tig is built to be used within Cumulus ecosystem. It is depending on cumulus CMA (Cumulus Documentation). Please refer to the Usage section for inputs and outputs. TIG itself is a lambda function which runs on top of CMA as its lambda layer.
Install poetry following the directions here: https://python-poetry.org/docs/#installation
- Be sure to install poetry in an isolated environment from the rest of your system.
- Be sure to use with a python version less than 3.12
- Jenkins pipeline template is applied to this project.
- development is based on poetry python environment.
- poetry run pytest // to run unit test
- poetry install // to install all dependencies defined in toml file
- poetry shell //to enter the poetry shell
- poetry build // to build the wheel
Unit Test can be run using the command
poetry run pytestUse cli helper script to generate thumbnails for each collection (NOTE: This automatically generates the config file and runs tig)
Go to dir `podaac/tig` and look at generate_thumbnails.sh script. Follow directions at top to setup.
Use cli to create a tig configuration for collections
generate_hitide_config --granule <granule_file> -dataset-id <collection short name> --include-image-variables <csv file image variables> --longitude <lon variable> --latitude <lat variable> --time <time variable> --footprint_strategy <footprint strategy>
granule: a sample granule file to generate the configuration for datset-id: collection short name include-image-variables: csv file of with image variable names and min max setting for each variable longitude: longitude variable include the group if they're in group defaults to longitude latitude: latitude variable include the group if they're in a group defaults to latitude time: time variable include the group if they're in a group defaults to time footprint_strategy: strategy to generate footprint will default to None options should be ["periodic", "linestring", "polar", "swot_linestring", "polarsides", "smap"]
Use cli to test thumbnail image generation for a granule with configuration file and palettes
tig --input_file <granule> --output_dir <output_dir> --config_file <config_file> --palette_dir <palette_dir>
Use cli to create a tig configuration for collections
generate_hitide_config --granule <granule_file> -dataset-id <collection short name> --include-image-variables <csv file image variables> --longitude <lon variable> --latitude <lat variable> --time <time variable> --footprint_strategy <footprint strategy>
granule: a sample granule file to generate the configuration for datset-id: collection short name include-image-variables: csv file of with image variable names and min max setting for each variable longitude: longitude variable include the group if they're in group defaults to longitude latitude: latitude variable include the group if they're in a group defaults to latitude time: time variable include the group if they're in a group defaults to time footprint_strategy: strategy to generate footprint will default to None options should be ["periodic", "linestring", "polar", "swot_linestring", "polarsides", "smap"]
** IN DEVELOPMENT **
Currently there is a regression test in the regression_test folder to run please use this command
Note palettes folder needs to be downloaded and included in the regression_test folder Were not clearing out any data after test future improvement to make it an option to clear or retain data as granules take awhile to download
pytest regression.py
variable: name of variable min: min value for variable max: max value for variable palette (optional): the palette to be used for the variable fill_missing (optional): if the generated images have missing pixel in images most likely resolution is to big, either lower resolution or we can fill in the pixels with surrounding pixel ppd (optional): resolution of the variable, must be an integer
Project using tig can include/use the tig as following:
module "tig_module" {
source = "https://cae-artifactory.jpl.nasa.gov/artifactory/general/gov/nasa/podaac/cumulus/tig-terraform/tig-terraform-0.3.0.zip"
// Lambda variables
prefix = var.prefix
image = var.tig_image
role = module.cumulus.lambda_processing_role_arn
cmr_environment = var.cmr_environment
subnet_ids = var.subnet_ids
security_group_ids = [aws_security_group.no_ingress_all_egress.id]
task_logs_retention_in_days = var.task_logs_retention_in_days
config_url = "https://hitide.podaac.earthdatacloud.nasa.gov/dataset-configs"
palette_url = "https://hitide.podaac.earthdatacloud.nasa.gov/palettes"
memory_size = var.tig_memory_size
}
and the module input variables explained as below.
| field name | type | default | values | description |
|---|---|---|---|---|
| prefix | string | (required) | A prefix string of lambda function. Ex. prefix = "sample" , created lambda : sample-tig | |
| region | string | (required) | AWS region where tig lambda is running upon. Ex. us-west-2 | |
| cmr_environment | string | (required) | dev, sit, ops | |
| config_bucket | string | (required) | bucket where dataset config resides | |
| config_dir | string | (required) | directory where dataset config file resides. dataset-config file follows the collection_shortname.cfg pattern. Ex. MODIS_A-JPL-L2P-v2019.0.cfg | |
| tig_output_bucket | string | (required) | bucket where tig file is created and written | |
| tig_output_dir | string | (required) | output directory of created tig(fp) file. file will be created as s3://tig_output_bucket/tig_output_dir/collection_short_name/granule_id.png. ex. s3://my-cumulus-internaldataset-tig/ MODIS_A-JPL-L2P-v2019.0/20200101000000-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0.png | |
| lambda_role | string | (required) | aws user role to run tig lambda | |
| layers | list(string) | (required) | list of layers' arn where tig runs upon. | |
| security_group_ids | list(string) | (required) | security group ids | |
| subnet_ids | list(string) | (required) | subnet ids where tig runs within | |
| config_url | string | the url of where to retrieve configurations | ||
| palette_url | string | the url of where to retrieve palettes |
ECS input variables optional
| field name | type | default | values | description |
|---|---|---|---|---|
| tig_ecs | bool | false | boolean to deploy ecs task | |
| cluster_arn | string | cumulus cluster arn | ||
| desired_count | number | 1 | number of ecs tig task to run | |
| log_destination_arn | string | A shared AWS:Log:Destination that receives logs in log_groups | ||
| ecs_cpu | number | 700 | cpu unit to allocate to a tig task | |
| ecs_memory_reservation | number | 1024 | memory unit to allocate to tig task |
Fargate input variables optional
| field name | type | default | values | description |
|---|---|---|---|---|
| tig_fargate | bool | false | boolean to deploy fargate task | |
| fargate_memory | number | 2048 | amount of memory to allocate for a single fargate task | |
| fargate_cpu | number | 1024 | amount of cpu to allocate for a single fargate task | |
| fargate_desired_count | number | 1 | desired count of how many fargate task | |
| fargate_min_capacity | number | 1 | minimum number of fargate task when scaling | |
| fargate_max_capacity | number | 1 | maximum number of fargate task when scaling | |
| scale_dimensions | map(string) | null | cloudwatch dimensions to scale on | |
| scale_up_cooldown | number | 60 | seconds before able to scaling up again | |
| scale_down_cooldown | number | 120 | seconds before able to scaling down again | |
| comparison_operator_scale_up | string | GreaterThanOrEqualToThreshold | The arithmetic operation to use when comparing the specified Statistic and Threshold | |
| evaluation_periods_scale_up | number | 1 | The number of periods over which data is compared to the specified threshold | |
| metric_name_scale_up | string | CPUUtilization | name of the metric | |
| namespace_scale_up | string | AWS/ECS | namespace for the alarm's associated metric | |
| period_scale_up | number | 60 | period in seconds over which the specified statistic is applied | |
| statistic_scale_up | string | Average | statistic to apply to the metric | |
| threshold_scale_up | number | 50 | threshold for statistic to compare against to trigger step | |
| scale_up_step_adjustment | list | step adjustment to make when scaling up fargate | ||
| comparison_operator_scale_down | string | |||
| evaluation_periods_scale_down | number | 1 | The number of periods over which data is compared to the specified threshold | |
| metric_name_scale_down | string | CPUUtilization | name of the metric | |
| namespace_scale_down | string | AWS/ECS | namespace for the alarm's associated metric | |
| period_scale_down | number | 60 | period in seconds over which the specified statistic is applied | |
| statistic_scale_down | string | Average | statistic to apply to the metric | |
| threshold_scale_down | number | 50 | threshold for statistic to compare against to trigger step | |
| scale_down_step_adjustment | list | step adjustment to make when scaling down fargate | ||
| fargate_iam_role | string | iam arn role for fargate |
module output variables
| field name | type | default | values | description |
|---|---|---|---|---|
| tig_function_name | string | (required) | The name of deployed tig lambda function | |
| tig_task_arn | string | (required) | tig lambda aws arn |
Cumulus message with granules payload. Example below
{
"granules": [
{
"files": [
{
"filename": "s3://bucket/file/with/checksum.dat",
"checksumType": "md5",
"checksum": "asdfdsa"
},
{
"filename": "s3://bucket/file/without/checksum.dat",
}
]
}
]
}- A tig file will be created under configured tig_output_bucket and tig-output-dir. filename as granuleId.png. Ex. s3://my-cumulus-internaldataset-tig/ MODIS_A-JPL-L2P-v2019.0/20200101000000-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0.png
- A file object will be appended to the files[] of processed granule. Example:
{
"granules": [
{
"granuleId": "20200101000000-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0",
"dataType": "MODIS_A-JPL-L2P-v2019.0",
"sync_granule_duration": 2603,
"files": [
{
"bucket": "my-protected",
"path": "MODIS_A-JPL-L2P-v2019.0/2020/001",
"filename": "s3://my-protected/MODIS_A-JPL-L2P-v2019.0/20200101000000-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0.nc",
"size": 18232098,
"name": "20200101000000-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0.nc",
"checksumType": "md5",
"checksum": "aa5204f125ae83847b3b80fa2e571b00",
"type": "data",
"url_path": "{cmrMetadata.CollectionReference.ShortName}",
"filepath": "MODIS_A-JPL-L2P-v2019.0/20200101000000-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0.nc",
"duplicate_found": true
},
{
"bucket": "my-public",
"path": "MODIS_A-JPL-L2P-v2019.0/2020/001",
"filename": "s3://my-public/MODIS_A-JPL-L2P-v2019.0/20200101000000-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0.nc.md5",
"size": 98,
"name": "20200101000000-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0.nc.md5",
"type": "metadata",
"url_path": "{cmrMetadata.CollectionReference.ShortName}",
"filepath": "MODIS_A-JPL-L2P-v2019.0/20200101000000-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0.nc.md5",
"duplicate_found": true
},
{
"bucket": "my-public",
"filename": "s3://my-public/MODIS_A-JPL-L2P-v2019.0/20200101000000-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0.cmr.json",
"size": 1617,
"name": "20200101000000-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0.cmr.json",
"type": "metadata",
"url_path": "{cmrMetadata.CollectionReference.ShortName}",
"filepath": "MODIS_A-JPL-L2P-v2019.0/20200101000000-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0.cmr.json",
"etag": "\"3e5b9259c5ee7eae5fe71467f151498b\""
},
{
"bucket": "my-internal",
"filename": "s3://my-internal/dataset-tig/MODIS_A-JPL-L2P-v2019.0/20200101000000-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0.png",
"filepath": "dataset-tig/MODIS_A-JPL-L2P-v2019.0/20200101000000-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0.png",
"size": 452,
"name": "20200101000000-JPL-L2P_GHRSST-SSTskin-MODIS_A-D-v02.0-fv01.0.png",
"type": "metadata"
}
],
}