Have a problem with Sphinxql. I try to match several fields and give each a weight.
select id
,item
,param1
,param2
,module_id
,param2_id
,date_change
,custom_rank
,weight() as rank
,in(site, 336935152)
and if(date_from, date_from, 1578400079) <= 1578400079
and if(date_to, date_to, 1578400079) >= 1578400079
and ((((module_id = 3674251022)and(param1_id = 455881287)and(param2_id = 4196041389)))) as cond1
,if(date_to, date_to, 1578400079) date_to_nvl
,if(date_from, date_from, 1578400079) date_from_nvl
from index
where MATCH('#(title,body) (search query)')
limit 0, 500
option max_matches = 500
,field_weights=(title=99999, body=1)
But weight doesn't matter. Even if I give 99999 weight to title and 1 to body and vice versa, query result doesn't change. My goal is to make sphinx, firstly match title and after body.
UPD: packedfactors() query result
+------+----------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| id | weight() | packedfactors() |
+------+----------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| 1055 | 1000631 | bm25=631, bm25a=0.650515, field_mask=3, doc_word_count=1, field0=(lcs=1, hit_count=1, word_count=1, tf_idf=0.158041, min_idf=0.158041, max_idf=0.158041, sum_idf=0.158041, min_hit_pos=1, min_best_span_pos=1, exact_hit=0, max_window_hits=1, min_gaps=0, exact_order=1, lccs=1, wlccs=0.158041, atc=0.000000), field1=(lcs=1, hit_count=5, word_count=1, tf_idf=0.790203, min_idf=0.158041, max_idf=0.158041, sum_idf=0.158041, min_hit_pos=3, min_best_span_pos=3, exact_hit=0, max_window_hits=1, min_gaps=0, exact_order=1, lccs=1, wlccs=0.158041, atc=0.000000), word0=(tf=6, idf=0.158041) |
| 1056 | 1000631 | bm25=631, bm25a=0.650515, field_mask=3, doc_word_count=1, field0=(lcs=1, hit_count=1, word_count=1, tf_idf=0.158041, min_idf=0.158041, max_idf=0.158041, sum_idf=0.158041, min_hit_pos=1, min_best_span_pos=1, exact_hit=0, max_window_hits=1, min_gaps=0, exact_order=1, lccs=1, wlccs=0.158041, atc=0.000000), field1=(lcs=1, hit_count=5, word_count=1, tf_idf=0.790203, min_idf=0.158041, max_idf=0.158041, sum_idf=0.158041, min_hit_pos=3, min_best_span_pos=3, exact_hit=0, max_window_hits=1, min_gaps=0, exact_order=1, lccs=1, wlccs=0.158041, atc=0.000000), word0=(tf=6, idf=0.158041) |
| 242 | 1000627 | bm25=627, bm25a=0.649095, field_mask=3, doc_word_count=1, field0=(lcs=1, hit_count=1, word_count=1, tf_idf=0.158041, min_idf=0.158041, max_idf=0.158041, sum_idf=0.158041, min_hit_pos=1, min_best_span_pos=1, exact_hit=0, max_window_hits=1, min_gaps=0, exact_order=1, lccs=1, wlccs=0.158041, atc=0.000000), field1=(lcs=1, hit_count=4, word_count=1, tf_idf=0.632162, min_idf=0.158041, max_idf=0.158041, sum_idf=0.158041, min_hit_pos=1, min_best_span_pos=1, exact_hit=0, max_window_hits=1, min_gaps=0, exact_order=1, lccs=1, wlccs=0.158041, atc=0.000000), word0=(tf=5, idf=0.158041) |
| 813 | 1000627 | bm25=627, bm25a=0.649095, field_mask=3, doc_word_count=1, field0=(lcs=1, hit_count=1, word_count=1, tf_idf=0.158041, min_idf=0.158041, max_idf=0.158041, sum_idf=0.158041, min_hit_pos=1, min_best_span_pos=1, exact_hit=0, max_window_hits=1, min_gaps=0, exact_order=1, lccs=1, wlccs=0.158041, atc=0.000000), field1=(lcs=1, hit_count=4, word_count=1, tf_idf=0.632162, min_idf=0.158041, max_idf=0.158041, sum_idf=0.158041, min_hit_pos=1, min_best_span_pos=1, exact_hit=0, max_window_hits=1, min_gaps=0, exact_order=1, lccs=1, wlccs=0.158041, atc=0.000000), word0=(tf=5, idf=0.158041) |
| 815 | 1000627 | bm25=627, bm25a=0.649095, field_mask=3, doc_word_count=1, field0=(lcs=1, hit_count=1, word_count=1, tf_idf=0.158041, min_idf=0.158041, max_idf=0.158041, sum_idf=0.158041, min_hit_pos=1, min_best_span_pos=1, exact_hit=0, max_window_hits=1, min_gaps=0, exact_order=1, lccs=1, wlccs=0.158041, atc=0.000000), field1=(lcs=1, hit_count=4, word_count=1, tf_idf=0.632162, min_idf=0.158041, max_idf=0.158041, sum_idf=0.158041, min_hit_pos=1, min_best_span_pos=1, exact_hit=0, max_window_hits=1, min_gaps=0, exact_order=1, lccs=1, wlccs=0.158041, atc=0.000000), word0=(tf=5, idf=0.158041) |
| 1054 | 1000627 | bm25=627, bm25a=0.649095, field_mask=3, doc_word_count=1, field0=(lcs=1, hit_count=1, word_count=1, tf_idf=0.158041, min_idf=0.158041, max_idf=0.158041, sum_idf=0.158041, min_hit_pos=1, min_best_span_pos=1, exact_hit=0, max_window_hits=1, min_gaps=0, exact_order=1, lccs=1, wlccs=0.158041, atc=0.000000), field1=(lcs=1, hit_count=4, word_count=1, tf_idf=0.632162, min_idf=0.158041, max_idf=0.158041, sum_idf=0.158041, min_hit_pos=1, min_best_span_pos=1, exact_hit=0, max_window_hits=1, min_gaps=0, exact_order=1, lccs=1, wlccs=0.158041, atc=0.000000), word0=(tf=5, idf=0.158041) |
| 334 | 1000621 | bm25=621, bm25a=0.647014, field_mask=3, doc_word_count=1, field0=(lcs=1, hit_count=1, word_count=1, tf_idf=0.158041, min_idf=0.158041, max_idf=0.158041, sum_idf=0.158041, min_hit_pos=1, min_best_span_pos=1, exact_hit=0, max_window_hits=1, min_gaps=0, exact_order=1, lccs=1, wlccs=0.158041, atc=0.000000), field1=(lcs=1, hit_count=3, word_count=1, tf_idf=0.474122, min_idf=0.158041, max_idf=0.158041, sum_idf=0.158041, min_hit_pos=1, min_best_span_pos=1, exact_hit=0, max_window_hits=1, min_gaps=0, exact_order=1, lccs=1, wlccs=0.158041, atc=0.000000), word0=(tf=4, idf=0.158041) |
| 335 | 1000621 | bm25=621, bm25a=0.647014, field_mask=3, doc_word_count=1, field0=(lcs=1, hit_count=1, word_count=1, tf_idf=0.158041, min_idf=0.158041, max_idf=0.158041, sum_idf=0.158041, min_hit_pos=1, min_best_span_pos=1, exact_hit=0, max_window_hits=1, min_gaps=0, exact_order=1, lccs=1, wlccs=0.158041, atc=0.000000), field1=(lcs=1, hit_count=3, word_count=1, tf_idf=0.474122, min_idf=0.158041, max_idf=0.158041, sum_idf=0.158041, min_hit_pos=1, min_best_span_pos=1, exact_hit=0, max_window_hits=1, min_gaps=0, exact_order=1, lccs=1, wlccs=0.158041, atc=0.000000), word0=(tf=4, idf=0.158041) |
| 510 | 1000621 | bm25=621, bm25a=0.647014, field_mask=3, doc_word_count=1, field0=(lcs=1, hit_count=1, word_count=1, tf_idf=0.158041, min_idf=0.158041, max_idf=0.158041, sum_idf=0.158041, min_hit_pos=8, min_best_span_pos=8, exact_hit=0, max_window_hits=1, min_gaps=0, exact_order=1, lccs=1, wlccs=0.158041, atc=0.000000), field1=(lcs=1, hit_count=3, word_count=1, tf_idf=0.474122, min_idf=0.158041, max_idf=0.158041, sum_idf=0.158041, min_hit_pos=10, min_best_span_pos=10, exact_hit=0, max_window_hits=1, min_gaps=0, exact_order=1, lccs=1, wlccs=0.158041, atc=0.000000), word0=(tf=4, idf=0.158041) |
| 1057 | 1000621 | bm25=621, bm25a=0.647014, field_mask=3, doc_word_count=1, field0=(lcs=1, hit_count=1, word_count=1, tf_idf=0.158041, min_idf=0.158041, max_idf=0.158041, sum_idf=0.158041, min_hit_pos=1, min_best_span_pos=1, exact_hit=0, max_window_hits=1, min_gaps=0, exact_order=1, lccs=1, wlccs=0.158041, atc=0.000000), field1=(lcs=1, hit_count=3, word_count=1, tf_idf=0.474122, min_idf=0.158041, max_idf=0.158041, sum_idf=0.158041, min_hit_pos=1, min_best_span_pos=1, exact_hit=0, max_window_hits=1, min_gaps=0, exact_order=1, lccs=1, wlccs=0.158041, atc=0.000000), word0=(tf=4, idf=0.158041) |
+------+----------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
Use PACKEDFACTORS() to understand what's going on, there may be different reasons why your ranking doesn't change.
Below you can see an example which shows how to use it and that for example for some documents stats are only exposed for one field of 2, because the keywords were found only in one field.
MySQL [(none)]> select id, weight(), packedfactors() from index where match('#(subject,body)manticore search') limit 5 option ranker=expr('sum(lcs*user_weight)*1000+bm25'), field_weights=(subject=1,body=10)\G
*************************** 1. row ***************************
id: 67020137501
weight(): 20614
packedfactors(): bm25=614, bm25a=0.67905319, field_mask=2, doc_word_count=2, field1=(lcs=2, hit_count=3, word_count=2, tf_idf=0.30208117, min_idf=0.07970884, max_idf=0.14266349, sum_idf=0.22237234, min_hit_pos=5, min_best_span_pos=17, exact_hit=0, max_window_hits=1, min_gaps=0, exact_order=1, lccs=2, wlccs=0.22237234, atc=0.02281057), word0=(tf=1, idf=0.14266349), word1=(tf=2, idf=0.07970884)
*************************** 2. row ***************************
id: 67020139037
weight(): 20614
packedfactors(): bm25=614, bm25a=0.67905319, field_mask=2, doc_word_count=2, field1=(lcs=2, hit_count=3, word_count=2, tf_idf=0.30208117, min_idf=0.07970884, max_idf=0.14266349, sum_idf=0.22237234, min_hit_pos=74, min_best_span_pos=86, exact_hit=0, max_window_hits=1, min_gaps=0, exact_order=1, lccs=2, wlccs=0.22237234, atc=0.02281057), word0=(tf=1, idf=0.14266349), word1=(tf=2, idf=0.07970884)
*************************** 3. row ***************************
id: 67164506141
weight(): 20601
packedfactors(): bm25=601, bm25a=0.67105567, field_mask=2, doc_word_count=2, field1=(lcs=2, hit_count=2, word_count=2, tf_idf=0.22237234, min_idf=0.07970884, max_idf=0.14266349, sum_idf=0.22237234, min_hit_pos=105, min_best_span_pos=105, exact_hit=0, max_window_hits=1, min_gaps=0, exact_order=1, lccs=2, wlccs=0.22237234, atc=0.02248834), word0=(tf=1, idf=0.14266349), word1=(tf=1, idf=0.07970884)
*************************** 4. row ***************************
id: 60360225821
weight(): 11653
packedfactors(): bm25=653, bm25a=0.70337892, field_mask=3, doc_word_count=2, field0=(lcs=1, hit_count=1, word_count=1, tf_idf=0.08168077, min_idf=0.08168077, max_idf=0.08168077, sum_idf=0.08168077, min_hit_pos=3, min_best_span_pos=3, exact_hit=0, max_window_hits=1, min_gaps=0, exact_order=0, lccs=1, wlccs=0.08168077, atc=0.000000), field1=(lcs=1, hit_count=4, word_count=1, tf_idf=0.60435468, min_idf=0.15108867, max_idf=0.15108867, sum_idf=0.15108867, min_hit_pos=61, min_best_span_pos=61, exact_hit=0, max_window_hits=1, min_gaps=0, exact_order=0, lccs=1, wlccs=0.15108867, atc=0.00027331), word0=(tf=4, idf=0.15108867), word1=(tf=1, idf=0.08168077)
*************************** 5. row ***************************
id: 59004972573
weight(): 11645
packedfactors(): bm25=645, bm25a=0.70018470, field_mask=3, doc_word_count=2, field0=(lcs=1, hit_count=1, word_count=1, tf_idf=0.08168077, min_idf=0.08168077, max_idf=0.08168077, sum_idf=0.08168077, min_hit_pos=11, min_best_span_pos=11, exact_hit=0, max_window_hits=1, min_gaps=0, exact_order=0, lccs=1, wlccs=0.08168077, atc=0.000000), field1=(lcs=1, hit_count=3, word_count=1, tf_idf=0.45326602, min_idf=0.15108867, max_idf=0.15108867, sum_idf=0.15108867, min_hit_pos=56, min_best_span_pos=56, exact_hit=0, max_window_hits=1, min_gaps=0, exact_order=0, lccs=1, wlccs=0.15108867, atc=0.00005638), word0=(tf=3, idf=0.15108867), word1=(tf=1, idf=0.08168077)
5 rows in set (0.01 sec)
MySQL [(none)]> select id, weight(), packedfactors() from index where match('#(subject,body)manticore search') limit 5 option ranker=expr('sum(lcs*user_weight)*1000+bm25'), field_weights=(subject=10,body=1)\G
*************************** 1. row ***************************
id: 60360225821
weight(): 11653
packedfactors(): bm25=653, bm25a=0.70337892, field_mask=3, doc_word_count=2, field0=(lcs=1, hit_count=1, word_count=1, tf_idf=0.08168077, min_idf=0.08168077, max_idf=0.08168077, sum_idf=0.08168077, min_hit_pos=3, min_best_span_pos=3, exact_hit=0, max_window_hits=1, min_gaps=0, exact_order=0, lccs=1, wlccs=0.08168077, atc=0.000000), field1=(lcs=1, hit_count=4, word_count=1, tf_idf=0.60435468, min_idf=0.15108867, max_idf=0.15108867, sum_idf=0.15108867, min_hit_pos=61, min_best_span_pos=61, exact_hit=0, max_window_hits=1, min_gaps=0, exact_order=0, lccs=1, wlccs=0.15108867, atc=0.00027331), word0=(tf=4, idf=0.15108867), word1=(tf=1, idf=0.08168077)
*************************** 2. row ***************************
id: 59004972573
weight(): 11645
packedfactors(): bm25=645, bm25a=0.70018470, field_mask=3, doc_word_count=2, field0=(lcs=1, hit_count=1, word_count=1, tf_idf=0.08168077, min_idf=0.08168077, max_idf=0.08168077, sum_idf=0.08168077, min_hit_pos=11, min_best_span_pos=11, exact_hit=0, max_window_hits=1, min_gaps=0, exact_order=0, lccs=1, wlccs=0.08168077, atc=0.000000), field1=(lcs=1, hit_count=3, word_count=1, tf_idf=0.45326602, min_idf=0.15108867, max_idf=0.15108867, sum_idf=0.15108867, min_hit_pos=56, min_best_span_pos=56, exact_hit=0, max_window_hits=1, min_gaps=0, exact_order=0, lccs=1, wlccs=0.15108867, atc=0.00005638), word0=(tf=3, idf=0.15108867), word1=(tf=1, idf=0.08168077)
*************************** 3. row ***************************
id: 52749413289
weight(): 11631
packedfactors(): bm25=631, bm25a=0.69566667, field_mask=3, doc_word_count=2, field0=(lcs=1, hit_count=1, word_count=1, tf_idf=0.14696111, min_idf=0.14696111, max_idf=0.14696111, sum_idf=0.14696111, min_hit_pos=1, min_best_span_pos=1, exact_hit=0, max_window_hits=1, min_gaps=0, exact_order=0, lccs=1, wlccs=0.14696111, atc=0.000000), field1=(lcs=1, hit_count=2, word_count=2, tf_idf=0.23519781, min_idf=0.08823670, max_idf=0.14696111, sum_idf=0.23519781, min_hit_pos=23, min_best_span_pos=23, exact_hit=0, max_window_hits=1, min_gaps=2, exact_order=0, lccs=1, wlccs=0.14696111, atc=0.00378523), word0=(tf=2, idf=0.14696111), word1=(tf=1, idf=0.08823670)
*************************** 4. row ***************************
id: 69779455599
weight(): 11609
packedfactors(): bm25=609, bm25a=0.68568671, field_mask=3, doc_word_count=2, field0=(lcs=1, hit_count=1, word_count=1, tf_idf=0.15835057, min_idf=0.15835057, max_idf=0.15835057, sum_idf=0.15835057, min_hit_pos=11, min_best_span_pos=11, exact_hit=0, max_window_hits=1, min_gaps=0, exact_order=0, lccs=1, wlccs=0.15835057, atc=0.000000), field1=(lcs=1, hit_count=1, word_count=1, tf_idf=0.08304220, min_idf=0.08304220, max_idf=0.08304220, sum_idf=0.08304220, min_hit_pos=32, min_best_span_pos=32, exact_hit=0, max_window_hits=1, min_gaps=0, exact_order=0, lccs=1, wlccs=0.08304220, atc=0.000000), word0=(tf=1, idf=0.15835057), word1=(tf=1, idf=0.08304220)
*************************** 5. row ***************************
id: 53174602295
weight(): 11605
packedfactors(): bm25=605, bm25a=0.67905343, field_mask=3, doc_word_count=2, field0=(lcs=1, hit_count=1, word_count=1, tf_idf=0.08168077, min_idf=0.08168077, max_idf=0.08168077, sum_idf=0.08168077, min_hit_pos=9, min_best_span_pos=9, exact_hit=0, max_window_hits=1, min_gaps=0, exact_order=0, lccs=1, wlccs=0.08168077, atc=0.000000), field1=(lcs=1, hit_count=1, word_count=1, tf_idf=0.15108867, min_idf=0.15108867, max_idf=0.15108867, sum_idf=0.15108867, min_hit_pos=35, min_best_span_pos=35, exact_hit=0, max_window_hits=1, min_gaps=0, exact_order=0, lccs=1, wlccs=0.15108867, atc=0.000000), word0=(tf=1, idf=0.15108867), word1=(tf=1, idf=0.08168077)
5 rows in set (0.01 sec)
Related
Here is my code:
sigma = 10;
sz = 20;
x = linspace(-sz / 2, sz / 2-1, sz);
gf = exp(-x .^ 2 / (2 * sigma ^ 2));
gf = gf / sum (gf); % normalize
f_filter = cconv(gf,f,length(f));
Basically I am Gaussian filtering original signal f. However, when I look at the filtered signal f_filter, there is a shift comparing the original signal f (See attached figure). I am not sure why this is happening. I would like to only smooth but not shift the orginal signal. Please help. Thanks.
my original signal f is here:
-0.0311
-0.0462
-0.0498
-0.0640
-0.0511
-0.0522
-0.0566
-0.0524
-0.0478
-0.0482
-0.0516
-0.0435
-0.0417
-0.0410
-0.0278
-0.0079
-0.0087
-0.0029
0.0105
0.0042
0.0046
0.0107
0.0119
0.0177
0.0077
0.0138
0.0114
0.0103
0.0089
0.0122
0.0122
0.0118
0.0041
0.0047
0.0062
0.0055
0.0033
0.0096
0.0062
-0.0013
0.0029
0.0112
0.0069
0.0160
0.0127
0.0131
0.0039
0.0116
0.0078
0.0018
0.0023
0.0133
0.0140
0.0135
0.0098
0.0100
0.0133
0.0131
0.0086
0.0114
0.0131
0.0175
0.0137
0.0157
0.0040
0.0136
0.0009
0.0049
0.0157
0.0104
0.0038
0.0039
0.0029
0.0126
0.0044
0.0055
0.0040
0.0091
-0.0023
0.0107
0.0151
0.0115
0.0135
0.0160
0.0071
0.0098
0.0094
0.0072
0.0079
0.0055
0.0155
0.0107
0.0108
0.0085
0.0099
0.0055
0.0078
0.0027
0.0121
0.0077
0.0062
0.0021
-0.0019
-0.0003
-0.0022
0.0059
0.0099
0.0114
0.0069
0.0038
0.0020
-0.0031
0.0024
-0.0025
-0.0004
0.0041
0.0059
0.0018
0.0033
0.0130
0.0131
0.0076
0.0084
0.0029
0.0086
0.0078
0.0054
0.0121
0.0101
0.0132
0.0115
0.0074
0.0070
0.0088
0.0017
-0.0003
-0.0060
0.0078
0.0100
0.0044
0.0017
0.0027
0.0062
0.0029
-0.0035
0.0032
0.0060
-0.0035
0.0081
0.0027
0.0043
0.0013
0.0049
0.0119
0.0273
0.0363
0.0435
0.0432
0.0357
0.0424
0.0318
0.0341
0.0354
0.0325
0.0263
0.0320
0.0312
0.0345
0.0407
0.0378
0.0376
0.0334
0.0381
0.0428
0.0375
0.0431
0.0403
0.0395
0.0308
0.0150
0.0006
0.0054
0.0002
0.0090
0.0075
0.0051
0.0067
0.0062
0.0108
0.0059
0.0095
0.0065
0.0087
0.0056
0.0136
0.0057
0.0079
0.0107
0.0106
0.0041
0.0032
0.0106
0.0091
0.0082
0.0025
0.0124
0.0035
0.0034
0.0097
0.0034
0.0050
0.0119
0.0087
0.0081
0.0118
0.0088
0.0050
0.0050
0.0057
0.0118
0.0122
0.0207
0.0112
0.0125
0.0083
0.0125
0.0140
0.0147
0.0237
0.0206
0.0141
0.0164
0.0189
0.0189
0.0136
0.0183
0.0195
0.0209
0.0154
0.0211
0.0254
0.0163
0.0249
0.0236
0.0262
0.0278
0.0285
0.0275
0.0212
0.0277
0.0211
0.0248
0.0289
0.0240
0.0266
0.0479
0.1744
0.4070
0.6818
0.8811
0.9859
0.9347
0.8441
0.7625
0.6396
0.4724
0.3639
0.3406
0.3406
0.3363
0.3318
0.3251
0.3287
0.3135
0.3122
0.3058
0.3103
0.3012
0.2974
0.2995
0.2941
0.2981
0.2968
0.2958
0.2938
0.2929
0.2926
0.2942
0.2982
0.2898
0.2940
0.2927
0.2950
0.2899
0.2979
0.2915
0.2961
0.2921
0.2931
0.2989
0.2941
0.2977
0.3041
0.3042
0.3086
0.3048
0.3069
0.3055
0.3123
0.3138
0.3128
0.3115
0.3092
0.3174
0.3152
0.3106
0.3080
0.3166
0.3109
0.3103
0.3135
0.3101
0.3133
0.3147
0.3044
0.2980
0.2972
0.3013
0.2980
0.3069
0.3932
0.6593
0.8921
1.1071
1.2763
1.3947
1.5076
1.6278
1.7452
1.7993
1.8287
1.8470
1.8957
1.9408
1.9791
2.0272
2.0686
2.0974
2.1335
2.1790
2.2134
2.2545
2.2903
2.3163
2.3585
2.3739
2.4126
2.4503
2.4787
2.5198
2.5447
2.5950
2.6228
2.6410
2.6812
2.7123
2.7557
2.8584
3.2480
3.5315
3.6808
3.7632
3.7471
3.7283
3.6692
3.6718
3.7756
3.9672
4.0376
3.9092
3.7276
3.6586
3.5948
3.6392
3.5671
3.6003
3.6194
3.6350
3.6624
3.6855
3.6958
3.9105
4.3880
5.1342
5.6176
6.3206
7.0392
7.3767
7.5715
7.6516
7.6469
7.5871
7.4591
7.6004
7.5532
7.3601
7.1487
5.9728
4.8974
4.5850
4.4268
4.3352
4.2887
4.3376
4.3182
4.2909
4.2777
4.2548
4.2677
4.2511
4.2817
4.3847
4.4418
4.4696
4.4932
4.4998
4.5151
4.5096
4.5278
4.5139
4.5020
4.4561
4.4067
4.3841
4.3638
4.3750
4.4366
4.5258
4.6565
4.6485
4.5836
4.5183
4.4583
4.3747
4.3509
4.2938
4.2823
4.2844
4.3135
4.3262
4.3255
4.2568
4.2011
4.1832
4.2278
4.2445
4.2409
4.2784
4.2917
4.3035
4.3015
4.3209
4.3204
4.3356
4.3287
4.3260
4.3483
4.3710
4.3798
4.3802
4.3805
4.5162
4.6906
5.0826
5.6588
6.0137
6.2436
6.5361
7.0790
7.6106
7.6410
7.4120
7.4535
7.2476
7.2596
7.1012
7.0986
6.9395
6.5633
5.8438
4.9434
4.6750
4.4320
4.3063
4.2096
4.0193
3.9698
4.0055
4.0218
4.0426
4.0688
4.0650
3.9793
3.9787
3.9766
3.9981
4.0405
4.0165
4.0290
4.0923
4.0897
4.0615
4.0258
4.0008
4.0274
4.0553
4.0646
4.0442
4.0477
3.9986
4.0354
4.0718
4.0563
4.0189
3.8631
3.8144
3.7736
3.8055
3.9730
4.0299
4.0148
3.8265
3.4675
3.3020
3.2474
3.2338
3.1986
3.1680
3.1289
3.0944
3.0523
3.0094
2.9510
2.9246
2.9057
2.8805
2.8545
2.8245
2.7690
2.7236
2.6833
2.6443
2.5969
2.5415
2.4684
2.4214
2.3699
2.3293
2.2513
2.1963
2.1285
2.0700
2.0209
1.9575
1.8658
1.6996
1.5120
1.4020
1.3087
1.2166
1.1441
1.0774
1.0226
0.9809
0.9448
0.8526
0.6915
0.4491
0.2842
0.2582
0.2570
0.2568
0.2609
0.2632
0.2581
0.2552
0.2539
0.2527
0.2578
0.2672
0.2701
0.2655
0.2658
0.2688
0.2761
0.2767
0.2738
0.2774
0.2801
0.2817
0.2803
0.2830
0.2828
0.2876
0.2952
0.2985
0.3016
0.3092
0.3130
0.3153
0.3182
0.3304
0.3471
0.3416
0.3476
0.3497
0.3453
0.3398
0.3448
0.3563
0.3511
0.3502
0.3481
0.3519
0.3573
0.3544
0.3512
0.3489
0.3499
0.3470
0.3533
0.3409
0.3556
0.3474
0.3435
0.3460
0.3519
0.3447
0.3395
0.3488
0.3473
0.3453
0.3433
0.3484
0.3526
0.3494
0.3607
0.3694
0.4126
0.4604
0.5004
0.5163
0.5328
0.5432
0.5506
0.5485
0.5605
0.5586
0.5622
0.5727
0.5804
0.5797
0.5666
0.5700
0.5696
0.5722
0.5715
0.5656
0.5572
0.5264
0.5156
0.5473
0.6286
0.7503
0.8715
0.8825
0.7507
0.5421
0.2869
0.1091
0.0423
0.0326
0.0343
0.0256
0.0231
0.0281
0.0298
0.0229
0.0283
0.0279
0.0270
0.0300
0.0245
0.0360
0.0280
0.0270
0.0232
0.0276
0.0270
0.0237
0.0197
0.0193
0.0172
0.0140
0.0093
0.0244
0.0226
0.0192
0.0145
0.0124
0.0167
0.0182
0.0111
0.0147
0.0081
0.0151
0.0130
0.0113
0.0131
0.0067
0.0028
0.0064
0.0069
0.0082
0.0075
0.0098
-0.0008
0.0037
0.0019
0.0060
0.0057
0.0033
0.0079
0.0122
0.0091
0.0067
-0.0038
0.0033
0.0013
0.0011
0.0034
0.0051
0.0009
-0.0001
-0.0005
0.0098
-0.0003
0.0067
0.0038
0.0106
0.0000
0.0126
0.0134
0.0090
0.0116
0.0083
0.0101
0.0152
0.0010
0.0068
0.0008
0.0053
0.0090
0.0087
0.0085
0.0054
0.0089
0.0077
0.0064
0.0046
0.0058
0.0025
0.0132
0.0088
0.0043
0.0052
0.0087
0.0122
0.0023
0.0066
0.0093
0.0042
0.0042
0.0138
0.0051
-0.0055
-0.0002
0.0048
0.0063
0.0076
0.0016
-0.0005
0.0086
0.0043
-0.0016
0.0100
0.0097
0.0042
0.0092
0.0051
0.0029
0.0044
0.0033
0.0073
0.0093
0.0077
0.0093
0.0021
0.0026
0.0093
0.0068
0.0039
0.0068
0.0041
0.0053
0.0037
0.0075
0.0016
0.0000
-0.0005
0.0073
0.0076
0.0049
0.0046
0.0087
0.0106
0.0072
0.0085
0.0036
0.0044
0.0043
0.0201
0.0076
0.0075
0.0134
0.0050
0.0071
0.0032
0.0055
0.0085
0.0046
0.0023
-0.0020
0.0027
0.0060
0.0066
0.0067
0.0014
0.0166
0.0067
0.0024
0.0072
0.0062
0.0081
0.0035
0.0077
0.0101
0.0045
0.0034
0.0144
0.0078
0.0065
0.0093
0.0181
0.0028
0.0050
0.0034
0.0063
0.0150
0.0035
0.0022
0.0079
0.0034
0.0110
0.0075
0.0058
0.0085
0.0152
0.0089
0.0060
0.0017
0.0041
0.0091
0.0072
-0.0109
0.0036
0.0063
0.0080
0.0037
0.0086
0.0097
0.0088
0.0016
0.0057
0.0059
0.0139
0.0061
0.0009
0.0059
0.0126
0.0117
0.0003
0.0060
0.0075
0.0073
0.0080
0.0154
0.0136
0.0121
0.0179
0.0150
0.0125
Instead of doing
f_filter = cconv(gf,f,length(f));
this does the trick:
f_filter = conv(gf,f);
f_filter = f_filter(sz/2+1:end-sz/2+1);
As suggested by #AnderBiguri you can use the option 'same' in your convolution fonction to preserve the original size of your array.
But if you apply a convolution with your normalized gaussian filter gf you will obtain a border effect.
To avoid the border effect you can apply the following tricks:
gf = exp(-x .^ 2 / (2 * sigma ^ 2)); %do not normalize gf now
f_filter = conv(f,gf,'same')./conv(ones(length(f),1),gf,'same') %normalization taking into account the lenght of the convolution
For example I've just transformed f into f = f+3
If we do not take into account the border effect we will obtain:
I have created a colourmap 'mycmap' which I want to use on every contourf in Matlab. How do this?
I have tried
[C,h]=contourf(Xrange,Y_range,capacity,14,'LineColor',mycmap');
but it doesn't work. My custom colour scale looks like:
mycmap =
0.9725 0.9725 0.9725
0.9442 0.9442 0.9442
0.9159 0.9159 0.9159
0.8876 0.8876 0.8876
0.8593 0.8593 0.8593
0.8310 0.8310 0.8310
0.8027 0.8027 0.8027
0.8002 0.8002 0.8002
0.7976 0.7976 0.7976
0.7950 0.7950 0.7950
0.7924 0.7924 0.7924
0.7204 0.7204 0.7204
0.6484 0.6484 0.6484
0.6484 0.6484 0.6484
0.6484 0.6484 0.6484
0.6329 0.6329 0.6329
0.6175 0.6175 0.6175
0.6021 0.6021 0.6021
0.5885 0.5885 0.5885
0.5750 0.5750 0.5750
0.5615 0.5615 0.5615
0.5480 0.5480 0.5480
0.5345 0.5345 0.5345
0.5210 0.5210 0.5210
0.5075 0.5075 0.5075
0.4940 0.4940 0.4940
0.4564 0.4564 0.4564
0.4188 0.4188 0.4188
0.4092 0.4092 0.4092
0.3995 0.3995 0.3995
0.3899 0.3899 0.3899
0.3802 0.3802 0.3802
0.3706 0.3706 0.3706
0.3609 0.3609 0.3609
0.3512 0.3512 0.3512
0.3416 0.3416 0.3416
0.3328 0.3328 0.3328
0.3239 0.3239 0.3239
0.3151 0.3151 0.3151
0.3063 0.3063 0.3063
0.2974 0.2974 0.2974
0.2886 0.2886 0.2886
0.2798 0.2798 0.2798
0.2771 0.2771 0.2771
0.2743 0.2743 0.2743
0.2716 0.2716 0.2716
0.2689 0.2689 0.2689
0.2661 0.2661 0.2661
0.2634 0.2634 0.2634
0.2607 0.2607 0.2607
0.2580 0.2580 0.2580
0.2552 0.2552 0.2552
0.2525 0.2525 0.2525
0.2498 0.2498 0.2498
0.2471 0.2471 0.2471
0.2196 0.2196 0.2196
0.1922 0.1922 0.1922
0.1647 0.1647 0.1647
0.1373 0.1373 0.1373
0.1098 0.1098 0.1098
0.0824 0.0824 0.0824
0.0549 0.0549 0.0549
0.0275 0.0275 0.0275
0 0 0
Thanks for helping.
You could invoke the colour map after the plotting...
[C,h]=contourf(Xrange,Y_range,capacity,14)
colormap(mycmap);
I have updated scala and sbt version to 2.12.0 and 0.13.8 respectively. Now every build fails with errors:
java.lang.VerifyError: Uninitialized object exists on backward branch 209
Exception Details:
Location:
scala/collection/immutable/HashMap$HashTrieMap.split()Lscala/collection/immutable/Seq; #249: goto
Reason:
Error exists in the bytecode
Bytecode:
0000000: 2ab6 0057 04a0 001e b200 afb2 00b4 04bd
0000010: 0002 5903 2a53 c000 b6b6 00ba b600 bec0
0000020: 00c0 b02a b600 31b8 003b 3c1b 04a4 015e
0000030: 1b05 6c3d 2a1b 056c 2ab6 0031 b700 c23e
0000040: 2ab6 0031 021d 787e 3604 2ab6 0031 0210
0000050: 201d 647c 7e36 05bb 0014 59b2 00b4 2ab6
0000060: 0033 c000 b6b6 00c6 b700 c91c b600 cd3a
0000070: 0619 06c6 001a 1906 b600 d1c0 007d 3a07
0000080: 1906 b600 d4c0 007d 3a08 a700 0dbb 00d6
0000090: 5919 06b7 00d9 bf19 073a 0919 083a 0abb
00000a0: 0002 5915 0419 09bb 0014 59b2 00b4 1909
00000b0: c000 b6b6 00c6 b700 c903 b800 df3a 0e3a
00000c0: 0d03 190d b900 e301 0019 0e3a 1136 1036
00000d0: 0f15 0f15 109f 0027 150f 0460 1510 190d
00000e0: 150f b900 e602 00c0 0005 3a17 1911 1917
00000f0: b800 ea3a 1136 1036 0fa7 ffd8 1911 b800
0000100: eeb7 005c 3a0b bb00 0259 1505 190a bb00
0000110: 1459 b200 b419 0ac0 00b6 b600 c6b7 00c9
0000120: 03b8 00df 3a13 3a12 0319 12b9 00e3 0100
0000130: 1913 3a16 3615 3614 1514 1515 9f00 2715
0000140: 1404 6015 1519 1215 14b9 00e6 0200 c000
0000150: 053a 1819 1619 18b8 00f1 3a16 3615 3614
0000160: a7ff d819 16b8 00ee b700 5c3a 0cb2 00f6
0000170: b200 b405 bd00 0259 0319 0b53 5904 190c
0000180: 53c0 00b6 b600 bab6 00f9 b02a b600 3303
0000190: 32b6 00fb b0
Stackmap Table:
same_frame(#35)
full_frame(#141,{Object[#2],Integer,Integer,Integer,Integer,Integer,Object[#105]},{})
append_frame(#151,Object[#125],Object[#125])
full_frame(#209,{Object[#2],Integer,Integer,Integer,Integer,Integer,Object[#105],Object[#125],Object[#125],Object[#125],Object[#125],Top,Top,Object[#20],Object[#55],Integer,Integer,Object[#103]},{Uninitialized[#159],Uninitialized[#159],Integer,Object[#125]})
full_frame(#252,{Object[#2],Integer,Integer,Integer,Integer,Integer,Object[#105],Object[#125],Object[#125],Object[#125],Object[#125],Top,Top,Object[#20],Object[#55],Integer,Integer,Object[#103]},{Uninitialized[#159],Uninitialized[#159],Integer,Object[#125]})
full_frame(#312,{Object[#2],Integer,Integer,Integer,Integer,Integer,Object[#105],Object[#125],Object[#125],Object[#125],Object[#125],Object[#2],Top,Object[#20],Object[#55],Integer,Integer,Object[#103],Object[#20],Object[#55],Integer,Integer,Object[#103]},{Uninitialized[#262],Uninitialized[#262],Integer,Object[#125]})
full_frame(#355,{Object[#2],Integer,Integer,Integer,Integer,Integer,Object[#105],Object[#125],Object[#125],Object[#125],Object[#125],Object[#2],Top,Object[#20],Object[#55],Integer,Integer,Object[#103],Object[#20],Object[#55],Integer,Integer,Object[#103]},{Uninitialized[#262],Uninitialized[#262],Integer,Object[#125]})
full_frame(#395,{Object[#2],Integer},{})
at scala.collection.immutable.HashMap$.scala$collection$immutable$HashMap$$makeHashTrieMap(HashMap.scala:179)
at scala.collection.immutable.HashMap$HashMap1.updated0(HashMap.scala:211)
at scala.collection.immutable.HashMap.$plus(HashMap.scala:59)
at scala.collection.immutable.HashMap.$plus(HashMap.scala:62)
at scala.collection.immutable.Map$Map4.updated(Map.scala:201)
at scala.collection.immutable.Map$Map4.$plus(Map.scala:202)
at scala.collection.immutable.Map$Map4.$plus(Map.scala:180)
at scala.collection.mutable.MapBuilder.$plus$eq(MapBuilder.scala:29)
at scala.collection.mutable.MapBuilder.$plus$eq(MapBuilder.scala:25)
at scala.collection.generic.Growable.$anonfun$$plus$plus$eq$1(Growable.scala:59)
at scala.collection.generic.Growable$$Lambda$19/924699145.apply(Unknown Source)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at scala.collection.generic.Growable.$plus$plus$eq(Growable.scala:59)
at scala.collection.generic.Growable.$plus$plus$eq$(Growable.scala:50)
at scala.collection.mutable.MapBuilder.$plus$plus$eq(MapBuilder.scala:25)
at scala.collection.generic.GenMapFactory.apply(GenMapFactory.scala:48)
at scala.sys.package$.env(package.scala:61)
at scala.tools.nsc.settings.ScalaSettings.defaultClasspath(ScalaSettings.scala:30)
at scala.tools.nsc.settings.ScalaSettings.defaultClasspath$(ScalaSettings.scala:30)
at scala.tools.nsc.settings.MutableSettings.defaultClasspath(MutableSettings.scala:19)
at scala.tools.nsc.settings.ScalaSettings.$init$(ScalaSettings.scala:60)
at scala.tools.nsc.settings.MutableSettings.<init>(MutableSettings.scala:20)
at scala.tools.nsc.Settings.<init>(Settings.scala:12)
at scala.tools.nsc.Driver.process(Driver.scala:41)
at scala.tools.nsc.Main.process(Main.scala)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:483)
at sbt.compiler.RawCompiler.apply(RawCompiler.scala:26)
at sbt.compiler.AnalyzingCompiler$$anonfun$compileSources$1$$anonfun$apply$2.apply(AnalyzingCompiler.scala:146)
at sbt.compiler.AnalyzingCompiler$$anonfun$compileSources$1$$anonfun$apply$2.apply(AnalyzingCompiler.scala:142)
at sbt.IO$.withTemporaryDirectory(IO.scala:291)
at sbt.compiler.AnalyzingCompiler$$anonfun$compileSources$1.apply(AnalyzingCompiler.scala:142)
at sbt.compiler.AnalyzingCompiler$$anonfun$compileSources$1.apply(AnalyzingCompiler.scala:139)
at sbt.IO$.withTemporaryDirectory(IO.scala:291)
at sbt.compiler.AnalyzingCompiler$.compileSources(AnalyzingCompiler.scala:139)
at sbt.compiler.ComponentCompiler$$anonfun$compileAndInstall$1.apply(ComponentCompiler.scala:63)
at sbt.compiler.ComponentCompiler$$anonfun$compileAndInstall$1.apply(ComponentCompiler.scala:60)
at sbt.IO$.withTemporaryDirectory(IO.scala:291)
at sbt.compiler.ComponentCompiler.compileAndInstall(ComponentCompiler.scala:60)
at sbt.compiler.ComponentCompiler$$anonfun$getLocallyCompiled$1.apply$mcV$sp(ComponentCompiler.scala:50)
at sbt.IfMissing$Define.apply(ComponentManager.scala:75)
at sbt.ComponentManager.sbt$ComponentManager$$createAndCache$1(ComponentManager.scala:39)
at sbt.ComponentManager$$anonfun$sbt$ComponentManager$$fromGlobal$1$1.apply(ComponentManager.scala:27)
at sbt.ComponentManager$$anonfun$sbt$ComponentManager$$fromGlobal$1$1.apply(ComponentManager.scala:26)
at sbt.ComponentManager$$anon$1.call(ComponentManager.scala:50)
at xsbt.boot.Locks$GlobalLock.withChannel$1(Locks.scala:93)
at xsbt.boot.Locks$GlobalLock.xsbt$boot$Locks$GlobalLock$$withChannelRetries$1(Locks.scala:78)
at xsbt.boot.Locks$GlobalLock$$anonfun$withFileLock$1.apply(Locks.scala:97)
at xsbt.boot.Using$.withResource(Using.scala:10)
at xsbt.boot.Using$.apply(Using.scala:9)
at xsbt.boot.Locks$GlobalLock.ignoringDeadlockAvoided(Locks.scala:58)
at xsbt.boot.Locks$GlobalLock.withLock(Locks.scala:48)
at xsbt.boot.Locks$.apply0(Locks.scala:31)
at xsbt.boot.Locks$.apply(Locks.scala:28)
at sbt.ComponentManager.lock(ComponentManager.scala:50)
at sbt.ComponentManager.lockGlobalCache(ComponentManager.scala:49)
at sbt.ComponentManager.sbt$ComponentManager$$fromGlobal$1(ComponentManager.scala:25)
at sbt.ComponentManager$$anonfun$files$1$$anonfun$apply$2.apply(ComponentManager.scala:44)
at sbt.ComponentManager$$anonfun$files$1$$anonfun$apply$2.apply(ComponentManager.scala:44)
at sbt.ComponentManager.sbt$ComponentManager$$getOrElse$1(ComponentManager.scala:32)
at sbt.ComponentManager$$anonfun$files$1.apply(ComponentManager.scala:44)
at sbt.ComponentManager$$anonfun$files$1.apply(ComponentManager.scala:44)
at sbt.ComponentManager$$anon$1.call(ComponentManager.scala:50)
at xsbt.boot.Locks$GlobalLock.withChannel$1(Locks.scala:93)
at xsbt.boot.Locks$GlobalLock.xsbt$boot$Locks$GlobalLock$$withChannelRetries$1(Locks.scala:78)
at xsbt.boot.Locks$GlobalLock$$anonfun$withFileLock$1.apply(Locks.scala:97)
at xsbt.boot.Using$.withResource(Using.scala:10)
at xsbt.boot.Using$.apply(Using.scala:9)
at xsbt.boot.Locks$GlobalLock.ignoringDeadlockAvoided(Locks.scala:58)
at xsbt.boot.Locks$GlobalLock.withLock(Locks.scala:48)
at xsbt.boot.Locks$.apply0(Locks.scala:31)
at xsbt.boot.Locks$.apply(Locks.scala:28)
at sbt.ComponentManager.lock(ComponentManager.scala:50)
at sbt.ComponentManager.lockLocalCache(ComponentManager.scala:47)
at sbt.ComponentManager.files(ComponentManager.scala:44)
at sbt.ComponentManager.file(ComponentManager.scala:53)
at sbt.compiler.ComponentCompiler.getLocallyCompiled(ComponentCompiler.scala:50)
at sbt.compiler.ComponentCompiler.apply(ComponentCompiler.scala:36)
at sbt.compiler.ComponentCompiler$$anon$1.apply(ComponentCompiler.scala:23)
at sbt.compiler.AnalyzingCompiler.loader(AnalyzingCompiler.scala:112)
at sbt.compiler.AnalyzingCompiler.getInterfaceClass(AnalyzingCompiler.scala:117)
at sbt.compiler.AnalyzingCompiler.call(AnalyzingCompiler.scala:98)
at sbt.compiler.AnalyzingCompiler.newCachedCompiler(AnalyzingCompiler.scala:56)
at sbt.compiler.AnalyzingCompiler.newCachedCompiler(AnalyzingCompiler.scala:51)
at sbt.compiler.CompilerCache$$anon$2.apply(CompilerCache.scala:47)
at sbt.compiler.AnalyzingCompiler.compile(AnalyzingCompiler.scala:39)
at sbt.compiler.MixedAnalyzingCompiler$$anonfun$compileScala$1$1.apply$mcV$sp(MixedAnalyzingCompiler.scala:51)
at sbt.compiler.MixedAnalyzingCompiler$$anonfun$compileScala$1$1.apply(MixedAnalyzingCompiler.scala:51)
at sbt.compiler.MixedAnalyzingCompiler$$anonfun$compileScala$1$1.apply(MixedAnalyzingCompiler.scala:51)
at sbt.compiler.MixedAnalyzingCompiler.timed(MixedAnalyzingCompiler.scala:75)
at sbt.compiler.MixedAnalyzingCompiler.compileScala$1(MixedAnalyzingCompiler.scala:50)
at sbt.compiler.MixedAnalyzingCompiler.compile(MixedAnalyzingCompiler.scala:65)
at sbt.compiler.IC$$anonfun$compileInternal$1.apply(IncrementalCompiler.scala:160)
at sbt.compiler.IC$$anonfun$compileInternal$1.apply(IncrementalCompiler.scala:160)
at sbt.inc.IncrementalCompile$$anonfun$doCompile$1.apply(Compile.scala:66)
at sbt.inc.IncrementalCompile$$anonfun$doCompile$1.apply(Compile.scala:64)
at sbt.inc.IncrementalCommon.cycle(IncrementalCommon.scala:31)
at sbt.inc.Incremental$$anonfun$1.apply(Incremental.scala:62)
at sbt.inc.Incremental$$anonfun$1.apply(Incremental.scala:61)
at sbt.inc.Incremental$.manageClassfiles(Incremental.scala:89)
at sbt.inc.Incremental$.compile(Incremental.scala:61)
at sbt.inc.IncrementalCompile$.apply(Compile.scala:54)
at sbt.compiler.IC$.compileInternal(IncrementalCompiler.scala:160)
at sbt.compiler.IC$.incrementalCompile(IncrementalCompiler.scala:138)
at sbt.Compiler$.compile(Compiler.scala:128)
at sbt.Compiler$.compile(Compiler.scala:114)
at sbt.Defaults$.sbt$Defaults$$compileIncrementalTaskImpl(Defaults.scala:814)
at sbt.Defaults$$anonfun$compileIncrementalTask$1.apply(Defaults.scala:805)
at sbt.Defaults$$anonfun$compileIncrementalTask$1.apply(Defaults.scala:803)
at scala.Function1$$anonfun$compose$1.apply(Function1.scala:47)
at sbt.$tilde$greater$$anonfun$$u2219$1.apply(TypeFunctions.scala:40)
at sbt.std.Transform$$anon$4.work(System.scala:63)
at sbt.Execute$$anonfun$submit$1$$anonfun$apply$1.apply(Execute.scala:226)
at sbt.Execute$$anonfun$submit$1$$anonfun$apply$1.apply(Execute.scala:226)
at sbt.ErrorHandling$.wideConvert(ErrorHandling.scala:17)
at sbt.Execute.work(Execute.scala:235)
at sbt.Execute$$anonfun$submit$1.apply(Execute.scala:226)
at sbt.Execute$$anonfun$submit$1.apply(Execute.scala:226)
at sbt.ConcurrentRestrictions$$anon$4$$anonfun$1.apply(ConcurrentRestrictions.scala:159)
at sbt.CompletionService$$anon$2.call(CompletionService.scala:28)
at java.util.concurrent.FutureTask.run(FutureTask.java:266)
at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511)
at java.util.concurrent.FutureTask.run(FutureTask.java:266)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
I am using jdk 1.8.0, OS X 10.11.6. Here code example:
object Main {
def main(args: Array[String]) {
println("test")
}
}
The same code builded fine with scala 2.10
UPD: build.sbt for 2.12 version
name := "hello_world_3"
version := "1.0"
scalaVersion := "2.12.0"
Problem was in minor JDK version. Scala 2.12 require's newer version of JDK then 1.8.0_111. So after JDK upgrade everything work. Thanks to #rumoku
a sample probability matrix:
ans =
0.1444 0.0456 0.0138 0.0126 0.0116 0.0107 0.0052
0.1444 0.0456 0.0138 0.0126 0.0116 0.0107 0.0052
0.1222 0.0386 0.0116 0.0106 0.0098 0.0091 0.0044
0.1444 0.0456 0.0138 0.0126 0.0116 0.0107 0.0052
0.1222 0.0386 0.0116 0.0106 0.0098 0.0091 0.0044
0.1889 0.0596 0.0180 0.0164 0.0151 0.0140 0.0067
0.1333 0.0421 0.0127 0.0116 0.0107 0.0099 0.0048
I have used dataSample and randSample to sample 128 time from my data which has A=(7,7) size in matlab:
datasample(A,128)
ans =
0.1333 0.0421 0.0127 0.0116 0.0107 0.0099 0.0048
0.1222 0.0386 0.0116 0.0106 0.0098 0.0091 0.0044
0.1889 0.0596 0.0180 0.0164 0.0151 0.0140 0.0067
0.1889 0.0596 0.0180 0.0164 0.0151 0.0140 0.0067
0.1333 0.0421 0.0127 0.0116 0.0107 0.0099 0.0048
0.1444 0.0456 0.0138 0.0126 0.0116 0.0107 0.0052
0.1222 0.0386 0.0116 0.0106 0.0098 0.0091 0.0044
...
However, I am interested in having those 128 sample of 7 (128,7) in binary format with two discrete values of 0 and 1:
[1 1 1 0 1 0 1]
I can write a loop and round-down/up those values to 0 and 1 with certain thresholds (i.e. 0.5), but that for sure will be noisy. Is there a function that can output the sampling in binary (0,1) in Matlab ?
I want to create a rocket shape by specifying a "cloud" of surface points and making MATLAB connect the points into rectangular patches (such that when there are many of these, it creates the illusion of a curved surface). How can I make MATLAB do this? I.e. in what order must I specify the points in order to make MATLAB patch them together correctly?
Here is what I have so far:
The above is using plot3; what I have is a long matrix of the following structure:
[x1 y1 z1
x2 y2 z2
.
.
.]
I would like to know how to make this into a surface. The full matrix is:
-0.2470 0.0380 0
-0.2470 0.0374 0.0066
-0.2470 0.0357 0.0130
-0.2470 0.0329 0.0190
-0.2470 0.0291 0.0244
-0.2470 0.0244 0.0291
-0.2470 0.0190 0.0329
-0.2470 0.0130 0.0357
-0.2470 0.0066 0.0374
-0.2470 0.0000 0.0380
-0.2470 -0.0066 0.0374
-0.2470 -0.0130 0.0357
-0.2470 -0.0190 0.0329
-0.2470 -0.0244 0.0291
-0.2470 -0.0291 0.0244
-0.2470 -0.0329 0.0190
-0.2470 -0.0357 0.0130
-0.2470 -0.0374 0.0066
-0.2470 -0.0380 0.0000
-0.2470 -0.0374 -0.0066
-0.2470 -0.0357 -0.0130
-0.2470 -0.0329 -0.0190
-0.2470 -0.0291 -0.0244
-0.2470 -0.0244 -0.0291
-0.2470 -0.0190 -0.0329
-0.2470 -0.0130 -0.0357
-0.2470 -0.0066 -0.0374
-0.2470 -0.0000 -0.0380
-0.2470 0.0066 -0.0374
-0.2470 0.0130 -0.0357
-0.2470 0.0190 -0.0329
-0.2470 0.0244 -0.0291
-0.2470 0.0291 -0.0244
-0.2470 0.0329 -0.0190
-0.2470 0.0357 -0.0130
-0.2470 0.0374 -0.0066
-0.1970 0.0380 0
-0.1970 0.0374 0.0066
-0.1970 0.0357 0.0130
-0.1970 0.0329 0.0190
-0.1970 0.0291 0.0244
-0.1970 0.0244 0.0291
-0.1970 0.0190 0.0329
-0.1970 0.0130 0.0357
-0.1970 0.0066 0.0374
-0.1970 0.0000 0.0380
-0.1970 -0.0066 0.0374
-0.1970 -0.0130 0.0357
-0.1970 -0.0190 0.0329
-0.1970 -0.0244 0.0291
-0.1970 -0.0291 0.0244
-0.1970 -0.0329 0.0190
-0.1970 -0.0357 0.0130
-0.1970 -0.0374 0.0066
-0.1970 -0.0380 0.0000
-0.1970 -0.0374 -0.0066
-0.1970 -0.0357 -0.0130
-0.1970 -0.0329 -0.0190
-0.1970 -0.0291 -0.0244
-0.1970 -0.0244 -0.0291
-0.1970 -0.0190 -0.0329
-0.1970 -0.0130 -0.0357
-0.1970 -0.0066 -0.0374
-0.1970 -0.0000 -0.0380
-0.1970 0.0066 -0.0374
-0.1970 0.0130 -0.0357
-0.1970 0.0190 -0.0329
-0.1970 0.0244 -0.0291
-0.1970 0.0291 -0.0244
-0.1970 0.0329 -0.0190
-0.1970 0.0357 -0.0130
-0.1970 0.0374 -0.0066
-0.1470 0.0380 0
-0.1470 0.0374 0.0066
-0.1470 0.0357 0.0130
-0.1470 0.0329 0.0190
-0.1470 0.0291 0.0244
-0.1470 0.0244 0.0291
-0.1470 0.0190 0.0329
-0.1470 0.0130 0.0357
-0.1470 0.0066 0.0374
-0.1470 0.0000 0.0380
-0.1470 -0.0066 0.0374
-0.1470 -0.0130 0.0357
-0.1470 -0.0190 0.0329
-0.1470 -0.0244 0.0291
-0.1470 -0.0291 0.0244
-0.1470 -0.0329 0.0190
-0.1470 -0.0357 0.0130
-0.1470 -0.0374 0.0066
-0.1470 -0.0380 0.0000
-0.1470 -0.0374 -0.0066
-0.1470 -0.0357 -0.0130
-0.1470 -0.0329 -0.0190
-0.1470 -0.0291 -0.0244
-0.1470 -0.0244 -0.0291
-0.1470 -0.0190 -0.0329
-0.1470 -0.0130 -0.0357
-0.1470 -0.0066 -0.0374
-0.1470 -0.0000 -0.0380
-0.1470 0.0066 -0.0374
-0.1470 0.0130 -0.0357
-0.1470 0.0190 -0.0329
-0.1470 0.0244 -0.0291
-0.1470 0.0291 -0.0244
-0.1470 0.0329 -0.0190
-0.1470 0.0357 -0.0130
-0.1470 0.0374 -0.0066
-0.0970 0.0380 0
-0.0970 0.0374 0.0066
-0.0970 0.0357 0.0130
-0.0970 0.0329 0.0190
-0.0970 0.0291 0.0244
-0.0970 0.0244 0.0291
-0.0970 0.0190 0.0329
-0.0970 0.0130 0.0357
-0.0970 0.0066 0.0374
-0.0970 0.0000 0.0380
-0.0970 -0.0066 0.0374
-0.0970 -0.0130 0.0357
-0.0970 -0.0190 0.0329
-0.0970 -0.0244 0.0291
-0.0970 -0.0291 0.0244
-0.0970 -0.0329 0.0190
-0.0970 -0.0357 0.0130
-0.0970 -0.0374 0.0066
-0.0970 -0.0380 0.0000
-0.0970 -0.0374 -0.0066
-0.0970 -0.0357 -0.0130
-0.0970 -0.0329 -0.0190
-0.0970 -0.0291 -0.0244
-0.0970 -0.0244 -0.0291
-0.0970 -0.0190 -0.0329
-0.0970 -0.0130 -0.0357
-0.0970 -0.0066 -0.0374
-0.0970 -0.0000 -0.0380
-0.0970 0.0066 -0.0374
-0.0970 0.0130 -0.0357
-0.0970 0.0190 -0.0329
-0.0970 0.0244 -0.0291
-0.0970 0.0291 -0.0244
-0.0970 0.0329 -0.0190
-0.0970 0.0357 -0.0130
-0.0970 0.0374 -0.0066
-0.0470 0.0380 0
-0.0470 0.0374 0.0066
-0.0470 0.0357 0.0130
-0.0470 0.0329 0.0190
-0.0470 0.0291 0.0244
-0.0470 0.0244 0.0291
-0.0470 0.0190 0.0329
-0.0470 0.0130 0.0357
-0.0470 0.0066 0.0374
-0.0470 0.0000 0.0380
-0.0470 -0.0066 0.0374
-0.0470 -0.0130 0.0357
-0.0470 -0.0190 0.0329
-0.0470 -0.0244 0.0291
-0.0470 -0.0291 0.0244
-0.0470 -0.0329 0.0190
-0.0470 -0.0357 0.0130
-0.0470 -0.0374 0.0066
-0.0470 -0.0380 0.0000
-0.0470 -0.0374 -0.0066
-0.0470 -0.0357 -0.0130
-0.0470 -0.0329 -0.0190
-0.0470 -0.0291 -0.0244
-0.0470 -0.0244 -0.0291
-0.0470 -0.0190 -0.0329
-0.0470 -0.0130 -0.0357
-0.0470 -0.0066 -0.0374
-0.0470 -0.0000 -0.0380
-0.0470 0.0066 -0.0374
-0.0470 0.0130 -0.0357
-0.0470 0.0190 -0.0329
-0.0470 0.0244 -0.0291
-0.0470 0.0291 -0.0244
-0.0470 0.0329 -0.0190
-0.0470 0.0357 -0.0130
-0.0470 0.0374 -0.0066
0.0030 0.0380 0
0.0030 0.0374 0.0066
0.0030 0.0357 0.0130
0.0030 0.0329 0.0190
0.0030 0.0291 0.0244
0.0030 0.0244 0.0291
0.0030 0.0190 0.0329
0.0030 0.0130 0.0357
0.0030 0.0066 0.0374
0.0030 0.0000 0.0380
0.0030 -0.0066 0.0374
0.0030 -0.0130 0.0357
0.0030 -0.0190 0.0329
0.0030 -0.0244 0.0291
0.0030 -0.0291 0.0244
0.0030 -0.0329 0.0190
0.0030 -0.0357 0.0130
0.0030 -0.0374 0.0066
0.0030 -0.0380 0.0000
0.0030 -0.0374 -0.0066
0.0030 -0.0357 -0.0130
0.0030 -0.0329 -0.0190
0.0030 -0.0291 -0.0244
0.0030 -0.0244 -0.0291
0.0030 -0.0190 -0.0329
0.0030 -0.0130 -0.0357
0.0030 -0.0066 -0.0374
0.0030 -0.0000 -0.0380
0.0030 0.0066 -0.0374
0.0030 0.0130 -0.0357
0.0030 0.0190 -0.0329
0.0030 0.0244 -0.0291
0.0030 0.0291 -0.0244
0.0030 0.0329 -0.0190
0.0030 0.0357 -0.0130
0.0030 0.0374 -0.0066
0.0530 0.0380 0
0.0530 0.0374 0.0066
0.0530 0.0357 0.0130
0.0530 0.0329 0.0190
0.0530 0.0291 0.0244
0.0530 0.0244 0.0291
0.0530 0.0190 0.0329
0.0530 0.0130 0.0357
0.0530 0.0066 0.0374
0.0530 0.0000 0.0380
0.0530 -0.0066 0.0374
0.0530 -0.0130 0.0357
0.0530 -0.0190 0.0329
0.0530 -0.0244 0.0291
0.0530 -0.0291 0.0244
0.0530 -0.0329 0.0190
0.0530 -0.0357 0.0130
0.0530 -0.0374 0.0066
0.0530 -0.0380 0.0000
0.0530 -0.0374 -0.0066
0.0530 -0.0357 -0.0130
0.0530 -0.0329 -0.0190
0.0530 -0.0291 -0.0244
0.0530 -0.0244 -0.0291
0.0530 -0.0190 -0.0329
0.0530 -0.0130 -0.0357
0.0530 -0.0066 -0.0374
0.0530 -0.0000 -0.0380
0.0530 0.0066 -0.0374
0.0530 0.0130 -0.0357
0.0530 0.0190 -0.0329
0.0530 0.0244 -0.0291
0.0530 0.0291 -0.0244
0.0530 0.0329 -0.0190
0.0530 0.0357 -0.0130
0.0530 0.0374 -0.0066
0.1030 0.0380 0
0.1030 0.0374 0.0066
0.1030 0.0357 0.0130
0.1030 0.0329 0.0190
0.1030 0.0291 0.0244
0.1030 0.0244 0.0291
0.1030 0.0190 0.0329
0.1030 0.0130 0.0357
0.1030 0.0066 0.0374
0.1030 0.0000 0.0380
0.1030 -0.0066 0.0374
0.1030 -0.0130 0.0357
0.1030 -0.0190 0.0329
0.1030 -0.0244 0.0291
0.1030 -0.0291 0.0244
0.1030 -0.0329 0.0190
0.1030 -0.0357 0.0130
0.1030 -0.0374 0.0066
0.1030 -0.0380 0.0000
0.1030 -0.0374 -0.0066
0.1030 -0.0357 -0.0130
0.1030 -0.0329 -0.0190
0.1030 -0.0291 -0.0244
0.1030 -0.0244 -0.0291
0.1030 -0.0190 -0.0329
0.1030 -0.0130 -0.0357
0.1030 -0.0066 -0.0374
0.1030 -0.0000 -0.0380
0.1030 0.0066 -0.0374
0.1030 0.0130 -0.0357
0.1030 0.0190 -0.0329
0.1030 0.0244 -0.0291
0.1030 0.0291 -0.0244
0.1030 0.0329 -0.0190
0.1030 0.0357 -0.0130
0.1030 0.0374 -0.0066
0.1530 0.0380 0
0.1530 0.0374 0.0066
0.1530 0.0357 0.0130
0.1530 0.0329 0.0190
0.1530 0.0291 0.0244
0.1530 0.0244 0.0291
0.1530 0.0190 0.0329
0.1530 0.0130 0.0357
0.1530 0.0066 0.0374
0.1530 0.0000 0.0380
0.1530 -0.0066 0.0374
0.1530 -0.0130 0.0357
0.1530 -0.0190 0.0329
0.1530 -0.0244 0.0291
0.1530 -0.0291 0.0244
0.1530 -0.0329 0.0190
0.1530 -0.0357 0.0130
0.1530 -0.0374 0.0066
0.1530 -0.0380 0.0000
0.1530 -0.0374 -0.0066
0.1530 -0.0357 -0.0130
0.1530 -0.0329 -0.0190
0.1530 -0.0291 -0.0244
0.1530 -0.0244 -0.0291
0.1530 -0.0190 -0.0329
0.1530 -0.0130 -0.0357
0.1530 -0.0066 -0.0374
0.1530 -0.0000 -0.0380
0.1530 0.0066 -0.0374
0.1530 0.0130 -0.0357
0.1530 0.0190 -0.0329
0.1530 0.0244 -0.0291
0.1530 0.0291 -0.0244
0.1530 0.0329 -0.0190
0.1530 0.0357 -0.0130
0.1530 0.0374 -0.0066
0.2030 0.0380 0
0.2030 0.0374 0.0066
0.2030 0.0357 0.0130
0.2030 0.0329 0.0190
0.2030 0.0291 0.0244
0.2030 0.0244 0.0291
0.2030 0.0190 0.0329
0.2030 0.0130 0.0357
0.2030 0.0066 0.0374
0.2030 0.0000 0.0380
0.2030 -0.0066 0.0374
0.2030 -0.0130 0.0357
0.2030 -0.0190 0.0329
0.2030 -0.0244 0.0291
0.2030 -0.0291 0.0244
0.2030 -0.0329 0.0190
0.2030 -0.0357 0.0130
0.2030 -0.0374 0.0066
0.2030 -0.0380 0.0000
0.2030 -0.0374 -0.0066
0.2030 -0.0357 -0.0130
0.2030 -0.0329 -0.0190
0.2030 -0.0291 -0.0244
0.2030 -0.0244 -0.0291
0.2030 -0.0190 -0.0329
0.2030 -0.0130 -0.0357
0.2030 -0.0066 -0.0374
0.2030 -0.0000 -0.0380
0.2030 0.0066 -0.0374
0.2030 0.0130 -0.0357
0.2030 0.0190 -0.0329
0.2030 0.0244 -0.0291
0.2030 0.0291 -0.0244
0.2030 0.0329 -0.0190
0.2030 0.0357 -0.0130
0.2030 0.0374 -0.0066
0.2530 0.0380 0
0.2530 0.0374 0.0066
0.2530 0.0357 0.0130
0.2530 0.0329 0.0190
0.2530 0.0291 0.0244
0.2530 0.0244 0.0291
0.2530 0.0190 0.0329
0.2530 0.0130 0.0357
0.2530 0.0066 0.0374
0.2530 0.0000 0.0380
0.2530 -0.0066 0.0374
0.2530 -0.0130 0.0357
0.2530 -0.0190 0.0329
0.2530 -0.0244 0.0291
0.2530 -0.0291 0.0244
0.2530 -0.0329 0.0190
0.2530 -0.0357 0.0130
0.2530 -0.0374 0.0066
0.2530 -0.0380 0.0000
0.2530 -0.0374 -0.0066
0.2530 -0.0357 -0.0130
0.2530 -0.0329 -0.0190
0.2530 -0.0291 -0.0244
0.2530 -0.0244 -0.0291
0.2530 -0.0190 -0.0329
0.2530 -0.0130 -0.0357
0.2530 -0.0066 -0.0374
0.2530 -0.0000 -0.0380
0.2530 0.0066 -0.0374
0.2530 0.0130 -0.0357
0.2530 0.0190 -0.0329
0.2530 0.0244 -0.0291
0.2530 0.0291 -0.0244
0.2530 0.0329 -0.0190
0.2530 0.0357 -0.0130
0.2530 0.0374 -0.0066
0.3030 0.0380 0
0.3030 0.0374 0.0066
0.3030 0.0357 0.0130
0.3030 0.0329 0.0190
0.3030 0.0291 0.0244
0.3030 0.0244 0.0291
0.3030 0.0190 0.0329
0.3030 0.0130 0.0357
0.3030 0.0066 0.0374
0.3030 0.0000 0.0380
0.3030 -0.0066 0.0374
0.3030 -0.0130 0.0357
0.3030 -0.0190 0.0329
0.3030 -0.0244 0.0291
0.3030 -0.0291 0.0244
0.3030 -0.0329 0.0190
0.3030 -0.0357 0.0130
0.3030 -0.0374 0.0066
0.3030 -0.0380 0.0000
0.3030 -0.0374 -0.0066
0.3030 -0.0357 -0.0130
0.3030 -0.0329 -0.0190
0.3030 -0.0291 -0.0244
0.3030 -0.0244 -0.0291
0.3030 -0.0190 -0.0329
0.3030 -0.0130 -0.0357
0.3030 -0.0066 -0.0374
0.3030 -0.0000 -0.0380
0.3030 0.0066 -0.0374
0.3030 0.0130 -0.0357
0.3030 0.0190 -0.0329
0.3030 0.0244 -0.0291
0.3030 0.0291 -0.0244
0.3030 0.0329 -0.0190
0.3030 0.0357 -0.0130
0.3030 0.0374 -0.0066
0.3530 0.0380 0
0.3530 0.0374 0.0066
0.3530 0.0357 0.0130
0.3530 0.0329 0.0190
0.3530 0.0291 0.0244
0.3530 0.0244 0.0291
0.3530 0.0190 0.0329
0.3530 0.0130 0.0357
0.3530 0.0066 0.0374
0.3530 0.0000 0.0380
0.3530 -0.0066 0.0374
0.3530 -0.0130 0.0357
0.3530 -0.0190 0.0329
0.3530 -0.0244 0.0291
0.3530 -0.0291 0.0244
0.3530 -0.0329 0.0190
0.3530 -0.0357 0.0130
0.3530 -0.0374 0.0066
0.3530 -0.0380 0.0000
0.3530 -0.0374 -0.0066
0.3530 -0.0357 -0.0130
0.3530 -0.0329 -0.0190
0.3530 -0.0291 -0.0244
0.3530 -0.0244 -0.0291
0.3530 -0.0190 -0.0329
0.3530 -0.0130 -0.0357
0.3530 -0.0066 -0.0374
0.3530 -0.0000 -0.0380
0.3530 0.0066 -0.0374
0.3530 0.0130 -0.0357
0.3530 0.0190 -0.0329
0.3530 0.0244 -0.0291
0.3530 0.0291 -0.0244
0.3530 0.0329 -0.0190
0.3530 0.0357 -0.0130
0.3530 0.0374 -0.0066
0.4030 0.0380 0
0.4030 0.0374 0.0066
0.4030 0.0357 0.0130
0.4030 0.0329 0.0190
0.4030 0.0291 0.0244
0.4030 0.0244 0.0291
0.4030 0.0190 0.0329
0.4030 0.0130 0.0357
0.4030 0.0066 0.0374
0.4030 0.0000 0.0380
0.4030 -0.0066 0.0374
0.4030 -0.0130 0.0357
0.4030 -0.0190 0.0329
0.4030 -0.0244 0.0291
0.4030 -0.0291 0.0244
0.4030 -0.0329 0.0190
0.4030 -0.0357 0.0130
0.4030 -0.0374 0.0066
0.4030 -0.0380 0.0000
0.4030 -0.0374 -0.0066
0.4030 -0.0357 -0.0130
0.4030 -0.0329 -0.0190
0.4030 -0.0291 -0.0244
0.4030 -0.0244 -0.0291
0.4030 -0.0190 -0.0329
0.4030 -0.0130 -0.0357
0.4030 -0.0066 -0.0374
0.4030 -0.0000 -0.0380
0.4030 0.0066 -0.0374
0.4030 0.0130 -0.0357
0.4030 0.0190 -0.0329
0.4030 0.0244 -0.0291
0.4030 0.0291 -0.0244
0.4030 0.0329 -0.0190
0.4030 0.0357 -0.0130
0.4030 0.0374 -0.0066
0.4530 0.0380 0
0.4530 0.0374 0.0066
0.4530 0.0357 0.0130
0.4530 0.0329 0.0190
0.4530 0.0291 0.0244
0.4530 0.0244 0.0291
0.4530 0.0190 0.0329
0.4530 0.0130 0.0357
0.4530 0.0066 0.0374
0.4530 0.0000 0.0380
0.4530 -0.0066 0.0374
0.4530 -0.0130 0.0357
0.4530 -0.0190 0.0329
0.4530 -0.0244 0.0291
0.4530 -0.0291 0.0244
0.4530 -0.0329 0.0190
0.4530 -0.0357 0.0130
0.4530 -0.0374 0.0066
0.4530 -0.0380 0.0000
0.4530 -0.0374 -0.0066
0.4530 -0.0357 -0.0130
0.4530 -0.0329 -0.0190
0.4530 -0.0291 -0.0244
0.4530 -0.0244 -0.0291
0.4530 -0.0190 -0.0329
0.4530 -0.0130 -0.0357
0.4530 -0.0066 -0.0374
0.4530 -0.0000 -0.0380
0.4530 0.0066 -0.0374
0.4530 0.0130 -0.0357
0.4530 0.0190 -0.0329
0.4530 0.0244 -0.0291
0.4530 0.0291 -0.0244
0.4530 0.0329 -0.0190
0.4530 0.0357 -0.0130
0.4530 0.0374 -0.0066
0.5030 0.0380 0
0.5030 0.0374 0.0066
0.5030 0.0357 0.0130
0.5030 0.0329 0.0190
0.5030 0.0291 0.0244
0.5030 0.0244 0.0291
0.5030 0.0190 0.0329
0.5030 0.0130 0.0357
0.5030 0.0066 0.0374
0.5030 0.0000 0.0380
0.5030 -0.0066 0.0374
0.5030 -0.0130 0.0357
0.5030 -0.0190 0.0329
0.5030 -0.0244 0.0291
0.5030 -0.0291 0.0244
0.5030 -0.0329 0.0190
0.5030 -0.0357 0.0130
0.5030 -0.0374 0.0066
0.5030 -0.0380 0.0000
0.5030 -0.0374 -0.0066
0.5030 -0.0357 -0.0130
0.5030 -0.0329 -0.0190
0.5030 -0.0291 -0.0244
0.5030 -0.0244 -0.0291
0.5030 -0.0190 -0.0329
0.5030 -0.0130 -0.0357
0.5030 -0.0066 -0.0374
0.5030 -0.0000 -0.0380
0.5030 0.0066 -0.0374
0.5030 0.0130 -0.0357
0.5030 0.0190 -0.0329
0.5030 0.0244 -0.0291
0.5030 0.0291 -0.0244
0.5030 0.0329 -0.0190
0.5030 0.0357 -0.0130
0.5030 0.0374 -0.0066
0.5530 0.0347 0
0.5530 0.0342 0.0060
0.5530 0.0326 0.0119
0.5530 0.0301 0.0174
0.5530 0.0266 0.0223
0.5530 0.0223 0.0266
0.5530 0.0174 0.0301
0.5530 0.0119 0.0326
0.5530 0.0060 0.0342
0.5530 0.0000 0.0347
0.5530 -0.0060 0.0342
0.5530 -0.0119 0.0326
0.5530 -0.0174 0.0301
0.5530 -0.0223 0.0266
0.5530 -0.0266 0.0223
0.5530 -0.0301 0.0174
0.5530 -0.0326 0.0119
0.5530 -0.0342 0.0060
0.5530 -0.0347 0.0000
0.5530 -0.0342 -0.0060
0.5530 -0.0326 -0.0119
0.5530 -0.0301 -0.0174
0.5530 -0.0266 -0.0223
0.5530 -0.0223 -0.0266
0.5530 -0.0174 -0.0301
0.5530 -0.0119 -0.0326
0.5530 -0.0060 -0.0342
0.5530 -0.0000 -0.0347
0.5530 0.0060 -0.0342
0.5530 0.0119 -0.0326
0.5530 0.0174 -0.0301
0.5530 0.0223 -0.0266
0.5530 0.0266 -0.0223
0.5530 0.0301 -0.0174
0.5530 0.0326 -0.0119
0.5530 0.0342 -0.0060
0.6030 0.0242 0
0.6030 0.0238 0.0042
0.6030 0.0227 0.0083
0.6030 0.0209 0.0121
0.6030 0.0185 0.0155
0.6030 0.0155 0.0185
0.6030 0.0121 0.0209
0.6030 0.0083 0.0227
0.6030 0.0042 0.0238
0.6030 0.0000 0.0242
0.6030 -0.0042 0.0238
0.6030 -0.0083 0.0227
0.6030 -0.0121 0.0209
0.6030 -0.0155 0.0185
0.6030 -0.0185 0.0155
0.6030 -0.0209 0.0121
0.6030 -0.0227 0.0083
0.6030 -0.0238 0.0042
0.6030 -0.0242 0.0000
0.6030 -0.0238 -0.0042
0.6030 -0.0227 -0.0083
0.6030 -0.0209 -0.0121
0.6030 -0.0185 -0.0155
0.6030 -0.0155 -0.0185
0.6030 -0.0121 -0.0209
0.6030 -0.0083 -0.0227
0.6030 -0.0042 -0.0238
0.6030 -0.0000 -0.0242
0.6030 0.0042 -0.0238
0.6030 0.0083 -0.0227
0.6030 0.0121 -0.0209
0.6030 0.0155 -0.0185
0.6030 0.0185 -0.0155
0.6030 0.0209 -0.0121
0.6030 0.0227 -0.0083
0.6030 0.0238 -0.0042
0.6530 0 0
0.6530 0 0
0.6530 0 0
0.6530 0 0
0.6530 0 0
0.6530 0 0
0.6530 0 0
0.6530 0 0
0.6530 0 0
0.6530 0 0
0.6530 0 0
0.6530 0 0
0.6530 0 0
0.6530 0 0
0.6530 0 0
0.6530 0 0
0.6530 0 0
0.6530 0 0
0.6530 0 0
0.6530 0 0
0.6530 0 0
0.6530 0 0
0.6530 0 0
0.6530 0 0
0.6530 0 0
0.6530 0 0
0.6530 0 0
0.6530 0 0
0.6530 0 0
0.6530 0 0
0.6530 0 0
0.6530 0 0
0.6530 0 0
0.6530 0 0
0.6530 0 0
0.6530 0 0
MATLAB plots the points in the same order as the vector that you supply. For example,
plot3([x1 x2 x3], [y1 y2 y3], [z1 z2 z3])
will connect [x1 y1 z1] to [x2 y2 z2], and connect [x2 y2 z2] to [x3 y3 z3]. If you want to create a rocket shape that is composed of several curves, then you will need to use one plot3 command for each curve.