Compute age- and length-binned weight proportions

Compute age- and length-binned weight proportions#

Import necessary modules#

With the biological data loaded and number proportions computed, we can now compute the weight proportions. The first step is to distribute binned weights over age, length, and sex across strata.

Distributed weights over age, length, and sex across strata#

The binned weight distributions are computed over values specific to aged (dict_df_bio["specimen"]) and unaged fish (dict_df_bio["length"]). This is done using the binned_weights function from the survey.proportions module and is done separately since the two datasets are processed somewhat differently before being combined in a later step. Here, we will use "stratum_ks" as our stratification definition.

# Pre-allocate a dictionary
ds_da_weight_dist = xr.Dataset()

# Aged
ds_da_weight_dist["aged"] = get_proportions.binned_weights(
    length_data=dict_df_bio["specimen"],
    include_filter={"sex": ["female", "male"]},
    interpolate_regression=False,
    group_columns=["stratum_ks", "sex", "age_bin"],
)

# Unaged
ds_da_weight_dist["unaged"] = get_proportions.binned_weights(
    length_data=dict_df_bio["length"],
    include_filter={"sex": ["female", "male"]},
    interpolate_regression=True,
    length_weight_data=da_binned_weight_table,
    group_columns=["stratum_ks", "sex"],
)

Just like the binned counts, this produces an xarray.Dataset comprising the aged and unaged data arrays.

Convert summed weights into weight proportions#

These binned weights are normalized into proportions using the weight_proportions function. However, there is a slightly different treatment done here due to the aforementioned separate processing of aged and unaged samples. So we first compute the weight proportions for aged-only animals, where we are using the summed combined weights (i.e. from the individual specimens and haul weight totals) to compute the proportions. This effectively represents the same quantity as proportion present in the number proportions xarray.Dataset dictionary.

# Initialize Dictionary container
dict_ds_weight_proportion = {}

# Aged
dict_ds_weight_proportion["aged"] = get_proportions.weight_proportions(
    weight_data=ds_da_weight_dist["aged"], 
    catch_data=dict_df_bio["catch"], 
    stratum_dim="stratum_ks"
)

This outputs an xarray.Dataset with the variable "proportion" that is stored in the container dictionary dict_da_weight_proportion.

<xarray.Dataset> Size: 128kB
Dimensions:     (length_bin: 40, stratum_ks: 9, sex: 2, age_bin: 22)
Coordinates:
  * length_bin  (length_bin) category 680B (1.0, 3.0] ... (79.0, 81.0]
  * stratum_ks  (stratum_ks) int64 72B 0 1 2 3 4 5 6 7 8
  * sex         (sex) object 16B 'female' 'male'
  * age_bin     (age_bin) category 374B (0.5, 1.5] (1.5, 2.5] ... (21.5, 22.5]
Data variables:
    proportion  (length_bin, stratum_ks, sex, age_bin) float64 127kB 0.0 ... 0.0

Scaling weight proportions for unaged fish#

The first step for the unaged weight proportions involves scaling the fitted binned weights from unaged fish, which requires a few additional steps. It incorporates the aged and unaged number proportions to estimate sex ratios, the binned weights computed for all fish, and the total catch weights.

# Compute the scaled weight proportions for unaged fish
dict_ds_weight_proportion["unaged"] = get_proportions.fitted_weight_proportions(
    weight_data=ds_da_weight_dist["unaged"],
    aged_weight_proportions=dict_ds_weight_proportion["aged"],
    number_proportions=dict_ds_number_proportion["unaged"],
    binned_weights=da_binned_weights_all,
    stratum_dim="stratum_ks"
)

<xarray.Dataset> Size: 7kB
Dimensions:     (stratum_ks: 9, length_bin: 40, sex: 2)
Coordinates:
  * stratum_ks  (stratum_ks) int64 72B 0 1 2 3 4 5 6 7 8
  * length_bin  (length_bin) category 680B (1.0, 3.0] ... (79.0, 81.0]
  * sex         (sex) object 16B 'female' 'male'
Data variables:
    proportion  (stratum_ks, length_bin, sex) float64 6kB 0.0 0.0 ... 0.0 0.0