from datetime import datetime, timedelta import numpy as np from re import sub from tools import dt2float def disp_progress(working_leo): from os.path import exists progfile = f'progress_rt_{working_leo}.npz' status = {-2: 'Aborted', -1: 'Queued', 0: 'Processing', 1: 'Completed'} if exists(progfile): print(' # Last Modified TAR File Token Status') print('=== =================== ================== ==========') with np.load(progfile,allow_pickle=True) as fid: for nn, (tarfile, progress, timetag) in enumerate(zip(fid['tarfiles'], fid['progress'], fid['timetag'])): tarseg = sub(r'.*_(\d{8}_\d{4}_.*)\.tar',r'\1',tarfile) print(f"{nn:3d} {timetag:%Y-%m-%d %H:%M:%S} {tarseg} {status[progress]}") else: print(f'{progfile} not found!') def disp_pod(podfile): import pickle from matplotlib import pyplot as plt dt0 = datetime.strptime(sub(r'.*\.(\d{4}\.\d{3})\..*',r'\1',podfile),'%Y.%j') figfile = sub(r'\.pkl',r'.png',podfile) colors = np.array([[int(color[ii:ii + 2], 16) / 255 for ii in range(1, 6, 2)] for color in plt.rcParams['axes.prop_cycle'].by_key()['color']]) with open(podfile, 'rb') as fid: pkldata = pickle.load(fid) dsat = pkldata['dsat'] dtime = pkldata['dtime'] dataall = pkldata['dataall'] plt.close('all') fig, (ax1, ax) = plt.subplots(2, 1, figsize = (12,9), gridspec_kw={'height_ratios': [1,8]}) plt.subplots_adjust(hspace = 0.01) time_range = [np.inf, -np.inf] for time in dtime: time_range = [min(time_range[0], dt2float(time[:1], dt0)[0]), max(time_range[1], dt2float(time[-1:], dt0)[0])] time_match = np.arange(time_range[0] + 30, time_range[1] - 149).astype('int32') idx_match = np.zeros_like(time_match, 'int') for dnum, (sat, time, data) in enumerate(zip(dsat, dtime, dataall)): idx = np.all(np.isfinite(data), axis = 1) idx1 = np.logical_and(np.isfinite(data[:, 0]), ~np.isfinite(data[:, 3])) idx2 = np.logical_and(~np.isfinite(data[:, 0]), np.isfinite(data[:, 3])) ax.plot(dt2float(time[~idx], dt0) / 3600, np.full(time[~idx].shape, dnum), '.', color=(colors[dnum % 10] + 2) / 3, zorder=2) ax.plot(dt2float(time[idx1], dt0) / 3600, np.full(time[idx1].shape, dnum-0.1), '.', ms=0.5, color=colors[dnum%10], zorder=2) ax.plot(dt2float(time[idx2], dt0) / 3600, np.full(time[idx2].shape, dnum+0.1), '.', ms=0.5, color=colors[dnum%10], zorder=2) ax.plot(dt2float(time[idx],dt0)/3600, np.full(time[idx].shape, dnum),'.',color=colors[dnum%10], zorder=2) ax.text(-2.2-(0 if dnum%2 else 1), dnum, sat, horizontalalignment='right', verticalalignment='center', color=colors[dnum%10], fontsize=12) time_nice = dt2float(time[idx],dt0).astype('int32') if time_nice.size == 0: continue idx = [0]+list(np.where(np.diff(time_nice) > 3)[0] + 1)+[len(time_nice)] for ii in range(len(idx)-1): idx_match[np.logical_and(time_match >= time_nice[idx[ii]] + 30, time_match <= time_nice[idx[ii + 1] - 1] - 149)] += 1 bad_time = time_match[idx_match==0] idx = [0] + list(np.where(np.diff(bad_time) > 1)[0] + 1) + [len(bad_time)] bad_time = bad_time.astype('float') / 3600 for ii in range(len(idx)-1): ax.add_patch( plt.Rectangle((bad_time[idx[ii]], -1), bad_time[idx[ii + 1] - 1] - bad_time[idx[ii]], len(dsat) + 1, facecolor=[0.9, 0.9, 0.9], zorder=1)) ax.grid(True) ax.set_xlabel(f'Hours of {dt0:%Y-%m-%d}') ax.set_xticks(range(-2,25)) ax.set_xlim((-2,24)) ax.set_ylim((-1,len(dsat))) ax.set_yticks(range(len(dsat))) ax.set_yticklabels([]) ax.tick_params(direction="in") ax1.plot(time_match.astype('float') / 3600, idx_match, 'k',linewidth=0.5) ax1.grid(True) ax1.set_xticks(range(-2,25)) ax1.set_xlim((-2,24)) ax1.set_xticks(range(-2, 25)) ax1.set_xticklabels([]) ax1.set_yticks(range(0, 30, 5)) ax1.set_ylim((0, 25)) ax1.set_title(sub(r'.*/(.*)\.pkl', r'\1', podfile)) ax1.tick_params(direction="in") fig.savefig(figfile, dpi = 300) plt.close('all') def disp_orb(orbfile): from matplotlib import pyplot as plt from os.path import isfile dt0 = datetime.strptime(sub(r'.*\.(\d{4}\.\d{3})\..*',r'\1',orbfile),'%Y.%j') figfile = sub(r'\.npz',r'.png',orbfile) colors = np.array([[int(color[ii:ii + 2], 16) / 255 for ii in range(1, 6, 2)] for color in plt.rcParams['axes.prop_cycle'].by_key()['color']]) with np.load(orbfile, allow_pickle=True) as fid: data_orb = fid['data'] dts_orb = fid['dts'] attfile = sub('leoOrb','leoAtt',orbfile) isatt = isfile(attfile) if isatt: with np.load(attfile, allow_pickle=True) as fid: data_att = fid['data'] dts_att = fid['dts'] vars_att = fid['vars'] data_att = data_att[:, ['att' in x for x in vars_att]] vars_att = vars_att[['att' in x for x in vars_att]] idx = np.cumsum(np.hstack(([False], np.any( np.abs(np.diff(data_att, axis=0) / np.diff(dt2float(dts_att, dt0))[:, None]) > 0.01, axis=1)))) % 2 == 1 data_att[idx] *= -1 plt.close('all') fig, axs = plt.subplots(4, 1, figsize = (12,9)) plt.subplots_adjust(hspace = 0.01) time_range = dt2float(dts_orb[[0,-1]], dt0) if isatt: time_range = [min(time_range[0], dt2float(dts_att[:1], dt0)[0]), max(time_range[1], dt2float(dts_att[-1:], dt0)[0])] t1 = np.hstack([time_range[:1],dt2float(dts_orb,dt0),time_range[1:]]) idx = np.where(np.diff(t1)>15)[0] t1 = np.array([t1[idx],t1[idx+1]]).T/3600 t2 = np.hstack([time_range[:1],dt2float(dts_att,dt0),time_range[1:]]) idx = np.where(np.diff(t2)>15)[0] t2 = np.array([t2[idx],t2[idx+1]]).T/3600 axs[0].plot(dt2float(dts_orb, dt0)/3600, data_orb[:, :3], '.', ms=1,markeredgewidth=0, zorder=2) axs[0].set_ylabel('Position (km)') axs[0].set_title(sub(r'.*/leo..._(.*)\.npz',r'\1 ',orbfile)+f'({dt0:%Y-%m-%d})') # sgn = np.sign(data_orb[:, 3]) # idx = sgn>0 # axs[1].plot(dt2float(dts_orb[idx], dt0), np.log10(data_orb[idx, 3]) + 6, '.', ms=1,markeredgewidth=0) # idx = sgn==0 # axs[1].plot(dt2float(dts_orb[idx], dt0), np.zeros_like(data_orb[idx, 3]), '.', ms=1, markeredgewidth=0) # idx = sgn<0 # axs[1].plot(dt2float(dts_orb[idx], dt0), -np.log10(-data_orb[idx, 3]) - 6, '.', ms=1, markeredgewidth=0) if max(np.abs(data_orb[:, 3])) > 0.002: yt_list = np.array([-0.005,-0.004,-0.003,-0.002,-0.001,0,0.001,0.002,0.003,0.004,0.005]) ytl_list = np.array(['-10','-1','-0.1', '-1E-2', '-1E-3', '0', '1E-3', '1E-2', '0.1', '1','10']) idx = data_orb[:, 3] > 0.001 if np.any(idx): data_orb[idx, 3] = 0.001 * (np.log10(data_orb[idx, 3]) + 4) idx = data_orb[:, 3] < -0.001 if np.any(idx): data_orb[idx, 3] = -0.001 * (np.log10(-data_orb[idx, 3]) + 4) axs[1].plot(dt2float(dts_orb, dt0)/3600, data_orb[:, 3], '.', ms=1, markeredgewidth=0, zorder=2) yl = axs[1].get_ylim() idx = np.logical_and(yt_list>=yl[0],yt_list