set_values

set_values(fs, values[, mode])

Creates a new fieldset with all the values in fs replaced by values.

Parameters

  • fs (Fieldset) – input fieldset

  • values (ndarray or list of ndarray) – values to be written into fs

  • mode (str) – resize mode. If specified must be set to “resize”.

Return type

Fieldset

If values is an ndarray the same values are set in each field of fs.

If values is a list of ndarray the list size must be same as the number of fields in fs.

The default behaviour is to produce an error if the number points in a field and the given ndarray are not the same. If, however, mode is specified and set to “resize” the resulting fieldset will be resized to have the same number of values as the ndarray. This can be a useful option when creating a new Fieldset from a template.

Missing values in the values are retained as missing values in the fieldset.

set_values(gpt, values)
set_values(gpt, index_or_name, values)

Creates a new Geopoints with the specified values column in gpt replaced by values.

Parameters

  • gpt (Geopoints) – input geopoints

  • index_or_name (number or str) – index or name of the values column to be replaced in gpt

  • values (number or list or ndarray) – values to be written into gpt

Return type

Geopoints

If index_or_name is specified and is a number it refers to the index of the column within the value columns (and not within all the columns in gpt). E.g. 0 means the first value column.

index_or_name has to be used for Geopoints of ‘ncols’ type. In all the other types the values column is uniquely identified.

If values is a number all the values are replaced with it. If values is a list or ndarray and is shorter than the geopoints count then only the first values that have a corresponding value in values are changed.

Example
import metview as mv

new_gpt_b = set_values(gpt_b,
            np.array([12.4, 13.3, 1.1]))

# update the 4th value column
new_gpt_c = set_values(gpt_c, 3,
            np.array([3.3, 4.4, 5.5]))

# update the column labelled "precip"
new_gpt_d = set_values(gpt_d, "precip",
            np.array([0.3, 0.2, 0.1]))

Note

set_values() generates a new geopoints object, leaving the original one intact. If you wish to modify the original object, then a more efficient way is to directly access the columns using the following syntax, following the examples above:

gpt['value'] = np.array([2.4, 13.3, 1.1])
gpt[name_of_column_4] = np.array([3.3, 4.4, 5.5])
gpt["precip"] = np.array([0.3, 0.2, 0.1])