Removed unnecessary todos and update chi_run

chi_pet/analysis/graph_lib.py

@@ -73,7 +73,7 @@ def sci_notation_latex(num_str, sig_figs=4, d_range=0, **kwargs):
     @param num_str: string of a number in exponential form
     @param sig_figs: The number of significant figures
     @param d_range: The exponent range before applying scientific notation.
-    @param **kwargs: TODO
+    @param **kwargs: 
     @return: latex string
 
     """

chi_pet/chi.py

@@ -37,7 +37,17 @@ def read_opts(self):
                 self.opts.states = list(yd.keys())
 
     def execute(self):
-        if self.opts.command == "launch":
+
+        if self.opts.command == 'create':
+            chi_root_node = ChiNode(self.opts.workdir, opts=self.opts)
+            chi_root_node.make_subnodes()
+
+        elif self.opts.command == 'run':
+            pass
+            c = ChiRun(self.opts)
+            c.run(self.opts)
+
+        elif self.opts.command == "launch":
             # # If no sim dirs are given find them all in simulations
             # if self.opts.launch == []:
             #     self.opts.launch = clib.find_dirs(wd / "simulations")
@@ -57,18 +67,8 @@ def execute(self):
             # running_path.touch()
             pass
 
-        elif self.opts.command == 'create':
-            # TODO NEXT implement this functionality
-            chi_root_node = ChiNode(self.opts.workdir, opts=self.opts)
-            chi_root_node.make_subnodes()
-
-        elif self.opts.command == 'run':
+        elif self.opts.prep:
             pass
-            c = ChiRun(self.opts)
-            c.Run(self.opts)
-
-        # elif self.opts.prep:
-        #     pass
         #     # leaf_lst = clib.find_leaf_dirs(self.opts.workdir)
         #     # for leaf_dir in leaf_lst:
         #     #     if self.opts.args_file:

chi_pet/chi_parse.py

@@ -18,7 +18,7 @@ def parse_chi_options():
     parent_parser.add_argument('-a', '--args_file', type=Path,
                                help='Name file that holds the program argument list.  (Used with --create option and --launch option)')
     parent_parser.add_argument('-d', '--workdir', type=Path,
-                               help='Name of the working directory where simulation will be run. Used with --run option only)')
+                               help='Name of the working directory where simulation will be created or run.')
     parent_parser.add_argument('-s', '--states', nargs='+', type=str,
                                help='Name of all the states the simulation will run eg. start, build, analyze, etc.')
 
@@ -32,6 +32,10 @@ def parse_chi_options():
     parser.add_argument('-rm', '--remove', nargs='+', metavar='FILE(s)', type=Path,
                         help='Removes FILEs from seed directories.')
 
+    # TODO add clean functionality
+    parser.add_argument('-C', '--clean', action='store_true',
+                        help='Remove subnode and data directory files from current work directory.')
+
     subparsers = parser.add_subparsers(dest='command')
 
     # CREATE options

chi_pet/chi_run.py

@@ -36,26 +36,39 @@ def run_args(workdir, state, args):
 
 class ChiRun(object):
     def __init__(self, opts):
-        self.opts = opts
+        self._opts = opts
 
-    def run(self, opts):
-        workdir_path = Path(opts.workdir)
-        args_file_path = workdir_path / opts.args_file
-        if not workdir_path.exists():
+    def run(self):
+        """Run simulation pipeline defined in the the args.yaml file defined as self.opts.args_file.
+
+        Parameters
+        ----------
+        opts : _type_
+            _description_
+
+        Raises
+        ------
+        FileNotFoundError
+            _description_
+        FileNotFoundError
+            _description_
+        """
+
+        # TODO NEXT Make these more useful for running simulations
+        if not self._opts.workdir.exists():
             raise FileNotFoundError(
                 "Run failed. Directory {} does not exists.".format(
-                    opts.workdir))
+                    self._opts.workdir))
 
-        elif not args_file_path.exists():
+        elif not self._opts._args_file.exists():
             raise FileNotFoundError(
-                "Run failed. args.yaml file not found in {}.".format(
-                    opts.workdir))
+                "Run failed. args.yaml file not found in {self._opts.workdir}.".format(
+                ))
         else:
-            with args_file_path.open('r') as f:
+            with self._opts.args_file.open('r') as f:
                 args_dict = load_yaml_in_order(f)
 
-        print(dump_yaml_in_order(args_dict, default_flow_style=False))
-
+        # print(dump_yaml_in_order(args_dict, default_flow_style=False))
         # Loop through all states in args.yaml.
         # TODO Add option to see if sim_action file exists and only run those states.
         for run_state, vals in args_dict.items():
@@ -93,4 +106,4 @@ def run_args(workdir, state, args):
     opts = parse_chi_options()
 
     c = ChiRun(opts)
-    c.execute(opts)
+    c.run(opts)

chi_pet/old/ChiParams.py

@@ -151,8 +151,6 @@ def UpdateParamValues(self):
     def UpdateShotgunParamValues(self):
         for cparam in self.chiparams:
             if cparam.values:
-                # TODO If two param value lists are different,
-                #     choose the smaller one.
                 if self.opts.n != cparam.GetNValues():
                     print("## Number of values of in {0}({1}) \
                     does not match nvars given({2}). Setting nvars to {1}.".format(
@@ -212,7 +210,7 @@ def MakeSimDirectoryDatabase(self, run_dir, gen, ind_lst=[]):
             sim_values += str(p[i]) + " "
             sim_name += p.format(p[i]) + "_"
 
-        #sim_name = sim_name[:-1] + str(random.uniform(0.0, 1.0))
+        # sim_name = sim_name[:-1] + str(random.uniform(0.0, 1.0))
         sim_name = sim_name[:-1]
         sim_values = sim_values[:-1]
         # print "sim_values: {}".format(sim_values)
@@ -367,23 +365,23 @@ def UpdateFitness(self, sim_dir, dotest=False):
             with open(data_file_path, 'r') as stream:
                 fitness_yaml = yaml.load(stream)
                 # Compile fitness information
-                #em_fitness = 0.0
-                #length_correlation_avg = 0.0
-                #fbiorientation = 0.0
-                #kinetochore_fitness = 0.0
+                # em_fitness = 0.0
+                # length_correlation_avg = 0.0
+                # fbiorientation = 0.0
+                # kinetochore_fitness = 0.0
 
                 # Get the EM and length correlation fitness information
-                #em_fitness = (fitness_yaml['short'] + fitness_yaml['med'] + fitness_yaml['long'])/3.
-                #length_correlation_avg = fitness_yaml['length_correlation_avg']
+                # em_fitness = (fitness_yaml['short'] + fitness_yaml['med'] + fitness_yaml['long'])/3.
+                # length_correlation_avg = fitness_yaml['length_correlation_avg']
 
                 # Get the chromosome fitness information, base it off chromosome seconds fraction key
                 # if 'chromosome_seconds_fraction' in fitness_yaml:
                 #    fbiorientation = fitness_yaml['fbiorientation']
                 #    kinetochore_fitness = np.mean([fitness_yaml['chromosome_kc_spb_distance'], fitness_yaml['chromosome_kc_spindle1d']])
 
                 # Combine total fitness in our special way
-                #total_fitness = em_fitness + 2.0*length_correlation_avg + 2.0*fbiorientation + kinetochore_fitness
-                #self.fitness[idx] = total_fitness
+                # total_fitness = em_fitness + 2.0*length_correlation_avg + 2.0*fbiorientation + kinetochore_fitness
+                # self.fitness[idx] = total_fitness
 
                 # Get what SpindleAnalysis thinks is the total fitness
                 total_fitness = fitness_yaml['FINAL_FITNESS']
@@ -553,7 +551,7 @@ def UpdateGeneticsRoulette(self):
             # Roulette wheel selection of the available parameters
             value1 = random.uniform(0.0, 1.0) * self.weight_sum1
             parent1_idx = -1
-            #cur_value1 = self.min_fitness
+            # cur_value1 = self.min_fitness
             for i in range(self.nparticles):
                 value1 -= (self.fitness[i] - self.min_genetics)
                 if value1 <= 0.0:
@@ -587,10 +585,10 @@ def UpdateGeneticsRoulette(self):
 
             # Two point crossover
             # Two point crossover
-            #self.parents[cidx1] = [parent1_idx, parent2_idx]
-            #self.parents[cidx2] = [parent1_idx, parent2_idx]
-            #cp1 = int(random.uniform(0, len(self.chiparams)))
-            #cp2 = int(random.uniform(0, len(self.chiparams)))
+            # self.parents[cidx1] = [parent1_idx, parent2_idx]
+            # self.parents[cidx2] = [parent1_idx, parent2_idx]
+            # cp1 = int(random.uniform(0, len(self.chiparams)))
+            # cp2 = int(random.uniform(0, len(self.chiparams)))
             # if cp2 < cp1:
             #    temp = cp1
             #    cp1 = cp2
@@ -703,7 +701,7 @@ def PrintSwarmBest(self):
         str2 += " {} ".format(self.gbest_better)
         print(str2)
 
-        #str3 = "{} {} ".format("gfull", self.gbest)
+        # str3 = "{} {} ".format("gfull", self.gbest)
         # for ichi in xrange(len(self.chiparams)):
         #    str3 += " {} ".format(self.gbestx[ichi].values[self.gbestid])
         # print str3

docker/Dockerfile

@@ -10,7 +10,6 @@ RUN apt update && \
 RUN git clone --recursive https://github.com/lamsoa729/chi_pet.git /root/chi_pet && \
     pip install -e /root/chi_pet
 
-# TODO add continuous integration