def __init_em_val(
self
): #formally creates the empdbvaltool module, waits for a signal to validate or close
if self.em_val == None:
self.em_val = EMPDBValWidget()
QtCore.QObject.connect(self.em_val, QtCore.SIGNAL("run_validate"),
self.on_em_validate_requested)
QtCore.QObject.connect(self.em_val, QtCore.SIGNAL("module_closed"),
self.on_em_val_closed)
def __init_em_val(self): #formally creates the empdbvaltool module, waits for a signal to validate or close
if self.em_val == None:
self.em_val = EMPDBValWidget()
QtCore.QObject.connect(self.em_val, QtCore.SIGNAL("run_validate"),self.on_em_validate_requested)
QtCore.QObject.connect(self.em_val, QtCore.SIGNAL("module_closed"),self.on_em_val_closed)
class E2ValidateMed():
def __init__(self):
self.em_val = None #will become the empdbvaltool module
self.plot3d = None #will become the emplot3d module
self.fh_stat = E2FoldHunterStat()
def start(self): #program starts here, inits the empdbvaltool module
if self.em_val == None:
self.__init_em_val()
get_application().show()
def __init_em_val(
self
): #formally creates the empdbvaltool module, waits for a signal to validate or close
if self.em_val == None:
self.em_val = EMPDBValWidget()
QtCore.QObject.connect(self.em_val, QtCore.SIGNAL("run_validate"),
self.on_em_validate_requested)
QtCore.QObject.connect(self.em_val, QtCore.SIGNAL("module_closed"),
self.on_em_val_closed)
def on_em_val_closed(self):
self.em_val = None
def __init_plot3d(self):
if self.plot3d == None:
self.plot3d = EMPlot3DWidget()
QtCore.QObject.connect(self.plot3d.plot_model,
QtCore.SIGNAL("view_transform"),
self.on_view_transform_requested)
QtCore.QObject.connect(self.plot3d.plot_model,
QtCore.SIGNAL("module_closed"),
self.on_plot3d_closed)
def on_view_transform_requested(self, new_pdb_file):
if self.em_val == None:
self.__init_em_val()
get_application().show()
self.em_val.set_pdb_file(str(new_pdb_file))
os.remove(str(new_pdb_file))
def on_plot3d_closed(self):
self.plot3d = None
def on_em_validate_requested(
self, mrc_file, pdb_file, trans, iso_thresh
): #if signal given to validate, creates emplot3d and displays graph
vals = {}
rotList = []
data = []
initPoint = []
try: #block user from view transforms from files that don't exist
f = open(pdb_file)
f.close()
except IOError:
print(
"Sorry, this pdb is only in temporary memory. Please write the transform to the hard drive to validate."
)
return
print(" ")
print(" ")
print(
"This process can take a few minutes depending on the number of transformations requested"
)
vals, rotList, b, data, initPoint = self.fh_stat.gen_data(
mrc_file, pdb_file, trans, iso_thresh)
if self.plot3d: get_application().close_specific(self.plot3d)
self.plot3d = None
if self.plot3d == None: #creates the emplot3d module after the data has been generated by fh_stat
self.__init_plot3d()
print("Note: The original probe is displayed by a cube")
print(" ")
get_application().show()
self.plot3d.plot_model.set_Vals(vals)
self.plot3d.plot_model.set_Rotations(rotList)
self.plot3d.plot_model.set_Probe(b)
self.plot3d.plot_model.set_data(data, "Results for Fit Validation")
self.plot3d.plot_model.set_data(
initPoint, "Original Probe",
shape="Cube") #note: original probe is displayed with a cube
self.plot2d(vals["score_1"], vals["score_2"], vals["score_3"], rotList)
def plot2d(self, s1, s2, s3, rotList):
'''
@s1: an array of all the transformations for the first scoring function (real space correlation)
@s2: an array of the scores of every transformation in the second function (atom inclusion)
@s3: an array of scores from the third function (volume inclusion)
@rotList: a list of all the random transformations
In all the graphs, it graphs all the random transformations as green dots, and the target probe as a red dot, so the user knows how
the target probe fares relative to the random transformations.
'''
from pylab import scatter, subplot, show, figure, colorbar
######## Graphing 1 - graph of 2 of the 3 scoring functions plotted against each other
g1 = figure(1)
g1a = subplot(311)
g1a.clear()
g1a.scatter(s1, s2, marker="o", color="green")
g1a.scatter(s1[:1], s2[:1], color="red")
g1a.set_xlabel("Real space correlation")
g1a.set_ylabel("Atom Inclusion Percentage")
g1b = subplot(312)
g1b.clear()
g1b.scatter(s3, s2, marker="o", color="green")
g1b.scatter(s3[:1], s2[:1], color="red")
g1b.set_xlabel("Volume Overlap")
g1b.set_ylabel("Atom Inclusion Percentage")
g1c = subplot(313)
g1c.clear()
g1c.scatter(s3, s1, marker="o", color="green")
g1c.scatter(s3[:1], s1[:1], color="red")
g1c.set_xlabel("Volume Overlap")
g1c.set_ylabel("Real space correlation")
# show()
####################################################
######### Graphing 2 - individual line grpahs for each scoring function
g2 = figure(2)
g2a = subplot(311)
g2a.clear()
blank = []
for i in range(0, len(s1)):
blank.append(0)
g2a.scatter(s1, blank, marker="o", color="green")
g2a.scatter(s1[:1], blank[:1], color="red")
g2a.set_xlabel("Real space correlation")
g2a.set_ylabel(" ")
g2b = subplot(312)
g2b.clear()
g2b.scatter(s2, blank, marker="o", color="green")
g2b.scatter(s2[:1], blank[:1], color="red")
g2b.set_xlabel("Atom Inclusion Percentage")
g2b.set_ylabel(" ")
g2c = subplot(313)
g2c.clear()
g2c.scatter(s3, blank, marker="o", color="green")
g2c.scatter(s3[:1], blank[:1], color="red")
g2c.set_xlabel("Volume Overlap")
g2c.set_ylabel(" ")
# show()
###################################################
########## Graphing 3 - line graph for cumulative z-score
g3 = figure(3)
g3a = subplot(111)
g3a.clear()
blank = []
s4 = []
for i in range(0, len(s1)):
blank.append(0)
te = s1[i] + s2[i] + s3[i]
s4.append(te)
s4_s = 0
val = 0
for i in range(0, len(s4)):
if (s4[i] > s4_s):
s4_s = s4[i]
val = i
valT = Transform(rotList[val].get_params("eman"))
g3a.scatter(s4, blank, marker="o", color="green")
g3a.scatter(s4[:1], blank[:1], color="red")
g3a.set_xlabel("Total z score")
g3a.set_ylabel(" ")
# show()
##################################################
######## Graphimg 4 - histogram for each scoring function
g4 = figure(4)
g4a = subplot(311)
g4a.clear()
g4a.hist(s1, 50, histtype='bar')
g4a.scatter(s1[:1], blank[:1], color="red")
g4a.set_xlabel("Real space correlation")
g4b = subplot(312)
g4b.clear()
g4b.hist(s2, 50, histtype='bar')
g4b.scatter(s2[:1], blank[:1], color="red")
g4b.set_xlabel("Atom Inclusion Percentage")
g4c = subplot(313)
g4c.clear()
g4c.hist(s3, 50, histtype='bar')
g4c.scatter(s3[:1], blank[:1], color="red")
g4c.set_xlabel("Volume inclusion")
show()
class E2ValidateMed():
def __init__(self):
self.em_val = None #will become the empdbvaltool module
self.plot3d = None #will become the emplot3d module
self.fh_stat = E2FoldHunterStat()
def start(self): #program starts here, inits the empdbvaltool module
if self.em_val == None:
self.__init_em_val()
get_application().show()
def __init_em_val(self): #formally creates the empdbvaltool module, waits for a signal to validate or close
if self.em_val == None:
self.em_val = EMPDBValWidget()
QtCore.QObject.connect(self.em_val, QtCore.SIGNAL("run_validate"),self.on_em_validate_requested)
QtCore.QObject.connect(self.em_val, QtCore.SIGNAL("module_closed"),self.on_em_val_closed)
def on_em_val_closed(self):
self.em_val = None
def __init_plot3d(self):
if self.plot3d == None:
self.plot3d = EMPlot3DWidget()
QtCore.QObject.connect(self.plot3d.plot_model, QtCore.SIGNAL("view_transform"),self.on_view_transform_requested)
QtCore.QObject.connect(self.plot3d.plot_model, QtCore.SIGNAL("module_closed"),self.on_plot3d_closed)
def on_view_transform_requested(self, new_pdb_file):
if self.em_val == None:
self.__init_em_val()
get_application().show()
self.em_val.set_pdb_file(str(new_pdb_file))
os.remove(str(new_pdb_file))
def on_plot3d_closed(self):
self.plot3d = None
def on_em_validate_requested(self, mrc_file, pdb_file, trans, iso_thresh): #if signal given to validate, creates emplot3d and displays graph
vals = {}
rotList = []
data = []
initPoint = []
try: #block user from view transforms from files that don't exist
f = open(pdb_file)
f.close()
except IOError:
print "Sorry, this pdb is only in temporary memory. Please write the transform to the hard drive to validate."
return
print " "
print " "
print "This process can take a few minutes depending on the number of transformations requested"
vals, rotList, b, data, initPoint = self.fh_stat.gen_data(mrc_file, pdb_file, trans, iso_thresh)
if self.plot3d: get_application().close_specific(self.plot3d)
self.plot3d = None
if self.plot3d == None: #creates the emplot3d module after the data has been generated by fh_stat
self.__init_plot3d()
print "Note: The original probe is displayed by a cube"
print " "
get_application().show()
self.plot3d.plot_model.set_Vals(vals)
self.plot3d.plot_model.set_Rotations(rotList)
self.plot3d.plot_model.set_Probe(b)
self.plot3d.plot_model.set_data(data,"Results for Fit Validation")
self.plot3d.plot_model.set_data(initPoint, "Original Probe",shape="Cube") #note: original probe is displayed with a cube
self.plot2d(vals["score_1"], vals["score_2"], vals["score_3"], rotList)
def plot2d(self, s1, s2, s3, rotList):
'''
@s1: an array of all the transformations for the first scoring function (real space correlation)
@s2: an array of the scores of every transformation in the second function (atom inclusion)
@s3: an array of scores from the third function (volume inclusion)
@rotList: a list of all the random transformations
In all the graphs, it graphs all the random transformations as green dots, and the target probe as a red dot, so the user knows how
the target probe fares relative to the random transformations.
'''
from pylab import scatter, subplot, show, figure, colorbar
######## Graphing 1 - graph of 2 of the 3 scoring functions plotted against each other
g1 = figure(1)
g1a = subplot(311)
g1a.clear()
g1a.scatter(s1,s2, marker ="o", color ="green")
g1a.scatter(s1[:1],s2[:1],color="red")
g1a.set_xlabel("Real space correlation")
g1a.set_ylabel("Atom Inclusion Percentage")
g1b = subplot(312)
g1b.clear()
g1b.scatter(s3,s2, marker = "o", color="green")
g1b.scatter(s3[:1],s2[:1],color="red")
g1b.set_xlabel("Volume Overlap")
g1b.set_ylabel("Atom Inclusion Percentage")
g1c=subplot(313)
g1c.clear()
g1c.scatter(s3,s1, marker = "o", color="green")
g1c.scatter(s3[:1],s1[:1],color="red")
g1c.set_xlabel("Volume Overlap")
g1c.set_ylabel("Real space correlation")
# show()
####################################################
######### Graphing 2 - individual line grpahs for each scoring function
g2 = figure(2)
g2a = subplot(311)
g2a.clear()
blank = []
for i in range(0,len(s1)):
blank.append(0)
g2a.scatter(s1,blank, marker ="o", color ="green")
g2a.scatter(s1[:1],blank[:1],color="red")
g2a.set_xlabel("Real space correlation")
g2a.set_ylabel(" ")
g2b = subplot(312)
g2b.clear()
g2b.scatter(s2,blank, marker = "o", color="green")
g2b.scatter(s2[:1],blank[:1],color="red")
g2b.set_xlabel("Atom Inclusion Percentage")
g2b.set_ylabel(" ")
g2c=subplot(313)
g2c.clear()
g2c.scatter(s3,blank, marker = "o", color="green")
g2c.scatter(s3[:1],blank[:1],color="red")
g2c.set_xlabel("Volume Overlap")
g2c.set_ylabel(" ")
# show()
###################################################
########## Graphing 3 - line graph for cumulative z-score
g3 = figure(3)
g3a = subplot(111)
g3a.clear()
blank = []
s4 =[]
for i in range(0,len(s1)):
blank.append(0)
te = s1[i] + s2[i] + s3[i]
s4.append(te)
s4_s=0
val = 0
for i in range(0, len(s4)):
if (s4[i]>s4_s):
s4_s = s4[i]
val = i
valT = Transform(rotList[val].get_params("eman"))
g3a.scatter(s4,blank, marker ="o", color ="green")
g3a.scatter(s4[:1],blank[:1],color="red")
g3a.set_xlabel("Total z score")
g3a.set_ylabel(" ")
# show()
##################################################
######## Graphimg 4 - histogram for each scoring function
g4 = figure(4)
g4a = subplot(311)
g4a.clear()
g4a.hist(s1,50, histtype='bar')
g4a.scatter(s1[:1],blank[:1],color="red")
g4a.set_xlabel("Real space correlation")
g4b = subplot(312)
g4b.clear()
g4b.hist(s2,50, histtype='bar')
g4b.scatter(s2[:1], blank[:1],color="red")
g4b.set_xlabel("Atom Inclusion Percentage")
g4c = subplot(313)
g4c.clear()
g4c.hist(s3,50, histtype='bar')
g4c.scatter(s3[:1], blank[:1],color="red")
g4c.set_xlabel("Volume inclusion")
show()
