Thanks Anthony,
Plotting all the data allows you to see the rate of fall of voltage ("%
power"), so I'm quite happy with the approach, rather than looking at
minima. Similarly, I found that using interpolate.plot gave the best
looking curves
I agree about the short variable names - I was sticking with the textbooks
on that but I should break free and use more descriptive names.
Thanks for the references, I'll follow up on them.
Regards,
Kevin.
On 31 August 2014 11:57, Anthony Briggs <[email protected]> wrote:
> Hi Kevin,
>
> Looks like it's just pulling those keys from the data, so something to
> convert those explicitly when you read it in would probably be the easiest
> way. There might be something in the read_csv function to convert data from
> a particular column, or you could try a dictionary comprehension per
> station (something along the lines of station_data = {reading['DT'][:10]:
> reading for reading in d[stn]}.
>
> You seem to have a lot of values for the same day though, so you'd either
> want to grab just the minimum, or do some sort of formatting in matplotlib.
>
> Another method would be to find the minimum value (or minimum 3) for a
> station and just report a warning if it's below a certain amount, rather
> than a graph which someone has to interpret.
>
> Final point - having a lot of one and two character variables makes it
> really hard to tell what your script is doing.
>
> Hope that helps,
>
> Anthony
>
>
>
> On 31 August 2014 11:11, Kevin Shackleton <[email protected]> wrote:
>
>> Hi,
>>
>> New to Melbourne PUG. Fairly new to Python.
>>
>> I have a script that interprets power levels at several automated
>> surveying total stations working at a site. The idea is to show that the
>> batteries have enough amp-hours to see the total station through a run of
>> cloudy days. After 2 or 3 years of battery life the battery amp-hour
>> capacity will have reduced to a point where the installation will run often
>> out of power before the solar panels kick in each morning. In practice
>> this is often affected by 3rd parties hanging extra electrical load on our
>> installation.
>>
>> The data looks like this:
>> 2014-06-16T18:40:20,HUT1,56
>> 2014-06-16T19:02:49,HUT2,15
>> 2014-06-16T20:16:12,HUT1,58
>> 2014-06-16T20:17:08,HUT2,11
>> 2014-06-16T20:51:17,HUT1,67
>> 2014-06-17T11:51:05,HUT1,100
>> 2014-06-17T11:51:07,HUT2,48
>> 2014-06-17T11:51:08,HUT3,57
>> where power level readings are coming from each of the huts (there are
>> actually 4 of them) at random times and with different data densities per
>> hut according to the cycle schedules. The power levels are percent. Some
>> total stations report in volts but that's another problem.
>>
>> My script solution is:
>> import pandas as pd
>> import matplotlib.pyplot as plt
>> df = pd.read_csv('Power_Log.csv',names=['DT','Station','Power'])
>> df2=df.groupby(['Station']) # set 'Station' as the data index
>> d = dict(iter(df2)) # make a dictionary including each station's data
>> for stn in d.keys():
>> plt.figure() # creates a new plot canvas
>> fig, ax = plt.subplots() # creates components of the plot
>> ax.set_ylabel('Power (%)',fontsize=12)
>> fig.subplots_adjust(bottom=0.15)
>> d[stn].interpolate().plot(x='DT',y='Power',rot=15,title='Power Level:
>> ' + stn)
>> ax.set_xlabel('Date-Time',fontsize=12)
>> plt.savefig('Station_Power_' + stn + '.png')
>>
>> Possibly a bit wasteful going from dataframe to dataframe to dictionary.
>>
>> This code knocks out nice graphs, except for one thing - the x ticks are
>> at unrounded positions and therefore have long labels.
>>
>> I'm getting nowhere with set_major_formatter and autofmt_xdate methods to
>> try to set the ticks to rounded days.
>>
>> And brilliant ideas here?
>>
>> Thanks,
>>
>> Kevin.
>>
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>>
>
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