heronyang

HOSTNAME=0.0.0.0
PORT=8000
GMAP_API_KEY="..."

heronyang

const dotenv = require('dotenv');
const express = require('express');

// Loads environment variables
dotenv.config();
const hostname = process.env.HOSTNAME;
const port = process.env.PORT;
const gmap_api_key = process.env.GMAP_API_KEY;

// Setups the server.

heronyang

INPUT = "merged_traffic_data.csv"
OUTPUT = "merged_traffic_data_for_fusion.csv"

with open(INPUT) as fin:
    with open(OUTPUT, 'w') as fout:
        is_first_line = True
        for line in fin:
            if is_first_line:
                fout.write(',location,death,injury,time\n')
                is_first_line = False

heronyang

heronyang

def extract_columns(data_source):
    dataframe = data_source.dataframe
    dataframe['death'] = dataframe['死亡受傷人數'].map(
        lambda x: x.split(';')[0][2:] if isinstance(x, str) else 0)
    dataframe['injury'] = dataframe['死亡受傷人數'].map(
        lambda x: x.split(';')[1][2:] if isinstance(x, str) else 0)
    dataframe.rename(columns={u'發生時間':'time'}, inplace=True)
    dataframe.rename(columns={u'經度':'lng'}, inplace=True)
    dataframe.rename(columns={u'緯度':'lat'}, inplace=True)
    dataframe = dataframe[pd.notnull(dataframe['lng'])]

heronyang

class CachableDataSource:

    def __init__(self, url):
        self.url = url
        self.cache_filepath = self.__get_cache_filepath(url)
        if os.path.isfile(self.cache_filepath):
            self.__load_cache()
        else:
            self.__load_dataframe()
            self.__save_cache()

heronyang

def main():
    
    # Downloads CSV files by giving the URLs, and store the downloaded raw
    # data in a object called CachableDataSource
    data_sources = [CachableDataSource(url) for url in TRAFFIC_ACCIDENT_DATA]
    
    # Extracts the columns we want from the downloaded data sources.
    dataframes = [extract_columns(ds) for ds in data_sources]
    
    # Merges all the dataframes into one big dataframe.

heronyang

TRAFFIC_ACCIDENT_DATA = [
    "https://www.npa.gov.tw/NPAGip/wSite/public/Data/f1551682182614.csv",
    "https://www.npa.gov.tw/NPAGip/wSite/public/Data/f1551682149786.csv"
]

heronyang

import pandas as pd

df_l = pd.read_csv('20180220.csv',
                   names=['TimeInterval', 'GantryFrom', 'GantryTo',
                          'VehicleType', 'SpaceMeanSpeed', '交通量'])
df_2l = pd.read_csv('20180219.csv',
                    names=['TimeInterval', 'GantryFrom', 'GantryTo',
                           'VehicleType', 'SpaceMeanSpeed', '交通量'])

def print_summary(df, field):

heronyang

#!/usr/bin/env python3
"""This script loads a pretrained InceptionV3 model, then retrains the
fully-connected layer using a given training data. Per epoch, one model with
weight values and one prediction on a given test data are saved to disk.

Input:
    - X_TRAIN_FILENAME: the location of training dataset file (.npy file)
    - Y_TRAIN_FILENAME: the location of training label file (.npy file)
    - X_TEST_FILENAME: the location of the dataset to predict (.npy file)
    - N_CLASSES: number of classes to classify