def doAction(self, umlObjects: List[OglObject],
selectedObjects: List[OglObject], umlFrame: UmlFrame):
"""
Args:
umlObjects: list of the uml objects of the diagram
selectedObjects: list of the selected objects
umlFrame: the frame of the diagram
"""
if len(selectedObjects) != 1:
dlg = MessageDialog(None,
_("You must select at most a single class"),
_("Warning"), OK | ICON_EXCLAMATION)
dlg.ShowModal()
dlg.Destroy()
return
filename: str = ToFastEdit.FAST_EDIT_TEMP_FILE
file: TextIO = open(filename, "w")
self.write(selectedObjects[0], file)
file.close()
if sysPlatform == PyutConstants.THE_GREAT_MAC_PLATFORM:
osSystem(f'open -W -a {self._editor} {filename}')
else:
osSystem(f'{self._editor} {filename}')
file = open(filename, "r")
self.read(selectedObjects[0], file)
file.close()
self._setProjectModified()
self._cleanupTempFile()
def timeout():
if (game.isPlay):
# "loop" du jeu
game.update()
if ((game.gameCounter % game.current_time_modulo) == 0):
ui.gameWidget.update()
game.gameCounter += 1
osSystem("clear")
print("STEP : " + str(game.gameCounter))
#print("BLUE SCORE : " + str(blueScore))
#print("RED SCORE : " + str(redScore))
#timer.stop()
timePeriod = game.current_time_period
def _launchAppropriateEditor(self, filename: str) -> None:
"""
Launches the configured editor in a platform appropriate way. Assumes this blocks
until the end-user exits the launched editor
Args:
filename: The file to pass to the editor
"""
if sysPlatform == PyutConstants.THE_GREAT_MAC_PLATFORM:
osSystem(f'open -W -a {self._editor} {filename}')
else:
osSystem(f'{self._editor} {filename}')
def _file_cleanup(fnamelist, include_directories=False):
""" """
for fname in fnamelist:
if osPathExists(str(fname)):
try:
osRemove(str(fname))
except OSError:
if osPathIsdir(str(fname)) and include_directories:
osSystem("rm -rf %s" % fname)
except:
# failed !? on cluster computing, this
# could be the case when an identical filename
# is created/owned by another user.
# I suspect this might happen for formatdb.log ;-)
pass
def _file_cleanup(fnamelist,include_directories=False):
""" """
for fname in fnamelist:
if osPathExists(str(fname)):
try:
osRemove(str(fname))
except OSError:
if osPathIsdir(str(fname)) and include_directories:
osSystem("rm -rf %s" % fname)
except:
# failed !? on cluster computing, this
# could be the case when an identical filename
# is created/owned by another user.
# I suspect this might happen for formatdb.log ;-)
pass
def uploadAsync(self, program):
self.boardBusy = True
try:
if self.board != -1:
self.board.close()
self.board = -1
result = osSystem(
"\"" + os.path.join(
os.path.normpath(
os.path.dirname(os.path.realpath(__file__)) + os.sep +
os.pardir) + "\\arduino\\arduino_debug") +
"\" --verbose --board arduino:avr:mega:cpu=atmega2560 --port "
+ str(self.port) + " --upload " +
self.programs[program]["path"])
if result == 0:
self.hasDirectProgram = self.programs[program][
"isDirectProgram"]
print("\r\nDONE !")
self.easyPopup(_('Success !'),
_('Upload finished successfully !'))
else:
self.easyPopup(
_('Oopsie...'),
_('Something went wrong, try again or report a bug') +
"\nError code " + str(result))
except Exception as e:
self.easyPopup(
_('Oopsie...'),
_('Something went wrong, try again or report a bug') + "\n" +
str(e))
self.boardBusy = False
self.refresh()
def _runPdfDiff(self, baseFileName: str, standardFileName) -> int:
status: int = osSystem(
f'{TestDiagramParent.EXTERNAL_PDF_DIFF_SCRIPT} {baseFileName} {standardFileName}'
)
return status
def _runDiff(self, baseFileName: str, standardFileName) -> int:
status: int = osSystem(
f'{TestDiagramParent.EXTERNAL_DIFF_PROGRAM} {baseFileName} {standardFileName}'
)
return status
def run_make_all_vs_all_fasta(infname,outfname,reportfname,verbose=False):
"""
Make pairwise combinations of sequences in input file with prep_launch.py
@type infname: string
@param infname: (absolute) path to protein multi fasta
@type outfname: string
@param outfname: (absolute) path to prep_launch.py output file
@type reportfname: string
@param reportfname: (absolute) path to prep_launch.py report file
@type verbose: Boolean
@param verbose: debugging messages to STDOUT (True) or not (False, default)
@attention: requires global variable EXECUTABLE_cexpander_ALLVSALL
@attention: this used to be called `make_all_vs_all_fasta.py`
@rtype: Boolean
@return: True
"""
command = "python %s %s %s %s" % (
EXECUTABLE_cexpander_ALLVSALL,
infname,
outfname,
reportfname,
)
if verbose: print command
_retval = osSystem(command)
return True
def run_make_all_vs_all_fasta(infname, outfname, reportfname, verbose=False):
"""
Make pairwise combinations of sequences in input file with prep_launch.py
@type infname: string
@param infname: (absolute) path to protein multi fasta
@type outfname: string
@param outfname: (absolute) path to prep_launch.py output file
@type reportfname: string
@param reportfname: (absolute) path to prep_launch.py report file
@type verbose: Boolean
@param verbose: debugging messages to STDOUT (True) or not (False, default)
@attention: requires global variable EXECUTABLE_cexpander_ALLVSALL
@attention: this used to be called `make_all_vs_all_fasta.py`
@rtype: Boolean
@return: True
"""
command = "python %s %s %s %s" % (
EXECUTABLE_cexpander_ALLVSALL,
infname,
outfname,
reportfname,
)
if verbose: print command
_retval = osSystem(command)
return True
def run_cbalignp(infname,outfname,commandline="",verbose=False):
"""
Run the cbalignp program by specifying input & output filenames etc.
@type infname: string
@param infname: (absolute) path to make_all_vs_all_fasta.py output file
@type outfname: string
@param outfname: (absolute) path to cbalignp output file
@type commandline: string
@param commandline: literal additional command line arguments for cbalignp
@type verbose: Boolean
@param verbose: print debugging messages to STDOUT (True) or not (False, default)
@attention: requires global variable EXECUTABLE_CEXPANDER_CBALIGNP
@attention: see cbalignp for (additional) command line options
@attention: only a subset of cbalignp commandline options are supported!
@rtype: Boolean
@return: True
"""
command = "%s -i %s %s > %s " % (
EXECUTABLE_CEXPANDER_CBALIGNP,
infname,
commandline,
outfname
)
if verbose: print command
_retval = osSystem(command)
return True
def run_cbalignp(infname, outfname, commandline="", verbose=False):
"""
Run the cbalignp program by specifying input & output filenames etc.
@type infname: string
@param infname: (absolute) path to make_all_vs_all_fasta.py output file
@type outfname: string
@param outfname: (absolute) path to cbalignp output file
@type commandline: string
@param commandline: literal additional command line arguments for cbalignp
@type verbose: Boolean
@param verbose: print debugging messages to STDOUT (True) or not (False, default)
@attention: requires global variable EXECUTABLE_CEXPANDER_CBALIGNP
@attention: see cbalignp for (additional) command line options
@attention: only a subset of cbalignp commandline options are supported!
@rtype: Boolean
@return: True
"""
command = "%s -i %s %s > %s " % (EXECUTABLE_CEXPANDER_CBALIGNP, infname,
commandline, outfname)
if verbose: print command
_retval = osSystem(command)
return True
def main():
global LEADERBOARD_CH_ID, QUEUE_CH_IDS, REPORT_CH_IDS
token = getDiscordToken()
if (token == ""):
token = updateDiscordToken(
input(
"No Discord Bot token found. Paste your Discord Bot token below and hit ENTER.\ntoken: "
))
# clear screen to hide token
if osName == "nt":
_ = osSystem("cls")
else:
_ = osSystem("clear")
AWS.init()
channels = getChannelIds()
LEADERBOARD_CH_ID = channels["leaderboard_channel"]
QUEUE_CH_IDS = channels["queue_channels"]
REPORT_CH_IDS = channels["report_channels"]
if (len(QUEUE_CH_IDS) > 0):
client.loop.create_task(stale_queue_timer())
else:
print(
"Stale queue feature disabled as no queue channel id was specified."
)
try:
client.run(token)
except discord.errors.LoginFailure:
print(
"! There was an error with the token you provided. Please verify your bot token and try again.\n"
"If you need help locating the token for your bot, visit https://www.writebots.com/discord-bot-token/"
)
sleep(5)
except Exception as e:
print(e)
def runcexpander(fname_fasta, cbalignp_commandline=" -y", output='binary'):
"""
Run the complete cascade of cexpander algorithms on an input multi fasta
file and return the output as a CexpanderOutput object
@type fname_fasta: string
@param fname_fasta: path to input multi fasta file
@type cbalignp_commandline: string
@param cbalignp_commandline: (extra) command line for cbalignp
@type min_cols: integer
@param min_cols: minimal number of uniformly matched positions (cols)
required to report transfer blocks for (>= 0)
@type projected_on: string
@param projected_on: apply fasta seqeunce header which to use for projection;
apply ':::' to do projections on all input sequences
@attention: requires global variable EXECUTABLE_cexpander_ALLVSALL
@attention: requires global variable EXECUTABLE_CEXPANDER_CBALIGNP
@attention: requires global variable EXECUTABLE_CEXPANDER_CEXPANDER
@attention: see cexpander_dr for (additional) command line options
@attention: only a subset of cexpander_dr commandline options are supported!
@rtype: CexpanderOutput object
@return: CexpanderOutput object
"""
if not fname_fasta: raise "NoProperFunctionArguments"
if not osPathIsfile(fname_fasta): raise "FileDoesNotExist"
# (0) create (~unique) filenames
uniquetag = get_random_string_tag()
fname_allvsall = ".".join([fname_fasta, uniquetag, "allvsall"])
fname_report = ".".join([fname_fasta, uniquetag, "report"])
fname_aligned = ".".join([fname_fasta, uniquetag, "aligned"])
fname_settings = ".".join([fname_fasta, uniquetag, "settings"])
fname_cexpander = ".".join([fname_fasta, uniquetag, "cexpander"])
# (1) create complete .fa -> cexpanderstring command
command = """
python %s %s %s %s;
%s -i %s %s > %s;
%s < %s;
""" % (
EXECUTABLE_CEXPANDER_ALLVSALL,
fname_fasta,
fname_allvsall,
fname_report,
EXECUTABLE_CEXPANDER_CBALIGNP,
fname_allvsall,
cbalignp_commandline,
fname_aligned,
EXECUTABLE_CEXPANDER_CEXPANDER,
fname_settings,
)
# (2) create fname_settings file
binorfloat = "$dumpcv"
if output == "float": binorfloat = "$dumpcvc"
fh = open(fname_settings, 'w')
content = "\n\n".join([
"$load\n%s\n%s" % (fname_report, fname_aligned),
"$addquery\n-1",
"$run",
"$dumpentries",
"$cv_linear",
"%s" % (binorfloat), # BINARY == $dumpcv, FLOAT = $dumpcvc
"$exit\n\n",
])
fh.write(content)
fh.close()
# (3) run the command
ci, co, ce = osPopen3(command)
ci.close()
# output of EXECUTABLE_CEXPANDER_ALLVSALL is cast to STDOUT as well!
cexpanderdata = co.read()
co.close()
error = ce.read()
ce.close()
# (4) parse fname_cexpander to CexpanderOutput object
cxpdr = parse_cexpander(cexpanderdata, fname_fasta)
# (5) cleanup files
osSystem("rm -f %s %s.%s.*" % (fname_fasta, fname_fasta, uniquetag))
# (6) return the output object
return cxpdr
def sim():
#VARIABLE INITIALIZATION
#CLINICAL VARIABLES
#Glucose level/concentration of the blood in mg/dL
glucose_blood_level = 100.0
#Glucose present in the blood in mg
glucose_blood = 4617.5
#Glycogen stored in the liver in mg
glycogen_liver = 100000
#Glycogen stored in the muscles in mg
glycogen_muscles = 500000
#Insulin present in the blood in μU/mL
insulin_blood = 15.0
#Glucagon present in the blood in in pg/mL
glucagon_blood = 20.00
#Amount of mg of carbohydrate that one unit of insulin will metabolize/cover
carb_insulin_ratio = 15000
#Grams of glycogen that one unit/one pg of glucagon will release
#NOT FACTUAL, FOR SIMULATION TESTING PURPOSES ONLY
glycogenolysis_ratio = 1
#Grams of glycogen that one gram of glucose will synthesize
#NOT FACTUAL, FOR SIMULATION TESTING PURPOSES ONLY
glycogenesis_ratio = 1
#Grams of glucose released from one gram of glycogen
#NOT FACTUAL, FOR SIMULATION TESTING PURPOSES ONLY
glycogen_to_glucose_ratio = 1
#Nonessential (burnable) fat stored in adipose tissue in grams
fat_nonessential = 16983.0
#Essential (non-burnable) fat stored in adipose tissue in grams
fat_essential = 2830.0
#Total fat in adipose tissue in grams
fat_total = 19813.0
#Number used to represent metabolic activity of the body
#NOT FACTUAL, FOR SIMULATION OF METABOLIC ACTIVITY
#TO ENHANCE REALISM OF SIMULATION ONLY
metabolic_rate = 3
#Volume of blood in dL
blood_volume = 46.175
#Insulin Sensitivity
insulin_sensitivity = 1
currentTime = 0
#DATA FOR GRAPHING
#Blood Glucose Level
bgDataX = []
bgDataY = []
#Blood Glucose Level within target range (crrently unused)
bgNormDataX = []
bgNormDataY = []
#Blood Glucose Level higher than tgarget range (currently unused)
bgHighDataX = []
bgHighDataY = []
#Blood GLucose Level lower than target range (currently unused)
bgLowDataX = []
bgLowDataY = []
#Blood Insulin Concentration
insulinDataX = []
insulinDataY = []
#Blood Glucagon Concentration
glucagonDataX = []
glucagonDataY = []
#Rate of Change Prediction
ROC_predictionX = []
ROC_predictionY = []
#Metabolic Rate
metabolic_rateX = []
metabolic_rateY = []
#GLycogen in liver
glycogenDataX = []
glycogenDataY = []
#Loop the program forever.
while (1 == 1):
#CALCULATE NUMBERS
currentTime += 1
#currentTime = str(datetime.now())
#currentTime = currentTime.replace("-", "")
#currentTime = currentTime.replace(":", "")
#currentTime = currentTime.replace(".", "")
#currentTime = currentTime.replace(" ", "")
#glucose_blood_level = (glucose_blood_level - insulin_blood + (glucagon_blood*12.5) - (metabolic_rate))
#- (carb_insulin_ratio*insulin_blood) + (glycogenolysis_ratio*glucagon_blood*glycogen_to_glucose_ratio))
if glycogen_liver > 0:
#Release glycogen & convert to glucose
glycogen_liver -= (glucagon_blood * glycogenolysis_ratio)
glucose_blood += (glucagon_blood * glycogenolysis_ratio *
glycogen_to_glucose_ratio)
if glycogen_liver < 100000:
#Absorb glucose & convert to glycogen
glycogen_liver += ((insulin_blood / 1000000) * carb_insulin_ratio *
glycogenesis_ratio)
glucose_blood -= ((insulin_blood / 1000000 * blood_volume / 10) *
carb_insulin_ratio)
glucose_blood -= metabolic_rate * 10
glucose_blood_level = (glucose_blood / blood_volume)
#glycogen_liver -+ (insulin_blood*
#insulin_blood = insulin_blood - insulin_blood
#glucagon_blood = glucagon_blood - glucagon_blood
if glucose_blood_level < 0.0:
glucose_blood_level = 0.0
insulinDataX.append(currentTime)
insulinDataY.append(insulin_blood)
glucagonDataX.append(currentTime)
glucagonDataY.append(glucagon_blood)
bgDataX.append(currentTime)
bgDataY.append(glucose_blood_level)
metabolic_rateX.append(currentTime)
metabolic_rateY.append(metabolic_rate)
glycogenDataX.append(currentTime)
glycogenDataY.append(glycogen_liver)
#nan = float('nan')
if glucose_blood_level >= 80 and glucose_blood_level <= 180:
bgNormDataX.append(currentTime)
bgNormDataY.append(glucose_blood_level)
if glucose_blood_level > 180:
bgHighDataX.append(currentTime)
bgHighDataY.append(glucose_blood_level)
if glucose_blood_level < 80:
bgLowDataX.append(currentTime)
bgLowDataY.append(glucose_blood_level)
if len(bgDataX) >= 3:
bgRateOfChange = ((bgDataY[-1] - bgDataY[-2]) +
(bgDataY[-2] - bgDataY[-3])) / 2
#Steady
#ROC Prediction
ROC_predictionX = []
ROC_predictionY = []
iterations = 1
for x in range(0, 6):
ROC_predictionX.append(currentTime + iterations)
ROC_predictionY.append(glucose_blood_level +
(bgRateOfChange * iterations))
iterations += 1
if bgRateOfChange < 1 and bgRateOfChange > -1:
ROC_arrows_WB = "→"
ROC_arrows = "-> steady"
#Rising
if bgRateOfChange >= 1 and bgRateOfChange < 2:
ROC_arrows_WB = "↗"
ROC_arrows = "/^ slowly rising"
if bgRateOfChange >= 2 and bgRateOfChange > 3:
ROC_arrows_WB = "↑"
ROC_arrows = "^|^ rising"
if bgRateOfChange >= 3:
ROC_arrows_WB = "⇈"
ROC_arrows = "^|^ ^|^ rapidly rising"
#Falling
if bgRateOfChange <= -1 and bgRateOfChange > -2:
ROC_arrows_WB = "↘"
ROC_arrows = "\\v slowly falling"
if bgRateOfChange <= -2 and bgRateOfChange > -3:
ROC_arrows_WB = "↓"
ROC_arrows = "v|v falling"
if bgRateOfChange <= -3:
ROC_arrows_WB = "⇊ RAPIDLY FALLING!"
ROC_arrows = "v|v v|v RAPIDLY FALLING /!\\"
else:
bgRateOfChange = 0
ROC_arrows_WB = "N/A"
ROC_arrows = "N/A"
ROC_widget = Paragraph(text=ROC_arrows_WB, width=50, height=25)
#UPDATE DISPLAY
#Clear the terminal window
if sysPlatform() == "Windows":
osSystem('cls')
else:
osSystem("clear")
#Print basic textual UI to the terminal window
print("GlucoSim v0.2.2-alpha")
print("2015 - 2017, created by John Kozlosky")
print("")
print("Blood glucose level: " + str(round(glucose_blood_level, 2)) +
"mg/dL " + ROC_arrows)
print("Blood glucose: " + str(round((glucose_blood / 1000), 2)) + "g")
print("Glycogen: " + str(round((glycogen_liver / 1000), 2)) +
"g hepatic, " + str(round(glycogen_muscles, 2) / 1000) +
"g muscular")
print("Blood insulin: " + str(round(insulin_blood, 2)) + "uU/mL")
print("Blood glucagon: " + str(round(glucagon_blood, 2)) + "pg/mL")
#print ("Fat: " + str(round(fat_total)) + "g total, " + str(round(fat_nonessential)) + "g nonessential, " + str(round(fat_essential)) + "g essential")
print("Metabolic activity level: " + str(round(metabolic_rate, 4)))
print("")
#Make graphs using Bokeh
#Blood glucose level
if len(bgDataX) >= 100:
bgGraph = figure() #x_axis_type="datetime")
bgGraph.line(bgDataX[-100:], bgDataY[-100:], color="black")
bgGraph.circle(bgDataX[-100:],
bgDataY[-100:],
fill_color="white",
line_color="black",
size=8)
bgGraph.circle(ROC_predictionX,
ROC_predictionY,
fill_color="white",
line_color="gray",
size=8)
#bgGraph.circle(bgNormDataX[0,100], bgNormDataY[0,100], fill_color = "white", line_color = "black", size = 8)
#bgGraph.circle(bgHighDataX[0,100], bgNormDataY[0,100], fill_color = "white", line_color = "yellow", size = 8)
#bgGraph.circle(bgLowDataX[0,100], bgLowDataY[0,100], fill_color = "white", line_color = "red", size = 8)
bgGraph.title.text = "Blood Glucose"
bgGraph.xaxis.axis_label = "Time"
bgGraph.yaxis.axis_label = "Blood Glucose (mg/dL)"
#bgGraph.yaxis.bounds = (0, 400)
else:
bgGraph = figure() #x_axis_type="datetime")
bgGraph.line(bgDataX, bgDataY, color="black")
bgGraph.circle(bgDataX,
bgDataY,
fill_color="white",
line_color="black",
size=8)
bgGraph.circle(ROC_predictionX,
ROC_predictionY,
fill_color="white",
line_color="gray",
size=8)
#bgGraph.circle(bgNormDataX, bgNormDataY, fill_color = "white", line_color = "black", size = 8)
#bgGraph.circle(bgHighDataX, bgNormDataY, fill_color = "white", line_color = "yellow", size = 8)
#bgGraph.circle(bgLowDataX, bgLowDataY, fill_color = "white", line_color = "red", size = 8)
bgGraph.title.text = "Blood Glucose"
bgGraph.xaxis.axis_label = "Time"
bgGraph.yaxis.axis_label = "Blood Glucose (mg/dL)"
#bgGraph.yaxis.bounds = (0, 400)
#Insulin
if len(insulinDataX) >= 100:
insulinGraph = figure() #x_axis_type="datetime")
insulinGraph.line(insulinDataX[-100:],
insulinDataY[-100:],
color="CadetBlue")
insulinGraph.circle(insulinDataX[-100:],
insulinDataY[-100:],
fill_color="white",
line_color="CadetBlue",
size=8)
insulinGraph.title.text = "Insulin"
insuinGraph.xaxis.axis_label = "Time"
insulinGraph.yaxis.axis_label = "Blood Insulin (μU/mL)"
#insulinGraph.yaxis.bounds = (0, 400)
else:
insulinGraph = figure() #x_axis_type="datetime")
insulinGraph.line(insulinDataX, insulinDataY, color="CadetBlue")
insulinGraph.circle(insulinDataX,
insulinDataY,
fill_color="white",
line_color="CadetBlue",
size=8)
insulinGraph.title.text = "Insulin"
insulinGraph.xaxis.axis_label = "Time"
insulinGraph.yaxis.axis_label = "Blood Insulin (μU/mL)"
#insulinGraph.yaxis.bounds = (0, 400)
#Glucagon
if len(glucagonDataX) >= 100:
glucagonGraph = figure() #x_axis_type="datetime"
glucagonGraph.line(glucagonDataX[-100:],
glucagonDataY[-100:],
color="GoldenRod")
glucagonGraph.circle(glucagonDataX[-100:],
glucagonDataY[-100:],
fill_color="white",
line_color="GoldenRod",
size=8)
glucagonGraph.title.text = "Glucagon"
glucagonGraph.xaxis.axis_label = "Time"
glucagonGraph.yaxis.axis_label = "Blood Glucagon (pg/mL)"
#glucagonGraph.yaxis.bounds = (0, 700)
else:
glucagonGraph = figure() #x_axis_type="datetime")
glucagonGraph.line(glucagonDataX, glucagonDataY, color="GoldenRod")
glucagonGraph.circle(glucagonDataX,
glucagonDataY,
fill_color="white",
line_color="GoldenRod",
size=8)
glucagonGraph.title.text = "Glucagon"
glucagonGraph.xaxis.axis_label = "Time"
glucagonGraph.yaxis.axis_label = "Blood Glucagon (pg/mL)"
#glucagonGraph.yaxis.bounds = (0, 700)
#Metabolic rate
if len(metabolic_rateX) >= 100:
metabolicGraph = figure()
metabolicGraph.line(metabolic_rateX[-100:],
metabolic_rateY[-100:],
color="black")
metabolicGraph.circle(metabolic_rateX[-100:],
metabolic_rateY[-100:],
fill_color="white",
line_color="black",
size=8)
metabolicGraph.title.text = "Metabolic Rate"
metabolicGraph.xaxis.axis_label = "Time"
metabolicGraph.yaxis.axis_label = "Metabolic Rate (simulated number)"
else:
metabolicGraph = figure()
metabolicGraph.line(metabolic_rateX,
metabolic_rateY,
color="black")
metabolicGraph.circle(metabolic_rateX,
metabolic_rateY,
fill_color="white",
line_color="black",
size=8)
metabolicGraph.title.text = "Metabolic Rate"
metabolicGraph.xaxis.axis_label = "Time"
metabolicGraph.yaxis.axis_label = "Metabolic Rate (simulated number)"
#Hepatic Glycogen
if len(glycogenDataX) >= 100:
glycogenGraph = figure()
glycogenGraph.line(glycogenDataX[-100:],
glycogenDataY[-100:],
color="black")
glycogenGraph.circle(glycogenDataX[-100:],
glycogenDataY[-100:],
fill_color="white",
line_color="black",
size=8)
glycogenGraph.title.text = "Hepatic Glycogen"
glycogenGraph.xaxis.axis_label = "Time"
glycogenGraph.yaxis.axis_label = "Hepatic Glycogen (mg)"
else:
glycogenGraph = figure()
glycogenGraph.line(glycogenDataX, glycogenDataY, color="black")
glycogenGraph.circle(glycogenDataX,
glycogenDataY,
fill_color="white",
line_color="black",
size=8)
glycogenGraph.title.text = "Hepatic Glycogen"
glycogenGraph.xaxis.axis_label = "Time"
glycogenGraph.yaxis.axis_label = "Hepatic Glycogen (mg)"
#Combine the blood glucose, insulin, and glucagon graphs into a grid
#grid = gridplot([bgGraph, insulinGraph, glucagonGraph], ncols=2, plot_width=600, plot_height=350)
grid = layout([
[bgGraph],
[insulinGraph, glucagonGraph],
[metabolicGraph, glycogenGraph],
[widgetbox(ROC_widget)],
],
sizing_mode='stretch_both')
#Write the graphs to an HTML file
output_file('infoGraph.html')
#GET USER COMMAND
command = str(input(">"))
if command == "set bg":
glucose_blood = float(input("New blood glucose: ")) * blood_volume
if command == "set insulin":
insulin_blood = float(input("New insulin level: "))
if command == "set glucagon":
glucagon_blood = float(input("New glucagon level: "))
if command == "set glycogen_liver":
glycogen_liver = float(input("New hapatic glycogen level: "))
if command == "set glycogen_muscles":
glycogen_muscles = float(input("New muscular glycogen level: "))
if command == "set fat_total":
fat_total = float(input("New total fat: "))
if command == "set fat_nonessential":
fat_nonessential = float(input("New nonessential fat: "))
if command == "set fat_essential":
fat_essential = float(input("New essential fat: "))
if command == "set metabolic_rate":
metabolic_rate = float(input("New metabolic activity level: "))
if command == "set blood_volume":
blood_volume = float(input("New blood volume: "))
#if command == "inject bolus":
#bolus = float(input("Novolog bolus size (U): "))
#if command == "ingest carb":
#carb = float(input("Ingest carbs (g): "))
if command == "show":
show(grid)
if command == "exit":
if input("Are you sure you want to exit? (y/n): ") == "y":
exit()
if command == "help":
print("SIMULATION CONTROL")
print("-----------------------")
print("set bg - set new value for blood glucose level")
print("set insulin - set new value for blood insulin level")
print("set glucagon - set new value for blood glucagon level")
print("set glycogen_liver - set new value for glycogen in liver")
print(
"set glycogen_muscles - set new value for glycogen in muscles")
print("set metabolic_rate - set metabolic activity level")
print("")
print("OTHER")
print("----------------------")
print("help - show this menu")
print("exit - exit the program")
print("show - display graphs")
print("")
osSystem('pause')
def clearConsole():
if osName == "nt":
osSystem("cls")
else:
osSystem("clear")
def qtrain():
global q_table_E1, q_table_E2, total_reward_bleu, total_reward_rouge
list_total_reward = [0 for agent in game.list_agent]
list_action = [None for agent in game.list_agent]
list_state = [None for agent in game.list_agent]
total_reward_bleu = [0 for i in range(TRAINING_N_EPISODE)]
total_reward_rouge = [0 for i in range(TRAINING_N_EPISODE)]
total_pommes_bleu = [0 for i in range(TRAINING_N_EPISODE)]
total_pommes_rouge = [0 for i in range(TRAINING_N_EPISODE)]
total_victimes_bleu = [0 for i in range(TRAINING_N_EPISODE)]
total_victimes_rouge = [0 for i in range(TRAINING_N_EPISODE)]
#Pour chaque partie
for i in range(TRAINING_N_EPISODE):
# variation du nombre des agents a chaque episode
#game.window_nb_agents_E1 = rd.randint(1, 5)
#game.window_nb_agents_E2 = rd.randint(0, 5)
game.reset()
#vider les lists
p = np.floor(1000 * i / TRAINING_N_EPISODE) / 10
list_total_reward = [0 for agent in game.list_agent]
list_action = [None for agent in game.list_agent]
list_state = [None for agent in game.list_agent]
osSystem("clear") #Vider la console
#Message affiché à chaque itération
runTime = np.floor(time.time() - startTime)
timeLeft = np.floor((TRAINING_N_EPISODE - i) * (runTime / (i or 1)))
runTime_h = int(runTime / 3600)
runTime_m = int(runTime / 60) % 60
runTime_s = int(runTime % 60)
timeLeft_h = int(timeLeft / 3600)
timeLeft_m = int(timeLeft / 60) % 60
timeLeft_s = int(timeLeft % 60)
runTime_h = (runTime_h < 10 and "0" or " ") + str(runTime_h)
runTime_m = (runTime_m < 10 and ":0" or ":") + str(runTime_m)
runTime_s = (runTime_s < 10 and ":0" or ":") + str(runTime_s)
timeLeft_h = (timeLeft_h < 10 and "0" or " ") + str(timeLeft_h)
timeLeft_m = (timeLeft_m < 10 and ":0" or ":") + str(timeLeft_m)
timeLeft_s = (timeLeft_s < 10 and ":0" or ":") + str(timeLeft_s)
print("Entrainement en cours...")
print("Episode : " + str(i) + "/" + str(TRAINING_N_EPISODE))
print("(" + str(p) + "%)")
print("Time elapsed: " + runTime_h + runTime_m + runTime_s)
print("Estimated time left: " + timeLeft_h + timeLeft_m + timeLeft_s)
print("\nQ_table sizes:")
print("BLUE: " + str(len(q_table_E1.keys())) + " RED: " +
str(len(q_table_E2.keys())))
#initialisation ETAT agent
for k in range(len(game.list_agent)):
list_state[k] = State(game.list_agent[k], game.objectsList)
#Stock les etats de chaque agent
#Debut partie
for j in range(TRAINING_N_STEP):
list_action = takeAllActions(list_state)
game.update()
updateQTable(list_state, list_action, list_total_reward)
#stockage des total rewards
for k in range(len(game.list_agent)):
if game.list_agent[k].team == 1: # BLEU
total_reward_bleu[
i] = total_reward_bleu[i] + list_total_reward[k]
total_pommes_bleu[i] = total_pommes_bleu[i] + game.list_agent[
k].pommesMangees
total_victimes_bleu[
i] = total_victimes_bleu[i] + game.list_agent[k].victimes
elif game.list_agent[k].team == 2: # ROUGE
total_reward_rouge[
i] = total_reward_rouge[i] + list_total_reward[k]
total_pommes_rouge[i] = total_pommes_rouge[
i] + game.list_agent[k].pommesMangees
total_victimes_rouge[
i] = total_victimes_rouge[i] + game.list_agent[k].victimes
#print("R_bleu = " + str(total_reward_bleu))
#print("R_rouge = " + str(total_reward_rouge))
print("P_bleu = " + str(total_pommes_bleu))
print("P_rouge = " + str(total_pommes_rouge))
print("V_bleu = " + str(total_victimes_bleu))
print("V_rouge = " + str(total_victimes_rouge))
#print("BLUE SCORE : " + str(blueScore))
#print("RED SCORE : " + str(redScore))
#timer.stop()
timePeriod = game.current_time_period
#timer.start(timePeriod)
app = QApplication(sys.argv)
game = Game()
if "-l" in sys.argv:
load_q_tables()
if "-t" in sys.argv:
game.isTraining = True
startTime = time.time()
qtrain()
save_q_tables()
#plt.plot(np.linspace(1,TRAINING_N_EPISODES,TRAINING_N_EPISODES,dtype = int), total_reward_bleu)
#plt.plot(np.linspace(1,TRAINING_N_EPISODES,TRAINING_N_EPISODES,dtype = int), total_reward_rouge)
#plt.show()
sys.exit()
else:
fenetre = Fenetre(game)
ui = Ui_MainWindow() # classe cree par QtDesigner
ui.setupUi(fenetre)
game.update_parametres()
osSystem("clear")
timer = QTimer()
timer.timeout.connect(timeout)
timePeriod = ui.SpinBoxTimePeriod.value()
timer.start(timePeriod)
sys.exit(app.exec_())
def sim():
#VARIABLE INITIALIZATION
#CLINICAL VARIABLES
#Glucose level/concentration of the blood in mg/dL
glucose_blood_level = 100.0
#Glucose present in the blood in mg
glucose_blood = 4617.5
#Glycogen stored in the liver in mg
glycogen_liver = 100000
#Glycogen stored in the muscles in mg
glycogen_muscles = 500000
#Insulin present in the blood in μU/mL
insulin_blood = 15.0
#Glucagon present in the blood in in pg/mL
glucagon_blood = 20.00
#Amount of mg of carbohydrate that one unit of insulin will metabolize/cover
carb_insulin_ratio = 15000
#Grams of glycogen that one unit/one pg of glucagon will release
#NOT FACTUAL, FOR SIMULATION TESTING PURPOSES ONLY
glycogenolysis_ratio = 1
#Grams of glycogen that one gram of glucose will synthesize
#NOT FACTUAL, FOR SIMULATION TESTING PURPOSES ONLY
glycogenesis_ratio = 1
#Grams of glucose released from one gram of glycogen
#NOT FACTUAL, FOR SIMULATION TESTING PURPOSES ONLY
glycogen_to_glucose_ratio = 1
#Nonessential (burnable) fat stored in adipose tissue in grams
fat_nonessential = 16983.0
#Essential (non-burnable) fat stored in adipose tissue in grams
fat_essential = 2830.0
#Total fat in adipose tissue in grams
fat_total = 19813.0
#Number used to represent metabolic activity of the body
#NOT FACTUAL, FOR SIMULATION OF METABOLIC ACTIVITY
#TO ENHANCE REALISM OF SIMULATION ONLY
metabolic_rate = 3
#Volume of blood in dL
blood_volume = 46.175
#Insulin Sensitivity
insulin_sensitivity = 1
currentTime = 0
#DATA FOR GRAPHING
#Blood Glucose Level
bgDataX = []
bgDataY = []
#Blood Glucose Level within target range (crrently unused)
bgNormDataX = []
bgNormDataY = []
#Blood Glucose Level higher than tgarget range (currently unused)
bgHighDataX = []
bgHighDataY = []
#Blood GLucose Level lower than target range (currently unused)
bgLowDataX = []
bgLowDataY = []
#Blood Insulin Concentration
insulinDataX = []
insulinDataY = []
#Blood Glucagon Concentration
glucagonDataX = []
glucagonDataY = []
#Rate of Change Prediction
ROC_predictionX = []
ROC_predictionY = []
#Metabolic Rate
metabolic_rateX = []
metabolic_rateY = []
#GLycogen in liver
glycogenDataX = []
glycogenDataY = []
#Loop the program forever.
while(1==1):
#CALCULATE NUMBERS
currentTime+=1
#currentTime = str(datetime.now())
#currentTime = currentTime.replace("-", "")
#currentTime = currentTime.replace(":", "")
#currentTime = currentTime.replace(".", "")
#currentTime = currentTime.replace(" ", "")
#glucose_blood_level = (glucose_blood_level - insulin_blood + (glucagon_blood*12.5) - (metabolic_rate))
#- (carb_insulin_ratio*insulin_blood) + (glycogenolysis_ratio*glucagon_blood*glycogen_to_glucose_ratio))
if glycogen_liver > 0:
#Release glycogen & convert to glucose
glycogen_liver -= (glucagon_blood*glycogenolysis_ratio)
glucose_blood += (glucagon_blood*glycogenolysis_ratio*glycogen_to_glucose_ratio)
if glycogen_liver < 100000:
#Absorb glucose & convert to glycogen
glycogen_liver += ((insulin_blood/1000000)*carb_insulin_ratio*glycogenesis_ratio)
glucose_blood -= ((insulin_blood/1000000*blood_volume/10)*carb_insulin_ratio)
glucose_blood -= metabolic_rate*10
glucose_blood_level = (glucose_blood/blood_volume)
#glycogen_liver -+ (insulin_blood*
#insulin_blood = insulin_blood - insulin_blood
#glucagon_blood = glucagon_blood - glucagon_blood
if glucose_blood_level < 0.0:
glucose_blood_level = 0.0
insulinDataX.append(currentTime)
insulinDataY.append(insulin_blood)
glucagonDataX.append(currentTime)
glucagonDataY.append(glucagon_blood)
bgDataX.append(currentTime)
bgDataY.append(glucose_blood_level)
metabolic_rateX.append(currentTime)
metabolic_rateY.append(metabolic_rate)
glycogenDataX.append(currentTime)
glycogenDataY.append(glycogen_liver)
#nan = float('nan')
if glucose_blood_level >= 80 and glucose_blood_level <= 180:
bgNormDataX.append(currentTime)
bgNormDataY.append(glucose_blood_level)
if glucose_blood_level > 180:
bgHighDataX.append(currentTime)
bgHighDataY.append(glucose_blood_level)
if glucose_blood_level < 80:
bgLowDataX.append(currentTime)
bgLowDataY.append(glucose_blood_level)
if len(bgDataX) >=3:
bgRateOfChange = ((bgDataY[-1] - bgDataY[-2]) + (bgDataY[-2] - bgDataY[-3]))/2
#Steady
#ROC Prediction
ROC_predictionX = []
ROC_predictionY = []
iterations = 1
for x in range (0,6):
ROC_predictionX.append(currentTime + iterations)
ROC_predictionY.append(glucose_blood_level + (bgRateOfChange*iterations))
iterations += 1
if bgRateOfChange < 1 and bgRateOfChange > -1:
ROC_arrows_WB = "→"
ROC_arrows = "-> steady"
#Rising
if bgRateOfChange >= 1 and bgRateOfChange < 2:
ROC_arrows_WB = "↗"
ROC_arrows = "/^ slowly rising"
if bgRateOfChange >= 2 and bgRateOfChange > 3:
ROC_arrows_WB = "↑"
ROC_arrows = "^|^ rising"
if bgRateOfChange >= 3:
ROC_arrows_WB = "⇈"
ROC_arrows = "^|^ ^|^ rapidly rising"
#Falling
if bgRateOfChange <= -1 and bgRateOfChange > -2:
ROC_arrows_WB = "↘"
ROC_arrows = "\\v slowly falling"
if bgRateOfChange <= -2 and bgRateOfChange > -3:
ROC_arrows_WB = "↓"
ROC_arrows = "v|v falling"
if bgRateOfChange <= -3:
ROC_arrows_WB = "⇊ RAPIDLY FALLING!"
ROC_arrows = "v|v v|v RAPIDLY FALLING /!\\"
else:
bgRateOfChange = 0
ROC_arrows_WB = "N/A"
ROC_arrows = "N/A"
ROC_widget = Paragraph(text=ROC_arrows_WB, width=50, height=25)
#UPDATE DISPLAY
#Clear the terminal window
if sysPlatform() == "Windows":
osSystem('cls')
else:
osSystem("clear")
#Print basic textual UI to the terminal window
print ("Diabetes/Body Energy Simulation Project")
print ("2015 - 2016, created by John Kozlosky")
print ("")
print ("Blood glucose level: " + str(round(glucose_blood_level, 2)) + "mg/dL " + ROC_arrows)
print ("Blood glucose: " + str(round((glucose_blood/1000), 2)) + "g")
print ("Glycogen: " + str(round((glycogen_liver/1000), 2)) + "g hepatic, " + str(round(glycogen_muscles, 2)/1000) + "g muscular")
print ("Blood insulin: " + str(round(insulin_blood, 2)) + "uU/mL")
print ("Blood glucagon: " + str(round(glucagon_blood, 2)) + "pg/mL")
#print ("Fat: " + str(round(fat_total)) + "g total, " + str(round(fat_nonessential)) + "g nonessential, " + str(round(fat_essential)) + "g essential")
print ("Metabolic activity level: " + str(round(metabolic_rate, 4)))
print ("")
#Make graphs using Bokeh
#Blood glucose level
if len(bgDataX) >= 100:
bgGraph = figure()#x_axis_type="datetime")
bgGraph.line(bgDataX[-100:], bgDataY[-100:], color = "black")
bgGraph.circle(bgDataX[-100:], bgDataY[-100:], fill_color = "white", line_color = "black", size = 8)
bgGraph.circle(ROC_predictionX, ROC_predictionY, fill_color = "white", line_color = "gray", size = 8)
#bgGraph.circle(bgNormDataX[0,100], bgNormDataY[0,100], fill_color = "white", line_color = "black", size = 8)
#bgGraph.circle(bgHighDataX[0,100], bgNormDataY[0,100], fill_color = "white", line_color = "yellow", size = 8)
#bgGraph.circle(bgLowDataX[0,100], bgLowDataY[0,100], fill_color = "white", line_color = "red", size = 8)
bgGraph.title.text = "Blood Glucose"
bgGraph.xaxis.axis_label = "Time"
bgGraph.yaxis.axis_label = "Blood Glucose (mg/dL)"
#bgGraph.yaxis.bounds = (0, 400)
else:
bgGraph = figure()#x_axis_type="datetime")
bgGraph.line(bgDataX, bgDataY, color = "black")
bgGraph.circle(bgDataX, bgDataY, fill_color = "white", line_color = "black", size = 8)
bgGraph.circle(ROC_predictionX, ROC_predictionY, fill_color = "white", line_color = "gray", size = 8)
#bgGraph.circle(bgNormDataX, bgNormDataY, fill_color = "white", line_color = "black", size = 8)
#bgGraph.circle(bgHighDataX, bgNormDataY, fill_color = "white", line_color = "yellow", size = 8)
#bgGraph.circle(bgLowDataX, bgLowDataY, fill_color = "white", line_color = "red", size = 8)
bgGraph.title.text = "Blood Glucose"
bgGraph.xaxis.axis_label = "Time"
bgGraph.yaxis.axis_label = "Blood Glucose (mg/dL)"
#bgGraph.yaxis.bounds = (0, 400)
#Insulin
if len(insulinDataX) >= 100:
insulinGraph = figure()#x_axis_type="datetime")
insulinGraph.line(insulinDataX[-100:], insulinDataY[-100:], color = "CadetBlue")
insulinGraph.circle(insulinDataX[-100:], insulinDataY[-100:], fill_color = "white", line_color = "CadetBlue", size = 8)
insulinGraph.title.text = "Insulin"
insuinGraph.xaxis.axis_label = "Time"
insulinGraph.yaxis.axis_label = "Blood Insulin (μU/mL)"
#insulinGraph.yaxis.bounds = (0, 400)
else:
insulinGraph = figure()#x_axis_type="datetime")
insulinGraph.line(insulinDataX, insulinDataY, color = "CadetBlue")
insulinGraph.circle(insulinDataX, insulinDataY, fill_color = "white", line_color = "CadetBlue", size = 8)
insulinGraph.title.text = "Insulin"
insulinGraph.xaxis.axis_label = "Time"
insulinGraph.yaxis.axis_label = "Blood Insulin (μU/mL)"
#insulinGraph.yaxis.bounds = (0, 400)
#Glucagon
if len(glucagonDataX) >= 100:
glucagonGraph = figure()#x_axis_type="datetime"
glucagonGraph.line(glucagonDataX[-100:], glucagonDataY[-100:], color = "GoldenRod")
glucagonGraph.circle(glucagonDataX[-100:], glucagonDataY[-100:], fill_color = "white", line_color = "GoldenRod", size = 8)
glucagonGraph.title.text = "Glucagon"
glucagonGraph.xaxis.axis_label = "Time"
glucagonGraph.yaxis.axis_label = "Blood Glucagon (pg/mL)"
#glucagonGraph.yaxis.bounds = (0, 700)
else:
glucagonGraph = figure()#x_axis_type="datetime")
glucagonGraph.line(glucagonDataX, glucagonDataY, color = "GoldenRod")
glucagonGraph.circle(glucagonDataX, glucagonDataY, fill_color = "white", line_color = "GoldenRod", size = 8)
glucagonGraph.title.text = "Glucagon"
glucagonGraph.xaxis.axis_label = "Time"
glucagonGraph.yaxis.axis_label = "Blood Glucagon (pg/mL)"
#glucagonGraph.yaxis.bounds = (0, 700)
#Metabolic rate
if len(metabolic_rateX) >= 100:
metabolicGraph = figure()
metabolicGraph.line(metabolic_rateX[-100:], metabolic_rateY[-100:], color = "black")
metabolicGraph.circle(metabolic_rateX[-100:], metabolic_rateY[-100:], fill_color = "white", line_color = "black", size = 8)
metabolicGraph.title.text = "Metabolic Rate"
metabolicGraph.xaxis.axis_label = "Time"
metabolicGraph.yaxis.axis_label = "Metabolic Rate (simulated number)"
else:
metabolicGraph = figure()
metabolicGraph.line(metabolic_rateX, metabolic_rateY, color = "black")
metabolicGraph.circle(metabolic_rateX, metabolic_rateY, fill_color = "white", line_color = "black", size = 8)
metabolicGraph.title.text = "Metabolic Rate"
metabolicGraph.xaxis.axis_label = "Time"
metabolicGraph.yaxis.axis_label = "Metabolic Rate (simulated number)"
#Hepatic Glycogen
if len(glycogenDataX) >= 100:
glycogenGraph = figure()
glycogenGraph.line(glycogenDataX[-100:], glycogenDataY[-100:], color = "black")
glycogenGraph.circle(glycogenDataX[-100:], glycogenDataY[-100:], fill_color = "white", line_color = "black", size = 8)
glycogenGraph.title.text = "Hepatic Glycogen"
glycogenGraph.xaxis.axis_label = "Time"
glycogenGraph.yaxis.axis_label = "Hepatic Glycogen (mg)"
else:
glycogenGraph = figure()
glycogenGraph.line(glycogenDataX, glycogenDataY, color = "black")
glycogenGraph.circle(glycogenDataX, glycogenDataY, fill_color = "white", line_color = "black", size = 8)
glycogenGraph.title.text = "Hepatic Glycogen"
glycogenGraph.xaxis.axis_label = "Time"
glycogenGraph.yaxis.axis_label = "Hepatic Glycogen (mg)"
#Combine the blood glucose, insulin, and glucagon graphs into a grid
#grid = gridplot([bgGraph, insulinGraph, glucagonGraph], ncols=2, plot_width=600, plot_height=350)
grid = layout([
[bgGraph],
[insulinGraph, glucagonGraph],
[metabolicGraph, glycogenGraph],
[widgetbox(ROC_widget)],
], sizing_mode='stretch_both')
#Write the graphs to an HTML file
output_file('infoGraph.html')
#GET USER COMMAND
command = str(input(">"))
if command == "set bg":
glucose_blood = float(input("New blood glucose: "))*blood_volume
if command == "set insulin":
insulin_blood = float(input("New insulin level: "))
if command == "set glucagon":
glucagon_blood = float(input("New glucagon level: "))
if command == "set glycogen_liver":
glycogen_liver = float(input("New hapatic glycogen level: "))
if command == "set glycogen_muscles":
glycogen_muscles = float(input("New muscular glycogen level: "))
if command == "set fat_total":
fat_total = float(input("New total fat: "))
if command == "set fat_nonessential":
fat_nonessential = float(input("New nonessential fat: "))
if command == "set fat_essential":
fat_essential = float(input("New essential fat: "))
if command == "set metabolic_rate":
metabolic_rate = float(input("New metabolic activity level: "))
if command == "set blood_volume":
blood_volume = float(input("New blood volume: "))
#if command == "inject bolus":
#bolus = float(input("Novolog bolus size (U): "))
#if command == "ingest carb":
#carb = float(input("Ingest carbs (g): "))
if command == "show":
show(grid)
if command == "exit":
if input("Are you sure you want to exit? (y/n): ") == "y":
exit()
if command == "help":
print("SIMULATION CONTROL")
print("-----------------------")
print("set bg - set new value for blood glucose level")
print("set insulin - set new value for blood insulin level")
print("set glucagon - set new value for blood glucagon level")
print("set glycogen_liver - set new value for glycogen in liver")
print("set glycogen_muscles - set new value for glycogen in muscles")
print("set metabolic_rate - set metabolic activity level")
print("")
print("OTHER")
print("----------------------")
print("help - show this menu")
print("exit - exit the program")
print("show - display graphs")
print("")
osSystem('pause')
def runcexpander(fname_fasta,cbalignp_commandline=" -y",output='binary'):
"""
Run the complete cascade of cexpander algorithms on an input multi fasta
file and return the output as a CexpanderOutput object
@type fname_fasta: string
@param fname_fasta: path to input multi fasta file
@type cbalignp_commandline: string
@param cbalignp_commandline: (extra) command line for cbalignp
@type min_cols: integer
@param min_cols: minimal number of uniformly matched positions (cols)
required to report transfer blocks for (>= 0)
@type projected_on: string
@param projected_on: apply fasta seqeunce header which to use for projection;
apply ':::' to do projections on all input sequences
@attention: requires global variable EXECUTABLE_cexpander_ALLVSALL
@attention: requires global variable EXECUTABLE_CEXPANDER_CBALIGNP
@attention: requires global variable EXECUTABLE_CEXPANDER_CEXPANDER
@attention: see cexpander_dr for (additional) command line options
@attention: only a subset of cexpander_dr commandline options are supported!
@rtype: CexpanderOutput object
@return: CexpanderOutput object
"""
if not fname_fasta: raise "NoProperFunctionArguments"
if not osPathIsfile(fname_fasta): raise "FileDoesNotExist"
# (0) create (~unique) filenames
uniquetag = get_random_string_tag()
fname_allvsall = ".".join([fname_fasta,uniquetag,"allvsall"])
fname_report = ".".join([fname_fasta,uniquetag,"report"])
fname_aligned = ".".join([fname_fasta,uniquetag,"aligned"])
fname_settings = ".".join([fname_fasta,uniquetag,"settings"])
fname_cexpander = ".".join([fname_fasta,uniquetag,"cexpander"])
# (1) create complete .fa -> cexpanderstring command
command = """
python %s %s %s %s;
%s -i %s %s > %s;
%s < %s;
""" % (
EXECUTABLE_CEXPANDER_ALLVSALL,
fname_fasta,
fname_allvsall,
fname_report,
EXECUTABLE_CEXPANDER_CBALIGNP,
fname_allvsall,
cbalignp_commandline,
fname_aligned,
EXECUTABLE_CEXPANDER_CEXPANDER,
fname_settings,
)
# (2) create fname_settings file
binorfloat = "$dumpcv"
if output == "float": binorfloat = "$dumpcvc"
fh = open(fname_settings,'w')
content = "\n\n".join( [
"$load\n%s\n%s" % (fname_report,fname_aligned),
"$addquery\n-1",
"$run",
"$dumpentries",
"$cv_linear",
"%s" % ( binorfloat ), # BINARY == $dumpcv, FLOAT = $dumpcvc
"$exit\n\n",
] )
fh.write(content)
fh.close()
# (3) run the command
ci,co,ce = osPopen3(command)
ci.close()
# output of EXECUTABLE_CEXPANDER_ALLVSALL is cast to STDOUT as well!
cexpanderdata = co.read()
co.close()
error = ce.read()
ce.close()
# (4) parse fname_cexpander to CexpanderOutput object
cxpdr = parse_cexpander(cexpanderdata,fname_fasta)
# (5) cleanup files
osSystem("rm -f %s %s.%s.*" % ( fname_fasta, fname_fasta,uniquetag ) )
# (6) return the output object
return cxpdr
