co.py 文件源码

def get_cal_coefficients(data):
    """The calibration coefficients for the AL5002 instrument drift
    inbetween calibrations. It is assumed that the drifting is linear
    and too take account of this new coefficients are calculated for
    each data point, which are then used to recalculate the CO concentrations.

    """

    sens = data['AL52CO_sens'].ravel()
    zero = data['AL52CO_zero'].ravel()
    utc_time = [time.mktime(num2date(i).timetuple()) for \
                i in data['mpl_timestamp'][:,0]]
    # create copies of sens and zero calibration coefficients
    sens_new, zero_new = sens[:], zero[:]
    # get calibration periods
    ix=np.where(sens[1:]-sens[:-1] != 0)[0]
    # remove nan values
    ix=ix[~np.isnan((sens[1:]-sens[:-1])[ix])]
    # ignore the first 100 data points
    ix=ix[ix>100]
    # the +2 is a dodgy way to make sure that the values have changed.
    # Apparently the zero and sens parameters do not change at
    # exactly the same time in the data stream
    ix=[10]+list(ix+2)+[sens.size-2]
    # loop over all calibration periods
    table=[]
    for i in range(len(ix)-1):
        ix1=ix[i]
        ix2=ix[i+1]
        sens_new[ix1:ix2]=np.interp(utc_time[ix1:ix2], np.float32([utc_time[ix1], utc_time[ix2]]), [sens[ix1], sens[ix2]])
        zero_new[ix1:ix2]=np.interp(utc_time[ix1:ix2], np.float32([utc_time[ix1], utc_time[ix2]]), [zero[ix1], zero[ix2]])
        # write calibration information to stdout
        timestamp_start=datetime.datetime.utcfromtimestamp(utc_time[ix1]).strftime('%Y-%m-%d %H:%M:%S')
        timestamp_end=datetime.datetime.utcfromtimestamp(utc_time[ix2]).strftime('%Y-%m-%d %H:%M:%S')
        if np.isnan(sens[ix1]):
            sens_string='   nan'
        else:
            sens_string='%6.2f' % (sens[ix1],)
        if np.isnan(zero[ix1]):
            zero_string='   nan'
        else:
        zero_string='%6i' % (zero[ix1],)
        table.append([timestamp_start, timestamp_end, sens_string, zero_string])

    return table