對Python中的多個文件執行分析

Question

我使用稱爲SHARPpy的模塊對天氣氣球數據執行天氣分析。數據爲.oax格式，因此我無法在此處提供示例。我知道如何從單個數據文件的數據中獲得所需的值，但我不確定如何輕鬆地爲多個文件（大約50個文件）執行此操作。以下是我的代碼，以及此代碼針對特定文件返回的示例。我怎樣才能自動執行此操作以使分析運行多個文件？

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 

輸出：

runfile('C:/Users/kirkj/tryit.py', wdir='C:/Users/kirkj') 
Most-Unstable CAPE: 1711.9340703 
Most-Unstable CIN: -76.4232980928 
Most-Unstable LCL: 936.427493357 
Most-Unstable LFC: 2268.41422348 
Most-Unstable EL: 11164.0 
Most-Unstable LI: -5.1505592459 
0-3 km Pressure-Weighted Mean Wind (kt): 3.90277462633 
0-6 km Shear (kt): 19.1904316825 
0-3 km Storm Relative Helicity [m2/s2]: 67.0917409854 
Effective Inflow Layer Bottom Height (m AGL): 0.0 
Effective Inflow Layer Top Height (m AGL): 2090.0 
Effective Inflow Layer SRH (m2/s2): 65.5651659762 
Effective Bulk Wind Difference: 14.2626808205 
C:/Users/kirkj/tryit.py:42: DeprecationWarning: converting an array with ndim > 0 to an index will result in an error in the future 
    ## put it all together for StringIO 

Answer 1

爲了擴大對@ PyNEwbie的回答是：

# 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))