class AssignAverageBMIRank(Scan):
mean_bmi_var: Decimal = SubjectValidator(data_type=Decimal, temporal=-1)
bmi_rank_var: Integer = SubjectValidator(data_type=Integer, temporal=-1)
def __post_init__(self):
self.ranked: Dict[str, int] = {}
def extract(self, composite: Dict) -> float:
mean_bmi = composites.get_property(composite, self.mean_bmi_var)
return mean_bmi
def analyze(self, extracts: Iterable[Tuple[str, Any]]) -> None:
# Initialize data structure for analyzed data
mean_bmi_dict: Dict[str, float] = {}
for composite_id, mean_bmi in extracts:
mean_bmi_dict[composite_id] = mean_bmi
people_ranked = list(
sorted(mean_bmi_dict.keys(), key=lambda k: -1 * mean_bmi_dict[k]))
for k, person in enumerate(people_ranked):
self.ranked[person] = k + 1
def alter(self, composite_id: str, composite: Dict) -> None:
rank = self.ranked[composite_id]
composites.put_property(composite, self.bmi_rank_var, rank)
class AssignMeanBMI(Change):
annual_bmi_var: Decimal = SubjectValidator(data_type=Decimal, temporal=1)
mean_bmi_var: Decimal = SubjectValidator(data_type=Decimal, temporal=-1)
def __call__(self, composite: Dict):
bmis = [
bmi for period, bmi in composites.get_all_observations(
composite, self.annual_bmi_var)
]
mean_bmi = numpy.average(bmis)
composites.put_property(composite, self.mean_bmi_var, mean_bmi)
class AssignRegressionStats(Change):
annual_weight_var: Decimal = SubjectValidator(data_type=Decimal, temporal=1)
weight_slope_var: Decimal = SubjectValidator(data_type=Decimal, temporal=-1)
weight_pval_var: Decimal = SubjectValidator(data_type=Decimal, temporal=-1)
def __call__(self, composite: Dict):
years = sorted([int(year) for year in composites.get_periods(composite)])
weights = [composites.get_observation(composite, str(year), self.annual_weight_var) for year in years]
slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(years, weights)
composites.put_property(composite, self.weight_slope_var, slope)
composites.put_property(composite, self.weight_pval_var, p_value)
class AssignAnnualBMI(Change):
annual_weight_var: Decimal = SubjectValidator(data_type=Decimal,
temporal=1)
height_var: Primitive = SubjectValidator(data_type=[Decimal, Integer],
temporal=-1)
annual_bmi_var: Decimal = SubjectValidator(data_type=Decimal, temporal=1)
def __call__(self, composite: Dict):
h_squared = composites.get_property(composite, self.height_var)**2
for period, weight in composites.get_all_observations(
composite, self.annual_weight_var):
bmi = weight / h_squared * 703
composites.put_observation(composite, period, self.annual_bmi_var,
bmi)
class ColorNameToRGB(Change):
"""Look up the RGB value for the color name specified by color_name_var, and store it in rgb_var."""
color_name_var: Text = SubjectValidator(data_type=Text)
rgb_var: Text = SubjectValidator(data_type=Text)
def __call__(self, composite: Dict):
# IRL, you'd have to handle nulls, decide how to deal with temporal variables, etc.
color_name_path = ["invariant"] + list(self.color_name_var.absolute_path)
color_name = nesteddicts.get(composite, color_name_path)
# At some point, I'll write a fancy retrieval/validation/assignment system, but that's not for the MVP
rgb_value = self.lookups["color_names"][color_name]
rgb_path = ["invariant"] + list(self.rgb_var.absolute_path)
nesteddicts.put(composite, rgb_path, rgb_value)
class AssignAverageBMIRank(Scan):
male_flag: Binary = SubjectValidator(data_type=Binary, temporal=-1)
mean_bmi_var: Decimal = SubjectValidator(data_type=Decimal, temporal=-1)
bmi_rank_gender_var: Integer = SubjectValidator(data_type=Integer, temporal=-1)
bmi_rank_overall_var: Integer = SubjectValidator(data_type=Integer, temporal=-1)
def __post_init__(self):
self.ranked: Dict[str, Dict[str, int]] = {}
def extract(self, composite: Dict) -> Tuple[bool, float]:
mean_bmi = composites.get_property(composite, self.mean_bmi_var)
is_male = composites.get_property(composite, self.male_flag)
return is_male, mean_bmi
def analyze(self, extracts: Iterable[Tuple[str, Any]]) -> None:
# Initialize data structure for analyzed data
mean_bmi_dict: Dict[str, Dict[str, float]] = {}
genders = ["male", "female", "overall"]
for gender in genders:
mean_bmi_dict[gender] = {}
self.ranked[gender] = {}
for composite_id, (is_male, mean_bmi) in extracts:
if is_male:
gender: str = "male"
else:
gender = "female"
mean_bmi_dict[gender][composite_id] = mean_bmi
mean_bmi_dict["overall"][composite_id] = mean_bmi
for gender in genders:
people_ranked = list(sorted(mean_bmi_dict[gender].keys(), key=lambda k: -1 * mean_bmi_dict[gender][k]))
for k, person in enumerate(people_ranked):
self.ranked[gender][person] = k + 1
def _get_gender(self, composite_id: str) -> str:
if composite_id in self.ranked["male"]:
return "male"
return "female"
def alter(self, composite_id: str, composite: Dict) -> None:
overall_rank = self.ranked["overall"][composite_id]
composites.put_property(composite, self.bmi_rank_overall_var, overall_rank)
gender: str = self._get_gender(composite_id)
gender_rank = self.ranked[gender][composite_id]
composites.put_property(composite, self.bmi_rank_gender_var, gender_rank)
class GeneratePersonDescription(Change):
color_name_var: Text = SubjectValidator(data_type=Text)
rgb_var: Text = SubjectValidator(data_type=Text)
person_name_var: Text = SubjectValidator(data_type=Text)
gender_var: Text = SubjectValidator(data_type=Text)
weight_gain_var: Decimal = SubjectValidator(data_type=Decimal)
sentence_var: Text = SubjectValidator(data_type=Text)
def get_pronoun(self, composite: Dict):
gender: str = get_value(composite, self.gender_var)
pronoun_mapping = {"male": "he", "female": "she"}
return pronoun_mapping[gender]
def __call__(self, composite: Dict):
template = "%s's favorite color is %s (%s). Over the observation period, %s gained %0.1f lbs."
name = get_value(composite, self.person_name_var)
color = get_value(composite, self.color_name_var)
rgb = get_value(composite, self.rgb_var)
weight_gain = get_value(composite, self.weight_gain_var)
pronoun = self.get_pronoun(composite)
sentence = template % (name, color, rgb, pronoun, weight_gain)
print(self.sentence_var)
sentence_path = ["invariant"] + list(self.sentence_var.absolute_path)
nesteddicts.put(composite, sentence_path, sentence)
class CalculateWeightGain(Change):
"""Determine the total weight gain over the observation period."""
weight_var: Decimal = SubjectValidator(data_type=Decimal)
weight_gain_var: Decimal = SubjectValidator(data_type=Decimal)
def __call__(self, composite: Dict):
periods = set(composite.keys()) - {"invariant"}
earliest = min(periods)
latest = max(periods)
weight_path = list(self.weight_var.absolute_path)
earliest_weight_path: list = [earliest] + weight_path
earliest_weight: float = nesteddicts.get(composite, earliest_weight_path)
latest_weight_path: list = [latest] + weight_path
latest_weight: float = nesteddicts.get(composite, latest_weight_path)
# I know, should have called it "weight change."
weight_gain = round(latest_weight - earliest_weight, 2)
weight_gain_path = ["invariant"] + list(self.weight_gain_var.absolute_path)
nesteddicts.put(composite, weight_gain_path, weight_gain)
class RetainOnlyFemales(Filter):
male_flag: Binary = SubjectValidator(data_type=Binary)
def passes(self, composite: Dict) -> bool:
male: bool = composites.get_property(composite, self.male_flag)
return not male