Schrodinger suites 2017

Atur jumlah dan catatanSchrodinger Suites 2018 for macOS Full VersionKondisi: BaruMin. Pemesanan: 1 BuahEtalase: Semua EtalaseSchrodinger Suites adalah perangkat lunak terkemuka untuk kimia komputasi. Program ini memberikan solusi lengkap dan layanan untuk semua cabang ilmu kehidupan dan ilmu material. Perusahaan telah membangun perangkat lunak ini sesuai dengan persyaratan yang tepat dari pelanggannya dan membantu para peneliti untuk menjadi lebih dekat dengan tujuan mereka meningkatkan kesehatan manusia dan meningkatkan kualitas hidup. Produk ini mencakup berbagai alat dan program dari pemodelan molekul hingga desain dan manufaktur obat. Aplikasi ini memiliki banyak hal untuk ditemukan dan menghasilkan bahan baru, dan peneliti dapat menggunakan program ini untuk belajar dan mendapatkan pengalaman di bidang ini. Fitur dan Fitur dari Suite Schrodinger Suites: - Pemodelan berbagai struktur molekul dan kimia - Pemodelan Struktur Biologi dan Biologi - Membuat model melalui sketsa dan mengubah nilai di berbagai jendela secara grafis - Lingkungan program grafis dan sederhana - Menganalisis dan mengevaluasi perilaku material terhadap kondisi sekitar, misalnya, dalam suhu yang sangat rendah atau tinggi - Studi struktur material pada tingkat atom - Kemampuan untuk melakukan berbagai jenis perhitungan komputasi pada struktur pemodelan - Kemungkinan untuk menganalisis bahan dan sumber yang baik untuk eksplorasi bahan baru dan struktur kimia - Cocok untuk aplikasi farmasi - Lihat data dan gambar menggunakan tabel grafik dan grafik dengan warna yang berbeda OS X 10.7 or Later x86_64 compatible processor. 4 GB memory per core.Ada masalah dengan produk ini?ULASAN PEMBELIBelum ada ulasan untuk produk iniBeli produk ini dan jadilah yang pertama memberikan ulasan

Has more details on thisprocess.Note that additional libraries installed within a virtualenv will not beavailable within Maestro’s interactive Python prompt.Also, as mentioned above, the Schrödinger suite is not compatible with condaenvironments.If you want to share third-party modules with multiple users or want a morepermanent set of modules, a virtual environment probably isn’t necessary.Instead, just install them to some standard directory and set yourPYTHONPATH to pick them up.To try a virtual environment yourself, run $SCHRODINGER/runschrodinger_virtualenv.py schrodinger.ve. This creates a new subdirectoryin the working directory called schrodinger.ve that contains a newvirtual environment.To activate your virtualenv, type source schrodinger.ve/bin/activateat the terminal for Posix systems or schrodinger.ve\Scripts\activateon Windows. (Source activate.csh if you are in a csh-compatible shell.)After activating the virtual environment, importing schrodinger modulesshould work with a bare python command. For example, python3 -c 'fromschrodinger import structure; print(structure.__file__)' should tell you wherethe schrodinger.structure module is installed.NoteOn Windows, bat is no longer supported and Powershell should beused instead.To leave the virtual environment, just run deactivate.In the virtual environment, the piputility is also provided, and can be used to install packages to the virtualenvironment. If you have a compatible C compiler, pip installreadline will install the readline module to the virtual environment.A Schrödinger virtualenv is tied to the release used to create it (given by thevalue of $SCHRODINGER). When you update to a new release, you willneed to create a new virtualenv and install your preferred packages into it.See the pip documentation on the pip freeze command for details on how toeasily capture and reproduce combinations of pip installed packages.Setting Up Your Code Editor¶Reading or writing code in a plain text editor like Notepad is not recommended,even for modest tasks. Modern code editors (such as VSCode or PyCharm) offersyntax highlighting, help with refactoring, integrated access to documentation,code completion, and more.In order to set up a Code Editor so that all its features work properly withSchrödinger software, you will need to set up a Virtual Environment, asdescribed above.As also noted above, Schrödinger virtualenvs are tied toparticular releases. When you update your release, you will need to generate anew virtualenv and point your editor at it.Footnotes1The modules in the schrodinger namespace arelocated in a $SCHRODINGER/mmshare-v* subdirectory that can befound by running $SCHRODINGER/run python3 -c 'import os,schrodinger; print(os.path.dirname(schrodinger.__file__))'. (Thisdirectory is$SCHRODINGER/internal/lib/python3.11/site-packages/schrodinger onLinux and Mac, and$SCHRODINGER/internal/lib/site-packages/schrodinger onWindows.)2For completeness, if the script argument to $SCHRODINGER/run does not have an explicitly specified path, the following locations are searched in order:The current working directory.The Schrödinger suite-wide executable directories($SCHRODINGER/internal/bin and $SCHRODINGER/internal/Scripts)The builtin Schrödinger executable directory($SCHRODINGER/mmshare-v*/$OS_CPU/bin for a given platformspecification $OS_CPU).The directory specified by the environment variableSCHRODINGER_SCRIPTS.The directory //scripts.The directory $SCHRODINGER/mmshare-vX.Y/python/common.The directory $SCHRODINGER/mmshare-vX.Y/python/scripts.Your PATH.

Jobcontrol is a way to allow tasks to run asynchronously, and provides supportfor starting tasks on different machines.For example, you may launch a task from a laptop (running Maestro) to a computenode, so that the task runs on several cores. Jobcontrol takes care oftransferring input files from your laptop to the cluster and collecting resultsand log files once the job is complete.Launching a job means running a command with -HOST . Ahost entry is currently defined in schrodinger.hosts files.Example:$SCHRODINGER/ligprep -imae in.mae -omae out.maeRunning with no arguments runs on localhost. Adding -HOST bolt_cpu would submitthe job to bolt.The jobcontrol module contains four major sections:Job data interaction - Deals with getting information about existingjobs.Job launching - Deals with starting a subjob.Job backend interaction - Provides utilities for a Python script runningas a job.Job Hosts.Job Model¶From the commandline perspective, a job consists of a short script that takescare of submitting the job, and will return with output of: JobId: If the command returns with a zero exit status and JobId, the job wassuccessfully started. This should take seconds for a small job, or the time tonegotiate start with the remote host. Then, the job is running in thebackground.Running code under jobcontrol¶Python scripts that run locally can be adapted to run remotely. jobcontrol willuse launchapi if the script defines afunction get_job_spec_from_args at the top level. $SCHRODINGER/run willuse the information returned from that function when a -HOST option isused. For example:$SCHRODINGER/run script.py -HOST localhost will execute the main functionunder jobcontrol on the localhost by using the information returned fromget_job_spec_from_args.Ordinary script¶For a script that executes normally (myscript.py), you only need to make surethat your script is importable as a module. In this example, myscript willsimply print out the hostname that the script is running on to show that ourscript that will have different outputs on different machines.import socketdef main(): print(socket.gethostname())if __name__ == "__main__": main()$SCHRODINGER/run myscript.py will print out your local hostname.Add jobcontrol API¶If we want to execute our script under jobcontrol, locally or remotely, we needto add a function at the top level that jobcontrol can use as a jobspecification. This function must be called get_job_spec_from_args. Here,we’re registering stderr and stdout so that we can see the output of thescript.import socketfrom schrodinger.job import launchapidef get_job_spec_from_args(argv): """ Return a JobSpecification necessary to run this script on a remote machine (e.g. under job control with the launch.py script). :type argv: list(str) :param argv: The list of command line arguments, including the script name at [0], matching $SCHRODINGER/run __file__ sys.argv """ job_builder = launchapi.JobSpecificationArgsBuilder(argv) job_builder.setStderr("myscript.log") job_builder.setStdout("myscript.log") return job_builder.getJobSpec()def main(): print(socket.gethostname())if __name__ == "__main__": main()Assuming that myscript.py is in the distribution on your local and remote computers:$SCHRODINGER/run myscript.py will print out your local hostname.$SCHRODINGER/run myscript.py -HOST bolt_cpu will log the

Jobcontrol is a way to allow tasks to run asynchronously, and provides supportfor starting tasks on different machines.For example, you may launch a task from a laptop (running Maestro) to a computenode, so that the task runs on several cores. Jobcontrol takes care oftransferring input files from your laptop to the cluster and collecting resultsand log files once the job is complete.Launching a job means running a command with -HOST . Ahost entry is currently defined in schrodinger.hosts files.Example:$SCHRODINGER/ligprep -imae in.mae -omae out.maeRunning with no arguments runs on localhost. Adding -HOST bolt_cpu would submitthe job to bolt.The jobcontrol module contains four major sections:Job data interaction - Deals with getting information about existingjobs.Job launching - Deals with starting a subjob.Job backend interaction - Provides utilities for a Python script runningas a job.Job Hosts.Job Model¶From the commandline perspective, a job consists of a short script that takescare of submitting the job, and will return with output of: JobId: If the command returns with a zero exit status and JobId, the job wassuccessfully started. This should take seconds for a small job, or the time tonegotiate start with the remote host. Then, the job is running in thebackground.Running code under jobcontrol¶Python scripts that run locally can be adapted to run remotely. jobcontrol willuse launchapi if the script defines afunction get_job_spec_from_args at the top level. $SCHRODINGER/run willuse the information returned from that function when a -HOST option isused. For example:$SCHRODINGER/run script.py -HOST localhost will execute the main functionunder jobcontrol on the localhost by using the information returned fromget_job_spec_from_args.Ordinary script¶For a script that executes normally (myscript.py), you only need to make surethat your script is importable as a module. In this example, myscript willsimply print out the hostname that the script is running on to show that ourscript that will have different outputs on different machines.import socketdef main(): print(socket.gethostname())if __name__ == "__main__": main()$SCHRODINGER/run myscript.py will print out your local hostname.Add jobcontrol API¶If we want to execute our script under jobcontrol, locally or remotely, we needto add a function at the top level that jobcontrol can use as a jobspecification. This function must be called get_job_spec_from_args. Here,we’re registering stderr and stdout so that we can see the output of thescript.import socketfrom schrodinger.job import launchapidef get_job_spec_from_args(argv): """ Return a JobSpecification necessary to run this script on a remote machine (e.g. under job control with the launch.py script). :type argv: list(str) :param argv: The list of command line arguments, including the script name at [0], matching $SCHRODINGER/run __file__ sys.argv """ job_builder = launchapi.JobSpecificationArgsBuilder(argv) job_builder.setStderr(“myscript.log”) job_builder.setStdout(“myscript.log”) return job_builder.getJobSpec()def main(): print(socket.gethostname())if __name__ == "__main__": main()Assuming that myscript.py is in the distribution on your local and remote computers:$SCHRODINGER/run myscript.py will print out your local hostname.$SCHRODINGER/run myscript.py -HOST bolt_cpu will log the

This.See below for more about installing additional moduleswhen working with Schrödinger’s Python.Running Schrödinger Scripts¶Individual Python scripts can be run via:$SCHRODINGER/run []The $SCHRODINGER/run command sets up environment variables neededfor executables and libraries shipped by Schrödinger to work properly. Itwill search a number of standard locations if the named script does not havean explicitly specified path. Along with a number of built in locations inthe SCHRODINGER directory 2, these are:The current working directory.The directory specified by the environment variableSCHRODINGER_SCRIPTS.The directory /scriptsX.Y, where is ~/.schrodinger on Linux and%LOCALAPPDATA%\Schrodinger on Windows.Your PATH.The Schrödinger script installation tools support installation intoSCHRODINGER_SCRIPTS (provided that you have write permission) andyour directory.Exploring Schrödinger Modules¶IPython and Jupyter Notebook¶An excellent way to explore Schrödinger modules is from a Python interactiveprompt. We recommend the IPython shell for this, which can be started with thecommand-line invocation:The IPython shell makes interactive exploration of code easy because itprovides tab completion and the ability to introspect code and doc stringsimmediately in the shell. There are many resources online to learn more aboutthese and other features of IPython.Note that an IPython shell is also provided from within Maestro (“Python Shell”in the Window menu).Jupyter Notebook is also available using the following command-lineinvocation:$SCHRODINGER/run jupyter notebookAccessing Your Own Modules¶This subsection can be skipped until you want to use modules that aren’tincluded in our distribution. (In addition to Schrödinger packages ourdistribution contains a number of useful third-party modules includingNumpy, SciPy, matplotlib, PyOpenGL, and BioPython.)The Schrödinger Python installation uses the PYTHONPATH environmentvariable in the same way as any other Python installation, so the easiest wayto access your own modules is by adding their directories to thePYTHONPATH. Note that these modules must be compatible with Python3.11 and compiled modules must be compatible with the Schrödinger installation(e.g. for Linux-x86 installations they must be 32-bit).If a SCHRODINGER_PYTHONPATH environment variable is present, ourPython distribution uses it in preference to the standardPYTHONPATH. This allows an incompatible local Python installationto coexist with our distribution. Because Maestro and other Schrödingerexecutables use Python, it is important to setSCHRODINGER_PYTHONPATH if your PYTHONPATH containsincompatible modules. Set it to an empty string to override thePYTHONPATH without specifying an alternate search path.Installing Additional Modules¶To install additional modules to a local directory for use withSchrödinger’s Python distribution, you can run $SCHRODINGER/runsetup.py install --prefix=$LOCAL_PY_PACKAGES on the setup.py fileprovided with the package. (For this to work, your$LOCAL_PY_PACKAGES/lib/python3.11/site-packages directory must existand be in your PYTHONPATH.) See Installing Python Modules for generalinformation on installing python packages.Per-user Virtual Environments for Installing Additional Modules¶We recommend virtual environments for users who want to experimentwith additional modules that are not shipped with Schrödinger Python. A Python“virtual environment” is an isolated, lightweight, user-local Pythoninstallation that can access the Schrödinger modules and to which users caneasily install additional Python modules. The venv Python module documentation

Hostname of bolt compute nodeRegister input and output files¶Files that are transferred from the launch machine to the compute machine needto be registered by job control. In this example, we have an input maestro fileand an output maestro file.import osimport sysfrom schrodinger import structurefrom schrodinger.job import launchapidef get_job_spec_from_args(argv): job_builder = launchapi.JobSpecificationArgsBuilder(argv) mae_file = argv[1] output_mae_file = os.path.basename(mae_file) + "processed.mae" job_builder.setInputFile(mae_file) job_builder.setOutputFile(output_mae_file) job_builder.setStderr("myscript.log") job_builder.setStdout("myscript.log") return job_builder.getJobSpec()def main(): output_file = os.path.basename(sys.argv[1]) + "processed.mae" with structure.StructureReader(sys.argv[1]) as reader: with structure.StructureWriter(output_file) as writer: for ct in reader: ct.title = ct.title + "processed" writer.append(ct)if __name__ == "__main__": main()Execute using: $SCHRODINGER/run myscript.py foo.mae -HOST localhostUsing a jobname¶Some jobs use the concept of a jobname, which is specified through command lineor maestro to to determine the names of log files for the job.import socketfrom schrodinger.job import launchapidef get_job_spec_from_args(argv): job_builder = launchapi.JobSpecificationArgsBuilder(argv, use_jobname_log=True) return job_builder.getJobSpec()def main(): print(socket.gethostname())if __name__ == "__main__": main()Execute using: $SCHRODINGER/run myscript.py -JOBNAME foo -HOST localhostMaestro Incorporation¶A single maestro file from a job can be marked for incorporation into maestro,meaning that those structures will show up in the project table.def get_job_spec_from_args(argv): job_builder = launchapi.JobSpecificationArgsBuilder(argv) job_builder.setOutputFile("foo.mae", incorporate=True) return job_builder.getJobSpec()Using $SCHRODINGER/run -FROM ¶Some scripts require $SCHRODINGER/run -FROM to run. In this case, wemark this when we a create JobSpecification:def get_job_spec_from_args(argv): job_builder = launchapi.JobSpecificationArgsBuilder(argv, schrodinger_product="scisol") return job_builder.getJobSpec()Integration into af2¶af2 is the framework that Schrodinger uses to write GUIs. ImplementgetJobSpec() in panel to create a job spec. We assume we want to executemyscript.py that we wrote above.:def getJobSpec(self): driver_path = 'myscript.py' cmd = [driver_path, self.input_selector.structFile()] return driver.get_job_spec_from_args(cmd)Integration with an Argument Parser¶An argument parser is useful when we want to document, validate, and accesscommand line arguments within a script. It is easy to integrate an argumentparser into a script that uses jobcontrol.import argparseimport osimport sysfrom schrodinger import structurefrom schrodinger.job import launchapifrom schrodinger.utils import cmdlinedef parse_args(argv): parser = argparse.ArgumentParser() parser.add_argument("inputfile", help="maestro file input") args = parser.parse_args(argv) return argsdef get_job_spec_from_args(argv): # first argument is this script args_namespace = parse_args(argv[1:]) job_builder = launchapi.JobSpecificationArgsBuilder(argv, use_jobname_log=True) job_builder.setInputFile(args_namespace.inputfile) jobname = os.path.splitext(os.path.basename(args_namespace.inputfile))[0] job_builder.setJobname(jobname) return job_builder.getJobSpec()def main(*argv): args = parse_args(argv) with structure.StructureReader(args.inputfile) as reader: for ct in reader: print(f"ct title={ct.title})if __name__ == '__main__': cmdline.main_wrapper(main, *sys.argv[1:])See documentation of full set of options using in code documentation.Introduction to JobDJ¶The JobDJ class is used to write driver scripts for “distributedjobs”, which involve one or more subjobs independently carrying outdifferent parts of a larger computation in parallel. JobDJ can submitindividual jobs to a queueing system (like SLURM or UGE) or an explicitlist of compute machines.JobDJ is a workflow tool that makes it possible to run multiple, potentiallysimultaneous jobs. It manages launching and state of all subjobs. It alsoprovides a mechanism to enforce dependencies between jobs.This document will only describe the most common use case for JobDJ,which is to run a number of independent