I am creating a MAW well and want to use it as an observation well to compare it later to field data, it should be screened over multiple layers. However, I am only getting the head value in the well of the very last timestep in my output file. Any ideas on how to get all timesteps in the output?
The FloPy manual says something about it needing to be in Output Control, but I can't figure out how to do that:
print_head (boolean) – print_head (boolean) keyword to indicate that the list of multi-aquifer well heads will be printed to the listing file for every stress period in which “HEAD PRINT” is specified in Output Control. If there is no Output Control option and PRINT_HEAD is specified, then heads are printed for the last time step of each stress period.
In the MODFLOW6 manual I see that it is possible to make a continuous output:
modflow6
My MAW definition looks like this:
maw = flopy.mf6.ModflowGwfmaw(gwf,
nmawwells=1,
packagedata=[0, Rwell, minbot, wellhead,'MEAN',OBS1welllayers],
connectiondata=OBS1connectiondata,
perioddata=[(0,'STATUS','ACTIVE')],
flowing_wells=False,
save_flows=True,
mover=True,
flow_correction=True,
budget_filerecord='OBS1wellbudget',
print_flows=True,
print_head=True,
head_filerecord='OBS1wellhead',
)
My output control looks like this:
oc = flopy.mf6.ModflowGwfoc(gwf,
budget_filerecord=budget_file,
head_filerecord=head_file,
saverecord=[('HEAD', 'ALL'), ('BUDGET', 'ALL'), ],
)
Hope this is all clear and someone can help me, thanks!
You need to initialise the MAW observations file... it's not done in the OC package.
You can find the scripts for the three MAW examples in the MF6 documentation here:
https://github.com/MODFLOW-USGS/modflow6-examples/tree/master/notebooks
It looks something like this:
obs_file = "{}.maw.obs".format(name)
csv_file = obs_file + ".csv"
obs_dict = {csv_file: [
("head", "head", (0,)),
("Q1", "maw", (0,), (0,)),
("Q2", "maw", (0,), (1,)),
("Q3", "maw", (0,), (2,)),
]}
maw.obs.initialize(filename=obs_file, digits=10, print_input=True, continuous=obs_dict)
created a linear regression model in which tried to find the weights(coefficients) and bias(y-intercept) thereby running the following code:-
reg.intercept_
reg.coef_
output
array([9.41523946, 9.41523946])
array([[-0.44871341, 0.20903483, 0.0142496 , 0.01288174, -0.14055166,
-0.17990912, -0.06054988, -0.08992433, -0.1454692 , -0.10144383,
-0.20062984, -0.12988747, -0.16859669, -0.12149035, -0.03336798,
-0.14690868, 0.32047333],
[-0.44871341, 0.20903483, 0.0142496 , 0.01288174, -0.14055166,
-0.17990912, -0.06054988, -0.08992433, -0.1454692 , -0.10144383,
-0.20062984, -0.12988747, -0.16859669, -0.12149035, -0.03336798,
-0.14690868, 0.32047333]])
getting the same values twice rather getting them only once and hence having difficulty in summarizing the weights
It could be that your input provides same columns as input for you reg. model.
Can you provide a sample of your dataset?
I have a pyspark dataframe as shown below
+--------------------+
| speed|
+--------------------+
|[5.59239, 2.51329...|
|[0.0191166, 0.169...|
|[0.561913, 0.4098...|
|[0.393343, 0.3580...|
|[0.118315, 0.1183...|
|[0.831407, 0.4470...|
|[1.49012e-08, 0.1...|
|[0.0411047, 0.152...|
|[0.620069, 0.8262...|
|[0.20373, 0.20373...|
+--------------------+
How can I write this dataframe to CSV such that I save it as it is shown above.Currently I tried coalesce but it saved as below
"[5.59239, 2.51329, 0.141536, 1.27485, 2.35138, 12.9668, 12.9668, 2.52421, 0.330804, 0.459188, 0.459188, 0.651573, 3.15373, 6.11923, 8.8445, 8.0871, 0.855173, 1.43534, 1.43534, 1.05988, 1.05988, 0.778344, 1.20522, 1.70414, 1.70414, 0.0795492, 1.10385, 1.4759, 1.64844, 0.82941, 1.11321, 1.37977, 0.849902, 1.24436, 1.24436, 0.698651, 0.791467, 0.636781, 0.666729, 0.666729, 0.45688, 0.45688, 0.158829, 2.12693, 29.8682, 29.8682, 9.62536, 3.40384, 2.51002, 1.55077, 1.01774, 0.922753, 0.922753, 0.0438924, 0.530669, 0.879573, 0.627267, 0.0532846, 0.0890066, 0.0884833, 0.140008, 0.147534, 0.0180038, 0.0132851, 0.112785, 0.112785, 0.22997, 0.22997, 0.0524423, 0.141886, 0.328422,............]"
But I want to save it in the format such that it is a proper excel file,with speed as column name and its values as a list of lists.
I dont want to use topandas() as it is memory intensive
If i have over emphasised/under emphasised sth,please let me know in the comments.
df.coalesce(1).write.option("header","true").csv("file:///s/tesing")
I resolved this!
df_Welding_amp.rdd.coalesce(1).saveAsTextFile('home/ram/file.csv')
Though I didnt get exactly as a list of lists, I was able to successfully get in row format as below
Row(speed='[5.59239, 2.51329, 0.141536, 1.27485, 2.35138, 12.9668, 12.9668, 2.52421, 0.330804, 0.459188, 0.459188, 0.651573, 3.15373, 6.11923, 8.8445, 8.0871, 0.855173, 1.43534, 1.43534, 1.05988, 1.05988, 0.778344, 1.20522, 1.70414, 1.70414, 0.0795492, 1.10385, 1.4759, 1.64844, 0.82941........
.....]
Row(speed='[0.0191166, 0.169978, 0.226254, 0.149923, 0.149923, 0.505102, 0.505102, 0.369975, 0.305384, 0.154693, 0.224818, 0.875909, 0.875909, 2.5506, 6.06761, 5.0829, 4.46667, 2.16333, 3.74257, 3.74257, 2.33873, 1.39336, 1.56772, 0.889895, 0.249284, 0.249284, 0.132409, 0.177825, 0.270215, 0.398466, 2.3726, 4.87186, 4.05198, 2.23753, 0.266356, 0.513157, 0.78962, 0.523164, 0.138469, 0.315834, 0.315834]
I am looking at the documentation of Decision Tree in Spark MLLib. Here is a line of code
data = MLUtils.loadLibSVMFile(sc, 'data/mllib/sample_libsvm_data.txt')
that loads the input data. When I opened the sample_libsv_data.txt file, one of the lines looked like:
0 128:51 129:159 130:253 131:159 132:50 155:48 156:238 157:252 158:252 159:252 160:237 182:54 183:227 184:253 185:252 186:239 187:233 188:252 189:57 190:6 208:10 209:60 210:224 211:252 212:253 213:252 214:202 215:84 216:252 217:253 218:122 236:163 237:252 238:252 239:252 240:253 241:252 242:252 243:96 244:189 245:253 246:167 263:51 264:238 265:253 266:253 267:190 268:114 269:253 270:228 271:47 272:79 273:255 274:168 290:48 291:238 292:252 293:252 294:179 295:12 296:75 297:121 298:21 301:253 302:243 303:50 317:38 318:165 319:253 320:233 321:208 322:84 329:253 330:252 331:165 344:7 345:178 346:252 347:240 348:71 349:19 350:28 357:253 358:252 359:195 372:57 373:252 374:252 375:63 385:253 386:252 387:195 400:198 401:253 402:190 413:255 414:253 415:196 427:76 428:246 429:252 430:112 441:253 442:252 443:148 455:85 456:252 457:230 458:25 467:7 468:135 469:253 470:186 471:12 483:85 484:252 485:223 494:7 495:131 496:252 497:225 498:71 511:85 512:252 513:145 521:48 522:165 523:252 524:173 539:86 540:253 541:225 548:114 549:238 550:253 551:162 567:85 568:252 569:249 570:146 571:48 572:29 573:85 574:178 575:225 576:253 577:223 578:167 579:56 595:85 596:252 597:252 598:252 599:229 600:215 601:252 602:252 603:252 604:196 605:130 623:28 624:199 625:252 626:252 627:253 628:252 629:252 630:233 631:145 652:25 653:128 654:252 655:253 656:252 657:141 658:37
I can understand that the first element is the class label (0) and I know about decision tree algorithm but I don't understand why each feature is like a tuple? Shouldn't we have just numbers representing features? What is the meaning of 128:51 as a feature value here?
128:51 as a feature value here means that there is value 51 in column 128. This is SVMLight format first introduced in svmlight and is good for representing sparse vectors. All indices that are not mentioned by name are omitted from the list and those features have 0 value. In other words, all columns from 1 to 127 are 0 in your example.
Note: the indexing of the columns in Spark sparse vectors like above starts from 0. So, there is a column with index 0, and 0:100 is a possible entry in the SVMLight format.
I am using sVM-light for binary classification.and I am using SVM in the learning mode.
I have my train.dat file ready.but when i run this command ,instead of creating file model ,it writes somethings in terminal:
my command:
./svm_learn example1/train.dat example1/model
output:
Scanning examples...done
Reading examples into memory...Feature numbers must be larger or equal to 1!!!
: Success
LINE: -1 0:1.0 6:1.0 16:1.0 18:1.0 28:1.0 29:1.0 31:1.0 48:1.0 58:1.0 73:1.0 82:1.0 93:1.0 95:1.0 106:1.0 108:1.0 118:1.0 121:1.0 122:1.0151:1.0 164:1.0 167:1.0 169:1.0 170:1.0 179:1.0 190:1.0 193:1.0 220:1.0 237:1.0250:1.0 252:1.0 267:1.0 268:1.0 269:1.0 278:1.0 283:1.0 291:1.0 300:1.0 305:1.0320:1.0 332:1.0 336:1.0 342:1.0 345:1.0 348:1.0 349:1.0 350:1.0 368:1.0 370:1.0384:1.0 390:1.0 394:1.0 395:1.0 396:1.0 397:1.0 400:1.0 401:1.0 408:1.0 416:1.0427:1.0 433:1.0 435:1.0 438:1.0 441:1.0 446:1.0 456:1.0 471:1.0 485:1.0 510:1.0523:1.0 525:1.0 526:1.0 532:1.0 540:1.0 553:1.0 567:1.0 568:1.0 581:1.0 583:1.0604:1.0 611:1.0 615:1.0 616:1.0 618:1.0 623:1.0 624:1.0 626:1.0 651:1.0 659:1.0677:1.0 678:1.0 683:1.0 690:1.0 694:1.0 699:1.0 713:1.0 714:1.0 720:1.0 722:1.0731:1.0 738:1.0 755:1.0 761:1.0 763:1.0 768:1.0 776:1.0 782:1.0 792:1.0 817:1.0823:1.0 827:1.0 833:1.0 834:1.0 838:1.0 842:1.0 848:1.0 851:1.0 863:1.0 867:1.0890:1.0 900:1.0 903:1.0 923:1.0 935:1.0 942:1.0 946:1.0 947:1.0 949:1.0 956:1.0962:1.0 965:1.0 968:1.0 983:1.0 986:1.0 987:1.0 990:1.0 998:1.0 1007:1.0 1014:1.0 1019:1.0 1022:1.0 1024:1.0 1029:1.0 1030:1.01032:1.0 1047:1.0 1054:1.0 1063:1.0 1069:1.0 1076:1.0 1085:1.0 1093:1.0 1098:1.0 1108:1.0 1109:1.01116:1.0 1120:1.0 1133:1.0 1134:1.0 1135:1.0 1138:1.0 1139:1.0 1144:1.0 1146:1.0 1148:1.0 1149:1.01161:1.0 1165:1.0 1169:1.0 1170:1.0 1177:1.0 1187:1.0 1194:1.0 1212:1.0 1214:1.0 1239:1.0 1243:1.01251:1.0 1257:1.0 1274:1.0 1278:1.0 1292:1.0 1297:1.0 1304:1.0 1319:1.0 1324:1.0 1325:1.0 1353:1.01357:1.0 1366:1.0 1374:1.0 1379:1.0 1392:1.0 1394:1.0 1407:1.0 1412:1.0 1414:1.0 1419:1.0 1433:1.01435:1.0 1437:1.0 1453:1.0 1463:1.0 1464:1.0 1469:1.0 1477:1.0 1481:1.0 1487:1.0 1506:1.0 1514:1.01519:1.0 1526:1.0 1536:1.0 1549:1.0 1551:1.0 1553:1.0 1561:1.0 1569:1.0 1578:1.0 1603:1.0 1610:1.01615:1.0 1617:1.0 1625:1.0 1638:1.0 1646:1.0 1663:1.0 1666:1.0 1672:1.0 1681:1.0 1690:1.0 1697:1.01699:1.0 1706:1.0 1708:1.0 1717:1.0 1719:1.0 1732:1.0 1737:1.0 1756:1.0 1766:1.0 1771:1.0 1789:1.01804:1.0 1805:1.0 1808:1.0 1814:1.0 1815:1.0 1820:1.0 1824:1.0 1832:1.0 1841:1.0 1844:1.0 1852:1.01861:1.0 1875:1.0 1899:1.0 1902:1.0 1904:1.0 1905:1.0 1917:1.0 1918:1.0 1919:1.0 1921:1.0 1926:1.01934:1.0 1937:1.0 1942:1.0 1956:1.0 1965:1.0 1966:1.0 1970:1.0 1971:1.0 1980:1.0 1995:1.0 2000:1.02009:1.0 2010:1.0 2012:1.0 2015:1.0 2018:1.0 2022:1.0 2047:1.0 2076:1.0 2082:1.0 2095:1.0 2108:1.02114:1.0 2123:1.0 2130:1.0 2133:1.0 2141:1.0 2142:1.0 2143:1.0 2148:1.0 2157:1.0 2160:1.0 2162:1.02170:1.0 2195:1.0 2199:1.0 2201:1.0 2202:1.0 2205:1.0 2211:1.0 2218:1.0
I dont know what to do.
p.s.when i make my train.dat very shorter ,everything works fine!!!
Thank you
From what I could interpret from the log, your training set has an issue.
The first few characters of the training row that has issue are
-1 0:1.0 6:1.0
The issue is not with the size but with feature indexing. You are starting your feature index at 0 (0:1) whereas svmlight requires that all feature index be equal or greater than 1.
Change the indexing to start at 1 and it should work fine.