Changeset 1647 in rotdif


Ignore:
Timestamp:
Feb 15, 2019, 2:20:23 PM (17 months ago)
Author:
yuexi
Message:

update

Files:
5 added
6 deleted
22 edited

Legend:

Unmodified
Added
Removed
  • appconfig.json

    r1527 r1647  
    11{
    2    "messaging" : {
    3       "zmqhostip" : "127.0.0.1",
    4       "tcphostip" : "127.0.0.1",
    5       "tcpport" : 30780,
    6       "udpport" : 30779,
    7       "wssport" : 80,
    8       "wsport" : 30777,
    9       "zmqport" : 30778,
    10       "udphostip" : "127.0.0.1"
    11    },
     2   "resourcedefault" : "local",
     3   "lockdir" : "/src/genapp/etc",
    124   "resources" : {
    135      "local" : ""
    146   },
     7   "messaging" : {
     8      "udpport" : 30779,
     9      "tcphostip" : "127.0.0.1",
     10      "tcptimeout" : "30",
     11      "udphostip" : "127.0.0.1",
     12      "wsport" : "30777",
     13      "wssport" : "80",
     14      "zmqhostip" : "127.0.0.1",
     15      "zmqport" : "30778",
     16      "tcpport" : "30780",
     17      "tcprport" : "30781"
     18   },
     19   "hostip" : "129.114.16.180",
    1520   "submitpolicy" : "login",
    16    "hostip" : "129.114.16.107",
    17    "lockdir" : "/src/genapp/etc",
    18    "hostname" : "rotdif.genapp.rocks",
    19    "resourcedefault" : "local"
     21   "hostname" : "rotdif.genapp.rocks"
    2022}
  • bin/indicator_chain.py

    r1529 r1647  
    44import re
    55
    6 def dock():
    7     for root, dirs, files in os.walk('./'):
    8         for name in files:
    9             filename = os.path.join(root, name)
    10             if '.txt' in filename:
    11                 exp_file = filename
    12     ##preprocess exp_file in multi-frequencies
     6def dock(relax_loc):
     7    #for root, dirs, files in os.walk('./'):
     8    #    for name in files:
     9    #        filename = os.path.join(root, name)
     10    #        if '.txt' in filename:   
     11    #            exp_file = filename
     12    exp_file = relax_loc
     13     ##preprocess exp_file in multi-frequencies
    1314    with open(exp_file) as in_f:
    1415        tmp_lists = in_f.read().splitlines()
  • bin/main_elm.py

    r1530 r1647  
    2525from math import exp, expm1, log10, log, log1p
    2626from subprocess import Popen, PIPE, STDOUT
     27from os.path import join
    2728
    2829def message_box(text,icon):
     
    160161    #save elm
    161162    elm_out = [str(base_dir) + "/" + 'ELM_prediction']   
     163    #save params
     164    with open(join(str(base_dir),"ELM_prediction")) as in_f:
     165        tmp = in_f.readlines()
     166    tmp[0]="Parameters\n"
     167    with open(join(str(base_dir),"ELM_params"),'w') as out_f:
     168        for items in tmp[:17]:
     169            out_f.write(items)
     170    elm_params = [str(base_dir)+"/"+"ELM_params"]
    162171    output_res[ 'elm_out' ] = elm_out
     172    output_res['elm_params'] = elm_params
    163173    print(json.dumps(output_res))
    164174
  • bin/main_elmdock.py

    r1531 r1647  
    4040from save_dock_elmdock import save_dock
    4141from indicator_chain import dock
     42from StringIO import StringIO
    4243
    4344if __name__ == "__main__":
    4445    color_list=[ '#1f77b4','#ff7f0e','#2ca02c','#d62728', '#9467bd',\
    4546'#8c564b', '#e377c2', '#7f7f7f', '#bcbd22','#17becf']
    46     to_dock = dock()
     47    json_variables = " "
     48    InitialDirectoryStr = os.path.abspath(os.path.dirname(sys.argv[0]))
     49    argv_io_string = StringIO(sys.argv[1])
     50    json_variables = json.load(argv_io_string)
     51    relax_location = json_variables['relax_location']
     52    relax_loc = relax_location[0]
     53    to_dock = dock(relax_loc)
    4754    output_res, base_dir, dyna_flag, elm_flag, elmdock_flag = rotdif(to_dock)
    48     exp_keys = exp_plot(color_list, dyna_flag, elm_flag, elmdock_flag)[1]
     55    exp_keys = exp_plot(color_list, dyna_flag, elm_flag, elmdock_flag,relax_loc)[1]
    4956    outputmd_arr_files = []
    5057    log_out = [str(base_dir) + "/" + 'elmdock_log.out']
    51    
    52     #ani_out = [str(base_dir) + "/"+ 'out_ani.py']
    53     #axi_out = [str(base_dir) + "/" + 'out_axial.py']
    54 
    5558    if dyna_flag == True:
    5659        output_res[ 'diso_plot' ] = diso_plot(color_list, exp_keys, to_dock, dyna_flag, elm_flag, elmdock_flag)
     
    6972        dock_pdb = [str(base_dir) + "/" + 'out_dock.pdb']
    7073        output_res[ 'pdb'] = dock_pdb
    71 
     74        view_pdb = str(base_dir) + "/" + 'out_dock.pdb'
     75        output_res['outputpdb'] = { "file" : view_pdb, "script" : "ribbon" }
     76       
     77        #user_pdb = [str(base_dir)+"/"+'diUb_AB.pdb']
    7278    output_res[ 'outputrotdif' ] = log_out
    73     #output_res[ 'ani_out' ] = ani_out
    74     #output_res[ 'axi_out' ] = axi_out
    75     #output_res[ 'exp_plot' ] = exp_plot(color_list, dyna_flag, elm_flag, elmdock_flag)[0]
    76     #output_res[ 'vec_plot' ] = vec_plot()
    77     #output_res[ 'chi2_plot' ] = chi2_plot()
    78     #output_res[ 'plot_2d' ] = plot_2d_3d(color_list, exp_keys)[0]
    79     #output_res[ 'plot_3d' ]  = plot_2d_3d(color_list, exp_keys)[1]
    80     #output_res[ 'iso_plot' ] = iso_plot(color_list,exp_keys, dyna_flag, elm_flag, elmdock_flag)
    81     #output_res[ 'axi_plot' ] = axi_plot(color_list,exp_keys, dyna_flag, elm_flag, elmdock_flag)
    82     #output_res[ 'ani_plot' ] = ani_plot(color_list,exp_keys, dyna_flag, elm_flag, elmdock_flag)
    8379    print (json.dumps(output_res))
  • bin/main_rotdif.py

    r1534 r1647  
    4040from save_dock import save_dock
    4141from indicator_chain import dock
     42from StringIO import StringIO
    4243
    4344if __name__ == "__main__":
    4445    color_list=[ '#1f77b4','#ff7f0e','#2ca02c','#d62728', '#9467bd',\
    4546'#8c564b', '#e377c2', '#7f7f7f', '#bcbd22','#17becf']
    46     to_dock = dock()
     47    json_variables = " "
     48    InitialDirectoryStr = os.path.abspath(os.path.dirname(sys.argv[0]))
     49    argv_io_string = StringIO(sys.argv[1])
     50    json_variables = json.load(argv_io_string)
     51    relax_location = json_variables['relax_location']
     52    relax_loc = relax_location[0]
     53    to_dock = dock(relax_loc)
    4754    output_res, base_dir, dyna_flag, elm_flag, elmdock_flag = rotdif(to_dock)
    48     exp_keys = exp_plot(color_list, dyna_flag, elm_flag, elmdock_flag)[1]
     55    exp_graph,exp_keys,exp_resi,dyna_resi = exp_plot(color_list, dyna_flag, elm_flag, elmdock_flag,relax_loc)
    4956    outputmd_arr_files = []
    5057    log_out = [str(base_dir) + "/" + 'rotdif_log.out']
     
    6875        dock_pdb = [str(base_dir) + "/" + 'out_dock.pdb']
    6976        output_res[ 'pdb'] = dock_pdb
     77        output_res['outputpdb'] = dock_pdb
    7078
     79    #get residule numbers for the vector plot
    7180    output_res[ 'outputrotdif' ] = log_out
    7281    output_res[ 'ani_out' ] = ani_out
    7382    output_res[ 'axi_out' ] = axi_out
    74     output_res[ 'exp_plot' ] = exp_plot(color_list, dyna_flag, elm_flag, elmdock_flag)[0]
    75     output_res[ 'vec_plot' ] = vec_plot()
     83    output_res[ 'exp_plot' ] = exp_graph
     84    output_res[ 'vec_plot' ] = vec_plot(exp_resi)
    7685    output_res[ 'chi2_plot' ] = chi2_plot()
    7786    output_res[ 'plot_2d' ] = plot_2d_3d(color_list, exp_keys)[0]
    7887    output_res[ 'plot_3d' ]  = plot_2d_3d(color_list, exp_keys)[1]
    79     output_res[ 'iso_plot' ] = iso_plot(color_list,exp_keys, dyna_flag, elm_flag, elmdock_flag)
    80     output_res[ 'axi_plot' ] = axi_plot(color_list,exp_keys, dyna_flag, elm_flag, elmdock_flag)
    81     output_res[ 'ani_plot' ] = ani_plot(color_list,exp_keys, dyna_flag, elm_flag, elmdock_flag)
     88    output_res[ 'iso_plot' ] = iso_plot(exp_resi, color_list,exp_keys, dyna_flag, elm_flag, elmdock_flag)
     89    output_res[ 'axi_plot' ] = axi_plot(exp_resi, color_list,exp_keys, dyna_flag, elm_flag, elmdock_flag)
     90    output_res[ 'ani_plot' ] = ani_plot(exp_resi, color_list,exp_keys, dyna_flag, elm_flag, elmdock_flag)
    8291    print (json.dumps(output_res))
  • bin/plot_ani.py

    r1535 r1647  
    33from preprocess_split import split_data
    44import statistics as stat
    5 def ani_plot(color_list, exp_keys, dyna_flag, elm_flag, elmdock_flag):
     5def ani_plot(exp_resi, color_list, exp_keys, dyna_flag, elm_flag, elmdock_flag):
    66    ani_output = split_data(dyna_flag, elm_flag, elmdock_flag)[2]
    77    ani_block = []
    8     for i in range(len(ani_output[0])):
     8    ind_a = []
     9    to_index = ani_output[0][0]
     10    for ii in range(len(exp_resi)):
     11        ind_a.append(to_index.index(int(exp_resi[ii])))
     12   
     13    to_loop = len(ani_output[0])
     14
     15    for i in range(to_loop):
     16        ani_output[0][i] = [ani_output[0][i][jj] for jj in ind_a]
     17        ani_output[1][i] = [ani_output[1][i][jj] for jj in ind_a]
     18        ani_output[2][i] = [ani_output[2][i][jj] for jj in ind_a]
     19        ani_output[3][i] = [ani_output[3][i][jj] for jj in ind_a]   
     20
    921        err_dev = stat.stdev(ani_output[3][i])
    1022        dif = list(map(operator.sub,ani_output[2][i],ani_output[1][i]))
     
    1325          "x": ani_output[1][i],
    1426          "y": ani_output[2][i],
     27          "text": ["residue "+ str(int(items)) for items in ani_output[0][i]],
    1528          "error_x": {
    1629            "array" : ani_output[3][i],
  • bin/plot_axi.py

    r1536 r1647  
    33from preprocess_split import split_data
    44import statistics as stat
    5 def axi_plot(color_list, exp_keys, dyna_flag, elm_flag, elmdock_flag):
     5def axi_plot(exp_resi,color_list, exp_keys, dyna_flag, elm_flag, elmdock_flag):
    66    axi_output = split_data(dyna_flag, elm_flag, elmdock_flag)[1]
    77    axi_block = []
    8     for i in range(len(axi_output[0])):
     8
     9    ind_a = []
     10    to_index = axi_output[0][0]
     11    for ii in range(len(exp_resi)):
     12        ind_a.append(to_index.index(int(exp_resi[ii])))
     13   
     14    to_loop = len(axi_output[0])
     15
     16    for i in range(to_loop):
     17        axi_output[0][i] = [axi_output[0][i][jj] for jj in ind_a]
     18        axi_output[1][i] = [axi_output[1][i][jj] for jj in ind_a]
     19        axi_output[2][i] = [axi_output[2][i][jj] for jj in ind_a]
     20        axi_output[3][i] = [axi_output[3][i][jj] for jj in ind_a]   
     21
    922        err_dev = stat.stdev(axi_output[3][i])
    1023        dif = list(map(operator.sub,axi_output[2][i],axi_output[1][i]))
     
    1326          "x": axi_output[1][i],
    1427          "y": axi_output[2][i],
     28          "text": ["residue "+ str(int(items)) for items in axi_output[0][i]],
    1529          "error_x": {
    1630            "array" : axi_output[3][i],
  • bin/plot_exp.py

    r1545 r1647  
    77from preprocess_split import split_data
    88
    9 def exp_plot(color_list, dyna_flag, elm_flag, elmdock_flag):
     9def exp_plot(color_list, dyna_flag, elm_flag, elmdock_flag, relax_loc):
    1010    iso_output = split_data(dyna_flag, elm_flag, elmdock_flag)[0]
    11     for root, dirs, files in os.walk('./'):
    12         for name in files:
    13             filename = os.path.join(root, name)
    14             if '.txt' in filename:
    15                 exp_file = filename
     11    exp_file = relax_loc
     12#    for root, dirs, files in os.walk('./'):
     13#        for name in files:
     14#            filename = os.path.join(root, name)
     15#            if '.txt' in filename:
     16#                exp_file = filename
    1617    ##preprocess exp_file in multi-frequencies
    1718    with open(exp_file) as in_f:
     
    2425columns = ["residue","chain","atom 1","atom 2","magnet","T1","T1 error","T2","T2 error","NOE","NOE error"])
    2526    resi = list(exp_pd["residue"])
    26     digi_resi = []
     27    exp_resi = []
     28    dyna_resi = []
    2729    #eliminate * in residue number
    2830    for list_elem in resi:
    29         digi_resi.extend(re.findall("\d+", list_elem))
    30     digi_resi = [int(elem) for elem in digi_resi]
    31     exp_pd["residue"] = digi_resi
     31        if("%" not in list_elem):
     32            dyna_resi.extend(re.findall("\d+", list_elem))
     33            if("*" not in list_elem):
     34                exp_resi.append(list_elem)
     35    #digi_resi = [int(elem) for elem in digi_resi]
     36    exp_pd = exp_pd[[x in dyna_resi for x in exp_pd["residue"]]]
     37    tmp_resi = [int(items) for items in list(exp_pd["residue"])]
     38    exp_pd['residue'] = tmp_resi
    3239    sort_exp = exp_pd.sort_values(['magnet', 'residue'], ascending=[True, True])
    3340    freq_set = set(list(exp_pd["magnet"]))
     
    331338        sys.stderr.write("More than 2 chains are not supported")
    332339
    333     return exp_plotly,exp_keys
     340    return exp_plotly,exp_keys,exp_resi,dyna_resi
  • bin/plot_exp_elmdock.py

    r1548 r1647  
    44import numpy
    55import os
     6import sys
    67#import our functions
    78from elmdock_split import split_data
    89
    9 def exp_plot(color_list, dyna_flag, elm_flag, elmdock_flag):
     10def exp_plot(color_list, dyna_flag, elm_flag, elmdock_flag, relax_loc):
    1011    iso_output = split_data(dyna_flag, elm_flag, elmdock_flag)[0]
    11     for root, dirs, files in os.walk('./'):
    12         for name in files:
    13             filename = os.path.join(root, name)
    14             if '.txt' in filename:
    15                 exp_file = filename
     12    #for root, dirs, files in os.walk('./'):
     13    #    for name in files:
     14    #        filename = os.path.join(root, name)
     15    #        if '.txt' in filename:
     16    #            exp_file = filename
     17    exp_file = relax_loc
    1618    ##preprocess exp_file in multi-frequencies
    1719    with open(exp_file) as in_f:
  • bin/plot_iso.py

    r1537 r1647  
    44import statistics as stat
    55
    6 def iso_plot(color_list, exp_keys, dyna_flag, elm_flag, elmdock_flag):
     6def iso_plot(exp_resi,color_list, exp_keys, dyna_flag, elm_flag, elmdock_flag):
    77    iso_output = split_data(dyna_flag, elm_flag, elmdock_flag)[0]
    88    iso_block = []
    9     for i in range(len(iso_output[0])):
     9    ind_a = []
     10    to_index = iso_output[0][0]
     11    for ii in range(len(exp_resi)):
     12        ind_a.append(to_index.index(int(exp_resi[ii])))
     13   
     14    to_loop = len(iso_output[0])
     15
     16    for i in range(to_loop):
     17        iso_output[0][i] = [iso_output[0][i][jj] for jj in ind_a]
     18        iso_output[1][i] = [iso_output[1][i][jj] for jj in ind_a]
     19        iso_output[2][i] = [iso_output[2][i][jj] for jj in ind_a]
     20        iso_output[3][i] = [iso_output[3][i][jj] for jj in ind_a]       
     21
    1022        err_dev = stat.stdev(iso_output[3][i])
    1123        dif = list(map(operator.sub,iso_output[2][i],iso_output[1][i]))
     
    1426          "x": iso_output[1][i],
    1527          "y": iso_output[2][i],
     28          "text": ["residue "+ str(int(items)) for items in iso_output[0][i]],
    1629          "error_x": {
    1730            "array" : iso_output[3][i],
  • bin/plot_vec.py

    r1538 r1647  
    11#!/usr/bin/python
    2 def vec_plot():
     2def vec_plot(resid):
    33    bond_vec = []
    44    with open('bond_vector') as in_f:
     
    2222        bond_z.append(break_vec[1])
    2323
     24    label = []
     25    break_1 = ""
     26    for ii in range(len(resid)):
     27        mylab = "residue " + str(int(resid[ii]))
     28        label.append(mylab)
     29        label.append(break_1)
     30        label.append(break_1)
     31
     32    #assert(len(label) == len(bond_x))
     33
    2434    vec_plotly = {
    2535    "data":[
     
    3040    "y": bond_y,
    3141    "z": bond_z,
     42    "text": label,
    3243    "line": {
    3344        "width": 6,
  • bin/preprocess_dyna_rho.py

    r1547 r1647  
    1818    output_pd = pd.DataFrame(data = data_list, columns = col_name)
    1919    resi = list(output_pd["residue"])
    20     digi_resi = []
    21     #eliminate * in residue number
     20    dyna_resi = []
     21    exp_resi = []
     22    ori_resi = []
     23    #deal with comment symbols in residue number
    2224    for list_elem in resi:
    23         digi_resi.extend(re.findall("\d+", list_elem))
    24     digi_resi = [int(elem) for elem in digi_resi]
    25     output_pd["residue"] = digi_resi
    26     sort_output = output_pd.sort_values(['freq', 'residue'], ascending=[True, True])
     25        if list_elem.startswith("%"):
     26            pass
     27        elif list_elem.startswith("*"):
     28            dyna_resi.append(list_elem[1:])
     29            exp_resi.append(list_elem)
     30        else:
     31            dyna_resi.append(list_elem)
     32            exp_resi.append(list_elem)
     33    ori_resi = dyna_resi
     34
     35    if dat_type == "exp":
     36        output_pd = output_pd[[x in exp_resi for x in ori_resi]]
     37    else:
     38        output_pd = output_pd[[x in dyna_resi for x in ori_resi]]
     39   
     40    resi_pd = output_pd['residue']
     41    int_resi = [int(items) for items in resi_pd]
     42    output_pd['residue'] = int_resi
     43    sort_output = output_pd.sort_values(['freq','chain','residue'], ascending=[True,True,True])
    2744    freq_set = set(list(sort_output["freq"]))
    2845    chain_set = set(list(sort_output["chain"]))
     
    4259        if dat_type =="exp":
    4360            for items in output_keys:
    44                 rho_resi_list = [float(ele) for ele in list(output_dict[items]["residue"])]   
     61                rho_resi_list = [int(ele) for ele in list(output_dict[items]["residue"])]   
    4562                rho_exp_list = [float(ele) for ele in list(output_dict[items]["rho_exp"])]
    4663                rho_pred_list = [float(ele) for ele in list(output_dict[items]["rho_pred"])]
     
    5370        elif dat_type =="dyna":
    5471        #all frequencies have the same dyanmics data
    55             resi_list = [float(ele) for ele in list(output_dict[output_keys[0]]["residue"])]
     72            resi_list = [int(ele) for ele in list(output_dict[output_keys[0]]["residue"])]
    5673            s2_list = [float(ele) for ele in list(output_dict[output_keys[0]]["s2"])]
    5774            tau_loc_list = [float(ele) for ele in list(output_dict[output_keys[0]]["tau_loc"])]
     
    7592        if dat_type =="exp":
    7693            for items in output_keys:
    77                 rho_resi_list = [float(ele) for ele in list(output_dict[items]["residue"])]   
     94                rho_resi_list = [int(ele) for ele in list(output_dict[output_keys[0]]["residue"])]   
    7895                rho_exp_list = [float(ele) for ele in list(output_dict[items]["rho_exp"])]
    7996                rho_pred_list = [float(ele) for ele in list(output_dict[items]["rho_pred"])]
     
    87104        #different chains have different dyanmics data
    88105            for items in output_keys:
    89                 resi_list = [float(ele) for ele in list(output_dict[output_keys[0]]["residue"])]
     106                resi_list = [int(ele) for ele in list(output_dict[output_keys[0]]["residue"])]
    90107                s2_list = [float(ele) for ele in list(output_dict[items]["s2"])]
    91108                tau_loc_list = [float(ele) for ele in list(output_dict[items]["tau_loc"])]
  • bin/run_rotdif_elmdock.py

    r1549 r1647  
    117117    path_to_live_log = str(base_dir) + "/" + 'elmdock_log.out'
    118118    error_string_md = ''
    119     timeout = 4 # seconds
     119    timeout = 30 # seconds
    120120
    121121    while True:
  • directives.json

    r1524 r1647  
    1 # this is a project directives file
    2 # comments are ok but must start the line in this JSON
    31{
    4   "title"                     : "ROTDIF",
    5 
    6 # the application name should not contain any special characters
    7   "application"               : "rotdif",
    8 
    9   "footer"                    : "Test footer",
    10   "footersize"                : "50px",
    11   "version"                   : "- Web",
    12 # choose your target languages to generate currently from [ "html5", "qt3", "qt4", "qt5", "java" ]
    13   "languages"                 : [ "html5"  ],
    14 
    15   "executable_path"           :
    16                                 {
    17                                  "html5" : "/opt/genapp/rotdif/bin",
    18                                  "qt3"   : "/opt/genapp/rotdif/bin",
    19                                  "qt4"   : "/opt/genapp/rotdif/bin",
    20                                  "qt5"   : "/opt/genapp/rotdif/bin",
    21                                  "java"  : "/opt/genapp/rotdif/bin"
    22                                 },
    23 
    24   "docroot"                   :
    25                                 {
    26                                  "html5" : "/var/www/html",
    27                                  "qt3"   : "/opt/genapp/rotdif/tmp",
    28                                  "qt4"   : "/opt/genapp/rotdif/tmp",
    29                                  "qt5"   : "/opt/genapp/rotdif/tmp",
    30                                  "java"  : "/opt/genapp/rotdif/tmp"
    31                                 },
    32 # these are helper editor and pdb viewer applications and can be changed
    33   "helper"                    : {
    34                                  "txt" :"vi",
    35                                  "pdb" :"rasmol"
    36                                 },
    37 # only uncomment this to have a "docs" tab in html5 which hyperlinks to your self created docs directory
    38 #  "docsbaseurl"               : "docs",
    39 
    40 # global hover help texts
    41   "help"                      : {
    42                                   "user_config" : "this is user config help"
    43                                  ,"register"    : "this is register help"
    44                                  ,"jobs"        : "this is jobs help"
    45                                  ,"files"       : "this is file help"
    46                                  ,"feedback"    : "this is feedback help"
    47                                  ,"docs"        : "this is docs help"
    48                                  ,"login"       : "this is login help"
    49                                  ,"logoff"      : "this is logoff help"
    50                                  ,"help"        : "this is help help"
    51                                  ,"project"     : "this is project help"
    52                                  ,"menu"        : "this is menu help"
    53                                  ,"submit"      : "this is help for the submit button"
    54                                  ,"reset"       : "this is help for the reset to default values button"
    55                                  ,"apptitle"    : "this is application help"
    56                                 },
    57 
    58   "textarea"                 : {
    59       "label"           : "Report:"
    60       ,"default"        : "header3"
    61       ,"verticalalign"  : "top"
    62       ,"cols"           : "110"
    63 # define rows to stop autosize
    64       ,"rows"           : "10"
    65       ,"help"           : "general textarea help"
    66   },
    67 
    68 #"usercolors" : "true",
    69 
    70 "style" : {
    71         "sidebarwidth"        : "280px"
    72         ,"sidebartextalign"   : "left"
    73         ,"sidebarleftpadding" : "10px"
    74         #,"backgroundimage"    : "Univ_Copenhagen11.jpg"
    75     },
    76 
    77 # "blue" colors
    78   "text_color_rgb"            : "220,210,210",
    79   "error_color_rgb"           : "255,0,0",
    80   "background_color_rgb"      : "0,0,95",
    81   "select_color_rgb"          : "200,128,0",
    82   "button_color_rgb"          : "228,255,250",
    83   "button_color_hex"          : "E4FFFA",
    84   "button_g_color_rgb"        : "128,170,150",
    85   "button_g_color_hex"        : "80AA06",
    86   "button_hover_color_rgb"    : "255,255,255",
    87   "button_hover_color_hex"    : "FFFFFF",
    88   "button_hover_g_color_rgb"  : "228,255,250",
    89   "button_hover_g_color_hex"  : "E4FFFA",
    90   "header1_color"             : "228,255,250",
    91   "header2_color"             : "228,255,250",
    92   "header3_color"             : "228,255,250",
    93   "header4_color"             : "228,255,250",
    94   "help_background_color_rgb" : "0,0,75",
    95   "help_text_color_rgb"       : "240,240,210"
    96 
    97 # paper colors (useful for screenshots for publications)
    98 #  "text_color_rgb"            : "0,0,0",
    99 #  "error_color_rgb"           : "255,0,0",
    100 #  "background_color_rgb"      : "255,255,255",
    101 #  "select_color_rgb"          : "200,128,0",
    102 #  "button_color_rgb"          : "192,192,255",
    103 #  "button_color_hex"          : "C0C0FF",
    104 #  "button_g_color_rgb"        : "255,255,255",
    105 #  "button_g_color_hex"        : "FFFFFF",
    106 #  "button_hover_color_rgb"    : "192,192,192",
    107 #  "button_hover_color_hex"    : "C0C0C0",
    108 #  "button_hover_g_color_rgb"  : "255,255,255",
    109 #  "button_hover_g_color_hex"  : "FFFFFF",
    110 #  "header1_color"             : "0,0,128",
    111 #  "header2_color"             : "0,0,128",
    112 #  "header3_color"             : "0,0,128",
    113 #  "header4_color"             : "0,0,128",
    114 #  "help_background_color_rgb" : "230,230,230",
    115 #  "help_text_color_rgb"       : "0,0,0"
    116 
    117 # debugging controls
    118 
    119   ,"debug"                   : {
    120 #         ,"jobmonitor"        : "true"
    121 #         ,"pid"               : "true"
    122 #         ,"cancel"            : "true"
    123 #         ,"license"           : "true"
    124 #         ,"help"              : "true"
    125 #         ,"_tree"             : "true"
    126 #         ,"valuen"            : "true"
    127 #        ,"valuenx"            : "true"
    128 #        ,"sync"               : "true"
    129 #        ,"spec"               : "true"
    130 #        ,"fp"                 : "true"
    131 #        ,"plottics"           : "true"
    132 #        ,"plottwod"           : "true"
    133 #        ,"data"               : "true"
    134 #        ,"textarea"           : "true"
    135 #        ,"values"             : "true"
    136 #        ,"repeat"             : "true"
    137 #        ,"repeathtml"         : "true"
    138 #        ,"hide"               : "true"
    139 #        ,"repeatdeps"         : "true"
    140 #        ,"repeatdepsobj"      : "true"
    141 #        ,"repeatlb"           : "true"
    142 #        ,"repeatcb"           : "true"
    143 #        ,"repeatinit"         : "true"
    144 #        ,"pull"               : "true"
    145 #        ,"ws"                 : "true"
    146 #        ,"msg"                : "true"
    147 #        ,"job"                : "true"
    148 #        ,"fc"                 : "true"
    149 #        ,"jc"                 : "true"
    150 #        ,"project"            : "true"
    151 #        ,"basemylog"          : "true"
    152 #        ,"event"              : "true"
    153 #        ,"altfile"            : "true"
    154 #        ,"valid"              : "true"
    155 #        ,"jsmol"              : "true"
    156    }
    157 
    158 #  uncomment to enable "captcha" human validation upon registration for html5/php target
    159 #  ,"enablecaptcha"                  : "true"
    160 
    161 #  uncomment to enable "login/register" splash entry for html5/php target
    162 #  ,"usesplash"                  : "true"
    163 
    164 # only uncomment if you have configured secure web sockets in your webserver
    165 #  ,"usewss"                  : "true"
    166 
    167 # the following are currently only meaningful for "html5" language
    168   ,"appconfig"                 : "/opt/genapp/rotdif/appconfig.json"
    169 
    170 # if you have a 32 bit mongo system "html5", comment this
    171   ,"mongojournal"               : "true"
    172 
    173 # optionally define a log directory for "html5" language
    174   ,"logdirectory"               : "_log"
    175    ,"usews" : "true"
     2   "help_text_color_rgb" : "240,240,210",
     3   "logdirectory" : "_log",
     4   "button_color_rgb" : "200,128,0",
     5   "mongo" : {},
     6   "debug" : {},
     7   "footersize" : "50px",
     8   "appconfig" : "/opt/genapp/rotdif/appconfig.json",
     9   "button_g_color_rgb" : "200,128,0",
     10   "button_hover_color_hex" : "FFFFFF",
     11   "help_background_color_rgb" : "0,0,75",
     12   "helper" : {
     13      "pdb" : "rasmol",
     14      "txt" : "vi"
     15   },
     16   "header1_color" : "228,255,250",
     17   "button_hover_color_rgb" : "255,255,255",
     18   "textarea" : {
     19      "verticalalign" : "top",
     20      "help" : "general textarea help",
     21      "default" : "header3",
     22      "label" : "Report:",
     23      "cols" : "110",
     24      "rows" : "10"
     25   },
     26   "header2_color" : "228,255,250",
     27   "version" : "- Web",
     28   "executable_path" : {
     29      "qt5" : "/opt/genapp/rotdif/bin",
     30      "qt3" : "/opt/genapp/rotdif/bin",
     31      "qt4" : "/opt/genapp/rotdif/bin",
     32      "java" : "/opt/genapp/rotdif/bin",
     33      "html5" : "/opt/genapp/rotdif/bin"
     34   },
     35   "button_hover_g_color_hex" : "FFFFFF",
     36   "header4_color" : "C88000",
     37   "text_color_rgb" : "0,0,0",
     38   "languages" : [
     39      "html5"
     40   ],
     41   "select_color_rgb" : "2E8B57",
     42   "style" : {
     43      "sidebartextalign" : "left",
     44      "sidebarleftpadding" : "10px",
     45      "sidebarwidth" : "280px"
     46   },
     47   "background_color_rgb" : "255,255,255",
     48   "button_g_color_hex" : "80AA06",
     49   "docroot" : {
     50      "qt3" : "/opt/genapp/rotdif/tmp",
     51      "qt5" : "/opt/genapp/rotdif/tmp",
     52      "qt4" : "/opt/genapp/rotdif/tmp",
     53      "html5" : "/var/www/html",
     54      "java" : "/opt/genapp/rotdif/tmp"
     55   },
     56   "header3_color" : "C88000",
     57   "button_color_hex" : "C88000",
     58   "application" : "rotdif",
     59   "error_color_rgb" : "255,0,0",
     60   "button_hover_g_color_rgb" : "C88000",
     61   "footer" : "ROTDIF-Web@2019",
     62   "title" : "ROTDIF",
     63   "usews" : "true",
     64   "mongojournal" : "true",
     65   "css" : "custom.css",
     66   "usesplash": "true",
     67   "splashdocs":"true"
    17668}
  • files/aboutrotdif.html

    r1471 r1647  
    11 <div style="width:800px;margin-left:100px">
    2     <p><span style="font-weight:bold;font-size:20pt;font-family:'Times New Roman'">Instructions</span></p>
     2    <p><span style="font-weight:bold;font-size:20pt;font-family:'Avenir'">Instructions</span></p>
    33
    44    <p style="text-align:left;margin-bottom:0.000000pt;margin-top:0.000000pt"></p>
    55   
    6     <p style="font-size:16pt;font-family:'Times New Roman'">
     6    <p style="font-size:16pt;font-family:'Avenir'">
    77    <i>RotDif</i> requires 2 input files:</p>
    88
    99    <p style="text-align:justify;margin-bottom:0.000000pt;margin-top:0.000000pt">
    1010   
    11     <span style="font-size:16pt;font-family:'Times New Roman'">
     11    <span style="font-size:16pt;font-family:'Avenir'">
    1212
    1313      <p>
     
    2727        <hr>
    2828
    29          <p><span style="font-weight:bold;font-size:20pt;font-family:'Times New Roman'">Example set of files</span></p>
     29         <p><span style="font-weight:bold;font-size:20pt;font-family:'Avenir'">Example set of files</span></p>
    3030       
    3131        <p> Relaxation files</p>
  • files/acknowledgements_my.html

    r1471 r1647  
    11<div style="width:1000px;margin-left:100px" >
    2 <p><span style="font-weight:bold;font-size:20pt;font-family:'Times New Roman'">Acknowledgments
     2<p><span style="font-weight:bold;font-size:20pt;font-family:'Avenir'">Acknowledgments
    33</span></p>
    4 
    5 
     4<div class="">
     5  <img src="https://drive.google.com/uc?id=1VUO5ds0TfXGZ8ARc6dUF90GzuGk0yUTn" width="400" height="200"></img>
     6  <img src="https://drive.google.com/uc?id=1MCe82b_VyJP7mFB_h_4fYFyCD8eKABmq" width="200" height="200"></img>
     7</div>
     8 
    69<p class="_normal" style=
    710"text-align:left;margin-bottom:8.000000pt;margin-top:0.000000pt"
    811awml:style="_Normal"><span style=
    9 "font-size:16pt;font-family:'Times New Roman'">This website was
    10 made possible by:</span></p>
    11 <p class="_normal" style=
    12 "text-align:center;margin-bottom:8.000000pt;margin-top:0.000000pt"
    13 awml:style="_Normal"><img style="width:125.0mm"
    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" title=""
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    16 <p class="_normal" style=
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    18 awml:style="_Normal"><a href="http://www.nsf.gov/"><span style=
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    20 http://www.nsf.gov/</span></a></p>
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    23 awml:style="_Normal"><img style="width:75.5mm"
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"
    25 title="" alt="" align=""></p>
    26 <p class="_normal" style=
    27 "text-align:center;margin-bottom:8.000000pt;margin-top:0.000000pt"
    28 awml:style="_Normal"><a href="http://www.ccpsas.org/"><span style=
    29 "font-size:18pt;font-family:'Times New Roman';text-decoration:underline;color:#0000ff">
    30 http://www.ccpsas.org/</span></a></p>
    31 <p></p>
    32 <p class="_normal" style=
    33 "text-align:justify;margin-bottom:8.000000pt;margin-top:0.000000pt"
    34 awml:style="_Normal"><span style="font-size:16pt;font-family:'Times New Roman'">Integration of
    35 the</span><span style=
    36 "font-style:italic;font-size:14pt"> ROTDIF </span> <span style=
    37 "font-size:16pt;font-family:'Times New Roman'">program into</span><span style=
    38 "font-style:italic;font-size:16pt;font-family:'Times New Roman'"> <a href="http://genapp.rocks/" target=
    39     "_blank" style="color:#0000ff">GenApp</a></span> <span style=
    40 "font-size:16pt;font-family:'Times New Roman'">scientific framework was performed by Dr. Alexey
    41 Savelyev and Dr. Emre Brookes (pictured below, from left to right).
    42 Dr. Savelyev is a postoctoral research associate, and Dr. Brookes
    43 is an assistant professor at the <a href=
    44 "http://www.biochem.uthscsa.edu/" target="_blank" style="color:#0000ff">Department of
    45 Biochemistry</a>, University of Texas Health Science Center, San
    46 Antonio, TX.</span></p>
    47 <p class="_normal" style=
    48 "text-align:center;margin-bottom:8.000000pt;margin-top:0.000000pt"
    49 awml:style="_Normal"><img style="width:95.5mm; height:55.9mm"
    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" title=""
    51 alt="" align=""></p>
     12"font-size:16pt;font-family:'Avenir'">ROTDIF-Web is made possible by the collaboration between researchers from University of Texas Health Science Center (UTHSC)
     13and University of Maryland (UMD).</span></p>
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    53     <p></p>
    54     <p style="text-align:center;margin-bottom:0.000000pt;margin-top:0.000000pt">
    55     <span style=
    56     "font-size:12pt;font-family:'Times New Roman'"></span><img style="width:72.2mm"
    57     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title="" alt="" align=""></p>
     15<p><span style="font-weight:bold;font-size:18pt;font-family:'Avenir'">Main Contributions
     16</span></p>
     17
     18<div style= "font-size:16pt;font-family:'Avenir'">
     19  <p>GenApp framework development: Dr. Emre Brookes@UTHSC</p>
     20  <p>ROTDIF integration: Yuexi Chen@UMD, Dr. Alexey Savelyev@UTHSC</p>
     21  <p>Feedback and suggestions: Dr. David Fushman@UMD, Andrew Boughton@UMD</p>
     22</div>
     23
     24<p><span style="font-weight:bold;font-size:18pt;font-family:'Avenir'">Funding
     25</span></p>
     26<div class="">
     27  <img src="https://drive.google.com/uc?id=1ldadw-cR_WgqeqzvnhHba_QCXfBuHyc1" width="150" height="150"></img>
     28  <img src="https://drive.google.com/uc?id=16xQfIud0tR1JcVXN6R3-ItoJm159q2dr" width="400" height="150"></img>
     29</div>
     30
     31<div style= "font-size:16pt;font-family:'Avenir'">
     32  <p>OAC-1739549 (to D.F.)</p>
     33  <p>OAC-1740087 (to E.B.)</p>
     34  <p>Science Gateways Community Institute (summer internship to Y.C.)</p>
     35</div>
    5836
    5937
    60 </div>
     38
     39 
  • files/official_disclaimer_my.html

    r1471 r1647  
    11<div style="width:850px;margin-left:100px;">
    2 <p><span style="font-weight:bold;font-size:20pt;font-family:'Times New Roman'">Disclaimer</span></p>
     2<p><span style="font-weight:bold;font-size:20pt;font-family:'Avenir'">Disclaimer</span></p>
    33
    44<p></p>
     
    77"text-align:justify;margin-bottom:0.0000in;margin-top:0.0000in;margin-right:0.0000in"
    88awml:style="Normal"><span style=
    9 "font-style:italic;font-size:16pt;font-family:'Times New Roman'">
     9"font-style:italic;font-size:16pt;font-family:'Avenir'">
    1010ROTDIF</span> <span style=
    11 "font-size:16pt;font-family:'Times New Roman'">is distributed "as is", without any warranty, including any implied warranty of merchantability or fitness for a particular use. The authors assume no responsibility for, and shall not be liable for, any special, indirect, or consequential damages, or any damages whatsoever, arising out of or in connection with the use of this software.
     11"font-size:16pt;font-family:'Avenir'">is distributed "as is", without any warranty, including any implied warranty of merchantability or fitness for a particular use. The authors assume no responsibility for, and shall not be liable for, any special, indirect, or consequential damages, or any damages whatsoever, arising out of or in connection with the use of this software.
    1212</p>
    1313</div>
  • files/welcome_my.html

    r1520 r1647  
    11 <div style="width:800px;margin-left:100px">
    2     <p><span style="font-weight:bold;font-size:20pt;font-family:'Times New Roman'">Welcome</span></p>
     2    <p><span style="font-weight:bold;font-size:20pt;font-family:'Avenir'">Welcome</span></p>
    33
    44    <p style="text-align:left;margin-bottom:0.000000pt;margin-top:0.000000pt"></p>
    55   
    6     <p style="font-size:16pt;font-family:'Times New Roman'">
    7     Welcome to ROTDIF, a package for processing, prediction, and rigid-body docking based on nuclear spin-relaxation!</p>
     6    <p style="font-size:16pt;font-family:'Avenir'">
     7    Welcome to ROTDIF, a package for processing, prediction, and rigid-body docking based on nuclear spin-relaxation data!</p>
    88
    99    <p style="text-align:justify;margin-bottom:0.000000pt;margin-top:0.000000pt">
    1010    <span style=
    11     "font-size:16pt;font-family:'Times New Roman'">
    12       The relaxation toolbox ROTDIF has several important capabilities.
    13       First, it consists of a new version of the ROTDIF program, which can now simultaneously analyze relaxation data for
    14       15N and 13C at several fields. The new ROTDIF 3.0 is orders of magnitude faster than the previous version, and is now able
    15       to accurately compute a fully anisotropic rotational diffusion tensor from a 250+ bond dataset in under 0.05 s. ROTDIF is also more robust,
    16       based on our simulations, when processing data with large conformational exchange contributions.
    17       Order parameters can also be rapidly computed. The toolbox also contains ELM3 and ELMDOCK, a program for ab initio prediction of the
    18       rotational diffusion tensor from molecular structure, and the
    19       associated docking program that orients and docks a two-domain system based on relaxation data. The prediction can be computed in less
    20       than 0.1 s, and docking can be performed within a few seconds on a standard laptop.
     11    "font-size:16pt;font-family:'Avenir'">
     12     ROTDIF-Web is a web-server that helps researchers investigate the amplitudes and time scales of internal motions in biological macromolecules (proteins and nucleic acids). On ROTDIF-Web, users can simultaneously analyze Nuclear Magnetic Resonance (NMR) relaxation data obtained at multiple magnetic fields and for different nuclei. ROTDIF-Web also includes powerful tools for ab initio prediction of rotational diffusion tensors from macromolecular structures and for building macromolecular complexes using rotational diffusion-guided docking. ROTDIF-Web incorporates all the features of portable ROTDIF 3.0 (written in Java) and extends data management and visualization features.
    2113    </span></p>
    2214
    2315    <p></p>
    24     <p> <u>If you use ROTDIF in your work, please cite: </u></p>
    25     <p style="color:#ffa500"> [1] Berlin K et al., Deriving quantitative dynamics information for proteins and RNAs using ROTDIF with a graphical user interface <span style="font-style:italic;font-size:14pt;font-family:'Times New Roman'"> J Biomol NMR </span> 2013 Dec;57(4):333-52 </p>
    26     <p style="color:#ffa500"> [2] Walker O et al., Efficient and accurate determination of the overall rotational diffusion tensor of a molecule from 15N relaxation data using computer program ROTDIF  <span style=
    27     "font-style:italic;font-size:14pt;font-family:'Times New Roman'"> J Magn Reson </span> 2004 Jun;168(2):336-45. </p>
     16    <p> If you use ROTDIF in your work, please cite: </p>
     17    <p style="color:#FFFFFF"> <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3939081/" target= "_new">[1] Konstantin Berlin, Andrew Longhini, T. Kwaku Dayie, David Fushman, Deriving quantitative dynamics information for proteins and RNAs using ROTDIF with a graphical user interface, <span style="font-style:italic;font-size:14pt;font-family:'Times New Roman'"> J Biomol NMR, </span> 2013 ;57(4):333-52 </a></p>
     18    <p style="color:#FFFFFF"><a href="https://www.ncbi.nlm.nih.gov/pubmed/15140445" target="_new"> [2] Olivier Walker, Ranjani Varadan, David Fushman, Efficient and accurate determination of the overall rotational diffusion tensor of a molecule from 15N relaxation data using computer program ROTDIF,  <span style=
     19    "font-style:italic;font-size:14pt;font-family:'Times New Roman'"> J Magn Reson, </span> 2004;168(2):336-45. </a></p>
    2820 </div>
     21
     22
  • menu.json

    r1520 r1647  
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    21         }
    22         ,{
    23             "id"      : "demo_menu",
    24             "label"   : "How to Use",
    25             "icon"    : "pngs/noicon.png",
    26             "autorun" : "demo",
    27             "modules" : [
    28                 {
    29                     "id"    : "demo",
    30                     "label" : "About ROTDIF",
    31                     "nobutton" : "true"
    32                 }
    33             ]
    34         }
     21        },
     22#       {
     23#            "id"      : "demo_menu",
     24#            "label"   : "How to Use",
     25#            "icon"    : "pngs/noicon.png",
     26#            "autorun" : "demo",
     27#            "modules" : [
     28#                {
     29#                    "id"    : "demo",
     30#                    "label" : "About ROTDIF",
     31#                   "nobutton" : "true"
     32#                }]
     33#          },
     34          {
     35             "id"    : "tutorial_menu",
     36             "label" : "ROTDIF tutorial",
     37             "icon": "pngs/noicon.png",
     38             "autorun":"tutorial",
     39             "modules"   :[
     40                 {
     41                  "id"       : "tutorial",
     42                  "label"    : "ROTDIF tutorial",
     43                  "nobutton" : "true"
     44                 }
     45            ]
     46         }
    3547        ,{
    3648            "id"      : "Acknowledgements_menu",
  • modules/elm.json

    r1520 r1647  
    2424               {
    2525                    "role"       : "input"
    26                     ,"id"         : "projectname"
    27                     ,"label"      : "Project name"
     26                    ,"id"         : "run_name"
     27                    ,"label"      : "run name"
    2828                    ,"type"       : "text"
    2929                    ,"required"   : "true"
    30                     ,"help"       : "Enter a project name"
    31                     ,"specifiedproject" : "Run_"
     30                    ,"help"       : "Enter a name for this task"
    3231                },
    3332################################################################
     
    120119                   "role"  : "output",
    121120                   "id"    : "elm_out",
    122                    "label" : "Download ELM prediction",
     121                   "label" : "Download ELM prediction &nbsp",
    123122                   "type"  : "file",
    124123                   "multiple": "true"
     124                  },
     125                  {
     126                   "role":"output",
     127                   "id":"elm_params",
     128                   "label":"Download ELM parameters &nbsp",
     129                   "type":"file",
     130                   "multiple":"true"
    125131                  }
     132                 
    126133            ],
    127134
  • modules/elmdock.json

    r1552 r1647  
    1 # this is a module file, any module specific info belongs here
     1        # this is a module file, any module specific info belongs here
    22{
    33    "moduleid" : "elmdock",
     
    2424               {
    2525                    "role"       : "input"
    26                     ,"id"         : "projectname"
    27                     ,"label"      : "Project name"
     26                    ,"id"         : "run_name"
     27                    ,"label"      : "run name"
    2828                    ,"type"       : "text"
    2929                    ,"required"   : "true"
    30                     ,"help"       : "Enter a project name"
    31                     ,"specifiedproject" : "Run_"
     30                    ,"help"       : "Enter a name to run this task"
    3231                },
    3332################################################################
     
    145144                   #"append"  : "on",
    146145                   "cols"    : 40
    147                   },
    148 
    149                 {
    150                    "role"    : "output",
    151                    "id"      : "live_log",
    152                    "label"   : "Live Log File:  ",
    153                    "type"    : "html"
    154                 },
     146                  },
    155147                   {
    156148                   "role"  : "output",
     
    163155                  # "role"  : "output",
    164156                  # "id"    : "elm_out",
    165                   # "label" : "Download ELM prediction",
     157                  # "label" : "Download ELM prediction ",
    166158                  # "type"  : "file",
    167159                  # "multiple": "true"
     
    170162                   "role"  : "output",
    171163                   "id"    : "elmdock_out",
    172                    "label" : "ELMDOCK results",
     164                   "label" : "ELMDOCK results &nbsp;&nbsp;",
    173165                   "type"  : "file",
    174166                   "multiple": "true"
     
    177169                   "role"  : "output",
    178170                   "id"    : "pdb",
    179                    "label" : "ELMDOCK PDB file",
     171                   "label" : "ELMDOCK PDB file ",
    180172                   "type"  : "file",
    181173                   "multiple": "true"
    182                   }
     174                  },
     175                   {
     176                   "role": "output",
     177                   "id" :"outputpdb",
     178                   "label":"Docking View",
     179                   "type"  : "atomicstructure",
     180                   "height" : "850",
     181                   "width" : "850"
     182                   }   
    183183            ],
    184184
  • modules/rotdif_all.json

    r1525 r1647  
    2525               {
    2626                    "role"       : "input"
    27                     ,"id"         : "projectname"
    28                     ,"label"      : "Project name"
     27                    ,"id"         : "run_name"
     28                    ,"label"      : "run name"
    2929                    ,"type"       : "text"
    3030                    ,"required"   : "true"
    31                     ,"help"       : "Enter a project name"
    32                     ,"specifiedproject" : "Run_"
     31                    ,"help"       : "Enter a name to run this task"
    3332                },
    3433################################################################
     
    153152                   "role"  : "output",
    154153                   "id"    : "axi_out",
    155                    "label" : "Save PyMol Axes: axially symmetric",
     154                   "label" : "Saved PyMol Axes: axially symmetric model",
    156155                   "type"  : "file",
    157156                   "multiple": "true"
     
    160159                   "role"  : "output",
    161160                   "id"    : "ani_out",
    162                    "label" : "Save PyMol Axes: full anisotropic",
     161                   "label" : "Saved PyMol Axes: fully anisotropic model",
    163162                   "type"  : "file",
    164163                   "multiple": "true"
     
    174173                   "role" : "output",
    175174                   "id" : "vec_plot",
    176                    "label" : "Bond Vectors Plot",
     175                   "label" : "Bond Orientations Plot",
    177176                   "showcollapse":"False",
    178177                   "height": "450px",
     
    218217                   "role"  : "output",
    219218                   "id"    : "iso_plot",
    220                    "label" : "Isotropic Model:",
     219                   "label" : "Isotropic Model Fit:",
    221220                   "showcollapse": "False",
    222221                   "type"  : "plotly"
     
    224223                {  "role"  : "output",
    225224                   "id"    : "axi_plot",
    226                    "label" : "Axially Symmetric Model:",
     225                   "label" : "Axially Symmetric Model Fit:",
    227226                   "showcollapse": "False",
    228227                   "type"  : "plotly"
     
    230229                {  "role"  : "output",
    231230                   "id"    : "ani_plot",
    232                    "label" : "Fully Anisotropic Model:",
     231                   "label" : "Fully Anisotropic Model Fit:",
    233232                   "showcollapse": "False",
    234233                   "type"  : "plotly"
     
    236235                {  "role"  : "output",
    237236                   "id"    : "diso_plot",
    238                    "label" : "Dynamics: Isotropic Model:",
     237                   "label" : "Dynamics: Isotropic Model Fit:",
    239238                   "showcollapse" : "False",
    240239                   "type"  : "plotly"
     
    242241                {  "role"  : "output",
    243242                   "id"    : "daxi_plot",
    244                    "label" : "Dynamics: Axially Symmetric Model:",
     243                   "label" : "Dynamics: Axially Symmetric Model Fit:",
    245244                   "showcollapse" : "False",
    246245                   "type"  : "plotly"
     
    248247                {  "role"  : "output",
    249248                   "id"    : "dani_plot",
    250                    "label" : "Dynamics: Fully Anisotropic Model:",
     249                   "label" : "Dynamics: Fully Anisotropic Model Fit:",
    251250                   "showcollapse" : "False",
    252251                   "type"  : "plotly"
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