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A simple Python API wrapper for the DarkSky weather API

Project description

DarkSkyAPI wrapper

The DarkSkyAPI weather wrapper is powered by DarkSky and provides an easy way to access weather details using Python 3.6.

Install

pip install darkskyapi-py

Changelog

v1.1.0

  • Minutely datablock added
  • Language support added
  • Exclusion of datablocks added
  • precipIntensityError datapoint added in DSF classes
  • The processed url is now an attribute of the client
  • It's no longer required to make seperate instances for currently, daily etc. Currently, daily, hourly and minutely are now properties of the DarkSkyClient class. Seperate instances can still be made for custom day, hour or minute values.

Usage V1.1.0

Client instance

First import the DarkSkyClient class from the DarkSkyAPI module. If you don't have an API key for the DarkSkyAPI yet, get one for free here. This will allow you to make a 1000 calls each day.

from DarkSkyAPI.DarkSkyAPI import DarkSkyClient

Next, create the client instance using the api_key as the first argument and a tuple containing the latitude and longitude of the location as the second argument. The third argument is optional and will set the units (Celsius/Fahrenheit). The fourth argument is optional and will set datablocks that you want to exclude (example: 'minutely'). The fifth argument is optional and will set the language of the weather summaries (English by default). The unit options are as follows:

  • auto: automatically select units based on geographic location
  • ca: same as si, except that windSpeed and windGust are in kilometers per hour
  • uk2: same as si, except that nearestStormDistance and visibility are in miles, and windSpeed and windGust in miles per hour.
  • us: Imperial units
  • si: International System of Units (default) If no units are provided it will default to "si".
client = DarkSkyClient(api_key, (lat, lon), units="si", exclude=["minutely", "hourly"], lang="nl")

The client instance already holds the raw weather response and can be accessed by client.raw_data.

client.raw_data

Additionally it also keeps track of the remaining api calls for the current day.

client.API_calls_remaining

The processed url can be obtained like this

client.url

Current data

All the data points of the current weather details are automatically set as attributes of the instance. This allows you to use the datapoints like attributes.

Current data is accessed in a number of ways. The easiest way is by using the currently property of the client instance

client.currently.temperature

The weekday can be accessed by calling the weekday attribute on the current instance. This will return the full weekday name (in English). To return the short weekday name (i.e Mon, Tue), use the weekday_short attribute.

client.currently.weekday
client.currently.weekday_short

Alternatively you can set a seperate instance like this

currently = client.get_current()
current.temperature

Daily, hourly and minutely data

To customize the amount of hours, days or minutes are returned, simply pass the n amount of hours/days/minute as an int to the method.

# Returns 6 days (including today)
daily = client.get_daily(6)
# Returns 24 hours (including current hour)
hourly = client.get_hourly(24)
# Returns 30 minutes (including current minute)
minutely = client.get_minutely(30)

The daily, hourly and minutely classes behave in the same way because they inherit from the same base class. Because of this, only the daily instance is documented. The terms hourly/daily/minutely and hour/day/minute can be used interchangeably.

The forecast datapoints can be accessed in various ways. To get the data by individual days or hours you can either use the day/hour/minute_n attribute or the data list of the forecast instance.

# Day attribute
client.daily.day_0['temperatureHigh']

# Daily data list
client.daily.data[0]['temperatureHigh']

All instances also have every date point set as a property. These properties hold a list of single datapoint values.

client.daily.temperatureHigh

Alternatively, there are several methods you can use to get data collections of one or more datapoints. These methods work on both the daily and hourly instance. The methods currently available are:

  • data_pair: Will return a list of value pair tuples containing firstly the datetime and secondly the value of the datapoint. This method accepts three arguments. The first argument is the datapoint (required). The second argument is the date_fmt parameter and will set the format of the datetime value (default - "%d-%m-%Y %H:%M"). The third argument is the graph argument, if set to True it wil return a graph-friendly dict of the datetime and values of the datapoint (default - False).
  • data_single: Will return a list of single datapoint values. This method accepts three arguments. The first argument is the datapoint you wan the values of. The second argument is a boolean that will convert the datapoint to percentages if set to True (default: False). The third argument is a boolean that will convert the datapoint to a datetime string if set to True (default: False).
  • data_combined: Will return a dict containing lists of datapoint values for each day/hour. This method accepts two arguments. The first is the list of datapoints. The second is the date_fmt incase the datapoint is time. If you don't provide a list of datapoints it will return all datapoints.
  • datetimes: Will return a list containing all the datetimes of the days/hours. This method accepts one argument which is the dateformat (default - "%d-%m-%Y %H:%M")

Data pair method

# Data pair default date format and no graph
client.daily.data_pair('temperatureHigh')

# Data pair weekday names date_fmt
client.daily.data_pair('temperatureHigh', date_fmt="%A")

# Data pair graph
client.daily.data_pair('temperatureHigh', graph=True)

Data single method

# Datapoint argument.
client.daily.data_single('temperatureHigh')

# Datapoint argument and to_percent argument set to True.
client.daily.data_single('precipProbability', to_percent=True)

# Datapoint argument and to_datetime argument set to True.
client.daily.data_single('precipIntensityMaxTime', to_datetime=True)

Data combined method

# Specified list of datapoints
client.daily.data_combined(['temperatureHigh', 'temperatureLow'], date_fmt="%H:%M")

# All data points
client.daily.data_combined()

Datetimes method

# Default date format
client.daily.datetimes()

# Weekday names date format (short)
client.daily.datetimes(date_fmt="%a")

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