import pandas as pd

import requests

# Utility functions

def clamp(x, a, b):

return max(a, min(x, b))

def map(x, a, b, c=0, d=1):

y = c + (x-a) / (b-a) * (d-c)

return clamp(y, c, d)

# functions to grap all station data

def clamp(x, a, b):

return max(a, min(x, b))

def map(x, a, b, c=0, d=1):

y = c + (x-a) / (b-a) * (d-c)

return clamp(y, c, d)

def get_station_data():

"""Fetch data from a URL and save it in a pandas DataFrame."""

# Send a GET request to the URL

response = requests.get("https://gbfs.lyft.com/gbfs/2.3/bkn/en/station_information.json")

# Raise an exception for HTTP errors

response.raise_for_status()

# Parse the JSON data from the response

json_data = response.json()

# Check if "data" and "stations" keys exist in the response

if "data" in json_data and "stations" in json_data["data"]:

stations = json_data["data"]["stations"]

else:

print("Expected data structure not found in the API response.")

return None

# Convert the list of stations into a pandas DataFrame

df = pd.DataFrame(stations)

# Print the columns to inspect what's available

print("Available columns:", df.columns)

# Select relevant columns

df = df[['station_id','name', 'lat', 'lon', 'capacity']]

minlat = min(df['lat'])

maxlat = max(df['lat'])

minlon = min(df['lon'])

maxlon = max(df['lon'])

df = df.assign(x=df.apply(lambda row: map(row['lat'], minlat, maxlat), axis=1))

df = df.assign(y=df.apply(lambda row: map(row['lon'], minlon, maxlon), axis=1))

return df.set_index('station_id')

df = get_station_data()

if df is not None:

print(df.head()) # Displaying the first few rows for clarity

df = get_station_data()

df

export_xy = df[['x', 'y']]

export_xy.to_csv('../data/all_station_normalized_locations.csv')

# Get real-time update data for ride starts and stops

def get_station_status(process_data=True):

"""Fetch data from a URL and save it in a pandas DataFrame."""

# Send a GET request to the URL

response = requests.get("https://gbfs.lyft.com/gbfs/2.3/bkn/en/station_status.json")

# Raise an exception for HTTP errors

response.raise_for_status()

# Parse the JSON data from the response

json_data = response.json()

# Check if "data" and "stations" keys exist in the response

if "data" in json_data and "stations" in json_data["data"]:

stations = json_data["data"]["stations"]

else:

print("Expected data structure not found in the API response.")

return None

# Convert the list of stations into a pandas DataFrame

df = pd.DataFrame(stations)

if process_data:

# Print the columns to inspect what's available

print("Available columns:", df.columns)

# Adjusting the columns based on availability and processing

df['station_status'] = df['is_installed'].apply(lambda x: "Installed" if x == 1 else "Not Installed")

df['legacy_id'] = df['station_id']

# Keeping only the columns you want to view

df = df[['station_id', 'num_bikes_available', 'num_ebikes_available', 'num_docks_available', 'station_status', 'legacy_id']]

return df.set_index('station_id')

# By default, data processing is turned on

df = get_station_status()

if df is not None:

print(df.head()) # Displaying the first few rows for clarity

def get_bike_changes(station_info_df, old_data=None):

changes = []

updated_data = get_station_status()

# Safety check

if 'station_id' not in updated_data.columns:

print("Error: station_id column not found in updated_data")

return updated_data, pd.DataFrame()

if old_data is None or 'station_id' not in old_data.columns:

return updated_data, changes

udf = updated_data.set_index('station_id')

odf = old_data.set_index('station_id')

for station_id in odf.index:

if station_id not in udf.index:

continue # This station wasn't in the updated data; skip it.

if udf.loc[station_id, 'num_bikes_available'] != odf.loc[station_id, 'num_bikes_available']:

changes.append(station_id)

changes_df = pd.DataFrame(index=changes)

changes_df = changes_df.join(station_info_df, how='left')

# Assuming gain_loss is the difference in num_bikes_available between old and updated

if changes: # Only compute if there are changes

changes_df['gain_loss'] = udf.loc[changes, 'num_bikes_available'] - odf.loc[changes, 'num_bikes_available']

else:

changes_df['gain_loss'] = []

return updated_data, changes_df[['name', 'x', 'y', 'gain_loss']]

station_data = get_station_data()

old, _ = get_bike_changes(station_data)

old.head()

updated, changes = get_bike_changes(station_data, old)

changes

from pythonosc import udp_client

#create a UDP client

client = udp_client.SimpleUDPClient("127.0.0.1", 9999)

client.send_message("/event", [3,5])