This notebook shows how to:
Before running the notebook:
First, uncomment the lines in the below cell and upgrade the pixiedust and bokeh packages. When this is done restart the kernel. You have to do this only once, or when there is an update available.
Then import the package needed to run this notebook.
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#!pip install --upgrade pixiedust
#!pip install --upgrade bokeh
In [2]:
import requests
import json
import pandas as pd
import numpy as np
from datetime import datetime
import time
import pixiedust
In [3]:
%%javascript
navigator.geolocation.getCurrentPosition(function(position) {
console.log(position.coords.latitude, position.coords.longitude);
setTimeout(function() {
IPython.notebook.kernel.execute('lat="' + position.coords.latitude + '";')
IPython.notebook.kernel.execute('lon="' + position.coords.longitude + '";')
},5000)});
Wait a few seconds to run the second cell to allow the above geolocation function to run.
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print(lat, lon)
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# @hidden_cell
# Weather company data API credentials
username='xxxxxx'
password='xxxxxx'
In [7]:
line='https://'+username+':'+password+\
'@twcservice.mybluemix.net/api/weather/v1/geocode/'+\
lat+'/'+lon+'/forecast/intraday/10day.json?&units=m'
r=requests.get(line)
weather = json.loads(r.text)
Uncomment and run the next cell to have a look what the json data file looks like.
In [8]:
#print json.dumps(weather, indent=4, sort_keys=True)
Convert the data into a DataFrame with each timestep on a new row. Convert the timestamp into a datetime format and drop the columns that are not needed. See this Cheat sheet for date format conversions. Finally, convert the data type into numeric.
In [9]:
df = pd.DataFrame.from_dict(weather['forecasts'][0],orient='index').transpose()
for forecast in weather['forecasts'][1:]:
df = pd.concat([df, pd.DataFrame.from_dict(forecast,orient='index').transpose()])
df['date'] = df['fcst_valid_local'].apply(lambda x: datetime.strptime(x, '%Y-%m-%dT%H:%M:%S+0100'))
df = df.drop(['expire_time_gmt','num','qualifier','qualifier_code'],1)
df = df.drop(['fcst_valid','fcst_valid_local','icon_extd','wdir_cardinal'],1)
df = df.drop(['subphrase_pt1','subphrase_pt2','subphrase_pt3','class'],1)
df = df.drop(['daypart_name','phrase_12char','phrase_22char','phrase_32char'],1)
df.dtypes
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In [10]:
df[['pop','wspd','rh','clds','wdir','temp']] = df[['pop','wspd','rh','clds','wdir','temp']].apply(pd.to_numeric)
df.dtypes
Out[10]:
As there seems to be an issue with the pop column (percentage of precipitation), create a new column rain.
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df['rain'] = df['pop'].as_matrix()
df=df.drop('pop',1)
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df.head()
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In [13]:
df = df.set_index('date',drop=False)
df.head()
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In [14]:
import matplotlib.pyplot as plt
import matplotlib
%matplotlib inline
fig, axes = plt.subplots(nrows=2, ncols=1, figsize=(14, 8))
df['temp'].plot(ax=axes[1], color='#6EEDD8',lw=3.0,sharex=True)
axes[1].set_title('Temperature',loc='left',fontsize=20)
df['rain'].plot(ax=axes[0], kind='bar', color='#C93D79',lw=2.0,sharex=True)
axes[0].set_title('Chance of rain',loc='left',fontsize=20)
Out[14]:
In [15]:
cities = [
('Exeter',50.7184,-3.5339),
('Truro',50.2632,-5.051),
('Carmarthen',51.8576,-4.3121),
('Norwich',52.6309,1.2974),
('Brighton And Hove',50.8225,-0.1372),
('Bristol',51.44999778,-2.583315472),
('Durham',54.7753,-1.5849),
('Llanidloes',52.4135,-3.5883),
('Penrith',54.6641,-2.7527),
('Jedburgh',55.4777,-2.5549),
('Coventry',52.42040367,-1.499996583),
('Edinburgh',55.94832786,-3.219090618),
('Cambridge',52.2053,0.1218),
('Glasgow',55.87440472,-4.250707236),
('Kingston upon Hull',53.7457,-0.3367),
('Leeds',53.83000755,-1.580017539),
('London',51.49999473,-0.116721844),
('Manchester',53.50041526,-2.247987103),
('Nottingham',52.97034426,-1.170016725),
('Aberdeen',57.1497,-2.0943),
('Fort Augustus',57.1448,-4.6805),
('Lairg',58.197,-4.6173),
('Oxford',51.7517,-1.2553),
('Inverey',56.9855,-3.5055),
('Shrewsbury',52.7069,-2.7527),
('Colwyn Bay',53.2932,-3.7276),
('Newton Stewart',54.9186,-4.5918),
('Portsmouth',50.80034751,-1.080022218)]
icons=[]
temps=[]
for city in cities:
lat = city[1]
lon = city[2]
line='https://'+username+':'+password+'@twcservice.mybluemix.net/api/weather/v1/geocode/'+str(lat)+'/'+str(lon)+'/observations.json?&units=m'
r=requests.get(line)
weather = json.loads(r.text)
icons=np.append(icons,weather['observation']['wx_icon'])
temps=np.append(temps,weather['observation']['temp'])
In [16]:
dfmap = pd.DataFrame(cities, columns=['city','lat','lon'])
dfmap['temp']=temps
dfmap['icon']=icons
dfmap.head()
Out[16]:
In [17]:
from mpl_toolkits.basemap import Basemap
from matplotlib.offsetbox import AnnotationBbox, OffsetImage
from matplotlib._png import read_png
from itertools import izip
import urllib
matplotlib.style.use('bmh')
fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(10, 12))
# background maps
m1 = Basemap(projection='mill',resolution=None,llcrnrlon=-7.5,llcrnrlat=49.84,urcrnrlon=2.5,urcrnrlat=59,ax=axes[0])
m1.drawlsmask(land_color='dimgrey',ocean_color='dodgerBlue',lakes=True)
m2 = Basemap(projection='mill',resolution=None,llcrnrlon=-7.5,llcrnrlat=49.84,urcrnrlon=2.5,urcrnrlat=59,ax=axes[1])
m2.drawlsmask(land_color='dimgrey',ocean_color='dodgerBlue',lakes=True)
# weather icons map
for [icon,city] in izip(icons,cities):
lat = city[1]
lon = city[2]
try:
pngfile=urllib.urlopen('https://github.com/ibm-cds-labs/python-notebooks/blob/master/weathericons/icon'+str(int(icon))+'.png?raw=true')
icon_hand = read_png(pngfile)
imagebox = OffsetImage(icon_hand, zoom=.15)
ab = AnnotationBbox(imagebox,m1(lon,lat),frameon=False)
axes[0].add_artist(ab)
except:
pass
# temperature map
for [temp,city] in izip(temps,cities):
lat = city[1]
lon = city[2]
if temp>12:
col='indigo'
elif temp>10:
col='darkmagenta'
elif temp>8:
col='red'
elif temp>6:
col='tomato'
elif temp>4:
col='turquoise'
x1, y1 = m2(lon,lat)
bbox_props = dict(boxstyle="round,pad=0.3", fc=col, ec=col, lw=2)
axes[1].text(x1, y1, temp, ha="center", va="center",
size=11,bbox=bbox_props)
plt.tight_layout()
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display(df)
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