diff --git a/spark/jobs/current_job.py b/spark/jobs/current_job.py index 10e2bc1f..288f4956 100644 --- a/spark/jobs/current_job.py +++ b/spark/jobs/current_job.py @@ -68,14 +68,18 @@ def main( complete_record["MODEL_QUALITY"] = orjson.dumps(model_quality).decode( "utf-8" ) - complete_record["STATISTICS"] = orjson.dumps(statistics).decode("utf-8") + complete_record["STATISTICS"] = statistics.model_dump_json( + serialize_as_any=True + ) complete_record["DATA_QUALITY"] = data_quality.model_dump_json( serialize_as_any=True ) complete_record["DRIFT"] = orjson.dumps(drift).decode("utf-8") case ModelType.MULTI_CLASS: statistics = calculate_statistics_current(current_dataset) - complete_record["STATISTICS"] = orjson.dumps(statistics).decode("utf-8") + complete_record["STATISTICS"] = statistics.model_dump_json( + serialize_as_any=True + ) schema = StructType( [ diff --git a/spark/jobs/metrics/statistics.py b/spark/jobs/metrics/statistics.py index aa3c93e9..d855f9b5 100644 --- a/spark/jobs/metrics/statistics.py +++ b/spark/jobs/metrics/statistics.py @@ -2,6 +2,8 @@ from models.reference_dataset import ReferenceDataset import pyspark.sql.functions as F +from models.statistics import Statistics + N_VARIABLES = "n_variables" N_OBSERVATION = "n_observations" MISSING_CELLS = "missing_cells" @@ -17,7 +19,7 @@ # FIXME generalize to one method def calculate_statistics_reference( reference_dataset: ReferenceDataset, -) -> dict[str, float]: +) -> Statistics: number_of_variables = len(reference_dataset.get_all_variables()) number_of_observations = reference_dataset.reference_count number_of_numerical = len(reference_dataset.get_numerical_variables()) @@ -79,12 +81,12 @@ def calculate_statistics_reference( .to_dict(orient="records")[0] ) - return stats + return Statistics(**stats) def calculate_statistics_current( current_dataset: CurrentDataset, -) -> dict[str, float]: +) -> Statistics: number_of_variables = len(current_dataset.get_all_variables()) number_of_observations = current_dataset.current_count number_of_numerical = len(current_dataset.get_numerical_variables()) @@ -146,4 +148,4 @@ def calculate_statistics_current( .to_dict(orient="records")[0] ) - return stats + return Statistics(**stats) diff --git a/spark/jobs/models/reference_dataset.py b/spark/jobs/models/reference_dataset.py index 37bf09c6..1b22e67a 100644 --- a/spark/jobs/models/reference_dataset.py +++ b/spark/jobs/models/reference_dataset.py @@ -2,7 +2,11 @@ from pyspark.ml.feature import StringIndexer from pyspark.sql import DataFrame -from pyspark.sql.types import DoubleType, StructField, StructType +from pyspark.sql.types import ( + DoubleType, + StructField, + StructType, +) from utils.models import ModelOut, ModelType, ColumnDefinition from utils.spark import apply_schema_to_dataframe diff --git a/spark/jobs/models/statistics.py b/spark/jobs/models/statistics.py new file mode 100644 index 00000000..b21cfa27 --- /dev/null +++ b/spark/jobs/models/statistics.py @@ -0,0 +1,17 @@ +from pydantic import BaseModel, ConfigDict + +from typing import Optional + + +class Statistics(BaseModel): + n_variables: int + n_observations: int + missing_cells: int + missing_cells_perc: Optional[float] + duplicate_rows: int + duplicate_rows_perc: Optional[float] + numeric: int + categorical: int + datetime: int + + model_config = ConfigDict(ser_json_inf_nan="null") diff --git a/spark/jobs/reference_job.py b/spark/jobs/reference_job.py index 678768e7..4500ab23 100644 --- a/spark/jobs/reference_job.py +++ b/spark/jobs/reference_job.py @@ -62,7 +62,9 @@ def main( complete_record["MODEL_QUALITY"] = orjson.dumps(model_quality).decode( "utf-8" ) - complete_record["STATISTICS"] = orjson.dumps(statistics).decode("utf-8") + complete_record["STATISTICS"] = statistics.model_dump_json( + serialize_as_any=True + ) complete_record["DATA_QUALITY"] = data_quality.model_dump_json( serialize_as_any=True ) @@ -73,7 +75,9 @@ def main( statistics = calculate_statistics_reference(reference_dataset) data_quality = metrics_service.calculate_data_quality() model_quality = metrics_service.calculate_model_quality() - complete_record["STATISTICS"] = orjson.dumps(statistics).decode("utf-8") + complete_record["STATISTICS"] = statistics.model_dump_json( + serialize_as_any=True + ) complete_record["DATA_QUALITY"] = data_quality.model_dump_json( serialize_as_any=True ) @@ -84,7 +88,12 @@ def main( metrics_service = ReferenceMetricsRegressionService( reference=reference_dataset ) + statistics = calculate_statistics_reference(reference_dataset) model_quality = metrics_service.calculate_model_quality() + + complete_record["STATISTICS"] = statistics.model_dump_json( + serialize_as_any=True + ) complete_record["MODEL_QUALITY"] = model_quality.model_dump_json( serialize_as_any=True ) diff --git a/spark/tests/binary_current_test.py b/spark/tests/binary_current_test.py index fa8d3c02..282ce6df 100644 --- a/spark/tests/binary_current_test.py +++ b/spark/tests/binary_current_test.py @@ -194,7 +194,7 @@ def test_calculation(spark_fixture, dataset): data_quality = metrics_service.calculate_data_quality() model_quality = metrics_service.calculate_model_quality_with_group_by_timestamp() - assert stats == my_approx( + assert stats.model_dump(serialize_as_any=True) == my_approx( { "categorical": 2, "datetime": 1, @@ -431,7 +431,7 @@ def test_calculation_current_joined(spark_fixture, current_joined): stats = calculate_statistics_current(current_dataset) data_quality = metrics_service.calculate_data_quality() - assert stats == my_approx( + assert stats.model_dump(serialize_as_any=True) == my_approx( { "categorical": 1, "datetime": 1, @@ -824,7 +824,7 @@ def test_calculation_complete(spark_fixture, complete_dataset): stats = calculate_statistics_current(current_dataset) data_quality = metrics_service.calculate_data_quality() - assert stats == my_approx( + assert stats.model_dump(serialize_as_any=True) == my_approx( { "missing_cells": 0, "missing_cells_perc": 0.0, @@ -973,7 +973,7 @@ def test_calculation_easy_dataset(spark_fixture, easy_dataset): stats = calculate_statistics_current(current_dataset) data_quality = metrics_service.calculate_data_quality() - assert stats == my_approx( + assert stats.model_dump(serialize_as_any=True) == my_approx( { "missing_cells": 0, "missing_cells_perc": 0.0, @@ -1122,7 +1122,7 @@ def test_calculation_dataset_cat_missing(spark_fixture, dataset_cat_missing): stats = calculate_statistics_current(current_dataset) data_quality = metrics_service.calculate_data_quality() - assert stats == my_approx( + assert stats.model_dump(serialize_as_any=True) == my_approx( { "missing_cells": 5, "missing_cells_perc": 6.25, @@ -1286,7 +1286,7 @@ def test_calculation_dataset_with_datetime(spark_fixture, dataset_with_datetime) stats = calculate_statistics_current(current_dataset) data_quality = metrics_service.calculate_data_quality() - assert stats == my_approx( + assert stats.model_dump(serialize_as_any=True) == my_approx( { "categorical": 2, "datetime": 1, @@ -1450,7 +1450,7 @@ def test_calculation_easy_dataset_bucket_test(spark_fixture, easy_dataset_bucket stats = calculate_statistics_current(current_dataset) data_quality = metrics_service.calculate_data_quality() - assert stats == my_approx( + assert stats.model_dump(serialize_as_any=True) == my_approx( { "missing_cells": 0, "missing_cells_perc": 0.0, @@ -1628,7 +1628,7 @@ def test_calculation_for_hour(spark_fixture, dataset_for_hour): data_quality = metrics_service.calculate_data_quality() model_quality = metrics_service.calculate_model_quality_with_group_by_timestamp() - assert stats == my_approx( + assert stats.model_dump(serialize_as_any=True) == my_approx( { "categorical": 2, "datetime": 1, @@ -1921,7 +1921,7 @@ def test_calculation_for_day(spark_fixture, dataset_for_day): data_quality = metrics_service.calculate_data_quality() model_quality = metrics_service.calculate_model_quality_with_group_by_timestamp() - assert stats == my_approx( + assert stats.model_dump(serialize_as_any=True) == my_approx( { "categorical": 2, "datetime": 1, @@ -2200,7 +2200,7 @@ def test_calculation_for_week(spark_fixture, dataset_for_week): data_quality = metrics_service.calculate_data_quality() model_quality = metrics_service.calculate_model_quality_with_group_by_timestamp() - assert stats == my_approx( + assert stats.model_dump(serialize_as_any=True) == my_approx( { "categorical": 2, "datetime": 1, @@ -2479,7 +2479,7 @@ def test_calculation_for_month(spark_fixture, dataset_for_month): data_quality = metrics_service.calculate_data_quality() model_quality = metrics_service.calculate_model_quality_with_group_by_timestamp() - assert stats == my_approx( + assert stats.model_dump(serialize_as_any=True) == my_approx( { "categorical": 2, "datetime": 1, diff --git a/spark/tests/binary_reference_test.py b/spark/tests/binary_reference_test.py index af275be7..06d7e64d 100644 --- a/spark/tests/binary_reference_test.py +++ b/spark/tests/binary_reference_test.py @@ -118,7 +118,7 @@ def test_calculation(spark_fixture, dataset): model_quality = metrics_service.calculate_model_quality() data_quality = metrics_service.calculate_data_quality() - assert stats == my_approx( + assert stats.model_dump(serialize_as_any=True) == my_approx( { "categorical": 2, "datetime": 1, @@ -300,7 +300,7 @@ def test_calculation_reference_joined(spark_fixture, reference_joined): model_quality = metrics_service.calculate_model_quality() data_quality = metrics_service.calculate_data_quality() - assert stats == my_approx( + assert stats.model_dump(serialize_as_any=True) == my_approx( { "categorical": 1, "datetime": 1, @@ -696,7 +696,7 @@ def test_calculation_complete(spark_fixture, complete_dataset): model_quality = metrics_service.calculate_model_quality() data_quality = metrics_service.calculate_data_quality() - assert stats == my_approx( + assert stats.model_dump(serialize_as_any=True) == my_approx( { "missing_cells": 0, "missing_cells_perc": 0.0, @@ -851,7 +851,7 @@ def test_calculation_easy_dataset(spark_fixture, easy_dataset): model_quality = metrics_service.calculate_model_quality() data_quality = metrics_service.calculate_data_quality() - assert stats == my_approx( + assert stats.model_dump(serialize_as_any=True) == my_approx( { "missing_cells": 0, "missing_cells_perc": 0.0, @@ -1005,7 +1005,7 @@ def test_calculation_dataset_cat_missing(spark_fixture, dataset_cat_missing): model_quality = metrics_service.calculate_model_quality() data_quality = metrics_service.calculate_data_quality() - assert stats == my_approx( + assert stats.model_dump(serialize_as_any=True) == my_approx( { "missing_cells": 5, "missing_cells_perc": 6.25, @@ -1182,7 +1182,7 @@ def test_calculation_dataset_with_datetime(spark_fixture, dataset_with_datetime) model_quality = metrics_service.calculate_model_quality() data_quality = metrics_service.calculate_data_quality() - assert stats == my_approx( + assert stats.model_dump(serialize_as_any=True) == my_approx( { "categorical": 2, "datetime": 1, @@ -1365,7 +1365,7 @@ def test_calculation_enhanced_data(spark_fixture, enhanced_data): model_quality = metrics_service.calculate_model_quality() data_quality = metrics_service.calculate_data_quality() - assert stats == my_approx( + assert stats.model_dump(serialize_as_any=True) == my_approx( { "missing_cells": 2996, "missing_cells_perc": 0.6241666666666668, @@ -1905,7 +1905,7 @@ def test_calculation_dataset_bool_missing(spark_fixture, dataset_bool_missing): model_quality = metrics_service.calculate_model_quality() data_quality = metrics_service.calculate_data_quality() - assert stats == my_approx( + assert stats.model_dump(serialize_as_any=True) == my_approx( { "missing_cells": 5, "missing_cells_perc": 6.25, diff --git a/spark/tests/multiclass_current_test.py b/spark/tests/multiclass_current_test.py index a0203eab..3d591d8a 100644 --- a/spark/tests/multiclass_current_test.py +++ b/spark/tests/multiclass_current_test.py @@ -96,7 +96,7 @@ def test_calculation_dataset_target_int(spark_fixture, dataset_target_int): stats = calculate_statistics_current(current_dataset) - assert stats == my_approx( + assert stats.model_dump(serialize_as_any=True) == my_approx( { "categorical": 2, "datetime": 1, @@ -148,7 +148,7 @@ def test_calculation_dataset_target_string(spark_fixture, dataset_target_string) stats = calculate_statistics_current(current_dataset) - assert stats == my_approx( + assert stats.model_dump(serialize_as_any=True) == my_approx( { "categorical": 4, "datetime": 1, @@ -200,7 +200,7 @@ def test_calculation_dataset_perfect_classes(spark_fixture, dataset_perfect_clas stats = calculate_statistics_current(current_dataset) - assert stats == my_approx( + assert stats.model_dump(serialize_as_any=True) == my_approx( { "categorical": 4, "datetime": 1, diff --git a/spark/tests/multiclass_reference_test.py b/spark/tests/multiclass_reference_test.py index 027dd6a6..e32a461f 100644 --- a/spark/tests/multiclass_reference_test.py +++ b/spark/tests/multiclass_reference_test.py @@ -83,7 +83,7 @@ def test_calculation_dataset_target_int(spark_fixture, dataset_target_int): data_quality = multiclass_service.calculate_data_quality() model_quality = multiclass_service.calculate_model_quality() - assert stats == my_approx( + assert stats.model_dump(serialize_as_any=True) == my_approx( { "categorical": 2, "datetime": 1, @@ -302,7 +302,7 @@ def test_calculation_dataset_target_string(spark_fixture, dataset_target_string) data_quality = multiclass_service.calculate_data_quality() model_quality = multiclass_service.calculate_model_quality() - assert stats == my_approx( + assert stats.model_dump(serialize_as_any=True) == my_approx( { "categorical": 4, "datetime": 1, @@ -521,7 +521,7 @@ def test_calculation_dataset_perfect_classes(spark_fixture, dataset_perfect_clas data_quality = multiclass_service.calculate_data_quality() model_quality = multiclass_service.calculate_model_quality() - assert stats == my_approx( + assert stats.model_dump(serialize_as_any=True) == my_approx( { "categorical": 4, "datetime": 1, diff --git a/spark/tests/regression_reference_test.py b/spark/tests/regression_reference_test.py index 3a3eb16b..2d44bf10 100644 --- a/spark/tests/regression_reference_test.py +++ b/spark/tests/regression_reference_test.py @@ -3,6 +3,7 @@ import pytest +from jobs.metrics.statistics import calculate_statistics_reference from jobs.utils.reference_regression import ReferenceMetricsRegressionService from jobs.models.reference_dataset import ReferenceDataset from jobs.utils.models import ( @@ -24,7 +25,8 @@ def reference_bike(spark_fixture, test_data_dir): ) -def test_calculation_dataset_target_int(spark_fixture, reference_bike): +@pytest.fixture() +def reference_dataset(spark_fixture, reference_bike): output = OutputType( prediction=ColumnDefinition(name="predictions", type=SupportedTypes.float), prediction_proba=None, @@ -45,7 +47,6 @@ def test_calculation_dataset_target_int(spark_fixture, reference_bike): ColumnDefinition(name="atemp", type=SupportedTypes.float), ColumnDefinition(name="hum", type=SupportedTypes.float), ColumnDefinition(name="windspeed", type=SupportedTypes.float), - ColumnDefinition(name="instant", type=SupportedTypes.int), ] model = ModelOut( uuid=uuid.uuid4(), @@ -64,10 +65,13 @@ def test_calculation_dataset_target_int(spark_fixture, reference_bike): updated_at=str(datetime.datetime.now()), ) - reference_dataset = ReferenceDataset( + yield ReferenceDataset( raw_dataframe=reference_bike, model=model, ) + + +def test_model_quality_metrics(spark_fixture, reference_dataset): assert reference_dataset.reference_count == 731 regression_service = ReferenceMetricsRegressionService(reference=reference_dataset) @@ -75,14 +79,33 @@ def test_calculation_dataset_target_int(spark_fixture, reference_bike): expected = my_approx( { - "mae": 126.6230232558139, - "mape": 33.33458358635063, - "mse": 42058.59416703146, - "rmse": 205.08192062449447, - "r2": 0.9106667318989127, - "adj_r2": 0.9091736967774461, - "var": 388091.1367098835, + "mae": 125.0137756497949, + "mape": 35.193142372738045, + "mse": 40897.76059849522, + "rmse": 202.2319475218869, + "r2": 0.9131323648676931, + "adj_r2": 0.9118033746222753, + "var": 393448.3132709007, } ) assert model_quality_metrics.model_dump() == expected + + +def test_statistics_metrics(spark_fixture, reference_dataset): + stats = calculate_statistics_reference(reference_dataset) + expected = my_approx( + { + "missing_cells": 0, + "missing_cells_perc": 0.0, + "duplicate_rows": 0, + "duplicate_rows_perc": 0.0, + "n_variables": 14, + "n_observations": 731, + "numeric": 13, + "categorical": 0, + "datetime": 1, + } + ) + + assert stats.model_dump(serialize_as_any=True) == expected diff --git a/spark/tests/resources/reference/regression/reference_bike.csv b/spark/tests/resources/reference/regression/reference_bike.csv index 2e1f7e95..af770c3b 100644 --- a/spark/tests/resources/reference/regression/reference_bike.csv +++ b/spark/tests/resources/reference/regression/reference_bike.csv @@ -1,732 +1,732 @@ instant,dteday,season,yr,mnth,holiday,weekday,workingday,weathersit,temp,atemp,hum,windspeed,predictions,ground_truth -1,1/1/2011,1,0,1,0,6,0,2,0.344167,0.363625,0.805833,0.160446,509.69,331 -2,1/2/2011,1,0,1,0,0,0,2,0.363478,0.353739,0.696087,0.248539,686.01,131 -3,1/3/2011,1,0,1,0,1,1,1,0.196364,0.189405,0.437273,0.248309,113.67,120 -4,1/4/2011,1,0,1,0,2,1,1,0.2,0.212122,0.590435,0.160296,92.59,108 -5,1/5/2011,1,0,1,0,3,1,1,0.226957,0.22927,0.436957,0.1869,106.64,82 -6,1/6/2011,1,0,1,0,4,1,1,0.204348,0.233209,0.518261,0.0895652,121.85,88 -7,1/7/2011,1,0,1,0,5,1,2,0.196522,0.208839,0.498696,0.168726,138.47,148 -8,1/8/2011,1,0,1,0,6,0,2,0.165,0.162254,0.535833,0.266804,112.05,68 -9,1/9/2011,1,0,1,0,0,0,1,0.138333,0.116175,0.434167,0.36195,163.53,54 -10,1/10/2011,1,0,1,0,1,1,1,0.150833,0.150888,0.482917,0.223267,53.27,41 -11,1/11/2011,1,0,1,0,2,1,2,0.169091,0.191464,0.686364,0.122132,54.36,43 -12,1/12/2011,1,0,1,0,3,1,1,0.172727,0.160473,0.599545,0.304627,34.97,25 -13,1/13/2011,1,0,1,0,4,1,1,0.165,0.150883,0.470417,0.301,48.06,38 -14,1/14/2011,1,0,1,0,5,1,1,0.16087,0.188413,0.537826,0.126548,79.31,54 -15,1/15/2011,1,0,1,0,6,0,2,0.233333,0.248112,0.49875,0.157963,287.25,222 -16,1/16/2011,1,0,1,0,0,0,1,0.231667,0.234217,0.48375,0.188433,257.6,251 -17,1/17/2011,1,0,1,1,1,0,2,0.175833,0.176771,0.5375,0.194017,134.96,117 -18,1/18/2011,1,0,1,0,2,1,2,0.216667,0.232333,0.861667,0.146775,126.54,9 -19,1/19/2011,1,0,1,0,3,1,2,0.292174,0.298422,0.741739,0.208317,152.63,78 -20,1/20/2011,1,0,1,0,4,1,2,0.261667,0.25505,0.538333,0.195904,110.72,83 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-722,12/22/2012,1,1,12,0,6,0,1,0.265833,0.236113,0.44125,0.407346,251.73,205 -723,12/23/2012,1,1,12,0,0,0,1,0.245833,0.259471,0.515417,0.133083,370.34,408 -724,12/24/2012,1,1,12,0,1,1,2,0.231304,0.2589,0.791304,0.0772304,173.05,174 -725,12/25/2012,1,1,12,1,2,0,2,0.291304,0.294465,0.734783,0.168726,390.9,440 -726,12/26/2012,1,1,12,0,3,1,3,0.243333,0.220333,0.823333,0.316546,64.51,9 -727,12/27/2012,1,1,12,0,4,1,2,0.254167,0.226642,0.652917,0.350133,220.77,247 -728,12/28/2012,1,1,12,0,5,1,2,0.253333,0.255046,0.59,0.155471,264.89,644 -729,12/29/2012,1,1,12,0,6,0,2,0.253333,0.2424,0.752917,0.124383,231.89,159 -730,12/30/2012,1,1,12,0,0,0,1,0.255833,0.2317,0.483333,0.350754,287.52,364 -731,12/31/2012,1,1,12,0,1,1,2,0.215833,0.223487,0.5775,0.154846,324.15,439 +1,2011-01-01,1,0,1,0,6,0,2,0.344167,0.363625,0.805833,0.160446,506.69,331 +2,2011-01-02,1,0,1,0,0,0,2,0.363478,0.353739,0.696087,0.248539,635.42,131 +3,2011-01-03,1,0,1,0,1,1,1,0.196364,0.189405,0.437273,0.248309,113.83,120 +4,2011-01-04,1,0,1,0,2,1,1,0.2,0.212122,0.590435,0.160296,95.03,108 +5,2011-01-05,1,0,1,0,3,1,1,0.226957,0.22927,0.436957,0.1869,104.1,82 +6,2011-01-06,1,0,1,0,4,1,1,0.204348,0.233209,0.518261,0.0895652,136.77,88 +7,2011-01-07,1,0,1,0,5,1,2,0.196522,0.208839,0.498696,0.168726,130.9,148 +8,2011-01-08,1,0,1,0,6,0,2,0.165,0.162254,0.535833,0.266804,100.39,68 +9,2011-01-09,1,0,1,0,0,0,1,0.138333,0.116175,0.434167,0.36195,161.6,54 +10,2011-01-10,1,0,1,0,1,1,1,0.150833,0.150888,0.482917,0.223267,52.61,41 +11,2011-01-11,1,0,1,0,2,1,2,0.169091,0.191464,0.686364,0.122132,54.54,43 +12,2011-01-12,1,0,1,0,3,1,1,0.172727,0.160473,0.599545,0.304627,34.11,25 +13,2011-01-13,1,0,1,0,4,1,1,0.165,0.150883,0.470417,0.301,46.14,38 +14,2011-01-14,1,0,1,0,5,1,1,0.16087,0.188413,0.537826,0.126548,68.29,54 +15,2011-01-15,1,0,1,0,6,0,2,0.233333,0.248112,0.49875,0.157963,257.48,222 +16,2011-01-16,1,0,1,0,0,0,1,0.231667,0.234217,0.48375,0.188433,253.11,251 +17,2011-01-17,1,0,1,1,1,0,2,0.175833,0.176771,0.5375,0.194017,132.76,117 +18,2011-01-18,1,0,1,0,2,1,2,0.216667,0.232333,0.861667,0.146775,152.52,9 +19,2011-01-19,1,0,1,0,3,1,2,0.292174,0.298422,0.741739,0.208317,158.84,78 +20,2011-01-20,1,0,1,0,4,1,2,0.261667,0.25505,0.538333,0.195904,116.11,83 +21,2011-01-21,1,0,1,0,5,1,1,0.1775,0.157833,0.457083,0.353242,76.25,75 +22,2011-01-22,1,0,1,0,6,0,1,0.0591304,0.0790696,0.4,0.17197,207.3,93 +23,2011-01-23,1,0,1,0,0,0,1,0.0965217,0.0988391,0.436522,0.2466,171.66,150 +24,2011-01-24,1,0,1,0,1,1,1,0.0973913,0.11793,0.491739,0.15833,75.92,86 +25,2011-01-25,1,0,1,0,2,1,2,0.223478,0.234526,0.616957,0.129796,167.34,186 +26,2011-01-26,1,0,1,0,3,1,3,0.2175,0.2036,0.8625,0.29385,43.15,34 +27,2011-01-27,1,0,1,0,4,1,1,0.195,0.2197,0.6875,0.113837,46.09,15 +28,2011-01-28,1,0,1,0,5,1,2,0.203478,0.223317,0.793043,0.1233,170.72,38 +29,2011-01-29,1,0,1,0,6,0,1,0.196522,0.212126,0.651739,0.145365,142.45,123 +30,2011-01-30,1,0,1,0,0,0,1,0.216522,0.250322,0.722174,0.0739826,196.17,140 +31,2011-01-31,1,0,1,0,1,1,2,0.180833,0.18625,0.60375,0.187192,49.55,42 +32,2011-02-01,1,0,2,0,2,1,2,0.192174,0.23453,0.829565,0.053213,182.08,47 +33,2011-02-02,1,0,2,0,3,1,2,0.26,0.254417,0.775417,0.264308,95.26,72 +34,2011-02-03,1,0,2,0,4,1,1,0.186957,0.177878,0.437826,0.277752,74.92,61 +35,2011-02-04,1,0,2,0,5,1,2,0.211304,0.228587,0.585217,0.127839,180.28,88 +36,2011-02-05,1,0,2,0,6,0,2,0.233333,0.243058,0.929167,0.161079,201.03,100 +37,2011-02-06,1,0,2,0,0,0,1,0.285833,0.291671,0.568333,0.1418,366.02,354 +38,2011-02-07,1,0,2,0,1,1,1,0.271667,0.303658,0.738333,0.0454083,186.74,120 +39,2011-02-08,1,0,2,0,2,1,1,0.220833,0.198246,0.537917,0.36195,84.22,64 +40,2011-02-09,1,0,2,0,3,1,2,0.134783,0.144283,0.494783,0.188839,59.29,53 +41,2011-02-10,1,0,2,0,4,1,1,0.144348,0.149548,0.437391,0.221935,70.96,47 +42,2011-02-11,1,0,2,0,5,1,1,0.189091,0.213509,0.506364,0.10855,135.62,149 +43,2011-02-12,1,0,2,0,6,0,1,0.2225,0.232954,0.544167,0.203367,254.52,288 +44,2011-02-13,1,0,2,0,0,0,1,0.316522,0.324113,0.457391,0.260883,561.73,397 +45,2011-02-14,1,0,2,0,1,1,1,0.415,0.39835,0.375833,0.417908,421.74,208 +46,2011-02-15,1,0,2,0,2,1,1,0.266087,0.254274,0.314348,0.291374,220.26,140 +47,2011-02-16,1,0,2,0,3,1,1,0.318261,0.3162,0.423478,0.251791,222.85,218 +48,2011-02-17,1,0,2,0,4,1,1,0.435833,0.428658,0.505,0.230104,335.41,259 +49,2011-02-18,1,0,2,0,5,1,1,0.521667,0.511983,0.516667,0.264925,984.07,579 +50,2011-02-19,1,0,2,0,6,0,1,0.399167,0.391404,0.187917,0.507463,1055.73,532 +51,2011-02-20,1,0,2,0,0,0,1,0.285217,0.27733,0.407826,0.223235,477.84,639 +52,2011-02-21,1,0,2,1,1,0,2,0.303333,0.284075,0.605,0.307846,283.46,195 +53,2011-02-22,1,0,2,0,2,1,1,0.182222,0.186033,0.577778,0.195683,65.41,74 +54,2011-02-23,1,0,2,0,3,1,1,0.221739,0.245717,0.423043,0.094113,148.39,139 +55,2011-02-24,1,0,2,0,4,1,2,0.295652,0.289191,0.697391,0.250496,132.79,100 +56,2011-02-25,1,0,2,0,5,1,2,0.364348,0.350461,0.712174,0.346539,254.06,120 +57,2011-02-26,1,0,2,0,6,0,1,0.2825,0.282192,0.537917,0.186571,432.56,424 +58,2011-02-27,1,0,2,0,0,0,1,0.343478,0.351109,0.68,0.125248,753.05,694 +59,2011-02-28,1,0,2,0,1,1,2,0.407273,0.400118,0.876364,0.289686,117.27,81 +60,2011-03-01,1,0,3,0,2,1,1,0.266667,0.263879,0.535,0.216425,161.42,137 +61,2011-03-02,1,0,3,0,3,1,1,0.335,0.320071,0.449583,0.307833,253.87,231 +62,2011-03-03,1,0,3,0,4,1,1,0.198333,0.200133,0.318333,0.225754,145.57,123 +63,2011-03-04,1,0,3,0,5,1,2,0.261667,0.255679,0.610417,0.203346,193.62,214 +64,2011-03-05,1,0,3,0,6,0,2,0.384167,0.378779,0.789167,0.251871,702.21,640 +65,2011-03-06,1,0,3,0,0,0,2,0.376522,0.366252,0.948261,0.343287,588.13,114 +66,2011-03-07,1,0,3,0,1,1,1,0.261739,0.238461,0.551304,0.341352,216.56,244 +67,2011-03-08,1,0,3,0,2,1,1,0.2925,0.3024,0.420833,0.12065,310.66,316 +68,2011-03-09,1,0,3,0,3,1,2,0.295833,0.286608,0.775417,0.22015,181.41,191 +69,2011-03-10,1,0,3,0,4,1,3,0.389091,0.385668,0.0,0.261877,449.07,46 +70,2011-03-11,1,0,3,0,5,1,2,0.316522,0.305,0.649565,0.23297,266.37,247 +71,2011-03-12,1,0,3,0,6,0,1,0.329167,0.32575,0.594583,0.220775,828.91,724 +72,2011-03-13,1,0,3,0,0,0,1,0.384348,0.380091,0.527391,0.270604,977.09,982 +73,2011-03-14,1,0,3,0,1,1,1,0.325217,0.332,0.496957,0.136926,343.25,359 +74,2011-03-15,1,0,3,0,2,1,2,0.317391,0.318178,0.655652,0.184309,254.91,289 +75,2011-03-16,1,0,3,0,3,1,2,0.365217,0.36693,0.776522,0.203117,289.19,321 +76,2011-03-17,1,0,3,0,4,1,1,0.415,0.410333,0.602917,0.209579,384.92,424 +77,2011-03-18,1,0,3,0,5,1,1,0.54,0.527009,0.525217,0.231017,1575.57,884 +78,2011-03-19,1,0,3,0,6,0,1,0.4725,0.466525,0.379167,0.368167,1630.82,1424 +79,2011-03-20,1,0,3,0,0,0,1,0.3325,0.32575,0.47375,0.207721,1128.02,1047 +80,2011-03-21,2,0,3,0,1,1,2,0.430435,0.409735,0.737391,0.288783,295.64,401 +81,2011-03-22,2,0,3,0,2,1,1,0.441667,0.440642,0.624583,0.22575,511.68,460 +82,2011-03-23,2,0,3,0,3,1,2,0.346957,0.337939,0.839565,0.234261,217.51,203 +83,2011-03-24,2,0,3,0,4,1,2,0.285,0.270833,0.805833,0.243787,169.93,166 +84,2011-03-25,2,0,3,0,5,1,1,0.264167,0.256312,0.495,0.230725,269.15,300 +85,2011-03-26,2,0,3,0,6,0,1,0.265833,0.257571,0.394167,0.209571,773.12,981 +86,2011-03-27,2,0,3,0,0,0,2,0.253043,0.250339,0.493913,0.1843,466.77,472 +87,2011-03-28,2,0,3,0,1,1,1,0.264348,0.257574,0.302174,0.212204,295.03,222 +88,2011-03-29,2,0,3,0,2,1,1,0.3025,0.292908,0.314167,0.226996,372.79,317 +89,2011-03-30,2,0,3,0,3,1,2,0.3,0.29735,0.646667,0.172888,218.23,168 +90,2011-03-31,2,0,3,0,4,1,3,0.268333,0.257575,0.918333,0.217646,172.12,179 +91,2011-04-01,2,0,4,0,5,1,2,0.3,0.283454,0.68625,0.258708,302.14,307 +92,2011-04-02,2,0,4,0,6,0,2,0.315,0.315637,0.65375,0.197146,755.73,898 +93,2011-04-03,2,0,4,0,0,0,1,0.378333,0.378767,0.48,0.182213,1508.42,1651 +94,2011-04-04,2,0,4,0,1,1,1,0.573333,0.542929,0.42625,0.385571,821.83,734 +95,2011-04-05,2,0,4,0,2,1,2,0.414167,0.39835,0.642083,0.388067,232.43,167 +96,2011-04-06,2,0,4,0,3,1,1,0.390833,0.387608,0.470833,0.263063,448.77,413 +97,2011-04-07,2,0,4,0,4,1,1,0.4375,0.433696,0.602917,0.162312,561.67,571 +98,2011-04-08,2,0,4,0,5,1,2,0.335833,0.324479,0.83625,0.226992,268.22,172 +99,2011-04-09,2,0,4,0,6,0,2,0.3425,0.341529,0.8775,0.133083,876.47,879 +100,2011-04-10,2,0,4,0,0,0,2,0.426667,0.426737,0.8575,0.146767,1234.71,1188 +101,2011-04-11,2,0,4,0,1,1,2,0.595652,0.565217,0.716956,0.324474,830.84,855 +102,2011-04-12,2,0,4,0,2,1,2,0.5025,0.493054,0.739167,0.274879,590.68,257 +103,2011-04-13,2,0,4,0,3,1,2,0.4125,0.417283,0.819167,0.250617,223.75,209 +104,2011-04-14,2,0,4,0,4,1,1,0.4675,0.462742,0.540417,0.1107,863.12,529 +105,2011-04-15,2,0,4,1,5,0,1,0.446667,0.441913,0.67125,0.226375,1191.99,642 +106,2011-04-16,2,0,4,0,6,0,3,0.430833,0.425492,0.888333,0.340808,483.23,121 +107,2011-04-17,2,0,4,0,0,0,1,0.456667,0.445696,0.479583,0.303496,1521.61,1558 +108,2011-04-18,2,0,4,0,1,1,1,0.5125,0.503146,0.5425,0.163567,712.41,669 +109,2011-04-19,2,0,4,0,2,1,2,0.505833,0.489258,0.665833,0.157971,493.43,409 +110,2011-04-20,2,0,4,0,3,1,1,0.595,0.564392,0.614167,0.241925,806.82,613 +111,2011-04-21,2,0,4,0,4,1,1,0.459167,0.453892,0.407083,0.325258,771.0,745 +112,2011-04-22,2,0,4,0,5,1,2,0.336667,0.321954,0.729583,0.219521,253.27,177 +113,2011-04-23,2,0,4,0,6,0,2,0.46,0.450121,0.887917,0.230725,1358.4,1462 +114,2011-04-24,2,0,4,0,0,0,2,0.581667,0.551763,0.810833,0.192175,2083.5,1710 +115,2011-04-25,2,0,4,0,1,1,1,0.606667,0.5745,0.776667,0.185333,762.59,773 +116,2011-04-26,2,0,4,0,2,1,1,0.631667,0.594083,0.729167,0.3265,723.88,678 +117,2011-04-27,2,0,4,0,3,1,2,0.62,0.575142,0.835417,0.3122,549.27,547 +118,2011-04-28,2,0,4,0,4,1,2,0.6175,0.578929,0.700833,0.320908,585.78,569 +119,2011-04-29,2,0,4,0,5,1,1,0.51,0.497463,0.457083,0.240063,1055.15,878 +120,2011-04-30,2,0,4,0,6,0,1,0.4725,0.464021,0.503333,0.235075,2072.64,1965 +121,2011-05-01,2,0,5,0,0,0,2,0.451667,0.448204,0.762083,0.106354,1418.8,1138 +122,2011-05-02,2,0,5,0,1,1,2,0.549167,0.532833,0.73,0.183454,809.69,847 +123,2011-05-03,2,0,5,0,2,1,2,0.616667,0.582079,0.697083,0.342667,712.07,603 +124,2011-05-04,2,0,5,0,3,1,2,0.414167,0.40465,0.737083,0.328996,236.3,255 +125,2011-05-05,2,0,5,0,4,1,1,0.459167,0.441917,0.444167,0.295392,678.22,614 +126,2011-05-06,2,0,5,0,5,1,1,0.479167,0.474117,0.59,0.228246,926.66,894 +127,2011-05-07,2,0,5,0,6,0,1,0.52,0.512621,0.54125,0.16045,1927.62,1612 +128,2011-05-08,2,0,5,0,0,0,1,0.528333,0.518933,0.631667,0.0746375,2325.81,1401 +129,2011-05-09,2,0,5,0,1,1,1,0.5325,0.525246,0.58875,0.176,701.43,664 +130,2011-05-10,2,0,5,0,2,1,1,0.5325,0.522721,0.489167,0.115671,758.88,694 +131,2011-05-11,2,0,5,0,3,1,1,0.5425,0.5284,0.632917,0.120642,643.15,550 +132,2011-05-12,2,0,5,0,4,1,1,0.535,0.523363,0.7475,0.189667,712.79,695 +133,2011-05-13,2,0,5,0,5,1,2,0.5125,0.4943,0.863333,0.179725,585.69,692 +134,2011-05-14,2,0,5,0,6,0,2,0.520833,0.500629,0.9225,0.13495,1113.61,902 +135,2011-05-15,2,0,5,0,0,0,2,0.5625,0.536,0.867083,0.152979,1553.64,1582 +136,2011-05-16,2,0,5,0,1,1,1,0.5775,0.550512,0.787917,0.126871,804.8,773 +137,2011-05-17,2,0,5,0,2,1,2,0.561667,0.538529,0.837917,0.277354,617.46,678 +138,2011-05-18,2,0,5,0,3,1,2,0.55,0.527158,0.87,0.201492,520.75,536 +139,2011-05-19,2,0,5,0,4,1,2,0.530833,0.510742,0.829583,0.108213,754.03,735 +140,2011-05-20,2,0,5,0,5,1,1,0.536667,0.529042,0.719583,0.125013,963.03,909 +141,2011-05-21,2,0,5,0,6,0,1,0.6025,0.571975,0.626667,0.12065,2243.29,2258 +142,2011-05-22,2,0,5,0,0,0,1,0.604167,0.5745,0.749583,0.148008,1785.61,1576 +143,2011-05-23,2,0,5,0,1,1,2,0.631667,0.590296,0.81,0.233842,681.7,836 +144,2011-05-24,2,0,5,0,2,1,2,0.66,0.604813,0.740833,0.207092,685.78,659 +145,2011-05-25,2,0,5,0,3,1,1,0.660833,0.615542,0.69625,0.154233,799.06,740 +146,2011-05-26,2,0,5,0,4,1,1,0.708333,0.654688,0.6775,0.199642,777.09,758 +147,2011-05-27,2,0,5,0,5,1,1,0.681667,0.637008,0.65375,0.240679,922.41,871 +148,2011-05-28,2,0,5,0,6,0,1,0.655833,0.612379,0.729583,0.230092,2112.77,2001 +149,2011-05-29,2,0,5,0,0,0,1,0.6675,0.61555,0.81875,0.213938,2160.18,2355 +150,2011-05-30,2,0,5,1,1,0,1,0.733333,0.671092,0.685,0.131225,1785.96,1549 +151,2011-05-31,2,0,5,0,2,1,1,0.775,0.725383,0.636667,0.111329,736.36,673 +152,2011-06-01,2,0,6,0,3,1,2,0.764167,0.720967,0.677083,0.207092,632.7,513 +153,2011-06-02,2,0,6,0,4,1,1,0.715,0.643942,0.305,0.292287,832.37,736 +154,2011-06-03,2,0,6,0,5,1,1,0.62,0.587133,0.354167,0.253121,992.73,898 +155,2011-06-04,2,0,6,0,6,0,1,0.635,0.594696,0.45625,0.123142,2066.57,1869 +156,2011-06-05,2,0,6,0,0,0,2,0.648333,0.616804,0.6525,0.138692,2226.46,1685 +157,2011-06-06,2,0,6,0,1,1,1,0.678333,0.621858,0.6,0.121896,892.64,673 +158,2011-06-07,2,0,6,0,2,1,1,0.7075,0.65595,0.597917,0.187808,875.34,763 +159,2011-06-08,2,0,6,0,3,1,1,0.775833,0.727279,0.622083,0.136817,712.06,676 +160,2011-06-09,2,0,6,0,4,1,2,0.808333,0.757579,0.568333,0.149883,647.42,563 +161,2011-06-10,2,0,6,0,5,1,1,0.755,0.703292,0.605,0.140554,896.59,815 +162,2011-06-11,2,0,6,0,6,0,1,0.725,0.678038,0.654583,0.15485,1882.27,1729 +163,2011-06-12,2,0,6,0,0,0,1,0.6925,0.643325,0.747917,0.163567,1945.87,1467 +164,2011-06-13,2,0,6,0,1,1,1,0.635,0.601654,0.494583,0.30535,897.85,863 +165,2011-06-14,2,0,6,0,2,1,1,0.604167,0.591546,0.507083,0.269283,800.38,727 +166,2011-06-15,2,0,6,0,3,1,1,0.626667,0.587754,0.471667,0.167912,1015.81,769 +167,2011-06-16,2,0,6,0,4,1,2,0.628333,0.595346,0.688333,0.206471,603.27,545 +168,2011-06-17,2,0,6,0,5,1,1,0.649167,0.600383,0.735833,0.143029,912.15,863 +169,2011-06-18,2,0,6,0,6,0,1,0.696667,0.643954,0.670417,0.119408,1893.15,1807 +170,2011-06-19,2,0,6,0,0,0,2,0.699167,0.645846,0.666667,0.102,1936.73,1639 +171,2011-06-20,2,0,6,0,1,1,2,0.635,0.595346,0.74625,0.155475,803.35,699 +172,2011-06-21,3,0,6,0,2,1,2,0.680833,0.637646,0.770417,0.171025,780.95,774 +173,2011-06-22,3,0,6,0,3,1,1,0.733333,0.693829,0.7075,0.172262,825.37,661 +174,2011-06-23,3,0,6,0,4,1,2,0.728333,0.693833,0.703333,0.238804,724.05,746 +175,2011-06-24,3,0,6,0,5,1,1,0.724167,0.656583,0.573333,0.222025,993.89,969 +176,2011-06-25,3,0,6,0,6,0,1,0.695,0.643313,0.483333,0.209571,2470.23,1782 +177,2011-06-26,3,0,6,0,0,0,1,0.68,0.637629,0.513333,0.0945333,2045.5,1920 +178,2011-06-27,3,0,6,0,1,1,2,0.6825,0.637004,0.658333,0.107588,894.11,854 +179,2011-06-28,3,0,6,0,2,1,1,0.744167,0.692558,0.634167,0.144283,786.38,732 +180,2011-06-29,3,0,6,0,3,1,1,0.728333,0.654688,0.497917,0.261821,892.91,848 +181,2011-06-30,3,0,6,0,4,1,1,0.696667,0.637008,0.434167,0.185312,1023.28,1027 +182,2011-07-01,3,0,7,0,5,1,1,0.7225,0.652162,0.39625,0.102608,1309.07,1246 +183,2011-07-02,3,0,7,0,6,0,1,0.738333,0.667308,0.444583,0.115062,2230.49,2204 +184,2011-07-03,3,0,7,0,0,0,2,0.716667,0.668575,0.6825,0.228858,2277.42,2282 +185,2011-07-04,3,0,7,1,1,0,2,0.726667,0.665417,0.637917,0.0814792,2665.03,3065 +186,2011-07-05,3,0,7,0,2,1,1,0.746667,0.696338,0.590417,0.126258,971.14,1031 +187,2011-07-06,3,0,7,0,3,1,1,0.72,0.685633,0.743333,0.149883,815.88,784 +188,2011-07-07,3,0,7,0,4,1,1,0.75,0.686871,0.65125,0.1592,975.68,754 +189,2011-07-08,3,0,7,0,5,1,2,0.709167,0.670483,0.757917,0.225129,835.05,692 +190,2011-07-09,3,0,7,0,6,0,1,0.733333,0.664158,0.609167,0.167912,2080.84,1988 +191,2011-07-10,3,0,7,0,0,0,1,0.7475,0.690025,0.578333,0.183471,1847.56,1743 +192,2011-07-11,3,0,7,0,1,1,1,0.7625,0.729804,0.635833,0.282337,766.85,723 +193,2011-07-12,3,0,7,0,2,1,1,0.794167,0.739275,0.559167,0.200254,696.78,662 +194,2011-07-13,3,0,7,0,3,1,1,0.746667,0.689404,0.631667,0.146133,876.53,748 +195,2011-07-14,3,0,7,0,4,1,1,0.680833,0.635104,0.47625,0.240667,910.7,888 +196,2011-07-15,3,0,7,0,5,1,1,0.663333,0.624371,0.59125,0.182833,1234.08,1318 +197,2011-07-16,3,0,7,0,6,0,1,0.686667,0.638263,0.585,0.208342,2294.18,2418 +198,2011-07-17,3,0,7,0,0,0,1,0.719167,0.669833,0.604167,0.245033,2103.6,2006 +199,2011-07-18,3,0,7,0,1,1,1,0.746667,0.703925,0.65125,0.215804,844.99,841 +200,2011-07-19,3,0,7,0,2,1,1,0.776667,0.747479,0.650417,0.1306,817.34,752 +201,2011-07-20,3,0,7,0,3,1,1,0.768333,0.74685,0.707083,0.113817,838.33,644 +202,2011-07-21,3,0,7,0,4,1,2,0.815,0.826371,0.69125,0.222021,669.41,632 +203,2011-07-22,3,0,7,0,5,1,1,0.848333,0.840896,0.580417,0.1331,932.3,562 +204,2011-07-23,3,0,7,0,6,0,1,0.849167,0.804287,0.5,0.131221,1251.39,987 +205,2011-07-24,3,0,7,0,0,0,1,0.83,0.794829,0.550833,0.169171,1349.38,1050 +206,2011-07-25,3,0,7,0,1,1,1,0.743333,0.720958,0.757083,0.0908083,708.93,568 +207,2011-07-26,3,0,7,0,2,1,1,0.771667,0.696979,0.540833,0.200258,763.0,750 +208,2011-07-27,3,0,7,0,3,1,1,0.775,0.690667,0.402917,0.183463,850.19,755 +209,2011-07-28,3,0,7,0,4,1,1,0.779167,0.7399,0.583333,0.178479,712.47,606 +210,2011-07-29,3,0,7,0,5,1,1,0.838333,0.785967,0.5425,0.174138,846.74,670 +211,2011-07-30,3,0,7,0,6,0,1,0.804167,0.728537,0.465833,0.168537,2004.15,1559 +212,2011-07-31,3,0,7,0,0,0,1,0.805833,0.729796,0.480833,0.164813,1931.26,1524 +213,2011-08-01,3,0,8,0,1,1,1,0.771667,0.703292,0.550833,0.156717,808.75,729 +214,2011-08-02,3,0,8,0,2,1,1,0.783333,0.707071,0.49125,0.20585,817.56,801 +215,2011-08-03,3,0,8,0,3,1,2,0.731667,0.679937,0.6575,0.135583,1013.68,467 +216,2011-08-04,3,0,8,0,4,1,2,0.71,0.664788,0.7575,0.19715,803.28,799 +217,2011-08-05,3,0,8,0,5,1,1,0.710833,0.656567,0.630833,0.184696,1064.28,1023 +218,2011-08-06,3,0,8,0,6,0,2,0.716667,0.676154,0.755,0.22825,1524.16,1521 +219,2011-08-07,3,0,8,0,0,0,1,0.7425,0.715292,0.752917,0.201487,1407.41,1298 +220,2011-08-08,3,0,8,0,1,1,1,0.765,0.703283,0.592083,0.192175,839.89,846 +221,2011-08-09,3,0,8,0,2,1,1,0.775,0.724121,0.570417,0.151121,869.8,907 +222,2011-08-10,3,0,8,0,3,1,1,0.766667,0.684983,0.424167,0.200258,878.82,884 +223,2011-08-11,3,0,8,0,4,1,1,0.7175,0.651521,0.42375,0.164796,1075.94,812 +224,2011-08-12,3,0,8,0,5,1,1,0.708333,0.654042,0.415,0.125621,1159.42,1051 +225,2011-08-13,3,0,8,0,6,0,2,0.685833,0.645858,0.729583,0.211454,1667.4,1504 +226,2011-08-14,3,0,8,0,0,0,2,0.676667,0.624388,0.8175,0.222633,1550.75,1338 +227,2011-08-15,3,0,8,0,1,1,1,0.665833,0.616167,0.712083,0.208954,797.26,775 +228,2011-08-16,3,0,8,0,2,1,1,0.700833,0.645837,0.578333,0.236329,790.11,721 +229,2011-08-17,3,0,8,0,3,1,1,0.723333,0.666671,0.575417,0.143667,811.63,668 +230,2011-08-18,3,0,8,0,4,1,1,0.711667,0.662258,0.654583,0.233208,751.17,639 +231,2011-08-19,3,0,8,0,5,1,2,0.685,0.633221,0.722917,0.139308,1006.23,797 +232,2011-08-20,3,0,8,0,6,0,1,0.6975,0.648996,0.674167,0.104467,1956.29,1914 +233,2011-08-21,3,0,8,0,0,0,1,0.710833,0.675525,0.77,0.248754,1412.14,1249 +234,2011-08-22,3,0,8,0,1,1,1,0.691667,0.638254,0.47,0.27675,914.63,833 +235,2011-08-23,3,0,8,0,2,1,1,0.640833,0.606067,0.455417,0.146763,1187.05,1281 +236,2011-08-24,3,0,8,0,3,1,1,0.673333,0.630692,0.605,0.253108,822.93,949 +237,2011-08-25,3,0,8,0,4,1,2,0.684167,0.645854,0.771667,0.210833,772.69,435 +238,2011-08-26,3,0,8,0,5,1,1,0.7,0.659733,0.76125,0.0839625,880.5,768 +239,2011-08-27,3,0,8,0,6,0,2,0.68,0.635556,0.85,0.375617,627.7,226 +240,2011-08-28,3,0,8,0,0,0,1,0.707059,0.647959,0.561765,0.304659,1557.85,1415 +241,2011-08-29,3,0,8,0,1,1,1,0.636667,0.607958,0.554583,0.159825,924.89,729 +242,2011-08-30,3,0,8,0,2,1,1,0.639167,0.594704,0.548333,0.125008,898.34,775 +243,2011-08-31,3,0,8,0,3,1,1,0.656667,0.611121,0.597917,0.0833333,1084.94,688 +244,2011-09-01,3,0,9,0,4,1,1,0.655,0.614921,0.639167,0.141796,840.57,783 +245,2011-09-02,3,0,9,0,5,1,2,0.643333,0.604808,0.727083,0.139929,937.58,875 +246,2011-09-03,3,0,9,0,6,0,1,0.669167,0.633213,0.716667,0.185325,1961.33,1935 +247,2011-09-04,3,0,9,0,0,0,1,0.709167,0.665429,0.742083,0.206467,2300.33,2521 +248,2011-09-05,3,0,9,1,1,0,2,0.673333,0.625646,0.790417,0.212696,1993.22,1236 +249,2011-09-06,3,0,9,0,2,1,3,0.54,0.5152,0.886957,0.343943,243.29,204 +250,2011-09-07,3,0,9,0,3,1,3,0.599167,0.544229,0.917083,0.0970208,356.8,118 +251,2011-09-08,3,0,9,0,4,1,3,0.633913,0.555361,0.939565,0.192748,345.26,153 +252,2011-09-09,3,0,9,0,5,1,2,0.65,0.578946,0.897917,0.124379,518.37,417 +253,2011-09-10,3,0,9,0,6,0,1,0.66,0.607962,0.75375,0.153608,1931.92,1750 +254,2011-09-11,3,0,9,0,0,0,1,0.653333,0.609229,0.71375,0.115054,1786.52,1633 +255,2011-09-12,3,0,9,0,1,1,1,0.644348,0.60213,0.692174,0.088913,796.91,690 +256,2011-09-13,3,0,9,0,2,1,1,0.650833,0.603554,0.7125,0.141804,815.39,701 +257,2011-09-14,3,0,9,0,3,1,1,0.673333,0.6269,0.697083,0.1673,727.56,647 +258,2011-09-15,3,0,9,0,4,1,2,0.5775,0.553671,0.709167,0.271146,529.98,428 +259,2011-09-16,3,0,9,0,5,1,2,0.469167,0.461475,0.590417,0.164183,872.18,742 +260,2011-09-17,3,0,9,0,6,0,2,0.491667,0.478512,0.718333,0.189675,1613.64,1434 +261,2011-09-18,3,0,9,0,0,0,1,0.5075,0.490537,0.695,0.178483,1538.28,1353 +262,2011-09-19,3,0,9,0,1,1,2,0.549167,0.529675,0.69,0.151742,873.57,691 +263,2011-09-20,3,0,9,0,2,1,2,0.561667,0.532217,0.88125,0.134954,458.23,438 +264,2011-09-21,3,0,9,0,3,1,2,0.595,0.550533,0.9,0.0964042,478.13,539 +265,2011-09-22,3,0,9,0,4,1,2,0.628333,0.554963,0.902083,0.128125,501.49,555 +266,2011-09-23,4,0,9,0,5,1,2,0.609167,0.522125,0.9725,0.0783667,363.0,258 +267,2011-09-24,4,0,9,0,6,0,2,0.606667,0.564412,0.8625,0.0783833,1630.17,1776 +268,2011-09-25,4,0,9,0,0,0,2,0.634167,0.572637,0.845,0.0503792,1729.5,1544 +269,2011-09-26,4,0,9,0,1,1,2,0.649167,0.589042,0.848333,0.1107,539.11,684 +270,2011-09-27,4,0,9,0,2,1,2,0.636667,0.574525,0.885417,0.118171,483.15,477 +271,2011-09-28,4,0,9,0,3,1,2,0.635,0.575158,0.84875,0.148629,511.23,480 +272,2011-09-29,4,0,9,0,4,1,1,0.616667,0.574512,0.699167,0.172883,722.68,653 +273,2011-09-30,4,0,9,0,5,1,1,0.564167,0.544829,0.6475,0.206475,982.32,830 +274,2011-10-01,4,0,10,0,6,0,2,0.41,0.412863,0.75375,0.292296,770.79,480 +275,2011-10-02,4,0,10,0,0,0,2,0.356667,0.345317,0.791667,0.222013,696.94,616 +276,2011-10-03,4,0,10,0,1,1,2,0.384167,0.392046,0.760833,0.0833458,323.48,330 +277,2011-10-04,4,0,10,0,2,1,1,0.484167,0.472858,0.71,0.205854,521.44,486 +278,2011-10-05,4,0,10,0,3,1,1,0.538333,0.527138,0.647917,0.17725,617.89,559 +279,2011-10-06,4,0,10,0,4,1,1,0.494167,0.480425,0.620833,0.134954,614.37,639 +280,2011-10-07,4,0,10,0,5,1,1,0.510833,0.504404,0.684167,0.0223917,1142.93,949 +281,2011-10-08,4,0,10,0,6,0,1,0.521667,0.513242,0.70125,0.0454042,2245.46,2235 +282,2011-10-09,4,0,10,0,0,0,1,0.540833,0.523983,0.7275,0.06345,2291.53,2397 +283,2011-10-10,4,0,10,1,1,0,1,0.570833,0.542925,0.73375,0.0423042,1914.49,1514 +284,2011-10-11,4,0,10,0,2,1,2,0.566667,0.546096,0.80875,0.143042,704.1,667 +285,2011-10-12,4,0,10,0,3,1,3,0.543333,0.517717,0.90625,0.24815,236.23,217 +286,2011-10-13,4,0,10,0,4,1,2,0.589167,0.551804,0.896667,0.141787,474.67,290 +287,2011-10-14,4,0,10,0,5,1,2,0.550833,0.529675,0.71625,0.223883,657.61,529 +288,2011-10-15,4,0,10,0,6,0,1,0.506667,0.498725,0.483333,0.258083,2038.95,1899 +289,2011-10-16,4,0,10,0,0,0,1,0.511667,0.503154,0.486667,0.281717,1888.11,1748 +290,2011-10-17,4,0,10,0,1,1,1,0.534167,0.510725,0.579583,0.175379,728.37,713 +291,2011-10-18,4,0,10,0,2,1,2,0.5325,0.522721,0.701667,0.110087,684.6,637 +292,2011-10-19,4,0,10,0,3,1,3,0.541739,0.513848,0.895217,0.243339,253.62,254 +293,2011-10-20,4,0,10,0,4,1,1,0.475833,0.466525,0.63625,0.422275,543.93,471 +294,2011-10-21,4,0,10,0,5,1,1,0.4275,0.423596,0.574167,0.221396,877.89,676 +295,2011-10-22,4,0,10,0,6,0,1,0.4225,0.425492,0.629167,0.0926667,2104.43,1499 +296,2011-10-23,4,0,10,0,0,0,1,0.421667,0.422333,0.74125,0.0995125,1728.19,1619 +297,2011-10-24,4,0,10,0,1,1,1,0.463333,0.457067,0.772083,0.118792,674.43,699 +298,2011-10-25,4,0,10,0,2,1,1,0.471667,0.463375,0.622917,0.166658,651.48,695 +299,2011-10-26,4,0,10,0,3,1,2,0.484167,0.472846,0.720417,0.148642,445.51,404 +300,2011-10-27,4,0,10,0,4,1,2,0.47,0.457046,0.812917,0.197763,569.54,240 +301,2011-10-28,4,0,10,0,5,1,2,0.330833,0.318812,0.585833,0.229479,371.83,456 +302,2011-10-29,4,0,10,0,6,0,3,0.254167,0.227913,0.8825,0.351371,248.99,57 +303,2011-10-30,4,0,10,0,0,0,1,0.319167,0.321329,0.62375,0.176617,766.59,885 +304,2011-10-31,4,0,10,0,1,1,1,0.34,0.356063,0.703333,0.10635,422.77,362 +305,2011-11-01,4,0,11,0,2,1,1,0.400833,0.397088,0.68375,0.135571,422.8,410 +306,2011-11-02,4,0,11,0,3,1,1,0.3775,0.390133,0.71875,0.0820917,403.37,370 +307,2011-11-03,4,0,11,0,4,1,1,0.408333,0.405921,0.702083,0.136817,404.91,318 +308,2011-11-04,4,0,11,0,5,1,2,0.403333,0.403392,0.6225,0.271779,551.32,470 +309,2011-11-05,4,0,11,0,6,0,1,0.326667,0.323854,0.519167,0.189062,1105.64,1156 +310,2011-11-06,4,0,11,0,0,0,1,0.348333,0.362358,0.734583,0.0920542,897.06,952 +311,2011-11-07,4,0,11,0,1,1,1,0.395,0.400871,0.75875,0.057225,357.14,373 +312,2011-11-08,4,0,11,0,2,1,1,0.408333,0.412246,0.721667,0.0690375,401.64,376 +313,2011-11-09,4,0,11,0,3,1,1,0.4,0.409079,0.758333,0.0621958,344.89,305 +314,2011-11-10,4,0,11,0,4,1,2,0.38,0.373721,0.813333,0.189067,332.78,190 +315,2011-11-11,4,0,11,1,5,0,1,0.324167,0.306817,0.44625,0.314675,536.43,440 +316,2011-11-12,4,0,11,0,6,0,1,0.356667,0.357942,0.552917,0.212062,1123.38,1275 +317,2011-11-13,4,0,11,0,0,0,1,0.440833,0.43055,0.458333,0.281721,1484.58,1004 +318,2011-11-14,4,0,11,0,1,1,1,0.53,0.524612,0.587083,0.306596,782.58,595 +319,2011-11-15,4,0,11,0,2,1,2,0.53,0.507579,0.68875,0.199633,675.74,449 +320,2011-11-16,4,0,11,0,3,1,3,0.456667,0.451988,0.93,0.136829,219.42,145 +321,2011-11-17,4,0,11,0,4,1,2,0.341667,0.323221,0.575833,0.305362,222.5,139 +322,2011-11-18,4,0,11,0,5,1,1,0.274167,0.272721,0.41,0.168533,290.69,245 +323,2011-11-19,4,0,11,0,6,0,1,0.329167,0.324483,0.502083,0.224496,1028.8,943 +324,2011-11-20,4,0,11,0,0,0,2,0.463333,0.457058,0.684583,0.18595,1108.9,787 +325,2011-11-21,4,0,11,0,1,1,3,0.4475,0.445062,0.91,0.138054,235.06,220 +326,2011-11-22,4,0,11,0,2,1,3,0.416667,0.421696,0.9625,0.118792,262.55,69 +327,2011-11-23,4,0,11,0,3,1,2,0.440833,0.430537,0.757917,0.335825,225.32,112 +328,2011-11-24,4,0,11,1,4,0,1,0.373333,0.372471,0.549167,0.167304,798.55,560 +329,2011-11-25,4,0,11,0,5,1,1,0.375,0.380671,0.64375,0.0988958,1091.6,1095 +330,2011-11-26,4,0,11,0,6,0,1,0.375833,0.385087,0.681667,0.0684208,1182.57,1249 +331,2011-11-27,4,0,11,0,0,0,1,0.459167,0.4558,0.698333,0.208954,1312.91,810 +332,2011-11-28,4,0,11,0,1,1,1,0.503478,0.490122,0.743043,0.142122,607.82,253 +333,2011-11-29,4,0,11,0,2,1,2,0.458333,0.451375,0.830833,0.258092,450.97,96 +334,2011-11-30,4,0,11,0,3,1,1,0.325,0.311221,0.613333,0.271158,310.0,188 +335,2011-12-01,4,0,12,0,4,1,1,0.3125,0.305554,0.524583,0.220158,282.73,182 +336,2011-12-02,4,0,12,0,5,1,1,0.314167,0.331433,0.625833,0.100754,297.19,268 +337,2011-12-03,4,0,12,0,6,0,1,0.299167,0.310604,0.612917,0.0957833,585.83,706 +338,2011-12-04,4,0,12,0,0,0,1,0.330833,0.3491,0.775833,0.0839583,795.78,634 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+365,2011-12-31,1,0,12,0,6,0,1,0.41,0.414121,0.615833,0.220154,926.29,665 +366,2012-01-01,1,1,1,0,0,0,1,0.37,0.375621,0.6925,0.192167,724.14,686 +367,2012-01-02,1,1,1,1,1,0,1,0.273043,0.252304,0.381304,0.329665,445.98,244 +368,2012-01-03,1,1,1,0,2,1,1,0.15,0.126275,0.44125,0.365671,80.83,89 +369,2012-01-04,1,1,1,0,3,1,2,0.1075,0.119337,0.414583,0.1847,91.23,95 +370,2012-01-05,1,1,1,0,4,1,1,0.265833,0.278412,0.524167,0.129987,195.88,140 +371,2012-01-06,1,1,1,0,5,1,1,0.334167,0.340267,0.542083,0.167908,342.46,307 +372,2012-01-07,1,1,1,0,6,0,1,0.393333,0.390779,0.531667,0.174758,1104.2,1070 +373,2012-01-08,1,1,1,0,0,0,1,0.3375,0.340258,0.465,0.191542,851.25,599 +374,2012-01-09,1,1,1,0,1,1,2,0.224167,0.247479,0.701667,0.0989,121.37,106 +375,2012-01-10,1,1,1,0,2,1,1,0.308696,0.318826,0.646522,0.187552,234.39,173 +376,2012-01-11,1,1,1,0,3,1,2,0.274167,0.282821,0.8475,0.131221,136.46,92 +377,2012-01-12,1,1,1,0,4,1,2,0.3825,0.381938,0.802917,0.180967,265.13,269 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+609,2012-08-31,3,1,8,0,5,1,1,0.764167,0.6894,0.5875,0.168533,1216.18,1433 +610,2012-09-01,3,1,9,0,6,0,2,0.753333,0.702654,0.638333,0.113187,2293.61,2352 +611,2012-09-02,3,1,9,0,0,0,2,0.696667,0.649,0.815,0.0640708,2368.83,2613 +612,2012-09-03,3,1,9,1,1,0,1,0.7075,0.661629,0.790833,0.151121,1975.17,1965 +613,2012-09-04,3,1,9,0,2,1,1,0.725833,0.686888,0.755,0.236321,812.92,867 +614,2012-09-05,3,1,9,0,3,1,1,0.736667,0.708983,0.74125,0.187808,807.37,832 +615,2012-09-06,3,1,9,0,4,1,2,0.696667,0.655329,0.810417,0.142421,671.12,611 +616,2012-09-07,3,1,9,0,5,1,1,0.703333,0.657204,0.73625,0.171646,1015.01,1045 +617,2012-09-08,3,1,9,0,6,0,2,0.659167,0.611121,0.799167,0.281104,1720.35,1557 +618,2012-09-09,3,1,9,0,0,0,1,0.61,0.578925,0.5475,0.224496,2481.06,2570 +619,2012-09-10,3,1,9,0,1,1,1,0.583333,0.565654,0.50375,0.258713,921.31,1118 +620,2012-09-11,3,1,9,0,2,1,1,0.5775,0.554292,0.52,0.0920542,1039.52,1070 +621,2012-09-12,3,1,9,0,3,1,1,0.599167,0.570075,0.577083,0.131846,984.38,1050 +622,2012-09-13,3,1,9,0,4,1,1,0.6125,0.579558,0.637083,0.0827208,1024.32,1054 +623,2012-09-14,3,1,9,0,5,1,1,0.633333,0.594083,0.6725,0.103863,1326.45,1379 +624,2012-09-15,3,1,9,0,6,0,1,0.608333,0.585867,0.501667,0.247521,2349.62,3160 +625,2012-09-16,3,1,9,0,0,0,1,0.58,0.563125,0.57,0.0901833,2195.75,2166 +626,2012-09-17,3,1,9,0,1,1,2,0.580833,0.55305,0.734583,0.151742,933.34,1022 +627,2012-09-18,3,1,9,0,2,1,2,0.623333,0.565067,0.8725,0.357587,510.41,371 +628,2012-09-19,3,1,9,0,3,1,1,0.5525,0.540404,0.536667,0.215175,800.06,788 +629,2012-09-20,3,1,9,0,4,1,1,0.546667,0.532192,0.618333,0.118167,953.95,939 +630,2012-09-21,3,1,9,0,5,1,1,0.599167,0.571971,0.66875,0.154229,1211.51,1250 +631,2012-09-22,3,1,9,0,6,0,1,0.65,0.610488,0.646667,0.283583,2256.35,2512 +632,2012-09-23,4,1,9,0,0,0,1,0.529167,0.518933,0.467083,0.223258,2338.91,2454 +633,2012-09-24,4,1,9,0,1,1,1,0.514167,0.502513,0.492917,0.142404,810.61,1001 +634,2012-09-25,4,1,9,0,2,1,1,0.55,0.544179,0.57,0.236321,833.54,845 +635,2012-09-26,4,1,9,0,3,1,1,0.635,0.596613,0.630833,0.2444,784.08,787 +636,2012-09-27,4,1,9,0,4,1,2,0.65,0.607975,0.690833,0.134342,751.06,751 +637,2012-09-28,4,1,9,0,5,1,2,0.619167,0.585863,0.69,0.164179,970.45,1045 +638,2012-09-29,4,1,9,0,6,0,1,0.5425,0.530296,0.542917,0.227604,2500.48,2589 +639,2012-09-30,4,1,9,0,0,0,1,0.526667,0.517663,0.583333,0.134958,2176.05,2015 +640,2012-10-01,4,1,10,0,1,1,2,0.520833,0.512,0.649167,0.0908042,779.68,763 +641,2012-10-02,4,1,10,0,2,1,3,0.590833,0.542333,0.871667,0.104475,371.85,315 +642,2012-10-03,4,1,10,0,3,1,2,0.6575,0.599133,0.79375,0.0665458,831.65,728 +643,2012-10-04,4,1,10,0,4,1,2,0.6575,0.607975,0.722917,0.117546,842.35,891 +644,2012-10-05,4,1,10,0,5,1,1,0.615,0.580187,0.6275,0.10635,1349.91,1516 +645,2012-10-06,4,1,10,0,6,0,1,0.554167,0.538521,0.664167,0.268025,2770.84,3031 +646,2012-10-07,4,1,10,0,0,0,2,0.415833,0.419813,0.708333,0.141162,1401.35,781 +647,2012-10-08,4,1,10,1,1,0,2,0.383333,0.387608,0.709583,0.189679,901.2,874 +648,2012-10-09,4,1,10,0,2,1,2,0.446667,0.438112,0.761667,0.1903,417.71,601 +649,2012-10-10,4,1,10,0,3,1,1,0.514167,0.503142,0.630833,0.187821,677.16,780 +650,2012-10-11,4,1,10,0,4,1,1,0.435,0.431167,0.463333,0.181596,791.95,834 +651,2012-10-12,4,1,10,0,5,1,1,0.4375,0.433071,0.539167,0.235092,920.6,1060 +652,2012-10-13,4,1,10,0,6,0,1,0.393333,0.391396,0.494583,0.146142,1949.49,2252 +653,2012-10-14,4,1,10,0,0,0,1,0.521667,0.508204,0.640417,0.278612,2240.74,2080 +654,2012-10-15,4,1,10,0,1,1,2,0.561667,0.53915,0.7075,0.296037,764.87,760 +655,2012-10-16,4,1,10,0,2,1,1,0.468333,0.460846,0.558333,0.182221,846.45,922 +656,2012-10-17,4,1,10,0,3,1,1,0.455833,0.450108,0.692917,0.101371,613.37,979 +657,2012-10-18,4,1,10,0,4,1,2,0.5225,0.512625,0.728333,0.236937,884.07,1008 +658,2012-10-19,4,1,10,0,5,1,2,0.563333,0.537896,0.815,0.134954,798.89,753 +659,2012-10-20,4,1,10,0,6,0,1,0.484167,0.472842,0.572917,0.117537,2529.15,2806 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+724,2012-12-24,1,1,12,0,1,1,2,0.231304,0.2589,0.791304,0.0772304,179.15,174 +725,2012-12-25,1,1,12,1,2,0,2,0.291304,0.294465,0.734783,0.168726,354.36,440 +726,2012-12-26,1,1,12,0,3,1,3,0.243333,0.220333,0.823333,0.316546,77.37,9 +727,2012-12-27,1,1,12,0,4,1,2,0.254167,0.226642,0.652917,0.350133,220.43,247 +728,2012-12-28,1,1,12,0,5,1,2,0.253333,0.255046,0.59,0.155471,238.72,644 +729,2012-12-29,1,1,12,0,6,0,2,0.253333,0.2424,0.752917,0.124383,219.06,159 +730,2012-12-30,1,1,12,0,0,0,1,0.255833,0.2317,0.483333,0.350754,269.62,364 +731,2012-12-31,1,1,12,0,1,1,2,0.215833,0.223487,0.5775,0.154846,298.24,439