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Commit 57d5ced2 authored by David Hendriks's avatar David Hendriks
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removed testing stuff. fixed example

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TODO.org deleted 100644 → 0
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* Todo list for the binary_c-python
** Logging functionality:
*** Idea
Idea is to be able to give a string via python that will be used in the through the libbinary_c.so so that log_every_timestep.c
The code below is the piece in log_every_timestep that uses it.
if(stardata->preferences->custom_output_function != NULL)
{
Dprint("calling custom output function %p\n",
stardata->preferences->custom_output_function);
stardata->preferences->custom_output_function(stardata);
}
So the function should recieve 'stardata' as input.
We can do that with providing a logging string alltogether, or generate a logging function
In either way, this should be passed to stardata->preferences->custom_output_function as a pointer to that function
*** Provide string for logging
In perl this is done in the following way:
**** code to input
And then to use it via
$population->set(
C_logging_code => '
PRINTF("MY_STELLAR_DATA %g %g %g %g\n",
stardata->model.time,
stardata->star[0].mass,
stardata->model.probability,
stardata->model.dt);
'
);
**** code to handle that input
sub binary_c_log_code
{
my ($code) = @_;
return "
#pragma push_macro(\"MAX\")
#pragma push_macro(\"MIN\")
#undef MAX
#undef MIN
#include \"binary_c.h\"
void custom_output_function(SV * x);
SV * custom_output_function_pointer(void);
SV * custom_output_function_pointer()
{
/*
* use PTR2UV to convert the function pointer
* &custom_output_function to an unsigned int,
* which is then converted to a Perl SV
*/
return (SV*)newSVuv(PTR2UV(&custom_output_function));
}
void custom_output_function(SV * x)
{
struct stardata_t * stardata = (struct stardata_t *)x;
$code;
}
#undef MAX
#undef MIN
#pragma pop_macro(\"MIN\")
#pragma pop_macro(\"MAX\")
";
}
Or use it via:
*** auto logging
We should also try to be able to have the code autogenerate some logging function via the following:
**** input in perl
$population->set( C_auto_logging => {
'MY_STELLAR_DATA' =>
[
'model.time',
'star[0].mass',
'model.probability',
'model.dt'
]
});
**** code to handle that input
Which is handled in perl via (see binary_grid2.pm
sub autogen_C_logging_code
{
# given a hash of arrays of variable names, where the hash
# key is the header, autogenerate PRINTF statements
my ($self) = @_;
my $code = undef;
if(defined $self->{_grid_options}->{C_auto_logging} &&
ref $self->{_grid_options}->{C_auto_logging} eq 'HASH'
)
{
$code = '';
foreach my $header (keys %{$self->{_grid_options}->{C_auto_logging}})
{
if(ref $self->{_grid_options}->{C_auto_logging}->{$header} eq 'ARRAY')
{
$code .= 'PRINTF("'.$header.' ';
foreach my $x (@{$self->{_grid_options}->{C_auto_logging}->{$header}})
{
$code .= '%g ';
}
$code .= '\n"';
foreach my $x (@{$self->{_grid_options}->{C_auto_logging}->{$header}})
{
$code .= ',((double)stardata->'.$x.')';
}
$code .= ');'
}
}
}
print "MADE AUTO CODE \n\n************************************************************\n\n$code\n\n************************************************************\n";
return $code;
}
*** DONE Make function in python that puts code into c function
CLOSED: [2019-10-31 Thu 11:13]
*** DONE Make function in python that generates c-function from a list of arguments
CLOSED: [2019-10-29 Tue 23:52]
*** DONE Resolve current issue malloc
CLOSED: [2019-11-08 Fri 11:12]
➜ binary_c-python git:(master) ✗ python python_API_test.py
Traceback (most recent call last):
File "python_API_test.py", line 3, in <module>
import binary_c
ImportError: /home/david/projects/binary_c_root/binary_c-python/libbinary_c_api.so: undefined symbol: MALLOC
I get this error when I am using the master version of binary_c with either branches of the python wrapper..
That went very deep haha. alot of memory allocation stuff
*** DONE Make sure this works with the last major release of binaryc
CLOSED: [2019-11-08 Fri 15:00]
*** DONE Finish testing a simpler case (see other repo)
CLOSED: [2019-11-08 Fri 09:37]
*** DONE Make master master work
CLOSED: [2019-11-08 Fri 15:00]
*** DONE Sync master with david_branch
CLOSED: [2019-11-08 Fri 15:00]
*** DONE make tag of old master branch for future reference
CLOSED: [2019-11-08 Fri 15:00]
*** DONE Implement the autogeneration of the library
CLOSED: [2019-11-08 Fri 15:48]
*** DONE Load all the things with the c-types
CLOSED: [2019-11-08 Fri 18:49]
*** DONE Implement new function for run_binary_with_custom_logging
CLOSED: [2019-11-08 Fri 21:19]
*** DONE Make new c function run_binary_with_custom_logging
CLOSED: [2019-11-08 Fri 21:19]
*** TODO Put in some new tests in the python test api
*** DONE Make sure the sharedlibs get written to the correct directory
CLOSED: [2019-11-10 Sun 00:21]
** General:
*** DONE Get a more reliable way of loading the default values (running a ./tbse echo or something?)
CLOSED: [2019-10-29 Tue 17:44]
*** DONE make routine that reads out all the lines, splits them into pieces and reads out the correct key
CLOSED: [2019-10-29 Tue 17:43]
*** TODO Put header and other source files in a dedicated directory
*** TODO Use sphinx or read the docs for auto generation of documentation
*** TODO Have the compiled files put into a build directory
*** TODO add pythonpath thing to readme
*** TODO make script that will set up binaryc automatically so that this can become an out of the box thing
*** TODO Test the importing of this code from different places
** Population ideas
*** TODO Queuing system and some multiprocessing to run many systems
*** TODO Consider rewriting the work that perl does
examples/examples_run_binary.py
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...@@ -34,7 +34,7 @@ def run_example_binary(): ...@@ -34,7 +34,7 @@ def run_example_binary():
output = binary_c.run_binary(argstring) output = binary_c.run_binary(argstring)
print (output) print (output)
# run_example_binary() run_example_binary()
def run_example_binary_with_run_system(): def run_example_binary_with_run_system():
""" """
...@@ -71,7 +71,7 @@ def run_example_binary_with_run_system(): ...@@ -71,7 +71,7 @@ def run_example_binary_with_run_system():
# Some routine to plot. # Some routine to plot.
# run_example_binary_with_run_system() run_example_binary_with_run_system()
def run_example_binary_with_custom_logging(): def run_example_binary_with_custom_logging():
...@@ -110,7 +110,7 @@ def run_example_binary_with_custom_logging(): ...@@ -110,7 +110,7 @@ def run_example_binary_with_custom_logging():
# Do whatever you like with the dataframe. # Do whatever you like with the dataframe.
print(df) print(df)
# run_example_binary_with_custom_logging() run_example_binary_with_custom_logging()
def run_example_binary_with_writing_logfile(): def run_example_binary_with_writing_logfile():
""" """
...@@ -119,9 +119,10 @@ def run_example_binary_with_writing_logfile(): ...@@ -119,9 +119,10 @@ def run_example_binary_with_writing_logfile():
import pandas as pd import pandas as pd
import numpy as np import numpy as np
import tempfile
# Run system. all arguments can be given as optional arguments. # Run system. all arguments can be given as optional arguments.
output = run_system(M_1=10, M_2=20, separation=0, orbital_period=100000000000, log_filename=os.getcwd()+'/test_log.txt') output = run_system(M_1=10, M_2=20, separation=0, orbital_period=100000000000, log_filename=tempfile.gettempdir()+'/test_log.txt')
# Catch results that start with a given header. (Mind that binary_c has to be configured to print them if your not using a custom logging function) # Catch results that start with a given header. (Mind that binary_c has to be configured to print them if your not using a custom logging function)
result_example_header = parse_output(output, 'example_header') result_example_header = parse_output(output, 'example_header')
...@@ -140,4 +141,4 @@ def run_example_binary_with_writing_logfile(): ...@@ -140,4 +141,4 @@ def run_example_binary_with_writing_logfile():
# Some routine to plot. # Some routine to plot.
# run_example_binary_with_writing_logfile() run_example_binary_with_writing_logfile()
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testing_examples/__init__.py deleted 100644 → 0
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testing_examples/example_notebook.ipynb deleted 100644 → 0
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testing_examples/full_evolution_with_plot.py deleted 100644 → 0
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import os, sys
import matplotlib.pyplot as plt
# Append root dir of this project to include functionality
sys.path.append(os.path.dirname(os.getcwd()))
import binary_c
from utils.defaults import physics_defaults
from utils.functions import create_arg_string
def example_with_loading_default_args():
"""
Example function loading the default physics args for a binary_c system. Got
it from the binary_grid2 perl module
This function works if binary_c is set to log something every timestep so that we can plot the evolution of a system
"""
# Load args
physics_args = physics_defaults.copy()
# Manually set M_1, M_2, orbital_period and separation values:
physics_args['M_1'] = 20
physics_args['M_2'] = 15
physics_args['separation'] = 0 # 0 = ignored, use period
physics_args['orbital_period'] = 4530.0
arg_string = create_arg_string(physics_args)
arg_string = f'binary_c {arg_string}'
buffer = ""
output = binary_c.run_binary(arg_string)
# Make some
results = {}
time_arr = []
mass_arr = []
mass_2_arr = []
# split output on newlines
for line in output.split('\n'):
# Skip any blank lines
if not line=='':
split_line = line.split()
header = split_line[0]
value_array = split_line[1:]
# Use parse data here:
if header=='TESTLOG':
# Add values to lists
time_arr.append(float(value_array[0]))
mass_arr.append(float(value_array[1]))
mass_2_arr.append(float(value_array[4]))
# Save in results dir
results['time'] = time_arr
results['mass'] = mass_arr
results['mass2'] = mass_2_arr
return results
results = example_with_loading_default_args()
# Plot some stuff
plt.plot(results['time'], results['mass'])
plt.plot(results['time'], results['mass2'])
plt.xscale('log')
plt.show()
testing_examples/run_system_with_custom_logging.py deleted 100644 → 0
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tests_and_snippets/.ipynb_checkpoints/test_noteboo-checkpoint.ipynb deleted 100644 → 0
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{
"cells": [],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 2
}
tests_and_snippets/snippets/arg_test.py deleted 100644 → 0
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import argparse
class grid(object):
def __init__(self, name):
self.name = name
self.grid_options = {}
def load_grid_options(self):
self.grid_options = {
'test1': 0,
'test2': 1,
}
def argparse(self):
"""
This function handles the arg parsing of the grid.
Make sure that every grid_option key/value is included in this,
preferably with an explanation on what that parameter will do
"""
parser = argparse.ArgumentParser(description='Arguments for the binary_c python wrapper grid')
# add arguments here
parser.add_argument('--test1', type=int, help='input for test1')
parser.add_argument('--test2', type=int, help='input for test2')
# Load the args from the cmdline
args = parser.parse_args()
# Copy current grid_option set
new_grid_options = self.grid_options.copy()
# loop over grid_options
for arg in vars(args):
# print ("arg: {arg} value: {value}".format(arg=arg, value=getattr(args, arg)))
# If an input has been given in the cmdline: override the previous value of grid_options
if getattr(args, arg):
new_grid_options[arg] = getattr(args, arg)
# Put the new grid options back
self.grid_options = new_grid_options.copy()
newgrid = grid('test')
newgrid.load_grid_options()
print(newgrid.grid_options)
newgrid.argparse()
print(newgrid.grid_options)
# Custom set a single value:
newgrid.grid_options['test2'] = 2
print(newgrid.grid_options)
# Custom set multiple values:
newgrid.grid_options.update({
'test1':4,
'test2':-2,
})
print(newgrid.grid_options)
tests_and_snippets/snippets/d.py deleted 100644 → 0
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import multiprocessing
import time
def doubler(number):
return number ** 2
def count(number):
nr = 0
for i in range(number):
nr += i
return number
if __name__ == '__main__':
numbers = range(2, 100000)
pool = multiprocessing.Pool(processes=6)
# print()
rs = pool.map_async(pool.map(count, numbers), range(len(numbers)))
pool.close() # No more work
while (True):
if (rs.ready()): break
remaining = rs._number_left
print("Waiting for", remaining, "tasks to complete...")
time.sleep(0.5)
\ No newline at end of file
tests_and_snippets/snippets/mp2.py deleted 100644 → 0
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import time
def basic_func(x):
if x == 0:
return 'zero'
elif x%2 == 0:
return 'even'
else:
return 'odd'
starttime = time.time()
for i in range(0,10):
y = i*i
time.sleep(2)
print('{} squared results in a/an {} number'.format(i, basic_func(y)))
print('That took {} seconds'.format(time.time() - starttime))
\ No newline at end of file
tests_and_snippets/snippets/mp3.py deleted 100644 → 0
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import time
import multiprocessing
def basic_func(x):
if x == 0:
return 'zero'
elif x%2 == 0:
return 'even'
else:
return 'odd'
def multiprocessing_func(x):
y = x*x
time.sleep(2)
print('{} squared results in a/an {} number'.format(x, basic_func(y)))
if __name__ == '__main__':
starttime = time.time()
processes = []
for i in range(0,100):
p = multiprocessing.Process(target=multiprocessing_func, args=(i,))
processes.append(p)
p.start()
for process in processes:
process.join()
print('That took {} seconds'.format(time.time() - starttime))
\ No newline at end of file
tests_and_snippets/snippets/mp3_pool.py deleted 100644 → 0
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import time
import multiprocessing
# https://medium.com/@urban_institute/using-multiprocessing-to-make-python-code-faster-23ea5ef996ba
def basic_func(x):
if x == 0:
return 'zero'
elif x%2 == 0:
return 'even'
else:
return 'odd'
def multiprocessing_func(x):
y = x*x
time.sleep(2)
print('{} squared results in a/an {} number'.format(x, basic_func(y)))
if __name__ == '__main__':
starttime = time.time()
pool = multiprocessing.Pool()
pool.map(multiprocessing_func, range(0,10))
pool.close()
print('That took {} seconds'.format(time.time() - starttime))
\ No newline at end of file
tests_and_snippets/snippets/multiprocessing_test.py deleted 100644 → 0
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from multiprocessing import Process, Queue
import time
import sys
def reader_proc(queue):
## Read from the queue; this will be spawned as a separate Process
while True:
msg = queue.get() # Read from the queue and do nothing
if (msg == 'DONE'):
break
def writer(count, queue):
## Write to the queue
for ii in range(0, count):
queue.put(ii) # Write 'count' numbers into the queue
queue.put('DONE')
if __name__=='__main__':
pqueue = Queue() # writer() writes to pqueue from _this_ process
for count in [10**4, 10**5, 10**6]:
### reader_proc() reads from pqueue as a separate process
reader_p = Process(target=reader_proc, args=((pqueue),))
reader_p.daemon = True
reader_p.start() # Launch reader_proc() as a separate python process
_start = time.time()
writer(count, pqueue) # Send a lot of stuff to reader()
reader_p.join() # Wait for the reader to finish
print("Sending {0} numbers to Queue() took {1} seconds".format(count,
(time.time() - _start)))
\ No newline at end of file
tests_and_snippets/testing_automatic_log_readout.py deleted 100644 → 0
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import os, sys, time
import matplotlib.pyplot as plt
from collections import defaultdict
import numpy as np
import pandas as pd
# sys.path.append('../')
import binary_c
from binaryc_python_utils.functions import create_arg_string, parse_output, run_system
"""
Script to test some auto reading out.
todo: make to hdf5
"""
start = time.time()
output = run_system(M_1=10, M_2=20, separation=0, orbital_period=100000000000)
result = parse_output(output, 'DAVID_SINGLE_ANALYSIS')
stop = time.time()
print("Took {:.2f}s to run single system".format(stop-start))
print("The following keys are present in the results:\n{}".format(result.keys()))
#### Now do whatever you want with it:
#t_res = np.asarray(result['t'], dtype=np.float64, order='C')
#m_res = np.asarray(result['mass'], dtype=np.float64, order='C')
# Cast the data into a dataframe.
df = pd.DataFrame.from_dict(result, dtype=np.float64)
sliced_df = df[df.t < 1000] # Cut off late parts of evolution
# print(sliced_df["t"])
plt.plot(sliced_df['omega'], sliced_df['radius'])
plt.xlabel('Time (Myr)')
plt.ylabel('omega (Rsol)')
plt.show()
\ No newline at end of file
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