#!/usr/bin/env python
# coding: utf-8

# # VacationPy
# ----
# 
# #### Note
# * Instructions have been included for each segment. You do not have to follow them exactly, but they are included to help you think through the steps.

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# Dependencies and Setup
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import requests
import gmaps
import os

# Import API key
from api_keys import g_key


# ### Store Part I results into DataFrame
# * Load the csv exported in Part I to a DataFrame

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weather_csv_file = "../WeatherPy/output_data/city_weather_data.csv"

weather_df = pd.read_csv(weather_csv_file)

weather_df.head()


# ### Humidity Heatmap
# * Configure gmaps.
# * Use the Lat and Lng as locations and Humidity as the weight.
# * Add Heatmap layer to map.

# In[34]:


# Configure gmaps with API key.
gmaps.configure(api_key=g_key)

# Convert Humidity to float and store
# Also, handle NaN values
weather_df = weather_df.dropna()
humidity = weather_df["Humidity"].astype(float)

# Store 'Latitude' and 'Longitude' into  locations. 
locations = weather_df[["Lat", "lng"]].astype(float)

# Create a humidity Heatmap layer
# Plot Heatmap
fig = gmaps.figure(center = [0,0] ,zoom_level = 2)
# Create and add heat layer 
heat_layer = gmaps.heatmap_layer(locations, weights=humidity,
                               dissipating=False, max_intensity=100,
                               point_radius = 4)
fig.add_layer(heat_layer)
#Display figure
fig


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# ### Create new DataFrame fitting weather criteria
# * Narrow down the cities to fit weather conditions.
# * Drop any rows will null values.

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# Narrow down the DataFrame to find your ideal weather condition.
filtered_weather_df = weather_df

# Drop any rows that don't contain all three conditions. Want to be sure the weather is ideal.

# A max temperature lower than 80 degrees but higher than 70.
filtered_weather_df = filtered_weather_df.loc[(filtered_weather_df["Max Temp"] < 80) & (filtered_weather_df["Max Temp"] > 70)]

# Wind speed less than 10 mph.
filtered_weather_df = filtered_weather_df.loc[filtered_weather_df["Wind Speed"] < 10]

# Zero cloudiness.
filtered_weather_df = filtered_weather_df.loc[filtered_weather_df["Cloudiness"] == 0]

# Drop any rows with null values
filtered_weather_df = filtered_weather_df.dropna()

filtered_weather_df



# ### Hotel Map
# * Store into variable named `hotel_df`.
# * Add a "Hotel Name" column to the DataFrame.
# * Set parameters to search for hotels with 5000 meters.
# * Hit the Google Places API for each city's coordinates.
# * Store the first Hotel result into the DataFrame.
# * Plot markers on top of the heatmap.

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hotel_df = filtered_weather_df

# params dictionary to update each iteration
params = {
    "radius": 5000,
    "types": "lodging",
    "key": g_key
}


for index, row in hotel_df.iterrows():
    # get lat, lng from df
    lat = row["Lat"]
    lng = row["lng"]

    # change location each iteration while leaving original params in place
    params["location"] = f"{lat},{lng}"
    
    base_url = "https://maps.googleapis.com/maps/api/place/nearbysearch/json"

    # assemble url and make API request
    print(f"Retrieving Results for Index {index}: {row['City']}.")
    response = requests.get(base_url, params=params).json()
    # print(json.dumps(response, indent=4, sort_keys=True))
    
    # extract results
    results = response['results']
    
    try:
        print(f"Closest hotel is {results[0]['name']}.")
        hotel_df.loc[index, 'Hotel Name'] = results[0]['name']
        
    except (KeyError, IndexError):
        print("Missing field/result... skipping.")
        
    print("------------")


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hotel_df


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# NOTE: Do not change any of the code in this cell
locations = hotel_df[["Lat", "lng"]]
markers = gmaps.marker_layer(locations)

# Using the template add the hotel marks to the heatmap
info_box_template = """
<dl>
<dt>Name</dt><dd>{Hotel Name}</dd>
<dt>City</dt><dd>{City}</dd>
<dt>Country</dt><dd>{Country}</dd>
</dl>
"""
# Store the DataFrame Row
# NOTE: be sure to update with your DataFrame name
hotel_info = [info_box_template.format(**row) for index, row in hotel_df.iterrows()]


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# Add marker layer ontop of heat map
hotel_layer = gmaps.symbol_layer(
    locations, fill_color='rgba(0, 150, 0, 0.4)',
    stroke_color='rgba(0, 0, 150, 0.4)', scale=2,
    info_box_content=hotel_info
)

# Display figure
fig.add_layer(markers)
fig.add_layer(hotel_layer)
fig


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