Latest Weather

John Gill

Comments

Source

Simple quick update latest weather

In [1]:
# Tell matplotlib to plot in line
%matplotlib inline

# import pandas
import pandas

# seaborn magically adds a layer of goodness on top of Matplotlib
# mostly this is just changing matplotlib defaults, but it does also
# provide some higher level plotting methods.
import seaborn

# Tell seaborn to set things up
seaborn.set()

def smooth(data, thresh=None):
    
    means = data.mean()

    if thresh is None:
        sds = data.std()
    else:
        sds = thresh
    
    delta = data - data.shift()
    
    good = delta[abs(delta) < sds]

    #print(good.describe())
    
    return delta.where(good, 0.0)
In [2]:
infile = "../files/pijessie_weather.csv"

!scp 192.168.0.127:Adafruit_Python_BMP/weather.csv $infile

weather.csv                                   100%   13MB   6.4MB/s   00:02
In [3]:
""" assume it is csv and let pandas do magic

  index_col tells it to use the 'date' column in the data
  as the row index, plotting picks up on this and uses the
  date on the x-axis

  The *parse_dates* bit just tells it to try and figure out
  the date/time in the columne labeled 'date'.
"""
data = pandas.read_csv(infile, index_col='date', parse_dates=['date'])
#data = smooth(data)

# smooth the data to filter out bad temps and pressures
#data.altitude = (smooth(data.altitude, 5.0).cumsum() + data.altitude[0])
#data.temp = (smooth(data.temp, 5.0).cumsum() + data.temp[0])
In [4]:
data.altitude.plot()
Out[4]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f5dc4afcb70>

Last 24 hours:

In [5]:
# reading is once a minute, so take last 24 * 60 readings
def plotem(data, n=-60):
    
    
    if n < 0:
        start = n
        end = len(data)
    else:
        start = 0
        end = n
        
    data[['temp', 'altitude', 'humidity']][n:].plot(subplots=True)
        
plotem(data, -24*60)
In [6]:
data.altitude[-8*60:].plot()
Out[6]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f5dc2992e10>

Last week

In [7]:
# reading is once a minute, so take last 7 * 24 * 60 readings
plotem(data, -7*24*60)
In [8]:
plotem(data)

Look at all the data

In [9]:
data.describe()
Out[9]:
temp pressure altitude sealevel_pressure humidity temp_dht
count 134436.000000 134436.000000 134436.000000 134436.000000 134430.000000 134430.000000
mean 27.861387 101168.977112 12.937777 101170.173919 68.160873 27.322403
std 1.599058 429.541753 33.097760 446.960346 9.849979 1.711857
min 22.300000 50042.000000 -73.255830 48765.000000 36.599998 1.200000
25% 27.000000 100935.000000 -10.068943 100937.000000 62.900002 26.400000
50% 28.000000 101151.000000 14.413017 101153.000000 69.199997 27.600000
75% 28.900000 101445.000000 32.352991 101447.000000 74.300003 28.500000
max 121.400000 117173.000000 2740.700691 102210.000000 98.000000 30.700001
In [10]:
data.tail()
Out[10]:
temp pressure altitude sealevel_pressure humidity temp_dht
date
2015-10-30 00:49:37.821883 28.3 100873 37.699238 100872 80.000000 26.400000
2015-10-30 00:50:38.421900 28.3 100864 38.200590 100873 80.000000 26.400000
2015-10-30 00:51:39.022023 28.4 100869 38.284151 100865 80.099998 26.500000
2015-10-30 00:52:39.622038 27.7 100860 38.534838 100868 80.000000 26.500000
2015-10-30 00:53:40.206103 27.4 100864 38.534838 100869 81.599998 26.299999

I currently have two temperature sensors:

  • DHT22 sensor which gives temperature and humidity.
  • BMP180 sensor which gives pressure and temperature.

The plot below shows the two temperature plots.

Both these sensors are currently in my study. For temperature and humidity I would like to have some readings from outside. If I can solder them to a phone jack then I can just run phone cable to where they need to be.

Below plots the current values from these sensors. This is handy for calibration.

In [11]:
data[['temp', 'temp_dht']].plot()
Out[11]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f5dc30cae48>

Dew Point

The warmer air is, the more moisture it can hold. The dew point is the temperature at which air would be totally saturated if it had as much moisture as it currently does.

Given the temperature and humidity the dew point can be calculated, the actual formula is pretty complex.

It is explained in more detail here: http://iridl.ldeo.columbia.edu/dochelp/QA/Basic/dewpoint.html

If you are interested in a simpler calculation that gives an approximation of dew point temperature if you know >the observed temperature and relative humidity, the following formula was proposed in a 2005 article by Mark G. >Lawrence in the Bulletin of the American Meteorological Society:

$$Td = T - ((100 - RH)/5.)$$
In [12]:
data['dewpoint'] = data.temp - ((100. - data.humidity)/5.)
In [13]:
data[['temp', 'dewpoint', 'humidity']].plot()
Out[13]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f5dc30aa3c8>
In [14]:
data[['temp', 'dewpoint', 'humidity']].plot(subplots=True)
Out[14]:
array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f5dc3064518>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x7f5dc32126d8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x7f5dc3330160>], dtype=object)
In [15]:
data[['temp', 'dewpoint']].plot()
Out[15]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f5dc0b35048>
In [16]:
data.altitude.plot()
Out[16]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f5dc1b8a668>

Comments

Comments powered by Disqus