# 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()

infile = "../files/pijessie_weather.csv"

!scp 192.168.0.127:Adafruit_Python_BMP/weather.csv $infile

weather.csv                                   100% 9735KB   4.8MB/s   00:02

""" 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'])

See the pressure plummet as Joaquin approaches Bermuda¶

Pressure is still dropping as of 17.50pm BDA time.

It looks like pressure was starting to bottom out around 10pm BDA time.

No surprise that is when my power went out :(

I will have to look into more reliable power for the raspberry pi for the next storm.

data.pressure[-1*24*24:].plot()

<matplotlib.axes._subplots.AxesSubplot at 0x7fe585742860>

# See how this compares to "normal" pressure
# Plot the last 10 days

data.pressure[-10*24*24:].plot()

<matplotlib.axes._subplots.AxesSubplot at 0x7fe58565db00>

data.tail()

!pwd

/home/jng/devel/peakrisk/posts

Bermuda Weather Radar¶

I found the Bermuda weather radar invaluable for watchin Joaquin

Below is a screenshot from 19.39 BDA time.

http://weather.bm/tools/animateimages.asp?name=RADAR_250KM_SRI

from IPython import display
display.Image('../galleries/Joaquin/joaquin.png')

I have had two simple raspberry pi weather stations running for a while now.

Both have pressure, temperature and humidity sensors.

One I have in the carefully controlled environment of my study, the other is hanging out of the window.

The study one is known as pijessie as it started life as a Raspberry Pi running the Jessie version of Raspbian.

The outside station is known as kittycam as I intend at some point to attach a camera so I can watch our cat come and go.

For a while I have been noticing that the pressure values have been quite a way apart. The software I am includes a conversion to altitude and I find these numbers more natural for me to think about.

The altitude conversion assumes the pressure at sea level is 1023.25 hPa, which is the mean pressure at sea level.

When the pressure is higher than this the altitude comes out below sea level, when pressure is lower than this above sea level.

As always, Wikipedia has good information on this: https://en.wikipedia.org/wiki/Atmospheric_pressure

For a while I had been noticing the two sensors giving values differing by about 10 metres altitude.

I had put this down to the sensors not being calibrated accurately, but also noticed that kittycam was more prone to weird glitches.

Now the glitches I put down to the fact I have one process collecting data every minute and another process creating a display on my laptop so I can glance over and see what the weather is doing. The latter was just polling the sensor every 10 minutes.

The code does not do anything smart like get a lock and my guess was that the two processes were occasionally trampling on each other's feet.

Long story short, I decided to take a closer look.

# Tell matplotlib to plot in line
%matplotlib inline

import datetime

# 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()

# input files: the data from the two sensors
infiles = ["../files/kittycam_weather.csv", "../files/pijessie_weather.csv"]

# Read the data

data = []

for infile in infiles:
    data.append(pandas.read_csv(infile, index_col='date', parse_dates=['date']))

# take a look at what we got
data[0].describe()

# plots are always good 

data[0].plot(subplots=True)

array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f1a6b1e4128>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x7f1a6a7e40f0>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x7f1a69ef79b0>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x7f1a6afef0f0>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x7f1a6ae8d7b8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x7f1a6aeb2908>], dtype=object)

Now the two sets of data have different indices since the processes collecting the data are not in sync.

So we need to align the data and then fill in missing values

# align returns two new dataframes, now aligned
d1, d2 = data[0].align(data[1])

# have a look, note the count is just the valid data.
# Things have been aligned, but missing values are set ton NaN
d1.describe()

# Use interpolation to fill in the missing values
d1 = d1.interpolate(method='time')
d2 = d2.interpolate(method='time')

# Now plot
d1.altitude.plot()
print(len(d1))

80476

# For convenience, add a new series to d1 with the altitude data from d2
d1['altitude2'] = d2.altitude

# Now plot the two
d1[['altitude', 'altitude2']][10000:30000].clip(-60,60).plot()

<matplotlib.axes._subplots.AxesSubplot at 0x7f1a6aa1fe48>

(d1.altitude - d1.altitude2)[10000:30000].clip(-20,15).plot()

<matplotlib.axes._subplots.AxesSubplot at 0x7f1a6a463630>

So we do have a difference around 5m. More interestingly, there seems to be some sort of daily pattern to the data.

Simple quick update latest weather

# 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)

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

""" 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])

data.altitude.plot()

<matplotlib.axes._subplots.AxesSubplot at 0x7f5dc4afcb70>

Last 24 hours:¶

# 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)

data.altitude[-8*60:].plot()

<matplotlib.axes._subplots.AxesSubplot at 0x7f5dc2992e10>

Last week¶

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

plotem(data)

Look at all the data¶

data.describe()

data.tail()

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.

data[['temp', 'temp_dht']].plot()

<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:

$$Td = T - ((100 - RH)/5.)$$

data['dewpoint'] = data.temp - ((100. - data.humidity)/5.)

data[['temp', 'dewpoint', 'humidity']].plot()

<matplotlib.axes._subplots.AxesSubplot at 0x7f5dc30aa3c8>

data[['temp', 'dewpoint', 'humidity']].plot(subplots=True)

array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f5dc3064518>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x7f5dc32126d8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x7f5dc3330160>], dtype=object)

data[['temp', 'dewpoint']].plot()

<matplotlib.axes._subplots.AxesSubplot at 0x7f5dc0b35048>

data.altitude.plot()

<matplotlib.axes._subplots.AxesSubplot at 0x7f5dc1b8a668>

Simple quick update latest weather

switched to new sensor 8am BDA 2015-08-21 Old sensor was giving systematic bias and other weirdness.

# Tell matplotlib to plot in line
%matplotlib inline

import datetime

# 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):
    """ This smoothing function is meant to spot and eliminate bad readings.
    
    """ 
    
    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)

infile = "../files/kittycam_weather.csv"

!scp 192.168.0.121:Adafruit_Python_BMP/weather.csv $infile

weather.csv                                   100% 5852KB   5.7MB/s   00:01

!wc ../files/weather.csv

  564  1127 56016 ../files/weather.csv

""" 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.plot()

<matplotlib.axes._subplots.AxesSubplot at 0x7f06f54f9f60>

# 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])
#data.altitude = data.altitude.clip(-100, 100)

data.altitude.plot()

<matplotlib.axes._subplots.AxesSubplot at 0x7f06f4aaa748>

data.tail()

data.describe()

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:

$$Td = T - ((100 - RH)/5.)$$

data['dewpoint'] = data.temp - ((100. - data.humidity)/5.)

Last 24 hours:¶

print(datetime.datetime.now())
print(data.ix[-1:])

2015-10-04 12:04:12.700504
                            temp  pressure    altitude  sealevel_pressure  \
date                                                                        
2015-10-04 15:03:06.546427  26.5     99758  130.525751              99773   

                             humidity  temp_dht  dewpoint  
date                                                       
2015-10-04 15:03:06.546427  82.400002      25.9     22.98

# reading is once a minute, so take last 24 * 60 readings
def plotem(data, n=-60, start=None):
        
    
    if n < 0:
        start = n
        end = len(data)
    else:
        start = start or 0
        end = start + n
        
    data[['temp', 'altitude', 'humidity', 'dewpoint']][start:end].plot(subplots=True)

# last few hours
plotem(data, -24*60)

Last week¶

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

plotem(data)

plotem(data, -5)

Look at all the data¶

data.describe()

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.

data[['temp', 'temp_dht']].plot()

<matplotlib.axes._subplots.AxesSubplot at 0x7f06f42c5e80>

data[['temp', 'dewpoint', 'humidity']].plot()

<matplotlib.axes._subplots.AxesSubplot at 0x7f06f431d908>

data[['temp', 'dewpoint', 'humidity']].plot(subplots=True)

array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f06f432a9e8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x7f06f4c719e8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x7f06f3ea7f60>], dtype=object)

data[['temp', 'dewpoint']].plot()

<matplotlib.axes._subplots.AxesSubplot at 0x7f06f41a6630>

from numpy import fft

data['fft_altitude'] = fft.fft(data.altitude)
data['fft_alt_real'] = data.fft_altitude.real
data['fft_alt_imag'] = data.fft_altitude.imag

data['alt_power'] = ((data.fft_alt_real ** 2.0) + (data.fft_alt_imag ** 2.0)) ** 0.5

import numpy
N = len(data)
TWELVE = N//(12*59)

def hours(pos):
    return N/(pos*60.) 


eleven_elevator = N/(12*59.0)   # Scottish elevator
print(eleven_elevator, TWELVE)
data['pos'] = numpy.arange(N)
data[['fft_alt_imag', 'fft_alt_real', 'alt_power']][TWELVE//3:2*TWELVE].plot()

85.14265536723164 85

<matplotlib.axes._subplots.AxesSubplot at 0x7f06f4034978>

data[TWELVE//3:2*TWELVE].plot(x='pos', y='alt_power')

<matplotlib.axes._subplots.AxesSubplot at 0x7f06f38c70b8>

data[TWELVE-4: TWELVE+10].plot(x='pos', y='alt_power')
print(TWELVE, eleven_elevator)

85 85.14265536723164

#FIXME - the data points are probably a fraction over a minute apart.
for x in range(TWELVE-5, TWELVE+5):
    print(x, "%6.4f" % hours(x))

80 12.5585
81 12.4035
82 12.2522
83 12.1046
84 11.9605
85 11.8198
86 11.6824
87 11.5481
88 11.4169
89 11.2886

data.tail(1)

data.tail(10*24*60).head(1)

hours(54)

18.605246913580245

data[TWELVE//3:3 + (2*TWELVE)].clip(-60000, 60000).plot(x='pos', y='alt_power')

<matplotlib.axes._subplots.AxesSubplot at 0x7f06f3f4e710>

xx = data.fft_altitude.copy()
xx[TWELVE:-TWELVE] = 0.0
data['smooth_alt'] = [x.real for x in fft.ifft(xx)]

data[['smooth_alt', 'altitude']].plot()

<matplotlib.axes._subplots.AxesSubplot at 0x7f06f3f2dcf8>

data.smooth_alt.plot()

<matplotlib.axes._subplots.AxesSubplot at 0x7f06f3f42470>

def power(x, y):
    
    return ((x ** 2.0) + (y ** 2.0)) ** 0.5

P = TWELVE
xx = data.fft_altitude.copy()
xx[:] = 0.0
xx[P:-P] = data.fft_altitude[P:-P]
data['twelve'] = [x.real for x in fft.ifft(xx)]
data['itwelve'] = [x.imag for x in fft.ifft(xx)]
data['twelve_pow'] = power(data.twelve, data.itwelve)
data['smoothed'] = data.altitude - data.twelve
data[['twelve', 'altitude', 'smoothed', 'smooth_alt']].plot(subplots=True)

array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f06f391c6d8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x7f06f31a07f0>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x7f06f3354eb8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x7f06f386e3c8>], dtype=object)

Fourier analysis¶

So, the story so far:

Looking to see if there is a relationship between the tidal pattern in pressure data and the moon.

The pressure data gives a waveform. The Fourier transform of this data we can converts the data from a time series to give us the underlying frequencies driving that data.

# Tell matplotlib to plot in line
%matplotlib inline
import pandas as pd

# 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()

from matplotlib import pyplot

import mpld3

# interactive plots -- except in ein

#mpld3.enable_notebook()

infile="../files/moon_weather.csv"

import numpy
from numpy import fft

data = pd.read_csv(infile, index_col='date', parse_dates=['date'])

data.altitude[:24480].plot()

<matplotlib.axes._subplots.AxesSubplot at 0x7f535e56dbe0>

data.describe()

24480/720.
#(60. * 12)

34.0

N = 24480
fft_altitude = fft.fft(data[:N].altitude)

# Plot the transform.  Most of the energy in the signal is at low frequencies
pd.Series(x.real for x in fft_altitude).clip(-1000, 1000)[:].plot()

<matplotlib.axes._subplots.AxesSubplot at 0x7f535e6e4908>

# spike of energy at location 34
pd.Series(x.real for x in fft_altitude)[20:40].plot()

<matplotlib.axes._subplots.AxesSubplot at 0x7f535e70bdd8>

# Convert location into period
point = 34
for p in (point-1, point, point+1):
    print(p)
    print("%8.3f %s" % (N/p, "minutes"))
    print("%8.3f %s" % (N/(p * 60), "hours"))
    print("%8.3f %s" % (N/(p * 60 * 24), "days"))

33
 741.818 minutes
  12.364 hours
   0.515 days
34
 720.000 minutes
  12.000 hours
   0.500 days
35
 699.429 minutes
  11.657 hours
   0.486 days

The spike of energy at location 34 corresponds to a signal with a period of 12 hours.

Note that the amount of data available limits the resolution of transform.

A peak at position 33 would give a period roughly 22 minutes longer, at 35 it would be 21 minutes shorter.

It looks like the tidal pattern in the pressure data has a period of 24 hours.

Hence, it looks like it is driven by the sun, not the moon.

# transform is complex, look at the imaginary component
pd.Series(x.imag for x in fft_altitude)[0:60].plot()

<matplotlib.axes._subplots.AxesSubplot at 0x7f535e620f98>

# remove 12 hour and higher freq components:
xx = fft_altitude.copy()
xx[point:-point] = 0.0
xx[int(point/2)] = 0.0
#xx[point+1:] = 0.0

smooth_altitude = fft.ifft(xx)

pd.Series(y.real for y in smooth_altitude).plot()

<matplotlib.axes._subplots.AxesSubplot at 0x7f535e4ccbe0>

df = pd.DataFrame()
df['altitude'] = data.altitude[:N]
df['smooth_altitude'] = [y.real for y in smooth_altitude]

df.plot(subplots=False)

<matplotlib.axes._subplots.AxesSubplot at 0x7f535e1801d0>

smdf = pd.DataFrame(dict(smooth_altitude=smooth_altitude, fft_alt=fft.fft(smooth_altitude)))
smdf.fft_alt[6:50].plot()

/home/jng/.virtualenvs/peakrisk2/lib/python3.4/site-packages/numpy/core/numeric.py:462: ComplexWarning: Casting complex values to real discards the imaginary part
  return array(a, dtype, copy=False, order=order)

<matplotlib.axes._subplots.AxesSubplot at 0x7f535dd28908>

# moon takes 29.53 days to be in same position relative to earth/sun

N/(29.53 * 24 * 60)

0.5756857433118862

pd.Series(fft.fft(df.smooth_altitude)

array([-931957.98424361    +0.j        ,  255374.61463530+17993.92648614j,
         92024.57013790+88700.72926733j, ...,
        -31904.27748675-39364.56851422j,   92024.57013790-88700.72926733j,
        255374.61463530-17993.92648614j])

import math
sine = [numpy.sin(x/math.pi) for x in range(1000)]

xfft = fft.fft(sine)

df = pd.DataFrame()
df['sine'] =sine
df['ffti'] = [x.imag for x in xfft]
df['fftr'] = [x.real for x in xfft]
df[:].plot(subplots=True)

array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f535e1fd908>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x7f535e0be6a0>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x7f535e306f60>], dtype=object)

df.describe()

df.head(20)

$$A_k = \sum_{m=0}^{n-1} a_m \exp\left\{-2\pi i{mk \over n}\right\} \qquad k = 0,\ldots,n-1.$$

$$a_m = \exp\{2\pi i\,f m\Delta t\}$$

where $\Delta t$ is the sampling interval

Nelson Mandela Day¶

July 18th is Nelson Mandela's birthday, he would have been 97.

The United Nations calls it Mandela Day, or international day for freedom, justice and democracy. They say:

Every year on 18 July — the day Nelson Mandela was born — the UN joins a call by the Nelson Mandela Foundation to devote 67 minutes of time to helping others, as a way to mark Nelson Mandela International Day.

For 67 years Nelson Mandela devoted his life to the service of humanity — as a human rights lawyer, a prisoner of conscience, an international peacemaker and the first democratically elected president of a free South Africa.

There is more here: http://www.un.org/en/events/mandeladay/

There are a number of events being organised in Bermuda.

It looks like things might kick off early with this event at Bermuda College:

On the Eve of Nelson Mandela Day

Collaboration by:

ADHT, Bermuda College, Chewstick, HRC, Imagine Bermuda

Presents

Another Kind of ‘Happy Hour’

Based @ the Bermuda College Library

FRIDAY, JULY 17TH 5.30 – 7.30 pm

MUSIC……….POETRY………STORY-SHARING

NETWORKING…….NETPLAYING

Meet and Greet,

Even stump your feet,

Although Challenging,

Remember, Life is Sweet!

There are others planning events, I will try to post details here.

I am thinking of going to different events and doing sixty-seven minutes of pi.

Trivia¶

import math
from ipython_doctester import test

# FIXME 
# ipython_doctester broken, might be this prob: https://github.com/catherinedevlin/ipython_doctester/pull/5
#@test
def prime(n):
    """ Simple primality tester

    Examples:
    
    >>> prime(15)
    False

    >>> prime(67)
    True
    """

    # Deal with some small numbers
    if n <= 2: return True

    factor = 2

    while factor < math.sqrt(n):
        if 0 == (n % factor): return False
        
        factor += 1

    return True

# Is 67 prime?
print("Is 67 prime?")
print(prime(67))

# Mandela would have been 97
print("Is 97 prime?")
print(prime(97))

Is 67 prime?
True
Is 97 prime?
True

Sixty-seven minutes of $\pi$¶

Angles are measured in different units. Mostly, people seem to use degrees.

A right angle is 90 degrees. The angles in a triangle add up to 180 degrees.

In mathematics it is often useful to use different units, radians.

$\pi$ radians is equal to 180 degrees. So the angles in a triangle add up to 180 degrees.

Now just as there are 60 minutes in an hour, there are also 60 minutes in a degree.

So, 67 minutes of $\pi$ is just 67 minutes from 180 degrees.

$$ x = \pi * 67 / (180 * 60)$$

# In python

math.pi * 67 / (180 * 60)

0.019489509980603347

# Now 67 minutes of pi number begins 0.01948.

1948 + 67

2015

So if Mandela had started his campaigning on his birthday in 1948 he would still be at it today.

The UN are just asking you to spend 67 minutes helping others on his birthday.

	temp	pressure	altitude	sealevel_pressure	humidity	temp_dht
date
2015-10-05 00:58:00.057913	26.2	98395	245.990163	98410	96.199997	25.799999
2015-10-05 00:59:00.673876	26.2	98383	247.865819	98389	96.099998	25.700001
2015-10-05 01:00:01.316217	26.2	98383	247.013206	98383	95.900002	25.700001
2015-10-05 01:01:04.485819	26.2	98408	247.098464	98395	95.900002	25.700001
2015-10-05 01:02:05.128270	26.1	98414	244.455788	98420	95.900002	25.700001

	temp	pressure	altitude	sealevel_pressure	humidity	temp_dht
count	42768.000000	42768.000000	42768.000000	42768.000000	42764.000000	42764.000000
mean	28.114357	101385.375842	-5.068642	101387.094487	76.806599	27.730374
std	2.205370	430.276252	36.334073	409.738250	8.512122	2.068583
min	22.800000	56117.000000	-1314.018026	67136.000000	29.500000	22.299999
25%	26.500000	101141.750000	-26.939647	101142.000000	71.599998	26.200001
50%	27.700000	101411.000000	-7.157439	101412.000000	77.099998	27.400000
75%	29.900000	101648.000000	15.246750	101649.000000	83.500000	29.299999
max	43.200000	102242.000000	3397.334521	118353.000000	94.300003	47.099998

	temp	pressure	altitude	sealevel_pressure	humidity	temp_dht
count	42768.000000	42768.000000	42768.000000	42768.000000	42764.000000	42764.000000
mean	28.114357	101385.375842	-5.068642	101387.094487	76.806599	27.730374
std	2.205370	430.276252	36.334073	409.738250	8.512122	2.068583
min	22.800000	56117.000000	-1314.018026	67136.000000	29.500000	22.299999
25%	26.500000	101141.750000	-26.939647	101142.000000	71.599998	26.200001
50%	27.700000	101411.000000	-7.157439	101412.000000	77.099998	27.400000
75%	29.900000	101648.000000	15.246750	101649.000000	83.500000	29.299999
max	43.200000	102242.000000	3397.334521	118353.000000	94.300003	47.099998

	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

	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

Hurricane Alex

Hurricane Joaquin pressure

See the pressure plummet as Joaquin approaches Bermuda¶

Bermuda Weather Radar¶

Walking in the Peak District

More fourier transforms in the atmosphere

Comparing pressure data from two sensors

Latest Weather

Last 24 hours:¶

Last week¶

Look at all the data¶

Dew Point¶

Latest Weather

Dew Point¶

Last 24 hours:¶

Last week¶

Look at all the data¶

Experimenting and building things

Machine Learning in the atmosphere

Fourier analysis¶

Sixty-seven Minutes

Nelson Mandela Day¶

Trivia¶

Sixty-seven minutes of $\pi$¶

	temp	pressure	altitude	sealevel_pressure	humidity	temp_dht
date
2015-10-04 14:59:04.029159	26.4	99788	128.924064	99791	82.500000	25.9
2015-10-04 15:00:04.671297	26.4	99808	127.069788	99808	82.500000	25.9
2015-10-04 15:01:05.262094	26.5	99791	129.429833	99788	82.500000	25.9
2015-10-04 15:02:05.904264	26.5	99766	129.935627	99767	82.599998	25.9
2015-10-04 15:03:06.546427	26.5	99758	130.525751	99773	82.400002	25.9

	temp	pressure	altitude	sealevel_pressure	humidity	temp_dht
count	60281.000000	60281.000000	60281.000000	60281.000000	60281.000000	60281.000000
mean	27.892394	101246.221778	6.548877	101247.144722	63.323885	27.275608
std	1.717909	315.289819	26.273290	315.299968	12.424934	1.718179
min	22.500000	99758.000000	-55.052172	99767.000000	28.700001	21.799999
25%	26.900000	101027.000000	-8.738071	101028.000000	58.099998	26.299999
50%	28.000000	101251.000000	6.079362	101252.000000	66.500000	27.500000
75%	29.000000	101430.000000	24.756020	101431.000000	71.599998	28.400000
max	31.400000	101985.000000	130.525751	101985.000000	88.500000	30.600000

	temp	pressure	altitude	sealevel_pressure	humidity	temp_dht	dewpoint	fft_altitude	fft_alt_real	fft_alt_imag	alt_power	pos
date
2015-09-24 10:03:41.189865	24.2	101185	11.91222	101187	35.799999	23.6	11.36	(-46.1226318013-240.511319049j)	-46.122632	-240.511319	244.893838	45881

	altitude	pressure	sealevel_pressure	temp	moon_phase	moon_glat	moon_glon	tide
count	30397.000000	0	0	30397.000000	30397.000000	30397.000000	30397.000000	30397.000000
mean	-31.879161	NaN	NaN	29.392555	58.143320	0.009210	3.272469	12.141895
std	20.589351	NaN	NaN	0.894486	36.001448	0.063526	1.943755	6.996605
min	-69.792586	NaN	NaN	27.200000	0.172035	-0.087831	0.000126	-0.087386
25%	-50.325302	NaN	NaN	28.700000	22.124449	-0.062341	1.274654	5.998249
50%	-34.975717	NaN	NaN	29.400000	69.537071	0.026644	3.743553	12.296909
75%	-10.941520	NaN	NaN	30.000000	91.296631	0.070193	4.961365	18.230285
max	8.379000	NaN	NaN	31.500000	99.811104	0.087505	6.283139	24.070675

	sine	ffti	fftr
count	1000.000000	1.000000e+03	1.000000e+03
mean	0.005197	-5.684342e-17	-9.947598e-17
std	0.706704	1.081976e+01	1.955480e+01
min	-0.999981	-1.990662e+02	-3.577702e+02
25%	-0.699680	-4.233174e-01	-5.654399e-02
50%	0.012879	-5.190293e-15	2.330771e-01
75%	0.709885	4.233174e-01	2.929166e-01
max	0.999983	1.990662e+02	1.862269e+02

	sine	ffti	fftr
0	0.000000	0.000000	5.197500
1	0.312962	0.052948	5.199377
2	0.594481	0.106019	5.205016
3	0.816273	0.159337	5.214444
4	0.956056	0.213027	5.227705
5	0.999785	0.267219	5.244862
6	0.943067	0.322045	5.265998
7	0.791600	0.377643	5.291215
8	0.560603	0.434158	5.320637
9	0.273282	0.491741	5.354412
10	-0.041494	0.550553	5.392711
11	-0.352102	0.610767	5.435733
12	-0.627335	0.672567	5.483709
13	-0.839540	0.736151	5.536901
14	-0.967398	0.801737	5.595609
15	-0.998063	0.869561	5.660173
16	-0.928453	0.939881	5.730982
17	-0.765564	1.012983	5.808478
18	-0.525759	1.089186	5.893164
19	-0.233132	1.168842	5.985611