The short description of the code describes the aim of the code, more information is within the file.
convert_fluxC_to_gC.py : To convert/calculate the gC/month from g/sq.m/s; system arguments take into consideration the model configuration and variable.
ecp_plot_diff_var_config.py: Calculating and plotting the Difference in fluxes.
ecp_plot_timeseries_config.py : To plot the global timeseries of a variable (includes rolling mean).
ssa.py : Contains the functions to calculate trend, seasonality, and anomalies using the SSA method.
ecp_decompose_mpi.py : The time series decomposition is carried out by the SSA. Removing trend and MAC from timeseries yields anomalies of the variable. (Can be run parallel!)
ecp_concatenate_decomp_over_lats_config.py : concatenate anomalies from 192 files created while decompostion.
ecp_extremes_analysis.py : To calculate extremes of a flux/variable with an option of chosing the model configuration. Allows us to compute extremes based on a given threshold value(s) as well.
ecp_correlation_methods_pixel_win.py : To computer the correlation cofficients when the TS is chosen differently ( cases are defined in the file as well as the docs). This version is designed to calculate for the time window of 25 years at every pixel and it also computes the statistics on the TCE and TCE_len.
ecp_dominant_climate_driver_correlation.py: Computes the dominant drivers of based on a p_value of 0.1 and correlation coefficient. It returns a ncfile of shape (6,18,13,192,288). 6: Ranks(0 - Most Dominant), 18 - Time windows of 25 years, 13- lag in months, 192/288: lat/lon).
ecp_dominant_climate_driver_correlation_graphs.py : Computes the frequency of occurance of the most dominant driver, spatial distribution, mean and standard deviation of the correlation coefficients for the whole globe and the CONUS; also outputs dataFrames.
ecp_correlation_drivers_triggers.py : Computes the correlation coefficients of the climate drivers and the triggers (locations of the begining of an event). The extreme event is defined as a time continuous extreme event where the max gap is 2 months and the minimum patch continuous extreme times are 3 months.
ecp_triggers_percent_distribution_dom_drivers.py : Plot percent distribution of dominant climate drivers for misc cases as well. It can perform calculation on the whole duration and not just triggers.
ecp_attr_corr_triggers_plotting.py : Plots the timeseries of the count of the dominant drivers for a particular lag for the complete time period (1850--2300).
ecp_shaded_extremes_drivers.py : The aim is to create a stacked graphs of gpp and climate drivers to identify the climate that led to an extreme event at that location. The TCE events are also shaded and we could focus on a particular TCE and see the detais.
ecp_dom_driver_RGB_tce.py : Ploting the RGB maps from the correlation nc file of all the drivers and their correlation cofficients.
ecp_IAV_extremes_drivers.py : To calculate the IAV of GPP at global and pixel level, the relative IAV i.e. IAV was computed after dividing the anomalies with trend of GPP. To calculate the IAV of Climate drivers pixel level. To calculate the IAV standard deviation was computed from 1850 to 1874, 1899, 1924 , ... , 2299.