make shorter CI test, update readme and contributing, make homogenous…

… format for time series data

CONTRIBUTING.md

@@ -10,7 +10,7 @@ To contribute to the pyMBE library, first one needs to be added as a member of t
 If you want to contribute to the development of the pyMBE library, please contact Dr. Pablo M. Blanco (pablb@ntnu.no)
 
 ## Rules to contribute to pyMBE
-Any new version of the code must reproduce all the data stored in `reference_data/`. 
-The scripts provided in `tests/` can be used to quickly set-up  simulations with pyMBE to reproduce such data sets.
+Any new version of the code must reproduce all the data stored in `testsuite/data`. 
+The scripts provided in `testsuite/` and in `samples/Beyer2024`can be used to quickly set-up  simulations with pyMBE to reproduce such data sets.
 All new code will be reviewed by at least one member of the pyMBE development team before being merged into the stable version of the library to ensure that a functional version of the code is always available.
 Class methods are sorted in alphabetical order.

README.md

@@ -20,17 +20,19 @@ A deprecated version of pyMBE compatible with ESPResSo v4.1.4 (under the histori
 
 - `docs/`: folder with the API documentation of pyMBE.
 - `figs/`: folder with various images used in the tutorials of pyMBE.
-- `handy_scripts/`: folder with various handy scripts and libraries.
+- `libs/`: folder with various libraries.
 - `logo/`: folder with the logo of pyMBE.
+- `mantainer/`: folder with various scripts used by the mantainers.
 - `parameters/`: folder with various sets of parameters from previous works.
 - `samples/`: folder with various sample scripts showcasing how to use pyMBE to setup different systems.
-- `tests/`: folder with several test scripts and data to check that new developments do not break pyMBE.
+- `testsuite/`: folder with several test scripts and data for continous integration of the library.
 - `tutorials/`: folder with the available tutorials on pyMBE.
 - `visualization/`: folder with helper scripts to aid the visualization of vtf trajectories from constant pH and Grand reaction simulations with [VMD](https://www.ks.uiuc.edu/Research/vmd/).6
 - `AUTHORS.md`: list of authors and contributors of pyMBE.
 - `CONTRIBUTING`: rules on how to contribute to pyMBE.
 - `LICENSE.md`: license of pyMBE.
 - `pyMBE.py`: source code of pyMBE
+- `requierements.txt`: list of requiered libraries to use pyMBE.
 
 ## Usage
 

lib/analysis.py

@@ -50,7 +50,7 @@ def analyze_time_series(path_to_datafolder):
                     index_list=[]
                     for key in data_dict.keys():
                         value_list.append(data_dict[key])
-                        index_list.append((key,))
+                        index_list.append((key,"value"))
                     analyzed_data = pd.concat([pd.Series(value_list, index=index_list), analyzed_data])
                     data = add_data_to_df(df=data,
                                         data_dict=analyzed_data.to_dict(),

samples/Beyer2024/peptide.py

@@ -50,17 +50,7 @@
 if mode not in valid_modes:
     raise ValueError(f"Mode {mode} is not currently supported, valid modes are {valid_modes}")
 
-# Simulation parameters
-if mode == "short-run":
-    Nsamples = 1000
-    MD_steps_per_sample = 1000
 
-elif mode == "long-run":
-    Nsamples = 5000
-    MD_steps_per_sample = 5000
-elif mode == "test":
-    Nsamples = 500
-    MD_steps_per_sample = 700
 
 SEED = 100
 dt = 0.01
@@ -96,6 +86,22 @@
     diameter_Cl=0.36*pmb.units.nm
     chain_length=len(sequence)
 
+# Simulation parameters
+if mode == "short-run":
+    Nsamples = 1000
+    MD_steps_per_sample = 1000
+elif mode == "long-run":
+    Nsamples = 5000
+    MD_steps_per_sample = 5000
+elif mode == "test":
+    Nsamples = 500
+    MD_steps_per_sample = 700
+    c_salt = 5e-3 * pmb.units.mol/ pmb.units.L
+    N_peptide_chains = 1
+else:
+    raise RuntimeError()
+
+
 pmb.define_peptide (name=sequence, sequence=sequence, model=model)
 
 # Solution parameters

testsuite/peptide_tests.py

@@ -8,7 +8,7 @@
 
 # Template of the test
 
-def run_peptide_test(script_path,test_pH_values,sequence,rtol,atol):
+def run_peptide_test(script_path,test_pH_values,sequence,rtol,atol,mode="test"):
     """
     Runs a set of tests for a given peptide sequence.
 
@@ -17,10 +17,14 @@ def run_peptide_test(script_path,test_pH_values,sequence,rtol,atol):
         test_pH_values(`lst`): List of pH values to be tested.
         sequence(`str`): Amino acid sequence of the peptide.
     """
+    valid_modes=["test","save"]
+    if mode not in valid_modes:
+        raise ValueError(f"Mode {mode} not supported, valid modes: {valid_modes}")
     # Get data folder
     time_series_folder_path=pmb.get_resource(f"samples/Beyer2024/time_series")
     # clean up data folder
-    os.system(f"rm {time_series_folder_path}/*")
+    if len(os.listdir(time_series_folder_path)):
+        os.system(f"rm {time_series_folder_path}/*")
     print(f"Running tests for {sequence}")
     for pH in (pbar := tqdm(test_pH_values)):
         pbar.set_description(f"pH = {pH}")
@@ -29,19 +33,28 @@ def run_peptide_test(script_path,test_pH_values,sequence,rtol,atol):
         os.system(run_command)
     # Analyze all time series
     data=analysis.analyze_time_series(path_to_datafolder=time_series_folder_path)
-    # Get reference test data
     data_path=pmb.get_resource(path="testsuite/peptide_tests_data")
-    ref_data=pd.read_csv(data_path+f"/{sequence}.csv", header=[0, 1], index_col=0)
-    # Check that the charge and radius of gyration are consistent
-    np.testing.assert_allclose(data["mean","charge"].to_numpy(), 
-                               ref_data["mean","charge"].to_numpy(), 
-                               rtol=rtol, 
-                               atol=atol)
-    np.testing.assert_allclose(data["mean","rg"].to_numpy(), 
-                               ref_data["mean","rg"].to_numpy(), 
-                               rtol=rtol, 
-                               atol=atol)
-    print(f"Test for {sequence} succesful")
+    if mode == "test":
+        # Get reference test data
+        ref_data=pd.read_csv(data_path+f"/{sequence}.csv", header=[0, 1])
+        # Check charge
+        test_charge=np.sort(data["mean","charge"].to_numpy())
+        ref_charge=np.sort(ref_data["mean","charge"].to_numpy())       
+        # Check that the charge and radius of gyration are consistent
+        np.testing.assert_allclose(test_charge, 
+                                    ref_charge, 
+                                    rtol=rtol, 
+                                    atol=atol)
+        np.testing.assert_allclose(data["mean","rg"].to_numpy(), 
+                                    ref_data["mean","rg"].to_numpy(), 
+                                    rtol=rtol, 
+                                    atol=atol)                               
+        print(f"Test for {sequence} succesful")
+    elif mode == "save":
+        # Save data for future testing
+        data.to_csv(f"{data_path}/{sequence}.csv", index=False)
+    else:
+        raise RuntimeError
 
 # Create an instance of pyMBE library
 pmb = pyMBE.pymbe_library()

testsuite/peptide_tests_data/EEEEEHHHHH.csv

@@ -1,5 +1,5 @@
 pH,sequence,n_blocks,block_size,mean,mean,err_mean,err_mean,n_eff,n_eff,tau_int,tau_int
-nan,nan,nan,nan,charge,rg,charge,rg,charge,rg,charge,rg
-7,EEEEEHHHHH,16.0,28.125,-3.46,2.2703462218107697,0.08485448511846783,0.011270236960158787,29.626400298799958,123.97658163444471,53.16204412785421,12.704012155120516
-3,EEEEEHHHHH,16.0,28.125,3.6155555555555554,2.2852353902531646,0.0659070291966048,0.00965865997785087,36.01484582149586,171.5617117303853,43.73196564107516,9.18037004963811
-11,EEEEEHHHHH,16.0,28.125,-5.0,2.376694528257385,0.0,0.009075495803215277,,180.56146365212345,,8.722791498127924
+value,value,nan,nan,charge,rg,charge,rg,charge,rg,charge,rg
+3,EEEEEHHHHH,16.0,28.125,3.74,2.2930545511345084,0.09668283176667573,0.012248005496732568,63.9924303361178,386.504157031771,24.61228604323432,4.074988512749192
+7,EEEEEHHHHH,16.0,28.125,-3.462222222222222,2.282215370136204,0.08152363902529337,0.02392826711272775,107.85755044145347,125.10472480386765,14.602593824798644,12.589452576659077
+11,EEEEEHHHHH,16.0,28.125,-5.0,2.379941392407537,0.0,0.015862071975736733,,271.3603994805873,,5.804089333038255

testsuite/peptide_tests_data/KKKKKDDDDD.csv

@@ -1,5 +1,5 @@
 pH,sequence,n_blocks,block_size,mean,mean,err_mean,err_mean,n_eff,n_eff,tau_int,tau_int
-nan,nan,nan,nan,charge,rg,charge,rg,charge,rg,charge,rg
-11,KKKKKDDDDD,16.0,28.125,-2.4394444444444443,2.2049612765357924,0.0715700444086051,0.012580800325214478,36.920026395901495,97.521880100548,42.65977448513195,16.150221862113444
-7,KKKKKDDDDD,16.0,28.125,0.0,2.0725310670590007,0.0,0.004655049123372929,,485.26077729437475,,3.2456775278971928
-3,KKKKKDDDDD,16.0,28.125,2.7888888888888888,2.2437294343122227,0.10015636535927931,0.009715071564187647,23.660790449758085,160.02409606208622,66.56582346133663,9.842267750871764
+value,value,nan,nan,charge,rg,charge,rg,charge,rg,charge,rg
+3,KKKKKDDDDD,16.0,28.125,2.92,2.24224133500218,0.10683895616817528,0.016899080996828193,66.55820017221221,218.86820380470385,23.66350045462667,7.196111507542911
+7,KKKKKDDDDD,16.0,28.125,0.0022222222222222222,2.061724695770055,0.002305347229885367,0.010022432237558167,418.13333333333344,337.82731077353264,3.7667410715105456,4.662145273062604
+11,KKKKKDDDDD,16.0,28.125,-2.537777777777778,2.2240114798138597,0.09417150320822036,0.016801648569199405,91.37898210342641,223.8836649942004,17.235910969676027,7.034903596361424

testsuite/peptide_tests_data/nDSHAKRHHGYKRKFHEKHHSHRGYc.csv

@@ -1,5 +1,5 @@
 pH,sequence,n_blocks,block_size,mean,mean,err_mean,err_mean,n_eff,n_eff,tau_int,tau_int
-nan,nan,nan,nan,charge,rg,charge,rg,charge,rg,charge,rg
-11,nDSHAKRHHGYKRKFHEKHHSHRGYc,16.0,28.125,-1.191111111111111,4.063464427616705,0.05348160750422651,0.08851343132097834,251.94488535923495,72.1482798903042,6.251367229735849,21.83004227445118
-7,nDSHAKRHHGYKRKFHEKHHSHRGYc,16.0,28.125,4.997777777777777,4.20850730205197,0.045342906001995156,0.11132315378572168,215.56745024938866,55.258072529405915,7.306297858105055,28.50262283752534
-3,nDSHAKRHHGYKRKFHEKHHSHRGYc,16.0,28.125,12.657777777777778,5.2393962478267655,0.060622215668095406,0.11362578274313505,161.98895235804122,60.20523413647287,9.722885277713772,26.160516151537188
+value,value,nan,nan,charge,rg,charge,rg,charge,rg,charge,rg
+7,nDSHAKRHHGYKRKFHEKHHSHRGYc,16.0,28.125,5.044444444444444,4.279252793720242,0.05798349244388049,0.09771700380997232,155.74642000226976,71.28000135030244,10.112591994161557,22.095959177862643
+3,nDSHAKRHHGYKRKFHEKHHSHRGYc,16.0,28.125,12.642222222222221,5.277371251802207,0.046374949253443896,0.11404609215228863,287.2696477457004,55.72484859428179,5.482653710177252,28.26387221796589
+11,nDSHAKRHHGYKRKFHEKHHSHRGYc,16.0,28.125,-1.1377777777777778,3.925277367120465,0.046895855319717836,0.0915024823081239,297.1873784737742,63.15113924203325,5.299686709855564,24.940167650783412