Examples with TYPE_LIFE com.ichi2.libanki.stats.Stats.AxisType.TYPE_LIFE used on opensource projects

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Example 1 with TYPE_LIFE

use of com.ichi2.libanki.stats.Stats.AxisType.TYPE_LIFE in project AnkiChinaAndroid by ankichinateam.

the class StudyOptionsFragment method calculateStat.

private double[] calculateStat(Collection col, long deckId) {
    // 计算已熟悉
    Stats stats = new Stats(col, deckId);
    stats.calculateCardTypes(TYPE_LIFE);
    return stats.getSeriesList()[0];
}
Also used : Stats(com.ichi2.libanki.stats.Stats)

Example 2 with TYPE_LIFE

use of com.ichi2.libanki.stats.Stats.AxisType.TYPE_LIFE in project AnkiChinaAndroid by ankichinateam.

the class AdvancedStatistics method calculateDueAsMetaInfo.

/**
 * Determine forecast statistics based on a computation or simulation of future reviews.
 * Returns all information required by stats.java to plot the 'forecast' chart based on these statistics.
 * The chart will display:
 * - The forecasted number of reviews per review type (relearn, mature, young, learn) as bars
 * - The forecasted number of cards in each state (new, young, mature) as lines
 * @param metaInfo Object which will be filled with all information required by stats.java to plot the 'forecast' chart and returned by this method.
 * @param type Type of 'forecast' chart for which to determine forecast statistics. Accepted values:
 *             Stats.TYPE_MONTH: Determine forecast statistics for next 30 days with 1-day chunks
 *             Stats.TYPE_YEAR:  Determine forecast statistics for next year with 7-day chunks
 *             Stats.TYPE_LIFE:  Determine forecast statistics for next 2 years with 30-day chunks
 * @param context Contains The collection which contains the decks to be simulated.
 *             Also used for access to the database and access to the creation time of the collection.
 *             The creation time of the collection is needed since due times of cards are relative to the creation time of the collection.
 *             So we could pass mCol here.
 * @param dids Deck id's
 * @return @see #metaInfo
 */
public StatsMetaInfo calculateDueAsMetaInfo(StatsMetaInfo metaInfo, Stats.AxisType type, Context context, String dids) {
    if (!AnkiDroidApp.getSharedPrefs(context).getBoolean("advanced_statistics_enabled", false)) {
        return metaInfo;
    }
    // To indicate that we calculated the statistics so that Stats.java knows that it shouldn't display the standard Forecast chart.
    Settings = new Settings(context);
    metaInfo.setStatsCalculated(true);
    Collection mCol = CollectionHelper.getInstance().getCol(context);
    double[][] mSeriesList;
    int[] mValueLabels;
    int[] mColors;
    int[] mAxisTitles;
    int mMaxCards = 0;
    int mMaxElements;
    double mFirstElement;
    double mLastElement = 0;
    int mZeroIndex = 0;
    double[][] mCumulative;
    double mMcount;
    mValueLabels = new int[] { R.string.statistics_relearn, R.string.statistics_mature, R.string.statistics_young, R.string.statistics_learn };
    mColors = new int[] { R.attr.stats_relearn, R.attr.stats_mature, R.attr.stats_young, R.attr.stats_learn };
    mAxisTitles = new int[] { type.ordinal(), R.string.stats_cards, R.string.stats_cumulative_cards };
    PlottableSimulationResult simuationResult = calculateDueAsPlottableSimulationResult(type, mCol, dids);
    ArrayList<int[]> dues = simuationResult.getNReviews();
    mSeriesList = new double[REVIEW_TYPE_COUNT_PLUS_1][dues.size()];
    for (int t = 0; t < dues.size(); t++) {
        int[] data = dues.get(t);
        int nReviews = data[REVIEW_TYPE_LEARN_PLUS_1] + data[REVIEW_TYPE_YOUNG_PLUS_1] + data[REVIEW_TYPE_MATURE_PLUS_1] + data[REVIEW_TYPE_RELEARN_PLUS_1];
        if (nReviews > mMaxCards)
            // Y-Axis: Max. value
            mMaxCards = nReviews;
        // In the bar-chart, the bars will be stacked on top of each other.
        // For the i^{th} bar counting from the bottom we therefore have to
        // provide the sum of the heights of the i^{th} bar and all bars below it.
        // X-Axis: Day / Week / Month
        mSeriesList[TIME][t] = data[TIME];
        mSeriesList[REVIEW_TYPE_LEARN_PLUS_1][t] = data[REVIEW_TYPE_LEARN_PLUS_1] + data[REVIEW_TYPE_YOUNG_PLUS_1] + data[REVIEW_TYPE_MATURE_PLUS_1] + // Y-Axis: # Cards
        data[REVIEW_TYPE_RELEARN_PLUS_1];
        mSeriesList[REVIEW_TYPE_YOUNG_PLUS_1][t] = data[REVIEW_TYPE_LEARN_PLUS_1] + data[REVIEW_TYPE_YOUNG_PLUS_1] + // Y-Axis: # Mature cards
        data[REVIEW_TYPE_MATURE_PLUS_1];
        mSeriesList[REVIEW_TYPE_MATURE_PLUS_1][t] = data[REVIEW_TYPE_LEARN_PLUS_1] + // Y-Axis: # Young
        data[REVIEW_TYPE_YOUNG_PLUS_1];
        // Y-Axis: # Learn
        mSeriesList[REVIEW_TYPE_RELEARN_PLUS_1][t] = data[REVIEW_TYPE_LEARN_PLUS_1];
        if (data[TIME] > mLastElement)
            // X-Axis: Max. value (only for TYPE_LIFE)
            mLastElement = data[TIME];
        if (data[TIME] == 0) {
            // Because we retrieve dues in the past and we should not cumulate them
            mZeroIndex = t;
        }
    }
    // # X values
    mMaxElements = dues.size() - 1;
    switch(type) {
        case TYPE_MONTH:
            // X-Axis: Max. value
            mLastElement = 31;
            break;
        case TYPE_YEAR:
            // X-Axis: Max. value
            mLastElement = 52;
            break;
        default:
    }
    // X-Axis: Min. value
    mFirstElement = 0;
    // Day starting at mZeroIndex, Cumulative # cards
    mCumulative = simuationResult.getNInState();
    mMcount = // Y-Axis: Max. cumulative value
    mCumulative[CARD_TYPE_NEW_PLUS_1][mCumulative[CARD_TYPE_NEW_PLUS_1].length - 1] + mCumulative[CARD_TYPE_YOUNG_PLUS_1][mCumulative[CARD_TYPE_YOUNG_PLUS_1].length - 1] + mCumulative[CARD_TYPE_MATURE_PLUS_1][mCumulative[CARD_TYPE_MATURE_PLUS_1].length - 1];
    // some adjustments to not crash the chartbuilding with empty data
    if (mMaxElements == 0) {
        mMaxElements = 10;
    }
    if (mMcount == 0) {
        mMcount = 10;
    }
    if (mFirstElement == mLastElement) {
        mFirstElement = 0;
        mLastElement = 6;
    }
    if (mMaxCards == 0)
        mMaxCards = 10;
    metaInfo.setmDynamicAxis(true);
    metaInfo.setmHasColoredCumulative(true);
    metaInfo.setmType(type);
    metaInfo.setmTitle(R.string.stats_forecast);
    metaInfo.setmBackwards(true);
    metaInfo.setmValueLabels(mValueLabels);
    metaInfo.setmColors(mColors);
    metaInfo.setmAxisTitles(mAxisTitles);
    metaInfo.setmMaxCards(mMaxCards);
    metaInfo.setmMaxElements(mMaxElements);
    metaInfo.setmFirstElement(mFirstElement);
    metaInfo.setmLastElement(mLastElement);
    metaInfo.setmZeroIndex(mZeroIndex);
    metaInfo.setmCumulative(mCumulative);
    metaInfo.setmMcount(mMcount);
    metaInfo.setmSeriesList(mSeriesList);
    metaInfo.setDataAvailable(dues.size() > 0);
    return metaInfo;
}
Also used : Collection(com.ichi2.libanki.Collection)

Example 3 with TYPE_LIFE

use of com.ichi2.libanki.stats.Stats.AxisType.TYPE_LIFE in project Anki-Android by ankidroid.

the class AdvancedStatistics method calculateDueAsMetaInfo.

/**
 * Determine forecast statistics based on a computation or simulation of future reviews.
 * Returns all information required by stats.java to plot the 'forecast' chart based on these statistics.
 * The chart will display:
 * - The forecasted number of reviews per review type (relearn, mature, young, learn) as bars
 * - The forecasted number of cards in each state (new, young, mature) as lines
 * @param metaInfo Object which will be filled with all information required by stats.java to plot the 'forecast' chart and returned by this method.
 * @param type Type of 'forecast' chart for which to determine forecast statistics. Accepted values:
 *             Stats.TYPE_MONTH: Determine forecast statistics for next 30 days with 1-day chunks
 *             Stats.TYPE_YEAR:  Determine forecast statistics for next year with 7-day chunks
 *             Stats.TYPE_LIFE:  Determine forecast statistics for next 2 years with 30-day chunks
 * @param context Contains The collection which contains the decks to be simulated.
 *             Also used for access to the database and access to the creation time of the collection.
 *             The creation time of the collection is needed since due times of cards are relative to the creation time of the collection.
 *             So we could pass mCol here.
 * @param dids Deck id's
 * @return @see #metaInfo
 */
public StatsMetaInfo calculateDueAsMetaInfo(StatsMetaInfo metaInfo, Stats.AxisType type, Context context, String dids) {
    if (!AnkiDroidApp.getSharedPrefs(context).getBoolean("advanced_statistics_enabled", false)) {
        return metaInfo;
    }
    // To indicate that we calculated the statistics so that Stats.java knows that it shouldn't display the standard Forecast chart.
    mSettings = new Settings(context);
    metaInfo.setStatsCalculated(true);
    Collection col = CollectionHelper.getInstance().getCol(context);
    int maxCards = 0;
    double lastElement = 0;
    int zeroIndex = 0;
    int[] valueLabels = { R.string.statistics_relearn, R.string.statistics_mature, R.string.statistics_young, R.string.statistics_learn };
    int[] colors = { R.attr.stats_relearn, R.attr.stats_mature, R.attr.stats_young, R.attr.stats_learn };
    int[] axisTitles = { type.ordinal(), R.string.stats_cards, R.string.stats_cumulative_cards };
    PlottableSimulationResult simuationResult = calculateDueAsPlottableSimulationResult(type, col, dids);
    ArrayList<int[]> dues = simuationResult.getNReviews();
    double[][] seriesList = new double[REVIEW_TYPE_COUNT_PLUS_1][dues.size()];
    for (int t = 0; t < dues.size(); t++) {
        int[] data = dues.get(t);
        int nReviews = data[REVIEW_TYPE_LEARN_PLUS_1] + data[REVIEW_TYPE_YOUNG_PLUS_1] + data[REVIEW_TYPE_MATURE_PLUS_1] + data[REVIEW_TYPE_RELEARN_PLUS_1];
        if (nReviews > maxCards)
            // Y-Axis: Max. value
            maxCards = nReviews;
        // In the bar-chart, the bars will be stacked on top of each other.
        // For the i^{th} bar counting from the bottom we therefore have to
        // provide the sum of the heights of the i^{th} bar and all bars below it.
        // X-Axis: Day / Week / Month
        seriesList[TIME][t] = data[TIME];
        seriesList[REVIEW_TYPE_LEARN_PLUS_1][t] = data[REVIEW_TYPE_LEARN_PLUS_1] + data[REVIEW_TYPE_YOUNG_PLUS_1] + data[REVIEW_TYPE_MATURE_PLUS_1] + // Y-Axis: # Cards
        data[REVIEW_TYPE_RELEARN_PLUS_1];
        seriesList[REVIEW_TYPE_YOUNG_PLUS_1][t] = data[REVIEW_TYPE_LEARN_PLUS_1] + data[REVIEW_TYPE_YOUNG_PLUS_1] + // Y-Axis: # Mature cards
        data[REVIEW_TYPE_MATURE_PLUS_1];
        seriesList[REVIEW_TYPE_MATURE_PLUS_1][t] = data[REVIEW_TYPE_LEARN_PLUS_1] + // Y-Axis: # Young
        data[REVIEW_TYPE_YOUNG_PLUS_1];
        // Y-Axis: # Learn
        seriesList[REVIEW_TYPE_RELEARN_PLUS_1][t] = data[REVIEW_TYPE_LEARN_PLUS_1];
        if (data[TIME] > lastElement)
            // X-Axis: Max. value (only for TYPE_LIFE)
            lastElement = data[TIME];
        if (data[TIME] == 0) {
            // Because we retrieve dues in the past and we should not cumulate them
            zeroIndex = t;
        }
    }
    // # X values
    int maxElements = dues.size() - 1;
    switch(type) {
        case TYPE_MONTH:
            // X-Axis: Max. value
            lastElement = 31;
            break;
        case TYPE_YEAR:
            // X-Axis: Max. value
            lastElement = 52;
            break;
        default:
    }
    // X-Axis: Min. value
    double firstElement = 0;
    // Day starting at zeroIndex, Cumulative # cards
    double[][] cumulative = simuationResult.getNInState();
    double count = // Y-Axis: Max. cumulative value
    cumulative[CARD_TYPE_NEW_PLUS_1][cumulative[CARD_TYPE_NEW_PLUS_1].length - 1] + cumulative[CARD_TYPE_YOUNG_PLUS_1][cumulative[CARD_TYPE_YOUNG_PLUS_1].length - 1] + cumulative[CARD_TYPE_MATURE_PLUS_1][cumulative[CARD_TYPE_MATURE_PLUS_1].length - 1];
    // some adjustments to not crash the chartbuilding with empty data
    if (maxElements == 0) {
        maxElements = 10;
    }
    if (count == 0) {
        count = 10;
    }
    if (firstElement == lastElement) {
        firstElement = 0;
        lastElement = 6;
    }
    if (maxCards == 0)
        maxCards = 10;
    metaInfo.setmDynamicAxis(true);
    metaInfo.setmHasColoredCumulative(true);
    metaInfo.setmType(type);
    metaInfo.setmTitle(R.string.stats_forecast);
    metaInfo.setmBackwards(true);
    metaInfo.setmValueLabels(valueLabels);
    metaInfo.setmColors(colors);
    metaInfo.setmAxisTitles(axisTitles);
    metaInfo.setmMaxCards(maxCards);
    metaInfo.setmMaxElements(maxElements);
    metaInfo.setmFirstElement(firstElement);
    metaInfo.setmLastElement(lastElement);
    metaInfo.setmZeroIndex(zeroIndex);
    metaInfo.setmCumulative(cumulative);
    metaInfo.setmMcount(count);
    metaInfo.setmSeriesList(seriesList);
    metaInfo.setDataAvailable(!dues.isEmpty());
    return metaInfo;
}
Also used : Collection(com.ichi2.libanki.Collection)

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Collection (com.ichi2.libanki.Collection)2 Stats (com.ichi2.libanki.stats.Stats)1
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