"""Generated message classes for monitoring version v1.

Manages your Stackdriver Monitoring data and configurations. Most projects
must be associated with a Stackdriver account, with a few exceptions as noted
on the individual method pages. The table entries below are presented in
alphabetical order, not in order of common use. For explanations of the
concepts found in the table entries, read the Stackdriver Monitoring
documentation.
"""
# NOTE: This file is autogenerated and should not be edited by hand.

from apitools.base.protorpclite import messages as _messages
from apitools.base.py import encoding
from apitools.base.py import extra_types


package = 'monitoring'


class Aggregation(_messages.Message):
  r"""Describes how to combine multiple time series to provide a different
  view of the data. Aggregation of time series is done in two steps. First,
  each time series in the set is aligned to the same time interval boundaries,
  then the set of time series is optionally reduced in number.Alignment
  consists of applying the per_series_aligner operation to each time series
  after its data has been divided into regular alignment_period time
  intervals. This process takes all of the data points in an alignment period,
  applies a mathematical transformation such as averaging, minimum, maximum,
  delta, etc., and converts them into a single data point per period.Reduction
  is when the aligned and transformed time series can optionally be combined,
  reducing the number of time series through similar mathematical
  transformations. Reduction involves applying a cross_series_reducer to all
  the time series, optionally sorting the time series into subsets with
  group_by_fields, and applying the reducer to each subset.The raw time series
  data can contain a huge amount of information from multiple sources.
  Alignment and reduction transforms this mass of data into a more manageable
  and representative collection of data, for example "the 95% latency across
  the average of all tasks in a cluster". This representative data can be more
  easily graphed and comprehended, and the individual time series data is
  still available for later drilldown. For more details, see Aggregating Time
  Series.

  Enums:
    CrossSeriesReducerValueValuesEnum: The reduction operation to be used to
      combine time series into a single time series, where the value of each
      data point in the resulting series is a function of all the already
      aligned values in the input time series.Not all reducer operations can
      be applied to all time series. The valid choices depend on the
      metric_kind and the value_type of the original time series. Reduction
      can yield a time series with a different metric_kind or value_type than
      the input time series.Time series data must first be aligned (see
      per_series_aligner) in order to perform cross-time series reduction. If
      cross_series_reducer is specified, then per_series_aligner must be
      specified, and must not be ALIGN_NONE. An alignment_period must also be
      specified; otherwise, an error is returned.
    PerSeriesAlignerValueValuesEnum: An Aligner describes how to bring the
      data points in a single time series into temporal alignment. Except for
      ALIGN_NONE, all alignments cause all the data points in an
      alignment_period to be mathematically grouped together, resulting in a
      single data point for each alignment_period with end timestamp at the
      end of the period.Not all alignment operations may be applied to all
      time series. The valid choices depend on the metric_kind and value_type
      of the original time series. Alignment can change the metric_kind or the
      value_type of the time series.Time series data must be aligned in order
      to perform cross-time series reduction. If cross_series_reducer is
      specified, then per_series_aligner must be specified and not equal to
      ALIGN_NONE and alignment_period must be specified; otherwise, an error
      is returned.

  Fields:
    alignmentPeriod: The alignment_period specifies a time interval, in
      seconds, that is used to divide the data in all the time series into
      consistent blocks of time. This will be done before the per-series
      aligner can be applied to the data.The value must be at least 60
      seconds. If a per-series aligner other than ALIGN_NONE is specified,
      this field is required or an error is returned. If no per-series aligner
      is specified, or the aligner ALIGN_NONE is specified, then this field is
      ignored.
    crossSeriesReducer: The reduction operation to be used to combine time
      series into a single time series, where the value of each data point in
      the resulting series is a function of all the already aligned values in
      the input time series.Not all reducer operations can be applied to all
      time series. The valid choices depend on the metric_kind and the
      value_type of the original time series. Reduction can yield a time
      series with a different metric_kind or value_type than the input time
      series.Time series data must first be aligned (see per_series_aligner)
      in order to perform cross-time series reduction. If cross_series_reducer
      is specified, then per_series_aligner must be specified, and must not be
      ALIGN_NONE. An alignment_period must also be specified; otherwise, an
      error is returned.
    groupByFields: The set of fields to preserve when cross_series_reducer is
      specified. The group_by_fields determine how the time series are
      partitioned into subsets prior to applying the aggregation operation.
      Each subset contains time series that have the same value for each of
      the grouping fields. Each individual time series is a member of exactly
      one subset. The cross_series_reducer is applied to each subset of time
      series. It is not possible to reduce across different resource types, so
      this field implicitly contains resource.type. Fields not specified in
      group_by_fields are aggregated away. If group_by_fields is not specified
      and all the time series have the same resource type, then the time
      series are aggregated into a single output time series. If
      cross_series_reducer is not defined, this field is ignored.
    perSeriesAligner: An Aligner describes how to bring the data points in a
      single time series into temporal alignment. Except for ALIGN_NONE, all
      alignments cause all the data points in an alignment_period to be
      mathematically grouped together, resulting in a single data point for
      each alignment_period with end timestamp at the end of the period.Not
      all alignment operations may be applied to all time series. The valid
      choices depend on the metric_kind and value_type of the original time
      series. Alignment can change the metric_kind or the value_type of the
      time series.Time series data must be aligned in order to perform cross-
      time series reduction. If cross_series_reducer is specified, then
      per_series_aligner must be specified and not equal to ALIGN_NONE and
      alignment_period must be specified; otherwise, an error is returned.
  """

  class CrossSeriesReducerValueValuesEnum(_messages.Enum):
    r"""The reduction operation to be used to combine time series into a
    single time series, where the value of each data point in the resulting
    series is a function of all the already aligned values in the input time
    series.Not all reducer operations can be applied to all time series. The
    valid choices depend on the metric_kind and the value_type of the original
    time series. Reduction can yield a time series with a different
    metric_kind or value_type than the input time series.Time series data must
    first be aligned (see per_series_aligner) in order to perform cross-time
    series reduction. If cross_series_reducer is specified, then
    per_series_aligner must be specified, and must not be ALIGN_NONE. An
    alignment_period must also be specified; otherwise, an error is returned.

    Values:
      REDUCE_NONE: No cross-time series reduction. The output of the Aligner
        is returned.
      REDUCE_MEAN: Reduce by computing the mean value across time series for
        each alignment period. This reducer is valid for DELTA and GAUGE
        metrics with numeric or distribution values. The value_type of the
        output is DOUBLE.
      REDUCE_MIN: Reduce by computing the minimum value across time series for
        each alignment period. This reducer is valid for DELTA and GAUGE
        metrics with numeric values. The value_type of the output is the same
        as the value_type of the input.
      REDUCE_MAX: Reduce by computing the maximum value across time series for
        each alignment period. This reducer is valid for DELTA and GAUGE
        metrics with numeric values. The value_type of the output is the same
        as the value_type of the input.
      REDUCE_SUM: Reduce by computing the sum across time series for each
        alignment period. This reducer is valid for DELTA and GAUGE metrics
        with numeric and distribution values. The value_type of the output is
        the same as the value_type of the input.
      REDUCE_STDDEV: Reduce by computing the standard deviation across time
        series for each alignment period. This reducer is valid for DELTA and
        GAUGE metrics with numeric or distribution values. The value_type of
        the output is DOUBLE.
      REDUCE_COUNT: Reduce by computing the number of data points across time
        series for each alignment period. This reducer is valid for DELTA and
        GAUGE metrics of numeric, Boolean, distribution, and string
        value_type. The value_type of the output is INT64.
      REDUCE_COUNT_TRUE: Reduce by computing the number of True-valued data
        points across time series for each alignment period. This reducer is
        valid for DELTA and GAUGE metrics of Boolean value_type. The
        value_type of the output is INT64.
      REDUCE_COUNT_FALSE: Reduce by computing the number of False-valued data
        points across time series for each alignment period. This reducer is
        valid for DELTA and GAUGE metrics of Boolean value_type. The
        value_type of the output is INT64.
      REDUCE_FRACTION_TRUE: Reduce by computing the ratio of the number of
        True-valued data points to the total number of data points for each
        alignment period. This reducer is valid for DELTA and GAUGE metrics of
        Boolean value_type. The output value is in the range 0.0, 1.0 and has
        value_type DOUBLE.
      REDUCE_PERCENTILE_99: Reduce by computing the 99th percentile
        (https://en.wikipedia.org/wiki/Percentile) of data points across time
        series for each alignment period. This reducer is valid for GAUGE and
        DELTA metrics of numeric and distribution type. The value of the
        output is DOUBLE.
      REDUCE_PERCENTILE_95: Reduce by computing the 95th percentile
        (https://en.wikipedia.org/wiki/Percentile) of data points across time
        series for each alignment period. This reducer is valid for GAUGE and
        DELTA metrics of numeric and distribution type. The value of the
        output is DOUBLE.
      REDUCE_PERCENTILE_50: Reduce by computing the 50th percentile
        (https://en.wikipedia.org/wiki/Percentile) of data points across time
        series for each alignment period. This reducer is valid for GAUGE and
        DELTA metrics of numeric and distribution type. The value of the
        output is DOUBLE.
      REDUCE_PERCENTILE_05: Reduce by computing the 5th percentile
        (https://en.wikipedia.org/wiki/Percentile) of data points across time
        series for each alignment period. This reducer is valid for GAUGE and
        DELTA metrics of numeric and distribution type. The value of the
        output is DOUBLE.
    """
    REDUCE_NONE = 0
    REDUCE_MEAN = 1
    REDUCE_MIN = 2
    REDUCE_MAX = 3
    REDUCE_SUM = 4
    REDUCE_STDDEV = 5
    REDUCE_COUNT = 6
    REDUCE_COUNT_TRUE = 7
    REDUCE_COUNT_FALSE = 8
    REDUCE_FRACTION_TRUE = 9
    REDUCE_PERCENTILE_99 = 10
    REDUCE_PERCENTILE_95 = 11
    REDUCE_PERCENTILE_50 = 12
    REDUCE_PERCENTILE_05 = 13

  class PerSeriesAlignerValueValuesEnum(_messages.Enum):
    r"""An Aligner describes how to bring the data points in a single time
    series into temporal alignment. Except for ALIGN_NONE, all alignments
    cause all the data points in an alignment_period to be mathematically
    grouped together, resulting in a single data point for each
    alignment_period with end timestamp at the end of the period.Not all
    alignment operations may be applied to all time series. The valid choices
    depend on the metric_kind and value_type of the original time series.
    Alignment can change the metric_kind or the value_type of the time
    series.Time series data must be aligned in order to perform cross-time
    series reduction. If cross_series_reducer is specified, then
    per_series_aligner must be specified and not equal to ALIGN_NONE and
    alignment_period must be specified; otherwise, an error is returned.

    Values:
      ALIGN_NONE: No alignment. Raw data is returned. Not valid if cross-
        series reduction is requested. The value_type of the result is the
        same as the value_type of the input.
      ALIGN_DELTA: Align and convert to DELTA. The output is delta = y1 -
        y0.This alignment is valid for CUMULATIVE and DELTA metrics. If the
        selected alignment period results in periods with no data, then the
        aligned value for such a period is created by interpolation. The
        value_type of the aligned result is the same as the value_type of the
        input.
      ALIGN_RATE: Align and convert to a rate. The result is computed as rate
        = (y1 - y0)/(t1 - t0), or "delta over time". Think of this aligner as
        providing the slope of the line that passes through the value at the
        start and at the end of the alignment_period.This aligner is valid for
        CUMULATIVE and DELTA metrics with numeric values. If the selected
        alignment period results in periods with no data, then the aligned
        value for such a period is created by interpolation. The output is a
        GAUGE metric with value_type DOUBLE.If, by "rate", you mean
        "percentage change", see the ALIGN_PERCENT_CHANGE aligner instead.
      ALIGN_INTERPOLATE: Align by interpolating between adjacent points around
        the alignment period boundary. This aligner is valid for GAUGE metrics
        with numeric values. The value_type of the aligned result is the same
        as the value_type of the input.
      ALIGN_NEXT_OLDER: Align by moving the most recent data point before the
        end of the alignment period to the boundary at the end of the
        alignment period. This aligner is valid for GAUGE metrics. The
        value_type of the aligned result is the same as the value_type of the
        input.
      ALIGN_MIN: Align the time series by returning the minimum value in each
        alignment period. This aligner is valid for GAUGE and DELTA metrics
        with numeric values. The value_type of the aligned result is the same
        as the value_type of the input.
      ALIGN_MAX: Align the time series by returning the maximum value in each
        alignment period. This aligner is valid for GAUGE and DELTA metrics
        with numeric values. The value_type of the aligned result is the same
        as the value_type of the input.
      ALIGN_MEAN: Align the time series by returning the mean value in each
        alignment period. This aligner is valid for GAUGE and DELTA metrics
        with numeric values. The value_type of the aligned result is DOUBLE.
      ALIGN_COUNT: Align the time series by returning the number of values in
        each alignment period. This aligner is valid for GAUGE and DELTA
        metrics with numeric or Boolean values. The value_type of the aligned
        result is INT64.
      ALIGN_SUM: Align the time series by returning the sum of the values in
        each alignment period. This aligner is valid for GAUGE and DELTA
        metrics with numeric and distribution values. The value_type of the
        aligned result is the same as the value_type of the input.
      ALIGN_STDDEV: Align the time series by returning the standard deviation
        of the values in each alignment period. This aligner is valid for
        GAUGE and DELTA metrics with numeric values. The value_type of the
        output is DOUBLE.
      ALIGN_COUNT_TRUE: Align the time series by returning the number of True
        values in each alignment period. This aligner is valid for GAUGE
        metrics with Boolean values. The value_type of the output is INT64.
      ALIGN_COUNT_FALSE: Align the time series by returning the number of
        False values in each alignment period. This aligner is valid for GAUGE
        metrics with Boolean values. The value_type of the output is INT64.
      ALIGN_FRACTION_TRUE: Align the time series by returning the ratio of the
        number of True values to the total number of values in each alignment
        period. This aligner is valid for GAUGE metrics with Boolean values.
        The output value is in the range 0.0, 1.0 and has value_type DOUBLE.
      ALIGN_PERCENTILE_99: Align the time series by using percentile
        aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting
        data point in each alignment period is the 99th percentile of all data
        points in the period. This aligner is valid for GAUGE and DELTA
        metrics with distribution values. The output is a GAUGE metric with
        value_type DOUBLE.
      ALIGN_PERCENTILE_95: Align the time series by using percentile
        aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting
        data point in each alignment period is the 95th percentile of all data
        points in the period. This aligner is valid for GAUGE and DELTA
        metrics with distribution values. The output is a GAUGE metric with
        value_type DOUBLE.
      ALIGN_PERCENTILE_50: Align the time series by using percentile
        aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting
        data point in each alignment period is the 50th percentile of all data
        points in the period. This aligner is valid for GAUGE and DELTA
        metrics with distribution values. The output is a GAUGE metric with
        value_type DOUBLE.
      ALIGN_PERCENTILE_05: Align the time series by using percentile
        aggregation (https://en.wikipedia.org/wiki/Percentile). The resulting
        data point in each alignment period is the 5th percentile of all data
        points in the period. This aligner is valid for GAUGE and DELTA
        metrics with distribution values. The output is a GAUGE metric with
        value_type DOUBLE.
      ALIGN_PERCENT_CHANGE: Align and convert to a percentage change. This
        aligner is valid for GAUGE and DELTA metrics with numeric values. This
        alignment returns ((current - previous)/previous) * 100, where the
        value of previous is determined based on the alignment_period.If the
        values of current and previous are both 0, then the returned value is
        0. If only previous is 0, the returned value is infinity.A 10-minute
        moving mean is computed at each point of the alignment period prior to
        the above calculation to smooth the metric and prevent false positives
        from very short-lived spikes. The moving mean is only applicable for
        data whose values are >= 0. Any values < 0 are treated as a missing
        datapoint, and are ignored. While DELTA metrics are accepted by this
        alignment, special care should be taken that the values for the metric
        will always be positive. The output is a GAUGE metric with value_type
        DOUBLE.
    """
    ALIGN_NONE = 0
    ALIGN_DELTA = 1
    ALIGN_RATE = 2
    ALIGN_INTERPOLATE = 3
    ALIGN_NEXT_OLDER = 4
    ALIGN_MIN = 5
    ALIGN_MAX = 6
    ALIGN_MEAN = 7
    ALIGN_COUNT = 8
    ALIGN_SUM = 9
    ALIGN_STDDEV = 10
    ALIGN_COUNT_TRUE = 11
    ALIGN_COUNT_FALSE = 12
    ALIGN_FRACTION_TRUE = 13
    ALIGN_PERCENTILE_99 = 14
    ALIGN_PERCENTILE_95 = 15
    ALIGN_PERCENTILE_50 = 16
    ALIGN_PERCENTILE_05 = 17
    ALIGN_PERCENT_CHANGE = 18

  alignmentPeriod = _messages.StringField(1)
  crossSeriesReducer = _messages.EnumField('CrossSeriesReducerValueValuesEnum', 2)
  groupByFields = _messages.StringField(3, repeated=True)
  perSeriesAligner = _messages.EnumField('PerSeriesAlignerValueValuesEnum', 4)


class Axis(_messages.Message):
  r"""A chart axis.

  Enums:
    ScaleValueValuesEnum: The axis scale. By default, a linear scale is used.

  Fields:
    label: The label of the axis.
    scale: The axis scale. By default, a linear scale is used.
  """

  class ScaleValueValuesEnum(_messages.Enum):
    r"""The axis scale. By default, a linear scale is used.

    Values:
      SCALE_UNSPECIFIED: Scale is unspecified. The view will default to
        LINEAR.
      LINEAR: Linear scale.
      LOG10: Logarithmic scale (base 10).
    """
    SCALE_UNSPECIFIED = 0
    LINEAR = 1
    LOG10 = 2

  label = _messages.StringField(1)
  scale = _messages.EnumField('ScaleValueValuesEnum', 2)


class ChartOptions(_messages.Message):
  r"""Options to control visual rendering of a chart.

  Enums:
    ModeValueValuesEnum: The chart mode.

  Fields:
    mode: The chart mode.
  """

  class ModeValueValuesEnum(_messages.Enum):
    r"""The chart mode.

    Values:
      MODE_UNSPECIFIED: Mode is unspecified. The view will default to COLOR.
      COLOR: The chart distinguishes data series using different color. Line
        colors may get reused when there are many lines in the chart.
      X_RAY: The chart uses the Stackdriver x-ray mode, in which each data set
        is plotted using the same semi-transparent color.
      STATS: The chart displays statistics such as average, median, 95th
        percentile, and more.
    """
    MODE_UNSPECIFIED = 0
    COLOR = 1
    X_RAY = 2
    STATS = 3

  mode = _messages.EnumField('ModeValueValuesEnum', 1)


class Column(_messages.Message):
  r"""Defines the layout properties and content for a column.

  Fields:
    weight: The relative weight of this column. The column weight is used to
      adjust the width of columns on the screen (relative to peers). Greater
      the weight, greater the width of the column on the screen. If omitted, a
      value of 1 is used while rendering.
    widgets: The display widgets arranged vertically in this column.
  """

  weight = _messages.IntegerField(1)
  widgets = _messages.MessageField('Widget', 2, repeated=True)


class ColumnLayout(_messages.Message):
  r"""A simplified layout that divides the available space into vertical
  columns and arranges a set of widgets vertically in each column.

  Fields:
    columns: The columns of content to display.
  """

  columns = _messages.MessageField('Column', 1, repeated=True)


class Dashboard(_messages.Message):
  r"""A Google Stackdriver dashboard. Dashboards define the content and layout
  of pages in the Stackdriver web application.

  Fields:
    columnLayout: The content is divided into equally spaced columns and the
      widgets are arranged vertically.
    displayName: The mutable, human-readable name.
    etag: etag is used for optimistic concurrency control as a way to help
      prevent simultaneous updates of a policy from overwriting each other. An
      etag is returned in the response to GetDashboard, and users are expected
      to put that etag in the request to UpdateDashboard to ensure that their
      change will be applied to the same version of the Dashboard
      configuration. The field should not be passed during dashboard creation.
    gridLayout: Content is arranged with a basic layout that re-flows a simple
      list of informational elements like widgets or tiles.
    name: The resource name of the dashboard.
    rowLayout: The content is divided into equally spaced rows and the widgets
      are arranged horizontally.
  """

  columnLayout = _messages.MessageField('ColumnLayout', 1)
  displayName = _messages.StringField(2)
  etag = _messages.StringField(3)
  gridLayout = _messages.MessageField('GridLayout', 4)
  name = _messages.StringField(5)
  rowLayout = _messages.MessageField('RowLayout', 6)


class DataSet(_messages.Message):
  r"""Groups a time series query definition with charting options.

  Enums:
    PlotTypeValueValuesEnum: How this data should be plotted on the chart.

  Fields:
    legendTemplate: A template string for naming TimeSeries in the resulting
      data set. This should be a string with interpolations of the form
      ${label_name}, which will resolve to the label's value.
    minAlignmentPeriod: Optional. The lower bound on data point frequency for
      this data set, implemented by specifying the minimum alignment period to
      use in a time series query For example, if the data is published once
      every 10 minutes, the min_alignment_period should be at least 10
      minutes. It would not make sense to fetch and align data at one minute
      intervals.
    plotType: How this data should be plotted on the chart.
    timeSeriesQuery: Fields for querying time series data from the Stackdriver
      metrics API.
  """

  class PlotTypeValueValuesEnum(_messages.Enum):
    r"""How this data should be plotted on the chart.

    Values:
      PLOT_TYPE_UNSPECIFIED: Plot type is unspecified. The view will default
        to LINE.
      LINE: The data is plotted as a set of lines (one line per series).
      STACKED_AREA: The data is plotted as a set of filled areas (one area per
        series), with the areas stacked vertically (the base of each area is
        the top of its predecessor, and the base of the first area is the X
        axis). Since the areas do not overlap, each is filled with a different
        opaque color.
      STACKED_BAR: The data is plotted as a set of rectangular boxes (one box
        per series), with the boxes stacked vertically (the base of each box
        is the top of its predecessor, and the base of the first box is the X
        axis). Since the boxes do not overlap, each is filled with a different
        opaque color.
      HEATMAP: The data is plotted as a heatmap. The series being plotted must
        have a DISTRIBUTION value type. The value of each bucket in the
        distribution is displayed as a color. This type is not currently
        available in the Stackdriver Monitoring application.
    """
    PLOT_TYPE_UNSPECIFIED = 0
    LINE = 1
    STACKED_AREA = 2
    STACKED_BAR = 3
    HEATMAP = 4

  legendTemplate = _messages.StringField(1)
  minAlignmentPeriod = _messages.StringField(2)
  plotType = _messages.EnumField('PlotTypeValueValuesEnum', 3)
  timeSeriesQuery = _messages.MessageField('TimeSeriesQuery', 4)


class DroppedLabels(_messages.Message):
  r"""A set of (label, value) pairs which were dropped during aggregation,
  attached to google.api.Distribution.Exemplars in google.api.Distribution
  values during aggregation.These values are used in combination with the
  label values that remain on the aggregated Distribution timeseries to
  construct the full label set for the exemplar values. The resulting full
  label set may be used to identify the specific task/job/instance (for
  example) which may be contributing to a long-tail, while allowing the
  storage savings of only storing aggregated distribution values for a large
  group.Note that there are no guarantees on ordering of the labels from
  exemplar-to-exemplar and from distribution-to-distribution in the same
  stream, and there may be duplicates. It is up to clients to resolve any
  ambiguities.

  Messages:
    LabelValue: Map from label to its value, for all labels dropped in any
      aggregation.

  Fields:
    label: Map from label to its value, for all labels dropped in any
      aggregation.
  """

  @encoding.MapUnrecognizedFields('additionalProperties')
  class LabelValue(_messages.Message):
    r"""Map from label to its value, for all labels dropped in any
    aggregation.

    Messages:
      AdditionalProperty: An additional property for a LabelValue object.

    Fields:
      additionalProperties: Additional properties of type LabelValue
    """

    class AdditionalProperty(_messages.Message):
      r"""An additional property for a LabelValue object.

      Fields:
        key: Name of the additional property.
        value: A string attribute.
      """

      key = _messages.StringField(1)
      value = _messages.StringField(2)

    additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True)

  label = _messages.MessageField('LabelValue', 1)


class Empty(_messages.Message):
  r"""A generic empty message that you can re-use to avoid defining duplicated
  empty messages in your APIs. A typical example is to use it as the request
  or the response type of an API method. For instance: service Foo {   rpc
  Bar(google.protobuf.Empty) returns (google.protobuf.Empty); } The JSON
  representation for Empty is empty JSON object {}.
  """



class Field(_messages.Message):
  r"""A single field of a message type.

  Enums:
    CardinalityValueValuesEnum: The field cardinality.
    KindValueValuesEnum: The field type.

  Fields:
    cardinality: The field cardinality.
    defaultValue: The string value of the default value of this field. Proto2
      syntax only.
    jsonName: The field JSON name.
    kind: The field type.
    name: The field name.
    number: The field number.
    oneofIndex: The index of the field type in Type.oneofs, for message or
      enumeration types. The first type has index 1; zero means the type is
      not in the list.
    options: The protocol buffer options.
    packed: Whether to use alternative packed wire representation.
    typeUrl: The field type URL, without the scheme, for message or
      enumeration types. Example:
      "type.googleapis.com/google.protobuf.Timestamp".
  """

  class CardinalityValueValuesEnum(_messages.Enum):
    r"""The field cardinality.

    Values:
      CARDINALITY_UNKNOWN: For fields with unknown cardinality.
      CARDINALITY_OPTIONAL: For optional fields.
      CARDINALITY_REQUIRED: For required fields. Proto2 syntax only.
      CARDINALITY_REPEATED: For repeated fields.
    """
    CARDINALITY_UNKNOWN = 0
    CARDINALITY_OPTIONAL = 1
    CARDINALITY_REQUIRED = 2
    CARDINALITY_REPEATED = 3

  class KindValueValuesEnum(_messages.Enum):
    r"""The field type.

    Values:
      TYPE_UNKNOWN: Field type unknown.
      TYPE_DOUBLE: Field type double.
      TYPE_FLOAT: Field type float.
      TYPE_INT64: Field type int64.
      TYPE_UINT64: Field type uint64.
      TYPE_INT32: Field type int32.
      TYPE_FIXED64: Field type fixed64.
      TYPE_FIXED32: Field type fixed32.
      TYPE_BOOL: Field type bool.
      TYPE_STRING: Field type string.
      TYPE_GROUP: Field type group. Proto2 syntax only, and deprecated.
      TYPE_MESSAGE: Field type message.
      TYPE_BYTES: Field type bytes.
      TYPE_UINT32: Field type uint32.
      TYPE_ENUM: Field type enum.
      TYPE_SFIXED32: Field type sfixed32.
      TYPE_SFIXED64: Field type sfixed64.
      TYPE_SINT32: Field type sint32.
      TYPE_SINT64: Field type sint64.
    """
    TYPE_UNKNOWN = 0
    TYPE_DOUBLE = 1
    TYPE_FLOAT = 2
    TYPE_INT64 = 3
    TYPE_UINT64 = 4
    TYPE_INT32 = 5
    TYPE_FIXED64 = 6
    TYPE_FIXED32 = 7
    TYPE_BOOL = 8
    TYPE_STRING = 9
    TYPE_GROUP = 10
    TYPE_MESSAGE = 11
    TYPE_BYTES = 12
    TYPE_UINT32 = 13
    TYPE_ENUM = 14
    TYPE_SFIXED32 = 15
    TYPE_SFIXED64 = 16
    TYPE_SINT32 = 17
    TYPE_SINT64 = 18

  cardinality = _messages.EnumField('CardinalityValueValuesEnum', 1)
  defaultValue = _messages.StringField(2)
  jsonName = _messages.StringField(3)
  kind = _messages.EnumField('KindValueValuesEnum', 4)
  name = _messages.StringField(5)
  number = _messages.IntegerField(6, variant=_messages.Variant.INT32)
  oneofIndex = _messages.IntegerField(7, variant=_messages.Variant.INT32)
  options = _messages.MessageField('Option', 8, repeated=True)
  packed = _messages.BooleanField(9)
  typeUrl = _messages.StringField(10)


class GaugeView(_messages.Message):
  r"""A gauge chart shows where the current value sits within a pre-defined
  range. The upper and lower bounds should define the possible range of values
  for the scorecard's query (inclusive).

  Fields:
    lowerBound: The lower bound for this gauge chart. The value of the chart
      should always be greater than or equal to this.
    upperBound: The upper bound for this gauge chart. The value of the chart
      should always be less than or equal to this.
  """

  lowerBound = _messages.FloatField(1)
  upperBound = _messages.FloatField(2)


class GridLayout(_messages.Message):
  r"""A basic layout divides the available space into vertical columns of
  equal width and arranges a list of widgets using a row-first strategy.

  Fields:
    columns: The number of columns into which the view's width is divided. If
      omitted or set to zero, a system default will be used while rendering.
    widgets: The informational elements that are arranged into the columns
      row-first.
  """

  columns = _messages.IntegerField(1)
  widgets = _messages.MessageField('Widget', 2, repeated=True)


class ListDashboardsResponse(_messages.Message):
  r"""The ListDashboards request.

  Fields:
    dashboards: The list of requested dashboards.
    nextPageToken: If there are more results than have been returned, then
      this field is set to a non-empty value. To see the additional results,
      use that value as page_token in the next call to this method.
  """

  dashboards = _messages.MessageField('Dashboard', 1, repeated=True)
  nextPageToken = _messages.StringField(2)


class MonitoringProjectsDashboardsCreateRequest(_messages.Message):
  r"""A MonitoringProjectsDashboardsCreateRequest object.

  Fields:
    dashboard: A Dashboard resource to be passed as the request body.
    parent: Required. The project on which to execute the request. The format
      is: projects/[PROJECT_ID_OR_NUMBER] The [PROJECT_ID_OR_NUMBER] must
      match the dashboard resource name.
  """

  dashboard = _messages.MessageField('Dashboard', 1)
  parent = _messages.StringField(2, required=True)


class MonitoringProjectsDashboardsDeleteRequest(_messages.Message):
  r"""A MonitoringProjectsDashboardsDeleteRequest object.

  Fields:
    name: Required. The resource name of the Dashboard. The format is:
      projects/[PROJECT_ID_OR_NUMBER]/dashboards/[DASHBOARD_ID]
  """

  name = _messages.StringField(1, required=True)


class MonitoringProjectsDashboardsGetRequest(_messages.Message):
  r"""A MonitoringProjectsDashboardsGetRequest object.

  Fields:
    name: Required. The resource name of the Dashboard. The format is one of:
      dashboards/[DASHBOARD_ID] (for system dashboards)
      projects/[PROJECT_ID_OR_NUMBER]/dashboards/[DASHBOARD_ID]  (for custom
      dashboards).
  """

  name = _messages.StringField(1, required=True)


class MonitoringProjectsDashboardsListRequest(_messages.Message):
  r"""A MonitoringProjectsDashboardsListRequest object.

  Fields:
    pageSize: A positive number that is the maximum number of results to
      return. If unspecified, a default of 1000 is used.
    pageToken: If this field is not empty then it must contain the
      nextPageToken value returned by a previous call to this method. Using
      this field causes the method to return additional results from the
      previous method call.
    parent: Required. The scope of the dashboards to list. The format is:
      projects/[PROJECT_ID_OR_NUMBER]
  """

  pageSize = _messages.IntegerField(1, variant=_messages.Variant.INT32)
  pageToken = _messages.StringField(2)
  parent = _messages.StringField(3, required=True)


class MonitoringProjectsDashboardsPatchRequest(_messages.Message):
  r"""A MonitoringProjectsDashboardsPatchRequest object.

  Fields:
    dashboard: A Dashboard resource to be passed as the request body.
    name: The resource name of the dashboard.
  """

  dashboard = _messages.MessageField('Dashboard', 1)
  name = _messages.StringField(2, required=True)


class Option(_messages.Message):
  r"""A protocol buffer option, which can be attached to a message, field,
  enumeration, etc.

  Messages:
    ValueValue: The option's value packed in an Any message. If the value is a
      primitive, the corresponding wrapper type defined in
      google/protobuf/wrappers.proto should be used. If the value is an enum,
      it should be stored as an int32 value using the
      google.protobuf.Int32Value type.

  Fields:
    name: The option's name. For protobuf built-in options (options defined in
      descriptor.proto), this is the short name. For example, "map_entry". For
      custom options, it should be the fully-qualified name. For example,
      "google.api.http".
    value: The option's value packed in an Any message. If the value is a
      primitive, the corresponding wrapper type defined in
      google/protobuf/wrappers.proto should be used. If the value is an enum,
      it should be stored as an int32 value using the
      google.protobuf.Int32Value type.
  """

  @encoding.MapUnrecognizedFields('additionalProperties')
  class ValueValue(_messages.Message):
    r"""The option's value packed in an Any message. If the value is a
    primitive, the corresponding wrapper type defined in
    google/protobuf/wrappers.proto should be used. If the value is an enum, it
    should be stored as an int32 value using the google.protobuf.Int32Value
    type.

    Messages:
      AdditionalProperty: An additional property for a ValueValue object.

    Fields:
      additionalProperties: Properties of the object. Contains field @type
        with type URL.
    """

    class AdditionalProperty(_messages.Message):
      r"""An additional property for a ValueValue object.

      Fields:
        key: Name of the additional property.
        value: A extra_types.JsonValue attribute.
      """

      key = _messages.StringField(1)
      value = _messages.MessageField('extra_types.JsonValue', 2)

    additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True)

  name = _messages.StringField(1)
  value = _messages.MessageField('ValueValue', 2)


class PickTimeSeriesFilter(_messages.Message):
  r"""Describes a ranking-based time series filter. Each input time series is
  ranked with an aligner. The filter will allow up to num_time_series time
  series to pass through it, selecting them based on the relative ranking.For
  example, if ranking_method is METHOD_MEAN,direction is BOTTOM, and
  num_time_series is 3, then the 3 times series with the lowest mean values
  will pass through the filter.

  Enums:
    DirectionValueValuesEnum: How to use the ranking to select time series
      that pass through the filter.
    RankingMethodValueValuesEnum: ranking_method is applied to each time
      series independently to produce the value which will be used to compare
      the time series to other time series.

  Fields:
    direction: How to use the ranking to select time series that pass through
      the filter.
    numTimeSeries: How many time series to allow to pass through the filter.
    rankingMethod: ranking_method is applied to each time series independently
      to produce the value which will be used to compare the time series to
      other time series.
  """

  class DirectionValueValuesEnum(_messages.Enum):
    r"""How to use the ranking to select time series that pass through the
    filter.

    Values:
      DIRECTION_UNSPECIFIED: Not allowed. You must specify a different
        Direction if you specify a PickTimeSeriesFilter.
      TOP: Pass the highest num_time_series ranking inputs.
      BOTTOM: Pass the lowest num_time_series ranking inputs.
    """
    DIRECTION_UNSPECIFIED = 0
    TOP = 1
    BOTTOM = 2

  class RankingMethodValueValuesEnum(_messages.Enum):
    r"""ranking_method is applied to each time series independently to produce
    the value which will be used to compare the time series to other time
    series.

    Values:
      METHOD_UNSPECIFIED: Not allowed. You must specify a different Method if
        you specify a PickTimeSeriesFilter.
      METHOD_MEAN: Select the mean of all values.
      METHOD_MAX: Select the maximum value.
      METHOD_MIN: Select the minimum value.
      METHOD_SUM: Compute the sum of all values.
      METHOD_LATEST: Select the most recent value.
    """
    METHOD_UNSPECIFIED = 0
    METHOD_MEAN = 1
    METHOD_MAX = 2
    METHOD_MIN = 3
    METHOD_SUM = 4
    METHOD_LATEST = 5

  direction = _messages.EnumField('DirectionValueValuesEnum', 1)
  numTimeSeries = _messages.IntegerField(2, variant=_messages.Variant.INT32)
  rankingMethod = _messages.EnumField('RankingMethodValueValuesEnum', 3)


class RatioPart(_messages.Message):
  r"""Describes a query to build the numerator or denominator of a
  TimeSeriesFilterRatio.

  Fields:
    aggregation: By default, the raw time series data is returned. Use this
      field to combine multiple time series for different views of the data.
    filter: Required. The monitoring filter that identifies the metric types,
      resources, and projects to query.
  """

  aggregation = _messages.MessageField('Aggregation', 1)
  filter = _messages.StringField(2)


class Row(_messages.Message):
  r"""Defines the layout properties and content for a row.

  Fields:
    weight: The relative weight of this row. The row weight is used to adjust
      the height of rows on the screen (relative to peers). Greater the
      weight, greater the height of the row on the screen. If omitted, a value
      of 1 is used while rendering.
    widgets: The display widgets arranged horizontally in this row.
  """

  weight = _messages.IntegerField(1)
  widgets = _messages.MessageField('Widget', 2, repeated=True)


class RowLayout(_messages.Message):
  r"""A simplified layout that divides the available space into rows and
  arranges a set of widgets horizontally in each row.

  Fields:
    rows: The rows of content to display.
  """

  rows = _messages.MessageField('Row', 1, repeated=True)


class Scorecard(_messages.Message):
  r"""A widget showing the latest value of a metric, and how this value
  relates to one or more thresholds.

  Fields:
    gaugeView: Will cause the scorecard to show a gauge chart.
    sparkChartView: Will cause the scorecard to show a spark chart.
    thresholds: The thresholds used to determine the state of the scorecard
      given the time series' current value. For an actual value x, the
      scorecard is in a danger state if x is less than or equal to a danger
      threshold that triggers below, or greater than or equal to a danger
      threshold that triggers above. Similarly, if x is above/below a warning
      threshold that triggers above/below, then the scorecard is in a warning
      state - unless x also puts it in a danger state. (Danger trumps
      warning.)As an example, consider a scorecard with the following four
      thresholds: {  value: 90,  category: 'DANGER',  trigger: 'ABOVE', }, {
      value: 70,  category: 'WARNING',  trigger: 'ABOVE', }, {  value: 10,
      category: 'DANGER',  trigger: 'BELOW', }, {  value: 20,  category:
      'WARNING',  trigger: 'BELOW', }Then: values less than or equal to 10
      would put the scorecard in a DANGER state, values greater than 10 but
      less than or equal to 20 a WARNING state, values strictly between 20 and
      70 an OK state, values greater than or equal to 70 but less than 90 a
      WARNING state, and values greater than or equal to 90 a DANGER state.
    timeSeriesQuery: Fields for querying time series data from the Stackdriver
      metrics API.
  """

  gaugeView = _messages.MessageField('GaugeView', 1)
  sparkChartView = _messages.MessageField('SparkChartView', 2)
  thresholds = _messages.MessageField('Threshold', 3, repeated=True)
  timeSeriesQuery = _messages.MessageField('TimeSeriesQuery', 4)


class SourceContext(_messages.Message):
  r"""SourceContext represents information about the source of a protobuf
  element, like the file in which it is defined.

  Fields:
    fileName: The path-qualified name of the .proto file that contained the
      associated protobuf element. For example:
      "google/protobuf/source_context.proto".
  """

  fileName = _messages.StringField(1)


class SpanContext(_messages.Message):
  r"""The context of a span, attached to Exemplars in Distribution values
  during aggregation.It contains the name of a span with format:
  projects/[PROJECT_ID_OR_NUMBER]/traces/[TRACE_ID]/spans/[SPAN_ID]

  Fields:
    spanName: The resource name of the span. The format is:
      projects/[PROJECT_ID_OR_NUMBER]/traces/[TRACE_ID]/spans/[SPAN_ID]
      [TRACE_ID] is a unique identifier for a trace within a project; it is a
      32-character hexadecimal encoding of a 16-byte array.[SPAN_ID] is a
      unique identifier for a span within a trace; it is a 16-character
      hexadecimal encoding of an 8-byte array.
  """

  spanName = _messages.StringField(1)


class SparkChartView(_messages.Message):
  r"""A sparkChart is a small chart suitable for inclusion in a table-cell or
  inline in text. This message contains the configuration for a sparkChart to
  show up on a Scorecard, showing recent trends of the scorecard's timeseries.

  Enums:
    SparkChartTypeValueValuesEnum: The type of sparkchart to show in this
      chartView.

  Fields:
    minAlignmentPeriod: The lower bound on data point frequency in the chart
      implemented by specifying the minimum alignment period to use in a time
      series query. For example, if the data is published once every 10
      minutes it would not make sense to fetch and align data at one minute
      intervals. This field is optional and exists only as a hint.
    sparkChartType: The type of sparkchart to show in this chartView.
  """

  class SparkChartTypeValueValuesEnum(_messages.Enum):
    r"""The type of sparkchart to show in this chartView.

    Values:
      SPARK_CHART_TYPE_UNSPECIFIED: Not allowed in well-formed requests.
      SPARK_LINE: The sparkline will be rendered as a small line chart.
      SPARK_BAR: The sparkbar will be rendered as a small bar chart.
    """
    SPARK_CHART_TYPE_UNSPECIFIED = 0
    SPARK_LINE = 1
    SPARK_BAR = 2

  minAlignmentPeriod = _messages.StringField(1)
  sparkChartType = _messages.EnumField('SparkChartTypeValueValuesEnum', 2)


class StandardQueryParameters(_messages.Message):
  r"""Query parameters accepted by all methods.

  Enums:
    FXgafvValueValuesEnum: V1 error format.
    AltValueValuesEnum: Data format for response.

  Fields:
    f__xgafv: V1 error format.
    access_token: OAuth access token.
    alt: Data format for response.
    callback: JSONP
    fields: Selector specifying which fields to include in a partial response.
    key: API key. Your API key identifies your project and provides you with
      API access, quota, and reports. Required unless you provide an OAuth 2.0
      token.
    oauth_token: OAuth 2.0 token for the current user.
    prettyPrint: Returns response with indentations and line breaks.
    quotaUser: Available to use for quota purposes for server-side
      applications. Can be any arbitrary string assigned to a user, but should
      not exceed 40 characters.
    trace: A tracing token of the form "token:<tokenid>" to include in api
      requests.
    uploadType: Legacy upload protocol for media (e.g. "media", "multipart").
    upload_protocol: Upload protocol for media (e.g. "raw", "multipart").
  """

  class AltValueValuesEnum(_messages.Enum):
    r"""Data format for response.

    Values:
      json: Responses with Content-Type of application/json
      media: Media download with context-dependent Content-Type
      proto: Responses with Content-Type of application/x-protobuf
    """
    json = 0
    media = 1
    proto = 2

  class FXgafvValueValuesEnum(_messages.Enum):
    r"""V1 error format.

    Values:
      _1: v1 error format
      _2: v2 error format
    """
    _1 = 0
    _2 = 1

  f__xgafv = _messages.EnumField('FXgafvValueValuesEnum', 1)
  access_token = _messages.StringField(2)
  alt = _messages.EnumField('AltValueValuesEnum', 3, default=u'json')
  callback = _messages.StringField(4)
  fields = _messages.StringField(5)
  key = _messages.StringField(6)
  oauth_token = _messages.StringField(7)
  prettyPrint = _messages.BooleanField(8, default=True)
  quotaUser = _messages.StringField(9)
  trace = _messages.StringField(10)
  uploadType = _messages.StringField(11)
  upload_protocol = _messages.StringField(12)


class Text(_messages.Message):
  r"""A widget that displays textual content.

  Enums:
    FormatValueValuesEnum: How the text content is formatted.

  Fields:
    content: The text content to be displayed.
    format: How the text content is formatted.
  """

  class FormatValueValuesEnum(_messages.Enum):
    r"""How the text content is formatted.

    Values:
      FORMAT_UNSPECIFIED: Format is unspecified. Defaults to MARKDOWN.
      MARKDOWN: The text contains Markdown formatting.
      RAW: The text contains no special formatting.
    """
    FORMAT_UNSPECIFIED = 0
    MARKDOWN = 1
    RAW = 2

  content = _messages.StringField(1)
  format = _messages.EnumField('FormatValueValuesEnum', 2)


class Threshold(_messages.Message):
  r"""Defines a threshold for categorizing time series values.

  Enums:
    ColorValueValuesEnum: The state color for this threshold. Color is not
      allowed in a XyChart.
    DirectionValueValuesEnum: The direction for the current threshold.
      Direction is not allowed in a XyChart.

  Fields:
    color: The state color for this threshold. Color is not allowed in a
      XyChart.
    direction: The direction for the current threshold. Direction is not
      allowed in a XyChart.
    label: A label for the threshold.
    value: The value of the threshold. The value should be defined in the
      native scale of the metric.
  """

  class ColorValueValuesEnum(_messages.Enum):
    r"""The state color for this threshold. Color is not allowed in a XyChart.

    Values:
      COLOR_UNSPECIFIED: Color is unspecified. Not allowed in well-formed
        requests.
      YELLOW: Crossing the threshold is "concerning" behavior.
      RED: Crossing the threshold is "emergency" behavior.
    """
    COLOR_UNSPECIFIED = 0
    YELLOW = 1
    RED = 2

  class DirectionValueValuesEnum(_messages.Enum):
    r"""The direction for the current threshold. Direction is not allowed in a
    XyChart.

    Values:
      DIRECTION_UNSPECIFIED: Not allowed in well-formed requests.
      ABOVE: The threshold will be considered crossed if the actual value is
        above the threshold value.
      BELOW: The threshold will be considered crossed if the actual value is
        below the threshold value.
    """
    DIRECTION_UNSPECIFIED = 0
    ABOVE = 1
    BELOW = 2

  color = _messages.EnumField('ColorValueValuesEnum', 1)
  direction = _messages.EnumField('DirectionValueValuesEnum', 2)
  label = _messages.StringField(3)
  value = _messages.FloatField(4)


class TimeSeriesFilter(_messages.Message):
  r"""A filter that defines a subset of time series data that is displayed in
  a widget. Time series data is fetched using the ListTimeSeries method.

  Fields:
    aggregation: By default, the raw time series data is returned. Use this
      field to combine multiple time series for different views of the data.
    filter: Required. The monitoring filter that identifies the metric types,
      resources, and projects to query.
    pickTimeSeriesFilter: Ranking based time series filter.
  """

  aggregation = _messages.MessageField('Aggregation', 1)
  filter = _messages.StringField(2)
  pickTimeSeriesFilter = _messages.MessageField('PickTimeSeriesFilter', 3)


class TimeSeriesFilterRatio(_messages.Message):
  r"""A pair of time series filters that define a ratio computation. The
  output time series is the pair-wise division of each aligned element from
  the numerator and denominator time series.

  Fields:
    denominator: The denominator of the ratio.
    numerator: The numerator of the ratio.
    pickTimeSeriesFilter: Ranking based time series filter.
    secondaryAggregation: Apply a second aggregation after the ratio is
      computed.
  """

  denominator = _messages.MessageField('RatioPart', 1)
  numerator = _messages.MessageField('RatioPart', 2)
  pickTimeSeriesFilter = _messages.MessageField('PickTimeSeriesFilter', 3)
  secondaryAggregation = _messages.MessageField('Aggregation', 4)


class TimeSeriesQuery(_messages.Message):
  r"""TimeSeriesQuery collects the set of supported methods for querying time
  series data from the Stackdriver metrics API.

  Fields:
    timeSeriesFilter: Filter parameters to fetch time series.
    timeSeriesFilterRatio: Parameters to fetch a ratio between two time series
      filters.
    unitOverride: The unit of data contained in fetched time series. If non-
      empty, this unit will override any unit that accompanies fetched data.
      The format is the same as the unit field in MetricDescriptor.
  """

  timeSeriesFilter = _messages.MessageField('TimeSeriesFilter', 1)
  timeSeriesFilterRatio = _messages.MessageField('TimeSeriesFilterRatio', 2)
  unitOverride = _messages.StringField(3)


class Type(_messages.Message):
  r"""A protocol buffer message type.

  Enums:
    SyntaxValueValuesEnum: The source syntax.

  Fields:
    fields: The list of fields.
    name: The fully qualified message name.
    oneofs: The list of types appearing in oneof definitions in this type.
    options: The protocol buffer options.
    sourceContext: The source context.
    syntax: The source syntax.
  """

  class SyntaxValueValuesEnum(_messages.Enum):
    r"""The source syntax.

    Values:
      SYNTAX_PROTO2: Syntax proto2.
      SYNTAX_PROTO3: Syntax proto3.
    """
    SYNTAX_PROTO2 = 0
    SYNTAX_PROTO3 = 1

  fields = _messages.MessageField('Field', 1, repeated=True)
  name = _messages.StringField(2)
  oneofs = _messages.StringField(3, repeated=True)
  options = _messages.MessageField('Option', 4, repeated=True)
  sourceContext = _messages.MessageField('SourceContext', 5)
  syntax = _messages.EnumField('SyntaxValueValuesEnum', 6)


class Widget(_messages.Message):
  r"""Widget contains a single dashboard component and configuration of how to
  present the component in the dashboard.

  Fields:
    blank: A blank space.
    scorecard: A scorecard summarizing time series data.
    text: A raw string or markdown displaying textual content.
    title: Optional. The title of the widget.
    xyChart: A chart of time series data.
  """

  blank = _messages.MessageField('Empty', 1)
  scorecard = _messages.MessageField('Scorecard', 2)
  text = _messages.MessageField('Text', 3)
  title = _messages.StringField(4)
  xyChart = _messages.MessageField('XyChart', 5)


class XyChart(_messages.Message):
  r"""A chart that displays data on a 2D (X and Y axes) plane.

  Fields:
    chartOptions: Display options for the chart.
    dataSets: The data displayed in this chart.
    thresholds: Threshold lines drawn horizontally across the chart.
    timeshiftDuration: The duration used to display a comparison chart. A
      comparison chart simultaneously shows values from two similar-length
      time periods (e.g., week-over-week metrics). The duration must be
      positive, and it can only be applied to charts with data sets of LINE
      plot type.
    xAxis: The properties applied to the X axis.
    yAxis: The properties applied to the Y axis.
  """

  chartOptions = _messages.MessageField('ChartOptions', 1)
  dataSets = _messages.MessageField('DataSet', 2, repeated=True)
  thresholds = _messages.MessageField('Threshold', 3, repeated=True)
  timeshiftDuration = _messages.StringField(4)
  xAxis = _messages.MessageField('Axis', 5)
  yAxis = _messages.MessageField('Axis', 6)


encoding.AddCustomJsonFieldMapping(
    StandardQueryParameters, 'f__xgafv', '$.xgafv')
encoding.AddCustomJsonEnumMapping(
    StandardQueryParameters.FXgafvValueValuesEnum, '_1', '1')
encoding.AddCustomJsonEnumMapping(
    StandardQueryParameters.FXgafvValueValuesEnum, '_2', '2')
