def lambda_handler(loc):
ap = AnalysisPeriod()
gmaps = googlemaps.Client(key='AIzaSyC7ZVjH0QZbR3T96_GO25dcP-woiA4hrvI')
# Geocode my hardcoded address
geocode_result = gmaps.geocode(loc)
addr = geocode_result[0]["formatted_address"]
loc = geocode_result[0]["geometry"]["location"]
wdd = returnWeatherDataDict(longitude=loc["lng"], latitude=loc["lat"])
df = pd.DataFrame({'row': wdd["dryBulbTemperature"]})
df['row'] = df['row'].astype('str')
data = pd.DataFrame(df.row.str.split(' at ', 1).tolist(),
columns=['temp', 'dt'])
data['dt'] = pd.to_datetime(data['dt'], format="%d %b %H:%M")
data['temp'] = data['temp'].astype('float64')
data['temp'] = data['temp'].apply(f)
#json_filename = sys.argv[2] + '.json'
#csv_filename = sys.argv[2] + '.csv'
j = data.to_json(orient='records')
r = dict(place=addr, data=j)
#data.to_csv(path_or_buf=csv_filename)
return r
def visualize(self):
currentWeatherData = self.epwLocations[self.currentIndex]
climateData = currentWeatherData.climateDataClass
longitude, latitude = climateData.longitude, climateData.latitude
weatherDataDict = returnWeatherDataDict(
longitude=longitude,
latitude=latitude,
plotGoogleMapPath='googlemap.html')
self.weatherDataDict = weatherDataDict
self.createHeatMap()
self.webView.setUrl(QUrl('googlemap.html'))
z = returnWindRose(weatherDataDict=weatherDataDict,
filepath='windrose.png')
x = QtGui.QPixmap('windrose.png')
self.lblWindrose.setPixmap(x)
self.lblWindrose.setScaledContents(True)
z = returnSunPath(weatherDataDict=weatherDataDict,
filepath='sunpath.png')
x = Image.open('sunpath.png')
y = Image.open('background.png').convert('RGBA')
l = Image.alpha_composite(y, x).save("composite.png")
self.labelSunpath.setPixmap(QtGui.QPixmap("composite.png"))
self.labelSunpath.setScaledContents(True)
# k = QtGui.QPixmap('background.png')
# self.labelSunpath.setPixmap(k)
self.labelSunpath.setScaledContents(True)
self.createHeatMap()
def returnWindRose(filepath, locationString=None, beaufortScale = None, divisions=None, Longitude=None, Latitude=None):
"""
:param filepath: file path to save (required)
:param locationString:
:param divisions:
:param Longitude:
:param Latitude:
:return:
"""
assert locationString or ((Longitude is not None) and (Latitude is not None)), "either locationString or Longitude and Latitude are required."
# call getWeatherData and get wind-speed and direction
windData = returnWeatherDataDict(locationString=locationString,longitude=Longitude,latitude=Latitude)
windSpeed = windData["windSpeed"]
windDirection = windData["windDirection"]
# define Calm (0 m/s) to filter it out later, and max-speed to create legend
Calm = windSpeed.count(0)
maxWind = int(max(windSpeed))
# define radial divisions for chart
Divisions = divisions
if Divisions == None:
Divisions = 8
else: Divisions = divisions
# define the angle division according to the divisions
def Angles():
angles = []
for i in range(0,36000, int(36000/Divisions)):
angles.append(float(i/100))
angles.append(360)
return angles
angleList = Angles()
# define the legend division according to max wind speed available
def legendList():
legend = []
for i in range(0,maxWind*100,int((maxWind*100)/10)):
legend.append(i/100)
return legend
WindSpeedLegend = legendList()
beaufort = [0.3,1.5,3.3,5.5,8.0,10.8,13.9,17.2,20.7,24.5,28.4,32.6]
# define scale
if beaufortScale is None or beaufortScale == False:
legendList = WindSpeedLegend
else: legendList = beaufort
# def wind_data(dir):
# wind_byDir = []
# for f in range(8760):
# if windSpeed[f] != 0:
# if windDirection[f] >= Angles()[dir] and windDirection[f] < Angles()[dir+1]:
# wind_byDir.append(windSpeed[f])
# return wind_byDir
# filter data for angle and windspeed defined above
def wind_data(dir):
wind_1 = []
wind_2 = []
wind_3 = []
wind_4 = []
wind_5 = []
wind_6 = []
wind_7 = []
wind_8 = []
wind_9 = []
wind_10 = []
wind_11 = []
wind_12 = []
if beaufortScale is None or beaufortScale == False:
for f in range(8760):
if windSpeed[f] != 0:
if windDirection[f] >= angleList[dir] and windDirection[f] < angleList[dir+1]:
if windSpeed[f] >= legendList[0] and windSpeed[f] < legendList[1]:
wind_1.append(windSpeed[f])
elif windSpeed[f] >= legendList[1] and windSpeed[f] < legendList[2]:
wind_2.append(windSpeed[f])
elif windSpeed[f] >= legendList[2] and windSpeed[f] < legendList[3]:
wind_3.append(windSpeed[f])
elif windSpeed[f] >= legendList[3] and windSpeed[f] < legendList[4]:
wind_4.append(windSpeed[f])
elif windSpeed[f] >= legendList[4] and windSpeed[f] < legendList[5]:
wind_5.append(windSpeed[f])
elif windSpeed[f] >= legendList[5] and windSpeed[f] < legendList[6]:
wind_6.append(windSpeed[f])
elif windSpeed[f] >= legendList[6] and windSpeed[f] < legendList[7]:
wind_7.append(windSpeed[f])
elif windSpeed[f] >= legendList[7] and windSpeed[f] < legendList[8]:
wind_8.append(windSpeed[f])
elif windSpeed[f] >= legendList[8] and windSpeed[f] < legendList[9]:
wind_9.append(windSpeed[f])
elif windSpeed[f] >= legendList[9]:
wind_10.append(windSpeed[f])
#return wind_1,wind_2,wind_3,wind_4,wind_5,wind_6,wind_7,wind_8,wind_9,wind_10
elif beaufortScale is True:
for f in range(8760):
if windSpeed[f] != 0:
if windDirection[f] >= angleList[dir] and windDirection[f] < angleList[dir+1]:
if windSpeed[f] >= legendList[0] and windSpeed[f] < legendList[1]:
wind_1.append(windSpeed[f])
elif windSpeed[f] >= legendList[1] and windSpeed[f] < legendList[2]:
wind_2.append(windSpeed[f])
elif windSpeed[f] >= legendList[2] and windSpeed[f] < legendList[3]:
wind_3.append(windSpeed[f])
elif windSpeed[f] >= legendList[3] and windSpeed[f] < legendList[4]:
wind_4.append(windSpeed[f])
elif windSpeed[f] >= legendList[4] and windSpeed[f] < legendList[5]:
wind_5.append(windSpeed[f])
elif windSpeed[f] >= legendList[5] and windSpeed[f] < legendList[6]:
wind_6.append(windSpeed[f])
elif windSpeed[f] >= legendList[6] and windSpeed[f] < legendList[7]:
wind_7.append(windSpeed[f])
elif windSpeed[f] >= legendList[7] and windSpeed[f] < legendList[8]:
wind_8.append(windSpeed[f])
elif windSpeed[f] >= legendList[8] and windSpeed[f] < legendList[9]:
wind_9.append(windSpeed[f])
elif windSpeed[f] >= legendList[9] and windSpeed[f] < legendList[10]:
wind_10.append(windSpeed[f])
elif windSpeed[f] >= legendList[10] and windSpeed[f] < legendList[11]:
wind_11.append(windSpeed[f])
elif windSpeed[f] >= legendList[11]:
wind_12.append(windSpeed[f])
return wind_1,wind_2,wind_3,wind_4,wind_5,wind_6,wind_7,wind_8,wind_9,wind_10,wind_11,wind_12
# create lists of frequencies for each wind speed
def frequence(test):
perce = []
for i in range(Divisions):
if wind_data(i)[test] == 0:
perce.append(0)
else:
perce.append(len(wind_data(i)[test])*100/(len(windSpeed)-Calm))
return perce
#
N = Divisions
theta = np.linspace(0.0, 2 * np.pi, N, endpoint=False)
# create arrays for each wind speed
if beaufortScale is not True:
radii0 = np.array(frequence(0))
radii1 = np.array(frequence(1))
radii2 = np.array(frequence(2))
radii3 = np.array(frequence(3))
radii4 = np.array(frequence(4))
radii5 = np.array(frequence(5))
radii6 = np.array(frequence(6))
radii7 = np.array(frequence(7))
radii8 = np.array(frequence(8))
radii9 = np.array(frequence(9))
elif beaufortScale is True:
radii0 = np.array(frequence(0))
radii1 = np.array(frequence(1))
radii2 = np.array(frequence(2))
radii3 = np.array(frequence(3))
radii4 = np.array(frequence(4))
radii5 = np.array(frequence(5))
radii6 = np.array(frequence(6))
radii7 = np.array(frequence(7))
radii8 = np.array(frequence(8))
radii9 = np.array(frequence(9))
radii10 = np.array(frequence(10))
radii11 = np.array(frequence(11))
#radii12 = np.array(frequence(12))
# add the arrays in order to stack bar charts later on
if beaufortScale is not True:
radiiCon1 = np.add(radii0,radii1)
radiiCon2 = np.add(radiiCon1,radii2)
radiiCon3 = np.add(radiiCon2,radii3)
radiiCon4 = np.add(radiiCon3,radii4)
radiiCon5 = np.add(radiiCon4,radii5)
radiiCon6 = np.add(radiiCon5,radii6)
radiiCon7 = np.add(radiiCon6,radii7)
radiiCon8 = np.add(radiiCon7,radii8)
if beaufortScale is True:
radiiCon1 = np.add(radii0, radii1)
radiiCon2 = np.add(radiiCon1, radii2)
radiiCon3 = np.add(radiiCon2, radii3)
radiiCon4 = np.add(radiiCon3, radii4)
radiiCon5 = np.add(radiiCon4, radii5)
radiiCon6 = np.add(radiiCon5, radii6)
radiiCon7 = np.add(radiiCon6, radii7)
radiiCon8 = np.add(radiiCon7, radii8)
radiiCon9 = np.add(radiiCon8, radii9)
radiiCon10 = np.add(radiiCon9, radii10)
radiiCon11 = np.add(radiiCon10, radii11)
# set bar charts in a radial array
width = np.pi / N*2
ax = plt.subplot(111, projection='polar')
ax.set_theta_zero_location("N")
ax.set_theta_direction(-1)
# create bar charts for each array
if beaufortScale is not True:
bars0 = ax.bar(theta, radii0, width=width, bottom=0.0, color="royalblue", label = legendList[0])
bars1 = ax.bar(theta, radii1, width=width, bottom=radii0, color="cornflowerblue", label = legendList[1])
bars2 = ax.bar(theta, radii2, width=width, bottom=radiiCon1, color="lightskyblue", label = legendList[2])
bars3 = ax.bar(theta, radii3, width=width, bottom=radiiCon2, color="aquamarine", label = legendList[3])
bars4 = ax.bar(theta, radii4, width=width, bottom=radiiCon3, color="paleturquoise", label = legendList[4])
bars5 = ax.bar(theta, radii5, width=width, bottom=radiiCon4, color="yellow", label = legendList[5])
bars6 = ax.bar(theta, radii6, width=width, bottom=radiiCon5, color="goldenrod", label = legendList[6])
bars7 = ax.bar(theta, radii7, width=width, bottom=radiiCon6, color="orange", label = legendList[7])
bars8 = ax.bar(theta, radii8, width=width, bottom=radiiCon7, color="orangered", label = legendList[8])
bars9 = ax.bar(theta, radii9, width=width, bottom=radiiCon8, color="red", label = legendList[9])
else:
bars0 = ax.bar(theta, radii0, width=width, bottom=0.0, color="royalblue", label="Calm")
bars1 = ax.bar(theta, radii1, width=width, bottom=radii0, color="cornflowerblue", label="Light Air")
bars2 = ax.bar(theta, radii2, width=width, bottom=radiiCon1, color="lightskyblue", label="Light Breeze")
bars3 = ax.bar(theta, radii3, width=width, bottom=radiiCon2, color="lightblue", label="Gentle Breeze")
bars4 = ax.bar(theta, radii4, width=width, bottom=radiiCon3, color="thistle", label="Moderate Breeze")
bars5 = ax.bar(theta, radii5, width=width, bottom=radiiCon4, color="peachpuff", label="Fresh Breeze")
bars6 = ax.bar(theta, radii6, width=width, bottom=radiiCon5, color="salmon", label="Strong Breeze")
bars7 = ax.bar(theta, radii7, width=width, bottom=radiiCon6, color="tomato", label="High Wind")
bars8 = ax.bar(theta, radii8, width=width, bottom=radiiCon7, color="orangered", label="Fresh Gale")
bars9 = ax.bar(theta, radii9, width=width, bottom=radiiCon8, color="red", label="Strong Gale")
bars10 = ax.bar(theta, radii10, width=width, bottom=radiiCon9, color="firebrick", label = "Violent Storm")
bars11 = ax.bar(theta, radii11, width=width, bottom=radiiCon10, color="brown", label="Hurricane")
#bars12 = ax.bar(theta, radii12, width=width, bottom=radiiCon11, color="maroon", label=legendList[12])
plt.xticks(np.radians(range(0, 360, 45)),
['N', 'NE', 'E', 'SE', 'S', 'SW', 'W', 'NW'])
# set size of chart by max frequency
plt.rgrids(range(1, int(np.amax(radiiCon8)*1.2), int(np.ceil(np.amax(radiiCon8)/5))), angle=290)
# set legend location and title
if beaufortScale is not True:
plt.legend(bbox_to_anchor=(1.05,1), loc=2, title=("Wind Speed [m/s]"), frameon=False)
else:
plt.legend(bbox_to_anchor=(1.05, 1), loc=2, title=("Beaufort Scale"), frameon=False)
plt.title("Wind Rose")
# for r, bar in zip(radii0, bars0):
# bar.set_facecolor(plt.cm.plasma(r / 10.))
# bar.set_alpha(0.5)
fig = matplotlib.pyplot.gcf()
fig.set_size_inches(8,5)
fig.savefig(filepath, dpi=300)
return filepath
from getWeatherData import returnWeatherDataDict
import matplotlib
#matplotlib.use("Qt4Agg")
import matplotlib.pyplot as plt
import numpy as np
import os
from utilities.colors import colorList
x = returnWeatherDataDict("Melbourne Australia")
drybulb = tuple(float(i) for i in x["dryBulbTemperature"])
# Working example:
color = colorList.index("magma")
valList = []
counter = 0
for hour in range(24):
newList = []
for date in range(365):
newList.append(drybulb[counter])
counter += 1
valList.append(newList)
plt.imshow(valList, interpolation="nearest", aspect=3, cmap=colorList[color])
plt.savefig("test/name.png")
def f(x):
x = x * 1.8 + 32
return float(x)
ap = AnalysisPeriod()
gmaps = googlemaps.Client(key='AIzaSyC7ZVjH0QZbR3T96_GO25dcP-woiA4hrvI')
# Geocode my hardcoded address
geocode_result = gmaps.geocode(sys.argv[1])
loc = geocode_result[0]["geometry"]["location"]
wdd = returnWeatherDataDict(longitude=loc["lng"], latitude=loc["lat"])
df = pd.DataFrame({'row': wdd["dryBulbTemperature"]})
df['row'] = df['row'].astype('str')
#print(df["row"].str.split(' at ', 1).tolist())
data = pd.DataFrame(df.row.str.split(' at ', 1).tolist(),
columns=['temp', 'dt'])
data['blanks'] = ""
data['dt'] = pd.to_datetime(data['dt'], format="%d %b %H:%M")
data['temp'] = data['temp'].astype('float64')
data['temp'] = data['temp'].apply(f)
json_filename = sys.argv[2] + '.json'
csv_filename = sys.argv[2] + '.csv'
data.to_json(path_or_buf=json_filename, orient='records', date_format='iso')
bx.set_theta_direction(-1)
plt.xticks(np.radians(range(0, 360, 45)),
['N', 'NE', 'E', 'SE', 'S', 'SW', 'W', 'NW'])
bx.grid(False)
bx.axes.get_yaxis().set_visible(False)
# general properties for the image saving
fig.set_size_inches(8, 8)
fig.savefig(filepath, dpi=300, transparent=True)
figB.set_size_inches(8, 8)
figB.savefig(backgroundPath, dpi=300)
plt.clf()
if __name__ == "__main__":
y = returnWeatherDataDict(locationString="Boston USA", plotGoogleMapPath='googleMap.html')
z = returnWindRose(y)
a = returnHeatMap(y,dataType="diffuseHorizontalRadiation",dataLabel="Diffuse Horizontal Radiation",
colormap='plasma')
b = returnHeatMap(y,dataType='dryBulbTemperature',dataLabel="Dry Bulb Temperature",
colormap='plasma')
c = returnSunPath(y,colormap='Blues')
dataDict = {"diffuseHorizontalRadiation": ("Diffuse Horizontal Radiation", 'inferno'),
'dryBulbTemperature': ("Dry Bulb Temperature", 'magma'),
'relativeHumidity': ("Relative Humidity", 'Blues'),
'globalHorizontalRadiation': ("Global Horizontal Radiation", 'Greens'),
'directNormalRadiation': ("Direct Normal Radiation", 'inferno'),
'diffuseHorizontalRadiation': ("Diffuse Horizontal Radiation", 'plasma'),
'horizontalInfraredRadiationIntensity': ("Horizontal Infrared Radiation Intensity", 'plasma'),
'globalHorizontalIlluminance': ("Global Horizontal Illuminance", 'plasma'),
At present the following outputs are supported: dryBulbTemperature, dewPointTemperature, relativeHumidity, windSpeed,
windDirection, globalHorizontalRadiation,directNormalRadiation,diffuseHorizontalRadiation,
horizontalInfraredRadiationIntensity,globalHorizontalIlluminance,directNormalIlluminance, diffuseHorizontalIlluminance"""
dataDict = {"diffuseHorizontalRadiation":("Diffuse Horizontal Radiation",'inferno'),
'dryBulbTemperature':("Dry Bulb Temperature",'magma'),
'relativeHumidity':("Relative Humidity",'Blues'),
'globalHorizontalRadiation':("Global Horizontal Radiation",'Greens'),
'directNormalRadiation':("Direct Normal Radiation",'inferno'),
'diffuseHorizontalRadiation':("Diffuse Horizontal Radiation",'plasma'),
'horizontalInfraredRadiationIntensity':("Horizontal Infrared Radiation Intensity",'plasma'),
'globalHorizontalIlluminance':("Global Horizontal Illuminance",'plasma'),
'directNormalIlluminance':("Direct Normal Illuminance",'Reds'),
'diffuseHorizontalIlluminance':("Diffuse Horizontal Illuminance",'plasma')}
x = returnWeatherDataDict("Timbuktu Africa")
for dataType,values in dataDict.items():
dataSet= tuple(float(i) for i in x[dataType])
valList = []
counter = 0
for hour in range(365):
newList = []
for date in range(24):
newList.append(dataSet[counter])
counter+=1
valList.append(newList)
y = np.transpose(valList)
plt.imshow(y, interpolation = "nearest", aspect = 4, cmap = values[1])