From 624048f5d8305041dceec04f894684956f18996c Mon Sep 17 00:00:00 2001 From: Daniel Wolfensberger Date: Thu, 22 Feb 2024 17:52:39 +0100 Subject: [PATCH 01/25] added sw version in odim outputs + changed default metranet reader to c --- rainforest/__init__.py | 12 +++++++++++- rainforest/qpe/qpe.py | 4 +++- 2 files changed, 14 insertions(+), 2 deletions(-) diff --git a/rainforest/__init__.py b/rainforest/__init__.py index 99b7be3..6d52d06 100644 --- a/rainforest/__init__.py +++ b/rainforest/__init__.py @@ -1,5 +1,15 @@ +import importlib.metadata as _importlib_metadata + from . import qpe from . import common from . import database from . import ml -from . import performance \ No newline at end of file +from . import performance + +# Get the version +try: + __version__ = _importlib_metadata.version("rainforest_mch") +except _importlib_metadata.PackageNotFoundError: + # package is not installed + __version__ = "0.0.0" + diff --git a/rainforest/qpe/qpe.py b/rainforest/qpe/qpe.py index 30523fc..030fbd4 100644 --- a/rainforest/qpe/qpe.py +++ b/rainforest/qpe/qpe.py @@ -33,7 +33,7 @@ from pyart.aux_io.odim_h5_writer import write_odim_grid_h5 from pyart.aux_io.odim_h5 import proj4_to_dict - +from .. import __version__ from ..common.logger import logger from ..common import constants from ..common.retrieve_data import retrieve_prod, get_COSMO_T, retrieve_hzt_prod, retrieve_prod_RT, retrieve_hzt_RT @@ -160,6 +160,7 @@ def _features_to_chgrid(features, features_labels, time, missing_files): grid.metadata['source'] = b'ORG:215, CTY:644, CMT:MeteoSwiss (Switzerland)' grid.metadata['version'] = b'H5rad 2.3' + grid.metadata['sw_version'] = __version__ # Add missing radar information quality = 'ADLPW' if len(missing_files) != 0: @@ -226,6 +227,7 @@ def _qpe_to_chgrid(qpe, time, missing_files, precision=2): grid.fields['radar_estimated_rain_rate']['prodname'] = 'CHRFO' grid.metadata['source'] = b'ORG:215, CTY:644, CMT:MeteoSwiss (Switzerland)' grid.metadata['version'] = b'H5rad 2.3' + grid.metadata['sw_version'] = __version__ # Add missing radar information quality = 'ADLPW' if len(missing_files) != 0: From 0bc6728884195d705eea7364170cc0932868ce7d Mon Sep 17 00:00:00 2001 From: Daniel Wolfensberger Date: Thu, 22 Feb 2024 18:53:12 +0100 Subject: [PATCH 02/25] solve circular import with __version__ --- rainforest/__init__.py | 10 +--------- rainforest/common/utils.py | 12 ++++++++++++ rainforest/qpe/qpe.py | 7 +++---- 3 files changed, 16 insertions(+), 13 deletions(-) diff --git a/rainforest/__init__.py b/rainforest/__init__.py index 6d52d06..18422a0 100644 --- a/rainforest/__init__.py +++ b/rainforest/__init__.py @@ -1,15 +1,7 @@ -import importlib.metadata as _importlib_metadata - from . import qpe from . import common from . import database from . import ml from . import performance -# Get the version -try: - __version__ = _importlib_metadata.version("rainforest_mch") -except _importlib_metadata.PackageNotFoundError: - # package is not installed - __version__ = "0.0.0" - +__version__ = common.utils.get_version() diff --git a/rainforest/common/utils.py b/rainforest/common/utils.py index 0f14cf5..63b6e7e 100644 --- a/rainforest/common/utils.py +++ b/rainforest/common/utils.py @@ -12,6 +12,7 @@ # Global imports import datetime import io +import importlib.metadata as _importlib_metadata import os from collections import OrderedDict import numpy as np @@ -22,11 +23,22 @@ import dask.dataframe as dd import re import numbers + # Local imports from .logger import logger from .wgs84_ch1903 import GPSConverter from . import constants +def get_version(): + # Get the version + try: + version = _importlib_metadata.version("rainforest_mch") + except _importlib_metadata.PackageNotFoundError: + # package is not installed + version = "0.0.0" + return version + + def hex_to_rgb(value): value = value.lstrip('#') lv = len(value) diff --git a/rainforest/qpe/qpe.py b/rainforest/qpe/qpe.py index 030fbd4..3cf1b0a 100644 --- a/rainforest/qpe/qpe.py +++ b/rainforest/qpe/qpe.py @@ -23,7 +23,6 @@ from pathlib import Path import os - from pathlib import Path from scipy.ndimage import gaussian_filter from scipy.signal import convolve2d @@ -33,7 +32,7 @@ from pyart.aux_io.odim_h5_writer import write_odim_grid_h5 from pyart.aux_io.odim_h5 import proj4_to_dict -from .. import __version__ +from ..common.utils import get_version from ..common.logger import logger from ..common import constants from ..common.retrieve_data import retrieve_prod, get_COSMO_T, retrieve_hzt_prod, retrieve_prod_RT, retrieve_hzt_RT @@ -160,7 +159,7 @@ def _features_to_chgrid(features, features_labels, time, missing_files): grid.metadata['source'] = b'ORG:215, CTY:644, CMT:MeteoSwiss (Switzerland)' grid.metadata['version'] = b'H5rad 2.3' - grid.metadata['sw_version'] = __version__ + grid.metadata['sw_version'] = get_version() # Add missing radar information quality = 'ADLPW' if len(missing_files) != 0: @@ -227,7 +226,7 @@ def _qpe_to_chgrid(qpe, time, missing_files, precision=2): grid.fields['radar_estimated_rain_rate']['prodname'] = 'CHRFO' grid.metadata['source'] = b'ORG:215, CTY:644, CMT:MeteoSwiss (Switzerland)' grid.metadata['version'] = b'H5rad 2.3' - grid.metadata['sw_version'] = __version__ + grid.metadata['sw_version'] = get_version() # Add missing radar information quality = 'ADLPW' if len(missing_files) != 0: From ed2ca81e0a528c46b0a5c81cbb62ca1359b0e102 Mon Sep 17 00:00:00 2001 From: Daniel Wolfensberger Date: Thu, 22 Feb 2024 19:46:33 +0100 Subject: [PATCH 03/25] added rainforest version to docs/conf.py --- docs/conf.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/docs/conf.py b/docs/conf.py index 56fc545..b447708 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -10,6 +10,7 @@ # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. # +import rainforest import os import sys sys.path.insert(0, os.path.abspath('.')) @@ -31,7 +32,7 @@ # ones. extensions = ['sphinx.ext.napoleon'] master_doc = 'index' - +version = rainforest.__version__ # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] From 2f9c8646d22d188da9d0d114ecd5f48c6bfeb516 Mon Sep 17 00:00:00 2001 From: Daniel Wolfensberger Date: Thu, 22 Feb 2024 19:46:33 +0100 Subject: [PATCH 04/25] added rainforest version to docs/conf.py --- docs/conf.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/docs/conf.py b/docs/conf.py index 56fc545..b447708 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -10,6 +10,7 @@ # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. # +import rainforest import os import sys sys.path.insert(0, os.path.abspath('.')) @@ -31,7 +32,7 @@ # ones. extensions = ['sphinx.ext.napoleon'] master_doc = 'index' - +version = rainforest.__version__ # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] From c45f060f0c1d09ce223ba7255f3909bcf8e220e7 Mon Sep 17 00:00:00 2001 From: Daniel Wolfensberger Date: Thu, 22 Feb 2024 19:51:18 +0100 Subject: [PATCH 05/25] updated version nb --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 8b9d14d..b25c94d 100644 --- a/setup.py +++ b/setup.py @@ -32,7 +32,7 @@ s = setup(name = "rainforest_mch", description = "RandomForest QPE python library", long_description = "A library to compute a gauge/radar database, train a RF QPE predictor and apply it to generate QPE fields", - version = "1.4.0", + version = "1.4.1", author = 'Rebecca Gugerli, Daniel Wolfensberger', author_email = ' rebecca.gugerli@epfl.ch, daniel.wolfensberger@meteoswiss.ch', license = 'GPL-3.0', From fc65fd7fd05565baccf1414a7230587c5e0e6bc0 Mon Sep 17 00:00:00 2001 From: Daniel Wolfensberger Date: Thu, 22 Feb 2024 19:51:18 +0100 Subject: [PATCH 06/25] updated version nb --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 8b9d14d..b25c94d 100644 --- a/setup.py +++ b/setup.py @@ -32,7 +32,7 @@ s = setup(name = "rainforest_mch", description = "RandomForest QPE python library", long_description = "A library to compute a gauge/radar database, train a RF QPE predictor and apply it to generate QPE fields", - version = "1.4.0", + version = "1.4.1", author = 'Rebecca Gugerli, Daniel Wolfensberger', author_email = ' rebecca.gugerli@epfl.ch, daniel.wolfensberger@meteoswiss.ch', license = 'GPL-3.0', From f84db720844615926abf1819d3c2e5a68780d685 Mon Sep 17 00:00:00 2001 From: gugerlir Date: Fri, 23 Feb 2024 09:51:40 +0000 Subject: [PATCH 07/25] make all datetime object timezone aware for real-time application --- rainforest/common/retrieve_data.py | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/rainforest/common/retrieve_data.py b/rainforest/common/retrieve_data.py index d4b9632..6b6121a 100644 --- a/rainforest/common/retrieve_data.py +++ b/rainforest/common/retrieve_data.py @@ -124,7 +124,9 @@ def retrieve_hzt_RT(tstep): # Sort filelist to most recent prediction content_filt = np.array([c for c in content_zip if c.endswith('800')]) times_filt = np.array([datetime.datetime.strptime(c[3:12], - '%y%j%H%M')+datetime.timedelta(hours=int(c[-2::])) for c in content_filt]) + '%y%j%H%M').replace(tzinfo=datetime.timezone.utc)+\ + datetime.timedelta(hours=int(c[-2::])) \ + for c in content_filt]) conditions = np.array([np.logical_and((t >= start_time), (t <= end_time)) for t in times_filt]) content_filt = content_filt[conditions] @@ -139,7 +141,8 @@ def retrieve_hzt_RT(tstep): if len(all_hours) != len(times_filt): content_times = np.array([datetime.datetime.strptime(c[3:12], - '%y%j%H%M')+datetime.timedelta(hours=int(c[-2::])) for c in content_zip]) + '%y%j%H%M').replace(tzinfo=datetime.timezone.utc)+\ + datetime.timedelta(hours=int(c[-2::])) for c in content_zip]) # Find time that is missing: for hh in all_hours: if not hh in times_filt: @@ -571,7 +574,8 @@ def retrieve_prod_RT(time, product_name, # Derive datetime of each file times_zip = np.array([datetime.datetime.strptime(c[3:12], - '%y%j%H%M') for c in content_zip]) + '%y%j%H%M').replace(tzinfo=datetime.timezone.utc) + for c in content_zip]) # Get a list of all files to retrieve conditions = (times_zip == time) From aa01ad02f989c764f54cdb843a2d3c1e2c4afda3 Mon Sep 17 00:00:00 2001 From: gugerlir Date: Fri, 23 Feb 2024 13:01:57 +0000 Subject: [PATCH 08/25] default metranet reader set to c-reader, adapted metadata of h5 output --- rainforest/common/radarprocessing.py | 2 +- rainforest/qpe/qpe.py | 40 +++++++++++++++++++++++----- 2 files changed, 34 insertions(+), 8 deletions(-) diff --git a/rainforest/common/radarprocessing.py b/rainforest/common/radarprocessing.py index 18f32c0..7391b4b 100644 --- a/rainforest/common/radarprocessing.py +++ b/rainforest/common/radarprocessing.py @@ -36,7 +36,7 @@ class Radar(object): single pyart radar instance as this was found to be faster in practice ''' def __init__(self, radname, polfiles, statusfile=None, vprfile=None, - temp_ref='TAIR', metranet_reader = 'python'): + temp_ref='TAIR', metranet_reader = 'C'): """ Creates an Radar class instance diff --git a/rainforest/qpe/qpe.py b/rainforest/qpe/qpe.py index 3cf1b0a..9781bbd 100644 --- a/rainforest/qpe/qpe.py +++ b/rainforest/qpe/qpe.py @@ -236,7 +236,17 @@ def _qpe_to_chgrid(qpe, time, missing_files, precision=2): qual_new = qual_new.replace(rad, '-') quality = qual_new grid.metadata['radar'] = quality.encode() - grid.metadata['nodes'] = 'WMO:06661,WMO:06699,WMO:06768,WMO:06726,WMO:06776' + + if '-' not in quality: + grid.metadata['nodes'] = 'WMO:06661,WMO:06699,WMO:06768,WMO:06726,WMO:06776' + else: + # 06661: Albis; 06699: Dôle; 06768: Lema; 06726: Plaine Morte; 06776: Weissfluh + all_wmo = ['WMO:06661','WMO:06699','WMO:06768','WMO:06726','WMO:06776'] + rad_wmo = [] + for ir, rad in enumerate(['A', 'D', 'L', 'P', 'W']): + if rad in quality: + rad_wmo.append(all_wmo[ir]) + grid.metadata['nodes'] = ','.join(rad_wmo) return grid @@ -423,8 +433,8 @@ def fetch_data(self, t0, t1 = None): self.radar_files[rad] = split_by_time(radfiles) self.status_files[rad] = split_by_time(statfiles) - except: - logger.error('Failed to retrieve data for radar {:s}'.format(rad)) + except Exception as error: + logger.error('Failed to retrieve data for radar {:s}. Error: {:s}'.format(rad, error)) # Retrieve iso0 height files if 'ISO0_HEIGHT' in self.cosmo_var: @@ -664,12 +674,28 @@ def compute(self, output_folder, t0, t1, timestep = 5, # Read raw radar file and create a RADAR object radobjects[rad] = Radar(rad, self.radar_files[rad][t], - self.status_files[rad][t]) + self.status_files[rad][t], + metranet_reader='C') + # try: + # radobjects[rad] = Radar(rad, self.radar_files[rad][t], + # self.status_files[rad][t], + # metranet_reader='C') + # except: + # try: + # wrnmsg = 'Could not read polar radar data for radar {:s}'.format(rad)+\ + # ' with c-reader (default), trying with python-reader' + # logger.warning(wrnmsg) + # radobjects[rad] = Radar(rad, self.radar_files[rad][t], + # self.status_files[rad][t], + # metranet_reader='python') + # except: + # logger.error('Could not read polar radar data of {:s}'.format(rad)) + # if problem with radar file, exclude it and add to missing files list if len(radobjects[rad].radarfields) == 0: self.missing_files[rad] = t - logger.info('Removing timestep {:s} of radar {:s}'.format(str(t), rad)) + logger.warning('Removing timestep {:s} of radar {:s}'.format(str(t), rad)) continue # Process the radar data @@ -680,8 +706,8 @@ def compute(self, output_folder, t0, t1, timestep = 5, radobjects[rad].snr_mask(self.config['SNR_THRESHOLD']) except Exception as e: self.missing_files[rad] = t - logger.info(e) - logger.info('Removing timestep {:s} of radar {:s}'.format(str(t), rad)) + logger.warning(e) + logger.warning('Removing timestep {:s} of radar {:s}'.format(str(t), rad)) continue radobjects[rad].compute_kdp(self.config['KDP_PARAMETERS']) From 33eafac266060f5438cd57ade508f44fa8ae6f5e Mon Sep 17 00:00:00 2001 From: gugerlir Date: Mon, 26 Feb 2024 10:14:15 +0000 Subject: [PATCH 09/25] modified error message when file retrieval does not work --- rainforest/qpe/qpe.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/rainforest/qpe/qpe.py b/rainforest/qpe/qpe.py index 9781bbd..fe6ff64 100644 --- a/rainforest/qpe/qpe.py +++ b/rainforest/qpe/qpe.py @@ -434,7 +434,8 @@ def fetch_data(self, t0, t1 = None): self.radar_files[rad] = split_by_time(radfiles) self.status_files[rad] = split_by_time(statfiles) except Exception as error: - logger.error('Failed to retrieve data for radar {:s}. Error: {:s}'.format(rad, error)) + error_msg = 'Failed to retrieve data for radar {:s}.\n'.format(rad)+ str(error) + logger.error(error_msg) # Retrieve iso0 height files if 'ISO0_HEIGHT' in self.cosmo_var: From 821900908db9a2c65eda3b075827bb0caae64359 Mon Sep 17 00:00:00 2001 From: gugerlir Date: Mon, 26 Feb 2024 13:50:54 +0000 Subject: [PATCH 10/25] modified logs for real-time --- rainforest/qpe/qpe.py | 11 ++++++++--- 1 file changed, 8 insertions(+), 3 deletions(-) diff --git a/rainforest/qpe/qpe.py b/rainforest/qpe/qpe.py index fe6ff64..2acbefa 100644 --- a/rainforest/qpe/qpe.py +++ b/rainforest/qpe/qpe.py @@ -661,6 +661,7 @@ def compute(self, output_folder, t0, t1, timestep = 5, """Part one - compute radar variables and mask""" # Begin compilation of radarobject + logger.info('Preparing all polar radar data') radobjects = {} for rad in self.config['RADARS']: # if radar does not exist, add to missing file list @@ -749,9 +750,11 @@ def compute(self, output_folder, t0, t1, timestep = 5, except: logger.error('No cleanup was defined, unzipped files remain in temp-folder') + logger.info('Processing all sweeps of all radars') for sweep in self.config['SWEEPS']: # Loop on sweeps - logger.info('---') - logger.info('Processing sweep ' + str(sweep)) + if not self.rt: + logger.info('---') + logger.info('Processing sweep ' + str(sweep)) for rad in self.config['RADARS']: # Loop on radars, A,D,L,P,W # If there is no radar file for the specific radar, continue to next radar @@ -762,7 +765,8 @@ def compute(self, output_folder, t0, t1, timestep = 5, logger.info('Processing sweep {} of {} - no data for this timestep!'.format(sweep, rad)) continue else: - logger.info('Processing radar ' + str(rad)) + if not self.rt: + logger.info('Processing radar ' + str(rad) + '; sweep '+str(sweep)) try: """Part two - retrieve radar data at every sweep""" @@ -849,6 +853,7 @@ def compute(self, output_folder, t0, t1, timestep = 5, """Part four - RF prediction""" + logger.info('Applying RF model to retrieve predictions') # Get QPE estimate # X: current time step; X_prev: previous timestep (t-5min) for k in self.models.keys(): From 7c1e4809e4d1b69da60f9def9c2298085823911e Mon Sep 17 00:00:00 2001 From: gugerlir Date: Mon, 26 Feb 2024 13:56:15 +0000 Subject: [PATCH 11/25] new release number --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index b25c94d..38f4044 100644 --- a/setup.py +++ b/setup.py @@ -32,7 +32,7 @@ s = setup(name = "rainforest_mch", description = "RandomForest QPE python library", long_description = "A library to compute a gauge/radar database, train a RF QPE predictor and apply it to generate QPE fields", - version = "1.4.1", + version = "1.4.2", author = 'Rebecca Gugerli, Daniel Wolfensberger', author_email = ' rebecca.gugerli@epfl.ch, daniel.wolfensberger@meteoswiss.ch', license = 'GPL-3.0', From 1ffc3a42dfea552d3fda56afaf6c1fe858fc0d96 Mon Sep 17 00:00:00 2001 From: Daniel Wolfensberger Date: Wed, 28 Feb 2024 12:47:59 +0100 Subject: [PATCH 12/25] small change to qpe grid for pyart ODIM writer consistency --- rainforest/qpe/qpe.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/rainforest/qpe/qpe.py b/rainforest/qpe/qpe.py index 2acbefa..086d604 100644 --- a/rainforest/qpe/qpe.py +++ b/rainforest/qpe/qpe.py @@ -132,7 +132,7 @@ def _features_to_chgrid(features, features_labels, time, missing_files): 1000 * np.max(Y_QPE_CENTERS)]]) - time_start = time - datetime.timedelta(seconds = 5 * 60) + time_start = time - datetime.timedelta(seconds = 5 * 60 + 1) grid.time['units'] = 'seconds since {:s}'.format( datetime.datetime.strftime(time_start, '%Y-%m-%dT%H:%M:%SZ')) @@ -203,7 +203,7 @@ def _qpe_to_chgrid(qpe, time, missing_files, precision=2): 1000 * np.max(Y_QPE_CENTERS)]]) - time_start = time - datetime.timedelta(seconds = 5 * 60) + time_start = time - datetime.timedelta(seconds = 5 * 60 + 1) grid.time['units'] = 'seconds since {:s}'.format( datetime.datetime.strftime(time_start, '%Y-%m-%dT%H:%M:%SZ')) From adcfe2b510fc20b04e3fd0f8f509d6f25cb47f49 Mon Sep 17 00:00:00 2001 From: Daniel Wolfensberger Date: Wed, 28 Feb 2024 20:50:09 +0100 Subject: [PATCH 13/25] Update setup.py --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 38f4044..178e398 100644 --- a/setup.py +++ b/setup.py @@ -32,7 +32,7 @@ s = setup(name = "rainforest_mch", description = "RandomForest QPE python library", long_description = "A library to compute a gauge/radar database, train a RF QPE predictor and apply it to generate QPE fields", - version = "1.4.2", + version = "1.4.3", author = 'Rebecca Gugerli, Daniel Wolfensberger', author_email = ' rebecca.gugerli@epfl.ch, daniel.wolfensberger@meteoswiss.ch', license = 'GPL-3.0', From 3f98baa47bf14d5d44618a3fb24d79e1547259d7 Mon Sep 17 00:00:00 2001 From: Daniel Wolfensberger Date: Mon, 11 Mar 2024 11:05:48 +0100 Subject: [PATCH 14/25] fix error leading to wrong start time in qpe.py --- rainforest/qpe/qpe.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/rainforest/qpe/qpe.py b/rainforest/qpe/qpe.py index 086d604..dba2609 100644 --- a/rainforest/qpe/qpe.py +++ b/rainforest/qpe/qpe.py @@ -132,11 +132,11 @@ def _features_to_chgrid(features, features_labels, time, missing_files): 1000 * np.max(Y_QPE_CENTERS)]]) - time_start = time - datetime.timedelta(seconds = 5 * 60 + 1) + time_start = time - datetime.timedelta(seconds = 5 * 60) grid.time['units'] = 'seconds since {:s}'.format( datetime.datetime.strftime(time_start, '%Y-%m-%dT%H:%M:%SZ')) - grid.time['data'] = np.arange(0, 5 *60) + grid.time['data'] = np.arange(0, 5 *60 + 1) grid.origin_latitude['data'] = 46.9524 grid.origin_longitude['data'] = 7.43958333 grid.projection = proj4_to_dict("+proj=somerc +lat_0=46.95240555555556 "+\ From ff2ef9b2b40dcadb31328f049c63384b861c729e Mon Sep 17 00:00:00 2001 From: Daniel Wolfensberger Date: Mon, 11 Mar 2024 11:05:48 +0100 Subject: [PATCH 15/25] fix error leading to wrong start time in qpe.py --- rainforest/qpe/qpe.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/rainforest/qpe/qpe.py b/rainforest/qpe/qpe.py index 3cf1b0a..4fe4570 100644 --- a/rainforest/qpe/qpe.py +++ b/rainforest/qpe/qpe.py @@ -136,7 +136,7 @@ def _features_to_chgrid(features, features_labels, time, missing_files): grid.time['units'] = 'seconds since {:s}'.format( datetime.datetime.strftime(time_start, '%Y-%m-%dT%H:%M:%SZ')) - grid.time['data'] = np.arange(0, 5 *60) + grid.time['data'] = np.arange(0, 5 *60 + 1) grid.origin_latitude['data'] = 46.9524 grid.origin_longitude['data'] = 7.43958333 grid.projection = proj4_to_dict("+proj=somerc +lat_0=46.95240555555556 "+\ From ae9de6b19c0789a626f363d40aefa4b302659b2b Mon Sep 17 00:00:00 2001 From: Daniel Wolfensberger Date: Mon, 11 Mar 2024 11:30:42 +0100 Subject: [PATCH 16/25] fix error leading to wrong start time in qpe.py --- rainforest/qpe/qpe.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/rainforest/qpe/qpe.py b/rainforest/qpe/qpe.py index 99b3944..f4026b2 100644 --- a/rainforest/qpe/qpe.py +++ b/rainforest/qpe/qpe.py @@ -132,7 +132,7 @@ def _features_to_chgrid(features, features_labels, time, missing_files): 1000 * np.max(Y_QPE_CENTERS)]]) - time_start = time - datetime.timedelta(seconds = 5 * 60 + 1) + time_start = time - datetime.timedelta(seconds = 5 * 60) grid.time['units'] = 'seconds since {:s}'.format( datetime.datetime.strftime(time_start, '%Y-%m-%dT%H:%M:%SZ')) @@ -203,11 +203,11 @@ def _qpe_to_chgrid(qpe, time, missing_files, precision=2): 1000 * np.max(Y_QPE_CENTERS)]]) - time_start = time - datetime.timedelta(seconds = 5 * 60 + 1) + time_start = time - datetime.timedelta(seconds = 5 * 60) grid.time['units'] = 'seconds since {:s}'.format( datetime.datetime.strftime(time_start, '%Y-%m-%dT%H:%M:%SZ')) - grid.time['data'] = np.arange(0, 5 *60) + grid.time['data'] = np.arange(0, 5 *60 + 1) grid.origin_latitude['data'] = 46.9524 grid.origin_longitude['data'] = 7.43958333 grid.projection = proj4_to_dict("+proj=somerc +lat_0=46.95240555555556 "+\ From 284a8cba0209e0a4428c162cad6f86e3ab655c94 Mon Sep 17 00:00:00 2001 From: Daniel Wolfensberger Date: Mon, 11 Mar 2024 11:30:42 +0100 Subject: [PATCH 17/25] fix error leading to wrong start time in qpe.py --- rainforest/qpe/qpe.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/rainforest/qpe/qpe.py b/rainforest/qpe/qpe.py index dba2609..f4026b2 100644 --- a/rainforest/qpe/qpe.py +++ b/rainforest/qpe/qpe.py @@ -203,11 +203,11 @@ def _qpe_to_chgrid(qpe, time, missing_files, precision=2): 1000 * np.max(Y_QPE_CENTERS)]]) - time_start = time - datetime.timedelta(seconds = 5 * 60 + 1) + time_start = time - datetime.timedelta(seconds = 5 * 60) grid.time['units'] = 'seconds since {:s}'.format( datetime.datetime.strftime(time_start, '%Y-%m-%dT%H:%M:%SZ')) - grid.time['data'] = np.arange(0, 5 *60) + grid.time['data'] = np.arange(0, 5 *60 + 1) grid.origin_latitude['data'] = 46.9524 grid.origin_longitude['data'] = 7.43958333 grid.projection = proj4_to_dict("+proj=somerc +lat_0=46.95240555555556 "+\ From d9b7243ab02e463eaf7d362cb239dc8bc8b27c7b Mon Sep 17 00:00:00 2001 From: Daniel Wolfensberger Date: Mon, 11 Mar 2024 11:38:58 +0100 Subject: [PATCH 18/25] modified test_qpe.py to also test time --- tests_ci/qpe/RFO/RFO222710510.h5 | Bin 0 -> 295630 bytes tests_ci/qpe/RF_dualpol/RFQ222710510.h5 | Bin 0 -> 298954 bytes tests_ci/qpe/test_qpe.py | 14 ++++++++++---- tests_cscs/summary.txt | 1 + 4 files changed, 11 insertions(+), 4 deletions(-) create mode 100644 tests_ci/qpe/RFO/RFO222710510.h5 create mode 100644 tests_ci/qpe/RF_dualpol/RFQ222710510.h5 diff --git a/tests_ci/qpe/RFO/RFO222710510.h5 b/tests_ci/qpe/RFO/RFO222710510.h5 new file mode 100644 index 0000000000000000000000000000000000000000..b51bdf9e31edb424fa22e99cadff1684d3821692 GIT binary patch literal 295630 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'{}{}.h5'.format(names[model], tstr))))) # Assertions + # Data + assert 'radar_estimated_rain_rate' in qpe.fields + assert qpe.fields['radar_estimated_rain_rate']['data'].shape == (1,640, 710) assert qpe_field.shape == (640, 710) or qpe_field.shape == (1,640, 710) - assert len(np.unique(qpe_field)) > 2 + assert len(np.unique(qpe.fields['radar_estimated_rain_rate']['data'])) > 2 - shutil.rmtree(str(Path(cwd, model))) + # Time + assert qpe.time['units'] == 'seconds since 2022-09-28T05:05:00Z' + assert qpe.time['data'] == [0, 300] \ No newline at end of file diff --git a/tests_cscs/summary.txt b/tests_cscs/summary.txt index 8b0e609..b9894c1 100644 --- a/tests_cscs/summary.txt +++ b/tests_cscs/summary.txt @@ -6,3 +6,4 @@ 20191019;1571443200;1571443200;1;304;tests_cscs/test_task_file.txt 20191019;1571443200;1571443200;1;304;tests_cscs/test_task_file.txt 20191019;1571443200;1571443200;1;304;tests_cscs/test_task_file.txt +20191019;1571443200;1571443200;1;304;tests_cscs/test_task_file.txt From 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am3DU5vlcVpJ2eA-WN6ktNs)ib_83oI4^dBj!Cq7^u#PI z>J+^$_+R>)8Cp81`Tf!bfjaW>`!<@JvJTRUZf!HabY-1fTfd)|ZaI;h;@w@mFK=Qm z+Pc2b~d_(q&Y&H)bzUW$q!rYwqD$ zk6pyY6dtEbGTc<=B=mj@FJ#$IMAz_)p6}KwWc}5k|L0z!MQ(ZKG@xR@h_Q>nh_NRT zcJzY*uq&4rk#1*DH-BHh?Dtjx>+oTE=J6RYetVz#O-Nah#92i5lJ^! zd8??t)K`l`E;}JC5S(!OCqLjjS}^_iFZl}v>d3e3TLTQpJIJ!j_{MYH<9uw2UuN*q z(Tdf76uB@}+U(6ye2l!>n_!gFUy|*xi)e4;pTHBpzqhGtZq(WDDAy0g*-DJ>KAOlQ zkH!tx^Caxd94&NQIVYe?@Txy&8`>XT*L0NSRL!suV*2q8h1?=zqhmrZLmZ=!i_@ha z6RI^@g_~qj_3VI(_1p(cY_#Z4a(2FoSuBPDMz_~T6^rjoSS^ME*Ik-!RWtz6$X^@H!tih2s20p zgaJ)wfR@uyIsdE5*2HdX)%1lBE^#VSs?)ymcZQ7RB3(M$_-2=>&{hCt+do z)(qY!xs)-QUO=4d)zO9b5-=HW*s+mqNq&61&&>WN4|sKP1hC{bMmeVBh@jNRvS6+U zIJMD=w6$oog6nQ$u9qka7mJ=&?Pr#TW{Jcs${<^~L z2^%w>rR)SuT95WuqQeXE$TO^nBU~D(D1DBkN6#pW7xzFd(osk(h5q{c^b_1|UVqwM z<$(@$iD_(m{mSbrR(yL}ndlV)^39`0a0<~5$4 zgyQyKK6bH*{W^QQ=4yPHN4Qi>!yV>bc(?{Jt+enbp1hwnx{&*k7M4o7S{HeMBl&Y@ z+uO;Io8I$;0MrB+`h4-wn#Bt>*%k1`QnMAIVfu92tn+SuXFLq@acRsn$+zwtxqoq)>3`7(SJmKrB0fT0V2YNq81BOe>TOecFocobE2<9V>1s=}z;9lR~w>ew55Q R`TdCevY>5>|7kn%KLDh@3gZ9( diff --git a/tests_ci/qpe/test_qpe.py b/tests_ci/qpe/test_qpe.py index 71e4f3e..4d6ec21 100644 --- a/tests_ci/qpe/test_qpe.py +++ b/tests_ci/qpe/test_qpe.py @@ -45,4 +45,6 @@ def test_qpe(): # Time assert qpe.time['units'] == 'seconds since 2022-09-28T05:05:00Z' - assert qpe.time['data'] == [0, 300] \ No newline at end of file + assert qpe.time['data'] == [0, 300] + + shutil.rmtree(str(Path(cwd, model))) \ No newline at end of file From e4d2a87ed00faa60247249851792133b4680d4d0 Mon Sep 17 00:00:00 2001 From: Daniel Wolfensberger Date: Mon, 11 Mar 2024 11:48:31 +0100 Subject: [PATCH 20/25] modified test_qpe.py to also test time --- tests_ci/qpe/test_qpe.py | 1 - 1 file changed, 1 deletion(-) diff --git a/tests_ci/qpe/test_qpe.py b/tests_ci/qpe/test_qpe.py index 4d6ec21..86763cb 100644 --- a/tests_ci/qpe/test_qpe.py +++ b/tests_ci/qpe/test_qpe.py @@ -40,7 +40,6 @@ def test_qpe(): # Data assert 'radar_estimated_rain_rate' in qpe.fields assert qpe.fields['radar_estimated_rain_rate']['data'].shape == (1,640, 710) - assert qpe_field.shape == (640, 710) or qpe_field.shape == (1,640, 710) assert len(np.unique(qpe.fields['radar_estimated_rain_rate']['data'])) > 2 # Time From a7c33bcdc2dc886ead309efbbd0d5201153f3801 Mon Sep 17 00:00:00 2001 From: Daniel Wolfensberger Date: Mon, 11 Mar 2024 11:48:31 +0100 Subject: [PATCH 21/25] modified test_qpe.py to also test time --- tests_ci/qpe/test_qpe.py | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/tests_ci/qpe/test_qpe.py b/tests_ci/qpe/test_qpe.py index 73a158b..2135b3f 100644 --- a/tests_ci/qpe/test_qpe.py +++ b/tests_ci/qpe/test_qpe.py @@ -36,7 +36,13 @@ def test_qpe(): qpe_field = qpe.data # Assertions - assert qpe_field.shape == (640, 710) or qpe_field.shape == (1,640, 710) - assert len(np.unique(qpe_field)) > 2 + # Data + assert 'radar_estimated_rain_rate' in qpe.fields + assert qpe.fields['radar_estimated_rain_rate']['data'].shape == (1,640, 710) + assert len(np.unique(qpe.fields['radar_estimated_rain_rate']['data'])) > 2 + + # Time + assert qpe.time['units'] == 'seconds since 2022-09-28T05:05:00Z' + assert qpe.time['data'] == [0, 300] shutil.rmtree(str(Path(cwd, model))) From b679e04cbe92bd7612a40209c2cff1bd19f0841b Mon Sep 17 00:00:00 2001 From: Daniel Wolfensberger Date: Mon, 11 Mar 2024 12:26:44 +0100 Subject: [PATCH 22/25] Update setup.py --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 178e398..3b176be 100644 --- a/setup.py +++ b/setup.py @@ -32,7 +32,7 @@ s = setup(name = "rainforest_mch", description = "RandomForest QPE python library", long_description = "A library to compute a gauge/radar database, train a RF QPE predictor and apply it to generate QPE fields", - version = "1.4.3", + version = "1.4.4", author = 'Rebecca Gugerli, Daniel Wolfensberger', author_email = ' rebecca.gugerli@epfl.ch, daniel.wolfensberger@meteoswiss.ch', license = 'GPL-3.0', From 3e3363afb84846f3e4da6c2f4d8ee359ffa66304 Mon Sep 17 00:00:00 2001 From: Rebecca Gugerli Date: Thu, 14 Mar 2024 10:33:01 +0100 Subject: [PATCH 23/25] Revert "Merge branch 'ensemble' into master" This reverts commit 209a931af4f3673ed8a5400c7d07045872516de9, reversing changes made to b679e04cbe92bd7612a40209c2cff1bd19f0841b. undo last merge --- rainforest/ml/rf.py | 1080 +++++++++++--------------------- rainforest/ml/rfdefinitions.py | 198 +----- rainforest/qpe/qpe.py | 96 +-- requirements.txt | 1 - 4 files changed, 388 insertions(+), 987 deletions(-) diff --git a/rainforest/ml/rf.py b/rainforest/ml/rf.py index 2f07f66..2047e66 100755 --- a/rainforest/ml/rf.py +++ b/rainforest/ml/rf.py @@ -5,6 +5,9 @@ """ # Global imports +from audioop import cross +import logging +logging.getLogger().setLevel(logging.INFO) import os import pickle import glob @@ -17,168 +20,14 @@ # Local imports from ..common import constants -from ..ml.utils import vert_aggregation, split_event, split_years -from ..ml.rfdefinitions import RandomForestRegressorBC, QuantileRandomForestRegressorBC, read_rf +from .utils import vert_aggregation, split_event, split_years +from .rfdefinitions import RandomForestRegressorBC from ..common.utils import perfscores, envyaml from ..common.graphics import plot_crossval_stats -from ..common.logger import logger - dir_path = os.path.dirname(os.path.realpath(__file__)) FOLDER_MODELS = Path(os.environ['RAINFOREST_DATAPATH'], 'rf_models') -def readInputData(input_location, tstart, tend, datalist=['gauge', 'radar', 'refer']): - - if 'gauge' in datalist: - gaugetab = pd.read_parquet(str(Path(input_location, 'gauge.parquet'))) - if 'radar' in datalist: - radartab = pd.read_parquet(str(Path(input_location, 'radar_x0y0.parquet'))) - if 'refer' in datalist: - refertab = pd.read_parquet(str(Path(input_location, 'reference_x0y0.parquet'))) - - grp = pickle.load(open(str(Path(input_location, 'grouping_idx_x0y0.p')),'rb')) - grp_hourly = grp['grp_hourly']; grp_vertical = grp['grp_vertical'] - - if tstart != None: - try: - tstart = datetime.datetime.strptime(tstart, - '%Y%m%d%H%M').replace(tzinfo=datetime.timezone.utc).timestamp() - except: - tstart = gaugetab['TIMESTAMP'].min() - logger.info('The format of tstart was wrong, taking the earliest date') - if tend != None: - try: - tend = datetime.datetime.strptime(tend, - '%Y%m%d%H%M').replace(tzinfo=datetime.timezone.utc).timestamp() - except: - tend = gaugetab['TIMESTAMP'].max() - logger.info('The format of tend was wrong, taking the earliest date') - - timevalid = gaugetab['TIMESTAMP'].copy().astype(bool) - vertvalid = radartab['TIMESTAMP'].copy().astype(bool) - - if (tstart != None): - timevalid[(gaugetab['TIMESTAMP'] < tstart)] = False - vertvalid[(radartab['TIMESTAMP'] < tstart)] = False - if (tend != None): - timevalid[(gaugetab['TIMESTAMP'] > tend)] = False - vertvalid[(radartab['TIMESTAMP'] > tend)] = False - - gaugetab = gaugetab[timevalid] - grp_hourly = grp_hourly[timevalid] - radartab = radartab[vertvalid] - grp_vertical = grp_vertical[vertvalid] - - if 'refer' in datalist: - refertab = refertab[timevalid] - return gaugetab, radartab, refertab, grp_hourly, grp_vertical - else: - return gaugetab, radartab, grp_hourly, grp_vertical - -def processFeatures(features_dic, radartab): - # currently the only supported additional features is zh (refl in linear units) - # and DIST_TO_RAD{A-D-L-W-P} (dist to individual radars) - # Get list of unique features names - if type(features_dic) == dict : - features = np.unique([item for sub in list(features_dic.values()) - for item in sub]) - else: - features = features_dic - - for f in features: - if 'zh' in f: - logger.info('Converting reflectivity {:s} from log [dBZ] to linear [mm^6 m^-3]'.format(f)) - try: - radartab[str(f)] = 10**(0.1 * radartab[f.replace('zh','ZH')+'_mean'].copy()) - except: - radartab[str(f)] = 10**(0.1 * radartab[f.replace('zh','ZH')].copy()) - elif 'zv' in f: - logger.info('Computing derived variable {:s}'.format(f)) - try: - radartab[str(f)] = 10**(0.1 * radartab[f.replace('zv','ZV')+'_mean'].copy()) - except: - radartab[str(f)] = 10**(0.1 * radartab[f.replace('zv','ZV')].copy()) - if 'DIST_TO_RAD' in f: - info_radar = constants.RADARS - vals = np.unique(radartab['RADAR']) - for val in vals: - dist = np.sqrt((radartab['X'] - info_radar['X'][val])**2+ - (radartab['Y'] - info_radar['Y'][val])**2) / 1000. - radartab['DIST_TO_RAD' + str(val)] = dist - - features = [str(f) for f in features] - - return radartab, features - -def getTempIDX(TempOBS, grp_hourly, test, train) : - - # Get reference values - T_test_60 = np.squeeze(np.array(pd.DataFrame(TempOBS[test]) - .groupby(grp_hourly[test]).mean())) - - T_train_60 = np.squeeze(np.array(pd.DataFrame(TempOBS[train]) - .groupby(grp_hourly[train]).mean())) - - # Dictionnary with all indices: - IDX = {} - - for tagg in ['10min', '60min']: - IDX[tagg] = {} - for data_type in ['test', 'train']: - IDX[tagg][data_type] = {} - for precip_type in ['all', 'liquid', 'solid']: - IDX[tagg][data_type][precip_type] = {} - - IDX['10min']['train']['liquid'] = TempOBS[train] >= constants.THRESHOLD_SOLID - IDX['10min']['train']['solid'] = TempOBS[train] < constants.THRESHOLD_SOLID - - IDX['60min']['train']['liquid'] = T_train_60 >= constants.THRESHOLD_SOLID - IDX['60min']['train']['solid'] = T_train_60 < constants.THRESHOLD_SOLID - - IDX['10min']['test']['liquid'] = TempOBS[test] >= constants.THRESHOLD_SOLID - IDX['10min']['test']['solid'] = TempOBS[test] < constants.THRESHOLD_SOLID - - IDX['60min']['test']['liquid'] = T_test_60 >= constants.THRESHOLD_SOLID - IDX['60min']['test']['solid'] = T_test_60 < constants.THRESHOLD_SOLID - - return IDX - - -def prepareScoreDic(modelnames, station_scores=True): - - all_scores = {'10min':{},'60min':{}} - all_stats = {'10min':{},'60min':{}} - - if station_scores == True: - all_station_scores = {'10min': {}, '60min': {}} - all_station_stats = {'10min': {}, '60min': {}} - - for tagg in ['10min', '60min']: - for model in modelnames: - all_scores[tagg][model] = {'train': {'solid':[],'liquid':[],'all':[]}, - 'test': {'solid':[],'liquid':[],'all':[]}} - all_stats[tagg][model] = {'train': {'solid':{},'liquid':{},'all':{}}, - 'test': {'solid':{},'liquid':{},'all':{}}} - if station_scores == True: - all_station_scores[tagg][model] = {'solid':{},'liquid':{},'all':{}} - all_station_stats[tagg][model] = {'solid':{},'liquid':{},'all':{}} - - if station_scores == False: - return all_scores, all_stats - else: - return all_scores, all_stats, all_station_scores, all_station_stats - -def calcScore(all_scores, obs, pred, idxTemp, bounds, - tagg = '10min', model='RFO', data_type='test'): - - for pp_type in all_scores[tagg][model][data_type].keys(): - idx = idxTemp[tagg][data_type][pp_type] - scores = perfscores(pred[idx], obs[idx], bounds=bounds) - all_scores[tagg][model][data_type][pp_type].append(scores) - - return all_scores - - class RFTraining(object): ''' This is the main class that allows to preparate data for random forest @@ -227,9 +76,9 @@ def __init__(self, db_location, input_location=None, self.db_location = db_location if not valid : - logger.info('Could not find valid input data from the folder {:s}'.format(input_location)) + logging.info('Could not find valid input data from the folder {:s}'.format(input_location)) # if force_regenerate_input or not valid: - # logger.info('The program will now compute this input data from the database, this takes quite some time') + # logging.info('The program will now compute this input data from the database, this takes quite some time') # self.prepare_input() def prepare_input(self, only_center=True, foldername_radar='radar'): @@ -265,7 +114,7 @@ def prepare_input(self, only_center=True, foldername_radar='radar'): """ if not os.path.exists(Path(self.db_location, foldername_radar)): - logger.error('Invalid foldername for radar data, please check') + logging.error('Invalid foldername for radar data, please check') if only_center: nx = [0] @@ -282,7 +131,7 @@ def prepare_input(self, only_center=True, foldername_radar='radar'): gauge = gauge.replace(-9999,np.nan) for x in nx: for y in ny: - logger.info('Processing neighbour {:d}{:d}'.format(x, y)) + logging.info('Processing neighbour {:d}{:d}'.format(x, y)) radar = dd.read_parquet(str(Path(self.db_location, foldername_radar, '*.parquet'))) refer = dd.read_parquet(str(Path(self.db_location, 'reference', @@ -389,7 +238,7 @@ def prepare_input(self, only_center=True, foldername_radar='radar'): # Save all to file # Save all to file - logger.info('Saving files to {}'.format(self.input_location)) + logging.info('Saving files to {}'.format(self.input_location)) refer.to_parquet(str(Path(self.input_location, 'reference_x{:d}y{:d}.parquet'.format(x,y))), compression = 'gzip', index = False) @@ -412,102 +261,7 @@ def prepare_input(self, only_center=True, foldername_radar='radar'): gauge.to_parquet(str(Path(self.input_location, 'gauge.parquet')), compression = 'gzip', index = False) - def prepare_input_vert_agg(self, aggregation_params, features=None, - tstart = None, tend = None, - output_folder = None): - """ - Calculates the vertical aggregation of the input features dic and saves - it to a file to run tests faster - - Parameters - ---------- - aggregation_params : dict - Dict with two keywords: beta and visib_weighting - If not given, the default is used: {'beta' : -0.5 , 'visib_weighting: True} - features : list - A list with features that will be aggregated to the ground, - e.g., ['RADAR', 'zh_VISIB_mean', - 'zv_VISIB_mean','KDP_mean','RHOHV_mean','T', 'HEIGHT','VISIB_mean']} - tstart : str - the starting time of the training time interval, default is to start - at the beginning of the time interval covered by the database - tend : str - the end time of the training time interval, default is to end - at the end of the time interval covered by the database - output_folder : str - Location where to store the trained models in pickle format, - if not provided it will store them in the standard location - str(Path(db_location, 'rf_input_data')) - """ - - if output_folder == None: - output_folder = self.input_location - if aggregation_params == None: - vert_agg_params = {'beta' : -0.5, 'visib_weighting' : 1} - else: - vert_agg_params = aggregation_params - - ############################################################################### - # Read and filter data - ############################################################################### - _, radartab, _, grp_vertical = \ - readInputData(self.input_location, tstart, tend, - datalist=['gauge', 'radar']) - - if features == None: - features = radartab.columns - - ############################################################################### - # Compute vertical aggregation and initialize model - ############################################################################### - radartab, features = processFeatures(features, radartab) - - ############################################################################### - # Compute data filter for each model - ############################################################################### - beta = vert_agg_params['beta'] - visib_weigh = vert_agg_params['visib_weighting'] - - vweights = 10**(beta * (radartab['HEIGHT']/1000.)) # vert. weights - - ############################################################################### - # Prepare training dataset - ############################################################################### - logger.info('Performing vertical aggregation of input features') - features_VERT_AGG = vert_aggregation(radartab[features], - vweights, grp_vertical,visib_weigh, - radartab['VISIB_mean']) - - ############################################################################### - # Fit - ############################################################################### - # create name of variables used in the model - features = [] - for f in features_VERT_AGG.columns: - if '_max' in f: - f = f.replace('_max','') - elif '_min' in f: - f = f.replace('_min','') - elif '_mean' in f: - f = f.replace('_mean','') - features.append(f) - - features_VERT_AGG['TIMESTAMP'] = radartab['TIMESTAMP'].groupby(grp_vertical).first() - features_VERT_AGG['STATION'] = radartab['STATION'].groupby(grp_vertical).first() - - features_VERT_AGG.to_parquet(str(Path(self.input_location, - 'feat_vert_agg_BETA_{:1.1f}_VisibWeigh_{:d}.parquet'.format(beta,visib_weigh))), - compression = 'gzip', index = False) - - filename = str(Path(self.input_location, \ - 'feat_vert_agg_BETA_{:1.1f}_VisibWeigh_{:d}_README.txt'.\ - format(beta,visib_weigh))) - with open(filename, 'w') as f: - f.write('Colnames\n'+ (';').join(list(features_VERT_AGG.columns)) + '\n') - f.write('Names in model\n' + (';').join(features) + '\n') - - def fit_models(self, config_file, features_dic, tstart = None, tend = None, output_folder = None): """ @@ -526,10 +280,10 @@ def fit_models(self, config_file, features_dic, tstart = None, tend = None, 'zv_VISIB_mean','KDP_mean','RHOHV_mean','T', 'HEIGHT','VISIB_mean']} will train a model with all these features that will then be stored under the name RF_dualpol_BC_.p in the ml/rf_models dir - tstart : str + tstart : datetime the starting time of the training time interval, default is to start at the beginning of the time interval covered by the database - tend : str + tend : datetime the end time of the training time interval, default is to end at the end of the time interval covered by the database output_folder : str @@ -544,40 +298,62 @@ def fit_models(self, config_file, features_dic, tstart = None, tend = None, try: config = envyaml(config_file) except: - logger.warning('Using default config as no valid config file was provided') + logging.warning('Using default config as no valid config file was provided') config_file = dir_path + '/default_config.yml' config = envyaml(config_file) + ####################################################################### + # Read data + ####################################################################### + + logging.info('Loading input data') + radartab = pd.read_parquet(str(Path(self.input_location, 'radar_x0y0.parquet'))) + gaugetab = pd.read_parquet(str(Path(self.input_location, 'gauge.parquet'))) + grp = pickle.load(open(str(Path(self.input_location, 'grouping_idx_x0y0.p')),'rb')) + grp_vertical = grp['grp_vertical'] + ############################################################################### - # Read and filter data + # Compute additional data if needed ############################################################################### - gaugetab, radartab, _, grp_vertical = \ - readInputData(self.input_location, tstart, tend, - datalist=['gauge', 'radar']) + + # currently the only supported additional features is zh (refl in linear units) + # and DIST_TO_RAD{A-D-L-W-P} (dist to individual radars) + # Get list of unique features names + features = np.unique([item for sub in list(features_dic.values()) + for item in sub]) - ############################################################################### - # Compute vertical aggregation and initialize model - ############################################################################### - radartab, features = processFeatures(features_dic, radartab) + for f in features: + if 'zh' in f: + logging.info('Computing derived variable {:s}'.format(f)) + radartab[f] = 10**(0.1 * radartab[f.replace('zh','ZH')]) + elif 'zv' in f: + logging.info('Computing derived variable {:s}'.format(f)) + radartab[f] = 10**(0.1 * radartab[f.replace('zv','ZV')]) + if 'DIST_TO_RAD' in f: + info_radar = constants.RADARS + vals = np.unique(radartab['RADAR']) + for val in vals: + dist = np.sqrt((radartab['X'] - info_radar['X'][val])**2+ + (radartab['Y'] - info_radar['Y'][val])**2) / 1000. + radartab['DIST_TO_RAD' + str(val)] = dist ############################################################################### # Compute data filter for each model ############################################################################### for model in features_dic.keys(): - logtime0 = datetime.datetime.now() vweights = 10**(config[model]['VERT_AGG']['BETA'] * (radartab['HEIGHT']/1000.)) # vert. weights filterconf = config[model]['FILTERING'].copy() - logger.info('Computing data filter') - logger.info('List of stations to ignore {:s}'.format(','.join(filterconf['STA_TO_REMOVE']))) - logger.info('Start time {:s}'.format(str(tstart))) - logger.info('End time {:s}'.format(str(tend))) - logger.info('ZH must be > {:f} if R <= {:f}'.format(filterconf['CONSTRAINT_MIN_ZH'][1], + logging.info('Computing data filter') + logging.info('List of stations to ignore {:s}'.format(','.join(filterconf['STA_TO_REMOVE']))) + logging.info('Start time {:s}'.format(str(tstart))) + logging.info('End time {:s}'.format(str(tend))) + logging.info('ZH must be > {:f} if R <= {:f}'.format(filterconf['CONSTRAINT_MIN_ZH'][1], filterconf['CONSTRAINT_MIN_ZH'][0])) - logger.info('ZH must be < {:f} if R <= {:f}'.format(filterconf['CONSTRAINT_MAX_ZH'][1], + logging.info('ZH must be < {:f} if R <= {:f}'.format(filterconf['CONSTRAINT_MAX_ZH'][1], filterconf['CONSTRAINT_MAX_ZH'][0])) ZH_agg = vert_aggregation(pd.DataFrame(radartab['ZH_mean']), @@ -595,6 +371,12 @@ def fit_models(self, config_file, features_dic, tstart = None, tend = None, invalid = np.logical_or(invalid,cond3) invalid = np.array(invalid) + if tend != None: + tend_unix = (tend - datetime.datetime(1970,1,1) ).total_seconds() + invalid[gaugetab['TIMESTAMP'] > tend_unix] = 1 + if tstart != None: + tstart_unix = (tstart - datetime.datetime(1970,1,1) ).total_seconds() + invalid[gaugetab['TIMESTAMP'] < tstart_unix] = 1 invalid[np.isnan(gaugetab['RRE150Z0'])] = 1 ############################################################################### @@ -603,7 +385,7 @@ def fit_models(self, config_file, features_dic, tstart = None, tend = None, gaugetab_train = gaugetab[~invalid].copy() - logger.info('Performing vertical aggregation of input features for model {:s}'.format(model)) + logging.info('Performing vertical aggregation of input features for model {:s}'.format(model)) features_VERT_AGG = vert_aggregation(radartab[features_dic[model]], vweights, grp_vertical, config[model]['VERT_AGG']['VISIB_WEIGHTING'], @@ -639,287 +421,26 @@ def fit_models(self, config_file, features_dic, tstart = None, tend = None, config[model]['FILTERING']['STA_INCLUDED'] = gaugetab['STATION'][~invalid].unique() config[model]['FILTERING']['CREATED'] = datetime.datetime.utcnow().strftime('%d %b %Y %H:%M UTC') - logger.info('') - logger.info('Training model on gauge data') + logging.info('') + logging.info('Training model on gauge data') - logger.info('Initializing random forest model {:s}'.format(model)) - if len(config[model]['QUANTILES']) == 0: - reg = RandomForestRegressorBC(degree = 1, + reg = RandomForestRegressorBC(degree = 1, bctype = config[model]['BIAS_CORR'], + visib_weighting = config[model]['VERT_AGG']['VISIB_WEIGHTING'], variables = features, beta = config[model]['VERT_AGG']['BETA'], - visib_weighting=config[model]['VERT_AGG']['VISIB_WEIGHTING'], + metadata = config[model]['FILTERING'], **config[model]['RANDOMFOREST_REGRESSOR']) - else: - reg = QuantileRandomForestRegressorBC(degree = 1, - bctype = config[model]['BIAS_CORR'], - variables = features, - beta = config[model]['VERT_AGG']['BETA'], - visib_weighting=config[model]['VERT_AGG']['VISIB_WEIGHTING'], - **config[model]['RANDOMFOREST_REGRESSOR']) - # reg = QuantileRegressionForest(**config[model]['RANDOMFOREST_REGRESSOR']) - # reg.bctype = config[model]['BIAS_CORR'] - # reg.variables = features - # reg.beta = config[model]['VERT_AGG']['BETA'] - # reg.visib_weighting=config[model]['VERT_AGG']['VISIB_WEIGHTING'] - - logger.info('Fitting random forest model {:s}'.format(model)) - - reg.fit(features_VERT_AGG[valid].to_numpy(), Y[valid]) - - logger.info('Model {} took {} minutes to be trained'.format(model, datetime.datetime.now()-logtime0)) + reg.fit(features_VERT_AGG[valid], Y[valid]) + out_name = str(Path(output_folder, '{:s}_BETA_{:2.1f}_BC_{:s}.p'.format(model, config[model]['VERT_AGG']['BETA'], config[model]['BIAS_CORR']))) - logger.info('Saving model to {:s}'.format(out_name)) + logging.info('Saving model to {:s}'.format(out_name)) pickle.dump(reg, open(out_name, 'wb')) - del reg - - -class RFModelEval(object): - ''' - This is the main class that allows to preparate data for random forest - training, train random forests and perform cross-validation of trained models - ''' - def __init__(self, db_location, input_location=None): - """ - Initializes the class to analyse the model and perform standard ML - evaluation on it - - Note that when calling this constructor the input data is only - generated for the central pixel (NX = NY = 0 = loc of gauge), if you - want to regenerate the inputs for all neighbour pixels, please - call the function self.prepare_input(only_center_pixel = False) - - Parameters - ---------- - db_location : str - Location of the main directory of the database (with subfolders - 'reference', 'gauge' and 'radar' on the filesystem) - input_location : str - Location of the prepared input data, if this data cannot be found - in this folder, it will be computed here, default is a subfolder - called rf_input_data within db_location - force_regenerate_input : bool - if True the input parquet files will always be regenerated from - the database even if already present in the input_location folder - """ - - if input_location == None: - input_location = str(Path(db_location, 'rf_input_data')) - - # Check if at least gauge.parquet, refer_x0y0.parquet and radar_x0y0.parquet - # are present - valid = True - if not os.path.exists(input_location): - valid = False - os.makedirs(input_location) - files = glob.glob(str(Path(input_location, '*'))) - files = [os.path.basename(f) for f in files] - if ('gauge.parquet' not in files or 'reference_x0y0.parquet' not in files - or 'radar_x0y0.parquet' not in files): - valid = False - - self.input_location = input_location - self.db_location = db_location - - if not valid : - logger.info('Could not find valid input data from the folder {:s}'.format(input_location)) - - def calc_model_predictions(self, models_dic, output_folder, model_folder=None, - tstart=None, tend=None, reference=['CPCH', 'RZC'], - station_obs = ['TRE200S0', 'DKL010Z0', 'FKL010Z0'], - ensemble = [] , quantile_dic = {}): - """_summary_ - - Parameters - ----------- - models_dic : dic - dic with modelname, filename of model - e.g.,model_dic = {'RFO': 'RFO_BETA_-0.5_BC_spline.p'} - output_folder : str - Path where the dataframe is stored - model_folder : str, optional - Path where model is stored. Defaults to None. - output_folder : str - Path to where to store the scores - tstart: str (YYYYMMDDHHMM) - A date to define a starting time for the input data - tend: str (YYYYMMDDHHMM) - A date to define the end of the input data - reference : list, optional - _description_. Defaults to ['CPCH', 'RZC']. - station_obs : list, optional - _description_. Defaults to ['TRE200S0', 'DKL1010Z0', 'FKL010Z0']. - ensemble : list - List with all models that a ensemble output is wished for - quantile_dic : dic - Dictionary with modelname and quantiles to extract, - e.g., quantile_dic = {'QuRFO': [0.05,0.10,0.5,0.9,0.95]} - """ - - # Get models and model path - modelnames = list(models_dic.keys()) - if model_folder == None: - MODEL_FOLDER = self.model_paths - else: - MODEL_FOLDER = model_folder - - # Get data from database, and filter it according to the time limitations - if len(reference) == 0 : - gaugetab, radartab, grp_hourly, grp_vertical = \ - readInputData(self.input_location, tstart, tend, datalist=['gauge', 'radar']) - else: - gaugetab, radartab, refertab, grp_hourly, grp_vertical =\ - readInputData(self.input_location, tstart, tend, datalist=['gauge', 'radar', 'refer']) - - ################################################################################# - # Read models and create features dictionary - ################################################################################# - regressors = {} - features_dic = {} - - for model in modelnames: - logger.info('Performing vertical aggregation of input features for model {:s}'.format(model)) - - # regressors[model] = pickle.load(open(Path(MODEL_FOLDER,features_dic[model]),'rb')) - regressors[model] = read_rf(models_dic[model], MODEL_FOLDER) - features = regressors[model].variables.copy() - features_dic[model] = features - - # As RADAR_prop will be calculated below, adding variable here: - # Compute additional data if needed - features_to_be_removed = [] - for f in features: - # Radar_prop is calculated with vert_aggregation - if f.startswith('RADAR_prop'): - features_to_be_removed.append(f) - - if len(features_to_be_removed) > 0: - for ftbr in features_to_be_removed: - features.remove(ftbr) - features.append('RADAR') - - regressors[model].features = features.copy() - - ############################################################################### - # Get linear units of reflectivity - ############################################################################### - radartab, features = processFeatures(features_dic, radartab) - - for colname in radartab.columns: - list_feat = np.unique([item for sub in list(features_dic.values()) - for item in sub]) - if colname.replace('_mean', '') in list_feat: - radartab.rename(columns = {colname:colname.replace('_mean','')}, inplace=True) - - ############################################################################### - # Compute vertical aggregation - ############################################################################### - features_VERT_AGG = {} - for im, model in enumerate(modelnames): - logger.info('Performing vertical aggregation of input features for model {:s}'.format(model)) - - beta = regressors[model].beta - visib_weighting = regressors[model].visib_weighting - - if (im > 0) and (beta == regressors[modelnames[im-1]].beta) \ - and (visib_weighting == regressors[modelnames[im-1]].visib_weighting) : - logger.info('Model {} has same vertical aggregation settings as {}, hence just copy aggregated 2D fields'.format(model, modelnames[im-1])) - features_VERT_AGG[model] = features_VERT_AGG[modelnames[im-1]].copy() - else: - vweights = 10**(beta*(radartab['HEIGHT']/1000.)) # vert. weights - try: - features_VERT_AGG[model] = vert_aggregation(radartab[features_dic[model]], - vweights, grp_vertical, - visib_weighting, - radartab['VISIB_mean']) - except: - features_VERT_AGG[model] = vert_aggregation(radartab[features_dic[model]], - vweights, grp_vertical, - visib_weighting, - radartab['VISIB']) - - ############################################################################### - # Clean and prepare data - ############################################################################### - test_not_ok = False - for iv, val in enumerate(radartab['s-tstamp'].groupby(grp_vertical).first()): - if gaugetab['s-tstamp'].iloc[iv] != val: - test_not_ok = True - print(gaugetab['s-tstamp'][iv]) - if test_not_ok: - logger.error('Time cut went wrong!!') - - valid = np.all(np.isfinite(features_VERT_AGG[modelnames[0]]), - axis = 1) - - for model in modelnames: - features_VERT_AGG[model] = features_VERT_AGG[model][valid] - - gaugetab = gaugetab[valid] - refertab = refertab[valid] - grp_hourly = grp_hourly[valid] - - ############################################################################### - # Assemble dataframe with data from database - ############################################################################### - logger.info('Assembling dataframe') - data_pred = pd.DataFrame({'TIMESTAMP': gaugetab['TIMESTAMP'], - 'STATION': gaugetab['STATION'], - 'RRE150Z0': gaugetab['RRE150Z0']*6, - 'IDX_HOURLY': grp_hourly}) - - for var in station_obs: - try: - data_pred[var] = gaugetab[var] - except: - logger.error('Could not add {}'.format(var)) - - for ref in reference: - try: - data_pred[ref] = refertab[ref] - except: - logger.error('Could not add {}'.format(ref)) - - ############################################################################### - # Calculate model values - ############################################################################### - for model in modelnames: - logger.info('Calculate estimates of {}'.format(model)) - - if model in quantile_dic.keys(): - R_pred_10 = regressors[model].predict(features_VERT_AGG[model][regressors[model].variables], - quantiles=quantile_dic[model]) - data_pred[model] = R_pred_10[0] - data_quant = pd.DataFrame(R_pred_10[1], - columns = [model+'_Q'+str(qu) for qu in quantile_dic[model]], - index = data_pred.index) - data_pred = pd.concat([data_pred, data_quant], axis=1) - else: - data_pred[model] = regressors[model].predict(\ - features_VERT_AGG[model][regressors[model].variables]) - - if (model in ensemble) & ('Qu' not in model) : - pred_ens = regressors[model].predict_ens(\ - features_VERT_AGG[model][regressors[model].variables]) - for tree in range(regressors[model].n_estimators): - data_pred['{}_E{}'.format(model, tree)] = pred_ens[:,tree] - # data_pred['{}_E{}'.format(model, tree)] = \ - # regressors[model].estimators_[tree].predict(\ - # features_VERT_AGG[model][regressors[model].variables]) - - ############################################################################### - # Save output - ############################################################################### - name_file = str(Path(output_folder, 'rfmodels_x0_y0.parquet')) - data_pred.to_parquet(name_file, compression='gzip', index=False) - logger.info('Saved file: {}'.format(name_file)) - - def feature_selection(self, features_dic, featuresel_configfile, output_folder, K=5, tstart=None, tend=None): """ @@ -947,14 +468,71 @@ def feature_selection(self, features_dic, featuresel_configfile, config = envyaml(featuresel_configfile) modelnames = list(features_dic.keys()) + ####################################################################### + # Read data + ####################################################################### + logging.info('Reading input data from {}'.format(self.input_location)) + radartab = pd.read_parquet(str(Path(self.input_location, 'radar_x0y0.parquet'))) + gaugetab = pd.read_parquet(str(Path(self.input_location, 'gauge.parquet'))) + grp = pickle.load(open(str(Path(self.input_location, 'grouping_idx_x0y0.p')),'rb')) + grp_vertical = grp['grp_vertical'] + grp_hourly = grp['grp_hourly'] + + ####################################################################### + # Filter time + ####################################################################### + if tstart != None: + try: + tstart = datetime.datetime.strptime(tstart, + '%Y%m%d%H%M').replace(tzinfo=datetime.timezone.utc).timestamp() + except: + tstart = gaugetab['TIMESTAMP'].min() + logging.info('The format of tstart was wrong, taking the earliest date') + if tend != None: + try: + tend = datetime.datetime.strptime(tend, + '%Y%m%d%H%M').replace(tzinfo=datetime.timezone.utc).timestamp() + except: + tend = gaugetab['TIMESTAMP'].max() + logging.info('The format of tend was wrong, taking the earliest date') + + timevalid = gaugetab['TIMESTAMP'].copy().astype(bool) + vertvalid = radartab['TIMESTAMP'].copy().astype(bool) + + if (tstart != None): + timevalid[(gaugetab['TIMESTAMP'] < tstart)] = False + vertvalid[(radartab['TIMESTAMP'] < tstart)] = False + if (tend != None): + timevalid[(gaugetab['TIMESTAMP'] > tend)] = False + vertvalid[(radartab['TIMESTAMP'] > tend)] = False + + gaugetab = gaugetab[timevalid] + grp_hourly = grp_hourly[timevalid] + radartab = radartab[vertvalid] + grp_vertical = grp_vertical[vertvalid] ############################################################################### - # Read data, filter time and get linear reflectivity units + # Compute additional data if needed ############################################################################### - gaugetab, radartab, _, grp_hourly, grp_vertical = \ - readInputData(self.input_location, tstart, tend) - - radartab, features = processFeatures(features_dic, radartab) + # currently the only supported additional features is zh (refl in linear units) + # and DIST_TO_RAD{A-D-L-W-P} (dist to individual radars) + # Get list of unique features names + features = np.unique([item for sub in list(features_dic.values()) + for item in sub]) + for f in features: + if 'zh' in f: + logging.info('Computing derived variable {:s}'.format(f)) + radartab[f] = 10**(0.1 * radartab[f.replace('zh','ZH')]) + elif 'zv' in f: + logging.info('Computing derived variable {:s}'.format(f)) + radartab[f] = 10**(0.1 * radartab[f.replace('zv','ZV')]) + if 'DIST_TO_RAD' in f: + info_radar = constants.RADARS + vals = np.unique(radartab['RADAR']) + for val in vals: + dist = np.sqrt((radartab['X'] - info_radar['X'][val])**2+ + (radartab['Y'] - info_radar['Y'][val])**2) / 1000. + radartab['DIST_TO_RAD' + str(val)] = dist ############################################################################### # Compute vertical aggregation @@ -962,11 +540,11 @@ def feature_selection(self, features_dic, featuresel_configfile, features_VERT_AGG = {} regressors = {} for im, model in enumerate(modelnames): - logger.info('Performing vertical aggregation of input features for model {:s}'.format(model)) + logging.info('Performing vertical aggregation of input features for model {:s}'.format(model)) if (im > 0) and (config[model]['VERT_AGG']['BETA'] == config[modelnames[im-1]]['VERT_AGG']['BETA']) \ and (config[model]['VERT_AGG']['VISIB_WEIGHTING'] == config[modelnames[im-1]]['VERT_AGG']['VISIB_WEIGHTING']): - logger.info('Model {} has same vertical aggregation settings as {}, hence just copy aggregated 2D fields'.format(model, modelnames[im-1])) + logging.info('Model {} has same vertical aggregation settings as {}, hence just copy aggregated 2D fields'.format(model, modelnames[im-1])) features_VERT_AGG[model] = features_VERT_AGG[modelnames[im-1]].copy() else: vweights = 10**(config[model]['VERT_AGG']['BETA'] * @@ -987,13 +565,18 @@ def feature_selection(self, features_dic, featuresel_configfile, valid = np.all(np.isfinite(features_VERT_AGG[modelnames[0]]), axis = 1) + # if (tstart != None): + # valid[(gaugetab['TIMESTAMP'] < tstart)] = False + # if (tend != None): + # valid[(gaugetab['TIMESTAMP'] > tend)] = False + test_not_ok = False for iv, val in enumerate(radartab['s-tstamp'].groupby(grp_vertical).first()): if gaugetab['s-tstamp'][iv] != val: test_not_ok = True print(gaugetab['s-tstamp'][iv]) if test_not_ok: - logger.error('Time cut went wrong!!') + logging.error('Time cut went wrong!!') for model in modelnames: features_VERT_AGG[model] = features_VERT_AGG[model][valid] @@ -1011,7 +594,7 @@ def feature_selection(self, features_dic, featuresel_configfile, if (K != None): K = list(range(K)) elif (K == None): - logger.info('Cross validation with random events defined but not specified, applying 5-fold CV') + logging.info('Cross validation with random events defined but not specified, applying 5-fold CV') K = list(range(5)) idx_testtrain = split_event(gaugetab['TIMESTAMP'].values, len(K)) @@ -1028,7 +611,7 @@ def feature_selection(self, features_dic, featuresel_configfile, scores[tagg][model][feat] = [] for k in K: - logger.info('Run {:d}/{:d}-{:d} of cross-validation'.format(k,np.nanmin(K),np.nanmax(K))) + logging.info('Run {:d}/{:d}-{:d} of cross-validation'.format(k,np.nanmin(K),np.nanmax(K))) test = idx_testtrain == k train = idx_testtrain != k @@ -1049,7 +632,7 @@ def feature_selection(self, features_dic, featuresel_configfile, rmse_ref_60 = perfscores(R_pred_60, R_test_60, bounds=None)['all']['RMSE'] for feat in features_VERT_AGG[model].keys(): - logger.info('Shuffling feature: {}'.format(feat)) + logging.info('Shuffling feature: {}'.format(feat)) # Shuffle input feature on test fraction, keep others untouched x_test = features_VERT_AGG[model][test].copy() x_test[feat] = np.random.permutation(x_test[feat].values) @@ -1076,7 +659,7 @@ def model_intercomparison(self, features_dic, intercomparison_configfile, bounds10 = [0,2,10,100], bounds60 = [0,2,10,100], cross_val_type='years', K=5, years=None, tstart=None, tend=None, station_scores=False, - save_model=False, save_output=False): + save_model=False): """ Does an intercomparison (cross-validation) of different RF models and reference products (RZC, CPC, ...) and plots the performance plots @@ -1110,7 +693,7 @@ def model_intercomparison(self, features_dic, intercomparison_configfile, at hourly time resolution [0,1,10,100] will give scores in range [0-1], [1-10] and [10-100] cross_val_type: str - Define how the split of events is done. Options are "random", + Define how the split of events is done. Options are "random events", "years" and "seasons" (TODO) K : int or None Number of splits in iterations do perform in the K fold cross-val @@ -1141,194 +724,267 @@ def model_intercomparison(self, features_dic, intercomparison_configfile, raise ValueError('Keys in features_dic are not all present in intercomparison config file!') if (cross_val_type == 'years') and (years == None): - logger.info('Cross validation years defined, but not specified, years from 2016-2021 used') - K = list(range(2016,2023,1)) + logging.info('Cross validation years defined, but not specified, years from 2016-2021 used') + K = list(range(2016,2022,1)) elif (cross_val_type == 'years') and (years != None): K = years - if (cross_val_type == 'random') and (K != None): + if (cross_val_type == 'random events') and (K != None): K = list(range(K)) - elif (cross_val_type == 'random') and (K == None): - logger.info('Cross validation with random events defined but not specified, applying 5-fold CV') + elif (cross_val_type == 'random events') and (K == None): + logging.info('Cross validation with random events defined but not specified, applying 5-fold CV') K = list(range(5)) + ####################################################################### + # Read data + ####################################################################### + logging.info('Reading input data from {}'.format(self.input_location)) + radartab = pd.read_parquet(str(Path(self.input_location, 'radar_x0y0.parquet'))) + refertab = pd.read_parquet(str(Path(self.input_location, 'reference_x0y0.parquet'))) + gaugetab = pd.read_parquet(str(Path(self.input_location, 'gauge.parquet'))) + grp = pickle.load(open(str(Path(self.input_location, 'grouping_idx_x0y0.p')),'rb')) + grp_vertical = grp['grp_vertical'] + grp_hourly = grp['grp_hourly'] + + ####################################################################### + # Filter time + ####################################################################### + if tstart != None: + try: + tstart = datetime.datetime.strptime(tstart, + '%Y%m%d%H%M').replace(tzinfo=datetime.timezone.utc).timestamp() + except: + tstart = gaugetab['TIMESTAMP'].min() + logging.info('The format of tstart was wrong, taking the earliest date') + if tend != None: + try: + tend = datetime.datetime.strptime(tend, + '%Y%m%d%H%M').replace(tzinfo=datetime.timezone.utc).timestamp() + except: + tend = gaugetab['TIMESTAMP'].max() + logging.info('The format of tend was wrong, taking the earliest date') ############################################################################### - # Read and filter data with time constraints + # Compute additional data if needed ############################################################################### - gaugetab, radartab, refertab, grp_hourly, grp_vertical = \ - readInputData(self.input_location, tstart, tend, - datalist=['gauge', 'radar', 'refer']) - - radartab, _ = processFeatures(features_dic, radartab) + # currently the only supported additional features is zh (refl in linear units) + # and DIST_TO_RAD{A-D-L-W-P} (dist to individual radars) + # Get list of unique features names + features = np.unique([item for sub in list(features_dic.values()) + for item in sub]) + for f in features: + if 'zh' in f: + logging.info('Computing derived variable {:s}'.format(f)) + radartab[f] = 10**(0.1 * radartab[f.replace('zh','ZH')]) + elif 'zv' in f: + logging.info('Computing derived variable {:s}'.format(f)) + radartab[f] = 10**(0.1 * radartab[f.replace('zv','ZV')]) + if 'DIST_TO_RAD' in f: + info_radar = constants.RADARS + vals = np.unique(radartab['RADAR']) + for val in vals: + dist = np.sqrt((radartab['X'] - info_radar['X'][val])**2+ + (radartab['Y'] - info_radar['Y'][val])**2) / 1000. + radartab['DIST_TO_RAD' + str(val)] = dist + ############################################################################### - # Compute vertical aggregation and initialize model + # Compute vertical aggregation ############################################################################### - features_VERT_AGG = {} regressors = {} - for im, model in enumerate(modelnames): - logger.info('Performing vertical aggregation of input features for model {:s}'.format(model)) + for model in modelnames: + logging.info('Performing vertical aggregation of input features for model {:s}'.format(model)) - # Save computational time if the input data is the same within the model - if (im > 0) and (config[model]['VERT_AGG']['BETA'] == config[modelnames[im-1]]['VERT_AGG']['BETA']) \ - and (config[model]['VERT_AGG']['VISIB_WEIGHTING'] == config[modelnames[im-1]]['VERT_AGG']['VISIB_WEIGHTING']): - logger.info('Model {} has same vertical aggregation settings as {}, hence just copy aggregated 2D fields'.format(model, modelnames[im-1])) - features_VERT_AGG[model] = features_VERT_AGG[modelnames[im-1]].copy() - else: - vweights = 10**(config[model]['VERT_AGG']['BETA'] * - (radartab['HEIGHT']/1000.)) # vert. weights - features_VERT_AGG[model] = vert_aggregation(radartab[features_dic[model]], - vweights, grp_vertical, - config[model]['VERT_AGG']['VISIB_WEIGHTING'], - radartab['VISIB_mean']) - - # Use either the classical RainForest model or the new one - if len(config[model]['QUANTILES']) == 0: - regressors[model] = RandomForestRegressorBC(degree = 1, + vweights = 10**(config[model]['VERT_AGG']['BETA'] * + (radartab['HEIGHT']/1000.)) # vert. weights + features_VERT_AGG[model] = vert_aggregation(radartab[features_dic[model]], + vweights, grp_vertical, + config[model]['VERT_AGG']['VISIB_WEIGHTING'], + radartab['VISIB_mean']) + + regressors[model] = RandomForestRegressorBC(degree = 1, bctype = config[model]['BIAS_CORR'], variables = features_dic[model], beta = config[model]['VERT_AGG']['BETA'], visib_weighting=config[model]['VERT_AGG']['VISIB_WEIGHTING'], **config[model]['RANDOMFOREST_REGRESSOR']) - else: - regressors[model] = QuantileRegressionForest(**config[model]['RANDOMFOREST_REGRESSOR']) - - ############################################################################### - # Remove nans within dataset - ############################################################################### + + # remove nans valid = np.all(np.isfinite(features_VERT_AGG[modelnames[0]]), axis = 1) + # if (tstart != None) and (tend == None): + # (gaugetab['TIMESTAMP'] >= tstart) + # elif (tstart == None) and (tend != None): + # timeperiod = (gaugetab['TIMESTAMP'] <= tend) + # elif (tstart != None) and (tend != None): + # timeperiod = (gaugetab['TIMESTAMP'] >= tstart) & (gaugetab['TIMESTAMP'] <= tend) + # else: + # timeperiod = valid + if (tstart != None): + valid[(gaugetab['TIMESTAMP'] < tstart)] = False + if (tend != None): + valid[(gaugetab['TIMESTAMP'] > tend)] = False + for model in modelnames: features_VERT_AGG[model] = features_VERT_AGG[model][valid] - + gaugetab = gaugetab[valid] refertab = refertab[valid] grp_hourly = grp_hourly[valid] - ############################################################################### # Get R, T and idx test/train - ############################################################################### R = np.array(gaugetab['RRE150Z0'] * 6) # Reference precip in mm/h R[np.isnan(R)] = 0 + T = np.array(gaugetab['TRE200S0']) # Reference temp in degrees + # features must have the same size as gauge - if cross_val_type == 'random': + if cross_val_type == 'random_events': idx_testtrain = split_event(gaugetab['TIMESTAMP'].values, len(K)) elif cross_val_type == 'years': idx_testtrain = split_years(gaugetab['TIMESTAMP'].values, years=K) else: - logger.error('Please define your cross validation separation') + logging.error('Please define your cross validation separation') + - ############################################################################### - # Initialize outputs - ############################################################################### modelnames.extend(reference_products) - if station_scores == False: - all_scores, all_stats = prepareScoreDic(modelnames, station_scores) - else: - all_scores, all_stats, all_station_scores, all_station_stats = \ - prepareScoreDic(modelnames, station_scores) - + all_scores = {'10min':{},'60min':{}} + all_stats = {'10min':{},'60min':{}} + + if station_scores == True: + all_station_scores = {'10min': {}, '60min': {}} + all_station_stats = {'10min': {}, '60min': {}} + ############################################################################### - # MAIN LOOP: CROSS VALIDATION + # Initialize outputs ############################################################################### - for ik, k in enumerate(K): - logger.info('Run {:d}/{:d}-{:d} of cross-validation'.format(k,np.nanmin(K),np.nanmax(K))) + for model in modelnames: + all_scores['10min'][model] = {'train': {'solid':[],'liquid':[],'all':[]}, + 'test': {'solid':[],'liquid':[],'all':[]}} + all_scores['60min'][model] = {'train': {'solid':[],'liquid':[],'all':[]}, + 'test': {'solid':[],'liquid':[],'all':[]}} + + all_stats['10min'][model] = {'train': {'solid':{},'liquid':{},'all':{}}, + 'test': {'solid':{},'liquid':{},'all':{}}} + + all_stats['60min'][model] = {'train': {'solid':{},'liquid':{},'all':{}}, + 'test': {'solid':{},'liquid':{},'all':{}}} + + if station_scores == True: + # for station scores we will limit the output to test data only + for timeagg in all_station_scores.keys(): + all_station_scores[timeagg][model] = {'solid':{},'liquid':{},'all':{}} + all_station_stats[timeagg][model] = {'solid':{},'liquid':{},'all':{}} + + + for k in K: + logging.info('Run {:d}/{:d}-{:d} of cross-validation'.format(k,np.nanmin(K),np.nanmax(K))) test = idx_testtrain == k train = idx_testtrain != k if cross_val_type == 'years': - logger.info('Time range for testing set: {} - {} with {:3.2f}% of datapoints'.format( + logging.info('Time range for testing set: {} - {} with {:3.2f}% of datapoints'.format( datetime.datetime.utcfromtimestamp(gaugetab['TIMESTAMP'][test].min()), datetime.datetime.utcfromtimestamp(gaugetab['TIMESTAMP'][test].max()), gaugetab['TIMESTAMP'][test].count()/ gaugetab['TIMESTAMP'].count()*100)) - - idxTemp = getTempIDX(T, grp_hourly, test, train) - R_obs_test_60 = np.squeeze(np.array(pd.DataFrame(R[test]) + # Get reference values + R_test_60 = np.squeeze(np.array(pd.DataFrame(R[test]) .groupby(grp_hourly[test]).mean())) - R_obs_train_60 = np.squeeze(np.array(pd.DataFrame(R[train]) + R_train_60 = np.squeeze(np.array(pd.DataFrame(R[train]) .groupby(grp_hourly[train]).mean())) - - idxTemp['10min']['test']['all'] = R[test].astype(bool) - idxTemp['10min']['train']['all'] = R[train].astype(bool) - idxTemp['60min']['train']['all'] = R_obs_train_60.astype(bool) - idxTemp['60min']['test']['all'] = R_obs_test_60.astype(bool) - + + T_test_60 = np.squeeze(np.array(pd.DataFrame(T[test]) + .groupby(grp_hourly[test]).mean())) + + T_train_60 = np.squeeze(np.array(pd.DataFrame(T[train]) + .groupby(grp_hourly[train]).mean())) + + + liq_10_train = T[train] >= constants.THRESHOLD_SOLID + sol_10_train = T[train] < constants.THRESHOLD_SOLID + liq_60_train = T_train_60 >= constants.THRESHOLD_SOLID + sol_60_train = T_train_60 < constants.THRESHOLD_SOLID + + liq_10_test = T[test] >= constants.THRESHOLD_SOLID + sol_10_test = T[test] < constants.THRESHOLD_SOLID + liq_60_test = T_test_60 >= constants.THRESHOLD_SOLID + sol_60_test = T_test_60 < constants.THRESHOLD_SOLID + # Fit every regression model for model in modelnames: - logger.info('Checking model {:s}'.format(model)) + logging.info('Checking model {:s}'.format(model)) + logging.info('Evaluating test error') + # 10 min + logging.info('at 10 min') # Performing fit if model not in reference_products: - logger.info('Training model on gauge data') - - regressors[model].fit(features_VERT_AGG[model][train].to_numpy(),R[train]) - - if len(config[model]['QUANTILES']) == 0 : - R_pred_10 = regressors[model].predict(features_VERT_AGG[model][test]) - else: - # In this case, R_pred_10 becomes a tuple - R_pred_10_all = regressors[model].predict(features_VERT_AGG[model][test].to_numpy(), - quantiles=config[model]['QUANTILES']) - - # Save output for analysis - R_pred_10 = R_pred_10_all[0] - - RFquantiles = np.column_stack([R_pred_10_all[0], R_pred_10_all[1]]) - header = ['mean'] + [str(qe) for qe in config[model]['QUANTILES']] - RFQ = pd.DataFrame(RFquantiles, columns=header) - RFQ['gauge'] = R[test] - filename = str(Path(output_folder, 'RFQuantiles_{}_K_fold_{}.parquet'.format(model, k))) - logger.info('Saving data to {:s}'.format(filename)) - RFQ.to_parquet(filename) + logging.info('Training model on gauge data') + regressors[model].fit(features_VERT_AGG[model][train],R[train]) + R_pred_10 = regressors[model].predict(features_VERT_AGG[model][test]) + if (save_model == True): regressors[model].variables = features_VERT_AGG[model].columns out_name = str(Path(output_folder, '{:s}_BETA_{:2.1f}_BC_{:s}_excl_{}.p'.format(model, config[model]['VERT_AGG']['BETA'], config[model]['BIAS_CORR'],k))) - logger.info('Saving model to {:s}'.format(out_name)) + logging.info('Saving model to {:s}'.format(out_name)) pickle.dump(regressors[model], open(out_name, 'wb')) else: R_pred_10 = refertab[model].values[test] - logger.info('Evaluating test error') - logger.info('at 10 min') - all_scores = calcScore(all_scores, obs=R[test], pred=R_pred_10, - idxTemp = idxTemp, bounds= bounds10, - tagg = '10min', model=model, data_type='test') + scores_solid = perfscores(R_pred_10[sol_10_test], + R[test][sol_10_test], + bounds = bounds10) + + all_scores['10min'][model]['test']['solid'].append(scores_solid) + + scores_liquid = perfscores(R_pred_10[liq_10_test], + R[test][liq_10_test], + bounds = bounds10) + all_scores['10min'][model]['test']['liquid'].append(scores_liquid) + + scores_all = perfscores(R_pred_10, + R[test], + bounds = bounds10) + all_scores['10min'][model]['test']['all'].append(scores_all) # 60 min - logger.info('at 60 min') + logging.info('at 60 min') R_pred_60 = np.squeeze(np.array(pd.DataFrame(R_pred_10) .groupby(grp_hourly[test]).mean())) - - all_scores = calcScore(all_scores, obs=R_obs_test_60, pred=R_pred_60, - idxTemp = idxTemp, bounds= bounds60, - tagg = '60min', model=model, data_type='test') + + scores_solid = perfscores(R_pred_60[sol_60_test], + R_test_60[sol_60_test], + bounds = bounds60) + all_scores['60min'][model]['test']['solid'].append(scores_solid) + + + scores_liquid = perfscores(R_pred_60[liq_60_test], + R_test_60[liq_60_test], + bounds = bounds60) + all_scores['60min'][model]['test']['liquid'].append(scores_liquid) + + scores_all = perfscores(R_pred_60, + R_test_60, + bounds = bounds60) + all_scores['60min'][model]['test']['all'].append(scores_all) if station_scores == True: - logger.info('Calculating station performances for model {}'.format(model)) + logging.info('Calculating station performances for model {}'.format(model)) stations_60 = np.array(gaugetab['STATION'][test] .groupby(grp_hourly[test]).first()) df = pd.DataFrame(columns=gaugetab['STATION'].unique(), - index = all_scores['60min'][model]['test']['all'][ik]['all'].keys()) - - # Fast fix - liq_10_test = idxTemp['10min']['test']['liquid'] - sol_10_test = idxTemp['10min']['test']['solid'] - liq_60_test = idxTemp['60min']['test']['liquid'] - sol_60_test = idxTemp['60min']['test']['solid'] - + index = scores_all['all'].keys()) for timeagg in all_station_scores.keys(): all_station_scores[timeagg][model]['all'][k] = df.copy() @@ -1344,83 +1000,80 @@ def model_intercomparison(self, features_dic, intercomparison_configfile, R[test][sta_idx])['all'] all_station_scores['10min'][model]['all'][k][sta] = scores_all_10 - scores_all_60 = perfscores(R_pred_60[sta_idx_60],R_obs_test_60[sta_idx_60])['all'] + scores_all_60 = perfscores(R_pred_60[sta_idx_60],R_test_60[sta_idx_60])['all'] all_station_scores['60min'][model]['all'][k][sta] = scores_all_60 del scores_all_10, scores_all_60 except: - logger.info('No performance score for {}'.format(sta)) + logging.info('No performance score for {}'.format(sta)) try: scores_liquid_10 = perfscores(R_pred_10[liq_10_test & sta_idx], R[test][liq_10_test & sta_idx])['all'] all_station_scores['10min'][model]['liquid'][k][sta] = scores_liquid_10 scores_liquid_60 = perfscores(R_pred_60[liq_60_test & sta_idx_60], - R_obs_test_60[liq_60_test & sta_idx_60])['all'] + R_test_60[liq_60_test & sta_idx_60])['all'] all_station_scores['60min'][model]['liquid'][k][sta] = scores_liquid_60 except: - logger.info('No performance score for liquid precip for {}'.format(sta)) + logging.info('No performance score for liquid precip for {}'.format(sta)) try: scores_solid_10 = perfscores(R_pred_10[sol_10_test & sta_idx], R[test][sol_10_test & sta_idx])['all'] all_station_scores['10min'][model]['solid'][k][sta] = scores_solid_10 scores_solid_60 = perfscores(R_pred_60[sol_60_test & sta_idx_60], - R_obs_test_60[sol_60_test & sta_idx_60])['all'] + R_test_60[sol_60_test & sta_idx_60])['all'] all_station_scores['60min'][model]['solid'][k][sta] = scores_solid_60 except: - logger.info('No performance score for solid precip for {}'.format(sta)) - - # Save output of training and testing data - if save_output: - if model not in reference_products: - data = features_VERT_AGG[model][test].copy() - data[model] = R_pred_10.copy() - else: - data = pd.DataFrame(pd.Series(R_pred_10, name=model)) - - data['gauge'] = R[test].copy() - data['TIMESTAMP'] = gaugetab['TIMESTAMP'][test] - data['STATION'] = gaugetab['STATION'][test] - - out_name = str(Path(output_folder, 'data_{}_test_10min_CVfold_{}.csv'.format(model,k))) - data.to_csv(out_name, index=False) - logger.info('Saving data to {:s}'.format(out_name)) - - data_60 = pd.DataFrame({'gauge': R_obs_test_60, model: R_pred_60}) - data_60['STATION'] = np.array(gaugetab['STATION'][test] - .groupby(grp_hourly[test]).first()) - data_60['TIMESTAMP'] = np.array(gaugetab['TIMESTAMP'][test] - .groupby(grp_hourly[test]).max()) - data_60['n_counts'] = np.squeeze(np.array(pd.DataFrame(R[test]) - .groupby(grp_hourly[test]).count())) - out_name = str(Path(output_folder, 'data_{}_test_60min_CVfold_{}.csv'.format(model,k))) - data_60.to_csv(out_name, index=False) - logger.info('Saving data to {:s}'.format(out_name)) + logging.info('No performance score for solid precip for {}'.format(sta)) # train - logger.info('Evaluating train error') + logging.info('Evaluating train error') # 10 min - logger.info('at 10 min') + logging.info('at 10 min') if model not in reference_products: R_pred_10 = regressors[model].predict(features_VERT_AGG[model][train]) else: R_pred_10 = refertab[model].values[train] - - all_scores = calcScore(all_scores, obs=R[train], pred=R_pred_10, - idxTemp = idxTemp, bounds= bounds60, - tagg = '10min', model=model, data_type='train') - - # 60 min - logger.info('at 60 min') + scores_solid = perfscores(R_pred_10[sol_10_train], + R[train][sol_10_train], + bounds = bounds10) + all_scores['10min'][model]['train']['solid'].append(scores_solid) + + scores_liquid = perfscores(R_pred_10[liq_10_train], + R[train][liq_10_train], + bounds = bounds10) + + all_scores['10min'][model]['train']['liquid'].append(scores_liquid) + + scores_all = perfscores(R_pred_10, + R[train], + bounds = bounds10) + all_scores['10min'][model]['train']['all'].append(scores_all) + + R_pred_60 = np.squeeze(np.array(pd.DataFrame(R_pred_10) .groupby(grp_hourly[train]).mean())) - - all_scores = calcScore(all_scores, obs=R_obs_train_60, pred=R_pred_60, - idxTemp = idxTemp, bounds= bounds60, - tagg = '60min', model=model, data_type='train') + + # 60 min + logging.info('at 60 min') + # Evaluate model 10 min + + scores_solid = perfscores(R_pred_60[sol_60_train], + R_train_60[sol_60_train], + bounds = bounds60) + all_scores['60min'][model]['train']['solid'].append(scores_solid) + + scores_liquid = perfscores(R_pred_60[liq_60_train], + R_train_60[liq_60_train], + bounds = bounds60) + all_scores['60min'][model]['train']['liquid'].append(scores_liquid) + + scores_all = perfscores(R_pred_60,R_train_60, + bounds = bounds60) + all_scores['60min'][model]['train']['all'].append(scores_all) # Compute statistics after the 5-fold cross validation @@ -1468,9 +1121,11 @@ def model_intercomparison(self, features_dic, intercomparison_configfile, perfs[score].std().rename('std')], axis=1) + if not os.path.exists(output_folder): os.makedirs(output_folder) + plot_crossval_stats(all_stats, output_folder) name_file = str(Path(output_folder, 'all_scores.p')) pickle.dump(all_scores, open(name_file, 'wb')) name_file = str(Path(output_folder, 'all_scores_stats.p')) @@ -1481,9 +1136,8 @@ def model_intercomparison(self, features_dic, intercomparison_configfile, pickle.dump(all_station_scores, open(name_file, 'wb')) name_file = str(Path(output_folder, 'all_station_stats.p')) pickle.dump(all_station_stats, open(name_file, 'wb')) - - plot_crossval_stats(all_stats, output_folder) - logger.info('Finished script and saved all scores to {}'.format(output_folder)) + + logging.info('Finished script and saved all scores to {}'.format(output_folder)) return all_scores, all_stats diff --git a/rainforest/ml/rfdefinitions.py b/rainforest/ml/rfdefinitions.py index 9856b12..ae52f88 100644 --- a/rainforest/ml/rfdefinitions.py +++ b/rainforest/ml/rfdefinitions.py @@ -14,7 +14,6 @@ import pickle import numpy as np from sklearn.ensemble import RandomForestRegressor -from doubt import QuantileRegressionForest import os from scipy.interpolate import UnivariateSpline from pathlib import Path @@ -84,8 +83,7 @@ def __init__(self, n_jobs=None, random_state=None, verbose=0, - warm_start=False, - max_samples=None): + warm_start=False): super().__init__(n_estimators = n_estimators, criterion = criterion, max_depth = max_depth, @@ -100,8 +98,7 @@ def __init__(self, n_jobs = n_jobs, random_state = random_state, verbose = verbose, - warm_start = warm_start, - max_samples=None) + warm_start = warm_start) self.degree = degree self.bctype = bctype @@ -187,198 +184,9 @@ def predict(self, X, round_func = None, bc = True): out = func(pred) out[out < 0] = 0 return round_func(out) - - def predict_ens(self, X, round_func = None, bc = True): - """ - Predict regression target for X. - The predicted regression target of an input sample is computed as the - mean predicted regression targets of the trees in the forest. - Parameters - ---------- - X : array-like or sparse matrix of shape (n_samples, n_features) - The input samples. Internally, its dtype will be converted to - ``dtype=np.float32``. If a sparse matrix is provided, it will be - converted into a sparse ``csr_matrix``. - round_func : lambda function - Optional function to apply to outputs (for example to discretize them - using MCH lookup tables). If not provided f(x) = x will be applied - (i.e. no function) - bc : bool - if True the bias correction function will be applied - - Returns - ------- - pred : array (n_samples,n_trees) - The predicted values. - """ - - if round_func == None: - round_func = lambda x: x - - func = lambda x: x - if bc: - if self.bctype in ['cdf','raw']: - func = lambda x : np.polyval(self.p,x) - elif self.bctype == 'spline': - func = lambda x : self.p(x) - - pred = np.empty(shape=[X.shape[0], self.n_estimators]) * np.nan - - for tree in range(self.n_estimators): - pred_tree = self.estimators_[tree].predict(X) - - out = func(pred_tree) - out[out < 0] = 0 - - pred[:,tree] = round_func(out) - - return pred - - -################## - -class QuantileRandomForestRegressorBC(QuantileRegressionForest): - ''' - This is an extension of the RandomForestRegressor regressor class of - sklearn that does additional bias correction, is able - to apply a rounding function to the outputs on the fly and adds a - bit of metadata: - - *bctype* : type of bias correction method - *variables* : name of input features - *beta* : weighting factor in vertical aggregation - *degree* : order of the polyfit used in some bias-correction methods - *metadata* : configuration setup used to train this model - - For *bc_type* tHe available methods are currently "raw": - simple linear fit between prediction and observation, "cdf": linear fit - between sorted predictions and sorted observations and "spline" : - spline fit between sorted predictions and sorted observations. Any - new method should be added in this class in order to be used. - - For any information regarding the parent class from the package doubt see - https://github.com/saattrupdan/doubt (used version: 4.3.1) - - ''' - def __init__(self, - variables, - beta, - visib_weighting, - degree = 1, - bctype = 'cdf', - metadata = {}, - n_estimators=100, - criterion="mse", - max_depth=None, - min_samples_split=2, - min_samples_leaf=1, - min_weight_fraction_leaf=0., - max_features="auto", - max_leaf_nodes=None, - min_impurity_decrease=0., - bootstrap=True, - oob_score=False, - n_jobs=None, - random_state=None, - verbose=0, - warm_start=False): - super().__init__(n_estimators = n_estimators, - criterion = criterion, - max_depth = max_depth, - min_samples_split = min_samples_split, - min_samples_leaf = min_samples_leaf, - min_weight_fraction_leaf = min_weight_fraction_leaf, - max_features = max_features, - max_leaf_nodes = max_leaf_nodes, - n_jobs = n_jobs, - random_state = random_state, - verbose = verbose) - - self.degree = degree - self.bctype = bctype - self.variables = variables - self.beta = beta - self.visib_weighting = visib_weighting - self.metadata = metadata - - def fit(self, X, y): - """ - Fit both estimator and a-posteriori bias correction - Parameters - ---------- - X (array-like or sparse matrix): - The input samples, of shape [n_samples, n_features]. Internally, it - will be converted to `dtype=np.float32` and if a sparse matrix is - provided to a sparse `csr_matrix`. - y (array-like): - The target values (class labels) as integers or strings, of shape - [n_samples] or [n_samples, n_outputs]. - - Returns - ------- - self : object - """ - - super().fit(X,y) - y_pred = super().predict(X) - if self.bctype in ['cdf','raw']: - if self.bctype == 'cdf': - x_ = np.sort(y_pred) - y_ = np.sort(y) - elif self.bctype == 'raw': - x_ = y_pred - y_ = y - self.p = _polyfit_no_inter(x_,y_,self.degree) - elif self.bctype == 'spline': - x_ = np.sort(y_pred) - y_ = np.sort(y) - _,idx = np.unique(x_, return_index = True) - self.p = UnivariateSpline(x_[idx], y_[idx]) - else: - self.p = 1 - - return - - def predict(self, X, round_func = None, bc = True): - """ - Predict regression target for X. - The predicted regression target of an input sample is computed as the - mean predicted regression targets of the trees in the forest. - Parameters - ---------- - X : array-like or sparse matrix of shape (n_samples, n_features) - The input samples. Internally, its dtype will be converted to - ``dtype=np.float32``. If a sparse matrix is provided, it will be - converted into a sparse ``csr_matrix``. - round_func : lambda function - Optional function to apply to outputs (for example to discretize them - using MCH lookup tables). If not provided f(x) = x will be applied - (i.e. no function) - bc : bool - if True the bias correction function will be applied - - Returns - ------- - y : array-like of shape (n_samples,) or (n_samples, n_outputs) - The predicted values. - """ - pred = super().predict(X) - - if round_func == None: - round_func = lambda x: x - - func = lambda x: x - if bc: - if self.bctype in ['cdf','raw']: - func = lambda x : np.polyval(self.p,x) - elif self.bctype == 'spline': - func = lambda x : self.p(x) - out = func(pred) - out[out < 0] = 0 - return round_func(out) ################## - + class MyCustomUnpickler(pickle.Unpickler): """ This is an extension of the pickle Unpickler that handles the diff --git a/rainforest/qpe/qpe.py b/rainforest/qpe/qpe.py index 273c7b4..f4026b2 100644 --- a/rainforest/qpe/qpe.py +++ b/rainforest/qpe/qpe.py @@ -3,10 +3,13 @@ """ Main function to compute the QPE estimations on the Swiss grid -Daniel Wolfensberger, MeteoSwiss/EPFL, daniel.wolfensberger@meteoswiss.ch -Rebecca Gugerli, EPFL/ MeteoSwiss -March 2023 +Daniel Wolfensberger +MeteoSwiss/EPFL +daniel.wolfensberger@epfl.ch +December 2019 +Modified by D. Wolfensberger and R. Gugerli +December 2022 """ @@ -20,7 +23,6 @@ from pathlib import Path import os -import h5py from pathlib import Path from scipy.ndimage import gaussian_filter from scipy.signal import convolve2d @@ -544,7 +546,7 @@ def save_features(self, features, features_labels, t, filepath): def compute(self, output_folder, t0, t1, timestep = 5, - basename = 'RFO%y%j%H%MVH', test_mode = False, ensembles = False): + basename = 'RFO%y%j%H%MVH', test_mode = False): """ Computes QPE values for a given time range and stores them in a folder, in a binary format @@ -658,7 +660,6 @@ def compute(self, output_folder, t0, t1, timestep = 5, logger.info('HZT fields for timestep {} missing, creating empty one'.format(t.strftime('%Y%m%d%H%M'))) """Part one - compute radar variables and mask""" - logger.info('Computing radar variables and masks') # Begin compilation of radarobject logger.info('Preparing all polar radar data') radobjects = {} @@ -846,15 +847,16 @@ def compute(self, output_folder, t0, t1, timestep = 5, W * isvalidzh_radsweep)),2) except: - logger.error('Could not compute sweep {:d}'.format(sweep)) raise + logger.error('Could not compute sweep {:d}'.format(sweep)) + pass + """Part four - RF prediction""" logger.info('Applying RF model to retrieve predictions') # Get QPE estimate # X: current time step; X_prev: previous timestep (t-5min) for k in self.models.keys(): - logger.info('Calculating prediction for model {}'.format(k)) model = self.models[k] X = [] for v in model.variables: @@ -890,64 +892,18 @@ def compute(self, output_folder, t0, t1, timestep = 5, # Convert radar data to precipitation estimates qpe = np.zeros((NBINS_X, NBINS_Y), dtype = np.float32).ravel() - - if len(self.config['QUANTILES'][k]) == 0: - try: - qpe[validrows] = self.models[k].predict(Xcomb[validrows,:]) - ensemble_qpe = np.zeros((NBINS_X, NBINS_Y, self.models[k].n_estimators)) - for tree in range(self.models[k].n_estimators): - qpe_temp = np.zeros((NBINS_X, NBINS_Y), dtype = np.float32).ravel() - qpe_temp[validrows] = self.models[k].estimators_[tree].predict(Xcomb[validrows,:]) - ensemble_qpe[:,:,tree] = np.reshape(qpe_temp, (NBINS_X, NBINS_Y)) - except: - logger.error('Model failed!') - raise - else: - try: - # This will render a tuple with the first element as a traditional - # Random Forest estimation, and second element a 2D array with all - # the quantile estimates - timelog0 = datetime.datetime.now() - qpe_all = self.models[k].predict(Xcomb[validrows,:], - quantiles = self.config['QUANTILES'][k]) - logger.info('Quantile prediction took {} '.format(datetime.datetime.now()-timelog0)) - qpe[validrows] = qpe_all[0] - - quantile_qpe = np.zeros((NBINS_X, NBINS_Y, len(self.config['QUANTILES'][k]))) - for iq, q in enumerate(self.config['QUANTILES'][k]): - qpe_temp = np.zeros((NBINS_X, NBINS_Y), dtype = np.float32).ravel() - qpe_temp[validrows] = qpe_all[1][:,iq] - quantile_qpe[:,:,iq] = np.reshape(qpe_temp, (NBINS_X, NBINS_Y)) - - if ensembles: - ensemble_qpe = np.zeros((NBINS_X, NBINS_Y, self.models[k].n_estimators)) - for tree in range(self.models[k].n_estimators): - qpe_temp = np.zeros((NBINS_X, NBINS_Y), dtype = np.float32).ravel() - qpe_temp[validrows] = self.models[k].estimators_[tree].predict(Xcomb[validrows,:]) - ensemble_qpe[:,:,tree] = np.reshape(qpe_temp, (NBINS_X, NBINS_Y)) - except: - logger.error('Model failed!') - raise + try: + qpe[validrows] = self.models[k].predict(Xcomb[validrows,:]) + except: + raise + logger.error('Model failed!') + pass - if ensembles: - filepath = output_folder + '/' + k+'_ENS' - if not os.path.exists(filepath): - os.mkdir(filepath) - filepath += '/' + tstr - f = h5py.File(filepath, 'w') - f.create_dataset('RFO_ENS', data=ensemble_qpe) - f.close() - logger.info('Continuing with next timestep as we dont apply a temporal integration') - continue - - # Traditional QPE output qpe = np.reshape(qpe, (NBINS_X, NBINS_Y)) - if self.config['SAVE_NON_POSTPROCESSED_DATA']: qpe_no_temp = qpe.copy() """Temporal disaggregation; Rescale qpe through rproxy""" - logger.info('Temporal disaggregation from 10-min to 5min') idx_zh = np.where(np.array(model.variables) == 'zh_VISIB')[0][0] @@ -993,6 +949,7 @@ def compute(self, output_folder, t0, t1, timestep = 5, logger.error("Pysteps is not available, no qpe disaggregation will be performed!") qpe_ac = _disaggregate(comp) + filepath = output_folder + '/' + k +'_AC/' if not os.path.exists(filepath): os.mkdir(filepath) @@ -1004,24 +961,7 @@ def compute(self, output_folder, t0, t1, timestep = 5, os.mkdir(filepath) filepath += '/' + tstr self.save_output(qpe, t, filepath) - - if ensembles: - filepath = output_folder + '/' + k+'_ENS' - if not os.path.exists(filepath): - os.mkdir(filepath) - filepath += '/' + tstr - f = h5py.File(filepath, 'w') - f.create_dataset('RFO_ENS', data=ensemble_qpe) - f.close() - - if len(self.config['QUANTILES'][k]) != 0 : - for iq, qu in enumerate(self.config['QUANTILES'][k]): - filepath = output_folder + '/' + k +'_Q{}/'.format(str(qu).replace('.','_')) - if not os.path.exists(filepath): - os.mkdir(filepath) - self.save_output(quantile_qpe[:,:,iq], t, filepath+tstr) - - + if self.config['SAVE_NON_POSTPROCESSED_DATA']: # Coding of folders (0 not applied, 1 applied) # T: temporal disaggregation diff --git a/requirements.txt b/requirements.txt index 123b557..40861fb 100644 --- a/requirements.txt +++ b/requirements.txt @@ -17,4 +17,3 @@ h5py pysteps xlrd pyproj -doubt \ No newline at end of file From f256815b295c0cc415018d1a478d8e0cc831667e Mon Sep 17 00:00:00 2001 From: Daniel Wolfensberger Date: Thu, 14 Mar 2024 13:25:40 +0100 Subject: [PATCH 24/25] added dask.config.set({'dataframe.query-planning': False}) to avoid new issue with dask_expr --- rainforest/common/utils.py | 2 ++ rainforest/database/retrieve_radar_data.py | 2 ++ rainforest/database/retrieve_reference_data.py | 2 ++ rainforest/performance/eval_get_estimates.py | 4 +++- 4 files changed, 9 insertions(+), 1 deletion(-) diff --git a/rainforest/common/utils.py b/rainforest/common/utils.py index 63b6e7e..6004266 100644 --- a/rainforest/common/utils.py +++ b/rainforest/common/utils.py @@ -20,6 +20,8 @@ from dateutil import parser import glob import yaml +import dask +dask.config.set({'dataframe.query-planning': False}) import dask.dataframe as dd import re import numbers diff --git a/rainforest/database/retrieve_radar_data.py b/rainforest/database/retrieve_radar_data.py index 0334307..fdc443e 100644 --- a/rainforest/database/retrieve_radar_data.py +++ b/rainforest/database/retrieve_radar_data.py @@ -14,6 +14,8 @@ import numpy as np import pandas as pd +import dask +dask.config.set({'dataframe.query-planning': False}) import dask.dataframe as dd import datetime import logging diff --git a/rainforest/database/retrieve_reference_data.py b/rainforest/database/retrieve_reference_data.py index 988ba5b..7f5ac80 100644 --- a/rainforest/database/retrieve_reference_data.py +++ b/rainforest/database/retrieve_reference_data.py @@ -18,6 +18,8 @@ import pandas as pd import datetime import logging +import pyart +dask.config.set({'dataframe.query-planning': False}) import dask.dataframe as dd logging.basicConfig(level=logging.INFO) diff --git a/rainforest/performance/eval_get_estimates.py b/rainforest/performance/eval_get_estimates.py index 5376dba..6c5c0ec 100644 --- a/rainforest/performance/eval_get_estimates.py +++ b/rainforest/performance/eval_get_estimates.py @@ -10,6 +10,8 @@ import numpy as np import pandas as pd from pathlib import Path +import dask +dask.config.set({'dataframe.query-planning': False}) import dask.dataframe as dd import copy import pickle @@ -435,4 +437,4 @@ def extractEstimatesFromMaps(self, slf_stations=False, tagg_hourly=True, save_ou with open(self.configfile,'a') as yamlfile: yaml.safe_dump({'FILE_60MIN' : save_file_60}, yamlfile) - return \ No newline at end of file + return From 55112a228d9f41ff96f38698530f5147fc7ed372 Mon Sep 17 00:00:00 2001 From: Daniel Wolfensberger Date: Thu, 14 Mar 2024 13:25:40 +0100 Subject: [PATCH 25/25] added dask.config.set({'dataframe.query-planning': False}) to avoid new issue with dask_expr --- rainforest/common/utils.py | 2 ++ rainforest/database/retrieve_radar_data.py | 2 ++ rainforest/database/retrieve_reference_data.py | 2 ++ rainforest/performance/eval_get_estimates.py | 4 +++- 4 files changed, 9 insertions(+), 1 deletion(-) diff --git a/rainforest/common/utils.py b/rainforest/common/utils.py index 63b6e7e..6004266 100644 --- a/rainforest/common/utils.py +++ b/rainforest/common/utils.py @@ -20,6 +20,8 @@ from dateutil import parser import glob import yaml +import dask +dask.config.set({'dataframe.query-planning': False}) import dask.dataframe as dd import re import numbers diff --git a/rainforest/database/retrieve_radar_data.py b/rainforest/database/retrieve_radar_data.py index 0334307..fdc443e 100644 --- a/rainforest/database/retrieve_radar_data.py +++ b/rainforest/database/retrieve_radar_data.py @@ -14,6 +14,8 @@ import numpy as np import pandas as pd +import dask +dask.config.set({'dataframe.query-planning': False}) import dask.dataframe as dd import datetime import logging diff --git a/rainforest/database/retrieve_reference_data.py b/rainforest/database/retrieve_reference_data.py index 988ba5b..7f5ac80 100644 --- a/rainforest/database/retrieve_reference_data.py +++ b/rainforest/database/retrieve_reference_data.py @@ -18,6 +18,8 @@ import pandas as pd import datetime import logging +import pyart +dask.config.set({'dataframe.query-planning': False}) import dask.dataframe as dd logging.basicConfig(level=logging.INFO) diff --git a/rainforest/performance/eval_get_estimates.py b/rainforest/performance/eval_get_estimates.py index 5376dba..6c5c0ec 100644 --- a/rainforest/performance/eval_get_estimates.py +++ b/rainforest/performance/eval_get_estimates.py @@ -10,6 +10,8 @@ import numpy as np import pandas as pd from pathlib import Path +import dask +dask.config.set({'dataframe.query-planning': False}) import dask.dataframe as dd import copy import pickle @@ -435,4 +437,4 @@ def extractEstimatesFromMaps(self, slf_stations=False, tagg_hourly=True, save_ou with open(self.configfile,'a') as yamlfile: yaml.safe_dump({'FILE_60MIN' : save_file_60}, yamlfile) - return \ No newline at end of file + return