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# 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()
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infile = "../files/pijessie_weather.csv"
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!scp 192.168.0.127:Adafruit_Python_BMP/weather.csv $infile
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""" 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'])
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.
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data.pressure[-1*24*24:].plot()
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# See how this compares to "normal" pressure
# Plot the last 10 days
data.pressure[-10*24*24:].plot()
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data.tail()
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!pwd
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
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from IPython import display
display.Image('../galleries/Joaquin/joaquin.png')
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