bSvlist = list() for n,par in enumerate(params): " synth data " out = vitas.main(par[0]) p3u = out[0] p3v = out[1] p3wU = out[2] # from 230deg p3z = out[3] p3ulist.extend(p3u[:16]) p3vlist.extend(p3v[:16]) " balloon data " infiles3,_ = so.get_sounding_files(par[1], homedir='/localdata') infiles3.sort() df = mf.parse_sounding2(infiles3[par[2]]) bSu = df.u.values bSv = df.v.values bSz = df.index.values """ reduce resolution of bS by averaging 100-m layer centered at p3 altitude """ bot = 25 top = 26 for z in p3z[:16]: center_idx = np.where(bSz==z*1000.)[0] if not center_idx: bSulist.append(np.nan) bSvlist.append(np.nan)
rcParams['xtick.labelsize'] = 15 rcParams['ytick.labelsize'] = 15 rcParams['legend.fontsize'] = 15 rcParams['axes.labelsize'] = 15 rcParams['legend.handletextpad'] = 0.2 rcParams['mathtext.default'] = 'sf' scale = 1.2 fig, axes = plt.subplots(2, 1, sharex=True, figsize=(5 * scale, 10 * scale)) axes[0].set_gid('(a) 23-24Jan01') axes[1].set_gid('(b) 17Feb01') nobs = ('n=7', 'n=11') infiles3, _ = so.get_sounding_files('3', homedir='/localdata') infiles7, _ = so.get_sounding_files('7', homedir='/localdata') cmap = discrete_cmap(7, base_cmap='Set1') color = (cmap(0), cmap(1)) infiles = (infiles3, infiles7) for n, ax in enumerate(axes): first = True for f in infiles[n]: df = mf.parse_sounding2(f) x = np.expand_dims(df.bvf_moist.values, axis=1) * 10000 y = np.expand_dims(df.index.values, axis=1) ax.plot(x, y, color=color[n], lw=0.5)
2, sharex=True, sharey=True, figsize=(8.5 * scale, 11 * scale)) axes = axes.flatten() axes[0].set_gid('(a) 12-14Jan03 (n={})') axes[1].set_gid('(b) 21-23Jan03 (n={})') axes[2].set_gid('(c) 15-16Feb03 (n={})') axes[3].set_gid('(d) 09Jan04 (n={})') axes[4].set_gid('(e) 02Feb04 (n={})') axes[5].set_gid('(f) 16-18Feb04 (n={})') axes[6].set_gid('(g) 25Feb04 (n={})') fig.delaxes(axes[-1]) infiles08, _ = so.get_sounding_files('8', homedir='/localdata') infiles09, _ = so.get_sounding_files('9', homedir='/localdata') infiles10, _ = so.get_sounding_files('10', homedir='/localdata') infiles11, _ = so.get_sounding_files('11', homedir='/localdata') infiles12, _ = so.get_sounding_files('12', homedir='/localdata') infiles13, _ = so.get_sounding_files('13', homedir='/localdata') infiles14, _ = so.get_sounding_files('14', homedir='/localdata') cmap = discrete_cmap(7, base_cmap='Set1') color = cmap(0) infiles = (infiles08, infiles09, infiles10, infiles11, infiles12, infiles13, infiles14) for ax, infile in zip(axes, infiles):
rcParams['xtick.labelsize'] = 15 rcParams['ytick.labelsize'] = 15 rcParams['legend.fontsize'] = 15 rcParams['axes.labelsize'] = 15 rcParams['legend.handletextpad'] = 0.2 rcParams['mathtext.default'] = 'sf' scale=1.2 fig,axes = plt.subplots(2,1,sharex=True,figsize=(5*scale,10*scale)) axes[0].set_gid('(a) 23-24Jan01') axes[1].set_gid('(b) 17Feb01') nobs=('n=7','n=11') infiles3,_ = so.get_sounding_files('3', homedir='/localdata') infiles7,_ = so.get_sounding_files('7', homedir='/localdata') cmap = discrete_cmap(7, base_cmap='Set1') color=(cmap(0),cmap(1)) infiles=(infiles3,infiles7) for n,ax in enumerate(axes): first = True for f in infiles[n]: df = mf.parse_sounding2(f) x = np.expand_dims(df.bvf_moist.values,axis=1)*10000 y = np.expand_dims(df.index.values,axis=1)
import matplotlib.pyplot as plt import numpy as np import sounding as so from scipy.ndimage.filters import gaussian_filter # homedir = '/Users/raulv/Documents' homedir = '/home/rvalenzuela' usr_case = None file_sound, usc = so.get_sounding_files(usr_case, homedir=homedir) ''' raw soundings vertically-interpolated ''' # # soundarray,_,_,_,_ = get_raw_array('thetaeq', file_sound) out = so.get_raw_array('bvf_moist', file_sound) soundarray, _, _, y, x, raw_dates = out title = 'BVFm raw' # make_imshow(soundarray,title,x,y,raw_dates) ''' time-interpolated ''' # soundarray2,_,_ = get_interp_array('u',files=file_sound) # soundarray2,_,_ = get_interp_array('v',files=file_sound) # soundarray2,_,_ = get_interp_array('DD',files=file_sound) # soundarray2,_,_ = get_interp_array('thetaeq',files=file_sound) out = so.get_interp_array('bvf_moist', files=file_sound) soundarray2, hgt, timestamp, raw_dates = out
Raul Valenzuela September, 2015 """ import matplotlib.pyplot as plt import numpy as np import sounding as so from scipy.ndimage.filters import gaussian_filter # homedir = '/Users/raulv/Documents' homedir = '/home/rvalenzuela' usr_case = None file_sound, usc = so.get_sounding_files(usr_case, homedir=homedir) ''' raw soundings vertically-interpolated ''' # # soundarray,_,_,_,_ = get_raw_array('thetaeq', file_sound) out = so.get_raw_array('bvf_moist', file_sound) soundarray, _, _, y, x, raw_dates = out title = 'BVFm raw' # make_imshow(soundarray,title,x,y,raw_dates) ''' time-interpolated ''' # soundarray2,_,_ = get_interp_array('u',files=file_sound) # soundarray2,_,_ = get_interp_array('v',files=file_sound) # soundarray2,_,_ = get_interp_array('DD',files=file_sound) # soundarray2,_,_ = get_interp_array('thetaeq',files=file_sound) out = so.get_interp_array('bvf_moist', files=file_sound) soundarray2, hgt, timestamp, raw_dates = out # make_imshow(soundarray2,'')