diff --git a/python/calibration.py b/python/calibration.py
deleted file mode 100644
index 73e2ed10..00000000
--- a/python/calibration.py
+++ /dev/null
@@ -1,86 +0,0 @@
-#!/usr/bin/env python
-import yaml
-from os import chdir
-from pathlib import Path
-from ngen.cal.configuration import General
-from ngen.cal.search import dds, dds_set, pso_search
-from ngen.cal.strategy import Algorithm
-from ngen.cal.agent import Agent
-
-from typing import TYPE_CHECKING
-if TYPE_CHECKING:
-    from typing import Mapping, Any
-
-def main(general: General, model_conf: Mapping[str, Any]):
-    #seed the random number generators if requested
-    if general.random_seed is not None:
-        import random
-        random.seed(general.random_seed)
-        import numpy as np
-        np.random.seed(general.random_seed)
-
-    print("Starting calib")
-
-    """
-    TODO calibrate each "catcment" independely, but there may be something interesting in grouping various formulation params
-    into a single variable vector and calibrating a set of heterogenous formultions...
-    """
-    start_iteration = 0
-    #Initialize the starting agent
-    agent = Agent(model_conf, general.workdir, general.log, general.restart, general.strategy.parameters)
-    if general.strategy.algorithm == Algorithm.dds:
-        func = dds_set #FIXME what about explicit/dds
-        start_iteration = general.start_iteration
-        if general.restart:
-            start_iteration = agent.restart()
-    elif general.strategy.algorithm == Algorithm.pso: #TODO how to restart PSO?
-        if agent.model.strategy != "uniform":
-            print("Can only use PSO with the uniform model strategy")
-            return
-        if general.restart:
-            print("Restart not supported for PSO search, starting at 0")
-        func = pso_search
-
-    print("Starting Iteration: {}".format(start_iteration))
-    # print("Starting Best param: {}".format(meta.best_params))
-    # print("Starting Best score: {}".format(meta.best_score))
-    print("Starting calibration loop")
-
-
-    #NOTE this assumes we calibrate each catchment independently, it may be possible to design an "aggregate" calibration
-    #that works in a more sophisticated manner.
-    if agent.model.strategy == 'explicit': #FIXME this needs a refactor...should be able to use a calibration_set with explicit loading
-        for catchment in agent.model.adjustables:
-            dds(start_iteration, general.iterations, catchment, agent)
-
-    elif agent.model.strategy == 'independent':
-        #for catchment_set in agent.model.adjustables:
-        func(start_iteration, general.iterations, agent)
-
-    elif agent.model.strategy == 'uniform':
-        #for catchment_set in agent.model.adjustables:
-        #    func(start_iteration, general.iterations, catchment_set, agent)
-        func(start_iteration, general.iterations, agent)
-
-if __name__ == "__main__":
-
-
-    import argparse
-
-    # get the command line parser
-    parser = argparse.ArgumentParser(
-        description='Calibrate catchments in NGEN NWM architecture.')
-    parser.add_argument('config_file', type=Path,
-                        help='The configuration yaml file for catchments to be operated on')
-
-    args = parser.parse_args()
-    
-    with open(args.config_file) as file:
-        conf = yaml.safe_load(file)
-    
-    general = General(**conf['general'])
-    # change directory to workdir
-    chdir(general.workdir)
-
-    #model = Model(model=conf['model']).model
-    main(general, conf['model'])