SAVE: working w/ zain irl

convert/fmp.py

@@ -1,103 +0,0 @@
-import pyodbc
-
-
-"""
-File Maker Pro Connection Object
-"""
-
-
-class FMP(object):
-    __connection_string = "DSN=chifmp;UID=hjohnson;PWD=hjohnson"
-
-    def __init__(self):
-        self.fmp_table_names = [
-            "clinics",
-            "patients",
-            "visits",
-            "diagnoses",
-            "diagnoses_made",
-            "meds",
-            "meds_dispensed",
-            # "settings",
-            # "globals",
-        ]
-
-        self.python_types_to_sql = {
-            "str": "VARCHAR",
-            "date": "TIMESTAMP",
-            "float64": "FLOAT",
-            "NoneType": "VARCHAR",
-        }
-
-        self.__connection = pyodbc.connect(FMP.__connection_string)
-
-    def fmp_table_names(self):
-        return self.fmp_table_names
-
-    @property
-    def connection(self):
-        return self.__connection
-
-
-class CHIFMPModel(object):
-    def __init__(self):
-        self.chifmp_table_names = [
-            "patients",
-            "visits",
-            "clinics",
-            "diagnoses",
-            "diagnoses_made",
-            "meds",
-            "meds_dispensed",
-            # "globals",
-            # "settings",
-        ]
-
-    def get_chifmp_table_names(self):
-        return self.chifmp_table_names
-
-    def _get_connection_string(self):
-        # import netrc
-
-        ## Add secret information in the `~/.netrc file
-        # machine chihaiti.fmp
-        #    account  chihaiti   #<- the name of the DSN to connetct to
-        #    login    fmp_database_name
-        #    password fmp_database_password
-        # netrcparser = netrc.netrc()
-        # mylogin, myaccount, mypassword = netrcparser.authenticators("chihaiti.fmp")
-        connection_string = "DSN=chifmp;UID=hjohnson;PWD=hjohnson"
-        print("Starting Connection with {0}".format(connection_string))
-        # del mylogin, myaccount, mypassword
-        return connection_string
-
-    def get_connnection(self):
-        import pyodbc
-
-        return pyodbc.connect(self._get_connection_string())
-
-
-class QueryElements(object):
-    """
-    Class to hold components used to construct a query
-
-    updates should be done like the following:
-
-    template = "UPDATE patients SET first_name = ? WHERE pk_patient_id = ?"
-    values = ('My"Name"', 12342)
-    cursor.execute(template, values);
-
-    """
-
-    def __init__(self, query_template, query_values):
-        self.query_template = query_template
-        self.query_values = query_values
-
-    def get_query_template(self):
-        return self.query_template
-
-    def get_query_values(self):
-        return self.query_values
-
-    def __str__(self):
-        return str(self.query_template) + " " + str(self.query_values)

convert/generate_json_from_fmp.py

@@ -1,38 +0,0 @@
-import json
-from datetime import datetime
-
-import pandas as pd
-import pyodbc
-
-from fmp import FMP
-
-db_json = dict()
-fmp_model = FMP()
-
-for tbl in fmp_model.fmp_table_names:
-    query = "select * from {tbl}".format(tbl=tbl)
-    df = pd.read_sql(query, fmp_model.connection)
-
-    print(tbl)
-    col_types = [
-        df.iloc[1, index].__class__.__name__ for index in range(len(df.columns))
-    ]
-    for elem in zip(df.columns, col_types):
-        print(elem)
-    print()
-
-    db_json[tbl] = {}
-    col_types = [
-        df.iloc[1, index].__class__.__name__ for index in range(len(df.columns))
-    ]
-    for elem in zip(df.columns, col_types):
-        db_json[tbl][elem[0]] = {}
-        db_json[tbl][elem[0]]["type"] = elem[1]
-        db_json[tbl][elem[0]]["pk"] = "pk" == elem[0][:2]
-        db_json[tbl][elem[0]]["fk"] = "fk" == elem[0][:2]
-
-
-curr_time = datetime.now().strftime("%Y%m%d_%H%M")
-
-with open("./models/{time}_model.json".format(time=curr_time), "w") as f:
-    json.dump(db_json, f, indent=4)

db/db.py

@@ -0,0 +1,19 @@
+from abc import ABC, abstractmethod
+
+
+class DB(ABC):
+    def __init__(self):
+        self._model = None
+        self.datetime_format = "%Y%m%d_%H%M"
+
+    @abstractmethod
+    def write_model(self):
+        pass
+
+    @abstractmethod
+    def parse_database(self) -> dict():
+        pass
+
+    @abstractmethod
+    def init_from_json(self, json_model: dict()):
+        pass

db/enter_psql.bash

@@ -0,0 +1 @@
+psql --host=database --username=user --dbname=database

db/fk.py

@@ -0,0 +1,9 @@
+query = "SELECT tc.table_schema, tc.constraint_name, tc.table_name, kcu.column_name, ccu.table_schema AS foreign_table_schema, ccu.table_name AS foreign_table_name, ccu.column_name AS foreign_column_name FROM information_schema.table_constraints AS tc JOIN information_schema.key_column_usage AS kcu ON tc.constraint_name = kcu.constraint_name AND tc.table_schema = kcu.table_schema JOIN information_schema.constraint_column_usage AS ccu ON ccu.constraint_name = tc.constraint_name AND ccu.table_schema = tc.table_schema WHERE tc.constraint_type = 'FOREIGN KEY' AND tc.table_name='patients';"
+
+from fmp import FMP
+import pandas as pd
+
+fmp_model = FMP()
+
+df = pd.read_sql(query, fmp_model.connection)
+print(df)

db/fk.sql

@@ -0,0 +1,17 @@
+SELECT
+    tc.table_schema, 
+    tc.constraint_name, 
+    tc.table_name, 
+    kcu.column_name, 
+    ccu.table_schema AS foreign_table_schema,
+    ccu.table_name AS foreign_table_name,
+    ccu.column_name AS foreign_column_name 
+FROM 
+    information_schema.table_constraints AS tc 
+    JOIN information_schema.key_column_usage AS kcu
+      ON tc.constraint_name = kcu.constraint_name
+      AND tc.table_schema = kcu.table_schema
+    JOIN information_schema.constraint_column_usage AS ccu
+      ON ccu.constraint_name = tc.constraint_name
+      AND ccu.table_schema = tc.table_schema
+WHERE tc.constraint_type = 'FOREIGN KEY' AND tc.table_name='patients';

db/fmp.py

@@ -0,0 +1,98 @@
+import json
+from datetime import datetime
+
+import pandas as pd
+import pyodbc
+from tqdm import tqdm
+
+from db import DB
+
+
+"""
+File Maker Pro Connection Object
+
+Desc:
+This file contains the primary logic for interacting with the file maker pro database
+"""
+
+
+class FMP(DB):
+    def __init__(self, dsn=None, username=None, password=None):
+        DB.__init__(self)
+        if dsn is None:
+            dsn = input("Enter the FMP DSN: ")
+        if username is None:
+            username = input("Enter your FMP username: ")
+        if password is None:
+            password = input("Enter your FMP password: ")
+
+        self.__dsn = dsn
+        self.__username = username
+        self.__password = password
+
+        self.__connection_string = "DSN={0};UID={1};PWD={2}".format(
+            self.__dsn, self.__username, self.__password
+        )
+
+        self.__fmp_table_names = [
+            "clinics",
+            "patients",
+            "visits",
+            "diagnoses",
+            "diagnoses_made",
+            "meds",
+            "meds_dispensed",
+            # "settings",
+            # "globals",
+        ]
+
+        self.__python_types_to_sql = {
+            "str": "VARCHAR",
+            "date": "TIMESTAMP",
+            "float64": "FLOAT",
+            "NoneType": "VARCHAR",
+        }
+
+        self.__pyodbc_connection = pyodbc.connect(self.__connection_string)
+
+    @property
+    def python_types_to_sql(self):
+        return self.__python_types_to_sql
+
+    @property
+    def fmp_table_names(self):
+        return self.__fmp_table_names
+
+    @property
+    def connection(self):
+        return self.__pyodbc_connection
+
+    # ABSTRACT BASE CLASS MEHTODS
+    def init_from_json(model_object):
+        self._model = model_object
+
+    def parse_database(self) -> dict():
+        self._model = dict()
+        for tbl in tqdm(self.fmp_table_names):
+            query = "select * from {tbl}".format(tbl=tbl)
+            df = pd.read_sql(query, self.connection)
+
+            self._model[tbl] = {}
+            col_types = [
+                df.iloc[1, index].__class__.__name__ for index in range(len(df.columns))
+            ]
+
+            for elem in zip(df.columns, col_types):
+                self._model[tbl][elem[0]] = {}
+                self._model[tbl][elem[0]]["type"] = elem[1]
+                self._model[tbl][elem[0]]["pk"] = "pk" == elem[0][:2]
+                self._model[tbl][elem[0]]["fk"] = "fk" == elem[0][:2]
+        return self._model
+
+    def write_model(self, model_object=None):
+        curr_time = datetime.now().strftime(self.datetime_format)
+        with open("./models/{time}_model.json".format(time=curr_time), "w") as f:
+            if model_object is not None:
+                json.dump(model_object, f, indent=4)
+            else:
+                json.dump(self._model, f, indent=4)

db/init-db.bash

@@ -0,0 +1,17 @@
+#!/usr/bin/env bash
+# Author: "hertz"
+# Date: 20200310
+
+# args:
+# 1 --
+# 2 --
+
+#if [[ $# -ne 1 ]]; then
+#    echo "Usage: $0 <param-1> <param-2> ..."
+#    exit
+#fi
+
+
+# script:
+
+

db/init/20200305_2357_init_db.sql

@@ -238,7 +238,7 @@ create table chi_20200305_2357.visits (
 create table chi_20200305_2357.diagnoses (
 	pk_diagnosis_id FLOAT primary key,
 	diagnosis_name VARCHAR ,
-	pk_diagnosis_key VARCHAR primary key,
+	pk_diagnosis_key VARCHAR ,
 	creation_date TIMESTAMP ,
 	creation_acct VARCHAR ,
 	modified_date TIMESTAMP ,
@@ -262,7 +262,7 @@ create table chi_20200305_2357.meds (
 	creation_acct VARCHAR ,
 	modified_date TIMESTAMP ,
 	modified_acct VARCHAR ,
-	pk_med_key VARCHAR primary key,
+	pk_med_key VARCHAR ,
 	med_type VARCHAR 
 );
 

db/init/20200309_1938_init_db.sql

@@ -0,0 +1,280 @@
+create schema chi_20200309_1938;
+create table chi_20200309_1938.clinics (
+	pk_clinic_id FLOAT primary key,
+	clinic_name VARCHAR ,
+	creation_date TIMESTAMP ,
+	creation_acct VARCHAR ,
+	modified_date TIMESTAMP ,
+	modified_acct VARCHAR 
+);
+
+create table chi_20200309_1938.patients (
+	pk_patient_id FLOAT primary key,
+	creation_date TIMESTAMP ,
+	creation_acct VARCHAR ,
+	modified_date TIMESTAMP ,
+	modified_acct VARCHAR ,
+	last_name VARCHAR ,
+	first_name VARCHAR ,
+	gender VARCHAR ,
+	face_photo VARCHAR ,
+	birthdate VARCHAR ,
+	phone VARCHAR ,
+	town VARCHAR ,
+	unique_name VARCHAR ,
+	unique_ID_name VARCHAR ,
+	nickname VARCHAR ,
+	needs_review VARCHAR ,
+	notes_modification_status VARCHAR ,
+	fk_geonameid VARCHAR 
+);
+
+create table chi_20200309_1938.visits (
+	pk_visit_id FLOAT primary key,
+	creation_date TIMESTAMP ,
+	creation_acct VARCHAR ,
+	modified_date TIMESTAMP ,
+	modified_acct VARCHAR ,
+	fk_patient_id FLOAT ,
+	fk_clinic_id FLOAT ,
+	visit_date TIMESTAMP ,
+	visit_age FLOAT ,
+	ob_pregnant VARCHAR ,
+	ob_last_menstrual_period VARCHAR ,
+	bp_systolic_blood_pressure FLOAT ,
+	bp_diastolic_blood_pressure FLOAT ,
+	bp_mean_arterial_pressure FLOAT ,
+	temp_in_fahrenheit FLOAT ,
+	pulse_rate FLOAT ,
+	weight_in_lbs FLOAT ,
+	height_in_inches FLOAT ,
+	glasses_given VARCHAR ,
+	sunglasses_given VARCHAR ,
+	dx_surgical_candidate VARCHAR ,
+	surgical_comments VARCHAR ,
+	visit_age_unit VARCHAR ,
+	chief_complaint VARCHAR ,
+	history_of_present_illness VARCHAR ,
+	visit_age_years FLOAT ,
+	exam_comments VARCHAR ,
+	tests_comments VARCHAR ,
+	tests_blood_sugar_result FLOAT ,
+	tests_pregnancy_result VARCHAR ,
+	tests_urine_glucose_result VARCHAR ,
+	tests_urine_protein_result VARCHAR ,
+	tests_urine_leukocytes_result VARCHAR ,
+	tests_urine_nitrites_result VARCHAR ,
+	ob_gravida FLOAT ,
+	ob_para FLOAT ,
+	ob_abortus FLOAT ,
+	ob_history VARCHAR ,
+	ob_wants_planning VARCHAR ,
+	albendazole_given VARCHAR ,
+	first_visit VARCHAR ,
+	known_med_allergies VARCHAR ,
+	meds_currently_taken VARCHAR ,
+	tests_pregnancy_given VARCHAR ,
+	tests_blood_sugar_given VARCHAR ,
+	tests_eye_exam_given VARCHAR ,
+	tests_urine_analysis_given VARCHAR ,
+	dx_asthma VARCHAR ,
+	dx_allergies VARCHAR ,
+	dx_anemia VARCHAR ,
+	dx_malnutrition VARCHAR ,
+	dx_diabetes VARCHAR ,
+	dx_pterygium VARCHAR ,
+	dx_cataract VARCHAR ,
+	dx_dry_eye VARCHAR ,
+	dx_conjunctivitis VARCHAR ,
+	dx_fever VARCHAR ,
+	dx_possible_malaria VARCHAR ,
+	dx_filariasis VARCHAR ,
+	dx_gastritis VARCHAR ,
+	dx_gerd VARCHAR ,
+	dx_gastroenteritis VARCHAR ,
+	dx_dysentery VARCHAR ,
+	dx_possible_cholera VARCHAR ,
+	dx_headache VARCHAR ,
+	dx_hernia VARCHAR ,
+	dx_hernia_type VARCHAR ,
+	dx_hypertension VARCHAR ,
+	dx_stroke VARCHAR ,
+	dx_angina VARCHAR ,
+	dx_chf VARCHAR ,
+	dx_musculoskeletal_pain VARCHAR ,
+	dx_otitis VARCHAR ,
+	dx_otitis_ear VARCHAR ,
+	dx_pneumonia VARCHAR ,
+	dx_sinusitis VARCHAR ,
+	dx_uri VARCHAR ,
+	dx_pregnant VARCHAR ,
+	dx_pregnant_weeks FLOAT ,
+	dx_atopic_derm VARCHAR ,
+	dx_fungal VARCHAR ,
+	dx_scabies VARCHAR ,
+	dx_impetigo VARCHAR ,
+	dx_tinea_capitus VARCHAR ,
+	dx_possible_std VARCHAR ,
+	dx_possible_tb VARCHAR ,
+	dx_vaginitis_candidal VARCHAR ,
+	dx_vaginitis_trich VARCHAR ,
+	dx_vaginitis_bv VARCHAR ,
+	dx_other VARCHAR ,
+	dx_other_text VARCHAR ,
+	tx_multivitamin VARCHAR ,
+	tx_pain_acetaminophen VARCHAR ,
+	tx_gi_omeprazole VARCHAR ,
+	tx_gi_pepto_bismol VARCHAR ,
+	tx_gi_cimetidine VARCHAR ,
+	tx_gi_tums VARCHAR ,
+	tx_gi_ors VARCHAR ,
+	tx_infections_albendazole VARCHAR ,
+	tx_infections_amoxicillin VARCHAR ,
+	tx_infections_azithromycin VARCHAR ,
+	tx_infections_bactrim VARCHAR ,
+	tx_infections_cipro VARCHAR ,
+	tx_infections_doxycycline VARCHAR ,
+	tx_infections_flagyl VARCHAR ,
+	tx_infections_fluconazole VARCHAR ,
+	tx_infections_griseofulvin VARCHAR ,
+	tx_infections_ivermectin VARCHAR ,
+	tx_infections_keflex VARCHAR ,
+	tx_infections_rocephin VARCHAR ,
+	tx_cardiac_amlodipine VARCHAR ,
+	tx_cardiac_aspirin VARCHAR ,
+	tx_cardiac_atenolol VARCHAR ,
+	tx_cardiac_hctz VARCHAR ,
+	tx_cardiac_lasix VARCHAR ,
+	tx_cardiac_enalapril VARCHAR ,
+	tx_topical_clotrimazole VARCHAR ,
+	tx_topical_hydrocortisone VARCHAR ,
+	tx_topical_silvadine VARCHAR ,
+	tx_topical_permethirine VARCHAR ,
+	tx_im_dexamethasone VARCHAR ,
+	tx_im_lasix VARCHAR ,
+	tx_misc_albuterol VARCHAR ,
+	tx_misc_benadryl VARCHAR ,
+	tx_misc_ferrous_sulfate VARCHAR ,
+	tx_misc_metformin VARCHAR ,
+	tx_misc_prednisolone VARCHAR ,
+	tx_misc_other VARCHAR ,
+	tx_misc_other_text VARCHAR ,
+	tx_comments VARCHAR ,
+	dx_uti VARCHAR ,
+	tx_multivitamin_varieties VARCHAR ,
+	tx_pain_acetaminophen_varieties VARCHAR ,
+	tx_infections_amoxicillin_varieties VARCHAR ,
+	tx_pain_ibuprofen VARCHAR ,
+	dx_comments VARCHAR ,
+	tx_infections_azithromycin_varieties VARCHAR ,
+	tx_infections_bactrim_varieties VARCHAR ,
+	tx_infections_keflex_varieties VARCHAR ,
+	tx_infections_rocephin_varieties VARCHAR ,
+	dx_vaginitis_general VARCHAR ,
+	dx_needs_to_see_dentist VARCHAR ,
+	dx_needs_to_see_eye_doctor VARCHAR ,
+	dx_healthy VARCHAR ,
+	provider VARCHAR ,
+	tx_saw_dentist VARCHAR ,
+	tx_saw_eye_doctor VARCHAR ,
+	tx_outside_referral VARCHAR ,
+	tx_outside_referral_text VARCHAR ,
+	tx_gi_zinc VARCHAR ,
+	tx_cardiac_methyldopa VARCHAR ,
+	tx_infections_augmentin VARCHAR ,
+	tx_infections_chloroquine VARCHAR ,
+	tx_infections_griseofuvin_varieties VARCHAR ,
+	tx_infections_nitrofurantoin VARCHAR ,
+	tx_topical_double_antibiotic VARCHAR ,
+	tx_misc_fluoxetine VARCHAR ,
+	tx_misc_ocp VARCHAR ,
+	tx_misc_phenytoin VARCHAR ,
+	dx_possible_parasites VARCHAR ,
+	tx_misc_iud VARCHAR ,
+	tests_id_test_given VARCHAR ,
+	tests_id_chlamydia_result VARCHAR ,
+	tests_id_hiv_result VARCHAR ,
+	tests_id_syphilis_result VARCHAR ,
+	tests_id_gonorrhea_result VARCHAR ,
+	tests_eye_exam_right_sphere VARCHAR ,
+	tests_eye_exam_right_cylinder VARCHAR ,
+	tests_eye_exam_right_axis VARCHAR ,
+	tests_eye_exam_right_bifocal VARCHAR ,
+	tests_eye_exam_left_sphere VARCHAR ,
+	tests_eye_exam_left_cylinder VARCHAR ,
+	tests_eye_exam_left_axis VARCHAR ,
+	tests_eye_exam_left_bifocal VARCHAR ,
+	tx_pain_ibuprofen_varieties VARCHAR ,
+	tx_misc_benadryl_varieties VARCHAR ,
+	tests_id_tb_result VARCHAR ,
+	tests_id_confirm_hiv_cd4 VARCHAR ,
+	tests_id_confirm_hiv_result VARCHAR ,
+	tests_id_confirm_hiv_arvt VARCHAR ,
+	tests_id_confirm_hiv_enrolled VARCHAR ,
+	tests_id_confirm_hiv_problems VARCHAR ,
+	tests_id_confirm_tb_result VARCHAR ,
+	tests_id_confirm_tb_comments VARCHAR ,
+	tests_id_confirm_hiv_enrolled_at VARCHAR ,
+	dx_depression VARCHAR ,
+	clinic_trip VARCHAR ,
+	tx_chw_referral VARCHAR ,
+	tx_misc_depo VARCHAR ,
+	tx_misc_paroxetine VARCHAR ,
+	tx_misc_keppra VARCHAR ,
+	dx_totalcount FLOAT ,
+	tx_totalcount FLOAT ,
+	tx_given_glasses VARCHAR ,
+	tx_misc_labetolol VARCHAR ,
+	tx_misc_carbamazepine VARCHAR ,
+	tx_misc_terazosin VARCHAR ,
+	tx_gi_ranitidine VARCHAR ,
+	tx_cardiac_lisinopril VARCHAR ,
+	tx_topical_triple_antibiotic VARCHAR ,
+	tx_misc_prednisone VARCHAR ,
+	clinic VARCHAR ,
+	notes_modification_status VARCHAR 
+);
+
+create table chi_20200309_1938.diagnoses (
+	pk_diagnosis_id FLOAT primary key,
+	diagnosis_name VARCHAR ,
+	pk_diagnosis_key VARCHAR ,
+	creation_date TIMESTAMP ,
+	creation_acct VARCHAR ,
+	modified_date TIMESTAMP ,
+	modified_acct VARCHAR 
+);
+
+create table chi_20200309_1938.diagnoses_made (
+	pk_diagnoses_made FLOAT primary key,
+	fk_visit_id FLOAT ,
+	fk_diagnosis_key VARCHAR ,
+	creation_date TIMESTAMP ,
+	creation_acct VARCHAR ,
+	modified_date TIMESTAMP ,
+	modified_acct VARCHAR 
+);
+
+create table chi_20200309_1938.meds (
+	pk_med_id FLOAT primary key,
+	med_name VARCHAR ,
+	creation_date TIMESTAMP ,
+	creation_acct VARCHAR ,
+	modified_date TIMESTAMP ,
+	modified_acct VARCHAR ,
+	pk_med_key VARCHAR ,
+	med_type VARCHAR 
+);
+
+create table chi_20200309_1938.meds_dispensed (
+	pk_meds_dispensed FLOAT primary key,
+	fk_visit_id FLOAT ,
+	fk_med_key VARCHAR ,
+	creation_date TIMESTAMP ,
+	creation_acct VARCHAR ,
+	modified_date TIMESTAMP ,
+	modified_acct VARCHAR ,
+	dispensed_total FLOAT ,
+	med_dosage VARCHAR 
+);
+

db/json_from_fmp.py

@@ -0,0 +1,30 @@
+import argparse
+import sys
+
+from fmp import FMP
+
+"""
+Generate JSON from file maker pro
+
+DESC: 
+This file will take in a data source name (dsn), username, and password, and generate to the 
+best of it's abilities a json file that represents the structure of the database
+"""
+
+
+def parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("-d", "--dsn", help="dsn name")
+    parser.add_argument("-u", "--username", help="username for dsn")
+    parser.add_argument("-p", "--password", help="password for dsn")
+    return parser.parse_args()
+
+
+def main(argv):
+    fmp = FMP(argv.dsn, argv.username, argv.password)
+    model = fmp.parse_database()
+    fmp.write_model(model)
+
+
+if __name__ == "__main__":
+    sys.exit(main(parse_args()))

db/postgres_init_from_json.py

@@ -0,0 +1,79 @@
+import argparse
+import json
+import os
+import sys
+from datetime import datetime
+
+from fmp import FMP
+
+db_json = dict()
+fmp_model = FMP()
+
+
+# 1st (sql file)
+# 2nd (write to sql connection)
+def write_command(string, out_file):
+    with open(out_file, "w") as f:
+        f.write(string)
+
+
+def parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("-m", "--model", required=True, help="JSON model file to seed")
+    args = parser.parse_args()
+    print(args)
+    if not os.path.isfile(os.path.abspath(args.model)):
+        raise ValueError("File: {0} doesn't exist".format(args.model))
+    return args
+
+
+def main(argv):
+    model_file = argv.model
+    with open(model_file, "r") as f:
+        data_model = json.load(f)
+
+    date_time = "_".join(os.path.split(os.path.abspath(model_file))[-1].split("_")[:2])
+    output_file = "./init/{date_time}_init_db.sql".format(date_time=date_time)
+
+    schema = "chi_{date_time}".format(date_time=date_time)
+    command = "create schema {schema};\n".format(schema=schema)
+    for table_name, attribute_dict in data_model.items():
+        command += "create table {schema}.{table_name} (\n".format(
+            schema=schema, table_name=table_name
+        )
+        features = list(attribute_dict.keys())
+        last_elem = features[-1]
+
+        # HACK: mark the first feautre name with 'pk' in
+        # the name as the primary key even if others exist.
+        pk_found = False
+        for feature in features:
+
+            extra_param_string = ""
+
+            if attribute_dict[feature]["pk"] and not pk_found:
+                extra_param_string += "primary key"
+                pk_found = True
+
+            # elif attribute_dict[feature]["fk"]:
+            #     extra_param_string += "foreign key"
+
+            command += "\t{attr_name} {attr_type} {extra_params}".format(
+                attr_name=feature,
+                attr_type=fmp_model.python_types_to_sql[
+                    attribute_dict[feature]["type"]
+                ],
+                extra_params=extra_param_string,
+            )
+            if feature != last_elem:
+                command += ",\n"
+            else:
+                command += "\n"
+
+        command += ");\n\n"
+
+    write_command(command, output_file)
+
+
+if __name__ == "__main__":
+    sys.exit(main(parse_args()))

db/postgres_seed_from_json.py

@@ -32,7 +32,7 @@ def main(argv):
     with open(model_file, "r") as f:
         data_model = json.load(f)
 
-    date_time = '_'.join(os.path.split(os.path.abspath(model_file))[-1].split('_')[:2])
+    date_time = "_".join(os.path.split(os.path.abspath(model_file))[-1].split("_")[:2])
     schema = "chi_{date_time}".format(date_time=date_time)
     command = "create schema {schema};\n".format(schema=schema)
     for table_name, attribute_dict in data_model.items():

db/psql.py

@@ -0,0 +1,7 @@
+# import some python ORM for SQl
+from db import DB
+
+
+class PSQL(DB):
+    def __init__(self,):
+        pass

db/seed-db.bash

@@ -0,0 +1,17 @@
+#!/usr/bin/env bash
+# Author: "hertz"
+# Date: 20200310
+
+# args:
+# 1 --
+# 2 --
+
+#if [[ $# -ne 1 ]]; then
+#    echo "Usage: $0 <param-1> <param-2> ..."
+#    exit
+#fi
+
+
+# script:
+
+

db/seed/.gitignore

@@ -0,0 +1,2 @@
+*
+!.gitignore

docker-compose.yml

@@ -13,7 +13,7 @@ services:
   database:
     image: "postgres" # use latest official postgres version
     env_file:
-      - database.env # configure postgres
+      - ./db/database.env # configure postgres
     volumes:
       - database-data:/var/lib/postgresql/data/ # persist data even if container shuts down
     ports: