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input_CLUStool.in
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input_CLUStool.in
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Input file for mainCLUStool.py.
IMPORTANT NOTE: the value of the keys has to be written in the line that immediately follows the [key] label. If you don't want to set a key, comment it with a '#': #[key]. Commented lines are not considered.
##################################################################
##################### REQUIRED INPUT PATHS/NAMES (have to be set!) #################
# Output data directory
[dir_OUTPUT]
/home/fabiano/Research/lavori/MedscopeEnsClus/
# Directory that contains all files to be analized
[INPUT_PATH]
/home/fabiano/DATA/Medscope/seasonal_forecasts_1d5/input_par167_1ens/
# Name of the variable to be extracted from the fields (2t, tprate)
[varname]
2t
# Is this a Medscope run? If True, once determined the season and the year, the code automatically sets exp_name, string_name, climat_file and climat_std.
[medscope_run]
True
# Season to be selected (options: JJA, DJF, DJFM, NDJFM, MAM, SON). The code accepts also single months, named in this way: Jan, Feb, Mar, ...
[season]
JJA
# Only for MEDSCOPE runs.
# Year of the prediction. (The year in which the season starts. For example, to analyze 2012 winter, year has to be set to 2011.)
[medscope_year_pred]
2017
# Only for non-MEDSCOPE runs.
# Common string to all file_names to be analized inside INPUT_PATH. If not specified all files in the directory will be considered.
[string_name]
spred_2003_may_ens
# Only for non-MEDSCOPE runs.
# Name of this run
[exp_name]
Aug_2003
# Overwrite output files if already present in the same folder? (if not sure, leave this as False)
[overwrite_output]
True
# Name of the dataset (ECEARTH, ERA, NCEP)
[model]
Medscope
##########################################################################
############## reference FILES ################
# Compare with the model climatology?
[clim_compare]
True
# File that contains the model climatology.
[climat_file]
/home/fabiano/DATA/Medscope/seasonal_forecasts_1d5/input_par167_1ens/climatology_mean_nov_1993-2016.nc
# File that contains the model climatology variance (std). If specified, the anomalies are plotted also in units of model sigma.
[climat_std]
/home/fabiano/DATA/Medscope/seasonal_forecasts_1d5/input_par167_1ens/climatology_std_nov_1993-2016.nc
# Sigma of the model. If the climat_std file is specified, there is no need to set this key. Or, it is better not to.
#[clim_sigma_value]
#7.0
# Compare with Observations?
[obs_compare]
True
# Observation file. Anomalies with respect to the observed climatology.
[obs_file]
/home/fabiano/DATA/Medscope/ERAInterim_1d5/ERAInterim_anomalies_167_grid150.nc
##########################################################################
############## options for EOFs/CLUSTERING ################
# Number of EOFs to be used in the decomposition:
[numpcs]
4
# Percentage of variance explained by the EOFs considered. Number of EOFs used in the decomposition is calculated by program.
#[perc]
80
# Number of clusters to be used:
[numclus]
4
# Try to determine the best number of clusters according to Dunn and Davies-Bouldin indexes? (If True, a plot of the various indexes is produced, but no final maps)
[check_best_numclus]
False
##########################################################################
############## Options for the analysis ################
# Atmospheric level at which the variable is extracted (if more levels are present)
#[level]
500
# Regional average ('EAT': Euro-Atlantic, 'PNA': Pacific North American, 'NH': Northern Hemisphere, 'Med': Mediterranean, 'Eu': Europe)
# Area to be selected
[area]
Med
# Data frequency (options: day, month)
[timestep]
month
# Type of pattern to consider. The choice is between: '**th_percentile', 'mean', 'maximum', 'std', 'trend'. For the percentile, the actual number has to be specified in the key value: if the 75th percentile is desired, the right key is '75th_percentile'.
[extreme]
mean
##########################################################################
################### Options for plots and visualization ###################
# Format of saved figures: (pdf or eps)
[fig_format]
pdf
# Number of color levels to be used in the contour plots.
[n_color_levels]
21
# Calibrate the color scale on the observed anomaly? (True gives true rendering of the observed/modeled difference, but may hide the difference between clusters if the observed anomaly is much larger.)
[fig_ref_to_obs]
False
# Draw contour lines?
[draw_contour_lines]
False
# Number of levels to be used for the contour lines.
[n_levels]
5
# Colormap used for the contour plots.
[cmap]
RdBu_r
# Colormap used for the cluster colors.
[cmap_cluster]
nipy_spectral
# Label for the colorbar.
[cb_label]
Temperature anomaly (K)
# Max number of ens. member plotted in the same figure:
[max_ens_in_fig]
30
# Use numbers to label points in the Taylor plots.
[taylor_w_numbers]
True