├── .git_archival.txt ├── .gitattributes ├── .github └── workflows │ └── python-publish.yml ├── .gitignore ├── IMProToo ├── __init__.py ├── core.py └── tools.py ├── LICENSE ├── README.md ├── examples ├── batch_convert_rawData.py └── batch_makeQuicklooks.py ├── pyproject.toml └── setup.py /.git_archival.txt: -------------------------------------------------------------------------------- 1 | ref-names: HEAD -> master, tag: 0.107 2 | -------------------------------------------------------------------------------- /.gitattributes: -------------------------------------------------------------------------------- 1 | .git_archival.txt export-subst 2 | -------------------------------------------------------------------------------- /.github/workflows/python-publish.yml: -------------------------------------------------------------------------------- 1 | # This workflow will upload a Python Package using Twine when a release is created 2 | # For more information see: https://help.github.com/en/actions/language-and-framework-guides/using-python-with-github-actions#publishing-to-package-registries 3 | 4 | # This workflow uses actions that are not certified by GitHub. 5 | # They are provided by a third-party and are governed by 6 | # separate terms of service, privacy policy, and support 7 | # documentation. 8 | 9 | name: Upload Python Package 10 | 11 | on: 12 | release: 13 | types: [published] 14 | 15 | permissions: 16 | contents: read 17 | 18 | jobs: 19 | deploy: 20 | 21 | runs-on: ubuntu-latest 22 | 23 | steps: 24 | - uses: actions/checkout@v3 25 | - name: Set up Python 26 | uses: actions/setup-python@v3 27 | with: 28 | python-version: '3.x' 29 | - name: Install dependencies 30 | run: | 31 | python -m pip install --upgrade pip 32 | pip install build 33 | - name: Build package 34 | run: python -m build 35 | - name: Publish package 36 | uses: pypa/gh-action-pypi-publish@27b31702a0e7fc50959f5ad993c78deac1bdfc29 37 | with: 38 | user: __token__ 39 | password: ${{ secrets.PYPI_API_TOKEN }} 40 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | *~ 2 | *.pyc 3 | *.backup 4 | *.png 5 | *.nc 6 | *.kate-swp 7 | .nfs* 8 | *FRIEDHOF.py 9 | IMProToo.egg-info 10 | build 11 | dist 12 | .DS_Store 13 | .eggs -------------------------------------------------------------------------------- /IMProToo/__init__.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | IMProToo 4 | Improved MRR Processing Tool 5 | """ 6 | from __future__ import division 7 | 8 | 9 | from .core import * 10 | from .core import __version__ 11 | from .tools import * 12 | -------------------------------------------------------------------------------- /IMProToo/core.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | ''' 3 | IMProToo 4 | Improved MRR Processing Tool 5 | 6 | Python toolkit to read, write and process MRR Data. Raw Data, Average and 7 | Instantaneous Data are supported. 8 | 9 | Copyright (C) 2011-2021 Maximilian Maahn, U Leipzig 10 | maximilian.maahn_AT_uni-leipzig.de 11 | https://github.com/maahn/IMProToo 12 | 13 | 14 | This program is free software: you can redistribute it and/or modify 15 | it under the terms of the GNU General Public License as published by 16 | the Free Software Foundation, either version 3 of the License, or 17 | any later version. 18 | 19 | This program is distributed in the hope that it will be useful, 20 | but WITHOUT ANY WARRANTY; without even the implied warranty of 21 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 22 | GNU General Public License for more details. 23 | 24 | You should have received a copy of the GNU General Public License 25 | along with this program. If not, see . 26 | 27 | ''' 28 | 29 | from __future__ import division 30 | from __future__ import print_function 31 | 32 | import numpy as np 33 | import gzip 34 | import re 35 | import datetime 36 | import calendar 37 | import time 38 | import glob 39 | from copy import deepcopy 40 | import warnings 41 | import sys 42 | import os 43 | import codecs 44 | 45 | from .tools import unix2date, date2unix, limitMaInidces, quantile 46 | from .tools import oneD2twoD, _get_netCDF_module 47 | 48 | try: 49 | from importlib.metadata import version, PackageNotFoundError 50 | py3 = True 51 | except ImportError: 52 | from pkg_resources import get_distribution, DistributionNotFound 53 | py3 = False 54 | 55 | if py3: 56 | try: 57 | __version__ = version("IMProToo") 58 | except PackageNotFoundError: 59 | # package is not installed 60 | pass 61 | else: 62 | try: 63 | __version__ = get_distribution("IMProToo").version 64 | except DistributionNotFound: 65 | # package is not installed 66 | pass 67 | 68 | 69 | class MrrZe: 70 | ''' 71 | class to calculate the 'real' MRR Ze from MRR raw data. The spectra are 72 | noise corrected and dealiased. see batch_convert_rawData.py for 73 | exemplary use 74 | ''' 75 | warnings.filterwarnings('always', '.*', UserWarning,) 76 | 77 | def __init__(self, rawData): 78 | 79 | if rawData.mrrRawCC == 0: 80 | print('WARNING: MRR calibration constant set to 0!') 81 | 82 | self.co = dict() 83 | 84 | # verbosity 85 | self.co["debug"] = 0 86 | 87 | # ######MRR Settings####### 88 | 89 | # mrr frequency, MRR after 2011 (or upgraded) use 24.23e9 90 | self.co["mrrFrequency"] = 24.15e9 # in Hz, 91 | # wavelength in m 92 | self.co["lamb"] = 299792458. / self.co["mrrFrequency"] 93 | # mrr calibration constant 94 | self.co["mrrCalibConst"] = rawData.mrrRawCC 95 | 96 | # do not change these values, unless you have a non standard MRR! 97 | # nyquist range minimum 98 | self.co["nyqVmin"] = 0 99 | # nyquist range maximum 100 | self.co["nyqVmax"] = 11.9301147 101 | # nyquist delta 102 | self.co["nyqVdelta"] = 0.1893669 103 | # list with nyquist velocities 104 | self.co["nyqVel"] = np.arange( 105 | self.co["nyqVmin"], 106 | self.co["nyqVmax"]+0.0001, 107 | self.co["nyqVdelta"] 108 | ) 109 | # spectral resolution 110 | self.co["widthSpectrum"] = 64 111 | # min height to be processed 112 | self.co["minH"] = 1 # start counting at 0 113 | # max height to be processed 114 | self.co["maxH"] = 31 # start counting at 0 115 | # no of processed heights 116 | self.co["noH"] = self.co["maxH"]+1 - self.co["minH"] 117 | # shape of spectrum for one time step 118 | self.co["specShape"] = (self.co["noH"], self.co["widthSpectrum"],) 119 | # input data MRR averaging time 120 | self.co["averagingTime"] = 10 121 | # |K**2| dielectric constant 122 | self.co["K2"] = 0.92 123 | 124 | # ######options for finding peaks####### 125 | 126 | # minimum width of a peak. if set to 4 instead of 3, more clutter is 127 | # removed, but sensitivity becomes worse. 128 | self.co["findPeak_minPeakWidth"] = 3 129 | # minimum standard deviation of of spectrum for peak 130 | # self.co["findPeak_minStdPerS"]/np.sqrt(self.co["averagingTime"]) 131 | self.co["findPeak_minStdPerS"] = 0.6 132 | # minimum difference of Doppler velocity from self.co["nyqVmax"]/2 for 133 | # peak 134 | self.co["findPeak_minWdiff"] = 0.2 135 | 136 | # ######options for getting peaks####### 137 | 138 | # method for finding peaks in the spectrum, either based on Hildebrand 139 | # and Sekhon, 1974 [hilde] or on the method of descending average 140 | # [descAve]. [hilde] is recommended 141 | self.co["getPeak_method"] = "hilde" # ["hilde","descAve"] 142 | # sometimes the first method fails and almost the whole spectrum is 143 | # found as a peak, so apply a second check based on the remaining 144 | # method from [hilde,descAve] 145 | self.co["getPeak_makeDoubleCheck"] = True 146 | # apply double check to peaks wider than xx*noOfSpec 147 | # wider real peaks can actually happen! These are usually bimodal 148 | # peaks, descending average method fails for them, thus the spectrum 149 | self.co["getPeak_makeDoubleCheck_minPeakWidth"] = 0.9 150 | # hilde method uses an extra buffer to avoid to large peaks. loop stops 151 | # first at spectrum >= self.co["getPeak_hildeExtraLimit"]*hilde_limit, 152 | # only one more bin is added if above self.co[ 153 | # "getPeak_hildeExtraLimit"]. More bins above self.co[ 154 | # "getPeak_hildeExtraLimit"] are ignored 155 | self.co["getPeak_hildeExtraLimit"] = 1.2 # times hildebrand limit 156 | # options for descAve method 157 | # window to calculate the average, if too large, it might go into the 158 | # next peak! if too small, it might not catch bimodal distributions 159 | self.co["getPeak_descAveCheckWidth"] = 10 160 | # descAve stops not before mean is smaller than self.co[ 161 | # "getPeak_descAveMinMeanWeight"] of the mean of the self.co[ 162 | # "getPeak_descAveCheckWidth"] smallest bins. make very big to turn off 163 | self.co["getPeak_descAveMinMeanWeight"] = 4 164 | 165 | # ####options for confirming peaks ########## 166 | # check whether time/height neighbors of a peak contain a peak as well 167 | self.co["confirmPeak_5x5boxCoherenceTest"] = True 168 | # maximum of other peaks must be within X Doppler-bins of the maximum 169 | # of the tested peak 170 | self.co["confirmPeak_5x5boxCoherenceTest_maxBinDistane"] = 10 171 | 172 | # ######general options####### 173 | 174 | # process only peaks in self.co["spectrumBorderMin"][height]: 175 | # self.co["spectrumBorderMax"][height] 176 | self.co["spectrumBorderMin"] = [5, 4, 3, 2, 2, 2, 2, 2, 2, 2, 177 | 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 178 | 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 5] 179 | self.co["spectrumBorderMax"] = [60, 61, 62, 63, 63, 63, 63, 63, 63, 180 | 63, 63, 63, 63, 63, 63, 63, 63, 63, 181 | 63, 63, 63, 63, 63, 63, 63, 63, 63, 182 | 63, 62, 61, 63] 183 | # interpolate spectrum in between 184 | self.co["interpolateSpectrum"] = True 185 | # extend also peaks to interpolated part 186 | self.co["fillInterpolatedPeakGaps"] = True 187 | # mask everything in these heights, since they are disturbed 188 | self.co["completelyMaskedHeights"] = [0, 1, 30] 189 | # first height with trustful peaks. Setting important for dealiasing 190 | # to avoid folding from completelyMaskedHeights into the first used# 191 | # height. 192 | self.co["firstUsedHeight"] = 2 193 | 194 | # ######dealiasing options####### 195 | 196 | # dealiase spectrum yes/no 197 | self.co["dealiaseSpectrum"] = True 198 | # save also non dealiased eta, Ze, W, Znoise specWidth, 199 | # peakVelLeftBorder, peakVelRightBorder 200 | self.co["dealiaseSpectrum_saveAlsoNonDealiased"] = True 201 | # make sure there is only one peak per height after dealiasing! 202 | self.co["dealiaseSpectrum_maxOnePeakPerHeight"] = True 203 | # dealiasing is based on comparison with reference velocity calculated 204 | # from reflectivity. v = A*Ze**B 205 | # Atlas et al. 1973 206 | self.co['dealiaseSpectrum_Ze-vRelationSnowA'] = 0.817 207 | # Atlas et al. 1973 208 | self.co['dealiaseSpectrum_Ze-vRelationSnowB'] = 0.063 209 | # Atlas et al. 1973 210 | self.co['dealiaseSpectrum_Ze-vRelationRainA'] = 2.6 211 | # Atlas et al. 1973 212 | self.co['dealiaseSpectrum_Ze-vRelationRainB'] = 0.107 213 | # trusted peak needs minimal Ze 214 | self.co['dealiaseSpectrum_trustedPeakminZeQuantile'] = 0.1 215 | # if you have interference, you don't want to start you dealiasing 216 | # procedure there 217 | self.co["dealiaseSpectrum_heightsWithInterference"] = [] 218 | # test coherence of dealiasesd velocity spectrum in time dimension. 219 | # try to refold short jumps. 220 | self.co["dealiaseSpectrum_makeCoherenceTest"] = True 221 | # if the height averaged velocity between to timesteps is larger than 222 | # this, it is tried to refold the spectrum 223 | self.co["dealiaseSpectrum_makeCoherenceTest_velocityThreshold"] = 8 224 | # if there are after coherence test still velocity jumps, mask 225 | # +/- timesteps 226 | self.co["dealiaseSpectrum_makeCoherenceTest_maskRadius"] = 10 227 | 228 | # ######netCDF options####### 229 | 230 | self.co["ncCreator"] = "IMProToo user" 231 | self.co["ncDescription"] = "MRR data processed with IMProToo" 232 | self.co["ncLocation"] = "" 233 | self.co["ncInstitution"] = "" 234 | 235 | # ######end of settings####### 236 | 237 | # special option to top processing in the middel and return results 238 | self.debugStopper = 0 239 | 240 | self.missingNumber = -9999. 241 | 242 | self.header = rawData.header 243 | self.time = rawData.mrrRawTime 244 | self.timezone = rawData.timezone 245 | self.H = rawData.mrrRawHeight[:, self.co["minH"]:self.co["maxH"]+1] 246 | self.TF = rawData.mrrRawTF[:, self.co["minH"]:self.co["maxH"]+1] 247 | self.rawSpectrum = rawData.mrrRawSpectrum[ 248 | :, self.co["minH"]:self.co["maxH"]+1 249 | ] 250 | self.noSpecPerTimestep = rawData.mrrRawNoSpec 251 | 252 | self.no_h = np.shape(self.H)[1] 253 | self.no_t = np.shape(self.time)[0] 254 | self.no_v = self.co["widthSpectrum"] 255 | 256 | self._shape2D = np.shape(self.H) 257 | self._shape3D = np.shape(self.rawSpectrum) 258 | 259 | self.qual = dict() 260 | 261 | return 262 | 263 | def averageSpectra(self, averagingTime): 264 | """ 265 | average spectra and other data. If averaging time is e.g. 60, the 266 | data with the timestamp 14:00 contains all measurements from 13:59:00 267 | to 13:59:59 (like MRR standard software) 268 | """ 269 | 270 | rawSpectra = self.rawSpectrum 271 | rawTimestamps = self.time 272 | heights = self.H 273 | TFs = self.TF 274 | noSpec = self.noSpecPerTimestep 275 | 276 | # find first entry 277 | startSeconds = unix2date(rawTimestamps[0]).second 278 | start = rawTimestamps[0] + averagingTime - startSeconds 279 | # find last minute 280 | endSeconds = unix2date(rawTimestamps[-1]).second 281 | end = rawTimestamps[-1] + 60 - endSeconds 282 | # make new time vector and 283 | rawTimestampsAve = np.ma.arange( 284 | start, end+averagingTime, averagingTime, dtype="int") 285 | 286 | # create new arrays 287 | newSpectraShape = list(rawSpectra.shape) 288 | newSpectraShape[0] = rawTimestampsAve.shape[0] 289 | rawSpectraAve = np.ma.zeros(newSpectraShape) * np.nan 290 | 291 | newTFsShape = list(TFs.shape) 292 | newTFsShape[0] = rawTimestampsAve.shape[0] 293 | TFsAve = np.ma.zeros(newTFsShape) * np.nan 294 | 295 | newHeightsShape = list(heights.shape) 296 | newHeightsShape[0] = rawTimestampsAve.shape[0] 297 | heightsAve = np.ma.zeros(newHeightsShape) * np.nan 298 | 299 | newNoSpecShape = (rawTimestampsAve.shape[0],) 300 | noSpecAve = np.ma.zeros(newNoSpecShape, dtype=int) 301 | 302 | # ugly loop trough new, averaged time vector! 303 | for t, timestamp in enumerate(rawTimestampsAve): 304 | # boolean array containing the wanted entries 305 | booleanTimes = (rawTimestamps < timestamp) * \ 306 | (rawTimestamps >= timestamp-averagingTime) 307 | aveLength = np.sum(booleanTimes) 308 | # proceed only if entries were found 309 | if aveLength != 0: 310 | # and if TF and heights are NOT changing and if heights are 311 | # not zero!! 312 | if ( 313 | np.all(TFs[booleanTimes] == TFs[booleanTimes][0]) and 314 | np.all(heights[booleanTimes] == heights[booleanTimes][0]) 315 | and np.logical_not(np.all(heights[booleanTimes] == 0)) 316 | ): 317 | # averaging: 318 | rawSpectraAve[t] = np.ma.average( 319 | rawSpectra[booleanTimes], axis=0) 320 | heightsAve[t] = np.ma.average( 321 | heights[booleanTimes], axis=0) 322 | TFsAve[t] = np.ma.average(TFs[booleanTimes], axis=0) 323 | noSpecAve[t] = np.ma.sum(noSpec[booleanTimes]) 324 | else: 325 | print("Skipping data due to changed MRR configuration!") 326 | else: 327 | rawSpectraAve[t] = np.nan 328 | heightsAve[t] = np.nan 329 | TFsAve[t] = np.nan 330 | noSpecAve[t] = 0 331 | print("No Data at " + str(unix2date(timestamp))) 332 | 333 | self.rawSpectrum = rawSpectraAve 334 | self.time = rawTimestampsAve 335 | self.H = heightsAve 336 | self.TF = TFsAve 337 | self.noSpecPerTimestep = noSpecAve.filled(0) 338 | 339 | self.no_t = np.shape(self.time)[0] 340 | self._shape2D = np.shape(self.H) 341 | self._shape3D = np.shape(self.rawSpectrum) 342 | 343 | self.co["averagingTime"] = averagingTime 344 | return 345 | 346 | def getSub(self, start, stop): 347 | """ 348 | cut out some spectra (for debugging) 349 | 350 | start,stop (int): border indices 351 | """ 352 | if stop == -1: 353 | stop = self._shape2D[0] 354 | 355 | self.rawSpectrum = self.rawSpectrum[start:stop] 356 | self.time = self.time[start:stop] 357 | self.H = self.H[start:stop] 358 | self.TF = self.TF[start:stop] 359 | self.noSpecPerTimestep = self.noSpecPerTimestep[start:stop] 360 | 361 | if len(self.noSpecPerTimestep) == 0: 362 | raise ValueError('getSub: No data lef!') 363 | 364 | self.no_t = np.shape(self.time)[0] 365 | self._shape2D = np.shape(self.H) 366 | self._shape3D = np.shape(self.rawSpectrum) 367 | 368 | return 369 | 370 | def rawToSnow(self): 371 | ''' 372 | core function for calculating Ze and other moments. Settings have 373 | to be set before 374 | ''' 375 | 376 | if self.co["mrrCalibConst"] == 0: 377 | raise IOError('ERROR: MRR calibration constant set to 0!') 378 | 379 | self.untouchedRawSpectrum = deepcopy(self.rawSpectrum) 380 | 381 | self.specVel = self.co["nyqVel"] 382 | self.specVel3D = np.zeros(self._shape3D) 383 | self.specVel3D[:] = self.specVel 384 | 385 | self.specIndex = np.arange(self.no_v) 386 | 387 | self._specBorderMask = np.ones(self.co["specShape"], dtype=bool) 388 | for h in range(self.co["noH"]): 389 | self._specBorderMask[h, self.co["spectrumBorderMin"] 390 | [h]:self.co["spectrumBorderMax"][h]] = False 391 | self._specBorderMask3D = np.ones(self._shape3D, dtype=bool) 392 | self._specBorderMask3D[:] = self._specBorderMask 393 | 394 | # but we have to apply the TF before we start anything: 395 | TF3D = np.zeros(self._shape3D) 396 | TF3D.T[:] = self.TF.T 397 | self.rawSpectrum = np.ma.masked_array( 398 | self.rawSpectrum.data / TF3D, self.rawSpectrum.mask) 399 | 400 | # 1)missing spectra 401 | missingMask = np.any(np.isnan(self.rawSpectrum.data), axis=-1) 402 | self.qual["incompleteSpectrum"] = missingMask 403 | # 2) Wdiff 404 | WdiffMask, self.wdiffs = self._testMeanW(self.rawSpectrum) 405 | 406 | # 3) std 407 | stdMask, self.stds = self._testStd(self.rawSpectrum) 408 | 409 | # join the results 410 | noiseMask = missingMask+(stdMask*WdiffMask) 411 | self.qual["spectrumVarianceTooLowForPeak"] = stdMask * \ 412 | WdiffMask # 2) no signal detected by variance test 413 | 414 | # make 3D noise Mask 415 | noiseMaskLarge = np.zeros(self._shape3D, dtype=bool).T 416 | noiseMaskLarge[:] = noiseMask.T 417 | noiseMaskLarge = noiseMaskLarge.T 418 | 419 | # we don't need the mask right now since missingMask contains all 420 | # mask entries 421 | self.rawSpectrum = self.rawSpectrum.data 422 | 423 | if self.debugStopper == 1: 424 | self.rawSpectrum = np.ma.masked_array( 425 | self.rawSpectrum, noiseMaskLarge) 426 | return 427 | # find the peak 428 | peakMask = np.ones(self._shape3D, dtype=bool) 429 | self.qual["usedSecondPeakAlgorithmDueToWidePeak"] = np.zeros( 430 | self._shape2D, dtype=bool) 431 | self.qual["peakTooThinn"] = np.zeros(self._shape2D, dtype=bool) 432 | for h in range(0, self.co["noH"]): 433 | # check whether there is anything to do 434 | if np.any(np.logical_not(noiseMaskLarge[:, h])): 435 | # get the peak 436 | specMins = self.co["spectrumBorderMin"][h] 437 | specMaxs = self.co["spectrumBorderMax"][h] 438 | res = self._getPeak( 439 | self.rawSpectrum[:, h, specMins:specMaxs][ 440 | ~noiseMask[:, h]], 441 | self.noSpecPerTimestep[~noiseMask[:, h]], 442 | h 443 | ) 444 | ( 445 | peakMask[:, h, specMins:specMaxs][~noiseMask[:, h]], 446 | self.qual["peakTooThinn"][:, h][~noiseMask[:, h]], 447 | self.qual["usedSecondPeakAlgorithmDueToWidePeak"][:, h][ 448 | ~noiseMask[:, h]] 449 | ) = res 450 | # apply results 451 | self.rawSpectrum = np.ma.masked_array(self.rawSpectrum, peakMask) 452 | 453 | # what is the noise, but _without_ the borders, we want in noise 3D 454 | # also 455 | noise = np.ma.masked_array(self.rawSpectrum.data, (np.logical_not( 456 | self.rawSpectrum.mask)+self._specBorderMask3D)) 457 | self.specNoise = np.ma.average(noise, axis=-1).filled(0) 458 | 459 | if self.debugStopper == 2: 460 | return 461 | 462 | if self.co["confirmPeak_5x5boxCoherenceTest"]: 463 | coherCheckNoiseMask = self._cleanUpNoiseMask(self.rawSpectrum) 464 | coherCheckNoiseMask3D = np.zeros(self._shape3D, dtype=bool) 465 | coherCheckNoiseMask3D.T[:] = coherCheckNoiseMask.T 466 | else: 467 | coherCheckNoiseMask = np.zeros(self._shape2D, dtype=bool) 468 | coherCheckNoiseMask3D = np.zeros(self._shape3D, dtype=bool) 469 | self.qual["peakRemovedByCoherenceTest"] = coherCheckNoiseMask * \ 470 | (~np.all(self.rawSpectrum.mask, axis=-1)) 471 | 472 | self.rawSpectrum.mask = self.rawSpectrum.mask + coherCheckNoiseMask3D 473 | if self.debugStopper == 3: 474 | return 475 | 476 | # since we have removed more noisy spectra we have to calculate the 477 | # noise again 478 | noise = np.ma.masked_array(self.rawSpectrum.data, (np.logical_not( 479 | self.rawSpectrum.mask)+self._specBorderMask3D)) 480 | self.specNoise = np.ma.average(noise, axis=-1).filled(0) 481 | self.specNoise_std = np.ma.std(noise, axis=-1).filled(0) 482 | self.specNoise3D = np.zeros_like(noise).filled(0) 483 | self.specNoise3D.T[:] = self.specNoise.T 484 | 485 | # remove the noise 486 | self.rawSpectrum = np.ma.masked_array( 487 | self.rawSpectrum.data - self.specNoise3D, self.rawSpectrum.mask) 488 | 489 | if self.co["interpolateSpectrum"]: 490 | # interpolate spectrum 491 | intSpectrum = deepcopy(self.rawSpectrum.data) 492 | ix = np.arange(len(self.rawSpectrum.ravel())) 493 | intSpectrum[self._specBorderMask3D] = np.interp( 494 | ix[self._specBorderMask3D.ravel()], 495 | ix[~self._specBorderMask3D.ravel()], 496 | self.rawSpectrum[~self._specBorderMask3D] 497 | ) 498 | 499 | self.rawSpectrum = np.ma.masked_array( 500 | intSpectrum, self.rawSpectrum.mask) 501 | self.qual["interpolatedSpectrum"] = np.ones( 502 | self._shape2D, dtype=bool) 503 | if self.debugStopper == 5: 504 | return 505 | else: 506 | self.qual["interpolatedSpectrum"] = np.zeros( 507 | self._shape2D, dtype=bool) 508 | 509 | if self.co["fillInterpolatedPeakGaps"]: 510 | ( 511 | self.rawSpectrum.mask, 512 | self.qual["filledInterpolatedPeakGaps"] 513 | ) = self._fillInterpolatedPeakGaps(self.rawSpectrum.mask) 514 | else: 515 | self.qual["filledInterpolatedPeakGaps"] = np.zeros( 516 | self._shape2D, dtype=bool) 517 | 518 | # calculate the (not dealiased) SNR 519 | self.SNR = (10*np.ma.log10(np.ma.sum(self.rawSpectrum, axis=-1) / 520 | (self.specNoise*self.co["widthSpectrum"]))).filled(-9999) 521 | 522 | if self.co["dealiaseSpectrum"] == True: 523 | 524 | if self.co["dealiaseSpectrum_saveAlsoNonDealiased"] == True: 525 | self.eta_noDA, self.Ze_noDA, self.W_noDA, self.etaNoiseAve_noDA_TBD, self.etaNoiseStd_noDA_TBD, self.specWidth_noDA, self.skewness_noDA, self.kurtosis_noDA, self.peakVelLeftBorder_noDA, self.peakVelRightBorder_noDA, self.leftSlope_noDA, self.rightSlope_noDA = self._calcEtaZeW( 526 | self.rawSpectrum, self.H, self.specVel3D, self.specNoise, self.specNoise_std) 527 | self.qual_noDA = deepcopy(self.qual) 528 | 529 | # can be deleted, is identical to self.etaNoise, because noise is not dealiased. 530 | del self.etaNoiseAve_noDA_TBD, self.etaNoiseStd_noDA_TBD 531 | 532 | self.rawSpectrum = self._dealiaseSpectrum(self.rawSpectrum) 533 | # since we don't want that spectrum from teh disturbed 1st range gate are folded into the secod on, peaks in the second one might be incomplete. try to make an entry in the quality mask. 534 | self.qual["peakMightBeIncomplete"] = np.zeros( 535 | self._shape2D, dtype=bool) 536 | self.qual["peakMightBeIncomplete"][:, self.co["firstUsedHeight"]][self.rawSpectrum.mask[:, self.co["firstUsedHeight"], 537 | self.co["widthSpectrum"]+self.co["spectrumBorderMin"][self.co["firstUsedHeight"]]] == False] = True 538 | 539 | # no dealiasing 540 | else: 541 | pass 542 | self.eta, self.Ze, self.W, self.etaNoiseAve, self.etaNoiseStd, self.specWidth, self.skewness, self.kurtosis, self.peakVelLeftBorder, self.peakVelRightBorder, self.leftSlope, self.rightSlope = self._calcEtaZeW( 543 | self.rawSpectrum, self.H, self.specVel3D, self.specNoise, self.specNoise_std) 544 | # make bin mask out of quality information 545 | self.qualityBin, self.qualityDesc = self.getQualityBinArray(self.qual) 546 | return 547 | 548 | def _testMeanW(self, rawSpectrum): 549 | ''' 550 | checks whether spectrum mean velocity is unequal to mean velocity (6m s^-1) 551 | ''' 552 | 553 | mask = deepcopy(rawSpectrum.mask) + self._specBorderMask3D 554 | spec = np.ma.masked_array(rawSpectrum.data, mask) 555 | velocity = np.ma.masked_array(self.specVel3D, self._specBorderMask3D) 556 | 557 | Wdiff = np.absolute(np.ma.average( 558 | velocity, axis=-1)-(np.ma.sum(velocity*spec, axis=-1)/np.sum(spec, axis=-1))) 559 | 560 | noiseMask = Wdiff.filled(0) < self.co["findPeak_minWdiff"] 561 | 562 | return noiseMask, Wdiff.filled(0) 563 | 564 | def _testStd(self, rawSpectrum): 565 | ''' 566 | checks whether spectrum passes variance limit 567 | ''' 568 | 569 | mask = deepcopy(rawSpectrum.mask) + self._specBorderMask3D 570 | spec = np.ma.masked_array(rawSpectrum.data, mask) 571 | 572 | std = (np.ma.std(spec, axis=-1)/np.ma.mean(spec, axis=-1)) 573 | 574 | # the 5.7 is because we have typically 5.7 spectra per second and this 575 | # quantitiy was defined with self.co["averagingTime"] instead of 576 | # self.noSpecPerTimestep before 577 | maxStd = self.co["findPeak_minStdPerS"] / \ 578 | np.sqrt(self.noSpecPerTimestep/5.7) 579 | 580 | return std.filled(0) < maxStd[:, np.newaxis], std.filled(0) 581 | 582 | def _findAddtionalPeaks(self, rawSpectrum): 583 | ''' 584 | This functio tries to find addtional peaks in the spectrum 585 | 586 | disabled since it gives too many false positives... 587 | 588 | ''' 589 | qual = np.zeros(self._shape2D, dtype=bool) 590 | 591 | # invert mask 592 | rawSpectrum = np.ma.masked_array(rawSpectrum.data, ~rawSpectrum.mask) 593 | self.co["findAddtionalPeaksThreshold"] = 15 594 | for tt in range(self.no_t): 595 | for hh in range(self.no_h): 596 | if hh in self.co["completelyMaskedHeights"]: 597 | continue 598 | greaterZero = 0 599 | for ii in range(self.co["spectrumBorderMin"][hh], self.co["spectrumBorderMax"][hh]): 600 | if greaterZero >= self.co["findAddtionalPeaksThreshold"]: 601 | qual[tt, hh] = True 602 | if rawSpectrum.mask[tt, hh, ii] == True or rawSpectrum.data[tt, hh, ii] <= 0: 603 | greaterZero = 0 604 | continue 605 | else: 606 | greaterZero += 1 607 | 608 | return qual 609 | 610 | def _cleanUpNoiseMask(self, spectrum): 611 | """ 612 | 11 of 5x5 points in height/time space must have a signal to be valid! 613 | 614 | @parameter spectrum (numpy masked float): spectrum + noiseMask to be applied to teh data 615 | @return - newMask (numpy boolean):numpy boolean noiseMask 616 | """ 617 | 618 | noiseMask = np.all(spectrum.mask, axis=-1) 619 | newMask = deepcopy(noiseMask) 620 | # make it bigger to cover edges for 5x5 test, 2 pixel border 621 | maxs = np.ma.masked_all((self.no_t+4, self.no_h+1)) 622 | maxs[2:-2, 2:-2] = np.ma.masked_array( 623 | np.ma.argmax(spectrum, axis=-1), noiseMask)[:, 2:30] 624 | 625 | highLimit = 11 626 | lowLimit = 9 627 | lowestLimit = 8 628 | 629 | hOffset = self.co["minH"] # since we don't start at zero height 630 | 631 | # loop through all points... 632 | for t in np.arange(self.no_t): 633 | # is it real signal? only if at least 11 of 25 neigbours have signal as well! 634 | # for h in np.arange(4,28): 635 | for h in np.arange(2, 30): 636 | if noiseMask[t, h] == False: 637 | tSM = t+2 # for subMaxs t needs to be 2 larger due to 2 pixel border! for h not neccesary, 2 pixel border at botztom already there 638 | subMaxs = maxs[tSM-2:tSM+3, h-2:h+3] 639 | thisMaxsDiff = 32-maxs[tSM, h] 640 | subMaxsNormed = limitMaInidces(subMaxs + thisMaxsDiff, 64) 641 | diffs = np.abs(subMaxsNormed - 32) 642 | 643 | if t in [0, self.no_t-1] or h in [2, 29]: 644 | limit = lowestLimit 645 | elif t in [1, self.no_t-2] or h in [3, 28]: 646 | limit = lowLimit 647 | else: 648 | limit = highLimit 649 | 650 | if np.ma.sum(diffs <= self.co["confirmPeak_5x5boxCoherenceTest_maxBinDistane"]) < limit: 651 | newMask[t, h] = True 652 | 653 | # kick out heights #0,1,30 654 | newMask[:, self.co["completelyMaskedHeights"]] = True 655 | 656 | self.qual["spectrumNotProcessed"] = np.zeros(self._shape2D, dtype=bool) 657 | self.qual["spectrumNotProcessed"][:, 658 | self.co["completelyMaskedHeights"]] = True 659 | 660 | return newMask 661 | 662 | def _getPeak(self, spectrum, noSpecs, h): 663 | """ 664 | get the peak of the spectrum, first getPeakHildebrand is used, if the spectrum is wider than 10 and makeDoubleCheck = True, also getPeakDescendingAve is used and the smaller one is taken! 665 | 666 | @parameter spectrum (numpy float64): (averaged, dealiased) raw data from MRR Raw data 667 | @parameter noSpecs (numpy float64):number of single spectras which belong to each average spectrum, usually 58* No of averaged spectra 668 | @paramter h, (int): height, for easier debugging 669 | @return - spectrum (numpy float64): masked(!) spectrum 670 | @return - qualiy (dict with array bool) 671 | """ 672 | t = time.time() 673 | quality = dict() 674 | 675 | specLength = np.shape(spectrum)[-1] 676 | # get maxima of reduced spectra 677 | iMax = np.argmax(spectrum, axis=-1) 678 | iMaxFlat = np.ravel(iMax) 679 | # arrays don't work, so make them flat 680 | spectrumFlat = np.reshape(spectrum, (-1, specLength)) 681 | 682 | if self.co["getPeak_method"] == "hilde": 683 | # get peak using Hildebrands method 684 | firstPeakMask = self._getPeakHildebrand( 685 | spectrumFlat, iMaxFlat, noSpecs, h) 686 | elif self.co["getPeak_method"] == "descAve": 687 | # get peak using Hildebrands method 688 | firstPeakMask = self._getPeakDescendingAve(spectrumFlat, iMaxFlat) 689 | else: 690 | raise ValueError("Unknown doubleCheckPreference: " + 691 | self.co["getPeak_method"]) 692 | 693 | peakMask = deepcopy(firstPeakMask) 694 | # look for wide peak and make a second check 695 | if self.co["getPeak_makeDoubleCheck"]: 696 | doubleCheck = np.sum(np.logical_not( 697 | firstPeakMask), axis=-1) > specLength * self.co["getPeak_makeDoubleCheck_minPeakWidth"] 698 | quality["veryWidePeakeUsedSecondPeakAlgorithm"] = doubleCheck 699 | if np.any(doubleCheck == True): 700 | #secondPeakMVeryWidePeakeUask = getPeakDescendingAve(spectrumFlat,iMaxFlat) 701 | secondPeakMask = np.zeros(np.shape(spectrumFlat), dtype=bool) 702 | if self.co["getPeak_method"] == "hilde": 703 | # get peak using desc Average method 704 | secondPeakMask[doubleCheck] = self._getPeakDescendingAve( 705 | spectrumFlat[doubleCheck], iMaxFlat[doubleCheck]) 706 | elif self.co["getPeak_method"] == "descAve": 707 | # get peak using Hildebrands method 708 | secondPeakMask[doubleCheck] = self._getPeakHildebrand( 709 | spectrumFlat[doubleCheck], iMaxFlat[doubleCheck], noSpecs[doubleCheck], h) 710 | peakMask[doubleCheck] = firstPeakMask[doubleCheck] + \ 711 | secondPeakMask[doubleCheck] 712 | else: 713 | quality["veryWidePeakeUsedSecondPeakAlgorithm"] = np.zeros( 714 | specLength, dtype=bool) 715 | # only peaks which are at least 3 bins wide, remove the others 716 | tooThinn = np.sum(np.logical_not(peakMask), axis=- 717 | 1) < self.co["findPeak_minPeakWidth"] 718 | peakMask[tooThinn] = True 719 | quality["peakTooThinn"] = tooThinn * (np.sum(~peakMask, axis=-1) != 0) 720 | 721 | if self.co["debug"] > 0: 722 | print("runtime", time.time()-t, "s") 723 | # spectrum 724 | return np.reshape(peakMask, np.shape(spectrum)), quality["peakTooThinn"], quality["veryWidePeakeUsedSecondPeakAlgorithm"] 725 | 726 | # get the border indices belonging to the hildebrand limit 727 | 728 | def _getPeakHildebrand(self, dataFlat, iMax, noSpecs, h): 729 | """ 730 | get the peak of the spectrum using Hildebrand algorithm. Note that this routine works 731 | 'the other way around' than e.g. pamtra's or pyart's Hildebrand routine. I.e. we start 732 | with the full spectrum and remove the largest bins instead of starting with the 733 | smallest values and adding larger ones. This is more robust for the MRR. also 734 | getPeak_hildeExtraLimit works better for MRR than teh traditional threshold definition from HS74. 735 | 736 | @parameter dataFlat (numpy float64): flat spectrum from MRR Raw data 737 | @parameter iMax (numpy float64): vector containing indices of the maxima 738 | @parameter Nspec (numpy float64): number of spectra of each averaged spectrum 739 | 740 | @return - iPeakMin, iMax (int float64): edges of each spectrum 741 | """ 742 | 743 | # first get the limit reflectivity 744 | limits = self._noiseHildebrand(dataFlat, noSpecs, h) 745 | maskHildebrand = np.ones(np.shape(dataFlat), dtype=bool) 746 | iPeakMax = deepcopy(iMax) 747 | iPeakMin = deepcopy(iMax) 748 | 749 | # not only uses extra limit, but also starts at the peak!, thus specturm is refolded around peak! 750 | 751 | # then get the edges of the peak as index of the spectrum 752 | for k in np.arange(iMax.shape[0]): 753 | # unmask the peak 754 | maskHildebrand[k, iMax[k]] = False 755 | 756 | spectrum = np.roll(dataFlat[k], -iMax[k]) 757 | mask = np.roll(maskHildebrand[k], -iMax[k]) 758 | # to the right 759 | for i in np.arange(1, dataFlat.shape[-1], 1): 760 | # unmask if above limit (=peak) 761 | if spectrum[i] > limits[k]*self.co["getPeak_hildeExtraLimit"]: 762 | mask[i] = False 763 | # else stop 764 | else: 765 | # unmask on last bin if between limits[k]*self.co["getPeak_hildeExtraLimit"] and limits[k], but stop in any case! 766 | if spectrum[i] > limits[k]: 767 | mask[i] = False 768 | break 769 | # to the left 770 | for i in np.arange(dataFlat.shape[-1]-1, 0-1, -1): 771 | if spectrum[i] > limits[k]*self.co["getPeak_hildeExtraLimit"]: 772 | mask[i] = False 773 | else: 774 | if spectrum[i] > limits[k]: 775 | mask[i] = False 776 | break 777 | 778 | dataFlat[k] = np.roll(spectrum, iMax[k]) 779 | maskHildebrand[k] = np.roll(mask, iMax[k]) 780 | 781 | return maskHildebrand 782 | 783 | def _noiseHildebrand(self, dataFlat, noSpecs, h, flat=True): 784 | """ 785 | #calculate the minimum reflectivity of the peak (or maximum of the noise) according to Hildebrand and Sekhon 786 | 787 | @parameter dataFlat (numpy masked array float64): flat spectrum from MRR Raw data 788 | @parameter Nspec (numpy float64): number of spectra of each averaged spectrum 789 | 790 | @return - limits (int float64): limit reflectivity of each spectrum 791 | """ 792 | 793 | specLength = np.shape(dataFlat)[-1] 794 | if flat == False: 795 | dataShape = np.shape(dataFlat)[0] 796 | dataFlat = np.reshape(dataFlat, (-1, specLength)) 797 | 798 | # sort the data 799 | dataFlat = np.ma.sort(dataFlat, axis=-1) 800 | 801 | # calculate all variances and means (that is cheaper than a loop!) 802 | # start with whole spectrum, then discard maximum, than second but next maximum etc. 803 | Dvar = np.zeros(dataFlat.shape) 804 | Dmean = np.zeros(dataFlat.shape) 805 | limits = np.zeros(np.shape(dataFlat[..., 0])) 806 | for i in np.arange(specLength-1, 1, -1): 807 | Dvar[..., i] = np.ma.var(dataFlat[..., 0:i], axis=-1) 808 | Dmean[..., i] = np.ma.mean(dataFlat[..., 0:i], axis=-1) 809 | # calculate the Hildebrand coefficient 810 | Dvar[Dvar == 0] = 0.0001 811 | Coefficient = ((Dmean**2) / Dvar) 812 | # check where hildebrands assumption is true 813 | for j in np.arange(np.shape(dataFlat)[0]): 814 | for i in np.arange(specLength-1, -1, -1): 815 | if Coefficient[j, i] >= noSpecs[j]: 816 | limits[j] = dataFlat[j, i-1] 817 | break 818 | 819 | if flat == False: 820 | limits = np.reshape(limits, (dataShape, self.co["noH"])) 821 | 822 | return limits 823 | 824 | def _getPeakDescendingAve(self, dataFlat, iMax): 825 | """ 826 | get the peak of the spectrum 827 | function iterates through the _not_ size-sorted spectrum from the maximum to the left and to the right and stops as soon as the average stops decreasing. 828 | 829 | @parameter dataFlat (numpy float64): flat spectrum from MRR Raw data 830 | @parameter iMax (numpy float64): vector containing indices of the maxima 831 | 832 | @return - iPeakMin, iMax (int float64): edges of each spectrum 833 | """ 834 | 835 | maskDescAve = np.ones(np.shape(dataFlat), dtype=bool) 836 | 837 | # iterate through spectras: 838 | for k in np.arange(iMax.shape[0]): 839 | # the rolling allow recognition also if 0 m s^-1 is crossed 840 | rolledSpectrum = np.roll(dataFlat[k], -iMax[k]) 841 | rolledMask = np.roll(maskDescAve[k], -iMax[k]) 842 | meanRightOld = np.ma.mean( 843 | rolledSpectrum[1:self.co["getPeak_descAveCheckWidth"]+1]) 844 | meanLeftOld = np.ma.mean( 845 | rolledSpectrum[-1:-(self.co["getPeak_descAveCheckWidth"]+1):-1]) 846 | minMeanToBreak = self.co["getPeak_descAveMinMeanWeight"] * np.mean( 847 | np.sort(dataFlat[k])[0:self.co["getPeak_descAveCheckWidth"]]) 848 | # unmask peak 849 | rolledMask[0] = False 850 | # to the right: 851 | for i in np.arange(1, dataFlat.shape[-1], 1): 852 | meanRight = np.ma.mean( 853 | rolledSpectrum[i:i+self.co["getPeak_descAveCheckWidth"]]) 854 | # is the average still decraesing? 855 | if meanRight <= meanRightOld or meanRight > minMeanToBreak: 856 | rolledMask[i] = False 857 | meanRightOld = meanRight 858 | else: 859 | break 860 | # to the left 861 | for i in np.arange(dataFlat.shape[-1]-1, 0-1, -1): 862 | meanLeft = np.ma.mean( 863 | rolledSpectrum[i:i-self.co["getPeak_descAveCheckWidth"]:-1]) 864 | # is the average still decraesing? 865 | if meanLeft <= meanLeftOld or meanLeft > minMeanToBreak: 866 | rolledMask[i] = False 867 | meanLeftOld = meanLeft 868 | else: 869 | break 870 | dataFlat[k] = np.roll(rolledSpectrum, iMax[k]) 871 | maskDescAve[k] = np.roll(rolledMask, iMax[k]) 872 | 873 | return maskDescAve 874 | 875 | def _fillInterpolatedPeakGaps(self, specMask): 876 | ''' 877 | Interpolate gaps of specMask around 0 m s^-1 between spectrumBorderMin and spectrumBorderMax in noH heights 878 | returns updated specMask and quality information 879 | ''' 880 | quality = np.zeros(self._shape2D, dtype=bool) 881 | for h in range(1, self.co["noH"]): 882 | # the ones with peaks at both sides around 0 m s^-1! 883 | peaksAroundZero = (specMask[:, h-1, self.co["spectrumBorderMax"][h-1]-1] == False) * ( 884 | specMask[:, h, self.co["spectrumBorderMin"][h]] == False) 885 | specMask[:, h, 0:self.co["spectrumBorderMin"] 886 | [h]][peaksAroundZero] = False 887 | specMask[:, h-1, self.co["spectrumBorderMax"] 888 | [h-1]:][peaksAroundZero] = False 889 | 890 | # the ones with peak at only one side, 891 | peaksAroundZeroHalfToLeft = (specMask[:, h-1, self.co["spectrumBorderMax"][h-1]-1] == True) * ( 892 | specMask[:, h, self.co["spectrumBorderMin"][h]] == False) 893 | peaksAroundZeroHalfToLeftBMin = (peaksAroundZeroHalfToLeft * ( 894 | self.rawSpectrum.data[:, h, 0:self.co["spectrumBorderMin"][h]] > self.specNoise3D[:, h, 0:self.co["spectrumBorderMin"][h]]).T).T 895 | peaksAroundZeroHalfToLeftBMax = (peaksAroundZeroHalfToLeft * ( 896 | self.rawSpectrum.data[:, h-1, self.co["spectrumBorderMax"][h-1]:] > self.specNoise3D[:, h, self.co["spectrumBorderMax"][h-1]:]).T).T 897 | specMask[:, h, 0:self.co["spectrumBorderMin"] 898 | [h]][peaksAroundZeroHalfToLeftBMin] = False 899 | specMask[:, h-1, self.co["spectrumBorderMax"] 900 | [h-1]:][peaksAroundZeroHalfToLeftBMax] = False 901 | 902 | peaksAroundZeroHalfToRight = (specMask[:, h-1, self.co["spectrumBorderMax"][h-1]-1] == False) * ( 903 | specMask[:, h, self.co["spectrumBorderMin"][h]] == True) 904 | peaksAroundZeroHalfToRightBMin = (peaksAroundZeroHalfToRight * ( 905 | self.rawSpectrum.data[:, h, 0:self.co["spectrumBorderMin"][h]] > self.specNoise3D[:, h-1, 0:self.co["spectrumBorderMin"][h]]).T).T 906 | peaksAroundZeroHalfToRightBMax = (peaksAroundZeroHalfToRight * ( 907 | self.rawSpectrum.data[:, h-1, self.co["spectrumBorderMax"][h-1]:] > self.specNoise3D[:, h-1, self.co["spectrumBorderMax"][h-1]:]).T).T 908 | specMask[:, h, 0:self.co["spectrumBorderMin"] 909 | [h]][peaksAroundZeroHalfToRightBMin] = False 910 | specMask[:, h-1, self.co["spectrumBorderMax"][h-1] :][peaksAroundZeroHalfToRightBMax] = False 911 | 912 | quality[:, h] = quality[:, h-1] = peaksAroundZero + \ 913 | peaksAroundZeroHalfToLeft + peaksAroundZeroHalfToRight 914 | 915 | return specMask, quality 916 | 917 | def _dealiaseSpectrum(self, rawSpectrum): 918 | ''' 919 | dealiase Spectrum 920 | 921 | input rawSpectrum 922 | output extendSpectrum with 192 bins 923 | ''' 924 | self.qual["severeProblemsDuringDA"] = np.zeros( 925 | self._shape2D, dtype=bool) 926 | 927 | # first locate peaks in raveld specturm 928 | self._allPeaks, self._allPeaksIndices, self._allPeaksMaxIndices, self._allPeaksVelMe, self._allPeaksHeight, self._allPeaksRefV, self._allPeaksZe = self._locatePeaks( 929 | rawSpectrum) 930 | 931 | # find one peaks and its veloci/heigth you trust 932 | self._trustedPeakNo, self._trustedPeakHeight, self._trustedPeakVel, self._trustedPeakHeightStart, self._trustedPeakHeightStop = self._getTrustedPeak( 933 | self._allPeaksZe, self._allPeaksVelMe, self._allPeaksRefV, self._allPeaksMaxIndices, self._allPeaksHeight) 934 | 935 | # now extend spectrum! 936 | extendedRawSpectrum = deepcopy(rawSpectrum.data) 937 | extendedRawSpectrum = np.concatenate((np.roll( 938 | extendedRawSpectrum, 1, axis=1), extendedRawSpectrum, np.roll(extendedRawSpectrum, -1, axis=1)), axis=2) 939 | 940 | # do not apply fo first range gates 941 | extendedRawSpectrum[:, 0, :self.co["widthSpectrum"]] = 0 942 | # and not to the last one 943 | extendedRawSpectrum[:, self.co["noH"] - 944 | 1, 2*self.co["widthSpectrum"]:] = 0 945 | extendedRawSpectrum = np.ma.masked_array(extendedRawSpectrum, True) 946 | 947 | # if wanted, save old values 948 | if self.co["dealiaseSpectrum_saveAlsoNonDealiased"] == True: 949 | self.specVel_noDA = deepcopy(self.specVel) 950 | self.specVel3D_noDA = deepcopy(self.specVel3D) 951 | self.specIndex_noDA = deepcopy(self.specIndex) 952 | self.no_v_noDA = deepcopy(self.no_v) 953 | 954 | # save new velocities 955 | self.specVel = np.array(list(self.co["nyqVel"] - self.co["widthSpectrum"]*self.co["nyqVdelta"])+list( 956 | self.co["nyqVel"])+list(self.co["nyqVel"] + self.co["widthSpectrum"]*self.co["nyqVdelta"])) 957 | self.specVel3D = np.zeros(np.shape(extendedRawSpectrum)) 958 | self.specVel3D[:] = self.specVel 959 | self.specIndex = np.arange(3*self.no_v) 960 | self.no_v = self.no_v * 3 961 | 962 | # extend spectrum to 192 bins and unmask best fitting peaks 963 | extendedRawSpectrum = self._findHeightsForPeaks(extendedRawSpectrum, self._trustedPeakNo, self._trustedPeakVel, self._trustedPeakHeight, 964 | self._trustedPeakHeightStart, self._trustedPeakHeightStop, self._allPeaks, self._allPeaksIndices, self._allPeaksVelMe, self._allPeaksHeight) 965 | 966 | if self.co["dealiaseSpectrum_makeCoherenceTest"]: 967 | # simple method to detect falsely folded peaks, works only for 1-2 outliers 968 | extendedRawSpectrum = self._deAlCoherence(extendedRawSpectrum) 969 | 970 | self.qual["spectrumIsDealiased"] = np.all( 971 | extendedRawSpectrum.mask[:, :, self.co["widthSpectrum"]:2*self.co["widthSpectrum"]] != rawSpectrum.mask[:, :], axis=-1) 972 | 973 | # still we don't want peaks at height 0,1,31 974 | extendedRawSpectrum.mask[:, self.co["completelyMaskedHeights"]] = True 975 | 976 | return extendedRawSpectrum 977 | 978 | def _locatePeaks(self, rawSpectrum): 979 | ''' 980 | ravel rawSpectrum and try to find one peak per height 981 | 982 | returns time dictonaries with: 983 | allPeaks - time dictonary with lists of the spectral reflectivities for each peak 984 | allPeaksIndices - related indices 985 | allPeaksMaxIndices - time dictonary maximum of each peak 986 | allPeaksVelMe - first guess peak velocity based on the last bin 987 | allPeaksHeight - first guess peak height based on the last bin 988 | allPeaksRefV - expected velocity of each peak based on Ze according to theory 989 | allPeaksZe - time dictonary with lists of first guess Ze for each peak 990 | 991 | ''' 992 | allPeaks = dict() 993 | allPeaksIndices = dict() 994 | allPeaksMaxIndices = dict() 995 | allPeaksVelMe = dict() 996 | allPeaksHeight = dict() 997 | allPeaksRefV = dict() 998 | allPeaksZe = dict() 999 | # get velocities of spectrum. we start negative, because first guess height is always defualt height of most right bin of peak 1000 | velMe = np.array(list( 1001 | self.co["nyqVel"] - self.co["widthSpectrum"]*self.co["nyqVdelta"])+list(self.co["nyqVel"])) 1002 | 1003 | for t in np.arange(self.no_t): 1004 | completeSpectrum = self.rawSpectrum[t].ravel() 1005 | 1006 | # skip if there are no peaks in the timestep 1007 | if np.all(completeSpectrum.mask) == True: 1008 | if self.co["debug"] > 4: 1009 | '_locatePeaks: nothing to do at', t 1010 | continue 1011 | 1012 | deltaH = self.H[t, 15] - self.H[t, 14] 1013 | 1014 | peaks = list() 1015 | peaksIndices = list() 1016 | peaksMaxIndices = list() 1017 | peaksVelMe = list() 1018 | peaksHeight = list() 1019 | peaksVref = list() 1020 | peaksZe = list() 1021 | 1022 | peakTmp = list() 1023 | peakTmpInd = list() 1024 | peaksStartIndices = list() 1025 | peaksEndIndices = list() 1026 | truncatingPeak = False 1027 | 1028 | # go through all bins 1029 | for ii, spec in enumerate(completeSpectrum): 1030 | # found peak! 1031 | withinPeak = (completeSpectrum.mask[ii] == False) and ( 1032 | truncatingPeak == False) 1033 | if withinPeak: 1034 | peakTmp.append(spec) 1035 | peakTmpInd.append(ii) 1036 | # if the peak length is now larger than the raw spectrum width, then this peak has 1037 | # wrapped around the entire width. Flag will cause the peak to be split in two, because 1038 | # the next step within the loop through completeSpectrum will have withinPeak False. 1039 | if len(peakTmp) >= self.co["widthSpectrum"]: 1040 | truncatingPeak = True 1041 | warnings.warn('Truncated peak early. Masked area has wrapped around spectrum width at ' + 1042 | 'timestemp ' + str(t) + ', bin number ' + str(ii)) 1043 | # 3found no peak, but teh last one has to be processed 1044 | elif len(peakTmp) >= self.co["findPeak_minPeakWidth"]: 1045 | # get the height of the LAST entry of the peak, uses int division // ! 1046 | peakTmpHeight = peakTmpInd[-1]//self.co["widthSpectrum"] 1047 | 1048 | # reconstruct the non folded indices shifted by 64! since peakTmpInd[-1] is reference 1049 | orgIndex = np.arange(peakTmpInd[-1] % self.co["widthSpectrum"]-len( 1050 | peakTmpInd), peakTmpInd[-1] % self.co["widthSpectrum"])+1+self.co["widthSpectrum"] 1051 | 1052 | # calculate a first guess Ze 1053 | etaSumTmp = np.sum( 1054 | peakTmp * np.array((self.co["mrrCalibConst"] * (peakTmpHeight**2 * deltaH)) / (1e20), dtype=float)) 1055 | # in rare cases, Ze is below Zero, maybey since the wrong peak is examined? 1056 | if etaSumTmp <= 0: 1057 | warnings.warn('negative (linear) Ze occured during dealiasing, peak removed at timestep '+str( 1058 | t)+', bin number ' + str(ii)+', most likely at height ' + str(peakTmpHeight)) 1059 | self.qual["severeProblemsDuringDA"][t, 1060 | peakTmpHeight] = True 1061 | peakTmp = list() 1062 | peakTmpInd = list() 1063 | continue 1064 | ZeTmp = 1e18*(self.co["lamb"]**4 * 1065 | etaSumTmp/(np.pi**5*self.co["K2"])) 1066 | 1067 | # guess doppler velocity 1068 | peakTmpSnowVel = self.co['dealiaseSpectrum_Ze-vRelationSnowA'] * \ 1069 | ZeTmp**self.co['dealiaseSpectrum_Ze-vRelationSnowB'] 1070 | peakTmpRainVel = self.co['dealiaseSpectrum_Ze-vRelationRainA'] * \ 1071 | ZeTmp**self.co['dealiaseSpectrum_Ze-vRelationRainB'] 1072 | peakTmpRefVel = (peakTmpSnowVel + peakTmpRainVel)/2. 1073 | 1074 | # save other features 1075 | peaksVref.append(peakTmpRefVel) 1076 | peaks.append(peakTmp) 1077 | peaksIndices.append(peakTmpInd) 1078 | peaksStartIndices.append(peakTmpInd[0]) 1079 | peaksEndIndices.append(peakTmpInd[-1]) 1080 | 1081 | peaksMaxIndices.append(np.argmax(peakTmp)+ii-len(peakTmp)) 1082 | peaksHeight.append(peakTmpHeight) 1083 | peaksVelMe.append( 1084 | np.sum((velMe[orgIndex[0]:orgIndex[-1]+1]*peakTmp))/np.sum(peakTmp)) 1085 | peaksZe.append(ZeTmp) 1086 | 1087 | peakTmp = list() 1088 | peakTmpInd = list() 1089 | truncatingPeak = False 1090 | # small peaks can show up again due to dealiasing, get rid of them: 1091 | elif len(peakTmp) > 0 and len(peakTmp) < self.co["findPeak_minPeakWidth"]: 1092 | peakTmp = list() 1093 | peakTmpInd = list() 1094 | truncatingPeak = False 1095 | # no peak 1096 | else: 1097 | continue 1098 | 1099 | # we want only ONE peak per range gate! 1100 | if self.co["dealiaseSpectrum_maxOnePeakPerHeight"]: 1101 | # get list with peaks, whcih are too much 1102 | peaksTbd = self._maxOnePeakPerHeight( 1103 | t, peaksStartIndices, peaksEndIndices, peaksZe) 1104 | # remove them 1105 | for peakTbd in np.sort(peaksTbd)[::-1]: 1106 | peaks.pop(peakTbd) 1107 | peaksIndices.pop(peakTbd) 1108 | peaksMaxIndices.pop(peakTbd) 1109 | peaksVelMe.pop(peakTbd) 1110 | peaksHeight.pop(peakTbd) 1111 | peaksVref.pop(peakTbd) 1112 | peaksZe.pop(peakTbd) 1113 | 1114 | # if anything was found, save it 1115 | if len(peaks) > 0: 1116 | allPeaks[t] = peaks 1117 | allPeaksIndices[t] = peaksIndices 1118 | allPeaksMaxIndices[t] = peaksMaxIndices 1119 | allPeaksVelMe[t] = peaksVelMe 1120 | allPeaksHeight[t] = peaksHeight 1121 | allPeaksRefV[t] = peaksVref 1122 | allPeaksZe[t] = peaksZe 1123 | # end for t 1124 | 1125 | return allPeaks, allPeaksIndices, allPeaksMaxIndices, allPeaksVelMe, allPeaksHeight, allPeaksRefV, allPeaksZe 1126 | 1127 | def _maxOnePeakPerHeight(self, t, peaksStartIndices, peaksEndIndices, peaksZe): 1128 | ''' 1129 | some height will contain more than one peak, try to find them 1130 | returns a list with peaks to be delteted 1131 | ''' 1132 | 1133 | peaksStartIndices = np.array(peaksStartIndices) 1134 | peaksEndIndices = np.array(peaksEndIndices) 1135 | peaksZeCopy = np.array(peaksZe) 1136 | 1137 | peaksTbd = list() 1138 | 1139 | for pp, peakStart in enumerate(peaksStartIndices): 1140 | deletePeaks = False 1141 | if peakStart == -9999: 1142 | continue # peak has been deleted 1143 | followingPeaks = (peaksStartIndices >= peakStart) * \ 1144 | (peaksStartIndices < peakStart+(1.5*self.co["widthSpectrum"])) 1145 | if (np.sum(followingPeaks) >= 3): 1146 | # if you have three peaks so close together it is cristal clear: 1147 | deletePeaks = True 1148 | elif (np.sum(followingPeaks) == 2): 1149 | # if you have only two they must be close together 1150 | secondPeak = np.where(followingPeaks)[0][1] 1151 | deletePeaks = ( 1152 | peaksEndIndices[secondPeak] - peakStart < self.co["widthSpectrum"]/2.) 1153 | if deletePeaks == True: 1154 | 1155 | # don't consider more than 3! the rest is hopefully caught by next loop! 1156 | Indices = np.where(followingPeaks)[0][0:3] 1157 | smallestZe = Indices[np.argmin(peaksZeCopy[Indices])] 1158 | peaksTbd.append(smallestZe) 1159 | 1160 | # these are needed for the loop, so they are only masked, not deleted 1161 | peaksStartIndices[peaksTbd[-1]] = -9999 1162 | peaksEndIndices[peaksTbd[-1]] = -9999 1163 | peaksZeCopy[peaksTbd[-1]] = 9999 1164 | 1165 | return peaksTbd 1166 | 1167 | def _getTrustedPeak(self, allPeaksZe, allPeaksVelMe, allPeaksRefV, allPeaksMaxIndices, allPeaksHeight): 1168 | ''' 1169 | find heigth and position of most trustfull peak 1170 | 1171 | allPeaksZe - time dictonary with lists of first guess Ze for each peak 1172 | allPeaksVelMe - first guess peak velocity based on the last bin 1173 | allPeaksRefV - expected velocity of each peak based on Ze according to theory 1174 | allPeaksMaxIndices - time dictonary maximum of each peak 1175 | allPeaksHeight - first guess peak height based on the last bin 1176 | 1177 | returns 1D time arrays 1178 | trustedPeakNo - no of trusted peaks (starting at bottom) 1179 | trustedPeakHeight - estimated height 1180 | trustedPeakVel - -estimated velocity 1181 | trustedPeakHeightStart, trustedPeakHeightStop - start and stop indices from 0:192 range 1182 | ''' 1183 | trustedPeakHeight = np.zeros(self.no_t, dtype=int) 1184 | trustedPeakVel = np.zeros(self.no_t) 1185 | trustedPeakNo = np.ones(self.no_t, dtype=int)*-9999 1186 | trustedPeakHeightStart = np.zeros(self.no_t, dtype=int) 1187 | trustedPeakHeightStop = np.zeros(self.no_t, dtype=int) 1188 | for t in np.arange(self.no_t): 1189 | # now process the found peaks 1190 | if t in list(self._allPeaks.keys()): 1191 | 1192 | # the trusted peak needs a certain minimal reflectivity to avoid confusion by interference etc, get the minimum threshold 1193 | averageZe = np.sum(allPeaksZe[t])/float(len(allPeaksZe[t])) 1194 | minZe = quantile( 1195 | self._allPeaksZe[t], self.co['dealiaseSpectrum_trustedPeakminZeQuantile']) 1196 | 1197 | peaksVelMe = np.array(allPeaksVelMe[t]) 1198 | peaksVels = np.array([peaksVelMe+self.co["nyqVdelta"]*self.co["widthSpectrum"], 1199 | peaksVelMe, peaksVelMe-self.co["nyqVdelta"]*self.co["widthSpectrum"]]) 1200 | refVels = np.array( 1201 | [allPeaksRefV[t], allPeaksRefV[t], allPeaksRefV[t]]) 1202 | # this difference between real velocity (thee different ones are tried: dealaisisnmg up, static or down) and expected Ze based velocityhas to be minimum to find trusted peak 1203 | diffs = np.abs(peaksVels - refVels) 1204 | 1205 | # mask small peaks, peaks which are in the firt processed range gate and peaks which are in self.co["dealiaseSpectrum_heightsWithInterference"] (e.g. disturbed by interference) 1206 | diffs = np.ma.masked_array(diffs, [allPeaksZe[t] <= minZe]*3) 1207 | tripplePeaksMaxIndices = np.array(3*[allPeaksMaxIndices[t]]) 1208 | # the first used height is a bit special, often peaks are incomplete,try to catch them to avoid trust them 1209 | diffs = np.ma.masked_array(diffs, (tripplePeaksMaxIndices >= self.co["firstUsedHeight"]*self.co["widthSpectrum"])*( 1210 | tripplePeaksMaxIndices < self.co["firstUsedHeight"]*(self.co["widthSpectrum"]*1.5))) 1211 | # now mask all other peaks which are found unlikely 1212 | for hh in self.co["dealiaseSpectrum_heightsWithInterference"]+self.co["completelyMaskedHeights"]: 1213 | diffs = np.ma.masked_array(diffs, (tripplePeaksMaxIndices >= hh*self.co["widthSpectrum"])*( 1214 | tripplePeaksMaxIndices < (hh+1)*self.co["widthSpectrum"])) 1215 | 1216 | # if we managed to mask all peaks, we have no choice but taking all 1217 | if np.all(diffs.mask == True): 1218 | diffs.mask[:] = False 1219 | if self.co["debug"] > 4: 1220 | print("managed to mask all peaks at " + str(t) + 1221 | " while trying to find most trustfull one during dealiasing.") 1222 | 1223 | # the minimum velocity difference tells wehther dealiasing goes up, down or is not applied 1224 | UpOrDn = np.ma.argmin(np.ma.min(diffs, axis=1)) 1225 | # get paramters for trusted peaks 1226 | trustedPeakNo[t] = np.ma.argmin(diffs[UpOrDn]) 1227 | # -1 to ensure that updraft is negative now!! 1228 | trustedPeakHeight[t] = allPeaksHeight[t][trustedPeakNo[t]] + UpOrDn-1 1229 | trustedPeakSpecShift = trustedPeakHeight[t] * \ 1230 | self.co["widthSpectrum"] - self.co["widthSpectrum"] 1231 | trustedPeakVel[t] = peaksVels[UpOrDn][trustedPeakNo[t]] 1232 | # transform back to height related spectrum 1233 | # in dimension of 0:192 #spectrum is extended to the left 1234 | trustedPeakHeightIndices = (np.array( 1235 | self._allPeaksIndices[t][trustedPeakNo[t]])-trustedPeakSpecShift)[[0, -1]] 1236 | trustedPeakHeightStart[t] = trustedPeakHeightIndices[0] 1237 | trustedPeakHeightStop[t] = trustedPeakHeightIndices[-1] 1238 | 1239 | return trustedPeakNo, trustedPeakHeight, trustedPeakVel, trustedPeakHeightStart, trustedPeakHeightStop 1240 | 1241 | def _findHeightsForPeaks(self, extendedRawSpectrum, trustedPeakNo, trustedPeakVel, trustedPeakHeight, trustedPeakHeightStart, trustedPeakHeightStop, allPeaks, allPeaksIndices, allPeaksVelMe, allPeaksHeight): 1242 | ''' 1243 | try to find the height of each peak by starting at the trusted peak 1244 | extendedRawSpectrum - extended to 192 bins, returned with new, dealiased mask 1245 | trustedPeakNo - trusted peak number of all peaks in time step 1246 | trustedPeakVel - most liekely velocity 1247 | trustedPeakHeight - most likely height 1248 | trustedPeakHeightStart, trustedPeakHeightStop - start/stop of peaks 1249 | allPeaks - time dictonary with lists of the spectral reflectivities for each peak 1250 | allPeaksIndices - related indices 1251 | allPeaksVelMe - first guess peak velocity based on the last bin 1252 | allPeaksHeight - first guess peak height based on the last bin 1253 | ''' 1254 | for t in np.arange(self.no_t): 1255 | if t in list(self._allPeaks.keys()): 1256 | extendedRawSpectrum[t, trustedPeakHeight[t], 1257 | trustedPeakHeightStart[t]:trustedPeakHeightStop[t]+1].mask = False 1258 | 1259 | peaksVelMe = np.array(allPeaksVelMe[t]) 1260 | # get all three possible velocities 1261 | peaksVels = np.array([peaksVelMe+self.co["nyqVdelta"]*self.co["widthSpectrum"], 1262 | peaksVelMe, peaksVelMe-self.co["nyqVdelta"]*self.co["widthSpectrum"]]) 1263 | 1264 | formerPeakVel = trustedPeakVel[t] 1265 | # loop through all peaks, starting at the trusted one 1266 | for jj in list(range(trustedPeakNo[t]-1, -1, -1))+list(range(trustedPeakNo[t]+1, len(allPeaks[t]))): 1267 | # To combine ascending and descending loop in one: 1268 | if jj == trustedPeakNo[t]+1: 1269 | formerPeakVel = trustedPeakVel[t] 1270 | # go up, stay or down? for which option fifference to former (trusted) peaks is smallest. 1271 | UpOrDn = np.argmin( 1272 | np.abs(peaksVels[:, jj] - formerPeakVel)) 1273 | # change height, indices and velocity accordingly 1274 | thisPeakHeight = allPeaksHeight[t][jj] + UpOrDn-1 1275 | if thisPeakHeight not in list(range(self.co["noH"])): 1276 | warnings.warn('Dealiasing failed! peak boundaries excced max/min height. time step '+str( 1277 | t)+', peak number ' + str(jj)+', tried to put at height ' + str(thisPeakHeight)) 1278 | self.qual["severeProblemsDuringDA"][t] = True 1279 | continue 1280 | thisPeakSpecShift = thisPeakHeight * \ 1281 | self.co["widthSpectrum"] - self.co["widthSpectrum"] 1282 | thisPeakVel = peaksVels[UpOrDn][jj] 1283 | thisPeakHeightIndices = np.array( 1284 | allPeaksIndices[t][jj])-thisPeakSpecShift 1285 | if np.any(thisPeakHeightIndices < 0) or np.any(thisPeakHeightIndices >= 3*self.co["widthSpectrum"]): 1286 | warnings.warn('Dealiasing failed! peak boundaries fall out of spectrum. time step '+str( 1287 | t)+', peak number ' + str(jj)+', most likely at height ' + str(thisPeakHeight)) 1288 | self.qual["severeProblemsDuringDA"][t] = True 1289 | 1290 | # check whether there is already a peak in the found height! 1291 | if np.all(extendedRawSpectrum[t, thisPeakHeight].mask == True): 1292 | if thisPeakHeight >= self.co["noH"] or thisPeakHeight < 0: 1293 | warnings.warn('Dealiasing reached max/min height... time step '+str( 1294 | t)+', peak number ' + str(jj)+', most likely at height ' + str(thisPeakHeight)) 1295 | self.qual["severeProblemsDuringDA"][t] = True 1296 | continue 1297 | # only if there is no peak yet!! 1298 | extendedRawSpectrum[t, thisPeakHeight, thisPeakHeightIndices[0] :thisPeakHeightIndices[-1]+1].mask = False 1299 | formerPeakVel = thisPeakVel 1300 | # if there is already a peak in the height, repeat the process, but take the second likely height/velocity 1301 | else: 1302 | if self.co["debug"] > 4: 1303 | print('DA: there is already a peak in found height, take second choice', 1304 | t, jj, thisPeakHeight, trustedPeakNo[t], trustedPeakHeight) 1305 | # otherwise take second choice! 1306 | formerPeakVelList = np.array([formerPeakVel]*3) 1307 | formerPeakVelList[UpOrDn] = 1e10 # make extremely big 1308 | UpOrDn2 = np.ma.argmin( 1309 | np.abs(peaksVels[:, jj] - formerPeakVelList)) 1310 | thisPeakHeight = allPeaksHeight[t][jj] + UpOrDn2-1 1311 | if thisPeakHeight not in list(range(self.co["noH"])): 1312 | warnings.warn('Dealiasing step 2 failed! peak boundaries excced max/min height. time step '+str( 1313 | t)+', peak number ' + str(jj)+', tried to put at height ' + str(thisPeakHeight)) 1314 | self.qual["severeProblemsDuringDA"][t] = True 1315 | continue 1316 | thisPeakSpecShift = thisPeakHeight * \ 1317 | self.co["widthSpectrum"] - self.co["widthSpectrum"] 1318 | thisPeakVel = peaksVels[UpOrDn2][jj] 1319 | thisPeakHeightIndices = np.array( 1320 | allPeaksIndices[t][jj])-thisPeakSpecShift 1321 | if np.any(thisPeakHeightIndices < 0) or np.any(thisPeakHeightIndices >= 3*self.co["widthSpectrum"]): 1322 | warnings.warn('Dealiasing step 2 failed! peak boundaries fall out of spectrum. time step '+str( 1323 | t)+', peak number ' + str(jj)+', most likely at height ' + str(thisPeakHeight)) 1324 | self.qual["severeProblemsDuringDA"][t] = True 1325 | if thisPeakHeight >= self.co["noH"] or thisPeakHeight < 0: 1326 | warnings.warn('Dealiasing reached max/min height... time step '+str( 1327 | t)+', peak number ' + str(jj)+', most likely at height ' + str(thisPeakHeight)) 1328 | self.qual["severeProblemsDuringDA"][t] = True 1329 | continue 1330 | # check again whether there is already a peak in the spectrum 1331 | if np.all(extendedRawSpectrum[t, thisPeakHeight].mask == True): 1332 | # next try 1333 | extendedRawSpectrum[t, thisPeakHeight, thisPeakHeightIndices[0] :thisPeakHeightIndices[-1]+1].mask = False 1334 | formerPeakVel = thisPeakVel 1335 | # if yes, give up 1336 | else: 1337 | warnings.warn('Could not find height of peak! time step '+str( 1338 | t)+', peak number ' + str(jj)+', most likely at height ' + str(thisPeakHeight)) 1339 | self.qual["severeProblemsDuringDA"][t] = True 1340 | 1341 | return extendedRawSpectrum 1342 | 1343 | def _deAlCoherence(self, newSpectrum): 1344 | ''' 1345 | make sure no weired foldings happend by looking for big jumps in the height-averaged velocity 1346 | if two jumps very closely together (<=3 peaks inbetween) are found, teh peaks inbetween are corrected 1347 | can make it worse if dealiasing produces zig-zag patterns. 1348 | 1349 | ''' 1350 | self.qual["DAdirectionCorrectedByCoherenceTest"] = np.zeros( 1351 | self._shape2D, dtype=bool) 1352 | meanVelocity = np.ma.average(np.ma.sum( 1353 | newSpectrum*self.specVel, axis=-1)/np.ma.sum(newSpectrum, axis=-1), axis=-1) 1354 | 1355 | velDiffs = np.diff(meanVelocity) 1356 | 1357 | # find velocity jumps 1358 | velDiffsBig = np.where( 1359 | velDiffs > self.co["dealiaseSpectrum_makeCoherenceTest_velocityThreshold"])[0] 1360 | velDiffsSmall = np.where( 1361 | velDiffs < -self.co["dealiaseSpectrum_makeCoherenceTest_velocityThreshold"])[0] 1362 | 1363 | # check whether there is an opposite one close by and collect time steps to be refolded 1364 | foldUp = list() 1365 | for ll in velDiffsBig: 1366 | if ll+1 in velDiffsSmall: 1367 | foldUp.append(ll+1) 1368 | continue 1369 | if ll+2 in velDiffsSmall: 1370 | foldUp.append(ll+1) 1371 | foldUp.append(ll+2) 1372 | continue 1373 | if ll+3 in velDiffsSmall: 1374 | foldUp.append(ll+1) 1375 | foldUp.append(ll+2) 1376 | foldUp.append(ll+3) 1377 | 1378 | updatedSpectrumMask = deepcopy(newSpectrum.mask) 1379 | 1380 | for tt in foldUp: 1381 | updatedSpectrumMask[tt] = np.roll(updatedSpectrumMask[tt].ravel( 1382 | ), 2 * self.co["widthSpectrum"]).reshape((self.co["noH"], 3*self.co["widthSpectrum"])) 1383 | # avoid that something is folded into the highest range gate 1384 | updatedSpectrumMask[tt, 0, :2*self.co["widthSpectrum"]] = True 1385 | self.qual["DAdirectionCorrectedByCoherenceTest"][tt, :] = True 1386 | if self.co["debug"] > 4: 1387 | print('coherenceTest corrected dealiasing upwards:', foldUp) 1388 | 1389 | newSpectrum = np.ma.masked_array(newSpectrum.data, updatedSpectrumMask) 1390 | 1391 | # now the same for the other folding direction 1392 | meanVelocity = np.ma.average(np.ma.sum( 1393 | newSpectrum*self.specVel, axis=-1)/np.ma.sum(newSpectrum, axis=-1), axis=-1) 1394 | 1395 | velDiffs = np.diff(meanVelocity) 1396 | 1397 | # find very big differences 1398 | velDiffsBig = np.where( 1399 | velDiffs > self.co["dealiaseSpectrum_makeCoherenceTest_velocityThreshold"])[0] 1400 | velDiffsSmall = np.where( 1401 | velDiffs < -self.co["dealiaseSpectrum_makeCoherenceTest_velocityThreshold"])[0] 1402 | 1403 | foldDn = list() 1404 | # check whether there is an opposite one close by and collect time steps to be refolded 1405 | for ll in velDiffsSmall: 1406 | if ll+1 in velDiffsBig: 1407 | foldDn.append(ll+1) 1408 | continue 1409 | if ll+2 in velDiffsBig: 1410 | foldDn.append(ll+1) 1411 | foldDn.append(ll+2) 1412 | continue 1413 | if ll+3 in velDiffsBig: 1414 | foldDn.append(ll+1) 1415 | foldDn.append(ll+2) 1416 | foldDn.append(ll+2) 1417 | 1418 | updatedSpectrumMask = deepcopy(newSpectrum.mask) 1419 | # change all peaks accordingly 1420 | for tt in foldDn: 1421 | # roll the mask! 1422 | updatedSpectrumMask[tt] = np.roll(updatedSpectrumMask[tt].ravel( 1423 | ), -2*self.co["widthSpectrum"]).reshape((self.co["noH"], 3*self.co["widthSpectrum"])) 1424 | # avoid that something is folded into the lowest range gate 1425 | updatedSpectrumMask[tt, -1, -2*self.co["widthSpectrum"]:] = True 1426 | self.qual["DAdirectionCorrectedByCoherenceTest"][tt, :] = True 1427 | if self.co["debug"] > 4: 1428 | print('coherenceTest corrected dealiasing Donwards:', foldDn) 1429 | 1430 | newSpectrum = np.ma.masked_array(newSpectrum.data, updatedSpectrumMask) 1431 | 1432 | # this method is very incompelte, so save still odd looking peaks in the quality mask: 1433 | # first, collect all height which should be treated, we don't want to find jumps of the interpolated area!: 1434 | includedHeights = list(set(range(self.co["maxH"])).difference(set( 1435 | self.co["completelyMaskedHeights"]+self.co["dealiaseSpectrum_heightsWithInterference"]))) 1436 | # now get the mean velocity of the profile 1437 | meanVelocity = np.ma.average(np.ma.sum( 1438 | newSpectrum[:, includedHeights]*self.specVel, axis=-1)/np.ma.sum(newSpectrum[:, includedHeights], axis=-1), axis=-1) 1439 | velDiffs = np.abs(np.diff(meanVelocity)) 1440 | # find all steps exceeding a min velocity threshold 1441 | crazyVelDiffs = np.where( 1442 | velDiffs > self.co["dealiaseSpectrum_makeCoherenceTest_velocityThreshold"])[0] 1443 | 1444 | self.qual["DAbigVelocityJumpDespiteCoherenceTest"] = np.zeros( 1445 | self._shape2D, dtype=bool) 1446 | # surrounding data has to be masked as well, take +- self.co["dealiaseSpectrum_makeCoherenceTest_maskRadius"] (default 20min) around suspicous data 1447 | for crazyVelDiff in crazyVelDiffs: 1448 | self.qual["DAbigVelocityJumpDespiteCoherenceTest"][crazyVelDiff-self.co["dealiaseSpectrum_makeCoherenceTest_maskRadius"] :crazyVelDiff+self.co["dealiaseSpectrum_makeCoherenceTest_maskRadius"]+1, :] = True 1449 | 1450 | return newSpectrum 1451 | 1452 | def _calcEtaZeW(self, rawSpectra, heights, velocities, noise, noise_std): 1453 | ''' 1454 | calculate the spectral moements and other spectral variables 1455 | ''' 1456 | 1457 | deltaH = oneD2twoD( 1458 | heights[..., 15]-heights[..., 14], heights.shape[-1], 1) 1459 | 1460 | # transponieren um multiplizieren zu ermoeglichen! 1461 | eta = (rawSpectra.data.T * np.array( 1462 | (self.co["mrrCalibConst"] * (heights**2 / deltaH)) / (1e20), dtype=float).T).T 1463 | eta = np.ma.masked_array(eta, rawSpectra.mask) 1464 | etaNoiseAve = noise * \ 1465 | (self.co["mrrCalibConst"] * (heights**2 / deltaH)) / 1e20 1466 | etaNoiseStd = noise_std * \ 1467 | (self.co["mrrCalibConst"] * (heights**2 / deltaH)) / 1e20 1468 | 1469 | # calculate Ze 1470 | Ze = 1e18*(self.co["lamb"]**4*np.ma.sum(eta, 1471 | axis=-1)/(np.pi**5*self.co["K2"])) 1472 | Ze = (10*np.ma.log10(Ze)).filled(-9999) 1473 | #Znoise = 1e18*(self.co["lamb"]**4*(etaNoise*self.co["widthSpectrum"])/(np.pi**5*self.co["K2"])) 1474 | #Znoise = 10*np.ma.log10(Znoise).filled(-9999) 1475 | 1476 | # no slicing neccesary due to mask! definign average value "my" 1477 | my = np.ma.sum(velocities*rawSpectra, axis=-1) / \ 1478 | np.ma.sum(rawSpectra, axis=-1) 1479 | 1480 | # normed weights 1481 | P = (rawSpectra.T/np.ma.sum(rawSpectra, axis=-1).T).T 1482 | x = velocities 1483 | 1484 | # http://mathworld.wolfram.com/CentralMoment.html 1485 | # T is neccessary due to different dimensions 1486 | mom2 = np.ma.sum(P*(x.T-my.T).T**2, axis=-1) 1487 | mom3 = np.ma.sum(P*(x.T-my.T).T**3, axis=-1) 1488 | mom4 = np.ma.sum(P*(x.T-my.T).T**4, axis=-1) 1489 | 1490 | # average fall velocity is my 1491 | W = my.filled(-9999) 1492 | # spec width is weighted std 1493 | specWidth = np.sqrt(mom2).filled(-9999) 1494 | # http://mathworld.wolfram.com S^-1kewness.html 1495 | skewness = (mom3/mom2**(3./2.)).filled(-9999) 1496 | # http://mathworld.wolfram.com/Kurtosis.html 1497 | kurtosis = (mom4/mom2**(2.)).filled(-9999) 1498 | 1499 | # get velocity at borders and max of peak 1500 | peakVelLeftBorder = self.specVel[np.argmin(rawSpectra.mask, axis=-1)] 1501 | peakVelRightBorder = self.specVel[len( 1502 | self.specVel) - np.argmin(rawSpectra.mask[..., ::-1], axis=-1) - 1] 1503 | peakVelMax = self.specVel[np.argmax(rawSpectra.filled(-9999), axis=-1)] 1504 | 1505 | # get the according indices 1506 | peakArgLeftBorder = np.argmin(rawSpectra.mask, axis=-1) 1507 | peakArgRightBorder = len( 1508 | self.specVel) - np.argmin(rawSpectra.mask[..., ::-1], axis=-1) - 1 1509 | 1510 | # to find the entries we have to flatten everything 1511 | etaSpectraFlat = eta.reshape((eta.shape[0]*eta.shape[1], eta.shape[2])) 1512 | 1513 | # no get the according values 1514 | peakEtaLeftBorder = 10*np.log10(etaSpectraFlat[list(range( 1515 | etaSpectraFlat.shape[0])), peakArgLeftBorder.ravel()].reshape(self._shape2D)) 1516 | peakEtaRightBorder = 10*np.log10(etaSpectraFlat[list(range( 1517 | etaSpectraFlat.shape[0])), peakArgRightBorder.ravel()].reshape(self._shape2D)) 1518 | 1519 | peakEtaMax = 10*np.log10(np.max(eta.filled(-9999), axis=-1)) 1520 | 1521 | leftSlope = (peakEtaMax - peakEtaLeftBorder) / \ 1522 | (peakVelMax - peakVelLeftBorder) 1523 | rightSlope = (peakEtaMax - peakEtaRightBorder) / \ 1524 | (peakVelMax - peakVelRightBorder) 1525 | 1526 | peakVelLeftBorder[Ze == -9999] = -9999 1527 | peakVelRightBorder[Ze == -9999] = -9999 1528 | leftSlope[Ze == -9999] = -9999 1529 | rightSlope[Ze == -9999] = -9999 1530 | leftSlope[np.isnan(leftSlope)] = -9999 1531 | rightSlope[np.isnan(rightSlope)] = -9999 1532 | 1533 | return eta, Ze, W, etaNoiseAve, etaNoiseStd, specWidth, skewness, kurtosis, peakVelLeftBorder, peakVelRightBorder, leftSlope, rightSlope 1534 | 1535 | def getQualityBinArray(self, qual): 1536 | ''' 1537 | convert the bool quality masks to one binary array 1538 | ''' 1539 | 1540 | binQual = np.zeros(self._shape2D, dtype=int) 1541 | qualFac = dict() 1542 | description = '' 1543 | description += 'A) usually, the following erros can be ignored (no. is position of bit): ' 1544 | qualFac["interpolatedSpectrum"] = 0b1 1545 | description += '1) spectrum interpolated around 0 and 12 m s^-1 ' 1546 | 1547 | qualFac["filledInterpolatedPeakGaps"] = 0b10 1548 | description += '2) peak streches over interpolated part ' 1549 | 1550 | qualFac["spectrumIsDealiased"] = 0b100 1551 | description += '3) peak is dealiased ' 1552 | 1553 | qualFac["usedSecondPeakAlgorithmDueToWidePeak"] = 0b1000 1554 | description += '4) first Algorithm to determine peak failed, used backup ' 1555 | 1556 | qualFac["DAdirectionCorrectedByCoherenceTest"] = 0b10000 1557 | description += '5) dealiasing went wrong, but is corrected ' 1558 | 1559 | description += 'B) reasons why a spectrum does NOT contain a peak: ' 1560 | qualFac["incompleteSpectrum"] = 0b10000000 1561 | description += '8) spectrum was incompletely recorded ' 1562 | 1563 | qualFac["spectrumVarianceTooLowForPeak"] = 0b100000000 1564 | description += '9) the variance test indicated no peak ' 1565 | 1566 | qualFac["spectrumNotProcessed"] = 0b1000000000 1567 | description += '10) spectrum is not processed due to according setting ' 1568 | 1569 | qualFac["peakTooThinn"] = 0b10000000000 1570 | description += '11) peak removed since not wide enough ' 1571 | 1572 | qualFac["peakRemovedByCoherenceTest"] = 0b100000000000 1573 | description += '12) peak removed, because too few neighbours show signal, too ' 1574 | 1575 | description += "C) thinks went seriously wrong, don't use data with these codes" 1576 | qualFac["peakMightBeIncomplete"] = 0b1000000000000000 1577 | description += '16) peak is at the very border to bad data ' 1578 | 1579 | qualFac["DAbigVelocityJumpDespiteCoherenceTest"] = 0b10000000000000000 1580 | description += '17) in this area there are still strong velocity jumps, indicates failed dealiasing ' 1581 | 1582 | qualFac["severeProblemsDuringDA"] = 0b100000000000000000 1583 | description += '18) during dealiasing, a warning was triggered, applied to whole columm ' 1584 | 1585 | for key in list(qual.keys()): 1586 | binQual[:] = binQual[:] + (qual[key] * qualFac[key]) 1587 | 1588 | return binQual, description 1589 | 1590 | def writeNetCDF(self, fname, varsToSave="all", ncForm="NETCDF3_CLASSIC"): 1591 | ''' 1592 | write the results to a netcdf file 1593 | 1594 | Input: 1595 | 1596 | fname: str filename with path 1597 | varsToSave list of variables of the profile to be saved. "all" saves all implmented ones 1598 | ncForm: str netcdf file format, possible values are NETCDF3_CLASSIC, NETCDF3_64BIT, NETCDF4_CLASSIC, and NETCDF4 for the python-netcdf4 package, NETCDF3 takes the "old" Scientific.IO.NetCDF module, which is a bit more convinient to install or as fall back option python-netcdf3 1599 | ''' 1600 | 1601 | nc, pyNc = _get_netCDF_module(ncForm=ncForm) 1602 | 1603 | # option dealiaseSpectrum_saveAlsoNonDealiased makes only sence, if spectrum is really dealiased: 1604 | saveAlsoNonDealiased = self.co["dealiaseSpectrum_saveAlsoNonDealiased"] and self.co["dealiaseSpectrum"] 1605 | 1606 | if pyNc: 1607 | cdfFile = nc.Dataset(fname, "w", format=ncForm) 1608 | else: 1609 | cdfFile = nc.NetCDFFile(fname, "w") 1610 | 1611 | # write meta data 1612 | cdfFile.title = 'Micro rain radar data processed with IMProToo' 1613 | cdfFile.comment = 'IMProToo has been developed for improved snow measurements. Note that this data has been processed regardless of precipitation type.' 1614 | cdfFile.institution = self.co["ncInstitution"] 1615 | cdfFile.contact_person = self.co["ncCreator"] 1616 | cdfFile.source = 'MRR-2' 1617 | cdfFile.location = self.co["ncLocation"] 1618 | cdfFile.history = 'Created with IMProToo v' + __version__ 1619 | cdfFile.author = 'Max Maahn' 1620 | cdfFile.processing_date = datetime.datetime.utcnow().strftime("%Y-%m-%d %H:%M:%S UTC") 1621 | cdfFile.reference = 'Maahn, M. and Kollias, P., 2012: Improved Micro Rain Radar snow measurements using Doppler spectra post-processing, Atmos. Meas. Tech., 5, 2661-2673, doi:10.5194/amt-5-2661-2012. ' 1622 | 1623 | cdfFile.properties = str(self.co) 1624 | cdfFile.mrrHeader = str(self.header) 1625 | 1626 | # make frequsnions 1627 | cdfFile.createDimension('time', int(self.no_t)) 1628 | cdfFile.createDimension('range', int(self.no_h)) 1629 | cdfFile.createDimension('velocity', int(self.no_v)) 1630 | if saveAlsoNonDealiased: 1631 | cdfFile.createDimension('velocity_noDA', int(self.no_v_noDA)) 1632 | 1633 | ncShape2D = ("time", "range",) 1634 | ncShape3D = ("time", "range", "velocity",) 1635 | ncShape3D_noDA = ("time", "range", "velocity_noDA",) 1636 | 1637 | fillVDict = dict() 1638 | # little cheat to avoid hundreds of if, else... 1639 | if pyNc: 1640 | fillVDict["fill_value"] = self.missingNumber 1641 | 1642 | nc_time = cdfFile.createVariable('time', 'i', ('time',), **fillVDict) 1643 | nc_time.description = "measurement time. Following Meteks convention, the dataset at e.g. 11:55 contains all recorded raw between 11:54:00 and 11:54:59 (if delta t = 60s)!" 1644 | nc_time.timezone = self.timezone 1645 | nc_time.units = 'seconds since 1970-01-01 00:00:00' 1646 | nc_time[:] = np.array(self.time.filled(self.missingNumber), dtype="i4") 1647 | # commented because of Ubuntu bug: https://bugs.launchpad.net/ubuntu/+source/python-scientific/+bug/1005571 1648 | #if not pyNc: nc_time._FillValue =int(self.missingNumber) 1649 | 1650 | nc_range = cdfFile.createVariable( 1651 | 'range', 'i', ('range',), **fillVDict) # = missingNumber) 1652 | nc_range.description = "range bins" 1653 | nc_range.units = '#' 1654 | nc_range[:] = np.arange(self.co["minH"], self.co["maxH"]+1, dtype="i4") 1655 | #if not pyNc: nc_range._FillValue =int(self.missingNumber) 1656 | 1657 | nc_velocity = cdfFile.createVariable( 1658 | 'velocity', 'f', ('velocity',), **fillVDict) 1659 | nc_velocity.description = "Doppler velocity bins. If dealiasing is applied, the spectra are triplicated" 1660 | nc_velocity.units = 'm s^-1' 1661 | nc_velocity[:] = np.array(self.specVel, dtype="f4") 1662 | #if not pyNc: nc_velocity._FillValue =float(self.missingNumber) 1663 | 1664 | if saveAlsoNonDealiased: 1665 | nc_velocity_noDA = cdfFile.createVariable( 1666 | 'velocity_noDA', 'f', ('velocity_noDA',), **fillVDict) 1667 | nc_velocity_noDA.description = "Original, non dealiased, Doppler velocity bins." 1668 | nc_velocity_noDA.units = 'm s^-1' 1669 | nc_velocity_noDA[:] = np.array(self.specVel_noDA, dtype="f4") 1670 | #if not pyNc: nc_velocity_noDA._FillValue =float(self.missingNumber) 1671 | 1672 | nc_height = cdfFile.createVariable( 1673 | 'height', 'f', ncShape2D, **fillVDict) # = missingNumber) 1674 | nc_height.description = "height above instrument" 1675 | nc_height.units = 'm' 1676 | nc_height[:] = np.array(self.H.filled(self.missingNumber), dtype="f4") 1677 | #if not pyNc: nc_height._FillValue =float(self.missingNumber) 1678 | 1679 | if (varsToSave == 'all' and saveAlsoNonDealiased) or "eta_noDA" in varsToSave: 1680 | nc_eta_noDA = cdfFile.createVariable( 1681 | 'eta_noDA', 'f', ncShape3D_noDA, **fillVDict) 1682 | nc_eta_noDA.description = "spectral reflectivities NOT dealiased" 1683 | nc_eta_noDA.units = "mm^6 m^-3" 1684 | nc_eta_noDA[:] = np.array(self.eta_noDA.data, dtype="f4") 1685 | #if not pyNc: nc_eta_noDA._FillValue =float(self.missingNumber) 1686 | 1687 | nc_etaMask_noDA = cdfFile.createVariable( 1688 | 'etaMask_noDA', 'i', ncShape3D_noDA, **fillVDict) 1689 | nc_etaMask_noDA.description = "noise mask of eta NOT dealiased, 0: signal, 1:noise" 1690 | nc_etaMask_noDA.units = "bool" 1691 | nc_etaMask_noDA[:] = np.array( 1692 | np.array(self.eta_noDA.mask, dtype=int), dtype="i4") 1693 | #if not pyNc: nc_etaMask_noDA._FillValue =int(self.missingNumber) 1694 | 1695 | if varsToSave == 'all' or "eta" in varsToSave: 1696 | nc_eta = cdfFile.createVariable('eta', 'f', ncShape3D, **fillVDict) 1697 | nc_eta.description = "spectral reflectivities. if dealiasing is applied, the spectra are triplicated, thus up to three peaks can occur from -12 to +24 m s^-1. However, only one peak is not masked in etaMask" 1698 | nc_eta.units = "mm^6 m^-3" 1699 | nc_eta[:] = np.array(self.eta.data, dtype="f4") 1700 | #if not pyNc: nc_eta._FillValue =float(self.missingNumber) 1701 | 1702 | nc_etaMask = cdfFile.createVariable( 1703 | 'etaMask', 'i', ncShape3D, **fillVDict) 1704 | nc_etaMask.description = "noise mask of eta, 0: signal, 1:noise" 1705 | nc_etaMask.units = "bool" 1706 | nc_etaMask[:] = np.array( 1707 | np.array(self.eta.mask, dtype=int), dtype="i4") 1708 | #if not pyNc: nc_etaMask._FillValue =int(self.missingNumber) 1709 | 1710 | if varsToSave == 'all' or "quality" in varsToSave: 1711 | qualArray, qualDescription = self.getQualityBinArray(self.qual) 1712 | 1713 | nc_qual = cdfFile.createVariable( 1714 | 'quality', 'i', ncShape2D, **fillVDict) 1715 | nc_qual.description = qualDescription 1716 | nc_qual.units = "bin" 1717 | nc_qual[:] = np.array(qualArray, dtype="i4") 1718 | #if not pyNc: nc_qual._FillValue =int(self.missingNumber) 1719 | 1720 | if varsToSave == 'all' or "TF" in varsToSave: 1721 | nc_TF = cdfFile.createVariable('TF', 'f', ncShape2D, **fillVDict) 1722 | nc_TF.description = "Transfer Function (see Metek's documentation)" 1723 | nc_TF.units = "-" 1724 | nc_TF[:] = np.array(self.TF.filled(self.missingNumber), dtype="f4") 1725 | #if not pyNc: nc_TF._FillValue =float(self.missingNumber) 1726 | 1727 | if (varsToSave == 'all' and saveAlsoNonDealiased) or "Ze_noDA" in varsToSave: 1728 | nc_ze_noDA = cdfFile.createVariable( 1729 | 'Ze_noDA', 'f', ncShape2D, **fillVDict) 1730 | nc_ze_noDA.description = "reflectivity of the most significant peak, not dealiased" 1731 | nc_ze_noDA.units = "dBz" 1732 | nc_ze_noDA[:] = np.array(self.Ze_noDA, dtype="f4") 1733 | #if not pyNc: nc_ze_noDA._FillValue =float(self.missingNumber) 1734 | 1735 | if varsToSave == 'all' or "Ze" in varsToSave: 1736 | nc_ze = cdfFile.createVariable('Ze', 'f', ncShape2D, **fillVDict) 1737 | nc_ze.description = "reflectivity of the most significant peak" 1738 | nc_ze.units = "dBz" 1739 | nc_ze[:] = np.array(self.Ze, dtype="f4") 1740 | #if not pyNc: nc_ze._FillValue =float(self.missingNumber) 1741 | 1742 | if (varsToSave == 'all' and saveAlsoNonDealiased) or "specWidth_noDA" in varsToSave: 1743 | nc_specWidth_noDA = cdfFile.createVariable( 1744 | 'spectralWidth_noDA', 'f', ncShape2D, **fillVDict) 1745 | nc_specWidth_noDA.description = "spectral width of the most significant peak, not dealiased" 1746 | nc_specWidth_noDA.units = "m s^-1" 1747 | nc_specWidth_noDA[:] = np.array(self.specWidth_noDA, dtype="f4") 1748 | #if not pyNc: nc_specWidth_noDA._FillValue =float(self.missingNumber) 1749 | 1750 | if varsToSave == 'all' or "specWidth" in varsToSave: 1751 | nc_specWidth = cdfFile.createVariable( 1752 | 'spectralWidth', 'f', ncShape2D, **fillVDict) 1753 | nc_specWidth.description = "spectral width of the most significant peak" 1754 | nc_specWidth.units = "m s^-1" 1755 | nc_specWidth[:] = np.array(self.specWidth, dtype="f4") 1756 | #if not pyNc: nc_specWidth._FillValue =float(self.missingNumber) 1757 | 1758 | if (varsToSave == 'all' and saveAlsoNonDealiased) or "skewness_noDA" in varsToSave: 1759 | nc_skewness_noDA = cdfFile.createVariable( 1760 | 'skewness_noDA', 'f', ncShape2D, **fillVDict) 1761 | nc_skewness_noDA.description = "Skewness of the most significant peak, not dealiased" 1762 | nc_skewness_noDA.units = "-" 1763 | nc_skewness_noDA[:] = np.array(self.skewness_noDA, dtype="f4") 1764 | #if not pyNc: nc_skewness_noDA._FillValue =float(self.missingNumber) 1765 | 1766 | if varsToSave == 'all' or "skewness" in varsToSave: 1767 | nc_skewness = cdfFile.createVariable( 1768 | 'skewness', 'f', ncShape2D, **fillVDict) 1769 | nc_skewness.description = "Skewness of the most significant peak" 1770 | nc_skewness.units = "-" 1771 | nc_skewness[:] = np.array(self.skewness, dtype="f4") 1772 | #if not pyNc: nc_skewness._FillValue =float(self.missingNumber) 1773 | 1774 | if (varsToSave == 'all' and saveAlsoNonDealiased) or "kurtosis_noDA" in varsToSave: 1775 | nc_kurtosis_noDA = cdfFile.createVariable( 1776 | 'kurtosis_noDA', 'f', ncShape2D, **fillVDict) 1777 | nc_kurtosis_noDA.description = "kurtosis of the most significant peak, not dealiased" 1778 | nc_kurtosis_noDA.units = "-" 1779 | nc_kurtosis_noDA[:] = np.array(self.kurtosis_noDA, dtype="f4") 1780 | #if not pyNc: nc_kurtosis_noDA._FillValue =float(self.missingNumber) 1781 | 1782 | if varsToSave == 'all' or "kurtosis" in varsToSave: 1783 | nc_kurtosis = cdfFile.createVariable( 1784 | 'kurtosis', 'f', ncShape2D, **fillVDict) 1785 | nc_kurtosis.description = "kurtosis of the most significant peak" 1786 | nc_kurtosis.units = "-" 1787 | nc_kurtosis[:] = np.array(self.kurtosis, dtype="f4") 1788 | #if not pyNc: nc_kurtosis._FillValue =float(self.missingNumber) 1789 | 1790 | if (varsToSave == 'all' and saveAlsoNonDealiased) or "peakVelLeftBorder_noDA" in varsToSave: 1791 | nc_peakVelLeftBorder_noDA = cdfFile.createVariable( 1792 | 'peakVelLeftBorder_noDA', 'f', ncShape2D, **fillVDict) 1793 | nc_peakVelLeftBorder_noDA.description = "Doppler velocity of the left border of the peak, not dealiased" 1794 | nc_peakVelLeftBorder_noDA.units = "m s^-1" 1795 | nc_peakVelLeftBorder_noDA[:] = np.array( 1796 | self.peakVelLeftBorder_noDA, dtype="f4") 1797 | #if not pyNc: nc_peakVelLeftBorder_noDA._FillValue =float(self.missingNumber) 1798 | 1799 | if varsToSave == 'all' or "peakVelLeftBorder" in varsToSave: 1800 | nc_peakVelLeftBorder = cdfFile.createVariable( 1801 | 'peakVelLeftBorder', 'f', ncShape2D, **fillVDict) 1802 | nc_peakVelLeftBorder.description = "Doppler velocity of the left border of the peak" 1803 | nc_peakVelLeftBorder.units = "m s^-1" 1804 | nc_peakVelLeftBorder[:] = np.array( 1805 | self.peakVelLeftBorder, dtype="f4") 1806 | #if not pyNc: nc_peakVelLeftBorder._FillValue =float(self.missingNumber) 1807 | 1808 | if (varsToSave == 'all' and saveAlsoNonDealiased) or "peakVelRightBorder_noDA" in varsToSave: 1809 | nc_peakVelRightBorder_noDA = cdfFile.createVariable( 1810 | 'peakVelRightBorder_noDA', 'f', ncShape2D, **fillVDict) 1811 | nc_peakVelRightBorder_noDA.description = "Doppler velocity of the right border of the peak, not dealiased" 1812 | nc_peakVelRightBorder_noDA.units = "m s^-1" 1813 | nc_peakVelRightBorder_noDA[:] = np.array( 1814 | self.peakVelRightBorder_noDA, dtype="f4") 1815 | #if not pyNc: nc_peakVelRightBorder_noDA._FillValue =float(self.missingNumber) 1816 | 1817 | if varsToSave == 'all' or "peakVelRightBorder" in varsToSave: 1818 | nc_peakVelRightBorder = cdfFile.createVariable( 1819 | 'peakVelRightBorder', 'f', ncShape2D, **fillVDict) 1820 | nc_peakVelRightBorder.description = "Doppler velocity of the right border of the peak" 1821 | nc_peakVelRightBorder.units = "m s^-1" 1822 | nc_peakVelRightBorder[:] = np.array( 1823 | self.peakVelRightBorder, dtype="f4") 1824 | #if not pyNc: nc_peakVelRightBorder._FillValue =float(self.missingNumber) 1825 | 1826 | if (varsToSave == 'all' and saveAlsoNonDealiased) or "leftSlope_noDA" in varsToSave: 1827 | nc_leftSlope_noDA = cdfFile.createVariable( 1828 | 'leftSlope_noDA', 'f', ncShape2D, **fillVDict) 1829 | nc_leftSlope_noDA.description = "Slope at the left side of the peak, not dealiased" 1830 | nc_leftSlope_noDA.units = "dB/(m s^-1)" 1831 | nc_leftSlope_noDA[:] = np.array(self.leftSlope_noDA, dtype="f4") 1832 | #if not pyNc: nc_leftSlope_noDA._FillValue =float(self.missingNumber) 1833 | 1834 | if varsToSave == 'all' or "leftSlope" in varsToSave: 1835 | nc_leftSlope = cdfFile.createVariable( 1836 | 'leftSlope', 'f', ncShape2D, **fillVDict) 1837 | nc_leftSlope.description = "Slope at the left side of the peak" 1838 | nc_leftSlope.units = "dB/(m s^-1)" 1839 | nc_leftSlope[:] = np.array(self.leftSlope, dtype="f4") 1840 | #if not pyNc: nc_leftSlope._FillValue =float(self.missingNumber) 1841 | 1842 | if (varsToSave == 'all' and saveAlsoNonDealiased) or "rightSlope_noDA" in varsToSave: 1843 | nc_rightSlope_noDA = cdfFile.createVariable( 1844 | 'rightSlope_noDA', 'f', ncShape2D, **fillVDict) 1845 | nc_rightSlope_noDA.description = "Slope at the right side of the peak, not dealiased" 1846 | nc_rightSlope_noDA.units = "dB/(m s^-1)" 1847 | nc_rightSlope_noDA[:] = np.array(self.rightSlope_noDA, dtype="f4") 1848 | #if not pyNc: nc_rightSlope_noDA._FillValue =float(self.missingNumber) 1849 | 1850 | if varsToSave == 'all' or "rightSlope" in varsToSave: 1851 | nc_rightSlope = cdfFile.createVariable( 1852 | 'rightSlope', 'f', ncShape2D, **fillVDict) 1853 | nc_rightSlope.description = "Slope at the right side of the peak" 1854 | nc_rightSlope.units = "dB/(m s^-1)" 1855 | nc_rightSlope[:] = np.array(self.rightSlope, dtype="f4") 1856 | #if not pyNc: nc_rightSlope._FillValue =float(self.missingNumber) 1857 | 1858 | if (varsToSave == 'all' and saveAlsoNonDealiased) or "W_noDA" in varsToSave: 1859 | nc_w_noDA = cdfFile.createVariable( 1860 | 'W_noDA', 'f', ncShape2D, **fillVDict) 1861 | nc_w_noDA.description = "Mean Doppler Velocity of the most significant peak, not dealiased" 1862 | nc_w_noDA.units = "m s^-1" 1863 | nc_w_noDA[:] = np.array(self.W_noDA, dtype="f4") 1864 | #if not pyNc: nc_w_noDA._FillValue =float(self.missingNumber) 1865 | 1866 | if varsToSave == 'all' or "W" in varsToSave: 1867 | nc_w = cdfFile.createVariable('W', 'f', ncShape2D, **fillVDict) 1868 | nc_w.description = "Mean Doppler Velocity of the most significant peak" 1869 | nc_w.units = "m s^-1" 1870 | nc_w[:] = np.array(self.W, dtype="f4") 1871 | #if not pyNc: nc_w._FillValue =float(self.missingNumber) 1872 | 1873 | if varsToSave == 'all' or "etaNoiseAve" in varsToSave: 1874 | nc_noiseAve = cdfFile.createVariable( 1875 | 'etaNoiseAve', 'f', ncShape2D, **fillVDict) 1876 | nc_noiseAve.description = "mean noise of one Doppler Spectrum in the same units as eta, never dealiased" 1877 | nc_noiseAve.units = "mm^6 m^-3" 1878 | nc_noiseAve[:] = np.array(self.etaNoiseAve, dtype="f4") 1879 | #if not pyNc: nc_noiseAve._FillValue =float(self.missingNumber) 1880 | 1881 | if varsToSave == 'all' or "etaNoiseStd" in varsToSave: 1882 | nc_noiseStd = cdfFile.createVariable( 1883 | 'etaNoiseStd', 'f', ncShape2D, **fillVDict) 1884 | nc_noiseStd.description = "std of noise of one Doppler Spectrum in the same units as eta, never dealiased" 1885 | nc_noiseStd.units = "mm^6 m^-3" 1886 | nc_noiseStd[:] = np.array(self.etaNoiseStd, dtype="f4") 1887 | #if not pyNc: nc_noiseStd._FillValue =float(self.missingNumber) 1888 | 1889 | if varsToSave == 'all' or "SNR" in varsToSave: 1890 | nc_SNR = cdfFile.createVariable('SNR', 'f', ncShape2D, **fillVDict) 1891 | nc_SNR.description = "signal to noise ratio of the most significant peak, never dealiased!" 1892 | nc_SNR.units = "dB" 1893 | nc_SNR[:] = np.array(self.SNR, dtype="f4") 1894 | #if not pyNc: nc_SNR._FillValue =float(self.missingNumber) 1895 | 1896 | cdfFile.close() 1897 | return 1898 | 1899 | 1900 | class mrrProcessedData: 1901 | ''' 1902 | Class to read MRR average or instantaneous data 1903 | includes function to save data to netcdf 1904 | 1905 | ''' 1906 | missingNumber = -9999 1907 | 1908 | def __init__(self, fname, debugLimit=0, maskData=True, verbosity=2, ncForm="NETCDF3_CLASSIC"): 1909 | """ 1910 | reads MRR Average or Instantaneous data. The data is not converted, no magic! The input files can be .gz compressed. Invalid or missing data is marked as nan 1911 | 1912 | @parameter fname (str or list): list of files or Filename, wildcards allowed, or 1913 | a single netCDF filename if reading from a file previously 1914 | created by mrrProcessedData.writeNetCDF() 1915 | @parameter debugLimit (int): stop after debugLimit timestamps 1916 | @parameter maskData (bool): mask nan's in arrays 1917 | @parameter verbosity (int): 0: silent exept warnings/errors, 2:verbose 1918 | @parameter ncForm (string): set netCDF format 1919 | 1920 | No return, but provides MRR dataset variables 1921 | """ 1922 | 1923 | # If this is a single filename input, and it is a netCDF 1924 | # (extension is nc or cdf), then read it directly and return. 1925 | if type(fname) is str: 1926 | if os.path.splitext(fname)[1] in ('.nc', '.cdf'): 1927 | 1928 | nc, pyNc = _get_netCDF_module(ncForm=ncForm) 1929 | 1930 | if pyNc: 1931 | cdfFile = nc.Dataset(fname, "r", format=ncForm) 1932 | else: 1933 | cdfFile = nc.NetCDFFile(fname, "r") 1934 | 1935 | self.header = cdfFile.getncattr('mrrHeader') 1936 | self.mrrTimestamps = cdfFile.variables['time'][:] 1937 | self.mrrH = cdfFile.variables['MRR_H'][:] 1938 | self.mrrTF = cdfFile.variables['MRR_TF'][:] 1939 | self.mrrF = cdfFile.variables['MRR_F'][:] 1940 | self.mrrD = cdfFile.variables['MRR_D'][:] 1941 | self.mrrN = cdfFile.variables['MRR_N'][:] 1942 | self.mrrK = cdfFile.variables['MRR_K'][:] 1943 | self.mrrCapitalZ = cdfFile.variables['MRR_Capital_Z'][:] 1944 | self.mrrSmallz = cdfFile.variables['MRR_Small_z'][:] 1945 | self.mrrPIA = cdfFile.variables['MRR_PIA'][:] 1946 | self.mrrRR = cdfFile.variables['MRR_RR'][:] 1947 | self.mrrLWC = cdfFile.variables['MRR_LWC'][:] 1948 | self.mrrW = cdfFile.variables['MRR_W'][:] 1949 | 1950 | cdfFile.close() 1951 | 1952 | self.shape2D = np.shape(self.mrrH) 1953 | self.shape3D = np.shape(self.mrrF) 1954 | 1955 | return 1956 | 1957 | # some helper functions! 1958 | def splitMrrAveData(string, debugTime, floatInt): 1959 | ''' 1960 | splits one line of mrr data into list 1961 | @parameter string (str) string of MRR data 1962 | @parameter debugTime (int) time for debug output 1963 | @parameter floatInt (type) convert float or integer 1964 | 1965 | @retrun array with mrr data 1966 | ''' 1967 | listOfData = list() 1968 | listOfData_append = listOfData.append 1969 | 1970 | i_start = 3 1971 | i_offset = 7 1972 | try: 1973 | for k in np.arange(i_start, i_offset*31, i_offset): 1974 | listOfData_append(mrrDataEsc( 1975 | string[k:k+i_offset], floatInt)) 1976 | except: 1977 | # try to fix MRR bug 1978 | print("repairing data at " + str(unix2date(debugTime))) 1979 | string = string.replace("10000.0", "10000.") 1980 | string = string.replace("1000.00", "1000.0") 1981 | string = string.replace("100.000", "100.00") 1982 | string = string.replace("10.0000", "10.000") 1983 | string = string.replace("1.00000", "1.0000") 1984 | listOfData = list() 1985 | listOfData_append = listOfData.append 1986 | for k in np.arange(i_start, i_offset*31, i_offset): 1987 | try: 1988 | listOfData_append(mrrDataEsc( 1989 | string[k:k+i_offset], floatInt)) 1990 | except: 1991 | print("######### Warning, Corrupt data at " + str(unix2date(debugTime) 1992 | ) + ", position "+str(k)+": " + string+" #########") 1993 | listOfData_append(np.nan) 1994 | return np.array(listOfData) 1995 | 1996 | def mrrDataEsc(string, floatInt): 1997 | """ 1998 | set invalid data to nan! 1999 | 2000 | @parameter string (str): string from mrr data 2001 | @parameter floatInt (function): int or float function 2002 | 2003 | @return - float or int number 2004 | """ 2005 | 2006 | if (string == " "*7) or (len(string) != 7): 2007 | return np.nan 2008 | else: 2009 | return floatInt(string) 2010 | 2011 | if type(fname) == list: 2012 | files = fname 2013 | else: 2014 | files = glob.glob(fname) 2015 | files.sort() 2016 | 2017 | foundAtLeastOneFile = False 2018 | 2019 | # go through all files 2020 | for f, file in enumerate(files): 2021 | if verbosity > 1: 2022 | print("%s of %s:" % (f+1, len(files)), file) 2023 | 2024 | # open file, gzip or ascii 2025 | try: 2026 | if file[-3:] == ".gz": 2027 | try: 2028 | allData = gzip.open(file, 'rt') 2029 | except: 2030 | print("could not open:", file) 2031 | raise IOError("could not open:" + file) 2032 | else: 2033 | try: 2034 | # without errors='ignore', post-processing script crashes 2035 | # when loading MRR raw file with some missing/corrupt data 2036 | # using codecs.open(... encoding='UTF-8' ...) as this seems to be 2037 | # the only method that works in python 2 and 3. 2038 | allData = codecs.open(file, 'r', encoding='UTF-8', errors='ignore') 2039 | except: 2040 | print("could not open:", file) 2041 | raise IOError("could not open:" + file) 2042 | 2043 | if len(allData.read(10)) == 0: 2044 | print(file, "empty!") 2045 | allData.close() 2046 | raise IOError("File empty") 2047 | else: 2048 | allData.seek(0) 2049 | i = 0 2050 | except IOError: 2051 | print("skipping...", file) 2052 | continue 2053 | 2054 | foundAtLeastOneFile = True 2055 | 2056 | # go through file and make a dictionary with timestamp as key and all corresponding lines of data as values 2057 | dataMRR = {} 2058 | prevDate = 0 2059 | tmpList = list() 2060 | for line in allData: 2061 | if line[0:3] == "MRR": 2062 | if i != 0: 2063 | dataMRR[prevDate] = tmpList 2064 | tmpList = [] 2065 | asciiDate = line[4:20] 2066 | # We must have UTC! 2067 | if (re.search("UTC", line) == None): 2068 | sys.exit("Warning, line must start with UTC!") 2069 | date = datetime.datetime(year=2000+int(asciiDate[0:2]), month=int(asciiDate[2:4]), day=int( 2070 | asciiDate[4:6]), hour=int(asciiDate[6:8]), minute=int(asciiDate[8:10]), second=int(asciiDate[10:12])) 2071 | date = int(date2unix(date)) 2072 | tmpList.append(line) 2073 | prevDate = date 2074 | else: 2075 | tmpList.append(line) 2076 | i += 1 2077 | 2078 | dataMRR[prevDate] = tmpList 2079 | allData.close() 2080 | 2081 | try: 2082 | del dataMRR[0] 2083 | print("Warning: some lines without timestamp") 2084 | except: 2085 | pass 2086 | 2087 | if debugLimit == 0: 2088 | debugLimit = len(list(dataMRR.keys())) 2089 | 2090 | # create arrays for data 2091 | aveTimestamps = np.array(np.sort(list(dataMRR.keys()))[ 2092 | 0:debugLimit], dtype=int) 2093 | aveH = np.ones((debugLimit, 31), dtype=float)*np.nan 2094 | aveTF = np.ones((debugLimit, 31), dtype=float)*np.nan 2095 | aveF = np.ones((debugLimit, 31, 64), dtype=float)*np.nan 2096 | aveN = np.ones((debugLimit, 31, 64), dtype=float)*np.nan 2097 | aveD = np.ones((debugLimit, 31, 64), dtype=float)*np.nan 2098 | aveK = np.ones((debugLimit, 31), dtype=float)*np.nan 2099 | aveCapitalZ = np.ones((debugLimit, 31), dtype=float)*np.nan 2100 | aveSmallz = np.ones((debugLimit, 31), dtype=float)*np.nan 2101 | avePIA = np.ones((debugLimit, 31), dtype=float)*np.nan 2102 | aveRR = np.ones((debugLimit, 31), dtype=float)*np.nan 2103 | aveLWC = np.ones((debugLimit, 31), dtype=float)*np.nan 2104 | aveW = np.ones((debugLimit, 31), dtype=float)*np.nan 2105 | 2106 | # go through timestamps and fill up arrays 2107 | for t, timestamp in enumerate(aveTimestamps[0:debugLimit]): 2108 | # print unix2date(timestamp) 2109 | dataSet = dataMRR[timestamp] 2110 | for dataLine in dataSet: 2111 | if dataLine[0:3] == "MRR": 2112 | # just one is stored, thus no array 2113 | self.header = dataLine[21:-2] 2114 | continue # print timestamp 2115 | elif dataLine[0:3] == "H ": 2116 | aveH[t, :] = splitMrrAveData( 2117 | dataLine, timestamp, float) 2118 | continue 2119 | elif dataLine[0:3] == "TF ": 2120 | aveTF[t, :] = splitMrrAveData( 2121 | dataLine, timestamp, float) 2122 | continue # print "TF" 2123 | elif dataLine[0:1] == "F": 2124 | try: 2125 | specBin = int(dataLine[1:3]) 2126 | except: 2127 | print("######### Warning, Corrupt data header at " + 2128 | str(unix2date(timestamp)) + ", " + dataLine+" #########") 2129 | continue 2130 | aveF[t, :, specBin] = splitMrrAveData( 2131 | dataLine, timestamp, float) 2132 | continue 2133 | elif dataLine[0:1] == "D": 2134 | try: 2135 | specBin = int(dataLine[1:3]) 2136 | except: 2137 | print("######### Warning, Corrupt data header at " + 2138 | str(unix2date(timestamp)) + ", " + dataLine+" #########") 2139 | continue 2140 | aveD[t, :, specBin] = splitMrrAveData( 2141 | dataLine, timestamp, float) 2142 | continue 2143 | elif dataLine[0:1] == "N": 2144 | try: 2145 | specBin = int(dataLine[1:3]) 2146 | except: 2147 | print("######### Warning, Corrupt data header at " + 2148 | str(unix2date(timestamp)) + ", " + dataLine+" #########") 2149 | continue 2150 | aveN[t, :, specBin] = splitMrrAveData( 2151 | dataLine, timestamp, float) 2152 | continue 2153 | elif dataLine[0:3] == "K ": 2154 | aveK[t, :] = splitMrrAveData( 2155 | dataLine, timestamp, float) 2156 | continue 2157 | elif dataLine[0:3] == "PIA": 2158 | avePIA[t, :] = splitMrrAveData( 2159 | dataLine, timestamp, float) 2160 | continue 2161 | elif dataLine[0:3] == "Z ": 2162 | aveCapitalZ[t, :] = splitMrrAveData( 2163 | dataLine, timestamp, float) 2164 | continue 2165 | elif dataLine[0:3] == "z ": 2166 | aveSmallz[t, :] = splitMrrAveData( 2167 | dataLine, timestamp, float) 2168 | continue 2169 | elif dataLine[0:3] == "RR ": 2170 | aveRR[t, :] = splitMrrAveData( 2171 | dataLine, timestamp, float) 2172 | continue 2173 | elif dataLine[0:3] == "LWC": 2174 | aveLWC[t, :] = splitMrrAveData( 2175 | dataLine, timestamp, float) 2176 | continue 2177 | elif dataLine[0:3] == "W ": 2178 | aveW[t, :] = splitMrrAveData( 2179 | dataLine, timestamp, float) 2180 | continue 2181 | elif len(dataLine) == 2: 2182 | continue 2183 | else: 2184 | print("? Line not recognized:", str( 2185 | unix2date(timestamp)), dataLine, len(dataLine)) 2186 | 2187 | # join arrays of different files 2188 | try: 2189 | self.mrrTimestamps = np.concatenate( 2190 | (self.mrrTimestamps, aveTimestamps), axis=0) 2191 | self.mrrH = np.concatenate((self.mrrH, aveH), axis=0) 2192 | self.mrrTF = np.concatenate((self.mrrTF, aveTF), axis=0) 2193 | self.mrrF = np.concatenate((self.mrrF, aveF), axis=0) 2194 | self.mrrN = np.concatenate((self.mrrN, aveN), axis=0) 2195 | self.mrrD = np.concatenate((self.mrrD, aveD), axis=0) 2196 | self.mrrK = np.concatenate((self.mrrK, aveK), axis=0) 2197 | self.mrrPIA = np.concatenate((self.mrrPIA, avePIA), axis=0) 2198 | self.mrrCapitalZ = np.concatenate( 2199 | (self.mrrCapitalZ, aveCapitalZ), axis=0) 2200 | self.mrrSmallz = np.concatenate( 2201 | (self.mrrSmallz, aveSmallz), axis=0) 2202 | self.mrrRR = np.concatenate((self.mrrRR, aveRR), axis=0) 2203 | self.mrrLWC = np.concatenate((self.mrrLWC, aveLWC), axis=0) 2204 | self.mrrW = np.concatenate((self.mrrW, aveW), axis=0) 2205 | except AttributeError: 2206 | self.mrrTimestamps = aveTimestamps 2207 | self.mrrH = aveH 2208 | self.mrrTF = aveTF 2209 | self.mrrF = aveF 2210 | self.mrrN = aveN 2211 | self.mrrD = aveD 2212 | self.mrrK = aveK 2213 | self.mrrPIA = avePIA 2214 | self.mrrCapitalZ = aveCapitalZ 2215 | self.mrrSmallz = aveSmallz 2216 | self.mrrRR = aveRR 2217 | self.mrrLWC = aveLWC 2218 | self.mrrW = aveW 2219 | if foundAtLeastOneFile == False: 2220 | print("NO DATA") 2221 | raise UnboundLocalError 2222 | try: 2223 | self.header 2224 | except: 2225 | print("did not find any MRR data in file!") 2226 | raise IOError("did not find any MRR data in file!") 2227 | del aveTimestamps, aveH, aveTF, aveF, aveN, aveD, aveK, avePIA, aveCapitalZ, aveSmallz, aveRR, aveLWC, aveW 2228 | 2229 | if maskData: 2230 | self.mrrTimestamps = np.ma.masked_array( 2231 | self.mrrTimestamps, np.isnan(self.mrrTimestamps)) 2232 | self.mrrH = np.ma.masked_array(self.mrrH, np.isnan(self.mrrH)) 2233 | self.mrrTF = np.ma.masked_array(self.mrrTF, np.isnan(self.mrrTF)) 2234 | self.mrrF = np.ma.masked_array(self.mrrF, np.isnan(self.mrrF)) 2235 | self.mrrN = np.ma.masked_array(self.mrrN, np.isnan(self.mrrN)) 2236 | self.mrrD = np.ma.masked_array(self.mrrD, np.isnan(self.mrrD)) 2237 | self.mrrK = np.ma.masked_array(self.mrrK, np.isnan(self.mrrK)) 2238 | self.mrrPIA = np.ma.masked_array( 2239 | self.mrrPIA, np.isnan(self.mrrPIA)) 2240 | self.mrrCapitalZ = np.ma.masked_array( 2241 | self.mrrCapitalZ, np.isnan(self.mrrCapitalZ)) 2242 | self.mrrSmallz = np.ma.masked_array( 2243 | self.mrrSmallz, np.isnan(self.mrrSmallz)) 2244 | self.mrrRR = np.ma.masked_array(self.mrrRR, np.isnan(self.mrrRR)) 2245 | self.mrrLWC = np.ma.masked_array( 2246 | self.mrrLWC, np.isnan(self.mrrLWC)) 2247 | self.mrrW = np.ma.masked_array(self.mrrW, np.isnan(self.mrrW)) 2248 | 2249 | self.shape2D = np.shape(self.mrrH) 2250 | self.shape3D = np.shape(self.mrrF) 2251 | 2252 | if verbosity > 0: 2253 | print("done reading") 2254 | # end def __init__ 2255 | 2256 | def writeNetCDF(self, fileOut, author="IMProToo", location="", institution="", ncForm="NETCDF3_CLASSIC"): 2257 | ''' 2258 | writes MRR Average or Instantaneous Data into Netcdf file 2259 | 2260 | @parameter fileOut (str): netCDF file name 2261 | @parameter author (str): Author for netCDF meta data (default:IMProToo) 2262 | @parameter location (str): Location of instrument for NetCDF Metadata (default: "") 2263 | @parameter institution (str): Institution to whom the instrument belongs (default: "") 2264 | @parameter ncForm (str): netCDF Format, possible values are NETCDF3_CLASSIC, NETCDF3_64BIT, NETCDF4_CLASSIC, and NETCDF4 for the python-netcdf4 package, NETCDF3 takes the "old" Scientific.IO.NetCDF module, which is a bit more convinient to install or as fall back option python-netcdf3 2265 | 2266 | ''' 2267 | 2268 | nc, pyNc = _get_netCDF_module(ncForm=ncForm) 2269 | 2270 | if pyNc: 2271 | cdfFile = nc.Dataset(fileOut, "w", format=ncForm) 2272 | else: 2273 | cdfFile = nc.NetCDFFile(fileOut, "w") 2274 | 2275 | fillVDict = dict() 2276 | # little cheat to avoid hundreds of if, else... 2277 | if pyNc: 2278 | fillVDict["fill_value"] = self.missingNumber 2279 | 2280 | print("writing %s ..." % (fileOut)) 2281 | # Attributes 2282 | cdfFile.history = 'Created with IMProToo v' + __version__ 2283 | cdfFile.author = 'Max Maahn' 2284 | cdfFile.processing_date = datetime.datetime.utcnow().strftime("%Y-%m-%d %H:%M:%S UTC") 2285 | cdfFile.reference = 'Maahn, M. and Kollias, P., 2012: Improved Micro Rain Radar snow measurements using Doppler spectra post-processing, Atmos. Meas. Tech., 5, 2661-2673, doi:10.5194/amt-5-2661-2012. ' 2286 | cdfFile.title = 'Micro rain radar averaged data (Metek standard output) converted to netcdf' 2287 | cdfFile.comment = 'This data is only valid in case of liquid precipitation. Note that this data has been processed regardless of precipitation type and additional external information about precipitation type is needed for correct interpretation of the measurements.' 2288 | cdfFile.institution = institution 2289 | cdfFile.contact_person = author 2290 | cdfFile.source = 'MRR-2' 2291 | cdfFile.location = location 2292 | cdfFile.mrrHeader = self.header 2293 | 2294 | # Dimensions 2295 | cdfFile.createDimension('MRR rangegate', 31) 2296 | cdfFile.createDimension('time', None) # allows Multifile read 2297 | cdfFile.createDimension('MRR spectralclass', 64) 2298 | 2299 | nc_times = cdfFile.createVariable('time', 'i', ('time',), **fillVDict) 2300 | nc_ranges = cdfFile.createVariable( 2301 | 'MRR rangegate', 'f', ('time', 'MRR rangegate',), **fillVDict) 2302 | nc_classes = cdfFile.createVariable( 2303 | 'MRR spectralclass', 'i', ('MRR spectralclass',)) 2304 | 2305 | nc_times.units = 'seconds since 1970-01-01 00:00:00' 2306 | nc_ranges.units = 'm' 2307 | nc_classes.units = 'none' 2308 | 2309 | # Create Variables 2310 | nc_h = cdfFile.createVariable( 2311 | 'MRR_H', 'f', ('time', 'MRR rangegate',), **fillVDict) 2312 | nc_h.units = 'm' 2313 | 2314 | nc_tf = cdfFile.createVariable( 2315 | 'MRR_TF', 'f', ('time', 'MRR rangegate',), **fillVDict) 2316 | nc_tf.units = 'none' 2317 | 2318 | nc_f = cdfFile.createVariable( 2319 | 'MRR_F', 'f', ('time', 'MRR rangegate', 'MRR spectralclass',), **fillVDict) 2320 | nc_f.units = 'dB' 2321 | 2322 | nc_d = cdfFile.createVariable( 2323 | 'MRR_D', 'f', ('time', 'MRR rangegate', 'MRR spectralclass',), **fillVDict) 2324 | nc_d.units = 'mm' 2325 | 2326 | nc_n = cdfFile.createVariable( 2327 | 'MRR_N', 'f', ('time', 'MRR rangegate', 'MRR spectralclass',), **fillVDict) 2328 | nc_n.units = 'm^-3 mm^-1' 2329 | 2330 | nc_k = cdfFile.createVariable( 2331 | 'MRR_K', 'f', ('time', 'MRR rangegate',), **fillVDict) 2332 | nc_k.units = 'dB' 2333 | 2334 | nc_capitalZ = cdfFile.createVariable( 2335 | 'MRR_Capital_Z', 'f', ('time', 'MRR rangegate',), **fillVDict) 2336 | nc_capitalZ.units = 'dBz' 2337 | 2338 | nc_smallz = cdfFile.createVariable( 2339 | 'MRR_Small_z', 'f', ('time', 'MRR rangegate',), **fillVDict) 2340 | nc_smallz.units = 'dBz' 2341 | 2342 | nc_pia = cdfFile.createVariable( 2343 | 'MRR_PIA', 'f', ('time', 'MRR rangegate',), **fillVDict) 2344 | nc_pia.units = 'dB' 2345 | 2346 | nc_rr = cdfFile.createVariable( 2347 | 'MRR_RR', 'f', ('time', 'MRR rangegate',), **fillVDict) 2348 | nc_rr.units = 'mm/h' 2349 | 2350 | nc_lwc = cdfFile.createVariable( 2351 | 'MRR_LWC', 'f', ('time', 'MRR rangegate',), **fillVDict) 2352 | nc_lwc.units = 'g/m^3' 2353 | 2354 | nc_w = cdfFile.createVariable( 2355 | 'MRR_W', 'f', ('time', 'MRR rangegate',), **fillVDict) 2356 | nc_w.units = 'm s^-1' 2357 | 2358 | # fill dimensions 2359 | nc_classes[:] = np.arange(0, 64, 1, dtype="i4") 2360 | nc_times[:] = np.array(self.mrrTimestamps, dtype="i4") 2361 | nc_ranges[:] = np.array(self.mrrH, dtype="f4") 2362 | 2363 | # fill data 2364 | nc_h[:] = np.array(self.mrrH, dtype="f4") 2365 | nc_tf[:] = np.array(self.mrrTF, dtype="f4") 2366 | nc_f[:] = np.array(self.mrrF, dtype="f4") 2367 | nc_d[:] = np.array(self.mrrD, dtype="f4") 2368 | nc_n[:] = np.array(self.mrrN, dtype="f4") 2369 | nc_k[:] = np.array(self.mrrK, dtype="f4") 2370 | nc_capitalZ[:] = np.array(self.mrrCapitalZ, dtype="f4") 2371 | nc_smallz[:] = np.array(self.mrrSmallz, dtype="f4") 2372 | nc_pia[:] = np.array(self.mrrPIA, dtype="f4") 2373 | nc_rr[:] = np.array(self.mrrRR, dtype="f4") 2374 | nc_lwc[:] = np.array(self.mrrLWC, dtype="f4") 2375 | nc_w[:] = np.array(self.mrrW, dtype="f4") 2376 | 2377 | # commented because of Ubuntu bug: https://bugs.launchpad.net/ubuntu/+source/python-scientific/+bug/1005571 2378 | # if not pyNc: 2379 | ##import pdb;pdb.set_trace() 2380 | #nc_ranges._FillValue =float(self.missingNumber) 2381 | #nc_tf._FillValue =float(self.missingNumber) 2382 | #nc_f._FillValue =float(self.missingNumber) 2383 | #nc_d._FillValue =float(self.missingNumber) 2384 | #nc_n._FillValue =float(self.missingNumber) 2385 | #nc_k._FillValue =float(self.missingNumber) 2386 | #nc_capitalZ._FillValue =float(self.missingNumber) 2387 | #nc_smallz._FillValue =float(self.missingNumber) 2388 | #nc_pia._FillValue =float(self.missingNumber) 2389 | #nc_rr._FillValue =float(self.missingNumber) 2390 | #nc_lwc._FillValue =float(self.missingNumber) 2391 | #nc_w._FillValue =float(self.missingNumber) 2392 | 2393 | cdfFile.close() 2394 | print("done") 2395 | # end def writeNetCDF 2396 | # end class MrrData 2397 | 2398 | 2399 | class mrrRawData: 2400 | ''' 2401 | Class to read MRR raw data 2402 | includes function to save data to netcdf 2403 | ''' 2404 | 2405 | missingNumber = -9999 2406 | 2407 | def __init__(self, fname, debugStart=0, debugLimit=0, maskData=True, ncForm="NETCDF3_CLASSIC"): 2408 | """ 2409 | reads MRR raw data. The data is not converted, no magic! The input files can be .gz compressed. 2410 | A single netCDF file can be input, that was previously created from mrrRawData.writeNetCDF() 2411 | Invalid or Missing data is marked as nan and masked 2412 | 2413 | Since MRR raw data can contains all teh data transfered on the serial bus, a lot warnings can be raised. Usually these can be ignored. 2414 | 2415 | @parameter fname (str or list): list of files or Filename, wildcards allowed! 2416 | a single netCDF filename if reading from a file previously 2417 | created by mrrProcessedData.writeNetCDF() 2418 | @parameter debugstart (int): start after debugstart timestamps 2419 | @parameter debugLimit (int): stop after debugLimit timestamps 2420 | @parameter ncForm (string): set netCDF format 2421 | 2422 | provides: 2423 | mrrRawTime (numpy int64): timestamps in seconds since 01-01-1970 (time) 2424 | mrrRawHeight (numpy float64): height levels (time*height) 2425 | mrrRawTF (numpy float64): Transfer function (time*height) 2426 | mrrRawSpectrum (numpy float64): spectral reflectivities of MRR raw data (time*height*velocity) 2427 | """ 2428 | 2429 | # only provided in newer Firmware, has to be guessed for older ones 2430 | self.defaultSpecPer10Sec = 58 2431 | self.timezone = None 2432 | 2433 | # If this is a single filename input, and it is a netCDF 2434 | # (extension is nc or cdf), then read it directly and return. 2435 | if type(fname) is str: 2436 | if os.path.splitext(fname)[1] in ('.nc', '.cdf'): 2437 | 2438 | nc, pyNc = _get_netCDF_module(ncForm=ncForm) 2439 | 2440 | if pyNc: 2441 | cdfFile = nc.Dataset(fname, "r", format=ncForm) 2442 | else: 2443 | cdfFile = nc.NetCDFFile(fname, "r") 2444 | 2445 | self.header = cdfFile.getncattr('mrrHeader') 2446 | self.mrrRawCC = cdfFile.getncattr('mrrCalibrationConstant') 2447 | self.mrrRawHeight = cdfFile.variables['MRR rangegate'][:] 2448 | self.mrrRawTime = cdfFile.variables['MRR time'][:] 2449 | self.mrrRawTF = cdfFile.variables['MRR_TF'][:] 2450 | self.mrrRawSpectrum = cdfFile.variables['MRR_Spectra'][:] 2451 | self.mrrRawNoSpec = cdfFile.variables['MRR_NoSpectra'][:] 2452 | 2453 | try: 2454 | self.timezone = str(cdfFile.variables['MRR time'].timezone) 2455 | except AttributeError: 2456 | # this can occur when loading a file created with an older 2457 | # version of IMProToo, before the timezone update. 2458 | warnings.warn("timezone attribute missing, assuming UTC") 2459 | self.timezone = "UTC" 2460 | 2461 | cdfFile.close() 2462 | 2463 | self.shape2D = np.shape(self.mrrRawHeight) 2464 | self.shape3D = np.shape(self.mrrRawSpectrum) 2465 | 2466 | return 2467 | 2468 | # some helper functions 2469 | def rawEsc(string, floatInt): 2470 | """ 2471 | set invalid data to nan! 2472 | 2473 | @parameter string (str): string from mrr data 2474 | @parameter floatInt (function): int or float function 2475 | 2476 | @return - float or int number 2477 | """ 2478 | 2479 | if (string == " "*9) or (len(string) != 9): 2480 | return np.nan 2481 | else: 2482 | return floatInt(string) 2483 | 2484 | def splitMrrRawData(string, debugTime, floatInt, startI): 2485 | ''' 2486 | splits one line of mrr raw data into list 2487 | @parameter string (str) string of MRR data 2488 | @parameter debugTime (int) time for debug output 2489 | @parameter floatInt (type) convert float or integer 2490 | @parameter startI (int) first data index, old file format 6, new 3 2491 | @retrun array with mrr data 2492 | ''' 2493 | 2494 | instData = list() 2495 | instData_append = instData.append 2496 | 2497 | for k in np.arange(startI, 9*32, 9): 2498 | try: 2499 | instData_append(rawEsc(string[k:k+9], floatInt)) 2500 | except: 2501 | print("######### Warning, Corrupt data at " + str(unix2date(debugTime)) + 2502 | ", " + str(timestamp) + ", position "+str(k)+": " + string+" #########") 2503 | instData_append(np.nan) 2504 | return np.array(instData) 2505 | 2506 | if type(fname) == list: 2507 | files = fname 2508 | else: 2509 | files = glob.glob(fname) 2510 | files.sort() 2511 | 2512 | foundAtLeastOneFile = False 2513 | 2514 | # go thorugh all files 2515 | for f, file in enumerate(files): 2516 | print("%s of %s:" % (f+1, len(files)), file) 2517 | # open file gz or ascii 2518 | try: 2519 | if file[-3:] == ".gz": 2520 | try: 2521 | allData = gzip.open(file, 'rt') 2522 | except: 2523 | print("could not open:", file) 2524 | raise IOError("could not open:" + file) 2525 | else: 2526 | try: 2527 | # without errors='ignore', post-processing script crashes 2528 | # when loading MRR raw file with some missing/corrupt data 2529 | # using codecs.open(... encoding='UTF-8' ...) as this seems to be 2530 | # the only method that works in python 2 and 3. 2531 | allData = codecs.open(file, 'r', encoding='UTF-8', errors='ignore') 2532 | except: 2533 | print("could not open:", file) 2534 | raise IOError("could not open:" + file) 2535 | 2536 | if len(allData.read(10)) == 0: 2537 | print(file, "empty!") 2538 | allData.close() 2539 | raise IOError("File empty") 2540 | else: 2541 | allData.seek(0) 2542 | i = 0 2543 | except IOError: 2544 | print("skipping...") 2545 | continue 2546 | 2547 | foundAtLeastOneFile = True 2548 | 2549 | # go through file and make a dictionary with timestamp as key and all corresponding lines of data as values 2550 | dataMRR = {} 2551 | prevDate = 0 2552 | tmpList = list() 2553 | 2554 | # preset, is changed in 8 lines if required 2555 | fileFormat = "new" 2556 | 2557 | for line in allData: 2558 | if line[0:2] == "T:" or line[0:3] == "MRR": 2559 | if i != 0: 2560 | dataMRR[prevDate] = tmpList 2561 | tmpList = [] 2562 | if line[0:2] == "T:": 2563 | asciiDate = line[2:14] # old mrr raw data 2564 | fileFormat = "old" # if there 2565 | elif line[0:4] == "MRR ": 2566 | asciiDate = line[4:16] # new mrr raw spectra 2567 | else: 2568 | raise IOError("must be either new or old file format!") 2569 | # Script wants UTC! 2570 | date = datetime.datetime(year=2000+int(asciiDate[0:2]), month=int(asciiDate[2:4]), day=int( 2571 | asciiDate[4:6]), hour=int(asciiDate[6:8]), minute=int(asciiDate[8:10]), second=int(asciiDate[10:12])) 2572 | date = int(date2unix(date)) 2573 | tmpList.append(line) 2574 | prevDate = date 2575 | else: 2576 | tmpList.append(line) 2577 | i += 1 2578 | # end for line 2579 | dataMRR[prevDate] = tmpList 2580 | allData.close() 2581 | 2582 | try: 2583 | del dataMRR[0] 2584 | warnings.warn("Warning: some lines without timestamp") 2585 | except: 2586 | pass 2587 | 2588 | if fileFormat == "new": 2589 | startIndex = 3 2590 | elif fileFormat == "old": 2591 | startIndex = 6 2592 | else: 2593 | raise IOError("must be either new or old file format!") 2594 | 2595 | if debugLimit == 0: 2596 | debugLimit = len(list(dataMRR.keys())) 2597 | 2598 | specLength = debugLimit - debugStart 2599 | 2600 | # create arrays for data 2601 | rawSpectra = np.ones((specLength, 32, 64), dtype=int)*np.nan 2602 | rawTimestamps = np.array(np.sort(list(dataMRR.keys()))[ 2603 | debugStart:debugLimit], dtype=int) 2604 | rawHeights = np.ones((specLength, 32), dtype=int)*np.nan 2605 | rawTFs = np.ones((specLength, 32), dtype=float)*np.nan 2606 | rawNoSpec = np.zeros(specLength, dtype=int) 2607 | 2608 | # default value - if the whole file is processed without ever setting mrrRawCC, this 2609 | # means the file is not usable for Ze calculations, but there is no workaround there. 2610 | self.mrrRawCC = 0 2611 | 2612 | # go through timestamps and fill up arrays 2613 | for t, timestamp in enumerate(rawTimestamps): 2614 | dataSet = dataMRR[timestamp] 2615 | for dataLine in dataSet: 2616 | if dataLine[0:2] == "T:" or dataLine[0:3] == "MRR": 2617 | # store the first or second header line as an example, but parse every one 2618 | # to check for the CC and number of spectra variables. The first header line 2619 | # of MRR data might be messed up after starting the MRR, so the second one 2620 | # is used if available. 2621 | if t in [0, 1]: 2622 | self.header = dataLine 2623 | headerLineCC, headerLineNumSpectra, timezone = self.parseHeaderLine( 2624 | dataLine, fileFormat) 2625 | if headerLineCC is not None: 2626 | self.mrrRawCC = headerLineCC 2627 | if headerLineNumSpectra is not None: 2628 | rawNoSpec[t] = headerLineNumSpectra 2629 | else: 2630 | # if fileFormat is "old", then the default value must always be taken; 2631 | # otherwise, use the value from the headerLine, if present, otherwise 2632 | # print a warning, since that means the headerLine had a problem. 2633 | if fileFormat == "new": 2634 | warnings.warn( 2635 | 'Warning, could not read number of Spectra, taking default instead: '+self.defaultSpecPer10Sec) 2636 | rawNoSpec[t] = self.defaultSpecPer10Sec 2637 | if self.timezone is None: 2638 | self.timezone = timezone 2639 | else: 2640 | assert self.timezone == timezone 2641 | 2642 | continue # print timestamp 2643 | elif dataLine[0:3] == "M:h" or dataLine[0] == "H": 2644 | rawHeights[t, :] = splitMrrRawData( 2645 | dataLine, timestamp, int, startIndex) 2646 | continue # print "H" 2647 | elif dataLine[0:4] == "M:TF" or dataLine[0:2] == "TF": 2648 | rawTFs[t, :] = splitMrrRawData( 2649 | dataLine, timestamp, float, startIndex) 2650 | continue # print "TF" 2651 | elif dataLine[0:3] == "M:f" or dataLine[0] == "F": 2652 | try: 2653 | if fileFormat == "old": 2654 | specBin = int(dataLine[3:5]) 2655 | else: 2656 | specBin = int(dataLine[1:3]) 2657 | except: 2658 | warnings.warn("######### Warning, Corrupt data header at " + str( 2659 | unix2date(timestamp)) + ", " + str(timestamp) + ", " + dataLine+" #########") 2660 | continue 2661 | rawSpectra[t, :, specBin] = splitMrrRawData( 2662 | dataLine, timestamp, int, startIndex) 2663 | continue 2664 | elif (dataLine[0:2] == "C:") or (dataLine[0:2] == "R:"): 2665 | continue 2666 | else: 2667 | warnings.warn("? Line not recognized:" + dataLine) 2668 | 2669 | # end for t,timestamp 2670 | 2671 | # discard spectra which are only partly valid! 2672 | 2673 | rawSpectra[np.any(np.isnan(rawSpectra), axis=2)] = np.nan 2674 | rawSpectra[np.any(np.isnan(rawTFs), axis=1)] = np.nan 2675 | rawSpectra[np.any(np.isnan(rawHeights), axis=1)] = np.nan 2676 | rawTFs[np.any(np.isnan(rawTFs), axis=1)] = np.nan 2677 | rawHeights[np.any(np.isnan(rawHeights), axis=1)] = np.nan 2678 | 2679 | # join arrays of different days 2680 | try: 2681 | self.mrrRawHeight = np.concatenate( 2682 | (self.mrrRawHeight, rawHeights), axis=0) 2683 | self.mrrRawTime = np.concatenate( 2684 | (self.mrrRawTime, rawTimestamps), axis=0) 2685 | self.mrrRawTF = np.concatenate((self.mrrRawTF, rawTFs), axis=0) 2686 | self.mrrRawSpectrum = np.concatenate( 2687 | (self.mrrRawSpectrum, rawSpectra), axis=0) 2688 | self.mrrRawNoSpec = np.concatenate( 2689 | (self.mrrRawNoSpec, rawNoSpec), axis=0) 2690 | except AttributeError: 2691 | self.mrrRawHeight = rawHeights 2692 | self.mrrRawTime = rawTimestamps 2693 | self.mrrRawTF = rawTFs 2694 | self.mrrRawSpectrum = rawSpectra 2695 | self.mrrRawNoSpec = rawNoSpec 2696 | # end try 2697 | # end for f,file 2698 | 2699 | if foundAtLeastOneFile == False: 2700 | raise UnboundLocalError("No files found: " + fname) 2701 | try: 2702 | self.header 2703 | except: 2704 | print("did not find any MRR data in file!") 2705 | raise IOError("did not find any MRR data in file!") 2706 | del rawHeights, rawTimestamps, rawTFs, rawSpectra 2707 | 2708 | if maskData: 2709 | self.mrrRawHeight = np.ma.masked_array( 2710 | self.mrrRawHeight, np.isnan(self.mrrRawHeight)) 2711 | self.mrrRawTime = np.ma.masked_array( 2712 | self.mrrRawTime, np.isnan(self.mrrRawTime)) 2713 | self.mrrRawTF = np.ma.masked_array( 2714 | self.mrrRawTF, np.isnan(self.mrrRawTF)) 2715 | self.mrrRawSpectrum = np.ma.masked_array( 2716 | self.mrrRawSpectrum, np.isnan(self.mrrRawSpectrum)) 2717 | 2718 | self.shape2D = np.shape(self.mrrRawHeight) 2719 | self.shape3D = np.shape(self.mrrRawSpectrum) 2720 | 2721 | # end def __init__ 2722 | 2723 | @staticmethod 2724 | def parseHeaderLine(headerLine, fileFormat): 2725 | ''' 2726 | Parses the raw data header line. 2727 | Tries to parse according to the fileFormat ("old", or "new") 2728 | Prints a warning if unsuccessful. 2729 | ''' 2730 | 2731 | tokens = headerLine.split() 2732 | 2733 | CC = None 2734 | numSpectra = None 2735 | 2736 | try: 2737 | idx = tokens.index('CC') 2738 | except: 2739 | warnings.warn('Warning, could not find Keyword CC in :'+headerLine) 2740 | else: 2741 | try: 2742 | CC = int(tokens[idx+1]) 2743 | except: 2744 | warnings.warn('Warning, could not read CC in: ' + headerLine) 2745 | 2746 | if fileFormat == "new": 2747 | if not tokens[2].startswith("UTC"): 2748 | raise IOError("ERROR, timestring must start with UTC!") 2749 | timezone = tokens[2] 2750 | if tokens[-1] != "RAW": 2751 | raise IOError("Was expecting MRR RAW DATA, found: "+tokens[-1]) 2752 | try: 2753 | idx = tokens.index('MDQ') 2754 | except: 2755 | warnings.warn( 2756 | 'Warning, could not find Keyword MDQ in :'+headerLine) 2757 | else: 2758 | try: 2759 | numSpectra = int(tokens[idx+2]) 2760 | except: 2761 | warnings.warn( 2762 | 'Warning, could not read number of Spectra: in ' + headerLine) 2763 | 2764 | elif fileFormat == "old": 2765 | if tokens[1] != "UTC": 2766 | raise IOError("ERROR, time must be UTC!") 2767 | timezone = tokens[1] 2768 | 2769 | else: 2770 | raise IOError("must be either new or old file format!") 2771 | 2772 | return CC, numSpectra, timezone 2773 | 2774 | def writeNetCDF(self, fileOut, author="IMProToo", description="MRR Raw Data", ncForm='NETCDF3_CLASSIC'): 2775 | ''' 2776 | writes MRR raw Data into Netcdf file 2777 | 2778 | @parameter fileOut (str): netCDF file name 2779 | @parameter author (str): Author for netCDF meta data (default:IMProToo) 2780 | @parameter description (str): Description for NetCDF Metadata (default: empty) 2781 | @parameter netcdfFormat (str): netcdf format, possible values are NETCDF3_CLASSIC, NETCDF3_64BIT, NETCDF4_CLASSIC, and NETCDF4 for the python-netcdf4 package, NETCDF3 takes the "old" Scientific.IO.NetCDF module, which is a bit more convinient to install or as fall back option python-netcdf3 2782 | ''' 2783 | 2784 | nc, pyNc = _get_netCDF_module(ncForm=ncForm) 2785 | 2786 | if pyNc: 2787 | cdfFile = nc.Dataset(fileOut, "w", format=ncForm) 2788 | else: 2789 | cdfFile = nc.NetCDFFile(fileOut, "w") 2790 | 2791 | print("writing %s ..." % (fileOut)) 2792 | # Attributes 2793 | cdfFile.history = 'Created ' + str(time.ctime(time.time())) 2794 | cdfFile.source = 'Created by '+author + ' with IMProToo v' + __version__ 2795 | cdfFile.mrrHeader = self.header 2796 | cdfFile.description = description 2797 | cdfFile.mrrCalibrationConstant = self.mrrRawCC 2798 | 2799 | fillVDict = dict() 2800 | # little cheat to avoid hundreds of if, else... 2801 | if pyNc: 2802 | fillVDict["fill_value"] = self.missingNumber 2803 | 2804 | # Dimensions 2805 | cdfFile.createDimension('MRR rangegate', 32) 2806 | cdfFile.createDimension('time', None) # allows Multifile read 2807 | cdfFile.createDimension('MRR spectralclass', 64) 2808 | 2809 | nc_times = cdfFile.createVariable( 2810 | 'MRR time', 'i', ('time',), **fillVDict) 2811 | nc_ranges = cdfFile.createVariable( 2812 | 'MRR rangegate', 'f', ('time', 'MRR rangegate',)) 2813 | nc_classes = cdfFile.createVariable( 2814 | 'MRR spectralclass', 'i', ('MRR spectralclass',), **fillVDict) 2815 | 2816 | nc_times.units = 'seconds since 1970-01-01 00:00:00' 2817 | nc_times.timezone = self.timezone 2818 | nc_ranges.units = 'm' 2819 | nc_classes.units = 'none' 2820 | 2821 | # Create Variables 2822 | nc_tf = cdfFile.createVariable( 2823 | 'MRR_TF', 'f', ('time', 'MRR rangegate',), **fillVDict) 2824 | nc_tf.units = 'none' 2825 | 2826 | nc_spectra = cdfFile.createVariable( 2827 | 'MRR_Spectra', 'f', ('time', 'MRR rangegate', 'MRR spectralclass',), **fillVDict) 2828 | nc_spectra.units = 'none' 2829 | 2830 | nc_noSpec = cdfFile.createVariable( 2831 | 'MRR_NoSpectra', 'i', ('time',), **fillVDict) 2832 | nc_noSpec.units = 'none' 2833 | 2834 | # fill dimensions 2835 | nc_classes[:] = np.array(np.arange(0, 64, 1), dtype="i4") 2836 | nc_times[:] = np.array(self.mrrRawTime, dtype="i4") 2837 | nc_ranges[:] = np.array(self.mrrRawHeight, dtype="f4") 2838 | 2839 | # fill data 2840 | nc_tf[:] = np.array(self.mrrRawTF, dtype="f4") 2841 | nc_spectra[:] = np.array(self.mrrRawSpectrum, dtype="f4") 2842 | nc_noSpec[:] = np.array(self.mrrRawNoSpec, dtype="i4") 2843 | 2844 | # commented because of Ubuntu bug: https://bugs.launchpad.net/ubuntu/+source/python-scientific/+bug/1005571 2845 | # if not pyNc: 2846 | #nc_noSpec._FillValue =int(self.missingNumber) 2847 | #nc_spectra._FillValue =float(self.missingNumber) 2848 | #nc_tf._FillValue =float(self.missingNumber) 2849 | #nc_ranges._FillValue =float(self.missingNumber) 2850 | 2851 | cdfFile.close() 2852 | print("done") 2853 | # end def write2NetCDF 2854 | # end class MrrData 2855 | -------------------------------------------------------------------------------- /IMProToo/tools.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | ''' 3 | IMProToo 4 | Improved MRR Processing Tool 5 | 6 | Python toolkit to read write and process MRR Data. Raw Data, Average and 7 | Instantaneous Data are supported. 8 | 9 | This file includes some helper functions 10 | 11 | Copyright (C) 2011-2021 Maximilian Maahn, U Leipzig 12 | maximilian.maahn_AT_uni-leipzig.de 13 | https://github.com/maahn/IMProToo 14 | 15 | This program is free software: you can redistribute it and/or modify 16 | it under the terms of the GNU General Public License as published by 17 | the Free Software Foundation, either version 3 of the License, or 18 | any later version. 19 | 20 | This program is distributed in the hope that it will be useful, 21 | but WITHOUT ANY WARRANTY; without even the implied warranty of 22 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 23 | GNU General Public License for more details. 24 | 25 | You should have received a copy of the GNU General Public License 26 | along with this program. If not, see . 27 | 28 | 29 | 30 | IMProTooTools: various helper functions 31 | 32 | 33 | ''' 34 | from __future__ import division 35 | from __future__ import print_function 36 | 37 | import numpy as np 38 | import time 39 | import datetime 40 | import calendar 41 | import warnings 42 | 43 | #warnings.filterwarnings('always', '.*', UserWarning,) 44 | 45 | 46 | def getManualQualityArray(inputFile, timeVector): 47 | ''' 48 | returns quality array for timevector using inputFile. See readQualityFile for details about inputFile. 49 | ''' 50 | startTimes, endTimes, comments = readQualityFile(inputFile) 51 | quality = np.zeros(timeVector.shape, dtype=bool) 52 | for startTime, endTime in zip(startTimes, endTimes): 53 | startTime = date2unix(startTime) 54 | endTime = date2unix(endTime) 55 | quality = quality + ((timeVector >= startTime) 56 | * (timeVector < endTime)) 57 | return quality 58 | 59 | 60 | def readQualityFile(inputFile): 61 | ''' 62 | reads manual quality control files, format is like ancient 'Hatpro" format: 63 | 64 | file format is: 65 | date, no of entries for this day, starttime (00.00 - 23:59), endtime (00:01 - 24:00), comment 66 | 120105 3 11.00 17.00 snow on dish 67 | 19.00 20.00 snow on dish 68 | 22.00 24.00 interference 69 | 120106 1 00.00 21.00 snow on dish 70 | #this is a comment 71 | 120107 1 00.00 24.00 maintenance 72 | 73 | ''' 74 | startTimes = list() 75 | endTimes = list() 76 | comments = list() 77 | 78 | belongsToOldDate = 1 79 | ll = -1 80 | 81 | f = open(inputFile, "r") 82 | for line in f.readlines(): 83 | ll += 1 84 | fields = line.split() 85 | try: 86 | # check for comments 87 | if fields[0][0:1] == '#': 88 | print('comment', ' '.join(fields)) 89 | continue 90 | # does the line hav its own timestamp? 91 | if belongsToOldDate == 1: 92 | startDate = fields[0] 93 | belongsToOldDate = int(fields[1]) 94 | startTime = fields[2] 95 | endTime = fields[3] 96 | comment = ' '.join(fields[4:]) 97 | else: 98 | startTime = fields[0] 99 | endTime = fields[1] 100 | comment = ' '.join(fields[2:]) 101 | belongsToOldDate -= 1 102 | # yes, 24.00 is a weired timestamp, correct this to 00.00 next day 103 | if endTime == '24.00': 104 | #import pdb;pdb.set_trace() 105 | endDate = datetime.datetime.strftime((datetime.datetime.strptime( 106 | startDate, '%y%m%d')+datetime.timedelta(1)), '%y%m%d') 107 | endTime = '00.00' 108 | else: 109 | endDate = startDate 110 | comment = ' '.join(fields[4:]) 111 | startTimes.append(datetime.datetime.strptime( 112 | startDate+' '+startTime, '%y%m%d %H.%M')) 113 | endTimes.append(datetime.datetime.strptime( 114 | endDate+' '+endTime, '%y%m%d %H.%M')) 115 | comments.append(comment) 116 | except: 117 | belongsToOldDate = 1 118 | warnings.warn('Could not read line no. '+str(ll) + str(fields)) 119 | 120 | f.close() 121 | 122 | return startTimes, endTimes, comments 123 | 124 | 125 | def date2unix(date): 126 | ''' 127 | converts datetime object to seconds since 01-01-1970 128 | ''' 129 | return calendar.timegm(date.timetuple()) 130 | 131 | 132 | def unix2date(unix): 133 | ''' 134 | converts seconds since 01-01-1970 to datetime object 135 | ''' 136 | 137 | return datetime.datetime.utcfromtimestamp(unix) 138 | 139 | 140 | def quantile(x, q, qtype=7, issorted=False): 141 | """ 142 | Args: 143 | x - input data 144 | q - quantile 145 | qtype - algorithm 146 | issorted- True if x already sorted. 147 | 148 | Compute quantiles from input array x given q.For median, 149 | specify q=0.5. 150 | 151 | References: 152 | http://reference.wolfram.com/mathematica/ref/Quantile.html 153 | http://wiki.r-project.org/rwiki/doku.php?id=rdoc:stats:quantile 154 | 155 | Author: 156 | Ernesto P.Adorio Ph.D. 157 | UP Extension Program in Pampanga, Clark Field. 158 | """ 159 | if not issorted: 160 | y = sorted(x) 161 | else: 162 | y = x 163 | if not (1 <= qtype <= 9): 164 | return None # error! 165 | 166 | # Parameters for the Hyndman and Fan algorithm 167 | abcd = [(0, 0, 1, 0), # inverse empirical distrib.function., R type 1 168 | (0.5, 0, 1, 0), # similar to type 1, averaged, R type 2 169 | (0.5, 0, 0, 0), # nearest order statistic,(SAS) R type 3 170 | 171 | (0, 0, 0, 1), # California linear interpolation, R type 4 172 | (0.5, 0, 0, 1), # hydrologists method, R type 5 173 | # mean-based estimate(Weibull method), (SPSS,Minitab), type 6 174 | (0, 1, 0, 1), 175 | (1, -1, 0, 1), # mode-based method,(S, S-Plus), R type 7 176 | (1.0/3, 1.0/3, 0, 1), # median-unbiased , R type 8 177 | (3/8.0, 0.25, 0, 1) # normal-unbiased, R type 9. 178 | ] 179 | 180 | a, b, c, d = abcd[qtype-1] 181 | n = len(x) 182 | g, j = np.modf(a + (n+b) * q - 1) 183 | if j < 0: 184 | return y[0] 185 | elif j >= n: 186 | return y[n-1] # oct. 8, 2010 y[n]???!! uncaught off by 1 error!!! 187 | 188 | j = int(np.floor(j)) 189 | if g == 0: 190 | return y[j] 191 | else: 192 | return y[j] + (y[j+1] - y[j]) * (c + d * g) 193 | 194 | 195 | def oneD2twoD(vector, shape2, axis): 196 | ''' 197 | helper function to convert 1D to 2D data 198 | ''' 199 | if axis == 0: 200 | matrix = np.zeros((shape2, len(vector))) 201 | for h in np.arange(shape2): 202 | matrix[h] = vector 203 | elif axis == 1: 204 | matrix = np.zeros((len(vector), shape2)) 205 | for h in np.arange(shape2): 206 | matrix[:, h] = vector 207 | else: 208 | raise ValueError("wrong axis") 209 | return matrix 210 | 211 | 212 | def limitMaInidces(iArray, max): 213 | ''' 214 | helper function to limit indices to certain interval 215 | lower limit 0 216 | max in python style -> actuallay max-1 217 | 218 | ''' 219 | reArray = np.ma.masked_all_like(iArray.ravel()) 220 | shape = iArray.shape 221 | for n, i in enumerate(iArray.ravel()): 222 | if i >= max: 223 | reArray[n] = i - max 224 | elif i < 0: 225 | reArray[n] = i + max 226 | else: 227 | reArray[n] = i 228 | return reArray.reshape(shape) 229 | 230 | 231 | def _get_netCDF_module(ncForm="NETCDF3"): 232 | ''' 233 | helper function to determine which netCDF module is loaded, to enable 234 | consistency between the various writeNetCDF methods. 235 | 236 | Returns both 'nc' (the netCDF module) and 'pyNC', a bool variable which 237 | controls how some attribute data is set, and how to create the netCDF file 238 | (since the function is different in the various netCDF modules.) 239 | ''' 240 | 241 | # most syntax is identical, but there is one nasty difference regarding the fillValue... 242 | if ncForm in ["NETCDF3_CLASSIC", "NETCDF3_64BIT", "NETCDF4_CLASSIC", "NETCDF4"]: 243 | import netCDF4 as nc 244 | pyNc = True 245 | elif ncForm in ["NETCDF3"]: 246 | try: 247 | import Scientific.IO.NetCDF as nc 248 | pyNc = False 249 | except: 250 | # fallback for netcdf3 with the same syntax as netcdf4! 251 | import netCDF3 as nc 252 | pyNc = True 253 | else: 254 | raise ValueError("Unknown nc form "+ncForm) 255 | 256 | return nc, pyNc 257 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # IMProToo - Improved Mrr Processing Tool 2 | 3 | 4 | 5 | IMProToo is an improved processing method for Micro Rain radar. It is especially suited for snow observations and provides besides other things effective reflectivity, Doppler velocity and spectral width. The method features a noise removal based on recognition of the most significant peak and a dynamic dealiasing routine which allows observations even if the Nyquist velocity range is exceeded. The software requires MRR "raw data", it does not work with Metek's standard products MRR "Averaged Data" or "Processed Data". 6 | 7 | Please note that this software was developed for observations at low SNR ratios such as snow, drizzle or light rain. Heavy rain, especially in combination with strong turbulence, might give wrong results. 8 | 9 | The software can be used under the GPL license 10 | 11 | ## What's new? 12 | 13 | ### 0.107 14 | * PyPI release, fixed installation from github archive through setuptools_scm_git_archive 15 | 16 | ### 0.106 17 | * Fixed Python 2.7 file reading and timezone bug (thanks to A. Merrelli) 18 | 19 | ### 0.105 20 | * Fixed Python 3 file reading bug (thanks to M. Bartolini) 21 | 22 | ### 0.104 23 | * Python 3 compatibility (2.7 still working) 24 | * Meta data bug fix 25 | 26 | ### 0.103 27 | * Non-UTC time stamps permitted 28 | * Fixed bug caused by numpy update 29 | 30 | ### 0.102 31 | * Various bug fixes, see https://github.com/maahn/IMProToo/issues/6 and https://github.com/maahn/IMProToo/issues/5 32 | 33 | ### 0.101 34 | * An installation routine is provided (See below). To avoid conflicts, please remove earlier versions manually before installing a newer version. 35 | 36 | ## How does it work 37 | 38 | The routine is described in 39 | Maahn, M. and Kollias, P.: Improved Micro Rain Radar snow measurements using Doppler spectra post-processing, Atmos. Meas. Tech. Discuss., 5, 4771-4808, doi:10.5194/amtd-5-4771-2012, 2012. http://www.atmos-meas-tech-discuss.net/5/4771/2012/amtd-5-4771-2012.html 40 | 41 | Please quote the article if you use the routine for your publication. 42 | 43 | ## How to install 44 | 45 | The software is developed for python 2.7 or 3.6+ and should run on any recent Linux system (and most likely also Mac OS X). Windows is currently not supported, but probably only minor changes are necessary. 46 | 47 | The following python packages are required: 48 | * numpy 49 | * scipy 50 | * matplotlib (for plotting only) 51 | * netcdf4-python http://code.google.com/p/netcdf4-python/ OR python-netcdf (for saving the results only) 52 | 53 | ## Installation 54 | 55 | IMProToo is available on PyPI, so it can be installed with 56 | ``` 57 | pip install IMProToo 58 | ``` 59 | in the terminal. 60 | 61 | ## How to use 62 | 63 | To use the toolkit, start python and import it: 64 | ``` 65 | import IMProToo 66 | ``` 67 | 68 | read the raw data file (can be gzip-compressed) 69 | ``` 70 | rawData = IMProToo.mrrRawData("mrrRawFile.mrr.gz") 71 | ``` 72 | 73 | create the IMProToo object and load rawData 74 | ``` 75 | processedSpec = IMProToo.MrrZe(rawData) 76 | ``` 77 | 78 | if needed, average rawData to 60s 79 | ``` 80 | processedSpec.averageSpectra(60) 81 | ``` 82 | 83 | all settings (e.g. creator attribute of netCDF file, dealiasing) are available in the 'processedSpec.co' dictionary and must be set before calculating Ze etc. See the source code for a description of the settings. 84 | ``` 85 | processedSpec.co["ncCreator"] = "M.Maahn, IGM University of Cologne" 86 | processedSpec.co["ncDescription"] = "MRR data from Cologne" 87 | processedSpec.co["dealiaseSpectrum"] = True 88 | ``` 89 | 90 | calculate Ze and other moments 91 | ``` 92 | processedSpec.rawToSnow() 93 | ``` 94 | 95 | write all variables to a netCDF file. 96 | ``` 97 | processedSpec.writeNetCDF("IMProToo_netCDF_file.nc",ncForm="NETCDF3_CLASSIC") 98 | ``` 99 | 100 | 101 | ## Questions 102 | In case of any questions, please don't hesitate to contact Maximilian Maahn: maximilian [dot] maahn [at] uni [dash] leipzig [dot] de 103 | -------------------------------------------------------------------------------- /examples/batch_convert_rawData.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | 3 | ''' 4 | Copyright (C) 2011-2021 Maximilian Maahn, U Leipzig 5 | maximilian.maahn_AT_uni-leipzig.de 6 | 7 | example script for converting mrrRaw data to netcdf using IMProToos 8 | ''' 9 | from __future__ import print_function 10 | 11 | import sys 12 | import numpy as np 13 | import glob 14 | import os 15 | import datetime 16 | import IMProToo 17 | import gzip 18 | 19 | 20 | version = IMProToo.__version__ 21 | 22 | 23 | if len(sys.argv) < 4: 24 | sys.exit('use: python batch_convert_rawData.py pathIn pathOut site') 25 | 26 | pathIn = sys.argv[1] 27 | pathOut = sys.argv[2] 28 | site = sys.argv[3] 29 | 30 | skipExisting = True 31 | 32 | print(pathIn) 33 | 34 | try: 35 | os.mkdir(pathOut) 36 | except OSError: 37 | pass 38 | 39 | # go through all gz compressed files in pathIn/year/month/ 40 | for nfile in np.sort(glob.glob(pathIn+"/*raw*")): 41 | # get the timestamp 42 | timestamp = None 43 | if nfile.split('.')[-1] == 'gz': 44 | f = gzip.open(nfile, 'rt') 45 | else: 46 | f = open(nfile, 'r') 47 | # Sometimes the first MRR timestamps are from the day before, so we cannot take the first date we found. get list of line breaks 48 | line_offset = [] 49 | offset = 0 50 | for line in f: 51 | line_offset.append(offset) 52 | offset += len(line) 53 | f.seek(0) 54 | 55 | # Now, to skip 20% of the file 56 | f.seek(line_offset[len(line_offset)//5]) 57 | 58 | # now find the date 59 | try: 60 | while True: 61 | string = str(f.readline()) 62 | if not string: 63 | break 64 | if string[:2] == "T:": 65 | timestamp = datetime.datetime.strptime( 66 | string[2:14], "%y%m%d%H%M%S").strftime("%Y%m%d") 67 | break 68 | elif string[:4] == "MRR ": 69 | timestamp = datetime.datetime.strptime( 70 | string[4:16], "%y%m%d%H%M%S").strftime("%Y%m%d") 71 | break 72 | 73 | finally: 74 | f.close() 75 | 76 | if timestamp is None: 77 | print("did not find MRR timesamp in %s, Skipping" % nfile) 78 | continue 79 | 80 | fileOut = pathOut+"/mrr_improtoo_"+version+"_"+site+"_"+timestamp+".nc" 81 | 82 | if skipExisting and (os.path.isfile(fileOut) or os.path.isfile(fileOut+".gz")): 83 | print("NetCDF file aready exists, skipping: ", timestamp, nfile, fileOut) 84 | continue 85 | 86 | print(timestamp, nfile, fileOut) 87 | 88 | # load raw data from file 89 | print("reading...", nfile) 90 | try: 91 | rawData = IMProToo.mrrRawData(nfile) 92 | except: 93 | print("could not read data") 94 | continue 95 | 96 | try: 97 | # convert rawData object 98 | processedSpec = IMProToo.MrrZe(rawData) 99 | # average rawData to 60s 100 | processedSpec.averageSpectra(60) 101 | # the MRR at 'lyr' was affected by interference for some days, dealiasing routine needs to know about that: 102 | if site == "lyr" and timestamp in ['20100620', '20100621', '20100622', '20100623', '20100624', '20100625', '20100626', '20100627', '20100628', '20100629', '20100630', '20100701', '20100702', '20100703', '20100704', '20100705', '20100706', '20100707']: 103 | processedSpec.co['dealiaseSpectrum_heightsWithInterference'] = processedSpec.co[ 104 | 'dealiaseSpectrum_heightsWithInterference'] + [25, 26, 27, 28, 29, 30] 105 | # creator attribute of netCDF file 106 | processedSpec.co["ncCreator"] = "M.Maahn, IGM University of Cologne" 107 | 108 | # calculate Ze and other moments 109 | processedSpec.rawToSnow() 110 | 111 | # write all variables to a netCDF file. 112 | print("writing...", fileOut) 113 | processedSpec.writeNetCDF(fileOut, ncForm="NETCDF3_CLASSIC") 114 | except Exception as error: 115 | print(str(error)) 116 | print("could not process data") 117 | continue 118 | -------------------------------------------------------------------------------- /examples/batch_makeQuicklooks.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | ''' 3 | Copyright (C) 2011-2021 Maximilian Maahn, U Leipzig 4 | maximilian.maahn_AT_uni-leipzig.de 5 | 6 | make quicklooks from IMProToo NetCDF files. 7 | 8 | 9 | use: python batch_makeQuicklooks.py pathIn pathOut site 10 | 11 | requires: 12 | 13 | numpy, matplotlib, netcdf4-python or python-netcdf 14 | 15 | ''' 16 | 17 | 18 | from __future__ import print_function 19 | from copy import deepcopy 20 | import matplotlib.font_manager as font_manager 21 | from matplotlib import rc, ticker 22 | from IMProToo.tools import * 23 | import IMProToo 24 | import string 25 | import random 26 | import matplotlib.pyplot as plt 27 | import matplotlib.mlab as mlab 28 | 29 | 30 | import sys 31 | import numpy as np 32 | import glob 33 | import calendar 34 | import datetime 35 | import os 36 | import matplotlib 37 | matplotlib.use('Agg') 38 | 39 | try: 40 | import netCDF4 as nc 41 | pyNc = True 42 | except: 43 | import Scientific.IO.NetCDF as nc 44 | pyNc = False 45 | 46 | tmpDir = "/tmp/" 47 | skipExisting = True 48 | 49 | 50 | def unix2timestamp(unix): 51 | return datetime.datetime.utcfromtimestamp(unix).strftime("%Y%m%d") 52 | 53 | 54 | def timestamp2unix(timestamp): 55 | return calendar.timegm(datetime.datetime(year=int(timestamp[0:4]), month=int(timestamp[4:6]), day=int(timestamp[6:8]), hour=0, minute=0, second=0).timetuple()) 56 | 57 | 58 | def quicklook(site, ncFile, imgFile, imgTitle): 59 | """ 60 | Makes Quicklooks of MRR data 61 | 62 | 63 | @parameter site (str): code for the site where the data was recorded (usually 3 letter) 64 | @parameter ncFile (str): netcdf file name incl. path, usually "path/mrr_site_yyyymmdd.nc" 65 | @parameter imgFile (str): image file name, incl. path, extensions determines file format (e.g. png, eps, pdf ...) 66 | @parameter imgTitle (str): plot title 67 | """ 68 | print("##### " + imgTitle + "######") 69 | tmpFile = False 70 | if ncFile.split(".")[-1] == "gz": 71 | tmpFile = True 72 | gzFile = deepcopy(ncFile) 73 | ncFile = tmpDir+"/maxLibs_netcdf_" + \ 74 | ''.join(random.choice(string.ascii_uppercase + string.digits) 75 | for x in range(5))+".tmp.nc" 76 | print('uncompressing', gzFile, "->", ncFile) 77 | os.system("zcat "+gzFile+">"+ncFile) 78 | else: 79 | print('opening', ncFile) 80 | 81 | if pyNc: 82 | ncData = nc.Dataset(ncFile, 'r') 83 | else: 84 | ncData = nc.NetCDFFile(ncFile, 'r') 85 | 86 | timestampsNew = ncData.variables["time"][:] 87 | HNew = ncData.variables["height"][:] 88 | ZeNew = ncData.variables["Ze"][:] 89 | noiseAveNew = ncData.variables["etaNoiseAve"][:] 90 | noiseStdNew = ncData.variables["etaNoiseStd"][:] 91 | spectralWidthNew = ncData.variables["spectralWidth"][:] 92 | WNew = ncData.variables["W"][:] 93 | qualityNew = ncData.variables["quality"][:] 94 | 95 | ncData.close() 96 | if (tmpFile): 97 | os.system("rm -f "+ncFile) 98 | 99 | date = unix2timestamp(timestampsNew[0]) 100 | starttime = timestamp2unix(date) 101 | endtime = starttime+60*60*24 102 | 103 | HNew[np.isnan(HNew)] = -9999 104 | ylim = [np.min(HNew[HNew != -9999]), np.max(HNew)] 105 | xlim = [starttime, endtime] 106 | timestampsNew = oneD2twoD(timestampsNew, ZeNew.shape[1], 1) 107 | 108 | fig = plt.figure(figsize=(10, 13)) 109 | 110 | sp1 = fig.add_subplot(511) 111 | sp1.set_title(imgTitle) 112 | levels = np.arange(-15, 40, 0.1) 113 | plotCF = sp1.contourf(timestampsNew, HNew, ZeNew, levels, extend="both") 114 | cbZe = plt.colorbar(plotCF) 115 | cbZe.set_label('MRR Ze [dBz]') 116 | sp1.set_ylim(ylim) 117 | sp1.set_xlim(xlim) 118 | 119 | sp1.axhline(HNew[-1, 2]) 120 | sp1.axhline(HNew[-1, 29]) 121 | 122 | sp2 = fig.add_subplot(512) 123 | levels = np.arange(-10, 18, 0.1) 124 | plotCF = sp2.contourf(timestampsNew, HNew, WNew, levels, extend="both") 125 | cbZe = plt.colorbar(plotCF) 126 | cbZe.set_label('MRR W [m/s]') 127 | sp2.set_ylim(ylim) 128 | sp2.set_xlim(xlim) 129 | 130 | sp2.axhline(HNew[-1, 2]) 131 | sp2.axhline(HNew[-1, 29]) 132 | 133 | sp3 = fig.add_subplot(513) 134 | levels = np.arange(0, 1.5, 0.1) 135 | plotCF = sp3.contourf(timestampsNew, HNew, 136 | spectralWidthNew, levels, extend="both") 137 | cbZe = plt.colorbar(plotCF) 138 | cbZe.set_label('spectralWidth [m/s]') 139 | sp3.set_ylim(ylim) 140 | sp3.set_xlim(xlim) 141 | 142 | sp3.axhline(HNew[-1, 2]) 143 | sp3.axhline(HNew[-1, 29]) 144 | 145 | sp4 = fig.add_subplot(514) 146 | levels = np.arange(1e-10, 1e-8, 2e-10) 147 | plotCF = sp4.contourf(timestampsNew, HNew, 148 | noiseAveNew, levels, extend="both") 149 | cbZe = plt.colorbar(plotCF) 150 | cbZe.set_label('mean spectral noise [1/m]') 151 | sp4.set_ylim(ylim) 152 | sp4.set_xlim(xlim) 153 | sp4.axhline(HNew[-1, 2]) 154 | sp4.axhline(HNew[-1, 29]) 155 | #import pdb;pdb.set_trace() 156 | 157 | sp5 = fig.add_subplot(515) 158 | levels = np.arange(20) 159 | for i in levels: 160 | levels[i] = 2**i 161 | plotCF = sp5.contourf(timestampsNew, HNew, qualityNew, 162 | levels, norm=matplotlib.colors.LogNorm()) 163 | cbZe = plt.colorbar(plotCF) 164 | cbZe.set_label('quality array') 165 | sp5.set_ylim(ylim) 166 | sp5.set_xlim(xlim) 167 | sp5.axhline(HNew[-1, 2]) 168 | sp5.axhline(HNew[-1, 29]) 169 | 170 | # sp1.set_xlim(np.min(timestampsNew),np.max(timestampsNew)) 171 | sp1.set_xticks(np.arange(sp1.get_xlim()[0], sp1.get_xlim()[1], 7200)) 172 | sp1.set_xticklabels([]) 173 | 174 | # sp2.set_xlim(np.min(timestampsNew),np.max(timestampsNew)) 175 | sp2.set_xticks(np.arange(sp1.get_xlim()[0], sp1.get_xlim()[1], 7200)) 176 | sp2.set_xticklabels([]) 177 | 178 | # sp3.set_xlim(np.min(timestampsNew),np.max(timestampsNew)) 179 | sp3.set_xticks(np.arange(sp1.get_xlim()[0], sp1.get_xlim()[1], 7200)) 180 | sp3.set_xticklabels([]) 181 | 182 | # sp4.set_xlim(np.min(timestampsNew),np.max(timestampsNew)) 183 | sp4.set_xticks(np.arange(sp1.get_xlim()[0], sp1.get_xlim()[1], 7200)) 184 | sp4.set_xticklabels([]) 185 | 186 | # pdb.set_trace() 187 | # sp5.set_xlim(np.min(timestampsNew)-60,np.max(timestampsNew)) 188 | sp5.set_xticks(np.arange(sp5.get_xlim()[0], sp5.get_xlim()[1]+7200, 7200)) 189 | niceDates = list() 190 | for timestamp in np.arange(sp5.get_xlim()[0], sp5.get_xlim()[1]+7200, 7200): 191 | niceDates.append( 192 | str(datetime.datetime.utcfromtimestamp(timestamp).strftime("%H:%M"))) 193 | sp5.set_xticklabels(niceDates) 194 | 195 | plt.subplots_adjust(hspace=0.02, left=0.085, right=0.78) 196 | 197 | plt.savefig(imgFile) 198 | print(imgFile) 199 | 200 | plt.close() 201 | return 202 | 203 | 204 | if len(sys.argv) < 4: 205 | print('use: batch_makeQuicklooks.py pathIn pathOut site') 206 | sys.exit() 207 | 208 | pathIn = sys.argv[1] 209 | pathOut = sys.argv[2] 210 | site = sys.argv[3] 211 | 212 | 213 | try: 214 | os.mkdir(pathOut) 215 | except OSError: 216 | pass 217 | 218 | 219 | for ncFile in np.sort(glob.glob(pathIn+"/*nc")): 220 | #import pdb;pdb.set_trace() 221 | date = ncFile.split("_")[-1].split(".")[0] 222 | print(date, ncFile) 223 | imgFile = pathOut + "/mrr_improtoo_" + \ 224 | IMProToo.__version__+'_'+site+"_"+date+".png" 225 | imgTitle = site + " " + date + " IMProToo " + IMProToo.__version__ 226 | 227 | if skipExisting and os.path.isfile(imgFile): 228 | print("Quicklook aready exists, skipping: ", date, ncFile, imgFile) 229 | continue 230 | 231 | quicklook(site, ncFile, imgFile, imgTitle) 232 | -------------------------------------------------------------------------------- /pyproject.toml: -------------------------------------------------------------------------------- 1 | # pyproject.toml 2 | [build-system] 3 | requires = ["setuptools>=42", "wheel", "setuptools_scm>=5.0.2"] -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | ''' 3 | pyOptimalEstimation 4 | 5 | Copyright (C) 2014-15 Maximilian Maahn, IGMK (mmaahn_(AT)_meteo.uni-koeln.de) 6 | http://gop.meteo.uni-koeln.de/software 7 | 8 | This program is free software: you can redistribute it and/or modify 9 | it under the terms of the GNU General Public License as published by 10 | the Free Software Foundation, either version 3 of the License, or 11 | any later version. 12 | 13 | This program is distributed in the hope that it will be useful, 14 | but WITHOUT ANY WARRANTY; without even the implied warranty of 15 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 16 | GNU General Public License for more details. 17 | 18 | You should have received a copy of the GNU General Public License 19 | along with this program. If not, see . 20 | 21 | ''' 22 | 23 | from setuptools import setup 24 | import io 25 | # read the contents of your README file 26 | from os import path 27 | this_directory = path.abspath(path.dirname(__file__)) 28 | with io.open(path.join(this_directory, 'README.md'), encoding='utf-8') as f: 29 | long_description = f.read() 30 | 31 | 32 | if __name__ == "__main__": 33 | setup( 34 | name='IMProToo', 35 | use_scm_version=True, 36 | packages=['IMProToo', ], 37 | license='GNU General Public License 3', 38 | author="Maximilian Maahn", 39 | long_description=long_description, 40 | long_description_content_type='text/markdown', 41 | classifiers=[ 42 | "Development Status :: 4 - Beta", 43 | "Topic :: Utilities", 44 | "License :: OSI Approved :: GNU General Public License v3 (GPLv3)", 45 | "Operating System :: OS Independent", 46 | "Programming Language :: Python", 47 | 'Intended Audience :: Science/Research', 48 | 'Topic :: Scientific/Engineering', 49 | ], 50 | install_requires=['numpy', 'matplotlib', 'netCDF4', 'scipy'], 51 | setup_requires=['setuptools_scm','setuptools_scm_git_archive'], 52 | ) 53 | --------------------------------------------------------------------------------