![]() ![]() *Īcfe_get_setting($name, ) Update Setting It is recommended to use this function in the acfe/init hook to avoid any undefined function error if the developer disable the ACF Extended plugin. It will simply prefix any setting name with acfe/ to target ACF Extended specific settings. This function is a cousin of the native acf_get_setting() helper. It can be used in hooks where the post_id is not available, like acf/load_field or acf/render_field. This helper is an universal function allowing to retrieve the current ACF Post ID in the back-end and front-end. $field_group = acfe_get_field_group_from_field($field) Get Post ID *Īcfe_get_field_group_from_field($field) $field = acf_get_field('my_field') This helper allows developer to retrieve the Field Group object from any field or sub field. * bool $format_value Whenever to format values or notĪcfe_get_fields($post_id, ) $values = acfe_get_fields(12) Get Field Group From Field This function is useful when dealing with advanced hooks like acf/pre_load_value. This function is a cousin of the native get_fields() helper, but instead of returning top level fields name, it will return field keys to produce an array comparable to the $_POST array returned during a post save. * string $selector Field name, key or empty (global error)Īcfe_add_validation_error(, ) acfe_add_validation_error('my_field', 'Field Custom error message') Īcfe_add_validation_error('', 'Global Custom error message') Get Fields Just like its cousin, it is possible to leave the selector field empty to throw a general error. It can be used within acf/validate_save_post & acfe/validate_save_post hooks. from import seasonal_decompose res = seasonal_decompose(data, model = "additive",period = 30) fig, (ax1,ax2,ax3) = plt.subplots(3,1, figsize=(15,8)) (ax=ax1,ylabel = "trend") (ax=ax2,ylabel = "seasoanlity") (ax=ax3,ylabel = "residual") plt.This function is a cousin of the native acf_add_validation_error() helper, but instead of using field key attribute, the field name or field key can be used as selector. I am going to use a stats model API for this purpose but one can use NumPy and Pandas as well to decompose the three parts of a time series -trend, seasonality, residual. Important note: make sure your data doesn’t have NA values, otherwise the ACF will fail.Ĭan we look at the trend and seasonality separately to dive deep into the data? We will focus on the points that lie beyond the blue region as they signify strong statistical significance. In terms of the month if I have to say then, high positive correlations for March, June, September, December, whereas Jan, Feb and April have negative correlations but that too vanishes with lag. ![]() Notice how the coefficient is high at lag 3, 6,9,12. X axis>lag in months, y axis>correlation coefficient So when performing ACF it is advisable to remove any trend present in the data and to make sure the data is stationary. Weak stationary - meaning no systematic change in the mean, variance, and no systematic fluctuation.
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