Measles03 Updates

model_measles03/Assets/python/builder_demographics.py

@@ -27,6 +27,7 @@ def demographicsBuilder():
     REF_CODE = gdata.var_params['ref_code']
     REF_YEAR = gdata.var_params['ref_year']
     NUM_NODES = gdata.var_params['num_nodes']
+    INIT_POP = gdata.var_params['init_pop']
 
     # Demographic reference data file
     dat_file = f'pop_dat_{REF_CODE}.csv'
@@ -43,7 +44,7 @@ def demographicsBuilder():
     node_id = 1
 
     for k1 in range(NUM_NODES):
-        node_pop = float(gdata.init_pop) / NUM_NODES
+        node_pop = float(INIT_POP) / NUM_NODES
         imp_rate = R0/6.0 * node_pop * 1.615e-7 * np.power(10.0, LOG10_IMP)
         nname = 'EXAMPLE:A{:05d}'.format(node_id)
         node_obj = Node(lat=0.0, lon=0.0, pop=node_pop,

model_measles03/Assets/python/builder_reports.py

@@ -4,7 +4,7 @@
 #
 # *****************************************************************************
 
-from emod_report_func import build_file, write_file
+from emod_report_func import build_file, write_file, report_strain
 
 # *****************************************************************************
 
@@ -15,7 +15,7 @@ def reportsBuilder():
     json_set = build_file()
 
     # Configurations
-    pass
+    report_strain(json_set, every_timestep=True)
 
     #  Write file
     write_file(json_set)

model_measles03/Assets/python/dtk_post_process.py

@@ -3,6 +3,7 @@
 # *****************************************************************************
 
 import json
+import os
 import sqlite3
 
 import global_data as gdata
@@ -11,7 +12,8 @@
 
 from emod_postproc_func import post_proc_poppyr, post_proc_prev, post_proc_cost
 from emod_constants import SQL_TIME, SQL_MCW, SQL_AGE, O_FILE, MO_DAYS, \
-                           SQL_FILE, BASE_YEAR
+                           SQL_FILE, BASE_YEAR, RST_FILE, RST_TIME, RST_NODE, \
+                           RST_TOT_INF
 
 # *****************************************************************************
 
@@ -32,6 +34,13 @@ def application(output_path):
     # Retain campaign cost output channel
     post_proc_cost(output_path, parsed_dat[key_str])
 
+    # Post-process strain reporter
+    infdat = np.loadtxt(os.path.join(output_path, RST_FILE),
+                        delimiter=',', skiprows=1, ndmin=2)
+
+    infcol = infdat[:, [RST_TIME, RST_NODE, RST_TOT_INF]]
+    parsed_dat[key_str]['rst'] = infcol.tolist()
+
     # Connect to SQL database; retreive new entries
     connection_obj = sqlite3.connect(SQL_FILE)
     cursor_obj = connection_obj.cursor()

model_measles03/Assets/python/global_data.py

@@ -18,8 +18,6 @@
 demog_object = None
 
 # Other stuff
-init_pop = 250000
-
 inf_prd_mean = 18.0
 
 brate_mult_x = None

model_measles03/experiment_none_ri70_nodes002/COMPS_ID.id

@@ -0,0 +1 @@
+38025497-0c3d-f111-92e2-000d3af5294c::Experiment

model_measles03/experiment_none_ri70/make01_param_dict.py → model_measles03/experiment_none_ri70_nodes002/make01_param_dict.py

@@ -30,7 +30,7 @@ def write_param_dict():
     # Setup
     param_dict = dict()
 
-    param_dict[EXP_NAME] = 'Measles03-NoSIAs-70RI'
+    param_dict[EXP_NAME] = 'Measles03-NoSIAs-70RI-Nodes002'
     param_dict[NUM_SIMS] = 250
     param_dict[EXP_V] = dict()
     param_dict[EXP_C] = dict()
@@ -57,6 +57,9 @@ def write_param_dict():
     P_CON['ref_code'] = 'COD'
     P_CON['ref_year'] = 2040
 
+    # Initial total population
+    P_CON['init_pop'] = 250000
+
     # Number of nodes
     P_CON['num_nodes'] = 2
 

model_measles03/experiment_none_ri70_nodes020/COMPS_ID.id

@@ -0,0 +1 @@
+6ca2e29e-0c3d-f111-92e2-000d3af5294c::Experiment

model_measles03/experiment_none_ri70_nodes020/make01_param_dict.py

@@ -0,0 +1,97 @@
+# *****************************************************************************
+#
+# *****************************************************************************
+
+import json
+import os
+import sys
+
+import numpy as np
+
+# Ought to go in emodpy
+sys.path.insert(0, os.path.abspath(os.path.join('..', '..', 'local_python')))
+from py_assets_common.emod_constants import EXP_C, EXP_V, EXP_NAME, NUM_SIMS, \
+                                            P_FILE
+
+# *****************************************************************************
+
+# This script makes a json dictionary that is used by the pre-processing script
+# in EMOD. Variable names defined here will be available to use in creating
+# the input files.
+
+# The pre-process script will open the json dict created by this method. For
+# everything with the EXP_V key, that script will assume a list and get a value
+# from that list based on the sim index. For everything with the EXP_C key, it
+# will assume a single value and copy that value.
+
+
+def write_param_dict():
+
+    # Setup
+    param_dict = dict()
+
+    param_dict[EXP_NAME] = 'Measles03-NoSIAs-70RI-Nodes020'
+    param_dict[NUM_SIMS] = 250
+    param_dict[EXP_V] = dict()
+    param_dict[EXP_C] = dict()
+
+    # Random number consistency
+    np.random.seed(4)
+
+    # Convenience naming
+    NSIMS = param_dict[NUM_SIMS]
+    P_VAR = param_dict[EXP_V]
+    P_CON = param_dict[EXP_C]
+
+    # Run number (EMOD random seed)
+    P_VAR['run_number'] = list(range(NSIMS))
+
+    # Infectivity
+    vals = 10.0 + np.random.gamma(30.0, scale=0.133, size=NSIMS)
+    P_VAR['R0'] = (np.round(vals, 2)).tolist()
+
+    # RI params
+    P_CON['MCV1'] = 0.70
+
+    # Reference country code and year for population pyramid
+    P_CON['ref_code'] = 'COD'
+    P_CON['ref_year'] = 2040
+
+    # Initial total population
+    P_CON['init_pop'] = 250000
+
+    # Number of nodes
+    P_CON['num_nodes'] = 20
+
+    # SIA parameters
+    P_CON['sia_start_year'] = 1000
+    P_CON['sia_interval_yrs'] = 3
+    P_CON['sia_coverage'] = 0.60
+    P_CON['sia_min_age_yr'] = 0.75
+    P_CON['sia_max_age_yr'] = 5.00
+
+    # Log10 of multiplier on exogeneous case importation
+    P_CON['log10_import_mult'] = 1.0
+
+    # RI params
+    P_CON['MCV1_age'] = 270.0
+    P_CON['Age_Take'] = True
+    P_CON['MCV2'] = 0.0
+
+    # Maternal protection effectiveness
+    P_CON['mat_factor'] = 1.0
+
+    # Write parameter dictionary
+    with open(P_FILE, 'w') as fid01:
+        json.dump(param_dict, fid01)
+
+    return None
+
+# *****************************************************************************
+
+
+if (__name__ == "__main__"):
+
+    write_param_dict()
+
+# *****************************************************************************

model_measles03/experiment_none_ri70_nodes020/make02_launch_sims.py

@@ -0,0 +1,27 @@
+# *****************************************************************************
+#
+# *****************************************************************************
+
+import os
+import sys
+
+# Ought to go in emodpy
+sys.path.insert(0, os.path.abspath(os.path.join('..', '..', 'local_python')))
+from emod_exp import start_exp
+from py_assets_common.emod_constants import P_FILE
+
+# *****************************************************************************
+
+# Paths
+PATH_EXP_DEF = os.path.abspath(P_FILE)
+PATH_PYTHON = os.path.abspath(os.path.join('..', 'Assets', 'python'))
+PATH_DATA = os.path.abspath(os.path.join('..', 'Assets', 'data'))
+
+# *****************************************************************************
+
+
+if (__name__ == "__main__"):
+
+    start_exp(PATH_PYTHON, PATH_DATA, PATH_EXP_DEF, short_queue=True)
+
+# ******************************************************************************

model_measles03/experiment_none_ri70_nodes020/make03_pool_brick.py

@@ -0,0 +1,19 @@
+# *****************************************************************************
+#
+# *****************************************************************************
+
+import os
+import sys
+
+# Ought to go in emodpy
+sys.path.insert(0, os.path.abspath(os.path.join('..', '..', 'local_python')))
+from emod_reduce import get_data_brick
+
+# *****************************************************************************
+
+
+if (__name__ == "__main__"):
+
+    get_data_brick()
+
+# *****************************************************************************

model_measles03/experiment_sia3yr60cv_ri70/make01_param_dict.py

@@ -57,6 +57,9 @@ def write_param_dict():
     P_CON['ref_code'] = 'COD'
     P_CON['ref_year'] = 2040
 
+    # Initial total population
+    P_CON['init_pop'] = 250000
+
     # Number of nodes
     P_CON['num_nodes'] = 2
 

model_measles03/figure_prevalence/make_fig_prevalence.py

@@ -0,0 +1,95 @@
+# *****************************************************************************
+
+import json
+import os
+import sys
+
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.patches as patch
+
+# Ought to go in emodpy
+sys.path.append(os.path.abspath(os.path.join('..', '..', 'local_python')))
+sys.path.append(os.path.abspath(os.path.join('..', 'Assets', 'python')))
+
+from py_assets_common.emod_constants import NUM_SIMS, P_FILE, POP_PYR, \
+                                            D_FILE, EXP_C
+from global_data import run_years, t_step_days
+
+# *****************************************************************************
+
+DIRNAMES = ['experiment_none_ri70_nodes002',
+            'experiment_none_ri70_nodes020']
+
+# *****************************************************************************
+
+
+def make_fig():
+
+    # Figures
+    fig01 = plt.figure(figsize=(8, 6))
+
+    # Figures - Sims - Prevalance
+    axs01 = fig01.add_subplot(1, 1, 1)
+    plt.sca(axs01)
+
+    axs01.grid(visible=True, which='major', ls='-', lw=0.5, label='')
+    axs01.grid(visible=True, which='minor', ls=':', lw=0.1)
+    axs01.set_axisbelow(True)
+
+    for dirname in DIRNAMES:
+
+        # Sim outputs
+        tpath = os.path.join('..', dirname)
+
+        with open(os.path.join(tpath, D_FILE)) as fid01:
+            data_brick = json.load(fid01)
+
+        with open(os.path.join(tpath, P_FILE)) as fid01:
+            param_dict = json.load(fid01)
+
+        NSIMS = int(param_dict[NUM_SIMS])
+        NNODES = int(param_dict[EXP_C]["num_nodes"])
+        tvec = np.arange(0, 365*run_years, t_step_days)
+
+        inf_dat = np.zeros((NSIMS, NNODES, len(tvec)))
+        pyr_mat = np.zeros((NSIMS, int(run_years)+1, 20))-1
+
+        for skey in data_brick:
+            if (not skey.isdigit()):
+                continue
+
+            sim_idx = int(skey)
+
+            inf_col = np.array(data_brick[skey]['rst'])  # (Time, Node, TotInf)
+            for k1 in range(inf_col.shape[0]):
+                time_idx = int(inf_col[k1, 0]/t_step_days)
+                node_idx = int(inf_col[k1, 1] - 1)  # Node indicies are 1-based
+                inf_dat[sim_idx, node_idx, time_idx] = inf_col[k1, 2]
+            pyr_mat[sim_idx, :, :] = np.array(data_brick[skey][POP_PYR])
+
+        fidx = (pyr_mat[:, 0, 0] >= 0)
+
+        non_zero_dat = (inf_dat[fidx, :, :] > 0)
+        non_zero_avg = np.mean(non_zero_dat, axis=(0,1))  # Lump sims and nodes
+
+        axs01.plot(tvec/365.0, non_zero_avg, linewidth=2,
+                   label='Nodes {:d}'.format(NNODES))
+        axs01.set_ylabel('Non-zero prevalence (fraction)', fontsize=16)
+        axs01.set_xlabel('Year', fontsize=16)
+
+    plt.tight_layout()
+    plt.legend()
+    plt.savefig('fig_prevalence_01.png'.format(dirname))
+    plt.close()
+
+    return None
+
+# *****************************************************************************
+
+
+if (__name__ == "__main__"):
+
+    make_fig()
+
+# *****************************************************************************