def fillCalcsandPropsDict(self):
self.checkedPropsDict = {
"melting_point": self.melting_point,
"boiling_point": self.boiling_point,
"water_sol": self.water_sol,
"vapor_press": self.vapor_press,
"mol_diss": self.mol_diss,
"ion_con": self.ion_con,
"henrys_law_con": self.henrys_law_con,
"kow_no_ph": self.kow_no_ph,
"kow_wph": self.kow_wph,
"kow_ph": self.kow_ph,
"koc": self.koc
}
# calculator checkboxes' values:
self.calcluatorsDict = {
"chemaxon": self.chemaxon,
"epi": self.epi,
"test": self.test,
"sparc": self.sparc,
"measured": self.measured
}
self.parent_image = data_walks.nodeWrapper(self.smiles, None, 250, 50, 'pchem_parent_wrap', 'svg', None)
# dict with keys of checked calculators and values of
# checked properties that are also available for said calculators
# format: { key: "calculator name", value: [checked property ids] }
self.checkedCalcsAndPropsDict = {}
for calcKey, calcValue in self.calcluatorsDict.items():
if calcValue == 'true' or calcValue == 'on':
# get checked parameters that are available for calculator
propList = []
for propKey, propValue in self.checkedPropsDict.items():
# find checked properties
if propValue == 'on' or propValue == 'true':
# check if property is available for calculator:
if pchemprop_parameters.pchempropAvailable(calcKey, propKey):
propList.append(propKey)
self.checkedCalcsAndPropsDict.update({calcKey: propList})
self.run_data = {
'title': "P-Chem Properties Output",
'jid': self.jid,
'time': datetime.datetime.strptime(self.jid, '%Y%m%d%H%M%S%f').strftime('%A, %Y-%B-%d %H:%M:%S'),
'chem_struct': self.chem_struct,
'smiles': self.smiles,
'name': self.name,
'formula': self.formula,
'mass': self.mass,
'exactMass': self.exact_mass
}
def parsePOST(request):
pdf_t = request.POST.get('pdf_t')
pdf_nop = request.POST.get('pdf_nop')
pdf_p = json.loads(request.POST.get('pdf_p'))
if 'pdf_json' in request.POST and request.POST['pdf_json']:
pdf_json = json.loads(request.POST.get('pdf_json'))
# pdf_json = request.POST.get('pdf_json')
else:
pdf_json = None
# Append strings and check if charts are present
final_str = pdf_t
if 'gentrans' in request.path:
headings = ['genKey', 'smiles', 'iupac', 'formula', 'mass', 'exactMass', 'routes']
calcs = ['chemaxon', 'epi', 'test', 'sparc', 'measured']
checkedCalcsAndProps = pdf_json['checkedCalcsAndProps']
products = pdf_json['nodes']
props = ['melting_point', 'boiling_point', 'water_sol', 'vapor_press',
'mol_diss', 'ion_con', 'henrys_law_con', 'kow_no_ph', 'kow_wph', 'koc']
for product in products:
product_image = data_walks.nodeWrapper(product['smiles'], None, 250, 100, product['genKey'], 'png', False)
product.update({'image': product_image})
# build object that's easy to make pchem table in template..
if 'pchemprops' in product:
rows = []
for prop in props:
data_row = []
kow_no_ph_list = []
kow_wph_list = []
data_row.append(prop)
calc_index = 1
for calc in calcs:
# pick out data by key, make sure not already there..
for data_obj in product['pchemprops']:
if data_obj['prop'] == prop and data_obj['calc'] == calc:
if prop == 'kow_no_ph' and calc == 'chemaxon':
# expecting same calc/prop with 3 methods..\
already_there = False
for item in kow_no_ph_list:
if data_obj['method'] == item['method']:
already_there = True
if not already_there:
kow_no_ph_list.append({
'method': data_obj['method'],
'data': round(data_obj['data'], 2)
})
elif prop == 'kow_wph' and calc == 'chemaxon':
# expecting same calc/prop with 3 methods..
already_there = False
for item in kow_no_ph_list:
if data_obj['method'] == item['method']:
already_there = True
if not already_there:
kow_wph_list.append({
'method': data_obj['method'],
'data': round(data_obj['data'], 2)
})
# account for kow_wph too!!!!!
# elif prop == 'ion_con' and calc == 'chemaxon':
elif prop == 'ion_con':
# is there also pKb?????
pka_string = ""
pka_index = 1
for pka in data_obj['data']['pKa']:
pka_string += "pka_{}: {} \n".format(pka_index, round(pka, 2))
pka_index += 1
data_row.append(pka_string)
else:
data_row.append(data_obj['data'])
if len(data_row) <= calc_index:
# should mean there wasn't data for calc-prop combo:
data_row.append('')
calc_index += 1
if prop == 'kow_no_ph' and calc == 'chemaxon':
# insert chemaxon's kow values if they exist:
kow_string = ""
for kow in kow_no_ph_list:
kow_string += "{} ({}) \n".format(kow['data'], kow['method'])
# data_row.insert(1, kow_string)
data_row[1] = kow_string
# kow_values = []
if prop == 'kow_wph' and calc == 'chemaxon':
# insert chemaxon's kow values if they exist:
kow_string = ""
for kow in kow_wph_list:
kow_string += "{} ({}) \n".format(kow['data'], kow['method'])
# data_row.insert(1, kow_string)
data_row[1] = kow_string
# kow_values = []
rows.append(data_row)
product['data'] = rows
final_str += buildMetaboliteTableForPDF().render(
Context(dict(headings=headings, checkedCalcsAndProps=checkedCalcsAndProps, products=products, props=props, calcs=calcs)))
final_str += """<br>"""
if (int(pdf_nop)>0):
for i in range(int(pdf_nop)):
final_str += """<img id="imgChart1" src="%s" />"""%(pdf_p[i])
# final_str = final_str + """<br>"""
# Styling
input_css="""
<style>
html {
background-color:#FFFFFF;
}
body {
font-family: 'Open Sans', sans-serif;
}
table, td, th {
border: 1px solid #666666;
}
#gentrans_table td {
/*word-wrap: normal;*/
max-width: 100px;
word-break: break-all;
}
table#inputsTable td {
max-width: 600px;
word-break: break-all;
}
th {text-align:center; padding:2px; font-size:11px;}
td {padding:2px; font-size:10px;}
h2 {font-size:13px; color:#79973F;}
h3 {font-size:12px; color:#79973F; margin-top: 8px;}
h4 {font-size:12px; color:#79973F; padding-top:30px;}
.pdfDiv {border: 1px solid #000000;}
div.tooltiptext {display: table; border: 1px solid #333333; margin: 8px; padding: 4px}
div {
background-color: #FFFFFF;
}
img {
background-color: #FFFFFF;
}
</style>
"""
input_str = input_css + final_str
return input_str
def wrap_molecule(propDict, height, width, scale):
"""
Wraps molecule image result (source url) with a table
and populates said table with molecular details.
Inputs: property dict (e.g., pka, taut image urls)
Outputs: name, iupac, forumula, mass data wrapped in table with image and name
"""
if not propDict:
return "<p>could not retrieve molecule image</p>"
key = None
if 'key' in propDict:
key = propDict['key']
# image = mark_safe(data_walks.nodeWrapper(propDict['smiles'], None, width, scale, key)) # displayed image
image = mark_safe(data_walks.nodeWrapper(propDict['smiles'], None, width, scale, key, 'svg')) # displayed image
formula = propDict['formula']
iupac = propDict['iupac']
mass = "{} g/mol".format(propDict['mass'])
smiles = propDict['smiles']
exactMass = "{} g/mol".format(propDict['exactMass'])
# call nodeWrapper again with 'png' image format and add it to infoDict
# with a image_pdf key or something....
# pdf_image = mark_safe(data_walks.nodeWrapper(propDict['smiles'], None, width, scale, key, None))
infoDict = {
"image": image,
"formula": formula,
"iupac": iupac,
"mass": mass,
"smiles": smiles,
"exactMass": exactMass
# 'pdf_image': pdf_image
}
# this is the actual image on the chemspec output, wrapped in a div:
html = """
<div class="chemspec_molecule nopdf">
"""
html += infoDict['image']
# html += infoDict['pdf_image']
html += """
</div>
"""
# this builds the larger-image popup:
# the Molecular/Structure class/object needs to be used as well
# as its location in CTS considered. Is it being in REST make sense?
# the following list needs to be in that class:
mol_props = ['formula', 'iupac', 'mass', 'smiles', 'exactMass']
# wrappedDict = data_walks.popupBuilder(infoDict, ['formula', 'iupac', 'mass', 'smiles'], key, 'Molecular Information') # popup table image
wrappedDict = data_walks.popupBuilder(infoDict, mol_props, key, 'Molecular Information') # popup table image
html += '<div class="tooltiptext ' + iupac + '">'
html += wrappedDict['html']
html += '</div>'
return html
# def makeLogFile(chemspec_obj):
# """
# Gathers data for log file and
# puts it in a hidden div to be downloaded
# by the user
# """
