Hey John - currently using matplotlib.mlab import csv2rec functionality in a
script.
Is there a tool or way to automate plotting of multiple y series contained
in a csv data file (data in columns, header is first row, x axis is time,
several y series) with varying column header names and varying numbers of
columns depending on the individual data file?
I particularly want to avoid manually typing individual series names -as
this information is contained in the header row for each column of data it
seems inefficient to have to type series names for plotting, only to have to
retype series names for the next csv file which contains different column
header names
Plotfile came close, but doesnt seem to automatically label individual
series by column header
eg file formats (varying headers, and numbers of columns):
file 1
elapsedtime,AS2data,AS45data,SE34data,VB56data
file 2
elapsedtime,AS09data,VB24data
John Hunter-4 wrote:
>
> <<support for native plotting of python date and datetime
> objects <<support for loading CSV files (or general space/tab/comma
> delimited
> files) into numpy record arrays, and the type conversions (int, float,
> date, etc...) >><<The function assumes there is a
> header row, and these strings will be munged to give valid python
> attribute names. It inspects the first checkrows lines after the
> header to try and infer the datatype and set the appropriate
> conversion function. >>
> Here is an example (svn only)
>
> from matplotlib.mlab import csv2rec
> from pylab import figure, show
>
> a = csv2rec('data/msft.csv')
> fig = figure()
> ax = fig.add_subplot(111)
> ax.plot(a.date, a.adj_close, '-')
> fig.autofmt_xdate()
> show()
>
> The autofmt_xdate is optional, but is a new function that does a few
> things you usually want in date plots: turns off tick labels in the
> upper subplots if any, rotates the tick labels on the lowest axes and
> right aligns them, and increases the bottom of the subplots adjust to
> make room for the rotated tick labels.
>
> Here is what the dtype looks like from the example above.
>
> In [3]: !head -3 data/msft.csv
> Date,Open,High,Low,Close,Volume,Adj. Close*
> 19-Sep-03,29.76,29.97,29.52,29.96,92433800,29.79
> 18-Sep-03,28.49,29.51,28.42,29.50,67268096,29.34
>
> In [4]: a = csv2rec('data/msft.csv')
>
> In [5]: a.dtype
> Out[5]: dtype([('date', '|O4'), ('open', '<f8'), ('high', '<f8'),
> ('low', '<f8'), ('close', '<f8'), ('volume', '<i4'), ('adj_close',
> '<f8')])
>
> In [6]: a.date[:2]
> Out[6]: array([2003-09-19 00:00:00, 2003-09-18 00:00:00], dtype=object)
>
> I'll probably add a few performance features to the csv2rec function,
> mainly to let you skip columns and supply conversion functions where
> desired because the autodate parser is pretty slow if you want to
> parse date strings, but this is enough to make it useful. Another
> useful feature will be able to support customizable type dependent
> NULL value conversion (eg convert to numpy.nan for floats,
> '0000-00-00' for dates, etc...)
>
> Record arrays are your friend; have fun!
> JDH
>
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