在python中对多个文件执行分析

Question

我正在使用一个名为sharpy的模块对气象气球数据执行天气分析。数据是.oax格式的，所以我不能在这里提供示例。我知道如何从单个数据文件的数据中获取所需的值，但我不确定如何轻松地对多个文件（大约50个文件）执行此操作。下面是我的代码，以及此代码为特定文件返回的示例。如何自动执行此操作以对多个文件运行分析？在

spc_file = open('X:/seabreezestormdays/201412090300_040842.oax', 'r').read()

import sharppy
import sharppy.sharptab.profile as profile
import sharppy.sharptab.interp as interp
import sharppy.sharptab.winds as winds
import sharppy.sharptab.utils as utils
import sharppy.sharptab.params as params
import sharppy.sharptab.thermo as thermo
import numpy as np
from StringIO import StringIO

def parseSPC(spc_file):
    ## read in the file
    data = np.array([l.strip() for l in spc_file.split('\n')])

    ## necessary index points
    title_idx = np.where( data == '%TITLE%')[0][0]
    start_idx = np.where( data == '%RAW%' )[0] + 1
    finish_idx = np.where( data == '%END%')[0]

    ## create the plot title
    data_header = data[title_idx + 1].split()
    location = data_header[0]
    time = data_header[1][:11]

    ## put it all together for StringIO
    full_data = '\n'.join(data[start_idx : finish_idx][:])
    sound_data = StringIO( full_data )

    ## read the data into arrays
    p, h, T, Td, wdir, wspd = np.genfromtxt( sound_data, delimiter=',', comments="%", unpack=True )

    return p, h, T, Td, wdir, wspd

pres, hght, tmpc, dwpc, wdir, wspd = parseSPC(spc_file)

prof = profile.create_profile(profile='default', pres=pres, hght=hght, tmpc=tmpc, \
                                    dwpc=dwpc, wspd=wspd, wdir=wdir, missing=-9999, strictQC=True)
msl_hght = prof.hght[prof.sfc] # Grab the surface height value
#print "SURFACE HEIGHT (m MSL):",msl_hght
agl_hght = interp.to_agl(prof, msl_hght) # Converts to AGL
#print "SURFACE HEIGHT (m AGL):", agl_hght
msl_hght = interp.to_msl(prof, agl_hght) # Converts to MSL
#print "SURFACE HEIGHT (m MSL):",msl_hght
sfcpcl = params.parcelx( prof, flag=1 ) # Surface Parcel
fcstpcl = params.parcelx( prof, flag=2 ) # Forecast Parcel
mupcl = params.parcelx( prof, flag=3 ) # Most-Unstable Parcel
mlpcl = params.parcelx( prof, flag=4 ) # 100 mb Mean Layer Parcel
print "Most-Unstable CAPE:", mupcl.bplus # J/kg
print "Most-Unstable CIN:", mupcl.bminus # J/kg
print "Most-Unstable LCL:", mupcl.lclhght # meters AGL
print "Most-Unstable LFC:", mupcl.lfchght # meters AGL
print "Most-Unstable EL:", mupcl.elhght # meters AGL
print "Most-Unstable LI:", mupcl.li5 # C
sfc = prof.pres[prof.sfc]
p3km = interp.pres(prof, interp.to_msl(prof, 3000.))
p6km = interp.pres(prof, interp.to_msl(prof, 6000.))
p1km = interp.pres(prof, interp.to_msl(prof, 1000.))
mean_3km = winds.mean_wind(prof, pbot=sfc, ptop=p3km)
sfc_6km_shear = winds.wind_shear(prof, pbot=sfc, ptop=p6km)
sfc_3km_shear = winds.wind_shear(prof, pbot=sfc, ptop=p3km)
sfc_1km_shear = winds.wind_shear(prof, pbot=sfc, ptop=p1km)
print "0-3 km Pressure-Weighted Mean Wind (kt):", utils.comp2vec(mean_3km[0], mean_3km[1])[1]
print "0-6 km Shear (kt):", utils.comp2vec(sfc_6km_shear[0], sfc_6km_shear[1])[1]
srwind = params.bunkers_storm_motion(prof)
#print "Bunker's Storm Motion (right-mover) [deg,kts]:", utils.comp2vec(srwind[0], srwind[1])
#print "Bunker's Storm Motion (left-mover) [deg,kts]:", utils.comp2vec(srwind[2], srwind[3])
srh3km = winds.helicity(prof, 0, 3000., stu = srwind[0], stv = srwind[1])
srh1km = winds.helicity(prof, 0, 1000., stu = srwind[0], stv = srwind[1])
print "0-3 km Storm Relative Helicity [m2/s2]:",srh3km[0]
stp_fixed = params.stp_fixed(sfcpcl.bplus, sfcpcl.lclhght, srh1km[0], utils.comp2vec(sfc_6km_shear[0], sfc_6km_shear[1])[1])
ship = params.ship(prof)
eff_inflow = params.effective_inflow_layer(prof)
ebot_hght = interp.to_agl(prof, interp.hght(prof, eff_inflow[0]))
etop_hght = interp.to_agl(prof, interp.hght(prof, eff_inflow[1]))
print "Effective Inflow Layer Bottom Height (m AGL):", ebot_hght
print "Effective Inflow Layer Top Height (m AGL):", etop_hght
effective_srh = winds.helicity(prof, ebot_hght, etop_hght, stu = srwind[0], stv = srwind[1])
print "Effective Inflow Layer SRH (m2/s2):", effective_srh[0]
ebwd = winds.wind_shear(prof, pbot=eff_inflow[0], ptop=eff_inflow[1])
ebwspd = utils.mag( ebwd[0], ebwd[1] )
print "Effective Bulk Wind Difference:", ebwspd
scp = params.scp(mupcl.bplus, effective_srh[0], ebwspd)
stp_cin = params.stp_cin(mlpcl.bplus, effective_srh[0], ebwspd, mlpcl.lclhght, mlpcl.bminus)
#print "Supercell Composite Parameter:", scp
#print "Significant Tornado Parameter (w/CIN):", stp_cin
#print "Significant Tornado Parameter (fixed):", stp_fixed

输出：

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Answer 1

扩展@PyNEwbie的答案：

# get all the .oax files from a directory
from glob import glob
files = glob.glob('X:/seebreezestromdays/*.oax')

# To get files from the command line, use the following instead:
#      files = sys.argv[1:]

# load all the data from a list of files.  
data = [parseSPC(f) for f in files]

# details for each file
for d in data:
    # The following is shorthand for:
    #    pres, hght, tmpc, dwpc, wdir, wspd = d
    #    show_one_file(pres, hght, tmpc, dwpc, wdir, wspd)
    show_one_file(*d)

# summary for all files
# The following is shortand for:
#    # make separate lists for each stat
#    pres, hght, tmpc, dwpc, wdir, wspd = zip(*data)
#    show_summary(pres, hght, tmpc, dwpc, wdir, wspd)
show_summary(*zip(*data))