Package com.cedarsoftware.util

Examples of com.cedarsoftware.util.CaseInsensitiveMap$CaseInsensitiveString


            {
                throw new IllegalArgumentException("JSON format must have a root 'ncube' field containing the String name of the NCube.");
            }
            NCube ncube = new NCube(cubeName);
            ncube.setVersion("file");
            ncube.metaProps = new CaseInsensitiveMap();
            ncube.metaProps.putAll(jsonNCube);
            ncube.metaProps.remove("ncube");
            ncube.metaProps.remove("defaultCellValue");
            ncube.metaProps.remove("defaultCellValueType");
            ncube.metaProps.remove("ruleMode");
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    /**
     * @return Map (case insensitive keys) containing meta (additional) properties for the n-cube.
     */
    public Map<String, Object> getMetaProperties()
    {
        Map ret = metaProps == null ? new CaseInsensitiveMap() : metaProps;
        return Collections.unmodifiableMap(ret);
    }
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     */
    public Object setMetaProperty(String key, Object value)
    {
        if (metaProps == null)
        {
            metaProps = new CaseInsensitiveMap();
        }
        return metaProps.put(key, value);
    }
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     */
    public void addMetaProperties(Map allAtOnce)
    {
        if (metaProps == null)
        {
            metaProps = new CaseInsensitiveMap();
        }
        metaProps.putAll(allAtOnce);
    }
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            {
                throw new IllegalArgumentException("JSON format must have a root 'ncube' field containing the String name of the NCube.");
            }
            NCube ncube = new NCube(cubeName);
            ncube.setVersion("file");
            ncube.metaProps = new CaseInsensitiveMap();
            ncube.metaProps.putAll(jsonNCube);
            ncube.metaProps.remove("ncube");
            ncube.metaProps.remove("defaultCellValue");
            ncube.metaProps.remove("defaultCellValueType");
            ncube.metaProps.remove("ruleMode");
            ncube.metaProps.remove("axes");
            ncube.metaProps.remove("cells");
            if (ncube.metaProps.size() < 1)
            {   // No additional props, don't even waste space for additional meta properties.
                ncube.metaProps = null;
            }

            String defType = (String) jsonNCube.get("defaultCellValueType");
            ncube.defaultCellValue = parseJsonValue(jsonNCube.get("defaultCellValue"), null, defType, false);
            ncube.ruleMode = getBoolean(jsonNCube, "ruleMode");

            if (!(jsonNCube.get("axes") instanceof JsonObject))
            {
                throw new IllegalArgumentException("Must specify a list of axes for the ncube, under the key 'axes' as [{axis 1}, {axis 2}, ... {axis n}].");
            }

            JsonObject axes = (JsonObject) jsonNCube.get("axes");
            Object[] items = axes.getArray();

            if (ArrayUtilities.isEmpty(items))
            {
                throw new IllegalArgumentException("Must be at least one axis defined in the JSON format.");
            }

            // Read axes
            for (Object item : items)
            {
                Map jsonAxis = (Map) item;
                String name = getString(jsonAxis, "name");
                AxisType type = AxisType.valueOf(getString(jsonAxis, "type"));
                boolean hasDefault = getBoolean(jsonAxis, "hasDefault");
                AxisValueType valueType = AxisValueType.valueOf(getString(jsonAxis, "valueType"));
                final int preferredOrder = getLong(jsonAxis, "preferredOrder").intValue();
                final Boolean multiMatch = getBoolean(jsonAxis, "multiMatch");
                Axis axis = new Axis(name, type, valueType, hasDefault, preferredOrder, multiMatch);
                ncube.addAxis(axis);
                axis.metaProps = new CaseInsensitiveMap();
                axis.metaProps.putAll(jsonAxis);

                axis.metaProps.remove("name");
                axis.metaProps.remove("type");
                axis.metaProps.remove("hasDefault");
                axis.metaProps.remove("valueType");
                axis.metaProps.remove("preferredOrder");
                axis.metaProps.remove("multiMatch");
                axis.metaProps.remove("columns");

                if (axis.metaProps.size() < 1)
                {
                    axis.metaProps = null;
                }

                if (!(jsonAxis.get("columns") instanceof JsonObject))
                {
                    throw new IllegalArgumentException("'columns' must be specified, axis '" + name + "', NCube '" + cubeName + "'");
                }
                JsonObject colMap = (JsonObject) jsonAxis.get("columns");

                if (!colMap.isArray())
                {
                     throw new IllegalArgumentException("'columns' must be an array, axis '" + name + "', NCube '" + cubeName + "'");
                }

                // Read columns
                Object[] cols = colMap.getArray();
                for (Object col : cols)
                {
                    Map jsonColumn = (Map) col;
                    Object value = jsonColumn.get("value");
                    String url = (String)jsonColumn.get("url");
                    String colType = (String) jsonColumn.get("type");
                    Object id = jsonColumn.get("id");

                    if (value == null)
                    {
                        if (id == null)
                        {
                            throw new IllegalArgumentException("Missing 'value' field on column or it is null, axis '" + name + "', NCube '" + cubeName + "'");
                        }
                        else
                        {   // Allows you to skip setting both id and value to the same value.
                            value = id;
                        }
                    }

                    boolean cache = true;

                    if (jsonColumn.containsKey("cache"))
                    {
                        if (jsonColumn.get("cache") instanceof Boolean)
                        {
                            cache = (Boolean) jsonColumn.get("cache");
                        }
                        else if (jsonColumn.get("cache") instanceof String)
                        {   // Allow setting it as a String too
                            cache = "true".equalsIgnoreCase((String)jsonColumn.get("cache"));
                        }
                        else
                        {
                            throw new IllegalArgumentException("'cache' parameter must be set to 'true' or 'false', or not used (defaults to 'true')");
                        }
                    }

                    Column colAdded;

                    if (type == AxisType.DISCRETE || type == AxisType.NEAREST)
                    {
                        colAdded = ncube.addColumn(axis.getName(), (Comparable) parseJsonValue(value, null, colType, false));
                    }
                    else if (type == AxisType.RANGE)
                    {
                        Object[] rangeItems = ((JsonObject)value).getArray();
                        if (rangeItems.length != 2)
                        {
                            throw new IllegalArgumentException("Range must have exactly two items, axis '" + name +"', NCube '" + cubeName + "'");
                        }
                        Comparable low = (Comparable) parseJsonValue(rangeItems[0], null, colType, false);
                        Comparable high = (Comparable) parseJsonValue(rangeItems[1], null, colType, false);
                        colAdded = ncube.addColumn(axis.getName(), new Range(low, high));
                    }
                    else if (type == AxisType.SET)
                    {
                        Object[] rangeItems = ((JsonObject)value).getArray();
                        RangeSet rangeSet = new RangeSet();
                        for (Object pt : rangeItems)
                        {
                            if (pt instanceof Object[])
                            {
                                Object[] rangeValues = (Object[]) pt;
                                Comparable low = (Comparable) parseJsonValue(rangeValues[0], null, colType, false);
                                Comparable high = (Comparable) parseJsonValue(rangeValues[1], null, colType, false);
                                Range range = new Range(low, high);
                                rangeSet.add(range);
                            }
                            else
                            {
                                rangeSet.add((Comparable)parseJsonValue(pt, null, colType, false));
                            }
                        }
                        colAdded = ncube.addColumn(axis.getName(), rangeSet);
                    }
                    else if (type == AxisType.RULE)
                    {
                        Object cmd = parseJsonValue(value, url, colType, cache);
                        if (!(cmd instanceof CommandCell))
                        {
                            throw new IllegalArgumentException("Column values on a RULE axis must be of type CommandCell, axis '" + name + "', NCube '" + cubeName + "'");
                        }
                        colAdded = ncube.addColumn(axis.getName(), (CommandCell)cmd);
                    }
                    else
                    {
                        throw new IllegalArgumentException("Unsupported Axis Type '" + type + "' for simple JSON input, axis '" + name + "', NCube '" + cubeName + "'");
                    }

                    if (id != null)
                    {
                        long sysId = colAdded.getId();
                        userIdToUniqueId.put(id, sysId);
                    }

                    colAdded.metaProps = new CaseInsensitiveMap();
                    colAdded.metaProps.putAll(jsonColumn);
                    colAdded.metaProps.remove("id");
                    colAdded.metaProps.remove("value");
                    colAdded.metaProps.remove("type");
                    colAdded.metaProps.remove("url");
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    {
        if (_headers == null)
        {
            _headers = new String[]{};
        }
        Map headerStrings = new CaseInsensitiveMap();
        for (String header : _headers)
        {
            headerStrings.put(header, null);
        }
        // Step 1. Sort axes from smallest to largest.
        // Hypercubes look best when the smaller axes are on the inside, and the larger axes are on the outside.
        List<Axis> axes = new ArrayList<Axis>(ncube.getAxes());
        Collections.sort(axes, new Comparator<Axis>()
        {
            public int compare(Axis a1, Axis a2)
            {
                return a2.size() - a1.size();
            }
        });

        // Step 2.  Now find an axis that is a good candidate for the single (top) axis.  This would be an axis
        // with the number of columns closest to 12.
        int smallestDelta = Integer.MAX_VALUE;
        int candidate = -1;
        int count = 0;

        for (Axis axis : axes)
        {
            if (headerStrings.keySet().contains(axis.getName()))
            {
                candidate = count;
                break;
            }
            int delta = abs(axis.size() - 12);
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    {
        if (headers == null)
        {
            headers = new String[]{};
        }
        Map headerStrings = new CaseInsensitiveMap();
        for (String header : headers)
        {
            headerStrings.put(header, null);
        }
        // Step 1. Sort axes from smallest to largest.
        // Hypercubes look best when the smaller axes are on the inside, and the larger axes are on the outside.
        List<Axis> axes = new ArrayList<Axis>(getAxes());
        Collections.sort(axes, new Comparator<Axis>()
        {
            public int compare(Axis a1, Axis a2)
            {
                return a2.size() - a1.size();
            }
        });

        // Step 2.  Now find an axis that is a good candidate for the single (top) axis.  This would be an axis
        // with the number of columns closest to 12.
        int smallestDelta = Integer.MAX_VALUE;
        int candidate = -1;
        int count = 0;

        for (Axis axis : axes)
        {
            if (headerStrings.keySet().contains(axis.getName()))
            {
                candidate = count;
                break;
            }
            int delta = abs(axis.size() - 12);
View Full Code Here

    {
        if (headers == null)
        {
            headers = new String[]{};
        }
        Map headerStrings = new CaseInsensitiveMap();
        for (String header : headers)
        {
            headerStrings.put(header, null);
        }
        // Step 1. Sort axes from smallest to largest.
        // Hypercubes look best when the smaller axes are on the inside, and the larger axes are on the outside.
        List<Axis> axes = new ArrayList<Axis>(getAxes());
        Collections.sort(axes, new Comparator<Axis>()
        {
            public int compare(Axis a1, Axis a2)
            {
                return a2.size() - a1.size();
            }
        });

        // Step 2.  Now find an axis that is a good candidate for the single (top) axis.  This would be an axis
        // with the number of columns closest to 12.
        int smallestDelta = Integer.MAX_VALUE;
        int candidate = -1;
        int count = 0;

        for (Axis axis : axes)
        {
            if (headerStrings.keySet().contains(axis.getName()))
            {
                candidate = count;
                break;
            }
            int delta = abs(axis.size() - 12);
View Full Code Here

    /**
     * @return Map (case insensitive keys) containing meta (additional) properties for the n-cube.
     */
    public Map<String, Object> getMetaProperties()
    {
        Map ret = metaProps == null ? new CaseInsensitiveMap() : metaProps;
        return Collections.unmodifiableMap(ret);
    }
View Full Code Here

    {
        if (_headers == null)
        {
            _headers = new String[]{};
        }
        Map headerStrings = new CaseInsensitiveMap();
        for (String header : _headers)
        {
            headerStrings.put(header, null);
        }
        // Step 1. Sort axes from smallest to largest.
        // Hypercubes look best when the smaller axes are on the inside, and the larger axes are on the outside.
        List<Axis> axes = new ArrayList<Axis>(ncube.getAxes());
        Collections.sort(axes, new Comparator<Axis>()
        {
            public int compare(Axis a1, Axis a2)
            {
                return a2.size() - a1.size();
            }
        });

        // Step 2.  Now find an axis that is a good candidate for the single (top) axis.  This would be an axis
        // with the number of columns closest to 12.
        int smallestDelta = Integer.MAX_VALUE;
        int candidate = -1;
        int count = 0;

        for (Axis axis : axes)
        {
            if (headerStrings.keySet().contains(axis.getName()))
            {
                candidate = count;
                break;
            }
            int delta = abs(axis.size() - 12);
View Full Code Here

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