Random Forest Analysis Giving NaNs - dataframe
I have a df that is all numeric values. Columns 1:1028 are the predictors and columns 1029:1033 are the responses. Here is a subset of this:
PhHAL9G636300 PhHAL9G639600 PhHAL9G640000 PhHAL9G642000 PhHAL9G643800 PhHAL9G645300 PhHAL9G646100 PhHAL9G646600 PhHALJ003900 Biomass Growth WaterLoss
PENW3 5.365778 2.98025485 5.495861 4.405202465 2.9113147 2.5418600 2.09767062 2.52296664 1.9087030 0.46125981 -13.4226665 -0.37320470
PENW1 3.490321 4.64568874 3.717329 3.604487984 2.1293068 2.2661013 -1.47617955 -0.83020824 -1.4564567 4.61259811 -14.4985291 -1.41470618
CERD2 4.602228 1.83881344 4.474194 2.395140203 3.7523682 -0.2886215 -0.73070022 2.79344405 -0.7981627 -0.76634289 5.5581189 15.96563076
COCW2 4.614825 1.59769640 5.498260 0.006269791 1.9118368 4.2591423 3.73962184 2.53800230 2.5671915 -0.13178852 -25.7182390 -0.07377302
PENW2 2.874244 3.33668026 2.686314 3.640362110 3.3344128 1.5625493 -1.92483779 0.85800308 -1.8455277 -0.09584619 -15.5743917 -0.99376599
NIGD3 2.534445 1.86024236 3.028953 3.531425944 4.2376438 2.0732650 -0.46514048 0.79555084 -0.5451820 1.05436439 12.0221867 21.04783002
PEND2 5.362056 2.64328649 4.464727 3.452061385 3.8409665 -0.5227258 -0.06950176 2.05314895 -1.5741809 0.68405104 4.6766551 11.99731270
CERD3 6.379754 3.02126477 5.958883 2.617817128 3.2571087 0.6389388 -1.54158346 2.70402517 -1.5102857 -0.98235901 4.4562891 15.74806130
PEND3 5.530008 3.49150186 4.664897 4.122665811 3.4805644 1.6611925 1.00556471 2.13871970 -0.5366320 0.42174575 3.2075488 12.88432662
NIGW3 4.183288 5.26329612 5.928775 8.194201254 1.4188970 3.3488663 -0.24100014 1.17649150 -0.3305682 0.29951936 1.1783257 1.30187685
SORW2 6.893395 3.84929409 7.853042 0.700629044 1.2822700 3.7429352 2.73703304 3.38057924 2.5338634 0.02995194 -12.5004986 -5.77946530
BISD3 7.044496 2.75794859 3.816874 2.703812532 2.6916801 2.3260304 3.37232732 2.31685090 1.7024061 -1.02864818 -0.6121276 14.66858209
COCD2 5.393332 3.05175638 5.822644 2.200587922 2.2212163 2.4246024 3.13408898 2.07709126 2.1863062 -0.10286482 -2.4485105 7.86349310
BISW2 2.174211 2.62450842 5.128353 4.037738498 1.3183220 0.9764650 0.53499762 1.02802526 0.4124477 1.55750066 -25.8719336 -13.89189391
SORD3 6.154951 2.22626768 4.676438 0.489662530 1.1602737 1.6238320 3.90773303 3.34912476 3.7395865 0.11315130 0.2693362 17.67178436
COCW3 4.341137 4.05631371 5.292476 2.505723413 0.4784145 0.1552958 2.35139206 2.34302308 1.9836908 -1.03034659 -25.2571550 -1.06753902
CERW1 4.980878 0.91666130 2.190792 1.724122567 3.0002243 -0.9078029 -1.30732267 1.90047369 -1.3084019 -0.11381736 -10.3487734 -0.48603403
NIGW1 4.310666 5.24869379 6.482000 6.341412520 2.6579484 3.7324397 -0.69538644 1.39456781 -0.7667490 -0.07787503 1.9467990 -2.35639710
SORD2 3.439050 1.01743984 3.608031 1.984325521 0.9697594 1.9438491 2.75019240 3.59450372 3.0152745 -0.14915399 1.8118978 19.30355526
BISW3 4.995399 3.02559441 5.391413 0.707718031 -0.0867396 2.1041361 3.57258520 2.88651590 2.2532781 1.48561601 -26.3330176 -12.63775254
SORD1 4.867184 3.58269882 5.082423 0.847579020 1.1842905 3.2943452 3.54584508 2.26684212 3.2791237 -1.80013432 -3.0361530 18.53648347
NIGD2 3.433412 1.38880580 5.344590 6.113465129 1.3389915 0.9967764 0.55527371 1.04742251 0.4318620 1.16237244 10.5530804 20.06039943
SORW3 6.832991 3.48434777 7.954499 -0.373941722 0.9673997 3.8959556 2.87342668 3.53076025 2.5194703 1.12619278 -7.8896590 -6.45557334
PEND1 4.536131 1.98241616 2.469180 2.938093546 4.6266296 -0.3003059 -1.62573524 1.53755316 -1.5869045 0.49889437 3.3544594 12.90013295
CERW3 3.947328 1.97539246 4.499408 1.135578151 2.1385166 -0.2011640 -0.65101772 -0.06185877 -0.7272633 -0.04193271 -7.1211857 -1.98319240
NIGD1 1.517705 -0.02588437 2.040182 4.303738855 3.0117854 -0.9594330 -1.35627738 0.18988023 -1.3514038 0.88463744 8.0555996 20.38861318
COCW1 4.705918 2.51742179 4.476741 0.975394641 1.2854224 3.7611179 3.28623937 2.48855442 3.5451750 0.60502910 -26.9477962 0.97206804
BISD2 4.838736 3.99032517 7.239421 2.461942761 2.4587895 2.0971745 3.19578030 2.09829508 1.4836582 -1.10579679 -0.9059489 14.44078502
I am following the IPMRF package manual (pg 7):
#IMP based on CIT-RF (party package)
library(randomForestSRC)
library(party)
mtcars.new <- mtcars
ntree<-500
da<-mtcars.new[,3:10]
mc.cf <- cforest(carb+ mpg+ cyl ~., data = mtcars.new,
control = cforest_unbiased(mtry = 8, ntree = 500))
#IPM case-wise computing with OOB with party
pupf<-ipmparty(mc.cf ,da,ntree)
#global IPM
pua<-apply(pupf,2,mean)
pua
But I am switching mtcars for my dataset. However, when I run mine, I get all NaNs in the output and I can not figure out what is wrong with my code. So far I've checked if everything is numeric, if it's a limit in predictors (tried 10 rather than 1028), changed mtcars to have negative values to check that since mine has negative values, but none of these seem to be the problem. Granted, please double check me just in case. Here is my code that I am running with my dataset:
phallii.cf = cforest(Biomass + Growth + WaterLoss ~., data= RFTrainData, control=cforest_unbiased(mtry=33, ntree=1000)) #mtry = p/3
da = RFTrainData[,1:1028] #predictor variables only
ntree=1000
phallii.ipm = ipmparty(phallii.cf, da, ntree)
If anyone has any ideas I would greatly appreciate it!
Related
get the index of string element in np1 while it has a substring in np2
my code is as below:
import numpy as np
keywordlist = ['cpp-4.8.5', 'CUnit-2.1.3', 'CUnit-devel', 'doxygen-1.8.5', 'e2fsprogs-1.42.9', 'e2fsprogs-libs', 'epel-release', 'fuse3-devel', 'fuse3-libs', 'gcc-4.8.5', 'gcc-c++', 'gcc-gfortran', 'ghc-array', 'ghc-base', 'ghc-bytestring', 'ghc-containers', 'ghc-deepseq', 'ghc-directory', 'ghc-filepath', 'ghc-json', 'ghc-mtl', 'ghc-old', 'ghc-parsec', 'ghc-pretty', 'ghc-regex', 'ghc-regex', 'ghc-ShellCheck', 'ghc-syb', 'ghc-text', 'ghc-time', 'ghc-transformers', 'ghc-unix', 'git-1.8.3.1', 'graphviz-2.30.1', 'help2man-1.41.1', 'ibacm-22.4', 'keyutils-libs', 'krb5-devel', 'krb5-libs', 'krb5-workstation', 'lcov-1.13', 'libaio-devel', 'libblkid-2.23.2', 'libcom_err-1.42.9', 'libcom_err-devel', 'libgcc-4.8.5', 'libgfortran-4.8.5', 'libgomp-4.8.5', 'libibumad-22.4', 'libibverbs-22.4', 'libiscsi-devel', 'libkadm5-1.15.1', 'libmount-2.23.2', 'libpmem-1.5.1', 'libpmemblk-1.5.1', 'libpmemblk-devel', 'libpmem-devel', 'libquadmath-4.8.5', 'libquadmath-devel', 'librdmacm-22.4', 'libselinux-2.5', 'libselinux-devel', 'libselinux-python', 'libselinux-utils', 'libsepol-devel', 'libsmartcols-2.23.2', 'libss-1.42.9', 'libstdc++-4.8.5', 'libstdc++-devel', 'libunwind-1.2', 'libunwind-devel', 'libuuid-2.23.2', 'libuuid-devel', 'libverto-devel', 'libXaw-1.0.13', 'libXScrnSaver-1.2.2', 'make-3.82', 'nasm-2.10.07', 'numactl-devel', 'numactl-libs', 'openssl-1.0.2k', 'openssl-devel', 'openssl-libs', 'pcre-devel', 'perl-Digest', 'perl-Digest', 'perl-GD', 'perl-Git', 'python-2.7.5', 'python2-pycodestyle', 'python-libs', 'rdma-core', 'rdma-core', 'sg3_utils-1.37', 'sg3_utils-libs', 'ShellCheck-0.3.8', 'util-linux', 'zlib-devel']
np1 = np.array(keywordlist)
# ['cpp-4.8.5' 'CUnit-2.1.3' 'CUnit-devel' 'doxygen-1.8.5' ... 'ShellCheck-0.3.8' 'util-linux' 'zlib-devel']
result = ['epel-release-7-12.noarch', 'rdma-core-22.4-5.el7.x86_64', 'cpp-4.8.5-44.el7.x86_64', 'doxygen-1.8.5-4.el7.x86_64', 'ghc-base-4.6.0.1-26.4.el7.x86_64', 'libuuid-2.23.2-65.el7.x86_64', 'python-libs-2.7.5-89.el7.x86_64', 'libkadm5-1.15.1-50.el7.x86_64', 'libmount-2.23.2-65.el7.x86_64', 'libquadmath-4.8.5-44.el7.x86_64', 'util-linux-2.23.2-65.el7.x86_64', 'libss-1.42.9-19.el7.x86_64', 'keyutils-libs-1.5.8-3.el7.x86_64', 'e2fsprogs-libs-1.42.9-19.el7.x86_64', 'ghc-pretty-1.1.1.0-26.4.el7.x86_64', 'libXaw-1.0.13-4.el7.x86_64', 'libselinux-2.5-15.el7.x86_64', 'libibverbs-22.4-5.el7.x86_64', 'libselinux-utils-2.5-15.el7.x86_64', 'libgomp-4.8.5-44.el7.x86_64', 'libblkid-2.23.2-65.el7.x86_64', 'gcc-c++-4.8.5-44.el7.x86_64', 'e2fsprogs-1.42.9-19.el7.x86_64', 'CUnit-devel-2.1.3-8.el7.x86_64', 'make-3.82-24.el7.x86_64', 'numactl-libs-2.0.12-5.el7.x86_64', 'perl-Git-1.8.3.1-23.el7_8.noarch', 'openssl-libs-1.0.2k-19.el7.x86_64', 'gcc-4.8.5-44.el7.x86_64', 'CUnit-2.1.3-8.el7.x86_64', 'ghc-syb-0.4.0-35.el7.x86_64', 'gcc-gfortran-4.8.5-44.el7.x86_64', 'libselinux-python-2.5-15.el7.x86_64', 'sg3_utils-libs-1.37-19.el7.x86_64', 'fuse3-libs-3.6.1-4.el7.x86_64', 'libquadmath-devel-4.8.5-44.el7.x86_64', 'libgfortran-4.8.5-44.el7.x86_64', 'krb5-workstation-1.15.1-50.el7.x86_64', 'librdmacm-22.4-5.el7.x86_64', 'sg3_utils-1.37-19.el7.x86_64', 'libsmartcols-2.23.2-65.el7.x86_64', 'fuse3-devel-3.6.1-4.el7.x86_64', 'python-2.7.5-89.el7.x86_64', 'openssl-1.0.2k-19.el7.x86_64', 'libgcc-4.8.5-44.el7.x86_64', 'libaio-devel-0.3.109-13.el7.x86_64', 'ghc-old-locale-1.0.0.5-26.4.el7.x86_64', 'libcom_err-1.42.9-19.el7.x86_64', 'git-1.8.3.1-23.el7_8.x86_64', 'krb5-libs-1.15.1-50.el7.x86_64']
np2 = np.array(result)
# ['epel-release-7-12.noarch' 'rdma-core-22.4-5.el7.x86_64' ... 'krb5-libs-1.15.1-50.el7.x86_64']
expectation = ['cpp-4.8.5-39.el7.x86_64', 'CUnit-2.1.3-8.el7.x86_64', 'CUnit-devel-2.1.3-8.el7.x86_64', 'doxygen-1.8.5-4.el7.x86_64', 'e2fsprogs-1.42.9-17.el7.x86_64', 'e2fsprogs-libs-1.42.9-17.el7.x86_64', 'epel-release-latest-7.noarch', 'fuse3-devel-3.6.1-4.el7.x86_64', 'fuse3-libs-3.6.1-4.el7.x86_64', 'gcc-4.8.5-39.el7.x86_64', 'gcc-c++-4.8.5-39.el7.x86_64', 'gcc-gfortran-4.8.5-39.el7.x86_64', 'ghc-array-0.4.0.1-26.4.el7.x86_64', 'ghc-base-4.6.0.1-26.4.el7.x86_64', 'ghc-bytestring-0.10.0.2-26.4.el7.x86_64', 'ghc-containers-0.5.0.0-26.4.el7.x86_64', 'ghc-deepseq-1.3.0.1-26.4.el7.x86_64', 'ghc-directory-1.2.0.1-26.4.el7.x86_64', 'ghc-filepath-1.3.0.1-26.4.el7.x86_64', 'ghc-json-0.7-4.el7.x86_64', 'ghc-mtl-2.1.2-27.el7.x86_64', 'ghc-old-locale-1.0.0.5-26.4.el7.x86_64', 'ghc-parsec-3.1.3-31.el7.x86_64', 'ghc-pretty-1.1.1.0-26.4.el7.x86_64', 'ghc-regex-base-0.93.2-29.el7.x86_64', 'ghc-regex-tdfa-1.1.8-11.el7.x86_64', 'ghc-ShellCheck-0.3.8-1.el7.x86_64', 'ghc-syb-0.4.0-35.el7.x86_64', 'ghc-text-0.11.3.1-2.el7.x86_64', 'ghc-time-1.4.0.1-26.4.el7.x86_64', 'ghc-transformers-0.3.0.0-34.el7.x86_64', 'ghc-unix-2.6.0.1-26.4.el7.x86_64', 'git-1.8.3.1-23.el7_8.x86_64', 'graphviz-2.30.1-21.el7.x86_64', 'help2man-1.41.1-3.el7.noarch', 'ibacm-22.4-2.el7_8.x86_64', 'keyutils-libs-devel-1.5.8-3.el7.x86_64', 'krb5-devel-1.15.1-46.el7.x86_64', 'krb5-libs-1.15.1-46.el7.x86_64', 'krb5-workstation-1.15.1-46.el7.x86_64', 'lcov-1.13-1.el7.noarch', 'libaio-devel-0.3.109-13.el7.x86_64', 'libblkid-2.23.2-63.el7.x86_64', 'libcom_err-1.42.9-17.el7.x86_64', 'libcom_err-devel-1.42.9-17.el7.x86_64', 'libgcc-4.8.5-39.el7.x86_64', 'libgfortran-4.8.5-39.el7.x86_64', 'libgomp-4.8.5-39.el7.x86_64', 'libibumad-22.4-2.el7_8.x86_64', 'libibverbs-22.4-2.el7_8.x86_64', 'libiscsi-devel-1.9.0-7.el7.x86_64', 'libkadm5-1.15.1-46.el7.x86_64', 'libmount-2.23.2-63.el7.x86_64', 'libpmem-1.5.1-2.1.el7.x86_64', 'libpmemblk-1.5.1-2.1.el7.x86_64', 'libpmemblk-devel-1.5.1-2.1.el7.x86_64', 'libpmem-devel-1.5.1-2.1.el7.x86_64', 'libquadmath-4.8.5-39.el7.x86_64', 'libquadmath-devel-4.8.5-39.el7.x86_64', 'librdmacm-22.4-2.el7_8.x86_64', 'libselinux-2.5-15.el7.x86_64', 'libselinux-devel-2.5-15.el7.x86_64', 'libselinux-python-2.5-15.el7.x86_64', 'libselinux-utils-2.5-15.el7.x86_64', 'libsepol-devel-2.5-10.el7.x86_64', 'libsmartcols-2.23.2-63.el7.x86_64', 'libss-1.42.9-17.el7.x86_64', 'libstdc++-4.8.5-39.el7.x86_64', 'libstdc++-devel-4.8.5-39.el7.x86_64', 'libunwind-1.2-2.el7.x86_64', 'libunwind-devel-1.2-2.el7.x86_64', 'libuuid-2.23.2-63.el7.x86_64', 'libuuid-devel-2.23.2-63.el7.x86_64', 'libverto-devel-0.2.5-4.el7.x86_64', 'libXaw-1.0.13-4.el7.x86_64', 'libXScrnSaver-1.2.2-6.1.el7.x86_64', 'make-3.82-24.el7.x86_64', 'nasm-2.10.07-7.el7.x86_64', 'numactl-devel-2.0.12-5.el7.x86_64', 'numactl-libs-2.0.12-5.el7.x86_64', 'openssl-1.0.2k-19.el7.x86_64', 'openssl-devel-1.0.2k-19.el7.x86_64', 'openssl-libs-1.0.2k-19.el7.x86_64', 'pcre-devel-8.32-17.el7.x86_64', 'perl-Digest-1.17-245.el7.noarch', 'perl-Digest-MD5-2.52-3.el7.x86_64', 'perl-GD-2.49-3.el7.x86_64', 'perl-Git-1.8.3.1-23.el7_8.noarch', 'python-2.7.5-88.el7.x86_64', 'python2-pycodestyle-2.5.0-1.el7.noarch', 'python-libs-2.7.5-88.el7.x86_64', 'rdma-core-22.4-2.el7_8.x86_64', 'rdma-core-devel-22.4-2.el7_8.x86_64', 'sg3_utils-1.37-19.el7.x86_64', 'sg3_utils-libs-1.37-19.el7.x86_64', 'ShellCheck-0.3.8-1.el7.x86_64', 'util-linux-2.23.2-63.el7.x86_64', 'zlib-devel-1.2.7-18.el7.x86_64']
np3 = np.array(expectation)
# ['cpp-4.8.5-39.el7.x86_64' 'CUnit-2.1.3-8.el7.x86_64' ... 'util-linux-2.23.2-63.el7.x86_64' 'zlib-devel-1.2.7-18.el7.x86_64']
ready = []
for i in keywordlist:
for j in result:
x = np.char.startswith(j, i)
if x:
ready.append(np3[np.where(np.char.startswith(np3, i))])
np4 = np.array(ready)
# [array(['cpp-4.8.5-39.el7.x86_64'], dtype='<U39') array(['CUnit-2.1.3-8.el7.x86_64'], dtype='<U39') ... array(['util-linux-2.23.2-63.el7.x86_64'], dtype='<U39')]
notready = [i for i in np3 if i not in np4]
print(f"not ready: {notready}")
The purpose is to use string format keyword in keyword list to examine its existence in all np2 elements.
If any element in np2 starts with any keyword, or keyword is the substring of any element in np2, get the index of element in expectation which also start with that keyword and form into np4.
Finally， get not ready which is made up of elements that are in np3 but not in np4.
To make my explanation more vividly, I have a bunch of rpm files to be installed, the list of expectation.
The keyword list catches the former two keywords of each rpm file name.
Result is the standard output of already installed rpm files.
Taking cpp-4.8.5 as an example, I can see cpp-4.8.5-44.el7.x86_64 in result, which means currently cpp-4.8.5-44.el7.x86_64 has been installed. So, cpp-4.8.5-39.el7.x86_64 in expectation can be removed, since cpp-4.8.5-*.rpm has been successfully installed. Next step, deal with the other left items in expectation.
My question is: there any easier or more efficient way to get the result equivalent to notready? maybe with any other numpy built-in methods, but not with for loop.
How to use e.findAt for similar geometrical models?
I used Abaqus macro recording when defining a set: by edge (20 deg). My end goal is to run the script for different .STEP models/geometries which are identical in every aspect except for a few parametric values. For each model I am using the same procedure for selecting the highlighted edges: create a set from edge-by-angle-20.
However, when I run it for other geometric models, it selects an entirely different e.getSequenceFromMask or as I should say entirely different sets for e.findAt after running the commands for the similar edge.
session.journalOptions.replayGeometry
session.journalOptions.setValues(replayGeometry=INDEX)
I am aware that the program has restrictions over findAt :
findAt initially uses the ACIS tolerance of 1E-6. As a result, findAt returns any edge that is at the arbitrary point specified or at a distance of less than 1E-6 from the arbitrary point.
Edge to be selected in all models which are similar with a few variations in dimensions
I would appreciate it if anyone can suggest how to ensure the same edges are selected for different geometries when using the e.findAt command.
My code:
#Sample for the same edges selected from different dimensioned CAD models
#CAD Model_1
edges = e.getSequenceFromMask(mask=(
'[#63002104 #88110 #10000480 #20402 #480c802 #10420070 #c4411',
' #70104842 #10022000 #1e2002 #30 #80820010 #20003 #18144186',
' #b000 ]',), )
p.Set(edges=edges, name='Set-2')
#Set 2:
mdb.models['Model-1'].parts['Circular_knit - 2, 3, 10'].edges.findAt(((2.808153, 3.86272, -0.231317),),((3.959929, 2.669325, -0.231317),),((3.581888, 1.932068, -0.134932),),((1.501975, 3.782442, -0.134931),),((2.094736, 4.291667, -0.231317),),((2.135971, 3.464163, -0.134931),),((2.705066, 3.040627, -0.134931),),((3.436245, 3.316406, -0.231317),),((3.19197, 2.524704, -0.134932),),((4.363292, 1.941137, -0.231317),),((3.862971, 1.280728, -0.134932),),((-0.318814, 3.68708, 0.0),),((1.317672, 4.590214, -0.231317),),((4.764061, 0.331739, -0.231318),),((4.74929, -0.500571, -0.231318),),((4.590214, -1.317672, -0.231317),),((4.634078, 1.15397, -0.231318),),((-0.590217, 4.023581, -0.132766),),((-0.331245, 4.765369, -0.229231),),((0.822343, 3.985795, -0.134932),),((0.500571, 4.74929, -0.231318),),((4.02668, 0.590473, -0.134933),),((4.068041, -0.117724, -0.134933),),((4.291667, -2.094736, -0.231317),),((3.464163, -2.135971, -0.134931),),((3.86272, -2.808153, -0.231317),),((1.932068, -3.581888, -0.134932),),((1.280728, -3.862971, -0.134932),),((1.15397, -4.634078, -0.231318),),((0.590473, -4.02668, -0.134933),),((-0.117724, -4.068041, -0.134933),),((3.985795, -0.822343, -0.134932),),((0.117724, 4.068041, -0.134933),),((-1.15397, 4.634078, -0.231318),),((-1.280728, 3.862971, -0.134932),),((-2.524704, 3.19197, -0.134932),),((-2.669325, 3.959929, -0.231317),),((-1.941137, 4.363292, -0.231317),),((-1.932068, 3.581888, -0.134932),),((3.782442, -1.501975, -0.134931),),((3.040627, -2.705066, -0.134931),),((3.316406, -3.436245, -0.231317),),((2.524704, -3.19197, -0.134932),),((2.669325, -3.959929, -0.231317),),((1.941137, -4.363292, -0.231317),),((0.331739, -4.764061, -0.231318),),((-0.500571, -4.74929, -0.231318),),((-3.464163, 2.135971, -0.134931),),((-4.291667, 2.094736, -0.231317),),((-3.316406, 3.436245, -0.231317),),((-3.040627, 2.705066, -0.134931),),((-3.86272, 2.808153, -0.231317),),((-1.317672, -4.590214, -0.231317),),((-0.822343, -3.985795, -0.134932),),((-4.764061, -0.331739, -0.231318),),((-3.959929, -2.669325, -0.231317),),((-3.862971, -1.280728, -0.134932),),((-3.782442, 1.501975, -0.134931),),((-4.590214, 1.317672, -0.231317),),((-3.985795, 0.822343, -0.134932),),((-2.135971, -3.464163, -0.134931),),((-4.74929, 0.500571, -0.231318),),((-4.068041, 0.117724, -0.134933),),((-4.02668, -0.590473, -0.134933),),((-4.634078, -1.15397, -0.231318),),((-4.363292, -1.941137, -0.231317),),((-3.581888, -1.932068, -0.134932),),((-3.19197, -2.524704, -0.134932),),((-3.436245, -3.316406, -0.231317),),((-2.705066, -3.040627, -0.134931),),((-2.094736, -4.291667, -0.231317),),((-1.501975, -3.782442, -0.134931),),((-2.808153, -3.86272, -0.231317),),)
#CAD Model_2
edges = e.getSequenceFromMask(mask=('[#ffffffff:12 #3fffff ]', ), )
p.Set(edges=edges, name='Set-2')
#Set_2:
mdb.models['Model-1'].parts['Circular_knit - 2, 3, 9'].edges.findAt(((3.609683, 0.972388, 1e-06),),((3.573439, 0.962676, 0.012491),),((3.76173, 1.266747, -0.034239),),((4.345677, 1.895909, -0.106922),),((3.369331, 1.576662, 0.046191),),((3.59632, 1.879201, -0.101049),),((4.017677, 2.705061, -0.092682),),((3.044359, 2.137787, 0.046191),),((3.950435, 2.621725, -0.106922),),((4.319341, 2.493773, 0.001066),),((3.51041, 1.897727, -0.09198),),((4.38626, 1.924207, -0.137251),),((3.786618, 1.25932, -0.091981),),((4.653757, 1.133308, -0.137251),),((3.94777, 0.582649, -0.091981),),((4.779853, 0.307975, -0.137252),),((4.987546, 0.0, 0.001066),),((3.968123, -0.096234, -0.034239),),((3.706257, -0.319236, 0.046191),),((4.054105, -0.170725, -0.101049),),((4.831941, 0.333813, -0.092682),),((3.705386, 0.329198, 0.046191),),((3.686317, 0.32753, 0.012491),),((3.591928, 0.967631, 0.046191),),((4.608877, 1.112487, -0.106922),),((3.924549, 0.594285, -0.034239),),((4.732039, 0.295263, -0.106922),),((4.68676, 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-0.106922),),((-3.695902, 1.447609, -0.034239),),((-4.547648, 1.340996, -0.106922),),((-3.576027, 0.953017, 0.012491),),((-3.908966, 0.802706, -0.09198),),((-4.760715, 0.526717, -0.137252),),((-3.724569, 0.320838, 1e-06),),((-4.816498, 0.510316, -0.092682),),((-3.962868, 0.872119, -0.101049),),((-3.612271, 0.962729, 1e-06),),((-2.633957, 2.626886, 0.046191),),((0.866078, -4.911774, 0.001066),),((1.133308, -4.653757, -0.137251),),((0.295263, -4.732039, -0.106922),),((-0.096234, -3.968123, -0.034239),),((-0.530933, -4.711421, -0.106922),),((-0.78383, -3.891127, -0.034239),),((-0.957971, -3.594516, 0.046191),),((-0.872119, -3.962868, -0.101049),),((-0.868796, -4.927189, 0.037363),),((-0.802706, -3.908966, -0.09198),),((-1.345405, -4.596926, -0.137251),),((-1.711195, -4.701469, 0.037363),),((-1.338939, -4.65471, -0.092682),),((0.3308, -3.723697, 1e-06),),((0.333813, -4.831941, -0.092682),),((0.0, -5.003198, 0.037363),),((0.0, -4.987546, 0.001066),),((0.868796, -4.927189, 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0.046191),),((-4.711421, 0.530933, -0.106922),),((-4.911774, 0.866078, 0.001066),),((-2.995885, 2.736711, -0.101049),),((0.32753, -3.686317, 0.012491),),((0.582649, -3.94777, -0.091981),),((-0.319236, -3.706257, 0.046191),),((-0.317568, -3.687189, 0.012491),),((-0.111726, -3.988971, -0.091981),),((0.307975, -4.779853, -0.137252),),((-0.866078, -4.911774, 0.001066),),((-0.526717, -4.760715, -0.137252),),((-0.320838, -3.724569, 1e-06),),((-0.510316, -4.816498, -0.092682),),((-0.953017, -3.576027, 0.012491),),((-0.962729, -3.612271, 1e-06),),((-1.705841, -4.68676, 0.001066),),((-1.469294, -3.710189, -0.091979),),((-2.123214, -4.293462, -0.137251),),((-2.091239, -3.398682, -0.091979),),((-2.83651, -3.859543, -0.137251),),((-2.649644, -2.983909, -0.091979),),((-3.463619, -3.308353, -0.137251),),((-3.820682, -3.205932, 0.001066),),((-3.101617, -2.476941, -0.034239),),((-3.044359, -2.137787, 0.046191),),((-3.215364, -2.475146, -0.101049),),((-3.832672, -3.215994, 0.037363),),((-3.127541, -2.478471, -0.091979),),((-3.985488, -2.656641, -0.137251),),((-4.332897, -2.501599, 0.037363),),((-3.59632, -1.879201, -0.101049),),((-3.38599, -1.58443, 1e-06),),((-3.351983, -1.568572, 0.012491),),((-3.484612, -1.90072, -0.034239),),((-4.319341, -2.493773, 0.001066),),((-1.547014, -3.751221, -0.101049),),((-1.575367, -3.390216, 1e-06),),((-1.559509, -3.356209, 0.012491),),((-1.567599, -3.373557, 0.046191),),((-0.170725, -4.054105, -0.101049),),((-3.751221, 1.547014, -0.101049),),((-3.968123, 0.096234, -0.034239),),((-5.003198, 0.0, 0.037363),),((-3.686317, -0.32753, 0.012491),),((-3.94777, -0.582649, -0.091981),),((-4.653757, -1.133308, -0.137251),),((-3.609683, -0.972388, 1e-06),),((-3.591928, -0.967631, 0.046191),),((-4.608877, -1.112487, -0.106922),),((-4.911774, -0.866078, 0.001066),),((-3.705386, -0.329198, 0.046191),),((-4.732039, -0.295263, -0.106922),),((-1.340996, -4.547648, -0.106922),),((-1.447609, -3.695902, -0.034239),),((-2.501599, -4.332897, 0.037363),),((-2.174904, -3.425595, -0.101049),),((-2.850196, -3.916052, -0.092682),),((-2.736711, -2.995885, -0.101049),),((-2.639883, -2.646954, 1e-06),),((-2.613351, -2.620422, 0.012491),),((-3.435161, -3.267881, -0.106922),),((-2.140139, -3.065152, 1e-06),),((-2.129595, -3.050095, 0.046191),),((-2.815512, -3.814744, -0.106922),),((-3.205932, -3.820682, 0.001066),),((-3.215994, -3.832672, 0.037363),),((-3.950435, -2.621725, -0.106922),),((-3.059416, -2.14833, 1e-06),),((-4.017677, -2.705061, -0.092682),),((-3.48691, -3.361627, -0.092682),),((-2.626886, -2.633957, 0.046191),),((-3.51041, -1.897727, -0.09198),),((-4.426368, -1.966302, -0.092682),),((-3.369331, -1.576662, 0.046191),),((-4.345677, -1.895909, -0.106922),),((-4.68676, -1.705841, 0.001066),),((-3.786618, -1.25932, -0.091981),),((-2.12688, -4.35149, -0.092682),),((-2.110314, -4.245698, -0.106922),),((-2.493773, -4.319341, 0.001066),),((-4.987546, 0.0, 0.001066),),((-4.927189, -0.868796, 0.037363),),((-4.700566, -1.167799, -0.092682),),((-3.573439, -0.962676, 0.012491),),((-3.868004, -1.226157, -0.101049),),((-3.76173, -1.266747, -0.034239),),((-3.924549, -0.594285, -0.034239),),((-4.02216, -0.535857, -0.101049),),((-2.62438, -2.9779, -0.034239),),((-2.118616, -3.034415, 0.012491),),((-2.067403, -3.388378, -0.034239),),((-3.028679, -2.126808, 0.012491),),((-4.38626, -1.924207, -0.137251),),((-4.701469, -1.711195, 0.037363),),)
Do not use getSequenceFromMask This method seems very appealing, however, Abaqus documentation does not cover how masks are formed, so it is not possible to artificially generate a mask for a set of objects. Use coordinates (findAt()) or indeces instead Most of the time if your geometry is complex and is generated without control over vertices/edges/etc numbering then the findAt() method is your only option (along with methods getByBoundingBox(...), getByBoundingCylinder(...) and getByBoundingSphere(...)). If searching edge-by-edge is not very efficient and getByBounding... methods do not allow you to select the right array of edges then you can try: 1) to find the first edge of your sequence; 2) then use (as you already tried) the getEdgesByEdgeAngle(...) method to find all adjacent edges. If you know for sure the numbering of your geometrical entities (vertices/edges/etc.) then you can access them by their index. So, using notations from your example, if edges of interest of your geometry have indexes from n to m then: part = mdb.models['Model-1'].parts['Circular_knit - 2, 3, 10'] set2 = part.Set(name='Set-2', edges=part.edges[n:m+1])
tryCatch crashes while nls fit
I am trying to fit a non-linear model using nls function. The model has a conditioning and works by groups therefore I try to implement a loop which covers over 300 combinations of starting values. I am using tryCatch but to my surprise the loop crashes, I guess because of the starting values or lower and upper bounds.
tryCatch(
for(r in 1:nrow(st4)){
halfLE3[[r]] <- nls(
inty ~ I(time < (position/velocity) + dinitial) * Inty_S0 +
(time >= (position/velocity) + dinitial) *
I(Intyf[probe] + (Inty_S0[probe] - Intyf) *
(exp(-Decay * (time - (position/velocity) + dinitial)))),
data = Data4,
algorithm = "port",
control = list(warnOnly = TRUE),
start = list(Decay=st4[r,3], dinitial=st4[r,2],
Intyf=rep(st4[r,4], length(levels(Data4$probe))),
Inty_S0=rep(st4[r,5], length(levels(Data4$probe))),
velocity=st4[r,1]),
lower = list(Decay= .1, dinitial=0.1, velocity=10, Intyf=0.1, inty_S0=.5),
upper = list(Decay= 1, dinitial=10, velocity=8000, Intyf=1, inty_S0=1.5)
)
}
,error = function(e) {e}
)
st4 is a dataframe with 300 combinations of starting values.
probe refers to group.
Do you have any idea why the loop crashes even using the tryCatch? Do you have any idea what I can improve? I used the nls.control changing the maxiter and other parameters but works same as the control I use here above.
I would be very grateful for any suggestion.
I fixed the issue. The tryCatch should be located inside the for loop adding a bracket.
for(r in 1:nrow(st4)){
tryCatch({
nls()......
},error = function(e) {e}
)
}
Update plot according to updated slider value wit Gtk
In Julia, I'd like to update a plot upon the change of value in a Gtk Slider. I understand that this has to do with the "change-value" signal in https://developer.gnome.org/gtk2/2.24/GtkRange.html#GtkRange-value-changed. However, as a beginner, I do not know how to implement the code
The “change-value” signal
gboolean
user_function (GtkRange *range,
GtkScrollType scroll,
gdouble value,
gpointer user_data)
to achieve what I wanted to do. Could anyone kindly provide an example how to use the "change-value" signal?
I know how to set up a window for the slider
sl = slider(1:11)
win = Window("Testing") |> (bx = Box(:v))
push!(bx, sl)
Gtk.showall(win)
I also know what kind of function I need to update the plot
function update(val)
int_val = int(val)
if Signal(sl) != int_val
x = range(0., 2*pi, step=0.01)
y = map(sin, x)
PyPlot.plot(x,Signal(sl)*y)
end
end
However, I don't know how or where I can trigger the "update" function to take actual action.
Thanks!
Thanks, liberforce for your advice!
Here is my minimal working example:
'''
using Gtk
# Set up scale (slider) and window
sl = GtkScale(false, 0:10)
win = Gtk.Window("Gain Selection") |> (bx = Gtk.Box(:v))
push!(bx, sl)
Gtk.showall(win)
# Connect with value-changed signal
id = signal_connect(sl, "value-changed") do widget
# Get scale value
sub = Gtk.GAccessor.adjustment(widget)
val = Gtk.get_gtk_property(sub,"value",Int64)
# Perform function related to the scale value.
println("Gain is changed to ", val)
end
push!(bx, sl)
Gtk.showall(win);
'''
Reference:
signal connect: http://juliagraphics.github.io/Gtk.jl/latest/manual/signals.html
GtkScale: https://lazka.github.io/pgi-docs/Gtk-3.0/classes/Scale.html
Extract scale value: https://discourse.julialang.org/t/how-to-get-the-current-value-of-a-gtk-scale-widget/15680
Julia Do-block: https://www.juliabloggers.com/julia-do-block-vs-python-with-statement/
How to get an outline view in sublime texteditor?
How do I get an outline view in sublime text editor for Windows? The minimap is helpful but I miss a traditional outline (a klickable list of all the functions in my code in the order they appear for quick navigation and orientation) Maybe there is a plugin, addon or similar? It would also be nice if you can shortly name which steps are neccesary to make it work. There is a duplicate of this question on the sublime text forums.
Hit CTRL+R, or CMD+R for Mac, for the function list. This works in Sublime Text 1.3 or above.
A plugin named Outline is available in package control, try it! https://packagecontrol.io/packages/Outline Note: it does not work in multi rows/columns mode. For multiple rows/columns work use this fork: https://github.com/vlad-wonderkidstudio/SublimeOutline
I use the fold all action. It will minimize everything to the declaration, I can see all the methods/functions, and then expand the one I'm interested in.
I briefly look at SublimeText 3 api and view.find_by_selector(selector) seems to be able to return a list of regions. So I guess that a plugin that would display the outline/structure of your file is possible. A plugin that would display something like this: Note: the function name display plugin could be used as an inspiration to extract the class/methods names or ClassHierarchy to extract the outline structure
If you want to be able to printout or save the outline the ctr / command + r is not very useful.
One can do a simple find all on the following grep ^[^\n]*function[^{]+{ or some variant of it to suit the language and situation you are working in.
Once you do the find all you can copy and paste the result to a new document and depending on the number of functions should not take long to tidy up.
The answer is far from perfect, particularly for cases when the comments have the word function (or it's equivalent) in them, but I do think it's a helpful answer.
With a very quick edit this is the result I got on what I'm working on now.
PathMaker.prototype.start = PathMaker.prototype.initiate = function(point){};
PathMaker.prototype.path = function(thePath){};
PathMaker.prototype.add = function(point){};
PathMaker.prototype.addPath = function(path){};
PathMaker.prototype.go = function(distance, angle){};
PathMaker.prototype.goE = function(distance, angle){};
PathMaker.prototype.turn = function(angle, distance){};
PathMaker.prototype.continue = function(distance, a){};
PathMaker.prototype.curve = function(angle, radiusX, radiusY){};
PathMaker.prototype.up = PathMaker.prototype.north = function(distance){};
PathMaker.prototype.down = PathMaker.prototype.south = function(distance){};
PathMaker.prototype.east = function(distance){};
PathMaker.prototype.west = function(distance){};
PathMaker.prototype.getAngle = function(point){};
PathMaker.prototype.toBezierPoints = function(PathMakerPoints, toSource){};
PathMaker.prototype.extremities = function(points){};
PathMaker.prototype.bounds = function(path){};
PathMaker.prototype.tangent = function(t, points){};
PathMaker.prototype.roundErrors = function(n, acurracy){};
PathMaker.prototype.bezierTangent = function(path, t){};
PathMaker.prototype.splitBezier = function(points, t){};
PathMaker.prototype.arc = function(start, end){};
PathMaker.prototype.getKappa = function(angle, start){};
PathMaker.prototype.circle = function(radius, start, end, x, y, reverse){};
PathMaker.prototype.ellipse = function(radiusX, radiusY, start, end, x, y , reverse/*, anchorPoint, reverse*/ ){};
PathMaker.prototype.rotateArc = function(path /*array*/ , angle){};
PathMaker.prototype.rotatePoint = function(point, origin, r){};
PathMaker.prototype.roundErrors = function(n, acurracy){};
PathMaker.prototype.rotate = function(path /*object or array*/ , R){};
PathMaker.prototype.moveTo = function(path /*object or array*/ , x, y){};
PathMaker.prototype.scale = function(path, x, y /* number X scale i.e. 1.2 for 120% */ ){};
PathMaker.prototype.reverse = function(path){};
PathMaker.prototype.pathItemPath = function(pathItem, toSource){};
PathMaker.prototype.merge = function(path){};
PathMaker.prototype.draw = function(item, properties){};