# Path: user_metrics/etl/aggregator.py
# def weighted_rate(iter, **kwargs):
#     """
#         Computes a weighted rate over the elements of the iterator.
#     """
# 
#     def weight_method_default(x):
#         return 1
# 
#     weight_idx = kwargs['weight_idx'] if 'weight_idx' in kwargs else 1
#     val_idx = kwargs['val_idx'] if 'val_idx' in kwargs else 1
#     weight_method = kwargs['weight_method'] if 'cmp_method' in kwargs else \
#         weight_method_default
# 
#     count = 0
#     total_weight = 0.0
#     weighted_sum = 0.0
#     for r in iter.__iter__():
#         try:
#             count += 1
#             weight = weight_method(r[weight_idx])
#             total_weight += r[weight_idx]
#             weighted_sum += weight * r[val_idx]
#         except IndexError:
#             continue
#         except TypeError:
#             continue
#     if count:
#         return [count, total_weight, weighted_sum / count]
#     else:
#         return [count, total_weight, 0.0]
# 
# def decorator_builder(header):
#     """
#         Decorator method to annotate aggregation methods to ensure the correct
#         data model is exposed by::
# 
#             metric_agg = decorator_builder(Metric.header())(metric_agg)
#     """
#     def eval_data_model(f):
#         def wrapper(metric, **kwargs):
#             if hasattr(metric, 'header'):
#                 header_arg = metric.header()
#                 if all(
#                         header_arg[i] == header[i]
#                         for i in range(len(header)-1)):
#                     return f(metric, **kwargs)
#             else:
#                 raise AggregatorError('This aggregator (%s) does not operate '
#                                       'on this data type.' % f.__name__)
#         return wrapper
#     return eval_data_model
# 
# def build_numpy_op_agg(agg_meta_list, metric_header, method_handle):
#     """
#         Builder method for ``numpy_op`` aggregator.
#     """
#     agg_method = numpy_op
#     agg_method = decorator_builder(metric_header)(agg_method)
#     agg_meta_header = [o.field_name for o in agg_meta_list]
# 
#     setattr(agg_method, METRIC_AGG_METHOD_FLAG, True)
#     setattr(agg_method, METRIC_AGG_METHOD_NAME, method_handle)
#     setattr(agg_method, METRIC_AGG_METHOD_HEAD, agg_meta_header)
#     setattr(agg_method, METRIC_AGG_METHOD_KWARGS,
#             {
#                 'agg_meta': agg_meta_list
#             }
#             )
#     return agg_method
# 
# def build_agg_meta(op_list, field_prefix_names):
#     """
#         Builder helper method for building module aggregators via
#         ``build_numpy_op_agg``.
#     """
#     return [AggregatorMeta(name + op.__name__, index, op)
#             for name, index in field_prefix_names.iteritems()
#             for op in op_list]
# 
# Path: user_metrics/metrics/users.py
# USER_METRIC_PERIOD_TYPE = enum(REGISTRATION='REGISTRATION', INPUT='INPUT')
# 
# Path: user_metrics/metrics/user_metric.py
# METRIC_AGG_METHOD_KWARGS = 'metric_agg_kwargs'


from copy import deepcopy
from dateutil.parser import parse as date_parse
from user_metrics.etl.aggregator import weighted_rate, decorator_builder, \
    build_numpy_op_agg, build_agg_meta
from numpy import median, min, max, mean, std
from user_metrics.metrics.users import USER_METRIC_PERIOD_TYPE as umpt
from user_metrics.utils import enum, format_mediawiki_timestamp
from user_metrics.metrics.user_metric import METRIC_AGG_METHOD_KWARGS
import user_metric as um
import edit_count as ec
# ==========================

# Build "rate" decorator
edit_rate_agg = weighted_rate
edit_rate_agg = decorator_builder(EditRate.header())(edit_rate_agg)

setattr(edit_rate_agg, um.METRIC_AGG_METHOD_FLAG, True)
setattr(edit_rate_agg, um.METRIC_AGG_METHOD_NAME, 'edit_rate_agg')
setattr(edit_rate_agg, um.METRIC_AGG_METHOD_HEAD, ['total_users',
                                                   'total_weight', 'rate'])
setattr(edit_rate_agg, um.METRIC_AGG_METHOD_KWARGS, {
    'val_idx': 2,
})

metric_header = EditRate.header()

field_prefixes = \
    {
        'count_': 1,
        'rate_': 2,
    }


# Build "dist" decorator
op_list = [sum, mean, std, median, min, max]
er_stats_agg = build_numpy_op_agg(build_agg_meta(op_list, field_prefixes),
                                  metric_header,
                                  'er_stats_agg')

agg_kwargs = getattr(er_stats_agg, METRIC_AGG_METHOD_KWARGS)

# Path: user_metrics/etl/aggregator.py
# def weighted_rate(iter, **kwargs):
#     """
#         Computes a weighted rate over the elements of the iterator.
#     """
# 
#     def weight_method_default(x):
#         return 1
# 
#     weight_idx = kwargs['weight_idx'] if 'weight_idx' in kwargs else 1
#     val_idx = kwargs['val_idx'] if 'val_idx' in kwargs else 1
#     weight_method = kwargs['weight_method'] if 'cmp_method' in kwargs else \
#         weight_method_default
# 
#     count = 0
#     total_weight = 0.0
#     weighted_sum = 0.0
#     for r in iter.__iter__():
#         try:
#             count += 1
#             weight = weight_method(r[weight_idx])
#             total_weight += r[weight_idx]
#             weighted_sum += weight * r[val_idx]
#         except IndexError:
#             continue
#         except TypeError:
#             continue
#     if count:
#         return [count, total_weight, weighted_sum / count]
#     else:
#         return [count, total_weight, 0.0]
# 
# def decorator_builder(header):
#     """
#         Decorator method to annotate aggregation methods to ensure the correct
#         data model is exposed by::
# 
#             metric_agg = decorator_builder(Metric.header())(metric_agg)
#     """
#     def eval_data_model(f):
#         def wrapper(metric, **kwargs):
#             if hasattr(metric, 'header'):
#                 header_arg = metric.header()
#                 if all(
#                         header_arg[i] == header[i]
#                         for i in range(len(header)-1)):
#                     return f(metric, **kwargs)
#             else:
#                 raise AggregatorError('This aggregator (%s) does not operate '
#                                       'on this data type.' % f.__name__)
#         return wrapper
#     return eval_data_model
# 
# def build_numpy_op_agg(agg_meta_list, metric_header, method_handle):
#     """
#         Builder method for ``numpy_op`` aggregator.
#     """
#     agg_method = numpy_op
#     agg_method = decorator_builder(metric_header)(agg_method)
#     agg_meta_header = [o.field_name for o in agg_meta_list]
# 
#     setattr(agg_method, METRIC_AGG_METHOD_FLAG, True)
#     setattr(agg_method, METRIC_AGG_METHOD_NAME, method_handle)
#     setattr(agg_method, METRIC_AGG_METHOD_HEAD, agg_meta_header)
#     setattr(agg_method, METRIC_AGG_METHOD_KWARGS,
#             {
#                 'agg_meta': agg_meta_list
#             }
#             )
#     return agg_method
# 
# def build_agg_meta(op_list, field_prefix_names):
#     """
#         Builder helper method for building module aggregators via
#         ``build_numpy_op_agg``.
#     """
#     return [AggregatorMeta(name + op.__name__, index, op)
#             for name, index in field_prefix_names.iteritems()
#             for op in op_list]
# 
# Path: user_metrics/metrics/users.py
# USER_METRIC_PERIOD_TYPE = enum(REGISTRATION='REGISTRATION', INPUT='INPUT')
# 
# Path: user_metrics/metrics/user_metric.py
# METRIC_AGG_METHOD_KWARGS = 'metric_agg_kwargs'


# Path: user_metrics/metrics/edit_rate.py
from copy import deepcopy
from dateutil.parser import parse as date_parse
from user_metrics.etl.aggregator import weighted_rate, decorator_builder, \
    build_numpy_op_agg, build_agg_meta
from numpy import median, min, max, mean, std
from user_metrics.metrics.users import USER_METRIC_PERIOD_TYPE as umpt
from user_metrics.utils import enum, format_mediawiki_timestamp
from user_metrics.metrics.user_metric import METRIC_AGG_METHOD_KWARGS
import user_metric as um
import edit_count as ec

# ==========================

# Build "rate" decorator
edit_rate_agg = weighted_rate
edit_rate_agg = decorator_builder(EditRate.header())(edit_rate_agg)

setattr(edit_rate_agg, um.METRIC_AGG_METHOD_FLAG, True)
setattr(edit_rate_agg, um.METRIC_AGG_METHOD_NAME, 'edit_rate_agg')
setattr(edit_rate_agg, um.METRIC_AGG_METHOD_HEAD, ['total_users',
                                                   'total_weight', 'rate'])
setattr(edit_rate_agg, um.METRIC_AGG_METHOD_KWARGS, {
    'val_idx': 2,
})

metric_header = EditRate.header()

field_prefixes = \
    {
        'count_': 1,
        'rate_': 2,
    }


# Build "dist" decorator
op_list = [sum, mean, std, median, min, max]
er_stats_agg = build_numpy_op_agg(build_agg_meta(op_list, field_prefixes),
                                  metric_header,
                                  'er_stats_agg')

agg_kwargs = getattr(er_stats_agg, METRIC_AGG_METHOD_KWARGS)

setattr(er_stats_agg, METRIC_AGG_METHOD_KWARGS, agg_kwargs)