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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