#pylint: disable=no-member
"""Data Preparation
Calculates and joins parameters required for pygypsy to a plot table
"""
from __future__ import division
import logging
import pandas as pd
from copy import deepcopy
from pygypsy.stand_density_factor import (
estimate_sdf_aw,
estimate_sdf_sb,
estimate_sdf_sw,
estimate_sdf_pl,
)
from pygypsy.density import (
estimate_density_aw,
estimate_density_sw,
estimate_density_sb,
estimate_density_pl,
)
from pygypsy.site_index import (
get_site_indices_from_dominant_species,
_estimate_site_index
)
from pygypsy.utils import (
_get_gypsy_valid_species,
_log_loop_progress,
_generate_fplot_dict,
_reclassify_and_sort_species,
)
from pygypsy.asaCompileAgeGivenSpSiHt import (
computeTreeAge,
ComputeGypsyTreeHeightGivenSiteIndexAndTotalAge,
ComputeGypsySiteIndex,
)
from pygypsy.exceptions import MinimumAgeError, ProportionsSumError
LOGGER = logging.getLogger(__name__)
def _populate_species_dict_with_indices(species_dict, estimated_site_indices):
"""Fill the dictionary with estimated SIs
Fill all the SIs to avoid IFs and loops. Some of them will not be
used.
:param species_dict: dictionary used to store params for all species
:param estimated_site_indices: array of estimated site indices from
get_species_site_indices function
..note: the dominant species in the estiamted_site_indices uses its
observed site index
"""
local_species_dict = deepcopy(species_dict)
for species, site_index in estimated_site_indices.items():
local_species_dict[species]['SI'] = site_index
return local_species_dict
# TODO: this is a bit deep in the stack to use the pandas data structure
# should refactor in future. instead pass tph and bph as args
[docs]def populate_species_dict(partial_species_list,
sorted_species_abbrev_perc_tuples_list,
dominant_species=None, row=None,
dominant_species_current_age=None,
dominant_species_current_height=None):
"""Fill partial fplot
Given fplot with proportion filled out, add age, top height, basal area,
density
:param partial_species_list:
:param sorted_species_abbrev_perc_tuples_list:
:param dominant_species:
:param dominant_species_current_age:
:param dominant_species_current_height:
"""
species_dict = deepcopy(partial_species_list)
outer_sorted_species_perc_list = sorted_species_abbrev_perc_tuples_list
for species in outer_sorted_species_perc_list:
# TODO: raise NoDominantSpeciesError and catch it where called instead of break
# confirmed with julianno
if species[1] == 0:
break
# TODO: this is always true on first pass of the loop
elif species[0] == dominant_species[0]:
species_dict[species[0]]['tage'] = dominant_species_current_age
species_dict[species[0]]['topHeight'] = dominant_species_current_height
species_dict[species[0]]['N'] = row['TPH'] * species[1] / 100
species_dict[species[0]]['BA'] = row['BPH'] * species[1] / 100
x_si = ComputeGypsySiteIndex(
species[0],
species_dict[species[0]]['topHeight'],
0,
species_dict[species[0]]['tage']
)
species_dict[species[0]]['bhage'] = x_si[0]
# if, after re-arranging the proportions, dom species is another
# one then we need to re-estimate everything even for the new
# dominant species
else:
si_sp = species_dict[species[0]]['SI']
species_dict[species[0]]['PCT'] = species[1]
species_dict[species[0]]['tage'] = computeTreeAge(
species[0],
treeHt=dominant_species_current_height,
treeSi=si_sp,
maxTreeAge=450
)
# density based on the proportion of the species
species_dict[species[0]]['N'] = row['TPH'] * species[1]/100
# Basal area from the species proportion as well
species_dict[species[0]]['BA'] = row['BPH'] * species[1]/100
# calling the ComputeGypsySiteIndex function, estimate bhage
x_si = ComputeGypsySiteIndex(
species[0], dominant_species_current_height, 0,
species_dict[species[0]]['tage']
)
species_dict[species[0]]['bhage'] = x_si[0]
return species_dict
def _prep_row(row, minimum_age=25):
'''Estimate properties required for gypsy simulation
:param row: row of data frame
:param minimum age: age below which plot should be skipped
:raises ProportionsSumError: if species proportions do not add to 1
:raises MinimumAgeError: if plot is less than minimum age
'''
species_abbrev_percent_list = [
(row['SP1'], row['PCT1']),
(row['SP2'], row['PCT2']),
(row['SP3'], row['PCT3']),
(row['SP4'], row['PCT4']),
(row['SP5'], row['PCT5'])
]
plot_dominant_species = row['SP1']
dominant_species_current_age = row['AGE']
dominant_species_current_height = row['HD']
check_prop = sum(zip(*species_abbrev_percent_list)[1])
if check_prop != 100:
raise ProportionsSumError(
'Species proportions do not add to 1: %s' % check_prop
)
if dominant_species_current_age < minimum_age:
msg = ('Dominant species age is less than minimum age %s;'
'skipping plot %s') % (minimum_age, row['id_l1'])
raise MinimumAgeError(msg)
site_index = _estimate_site_index(
plot_dominant_species,
dominant_species_current_age,
dominant_species_current_height
)
temp_dominant_species = _get_gypsy_valid_species(plot_dominant_species)
gypsy_site_indices = get_site_indices_from_dominant_species(
temp_dominant_species,
site_index
)
empty_species_dict = _generate_fplot_dict()
species_dict = _populate_species_dict_with_indices(empty_species_dict,
gypsy_site_indices)
outer_sorted_species_perc_list, outer_species_perc_dict = \
_reclassify_and_sort_species(species_abbrev_percent_list)
species_dict['Aw']['PCT'] = outer_species_perc_dict['Aw']
species_dict['Pl']['PCT'] = outer_species_perc_dict['Pl']
species_dict['Sw']['PCT'] = outer_species_perc_dict['Sw']
species_dict['Sb']['PCT'] = outer_species_perc_dict['Sb']
dominant_species = outer_sorted_species_perc_list[0]
species_dict = populate_species_dict(
species_dict, outer_sorted_species_perc_list,
dominant_species=dominant_species,
row=row,
dominant_species_current_age=dominant_species_current_age,
dominant_species_current_height=dominant_species_current_height
)
density_aw = species_dict['Aw']['N']
density_sw = species_dict['Sw']['N']
density_pl = species_dict['Pl']['N']
density_sb = species_dict['Sb']['N']
tage_aw = species_dict['Aw']['tage']
tage_sw = species_dict['Sw']['tage']
tage_pl = species_dict['Pl']['tage']
tage_sb = species_dict['Sb']['tage']
bhage_aw = species_dict['Aw']['bhage']
bhage_sw = species_dict['Sw']['bhage']
bhage_pl = species_dict['Pl']['bhage']
bhage_sb = species_dict['Sb']['bhage']
y2bh_aw = tage_aw - bhage_aw
y2bh_sw = tage_sw - bhage_sw
y2bh_pl = tage_pl - bhage_pl
y2bh_sb = tage_sb - bhage_sb
site_index_aw = species_dict['Aw']['SI']
site_index_sw = species_dict['Sw']['SI']
site_index_pl = species_dict['Pl']['SI']
site_index_sb = species_dict['Sb']['SI']
basal_area_aw = species_dict['Aw']['BA']
basal_area_sb = species_dict['Sb']['BA']
basal_area_sw = species_dict['Sw']['BA']
basal_area_pl = species_dict['Pl']['BA']
top_height_aw = ComputeGypsyTreeHeightGivenSiteIndexAndTotalAge(
'Aw', site_index_aw, tage_aw
)
top_height_sb = ComputeGypsyTreeHeightGivenSiteIndexAndTotalAge(
'Sb', site_index_sb, tage_sb
)
top_height_pl = ComputeGypsyTreeHeightGivenSiteIndexAndTotalAge(
'Pl', site_index_pl, tage_pl
)
top_height_sw = ComputeGypsyTreeHeightGivenSiteIndexAndTotalAge(
'Sw', site_index_sw, tage_sw
)
density_bh_aw, sdf_aw0 = estimate_sdf_aw(
'Aw', site_index_aw, bhage_aw, density_aw
)
density_bh_sb, sdf_sb0 = estimate_sdf_sb(
'Sb', site_index_sb, tage_sb, density_sb
)
density_bh_sw, sdf_sw0 = estimate_sdf_sw(
'Sw', site_index_sw, tage_sw, sdf_aw0, density_sw
)
density_bh_pl, sdf_pl0 = estimate_sdf_pl(
'Pl', site_index_pl, tage_pl,
sdf_aw0, sdf_sw0, sdf_sb0, density_pl
)
initial_density_aw = estimate_density_aw(
sdf_aw0, 0, site_index_aw
)
initial_density_sb = estimate_density_sb(
sdf_sb0, 0, site_index_sb
)
initial_density_sw = estimate_density_sw(
sdf_sw0, sdf_aw0, 0, site_index_sw
)
initial_density_pl = estimate_density_pl(
sdf_aw0, sdf_sw0, sdf_sb0, sdf_pl0, 0, site_index_pl
)
plot_properties = {
'SI_Aw': site_index_aw,
'SI_Sw': site_index_sw,
'SI_Pl': site_index_pl,
'SI_Sb': site_index_sb,
'N_Aw': density_aw,
'N_Sw': density_sw,
'N_Pl': density_pl,
'N_Sb': density_sb,
'y2bh_Aw': y2bh_aw,
'y2bh_Sw': y2bh_sw,
'y2bh_Pl': y2bh_pl,
'y2bh_Sb': y2bh_sb,
'tage_Aw': tage_aw,
'tage_Sw': tage_sw,
'tage_Pl': tage_pl,
'tage_Sb': tage_sb,
'BA_Aw': basal_area_aw,
'BA_Sw': basal_area_sw,
'BA_Pl': basal_area_pl,
'BA_Sb': basal_area_sb,
'SDF_Aw': sdf_aw0,
'SDF_Sw': sdf_sw0,
'SDF_Pl': sdf_pl0,
'SDF_Sb': sdf_sb0,
'N0_Aw': initial_density_aw,
'N0_Sb': initial_density_sb,
'N0_Sw': initial_density_sw,
'N0_Pl': initial_density_pl,
'StumpDOB_Aw': species_dict['Aw']['StumpDOB'],
'StumpDOB_Sb': species_dict['Sb']['StumpDOB'],
'StumpDOB_Sw': species_dict['Sw']['StumpDOB'],
'StumpDOB_Pl': species_dict['Pl']['StumpDOB'],
'StumpHeight_Aw': species_dict['Aw']['StumpHeight'],
'StumpHeight_Sb': species_dict['Sb']['StumpHeight'],
'Stumpheight_Sw': species_dict['Sw']['StumpHeight'],
'StumpHeight_Pl': species_dict['Pl']['StumpHeight'],
'TopDib_Aw': species_dict['Aw']['TopDib'],
'TopDib_Sb': species_dict['Sb']['TopDib'],
'TopDib_Sw': species_dict['Sw']['TopDib'],
'TopDib_Pl': species_dict['Pl']['TopDib'],
'topHeight_Aw': top_height_aw,
'topHeight_Sw': top_height_sw,
'topHeight_Sb': top_height_sb,
'topHeight_Pl': top_height_pl
}
return plot_properties
[docs]def prep_standtable(data, **kwargs):
'''Define site_index of all other species given the dominant species
The site index is only defined for the dominant species in a plot. This
function uses the dominant species and its site index to estimate the site
indices for other species. It estimates other values and returns a data
frame of all of the values for all plots input.
estimates site_index Dom from inventory Age and HD
this site_index is used to estimate the site index of the other species
for example:
- FD is dom species take HD and Age
- assume FD is Sw generate site_index for Sw
- get the SW site_index
- calculate the SIs for other species from the conversion formulas
:param data: input data frame
:param kwargs: additional arguments to :func:`_prep_row`
'''
plot_dict = {}
n_rows = data.shape[0]
for i, row in data.iterrows():
_log_loop_progress(i, n_rows)
plot_id = row['id_l1']
try:
prepped_plot = _prep_row(row, **kwargs)
plot_dict[plot_id] = prepped_plot
except MinimumAgeError as err:
LOGGER.error(err.message)
except ProportionsSumError as err:
LOGGER.error(err.message)
except Exception as err: #pylint: disable=broad-except
LOGGER.critical('Unhandled error: %s', err.message)
plot_df = pd.DataFrame(plot_dict)
plot_df = plot_df.transpose()
return plot_df
