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4.3. Number Of Horizontal Gridpoints
Is Required: TRUE Type: INTEGER Cardinality: 1.1
Total number of horizontal (XY) points (or degrees of freedom) on computational grid. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.key_properties.resolution.number_of_horizontal_gridpoints')
# PROPERTY VALUE:
# Set as follows: DOC.set_value(value)
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
5. Key Properties --> Tuning Applied
Tuning applied to sea ice model component
5.1. Description
Is Required: TRUE Type: STRING Cardinality: 1.1
General overview description of tuning: explain and motivate the main targets and metrics retained. Document the relative weight given to climate performance metrics versus process oriented metrics, and on the possible conflicts with parameterization level tuning. In particular describe any struggle with a parameter value that required pushing it to its limits to solve a particular model deficiency. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.key_properties.tuning_applied.description')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
5.2. Target
Is Required: TRUE Type: STRING Cardinality: 1.1
What was the aim of tuning, e.g. correct sea ice minima, correct seasonal cycle. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.key_properties.tuning_applied.target')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
5.3. Simulations
Is Required: TRUE Type: STRING Cardinality: 1.1
*Which simulations had tuning applied, e.g. all, not historical, only pi-control? * | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.key_properties.tuning_applied.simulations')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
5.4. Metrics Used
Is Required: TRUE Type: STRING Cardinality: 1.1
List any observed metrics used in tuning model/parameters | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.key_properties.tuning_applied.metrics_used')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
5.5. Variables
Is Required: FALSE Type: STRING Cardinality: 0.1
Which variables were changed during the tuning process? | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.key_properties.tuning_applied.variables')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
6. Key Properties --> Key Parameter Values
Values of key parameters
6.1. Typical Parameters
Is Required: FALSE Type: ENUM Cardinality: 0.N
What values were specificed for the following parameters if used? | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.key_properties.key_parameter_values.typical_parameters')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "Ice strength (P*) in units of N m{-2}"
# "Snow conductivity (ks) in units of W m{-1} K{-1} "
# "Minimum thickness of ice created in leads (h0) in units of m"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
6.2. Additional Parameters
Is Required: FALSE Type: STRING Cardinality: 0.N
If you have any additional paramterised values that you have used (e.g. minimum open water fraction or bare ice albedo), please provide them here as a comma separated list | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.key_properties.key_parameter_values.additional_parameters')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
7. Key Properties --> Assumptions
Assumptions made in the sea ice model
7.1. Description
Is Required: TRUE Type: STRING Cardinality: 1.N
General overview description of any key assumptions made in this model. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.key_properties.assumptions.description')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
7.2. On Diagnostic Variables
Is Required: TRUE Type: STRING Cardinality: 1.N
Note any assumptions that specifically affect the CMIP6 diagnostic sea ice variables. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.key_properties.assumptions.on_diagnostic_variables')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
7.3. Missing Processes
Is Required: TRUE Type: STRING Cardinality: 1.N
List any key processes missing in this model configuration? Provide full details where this affects the CMIP6 diagnostic sea ice variables? | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.key_properties.assumptions.missing_processes')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
8. Key Properties --> Conservation
Conservation in the sea ice component
8.1. Description
Is Required: TRUE Type: STRING Cardinality: 1.1
Provide a general description of conservation methodology. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.key_properties.conservation.description')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
8.2. Properties
Is Required: TRUE Type: ENUM Cardinality: 1.N
Properties conserved in sea ice by the numerical schemes. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.key_properties.conservation.properties')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "Energy"
# "Mass"
# "Salt"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
8.3. Budget
Is Required: TRUE Type: STRING Cardinality: 1.1
For each conserved property, specify the output variables which close the related budgets. as a comma separated list. For example: Conserved property, variable1, variable2, variable3 | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.key_properties.conservation.budget')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
8.4. Was Flux Correction Used
Is Required: TRUE Type: BOOLEAN Cardinality: 1.1
Does conservation involved flux correction? | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.key_properties.conservation.was_flux_correction_used')
# PROPERTY VALUE:
# Set as follows: DOC.set_value(value)
# Valid Choices:
# True
# False
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
8.5. Corrected Conserved Prognostic Variables
Is Required: TRUE Type: STRING Cardinality: 1.1
List any variables which are conserved by more than the numerical scheme alone. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.key_properties.conservation.corrected_conserved_prognostic_variables')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
9. Grid --> Discretisation --> Horizontal
Sea ice discretisation in the horizontal
9.1. Grid
Is Required: TRUE Type: ENUM Cardinality: 1.1
Grid on which sea ice is horizontal discretised? | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.grid')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "Ocean grid"
# "Atmosphere Grid"
# "Own Grid"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
9.2. Grid Type
Is Required: TRUE Type: ENUM Cardinality: 1.1
What is the type of sea ice grid? | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.grid_type')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "Structured grid"
# "Unstructured grid"
# "Adaptive grid"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
9.3. Scheme
Is Required: TRUE Type: ENUM Cardinality: 1.1
What is the advection scheme? | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.scheme')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "Finite differences"
# "Finite elements"
# "Finite volumes"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
9.4. Thermodynamics Time Step
Is Required: TRUE Type: INTEGER Cardinality: 1.1
What is the time step in the sea ice model thermodynamic component in seconds. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.thermodynamics_time_step')
# PROPERTY VALUE:
# Set as follows: DOC.set_value(value)
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
9.5. Dynamics Time Step
Is Required: TRUE Type: INTEGER Cardinality: 1.1
What is the time step in the sea ice model dynamic component in seconds. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.dynamics_time_step')
# PROPERTY VALUE:
# Set as follows: DOC.set_value(value)
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
9.6. Additional Details
Is Required: FALSE Type: STRING Cardinality: 0.1
Specify any additional horizontal discretisation details. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.additional_details')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
10. Grid --> Discretisation --> Vertical
Sea ice vertical properties
10.1. Layering
Is Required: TRUE Type: ENUM Cardinality: 1.N
What type of sea ice vertical layers are implemented for purposes of thermodynamic calculations? | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.grid.discretisation.vertical.layering')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "Zero-layer"
# "Two-layers"
# "Multi-layers"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
10.2. Number Of Layers
Is Required: TRUE Type: INTEGER Cardinality: 1.1
If using multi-layers specify how many. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.grid.discretisation.vertical.number_of_layers')
# PROPERTY VALUE:
# Set as follows: DOC.set_value(value)
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
10.3. Additional Details
Is Required: FALSE Type: STRING Cardinality: 0.1
Specify any additional vertical grid details. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.grid.discretisation.vertical.additional_details')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
11. Grid --> Seaice Categories
What method is used to represent sea ice categories ?
11.1. Has Mulitple Categories
Is Required: TRUE Type: BOOLEAN Cardinality: 1.1
Set to true if the sea ice model has multiple sea ice categories. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.grid.seaice_categories.has_mulitple_categories')
# PROPERTY VALUE:
# Set as follows: DOC.set_value(value)
# Valid Choices:
# True
# False
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
11.2. Number Of Categories
Is Required: TRUE Type: INTEGER Cardinality: 1.1
If using sea ice categories specify how many. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.grid.seaice_categories.number_of_categories')
# PROPERTY VALUE:
# Set as follows: DOC.set_value(value)
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
11.3. Category Limits
Is Required: TRUE Type: STRING Cardinality: 1.1
If using sea ice categories specify each of the category limits. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.grid.seaice_categories.category_limits')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
11.4. Ice Thickness Distribution Scheme
Is Required: TRUE Type: STRING Cardinality: 1.1
Describe the sea ice thickness distribution scheme | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.grid.seaice_categories.ice_thickness_distribution_scheme')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
11.5. Other
Is Required: FALSE Type: STRING Cardinality: 0.1
If the sea ice model does not use sea ice categories specify any additional details. For example models that paramterise the ice thickness distribution ITD (i.e there is no explicit ITD) but there is assumed distribution and fluxes are computed accordingly. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.grid.seaice_categories.other')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
12. Grid --> Snow On Seaice
Snow on sea ice details
12.1. Has Snow On Ice
Is Required: TRUE Type: BOOLEAN Cardinality: 1.1
Is snow on ice represented in this model? | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.grid.snow_on_seaice.has_snow_on_ice')
# PROPERTY VALUE:
# Set as follows: DOC.set_value(value)
# Valid Choices:
# True
# False
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
12.2. Number Of Snow Levels
Is Required: TRUE Type: INTEGER Cardinality: 1.1
Number of vertical levels of snow on ice? | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.grid.snow_on_seaice.number_of_snow_levels')
# PROPERTY VALUE:
# Set as follows: DOC.set_value(value)
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
12.3. Snow Fraction
Is Required: TRUE Type: STRING Cardinality: 1.1
Describe how the snow fraction on sea ice is determined | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.grid.snow_on_seaice.snow_fraction')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
12.4. Additional Details
Is Required: FALSE Type: STRING Cardinality: 0.1
Specify any additional details related to snow on ice. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.grid.snow_on_seaice.additional_details')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
13. Dynamics
Sea Ice Dynamics
13.1. Horizontal Transport
Is Required: TRUE Type: ENUM Cardinality: 1.1
What is the method of horizontal advection of sea ice? | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.dynamics.horizontal_transport')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "Incremental Re-mapping"
# "Prather"
# "Eulerian"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
13.2. Transport In Thickness Space
Is Required: TRUE Type: ENUM Cardinality: 1.1
What is the method of sea ice transport in thickness space (i.e. in thickness categories)? | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.dynamics.transport_in_thickness_space')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "Incremental Re-mapping"
# "Prather"
# "Eulerian"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
13.3. Ice Strength Formulation
Is Required: TRUE Type: ENUM Cardinality: 1.1
Which method of sea ice strength formulation is used? | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.dynamics.ice_strength_formulation')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "Hibler 1979"
# "Rothrock 1975"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
13.4. Redistribution
Is Required: TRUE Type: ENUM Cardinality: 1.N
Which processes can redistribute sea ice (including thickness)? | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.dynamics.redistribution')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "Rafting"
# "Ridging"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
13.5. Rheology
Is Required: TRUE Type: ENUM Cardinality: 1.1
Rheology, what is the ice deformation formulation? | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.dynamics.rheology')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "Free-drift"
# "Mohr-Coloumb"
# "Visco-plastic"
# "Elastic-visco-plastic"
# "Elastic-anisotropic-plastic"
# "Granular"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
14. Thermodynamics --> Energy
Processes related to energy in sea ice thermodynamics
14.1. Enthalpy Formulation
Is Required: TRUE Type: ENUM Cardinality: 1.1
What is the energy formulation? | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.thermodynamics.energy.enthalpy_formulation')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "Pure ice latent heat (Semtner 0-layer)"
# "Pure ice latent and sensible heat"
# "Pure ice latent and sensible heat + brine heat reservoir (Semtner 3-layer)"
# "Pure ice latent and sensible heat + explicit brine inclusions (Bitz and Lipscomb)"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
14.2. Thermal Conductivity
Is Required: TRUE Type: ENUM Cardinality: 1.1
What type of thermal conductivity is used? | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.thermodynamics.energy.thermal_conductivity')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "Pure ice"
# "Saline ice"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
14.3. Heat Diffusion
Is Required: TRUE Type: ENUM Cardinality: 1.1
What is the method of heat diffusion? | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.thermodynamics.energy.heat_diffusion')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "Conduction fluxes"
# "Conduction and radiation heat fluxes"
# "Conduction, radiation and latent heat transport"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
14.4. Basal Heat Flux
Is Required: TRUE Type: ENUM Cardinality: 1.1
Method by which basal ocean heat flux is handled? | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.thermodynamics.energy.basal_heat_flux')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "Heat Reservoir"
# "Thermal Fixed Salinity"
# "Thermal Varying Salinity"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
14.5. Fixed Salinity Value
Is Required: FALSE Type: FLOAT Cardinality: 0.1
If you have selected {Thermal properties depend on S-T (with fixed salinity)}, supply fixed salinity value for each sea ice layer. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.thermodynamics.energy.fixed_salinity_value')
# PROPERTY VALUE:
# Set as follows: DOC.set_value(value)
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
14.6. Heat Content Of Precipitation
Is Required: TRUE Type: STRING Cardinality: 1.1
Describe the method by which the heat content of precipitation is handled. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.thermodynamics.energy.heat_content_of_precipitation')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
14.7. Precipitation Effects On Salinity
Is Required: FALSE Type: STRING Cardinality: 0.1
If precipitation (freshwater) that falls on sea ice affects the ocean surface salinity please provide further details. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.thermodynamics.energy.precipitation_effects_on_salinity')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
15. Thermodynamics --> Mass
Processes related to mass in sea ice thermodynamics
15.1. New Ice Formation
Is Required: TRUE Type: STRING Cardinality: 1.1
Describe the method by which new sea ice is formed in open water. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.thermodynamics.mass.new_ice_formation')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
15.2. Ice Vertical Growth And Melt
Is Required: TRUE Type: STRING Cardinality: 1.1
Describe the method that governs the vertical growth and melt of sea ice. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.thermodynamics.mass.ice_vertical_growth_and_melt')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
15.3. Ice Lateral Melting
Is Required: TRUE Type: ENUM Cardinality: 1.1
What is the method of sea ice lateral melting? | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.thermodynamics.mass.ice_lateral_melting')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "Floe-size dependent (Bitz et al 2001)"
# "Virtual thin ice melting (for single-category)"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
15.4. Ice Surface Sublimation
Is Required: TRUE Type: STRING Cardinality: 1.1
Describe the method that governs sea ice surface sublimation. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.thermodynamics.mass.ice_surface_sublimation')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
15.5. Frazil Ice
Is Required: TRUE Type: STRING Cardinality: 1.1
Describe the method of frazil ice formation. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.thermodynamics.mass.frazil_ice')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
16. Thermodynamics --> Salt
Processes related to salt in sea ice thermodynamics.
16.1. Has Multiple Sea Ice Salinities
Is Required: TRUE Type: BOOLEAN Cardinality: 1.1
Does the sea ice model use two different salinities: one for thermodynamic calculations; and one for the salt budget? | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.thermodynamics.salt.has_multiple_sea_ice_salinities')
# PROPERTY VALUE:
# Set as follows: DOC.set_value(value)
# Valid Choices:
# True
# False
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
16.2. Sea Ice Salinity Thermal Impacts
Is Required: TRUE Type: BOOLEAN Cardinality: 1.1
Does sea ice salinity impact the thermal properties of sea ice? | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.thermodynamics.salt.sea_ice_salinity_thermal_impacts')
# PROPERTY VALUE:
# Set as follows: DOC.set_value(value)
# Valid Choices:
# True
# False
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
17. Thermodynamics --> Salt --> Mass Transport
Mass transport of salt
17.1. Salinity Type
Is Required: TRUE Type: ENUM Cardinality: 1.1
How is salinity determined in the mass transport of salt calculation? | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.thermodynamics.salt.mass_transport.salinity_type')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "Constant"
# "Prescribed salinity profile"
# "Prognostic salinity profile"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
17.2. Constant Salinity Value
Is Required: FALSE Type: FLOAT Cardinality: 0.1
If using a constant salinity value specify this value in PSU? | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.thermodynamics.salt.mass_transport.constant_salinity_value')
# PROPERTY VALUE:
# Set as follows: DOC.set_value(value)
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
17.3. Additional Details
Is Required: FALSE Type: STRING Cardinality: 0.1
Describe the salinity profile used. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.thermodynamics.salt.mass_transport.additional_details')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
18. Thermodynamics --> Salt --> Thermodynamics
Salt thermodynamics
18.1. Salinity Type
Is Required: TRUE Type: ENUM Cardinality: 1.1
How is salinity determined in the thermodynamic calculation? | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.thermodynamics.salt.thermodynamics.salinity_type')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "Constant"
# "Prescribed salinity profile"
# "Prognostic salinity profile"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
18.2. Constant Salinity Value
Is Required: FALSE Type: FLOAT Cardinality: 0.1
If using a constant salinity value specify this value in PSU? | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.thermodynamics.salt.thermodynamics.constant_salinity_value')
# PROPERTY VALUE:
# Set as follows: DOC.set_value(value)
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
18.3. Additional Details
Is Required: FALSE Type: STRING Cardinality: 0.1
Describe the salinity profile used. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.thermodynamics.salt.thermodynamics.additional_details')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
19. Thermodynamics --> Ice Thickness Distribution
Ice thickness distribution details.
19.1. Representation
Is Required: TRUE Type: ENUM Cardinality: 1.1
How is the sea ice thickness distribution represented? | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.thermodynamics.ice_thickness_distribution.representation')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "Explicit"
# "Virtual (enhancement of thermal conductivity, thin ice melting)"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
20. Thermodynamics --> Ice Floe Size Distribution
Ice floe-size distribution details.
20.1. Representation
Is Required: TRUE Type: ENUM Cardinality: 1.1
How is the sea ice floe-size represented? | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.thermodynamics.ice_floe_size_distribution.representation')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "Explicit"
# "Parameterised"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
20.2. Additional Details
Is Required: FALSE Type: STRING Cardinality: 0.1
Please provide further details on any parameterisation of floe-size. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.thermodynamics.ice_floe_size_distribution.additional_details')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
21. Thermodynamics --> Melt Ponds
Characteristics of melt ponds.
21.1. Are Included
Is Required: TRUE Type: BOOLEAN Cardinality: 1.1
Are melt ponds included in the sea ice model? | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.thermodynamics.melt_ponds.are_included')
# PROPERTY VALUE:
# Set as follows: DOC.set_value(value)
# Valid Choices:
# True
# False
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
21.2. Formulation
Is Required: TRUE Type: ENUM Cardinality: 1.1
What method of melt pond formulation is used? | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.thermodynamics.melt_ponds.formulation')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "Flocco and Feltham (2010)"
# "Level-ice melt ponds"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
21.3. Impacts
Is Required: TRUE Type: ENUM Cardinality: 1.N
What do melt ponds have an impact on? | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.thermodynamics.melt_ponds.impacts')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "Albedo"
# "Freshwater"
# "Heat"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
22. Thermodynamics --> Snow Processes
Thermodynamic processes in snow on sea ice
22.1. Has Snow Aging
Is Required: TRUE Type: BOOLEAN Cardinality: 1.N
Set to True if the sea ice model has a snow aging scheme. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.has_snow_aging')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value(value)
# Valid Choices:
# True
# False
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
22.2. Snow Aging Scheme
Is Required: FALSE Type: STRING Cardinality: 0.1
Describe the snow aging scheme. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.snow_aging_scheme')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
22.3. Has Snow Ice Formation
Is Required: TRUE Type: BOOLEAN Cardinality: 1.N
Set to True if the sea ice model has snow ice formation. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.has_snow_ice_formation')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value(value)
# Valid Choices:
# True
# False
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
22.4. Snow Ice Formation Scheme
Is Required: FALSE Type: STRING Cardinality: 0.1
Describe the snow ice formation scheme. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.snow_ice_formation_scheme')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
22.5. Redistribution
Is Required: TRUE Type: STRING Cardinality: 1.1
What is the impact of ridging on snow cover? | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.redistribution')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
22.6. Heat Diffusion
Is Required: TRUE Type: ENUM Cardinality: 1.1
What is the heat diffusion through snow methodology in sea ice thermodynamics? | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.heat_diffusion')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "Single-layered heat diffusion"
# "Multi-layered heat diffusion"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
23. Radiative Processes
Sea Ice Radiative Processes
23.1. Surface Albedo
Is Required: TRUE Type: ENUM Cardinality: 1.1
Method used to handle surface albedo. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.radiative_processes.surface_albedo')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "Delta-Eddington"
# "Parameterized"
# "Multi-band albedo"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
23.2. Ice Radiation Transmission
Is Required: TRUE Type: ENUM Cardinality: 1.N
Method by which solar radiation through sea ice is handled. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.seaice.radiative_processes.ice_radiation_transmission')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "Delta-Eddington"
# "Exponential attenuation"
# "Ice radiation transmission per category"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
First, find the relative maxima of the spectrum. | from scipy.signal import argrelextrema
maxes = argrelextrema(spectrum, np.greater, order=2)
print maxes[0]
for x in maxes[0]:
plt.axvline(x)
plt.plot(spectrum, color='r') | peak-detection.ipynb | greenca/diy-spectrometer | mit |
This clearly gives us way too many local maxima.
So, next, we try the find_peaks_cwt function from scipy.signal, which uses wavelets. | from scipy.signal import find_peaks_cwt
cwt_peaks = find_peaks_cwt(spectrum, np.arange(10,15))
print cwt_peaks
for x in cwt_peaks:
plt.axvline(x)
plt.plot(spectrum, color='r') | peak-detection.ipynb | greenca/diy-spectrometer | mit |
This is better, in that we lost all the spurious values, but it doesn't match that well, and we don't get the
double-peak (near 150) anymore.
https://gist.github.com/endolith/250860 has a python translation of a matlab peak-detection script. Downloaded as peakdetect.py | import peakdetect
peaks, valleys = peakdetect.peakdet(spectrum, 3)
print peaks
for index, val in peaks:
plt.axvline(index)
plt.plot(spectrum, color='r') | peak-detection.ipynb | greenca/diy-spectrometer | mit |
This is a pretty decent result, which we should be able to use for matching with known spectra.
Calibration
The sample spectrum above is for a fluorescent lamp. This is a known spectrum, that we can use for calibration. Here is a labelled plot of the spectrum:
"Fluorescent lighting spectrum peaks labelled". Licensed under CC BY-SA 3.0 via Wikimedia Commons - http://commons.wikimedia.org/wiki/File:Fluorescent_lighting_spectrum_peaks_labelled.gif#/media/File:Fluorescent_lighting_spectrum_peaks_labelled.gif
Visually, this appears to match pretty well with our spectrum. We calibrate the x-axis by matching two points with the known spectrum. Let's use the strongest two peaks: 5, at 546.5 nm (from Mercury) and 12, at 611.6 nm (from Europium). In our spectrum, peak 4 has a higher intensity than peak 12, but we'll use peak 12 anyway, because peaks 4 and 5 are too close together to get an accurate calibration. | intensities = sorted([intensity for index, intensity in peaks])
peak5 = [index for index, intensity in peaks if intensity == intensities[-1]][0]
peak12 = [index for index, intensity in peaks if intensity == intensities[-3]][0]
print peak5, peak12 | peak-detection.ipynb | greenca/diy-spectrometer | mit |
Linear scale between index numbers and wavelengths:
<br>wavelength = m*index + b | peak5_wl = 546.5
peak12_wl = 611.6
m = (peak12_wl - peak5_wl)/(peak12 - peak5)
b = peak5_wl - m*peak5
print m, b
wavelengths = [m*index + b for index in range(len(spectrum))]
plt.plot(wavelengths, spectrum)
plt.xlabel('Wavelength (nm)')
plt.ylabel('Intensity') | peak-detection.ipynb | greenca/diy-spectrometer | mit |
2. Download Associations, if necessary | # Get ftp://ftp.ncbi.nlm.nih.gov/gene/DATA/gene2go.gz
from goatools.base import download_ncbi_associations
gene2go = download_ncbi_associations() | notebooks/cell_cycle.ipynb | tanghaibao/goatools | bsd-2-clause |
3. Read associations
Normally, when reading associations, GeneID2GOs are returned. We get the reverse, GO2GeneIDs, by adding the key-word arg, "go2geneids=True" to the call to read_ncbi_gene2go. | from goatools.anno.genetogo_reader import Gene2GoReader
objanno = Gene2GoReader("gene2go", taxids=[9606])
go2geneids_human = objanno.get_id2gos(namespace='BP', go2geneids=True)
print("{N:} GO terms associated with human NCBI Entrez GeneIDs".format(N=len(go2geneids_human))) | notebooks/cell_cycle.ipynb | tanghaibao/goatools | bsd-2-clause |
4. Initialize Gene-Search Helper | from goatools.go_search import GoSearch
srchhelp = GoSearch("go-basic.obo", go2items=go2geneids_human) | notebooks/cell_cycle.ipynb | tanghaibao/goatools | bsd-2-clause |
5. Find human all genes related to "cell cycle"
5a. Prepare "cell cycle" text searches
We will need to search for both cell cycle and cell cycle-independent. Those GOs that contain the text cell cycle-independent are specifically not related to cell cycle and must be removed from our list of cell cycle GO terms. | import re
# Compile search pattern for 'cell cycle'
cell_cycle_all = re.compile(r'cell cycle', flags=re.IGNORECASE)
cell_cycle_not = re.compile(r'cell cycle.independent', flags=re.IGNORECASE) | notebooks/cell_cycle.ipynb | tanghaibao/goatools | bsd-2-clause |
5b. Find NCBI Entrez GeneIDs related to "cell cycle" | # Find ALL GOs and GeneIDs associated with 'cell cycle'.
# Details of search are written to a log file
fout_allgos = "cell_cycle_gos_human.log"
with open(fout_allgos, "w") as log:
# Search for 'cell cycle' in GO terms
gos_cc_all = srchhelp.get_matching_gos(cell_cycle_all, prt=log)
# Find any GOs matching 'cell cycle-independent' (e.g., "lysosome")
gos_no_cc = srchhelp.get_matching_gos(cell_cycle_not, gos=gos_cc_all, prt=log)
# Remove GO terms that are not "cell cycle" GOs
gos = gos_cc_all.difference(gos_no_cc)
# Add children GOs of cell cycle GOs
gos_all = srchhelp.add_children_gos(gos)
# Get Entrez GeneIDs for cell cycle GOs
geneids = srchhelp.get_items(gos_all)
print("{N} human NCBI Entrez GeneIDs related to 'cell cycle' found.".format(N=len(geneids)))
| notebooks/cell_cycle.ipynb | tanghaibao/goatools | bsd-2-clause |
6. Print the "cell cycle" protein-coding gene Symbols
In this example, the background is all human protein-codinge genes.
Follow the instructions in the background_genes_ncbi notebook to download a set of background population genes from NCBI. | from genes_ncbi_9606_proteincoding import GENEID2NT
for geneid in geneids: # geneids associated with cell-cycle
nt = GENEID2NT.get(geneid, None)
if nt is not None:
print("{Symbol:<10} {desc}".format(
Symbol = nt.Symbol,
desc = nt.description)) | notebooks/cell_cycle.ipynb | tanghaibao/goatools | bsd-2-clause |
First checking the avg RMSE for Linear Regression | clf = LinearRegression()
scores = cross_val_score(clf, X_, y, cv=5, scoring='neg_mean_squared_error')
for i in range(0,5):
scores[i] = sqrt(-1*scores[i])
print(scores)
avg_rmse = scores.mean()
print("\n\nAvg RMSE is ",scores.mean()) | Baseline 2.ipynb | AkshanshChahal/BTP | mit |
Epsilon-Support Vector Regression (SVR)
RBF Kernel | # 5 Fold CV, to calculate avg RMSE
clf = SVR(C=500000.0, epsilon=0.1, kernel='rbf', gamma=0.0008)
scores = cross_val_score(clf, X_, y.values.ravel(), cv=5, scoring='neg_mean_squared_error')
for i in range(0,5):
scores[i] = sqrt(-1*scores[i])
print(scores)
avg_rmse = scores.mean()
print("\n\nAvg RMSE is ",scores.mean())
# Just the 4 original features (no soil data)
X_old = X[["X1_zscore", "X2_zscore", "X3_zscore", "X4_zscore"]]
# 5 Fold CV, to calculate avg RMSE
clf = SVR(C=1000.0, epsilon=0.1, kernel='rbf', gamma=0.027)
scores = cross_val_score(clf, X_old, y.values.ravel(), cv=5, scoring='neg_mean_squared_error')
for i in range(0,5):
scores[i] = sqrt(-1*scores[i])
print(scores)
avg_rmse = scores.mean()
print("\n\nAvg RMSE is ",scores.mean()) | Baseline 2.ipynb | AkshanshChahal/BTP | mit |
SVR : 927
LR : 1018
SVR (RBF kernel) works better than Linear Regression.
Also, the soil feature, for now, does more harm than good (Phosphorous content)
Lets check the importance of Rain Data | # Just 2 features (no rain data)
X_nr = X[["X1_zscore", "X2_zscore"]]
# 5 Fold CV, to calculate avg RMSE
clf = SVR(C=1000.0, epsilon=0.1, kernel='rbf', gamma=0.027)
scores = cross_val_score(clf, X_nr, y.values.ravel(), cv=5, scoring='neg_mean_squared_error')
for i in range(0,5):
scores[i] = sqrt(-1*scores[i])
print(scores)
avg_rmse = scores.mean()
print("\n\nAvg RMSE is ",scores.mean()) | Baseline 2.ipynb | AkshanshChahal/BTP | mit |
The Rain data does helps us
Lets try for SVR with other kernels ...
Degree 3 Polynomial | # 5 Fold CV, to calculate avg RMSE
clf = SVR(kernel='poly', gamma='auto', degree=3, coef0=2)
scores = cross_val_score(clf, X_old, y.values.ravel(), cv=5, scoring='neg_mean_squared_error')
for i in range(0,5):
scores[i] = sqrt(-1*scores[i])
print(scores)
avg_rmse = scores.mean()
print("\n\nAvg RMSE is ",scores.mean()) | Baseline 2.ipynb | AkshanshChahal/BTP | mit |
Polynomial Kernel also does better than Linear Regression
Degree 4 Polynomial | # 5 Fold CV, to calculate avg RMSE
clf = SVR(kernel='poly', gamma='auto', degree=4, coef0=2)
scores = cross_val_score(clf, X_old, y.values.ravel(), cv=5, scoring='neg_mean_squared_error')
for i in range(0,5):
scores[i] = sqrt(-1*scores[i])
print(scores)
avg_rmse = scores.mean()
print("\n\nAvg RMSE is ",scores.mean()) | Baseline 2.ipynb | AkshanshChahal/BTP | mit |
To get the monthly traffic data on English Wikipedia from January 2008 through September 2017, we need to use 2 API endpoints, the Pagecounts API and the Pageviews API. The Pagecounts API provides monthy desktop and mobile traffic data from January 2008 through July 2016, and the Pageviews API provides monthy desktop, mobile-web, and mobile-app traffic data from July 2015 through September 2017. Once the user finishes the parameter settings for the API request, the traffic data will be returned in JSON format. The codes below will get you all pagecounts for English Wikipedia accessed through desktop from January 2008 through July 2016. | # Collect desktop traffic data from January 2008 through July 2016 using the Pagecounts API
endpoint_pagecounts = 'https://wikimedia.org/api/rest_v1/metrics/legacy/pagecounts/aggregate/{project}/{access}/{granularity}/{start}/{end}'
params_pc_desktop = {
'project' : 'en.wikipedia.org',
'access' : 'desktop-site',
'granularity' : 'monthly',
'start' : '2008010100',
'end' : '2016080100'#use the first day of the following month to ensure a full month of data is collected
}
api_call = requests.get(endpoint_pagecounts.format(**params_pc_desktop))
response_pc_desktop = api_call.json()
with open('pagecounts_desktop-site_200801-201607.json', 'w') as outfile:
json.dump(response_pc_desktop, outfile)
| hcds-a1-data-curation.ipynb | HWNi/data-512-a1 | mit |
The codes below will get you all pagecounts for English Wikipedia accessed through mobile from January 2008 through July 2016. | # Collect mobile traffic data from January 2008 through July 2016 using the Pagecounts API
endpoint_pagecounts = 'https://wikimedia.org/api/rest_v1/metrics/legacy/pagecounts/aggregate/{project}/{access}/{granularity}/{start}/{end}'
params_pc_mobile = {
'project' : 'en.wikipedia.org',
'access' : 'mobile-site',
'granularity' : 'monthly',
'start' : '2008010100',
'end' : '2016080100'
}
api_call = requests.get(endpoint_pagecounts.format(**params_pc_mobile))
response_pc_mobile = api_call.json()
with open('pagecounts_mobile-site_200801-201607.json', 'w') as outfile:
json.dump(response_pc_mobile, outfile)
| hcds-a1-data-curation.ipynb | HWNi/data-512-a1 | mit |
The codes below will get you all pageviews for English Wikipedia accessed through desktop from July 2015 through September 2017. Note that the data doesn't count traffic by web crawlers or spiders. | # Collect desktop traffic data from July 2015 through September 2017 using the Pageviews API
endPoint_pageviews = 'https://wikimedia.org/api/rest_v1/metrics/pageviews/aggregate/{project}/{access}/{agent}/{granularity}/{start}/{end}'
headers = {'User-Agent' : 'https://github.com/HWNi', 'From' : '[email protected]'}
params_pv_desktop = {
'project' : 'en.wikipedia.org',
'access' : 'desktop',
'agent' : 'user',
'granularity' : 'monthly',
'start' : '2015070100',
'end' : '2017100100'
}
api_call = requests.get(endPoint_pageviews.format(**params_pv_desktop))
response_pv_desktop = api_call.json()
with open('pageviews_desktop_201507-201709.json', 'w') as outfile:
json.dump(response_pv_desktop, outfile)
| hcds-a1-data-curation.ipynb | HWNi/data-512-a1 | mit |
The codes below will get you all pageviews for English Wikipedia accessed through mobile website from July 2015 through September 2017. Again, note that the data doesn't count traffic by web crawlers or spiders. | # Collect mobile web traffic data from July 2015 through September 2017 using the Pageviews API
endPoint_pageviews = 'https://wikimedia.org/api/rest_v1/metrics/pageviews/aggregate/{project}/{access}/{agent}/{granularity}/{start}/{end}'
headers = {'User-Agent' : 'https://github.com/HWNi', 'From' : '[email protected]'}
params_pv_mobile_web = {
'project' : 'en.wikipedia.org',
'access' : 'mobile-web',
'agent' : 'user',
'granularity' : 'monthly',
'start' : '2015070100',
'end' : '2017100100'
}
api_call = requests.get(endPoint_pageviews.format(**params_pv_mobile_web))
response_pv_mobile_web = api_call.json()
with open('pageviews_mobile-web_201507-201709.json', 'w') as outfile:
json.dump(response_pv_mobile_web, outfile)
| hcds-a1-data-curation.ipynb | HWNi/data-512-a1 | mit |
The codes below will get you all pageviews for English Wikipedia accessed through mobile app from July 2015 through September 2017. Again, note that the data doesn't count traffic by web crawlers or spiders. | # Collect mobile app traffic data from July 2015 through September 2017 using the Pageviews API
endPoint_pageviews = 'https://wikimedia.org/api/rest_v1/metrics/pageviews/aggregate/{project}/{access}/{agent}/{granularity}/{start}/{end}'
headers = {'User-Agent' : 'https://github.com/HWNi', 'From' : '[email protected]'}
params_pv_mobile_app = {
'project' : 'en.wikipedia.org',
'access' : 'mobile-app',
'agent' : 'user',
'granularity' : 'monthly',
'start' : '2015070100',
'end' : '2017100100'
}
api_call = requests.get(endPoint_pageviews.format(**params_pv_mobile_app))
response_pv_mobile_app = api_call.json()
with open('pageviews_mobile-app_201507-201709.json', 'w') as outfile:
json.dump(response_pv_mobile_app, outfile)
| hcds-a1-data-curation.ipynb | HWNi/data-512-a1 | mit |
Step 2: Data processing
Now, we have 5 JSON files containing the traffic data we're interested in. In this step, we first iterate these 5 JSON files one by one and combine the data into a Python dictionary. Eventually, the key of the dictionary will be the list of time stamps (from January 2008 to September 2017). For each key (time stamp), we will append a list which contains 5 values: pagecounts accessed through desktop, pagecounts accessed through mobile, pageviews accessed through desktop, pageviews accessed through mobile web, and pageviews accessed through mobile app. | data_cleaned = {}
for item in response_pc_desktop['items']:
timeStamp = item['timestamp']
data_cleaned[timeStamp] = [item['count'], 0, 0, 0, 0]
for item in response_pc_mobile['items']:
timeStamp = item['timestamp']
if timeStamp in data_cleaned:
data_cleaned[timeStamp][1] = item['count']
else:
data_cleaned[timeStamp] = [0, item['count'], 0, 0, 0]
for item in response_pv_desktop['items']:
timeStamp = item['timestamp']
if timeStamp in data_cleaned:
data_cleaned[timeStamp][2] = item['views']
else:
data_cleaned[timeStamp] = [0, 0, item['views'], 0, 0]
for item in response_pv_mobile_web['items']:
timeStamp = item['timestamp']
if timeStamp in data_cleaned:
data_cleaned[timeStamp][3] = item['views']
else:
data_cleaned[timeStamp] = [0, 0, 0, item['views'], 0]
for item in response_pv_mobile_app['items']:
timeStamp = item['timestamp']
if timeStamp in data_cleaned:
data_cleaned[timeStamp][4] = item['views']
else:
data_cleaned[timeStamp] = [0, 0, 0, 0, item['views']] | hcds-a1-data-curation.ipynb | HWNi/data-512-a1 | mit |
After we get the dictionary, we could convert it into a Pandas dataframe and save the dataframe to a csv file | df = pd.DataFrame.from_dict(data_cleaned, orient='index')
df_result = pd.DataFrame
df['timestamp'] = df.index
df['year'] = [t[0:4] for t in df['timestamp']]
df['month'] = [t[4:6] for t in df['timestamp']]
df['pagecount_all_views'] = df[0] + df[1]
df['pagecount_desktop_views'] = df[0]
df['pagecount_mobile_views'] = df[1]
df['pageview_all_views'] = df[2] + df[3] + df[4]
df['pageview_desktop_views'] = df[2]
df['pageview_mobile_views'] = df[3] + df[4]
df = df.loc[:, 'year' : 'pageview_mobile_views']
df.to_csv('en-wikipedia_traffic_200801-201709.csv', index=False)
df | hcds-a1-data-curation.ipynb | HWNi/data-512-a1 | mit |
Step 3: Analysis
In the final step, we make a time series plot for the data we processed before. The X-axis of the plot will be a date range, and Y-axis of the plot will be the amount of traffic times 1 million(The author downscaled the traffic data by 1 million). | dateRange = pd.date_range('2008-01', '2017-10', freq='M')
scale = 1e-6
sns.set_style("whitegrid")
fig = plt.figure(figsize=(18, 12))
plt.plot(dateRange, df['pagecount_all_views'] * scale, linestyle = ':')
plt.plot(dateRange, df['pagecount_desktop_views'] * scale)
plt.plot(dateRange, df['pagecount_mobile_views'] * scale)
plt.plot(dateRange, df['pageview_all_views'] * scale, linestyle = ':')
plt.plot(dateRange, df['pagecount_desktop_views'] * scale)
plt.plot(dateRange, df['pagecount_mobile_views'] * scale)
plt.legend()
plt.xlabel('Year')
plt.ylabel('Amount of Traffic (* 1,000,000)')
fig.savefig('en-wikipedia_traffic_200801-201709.jpg') | hcds-a1-data-curation.ipynb | HWNi/data-512-a1 | mit |
Play with some basic functions adapted from tide data functions
Query Builder | def query_builder(start_dt, end_dt, station, offset= 1):
"""Function accepts: a start and end datetime string in the form 'YYYYMMDD mm:ss'
which are <= 1 year apart, a station ID, and an offset.
Function assembles a query parameters/arguments dict and returns an API query and the
query dictionary (query_dict). The relevant base URL is the NCDC endpoint
'http://www.ncdc.noaa.gov/cdo-web/api/v2/data?'."""
import urllib
# API endpoint
base_url= 'http://www.ncdc.noaa.gov/cdo-web/api/v2/data?'
# dict of NOAA query parameters/arguments
query_dict = dict(startdate= start_dt, enddate= end_dt, stationid= station,
offset= offset, datasetid= 'GHCND', limit= 1000)
# encode arguments
encoded_args = urllib.urlencode(query_dict)
# query
query = base_url + encoded_args
# decode url % (reconvert reserved characters to utf8 string)
query= urllib.unquote(query)
# create and return query from base url and encoded arguments
return query, query_dict
query_1, query_dict= query_builder('2014-01-01', '2015-01-01', station= 'GHCND:USW00023174')
print(query_1)
query_2, query_dict= query_builder('2014-01-01', '2015-01-01', station= 'GHCND:USW00023174', offset= 1001)
print(query_2) | NOAA_sandbox.ipynb | baumanab/noaa_requests | gpl-3.0 |
Offset Generator | def offsetter(response):
"""
Function accepts a restful query response (JSON)
Function returns a dictionary of offsets to pull the entire query set
where the set is limited to 1000 records per query. Function also
returns a record count for use in validation.
"""
# get repeats and repeat range
import math
count= response['metadata']['resultset']['count']
repeats= math.ceil(count/1000.)
repeat_range= range(int(repeats))
# get offsets dictionary
offset= 1
offsets= [1]
for item in repeat_range[1:]:
offset += 1000
offsets.append(offset)
# zip up the results and convert to dictionary
offset_dict= dict(zip(repeat_range[1:], offsets[1:])) # the first call has been done already to get meta
return offset_dict, count # for quality control
| NOAA_sandbox.ipynb | baumanab/noaa_requests | gpl-3.0 |
Query Generator
TODO
refactor with a decorator
make key an attribute that can be hidden | def execute_query(query):
"""
Function accepts an NOAA query for daily summaries for a specfic location
and executes the query.
Function returns a response (JSON)
"""
url = query
# replace token with token provided by NOAA. Enter token as string
headers = {'token': NOAA_Token_Here} # https://www.ncdc.noaa.gov/cdo-web/token
response = requests.get(url, headers = headers)
response = response.json()
return response
working_1= execute_query(query_1)['results']
working_2 = execute_query(query_2)['results'] | NOAA_sandbox.ipynb | baumanab/noaa_requests | gpl-3.0 |
Subsets and Splits