"""Honeybee PointGroup and TestPointGroup."""
from __future__ import division
from ..utilcol import random_name
from ..dataoperation import match_data
from ..schedule import Schedule
from ..futil import write_to_file_by_name
from .analysispoint import AnalysisPoint
from ..exception import EmptyFileError
import os
try:
from itertools import izip as zip
except:
pass
from collections import namedtuple, OrderedDict
import sys
if (sys.version_info >= (3, 0)):
xrange = range
readmode = 'r'
else:
readmode = 'rb'
[docs]class AnalysisGrid(object):
"""A grid of analysis points.
Attributes:
analysis_points: A collection of analysis points.
"""
__slots__ = ('_analysis_points', '_name', '_sources', '_wgroups', '_directFiles',
'_totalFiles')
def __init__(self, analysis_points, name=None, window_groups=None):
"""Initialize a AnalysisPointGroup.
analysis_points: A collection of AnalysisPoints.
name: A unique name for this AnalysisGrid.
window_groups: A collection of window_groups which contribute to this grid.
This input is only meaningful in studies such as daylight coefficient
and multi-phase studies that the contribution of each source will be
calculated separately (default: None).
"""
self.name = name
# name of sources and their state. It's only meaningful in multi-phase daylight
# analysis. In analysis for a single time it will be {None: [None]}
self._sources = OrderedDict()
if window_groups:
self._wgroups = tuple(wg.name for wg in window_groups)
else:
self._wgroups = ()
for ap in analysis_points:
assert hasattr(ap, '_dir'), \
'{} is not an AnalysisPoint.'.format(ap)
self._analysis_points = analysis_points
self._directFiles = [] # list of results files
self._totalFiles = [] # list of results files
[docs] @classmethod
def from_json(cls, ag_json):
"""Create an analysis grid from json objects."""
analysis_points = tuple(AnalysisPoint.from_json(pt)
for pt in ag_json["analysis_points"])
return cls(analysis_points=analysis_points, name=ag_json["name"],
window_groups=None)
[docs] @classmethod
def from_points_and_vectors(cls, points, vectors=None,
name=None, window_groups=None):
"""Create an analysis grid from points and vectors.
Args:
points: A flatten list of (x, y ,z) points.
vectors: An optional list of (x, y, z) for direction of test points.
If not provided a (0, 0, 1) vector will be assigned.
"""
vectors = vectors or ()
points, vectors = match_data(points, vectors, (0, 0, 1))
aps = tuple(AnalysisPoint(pt, v) for pt, v in zip(points, vectors))
return cls(aps, name, window_groups)
[docs] @classmethod
def from_file(cls, file_path, start_line=None, end_line=None):
"""Create an analysis grid from a pts file.
Args:
file_path: Full path to points file
start_line: Start line (default: 0)
end_line: End line as an integer (default: last line in file)
"""
start_line = int(start_line) if start_line else 0
try:
end_line = int(end_line)
except TypeError:
end_line = float('+inf')
line_count = end_line - start_line + 1
assert os.path.isfile(file_path), "Can't find {}.".format(file_path)
ap = AnalysisPoint # load analysis point locally for better performance
points = []
with open(file_path, readmode) as inf:
for i in range(start_line):
next(inf)
for count, l in enumerate(inf):
if not count < line_count:
break
points.append(ap.from_raw_values(*l.split()))
return cls(points)
@property
def isAnalysisGrid(self):
"""Return True for AnalysisGrid."""
return True
@property
def name(self):
"""AnalysisGrid name."""
return self._name
@name.setter
def name(self, n):
self._name = n or random_name()
@property
def window_groups(self):
"""A list of window group names that are related to this analysis grid."""
return self._wgroups
@window_groups.setter
def window_groups(self, wgs):
self._wgroups = tuple(wg.name for wg in wgs)
@property
def points(self):
"""A generator of points as x, y, z."""
return (ap.location for ap in self._analysis_points)
@property
def vectors(self):
"""Get generator of vectors as x, y , z."""
return (ap.direction for ap in self._analysis_points)
@property
def analysis_points(self):
"""Return a list of analysis points."""
return self._analysis_points
@property
def sources(self):
"""Get sorted list fo sources."""
if not self._sources:
return self.analysis_points[0].sources
else:
srcs = list(range(len(self._sources)))
for name, d in self._sources.items():
srcs[d['id']] = name
return srcs
@property
def has_values(self):
"""Check if this analysis grid has result values."""
return self.analysis_points[0].has_values
@property
def has_direct_values(self):
"""Check if direct values are available for this point.
In point-in-time and 3phase recipes only total values are available.
"""
return self.analysis_points[0].has_direct_values
@property
def hoys(self):
"""Return hours of the year for results if any."""
return self.analysis_points[0].hoys
@property
def is_results_point_in_time(self):
"""Return True if the grid has the results only for an hour."""
return len(self.hoys) == 1
@property
def result_files(self):
"""Return result files as a list [[total files], [direct files]]."""
return self._totalFiles, self._directFiles
[docs] def add_result_files(self, file_path, hoys, start_line=None, is_direct=False,
header=True, mode=0):
"""Add new result files to grid.
Use this methods if you want to get annual metrics without loading the values
for each point. This method is only useful for cases with no window groups and
dynamic blind states. After adding the files you can call 'annualMetrics' method.
"""
ResultFile = namedtuple(
'ResultFile', ('path', 'hoys', 'start_line', 'header', 'mode'))
inf = ResultFile(file_path, hoys, start_line, header, mode)
if is_direct:
self._directFiles.append(inf)
else:
self._totalFiles.append(inf)
[docs] def set_values(self, hoys, values, source=None, state=None, is_direct=False):
# assign the values to points
for count, hourlyValues in enumerate(values):
self.analysis_points[count].set_values(
hourlyValues, hoys, source, state, is_direct)
[docs] def set_values_from_file(self, file_path, hoys=None, source=None, state=None,
start_line=None, is_direct=False, header=True,
check_point_count=True, mode=0):
"""Load values for test points from a file.
Args:
file_path: Full file path to the result file.
hoys: A collection of hours of the year for the results. If None the
default will be range(0, len(results)).
source: Name of the source.
state: Name of the state.
start_line: Number of start lines after the header from 0 (default: 0).
is_direct: A Boolean to declare if the results is direct illuminance
(default: False).
header: A Boolean to declare if the file has header (default: True).
mode:
0 > load the values
1 > load values as binary. Any non-zero value
will be 1. This is useful for studies such as sunlight hours.
2 or Greater > Load results divided by mode value, which can be 2 or
any other value larger than 2. Use this mode for daylight factor or
radiation analysis.
e.g. For daylight factor that you run an illuminance study under an
overcast sky with total horizontal illuminance of 10000 lux. Now to
get daylight factor you should divide the values by 100 ( 10000 / 100).
In that case you can use mode = 100 to get the correct values.
"""
if os.path.getsize(file_path) < 2:
raise EmptyFileError(file_path)
st = start_line or 0
with open(file_path, readmode) as inf:
if header:
inf, _ = self.parse_header(inf, st, hoys, check_point_count)
self.add_result_files(file_path, hoys, st, is_direct, header, mode)
for i in xrange(st):
next(inf)
end = len(self._analysis_points)
if mode == 0:
values = (tuple(int(float(r)) for r in next(inf).split())
for count in xrange(end))
elif mode == 1:
# binary 0-1 (useful for solaraccess studies)
values = (tuple(1 if float(r) > 0 else 0 for r in next(inf).split())
for count in xrange(end))
else:
# divide values by mode (useful for daylight factor calculation)
values = (tuple(float(r) / mode for r in next(inf).split())
for count in xrange(end))
# assign the values to points
for count, hourlyValues in enumerate(values):
self.analysis_points[count].set_values(
hourlyValues, hoys, source, state, is_direct)
[docs] def set_coupled_values_from_file(
self, total_file_path, direct_file_path, hoys=None, source=None, state=None,
start_line=None, header=True, check_point_count=True, mode=0):
"""Load direct and total values for test points from two files.
Args:
file_path: Full file path to the result file.
hoys: A collection of hours of the year for the results. If None the
default will be range(0, len(results)).
source: Name of the source.
state: Name of the state.
start_line: Number of start lines after the header from 0 (default: 0).
header: A Boolean to declare if the file has header (default: True).
mode: 0 > load the values 1 > load values as binary. Any non-zero value
will be 1. This is useful for studies such as sunlight hours. 2 >
load the values divided by mode number. Use this mode for daylight
factor or radiation analysis.
"""
for file_path in (total_file_path, direct_file_path):
if os.path.getsize(file_path) < 2:
raise EmptyFileError(file_path)
st = start_line or 0
with open(total_file_path, readmode) as inf, open(direct_file_path, readmode) as dinf:
if header:
inf, _ = self.parse_header(inf, st, hoys, check_point_count)
dinf, _ = self.parse_header(dinf, st, hoys, check_point_count)
self.add_result_files(total_file_path, hoys, st, False, header, mode)
self.add_result_files(direct_file_path, hoys, st, True, header, mode)
for i in xrange(st):
next(inf)
next(dinf)
end = len(self._analysis_points)
if mode == 0:
coupled_values = (
tuple((int(float(r)), int(float(d))) for r, d in
zip(next(inf).split(), next(dinf).split()))
for count in xrange(end))
elif mode == 1:
# binary 0-1
coupled_values = (tuple(
(int(float(1 if float(r) > 0 else 0)),
int(float(1 if float(d) > 0 else 0)))
for r, d in zip(next(inf).split(), next(dinf).split()))
for count in xrange(end))
else:
# divide values by mode (useful for daylight factor calculation)
coupled_values = (
tuple((float(r) / mode, float(d) / mode) for r, d in
zip(next(inf).split(), next(dinf).split()))
for count in xrange(end))
# assign the values to points
for count, hourlyValues in enumerate(coupled_values):
self.analysis_points[count].set_coupled_values(
hourlyValues, hoys, source, state)
[docs] def combined_value_by_id(self, hoy=None, blinds_state_ids=None):
"""Get combined value from all sources based on state_id.
Args:
hoy: hour of the year.
blinds_state_ids: List of state ids for all the sources for an hour. If you
want a source to be removed set the state to -1.
Returns:
total, direct values.
"""
if self.digit_sign == 1:
self.load_values_from_files()
hoy = hoy or self.hoys[0]
return (p.combined_value_by_id(hoy, blinds_state_ids) for p in self)
[docs] def combined_values_by_id(self, hoys=None, blinds_state_ids=None):
"""Get combined value from all sources based on state_ids.
Args:
hoys: A collection of hours of the year.
blinds_state_ids: List of state ids for all the sources for input hoys. If
you want a source to be removed set the state to -1.
Returns:
Return a generator for (total, direct) values.
"""
if self.digit_sign == 1:
self.load_values_from_files()
return (p.combined_value_by_id(hoys, blinds_state_ids) for p in self)
[docs] def sum_values_by_id(self, hoys=None, blinds_state_ids=None):
"""Get sum of value for all the hours.
This method is mostly useful for radiation and solar access analysis.
Args:
hoys: A collection of hours of the year.
blinds_state_ids: List of state ids for all the sources for input hoys. If
you want a source to be removed set the state to -1.
Returns:
Return a collection of sum values as (total, direct) values.
"""
if self.digit_sign == 1:
self.load_values_from_files()
return (p.sum_values_by_id(hoys, blinds_state_ids) for p in self)
[docs] def max_values_by_id(self, hoys=None, blinds_state_ids=None):
"""Get maximum value for all the hours.
Args:
hoys: A collection of hours of the year.
blinds_state_ids: List of state ids for all the sources for input hoys. If
you want a source to be removed set the state to -1.
Returns:
Return a tuple for sum of (total, direct) values.
"""
if self.digit_sign == 1:
self.load_values_from_files()
return (p.max_values_by_id(hoys, blinds_state_ids) for p in self)
[docs] def annual_metrics(self, da_threshhold=None, udi_min_max=None, blinds_state_ids=None,
occ_schedule=None):
"""Calculate annual metrics.
Daylight autonomy, continious daylight autonomy and useful daylight illuminance.
Args:
da_threshhold: Threshhold for daylight autonomy in lux (default: 300).
udi_min_max: A tuple of min, max value for useful daylight illuminance
(default: (100, 3000)).
blinds_state_ids: List of state ids for all the sources for input hoys. If
you want a source to be removed set the state to -1.
occ_schedule: An annual occupancy schedule.
Returns:
Daylight autonomy, Continious daylight autonomy, Useful daylight illuminance,
Less than UDI, More than UDI
"""
results_loaded = True
if not self.has_values and not self.result_files[0]:
raise ValueError('No values are assigned to this analysis grid.')
elif not self.has_values:
# results are not loaded but are available
assert len(self.result_files[0]) == 1, \
ValueError(
'Annual recipe can currently only handle '
'a single merged result file.'
)
results_loaded = False
print('Loading the results from result files.')
res = ([], [], [], [], [])
da_threshhold = da_threshhold or 300.0
udi_min_max = udi_min_max or (100, 3000)
hoys = self.hoys
occ_schedule = occ_schedule or Schedule.eight_am_to_six_pm()
if results_loaded:
blinds_state_ids = blinds_state_ids or [[0] * len(self.sources)] * len(hoys)
for sensor in self.analysis_points:
for c, r in enumerate(sensor.annual_metrics(da_threshhold,
udi_min_max,
blinds_state_ids,
occ_schedule
)):
res[c].append(r)
else:
# This is a method for annual recipe to load the results line by line
# which unlike the other method doesn't load all the values to the memory
# at once.
blinds_state_ids = [[0] * len(self.sources)] * len(hoys)
calculate_annual_metrics = self.analysis_points[0]._calculate_annual_metrics
for file_data in self.result_files[0]:
file_path, hoys, start_line, header, mode = file_data
# read the results line by line and caluclate the values
if os.path.getsize(file_path) < 2:
raise EmptyFileError(file_path)
assert mode == 0, \
TypeError(
'Annual results can only be calculated from '
'illuminance studies.')
st = start_line or 0
with open(file_path, readmode) as inf:
if header:
inf, _ = self.parse_header(inf, st, hoys, False)
for i in xrange(st):
next(inf)
end = len(self._analysis_points)
# load one line at a time
for count in xrange(end):
values = (int(float(r)) for r in next(inf).split())
for c, r in enumerate(
calculate_annual_metrics(
values, hoys, da_threshhold, udi_min_max,
blinds_state_ids, occ_schedule)):
res[c].append(r)
return res
[docs] def spatial_daylight_autonomy(self, da_threshhold=None, target_da=None,
blinds_state_ids=None, occ_schedule=None):
"""Calculate Spatial Daylight Autonomy (sDA).
Args:
da_threshhold: Minimum illuminance threshhold for daylight (default: 300).
target_da: Minimum threshhold for daylight autonomy in percentage
(default: 50%).
blinds_state_ids: List of state ids for all the sources for input hoys. If
you want a source to be removed set the state to -1.
occ_schedule: An annual occupancy schedule.
Returns:
sDA: Spatial daylight autonomy as percentage of analysis points.
DA: Daylight autonomy for each analysis point.
Problematic points: List of problematic points.
"""
results_loaded = True
if not self.has_values and not self.result_files[0]:
raise ValueError('No values are assigned to this analysis grid.')
elif not self.has_values:
# results are not loaded but are available
assert len(self.result_files[0]) == 1, \
ValueError(
'Annual recipe can currently only handle '
'a single merged result file.'
)
results_loaded = False
print('Loading the results from result files.')
res = ([], [])
da_threshhold = da_threshhold or 300.0
target_da = target_da or 50.0
hoys = self.hoys
occ_schedule = occ_schedule or Schedule.eight_am_to_six_pm()
if results_loaded:
blinds_state_ids = blinds_state_ids or [[0] * len(self.sources)] * len(hoys)
for sensor in self.analysis_points:
for c, r in enumerate(sensor.daylight_autonomy(da_threshhold,
blinds_state_ids,
occ_schedule
)):
res[c].append(r)
else:
# This is a method for annual recipe to load the results line by line
# which unlike the other method doesn't load all the values to the memory
# at once.
blinds_state_ids = [[0] * len(self.sources)] * len(hoys)
calculate_daylight_autonomy = \
self.analysis_points[0]._calculate_daylight_autonomy
for file_data in self.result_files[0]:
file_path, hoys, start_line, header, mode = file_data
# read the results line by line and caluclate the values
if os.path.getsize(file_path) < 2:
raise EmptyFileError(file_path)
assert mode == 0, \
TypeError(
'Annual results can only be calculated from '
'illuminance studies.')
st = start_line or 0
with open(file_path, readmode) as inf:
if header:
inf, _ = self.parse_header(inf, st, hoys, False)
for i in xrange(st):
next(inf)
end = len(self._analysis_points)
# load one line at a time
for count in xrange(end):
values = (int(float(r)) for r in next(inf).split())
for c, r in enumerate(
calculate_daylight_autonomy(
values, hoys, da_threshhold,
blinds_state_ids, occ_schedule)):
res[c].append(r)
daylight_autonomy = res[0]
problematic_points = []
for pt, da in zip(self.analysis_points, daylight_autonomy):
if da < target_da:
problematic_points.append(pt)
try:
sda = (1 - len(problematic_points) / len(self.analysis_points)) * 100
except ZeroDivisionError:
sda = 0
return sda, daylight_autonomy, problematic_points
[docs] def annual_sunlight_exposure(self, threshhold=None, blinds_state_ids=None,
occ_schedule=None, target_hours=None, target_area=None):
"""Annual Solar Exposure (ASE)
As per IES-LM-83-12 ase is the percent of sensors that are
found to be exposed to more than 1000lux of direct sunlight for
more than 250hrs per year. For LEED credits No more than 10% of
the points in the grid should fail this measure.
Args:
threshhold: Threshhold for for solar exposure in lux (default: 1000).
blinds_state_ids: List of state ids for all the sources for input hoys.
If you want a source to be removed set the state to -1. ase must
be calculated without dynamic blinds but you can use this option
to study the effect of different blind states.
occ_schedule: An annual occupancy schedule.
target_hours: Minimum targe hours for each point (default: 250).
target_area: Minimum target area percentage for this grid (default: 10).
Returns:
Success as a Boolean, ase values for each point, Percentage area,
Problematic points, Problematic hours for each point
"""
results_loaded = True
if not self.has_direct_values and not self.result_files[1]:
raise ValueError(
'Direct values are not available to calculate ASE.\nIn most of the cases'
' this is because you are using a point in time recipe or the three-'
'phase recipe. You should use one of the daylight coefficient based '
'recipes or the 5 phase recipe instead.')
elif not self.has_direct_values:
# results are not loaded but are available
assert len(self.result_files[1]) == 1, \
ValueError(
'Annual recipe can currently only handle '
'a single merged result file.'
)
results_loaded = False
print('Loading the results from result files.')
res = ([], [], [])
threshhold = threshhold or 1000
target_hours = target_hours or 250
target_area = target_area or 10
hoys = self.hoys
occ_schedule = occ_schedule or set(hoys)
if results_loaded:
blinds_state_ids = blinds_state_ids or [[0] * len(self.sources)] * len(hoys)
for sensor in self.analysis_points:
for c, r in enumerate(sensor.annual_sunlight_exposure(threshhold,
blinds_state_ids,
occ_schedule,
target_hours
)):
res[c].append(r)
else:
# This is a method for annual recipe to load the results line by line
# which unlike the other method doesn't load all the values to the memory
# at once.
blinds_state_ids = [[0] * len(self.sources)] * len(hoys)
calculate_annual_sunlight_exposure = \
self.analysis_points[0]._calculate_annual_sunlight_exposure
for file_data in self.result_files[1]:
file_path, hoys, start_line, header, mode = file_data
# read the results line by line and caluclate the values
if os.path.getsize(file_path) < 2:
raise EmptyFileError(file_path)
assert mode == 0, \
TypeError(
'Annual results can only be calculated from '
'illuminance studies.')
st = start_line or 0
with open(file_path, readmode) as inf:
if header:
inf, _ = self.parse_header(inf, st, hoys, False)
for i in xrange(st):
next(inf)
end = len(self._analysis_points)
# load one line at a time
for count in xrange(end):
values = (int(float(r)) for r in next(inf).split())
for c, r in enumerate(
calculate_annual_sunlight_exposure(
values, hoys, threshhold, blinds_state_ids, occ_schedule,
target_hours)):
res[c].append(r)
# calculate ase for the grid
ap = self.analysis_points # create a local copy of points for better performance
problematic_point_count = 0
problematic_points = []
problematic_hours = []
ase_values = []
for i, (success, ase, pHours) in enumerate(zip(*res)):
ase_values.append(ase) # collect annual ase values for each point
if success:
continue
problematic_point_count += 1
problematic_points.append(ap[i])
problematic_hours.append(pHours)
per_problematic = 100 * problematic_point_count / len(ap)
return per_problematic < target_area, ase_values, per_problematic, \
problematic_points, problematic_hours
[docs] def parse_blind_states(self, blinds_state_ids):
"""Parse input blind states.
The method tries to convert each state to a tuple of a list. Use this method
to parse the input from plugins.
Args:
blinds_state_ids: List of state ids for all the sources for an hour. If you
want a source to be removed set the state to -1. If not provided
a longest combination of states from sources (window groups) will
be used. Length of each item in states should be equal to number
of sources.
"""
return self.analysis_points[0].parse_blind_states(blinds_state_ids)
[docs] def load_values_from_files(self):
"""Load grid values from self.result_files."""
# remove old results
for ap in self._analysis_points:
ap._sources = OrderedDict()
ap._values = []
r_files = self.result_files[0][:]
d_files = self.result_files[1][:]
self._totalFiles = []
self._directFiles = []
# pass
if r_files and d_files:
# both results are available
for rf, df in zip(r_files, d_files):
rfPath, hoys, start_line, header, mode = rf
dfPath, hoys, start_line, header, mode = df
fn = os.path.split(rfPath)[-1][:-4].split("..")
source = fn[-2]
state = fn[-1]
print(
'\nloading total and direct results for {} AnalysisGrid'
' from {}::{}\n{}\n{}\n'.format(
self.name, source, state, rfPath, dfPath))
self.set_coupled_values_from_file(
rfPath, dfPath, hoys, source, state, start_line, header,
False, mode
)
elif r_files:
for rf in r_files:
rfPath, hoys, start_line, header, mode = rf
fn = os.path.split(rfPath)[-1][:-4].split("..")
source = fn[-2]
state = fn[-1]
print('\nloading the results for {} AnalysisGrid form {}::{}\n{}\n'
.format(self.name, source, state, rfPath))
self.set_values_from_file(
rf, hoys, source, state, start_line, is_direct=False,
header=header, check_point_count=False, mode=mode
)
elif d_files:
for rf in d_files:
rfPath, hoys, start_line, header, mode = rf
fn = os.path.split(rfPath)[-1][:-4].split("..")
source = fn[-2]
state = fn[-1]
print('\nloading the results for {} AnalysisGrid form {}::{}\n{}\n'
.format(self.name, source, state, rfPath))
self.set_values_from_file(
rf, hoys, source, state, start_line, is_direct=True,
header=header, check_point_count=False, mode=mode
)
[docs] def unload(self):
"""Remove all the sources and values from analysis_points."""
self._totalFiles = []
self._directFiles = []
for ap in self._analysis_points:
ap._sources = OrderedDict()
ap._values = []
[docs] def duplicate(self):
"""Duplicate AnalysisGrid."""
aps = tuple(ap.duplicate() for ap in self._analysis_points)
dup = AnalysisGrid(aps, self._name)
dup._sources = aps[0]._sources
dup._wgroups = self._wgroups
return dup
[docs] def to_rad_string(self):
"""Return analysis points group as a Radiance string."""
return "\n".join((ap.to_rad_string() for ap in self._analysis_points))
[docs] def write(self, folder, filename=None, mkdir=False):
"""write analysis grid to file."""
name = filename or self.name + '.pts'
return write_to_file_by_name(folder, name, self.to_rad_string() + '\n', mkdir)
[docs] def ToString(self):
"""Overwrite ToString .NET method."""
return self.__repr__()
[docs] def to_json(self):
"""Create json object from analysisGrid."""
analysis_points = [ap.to_json() for ap in self.analysis_points]
return {
"name": self._name,
"analysis_points": analysis_points
}
def __add__(self, other):
"""Add two analysis grids and create a new one.
This method won't duplicate the analysis points.
"""
assert isinstance(other, AnalysisGrid), \
TypeError('Expected an AnalysisGrid not {}.'.format(type(other)))
assert self.hoys == other.hoys, \
ValueError('Two analysis grid must have the same hoys.')
if not self.has_values:
sources = self._sources.update(other._sources)
else:
assert self._sources == other._sources, \
ValueError(
'Two analysis grid with values must have the same window_groups.'
)
sources = self._sources
points = self.analysis_points + other.analysis_points
name = '{}+{}'.format(self.name, other.name)
addition = AnalysisGrid(points, name)
addition._sources = sources
return addition
def __len__(self):
"""Number of points in this group."""
return len(self._analysis_points)
def __getitem__(self, index):
"""Get value for an index."""
return self._analysis_points[index]
def __iter__(self):
"""Iterate points."""
return iter(self._analysis_points)
def __str__(self):
"""String repr."""
return self.to_rad_string()
@property
def digit_sign(self):
if not self.has_values:
if len(self.result_files[0]) + len(self.result_files[1]) == 0:
# only x, y, z datat is available
return 0
else:
# results are available but are not loaded yet
return 1
elif self.is_results_point_in_time:
# results is loaded for a single hour
return 2
else:
# results is loaded for multiple hours
return 3
@property
def _sign(self):
if not self.has_values:
if len(self.result_files[0]) + len(self.result_files[1]) == 0:
# only x, y, z datat is available
return '[.]'
else:
# results are available but are not loaded yet
return '[/]'
elif self.is_results_point_in_time:
# results is loaded for a single hour
return '[+]'
else:
# results is loaded for multiple hours
return '[*]'
def __repr__(self):
"""Return analysis points and directions."""
return 'AnalysisGrid::{}::#{}::{}'.format(
self._name, len(self._analysis_points), self._sign
)