Merge pull request #161 from deepmodeling/devel

merge devel in to master

.gitignore

@@ -35,3 +35,5 @@ dpgen.egg-info
 */__pycache__
 *.swp
 .eggs
+.coverage
+dbconfig.json

README.md

@@ -75,29 +75,42 @@ and if everything works, it gives
 ```
 DeepModeling
 ------------
+Version: 0.5.1.dev53+gddbeee7.d20191020
+Date:    Oct-07-2019
+Path:    /home/me/miniconda3/envs/py363/lib/python3.6/site-packages/dpgen-0.5.1.dev53+gddbeee7.d20191020-py3.6.egg/dpgen
 
-Version: 0.2.0
-Path:    /home/wanghan/.local/lib/python3.6/site-packages/dpgen-0.1.0-py3.6.egg/dpgen
-Date:    Aug 13, 2019
-
-usage: dpgen [-h] {init_surf,init_bulk,run,test,db} ...
+Dependency
+------------
+     numpy     1.17.2   /home/me/miniconda3/envs/py363/lib/python3.6/site-packages/numpy
+    dpdata     0.1.10   /home/me/miniconda3/envs/py363/lib/python3.6/site-packages/dpdata-0.1.10-py3.6.egg/dpdata
+  pymatgen   2019.7.2   /home/me/miniconda3/envs/py363/lib/python3.6/site-packages/pymatgen
+     monty      2.0.4   /home/me/miniconda3/envs/py363/lib/python3.6/site-packages/monty
+       ase     3.17.0   /home/me/miniconda3/envs/py363/lib/python3.6/site-packages/ase-3.17.0-py3.6.egg/ase
+  paramiko      2.6.0   /home/me/miniconda3/envs/py363/lib/python3.6/site-packages/paramiko
+ custodian  2019.2.10   /home/me/miniconda3/envs/py363/lib/python3.6/site-packages/custodian
+
+Description
+------------
+usage: dpgen [-h] {init_surf,init_bulk,run,run/report,test,db} ...
 
 dpgen is a convenient script that uses DeepGenerator to prepare initial data,
 drive DeepMDkit and analyze results. This script works based on several sub-
 commands with their own options. To see the options for the sub-commands, type
 "dpgen sub-command -h".
 
 positional arguments:
-  {init_surf,init_bulk,run,test,db}
+  {init_surf,init_bulk,run,run/report,test,db}
     init_surf           Generating initial data for surface systems.
     init_bulk           Generating initial data for bulk systems.
-    run                 Main process of Deep Generator.
+    run                 Main process of Deep Potential Generator.
+    run/report          Report the systems and the thermodynamic conditions of
+                        the labeled frames.
     test                Auto-test for Deep Potential.
-    db                  Collecting data from DP-GEN.
-
+    db                  Collecting data from Deep Generator.
 
 optional arguments:
   -h, --help            show this help message and exit
+
 ```
 
 
@@ -108,8 +121,10 @@ optional arguments:
 You may prepare initial data for bulk systems with VASP by:
 
 ```bash
-dpgen init_bulk PARAM MACHINE
+dpgen init_bulk PARAM [MACHINE]
 ```
+The MACHINE configure file is optional. If this parameter exists, then the optimization 
+tasks or MD tasks will be submitted automatically according to MACHINE.json.
 
 Basically `init_bulk` can be devided into four parts , denoted as `stages` in `PARAM`:
 1. Relax in folder `00.place_ele`
@@ -119,6 +134,8 @@ Basically `init_bulk` can be devided into four parts , denoted as `stages` in `P
 
 All stages must be **in order**. One doesn't need to run all stages. For example, you may run stage 1 and 2, generating supercells as starting point of exploration in `dpgen run`.
 
+If MACHINE is None, there should be only one stage in stages. Corresponding tasks will be generated, but user's intervention should be involved in, to manunally run the scripts.
+
 Following is an example for `PARAM`, which generates data from a typical structure hcp.
 ```json
 {
@@ -176,8 +193,10 @@ The bold notation of key (such as **Elements**) means that it's a necessary key.
 You may prepare initial data for surface systems with VASP by:
 
 ```bash
-dpgen init_surf PARAM MACHINE
+dpgen init_surf PARAM [MACHINE]
 ```
+The MACHINE configure file is optional. If this parameter exists, then the optimization
+tasks or MD tasks will be submitted automatically according to MACHINE.json.
 
 Basically `init_surf` can be devided into two parts , denoted as `stages` in `PARAM`:
 1. Build specific surface in folder `00.place_ele`
@@ -199,7 +218,7 @@ Following is an example for `PARAM`, which generates data from a typical structu
     2,
     2
   ],
-  "z_min": 9,
+  "layer_numb": 3,
   "vacuum_max": 9,
   "vacuum_resol": [
     0.5,
@@ -251,7 +270,7 @@ The bold notation of key (such as **Elements**) means that it's a necessary key.
 | **Elements** | List of String | ["Mg"] | Atom types
 |  **cell_type** | String  | "hcp" | Specifying which typical structure to be generated. **Options** include fcc, hcp, bcc, sc, diamond.
 | **latt** | Float | 4.479 | Lattice constant for single cell.
-| **z_min** | Float | 9 | Thickness of slab (Angstrom).
+| **layer_numb** | Integer | 3 | Number of equavilent layers of slab.
 | **vacuum_max** | Float | 9 | Maximal thickness of vacuum (Angstrom).
 | **vacuum_resol** | List of float | [0.5, 1 ] | Interval of thichness of vacuum. If size of `vacuum_resol` is 1, the interval is fixed to its value. If size of `vacuum_resol` is 2, the interval is `vacuum_resol[0]` before `mid_point`, otherwise `vacuum_resol[1]` after `mid_point`.
 | **millers** | List of list of Integer | [[1,0,0]] | Miller indices. 
@@ -431,6 +450,7 @@ The bold notation of key (such aas **type_map**) means that it's a necessary key
 | *#Basics*
 | **type_map** | List of string | ["H", "C"] | Atom types
 | **mass_map** | List of float |  [1, 12] | Standard atom weights.
+| **use_ele_temp** | int | 0 | Currently only support fp_style vasp. 0(default): no electron temperature. 1: eletron temperature as frame parameter. 2: electron temperature as atom parameter.
 | *#Data*
  | init_data_prefix | String | "/sharedext4/.../data/" | Prefix of initial data directories
  | ***init_data_sys*** | List of string|["CH4.POSCAR.01x01x01/.../deepmd"] |Directories of initial data. You may use either absolute or relative path here.

dpgen/.gitignore

@@ -0,0 +1,2 @@
+_date.py
+_version.py

dpgen/auto_test/cmpt_00_equi.py

@@ -47,7 +47,7 @@ def comput_lmp_nev(conf_dir, task_name, write_stable = False) :
         return None, None, None
 
 def comput_vasp_nev(jdata, conf_dir, write_stable = False) :
-    kspacing = jdata['vasp_params']['kspacing']
+
     conf_path = re.sub('confs', global_equi_name, conf_dir)
     conf_path = os.path.abspath(conf_path)
     poscar = os.path.join(conf_path, 'POSCAR')
@@ -59,6 +59,7 @@ def comput_vasp_nev(jdata, conf_dir, write_stable = False) :
     if 'relax_incar' in jdata.keys():
         vasp_str='vasp-relax_incar'
     else:
+        kspacing = jdata['vasp_params']['kspacing']
         vasp_str='vasp-k%.2f' % kspacing
 
     ener_shift = comput_e_shift(poscar, vasp_str)

dpgen/auto_test/cmpt_01_eos.py

@@ -1,22 +1,31 @@
 #!/usr/bin/env python3
 
 import os, glob, argparse, json, re
+import dpgen.auto_test.lib.util as util
 import dpgen.auto_test.lib.lammps as lammps
 import dpgen.auto_test.lib.vasp as vasp
 
 global_task_name = '01.eos'
 
-def comput_lmp_eos(conf_dir, task_name) :
+def comput_lmp_eos(jdata,conf_dir, task_name) :
     conf_path = re.sub('confs', global_task_name, conf_dir)
     conf_path = os.path.abspath(conf_path)
     conf_path = os.path.join(conf_path, task_name)
     vol_paths = glob.glob(os.path.join(conf_path, 'vol-*'))
     vol_paths.sort()
+    result = os.path.join(conf_path,'result')
     print('Vpa(A^3)\tEpA(eV)')
-    for ii in vol_paths :
-        log_lammps = os.path.join(ii, 'log.lammps')
-        natoms, epa, vpa = lammps.get_nev(log_lammps)
-        print(vpa, epa)
+    with open(result,'w') as fp:
+        fp.write('conf_dir:%s\n VpA(A^3)  EpA(eV)\n'% (conf_dir))
+        for ii in vol_paths :
+            log_lammps = os.path.join(ii, 'log.lammps')
+            natoms, epa, vpa = lammps.get_nev(log_lammps)
+            print(vpa, epa)
+            fp.write('%7.3f  %8.4f \n' % (vpa,epa))
+    fp.close()
+    if 'upload_username' in jdata.keys() and task_name =='deepmd':
+        upload_username=jdata['upload_username']
+        util.insert_data('eos','deepmd',upload_username,result)
 
 def comput_vasp_eos(jdata, conf_dir) :
     conf_path = re.sub('confs', global_task_name, conf_dir)
@@ -30,17 +39,25 @@ def comput_vasp_eos(jdata, conf_dir) :
     task_path = os.path.join(conf_path, vasp_str)
     vol_paths = glob.glob(os.path.join(task_path, 'vol-*'))
     vol_paths.sort()
+    result = os.path.join(task_path,'result')
     print('Vpa(A^3)\tEpA(eV)')
-    for ii in vol_paths :
-        outcar = os.path.join(ii, 'OUTCAR')
-        natoms, epa, vpa = vasp.get_nev(outcar)
-        print(vpa, epa)
+    with open(result,'w') as fp:
+        fp.write('conf_dir:%s\n VpA(A^3)  EpA(eV)\n'% (conf_dir))
+        for ii in vol_paths :
+            outcar = os.path.join(ii, 'OUTCAR')
+            natoms, epa, vpa = vasp.get_nev(outcar)
+            print(vpa, epa)
+            fp.write('%7.3f  %8.4f \n' % (vpa,epa))
+    fp.close()
+    if 'upload_username' in jdata.keys():
+        upload_username=jdata['upload_username']
+        util.insert_data('eos','vasp',upload_username,result)
 
 def _main():
     parser = argparse.ArgumentParser(
         description="cmpt 01.eos")
     parser.add_argument('TASK', type=str,
-                        choices = ['vasp', 'deepmd', 'meam'], 
+                        choices = ['vasp', 'deepmd', 'meam'],
                         help='the task of generation, vasp or lammps')
     parser.add_argument('PARAM', type=str,
                         help='json parameter file')
@@ -52,14 +69,13 @@ def _main():
         jdata = json.load (fp)
 
     if args.TASK == 'vasp':
-        comput_vasp_eos(jdata, args.CONF)               
+        comput_vasp_eos(jdata, args.CONF)
     elif args.TASK == 'deepmd' :
-        comput_lmp_eos(args.CONF, args.TASK)
+        comput_lmp_eos(jdata,args.CONF, args.TASK)
     elif args.TASK == 'meam' :
-        comput_lmp_eos(args.CONF, args.TASK)
+        comput_lmp_eos(jdata,args.CONF, args.TASK)
     else :
         raise RuntimeError("unknow task ", args.TASK)
 
 if __name__ == '__main__' :
     _main()
-

dpgen/auto_test/cmpt_02_elastic.py

@@ -5,14 +5,33 @@
 import numpy as np
 import dpgen.auto_test.lib.vasp as vasp
 import dpgen.auto_test.lib.lammps as lammps
+import dpgen.auto_test.lib.util as util
 from pymatgen.analysis.elasticity.elastic import ElasticTensor
 from pymatgen.analysis.elasticity.strain import Strain
 from pymatgen.analysis.elasticity.stress import Stress
 
 global_equi_name = '00.equi'
 global_task_name = '02.elastic'
 
-def print_et (et): 
+def result_et(et,conf_dir,result):
+    with open(result,'w') as fp:
+        fp.write('conf_dir:%s\n'% (conf_dir))
+        for ii in range(6) :
+            for jj in range(6) :
+                fp.write ("%7.2f " % (et.voigt[ii][jj] / 1e4))
+            fp.write('\n')
+        BV = et.k_voigt / 1e4
+        GV = et.g_voigt / 1e4
+        EV = 9*BV*GV/(3*BV+GV)
+        uV = 0.5*(3*BV-2*GV)/(3*BV+GV)
+        fp.write("# Bulk   Modulus BV = %.2f GPa\n" % (BV))
+        fp.write("# Shear  Modulus GV = %.2f GPa\n" % (GV))
+        fp.write("# Youngs Modulus EV = %.2f GPa\n" % (EV))
+        fp.write("# Poission Ratio uV = %.2f \n" % (uV))
+    fp.close()
+
+
+def print_et (et):
     for ii in range(6) :
         for jj in range(6) :
             sys.stdout.write ("%7.2f " % (et.voigt[ii][jj] / 1e4))
@@ -27,17 +46,15 @@ def print_et (et):
     print("# Poission Ratio uV = %.2f " % (uV))
 
 def cmpt_vasp(jdata, conf_dir) :
-    fp_params = jdata['vasp_params']
-    kspacing = fp_params['kspacing']
-    kgamma = fp_params['kgamma']
-
     conf_path = os.path.abspath(conf_dir)
     conf_poscar = os.path.join(conf_path, 'POSCAR')
     task_path = re.sub('confs', global_task_name, conf_path)
     if 'relax_incar' in jdata.keys():
         vasp_str='vasp-relax_incar'
     else:
-        vasp_str='vasp-k%.2f' % kspacing 
+        fp_params = jdata['vasp_params']
+        kspacing = fp_params['kspacing']
+        vasp_str='vasp-k%.2f' % kspacing
     task_path = os.path.join(task_path, vasp_str)
 
     equi_stress = Stress(np.loadtxt(os.path.join(task_path, 'equi.stress.out')))
@@ -55,14 +72,16 @@ def cmpt_vasp(jdata, conf_dir) :
     et = ElasticTensor.from_independent_strains(lst_strain, lst_stress, eq_stress = equi_stress, vasp = False)
     # et = ElasticTensor.from_independent_strains(lst_strain, lst_stress, eq_stress = None)
     # bar to GPa
-    # et = -et / 1e4 
+    # et = -et / 1e4
     print_et(et)
+    result = os.path.join(task_path,'result')
+    result_et(et,conf_dir,result)
+    if 'upload_username' in jdata.keys():
+        upload_username=jdata['upload_username']
+        util.insert_data('elastic','vasp',upload_username,result)
 
-def cmpt_deepmd_lammps(jdata, conf_dir, task_name) :
-    deepmd_model_dir = jdata['deepmd_model_dir']
-    deepmd_type_map = jdata['deepmd_type_map']
-    ntypes = len(deepmd_type_map)    
 
+def cmpt_deepmd_lammps(jdata, conf_dir, task_name) :
     conf_path = os.path.abspath(conf_dir)
     conf_poscar = os.path.join(conf_path, 'POSCAR')
     task_path = re.sub('confs', global_task_name, conf_path)
@@ -82,8 +101,15 @@ def cmpt_deepmd_lammps(jdata, conf_dir, task_name) :
     et = ElasticTensor.from_independent_strains(lst_strain, lst_stress, eq_stress = equi_stress, vasp = False)
     # et = ElasticTensor.from_independent_strains(lst_strain, lst_stress, eq_stress = None)
     # bar to GPa
-    # et = -et / 1e4 
+    # et = -et / 1e4
     print_et(et)
+    result = os.path.join(task_path,'result')
+    result_et(et,conf_dir,task_path)
+    if 'upload_username' in jdata.keys() and task_name=='deepmd':
+        upload_username=jdata['upload_username']
+        util.insert_data('elastic','deepmd',upload_username,result)
+
+
 
 def _main() :
     parser = argparse.ArgumentParser(
@@ -101,15 +127,13 @@ def _main() :
 
     print('# generate %s task with conf %s' % (args.TASK, args.CONF))
     if args.TASK == 'vasp':
-        cmpt_vasp(jdata, args.CONF)               
+        cmpt_vasp(jdata, args.CONF)
     elif args.TASK == 'deepmd' :
         cmpt_deepmd_lammps(jdata, args.CONF, args.TASK)
     elif args.TASK == 'meam' :
         cmpt_deepmd_lammps(jdata, args.CONF, args.TASK)
     else :
         raise RuntimeError("unknow task ", args.TASK)
-    
+
 if __name__ == '__main__' :
     _main()
-
-

dpgen/auto_test/cmpt_03_vacancy.py

@@ -26,8 +26,6 @@ def comput_e_shift(poscar, task_name) :
     return ener_shift 
 
 def cmpt_vasp(jdata, conf_dir, supercell) :
-    fp_params = jdata['vasp_params']
-    kspacing = fp_params['kspacing']
 
     if 'relax_incar' in jdata.keys():
         vasp_str='vasp-relax_incar'

dpgen/auto_test/cmpt_04_interstitial.py

@@ -17,8 +17,6 @@ def cmpt_vasp(jdata, conf_dir, supercell, insert_ele) :
         _cmpt_vasp(jdata, conf_dir, supercell, ii)
 
 def _cmpt_vasp(jdata, conf_dir, supercell, insert_ele) :
-    fp_params = jdata['vasp_params']
-    kspacing = fp_params['kspacing']
 
     if 'relax_incar' in jdata.keys():
         vasp_str='vasp-relax_incar'
@@ -56,12 +54,10 @@ def cmpt_deepmd_reprod_traj(jdata, conf_dir, supercell, insert_ele, task_name) :
         _cmpt_deepmd_reprod_traj(jdata, conf_dir, supercell, ii, task_name)
 
 def _cmpt_deepmd_reprod_traj(jdata, conf_dir, supercell, insert_ele, task_name) :
-    fp_params = jdata['vasp_params']
-    kspacing = fp_params['kspacing']
-
     if 'relax_incar' in jdata.keys():
         vasp_str='vasp-relax_incar'
     else:
+        kspacing = jdata['vasp_params']['kspacing']
         vasp_str='vasp-k%.2f' % kspacing
 
     conf_path = os.path.abspath(conf_dir)