simonkassel

simonkassel

source_id,dest_id,source_name,dest_name,name_score,source_addr,dest_addr,addr_score,avg_score
hifld_-_4129406,hcris_-_423027,TRIDENT MEDICAL CENTER,ENCOMPASS HEALTH REHABILITATION HOSP,31.034482758620683,9330 MEDICAL PLAZA DR,9181 MEDCOM STREET,41.02564102564102,36.03006189213085
hifld_-_4129406,dh_-_4664,TRIDENT MEDICAL CENTER,Encompass Health Rehabilitation Hospital of Charleston,31.57894736842105,9330 MEDICAL PLAZA DR,9181 Medcom St ,40.0,35.78947368421053
hifld_-_3829406,dh_-_3359,HEALTHSOUTH REHABILITATION HOSPITAL OF CHARLESTON,Trident Medical Center,33.80281690140845,9181 MEDCOM ST,9330 Medical Plaza Dr ,40.0,36.901408450704224
hifld_-_7429505,hcris_-_422007,CAROLINAS HOSPITAL SYSTEM,REGENCY HOSPITAL OF FLORENCE,49.056603773584904,805 PAMPLICO HWY,121 EAST CEDAR STREET FOURTH FLOOR,19.999999999999996,34.52830188679245
hifld_-_2929505,dh_-_3373,WOMEN'S CENTER OF CAROLINAS HOSPITAL SYSTEM,Florence Medical Center (FKA Carolinas Hospital System - Florence),69.72477064220183,1590 FREEDOM BLVD,805 Pamplico H

simonkassel

{
    "Estimated Beds Available (Conventional) - Lower": [0.0, 1.0, 2.0, 7.0, 212.0],
    "Estimated Beds Available (Conventional) - Upper": [0.0, 1.0, 2.0, 5.0, 14.0, 425.0],
    "Estimated Additional Ventilators Required (Conventional) - Lower": [0.0],
    "Estimated Additional Ventilators Required (Conventional) - Upper": [0.0],
    "Physicians Required to Staff Beds (Conventional) - Lower": [1.0, 2.0, 3.0, 5.0, 117.0],
    "Physicians Required to Staff Beds (Conventional) - Upper": [1.0, 2.0, 3.0, 5.0, 117.0],
    "Respiratory Therapists Required to Staff Beds (Conventional) - Lower": [1.0, 2.0, 3.0, 5.0, 117.0],
    "Respiratory Therapists Required to Staff Beds (Conventional) - Upper": [1.0, 2.0, 3.0, 5.0, 118.0],
    "Critical Care Nurses Required to Staff Beds (Conventional) - Lower": [1.0, 2.0, 3.0, 5.0, 118.0],

simonkassel

simonkassel

# Libraries Included:
# Numpy, Scipy, Scikit, Pandas

import pandas as pd
import numpy as np

d = {'col1': [1, 2, 3, 4], 'col2': [5, 6, 7, 8], 'col3': ['a','b','a','b']}
df = pd.DataFrame(d)

# 1) subset the dataframe to just include rows with values of 'a' in col3

simonkassel

import os
import sys
import boto3
import shutil
import rasterio

from copy import copy
from shapely.geometry import box

# geopandas, tqdm and joblib must be pip-installed in 

simonkassel

import os
import geopandas as gpd
import pandas as pd

if os.path.isdir('tracts'):
	os.system('rm -r tracts')
os.mkdir('tracts')

shapefile_uris = []
for i in range(1, 57):

simonkassel

# SSD with Mobilenet v1, configured for ships dataset.
# Users should configure the fine_tune_checkpoint field in the train config as
# well as the label_map_path and input_path fields in the train_input_reader and
# eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that
# should be configured.

model {
  ssd {
    num_classes: 1
    box_coder {

simonkassel

# SSD with Mobilenet v1, configured for ships dataset.
# Users should configure the fine_tune_checkpoint field in the train config as
# well as the label_map_path and input_path fields in the train_input_reader and
# eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that
# should be configured.

model {
  ssd {
    num_classes: 1
    box_coder {

simonkassel

#!/usr/bin/python3

"""
Split tiffs into training and test sets and insert into workflow config

This script assumes the following:
    - Existing workflow config to insert train/test data specifications
      into
    - Input tiffs and ground truth label stores are located below the
      "raw" directory specified in the base workflow config