def simulation_step(self, iteration, instance):
        '''
        function : In iter=1, use the input files from pre_loop, else use the outputs of the analysis stage in the
        previous iteration. Run gromacs in each instance using these files.

        gromacs :-

                Purpose : Run the gromacs simulation on each of the smaller files. Parameter files and executables are input
                            from pre_loop. There are 'numCUs' number of instances of gromacs per iteration.

                Arguments : --grompp    = gromacs parameters filename
                            --topol     = topology filename
        '''

        #----------------------------------------------------------------------------------------------------------
        # GROMPP kernel

        k1 = Kernel(name="custom.grompp")
        k1.arguments = [
            "--mdp={0}".format(os.path.basename(Kconfig.mdp_file)),
            "--gro=start.gro",
            "--top={0}".format(os.path.basename(Kconfig.top_file)),
            "--tpr=topol.tpr"
        ]

        k1.link_input_data = [
            '$SHARED/{0} > {0}'.format(os.path.basename(Kconfig.mdp_file)),
            '$SHARED/{0} > {0}'.format(os.path.basename(Kconfig.top_file))
        ]

        k1.copy_output_data = [
            'topol.tpr > $SHARED/iter_{1}/topol_{0}.tpr'.format(
                instance - 1, iteration - 1)
        ]

        if (iteration - 1 == 0):
            k1.link_input_data.append(
                '$PRE_LOOP/temp/start{0}.gro > start.gro'.format(instance - 1))

        else:
            k1.link_input_data.append(
                '$ANALYSIS_ITERATION_{0}_INSTANCE_1/temp/start{1}.gro > start.gro'
                .format(iteration - 1, instance - 1))
        #----------------------------------------------------------------------------------------------------------

        #----------------------------------------------------------------------------------------------------------
        # MDRUN kernel

        k2 = Kernel(name="custom.mdrun")
        k2.arguments = ["--size=1", "--tpr=topol.tpr", "--out=out.gro"]

        k2.link_input_data = [
            '$SHARED/iter_{1}/topol_{0}.tpr > topol.tpr'.format(
                instance - 1, iteration - 1)
        ]
        #----------------------------------------------------------------------------------------------------------

        return [k1, k2]
Exemplo n.º 2
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    def analysis_step(self, iteration, instance):

        k1 = Kernel(name="misc.randval")
        k1.arguments = ["--upperlimit=3", "--filename=iters.dat"]
        k1.download_output_data = "iters.dat"

        k2 = Kernel(name="misc.randval")
        k2.arguments = ["--upperlimit=16", "--filename=sims.dat"]
        k2.download_output_data = "sims.dat"

        return [k1, k2]
Exemplo n.º 3
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    def prepare_replica_for_exchange(self, replica):
        """Prepares md.re_exchange kernel to launch namd_matrix_calculator.py 
        script on target resource in order to populate columns of swap matrix.

        Arguments:
        replica - object representing a given replica and it's attributes
 
        Returns:
        k - an instance of Kernel class
        """

        basename = self.inp_basename[:-5]

        matrix_col = "matrix_column_{cycle}_{replica}.dat"\
                     .format(cycle=replica.cycle-1, replica=replica.id )

        k = Kernel(name="md.re_exchange")
        k.arguments = [
            "--calculator=namd_matrix_calculator.py",
            "--replica_id=" + str(replica.id),
            "--replica_cycle=" + str(replica.cycle - 1),
            "--replicas=" + str(self.replicas),
            "--replica_basename=" + str(basename)
        ]

        k.upload_input_data = "namd_matrix_calculator.py"
        k.download_output_data = matrix_col

        return k
 def simulation_step(self, iteration, instance):
     k = Kernel(name="misc.mkfile")
     k.arguments = [
         "--size=1000",
         "--filename=simulation-{0}-{1}.dat".format(iteration, instance)
     ]
     return k
    def simulation_step(self, iteration, instance):
        '''
        function : In iter=1, use the input files from pre_loop, else use the outputs of the analysis stage in the
        previous iteration. Run gromacs in each instance using these files.

        gromacs :-

                Purpose : Run the gromacs simulation on each of the smaller files. Parameter files and executables are input
                            from pre_loop. There are 'numCUs' number of instances of gromacs per iteration.

                Arguments : --grompp    = gromacs parameters filename
                            --topol     = topology filename
        '''

        gromacs = Kernel(name="md.gromacs")
        gromacs.arguments = [
            "--grompp={0}".format(os.path.basename(Kconfig.mdp_file)),
            "--topol={0}".format(os.path.basename(Kconfig.top_file))
        ]
        gromacs.link_input_data = [
            '$PRE_LOOP/{0} > {0}'.format(os.path.basename(Kconfig.mdp_file)),
            '$PRE_LOOP/{0} > {0}'.format(os.path.basename(Kconfig.top_file)),
            '$PRE_LOOP/run.py > run.py'
        ]

        if (iteration - 1 == 0):
            gromacs.link_input_data.append(
                '$PRE_LOOP/temp/start{0}.gro > start.gro'.format(instance - 1))

        else:
            gromacs.link_input_data.append(
                '$ANALYSIS_ITERATION_{0}_INSTANCE_1/temp/start{1}.gro > start.gro'
                .format(iteration - 1, instance - 1))

        return gromacs
    def analysis_step(self, iteration, instance):
        """In the analysis step, we take the previously generated simulation
           output and perform a Levenshtein distance calculation between it
           and the 'reference' file.

           ..note:: The placeholder ``$PRE_LOOP`` used in ``link_input_data`` is
                    a reference to the working directory of pre_loop.
                    The placeholder ``$PREV_SIMULATION`` used in ``link_input_data``
                    is a reference to the working directory of the previous
                    simulation step.

                    It is also possible to reference a specific
                    simulation step using ``$SIMULATION_N`` or all simulations
                    via ``$SIMULATIONS``. Analogous placeholders exist for
                    ``ANALYSIS``.
        """
        input_filename  = "simulation-{0}-{1}.dat".format(iteration, instance)
        output_filename = "analysis-{0}-{1}.dat".format(iteration, instance)

        k = Kernel(name="misc.levenshtein")
        k.link_input_data      = ["$PRE_LOOP/reference.dat", "$SIMULATION_ITERATION_{1}_INSTANCE_{2}/{0}".format(input_filename,iteration,instance),"$PRE_LOOP/levenshtein.py"]
        k.arguments            = ["--inputfile1=reference.dat",
                                  "--inputfile2={0}".format(input_filename),
                                  "--outputfile={0}".format(output_filename)]
        k.download_output_data = output_filename
        return k
Exemplo n.º 7
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    def pre_loop(self):

        k = Kernel(name="misc.chksum")
        k.arguments            = ["--inputfile=UTF-8-demo.txt", "--outputfile=checksum.sha1"]
        k.download_input_data  = "htpttpt://malformed.url"
        k.download_output_data = "checksum.sha1"
        return k
 def set2element_initialization(self,element):
     # Creating an ASCII file by using the misc.mkfile kernel. Each file represents
     # a element of the set.
     print "Creating Element {0}".format(element)
     k = Kernel(name = "misc.mkfile")
     k.arguments = ["--size=10000", "--filename=newfile_{0}.dat".format(element)]
     return k
    def element_comparison(self, element1, element2):
        """In the comparison, we take the previously generated files
           and perform a difference between those files. Each file coresponds to
           an elements of the set.

        """

        input_filename1 = "asciifile-{0}.dat".format(element1)
        input_filename2 = "asciifile-{0}.dat".format(element2)
        output_filename = "comparison-{0}-{1}.log".format(element1, element2)
        print "Comparing {0} with {1}. Saving result in {2}".format(
            input_filename1, input_filename2, output_filename)

        # Compare the previously generated files with the misc.diff kernel and
        # write the result of each comparison to a specific output file.

        k = Kernel(name="misc.diff")
        k.arguments = [
            "--inputfile1={0}".format(input_filename1),
            "--inputfile2={0}".format(input_filename2),
            "--outputfile={0}".format(output_filename)
        ]

        # Download the result files.
        k.download_output_data = output_filename
        return k
 def simulation_step(self, iteration, instance):
     """In the simulation step we
     """
     k = Kernel(name="misc.mkfile")
     k.arguments = ["--size=1000", "--filename=asciifile.dat"]
     k.exists_remote = ['asciifile.dat']
     return [k]
    def prepare_replica_for_md(self, replica):
        """Specifies input and output files and passes them to kernel

        Arguments:
        replica - object representing a given replica and it's associated
        parameters
        """
        input_name = self.inp_basename + "_" + \
                     str(replica.id) + "_" + \
                     str(replica.cycle) + ".md"
        output_name = self.inp_basename + "_" + \
                      str(replica.id) + "_" + \
                      str(replica.cycle) + ".out"

        k = Kernel(name="misc.ccount")
        k.arguments = ["--inputfile=" + \
                       input_name + " " + \
                       self.sh_file, "--outputfile=" + \
                       output_name]
        # no need to specify shared data here
        # everything in shared_files list will be staged in
        k.upload_input_data = [input_name]
        k.download_output_data = output_name

        replica.cycle = replica.cycle + 1
        return k
    def pre_loop(self):
        '''
        function : transfers input files and intermediate executables

        pre_grlsd_loop :-
                Purpose : Transfers files, Split the input file into smaller files to be used by each of the
                            gromacs instances in the first iteration.

                Arguments : --inputfile = file to be split
                            --numCUs    = number of simulation instances/ number of smaller files
        '''
        k = Kernel(name="md.pre_grlsd_loop")
        k.upload_input_data = [
            Kconfig.md_input_file, Kconfig.lsdm_config_file, Kconfig.top_file,
            Kconfig.mdp_file, '{0}/spliter.py'.format(Kconfig.misc_loc),
            '{0}/gro.py'.format(Kconfig.misc_loc),
            '{0}/run.py'.format(Kconfig.misc_loc),
            '{0}/pre_analyze.py'.format(Kconfig.misc_loc),
            '{0}/post_analyze.py'.format(Kconfig.misc_loc),
            '{0}/selection.py'.format(Kconfig.misc_loc),
            '{0}/reweighting.py'.format(Kconfig.misc_loc)
        ]
        k.arguments = [
            "--inputfile={0}".format(os.path.basename(Kconfig.md_input_file)),
            "--numCUs={0}".format(Kconfig.num_CUs)
        ]
        return k
Exemplo n.º 13
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 def simulation_step(self, iteration, instance):
     """In the simulation step we simply create files with 1000 characters.
     """
     k = Kernel(name="misc.mkfile")
     k.arguments = ["--size=1000", "--filename=asciifile-{0}.dat".format(instance)]
     k.download_output_data = ['asciifile-{0}.dat > iter{1}/asciifile-{0}.dat'.format(instance,iteration)]
     return [k]
Exemplo n.º 14
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    def element_comparison(self, elements1, elements2):
        """In the comparison, we take the previously generated modified trajectory
           and perform a Hausdorff distance calculation between all the unique pairs
           of trajectories
        """
        input_filenames1 = ["traj_flat%d.npz.npy" % (el1) for el1 in elements1]
        input_filenames2 = ["traj_flat%d.npz.npy" % (el2) for el2 in elements2]
        output_filename = "comparison-%03d-%03d.dat" % (elements1[0],
                                                        elements2[0])

        print "Element Comparison {0} - {1}".format(elements1, elements2)

        k = Kernel(name="my.hausdorff")
        k.arguments = [
            "--dist_file=hausdorff_kernel.py",
            "--inputfile1={0}".format(input_filenames1),
            "--inputfile2={0}".format(input_filenames2),
            "--outputfile={0}".format(output_filename)
        ]
        k.upload_input_data = ["hausdorff_kernel.py"]

        # If the input data are in in a web server use the following
        # k.download_input_data = ["/<PATH>/<TO>/<WEB>?<SERVER>/<WITH>/hausdorff_kernel.py > hausdorff_kernel.py"]
        # If the input data are in a folder to the target machine use the following
        # k.link_input_data = ["/<PATH>/<TO>/<FOLDER>/<WITH>/hausdorff_kernel.py > hausdorff_kernel.py"]

        # The result files comparison-x-y.dat are downloaded.
        k.download_output_data = output_filename

        print "Element Comparison Finished {0} - {1}".format(
            elements1, elements2)

        return k
Exemplo n.º 15
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 def stage_1(self, instance):
     k = Kernel(name="synapse.sample")
     k.arguments = [
         "--path=$HOME/ves/synapse_local", "--mode=sample", "--flops=1000",
         "--samples=1"
     ]
     return k
Exemplo n.º 16
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    def stage_2(self, instance):
        k2 = Kernel(name="md.gromacs")
        k2.link_input_data = ['$STAGE_1/in.tpr > in.tpr']
        k2.executable = ['path/to/gromacs/gmx']
        k2.arguments = ['mdrun', '-s', 'in.tpr', '-deffnm', 'out']
        k2.cores = 1

        return k2
Exemplo n.º 17
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    def step_2(self, instances):

        k = Kernel(name="misc.cat")
        k.upload_input_data = ['./output_file_2.txt > file2.txt']
        k.copy_input_data = ['$STEP_1/temp.txt > file1.txt']
        k.arguments = ["--file1=file1.txt", "--file2=file2.txt"]
        k.download_output_data = ['./file1.txt > output_file.txt']
        return k
Exemplo n.º 18
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 def step_1(self, instance):
     k = Kernel(name="misc.hello")
     k.upload_input_data = ['./input_file.txt > temp.txt']
     k.arguments = ["--file=temp.txt"]
     k.download_output_data = [
         './temp.txt > output_file_{0}.txt'.format(instance)
     ]
     return k
 def step_1(self, instance):
     k = Kernel(name="misc.hello")
     k.upload_input_data = ['./input_file.txt > temp.txt']
     k.arguments = ["--file=temp.txt"]
     k.download_output_data = [
         './temp.txt > /var/lib/jenkins/workspace/EnsembleMDTesting/temp_results/remote_file.txt'
     ]
     return k
Exemplo n.º 20
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    def stage_1(self, instances):
        """This step sleeps for 60 seconds."""

        k = Kernel(name="spark")
        k.upload_input_data = ['leafletfinder.py','traj_positions.npy']
        k.arguments = ["--exec-mem=60g","--driver-mem=30g",
        "--max-result-size=25g","--spark-script=leafletfinder.py","--input-file=traj_positions.npy","--partitions=378"]
        return k
Exemplo n.º 21
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 def step_1(self, instance):
     """The first step of the pipeline creates a 1 MB ASCI file.
     """
     k = Kernel(name="misc.mkfile")
     k.arguments = [
         "--size=1000000", "--filename=asciifile-{0}.dat".format(instance)
     ]
     return k
Exemplo n.º 22
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 def pre_loop(self):
     """pre_loop is executed before the main simulation-analysis loop is
        started. In this example we create an initial 1 kB random ASCII file
        that we use as the reference for all analysis steps.
     """
     k = Kernel(name="misc.mkfile")
     k.arguments = ["--size=1000", "--filename=reference.dat"]
     return k
 def simulation_step(self, iteration, instance):
     """The simulation step generates a 1 kB file containing random ASCII
        characters that is compared against the 'reference' file in the
        subsequent analysis step.
     """
     k = Kernel(name="misc.mkfile")
     k.arguments = ["--size=1000", "--filename=simulation-{0}-{1}.dat".format(iteration, instance)]
     return k
Exemplo n.º 24
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 def analysis_step(self, iteration, instance):
     """ In the analysis step, we use the 'randval' kernel to output a random number within 
     the upperlimit. The output is simply a number (and no other messages). Hence, we do not mention
     and extraction scripts. The pattern automatically picks up the number.
     """
     k = Kernel(name="misc.randval")
     k.arguments = ["--upperlimit=16"]
     return [k]
Exemplo n.º 25
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 def step_1(self, instance):
     k = Kernel(name="misc.chksum")
     k.arguments = [
         "--inputfile=UTF-8-demo.txt",
         "--outputfile=checksum{0}.sha1".format(instance)
     ]
     k.download_input_data = "http://testing.saga-project.org/cybertools/UTF-8-demo.txt"
     k.download_output_data = "checksum{0}.sha1".format(instance)
Exemplo n.º 26
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 def test_NoKernelPluginError(self):
     from radical.ensemblemd import Kernel
     from radical.ensemblemd.exceptions import NoKernelPluginError
     try:
         Kernel(name="random")
     except NoKernelPluginError, er:
         print 'NoKernelPluginError: Passed: ', er
         assert "Couldn't find a kernel plug-in named 'random'" in er
Exemplo n.º 27
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 def test_TypeError(self):
     from radical.ensemblemd import Kernel
     from radical.ensemblemd.exceptions import TypeError
     try:
         Kernel(name=1)
     except TypeError, er:
         print 'TypeError: Passed: ', er
         assert "Expected (base) type <type 'str'>, but got <type 'int'>." in er
Exemplo n.º 28
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 def step_1(self, instance):
     k = Kernel(name="misc.chksum")
     k.arguments = [
         "--inputfile={0}".format(self._checksum_inputfile),
         "--outputfile={0}".format(self._download_output)
     ]
     k.upload_input_data = self._upload_directives
     k.download_output_data = self._download_output
     return k
Exemplo n.º 29
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 def simulation_step(self, iteration, instance):
     """In the simulation step we
     """
     k = Kernel(name="misc.mkfile")
     k.arguments = [
         "--size=1000", "--filename=asciifile-{0}.dat".format(instance)
     ]
     k.download_output_data = ['asciifile-{0}.dat'.format(instance)]
     return [k]
Exemplo n.º 30
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 def stage_2(self, instance):
     k = Kernel(name="misc.ccount")
     k.arguments = [
         "--inputfile=asciifile-{0}.dat".format(instance),
         "--outputfile=cfreqs-{0}.dat".format(instance)
     ]
     k.copy_input_data = "$STAGE_1/asciifile-{0}.dat".format(instance)
     k.download_output_data = "cfreqs-{0}.dat".format(instance)
     return k