From 64fb2174c7470e06f78de980d2c9e604b2639a05 Mon Sep 17 00:00:00 2001 From: Jinyu Xie Date: Wed, 15 May 2024 23:10:38 -0700 Subject: [PATCH] Revert "Fix random init state" --- .gitignore | 2 +- .../results/2017-12-31_17-46-32/BG_trace.png | Bin 0 -> 105073 bytes examples/results/2017-12-31_17-46-32/CVGA.png | Bin 0 -> 39667 bytes .../2017-12-31_17-46-32/CVGA_stats.csv | 2 + .../2017-12-31_17-46-32/adolescent#001.csv | 482 ++++++++++++++++++ .../2017-12-31_17-46-32/adolescent#002.csv | 482 ++++++++++++++++++ .../2017-12-31_17-46-32/adolescent#003.csv | 482 ++++++++++++++++++ .../2017-12-31_17-46-32/adolescent#004.csv | 482 ++++++++++++++++++ .../2017-12-31_17-46-32/adolescent#005.csv | 482 ++++++++++++++++++ .../2017-12-31_17-46-32/adolescent#006.csv | 482 ++++++++++++++++++ .../2017-12-31_17-46-32/adolescent#007.csv | 482 ++++++++++++++++++ .../2017-12-31_17-46-32/adolescent#008.csv | 482 ++++++++++++++++++ .../2017-12-31_17-46-32/adolescent#009.csv | 482 ++++++++++++++++++ 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results/sysid_results/risk_trace.csv | 91 ---- results/sysid_results/zone_stats.png | Bin 30408 -> 0 bytes setup.py | 2 +- simglucose/__init__.py | 6 + simglucose/controller/mpc_ctrller.py | 216 -------- simglucose/controller/sys_id_controller.py | 34 -- simglucose/envs/simglucose_gym_env.py | 52 +- simglucose/patient/t1dpatient.py | 7 +- simglucose/simulation/user_interface.py | 15 +- 85 files changed, 14627 insertions(+), 14941 deletions(-) create mode 100644 examples/results/2017-12-31_17-46-32/BG_trace.png create mode 100644 examples/results/2017-12-31_17-46-32/CVGA.png create mode 100644 examples/results/2017-12-31_17-46-32/CVGA_stats.csv create mode 100644 examples/results/2017-12-31_17-46-32/adolescent#001.csv create mode 100644 examples/results/2017-12-31_17-46-32/adolescent#002.csv create mode 100644 examples/results/2017-12-31_17-46-32/adolescent#003.csv create mode 100644 examples/results/2017-12-31_17-46-32/adolescent#004.csv create mode 100644 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zn2Z8E(o>!)YN5P+qj$j{V`JfuK+OHW;@-G+*8>EF3Hu>gM+J)}UUc-iaDe7qnmkXm zyx%ULN!wytTlM70qGbuyMOCf$*Ts>|vy3A0WCQG*!+~ttk^g9!d;I_Uf7_b6O)@i7 V9`m0w{U!V}*<{+pqsGn~{s-{p8(sha literal 0 HcmV?d00001 diff --git a/examples/run_gymnasium.py b/examples/run_gymnasium.py index 03530ead..c31e9e38 100644 --- a/examples/run_gymnasium.py +++ b/examples/run_gymnasium.py @@ -4,7 +4,7 @@ register( id="simglucose/adolescent2-v0", entry_point="simglucose.envs:T1DSimGymnaisumEnv", - max_episode_steps=1000, + max_episode_steps=10, kwargs={"patient_name": "adolescent#002"}, ) diff --git a/examples/run_mpc.py b/examples/run_mpc.py deleted file mode 100644 index 8f494430..00000000 --- a/examples/run_mpc.py +++ /dev/null @@ -1,42 +0,0 @@ -import pandas as pd -from datetime import timedelta, datetime -from simglucose.controller.mpc_ctrller import PatientModel, MPCController -from simglucose.simulation.user_interface import simulate -from simglucose.simulation.scenario_gen import RandomScenario - -RESULT_PATH = './results/sysid_results/adolescent#001.csv' -df_result = pd.read_csv(RESULT_PATH) -df_result = df_result.iloc[:-1, :] -patient_name = 'adolescent#001' -na = 3 -nb = 5 -nc = 5 -X = df_result.loc[:, ['insulin', 'CHO']].values -y = df_result['CGM'].values -mdl = PatientModel(patient_name, na, nb, nc, X, y) - -# print(mdl.params) -# print(mdl.alpha) -# print(mdl.beta) - -N = 60 # horizon 5hr -Q = 1 # -QN = 10 # final cost -R = 1000 # input penality - -ctrller = MPCController(patient_name, mdl, N, Q, QN, R, insulin_max=1) - -SIM_RESULT_PATH = './results/mpc_results' -start_time = datetime.combine(datetime.now().date(), - datetime.min.time()) + timedelta(hours=7) -simulate(sim_time=timedelta(hours=24), - scenario=RandomScenario(start_time=start_time, seed=1), - patient_names=['adolescent#001'], - cgm_name='Dexcom', - cgm_seed=1, - insulin_pump_name='Insulet', - controller=ctrller, - start_time=start_time, - save_path=SIM_RESULT_PATH, - animate=True, - parallel=False) diff --git a/examples/run_sys_id_experiments.py b/examples/run_sys_id_experiments.py deleted file mode 100644 index 00de049f..00000000 --- a/examples/run_sys_id_experiments.py +++ /dev/null @@ -1,22 +0,0 @@ -from datetime import timedelta, datetime -from simglucose.controller.sys_id_controller import SysIDController -from simglucose.simulation.user_interface import simulate -from simglucose.simulation.scenario import CustomScenario - -RESULT_PATH = './results/sysid_results' - -start_time = datetime.combine(datetime.now().date(), datetime.min.time()) - -insulin_time = [start_time + timedelta(hours=10)] -insulin_amount = [0.5] -sysid_controller = SysIDController(insulin_time, insulin_amount) - -simulate(sim_time=timedelta(hours=24), - scenario=CustomScenario(start_time=start_time, - scenario=[(start_time + timedelta(hours=7), - 30)]), - controller=sysid_controller, - start_time=start_time, - save_path=RESULT_PATH, - animate=True, - parallel=True) diff --git a/results/sysid_results/BG_trace.png 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00:00:00,141.51056155254895,140.67086831390878,,,0.0,1.8328730497372092,1.8328730497372092 diff --git a/results/sysid_results/adult#006.csv b/results/sysid_results/adult#006.csv deleted file mode 100644 index cebffba3..00000000 --- a/results/sysid_results/adult#006.csv +++ /dev/null @@ -1,482 +0,0 @@ -Time,BG,CGM,CHO,insulin,LBGI,HBGI,Risk -2021-10-16 00:00:00,135.64,152.41394936879047,0.0,0.028741666666666665,0.0,1.2171253297619526,1.2171253297619526 -2021-10-16 00:03:00,135.64000000000118,148.2020223220473,0.0,0.028741666666666665,0.0,1.217125329762065,1.217125329762065 -2021-10-16 00:06:00,135.64000000011848,145.8593732464935,0.0,0.028741666666666665,0.0,1.2171253297733429,1.2171253297733429 -2021-10-16 00:09:00,135.64000000135246,144.0927095086945,0.0,0.028741666666666665,0.0,1.2171253298920246,1.2171253298920246 -2021-10-16 00:12:00,135.64000000706653,142.72895022625744,0.0,0.028741666666666665,0.0,1.2171253304416005,1.2171253304416005 -2021-10-16 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-HBGI,adult#007,1.1807868430401143,1.1806712853920558,3.7365254203881184,7.962153795303859,0.0,0.0,0.018298536103792503,0.3469879688441051,0.5471874125586964 -HBGI,adult#008,2.031446280798374,2.0311498390518428,4.100252037466976,7.273016792058362,1.1544771030180587,0.5369860199251915,0.7252046883765034,1.0798120910780016,1.271386259472838 -HBGI,adult#009,2.253729632445686,2.2533400526423577,5.333947522916286,15.020452255088179,5.522540695694317,2.2386373096875865,1.5839088740288636,1.5978337436861794,1.6820699196907765 -HBGI,adult#010,3.2741419086407513,3.274849638688507,8.429973920182213,15.096414993818668,4.263062666026038,2.3405560461928094,2.5396294424445505,2.8793057364167636,3.01006013725226 -HBGI,child#001,1.8001230102743395,1.8122934831255442,13.714964512046187,18.339040298608843,0.0,0.0,0.0,0.060431202687747654,0.37590707896471587 -HBGI,child#002,2.0509269391409894,2.052789725811535,5.951538508531406,17.927939446304,0.0,0.0,0.0,0.0006955513668499913,0.15626360939081818 -HBGI,child#003,1.0475554672832916,1.043865867198265,9.7538961533867,18.70234598698582,0.0,0.0,0.0,0.0,0.0 -HBGI,child#004,1.03645639288742,1.0271475691796492,5.954544282984556,8.336211399132829,0.0,0.0,0.0,0.0,0.0 -HBGI,child#005,1.4397554928402574,1.442845935538935,4.285757209632765,17.69775519069357,0.0,0.0,0.01899736498689999,0.36932700720541345,0.6083180315805213 -HBGI,child#006,1.8889629849647611,1.8907357589637666,10.026974623249465,21.174787532528075,0.0,0.0,0.0,0.4397767433981802,0.8800009528593588 -HBGI,child#007,1.0099454111545938,1.0120499989050684,6.577879508464642,12.042796493897928,0.0,0.0,0.22518200236324815,0.7136145680978628,0.8610042417095307 -HBGI,child#008,3.0632346653066564,3.0640276794758377,18.583723279810066,34.57320031572373,0.17615033613896225,0.0,0.21392488041314917,1.372178043944933,1.950364461477089 -HBGI,child#009,1.3778825823641059,1.375929871872773,7.834796609144515,3.0206819203949333,0.0,0.0,0.22809675742290741,0.926367267509735,1.1376622107219077 -HBGI,child#010,1.2926752644056194,1.2931198539964215,9.6641008963445,18.92003276995586,0.0,0.0,0.09196184103695221,0.6423135312699775,0.8766608222647554 -Risk Index,adolescent#001,2.7553696010306004,2.7561324616061182,3.859440073776011,6.479090318142858,0.9346233825122363,0.9172083304428127,1.836512228411222,2.418915234013509,2.577236413255113 -Risk Index,adolescent#002,3.2153042853247538,3.215127861701949,11.318210622291513,23.97730684615651,5.2761951993061595,2.11330117309679,2.2374517292198526,2.6962607357321784,2.8802074097228703 -Risk Index,adolescent#003,2.3546507198941784,2.3517767543160164,6.406135908873514,12.677116635476606,0.047575957946449025,0.20794846478057433,0.03316203870915283,0.5817862737993375,0.9545185380097692 -Risk Index,adolescent#004,2.207541635387405,2.2086126909156514,6.793864240789876,17.210759523620634,1.8155549504892265,0.18215512361432734,0.39082046337477366,0.8648255095892812,1.1307315870783512 -Risk Index,adolescent#005,1.5604628202587003,1.5598849216537234,5.851622668887665,16.691246182563553,1.2890375759125967,0.23143888359123507,0.6338477989818947,1.086301613716562,1.253998742678564 -Risk Index,adolescent#006,1.1252029662458949,1.1247866289141915,5.5003167381912785,6.489922171748298,0.0491734898513104,0.7055902142768488,1.057854609077545,1.1153850274987203,1.1217583469603356 -Risk Index,adolescent#007,2.263647580488751,2.2640318118074645,9.229860375796328,17.570271468197216,1.375498974950566,0.9942850299533326,1.4453979430257566,1.8034648279788752,1.9417092800559421 -Risk Index,adolescent#008,2.1588805893218765,2.158039925847348,9.340269542255626,22.241326312203324,2.632306594884215,1.2578504008518285,1.777192264774818,2.05641435266057,2.1140017020331667 -Risk Index,adolescent#009,1.4494221431626024,1.4489310501321113,5.219130738838781,7.5913814388593925,0.03172550011283396,0.00033013947539132485,0.35316341732116757,0.9011652292485435,1.110351029439656 -Risk Index,adolescent#010,2.228093005328218,2.2279729997622986,5.520835373590605,18.862263292256536,1.3191617545415566,0.002763142138900188,0.24803109719689964,0.9255149408047798,1.2716687646346667 -Risk Index,adult#001,1.5108776953447527,1.5106267352979992,4.062425813420001,8.101664132014314,2.523023139903424,0.9332486051637828,0.7168116136706033,0.8010009977328771,0.8810392347040272 -Risk Index,adult#002,1.2960534263625418,1.2961340205683691,2.923736836584247,7.034220005191354,1.2598718466197065,0.7135387347528885,0.8171772871509115,0.9030262020406766,0.9427222083828015 -Risk Index,adult#003,2.5060754304680772,2.5048436609535103,7.487062364838234,12.077298306590919,1.2196650712162644,0.8420395870439279,1.6778902680575145,2.2053119237775602,2.343402837378546 -Risk Index,adult#004,2.978209945273298,2.9756053270957232,8.451334277083111,14.148056156254063,0.21484620396054022,0.12555546257950478,0.9533249750923825,1.8254195238555504,2.1606008881226972 -Risk Index,adult#005,1.9658888682563873,1.9655011630371808,4.773163869359271,10.564733168723698,2.889487787442299,1.4715390135771846,1.5441244092925852,1.754119254113828,1.8328730497372092 -Risk Index,adult#006,1.2171411927573836,1.2172842723235247,3.7963562946132914,12.976571559354422,4.2490079804949055,1.3959836452546173,1.0307777756196275,1.0358297929008329,1.061338744197731 -Risk Index,adult#007,1.1807868430401143,1.1806712853920558,3.7365254203881184,7.962153795303859,0.039722680636219636,0.27749289910703345,0.018298536103792503,0.3469879688441051,0.5471874125586964 -Risk Index,adult#008,2.031446280798374,2.0311498390518428,4.100252037466976,7.273016792058362,1.1544771030180587,0.5369860199251915,0.7252046883765034,1.0798120910780016,1.271386259472838 -Risk Index,adult#009,2.253729632445686,2.2533400526423577,5.333947522916286,15.020452255088179,5.522540695694317,2.2386373096875865,1.5839088740288636,1.5978337436861794,1.6820699196907765 -Risk Index,adult#010,3.2741419086407513,3.274849638688507,8.429973920182213,15.096414993818668,4.263062666026038,2.3405560461928094,2.5396294424445505,2.8793057364167636,3.01006013725226 -Risk Index,child#001,1.8001230102743395,1.8122934831255442,13.714964512046187,18.339040298608843,38.49610620199929,14.08975545780941,1.041614333286715,0.060431202687747654,0.37590707896471587 -Risk Index,child#002,2.0509269391409894,2.052789725811535,5.951538508531406,17.927939446304,0.3959031093795356,3.4415085597119495,0.8242138177967753,0.0006955513668499913,0.15626360939081818 -Risk Index,child#003,1.0475554672832916,1.043865867198265,9.7538961533867,18.70234598698582,1.6584845810809081,0.9040551888904549,0.3382917579486538,0.11330570111493792,0.04385137163864166 -Risk Index,child#004,1.03645639288742,1.0271475691796492,5.954544282984556,8.336211399132829,63.917563884083194,34.02959653825751,10.161640373837557,2.6979896710278224,1.198962121280497 -Risk Index,child#005,1.4397554928402574,1.442845935538935,4.285757209632765,17.69775519069357,0.1975026281310063,0.2757033701880793,0.01899736498689999,0.36932700720541345,0.6083180315805213 -Risk Index,child#006,1.8889629849647611,1.8907357589637666,10.026974623249465,21.174787532528075,3.775779438397604,3.562033939929811,0.07587324132231946,0.4397767433981802,0.8800009528593588 -Risk Index,child#007,1.0099454111545938,1.0120499989050684,6.577879508464642,12.042796493897928,0.9536303726233417,0.1915688010082197,0.22518200236324815,0.7136145680978628,0.8610042417095307 -Risk Index,child#008,3.0632346653066564,3.0640276794758377,18.583723279810066,34.57320031572373,0.17615033613896225,0.21951907965229725,0.21392488041314917,1.372178043944933,1.950364461477089 -Risk Index,child#009,1.3778825823641059,1.375929871872773,7.834796609144515,3.0206819203949333,13.677303441751521,0.9936876044041505,0.22809675742290741,0.926367267509735,1.1376622107219077 -Risk 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version="0.2.9", description="A Type-1 Diabetes Simulator as a Reinforcement Learning Environment in OpenAI gym or rllab (python implementation of UVa/Padova Simulator)", url="https://github.com/jxx123/simglucose", author="Jinyu Xie", diff --git a/simglucose/__init__.py b/simglucose/__init__.py index e69de29b..ea4ffe53 100644 --- a/simglucose/__init__.py +++ b/simglucose/__init__.py @@ -0,0 +1,6 @@ +from gym.envs.registration import register + +register( + id='simglucose-v0', + entry_point='simglucose.envs:T1DSimEnv', +) diff --git a/simglucose/controller/mpc_ctrller.py b/simglucose/controller/mpc_ctrller.py deleted file mode 100644 index 19d631dc..00000000 --- a/simglucose/controller/mpc_ctrller.py +++ /dev/null @@ -1,216 +0,0 @@ -from .base import Controller -from .base import Action -import cvxpy as cp -import logging -from scipy import sparse -import numpy as np -from fireTS.models import NARX -from sklearn.linear_model import LinearRegression -# from sklearn.preprocessing import StandardScaler -from simglucose.utils import fetch_patient_params, fetch_patient_quest - -logger = logging.getLogger(__name__) - - -class PatientModel: - NU = 1 - - def __init__(self, patient_name, na, nb, nc, X, y): - """ - params - parameters of a linear ARX model. params should be an array - with first na parameters representing the coefficients of the - auto-regressors, and the rest of the parameters representing the - coefficients of the exogenous inputs. - """ - assert na > 0 and nb > 0 - self.patient_name = patient_name - self._na = na # auto order - self._nb = nb # insulin order - self._nc = nc # meal order - # self.scaler = StandardScaler() - self.quests = fetch_patient_quest(patient_name) - self.patient_params = fetch_patient_params(patient_name) - self.mdl = NARX(LinearRegression(), auto_order=na, exog_order=[nb, nc]) - self._fit(X, y) - self._A, self._B = self._construct_state_matrix() - - def _construct_state_matrix(self): - A = sparse.lil_matrix(np.zeros([self.nx, self.nx])) - A[0, :self.na] = self.alpha - A[1:self.na, :(self.na - 1)] = np.identity(self.na - 1) - if self.nb > 1: - A[0, self.na:] = self.beta[1:] - A[(self.na + 1):(self.na + self.nb - 1), - self.na:(self.na + self.nb - 2)] = np.identity(self.nb - 2) - - # Assume there is only 1 input. - B = sparse.lil_matrix(np.zeros([self.nx, self.nu])) - B[0] = self.beta[0] - B[self.na] = 1 - return A, B - - @property - def basal(self): - if self.patient_params: - # unit: pmol/(L*kg) - u2ss = self.patient_params['u2ss'] - # unit: kg - BW = self.patient_params['BW'] - else: - # NOTE: This need to be revisited. Does it make sense to use - # average? Use average stats if the params is missing. - u2ss = 1.43 # unit: pmol/(L*kg) - BW = 57.0 # unit: kg - - basal = u2ss * BW / 6000 # unit: U/min - return basal - - @property - def Gb(self): - """ - Subcutaneous BG value at the stable state. - """ - return self.patient_params['Gb'] - - def _fit(self, X, y): - """ - X: [insulin, meal] - y: CGM readings - """ - # Centered at the balance points. - X[:, 0] = X[:, 0] - self.basal - y = y - self.Gb - - # TODO: verify if standardizing the data helps with the modeling. - # X, y = self.scaler.fit_transform(X, y) - self.mdl.fit(X, y) - - @property - def params(self): - # TODO: expose base_estimator coefficients API at the fireTS model level. - # Going into the internal interface is not a good practice. - return self.mdl.base_estimator.coef_ - - @property - def alpha(self): - return self.params[:self.na] - - @property - def beta(self): - # NOTE: This assumes the rest of the parameters are the coefficients - # for insulin. This need to be modified if the model is no longer a - # simple linear ARX model. - return self.params[self.na:(self.na + self.nb)] - - @property - def na(self): - return self._na - - @property - def nb(self): - return self._nb - - @property - def A(self) -> sparse.lil_matrix: - return self._A - - @property - def B(self) -> sparse.lil_matrix: - return self._B - - @property - def nx(self): - return self.na + self.nb - 1 - - @property - def nu(self): - # Number of inputs is fixed to 1. - return self.NU - - -class MPCController(Controller): - def __init__(self, - patient_name, - model, - N, - Q, - QN, - R, - target=140, - insulin_max=5): - self.patient_name = patient_name - self.N = N - self.Q = Q - self.QN = QN - self.R = R - self.target = target - self.quests = fetch_patient_quest(patient_name) - self.patient_params = fetch_patient_params(patient_name) - self.u_max = insulin_max # U/min - self.model = model - self.reset() - - @property - def basal(self): - if self.patient_params: - # unit: pmol/(L*kg) - u2ss = self.patient_params['u2ss'] - # unit: kg - BW = self.patient_params['BW'] - else: - # NOTE: This need to be revisited. Does it make sense to use - # average? Use average stats if the params is missing. - u2ss = 1.43 # unit: pmol/(L*kg) - BW = 57.0 # unit: kg - - basal = u2ss * BW / 6000 # unit: U/min - return basal - - @property - def Gb(self): - """ - Subcutaneous BG value at the stable state. - """ - return self.patient_params['Gb'] - - def _define_opt_problem(self): - obj = 0 - u_min = -self.basal - yr = self.target - self.Gb - constraints = [self.x[:, 0] == self.x_init] - - for k in range(self.N): - # obj += self.Q * cp.norm(self.x[0, k] - yr, 2) + self.R * cp.norm( - # self.u[:, k], 2) - obj += self.Q * cp.norm(self.x[0, k] - yr, 2) - if k > 0: - obj += self.R * cp.norm(self.u[:, k] - self.u[:, k - 1], 2) - constraints += [ - self.x[:, k + 1] == self.model.A @ self.x[:, k] + - self.model.B @ self.u[:, k] - ] - constraints += [u_min <= self.u[:, k], self.u[:, k] <= self.u_max] - obj += self.QN * cp.norm(self.x[0, self.N] - yr, 2) - return cp.Problem(cp.Minimize(obj), constraints) - - def policy(self, observation, reward, done, **kwargs): - self.state[1:self.model.na] = self.state[:(self.model.na - 1)] - self.state[0] = observation.CGM - self.Gb - self.state[(self.model.na + 1):] = self.state[self.model.na:-1] - self.state[self.model.na] = self.prev_input - - self.x_init.value = self.state - self.prob.solve(solver=cp.ECOS, warm_start=True, verbose=True) - - opt_input = max(self.u[:, 0].value + self.basal, 0) - self.prev_input = opt_input - print(f'opt insulin: {opt_input}') - return Action(basal=opt_input, bolus=0) - - def reset(self): - self.state = np.zeros(self.model.nx) - self.prev_input = 0.0 - self.x_init = cp.Parameter(self.model.nx) - self.x = cp.Variable((self.model.nx, self.N + 1)) - self.u = cp.Variable((self.model.nu, self.N)) - self.prob = self._define_opt_problem() diff --git a/simglucose/controller/sys_id_controller.py b/simglucose/controller/sys_id_controller.py deleted file mode 100644 index 4e05c5dc..00000000 --- a/simglucose/controller/sys_id_controller.py +++ /dev/null @@ -1,34 +0,0 @@ -from re import L -from .base import Controller -from .base import Action -from simglucose.utils import fetch_patient_params - - -class SysIDController(Controller): - """ - This controller is just used to collect data for system identification. - It tries to stimulate the system in a proper way to get useful information of the system. - """ - def __init__(self, insulin_time, insulin_amount): - assert len(insulin_time) == len(insulin_amount) - self.insulin_time = insulin_time - self.insulin_amount = insulin_amount - self.reset() - - def policy(self, observation, reward, done, **info): - time = info['time'] - if not self.basal: - patient_params = fetch_patient_params(info['patient_name']) - u2ss = patient_params['u2ss'] - BW = patient_params['BW'] - self.basal = u2ss * BW / 6000 # unit: U/min - - action = Action(basal=self.basal, bolus=0.0) - if time in self.insulin_time: - action = Action( - basal=self.basal, - bolus=self.insulin_amount[self.insulin_time.index(time)]) - return action - - def reset(self): - self.basal = None diff --git a/simglucose/envs/simglucose_gym_env.py b/simglucose/envs/simglucose_gym_env.py index 0bc8ca4f..aab5e394 100644 --- a/simglucose/envs/simglucose_gym_env.py +++ b/simglucose/envs/simglucose_gym_env.py @@ -12,8 +12,10 @@ from datetime import datetime import gymnasium + PATIENT_PARA_FILE = pkg_resources.resource_filename( - "simglucose", "params/vpatient_params.csv") + "simglucose", "params/vpatient_params.csv" +) class T1DSimEnv(gym.Env): @@ -26,11 +28,9 @@ class T1DSimEnv(gym.Env): SENSOR_HARDWARE = "Dexcom" INSULIN_PUMP_HARDWARE = "Insulet" - def __init__(self, - patient_name=None, - custom_scenario=None, - reward_fun=None, - seed=None): + def __init__( + self, patient_name=None, custom_scenario=None, reward_fun=None, seed=None + ): """ patient_name must be 'adolescent#001' to 'adolescent#010', or 'adult#001' to 'adult#010', or 'child#001' to 'child#010' @@ -79,19 +79,20 @@ def _create_env(self): if isinstance(self.patient_name, list): patient_name = self.np_random.choice(self.patient_name) - patient = T1DPatient.withName(patient_name, - random_init_bg=True, - seed=seed4) + patient = T1DPatient.withName(patient_name, random_init_bg=True, seed=seed4) else: - patient = T1DPatient.withName(self.patient_name, - random_init_bg=True, - seed=seed4) + patient = T1DPatient.withName( + self.patient_name, random_init_bg=True, seed=seed4 + ) if isinstance(self.custom_scenario, list): scenario = self.np_random.choice(self.custom_scenario) else: - scenario = (RandomScenario(start_time=start_time, seed=seed3) if - self.custom_scenario is None else self.custom_scenario) + scenario = ( + RandomScenario(start_time=start_time, seed=seed3) + if self.custom_scenario is None + else self.custom_scenario + ) sensor = CGMSensor.withName(self.SENSOR_HARDWARE, seed=seed2) pump = InsulinPump.withName(self.INSULIN_PUMP_HARDWARE) @@ -108,11 +109,11 @@ def _close(self): @property def action_space(self): ub = self.env.pump._params["max_basal"] - return spaces.Box(low=0, high=ub, shape=(1, )) + return spaces.Box(low=0, high=ub, shape=(1,)) @property def observation_space(self): - return spaces.Box(low=0, high=1000, shape=(1, )) + return spaces.Box(low=0, high=1000, shape=(1,)) @property def max_basal(self): @@ -120,7 +121,7 @@ def max_basal(self): class T1DSimGymnaisumEnv(gymnasium.Env): - metadata = {"render_modes": ["human"], "render_fps": 60} + metadata = {"render_modes": ["human"]} MAX_BG = 1000 def __init__( @@ -139,14 +140,12 @@ def __init__( reward_fun=reward_fun, seed=seed, ) - self.observation_space = gymnasium.spaces.Box(low=0, - high=self.MAX_BG, - shape=(1, ), - dtype=np.float32) - self.action_space = gymnasium.spaces.Box(low=0, - high=self.env.max_basal, - shape=(1, ), - dtype=np.float32) + self.observation_space = gymnasium.spaces.Box( + low=0, high=self.MAX_BG, shape=(1,), dtype=np.float32 + ) + self.action_space = gymnasium.spaces.Box( + low=0, high=self.env.max_basal, shape=(1,), dtype=np.float32 + ) def step(self, action): obs, reward, done, info = self.env.step(action) @@ -160,8 +159,7 @@ def step(self, action): # ) # Once the max_episode_steps is set, the truncated value will be overridden. truncated = False - return np.array([obs.CGM], - dtype=np.float32), reward, done, truncated, info + return np.array([obs.CGM], dtype=np.float32), reward, done, truncated, info def reset(self, seed=None, options=None): super().reset(seed=seed) diff --git a/simglucose/patient/t1dpatient.py b/simglucose/patient/t1dpatient.py index 217e73dc..3f233556 100644 --- a/simglucose/patient/t1dpatient.py +++ b/simglucose/patient/t1dpatient.py @@ -124,7 +124,10 @@ def model(t, x, action, params, last_Qsto, last_foodtaken): # Glucose in the stomach qsto = x[0] + x[1] - Dbar = last_Qsto + last_foodtaken + # NOTE: Dbar is in unit mg, hence last_foodtaken needs to be converted + # from mg to g. See https://github.com/jxx123/simglucose/issues/41 for + # details. + Dbar = last_Qsto + last_foodtaken * 1000 # unit: mg # Stomach solid dxdt[0] = -params.kmax * x[0] + d @@ -246,7 +249,7 @@ def reset(self): Reset the patient state to default intial state ''' if self._init_state is None: - self.init_state = np.copy(self._params.iloc[2:15].values) + self.init_state = self._params.iloc[2:15] else: self.init_state = self._init_state diff --git a/simglucose/simulation/user_interface.py b/simglucose/simulation/user_interface.py index 4b561d43..a6f460fc 100644 --- a/simglucose/simulation/user_interface.py +++ b/simglucose/simulation/user_interface.py @@ -262,12 +262,9 @@ def pick_controller(): return controller -def pick_save_path(use_default=False): - if not use_default: - foldername = input('Folder name to save results [default]: ') - if foldername == 'default' or foldername == '': - foldername = datetime.now().strftime('%Y-%m-%d_%H-%M-%S') - else: +def pick_save_path(): + foldername = input('Folder name to save results [default]: ') + if foldername == 'default' or foldername == '': foldername = datetime.now().strftime('%Y-%m-%d_%H-%M-%S') save_path = os.path.join(os.path.abspath('./results/'), foldername) @@ -311,7 +308,7 @@ def simulate(sim_time=None, cgm_seed=None, insulin_pump_name=None, start_time=None, - save_path='default', + save_path=None, animate=None, parallel=None): ''' @@ -341,10 +338,6 @@ def simulate(sim_time=None, if save_path is None: save_path = pick_save_path() - elif save_path == 'default': - save_path = pick_save_path(use_default=True) - elif save_path == 'None': - save_path = None if sim_time is None: sim_time = timedelta(