import csv

# Load the CSV
inv = []
with open("/home/trapezoid/code/foo/inv") as f:
    csv_r = csv.DictReader(f, fieldnames=["County", "Serial", "Model", "Type"], quotechar='"', quoting=csv.QUOTE_ALL)
    for r in csv_r:
        inv.append(r)

# Create a list of just county names
all_county_names = [r['County'] for r in inv]

# Create a dict to hold the data as we iterate through the county hardware
county_data = {}

# Iterate through each county, and create a nested dictionary. Top level dict keys are the county names, the nested dict is a map of hardware type to hardware count
for county in all_county_names:
    county_inv = [r for r in inv if r['County'] == county]
    county_counts = defaultdict(int)
    for x in county_inv:
        if not x['Type']:
            x['Type'] = ''
        # Making things easier by concatenating the HW model to HW type, since the same HW can sometimes be used for different things (i.e., ADJ clients and EMS clients)
        county_counts[x['Model'] + '-' + x['Type']] += 1
    county_data[county] = county_counts

# Create an empty list to hold the names of any counties that match the hardware in Mesa
full_matches = []

# Iterate through the county data dict, and compare the HW types for each county to the HW types used in Mesa. If there's an match, add the county name to the full_matches list
for k,v in county_data.items():
    matches = []
    for kk, vv in v.items():
        if kk in county_data['Mesa'].keys():
            matches.append(kk)
        if matches == list(county_data['Mesa'].keys()):
            full_matches.append(k)

print(full_matches)