def parse_html(html):
soup = BeautifulSoup(html, "html.parser")
data = {
"lots": [],
"data_source": data_source,
"last_updated": convert_date(soup.find(id="P1_LAST_UPDATE").text, "%d.%m.%Y %H:%M:%S")
}
for table in soup.find_all("table"):
if table["summary"] != "":
region = table["summary"]
for lot_row in table.find_all("tr"):
if lot_row.find("th") is not None:
continue
state_div = lot_row.find("div")
if "green" in state_div["class"]:
state = "open"
elif "yellow" in state_div["class"]:
state = "open"
elif "red" in state_div["class"]:
state = "open"
elif "park-closed" in state_div["class"]:
state = "closed"
else:
state = "nodata"
lot_name = lot_row.find("td", {"headers": "BEZEICHNUNG"}).text
try:
free = int(lot_row.find("td", {"headers": "FREI"}).text)
except ValueError:
free = 0
try:
total = int(lot_row.find("td", {"headers": "KAPAZITAET"}).text)
except ValueError:
total = get_most_lots_from_known_data("Dresden", lot_name)
id = generate_id(__file__, lot_name)
forecast = os.path.isfile("forecast_data/" + id + ".csv")
data["lots"].append({
"coords": geodata.coords(lot_name),
"name": lot_name,
"total": total,
"free": free,
"state": state,
"id": id,
"lot_type": type_map.get(lot_name, ""),
"address": address_map.get(lot_name, ""),
"forecast": forecast,
"region": region
})
return data
def parse_website_app(html):
soup = BeautifulSoup(html, "html.parser")
date_field = soup.find(id="P1_LAST_UPDATE").text
last_updated = convert_date(date_field, "%d.%m.%Y %H:%M:%S")
data = {
"lots": [],
"last_updated": last_updated
}
for table in soup.find_all("table"):
if table["summary"] != "":
region = table["summary"]
if region == "Busparkplätze":
continue
for lot_row in table.find_all("tr"):
if lot_row.find("th") is not None:
continue
cls = lot_row.find("div")["class"]
state = "nodata"
if "green" in cls or "yellow" in cls or "red" in cls:
state = "open"
elif "park-closed" in cls:
state = "closed"
lot_name = lot_row.find("td", {"headers": "BEZEICHNUNG"}).text
try:
col = lot_row.find("td", {"headers": "FREI"})
free = int(col.text)
except ValueError:
free = 0
try:
col = lot_row.find("td", {"headers": "KAPAZITAET"})
total = int(col.text)
except ValueError:
total = get_most_lots_from_known_data("Dresden", lot_name)
lot = geodata.lot(lot_name)
forecast = os.path.isfile("forecast_data/" + lot.id + ".csv")
data["lots"].append({
"coords": lot.coords,
"name": lot_name,
"total": total,
"free": free,
"state": state,
"id": lot.id,
"lot_type": lot.type,
"address": lot.address,
"forecast": forecast,
"region": region
})
return data
def parse_html(html):
soup = BeautifulSoup(html, "html.parser")
date_field = soup.find(id="P1_LAST_UPDATE").text
last_updated = convert_date(date_field, "%d.%m.%Y %H:%M:%S")
data = {
"lots": [],
"last_updated": last_updated
}
for table in soup.find_all("table"):
if table["summary"] != "":
region = table["summary"]
for lot_row in table.find_all("tr"):
if lot_row.find("th") is not None:
continue
cls = lot_row.find("div")["class"]
state = "nodata"
if "green" in cls or "yellow" in cls or "red" in cls:
state = "open"
elif "park-closed" in cls:
state = "closed"
lot_name = lot_row.find("td", {"headers": "BEZEICHNUNG"}).text
try:
col = lot_row.find("td", {"headers": "FREI"})
free = int(col.text)
except ValueError:
free = 0
try:
col = lot_row.find("td", {"headers": "KAPAZITAET"})
total = int(col.text)
except ValueError:
total = get_most_lots_from_known_data("Dresden", lot_name)
lot = geodata.lot(lot_name)
forecast = os.path.isfile("forecast_data/" + lot.id + ".csv")
data["lots"].append({
"coords": lot.coords,
"name": lot_name,
"total": total,
"free": free,
"state": state,
"id": lot.id,
"lot_type": lot.type,
"address": lot.address,
"forecast": forecast,
"region": region
})
return data
def parse_html(html):
soup = BeautifulSoup(html, "html.parser")
data = {
"last_updated": convert_date(soup.find("tr").find("strong").text, "Stand: %d.%m.%Y, %H:%M Uhr"),
"data_source": data_source,
"lots": []
}
rows = soup.find_all("tr")
rows = rows[1:]
region_header = ""
for row in rows:
if len(row.find_all("th")) > 0:
# This is a header row, save it for later
region_header = row.find("th", {"class": "head1"}).text
else:
if row.find("td").text == "Gesamt":
continue
# This is a parking lot row
raw_lot_data = row.find_all("td")
if len(raw_lot_data) == 2:
type_and_name = process_name(raw_lot_data[0].text)
data["lots"].append({
"name": type_and_name[1],
"type": type_and_name[0],
"total": get_most_lots_from_known_data("Lübeck", type_and_name[1]),
"free": 0,
"region": region_header,
"state": process_state_map.get(raw_lot_data[1].text, ""),
"coords": geodata.coords(type_and_name[1]),
"id": generate_id(__file__, type_and_name[1]),
"forecast": False
})
elif len(raw_lot_data) == 4:
type_and_name = process_name(raw_lot_data[0].text)
data["lots"].append({
"name": type_and_name[1],
"type": type_and_name[0],
"total": int(raw_lot_data[1].text),
"free": int(raw_lot_data[2].text),
"region": region_header,
"state": "open",
"coords": geodata.coords(type_and_name[1]),
"id": generate_id(__file__, type_and_name[1]),
"forecast": False
})
return data
def parse_html(html):
soup = BeautifulSoup(html, "html.parser")
date_field = soup.find("tr").find("strong").text
last_updated = convert_date(date_field, "Stand: %d.%m.%Y, %H:%M Uhr")
data = {
"last_updated": last_updated,
"lots": []
}
rows = soup.find_all("tr")
rows = rows[1:]
region_header = ""
for row in rows:
if len(row.find_all("th")) > 0:
# This is a header row, save it for later
region_header = row.find("th", {"class": "head1"}).text
else:
if row.find("td").text == "Gesamt":
continue
# This is a parking lot row
raw_lot_data = row.find_all("td")
type_and_name = process_name(raw_lot_data[0].text)
if len(raw_lot_data) == 2:
total = get_most_lots_from_known_data("Lübeck",
type_and_name[1])
free = 0
state = process_state_map.get(raw_lot_data[1].text, "")
elif len(raw_lot_data) == 4:
total = int(raw_lot_data[1].text)
free = int(raw_lot_data[2].text)
state = "open"
lot = geodata.lot(type_and_name[1])
data["lots"].append({
"name": lot.name,
"lot_type": type_and_name[0],
"total": total,
"free": free,
"region": region_header,
"state": state,
"coords": lot.coords,
"id": lot.id,
"forecast": False
})
return data
def parse_website(html):
soup = BeautifulSoup(html, "html.parser")
for h3 in soup.find_all("h3"):
if h3.text == "Letzte Aktualisierung":
last_updated = convert_date(h3.find_next_sibling("div").text, "%d.%m.%Y %H:%M:%S")
data = {
"lots": [],
"last_updated": last_updated
}
for table in soup.find_all("table"):
thead = table.find("thead")
if not thead:
continue
region = table.find("thead").find("tr").find_all("th")[1].find("div").text
if region == "Busparkplätze":
continue
for tr in table.find("tbody").find_all("tr"):
td = tr.find_all("td")
name = tr.find("a").text
lot = geodata.lot(name)
try:
total = int(td[2].find_all("div")[1].text)
except ValueError:
total = get_most_lots_from_known_data("Dresden", name)
try:
free = int(td[3].find_all("div")[1].text)
valid_free = True
except ValueError:
valid_free = False
free = 0
if "park-closed" in td[0]["class"]:
state = "closed"
elif "blue" in td[0]["class"] and not valid_free:
state = "nodata"
else:
state = "open"
data["lots"].append({
"coords": lot.coords,
"name": name,
"total": total,
"free": free,
"state": state,
"id": lot.id,
"lot_type": lot.type,
"address": lot.address,
"forecast": os.path.isfile("forecast_data/" + lot.id + ".csv"),
"region": region
})
return data
def parse_html(html):
soup = BeautifulSoup(html, "html.parser")
date_field = soup.find("tr").find("strong").text
last_updated = convert_date(date_field, "Stand: %d.%m.%Y, %H:%M Uhr")
data = {"last_updated": last_updated, "lots": []}
rows = soup.find_all("tr")
rows = rows[1:]
region_header = ""
for row in rows:
if len(row.find_all("th")) > 0:
# This is a header row, save it for later
region_header = row.find("th", {"class": "head1"}).text
else:
if row.find("td").text == "Gesamt":
continue
# This is a parking lot row
raw_lot_data = row.find_all("td")
type_and_name = process_name(raw_lot_data[0].text)
if len(raw_lot_data) == 2:
total = get_most_lots_from_known_data("Lübeck",
type_and_name[1])
free = 0
state = process_state_map.get(raw_lot_data[1].text, "")
elif len(raw_lot_data) == 4:
total = int(raw_lot_data[1].text)
free = int(raw_lot_data[2].text)
state = "open"
lot = geodata.lot(type_and_name[1])
data["lots"].append({
"name": lot.name,
"lot_type": type_and_name[0],
"total": total,
"free": free,
"region": region_header,
"state": state,
"coords": lot.coords,
"id": lot.id,
"forecast": False
})
return data
def parse_html(html):
if geodata.private_data:
api_data = json.loads(html)
dt = time.strptime(api_data[0]["timestamp"].split(".")[0], "%Y-%m-%dT%H:%M:%S")
ts = time.gmtime(time.mktime(dt))
data = {
"lots": [],
"last_updated": time.strftime("%Y-%m-%dT%H:%M:%S", ts)
}
status = ['open', 'closed', 'unknown']
id_lots = {geodata.lots[n].aux: geodata.lots[n] for n in geodata.lots}
for dataset in api_data:
try:
lot = id_lots[dataset['id']]
forecast = os.path.isfile("forecast_data/" + lot.id + ".csv")
data["lots"].append({
"coords": lot.coords,
"name": lot.name,
"total": lot.total,
"free": max(lot.total - dataset["belegung"], 0),
"state": status[dataset["status"] - 1],
"id": lot.id,
"lot_type": lot.type,
"address": lot.address,
"forecast": forecast,
"region": ""
})
except KeyError:
pass
else:
#use website
soup = BeautifulSoup(html, "html.parser")
date_field = soup.find(id="P1_LAST_UPDATE").text
last_updated = convert_date(date_field, "%d.%m.%Y %H:%M:%S")
data = {
"lots": [],
"last_updated": last_updated
}
for table in soup.find_all("table"):
if table["summary"] != "":
region = table["summary"]
if region == "Busparkplätze":
continue
for lot_row in table.find_all("tr"):
if lot_row.find("th") is not None:
continue
cls = lot_row.find("div")["class"]
state = "nodata"
if "green" in cls or "yellow" in cls or "red" in cls:
state = "open"
elif "park-closed" in cls:
state = "closed"
lot_name = lot_row.find("td", {"headers": "BEZEICHNUNG"}).text
try:
col = lot_row.find("td", {"headers": "FREI"})
free = int(col.text)
except ValueError:
free = 0
try:
col = lot_row.find("td", {"headers": "KAPAZITAET"})
total = int(col.text)
except ValueError:
total = get_most_lots_from_known_data("Dresden", lot_name)
lot = geodata.lot(lot_name)
forecast = os.path.isfile("forecast_data/" + lot.id + ".csv")
data["lots"].append({
"coords": lot.coords,
"name": lot_name,
"total": total,
"free": free,
"state": state,
"id": lot.id,
"lot_type": lot.type,
"address": lot.address,
"forecast": forecast,
"region": region
})
return data
def parse_html(html):
soup = BeautifulSoup(html, "html.parser")
data = {
"lots": [],
"data_source":
data_source,
"last_updated":
convert_date(soup.find(id="P1_LAST_UPDATE").text, "%d.%m.%Y %H:%M:%S")
}
for table in soup.find_all("table"):
if table["summary"] != "":
region = table["summary"]
for lot_row in table.find_all("tr"):
if lot_row.find("th") is not None:
continue
state_div = lot_row.find("div")
if "green" in state_div["class"]:
state = "open"
elif "yellow" in state_div["class"]:
state = "open"
elif "red" in state_div["class"]:
state = "open"
elif "park-closed" in state_div["class"]:
state = "closed"
else:
state = "nodata"
lot_name = lot_row.find("td", {"headers": "BEZEICHNUNG"}).text
try:
free = int(lot_row.find("td", {"headers": "FREI"}).text)
except ValueError:
free = 0
try:
total = int(
lot_row.find("td", {
"headers": "KAPAZITAET"
}).text)
except ValueError:
total = get_most_lots_from_known_data("Dresden", lot_name)
id = generate_id(__file__, lot_name)
forecast = os.path.isfile("forecast_data/" + id + ".csv")
data["lots"].append({
"coords": geodata.coords(lot_name),
"name": lot_name,
"total": total,
"free": free,
"state": state,
"id": id,
"lot_type": type_map.get(lot_name, ""),
"address": address_map.get(lot_name, ""),
"forecast": forecast,
"region": region
})
return data
def parse_html(html):
if geodata.private_data:
api_data = json.loads(html)
dt = time.strptime(api_data[0]["timestamp"].split(".")[0],
"%Y-%m-%dT%H:%M:%S")
ts = time.gmtime(time.mktime(dt))
data = {
"lots": [],
"last_updated": time.strftime("%Y-%m-%dT%H:%M:%S", ts)
}
status = ['open', 'closed', 'unknown']
id_lots = {geodata.lots[n].aux: geodata.lots[n] for n in geodata.lots}
for dataset in api_data:
try:
lot = id_lots[dataset['id']]
forecast = os.path.isfile("forecast_data/" + lot.id + ".csv")
data["lots"].append({
"coords":
lot.coords,
"name":
lot.name,
"total":
lot.total,
"free":
max(lot.total - dataset["belegung"], 0),
"state":
status[dataset["status"] - 1],
"id":
lot.id,
"lot_type":
lot.type,
"address":
lot.address,
"forecast":
forecast,
"region":
""
})
except KeyError:
pass
else:
#use website
soup = BeautifulSoup(html, "html.parser")
date_field = soup.find(id="P1_LAST_UPDATE").text
last_updated = convert_date(date_field, "%d.%m.%Y %H:%M:%S")
data = {"lots": [], "last_updated": last_updated}
for table in soup.find_all("table"):
if table["summary"] != "":
region = table["summary"]
if region == "Busparkplätze":
continue
for lot_row in table.find_all("tr"):
if lot_row.find("th") is not None:
continue
cls = lot_row.find("div")["class"]
state = "nodata"
if "green" in cls or "yellow" in cls or "red" in cls:
state = "open"
elif "park-closed" in cls:
state = "closed"
lot_name = lot_row.find("td", {
"headers": "BEZEICHNUNG"
}).text
try:
col = lot_row.find("td", {"headers": "FREI"})
free = int(col.text)
except ValueError:
free = 0
try:
col = lot_row.find("td", {"headers": "KAPAZITAET"})
total = int(col.text)
except ValueError:
total = get_most_lots_from_known_data(
"Dresden", lot_name)
lot = geodata.lot(lot_name)
forecast = os.path.isfile("forecast_data/" + lot.id +
".csv")
data["lots"].append({
"coords": lot.coords,
"name": lot_name,
"total": total,
"free": free,
"state": state,
"id": lot.id,
"lot_type": lot.type,
"address": lot.address,
"forecast": forecast,
"region": region
})
return data
