Catalog of Custom Functions

These functions encapsulate complex python code, allowing you to implement sophisticated data manipulations and calculations with ease.

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Last updated 13 hours ago

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Catalog of Custom Functions

These functions encapsulate complex python code, allowing you to implement sophisticated data manipulations and calculations with ease.

GeoPard offers a comprehensive Catalog of Custom Functions designed to enhance the readability and functionality of Equation-based Analytics. These functions encapsulate complex python code, allowing you to implement sophisticated data manipulations and calculations with ease.

Enter Equation screen

The list of available pre-built functions to create more intuitive and maintainable Equations within the GeoPard platform:

geopard.fill_gaps_with_k_neighbors(input_data, k=3) This function restores data gaps or zeros in a dataset using the K-Neighbors algorithm. By specifying the input_data variable (as Dataset with the selected attribute) and the number of neighbors k, you can seamlessly fill missing values, ensuring data continuity and integrity.
geopard.determine_data_similarity(data_layer_1, data_layer_2) Use this function to calculate the per-pixel similarity between two Datasets. By providing variables data_layer_1 and data_layer_2 associated with Datasets, you can generate a similarity map with values ranging from 0 to 1, facilitating comparative studies and pattern recognition.
geopard.determine_low_high_similarity(data_layer_1, data_layer_2) This function assesses the low-high similarity between two Datasets. By inputting variables data_layer_1 and data_layer_2 associated with Datasets, you receive a categorized similarity map indicating combinations such as low-low, low-high, high-low, and high-high, which is useful for nuanced data classification.
geopard.get_value_for_zone(data_layer, zones_layer, zone_id) Extract values from the Dataset attribute for a selected Zone using this function. By providing data_layer representing Dataset attribute, zones_layer representing Dataset with Zones, and zone_id as a Zone id number, you can isolate and analyze attributes like yield, application rates, or seed rates within a designated geographical area (zone).
geopard.drop_value(data_layer, value_to_drop) This function allows you to remove specific values from a Dataset attribute. By specifying data_layer associated with a Dataset attribute and value_to_drop as a number, you can cleanse the Dataset attribute data by eliminating them from the result (technically replacing unwanted values with NaN).
geopard.normalize_data(data_layer) Normalize your Dataset attribute efficiently with this function. By inputting data_layer associated with the Dataset attribute, you can scale the data to a standardized range from 0 to 1, facilitating comparison and integration across different Datasets.
geopard.calculate_total_applied_fertilizer(application_list, active_ingredient_coefficient_list) Calculate the Total Applied Fertilizer in units per area (for example in kg/ha, l/ha, gal/ac, etc). By providing application_list of Datasets with AppliedRate attributes and corresponding active_ingredient_coefficient_list with fertilizer products to get the actually total applied fertilizer in units (for example in kg, l, gal, etc).
geopard.calculate_total_applied_nitrogen(application_list, active_ingredient_coefficient_list) Calculate the Total Applied Nitrogen in kg/ha using this function. By providing application_list of Datasets with AppliedRate attributes and a corresponding active_ingredient_coefficient_list with nitrogen products to convert actual nitrogen to kg/ha, you can accurately compute Total Applied Nitrogen, essential for agricultural planning and sustainability assessments. The output is used as N_total_applied ingeopard.calculate_nitrogen_use_efficiency (function 10).
geopard.calculate_nitrogen_uptake(yield_wet_tha, moisture_pct, protein_pct, protein_crop_correction_coefficient) Determine Nitrogen Uptake in kg/ha with this function. By supplying yield_wet_tha, moisture_pct, protein_pct from Yield Dataset and a protein_crop_correction_coefficient represented the linkage protein to uptaken nitrogen, you can assess the Nitrogen Use Efficiency in crop production. The output is used as N_uptake ingeopard.calculate_nitrogen_use_efficiency (function 10).
geopard.calculate_nitrogen_use_efficiency(N_total_applied, N_uptake) Evaluate Nitrogen Use Efficiency as a percentage using this function. By inputting N_total_applied and N_uptake variables (from functions 8 and 9), you can measure the effectiveness of nitrogen application, aiding in optimizing fertilizer usage.
geopard.calculate_costs(application_rate_list, price_per_unit_list) Compute Total Costs based on application rates and prices with this function. By providing a application_rate_list of Datasets with the AppliedRate attributes and a corresponding price_per_unit_list, you can aggregate expenses related to various agricultural activities, supporting budget management and financial planning. The output is used as costs ingeopard.calculate_profit (function 13).
geopard.calculate_revenue(yield_as_mass, yield_price_per_unit) Calculate Revenue from the Yield Dataset using this function. By inputting yield_as_mass associated with the Yield Dataset attribute and the yield_price_per_unit, you can estimate income generated from crop production, facilitating economic evaluations. The output is used as costs ingeopard.calculate_profit (function 13).
geopard.calculate_profit(revenue, costs) Determine Profit by subtracting Costs from Revenue using this function. By providing the revenue and costs variables (from functions 11 and 12), you can easily compute the financial gain from their agricultural operations, supporting profitability analysis and strategic decision-making.
geopard.fill_value_for_range(input, min_value, max_value, value_to_fill) This function filters values within a specified range in the input array. By providing the input array, along with optional min_value and optional max_value thresholds, you can isolate values that fall within the desired range. The value_to_fill parameter allows for replacing out-of-range values with a specified value, enhancing data filtering and normalization processes.

PreviousCatalog of Predefined Agronomic Equations NextTopography

Last updated 13 hours ago

Was this helpful?

Enter Equation screen

The list of available pre-built functions to create more intuitive and maintainable Equations within the GeoPard platform:

geopard.fill_gaps_with_k_neighbors(input_data, k=3) This function restores data gaps or zeros in a dataset using the K-Neighbors algorithm. By specifying the input_data variable (as Dataset with the selected attribute) and the number of neighbors k, you can seamlessly fill missing values, ensuring data continuity and integrity.
geopard.determine_data_similarity(data_layer_1, data_layer_2) Use this function to calculate the per-pixel similarity between two Datasets. By providing variables data_layer_1 and data_layer_2 associated with Datasets, you can generate a similarity map with values ranging from 0 to 1, facilitating comparative studies and pattern recognition.
geopard.determine_low_high_similarity(data_layer_1, data_layer_2) This function assesses the low-high similarity between two Datasets. By inputting variables data_layer_1 and data_layer_2 associated with Datasets, you receive a categorized similarity map indicating combinations such as low-low, low-high, high-low, and high-high, which is useful for nuanced data classification.
geopard.get_value_for_zone(data_layer, zones_layer, zone_id) Extract values from the Dataset attribute for a selected Zone using this function. By providing data_layer representing Dataset attribute, zones_layer representing Dataset with Zones, and zone_id as a Zone id number, you can isolate and analyze attributes like yield, application rates, or seed rates within a designated geographical area (zone).
geopard.drop_value(data_layer, value_to_drop) This function allows you to remove specific values from a Dataset attribute. By specifying data_layer associated with a Dataset attribute and value_to_drop as a number, you can cleanse the Dataset attribute data by eliminating them from the result (technically replacing unwanted values with NaN).
geopard.normalize_data(data_layer) Normalize your Dataset attribute efficiently with this function. By inputting data_layer associated with the Dataset attribute, you can scale the data to a standardized range from 0 to 1, facilitating comparison and integration across different Datasets.
geopard.calculate_total_applied_fertilizer(application_list, active_ingredient_coefficient_list) Calculate the Total Applied Fertilizer in units per area (for example in kg/ha, l/ha, gal/ac, etc). By providing application_list of Datasets with AppliedRate attributes and corresponding active_ingredient_coefficient_list with fertilizer products to get the actually total applied fertilizer in units (for example in kg, l, gal, etc).
geopard.calculate_total_applied_nitrogen(application_list, active_ingredient_coefficient_list) Calculate the Total Applied Nitrogen in kg/ha using this function. By providing application_list of Datasets with AppliedRate attributes and a corresponding active_ingredient_coefficient_list with nitrogen products to convert actual nitrogen to kg/ha, you can accurately compute Total Applied Nitrogen, essential for agricultural planning and sustainability assessments. The output is used as N_total_applied ingeopard.calculate_nitrogen_use_efficiency (function 10).
geopard.calculate_nitrogen_uptake(yield_wet_tha, moisture_pct, protein_pct, protein_crop_correction_coefficient) Determine Nitrogen Uptake in kg/ha with this function. By supplying yield_wet_tha, moisture_pct, protein_pct from Yield Dataset and a protein_crop_correction_coefficient represented the linkage protein to uptaken nitrogen, you can assess the Nitrogen Use Efficiency in crop production. The output is used as N_uptake ingeopard.calculate_nitrogen_use_efficiency (function 10).
geopard.calculate_nitrogen_use_efficiency(N_total_applied, N_uptake) Evaluate Nitrogen Use Efficiency as a percentage using this function. By inputting N_total_applied and N_uptake variables (from functions 8 and 9), you can measure the effectiveness of nitrogen application, aiding in optimizing fertilizer usage.
geopard.calculate_costs(application_rate_list, price_per_unit_list) Compute Total Costs based on application rates and prices with this function. By providing a application_rate_list of Datasets with the AppliedRate attributes and a corresponding price_per_unit_list, you can aggregate expenses related to various agricultural activities, supporting budget management and financial planning. The output is used as costs ingeopard.calculate_profit (function 13).
geopard.calculate_revenue(yield_as_mass, yield_price_per_unit) Calculate Revenue from the Yield Dataset using this function. By inputting yield_as_mass associated with the Yield Dataset attribute and the yield_price_per_unit, you can estimate income generated from crop production, facilitating economic evaluations. The output is used as costs ingeopard.calculate_profit (function 13).
geopard.calculate_profit(revenue, costs) Determine Profit by subtracting Costs from Revenue using this function. By providing the revenue and costs variables (from functions 11 and 12), you can easily compute the financial gain from their agricultural operations, supporting profitability analysis and strategic decision-making.
geopard.fill_value_for_range(input, min_value, max_value, value_to_fill) This function filters values within a specified range in the input array. By providing the input array, along with optional min_value and optional max_value thresholds, you can isolate values that fall within the desired range. The value_to_fill parameter allows for replacing out-of-range values with a specified value, enhancing data filtering and normalization processes.