From 5d24110325adf5aeaae00d28498e2a41dd4cc63d Mon Sep 17 00:00:00 2001
From: David Hendriks <davidhendriks93@gmail.com>
Date: Mon, 10 Feb 2020 16:43:46 +0000
Subject: [PATCH] added some plots, restructured routine for scalingplots,
fixing the phasevol part of the grid generator
---
binarycpython/utils/grid.py | 20 +-
binarycpython/utils/spacing_functions.py | 6 +-
test_systems/README.md | 3 +-
tests/population/grid_tests.py | 91 +-
tests/population/scaling/plot_scaling.py | 327 ++-
.../scaling_plots/speedup_scaling_Astro1.eps | 2 +-
.../scaling_plots/speedup_scaling_Astro1.pdf | Bin 16209 -> 16209 bytes
.../total_time_scaling_Astro1.eps | 2552 +++++++++++++++++
.../total_time_scaling_Astro1.pdf | Bin 0 -> 15709 bytes
.../total_time_scaling_Astro1.png | Bin 0 -> 58634 bytes
10 files changed, 2795 insertions(+), 206 deletions(-)
create mode 100644 tests/population/scaling/scaling_plots/total_time_scaling_Astro1.eps
create mode 100644 tests/population/scaling/scaling_plots/total_time_scaling_Astro1.pdf
create mode 100644 tests/population/scaling/scaling_plots/total_time_scaling_Astro1.png
diff --git a/binarycpython/utils/grid.py b/binarycpython/utils/grid.py
index f762a75fb..c69bc7145 100644
--- a/binarycpython/utils/grid.py
+++ b/binarycpython/utils/grid.py
@@ -484,7 +484,8 @@ class Population(object):
# TODO: Unset custom logging code
# TODO: Unset function memory adress
- print(self.grid_options["custom_logging_func_memaddr"])
+ # print(self.grid_options["custom_logging_func_memaddr"])
+
# remove shared library files
if self.grid_options["custom_logging_shared_library_file"]:
@@ -1007,11 +1008,24 @@ class Population(object):
+ "\n"
)
+ code_string += (
+ indent * depth
+ + "sampled_values_{} = {}".format(grid_variable["name"], grid_variable["spacingfunc"])
+ + "\n"
+ )
+
+ # TODO: Make clear that the phasevol only works good if you sample linearly in that thing.
+ code_string += (
+ indent * depth
+ + "phasevol_{} = sampled_values_{}[1]-sampled_values_{}[0]".format(grid_variable["name"], grid_variable["name"], grid_variable["name"])
+ + "\n"
+ )
+
# Adding for loop structure
code_string += (
indent * depth
- + "for {} in {}:".format(
- grid_variable["name"], grid_variable["spacingfunc"]
+ + "for {} in sampled_values_{}:".format(
+ grid_variable["name"], grid_variable["name"]
)
+ "\n"
)
diff --git a/binarycpython/utils/spacing_functions.py b/binarycpython/utils/spacing_functions.py
index b8f70c3ba..fbd3d35c7 100644
--- a/binarycpython/utils/spacing_functions.py
+++ b/binarycpython/utils/spacing_functions.py
@@ -1,2 +1,4 @@
-def const():
- return
+import numpy as np
+
+def const(min, max, steps):
+ return np.linspace(min, max, steps)
diff --git a/test_systems/README.md b/test_systems/README.md
index c04d3e104..45fd1d781 100644
--- a/test_systems/README.md
+++ b/test_systems/README.md
@@ -1,2 +1 @@
-#
-This directory is mean to contain script to generate test systems.
+# This directory is meant to contain script to generate test systems.
diff --git a/tests/population/grid_tests.py b/tests/population/grid_tests.py
index a0f4e9761..3b45b999c 100644
--- a/tests/population/grid_tests.py
+++ b/tests/population/grid_tests.py
@@ -252,20 +252,20 @@ test_pop.set(
####
# Dry run:
-# test_pop.set(verbose=1)
-# test_pop.add_grid_variable(
-# name="lnm1",
-# longname="log primary mass",
-# valuerange=[10, 20],
-# resolution="10",
-# spacingfunc="np.linspace(0.213, 10.2, 10)",
-# precode="M_1=math.exp(lnm1)",
-# probdist="flat(M_1)",
-# # probdist='self.custom_options["extra_prob_function"](M_1)',
-# dphasevol="",
-# parameter_name="M_1",
-# condition="",
-# )
+test_pop.set(verbose=1)
+test_pop.add_grid_variable(
+ name="lnm1",
+ longname="log primary mass",
+ valuerange=[10, 20],
+ resolution="10",
+ spacingfunc="np.linspace(0.213, 10.2, 10)",
+ precode="M_1=math.exp(lnm1)",
+ probdist="flat(M_1)",
+ # probdist='self.custom_options["extra_prob_function"](M_1)',
+ dphasevol="",
+ parameter_name="M_1",
+ condition="",
+)
# ### Grid generating test.
# test_pop.add_grid_variable(
@@ -281,7 +281,7 @@ test_pop.set(
# condition='self.grid_options["binary"]==0',
# )
-# test_pop.generate_grid_code(dry_run=True)
+test_pop.generate_grid_code(dry_run=True)
# test_pop.load_grid_function()
@@ -290,34 +290,39 @@ test_pop.set(
####
# testing population:
-test_pop.set(verbose=1, amt_cores=4)
-test_pop.add_grid_variable(
- name="lnm1",
- longname="log primary mass",
- valuerange=[10, 20],
- resolution="10",
- spacingfunc="np.linspace(0.213, 10.2, 10)",
- precode="M_1=math.exp(lnm1)",
- probdist="flat(M_1)",
- # probdist='self.custom_options["extra_prob_function"](M_1)',
- dphasevol="",
- parameter_name="M_1",
- condition="",
-)
+# test_pop.set(verbose=1,
+# amt_cores=1,
+# binary=0,
+# )
+# test_pop.add_grid_variable(
+# name="M_1",
+# longname="log primary mass",
+# valuerange=[10, 20],
+# resolution="10",
+# spacingfunc="const(10, 20, 4)",
+# precode="",
+# # precode="M_1=math.exp(lnm1)",
+# probdist="flat(M_1)",
+# # probdist='self.custom_options["extra_prob_function"](M_1)',
+# dphasevol="dlnm1",
+# parameter_name="M_1",
+# condition="",
+# )
-### Grid generating test.
-test_pop.add_grid_variable(
- name="period",
- longname="period",
- valuerange=["M_1", 20],
- resolution="10",
- spacingfunc="np.linspace(1, 10, 10)",
- precode="orbital_period = period**2",
- probdist="flat(orbital_period)",
- parameter_name="orbital_period",
- dphasevol="",
- condition='self.grid_options["binary"]==0',
-)
+# ### Grid generating test.
+# test_pop.add_grid_variable(
+# name="period",
+# longname="period",
+# valuerange=["M_1", 20],
+# resolution="10",
+# spacingfunc="",
+# spacingfunc="np.linspace(1, 10, 10)",
+# precode="orbital_period = period**2",
+# probdist="flat(orbital_period)",
+# parameter_name="orbital_period",
+# dphasevol="dper",
+# condition='self.grid_options["binary"]==0',
+# )
-test_pop.test_evolve_population()
+# test_pop.test_evolve_population()
# test_pop.generate_grid_code(dry_run=True)
diff --git a/tests/population/scaling/plot_scaling.py b/tests/population/scaling/plot_scaling.py
index fb72b1cf4..13cd47162 100644
--- a/tests/population/scaling/plot_scaling.py
+++ b/tests/population/scaling/plot_scaling.py
@@ -8,10 +8,176 @@ import numpy as np
def calc_mean_and_std(arr):
return np.mean(arr), np.std(arr)
+def calculate_results(results, unique_amt_cores, unique_amt_systems):
+ """
+ Function to calculate the numbers
+ """
+
+ calculated_results = []
+ for i in unique_amt_cores:
+ for j in unique_amt_systems:
+ (
+ total_time_sequential_list,
+ total_time_multiprocessing_list,
+ total_ratio_list,
+ ) = ([], [], [])
+
+ for res in results:
+ if (res[0] == i) & (res[1] == j):
+ total_time_sequential_list.append(res[2])
+ total_time_multiprocessing_list.append(res[3])
+ total_ratio_list.append(res[4])
+
+ if (
+ (total_time_sequential_list)
+ and (total_time_multiprocessing_list)
+ and (total_ratio_list)
+ ):
+ # calculate stuff
+ mean_time_sequential, std_sequential = calc_mean_and_std(
+ np.array(total_time_sequential_list)
+ )
+ mean_time_multiprocessing, std_multiprocessing = calc_mean_and_std(
+ np.array(total_time_multiprocessing_list)
+ )
+ mean_ratio, std_ratio = calc_mean_and_std(np.array(total_ratio_list))
+
+ # make dict
+ res_dict = {
+ "cores": i,
+ "systems": j,
+ "mean_time_sequential": mean_time_sequential,
+ "mean_time_multiprocessing": mean_time_multiprocessing,
+ "mean_ratio": mean_ratio,
+ "std_sequential": std_sequential,
+ "std_multiprocessing": std_multiprocessing,
+ "std_ratio": std_ratio,
+ "total_runs": len(total_time_sequential_list),
+ }
+
+ calculated_results.append(res_dict)
+
+ return calculated_results
+
+def plot_speedup_and_efficiency(calculated_results, unique_amt_cores, unique_amt_systems):
+ x_position_shift = 0
+ y_position_shift = -0.05
+ max_speedup = 0
+
+ # https://stackoverflow.com/questions/46323530/matplotlib-plot-two-x-axes-one-linear-and-one-with-logarithmic-ticks
+ fig, ax1 = plt.subplots()
+ ax2 = ax1.twinx()
+ for amt_systems in unique_amt_systems:
+
+ cores = []
+ speedup = []
+ std = []
+ efficiency = []
+
+ for el in calculated_results:
+ if el["systems"] == amt_systems:
+
+ cores.append(el["cores"] + x_position_shift)
+ speedup.append(el["mean_ratio"])
+ std.append(el["std_ratio"])
+ efficiency.append(el["mean_ratio"] / el["cores"])
+
+ if el["mean_ratio"] > max_speedup:
+ max_speedup = el["mean_ratio"]
+
+ # add number of runs its based on
+ ax1.text(
+ el["cores"] + x_position_shift + 0.01,
+ el["mean_ratio"] + y_position_shift,
+ el["total_runs"],
+ )
+
+ ax1.errorbar(
+ cores,
+ speedup,
+ std,
+ linestyle="None",
+ marker="^",
+ label="Speed up & efficiency of {} systems".format(amt_systems),
+ )
+
+ ax2.plot(cores, efficiency, alpha=0.5)
+ x_position_shift += 0.1
+
+ ax1.set_title(
+ "Speed up ratio vs amount of cores for different amounts of systems on {}".format(
+ name_testcase
+ )
+ )
+ ax1.set_xlabel("Amount of cores used")
+ ax1.set_ylabel("Speed up ratio (time_linear/time_parallel)")
+
+ ax1.set_xlim(0, max(unique_amt_cores) + 4)
+ ax1.set_ylim(0, max_speedup + 2)
+ ax2.set_ylim(0, 1)
+
+ ax1.grid()
+ ax1.legend(loc=4)
+ fig.savefig(os.path.join(img_dir, "speedup_scaling_{}.{}".format(name_testcase, "png")))
+ fig.savefig(os.path.join(img_dir, "speedup_scaling_{}.{}".format(name_testcase, "pdf")))
+ fig.savefig(os.path.join(img_dir, "speedup_scaling_{}.{}".format(name_testcase, "eps")))
+ plt.show()
+
+def plot_runtime(calculated_results, unique_amt_cores, unique_amt_systems):
+ fig = plt.figure()
+ ax = fig.add_subplot(111)
+
+ color_options = ['blue', 'green', 'red', 'black', 'yellow', 'magenta']
+
+ x_position_shift = 0
+ y_position_shift = -0.05
+
+ colors = [color_options[i] for i in range(len(unique_amt_systems))]
+ for i, amt_systems in enumerate(unique_amt_systems):
+
+ cores = []
+
+ mean_times_linear = []
+ mean_times_multiprocessing = []
+ std_linear = []
+ std_mp = []
+
+ for el in calculated_results:
+ if el['systems']==amt_systems:
+ cores.append(el['cores'] + x_position_shift)
+
+ mean_times_linear.append(el['mean_time_sequential'])
+ std_linear.append(el['std_sequential'])
+
+ mean_times_multiprocessing.append(el['mean_time_multiprocessing'])
+ std_mp.append(el['std_multiprocessing'])
+
+ # # add number of runs its based on
+ # plt.text(el['cores'] + x_position_shift+0.01, el['mean_ratio']+y_position_shift, el['total_runs'])
+
+ ax.errorbar(cores, mean_times_linear, std_linear, color=colors[i], linewidth=1, marker='P', label='{} systems linear run'.format(amt_systems))
+ ax.errorbar(cores, mean_times_multiprocessing, std_mp, linewidth=1, color=colors[i], marker='d', label='{} systems MP run'.format(amt_systems))
+ x_position_shift += 0.1
+
+ ax.set_title("Total time for sequential and MP for different amounts of systems on {}".format(name_testcase))
+ ax.set_xlabel("Amount of cores used")
+ ax.set_ylabel("Total time taken (s)")
+
+ ax.set_xlim(0, max(unique_amt_cores) + 20)
+ ax.set_yscale('log')
+ ax.grid()
+ ax.legend()
+
+ fig.savefig(os.path.join(img_dir, 'total_time_scaling_{}.{}'.format(name_testcase, 'png')))
+ fig.savefig(os.path.join(img_dir, 'total_time_scaling_{}.{}'.format(name_testcase, 'pdf')))
+ fig.savefig(os.path.join(img_dir, 'total_time_scaling_{}.{}'.format(name_testcase, 'eps')))
+
+ plt.show()
-####
+#####################
+
# Configure
result_file = "comparison_result.dat"
result_file = "comparison_result_astro1.dat"
@@ -27,7 +193,7 @@ with open(result_file, "r") as f:
res.append(res[-2] / res[-1])
results.append(res)
-# make dataframe
+# make dataframe and set correct headers.
headers = [
"cores",
"total_systems",
@@ -44,161 +210,12 @@ unique_amt_cores = df["cores"].unique()
unique_amt_systems = df["total_systems"].unique()
# Create dictionary with calculated means and stdevs etc
-calculated_results = []
-for i in unique_amt_cores:
- for j in unique_amt_systems:
- (
- total_time_sequential_list,
- total_time_multiprocessing_list,
- total_ratio_list,
- ) = ([], [], [])
-
- for res in results:
- if (res[0] == i) & (res[1] == j):
- total_time_sequential_list.append(res[2])
- total_time_multiprocessing_list.append(res[3])
- total_ratio_list.append(res[4])
-
- if (
- (total_time_sequential_list)
- and (total_time_multiprocessing_list)
- and (total_ratio_list)
- ):
- # calculate stuff
- mean_time_sequential, std_sequential = calc_mean_and_std(
- np.array(total_time_sequential_list)
- )
- mean_time_multiprocessing, std_multiprocessing = calc_mean_and_std(
- np.array(total_time_multiprocessing_list)
- )
- mean_ratio, std_ratio = calc_mean_and_std(np.array(total_ratio_list))
-
- # make dict
- res_dict = {
- "cores": i,
- "systems": j,
- "mean_time_sequential": mean_time_sequential,
- "mean_time_multiprocessing": mean_time_multiprocessing,
- "mean_ratio": mean_ratio,
- "std_sequential": std_sequential,
- "std_multiprocessing": std_multiprocessing,
- "std_ratio": std_ratio,
- "total_runs": len(total_time_sequential_list),
- }
-
- calculated_results.append(res_dict)
+calculated_results = calculate_results(results, unique_amt_cores, unique_amt_systems)
#############################################################################################################################
# Plot speed up and efficiency.
-x_position_shift = 0
-y_position_shift = -0.05
-max_speedup = 0
-# https://stackoverflow.com/questions/46323530/matplotlib-plot-two-x-axes-one-linear-and-one-with-logarithmic-ticks
-fig, ax1 = plt.subplots()
-ax2 = ax1.twinx()
-for amt_systems in unique_amt_systems:
-
- cores = []
- speedup = []
- std = []
- efficiency = []
-
- for el in calculated_results:
- if el["systems"] == amt_systems:
-
- cores.append(el["cores"] + x_position_shift)
- speedup.append(el["mean_ratio"])
- std.append(el["std_ratio"])
- efficiency.append(el["mean_ratio"] / el["cores"])
-
- if el["mean_ratio"] > max_speedup:
- max_speedup = el["mean_ratio"]
-
- # add number of runs its based on
- ax1.text(
- el["cores"] + x_position_shift + 0.01,
- el["mean_ratio"] + y_position_shift,
- el["total_runs"],
- )
-
- ax1.errorbar(
- cores,
- speedup,
- std,
- linestyle="None",
- marker="^",
- label="Speed up & efficiency of {} systems".format(amt_systems),
- )
- ax2.plot(cores, efficiency, alpha=0.5)
- x_position_shift += 0.1
+plot_speedup_and_efficiency(calculated_results, unique_amt_cores, unique_amt_systems)
-ax1.set_title(
- "Speed up ratio vs amount of cores for different amounts of systems on {}".format(
- name_testcase
- )
-)
-ax1.set_xlabel("Amount of cores used")
-ax1.set_ylabel("Speed up ratio (time_linear/time_parallel)")
-
-ax1.set_xlim(0, max(unique_amt_cores) + 4)
-ax1.set_ylim(0, max_speedup + 2)
-ax2.set_ylim(0, 1)
-
-ax1.grid()
-ax1.legend(loc=4)
-fig.savefig(os.path.join(img_dir, "speedup_scaling_{}.{}".format(name_testcase, "png")))
-fig.savefig(os.path.join(img_dir, "speedup_scaling_{}.{}".format(name_testcase, "pdf")))
-fig.savefig(os.path.join(img_dir, "speedup_scaling_{}.{}".format(name_testcase, "eps")))
-plt.show()
-
-#####
+########################################################################################
# Plot run time
-# fig = plt.figure()
-# ax = fig.add_subplot(111)
-
-# color_options = ['blue', 'green', 'red', 'black', 'yellow', 'magenta']
-
-# x_position_shift = 0
-# y_position_shift = -0.05
-
-# colors = [color_options[i] for i in range(len(unique_amt_systems))]
-# for i, amt_systems in enumerate(unique_amt_systems):
-
-# cores = []
-
-# mean_times_linear = []
-# mean_times_multiprocessing = []
-# std_linear = []
-# std_mp = []
-
-# for el in calculated_results:
-# if el['systems']==amt_systems:
-# cores.append(el['cores'] + x_position_shift)
-
-# mean_times_linear.append(el['mean_time_sequential'])
-# std_linear.append(el['std_sequential'])
-
-# mean_times_multiprocessing.append(el['mean_time_multiprocessing'])
-# std_mp.append(el['std_multiprocessing'])
-
-# # # add number of runs its based on
-# # plt.text(el['cores'] + x_position_shift+0.01, el['mean_ratio']+y_position_shift, el['total_runs'])
-
-# ax.errorbar(cores, mean_times_linear, std_linear, color=colors[i], linewidth=1, marker='P', label='{} systems linear run'.format(amt_systems))
-# ax.errorbar(cores, mean_times_multiprocessing, std_multiprocessing, linewidth=1, color=colors[i], marker='d', label='{} systems MP run'.format(amt_systems))
-# x_position_shift += 0.1
-
-# ax.set_title("Total time for sequential and MP for different amounts of systems on {}".format(name_testcase))
-# ax.set_xlabel("Amount of cores used")
-# ax.set_ylabel("Total time taken (s)")
-
-# ax.set_xlim(0, max(unique_amt_cores) + 20)
-# ax.set_yscale('log')
-# ax.grid()
-# ax.legend()
-
-# fig.savefig(os.path.join(img_dir, 'total_time_scaling_{}.{}'.format(name_testcase, 'png')))
-# fig.savefig(os.path.join(img_dir, 'total_time_scaling_{}.{}'.format(name_testcase, 'pdf')))
-# fig.savefig(os.path.join(img_dir, 'total_time_scaling_{}.{}'.format(name_testcase, 'eps')))
-
-# plt.show()
+plot_runtime(calculated_results, unique_amt_cores, unique_amt_systems)
\ No newline at end of file
diff --git a/tests/population/scaling/scaling_plots/speedup_scaling_Astro1.eps b/tests/population/scaling/scaling_plots/speedup_scaling_Astro1.eps
index 49d71a374..2ba6d8cda 100644
--- a/tests/population/scaling/scaling_plots/speedup_scaling_Astro1.eps
+++ b/tests/population/scaling/scaling_plots/speedup_scaling_Astro1.eps
@@ -1,7 +1,7 @@
%!PS-Adobe-3.0 EPSF-3.0
%%Title: scaling_plots/speedup_scaling_Astro1.eps
%%Creator: matplotlib version 3.1.2, http://matplotlib.org/
-%%CreationDate: Thu Jan 30 13:38:14 2020
+%%CreationDate: Mon Feb 10 16:42:49 2020
%%Orientation: portrait
%%BoundingBox: 75.6 223.20000000000002 536.4 568.8
%%EndComments
diff --git a/tests/population/scaling/scaling_plots/speedup_scaling_Astro1.pdf b/tests/population/scaling/scaling_plots/speedup_scaling_Astro1.pdf
index eac5b25473077a3c03938c7fdad08fde50edc8f6..21bc02885c8f00406eddbda9d64c663fa8faa284 100644
GIT binary patch
delta 22
dcmcaucd>4Rtre${p@E^9iIIurW;ZKtRsdnz2HyYx
delta 22
dcmcaucd>4Rtre%Cv4Nqnv4x@WW;ZKtRsdnP2H5}r
diff --git a/tests/population/scaling/scaling_plots/total_time_scaling_Astro1.eps b/tests/population/scaling/scaling_plots/total_time_scaling_Astro1.eps
new file mode 100644
index 000000000..c2e7d28f2
--- /dev/null
+++ b/tests/population/scaling/scaling_plots/total_time_scaling_Astro1.eps
@@ -0,0 +1,2552 @@
+%!PS-Adobe-3.0 EPSF-3.0
+%%Title: scaling_plots/total_time_scaling_Astro1.eps
+%%Creator: matplotlib version 3.1.2, http://matplotlib.org/
+%%CreationDate: Mon Feb 10 16:42:52 2020
+%%Orientation: portrait
+%%BoundingBox: 75.6 223.20000000000002 536.4 568.8
+%%EndComments
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--
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