added progress bar to parallel loop, initialize pop and update pop, a…

…dded __main__ function

.gitignore

@@ -10,4 +10,5 @@ __pycache__/
 docs/_build
 docs/_static
 .DS_Store
-.cache/
+.cache/
+*.egg-info

.travis.yml

@@ -1,7 +1,6 @@
 language: python
 python:
   - "2.7"
-  - "3.4"
   - "3.5"
   - "3.6"
 matrix:
@@ -11,8 +10,6 @@ matrix:
       sudo: true
   allow_failures:
     - python: "2.7"
-    - python: "3.4"
-    - python: "3.5"
 # command to install dependencies
 install:
 # add new stuff to requirements file, not here

gastop/__init__.py

@@ -1,7 +1,7 @@
-from gastop.truss import Truss
-from gastop.evaluator import Evaluator
-from gastop.crossover import Crossover
-from gastop.mutator import Mutator
-from gastop.selector import Selector
-from gastop.fitness import FitnessFunction
-from gastop.genalg import GenAlg
+from .truss import Truss
+from .evaluator import Evaluator
+from .crossover import Crossover
+from .mutator import Mutator
+from .selector import Selector
+from .fitness import FitnessFunction
+from .genalg import GenAlg

gastop/__main__.py

@@ -0,0 +1,98 @@
+def main():
+    import numpy as np
+    import argparse
+    from gastop import GenAlg, utilities
+
+    parser = argparse.ArgumentParser(prog='gastop',
+                                     description="A Genetic Algorithm for Structural Design and Topological Optimization. See full documentation at gastop.readthedocs.io")
+    parser.add_argument("config_path", help="file path to gastop config file")
+    parser.add_argument("-p", "--pop_size", type=int,
+                        help="population size. If not specified, defaults to what is in config.", metavar='')
+    parser.add_argument("-g", "--num_gens", type=int,
+                        help="number of generations. If not specified, defaults to what is in config.", metavar='')
+    parser.add_argument("-t", "--num_threads", type=int,
+                        help="number of threads to use. If not specified, defaults to what is in config.", metavar='')
+    group = parser.add_mutually_exclusive_group()
+    group.add_argument("-q", "--quiet", action="store_true",
+                       help="hide progress display window")
+    group.add_argument("-d", "--display", action='store_true',
+                       help="show progress display window")
+    args = parser.parse_args()
+
+    config = utilities.init_file_parser(args.config_path)
+
+    if args.display:
+        progress_display == 2
+    elif args.quiet:
+        progress_display == 1
+    else:
+        progress_display = None
+
+    if args.num_threads:
+        num_threads = args.num_threads
+    else:
+        num_threads = config['ga_params']['num_threads']
+
+    if args.num_gens:
+        num_generations = args.num_gens
+    else:
+        num_generations = config['ga_params']['num_generations']
+
+    if args.pop_size:
+        pop_size = args.pop_size
+    else:
+        pop_size = config['ga_params']['pop_size']
+
+    # Create the Genetic Algorithm Object
+    ga = GenAlg(config)
+    ga.initialize_population(pop_size)
+    best, progress_history = ga.run(num_generations=num_generations,
+                                    progress_display=progress_display,
+                                    num_threads=num_threads)
+
+    con = best.edges.copy()
+    matl = best.properties.copy()
+
+    # remove self connected edges and duplicate members
+    matl = matl[(con[:, 0]) >= 0]
+    con = con[(con[:, 0]) >= 0]
+    matl = matl[(con[:, 1]) >= 0]
+    con = con[(con[:, 1]) >= 0]
+    con = con.astype(int)
+
+    np.set_printoptions(precision=2)
+
+    print('\n')
+    print('Nodes:')
+    print('     x     y     z   ')
+    print(best.rand_nodes)
+
+    print('\n')
+    print('Edges:')
+    print(con)
+
+    print('\n')
+    print('Properties:')
+    print(matl)
+
+    print('\n')
+    print('Mass: %.3f kg' % best.mass)
+
+    print('\n')
+    print('FoS:')
+    print(best.fos)
+
+    print('\n')
+    print('Deflections (m): ')
+    print('      Dx        Dy        Dz        Rx        Ry        Rz        ')
+    print(best.deflection[:, :, 0])
+
+    if progress_display == 2:
+        best.plot(domain=config['random_params']['domain'].T,
+                  loads=config['evaluator_params']['boundary_conditions']['loads'],
+                  fixtures=config['evaluator_params']['boundary_conditions']['fixtures'],
+                  deflection=True, load_scale=.001, def_scale=100)
+
+
+if __name__ == '__main__':
+    main()

gastop/genalg.py

@@ -118,7 +118,7 @@ def generate_random(self, _):  # Dan
 
         return Truss(user_spec_nodes, new_nodes, new_edges, new_properties)
 
-    def initialize_population(self, pop_size):
+    def initialize_population(self, pop_size=None):
         '''Initializes population with randomly creates Truss objects
 
         Args:
@@ -127,7 +127,10 @@ def initialize_population(self, pop_size):
         Returns:
             Updated self.population
         '''
-        self.ga_params['pop_size'] = pop_size
+        if pop_size:
+            self.ga_params['pop_size'] = pop_size
+        else:
+            pop_size = self.ga_params['pop_size']
 
         # Try 1: t= 0.298
         # self.population = [self.generate_random() for i in range(pop_size)]
@@ -146,7 +149,8 @@ def initialize_population(self, pop_size):
 
         # Try 4: t=0.232
         pool = Pool()
-        self.population = pool.map(self.generate_random, range(pop_size))
+        self.population = list(tqdm(pool.imap(
+            self.generate_random, range(pop_size), int(np.sqrt(pop_size))), total=pop_size, leave=False, desc='Initializing Population', position=0))
         pool.close()
         pool.join()
 
@@ -167,39 +171,43 @@ def run(self, num_generations=None, progress_display=1, num_threads=None):
             ax1 = fig.add_subplot(1, 1, 1)
             plt.ylabel('fos')
             plt.xlabel('iteration')
-        #
+
+        # wrapper function for evaluation and scoring
+        def score(truss):
+            return self.fitness_function(self.evaluator(truss))
 
         # Loop over all generations:
 
         # Without any parallelization:
-        # Try 1: time =
         if num_threads == 1:
             for current_gen in tqdm(range(num_generations), desc='Overall', position=0):
                 self.ga_params['current_generation'] = current_gen
                 # Loop over all trusses -> PARALLELIZE. Later
-                for current_truss in tqdm(self.population, desc='Current Generation', leave=False, position=1):
-                    # Run evaluator method. Will store refitness_scoresults in Truss Object
+                for current_truss in tqdm(self.population, desc='Evaluating', position=1):
                     self.evaluator(current_truss)
-                    # Assigns numerical score to each truss
+                for current_truss in tqdm(self.population, desc='Scoring', position=1):
                     self.fitness_function(current_truss)
-                # print(current_truss.fos)
                 if progress_display == 2:
                     self.progress_monitor(current_gen, progress_display, ax1)
                 self.update_population()  # Determine which members to
                 if progress_display == 2:
                     plt.show()  # sfr, keep plot from closing right after this completes, terminal will hang until this is closed
             return self.population[0], self.pop_progress
 
+        # With parallelization
         else:
-            # With parallelization
-            # Try 2: time =
-            for current_gen in tqdm(range(num_generations)):
-            #for current_gen in tqdm_notebook(range(num_generations),desc='Generation'):
+            if self.ga_params['pop_size'] < 1e4:
+                chunksize = self.ga_params['pop_size']/100
+            else:
+                chunksize = int(np.sqrt(self.ga_params['pop_size']))
+            for current_gen in tqdm(range(num_generations), desc='Overall', position=0):
+                # for current_gen in tqdm_notebook(range(num_generations),desc='Generation'):
                 self.ga_params['current_generation'] = current_gen
                 pool = Pool(num_threads)
-                self.population = pool.map(self.evaluator, self.population)
-                self.population = pool.map(
-                    self.fitness_function, self.population)
+                self.population = list(tqdm(pool.imap(
+                    self.evaluator, self.population, chunksize), total=self.ga_params['pop_size'], desc='Evaluating', position=1))
+                self.population = list(tqdm(pool.imap(
+                    self.fitness_function, self.population, chunksize), total=self.ga_params['pop_size'], desc='Scoring', position=1))
                 pool.close()
                 pool.join()
                 if progress_display == 2:
@@ -309,6 +317,8 @@ def update_population(self):  # Cristian
         # Save most fit trusses as elites
         pop_elite = population[:num_elite]
 
+        pbar = tqdm(total=(num_crossover+num_mutation+num_random),
+                    desc='Updating', position=1)
         # Portion of new population formed by crossover
         pop_crossover = []
         for i in range(0, num_crossover, 2):
@@ -318,6 +328,7 @@ def update_population(self):  # Cristian
             parent2 = population[parentindex2]
             child1, child2 = crossover(parent1, parent2)
             pop_crossover.extend((child1, child2))
+            pbar.update(2)
 
         # Portion of new population formed by mutation
         pop_mutation = []
@@ -326,9 +337,13 @@ def update_population(self):  # Cristian
             parent = population[parentindex]
             child = mutator(parent)
             pop_mutation.append(child)
+            pbar.update()
 
         # Create new random trusses with remaining spots in generation
-        pop_random = [self.generate_random(2) for i in range(num_random)]
+        pop_random = []
+        for i in range(num_random):
+            pop_random.append(self.generate_random(2))
+            pbar.update()
 
         # Append separate lists to form new generation
         population = pop_elite + pop_crossover + pop_mutation + pop_random

requirements.txt

@@ -2,7 +2,7 @@
 --index-url https://pypi.python.org/simple/
 
 numpy >= 1.13.0
-matplotlib >= 2.0.0
+matplotlib >= 3.0.0
 configobj >= 5.0.6
 tqdm >= 4.28.1
 pytest >=3.0.5

setup.py

@@ -34,15 +34,15 @@
     # There are some restrictions on what makes a valid project name
     # specification here:
     # https://packaging.python.org/specifications/core-metadata/#name
-    name='GASTOp',  # Required
+    name='gastop',  # Required
 
     # Versions should comply with PEP 440:
     # https://www.python.org/dev/peps/pep-0440/
     #
     # For a discussion on single-sourcing the version across setup.py and the
     # project code, see
     # https://packaging.python.org/en/latest/single_source_version.html
-    version='0.1.0',  # Required
+    version='0.2.9',  # Required
 
     # This is a one-line description or tagline of what your project does. This
     # corresponds to the "Summary" metadata field:
@@ -83,7 +83,7 @@
 
     # This should be a valid email address corresponding to the author listed
     # above.
-    author_email='wconlin@princeton.edu',  # Optional
+    # author_email=
 
     # Classifiers help users find your project by categorizing it.
     #
@@ -129,7 +129,8 @@
     #
     #   py_modules=["my_module"],
     #
-    packages=find_packages(exclude=['docs', 'tests']),  # Required
+    packages=find_packages(
+        exclude=['docs', 'tests', 'profiling', 'junk']),  # Required
 
     # This field lists other packages that your project depends on to run.
     # Any package you put here will be installed by pip when your project is
@@ -177,7 +178,7 @@
     # executes the function `main` from this package when invoked:
     entry_points={  # Optional
         'console_scripts': [
-            'gastop=gastop:main',
+            'gastop=gastop.__main__:main',
         ],
     },