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Updated README and cleaned up

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Trung Nguyen
2024-07-21 10:06:37 -05:00
parent e8f09bfb02
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40
tools/regression-tests/README
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@ -1,20 +1,36 @@
The script `run_tests.py` in this folder is used to perform regression tests using in-place example scripts.
The script `run_tests.py` in this folder is used to perform regression tests
using in-place example scripts.
What this single script does is to launch the selected LAMMPS binary using a testing configuration defined in a `.yaml` file (e.g., `config.yaml`) for the set of input scripts inside the given `examples/` subfolders, and compare the output thermo with that in the existing log file with the same number of procs. If there are multiple log files with the same input script (e.g., `log.melt.*.g++.1` and `log.melt.*.g++.4`), the one with highest number of procs is chosen.
What this single script does is to launch the selected LAMMPS binary
using a testing configuration defined in a `.yaml` file (e.g., `config.yaml`)
for the set of input scripts inside the given `examples/` subfolders,
and compare the output thermo with that in the existing log file with the same number of procs.
If there are multiple log files with the same input script (e.g., `log.melt.*.g++.1` and `log.melt.*.g++.4`),
the one with the highest number of procs is chosen.
The output includes the number of passed and failed tests and an `output.xml` file in the JUnit XML format for downstream reporting. The output and error of any crashed runs are logged.
The output includes the number of passed and failed tests and
an `output.xml` file in the JUnit XML format for downstream reporting.
The output and error of any crashed runs are logged.
A test with an input script is considered passed when the given LAMMPS binary produces thermo output quantities consistent with those in the reference log file within the specified tolerances in the test configuration `.yaml` file.
A test with an input script is considered passed when the given LAMMPS binary produces
thermo output quantities consistent with those in the reference log file
within the specified tolerances in the test configuration `.yaml` file.
With the current features, users can:
+ launch tests with `mpirun` with multiple procs
+ specify which LAMMPS binary version to test (e.g., the version with their new code or those from `lammps-testing`)
+ specify which LAMMPS binary version to test (e.g., the version from a commit, or those from `lammps-testing`)
+ specify the examples subfolders (thus the reference log files) seperately (e.g. from other LAMMPS versions or commits)
+ specify tolerances for individual quantities for any input script to override the global values
+ launch tests with `mpirun` with all supported command line features (multiple procs, multiple paritions, and suffices)
+ skip certain input files if not interested, or no reference log file exists
+ simplify the main LAMMPS builds, as long as a LAMMPS binary is available
Limitations:
- input scripts use thermo style multi (e.g., examples/peptide) do not work with the expected thermo output format
- input scripts that require partition runs (e.g. examples/neb) need a separate config file, e.g. "args: --partition 2x1"
- testing accelerator packages (GPU, INTEL, KOKKOS, OPENMP) need separate config files, "args: -sf omp -pk omp 4"
TODO:
+ keep track of the testing progress to resume the testing from the last checkpoint
@ -22,6 +38,14 @@ TODO:
split the list of input scripts into separate runs (there are 800+ input script under the top-level examples)
+ be able to be invoked from run_tests in the lammps-testing infrastruture
The following Python packages need to be installed into an activated environment:
python3 -m venv testing-env
source testing-env/bin/activate
pip install numpy pyyaml junit_xml
Example uses:
1. Simple use with the provided `tools/regression-tests/config.yaml` and the `examples/` folder at the top level:
@ -38,6 +62,10 @@ Example uses:
--example-folders="/path/to/examples/folder1;/path/to/examples/folder2" \
--config-file=/path/to/config/file/config.yaml
4) Test a LAMMPS binary with the whole top-level /examples folder in a LAMMPS source tree
python3 run_tests.py --lmp-bin=/path/to/lmp_binary --example-top-level=/path/to/lammps/examples
An example of the test configuration `config.yaml` is given as below.
---

682
tools/regression-tests/run_tests.py
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@ -1,23 +1,37 @@
#!/usr/bin/env python3
'''
pip install numpy pyyaml junit_xml
UPDATE: July 5, 2024:
UPDATE: July 21, 2024:
Launching the LAMMPS binary under testing using a configuration defined in a yaml file (e.g. config.yaml).
Comparing the output thermo with that in the existing log file (with the same nprocs)
+ data in the log files are extracted and converted into yaml data structure
+ using the in place input scripts, no need to add REG markers to the input scripts
This way we can:
+ launch tests with mpirun with multiple procs
+ specify what LAMMPS binary version to test (e.g., testing separate builds)
+ simplify the build configuration (no need to build the Python module)
With the current features, users can:
+ specify which LAMMPS binary version to test (e.g., the version from a commit, or those from `lammps-testing`)
+ specify the examples subfolders (thus the reference log files) seperately (e.g. from other LAMMPS versions or commits)
+ specify tolerances for individual quantities for any input script to override the global values
+ launch tests with `mpirun` with all supported command line features (multiple procs, multiple paritions, and suffices)
+ skip certain input files if not interested, or no reference log file exists
+ simplify the main LAMMPS builds, as long as a LAMMPS binary is available
TODO:
+ distribute the input list across multiple processes via multiprocessing
Limitations:
- input scripts use thermo style multi (e.g., examples/peptide) do not work with the expected thermo output format
- input scripts that require partition runs (e.g. examples/neb) need a separate config file, e.g. "args: --partition 2x1"
- testing accelerator packages (GPU, INTEL, KOKKOS, OPENMP) need separate config files, "args: -sf omp -pk omp 4"
Example usage:
TODO:
+ keep track of the testing progress to resume the testing from the last checkpoint
+ distribute the input list across multiple processes via multiprocessing, or
split the list of input scripts into separate runs (there are 800+ input script under the top-level examples)
+ be able to be invoked from run_tests in the lammps-testing infrastruture
The following Python packages need to be installed into an activated environment:
python3 -m venv testing-env
source testing-env/bin/activate
pip install numpy pyyaml junit_xml
Example usage:
1) Simple use (using the provided tools/regression-tests/config.yaml and the examples/ folder at the top level)
python3 run_tests.py --lmp-bin=/path/to/lmp_binary
2) Use a custom testing configuration
@ -26,6 +40,8 @@ UPDATE: July 5, 2024:
python3 run_tests.py --lmp-bin=/path/to/lmp_binary \
--example-folders="/path/to/examples/folder1;/path/to/examples/folder2" \
--config-file=/path/to/config/file/config.yaml
4) Test a LAMMPS binary with the whole top-level /examples folder in a LAMMPS source tree
python3 run_tests.py --lmp-bin=/path/to/lmp_binary --example-top-level=/path/to/lammps/examples
'''
import os
@ -39,8 +55,8 @@ from multiprocessing import Pool
import logging
# need "pip install numpy pyyaml"
import yaml
import numpy as np
import yaml
# need "pip install junit_xml"
from junit_xml import TestSuite, TestCase
@ -60,27 +76,29 @@ class TestResult:
'''
get the thermo output from a log file with thermo style yaml
yamlFileName: input YAML file with thermo structured
as described in https://docs.lammps.org/Howto_structured_data.html
as described in https://docs.lammps.org/Howto_structured_data.html
return: thermo, which is a list containing a dictionary for each run
where the tag "keywords" maps to the list of thermo header strings
and the tag “data” has a list of lists where the outer list represents the lines
of output and the inner list the values of the columns matching the header keywords for that step.
where the tag "keywords" maps to the list of thermo header strings
and the tag data has a list of lists where the outer list represents the lines
of output and the inner list the values of the columns matching the header keywords for that step.
'''
def extract_thermo(yamlFileName):
docs = ""
with open(yamlFileName) as f:
for line in f:
m = re.search(r"^(keywords:.*$|data:$|---$|\.\.\.$| - \[.*\]$)", line)
if m: docs += m.group(0) + '\n'
thermo = list(yaml.load_all(docs, Loader=Loader))
return thermo
docs = ""
with open(yamlFileName) as f:
for line in f:
m = re.search(r"^(keywords:.*$|data:$|---$|\.\.\.$| - \[.*\]$)", line)
if m: docs += m.group(0) + '\n'
thermo = list(yaml.load_all(docs, Loader=Loader))
return thermo
'''
Convert an existing log.lammps file into a thermo yaml style log
inputFileName = a provided log file in an examples folder (e.g. examples/melt/log.8Apr21.melt.g++.4)
return a YAML data structure as if loaded from a thermo yaml file
Convert an existing log file into a thermo yaml style log
inputFileName = a provided log file in an examples folder (e.g. examples/melt/log.8Apr21.melt.g++.4)
return a YAML data structure as if loaded from a thermo yaml file
'''
def extract_data_to_yaml(inputFileName):
with open(inputFileName, 'r') as file:
@ -174,10 +192,10 @@ def get_lammps_build_configuration(lmp_binary):
return packages.split(" "), operating_system, GitInfo, compile_flags
'''
launch LAMMPS using the configuration defined in the dictionary config with an input file
TODO:
- generate new reference values if needed
- wrap subprocess with try/catch to handle exceptions
launch LAMMPS using the configuration defined in the dictionary config with an input file
TODO:
- generate new reference values if needed
- wrap subprocess with try/catch to handle exceptions
'''
def execute(lmp_binary, config, input_file_name, generate_ref_yaml=False):
cmd_str = config['mpiexec'] + " " + config['mpiexec_numproc_flag'] + " " + config['nprocs'] + " "
@ -188,7 +206,7 @@ def execute(lmp_binary, config, input_file_name, generate_ref_yaml=False):
return cmd_str, p.stdout, p.stderr, p.returncode
'''
split a list into a list of N sublists
split a list into a list of N sublists
'''
def divide_into_N(original_list, N):
size = np.ceil(len(original_list) / N)
@ -200,10 +218,11 @@ def divide_into_N(original_list, N):
b.append(l)
return b
'''
inputFileName: LAMMPS input file with comments #REG:ADD and #REG:SUB as markers
outputFileName: modified input file ready for testing
process the #REG markers in an input script, add/replace with what follows each marker
inputFileName: LAMMPS input file with comments #REG:ADD and #REG:SUB as markers
outputFileName: modified input file ready for testing
'''
def process_markers(inputFileName, outputFileName):
# read in the script
@ -231,12 +250,12 @@ def process_markers(inputFileName, outputFileName):
'''
attempt to insert the #REG markers before each run command
#REG:ADD thermo 10
#REG:ADD thermo_style yaml
attempt to insert the #REG markers before each run command
#REG:ADD thermo 10
#REG:ADD thermo_style yaml
inputFileName: provided LAMMPS input file
outputFileName: modified input file ready for testing
inputFileName: provided LAMMPS input file
outputFileName: modified input file ready for testing
'''
def generate_markers(inputFileName, outputFileName):
# read in the script
@ -268,273 +287,272 @@ def has_markers(inputFileName):
return False
'''
Iterate over a list of input files using the given lmp_binary, the testing configuration
return test results, as a list of TestResult instances
Iterate over a list of input files using the given lmp_binary, the testing configuration
return test results, as a list of TestResult instances
def func(input1, input2, output):
# do smth
return result
To map a function to individual workers:
# args is a list of Ncores tuples, each tuple contains the arguments passed to the function executed by a worker
args = []
for i in range(num_workers):
args.append((input1, input2, output))
def func(input1, input2, output):
# do smth
return result
with Pool(num_workers) as pool:
results = pool.starmap(func, args)
# args is a list of num_workers tuples, each tuple contains the arguments passed to the function executed by a worker
args = []
for i in range(num_workers):
args.append((input1, input2, output))
with Pool(num_workers) as pool:
results = pool.starmap(func, args)
'''
def iterate(lmp_binary, input_list, config, results, removeAnnotatedInput=False):
EPSILON = np.float64(config['epsilon'])
nugget = float(config['nugget'])
def iterate(lmp_binary, input_list, config, results, removeAnnotatedInput=False, output=None):
EPSILON = np.float64(config['epsilon'])
nugget = float(config['nugget'])
num_tests = len(input_list)
num_passed = 0
test_id = 0
num_tests = len(input_list)
num_passed = 0
test_id = 0
# using REG-commented input scripts
using_markers = False
# using REG-commented input scripts, now turned off (False)
using_markers = False
# iterate over the input scripts
for input in input_list:
# iterate over the input scripts
for input in input_list:
# skip the input file if listed
if 'skip' in config:
if input in config['skip']:
logger.info(f"SKIPPED: {input} as specified in the configuration file {configFileName}")
# skip the input file if listed
if 'skip' in config:
if input in config['skip']:
logger.info(f"SKIPPED: {input} as specified in the configuration file {configFileName}")
test_id = test_id + 1
continue
str_t = "\nRunning " + input + f" ({test_id+1}/{num_tests})"
result = TestResult(name=input, output="", time="", status="passed")
if using_markers == True:
input_test = 'test.' + input
if os.path.isfile(input) == True:
if has_markers(input):
process_markers(input, input_test)
else:
print(f"WARNING: {input} does not have REG markers")
input_markers = input + '.markers'
# if the input file with the REG markers does not exist
# attempt to plug in the REG markers before each run command
if os.path.isfile(input_markers) == False:
cmd_str = "cp " + input + " " + input_markers
os.system(cmd_str)
generate_markers(input, input_markers)
process_markers(input_markers, input_test)
str_t = "\nRunning " + input_test + f" ({test_id+1}/{num_tests})"
else:
input_test = input
print(str_t)
print(f"-"*len(str_t))
logger.info(str_t)
logger.info(f"-"*len(str_t))
# check if a log file exists in the current folder: log.DDMMMYY.basename.[nprocs]
basename = input_test.replace('in.','')
logfile_exist = False
# if there are multiple log files for different number of procs, pick the maximum number
cmd_str = "ls log.*"
p = subprocess.run(cmd_str, shell=True, text=True, capture_output=True)
logfile_list = p.stdout.split('\n')
logfile_list.remove('')
max_np = 1
for file in logfile_list:
# looks for pattern log.{date}.{basename}.g++.{nprocs}
# get the date from the log files
date = file.split('.',2)[1]
pattern = f'log.{date}.{basename}.*'
if fnmatch.fnmatch(file, pattern):
p = file.rsplit('.', 1)
if max_np < int(p[1]):
max_np = int(p[1])
logfile_exist = True
thermo_ref_file = file
# if the maximum number of procs is different from the value in the configuration file
# then override the setting for this input script
saved_nprocs = config['nprocs']
if max_np != int(config['nprocs']):
config['nprocs'] = str(max_np)
if logfile_exist:
thermo_ref = extract_data_to_yaml(thermo_ref_file)
num_runs_ref = len(thermo_ref)
else:
logger.info(f"Cannot find a reference log file {thermo_ref_file} for {input_test}.")
# try to read in the thermo yaml output from the working directory
thermo_ref_file = 'thermo.' + input + '.yaml'
file_exist = os.path.isfile(thermo_ref_file)
if file_exist == True:
thermo_ref = extract_thermo(thermo_ref_file)
num_runs_ref = len(thermo_ref)
else:
logger.info(f"SKIPPED: {thermo_ref_file} does not exist in the working directory.")
result.status = "skipped due to missing the log file"
results.append(result)
test_id = test_id + 1
continue
# or more customizable with config.yaml
cmd_str, output, error, returncode = execute(lmp_binary, config, input_test)
# restore the nprocs value in the configuration
config['nprocs'] = saved_nprocs
# process error code from the run
if os.path.isfile("log.lammps") == False:
logger.info(f"ERROR: No log.lammps generated with {input_test} with return code {returncode}. Check the {log_file} for the run output.\n")
logger.info(f"\n{input_test}:")
logger.info(f"\n{error}")
test_id = test_id + 1
continue
str_t = "\nRunning " + input + f" ({test_id+1}/{num_tests})"
# process thermo output from the run
thermo = extract_data_to_yaml("log.lammps")
result = TestResult(name=input, output="", time="", status="passed")
if using_markers == True:
input_test = 'test.' + input
if os.path.isfile(input) == True:
if has_markers(input):
process_markers(input, input_test)
num_runs = len(thermo)
if num_runs == 0:
logger.info(f"The run terminated with {input_test} gives the following output:\n")
logger.info(f"\n{output}")
if "Unrecognized" in output:
result.status = "unrecognized command"
else:
print(f"WARNING: {input} does not have REG markers")
input_markers = input + '.markers'
# if the input file with the REG markers does not exist
# attempt to plug in the REG markers before each run command
if os.path.isfile(input_markers) == False:
cmd_str = "cp " + input + " " + input_markers
os.system(cmd_str)
generate_markers(input, input_markers)
process_markers(input_markers, input_test)
str_t = "\nRunning " + input_test + f" ({test_id+1}/{num_tests})"
else:
input_test = input
print(str_t)
print(f"-"*len(str_t))
logger.info(str_t)
logger.info(f"-"*len(str_t))
result.status = "error"
logger.info(f"ERROR: Failed with {input_test} due to {result.status}.\n")
results.append(result)
test_id = test_id + 1
continue
# check if a log file exists in the current folder: log.DDMMMYY.basename.[nprocs]
basename = input_test.replace('in.','')
logfile_exist = False
# if there are multiple log files for different number of procs, pick the maximum number
cmd_str = "ls log.*"
p = subprocess.run(cmd_str, shell=True, text=True, capture_output=True)
logfile_list = p.stdout.split('\n')
logfile_list.remove('')
max_np = 1
for file in logfile_list:
# looks for pattern log.{date}.{basename}.g++.{nprocs}
# get the date from the log files
date = file.split('.',2)[1]
pattern = f'log.{date}.{basename}.*'
if fnmatch.fnmatch(file, pattern):
p = file.rsplit('.', 1)
if max_np < int(p[1]):
max_np = int(p[1])
logfile_exist = True
thermo_ref_file = file
# if the maximum number of procs is different from the value in the configuration file
# then override the setting for this input script
saved_nprocs = config['nprocs']
if max_np != int(config['nprocs']):
config['nprocs'] = str(max_np)
if logfile_exist:
thermo_ref = extract_data_to_yaml(thermo_ref_file)
num_runs_ref = len(thermo_ref)
else:
logger.info(f"Cannot find reference log file with {pattern}.")
# try to read in the thermo yaml output from the working directory
thermo_ref_file = 'thermo.' + input + '.yaml'
file_exist = os.path.isfile(thermo_ref_file)
if file_exist == True:
thermo_ref = extract_thermo(thermo_ref_file)
num_runs_ref = len(thermo_ref)
else:
logger.info(f"SKIPPED: {thermo_ref_file} does not exist in the working directory.")
result.status = "skipped due to missing the log file"
print(f"Comparing thermo output from log.lammps against the reference log file {thermo_ref_file}")
if num_runs != num_runs_ref:
logger.info(f"ERROR: Number of runs in log.lammps ({num_runs}) is not the same as that in the reference log ({num_runs_ref})")
result.status = "error"
results.append(result)
test_id = test_id + 1
continue
# using the LAMMPS python module (for single-proc runs)
# lmp = lammps()
# lmp.file(input_test)
# or more customizable with config.yaml
cmd_str, output, error, returncode = execute(lmp_binary, config, input_test)
# restore the nprocs value in the configuration
config['nprocs'] = saved_nprocs
# process error code from the run
if os.path.isfile("log.lammps") == False:
logger.info(f"ERROR: No log.lammps generated with {input_test} with return code {returncode}. Check the {log_file} for the run output.\n")
logger.info(f"\n{input_test}:")
logger.info(f"\n{error}")
test_id = test_id + 1
continue
# process thermo output
thermo = extract_data_to_yaml("log.lammps")
num_runs = len(thermo)
if num_runs == 0:
# comparing output vs reference values
width = 20
if verbose == True:
print("Quantities".ljust(width) + "Output".center(width) + "Reference".center(width) +
"Abs Diff Check".center(width) + "Rel Diff Check".center(width))
logger.info(f"The run terminated with {input_test} gives the following output:\n")
logger.info(f"\n{output}")
if "Unrecognized" in output:
result.status = "unrecognized command"
else:
result.status = "error"
logger.info(f"ERROR: Failed with {input_test} due to {result.status}.\n")
# check if overrides for this input scipt is specified
overrides = {}
if 'overrides' in config:
if input_test in config['overrides']:
overrides = config['overrides'][input_test]
# iterate through all num_runs
num_abs_failed = 0
num_rel_failed = 0
failed_abs_output = []
failed_rel_output = []
num_checks = 0
for irun in range(num_runs):
num_fields = len(thermo[irun]['keywords'])
# get the total number of the thermo output lines
nthermo_steps = len(thermo[irun]['data'])
# get the output at the last timestep
thermo_step = nthermo_steps - 1
# iterate over the fields
for i in range(num_fields):
quantity = thermo[irun]['keywords'][i]
val = thermo[irun]['data'][thermo_step][i]
ref = thermo_ref[irun]['data'][thermo_step][i]
abs_diff = abs(float(val) - float(ref))
if abs(float(ref)) > EPSILON:
rel_diff = abs(float(val) - float(ref))/abs(float(ref))
else:
rel_diff = abs(float(val) - float(ref))/abs(float(ref)+nugget)
abs_diff_check = "PASSED"
rel_diff_check = "PASSED"
if quantity in config['tolerance'] or quantity in overrides:
if quantity in config['tolerance']:
abs_tol = float(config['tolerance'][quantity]['abs'])
rel_tol = float(config['tolerance'][quantity]['rel'])
# overrides the global tolerance values if specified
if quantity in overrides:
abs_tol = float(overrides[quantity]['abs'])
rel_tol = float(overrides[quantity]['rel'])
num_checks = num_checks + 2
if abs_diff > abs_tol:
abs_diff_check = "FAILED"
reason = f"Run {irun}: {quantity}: actual ({abs_diff:0.2e}) > expected ({abs_tol:0.2e})"
failed_abs_output.append(f"{reason}")
num_abs_failed = num_abs_failed + 1
if rel_diff > rel_tol:
rel_diff_check = "FAILED"
reason = f"Run {irun}: {quantity}: actual ({rel_diff:0.2e}) > expected ({rel_tol:0.2e})"
failed_rel_output.append(f"{reason}")
num_rel_failed = num_rel_failed + 1
else:
# N/A means that tolerances are not defined in the config file
abs_diff_check = "N/A"
rel_diff_check = "N/A"
if verbose == True and abs_diff_check != "N/A" and rel_diff_check != "N/A":
print(f"{thermo[irun]['keywords'][i].ljust(width)} {str(val).rjust(20)} {str(ref).rjust(20)} "
"{abs_diff_check.rjust(20)} {rel_diff_check.rjust(20)}")
if num_abs_failed > 0:
print(f"{num_abs_failed} absolute diff checks failed with the specified tolerances.")
result.status = "failed"
if verbose == True:
for i in failed_abs_output:
print(f"- {i}")
if num_rel_failed > 0:
print(f"{num_rel_failed} relative diff checks failed with the specified tolerances.")
result.status = "failed"
if verbose == True:
for i in failed_rel_output:
print(f"- {i}")
if num_abs_failed == 0 and num_rel_failed == 0:
print(f"All {num_checks} checks passed.")
result.status = "passed"
num_passed = num_passed + 1
results.append(result)
str_t = f"Finished " + input_test
print(str_t)
print("-"*(5*width+4))
test_id = test_id + 1
continue
print(f"Comparing thermo output from log.lammps against the reference log file {thermo_ref_file}")
if num_runs != num_runs_ref:
logger.info(f"ERROR: Number of runs in log.lammps ({num_runs}) is not the same as that in the reference log ({num_runs_ref})")
result.status = "error"
results.append(result)
test_id = test_id + 1
continue
# remove the annotated input script
if removeAnnotatedInput == True:
cmd_str = "rm " + input_test
os.system(cmd_str)
# comparing output vs reference values
width = 20
if verbose == True:
print("Quantities".ljust(width) + "Output".center(width) + "Reference".center(width) +
"Abs Diff Check".center(width) + "Rel Diff Check".center(width))
# check if overrides for this input scipt is specified
overrides = {}
if 'overrides' in config:
if input_test in config['overrides']:
overrides = config['overrides'][input_test]
# iterate through all num_runs
num_abs_failed = 0
num_rel_failed = 0
failed_abs_output = []
failed_rel_output = []
num_checks = 0
for irun in range(num_runs):
num_fields = len(thermo[irun]['keywords'])
# get the total number of the thermo output lines
nthermo_steps = len(thermo[irun]['data'])
# get the output at the last timestep
thermo_step = nthermo_steps - 1
# iterate over the fields
for i in range(num_fields):
quantity = thermo[irun]['keywords'][i]
val = thermo[irun]['data'][thermo_step][i]
ref = thermo_ref[irun]['data'][thermo_step][i]
abs_diff = abs(float(val) - float(ref))
if abs(float(ref)) > EPSILON:
rel_diff = abs(float(val) - float(ref))/abs(float(ref))
else:
rel_diff = abs(float(val) - float(ref))/abs(float(ref)+nugget)
abs_diff_check = "PASSED"
rel_diff_check = "PASSED"
if quantity in config['tolerance'] or quantity in overrides:
if quantity in config['tolerance']:
abs_tol = float(config['tolerance'][quantity]['abs'])
rel_tol = float(config['tolerance'][quantity]['rel'])
# overrides the global tolerance values if specified
if quantity in overrides:
abs_tol = float(overrides[quantity]['abs'])
rel_tol = float(overrides[quantity]['rel'])
num_checks = num_checks + 2
if abs_diff > abs_tol:
abs_diff_check = "FAILED"
reason = f"Run {irun}: {quantity}: actual ({abs_diff:0.2e}) > expected ({abs_tol:0.2e})"
failed_abs_output.append(f"{reason}")
num_abs_failed = num_abs_failed + 1
if rel_diff > rel_tol:
rel_diff_check = "FAILED"
reason = f"Run {irun}: {quantity}: actual ({rel_diff:0.2e}) > expected ({rel_tol:0.2e})"
failed_rel_output.append(f"{reason}")
num_rel_failed = num_rel_failed + 1
else:
# N/A means that tolerances are not defined in the config file
abs_diff_check = "N/A"
rel_diff_check = "N/A"
if verbose == True and abs_diff_check != "N/A" and rel_diff_check != "N/A":
print(f"{thermo[irun]['keywords'][i].ljust(width)} {str(val).rjust(20)} {str(ref).rjust(20)} "
"{abs_diff_check.rjust(20)} {rel_diff_check.rjust(20)}")
if num_abs_failed > 0:
print(f"{num_abs_failed} absolute diff checks failed with the specified tolerances.")
result.status = "failed"
if verbose == True:
for i in failed_abs_output:
print(f"- {i}")
if num_rel_failed > 0:
print(f"{num_rel_failed} relative diff checks failed with the specified tolerances.")
result.status = "failed"
if verbose == True:
for i in failed_rel_output:
print(f"- {i}")
if num_abs_failed == 0 and num_rel_failed == 0:
print(f"All {num_checks} checks passed.")
result.status = "passed"
num_passed = num_passed + 1
results.append(result)
str_t = f"Finished " + input_test
print(str_t)
print("-"*(5*width+4))
test_id = test_id + 1
# remove the annotated input script
if removeAnnotatedInput == True:
cmd_str = "rm " + input_test
os.system(cmd_str)
return num_passed
return num_passed
'''
TODO:
- automate annotating the example input scripts if thermo style is multi (e.g. examples/peptide)
'''
if __name__ == "__main__":
@ -627,24 +645,15 @@ if __name__ == "__main__":
p = subprocess.run(cmd_str, shell=True, text=True, capture_output=True)
input_list = p.stdout.split('\n')
input_list.remove("")
# find out which folder to cd into to run the input script
for input in input_list:
folder = input.rsplit('/', 1)[0]
folder_list.append(folder)
print(f"There are {len(input_list)} input scripts in total under the {example_toplevel} folder.")
# divide the list of input scripts into num_workers chunks
sublists = divide_into_N(input_list, num_workers)
'''
# getting the list of all the subfolders
cmd_str = f"ls -d {example_toplevel} "
p = subprocess.run(cmd_str, shell=True, text=True, capture_output=True)
folder_list = p.stdout.split('\n')
folder_list.remove("")
print(f"There are {len(folder_list)} subfolders in total under the {example_toplevel} folder.")
# divide the list of subfolders into num_workers chunks
sublists = divide_into_N(folder_list, num_workers)
'''
# if only statistics, not running anything
if dry_run == True:
@ -655,10 +664,12 @@ if __name__ == "__main__":
test_cases = []
# if the example folders are not specified from the command-line argument --example-folders
# then use the --example-top-folder
# then use the path from --example-top-folder
if len(example_subfolders) == 0:
# input file list
# get the input file list, for now the first in the sublist
# TODO: generate a list of tuples, each tuple contains a folder list for a worker,
# then use multiprocessing.Pool starmap()
folder_list = []
for input in sublists[0]:
folder = input.rsplit('/', 1)[0]
@ -668,82 +679,9 @@ if __name__ == "__main__":
example_subfolders = folder_list
'''
example_subfolders = sublists[0]
'''
'''
example_subfolders.append("../../examples/melt")
example_subfolders.append('../../examples/flow')
example_subfolders.append('../../examples/indent')
example_subfolders.append('../../examples/shear')
example_subfolders.append('../../examples/steinhardt')
# prd log file parsing issue
# neb log file parsing issue
# snap log files obsolete?
# append the example subfolders depending on the installed packages
if 'ASPHERE' in packages:
#example_subfolders.append('../../examples/ASPHERE/ellipsoid')
example_subfolders.append('../../examples/ellipse')
if 'CORESHELL' in packages:
example_subfolders.append('../../examples/coreshell')
if 'MOLECULE' in packages:
example_subfolders.append('../../examples/micelle')
# peptide thermo_style as multi
#example_subfolders.append('../../examples/peptide')
if 'GRANULAR' in packages:
example_subfolders.append('../../examples/granular')
example_subfolders.append('../../examples/pour')
if 'AMOEBA' in packages:
example_subfolders.append('../../examples/amoeba')
if 'BODY' in packages:
example_subfolders.append('../../examples/body')
if 'BPM' in packages:
example_subfolders.append('../../examples/bpm/impact')
example_subfolders.append('../../examples/bpm/pour')
if 'COLLOID' in packages:
example_subfolders.append('../../examples/colloid')
if 'CRACK' in packages:
example_subfolders.append('../../examples/crack')
if 'DIELECTRIC' in packages:
example_subfolders.append('../../examples/PACKAGES/dielectric')
if 'DIPOLE' in packages:
example_subfolders.append('../../examples/dipole')
if 'DPD-BASIC' in packages:
example_subfolders.append('../../examples/PACKAGES/dpd-basic/dpd')
example_subfolders.append('../../examples/PACKAGES/dpd-basic/dpdext')
example_subfolders.append('../../examples/PACKAGES/dpd-basic/dpd_tstat')
example_subfolders.append('../../examples/PACKAGES/dpd-basic/dpdext_tstat')
if 'MANYBODY' in packages:
example_subfolders.append('../../examples/tersoff')
example_subfolders.append('../../examples/vashishta')
example_subfolders.append('../../examples/threebody')
if 'RIGID' in packages:
example_subfolders.append('../../examples/rigid')
if 'SNAP' in packages:
example_subfolders.append('../../examples/snap')
if 'SRD' in packages:
example_subfolders.append('../../examples/srd')
'''
all_results = []
# default setting
# default setting is to use inplace_input
if inplace_input == True:
# save current working dir
p = subprocess.run("pwd", shell=True, text=True, capture_output=True)
@ -754,7 +692,6 @@ if __name__ == "__main__":
# change dir to a folder under examples/, need to use os.chdir()
# TODO: loop through the subfolders under examples/, depending on the installed packages
'''
args = []
for i in range(num_workers):
@ -766,7 +703,6 @@ if __name__ == "__main__":
total_tests = 0
passed_tests = 0
for directory in example_subfolders:
p = subprocess.run("pwd", shell=True, text=True, capture_output=True)
@ -786,6 +722,7 @@ if __name__ == "__main__":
num_passed = iterate(lmp_binary, input_list, config, results)
passed_tests += num_passed
# append the results to the all_results list
all_results.extend(results)
# get back to the working dir
@ -798,12 +735,15 @@ if __name__ == "__main__":
results = []
passed_tests = iterate(input_list, config, results)
all_results.extend(results)
# print out summary
print("Summary:")
print(f" - {passed_tests} numerical tests passed / {total_tests} tests")
print(f" - Details are given in {output_file}.")
# generate a JUnit XML file
# optional: need to check if junit_xml packaged is already installed in the env
# generate a JUnit XML file
with open(output_file, 'w') as f:
test_cases = []
for result in all_results: