Calculating equally spaced values over a range

Any help would be really appreciated,
I've bee asked: Calculate Range for a sequence of 101 equally-spaced angles in the range from -90 to +90.
Basically I need 101 values equally spaced between -90 and 90 each of these values needs inserted into a range formula and graph. 
This is the vi im working on, the unassigned T input variables on my forumula nodes are the angle values I need to calculate.    

Or use the Ramp Pattern function to create a 1D array of equally distributed values between two limits (-90 and 90).
Thoric (CLA, CLED, CTD and LabVIEW Champion)

Similar Messages

  • SUM(Case how to use this structure to get average values over date range

    I am using:
    Oracle SQL Developer (3.0.04) Build MAin-04.34 Oracle Database 11g Enterprise Edition 11.2.0.1.0 - 64bit Production
    How do I use the sum function with a case structure inside.
    so I have data that looks like has an ID, date, and value. I am looking to get the 7 day average for the date range of 4/1/2013 thru 4/20/2013
    with t as (
    select 1 ID_Key,to_date('4/1/2013','mm-dd-yyyy') date_val, 10 Value_num from dual union all
    select 1 ID_key,to_date('4/2/2013','mm-dd-yyyy'), 15 from dual union all
    select 1 ID_key,to_date('4/3/2013','mm-dd-yyyy'), 20 from dual union all
    select 1 ID_key,to_date('4/5/2013','mm-dd-yyyy'), 0 from dual union all
    select 1 ID_key,to_date('4/8/2013','mm-dd-yyyy'), 12 from dual union all
    select 1 ID_key,to_date('4/9/2013','mm-dd-yyyy'), 8 from dual union all
    select 1 ID_key,to_date('4/10/2013','mm-dd-yyyy'), 6 from dual union all
    select 1 ID_key,to_date('4/12/2013','mm-dd-yyyy'), 10 from dual union all
    select 1 ID_key,to_date('4/13/2013','mm-dd-yyyy'), 0 from dual union all
    select 1 ID_key,to_date('4/14/2013','mm-dd-yyyy'), 0 from dual union all
    select 1 ID_key,to_date('4/15/2013','mm-dd-yyyy'), 10 from dual union all
    select 1 ID_key,to_date('4/16/2013','mm-dd-yyyy'), 5 from dual union all
    select 1 ID_key,to_date('4/17/2013','mm-dd-yyyy'), 2 from dual union all
    select 1 ID_key,to_date('4/20/2013','mm-dd-yyyy'), 3 from dual union all
    select 2 ID_key,to_date('4/3/2013','mm-dd-yyyy'), 12 from dual union all
    select 2 ID_key,to_date('4/5/2013','mm-dd-yyyy'), 15 from dual union all
    select 2 ID_key,to_date('4/6/2013','mm-dd-yyyy'), 5 from dual union all
    select 2 ID_key,to_date('4/7/2013','mm-dd-yyyy'), 7 from dual union all
    select 2 ID_key,to_date('4/9/2013','mm-dd-yyyy'), 10 from dual union all
    select 2 ID_key,to_date('4/11/2013','mm-dd-yyyy'), 5 from dual union all
    select 2 ID_key,to_date('4/12/2013','mm-dd-yyyy'), 0 from dual union all
    select 2 ID_key,to_date('4/13/2013','mm-dd-yyyy'), 0 from dual union all
    select 2 ID_key,to_date('4/15/2013','mm-dd-yyyy'), 6 from dual union all
    select 2 ID_key,to_date('4/16/2013','mm-dd-yyyy'), 8 from dual union all
    select 2 ID_key,to_date('4/17/2013','mm-dd-yyyy'), 0 from dual union all
    select 2 ID_key,to_date('4/18/2013','mm-dd-yyyy'), 10 from dual union all
    select 2 ID_key,to_date('4/19/2013','mm-dd-yyyy'), 5 from dual
    )**Please let me know if the table does not load.
    I would like to get the 7 day average as long as there is date for that row has enough previous dates, it not then it will return null.
    the results should look like this
    ID_Key      date_val     Value_num     7Day_Avg     7Day_Avg2
    1     4/1/2013     10          null          null
    1     4/2/2013     15          null          null
    1     4/3/2013     20          null          null
    1     4/5/2013     0          null          null
    1     4/8/2013     12          6.71          11.75
    1     4/9/2013     8          5.71          10.00
    1     4/10/2013     6          3.71          6.50
    1     4/12/2013     10          5.14          9.00
    1     4/13/2013     0          5.14          7.20
    1     4/14/2013     0          5.14          6.00
    1     4/15/2013     10          4.86          5.67
    1     4/16/2013     5          4.42          5.17
    1     4/17/2013     2          3.85          4.50
    1     4/20/2013     3          2.86          4.00
    2     4/3/2013     12          null          null
    2     4/5/2013     15          null          null
    2     4/6/2013     5          null          null
    2     4/7/2013     7          5.57          9.75
    2     4/9/2013     10          7.00          9.80
    2     4/11/2013     5          6.00          8.40
    2     4/12/2013     0          3.86          5.40
    2     4/13/2013     0          3.14          4.40
    2     4/15/2013     6          3.00          4.20
    2     4/16/2013     8          2.71          3.80
    2     4/17/2013     0          2.71          3.17
    2     4/18/2013     10          3.43          4.00
    2     4/19/2013     5          4.14          4.83As you may notice, there are gaps in the dates, so the value are then treated as zeros for the 7Day_Avg and then ignored for teh 7Day_Avg2 (not counted as number of days averaged do to no valu_num row)
    I was trying something like this to start, but getting error "missing keyword"
    select
    t.*/,
    sum(
          case
            when date_val between :day2 - 6 and :day2
            then value_num between date_val - 6 and date_val
            else null
            end
            as 7Day_avg
    form tShould I have the case structure outside the sum function?
    Any thoughts??
    Edited by: 1004407 on Jun 7, 2013 11:06 AM

    Hi,
    If you want the average of the last 7 days, including the current day, then then RANGE should be 6 PRECEDING, not 7.
    Try this:
    WITH     got_min_date_val AS
            SELECT  id_key, date_val, value_num
            ,       MIN (date_val) OVER () AS min_date_val
            FROM    t
            WHERE  date_val BETWEEN TO_DATE ('04-01-2013', 'mm-dd-yyyy')
                             AND   TO_DATE ('04-20-2013', 'mm-dd-yyyy')
    SELECT    id_key, date_val, value_num
    ,         CASE
                  WHEN  date_val >= min_date_val + 6
                  THEN  SUM (value_num) OVER ( PARTITION BY  id_key
                                               ORDER BY      date_val
                                               RANGE         6 PRECEDING
                        / 7
              END  AS avg_7_day
    ,         CASE
                  WHEN  date_val >= min_date_val + 6
                  THEN  AVG (value_num) OVER ( PARTITION BY  id_key
                                               ORDER BY      date_val
                                               RANGE         6 PRECEDING
              END   AS avg_7_day_2
    FROM      got_min_date_val
    ORDER BY  id_key
    ,         date_val
    Output:
       ID_KEY DATE_VAL   VALUE_NUM  AVG_7_DAY  AVG_7_DAY_2
             1 01-APR-13         10
             1 02-APR-13         15
             1 03-APR-13         20
             1 05-APR-13          0
             1 08-APR-13         12       6.71        11.75
             1 09-APR-13          8       5.71        10.00
             1 10-APR-13          6       3.71         6.50
             1 12-APR-13         10       5.14         9.00
             1 13-APR-13          0       5.14         7.20
             1 14-APR-13          0       5.14         6.00
             1 15-APR-13         10       4.86         5.67
             1 16-APR-13          5       4.43         5.17
             1 17-APR-13          2       3.86         4.50
             1 20-APR-13          3       2.86         4.00
             2 03-APR-13         12
             2 05-APR-13         15
             2 06-APR-13          5
             2 07-APR-13          7       5.57         9.75
             2 09-APR-13         10       7.00         9.80
             2 11-APR-13          5       6.00         8.40
             2 12-APR-13          0       3.86         5.40
             2 13-APR-13          0       3.14         4.40
             2 15-APR-13          6       3.00         4.20
             2 16-APR-13          8       2.71         3.80
             2 17-APR-13          0       2.71         3.17
             2 18-APR-13         10       3.43         4.00
             2 19-APR-13          5       4.14         4.83
    Message was edited by: FrankKulash
    Sorry; I meant to reply to OP, not to Greg

  • DP macros, calculating average values over a period of time

    Hello
    I have a key figure row, for the future i want this key figure to contain the average of a different key figure row over the last year.
    How would you go about calculating the average value of a key figure row over a period of time and then assigning this value to another key figure?
    I've tried variatons of AVG() and SUM() & SUM_CALC a but none of them seem to get me anywhere, i may not understand completely how rows & values work so any tips would be helpful.
    Iin pseudo-logic: what i need to do is:
    Calculate the average value of a key figure row over a given period (the last year)
    Store this value somewhere, in regular programming it'd be a variable of some kind.
    Assign this value to another key figure row 18 months into the future.
    Regards
    Simon Pedersen

    <H5>Hi Simon,
    If you are a technical guy, you can create a BADI implementation for that macro and manipulate the matrix data like the way you want.
    the procedure to implement a BADI is
    1.SPRO --> SAP SCM - Implementation Guide --> Advanced Planning and Optimization --> Supply Chain Planning --> Demand Planning (DP) --> Business Add-Ins (BAdIs) --> MacroBuilder --> Additional Functions for Macros.
    create a new implementation by copying the class for the BADI defenition '/SAPAPO/ADVX' and write your own code in the method '/SAPAPO/IF_EX_ADVX~USER_EXIT_MACRO '. There is a sample code and proceedure explaining how to handle the data in the internal tables C_T_TAB and C_T_TAB_OLD. the calculations can be made with help of I_T_LINES, I_T_COLS which are rows and columns tables.
    find out the the row and columns of the grid to be read and do calculation and then put the result in the desired cell.
    Please let me know if you need further assistance.
    Regards,
    Srini.
    Award points for the helpful answers <H5>

  • How to calculate query value on extended range of data

    Hi,
    This is my first post, so let me greet all forum users. I'm reading the forum for few weeks and I'm really impressed with the force of this community.
    My question is based on some real problems but I'll ask in general:
    In a BEx query - is it possible (and how?) in a result cell to obtain value that is calculated based on some more extended range of infoprovider records than it results from all the characteristics related to that cell?
    The question is somehow about a reverse case to the restriction in Selection. I'm asking about a kind of 'extension' of selection.
    Example for the question follows (it is somehow simple, I put it here just to picture the problem, note: the question is more general!)
    cube: ch: 0CALMONTH, 0MATERIAL, 0PLANT
          kf: 0QUANTITY (ex. of outgoing deliveries)
    What I need is to obtain in any query the SUM(0QUANTITY) over all 0PLANT (this can lead to knowing the 'activity' of the specific plant by calculating the share. that activity can be calculated at diferent level of detail).
    The 'dream' query would look like:
    rows: 0CALMONTH, 0MATERIAL, 0PLANT      
    cols: 0QUANTITY  SUM_OVER_0PLANT
    resulting in ex:
    0CALMONTH  0MATERIAL  0PLANT  0QUANTITY  SUM_OVER_0PLANT  PLANT_ACTIVITY
    2005.01    00001      P100    10         30               1/3
    2005.01    00001      P200    20         30               2/3
    2005.01    00002      P100    30         70               3/7
    2005.01    00002      P200    40         70               4/7
    2005.02    00001      P100    50         110              5/11
    2005.02    00001      P200    60         110              6/11
    2005.02    00002      P100    70         150              7/15
    2005.02    00002      P200    80         150              8/15
    after removing drill 0MATERIAL:
    0CALMONTH  0PLANT  0QUANTITY  SUM_OVER_0PLANT  PLANT_ACTIVITY
    2005.01    P100    40         100              4/10
    2005.01    P200    60         100              6/10
    2005.02    P100    120        260              12/26
    2005.02    P200    140        260              14/26
    after removing drill 0CALMONTH:
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    P100    160        360              16/36
    P200    200        360              20/36
    For this specific case I investigated several solutions:
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    2) SUM can be directly precalculated in the cube. Well, I'd prefer Bex only...
    Please keep in mind also following:
    1) there may be a need to refer to the data that is outside prompt/filtering/restricting range.
    2) I want to preserve all the OLAP freedom, so all the solution should be a query with some tricky formula/kf/??? to be used by user in any situation and producing right result.
    I hope I'm not demanding to much...
    (now after this long example please have a look at the question again!)
    Regards,
    Mirek

    Hi Ashwin,
    while using SUMCT I loose the freedom of OLAP. SUMCT calculates properly SUM_OVER_0PLANT only when the 0PLANT is the lowest drillin level. If this is not the case (ie. some other ch is the lowest level or 0PLANT results are suppressed) the approach won't provide right result.
    sure I will reward all helpfull posts.
    regards,
    Mirek

  • Equal spacing between the images of uniformed height but variable widths with AS3?

    I have to figure out how to insert images with variable width with equal spacing intervals.
    Here is where I got so far:
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              trace("build Scroller");
              for (var item:uint = 0; item<imageList.length();item++) {
                        var thisOne:MovieClip = new MovieClip();
      var currentX = 90;
                        var spaceBetween = 20;
      var currentImageWidth = Number(imageList[item].attribute("width"));
    thisOne.x = currentX;
    thisOne.x = (currentImageWidth+spaceBetween)*item;
    I can see that my images are being spread out on the page and if I change the number in var spaceBetween it affects the spacing. However the spacing is not uniformed. I can not figure why. Perhaps because I can not properly retrieve the image widths from the xml file. I assigned a width in xml file in the following manner:
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    <image src="appThmb_imgs/A-illuminatorUpLit_xsm.jpg" title="UpDownGlowingVase" url="http://www.888acolyte.com" width="132"/>
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    Thank You for your reply.
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    var xmlData:XML = new XML();
    var xmlPath:String = "app_thmbs_imgsModfd.xml";
    xmlLoader.load(new URLRequest(xmlPath));
    trace("loading xml from: " + xmlPath);
    xmlLoader.addEventListener(Event.COMPLETE, LoadXML);
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              trace("xml load complete");
              xmlData = new XML(e.target.data);
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    var scroller:MovieClip = new MovieClip();
    this.addChild(scroller);
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              trace("build Scroller");
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              for (var item:uint = 0; item<imageList.length();item++) {
                        var thisOne:MovieClip = new MovieClip();
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                        nextX=int(imageList[item].attribute("width"))+spaceBetween;
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                        var blackBox:Sprite = new Sprite();
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                        thisOne.src = imageList[item].attribute("src");
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              trace("over"+e.currentTarget.title);
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              trace("out"+e.currentTarget.title);
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        maxH = maxH == 0 ? maxW : maxH;
        mc.width = maxW;
        mc.height = 110;
              mc.scaleX=mc.scaleY
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                        mc.x = (maxW - mc.width) / 2;
              if (centerVert){
                        mc.y = (maxH - mc.height) / 2;

  • How to get top 11 values per date range

    I want to get the top 11 values by date range.
    Sample Data
    CREATE TABLE SAMPLE_DATA
        DOMAIN_NAME VARCHAR2(100),
        QTD         NUMBER,
        LOAD_DATE   DATE
    -- Insert
    BEGIN
      FOR lc IN 1..20
      LOOP
        FOR ld IN 1..30
        LOOP
          INSERT
          INTO SAMPLE_DATA VALUES
              'DM_'
              ||lc,
              round(dbms_random.value(0,1000)),
              SYSDATE-ld
        END LOOP;
      END LOOP;
    COMMIT;
    END;
    SELECT *
    FROM
      (SELECT DOMAIN_NAME,
        QTD,
        LOAD_DATE
      FROM
        (SELECT DOMAIN_NAME,
          QTD,
          LOAD_DATE
        FROM SAMPLE_DATA
        WHERE LOAD_DATE = TRUNC(SYSDATE-3)
        ORDER BY QTD DESC
      WHERE ROWNUM <=10
      UNION ALL
      SELECT 'Others' DOMAIN_NAME,
        SUM(QTD) QTD,
        LOAD_DATE
      FROM
        (SELECT DOMAIN_NAME,
          QTD,
          LOAD_DATE
        FROM
          (SELECT rownum rn,
            DOMAIN_NAME,
            QTD,
            LOAD_DATE
          FROM
            (SELECT DOMAIN_NAME,
              QTD,
              LOAD_DATE
            FROM SAMPLE_DATA
            WHERE LOAD_DATE = TRUNC(SYSDATE-3)
            ORDER BY QTD DESC
        WHERE rn > 10
      GROUP BY LOAD_DATE
    ORDER BY QTD DESC
    -- Result
    DOMAIN_NAME                 QTD                         LOAD_DATE                  
    Others                      2888                        24/03/13                   
    DM_1                        1000                        24/03/13                   
    DM_20                       933                         24/03/13                   
    DM_11                       913                         24/03/13                   
    DM_3                        743                         24/03/13                   
    DM_13                       572                         24/03/13                   
    DM_12                       568                         24/03/13                   
    DM_9                        564                         24/03/13                   
    DM_6                        505                         24/03/13                   
    DM_5                        504                         24/03/13                   
    DM_2                        480                         24/03/13    
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    e.g
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    DOMAIN_NAME                 QTD                         LOAD_DATE                  
    Others                      2888                        24/03/13                   
    DM_1                        1000                        24/03/13                   
    DM_20                       933                         24/03/13                   
    DM_11                       913                         24/03/13                   
    DM_3                        743                         24/03/13                   
    DM_13                       572                         24/03/13                   
    DM_12                       568                         24/03/13                   
    DM_9                        564                         24/03/13                   
    DM_6                        505                         24/03/13                   
    DM_5                        504                         24/03/13                   
    DM_2                        480                         24/03/13                     
    Others                      1948                        25/03/13                   
    DM_1                        807                         25/03/13                   
    DM_8                        764                         25/03/13                   
    DM_7                        761                         25/03/13                   
    DM_11                       656                         25/03/13                   
    DM_18                       611                         25/03/13                   
    DM_17                       523                         25/03/13                   
    DM_14                       467                         25/03/13                   
    DM_19                       447                         25/03/13                   
    DM_15                       437                         25/03/13                   
    DM_6                        380                         25/03/13             Thank you in advance.

    I got the solution. Just sharing.
    I used analytic functions that make my job easy.
    Sample Data
    DOMAIN_NAME                 QTD                         LOAD_DATE                  
    DM_1                        807                         25/03/2013                 
    DM_1                        1000                        24/03/2013                 
    DM_2                        226                         25/03/2013                 
    DM_2                        480                         24/03/2013                 
    DM_3                        244                         25/03/2013                 
    DM_3                        743                         24/03/2013                 
    DM_4                        48                          25/03/2013                 
    DM_4                        413                         24/03/2013                 
    DM_5                        164                         25/03/2013                 
    DM_5                        504                         24/03/2013                 
    DM_6                        380                         25/03/2013                 
    DM_6                        505                         24/03/2013                 
    DM_7                        761                         25/03/2013                 
    DM_7                        212                         24/03/2013                 
    DM_8                        764                         25/03/2013                 
    DM_8                        308                         24/03/2013                 
    DM_9                        354                         25/03/2013                 
    DM_9                        564                         24/03/2013                 
    DM_10                       214                         25/03/2013                 
    DM_10                       367                         24/03/2013                 
    DM_11                       656                         25/03/2013                 
    DM_11                       913                         24/03/2013                 
    DM_12                       37                          25/03/2013                 
    DM_12                       568                         24/03/2013                 
    DM_13                       332                         25/03/2013                 
    DM_13                       572                         24/03/2013                 
    DM_14                       467                         25/03/2013                 
    DM_14                       87                          24/03/2013                 
    DM_15                       437                         25/03/2013                 
    DM_15                       450                         24/03/2013                 
    DM_16                       238                         25/03/2013                 
    DM_16                       299                         24/03/2013                 
    DM_17                       523                         25/03/2013                 
    DM_17                       143                         24/03/2013                 
    DM_18                       611                         25/03/2013                 
    DM_18                       145                         24/03/2013                 
    DM_19                       447                         25/03/2013                 
    DM_19                       464                         24/03/2013                 
    DM_20                       91                          25/03/2013                 
    DM_20                       933                         24/03/2013                  Top 11 QTD of DOMAIN_NAME per Data Range.
    SELECT *
    FROM
      (SELECT DOMAIN_NAME,
        QTD,
        LOAD_DATE
      FROM
        (SELECT LOAD_DATE,
          DOMAIN_NAME ,
          QTD,
          (DENSE_RANK() OVER (PARTITION BY LOAD_DATE ORDER BY QTD DESC )) AS RANK_QTD
        FROM SAMPLE_DATA
        WHERE trunc(load_date) BETWEEN '24/03/2013' AND '25/03/2013'
      WHERE RANK_QTD <= 10
      UNION ALL
      SELECT 'Others',
        SUM(QTD) AS QTD,
        LOAD_DATE
      FROM
        (SELECT LOAD_DATE,
          DOMAIN_NAME ,
          QTD,
          (DENSE_RANK() OVER (PARTITION BY LOAD_DATE ORDER BY QTD DESC )) AS RANK_QTD
        FROM SAMPLE_DATA
        WHERE trunc(load_date) BETWEEN '24/03/2013' AND '25/03/2013'
      WHERE RANK_QTD > 10
      GROUP BY LOAD_DATE
    ORDER BY LOAD_DATE ASC,
      QTD DESC
    DOMAIN_NAME                 QTD                         LOAD_DATE                  
    Others                      2888                        24/03/2013                 
    DM_1                        1000                        24/03/2013                 
    DM_20                       933                         24/03/2013                 
    DM_11                       913                         24/03/2013                 
    DM_3                        743                         24/03/2013                 
    DM_13                       572                         24/03/2013                 
    DM_12                       568                         24/03/2013                 
    DM_9                        564                         24/03/2013                 
    DM_6                        505                         24/03/2013                 
    DM_5                        504                         24/03/2013                 
    DM_2                        480                         24/03/2013                 
    Others                      1948                        25/03/2013                 
    DM_1                        807                         25/03/2013                 
    DM_8                        764                         25/03/2013                 
    DM_7                        761                         25/03/2013                 
    DM_11                       656                         25/03/2013                 
    DM_18                       611                         25/03/2013                 
    DM_17                       523                         25/03/2013                 
    DM_14                       467                         25/03/2013                 
    DM_19                       447                         25/03/2013                 
    DM_15                       437                         25/03/2013                 
    DM_6                        380                         25/03/2013 

  • Select just the values between min and max of an accumulated value over day

    Hello Forum,
    a value is accumulated over a day and over a period of time. Next day the value is reseted and starts again to be accumulated:
    with sampledata as (select to_date('09.09.2012 00:04:08', 'dd.mm.yyyy hh24:mi:ss') ts, 120 val from dual union all
                       select to_date('09.09.2012 01:03:08', 'dd.mm.yyyy hh24:mi:ss') ts, 120 val from dual union all
                       select to_date('09.09.2012 02:54:11', 'dd.mm.yyyy hh24:mi:ss') ts, 120 val from dual union all
                       select to_date('09.09.2012 03:04:08', 'dd.mm.yyyy hh24:mi:ss') ts, 120 val from dual union all
                       select to_date('09.09.2012 04:04:19', 'dd.mm.yyyy hh24:mi:ss') ts, 120 val from dual union all
                       select to_date('09.09.2012 05:04:20', 'dd.mm.yyyy hh24:mi:ss') ts, 120 val from dual union all
                       select to_date('09.09.2012 06:12:02', 'dd.mm.yyyy hh24:mi:ss') ts, 23 val from dual union all
                       select to_date('09.09.2012 07:12:03', 'dd.mm.yyyy hh24:mi:ss') ts, 29 val from dual union all
                       select to_date('09.09.2012 08:12:04', 'dd.mm.yyyy hh24:mi:ss') ts, 30 val from dual union all
                       select to_date('09.09.2012 09:12:11', 'dd.mm.yyyy hh24:mi:ss') ts, 45 val from dual union all
                       select to_date('09.09.2012 10:12:12', 'dd.mm.yyyy hh24:mi:ss') ts, 60 val from dual union all
                       select to_date('09.09.2012 11:12:13', 'dd.mm.yyyy hh24:mi:ss') ts, 75 val from dual union all
                       select to_date('09.09.2012 12:21:24', 'dd.mm.yyyy hh24:mi:ss') ts, 95 val from dual union all
                       select to_date('09.09.2012 13:21:26', 'dd.mm.yyyy hh24:mi:ss') ts, 120 val from dual union all
                       select to_date('09.09.2012 14:21:27', 'dd.mm.yyyy hh24:mi:ss') ts, 142 val from dual union all
                       select to_date('09.09.2012 15:21:30', 'dd.mm.yyyy hh24:mi:ss') ts, 142 val from dual union all
                       select to_date('09.09.2012 16:21:32', 'dd.mm.yyyy hh24:mi:ss') ts, 142 val from dual union all
                       select to_date('09.09.2012 17:21:33', 'dd.mm.yyyy hh24:mi:ss') ts, 142 val from dual union all
                       select to_date('09.09.2012 21:21:33', 'dd.mm.yyyy hh24:mi:ss') ts, 142 val from dual union all
                       select to_date('09.09.2012 23:21:33', 'dd.mm.yyyy hh24:mi:ss') ts, 142 val from dual union all
                       select to_date('10.09.2012 00:04:08', 'dd.mm.yyyy hh24:mi:ss') ts, 142 val from dual union all
                       select to_date('10.09.2012 01:03:08', 'dd.mm.yyyy hh24:mi:ss') ts, 142 val from dual union all
                       select to_date('10.09.2012 02:54:11', 'dd.mm.yyyy hh24:mi:ss') ts, 142 val from dual union all
                       select to_date('10.09.2012 03:04:08', 'dd.mm.yyyy hh24:mi:ss') ts, 142 val from dual union all
                       select to_date('10.09.2012 04:04:19', 'dd.mm.yyyy hh24:mi:ss') ts, 142 val from dual union all
                       select to_date('10.09.2012 05:04:20', 'dd.mm.yyyy hh24:mi:ss') ts, 142 val from dual union all
                       select to_date('10.09.2012 06:12:02', 'dd.mm.yyyy hh24:mi:ss') ts, 14 val from dual union all
                       select to_date('10.09.2012 07:12:03', 'dd.mm.yyyy hh24:mi:ss') ts, 34 val from dual union all
                       select to_date('10.09.2012 08:12:04', 'dd.mm.yyyy hh24:mi:ss') ts, 58 val from dual union all
                       select to_date('10.09.2012 09:12:11', 'dd.mm.yyyy hh24:mi:ss') ts, 70 val from dual union all
                       select to_date('10.09.2012 10:12:12', 'dd.mm.yyyy hh24:mi:ss') ts, 120 val from dual union all
                       select to_date('10.09.2012 11:12:13', 'dd.mm.yyyy hh24:mi:ss') ts, 142 val from dual union all
                       select to_date('10.09.2012 12:21:24', 'dd.mm.yyyy hh24:mi:ss') ts, 153 val from dual union all
                       select to_date('10.09.2012 13:21:26', 'dd.mm.yyyy hh24:mi:ss') ts, 153 val from dual union all
                       select to_date('10.09.2012 14:21:27', 'dd.mm.yyyy hh24:mi:ss') ts, 153 val from dual union all
                       select to_date('10.09.2012 15:21:30', 'dd.mm.yyyy hh24:mi:ss') ts, 153 val from dual union all
                       select to_date('10.09.2012 16:21:32', 'dd.mm.yyyy hh24:mi:ss') ts, 153 val from dual union all
                       select to_date('10.09.2012 21:21:33', 'dd.mm.yyyy hh24:mi:ss') ts, 153 val from dual)
    select   ts, val
    from     sampledata
    order by ts asc;How should I change the select statement to skip all data sets before the first minimum and the duplicates after the maximum of a day in order to get such a result:
    TS     VAL
    09.09.12 06:12     23
    09.09.12 07:12     29
    09.09.12 08:12     30
    09.09.12 09:12     45
    09.09.12 10:12     60
    09.09.12 11:12     75
    09.09.12 12:21     95
    09.09.12 13:21     120
    09.09.12 14:21     142
    10.09.12 06:12     14
    10.09.12 07:12     34
    10.09.12 08:12     58
    10.09.12 09:12     70
    10.09.12 10:12     120
    10.09.12 11:12     142
    10.09.12 12:21     153Thank you

    This solution works perfectly when the accumulated value has its low and its high on the same day. But I found out :( , that there is also data, which has its low yesterday and its high today. For a better understandig of the case, there is a machine, wich is working over 3 Shifts with irregular start and end time. For example Shift1 cann start at 5:50 or at 7:15. The accumulated value of the worked time is accumuated for each shift extra. This solution works for the shift 1 (approximate between 06:00-14:00) and for the shift 2(approximate between 14:00-22:00), because there is the low and the high of the accumulated value on the same day. This solution does not work for the shif 3(approximate between 22:00-06:00), because the high of the accumulated value is or can be the next day.
    So the thread title should be: "Select just the values between min and max of an accumulated value over the same day(today) or over two successive days (yesterday and today)
    Sampledata for shift 1 or shift 2:
    {code}
    with sampledata as (select to_date('09.09.2012 00:04:08', 'dd.mm.yyyy hh24:mi:ss') ts, 120 val from dual union all
    select to_date('09.09.2012 01:03:08', 'dd.mm.yyyy hh24:mi:ss') ts, 120 val from dual union all
    select to_date('09.09.2012 02:54:11', 'dd.mm.yyyy hh24:mi:ss') ts, 120 val from dual union all
    select to_date('09.09.2012 03:04:08', 'dd.mm.yyyy hh24:mi:ss') ts, 120 val from dual union all
    select to_date('09.09.2012 04:04:19', 'dd.mm.yyyy hh24:mi:ss') ts, 120 val from dual union all
    select to_date('09.09.2012 05:04:20', 'dd.mm.yyyy hh24:mi:ss') ts, 120 val from dual union all
    select to_date('09.09.2012 06:12:02', 'dd.mm.yyyy hh24:mi:ss') ts, 23 val from dual union all
    select to_date('09.09.2012 07:12:03', 'dd.mm.yyyy hh24:mi:ss') ts, 29 val from dual union all
    select to_date('09.09.2012 08:12:04', 'dd.mm.yyyy hh24:mi:ss') ts, 30 val from dual union all
    select to_date('09.09.2012 09:12:11', 'dd.mm.yyyy hh24:mi:ss') ts, 45 val from dual union all
    select to_date('09.09.2012 10:12:12', 'dd.mm.yyyy hh24:mi:ss') ts, 60 val from dual union all
    select to_date('09.09.2012 11:12:13', 'dd.mm.yyyy hh24:mi:ss') ts, 75 val from dual union all
    select to_date('09.09.2012 12:21:24', 'dd.mm.yyyy hh24:mi:ss') ts, 95 val from dual union all
    select to_date('09.09.2012 13:21:26', 'dd.mm.yyyy hh24:mi:ss') ts, 120 val from dual union all
    select to_date('09.09.2012 14:21:27', 'dd.mm.yyyy hh24:mi:ss') ts, 142 val from dual union all
    select to_date('09.09.2012 15:21:30', 'dd.mm.yyyy hh24:mi:ss') ts, 142 val from dual union all
    select to_date('09.09.2012 16:21:32', 'dd.mm.yyyy hh24:mi:ss') ts, 142 val from dual union all
    select to_date('09.09.2012 17:21:33', 'dd.mm.yyyy hh24:mi:ss') ts, 142 val from dual union all
    select to_date('09.09.2012 21:21:33', 'dd.mm.yyyy hh24:mi:ss') ts, 142 val from dual union all
    select to_date('09.09.2012 23:21:33', 'dd.mm.yyyy hh24:mi:ss') ts, 142 val from dual union all
    select to_date('10.09.2012 00:04:08', 'dd.mm.yyyy hh24:mi:ss') ts, 142 val from dual union all
    select to_date('10.09.2012 01:03:08', 'dd.mm.yyyy hh24:mi:ss') ts, 142 val from dual union all
    select to_date('10.09.2012 02:54:11', 'dd.mm.yyyy hh24:mi:ss') ts, 142 val from dual union all
    select to_date('10.09.2012 03:04:08', 'dd.mm.yyyy hh24:mi:ss') ts, 142 val from dual union all
    select to_date('10.09.2012 04:04:19', 'dd.mm.yyyy hh24:mi:ss') ts, 142 val from dual union all
    select to_date('10.09.2012 05:04:20', 'dd.mm.yyyy hh24:mi:ss') ts, 142 val from dual union all
    select to_date('10.09.2012 06:12:02', 'dd.mm.yyyy hh24:mi:ss') ts, 143 val from dual union all
    select to_date('10.09.2012 07:12:03', 'dd.mm.yyyy hh24:mi:ss') ts, 144 val from dual union all
    select to_date('10.09.2012 08:12:04', 'dd.mm.yyyy hh24:mi:ss') ts, 145 val from dual union all
    select to_date('10.09.2012 09:12:11', 'dd.mm.yyyy hh24:mi:ss') ts, 146 val from dual union all
    select to_date('10.09.2012 10:12:12', 'dd.mm.yyyy hh24:mi:ss') ts, 147 val from dual union all
    select to_date('10.09.2012 11:12:13', 'dd.mm.yyyy hh24:mi:ss') ts, 148 val from dual union all
    select to_date('10.09.2012 12:21:24', 'dd.mm.yyyy hh24:mi:ss') ts, 153 val from dual union all
    select to_date('10.09.2012 13:21:26', 'dd.mm.yyyy hh24:mi:ss') ts, 153 val from dual union all
    select to_date('10.09.2012 14:21:27', 'dd.mm.yyyy hh24:mi:ss') ts, 153 val from dual union all
    select to_date('10.09.2012 15:21:30', 'dd.mm.yyyy hh24:mi:ss') ts, 153 val from dual union all
    select to_date('10.09.2012 16:21:32', 'dd.mm.yyyy hh24:mi:ss') ts, 153 val from dual union all
    select to_date('10.09.2012 21:21:33', 'dd.mm.yyyy hh24:mi:ss') ts, 153 val from dual)
    , got_analytics     AS
         SELECT ts, val
         ,     MIN (val) OVER ( PARTITION BY TRUNC (ts)
                        ORDER BY      ts DESC
                        )      AS min_val_after
         ,     CASE
              WHEN ROW_NUMBER () OVER ( PARTITION BY TRUNC (ts)
                             ORDER BY     val
                             ,      ts     
                             ) = 1          
              THEN -1 -- Impossibly low val
              ELSE LAG (val) OVER ( PARTITION BY TRUNC (ts)
                             ORDER BY      ts
              END           AS prev_val
         ,     MIN (val) OVER (PARTITION BY     TRUNC (ts))
                        AS low_val_today
         ,     NVL ( LAST_VALUE (val) OVER ( ORDER BY ts
                             RANGE BETWEEN UNBOUNDED PRECEDING
                                  AND     ts - TRUNC (ts) PRECEDING
              , -1
              )          AS last_val_yesterday
         FROM sampledata
    SELECT     ts
    ,     val
    FROM     got_analytics
    WHERE     val          <= min_val_after
    AND     val          > prev_val
    AND     (      val     > low_val_today
         OR     val     != last_val_yesterday
    ORDER BY ts
    {code}
    with the expected results:
    {code}
    1     09.09.2012 06:12:02     23
    2     09.09.2012 07:12:03     29
    3     09.09.2012 08:12:04     30
    4     09.09.2012 09:12:11     45
    5     09.09.2012 10:12:12     60
    6     09.09.2012 11:12:13     75
    7     09.09.2012 12:21:24     95
    8     09.09.2012 13:21:26     120
    9     09.09.2012 14:21:27     142
    10     10.09.2012 06:12:02     143
    11     10.09.2012 07:12:03     144
    12     10.09.2012 08:12:04     145
    13     10.09.2012 09:12:11     146
    14     10.09.2012 10:12:12     147
    15     10.09.2012 11:12:13     148
    16     10.09.2012 12:21:24     153
    {code}
    And the sampledata for shift 3 is:
    {code}
    with sampledata as (select to_date('08.09.2012 00:04:08', 'dd.mm.yyyy hh24:mi:ss') ts, 23 val from dual union ALL
    select to_date('08.09.2012 02:04:08', 'dd.mm.yyyy hh24:mi:ss') ts, 45 val from dual union all
    select to_date('08.09.2012 05:03:08', 'dd.mm.yyyy hh24:mi:ss') ts, 78 val from dual union all
    select to_date('08.09.2012 06:54:11', 'dd.mm.yyyy hh24:mi:ss') ts, 90 val from dual union all
    select to_date('08.09.2012 08:04:08', 'dd.mm.yyyy hh24:mi:ss') ts, 90 val from dual union all
    select to_date('08.09.2012 10:04:19', 'dd.mm.yyyy hh24:mi:ss') ts, 90 val from dual union all
    select to_date('08.09.2012 12:04:20', 'dd.mm.yyyy hh24:mi:ss') ts, 90 val from dual union all
    select to_date('08.09.2012 16:12:02', 'dd.mm.yyyy hh24:mi:ss') ts, 90 val from dual union all
    select to_date('08.09.2012 17:12:03', 'dd.mm.yyyy hh24:mi:ss') ts, 90 val from dual union all
    select to_date('08.09.2012 19:12:04', 'dd.mm.yyyy hh24:mi:ss') ts, 90 val from dual union all
    select to_date('08.09.2012 21:12:11', 'dd.mm.yyyy hh24:mi:ss') ts, 90 val from dual union all
    select to_date('08.09.2012 22:00:12', 'dd.mm.yyyy hh24:mi:ss') ts, 24 val from dual union all
    select to_date('08.09.2012 22:12:13', 'dd.mm.yyyy hh24:mi:ss') ts, 40 val from dual union all
    select to_date('08.09.2012 23:21:24', 'dd.mm.yyyy hh24:mi:ss') ts, 68 val from dual union all
    select to_date('09.09.2012 01:03:08', 'dd.mm.yyyy hh24:mi:ss') ts, 79 val from dual union all
    select to_date('09.09.2012 02:54:11', 'dd.mm.yyyy hh24:mi:ss') ts, 124 val from dual union all
    select to_date('09.09.2012 03:04:08', 'dd.mm.yyyy hh24:mi:ss') ts, 125 val from dual union all
    select to_date('09.09.2012 04:04:19', 'dd.mm.yyyy hh24:mi:ss') ts, 126 val from dual union all
    select to_date('09.09.2012 05:04:20', 'dd.mm.yyyy hh24:mi:ss') ts, 127 val from dual union all
    select to_date('09.09.2012 06:12:02', 'dd.mm.yyyy hh24:mi:ss') ts, 127 val from dual union all
    select to_date('09.09.2012 07:12:03', 'dd.mm.yyyy hh24:mi:ss') ts, 127 val from dual union all
    select to_date('09.09.2012 08:12:04', 'dd.mm.yyyy hh24:mi:ss') ts, 127 val from dual union all
    select to_date('09.09.2012 09:12:11', 'dd.mm.yyyy hh24:mi:ss') ts, 127 val from dual union all
    select to_date('09.09.2012 10:12:12', 'dd.mm.yyyy hh24:mi:ss') ts, 127 val from dual union all
    select to_date('09.09.2012 11:12:13', 'dd.mm.yyyy hh24:mi:ss') ts, 127 val from dual union all
    select to_date('09.09.2012 12:21:24', 'dd.mm.yyyy hh24:mi:ss') ts, 127 val from dual union all
    select to_date('09.09.2012 13:21:26', 'dd.mm.yyyy hh24:mi:ss') ts, 127 val from dual union all
    select to_date('09.09.2012 14:21:27', 'dd.mm.yyyy hh24:mi:ss') ts, 127 val from dual union all
    select to_date('09.09.2012 15:21:30', 'dd.mm.yyyy hh24:mi:ss') ts, 127 val from dual union all
    select to_date('09.09.2012 16:21:32', 'dd.mm.yyyy hh24:mi:ss') ts, 127 val from dual union all
    select to_date('09.09.2012 17:21:33', 'dd.mm.yyyy hh24:mi:ss') ts, 127 val from dual union all
    select to_date('09.09.2012 21:21:33', 'dd.mm.yyyy hh24:mi:ss') ts, 127 val from dual union ALL
    select to_date('09.09.2012 22:21:33', 'dd.mm.yyyy hh24:mi:ss') ts, 5 val from dual union ALL
    select to_date('09.09.2012 22:51:33', 'dd.mm.yyyy hh24:mi:ss') ts, 23 val from dual union all
    select to_date('09.09.2012 23:21:33', 'dd.mm.yyyy hh24:mi:ss') ts, 40 val from dual union all
    select to_date('10.09.2012 00:04:08', 'dd.mm.yyyy hh24:mi:ss') ts, 50 val from dual union all
    select to_date('10.09.2012 01:03:08', 'dd.mm.yyyy hh24:mi:ss') ts, 60 val from dual union all
    select to_date('10.09.2012 02:54:11', 'dd.mm.yyyy hh24:mi:ss') ts, 78 val from dual union all
    select to_date('10.09.2012 03:04:08', 'dd.mm.yyyy hh24:mi:ss') ts, 142 val from dual union all
    select to_date('10.09.2012 04:04:19', 'dd.mm.yyyy hh24:mi:ss') ts, 145 val from dual union all
    select to_date('10.09.2012 05:04:20', 'dd.mm.yyyy hh24:mi:ss') ts, 146 val from dual union all
    select to_date('10.09.2012 06:12:02', 'dd.mm.yyyy hh24:mi:ss') ts, 146 val from dual union all
    select to_date('10.09.2012 07:12:03', 'dd.mm.yyyy hh24:mi:ss') ts, 146 val from dual union all
    select to_date('10.09.2012 08:12:04', 'dd.mm.yyyy hh24:mi:ss') ts, 146 val from dual union all
    select to_date('10.09.2012 09:12:11', 'dd.mm.yyyy hh24:mi:ss') ts, 146 val from dual union all
    select to_date('10.09.2012 10:12:12', 'dd.mm.yyyy hh24:mi:ss') ts, 146 val from dual union all
    select to_date('10.09.2012 11:12:13', 'dd.mm.yyyy hh24:mi:ss') ts, 146 val from dual union all
    select to_date('10.09.2012 12:21:24', 'dd.mm.yyyy hh24:mi:ss') ts, 146 val from dual union all
    select to_date('10.09.2012 13:21:26', 'dd.mm.yyyy hh24:mi:ss') ts, 146 val from dual union all
    select to_date('10.09.2012 14:21:27', 'dd.mm.yyyy hh24:mi:ss') ts, 146 val from dual union all
    select to_date('10.09.2012 15:21:30', 'dd.mm.yyyy hh24:mi:ss') ts, 146 val from dual union all
    select to_date('10.09.2012 16:21:32', 'dd.mm.yyyy hh24:mi:ss') ts, 146 val from dual union all
    select to_date('10.09.2012 21:21:33', 'dd.mm.yyyy hh24:mi:ss') ts, 146 val from dual)
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    ORDER BY ts DESC
    ) AS min_val_after
    , CASE
    WHEN ROW_NUMBER () OVER ( PARTITION BY TRUNC (ts)
    ORDER BY val
    , ts
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    THEN -1 -- Impossibly low val
    ELSE LAG (val) OVER ( PARTITION BY TRUNC (ts)
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    AND ts - TRUNC (ts) PRECEDING
    , -1
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    FROM sampledata
    SELECT ts
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    FROM got_analytics
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    {code}
    with the unexpected results:
    {code}
    - ts val
    1     08.09.2012 00:04:08     23
    2     08.09.2012 22:12:13     40
    3     08.09.2012 23:21:24     68
    4     09.09.2012 22:21:33     5
    5     09.09.2012 22:51:33     23
    6     09.09.2012 23:21:33     40
    7     10.09.2012 00:04:08     50
    8     10.09.2012 01:03:08     60
    9     10.09.2012 02:54:11     78
    10     10.09.2012 03:04:08     142
    11     10.09.2012 04:04:19     145
    12     10.09.2012 05:04:20     146
    {code}
    The result should be:
    {code}
    - ts val
    1     08.09.2012 00:04:08     23
    2     08.09.2012 02:04:08     45
    3     08.09.2012 05:03:08     78
    4     08.09.2012 06:54:11     90
    5     08.09.2012 22:00:12     24
    6     08.09.2012 22:12:13     40
    7     08.09.2012 23:21:24     68
    8     09.09.2012 01:03:08     79
    9     09.09.2012 02:54:11     124
    10     09.09.2012 03:04:08     125
    11     09.09.2012 04:04:19     126
    12     09.09.2012 05:04:20     127
    13     09.09.2012 22:21:33     5
    14     09.09.2012 22:51:33     23
    15     09.09.2012 23:21:33     40
    16     10.09.2012 00:04:08     50
    17     10.09.2012 01:03:08     60
    18     10.09.2012 02:54:11     78
    19     10.09.2012 03:04:08     142
    20     10.09.2012 04:04:19     145
    21     10.09.2012 05:04:20     146
    {code}
    Thank you for your help!

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