Software Attributes

Software Attributes#

The sections here tabulate the software attribute database. Each attribute (column) is described in the Attribute Schema. Some columns contain lists of strings which means that they cannot be fully sorted. Instead, they can be filtered by searching for a string in the search box

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from pathlib import Path
import yaml
from itables import init_notebook_mode
import pandas as pd

init_notebook_mode(all_interactive=True)

software_attr_dir = Path("..", "..", "software_attributes")
model_list_inputs = yaml.safe_load( open(software_attr_dir / "database_list.yaml", "r") )
models = model_list_inputs["active"]

model_attributes_map = {
    model: yaml.safe_load( open(Path("..", "..", "software_attributes", "database", f"{model}.yaml"), "r") )
    for model in models
}

df = pd.DataFrame.from_dict(model_attributes_map, orient="index")
# print(list(df.columns))
# print(df)

General#

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# Get all columns that begin with "does_"
does_columns = [col for col in df.columns if col.startswith("does_")]

# Make a list of all the "does_" columns that are True for each row dropping the "does_" prefix
# Add this to the dataframe
for i, row in df.iterrows():
    does_list = [col.replace("does_", "") for col in does_columns if row[col]]
    # print(f"{i}: {does_list}")
    df.at[i, "does"] = does_list

general_df = df[[
    "github_account",
    "github_repo",
    "does",
    "active_funding",
    "development_status",
    "programming_language",
    "license_type",
    "data_collection_methods",
    "last_release_date"
]]
general_df
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Turbine Modeling#

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turbine_df = df[[
    "scope",
    "system_location",
    "time_domain",
    "turbine_model",
    "does_loads",
    "does_design_optimization",
    "does_costs",
]]
turbine_df = turbine_df[ pd.DataFrame( turbine_df["scope"].tolist() ).isin( ["turbine", "any"] ).any(axis=1).values ]
turbine_df
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Farm Modeling#

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farm_df = df[[
    "scope",
    "system_location",
    "time_domain",
    "turbine_model",
    "wake_model",
    "does_loads",
    "does_design_optimization",
    "does_costs",
]]
farm_df = farm_df[ pd.DataFrame( farm_df["scope"].tolist() ).isin( ["farm", "any"] ).any(axis=1).values ]
farm_df.dropna()
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Cost Modeling#

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cost_df = df[[
    "scope",
    "system_location",
    "does_design_optimization",
    "does_costs",
    "tags",
]]
cost_df = cost_df[ cost_df["does_costs"] == True]
cost_df.dropna()
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Full Suite Modeling Comparison#

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modeling_df = df[[
    "scope",
    "system_location",
    "time_domain",
    "turbine_model",
    "wake_model",
    "does_controls",
    "does_costs",
    "does_data_analysis",
    "does_design",
    "does_design_optimization",
    "does_simulation",
    "does_loads",
    "tags",
]]
modeling_df
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Distribution#

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distribution_df = df[[
    "supported_platforms",
    "package_manager",
    "package_manager_url",
    "installation_complexity",
]]
distribution_df
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Documentation#

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documentation_df = df[[
    "documentation_url",
    "has_user_docs",
    "has_developer_docs",
    "has_api_docs",
    "has_cli_docs",
    "has_installation_docs",
    "has_getting_started_docs",
    "has_examples_tutorials",
    "has_design_intent_statement",
    "readability",
    "project_coverage",
]]
documentation_df
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Testing#

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testing_df = df[[
    "testing_framework",
    "continuous_testing",
    "test_coverage",
    "test_documentation",
    "performance_tests",
]]
testing_df
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Interfaces#

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interfaces_df = df[[
    "first_class_ui",
    "has_cli",
    "language_interfaces",
    "feature_accessibility",
]]
interfaces_df
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Input / Output#

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io_df = df[[
    "filetypes",
    "file_structure_stability",
]]
io_df
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