Docker and TSA

[Matthew Clark]

Hello all,

I have been trying to run force-higher-level TSA on multiple machines (2 PCs with WSL/Ubuntu 18.04, the other an older Linux Ubuntu 18.04, with Docker.

I get this error "Segmentation fault (core dumped)".  

I tried this with the bare minimum of processing:

NTHREAD_READ = 2

NTHREAD_COMPUTE = 2

NTHREAD_WRITE = 2

Any ideas?

- Matt

[david frantz]

Hi Matt,

segmentation faults are bad indeed, and tricky to cure from remote as the code immediately crashes without a proper error message.. 

Most frequently, segfaults in FORCE are occurring when some input is unexpected, which is also a difficult thing to catch.

Could you please post the full parameter file that you used? If you have used additional files (tile lists, endmembers etc.) can you upload these as well?
Cheers,

David

[Matthew Clark]

Hi David,

Below is the parameter file. 

++PARAM_TSA_START++

# INPUT/OUTPUT DIRECTORIES

# ------------------------------------------------------------------------

# Lower Level datapool (parent directory of tiled input data)

# Type: full directory path

DIR_LOWER = /opt/data/sonoma/level2

# Higher Level datapool (parent directory of tiled output data)

# Type: full directory path

DIR_HIGHER = /opt/data/sonoma/tsa

# MASKING

# ------------------------------------------------------------------------

# Analysis Mask datapool (parent directory of tiled analysis masks)

# If no analsys mask should be applied, give NULL.

# Type: full directory path

DIR_MASK = NULL

# Basename of analysis masks (e.g. WATER-MASK.tif).

# Masks need to be binary with 0 = off / 1 = on

# Type: Basename of file

BASE_MASK = NULL

# OUTPUT OPTIONS

# ------------------------------------------------------------------------

# Output format, which is either uncompressed flat binary image format aka

# ENVI Standard or GeoTiff. GeoTiff images are compressed with LZW and hori-

# zontal differencing; BigTiff support is enabled; the Tiff is structured 

# with striped blocks according to the TILE_SIZE (X) and BLOCK_SIZE (Y) speci-

# fications. Metadata are written to the ENVI header or directly into the Tiff

# to the FORCE domain. If the size of the metadata exceeds the Tiff's limit,

# an external .aux.xml file is additionally generated.

# Type: Character. Valid values: {ENVI,GTiff}

OUTPUT_FORMAT = GTiff

# This parameter controls whether the output is written as multi-band image, or

# if the stack will be exploded into single-band files.

# Type: Logical. Valid values: {TRUE,FALSE}

OUTPUT_EXPLODE = FALSE

# PARALLEL PROCESSING

# ------------------------------------------------------------------------

# This module is using a streaming mechanism to speed up processing. There

# are three processing teams (3 Threads) that simultaneously handle Input,

# Processing, and Output. Example: when Block 2 is being processed, data

# from Block 3 are already being input and results from Block 1 are being

# output. Each team can have multiple sub-threads to speed up the work. The

# number of threads to use for each team is given by following parameters.

# Type: Integer. Valid range: [1,...

NTHREAD_READ = 2

NTHREAD_COMPUTE = 2

NTHREAD_WRITE = 2

# PROCESSING EXTENT AND RESOLUTION

# ------------------------------------------------------------------------

# Analysis extent, given in tile numbers (see tile naming)

# Each existing tile falling into this square extent will be processed

# A shapefile of the tiles can be generated with force-tabulate-grid

# Type: Integer list. Valid range: [-999,9999]

X_TILE_RANGE = 0001 0006

Y_TILE_RANGE = 0001 0005

# Allow-list of tiles. Can be used to further limit the analysis extent to

# non-square extents. The allow-list is intersected with the analysis extent,

# i.e. only tiles included in both the analysis extent AND the allow-list will

# be processed.

# Optional. If NULL, the complete analysis extent is processed

# Type: full file path

FILE_TILE = NULL

# This parameter can be used to override the default blocksize of the input

# images (as specified in the datacube-definition.prj file). This can be

# necessary if the default blocksize is too large for your system and you

# cannot fit all necessary data into RAM. Note that processing of larger

# blocksizes is more efficient. The tilesize must be dividable by the blocksize

# without remainder. Set to 0, to use the default blocksize

# Type: Double. Valid range: 0 or [RESOLUTION,TILE_SIZE]

BLOCK_SIZE = 0

# Analysis resolution. The tile (and block) size must be dividable by this

# resolution without remainder, e.g. 30m resolution with 100km tiles is not possible

# Type: Double. Valid range: ]0,BLOCK_SIZE]

RESOLUTION = 30

# How to reduce spatial resolution for cases when the image resolution is higher

# than the analysis resolution. If FALSE, the resolution is degraded using Nearest

# Neighbor resampling (fast). If TRUE, an approx. Point Spread Function (Gaussian

# lowpass with FWHM = analysis resolution) is used to approximate the acquisition

# of data at lower spatial resolution

# Type: Logical. Valid values: {TRUE,FALSE}

REDUCE_PSF = FALSE

# If you have spatially enhanced some Level 2 ARD using the FORCE Level 2 ImproPhe

# module, this switch specifies whether the data are used at original (FALSE) or

# enhanced spatial resolution (TRUE). If there are no improphe'd products, this

# switch doesn't have any effect

# Type: Logical. Valid values: {TRUE,FALSE}

USE_L2_IMPROPHE = FALSE

# SENSOR ALLOW-LIST

# ------------------------------------------------------------------------

# Sensors to be used in the analysis. Multi-sensor analyses are restricted

# to the overlapping bands. Following sensors are available: LND04 (6-band

# Landsat 4 TM), LND05 (6-band Landsat 5 TM), LND07 (6-band Landsat 7 ETM+),

# LND08 (6-band Landsat 8 OLI), SEN2A (10-band Sentinel-2A), SEN2B (10-band

# Sentinel-2B), sen2a (4-band Sentinel-2A), sen2b (4-band Sentinel-2B),

# S1AIA (2-band VV-VH Sentinel-1A IW ascending), S1BIA (2-band VV-VH Senti-

# nel-1B IW ascending), S1AID (2-band VV-VH Sentinel-1A IW descending), S1BID

# (2-band VV-VH Sentinel-1B IW descending), MOD01 (7-band Terra MODIS), MOD02.

# (7-band Aqua MODIS).

# The resulting outputs are named according to their band designation, i.e. 

# LNDLG ((6-band Landsat legacy bands), SEN2L (10-band Sentinel-2 land surface

# bands), SEN2H (4-band Sentinel-2 high-res bands), R-G-B (3-band visual) or

# VVVHP (VV/VH polarized), MODIS (7-band MODIS).

# BAP Composites with such a band designation can be input again (e.g. 

# SENSORS = LNDLG).

# Type: Character list. Valid values: {LND04,LND05,LND07,LND08,SEN2A,

#   SEN2B,sen2a,sen2b,S1AIA,S1BIA,S1AID,S1BID,MOD01,MOD02,LNDLG,SEN2L,SEN2H,R-G-B,VVVHP,MODIS}

SENSORS = LND07 LND08 SEN2A SEN2B

# QAI SCREENING

# ------------------------------------------------------------------------

# This list controls, which QAI flags are masked out before doing the analysis.

# Type: Character list. Valid values: {NODATA,CLOUD_OPAQUE,CLOUD_BUFFER,

#   CLOUD_CIRRUS,CLOUD_SHADOW,SNOW,WATER,AOD_FILL,AOD_HIGH,AOD_INT,SUBZERO,

#   SATURATION,SUN_LOW,ILLUMIN_NONE,ILLUMIN_POOR,ILLUMIN_LOW,SLOPED,WVP_NONE}

SCREEN_QAI = NODATA

# Threshold for removing outliers. Triplets of observations are used to determine

# the overall noise in the time series by computinglinearly interpolating between

# the bracketing observations. The RMSE of the residual between the middle value

# and the interpolation is the overall noise. Any observations, which have a

# residual larger than a multiple of the noise are iteratively filtered out

# (ABOVE_NOISE). Lower/Higher values filter more aggressively/conservatively.

# Likewise, any masked out observation (as determined by the SCREEN_QAI filter)

# can be restored if its residual is lower than a multiple of the noise

# (BELOW_NOISE). Higher/Lower values will restore observations more aggres-

# sively/conservative. Give 0 to both parameters to disable the filtering.

# Type: Float. Valid range: [0,...

ABOVE_NOISE = 3

BELOW_NOISE = 1

# PROCESSING TIMEFRAME

# ------------------------------------------------------------------------

# Time extent for the analysis. All data between these dates will be used in

# the analysis.

# Type: Date list. Format: YYYY-MM-DD

DATE_RANGE = 2016-09-22 2021-02-24

# DOY range for filtering the time extent. Day-of-Years that are outside of

# the given interval will be ignored. Example: DATE_RANGE = 2010-01-01 

# 2019-12-31, DOY_RANGE = 91 273 will use all April-Sepember observations from

# 2010-2019. If you want to extend this window over years give DOY min > 

# DOY max. Example: DATE_RANGE = 2010-01-01 2019-12-31, DOY_RANGE = 274 90 

# will use all October-March observations from 2010-2019.

# Type: Integer list. Valid values: [1,365]

DOY_RANGE = 274 273

# SPECTRAL INDEX

# ------------------------------------------------------------------------

# Perform the time series analysis using the specified band or index.

# Multiple indices can be processed ar once to avoid multiple reads of the

# same file. Only necessary bands will be input. You will be alerted if the

# index cannot be computed based on the requested SENSORS. The index SMA is

# a linear spectral mixture analysis and is dependent on the parameters

# specified in the SPECTRAL MIXTURE ANALYSIS section below.

# Type: Character list. Valid values: {BLUE,GREEN,RED,NIR,SWIR1,SWIR2,RE1,

#   RE2,RE3,BNIR,NDVI,EVI,NBR,NDTI,ARVI,SAVI,SARVI,TC-BRIGHT,TC-GREEN,TC-WET,

#   TC-DI,NDBI,NDWI,MNDWI,NDMI,NDSI,SMA}

INDEX = NDVI EVI

# Standardize the TSS time series with pixel mean and/or standard deviation?

# Type: Logical. Valid values: {NONE,NORMALIZE,CENTER}

STANDARDIZE_TSS = NONE

# Output the quality-screened Time Series Stack? This is a layer stack of

# index values for each date.

# Type: Logical. Valid values: {TRUE,FALSE}

OUTPUT_TSS = FALSE

# SPECTRAL MIXTURE ANALYSIS

# ------------------------------------------------------------------------

# This block only applies if INDEX includes SMA

# ------------------------------------------------------------------------

# Endmember file holding the endmembers according to the SENSORS band subset

# Type: full file path

FILE_ENDMEM  = NULL

# Sum-to-One constrained unmixing?

# Type: Logical. Valid values: {TRUE,FALSE}

SMA_SUM_TO_ONE = TRUE

# Non-negativity constrained unmixing?

# Type: Logical. Valid values: {TRUE,FALSE}

SMA_NON_NEG = TRUE

# Apply shade normalization? If TRUE, the last endmember FILE_ENDMEM needs

# to be the shade spectrum

# Type: Logical. Valid values: {TRUE,FALSE}

SMA_SHD_NORM = TRUE

# Endmember to be used for the analysis. This number refers to the column,

# in which the desired endmember is stored (FILE_ENDMEM).

# Type: Integer. Valid range: [1,NUMBER_OF_ENDMEMBERS]

SMA_ENDMEMBER = 1

# Output the SMA model Error? This is a layer stack of model RMSE for

# each date.

# Type: Logical. Valid values: {TRUE,FALSE}

OUTPUT_RMS = FALSE

# INTERPOLATION PARAMETERS

# ------------------------------------------------------------------------

# Interpolation method. You can choose between no, linear, moving average

# or Radial Basis Function Interpolation.

# Type: Character. Valid values: {NONE,LINEAR,MOVING,RBF}

INTERPOLATE = RBF

# Max temporal distance for the moving average filter in days. For each

# interpolation date, MOVING_MAX days before and after are considered.

# Type: Integer. Valid range: [1,365]

MOVING_MAX = 16

# Sigma (width of the Gaussian bell) for the RBF filter in days. For each

# interpolation date, a Gaussian kernel is used to smooth the observations.

# The smoothing effect is stronger with larger kernels and the chance of

# having nodata values is lower. Smaller kernels will follow the time series

# more closely but the chance of having nodata values is larger. Multiple

# kernels can be combined to take advantage of both small and large kernel

# sizes. The kernels are weighted according to the data density within each

# kernel.

# Type: Integer list. Valid range: [1,365]

RBF_SIGMA = 8 16 32

# Cutoff density for the RBF filter. The Gaussian kernels have infinite width,

# which is computationally slow, and doesn't make much sense as observations

# that are way too distant (in terms of time) are considered. Thus, the

# tails of the kernel are cut off. This parameter specifies, which percen-

# tage of the area under the Gaussian should be used.

# Type: Float. Valid range: ]0,1]

RBF_CUTOFF = 0.95

# This parameter gives the interpolation step in days.

# Type: Integer. Valid range: [1,...

INT_DAY = 16

# Standardize the TSI time series with pixel mean and/or standard deviation?

# Type: Logical. Valid values: {NONE,NORMALIZE,CENTER}

STANDARDIZE_TSI = NONE

# Output the Time Series Interpolation? This is a layer stack of index

# values for each interpolated date. Note that interpolation will be per-

# formed even if OUTPUT_TSI = FALSE - unless you specify INTERPOLATE = NONE.

# Type: Logical. Valid values: {TRUE,FALSE}

OUTPUT_TSI = FALSE

# PYTHON-PLUGIN PARAMETERS

# ------------------------------------------------------------------------

# This file specifies the file holding user-provided python code. You can skip this

# by setting FILE_PYTHON = NULL, but this requires OUTPUT_PYP = FALSE.

# Type: full file path

FILE_PYTHON = NULL

# Output the results provided by the python-plugin? If TRUE, FILE_PYTHON must exist.

# Type: Logical. Valid values: {TRUE,FALSE}

OUTPUT_PYP = FALSE

# SPECTRAL TEMPORAL METRICS

# ------------------------------------------------------------------------

# Output Spectral Temporal Metrics? The remaining parameters in this block

# are only evaluated if TRUE

# Type: Logical. Valid values: {TRUE,FALSE}

OUTPUT_STM = TRUE

# Which Spectral Temporal Metrics should be computed? The STM output files

# will have as many bands as you specify metrics (in the same order).

# Currently available statistics are the average, standard deviation, mini-

# mum, maximum, range, skewness, kurtosis, any quantile from 1-99%, and

# interquartile range. Note that median is Q50.

# Type: Character list. Valid values: {MIN,Q01-Q99,MAX,AVG,STD,RNG,IQR,SKW,KRT,NUM}

STM = MIN Q25 Q50 Q75 MAX AVG STD RNG 

# FOLDING PARAMETERS

# ------------------------------------------------------------------------

# Which statistic should be used for folding the time series? This parameter

# is only evaluated if one of the following outputs in this block is requested.

# Currently available statistics are the average, standard deviation, mini-

# mum, maximum, range, skewness, kurtosis, median, 10/25/75/90% quantiles,

# and interquartile range

# Type: Character. Valid values: {MIN,Q10,Q25,Q50,Q75,Q90,MAX,AVG,STD,

#   RNG,IQR,SKW,KRT,NUM

FOLD_TYPE = MIN Q25 Q50 Q75 MAX AVG STD RNG

# Standardize the FB* time series with pixel mean and/or standard deviation?

# Type: Logical. Valid values: {NONE,NORMALIZE,CENTER}

STANDARDIZE_FOLD = NONE

# Output the Fold-by-Year/Quarter/Month/Week/DOY time series? These are layer

# stacks of folded index values for each year, quarter, month, week or DOY.

# Type: Logical. Valid values: {TRUE,FALSE}

OUTPUT_FBY = TRUE

OUTPUT_FBQ = TRUE

OUTPUT_FBM = FALSE

OUTPUT_FBW = FALSE

OUTPUT_FBD = FALSE

# Compute and output a linear trend analysis on any of the folded time series?

# Note that the OUTPUT_FBX parameters don't need to be TRUE to do this.

# See also the TREND PARAMETERS block below.

# Type: Logical. Valid values: {TRUE,FALSE}

OUTPUT_TRY = FALSE

OUTPUT_TRQ = FALSE

OUTPUT_TRM = FALSE

OUTPUT_TRW = FALSE

OUTPUT_TRD = FALSE

# Compute and output an extended Change, Aftereffect, Trend (CAT) analysis on

# any of the folded time series?

# Note that the OUTPUT_FBX parameters don't need to be TRUE to do this.

# See also the TREND PARAMETERS block below.

# Type: Logical. Valid values: {TRUE,FALSE}

OUTPUT_CAY = TRUE

OUTPUT_CAQ = TRUE

OUTPUT_CAM = TRUE

OUTPUT_CAW = TRUE

OUTPUT_CAD = TRUE

# LAND SURFACE PHENOLOGY PARAMETERS - SPLITS-BASED

# ------------------------------------------------------------------------

# The Land Surface Phenology (LSP) options are only available if FORCE was

# compiled with SPLITS (see installation section in the FORCE user guide).

# ------------------------------------------------------------------------

# For estimating LSP for one year, some data from the previous/next year

# need to be considered to find the seasonal minima, which define a season.

# The parameters are given in DOY, i.e. LSP_DOY_PREV_YEAR = 273, and 

# LSP_DOY_NEXT_YEAR = 91 will use all observations from October (Year-1)

# to March (Year+1)

# Type: Integer. Valid range: [1,365]

LSP_DOY_PREV_YEAR = 274

LSP_DOY_NEXT_YEAR = 273

# Seasonality is of Northern-, Southern-hemispheric or of mixed type? If

# mixed, the code will attempt to estimate the type on a per-pixel basis.

# Type: Character. Valid values: {NORTH,SOUTH,MIXED}

LSP_HEMISPHERE = NORTH

# How many segments per year should be used for the spline fitting? More

# segments follow the seasonality more closely, less segments smooth the

# time series stronger.

# Type: Integer. Valid range: [1,...

LSP_N_SEGMENT = 8

# Amplitude threshold for detecing Start, and End of Season, i.e. the date,

# at which xx% of the amplitude is observed

# Type: Float. Valid range: ]0,1[

LSP_AMP_THRESHOLD = 0.2

# LSP won't be derived if the seasonal index values do not exceed following

# value. This is useful to remove unvegetated surfaces.

# Type: Integer. Valid range: [-10000,10000]

LSP_MIN_VALUE = 500

# LSP won't be derived if the seasonal amplitude is below following value

# This is useful to remove surfaces that do not have a seasonality.

# Type: Integer. Valid range: [0,10000]

LSP_MIN_AMPLITUDE = 500

# Which Phenometrics should be computed? There will be a LSP output file for

# each metric (with years as bands).

# Currently available are the dates of the early minimum, start of season,

# rising inflection, peak of season, falling inflection, end of season, late

# minimum; lengths of the total season, green season; values of the early

# minimum, start of season, rising inflection, peak of season, falling 

# inflection, end of season, late minimum, base level, seasonal amplitude;

# integrals of the total season, base level, base+total, green season; rates

# of averahe rising, average falling, maximum rising, maximum falling.

# Type: Character list. Valid values: {DEM,DSS,DRI,DPS,DFI,DES,DLM,LTS,LGS,

#   VEM,VSS,VRI,VPS,VFI,VES,VLM,VBL,VSA,IST,IBL,IBT,IGS,RAR,RAF,RMR,RMF}

LSP = DEM DSS DRI DPS DFI DES DLM LTS LGS VEM VSS VRI VPS VFI VES VLM VBL VSA IST IBL IBT IGS RAR RAF RMR RMF

# Standardize the LSP time series with pixel mean and/or standard deviation?

# Type: Logical. Valid values: {NONE,NORMALIZE,CENTER}

STANDARDIZE_LSP = NONE

# Output the Spline fit? This is a layer stack of fitted index values for

# interpolated date.

# Type: Logical. Valid values: {TRUE,FALSE}

OUTPUT_SPL = TRUE

# Output the Phenometrics? These are layer stacks per phenometric with as many

# bands as years (excluding one year at the beginning/end of the time series.

# Type: Logical. Valid values: {TRUE,FALSE}

OUTPUT_LSP = TRUE

# Compute and output a linear trend analysis on the requested Phenometric time

# series? Note that the OUTPUT_LSP parameters don't need to be TRUE to do this.

# See also the TREND PARAMETERS block below.

# Type: Logical. Valid values: {TRUE,FALSE}

OUTPUT_TRP = FALSE

# Compute and output an extended Change, Aftereffect, Trend (CAT) analysis on

# the requested Phenometric time series?

# Note that the OUTPUT_LSP parameters don't need to be TRUE to do this.

# See also the TREND PARAMETERS block below.

# Type: Logical. Valid values: {TRUE,FALSE}

OUTPUT_CAP = TRUE

# LAND SURFACE PHENOLOGY PARAMETERS - POLAR-BASED

# ------------------------------------------------------------------------

# Threshold for detecing Start of Season in the cumulative time series.

# Type: Float. Valid range: ]0,1[

POL_START_THRESHOLD = 0.2

# Threshold for detecing Mid of Season in the cumulative time series.

# Type: Float. Valid range: ]0,1[

POL_MID_THRESHOLD = 0.5

# Threshold for detecing End of Season in the cumulative time series.

# Type: Float. Valid range: ]0,1[

POL_END_THRESHOLD = 0.8

# Should the start of each phenological year be adapated?

# If FALSE, the start is static, i.e. Date of Early Minimum and Date of Late

# Minimum are the same for all years and 365 days apart. If TRUE, they differ

# from year to year and a phenological year is not forced to be 365 days long.

# Type: Logical. Valid values: {TRUE,FALSE}

POL_ADAPTIVE = TRUE

# Which Polarmetrics should be computed? There will be a POL output file for

# each metric (with years as bands).

# Currently available are the dates of the early minimum, late minimum, peak of season,

# start of season, mid of season, end of season, early average vector, average vector,

# late average vector; lengths of the total season, green season, between averge vectors;

# values of the early minimum, late minimum, peak of season, start of season, mid of season,

# end of season, early average vector, average vector, late average vector, base level,

# green amplitude, seasonal amplitude, peak amplitude, green season mean , green season

# variability, dates of start of phenological year, difference between start of phenological

# year and its longterm average; integrals of the total season, base level, base+total,

# green season, rising rate, falling rate; rates of average rising, average falling, maximum

# rising, maximum falling.

# Type: Character list. Valid values: {DEM,DLM,DPS,DSS,DMS,DES,DEV,DAV,DLV,LTS,

#   LGS,LGV,VEM,VLM,VPS,VSS,VMS,VES,VEV,VAV,VLV,VBL,VGA,VSA,VPA,VGM,VGV,DPY,DPV,

#   IST,IBL,IBT,IGS,IRR,IFR,RAR,RAF,RMR,RMF}

POL = VSS VPS VES VSA RMR IGS

# Standardize the POL time series with pixel mean and/or standard deviation?

# Type: Logical. Valid values: {NONE,NORMALIZE,CENTER}

STANDARDIZE_POL = NONE

# Output the polar-transformed time series? These are layer stack of cartesian X-

# and Y-coordinates for each interpolated date. This results in two files, product

# IDs are PCX and PCY.

# Type: Logical. Valid values: {TRUE,FALSE}

OUTPUT_PCT = FALSE

# Output the Polarmetrics? These are layer stacks per polarmetric with as many

# bands as years.

# Type: Logical. Valid values: {TRUE,FALSE}

OUTPUT_POL = FALSE

# Compute and output a linear trend analysis on the requested Polarmetric time

# series? Note that the OUTPUT_POL parameters don't need to be TRUE to do this.

# See also the TREND PARAMETERS block below.

# Type: Logical. Valid values: {TRUE,FALSE}

OUTPUT_TRO = FALSE

# Compute and output an extended Change, Aftereffect, Trend (CAT) analysis on

# the requested Polarmetric time series?

# Note that the OUTPUT_POL parameters don't need to be TRUE to do this.

# See also the TREND PARAMETERS block below.

# Type: Logical. Valid values: {TRUE,FALSE}

OUTPUT_CAO = FALSE

# TREND PARAMETERS

# ------------------------------------------------------------------------

# This parameter specifies the tail-type used for significance testing of

# the slope in the trend analysis. A left-, two-, or right-tailed t-test

# is performed.

# Type: Character. Valid values: {LEFT,TWO,RIGHT}

TREND_TAIL = TWO

# Confidence level for significance testing of the slope in the trend analysis

# Type: Float. Valid range: [0,1]

TREND_CONF = 0.95

++PARAM_TSA_END++

- Matt

[david frantz]

Hi Matt,

can you please test if this here is causing the issue:

FOLD_TYPE = MIN Q25 Q50 Q75 MAX AVG STD RNG

FOLD_TYPE currently only supports one metric, but I assumed that the first one would simply be taken.

If this does not do the trick, can you try without the SPLITS options?

Cheers,

David

[Matthew Clark]

Thanks, David. 

I tried setting FOLD_TYPE = AVG and it failed. However, I then turned off all folding parameters and it's working (including SPLITS) on a hefty Windows server with Docker. The folding wasn't necessary as I'm more interested in the SPLITS metrics so I'm good to go. Thank you!

- Matt

[david frantz]

Thanks for digging deeper Matt.

If it isn't too much trouble, could you please provide me with a minimal dataset that is causing this crash? 

Since you are using Docker, you are using the latest version, right?

Another tip: If you want to look into phenometrics, I suggest trying out the polar implementation. I consider this implementation more robust.

Cheers,

David