814084,0.0102735694218945,0.0062657048486255,0.0101673337340841,-0.845574958949119,0.00987181376906,0.0119910191329753,0.0073893955302256,0.0109430189035921,0.0077368725676862,0.005811612494201,-0.843882439484597,0.0,0.0,-0.0042469982750497,-0.0041162398552107,-0.0057574132036179,-0.0094830978502682,-0.0716865063652252,-0.0148812659833063,0.0,-0.0083679925853788,0,-0.0048695809392189,0.0,0.0,-0.011300394791737,-0.0068505983125122,-0.0026091905205557,0.0,-0.0084967219276929,0.0,-0.11028874754375,0.0,0.0,0.0,-0.0223302445128593,0.0,0.0,-0.708175703196676,0.0,0.0,0.0,0.0
diff --git a/pyincore/analyses/mlenabledcgeslc/__init__.py b/pyincore/analyses/mlenabledcgeslc/__init__.py
new file mode 100644
index 000000000..b251e805f
--- /dev/null
+++ b/pyincore/analyses/mlenabledcgeslc/__init__.py
@@ -0,0 +1,7 @@
+# Copyright (c) 2024 University of Illinois and others. All rights reserved.
+#
+# This program and the accompanying materials are made available under the
+# terms of the Mozilla Public License v2.0 which accompanies this distribution,
+# and is available at https://www.mozilla.org/en-US/MPL/2.0/
+
+from pyincore.analyses.mlenabledcgeslc.mlcgeslc import MlEnabledCgeSlc
\ No newline at end of file
diff --git a/pyincore/analyses/mlenabledcgeslc/baseKAP.csv b/pyincore/analyses/mlenabledcgeslc/baseKAP.csv
new file mode 100644
index 000000000..69fe1c1b6
--- /dev/null
+++ b/pyincore/analyses/mlenabledcgeslc/baseKAP.csv
@@ -0,0 +1,75 @@
+,sector,baseKAP
+0,AG_MI_R1,1.32343
+1,UTIL_R1,86.60789
+2,CONS_R1,6.44545
+3,MANU_R1,147.3874
+4,COMMER_R1,1413.324
+5,EDU_R1,3.195087
+6,HEALTH_R1,26.30652
+7,ART_ACC_R1,214.9359
+8,RELIG_R1,136.7312
+9,AG_MI_R2,0.226855
+10,UTIL_R2,163.0497
+11,CONS_R2,13.16167
+12,MANU_R2,1.96456
+13,COMMER_R2,4347.564
+14,EDU_R2,1077.981
+15,HEALTH_R2,267.174
+16,ART_ACC_R2,1170.836
+17,RELIG_R2,286.8778
+18,UTIL_R3,100.4826
+19,CONS_R3,104.0633
+20,MANU_R3,135.9967
+21,COMMER_R3,6324.506
+22,EDU_R3,28.54747
+23,HEALTH_R3,207.7495
+24,ART_ACC_R3,269.0348
+25,RELIG_R3,270.366
+26,UTIL_R4,17.64995
+27,CONS_R4,2.718651
+28,MANU_R4,2.387753
+29,COMMER_R4,1514.915
+30,EDU_R4,15.98366
+31,HEALTH_R4,777.8064
+32,ART_ACC_R4,165.8875
+33,RELIG_R4,45.82616
+34,UTIL_R5,40.22423
+35,CONS_R5,0.732834
+36,MANU_R5,706.6362
+37,COMMER_R5,3101.648
+38,EDU_R5,36.60337
+39,HEALTH_R5,419.9681
+40,ART_ACC_R5,501.4994
+41,RELIG_R5,86.28852
+42,AG_MI_R6,0.671073
+43,UTIL_R6,28.3906
+44,CONS_R6,5.839408
+45,MANU_R6,40.35103
+46,COMMER_R6,1224.491
+47,EDU_R6,18.75531
+48,HEALTH_R6,286.8313
+49,ART_ACC_R6,52.39905
+50,RELIG_R6,8.885036
+51,AG_MI_R7,0.085555
+52,UTIL_R7,2.574088
+53,CONS_R7,0.615061
+54,MANU_R7,3.1915
+55,COMMER_R7,492.5688
+56,EDU_R7,1.132406
+57,HEALTH_R7,111.1063
+58,ART_ACC_R7,19.894
+59,RELIG_R7,10.45709
+60,HS1_R1,1951.061
+61,HS2_R1,293.8371
+62,HS1_R2,2798.116
+63,HS2_R2,1491.191
+64,HS1_R3,4080.699
+65,HS2_R3,1132.364
+66,HS1_R4,3968.163
+67,HS2_R4,1036.572
+68,HS1_R5,6457.689
+69,HS2_R5,1235.736
+70,HS1_R6,3945.569
+71,HS2_R6,702.0965
+72,HS1_R7,2574.569
+73,HS2_R7,310.0058
diff --git a/pyincore/analyses/mlenabledcgeslc/mlcgeslc.py b/pyincore/analyses/mlenabledcgeslc/mlcgeslc.py
new file mode 100644
index 000000000..6d1ca4e1b
--- /dev/null
+++ b/pyincore/analyses/mlenabledcgeslc/mlcgeslc.py
@@ -0,0 +1,179 @@
+# Copyright (c) 2024 University of Illinois and others. All rights reserved.
+#
+# This program and the accompanying materials are made available under the
+# terms of the Mozilla Public License v2.0 which accompanies this distribution,
+# and is available at https://www.mozilla.org/en-US/MPL/2.0/
+
+import numpy as np
+import os
+import pandas as pd
+
+from pyincore import globals as pyglobals
+from pyincore.client import IncoreClient
+from pyincore.analyses.core_cge_ml import CoreCGEML
+from pyincore.utils import parse_files
+
+logger = pyglobals.LOGGER
+
+
+class MlEnabledCgeSlc(CoreCGEML):
+
+    model = "Machine Learning Enabled Computable General Equilibrium - Salt Lake City "
+
+    # Coefficients files
+    DDS_coefficients_file = "DDS_coefficients.csv"
+    DY_coefficients_file = "DY_coefficients.csv"
+    MIGT_coefficients_file = "MIGT_coefficients.csv"
+    DFFD_coefficients_file = "DFFD_coefficients.csv"
+
+    # Base value files
+    DS_base_val_file = "DS_base_val.csv"
+    GI_base_val_file = "GI_base_val.csv"
+    HH_base_val_file = "HH_base_val.csv"
+    FD_base_val_file = "FD_base_val.csv"
+
+    Base_KAP_file = "baseKAP.csv"
+
+    base_file_path = os.path.join(
+        pyglobals.PYINCORE_PACKAGE_HOME,
+        "analyses",
+        "mlenabledcgeslc",
+    )
+
+    model_filenames = {
+        "ds": os.path.join(
+            base_file_path,
+            DDS_coefficients_file,
+        ),
+        "dy": os.path.join(
+            base_file_path,
+            DY_coefficients_file,
+        ),
+        "migt": os.path.join(
+            base_file_path,
+            MIGT_coefficients_file,
+        ),
+        "dffd": os.path.join(
+            base_file_path,
+            DFFD_coefficients_file,
+        ),
+    }
+
+    filenames = [
+        os.path.join(
+            base_file_path,
+            DS_base_val_file,
+        ),
+        os.path.join(
+            base_file_path,
+            GI_base_val_file,
+        ),
+        os.path.join(
+            base_file_path,
+            HH_base_val_file,
+        ),
+        os.path.join(
+            base_file_path,
+            FD_base_val_file,
+        ),
+        os.path.join(
+            base_file_path,
+            Base_KAP_file,
+        ),
+    ]
+
+    def __init__(self, incore_client: IncoreClient):
+        sectors, base_cap_factors, base_cap, model_coeffs, cap_shock_sectors = (
+            parse_files(self.model_filenames, self.filenames)
+        )
+        self.base_cap_factors = base_cap_factors
+        self.base_cap = base_cap
+        self.model_coeffs = model_coeffs
+        self.cap_shock_sectors = cap_shock_sectors
+        super(MlEnabledCgeSlc, self).__init__(
+            incore_client, sectors, labor_groups=[f"L{gp}" for gp in range(1, 5)]
+        )  # 4 labor groups
+
+    def run(self) -> bool:
+        logger.info(f"Running {self.model} model...")
+        sector_shocks = pd.read_csv(
+            self.get_input_dataset("sector_shocks").get_file_path("csv")
+        )
+        # arrange the capital shocks in the same order as the sectors
+        shocks = []
+
+        for sector in self.cap_shock_sectors:
+            if sector.upper() not in [v.upper() for v in sector_shocks["sector"]]:
+                raise ValueError(
+                    f"Sector {sector} not found in the sector shocks file with\n {sector_shocks['sector']} sectors.\n" + 
+                    "Please make sure you have used the correct capital shocks"
+                )
+            shocks.append(
+                sector_shocks.loc[sector_shocks["sector"] == sector.upper()]["shock"]
+            )
+        capital_shocks = np.array(shocks, dtype=np.float64).reshape(1, -1)
+        # logger.info(f"capital_shocks shape: {capital_shocks.shape}")
+
+        super().run_core_cge_ml(
+            self.base_cap,
+            capital_shocks,
+            self.model_coeffs,
+            self.base_cap_factors,
+        )
+        logger.info(f"Running {self.model} model completed.")
+
+        return True
+
+    def get_spec(self):
+        return {
+            "name": "Salt-Lake-cge",
+            "description": "CGE model for Salt Lake City.",
+            "input_parameters": [
+                {
+                    "id": "result_name",
+                    "required": False,
+                    "description": "Result CSV dataset name prefix",
+                    "type": str,
+                }
+            ],
+            "input_datasets": [
+                {
+                    "id": "sector_shocks",
+                    "required": True,
+                    "description": "Aggregation of building functionality states to capital shocks per sector",
+                    "type": ["incore:capitalShocks"],
+                }
+            ],
+            "output_datasets": [
+                {
+                    "id": "domestic-supply",
+                    "parent_type": "",
+                    "description": "CSV file of resulting domestic supply",
+                    "type": "incore:Employment",
+                },
+                {
+                    "id": "gross-income",
+                    "parent_type": "",
+                    "description": "CSV file of resulting gross income",
+                    "type": "incore:Employment",
+                },
+                {
+                    "id": "pre-disaster-factor-demand",
+                    "parent_type": "",
+                    "description": "CSV file of factor demand before disaster",
+                    "type": "incore:FactorDemand",
+                },
+                {
+                    "id": "post-disaster-factor-demand",
+                    "parent_type": "",
+                    "description": "CSV file of resulting factor-demand",
+                    "type": "incore:FactorDemand",
+                },
+                {
+                    "id": "household-count",
+                    "parent_type": "",
+                    "description": "CSV file of household count",
+                    "type": "incore:HouseholdCount",
+                },
+            ],
+        }
diff --git a/pyincore/client.py b/pyincore/client.py
index 980c80f38..ee64aeae9 100644
--- a/pyincore/client.py
+++ b/pyincore/client.py
@@ -3,16 +3,15 @@
 # This program and the accompanying materials are made available under the
 # terms of the Mozilla Public License v2.0 which accompanies this distribution,
 # and is available at https://www.mozilla.org/en-US/MPL/2.0/
-
+import base64
 import getpass
 import hashlib
 import json
 import os
 import shutil
 import urllib.parse
-
+from datetime import datetime, timezone
 import requests
-
 from pyincore import globals as pyglobals
 from pyincore.utils import return_http_response
 
@@ -261,6 +260,22 @@ def store_authorization_in_file(self, authorization: str):
         except IOError as e:
             logger.warning(e)
 
+    def is_token_expired(self, token):
+        """Check if the token has expired
+
+        Returns:
+             True if the token has expired, False otherwise
+        """
+        # Split the token to get payload
+        _, payload_encoded, _ = token.split('.')
+        # Decode the payload
+        payload = base64.urlsafe_b64decode(payload_encoded + '==')  # Padding just in case
+        payload_json = json.loads(payload)
+        now = datetime.now(timezone.utc)
+        current_time = now.timestamp()
+        # Compare current time with exp claim
+        return current_time > payload_json['exp']
+
     def retrieve_token_from_file(self):
         """Attempts to retrieve authorization from a local file, if it exists.
 
@@ -276,9 +291,7 @@ def retrieve_token_from_file(self):
                 with open(self.token_file, 'r') as f:
                     auth = f.read().splitlines()
                     # check if token is valid
-                    userinfo_url = urllib.parse.urljoin(self.service_url, pyglobals.KEYCLOAK_USERINFO_PATH)
-                    r = requests.get(userinfo_url, headers={'Authorization': auth[0]})
-                    if r.status_code != 200:
+                    if self.is_token_expired(auth[0]):
                         return None
                 return auth[0]
             except IndexError:
diff --git a/pyincore/dfr3service.py b/pyincore/dfr3service.py
index d8b89c807..68480b24f 100644
--- a/pyincore/dfr3service.py
+++ b/pyincore/dfr3service.py
@@ -190,6 +190,7 @@ def match_inventory(self, mapping: MappingSet, inventories: list, entry_key: Opt
         """
 
         dfr3_sets = {}
+        dfr3_sets_cache = {}
 
         # find default mapping entry key if not provided
         if entry_key is None:
@@ -214,40 +215,58 @@ def match_inventory(self, mapping: MappingSet, inventories: list, entry_key: Opt
             retrofit_entry_key = inventory["properties"]["retrofit_k"] if "retrofit_k" in \
                                                                           inventory["properties"] else None
 
-            for m in mapping.mappings:
-                # for old format rule matching [[]]
-                # [[ and ] or [ and ]]
-                if isinstance(m.rules, list):
-                    if self._property_match_legacy(rules=m.rules, properties=inventory["properties"]):
-                        if retrofit_entry_key is not None and retrofit_entry_key in m.entry.keys():
-                            curve = m.entry[retrofit_entry_key]
-                        else:
-                            curve = m.entry[entry_key]
-                        dfr3_sets[inventory['id']] = curve
-
-                        # if it's string:id; then need to fetch it from remote and cast to dfr3curve object
-                        if isinstance(curve, str) and curve not in matched_curve_ids:
-                            matched_curve_ids.append(curve)
-
-                        # use the first match
-                        break
-
-                # for new format rule matching {"AND/OR":[]}
-                # {"AND": [xx, "OR": [yy, yy], "AND": {"OR":["zz", "zz"]]}
-                elif isinstance(m.rules, dict):
-                    if self._property_match(rules=m.rules, properties=inventory["properties"]):
-                        if retrofit_entry_key is not None and retrofit_entry_key in m.entry.keys():
-                            curve = m.entry[retrofit_entry_key]
-                        else:
-                            curve = m.entry[entry_key]
-                        dfr3_sets[inventory['id']] = curve
+            cached_curve = self._check_cache(dfr3_sets_cache, inventory["properties"])
 
-                        # if it's string:id; then need to fetch it from remote and cast to dfr3 curve object
-                        if isinstance(curve, str) and curve not in matched_curve_ids:
-                            matched_curve_ids.append(curve)
+            if cached_curve is not None:
+                dfr3_sets[inventory['id']] = cached_curve
 
-                        # use the first match
-                        break
+            else:
+                for m in mapping.mappings:
+                    # for old format rule matching [[]]
+                    # [[ and ] or [ and ]]
+                    if isinstance(m.rules, list):
+                        if self._property_match_legacy(rules=m.rules, properties=inventory["properties"]):
+                            if retrofit_entry_key is not None and retrofit_entry_key in m.entry:
+                                curve = m.entry[retrofit_entry_key]
+                            else:
+                                curve = m.entry[entry_key]
+                                
+                            dfr3_sets[inventory['id']] = curve
+
+                            matched_properties_dict = self._convert_properties_to_dict(m.rules, inventory["properties"])
+
+                            if retrofit_entry_key is not None:
+                                matched_properties_dict["retrofit_k"] = retrofit_entry_key
+                            # Add the matched inventory properties so other matching inventory can avoid rule matching
+                            dfr3_sets_cache[curve] = matched_properties_dict
+
+                            # if it's string:id; then need to fetch it from remote and cast to dfr3curve object
+                            if isinstance(curve, str) and curve not in matched_curve_ids:
+                                matched_curve_ids.append(curve)
+
+                            # use the first match
+                            break
+
+                    # for new format rule matching {"AND/OR":[]}
+                    # {"AND": [xx, "OR": [yy, yy], "AND": {"OR":["zz", "zz"]]}
+                    elif isinstance(m.rules, dict):
+                        if self._property_match(rules=m.rules, properties=inventory["properties"]):
+                            if retrofit_entry_key is not None and retrofit_entry_key in m.entry:
+                                curve = m.entry[retrofit_entry_key]
+                            else:
+                                curve = m.entry[entry_key]
+                            dfr3_sets[inventory['id']] = curve
+
+                            matched_properties_dict = self._convert_properties_to_dict(m.rules, inventory["properties"])
+                            # Add the matched inventory properties so other matching inventory can avoid rule matching
+                            dfr3_sets_cache[curve] = matched_properties_dict
+
+                            # if it's string:id; then need to fetch it from remote and cast to dfr3 curve object
+                            if isinstance(curve, str) and curve not in matched_curve_ids:
+                                matched_curve_ids.append(curve)
+
+                            # use the first match
+                            break
 
         batch_dfr3_sets = self.batch_get_dfr3_set(matched_curve_ids)
 
@@ -333,6 +352,71 @@ def match_list_of_dicts(self, mapping: MappingSet, inventories: list, entry_key:
 
         return dfr3_sets
 
+    @staticmethod
+    def _check_cache(dfr3_sets_dict, properties):
+        """A method to see if we already have matched an inventory with the same properties to a fragility curve
+
+                Args:
+                    dfr3_sets_dict (dict): {"fragility-curve-id-1": {"struct_typ": "W1", "no_stories": "2"}, etc.}
+                    properties (obj): A fiona Properties object that contains properties of the inventory row.
+
+                Returns:
+                    Fragility curve id if a match is found 
+
+        """
+        if not dfr3_sets_dict:
+            return None
+
+        retrofit_entry_key = properties["retrofit_k"] if "retrofit_k" in properties else None
+        for entry_key in dfr3_sets_dict:
+            inventory_dict = {}
+            entry_dict = dfr3_sets_dict[entry_key]
+            for rule_key in entry_dict:
+                inventory_dict[rule_key] = properties[rule_key]
+
+            if retrofit_entry_key is not None:
+                inventory_dict["retrofit_k"] = retrofit_entry_key
+
+            if entry_dict == inventory_dict:
+                return entry_key
+
+    @staticmethod
+    def _convert_properties_to_dict(rules, properties):
+        """A method to convert properties to a dictionary with only the matched values in the rule set
+
+        Args:
+            rules (obj): [[A and B] or [C and D]]
+            properties (dict): A dictionary that contains properties of the inventory row.
+
+        Returns:
+            Dictionary of property values for the inventory object so the matched fragility can be cached
+
+        """
+        matched_properties = {}
+        # Handle legacy rules
+        if isinstance(rules, list):
+            for i, and_rules in enumerate(rules):
+                for j, rule in enumerate(and_rules):
+                    matched_properties.update(Dfr3Service._eval_property_from_inventory(rule, properties))
+        elif isinstance(rules, dict):
+            # Handles new style of rules
+            boolean = list(rules.keys())[0]  # AND or OR
+            criteria = rules[boolean]
+
+            for criterion in criteria:
+                # Recursively parse and evaluate the rules with boolean
+                if isinstance(criterion, dict):
+                    for criteria in criterion:
+                        for rule_criteria in criterion[criteria]:
+                            matched_properties.update(Dfr3Service._eval_property_from_inventory(rule_criteria,
+                                                                                                properties))
+                elif isinstance(criterion, str):
+                    matched_properties.update(Dfr3Service._eval_property_from_inventory(criterion, properties))
+                else:
+                    raise ValueError("Cannot evaluate criterion, unsupported format!")
+
+        return matched_properties
+
     @staticmethod
     def _property_match_legacy(rules, properties):
         """A method to determine whether current set of rules rules applied to the inventory row (legacy rule format).
@@ -352,16 +436,10 @@ def _property_match_legacy(rules, properties):
 
         else:
             # rules = [[A and B], OR [C and D], OR [E and F]]
-            or_matched = [False for i in range(len(rules))]  # initiate all false state outer list
-            for i, and_rules in enumerate(rules):
-                and_matched = [False for j in range(len(and_rules))]  # initialte all false state for inner list
-                for j, rule in enumerate(and_rules):
-                    # evaluate, return True or False. And place it in the corresponding place
-                    and_matched[j] = Dfr3Service._eval_criterion(rule, properties)
-
-                # for inner list, AND boolean applied
-                if all(and_matched):
-                    or_matched[i] = True
+            or_matched = [
+                all(map(lambda rule: Dfr3Service._eval_criterion(rule, properties), and_rules))
+                for and_rules in rules
+            ]
 
         # for outer list, OR boolean is appied
         return any(or_matched)
@@ -404,6 +482,25 @@ def _property_match(rules, properties):
             else:
                 raise ValueError("boolean " + boolean + " not supported!")
 
+    @staticmethod
+    def _eval_property_from_inventory(rule, properties):
+        """A method to evaluate individual rule and get the property from the inventory properties.
+
+               Args:
+                   rule (str): # e.g. "int no_stories EQ 1",
+                   properties (dict): dictionary of properties of an inventory item. e.g. {"guid":xxx,
+                   "num_stories":xxx, ...}
+
+               Returns:
+                    dictionary entry with the inventory property value that matched the rule
+
+        """
+        elements = rule.split(" ", 3)
+        property_key = elements[1]
+
+        matched_props = {property_key: properties[property_key]}
+        return matched_props
+
     @staticmethod
     def _eval_criterion(rule, properties):
         """A method to evaluate individual rule and see if it appies to a certain inventory row.
@@ -425,18 +522,18 @@ def _eval_criterion(rule, properties):
         # e.g. "java.lang.String struct_typ EQUALS W2"
 
         rule_type = elements[0]  # e.g. int, str, double, java.lang.String, etc...
-        if rule_type not in known_types.keys():
+        if rule_type not in known_types:
             raise ValueError(rule_type + " Unknown. Cannot parse the rules of this mapping!")
 
         rule_key = elements[1]  # e.g. no_storeis, year_built, etc...
 
         rule_operator = elements[2]  # e.g. EQ, GE, LE, EQUALS
-        if rule_operator not in known_operators.keys():
+        if rule_operator not in known_operators:
             raise ValueError(rule_operator + " Unknown. Cannot parse the rules of this mapping!")
 
         rule_value = elements[3].strip('\'').strip('\"')
 
-        if rule_key in properties.keys():
+        if rule_key in properties:
             # validate if the rule is written correctly by comparing variable type, e.g. no_stories properties
             # should be integer
             if isinstance(properties[rule_key], eval(known_types[rule_type])):
diff --git a/pyincore/globals.py b/pyincore/globals.py
index 696ea5246..84232d92b 100644
--- a/pyincore/globals.py
+++ b/pyincore/globals.py
@@ -10,7 +10,7 @@
 import os
 import shutil
 
-PACKAGE_VERSION = "1.17.0"
+PACKAGE_VERSION = "1.18.0"
 
 INCORE_API_PROD_URL = "https://incore.ncsa.illinois.edu"
 INCORE_API_DEV_URL = "https://incore-dev.ncsa.illinois.edu"
diff --git a/pyincore/models/dfr3curve.py b/pyincore/models/dfr3curve.py
index 95b7c5e69..a42b61637 100644
--- a/pyincore/models/dfr3curve.py
+++ b/pyincore/models/dfr3curve.py
@@ -82,8 +82,9 @@ def solve_curve_expression(self, hazard_values: dict, curve_parameters: dict, **
             else:
                 conditions_met = []
                 for condition in rule["condition"]:
-                    if evaluateexpression.evaluate(condition, parameters):
-                        conditions_met.append(True)
+                    eval_result = evaluateexpression.evaluate(condition, parameters)
+                    if not math.isnan(eval_result) and eval_result:
+                        conditions_met.append(eval_result)
                     else:
                         conditions_met.append(False)
                         break
diff --git a/pyincore/utils/__init__.py b/pyincore/utils/__init__.py
index 93eb09efa..c8d0703bd 100644
--- a/pyincore/utils/__init__.py
+++ b/pyincore/utils/__init__.py
@@ -1,1 +1,2 @@
-from pyincore.utils.http_util import return_http_response
\ No newline at end of file
+from pyincore.utils.http_util import return_http_response
+from pyincore.utils.cge_ml_file_util import parse_coeff, parse_csv, parse_base_vals, parse_files
\ No newline at end of file
diff --git a/pyincore/utils/analysisutil.py b/pyincore/utils/analysisutil.py
index 669bd4899..fabf97ce9 100644
--- a/pyincore/utils/analysisutil.py
+++ b/pyincore/utils/analysisutil.py
@@ -261,14 +261,17 @@ def adjust_damage_for_liquefaction(limit_state_probabilities, ground_failure_pro
         keys = list(limit_state_probabilities.keys())
         adjusted_limit_state_probabilities = collections.OrderedDict()
 
-        for i in range(len(keys)):
+        ground_failure_probabilities_len = len(ground_failure_probabilities)
+        keys_len = len(keys)
+
+        for i in range(keys_len):
             # check and see...if we are trying to use the last ground failure
             # number for something other than the
             # last limit-state-probability, then we should use the
             # second-to-last probability of ground failure instead.
 
-            if i > len(ground_failure_probabilities) - 1:
-                prob_ground_failure = ground_failure_probabilities[len(ground_failure_probabilities) - 2]
+            if i > ground_failure_probabilities_len - 1:
+                prob_ground_failure = ground_failure_probabilities[ground_failure_probabilities_len - 2]
             else:
                 prob_ground_failure = ground_failure_probabilities[i]
 
diff --git a/pyincore/utils/cge_ml_file_util.py b/pyincore/utils/cge_ml_file_util.py
new file mode 100644
index 000000000..f16319d3e
--- /dev/null
+++ b/pyincore/utils/cge_ml_file_util.py
@@ -0,0 +1,159 @@
+from typing import Tuple, List, Dict
+
+import pandas as pd
+import numpy as np
+import os
+
+from pyincore import globals as pyglobals
+
+logger = pyglobals.LOGGER
+
+
+def parse_coeff(
+    filenames: Dict[str, str]
+) -> Tuple[Dict[str, np.ndarray], Dict[str, List[str]], Dict[str, List[str]]]:
+    """parse_coeff Function to parse the model coefficients.
+
+    Parameters
+    ----------
+    filenames : Dict[str, str]
+        Dictionary containing the factor name and the path to the coefficient file
+
+    Returns
+    -------
+    Tuple[Dict[str, np.ndarray], Dict[str, List[str]], Dict[str, List[str]]]
+        Tuple containing three dictionaries. The first dictionary contains the model coefficients for
+        each factor, the second dictionary contains the sectors for each factor, and the third dictionary
+        contains the order of the sectors for each factor for models.
+    """
+
+    model_coeffs: Dict[str, np.ndarray] = {}
+    sectors: Dict[str, List[str]] = {}
+    base_cap_sector_order: Dict[str, List[str]] = {}
+
+    for factor, filename in filenames.items():
+        logger.info(f"Parsing {filename}")
+        model_coeff_df: pd.DataFrame = pd.read_csv(filename)
+        sectors[factor] = list(model_coeff_df.columns[5:])
+        base_cap_sector_order[factor] = [
+            s.split(" ")[-1].upper() for s in list(model_coeff_df["Model_Name"])[1:]
+        ]
+        model_coeffs[factor] = np.float64(
+            model_coeff_df[model_coeff_df.columns[4:]].to_numpy()[1:, :]
+        )  # skip the first row as it contains the total value model and its not needed in output
+        # logger.info(f"factor: {factor} - model_coeff shape: {model_coeffs[factor].shape}")
+
+    return model_coeffs, sectors, base_cap_sector_order
+
+
+def parse_csv(file_name: str, sectors: List[str]) -> np.ndarray:
+    """parse_csv Utility function to parse the csv files
+
+    Parameters
+    ----------
+    file_name : str
+        Path to the csv file
+
+    Returns
+    -------
+    np.ndarray
+        Numpy array containing the data from the csv file
+    """
+
+    logger.info(f"Parsing {file_name}")
+    df = pd.read_csv(file_name)
+
+    col_name = df.columns[1]
+    val_col = df.columns[2]
+
+    # Convert 'sectors' column to categorical with the desired order
+    df[col_name] = pd.Categorical(df[col_name], categories=sectors, ordered=True)
+
+    # Sort the DataFrame based on the 'names' column
+    df = df.sort_values(by=col_name)
+
+    return np.array(df[val_col], dtype=np.float64)
+
+
+def parse_base_vals(
+    filenames: List[str],
+    ds_sectors: List[str],
+    base_cap_sector_order: Dict[str, List[str]],
+) -> Tuple[List[np.ndarray], np.ndarray]:
+    """parse_base_vals parse_base_vals will parse the base values from the input file and return them as numpy arrays
+
+    Parameters
+    ----------
+    filenames : List[str]
+        Paths to the .csv files containing the base values. It has to be organized this order, starting from:
+        1. Domestic Supply
+        2. Gross Income
+        3. Household Count
+        4. Factor Demand
+        5. Base Capital
+
+    Returns
+    -------
+
+    Tuple[np.ndarray, np.ndarray]
+        Tuple containing two numpy arrays. The first array is the base capital for different factors and the second is the base capital
+    """
+
+    base_cap_factors: List[np.ndarray] = []
+    base_cap: np.ndarray = parse_csv(filenames[-1], ds_sectors).reshape(
+        1, -1
+    )  # 1 x K array K = number of sectors in the model
+    # logger.info(f"base_cap shape: {base_cap.shape}")
+
+    for filename, sector_order in zip(filenames[:-1], base_cap_sector_order.values()):
+        base_cap_factors.append(
+            parse_csv(filename, sector_order).reshape(-1, 1)
+        )  # k_i x 1 array k_i = number of sectors k for a factor i
+        # logger.info(f"{os.path.basename(filename)} shape: {base_cap_factors[-1].shape}")
+
+    return base_cap_factors, base_cap
+
+
+def parse_files(
+    model_filenames: Dict[str, str], filenames: List[str]
+) -> Tuple[Dict[str, List[str]], np.ndarray, np.ndarray, Dict[str, np.ndarray]]:
+    """parse_files Utility function to parse the input files
+
+    Parameters
+    ----------
+    model_filenames : Dict[str, str]
+        Dictionary containing the factor name and the path to the coefficient file
+    filenames : List[str]
+        Paths to the .csv files containing the base values. It has to be organized this order, starting from:
+            1. Domestic Supply
+            2. Gross Income
+            3. Household Count
+            4. Factor Demand
+            5. Base Capital
+
+    Returns
+    -------
+    Tuple[Dict[str, List[str]], np.ndarray, np.ndarray, Dict[str, np.ndarray]]
+        Returns a tuple containing the following:
+        1. sectors: Dictionary containing the sectors for each factor
+        2. base_cap_factors: List of numpy arrays containing the base capital for each factor
+        3. base_cap: Numpy array containing the base capital
+        4. model_coeffs: Dictionary containing the model coefficients for each factor
+        5. sectors["ds"]: List of sectors for the domestic supply
+    """
+    logger.info("Parsing input files...")
+
+    model_coeffs, sectors, base_cap_sector_ordering = parse_coeff(model_filenames)
+
+    base_cap_factors, base_cap = parse_base_vals(
+        filenames, sectors["ds"], base_cap_sector_ordering
+    )
+    logger.info("Parsing input files completed.")
+
+    return (
+        base_cap_sector_ordering,
+        base_cap_factors,
+        base_cap,
+        model_coeffs,
+        sectors["ds"],
+    )
diff --git a/pyincore/utils/datasetutil.py b/pyincore/utils/datasetutil.py
index d0009ad5a..2ae644f2b 100644
--- a/pyincore/utils/datasetutil.py
+++ b/pyincore/utils/datasetutil.py
@@ -93,7 +93,7 @@ def construct_updated_inventories(inventory_dataset: Dataset, add_info_dataset:
 
             # if additional information e.g. Retrofit presented, merge inventory properties with that additional
             # information
-            inventory_df = pd.merge(inventory_df, add_info_df, left_index=True, right_index=True, how='inner')
+            inventory_df = pd.merge(inventory_df, add_info_df, left_index=True, right_index=True, how='left')
 
             # prepare retrofit definition into pandas dataframe; need to work with retrofit
             if len(mapping.mappingEntryKeys) > 0:
@@ -102,7 +102,7 @@ def construct_updated_inventories(inventory_dataset: Dataset, add_info_dataset:
                 mapping_entry_keys_df.columns = [col + '_mappingEntryKey' for col in mapping_entry_keys_df.columns]
                 mapping_entry_keys_df.set_index('name_mappingEntryKey', inplace=True)
                 inventory_df = pd.merge(inventory_df, mapping_entry_keys_df, left_on='retrofit_key', right_index=True,
-                                        how='inner')
+                                        how='left')
                 inventory_df.drop(columns=['defaultKey_mappingEntryKey'], inplace=True)
             else:
                 raise ValueError("Missing proper definition for mappingEntryKeys in the mapping!")
diff --git a/pyincore/utils/evaluateexpression.py b/pyincore/utils/evaluateexpression.py
index dba6c70db..7ead79cb3 100644
--- a/pyincore/utils/evaluateexpression.py
+++ b/pyincore/utils/evaluateexpression.py
@@ -1,6 +1,7 @@
 import math
 import scipy
 import numpy
+import decimal
 
 INVALID_NAMES = ["exec", "func", "eval", "type", "isinstance", "getattr", "setattr", "repr",
                  "compile", "open"]
@@ -29,9 +30,19 @@ def evaluate(expression: str, parameters: dict = {}):
             raise NameError(f"Using '{parameter}' is not allowed.")
 
     # TODO figure out a better way of doing this. Can we import the packages here directly?
-    safe_globals = {"__builtins__": {"min": min, "max": max}, "scipy": globals()["scipy"], "numpy": globals()["numpy"],
-                    "math": globals()["math"]}
+    safe_globals = {"__builtins__": {"min": min,
+                                     "max": max,
+                                     "round": round,
+                                     "sum": sum,
+                                     "abs": abs,
+                                     "pow": pow},
+                    "scipy": globals()["scipy"],
+                    "numpy": globals()["numpy"],
+                    "math": globals()["math"],
+                    "decimal": globals()["decimal"]}
     try:
         return eval(code, safe_globals, parameters)
-    except:
+    except Exception as e:
+        print(f"An unexpected error occurred: {e}")
         return math.nan
+
diff --git a/pyincore/utils/popdisloutputprocess.py b/pyincore/utils/popdisloutputprocess.py
index 262012d0a..50ff6f0a9 100644
--- a/pyincore/utils/popdisloutputprocess.py
+++ b/pyincore/utils/popdisloutputprocess.py
@@ -46,31 +46,28 @@ def __init__(self, pop_disl_result, pop_disl_result_path=None,
                 pd_result_flag = pd_result[(pd_result["guid"].notnull()) &
                                            (pd_result["numprec"].notnull()) &
                                            (pd_result["plcname10"] == filter_name)]
-                # only keep guid, place and dislocated
-                pd_result_shp = pd_result[(pd_result["dislocated"]) &
-                                          (pd_result["guid"].notnull()) &
+                # only keep guid and place
+                pd_result_shp = pd_result[(pd_result["guid"].notnull()) &
                                           (pd_result["numprec"].notnull()) &
                                           (pd_result["plcname10"] == filter_name)]
             else:
                 pd_result_flag = pd_result[(pd_result["guid"].notnull()) &
                                            (pd_result["numprec"].notnull())]
-                # only keep guid and dislocated
-                pd_result_shp = pd_result[(pd_result["dislocated"]) &
-                                          (pd_result["guid"].notnull()) &
+                # only keep guid
+                pd_result_shp = pd_result[(pd_result["guid"].notnull()) &
                                           (pd_result["numprec"].notnull())]
         else:
             if filter_name:
                 pd_result_flag = pd_result[(pd_result["numprec"].notnull()) &
                                            (pd_result["plcname10"] == filter_name)]
-                # only keep guid, place and dislocated
-                pd_result_shp = pd_result[(pd_result["dislocated"]) &
-                                          (pd_result["numprec"].notnull()) &
+                # only keep guid and place
+                pd_result_shp = pd_result[(pd_result["numprec"].notnull()) &
                                           (pd_result["plcname10"] == filter_name)]
             else:
                 pd_result_flag = pd_result[(pd_result["numprec"].notnull())]
-                # only keep guid and dislocated
-                pd_result_shp = pd_result[(pd_result["dislocated"]) &
-                                          (pd_result["numprec"].notnull())]
+                # only keep guid
+                pd_result_shp = pd_result[(pd_result["numprec"].notnull())]
+
         self.vacant_disl = vacant_disl
         self.pop_disl_result = pd_result_flag
         self.pop_disl_result_shp = pd_result_shp
@@ -89,7 +86,17 @@ def get_heatmap_shp(self, filename="pop-disl-numprec.shp"):
 
         # save as shapefile
         gdf = gpd.GeoDataFrame(df, crs='epsg:4326')
-        gdf = gdf[["guid", "numprec", "geometry"]]
+        gdf = gdf[["guid", "numprec", "geometry", "dislocated"]]
+
+        # keep original dislocated results
+        gdf["numprec_dislocated"] = gdf["numprec"].copy()
+
+        # set numprec = 0 if dislocated is False
+        gdf.loc[~gdf["dislocated"], "numprec_dislocated"] = 0
+
+        # set numprec numprec_dislocated and  as integer
+        gdf["numprec"] = gdf["numprec"].fillna(0).astype(int)
+        gdf["numprec_dislocated"] = gdf["numprec_dislocated"].fillna(0).astype(int)
         gdf.to_file(filename)
 
         return filename
diff --git a/requirements.txt b/requirements.txt
index 1c769cd14..67803f1a7 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -14,4 +14,4 @@ rasterio>=1.3.9
 requests>=2.31.0
 rtree>=1.1.0
 scipy>=1.11.3
-shapely>=2.0.2
\ No newline at end of file
+shapely>=2.0.2
diff --git a/setup.py b/setup.py
index c289a9706..675317bed 100644
--- a/setup.py
+++ b/setup.py
@@ -7,7 +7,7 @@
 from setuptools import setup, find_packages
 
 # version number of pyincore
-version = '1.17.0'
+version = '1.18.0'
 
 with open("README.rst", encoding="utf-8") as f:
     readme = f.read()
@@ -49,7 +49,7 @@
     packages=find_packages(where=".", exclude=["*.tests", "*.tests.*", "tests.*", "tests"]),
     include_package_data=True,
     package_data={
-        '': ['*.ini']
+        '': ['*.ini', "*.csv"]
     },
 
     python_requires=">=3.9",


Test patch:
diff --git a/tests/pyincore/analyses/buildingdamage/test_slc_buildingdamage.py b/tests/pyincore/analyses/buildingdamage/test_slc_buildingdamage.py
new file mode 100644
index 000000000..ad7215cd4
--- /dev/null
+++ b/tests/pyincore/analyses/buildingdamage/test_slc_buildingdamage.py
@@ -0,0 +1,36 @@
+from pyincore import IncoreClient, FragilityService, MappingSet, Earthquake, HazardService, DataService
+from pyincore.analyses.buildingdamage import BuildingDamage
+import time
+
+
+if __name__ == "__main__":
+    client = IncoreClient()
+
+    # Initiate fragility service
+    fragility_services = FragilityService(client)
+    hazard_services = HazardService(client)
+    data_services = DataService(client)
+
+    # Analysis setup
+    start_time = time.time()
+    bldg_dmg = BuildingDamage(client)
+
+    mapping_set = MappingSet(fragility_services.get_mapping("6309005ad76c6d0e1f6be081"))
+    bldg_dmg.set_input_dataset('dfr3_mapping_set', mapping_set)
+
+    bldg_dmg.load_remote_input_dataset("buildings", "62fea288f5438e1f8c515ef8")  # Salt Lake County All Building
+    bldg_dmg.set_parameter("result_name", "SLC_bldg_dmg_no_retrofit-withLIQ7.1")
+
+    eq = Earthquake.from_hazard_service("640a03ea73a1642180262450", hazard_services)  # Mw 7.1
+    # eq = Earthquake.from_hazard_service("64108b6486a52d419dd69a41", hazard_services) #  Mw 7.0
+    bldg_dmg.set_input_hazard("hazard", eq)
+
+    bldg_dmg.set_parameter("use_liquefaction", True)
+    bldg_dmg.set_parameter("liquefaction_geology_dataset_id", "62fe9ab685ac6b569e372429")
+    bldg_dmg.set_parameter("num_cpu", 8)
+
+    # Run building damage without liquefaction
+    bldg_dmg.run_analysis()
+
+    end_time = time.time()
+    print(f"total runtime: {end_time - start_time}")
\ No newline at end of file
diff --git a/tests/pyincore/analyses/mlenabledcgeslc/test_mlcgeslc.py b/tests/pyincore/analyses/mlenabledcgeslc/test_mlcgeslc.py
new file mode 100644
index 000000000..9f90de2c9
--- /dev/null
+++ b/tests/pyincore/analyses/mlenabledcgeslc/test_mlcgeslc.py
@@ -0,0 +1,14 @@
+from pyincore import IncoreClient, globals as pyglobals
+from pyincore.analyses.mlenabledcgeslc import MlEnabledCgeSlc
+
+client = IncoreClient(pyglobals.INCORE_API_DEV_URL)
+
+mlcgeslc = MlEnabledCgeSlc(client)
+
+sector_shocks = "65fdeed7e42f3b0da56c4eef"
+
+mlcgeslc.load_remote_input_dataset("sector_shocks", sector_shocks)
+# optional
+mlcgeslc.set_parameter("result_name", "slc_7_region")
+
+mlcgeslc.run_analysis()
\ No newline at end of file
diff --git a/tests/pyincore/models/test_dfr3curve.py b/tests/pyincore/models/test_dfr3curve.py
index b56693a3d..dd309393e 100644
--- a/tests/pyincore/models/test_dfr3curve.py
+++ b/tests/pyincore/models/test_dfr3curve.py
@@ -1,9 +1,11 @@
 import json
+import math
 import os
 import collections
 
 import numpy
 import pytest
+from pyincore.utils import evaluateexpression
 
 from pyincore import globals as pyglobals, FragilityCurveSet, RepairCurveSet,  RestorationCurveSet, AnalysisUtil
 import numpy as np
@@ -55,17 +57,20 @@ def get_repair_set(repair_dir: str):
     repair_set = RepairCurveSet(repair_curveset)
     return repair_set
 
+
 def get_remote_repair_set(repair_id: str):
     dfr3svc = pytest.repairsvc
     repair_set = RepairCurveSet(dfr3svc.get_dfr3_set(repair_id))
     return repair_set
 
+
 def get_restoration_set(restoration_dir: str):
     with open(os.path.join(pyglobals.TEST_DATA_DIR, restoration_dir), 'r', encoding='utf-8') as f:
         restoration_curveset = json.load(f)
     restoration_set = RestorationCurveSet(restoration_curveset)
     return restoration_set
 
+
 @pytest.mark.parametrize("fragility_set,hazard_values,args,expected", [
     (get_fragility_set("fragility_curve.json"), {}, {}, 0.2619967240482869),
     (get_fragility_set("fragility_curve.json"), {"surgeLevel": 6, "waveHeight": 4}, {}, 1.0),
@@ -137,6 +142,7 @@ def test_calculate_restoration_rates(restoration_set, args, expected):
     else:
         assert False
 
+
 @pytest.mark.parametrize("restoration_set,args,expected", [
     (get_restoration_set("pipe_restorationset.json"), {"break_rate": 0.2, "leak_rate": 0.4,
                                                        "pipe_length": 80, "num_workers": 8}, 3.3000000000000003)
@@ -149,3 +155,17 @@ def test_calculate_pipeline_restoration_rates(restoration_set, args, expected):
     else:
         assert result["RT"] == expected
 
+
+def test_fragility_eval():
+    expression = "round(ffe_elev) == 0"
+    parameters = {"ffe_elev": 0.1}
+    assert evaluateexpression.evaluate(expression, parameters) is True
+    expression = "sum((ffe_elev, g_elev)) == 3"
+
+    parameters = {"ffe_elev": 1, "g_elev": 2}
+    assert evaluateexpression.evaluate(expression, parameters) is True
+
+    # test unsupported built-in functions
+    expression = "bytes(1)"
+    parameters = {}
+    assert math.isnan(evaluateexpression.evaluate(expression, parameters)) is True
diff --git a/tests/pyincore/utils/test_dataprocessutil.py b/tests/pyincore/utils/test_dataprocessutil.py
index 3d36c0cb5..83b888cf9 100644
--- a/tests/pyincore/utils/test_dataprocessutil.py
+++ b/tests/pyincore/utils/test_dataprocessutil.py
@@ -15,7 +15,7 @@ def client():
 def test_get_mapped_result(client):
     bldg_dataset_id = "5f9091df3e86721ed82f701d"
     bldg_dmg_dataset_id = "5f9868c00ace240b22a7f2a5"
-    bldg_func_state_dataset_id = "642f66e5f27db6680103c4ad"
+    bldg_func_state_dataset_id = "660d9435ce705a7e547a664e"
     archetype_id = "5fca915fb34b193f7a44059b"
 
     dmg_ret_json, func_ret_json, max_state_df = util.get_mapped_result_from_dataset_id(
@@ -35,7 +35,7 @@ def test_get_mapped_result_from_analysis(client):
 
     archetype_id = "5fca915fb34b193f7a44059b"
 
-    bldg_func_state_dataset_id = "642f66e5f27db6680103c4ad"
+    bldg_func_state_dataset_id = "660d9435ce705a7e547a664e"
     bldg_func_state_dataset = Dataset.from_data_service(bldg_func_state_dataset_id, DataService(client))
 
     dmg_ret_json, func_ret_json, max_state_df = util.get_mapped_result_from_analysis(
@@ -90,7 +90,7 @@ def test_joplin_mcs_cluster(client):
 def test_joplin_bldg_func_cluster(client):
     _functionality_cluster(client, archetype_mapping="5fca915fb34b193f7a44059b",
                            building_dataset_id="5fa0b132cc6848728b66948d",
-                           bldg_func_state_id="642f66e5f27db6680103c4ad",
+                           bldg_func_state_id="660d9435ce705a7e547a664e",
                            arch_column="archetype",
                            title="joplin_bldg_func")
 
@@ -98,7 +98,7 @@ def test_joplin_bldg_func_cluster(client):
 def test_galveston_mcs_cluster(client):
     _functionality_cluster(client, archetype_mapping="6420befbb18d026e7c7dbafc",
                            building_dataset_id="63ff69a96d3b2a308baaca12",
-                           bldg_func_state_id="642f6230f27db66801038964",
+                           bldg_func_state_id="660d95bece705a7e547a6654",
                            arch_column="arch_wind",
                            title="galveston_mcs")
 
@@ -106,6 +106,6 @@ def test_galveston_mcs_cluster(client):
 def test_galveston_bldg_func_cluster(client):
     _functionality_cluster(client, archetype_mapping="6420befbb18d026e7c7dbafc",
                            building_dataset_id="63ff69a96d3b2a308baaca12",
-                           bldg_func_state_id="642f6495f27db66801039434",
+                           bldg_func_state_id="660d97bfce705a7e547a6659",
                            arch_column="arch_wind",
                            title="galveston_bldg_func")