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Posted to commits@climate.apache.org by jo...@apache.org on 2014/11/10 17:05:54 UTC
[07/10] climate git commit: CLIMATE-551 - Drop old RCMET toolkit from
codebase
http://git-wip-us.apache.org/repos/asf/climate/blob/8c142c35/rcmet/src/main/python/rcmes/cli/do_rcmes_processing_sub.py
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diff --git a/rcmet/src/main/python/rcmes/cli/do_rcmes_processing_sub.py b/rcmet/src/main/python/rcmes/cli/do_rcmes_processing_sub.py
deleted file mode 100644
index 240beb7..0000000
--- a/rcmet/src/main/python/rcmes/cli/do_rcmes_processing_sub.py
+++ /dev/null
@@ -1,734 +0,0 @@
-#
-# Licensed to the Apache Software Foundation (ASF) under one or more
-# contributor license agreements. See the NOTICE file distributed with
-# this work for additional information regarding copyright ownership.
-# The ASF licenses this file to You under the Apache License, Version 2.0
-# (the "License"); you may not use this file except in compliance with
-# the License. You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-#!/usr/local/bin/python
-"""
- PENDING DEPRICATION - YOU SHOULD INSTEAD USE THE rcmet.py within the bin dir
-
- Module that is used to lauch the rcmes processing from the rcmet_ui.py
- script.
-"""
-
-import os, sys
-import datetime
-import numpy
-import numpy.ma as ma
-import toolkit.plots as plots
-import mpl_toolkits.basemap.cm as cm
-import matplotlib.pyplot as plt
-import storage.db as db
-import storage.files as files
-import toolkit.process as process
-import toolkit.metrics as metrics
-
-def do_rcmes(settings, params, model, mask, options):
- '''
- Routine to perform full end-to-end RCMET processing.
-
- i) retrieve observations from the database
- ii) load in model data
- iii) temporal regridding
- iv) spatial regridding
- v) area-averaging
- vi) seasonal cycle compositing
- vii) metric calculation
- viii) plot production
-
- Input:
- 5 dictionaries which contain a huge argument list with all of the user options
- (which can be collected from the GUI)
-
- settings - dictionary of rcmes run settings::
-
- settings = {"cacheDir": string describing directory path,
- "workDir": string describing directory path,
- "fileList": string describing model file name + path }
-
- params - dictionary of rcmes run parameters::
-
- params = {"obsDatasetId": int( db dataset id ),
- "obsParamId": int( db parameter id ),
- "startTime": datetime object (needs to change to string + decode),
- "endTime": datetime object (needs to change to string + decode),
- "latMin": float,
- "latMax": float,
- "lonMin": float,
- "lonMax": float }
-
- model - dictionary of model parameters::
-
- model = {"varName": string describing name of variable to evaluate (as written in model file),
- "timeVariable": string describing name of time variable (as written in model file),
- "latVariable": string describing name of latitude variable (as written in model file),
- "lonVariable": string describing name of longitude variable (as written in model file) }
-
- mask - dictionary of mask specific options (only used if options['mask']=True)::
-
- mask = {"latMin": float,
- "latMax": float,
- "lonMin": float,
- "lonMax": float}
-
- options - dictionary full of different user supplied options::
-
- options = {"regrid": str( 'obs' | 'model' | 'regular' ),
- "timeRegrid": str( 'full' | 'annual' | 'monthly' | 'daily' ),
- "seasonalCycle": Boolean,
- "metric": str('bias'|'mae'|'acc'|'pdf'|'patcor'|'rms'|'diff'),
- "plotTitle": string describing title to use in plot graphic,
- "plotFilename": basename of file to use for plot graphic i.e. {plotFilename}.png,
- "mask": Boolean,
- "precip": Boolean }
-
- Output: image files of plots + possibly data
- '''
-
- # check the number of model data files
- if len(settings['fileList']) < 1: # no input data file
- print 'No input model data file. EXIT'
- sys.exit()
- # assign parameters that must be preserved throughout the process
- if options['mask'] == True:
- options['seasonalCycle'] = True
-
- ###########################################################################
- # Part 1: retrieve observation data from the database
- # NB. automatically uses local cache if already retrieved.
- ###########################################################################
- rcmedData = getDataFromRCMED( params, settings, options )
-
- ###########################################################################
- # Part 2: load in model data from file(s)
- ###########################################################################
- modelData = getDataFromModel( model, settings )
-
- ###########################################################################
- # Deal with some precipitation specific options
- # i.e. adjust units of model data and set plot color bars suitable for precip
- ###########################################################################
- # AG 06/12/1013: Need to revise how we select colormaps in the future
- colorbar = None
- if options['precip'] == True:
- modelData['data'] = modelData['data']*86400. # convert from kgm-2s-1 into mm/day
- colorbar = cm.s3pcpn
-
- # set color bar suitable for MODIS cloud data
- if params['obsParamId'] == 31:
- colorbar = plt.cm.gist_gray
-
- diffcolorbar = cm.GMT_polar
-
- ##################################################################################################################
- # Extract sub-selection of model data for required time range.
- # e.g. a single model file may contain data for 20 years,
- # but the user may have selected to only analyse data between 2003 and 2004.
- ##################################################################################################################
-
- # Make list of indices where modelData['times'] are between params['startTime'] and params['endTime']
- modelTimeOverlap = numpy.logical_and((numpy.array(modelData['times'])>=params['startTime']),
- (numpy.array(modelData['times'])<=params['endTime']))
-
- # Make subset of modelData['times'] using full list of times and indices calculated above
- modelData['times'] = list(numpy.array(modelData['times'])[modelTimeOverlap])
-
- # Make subset of modelData['data'] using full model data and indices calculated above
- modelData['data'] = modelData['data'][modelTimeOverlap, :, :]
-
- ##################################################################################################################
- # Part 3: Temporal regridding
- # i.e. model data may be monthly, and observation data may be daily.
- # We need to compare like with like so the User Interface asks what time unit the user wants to work with
- # e.g. the user may select that they would like to regrid everything to 'monthly' data
- # in which case, the daily observational data will be averaged onto monthly data
- # so that it can be compared directly with the monthly model data.
- ##################################################################################################################
- print 'Temporal Regridding Started'
-
- if(options['timeRegrid']):
- # Run both obs and model data through temporal regridding routine.
- # NB. if regridding not required (e.g. monthly time units selected and model data is already monthly),
- # then subroutine detects this and returns data untouched.
- rcmedData['data'], newObsTimes = process.calc_average_on_new_time_unit(rcmedData['data'],
- rcmedData['times'],
- unit=options['timeRegrid'])
-
- modelData['data'], newModelTimes = process.calc_average_on_new_time_unit(modelData['data'],
- modelData['times'],
- unit=options['timeRegrid'])
-
- # Set a new 'times' list which describes the common times used for both model and obs after the regrid.
- if newObsTimes == newModelTimes:
- times = newObsTimes
-
- ###########################################################################
- # Catch situations where after temporal regridding the times in model and obs don't match.
- # If this occurs, take subset of data from times common to both model and obs only.
- # e.g. imagine you are looking at monthly model data,
- # the model times are set to the 15th of each month.
- # + you are comparing against daily obs data.
- # If you set the start date as Jan 1st, 1995 and the end date as Jan 1st, 1996
- # -then system will load all model data in this range with the last date as Dec 15th, 1995
- # loading the daily obs data from the database will have a last data item as Jan 1st, 1996.
- # If you then do temporal regridding of the obs data from daily -> monthly (to match the model)
- # Then there will be data for Jan 96 in the obs, but only up to Dec 95 for the model.
- # This section of code deals with this situation by only looking at data
- # from the common times between model and obs after temporal regridding.
- ###########################################################################
- if newObsTimes != newModelTimes:
- print 'Warning: after temporal regridding, times from observations and model do not match'
- print 'Check if this is unexpected.'
- print 'Proceeding with data from times common in both model and obs.'
-
- # Create empty lists ready to store data
- times = []
- tempModelData = []
- tempObsData = []
-
- # Loop through each time that is common in both model and obs
- for commonTime in numpy.intersect1d(newObsTimes, newModelTimes):
- # build up lists of times, and model and obs data for each common time
- # NB. use lists for data for convenience (then convert to masked arrays at the end)
- times.append(newObsTimes[numpy.where(numpy.array(newObsTimes) == commonTime)[0][0]])
- tempModelData.append(modelData['data'][numpy.where(numpy.array(newModelTimes) == commonTime)[0][0], :, :])
- tempObsData.append(rcmedData['data'][numpy.where(numpy.array(newObsTimes) == commonTime)[0][0], :, :])
-
- # Convert data arrays from list back into full 3d arrays.
- modelData['data'] = ma.array(tempModelData)
- rcmedData['data'] = ma.array(tempObsData)
-
- # Reset all time lists so representative of the data actually used.
- newObsTimes = times
- newModelTimes = times
- rcmedData['times'] = times
- modelData['times'] = times
-
- ##################################################################################################################
- # Part 4: spatial regridding
- # The model and obs are rarely on the same grid.
- # To compare the two, you need them to be on the same grid.
- # The User Interface asked the user if they'd like to regrid everything to the model grid or the obs grid.
- # Alternatively, they could chose to regrid both model and obs onto a third regular lat/lon grid as defined
- # by parameters that they enter.
- #
- # NB. from this point on in the code, the 'lats' and 'lons' arrays are common to
- # both rcmedData['data'] and modelData['data'].
- ##################################################################################################################
-
- ##################################################################################################################
- # either i) Regrid obs data to model grid.
- ##################################################################################################################
- if options['regrid'] == 'model':
- # User chose to regrid observations to the model grid
- modelData['data'], rcmedData['data'], lats, lons = process.regrid_wrapper('0', rcmedData['data'],
- rcmedData['lats'],
- rcmedData['lons'],
- modelData['data'],
- modelData['lats'],
- modelData['lons'])
-
- ##################################################################################################################
- # or ii) Regrid model data to obs grid.
- ##################################################################################################################
- if options['regrid'] == 'obs':
- # User chose to regrid model data to the observation grid
-
- modelData['data'], rcmedData['data'], lats, lons = process.regrid_wrapper('1', rcmedData['data'],
- rcmedData['lats'],
- rcmedData['lons'],
- modelData['data'],
- modelData['lats'],
- modelData['lons'])
-
- ##################################################################################################################
- # or iii) Regrid both model data and obs data to new regular lat/lon grid.
- ##################################################################################################################
- if options['regrid'] == 'regular':
- # User chose to regrid both model and obs data onto a newly defined regular lat/lon grid
- # Construct lats, lons from grid parameters
-
- # Create 1d lat and lon arrays
- # AG 06/21/2013: These variables are undefined, where are they generated from?
- lat = numpy.arange(nLats)*dLat+Lat0
- lon = numpy.arange(nLons)*dLon+Lon0
-
- # Combine 1d lat and lon arrays into 2d arrays of lats and lons
- lons, lats = numpy.meshgrid(lon, lat)
-
- ###########################################################################################################
- # Regrid model data for every time
- # NB. store new data in a list and convert back to an array at the end.
- ###########################################################################################################
- tmpModelData = []
-
- timeCount = modelData['data'].shape[0]
- for t in numpy.arange(timeCount):
- tmpModelData.append(process.do_regrid(modelData['data'][t, :, :],
- modelData['lats'][:, :],
- modelData['lons'][:, :],
- rcmedData['lats'][:, :],
- rcmedData['lons'][:, :]))
-
- # Convert list back into a masked array
- modelData['data'] = ma.array(tmpModelData)
-
- ###########################################################################################################
- # Regrid obs data for every time
- # NB. store new data in a list and convert back to an array at the end.
- ###########################################################################################################
- tempObsData = []
- timeCount = rcmedData['data'].shape[0]
- for t in numpy.arange(timeCount):
- tempObsData.append(process.do_regrid(rcmedData['data'][t, :, :],
- rcmedData['lats'][:, :],
- rcmedData['lons'][:, :],
- modelData['lats'][:, :], modelData['lons'][:, :]))
-
- # Convert list back into a masked array
- rcmedData['data'] = ma.array(tempObsData)
-
- ##################################################################################################################
- # (Optional) Part 5: area-averaging
- #
- # RCMET has the ability to either calculate metrics at every grid point,
- # or to calculate metrics for quantities area-averaged over a defined (masked) region.
- #
- # If the user has selected to perform area-averaging,
- # then they have also selected how they want to define
- # the area to average over.
- # The options were:
- # -define masked region using regular lat/lon bounding box parameters
- # -read in masked region from file
- #
- # either i) Load in the mask file (if required)
- # or ii) Create the mask using latlonbox
- # then iii) Do the area-averaging
- #
- ###############################################################################################################
- if options['mask'] == True: # i.e. define regular lat/lon box for area-averaging
- print 'Using Latitude/Longitude Mask for Area Averaging'
-
- ###############################################################################################################
- # Define mask using regular lat/lon box specified by users (i.e. ignore regions where mask = True)
- ###############################################################################################################
- mask = numpy.logical_or(numpy.logical_or(lats<=mask['latMin'], lats>=mask['latMax']),
- numpy.logical_or(lons<=mask['lonMin'], lons>=mask['lonMax']))
-
- ######################m########################################################################################
- # Calculate area-weighted averages within this region and store in new lists
- ###############################################################################################################
- modelStore = []
- timeCount = modelData['data'].shape[0]
- for t in numpy.arange(timeCount):
- modelStore.append(process.calc_area_mean(modelData['data'][t, :, :], lats, lons, mymask=mask))
-
- obsStore = []
- timeCount = rcmedData['data'].shape[0]
- for t in numpy.arange(timeCount):
- obsStore.append(process.calc_area_mean(rcmedData['data'][t, :, :], lats, lons, mymask=mask))
-
- ###############################################################################################################
- # Now overwrite data arrays with the area-averaged values
- ###############################################################################################################
- modelData['data'] = ma.array(modelStore)
- rcmedData['data'] = ma.array(obsStore)
-
- ###############################################################################################################
- # Free-up some memory by overwriting big variables
- ###############################################################################################################
- obsStore = 0
- modelStore = 0
-
- ##############################################################################################################
- # NB. if area-averaging has been performed then the dimensions of the data arrays will have changed from 3D to 1D
- # i.e. only one value per time.
- ##############################################################################################################
-
- ##############################################################################################################
- # (Optional) Part 6: seasonal cycle compositing
- #
- # RCMET has the ability to calculate seasonal average values from a long time series of data.
- #
- # e.g. for monthly data going from Jan 1980 - Dec 2010
- # If the user selects to do seasonal cycle compositing,
- # this section calculates the mean of all Januarys, mean of all Februarys, mean of all Marchs etc
- # -result has 12 times.
- #
- # NB. this works with incoming 3D data or 1D data (e.g. time series after avea-averaging).
- #
- # If no area-averaging has been performed in Section 5,
- # then the incoming data is 3D, and the outgoing data will also be 3D,
- # but with the number of times reduced to 12
- # i.e. you will get 12 map plots each one showing the average values for a month. (all Jans, all Febs etc)
- #
- #
- # If area-averaging has been performed in Section 5,
- # then the incoming data is 1D, and the outgoing data will also be 1D,
- # but with the number of times reduced to 12
- # i.e. you will get a time series of 12 data points
- # each one showing the average values for a month. (all Jans, all Febs etc).
- #
- ##################################################################################################################
- if options['seasonalCycle'] == True:
- print 'Compositing data to calculate seasonal cycle'
-
- modelData['data'] = metrics.calcAnnualCycleMeans(modelData['data'])
- rcmedData['data'] = metrics.calcAnnualCycleMeans(rcmedData['data'])
-
- ##################################################################################################################
- # Part 7: metric calculation
- # Calculate performance metrics comparing rcmedData['data'] and modelData['data'].
- # All output is stored in metricData regardless of what metric was calculated.
- #
- # NB. the dimensions of metricData will vary depending on the dimensions of the incoming data
- # *and* on the type of metric being calculated.
- #
- # e.g. bias between incoming 1D model and 1D obs data (after area-averaging) will be a single number.
- # bias between incoming 3D model and 3D obs data will be 2D, i.e. a map of mean bias.
- # correlation coefficient between incoming 3D model and 3D obs data will be 1D time series.
- #
- ##################################################################################################################
-
- if options['metric'] == 'bias':
- metricData = metrics.calcBias(modelData['data'], rcmedData['data'])
- metricTitle = 'Bias'
-
- if options['metric'] == 'mae':
- metricData = metrics.calcBiasAveragedOverTime(modelData['data'], rcmedData['data'], 'abs')
- metricTitle = 'Mean Absolute Error'
-
- if options['metric'] == 'rms':
- metricData = metrics.calcRootMeanSquaredDifferenceAveragedOverTime(modelData['data'], rcmedData['data'])
- metricTitle = 'RMS error'
-
- #if options['metric'] == 'patcor':
- #metricData = metrics.calc_pat_cor2D(modelData['data'], rcmedData['data'])
- #metricTitle = 'Pattern Correlation'
-
-
- if options['metric'] == 'pdf':
- metricData = metrics.calcPdf(modelData['data'], rcmedData['data'])
- metricTitle = 'Probability Distribution Function'
-
- if options['metric'] == 'coe':
- metricData = metrics.calcNashSutcliff(modelData['data'], rcmedData['data'])
- metricTitle = 'Coefficient of Efficiency'
-
- if options['metric'] == 'stddev':
- metricData = metrics.calcTemporalStdev(modelData['data'])
- data2 = metrics.calcTemporalStdev(rcmedData['data'])
- metricTitle = 'Standard Deviation'
-
- ##################################################################################################################
- # Part 8: Plot production
- #
- # Produce plots of metrics and obs, model data.
- # Type of plot produced depends on dimensions of incoming data.
- # e.g. 1D data is plotted as a time series.
- # 2D data is plotted as a map.
- # 3D data is plotted as a sequence of maps.
- #
- ##################################################################################################################
-
- ##################################################################################################################
- # 1 dimensional data, e.g. Time series plots
- ##################################################################################################################
- if metricData.ndim == 1:
- print 'Producing time series plots ****'
- print metricData
- yearLabels = True
- # mytitle = 'Area-average model v obs'
-
- ################################################################################################################
- # If producing seasonal cycle plots, don't want to put year labels on the time series plots.
- ################################################################################################################
- if options['seasonalCycle'] == True:
- yearLabels = False
- mytitle = 'Annual cycle: area-average model v obs'
- # Create a list of datetimes to represent the annual cycle, one per month.
- times = []
- for m in xrange(12):
- times.append(datetime.datetime(2000, m+1, 1, 0, 0, 0, 0))
-
- ###############################################################################################
- # Special case for pattern correlation plots. TODO: think of a cleaner way of doing this.
- # Only produce these plots if the metric is NOT pattern correlation.
- ###############################################################################################
-
- # TODO - Clean up this if statement. We can use a list of values then ask if not in LIST...
- #KDW: change the if statement to if else to accommodate the 2D timeseries plots
- if (options['metric'] != 'patcor')&(options['metric'] != 'acc')&(options['metric'] != 'nacc')&(options['metric'] != 'coe')&(options['metric'] != 'pdf'):
- # for anomaly and pattern correlation,
- # can't plot time series of model, obs as these are 3d fields
- # ^^ This is the reason modelData['data'] has been swapped for metricData in
- # the following function
- # TODO: think of a cleaner way of dealing with this.
-
- ###########################################################################################
- # Produce the time series plots with two lines: obs and model
- ###########################################################################################
- print 'two line timeseries'
- # mytitle = options['plotTitle']
- mytitle = 'Area-average model v obs'
- if options['plotTitle'] == 'default':
- mytitle = metricTitle+' model & obs'
- #plots.draw_time_series_plot(modelData['data'],times,options['plotFilename']+'both',
- # settings['workDir'],data2=rcmedData['data'],mytitle=mytitle,
- # ytitle='Y',xtitle='time',
- # year_labels=yearLabels)
- plots.draw_time_series_plot(metricData, times, options['plotFilename']+'both',
- settings['workDir'], data2, mytitle=mytitle,
- ytitle='Y', xtitle='time',
- year_labels=yearLabels)
-
- else:
- ###############################################################################################
- # Produce the metric time series plot (one line only)
- ###############################################################################################
- mytitle = options['plotTitle']
- if options['plotTitle'] == 'default':
- mytitle = metricTitle+' model v obs'
- print 'one line timeseries'
- plots.draw_time_series_plot(metricData, times, options['plotFilename'],
- settings['workDir'], mytitle=mytitle, ytitle='Y', xtitle='time',
- year_labels=yearLabels)
-
- ###############################################################################################
- # 2 dimensional data, e.g. Maps
- ###############################################################################################
- if metricData.ndim == 2:
-
- ###########################################################################################
- # Calculate color bar ranges for data such that same range is used in obs and model plots
- # for like-with-like comparison.
- ###########################################################################################
- mymax = max(rcmedData['data'].mean(axis=0).max(), modelData['data'].mean(axis=0).max())
- mymin = min(rcmedData['data'].mean(axis=0).min(), modelData['data'].mean(axis=0).min())
-
- ###########################################################################################
- # Time title labels need their format adjusting depending on the temporal regridding used,
- # e.g. if data are averaged to monthly,
- # then want to write 'Jan 2002', 'Feb 2002', etc instead of 'Jan 1st, 2002', 'Feb 1st, 2002'
- #
- # Also, if doing seasonal cycle compositing
- # then want to write 'Jan','Feb','Mar' instead of 'Jan 2002','Feb 2002','Mar 2002' etc
- # as data are representative of all Jans, all Febs etc.
- ###########################################################################################
- if(options['timeRegrid'] == 'daily'):
- timeFormat = "%b %d, %Y"
- if(options['timeRegrid'] == 'monthly'):
- timeFormat = "%b %Y"
- if(options['timeRegrid'] == 'annual'):
- timeFormat = "%Y"
- if(options['timeRegrid'] == 'full'):
- timeFormat = "%b %d, %Y"
-
- ###########################################################################################
- # Special case: when plotting bias data, we also like to plot the mean obs and mean model data.
- # In this case, we need to calculate new time mean values for both obs and model.
- # When doing this time averaging, we also need to deal with missing data appropriately.
- #
- # Classify missing data resulting from multiple times (using threshold data requirment)
- # i.e. if the working time unit is monthly data, and we are dealing with multiple months of data
- # then when we show mean of several months, we need to decide what threshold of missing data we tolerate
- # before classifying a data point as missing data.
- ###########################################################################################
-
- ###########################################################################################
- # Calculate time means of model and obs data
- ###########################################################################################
- modelDataMean = modelData['data'].mean(axis=0)
- obsDataMean = rcmedData['data'].mean(axis=0)
-
- ###########################################################################################
- # Calculate missing data masks using tolerance threshold of missing data going into calculations
- ###########################################################################################
- obsDataMask = process.create_mask_using_threshold(rcmedData['data'], threshold=0.75)
- modelDataMask = process.create_mask_using_threshold(modelData['data'], threshold=0.75)
-
- ###########################################################################################
- # Combine data and masks into masked arrays suitable for plotting.
- ###########################################################################################
- modelDataMean = ma.masked_array(modelDataMean, modelDataMask)
- obsDataMean = ma.masked_array(obsDataMean, obsDataMask)
-
- ###########################################################################################
- # Plot model data
- ###########################################################################################
- mytitle = 'Model data: mean between %s and %s' % ( modelData['times'][0].strftime(timeFormat),
- modelData['times'][-1].strftime(timeFormat) )
- myfname = os.path.join(options['workDir'], options['plotFilename']+'model')
-
- plots.draw_cntr_map_single(modelDataMean, lats, lons, mymin, mymax, mytitle, myfname, cMap = colorbar)
-
- ###########################################################################################
- # Plot obs data
- ###########################################################################################
- mytitle = 'Obs data: mean between %s and %s' % ( rcmedData['times'][0].strftime(timeFormat),
- rcmedData['times'][-1].strftime(timeFormat) )
- myfname = os.path.join(options['workDir'], options['plotFilename']+'obs')
- plots.draw_cntr_map_single(obsDataMean, lats, lons, mymin, mymax, mytitle, myfname, cMap = colorbar)
-
-
- ###########################################################################################
- # Plot metric
- ###########################################################################################
- mymax = metricData.max()
- mymin = metricData.min()
-
- mytitle = options['plotTitle']
-
- if options['plotTitle'] == 'default':
- mytitle = metricTitle+' model v obs %s to %s' % ( rcmedData['times'][0].strftime(timeFormat),
- rcmedData['times'][-1].strftime(timeFormat) )
- myfname = os.path.join(options['workDir'], options['plotFilename'])
- plots.draw_cntr_map_single(metricData, lats, lons, mymin, mymax, mytitle, myfname, cMap = diffcolorbar)
-
- ###############################################################################################
- # 3 dimensional data, e.g. sequence of maps
- ###############################################################################################
- if metricData.ndim == 3:
- print 'Generating series of map plots, each for a different time.'
- for t in numpy.arange(rcmedData['data'].shape[0]):
-
- #######################################################################################
- # Calculate color bar ranges for data such that same range is used in obs and model plots
- # for like-with-like comparison.
- #######################################################################################
- colorRangeMax = max(rcmedData['data'][t, :, :].max(), modelData['data'][t, :, :].max())
- colorRangeMin = min(rcmedData['data'][t, :, :].min(), modelData['data'][t, :, :].min())
-
- # Setup the timeTitle
- timeSlice = times[t]
- timeTitle = createTimeTitle( options, timeSlice, rcmedData, modelData )
-
- #######################################################################################
- # Plot model data
- #######################################################################################
- mytitle = 'Model data: mean '+timeTitle
- myfname = os.path.join(settings['workDir'], options['plotFilename']+'model'+str(t))
- plots.draw_cntr_map_single(modelData['data'][t, :, :], lats, lons, colorRangeMin, colorRangeMax,
- mytitle, myfname, cMap = colorbar)
-
- #######################################################################################
- # Plot obs data
- #######################################################################################
- mytitle = 'Obs data: mean '+timeTitle
- myfname = os.path.join(settings['workDir'], options['plotFilename']+'obs'+str(t))
- plots.draw_cntr_map_single(rcmedData['data'][t, :, :], lats, lons, colorRangeMin, colorRangeMax,
- mytitle, myfname, cMap = colorbar)
-
- #######################################################################################
- # Plot metric
- #######################################################################################
- mytitle = options['plotTitle']
- myfname = os.path.join(settings['workDir'], options['plotFilename']+str(t))
-
- if options['plotTitle'] == 'default':
- mytitle = metricTitle +' model v obs : '+timeTitle
-
- colorRangeMax = metricData.max()
- colorRangeMin = metricData.min()
- plots.draw_cntr_map_single(metricData[t, :, :], lats, lons, colorRangeMin, colorRangeMax,
- mytitle, myfname, cMap = diffcolorbar)
-
-
-def getDataFromRCMED( params, settings, options ):
- """
- This function takes in the params, settings, and options dictionaries and will return an rcmedData dictionary.
-
- return:
- rcmedData = {"lats": 1-d numpy array of latitudes,
- "lons": 1-d numpy array of longitudes,
- "levels": 1-d numpy array of height/pressure levels (surface based data will have length == 1),
- "times": list of python datetime objects,
- "data": masked numpy arrays of data values}
- """
- rcmedData = {}
- obsLats, obsLons, obsLevs, obsTimes, obsData = db.extractData(params['obsDatasetId'],
- params['obsParamId'],
- params['latMin'],
- params['latMax'],
- params['lonMin'],
- params['lonMax'],
- params['startTime'],
- params['endTime'],
- settings['cacheDir'],
- options['timeRegrid'])
- rcmedData['lats'] = obsLats
- rcmedData['lons'] = obsLons
- rcmedData['levels'] = obsLevs
- rcmedData['times'] = obsTimes
- rcmedData['data'] = obsData
-
- return rcmedData
-
-def getDataFromModel( model, settings ):
- """
- This function takes in the model and settings dictionaries and will return a model data dictionary.
-
- return:
- model = {"lats": 1-d numpy array of latitudes,
- "lons": 1-d numpy array of longitudes,
- "times": list of python datetime objects,
- "data": numpy array containing data from all files}
- """
- model = files.read_data_from_file_list(settings['fileList'],
- model['varName'],
- model['timeVariable'],
- model['latVariable'],
- model['lonVariable'])
- return model
-
-##################################################################################################################
-# Processing complete
-##################################################################################################################
-
-def createTimeTitle( options, timeSlice, rcmedData, modelData ):
- """
- Function that takes in the options dictionary and a specific timeSlice.
-
- Return: string timeTitle properly formatted based on the 'timeRegrid' and 'seasonalCycle' options value.
-
- Time title labels need their format adjusting depending on the temporal regridding used
-
- e.g. if data are averaged to monthly, then want to write 'Jan 2002',
- 'Feb 2002', etc instead of 'Jan 1st, 2002', 'Feb 1st, 2002'
-
- Also, if doing seasonal cycle compositing then want to write 'Jan','Feb',
- 'Mar' instead of 'Jan 2002', 'Feb 2002','Mar 2002' etc as data are
- representative of all Jans, all Febs etc.
- """
- if(options['timeRegrid'] == 'daily'):
- timeTitle = timeSlice.strftime("%b %d, %Y")
- if options['seasonalCycle'] == True:
- timeTitle = timeSlice.strftime("%b %d (all years)")
-
- if(options['timeRegrid'] == 'monthly'):
- timeTitle = timeSlice.strftime("%b %Y")
- if options['seasonalCycle'] == True:
- timeTitle = timeSlice.strftime("%b (all years)")
-
- if(options['timeRegrid'] == 'annual'):
- timeTitle = timeSlice.strftime("%Y")
-
- if(options['timeRegrid'] == 'full'):
- minTime = min(min(rcmedData['times']), min(modelData['times']))
- maxTime = max(max(rcmedData['times']), max(modelData['times']))
- timeTitle = minTime.strftime("%b %d, %Y")+' to '+maxTime.strftime("%b %d, %Y")
-
- return timeTitle
-
-
http://git-wip-us.apache.org/repos/asf/climate/blob/8c142c35/rcmet/src/main/python/rcmes/cli/rcmet20_cordexAF.py
----------------------------------------------------------------------
diff --git a/rcmet/src/main/python/rcmes/cli/rcmet20_cordexAF.py b/rcmet/src/main/python/rcmes/cli/rcmet20_cordexAF.py
deleted file mode 100755
index c2efd4d..0000000
--- a/rcmet/src/main/python/rcmes/cli/rcmet20_cordexAF.py
+++ /dev/null
@@ -1,996 +0,0 @@
-#
-# Licensed to the Apache Software Foundation (ASF) under one or more
-# contributor license agreements. See the NOTICE file distributed with
-# this work for additional information regarding copyright ownership.
-# The ASF licenses this file to You under the Apache License, Version 2.0
-# (the "License"); you may not use this file except in compliance with
-# the License. You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-#!/usr/local/bin/python
-
-# 0. Keep both Peter's original and modified libraries
-
-# Python Standard Lib Imports
-import argparse
-import ConfigParser
-import datetime
-import glob
-import os
-import sys
-import json
-
-# 3rd Party Modules
-import numpy as np
-import numpy.ma as ma
-
-# RCMES Imports
-# Appending rcmes via relative path
-#sys.path.append(os.path.abspath('../.'))
-import storage.files_v12
-import storage.rcmed as db
-import toolkit.do_data_prep
-import toolkit.do_metrics_20
-import toolkit.process as process
-from classes import Settings, Model, BoundingBox, SubRegion, GridBox
-
-parser = argparse.ArgumentParser(description='Regional Climate Model Evaluation Toolkit. Use -h for help and options')
-parser.add_argument('-c', '--config', dest='CONFIG', help='Path to an evaluation configuration file')
-args = parser.parse_args()
-
-
-def getSettings(settings):
- """
- This function will collect 2 parameters from the user about the RCMET run they have started.
-
- Input::
- settings - Empty Python Dictionary they will be used to store the user supplied inputs
-
- Output::
- None - The user inputs will be added to the supplied dictionary.
- """
- settings['workDir'] = os.path.abspath(raw_input('Please enter workDir:\n> '))
- if os.path.isdir(settings['workDir']):
- pass
- else:
- makeDirectory(settings['workDir'])
-
- settings['cacheDir'] = os.path.abspath(raw_input('Please enter cacheDir:\n> '))
- if os.path.isdir(settings['cacheDir']):
- pass
- else:
- makeDirectory(settings['cacheDir'])
-
-def setSettings(settings, config):
- """
- This function is used to set the values within the 'SETTINGS' dictionary when a user provides an external
- configuration file.
-
- Input::
- settings - Python Dictionary object that will collect the key : value pairs
- config - A configparse object that contains the external config values
-
- Output::
- None - The settings dictionary will be updated in place.
- """
- pass
-
-def makeDirectory(directory):
- print "%s doesn't exist. Trying to create it now." % directory
- try:
- os.mkdir(directory)
- except OSError:
- print "This program cannot create dir: %s due to permission issues." % directory
- sys.exit()
-
-def rcmet_cordexAF():
- """
- Command Line User interface for RCMET.
- Collects user options then runs RCMET to perform processing.
- Duplicates job of GUI.
- Peter Lean March 2011
-
- Jul 2, 2011
- Modified to process multiple models
- Follow the logical variable "GUI" for interactive operations
-
- July 6, 2012: Jinwon Kim
- * This version works with do_rcmes_processing_sub_v12cmip5multi.py *
- Re-gridded data output options include both binary and netCDF.
- Interpolation of both model and obs data onto a user-define grid system has been completed.
- Allow generic treatment of both multiple model and observation data
- * longitudes/latitudes are defined for individual datasets
- * the metadata for observations will utilized Cameron's updates
- Still works for the global observation coverage scheme (may involve missing/bad values)
- * this version requires that all obs data are to be defined at the same temporal grid (monthly, daily)
- * this version requires that all mdl data are to be defined at the same temporal grid (monthly, daily)
- """
- print 'Start RCMET'
-
-
- """ COMMENTED OUT UN-USED CODE
- # Specify GUI or nonGUI version [True/False]
- GUI = False
- user_input = int(raw_input('Enter interactive/specified run: [0/1]: \n> '))
- if user_input == 0:
- GUI = True
-
- # 1. Prescribe the directories and variable names for processing
- #dir_rcmet = '/nas/share3-wf/jinwonki/rcmet' # The path to the python script to process the cordex-AF data
- if GUI:
- workdir = os.path.abspath(raw_input('Please enter workdir:\n> '))
- cachedir = os.path.abspath(raw_input('Please enter cachedir:\n> '))
- mdlDataDir = os.path.abspath(raw_input('Enter the model data directory (e.g., ~/data/cordex-af):\n> '))
- modelVarName = raw_input('Enter the model variable name from above:\n> ') # Input model variable name
- modelLatVarName = raw_input('Enter the Latitude variable name:\n> ') # Input model variable name
- modelLonVarName = raw_input('Enter the Longitude variable name:\n> ') # Input model variable name
- modelTimeVarName = raw_input('Enter the Time variable name:\n> ') # Input model variable name
- mdlTimeStep = raw_input('Enter the model Time step (e.g., daily, monthly):\n> ') # Input model variable name
- else:
- modelVarName = 'pr'
- #modelVarName='tas'
- #modelVarName='tasmax'
- #modelVarName='tasmin'
- #modelVarName='clt'
- mdlTimeStep = 'monthly'
- modelLatVarName = 'lat'
- modelLonVarName = 'lon'
- modelTimeVarName = 'time' # mdl var names for lat, long, & time coords
- workdir = '../cases/cordex-af/wrk2'
- cachedir = '../cases/cordex-af/cache'
- mdlDataDir = '/nas/share4-cf/jinwonki/data/cordex-af'
- if modelVarName == 'pr':
- precipFlag = True
- else:
- precipFlag = False
- """
- # 2. Metadata for the RCMED database
-
- # TODO: WORK OUT THE RCMED PARAMETERS API USAGE - Prolly need to move this into a PARAMETERS Object
- """ COMMENTED OUT HARDCODED VALUES
- try:
- parameters = db.getParams()
- except Exception:
- sys.exit()
-
- datasets = [parameter['longname'] for parameter in parameters]
-
- # NOTE: the list must be updated whenever a new dataset is added to RCMED (current as of 11/22/2011)
- db_datasets = ['TRMM', 'ERA-Interim', 'AIRS', 'MODIS', 'URD', 'CRU3.0', 'CRU3.1']
- db_dataset_ids = [3, 1, 2, 5, 4, 6, 10]
- db_dataset_startTimes = [datetime.datetime(1998, 1, 1, 0, 0, 0, 0), datetime.datetime(1989, 01, 01, 0, 0, 0, 0), datetime.datetime(2002, 8, 31, 0, 0, 0, 0), \
- datetime.datetime(2000, 2, 24, 0, 0, 0, 0), datetime.datetime(1948, 1, 1, 0, 0, 0, 0), datetime.datetime(1901, 1, 1, 0, 0, 0, 0), \
- datetime.datetime(1901, 1, 1, 0, 0, 0, 0)]
- db_dataset_endTimes = [datetime.datetime(2010, 1, 1, 0, 0, 0, 0), datetime.datetime(2009, 12, 31, 0, 0, 0, 0), datetime.datetime(2010, 1, 1, 0, 0, 0, 0), \
- datetime.datetime(2010, 5, 30, 0, 0, 0, 0), datetime.datetime(2010, 1, 1, 0, 0, 0, 0), datetime.datetime(2006, 12, 1, 0, 0, 0, 0), \
- datetime.datetime(2009, 12, 31, 0, 0, 0, 0)] #adjusted the last end_time to 31-DEC-2009 instead of 01-DEC-2009
- db_parameters = [['pr_day', 'pr_mon'], ['T2m', 'Tdew2m'], ['T2m'], ['cldFrac'], ['pr_day'], ['T2m', 'T2max', 'T2min', 'pr'], ['pr', 'T2m', 'T2max', 'T2min', 'cldFrac']]
- db_parameter_ids = [[14, 36], [12, 13], [15], [31], [30], [33, 34, 35, 32], [37, 38, 39, 41, 42]]
-
- # Assign the obs dataset & and its attributes from the RCNMED dataset/parameter list above
- idObsDat = []
- idObsDatPara = []
- obsTimeStep = []
-
- if GUI:
- for n in np.arange(len(db_datasets)):
- print n, db_datasets[n]
-
- numOBSs = int(raw_input('Enter the number of observed datasets to be utilized:\n> '))
- # assign the obs dataset id and the parameter id defined within the dataset into the lists "idObsDat" & "idObsDatPara".
- for m in np.arange(numOBSs):
- idObsDat.append(input=int(raw_input('Enter the observed dataset number from above:\n> ')))
- for l in np.arange(len(db_parameters[input])):
- print l, db_parameters[idObsDat][l]
-
- idObsDatPara.append(int(raw_input('Enter the observed data parameter from above:\n> ')))
- else:
- numOBSs = 2
- idObsDat = [0, 6]
- idObsDatPara = [1, 0]
- obsTimeStep = ['monthly', 'monthly']
- #numOBSs=1; idObsDat=[6]; idObsDatPara=[0]; obsTimeStep=['monthly']
- #numOBSs=1; idObsDat=[5]; idObsDatPara=[3]; obsTimeStep=['monthly']
- #numOBSs=1; idObsDat=[0]; idObsDatPara=[1]; obsTimeStep=['monthly']
- ##### Data table to be replace with the use of metadata #################################
- #idObsDat=0; idObsDatPara=0; obsTimeStep='monthly' # TRMM daily
- #idObsDat=0; idObsDatPara=1; obsTimeStep='monthly' # TRMM monthly
- #idObsDat=3; idObsDatPara=0; obsTimeStep='monthly' # MODIS cloud fraction
- #idObsDat=5; idObsDatPara=0; obsTimeStep='monthly' # CRU3.0 - t2bar
- #idObsDat=5; idObsDatPara=1; obsTimeStep='monthly' # CRU3.0 - t2max
- #idObsDat=5; idObsDatPara=2; obsTimeStep='monthly' # CRU3.0 - t2min
- #idObsDat=5; idObsDatPara=3; obsTimeStep='monthly' # CRU3.0 - pr
- #idObsDat=6; idObsDatPara=0; obsTimeStep='monthly' # CRU3.1 - pr
- #idObsDat=6; idObsDatPara=1; obsTimeStep='monthly' # CRU3.1 - t2bar
- #idObsDat=6; idObsDatPara=2; obsTimeStep='monthly' # CRU3.1 - t2max
- #idObsDat=6; idObsDatPara=3; obsTimeStep='monthly' # CRU3.1 - t2min
- #idObsDat=6; idObsDatPara=4; obsTimeStep='monthly' # CRU3.1 - cloud fraction
- ##### Data table to be replace with the use of metadata #################################
- # assign observed data info: all variables are 'list'
- obsDataset = []
- data_type = []
- obsDatasetId = []
- obsParameterId = []
- obsStartTime = []
- obsEndTime = []
- obsList = []
-
- for m in np.arange(numOBSs):
- obsDataset.append(db_datasets[idObsDat[m]])# obsDataset=db_datasets[idObsDat[m]]
- data_type.append(db_parameters[idObsDat[m]][idObsDatPara[m]])# data_type = db_parameters[idObsDat[m]][idObsDatPara[m]]
- obsDatasetId.append(db_dataset_ids[idObsDat[m]])# obsDatasetId = db_dataset_ids[idObsDat[m]]
- obsParameterId.append(db_parameter_ids[idObsDat[m]][idObsDatPara[m]])# obsParameterId = db_parameter_ids[idObsDat[m]][idObsDatPara[m]]
- obsStartTime.append(db_dataset_startTimes[idObsDat[m]])# obsStartTime = db_dataset_startTimes[idObsDat[m]]
- obsEndTime.append(db_dataset_endTimes[idObsDat[m]])# obsEndTime = db_dataset_endTimes[idObsDat[m]]
- obsList.append(db_datasets[idObsDat[m]] + '_' + db_parameters[idObsDat[m]][idObsDatPara[m]])
- TRMM_pr_mon
- CRU3.1_pr
-
- print'obsDatasetId,obsParameterId,obsList,obsStartTime,obsEndTime= ', obsDatasetId, obsParameterId, obsStartTime, obsEndTime# return -1
- obsStartTmax = max(obsStartTime)
- obsEndTmin = min(obsEndTime)
-
- ###################################################################
- # 3. Load model data and assign model-related processing info
- ###################################################################
- # 3a: construct the list of model data files
- if GUI:
- FileList_instructions = raw_input('Enter model file (specify multiple files using wildcard: e.g., *pr.nc):\n> ')
- else:
- FileList_instructions = '*' + modelVarName + '.nc'
- #FileList_instructions = '*' + 'ARPEGE51' + '*' + modelVarName + '.nc'
- FileList_instructions = mdlDataDir + '/' + FileList_instructions
- FileList = glob.glob(FileList_instructions)
- n_infiles = len(FileList)
- #print FileList_instructions,n_infiles,FileList
-
- # 3b: (1) Attempt to auto-detect latitude and longitude variable names (removed in rcmes.files_v12.find_latlon_var_from_file)
- # (2) Find lat,lon limits from first file in FileList (active)
- file_type = 'nc'
- laName = modelLatVarName
- loName = modelLonVarName
- latMin = ma.zeros(n_infiles)
- latMax = ma.zeros(n_infiles)
- lonMin = ma.zeros(n_infiles)
- lonMax = ma.zeros(n_infiles)
-
- for n in np.arange(n_infiles):
- ifile = FileList[n]
- status, latMin[n], latMax[n], lonMin[n], lonMax[n] = storage.files_v12.find_latlon_var_from_file(ifile, file_type, laName, loName)
- print 'Min/Max Lon & Lat: ', n, lonMin[n], lonMax[n], latMin[n], latMax[n]
- if GUI:
- instruction = raw_input('Do the long/lat ranges all model files match? (y/n)\n> ')
-
- else:
- instruction = 'y'
- print instruction
- if instruction != 'y':
- print 'Long & lat ranges of model data files do not match: EXIT'; return -1
- latMin = latMin[0]
- latMax = latMax[0]
- lonMin = lonMin[0]
- lonMax = lonMax[0]
- print 'Min/Max Lon & Lat:', lonMin, lonMax, latMin, latMax
- print ''
-
-
-
- # TODO: Work out how to handle when model files have different ranges for Latitude, Longitude or Time
-
- # 3c: Decode model times into a python datetime object (removed in rcmes.process_v12.decode_model_times; var name is hardwired in 1.)
- # Check the length of model data period. Retain only the files that contain the entire 20yr records
- # Also specify the model data time step. Not used for now, but will be used to control the selection of the obs data (4) & temporal regridding (7).
- # Note July 25, 2011: model selection for analysis is moved and is combined with the determination of the evaluation period
- timeName = modelTimeVarName
- mdldataTimeStep = 'monthly'
- file_type = 'nc'
- n_mos = ma.zeros(n_infiles)
- newFileList = []
- mdlStartT = []
- mdlEndT = []
- mdlName = []
- k = 0
-
- for n in np.arange(n_infiles):
- # extract model names for identification
- # Provided that model results are named as
- # mdlDataDir/projectName_mdlName_(some other information)_variableName.nc
- ifile = FileList[n]
- name = ifile[len(mdlDataDir)+1:len(mdlDataDir)+20] # +1 excludes '/'
- name_wo_project = name[name.find('_')+1:] # file name without its project name
-
- mdlName.append(name_wo_project[0:name_wo_project.find('_')]) # print'model name= ',name[0:name.find('_')]
- # extract the temporal coverage of each model data file and the related time parameters
-
- modelTimes = process.getModelTimes(ifile, timeName)
-
- # NOW WE HAVE MODEL TIMES...WHAT ARE THEY USED FOR???
-
- # THIS APPEARS TO BE A MONTHLY SPECIFIC IMPLEMENTATAION DETAIL
- n_mos[n] = len(modelTimes)
-
- # PARSE OUT THE Min(YEAR and MONTH) and Max(YEAR and MONTH)
- # Could this merely be a MinTime and MaxTime so essentially a TimeRange?
-
-
- y0 = min(modelTimes).strftime("%Y")
- m0 = min(modelTimes).strftime("%m")
- y1 = max(modelTimes).strftime("%Y")
- m1 = max(modelTimes).strftime("%m")
-
-
-
- if mdlTimeStep == 'monthly':
- d0 = 1
- d1 = 1
- else:
- d0 = min(modelTimes).strftime("%d")
- d1 = max(modelTimes).strftime("%d")
-
- minMdlT = datetime.datetime(int(y0), int(m0), int(d0), 0, 0, 0, 0)
- maxMdlT = datetime.datetime(int(y1), int(m1), int(d1), 0, 0, 0, 0)
-
- # AFTER all the Datetime to string to int and back to datetime, we are left with the ModelTimeStart and ModelTimeEnd
- mdlStartT.append(minMdlT)
- mdlEndT.append(maxMdlT)
-
- print 'Mdl Times decoded: n= ', n, ' Name: ', mdlName[n], ' length= ', len(modelTimes), \
- ' 1st mdl time: ', mdlStartT[n].strftime("%Y/%m"), ' Lst mdl time: ', mdlEndT[n].strftime("%Y/%m")
-
- #print 'mdlStartT'; print mdlStartT; print 'mdlEndT'; print mdlEndT
- #print max(mdlStartT),min(mdlEndT)
-
- # get the list of models to be evaluated and the period of evaluation
- # July 25, 2011: the selection of model and evaluation period are modified:
- # 1. Default: If otherwise specified, select the longest overlapping period and exclude the model outputs that do not cover the default period
- # 2. MaxMdl : Select the max number of models for evaluation. The evaluation period may be reduced
- # 3. PrdSpc : The evaluation period is specified and the only data files that cover the specified period are included for evaluation.
- # 4. Note that the analysis period is limited to the full annual cycle, i.e., starts in Jan and ends in Dec.
- # 5: Select the period for evaluation/analysis (defaults to overlapping times between model and obs)
- # 5a: First calculate the overlapping period
- startTime = []
- endTime = []
-
- for n in np.arange(n_infiles):
- startTime.append(max(mdlStartT[n], obsStartTmax))
- endTime.append(min(mdlEndT[n], obsEndTmin))
-
- #print n,mdlStartT[n],mdlEndT[n],startTime[n],endTime[n]
- yy = int(startTime[n].strftime("%Y"))
- mm = int(startTime[n].strftime("%m"))
-
- if mm != 1:
- yy = yy + 1
- mm = 1
-
- startTime[n] = datetime.datetime(int(yy), int(mm), 1, 0, 0, 0, 0)
- yy = int(endTime[n].strftime("%Y"))
- mm = int(endTime[n].strftime("%m"))
-
- if mm != 12:
- yy = yy - 1
- mm = 12
-
- endTime[n] = datetime.datetime(int(yy), int(mm), 1, 0, 0, 0, 0)
- print mdlName[n], ' common start/end time: ', startTime[n], endTime[n]
-
- maxAnlT0 = min(startTime)
- maxAnlT1 = max(endTime)
- minAnlT0 = max(startTime)
- minAnlT1 = min(endTime)
- #print startTime; print endTime
- print 'max common period: ', maxAnlT0, '-', maxAnlT1; print 'min common period: ', minAnlT0, '-', minAnlT1
-
- # 5b: Determine the evaluation period and the models to be evaluated
- if GUI:
- print 'Select evaluation period. Depending on the selected period, the number of models may vary. See above common start/end times'
- print 'Enter: 1 for max common period, 2 for min common period, 3 for your own choice: Note that all period starts from Jan and end at Dec'
- choice = int(raw_input('Enter your choice from above [1,2,3] \n> '))
- else:
- choice = 3
- if choice == 1:
- startTime = maxAnlT0
- endTime = maxAnlT1
- print 'Maximum(model,obs) period is selected. Some models will be dropped from evaluation'
-
- if choice == 2:
- startTime = minAnlT0
- endTime = minAnlT1
- print 'Minimum(model,obs) period is selected. All models will be evaluated except there are problems'
-
- if choice == 3:
- startYear = int(raw_input('Enter start year YYYY \n'))
- endYear = int(raw_input('Enter end year YYYY \n'))
-
- if startYear < int(maxAnlT0.strftime("%Y")):
- print 'Your start year is earlier than the available data period: EXIT; return -1'
-
- if endYear > int(maxAnlT1.strftime("%Y")):
- print 'Your end year is later than the available data period: EXIT; return -1'
-
- # CGOODALE - Updating the Static endTime to be 31-DEC
- startTime = datetime.datetime(startYear, 1, 1, 0, 0)
- endTime = datetime.datetime(endYear, 12, 31, 0, 0)
- print 'Evaluation will be performed for a user-selected period'
-
- print 'Final: startTime/endTime: ', startTime, '/', endTime
-
-
- # select model data for analysis and analysis period
- k = 0
- newFileList = []
- name = []
- print 'n_infiles= ', n_infiles
- for n in np.arange(n_infiles):
- ifile = FileList[n]
- nMos = n_mos[n]
- print mdlName[n], n_mos[n], mdlStartT[n], startTime, mdlEndT[n], endTime
-
- # LOOP OVER THE MODEL START TIMES AND DETERMINE WHICH TO KEEP based on user entered Start/End Years
-
- if mdlStartT[n] <= startTime and mdlEndT[n] >= endTime:
- newFileList.append(ifile)
- name.append(mdlName[n])
- k += 1
- FileList = newFileList
- newFileList = 0
- FileList.sort()
- print 'the number of select files = ', len(FileList)
- mdlName = name
- numMDLs = len(FileList)
-
- for n in np.arange(numMDLs):
- print n, mdlName[n], FileList[n]
-
- # 6: Select spatial regridding options
- # PULLED DOWN INTO THE MAIN Loop
- regridOption = 2 # for multi-model cases, this option can be selected only when all model data are on the same grid system.
- naLons = 1
- naLats = 1
- dLon = 0.5
- dLat = 0.5 # these are dummies for regridOption = 1 & 2
-
- if GUI:
- print 'Spatial regridding options: '
- print '[0] Use Observational grid'
- print '[1] Use Model grid'
- print '[2] Define new regular lat/lon grid to use'
- regridOption = int(raw_input('Please make a selection from above:\n> '))
-
- if np.logical_or(regridOption > 2, regridOption < 0):
- print 'Error: Non-existing spatial regridding option. EXIT'; return -1, -1, -1, -1
- # specify the regridding option
- if regridOption == 0:
- regridOption = 'obs'
- if regridOption == 1:
- regridOption = 'model'
- # If requested, get new grid parameters: min/max long & lat values and their uniform increments; the # of longs and lats
-
- if regridOption == 2:
- regridOption = 'regular'
- dLon = 0.44
- dLat = 0.44
- lonMin = -24.64
- lonMax = 60.28
- latMin = -45.76
- latMax = 42.24
- naLons = int((lonMax - lonMin + 1.e-5 * dLon) / dLon) + 1
- naLats = int((latMax - latMin + 1.e-5 * dLat) / dLat) + 1
-
- if GUI:
- if regridOption == 2:
- regridOption = 'regular'
- lonMin = float(raw_input('Please enter the longitude at the left edge of the domain:\n> '))
- lonMax = float(raw_input('Please enter the longitude at the right edge of the domain:\n> '))
- latMin = float(raw_input('Please enter the latitude at the lower edge of the domain:\n> '))
- latMax = float(raw_input('Please enter the latitude at the upper edge of the domain:\n> '))
- dLon = float(raw_input('Please enter the longitude spacing (in degrees) e.g. 0.5:\n> '))
- dLat = float(raw_input('Please enter the latitude spacing (in degrees) e.g. 0.5:\n> '))
- nLons = int((lonMax - lonMin + 1.e-5 * dLon) / dLon) + 1
- nLats = int((latMax - latMin + 1.e-5 * dLat) / dLat) + 1
-
- print 'Spatial re-grid data on the ', regridOption, ' grid'
-
-
- # 7: Temporal regridding: Bring the model and obs data to the same temporal grid for comparison
- # (e.g., daily vs. daily; monthly vs. monthly)
- timeRegridOption = 2
- if GUI == True:
- print 'Temporal regridding options: i.e. averaging from daily data -> monthly data'
- print 'The time averaging will be performed on both model and observational data.'
- print '[0] Calculate time mean for full period.'
- print '[1] Calculate annual means'
- print '[2] Calculate monthly means'
- print '[3] Calculate daily means (from sub-daily data)'
- timeRegridOption = int(raw_input('Please make a selection from above:\n> '))
- # non-existing option is selected
- if np.logical_or(timeRegridOption > 3, timeRegridOption < 0):
- print 'Error: ', timeRegridOption, ' is a non-existing temporal regridding option. EXIT'; return -1, -1, -1, -1
- # specify the temporal regridding option
- if timeRegridOption == 0:
- timeRegridOption = 'mean over all times: i.e., annual-mean climatology'
-
- if timeRegridOption == 1:
- timeRegridOption = 'annual'
-
- if timeRegridOption == 2:
- timeRegridOption = 'monthly'
-
- if timeRegridOption == 3:
- timeRegridOption = 'daily'
-
- print 'timeRegridOption= ', timeRegridOption
-
-
- #******************************************************************************************************************
- # 8: Select whether to perform Area-Averaging over masked region
- # If choice != 'y', the analysis/evaluation will be performed at every grid points within the analysis domain
- #******************************************************************************************************************
- numSubRgn = 21
- subRgnLon0 = ma.zeros(numSubRgn)
- subRgnLon1 = ma.zeros(numSubRgn)
- subRgnLat0 = ma.zeros(numSubRgn)
- subRgnLat1 = ma.zeros(numSubRgn)
- # 21 rgns: SMHI11 + W+C+E. Mediterrenean (JK) + 3 in UCT (Western Sahara, Somalia, Madagascar) + 4 in Mideast
- subRgnLon0 = [-10.0, 0.0, 10.0, 20.0, -19.3, 15.0, -10.0, -10.0, 33.9, 44.2, 10.0, 10.0, 30.0, 13.6, 13.6, 20.0, 43.2, 33.0, 45.0, 43.0, 50.0] # HYB 21 rgns
- subRgnLon1 = [ 0.0, 10.0, 20.0, 33.0, -10.2, 30.0, 10.0, 10.0, 40.0, 51.8, 25.0, 25.0, 40.0, 20.0, 20.0, 35.7, 50.3, 40.0, 50.0, 50.0, 58.0] # HYB 21 rgns
- subRgnLat0 = [ 29.0, 29.0, 25.0, 25.0, 12.0, 15.0, 7.3, 5.0, 6.9, 2.2, 0.0, -10.0, -15.0, -27.9, -35.0, -35.0, -25.8, 25.0, 28.0, 13.0, 20.0] # HYB 21 rgns
- subRgnLat1 = [ 36.5, 37.5, 32.5, 32.5, 20.0, 25.0, 15.0, 7.3, 15.0, 11.8, 10.0, 0.0, 0.0, -21.4, -27.9, -21.4, -11.7, 35.0, 35.0, 20.0, 27.5] # HYB 21 rgns
- subRgnName = ['R01', 'R02', 'R03', 'R04', 'R05', 'R06', 'R07', 'R08', 'R09', 'R10', 'R11', 'R12', 'R13', 'R14', 'R15', 'R16', 'R17', 'R18', 'R19', 'R20', 'R21'] # HYB 21 rgns
- print subRgnName
-
- maskOption = 0
- maskLonMin = 0
- maskLonMax = 0
- maskLatMin = 0
- maskLatMax = 0
- rgnSelect = 0
-
- choice = 'y'
-
- if GUI:
- choice = raw_input('Do you want to calculate area averages over a masked region of interest? [y/n]\n> ').lower()
- if choice == 'y':
- maskOption = 1
- #print '[0] Load spatial mask from file.'
- #print '[1] Enter regular lat/lon box to use as mask.'
- #print '[2] Use pre-determined mask ranges'
- #try:
- # maskInputChoice = int(raw_input('Please make a selection from above:\n> '))
- #if maskInputChoice==0: # Read mask from file
- # maskFile = raw_input('Please enter the file containing the mask data (including full path):\n> ')
- # maskFileVar = raw_input('Please enter variable name of the mask data in the file:\n> ')
- #if maskInputChoice==1:
- # maskLonMin = float(raw_input('Please enter the longitude at the left edge of the mask region:\n> '))
- # maskLonMax = float(raw_input('Please enter the longitude at the right edge of the mask region:\n> '))
- # maskLatMin = float(raw_input('Please enter the latitude at the lower edge of the mask region:\n> '))
- # maskLatMax = float(raw_input('Please enter the latitude at the upper edge of the mask region:\n> '))
- ## maskInputChoice = 0/1: Load spatial mask from file/specifify with long,lat range'
-
-
- if choice == 'y':
- maskOption = 1
- maskInputChoice = 1
- if maskInputChoice == 1:
- for n in np.arange(numSubRgn):
- print 'Subregion [', n, '] ', subRgnName[n], subRgnLon0[n], 'E - ', subRgnLon1[n], ' E: ', subRgnLat0[n], 'N - ', subRgnLat1[n], 'N'
- rgnSelect = 3
- if GUI:
- rgnSelect = raw_input('Select the region for which regional-mean timeseries are to be analyzed\n')
-
- #if maskInputChoice==0: # Read mask from file
- # maskFile = 'maskFileNameTBD'
- # maskFileVar = 'maskFileVarTBD'
-
- # 9. Select properties to evaluate/analyze
- # old Section 8: Select: calculate seasonal cycle composites
-
- seasonalCycleOption = 'y'
- if GUI:
- seasonalCycleOption = raw_input('Composite the data to show seasonal cycles? [y/n]\n> ').lower()
- if seasonalCycleOption == 'y':
- seasonalCycleOption = 1
- else:
- seasonalCycleOption = 0
-
-
- # Section 9: Select Peformance Metric
- choice = 0
- if GUI:
- print 'Metric options'
- print '[0] Bias: mean bias across full time range'
- print '[1] Mean Absolute Error: across full time range'
- print '[2] Difference: calculated at each time unit'
- print '[3] Anomaly Correlation> '
- print '[4] Pattern Correlation> '
- print '[5] TODO: Probability Distribution Function similarity score'
- print '[6] RMS error'
- choice = int(raw_input('Please make a selection from the options above\n> '))
- # assign the metrics to be calculated
- if choice == 0:
- metricOption = 'bias'
-
- if choice == 1:
- metricOption = 'mae'
-
- if choice == 2:
- metricOption = 'difference'
-
- if choice == 3:
- metricOption = 'acc'
-
- if choice == 4:
- metricOption = 'patcor'
-
- if choice == 5:
- metricOption = 'pdf'
-
- if choice == 6:
- metricOption = 'rms'
-
-
- # Select output option
- FoutOption = 0
- if GUI:
- choice = raw_input('Option for output files of obs/model data: Enter no/bn/nc\n> ').lower()
- if choice == 'no':
- FoutOption = 0
- if choice == 'bn':
- FoutOption = 1
- if choice == 'nc':
- FoutOption = 2
-
- ###################################################################################################
- # Section 11: Select Plot Options
- ###################################################################################################
-
-
- modifyPlotOptions = 'no'
- plotTitle = modelVarName + '_'
- plotFilenameStub = modelVarName + '_'
-
- if GUI:
- modifyPlotOptions = raw_input('Do you want to modify the default plot options? [y/n]\n> ').lower()
-
- if modifyPlotOptions == 'y':
- plotTitle = raw_input('Enter the plot title:\n> ')
- plotFilenameStub = raw_input('Enter the filename stub to use, without suffix e.g. files will be named <YOUR CHOICE>.png\n> ')
-
-
-
- print'------------------------------'
- print'End of preprocessor: Run RCMET'
- print'------------------------------'
-
- """
-
-
- # Section 13: Run RCMET, passing in all of the user options
-
- # TODO: **Cameron** Add an option to write a file that includes all options selected before this step to help repeating the same analysis.
- # read-in and regrid both obs and model data onto a common grid system (temporally & spatially).
- # the data are passed to compute metrics and plotting
- # numOBSs & numMDLs will be increased by +1 for multiple obs & mdls, respectively, to accomodate obs and model ensembles
- # nT: the number of time steps in the data
-
-
-# numOBS, numMDL, nT, ngrdY, ngrdX, Times, obsData, mdlData, obsRgn, mdlRgn, obsList, mdlList = toolkit.do_data_prep.prep_data(\
-# cachedir, workdir, \
-# obsList, obsDatasetId, obsParameterId, \
-# startTime, endTime, \
-# latMin, latMax, lonMin, lonMax, dLat, dLon, naLats, naLons, \
-# FileList, \
-# numSubRgn, subRgnLon0, subRgnLon1, subRgnLat0, subRgnLat1, subRgnName, \
-# modelVarName, precipFlag, modelTimeVarName, modelLatVarName, modelLonVarName, \
-# regridOption, timeRegridOption, maskOption, FoutOption)
-
- """
- Parameter to Object Mapping
- cachedir = settings.cacheDir
- workdir = settings.cacheDir
- obsList = obsDatasetList.each['longname']
- """
-
- numOBS, numMDL, nT, ngrdY, ngrdX, Times, obsData, mdlData, obsRgn, mdlRgn, obsList, mdlList = toolkit.do_data_prep(\
- settings, obsDatasetList, gridBox, models, subRegionTuple)
-
- """
- print 'Input and regridding of both obs and model data are completed. now move to metrics calculations'
- # Input and regridding of both obs and model data are completed. now move to metrics calculations
-
- print '-----------------------------------------------'
- print 'mdlID numMOs mdlStartTime mdlEndTime fileName'
- print '-----------------------------------------------'
-
- """
- mdlSelect = numMDL - 1 # numMDL-1 corresponds to the model ensemble
-
- """
- if GUI:
- n = 0
- while n < len(mdlList):
- print n, n_mos[n], mdlStartT[n], mdlEndT[n], FileList[n][35:]
- n += 1
- ask = 'Enter the model ID to be evaluated from above: ', len(FileList), ' for the model-ensemble: \n'
- mdlSelect = int(raw_input(ask))
-
- print '----------------------------------------------------------------------------------------------------------'
-
-
- if maskOption == 1:
- seasonalCycleOption = 1
-
- # TODO: This seems like we can just use numOBS to compute obsSelect (obsSelect = numbOBS -1)
- if numOBS == 1:
- obsSelect = 1
- else:
- #obsSelect = 1 # 1st obs (TRMM)
- #obsSelect = 2 # 2nd obs (CRU3.1)
- obsSelect = numOBS # obs ensemble
-
- obsSelect = obsSelect - 1 # convert to fit the indexing that starts from 0
-
- toolkit.do_metrics_20.metrics_plots(numOBS, numMDL, nT, ngrdY, ngrdX, Times, obsData, mdlData, obsRgn, mdlRgn, obsList, mdlList, \
- workdir, \
- mdlSelect, obsSelect, \
- numSubRgn, subRgnName, rgnSelect, \
- obsParameterId, precipFlag, timeRegridOption, maskOption, seasonalCycleOption, metricOption, \
- plotTitle, plotFilenameStub)
- """
-
-def generateModels(modelConfig):
- """
- This function will return a list of Model objects that can easily be used for
- metric computation and other processing tasks.
-
- Input::
- modelConfig - list of ('key', 'value') tuples. Below is a list of valid keys
- filenamepattern - string i.e. '/nas/run/model/output/MOD*precip*.nc'
- latvariable - string i.e. 'latitude'
- lonvariable - string i.e. 'longitude'
- timevariable - string i.e. 't'
- timestep - string 'monthly' | 'daily' | 'annual'
- varname - string i.e. 'pr'
-
- Output::
- modelList - List of Model objects
- """
- # Setup the config Data Dictionary to make parsing easier later
- configData = {}
- for entry in modelConfig:
- configData[entry[0]] = entry[1]
-
- modelFileList = None
- for keyValTuple in modelConfig:
- if keyValTuple[0] == 'filenamePattern':
- modelFileList = glob.glob(keyValTuple[1])
-
- # Remove the filenamePattern from the dict since it is no longer used
- configData.pop('filenamePattern')
-
- models = []
- for modelFile in modelFileList:
- configData['filename'] = modelFile
- model = Model(**configData)
- models.append(model)
-
- return models
-
-def generateSettings(settingsConfig):
- """
- Helper function to decouple the argument parsing from the Settings object creation
-
- Input::
- settingsConfig - list of ('key', 'value') tuples.
- workdir - string i.e. '/nas/run/rcmet/work/'
- cachedir - string i.e. '/tmp/rcmet/cache/'
- Output::
- settings - Settings Object
- """
- # Setup the config Data Dictionary to make parsing easier later
- configData = {}
- for entry in settingsConfig:
- configData[entry[0]] = entry[1]
-
- return Settings(**configData)
-
-def generateDatasets(rcmedConfig):
- """
- Helper function to decouple the argument parsing from the RCMEDDataset object creation
-
- Input::
- rcmedConfig - list of ('key', 'value') tuples.
- obsDatasetId=3,10
- obsParamId=36,32
- obsTimeStep=monthly,monthly
-
- Output::
- datasets - list of RCMEDDataset Objects
- # Setup the config Data Dictionary to make parsing easier later
- """
- delimiter = ','
- configData = {}
- for entry in rcmedConfig:
- if delimiter in entry[1]:
- # print 'delim found - %s' % entry[1]
- valueList = entry[1].split(delimiter)
- configData[entry[0]] = valueList
- else:
- configData[entry[0]] = entry[1]
-
- return configData
-
-def tempGetYears():
- startYear = int(raw_input('Enter start year YYYY \n'))
- endYear = int(raw_input('Enter end year YYYY \n'))
- # CGOODALE - Updating the Static endTime to be 31-DEC
- startTime = datetime.datetime(startYear, 1, 1, 0, 0)
- endTime = datetime.datetime(endYear, 12, 31, 0, 0)
- return (startTime, endTime)
-
-if __name__ == "__main__":
-
- if args.CONFIG:
- print 'Running using config file: %s' % args.CONFIG
- # Parse the Config file
- userConfig = ConfigParser.SafeConfigParser()
- userConfig.optionxform = str # This is so the case is preserved on the items in the config file
- userConfig.read(args.CONFIG)
- settings = generateSettings(userConfig.items('SETTINGS'))
- models = generateModels(userConfig.items('MODEL'))
- datasets = generateDatasets(userConfig.items('RCMED'))
-
- # Go get the parameter listing from the database
- try:
- params = db.getParams()
- except Exception:
- sys.exit()
-
- obsDatasetList = []
- for param_id in datasets['obsParamId']:
- for param in params:
- if param['parameter_id'] == int(param_id):
- obsDatasetList.append(param)
- else:
- pass
-
- # TODO: Find a home for the regrid parameters in the CONFIG file
- # Setup the Regridding Options
- regridOption = 'regular'
- # dLon = 0.44 - Provided in the SETTINGS config section
- # dLat = 0.44
- lonMin = -24.64
- lonMax = 60.28
- latMin = -45.76
- latMax = 42.24
- # Create a Grid Box Object that extends the bounding box Object
- gridBox = GridBox(latMin, lonMin, latMax, lonMax, settings.gridLonStep, settings.gridLatStep)
-
- """ These can now be accessed from the gridBox object using gridBox.latCount and gridBox.lonCount attributes
- naLons = int((gridBox.lonMax - gridBox.lonMin + 1.e-5 * settings.gridLonStep) / settings.gridLonStep) + 1
- print naLons
- print int((gridBox.lonMax - gridBox.lonMin) / gridBox.lonStep) + 1
- naLats = int((gridBox.latMax - gridBox.latMin + 1.e-5 * settings.gridLatStep) / settings.gridLatStep) + 1
- """
- timeRegridOption = settings.temporalGrid
-
- # TODO: How do we support n subregions as Jinwon has below?
-
- numSubRgn = 21
-# subRgnLon0 = ma.zeros(numSubRgn)
-# subRgnLon1 = ma.zeros(numSubRgn)
-# subRgnLat0 = ma.zeros(numSubRgn)
-# subRgnLat1 = ma.zeros(numSubRgn)
- # 21 rgns: SMHI11 + W+C+E. Mediterrenean (JK) + 3 in UCT (Western Sahara, Somalia, Madagascar) + 4 in Mideast
- subRgnLon0 = [-10.0, 0.0, 10.0, 20.0, -19.3, 15.0, -10.0, -10.0, 33.9, 44.2, 10.0, 10.0, 30.0, 13.6, 13.6, 20.0, 43.2, 33.0, 45.0, 43.0, 50.0] # HYB 21 rgns
- subRgnLon1 = [ 0.0, 10.0, 20.0, 33.0, -10.2, 30.0, 10.0, 10.0, 40.0, 51.8, 25.0, 25.0, 40.0, 20.0, 20.0, 35.7, 50.3, 40.0, 50.0, 50.0, 58.0] # HYB 21 rgns
- subRgnLat0 = [ 29.0, 29.0, 25.0, 25.0, 12.0, 15.0, 7.3, 5.0, 6.9, 2.2, 0.0, -10.0, -15.0, -27.9, -35.0, -35.0, -25.8, 25.0, 28.0, 13.0, 20.0] # HYB 21 rgns
- subRgnLat1 = [ 36.5, 37.5, 32.5, 32.5, 20.0, 25.0, 15.0, 7.3, 15.0, 11.8, 10.0, 0.0, 0.0, -21.4, -27.9, -21.4, -11.7, 35.0, 35.0, 20.0, 27.5] # HYB 21 rgns
- subRgnName = ['R01', 'R02', 'R03', 'R04', 'R05', 'R06', 'R07', 'R08', 'R09', 'R10', 'R11', 'R12', 'R13', 'R14', 'R15', 'R16', 'R17', 'R18', 'R19', 'R20', 'R21'] # HYB 21 rgns
- print subRgnName
-
- subRegionTuple = (numSubRgn, subRgnLon0, subRgnLon1, subRgnLat0, subRgnLat1, subRgnName)
-
-
- rgnSelect = 3
- maskOption = settings.maskOption
-
- bbox = BoundingBox(subRgnLat0[rgnSelect],
- subRgnLon0[rgnSelect],
- subRgnLat1[rgnSelect],
- subRgnLon1[rgnSelect])
-
- regionMask = SubRegion(subRgnName[rgnSelect], bbox)
-
- # Using a 'mask' instance of the BoundingBox object
-# maskLonMin = 0
-# maskLonMax = 0
-# maskLatMin = 0
-# maskLatMax = 0
-
- choice = 'y'
-
- # THIS JUST MEANS A USER DEFINED MASK IS BEING USED (basically from the hardcoded values listed above (line 819 ish)
- maskInputChoice = 1
-
- if maskInputChoice == 1:
- for n in np.arange(numSubRgn):
- print 'Subregion [', n, '] ', subRgnName[n], subRgnLon0[n], 'E - ', subRgnLon1[n], ' E: ', subRgnLat0[n], 'N - ', subRgnLat1[n], 'N'
- rgnSelect = 3
-
- # Section 9: Select Peformance Metric
- metricOption = 'bias'
- FoutOption = 0
-
- # Section 11: Select Plot Options
- # TODO: Using first model in models since Var name is the same across all
- modifyPlotOptions = 'no'
- plotTitle = models[0].varName + '_'
- plotFilenameStub = models[0].varName + '_'
-
- print'------------------------------'
- print'End of preprocessor: Run RCMET'
- print'------------------------------'
-
- numOBS, numMDL, nT, ngrdY, ngrdX, Times, obsData, mdlData, obsRgn, mdlRgn, obsList, mdlList = toolkit.do_data_prep.prep_data(settings, obsDatasetList, gridBox, models, subRegionTuple)
-
-
- print 'Input and regridding of both obs and model data are completed. now move to metrics calculations'
-
- """FROM THE UPPER SECTION OF CODE"""
-
- mdlSelect = numMDL - 1 # numMDL-1 corresponds to the model ensemble
-
- """ Disregard GUI block for now
- if GUI:
- n = 0
- while n < len(mdlList):
- print n, n_mos[n], mdlStartT[n], mdlEndT[n], FileList[n][35:]
- n += 1
- ask = 'Enter the model ID to be evaluated from above: ', len(FileList), ' for the model-ensemble: \n'
- mdlSelect = int(raw_input(ask))
-
- print '----------------------------------------------------------------------------------------------------------'
- """
-
- if maskOption:
- seasonalCycleOption = True
-
- # TODO: This seems like we can just use numOBS to compute obsSelect (obsSelect = numbOBS -1)
- if numOBS == 1:
- obsSelect = 1
- else:
- #obsSelect = 1 # 1st obs (TRMM)
- #obsSelect = 2 # 2nd obs (CRU3.1)
- obsSelect = numOBS # obs ensemble
-
- obsSelect = obsSelect - 1 # convert to fit the indexing that starts from 0
-
-
-
- # TODO: Undo the following code when refactoring later
- obsParameterId = [str(x['parameter_id']) for x in obsDatasetList]
- precipFlag = models[0].precipFlag
-
- toolkit.do_metrics_20.metrics_plots(numOBS, numMDL, nT, ngrdY, ngrdX, Times, obsData, mdlData, obsRgn, mdlRgn, obsList, mdlList, \
- settings.workDir, \
- mdlSelect, obsSelect, \
- numSubRgn, subRgnName, rgnSelect, \
- obsParameterId, precipFlag, timeRegridOption, maskOption, seasonalCycleOption, metricOption, \
- plotTitle, plotFilenameStub)
-
-
-
- else:
- print 'Interactive mode has been enabled'
- #getSettings(SETTINGS)
- print "But isn't implemented. Try using the -c option instead"
-
- #rcmet_cordexAF()