EnhanceScaleMissing (Public javadoc for netcdf-java version 5.0.0-beta2)

All Superinterfaces:

IsMissingEvaluator

All Known Implementing Classes:

CoordinateAxis, CoordinateAxis1D, CoordinateAxis1DTime, CoordinateAxis2D, VariableDS
```
public interface EnhanceScaleMissing
extends IsMissingEvaluator
```
A Variable decorator that handles missing data, and scale/offset packed data. Specifically, this handles:
- packed data using scale_factor and add_offset
- invalid/missing data using valid_min, valid_max, valid_range, missing_value or _FillValue
if those "standard attributes" are present.
Standard Use

Implementation rules for missing data. Here "missing data" is a general name for invalid/never written/missing values. Use this interface when you dont need to distinguish these variants. See below for a lower-level interface if you do need to distinguish between them.
1. By default, hasMissing() is true if any of hasInvalidData(), hasFillData() or hasMissingValue() are true (see below). You can modify this behavior in the constuctor or by calling setInvalidDataIsMissing(), setFillDataIsMissing(), or setMissingValueIsMissing().
2. Test specific values through isMissing(double val). Note that the data is converted and compared as a double.
3. Data values of float or double NaN are considered missing data and will return true if called with isMissing(). (However hasMissing() will not detect if you are setting NaNs yourself).
4. if you do not need to distinguish between _FillValue, missing_value and invalid, then set useNaNs = true in the constructor. When the Variable element type is float or double (or is set to double because its packed), then this sets isMissing() data values to NaNs, which makes further comparisions more efficient.
Implementation rules for scale/offset (no missing data)
1. If scale_factor and/or add_offset variable attributes are present, then this is a "packed" Variable.
2. The Variable element type is converted in the following way:
  - The dataType is set to the widest type of 1) the data type 2) the scale_factor attribute type and 3) the add_offset attribute type. (byte < short < int < float < double ).
3. external (packed) data is converted to internal (unpacked) data transparently during the read() call.
Implementation rules for missing data with scale/offset
1. Always converted to a float or double type.
2. _FillValue and missing_value values are always in the units of the external (packed) data.
3. If valid_range is the same type as scale_factor (actually the wider of scale_factor and add_offset) and this is wider than the external data, then it will be interpreted as being in the units of the internal (unpacked) data. Otherwise it is in the units of the external (packed) data.
4. The dataType is set to float if all attributes used are float (scale_factor, add_offset valid_min, valid_max, valid_range, missing_data and _FillValue) otherwise the dataType is set to double.
Low Level Access
The following provide more direct access to missing/invalid data. These are mostly convenience routines for checking the standard attributes. If you set useNaNs = true in the constructor, these routines cannot be used when the data has type float or double.
Implementation rules for valid_range
1. if valid_range is present, valid_min and valid_max attributes are ignored. Otherwise, the valid_min and/or valid_max is used to construct a valid range. If any of these exist, hasInvalidData() is true.
2. To test a specific value, call isInvalidData(). Note that the data is converted and compared as a double. Or get the range through getValidMin() and getValidMax().
Implementation rules for _FillData
1. if the _FillData attribute is present, it must have a scalar value of the same type as the data. In this case, hasFillData() returns true.
2. Test specific values through isFillValue(). Note that the data is converted and compared as a double.
Implementation rules for missing_value
1. if the missing_value attribute is present, it must have a scalar or vector value of the same type as the data. In this case, hasMissingValue() returns true.
2. Test specific values through isMissingValue(). Note that the data is converted and compared as a double.
Strategies for using EnhanceScaleMissing
1. Low-level: use the is/has InvalidData/FillData/missingValue routines to "roll-your own" tests for the various kinds of missing/invalid data.
2. Standard: use is/hasMissing() to test for missing data when you dont need to distinguish between the variants. Use the setXXXisMissing() to customize the behavior if needed.
3. Efficient : If you expect to scan more than once for missing values, and you are not distinguishing between InvalidData/FillData/missingValue, then set useNaNs in the constructor. This sets isMissing() data values to NaNs when reading, and subsequent checks are more efficient.

Method Summary

All Methods Instance Methods Abstract Methods
Modifier and Type	Method and Description
`Array`	`convertScaleOffsetMissing(Array data)` Convert data if hasScaleOffset, using scale and offset.
`double`	`convertScaleOffsetMissing(byte value)` Convert this byte value to a double, using scale/offset/missing value if applicable
`double`	`convertScaleOffsetMissing(double value)` Convert this double value using scale/offset/missing value if applicable
`double`	`convertScaleOffsetMissing(int value)` Convert this int value to a double, using scale/offset/missing value if applicable
`double`	`convertScaleOffsetMissing(long value)` Convert this long value to a double, using scale/offset/missing value if applicable
`double`	`convertScaleOffsetMissing(short value)` Convert this short value to a double, using scale/offset/missing value if applicable
`double`	`getFillValue()` return value of _FillValue attribute
`double[]`	`getMissingValues()` return values of missing_value attributes
`boolean`	`getUseNaNs()` get whether to use NaNs for missing float/double values (for efficiency)
`double`	`getValidMax()` return the maximum value in the valid range
`double`	`getValidMin()` return the minimum value in the valid range
`boolean`	`hasFillValue()` true if Variable has _FillValue attribute
`boolean`	`hasInvalidData()` true if Variable has valid_range, valid_min or valid_max attributes
`boolean`	`hasMissing()` true if Variable has missing data values
`boolean`	`hasMissingValue()` true if Variable has missing_value attribute
`boolean`	`hasScaleOffset()` true if Variable data will be converted using scale and offet
`boolean`	`isFillValue(double val)` return true if val equals the _FillValue
`boolean`	`isInvalidData(double val)` return true if val is outside the valid range
`boolean`	`isMissing(double val)` true if val is a missing data value.
`boolean`	`isMissingFast(double val)` true if val is a missing data value.
`boolean`	`isMissingValue(double val)` return true if val equals a missing_value
`void`	`setFillValueIsMissing(boolean b)` set if _FillValue is considered isMissing(); better set in constructor if possible
`void`	`setInvalidDataIsMissing(boolean b)` set if valid_range is considered isMissing(); better set in constructor if possible
`void`	`setMissingDataIsMissing(boolean b)` set if missing_data is considered isMissing(); better set in constructor if possible
`void`	`setUseNaNs(boolean useNaNs)` set whether to use NaNs for missing float/double values, for efficiency

- Method Detail
  - hasMissing
```
boolean hasMissing()
```
    true if Variable has missing data values
    
    Specified by:
    
    hasMissing in interface IsMissingEvaluator
    
    Returns:
    
    true if Variable has missing data values
  - isMissing
```
boolean isMissing(double val)
```
    true if val is a missing data value.
    
    Specified by:
    
    isMissing in interface IsMissingEvaluator
    
    Parameters:
    
    val - unpacked value
    
    Returns:
    
    true if this value is a misssing value
  - isMissingFast
```
boolean isMissingFast(double val)
```
    true if val is a missing data value. if useNaNs is true (default) and the variable is of type float or double, then assume that missing values have been set to NaN, which speeds up the test considerably.
    
    Parameters:
    
    val - unpacked value
    
    Returns:
    
    true if this value is a misssing value
  - hasInvalidData
```
boolean hasInvalidData()
```
    true if Variable has valid_range, valid_min or valid_max attributes
  - getValidMin
```
double getValidMin()
```
    return the minimum value in the valid range
  - getValidMax
```
double getValidMax()
```
    return the maximum value in the valid range
  - isInvalidData
```
boolean isInvalidData(double val)
```
    return true if val is outside the valid range
  - hasFillValue
```
boolean hasFillValue()
```
    true if Variable has _FillValue attribute
  - getFillValue
```
double getFillValue()
```
    return value of _FillValue attribute
  - isFillValue
```
boolean isFillValue(double val)
```
    return true if val equals the _FillValue
  - hasMissingValue
```
boolean hasMissingValue()
```
    true if Variable has missing_value attribute
  - getMissingValues
```
double[] getMissingValues()
```
    return values of missing_value attributes
  - isMissingValue
```
boolean isMissingValue(double val)
```
    return true if val equals a missing_value
  - setFillValueIsMissing
```
void setFillValueIsMissing(boolean b)
```
    set if _FillValue is considered isMissing(); better set in constructor if possible
  - setInvalidDataIsMissing
```
void setInvalidDataIsMissing(boolean b)
```
    set if valid_range is considered isMissing(); better set in constructor if possible
  - setMissingDataIsMissing
```
void setMissingDataIsMissing(boolean b)
```
    set if missing_data is considered isMissing(); better set in constructor if possible
  - setUseNaNs
```
void setUseNaNs(boolean useNaNs)
```
    set whether to use NaNs for missing float/double values, for efficiency
  - getUseNaNs
```
boolean getUseNaNs()
```
    get whether to use NaNs for missing float/double values (for efficiency)
  - hasScaleOffset
```
boolean hasScaleOffset()
```
    true if Variable data will be converted using scale and offet
  - convertScaleOffsetMissing
```
Array convertScaleOffsetMissing(Array data)
                         throws java.io.IOException
```
    Convert data if hasScaleOffset, using scale and offset. Also if useNaNs = true, return NaN if value is missing data.
    
    Parameters:
    
    data - convert this
    
    Returns:
    
    converted data.
    
    Throws:
    
    java.io.IOException
  - convertScaleOffsetMissing
```
double convertScaleOffsetMissing(byte value)
```
    Convert this byte value to a double, using scale/offset/missing value if applicable
  - convertScaleOffsetMissing
```
double convertScaleOffsetMissing(short value)
```
    Convert this short value to a double, using scale/offset/missing value if applicable
  - convertScaleOffsetMissing
```
double convertScaleOffsetMissing(int value)
```
    Convert this int value to a double, using scale/offset/missing value if applicable
  - convertScaleOffsetMissing
```
double convertScaleOffsetMissing(long value)
```
    Convert this long value to a double, using scale/offset/missing value if applicable
  - convertScaleOffsetMissing
```
double convertScaleOffsetMissing(double value)
```
    Convert this double value using scale/offset/missing value if applicable

Interface EnhanceScaleMissing

Standard Use

Low Level Access

Strategies for using EnhanceScaleMissing

Method Summary

Method Detail

hasMissing

isMissing

isMissingFast

hasInvalidData

getValidMin

getValidMax

isInvalidData

hasFillValue

getFillValue

isFillValue

hasMissingValue

getMissingValues

isMissingValue

setFillValueIsMissing

setInvalidDataIsMissing

setMissingDataIsMissing

setUseNaNs

getUseNaNs

hasScaleOffset

convertScaleOffsetMissing

convertScaleOffsetMissing

convertScaleOffsetMissing

convertScaleOffsetMissing

convertScaleOffsetMissing

convertScaleOffsetMissing