easy_vic_build.tools.params_func.Scaling_operator

Statistical scaling operators used by parameter aggregation workflows.

Functions

multiply(x, y)

Multiply two numeric values.

Classes

Scaling_operator()

Collection of static scaling operators for array-like data.
easy_vic_build.tools.params_func.Scaling_operator.multiply(x, y)[source]

Multiply two numeric values.

Parameters:

  • x (float) – Left operand.

  • y (float) – Right operand.

Returns:

Product of x and y.

Return type:

float

class easy_vic_build.tools.params_func.Scaling_operator.Scaling_operator[source]

Bases: object

Collection of static scaling operators for array-like data.

static Harmonic_mean(data)[source]

Compute harmonic mean.

Parameters:

data (array-like) – Input numeric data.

Returns:

Harmonic mean value.

Return type:

float

static Arithmetic_mean(data)[source]

Compute arithmetic mean.

Parameters:

data (array-like) – Input numeric data.

Returns:

Arithmetic mean value.

Return type:

float

static Arithmetic_max(data)[source]

Compute maximum value ignoring NaN.

Parameters:

data (array-like) – Input numeric data.

Returns:

Maximum value.

Return type:

float

static Arithmetic_min(data)[source]

Compute minimum value ignoring NaN.

Parameters:

data (array-like) – Input numeric data.

Returns:

Minimum value.

Return type:

float

static Geometric_mean(data)[source]

Compute geometric mean.

Parameters:

data (array-like) – Input numeric data.

Returns:

Geometric mean value.

Return type:

float

static Maximum_difference(data)[source]

Compute range as max(data) - min(data).

Parameters:

data (array-like) – Input numeric data.

Returns:

Difference between maximum and minimum values.

Return type:

float

static Majority(data)[source]

Compute the most frequent value.

Parameters:

data (array-like) – Input data.

Returns:

Most frequent value, or np.nan when valid data is empty.

Return type:

float