Example #1
0
                    """

# master table
atoms = Table(
    'atoms',
    metadata,
    Column('atom_id', Integer, primary_key=True),
    Column('biomolecule_id', Integer, nullable=False),
    Column('residue_id', Integer, nullable=False),
    Column('path', PTree),
    Column('atom_serial', Integer, nullable=False),
    Column('group_pdb', String(7), nullable=False),  # HETATM/ATOM
    Column('atom_name', String(4), nullable=False),
    Column('alt_loc', String(1), nullable=False),
    Column('coords', Vector3D, nullable=False),
    Column('occupancy', Float(3, 2), nullable=False),
    Column('b_factor', Float(4, 2), nullable=False),
    Column('element', String(2)),
    Column('hyb', SmallInteger, nullable=False),
    Column('tripos_atom_type', String(5)),
    Column('is_donor',
           Boolean(create_constraint=False),
           DefaultClause('false'),
           nullable=False),
    Column('is_acceptor',
           Boolean(create_constraint=False),
           DefaultClause('false'),
           nullable=False),
    Column('is_aromatic',
           Boolean(create_constraint=False),
           DefaultClause('false'),
Example #2
0
100xp
Previously, we used the Table object to reflect a table from an existing database, but what if we want to create a new table? We still use the Table object; however, we replace the autoload keyword arguments with Column objects. The Column object takes a name, a SQLAlchemy type with an optional format, and optional keyword arguments for different constraints. With the table defined, we're now ready to create the table in the database by using the create_all method on metadata and supplying the engine as the only parameter.

When building the table, recall how in the video we passed in 255 as the maximum length of a String, but there were no such constraints for other types.

Instructions
Import Table, Column, String, Integer, Float, Boolean from sqlalchemy.
Build a new table called data with a name (String), count (Integer), amount(Float), and valid (Boolean) columns.
Create the table in the database.
"""
# Import Table, Column, String, Integer, Float, Boolean from sqlalchemy
from sqlalchemy import Table, Column, String, Integer, Float, Boolean

# Define a new table with a name, count, amount, and valid column: data
data = Table('data', metadata, Column('name', String(255)),
             Column('count', Integer()), Column('amount', Float()),
             Column('valid', Boolean()))

# Use the metadata to create the table
metadata.create_all(engine)

# Print table repr
print(repr(data))
""" sortie Ipython
<script.py> output:
    Table('data', MetaData(bind=None), Column('name', String(length=255), table=<data>), Column('count', Integer(), table=<data>), Column('amount', Float(), table=<data>), Column('valid', Boolean(), table=<data>), schema=None)
"""
"""  
Constraints and Data Defaults
100xp
Often, you need to make sure that a column is unique, nullable, a positive value, or related to a column in another table. These are called constraints. Many constraints are keywords on the column itself; however, they can also be passed directly to the Table object as well. In addition to constraints, you can also set a default value for the column if no data is passed to it via the default keyword on the column. There is also an onupdate keyword for setting the column value when the row is updated. This is extremely useful for keeping datetime stamps for auditing purposes.
Example #3
0
db = Database(credentials=credentials)
db_schema = None  #  set if you are not using the default
'''
To do anything with IoT Platform Analytics, you will need one or more entity type.
You can create entity types through the IoT Platform or using the python API as shown below.
The database schema is only needed if you are not using the default schema. You can also rename the timestamp.
'''
entity_name = 'Turbines'
# dash100462  Used in dev2
db_schema = 'dash100462'
# db_schema = None  # replace if you are not using the default schema
#db.drop_table(entity_name, schema = db_schema)

entity = EntityType(
    entity_name, db, Column('Turbine_ID', String(50)),
    Column('Temperature', Float()), Column('Pressure', Float()),
    Column('Volume', Float()),
    DemoHTTPPreload(request='GET',
                    url='internal_test',
                    output_item='http_preload_done'),
    bif.PythonExpression(expression='df["Temperature"]*df["Pressure"]',
                         output_name='Volume'), **{
                             '_timestamp': 'evt_timestamp',
                             '_db_schema': db_schema
                         })
'''
When creating an EntityType object you will need to specify the name of the entity, the database
object that will contain entity data
After creating an EntityType you will need to register it so that it visible in the UI.
To also register the functions and constants associated with the entity type, specify
'publish_kpis' = True.
def load_unicode_test_data():
    """Loading unicode test dataset from a csv file in the repo"""
    data = get_example_data("unicode_utf8_unixnl_test.csv",
                            is_gzip=False,
                            make_bytes=True)
    df = pd.read_csv(data, encoding="utf-8")
    # generate date/numeric data
    df["dttm"] = datetime.datetime.now().date()
    df["value"] = [random.randint(1, 100) for _ in range(len(df))]
    df.to_sql(  # pylint: disable=no-member
        "unicode_test",
        db.engine,
        if_exists="replace",
        chunksize=500,
        dtype={
            "phrase": String(500),
            "short_phrase": String(10),
            "with_missing": String(100),
            "dttm": Date(),
            "value": Float(),
        },
        index=False,
    )
    print("Done loading table!")
    print("-" * 80)

    print("Creating table [unicode_test] reference")
    obj = db.session.query(TBL).filter_by(table_name="unicode_test").first()
    if not obj:
        obj = TBL(table_name="unicode_test")
    obj.main_dttm_col = "dttm"
    obj.database = utils.get_or_create_main_db()
    db.session.merge(obj)
    db.session.commit()
    obj.fetch_metadata()
    tbl = obj

    slice_data = {
        "granularity_sqla": "dttm",
        "groupby": [],
        "metric": {
            "aggregate": "SUM",
            "column": {
                "column_name": "value"
            },
            "expressionType": "SIMPLE",
            "label": "Value",
        },
        "row_limit": config.get("ROW_LIMIT"),
        "since": "100 years ago",
        "until": "now",
        "where": "",
        "viz_type": "word_cloud",
        "size_from": "10",
        "series": "short_phrase",
        "size_to": "70",
        "rotation": "square",
        "limit": "100",
    }

    print("Creating a slice")
    slc = Slice(
        slice_name="Unicode Cloud",
        viz_type="word_cloud",
        datasource_type="table",
        datasource_id=tbl.id,
        params=get_slice_json(slice_data),
    )
    merge_slice(slc)

    print("Creating a dashboard")
    dash = db.session.query(Dash).filter_by(
        dashboard_title="Unicode Test").first()

    if not dash:
        dash = Dash()
    js = """\
{
    "CHART-Hkx6154FEm": {
        "children": [],
        "id": "CHART-Hkx6154FEm",
        "meta": {
            "chartId": 2225,
            "height": 30,
            "sliceName": "slice 1",
            "width": 4
        },
        "type": "CHART"
    },
    "GRID_ID": {
        "children": [
            "ROW-SyT19EFEQ"
        ],
        "id": "GRID_ID",
        "type": "GRID"
    },
    "ROOT_ID": {
        "children": [
            "GRID_ID"
        ],
        "id": "ROOT_ID",
        "type": "ROOT"
    },
    "ROW-SyT19EFEQ": {
        "children": [
            "CHART-Hkx6154FEm"
        ],
        "id": "ROW-SyT19EFEQ",
        "meta": {
            "background": "BACKGROUND_TRANSPARENT"
        },
        "type": "ROW"
    },
    "DASHBOARD_VERSION_KEY": "v2"
}
    """
    dash.dashboard_title = "Unicode Test"
    pos = json.loads(js)
    update_slice_ids(pos, [slc])
    dash.position_json = json.dumps(pos, indent=4)
    dash.slug = "unicode-test"
    dash.slices = [slc]
    db.session.merge(dash)
    db.session.commit()
Example #5
0
 def test_float_setinputsizes(self):
     self._test_setinputsizes(Float(15), 25.34534, None)
Example #6
0
 def test_compare_float_text(self):
     self._compare_default_roundtrip(
         Float(),
         text("5.2"),
     )
Example #7
0
class Quote(Base):
    __tablename__ = 'quotes'
    id = Column(Integer, primary_key=True)

    stock_id = Column(ForeignKey('stocks.id'))
    stock = relationship('Stock', back_populates='quotes')
    symbol = Column(String(30))
    symbol_name = Column(String(255))
    stock_exchange = Column(String(30))
    trade_currency = Column(String(30))
    date_inserted = Column(DateTime, default=now)
    date_dividend_ex = Column(DateTime)
    dividend_pay_date = Column(DateTime)
    dividend_share = Column(Float())
    dividend_yield = Column(Float())
    date_last_traded = Column(DateTime)
    ebitda = Column(Float())
    eps_current = Column(Float())
    eps_current_year = Column(Float())
    eps_next_quarter = Column(Float())
    eps_next_year = Column(Float())
    eps_price_est_current_year = Column(Float())
    eps_price_est_next_year = Column(Float())
    errored_symbol = Column(Boolean(), default=False)
    limit_high = Column(Float())
    limit_low = Column(Float())
    market_cap = Column(BigInteger)
    pct_change_200_day_avg = Column(Float(precision=4))
    pct_change_50_day_avg = Column(Float(precision=4))
    pct_change_current = Column(Float(precision=4))
    pct_change_today = Column(Float(precision=4))
    pct_change_year_high = Column(Float(precision=4))
    pct_change_year_low = Column(Float(precision=4))
    pct_day_change = Column(Float(precision=4))
    pe_ratio = Column(Float(precision=4))
    peg_ratio = Column(Float(precision=4))
    price_200_day_moving_avg = Column(Float(precision=4))
    price_50_day_moving_avg = Column(Float(precision=4))
    price_ask = Column(Float(precision=4))
    price_bid = Column(Float(precision=4))
    price_book = Column(Float(precision=4))
    price_book_value = Column(Float(precision=4))
    price_change_200_day_avg = Column(Float(precision=4))
    price_change_50_day_avg = Column(Float(precision=4))
    price_change_year_high = Column(Float(precision=4))
    price_change_year_low = Column(Float(precision=4))
    price_day_change = Column(Float(precision=4))
    price_day_high = Column(Float(precision=4))
    price_day_low = Column(Float(precision=4))
    price_day_open = Column(Float(precision=4))
    price_last_close = Column(Float(precision=4))
    price_last_trade = Column(Float(precision=4))
    price_sales = Column(Float(precision=4))
    price_year_high = Column(Float(precision=4))
    price_year_low = Column(Float(precision=4))
    price_year_target = Column(Float(precision=4))
    range_day = Column(String(30))
    range_year = Column(String(30))
    short_ratio = Column(Float(precision=4))
    volume_day = Column(Integer)
    volume_day_avg = Column(Integer)

    def __repr__(self):
        return """Quote(symbol='{}', symbol_name='{}', date_last_traded='{}',
               price_last_trade='{}', price_last_close='{}', date_inserted='{}'""".format(
            self.symbol, self.symbol_name, self.date_last_traded,
            self.price_last_trade, self.price_last_close, self.date_inserted)
Example #8
0
def load_energy():
    """Loads an energy related dataset to use with sankey and graphs"""
    tbl_name = 'energy_usage'
    with gzip.open(os.path.join(DATA_FOLDER, 'energy.json.gz')) as f:
        pdf = pd.read_json(f)
    pdf.to_sql(
        tbl_name,
        db.engine,
        if_exists='replace',
        chunksize=500,
        dtype={
            'source': String(255),
            'target': String(255),
            'value': Float(),
        },
        index=False)

    print("Creating table [wb_health_population] reference")
    tbl = db.session.query(TBL).filter_by(table_name=tbl_name).first()
    if not tbl:
        tbl = TBL(table_name=tbl_name)
    tbl.description = "Energy consumption"
    tbl.is_featured = True
    tbl.database = utils.get_or_create_main_db(caravel)
    db.session.merge(tbl)
    db.session.commit()
    tbl.fetch_metadata()

    merge_slice(
        Slice(
            slice_name="Energy Sankey",
            viz_type='sankey',
            datasource_type='table',
            datasource_id=tbl.id,
            params=textwrap.dedent("""\
            {
                "collapsed_fieldsets": "",
                "datasource_id": "3",
                "datasource_name": "energy_usage",
                "datasource_type": "table",
                "flt_col_0": "source",
                "flt_eq_0": "",
                "flt_op_0": "in",
                "groupby": [
                    "source",
                    "target"
                ],
                "having": "",
                "metric": "sum__value",
                "row_limit": "5000",
                "slice_id": "",
                "slice_name": "Energy Sankey",
                "viz_type": "sankey",
                "where": ""
            }
            """))
    )

    merge_slice(
        Slice(
            slice_name="Energy Force Layout",
            viz_type='directed_force',
            datasource_type='table',
            datasource_id=tbl.id,
            params=textwrap.dedent("""\
            {
                "charge": "-500",
                "collapsed_fieldsets": "",
                "datasource_id": "1",
                "datasource_name": "energy_usage",
                "datasource_type": "table",
                "flt_col_0": "source",
                "flt_eq_0": "",
                "flt_op_0": "in",
                "groupby": [
                    "source",
                    "target"
                ],
                "having": "",
                "link_length": "200",
                "metric": "sum__value",
                "row_limit": "5000",
                "slice_id": "229",
                "slice_name": "Force",
                "viz_type": "directed_force",
                "where": ""
            }
            """))
    )
    merge_slice(
        Slice(
            slice_name="Heatmap",
            viz_type='heatmap',
            datasource_type='table',
            datasource_id=tbl.id,
            params=textwrap.dedent("""\
            {
                "all_columns_x": "source",
                "all_columns_y": "target",
                "canvas_image_rendering": "pixelated",
                "collapsed_fieldsets": "",
                "datasource_id": "1",
                "datasource_name": "energy_usage",
                "datasource_type": "table",
                "flt_col_0": "source",
                "flt_eq_0": "",
                "flt_op_0": "in",
                "having": "",
                "linear_color_scheme": "blue_white_yellow",
                "metric": "sum__value",
                "normalize_across": "heatmap",
                "slice_id": "229",
                "slice_name": "Heatmap",
                "viz_type": "heatmap",
                "where": "",
                "xscale_interval": "1",
                "yscale_interval": "1"
            }
            """))
    )
Example #9
0
def load_unicode_test_data():
    """Loading unicode test dataset from a csv file in the repo"""
    df = pd.read_csv(os.path.join(DATA_FOLDER, 'unicode_utf8_unixnl_test.csv'),
                     encoding="utf-8")
    # generate date/numeric data
    df['date'] = datetime.datetime.now().date()
    df['value'] = [random.randint(1, 100) for _ in range(len(df))]
    df.to_sql(
        'unicode_test',
        db.engine,
        if_exists='replace',
        chunksize=500,
        dtype={
            'phrase': String(500),
            'short_phrase': String(10),
            'with_missing': String(100),
            'date': Date(),
            'value': Float(),
        },
        index=False)
    print("Done loading table!")
    print("-" * 80)

    print("Creating table [unicode_test] reference")
    obj = db.session.query(TBL).filter_by(table_name='unicode_test').first()
    if not obj:
        obj = TBL(table_name='unicode_test')
    obj.main_dttm_col = 'date'
    obj.database = utils.get_or_create_main_db(caravel)
    obj.is_featured = False
    db.session.merge(obj)
    db.session.commit()
    obj.fetch_metadata()
    tbl = obj

    slice_data = {
        "datasource_id": "3",
        "datasource_name": "unicode_test",
        "datasource_type": "table",
        "flt_op_1": "in",
        "granularity": "date",
        "groupby": [],
        "metric": 'sum__value',
        "row_limit": config.get("ROW_LIMIT"),
        "since": "100 years ago",
        "until": "now",
        "where": "",
        "viz_type": "word_cloud",
        "size_from": "10",
        "series": "short_phrase",
        "size_to": "70",
        "rotation": "square",
        "limit": "100",
    }

    print("Creating a slice")
    slc = Slice(
        slice_name="Unicode Cloud",
        viz_type='word_cloud',
        datasource_type='table',
        datasource_id=tbl.id,
        params=get_slice_json(slice_data),
    )
    merge_slice(slc)

    print("Creating a dashboard")
    dash = db.session.query(Dash).filter_by(dashboard_title="Unicode Test").first()

    if not dash:
        dash = Dash()
    pos = {
        "size_y": 4,
        "size_x": 4,
        "col": 1,
        "row": 1,
        "slice_id": slc.id,
    }
    dash.dashboard_title = "Unicode Test"
    dash.position_json = json.dumps([pos], indent=4)
    dash.slug = "unicode-test"
    dash.slices = [slc]
    db.session.merge(dash)
    db.session.commit()
Example #10
0
class Place(BaseModel, Base):
    """This is the class for Place
    Attributes:
        city_id: city id
        user_id: user id
        name: name input
        description: string of description
        number_rooms: number of room in int
        number_bathrooms: number of bathrooms in int
        max_guest: maximum guest in int
        price_by_night:: pice for a staying in int
        latitude: latitude in flaot
        longitude: longitude in float
        amenity_ids: list of Amenity ids
    """

    if os_type_storage == "db":
        __tablename__ = "places"
        city_id = Column(String(60), ForeignKey('cities.id'), nullable=False)
        user_id = Column(String(60), ForeignKey('users.id'), nullable=False)
        name = Column(String(128), nullable=False)
        description = Column(String(1024), nullable=True)
        number_rooms = Column(Integer, default=0, nullable=False)
        number_bathrooms = Column(Integer, default=0, nullable=False)
        max_guest = Column(Integer, default=0, nullable=False)
        price_by_night = Column(Integer, default=0, nullable=False)
        latitude = Column(Float(), nullable=True)
        longitude = Column(Float(), nullable=True)
        reviews = relationship('Review',
                               cascade="all, delete",
                               backref='place')
        amenities = relationship("Amenity",
                                 secondary=place_amenity,
                                 viewonly=False,
                                 backref='place_amenities')
    else:
        city_id = ""
        user_id = ""
        name = ""
        description = ""
        number_rooms = 0
        number_bathrooms = 0
        max_guest = 0
        price_by_night = 0
        latitude = 0.0
        longitude = 0.0
        amenity_ids = []

        @property
        def get_reviews(self):
            my_list = []
            reviews_dict = models.storage.all(Review)
            for key, value in reviews_dict.items():
                if self.id == reviews_dict['place_id']:
                    my_list.append(value)
            return (my_list)

        @property
        def amenities(self):
            my_list = []
            amen_dict = models.storage.all(models.amenity.Amenity)
            amen_ids = amen_dict['amenity_ids']
            for item in amenity_ids:
                if self.id == item:
                    my_list.append(item)
            return (my_list)

        @amenities.setter
        def amenities(self, obj):
            if isinstance(obj, Amenity):
                self.amenity_ids.append(obj.id)
Example #11
0
class FundStatsHF(Base):
    __tablename__ = 'fund_statistics'
    __table_args__ = {'schema': 'cbaas'}
    id = Column(Integer, primary_key=True)
    created = Column(String(255))
    dateAdd = Column(DateTime)
    lastUpdated = Column(DateTime)
    lastUpdatedby = Column(String(255))
    fund_id = Column(Integer)
    S_INFO_WINDCODE = Column(String(255))
    as_of_date_ = Column(Date)
    aum = Column(Float(6))
    hasExceptio = Column(Integer)
    latest = Column(Integer)
    nav = Column(Float(6))
    pre_sycamore_scoring_ty = Column(Integer)
    pre_sycamore_scoring_value = Column(Float(6))
    rawDate = Column(Date)
    rawReturns = Column(Float(6))
    rawRiskFreeRate = Column(Float(6))
    reCalculate = Column(Float(6))
    return_ = Column(Float(6))
    start_date_ = Column(Date)
    sycamore_scoring_ty = Column(Integer)
    sycamore_scoring_value = Column(Float(6))
    CAPM_Alpha1Y = Column(Float(6))
    CAPM_Alpha2Y = Column(Float(6))
    CAPM_Alpha3M = Column(Float(6))
    CAPM_Alpha3Y = Column(Float(6))
    CAPM_Alpha5Y = Column(Float(6))
    CAPM_Alpha6M = Column(Float(6))
    CAPM_Beta1Y = Column(Float(6))
    CAPM_Beta2Y = Column(Float(6))
    CAPM_Beta3M = Column(Float(6))
    CAPM_Beta3Y = Column(Float(6))
    CAPM_Beta5Y = Column(Float(6))
    CAPM_Beta6M = Column(Float(6))
    GII_Alpha1Y = Column(Float(6))
    GII_Alpha2Y = Column(Float(6))
    GII_Alpha3M = Column(Float(6))
    GII_Alpha3Y = Column(Float(6))
    GII_Alpha5Y = Column(Float(6))
    GII_Alpha6M = Column(Float(6))
    GII_Alpha_tstat1Y = Column(Float(6))
    GII_Alpha_tstat2Y = Column(Float(6))
    GII_Alpha_tstat3M = Column(Float(6))
    GII_Alpha_tstat3Y = Column(Float(6))
    GII_Alpha_tstat5Y = Column(Float(6))
    GII_Alpha_tstat6M = Column(Float(6))
    GII_Beta1Y = Column(Float(6))
    GII_Beta2Y = Column(Float(6))
    GII_Beta3M = Column(Float(6))
    GII_Beta3Y = Column(Float(6))
    GII_Beta5Y = Column(Float(6))
    GII_Beta6M = Column(Float(6))
    GII_Gamma1Y = Column(Float(6))
    GII_Gamma2Y = Column(Float(6))
    GII_Gamma3M = Column(Float(6))
    GII_Gamma3Y = Column(Float(6))
    GII_Gamma5Y = Column(Float(6))
    GII_Gamma6M = Column(Float(6))
    GII_gamma_tstat1Y = Column(Float(6))
    GII_gamma_tstat2Y = Column(Float(6))
    GII_gamma_tstat3M = Column(Float(6))
    GII_gamma_tstat3Y = Column(Float(6))
    GII_gamma_tstat5Y = Column(Float(6))
    GII_gamma_tstat6M = Column(Float(6))
    MH_Alpha1Y = Column(Float(6))
    MH_Alpha2Y = Column(Float(6))
    MH_Alpha3M = Column(Float(6))
    MH_Alpha3Y = Column(Float(6))
    MH_Alpha5Y = Column(Float(6))
    MH_Alpha6M = Column(Float(6))
    MH_Gamma1Y = Column(Float(6))
    MH_Gamma2Y = Column(Float(6))
    MH_Gamma3M = Column(Float(6))
    MH_Gamma3Y = Column(Float(6))
    MH_Gamma5Y = Column(Float(6))
    MH_Gamma6M = Column(Float(6))
    MH_Gamma_tstat1Y = Column(Float(6))
    MH_Gamma_tstat2Y = Column(Float(6))
    MH_Gamma_tstat3M = Column(Float(6))
    MH_Gamma_tstat3Y = Column(Float(6))
    MH_Gamma_tstat5Y = Column(Float(6))
    MH_Gamma_tstat6M = Column(Float(6))
    TM_Alpha1Y = Column(Float(6))
    TM_Alpha2Y = Column(Float(6))
    TM_Alpha3M = Column(Float(6))
    TM_Alpha3Y = Column(Float(6))
    TM_Alpha5Y = Column(Float(6))
    TM_Alpha6M = Column(Float(6))
    TM_Gamma1Y = Column(Float(6))
    TM_Gamma2Y = Column(Float(6))
    TM_Gamma3M = Column(Float(6))
    TM_Gamma3Y = Column(Float(6))
    TM_Gamma5Y = Column(Float(6))
    TM_Gamma6M = Column(Float(6))
    TM_Gamma_tstat1Y = Column(Float(6))
    TM_Gamma_tstat2Y = Column(Float(6))
    TM_Gamma_tstat3M = Column(Float(6))
    TM_Gamma_tstat3Y = Column(Float(6))
    TM_Gamma_tstat5Y = Column(Float(6))
    TM_Gamma_tstat6M = Column(Float(6))
    activePremium1Y = Column(Float(6))
    activePremium2Y = Column(Float(6))
    activePremium3M = Column(Float(6))
    activePremium3Y = Column(Float(6))
    activePremium5Y = Column(Float(6))
    activePremium6M = Column(Float(6))
    annualizedAlpha1Y = Column(Float(6))
    annualizedAlpha2Y = Column(Float(6))
    annualizedAlpha3M = Column(Float(6))
    annualizedAlpha3Y = Column(Float(6))
    annualizedAlpha5Y = Column(Float(6))
    annualizedAlpha6M = Column(Float(6))
    appraisalRatio1Y = Column(Float(6))
    appraisalRatio2Y = Column(Float(6))
    appraisalRatio3M = Column(Float(6))
    appraisalRatio3Y = Column(Float(6))
    appraisalRatio5Y = Column(Float(6))
    appraisalRatio6M = Column(Float(6))
    autoCorrelation1Y = Column(Float(6))
    autoCorrelation2Y = Column(Float(6))
    autoCorrelation3M = Column(Float(6))
    autoCorrelation3Y = Column(Float(6))
    autoCorrelation5Y = Column(Float(6))
    autoCorrelation6M = Column(Float(6))
    calmarRatio1Y = Column(Float(6))
    calmarRatio2Y = Column(Float(6))
    calmarRatio3M = Column(Float(6))
    calmarRatio3Y = Column(Float(6))
    calmarRatio5Y = Column(Float(6))
    calmarRatio6M = Column(Float(6))
    conditionalVar_nonParametric1Y = Column(Float(6))
    conditionalVar_nonParametric2Y = Column(Float(6))
    conditionalVar_nonParametric3M = Column(Float(6))
    conditionalVar_nonParametric3Y = Column(Float(6))
    conditionalVar_nonParametric5Y = Column(Float(6))
    conditionalVar_nonParametric6M = Column(Float(6))
    d4Ratio1Y = Column(Float(6))
    d4Ratio2Y = Column(Float(6))
    d4Ratio3M = Column(Float(6))
    d4Ratio3Y = Column(Float(6))
    d4Ratio5Y = Column(Float(6))
    d4Ratio6M = Column(Float(6))
    d5Ratio1Y = Column(Float(6))
    d5Ratio2Y = Column(Float(6))
    d5Ratio3M = Column(Float(6))
    d5Ratio3Y = Column(Float(6))
    d5Ratio5Y = Column(Float(6))
    d5Ratio6M = Column(Float(6))
    downsideDeviationRatio1Y = Column(Float(6))
    downsideDeviationRatio2Y = Column(Float(6))
    downsideDeviationRatio3M = Column(Float(6))
    downsideDeviationRatio3Y = Column(Float(6))
    downsideDeviationRatio5Y = Column(Float(6))
    downsideDeviationRatio6M = Column(Float(6))
    excessReturnsRatio1Y = Column(Float(6))
    excessReturnsRatio2Y = Column(Float(6))
    excessReturnsRatio3M = Column(Float(6))
    excessReturnsRatio3Y = Column(Float(6))
    excessReturnsRatio5Y = Column(Float(6))
    excessReturnsRatio6M = Column(Float(6))
    ff3_alpha1Y = Column(Float(6))
    ff3_alpha2Y = Column(Float(6))
    ff3_alpha3M = Column(Float(6))
    ff3_alpha3Y = Column(Float(6))
    ff3_alpha5Y = Column(Float(6))
    ff3_alpha6M = Column(Float(6))
    ff3_rsq1Y = Column(Float(6))
    ff3_rsq2Y = Column(Float(6))
    ff3_rsq3M = Column(Float(6))
    ff3_rsq3Y = Column(Float(6))
    ff3_rsq5Y = Column(Float(6))
    ff3_rsq6M = Column(Float(6))
    ff3_sysrisk1Y = Column(Float(6))
    ff3_sysrisk2Y = Column(Float(6))
    ff3_sysrisk3M = Column(Float(6))
    ff3_sysrisk3Y = Column(Float(6))
    ff3_sysrisk5Y = Column(Float(6))
    ff3_sysrisk6M = Column(Float(6))
    ff3_tstat1Y = Column(Float(6))
    ff3_tstat2Y = Column(Float(6))
    ff3_tstat3M = Column(Float(6))
    ff3_tstat3Y = Column(Float(6))
    ff3_tstat5Y = Column(Float(6))
    ff3_tstat6M = Column(Float(6))
    fungHsiehAlpha1Y = Column(Float(6))
    fungHsiehAlpha2Y = Column(Float(6))
    fungHsiehAlpha3M = Column(Float(6))
    fungHsiehAlpha3Y = Column(Float(6))
    fungHsiehAlpha5Y = Column(Float(6))
    fungHsiehAlpha6M = Column(Float(6))
    fungHsiehAlphatStat1Y = Column(Float(6))
    fungHsiehAlphatStat2Y = Column(Float(6))
    fungHsiehAlphatStat3M = Column(Float(6))
    fungHsiehAlphatStat3Y = Column(Float(6))
    fungHsiehAlphatStat5Y = Column(Float(6))
    fungHsiehAlphatStat6M = Column(Float(6))
    fungHsiehRsq1Y = Column(Float(6))
    fungHsiehRsq2Y = Column(Float(6))
    fungHsiehRsq3M = Column(Float(6))
    fungHsiehRsq3Y = Column(Float(6))
    fungHsiehRsq5Y = Column(Float(6))
    fungHsiehRsq6M = Column(Float(6))
    fungHsiehSysRisk1Y = Column(Float(6))
    fungHsiehSysRisk2Y = Column(Float(6))
    fungHsiehSysRisk3M = Column(Float(6))
    fungHsiehSysRisk3Y = Column(Float(6))
    fungHsiehSysRisk5Y = Column(Float(6))
    fungHsiehSysRisk6M = Column(Float(6))
    informationRatio1Y = Column(Float(6))
    informationRatio2Y = Column(Float(6))
    informationRatio3M = Column(Float(6))
    informationRatio3Y = Column(Float(6))
    informationRatio5Y = Column(Float(6))
    informationRatio6M = Column(Float(6))
    kurtosis1Y = Column(Float(6))
    kurtosis2Y = Column(Float(6))
    kurtosis3M = Column(Float(6))
    kurtosis3Y = Column(Float(6))
    kurtosis5Y = Column(Float(6))
    kurtosis6M = Column(Float(6))
    maxDrawdownRatio1Y = Column(Float(6))
    maxDrawdownRatio2Y = Column(Float(6))
    maxDrawdownRatio3M = Column(Float(6))
    maxDrawdownRatio3Y = Column(Float(6))
    maxDrawdownRatio5Y = Column(Float(6))
    maxDrawdownRatio6M = Column(Float(6))
    mean1Y = Column(Float(6))
    mean2Y = Column(Float(6))
    mean3M = Column(Float(6))
    mean3Y = Column(Float(6))
    mean5Y = Column(Float(6))
    mean6M = Column(Float(6))
    median1Y = Column(Float(6))
    median2Y = Column(Float(6))
    median3M = Column(Float(6))
    median3Y = Column(Float(6))
    median5Y = Column(Float(6))
    median6M = Column(Float(6))
    r3Ratio1Y = Column(Float(6))
    r3Ratio2Y = Column(Float(6))
    r3Ratio3M = Column(Float(6))
    r3Ratio3Y = Column(Float(6))
    r3Ratio5Y = Column(Float(6))
    r3Ratio6M = Column(Float(6))
    returns1Y = Column(Float(6))
    returns2Y = Column(Float(6))
    returns3M = Column(Float(6))
    returns3Y = Column(Float(6))
    returns5Y = Column(Float(6))
    returns6M = Column(Float(6))
    returns1M = Column(Float(6))
    returnsMTD = Column(Float(6))
    returnsTotal = Column(Float(6))
    returnsYTD = Column(Float(6))
    riskAdjustedReturns1Y = Column(Float(6))
    riskAdjustedReturns2Y = Column(Float(6))
    riskAdjustedReturns3M = Column(Float(6))
    riskAdjustedReturns3Y = Column(Float(6))
    riskAdjustedReturns5Y = Column(Float(6))
    riskAdjustedReturns6M = Column(Float(6))
    riskAdjustedReturns = Column(Float(6))
    riskAdjustedReturnsMTD = Column(Float(6))
    riskAdjustedReturnsTotal = Column(Float(6))
    riskAdjustedReturnsYTD = Column(Float(6))
    sharpRatio1Y = Column(Float(6))
    sharpRatio2Y = Column(Float(6))
    sharpRatio3M = Column(Float(6))
    sharpRatio3Y = Column(Float(6))
    sharpRatio5Y = Column(Float(6))
    sharpRatio6M = Column(Float(6))
    skewness1Y = Column(Float(6))
    skewness2Y = Column(Float(6))
    skewness3M = Column(Float(6))
    skewness3Y = Column(Float(6))
    skewness5Y = Column(Float(6))
    skewness6M = Column(Float(6))
    sortinoRatio1Y = Column(Float(6))
    sortinoRatio2Y = Column(Float(6))
    sortinoRatio3M = Column(Float(6))
    sortinoRatio3Y = Column(Float(6))
    sortinoRatio5Y = Column(Float(6))
    sortinoRatio6M = Column(Float(6))
    standardDeviationRatio1Y = Column(Float(6))
    standardDeviationRatio2Y = Column(Float(6))
    standardDeviationRatio3M = Column(Float(6))
    standardDeviationRatio3Y = Column(Float(6))
    standardDeviationRatio5Y = Column(Float(6))
    standardDeviationRatio6M = Column(Float(6))
    tailRisk_nonParametric1Y = Column(Float(6))
    tailRisk_nonParametric2Y = Column(Float(6))
    tailRisk_nonParametric3M = Column(Float(6))
    tailRisk_nonParametric3Y = Column(Float(6))
    tailRisk_nonParametric5Y = Column(Float(6))
    tailRisk_nonParametric6M = Column(Float(6))
    trackingError1Y = Column(Float(6))
    trackingError2Y = Column(Float(6))
    trackingError3M = Column(Float(6))
    trackingError3Y = Column(Float(6))
    trackingError5Y = Column(Float(6))
    trackingError6M = Column(Float(6))
    treynorRatio1Y = Column(Float(6))
    treynorRatio2Y = Column(Float(6))
    treynorRatio3M = Column(Float(6))
    treynorRatio3Y = Column(Float(6))
    treynorRatio5Y = Column(Float(6))
    treynorRatio6M = Column(Float(6))
    upsideDeviationRatio1Y = Column(Float(6))
    upsideDeviationRatio2Y = Column(Float(6))
    upsideDeviationRatio3M = Column(Float(6))
    upsideDeviationRatio3Y = Column(Float(6))
    upsideDeviationRatio5Y = Column(Float(6))
    upsideDeviationRatio6M = Column(Float(6))
    var_nonParametric1Y = Column(Float(6))
    var_nonParametric2Y = Column(Float(6))
    var_nonParametric3M = Column(Float(6))
    var_nonParametric3Y = Column(Float(6))
    var_nonParametric5Y = Column(Float(6))
    var_nonParametric6M = Column(Float(6))
Example #12
0
FutureFactor = Table(
    "future_factor",
    metadata,
    Column("id", Integer, primary_key=True),
    Column("futureId", Integer),
    Column("factorId", Integer),
    Column("tableNames", String(500)),  # perhaps exceed the limitation
    Column("modelId", Integer),
    Column("name", String(100)),
    Column("version", String(50)),
    Column("component", String(1000)),
    Column("fCate", Integer),
    Column("generateTime", DateTime),
    Column("onlineTime", DateTime),
    Column("predict", Integer),
    Column("predictValue", Float(precision=6)),
    Column("confidence", Float(precision=3)),
    Column("accuracy1m", Float(precision=3)),
    Column("accuracy3m", Float(precision=3)),
    Column("accuracy6m", Float(precision=3)),
    Column("accuracy12m", Float(precision=3)),
    Column("trainInfo", String(1000)),
    Column("testInfo", String(1000)),
    Column("loadIndex", Float(precision=3)),
    Column("corrIndex", Float(precision=3)),
    Column("corr", String(255)),
    Column("mi", String(255)),
    Column("heatMap", String(255)),
    Column("enable", Integer),
    Column("status", Integer))
Example #13
0
class Exposure(Base):
    __tablename__ = 'risk_exposure'
    __table_args__ = (Index('trade_date', 'security_code', unique=True), )
    trade_date = Column(DateTime, primary_key=True, nullable=False)
    # symbol = Column(String, primary_key=True, nullable=False)
    security_code = Column(VARCHAR(24), primary_key=True)
    BETA = Column(Float(53))
    MOMENTUM = Column(Float(53))
    SIZE = Column(Float(53))
    EARNYILD = Column(Float(53))
    RESVOL = Column(Float(53))
    GROWTH = Column(Float(53))
    BTOP = Column(Float(53))
    LEVERAGE = Column(Float(53))
    LIQUIDTY = Column(Float(53))
    SIZENL = Column(Float(53))
    Bank = Column(Float(53))
    RealEstate = Column(Float(53))
    Health = Column(Float(53))
    Transportation = Column(Float(53))
    Mining = Column(Float(53))
    NonFerMetal = Column(Float(53))
    HouseApp = Column(Float(53))
    LeiService = Column(Float(53))
    MachiEquip = Column(Float(53))
    BuildDeco = Column(Float(53))
    CommeTrade = Column(Float(53))
    CONMAT = Column(Float(53))
    Auto = Column(Float(53))
    Textile = Column(Float(53))
    FoodBever = Column(Float(53))
    Electronics = Column(Float(53))
    Computer = Column(Float(53))
    LightIndus = Column(Float(53))
    Utilities = Column(Float(53))
    Telecom = Column(Float(53))
    AgriForest = Column(Float(53))
    CHEM = Column(Float(53))
    Media = Column(Float(53))
    IronSteel = Column(Float(53))
    NonBankFinan = Column(Float(53))
    ELECEQP = Column(Float(53))
    AERODEF = Column(Float(53))
    Conglomerates = Column(Float(53))
    COUNTRY = Column(Float(53))
Example #14
0
class Headers(Base):
    __tablename__ = "headers"

    id = Column(Integer, primary_key=True)
    filetype = Column(String(67))
    instrume = Column(String(3))
    rootname = Column(String(9))
    imagetyp = Column(String(20))
    targname = Column(String(67))
    ra_targ = Column(Float(20))
    dec_targ = Column(Float(20))
    proposid = Column(Integer)
    qualcom1 = Column(String(67))
    qualcom2 = Column(String(67))
    qualcom3 = Column(String(67))
    quality = Column(String(67))
    opus_ver = Column(String(30))
    postarg1 = Column(Float(32))
    postarg2 = Column(Float)
    cal_ver = Column(String(30))
    proctime = Column(Float)

    obstype = Column(String(20))
    obsmode = Column(String(20))
    exptype = Column(String(20))
    detector = Column(String(20))
    segment = Column(String(20))
    detecthv = Column(String(20))
    life_adj = Column(Integer)
    fppos = Column(Integer)
    exp_num = Column(Integer)
    cenwave = Column(Integer)
    propaper = Column(String(20))
    apmpos = Column(String(20))
    aperxpos = Column(Float)
    aperypos = Column(Float)
    aperture = Column(String(8))
    opt_elem = Column(String(7))
    shutter = Column(String(20))
    extended = Column(String(20))
    obset_id = Column(String(2))
    asn_id = Column(String(9))
    asn_tab = Column(String(18))
    randseed = Column(BigInteger)
    asn_mtyp = Column(String(20))
    overflow = Column(Integer)
    nevents = Column(Integer)
    neventsa = Column(Float)
    neventsb = Column(Float)
    dethvla = Column(Integer)
    dethvlb = Column(Integer)
    deventa = Column(Float)
    deventb = Column(Float)
    feventa = Column(Float)
    feventb = Column(Float)
    hvlevela = Column(Integer)
    hvlevelb = Column(Integer)
    dpixel1a = Column(Float)
    dpixel1b = Column(Float)
    date_obs = Column(String(10))
    time_obs = Column(String(8))
    expstart = Column(Float)
    expend = Column(Float)
    exptime = Column(Float)
    numflash = Column(Integer)
    ra_aper = Column(Float)
    dec_aper = Column(Float)
    shift1a = Column(Float)
    shift2a = Column(Float)
    shift1b = Column(Float)
    shift2b = Column(Float)
    shift1c = Column(Float)
    shift2c = Column(Float)

    sp_loc_a = Column(Float)
    sp_loc_b = Column(Float)
    sp_loc_c = Column(Float)
    sp_nom_a = Column(Float)
    sp_nom_b = Column(Float)
    sp_nom_c = Column(Float)
    sp_off_a = Column(Float)
    sp_off_b = Column(Float)
    sp_off_c = Column(Float)
    sp_err_a = Column(Float)
    sp_err_b = Column(Float)
    sp_err_c = Column(Float)

    #NUV keywords
    dethvl = Column(Float)
    cycle = Column(Integer)

    file_id = Column(Integer, ForeignKey('files.id'))
    #file = relationship("Files", backref=backref('headers', order_by=id))

    __table_args__ = (Index('idx_rootname', 'rootname', unique=False), )
    __table_args__ = (Index('idx_config', 'segment', 'fppos', 'cenwave', 'opt_elem', unique=False), )
Example #15
0
 def result(self, context: TelRootContext) -> TelQueryResult:
     return TelQueryResult(literal(self._value, Float()), dialect=context.husky_dialect)
Example #16
0
def load_long_lat_data():
    """Loading lat/long data from a csv file in the repo"""
    with gzip.open(os.path.join(DATA_FOLDER, 'san_francisco.csv.gz')) as f:
        pdf = pd.read_csv(f, encoding="utf-8")
    pdf['date'] = datetime.datetime.now().date()
    pdf['occupancy'] = [random.randint(1, 6) for _ in range(len(pdf))]
    pdf['radius_miles'] = [random.uniform(1, 3) for _ in range(len(pdf))]
    pdf.to_sql(
        'long_lat',
        db.engine,
        if_exists='replace',
        chunksize=500,
        dtype={
            'longitude': Float(),
            'latitude': Float(),
            'number': Float(),
            'street': String(100),
            'unit': String(10),
            'city': String(50),
            'district': String(50),
            'region': String(50),
            'postcode': Float(),
            'id': String(100),
            'date': Date(),
            'occupancy': Float(),
            'radius_miles': Float(),
        },
        index=False)
    print("Done loading table!")
    print("-" * 80)

    print("Creating table reference")
    obj = db.session.query(TBL).filter_by(table_name='long_lat').first()
    if not obj:
        obj = TBL(table_name='long_lat')
    obj.main_dttm_col = 'date'
    obj.database = utils.get_or_create_main_db(caravel)
    obj.is_featured = False
    db.session.merge(obj)
    db.session.commit()
    obj.fetch_metadata()
    tbl = obj

    slice_data = {
        "datasource_id": "7",
        "datasource_name": "long_lat",
        "datasource_type": "table",
        "granularity": "day",
        "since": "2014-01-01",
        "until": "2016-12-12",
        "where": "",
        "viz_type": "mapbox",
        "all_columns_x": "LON",
        "all_columns_y": "LAT",
        "mapbox_style": "mapbox://styles/mapbox/light-v9",
        "all_columns": ["occupancy"],
        "row_limit": 500000,
    }

    print("Creating a slice")
    slc = Slice(
        slice_name="Mapbox Long/Lat",
        viz_type='mapbox',
        datasource_type='table',
        datasource_id=tbl.id,
        params=get_slice_json(slice_data),
    )
    merge_slice(slc)
Example #17
0
 def test_compare_float_str(self):
     self._compare_default_roundtrip(
         Float(),
         "5.2",
     )
Example #18
0
def load_long_lat_data(only_metadata: bool = False,
                       force: bool = False) -> None:
    """Loading lat/long data from a csv file in the repo"""
    tbl_name = "long_lat"
    database = utils.get_example_database()
    table_exists = database.has_table_by_name(tbl_name)

    if not only_metadata and (not table_exists or force):
        data = get_example_data("san_francisco.csv.gz", make_bytes=True)
        pdf = pd.read_csv(data, encoding="utf-8")
        start = datetime.datetime.now().replace(hour=0,
                                                minute=0,
                                                second=0,
                                                microsecond=0)
        pdf["datetime"] = [
            start + datetime.timedelta(hours=i * 24 / (len(pdf) - 1))
            for i in range(len(pdf))
        ]
        pdf["occupancy"] = [random.randint(1, 6) for _ in range(len(pdf))]
        pdf["radius_miles"] = [random.uniform(1, 3) for _ in range(len(pdf))]
        pdf["geohash"] = pdf[["LAT",
                              "LON"]].apply(lambda x: geohash.encode(*x),
                                            axis=1)
        pdf["delimited"] = pdf["LAT"].map(str).str.cat(pdf["LON"].map(str),
                                                       sep=",")
        pdf.to_sql(
            tbl_name,
            database.get_sqla_engine(),
            if_exists="replace",
            chunksize=500,
            dtype={
                "longitude": Float(),
                "latitude": Float(),
                "number": Float(),
                "street": String(100),
                "unit": String(10),
                "city": String(50),
                "district": String(50),
                "region": String(50),
                "postcode": Float(),
                "id": String(100),
                "datetime": DateTime(),
                "occupancy": Float(),
                "radius_miles": Float(),
                "geohash": String(12),
                "delimited": String(60),
            },
            index=False,
        )
        print("Done loading table!")
        print("-" * 80)

    print("Creating table reference")
    table = get_table_connector_registry()
    obj = db.session.query(table).filter_by(table_name=tbl_name).first()
    if not obj:
        obj = table(table_name=tbl_name)
    obj.main_dttm_col = "datetime"
    obj.database = database
    obj.filter_select_enabled = True
    db.session.merge(obj)
    db.session.commit()
    obj.fetch_metadata()
    tbl = obj

    slice_data = {
        "granularity_sqla": "day",
        "since": "2014-01-01",
        "until": "now",
        "viz_type": "mapbox",
        "all_columns_x": "LON",
        "all_columns_y": "LAT",
        "mapbox_style": "mapbox://styles/mapbox/light-v9",
        "all_columns": ["occupancy"],
        "row_limit": 500000,
    }

    print("Creating a slice")
    slc = Slice(
        slice_name="Mapbox Long/Lat",
        viz_type="mapbox",
        datasource_type="table",
        datasource_id=tbl.id,
        params=get_slice_json(slice_data),
    )
    misc_dash_slices.add(slc.slice_name)
    merge_slice(slc)
Example #19
0
 def test_compare_float_no_diff6(self):
     self._compare_default_roundtrip(Float(),
                                     "5",
                                     text("5.0"),
                                     diff_expected=False)
Example #20
0
class UT_table:
    # connection string postgres"
    internal_connection = Connection()

    # create engine and metadata

    engine = create_engine(internal_connection.conn_str(),
                           echo=False,
                           convert_unicode=True)
    metadata = MetaData(engine)

    # define tables
    ut_table = Table(
        'ut_table',
        metadata,
        Column('id_ut', Integer, primary_key=True),  # 0
        Column('progetto', String(100)),  # 1
        Column('nr_ut', Integer),  # 2
        Column('ut_letterale', String(100)),  # 3
        Column('def_ut', String(100)),  # 4
        Column('descrizione_ut', Text),  # 5
        Column('interpretazione_ut', String(100)),  # 6
        Column('nazione', String(100)),  # 7
        Column('regione', String(100)),  # 8
        Column('provincia', String(100)),  # 9
        Column('comune', String(100)),  # 10
        Column('frazione', String(100)),  # 11
        Column('localita', String(100)),  # 12
        Column('indirizzo', String(100)),  # 13
        Column('nr_civico', String(100)),  # 14
        Column('carta_topo_igm', String(100)),  # 15
        Column('carta_ctr', String(100)),  # 16
        Column('coord_geografiche', String(100)),  # 17
        Column('coord_piane', String(100)),  # 18
        Column('quota', Float(3, 2)),  # 19
        Column('andamento_terreno_pendenza', String(100)),  # 20
        Column('utilizzo_suolo_vegetazione', String(100)),  # 21
        Column('descrizione_empirica_suolo', Text),  # 22
        Column('descrizione_luogo', Text),  # 23
        Column('metodo_rilievo_e_ricognizione', String(100)),  # 24
        Column('geometria', String(100)),  # 25
        Column('bibliografia', Text),  # 26
        Column('data', String(100)),  # 27
        Column('ora_meteo', String(100)),  # 28
        Column('responsabile', String(100)),  # 29
        Column('dimensioni_ut', String(100)),  # 30
        Column('rep_per_mq', String(100)),  # 31
        Column('rep_datanti', String(100)),  # 32
        Column('periodo_I', String(100)),  # 33
        Column('datazione_I', String(100)),  # 34
        Column('interpretazione_I', String(100)),  # 35
        Column('periodo_II', String(100)),  # 36
        Column('datazione_II', String(100)),  # 37
        Column('interpretazione_II', String(100)),  # 38
        Column('documentazione', Text),  # 39
        Column('enti_tutela_vincoli', String(100)),  # 40
        Column('indagini_preliminari', String(100)),  # 41

        # explicit/composite unique constraint.  'name' is optional.
        UniqueConstraint('progetto',
                         'nr_ut',
                         'ut_letterale',
                         name='ID_ut_unico'))

    metadata.create_all(engine)
# Create an engine that connects to the census.sqlite file: engine
engine = create_engine('sqlite:///MyStocks.sqlite')

metadata = MetaData()

# Print table names
print(engine.table_names())

# Define a new table with a name, count, amount, and valid column: data
data = Table(
    'Stocks',
    metadata,
    Column('Period', String(20)),  # Monthly, Weekly, Daily, Hourly, Minutely
    Column('Symbol', String(20)),
    Column('Date', Date()),
    Column('Open', Float()),
    Column('High', Float()),
    Column('Low', Float()),
    Column('Close', Float()),
    Column('Volume', Float()),
    Column('BigMove', Boolean(), default=False),
    Column('Direction', Integer(),
           default=0)  # 1 = up, -1 = down, 0 = default (no big move)
)

# Define a new table with a name, count, amount, and valid column: data
# data = Table('Company', metadata,
#         Column('Symbol', String(10), unique=True),
#         Column('Name', String(100), unique=True),
#         Column('Sector', String(100)),
#         Column('Year', Integer()),
Example #22
0
class RoadWeatherDB(DeclarativeBase):
    __tablename__ = "road_table"

    id = Column(Integer, primary_key=True)
    precipitation = Column('precipitation', String(40))
    air_temperature_trend = Column('air_temperature_trend', String(40))
    dew_point = Column('dew_point', Float())
    freezing_point2 = Column('freezing_point2', Float())
    road_warning1 = Column('road_warning1', String(40))
    road_warning2 = Column('road_warning2', String(40))
    freezing_point1 = Column('freezing_point1', Float())
    intensity = Column('intensity', Float())
    visibility = Column('visibility', Float())
    road_temperature1_trend = Column('road_temperature1_trend', Float())
    road_temperature2_trend = Column('road_temperature2_trend', Float())
    air_temperature = Column('air_temperature', Float())
    time = Column('time', String(40))
    road_condition2 = Column('road_condition2', String(40))
    air_humidity = Column('air_humidity', Float())
    station_name = Column('station_name', String(100))
    road_temperature2 = Column('road_temperature2', Float())
    road_condition1 = Column('road_condition1', String(40))
    road_temperature1 = Column('road_temperature1', Float())
Example #23
0
class Indicator(BaseModel):  # 继承生成的orm基类
    __tablename__ = "indicator"  # 表名
    bar_id = Column(Integer, ForeignKey("bar_daily.id"))
    ma5 = Column(Float(4))
    ma10 = Column(Float(4))
    ma30 = Column(Float(4))
    ma60 = Column(Float(4))
    ma120 = Column(Float(4))
    ma240 = Column(Float(4))
    boll_up = Column(Float(4))
    boll_mb = Column(Float(4))
    boll_dn = Column(Float(4))
    macd = Column(Float(4))
    rsi = Column(Float(4))
Example #24
0
 def test_float_as_decimal(self):
     self._do_test(
         Float(precision=8, asdecimal=True),
         [15.7563, decimal.Decimal("15.7563"), None],
         [decimal.Decimal("15.7563"), None],
     )
Example #25
0
    def test_numerics_broken_inspection(self):
        """Numeric scenarios where Oracle type info is 'broken',
        returning us precision, scale of the form (0, 0) or (0, -127).
        We convert to Decimal and let int()/float() processors take over.

        """

        metadata = self.metadata

        # this test requires cx_oracle 5

        foo = Table(
            "foo",
            metadata,
            Column("idata", Integer),
            Column("ndata", Numeric(20, 2)),
            Column("ndata2", Numeric(20, 2)),
            Column("nidata", Numeric(5, 0)),
            Column("fdata", Float()),
        )
        foo.create()

        foo.insert().execute({
            "idata": 5,
            "ndata": decimal.Decimal("45.6"),
            "ndata2": decimal.Decimal("45.0"),
            "nidata": decimal.Decimal("53"),
            "fdata": 45.68392,
        })

        stmt = "SELECT idata, ndata, ndata2, nidata, fdata FROM foo"

        row = testing.db.execute(stmt).fetchall()[0]
        eq_(
            [type(x) for x in row],
            [int, decimal.Decimal, decimal.Decimal, int, float],
        )
        eq_(
            row,
            (
                5,
                decimal.Decimal("45.6"),
                decimal.Decimal("45"),
                53,
                45.683920000000001,
            ),
        )

        # with a nested subquery,
        # both Numeric values that don't have decimal places, regardless
        # of their originating type, come back as ints with no useful
        # typing information beyond "numeric".  So native handler
        # must convert to int.
        # this means our Decimal converters need to run no matter what.
        # totally sucks.

        stmt = """
        SELECT
            (SELECT (SELECT idata FROM foo) FROM DUAL) AS idata,
            (SELECT CAST((SELECT ndata FROM foo) AS NUMERIC(20, 2)) FROM DUAL)
            AS ndata,
            (SELECT CAST((SELECT ndata2 FROM foo) AS NUMERIC(20, 2)) FROM DUAL)
            AS ndata2,
            (SELECT CAST((SELECT nidata FROM foo) AS NUMERIC(5, 0)) FROM DUAL)
            AS nidata,
            (SELECT CAST((SELECT fdata FROM foo) AS FLOAT) FROM DUAL) AS fdata
        FROM dual
        """
        row = testing.db.execute(stmt).fetchall()[0]
        eq_(
            [type(x) for x in row],
            [int, decimal.Decimal, int, int, decimal.Decimal],
        )
        eq_(
            row,
            (5, decimal.Decimal("45.6"), 45, 53, decimal.Decimal("45.68392")),
        )

        row = testing.db.execute(
            text(
                stmt,
                typemap={
                    "idata": Integer(),
                    "ndata": Numeric(20, 2),
                    "ndata2": Numeric(20, 2),
                    "nidata": Numeric(5, 0),
                    "fdata": Float(),
                },
            )).fetchall()[0]
        eq_(
            [type(x) for x in row],
            [int, decimal.Decimal, decimal.Decimal, decimal.Decimal, float],
        )
        eq_(
            row,
            (
                5,
                decimal.Decimal("45.6"),
                decimal.Decimal("45"),
                decimal.Decimal("53"),
                45.683920000000001,
            ),
        )

        stmt = """
        SELECT
                anon_1.idata AS anon_1_idata,
                anon_1.ndata AS anon_1_ndata,
                anon_1.ndata2 AS anon_1_ndata2,
                anon_1.nidata AS anon_1_nidata,
                anon_1.fdata AS anon_1_fdata
        FROM (SELECT idata, ndata, ndata2, nidata, fdata
        FROM (
            SELECT
                (SELECT (SELECT idata FROM foo) FROM DUAL) AS idata,
                (SELECT CAST((SELECT ndata FROM foo) AS NUMERIC(20, 2))
                FROM DUAL) AS ndata,
                (SELECT CAST((SELECT ndata2 FROM foo) AS NUMERIC(20, 2))
                FROM DUAL) AS ndata2,
                (SELECT CAST((SELECT nidata FROM foo) AS NUMERIC(5, 0))
                FROM DUAL) AS nidata,
                (SELECT CAST((SELECT fdata FROM foo) AS FLOAT) FROM DUAL)
                AS fdata
            FROM dual
        )
        WHERE ROWNUM >= 0) anon_1
        """
        row = testing.db.execute(stmt).fetchall()[0]
        eq_(
            [type(x) for x in row],
            [int, decimal.Decimal, int, int, decimal.Decimal],
        )
        eq_(
            row,
            (5, decimal.Decimal("45.6"), 45, 53, decimal.Decimal("45.68392")),
        )

        row = testing.db.execute(
            text(
                stmt,
                typemap={
                    "anon_1_idata": Integer(),
                    "anon_1_ndata": Numeric(20, 2),
                    "anon_1_ndata2": Numeric(20, 2),
                    "anon_1_nidata": Numeric(5, 0),
                    "anon_1_fdata": Float(),
                },
            )).fetchall()[0]
        eq_(
            [type(x) for x in row],
            [int, decimal.Decimal, decimal.Decimal, decimal.Decimal, float],
        )
        eq_(
            row,
            (
                5,
                decimal.Decimal("45.6"),
                decimal.Decimal("45"),
                decimal.Decimal("53"),
                45.683920000000001,
            ),
        )

        row = testing.db.execute(
            text(
                stmt,
                typemap={
                    "anon_1_idata": Integer(),
                    "anon_1_ndata": Numeric(20, 2, asdecimal=False),
                    "anon_1_ndata2": Numeric(20, 2, asdecimal=False),
                    "anon_1_nidata": Numeric(5, 0, asdecimal=False),
                    "anon_1_fdata": Float(asdecimal=True),
                },
            )).fetchall()[0]
        eq_([type(x) for x in row],
            [int, float, float, float, decimal.Decimal])
        eq_(row, (5, 45.6, 45, 53, decimal.Decimal("45.68392")))
Example #26
0
class CompetenceOption(ConfigurableOption):
    """
    A competence model (both for the main one and the sub-competences)

    :param int required_id: The id of the bareme element needed
    """
    __table_args__ = default_table_args
    __colanderalchemy_config__ = {
        "title":
        u"Grille de compétences",
        "seq_widget_options": {
            "add_subitem_text_template": u"Ajouter une compétence",
        },
        "validation_msg":
        u"La grille de compétences a bien été configurée",
        "help_msg":
        u"Définissez des compétences, celles-ci sont \
composées: <ul><li>D'un libellé</li>\
<li>D'un ensemble de sous-compétences</li></ul>"
    }
    id = get_id_foreignkey_col('configurable_option.id')
    # To be removed in 3.2
    requirement = Column(Float(),
                         default=0,
                         info={'colanderalchemy': {
                             "exclude": True
                         }})
    children = relationship(
        "CompetenceSubOption",
        primaryjoin="CompetenceOption.id==CompetenceSubOption.parent_id",
        info={
            'colanderalchemy': {
                'title':
                u"Sous-compétences associées",
                "widget":
                deform.widget.SequenceWidget(
                    add_subitem_text_template=u"Ajouter une \
sous-compétence",
                    min_len=1,
                )
            },
        },
        back_populates='parent',
    )

    @classmethod
    def query(cls, active=True, *args):
        query = super(CompetenceOption, cls).query(*args)
        query = query.filter_by(active=active)
        return query

    def __json__(self, request):
        return dict(
            id=self.id,
            label=self.label,
            requirements=[req.__json__(request) for req in self.requirement],
            children=[child.__json__(request) for child in self.children],
        )

    @classmethod
    def __radar_datas__(cls, deadline_id):
        result = []
        for option in cls.query():
            result.append({
                'axis': option.label,
                'value': option.get_requirement(deadline_id)
            })
        return result

    def get_requirement(self, deadline_id):
        for req in self.requirements:
            if req.deadline_id == deadline_id:
                return req.requirement
        return 0
Example #27
0
class Filingtype(RefTypeMixin, AuditMixin, Model):
    __tablename__ = 'filingtype'

    id = Column(Integer, primary_key=True, autoincrement=True)
    cost = Column(Float(53), nullable=False)
    perpagecost = Column(Float(53), nullable=False)
Example #28
0
class CompetenceRequirement(DBBASE):
    __colanderalchemy_config__ = {
        "title": u"Niveau de référence de la grille de compétence",
        "validation_msg": u"Les niveaux de référence de la grille de \
compétences ont bien été configurés",
        "help_msg": u"Pour chaque compétence, définissez les niveaux de \
référence à chaque échéance.",
        "seq_widget_options": {
            "add_subitem_text_template": u"Ajouter un niveau de référence",
        },
    }
    competence_id = Column(
        ForeignKey('competence_option.id'),
        primary_key=True,
        info={'colanderalchemy': get_hidden_field_conf()},
    )
    deadline_id = Column(
        ForeignKey('competence_deadline.id'),
        primary_key=True,
        info={'colanderalchemy': get_hidden_field_conf()},
    )
    requirement = Column(Float(),
                         default=0,
                         info={
                             'colanderalchemy': {
                                 "title":
                                 u"Niveau de référence",
                                 "widget":
                                 get_deferred_model_select(
                                     CompetenceScale,
                                     mandatory=True,
                                     keys=('value', 'label'),
                                 )
                             }
                         })

    competence = relationship(
        'CompetenceOption',
        backref=backref('requirements',
                        info={
                            'colanderalchemy': {
                                "title":
                                u"Niveaux de référence pour cette compétence",
                            },
                        }),
        info={
            'colanderalchemy': {
                "exclude": True
            },
        })
    deadline = relationship("CompetenceDeadline",
                            backref=backref('requirements',
                                            info={
                                                'colanderalchemy': {
                                                    "exclude": True
                                                },
                                            }),
                            info={
                                'colanderalchemy': {
                                    "exclude": True
                                },
                            })

    def __json__(self, request):
        return dict(
            deadline_id=self.deadline_id,
            competence_id=self.competence_id,
            reqirement=self.requirement,
            deadline_label=self.deadline.label,
        )
Example #29
0
def upgrade(migrate_engine):
    meta = MetaData(bind=migrate_engine)
    meter = Table('meter', meta, autoload=True)
    meter.c.counter_volume.alter(type=Float(53))
Example #30
0
class Uqer(Base):
    __tablename__ = 'uqer'
    __table_args__ = (Index('uqer_idx', 'trade_date', 'code', unique=True), )

    trade_date = Column(DateTime, primary_key=True, nullable=False)
    code = Column(Integer, primary_key=True, nullable=False)
    AccountsPayablesTDays = Column(Float(53))
    AccountsPayablesTRate = Column(Float(53))
    AdminiExpenseRate = Column(Float(53))
    ARTDays = Column(Float(53))
    ARTRate = Column(Float(53))
    ASSI = Column(Float(53))
    BLEV = Column(Float(53))
    BondsPayableToAsset = Column(Float(53))
    CashRateOfSales = Column(Float(53))
    CashToCurrentLiability = Column(Float(53))
    CMRA = Column(Float(53))
    CTOP = Column(Float(53))
    CTP5 = Column(Float(53))
    CurrentAssetsRatio = Column(Float(53))
    CurrentAssetsTRate = Column(Float(53))
    CurrentRatio = Column(Float(53))
    DAVOL10 = Column(Float(53))
    DAVOL20 = Column(Float(53))
    DAVOL5 = Column(Float(53))
    DDNBT = Column(Float(53))
    DDNCR = Column(Float(53))
    DDNSR = Column(Float(53))
    DebtEquityRatio = Column(Float(53))
    DebtsAssetRatio = Column(Float(53))
    DHILO = Column(Float(53))
    DilutedEPS = Column(Float(53))
    DVRAT = Column(Float(53))
    EBITToTOR = Column(Float(53))
    EGRO = Column(Float(53))
    EMA10 = Column(Float(53))
    EMA120 = Column(Float(53))
    EMA20 = Column(Float(53))
    EMA5 = Column(Float(53))
    EMA60 = Column(Float(53))
    EPS = Column(Float(53))
    EquityFixedAssetRatio = Column(Float(53))
    EquityToAsset = Column(Float(53))
    EquityTRate = Column(Float(53))
    ETOP = Column(Float(53))
    ETP5 = Column(Float(53))
    FinancialExpenseRate = Column(Float(53))
    FinancingCashGrowRate = Column(Float(53))
    FixAssetRatio = Column(Float(53))
    FixedAssetsTRate = Column(Float(53))
    GrossIncomeRatio = Column(Float(53))
    HBETA = Column(Float(53))
    HSIGMA = Column(Float(53))
    IntangibleAssetRatio = Column(Float(53))
    InventoryTDays = Column(Float(53))
    InventoryTRate = Column(Float(53))
    InvestCashGrowRate = Column(Float(53))
    LCAP = Column(Float(53))
    LFLO = Column(Float(53))
    LongDebtToAsset = Column(Float(53))
    LongDebtToWorkingCapital = Column(Float(53))
    LongTermDebtToAsset = Column(Float(53))
    MA10 = Column(Float(53))
    MA120 = Column(Float(53))
    MA20 = Column(Float(53))
    MA5 = Column(Float(53))
    MA60 = Column(Float(53))
    MAWVAD = Column(Float(53))
    MFI = Column(Float(53))
    MLEV = Column(Float(53))
    NetAssetGrowRate = Column(Float(53))
    NetProfitGrowRate = Column(Float(53))
    NetProfitRatio = Column(Float(53))
    NOCFToOperatingNI = Column(Float(53))
    NonCurrentAssetsRatio = Column(Float(53))
    NPParentCompanyGrowRate = Column(Float(53))
    NPToTOR = Column(Float(53))
    OperatingExpenseRate = Column(Float(53))
    OperatingProfitGrowRate = Column(Float(53))
    OperatingProfitRatio = Column(Float(53))
    OperatingProfitToTOR = Column(Float(53))
    OperatingRevenueGrowRate = Column(Float(53))
    OperCashGrowRate = Column(Float(53))
    OperCashInToCurrentLiability = Column(Float(53))
    PB = Column(Float(53))
    PCF = Column(Float(53))
    PE = Column(Float(53))
    PS = Column(Float(53))
    PSY = Column(Float(53))
    QuickRatio = Column(Float(53))
    REVS10 = Column(Float(53))
    REVS20 = Column(Float(53))
    REVS5 = Column(Float(53))
    ROA = Column(Float(53))
    ROA5 = Column(Float(53))
    ROE = Column(Float(53))
    ROE5 = Column(Float(53))
    RSI = Column(Float(53))
    RSTR12 = Column(Float(53))
    RSTR24 = Column(Float(53))
    SalesCostRatio = Column(Float(53))
    SaleServiceCashToOR = Column(Float(53))
    SUE = Column(Float(53))
    TaxRatio = Column(Float(53))
    TOBT = Column(Float(53))
    TotalAssetGrowRate = Column(Float(53))
    TotalAssetsTRate = Column(Float(53))
    TotalProfitCostRatio = Column(Float(53))
    TotalProfitGrowRate = Column(Float(53))
    VOL10 = Column(Float(53))
    VOL120 = Column(Float(53))
    VOL20 = Column(Float(53))
    VOL240 = Column(Float(53))
    VOL5 = Column(Float(53))
    VOL60 = Column(Float(53))
    WVAD = Column(Float(53))
    REC = Column(Float(53))
    DAREC = Column(Float(53))
    GREC = Column(Float(53))
    FY12P = Column(Float(53))
    DAREV = Column(Float(53))
    GREV = Column(Float(53))
    SFY12P = Column(Float(53))
    DASREV = Column(Float(53))
    GSREV = Column(Float(53))
    FEARNG = Column(Float(53))
    FSALESG = Column(Float(53))
    TA2EV = Column(Float(53))
    CFO2EV = Column(Float(53))
    ACCA = Column(Float(53))
    DEGM = Column(Float(53))
    SUOI = Column(Float(53))
    EARNMOM = Column(Float(53))
    FiftyTwoWeekHigh = Column(Float(53))
    Volatility = Column(Float(53))
    Skewness = Column(Float(53))
    ILLIQUIDITY = Column(Float(53))
    BackwardADJ = Column(Float(53))
    MACD = Column(Float(53))
    ADTM = Column(Float(53))
    ATR14 = Column(Float(53))
    ATR6 = Column(Float(53))
    BIAS10 = Column(Float(53))
    BIAS20 = Column(Float(53))
    BIAS5 = Column(Float(53))
    BIAS60 = Column(Float(53))
    BollDown = Column(Float(53))
    BollUp = Column(Float(53))
    CCI10 = Column(Float(53))
    CCI20 = Column(Float(53))
    CCI5 = Column(Float(53))
    CCI88 = Column(Float(53))
    KDJ_K = Column(Float(53))
    KDJ_D = Column(Float(53))
    KDJ_J = Column(Float(53))
    ROC6 = Column(Float(53))
    ROC20 = Column(Float(53))
    SBM = Column(Float(53))
    STM = Column(Float(53))
    UpRVI = Column(Float(53))
    DownRVI = Column(Float(53))
    RVI = Column(Float(53))
    SRMI = Column(Float(53))
    ChandeSD = Column(Float(53))
    ChandeSU = Column(Float(53))
    CMO = Column(Float(53))
    DBCD = Column(Float(53))
    ARC = Column(Float(53))
    OBV = Column(Float(53))
    OBV6 = Column(Float(53))
    OBV20 = Column(Float(53))
    TVMA20 = Column(Float(53))
    TVMA6 = Column(Float(53))
    TVSTD20 = Column(Float(53))
    TVSTD6 = Column(Float(53))
    VDEA = Column(Float(53))
    VDIFF = Column(Float(53))
    VEMA10 = Column(Float(53))
    VEMA12 = Column(Float(53))
    VEMA26 = Column(Float(53))
    VEMA5 = Column(Float(53))
    VMACD = Column(Float(53))
    VOSC = Column(Float(53))
    VR = Column(Float(53))
    VROC12 = Column(Float(53))
    VROC6 = Column(Float(53))
    VSTD10 = Column(Float(53))
    VSTD20 = Column(Float(53))
    KlingerOscillator = Column(Float(53))
    MoneyFlow20 = Column(Float(53))
    AD = Column(Float(53))
    AD20 = Column(Float(53))
    AD6 = Column(Float(53))
    CoppockCurve = Column(Float(53))
    ASI = Column(Float(53))
    ChaikinOscillator = Column(Float(53))
    ChaikinVolatility = Column(Float(53))
    EMV14 = Column(Float(53))
    EMV6 = Column(Float(53))
    plusDI = Column(Float(53))
    minusDI = Column(Float(53))
    ADX = Column(Float(53))
    ADXR = Column(Float(53))
    Aroon = Column(Float(53))
    AroonDown = Column(Float(53))
    AroonUp = Column(Float(53))
    DEA = Column(Float(53))
    DIFF = Column(Float(53))
    DDI = Column(Float(53))
    DIZ = Column(Float(53))
    DIF = Column(Float(53))
    MTM = Column(Float(53))
    MTMMA = Column(Float(53))
    PVT = Column(Float(53))
    PVT6 = Column(Float(53))
    PVT12 = Column(Float(53))
    TRIX5 = Column(Float(53))
    TRIX10 = Column(Float(53))
    UOS = Column(Float(53))
    MA10RegressCoeff12 = Column(Float(53))
    MA10RegressCoeff6 = Column(Float(53))
    PLRC6 = Column(Float(53))
    PLRC12 = Column(Float(53))
    SwingIndex = Column(Float(53))
    Ulcer10 = Column(Float(53))
    Ulcer5 = Column(Float(53))
    Hurst = Column(Float(53))
    ACD6 = Column(Float(53))
    ACD20 = Column(Float(53))
    EMA12 = Column(Float(53))
    EMA26 = Column(Float(53))
    APBMA = Column(Float(53))
    BBI = Column(Float(53))
    BBIC = Column(Float(53))
    TEMA10 = Column(Float(53))
    TEMA5 = Column(Float(53))
    MA10Close = Column(Float(53))
    AR = Column(Float(53))
    BR = Column(Float(53))
    ARBR = Column(Float(53))
    CR20 = Column(Float(53))
    MassIndex = Column(Float(53))
    BearPower = Column(Float(53))
    BullPower = Column(Float(53))
    Elder = Column(Float(53))
    NVI = Column(Float(53))
    PVI = Column(Float(53))
    RC12 = Column(Float(53))
    RC24 = Column(Float(53))
    JDQS20 = Column(Float(53))
    Variance20 = Column(Float(53))
    Variance60 = Column(Float(53))
    Variance120 = Column(Float(53))
    Kurtosis20 = Column(Float(53))
    Kurtosis60 = Column(Float(53))
    Kurtosis120 = Column(Float(53))
    Alpha20 = Column(Float(53))
    Alpha60 = Column(Float(53))
    Alpha120 = Column(Float(53))
    Beta20 = Column(Float(53))
    Beta60 = Column(Float(53))
    Beta120 = Column(Float(53))
    SharpeRatio20 = Column(Float(53))
    SharpeRatio60 = Column(Float(53))
    SharpeRatio120 = Column(Float(53))
    TreynorRatio20 = Column(Float(53))
    TreynorRatio60 = Column(Float(53))
    TreynorRatio120 = Column(Float(53))
    InformationRatio20 = Column(Float(53))
    InformationRatio60 = Column(Float(53))
    InformationRatio120 = Column(Float(53))
    GainVariance20 = Column(Float(53))
    GainVariance60 = Column(Float(53))
    GainVariance120 = Column(Float(53))
    LossVariance20 = Column(Float(53))
    LossVariance60 = Column(Float(53))
    LossVariance120 = Column(Float(53))
    GainLossVarianceRatio20 = Column(Float(53))
    GainLossVarianceRatio60 = Column(Float(53))
    GainLossVarianceRatio120 = Column(Float(53))
    RealizedVolatility = Column(Float(53))
    REVS60 = Column(Float(53))
    REVS120 = Column(Float(53))
    REVS250 = Column(Float(53))
    REVS750 = Column(Float(53))
    REVS5m20 = Column(Float(53))
    REVS5m60 = Column(Float(53))
    REVS5Indu1 = Column(Float(53))
    REVS20Indu1 = Column(Float(53))
    Volumn1M = Column(Float(53))
    Volumn3M = Column(Float(53))
    Price1M = Column(Float(53))
    Price3M = Column(Float(53))
    Price1Y = Column(Float(53))
    Rank1M = Column(Float(53))
    CashDividendCover = Column(Float(53))
    DividendCover = Column(Float(53))
    DividendPaidRatio = Column(Float(53))
    RetainedEarningRatio = Column(Float(53))
    CashEquivalentPS = Column(Float(53))
    DividendPS = Column(Float(53))
    EPSTTM = Column(Float(53))
    NetAssetPS = Column(Float(53))
    TORPS = Column(Float(53))
    TORPSLatest = Column(Float(53))
    OperatingRevenuePS = Column(Float(53))
    OperatingRevenuePSLatest = Column(Float(53))
    OperatingProfitPS = Column(Float(53))
    OperatingProfitPSLatest = Column(Float(53))
    CapitalSurplusFundPS = Column(Float(53))
    SurplusReserveFundPS = Column(Float(53))
    UndividedProfitPS = Column(Float(53))
    RetainedEarningsPS = Column(Float(53))
    OperCashFlowPS = Column(Float(53))
    CashFlowPS = Column(Float(53))
    NetNonOIToTP = Column(Float(53))
    NetNonOIToTPLatest = Column(Float(53))
    PeriodCostsRate = Column(Float(53))
    InterestCover = Column(Float(53))
    NetProfitGrowRate3Y = Column(Float(53))
    NetProfitGrowRate5Y = Column(Float(53))
    OperatingRevenueGrowRate3Y = Column(Float(53))
    OperatingRevenueGrowRate5Y = Column(Float(53))
    NetCashFlowGrowRate = Column(Float(53))
    NetProfitCashCover = Column(Float(53))
    OperCashInToAsset = Column(Float(53))
    CashConversionCycle = Column(Float(53))
    OperatingCycle = Column(Float(53))
    PEG3Y = Column(Float(53))
    PEG5Y = Column(Float(53))
    PEIndu = Column(Float(53))
    PBIndu = Column(Float(53))
    PSIndu = Column(Float(53))
    PCFIndu = Column(Float(53))
    PEHist20 = Column(Float(53))
    PEHist60 = Column(Float(53))
    PEHist120 = Column(Float(53))
    PEHist250 = Column(Float(53))
    StaticPE = Column(Float(53))
    ForwardPE = Column(Float(53))
    EnterpriseFCFPS = Column(Float(53))
    ShareholderFCFPS = Column(Float(53))
    ROEDiluted = Column(Float(53))
    ROEAvg = Column(Float(53))
    ROEWeighted = Column(Float(53))
    ROECut = Column(Float(53))
    ROECutWeighted = Column(Float(53))
    ROIC = Column(Float(53))
    ROAEBIT = Column(Float(53))
    ROAEBITTTM = Column(Float(53))
    OperatingNIToTP = Column(Float(53))
    OperatingNIToTPLatest = Column(Float(53))
    InvestRAssociatesToTP = Column(Float(53))
    InvestRAssociatesToTPLatest = Column(Float(53))
    NPCutToNP = Column(Float(53))
    SuperQuickRatio = Column(Float(53))
    TSEPToInterestBearDebt = Column(Float(53))
    DebtTangibleEquityRatio = Column(Float(53))
    TangibleAToInteBearDebt = Column(Float(53))
    TangibleAToNetDebt = Column(Float(53))
    NOCFToTLiability = Column(Float(53))
    NOCFToInterestBearDebt = Column(Float(53))
    NOCFToNetDebt = Column(Float(53))
    TSEPToTotalCapital = Column(Float(53))
    InteBearDebtToTotalCapital = Column(Float(53))
    NPParentCompanyCutYOY = Column(Float(53))
    SalesServiceCashToORLatest = Column(Float(53))
    CashRateOfSalesLatest = Column(Float(53))
    NOCFToOperatingNILatest = Column(Float(53))
    TotalAssets = Column(Float(53))
    MktValue = Column(Float(53))
    NegMktValue = Column(Float(53))
    TEAP = Column(Float(53))
    NIAP = Column(Float(53))
    TotalFixedAssets = Column(Float(53))
    IntFreeCL = Column(Float(53))
    IntFreeNCL = Column(Float(53))
    IntCL = Column(Float(53))
    IntDebt = Column(Float(53))
    NetDebt = Column(Float(53))
    NetTangibleAssets = Column(Float(53))
    WorkingCapital = Column(Float(53))
    NetWorkingCapital = Column(Float(53))
    TotalPaidinCapital = Column(Float(53))
    RetainedEarnings = Column(Float(53))
    OperateNetIncome = Column(Float(53))
    ValueChgProfit = Column(Float(53))
    NetIntExpense = Column(Float(53))
    EBIT = Column(Float(53))
    EBITDA = Column(Float(53))
    EBIAT = Column(Float(53))
    NRProfitLoss = Column(Float(53))
    NIAPCut = Column(Float(53))
    FCFF = Column(Float(53))
    FCFE = Column(Float(53))
    DA = Column(Float(53))
    TRevenueTTM = Column(Float(53))
    TCostTTM = Column(Float(53))
    RevenueTTM = Column(Float(53))
    CostTTM = Column(Float(53))
    GrossProfitTTM = Column(Float(53))
    SalesExpenseTTM = Column(Float(53))
    AdminExpenseTTM = Column(Float(53))
    FinanExpenseTTM = Column(Float(53))
    AssetImpairLossTTM = Column(Float(53))
    NPFromOperatingTTM = Column(Float(53))
    NPFromValueChgTTM = Column(Float(53))
    OperateProfitTTM = Column(Float(53))
    NonOperatingNPTTM = Column(Float(53))
    TProfitTTM = Column(Float(53))
    NetProfitTTM = Column(Float(53))
    NetProfitAPTTM = Column(Float(53))
    SaleServiceRenderCashTTM = Column(Float(53))
    NetOperateCFTTM = Column(Float(53))
    NetInvestCFTTM = Column(Float(53))
    NetFinanceCFTTM = Column(Float(53))
    GrossProfit = Column(Float(53))
    Beta252 = Column(Float(53))
    RSTR504 = Column(Float(53))
    EPIBS = Column(Float(53))
    CETOP = Column(Float(53))
    DASTD = Column(Float(53))
    CmraCNE5 = Column(Float(53))
    HsigmaCNE5 = Column(Float(53))
    SGRO = Column(Float(53))
    EgibsLong = Column(Float(53))
    STOM = Column(Float(53))
    STOQ = Column(Float(53))
    STOA = Column(Float(53))
    NLSIZE = Column(Float(53))