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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)
I have a dataframe which is of length 177 and I want to calculate and plot the partial auto-correlation function (PACF).
I have the data imported etc and I do:
from statsmodels.tsa.stattools import pacf
ys = pacf(data[key][array].diff(1).dropna(), alpha=0.05, nlags=176, method="ywunbiased")
xs = range(lags+1)
plt.figure()
plt.scatter(xs,ys[0])
plt.grid()
plt.vlines(xs, 0, ys[0])
plt.plot(ys[1])
The method used results in numbers greater than 1 for very long lags (90ish) which is incorrect and I get a RuntimeWarning: invalid value encountered in sqrtreturn rho, np.sqrt(sigmasq) but since I can't see their source code I don't know what this means.
To be honest, when I search for PACF, all the examples only carry out PACF up to 40 lags or 60 or so and they never have any significant PACF after lag=2 and so I couldn't compare to other examples either.
But when I use:
method="ols"
# or
method="ywmle"
the numbers are corrected. So it must be the algo they use to solve it.
I tried importing inspect and getsource method but its useless it just shows that it uses another package and I can't find that.
If you also know where the problem arises from, I would really appreciate the help.
For your reference, the values for data[key][array] are:
[1131.130005, 1144.939941, 1126.209961, 1107.300049, 1120.680054, 1140.839966, 1101.719971, 1104.23999, 1114.579956, 1130.199951, 1173.819946, 1211.920044, 1181.27002, 1203.599976, 1180.589966, 1156.849976, 1191.5, 1191.329956, 1234.180054, 1220.329956, 1228.810059, 1207.01001, 1249.47998, 1248.290039, 1280.079956, 1280.660034, 1294.869995, 1310.609985, 1270.089966, 1270.199951, 1276.660034, 1303.819946, 1335.849976, 1377.939941, 1400.630005, 1418.300049, 1438.23999, 1406.819946, 1420.859985, 1482.369995, 1530.619995, 1503.349976, 1455.27002, 1473.98999, 1526.75, 1549.380005, 1481.140015, 1468.359985, 1378.550049, 1330.630005, 1322.699951, 1385.589966, 1400.380005, 1280.0, 1267.380005, 1282.829956, 1166.359985, 968.75, 896.23999, 903.25, 825.880005, 735.090027, 797.869995, 872.8099980000001, 919.1400150000001, 919.320007, 987.4799800000001, 1020.6199949999999, 1057.079956, 1036.189941, 1095.630005, 1115.099976, 1073.869995, 1104.48999, 1169.430054, 1186.689941, 1089.410034, 1030.709961, 1101.599976, 1049.329956, 1141.199951, 1183.26001, 1180.550049, 1257.640015, 1286.119995, 1327.219971, 1325.829956, 1363.609985, 1345.199951, 1320.640015, 1292.280029, 1218.890015, 1131.420044, 1253.300049, 1246.959961, 1257.599976, 1312.410034, 1365.680054, 1408.469971, 1397.910034, 1310.329956, 1362.160034, 1379.319946, 1406.579956, 1440.670044, 1412.160034, 1416.180054, 1426.189941, 1498.109985, 1514.680054, 1569.189941, 1597.569946, 1630.73999, 1606.280029, 1685.72998, 1632.969971, 1681.550049, 1756.540039, 1805.810059, 1848.359985, 1782.589966, 1859.449951, 1872.339966, 1883.949951, 1923.569946, 1960.22998, 1930.6700440000002, 2003.369995, 1972.290039, 2018.050049, 2067.560059, 2058.899902, 1994.9899899999998, 2104.5, 2067.889893, 2085.51001, 2107.389893, 2063.110107, 2103.840088, 1972.180054, 1920.030029, 2079.360107, 2080.409912, 2043.939941, 1940.2399899999998, 1932.22998, 2059.73999, 2065.300049, 2096.949951, 2098.860107, 2173.600098, 2170.949951, 2168.27002, 2126.149902, 2198.810059, 2238.830078, 2278.8701170000004, 2363.639893, 2362.719971, 2384.199951, 2411.800049, 2423.409912, 2470.300049, 2471.649902, 2519.360107, 2575.26001, 2584.840088, 2673.610107, 2823.810059, 2713.830078, 2640.8701170000004, 2648.050049, 2705.27002, 2718.3701170000004, 2816.290039, 2901.52002, 2913.97998]
Your time series is pretty clearly not stationary, so that Yule-Walker assumptions are violated.
More generally, PACF is usually appropriate with stationary time series. You might difference your data first, before considering the partial autocorrelations.
I'm implementing some RL in PyTorch and had to write my own mse_loss function (which I found on Stackoverflow ;) ).
The loss function is:
def mse_loss(input_, target_):
return torch.sum(
(input_ - target_) * (input_ - target_)) / input_.data.nelement()
Now, in my training loop, the first input is something like:
tensor([-1.7610e+10]), tensor([-6.5097e+10])
With this input I'll get the error:
Unable to get repr for <class 'torch.Tensor'>
Computing a = (input_ - target_) works fine, while b = a * a respectively b = torch.pow(a, 2) will fail with the error metioned above.
Does anyone know a fix for this?
Thanks a lot!
Update:
I just tried using torch.nn.functional.mse_loss which will result in the same error..
I had the same error,when I use the below code
criterion = torch.nn.CrossEntropyLoss().cuda()
output=output.cuda()
target=target.cuda()
loss=criterion(output, target)
but I finally found my wrong：output is like tensor([[0.5746,0.4254]]) and target is like tensor([2]),the number 2 is out of indice of output
when I not use GPU,this error message is:
RuntimeError: Assertion `cur_target >= 0 && cur_target < n_classes' failed. at /opt/conda/conda-bld/pytorch-nightly_1547458468907/work/aten/src/THNN/generic/ClassNLLCriterion.c:93
Are you using a GPU ?
I had simillar problem (but I was using gather operations), and when I moved my tensors to CPU I could get a proper error message. I fixed the error, switched back to GPU and it was alright.
Maybe pytorch has trouble outputing the correct error when it comes from inside the GPU.
I have a word2vec model that I created in PySpark. The model is saved as a .parquet file. I want to be able to access and query the model (or the words and word vectors) using vanilla Python because I am building a flask app that will allow a user to enter words of interest for finding synonyms.
I've extracted the words and word vectors, but I've noticed that while I have approximately 7000 unique words, my word vectors have a length of 100. For example, here are two words "serious" and "breaks". Their vectors only have a length of 100. Why is this? How is it able to then reconstruct the entire vector space with only 100 values for each word? Is it simply only giving me the top 100 or the first 100 values?
vectors.take(2)
Out[48]:
[Row(word=u'serious', vector=DenseVector([0.0784, -0.0882, -0.0342, -0.0153, 0.0223, 0.1034, 0.1218, -0.0814, -0.0198, -0.0325, -0.1024, -0.2412, -0.0704, -0.1575, 0.0342, -0.1447, -0.1687, 0.0673, 0.1248, 0.0623, -0.0078, -0.0813, 0.0953, -0.0213, 0.0031, 0.0773, -0.0246, -0.0822, -0.0252, -0.0274, -0.0288, 0.0403, -0.0419, -0.1122, -0.0397, 0.0186, -0.0038, 0.1279, -0.0123, 0.0091, 0.0065, 0.0884, 0.0899, -0.0479, 0.0328, 0.0171, -0.0962, 0.0753, -0.187, 0.034, -0.1393, -0.0575, -0.019, 0.0151, -0.0205, 0.0667, 0.0762, -0.0365, -0.025, -0.184, -0.0118, -0.0964, 0.1744, 0.0563, -0.0413, -0.054, -0.1764, -0.087, 0.0747, -0.022, 0.0778, -0.0014, -0.1313, -0.1133, -0.0669, 0.0007, -0.0378, -0.1093, -0.0732, 0.1494, -0.0815, -0.0137, 0.1009, -0.0057, 0.0195, 0.0085, 0.025, 0.0064, 0.0076, 0.0676, 0.1663, -0.0078, 0.0278, 0.0519, -0.0615, -0.0833, 0.0643, 0.0032, -0.0882, 0.1033])),
Row(word=u'breaks', vector=DenseVector([0.0065, 0.0027, -0.0121, 0.0296, -0.0467, 0.0297, 0.0499, 0.0843, 0.1027, 0.0179, -0.014, 0.0586, 0.06, 0.0534, 0.0391, -0.0098, -0.0266, -0.0422, 0.0188, 0.0065, -0.0309, 0.0038, -0.0458, -0.0252, 0.0428, 0.0046, -0.065, -0.0822, -0.0555, -0.0248, -0.0288, -0.0016, 0.0334, -0.0028, -0.0718, -0.0571, -0.0668, -0.0073, 0.0658, -0.0732, 0.0976, -0.0255, -0.0712, 0.0899, 0.0065, -0.04, 0.0964, 0.0356, 0.0142, 0.0857, 0.0669, -0.038, -0.0728, -0.0446, 0.1194, -0.056, 0.1022, 0.0459, -0.0343, -0.0861, -0.0943, -0.0435, -0.0573, 0.0229, 0.0368, 0.085, -0.0218, -0.0623, 0.0502, -0.0645, 0.0247, -0.0371, -0.0785, 0.0371, -0.0047, 0.0012, 0.0214, 0.0669, 0.049, -0.0294, -0.0272, 0.0642, -0.006, -0.0804, -0.06, 0.0719, -0.0109, -0.0272, -0.0366, 0.0041, 0.0556, 0.0108, 0.0624, 0.0134, -0.0094, 0.0219, 0.0164, -0.0545, -0.0055, -0.0193]))]
Any thoughts on the best way to reconstruct this model in vanilla python?
Just to improve on the comment by zero323, for anyone else who arrives here.
Word2Vec has a default setting to create word vectors of 100dims. To change this:
model = Word2Vec(sentences, size=300)
when initializing the model will create vectors of 300 dimensions.
I think the problem lays with your minCount parameter value for the Word2Vec model.
If this value is too high, less words get used in the training of the model resulting in a words vector of only 100.
7000 unique words is not a lot.
Try setting the minCount lower than the default 5.
model.setMinCount(value)
https://spark.apache.org/docs/latest/api/python/pyspark.ml.html?highlight=word2vec#pyspark.ml.feature.Word2Vec
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.