ThomasTheMaker/arc-stack-python · Datasets at Hugging Face

cc8abb2907e577ffd204f1ea8583e97ab1d687f1

410

py

Python

lib/googlecloudsdk/compute/subcommands/http_health_checks/__init__.py

IsaacHuang/google-cloud-sdk

52afa5d1a75dff08f4f5380c5cccc015bf796ca5

[ "Apache-2.0" ]

null

lib/googlecloudsdk/compute/subcommands/http_health_checks/__init__.py

IsaacHuang/google-cloud-sdk

52afa5d1a75dff08f4f5380c5cccc015bf796ca5

[ "Apache-2.0" ]

null

lib/googlecloudsdk/compute/subcommands/http_health_checks/__init__.py

IsaacHuang/google-cloud-sdk

52afa5d1a75dff08f4f5380c5cccc015bf796ca5

[ "Apache-2.0" ]

2

2020-07-25T05:03:06.000Z

2020-11-04T04:55:57.000Z

# Copyright 2014 Google Inc. All Rights Reserved. """Commands for reading and manipulating HTTP health checks.""" from googlecloudsdk.calliope import base class HttpHealthChecks(base.Group): """Read and manipulate HTTP health checks for load balanced instances.""" HttpHealthChecks.detailed_help = { 'brief': ('Read and manipulate HTTP health checks for load balanced ' 'instances') }

27.333333

75

0.736585

cc8c3ccbb426c49972b991935c3bbbd09c451c5d

52,014

py

Python

pycofe/tasks/basic.py

ekr-ccp4/jsCoFE

b9424733fb567938927509bc667ef24ed60ddd8c

[ "MIT" ]

null

pycofe/tasks/basic.py

ekr-ccp4/jsCoFE

b9424733fb567938927509bc667ef24ed60ddd8c

[ "MIT" ]

null

pycofe/tasks/basic.py

ekr-ccp4/jsCoFE

b9424733fb567938927509bc667ef24ed60ddd8c

[ "MIT" ]

1

2021-02-25T06:54:15.000Z

##!/usr/bin/python # # ============================================================================ # # 06.02.18 <-- Date of Last Modification. # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # ---------------------------------------------------------------------------- # # BASIC TASK WRAPPER # # Command-line: N/A # # Copyright (C) Eugene Krissinel, Andrey Lebedev 2017-2018 # # ============================================================================ # # Function list: # # TaskDriver:: # report_page_id () # setReportWidget ( widgetId ) # resetReportPage () # log_page_id () # err_page_id () # file_stdout_path () # file_stderr_path () # file_stdin_path () # reportDir () # outputDir () # inputDir () # reportDocumentName() # refmac_section () # refmac_report () # importDir () # import_summary_id () # python native imports import os import sys import shutil import traceback # ccp4-python imports import pyrvapi import pyrvapi_ext.parsers # pycofe imports from pycofe.dtypes import dtype_template, dtype_xyz, dtype_structure, databox from pycofe.dtypes import dtype_ensemble, dtype_hkl, dtype_ligand from pycofe.dtypes import dtype_sequence from pycofe.proc import edmap, import_merged from pycofe.varut import signal, jsonut, command # ============================================================================ # driver class class TaskDriver(object): # ======================================================================== # common definitions rvrow = 0 # current report row _rvrow_bak = 0 # saved report row _report_widget_id = "report_page" _scriptNo = 0 # input script counter def report_page_id (self): return self._report_widget_id def setReportWidget ( self,widgetId,row=0 ): self._rvrow_bak = self.rvrow self.rvrow = row self._report_widget_id = widgetId return self._rvrow_bak def resetReportPage ( self,row=-1): rvrow = self.rvrow if row<0: self.rvrow = self._rvrow_bak else: self.rvrow = row self._report_widget_id = "report_page" return rvrow def log_page_id (self): return "log_page" def err_page_id (self): return "err_page" def traceback_page_id (self): return "python_exception" def file_stdout_path (self): return "_stdout.log" # reserved name, used in NC def file_stderr_path (self): return "_stderr.log" def file_stdin_path (self): return "_stdin." + str(self._scriptNo) + ".script" def reportDir (self): return "report" # in current directory ( job_dir ) def outputDir (self): return "output" # in current directory ( job_dir ) def inputDir (self): return "input" # in current directory ( job_dir ) def reportDocumentName(self): return "rvapi_document" def refmac_section (self): return "refmac_section" def refmac_report (self): return "refmac_report" def importDir (self): return "uploads" # in current directory ( job_dir ) def import_summary_id (self): return "import_summary_id" # summary table id # ======================================================================== # class variables exeType = None job_dir = None job_id = None # create output data list structure outputDataBox = databox.DataBox() # standard output file handlers file_stdout = None file_stderr = None file_stdin = None # task and input data task = None input_data = None outputFName = "" # report parsers log_parser = None generic_parser_summary = {} # data register counter dataSerialNo = 0 summary_row = 0 # current row in import summary table summary_row_0 = 0 # reference row in import summary table widget_no = 0 # widget Id unificator navTreeId = "" # navigation tree Id # ======================================================================== # cofe config # This needs to be obtained from the jscofe config-file. # maintainerEmail = None maintainerEmail = "my.name@gmail.com" # ======================================================================== # initiaisation def __init__ ( self,title_str,module_name,options={}, args=None ): # # options = { // all optional # report_page : { show : True, name : "Report" }, # log_page : { show : True, name : "Log file" }, # err_page : { show : True, name : "Errors" }, # nav_tree : { id : treeId, name : "Workflow" } # // will do nav tree instead of tabs if given # } # args = optional replacement for sys.argv to allow this class to be # called from within other Python programs (such as tests) # def getOption(name1,name2,default): try: return options[name1][name2] except: return default # clear signal file; this is mostly for command-line debugging, the signal # should be cleared in JS layer before this script is invoked signal.CofeSignal.clear() # get command line arguments and change to job directory; keep all file names # relative to job directory, this is a must if args is None: args = sys.argv[1:] self.exeType = args[0] self.job_dir = args[1] self.job_id = args[2] # set scratch area if necessary if self.exeType=="SGE" and "TMP" in os.environ: os.environ["CCP4_SCR"] = os.environ["TMP"] if "CCP4_SCR" in os.environ: os.environ["TMPDIR"] = os.environ["CCP4_SCR"] # always make job directory current os.chdir ( self.job_dir ) # initialise execution logs self.file_stdout = open ( self.file_stdout_path(),'w' ) self.file_stderr = open ( self.file_stderr_path(),'w' ) # fetch task data self.task = jsonut.readjObject ( "job.meta" ) if self.task is None: self.file_stdout.write ( "\n\n *** task read failed in '" + module_name + "'\n\n" ) self.file_stderr.write ( "\n\n *** task read failed in '" + module_name + "'\n\n" ) print " task read failed in '" + module_name + "'" raise signal.TaskReadFailure() self.input_data = databox.readDataBox ( self.inputDir() ) if self.task.uoname: self.outputFName = self.task.uoname else: self.outputFName = self.task.oname # print title in standard logs if title_str: tstr = title_str else: tstr = self.task.title self.file_stdout.write ( "[" + self.job_id.zfill(4) + "] " + tstr.upper() + "\n\n" ) self.file_stderr.write ( " " ) # initialise HTML report document; note that we use absolute path for # the report directory, which is necessary for passing RVAPI document # to applications via creation of the rvapi_document file with # pyrvapi.rvapi_store_document2(..) # Make a tree or tab layout if "nav_tree" in options: pyrvapi.rvapi_init_document ( "jscofe_report", # document Id os.path.join(os.getcwd(),self.reportDir()), # report directory (reserved) "Title", # title (immaterial) 1, # HTML report to be produced 0, # Report will have tabs "jsrview", # where to look for js support (reserved) None,None, "task.tsk", "i2.xml" ) self.navTreeId = options["nav_tree"]["id"] pyrvapi.rvapi_add_tree_widget ( self.navTreeId, # tree widget reference (Id) options["nav_tree"]["name"], # tree widget title "body", # reference to widget holder (grid) 0, # holder row 0, # holder column 1, # row span 1 # column span ) pyrvapi.rvapi_set_tab_proxy ( self.navTreeId,"" ) else: pyrvapi.rvapi_init_document ( "jscofe_report", # document Id os.path.join(os.getcwd(),self.reportDir()), # report directory (reserved) "Title", # title (immaterial) 1, # HTML report to be produced 4, # Report will have tabs "jsrview", # where to look for js support (reserved) None,None, "task.tsk", "i2.xml" ) self.rvrow = 0; focus = True if getOption("report_page","show",True): pyrvapi.rvapi_add_tab ( self.report_page_id(), getOption("report_page","name","Report"),focus ) self.putTitle ( tstr ) focus = False if getOption("log_page","show",True): if self.navTreeId: pyrvapi.rvapi_set_tab_proxy ( self.navTreeId,self.report_page_id() ) pyrvapi.rvapi_add_tab ( self.log_page_id(), getOption("log_page","name","Log file"),focus ) pyrvapi.rvapi_append_content ( os.path.join("..",self.file_stdout_path()+'?capsize'), True,self.log_page_id() ) focus = False if getOption("err_page","show",True): if self.navTreeId: pyrvapi.rvapi_set_tab_proxy ( self.navTreeId,self.report_page_id() ) pyrvapi.rvapi_add_tab ( self.err_page_id(), getOption("err_page","name","Errors"),focus ) pyrvapi.rvapi_append_content ( os.path.join("..",self.file_stderr_path()), True,self.err_page_id() ) if self.navTreeId: pyrvapi.rvapi_set_tab_proxy ( self.navTreeId,"" ) pyrvapi.rvapi_flush() return # ============================================================================ def getOFName ( self,extention,modifier=-1 ): if modifier >= 0: return self.outputFName + "." + str(modifier).zfill(3) + extention else: return self.outputFName + extention def getCIFOFName ( self,modifier=-1 ): return self.getOFName ( ".cif",modifier ) def getXYZOFName ( self,modifier=-1 ): return self.getOFName ( ".pdb",modifier ) def getMTZOFName ( self,modifier=-1 ): return self.getOFName ( ".mtz",modifier ) def getMapOFName ( self,modifier=-1 ): return self.getOFName ( ".map",modifier ) def getDMapOFName ( self,modifier=-1 ): return self.getOFName ( ".diff.map",modifier ) # ============================================================================ def getWidgetId ( self,wid ): widgetId = wid + "_" + str(self.widget_no) self.widget_no += 1 return widgetId # ============================================================================ def checkPDB(self): if "PDB_DIR" not in os.environ: pyrvapi.rvapi_set_text ( "Error: jsCoFE is not configured to work with PDB archive." + \ "Please look for support.", self.report_page_id(),self.rvrow,0,1,1 ) self.fail ( " *** Error: jsCofe is not configured to work with PDB archive \n" + \ " Please look for support\n","No PDB configured" ) return # ============================================================================ def insertReportTab ( self,title_str,focus=True ): pyrvapi.rvapi_insert_tab ( self.report_page_id(),title_str, self.log_page_id(),focus ) self.rvrow = 0; self.putTitle ( title_str ) pyrvapi.rvapi_flush () return def putMessage ( self,message_str ): pyrvapi.rvapi_set_text ( message_str,self.report_page_id(),self.rvrow,0,1,1 ) self.rvrow += 1 return def putMessage1 ( self,pageId,message_str,row,colSpan=1 ): pyrvapi.rvapi_set_text ( message_str,pageId,row,0,1,colSpan ) return def putMessageLF ( self,message_str ): pyrvapi.rvapi_set_text ( "" + message_str + "",self.report_page_id(),self.rvrow,0,1,1 ) self.rvrow += 1 return def putWaitMessageLF ( self,message_str ): gridId = "wait_message_" + str(self.widget_no) pyrvapi.rvapi_add_grid ( gridId,False,self.report_page_id(),self.rvrow,0,1,1 ) pyrvapi.rvapi_set_text ( "" + message_str + "",gridId,0,0,1,1 ) pyrvapi.rvapi_set_text ( "<div class='activity_bar'/>",gridId,0,1,1,1 ) self.widget_no += 1 return def putTitle ( self,title_str ): if self.rvrow>0: pyrvapi.rvapi_set_text ( " ",self.report_page_id(),self.rvrow,0,1,1 ) self.rvrow += 1 self.putTitle1 ( self.report_page_id(),title_str,self.rvrow,1 ) self.rvrow += 1 return def insertTab ( self,tabId,tabName,content,focus=False ): pyrvapi.rvapi_insert_tab ( tabId,tabName,self.log_page_id(),focus ) if content: pyrvapi.rvapi_append_content ( content,True,tabId ) return def flush(self): pyrvapi.rvapi_flush() return def putTitle1 ( self,pageId,title_str,row,colSpan=1 ): pyrvapi.rvapi_set_text ( "<h2>[" + self.job_id.zfill(4) + "] " + title_str + "</h2>", pageId,row,0,1,colSpan ) return # ============================================================================ def putPanel ( self,panel_id ): pyrvapi.rvapi_add_panel ( panel_id,self.report_page_id(),self.rvrow,0,1,1 ) self.rvrow += 1 return def putFieldset ( self,fset_id,title ): pyrvapi.rvapi_add_fieldset ( fset_id,title,self.report_page_id(),self.rvrow,0,1,1 ) self.rvrow += 1 return def putSection ( self,sec_id,sectionName,openState_bool=False ): pyrvapi.rvapi_add_section ( sec_id,sectionName,self.report_page_id(), self.rvrow,0,1,1,openState_bool ) self.rvrow += 1 return # ============================================================================ # define basic HTML report functions def putSummaryLine ( self,line0,line1,line2 ): if self.import_summary_id(): pyrvapi.rvapi_put_table_string ( self.import_summary_id(),line0,self.summary_row,0 ) pyrvapi.rvapi_put_table_string ( self.import_summary_id(),line1,self.summary_row,1 ) pyrvapi.rvapi_put_table_string ( self.import_summary_id(),line2,self.summary_row,2 ) self.summary_row_0 = self.summary_row self.summary_row += 1 return def addSummaryLine ( self,line1,line2 ): if self.import_summary_id(): pyrvapi.rvapi_put_table_string ( self.import_summary_id(),line1,self.summary_row,0 ) pyrvapi.rvapi_put_table_string ( self.import_summary_id(),line2,self.summary_row,1 ) self.summary_row += 1 pyrvapi.rvapi_shape_table_cell ( self.import_summary_id(),self.summary_row_0,0,"","","", self.summary_row-self.summary_row_0,1 ); return def putSummaryLine_red ( self,line0,line1,line2 ): if self.import_summary_id(): pyrvapi.rvapi_put_table_string ( self.import_summary_id(),line0,self.summary_row,0 ) pyrvapi.rvapi_put_table_string ( self.import_summary_id(),line1,self.summary_row,1 ) pyrvapi.rvapi_put_table_string ( self.import_summary_id(),line2,self.summary_row,2 ) pyrvapi.rvapi_shape_table_cell ( self.import_summary_id(),self.summary_row,0,"", "text-align:left;color:maroon;","",1,1 ) pyrvapi.rvapi_shape_table_cell ( self.import_summary_id(),self.summary_row,1,"", "text-align:left;color:maroon;","",1,1 ) pyrvapi.rvapi_shape_table_cell ( self.import_summary_id(),self.summary_row,2,"", "text-align:left;color:maroon;","",1,1 ) self.summary_row += 1 return def putTable ( self,tableId,title_str,holderId,row,mode=100 ): pyrvapi.rvapi_add_table ( tableId,title_str,holderId,row,0,1,1, mode ) pyrvapi.rvapi_set_table_style ( tableId, "table-blue","text-align:left;" ) return def setTableHorzHeaders ( self,tableId,header_list,tooltip_list ): for i in range(len(header_list)): pyrvapi.rvapi_put_horz_theader ( tableId,header_list[i], tooltip_list[i],i ) return def putTableLine ( self,tableId,header,tooltip,line,row ): pyrvapi.rvapi_put_vert_theader ( tableId,header,tooltip,row ) if line: pyrvapi.rvapi_put_table_string ( tableId,line,row,0 ) pyrvapi.rvapi_shape_table_cell ( tableId,row,0,"", "text-align:left;width:100%;white-space:nowrap;" + \ "font-family:\"Courier\";text-decoration:none;" + \ "font-weight:normal;font-style:normal;width:auto;", "",1,1 ); return row+1 # ============================================================================ def open_stdin ( self ): self.file_stdin = open ( self.file_stdin_path(),"w" ) return def write_stdin ( self,S ): self.file_stdin.write ( S ) return def close_stdin ( self ): self.file_stdin.close() return # ============================================================================ def writeKWParameter ( self,item ): if item.visible: if (item.type == "integer" or item.type == "real"): self.file_stdin.write ( item.keyword + " " + str(item.value) + "\n" ) elif (item.type == "integer_" or item.type == "real_") and (item.value != ""): self.file_stdin.write ( item.keyword + " " + str(item.value) + "\n" ) elif (item.type == "combobox"): self.file_stdin.write ( item.keyword + " " + item.value + "\n" ) elif (item.type == "checkbox"): if item.value: self.file_stdin.write ( item.keyword + " " + item.translate[1] + "\n" ) else: self.file_stdin.write ( item.keyword + " " + item.translate[0] + "\n" ) return def putKWParameter ( self,item ): if item.visible: if item.type=="checkbox": if item.value: return item.keyword + "\n" else: return "" else: return item.keyword + " " + str(item.value) + "\n" else: return "" def getKWParameter ( self,keyword,item ): if item.visible: if item.type=="checkbox": if item.value: return " " + keyword else: return "" else: v = str(item.value) if v: if keyword.endswith("=") or keyword.endswith("::"): return " " + keyword + v else: return " " + keyword + " " + v else: return "" def getKWItem ( self,item ): if item.visible: if item.type=="checkbox": if hasattr(item,'translate'): if item.value: return " " + item.keyword + str(item.translate[1]) else: return " " + item.keyword + str(item.translate[0]) elif item.value: return " " + item.keyword else: return "" else: v = str(item.value) if v and v!="_blank_": if item.keyword.endswith("=") or item.keyword.endswith("::"): return " " + item.keyword + v else: return " " + item.keyword + " " + v return "" def getParameter ( self,item,checkVisible=True ): if item.visible or not checkVisible: return str(item.value) return "" """ if (item.type == "integer" or item.type == "real"): return str(item.value) elif (item.type == "integer_" or item.type == "real_") and (item.value != ""): return str(item.value) else: return str(item.value) return "" """ # ============================================================================ def makeClass ( self,dict ): return databox.make_class ( dict ) # ============================================================================ def unsetLogParser ( self ): self.file_stdout.flush() self.log_parser = None pyrvapi.rvapi_flush() #self.file_stdout = open ( self.file_stdout_path(),'a' ) return def setGenericLogParser ( self,panel_id,split_sections_bool,graphTables=False ): self.putPanel ( panel_id ) #self.generic_parser_summary = {} self.log_parser = pyrvapi_ext.parsers.generic_parser ( panel_id,split_sections_bool, summary=self.generic_parser_summary, graph_tables=graphTables ) pyrvapi.rvapi_flush() return def setMolrepLogParser ( self,panel_id ): self.putPanel ( panel_id ) self.log_parser = pyrvapi_ext.parsers.molrep_parser ( panel_id ) pyrvapi.rvapi_flush() return # ============================================================================ def stampFileName ( self,serNo,fileName ): return dtype_template.makeFileName ( self.job_id,serNo,fileName ) def makeDataId ( self,serNo ): return dtype_template.makeDataId ( self.job_id,serNo ) # ============================================================================ def storeReportDocument(self,meta_str): if meta_str: pyrvapi.rvapi_put_meta ( meta_str ) pyrvapi.rvapi_store_document2 ( self.reportDocumentName() ) return def restoreReportDocument(self): pyrvapi.rvapi_restore_document2 ( self.reportDocumentName() ) return pyrvapi.rvapi_get_meta() # ============================================================================ def makeFullASUSequenceFile ( self,seq_list,title,fileName ): combseq = "" for s in seq_list: seqstring = self.makeClass(s).getSequence ( self.inputDir() ) for i in range(s.ncopies): combseq += seqstring dtype_sequence.writeSeqFile ( fileName,title,combseq ) return # ============================================================================ def runApp ( self,appName,cmd,quitOnError=True ): input_script = None if self.file_stdin: input_script = self.file_stdin_path() self._scriptNo += 1 rc = command.call ( appName,cmd,"./",input_script, self.file_stdout,self.file_stderr,self.log_parser ) self.file_stdin = None if rc.msg and quitOnError: raise signal.JobFailure ( rc.msg ) return rc # ============================================================================ def calcEDMap ( self,xyzPath,hklData,libPath,filePrefix ): edmap.calcEDMap ( xyzPath,os.path.join(self.inputDir(),hklData.files[0]), libPath,hklData.dataset,filePrefix,self.job_dir, self.file_stdout,self.file_stderr,self.log_parser ) return [ filePrefix + edmap.file_pdb (), filePrefix + edmap.file_mtz (), filePrefix + edmap.file_map (), filePrefix + edmap.file_dmap() ] def calcAnomEDMap ( self,xyzPath,hklData,anom_form,filePrefix ): edmap.calcAnomEDMap ( xyzPath,os.path.join(self.inputDir(),hklData.files[0]), hklData.dataset,anom_form,filePrefix,self.job_dir, self.file_stdout,self.file_stderr,self.log_parser ) return [ filePrefix + edmap.file_pdb(), filePrefix + edmap.file_mtz(), filePrefix + edmap.file_map(), filePrefix + edmap.file_dmap() ] def calcCCP4Maps ( self,mtzPath,filePrefix,source_key="refmac" ): edmap.calcCCP4Maps ( mtzPath,filePrefix,self.job_dir, self.file_stdout,self.file_stderr, source_key,self.log_parser ) return [ filePrefix + edmap.file_map(), filePrefix + edmap.file_dmap() ] def finaliseStructure ( self,xyzPath,name_pattern,hkl,libPath,associated_data_list, subtype,openState_bool=False, title="Output Structure" ): # subtype = 0: copy subtype from associated data # = 1: set MR subtype # = 2: set EP subtype structure = None if os.path.isfile(xyzPath): sec_id = self.refmac_section() + "_" + str(self.widget_no) self.putSection ( sec_id,"Electron Density Calculations with Refmac", openState_bool ) panel_id = self.refmac_report() + "_" + str(self.widget_no) pyrvapi.rvapi_add_panel ( panel_id,sec_id,0,0,1,1 ) #self.log_parser = pyrvapi_ext.parsers.generic_parser ( panel_id,False ) self.log_parser = pyrvapi_ext.parsers.generic_parser ( panel_id,False, summary=self.generic_parser_summary, graph_tables=False ) fnames = self.calcEDMap ( xyzPath,hkl,libPath,name_pattern ) # Register output data. This moves needful files into output directory # and puts the corresponding metadata into output databox structure = self.registerStructure ( fnames[0],fnames[1],fnames[2],fnames[3],libPath ) if structure: structure.addDataAssociation ( hkl.dataId ) structure.setRefmacLabels ( hkl ) for i in range(len(associated_data_list)): if associated_data_list[i]: structure.addDataAssociation ( associated_data_list[i].dataId ) if subtype==0: for i in range(len(associated_data_list)): if associated_data_list[i]: structure.copySubtype ( associated_data_list[i] ) elif subtype==1: structure.addMRSubtype() else: structure.addEPSubtype() structure.addXYZSubtype() if title!="": self.putTitle ( title ) self.putMessage ( " " ) self.putStructureWidget ( "structure_btn_", "Structure and electron density", structure ) else: self.putTitle ( "No Solution Found" ) self.widget_no += 1 return structure def finaliseAnomSubstructure ( self,xyzPath,name_pattern,hkl, associated_data_list, anom_form,openState_bool=False, title="" ): anom_structure = self.finaliseAnomSubstructure1 ( xyzPath,name_pattern, hkl,associated_data_list,anom_form, self.report_page_id(),self.rvrow, openState_bool,title ) self.rvrow += 2 if anom_structure: self.rvrow += 1 if title: self.rvrow += 1 return anom_structure def finaliseAnomSubstructure1 ( self,xyzPath,name_pattern,hkl, associated_data_list,anom_form,pageId, row,openState_bool=False,title="" ): sec_id = self.refmac_section() + "_" + str(self.widget_no) row1 = row pyrvapi.rvapi_add_section ( sec_id, "Anomalous Electron Density Calculations with Refmac", pageId,row1,0,1,1,openState_bool ) row1 += 1 panel_id = self.refmac_report() + "_" + str(self.widget_no) pyrvapi.rvapi_add_panel ( panel_id,sec_id,0,0,1,1 ) #self.log_parser = pyrvapi_ext.parsers.generic_parser ( panel_id,False ) self.log_parser = pyrvapi_ext.parsers.generic_parser ( panel_id,False, summary=self.generic_parser_summary, graph_tables=False ) fnames = self.calcAnomEDMap ( xyzPath,hkl,anom_form,name_pattern ) anom_structure = self.registerStructure ( fnames[0],fnames[1],fnames[2],fnames[3],None ) if anom_structure: anom_structure.addDataAssociation ( hkl.dataId ) anom_structure.setRefmacLabels ( hkl ) for i in range(len(associated_data_list)): if associated_data_list[i]: structure.addDataAssociation ( associated_data_list[i].dataId ) anom_structure.setAnomSubstrSubtype() # anomalous maps self.putMessage1 ( pageId," ",row1,1 ) row1 += 1 if title!="": self.putTitle1 ( pageId,title,row1,1 ) row1 += 1 openState = -1 if openState_bool: openState = 1 self.putStructureWidget1 ( pageId,"anom_structure_btn_", "Anomalous substructure and electron density", anom_structure,openState,row1,1 ) return anom_structure else: self.putTitle1 ( pageId,"No Anomalous Structure Found",row1,1 ) return None def finaliseLigand ( self,code,xyzPath,cifPath,openState_bool=False, title="Ligand Structure" ): ligand = None if os.path.isfile(xyzPath): # Register output data. This moves needful files into output directory # and puts the corresponding metadata into output databox ligand = self.registerLigand ( xyzPath,cifPath ) if ligand: if title!="": self.putTitle ( title ) ligand.code = code self.putLigandWidget ( "ligand_btn_","Ligand structure", ligand ) else: self.putTitle ( "No Ligand Formed" ) self.widget_no += 1 return ligand # ============================================================================ def putInspectButton ( self,dataObject,title,gridId,row,col ): buttonId = "inspect_data_" + str(self.widget_no) self.widget_no += 1 pyrvapi.rvapi_add_button ( buttonId,title,"{function}", "window.parent.rvapi_inspectData(" + self.job_id +\ ",'" + dataObject._type + "','" + dataObject.dataId + "')", False,gridId, row,col,1,1 ) return # ============================================================================ def putRevisionWidget ( self,gridId,row,message,revision ): buttonId = "inspect_" + str(self.widget_no) self.widget_no += 1 pyrvapi.rvapi_add_button ( buttonId,"Inspect","{function}", "window.parent.rvapi_inspectData(" + self.job_id +\ ",'DataRevision','" + revision.dataId + "')", False,gridId, row,0,1,1 ) pyrvapi.rvapi_set_text ( message,gridId, row,1,1,1 ) pyrvapi.rvapi_set_text ( "\"" + revision.dname + "\"", gridId, row,2,1,1 ) return def registerRevision ( self,revision,serialNo=1,title="Structure Revision", message="New structure revision name:", gridId = "" ): revision.makeRevDName ( self.job_id,serialNo,self.outputFName ) revision.register ( self.outputDataBox ) if title: self.putTitle ( title ) grid_id = gridId if len(gridId)<=0: grid_id = "revision_" + str(self.widget_no) self.widget_no += 1 pyrvapi.rvapi_add_grid ( grid_id,False,self.report_page_id(),self.rvrow,0,1,1 ) self.putRevisionWidget ( grid_id,0,message,revision ) self.rvrow += 1 return grid_id #def registerRevision1 ( self,revision,serialNo,pageId,row,title="Structure Revision" ): # revision.makeRevDName ( self.job_id,serialNo,self.outputFName ) # revision.register ( self.outputDataBox ) # self.putTitle1 ( pageId,title,row,1 ) # self.putMessage1 ( pageId,"New structure revision name: " +\ # "\"" + revision.dname + "\"", # row+1,1 ) # return def registerStructure ( self,xyzPath,mtzPath,mapPath,dmapPath, libPath=None,copy=False ): self.dataSerialNo += 1 structure = dtype_structure.register ( xyzPath,mtzPath,mapPath,dmapPath,libPath, self.dataSerialNo ,self.job_id, self.outputDataBox,self.outputDir(), copy=copy ) if not structure: self.file_stderr.write ( " NONE STRUCTURE" ) self.file_stderr.flush() else: structure.putXYZMeta ( self.outputDir(),self.file_stdout, self.file_stderr,None ) return structure def _move_file_to_output_dir ( self,fpath,fname_dest ): if os.path.isfile(fpath): fpath_dest = os.path.join ( self.outputDir(),fname_dest ) if not os.path.isfile(fpath_dest): os.rename ( fpath,fpath_dest ) return True return False def registerStructure1 ( self,xyzPath,mtzPath,mapPath,dmapPath,libPath,regName ): self.dataSerialNo += 1 structure = dtype_structure.register1 ( xyzPath,mtzPath,mapPath,dmapPath,libPath, regName,self.dataSerialNo,self.job_id, self.outputDataBox ) if not structure: self.file_stderr.write ( " NONE STRUCTURE\n" ) self.file_stderr.flush() else: self._move_file_to_output_dir ( xyzPath ,structure.files[0] ) self._move_file_to_output_dir ( mtzPath ,structure.files[1] ) if (len(structure.files)>2) and structure.files[2]: self._move_file_to_output_dir ( mapPath ,structure.files[2] ) if len(structure.files)>3 and structure.files[3]: self._move_file_to_output_dir ( dmapPath,structure.files[3] ) if len(structure.files)>4 and structure.files[4]: self._move_file_to_output_dir ( libPath,structure.files[4] ) structure.putXYZMeta ( self.outputDir(),self.file_stdout, self.file_stderr,None ) return structure def registerLigand ( self,xyzPath,cifPath,copy=False ): self.dataSerialNo += 1 ligand = dtype_ligand.register ( xyzPath,cifPath, self.dataSerialNo ,self.job_id, self.outputDataBox,self.outputDir(), copy=copy ) if not ligand: self.file_stderr.write ( " NONE LIGAND" ) self.file_stderr.flush() return ligand # ---------------------------------------------------------------------------- def putHKLWidget ( self,widgetId,title_str,hkl,openState=-1 ): self.putHKLWidget1 ( self.report_page_id(),widgetId + str(self.widget_no), title_str,hkl,openState,self.rvrow,1 ) self.rvrow += 2 self.widget_no += 1 return def putHKLWidget1 ( self,pageId,widgetId,title_str,hkl,openState,row,colSpan ): self.putMessage1 ( pageId,"Assigned name: " + hkl.dname, row,0,colSpan ) pyrvapi.rvapi_add_data ( widgetId + str(self.widget_no),title_str, # always relative to job_dir from job_dir/html os.path.join("..",self.outputDir(),hkl.files[0]), "hkl:hkl",pageId,row+1,0,1,colSpan,openState ) self.widget_no += 1 return row + 2 def putStructureWidget ( self,widgetId,title_str,structure,openState=-1 ): self.putStructureWidget1 ( self.report_page_id(), widgetId + str(self.widget_no),title_str, structure,openState,self.rvrow,1 ) self.rvrow += 2 self.widget_no += 1 return def putStructureWidget1 ( self,pageId,widgetId,title_str,structure,openState,row,colSpan ): self.putMessage1 ( pageId,"Assigned name: " + structure.dname + " ", row,1 ) wId = widgetId + str(self.widget_no) self.widget_no += 1 type = ["xyz","hkl:map","hkl:ccp4_map","hkl:ccp4_dmap","LIB"] created = False for i in range(len(structure.files)): if structure.files[i]: if not created: pyrvapi.rvapi_add_data ( wId,title_str, # always relative to job_dir from job_dir/html os.path.join("..",self.outputDir(),structure.files[i]), type[i],pageId,row+1,0,1,colSpan,openState ) created = True else: pyrvapi.rvapi_append_to_data ( wId, # always relative to job_dir from job_dir/html os.path.join("..",self.outputDir(),structure.files[i]), type[i] ) return row+2 # ============================================================================ def putLigandWidget ( self,widgetId,title_str,ligand,openState=-1 ): self.putLigandWidget1 ( self.report_page_id(), widgetId + str(self.widget_no),title_str, ligand,openState,self.rvrow,1 ) self.rvrow += 2 self.widget_no += 1 return def putLigandWidget1 ( self,pageId,widgetId,title_str,ligand,openState,row,colSpan ): wId = widgetId + str(self.widget_no) self.putMessage1 ( pageId,"Assigned name: " + ligand.dname + " ", row,1 ) pyrvapi.rvapi_add_data ( wId,title_str, # always relative to job_dir from job_dir/html os.path.join("..",self.outputDir(),ligand.files[0]), "xyz",pageId,row+1,0,1,colSpan,openState ) if len(ligand.files) > 1: pyrvapi.rvapi_append_to_data ( wId, # always relative to job_dir from job_dir/html os.path.join("..",self.outputDir(),ligand.files[1]), "LIB" ) self.widget_no += 1 return row+2 # ============================================================================ def registerXYZ ( self,xyzPath,checkout=True ): self.dataSerialNo += 1 if checkout: xyz = dtype_xyz.register ( xyzPath,self.dataSerialNo,self.job_id, self.outputDataBox,self.outputDir() ) else: xyz = dtype_xyz.register ( xyzPath,self.dataSerialNo,self.job_id, None,self.outputDir() ) if not xyz: self.file_stderr.write ( " NONE XYZ DATA\n" ) self.file_stderr.flush() return xyz """ def registerHKL ( self,mtzPath ): self.dataSerialNo += 1 hkl = dtype_hkl.register ( mtzPath,self.dataSerialNo,self.job_id, self.outputDataBox,self.outputDir() ) if not hkl: self.file_stderr.write ( " NONE HKL DATA\n" ) self.file_stderr.flush() return hkl """ # ---------------------------------------------------------------------------- def putXYZWidget ( self,widgetId,title_str,xyz,openState=-1 ): pyrvapi.rvapi_add_data ( widgetId,title_str, # always relative to job_dir from job_dir/html os.path.join("..",self.outputDir(),xyz.files[0]), "xyz",secId,secrow,0,1,1,-1 ) return # ============================================================================ def registerEnsemble ( self,sequence,ensemblePath,checkout=True ): self.dataSerialNo += 1 if checkout: ensemble = dtype_ensemble.register ( sequence,ensemblePath, self.dataSerialNo,self.job_id, self.outputDataBox, self.outputDir() ) else: ensemble = dtype_ensemble.register ( sequence,ensemblePath, self.dataSerialNo,self.job_id, None,self.outputDir() ) if not ensemble: self.file_stderr.write ( " NONE ENSEMBLE DATA\n" ) self.file_stderr.flush() else: ensemble.putXYZMeta ( self.outputDir(),self.file_stdout, self.file_stderr,None ) return ensemble # ---------------------------------------------------------------------------- def putEnsembleWidget ( self,widgetId,title_str,ensemble,openState=-1 ): self.putEnsembleWidget1 ( self.report_page_id(),widgetId,title_str, ensemble,openState,self.rvrow,1 ) self.rvrow += 2 return def putEnsembleWidget1 ( self,pageId,widgetId,title_str,ensemble,openState,row,colSpan ): self.putMessage1 ( pageId,"Assigned name: " +\ ensemble.dname + "  ", row,1 ) pyrvapi.rvapi_add_data ( widgetId,title_str, # always relative to job_dir from job_dir/html os.path.join("..",self.outputDir(),ensemble.files[0]), "xyz",pageId,row+1,0,1,colSpan,openState ) return row+2 # ============================================================================ def checkSpaceGroupChanged ( self,sol_spg,hkl,mtzfilepath ): # Parameters: # sol_spg a string with space group obtained from solution's XYZ file # hkl HKL class of reflection data used to produce the XYZ file # mtzfilepath path to solution's MTZ file (with possibly changed SpG) # # Returns: # None if space group has not changed # (newMTZPath,newHKL) path to new HKL file and HKL class if SpG changed # the following will provide for import of generated HKL dataset(s) # def importDir (self): return "./" # import from working directory # def import_summary_id(self): return None # don't make summary table solSpg = sol_spg.replace(" ", "") if solSpg and (solSpg!=hkl.getSpaceGroup().replace(" ", "")): self.putMessage ( "Space Group changed to " +\ sol_spg + "" ) rvrow0 = self.rvrow self.rvrow += 1 #if not self.generic_parser_summary: # self.generic_parser_summary = {} self.generic_parser_summary["z01"] = {'SpaceGroup':sol_spg} newHKLFPath = self.getOFName ( "_" + solSpg + "_" + hkl.files[0],-1 ) os.rename ( mtzfilepath,newHKLFPath ) self.files_all = [ newHKLFPath ] import_merged.run ( self,"New reflection dataset details" ) if dtype_hkl.dtype() in self.outputDataBox.data: sol_hkl = self.outputDataBox.data[dtype_hkl.dtype()][0] pyrvapi.rvapi_set_text ( "New reflection dataset created: " +\ sol_hkl.dname,self.report_page_id(),rvrow0,0,1,1 ) self.putMessage ( "Consider re-merging your original dataset using " +\ "this new one as a reference" ) # Copy new reflection file to input directory in order to serve # Refmac job(s) (e.g. as part of self.finaliseStructure()). The # job needs reflection data for calculating Rfree, other stats # and density maps. shutil.copy2 ( os.path.join(self.outputDir(),sol_hkl.files[0]), self.inputDir() ) return (newHKLFPath,sol_hkl) else: self.putMessage ( "Data registration error -- report to developers." ) else: return None def checkSpaceGroupChanged1 ( self,sol_spg,hkl_list ): # reindexing of array HKL dataset, returns None if space group does # not change solSpg = sol_spg.replace(" ", "") if solSpg and (solSpg!=hkl_list[0].getSpaceGroup().replace(" ", "")): self.putMessage ( "Space Group changed to " +\ sol_spg + "" ) #rvrow0 = self.rvrow self.rvrow += 1 self.generic_parser_summary["z01"] = {'SpaceGroup':sol_spg} # prepare script for reindexing self.open_stdin () self.write_stdin ( "SYMM \"" + sol_spg + "\"\n" ) self.close_stdin () f_stdin = self.file_stdin self.unsetLogParser() # make list of files to import self.files_all = [] for i in range(len(hkl_list)): # make new hkl file name newHKLFPath = self.getOFName ( "_" + solSpg + "_" + hkl_list[i].files[0],-1 ) # make command-line parameters for reindexing cmd = [ "hklin" ,hkl_list[i].getFilePath(self.inputDir()), "hklout",newHKLFPath ] # run reindex self.file_stdin = f_stdin # for repeat use of input script file self.runApp ( "reindex",cmd ) if os.path.isfile(newHKLFPath): self.files_all.append ( newHKLFPath ) else: self.putMessage ( "Error: cannot reindex " + hkl_list[i].dname ) import_merged.run ( self,"New reflection datasets" ) return self.outputDataBox.data[hkl_list[0]._type] else: return None # ============================================================================ def success(self): if self.task and self.generic_parser_summary: self.task.scores = self.generic_parser_summary with open('job.meta','w') as file_: file_.write ( self.task.to_JSON() ) self.rvrow += 1 self.putMessage ( " " ) # just to make extra space after report self.outputDataBox.save ( self.outputDir() ) pyrvapi.rvapi_flush () self.file_stdout.close() self.file_stderr.close() raise signal.Success() def fail ( self,pageMessage,signalMessage ): if self.task and self.generic_parser_summary: self.task.scores = self.generic_parser_summary with open('job.meta','w') as file_: file_.write ( self.task.to_JSON() ) self.putMessage ( " " ) # just to make extra space after report pyrvapi.rvapi_set_text ( pageMessage,self.report_page_id(),self.rvrow,0,1,1 ) pyrvapi.rvapi_flush () msg = pageMessage.replace("","").replace("","").replace("","") \ .replace("","").replace(" ","\n").replace("","\n") self.file_stdout.write ( msg + "\n" ) self.file_stderr.write ( msg + "\n" ) self.file_stdout.close () self.file_stderr.close () raise signal.JobFailure ( signalMessage ) def python_fail_tab ( self ): trace = ''.join( traceback.format_exception( *sys.exc_info() ) ) msg = '<h2>Job Driver Failure</h2>' msg += 'Catched error:<pre>' + trace + '</pre>' msg += """ This is an internal error, which may be caused by different sort of hardware and network malfunction, but most probably due to a bug or not anticipated properties of input data. """ if self.maintainerEmail: msg += """ You may contribute to the improvement of jsCoFE by sending this message together with input data and task description to """ msg += self.maintainerEmail page_id = self.traceback_page_id() pyrvapi.rvapi_add_tab ( page_id, "Error Trace", True ) pyrvapi.rvapi_set_text ( msg, page_id, 0, 0, 1, 1 ) def start(self): try: self.run() except signal.Success, s: signal_obj = s except signal.CofeSignal, s: self.python_fail_tab() signal_obj = s except: self.python_fail_tab() signal_obj = signal.JobFailure() else: signal_obj = signal.Success() finally: pass signal_obj.quitApp()

40.103315

100

0.514381

cc8c90a4501fe9ab990f2ab191504461fa54d0c3

154

py

Python

models/__init__.py

Celiali/FixMatch

fe55ea9daf353aead58b630da96f357eee728a82

[ "MIT" ]

12

2020-12-07T04:24:58.000Z

2022-02-16T15:33:26.000Z

models/__init__.py

Celiali/FixMatch

fe55ea9daf353aead58b630da96f357eee728a82

[ "MIT" ]

1

2021-07-15T23:02:22.000Z

models/__init__.py

Celiali/FixMatch

fe55ea9daf353aead58b630da96f357eee728a82

[ "MIT" ]

1

2021-07-14T10:21:48.000Z

from .wideresnet import * from .wideresnet_lk import * WRN_MODELS = { 'WideResNet':WideResNet, 'WideResNet_Lk': WideResNet_Lk, }

19.25

39

0.642857

cc8d3cb600d5b30b2c38ef88bbba0a6183336176

8,954

py

Python

sp3-diffusion/extract-convert.py

robfairh/npre555-cp03

2aea7ae2df4a720c5d09003f98192f8986a6d107

[ "BSD-3-Clause" ]

null

sp3-diffusion/extract-convert.py

robfairh/npre555-cp03

2aea7ae2df4a720c5d09003f98192f8986a6d107

[ "BSD-3-Clause" ]

2

2021-01-04T12:29:30.000Z

2021-02-01T11:13:45.000Z

sp3-diffusion/extract-convert.py

robfairh/npre555-cp03

2aea7ae2df4a720c5d09003f98192f8986a6d107

[ "BSD-3-Clause" ]

null

# This script is based on moltres/python/extractSerpent2GCs.py import os import numpy as np import argparse import subprocess import serpentTools as sT def makePropertiesDir( outdir, filebase, mapFile, unimapFile, serp1=False, fromMain=False): """ Takes in a mapping from branch names to material temperatures, then makes a properties directory. Serp1 means that the group transfer matrix is transposed.""" # the constants moltres looks for: goodStuff = ['Tot', 'Sp0', 'Sp2', 'Fiss', 'Nsf', 'Kappa', 'Sp1', 'Sp3', 'Invv', 'Chit', 'Chip', 'Chid', 'BETA_EFF', 'lambda'] goodMap = dict([(thing, 'inf' + thing) for thing in goodStuff]) goodMap['BETA_EFF'] = 'betaEff' goodMap['lambda'] = 'lambda' # map material names to universe names from serpent with open(unimapFile) as fh: uniMap = [] for line in fh: uniMap.append(tuple(line.split())) # this now maps material names to serpent universes uniMap = dict(uniMap) # list of material names inmats = list(uniMap.keys()) print("Making properties for materials:") print(inmats) coeList = dict([(mat, sT.read(mat + '.coe')) for mat in inmats]) # primary branch to temp mapping branch2TempMapping = open(mapFile) # Check if calculation uses 6 neutron precursor groups. # This prevents writing of excess zeros. Check if any # entries in the 7th and 8th group precursor positions # are nonzero, if so, use 8 groups. use8Groups = False for line in branch2TempMapping: item, temp = tuple(line.split()) for mat in inmats: if mat in item: currentMat = mat break strData = coeList[currentMat].branches[item].universes[ uniMap[currentMat], 0, 0, None].gc[goodMap['BETA_EFF']] strData = strData[1:9] if np.any(strData[-2:] != 0.0): use8Groups = True # Now loop through a second time branch2TempMapping.close() branch2TempMapping = open(mapFile) for line in branch2TempMapping: item, temp = tuple(line.split()) for mat in inmats: if mat in item: currentMat = mat break else: print('Considered materials: {}'.format(inmats)) raise Exception( 'Couldnt find a material corresponding to branch {}'.format( item)) try: totxsdata = coeList[currentMat].branches[item].universes[ uniMap[currentMat], 0, 0, None].infExp[goodMap['Tot']] sp0xsdata = coeList[currentMat].branches[item].universes[ uniMap[currentMat], 0, 0, None].infExp[goodMap['Sp0']] sp1xsdata = coeList[currentMat].branches[item].universes[ uniMap[currentMat], 0, 0, None].infExp[goodMap['Sp1']] sp2xsdata = coeList[currentMat].branches[item].universes[ uniMap[currentMat], 0, 0, None].infExp[goodMap['Sp2']] sp3xsdata = coeList[currentMat].branches[item].universes[ uniMap[currentMat], 0, 0, None].infExp[goodMap['Sp3']] G = len(totxsdata) remxs0g = totxsdata - sp0xsdata.reshape((G, G)).diagonal() remxs1g = totxsdata - sp1xsdata.reshape((G, G)).diagonal() remxs2g = totxsdata - sp2xsdata.reshape((G, G)).diagonal() remxs3g = totxsdata - sp3xsdata.reshape((G, G)).diagonal() with open(outdir + '/' + filebase + '_' + currentMat + '_DIFFCOEFA.txt', 'a') as fh: strData = 1./3./remxs1g strData = ' '.join( [str(dat) for dat in strData]) if isinstance( strData, np.ndarray) else strData fh.write(str(temp) + ' ' + strData) fh.write('\n') with open(outdir + '/' + filebase + '_' + currentMat + '_DIFFCOEFB.txt', 'a') as fh: strData = 9./35./remxs3g strData = ' '.join( [str(dat) for dat in strData]) if isinstance( strData, np.ndarray) else strData fh.write(str(temp) + ' ' + strData) fh.write('\n') with open(outdir + '/' + filebase + '_' + currentMat + '_REMXSA.txt', 'a') as fh: strData = remxs0g strData = ' '.join( [str(dat) for dat in strData]) if isinstance( strData, np.ndarray) else strData fh.write(str(temp) + ' ' + strData) fh.write('\n') with open(outdir + '/' + filebase + '_' + currentMat + '_REMXSB.txt', 'a') as fh: strData = remxs2g + 4./5*remxs0g strData = ' '.join( [str(dat) for dat in strData]) if isinstance( strData, np.ndarray) else strData fh.write(str(temp) + ' ' + strData) fh.write('\n') with open(outdir + '/' + filebase + '_' + currentMat + '_COUPLEXSA.txt', 'a') as fh: strData = 2*remxs0g strData = ' '.join( [str(dat) for dat in strData]) if isinstance( strData, np.ndarray) else strData fh.write(str(temp) + ' ' + strData) fh.write('\n') with open(outdir + '/' + filebase + '_' + currentMat + '_COUPLEXSB.txt', 'a') as fh: strData = 2./5*remxs0g strData = ' '.join( [str(dat) for dat in strData]) if isinstance( strData, np.ndarray) else strData fh.write(str(temp) + ' ' + strData) fh.write('\n') for coefficient in ['Chit', 'Chip', 'Chid', 'Fiss', 'Nsf', 'Sp0', 'Kappa', 'Invv', 'BETA_EFF', 'lambda']: with open(outdir + '/' + filebase + '_' + currentMat + '_' + coefficient.upper() + '.txt', 'a') as fh: if coefficient == 'lambda' or coefficient == 'BETA_EFF': strData = coeList[currentMat].branches[ item].universes[ uniMap[currentMat], 0, 0, None].gc[ goodMap[coefficient]] # some additional formatting is needed here strData = strData[1:9] # Cut off group 7 and 8 precursor params in 6 # group calcs if not use8Groups: strData = strData[0:6] else: strData = coeList[currentMat].branches[ item].universes[ uniMap[currentMat], 0, 0, None].infExp[ goodMap[coefficient]] strData = ' '.join( [str(dat) for dat in strData]) if isinstance( strData, np.ndarray) else strData fh.write(str(temp) + ' ' + strData) fh.write('\n') except KeyError: raise Exception('Check your mapping and secondary branch files.') if __name__ == '__main__': # make it act like a nice little terminal program parser = argparse.ArgumentParser( description='Extracts Serpent 2 group constants, \ and puts them in a directory suitable for moltres.') parser.add_argument('outDir', metavar='o', type=str, nargs=1, help='name of directory to write properties to.') parser.add_argument('fileBase', metavar='f', type=str, nargs=1, help='File base name to give moltres') parser.add_argument( 'mapFile', metavar='b', type=str, nargs=1, help='File that maps branches to temperatures') parser.add_argument( 'universeMap', metavar='u', type=str, nargs=1, help='File that maps material names to serpent universe') parser.add_argument( '--serp1', dest='serp1', action='store_true', help='use this flag for serpent 1 group transfer matrices') parser.set_defaults(serp1=False) args = parser.parse_args() # these are unpacked, so it fails if they werent passed to the script outdir = args.outDir[0] fileBase = args.fileBase[0] mapFile = args.mapFile[0] unimapFile = args.universeMap[0] makePropertiesDir(outdir, fileBase, mapFile, unimapFile, serp1=args.serp1, fromMain=True) print("Successfully made property files in directory {}.".format(outdir))

38.594828

78

0.52256

cc8e5699c6d924eefec86f5ef76ba0da8a0749bf

63,581

py

Python

ms1searchpy/main.py

markmipt/ms1searchpy

1fae3ba9ca25ac151b34110d333820f0a063ee11

[ "Apache-2.0" ]

6

2020-01-28T12:29:02.000Z

2022-02-01T14:43:44.000Z

ms1searchpy/main.py

markmipt/ms1searchpy

1fae3ba9ca25ac151b34110d333820f0a063ee11

[ "Apache-2.0" ]

3

2021-07-30T01:28:05.000Z

2021-11-25T09:14:31.000Z

ms1searchpy/main.py

markmipt/ms1searchpy

1fae3ba9ca25ac151b34110d333820f0a063ee11

[ "Apache-2.0" ]

2

2020-07-23T10:01:10.000Z

2021-05-04T12:46:04.000Z

import os from . import utils import numpy as np from scipy.stats import scoreatpercentile from scipy.optimize import curve_fit from scipy import exp import operator from copy import copy, deepcopy from collections import defaultdict, Counter import re from pyteomics import parser, mass, fasta, auxiliary as aux, achrom try: from pyteomics import cmass except ImportError: cmass = mass import subprocess from sklearn import linear_model import tempfile import pandas as pd import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt from multiprocessing import Queue, Process, cpu_count from itertools import chain try: import seaborn seaborn.set(rc={'axes.facecolor':'#ffffff'}) seaborn.set_style('whitegrid') except: pass from .utils import calc_sf_all, recalc_spc import lightgbm as lgb import pandas as pd from sklearn.model_selection import train_test_split from scipy.stats import zscore, spearmanr import pandas as pd from pyteomics import pepxml, achrom, auxiliary as aux, mass, fasta, mzid, parser from pyteomics import electrochem import numpy as np import random SEED = 42 from sklearn.model_selection import train_test_split from os import path, mkdir from collections import Counter, defaultdict import warnings import pylab as plt warnings.formatwarning = lambda msg, *args, **kw: str(msg) + '\n' import pandas as pd from sklearn.model_selection import train_test_split, KFold import os from collections import Counter, defaultdict from scipy.stats import scoreatpercentile from sklearn.isotonic import IsotonicRegression import warnings import numpy as np import matplotlib import numpy import pandas import random import sklearn import matplotlib.pyplot as plt from sklearn import ( feature_extraction, feature_selection, decomposition, linear_model, model_selection, metrics, svm ) import scipy from scipy.stats import rankdata from copy import deepcopy import csv from scipy.stats import rankdata import lightgbm as lgb import numpy as np import pandas as pd import matplotlib.pyplot as plt from itertools import chain import time as timemodule import ast from sklearn import metrics SEED = 50 def worker_RT(qin, qout, shift, step, RC=False, elude_path=False, ns=False, nr=False, win_sys=False): pepdict = dict() if elude_path: outtrain_name = os.path.join(tempfile.gettempdir(), os.urandom(24).hex()) outtrain = open(outtrain_name, 'w') outres_name = os.path.join(tempfile.gettempdir(), os.urandom(24).hex()) for seq, RT in zip(ns, nr): outtrain.write(seq + '\t' + str(RT) + '\n') outtrain.close() outtest_name = os.path.join(tempfile.gettempdir(), os.urandom(24).hex()) outtest = open(outtest_name, 'w') maxval = len(qin) start = 0 while start + shift < maxval: item = qin[start+shift] outtest.write(item + '\n') start += step outtest.close() subprocess.call([elude_path, '-t', outtrain_name, '-e', outtest_name, '-a', '-o', outres_name]) for x in open(outres_name).readlines()[3:]: seq, RT = x.strip().split('\t') pepdict[seq] = float(RT) else: maxval = len(qin) start = 0 while start + shift < maxval: item = qin[start+shift] pepdict[item] = achrom.calculate_RT(item, RC) start += step if win_sys: return pepdict else: qout.put(pepdict) qout.put(None) def final_iteration(resdict, mass_diff, rt_diff, pept_prot, protsN, base_out_name, prefix, isdecoy, isdecoy_key, escore, fdr, nproc, fname=False): n = nproc prots_spc_basic = dict() p1 = set(resdict['seqs']) pep_pid = defaultdict(set) pid_pep = defaultdict(set) banned_dict = dict() for pep, pid in zip(resdict['seqs'], resdict['ids']): pep_pid[pep].add(pid) pid_pep[pid].add(pep) if pep in banned_dict: banned_dict[pep] += 1 else: banned_dict[pep] = 1 if len(p1): prots_spc_final = dict() prots_spc_copy = False prots_spc2 = False unstable_prots = set() p0 = False names_arr = False tmp_spc_new = False decoy_set = False while 1: if not prots_spc2: best_match_dict = dict() n_map_dict = defaultdict(list) for k, v in protsN.items(): n_map_dict[v].append(k) decoy_set = set() for k in protsN: if isdecoy_key(k): decoy_set.add(k) decoy_set = list(decoy_set) prots_spc2 = defaultdict(set) for pep, proteins in pept_prot.items(): if pep in p1: for protein in proteins: prots_spc2[protein].add(pep) for k in protsN: if k not in prots_spc2: prots_spc2[k] = set([]) prots_spc2 = dict(prots_spc2) unstable_prots = set(prots_spc2.keys()) top100decoy_N = sum([val for key, val in protsN.items() if isdecoy_key(key)]) names_arr = np.array(list(prots_spc2.keys())) n_arr = np.array([protsN[k] for k in names_arr]) tmp_spc_new = dict((k, len(v)) for k, v in prots_spc2.items()) top100decoy_score_tmp = [tmp_spc_new.get(dprot, 0) for dprot in decoy_set] top100decoy_score_tmp_sum = float(sum(top100decoy_score_tmp)) tmp_spc = tmp_spc_new prots_spc = tmp_spc_new if not prots_spc_copy: prots_spc_copy = deepcopy(prots_spc) for idx, v in enumerate(decoy_set): if v in unstable_prots: top100decoy_score_tmp_sum -= top100decoy_score_tmp[idx] top100decoy_score_tmp[idx] = prots_spc.get(v, 0) top100decoy_score_tmp_sum += top100decoy_score_tmp[idx] p = float(sum(top100decoy_score_tmp)) / top100decoy_N p = top100decoy_score_tmp_sum / top100decoy_N n_change = set(protsN[k] for k in unstable_prots) for n_val in n_change: for k in n_map_dict[n_val]: v = prots_spc[k] if n_val not in best_match_dict or v > prots_spc[best_match_dict[n_val]]: best_match_dict[n_val] = k n_arr_small = [] names_arr_small = [] v_arr_small = [] for k, v in best_match_dict.items(): n_arr_small.append(k) names_arr_small.append(v) v_arr_small.append(prots_spc[v]) prots_spc_basic = dict() all_pvals = calc_sf_all(np.array(v_arr_small), n_arr_small, p) for idx, k in enumerate(names_arr_small): prots_spc_basic[k] = all_pvals[idx] if not p0: p0 = float(p) prots_spc_tmp = dict() v_arr = np.array([prots_spc[k] for k in names_arr]) all_pvals = calc_sf_all(v_arr, n_arr, p) for idx, k in enumerate(names_arr): prots_spc_tmp[k] = all_pvals[idx] sortedlist_spc = sorted(prots_spc_tmp.items(), key=operator.itemgetter(1))[::-1] with open(base_out_name + '_proteins_full_noexclusion.tsv', 'w') as output: output.write('dbname\tscore\tmatched peptides\ttheoretical peptides\n') for x in sortedlist_spc: output.write('\t'.join((x[0], str(x[1]), str(prots_spc_copy[x[0]]), str(protsN[x[0]]))) + '\n') best_prot = utils.keywithmaxval(prots_spc_basic) best_score = prots_spc_basic[best_prot] unstable_prots = set() if best_prot not in prots_spc_final: prots_spc_final[best_prot] = best_score banned_pids = set() for pep in prots_spc2[best_prot]: for pid in pep_pid[pep]: banned_pids.add(pid) for pid in banned_pids: for pep in pid_pep[pid]: banned_dict[pep] -= 1 if banned_dict[pep] == 0: for bprot in pept_prot[pep]: tmp_spc_new[bprot] -= 1 unstable_prots.add(bprot) else: v_arr = np.array([prots_spc[k] for k in names_arr]) all_pvals = calc_sf_all(v_arr, n_arr, p) for idx, k in enumerate(names_arr): prots_spc_basic[k] = all_pvals[idx] for k, v in prots_spc_basic.items(): if k not in prots_spc_final: prots_spc_final[k] = v break prot_fdr = aux.fdr(prots_spc_final.items(), is_decoy=isdecoy) if prot_fdr >= 12.5 * fdr: v_arr = np.array([prots_spc[k] for k in names_arr]) all_pvals = calc_sf_all(v_arr, n_arr, p) for idx, k in enumerate(names_arr): prots_spc_basic[k] = all_pvals[idx] for k, v in prots_spc_basic.items(): if k not in prots_spc_final: prots_spc_final[k] = v break prots_spc_basic2 = copy(prots_spc_final) prots_spc_final = dict() prots_spc_final2 = dict() if n == 0: try: n = cpu_count() except NotImplementedError: n = 1 if n == 1 or os.name == 'nt': qin = [] qout = [] for mass_koef in range(10): rtt_koef = mass_koef qin.append((mass_koef, rtt_koef)) qout = worker(qin, qout, mass_diff, rt_diff, resdict, protsN, pept_prot, isdecoy_key, isdecoy, fdr, prots_spc_basic2, True) for item, item2 in qout: if item2: prots_spc_copy = item2 for k in protsN: if k not in prots_spc_final: prots_spc_final[k] = [item.get(k, 0.0), ] else: prots_spc_final[k].append(item.get(k, 0.0)) else: qin = Queue() qout = Queue() for mass_koef in range(10): rtt_koef = mass_koef qin.put((mass_koef, rtt_koef)) for _ in range(n): qin.put(None) procs = [] for proc_num in range(n): p = Process(target=worker, args=(qin, qout, mass_diff, rt_diff, resdict, protsN, pept_prot, isdecoy_key, isdecoy, fdr, prots_spc_basic2)) p.start() procs.append(p) for _ in range(n): for item, item2 in iter(qout.get, None): if item2: prots_spc_copy = item2 for k in protsN: if k not in prots_spc_final: prots_spc_final[k] = [item.get(k, 0.0), ] else: prots_spc_final[k].append(item.get(k, 0.0)) for p in procs: p.join() for k in prots_spc_final.keys(): prots_spc_final[k] = np.mean(prots_spc_final[k]) prots_spc = deepcopy(prots_spc_final) sortedlist_spc = sorted(prots_spc.items(), key=operator.itemgetter(1))[::-1] with open(base_out_name + '_proteins_full.tsv', 'w') as output: output.write('dbname\tscore\tmatched peptides\ttheoretical peptides\n') for x in sortedlist_spc: output.write('\t'.join((x[0], str(x[1]), str(prots_spc_copy[x[0]]), str(protsN[x[0]]))) + '\n') checked = set() for k, v in list(prots_spc.items()): if k not in checked: if isdecoy_key(k): if prots_spc.get(k.replace(prefix, ''), -1e6) > v: del prots_spc[k] checked.add(k.replace(prefix, '')) else: if prots_spc.get(prefix + k, -1e6) > v: del prots_spc[k] checked.add(prefix + k) filtered_prots = aux.filter(prots_spc.items(), fdr=fdr, key=escore, is_decoy=isdecoy, remove_decoy=True, formula=1, full_output=True, correction=1) if len(filtered_prots) < 1: filtered_prots = aux.filter(prots_spc.items(), fdr=fdr, key=escore, is_decoy=isdecoy, remove_decoy=True, formula=1, full_output=True, correction=0) identified_proteins = 0 for x in filtered_prots: identified_proteins += 1 print('TOP 5 identified proteins:') print('dbname\tscore\tnum matched peptides\tnum theoretical peptides') for x in filtered_prots[:5]: print('\t'.join((str(x[0]), str(x[1]), str(int(prots_spc_copy[x[0]])), str(protsN[x[0]])))) print('results:%s;number of identified proteins = %d' % (base_out_name, identified_proteins, )) # print('R=', r) with open(base_out_name + '_proteins.tsv', 'w') as output: output.write('dbname\tscore\tmatched peptides\ttheoretical peptides\n') for x in filtered_prots: output.write('\t'.join((x[0], str(x[1]), str(prots_spc_copy[x[0]]), str(protsN[x[0]]))) + '\n') if fname: fig = plt.figure(figsize=(16, 12)) DPI = fig.get_dpi() fig.set_size_inches(2000.0/float(DPI), 2000.0/float(DPI)) df0 = pd.read_table(os.path.splitext(fname)[0].replace('.features', '') + '.features' + '.tsv') # Features RT distribution # TODO add matched features and matched to 1% FDR proteins features ax = fig.add_subplot(3, 1, 1) bns = np.arange(0, df0['rtApex'].max() + 1, 1) ax.hist(df0['rtApex'], bins = bns) ax.set_xlabel('RT, min', size=16) ax.set_ylabel('# features', size=16) # Features mass distribution # TODO add matched features and matched to 1% FDR proteins features ax = fig.add_subplot(3, 1, 2) bns = np.arange(0, df0['massCalib'].max() + 6, 5) ax.hist(df0['massCalib'], bins = bns) ax.set_xlabel('neutral mass, Da', size=16) ax.set_ylabel('# features', size=16) # Features intensity distribution # TODO add matched features and matched to 1% FDR proteins features ax = fig.add_subplot(3, 1, 3) bns = np.arange(np.log10(df0['intensityApex'].min()) - 0.5, np.log10(df0['intensityApex'].max()) + 0.5, 0.5) ax.hist(np.log10(df0['intensityApex']), bins = bns) ax.set_xlabel('log10(Intensity)', size=16) ax.set_ylabel('# features', size=16) plt.savefig(base_out_name + '.png') def noisygaus(x, a, x0, sigma, b): return a * exp(-(x - x0) ** 2 / (2 * sigma ** 2)) + b def calibrate_mass(bwidth, mass_left, mass_right, true_md): bbins = np.arange(-mass_left, mass_right, bwidth) H1, b1 = np.histogram(true_md, bins=bbins) b1 = b1 + bwidth b1 = b1[:-1] popt, pcov = curve_fit(noisygaus, b1, H1, p0=[1, np.median(true_md), 1, 1]) mass_shift, mass_sigma = popt[1], abs(popt[2]) return mass_shift, mass_sigma, pcov[0][0] def calibrate_RT_gaus(bwidth, mass_left, mass_right, true_md): bbins = np.arange(-mass_left, mass_right, bwidth) H1, b1 = np.histogram(true_md, bins=bbins) b1 = b1 + bwidth b1 = b1[:-1] popt, pcov = curve_fit(noisygaus, b1, H1, p0=[1, np.median(true_md), bwidth * 5, 1]) mass_shift, mass_sigma = popt[1], abs(popt[2]) return mass_shift, mass_sigma, pcov[0][0] def process_file(args): utils.seen_target.clear() utils.seen_decoy.clear() args = utils.prepare_decoy_db(args) return process_peptides(args) def peptide_processor(peptide, **kwargs): seqm = peptide results = [] m = cmass.fast_mass(seqm, aa_mass=kwargs['aa_mass']) + kwargs['aa_mass'].get('Nterm', 0) + kwargs['aa_mass'].get('Cterm', 0) acc_l = kwargs['acc_l'] acc_r = kwargs['acc_r'] dm_l = acc_l * m / 1.0e6 if acc_r == acc_l: dm_r = dm_l else: dm_r = acc_r * m / 1.0e6 start = nmasses.searchsorted(m - dm_l) end = nmasses.searchsorted(m + dm_r) for i in range(start, end): peak_id = ids[i] I = Is[i] massdiff = (m - nmasses[i]) / m * 1e6 mods = 0 results.append((seqm, massdiff, mods, i)) return results def prepare_peptide_processor(fname, args): global nmasses global rts global charges global ids global Is global Scans global Isotopes global mzraw global avraw global imraw min_ch = args['cmin'] max_ch = args['cmax'] min_isotopes = args['i'] min_scans = args['sc'] print('Reading spectra ...') df_features = utils.iterate_spectra(fname, min_ch, max_ch, min_isotopes, min_scans) # Sort by neutral mass df_features = df_features.sort_values(by='massCalib') nmasses = df_features['massCalib'].values rts = df_features['rtApex'].values charges = df_features['charge'].values ids = df_features['id'].values Is = df_features['intensityApex'].values Scans = df_features['nScans'].values Isotopes = df_features['nIsotopes'].values mzraw = df_features['mz'].values avraw = np.zeros(len(df_features)) if len(set(df_features['FAIMS'])) > 1: imraw = df_features['FAIMS'].values else: imraw = df_features['ion_mobility'].values print('Number of peptide isotopic clusters: %d' % (len(nmasses), )) fmods = args['fmods'] aa_mass = mass.std_aa_mass if fmods: for mod in fmods.split(','): m, aa = mod.split('@') if aa == '[': aa_mass['Nterm'] = float(m) elif aa == ']': aa_mass['Cterm'] = float(m) else: aa_mass[aa] += float(m) acc_l = args['ptol'] acc_r = args['ptol'] return {'aa_mass': aa_mass, 'acc_l': acc_l, 'acc_r': acc_r, 'args': args}, df_features def peptide_processor_iter_isoforms(peptide, **kwargs): out = [] out.append(peptide_processor(peptide, **kwargs)) return out def get_results(ms1results): resdict = dict() labels = [ 'seqs', 'md', 'mods', 'iorig', # 'rt', # 'ids', # 'Is', # 'Scans', # 'Isotopes', # 'mzraw', # 'av', # 'ch', # 'im', ] for label, val in zip(labels, zip(*ms1results)): resdict[label] = np.array(val) return resdict def filter_results(resultdict, idx): tmp = dict() for label in resultdict: tmp[label] = resultdict[label][idx] return tmp def process_peptides(args): fname = args['file'] fdr = args['fdr'] / 100 min_isotopes_calibration = args['ci'] try: outpath = args['outpath'] except: outpath = False if outpath: base_out_name = os.path.splitext(os.path.join(outpath, os.path.basename(fname)))[0] else: base_out_name = os.path.splitext(fname)[0] out_log = open(base_out_name + '_log.txt', 'w') out_log.close() out_log = open(base_out_name + '_log.txt', 'w') elude_path = args['elude'] elude_path = elude_path.strip() deeplc_path = args['deeplc'] deeplc_path = deeplc_path.strip() calib_path = args['pl'] calib_path = calib_path.strip() if calib_path and args['ts']: args['ts'] = 0 print('Two-stage RT prediction does not work with list of MS/MS identified peptides...') args['enzyme'] = utils.get_enzyme(args['e']) ms1results = [] peps = utils.peptide_gen(args) kwargs, df_features = prepare_peptide_processor(fname, args) func = peptide_processor_iter_isoforms print('Running the search ...') for y in utils.multimap(1, func, peps, **kwargs): for result in y: if len(result): ms1results.extend(result) prefix = args['prefix'] protsN, pept_prot = utils.get_prot_pept_map(args) resdict = get_results(ms1results) del ms1results resdict['mc'] = np.array([parser.num_sites(z, args['enzyme']) for z in resdict['seqs']]) isdecoy = lambda x: x[0].startswith(prefix) isdecoy_key = lambda x: x.startswith(prefix) escore = lambda x: -x[1] e_ind = np.array([Isotopes[iorig] for iorig in resdict['iorig']]) >= min_isotopes_calibration # e_ind = resdict['Isotopes'] >= min_isotopes_calibration # e_ind = resdict['Isotopes'] >= 1 resdict2 = filter_results(resdict, e_ind) e_ind = resdict2['mc'] == 0 resdict2 = filter_results(resdict2, e_ind) p1 = set(resdict2['seqs']) if len(p1): prots_spc2 = defaultdict(set) for pep, proteins in pept_prot.items(): if pep in p1: for protein in proteins: prots_spc2[protein].add(pep) for k in protsN: if k not in prots_spc2: prots_spc2[k] = set([]) prots_spc = dict((k, len(v)) for k, v in prots_spc2.items()) names_arr = np.array(list(prots_spc.keys())) v_arr = np.array(list(prots_spc.values())) n_arr = np.array([protsN[k] for k in prots_spc]) top100decoy_score = [prots_spc.get(dprot, 0) for dprot in protsN if isdecoy_key(dprot)] top100decoy_N = [val for key, val in protsN.items() if isdecoy_key(key)] p = np.mean(top100decoy_score) / np.mean(top100decoy_N) print('p=%s' % (np.mean(top100decoy_score) / np.mean(top100decoy_N))) prots_spc = dict() all_pvals = calc_sf_all(v_arr, n_arr, p) for idx, k in enumerate(names_arr): prots_spc[k] = all_pvals[idx] checked = set() for k, v in list(prots_spc.items()): if k not in checked: if isdecoy_key(k): if prots_spc.get(k.replace(prefix, ''), -1e6) > v: del prots_spc[k] checked.add(k.replace(prefix, '')) else: if prots_spc.get(prefix + k, -1e6) > v: del prots_spc[k] checked.add(prefix + k) filtered_prots = aux.filter(prots_spc.items(), fdr=0.05, key=escore, is_decoy=isdecoy, remove_decoy=True, formula=1, full_output=True) identified_proteins = 0 for x in filtered_prots: identified_proteins += 1 print('results for default search: number of identified proteins = %d' % (identified_proteins, )) print('Running mass recalibration...') e_ind = resdict['mc'] == 0 resdict2 = filter_results(resdict, e_ind) true_md = [] true_isotopes = [] true_seqs = [] true_prots = set(x[0] for x in filtered_prots) for pep, proteins in pept_prot.items(): if any(protein in true_prots for protein in proteins): true_seqs.append(pep) e_ind = np.in1d(resdict2['seqs'], true_seqs) true_seqs = resdict2['seqs'][e_ind] true_md.extend(resdict2['md'][e_ind]) true_md = np.array(true_md) # true_isotopes.extend(resdict2['Isotopes'][e_ind]) true_isotopes.extend(np.array([Isotopes[iorig] for iorig in resdict2['iorig']])[e_ind]) true_isotopes = np.array(true_isotopes) true_intensities = np.array([Is[iorig] for iorig in resdict2['iorig']])[e_ind] # true_intensities = np.array(resdict2['Is'][e_ind]) # true_rt = np.array(resdict2['rt'][e_ind]) # true_mz = np.array(resdict2['mzraw'][e_ind]) true_rt = np.array([rts[iorig] for iorig in resdict2['iorig']])[e_ind] true_mz = np.array([mzraw[iorig] for iorig in resdict2['iorig']])[e_ind] df1 = pd.DataFrame() df1['mass diff'] = true_md df1['mz'] = true_mz df1['RT'] = true_rt df1['Intensity'] = true_intensities df1['seqs'] = true_seqs df1['orig_md'] = true_md mass_left = args['ptol'] mass_right = args['ptol'] try: mass_shift, mass_sigma, covvalue = calibrate_mass(0.001, mass_left, mass_right, true_md) except: mass_shift, mass_sigma, covvalue = calibrate_mass(0.01, mass_left, mass_right, true_md) print('Calibrated mass shift: ', mass_shift) print('Calibrated mass sigma in ppm: ', mass_sigma) out_log.write('Calibrated mass shift: %s\n' % (mass_shift, )) out_log.write('Calibrated mass sigma in ppm: %s\n' % (mass_sigma, )) e_all = abs(resdict['md'] - mass_shift) / (mass_sigma) r = 3.0 e_ind = e_all <= r resdict = filter_results(resdict, e_ind) zs_all = e_all[e_ind] ** 2 e_ind = np.array([Isotopes[iorig] for iorig in resdict['iorig']]) >= min_isotopes_calibration resdict2 = filter_results(resdict, e_ind) e_ind = resdict2['mc'] == 0 resdict2 = filter_results(resdict2, e_ind) p1 = set(resdict2['seqs']) prots_spc2 = defaultdict(set) for pep, proteins in pept_prot.items(): if pep in p1: for protein in proteins: prots_spc2[protein].add(pep) for k in protsN: if k not in prots_spc2: prots_spc2[k] = set([]) prots_spc = dict((k, len(v)) for k, v in prots_spc2.items()) names_arr = np.array(list(prots_spc.keys())) v_arr = np.array(list(prots_spc.values())) n_arr = np.array([protsN[k] for k in prots_spc]) top100decoy_score = [prots_spc.get(dprot, 0) for dprot in protsN if isdecoy_key(dprot)] top100decoy_N = [val for key, val in protsN.items() if isdecoy_key(key)] p = np.mean(top100decoy_score) / np.mean(top100decoy_N) print('p=%s' % (np.mean(top100decoy_score) / np.mean(top100decoy_N))) prots_spc = dict() all_pvals = calc_sf_all(v_arr, n_arr, p) for idx, k in enumerate(names_arr): prots_spc[k] = all_pvals[idx] checked = set() for k, v in list(prots_spc.items()): if k not in checked: if isdecoy_key(k): if prots_spc.get(k.replace(prefix, ''), -1e6) > v: del prots_spc[k] checked.add(k.replace(prefix, '')) else: if prots_spc.get(prefix + k, -1e6) > v: del prots_spc[k] checked.add(prefix + k) filtered_prots = aux.filter(prots_spc.items(), fdr=0.05, key=escore, is_decoy=isdecoy, remove_decoy=True, formula=1, full_output=True) identified_proteins = 0 for x in filtered_prots: identified_proteins += 1 print('results for default search after mass calibration: number of identified proteins = %d' % (identified_proteins, )) print('Running RT prediction...') e_ind = np.array([Isotopes[iorig] for iorig in resdict['iorig']]) >= min_isotopes_calibration # e_ind = resdict['Isotopes'] >= min_isotopes_calibration # e_ind = resdict['Isotopes'] >= 1 resdict2 = filter_results(resdict, e_ind) e_ind = resdict2['mc'] == 0 resdict2 = filter_results(resdict2, e_ind) true_seqs = [] true_rt = [] true_isotopes = [] true_prots = set(x[0] for x in filtered_prots)#[:5]) for pep, proteins in pept_prot.items(): if any(protein in true_prots for protein in proteins): true_seqs.append(pep) e_ind = np.in1d(resdict2['seqs'], true_seqs) true_seqs = resdict2['seqs'][e_ind] true_rt.extend(np.array([rts[iorig] for iorig in resdict2['iorig']])[e_ind]) # true_rt.extend(resdict2['rt'][e_ind]) true_rt = np.array(true_rt) true_isotopes.extend(np.array([Isotopes[iorig] for iorig in resdict2['iorig']])[e_ind]) # true_isotopes.extend(resdict2['Isotopes'][e_ind]) true_isotopes = np.array(true_isotopes) e_all = abs(resdict2['md'][e_ind] - mass_shift) / (mass_sigma) zs_all_tmp = e_all ** 2 e_ind = true_isotopes >= min_isotopes_calibration true_seqs = true_seqs[e_ind] true_rt = true_rt[e_ind] true_isotopes = true_isotopes[e_ind] zs_all_tmp = zs_all_tmp[e_ind] e_ind = np.argsort(zs_all_tmp) true_seqs = true_seqs[e_ind] true_rt = true_rt[e_ind] true_isotopes = true_isotopes[e_ind] true_seqs = true_seqs[:2500] true_rt = true_rt[:2500] true_isotopes = true_isotopes[:2500] best_seq = defaultdict(list) newseqs = [] newRTs = [] for seq, RT in zip(true_seqs, true_rt): best_seq[seq].append(RT) for k, v in best_seq.items(): newseqs.append(k) newRTs.append(np.median(v)) true_seqs = np.array(newseqs) true_rt = np.array(newRTs) if calib_path: df1 = pd.read_csv(calib_path, sep='\t') true_seqs2 = df1['peptide'].values true_rt2 = df1['RT exp'].values else: true_seqs2 = true_seqs true_rt2 = true_rt if args['ts'] != 2 and deeplc_path: outtrain_name = os.path.join(tempfile.gettempdir(), os.urandom(24).hex()) outtrain = open(outtrain_name, 'w') outcalib_name = os.path.join(tempfile.gettempdir(), os.urandom(24).hex()) outcalib = open(outcalib_name, 'w') outres_name = os.path.join(tempfile.gettempdir(), os.urandom(24).hex()) ns = true_seqs nr = true_rt print('Peptides used for RT prediction: %d' % (len(ns), )) ns2 = true_seqs2 nr2 = true_rt2 outtrain.write('seq,modifications,tr\n') for seq, RT in zip(ns2, nr2): mods_tmp = '|'.join([str(idx+1)+'|Carbamidomethyl' for idx, aa in enumerate(seq) if aa == 'C']) outtrain.write(seq + ',' + str(mods_tmp) + ',' + str(RT) + '\n') outtrain.close() outcalib.write('seq,modifications,tr\n') for seq, RT in zip(ns, nr): mods_tmp = '|'.join([str(idx+1)+'|Carbamidomethyl' for idx, aa in enumerate(seq) if aa == 'C']) outcalib.write(seq + ',' + str(mods_tmp) + ',' + str(RT) + '\n') outcalib.close() subprocess.call([deeplc_path, '--file_pred', outcalib_name, '--file_cal', outtrain_name, '--file_pred_out', outres_name]) pepdict = dict() train_RT = [] train_seq = [] for x in open(outres_name).readlines()[1:]: _, seq, _, RTexp, RT = x.strip().split(',') pepdict[seq] = float(RT) train_seq.append(seq) train_RT.append(float(RTexp)) train_RT = np.array(train_RT) RT_pred = np.array([pepdict[s] for s in train_seq]) rt_diff_tmp = RT_pred - train_RT RT_left = -min(rt_diff_tmp) RT_right = max(rt_diff_tmp) try: start_width = (scoreatpercentile(rt_diff_tmp, 95) - scoreatpercentile(rt_diff_tmp, 5)) / 100 XRT_shift, XRT_sigma, covvalue = calibrate_RT_gaus(start_width, RT_left, RT_right, rt_diff_tmp) except: start_width = (scoreatpercentile(rt_diff_tmp, 95) - scoreatpercentile(rt_diff_tmp, 5)) / 50 XRT_shift, XRT_sigma, covvalue = calibrate_RT_gaus(start_width, RT_left, RT_right, rt_diff_tmp) if np.isinf(covvalue): XRT_shift, XRT_sigma, covvalue = calibrate_RT_gaus(0.1, RT_left, RT_right, rt_diff_tmp) if np.isinf(covvalue): XRT_shift, XRT_sigma, covvalue = calibrate_RT_gaus(1.0, RT_left, RT_right, rt_diff_tmp) print('Calibrated RT shift: ', XRT_shift) print('Calibrated RT sigma: ', XRT_sigma) aa, bb, RR, ss = aux.linear_regression(RT_pred, train_RT) else: if args['ts'] != 2 and elude_path: outtrain_name = os.path.join(tempfile.gettempdir(), os.urandom(24).hex()) outtrain = open(outtrain_name, 'w') outcalib_name = os.path.join(tempfile.gettempdir(), os.urandom(24).hex()) outcalib = open(outcalib_name, 'w') outres_name = os.path.join(tempfile.gettempdir(), os.urandom(24).hex()) ns = true_seqs nr = true_rt print('Peptides used for RT prediction: %d' % (len(ns), )) ns2 = true_seqs2 nr2 = true_rt2 for seq, RT in zip(ns, nr): outtrain.write(seq + '\t' + str(RT) + '\n') outtrain.close() for seq, RT in zip(ns, nr): outcalib.write(seq + '\t' + str(RT) + '\n') outcalib.close() subprocess.call([elude_path, '-t', outtrain_name, '-e', outcalib_name, '-a', '-g', '-o', outres_name]) pepdict = dict() train_RT = [] train_seq = [] for x in open(outres_name).readlines()[3:]: seq, RT, RTexp = x.strip().split('\t') pepdict[seq] = float(RT) train_seq.append(seq) train_RT.append(float(RTexp)) train_RT = np.array(train_RT) rt_diff_tmp = RT_pred - train_RT RT_left = -min(rt_diff_tmp) RT_right = max(rt_diff_tmp) start_width = (scoreatpercentile(rt_diff_tmp, 95) - scoreatpercentile(rt_diff_tmp, 5)) / 50 XRT_shift, XRT_sigma, covvalue = calibrate_RT_gaus(start_width, RT_left, RT_right, rt_diff_tmp) if np.isinf(covvalue): XRT_shift, XRT_sigma, covvalue = calibrate_RT_gaus(0.1, RT_left, RT_right, rt_diff_tmp) if np.isinf(covvalue): XRT_shift, XRT_sigma, covvalue = calibrate_RT_gaus(1.0, RT_left, RT_right, rt_diff_tmp) print('Calibrated RT shift: ', XRT_shift) print('Calibrated RT sigma: ', XRT_sigma) aa, bb, RR, ss = aux.linear_regression(RT_pred, train_RT) else: ns = true_seqs nr = true_rt ns2 = true_seqs2 nr2 = true_rt2 RC = achrom.get_RCs_vary_lcp(ns2, nr2) RT_pred = np.array([achrom.calculate_RT(s, RC) for s in ns]) train_RT = nr aa, bb, RR, ss = aux.linear_regression(RT_pred, nr) rt_diff_tmp = RT_pred - nr RT_left = -min(rt_diff_tmp) RT_right = max(rt_diff_tmp) start_width = (scoreatpercentile(rt_diff_tmp, 95) - scoreatpercentile(rt_diff_tmp, 5)) / 50 XRT_shift, XRT_sigma, covvalue = calibrate_RT_gaus(start_width, RT_left, RT_right, rt_diff_tmp) if np.isinf(covvalue): XRT_shift, XRT_sigma, covvalue = calibrate_RT_gaus(0.1, RT_left, RT_right, rt_diff_tmp) if np.isinf(covvalue): XRT_shift, XRT_sigma, covvalue = calibrate_RT_gaus(1.0, RT_left, RT_right, rt_diff_tmp) print('Calibrated RT shift: ', XRT_shift) print('Calibrated RT sigma: ', XRT_sigma) print(aa, bb, RR, ss) best_sigma = XRT_sigma RT_sigma = XRT_sigma else: print('No matches found') if args['ts']: print('Running second stage RT prediction...') ns = np.array(ns) nr = np.array(nr) idx = np.abs((rt_diff_tmp) - XRT_shift) <= 3 * XRT_sigma ns = ns[idx] nr = nr[idx] if deeplc_path: outtrain_name = os.path.join(tempfile.gettempdir(), os.urandom(24).hex()) outtrain = open(outtrain_name, 'w') outres_name = os.path.join(tempfile.gettempdir(), os.urandom(24).hex()) print('Peptides used for RT prediction: %d' % (len(ns), )) ll = len(ns) ns = ns[:ll] nr = nr[:ll] outtrain.write('seq,modifications,tr\n') for seq, RT in zip(ns, nr): mods_tmp = '|'.join([str(idx+1)+'|Carbamidomethyl' for idx, aa in enumerate(seq) if aa == 'C']) outtrain.write(seq + ',' + str(mods_tmp) + ',' + str(RT) + '\n') outtrain.close() subprocess.call([deeplc_path, '--file_pred', outtrain_name, '--file_cal', outtrain_name, '--file_pred_out', outres_name]) pepdict = dict() train_RT = [] train_seq = [] for x in open(outres_name).readlines()[1:]: _, seq, _, RTexp, RT = x.strip().split(',') pepdict[seq] = float(RT) train_seq.append(seq) train_RT.append(float(RTexp)) train_RT = np.array(train_RT) RT_pred = np.array([pepdict[s] for s in train_seq]) rt_diff_tmp = RT_pred - train_RT RT_left = -min(rt_diff_tmp) RT_right = max(rt_diff_tmp) try: start_width = (scoreatpercentile(rt_diff_tmp, 95) - scoreatpercentile(rt_diff_tmp, 5)) / 100 XRT_shift, XRT_sigma, covvalue = calibrate_RT_gaus(start_width, RT_left, RT_right, rt_diff_tmp) except: start_width = (scoreatpercentile(rt_diff_tmp, 95) - scoreatpercentile(rt_diff_tmp, 5)) / 50 XRT_shift, XRT_sigma, covvalue = calibrate_RT_gaus(start_width, RT_left, RT_right, rt_diff_tmp) if np.isinf(covvalue): XRT_shift, XRT_sigma, covvalue = calibrate_RT_gaus(0.1, RT_left, RT_right, rt_diff_tmp) if np.isinf(covvalue): XRT_shift, XRT_sigma, covvalue = calibrate_RT_gaus(1.0, RT_left, RT_right, rt_diff_tmp) print('Calibrated RT shift: ', XRT_shift) print('Calibrated RT sigma: ', XRT_sigma) aa, bb, RR, ss = aux.linear_regression(RT_pred, train_RT) else: if elude_path: outtrain_name = os.path.join(tempfile.gettempdir(), os.urandom(24).hex()) outtrain = open(outtrain_name, 'w') outres_name = os.path.join(tempfile.gettempdir(), os.urandom(24).hex()) print(len(ns)) ll = len(ns) ns = ns[:ll] nr = nr[:ll] for seq, RT in zip(ns, nr): outtrain.write(seq + '\t' + str(RT) + '\n') outtrain.close() subprocess.call([elude_path, '-t', outtrain_name, '-e', outtrain_name, '-a', '-g', '-o', outres_name]) pepdict = dict() train_RT = [] train_seq = [] for x in open(outres_name).readlines()[3:]: seq, RT, RTexp = x.strip().split('\t') pepdict[seq] = float(RT) train_seq.append(seq) train_RT.append(float(RTexp)) train_RT = np.array(train_RT) RT_pred = np.array([pepdict[s] for s in train_seq]) rt_diff_tmp = RT_pred - train_RT RT_left = -min(rt_diff_tmp) RT_right = max(rt_diff_tmp) start_width = (scoreatpercentile(rt_diff_tmp, 95) - scoreatpercentile(rt_diff_tmp, 5)) / 50 XRT_shift, XRT_sigma, covvalue = calibrate_RT_gaus(start_width, RT_left, RT_right, rt_diff_tmp) if np.isinf(covvalue): XRT_shift, XRT_sigma, covvalue = calibrate_RT_gaus(0.1, RT_left, RT_right, rt_diff_tmp) if np.isinf(covvalue): XRT_shift, XRT_sigma, covvalue = calibrate_RT_gaus(1.0, RT_left, RT_right, rt_diff_tmp) print('Calibrated RT shift: ', XRT_shift) print('Calibrated RT sigma: ', XRT_sigma) aa, bb, RR, ss = aux.linear_regression(RT_pred, train_RT) else: RC = achrom.get_RCs_vary_lcp(ns, nr) RT_pred = np.array([achrom.calculate_RT(s, RC) for s in ns]) aa, bb, RR, ss = aux.linear_regression(RT_pred, nr) rt_diff_tmp = RT_pred - nr RT_left = -min(rt_diff_tmp) RT_right = max(rt_diff_tmp) start_width = (scoreatpercentile(rt_diff_tmp, 95) - scoreatpercentile(rt_diff_tmp, 5)) / 50 XRT_shift, XRT_sigma, covvalue = calibrate_RT_gaus(start_width, RT_left, RT_right, rt_diff_tmp) if np.isinf(covvalue): XRT_shift, XRT_sigma, covvalue = calibrate_RT_gaus(0.1, RT_left, RT_right, rt_diff_tmp) if np.isinf(covvalue): XRT_shift, XRT_sigma, covvalue = calibrate_RT_gaus(1.0, RT_left, RT_right, rt_diff_tmp) print('Calibrated RT shift: ', XRT_shift) print('Calibrated RT sigma: ', XRT_sigma) print(aa, bb, RR, ss) best_sigma = XRT_sigma RT_sigma = XRT_sigma out_log.write('Calibrated RT shift: %s\n' % (XRT_shift, )) out_log.write('Calibrated RT sigma: %s\n' % (XRT_sigma, )) out_log.close() p1 = set(resdict['seqs']) n = args['nproc'] if deeplc_path: pepdict = dict() outtrain_name = os.path.join(tempfile.gettempdir(), os.urandom(24).hex()) outtrain = open(outtrain_name, 'w') outres_name = os.path.join(tempfile.gettempdir(), os.urandom(24).hex()) outtrain.write('seq,modifications,tr\n') for seq, RT in zip(ns, nr): mods_tmp = '|'.join([str(idx+1)+'|Carbamidomethyl' for idx, aa in enumerate(seq) if aa == 'C']) outtrain.write(seq + ',' + str(mods_tmp) + ',' + str(RT) + '\n') outtrain.close() outtest_name = os.path.join(tempfile.gettempdir(), os.urandom(24).hex()) outtest = open(outtest_name, 'w') outtest.write('seq,modifications\n') for seq in p1: mods_tmp = '|'.join([str(idx+1)+'|Carbamidomethyl' for idx, aa in enumerate(seq) if aa == 'C']) outtest.write(seq + ',' + str(mods_tmp) + '\n') outtest.close() if args['deeplc_library']: print('Using deeplc library...') subprocess.call([deeplc_path, '--file_pred', outtest_name, '--file_cal', outtrain_name, '--file_pred_out', outres_name, '--use_library', args['deeplc_library'], '--write_library']) else: subprocess.call([deeplc_path, '--file_pred', outtest_name, '--file_cal', outtrain_name, '--file_pred_out', outres_name]) for x in open(outres_name).readlines()[1:]: _, seq, _, RT = x.strip().split(',') pepdict[seq] = float(RT) else: if n == 1 or os.name == 'nt': qin = list(p1) qout = [] if elude_path: pepdict = worker_RT(qin, qout, 0, 1, False, elude_path, ns, nr, True) else: pepdict = worker_RT(qin, qout, 0, 1, RC, False, False, False, True) else: qin = list(p1) qout = Queue() procs = [] for i in range(n): if elude_path: p = Process(target=worker_RT, args=(qin, qout, i, n, False, elude_path, ns, nr)) else: p = Process(target=worker_RT, args=(qin, qout, i, n, RC, False, False, False)) p.start() procs.append(p) pepdict = dict() for _ in range(n): for item in iter(qout.get, None): for k, v in item.items(): pepdict[k] = v for p in procs: p.join() rt_pred = np.array([pepdict[s] for s in resdict['seqs']]) rt_diff = np.array([rts[iorig] for iorig in resdict['iorig']]) - rt_pred # rt_diff = resdict['rt'] - rt_pred e_all = (rt_diff) ** 2 / (RT_sigma ** 2) r = 9.0 e_ind = e_all <= r resdict = filter_results(resdict, e_ind) rt_diff = rt_diff[e_ind] rt_pred = rt_pred[e_ind] with open(base_out_name + '_protsN.tsv', 'w') as output: output.write('dbname\ttheor peptides\n') for k, v in protsN.items(): output.write('\t'.join((k, str(v))) + '\n') with open(base_out_name + '_PFMs.tsv', 'w') as output: output.write('sequence\tmass diff\tRT diff\tpeak_id\tIntensity\tnScans\tnIsotopes\tproteins\tm/z\tRT\taveragineCorr\tcharge\tion_mobility\n') # for seq, md, rtd, peak_id, I, nScans, nIsotopes, mzr, rtr, av, ch, im in zip(resdict['seqs'], resdict['md'], rt_diff, resdict['ids'], resdict['Is'], resdict['Scans'], resdict['Isotopes'], resdict['mzraw'], resdict['rt'], resdict['av'], resdict['ch'], resdict['im']): for seq, md, rtd, iorig in zip(resdict['seqs'], resdict['md'], rt_diff, resdict['iorig']): peak_id = ids[iorig] I = Is[iorig] nScans = Scans[iorig] nIsotopes = Isotopes[iorig] mzr = mzraw[iorig] rtr = rts[iorig] av = avraw[iorig] ch = charges[iorig] im = imraw[iorig] output.write('\t'.join((seq, str(md), str(rtd), str(peak_id), str(I), str(nScans), str(nIsotopes), ';'.join(pept_prot[seq]), str(mzr), str(rtr), str(av), str(ch), str(im))) + '\n') e_ind = resdict['mc'] == 0 resdict = filter_results(resdict, e_ind) rt_diff = rt_diff[e_ind] rt_pred = rt_pred[e_ind] mass_diff = (resdict['md'] - mass_shift) / (mass_sigma) rt_diff = (np.array([rts[iorig] for iorig in resdict['iorig']]) - rt_pred) / RT_sigma # rt_diff = (resdict['rt'] - rt_pred) / RT_sigma prefix = 'DECOY_' isdecoy = lambda x: x[0].startswith(prefix) isdecoy_key = lambda x: x.startswith(prefix) escore = lambda x: -x[1] SEED = 42 # Hyperparameter grid param_grid = { 'boosting_type': ['gbdt', ], 'num_leaves': list(range(10, 1000)), 'learning_rate': list(np.logspace(np.log10(0.001), np.log10(0.05), base = 10, num = 1000)), 'metric': ['rmse', ], 'verbose': [-1, ], 'num_threads': [args['nproc'], ], } def get_X_array(df, feature_columns): return df.loc[:, feature_columns].values def get_Y_array_pfms(df): return df.loc[:, 'decoy'].values def get_features_pfms(dataframe): feature_columns = dataframe.columns columns_to_remove = [] banned_features = { 'iorig', 'ids', 'seqs', 'decoy', 'preds', 'av', 'Scans', 'proteins', 'peptide', 'md', } for feature in feature_columns: if feature in banned_features: columns_to_remove.append(feature) feature_columns = feature_columns.drop(columns_to_remove) return feature_columns def objective_pfms(df, hyperparameters, iteration, threshold=0): """Objective function for grid and random search. Returns the cross validation score from a set of hyperparameters.""" all_res = [] groups = df['peptide'] ix = df.index.values unique = np.unique(groups) np.random.RandomState(SEED).shuffle(unique) result = [] for split in np.array_split(unique, 3): mask = groups.isin(split) train, test = ix[~mask], ix[mask] train_df = df.iloc[train] test_df = df.iloc[test] feature_columns = get_features_pfms(df) model = get_cat_model_final_pfms(train_df, hyperparameters, feature_columns) df.loc[mask, 'preds'] = model.predict(get_X_array(df.loc[mask, :], feature_columns)) train_df = df.iloc[train] test_df = df.iloc[test] fpr, tpr, thresholds = metrics.roc_curve(get_Y_array_pfms(test_df), test_df['preds']) shr_v = metrics.auc(fpr, tpr) # shr_v = len(aux.filter(test_df, fdr=0.25, key='preds', is_decoy='decoy')) all_res.append(shr_v) # print(shr_v) if shr_v < threshold: all_res = [0, ] break shr_v = np.mean(all_res) # print(shr_v) # print('\n') return [shr_v, hyperparameters, iteration, all_res] def random_search_pfms(df, param_grid, out_file, max_evals): """Random search for hyperparameter optimization. Writes result of search to csv file every search iteration.""" threshold = 0 # Dataframe for results results = pd.DataFrame(columns = ['sharpe', 'params', 'iteration', 'all_res'], index = list(range(max_evals))) for i in range(max_evals): print('%d/%d' % (i+1, max_evals)) # Choose random hyperparameters random_params = {k: random.sample(v, 1)[0] for k, v in param_grid.items()} # Evaluate randomly selected hyperparameters eval_results = objective_pfms(df, random_params, i, threshold) results.loc[i, :] = eval_results threshold = max(threshold, np.mean(eval_results[3]) - 3 * np.std(eval_results[3])) # open connection (append option) and write results of_connection = open(out_file, 'a') writer = csv.writer(of_connection) writer.writerow(eval_results) # make sure to close connection of_connection.close() # Sort with best score on top results.sort_values('sharpe', ascending = False, inplace = True) results.reset_index(inplace = True) return results def get_cat_model_pfms(df, hyperparameters, feature_columns, train, test): feature_columns = list(feature_columns) dtrain = lgb.Dataset(get_X_array(train, feature_columns), get_Y_array_pfms(train), feature_name=feature_columns, free_raw_data=False) dvalid = lgb.Dataset(get_X_array(test, feature_columns), get_Y_array_pfms(test), feature_name=feature_columns, free_raw_data=False) np.random.seed(SEED) evals_result = {} model = lgb.train(hyperparameters, dtrain, num_boost_round=5000, valid_sets=(dvalid,), valid_names=('valid',), verbose_eval=False, early_stopping_rounds=20, evals_result=evals_result) return model def get_cat_model_final_pfms(df, hyperparameters, feature_columns): feature_columns = list(feature_columns) train = df dtrain = lgb.Dataset(get_X_array(train, feature_columns), get_Y_array_pfms(train), feature_name=feature_columns, free_raw_data=False) np.random.seed(SEED) model = lgb.train(hyperparameters, dtrain, num_boost_round=100) return model df1 = pd.DataFrame() for k in resdict.keys(): df1[k] = resdict[k] df1['ids'] = df1['iorig'].apply(lambda x: ids[x]) df1['Is'] = df1['iorig'].apply(lambda x: Is[x]) df1['Scans'] = df1['iorig'].apply(lambda x: Scans[x]) df1['Isotopes'] = df1['iorig'].apply(lambda x: Isotopes[x]) df1['mzraw'] = df1['iorig'].apply(lambda x: mzraw[x]) df1['rt'] = df1['iorig'].apply(lambda x: rts[x]) df1['av'] = df1['iorig'].apply(lambda x: avraw[x]) df1['ch'] = df1['iorig'].apply(lambda x: charges[x]) df1['im'] = df1['iorig'].apply(lambda x: imraw[x]) df1['mass_diff'] = mass_diff df1['rt_diff'] = rt_diff df1['decoy'] = df1['seqs'].apply(lambda x: all(z.startswith(prefix) for z in pept_prot[x])) df1['peptide'] = df1['seqs'] mass_dict = {} pI_dict = {} charge_dict = {} for pep in set(df1['peptide']): try: mass_dict[pep] = mass.fast_mass2(pep) pI_dict[pep] = electrochem.pI(pep) charge_dict[pep] = electrochem.charge(pep, pH=7.0) except: mass_dict[pep] = 0 pI_dict[pep] = 0 charge_dict[pep] = 0 df1['plen'] = df1['peptide'].apply(lambda z: len(z)) df1['mass'] = df1['peptide'].apply(lambda x: mass_dict[x]) df1['pI'] = df1['peptide'].apply(lambda x: pI_dict[x]) df1['charge_theor'] = df1['peptide'].apply(lambda x: charge_dict[x]) df1['rt_diff_abs'] = df1['rt_diff'].abs() df1['rt_diff_abs_pdiff'] = df1['rt_diff_abs'] - df1.groupby('ids')['rt_diff_abs'].transform('median') df1['rt_diff_abs_pnorm'] = df1['rt_diff_abs'] / (df1.groupby('ids')['rt_diff_abs'].transform('sum') + 1e-2) df1['id_count'] = df1.groupby('ids')['mass_diff'].transform('count') df1['seq_count'] = df1.groupby('peptide')['mass_diff'].transform('count') df1t5 = df1.sort_values(by='Is', ascending=False).copy() df1t5 = df1t5.drop_duplicates(subset='peptide', keep='first') if args['ml']: print('Start Machine Learning on PFMs...') print('Features used for MachineLearning: ', get_features_pfms(df1)) MAX_EVALS = 25 out_file = 'test_randomCV_PFMs_2.tsv' of_connection = open(out_file, 'w') writer = csv.writer(of_connection) # Write column names headers = ['auc', 'params', 'iteration', 'all_res'] writer.writerow(headers) of_connection.close() random_results = random_search_pfms(df1, param_grid, out_file, MAX_EVALS) random_results = pd.read_csv(out_file) random_results = random_results[random_results['auc'] != 'auc'] random_results['params'] = random_results['params'].apply(lambda x: ast.literal_eval(x)) convert_dict = {'auc': float, } random_results = random_results.astype(convert_dict) bestparams = random_results.sort_values(by='auc',ascending=False)['params'].values[0] bestparams['num_threads'] = args['nproc'] print(random_results.sort_values(by='auc',ascending=False)['auc'].values[0]) groups = df1['peptide'] ix = df1.index.values unique = np.unique(groups) np.random.RandomState(SEED).shuffle(unique) result = [] for split in np.array_split(unique, 3): mask = groups.isin(split) train, test = ix[~mask], ix[mask] train_df = df1.iloc[train] test_df = df1.iloc[test] feature_columns = list(get_features_pfms(train_df)) model = get_cat_model_final_pfms(train_df, bestparams, feature_columns) df1.loc[test, 'preds'] = model.predict(get_X_array(test_df, feature_columns)) else: df1['preds'] = np.power(df1['mass_diff'], 2) + np.power(df1['rt_diff'], 2) df1['qpreds'] = pd.qcut(df1['preds'], 10, labels=range(10)) df1['proteins'] = df1['seqs'].apply(lambda x: ';'.join(pept_prot[x])) df1.to_csv(base_out_name + '_PFMs_ML.tsv', sep='\t', index=False) resdict['qpreds'] = df1['qpreds'].values resdict['ids'] = df1['ids'].values mass_diff = resdict['qpreds'] rt_diff = resdict['qpreds'] p1 = set(resdict['seqs']) prots_spc2 = defaultdict(set) for pep, proteins in pept_prot.items(): if pep in p1: for protein in proteins: prots_spc2[protein].add(pep) for k in protsN: if k not in prots_spc2: prots_spc2[k] = set([]) prots_spc = dict((k, len(v)) for k, v in prots_spc2.items()) names_arr = np.array(list(prots_spc.keys())) v_arr = np.array(list(prots_spc.values())) n_arr = np.array([protsN[k] for k in prots_spc]) top100decoy_score = [prots_spc.get(dprot, 0) for dprot in protsN if isdecoy_key(dprot)] top100decoy_N = [val for key, val in protsN.items() if isdecoy_key(key)] p = np.mean(top100decoy_score) / np.mean(top100decoy_N) print('p=%s' % (np.mean(top100decoy_score) / np.mean(top100decoy_N))) prots_spc = dict() all_pvals = calc_sf_all(v_arr, n_arr, p) for idx, k in enumerate(names_arr): prots_spc[k] = all_pvals[idx] final_iteration(resdict, mass_diff, rt_diff, pept_prot, protsN, base_out_name, prefix, isdecoy, isdecoy_key, escore, fdr, args['nproc'], fname) def worker(qin, qout, mass_diff, rt_diff, resdict, protsN, pept_prot, isdecoy_key, isdecoy, fdr, prots_spc_basic2, win_sys=False): for item in (iter(qin.get, None) if not win_sys else qin): mass_koef, rtt_koef = item e_ind = mass_diff <= mass_koef resdict2 = filter_results(resdict, e_ind) features_dict = dict() for pep in set(resdict2['seqs']): for bprot in pept_prot[pep]: prot_score = prots_spc_basic2[bprot] if prot_score > features_dict.get(pep, [-1, ])[-1]: features_dict[pep] = (bprot, prot_score) prots_spc_basic = dict() p1 = set(resdict2['seqs']) pep_pid = defaultdict(set) pid_pep = defaultdict(set) banned_dict = dict() for pep, pid in zip(resdict2['seqs'], resdict2['ids']): # for pep, pid in zip(resdict2['seqs'], [ids[iorig] for iorig in resdict2['iorig']]): pep_pid[pep].add(pid) pid_pep[pid].add(pep) if pep in banned_dict: banned_dict[pep] += 1 else: banned_dict[pep] = 1 if len(p1): prots_spc_final = dict() prots_spc_copy = False prots_spc2 = False unstable_prots = set() p0 = False names_arr = False tmp_spc_new = False decoy_set = False while 1: if not prots_spc2: best_match_dict = dict() n_map_dict = defaultdict(list) for k, v in protsN.items(): n_map_dict[v].append(k) decoy_set = set() for k in protsN: if isdecoy_key(k): decoy_set.add(k) decoy_set = list(decoy_set) prots_spc2 = defaultdict(set) for pep, proteins in pept_prot.items(): if pep in p1: for protein in proteins: if protein == features_dict[pep][0]: prots_spc2[protein].add(pep) for k in protsN: if k not in prots_spc2: prots_spc2[k] = set([]) prots_spc2 = dict(prots_spc2) unstable_prots = set(prots_spc2.keys()) top100decoy_N = sum([val for key, val in protsN.items() if isdecoy_key(key)]) names_arr = np.array(list(prots_spc2.keys())) n_arr = np.array([protsN[k] for k in names_arr]) tmp_spc_new = dict((k, len(v)) for k, v in prots_spc2.items()) top100decoy_score_tmp = [tmp_spc_new.get(dprot, 0) for dprot in decoy_set] top100decoy_score_tmp_sum = float(sum(top100decoy_score_tmp)) tmp_spc = tmp_spc_new prots_spc = tmp_spc_new if not prots_spc_copy: prots_spc_copy = deepcopy(prots_spc) for idx, v in enumerate(decoy_set): if v in unstable_prots: top100decoy_score_tmp_sum -= top100decoy_score_tmp[idx] top100decoy_score_tmp[idx] = prots_spc.get(v, 0) top100decoy_score_tmp_sum += top100decoy_score_tmp[idx] p = float(sum(top100decoy_score_tmp)) / top100decoy_N p = top100decoy_score_tmp_sum / top100decoy_N if not p0: p0 = float(p) n_change = set(protsN[k] for k in unstable_prots) for n_val in n_change: for k in n_map_dict[n_val]: v = prots_spc[k] if n_val not in best_match_dict or v > prots_spc[best_match_dict[n_val]]: best_match_dict[n_val] = k n_arr_small = [] names_arr_small = [] v_arr_small = [] for k, v in best_match_dict.items(): n_arr_small.append(k) names_arr_small.append(v) v_arr_small.append(prots_spc[v]) prots_spc_basic = dict() all_pvals = calc_sf_all(np.array(v_arr_small), n_arr_small, p) for idx, k in enumerate(names_arr_small): prots_spc_basic[k] = all_pvals[idx] best_prot = utils.keywithmaxval(prots_spc_basic) best_score = prots_spc_basic[best_prot] unstable_prots = set() if best_prot not in prots_spc_final: prots_spc_final[best_prot] = best_score banned_pids = set() for pep in prots_spc2[best_prot]: for pid in pep_pid[pep]: banned_pids.add(pid) for pid in banned_pids: for pep in pid_pep[pid]: banned_dict[pep] -= 1 if banned_dict[pep] == 0: best_prot_val = features_dict[pep][0] for bprot in pept_prot[pep]: if bprot == best_prot_val: tmp_spc_new[bprot] -= 1 unstable_prots.add(bprot) else: v_arr = np.array([prots_spc[k] for k in names_arr]) all_pvals = calc_sf_all(v_arr, n_arr, p) for idx, k in enumerate(names_arr): prots_spc_basic[k] = all_pvals[idx] for k, v in prots_spc_basic.items(): if k not in prots_spc_final: prots_spc_final[k] = v break try: prot_fdr = aux.fdr(prots_spc_final.items(), is_decoy=isdecoy) except ZeroDivisionError: prot_fdr = 100.0 if prot_fdr >= 12.5 * fdr: v_arr = np.array([prots_spc[k] for k in names_arr]) all_pvals = calc_sf_all(v_arr, n_arr, p) for idx, k in enumerate(names_arr): prots_spc_basic[k] = all_pvals[idx] for k, v in prots_spc_basic.items(): if k not in prots_spc_final: prots_spc_final[k] = v break if mass_koef == 9: item2 = prots_spc_copy else: item2 = False if not win_sys: qout.put((prots_spc_final, item2)) else: qout.append((prots_spc_final, item2)) if not win_sys: qout.put(None) else: return qout

37.466706

276

0.564225

cc8e8a561ef5687c9cc7d4a828c190e58f83f661

1,099

py

Python

waitinglist/urls.py

konradb/django-waitinglist

81e2b3ba81d96c377f4167b0bbd01bb023f7b97a

[ "MIT" ]

null

waitinglist/urls.py

konradb/django-waitinglist

81e2b3ba81d96c377f4167b0bbd01bb023f7b97a

[ "MIT" ]

null

waitinglist/urls.py

konradb/django-waitinglist

81e2b3ba81d96c377f4167b0bbd01bb023f7b97a

[ "MIT" ]

null

from django.conf.urls import url from django.views.generic import TemplateView from . import views urlpatterns = [ url(r"^list_signup/$", views.list_signup, name="waitinglist_list_signup"), url(r"^ajax_list_signup/$", views.ajax_list_signup, name="waitinglist_ajax_list_signup"), url(r"^survey/thanks/$", TemplateView.as_view(template_name="waitinglist/thanks.html"), name="waitinglist_thanks"), url(r"^survey/(?P<code>.*)/$", views.survey, name="waitinglist_survey"), url(r"^success/$", TemplateView.as_view(template_name="waitinglist/success.html"), name="waitinglist_success"), url(r"^cohorts/$", views.cohort_list, name="waitinglist_cohort_list"), url(r"^cohorts/create/$", views.cohort_create, name="waitinglist_cohort_create"), url(r"^cohorts/cohort/(\d+)/$", views.cohort_detail, name="waitinglist_cohort_detail"), url(r"^cohorts/cohort/(\d+)/add_member/$", views.cohort_member_add, name="waitinglist_cohort_member_add"), url(r"^cohorts/cohort/(\d+)/send_invitations/$", views.cohort_send_invitations, name="waitinglist_cohort_send_invitations"), ]

57.842105

128

0.744313

cc8eeef4e955218a7296a121d1dfb2153b85aa22

1,162

py

Python

nokogiri/tqdm_load.py

nat-chan/nokogiri

476c2989e2fbe80b2937923fbef0211459ec738f

[ "MIT" ]

null

nokogiri/tqdm_load.py

nat-chan/nokogiri

476c2989e2fbe80b2937923fbef0211459ec738f

[ "MIT" ]

null

nokogiri/tqdm_load.py

nat-chan/nokogiri

476c2989e2fbe80b2937923fbef0211459ec738f

[ "MIT" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- from pathlib import Path from nokogiri.which_env import which_env from pickle import Unpickler class TQDMBytesReader(object): def __init__(self, fd, tqdm, total, desc=''): self.fd = fd self.tqdm = tqdm(total=total) self.tqdm.set_description(desc) def read(self, size=-1): bytes = self.fd.read(size) self.tqdm.update(len(bytes)) return bytes def readline(self): bytes = self.fd.readline() self.tqdm.update(len(bytes)) return bytes def __enter__(self): self.tqdm.__enter__() return self def __exit__(self, *args, **kwargs): return self.tqdm.__exit__(*args, **kwargs) def tqdm_load(fname, tqdm=None, desc=''): if tqdm == None: if which_env() == which_env.JUPYTER: from tqdm.notebook import tqdm else: from tqdm import tqdm with open(fname, "rb") as fd: total = Path(fname).stat().st_size with TQDMBytesReader(fd, tqdm=tqdm, total=total, desc=desc) as pbfd: up = Unpickler(pbfd) obj = up.load() return obj

31.405405

77

0.60241

cc930b4f7f09f8fa1727fc2d776abafb2a109c6e

5,991

py

Python

tests/test_contrib/test_prepredict.py

uricod/yellowbrick

6fb2e9b7e5b2998c6faa4dcca81a4b0f91bf29b4

[ "Apache-2.0" ]

1

2017-03-03T03:26:54.000Z

tests/test_contrib/test_prepredict.py

uricod/yellowbrick

6fb2e9b7e5b2998c6faa4dcca81a4b0f91bf29b4

[ "Apache-2.0" ]

1

2021-11-10T18:06:19.000Z

tests/test_contrib/test_prepredict.py

uricod/yellowbrick

6fb2e9b7e5b2998c6faa4dcca81a4b0f91bf29b4

[ "Apache-2.0" ]

null

# tests.test_contrib.test_prepredict # Test the prepredict estimator. # # Author: Benjamin Bengfort <benjamin@bengfort.com> # Created: Mon Jul 12 07:07:33 2021 -0400 # # ID: test_prepredict.py [] benjamin@bengfort.com $ """ Test the prepredict estimator. """ ########################################################################## ## Imports ########################################################################## import pytest from io import BytesIO from tests.fixtures import Dataset, Split from tests.base import IS_WINDOWS_OR_CONDA, VisualTestCase from sklearn.naive_bayes import GaussianNB from sklearn.cluster import MiniBatchKMeans from sklearn.linear_model import LinearRegression from sklearn.model_selection import train_test_split as tts from sklearn.datasets import make_classification, make_regression, make_blobs from yellowbrick.contrib.prepredict import * from yellowbrick.regressor import PredictionError from yellowbrick.classifier import ClassificationReport import numpy as np # Set random state np.random.seed() ########################################################################## ## Fixtures ########################################################################## @pytest.fixture(scope="class") def multiclass(request): """ Creates a random multiclass classification dataset fixture """ X, y = make_classification( n_samples=500, n_features=20, n_informative=8, n_redundant=2, n_classes=6, n_clusters_per_class=3, random_state=87, ) X_train, X_test, y_train, y_test = tts(X, y, test_size=0.2, random_state=93) dataset = Dataset(Split(X_train, X_test), Split(y_train, y_test)) request.cls.multiclass = dataset @pytest.fixture(scope="class") def continuous(request): """ Creates a random continuous regression dataset fixture """ X, y = make_regression( n_samples=500, n_features=22, n_informative=8, random_state=42, noise=0.2, bias=0.2, ) X_train, X_test, y_train, y_test = tts(X, y, test_size=0.2, random_state=11) # Set a class attribute for regression request.cls.continuous = Dataset(Split(X_train, X_test), Split(y_train, y_test)) @pytest.fixture(scope="class") def blobs(request): """ Create a random blobs clustering dataset fixture """ X, y = make_blobs( n_samples=1000, n_features=12, centers=6, shuffle=True, random_state=42 ) # Set a class attribute for blobs request.cls.blobs = Dataset(X, y) ########################################################################## ## Tests ########################################################################## @pytest.mark.usefixtures("multiclass") @pytest.mark.usefixtures("continuous") @pytest.mark.usefixtures("blobs") class TestPrePrePredictEstimator(VisualTestCase): """ Pre-predict contrib tests. """ @pytest.mark.xfail( IS_WINDOWS_OR_CONDA, reason="image comparison failure on Conda 3.8 and 3.9 with RMS 19.307", ) def test_prepredict_classifier(self): """ Test the prepredict estimator with classification report """ # Make prepredictions X, y = self.multiclass.X, self.multiclass.y y_pred = GaussianNB().fit(X.train, y.train).predict(X.test) # Create prepredict estimator with prior predictions estimator = PrePredict(y_pred, CLASSIFIER) assert estimator.fit(X.train, y.train) is estimator assert estimator.predict(X.train) is y_pred assert estimator.score(X.test, y.test) == pytest.approx(0.41, rel=1e-3) # Test that a visualizer works with the pre-predictions. viz = ClassificationReport(estimator) viz.fit(None, y.train) viz.score(None, y.test) viz.finalize() self.assert_images_similar(viz) def test_prepredict_regressor(self): """ Test the prepredict estimator with a prediction error plot """ # Make prepredictions X, y = self.continuous.X, self.continuous.y y_pred = LinearRegression().fit(X.train, y.train).predict(X.test) # Create prepredict estimator with prior predictions estimator = PrePredict(y_pred, REGRESSOR) assert estimator.fit(X.train, y.train) is estimator assert estimator.predict(X.train) is y_pred assert estimator.score(X.test, y.test) == pytest.approx(0.9999983124154966, rel=1e-2) # Test that a visualizer works with the pre-predictions. viz = PredictionError(estimator) viz.fit(X.train, y.train) viz.score(X.test, y.test) viz.finalize() self.assert_images_similar(viz, tol=10.0) def test_prepredict_clusterer(self): """ Test the prepredict estimator with a silhouette visualizer """ X = self.blobs.X y_pred = MiniBatchKMeans(random_state=831).fit(X).predict(X) # Create prepredict estimator with prior predictions estimator = PrePredict(y_pred, CLUSTERER) assert estimator.fit(X) is estimator assert estimator.predict(X) is y_pred assert estimator.score(X) == pytest.approx(0.5477478541994333, rel=1e-2) # NOTE: there is currently no cluster visualizer that can take advantage of # the prepredict utility since they all require learned attributes. def test_load(self): """ Test the various ways that prepredict loads data """ # Test callable ppe = PrePredict(lambda: self.multiclass.y.test) assert ppe._load() is self.multiclass.y.test # Test file-like object, assume that str and pathlib.Path work similarly f = BytesIO() np.save(f, self.continuous.y.test) f.seek(0) ppe = PrePredict(f) assert np.array_equal(ppe._load(), self.continuous.y.test) # Test direct array-like completed in other tests.

32.037433

93

0.624604

cc9334543aeeb4519b2ac91825f9b078d9f16ee4

94

py

Python

src6/1/string3.py

pjackson3/cs50

4cf8ca67abfc293d4dbb9bf5a1cb742d74ca7a31

[ "MIT" ]

null

src6/1/string3.py

pjackson3/cs50

4cf8ca67abfc293d4dbb9bf5a1cb742d74ca7a31

[ "MIT" ]

null

src6/1/string3.py

pjackson3/cs50

4cf8ca67abfc293d4dbb9bf5a1cb742d74ca7a31

[ "MIT" ]

1

2020-11-24T23:25:26.000Z

# input and print, with format strings s = input("What's your name?\n") print(f"hello, {s}")

18.8

38

0.659574

cc93993829d4a8f1dc6a847d22783d8908c2b006

2,259

py

Python

examples/metrics/fc/example_idtxl_wrapper_multi.py

HelmchenLabSoftware/mesostat-dev

8baa7120b892fe0df893cdcf0f20f49876643d75

[ "MIT" ]

null

examples/metrics/fc/example_idtxl_wrapper_multi.py

HelmchenLabSoftware/mesostat-dev

8baa7120b892fe0df893cdcf0f20f49876643d75

[ "MIT" ]

null

examples/metrics/fc/example_idtxl_wrapper_multi.py

HelmchenLabSoftware/mesostat-dev

8baa7120b892fe0df893cdcf0f20f49876643d75

[ "MIT" ]

null

import os, sys import numpy as np import matplotlib.pyplot as plt # Export library path rootname = "mesoscopic-functional-connectivity" thispath = os.path.dirname(os.path.abspath(__file__)) rootpath = os.path.join(thispath[:thispath.index(rootname)], rootname) print("Appending project path", rootpath) sys.path.append(rootpath) from codes.lib.info_metrics.info_metrics_generic import parallel_metric_2d from codes.lib.models.test_lib import dynsys from codes.lib.sweep_lib import DataSweep ############################ # Parameters ############################ # DynSys parameters dynsysParam = { 'nNode' : 4, # Number of variables 'nData' : 4000, # Number of timesteps 'nTrial' : 20, # Number of trials 'dt' : 50, # ms, timestep 'tau' : 500, # ms, timescale of each mesoscopic area 'inpT' : 100, # Period of input oscillation 'inpMag' : 0.0, # Magnitude of the periodic input 'std' : 0.2, # STD of neuron noise } # IDTxl parameters idtxlParam = { 'dim_order' : 'rps', 'cmi_estimator' : 'JidtGaussianCMI', 'max_lag_sources' : 5, 'min_lag_sources' : 1, 'window' : 50 } ############################ # Data ############################ nSweep = 10 data = dynsys(dynsysParam) #[trial x channel x time] print("Generated data of shape", data.shape) methods = ['BivariateTE', 'MultivariateTE'] dataSweep1 = DataSweep(data, idtxlParam, nSweepMax=nSweep) timeIdxs = dataSweep1.get_target_time_idxs() # print(timeIdxs) # # from codes.lib.sweep_lib import Sweep2D # # sweeper = Sweep2D(dataSweep1.iterator(), methods, idtxlParam["dim_order"], parTarget=True) # # for i, (method, data, iTrg) in enumerate(sweeper.iterator()): # print(i, method, data.shape, iTrg) results = parallel_metric_2d(dataSweep1.iterator(), "idtxl", methods, idtxlParam, nCore=None) fig, ax = plt.subplots(nrows=nSweep, ncols=2) fig.suptitle("TE computation for several windows of the data") for iMethod, method in enumerate(methods): ax[0][iMethod].set_title(method) print(results[method].shape) for iSweep in range(nSweep): ax[iSweep][0].set_ylabel("time="+str(timeIdxs[iSweep])) ax[iSweep][iMethod].imshow(results[method][iSweep][0]) plt.show()

29.723684

93

0.660912

cc97881f01338acc3697d0a0591a9ee19e6dbad9

1,747

py

Python

project.py

blendacosta/CIP_FinalProject

8f50298d1b35cffe4569f5a78a158d6d699fa532

[ "MIT" ]

null

project.py

blendacosta/CIP_FinalProject

8f50298d1b35cffe4569f5a78a158d6d699fa532

[ "MIT" ]

null

project.py

blendacosta/CIP_FinalProject

8f50298d1b35cffe4569f5a78a158d6d699fa532

[ "MIT" ]

null

''' A Gameplay Mechanic [TS4] By CozyGnomes (https://cozygnomes.tumblr.com/) This is an existing document that contains several things you can do in your gameplay on TS4. This program comes with the intention of automatically generating the related phrase without having to search for each number (as instructed in the original document). Project by Blenda C ''' import random FILE_NAME = 'gpmechanic.txt' def get_mechanics(): # read the file containing the mechanics list_mechanics = [] with open(FILE_NAME, encoding='utf-8') as file: for line in file: # for-each loop gives lines one at a time list_mechanics.append(line.strip()) # strip removes whitespace at the start or end return list_mechanics def introduction(): # gives the introduction and instructions to the user print("") print("A Gameplay Mechanic [TS4] by CozyGnomes") print("Every time you press enter, a new suggestion for your gameplay will be generated. (to exit enter 0)") def main(): introduction() list_mechanics = get_mechanics() chosen_value = random.choice(list_mechanics) # comes with ‘import random’ print("You should...") print('') print(chosen_value) # here is the verification if the user wants to generate an alternative mechanic or wants to leave while True: user_input = input("") if user_input == '': print("or...") chosen_value = random.choice(list_mechanics) print(chosen_value) elif user_input == '0': break # once generated, a final message print('') print("Now it's time to do it! Good luck!") if __name__ == '__main__': main()

37.170213

112

0.661133

cc9a005a14d0a98035518428a505d967d10c254e

101

py

Python

Section 18/8.Document-function-with-keyword-based-arguments.py

airbornum/-Complete-Python-Scripting-for-Automation

bc053444f8786259086269ca1713bdb10144dd74

[ "MIT" ]

18

2020-04-13T03:14:06.000Z

2022-03-09T18:54:41.000Z

Section 18/8.Document-function-with-keyword-based-arguments.py

airbornum/-Complete-Python-Scripting-for-Automation

bc053444f8786259086269ca1713bdb10144dd74

[ "MIT" ]

null

Section 18/8.Document-function-with-keyword-based-arguments.py

airbornum/-Complete-Python-Scripting-for-Automation

bc053444f8786259086269ca1713bdb10144dd74

[ "MIT" ]

22

2020-04-29T21:12:42.000Z

2022-03-17T18:19:54.000Z

def display(a,b): print(f'a={a}') return None display(3,4) display(a=3,b=4) display(b=4,a=3)

14.428571

18

0.60396

cc9b021bad66790ffb26790ca7f0b624083f6bad

830

py

Python

test_dataloader.py

dsoselia/Continual-Learning-Benchmark

b3393aa9eb9a6956a6560ee15904a2cdb046e4c7

[ "MIT" ]

null

test_dataloader.py

dsoselia/Continual-Learning-Benchmark

b3393aa9eb9a6956a6560ee15904a2cdb046e4c7

[ "MIT" ]

null

test_dataloader.py

dsoselia/Continual-Learning-Benchmark

b3393aa9eb9a6956a6560ee15904a2cdb046e4c7

[ "MIT" ]

null

# %% from collections import OrderedDict import dataloaders.base from dataloaders.datasetGen import SplitGen, PermutedGen train_dataset, val_dataset = dataloaders.base.__dict__["CIFAR10"]('data', False) # %% print(train_dataset) # %% train_dataset_splits, val_dataset_splits, task_output_space = SplitGen(train_dataset, val_dataset, first_split_sz=2, other_split_sz=2, rand_split=False, remap_class=not False) # %% for X, y , cl in train_dataset_splits["4"]: print(X.shape) print("cl") print(y) break # %% # %%

28.62069

98

0.474699

cc9ccfbe858fbd860be65559e189e269ad336b72

4,631

py

Python

zsalt/pretty_redshift.py

dmnielsen/zSALT

938904ef8f331d5e047bd00a04a4424e982552e2

[ "BSD-3-Clause" ]

null

zsalt/pretty_redshift.py

dmnielsen/zSALT

938904ef8f331d5e047bd00a04a4424e982552e2

[ "BSD-3-Clause" ]

null

zsalt/pretty_redshift.py

dmnielsen/zSALT

938904ef8f331d5e047bd00a04a4424e982552e2

[ "BSD-3-Clause" ]

null

import sys import pyfits import numpy as np from PySpectrograph import Spectrum from PySpectrograph.Utilities.fit import interfit import pylab as pl def ncor(x, y): """Calculate the normalized correlation of two arrays""" d=np.correlate(x,x)*np.correlate(y,y) if d<=0: return 0 return np.correlate(x,y)/d**0.5 def xcor_redshift(spectra, template, sub=False, z1=0, z2=1, zstep=0.001): """Meaure the redshift of a spectra by cross correlating it with a template returns an array of correlation values """ zvalue=np.arange(z1,z2,zstep) cc_arr=np.zeros(len(zvalue)) sflux=continuum_subtract(spectra) tflux=continuum_subtract(template) for i,z in enumerate(zvalue): nflux=np.interp(spectra.wavelength, template.wavelength*(1+z), tflux) cc_arr[i]=ncor(sflux, nflux) return zvalue, cc_arr def continuum_subtract(spec, function='polynomial', order=7): """Fit a function to a spectra and subtract the continuum""" wc=interfit(spec.wavelength, spec.flux, function=function, order=order) wc.interfit() return spec.flux-wc(spec.wavelength) def loadtext(infile): warr, farr=np.loadtxt(infile, usecols=(0,1), unpack=True) spec=Spectrum.Spectrum(warr, farr, stype='continuum') return spec def loadiraf(hdu): farr=hdu[0].data xarr=np.arange(len(farr)) warr=hdu[0].header['CRVAL1']+hdu[0].header['CDELT1']*(xarr+hdu[0].header['CRPIX1']) mask=(farr>10) spec=Spectrum.Spectrum(warr[mask], farr[mask], stype='continuum') return spec def loadsdss(hdu): farr=hdu[0].data[0] print len(farr) xarr=np.arange(len(farr)) warr=10**(hdu[0].header['CRVAL1']+hdu[0].header['CD1_1']*(xarr+1)) spec=Spectrum.Spectrum(warr, farr, stype='continuum') return spec def readlinelist(infile): line_wave=[] line_name=[] for lines in open(infile).readlines(): l=lines.split() line_wave.append(l[0]) line_name.append(l[1]) return line_wave, line_name if __name__=='__main__': if sys.argv[1].count('fits'): hdu=pyfits.open(sys.argv[1]) spec=loadiraf(hdu) else: spec=loadtext(sys.argv[1]) thdu=pyfits.open(sys.argv[2]) zc=float(sys.argv[3]) template=loadsdss(thdu) z1=max(0,zc-0.15) z2=max(0,zc+0.15) z_arr, cc_arr=xcor_redshift(spec, template, z1=z1, z2=z2, zstep=0.0001) z=z_arr[cc_arr.argmax()] print z #z_arr, cc_arr=xcor_redshift(spec, template, z1=z-0.05, z2=z+0.05, zstep=0.0001) #z=z_arr[cc_arr.argmax()] #print z pl.figure() sp=pl.axes([0.15,0.15,0.8,0.8]) cflux=np.convolve(spec.flux, np.ones(10), mode='same') #cflux*=1000e16 sp.plot(spec.wavelength, cflux, color='#000000') coef=np.polyfit(spec.wavelength, cflux, 3) #sp.plot(spec.wavelength, np.polyval(coef, spec.wavelength)) nflux=np.interp(spec.wavelength, (1+z)*template.wavelength, template.flux) tcoef=np.polyfit(spec.wavelength, nflux*cflux.mean()/nflux.mean(), 2) #ratio=cflux.mean()/nflux.mean()#*np.polyval(coef, spec.wavelength)/np.polyval(tcoef, spec.wavelength) ratio=cflux.mean()/nflux.mean()*np.polyval(coef, spec.wavelength)/np.polyval(tcoef, spec.wavelength) #sp.plot(spec.wavelength, nflux*ratio*0.5-0.4e-16, color='#FF0000') sp.plot(spec.wavelength, nflux*ratio, color='#FF0000') #sp.plot(spec.wavelength, np.polyval(tcoef, spec.wavelength)) #pl.plot((1+z)*template.wavelength, template.flux*spec.flux.mean()/template.flux.mean()) spname=sys.argv[1].split('_')[0] #sp.set_ylim([0,2000]) x1,x2=sp.get_xlim() y1,y2=sp.get_ylim() print y1,y2, x1,x2 line_wave, line_name=readlinelist('redshift/sdss.linelist') dx=10 for w,n in zip(line_wave, line_name): w=float(w)*(1+z) if w>x1 and w< x2: sp.plot([w,w],[y1,y2],ls='--', color='#AAAAAA') sp.text(w, y2-dx, '$%s$' % n.replace('_', '\\'), color='#AAAAAA', fontsize=8) #if dx<300: # dx+=100 #else: # dx=100 spname=sys.argv[4] sp.text(4500,0.8*y2,spname, fontsize=24) sp.text(4500,0.70*y2,'z=%5.4f' % zc, fontsize=24) sp.set_ylabel('Counts') sp.set_xlabel('$\lambda \ (\AA)$') if len(sys.argv)>5: sy1=float(sys.argv[5]) sy2=float(sys.argv[6]) else: sy1=0.7 sy2=0.7 if False: cc=pl.axes([sy1, sy2,0.2,0.2]) cc.plot(z_arr, cc_arr, color='#777777') xticks=np.arange(100*z1,100*z2+1,10, dtype=int)/100.0 print xticks cc.set_xticks(xticks) cc.set_yticklabels([]) cc.set_xlabel('z') cc.set_title('X-corr Function') pl.savefig(spname+'.png') pl.show()

30.668874

105

0.649536

cc9d27fc8106d84826a9bc91142a53251a35c56a

62,936

py

Python

niimpy/preprocess.py

CxAalto/niimpy

ffd20f9c6aba1671d9035f47715d1649ced0e6e7

[ "MIT" ]

5

2021-11-23T12:05:23.000Z

2022-02-11T12:57:50.000Z

niimpy/preprocess.py

niima-project/niimpy

975470507b1f8836d9e29d43601e345612b06a62

[ "MIT" ]

62

2021-07-16T09:17:18.000Z

2022-03-16T11:27:50.000Z

niimpy/preprocess.py

niima-project/niimpy

975470507b1f8836d9e29d43601e345612b06a62

[ "MIT" ]

6

2021-09-07T13:06:57.000Z

2022-03-14T11:26:30.000Z

################################################################################ # This is the main file for preprocessing smartphone sensor data # # # # Contributors: Anna Hakala & Ana Triana # ################################################################################ import niimpy import numpy as np import pandas as pd from pandas import Series import matplotlib.pyplot as plt import seaborn as sns import time import datetime import pytz import niimpy.aalto # backwards compatibility aliases from .screen import screen_off, screen_duration def date_range(df, begin, end): """Extract out a certain date range from a DataFrame. Extract out a certain data range from a dataframe. The index must be the dates, and the index must be sorted. """ # TODO: is this needed? Do normal pandas operation, timestamp # checking is not really needed (and limits the formats that can # be used, pandas can take more than pd.Timestamp) if(begin!=None): assert isinstance(begin,pd.Timestamp),"begin not given in timestamp format" else: begin = df.index[0] if(end!= None): assert isinstance(end,pd.Timestamp),"end not given in timestamp format" else: end = df.index[-1] df_new = df.loc[begin:end] return df_new # Above this point is function that should *stay* in preprocess.py # Below this is functions that may or may not be moved. def get_subjects(database): """ Returns a list of the subjects in the database Parameters ---------- database: database """ # TODO: deprecate, user should do ['user'].unique() on dataframe themselves assert isinstance(database, niimpy.database.Data1),"database not given in Niimpy database format" questions = database.raw(table='AwareHyksConverter', user=niimpy.ALL) subjects=list(questions.user.unique()) return subjects def get_phq9(database,subject): """ Returns the phq9 scores from the databases per subject Parameters ---------- database: database user: string Returns ------- phq9: Dataframe with the phq9 score """ # TODO: Most of this logic can be moved to sum_survey_cores assert isinstance(database, niimpy.database.Data1),"database not given in Niimpy database format" assert isinstance(subject, str),"user not given in string format" phq9 = niimpy.aalto.phq9_raw(database) phq9 = phq9[phq9['user']==subject] phq9 = phq9.drop(['user','source'],axis=1) phq9 = phq9.sort_index() phq9 = phq9.reset_index().drop_duplicates(subset=['index','id'],keep='first').set_index('index') phq9 = phq9.groupby(phq9.index)['answer'].sum() phq9 = phq9.to_frame() return phq9 #surveys def daily_affect_variability(questions, subject=None): """ Returns two DataFrames corresponding to the daily affect variability and mean daily affect, both measures defined in the OLO paper available in 10.1371/journal.pone.0110907. In brief, the mean daily affect computes the mean of each of the 7 questions (e.g. sad, cheerful, tired) asked in a likert scale from 0 to 7. Conversely, the daily affect viariability computes the standard deviation of each of the 7 questions. NOTE: This function aggregates data by day. Parameters ---------- questions: DataFrame with subject data (or database for backwards compatibility) subject: string, optional (backwards compatibility only, in the future do filtering before). Returns ------- DLA_mean: mean of the daily affect DLA_std: standard deviation of the daily affect """ # TODO: The daily summary (mean/std) seems useful, can we generalize? # Backwards compatibilty if a database was passed if isinstance(questions, niimpy.database.Data1): questions = questions.raw(table='AwareHyksConverter', user=subject) # Maintain backwards compatibility in the case subject was passed and # questions was *not* a dataframe. elif isinstance(subject, string): questions = questions[questions['user'] == subject] questions=questions[(questions['id']=='olo_1_1') | (questions['id']=='olo_1_2') | (questions['id']=='olo_1_3') | (questions['id']=='olo_1_4') | (questions['id']=='olo_1_5') | (questions['id']=='olo_1_6') | (questions['id']=='olo_1_7') | (questions['id']=='olo_1_8')] questions['answer']=pd.to_numeric(questions['answer']) questions = questions.drop(['device', 'time', 'user'], axis=1) if (pd.Timestamp.tzname(questions.index[0]) != 'EET'): if pd.Timestamp.tzname(questions.index[0]) != 'EEST': questions.index = pd.to_datetime(questions.index).tz_localize('Europe/Helsinki') questions=questions.drop_duplicates(subset=['datetime','id'],keep='first') questions=questions.pivot_table(index='datetime', columns='id', values='answer') questions=questions.rename(columns={'olo_1_1': 'cheerful', 'olo_1_2': 'tired','olo_1_3': 'content', 'olo_1_4': 'nervous','olo_1_5': 'tranquil', 'olo_1_6': 'sad', 'olo_1_7': 'excited', 'olo_1_8': 'active'}) questions = questions.reset_index() DLA = questions.copy() questions['date_minus_time'] = questions['datetime'].apply( lambda questions : datetime.datetime(year=questions.year, month=questions.month, day=questions.day)) questions.set_index(questions["date_minus_time"],inplace=True) DLA_std = questions.resample('D').std()#), how='std') DLA_std=DLA_std.rename(columns={'date_minus_time': 'datetime'}) DLA_std.index = pd.to_datetime(DLA_std.index).tz_localize('Europe/Helsinki') DLA_mean = questions.resample('D').mean() DLA_mean=DLA_mean.rename(columns={'date_minus_time': 'datetime'}) DLA_mean.index = pd.to_datetime(DLA_mean.index).tz_localize('Europe/Helsinki') return DLA_std, DLA_mean #Ambient Noise def ambient_noise(noise, subject, begin=None, end=None): """ Returns a Dataframe with 5 possible computations regarding the noise ambient plug-in: average decibels, average frequency, number of times when there was noise in the day, number of times when there was a loud noise in the day (>70dB), and number of times when the noise matched the speech noise level and frequency (65Hz < freq < 255Hz and dB>50 ) NOTE: This function aggregates data by day. Parameters ---------- noise: DataFrame with subject data (or database for backwards compatibility) subject: string, optional (backwards compatibility only, in the future do filtering before). begin: datetime, optional end: datetime, optional Returns ------- avg_noise: Dataframe """ # TODO: move to niimpy.noise # TODO: add arguments for frequency/decibels/silence columns # Backwards compatibilty if a database was passed if isinstance(noise, niimpy.database.Data1): noise = noise.raw(table='AwareAmbientNoise', user=subject) # Maintain backwards compatibility in the case subject was passed and # questions was *not* a dataframe. elif isinstance(subject, string): noise = noise[noise['user'] == subject] # Shrink the dataframe down to only what we need noise = noise[['double_frequency', 'is_silent', 'double_decibels', 'datetime']] # Extract the data range (In the future should be done before this function # is called.) if begin is not None or end is not None: noise = date_range(noise, begin, end) noise['is_silent']=pd.to_numeric(noise['is_silent']) loud = noise[noise.double_decibels>70] #check if environment was noisy speech = noise[noise['double_frequency'].between(65, 255)] speech = speech[speech.is_silent==0] #check if there was a conversation silent=noise[noise.is_silent==0] #This is more what moments there are noise in the environment. avg_noise=noise.resample('D', on='datetime').mean() #average noise avg_noise=avg_noise.drop(['is_silent'],axis=1) if not silent.empty: silent=silent.resample('D', on='datetime').count() silent = silent.drop(['double_decibels','double_frequency','datetime'],axis=1) silent=silent.rename(columns={'is_silent':'noise'}) avg_noise = avg_noise.merge(silent, how='outer', left_index=True, right_index=True) if not loud.empty: loud=loud.resample('D', on='datetime').count() loud = loud.drop(['double_decibels','double_frequency','datetime'],axis=1) loud=loud.rename(columns={'is_silent':'loud'}) avg_noise = avg_noise.merge(loud, how='outer', left_index=True, right_index=True) if not speech.empty: speech=speech.resample('D', on='datetime').count() speech = speech.drop(['double_decibels','double_frequency','datetime'],axis=1) speech=speech.rename(columns={'is_silent':'speech'}) avg_noise = avg_noise.merge(speech, how='outer', left_index=True, right_index=True) return avg_noise #Application def shutdown_info(database,subject,begin=None,end=None): """ Returns a DataFrame with the timestamps of when the phone has shutdown. NOTE: This is a helper function created originally to preprocess the application info data Parameters ---------- database: Niimpy database user: string begin: datetime, optional end: datetime, optional Returns ------- shutdown: Dataframe """ bat = niimpy.read._get_dataframe(database, table='AwareBattery', user=subject) bat = niimpy.filter_dataframe(bat, begin=begin, end=end) # TODO: move to niimpy.battery if 'datetime' in bat.columns: bat = bat[['battery_status', 'datetime']] else: bat = bat[['battery_status']] bat=bat.loc[begin:end] bat['battery_status']=pd.to_numeric(bat['battery_status']) shutdown = bat[bat['battery_status'].between(-3, 0, inclusive=False)] return shutdown def get_seconds(time_delta): """ Converts the timedelta to seconds NOTE: This is a helper function Parameters ---------- time_delta: Timedelta """ return time_delta.dt.seconds def app_duration(database,subject,begin=None,end=None,app_list_path=None): """ Returns two DataFrames contanining the duration and number of events per group of apps, e.g. number of times a person used communication apps like WhatsApp, Telegram, Messenger, sms, etc. and for how long these apps were used in a day (in seconds). Parameters ---------- database: Niimpy database user: string begin: datetime, optional end: datetime, optional app_list_path: path to the csv file where the apps are classified into groups Returns ------- duration: Dataframe count: Dataframe """ assert isinstance(database, niimpy.database.Data1),"database not given in Niimpy database format" assert isinstance(subject, str),"user not given in string format" app = database.raw(table='AwareApplicationNotifications', user=subject) if(begin!=None): assert isinstance(begin,pd.Timestamp),"begin not given in timestamp format" else: begin = app.iloc[0]['datetime'] if(end!= None): assert isinstance(end,pd.Timestamp),"end not given in timestamp format" else: end = app.iloc[len(app)-1]['datetime'] if(app_list_path==None): app_list_path = '/m/cs/scratch/networks-nima/ana/niima-code/Datastreams/Phone/apps_group.csv' app = app.drop(columns=['device','user','time','defaults','sound','vibrate']) app=app.loc[begin:end] #Classify the apps into groups app_list=pd.read_csv(app_list_path) app['group']=np.nan for index, row in app.iterrows(): group=app_list.isin([row['application_name']]).any() group=group.reset_index() if (not any(group[0])): app.loc[index,'group']=10 else: app.loc[index,'group']=group.index[group[0] == True].tolist()[0] #Insert missing data due to phone being shut down shutdown = shutdown_info(database,subject,begin,end) if not shutdown.empty: shutdown['group']=11 shutdown['battery_status'] = 'off' app = app.merge(shutdown, how='outer', left_index=True, right_index=True) app['application_name'] = app['application_name'].replace(np.nan, 'off', regex=True) app['group_x'] = app['group_x'].replace(np.nan, 11, regex=True) app = app.drop(['battery_status','group_y'], axis=1) dates=app.datetime_x.combine_first(app.datetime_y) app['datetime']=dates app = app.drop(['datetime_x','datetime_y'], axis=1) app=app.rename(columns={'group_x':'group'}) #Insert missing data due to the screen being off screen=screen_off(database,subject,begin,end) if not screen.empty: app = app.merge(screen, how='outer', left_index=True, right_index=True) app['application_name'] = app['application_name'].replace(np.nan, 'off', regex=True) app['group'] = app['group'].replace(np.nan, 11, regex=True) del app['screen_status'] dates=app.datetime_x.combine_first(app.datetime_y) app['datetime']=dates app = app.drop(['datetime_x','datetime_y'], axis=1) #Insert missing data caught by sms but unknown cause sms = database.raw(table='AwareMessages', user=subject) sms = sms.drop(columns=['device','user','time','trace']) sms = sms.drop_duplicates(subset=['datetime','message_type'],keep='first') sms = sms[sms.message_type=='outgoing'] sms = sms.loc[begin:end] if not sms.empty: app = app.merge(sms, how='outer', left_index=True, right_index=True) app['application_name'] = app['application_name'].replace(np.nan, 'sms', regex=True) app['group'] = app['group'].replace(np.nan, 2, regex=True) del app['message_type'] dates=app.datetime_x.combine_first(app.datetime_y) app['datetime']=dates app = app.drop(['datetime_x','datetime_y'], axis=1) #Insert missing data caught by calls but unknown cause call = database.raw(table='AwareCalls', user=subject) if not call.empty: call = call.drop(columns=['device','user','time','trace']) call = call.drop_duplicates(subset=['datetime','call_type'],keep='first') call['call_duration'] = pd.to_timedelta(call.call_duration.astype(int), unit='s') call = call.loc[begin:end] dummy = call.datetime+call.call_duration dummy = pd.Series.to_frame(dummy) dummy['finish'] = dummy[0] dummy = dummy.set_index(0) call = call.merge(dummy, how='outer', left_index=True, right_index=True) dates=call.datetime.combine_first(call.finish) call['datetime']=dates call = call.drop(columns=['call_duration','finish']) app = app.merge(call, how='outer', left_index=True, right_index=True) app.group = app.group.fillna(2) app.application_name = app.application_name.fillna('call') dates=app.datetime_x.combine_first(app.datetime_y) app['datetime']=dates app = app.drop(columns=['datetime_x','datetime_y','call_type']) #Calculate the app duration per group app['duration']=np.nan app['duration']=app['datetime'].diff() app['duration'] = app['duration'].shift(-1) app['datetime'] = app['datetime'].dt.floor('d') duration=pd.pivot_table(app,values='duration',index='datetime', columns='group', aggfunc=np.sum) count=pd.pivot_table(app,values='duration',index='datetime', columns='group', aggfunc='count') duration.columns = duration.columns.map({0.0: 'sports', 1.0: 'games', 2.0: 'communication', 3.0: 'social_media', 4.0: 'news', 5.0: 'travel', 6.0: 'shop', 7.0: 'entretainment', 8.0: 'work_study', 9.0: 'transportation', 10.0: 'other', 11.0: 'off'}) count.columns = count.columns.map({0.0: 'sports', 1.0: 'games', 2.0: 'communication', 3.0: 'social_media', 4.0: 'news', 5.0: 'travel', 6.0: 'shop', 7.0: 'entretainment', 8.0: 'work_study', 9.0: 'transportation', 10.0: 'other', 11.0: 'off'}) duration = duration.apply(get_seconds,axis=1) return duration, count #Communication def call_info(database,subject,begin=None,end=None): """ Returns a DataFrame contanining the duration and number of events per type of calls (outgoing, incoming, missed). The Dataframe summarizes the duration of the incoming/outgoing calls in seconds, number of those events, and how long (in seconds) the person has spoken to the top 5 contacts (most frequent) Parameters ---------- database: Niimpy database user: string begin: datetime, optional end: datetime, optional Returns ------- duration: Dataframe """ assert isinstance(database, niimpy.database.Data1),"database not given in Niimpy database format" assert isinstance(subject, str),"user not given in string format" call = database.raw(table='AwareCalls', user=subject) if(begin!=None): assert isinstance(begin,pd.Timestamp),"begin not given in timestamp format" else: begin = call.iloc[0]['datetime'] if(end!= None): assert isinstance(end,pd.Timestamp),"end not given in timestamp format" else: end = call.iloc[len(call)-1]['datetime'] call = call.drop(columns=['device','user','time']) call = call.loc[begin:end] call['datetime'] = call['datetime'].dt.floor('d') call['call_duration']=pd.to_numeric(call['call_duration']) duration = call.groupby(['datetime']).sum() missed_calls = call.loc[(call['call_type'] == 'missed')].groupby(['datetime']).count() outgoing_calls = call.loc[(call['call_type'] == 'outgoing')].groupby(['datetime']).count() incoming_calls = call.loc[(call['call_type'] == 'incoming')].groupby(['datetime']).count() duration['call_missed'] = missed_calls['call_type'] duration['call_outgoing'] = outgoing_calls['call_type'] duration['call_incoming'] = incoming_calls['call_type'] duration2 = call.pivot_table(index='datetime', columns='call_type', values='call_duration',aggfunc='sum') if ('incoming' in duration2.columns): duration2 = duration2.rename(columns={'incoming': 'call_incoming_duration'}) if ('outgoing' in duration2.columns): duration2 = duration2.rename(columns={'outgoing': 'call_outgoing_duration'}) if ('missed' in duration2.columns): duration2 = duration2.drop(columns=['missed']) duration = duration.merge(duration2, how='outer', left_index=True, right_index=True) duration = duration.fillna(0) if ('missed_y' in duration.columns): duration = duration.drop(columns=['missed_y']) #duration.columns = ['total_call_duration', 'call_missed', 'call_outgoing', 'call_incoming', 'call_incoming_duration', 'call_outgoing_duration'] #Now let's calculate something more sophisticated... Let's see trace = call.groupby(['trace']).count() trace = trace.sort_values(by=['call_type'], ascending=False) top5 = trace.index.values.tolist()[:5] call['frequent']=0 call = call.reset_index() call = call.rename(columns={'index': 'date'}) for index, row in call.iterrows(): if (call.loc[index,'trace'] in top5): call.loc[index,'frequent']=1 call['frequent'] = call['frequent'].astype(str) duration2 = call.pivot_table(index='date', columns=['call_type','frequent'], values='call_duration',aggfunc='sum') duration2.columns = ['_'.join(col) for col in duration2.columns] duration2 = duration2.reset_index() #duration2.columns = ['datetime','incoming_0','incoming_1','missed_0','missed_1','outgoing_0','outgoing_1'] duration2['datetime'] = duration2['date'].dt.floor('d') duration2 = duration2.groupby(['datetime']).sum() if ('incoming_0' in duration2.columns): duration2 = duration2.drop(columns=['incoming_0']) if ('missed_0' in duration2.columns): duration2 = duration2.drop(columns=['missed_0']) if ('missed_1' in duration2.columns): duration2 = duration2.drop(columns=['missed_1']) if ('outgoing_0' in duration2.columns): duration2 = duration2.drop(columns=['outgoing_0']) duration = duration.merge(duration2, how='outer', left_index=True, right_index=True) duration = duration.rename(columns={'incoming_1': 'incoming_duration_top5', 'outgoing_1': 'outgoing_duration_top5'}) return duration def sms_info(database,subject,begin=None,end=None): """ Returns a DataFrame contanining the number of events per type of messages SMS (outgoing, incoming). The Dataframe summarizes the number of the incoming/outgoing sms and how many of those correspond to the top 5 contacts (most frequent with whom the subject exchanges texts) Parameters ---------- database: Niimpy database user: string begin: datetime, optional end: datetime, optional Returns ------- sms_stats: Dataframe """ assert isinstance(database, niimpy.database.Data1),"database not given in Niimpy database format" assert isinstance(subject, str),"user not given in string format" sms = database.raw(table='AwareMessages', user=subject) if(begin!=None): assert isinstance(begin,pd.Timestamp),"begin not given in timestamp format" else: begin = sms.iloc[0]['datetime'] if(end!= None): assert isinstance(end,pd.Timestamp),"end not given in timestamp format" else: end = sms.iloc[len(sms)-1]['datetime'] sms = sms.drop(columns=['device','user','time']) sms['datetime'] = sms['datetime'].dt.floor('d') sms = sms.loc[begin:end] if (len(sms)>0): sms_stats = sms.copy() sms_stats['dummy'] = 1 sms_stats = sms_stats.pivot_table(index='datetime', columns='message_type', values='dummy',aggfunc='sum') #Now let's move to somethign more sophisticated trace = sms.groupby(['trace']).count() trace = trace.sort_values(by=['message_type'], ascending=False) top5 = trace.index.values.tolist()[:5] sms['frequent']=0 sms = sms.reset_index() sms = sms.rename(columns={'index': 'date'}) for index, row in sms.iterrows(): if (sms.loc[index,'trace'] in top5): sms.loc[index,'frequent']=1 sms['frequent'] = sms['frequent'].astype(str) sms['dummy']=1 dummy = sms.pivot_table(index='date', columns=['message_type','frequent'], values='dummy',aggfunc='sum') dummy.columns = ['_'.join(col) for col in dummy.columns] dummy = dummy.reset_index() dummy['datetime'] = dummy['date'].dt.floor('d') dummy = dummy.groupby(['datetime']).sum() if ('incoming_0' in dummy.columns): dummy = dummy.drop(columns=['incoming_0']) if ('outgoing_0' in dummy.columns): dummy = dummy.drop(columns=['outgoing_0']) sms_stats = sms_stats.merge(dummy, how='outer', left_index=True, right_index=True) sms_stats = sms_stats.rename(columns={'incoming_1': 'sms_incoming_top5', 'outgoing_1': 'sms_outgoing_top5'}) sms_stats = sms_stats.fillna(0) if ('incoming' in sms_stats.columns): sms_stats = sms_stats.rename(columns={'incoming': 'sms_incoming'}) if ('outgoing' in sms_stats.columns): sms_stats = sms_stats.rename(columns={'outgoing': 'sms_outgoing'}) return sms_stats else: sms_stats = pd.DataFrame() return sms_stats def sms_duration(database,subject,begin,end): """ Returns a DataFrame contanining the duration per type of messages SMS (outgoing, incoming). The Dataframe summarizes the calculated duration of the incoming/outgoing sms and the lags (i.e. the period between receiving a message and reading/writing a reply). NOTE: The foundation of this function is still weak and needs discussion Parameters ---------- database: Niimpy database user: string begin: datetime, optional end: datetime, optional Returns ------- reading: Dataframe writing: Dataframe """ assert isinstance(database, niimpy.database.Data1),"database not given in Niimpy database format" assert isinstance(subject, str),"user not given in string format" app = database.raw(table='AwareApplicationNotifications', user=subject) if(begin!=None): assert isinstance(begin,pd.Timestamp),"begin not given in timestamp format" else: begin = app.iloc[0]['datetime'] if(end!= None): assert isinstance(end,pd.Timestamp),"end not given in timestamp format" else: end = app.iloc[len(app)-1]['datetime'] app = app.drop(columns=['device','user','time','defaults','sound','vibrate']) #Insert missing data due to phone being shut down shutdown = shutdown_info(database,subject,begin,end) shutdown=shutdown.rename(columns={'battery_status':'application_name'}) shutdown['application_name'] = 'off' app = app.merge(shutdown, how='outer', left_index=True, right_index=True) app['application_name_x'] = app['application_name_x'].replace(np.nan, 'off', regex=True) del app['application_name_y'] dates=app.datetime_x.combine_first(app.datetime_y) app['datetime']=dates app = app.drop(['datetime_x','datetime_y'],axis=1) app=app.rename(columns={'application_name_x':'application_name'}) #Insert missing data due to the screen being off screen=screen_off(database,subject,begin,end) app = app.merge(screen, how='outer', left_index=True, right_index=True) app['application_name'] = app['application_name'].replace(np.nan, 'off', regex=True) del app['screen_status'] dates=app.datetime_x.combine_first(app.datetime_y) app['datetime']=dates app = app.drop(['datetime_x','datetime_y'],axis=1) app = app.drop_duplicates(subset=['datetime','application_name'],keep='first') #Insert missing data caught by sms but unknown cause sms = database.raw(table='AwareMessages', user=subject) sms = sms.drop(columns=['device','user','time','trace']) sms = sms.drop_duplicates(subset=['datetime','message_type'],keep='first') #sms = sms[sms.message_type=='outgoing'] app = app.merge(sms, how='outer', left_index=True, right_index=True) app.loc[app['application_name'].isnull(),'application_name'] = app['message_type'] del app['message_type'] dates=app.datetime_x.combine_first(app.datetime_y) app['datetime']=dates app = app.drop(['datetime_x','datetime_y'],axis=1) #Calculate the app duration app['duration']=np.nan app['duration']=app['datetime'].diff() app['duration'] = app['duration'].shift(-1) #Select the text applications only sms_app_name = ['Messages','Mensajería','MensajerÃa','Viestit','incoming','outgoing'] app = app[app['application_name'].isin(sms_app_name)] sms_app_name = ['Messages','Mensajería','MensajerÃa','Viestit'] app['application_name'].loc[(app['application_name'].isin(sms_app_name))] = 'messages' app['group']=np.nan for i in range(len(app)-1): if (app.application_name[i]=='incoming' and app.application_name[i+1]=='messages'): app.group[i+1]=1 elif (app.application_name[i]=='messages' and app.application_name[i+1]=='outgoing'): app.group[i+1]=2 else: app.group[i+1]=0 app['lags'] = app['datetime'].diff() app['datetime'] = app['datetime'].dt.floor('d') app=app.loc[begin:end] reading = app.loc[(app['group']==1)] if (len(reading)>0): reading = pd.pivot_table(reading,values=['duration','lags'],index='datetime', columns='application_name', aggfunc=np.sum) reading.columns = ['reading_duration','reading_lags'] reading = reading.apply(get_seconds,axis=1) writing = app.loc[(app['group']==2)] if (len(writing)>0): for i in range(len(writing)-1): if (writing.lags[i].seconds<15 or writing.lags[i].seconds>120): writing.lags[i] = datetime.datetime.strptime('00:05', "%M:%S") - datetime.datetime.strptime("00:00", "%M:%S") del writing['duration'] writing = writing.rename(columns={'lags':'writing_duration'}) writing = pd.pivot_table(writing,values='writing_duration',index='datetime', columns='application_name', aggfunc=np.sum) writing = writing.apply(get_seconds,axis=1) return reading, writing def communication_info(database,subject,begin=None,end=None): """ Returns a DataFrame contanining all the information extracted from communication's events (calls, sms, and communication apps like WhatsApp, Telegram, Messenger, etc.). Regarding calls, this function contains the duration of the incoming/outgoing calls in seconds, number of those events, and how long (in seconds) the person has spoken to the top 5 contacts (most frequent). Regarding the SMSs, this function contains the number of incoming /outgoing events, and the top 5 contacts (most frequent). Aditionally, we also include the calculated duration of the incoming/outgoing sms and the lags (i.e. the period between receiving a message and reading/writing a reply). Regarding the app, the duration of communication events is summarized. This function also sums all the different durations (calls, SMSs, apps) and provides the duration (in seconds) that a person spent communicating during the day. Parameters ---------- database: Niimpy database user: string begin: datetime, optional end: datetime, optional Returns ------- call_summary: Dataframe """ assert isinstance(database, niimpy.database.Data1),"database not given in Niimpy database format" assert isinstance(subject, str),"user not given in string format" app = database.raw(table='AwareApplicationNotifications', user=subject) if(begin!=None): assert isinstance(begin,pd.Timestamp),"begin not given in timestamp format" else: begin = app.iloc[0]['datetime'] if(end!= None): assert isinstance(end,pd.Timestamp),"end not given in timestamp format" else: end = app.iloc[len(app)-1]['datetime'] duration_app, count_app = app_duration(database,subject,begin,end) call_summary = call_info(database,subject,begin,end) sms_summary = sms_info(database,subject,begin,end) #reading, writing = sms_duration(database,subject,begin,end) if (not sms_summary.empty): call_summary = call_summary.merge(sms_summary, how='outer', left_index=True, right_index=True) call_summary = call_summary.fillna(0) #Now let's see if there is any info from the apps worth bringin back if ('communication' in duration_app.columns): #2 is the number for communication apps comm_app = duration_app['communication']#.dt.seconds comm_app = comm_app.fillna(0) comm_app = comm_app.to_frame() if ('social_media' in duration_app.columns): #2 is the number for communication apps social_app = duration_app['social_media']#.dt.seconds social_app = social_app.fillna(0) social_app = social_app.to_frame() try: social_app try: comm_app comm_app = comm_app.merge(social_app, how='outer', left_index=True, right_index=True) except NameError: comm_app = social_app except NameError: pass try: comm_app call_summary = call_summary.merge(comm_app, how='outer', left_index=True, right_index=True) except NameError: pass call_summary = call_summary.fillna(0) if ('communication' in call_summary.columns): call_summary['total_comm_duration'] = call_summary['call_duration']+call_summary['communication'] if (('social_media' in call_summary.columns) and ('communication' in call_summary.columns)): call_summary['total_comm_duration'] = call_summary['call_duration']+call_summary['social_media']+call_summary['communication'] if ('communication' in call_summary.columns): call_summary=call_summary.rename(columns={'communication':'comm_apps_duration'}) if ('social_media' in call_summary.columns): call_summary=call_summary.rename(columns={'social_media':'social_apps_duration'}) #Now let's see if there is any info from the sms duration '''if (len(reading)>0): reading['reading_duration'] = reading['reading_duration']#.dt.seconds reading['reading_lags'] = reading['reading_lags']#.dt.seconds call_summary = call_summary.merge(reading, how='outer', left_index=True, right_index=True) call_summary = call_summary.fillna(0) call_summary['total_comm_duration'] = call_summary['total_comm_duration']+call_summary['reading_duration'] if (len(writing)>0): writing=writing.rename(columns={'outgoing':'writing_duration'}) writing['writing_duration'] = writing['writing_duration']#.dt.seconds call_summary = call_summary.merge(writing, how='outer', left_index=True, right_index=True) call_summary = call_summary.fillna(0) call_summary['total_comm_duration'] = call_summary['total_comm_duration']+call_summary['writing_duration']''' return call_summary #Occurrences def occurrence_call_sms(database,subject,begin=None,end=None): """ Returns a DataFrame contanining the number of events that occur in a day for call and sms. The events are binned in 12-minutes, i.e. if there is an event at 11:05 and another one at 11:45, 2 occurences happened in one hour. Then, the sum of these occurences yield the number per day. Parameters ---------- database: Niimpy database user: string begin: datetime, optional end: datetime, optional Returns ------- event: Dataframe """ assert isinstance(database, niimpy.database.Data1),"database not given in Niimpy database format" assert isinstance(subject, str),"user not given in string format" call = database.raw(table='AwareCalls', user=subject) if not call.empty: if(begin!=None): assert isinstance(begin,pd.Timestamp),"begin not given in timestamp format" else: begin = call.iloc[0]['datetime'] if(end!= None): assert isinstance(end,pd.Timestamp),"end not given in timestamp format" else: end = call.iloc[len(call)-1]['datetime'] call = call.drop(columns=['device','user','time']) call = call.loc[begin:end] sms = database.raw(table='AwareMessages', user=subject) if not sms.empty: if(begin!=None): assert isinstance(begin,pd.Timestamp),"begin not given in timestamp format" else: begin = call.iloc[0]['datetime'] if(end!= None): assert isinstance(end,pd.Timestamp),"end not given in timestamp format" else: end = call.iloc[len(call)-1]['datetime'] sms = sms.drop(columns=['device','user','time']) sms = sms.loc[begin:end] if not call.empty: if not sms.empty: call_sms = call.merge(sms, how='outer', left_index=True, right_index=True) times = pd.DatetimeIndex.to_series(call_sms.index,keep_tz=True) else: times = pd.DatetimeIndex.to_series(call.index,keep_tz=True) if not sms.empty: if not call.empty: call_sms = sms.merge(call, how='outer', left_index=True, right_index=True) times = pd.DatetimeIndex.to_series(call_sms.index,keep_tz=True) else: times = pd.DatetimeIndex.to_series(sms.index,keep_tz=True) event=niimpy.util.occurrence(times) event = event.groupby(['day']).sum() event = event.drop(columns=['hour']) return event def occurrence_call_sms_apps(database,subject,begin=None,end=None,app_list_path=None,comm_app_list_path=None): """ Returns a DataFrame contanining the number of events that occur in a day for calls, sms, and communication apps. The events are binned in 12-minutes, i.e. if there is an event at 11:05 and another one at 11:45, 2 occurences happened in one hour. Then, the sum of these occurences yield the number per day. Parameters ---------- database: Niimpy database user: string begin: datetime, optional end: datetime, optional app_list_path: path to the file where the apps are classified into groups comm_app_list_path:path to the file where the communication apps are listed Returns ------- event: Dataframe """ assert isinstance(database, niimpy.database.Data1),"database not given in Niimpy database format" assert isinstance(subject, str),"user not given in string format" call = database.raw(table='AwareCalls', user=subject) if not call.empty: if(begin!=None): assert isinstance(begin,pd.Timestamp),"begin not given in timestamp format" else: begin = call.iloc[0]['datetime'] if(end!= None): assert isinstance(end,pd.Timestamp),"end not given in timestamp format" else: end = call.iloc[len(call)-1]['datetime'] call = call.drop(columns=['device','user','time']) call = call.loc[begin:end] sms = database.raw(table='AwareMessages', user=subject) if not sms.empty: if(begin!=None): assert isinstance(begin,pd.Timestamp),"begin not given in timestamp format" else: begin = sms.iloc[0]['datetime'] if(end!= None): assert isinstance(end,pd.Timestamp),"end not given in timestamp format" else: end = sms.iloc[len(sms)-1]['datetime'] sms = sms.drop(columns=['device','user','time']) sms = sms.loc[begin:end] app = database.raw(table='AwareApplicationNotifications', user=subject) if (app_list_path==None): app_list_path='/m/cs/scratch/networks-nima/ana/niima-code/Datastreams/Phone/apps_group.csv' if (comm_app_list_path==None): comm_app_list_path='/m/cs/scratch/networks-nima/ana/niima-code/Datastreams/Phone/comm_apps.csv' if not app.empty: if(begin!=None): assert isinstance(begin,pd.Timestamp),"begin not given in timestamp format" else: begin = app.iloc[0]['datetime'] if(end!= None): assert isinstance(end,pd.Timestamp),"end not given in timestamp format" else: end = app.iloc[len(app)-1]['datetime'] app = app.drop(columns=['device','user','time','defaults','sound','vibrate']) app = app.loc[begin:end] app_list=pd.read_csv(app_list_path) app['group']=np.nan for index, row in app.iterrows(): group=app_list.isin([row['application_name']]).any() group=group.reset_index() if (not any(group[0])): app.loc[index,'group']=10 else: app.loc[index,'group']=group.index[group[0] == True].tolist()[0] app = app.loc[app['group'] == 2] comm_app_list = pd.read_csv(comm_app_list_path) comm_app_list = comm_app_list['Communication'].tolist() app = app[~app.application_name.isin(comm_app_list)] if not call.empty: if not sms.empty: event = call.merge(sms, how='outer', left_index=True, right_index=True) else: event = call else: if not sms.empty: event = sms else: event= pd.DataFrame() if not app.empty: if not event.empty: event = event.merge(app, how='outer', left_index=True, right_index=True) else: event=app if not event.empty: times = pd.DatetimeIndex.to_series(event.index,keep_tz=True) event=niimpy.util.occurrence(times) event = event.groupby(['day']).sum() event = event.drop(columns=['hour']) return event def occurrence_call_sms_social(database,subject,begin=None,end=None,app_list_path=None,comm_app_list_path=None): """ Returns a DataFrame contanining the number of events that occur in a day for calls, sms, and social and communication apps. The events are binned in 12-minutes, i.e. if there is an event at 11:05 and another one at 11:45, 2 occurences happened in one hour. Then, the sum of these occurences yield the number per day. Parameters ---------- database: Niimpy database user: string begin: datetime, optional end: datetime, optional app_list_path: path to the file where the apps are classified into groups comm_app_list_path:path to the file where the communication apps are listed Returns ------- event: Dataframe """ assert isinstance(database, niimpy.database.Data1),"database not given in Niimpy database format" assert isinstance(subject, str),"user not given in string format" call = database.raw(table='AwareCalls', user=subject) if not call.empty: if(begin!=None): assert isinstance(begin,pd.Timestamp),"begin not given in timestamp format" else: begin = call.iloc[0]['datetime'] if(end!= None): assert isinstance(end,pd.Timestamp),"end not given in timestamp format" else: end = call.iloc[len(call)-1]['datetime'] call = call.drop(columns=['device','user','time']) call = call.loc[begin:end] sms = database.raw(table='AwareMessages', user=subject) if not sms.empty: if(begin!=None): assert isinstance(begin,pd.Timestamp),"begin not given in timestamp format" else: begin = sms.iloc[0]['datetime'] if(end!= None): assert isinstance(end,pd.Timestamp),"end not given in timestamp format" else: end = sms.iloc[len(sms)-1]['datetime'] sms = sms.drop(columns=['device','user','time']) sms = sms.loc[begin:end] app = database.raw(table='AwareApplicationNotifications', user=subject) if(app_list_path==None): app_list_path='/m/cs/scratch/networks-nima/ana/niima-code/Datastreams/Phone/apps_group.csv' if (comm_app_list_path==None): comm_app_list_path='/m/cs/scratch/networks-nima/ana/niima-code/Datastreams/Phone/comm_apps.csv' if not app.empty: if(begin!=None): assert isinstance(begin,pd.Timestamp),"begin not given in timestamp format" else: begin = app.iloc[0]['datetime'] if(end!= None): assert isinstance(end,pd.Timestamp),"end not given in timestamp format" else: end = app.iloc[len(app)-1]['datetime'] app = app.drop(columns=['device','user','time','defaults','sound','vibrate']) app = app.loc[begin:end] app_list=pd.read_csv(app_list_path) app['group']=np.nan for index, row in app.iterrows(): group=app_list.isin([row['application_name']]).any() group=group.reset_index() if (not any(group[0])): app.loc[index,'group']=10 else: app.loc[index,'group']=group.index[group[0] == True].tolist()[0] app = app.loc[(app['group'] == 2) | (app['group'] == 3)] comm_app_list = pd.read_csv(comm_app_list_path) comm_app_list = comm_app_list['Communication'].tolist() app = app[~app.application_name.isin(comm_app_list)] if not call.empty: if not sms.empty: event = call.merge(sms, how='outer', left_index=True, right_index=True) else: event = call else: if not sms.empty: event = sms else: event= pd.DataFrame() if not app.empty: if not event.empty: event = event.merge(app, how='outer', left_index=True, right_index=True) else: event=app if not event.empty: times = pd.DatetimeIndex.to_series(event.index,keep_tz=True) event=niimpy.util.occurrence(times) event = event.groupby(['day']).sum() event = event.drop(columns=['hour']) event.index = pd.to_datetime(event.index).tz_localize('Europe/Helsinki') return event #Location def location_data(database,subject,begin=None,end=None): """ Reads the readily, preprocessed location data from the right database. The data already contains the aggregation of the GPS data (more info here: https://github.com/digitraceslab/koota-server/blob/master/kdata/converter.py). Parameters ---------- database: Niimpy database user: string begin: datetime, optional end: datetime, optional Returns ------- location: Dataframe """ assert isinstance(database, niimpy.database.Data1),"database not given in Niimpy database format" assert isinstance(subject, str),"user not given in string format" location = database.raw(table='AwareLocationDay', user=subject) location = location.drop(['device','user'],axis=1) location=location.drop_duplicates(subset=['day'],keep='first') location['day']=pd.to_datetime(location['day'], format='%Y-%m-%d') location=location.set_index('day') location.index = pd.to_datetime(location.index).tz_localize('Europe/Helsinki') if(begin!=None): assert isinstance(begin,pd.Timestamp),"begin not given in timestamp format" else: begin = location.index[0] if(end!= None): assert isinstance(end,pd.Timestamp),"end not given in timestamp format" else: end = location.index[-1] location=location.loc[begin:end] return location #Screen def get_battery_data(battery, user=None, start = None, end = None): """ Returns a DataFrame with battery data for a user. Parameters ---------- battery: DataFrame with battery data user: string, optional start: datetime, optional end: datetime, optional """ assert isinstance(battery, pd.core.frame.DataFrame), "data is not a pandas DataFrame" if(user!= None): assert isinstance(user, str),"user not given in string format" battery_data = battery[(battery['user']==user)] else: battery_data = battery if(start!=None): start = pd.to_datetime(start) else: start = battery_data.iloc[0]['datetime'] if(end!= None): end = pd.to_datetime(end) else: end = battery_data.iloc[len(battery_data)-1]['datetime'] battery_data = battery_data[(battery_data['datetime']>=start) & (battery_data['datetime']<=end)] battery_data['battery_level'] = pd.to_numeric(battery_data['battery_level']) #df['column'].fillna(pd.Timedelta(seconds=0)) #df.dropna() battery_data = battery_data.drop_duplicates(subset=['datetime','user','device'],keep='last') battery_data = battery_data.drop(['user','device','time','datetime'],axis=1) return battery_data def battery_occurrences(battery_data, user=None, start=None, end=None, battery_status = False, days= 0, hours=6, minutes=0, seconds=0,milli=0, micro=0, nano=0): """ Returns a dataframe showing the amount of battery data points found between a given interval and steps. The default interval is 6 hours. Parameters ---------- battery_data: Dataframe user: string, optional start: datetime, optional end: datetime, optional battery_status: boolean, optional """ assert isinstance(battery_data, pd.core.frame.DataFrame), "data is not a pandas DataFrame" assert isinstance(user, (type(None), str)),"user not given in string format" if(user!= None): ocurrence_data = battery_data[(battery_data['user']==user)] else: occurrence_data = battery_data occurrence_data = occurrence_data.drop_duplicates(subset=['datetime','device'],keep='last') if(start==None): start = occurrence_data.iloc[0]['datetime'] start = pd.to_datetime(start) td = pd.Timedelta(days=days,hours=hours,minutes=minutes,seconds=seconds,milliseconds=milli,microseconds=micro,nanoseconds=nano) delta = start+td if(end==None): end = occurrence_data.iloc[len(occurrence_data)-1]['datetime'] end = pd.to_datetime(end) idx_range = np.floor((end-start)/td).astype(int) idx = pd.date_range(start, periods = idx_range, freq=td) if ((battery_status == True) & ('battery_status' in occurrence_data.columns)): occurrences = pd.DataFrame(np.nan, index = idx,columns=list(['start','end','occurrences','battery_status'])) for i in range(idx_range): idx_dat = occurrence_data.loc[(occurrence_data['datetime']>start) & (occurrence_data['datetime']<=delta)] battery_status = occurrence_data.loc[(occurrence_data['datetime']>start) & (occurrence_data['datetime']<=delta) & ((occurrence_data['battery_status']=='-1')|(occurrence_data['battery_status']=='-2')|(occurrence_data['battery_status']=='-3'))] occurrences.iloc[i] = [start, delta,len(idx_dat), len(battery_status)] start = start + td delta = start + td else: occurrences = pd.DataFrame(np.nan, index = idx,columns=list(['start','end','occurrences'])) for i in range(idx_range): idx_dat = occurrence_data.loc[(occurrence_data['datetime']>start) & (occurrence_data['datetime']<=delta)] occurrences.iloc[i] = [start, delta,len(idx_dat)] start = start + td delta = start + td return occurrences def battery_gaps(data, min_duration_between = None): '''Returns a DataFrame including all battery data and showing the delta between consecutive battery timestamps. The minimum size of the considered deltas can be decided with the min_duration_between parameter. Parameters ---------- data: dataframe with date index min_duration_between: Timedelta, for example, pd.Timedelta(hours=6) ''' assert isinstance(data, pd.core.frame.DataFrame), "data is not a pandas DataFrame" assert isinstance(data.index, pd.core.indexes.datetimes.DatetimeIndex), "data index is not DatetimeIndex" gaps = data.copy() gaps['tvalue'] = gaps.index gaps['delta'] = (gaps['tvalue']-gaps['tvalue'].shift()).fillna(pd.Timedelta(seconds=0)) if(min_duration_between!=None): gaps = gaps[gaps['delta']>=min_duration_between] return gaps def battery_charge_discharge(data): '''Returns a DataFrame including all battery data and showing the charge/discharge between each timestamp. Parameters ---------- data: dataframe with date index ''' assert isinstance(data, pd.core.frame.DataFrame), "data is not a pandas DataFrame" assert isinstance(data.index, pd.core.indexes.datetimes.DatetimeIndex), "data index is not DatetimeIndex" charge = data.copy() charge['battery_level'] = pd.to_numeric(charge['battery_level']) charge['tvalue'] = charge.index charge['tdelta'] = (charge['tvalue']-charge['tvalue'].shift()).fillna(pd.Timedelta(seconds=0)) charge['bdelta'] = (charge['battery_level']-charge['battery_level'].shift()).fillna(0) charge['charge/discharge']= ((charge['bdelta'])/((charge['tdelta']/ pd.Timedelta(seconds=1)))) return charge def find_real_gaps(battery_data,other_data,start=None, end=None, days= 0, hours=6, minutes=0, seconds=0,milli=0, micro=0, nano=0): """ Returns a dataframe showing the gaps found both in the battery data and the other data. The default interval is 6 hours. Parameters ---------- battery_data: Dataframe other_data: Dataframe The data you want to compare with start: datetime, optional end: datetime, optional """ assert isinstance(battery_data, pd.core.frame.DataFrame), "battery_data is not a pandas DataFrame" assert isinstance(other_data, pd.core.frame.DataFrame), "other_data is not a pandas DataFrame" assert isinstance(battery_data.index, pd.core.indexes.datetimes.DatetimeIndex), "battery_data index is not DatetimeIndex" assert isinstance(other_data.index, pd.core.indexes.datetimes.DatetimeIndex), "other_data index is not DatetimeIndex" if(start!=None): start = pd.to_datetime(start) else: start = battery_data.index[0] if (battery_data.index[0]<= other_data.index[0]) else other_data.index[0] if(end!=None): end = pd.to_datetime(end) else: end = battery_data.index[-1] if (battery_data.index[-1]>= other_data.index[-1]) else other_data.index[-1] battery = battery_occurrences(battery_data, start=start,end=end,days=days,hours=hours,minutes=minutes,seconds=seconds,milli=milli,micro=micro,nano=nano) battery.rename({'occurrences': 'battery_occurrences'}, axis=1, inplace = True) other = battery_occurrences(other_data, start=start,days=days,hours=hours,minutes=minutes,seconds=seconds,milli=milli,micro=micro,nano=nano) mask = (battery['battery_occurrences']==0)&(other['occurrences']==0) gaps = pd.concat([battery[mask],other[mask]['occurrences']],axis=1, sort=False) return gaps def find_non_battery_gaps(battery_data,other_data,start=None, end=None, days= 0, hours=6, minutes=0, seconds=0,milli=0, micro=0, nano=0): """ Returns a dataframe showing the gaps found only in the other data. The default interval is 6 hours. Parameters ---------- battery_data: Dataframe other_data: Dataframe The data you want to compare with start: datetime, optional end: datetime, optional """ assert isinstance(battery_data, pd.core.frame.DataFrame), "battery_data is not a pandas DataFrame" assert isinstance(other_data, pd.core.frame.DataFrame), "other_data is not a pandas DataFrame" assert isinstance(battery_data.index, pd.core.indexes.datetimes.DatetimeIndex), "battery_data index is not DatetimeIndex" assert isinstance(other_data.index, pd.core.indexes.datetimes.DatetimeIndex), "other_data index is not DatetimeIndex" if(start!=None): start = pd.to_datetime(start) else: start = battery_data.index[0] if (battery_data.index[0]<= other_data.index[0]) else other_data.index[0] if(end!=None): end = pd.to_datetime(end) else: end = battery_data.index[-1] if (battery_data.index[-1]>= other_data.index[-1]) else other_data.index[-1] battery = battery_occurrences(battery_data, start=start,end=end,days=days,hours=hours,minutes=minutes,seconds=seconds,milli=milli,micro=micro,nano=nano) battery.rename({'occurrences': 'battery_occurrences'}, axis=1, inplace = True) other = battery_occurrences(other_data, start=start,days=days,hours=hours,minutes=minutes,seconds=seconds,milli=milli,micro=micro,nano=nano) mask = (battery['battery_occurrences']>10)&(other['occurrences']==0) gaps = pd.concat([battery[mask],other[mask]['occurrences']],axis=1, sort=False) return gaps def find_battery_gaps(battery_data,other_data,start=None, end=None, days= 0, hours=6, minutes=0, seconds=0,milli=0, micro=0, nano=0): """ Returns a dataframe showing the gaps found only in the battery data. The default interval is 6 hours. Parameters ---------- battery_data: Dataframe other_data: Dataframe The data you want to compare with start: datetime, optional end: datetime, optional """ assert isinstance(battery_data, pd.core.frame.DataFrame), "battery_data is not a pandas DataFrame" assert isinstance(other_data, pd.core.frame.DataFrame), "other_data is not a pandas DataFrame" assert isinstance(battery_data.index, pd.core.indexes.datetimes.DatetimeIndex), "battery_data index is not DatetimeIndex" assert isinstance(other_data.index, pd.core.indexes.datetimes.DatetimeIndex), "other_data index is not DatetimeIndex" if(start!=None): start = pd.to_datetime(start) else: start = battery_data.index[0] if (battery_data.index[0]<= other_data.index[0]) else other_data.index[0] if(end!=None): end = pd.to_datetime(end) else: end = battery_data.index[-1] if (battery_data.index[-1]>= other_data.index[-1]) else other_data.index[-1] battery = battery_occurrences(battery_data, start=start,end=end,days=days,hours=hours,minutes=minutes,seconds=seconds,milli=milli,micro=micro,nano=nano) battery.rename({'occurrences': 'battery_occurrences'}, axis=1, inplace = True) other = battery_occurrences(other_data, start=start,end=end,days=days,hours=hours,minutes=minutes,seconds=seconds,milli=milli,micro=micro,nano=nano) mask = (battery['battery_occurrences']==0)&(other['occurrences']>0) gaps = pd.concat([battery[mask],other[mask]['occurrences']],axis=1, sort=False) return gaps def missing_data_format(question,keep_values=False): """ Returns a series of timestamps in the right format to allow missing data visualization . Parameters ---------- question: Dataframe """ question['date'] = question.index question['date'] = question['date'].apply( lambda question : datetime.datetime(year=question.year, month=question.month, day=question.day)) question = question.drop_duplicates(subset=['date'],keep='first') question = question.set_index(['date']) if (keep_values == False): question['answer'] = 1 question = question.T.squeeze() return question def screen_missing_data(database,subject,begin=None,end=None): """ Returns a DataFrame contanining the percentage (range [0,1]) of loss data calculated based on the transitions of screen status. In general, if screen_status(t) == screen_status(t+1), we declared we have at least one missing point. Parameters ---------- database: Niimpy database user: string begin: datetime, optional end: datetime, optional Returns ------- count: Dataframe """ assert isinstance(database, niimpy.database.Data1),"database not given in Niimpy database format" assert isinstance(subject, str),"usr not given in string format" screen = database.raw(table='AwareScreen', user=subject) if(begin!=None): assert isinstance(begin,pd.Timestamp),"begin not given in timestamp format" else: begin = screen.iloc[0]['datetime'] if(end!= None): assert isinstance(end,pd.Timestamp),"end not given in timestamp format" else: end = screen.iloc[len(screen)-1]['datetime'] screen=screen.drop_duplicates(subset=['datetime'],keep='first') screen = screen.drop(['device','user','time'],axis=1) screen=screen.loc[begin:end] screen['screen_status']=pd.to_numeric(screen['screen_status']) #Include the missing points that are due to shutting down the phone shutdown = shutdown_info(database,subject,begin,end) shutdown=shutdown.rename(columns={'battery_status':'screen_status'}) shutdown['screen_status']=0 screen = screen.merge(shutdown, how='outer', left_index=True, right_index=True) screen['screen_status'] = screen.fillna(0)['screen_status_x'] + screen.fillna(0)['screen_status_y'] screen = screen.drop(['screen_status_x','screen_status_y'],axis=1) dates=screen.datetime_x.combine_first(screen.datetime_y) screen['datetime']=dates screen = screen.drop(['datetime_x','datetime_y'],axis=1) #Detect missing data points screen['missing']=0 screen['next']=screen['screen_status'].shift(-1) screen['dummy']=screen['screen_status']-screen['next'] screen['missing'] = np.where(screen['dummy']==0, 1, 0) screen['missing'] = screen['missing'].shift(1) screen = screen.drop(['dummy','next'], axis=1) screen = screen.fillna(0) screen['datetime'] = screen['datetime'].apply( lambda screen : datetime.datetime(year=screen.year, month=screen.month, day=screen.day)) screen = screen.drop(['screen_status'], axis=1) count=pd.pivot_table(screen,values='missing',index='datetime', aggfunc='count') count = screen.groupby(['datetime','missing'])['missing'].count().unstack(fill_value=0) count['missing'] = count[1.0]/(count[0.0]+count[1.0]) count = count.drop([0.0,1.0], axis=1) if (pd.Timestamp.tzname(count.index[0]) != 'EET'): if pd.Timestamp.tzname(count.index[0]) != 'EEST': count.index = pd.to_datetime(count.index).tz_localize('Europe/Helsinki') return count def missing_noise(database,subject,begin=None,end=None): """ Returns a Dataframe with the estimated missing data from the ambient noise sensor. NOTE: This function aggregates data by day. Parameters ---------- database: Niimpy database user: string begin: datetime, optional end: datetime, optional Returns ------- avg_noise: Dataframe """ assert isinstance(database, niimpy.database.Data1),"database not given in Niimpy database format" assert isinstance(subject, str),"user not given in string format" noise = database.raw(table='AwareAmbientNoise', user=subject) if(begin!=None): assert isinstance(begin,pd.Timestamp),"begin not given in timestamp format" else: begin = noise.iloc[0]['datetime'] if(end!= None): assert isinstance(end,pd.Timestamp),"end not given in timestamp format" else: end = noise.iloc[len(noise)-1]['datetime'] noise = noise.drop(['device','user','time','double_silence_threshold','double_rms','blob_raw','is_silent','double_frequency'],axis=1) noise = noise.loc[begin:end] noise['duration'] = noise['datetime'].diff() noise['duration'] = get_seconds(noise['duration']) noise = noise.iloc[1:] shutdown = shutdown_info(database,subject,begin,end) shutdown=shutdown.rename(columns={'battery_status':'duration'}) noise = noise.merge(shutdown, how='outer', left_index=True, right_index=True) noise['duration_x'] = noise.fillna(0)['duration_x'] + noise.fillna(0)['duration_y'] noise=noise.rename(columns={'duration_x':'duration'}) dates=noise.datetime_x.combine_first(noise.datetime_y) noise['datetime']=dates noise = noise.drop(['datetime_x','datetime_y'],axis=1) noise=noise.drop(['double_decibels', 'duration_y'],axis=1) noise['missing'] = np.where(noise['duration']>=1860, 1, 0) #detect the missing points noise['dummy'] = noise.missing.shift(-2) #assumes that everytime the cellphone shuts down, two timestamps are generated with -1 in the battery_health noise['dummy'] = noise.dummy*noise.duration noise['dummy'] = noise.dummy.shift(2) noise['missing'] = np.where(noise['missing']==1, np.round(noise['duration']/1800), 0) #calculate the number of datapoints missing noise = noise.drop(noise[noise.dummy==-1].index) #delete those missing datapoints due to the phone being shut down noise = noise.drop(['duration', 'datetime', 'dummy'],axis=1) return noise

42.182306

270

0.668981

cc9e3bc94eb629f76a855b39c6d9abe102c7440e

15,570

py

Python

analog-voltage-control/analogVoltageController.py

jsbangsund/measurement-automation-tools

d2c2fd58b3a6884945081cb9a9ad87366da4a10e

[ "MIT" ]

2

2018-09-27T09:47:47.000Z

2022-03-24T09:53:04.000Z

analog-voltage-control/analogVoltageController.py

jsbangsund/measurement-automation-tools

d2c2fd58b3a6884945081cb9a9ad87366da4a10e

[ "MIT" ]

null

analog-voltage-control/analogVoltageController.py

jsbangsund/measurement-automation-tools

d2c2fd58b3a6884945081cb9a9ad87366da4a10e

[ "MIT" ]

null

# imports import visa import numpy as np import os import csv import time import datetime import tkinter as tk from tkinter.filedialog import askopenfilename, askdirectory from tkinter.ttk import Frame, Button, Style,Treeview, Scrollbar, Checkbutton from functools import partial import serial # This app uses an arduino to output two analog voltage channels from 0 to ~3.3V # These output voltages are used to control flow rate on mass flow controllers class VoltageController(Frame): def __init__(self,parent): #### USER DEFINED self.arduinoAddress = 'COM5' self.window_title = "Mass Flow Control" self.channels = ["A","B"] self.V_calibration = {i:None for i in self.channels} # initialize correction factor self.show_keithley = True self.smu_address_default = "" self.smu_address = "" self.complianceV=5 self.max_V_out = 3.2467 # Measured maximum output voltage self.upper_reference_V = 4.097 # Measured reference output from LM4040 #### End user defined parameters self.arduino = serial.Serial(self.arduinoAddress,9600) Frame.__init__(self, parent) self.parent = parent self.configure_gui() def configure_gui(self): # Master Window self.parent.title(self.window_title) self.style = Style() self.style.theme_use("default") # Test Mode Frame frame_setpoints=Frame(self) frame_setpoints.pack() self.s_setpoints = {} self.b_set_setpoints = {} self.l_actual_flow = {} self.l_integer = {} tk.Label(frame_setpoints,text="Setpoint").grid(row=0,column=1,sticky=tk.W,padx=1, pady=1) tk.Label(frame_setpoints,text="Actual").grid(row=0,column=2,sticky=tk.W,padx=1, pady=1) tk.Label(frame_setpoints,text="Integer").grid(row=0,column=3,sticky=tk.W,padx=1, pady=1) for i,ch in enumerate(self.channels): self.s_setpoints[ch] = tk.StringVar() tk.Label(frame_setpoints,text="Channel " + str(ch) + " (SCCM)" ).grid(row=i+1,column=0,sticky=tk.W,padx=1, pady=1)#.pack(side=tk.LEFT) tk.Entry(frame_setpoints,textvariable=self.s_setpoints[ch],width=10 ).grid(row=i+1,column=1,sticky=tk.W) self.l_actual_flow[ch] = tk.Label(frame_setpoints,text="00.00") self.l_actual_flow[ch].grid(row=i+1,column=2,sticky=tk.W,padx=1, pady=1) self.l_integer[ch] = tk.Label(frame_setpoints,text="0000") self.l_integer[ch].grid(row=i+1,column=3,sticky=tk.W,padx=1, pady=1) self.b_set_setpoints[ch] = Button( frame_setpoints,text="Set", command=partial(self.set_setpoint,ch)) self.b_set_setpoints[ch].grid(row=i+1,column=4,sticky=tk.W,padx=1, pady=1) # Source control buttons frame_buttons=Frame(self) frame_buttons.pack() # Turn on all sources tk.Button(frame_buttons,text="Turn Sources On", bg="lime", command=self.turn_on_sources).grid(row=0,column=0,sticky=tk.W,padx=1, pady=1) # Set all sources to zero tk.Button(frame_buttons,text="Set Sources to 0", bg="red", command=self.turn_off_sources).grid(row=0,column=1,sticky=tk.W,padx=1, pady=1) # Functions for measuring with Keithley if self.show_keithley: self.rm = visa.ResourceManager() self.resources = self.rm.list_resources() self.configure_keithley_widgets() # Style Configuration Style().configure("defaultState.TButton", foreground='black', background='light grey') Style().configure("onState.TButton", foreground='black', background='red') Style().map("onState.TButton", background=[('disabled', 'grey'), ('pressed', 'red3'), ('active', 'red2')]) self.pack(fill=tk.BOTH, expand=1) def configure_keithley_widgets(self): frame_keithley = Frame(self) frame_keithley.pack() self.l_smu_address = tk.Label(frame_keithley, text='Pick SMU address:') self.l_smu_address.grid(row=0, column=0, sticky=tk.W) self.s_smu_address = tk.StringVar() self.s_smu_address.set(self.smu_address) self.o_smu_address = tk.OptionMenu( frame_keithley, self.s_smu_address,*self.resources, command=self.connect_to_smu) self.o_smu_address.grid(row=0,column=1, sticky=tk.W) self.configure_resource_optionmenu() ##### Connect buttons #self.c_connect = Frame(self.c_top) #self.c_connect.pack(fill=X, expand=True) self.b_connect = tk.Button(frame_keithley, command=self.connect_to_smu) self.b_connect.configure(text="Connect", background= "yellow") self.b_connect.grid(row=0, column=2, sticky=tk.E, padx=5) self.b_calibrate_channelA = tk.Button(frame_keithley, command=partial(self.calibrate_channels,"A")) self.b_calibrate_channelA.configure(text="Calibrate A", background= "grey") self.b_calibrate_channelA.grid(row=0, column=3, sticky=tk.E, padx=5) self.b_calibrate_channelB = tk.Button(frame_keithley, command=partial(self.calibrate_channels,"B")) self.b_calibrate_channelB.configure(text="Calibrate B", background= "grey") self.b_calibrate_channelB.grid(row=0, column=4, sticky=tk.E, padx=5) tk.Label(frame_keithley, text='Voltage Reading:').grid(row=1,column=0,padx=5,sticky=tk.E) self.l_voltage = tk.Label(frame_keithley, text='000.0 mV') self.l_voltage.grid(row=1,column=1,padx=5,sticky=tk.W) def configure_resource_optionmenu(self): # Only display keithley or GPIB addresses # Keithley addresses have form USB0::0x05E6::0x26##::7 digit SN::INSTR self.display_resources = [] for resource in self.resources: if ('USB0::0x05E6::0x26' in resource) or ('GPIB0' in resource): # Add the resource address and vendor info to the option menu hardware_info = self.get_hardware_label(resource) if not hardware_info=='Unknown': self.display_resources.append(resource + '--' + hardware_info) # https://stackoverflow.com/questions/28412496/updating-optionmenu-from-list menu = self.o_smu_address["menu"] menu.delete(0, "end") for string in self.display_resources: menu.add_command(label=string, command=lambda value=string.split('--')[0]: self.s_smu_address.set(value)) # reset address to default self.s_smu_address.set(self.smu_address_default) def get_hardware_label(self,resource): # Check for known hardware types and make a label try: r = self.rm.open_resource(resource) hardware_info = r.query("*IDN?") if 'OK\r\n' in hardware_info: # The "OK\r\n" message is sent as a handshake from Obis lasers # Turn hand-shaking off and then ask for the info again r.write('system:communicate:handshaking OFF') hardware_info = r.query("*IDN?") # Check for known instruments if 'Keithley' in hardware_info: model_number = hardware_info.split(',')[1].split(' Model ')[1] serial_number = hardware_info.split(',')[2][1:] label = 'Keithley ' + model_number + ' SN: ' + serial_number elif 'Stanford' in hardware_info: label = 'Lock-in Amplifier ' + hardware_info.split(',')[1] elif 'HEWLETT' in hardware_info: label = 'Parameter Analyzer ' + hardware_info.split(',')[1] elif 'Coherent' in hardware_info: wavelength = r.query('system:information:wavelength?') if 'OK' in wavelength: r.write('system:communicate:handshaking OFF') wavelength = r.query('system:information:wavelength?') label = 'Coherent ' + wavelength.strip() + 'nm laser' else: label = 'Unknown: ' + hardware_info.strip() r.close() except Exception as e: #print(e) label='Unknown' return label def connect_to_smu(self): self.smu_address = self.s_smu_address.get() self.keithley = self.rm.open_resource(self.smu_address) self.initializeKeithley(self.keithley) print('keithley connected') self.b_connect.configure(background='green2') def calibrate_channels(self,ch): print("calibrating channel " + ch) setpoints = np.arange(0,4096,1) voltages = np.zeros(setpoints.shape) self.keithley.write('smua.source.leveli=0') self.keithley.write('smua.source.output=1') self.keithley.write('smua.measure.autorangev=1') for i,setpt in enumerate(setpoints): self.arduino.write((str(ch)+str(setpt)+'\n').encode()) self.l_actual_flow[ch].configure(text=str(setpt)) time.sleep(0.2) voltages[i] = self.readVoltage() self.l_voltage.configure(text="{:.2f}".format(voltages[i]*1e3) + " mV") self.parent.update() np.savetxt('Channel'+ch+'_calibration.csv',np.vstack((setpoints,voltages)).T, delimiter=',',header='Setpoints,Voltages') def prep_measure_stability(self,ch): self.start_time = time.time() self.set_setpoint(ch) # Turn on desired set-point self.file = 'Channel'+ch+'_stability.csv' header="Elapsed Time (hr),Output Voltage (V)\n" with open(self.file, 'a') as f: f.write(header) self.measure_stability(ch) def measure_stability(self,ch): voltage = self.readVoltage() # read voltage self.onTime = time.time() - self.startTime # record onTime self.l_voltage.configure(text="{:.2f}".format(voltages[i]*1e3) + " mV") # Update file with open(self.file, 'a') as f: f.write(str(self.onTime/3600.0)+','+ str(voltage)+'\n') # Update once every 20 seconds self.parent.after(int(20 * 1000), partial(self.measure_stability,ch)) def set_setpoint(self,ch): integer,actual_flow = self.convert_sccm_to_int(float(self.s_setpoints[ch].get()),ch) self.l_actual_flow[ch].configure(text='{:.2f}'.format(float(actual_flow))) self.l_integer[ch].configure(text=str(integer)) self.arduino.write((str(ch)+str(integer)+'\n').encode()) def convert_sccm_to_int(self,sccm,ch): # Get calibration if not yet loaded if self.V_calibration[ch] is None: self.V_calibration[ch] = np.genfromtxt("Channel"+ch+"_calibration.csv", delimiter=',',skip_header=1) # Maximum SCCM output is 200 # the upper reference output voltage is given by the LM4040 sccm_per_volt = 200 / self.upper_reference_V V_out = sccm / sccm_per_volt # Needed output voltage if V_out > self.max_V_out: print('Maximum output voltage exceeded') V_out = self.max_V_out idx_min = np.abs(V_out - self.V_calibration[ch][:,1]).argmin() integer = int(self.V_calibration[ch][idx_min,0]) #V_out * 4096 / self.max_V_out / MCF actual_flow=self.V_calibration[ch][idx_min,1]*sccm_per_volt return integer,actual_flow def turn_off_sources(self): for ch in self.channels: self.arduino.write((str(ch)+str(0)+'\n').encode()) def turn_on_sources(self): for ch in self.channels: self.set_setpoint(ch) def initializeKeithley(self,keithley): keithley.write('reset()') keithley.timeout = 4000 # ms keithley.write('errorqueue.clear()') ch = 'a' keithley.write( 'smu'+ch+'.reset()') keithley.write( 'smu'+ch+'.measure.count=20') keithley.write( 'smu'+ch+'.measure.nplc=1') keithley.write( 'smu'+ch+'.nvbuffer1.appendmode=0') keithley.write( 'smu'+ch+'.nvbuffer1.clear()') keithley.write( 'smu'+ch+'.source.func=0') # 0 is output_DCAMPS, 1 is output_DCVOLTS keithley.write( 'smu'+ch+'.source.limitv='+str(self.complianceV)) keithley.write( 'smu'+ch+'.source.leveli=0') keithley.write( 'smu'+ch+'.source.output=0') keithley.write( 'smu'+ch+'.measure.autorangev=1') ch = 'b' keithley.write( 'smu'+ch+'.reset()') keithley.write( 'smu'+ch+'.measure.count=10') keithley.write( 'smu'+ch+'.measure.nplc=1') keithley.write( 'smu'+ch+'.nvbuffer1.appendmode=0') keithley.write( 'smu'+ch+'.nvbuffer1.clear()') keithley.write( 'smu'+ch+'.source.func=1') # 0 is output_DCAMPS, 1 is output_DCVOLTS keithley.write( 'smu'+ch+'.source.levelv=0') keithley.write( 'smu'+ch+'.measure.autorangei=1') keithley.write( 'smu'+ch+'.source.output=1') print('keithley initialized') def turnCurrentOn(self,I): print('current turned on') self.keithley.write( 'smua.source.leveli='+str(I)) self.keithley.write( 'smua.source.output=1') def turnCurrentOff(self): print('current turned off') self.keithley.write( 'smua.source.output=0') def turnVoltageOn(self,V): #print('voltage turned on') self.keithley.write( 'smua.source.func=1') # 0 is output_DCAMPS, 1 is output_DCVOLTS self.keithley.write( 'smua.source.levelv='+str(V)) self.keithley.write( 'smua.source.output=1') def turnVoltageOff(self): #print('voltage turned off') self.keithley.write( 'smua.source.levelv=0') self.keithley.write( 'smua.source.func=0') # 0 is output_DCAMPS, 1 is output_DCVOLTS self.keithley.write( 'smua.source.output=0') # reads the voltage from keithley of the specified device def readVoltage(self): self.keithley.write('smua.nvbuffer1.clear()') self.keithley.write('smua.measure.v(smua.nvbuffer1)') sig = self.keithley.query('printbuffer(1,smua.nvbuffer1.n,smua.nvbuffer1)') sig=[float(v) for v in sig.split(',')] return np.mean(sig) def readCurrent(self): self.keithley.write('smua.measure.autorangei=1') self.keithley.write('smua.nvbuffer1.clear()') self.keithley.write('smua.measure.i(smua.nvbuffer1)') sig = self.keithley.query('printbuffer(1,smua.nvbuffer1.n,smua.nvbuffer1)') sig=[float(v) for v in sig.split(',')] return np.mean(sig) # reads device photodiode signal measured by keithley from channel b def keithleyDiodeRead(self,keithley): holder = [] for x in range(0,self.keithleyReadingCount): keithley.write('smub.nvbuffer1.clear()') keithley.write('smub.measure.i(smub.nvbuffer1)') sig = keithley.query('printbuffer(1,smub.nvbuffer1.n,smub.nvbuffer1)') sig=[float(v) for v in sig.split(',')] holder.append(sig) return np.mean(holder),np.std(holder) def main(): root = tk.Tk() app = VoltageController(root) root.mainloop() #app.keithley.close() app.arduino.close() if __name__ == '__main__': main()

48.504673

107

0.617213

cc9ffda493fbb21c5940dac2d68d30da4ab49dc6

12,522

py

Python

cnn_theano_overlap.py

nanfeng1101/DBQA

56b0a320e4641f46e80db78039d4ca79e9037d7e

[ "MIT" ]

7

2017-08-14T02:40:04.000Z

2019-04-24T13:44:44.000Z

cnn_theano_overlap.py

nanfeng1101/DBQA

56b0a320e4641f46e80db78039d4ca79e9037d7e

[ "MIT" ]

null

cnn_theano_overlap.py

nanfeng1101/DBQA

56b0a320e4641f46e80db78039d4ca79e9037d7e

[ "MIT" ]

2

2018-05-19T01:26:38.000Z

2019-12-04T07:58:09.000Z

#-*- coding:utf-8 -*- __author__ = "ChenJun" import theano import theano.tensor as T import numpy as np import cPickle as pickle from theano_models.qa_cnn import CNNModule from theano_models.layers import InteractLayer, MLP, MLPDropout, BatchNormLayer from theano_models.optimizer import Optimizer from data_process.load_data import data_loader from collections import OrderedDict from qa_score import qa_evaluate from weighted_model import ensemble import warnings warnings.filterwarnings("ignore") SEED = 3435 rng = np.random.RandomState(SEED) def get_overlap(path,length): train_overlap = pickle.load(open(path+"pkl/overlap01-train.pkl","r")) valid_overlap = pickle.load(open(path+"pkl/overlap01-valid.pkl","r")) test_overlap = pickle.load(open(path+"pkl/overlap01-test.pkl","r")) train_overlap_q, train_overlap_a = train_overlap[:,0], train_overlap[:,1] valid_overlap_q, valid_overlap_a = valid_overlap[:,0], valid_overlap[:,1] test_overlap_q, test_overlap_a = test_overlap[:,0], test_overlap[:,1] print "overlap01 feature shape: ", train_overlap_q.shape, valid_overlap_q.shape, test_overlap_a.shape train_overlap_q = theano.shared(value=train_overlap_q.reshape((train_overlap.shape[0], 1, length,1)),borrow=True) train_overlap_a = theano.shared(value=train_overlap_a.reshape((train_overlap.shape[0], 1, length, 1)), borrow=True) valid_overlap_q = theano.shared(value=valid_overlap_q.reshape((valid_overlap.shape[0], 1, length, 1)), borrow=True) valid_overlap_a = theano.shared(value=valid_overlap_a.reshape((valid_overlap.shape[0], 1, length, 1)), borrow=True) test_overlap_q = theano.shared(value=test_overlap_q.reshape((test_overlap.shape[0], 1, length, 1)), borrow=True) test_overlap_a = theano.shared(value=test_overlap_a.reshape((test_overlap.shape[0], 1, length, 1)), borrow=True) return [(train_overlap_q,valid_overlap_q,test_overlap_q),(train_overlap_a,valid_overlap_a,test_overlap_a)] def build_model(batch_size,img_h,img_w,filter_windows,filter_num,n_in,n_hidden,n_out,L1_reg,L2_reg,conv_non_linear,learning_rate,n_epochs,random=False,non_static=False): """ build cnn model for QA. :param batch_size: batch_size :param img_h: sentence length :param img_w: word vector dimension [100] :param filter_windows: filter window sizes :param filter_num: the number of feature maps (per filter window) :param n_in: num of input units :param n_hidden: num of hidden units :param n_out: num of out units :param L1_reg: mlp L1 loss :param L2_reg: mlp L2 loss :param conv_non_linear: activation :param learning_rate: learning rate :param n_epochs: num of epochs :param random: bool, use random embedding or trained embedding :param non_static: bool, use word embedding for param or not :return: """ global rng ############### # LOAD DATA # ############### print "loading the data... " path = "/Users/chenjun/PycharmProjects/DBQA/" loader = data_loader(path+"pkl/data-train-nn.pkl",path+"pkl/data-valid-nn.pkl",path+"pkl/data-test-nn.pkl", path+"pkl/index2vec.pkl") valid_group_list = pickle.load(open(path+"pkl/valid_group.pkl")) test_group_list = [int(x.strip()) for x in open(path + "data/dbqa-data-test.txt.group")] datasets, emb_words = loader.get_input_by_model(model="theano",random=random) train_q_data, valid_q_data, test_q_data = datasets[0] train_a_data, valid_a_data, test_a_data = datasets[1] train_l_data, valid_l_data, test_l_data = datasets[2] features = get_overlap(path, length=img_h) train_overlap_q, valid_overlap_q, test_overlap_q = features[0] train_overlap_a, valid_overlap_a, test_overlap_a = features[1] # calculate the number of batches n_train_batches = train_q_data.get_value(borrow=True).shape[0] // batch_size n_valid_batches = valid_q_data.get_value(borrow=True).shape[0] // batch_size n_test_batches = test_q_data.get_value(borrow=True).shape[0] // batch_size print "batch_size: %i, n_train_batches: %i, n_valid_batches: %i, n_test_batches: %i" % (batch_size, n_train_batches, n_valid_batches, n_test_batches) ############### # BUILD MODEL # ############### print "building the model... " # define the input variable index = T.lscalar(name="index") drop_rate = T.fscalar(name="drop_rate") x1 = T.matrix(name='x1', dtype='int64') x2 = T.matrix(name='x2', dtype='int64') y = T.lvector(name='y') x1_overlap = T.tensor4(name="x1_overlap", dtype='float32') x2_overlap = T.tensor4(name="x2_overlap", dtype='float32') # transfer input to vector with embedding. _x1 = emb_words[x1.flatten()].reshape((x1.shape[0], 1, img_h, img_w - 1)) emb_x1 = T.concatenate([_x1, x1_overlap], axis=3) _x2 = emb_words[x2.flatten()].reshape((x2.shape[0], 1, img_h, img_w - 1)) emb_x2 = T.concatenate([_x2, x2_overlap], axis=3) # conv_layer conv_layers = [] q_input = [] a_input = [] for i, filter_h in enumerate(filter_windows): filter_w = img_w filter_shape = (filter_num, 1, filter_h, filter_w) pool_size = (img_h - filter_h + 1, img_w - filter_w + 1) conv_layer = CNNModule(rng, filter_shape=filter_shape, pool_size=pool_size, non_linear=conv_non_linear) q_conv_output, a_conv_output = conv_layer(emb_x1, emb_x2) q_conv_output = q_conv_output.flatten(2) # [batch_size * filter_num] a_conv_output = a_conv_output.flatten(2) # [batch_size * filter_num] q_input.append(q_conv_output) a_input.append(a_conv_output) conv_layers.append(conv_layer) q_input = T.concatenate(q_input, axis=1) # batch_size*(filter_num*len(filter_windows)) a_input = T.concatenate(a_input, axis=1) # batch_size*(filter_num*len(filter_windows)) num_filters = len(filter_windows) * filter_num interact_layer = InteractLayer(rng, num_filters, num_filters, dim=n_in) qa_vec = interact_layer(q_input, a_input) bn_layer = BatchNormLayer(n_in=n_in, inputs=qa_vec) # classifier = MLP(rng,input=bn_layer.out,n_in=n_in,n_hidden=n_hidden,n_out=n_out) classifier = MLPDropout(rng, input=bn_layer.out, n_in=n_in, n_hidden=n_hidden, n_out=n_out, dropout_rate=drop_rate) # model params params = classifier.params + interact_layer.params + bn_layer.params for i in xrange(len(conv_layers)): params += conv_layers[i].params if non_static: print "---CNN-NON-STATIC---" params += [emb_words] else: print "---CNN-STATIC---" opt = Optimizer() cost = ( classifier.cross_entropy(y) + L1_reg * classifier.L1 + L2_reg * classifier.L2_sqr ) # updates = opt.sgd_updates_adadelta(params, cost, 0.95, 1e-6, 9) updates = opt.RMSprop(params, cost) train_model = theano.function( inputs=[index, drop_rate], updates=updates, outputs=cost, givens={ x1: train_q_data[index * batch_size:(index + 1) * batch_size], x2: train_a_data[index * batch_size:(index + 1) * batch_size], y: train_l_data[index * batch_size:(index + 1) * batch_size], x1_overlap: train_overlap_q[index * batch_size: (index + 1) * batch_size], x2_overlap: train_overlap_a[index * batch_size: (index + 1) * batch_size] }, ) valid_model = theano.function( inputs=[index, drop_rate], outputs=classifier.pred_prob(), givens={ x1: valid_q_data[index * batch_size:(index + 1) * batch_size], x2: valid_a_data[index * batch_size:(index + 1) * batch_size], x1_overlap: valid_overlap_q[index * batch_size: (index + 1) * batch_size], x2_overlap: valid_overlap_a[index * batch_size: (index + 1) * batch_size] }, ) test_model = theano.function( inputs=[index, drop_rate], outputs=classifier.pred_prob(), givens={ x1: test_q_data[index * batch_size:(index + 1) * batch_size], x2: test_a_data[index * batch_size:(index + 1) * batch_size], x1_overlap: test_overlap_q[index * batch_size: (index + 1) * batch_size], x2_overlap: test_overlap_a[index * batch_size: (index + 1) * batch_size] }, ) ############### # TRAIN MODEL # ############### print('training the model...') epoch = 0 valid_dic = OrderedDict() eval_dic = OrderedDict() while epoch < n_epochs: epoch += 1 batch_cost = 0. for batch_index1 in xrange(n_train_batches): batch_cost += train_model(batch_index1, 0.5) # drop if batch_index1 % 100 == 0: print ('epoch %i/%i, batch %i/%i, cost %f') % ( epoch, n_epochs, batch_index1, n_train_batches, batch_cost / n_train_batches) ############### # VALID MODEL # ############### valid_score_data = [] for batch_index2 in xrange(n_valid_batches): batch_pred = valid_model(batch_index2, 0.0) # drop valid_score_data.append(batch_pred) valid_score_list = (np.concatenate(np.asarray(valid_score_data), axis=0)).tolist() valid_label_list = valid_l_data.get_value(borrow=True).tolist() for i in xrange(len(valid_score_list), len(valid_label_list)): valid_score_list.append(np.random.random()) _eval = qa_evaluate(valid_score_list, valid_label_list, valid_group_list, label=1, mod="mrr") print "---valid mrr: ", _eval valid_dic[str(epoch) + "-" + str(batch_index1)] = _eval ############### # TEST MODEL # ############### test_score_data = [] for batch_index3 in xrange(n_test_batches): batch_pred = test_model(batch_index3, 0.0) # drop test_score_data.append(batch_pred) test_score_list = (np.concatenate(np.asarray(test_score_data), axis=0)).tolist() test_label_list = test_l_data.get_value(borrow=True).tolist() for i in xrange(len(test_score_list), len(test_label_list)): test_score_list.append(np.random.random()) _eval = qa_evaluate(test_score_list, test_label_list, test_group_list, label=1, mod="mrr") print "---test mrr: ", _eval eval_dic[str(epoch) + "-" + str(batch_index1)] = _eval pickle.dump(valid_score_list, open(path + "result/cnn-overlap-valid.pkl." + str(epoch) + "-" + str(batch_index1), "w")) pickle.dump(test_score_list, open(path + "result/cnn-overlap-test.pkl."+str(epoch)+"-"+str(batch_index1), "w")) pickle.dump(test_label_list, open(path + "result/test_label.pkl", "w")) pickle.dump(valid_label_list, open(path + "result/valid_label.pkl", "w")) _valid_dic = sorted(valid_dic.items(), key=lambda x: x[1])[-10:] _eval_dic = sorted(eval_dic.items(), key=lambda x: x[1])[-10:] print "valid dic: ", _valid_dic print "eval dic: ", _eval_dic valid_score_file = [path+"result/cnn-overlap-valid.pkl."+x[0] for x in _valid_dic] test_score_file = [path + "result/cnn-overlap-test.pkl." + x[0] for x in _valid_dic] ###from valid valid_label_file = path + "result/valid_label.pkl" test_label_file = path + "result/test_label.pkl" test_ensemble_file = path + "result/test_ensemble_overlap.pkl" valid_ensemble_file = path + "result/valid_ensemble_overlap.pkl" valid_mrr = ensemble(valid_score_file, valid_label_file, valid_group_list, valid_ensemble_file) test_mrr = ensemble(test_score_file, test_label_file, test_group_list, test_ensemble_file) print "---ensemble valid mrr: ", valid_mrr print "---ensemble test mrr: ", test_mrr if __name__ == "__main__": build_model(batch_size=64, img_h=50, img_w=51, filter_windows=[3, 1, 2], filter_num=128, n_in=20, n_hidden=128, n_out=2, L1_reg=0.00, L2_reg=0.0001, conv_non_linear="relu", learning_rate=0.001, n_epochs=3, random=False, non_static=False)

47.977011

169

0.645504

cca08eaaa789f720d1578ebb6f7b99357f1739e1

7,298

py

Python

marble-plotter-dous/src/main/python/server.py

paser4se/marble

35da5ac8ff24ff7f30a135cbabf57f60f06f54e3

[ "Apache-2.0" ]

2

2017-06-05T13:06:06.000Z

2021-06-23T13:53:33.000Z

marble-plotter-dous/src/main/python/server.py

paser4se/marble

35da5ac8ff24ff7f30a135cbabf57f60f06f54e3

[ "Apache-2.0" ]

35

2015-07-21T15:09:24.000Z

2020-07-08T09:06:08.000Z

marble-plotter-dous/src/main/python/server.py

paser4se/marble

35da5ac8ff24ff7f30a135cbabf57f60f06f54e3

[ "Apache-2.0" ]

5

2015-06-16T09:40:39.000Z

2020-11-05T08:13:57.000Z

# Rest Server from flask import Flask, jsonify, abort, request # Eureka client from eureka.client import EurekaClient # Background tasks import threading import atexit import logging import socket import netifaces as ni import sys import os import time # Plotter libs from io import BytesIO import pymongo from pymongo import MongoClient import numpy as np import matplotlib.pyplot as plt import pandas as pd import base64 import datetime DATABASE_NAME = 'marble' POSTS_COLLECTION = 'posts' PROCESSED_POSTS_COLLECTION = 'processed_posts' pool_time = 5 # Seconds # variables that are accessible from anywhere commonDataStruct = {} # lock to control access to variable dataLock = threading.Lock() # thread handler yourThread = threading.Thread() logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) # Global variables app_name = "plotter-dous" try: ni.ifaddresses('eth0') app_ip = ni.ifaddresses('eth0')[2][0]['addr'] except Exception: app_ip = "localhost" app_host = socket.getfqdn() app_port = 8084 secure_app_port = 8443 eureka_url = "http://registry:1111/eureka/" def create_app(): app = Flask(__name__) def interrupt(): global yourThread yourThread.cancel() def doStuff(): global commonDataStruct global yourThread with dataLock: # TODO: Handle what happens when eureka goes down try: commonDataStruct['ec'].heartbeat() except Exception: logger.info("Registering to Eureka...") try: commonDataStruct['ec'].register(initial_status="UP") logger.info("Registered to Eureka.") commonDataStruct['ec'].heartbeat() except Exception as e: logger.warning( "Caught exception while trying to register in Eureka: " + str(e) + ". Will retry again shortly.") exc_type, exc_obj, exc_tb = sys.exc_info() fname = os.path.split( exc_tb.tb_frame.f_code.co_filename)[1] print((exc_type, fname, exc_tb.tb_lineno)) # Set the next thread to happen yourThread = threading.Timer(pool_time, doStuff, ()) yourThread.start() def doStuffStart(): # Do initialisation stuff here # no spaces or underscores, this needs to be url-friendly commonDataStruct['ec'] = EurekaClient(app_name, ip_address=app_ip, eureka_url=eureka_url, eureka_domain_name="", data_center="MyOwn", port=app_port, secure_port=None, use_dns=False, region="none", prefer_same_zone=False, context="", host_name=app_host, vip_address=app_name, secure_vip_address=app_name) global yourThread # Create your thread yourThread = threading.Timer(pool_time, doStuff, ()) yourThread.start() # Initiate doStuffStart() # When you kill Flask (SIGTERM), clear the trigger for the next thread atexit.register(interrupt) return app class ChartResponse(object): def __init__(self, name, description="", type="Image", customType=None, jobId=None, options={}, data={}, images={}): self.id = None self.name = name self.description = description self.type = type self.customType = customType self.jobId = jobId self.options = options self.data = data self.images = images self.createdAt = None def plotTopic(topicName, options): polarity = None chartName = options['title'] chartDescription = options['description'] client = MongoClient('mongodb', 27017) db = client[DATABASE_NAME] posts_collection = db.get_collection(POSTS_COLLECTION) processed_posts_collection = db.get_collection(PROCESSED_POSTS_COLLECTION) invalid_plot = False if (options['type'] == "scatter"): logger.debug("Plotting scatter.") collection = options.get('collection', PROCESSED_POSTS_COLLECTION) point_size = options.get('point_size', 2) color = options.get('color', 'green') y_axis_field = options.get('y_axis_field', 'polarity') y_min = options.get('y_min', None) y_max = options.get('y_max', None) if (collection == POSTS_COLLECTION): posts = posts_collection.find( {'topicName': topicName}).sort('createdAt', pymongo.ASCENDING) else: posts = processed_posts_collection.find( {'topicName': topicName}).sort('createdAt', pymongo.ASCENDING) dates_axis = [] y_axis = [] for post in posts: if (y_axis_field in post): dates_axis.append(post['createdAt']) y_axis.append(post[y_axis_field]) dates = [pd.to_datetime(d) for d in dates_axis] fig = plt.figure(1, figsize=(11, 6)) plt.title(chartName) plt.xlabel('createdAt') plt.ylabel(y_axis_field) # the scatter plot: axScatter = plt.subplot(111) axScatter.scatter(x=dates, y=y_axis, s=point_size, color=color) # set axes range plt.xlim(dates[0], dates[len(dates) - 1]) if y_min == None: y_min = min(y_axis) if y_max == None: y_max = max(y_axis) plt.ylim(y_min, y_max) my_plot = plt.gcf() imgdata = BytesIO() # my_plot.show() my_plot.savefig(imgdata, format='png') encoded_chart = base64.b64encode(imgdata.getvalue()) else: invalid_plot = True client.close() if invalid_plot: return None singleChart = { "id": None, "name": chartName, "description": chartDescription, "type": "Figure List", "customType": "", "jobId": None, "options": {}, "data": {}, "figures": [encoded_chart.decode('ascii')], #"figures": [], "createdAt": None } response = { "charts": [ singleChart ] } return response app = create_app() @app.route('/api/plot', methods=['POST']) def process(): print(request) if not request.json or not 'topicName' or not 'options' in request.json: abort(400) response = plotTopic( request.json['topicName'], request.json.get('options', {})) if (response != None): return jsonify(response), 200 else: return "", 500 if __name__ == '__main__': app.run(host="0.0.0.0", port=app_port) # plotTopic("Apple Microsoft", { # 'title': 'Titlte', 'description': 'Dscription'}) #input("Press Enter to continue...")

29.075697

121

0.565908

cca2291d6cc0c07dfc8cf1b487d638a6d05c267b

3,274

py

Python

dependencies/src/4Suite-XML-1.0.2/test/Xml/Xslt/Borrowed/sm_19991228.py

aleasims/Peach

bb56841e943d719d5101fee0a503ed34308eda04

[ "MIT" ]

null

dependencies/src/4Suite-XML-1.0.2/test/Xml/Xslt/Borrowed/sm_19991228.py

aleasims/Peach

bb56841e943d719d5101fee0a503ed34308eda04

[ "MIT" ]

null

dependencies/src/4Suite-XML-1.0.2/test/Xml/Xslt/Borrowed/sm_19991228.py

aleasims/Peach

bb56841e943d719d5101fee0a503ed34308eda04

[ "MIT" ]

1

2020-07-26T03:57:45.000Z

#Example for Jon Smirl on 28 Dec 1999, originally by Steve Muench, with improvements by Mike Brown and Jeremy Richman from Xml.Xslt import test_harness sheet_1 = """<xsl:stylesheet xmlns:xsl="http://www.w3.org/1999/XSL/Transform" version="1.0"> <xsl:template match="/"> <html> <head/> <body> <xsl:apply-templates/> </body> </html> </xsl:template> <xsl:template match="p"> <xsl:apply-templates/> </xsl:template> <xsl:template match="programlisting"> <xsl:call-template name="br-replace"> <xsl:with-param name="word" select="."/> </xsl:call-template> </xsl:template> <xsl:template name="br-replace"> <xsl:param name="word"/>  <xsl:variable name="cr"><xsl:text> </xsl:text></xsl:variable> <xsl:choose> <xsl:when test="contains($word,$cr)"> <xsl:value-of select="substring-before($word,$cr)"/> <xsl:call-template name="br-replace"> <xsl:with-param name="word" select="substring-after($word,$cr)"/> </xsl:call-template> </xsl:when> <xsl:otherwise> <xsl:value-of select="$word"/> </xsl:otherwise> </xsl:choose> </xsl:template> </xsl:stylesheet>""" sheet_2 = """<xsl:stylesheet xmlns:xsl="http://www.w3.org/1999/XSL/Transform" version="1.0"> <xsl:template match="/"> <html> <head/> <body> <xsl:apply-templates/> </body> </html> </xsl:template> <xsl:template match="p"> <xsl:apply-templates/> </xsl:template> <xsl:template match="programlisting"> <xsl:apply-templates/> </xsl:template> <xsl:template match="programlisting/text()[contains(.,'
')]"> <xsl:call-template name="br-replace"> <xsl:with-param name="text" select="."/> </xsl:call-template> </xsl:template> <xsl:template name="br-replace"> <xsl:param name="text"/>  <xsl:choose> <xsl:when test="contains($text, '
')"> <xsl:value-of select="substring-before($text, '
')"/> <xsl:call-template name="br-replace"> <xsl:with-param name="text" select="substring-after($text, '
')"/> </xsl:call-template> </xsl:when> <xsl:otherwise> <xsl:value-of select="$text"/> </xsl:otherwise> </xsl:choose> </xsl:template> </xsl:stylesheet>""" source_1="""<doc> This is some text. <programlisting><![CDATA[This is a paragraph with some newlines does it work?]]></programlisting> </doc>""" expected_1 = """<html> <head> <meta http-equiv='Content-Type' content='text/html; charset=iso-8859-1'> </head> <body> This is some text. This is a paragraph with some newlines does it work? </body> </html>""" def Test(tester): source = test_harness.FileInfo(string=source_1) sheet = test_harness.FileInfo(string=sheet_1) test_harness.XsltTest(tester, source, [sheet], expected_1, title='test 1') source = test_harness.FileInfo(string=source_1) sheet = test_harness.FileInfo(string=sheet_2) test_harness.XsltTest(tester, source, [sheet], expected_1, title='test 2') return

25.578125

117

0.640806

cca2be577b6cb5b5ef2f77c8c187c1f9904195fb

2,687

py

Python

django_docutils/favicon/rst/transforms/favicon.py

tony/django-docutils

ed3e089728507a0f579a62bcb182f283bc59929c

[ "MIT" ]

10

2017-04-28T00:19:10.000Z

2020-07-22T15:27:09.000Z

django_docutils/favicon/rst/transforms/favicon.py

tony/django-docutils

ed3e089728507a0f579a62bcb182f283bc59929c

[ "MIT" ]

231

2017-01-17T04:47:51.000Z

2022-03-30T03:03:42.000Z

django_docutils/favicon/rst/transforms/favicon.py

tony/django-docutils

ed3e089728507a0f579a62bcb182f283bc59929c

[ "MIT" ]

1

2019-01-25T14:42:15.000Z

import tldextract from django.db.models import Q from docutils import nodes from docutils.transforms import Transform from django_docutils.favicon.models import get_favicon_model from ..nodes import icon Favicon = get_favicon_model() def resolve_favicon(url): """Given a URL to a website, see if a Favicon exists in db. URL will be resolved to a fqdn for a key lookup. :param url: URL to any page on a website :type url: str :returns: Full Storage based favicon url path, or None :rtype: str|None """ # e.g. forums.bbc.co.uk fqdn = tldextract.extract(url).fqdn try: return Favicon.objects.get(domain=fqdn).favicon.url except (ValueError, Favicon.DoesNotExist): return None class FaviconTransform(Transform): #: run after based.app.references.rst.transforms.xref default_priority = 20 def apply(self): q = Q() # first run, iterate through references, extract FQDN's, add to query for node in self.document.traverse(plain_references): q.add(Q(domain__exact=tldextract.extract(node['refuri']).fqdn), Q.OR) # pull all fqdn's with a favicon favicons = Favicon.objects.filter(q) for node in self.document.traverse(plain_references): fqdn = tldextract.extract(node['refuri']).fqdn try: favicon_url = next( # Find favicon matching fqdn (f.favicon.url for f in favicons if f.domain == fqdn), None ) except ValueError: # no favicon exists for fqdn favicon_url = None if favicon_url: nodecopy = node.deepcopy() ico = icon( '', '', style=f'background-image: url({favicon_url})', classes=['ico'], ) nodecopy.insert(0, ico) node.replace_self(nodecopy) def plain_references(node): """Docutils traversal: Only return references with URI's, skip xref's If a nodes.reference already has classes, it's an icon class from xref, so skip that. If a nodes.reference has no 'refuri', it's junk, skip. Docutils node.traverse condition callback :returns: True if it's a URL we want to lookup favicons for :rtype: bool """ if isinstance(node, nodes.reference): # skip nodes already with xref icon classes or no refuri no_classes = 'classes' not in node or not node['classes'] has_refuri = 'refuri' in node if no_classes and has_refuri and node['refuri'].startswith('http'): return True return False

30.885057

81

0.61965

cca383b23457a782befbce9c96268d9161502ad0

1,733

py

Python

xnmt/model_context.py

marcintustin/xnmt

f315fc5e493d25746bbde46d2c89cea3410d43df

[ "Apache-2.0" ]

null

xnmt/model_context.py

marcintustin/xnmt

f315fc5e493d25746bbde46d2c89cea3410d43df

[ "Apache-2.0" ]

null

xnmt/model_context.py

marcintustin/xnmt

f315fc5e493d25746bbde46d2c89cea3410d43df

[ "Apache-2.0" ]

null

import dynet as dy import os from xnmt.serializer import Serializable class ModelContext(Serializable): yaml_tag = u'!ModelContext' def __init__(self): self.dropout = 0.0 self.weight_noise = 0.0 self.default_layer_dim = 512 self.dynet_param_collection = None self.serialize_params = ["dropout", "weight_noise", "default_layer_dim"] def update(self, other): for param in self.serialize_params: setattr(self, param, getattr(other, param)) class PersistentParamCollection(object): def __init__(self, model_file, save_num_checkpoints=1): self.model_file = model_file self.param_col = dy.Model() self.is_saved = False assert save_num_checkpoints >= 1 or (model_file is None and save_num_checkpoints==0) if save_num_checkpoints>0: self.data_files = [self.model_file + '.data'] for i in range(1,save_num_checkpoints): self.data_files.append(self.model_file + '.data.' + str(i)) def revert_to_best_model(self): self.param_col.populate(self.model_file + '.data') def save(self, fname=None): if fname: assert fname == self.data_files[0], "%s != %s" % (fname + '.data', self.data_files[0]) if not self.is_saved: self.remove_existing_history() self.shift_safed_checkpoints() self.param_col.save(self.data_files[0]) self.is_saved = True def remove_existing_history(self): for fname in self.data_files[1:]: if os.path.exists(fname): os.remove(fname) def shift_safed_checkpoints(self): for i in range(len(self.data_files)-1)[::-1]: if os.path.exists(self.data_files[i]): os.rename(self.data_files[i], self.data_files[i+1]) def load_from_data_file(self, datafile): self.param_col.populate(datafile)

38.511111

100

0.709175

cca508227383d8fc79a193435ae1d2995d0eb8b7

3,100

py

Python

utils.py

Akshayvm98/Django-School

723d52db2cd3bc7665680a3adaf8687f97836d48

[ "MIT" ]

26

2015-08-04T00:13:27.000Z

2021-03-19T01:01:14.000Z

utils.py

Akshayvm98/Django-School

723d52db2cd3bc7665680a3adaf8687f97836d48

[ "MIT" ]

null

utils.py

Akshayvm98/Django-School

723d52db2cd3bc7665680a3adaf8687f97836d48

[ "MIT" ]

28

2015-01-19T15:10:15.000Z

2020-10-27T11:22:21.000Z

import csv from django.http import HttpResponse, HttpResponseForbidden from django.template.defaultfilters import slugify from django.db.models.loading import get_model def export(qs, fields=None): model = qs.model response = HttpResponse(mimetype='text/csv') response['Content-Disposition'] = 'attachment; filename=%s.csv' % slugify(model.__name__) writer = csv.writer(response) # Write headers to CSV file if fields: headers = fields else: headers = [] for field in model._meta.fields: headers.append(field.name) writer.writerow(headers) # Write data to CSV file for obj in qs: row = [] for field in headers: if field in headers: val = getattr(obj, field) if callable(val): val = val() row.append(val) writer.writerow(row) # Return CSV file to browser as download return response def admin_list_export(request, model_name, app_label, queryset=None, fields=None, list_display=True): """ Put the following line in your urls.py BEFORE your admin include (r'^admin/(?P<app_label>[\d\w]+)/(?P<model_name>[\d\w]+)/csv/', 'util.csv_view.admin_list_export'), """ if not request.user.is_staff: return HttpResponseForbidden() if not queryset: model = get_model(app_label, model_name) queryset = model.objects.all() filters = dict() for key, value in request.GET.items(): if key not in ('ot', 'o'): filters[str(key)] = str(value) if len(filters): queryset = queryset.filter(**filters) if not fields and list_display: from django.contrib import admin ld = admin.site._registry[queryset.model].list_display if ld and len(ld) > 0: fields = ld ''' if not fields: if list_display and len(queryset.model._meta.admin.list_display) > 1: fields = queryset.model._meta.admin.list_display else: fields = None ''' return export(queryset, fields) """ Create your own change_list.html for your admin view and put something like this in it: {% block object-tools %} <ul class="object-tools"> <li><a href="csv/{%if request.GET%}?{{request.GET.urlencode}}{%endif%}" class="addlink">Export to CSV</a></li> {% if has_add_permission %} <li><a href="add/{% if is_popup %}?_popup=1{% endif %}" class="addlink">{% blocktrans with cl.opts.verbose_name|escape as name %}Add {{ name }}{% endblocktrans %}</a></li> {% endif %} </ul> {% endblock %} """ import datetime from django.http import HttpResponseRedirect #, HttpResponse from django.shortcuts import render_to_response from django.template import RequestContext from django.contrib.auth.decorators import login_required def mail(request, app_label, model_name): context = { 'app_label':app_label, 'model_name':model_name, } return render_to_response('mail.html', context, context_instance=RequestContext(request))

36.470588

179

0.639032

cca9c51d89c5109307f0fb8fc9ff82831401ed2e

1,668

py

Python

hard-gists/a027a9fc5aac66e6a382/snippet.py

jjhenkel/dockerizeme

eaa4fe5366f6b9adf74399eab01c712cacaeb279

[ "Apache-2.0" ]

21

2019-07-08T08:26:45.000Z

2022-01-24T23:53:25.000Z

hard-gists/a027a9fc5aac66e6a382/snippet.py

jjhenkel/dockerizeme

eaa4fe5366f6b9adf74399eab01c712cacaeb279

[ "Apache-2.0" ]

5

2019-06-15T14:47:47.000Z

2022-02-26T05:02:56.000Z

hard-gists/a027a9fc5aac66e6a382/snippet.py

jjhenkel/dockerizeme

eaa4fe5366f6b9adf74399eab01c712cacaeb279

[ "Apache-2.0" ]

17

2019-05-16T03:50:34.000Z

2021-01-14T14:35:12.000Z

# -*- coding: utf-8 -*- # Author: Massimo Menichinelli # Homepage: http://www.openp2pdesign.org # License: MIT # import wx import serial # A new custom class that extends the wx.Frame class MyFrame(wx.Frame): def __init__(self, parent, title): super(MyFrame, self).__init__(parent, title=title, size=(250, 150)) # Attach the paint event to the frame self.Bind(wx.EVT_PAINT, self.OnPaint) # Create a timer for redrawing the frame every 100 milliseconds self.Timer = wx.Timer(self) self.Timer.Start(100) self.Bind(wx.EVT_TIMER, self.OnPaint) # Show the frame self.Centre() self.Show() def OnPaint(self, event=None): # Create the paint surface dc = wx.PaintDC(self) # Refresh the display self.Refresh() # Get data from serial port value = arduino.readline() # Draw the serial data # Set up colors: thickness = 4 border_color = "#990000" fill_color = "#FF944D" dc.SetPen(wx.Pen(border_color, thickness)) dc.SetBrush(wx.Brush(fill_color)) # Draw a line dc.DrawLine(50, 40, 50+value, 40) # Draw a rectangle dc.DrawRectangle(50,50,value,50) # Main program if __name__ == '__main__': # Connect to serial port first try: arduino = serial.Serial('/dev/tty.usbmodem1421', 9600) except: print "Failed to connect" exit() # Create and launch the wx interface app = wx.App() MyFrame(None, 'Serial data test') app.MainLoop() # Close the serial connection arduino.close()

23.492958

71

0.601918

ccae60674a2d5d72a4d3496efd86de39ff78e0dc

13,807

py

Python

src/python/pants/backend/docker/util_rules/docker_build_context_test.py

asherf/pants

e010b93c4123b4446a631cac5db0b7ea15634686

[ "Apache-2.0" ]

null

src/python/pants/backend/docker/util_rules/docker_build_context_test.py

asherf/pants

e010b93c4123b4446a631cac5db0b7ea15634686

[ "Apache-2.0" ]

14

2021-05-03T13:54:41.000Z

2022-03-30T10:20:58.000Z

src/python/pants/backend/docker/util_rules/docker_build_context_test.py

asherf/pants

e010b93c4123b4446a631cac5db0b7ea15634686

[ "Apache-2.0" ]

null

# Copyright 2021 Pants project contributors (see CONTRIBUTORS.md). # Licensed under the Apache License, Version 2.0 (see LICENSE). from __future__ import annotations from textwrap import dedent from typing import Any, ContextManager import pytest from pants.backend.docker.subsystems.dockerfile_parser import rules as parser_rules from pants.backend.docker.target_types import DockerImageTarget from pants.backend.docker.util_rules.docker_build_args import docker_build_args from pants.backend.docker.util_rules.docker_build_context import ( DockerBuildContext, DockerBuildContextRequest, DockerVersionContext, ) from pants.backend.docker.util_rules.docker_build_context import rules as context_rules from pants.backend.docker.util_rules.docker_build_env import docker_build_environment_vars from pants.backend.docker.util_rules.dockerfile import rules as dockerfile_rules from pants.backend.python import target_types_rules from pants.backend.python.goals import package_pex_binary from pants.backend.python.goals.package_pex_binary import PexBinaryFieldSet from pants.backend.python.target_types import PexBinary from pants.backend.python.util_rules import pex_from_targets from pants.backend.shell.target_types import ShellSourcesGeneratorTarget, ShellSourceTarget from pants.backend.shell.target_types import rules as shell_target_types_rules from pants.core.goals.package import BuiltPackage from pants.core.target_types import FilesGeneratorTarget from pants.core.target_types import rules as core_target_types_rules from pants.engine.addresses import Address from pants.engine.fs import Snapshot from pants.engine.internals.scheduler import ExecutionError from pants.testutil.pytest_util import no_exception from pants.testutil.rule_runner import QueryRule, RuleRunner @pytest.fixture def rule_runner() -> RuleRunner: rule_runner = RuleRunner( rules=[ *context_rules(), *core_target_types_rules(), *dockerfile_rules(), *package_pex_binary.rules(), *parser_rules(), *pex_from_targets.rules(), *shell_target_types_rules(), *target_types_rules.rules(), docker_build_args, docker_build_environment_vars, QueryRule(BuiltPackage, [PexBinaryFieldSet]), QueryRule(DockerBuildContext, (DockerBuildContextRequest,)), ], target_types=[ DockerImageTarget, FilesGeneratorTarget, PexBinary, ShellSourcesGeneratorTarget, ShellSourceTarget, ], ) return rule_runner def assert_build_context( rule_runner: RuleRunner, address: Address, *, expected_files: list[str], expected_version_context: dict[str, dict[str, str]] | None = None, pants_args: list[str] | None = None, runner_options: dict[str, Any] | None = None, ) -> None: if runner_options is None: runner_options = {} runner_options.setdefault("env_inherit", set()).update({"PATH", "PYENV_ROOT", "HOME"}) rule_runner.set_options(pants_args or [], **runner_options) context = rule_runner.request( DockerBuildContext, [ DockerBuildContextRequest( address=address, build_upstream_images=False, ) ], ) snapshot = rule_runner.request(Snapshot, [context.digest]) assert sorted(expected_files) == sorted(snapshot.files) if expected_version_context is not None: assert context.version_context == DockerVersionContext.from_dict(expected_version_context) def test_file_dependencies(rule_runner: RuleRunner) -> None: rule_runner.write_files( { # img_A -> files_A # img_A -> img_B "src/a/BUILD": dedent( """\ docker_image(name="img_A", dependencies=[":files_A", "src/b:img_B"]) files(name="files_A", sources=["files/**"]) """ ), "src/a/Dockerfile": "FROM base", "src/a/files/a01": "", "src/a/files/a02": "", # img_B -> files_B "src/b/BUILD": dedent( """\ docker_image(name="img_B", dependencies=[":files_B"]) files(name="files_B", sources=["files/**"]) """ ), "src/b/Dockerfile": "FROM base", "src/b/files/b01": "", "src/b/files/b02": "", # Mixed "src/c/BUILD": dedent( """\ docker_image(name="img_C", dependencies=["src/a:files_A", "src/b:files_B"]) """ ), "src/c/Dockerfile": "FROM base", } ) # We want files_B in build context for img_B assert_build_context( rule_runner, Address("src/b", target_name="img_B"), expected_files=["src/b/Dockerfile", "src/b/files/b01", "src/b/files/b02"], ) # We want files_A in build context for img_A, but not files_B assert_build_context( rule_runner, Address("src/a", target_name="img_A"), expected_files=["src/a/Dockerfile", "src/a/files/a01", "src/a/files/a02"], ) # Mixed. assert_build_context( rule_runner, Address("src/c", target_name="img_C"), expected_files=[ "src/c/Dockerfile", "src/a/files/a01", "src/a/files/a02", "src/b/files/b01", "src/b/files/b02", ], ) def test_files_out_of_tree(rule_runner: RuleRunner) -> None: # src/a:img_A -> res/static:files rule_runner.write_files( { "src/a/BUILD": dedent( """\ docker_image(name="img_A", dependencies=["res/static:files"]) """ ), "res/static/BUILD": dedent( """\ files(name="files", sources=["!BUILD", "**/*"]) """ ), "src/a/Dockerfile": "FROM base", "res/static/s01": "", "res/static/s02": "", "res/static/sub/s03": "", } ) assert_build_context( rule_runner, Address("src/a", target_name="img_A"), expected_files=[ "src/a/Dockerfile", "res/static/s01", "res/static/s02", "res/static/sub/s03", ], ) def test_packaged_pex_path(rule_runner: RuleRunner) -> None: # This test is here to ensure that we catch if there is any change in the generated path where # built pex binaries go, as we rely on that for dependency inference in the Dockerfile. rule_runner.write_files( { "src/docker/BUILD": """docker_image(dependencies=["src/python/proj/cli:bin"])""", "src/docker/Dockerfile": """FROM python""", "src/python/proj/cli/BUILD": """pex_binary(name="bin", entry_point="main.py")""", "src/python/proj/cli/main.py": """print("cli main")""", } ) assert_build_context( rule_runner, Address("src/docker", target_name="docker"), expected_files=["src/docker/Dockerfile", "src.python.proj.cli/bin.pex"], ) def test_version_context_from_dockerfile(rule_runner: RuleRunner) -> None: rule_runner.write_files( { "src/docker/BUILD": "docker_image()", "src/docker/Dockerfile": dedent( """\ FROM python:3.8 FROM alpine as interim FROM interim FROM scratch:1-1 as output """ ), } ) assert_build_context( rule_runner, Address("src/docker"), expected_files=["src/docker/Dockerfile"], expected_version_context={ "baseimage": {"tag": "3.8"}, "stage0": {"tag": "3.8"}, "interim": {"tag": "latest"}, "stage2": {"tag": "latest"}, "output": {"tag": "1-1"}, }, ) def test_synthetic_dockerfile(rule_runner: RuleRunner) -> None: rule_runner.write_files( { "src/docker/BUILD": dedent( """\ docker_image( instructions=[ "FROM python:3.8", "FROM alpine as interim", "FROM interim", "FROM scratch:1-1 as output", ] ) """ ), } ) assert_build_context( rule_runner, Address("src/docker"), expected_files=["src/docker/Dockerfile.docker"], expected_version_context={ "baseimage": {"tag": "3.8"}, "stage0": {"tag": "3.8"}, "interim": {"tag": "latest"}, "stage2": {"tag": "latest"}, "output": {"tag": "1-1"}, }, ) def test_shell_source_dependencies(rule_runner: RuleRunner) -> None: rule_runner.write_files( { "src/docker/BUILD": dedent( """\ docker_image(dependencies=[":entrypoint", ":shell"]) shell_source(name="entrypoint", source="entrypoint.sh") shell_sources(name="shell", sources=["scripts/**/*.sh"]) """ ), "src/docker/Dockerfile": "FROM base", "src/docker/entrypoint.sh": "", "src/docker/scripts/s01.sh": "", "src/docker/scripts/s02.sh": "", "src/docker/scripts/random.file": "", } ) assert_build_context( rule_runner, Address("src/docker"), expected_files=[ "src/docker/Dockerfile", "src/docker/entrypoint.sh", "src/docker/scripts/s01.sh", "src/docker/scripts/s02.sh", ], ) def test_build_arg_defaults_from_dockerfile(rule_runner: RuleRunner) -> None: # Test that only explicitly defined build args in the BUILD file or pants configuraiton use the # environment for its values. rule_runner.write_files( { "src/docker/BUILD": dedent( """\ docker_image( extra_build_args=[ "base_version", ] ) """ ), "src/docker/Dockerfile": dedent( """\ ARG base_name=python ARG base_version=3.8 FROM ${base_name}:${base_version} ARG NO_DEF ENV opt=${NO_DEF} """ ), } ) assert_build_context( rule_runner, Address("src/docker"), runner_options={ "env": { "base_name": "no-effect", "base_version": "3.9", }, }, expected_files=["src/docker/Dockerfile"], expected_version_context={ "baseimage": {"tag": "${base_version}"}, "stage0": {"tag": "${base_version}"}, "build_args": { # `base_name` is not listed here, as it was not an explicitly defined build arg. "base_version": "3.9", }, }, ) @pytest.mark.parametrize( "dockerfile_arg_value, extra_build_arg_value, expect", [ pytest.param(None, None, no_exception(), id="No args defined"), pytest.param( None, "", pytest.raises(ExecutionError, match=r"variable 'MY_ARG' is undefined"), id="No default value for build arg", ), pytest.param(None, "some default value", no_exception(), id="Default value for build arg"), pytest.param("", None, no_exception(), id="No build arg defined, and ARG without default"), pytest.param( "", "", pytest.raises(ExecutionError, match=r"variable 'MY_ARG' is undefined"), id="No default value from ARG", ), pytest.param( "", "some default value", no_exception(), id="Default value for build arg, ARG present" ), pytest.param( "some default value", None, no_exception(), id="No build arg defined, only ARG" ), pytest.param("some default value", "", no_exception(), id="Default value from ARG"), pytest.param( "some default value", "some other default", no_exception(), id="Default value for build arg, ARG default", ), ], ) def test_undefined_env_var_behavior( rule_runner: RuleRunner, dockerfile_arg_value: str | None, extra_build_arg_value: str | None, expect: ContextManager, ) -> None: dockerfile_arg = "" if dockerfile_arg_value is not None: dockerfile_arg = "ARG MY_ARG" if dockerfile_arg_value: dockerfile_arg += f"={dockerfile_arg_value}" extra_build_args = "" if extra_build_arg_value is not None: extra_build_args = 'extra_build_args=["MY_ARG' if extra_build_arg_value: extra_build_args += f"={extra_build_arg_value}" extra_build_args += '"],' rule_runner.write_files( { "src/docker/BUILD": dedent( f"""\ docker_image( {extra_build_args} ) """ ), "src/docker/Dockerfile": dedent( f"""\ FROM python:3.8 {dockerfile_arg} """ ), } ) with expect: assert_build_context( rule_runner, Address("src/docker"), expected_files=["src/docker/Dockerfile"], )

32.640662

99

0.557833

ccb0eb936ec41c62e8c05807faa25bca2c7ad40d

48

py

Python

tests/extensions/artificial_intelligence/ai_model/__init__.py

chr0m1ng/blip-sdk-js

74f404a3ef048a100cc330f78a9751572e31fb0b

[ "MIT" ]

null

tests/extensions/artificial_intelligence/ai_model/__init__.py

chr0m1ng/blip-sdk-js

74f404a3ef048a100cc330f78a9751572e31fb0b

[ "MIT" ]

9

2021-05-27T21:08:23.000Z

2021-06-14T20:10:10.000Z

tests/extensions/artificial_intelligence/ai_model/__init__.py

chr0m1ng/blip-sdk-python

74f404a3ef048a100cc330f78a9751572e31fb0b

[ "MIT" ]

null

from .test_ai_model import TestAiModelExtension

24

47

0.895833

ccb0f6edadd14edc784ea01c369c3b62583b31ca

5,100

py

Python

src/rescuexport/dal.py

karlicoss/rescuexport

69f5275bfa7cb39a1ba74b99312b605ba340916a

[ "MIT" ]

6

2019-11-28T10:56:53.000Z

2022-01-10T21:07:40.000Z

src/rescuexport/dal.py

karlicoss/rescuexport

69f5275bfa7cb39a1ba74b99312b605ba340916a

[ "MIT" ]

null

src/rescuexport/dal.py

karlicoss/rescuexport

69f5275bfa7cb39a1ba74b99312b605ba340916a

[ "MIT" ]

1

2020-12-08T14:16:53.000Z

#!/usr/bin/env python3 import logging from pathlib import Path import json from datetime import datetime, timedelta from typing import Set, Sequence, Any, Iterator from dataclasses import dataclass from .exporthelpers.dal_helper import PathIsh, Json, Res, datetime_naive from .exporthelpers.logging_helper import LazyLogger logger = LazyLogger(__package__) seconds = int _DT_FMT = '%Y-%m-%dT%H:%M:%S' @dataclass class Entry: dt: datetime_naive ''' Ok, it definitely seems local, by the looks of the data. https://www.rescuetime.com/apidoc#analytic-api-reference "defined by the user’s selected time zone" -- not sure what it means, but another clue I suppose Note that the manual export has something like -08:00, but it's the time is same as local -- doesn't make any sense... ''' duration_s: seconds activity: str @classmethod def from_row(cls, row: Json) -> 'Entry': # COL_DT = 0 # COL_DUR = 1 # COL_ACTIVITY = 3 # todo I think cols are fixed so could speed up lookup? Not really necessary at te moment though COL_DT = 'Date' COL_DUR = 'Time Spent (seconds)' COL_ACTIVITY = 'Activity' dt_s = row[COL_DT] dur = row[COL_DUR] activity = row[COL_ACTIVITY] dt = datetime.strptime(dt_s, _DT_FMT) return cls(dt=dt, duration_s=dur, activity=activity) class DAL: def __init__(self, sources: Sequence[PathIsh]) -> None: # todo not sure if should sort -- probably best to rely on get_files? self.sources = [p if isinstance(p, Path) else Path(p) for p in sources] def raw_entries(self) -> Iterator[Res[Json]]: # todo rely on more_itertools for it? emitted: Set[Any] = set() last = None for src in self.sources: # todo parse in multiple processes?? try: j = json.loads(src.read_text()) except Exception as e: ex = RuntimeError(f'While processing {src}') ex.__cause__ = e yield ex continue headers = j['row_headers'] rows = j['rows'] total = len(rows) unique = 0 for row in rows: frow = tuple(row) # freeze for hashing if frow in emitted: continue drow = dict(zip(headers, row)) if last is not None and drow['Date'] < last['Date']: # pylint: disable=unsubscriptable-object yield RuntimeError(f'Expected\n{drow}\nto be later than\n{last}') # TODO ugh, for couple of days it was pretty bad, lots of duplicated entries.. # for now, just ignore it else: yield drow emitted.add(frow) unique += 1 last = drow logger.debug(f"{src}: filtered out {total - unique:<6} of {total:<6}. Grand total: {len(emitted)}") def entries(self) -> Iterator[Res[Entry]]: for row in self.raw_entries(): if isinstance(row, Exception): yield row continue cur = Entry.from_row(row) yield cur from typing import Iterable # todo quick test (dal helper aided: check that DAL can handle fake data) def fake_data_generator(rows=100, seed=123) -> Json: # todo ok, use faker/mimesis here?? from random import Random r = Random(seed) def row_gen(): base = datetime(year=2000, month=1, day=1) cur = base emitted = 0 i = 0 while emitted < rows: i += 1 sleeping = 1 <= cur.hour <= 8 if sleeping: cur = cur + timedelta(hours=2) continue # do something during that period duration = r.randint(10, 500) if r.choice([True, False]): emitted += 1 yield [ cur.strftime(_DT_FMT), duration, 1, f'Activity {i % 10}', 'Category {i % 3}', i % 2, ] cur += timedelta(seconds=duration) return { "notes": "data is an array of arrays (rows), column names for rows in row_headers", "row_headers": ["Date", "Time Spent (seconds)", "Number of People", "Activity", "Category", "Productivity"], "rows": list(row_gen()) } def main() -> None: # todo adapt for dal_helper? import argparse p = argparse.ArgumentParser() p.add_argument('path', type=Path) args = p.parse_args() files = list(sorted(args.path.glob('*.json'))) model = DAL(files) count = 0 for x in model.entries(): if isinstance(x, Exception): logger.error(x) else: count += 1 if count % 10000 == 0: logger.info('Processed %d entries', count) # print(x) if __name__ == '__main__': main()

31.097561

122

0.550392

ccb20b99e03872ed73a498ac6c05de75181ce2b4

1,287

py

Python

MuonAnalysis/MuonAssociators/python/muonHLTL1Match_cfi.py

ckamtsikis/cmssw

ea19fe642bb7537cbf58451dcf73aa5fd1b66250

[ "Apache-2.0" ]

852

2015-01-11T21:03:51.000Z

2022-03-25T21:14:00.000Z

MuonAnalysis/MuonAssociators/python/muonHLTL1Match_cfi.py

ckamtsikis/cmssw

ea19fe642bb7537cbf58451dcf73aa5fd1b66250

[ "Apache-2.0" ]

30,371

2015-01-02T00:14:40.000Z

2022-03-31T23:26:05.000Z

MuonAnalysis/MuonAssociators/python/muonHLTL1Match_cfi.py

ckamtsikis/cmssw

ea19fe642bb7537cbf58451dcf73aa5fd1b66250

[ "Apache-2.0" ]

3,240

2015-01-02T05:53:18.000Z

2022-03-31T17:24:21.000Z

import FWCore.ParameterSet.Config as cms from MuonAnalysis.MuonAssociators.muonL1Match_cfi import * muonHLTL1Match = cms.EDProducer("HLTL1MuonMatcher", muonL1MatcherParameters, # Reconstructed muons src = cms.InputTag("muons"), # L1 Muon collection, and preselection on that collection matched = cms.InputTag("patTrigger"), # Requests to select the object matchedCuts = cms.string('coll("hltL1extraParticles")'), # 90% compatible with documentation at SWGuidePATTrigger#Module_Configuration_AN1 # andOr = cms.bool( False ), # if False, do the 'AND' of the conditions below; otherwise, do the OR # filterIdsEnum = cms.vstring( '*' ), # filterIds = cms.vint32( 0 ), # filterLabels = cms.vstring( '*' ), # pathNames = cms.vstring( '*' ), # collectionTags = cms.vstring( 'hltL1extraParticles' ), resolveAmbiguities = cms.bool( True ), # if True, no more than one reco object can be matched to the same L1 object; precedence is given to the reco ones coming first in the list # Fake filter lavels for the object propagated to the second muon station setPropLabel = cms.string("propagatedToM2"), # Write extra ValueMaps writeExtraInfo = cms.bool(True), )

40.21875

186

0.679876

ccb315a349fc8cff12751a9ddd2a5e9db7858230

632

py

Python

Curso_em_Video_py3/ex082.py

Rodrigo98Matos/Projetos_py

6428e2c09d28fd8a717743f4434bc788e7d7d3cc

[ "MIT" ]

1

2021-05-11T12:39:43.000Z

Curso_em_Video_py3/ex082.py

Rodrigo98Matos/Projetos_py

6428e2c09d28fd8a717743f4434bc788e7d7d3cc

[ "MIT" ]

null

Curso_em_Video_py3/ex082.py

Rodrigo98Matos/Projetos_py

6428e2c09d28fd8a717743f4434bc788e7d7d3cc

[ "MIT" ]

null

lista = list() while True: lista.append(int(input("Digite um número inteiro:\t"))) while True: p = str(input("Digitar mais números?\t").strip())[0].upper() if p in 'SN': break else: print("\033[31mDigite uma opção válida!\033[m") if p == 'N': break par = list() impar = list() for n in lista: if n % 2 == 0: par.append(n) else: impar.append(n) par.sort() impar.sort() if 0 in par: par.remove(0) print(f"Entre os números \033[32m{lista}\033[m, os números pares são: \033[33m{par}\033[m e os números ímpares são: \033[34m{impar}\033[m!")

26.333333

140

0.568038

ccb4877c2d894de84894acf89db676779bb0786b

1,212

py

Python

census_data_downloader/tables/percapitaincome.py

JoeGermuska/census-data-downloader

0098b9e522b78ad0e30301c9845ecbcc903c62e4

[ "MIT" ]

170

2019-04-01T01:41:42.000Z

2022-03-25T21:22:06.000Z

census_data_downloader/tables/percapitaincome.py

JoeGermuska/census-data-downloader

0098b9e522b78ad0e30301c9845ecbcc903c62e4

[ "MIT" ]

68

2019-03-31T22:52:43.000Z

2021-08-30T16:33:54.000Z

census_data_downloader/tables/percapitaincome.py

JoeGermuska/census-data-downloader

0098b9e522b78ad0e30301c9845ecbcc903c62e4

[ "MIT" ]

34

2019-04-02T17:57:16.000Z

2022-03-28T17:22:35.000Z

#! /usr/bin/env python # -*- coding: utf-8 -* import collections from census_data_downloader.core.tables import BaseTableConfig from census_data_downloader.core.decorators import register @register class PerCapitaIncomeDownloader(BaseTableConfig): # inflation adjusted PROCESSED_TABLE_NAME = "percapitaincome" UNIVERSE = "total population" RAW_TABLE_NAME = 'B19301' RAW_FIELD_CROSSWALK = collections.OrderedDict({ '001': "per_capita_income" }) @register class PerCapitaIncomeLatinoDownloader(PerCapitaIncomeDownloader): # inflation adjusted PROCESSED_TABLE_NAME = "percapitaincomelatino" RAW_TABLE_NAME = 'B19301I' @register class PerCapitaIncomeWhiteDownloader(PerCapitaIncomeDownloader): # inflation adjusted PROCESSED_TABLE_NAME = "percapitaincomewhite" RAW_TABLE_NAME = 'B19301H' @register class PerCapitaIncomeBlackDownloader(PerCapitaIncomeDownloader): # inflation adjusted PROCESSED_TABLE_NAME = "percapitaincomeblack" RAW_TABLE_NAME = 'B19301B' @register class PerCapitaIncomeAsianDownloader(PerCapitaIncomeDownloader): # inflation adjusted PROCESSED_TABLE_NAME = "percapitaincomeasian" RAW_TABLE_NAME = 'B19301D'

26.933333

65

0.782178

ccb69ce91869d886c281dd5c097515346b3bb141

844

py

Python

pyskel_bc/cli.py

smnorris/pyskel_bc

d8bdec3e15da6268c9b6a0f3be1fdd6af9737d21

[ "Apache-2.0" ]

null

pyskel_bc/cli.py

smnorris/pyskel_bc

d8bdec3e15da6268c9b6a0f3be1fdd6af9737d21

[ "Apache-2.0" ]

null

pyskel_bc/cli.py

smnorris/pyskel_bc

d8bdec3e15da6268c9b6a0f3be1fdd6af9737d21

[ "Apache-2.0" ]

null

# Copyright 2018 Province of British Columbia # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Skeleton of a CLI import click import pyskel_bc @click.command('pyskel_bc') @click.argument('count', type=int, metavar='N') def cli(count): """Echo a value `N` number of times""" for i in range(count): click.echo(pyskel_bc.has_legs)

30.142857

76

0.740521

ccb6b1cc30ddbfae5e44890354c45b22ed3bc398

757

py

Python

solutions/python3/558.py

sm2774us/amazon_interview_prep_2021

f580080e4a6b712b0b295bb429bf676eb15668de

[ "MIT" ]

42

2020-08-02T07:03:49.000Z

2022-03-26T07:50:15.000Z

solutions/python3/558.py

ajayv13/leetcode

de02576a9503be6054816b7444ccadcc0c31c59d

[ "MIT" ]

null

solutions/python3/558.py

ajayv13/leetcode

de02576a9503be6054816b7444ccadcc0c31c59d

[ "MIT" ]

40

2020-02-08T02:50:24.000Z

2022-03-26T15:38:10.000Z

class Solution: def intersect(self, q1, q2): if q1.isLeaf: return q1.val and q1 or q2 elif q2.isLeaf: return q2.val and q2 or q1 else: tLeft = self.intersect(q1.topLeft, q2.topLeft) tRight = self.intersect(q1.topRight, q2.topRight) bLeft = self.intersect(q1.bottomLeft, q2.bottomLeft) bRight = self.intersect(q1.bottomRight, q2.bottomRight) if tLeft.isLeaf and tRight.isLeaf and bLeft.isLeaf and bRight.isLeaf and tLeft.val == tRight.val == bLeft.val == bRight.val: node = Node(tLeft.val, True, None, None, None, None) else: node = Node(False, False, tLeft, tRight, bLeft, bRight) return node

47.3125

136

0.587847

ccb7c733b68470d611b304324e4c358b41fac520

1,442

py

Python

project/encryption_utils.py

v2121/redshift-django

b6d66b2ee0443f039cfbad9f53c4ffd4efd156ec

[ "CC0-1.0" ]

null

project/encryption_utils.py

v2121/redshift-django

b6d66b2ee0443f039cfbad9f53c4ffd4efd156ec

[ "CC0-1.0" ]

null

project/encryption_utils.py

v2121/redshift-django

b6d66b2ee0443f039cfbad9f53c4ffd4efd156ec

[ "CC0-1.0" ]

null

import Crypto.Random from Crypto.Cipher import AES import hashlib import base64 SALT_SIZE = 16 AES_MULTIPLE = 16 NUMBER_OF_ITERATIONS = 20 def generate_key(password, salt): key = password.encode('utf-8') + salt for i in range(NUMBER_OF_ITERATIONS): key = hashlib.sha256(str(key).encode('utf-8')).digest() return key def pad_text(text, multiple): extra_bytes = len(text) % multiple padding_size = multiple - extra_bytes padding = chr(padding_size) * padding_size padded_text = text + padding return padded_text def unpad_text(padded_text): padding_size = padded_text[-1] text = padded_text[:-padding_size] return text def encrypt(plaintext, password): salt = Crypto.Random.get_random_bytes(SALT_SIZE) key = generate_key(password, salt) cipher = AES.new(key, AES.MODE_ECB) padded_plaintext = pad_text(plaintext, AES_MULTIPLE) ciphertext = cipher.encrypt(padded_plaintext) ciphertext_with_salt = salt + ciphertext return str(base64.b64encode(ciphertext_with_salt), 'utf-8') def decrypt(ciphertext, password): ciphertext = base64.b64decode(ciphertext) salt = ciphertext[0:SALT_SIZE] ciphertext_sans_salt = ciphertext[SALT_SIZE:] key = generate_key(password, salt) cipher = AES.new(key, AES.MODE_ECB) padded_plaintext = cipher.decrypt(ciphertext_sans_salt) plaintext = unpad_text(padded_plaintext) return str(plaintext, 'utf-8')

31.347826

63

0.731623

ccb976a45584bf29937e03ff9e1aef9ce04de815

462

py

Python

core/xml/utils.py

Magoli1/carla-pre-crash-scenario-generator

46666309a55e9aab59cebeda33aca7723c1d360c

[ "MIT" ]

1

2022-03-30T08:28:53.000Z

core/xml/utils.py

Magoli1/carla-pre-crash-scenario-generator

46666309a55e9aab59cebeda33aca7723c1d360c

[ "MIT" ]

null

core/xml/utils.py

Magoli1/carla-pre-crash-scenario-generator

46666309a55e9aab59cebeda33aca7723c1d360c

[ "MIT" ]

null

from xml.etree import ElementTree from xml.dom import minidom def get_pretty_xml(tree): """Transforms and pretty-prints a given xml-tree to string :param tree: XML-tree :type tree: object :returns: (Prettified) stringified version of the passed xml-tree :rtype: str """ stringified_tree = ElementTree.tostring(tree.getroot(), 'utf-8') minidoc = minidom.parseString(stringified_tree) return minidoc.toprettyxml(indent=" ")

28.875

69

0.71645

ccba7105c97f04202293ba5c9e3bb9286a1e1c30

1,745

py

Python

tests/test_main.py

giulionf/GetOldTweets3

038b8fed7da27300e6c611d3c0fd617588075a58

[ "MIT" ]

null

tests/test_main.py

giulionf/GetOldTweets3

038b8fed7da27300e6c611d3c0fd617588075a58

[ "MIT" ]

null

tests/test_main.py

giulionf/GetOldTweets3

038b8fed7da27300e6c611d3c0fd617588075a58

[ "MIT" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- import os import sys import time import GetOldTweets3 as Got3 if sys.version_info[0] < 3: raise Exception("Python 2.x is not supported. Please upgrade to 3.x") sys.path.append(os.path.join(os.path.dirname(__file__), "..")) def test_username(): tweet_criteria = Got3.manager.TweetCriteria() \ .set_username('barackobama') \ .set_max_tweets(1) tweet = Got3.manager.TweetManager.get_tweets(tweet_criteria)[0] assert tweet.username == 'BarackObama' def test_query_search(): tweet_criteria = Got3.manager.TweetCriteria().set_query_search('#europe #refugees') \ .set_since("2015-05-01") \ .set_until("2015-09-30") \ .set_max_tweets(1) tweet = Got3.manager.TweetManager.get_tweets(tweet_criteria)[0] assert tweet.hashtags.lower() == '#europe #refugees' def test_mass_fetch_concurrent(): time1 = time.time() tweet_criteria = Got3.manager.TweetCriteria().set_query_search('#europe #refugees') \ .set_since("2015-05-01") \ .set_until("2015-09-30") \ .set_max_tweets(500) tweets = Got3.manager.ConcurrentTweetManager.get_tweets(tweet_criteria, worker_count=25) print("Time Needed Concurrent: {} Secs".format((time.time() - time1))) assert len(tweets) <= 1000 def test_mass_fetch_non_concurrent(): time1 = time.time() tweet_criteria = Got3.manager.TweetCriteria().set_query_search('#europe #refugees') \ .set_since("2015-05-01") \ .set_until("2015-09-30") \ .set_max_tweets(500) tweets = Got3.manager.TweetManager.get_tweets(tweet_criteria) print("Time Needed Non Concurrent: {} Secs".format((time.time() - time1))) assert len(tweets) <= 1000

33.557692

92

0.681948

ccbaa690673254ea1cda4e119201ebd6a7af3c57

421

py

Python

extsum/__main__.py

streof/extsum

45949a6a6aff65ceaf2a4bdc70f5b4d9660fca5f

[ "MIT" ]

null

extsum/__main__.py

streof/extsum

45949a6a6aff65ceaf2a4bdc70f5b4d9660fca5f

[ "MIT" ]

null

extsum/__main__.py

streof/extsum

45949a6a6aff65ceaf2a4bdc70f5b4d9660fca5f

[ "MIT" ]

null

import extsum as ext URL = "https://i.picsum.photos/id/42/1/1.jpg" # Uncomment for random Picsum photo # URL = "https://picsum.photos/1/1" if __name__ == '__main__': # Init photo = ext.Load(URL) photo_parsed = ext.Parse(photo) # Print found ID (if any) id_found = photo_parsed.find_id() if id_found is None: print("Couldn't find any ID") else: print(f"Found ID {id_found}")

23.388889

45

0.631829

ccbaec8553da0b692edf4bc23f5c78797c64aa03

931

py

Python

rplugin/python3/denite/kind/lab_browse.py

lighttiger2505/denite-lab

611e5e081d049d79999a8c0a0f38c2466d8ca970

[ "MIT" ]

1

2018-02-26T15:27:03.000Z

rplugin/python3/denite/kind/lab_browse.py

lighttiger2505/denite-lab

611e5e081d049d79999a8c0a0f38c2466d8ca970

[ "MIT" ]

null

rplugin/python3/denite/kind/lab_browse.py

lighttiger2505/denite-lab

611e5e081d049d79999a8c0a0f38c2466d8ca970

[ "MIT" ]

null

import subprocess from .base import Base class Kind(Base): def __init__(self, vim): super().__init__(vim) self.name = 'lab_browse' self.default_action = 'lab_browse' def action_lab_browse(self, context): for target in context['targets']: iid = target['word'] command = ['lab', 'browse', iid] process = subprocess.Popen(command, cwd=context['path'], universal_newlines=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE) try: output, err_output = process.communicate(timeout=15) except subprocess.TimeoutExpired: process.kill() output, err_output = process.communicate() exit_code = process.returncode if exit_code != 0: print('error')

30.032258

64

0.525242

ccbbb657919673062f15afd66c5a069d9e36de11

3,358

py

Python

Betsy/Betsy/modules/plot_sample_pca.py

jefftc/changlab

11da8c415afefcba0b0216238387c75aeb3a56ac

[ "MIT" ]

9

2017-01-13T02:38:41.000Z

2021-04-08T00:44:39.000Z

Betsy/Betsy/modules/plot_sample_pca.py

jefftc/changlab

11da8c415afefcba0b0216238387c75aeb3a56ac

[ "MIT" ]

null

Betsy/Betsy/modules/plot_sample_pca.py

jefftc/changlab

11da8c415afefcba0b0216238387c75aeb3a56ac

[ "MIT" ]

4

2017-01-05T16:25:25.000Z

2019-12-12T20:07:38.000Z

from Module import AbstractModule class Module(AbstractModule): def __init__(self): AbstractModule.__init__(self) def run( self, network, antecedents, out_attributes, user_options, num_cores, outfile): from genomicode import filelib from genomicode import parallel from Betsy import module_utils as mlib in_data = antecedents metadata = {} ## data_node, cls_node = antecedents ## a, b, c = read_label_file.read(cls_node.identifier) ## if len(a) > 1: ## colors = [] ## for i in range(5): ## colors.append(cm.hot(i / 5.0, 1)) ## colors.append(cm.autumn(i / 5.0, i)) ## colors.append(cm.cool(i / 5.0, i)) ## colors.append(cm.jet(i / 5.0, i)) ## colors.append(cm.spring(i / 5.0, i)) ## colors.append(cm.prism(i / 5.0, i)) ## colors.append(cm.summer(i / 5.0, i)) ## colors.append(cm.winter(i / 5.0, i)) ## opts = [colors[int(i)] for i in b] ## legend = [c[int(i)] for i in b] ## plot_pca(data_node.identifier, outfile, opts, legend) #num_genes = mlib.get_user_option( # user_options, "pca_num_genes", type=int) #assert num_genes >= 5 and num_genes < 1E5 #metadata["num_genes"] = num_genes pcaplot = mlib.get_config("pcaplot", which_assert_file=True) prism_file = "prism.txt" row_pc_file = "row_components.txt" col_pc_file = "col_components.txt" sq = parallel.quote cmd = [ sq(pcaplot), "--label", #"-g", num_genes, "--prism_file", prism_file, "--row_pc_file", row_pc_file, "--col_pc_file", col_pc_file, sq(in_data.identifier), sq(outfile), ] cmd = " ".join(map(str, cmd)) parallel.sshell(cmd) metadata["commands"] = [cmd] filelib.assert_exists_nz(outfile) return metadata def name_outfile(self, antecedents, user_options): return "pca.png" ## def plot_pca(filename, result_fig, opts='b', legend=None): ## import arrayio ## from genomicode import jmath, mplgraph ## from genomicode import filelib ## R = jmath.start_R() ## jmath.R_equals(filename, 'filename') ## M = arrayio.read(filename) ## labels = M._col_names['_SAMPLE_NAME'] ## data = M.slice() ## jmath.R_equals(data, 'X') ## R('NUM.COMPONENTS <- 2') ## R('S <- svd(X)') ## R('U <- S$u[,1:NUM.COMPONENTS]') ## R('D <- S$d[1:NUM.COMPONENTS]') ## # Project the data onto the first 2 components. ## R('x <- t(X) %*% U %*% diag(D)') ## x1 = R['x'][0:M.ncol()] ## x2 = R['x'][M.ncol():] ## xlabel = 'Principal Component 1' ## ylabel = 'Principal Component 2' ## if len(opts) > 1: ## fig = mplgraph.scatter( ## x1, x2, xlabel=xlabel, ylabel=ylabel, color=opts, ## legend=legend) ## else: ## fig = mplgraph.scatter( ## x1, x2, xlabel=xlabel, ylabel=ylabel, color=opts, ## label=labels) ## fig.savefig(result_fig) ## assert filelib.exists_nz(result_fig), 'the plot_pca.py fails'

32.921569

76

0.532758

ccbc5e88899c66f97d3d1a9a12e0ddc302dbc43e

1,098

py

Python

scripts/seg_vs_dog_plots.py

AbigailMcGovern/platelet-segmentation

46cd87b81fc44473b07a2bebed1e6134b2582348

[ "BSD-3-Clause" ]

1

2022-02-01T23:40:38.000Z

scripts/seg_vs_dog_plots.py

AbigailMcGovern/platelet-segmentation

46cd87b81fc44473b07a2bebed1e6134b2582348

[ "BSD-3-Clause" ]

3

2021-03-12T02:03:15.000Z

2021-03-31T00:39:05.000Z

scripts/seg_vs_dog_plots.py

AbigailMcGovern/platelet-segmentation

46cd87b81fc44473b07a2bebed1e6134b2582348

[ "BSD-3-Clause" ]

1

2021-04-06T23:23:32.000Z

from plots import plot_experiment_APs, plot_experiment_no_diff, experiment_VI_plots import os # paths, names, title, out_dir, out_name data_dir = '/Users/amcg0011/Data/pia-tracking/dl-results/210512_150843_seed_z-1_y-1_x-1_m_centg' suffix = 'seed_z-1_y-1_x-1_m_centg' out_dir = os.path.join(data_dir, 'DL-vs-Dog') ap_paths = [os.path.join(data_dir, suffix + '_validation_AP.csv'), os.path.join(data_dir, 'DoG-segmentation_average_precision.csv')] nd_paths = [os.path.join(data_dir, 'seed_z-1_y-1_x-1_m_centg_validation_metrics.csv'), os.path.join(data_dir, 'DoG-segmentation_metrics.csv')] vi_paths = [ os.path.join(data_dir, 'seed_z-1_y-1_x-1_m_centgvalidation_VI.csv'), os.path.join(data_dir, 'seed_z-1_y-1_x-1_m_centgvalidation_VI_DOG-seg.csv') ] #plot_experiment_APs(ap_paths, ['DL', 'DoG'], 'Average precision: DL vs Dog', out_dir, 'AP_DL-vs-Dog') #plot_experiment_no_diff(nd_paths, ['DL', 'DoG'], 'Number difference: DL vs Dog', out_dir, 'ND_DL-vs-Dog') experiment_VI_plots(vi_paths, ['DL', 'DoG'], 'VI Subscores: DL vs DoG', 'VI_DL-vs-DoG', out_dir)

64.588235

106

0.736794

ccbcc99c190554ac665c0c7ffb46b19b240c78b3

2,925

py

Python

src/pyfme/models/state/velocity.py

gaofeng2020/PyFME

26b76f0622a8dca0e24eb477a6fb4a8b2aa604d7

[ "MIT" ]

199

2015-12-29T19:49:42.000Z

2022-03-19T14:31:24.000Z

src/pyfme/models/state/velocity.py

gaofeng2020/PyFME

26b76f0622a8dca0e24eb477a6fb4a8b2aa604d7

[ "MIT" ]

126

2015-09-23T11:15:42.000Z

2020-07-29T12:27:22.000Z

src/pyfme/models/state/velocity.py

gaofeng2020/PyFME

26b76f0622a8dca0e24eb477a6fb4a8b2aa604d7

[ "MIT" ]

93

2015-12-26T13:02:29.000Z

2022-03-19T14:31:13.000Z

""" Python Flight Mechanics Engine (PyFME). Copyright (c) AeroPython Development Team. Distributed under the terms of the MIT License. Velocity -------- The aircraft state has """ from abc import abstractmethod import numpy as np # TODO: think about generic changes from body to horizon that could be used for # velocity, accelerations... # If also changes from attitude of elements in the body (such as sensors) to # body and horizon coordinates are implemented it would be useful! from pyfme.utils.coordinates import body2hor, hor2body class Velocity: """Velocity Attributes ---------- vel_body : ndarray, shape(3) (u [m/s], v [m/s], w [m/s]) u v w vel_NED : ndarray, shape(3) (v_north [m/s], v_east [m/s], v_down [m/s]) v_north v_east v_down """ def __init__(self): # Body axis self._vel_body = np.zeros(3) # m/s # Local horizon (NED) self._vel_NED = np.zeros(3) # m/s @abstractmethod def update(self, coords, attitude): raise NotImplementedError @property def vel_body(self): return self._vel_body @property def u(self): return self.vel_body[0] @property def v(self): return self.vel_body[1] @property def w(self): return self.vel_body[2] @property def vel_NED(self): return self._vel_NED @property def v_north(self): return self._vel_NED[0] @property def v_east(self): return self._vel_NED[1] @property def v_down(self): return self._vel_NED[2] @property def value(self): """Only for testing purposes""" return np.hstack((self.vel_body, self.vel_NED)) class BodyVelocity(Velocity): def __init__(self, u, v, w, attitude): # TODO: docstring super().__init__() self.update(np.array([u, v, w]), attitude) def update(self, value, attitude): self._vel_body[:] = value self._vel_NED = body2hor(value, attitude.theta, attitude.phi, attitude.psi) # m/s def __repr__(self): return f"u: {self.u:.2f} m/s, v: {self.v:.2f} m/s, w: {self.w:.2f} m/s" class NEDVelocity(Velocity): def __init__(self, vn, ve, vd, attitude): # TODO: docstring super().__init__() self.update(np.array([vn, ve, vd]), attitude) def update(self, value, attitude): self._vel_NED[:] = value self._vel_body = hor2body(value, attitude.theta, attitude.phi, attitude.psi) # m/s def __repr__(self): return (f"V_north: {self.v_north:.2f} m/s," f"V_east: {self.v_east:.2f} m/s, " f"V_down: {self.v_down:.2f} m/s")

23.58871

79

0.563077

ccbe05761dc731218eefcccd4c39733dc739862d

68

py

Python

tests/CompileTests/Python_tests/test2011_029.py

maurizioabba/rose

7597292cf14da292bdb9a4ef573001b6c5b9b6c0

[ "BSD-3-Clause" ]

488

2015-01-09T08:54:48.000Z

2022-03-30T07:15:46.000Z

tests/CompileTests/Python_tests/test2011_029.py

sujankh/rose-matlab

7435d4fa1941826c784ba97296c0ec55fa7d7c7e

[ "BSD-3-Clause" ]

174

2015-01-28T18:41:32.000Z

2022-03-31T16:51:05.000Z

tests/CompileTests/Python_tests/test2011_029.py

sujankh/rose-matlab

7435d4fa1941826c784ba97296c0ec55fa7d7c7e

[ "BSD-3-Clause" ]

146

2015-04-27T02:48:34.000Z

2022-03-04T07:32:53.000Z

# test while statements while 0: 1 while 0: 2 else: 3

6.8

23

0.558824

ccbffdcc60fb438aee22b3f831c544750ef569d6

246

py

Python

minds/sections/__init__.py

Atherz97/minds-bot

965f44ba39ad816947e4bcba051a86fa1c5ee05c

[ "MIT" ]

3

2020-05-01T10:59:59.000Z

2022-01-18T01:40:34.000Z

minds/sections/__init__.py

Atherz97/minds-bot

965f44ba39ad816947e4bcba051a86fa1c5ee05c

[ "MIT" ]

null

minds/sections/__init__.py

Atherz97/minds-bot

965f44ba39ad816947e4bcba051a86fa1c5ee05c

[ "MIT" ]

null

from minds.sections.newsfeed import NewsfeedAPI from minds.sections.channel import ChannelAPI from minds.sections.notifications import NotificationsAPI from minds.sections.posting import PostingAPI from minds.sections.interact import InteractAPI

41

57

0.878049

ccc14d33e30db584c95153ccde86ea32ce7c48be

8,331

py

Python

wavelet_prosody_toolkit/wavelet_prosody_toolkit/cwt_global_spectrum.py

eugenemfu/TTS_HW

34b3a32da2904578ddbd86bfd9529798cc3a1e9f

[ "BSD-3-Clause" ]

115

2019-08-06T08:34:33.000Z

2022-02-15T09:44:40.000Z

wavelet_prosody_toolkit/wavelet_prosody_toolkit/cwt_global_spectrum.py

eugenemfu/TTS_HW

34b3a32da2904578ddbd86bfd9529798cc3a1e9f

[ "BSD-3-Clause" ]

11

2019-08-13T15:27:07.000Z

2022-03-28T15:59:39.000Z

wavelet_prosody_toolkit/wavelet_prosody_toolkit/cwt_global_spectrum.py

eugenemfu/TTS_HW

34b3a32da2904578ddbd86bfd9529798cc3a1e9f

[ "BSD-3-Clause" ]

32

2019-01-30T12:00:15.000Z

2022-03-28T10:06:39.000Z

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ AUTHOR - Antti Suni <antti.suni@helsinki.fi> - Sébastien Le Maguer <lemagues@tcd.ie> DESCRIPTION usage: cwt_global_spectrum.py [-h] [-v] [-o OUTPUT] [-P] input_file Tool for extracting global wavelet spectrum of speech envelope introduced for second language fluency estimation in the following paper: @inproceedings{suni2019characterizing, title={Characterizing second language fluency with global wavelet spectrum}, author={Suni, Antti and Kallio, Heini and Benu{\v{s}}, {\v{S}}tefan and {\v{S}}imko, Juraj}, booktitle={International Congress of Phonetic Sciences}, pages={1947--1951}, year={2019}, organization={Australasian Speech Science and Technology Association Inc.} } positional arguments: input_file Input signal or F0 file optional arguments: -h, --help show this help message and exit -v, --verbosity increase output verbosity -o OUTPUT, --output OUTPUT output directory for analysis or filename for synthesis. (Default: input_file directory [Analysis] or <input_file>.f0 [Synthesis]) -P, --plot Plot the results You should be able to see peak around 4Hz, corresponding to syllable rate. For longer speech files, lower frequency peaks related to phrasing should appear. Synthetic test file with 8Hz, 4Hz and 1Hz components is included in sample directory. LICENSE See https://github.com/asuni/wavelet_prosody_toolkit/blob/master/LICENSE.txt """ # System/default import sys import os # Arguments import argparse # Messaging/logging import traceback import time import logging # Math/plot import numpy as np import matplotlib.ticker import matplotlib.pyplot as plt # Libraries from wavelet_prosody_toolkit.prosody_tools import cwt_utils as cwt_utils from wavelet_prosody_toolkit.prosody_tools import misc as misc from wavelet_prosody_toolkit.prosody_tools import energy_processing as energy_processing ############################################################################### # global constants ############################################################################### LEVEL = [logging.WARNING, logging.INFO, logging.DEBUG] ############################################################################### # Functions ############################################################################### def calc_global_spectrum(wav_file, period=5, n_scales=60, plot=False): """ """ # Extract signal envelope, scale and normalize (fs, waveform) = misc.read_wav(wav_file) waveform = misc.resample(waveform, fs, 16000) energy = energy_processing.extract_energy(waveform, min_freq=30, method="hilbert") energy[energy<0] = 0 energy = np.cbrt(energy+0.1) params = misc.normalize_std(energy) # perform continous wavelet transform on envelope with morlet wavelet # increase _period to get sharper spectrum matrix, scales, freq = cwt_utils.cwt_analysis(params, first_freq = 16, num_scales = n_scales, scale_distance = 0.1,period=period, mother_name="Morlet",apply_coi=True) # power, arbitrary scaling to prevent underflow p_matrix = (abs(matrix)**2).astype('float32')*1000.0 power_spec = np.nanmean(p_matrix,axis=1) if plot: f, wave_pics = plt.subplots(1, 2, gridspec_kw = {'width_ratios':[5, 1]}, sharey=True) f.subplots_adjust(hspace=10) f.subplots_adjust(wspace=0) wave_pics[0].set_ylim(0, n_scales) wave_pics[0].set_xlabel("Time(m:s)") wave_pics[0].set_ylabel("Frequency(Hz)") wave_pics[1].set_xlabel("power") wave_pics[1].tick_params(labelright=True) fname = os.path.basename(wav_file) title = "CWT Morlet(p="+str(period)+") global spectrum, "+ fname wave_pics[0].contourf(p_matrix, 100) wave_pics[0].set_title(title, loc="center") wave_pics[0].plot(params*3, color="white",alpha=0.5) freq_labels = [round(x,3) if (np.isclose(x, round(x)) or (x < 2 and np.isclose(x*100., round(x*100))) or (x < 0.5 and np.isclose(x*10000., round(x*10000)))) else "" for x in list(freq)] wave_pics[0].set_yticks(np.linspace(0, len(freq_labels)-1, len(freq_labels))) wave_pics[0].set_yticklabels(freq_labels) formatter = matplotlib.ticker.FuncFormatter(lambda ms, x: time.strftime('%M:%S', time.gmtime(ms // 200))) wave_pics[0].xaxis.set_major_formatter(formatter) wave_pics[1].grid(axis="y") wave_pics[1].plot(power_spec,np.linspace(0,len(power_spec), len(power_spec)),"-") plt.show() return (power_spec, freq) ############################################################################### # Main function ############################################################################### def main(): """Main entry function """ global args period = 5 n_scales = 60 # Compute the global spectrum (power_spec, freq) = calc_global_spectrum(args.wav_file, period, n_scales, args.plot) # save spectrum and associated frequencies for further processing output_dir = os.path.dirname(args.wav_file) if args.output_dir is not None: output_dir = args.output_dir os.makedirs(output_dir, exist_ok=True) basename = os.path.join(output_dir, os.path.splitext(os.path.basename(args.wav_file))[0]) np.savetxt(basename+".spec.txt", power_spec, fmt="%.5f", newline= " ") np.savetxt(basename+".freqs.txt", freq, fmt="%.5f", newline= " ") ############################################################################### # Envelopping ############################################################################### if __name__ == '__main__': try: parser = argparse.ArgumentParser(description="") # Add options parser.add_argument("-l", "--log_file", default=None, help="Logger file") parser.add_argument("-o", "--output_dir", default=None, type=str, help="The output directory (if not defined, use the same directory than the wave file)") parser.add_argument("-P", "--plot", default=False, action="store_true", help="Plot the results") parser.add_argument("-v", "--verbosity", action="count", default=0, help="increase output verbosity") # Add arguments parser.add_argument("wav_file", help="The input wave file") # Parsing arguments args = parser.parse_args() # create logger and formatter logger = logging.getLogger() formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') # Verbose level => logging level log_level = args.verbosity if (args.verbosity >= len(LEVEL)): log_level = len(LEVEL) - 1 logger.setLevel(log_level) logging.warning("verbosity level is too high, I'm gonna assume you're taking the highest (%d)" % log_level) else: logger.setLevel(LEVEL[log_level]) # create console handler ch = logging.StreamHandler() ch.setFormatter(formatter) logger.addHandler(ch) # create file handler if args.log_file is not None: fh = logging.FileHandler(args.log_file) logger.addHandler(fh) # Debug time start_time = time.time() logger.info("start time = " + time.asctime()) # Running main function <=> run application main() # Debug time logging.info("end time = " + time.asctime()) logging.info('TOTAL TIME IN MINUTES: %02.2f' % ((time.time() - start_time) / 60.0)) # Exit program sys.exit(0) except KeyboardInterrupt as e: # Ctrl-C raise e except SystemExit: # sys.exit() pass except Exception as e: logging.error('ERROR, UNEXPECTED EXCEPTION') logging.error(str(e)) traceback.print_exc(file=sys.stderr) sys.exit(-1) else: print("usage: cwt_global_spectrum.py <audiofile>")

35.451064

171

0.591886

ccc1a90b2d04001d5b226461f9c2d2ad26294437

4,177

py

Python

sdlc.py

phillipfurtado/mysearchengine

d28ad99b2729fba9df3f5f606c9aeafa2b257280

[ "Apache-2.0" ]

null

sdlc.py

phillipfurtado/mysearchengine

d28ad99b2729fba9df3f5f606c9aeafa2b257280

[ "Apache-2.0" ]

null

sdlc.py

phillipfurtado/mysearchengine

d28ad99b2729fba9df3f5f606c9aeafa2b257280

[ "Apache-2.0" ]

null

import sys import math import operator import re from os import listdir from os.path import isfile, join from PIL import Image IMAGE_WIDTH = 355 IMAGE_HEIGHT = 355 BORDER_DISCOUNT = 0.11 BLOCK_SIZE = 36 BLOCKS_SIZE = BLOCK_SIZE * BLOCK_SIZE X_BLOCK_SIZE = IMAGE_WIDTH / BLOCK_SIZE; Y_BLOCK_SIZE = IMAGE_HEIGHT / BLOCK_SIZE; NORTH_POINT = IMAGE_HEIGHT * BORDER_DISCOUNT WEST_POINT = IMAGE_WIDTH * BORDER_DISCOUNT SOUTH_POINT = IMAGE_HEIGHT - WEST_POINT EAST_POINT = IMAGE_WIDTH - WEST_POINT HALF_WIDTH = IMAGE_WIDTH / 2 HALF_HEIGHT = IMAGE_HEIGHT / 2 def getImageRegion(x, y): if x >= WEST_POINT and x <= EAST_POINT and y >= NORTH_POINT and y <= SOUTH_POINT: return "E" if x <= HALF_WIDTH and y < HALF_HEIGHT: return "A" if x <= HALF_WIDTH and y >= HALF_HEIGHT: return "D" if x > HALF_WIDTH and y <= HALF_HEIGHT: return "B" return "C" def convertHistogramIntoWords(histogram): newHistogram = {} #necessary? for key, value in enumerate(histogram): newHistogram[key] = value newHistogram = sorted(newHistogram.items(), key=operator.itemgetter(1), reverse=True) len95percent = int(len(newHistogram) * 0.95) words = '' count = 0 for key,value in newHistogram: if count == len95percent: break words += 'x' + str(key) ++count return words def generateSDLCDescriptorSample(image): lowColorImg = image.convert('P') yLeft = 0 yRight = Y_BLOCK_SIZE imageDescription = [] for y in range(0, BLOCK_SIZE): xLeft = 0 xRight = X_BLOCK_SIZE for x in range(0, BLOCK_SIZE): imageBlock = lowColorImg.transform((BLOCK_SIZE, BLOCK_SIZE), Image.EXTENT, (xLeft, yLeft, xRight, yRight)) imageHistogram = imageBlock.histogram() blockDescription = getImageRegion(xRight, yRight) + convertHistogramIntoWords(imageHistogram) imageDescription.append(blockDescription) xLeft += X_BLOCK_SIZE xRight += X_BLOCK_SIZE yLeft += Y_BLOCK_SIZE yRight += Y_BLOCK_SIZE return ' '.join(imageDescription) def persistSample(path, sdlc): fo = open(path, "wb") fo.write(sdlc) fo.close() def main(argv): print "Welcome to SDLC Descriptor Python implementation." imageCollectionPath = "data/colecaoDafitiPosthaus" queriesCollectionPath = "data/queries" print "Verifying datafiPosthaus and query collections." imageList = [ f for f in listdir(imageCollectionPath) if isfile(join(imageCollectionPath, f)) ] queryImageList = [ f for f in listdir(queriesCollectionPath) if isfile(join(queriesCollectionPath, f)) and re.match(r'[0-9]+.*\.jpg', f) ] print "Running SDLC Descriptor over DafitiPosthaus collection." print "This will take a time, make sure to have at least 20GB of free space on disk." for imagePath in queryImageList: imageFile = queriesCollectionPath + "/" + imagePath; try: im = Image.open(imageFile) sdlcImage = generateSDLCDescriptorSample(im) sdlcImagePath = queriesCollectionPath + "/" + imagePath.split(".")[0] + ".sdlc" persistSample(sdlcImagePath, sdlcImage) except IOError: pass print "Finished the second collection." print "Checkout for files with extersion .sdlc on each collection folder." for imagePath in imageList: imageFile = imageCollectionPath + "/" + imagePath; try: im = Image.open(imageFile) sdlcImage = generateSDLCDescriptorSample(im) sdlcImagePath = imageCollectionPath + "/" + imagePath.split(".")[0] + ".sdlc" persistSample(sdlcImagePath, sdlcImage) except IOError: pass print "Finished the first collection." print "Running SDLC Descriptor over query collection." if __name__ == '__main__': main(sys.argv)

27.123377

142

0.627484

ccc2302b262199c91606683ac0c0ea01d97056a4

1,649

py

Python

betahex/training/supervised.py

StarvingMarvin/betahex

0626cf4d003e94423f34f3d83149702a5557ddb8

[ "MIT" ]

2

2019-03-17T07:09:14.000Z

2020-05-04T17:40:51.000Z

betahex/training/supervised.py

StarvingMarvin/betahex

0626cf4d003e94423f34f3d83149702a5557ddb8

[ "MIT" ]

null

betahex/training/supervised.py

StarvingMarvin/betahex

0626cf4d003e94423f34f3d83149702a5557ddb8

[ "MIT" ]

null

import tensorflow as tf from tensorflow.contrib import learn from betahex.features import Features from betahex.training.common import make_train_model, make_policy_input_fn, accuracy from betahex.models import MODEL tf.logging.set_verbosity(tf.logging.INFO) def main(unused_argv): # Load training and eval data feat = Features(13, MODEL['features']) model_fn = make_train_model( feat, policy_filters=MODEL['filters'], policy_shape=MODEL['shape'], learning_rate=2e-3, learn_rate_decay=.98, optimizer="Adam", regularization_scale=MODEL['regularization_scale'] ) config = learn.RunConfig( save_checkpoints_steps=1000, save_checkpoints_secs=None, save_summary_steps=100 ) est = learn.Estimator( model_fn=model_fn, model_dir="data/tf/models/supervised/%s-l2e-3-d.98adam" % MODEL['name'], config=config ) train_in = make_policy_input_fn(feat, ["data/tf/features/train.tfrecords"], 64) eval_in = make_policy_input_fn(feat, ["data/tf/features/eval.tfrecords"], 32) fouls = 0 for i in range(40): est.fit( input_fn=train_in, steps=2000 ) metrics = { "accuracy": learn.MetricSpec( metric_fn=accuracy, prediction_key="classes") } eval_result = est.evaluate(input_fn=eval_in, metrics=metrics, steps=200) if eval_result['accuracy'] < 1e-2 or eval_result['loss'] > 16: fouls += 1 if fouls > 3: break if __name__ == '__main__': tf.app.run()

25.765625

84

0.627653

ccc373e2e0f33ced1981ea3d8ae722d60f288cc2

9,747

py

Python

MeterToTransPairingScripts.py

sandialabs/distribution-system-model-calibration

55493cc03b8ebcc5a0f2e7d2ff9092cb2e608f90

[ "BSD-3-Clause" ]

1

2021-11-12T21:30:35.000Z

MeterTransformerPairing/MeterToTransPairingScripts.py

sandialabs/distribution-system-model-calibration

55493cc03b8ebcc5a0f2e7d2ff9092cb2e608f90

[ "BSD-3-Clause" ]

null

MeterTransformerPairing/MeterToTransPairingScripts.py

sandialabs/distribution-system-model-calibration

55493cc03b8ebcc5a0f2e7d2ff9092cb2e608f90

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- """ BSD 3-Clause License Copyright 2021 National Technology & Engineering Solutions of Sandia, LLC (NTESS). Under the terms of Contract DE-NA0003525 with NTESS, the U.S. Government retains certain rights in this software. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. """ ############################################################################## # Import standard Python libraries import sys import numpy as np #import datetime #from copy import deepcopy from pathlib import Path import pandas as pd # Import custom libraries import M2TUtils import M2TFuncs ############################################################################### ############################################################################### # Input Data Notes # custIDInput: list of str (customers) - the list of customer IDs as strings # transLabelsTrue: numpy array of int (1,customers) - the transformer labels for each customer as integers. This is the ground truth transformer labels # transLabelsErrors: numpy array of int (1,customers) - the transformer labels for each customer which may contain errors. # In the sample data, customer_3 transformer was changed from 1 to 2 and customer_53 transformer was changed from 23 to 22 # voltageInput: numpy array of float (measurements,customers) - the raw voltage AMI measurements for each customer in Volts # pDataInput: numpy array of float (measurements, customers) - the real power measurements for each customer in Watts # qDataInput: numpy array of float (measurements, customers) - the reactive power measurements for each customer in VAr # Note that the indexing of all variables above should match in the customer index, i.e. custIDInput[0], transLabelsInput[0,0], voltageInput[:,0], pDataInput[:,0], and qDataInput[:,0] should all be the same customer ############################################################################### # Load Sample data currentDirectory = Path.cwd() filePath = Path(currentDirectory.parent,'SampleData') filename = Path(filePath,'VoltageData_AMI.npy') voltageInput = np.load(filename) filename = Path(filePath,'RealPowerData_AMI.npy') pDataInput = np.load(filename) filename = Path(filePath,'ReactivePowerData_AMI.npy') qDataInput = np.load(filename) filename = Path(filePath,'TransformerLabelsTrue_AMI.npy') transLabelsTrue = np.load(filename) filename = Path(filePath,'TransformerLabelsErrors_AMI.npy') transLabelsErrors = np.load(filename) filename = Path(filePath,'CustomerIDs_AMI.npy') custIDInput = list(np.load(filename)) ############################################################################### ############################################################################### # Data pre-processing # Convert the raw voltage measurements into per unit and difference (delta voltage) representation vPU = M2TUtils.ConvertToPerUnit_Voltage(voltageInput) vDV = M2TUtils.CalcDeltaVoltage(vPU) ############################################################################## # # Error Flagging Section - Correlation Coefficient Analysis # Calculate CC Matrix ccMatrix,noVotesIndex,noVotesIDs = M2TUtils.CC_EnsMedian(vDV,windowSize=384,custID=custIDInput) # The function CC_EnsMedian takes the median CC across windows in the dataset. # This is mainly done to deal with the issue of missing measurements in the dataset # If your data does not have missing measurements you could use numpy.corrcoef directly # Do a sweep of possible CC Thresholds and rank the flagged results notMemberVector = [0.25,0.26,0.27,0.28,0.29,0.30,0.31,0.32,0.33,0.34,0.35,0.36,0.37,0.38,0.39,0.4,0.41,0.42,0.43,0.44,0.45,0.46,0.47,0.48,0.49,0.50,0.51,0.52,0.53,0.54,0.55,0.56,0.57,0.58,0.59,0.60,0.61,0.62,0.63,0.64,0.65,0.66,0.67,0.68,0.69,0.70,0.71,0.72,0.73,0.74,0.75,0.76,0.78,0.79,0.80,0.81,0.82,0.83,0.84,0.85,0.86,0.87,0.88,0.90,0.91] allFlaggedTrans, allNumFlagged, rankedFlaggedTrans, rankedTransThresholds = M2TFuncs.RankFlaggingBySweepingThreshold(transLabelsErrors,notMemberVector,ccMatrix) # Plot the number of flagged transformers for all threshold values M2TUtils.PlotNumFlaggedTrans_ThresholdSweep(notMemberVector,allNumFlagged,transLabelsErrors,savePath=-1) # The main output from this Error Flagging section is rankedFlaggedTrans which # contains the list of flagged transformers ranked by correlation coefficient. # Transformers at the beginning of the list were flagged with lower CC, indicating # higher confidence that those transformers do indeed have errors. ############################################################################## # # Transformer Assignment Section - Linear Regression Steps # # Calculate the pairwise linear regression r2Affinity,rDist,xDist,regRDistIndiv,regXDistIndiv,mseMatrix = M2TUtils.ParamEst_LinearRegression(voltageInput,pDataInput,qDataInput) additiveFactor = 0.02 minMSE, mseThreshold = M2TUtils.FindMinMSE(mseMatrix,additiveFactor) #This sets the mse threshold based on adding a small amount to the smallest MSE value in the pairwise MSE matrix # Alternatively you could set the mse threshold manually #mseThreshold = 0.3 # Plot CDF for adjusted reactance distance replacementValue = np.max(np.max(xDist)) xDistAdjusted = M2TFuncs.AdjustDistFromThreshold(mseMatrix,xDist,mseThreshold, replacementValue) # Select a particular set of ranked results using a correlation coefficient threshold notMemberThreshold=0.5 flaggingIndex = np.where(np.array(notMemberVector)==notMemberThreshold)[0][0] flaggedTrans = allFlaggedTrans[flaggingIndex] predictedTransLabels,allChangedIndices,allChangedOrgTrans,allChangedPredTrans = M2TFuncs.CorrectFlaggedTransErrors(flaggedTrans,transLabelsErrors,custIDInput,ccMatrix,notMemberThreshold, mseMatrix,xDistAdjusted,reactanceThreshold=0.046) # predictedTransLabels: numpy array of int (1,customers) - the predicted labels # for each customer. Positive labels will be unchanged from the original # set of transformer labels. Negative labels will be new transformer groupings # which should be the correct groups of customers served by a particular # transformer but will require mapping back to a particular physical transformer. # In the sample data customer_4 was injected with an incorrect label and should now be grouped with customer_5 and customer_6 # customer_53 was also injected with an incorrect label and should now be grouped with customer_54 and customer_55 print('Meter to Transformer Pairing Algorithm Results') M2TUtils.PrettyPrintChangedCustomers(predictedTransLabels,transLabelsErrors,custIDInput) # This function calculates two transformer level metrics of accuracy that we have been using # incorrectTrans is a list of incorrect transformers where incorrect means customers added or omitted to the correct grouping # This defines Transformer Accuracy, i.e. the number of correct transformers out of the total transformers # incorrectPairedIDs lists the customers from incorrect trans which allows us to define # Customer Pairing Accuracy which is the number of customers in the correct groupings, i.e. no customers added or omitted from the grouping incorrectTrans,incorrectPairedIndices, incorrectPairedIDs= M2TUtils.CalcTransPredErrors(predictedTransLabels,transLabelsTrue,custIDInput,singleCustMarker=-999) print('') print('Ground Truth Results') print('Transformers with incorrect groupings:') print(incorrectTrans) # In the sample data, these will be empty because all customers were correctly grouped together by their service transformer. # Write output to a csv file df = pd.DataFrame() df['customer ID'] = custIDInput df['Original Transformer Labels (with errors)'] = transLabelsErrors[0,:] df['Predicted Transformer Labels'] = predictedTransLabels[0,:] df['Actual Transformer Labels'] = transLabelsTrue[0,:] df.to_csv('outputs_PredictedTransformerLabels.csv') print('Predicted transformer labels written to outputs_PredictedTransformerLabels.csv') df = pd.DataFrame() df['Ranked Flagged Transformers'] = flaggedTrans df.to_csv('outputs_RankedFlaggedTransformers.csv') print('Flagged and ranked transformers written to outputs_RankedFlaggedTransformers.csv')

48.492537

344

0.733149

ccc51ab14667f22cd9b5eaba86b2724ac98a38f4

10,726

py

Python

mcdc_tnt/pyk_kernels/all/advance.py

jpmorgan98/MCDC-TNT-2

c437596097caa9af56df95213e7f64db38aac40e

[ "BSD-3-Clause" ]

1

2022-02-26T02:12:12.000Z

mcdc_tnt/pyk_kernels/all/advance.py

jpmorgan98/MCDC-TNT-2

c437596097caa9af56df95213e7f64db38aac40e

[ "BSD-3-Clause" ]

null

mcdc_tnt/pyk_kernels/all/advance.py

jpmorgan98/MCDC-TNT-2

c437596097caa9af56df95213e7f64db38aac40e

[ "BSD-3-Clause" ]

1

2022-02-09T22:39:42.000Z

import math import numpy as np import pykokkos as pk @pk.workload class Advance_cycle: def __init__(self, num_part, p_pos_x, p_pos_y, p_pos_z, p_dir_y, p_dir_z, p_dir_x, p_mesh_cell, p_speed, p_time, dx, mesh_total_xsec, L, p_dist_travled, p_end_trans, rands): self.p_pos_x: pk.View1D[pk.double] = p_pos_x self.p_pos_y: pk.View1D[pk.double] = p_pos_y self.p_pos_z: pk.View1D[pk.double] = p_pos_z self.p_dir_y: pk.View1D[pk.double] = p_dir_y self.p_dir_z: pk.View1D[pk.double] = p_dir_z self.p_dir_x: pk.View1D[pk.double] = p_dir_x self.p_mesh_cell: pk.View1D[int] = p_mesh_cell self.p_speed: pk.View1D[pk.double] = p_speed self.p_time: pk.View1D[pk.double] = p_time self.dx: pk.double = dx self.L: pk.double = L #print(dx) #print(L) self.num_part: int = num_part self.mesh_total_xsec: pk.View1D[pk.double] = mesh_total_xsec self.p_dist_travled: pk.View1D[pk.double] = p_dist_travled self.p_end_trans: pk.View1D[int] = p_end_trans self.rands: pk.View1D[pk.double] = rands @pk.main def run(self): pk.parallel_for(self.num_part, self.advanceCycle_wu) @pk.workunit def advanceCycle_wu(self, i: int): kicker: pk.double = 1e-8 if (self.p_end_trans[i] == 0): if (self.p_pos_x[i] < 0): #exited rhs self.p_end_trans[i] = 1 elif (self.p_pos_x[i] >= self.L): #exited lhs self.p_end_trans[i] = 1 else: dist: pk.double = -math.log(self.rands[i]) / self.mesh_total_xsec[self.p_mesh_cell[i]] #pk.printf('%d %f %f %f\n', i, dist, rands[i], mesh_total_xsec[p_mesh_cell[i]]) #p_dist_travled[i] = dist x_loc: pk.double = (self.p_dir_x[i] * dist) + self.p_pos_x[i] LB: pk.double = self.p_mesh_cell[i] * self.dx RB: pk.double = LB + self.dx if (x_loc < LB): #move partilce into cell at left self.p_dist_travled[i] = (LB - self.p_pos_x[i])/self.p_dir_x[i] + kicker self.p_mesh_cell[i] -= 1 elif (x_loc > RB): #move particle into cell at right self.p_dist_travled[i] = (RB - self.p_pos_x[i])/self.p_dir_x[i] + kicker self.p_mesh_cell[i] += 1 else: #move particle in cell self.p_dist_travled[i] = dist self.p_end_trans[i] = 1 #pk.printf('%d: x pos before step %f\n', i, p_pos_x[i]) self.p_pos_x[i] = self.p_dir_x[i]*self.p_dist_travled[i] + self.p_pos_x[i] self.p_pos_y[i] = self.p_dir_y[i]*self.p_dist_travled[i] + self.p_pos_y[i] self.p_pos_z[i] = self.p_dir_z[i]*self.p_dist_travled[i] + self.p_pos_z[i] #pk.printf('%d: x pos after step: %f should be: %f\n', i, p_pos_x[i], (temp_x)) self.p_time[i] += dist/self.p_speed[i] @pk.workload class DistTraveled: def __init__(self, num_part, max_mesh_index, mesh_dist_traveled_pk, mesh_dist_traveled_squared_pk, p_dist_travled, mesh, p_end_trans, clever_out): self.num_part: int = num_part self.max_mesh_index: int = max_mesh_index self.mesh_dist_traveled_pk: pk.View1D[pk.double] = mesh_dist_traveled_pk self.mesh_dist_traveled_squared_pk: pk.View1D[pk.double] = mesh_dist_traveled_squared_pk self.p_dist_travled: pk.View1D[pk.double] = p_dist_travled self.mesh: pk.View1D[int] = mesh self.p_end_trans: pk.View1D[int] = p_end_trans self.clever_out: pk.View1D[int] = clever_out @pk.main def distTraveled_main(self): end_flag: int = 1 cur_cell: int = 0 summer: int = 0 #pk.printf('1 %d\n', cur_cell) #pk.printf('3 %f\n', mesh_dist_traveled_pk[cur_cell]) for i in range(self.num_part): cur_cell = int(self.mesh[i]) if (0 < cur_cell) and (cur_cell < self.max_mesh_index): self.mesh_dist_traveled_pk[cur_cell] += self.p_dist_travled[i] self.mesh_dist_traveled_squared_pk[cur_cell] += self.p_dist_travled[i]**2 if self.p_end_trans[i] == 0: end_flag = 0 summer += p_end_trans[i] clever_out[0] = end_flag clever_out[1] = summer #@pk.workunit #def CellSum # for i in range(num_parts) #@profile def Advance(p_pos_x, p_pos_y, p_pos_z, p_mesh_cell, dx, p_dir_y, p_dir_z, p_dir_x, p_speed, p_time, num_part, mesh_total_xsec, mesh_dist_traveled, mesh_dist_traveled_squared, L): max_mesh_index = int(len(mesh_total_xsec)-1) p_end_trans: pk.View1D[int] = pk.View([num_part], int) #flag p_end_trans.fill(0) p_dist_travled: pk.View1D[pk.double] = pk.View([num_part], pk.double) clever_out: pk.View1D[int] = pk.View([4], int) end_flag = 0 cycle_count = 0 while end_flag == 0: #allocate randoms summer = 0 rands_np = np.random.random([num_part]) rands = pk.from_numpy(rands_np) #vector of indicies for particle transport p = pk.RangePolicy(pk.get_default_space(), 0, num_part) p_dist_travled.fill(0) pre_p_mesh = p_mesh_cell L = float(L) #space = pk.ExecutionSpace.OpenMP pk.execute(pk.ExecutionSpace.OpenMP, Advance_cycle(num_part, p_pos_x, p_pos_y, p_pos_z, p_dir_y, p_dir_z, p_dir_x, p_mesh_cell, p_speed, p_time, dx, mesh_total_xsec, L, p_dist_travled, p_end_trans, rands))#pk for number still in transport pk.execute(pk.ExecutionSpace.OpenMP, DistTraveled(num_part, max_mesh_index, mesh_dist_traveled, mesh_dist_traveled_squared, p_dist_travled, pre_p_mesh, p_end_trans, clever_out)) end_flag = clever_out[0] summer = clever_out[1] #print(cycle_count) if (cycle_count > int(1e3)): print("************ERROR**********") print(" Max itter hit") print(p_end_trans) print() print() return() cycle_count += 1 print("Advance Complete:......{1}% ".format(cycle_count, int(100*summer/num_part)), end = "\r") print() @pk.workload class StillIn: def __init__(self, p_pos_x, surface_distances, p_alive, num_part, clever_out): self.p_pos_x: pk.View1D[pk.double] = p_pos_x self.clever_out: pk.View1D[int] = clever_out self.surface_distances: pk.View1D[pk.double] = surface_distances self.p_alive: pk.View1D[int] = p_alive self.num_part: int = num_part @pk.main def run(self): tally_left: int = 0 tally_right: int = 0 for i in range(self.num_part): #exit at left if self.p_pos_x[i] <= 0: tally_left += 1 self.p_alive[i] = 0 elif self.p_pos_x[i] >= 1: tally_right += 1 self.p_alive[i] = 0 self.clever_out[0] = tally_left self.clever_out[1] = tally_right def speedTestAdvance(): # Position num_part = int(1e8) phase_parts = num_parts p_pos_x_np = np.zeros(phase_parts, dtype=float) p_pos_y_np = np.zeros(phase_parts, dtype=float) p_pos_z_np = np.zeros(phase_parts, dtype=float) p_pos_x = pk.from_numpy(p_pos_x_np) p_pos_y = pk.from_numpy(p_pos_y_np) p_pos_z = pk.from_numpy(p_pos_z_np) # Direction p_dir_x_np = np.zeros(phase_parts, dtype=float) p_dir_y_np = np.zeros(phase_parts, dtype=float) p_dir_z_np = np.zeros(phase_parts, dtype=float) p_dir_x = pk.from_numpy(p_dir_x_np) p_dir_y = pk.from_numpy(p_dir_y_np) p_dir_z = pk.from_numpy(p_dir_z_np) # Speed p_speed_np = np.zeros(phase_parts, dtype=float) p_speed = pk.from_numpy(p_speed_np) # Time p_time_np = np.zeros(phase_parts, dtype=float) p_time = pk.from_numpy(p_time_np) # Region p_mesh_cell_np = np.zeros(phase_parts, dtype=np.int32) p_mesh_cell = pk.from_numpy(p_mesh_cell_np) # Flags p_alive_np = np.full(phase_parts, False, dtype=np.int32) p_alive = pk.from_numpy(p_alive_np) kernels.Advance(p_pos_x, p_pos_y, p_pos_z, p_mesh_cell, dx, p_dir_y, p_dir_z, p_dir_x, p_speed, p_time, num_part, mesh_total_xsec, mesh_dist_traveled, mesh_dist_traveled_squared, surface_distances[len(surface_distances)-1]) """ def test_Advance(): L = 1 dx = .25 N_m = 4 num_part = 6 p_pos_x = np.array([-.01, 0, .1544, .2257, .75, 1.1]) p_pos_y = 2.1*np.ones(num_part) p_pos_z = 3.4*np.ones(num_part) p_mesh_cell = np.array([-1, 0, 0, 1, 3, 4], dtype=int) p_dir_x = np.ones(num_part) p_dir_x[0] = -1 p_dir_y = np.zeros(num_part) p_dir_z = np.zeros(num_part) p_speed = np.ones(num_part) p_time = np.zeros(num_part) p_alive = np.ones(num_part, bool) p_alive[5] = False particle_speed = 1 mesh_total_xsec = np.array([0.1,1,.1,100]) mesh_dist_traveled_squared = np.zeros(N_m) mesh_dist_traveled = np.zeros(N_m) [p_pos_x, p_pos_y, p_pos_z, p_mesh_cell, p_dir_y, p_dir_z, p_dir_x, p_speed, p_time, mesh_dist_traveled, mesh_dist_traveled_squared] = Advance(p_pos_x, p_pos_y, p_pos_z, p_mesh_cell, dx, p_dir_y, p_dir_z, p_dir_x, p_speed, p_time, num_part, mesh_total_xsec, mesh_dist_traveled, mesh_dist_traveled_squared, L) assert (np.sum(mesh_dist_traveled) > 0) assert (np.sum(mesh_dist_traveled_squared) > 0) assert (p_pos_x[0] == -.01) assert (p_pos_x[5] == 1.1) assert (p_pos_x[1:4].all() > .75) """ def test_StillIn(): num_part = 7 surface_distances = [0,.25,.75,1] p_pos_x = np.array([-.01, 0, .1544, .2257, .75, 1.1, 1]) p_alive = np.ones(num_part, bool) [p_alive, tally_left, tally_right] = StillIn(p_pos_x, surface_distances, p_alive, num_part) assert(p_alive[0] == False) assert(p_alive[5] == False) assert(tally_left == 2) assert(tally_right == 2) assert(p_alive[2:4].all() == True) if __name__ == '__main__': speedTestAdvance()

35.516556

312

0.591274

ccc5fd6159d96d352e343e6b001e8aa4a53ede03

514

py

Python

DGF/auth/_authenticator.py

LonguCodes/DGF

bd344eff34cbe6438f71631e2cc103f1c4584e09

[ "MIT" ]

null

DGF/auth/_authenticator.py

LonguCodes/DGF

bd344eff34cbe6438f71631e2cc103f1c4584e09

[ "MIT" ]

null

DGF/auth/_authenticator.py

LonguCodes/DGF

bd344eff34cbe6438f71631e2cc103f1c4584e09

[ "MIT" ]

null

from django.conf import settings from ..defaults import AUTHENTICATORS from ..utils import get_module authenticators = [] all_args = [] def collect_authenticators(): global authenticators, all_args authenticators = list(map(get_module, getattr(settings, 'GRAHENE_ADDONS_AUTHENTICATORS', AUTHENTICATORS))) for authenticator in authenticators: authenticator.args = list(map(str.lower, authenticator.args)) all_args = [arg for authenticator in authenticators for arg in authenticator.args]

34.266667

110

0.774319

ccc6026c8a78ded0d003eb1ba605983fe1d65590

1,123

py

Python

facerec/face_util.py

seanbenhur/sih2020

f8b5988425185ed3c85872b98b622932895f932b

[ "MIT" ]

null

facerec/face_util.py

seanbenhur/sih2020

f8b5988425185ed3c85872b98b622932895f932b

[ "MIT" ]

null

facerec/face_util.py

seanbenhur/sih2020

f8b5988425185ed3c85872b98b622932895f932b

[ "MIT" ]

null

import face_recognition as fr def compare_faces(file1, file2): """ Compare two images and return True / False for matching. """ # Load the jpg files into numpy arrays image1 = fr.load_image_file(file1) image2 = fr.load_image_file(file2) # Get the face encodings for each face in each image file # Assume there is only 1 face in each image, so get 1st face of an image. image1_encoding = fr.face_encodings(image1)[0] image2_encoding = fr.face_encodings(image2)[0] # results is an array of True/False telling if the unknown face matched anyone in the known_faces array results = fr.compare_faces([image1_encoding], image2_encoding) return results[0] # Each face is tuple of (Name,sample image) known_faces = [('Stark','sample_images/stark.jpg'), ('Hannah','sample_images/hannah.jpg'), ] def face_rec(file): """ Return name for a known face, otherwise return 'Uknown'. """ for name, known_file in known_faces: if compare_faces(known_file,file): return name return 'Unknown'

33.029412

107

0.664292

ccc6e968fc2455af2569e67afae509ff5c1e5fc0

3,354

py

Python

src/correlation/dataset_correlation.py

sakdag/crime-data-analysis

9c95238c6aaf1394f68be59e26e8c6d75f669d7e

[ "MIT" ]

null

src/correlation/dataset_correlation.py

sakdag/crime-data-analysis

9c95238c6aaf1394f68be59e26e8c6d75f669d7e

[ "MIT" ]

null

src/correlation/dataset_correlation.py

sakdag/crime-data-analysis

9c95238c6aaf1394f68be59e26e8c6d75f669d7e

[ "MIT" ]

null

import numpy as np import pandas as pd import pyproj as pyproj import shapely as shapely from shapely.geometry import Point from haversine import haversine import src.config.column_names as col_names def correlate_and_save(crime_df: pd.DataFrame, census_df: pd.DataFrame, file_name: str, correlation_mode: str): # Using geolocation information in census dataset, add zipcode column to the crime dataset if correlation_mode == 'euclidean': add_zip_code_column_using_euclidean(crime_df, census_df) else: add_zip_code_column_using_haversine(crime_df, census_df) # Save crime_df.to_csv(file_name, index=False) # Finds nearest zipcode geolocation to the crime location, then adds this zipcode to the crime dataset def add_zip_code_column_using_haversine(crime_df: pd.DataFrame, census_df: pd.DataFrame): crime_df[col_names.ZIP_CODE] = np.nan for index, row in crime_df.iterrows(): nearest_zip_code = np.nan nearest_zip_code_distance = -1 for census_index, census_row in census_df.iterrows(): distance = haversine((row[col_names.LATITUDE], row[col_names.LONGITUDE]), (census_row[col_names.LATITUDE], census_row[col_names.LONGITUDE])) if nearest_zip_code_distance == -1 or distance < nearest_zip_code_distance: nearest_zip_code = census_row[col_names.ZIP_CODE] nearest_zip_code_distance = distance crime_df.loc[index, col_names.ZIP_CODE] = nearest_zip_code def add_zip_code_column_using_euclidean(crime_df: pd.DataFrame, census_df: pd.DataFrame): crime_df[col_names.ZIP_CODE] = np.nan wgs84_proj = pyproj.CRS('EPSG:4326') los_angeles_proj = pyproj.CRS('EPSG:6423') project_los_angeles = pyproj.Transformer.from_crs(wgs84_proj, los_angeles_proj, always_xy=True).transform census_list = list() for census_index, census_row in census_df.iterrows(): point2_transformed = shapely.ops.transform(project_los_angeles, Point(census_row[col_names.LATITUDE], census_row[col_names.LONGITUDE])) census_list.append((census_row, point2_transformed)) for index, row in crime_df.iterrows(): nearest_zip_code = np.nan nearest_zip_code_distance = -1 point1_transformed = shapely.ops.transform(project_los_angeles, Point(row[col_names.LATITUDE], row[col_names.LONGITUDE])) for census_data in census_list: distance = point1_transformed.distance(census_data[1]) if nearest_zip_code_distance == -1 or distance < nearest_zip_code_distance: nearest_zip_code = census_data[0][col_names.ZIP_CODE] nearest_zip_code_distance = distance crime_df.loc[index, col_names.ZIP_CODE] = nearest_zip_code def merge_crime_and_census(crime_df: pd.DataFrame, census_df: pd.DataFrame, file_name: str): merged_df = crime_df.merge(census_df, on=col_names.ZIP_CODE, how='inner') redundant_columns = ['Latitude_x', 'Latitude_y', 'Longitude_x', 'Longitude_y', col_names.TOTAL_HOUSEHOLDS, col_names.AVERAGE_HOUSEHOLD_SIZE] merged_df.drop(columns=redundant_columns, inplace=True) # Save merged_df.to_csv(file_name, index=False)

46.583333

129

0.707812

cccac18c6d9e0d8ce330a9adb50aea424e46871f

197

py

Python

Function/xargs.py

tusharxoxoxo/Python-practice

2d75750029655c65271fd51a8d175285719a62e6

[ "MIT" ]

null

Function/xargs.py

tusharxoxoxo/Python-practice

2d75750029655c65271fd51a8d175285719a62e6

[ "MIT" ]

null

Function/xargs.py

tusharxoxoxo/Python-practice

2d75750029655c65271fd51a8d175285719a62e6

[ "MIT" ]

null

def increment(*numbers): print(numbers) def dance(*numbers): total = 1 for number in numbers: total *= number return total increment(3,4,5,6) print(dance(1,2,3,4,5,6))

13.133333

26

0.614213

cccd71a65c5bf63a13dfaa2062a6d5203e62b9ef

2,386

py

Python

blog/resources/tag.py

espstan/fBlog

7c63d117a3bbae3da80b3e8d7f731ae89036eb0d

[ "MIT" ]

2

2019-06-17T13:55:36.000Z

2019-06-19T22:40:06.000Z

blog/resources/tag.py

espstan/fBlog

7c63d117a3bbae3da80b3e8d7f731ae89036eb0d

[ "MIT" ]

35

2019-06-17T07:00:49.000Z

2020-02-17T09:41:53.000Z

blog/resources/tag.py

espstan/fBlog

7c63d117a3bbae3da80b3e8d7f731ae89036eb0d

[ "MIT" ]

null

from flask_restful import Resource from flask_restful import reqparse from sqlalchemy.exc import SQLAlchemyError from config import Configuration from models.tag import TagModel as TM class Tag(Resource): parser = reqparse.RequestParser() parser.add_argument('name', type=str, required=True, help='This field cannot be blank.') def post(self): data = Tag.parser.parse_args() name = data['name'] if len(name) > Configuration.MAX_TAG_NAME_SIZE: return {'message': 'A name\'s length is more than {}'.format(Configuration.MAX_TAG_NAME_SIZE)} if TM.query.filter(TM.name == name).first(): return {'message': 'Tag \'{}\' already exists'.format(name)} tag = TM(name=name) try: tag.save_to_db() except SQLAlchemyError as e: err = str(e.__class__.__name__) return {'message': '{}'.format(err)}, 500 return tag.get_json(), 201 def put(self): data = Tag.parser.parse_args() name = data['name'] if len(name) > Configuration.MAX_TAG_NAME_SIZE: return {'message': 'A tag\'s length is more than {}'.format(Configuration.MAX_TAG_NAME_SIZE)} tag = TM.find_by_name(name) if not tag: tag = TM(name=name) else: if not TM.query.filter(TM.name == name).first(): tag.name = name else: return {'message': 'Tag name \'{}\' already exists'.format(data['name'])} try: tag.save_to_db() except SQLAlchemyError as e: err = str(e.__class__.__name__) return {'message': '{}'.format(err)}, 500 return tag.get_json(), 201 def delete(self): data = Tag.parser.parse_args() name = data['name'] tag = TM.find_by_name(name) if tag: try: tag.delete_from_db() except SQLAlchemyError as e: err = str(e.__class__.__name__) return {'message': '{}'.format(err)}, 500 return {'message': 'Tag was deleted'} return {'message': 'Tag with name: \'{}\' was not found'.format(name)} class TagList(Resource): def get(self): return {'tags': [tag.get_json() for tag in TM.query.all()]}

31.394737

106

0.55658

ccd003a4c9bdfc241fd64f093ae2afa4fde92196

2,198

py

Python

Torrents/migrations/0001_initial.py

tabish-ali/torrent_site

58e2e7f943a28b16134b26f450b54e9a066e7114

[ "MIT" ]

1

2019-12-17T07:36:46.000Z

Torrents/migrations/0001_initial.py

tabish-ali/torrent_site

58e2e7f943a28b16134b26f450b54e9a066e7114

[ "MIT" ]

null

Torrents/migrations/0001_initial.py

tabish-ali/torrent_site

58e2e7f943a28b16134b26f450b54e9a066e7114

[ "MIT" ]

null

# Generated by Django 3.0.7 on 2020-06-06 16:06 from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='Comments', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('user_id', models.IntegerField()), ('post_id', models.IntegerField()), ('user_name', models.CharField(max_length=50)), ('comment', models.CharField(max_length=1000)), ('user_avatar', models.CharField(max_length=250)), ('created_at', models.DateTimeField(auto_now_add=True)), ], ), migrations.CreateModel( name='Likes', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('user_id', models.IntegerField()), ('post_id', models.IntegerField()), ('liked', models.BooleanField()), ('unliked', models.BooleanField()), ('liked_by', models.CharField(blank=True, max_length=50)), ('unliked_by', models.CharField(blank=True, max_length=50)), ], ), migrations.CreateModel( name='UploadTorrents', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('torrent_name', models.CharField(max_length=50)), ('torrent_description', models.CharField(max_length=250)), ('torrent_image', models.ImageField(null=True, upload_to='static/torrent_images/')), ('torrent_file', models.FileField(upload_to='static/torrent-files/')), ('likes', models.IntegerField(default=False)), ('unlikes', models.IntegerField(default=False)), ('uploader_name', models.CharField(max_length=50)), ('user_id', models.IntegerField(default=False)), ], ), ]

41.471698

114

0.560055

ccd0e580fc925afb48c3d9bac3c4c4c1b73ecf0c

5,493

py

Python

Agent.py

594zyc/CMCC_DialogSystem

9d85f4c319677bf5e682a562041b908fc3135a17

[ "Apache-2.0" ]

1

2019-11-20T16:36:30.000Z

Agent.py

594zyc/CMCC_DialogSystem

9d85f4c319677bf5e682a562041b908fc3135a17

[ "Apache-2.0" ]

null

Agent.py

594zyc/CMCC_DialogSystem

9d85f4c319677bf5e682a562041b908fc3135a17

[ "Apache-2.0" ]

null

""" 结合所有的 Manager,实现text-in text-out的交互式 agent 的接口 """ import os import sys import time import argparse import logging BASE_DIR = os.path.dirname(os.path.abspath(__file__)) sys.path.append(os.path.join(BASE_DIR, '../..')) os.environ["CUDA_VISIBLE_DEVICES"]="1" from DM.DST.StateTracking import DialogStateTracker from DM.policy.RuleMapping import RulePolicy from data.DataManager import DataManager from NLU.NLUManager import NLUManager from NLG.NLGManager import rule_based_NLG parser = argparse.ArgumentParser() parser.add_argument('-p', '--print', type=bool, default=True, help='print details') FLAGS= parser.parse_args() UserPersonal = { "已购业务": ["180元档幸福流量年包", "18元4G飞享套餐升级版"], # 这里应该是完整的业务的信息dict "套餐使用情况": "剩余流量 11.10 GB，剩余通话 0 分钟，话费余额 110.20 元，本月已产生话费 247.29 元", "号码": "18811369685", "归属地" : "北京", "品牌": "动感地带", "是否转品牌过渡期": "否", "话费查询": "话费余额 110.20 元", "流量查询": "剩余流量 11.10 GB", "订购时间": "订购时间 2017-04-04，生效时间 2017-05-01", "是否停机": "否", "话费充值": "请登录网上营业厅、微厅或 APP 充值", "流量充值": "请登录网上营业厅、微厅或 APP 充值", "账单查询": "请登录网上营业厅、微厅或 APP 查询" } NLU_save_path_dict = { 'domain': os.path.join(BASE_DIR, 'NLU/DomDect/model/ckpt'), 'useract': os.path.join(BASE_DIR, 'NLU/UserAct/model/ckpt'), 'slotfilling': os.path.join(BASE_DIR, 'NLU/SlotFilling/model/ckpt'), 'entity': os.path.join(BASE_DIR, 'NLU/ER/entity_list.txt'), 'sentiment': os.path.join(BASE_DIR, 'NLU/SentiDect') } class DialogAgent: def __init__(self): self.history_savedir = None self.detail_savedir = None self.logger = None self.user = self.create_user() self.rule_policy = RulePolicy() self.dst = DialogStateTracker(UserPersonal, FLAGS.print, self.logger) self.data_manager = DataManager(os.path.join(BASE_DIR, 'data/tmp')) self.nlu_manager = NLUManager(NLU_save_path_dict) # self.nlg_template = NLG_template self.turn_num = 1 self.dialog_history = [] def create_user(self): user_name = input("请输入您的用户名：") user_path = os.path.join(BASE_DIR, 'user', user_name) log_path = os.path.join(user_path, 'log') if not os.path.exists(user_path): os.mkdir(user_path) os.mkdir(log_path) self.history_savedir = user_path + '/dialogs.txt' log_name = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime()) self.detail_savedir = log_path +'/' + log_name + '.log' self.logger = self.create_logger(self.detail_savedir) return user_name def create_logger(self, logdir): fmt = '%(message)s' # datefmt = "%y-%m-%d %H:%M:%S" logging.basicConfig(level=logging.INFO, format=fmt) # datefmt=datefmt) logger = logging.getLogger('mylogger') logger.setLevel(logging.INFO) fh = logging.FileHandler(logdir) fh.setLevel(logging.INFO) logger.addHandler(fh) return logger def run(self): if FLAGS.print: self.logger.info('对话记录时间：'+time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())) try: while True: user_utter = input("用户输入：") if FLAGS.print: with open(self.detail_savedir, 'a') as f: f.write('-------------- Turn ' + str(self.turn_num) + '--------------\n') f.write('用户：' + user_utter + '\n') self.dialog_history.append('用户：' + user_utter) if user_utter in ['restart' , '重来' , '重新开始']: self.dst = DialogStateTracker(UserPersonal, FLAGS.print, self.logger) self.rule_policy = RulePolicy() if FLAGS.print: self.logger.info('对话状态已重置') else: print('对话状态已重置') continue if '再见' in user_utter or '结束' in user_utter or '谢谢' in user_utter: self.close() break nlu_results = self.nlu_manager.get_NLU_results(user_utter, self.data_manager) self.dst.update(nlu_results, self.rule_policy, self.data_manager) reply = rule_based_NLG(self.dst) if FLAGS.print: self.logger.info('系统:' + reply + '\n') else: print('系统:', reply, '\n') self.dialog_history.append('系统：' + reply) self.turn_num += 1 except KeyboardInterrupt: self.close() def close(self): self.nlu_manager.close() reply = '感谢您的使用，再见！' if FLAGS.print: self.logger.info('系统:' + reply + '\n') else: print('系统:', reply, '\n') with open(os.path.join(BASE_DIR, self.history_savedir), 'a') as f: f.write('对话记录时间：') f.write(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())+'\n\n') for dialog in self.dialog_history: dialog = '\n'.join(dialog.split()) f.write(dialog+'\n\n') f.write('系统：感谢您的使用，再见！\n') f.write('————————————————————————————————\n') if __name__ == '__main__': agent = DialogAgent() agent.run()

34.54717

97

0.54178

ccd2091aa554752b80be765a8333a7d8212199e1

1,712

py

Python

clu/constants/exceptions.py

fish2000/CLU

80bc2df5f001b5639d79ba979e19ec77a9931425

[ "BSD-3-Clause" ]

1

2019-07-02T08:17:59.000Z

clu/constants/exceptions.py

fish2000/CLU

80bc2df5f001b5639d79ba979e19ec77a9931425

[ "BSD-3-Clause" ]

13

2019-12-17T02:28:30.000Z

2021-11-17T03:46:10.000Z

clu/constants/exceptions.py

fish2000/CLU

80bc2df5f001b5639d79ba979e19ec77a9931425

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- from __future__ import print_function """ CLU’s Custom exception classes live here """ class BadDotpathWarning(Warning): """ Conversion from a path to a dotpath resulted in a bad dotpath … likely there are invalid characters like dashes in there. """ pass class CDBError(Exception): """ A problem with a compilation database """ pass class ConfigurationError(Exception): """ An error that occurred in the course of macro configuration """ pass class ExecutionError(Exception): """ An error during the execution of a shell command """ pass class ExportError(NameError): """ An error during the preparation of an item for export """ pass class ExportWarning(Warning): """ A non-life-threatening condition that occured during an export """ pass class FilesystemError(Exception): """ An error that occurred while mucking about with the filesystem """ pass class KeyValueError(ValueError): """ An error raised in the clu.keyvalue API """ pass class Nondeterminism(Exception): """ An error indicating a “heisenbug” – a nondeterministic problem. """ class UnusedValueWarning(Warning): """ A warning issued when an AppDirs instance is initialized using a value or values that aren’t currently utilized on the platform upon which we are currently running """ pass __all__ = ('BadDotpathWarning', 'CDBError', 'ConfigurationError', 'ExecutionError', 'FilesystemError', 'ExportError', 'ExportWarning', 'KeyValueError', 'Nondeterminism', 'UnusedValueWarning') __dir__ = lambda: list(__all__)

28.065574

74

0.672313

ccd4b8a090334276bc85ce2b13557564dea0406a

231

py

Python

setup.py

seakarki/vehicle_accident_prediction

a921015104865dc1e4901fb1581f7822bef75f06

[ "FTL" ]

null

setup.py

seakarki/vehicle_accident_prediction

a921015104865dc1e4901fb1581f7822bef75f06

[ "FTL" ]

null

setup.py

seakarki/vehicle_accident_prediction

a921015104865dc1e4901fb1581f7822bef75f06

[ "FTL" ]

null

from setuptools import find_packages, setup setup( name='src', packages=find_packages(), version='0.1.0', description='Prediction of likely occurance of Vehicle Accident', author='sea karki', license='', )

21

69

0.679654

ccd5ca4d81ce2bd9480bed7ee3a94756e7ada584

6,543

py

Python

utils/revOrm.py

NSLS-II/aphla

ceb5410dc836a8fb16321b6dc5e10d442be765c5

[ "BSD-3-Clause" ]

null

utils/revOrm.py

NSLS-II/aphla

ceb5410dc836a8fb16321b6dc5e10d442be765c5

[ "BSD-3-Clause" ]

1

2020-02-17T18:56:18.000Z

2020-02-20T17:06:20.000Z

utils/revOrm.py

NSLS-II/aphla

ceb5410dc836a8fb16321b6dc5e10d442be765c5

[ "BSD-3-Clause" ]

1

2021-03-08T16:07:11.000Z

#!/usr/bin/env python import sys, time, os sys.path.append('../src') import numpy as np import matplotlib.pylab as plt import hla from hla import caget, caput def compare(o1, o2): print o1 print o2 npoint, nbpm, ntrim = np.shape(o1._rawmatrix) print "checking bpm" for i in range(nbpm): # bpm name if o1.bpm[i][0] != o2.bpm[i][0] or \ o1.bpm[i][1] != o2.bpm[i][1] or \ o1.bpm[i][2] != o2.bpm[i][2]: print i, o1.bpm[i], o2.bpm[i] print "checking trim" for i in range(ntrim): # bpm name if o1.trim[i][0] != o2.trim[i][0] or \ o1.trim[i][1] != o2.trim[i][1] or \ o1.trim[i][2] != o2.trim[i][2]: print i, o1.trim[i], o2.trim[i] print "checking m" ndiff = 0 for i in range(nbpm): #if ndiff > 20: break for j in range(ntrim): if o1._mask[i,j] and o2._mask[i,j]: continue if o1.bpm[i][1] != o1.trim[j][1]: continue if o1._mask[i,j] or o2._mask[i,j]: print "skip", i,j,o1.bpm[i][0], o1.trim[j][0] continue if abs(o1.m[i,j]) < 5e-3: continue if o2._mask[i,j]: continue if abs((o1.m[i,j] - o2.m[i,j])/o2.m[i,j]) > .05: print i, j, o1.bpm[i][0], o1.trim[j][0], \ o1.m[i,j], o2.m[i,j] plt.clf() plt.plot(o1._rawkick[j,1:-1], o1._rawmatrix[1:-1,i,j], '-o') plt.plot(o2._rawkick[j,1:-1], o2._rawmatrix[1:-1,i,j], '-x') plt.title("%s/%s (%s/%s) %.2e %.2e" % ( o1.bpm[i][0], o1.trim[j][0], o1.bpm[i][1], o1.trim[j][1], o1.m[i,j], o2.m[i,j])) plt.savefig('orm-compare-t%04d-b%04d.png' % (j,i)) ndiff = ndiff + 1 def filter_orm(f): orm = hla.measorm.Orm([], []) orm.load(f) orm.checkLinearity() def merge_orm(f1, f2): orm1 = hla.measorm.Orm([], []) orm2 = hla.measorm.Orm([], []) orm1.load(f1) orm2.load(f2) compare(orm1, orm2) #orm1.update(orm2) #orm1.save("orm.pkl") def mask_orm(f, sk): orm = hla.measorm.Orm([], []) orm.load(f) for j in sk: orm._mask[:,j] = 1 orm.save(f) def test_orbit(f): orm2 = hla.measorm.Orm([], []) if not os.path.exists(f): return True orm2.load(f) print orm2 print "delay: ", orm2.TSLEEP npoint, nbpm, ntrim = np.shape(orm2._rawmatrix) for i in range(5): #itrim = np.random.randint(ntrim) itrim = 0 while True: ibpm = np.random.randint(nbpm) if orm2.trim[itrim][1] == orm2.bpm[ibpm][1]: break #print hla.getOrbit() bpmrb = orm2.bpm[ibpm][2] trimsp = orm2.trim[itrim][3] x0 = caget(bpmrb) k = caget(trimsp) dk = 1e-4 caput(trimsp, k + dk) time.sleep(orm2.TSLEEP) dx = orm2.m[ibpm,itrim]*dk x1 = caget(bpmrb) print trimsp, "% .2e" % k, bpmrb, \ "% .4e % .4e % .4e % .4e" % (x0, x1, x1-x0, dx) caput(trimsp, k) time.sleep(orm2.TSLEEP) plt.clf() plt.plot(orm2._rawkick[itrim, 1:-1], orm2._rawmatrix[1:-1,ibpm,itrim], '-o') plt.plot([k, k+dk], [x0, x1], '-x') plt.grid(True) plt.title("%s.%s/%s.%s" % (orm2.bpm[ibpm][0], orm2.bpm[ibpm][1], orm2.trim[itrim][0], orm2.trim[itrim][1])) plt.savefig('orm-test-%03d.png' % i) return True plt.clf() plt.plot(ratio, '-o') plt.ylabel("[(x1-x0)-dx]/x0") plt.savefig("orm-orbit-reproduce-1.png") plt.clf() plt.plot(x0, '--', label='orbit 0') plt.plot(x1, '-', label='orbit 1') plt.plot(x0+dx, 'x', label='ORM predict') plt.ylabel("orbit") plt.savefig("orm-orbit-reproduce-2.png") for i in range(len(bpm)): if x0[i]+dx[i] - x1[i] > 1e-4: print "Not agree well:", i,bpm[i], x0[i]+dx[i], x1[i] print "Done", time.time() def update_orm(f): orm = hla.measorm.Orm([], []) if not os.path.exists(f): return True orm.load(f) npoint, nbpm, ntrim = np.shape(orm._rawmatrix) bpm = list(set([b[0] for i,b in enumerate(orm.bpm)])) #orm.measure_update(bpm=bpm, trim = ['CXHG2C30A', 'CXHG2C06A', 'CXHG2C10A', 'CXL1G6C12B', 'CXHG2C14A', 'CXHG2C18A', 'CXHG2C20A', 'CXHG2C22A', 'CXHG2C24A', 'CXHG2C26A', 'FYL1G1C21A'], verbose=1) orm.checkLinearity(verbose=1) #orm.save("orm-full-update.pkl") def correct_orbit(f): orm = hla.orm.Orm([], []) if not os.path.exists(f): return True orm.load(f) hla.reset_trims() time.sleep(5) npoint, nbpm, ntrim = np.shape(orm._rawmatrix) bpm = [] for c in range(10): cc = "C%02d" % c b = hla.getGroupMembers(['*', 'BPMX', cc], op='intersection') print cc,b bpm.extend(b) print len(bpm), bpm ntrim_used = ntrim m = np.zeros((len(bpm), ntrim_used), 'd') sel = [0] * nbpm bpmpv = [] bpmfullpv = [b[2] for i,b in enumerate(orm.bpm)] for i,b in enumerate(orm.bpm): if not b[0] in bpm: continue sel[i] = 1 bpmpv.append(b[2]) v0 = np.array(hla.caget(bpmfullpv)) v = np.array(hla.caget(bpmpv)) plt.clf() plt.plot(v0, 'r-') plt.plot(v, 'ro') plt.savefig("orbit-00.png") m = np.compress(sel, orm.m[:,:ntrim_used], axis=0) print np.shape(m) #u,s,vh = np.linalg.svd(m) #print u, s, vh dk, resids, rank, s = np.linalg.lstsq(m, -1.0*v) print dk trimpv = [t[3] for i,t in enumerate(orm.trim)] print [t[0] for i,t in enumerate(orm.trim)] hla.caput(trimpv[:ntrim_used], dk) for i in range(1,10): time.sleep(2) plt.clf() v1 = np.array(hla.caget(bpmfullpv)) plt.plot(v1, '-o') plt.savefig("orbit-%02d.png" % i) if __name__ == "__main__": #filter_orm('../test/dat/orm-full-0179.pkl') #filter_orm('../test/dat/orm-full-0181.pkl') #filter_orm('../test/dat/orm-full.pkl') #filter_orm('../test/dat/orm-sub1.pkl') #mask_orm('../test/dat/orm-full-0179.pkl', [52, 90, 141, 226, 317, 413]) #merge_orm('../test/dat/orm-full-0179.pkl', # '../test/dat/orm-full-0181.pkl') #merge_orm('../test/dat/orm-full-0181.pkl', 'orm.pkl') #test_orbit('../test/dat/orm-full-0181.pkl') #update_orm('../test/dat/orm-full-0184.pkl') #update_orm('orm-full-update.pkl') correct_orbit('orm-full-update.pkl')

31.917073

197

0.52346

ae9f5ca026e9179a91eee39f486434f7618b47ec

1,698

py

Python

Object Detection and Tracking HSV color space.py

shivtejshete/Advanced_Image_Processing

b5a7ef94a44ab0b3bd9fa4a70d843099af70079e

[ "MIT" ]

null

Object Detection and Tracking HSV color space.py

shivtejshete/Advanced_Image_Processing

b5a7ef94a44ab0b3bd9fa4a70d843099af70079e

[ "MIT" ]

null

Object Detection and Tracking HSV color space.py

shivtejshete/Advanced_Image_Processing

b5a7ef94a44ab0b3bd9fa4a70d843099af70079e

[ "MIT" ]

null

import numpy as np import cv2 #sample function - dummy callback function def nothing(x): pass #capture live video cap = cv2.VideoCapture(0) #window for trackbars cv2.namedWindow('Track') #defining trackbars to control HSV values of given video stream cv2.createTrackbar('L_HUE', 'Track', 0, 255, nothing) cv2.createTrackbar('L_Sat', 'Track', 0, 255, nothing) cv2.createTrackbar('L_Val', 'Track', 0, 255, nothing) cv2.createTrackbar('H_HUE', 'Track', 255, 255, nothing) cv2.createTrackbar('H_Sat', 'Track', 255, 255, nothing) cv2.createTrackbar('H_Val', 'Track', 255, 255, nothing) while cap.isOpened()==True : #read the video feed _, frame = cap.read() cv2.imshow('Actual_Feed', frame) #get current trackbar positions for every frame l_hue = cv2.getTrackbarPos('L_HUE', 'Track') l_sat = cv2.getTrackbarPos('L_Sat', 'Track') l_val = cv2.getTrackbarPos('L_Val', 'Track') h_hue = cv2.getTrackbarPos('H_HUE', 'Track') h_sat = cv2.getTrackbarPos('H_Sat', 'Track') h_val = cv2.getTrackbarPos('H_Val', 'Track') #convert the captured frame into HSV hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV) #Hue(0), Saturation(1) and Value(2) # print(hsv.shape) #trim video feed HSV to a range lower_bound = np.array([l_hue, l_sat, l_val]) upper_bound = np.array([h_hue, h_sat, h_val]) mask = cv2.inRange(hsv, lower_bound,upper_bound ) frame = cv2.bitwise_and(frame, frame,mask=mask) cv2.imshow('frame', frame) cv2.imshow('mask', mask) # cv2.imshow('converted', hsv) key = cv2.waitKey(1) if key==27: break cap.release() cv2.destroyAllWindows()

29.789474

86

0.6596

ae9fdc83392f9acd375ec6b842e275792831330a

15,934

py

Python

tools/run_tests/xds_k8s_test_driver/tests/url_map/fault_injection_test.py

echo80313/grpc

93cdc8b77e7b3fe4a3afec1c9c7e29b3f02ec3cf

[ "Apache-2.0" ]

null

tools/run_tests/xds_k8s_test_driver/tests/url_map/fault_injection_test.py

echo80313/grpc

93cdc8b77e7b3fe4a3afec1c9c7e29b3f02ec3cf

[ "Apache-2.0" ]

4

2022-02-27T18:59:37.000Z

2022-02-27T18:59:53.000Z

tools/run_tests/xds_k8s_test_driver/tests/url_map/fault_injection_test.py

echo80313/grpc

93cdc8b77e7b3fe4a3afec1c9c7e29b3f02ec3cf

[ "Apache-2.0" ]

null

# Copyright 2021 The gRPC Authors # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import logging import time from typing import Tuple from absl import flags from absl.testing import absltest import grpc from framework import xds_url_map_testcase from framework.test_app import client_app # Type aliases HostRule = xds_url_map_testcase.HostRule PathMatcher = xds_url_map_testcase.PathMatcher GcpResourceManager = xds_url_map_testcase.GcpResourceManager DumpedXdsConfig = xds_url_map_testcase.DumpedXdsConfig RpcTypeUnaryCall = xds_url_map_testcase.RpcTypeUnaryCall RpcTypeEmptyCall = xds_url_map_testcase.RpcTypeEmptyCall XdsTestClient = client_app.XdsTestClient ExpectedResult = xds_url_map_testcase.ExpectedResult logger = logging.getLogger(__name__) flags.adopt_module_key_flags(xds_url_map_testcase) # The first batch of RPCs don't count towards the result of test case. They are # meant to prove the communication between driver and client is fine. _NUM_RPCS = 10 _LENGTH_OF_RPC_SENDING_SEC = 16 # We are using sleep to synchronize test driver and the client... Even though # the client is sending at QPS rate, we can't assert that exactly QPS * # SLEEP_DURATION number of RPC is finished. The final completed RPC might be # slightly more or less. _NON_RANDOM_ERROR_TOLERANCE = 0.01 # For random generator involved test cases, we want to be more loose about the # final result. Otherwise, we will need more test duration (sleep duration) and # more accurate communication mechanism. The accurate of random number # generation is not the intention of this test. _ERROR_TOLERANCE = 0.2 _DELAY_CASE_APPLICATION_TIMEOUT_SEC = 1 _BACKLOG_WAIT_TIME_SEC = 20 def _build_fault_injection_route_rule(abort_percentage: int = 0, delay_percentage: int = 0): return { 'priority': 0, 'matchRules': [{ 'fullPathMatch': '/grpc.testing.TestService/UnaryCall' }], 'service': GcpResourceManager().default_backend_service(), 'routeAction': { 'faultInjectionPolicy': { 'abort': { 'httpStatus': 401, 'percentage': abort_percentage, }, 'delay': { 'fixedDelay': { 'seconds': '20' }, 'percentage': delay_percentage, } } }, } def _wait_until_backlog_cleared(test_client: XdsTestClient, timeout: int = _BACKLOG_WAIT_TIME_SEC): """ Wait until the completed RPC is close to started RPC. For delay injected test cases, there might be a backlog of RPCs due to slow initialization of the client. E.g., if initialization took 20s and qps is 25, then there will be a backlog of 500 RPCs. In normal test cases, this is fine, because RPCs will fail immediately. But for delay injected test cases, the RPC might linger much longer and affect the stability of test results. """ logger.info('Waiting for RPC backlog to clear for %d seconds', timeout) deadline = time.time() + timeout while time.time() < deadline: stats = test_client.get_load_balancer_accumulated_stats() ok = True for rpc_type in [RpcTypeUnaryCall, RpcTypeEmptyCall]: started = stats.num_rpcs_started_by_method.get(rpc_type, 0) completed = stats.num_rpcs_succeeded_by_method.get( rpc_type, 0) + stats.num_rpcs_failed_by_method.get(rpc_type, 0) # We consider the backlog is healthy, if the diff between started # RPCs and completed RPCs is less than 1.5 QPS. if abs(started - completed) > xds_url_map_testcase.QPS.value * 1.1: logger.info( 'RPC backlog exist: rpc_type=%s started=%s completed=%s', rpc_type, started, completed) time.sleep(_DELAY_CASE_APPLICATION_TIMEOUT_SEC) ok = False else: logger.info( 'RPC backlog clear: rpc_type=%s started=%s completed=%s', rpc_type, started, completed) if ok: # Both backlog of both types of RPCs is clear, success, return. return raise RuntimeError('failed to clear RPC backlog in %s seconds' % timeout) class TestZeroPercentFaultInjection(xds_url_map_testcase.XdsUrlMapTestCase): @staticmethod def url_map_change( host_rule: HostRule, path_matcher: PathMatcher) -> Tuple[HostRule, PathMatcher]: path_matcher["routeRules"] = [ _build_fault_injection_route_rule(abort_percentage=0, delay_percentage=0) ] return host_rule, path_matcher def xds_config_validate(self, xds_config: DumpedXdsConfig): self.assertNumEndpoints(xds_config, 1) filter_config = xds_config.rds['virtualHosts'][0]['routes'][0][ 'typedPerFilterConfig']['envoy.filters.http.fault'] self.assertEqual('20s', filter_config['delay']['fixedDelay']) self.assertEqual( 0, filter_config['delay']['percentage'].get('numerator', 0)) self.assertEqual('MILLION', filter_config['delay']['percentage']['denominator']) self.assertEqual(401, filter_config['abort']['httpStatus']) self.assertEqual( 0, filter_config['abort']['percentage'].get('numerator', 0)) self.assertEqual('MILLION', filter_config['abort']['percentage']['denominator']) def rpc_distribution_validate(self, test_client: XdsTestClient): self.configure_and_send(test_client, rpc_types=(RpcTypeUnaryCall,), num_rpcs=_NUM_RPCS) self.assertRpcStatusCode(test_client, expected=(ExpectedResult( rpc_type=RpcTypeUnaryCall, status_code=grpc.StatusCode.OK, ratio=1),), length=_LENGTH_OF_RPC_SENDING_SEC, tolerance=_NON_RANDOM_ERROR_TOLERANCE) class TestNonMatchingFaultInjection(xds_url_map_testcase.XdsUrlMapTestCase): """EMPTY_CALL is not fault injected, so it should succeed.""" @staticmethod def client_init_config(rpc: str, metadata: str): # Python interop client will stuck if the traffic is slow (in this case, # 20s injected). The purpose of this test is examining the un-injected # traffic is not impacted, so it's fine to just send un-injected # traffic. return 'EmptyCall', metadata @staticmethod def url_map_change( host_rule: HostRule, path_matcher: PathMatcher) -> Tuple[HostRule, PathMatcher]: path_matcher["routeRules"] = [ _build_fault_injection_route_rule(abort_percentage=100, delay_percentage=100) ] return host_rule, path_matcher def xds_config_validate(self, xds_config: DumpedXdsConfig): self.assertNumEndpoints(xds_config, 1) # The first route rule for UNARY_CALL is fault injected self.assertEqual( "/grpc.testing.TestService/UnaryCall", xds_config.rds['virtualHosts'][0]['routes'][0]['match']['path']) filter_config = xds_config.rds['virtualHosts'][0]['routes'][0][ 'typedPerFilterConfig']['envoy.filters.http.fault'] self.assertEqual('20s', filter_config['delay']['fixedDelay']) self.assertEqual(1000000, filter_config['delay']['percentage']['numerator']) self.assertEqual('MILLION', filter_config['delay']['percentage']['denominator']) self.assertEqual(401, filter_config['abort']['httpStatus']) self.assertEqual(1000000, filter_config['abort']['percentage']['numerator']) self.assertEqual('MILLION', filter_config['abort']['percentage']['denominator']) # The second route rule for all other RPCs is untouched self.assertNotIn( 'envoy.filters.http.fault', xds_config.rds['virtualHosts'][0]['routes'][1].get( 'typedPerFilterConfig', {})) def rpc_distribution_validate(self, test_client: XdsTestClient): self.assertRpcStatusCode(test_client, expected=(ExpectedResult( rpc_type=RpcTypeEmptyCall, status_code=grpc.StatusCode.OK, ratio=1),), length=_LENGTH_OF_RPC_SENDING_SEC, tolerance=_NON_RANDOM_ERROR_TOLERANCE) @absltest.skip('20% RPC might pass immediately, reason unknown') class TestAlwaysDelay(xds_url_map_testcase.XdsUrlMapTestCase): @staticmethod def url_map_change( host_rule: HostRule, path_matcher: PathMatcher) -> Tuple[HostRule, PathMatcher]: path_matcher["routeRules"] = [ _build_fault_injection_route_rule(abort_percentage=0, delay_percentage=100) ] return host_rule, path_matcher def xds_config_validate(self, xds_config: DumpedXdsConfig): self.assertNumEndpoints(xds_config, 1) filter_config = xds_config.rds['virtualHosts'][0]['routes'][0][ 'typedPerFilterConfig']['envoy.filters.http.fault'] self.assertEqual('20s', filter_config['delay']['fixedDelay']) self.assertEqual(1000000, filter_config['delay']['percentage']['numerator']) self.assertEqual('MILLION', filter_config['delay']['percentage']['denominator']) def rpc_distribution_validate(self, test_client: XdsTestClient): self.configure_and_send(test_client, rpc_types=(RpcTypeUnaryCall,), num_rpcs=_NUM_RPCS, app_timeout=_DELAY_CASE_APPLICATION_TIMEOUT_SEC) _wait_until_backlog_cleared(test_client) self.assertRpcStatusCode( test_client, expected=(ExpectedResult( rpc_type=RpcTypeUnaryCall, status_code=grpc.StatusCode.DEADLINE_EXCEEDED, ratio=1),), length=_LENGTH_OF_RPC_SENDING_SEC, tolerance=_NON_RANDOM_ERROR_TOLERANCE) class TestAlwaysAbort(xds_url_map_testcase.XdsUrlMapTestCase): @staticmethod def url_map_change( host_rule: HostRule, path_matcher: PathMatcher) -> Tuple[HostRule, PathMatcher]: path_matcher["routeRules"] = [ _build_fault_injection_route_rule(abort_percentage=100, delay_percentage=0) ] return host_rule, path_matcher def xds_config_validate(self, xds_config: DumpedXdsConfig): self.assertNumEndpoints(xds_config, 1) filter_config = xds_config.rds['virtualHosts'][0]['routes'][0][ 'typedPerFilterConfig']['envoy.filters.http.fault'] self.assertEqual(401, filter_config['abort']['httpStatus']) self.assertEqual(1000000, filter_config['abort']['percentage']['numerator']) self.assertEqual('MILLION', filter_config['abort']['percentage']['denominator']) def rpc_distribution_validate(self, test_client: XdsTestClient): self.configure_and_send(test_client, rpc_types=(RpcTypeUnaryCall,), num_rpcs=_NUM_RPCS) self.assertRpcStatusCode( test_client, expected=(ExpectedResult( rpc_type=RpcTypeUnaryCall, status_code=grpc.StatusCode.UNAUTHENTICATED, ratio=1),), length=_LENGTH_OF_RPC_SENDING_SEC, tolerance=_NON_RANDOM_ERROR_TOLERANCE) class TestDelayHalf(xds_url_map_testcase.XdsUrlMapTestCase): @staticmethod def url_map_change( host_rule: HostRule, path_matcher: PathMatcher) -> Tuple[HostRule, PathMatcher]: path_matcher["routeRules"] = [ _build_fault_injection_route_rule(abort_percentage=0, delay_percentage=50) ] return host_rule, path_matcher def xds_config_validate(self, xds_config: DumpedXdsConfig): self.assertNumEndpoints(xds_config, 1) filter_config = xds_config.rds['virtualHosts'][0]['routes'][0][ 'typedPerFilterConfig']['envoy.filters.http.fault'] self.assertEqual('20s', filter_config['delay']['fixedDelay']) self.assertEqual(500000, filter_config['delay']['percentage']['numerator']) self.assertEqual('MILLION', filter_config['delay']['percentage']['denominator']) def rpc_distribution_validate(self, test_client: XdsTestClient): self.configure_and_send(test_client, rpc_types=(RpcTypeUnaryCall,), num_rpcs=_NUM_RPCS, app_timeout=_DELAY_CASE_APPLICATION_TIMEOUT_SEC) _wait_until_backlog_cleared(test_client) self.assertRpcStatusCode( test_client, expected=(ExpectedResult( rpc_type=RpcTypeUnaryCall, status_code=grpc.StatusCode.DEADLINE_EXCEEDED, ratio=0.5),), length=_LENGTH_OF_RPC_SENDING_SEC, tolerance=_ERROR_TOLERANCE) class TestAbortHalf(xds_url_map_testcase.XdsUrlMapTestCase): @staticmethod def url_map_change( host_rule: HostRule, path_matcher: PathMatcher) -> Tuple[HostRule, PathMatcher]: path_matcher["routeRules"] = [ _build_fault_injection_route_rule(abort_percentage=50, delay_percentage=0) ] return host_rule, path_matcher def xds_config_validate(self, xds_config: DumpedXdsConfig): self.assertNumEndpoints(xds_config, 1) filter_config = xds_config.rds['virtualHosts'][0]['routes'][0][ 'typedPerFilterConfig']['envoy.filters.http.fault'] self.assertEqual(401, filter_config['abort']['httpStatus']) self.assertEqual(500000, filter_config['abort']['percentage']['numerator']) self.assertEqual('MILLION', filter_config['abort']['percentage']['denominator']) def rpc_distribution_validate(self, test_client: XdsTestClient): self.configure_and_send(test_client, rpc_types=(RpcTypeUnaryCall,), num_rpcs=_NUM_RPCS) self.assertRpcStatusCode( test_client, expected=(ExpectedResult( rpc_type=RpcTypeUnaryCall, status_code=grpc.StatusCode.UNAUTHENTICATED, ratio=0.5),), length=_LENGTH_OF_RPC_SENDING_SEC, tolerance=_ERROR_TOLERANCE) if __name__ == '__main__': absltest.main()

43.654795

80

0.622756

ae9ff35726e01cb8b94b132adc55d816f0af6cd8

2,008

py

Python

funda/spiders/FundaListings.py

royzwambag/funda-scraper

c288ae3521731d1fa12fbde7acc10d725048603a

[ "MIT" ]

1

2021-02-21T22:42:43.000Z

funda/spiders/FundaListings.py

royzwambag/funda-scraper

c288ae3521731d1fa12fbde7acc10d725048603a

[ "MIT" ]

null

funda/spiders/FundaListings.py

royzwambag/funda-scraper

c288ae3521731d1fa12fbde7acc10d725048603a

[ "MIT" ]

1

2021-12-01T12:18:54.000Z

import scrapy from ..items import BasicHouseItem from ..utils import generate_url class FundaListingsSpider(scrapy.Spider): name = 'funda_listings' allowed_domains = ["funda.nl"] custom_settings = { "FEED_EXPORT_FIELDS": ["address", "zipcode", "price", "house_size", "plot_size", "rooms", "url", "image", "funda_id"] } def start_requests(self): return [scrapy.FormRequest(generate_url())] def parse(self, response): result_blocks = response.css("ol.search-results") for result_block in result_blocks: results = result_block.css("li.search-result") for result in results: url = result.css("div.search-result__header-title-col a::attr(href)").get() address = result.css("h2.search-result__header-title::text").get().strip() zipcode = result.css("h4.search-result__header-subtitle::text").get().strip() price = result.css("span.search-result-price::text").get() properties = result.css(".search-result-kenmerken") house_size = properties.css("span[title*='Gebruiksoppervlakte wonen']::text").get() plot_size = properties.css("span[title*='Perceeloppervlakte']::text").get() rooms = properties.css("li::text")[-1].get() image = result.css(".search-result-image img::attr(src)").get() funda_id = result.css("div.search-result__header-title-col a::attr(data-search-result-item-anchor)").get() items = BasicHouseItem() items['url'] = f"https://www.funda.nl/{url}" items['address'] = address items['zipcode'] = zipcode items['price'] = price items['house_size'] = house_size items['plot_size'] = plot_size items['rooms'] = rooms items['image'] = image items['funda_id'] = funda_id yield items

44.622222

125

0.580677

aea00ef15d75bbb6792d81d979d452ecd7795555

754

py

Python

wfdb/__init__.py

melbourne-cdth/wfdb-python

a36c22e12f8417ff18e57dbe54b7180dd183ec66

[ "MIT" ]

null

wfdb/__init__.py

melbourne-cdth/wfdb-python

a36c22e12f8417ff18e57dbe54b7180dd183ec66

[ "MIT" ]

null

wfdb/__init__.py

melbourne-cdth/wfdb-python

a36c22e12f8417ff18e57dbe54b7180dd183ec66

[ "MIT" ]

null

from wfdb.io.record import (Record, MultiRecord, rdheader, rdrecord, rdsamp, wrsamp, dl_database, edf2mit, mit2edf, wav2mit, mit2wav, wfdb2mat, csv2mit, sampfreq, signame, wfdbdesc, wfdbtime, sigavg) from wfdb.io.annotation import (Annotation, rdann, wrann, show_ann_labels, show_ann_classes, ann2rr, rr2ann, csv2ann, rdedfann, mrgann) from wfdb.io.download import get_dbs, get_record_list, dl_files, set_db_index_url from wfdb.plot.plot import plot_items, plot_wfdb, plot_all_records from wfdb.plot.plot_plotly import plot_items_pl, plot_wfdb_pl, plot_all_records_pl from wfdb.version import __version__

58

82

0.66313

aea011bdabd2351b652997696862bc1051c59d81

647

py

Python

reports/migrations/0103_auto_20190224_1000.py

CMU-TRP/podd-api

6eb5c4598f848f75d131287163cd9babf2a0a0fc

[ "MIT" ]

3

2020-04-26T06:28:50.000Z

2021-04-05T08:02:26.000Z

reports/migrations/0103_auto_20190224_1000.py

CMU-TRP/podd-api

6eb5c4598f848f75d131287163cd9babf2a0a0fc

[ "MIT" ]

10

2020-06-05T17:36:10.000Z

2022-03-11T23:16:42.000Z

reports/migrations/0103_auto_20190224_1000.py

CMU-TRP/podd-api

6eb5c4598f848f75d131287163cd9babf2a0a0fc

[ "MIT" ]

5

2021-04-08T08:43:49.000Z

2021-11-27T06:36:46.000Z

# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.db import models, migrations class Migration(migrations.Migration): dependencies = [ ('reports', '0102_recordspec_group_key'), ] operations = [ migrations.AlterField( model_name='reportimage', name='image_url', field=models.URLField(max_length=800), preserve_default=True, ), migrations.AlterField( model_name='reportimage', name='thumbnail_url', field=models.URLField(max_length=800), preserve_default=True, ), ]

23.962963

50

0.591963

aea0f505ac0ae8958de0fe2ef7a2f11f420d2940

936

py

Python

CodeFights2.py

sassy224/challenges

3e4c9c6fe19351f2e47408967bf1bd3f4ca63bb7

[ "MIT" ]

null

CodeFights2.py

sassy224/challenges

3e4c9c6fe19351f2e47408967bf1bd3f4ca63bb7

[ "MIT" ]

null

CodeFights2.py

sassy224/challenges

3e4c9c6fe19351f2e47408967bf1bd3f4ca63bb7

[ "MIT" ]

null

""" You are given a digital number written down on a sheet of paper. Your task is to figure out if you rotate the given sheet of paper by 180 degrees would the number still look exactly the same. input: "1" output: false input: "29562" output: true input: "77" output: false """ def digital_number(number): """ >>> digital_number('1') False >>> digital_number('29562') True >>> digital_number('77') False >>> digital_number('000') True The trick here is you need to understand what rotation is: https://en.wikipedia.org/wiki/Seven-segment_display_character_representations In short: 0 -> 0 2 -> 2 5 -> 5 6 -> 9 8 -> 8 9 -> 6 Other numbers can't be rotated into another number """ rule = {'0':'0', '2':'2', '5':'5', '6':'9', '8':'8', '9':'6'} new_str = [rule.get(digit, "x") for digit in number ] return number == "".join(new_str[::-1]) if __name__ == "__main__": import doctest doctest.testmod()

21.767442

126

0.654915

aea133a870e185a7ab120fb1bfa33b1fd3b07e6f

5,196

py

Python

AKSDataOpsDemo/scripts/evaluate.py

cloudmelon/aks-severless

ade3b6a110444c09bc4c0ff44cb232cb09ddebe7

[ "MIT" ]

6

2019-12-06T22:55:41.000Z

2019-12-10T23:57:40.000Z

AKSDataOpsDemo/scripts/evaluate.py

cloudmelon/aks-severless

ade3b6a110444c09bc4c0ff44cb232cb09ddebe7

[ "MIT" ]

null

AKSDataOpsDemo/scripts/evaluate.py

cloudmelon/aks-severless

ade3b6a110444c09bc4c0ff44cb232cb09ddebe7

[ "MIT" ]

2

2019-12-26T16:25:44.000Z

2020-09-02T22:43:41.000Z

import argparse import os, json, sys import azureml.core from azureml.core import Workspace from azureml.core import Experiment from azureml.core.model import Model import azureml.core from azureml.core import Run from azureml.core.webservice import AciWebservice, Webservice from azureml.core.conda_dependencies import CondaDependencies from azureml.core.image import ContainerImage from azureml.core import Image print("In evaluate.py") parser = argparse.ArgumentParser("evaluate") parser.add_argument("--model_name", type=str, help="model name", dest="model_name", required=True) parser.add_argument("--image_name", type=str, help="image name", dest="image_name", required=True) parser.add_argument("--output", type=str, help="eval output directory", dest="output", required=True) args = parser.parse_args() print("Argument 1: %s" % args.model_name) print("Argument 2: %s" % args.image_name) print("Argument 3: %s" % args.output) run = Run.get_context() ws = run.experiment.workspace print('Workspace configuration succeeded') model_list = Model.list(ws, name = args.model_name) latest_model = sorted(model_list, reverse=True, key = lambda x: x.created_time)[0] latest_model_id = latest_model.id latest_model_name = latest_model.name latest_model_version = latest_model.version latest_model_path = latest_model.get_model_path(latest_model_name, _workspace=ws) print('Latest model id: ', latest_model_id) print('Latest model name: ', latest_model_name) print('Latest model version: ', latest_model_version) print('Latest model path: ', latest_model_path) latest_model_run_id = latest_model.tags.get("run_id") print('Latest model run id: ', latest_model_run_id) latest_model_run = Run(run.experiment, run_id = latest_model_run_id) latest_model_accuracy = latest_model_run.get_metrics().get("acc") print('Latest model accuracy: ', latest_model_accuracy) ws_list = Webservice.list(ws, model_name = latest_model_name) print('webservice list') print(ws_list) deploy_model = False current_model = None if(len(ws_list) > 0): webservice = ws_list[0] try: image_id = webservice.tags['image_id'] image = Image(ws, id = image_id) current_model = image.models[0] print('Found current deployed model!') except: deploy_model = True print('Image id tag not found!') else: deploy_model = True print('No deployed webservice for model: ', latest_model_name) current_model_accuracy = -1 # undefined if current_model != None: current_model_run = Run(run.experiment, run_id = current_model.tags.get("run_id")) current_model_accuracy = current_model_run.get_metrics().get("acc") print('accuracies') print(latest_model_accuracy, current_model_accuracy) if latest_model_accuracy > current_model_accuracy: deploy_model = True print('Current model performs better and will be deployed!') else: print('Current model does NOT perform better and thus will NOT be deployed!') eval_info = {} eval_info["model_name"] = latest_model_name eval_info["model_version"] = latest_model_version eval_info["model_path"] = latest_model_path eval_info["model_acc"] = latest_model_accuracy eval_info["deployed_model_acc"] = current_model_accuracy eval_info["deploy_model"] = deploy_model eval_info["image_name"] = args.image_name eval_info["image_id"] = "" os.makedirs(args.output, exist_ok=True) eval_filepath = os.path.join(args.output, 'eval_info.json') if deploy_model == False: with open(eval_filepath, "w") as f: json.dump(eval_info, f) print('eval_info.json saved') print('Model did not meet the accuracy criteria and will not be deployed!') print('Exiting') sys.exit(0) # Continue to package Model and create image print('Model accuracy has met the criteria!') print('Proceeding to package model and create the image...') print('Updating scoring file with the correct model name') with open('score.py') as f: data = f.read() with open('score_fixed.py', "w") as f: f.write(data.replace('MODEL-NAME', args.model_name)) #replace the placeholder MODEL-NAME print('score_fixed.py saved') # create a Conda dependencies environment file print("Creating conda dependencies file locally...") conda_packages = ['numpy'] pip_packages = ['tensorflow==1.12.2', 'keras==2.2.4', 'azureml-sdk', 'azureml-monitoring'] mycondaenv = CondaDependencies.create(conda_packages=conda_packages, pip_packages=pip_packages) conda_file = 'scoring_dependencies.yml' with open(conda_file, 'w') as f: f.write(mycondaenv.serialize_to_string()) # create container image configuration print("Creating container image configuration...") image_config = ContainerImage.image_configuration(execution_script = 'score_fixed.py', runtime = 'python', conda_file = conda_file) print("Creating image...") image = Image.create(name=args.image_name, models=[latest_model], image_config=image_config, workspace=ws) # wait for image creation to finish image.wait_for_creation(show_output=True) eval_info["image_id"] = image.id with open(eval_filepath, "w") as f: json.dump(eval_info, f) print('eval_info.json saved')

33.960784

106

0.746151

aea32992cb3ebb2d5d93b2c9d53e7b2172d6b5d0

2,048

py

Python

tools/instruction.py

mzins/MIPS-CPU-SIM

0f6723c668266447035c5010c67abdd041324d1a

[ "MIT" ]

null

tools/instruction.py

mzins/MIPS-CPU-SIM

0f6723c668266447035c5010c67abdd041324d1a

[ "MIT" ]

null

tools/instruction.py

mzins/MIPS-CPU-SIM

0f6723c668266447035c5010c67abdd041324d1a

[ "MIT" ]

null

from tools import instruction_helpers from tools.errors import InstructionNotFound from logs.logconfig import log_config LOG = log_config() class Instruction: binary_instruction = "" def __init__(self): inst = raw_input("Please type a single command\n") self.opcode = None self.binary_instruction = self.parse_instruction(inst) def parse_instruction(self, instruction=""): instruction = instruction.replace(",","") instruction = instruction.upper() instruction_parts = instruction.split(" ") LOG.info('<note> INSTRUCTION UNITS ARE {}'.format(instruction_parts)) self.opcode = instruction_parts[0] type = instruction_helpers.type_finder(opcode=self.opcode) if type == "I": LOG.info('<note> FOUND I TYPE INSTRUCTION') opcode = instruction_helpers.i_type_look_up.get(instruction_parts[0]).get('OPCODE') rs = instruction_helpers.to_binary(instruction_parts[2]) rt = instruction_helpers.to_binary(instruction_parts[1]) imm = instruction_helpers.immediate_to_binary(instruction_parts[3]) binary_instruction = "{}{}{}{}".format(opcode, rs, rt, imm) elif type == "R": LOG.info('<note> FOUND R TYPE INSTRUCTION') opcode = instruction_helpers.r_type_look_up.get(instruction_parts[0]).get('OPCODE') rs = instruction_helpers.to_binary(instruction_parts[2]) rt = instruction_helpers.to_binary(instruction_parts[3]) rd = instruction_helpers.to_binary(instruction_parts[1]) func_code = instruction_helpers.r_type_look_up.get(instruction_parts[0]).get('FUNCTION') binary_instruction = "{}{}{}{}{}{}".format(opcode,rs, rt, rd, "00000", func_code) elif type == "J": LOG.info('<note>FOUND J TYPE INSTRUCTION') else: raise InstructionNotFound LOG.info('<note> BINARY INSTRUCTION {}\n'.format(binary_instruction)) return binary_instruction

37.236364

100

0.657715

aea38d32840ca9cfc7b545135134ea487ccba8a9

19,377

py

Python

projects/views.py

msherman64/portal

e5399ef2ed3051d7c9a46c660f028c666ae22ca6

[ "Apache-2.0" ]

null

projects/views.py

msherman64/portal

e5399ef2ed3051d7c9a46c660f028c666ae22ca6

[ "Apache-2.0" ]

null

projects/views.py

msherman64/portal

e5399ef2ed3051d7c9a46c660f028c666ae22ca6

[ "Apache-2.0" ]

null

from django.shortcuts import render from django.contrib.auth.decorators import login_required from chameleon.decorators import terms_required from django.contrib import messages from django.http import ( Http404, HttpResponseForbidden, HttpResponse, HttpResponseRedirect, HttpResponseNotAllowed, JsonResponse, ) from django.core.urlresolvers import reverse from django.core.exceptions import PermissionDenied from django import forms from datetime import datetime from django.conf import settings from .models import Project, ProjectExtras from projects.serializer import ProjectExtrasJSONSerializer from django.contrib.auth.models import User from django.views.decorators.http import require_POST from .forms import ( ProjectCreateForm, ProjectAddUserForm, AllocationCreateForm, EditNicknameForm, AddBibtexPublicationForm, ) from django.db import IntegrityError import re import logging import json from keystoneclient.v3 import client as ks_client from keystoneauth1 import adapter from django.conf import settings import uuid import sys from chameleon.keystone_auth import admin_ks_client, sync_projects, get_user from util.project_allocation_mapper import ProjectAllocationMapper logger = logging.getLogger("projects") def project_pi_or_admin_or_superuser(user, project): if user.is_superuser: return True if user.groups.filter(name="Allocation Admin").count() == 1: return True if user.username == project.pi.username: return True return False def project_member_or_admin_or_superuser(user, project, project_user): if project_pi_or_admin_or_superuser(user, project): return True for pu in project_user: if user.username == pu.username: return True return False @login_required def user_projects(request): context = {} username = request.user.username mapper = ProjectAllocationMapper(request) user = mapper.get_user(username) context["is_pi_eligible"] = user["piEligibility"].lower() == "eligible" context["username"] = username context["projects"] = mapper.get_user_projects(username, to_pytas_model=True) return render(request, "projects/user_projects.html", context) @login_required def view_project(request, project_id): mapper = ProjectAllocationMapper(request) try: project = mapper.get_project(project_id) if project.source != "Chameleon": raise Http404("The requested project does not exist!") except Exception as e: logger.error(e) raise Http404("The requested project does not exist!") form = ProjectAddUserForm() nickname_form = EditNicknameForm() pubs_form = AddBibtexPublicationForm() if request.POST and project_pi_or_admin_or_superuser(request.user, project): form = ProjectAddUserForm() if "add_user" in request.POST: form = ProjectAddUserForm(request.POST) if form.is_valid(): try: add_username = form.cleaned_data["username"] if mapper.add_user_to_project(project, add_username): sync_project_memberships(request, add_username) messages.success( request, f'User "{add_username}" added to project!' ) form = ProjectAddUserForm() except Exception as e: logger.exception("Failed adding user") messages.error( request, ( "Unable to add user. Confirm that the username is " "correct and corresponds to a current Chameleon user." ), ) else: messages.error( request, ( "There were errors processing your request. " "Please see below for details." ), ) elif "del_user" in request.POST: try: del_username = request.POST["username"] # Ensure that it's not possible to remove the PI if del_username == project.pi.username: raise PermissionDenied( "Removing the PI from the project is not allowed." ) if mapper.remove_user_from_project(project, del_username): sync_project_memberships(request, del_username) messages.success( request, 'User "%s" removed from project' % del_username ) except PermissionDenied as exc: messages.error(request, exc) except: logger.exception("Failed removing user") messages.error( request, "An unexpected error occurred while attempting " "to remove this user. Please try again", ) elif "nickname" in request.POST: nickname_form = edit_nickname(request, project_id) users = mapper.get_project_members(project) if not project_member_or_admin_or_superuser(request.user, project, users): raise PermissionDenied for a in project.allocations: if a.start and isinstance(a.start, str): a.start = datetime.strptime(a.start, "%Y-%m-%dT%H:%M:%SZ") if a.dateRequested: if isinstance(a.dateRequested, str): a.dateRequested = datetime.strptime( a.dateRequested, "%Y-%m-%dT%H:%M:%SZ" ) if a.dateReviewed: if isinstance(a.dateReviewed, str): a.dateReviewed = datetime.strptime(a.dateReviewed, "%Y-%m-%dT%H:%M:%SZ") if a.end: if isinstance(a.end, str): a.end = datetime.strptime(a.end, "%Y-%m-%dT%H:%M:%SZ") user_mashup = [] for u in users: user = { "username": u.username, "role": u.role, } try: portal_user = User.objects.get(username=u.username) user["email"] = portal_user.email user["first_name"] = portal_user.first_name user["last_name"] = portal_user.last_name except User.DoesNotExist: logger.info("user: " + u.username + " not found") user_mashup.append(user) return render( request, "projects/view_project.html", { "project": project, "project_nickname": project.nickname, "users": user_mashup, "is_pi": request.user.username == project.pi.username, "form": form, "nickname_form": nickname_form, "pubs_form": pubs_form, }, ) def set_ks_project_nickname(chargeCode, nickname): for region in list(settings.OPENSTACK_AUTH_REGIONS.keys()): ks_admin = admin_ks_client(region=region) project_list = ks_admin.projects.list(domain=ks_admin.user_domain_id) project = [ this for this in project_list if getattr(this, "charge_code", None) == chargeCode ] logger.info( "Assigning nickname {0} to project with charge code {1} at {2}".format( nickname, chargeCode, region ) ) if project and project[0]: project = project[0] ks_admin.projects.update(project, name=nickname) logger.info( "Successfully assigned nickname {0} to project with charge code {1} at {2}".format( nickname, chargeCode, region ) ) def sync_project_memberships(request, username): """Re-sync a user's Keystone project memberships. This calls utils.auth.keystone_auth.sync_projects under the hood, which will dynamically create missing projects as well. Args: request (Request): the parent request; used for region detection. username (str): the username to sync memberships for. Return: List[keystone.Project]: a list of Keystone projects the user is a member of. """ mapper = ProjectAllocationMapper(request) try: ks_admin = admin_ks_client(request=request) ks_user = get_user(ks_admin, username) if not ks_user: logger.error( ( "Could not fetch Keystone user for {}, skipping membership syncing".format( username ) ) ) return active_projects = mapper.get_user_projects( username, alloc_status=["Active"], to_pytas_model=True ) return sync_projects(ks_admin, ks_user, active_projects) except Exception as e: logger.error("Could not sync project memberships for %s: %s", username, e) return [] @login_required @terms_required("project-terms") def create_allocation(request, project_id, allocation_id=-1): mapper = ProjectAllocationMapper(request) user = mapper.get_user(request.user.username) if user["piEligibility"].lower() != "eligible": messages.error( request, "Only PI Eligible users can request allocations. If you would " "like to request PI Eligibility, please " '<a href="/user/profile/edit/">submit a PI Eligibility ' "request</a>.", ) return HttpResponseRedirect(reverse("projects:user_projects")) project = mapper.get_project(project_id) allocation = None allocation_id = int(allocation_id) if allocation_id > 0: for a in project.allocations: if a.id == allocation_id: allocation = a # goofiness that we should clean up later; requires data cleansing abstract = project.description if "--- Supplemental details ---" in abstract: additional = abstract.split("\n\n--- Supplemental details ---\n\n") abstract = additional[0] additional = additional[1].split("\n\n--- Funding source(s) ---\n\n") justification = additional[0] if len(additional) > 1: funding_source = additional[1] else: funding_source = "" elif allocation: justification = allocation.justification if "--- Funding source(s) ---" in justification: parts = justification.split("\n\n--- Funding source(s) ---\n\n") justification = parts[0] funding_source = parts[1] else: funding_source = "" else: justification = "" funding_source = "" if request.POST: form = AllocationCreateForm( request.POST, initial={ "description": abstract, "supplemental_details": justification, "funding_source": funding_source, }, ) if form.is_valid(): allocation = form.cleaned_data.copy() allocation["computeRequested"] = 20000 # Also update the project project.description = allocation.pop("description", None) supplemental_details = allocation.pop("supplemental_details", None) logger.error(supplemental_details) funding_source = allocation.pop("funding_source", None) # if supplemental_details == None: # raise forms.ValidationError("Justifcation is required") # This is required if not supplemental_details: supplemental_details = "(none)" logger.error(supplemental_details) if funding_source: allocation[ "justification" ] = "%s\n\n--- Funding source(s) ---\n\n%s" % ( supplemental_details, funding_source, ) else: allocation["justification"] = supplemental_details allocation["projectId"] = project_id allocation["requestorId"] = mapper.get_portal_user_id(request.user.username) allocation["resourceId"] = "39" if allocation_id > 0: allocation["id"] = allocation_id try: logger.info( "Submitting allocation request for project %s: %s" % (project.id, allocation) ) updated_project = mapper.save_project(project.as_dict()) mapper.save_allocation( allocation, project.chargeCode, request.get_host() ) messages.success(request, "Your allocation request has been submitted!") return HttpResponseRedirect( reverse("projects:view_project", args=[updated_project["id"]]) ) except: logger.exception("Error creating allocation") form.add_error( "__all__", "An unexpected error occurred. Please try again" ) else: form.add_error( "__all__", "There were errors processing your request. " "Please see below for details.", ) else: form = AllocationCreateForm( initial={ "description": abstract, "supplemental_details": justification, "funding_source": funding_source, } ) context = { "form": form, "project": project, "alloc_id": allocation_id, "alloc": allocation, } return render(request, "projects/create_allocation.html", context) @login_required @terms_required("project-terms") def create_project(request): mapper = ProjectAllocationMapper(request) form_args = {"request": request} user = mapper.get_user(request.user.username) if user["piEligibility"].lower() != "eligible": messages.error( request, "Only PI Eligible users can create new projects. " "If you would like to request PI Eligibility, please " '<a href="/user/profile/edit/">submit a PI Eligibility ' "request</a>.", ) return HttpResponseRedirect(reverse("projects:user_projects")) if request.POST: form = ProjectCreateForm(request.POST, **form_args) if form.is_valid(): # title, description, typeId, fieldId project = form.cleaned_data.copy() # let's check that any provided nickname is unique project["nickname"] = project["nickname"].strip() nickname_valid = ( project["nickname"] and ProjectExtras.objects.filter(nickname=project["nickname"]).count() < 1 and Project.objects.filter(nickname=project["nickname"]).count() < 1 ) if not nickname_valid: form.add_error("__all__", "Project nickname unavailable") return render(request, "projects/create_project.html", {"form": form}) project.pop("accept_project_terms", None) # pi pi_user_id = mapper.get_portal_user_id(request.user.username) project["piId"] = pi_user_id # allocations allocation = { "resourceId": 39, "requestorId": pi_user_id, "computeRequested": 20000, } supplemental_details = project.pop("supplemental_details", None) funding_source = project.pop("funding_source", None) # if supplemental_details == None: # raise forms.ValidationError("Justifcation is required") if not supplemental_details: supplemental_details = "(none)" if funding_source: allocation[ "justification" ] = "%s\n\n--- Funding source(s) ---\n\n%s" % ( supplemental_details, funding_source, ) else: allocation["justification"] = supplemental_details project["allocations"] = [allocation] # startup project["typeId"] = 2 # source project["source"] = "Chameleon" try: created_project = mapper.save_project(project, request.get_host()) logger.info("newly created project: " + json.dumps(created_project)) messages.success(request, "Your project has been created!") return HttpResponseRedirect( reverse("projects:view_project", args=[created_project["id"]]) ) except: logger.exception("Error creating project") form.add_error( "__all__", "An unexpected error occurred. Please try again" ) else: form.add_error( "__all__", "There were errors processing your request. " "Please see below for details.", ) else: form = ProjectCreateForm(**form_args) return render(request, "projects/create_project.html", {"form": form}) @login_required def edit_project(request): context = {} return render(request, "projects/edit_project.html", context) @require_POST def edit_nickname(request, project_id): mapper = ProjectAllocationMapper(request) project = mapper.get_project(project_id) if not project_pi_or_admin_or_superuser(request.user, project): messages.error(request, "Only the project PI can update nickname.") return EditNicknameForm() form = EditNicknameForm(request.POST) if form.is_valid(request): # try to update nickname try: nickname = form.cleaned_data["nickname"] ProjectAllocationMapper.update_project_nickname(project_id, nickname) form = EditNicknameForm() set_ks_project_nickname(project.chargeCode, nickname) messages.success(request, "Update Successful") except: messages.error(request, "Nickname not available") else: messages.error(request, "Nickname not available") return form def get_extras(request): provided_token = request.GET.get("token") if request.GET.get("token") else None stored_token = getattr(settings, "PROJECT_EXTRAS_API_TOKEN", None) if not provided_token or not stored_token or provided_token != stored_token: logger.error("Project Extras json api Access Token validation failed") return HttpResponseForbidden() logger.info("Get all project extras json endpoint requested") response = {"status": "success"} try: serializer = ProjectExtrasJSONSerializer() response["message"] = "" extras = json.loads(serializer.serialize(ProjectExtras.objects.all())) response["result"] = extras except ProjectExtras.DoesNotExist: response["message"] = "Does not exist." response["result"] = None return JsonResponse(response)

35.489011

95

0.588533

aea745b7da47bd3d0550f56fe6d42adc49716eb3

268

py

Python

Easy/find_the_distance_value_between_two_arrays.py

BrynjarGeir/LeetCode

dbd57e645c5398dec538b6466215b61491c8d1d9

[ "MIT" ]

null

Easy/find_the_distance_value_between_two_arrays.py

BrynjarGeir/LeetCode

dbd57e645c5398dec538b6466215b61491c8d1d9

[ "MIT" ]

null

Easy/find_the_distance_value_between_two_arrays.py

BrynjarGeir/LeetCode

dbd57e645c5398dec538b6466215b61491c8d1d9

[ "MIT" ]

null

class Solution: def findTheDistanceValue(self, arr1: List[int], arr2: List[int], d: int) -> int: distance = 0 for element in arr1: if not any(c for c in arr2 if abs(c-element) <= d): distance += 1 return distance

38.285714

84

0.559701

aea75e1eb83214dedb643e0e6cd5c7568f6bf1b4

2,193

py

Python

listeners/pipe.py

ggilestro/majordomo

d111c1dd1a4c4b8d2cdaa9651b51ece60a1b648d

[ "MIT" ]

null

listeners/pipe.py

ggilestro/majordomo

d111c1dd1a4c4b8d2cdaa9651b51ece60a1b648d

[ "MIT" ]

null

listeners/pipe.py

ggilestro/majordomo

d111c1dd1a4c4b8d2cdaa9651b51ece60a1b648d

[ "MIT" ]

null

#!/usr/bin/env python2 # -*- coding: utf-8 -*- # # pipe.py # # Copyright 2014 Giorgio Gilestro <gg@kozak> # # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to the Free Software # Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, # MA 02110-1301, USA. # # # Listen from pipefile # e.g.: echo "TEST COMMAND" > /tmp/pipefile import os, tempfile import logging import threading class pipe(): def __init__(self, pipefile, queue, actions): """ Reads from a pipe """ self.pipefile = pipefile self.queue = queue actions["pipe"] = {} self.__makefifo() self.listening_thread = threading.Thread(target=self.listen_from_pipe) #self.listening_thread.daemon = True self.isListening = True self.listening_thread.start() def transmit(self, received): """ """ cmd = ("pipe", received) self.queue.put(cmd) def __makefifo(self): """ """ try: os.mkfifo(self.pipefile) logging.debug("Listening to FIFO Pipe at %s" % self.pipefile) return True except: logging.debug("Error creating FIFO Pipe %s. File already existing?" % self.pipefile) return False def listen_from_pipe(self): """ """ while self.isListening: logging.debug("Listening from PIPE %s" % self.pipefile) with open(self.pipefile) as fifo: self.transmit(fifo.read().strip()) if __name__ == '__main__': p = pipe("pipefile", "none")

28.480519

96

0.617419

aea97cde74c5b76516a1287e08c111bed632fb99

1,608

py

Python

scripts/android-patch.py

rasaha91/react-native-macos

5da5441c1d98596683590bc076541560db61ff82

[ "CC-BY-4.0", "MIT" ]

2,114

2020-05-06T10:05:45.000Z

2022-03-31T23:19:28.000Z

scripts/android-patch.py

rasaha91/react-native-macos

5da5441c1d98596683590bc076541560db61ff82

[ "CC-BY-4.0", "MIT" ]

623

2020-05-05T21:24:26.000Z

2022-03-30T21:00:31.000Z

scripts/android-patch.py

rasaha91/react-native-macos

5da5441c1d98596683590bc076541560db61ff82

[ "CC-BY-4.0", "MIT" ]

85

2020-05-05T23:09:40.000Z

2022-03-29T10:12:42.000Z

import os import sys # A Python script that can be used to determine which files that require # patching have been touched between two points in the repo. def shell(command): stream = os.popen(command) result = stream.read() stream.close() return result def get_patches(): patches = {} for file in shell('find android-patches/patches -type f').splitlines(): slash_indices = [i for (i, c) in enumerate(file) if c == '/'] if len(slash_indices) < 3: continue patch_name = file[slash_indices[1]+1:slash_indices[2]] filename = file[slash_indices[2]+1:] if patch_name not in patches: patches[patch_name] = [] patches[patch_name].append(filename) return patches def get_touched_files(branch_from, branch_to): files = [] command = 'git diff --name-status {0} {1}'.format(branch_from, branch_to) for line in shell(command).splitlines(): files.append(line.split('\t')[-1]) return files if __name__ == '__main__': if len(sys.argv) != 3: sys.stderr.write('Usage: android-patch.py <commit> <commit>') sys.exit(1) patches = get_patches() touched_files = set(get_touched_files(sys.argv[1], sys.argv[2])) for patch_name in sorted(patches.keys()): patched_and_touched = [file for file in patches[patch_name] \ if file in touched_files] if len(patched_and_touched) > 0: print('\033[4m{0}\033[0m'.format(patch_name)) for file in patched_and_touched: print('* {0}'.format(file))

34.212766

77

0.625622

aeaa0a335119f48bebc88e35cf1698370503f3a0

259

py

Python

pyautofinance/common/strategies/usable_strategies/live_trading_test_strategy.py

webclinic017/PyAutoFinance

532cb1c5418dd9eeb07f2f08646170cde1fe0303

[ "MIT" ]

null

pyautofinance/common/strategies/usable_strategies/live_trading_test_strategy.py

webclinic017/PyAutoFinance

532cb1c5418dd9eeb07f2f08646170cde1fe0303

[ "MIT" ]

null

pyautofinance/common/strategies/usable_strategies/live_trading_test_strategy.py

webclinic017/PyAutoFinance

532cb1c5418dd9eeb07f2f08646170cde1fe0303

[ "MIT" ]

1

2022-02-24T09:18:13.000Z

from pyautofinance.common.strategies.bracket_strategy import BracketStrategy class LiveTradingTestStrategy(BracketStrategy): def _open_long_condition(self) -> bool: return True def _open_short_condition(self) -> bool: return False

23.545455

76

0.760618

aeaabc6f5b0d5b7cf3ded3141d91ff7d3817bb49

5,624

py

Python

src/example_publish_pypi_medium/asm-enforce-ready-signatures.py

lrhazi/example-publish-pypi

135bc75b0e37225b1879cb79d644f709977f1f3d

[ "MIT" ]

null

src/example_publish_pypi_medium/asm-enforce-ready-signatures.py

lrhazi/example-publish-pypi

135bc75b0e37225b1879cb79d644f709977f1f3d

[ "MIT" ]

null

src/example_publish_pypi_medium/asm-enforce-ready-signatures.py

lrhazi/example-publish-pypi

135bc75b0e37225b1879cb79d644f709977f1f3d

[ "MIT" ]

null

import json from docopt import docopt from bigip_utils.logger import logger from bigip_utils.bigip import * # # This script enforces all attack signatures that are ready to be enforced: # https://support.f5.com/csp/article/K60640453?utm_source=f5support&utm_medium=RSS # __doc__ = """ Usage: enforce-ready-signatures.py [-hvndsb] [-p POLICY_NAME] -l LIST_FILE Options: -h --help Show this screen. -v --version Show version. -n --dry-run Show actions. Do not execute them. -s --sync Sync devices after changes. -b --backup-config Create and download a UCS file. -d --dev-devices-only Skip non DEV devices. -l LIST_FILE --list-file=LIST_FILE CSV file with list of bigips. Format: hostname,ip,username,password -p POLICY_NAME --policy-name=POLICY_NAME Name of a policy to act on. [default: all] """ VERSION = "0.2" def enforce_ready_signatures(bigip, id): params = { '$select': '', '$filter': 'hasSuggestions eq false AND wasUpdatedWithinEnforcementReadinessPeriod eq false and performStaging eq true', } data = {'performStaging': 'false'} url_base_asm = f'https://{bigip.ip}/mgmt/tm/asm/policies/{id}/signatures' json_data = bigip.patch(url_base_asm, params=params, data=json.dumps(data)) count = int(json_data.get('totalItems', 0)) return count def get_ready_signatures_count(bigip, id): params = { '$select': '', '$filter': 'hasSuggestions eq false AND wasUpdatedWithinEnforcementReadinessPeriod eq false and performStaging eq true', '$top': '1', } url_base_asm = f'https://{bigip.ip}/mgmt/tm/asm/policies/{id}/signatures' json_data = bigip.get(url_base_asm, params=params) # for d in json_data['items']: # results[d['signatureReference']['name']] = d['signatureReference']['signatureId'] count = int(json_data.get('totalPages', 0)) return count def process_device(bigip, dry_run=True, policy=None, sync_device_group=None): policies_virtuals = get_virtuals_asm_policies(bigip) policies=bigip.get_asm_policies() enforced_signatures_count = 0 ready_signatures = {} for i in policies: if(i['type'] == 'parent'): continue policy_id = i['id'] policy_name = i['name'] policy_virtuals = policies_virtuals[policy_name] if not policy == 'all' and not policy == policy_name: continue if(i['enforcementMode'] == 'blocking'): ready_signatures[policy_name] = get_ready_signatures_count( bigip, policy_id) if ready_signatures[policy_name] and dry_run: logger.info( f"{bigip.hostname}: [DRY-RUN] : {policy_name}: Enforcing {ready_signatures[policy_name]} ready attack signatures. VIPs={len(policy_virtuals)}") elif ready_signatures[policy_name]: logger.info( f"{bigip.hostname}: {policy_name}: Enforcing {ready_signatures[policy_name]} ready attack signatures. VIPs={len(policy_virtuals)}") count = enforce_ready_signatures(bigip, policy_id) if count: r = apply_asm_policy(bigip, policy_id) if not r: logger.error( f"{bigip.hostname}: Applying policy {policy_name} did not complete successfully.") enforced_signatures_count += count if enforced_signatures_count and sync_device_group: logger.info(f"{bigip.hostname}: Syncing device group.") sync_devices(bigip, device_group=sync_device_group) return enforced_signatures_count if __name__ == "__main__": arguments = docopt(__doc__, version=VERSION) devices_file = arguments['--list-file'] dry_run = arguments['--dry-run'] dev_only = arguments['--dev-devices-only'] policy_name = arguments['--policy-name'] sync = arguments['--sync'] backup_config = arguments['--backup-config'] for (hostname, ip, username, password) in get_bigips(devices_file, dev_only=dev_only): b = BigIP(hostname, username, password, ip=ip, verify_ssl=False) logger.info( f"{b.hostname}: Started. Policy: {policy_name} Dry-Run: {dry_run}") proceed = True check_active(b) device_group = get_asm_sync_group(b) if not device_group and not check_standalone(b): logger.error( f"{b.hostname}: Could not find ASM device group name. {device_group}") proceed = False elif device_group: logger.info(f"{b.hostname}: Sync Device Group: {device_group}") if (not b.token): logger.warning( f'{b.hostname}: Unable to obtain authentication token') proceed = False if not check_active(b): logger.warning(f'{b.hostname}: Not active, skipping device.') proceed = False enforced_signatures_count = 0 get_ucs(b,overwrite=True) if proceed: if backup_config and not dry_run: get_ucs(b,overwrite=True) enforced_signatures_count = process_device( b, dry_run=dry_run, policy=policy_name, sync_device_group=device_group) logger.info( f"{b.hostname}: Finished. enforced signatures count: {enforced_signatures_count}") logger.info("Done.")

43.9375

163

0.618243

aeabb21d13fb47406449b6804d29f4983877b33d

13,358

py

Python

farb/sysinstall.py

samskivert/farbot

d88f16dcbd23d7ca3b7fdcf341c9346c0ab21bb8

[ "BSD-3-Clause" ]

null

farb/sysinstall.py

samskivert/farbot

d88f16dcbd23d7ca3b7fdcf341c9346c0ab21bb8

[ "BSD-3-Clause" ]

null

farb/sysinstall.py

samskivert/farbot

d88f16dcbd23d7ca3b7fdcf341c9346c0ab21bb8

[ "BSD-3-Clause" ]

null

# sysinstall.py vi:ts=4:sw=4:expandtab: # # Copyright (c) 2006-2008 Three Rings Design, Inc. # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions # are met: # 1. Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # 2. Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # 3. Neither the name of the copyright owner nor the names of contributors # may be used to endorse or promote products derived from this software # without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. import copy import os import string import farb class ConfigSection(object): """ Abstract class implementing re-usable functions for install.cfg(8) configuration sections. """ def _serializeOptions(self, output): """ Serialize all install.cfg options for this section and the to an output file. Concrete subclasses MUST provide a sectionOptions list as a class attribute. This list must contain all valid install.cfg options for the section, in the order required by sysinstall(8). Given the sectionOptions list, this implementation will introspect 'self' for attributes with names that match the sectionOptions. Any available attributes will be used, and any missing attributes will be ignored. @param output: Open, writable file handle """ for option in self.sectionOptions: if hasattr(self, option): output.write('%s=%s\n' % (option, getattr(self, option))) def _serializeCommands(self, output, commands=None): """ Write out all commands listed in the sectionCommands class attribute. @param output: Open, writable file handle @param commands: Commands to output. Defaults to sectionCommands. """ if (not commands): commands = self.sectionCommands for command in commands: output.write('%s\n' % (command)) class NetworkConfig(ConfigSection): """ install.cfg(8) network configuration section. """ # Section option names sectionOptions = ( 'hostname', # New Server's Host Name 'domainname', # New Server's Domain Name 'netDev', # Network Interface 'nfs', # NFS Installation Media 'tryDHCP' # DHCP an address ) # Default option values tryDHCP = 'YES' # Section commands sectionCommands = ( 'mediaSetNFS', ) def __init__(self, section, config): """ Initialize network configuration for a given installation. @param section: ZConfig Installation section @param config: ZConfig Farbot Config """ # Install-specific Options self.hostname = section.hostname self.domainname = section.domain self.netDev = section.networkdevice # FarBot-wide Options self.nfshost = config.Releases.nfshost self.nfspath = os.path.join(config.Releases.installroot, section.release.lower()) self.nfs = self.nfshost + ':' + self.nfspath def serialize(self, output): self._serializeOptions(output) self._serializeCommands(output) class DistSetConfig(ConfigSection): """ install.cfg(8) distribution set configuration section. """ # Section option names sectionOptions = ( 'dists', # Install these distribution sets ) # Section commands sectionCommands = ( 'distSetCustom', ) def __init__(self, release, config): """ Initialize distribution set configuration for a given installation. @param release: ZConfig Release section @param config: ZConfig Farbot Config """ # Flatten lists of dists, source dists, and kernel dists, inserting the # sub lists after src or kernels. Not sure if it really necessary to have # those sub lists in that exact location, but let's be safe. self.dists = copy.copy(release.dists) if self.dists.count('src') > 0: self.dists.insert(self.dists.index('src') + 1, string.join(release.sourcedists)) if self.dists.count('kernels') > 0: self.dists.insert(self.dists.index('kernels') + 1, string.join(release.kerneldists)) self.dists = string.join(self.dists) def serialize(self, output): self._serializeOptions(output) self._serializeCommands(output) class DiskLabelConfig(ConfigSection): """ install.cfg(8) FreeBSD labels (partition) configuration section. """ # Section option names are generated # Section commands sectionCommands = ( 'diskLabelEditor', ) def __init__(self, section, diskDevice): """ Initialize a disk label configuration for a given partition map and device. @param section: ZConfig PartitionMap section @param diskDevice: Device to label (eg ad0s1) """ # Section option names are generated self.sectionOptions = [] self.diskDevice = diskDevice # Grab our partition map for part in section.Partition: # Build device + slice + partition number, and append it to # sectionOptions slice = self.diskDevice + '-' + part.getSectionName() self.sectionOptions.append(slice) # Partition settings if (part.softupdates): setattr(self, slice, "%s %d %s 1" % (part.type, part.size, part.mount)) else: setattr(self, slice, "%s %d %s" % (part.type, part.size, part.mount)) # Ensure that partitions are in order (1 ... 9) self.sectionOptions.sort() def serialize(self, output): self._serializeOptions(output) self._serializeCommands(output) class DiskPartitionConfig(ConfigSection): """ install.cfg(8) BIOS partition configuration section. """ # Section option names sectionOptions = ( 'disk', # Disk to partition 'partition', # Partitioning method 'bootManager', # Boot manage to install ) # We hardcode the use of the entire disk partition = 'all' # Hardcode the use of the boot manager, too bootManager = 'standard' # Section commands sectionCommands = ( 'diskPartitionEditor', ) def __init__(self, section, config): """ Initialize a disk partition configuration for a given disk section. @param section: ZConfig Disk section @param config: ZConfig Farbot Config """ self.disk = section.getSectionName() # Grab our partition map # If it doesn't exist, complain loudly self.diskLabelConfig = None for map in config.Partitions.PartitionMap: if (section.partitionmap.lower() == map.getSectionName()): # Set up the disk labels. Always s1! self.diskLabelConfig = DiskLabelConfig(map, self.disk + 's1') break def serialize(self, output): self._serializeOptions(output) self._serializeCommands(output) self.diskLabelConfig.serialize(output) class SystemCommandConfig(ConfigSection): """ install.cfg(8) system command configuration section. """ # Section option names sectionOptions = ( 'command', # Command name and arguments ) # Section commands sectionCommands = ( 'system', ) def __init__(self, cmd): """ Initialize system command configuration for a given installation. @param section: ZConfig command key value """ # Build command + options self.cmd = cmd setattr(self, 'command', "%s" % (cmd)) def serialize(self, output): self._serializeOptions(output) self._serializeCommands(output) class PackageConfig(SystemCommandConfig): """ install.cfg(8) package install configuration section. Sysinstall's dependency handling is seriously broken, relying on an INDEX that doesn't necessarily reflect reality. We skip the sysinstall package installation code entirely and use a SystemCommand to call pkg_add(8) ourselves post-install. """ installPackageScript = os.path.join('/dist', os.path.basename(farb.INSTALL_PACKAGE_SH)) def __init__(self, section): """ Initialize package install configuration for a given installation. @param section: ZConfig Package section """ # /dist/install_package.sh <package name> self.package = section.package cmd = "%s %s" % (self.installPackageScript, self.package) super(PackageConfig, self).__init__(cmd) class InstallationConfig(ConfigSection): """ InstallationConfig instances represent a complete install.cfg file for sysinstall(8) """ # Section option names sectionOptions = ( 'debug', 'nonInteractive', 'noWarn' ) # Defaults debug = 'YES' nonInteractive = 'YES' noWarn = 'YES' # Commands needed to start up the interactive partitioner interactivePartitionCommands = ( 'diskInteractive="YES"', # Partition and label disks interactively 'diskPartitionEditor', # Run disk partition (MBR) editor 'diskLabelEditor' # Run disk label editor ) # Pre-package commands prePackageCommands = ( 'diskLabelCommit', # Write disk labels to disk 'installCommit' # Write install distribution to disk ) # Section commands sectionCommands = ( 'shutdown', ) def __init__(self, section, config): """ Initialize a new installation configuration. @param section: ZConfig Installation section @param config: ZConfig Farbot Config """ self.name = section.getSectionName() # Network configuration self.networkConfig = NetworkConfig(section, config) # Distribution sets for release in config.Releases.Release: if release.getSectionName() == section.release.lower(): self.distSetConfig = DistSetConfig(release, config) break # Disks (Partitions and Labels) self.diskPartitionConfigs = [] for disk in section.Disk: diskPartitionConfig = DiskPartitionConfig(disk, config) self.diskPartitionConfigs.append(diskPartitionConfig) # Packages self.packageConfigs = [] for psetName in section.packageset: foundPset = False for pset in config.PackageSets.PackageSet: if (psetName.lower() == pset.getSectionName()): foundPset = True break for package in pset.Package: pkgc = PackageConfig(package) self.packageConfigs.append(pkgc) # System Commands self.systemCommandConfigs = [] if (section.PostInstall): for cmd in section.PostInstall.command: systemCommandConfig = SystemCommandConfig(cmd) self.systemCommandConfigs.append(systemCommandConfig) def serialize(self, output): # Global configuration options self._serializeOptions(output) # Network configuration self.networkConfig.serialize(output) # Select distribution sets self.distSetConfig.serialize(output) # Disk formatting for disk in self.diskPartitionConfigs: disk.serialize(output) # If we have no diskPartitionConfigs, partition interactively if len(self.diskPartitionConfigs) == 0: self._serializeCommands(output, commands=self.interactivePartitionCommands) # Commit installation to disk self._serializeCommands(output, commands=self.prePackageCommands) # Packages for pkgc in self.packageConfigs: pkgc.serialize(output) # System Commands for scc in self.systemCommandConfigs: scc.serialize(output) # Global commands self._serializeCommands(output)

33.562814

96

0.643584

aeac7c6dc8a5f5e913ca2483b61751b8f3dca05f

1,233

py

Python

dynamo_consistency/dynamo/v1/siteinfo.py

dabercro/dynamo-consistency

f37dfebe781a833e9ae30869d8f57be79b4f583c

[ "MIT" ]

null

dynamo_consistency/dynamo/v1/siteinfo.py

dabercro/dynamo-consistency

f37dfebe781a833e9ae30869d8f57be79b4f583c

[ "MIT" ]

1

2018-02-20T21:21:14.000Z

dynamo_consistency/dynamo/v1/siteinfo.py

dabercro/dynamo-consistency

f37dfebe781a833e9ae30869d8f57be79b4f583c

[ "MIT" ]

2

2018-06-25T11:27:45.000Z

2021-05-13T20:32:36.000Z

""" This module is for fetching information from dynamo about different sites """ from .inventory import _get_inventory def _small_query(*args): """ This is a wrapper function for opening and closing inventory connection :param args: arguments to pass to query :returns: Result of the query :rtype: list """ mysql_reg = _get_inventory() sql = args[0] result = mysql_reg.query(sql, *(args[1:])) mysql_reg.close() return result def site_list(): """ :returns: The list of sites dynamo is storing :rtype: list """ return _small_query('SELECT name FROM sites') _READY = None # A cached list of ready sites def ready_sites(): """ :returns: Set of sites that are in ready status :rtype: set """ global _READY # pylint: disable=global-statement if _READY is None: _READY = set(_small_query('SELECT name FROM sites WHERE status = "ready"')) return _READY def get_gfal_location(site): """ :param str site: A site that we want to list with GFAL :returns: The host and path needed by the gfal-ls command :rtype: str """ return _small_query('SELECT backend FROM sites WHERE name=%s', site)[0]

20.898305

83

0.652879

aeadb61a2cb83bca3659659ea3f7918d3b60f93f

1,163

py

Python

core/management/commands/onetime_fix_cover_paths.py

flaiming/bookfinder

59154d106b62680668087e46eca9c0bf9cdaf336

[ "MIT" ]

null

core/management/commands/onetime_fix_cover_paths.py

flaiming/bookfinder

59154d106b62680668087e46eca9c0bf9cdaf336

[ "MIT" ]

null

core/management/commands/onetime_fix_cover_paths.py

flaiming/bookfinder

59154d106b62680668087e46eca9c0bf9cdaf336

[ "MIT" ]

null

import shutil import os from django.core.management.base import BaseCommand, CommandError from django.conf import settings from .models import BookCover, get_book_cover_image_path class Command(BaseCommand): help = "" def handle(self, *args, **options): qs = BookCover.objects.exclude(image__iregex=r"^covers/[a-z0-9_-]/[a-z0-9_-]/") print(qs.count()) counter = 0 for cover in qs: path = cover.image.path name = cover.image.name.split("/")[-1].replace(".peg", ".jpeg") new_path = get_book_cover_image_path(cover, name) #print(path, "-->", new_path) new_path_abs = os.path.join(settings.MEDIA_ROOT, new_path) try: os.makedirs("/".join(new_path_abs.split("/")[:-1])) except OSError: pass try: shutil.copyfile(path, new_path_abs) except shutil.SameFileError: pass cover.image.name = new_path cover.save() os.remove(path) counter += 1 if counter % 1000 == 0: print(counter)

31.432432

87

0.55632

aeafb1f7e55c1238f2645e1019c684aa0c10629d

1,002

py

Python

src/gafaelfawr/schema/admin_history.py

slaclab/gafaelfawr

7a64b0f159003d4745531c89d5b0f7d9777f7bce

[ "MIT" ]

null

src/gafaelfawr/schema/admin_history.py

slaclab/gafaelfawr

7a64b0f159003d4745531c89d5b0f7d9777f7bce

[ "MIT" ]

5

2019-06-18T17:28:52.000Z

2020-04-20T22:15:54.000Z

src/gafaelfawr/schema/admin_history.py

slaclab/gafaelfawr

7a64b0f159003d4745531c89d5b0f7d9777f7bce

[ "MIT" ]

1

2019-06-25T16:04:57.000Z

"""The admin_history database table. This is a stopgap representation of changes to the admin table until we have a group system and a group-based authorization system up and running. """ from __future__ import annotations from datetime import datetime from sqlalchemy import Column, DateTime, Enum, Index, Integer, String from sqlalchemy.dialects import postgresql from ..models.history import AdminChange from .base import Base __all__ = ["AdminHistory"] class AdminHistory(Base): __tablename__ = "admin_history" id: int = Column(Integer, primary_key=True) username: str = Column(String(64), nullable=False) action: AdminChange = Column(Enum(AdminChange), nullable=False) actor: str = Column(String(64), nullable=False) ip_address: str = Column( String(64).with_variant(postgresql.INET, "postgresql"), nullable=False ) event_time: datetime = Column(DateTime, nullable=False) __table_args__ = (Index("admin_history_by_time", "event_time", "id"),)

30.363636

78

0.746507

aeb200362d7e9aa9bfe424f4789c60841fc0fb5c

316

py

Python

app/utils/build_handled_query.py

msolorio/flask_world_api

a2d5394618b736aa7d5d5e75a422dbe9e5713533

[ "MIT" ]

1

2022-02-24T04:37:04.000Z

app/utils/build_handled_query.py

msolorio/flask_world_api

a2d5394618b736aa7d5d5e75a422dbe9e5713533

[ "MIT" ]

null

app/utils/build_handled_query.py

msolorio/flask_world_api

a2d5394618b736aa7d5d5e75a422dbe9e5713533

[ "MIT" ]

null

import traceback from ..models import ServerError def build_handled_query(query): def handled_query(*kwargs, **args): try: return query(*kwargs, **args) except: print('Stack Trace ==>', traceback.format_exc()) return ServerError() return handled_query

21.066667

60

0.617089

aeb2c65fe32f5a74d10e3aa724b39a7ca6e18fa4

8,153

py

Python

wpscan_out_parse/parser/_cli_parser.py

clivewalkden/wpscan_out_parse

dff46aa2e98390afc79b7bb622eb4c01d066fbb5

[ "MIT" ]

1

2021-06-24T08:35:15.000Z

wpscan_out_parse/parser/_cli_parser.py

clivewalkden/wpscan_out_parse

dff46aa2e98390afc79b7bb622eb4c01d066fbb5

[ "MIT" ]

null

wpscan_out_parse/parser/_cli_parser.py

clivewalkden/wpscan_out_parse

dff46aa2e98390afc79b7bb622eb4c01d066fbb5

[ "MIT" ]

null

import re from typing import Any, Dict, Sequence, List, Optional, Tuple from .base import Parser from .components import InterestingFinding from .results import WPScanResults #################### CLI PARSER ###################### class WPScanCliParser(Parser): """Main interface to parse WPScan CLI output. - wpscan_output: WPScan output as string. - false_positives_strings: List of false positive strings. """ def __init__(self, wpscan_output:str, false_positives_strings:Optional[Sequence[str]]=None) -> None: if not wpscan_output: wpscan_output = "" # Parser config: false positives string and verbosity (not available with cli parser) parser_config = dict( false_positives_strings=false_positives_strings, show_all_details=False ) super().__init__({'output':wpscan_output}, **parser_config) self._infos, self._warnings, self._alerts = self.parse_cli(wpscan_output) def get_infos(self) -> Sequence[str]: """ Return all the parsed infos""" return self._infos def get_warnings(self) -> Sequence[str]: """ Return all the parsed warnings""" return self._warnings def get_alerts(self)-> Sequence[str]: """ Return all the parsed alerts""" return self._alerts def _parse_cli_toogle(self, line:str, warning_on:bool, alert_on:bool) -> Tuple[bool, bool]: # Color parsing if "33m[!]" in line: warning_on = True elif "31m[!]" in line: alert_on = True # No color parsing Warnings string are hard coded here elif "[!]" in line and any( [ m in line for m in [ "The version is out of date", "No WPVulnDB API Token given", "You can get a free API token", ] ] ): warning_on = True elif "[!]" in line: alert_on = True # Both method with color and no color apply supplementary proccessing # Warning for insecure Wordpress and based on interesting findings strings if any( string in line for string in ["Insecure", "Outdated"] + InterestingFinding.INTERESTING_FINDING_WARNING_STRINGS ): warning_on = True # Trigger alert based on interesting finding alert strings if any( string in line for string in InterestingFinding.INTERESTING_FINDING_ALERT_STRINGS ): alert_on = True # Lower voice of Vulnerabilities found but not plugin version if "The version could not be determined" in line and alert_on: alert_on = False warning_on = True return (warning_on, alert_on) def _ignore_false_positives(self, infos:List[str], warnings:List[str], alerts:List[str]) -> Tuple[List[str], List[str], List[str]]: """Process false positives""" for alert in warnings + alerts: if self.is_false_positive(alert): try: alerts.remove(alert) except ValueError: warnings.remove(alert) infos.append("[False positive]\n{}".format(alert)) return infos, warnings, alerts def parse_cli(self, wpscan_output:str) -> Tuple[List[str], List[str], List[str]]: """Parse the ( messages, warnings, alerts ) from WPScan CLI output string. Return results as tuple( messages, warnings, alerts ).""" # Init scan messages (messages, warnings, alerts) = ([], [], []) # Init messages toogles warning_on, alert_on = False, False message_lines = [] current_message = "" # Every blank ("") line will be considered as a message separator for line in wpscan_output.splitlines() + [""]: # Parse all output lines and build infos, warnings and alerts line = line.strip() # Parse line warning_on, alert_on = self._parse_cli_toogle(line, warning_on, alert_on) # Remove colorization anyway after parsing line = re.sub(r"(\x1b|\[[0-9][0-9]?m)", "", line) # Append line to message. Handle the begin of the message case message_lines.append(line) # Build message current_message = "\n".join( [m for m in message_lines if m not in ["", "|"]] ).strip() # Message separator just a white line. # Only if the message if not empty. if line.strip() not in [""] or current_message.strip() == "": continue # End of the message # Post process message to separate ALERTS into different messages of same status and add rest of the infos to warnings if (alert_on or warning_on) and any( s in current_message for s in ["vulnerabilities identified", "vulnerability identified"] ): messages_separated = [] msg: List[str] = [] for l in message_lines + ["|"]: if l.strip() == "|": messages_separated.append( "\n".join([m for m in msg if m not in ["", "|"]]) ) msg = [] msg.append(l) # Append Vulnerabilities messages to ALERTS and other infos in one message vulnerabilities = [ m for m in messages_separated if "| [!] Title" in m.splitlines()[0] ] # Add the plugin infos to warnings or false positive if every vulnerabilities are ignore plugin_infos = "\n".join( [ m for m in messages_separated if "| [!] Title" not in m.splitlines()[0] ] ) if ( len([v for v in vulnerabilities if not self.is_false_positive(v)]) > 0 and "The version could not be determined" in plugin_infos ): warnings.append( plugin_infos + "\nAll known vulnerabilities are listed" ) else: messages.append(plugin_infos) if alert_on: alerts.extend(vulnerabilities) elif warning_on: warnings.extend(vulnerabilities) elif warning_on: warnings.append(current_message) else: messages.append(current_message) message_lines = [] current_message = "" # Reset Toogle Warning/Alert warning_on, alert_on = False, False return self._ignore_false_positives(messages, warnings, alerts) def get_error(self) -> Optional[str]: if "Scan Aborted" in self.data.get('output', ''): return "WPScan failed: {}".format( "\n".join( line for line in self.data.get('output', '').splitlines() if "Scan Aborted" in line ) ) else: return None def get_results(self) -> WPScanResults: """ Returns a dictionnary structure like :: { 'infos':[], 'warnings':[], 'alerts':[], 'summary':{ 'table':None, 'line':'WPScan result summary: alerts={}, warnings={}, infos={}, error={}' }, 'error':None } """ results = WPScanResults() results["infos"] = self.get_infos() results["warnings"] = self.get_warnings() results["alerts"] = self.get_alerts() results["summary"]["line"] = self.get_summary_line() results["error"] = self.get_error() return results

36.725225

135

0.531461

aeb3088e188323526fd71e3f97c23bb2c556a657

1,095

py

Python

backend/backend/views.py

synw/django-mqueue-livefeed

d4e8cc2a4c1014f1d7322c297f8f50db9ab8540f

[ "MIT" ]

9

2016-08-30T16:15:15.000Z

2022-03-22T17:17:01.000Z

backend/backend/views.py

synw/django-mqueue-livefeed

d4e8cc2a4c1014f1d7322c297f8f50db9ab8540f

[ "MIT" ]

null

backend/backend/views.py

synw/django-mqueue-livefeed

d4e8cc2a4c1014f1d7322c297f8f50db9ab8540f

[ "MIT" ]

2

2017-01-20T22:03:53.000Z

2017-12-20T11:43:38.000Z

from typing import Any, Dict, Union from django.conf import settings from django.http.response import HttpResponseBase, HttpResponseRedirect from django.views.generic import TemplateView from django.contrib.auth.views import LoginView, redirect_to_login class LoginView(LoginView): template_name = "login_form.html" class IndexView(TemplateView): template_name = "index.html" def dispatch( self, request, *args, **kwargs ) -> Union[HttpResponseRedirect, HttpResponseBase]: if self.request.user.is_superuser is False: # type: ignore # no superuser no app return redirect_to_login("/", "/login/") return super(IndexView, self).dispatch(request, *args, **kwargs) def get_context_data(self, **kwargs) -> Dict[str, Any]: context = super(IndexView, self).get_context_data(**kwargs) uri = settings.CENTRIFUGO_HOST.replace("https", "wss").replace("http", "ws") if settings.CENTRIFUGO_PORT is not None: uri += f":{settings.CENTRIFUGO_PORT}" context["uri"] = uri return context

36.5

84

0.689498

aeb3a40599bade079c5b02f2bf2cc33038b720fa

3,220

py

Python

tests/animation/generator/test_animation.py

OrangeUtan/MCMetagen

0293ea14bf1c6b1bae58741f9876ba662930b43d

[ "MIT" ]

null

tests/animation/generator/test_animation.py

OrangeUtan/MCMetagen

0293ea14bf1c6b1bae58741f9876ba662930b43d

[ "MIT" ]

null

tests/animation/generator/test_animation.py

OrangeUtan/MCMetagen

0293ea14bf1c6b1bae58741f9876ba662930b43d

[ "MIT" ]

null

import pytest from mcanitexgen.animation.generator import Animation, GeneratorError def frame(index: int, time: int): return {"index": index, "time": time} class Test_append: def test(self): anim1 = Animation(0, 10, [frame(0, 10)]) anim2 = Animation(10, 20, [frame(0, 10)]) anim1.append(anim2) assert anim1 == Animation(0, 20, [frame(0, 10), frame(0, 10)]) @pytest.mark.parametrize( "anim1, anim2, result", [ ( Animation(0, 10, [frame(0, 10)]), Animation(11, 20, [frame(0, 9)]), Animation(0, 20, [frame(0, 11), frame(0, 9)]), ), ( Animation(0, 10, [frame(0, 10)]), Animation(30, 40, [frame(0, 10)]), Animation(0, 40, [frame(0, 30), frame(0, 10)]), ), ], ) def test_fill_time_gap_between_animations( self, anim1: Animation, anim2: Animation, result: Animation ): anim1.append(anim2) assert anim1 == result def test_time_ranges_overlap(self): anim1 = Animation(0, 10, [frame(0, 10)]) anim2 = Animation(5, 15, [frame(0, 10)]) with pytest.raises(GeneratorError, match=".*starts before the other.*"): anim1.append(anim2) class Test_add_frame: @pytest.mark.parametrize( "anim, index, start, end, result", [ (Animation(0, 0), 0, 0, 10, Animation(0, 10, [frame(0, 10)])), ], ) def test(self, anim: Animation, index, start, end, result): anim.add_frame(index, start, end) assert anim == result @pytest.mark.parametrize( "anim, index, start, end, result", [ (Animation(0, 0), 0, 10, 25, Animation(10, 25, [frame(0, 15)])), (Animation(10, 10), 0, 20, 30, Animation(20, 30, [frame(0, 10)])), ], ) def test_add_frame_with_start_to_empty_animation( self, anim: Animation, index, start, end, result ): anim.add_frame(index, start, end) assert anim == result @pytest.mark.parametrize( "anim, index, start, end, result", [ ( Animation(0, 10, [frame(0, 10)]), 0, 20, 30, Animation(0, 30, [frame(0, 20), frame(0, 10)]), ), ( Animation(20, 40, [frame(0, 5), frame(0, 5)]), 0, 60, 70, Animation(20, 70, [frame(0, 5), frame(0, 25), frame(0, 10)]), ), ], ) def test_fill_time_gap(self, anim: Animation, index, start, end, result): anim.add_frame(index, start, end) assert anim == result @pytest.mark.parametrize( "start, end", [ (0, 0), (10, 10), (12, 11), (-4, -5), (-6, -5), ], ) def test_invalid_start_and_end(self, start, end): anim = Animation(0, 0) with pytest.raises( GeneratorError, match=f"Illegal start and end for frame: '{start}' '{end}'" ): anim.add_frame(0, start, end)

29.009009

87

0.491304

aeb447aba93643942a7e8b9f82a86a22f927bf04

2,214

py

Python

bin/mircx_polsplit.py

jdmonnier/mircx_mystic

45bf8491117674157b39f49cfe0c92c5ec6da500

[ "MIT" ]

1

2022-01-13T19:32:51.000Z

bin/mircx_polsplit.py

jdmonnier/mircx_mystic

45bf8491117674157b39f49cfe0c92c5ec6da500

[ "MIT" ]

null

bin/mircx_polsplit.py

jdmonnier/mircx_mystic

45bf8491117674157b39f49cfe0c92c5ec6da500

[ "MIT" ]

null

#! /usr/bin/env python """ This is a "quick and dirty" solution to getting polarization data through the pipeline. This script creates new fits files with independent polarization states. Make sure you have plenty of diskspace. """ from __future__ import print_function import argparse import os from time import sleep from astropy.io import fits from tqdm import tqdm parser = argparse.ArgumentParser(description='Process MIRC-X raw data files') parser.add_argument("--no-warn", action="store_true") parser.add_argument("--crop-bad", action="store_true") parser.add_argument("files", nargs="+", help="File(s) to process") args = parser.parse_args() if not args.no_warn: print("Warning: Make sure you have plenty of disk space; this is going to hurt.") print("(Hint: ^C while you still can! Sleeping 10 seconds for your benefit.)") sleep(10) for dir in ["pol1", "pol2"]: try: os.mkdir(dir) except FileExistsError: if os.path.isdir(dir): print("Warning: directory `" + dir + "` already exists") else: raise FileExistsError("Looks like you have a file named `" + dir + "`; please remove it.") def polstate(file, state): f = fits.open(file) f[0].header["POLSTATE"] = state f[0].header["CONF_NA"] = "H_PRISM50" # TEMPORARY FIX rows = f[0].header["CROPROWS"].split(",") if len(rows) != 2: raise ValueError("There must be exactly 2 detector regions. Is this a polarization data file?") span = 1 - eval(rows[0]) # 50-50 chance it should be rows[1] if state == 1: f[0].data = f[0].data[:,:,:span,:] elif state == 2: if args.crop_bad: f[0].data = f[0].data[:,:,span:-2,:] else: f[0].data = f[0].data[:,:,span:,:] else: raise ValueError("`state` (2nd arg of fcn `polstate`) must have the value either 1 or 2") path = "pol" + str(state) + "/" + file f.writeto(path) f.close() os.system("fpack " + path) os.remove(path) for file in tqdm(args.files): fz = file[-3:] == ".fz" if fz: os.system("funpack " + file) file = file[:-3] polstate(file, 1) polstate(file, 2) if fz: os.remove(file)

32.558824

103

0.62421

aeb56753078f68e7ebf914dfe3362d2ce395b9ab

44

py

Python

holoprot/models/__init__.py

vsomnath/holoprot

9bd6c58491eec701db94ce12f8e15e2143e202b9

[ "MIT" ]

10

2022-01-19T19:01:35.000Z

2022-03-21T13:04:59.000Z

holoprot/models/__init__.py

vsomnath/holoprot

9bd6c58491eec701db94ce12f8e15e2143e202b9

[ "MIT" ]

null

holoprot/models/__init__.py

vsomnath/holoprot

9bd6c58491eec701db94ce12f8e15e2143e202b9

[ "MIT" ]

3

2022-01-11T16:21:32.000Z

2022-03-11T15:33:57.000Z

from holoprot.models.trainer import Trainer

22

43

0.863636

aeb5bac5587511509d5243a09d8d7dc4620f3a3a

3,233

py

Python

mars/dataframe/datasource/core.py

HarshCasper/mars

4c12c968414d666c7a10f497bc22de90376b1932

[ "Apache-2.0" ]

2

2019-03-29T04:11:10.000Z

2020-07-08T10:19:54.000Z

mars/dataframe/datasource/core.py

HarshCasper/mars

4c12c968414d666c7a10f497bc22de90376b1932

[ "Apache-2.0" ]

null

mars/dataframe/datasource/core.py

HarshCasper/mars

4c12c968414d666c7a10f497bc22de90376b1932

[ "Apache-2.0" ]

null

# Copyright 1999-2020 Alibaba Group Holding Ltd. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from ...context import get_context from ...serialize import Int64Field, KeyField from ...tiles import TilesError from ..operands import DataFrameOperand, DataFrameOperandMixin class HeadOptimizedDataSource(DataFrameOperand, DataFrameOperandMixin): __slots__ = '_tiled', # Data source op that optimized for head, # First, it will try to trigger first_chunk.head() and raise TilesError, # When iterative tiling is triggered, # check if the first_chunk.head() meets requirements. _nrows = Int64Field('nrows') # for chunk _first_chunk = KeyField('first_chunk') @property def nrows(self): return self._nrows @property def first_chunk(self): return getattr(self, '_first_chunk', None) @classmethod def _tile(cls, op): # pragma: no cover raise NotImplementedError @classmethod def _tile_head(cls, op: "HeadOptimizedDataSource"): if op.first_chunk is None: op._tiled = tiled = cls._tile(op) chunks = tiled[0].chunks err = TilesError('HeadOrTailOptimizeDataSource requires ' 'some dependencies executed first') op._first_chunk = chunk = chunks[0] err.partial_tiled_chunks = [chunk.data] raise err else: tiled = op._tiled chunks = tiled[0].chunks del op._tiled ctx = get_context() chunk_shape = ctx.get_chunk_metas([op.first_chunk.key])[0].chunk_shape # reset first chunk op._first_chunk = None for c in chunks: c.op._first_chunk = None if chunk_shape[0] == op.nrows: # the first chunk has enough data tiled[0]._nsplits = tuple((s,) for s in chunk_shape) chunks[0]._shape = chunk_shape tiled[0]._chunks = chunks[:1] tiled[0]._shape = chunk_shape else: for chunk in tiled[0].chunks: chunk.op._nrows = None # otherwise tiled = [tiled[0].iloc[:op.nrows]._inplace_tile()] return tiled @classmethod def tile(cls, op: "HeadOptimizedDataSource"): if op.nrows is not None: return cls._tile_head(op) else: return cls._tile(op) class ColumnPruneSupportedDataSourceMixin(DataFrameOperandMixin): __slots__ = () def get_columns(self): # pragma: no cover raise NotImplementedError def set_pruned_columns(self, columns): # pragma: no cover raise NotImplementedError

33.677083

82

0.631921

aeb5f63f02d815be3691dc63ea53c88a38fdabc3

1,759

py

Python

scripts/chengyu.py

cfeibiao/chinese-xinhua

fc39a885e6bbd6d79576997bea53682af4e8f596

[ "MIT" ]

9,321

2018-02-10T09:17:33.000Z

2022-03-31T11:39:38.000Z

scripts/chengyu.py

cfeibiao/chinese-xinhua

fc39a885e6bbd6d79576997bea53682af4e8f596

[ "MIT" ]

48

2018-07-30T12:35:49.000Z

2022-03-11T03:49:24.000Z

scripts/chengyu.py

cfeibiao/chinese-xinhua

fc39a885e6bbd6d79576997bea53682af4e8f596

[ "MIT" ]

2,255

2018-03-12T09:54:37.000Z

2022-03-31T10:17:47.000Z

# -*- coding: utf-8 -*- """ author: pwxcoo date: 2018-02-05 description: 抓取下载成语并保存 """ import requests, json from bs4 import BeautifulSoup def downloader(url): """ 下载成语并保存 """ response = requests.get(url) if response.status_code != 200: print(f'{url} is failed!') return print(f'{url} is parsing') html = BeautifulSoup(response.content.decode('gbk', errors='ignore'), "lxml") table = html.find_all('table')[-2] prefix = 'http://www.zd9999.com' words = [prefix + a.get('href') for a in table.find_all('a')] res = [] for i in range(0, len(words)): response = requests.get(words[i]) print(f'{[words[i]]} is parsing') if response.status_code != 200: print(f'{words[i]} is failed!') continue wordhtml = BeautifulSoup(response.content.decode('gbk', errors='ignore'), "lxml") explanation = wordhtml.find_all('table')[-3].find_all('tr') res.append({'word':explanation[0].text.strip(),\ 'pinyin': explanation[1].find_all('tr')[0].find_all('td')[1].text.strip(),\ 'explanation': explanation[1].find_all('tr')[1].find_all('td')[1].text.strip(),\ 'derivation': explanation[1].find_all('tr')[2].find_all('td')[1].text.strip(),\ 'example': explanation[1].find_all('tr')[3].find_all('td')[1].text.strip()}) return res if __name__ == '__main__': res = downloader('http://www.zd9999.com/cy/') for i in range(2, 199): res += downloader(f'http://www.zd9999.com/cy/index_{i}.htm') print(len(res)) with open('chengyu.json', mode='w+', encoding='utf-8') as json_file: json.dump(res, json_file, ensure_ascii=False)

32.574074

100

0.582149

aeb856b22c775bb3a156957570f8b200275c77c9

21,563

py

Python

data/prepare_data_3dhp.py

YxZhxn/Ray3D

cc19cf1a8471b8464c172808e77d6305a3218a3c

[ "Apache-2.0" ]

23

2022-03-22T05:09:34.000Z

2022-03-31T08:24:43.000Z

data/prepare_data_3dhp.py

YxZhxn/Ray3D

cc19cf1a8471b8464c172808e77d6305a3218a3c

[ "Apache-2.0" ]

4

2022-03-22T05:11:48.000Z

2022-03-30T03:27:10.000Z

data/prepare_data_3dhp.py

YxZhxn/Ray3D

cc19cf1a8471b8464c172808e77d6305a3218a3c

[ "Apache-2.0" ]

2

2022-03-23T09:47:50.000Z

2022-03-25T01:12:18.000Z

import os import sys import copy import ipdb import json import mat73 import numpy as np import scipy.io as sio sys.path.append(os.path.join(os.path.dirname(__file__), '..')) from lib.camera.camera import CameraInfoPacket, catesian2homogenous rot = np.array([[1, 0, 0], [0, 0, -1], [0, 1, 0]]) # rotate along the x axis 90 degrees mpii_3dhp_cameras_intrinsic_params = [ { 'id': '0', 'center': [1024.704, 1051.394], 'focal_length': [1497.693, 1497.103], 'res_w': 2048, 'res_h': 2048, 'azimuth': 70, }, { 'id': '1', 'center': [1030.519, 1052.626], 'focal_length': [1495.217, 1495.52], 'res_w': 2048, 'res_h': 2048, 'azimuth': 70, }, { 'id': '2', 'center': [983.8873, 987.5902], 'focal_length': [1495.587, 1497.828], 'res_w': 2048, 'res_h': 2048, 'azimuth': 70, }, { 'id': '3', 'center': [1029.06, 1041.409], 'focal_length': [1495.886, 1496.033], 'res_w': 2048, 'res_h': 2048, 'azimuth': 70, }, { 'id': '4', 'center': [987.6075, 1019.069], 'focal_length': [1490.952, 1491.108], 'res_w': 2048, 'res_h': 2048, 'azimuth': 70, }, { 'id': '5', 'center': [1012.331, 998.5009], 'focal_length': [1500.414, 1499.971], 'res_w': 2048, 'res_h': 2048, 'azimuth': 70, }, { 'id': '6', 'center': [999.7319, 1010.251], 'focal_length': [1498.471, 1498.8], 'res_w': 2048, 'res_h': 2048, 'azimuth': 70, }, { 'id': '7', 'center': [987.2716, 976.8773], 'focal_length': [1498.831, 1499.674], 'res_w': 2048, 'res_h': 2048, 'azimuth': 70, }, { 'id': '8', 'center': [1017.387, 1043.032], 'focal_length': [1500.172, 1500.837], 'res_w': 2048, 'res_h': 2048, 'azimuth': 70, }, { 'id': '9', 'center': [1010.423, 1037.096], 'focal_length': [1501.554, 1501.9], 'res_w': 2048, 'res_h': 2048, 'azimuth': 70, }, { 'id': '10', 'center': [1041.614, 997.0433], 'focal_length': [1498.423, 1498.585], 'res_w': 2048, 'res_h': 2048, 'azimuth': 70, }, { 'id': '11', 'center': [1009.802, 999.9984], 'focal_length': [1495.779, 1493.703], 'res_w': 2048, 'res_h': 2048, 'azimuth': 70, }, { 'id': '12', 'center': [1000.56, 1014.975], 'focal_length': [1501.326, 1501.491], 'res_w': 2048, 'res_h': 2048, 'azimuth': 70, }, { 'id': '13', 'center': [1005.702, 1004.214], 'focal_length': [1496.961, 1497.378], 'res_w': 2048, 'res_h': 2048, 'azimuth': 70, } ] mpii_3dhp_cameras_extrinsic_params = [ { 'translation': [-0.5628666, 1.3981379999999999, 3.852623], 'R': [ [0.9650164, 0.00488022, 0.262144], [-0.004488356, -0.9993728, 0.0351275], [0.262151, -0.03507521, -0.9643893] ] }, { 'translation': [-1.429856, 0.7381779, 4.897966], 'R': [ [0.6050639, -0.02184232, 0.7958773], [-0.22647, -0.9630526, 0.1457429], [0.7632883, -0.2684261, -0.587655] ] }, { 'translation': [0.05725702, 1.307287, 2.7998220000000003], 'R': [ [-0.3608179, -0.009492658, 0.932588], [-0.0585942, -0.9977421, -0.03282591], [0.9307939, -0.06648842, 0.359447] ] }, { 'translation': [-0.2848168, 0.8079184, 3.1771599999999998], 'R': [ [-0.0721105, -0.04817664, 0.9962325], [-0.4393254, -0.8951841, -0.07508985], [0.895429, -0.443085, 0.04338695] ] }, { 'translation': [-1.563911, 0.8019607999999999, 3.5173159999999997], 'R': [ [0.3737275, 0.09688602, 0.9224646], [-0.009716132, -0.9940662, 0.1083427], [0.9274878, -0.04945343, -0.3705685] ] }, { 'translation': [0.35841340000000005, 0.9945657999999999, 3.439832], 'R': [ [-0.3521056, 0.01328985, -0.9358659], [-0.04961938, -0.9987582, 0.004485628], [-0.9346441, 0.0480165, 0.3523278] ] }, { 'translation': [0.5694388, 0.528871, 3.6873690000000003], 'R': [ [-0.9150326, -0.04843184, 0.4004618], [-0.1804886, -0.8386868, -0.5138369], [0.3607481, -0.5424563, 0.7586845] ] }, { 'translation': [1.378866, 1.270781, 2.631567], 'R': [ [-0.9995936, 0.02847456, 0.001368653], [-0.02843213, -0.9992908, 0.0246889], [0.002070688, 0.02463995, 0.9996943] ] }, { 'translation': [0.2213543, 0.65987, 3.644688], 'R': [ [0.000575281, 0.06160985, -0.9981001], [0.2082146, -0.9762325, -0.06013997], [-0.978083, -0.2077844, -0.01338968] ] }, { 'translation': [0.38862169999999996, 0.1375452, 4.216635], 'R': [ [0.04176839, 0.00780962, -0.9990969], [0.5555364, -0.831324, 0.01672664], [-0.8304425, -0.5557333, -0.03906159] ] }, { 'translation': [1.167962, 0.6176362000000001, 4.472351], 'R': [ [-0.8970265, 0.1361548, -0.4204822], [0.09417118, -0.8706428, -0.4828178], [-0.4318278, -0.4726976, 0.7681679] ] }, { 'translation': [0.1348272, 0.2515094, 4.570244], 'R': [ [0.9170455, 0.1972746, -0.3465695], [0.1720879, 0.5882171, 0.7901813], [0.3597408, -0.7842726, 0.5054733] ] }, { 'translation': [0.4124695, 0.5327588, 4.887095], 'R': [ [-0.7926738, 0.1323657, 0.5951031], [-0.396246, 0.6299778, -0.66792], [-0.4633114, -0.7652499, -0.4469175] ] }, { 'translation': [0.8671278, 0.8274571999999999, 3.985159], 'R': [ [-0.8701088, -0.09522671, -0.4835728], [0.4120245, 0.3978655, -0.8197188], [0.270456, -0.9124883, -0.3069505] ] } ] subjects = [ 'S1_Seq1_0', 'S1_Seq1_1', 'S1_Seq1_2', 'S1_Seq1_3', 'S1_Seq1_4', 'S1_Seq1_5', 'S1_Seq1_6', 'S1_Seq1_7', 'S1_Seq1_8', 'S1_Seq1_9', 'S1_Seq1_10', 'S1_Seq1_11', 'S1_Seq1_12', 'S1_Seq1_13', 'S1_Seq2_0', 'S1_Seq2_1', 'S1_Seq2_2', 'S1_Seq2_3', 'S1_Seq2_4', 'S1_Seq2_5', 'S1_Seq2_6', 'S1_Seq2_7', 'S1_Seq2_8', 'S1_Seq2_9', 'S1_Seq2_10', 'S1_Seq2_11', 'S1_Seq2_12', 'S1_Seq2_13', 'S2_Seq1_0', 'S2_Seq1_1', 'S2_Seq1_2', 'S2_Seq1_3', 'S2_Seq1_4', 'S2_Seq1_5', 'S2_Seq1_6', 'S2_Seq1_7', 'S2_Seq1_8', 'S2_Seq1_9', 'S2_Seq1_10', 'S2_Seq1_11', 'S2_Seq1_12', 'S2_Seq1_13', 'S2_Seq2_0', 'S2_Seq2_1', 'S2_Seq2_2', 'S2_Seq2_3', 'S2_Seq2_4', 'S2_Seq2_5', 'S2_Seq2_6', 'S2_Seq2_7', 'S2_Seq2_8', 'S2_Seq2_9', 'S2_Seq2_10', 'S2_Seq2_11', 'S2_Seq2_12', 'S2_Seq2_13', 'S3_Seq1_0', 'S3_Seq1_1', 'S3_Seq1_2', 'S3_Seq1_3', 'S3_Seq1_4', 'S3_Seq1_5', 'S3_Seq1_6', 'S3_Seq1_7', 'S3_Seq1_8', 'S3_Seq1_9', 'S3_Seq1_10', 'S3_Seq1_11', 'S3_Seq1_12', 'S3_Seq1_13', 'S3_Seq2_0', 'S3_Seq2_1', 'S3_Seq2_2', 'S3_Seq2_3', 'S3_Seq2_4', 'S3_Seq2_5', 'S3_Seq2_6', 'S3_Seq2_7', 'S3_Seq2_8', 'S3_Seq2_9', 'S3_Seq2_10', 'S3_Seq2_11', 'S3_Seq2_12', 'S3_Seq2_13', 'S4_Seq1_0', 'S4_Seq1_1', 'S4_Seq1_2', 'S4_Seq1_3', 'S4_Seq1_4', 'S4_Seq1_5', 'S4_Seq1_6', 'S4_Seq1_7', 'S4_Seq1_8', 'S4_Seq1_9', 'S4_Seq1_10', 'S4_Seq1_11', 'S4_Seq1_12', 'S4_Seq1_13', 'S4_Seq2_0', 'S4_Seq2_1', 'S4_Seq2_2', 'S4_Seq2_3', 'S4_Seq2_4', 'S4_Seq2_5', 'S4_Seq2_6', 'S4_Seq2_7', 'S4_Seq2_8', 'S4_Seq2_9', 'S4_Seq2_10', 'S4_Seq2_11', 'S4_Seq2_12', 'S4_Seq2_13', 'S5_Seq1_0', 'S5_Seq1_1', 'S5_Seq1_2', 'S5_Seq1_3', 'S5_Seq1_4', 'S5_Seq1_5', 'S5_Seq1_6', 'S5_Seq1_7', 'S5_Seq1_8', 'S5_Seq1_9', 'S5_Seq1_10', 'S5_Seq1_11', 'S5_Seq1_12', 'S5_Seq1_13', 'S5_Seq2_0', 'S5_Seq2_1', 'S5_Seq2_2', 'S5_Seq2_3', 'S5_Seq2_4', 'S5_Seq2_5', 'S5_Seq2_6', 'S5_Seq2_7', 'S5_Seq2_8', 'S5_Seq2_9', 'S5_Seq2_10', 'S5_Seq2_11', 'S5_Seq2_12', 'S5_Seq2_13', 'S6_Seq1_0', 'S6_Seq1_1', 'S6_Seq1_2', 'S6_Seq1_3', 'S6_Seq1_4', 'S6_Seq1_5', 'S6_Seq1_6', 'S6_Seq1_7', 'S6_Seq1_8', 'S6_Seq1_9', 'S6_Seq1_10', 'S6_Seq1_11', 'S6_Seq1_12', 'S6_Seq1_13', 'S6_Seq2_0', 'S6_Seq2_1', 'S6_Seq2_2', 'S6_Seq2_3', 'S6_Seq2_4', 'S6_Seq2_5', 'S6_Seq2_6', 'S6_Seq2_7', 'S6_Seq2_8', 'S6_Seq2_9', 'S6_Seq2_10', 'S6_Seq2_11', 'S6_Seq2_12', 'S6_Seq2_13', 'S7_Seq1_0', 'S7_Seq1_1', 'S7_Seq1_2', 'S7_Seq1_3', 'S7_Seq1_4', 'S7_Seq1_5', 'S7_Seq1_6', 'S7_Seq1_7', 'S7_Seq1_8', 'S7_Seq1_9', 'S7_Seq1_10', 'S7_Seq1_11', 'S7_Seq1_12', 'S7_Seq1_13', 'S7_Seq2_0', 'S7_Seq2_1', 'S7_Seq2_2', 'S7_Seq2_3', 'S7_Seq2_4', 'S7_Seq2_5', 'S7_Seq2_6', 'S7_Seq2_7', 'S7_Seq2_8', 'S7_Seq2_9', 'S7_Seq2_10', 'S7_Seq2_11', 'S7_Seq2_12', 'S7_Seq2_13', 'S8_Seq1_0', 'S8_Seq1_1', 'S8_Seq1_2', 'S8_Seq1_3', 'S8_Seq1_4', 'S8_Seq1_5', 'S8_Seq1_6', 'S8_Seq1_7', 'S8_Seq1_8', 'S8_Seq1_9', 'S8_Seq1_10', 'S8_Seq1_11', 'S8_Seq1_12', 'S8_Seq1_13', 'S8_Seq2_0', 'S8_Seq2_1', 'S8_Seq2_2', 'S8_Seq2_3', 'S8_Seq2_4', 'S8_Seq2_5', 'S8_Seq2_6', 'S8_Seq2_7', 'S8_Seq2_8', 'S8_Seq2_9', 'S8_Seq2_10', 'S8_Seq2_11', 'S8_Seq2_12', 'S8_Seq2_13', 'TS1', 'TS3', 'TS4' ] camera_params = dict() for sbj in subjects: if sbj.startswith('S'): subject, seq, cid = sbj.split('_') cid = int(cid) camera_meta = dict() camera_meta.update(mpii_3dhp_cameras_extrinsic_params[cid]) camera_meta.update(mpii_3dhp_cameras_intrinsic_params[cid]) camera_params[sbj] = [camera_meta] if sbj.startswith('T'): camera_meta = dict() camera_meta.update(mpii_3dhp_cameras_extrinsic_params[8]) camera_meta.update(mpii_3dhp_cameras_intrinsic_params[8]) camera_params[sbj] = [camera_meta] def read_ann(ann_file, mode): """ :param ann_file: :param mode: :return: """ if mode == 'train': return sio.loadmat(ann_file) if mode == 'test': return mat73.loadmat(ann_file) def read_cali(cali_file, vid_idx, mode): """ :param cali_file: :param vid_idx: :return: """ Ks, Rs, Ts = [], [], [] if mode == 'train': file = open(cali_file, 'r') content = file.readlines() for vid_i in vid_idx: K = np.array([float(s) for s in content[vid_i * 7 + 5][11:-2].split()]) K = np.reshape(K, (4, 4))[:3, :3] RT = np.array([float(s) for s in content[vid_i * 7 + 6][11:-2].split()]) RT = np.reshape(RT, (4, 4)) R = RT[:3, :3] R = R @ np.linalg.inv(rot) T = RT[:3, 3] / 1000 # mm to m Ks.append(K) Rs.append(R) Ts.append(T) if mode == 'test': raise NotImplementedError return Ks, Rs, Ts if __name__ == '__main__': # REFERENCE: https://github.com/nkolot/SPIN/blob/master/datasets/preprocess/mpi_inf_3dhp.py data_root = '/ssd/yzhan/data/benchmark/3D/mpi_inf_3dhp' res_w = 2048 res_h = 2048 # train train_subjects = ['S1', 'S2', 'S3', 'S4', 'S5', 'S6', 'S7', 'S8'] sequences = ['Seq1', 'Seq2'] video_list = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13] train_kpt_idx = [4, 23, 24, 25, 18, 19, 20, 3, 5, 6, 7, 9, 10, 11, 14, 15, 16] # test test_subjects = ['TS1', 'TS3', 'TS4'] # drop TS2, due to inaccurate extrinsic test_kpt_idx = [14, 8, 9, 10, 11, 12, 13, 15, 1, 16, 0, 5, 6, 7, 2, 3, 4] METADATA = { 'layout': '3dhp', 'num_joints': 17, 'keypoints_symmetry': [[4, 5, 6, 11, 12, 13], [1, 2, 3, 14, 15, 16]] } data_3d = {} data_2d = {} intrinsics = {} extrinsics = {} for sbj in train_subjects: for seq in sequences: ann_meta = read_ann(os.path.join(data_root, sbj, seq, 'annot.mat'), mode='train') valid_cameras = ann_meta['cameras'].reshape(-1).tolist() valid_frames = ann_meta['frames'].reshape(-1).tolist() kpts_2d = ann_meta['annot2'] kpts_3d = ann_meta['annot3'] Ks, Rs, Ts = read_cali(os.path.join(data_root, sbj, seq, 'camera.calibration'), video_list, mode='train') assert len(Ks) == len(Rs) == len(Ts) == len(valid_cameras), 'camera miss match' for cam_idx in valid_cameras: subject = '{}_{}_{}'.format(sbj, seq, cam_idx) joints_2d = kpts_2d[cam_idx, 0][:len(valid_frames)].reshape(len(valid_frames), -1, 2)[:, train_kpt_idx] joints_3d = kpts_3d[cam_idx, 0][:len(valid_frames)].reshape(len(valid_frames), -1, 3)[:, train_kpt_idx] joints_3d /= 1000 # mm to m valid_joints_2d = list() valid_joints_3d = list() valid_file_names = list() num_invalid_frame = 0 for frame_idx in range(len(valid_frames)): joint_2d = joints_2d[frame_idx] joint_3d = joints_3d[frame_idx] x_in = np.logical_and(joint_2d[:, 0] < res_w, joint_2d[:, 0] >= 0) y_in = np.logical_and(joint_2d[:, 1] < res_h, joint_2d[:, 1] >= 0) ok_pts = np.logical_and(x_in, y_in) if np.sum(ok_pts) < len(train_kpt_idx): num_invalid_frame += 1 continue frame_name = os.path.join(data_root, sbj, seq, 'imageSequence', 'video_{}'.format(cam_idx), 'img_%06d.jpg' % (frame_idx + 1)) if not os.path.exists(frame_name): num_invalid_frame += 1 continue valid_joints_2d.append(joint_2d) valid_joints_3d.append(joint_3d) valid_file_names.append('img_%06d.jpg' % (frame_idx + 1)) print('sbj -> {}, seq -> {}, camera -> {}, total frames -> {}, invalid frames -> {}'.format( sbj, seq, cam_idx, len(valid_frames), num_invalid_frame) ) valid_joints_2d = np.array(valid_joints_2d) valid_joints_3d = np.array(valid_joints_3d) assert valid_joints_2d.shape[0] == valid_joints_3d.shape[0] == len(valid_frames) - num_invalid_frame data_3d.setdefault(subject, dict()) data_3d[subject].setdefault('Action', list()) data_3d[subject]['Action'] = valid_joints_3d data_2d.setdefault(subject, dict()) data_2d[subject].setdefault('Action', list()) data_2d[subject]['Action'].append( { 'file_name': valid_file_names, 'positions_2d': valid_joints_2d } ) intrinsics.setdefault(subject, [Ks[cam_idx].tolist()]) extrinsics.setdefault(subject, [Rs[cam_idx].tolist(), Ts[cam_idx].tolist()]) for sbj in test_subjects: ann_meta = read_ann(os.path.join(data_root, sbj, 'annot_data.mat'), mode='test') valid_frames = ann_meta['valid_frame'].reshape(-1).tolist() kpts_2d = ann_meta['annot2'].transpose(2, 1, 0)[:, test_kpt_idx] kpts_3d = ann_meta['annot3'].transpose(2, 1, 0)[:, test_kpt_idx] kpts_3d /= 1000 # mm to m valid_joints_2d = list() valid_joints_3d = list() valid_file_names = list() num_invalid_frame = 0 for frame_idx, flag in enumerate(valid_frames): if flag == 0: num_invalid_frame += 1 continue joint_2d = kpts_2d[frame_idx] joint_3d = kpts_3d[frame_idx] x_in = np.logical_and(joint_2d[:, 0] < res_w, joint_2d[:, 0] >= 0) y_in = np.logical_and(joint_2d[:, 1] < res_h, joint_2d[:, 1] >= 0) ok_pts = np.logical_and(x_in, y_in) if np.sum(ok_pts) < len(train_kpt_idx): num_invalid_frame += 1 continue frame_name = os.path.join(data_root, sbj, 'imageSequence', 'img_%06d.jpg' % (frame_idx + 1)) if not os.path.exists(frame_name): num_invalid_frame += 1 continue valid_joints_2d.append(joint_2d) valid_joints_3d.append(joint_3d) valid_file_names.append('img_%06d.jpg' % (frame_idx + 1)) print('sbj -> {}, total frames -> {}, invalid frames -> {}'.format( sbj, len(valid_frames), num_invalid_frame) ) valid_joints_2d = np.array(valid_joints_2d) valid_joints_3d = np.array(valid_joints_3d) try: assert valid_joints_2d.shape[0] == valid_joints_3d.shape[0] == len(valid_frames) - num_invalid_frame except: ipdb.set_trace() data_3d.setdefault(sbj, dict()) data_3d[sbj].setdefault('Action', list()) data_3d[sbj]['Action'] = valid_joints_3d data_2d.setdefault(sbj, dict()) data_2d[sbj].setdefault('Action', list()) data_2d[sbj]['Action'].append( { 'file_name': valid_file_names, 'positions_2d': valid_joints_2d } ) _cameras = copy.deepcopy(camera_params) for cameras in _cameras.values(): for i, cam in enumerate(cameras): for k, v in cam.items(): if k not in ['id', 'res_w', 'res_h']: cam[k] = np.array(v, dtype='float32') camera_info = dict() for subject in _cameras: camera_info.setdefault(subject, list()) for cam in _cameras[subject]: if 'translation' not in cam: continue K = np.eye(3, dtype=np.float) K[0, 0] = cam['focal_length'][0] K[1, 1] = cam['focal_length'][1] K[0, 2] = cam['center'][0] K[1, 2] = cam['center'][1] R = cam['R'] t = np.array(cam['translation'], dtype=np.float64).reshape(3, 1) camera_info[subject].append(CameraInfoPacket(P=None, K=K, R=R, t=t, res_w=cam['res_w'], res_h=cam['res_h'], azimuth=cam['azimuth'], dist_coeff=None, undistort=False)) new_camera_info = dict() for subject in _cameras: new_camera_info.setdefault(subject, list()) for cam in _cameras[subject]: if 'translation' not in cam: continue K = np.eye(3, dtype=np.float) K[0, 0] = cam['focal_length'][0] K[1, 1] = cam['focal_length'][1] K[0, 2] = cam['center'][0] K[1, 2] = cam['center'][1] R = cam['R'] R = R @ np.linalg.inv(rot) t = np.array(cam['translation'], dtype=np.float64).reshape(3, 1) if subject.startswith('S'): cid = int(subject.split('_')[-1]) else: cid = 8 try: assert np.sum(K - Ks[cid]) < 1e-3 except: ipdb.set_trace() assert np.sum(R - Rs[cid]) < 1e-6 assert np.sum(t.reshape(3) - Ts[cid]) < 1e-6 new_camera_info[subject].append(CameraInfoPacket(P=None, K=K, R=R, t=t, res_w=cam['res_w'], res_h=cam['res_h'], azimuth=cam['azimuth'], dist_coeff=None, undistort=False)) for ky in subjects: joint_2d, file_names = data_2d[ky]['Action'][0]['positions_2d'], data_2d[ky]['Action'][0]['file_name'] joint_3d = data_3d[ky]['Action'] cam = camera_info[ky][0] new_cam = new_camera_info[ky][0] world_3d = cam.camera2world(joint_3d) world_3d_update = world_3d.copy() for idx in range(world_3d.shape[0]): world_3d_update[idx] = (rot @ world_3d[idx].T).T projected_2d = new_cam.project(catesian2homogenous(world_3d_update)) error = np.sum(joint_2d - projected_2d) print('{} error: {}'.format(ky, error/world_3d_update.shape[0])) data_3d[ky]['Action'] = world_3d_update np.savez(os.path.join(data_root, 'data_2d_3dhp_gt.npz'), metadata=METADATA, positions_2d=data_2d) np.savez(os.path.join(data_root, 'data_3d_3dhp.npz'), positions_3d=data_3d) json.dump(intrinsics, open(os.path.join(data_root, 'intrinsic.json'), 'w'), indent=4) json.dump(extrinsics, open(os.path.join(data_root, 'extrinsic.json'), 'w'), indent=4)

37.177586

119

0.518202

aeb984e53fbe4f0422547372387cc06ffdabc996

1,512

py

Python

alipay/aop/api/response/KoubeiServindustryLeadsRecordBatchqueryResponse.py

antopen/alipay-sdk-python-all

8e51c54409b9452f8d46c7bb10eea7c8f7e8d30c

[ "Apache-2.0" ]

213

2018-08-27T16:49:32.000Z

2021-12-29T04:34:12.000Z

alipay/aop/api/response/KoubeiServindustryLeadsRecordBatchqueryResponse.py

antopen/alipay-sdk-python-all

8e51c54409b9452f8d46c7bb10eea7c8f7e8d30c

[ "Apache-2.0" ]

29

2018-09-29T06:43:00.000Z

2021-09-02T03:27:32.000Z

alipay/aop/api/response/KoubeiServindustryLeadsRecordBatchqueryResponse.py

antopen/alipay-sdk-python-all

8e51c54409b9452f8d46c7bb10eea7c8f7e8d30c

[ "Apache-2.0" ]

59

2018-08-27T16:59:26.000Z

2022-03-25T10:08:15.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- import json from alipay.aop.api.response.AlipayResponse import AlipayResponse from alipay.aop.api.domain.LeadsOrderInfo import LeadsOrderInfo class KoubeiServindustryLeadsRecordBatchqueryResponse(AlipayResponse): def __init__(self): super(KoubeiServindustryLeadsRecordBatchqueryResponse, self).__init__() self._leads_order_info_list = None self._total_count = None @property def leads_order_info_list(self): return self._leads_order_info_list @leads_order_info_list.setter def leads_order_info_list(self, value): if isinstance(value, list): self._leads_order_info_list = list() for i in value: if isinstance(i, LeadsOrderInfo): self._leads_order_info_list.append(i) else: self._leads_order_info_list.append(LeadsOrderInfo.from_alipay_dict(i)) @property def total_count(self): return self._total_count @total_count.setter def total_count(self, value): self._total_count = value def parse_response_content(self, response_content): response = super(KoubeiServindustryLeadsRecordBatchqueryResponse, self).parse_response_content(response_content) if 'leads_order_info_list' in response: self.leads_order_info_list = response['leads_order_info_list'] if 'total_count' in response: self.total_count = response['total_count']

35.162791

120

0.70172

aeba289fa36713ab8da25a19a9e51eb95799d065

6,125

py

Python

iadmin/filters.py

saxix/django-iadmin

675317e8f0b4142eaf351595da27c065637a83ba

[ "BSD-1-Clause" ]

1

2015-06-23T09:24:12.000Z

iadmin/filters.py

saxix/django-iadmin

675317e8f0b4142eaf351595da27c065637a83ba

[ "BSD-1-Clause" ]

null

iadmin/filters.py

saxix/django-iadmin

675317e8f0b4142eaf351595da27c065637a83ba

[ "BSD-1-Clause" ]

null

from django.contrib.admin.filters import RelatedFieldListFilter, AllValuesFieldListFilter from django.db import models from django.db.models.query_utils import Q from django.utils.translation import ugettext as _ from django.utils.encoding import smart_unicode class CellFilter(object): title = "" menu_labels = {'lt': _('Less than'), 'gt': _('Greater than'), 'lte': _('Less or equals than'), 'gte': _('Greater or equals than'), 'exact': _('Equals to'), 'not': _('Not equals to'), 'rem': _('Remove filter')} def __init__(self, field, request, params, model, model_admin, field_path, column=None): self.column = column or field_path or field.name self.col_operators = model_admin.cell_filter_operators.get(field.name, ('exact', 'not')) self.seed = field_path def __repr__(self): return "<%s for `%s` as %s>" % (self.__class__.__name__, self.column, id(self)) def is_active(self, cl): active_filters = cl.params.keys() for x in self.expected_parameters(): if x in active_filters: return True return False def has_output(self): return True def get_menu_item_for_op(self, op): return CellFilter.menu_labels.get(op), '%s__%s' % (self.seed, op) def expected_parameters(self): expected_parameters = [] for op in self.col_operators: filter = '%s__%s' % (self.seed, op) expected_parameters.append(filter) return expected_parameters class ChoicesCellFilter(CellFilter, AllValuesFieldListFilter): pass class BooleanCellFilter(CellFilter, AllValuesFieldListFilter): def __init__(self, field, request, params, model, model_admin, field_path, column=None): self.col_operators = model_admin.cell_filter_operators.get(field.name, ('exact', 'not')) super(BooleanCellFilter, self).__init__(field, request, params, model, model_admin, field_path, column) def get_menu_item_for_op(self, op): if op in ('exact', ''): return _('Yes'), self.seed else: return _('No'), '%s__not' % self.seed def expected_parameters(self): expected_parameters = [] ops = [op for op in self.col_operators if op != 'exact'] expected_parameters.append(self.seed) for op in ops: filter = '%s__%s' % (self.seed, op) expected_parameters.append(filter) return expected_parameters class FieldCellFilter(CellFilter, AllValuesFieldListFilter): def get_menu_item_for_op(self, op): if op == 'exact': return CellFilter.menu_labels.get(op), self.seed return CellFilter.menu_labels.get(op), '%s__%s' % (self.seed, op) def expected_parameters(self): expected_parameters = [] ops = [op for op in self.col_operators if op != 'exact'] expected_parameters.append(self.seed) for op in ops: filter = '%s__%s' % (self.seed, op) expected_parameters.append(filter) return expected_parameters class RelatedFieldCellFilter(RelatedFieldListFilter, CellFilter): def __init__(self, field, request, params, model, model_admin, field_path, column=None): super(RelatedFieldCellFilter, self).__init__(field, request, params, model, model_admin, field_path) self.column = column or field_path or field.name self.col_operators = model_admin.cell_filter_operators.get(field.name, ('exact', 'not')) self.seed = "__".join(self.lookup_kwarg.split('__')[:-1]) class AllValuesComboFilter(AllValuesFieldListFilter): template = 'iadmin/filters/combobox.html' class RelatedFieldComboFilter(RelatedFieldListFilter): template = 'iadmin/filters/fieldcombobox.html' class RelatedFieldRadioFilter(RelatedFieldListFilter): template = 'iadmin/filters/fieldradio.html' class RelatedFieldCheckBoxFilter(RelatedFieldListFilter): template = 'iadmin/filters/fieldcheckbox.html' def __init__(self, field, request, params, model, model_admin, field_path): super(RelatedFieldCheckBoxFilter, self).__init__(field, request, params, model, model_admin, field_path) self.lookup_val = request.GET.getlist(self.lookup_kwarg, []) def queryset(self, request, queryset): if not len(self.lookup_val): return queryset filters = [] for val in self.lookup_val: filters.append(Q(**{self.lookup_kwarg: val})) query = filters.pop() for item in filters: query |= item return queryset.filter(query) def choices(self, cl): from django.contrib.admin.views.main import EMPTY_CHANGELIST_VALUE yield { 'selected': not len(self.lookup_val) and not self.lookup_val_isnull, 'query_string': cl.get_query_string({}, [self.lookup_kwarg, self.lookup_kwarg_isnull]), 'display': _('All'), } yield { 'selected': self.lookup_val_isnull, 'query_string': cl.get_query_string({self.lookup_kwarg_isnull: 1}, [self.lookup_kwarg, self.lookup_kwarg_isnull]), 'display': _('None'), } for pk_val, val in self.lookup_choices: yield { 'selected': smart_unicode(pk_val) in self.lookup_val, 'query_string': cl.get_query_string({ self.lookup_kwarg: pk_val, }, [self.lookup_kwarg_isnull]), 'display': val, } if (isinstance(self.field, models.related.RelatedObject) and self.field.field.null or hasattr(self.field, 'rel') and self.field.null): yield { 'selected': bool(self.lookup_val_isnull), 'query_string': cl.get_query_string({ self.lookup_kwarg_isnull: 'True', }, [self.lookup_kwarg]), 'display': EMPTY_CHANGELIST_VALUE, }

37.576687

112

0.629388

aeba984159365e20b71a8671ae068606c6f145f8

332

py

Python

db.py

amkudr/roadbotproject

f3a0cd10b85bc43e8c823b8670c8b4801e9ed3ab

[ "MIT" ]

null

db.py

amkudr/roadbotproject

f3a0cd10b85bc43e8c823b8670c8b4801e9ed3ab

[ "MIT" ]

1

2021-06-21T12:41:08.000Z

db.py

amkudr/roadbotproject

f3a0cd10b85bc43e8c823b8670c8b4801e9ed3ab

[ "MIT" ]

1

2021-06-21T08:01:12.000Z

from sqlalchemy import create_engine from sqlalchemy.ext.declarative import declarative_base from sqlalchemy.orm import scoped_session, sessionmaker import settings engine = create_engine(settings.API_DATA) db_session = scoped_session(sessionmaker(bind=engine)) Base = declarative_base() Base.query = db_session.query_property()

27.666667

55

0.843373

aebb903fbc8412bafe91beffd2ed946df7885f12

132

py

Python

python/Numpy/np-arrays.py

cdrowley/hackerrank

cc5c925327cd3ce5b52c1614b814da75d42cca72

[ "MIT" ]

null

python/Numpy/np-arrays.py

cdrowley/hackerrank

cc5c925327cd3ce5b52c1614b814da75d42cca72

[ "MIT" ]

null

python/Numpy/np-arrays.py

cdrowley/hackerrank

cc5c925327cd3ce5b52c1614b814da75d42cca72

[ "MIT" ]

null

# https://www.hackerrank.com/challenges/np-arrays/problem import numpy def arrays(arr): return numpy.array(arr, float)[::-1]

16.5

57

0.712121

aebbeb314ce75549bd49f12026710823e4b7c6f5

2,738

py

Python

pycfg.py

rhum1s/scripting_tools

c354a8a3bd38ca8aeaaa91405aa1c4ce703ef36b

[ "MIT" ]

null

pycfg.py

rhum1s/scripting_tools

c354a8a3bd38ca8aeaaa91405aa1c4ce703ef36b

[ "MIT" ]

null

pycfg.py

rhum1s/scripting_tools

c354a8a3bd38ca8aeaaa91405aa1c4ce703ef36b

[ "MIT" ]

null

# -*- coding:utf-8 -*- """ PyCfg - R. Souweine, 2016. """ import sys import os import ConfigParser from datetime import datetime class Cfg(): def __init__(self, cfg_file, cfg_sections, debug=False): """ Lecture d'un fichier de configuation type. """ if os.path.isfile(cfg_file): self.cfg_file = cfg_file self.cfg_sections = cfg_sections self.config = ConfigParser.ConfigParser() self.config.read(cfg_file) self.debug = debug # Dictionnaire des paramètres des sections de config désirées config_params = {} for section in self.config.sections(): if section in self.cfg_sections: config_params.update(self.ConfigSectionMap(self.config, section)) # Passage des paramètres en attribut de l'objet Cfg for key in config_params: setattr(self, key, config_params[key]) else: self.error("%s is doesn't exists." % cfg_file, exit=True) if self.debug is True: print "Fichier de configuration lu." def ConfigSectionMap(self, config_object, section): """ Mapping des sections d'un fichier de config. """ section_dict = {} options = config_object.options(section) for option in options: try: section_dict[option] = config_object.get(section, option) if section_dict[option] == -1: self.warning("skip: %s" % option) except: self.warning("exception on %s!" % option) section_dict[option] = None return section_dict def show_config(self): parametres_caches = ("cfg_file", "cfg_sections", "config", "debug", "None") print "Paramètres de configuration" print "----------------------------" for key in self.__dict__: if key not in (parametres_caches) and key is not None: print key, "-", self.__dict__[key] print "" def info(self, msg): print "%s PgDb > %s" % (datetime.now().strftime('%Y-%m-%d %H:%M'), msg) def warning(self, msg): print "%s PgDb > WARNING: %s" % (datetime.now().strftime('%Y-%m-%d %H:%M'), msg) def error(self, msg, exit=False): print "%s PgDb > ERROR: %s" % (datetime.now().strftime('%Y-%m-%d %H:%M'), msg) if exit is True: sys.exit() if __name__ == "__main__": c = Cfg("configs/global.cfg", ["inv", "general"]) print c.show_config()

34.225

88

0.528853

aebcb1ae1aa0b22cd2b6d96651c21aa61504ef82

504

py

Python

DGF/resolvers/change.py

LonguCodes/DGF

bd344eff34cbe6438f71631e2cc103f1c4584e09

[ "MIT" ]

null

DGF/resolvers/change.py

LonguCodes/DGF

bd344eff34cbe6438f71631e2cc103f1c4584e09

[ "MIT" ]

null

DGF/resolvers/change.py

LonguCodes/DGF

bd344eff34cbe6438f71631e2cc103f1c4584e09

[ "MIT" ]

null

from .utils import get_filters, get_values, get_relations, set_values, set_relations def default_resolve(schema, model, data, **kwargs): filters = get_filters(schema, data) return model.objects.filter(**filters) def default_execute(schema, data, raw_data, **kwargs): values = get_values(schema, raw_data) relations = get_relations(schema, raw_data) for model in data: set_values(model, values) set_relations(model, relations) model.save() return data

28

84

0.712302

aebe95d42dfd494b017b2be2cdcdbc8f022206bc

212

py

Python

11 Februari 2021/7_Ceil and Floor.py

FarrelRamdhani/KelasInformatika

4c43c0405f335447b10aab74afec627df23b919f

[ "MIT" ]

null

11 Februari 2021/7_Ceil and Floor.py

FarrelRamdhani/KelasInformatika

4c43c0405f335447b10aab74afec627df23b919f

[ "MIT" ]

null

11 Februari 2021/7_Ceil and Floor.py

FarrelRamdhani/KelasInformatika

4c43c0405f335447b10aab74afec627df23b919f

[ "MIT" ]

null

from math import ceil, floor, trunc x = 1.4 y = 2.6 print(floor(x), floor(y)) print(floor(-x), floor(-y)) print(ceil(x), ceil(y)) print(ceil(-x), ceil(-y)) print(trunc(x), trunc(y)) print(trunc(-x), trunc(-y))

17.666667

35

0.622642

aebf8e2ff0886eb00e8aeefe34ba66669b5dca4c

513

py

Python

clients/python/rest.py

BernardNotarianni/barrel-aof17

02b959aa38751f9912aa1b0dc5d112b76b727585

[ "Apache-2.0" ]

1

2017-09-11T00:35:49.000Z

clients/python/rest.py

BernardNotarianni/barrel-aof17

02b959aa38751f9912aa1b0dc5d112b76b727585

[ "Apache-2.0" ]

null

clients/python/rest.py

BernardNotarianni/barrel-aof17

02b959aa38751f9912aa1b0dc5d112b76b727585

[ "Apache-2.0" ]

null

#!/usr/bin/env python import requests import json store = 'http://localhost:8080/source' print "post a document" doc = {'name': 'tom'} headers = {'Content-Type': 'application/json'} r = requests.post(store, headers=headers, data=json.dumps(doc)) print r.status_code docid = r.json()['id'] revid = r.json()['rev'] print "get the document" r = requests.get(store + '/' + docid) print r.status_code print "delete the document" r = requests.delete(store + '/' + docid + '?rev=' + revid) print r.status_code

18.321429

63

0.678363

aec0c6a44b056aadc36e40e0073e2808b0d0bb55

10,463

py

Python

mvg_distributions/sqrt_gamma_gaussian.py

Liang813/tf_mvg

01bc681a8b3aac5dcf0837d481b963f4968eb777

[ "MIT" ]

21

2019-04-04T07:46:54.000Z

2021-12-15T18:06:35.000Z

mvg_distributions/sqrt_gamma_gaussian.py

Liang813/tf_mvg

01bc681a8b3aac5dcf0837d481b963f4968eb777

[ "MIT" ]

8

2019-03-01T10:08:30.000Z

2021-10-04T13:00:11.000Z

mvg_distributions/sqrt_gamma_gaussian.py

Liang813/tf_mvg

01bc681a8b3aac5dcf0837d481b963f4968eb777

[ "MIT" ]

7

2019-12-18T23:41:44.000Z

2021-11-21T10:15:48.000Z

import numpy as np import tensorflow as tf from tensorflow_probability.python.distributions import seed_stream import tensorflow_probability as tfp import mvg_distributions.covariance_representations as cov_rep from mvg_distributions.gamma import SqrtGamma tfd = tfp.distributions tfb = tfp.bijectors class SqrtGammaGaussian(tfd.Distribution): def __init__(self, df, log_diag_scale, add_mode_correction=False, validate_args=False, allow_nan_stats=True, name="SqrtGammaGaussian"): """ Square root Gamma-Gaussian distribution, this is equivalent to a Cholesky-Wishart distribution with a diagonal scale matrix. Thus it has the same hyper-parameters, as a the Cholesky-Wishart distribution. This distribution expects as input Cholesky Precision matrices. Moreover, it assumes that the diagonal elements in the matrix are log(values). Args: The distribution is defined for batch (b) of M (pxp) matrices, forming a tensor of [b, p, p] df: degrees of freedom, a tensor of [b], the values in it must be df > p - 1 log_diag_scale: a tensor of [b, p] with the log diagonal values of the matrix S add_mode_correction: bool, if using the distribution as a prior, setting this to True will add a correction factor to log_diag_scale, such that the log_prob will have the maximum in S validate_args: allow_nan_stats: name: """ parameters = locals() with tf.name_scope(name=name): df = tf.convert_to_tensor(df) log_diag_scale = tf.convert_to_tensor(log_diag_scale) assert df.shape.ndims == 1 assert log_diag_scale.shape.ndims == 2 self._df = df self._log_diag_scale = log_diag_scale graph_parents = [df, log_diag_scale] self.p = self.log_diag_scale.shape[1].value if self.p is None: self.p = tf.shape(self.log_diag_scale)[1] self._mode_correction_factor(add_mode_correction) self._sqrt_gamma_dist = None self._normal_dist = None super().__init__(dtype=self.df.dtype, reparameterization_type=tf.distributions.FULLY_REPARAMETERIZED, validate_args=validate_args, allow_nan_stats=allow_nan_stats, parameters=parameters, graph_parents=graph_parents, name=name) @property def sqrt_gamma_dist(self): if self._sqrt_gamma_dist is None: half_df = 0.5 * self.df # [b] # 0.0 to 0.5 - 0.5 p, then add 0.5 * df to all a = np.linspace(0.0, 0.5 - 0.5 * self.p, self.p, dtype=np.float32) # [n] a = a[np.newaxis, :] + half_df[:, tf.newaxis] # [b, n] b = 0.5 / tf.exp(self.log_diag_scale) # [b, n] self._sqrt_gamma_dist = SqrtGamma(concentration=a, rate=b) return self._sqrt_gamma_dist @property def normal_dist(self): if self._normal_dist is None: sqrt_diag_scale = tf.exp(0.5 * self.log_diag_scale) sqrt_diag_scale = tf.tile(sqrt_diag_scale[:, :, tf.newaxis], (1, 1, self.p)) # [b, n, n] self._normal_dist = tfd.Normal(loc=0, scale=sqrt_diag_scale) # [b, n, n] return self._normal_dist @property def log_diag_scale(self): return self._log_diag_scale @property def df(self): return self._df def _mode_correction_factor(self, add_mode_correction): if add_mode_correction: # corrected_diag_scale = diag_scale * ((p - 1)/(tf.range(p) * (1 - p) + (p - 1) * (df - 1))) correction_factor = tf.log(self.p - 1.) p_range = tf.range(self.p, dtype=self._log_diag_scale.dtype)[tf.newaxis, :] correction_factor -= tf.log(p_range * (1. - self.p) + (self.p - 1.) * (self.df[:, tf.newaxis] - 1.)) self._log_diag_scale += correction_factor def _log_prob_sqrt_gamma(self, x): log_diag_prob = self.sqrt_gamma_dist.log_prob(tf.matrix_diag_part(x)) return tf.reduce_sum(log_diag_prob, axis=1) def _log_prob_normal(self, x): log_off_diag_prob = self.normal_dist.log_prob(x) off_diag_mask = tf.ones(shape=tf.shape(x)) off_diag_mask = tf.matrix_band_part(off_diag_mask, -1, 0) off_diag_mask = tf.matrix_set_diag(off_diag_mask, tf.zeros(shape=tf.shape(x)[:-1])) log_off_diag_prob *= off_diag_mask return tf.reduce_sum(log_off_diag_prob, axis=[1, 2]) def _log_prob(self, x): log_diag_prob = self._log_prob_sqrt_gamma(x) log_off_diag_prob = self._log_prob_normal(x) return log_diag_prob + log_off_diag_prob def _batch_shape_tensor(self): return tf.shape(self.log_diag_scale)[0] def _batch_shape(self): return self.log_diag_scale.shape[0:1] def _event_shape_tensor(self): event_dim = tf.shape(self.log_diag_scale)[1] return tf.stack([event_dim, event_dim]) def _event_shape(self): event_dim = self.log_diag_scale.shape[1] return tf.TensorShape([event_dim, event_dim]) def _sample_n(self, n, seed=None): stream = seed_stream.SeedStream(seed=seed, salt="Wishart") # Sample a normal full matrix x = self.normal_dist.sample(sample_shape=n, seed=stream()) # Sample the log diagonal log_g = self.sqrt_gamma_dist.sample(sample_shape=n, seed=stream()) # Discard the upper triangular part x = tf.matrix_band_part(x, -1, 0) # Set the diagonal x = tf.matrix_set_diag(x, log_g) return x class SparseSqrtGammaGaussian(SqrtGammaGaussian): def __init__(self, df, log_diag_scale, add_mode_correction=False, validate_args=False, allow_nan_stats=True, name="SparseSqrtGammaGaussian"): """ Sparse square root Gamma-Gaussian distribution, this is equivalent to a Cholesky-Wishart distribution with a diagonal scale matrix and with a sparsity correction factor. Thus it has the same hyper-parameters, as a the Cholesky-Wishart distribution. Args: The distribution is defined for batch (b) of M (pxp) matrices, forming a tensor of [b, p, p] df: degrees of freedom, a tensor of [b], the values in it must be df > p - 1 log_diag_scale: a tensor of [b, p] with the log diagonal values of the matrix S add_mode_correction: bool, if using the distribution as a prior, setting this to True will add a correction factor to log_diag_scale, such that the log_prob will have the maximum in S validate_args: allow_nan_stats: name: """ super().__init__(df, log_diag_scale, add_mode_correction=add_mode_correction, validate_args=validate_args, allow_nan_stats=allow_nan_stats, name=name) @staticmethod def _convert_to_cov_obj(value): if not isinstance(value, cov_rep.PrecisionConvCholFilters): value = tf.convert_to_tensor(value, name="value") log_prob_shape = () if value.shape.ndims == 2: # Add batch dimension value = tf.expand_dims(value, axis=0) if value.shape.ndims == 3: log_prob_shape = tf.shape(value)[0:1] if value.shape.ndims == 4: # Collapse batch and sample dimension shape = tf.shape(value) log_prob_shape = shape[0:2] new_shape = [log_prob_shape[0] * log_prob_shape[1]] new_shape = tf.concat((new_shape, shape[2:]), axis=0) value = tf.reshape(value, new_shape) value = cov_rep.PrecisionCholesky(chol_precision=value) else: log_prob_shape = value.sample_shape[0:1] return value, log_prob_shape @property def normal_dist(self): if self._normal_dist is None: sqrt_diag_scale = tf.exp(0.5 * self.log_diag_scale) sqrt_diag_scale = sqrt_diag_scale[:, :, tf.newaxis] # [b, n, 1] self._normal_dist = tfd.Normal(loc=0, scale=sqrt_diag_scale) # [b, n, 1] return self._normal_dist def _call_log_prob(self, value, name, **kwargs): with self._name_scope(name): value, log_prob_shape = self._convert_to_cov_obj(value) try: log_prob = self._log_prob(value) return tf.reshape(log_prob, log_prob_shape) except NotImplementedError as original_exception: try: log_prob = tf.log(self._prob(value)) return tf.reshape(log_prob, log_prob_shape) except NotImplementedError: raise original_exception def _log_prob_sqrt_gamma(self, x): log_diag_prob = self.sqrt_gamma_dist.log_prob(x.log_diag_chol_precision) return tf.reduce_sum(log_diag_prob, axis=1) def _log_prob_normal(self, x): if isinstance(x, cov_rep.PrecisionConvCholFilters): nb = x.recons_filters_precision.shape[2].value # Get the elements in matrix [b, n, n] after they've been aligned per row, this is a [b, n, nb] tensor # that if it were reshaped to [b, n_w, n_h, n_b], the vector [b, i, j, :] contain the values of # the kth row in the matrix, where k corresponds to the i,j pixel. # For each row, we discard the leading zeros and the diagonal element off_diag_elements_aligned = x.recons_filters_precision_aligned[:, :, nb // 2 + 1:] log_off_diag_prob = self.normal_dist.log_prob(off_diag_elements_aligned) # Some elements in recons_filters_precision get zeroed out due to the zero padding for elements out of the # image in the convolution operator, thus they are not part of the Cholesky matrix. # Do not take into account those elements for the log probability computation off_diag_mask_aligned = x.off_diag_mask_compact_aligned() # log_off_diag_prob is [b, n, nb // 2 + 1], off_diag_mask is [n, nb] log_off_diag_prob *= off_diag_mask_aligned[tf.newaxis, :, nb // 2 + 1:] log_off_diag_prob = tf.reduce_sum(log_off_diag_prob, axis=[1, 2]) else: log_off_diag_prob = super()._log_prob_normal(x.chol_precision) return log_off_diag_prob

43.057613

119

0.640925

aec3ddbaacba1c772ca5cc048314681c7330681e

3,606

py

Python

anymail/webhooks/sendinblue.py

alee/django-anymail

acca6a46e17143caadb0445d7bdeb2f1eb00b71b

[ "BSD-3-Clause" ]

1,324

2016-03-10T04:57:52.000Z

2022-03-31T15:14:58.000Z

anymail/webhooks/sendinblue.py

alee/django-anymail

acca6a46e17143caadb0445d7bdeb2f1eb00b71b

[ "BSD-3-Clause" ]

208

2016-03-10T03:40:59.000Z

2022-03-22T23:16:08.000Z

anymail/webhooks/sendinblue.py

alee/django-anymail

acca6a46e17143caadb0445d7bdeb2f1eb00b71b

[ "BSD-3-Clause" ]

129

2016-03-10T09:24:52.000Z

2022-02-07T05:37:24.000Z

import json from datetime import datetime from django.utils.timezone import utc from .base import AnymailBaseWebhookView from ..signals import AnymailTrackingEvent, EventType, RejectReason, tracking class SendinBlueTrackingWebhookView(AnymailBaseWebhookView): """Handler for SendinBlue delivery and engagement tracking webhooks""" esp_name = "SendinBlue" signal = tracking def parse_events(self, request): esp_event = json.loads(request.body.decode('utf-8')) return [self.esp_to_anymail_event(esp_event)] # SendinBlue's webhook payload data doesn't seem to be documented anywhere. # There's a list of webhook events at https://apidocs.sendinblue.com/webhooks/#3. event_types = { # Map SendinBlue event type: Anymail normalized (event type, reject reason) "request": (EventType.QUEUED, None), # received even if message won't be sent (e.g., before "blocked") "delivered": (EventType.DELIVERED, None), "hard_bounce": (EventType.BOUNCED, RejectReason.BOUNCED), "soft_bounce": (EventType.BOUNCED, RejectReason.BOUNCED), "blocked": (EventType.REJECTED, RejectReason.BLOCKED), "spam": (EventType.COMPLAINED, RejectReason.SPAM), "invalid_email": (EventType.BOUNCED, RejectReason.INVALID), "deferred": (EventType.DEFERRED, None), "opened": (EventType.OPENED, None), # see also unique_opened below "click": (EventType.CLICKED, None), "unsubscribe": (EventType.UNSUBSCRIBED, None), "list_addition": (EventType.SUBSCRIBED, None), # shouldn't occur for transactional messages "unique_opened": (EventType.OPENED, None), # you'll *also* receive an "opened" } def esp_to_anymail_event(self, esp_event): esp_type = esp_event.get("event") event_type, reject_reason = self.event_types.get(esp_type, (EventType.UNKNOWN, None)) recipient = esp_event.get("email") try: # SendinBlue supplies "ts", "ts_event" and "date" fields, which seem to be based on the # timezone set in the account preferences (and possibly with inconsistent DST adjustment). # "ts_epoch" is the only field that seems to be consistently UTC; it's in milliseconds timestamp = datetime.fromtimestamp(esp_event["ts_epoch"] / 1000.0, tz=utc) except (KeyError, ValueError): timestamp = None tags = [] try: # If `tags` param set on send, webhook payload includes 'tags' array field. tags = esp_event['tags'] except KeyError: try: # If `X-Mailin-Tag` header set on send, webhook payload includes single 'tag' string. # (If header not set, webhook 'tag' will be the template name for template sends.) tags = [esp_event['tag']] except KeyError: pass try: metadata = json.loads(esp_event["X-Mailin-custom"]) except (KeyError, TypeError): metadata = {} return AnymailTrackingEvent( description=None, esp_event=esp_event, event_id=None, # SendinBlue doesn't provide a unique event id event_type=event_type, message_id=esp_event.get("message-id"), metadata=metadata, mta_response=esp_event.get("reason"), recipient=recipient, reject_reason=reject_reason, tags=tags, timestamp=timestamp, user_agent=None, click_url=esp_event.get("link"), )

42.928571

111

0.640044

aec6b13b4c7b4c30dffc5f72de017a641a160409

2,232

py

Python

authentication/strategy.py

mitodl/bootcamp-ecommerce

ba7d6aefe56c6481ae2a5afc84cdd644538b6d50

[ "BSD-3-Clause" ]

2

2018-06-20T19:37:03.000Z

2021-01-06T09:51:40.000Z

authentication/strategy.py

mitodl/bootcamp-ecommerce

ba7d6aefe56c6481ae2a5afc84cdd644538b6d50

[ "BSD-3-Clause" ]

1,226

2017-02-23T14:52:28.000Z

2022-03-29T13:19:54.000Z

authentication/strategy.py

mitodl/bootcamp-ecommerce

ba7d6aefe56c6481ae2a5afc84cdd644538b6d50

[ "BSD-3-Clause" ]

3

2017-03-20T03:51:27.000Z

2021-03-19T15:54:31.000Z

"""Custom strategy""" from urllib.parse import urlencode from django.db import transaction from social_django.strategy import DjangoStrategy from main import features from profiles.models import LegalAddress, Profile class BootcampDjangoStrategy(DjangoStrategy): """Abstract strategy for botcamp app""" def redirect_with_partial(self, url, backend, partial_token): """Redirect to the specified url with a partial token""" qs = urlencode({"backend": backend, "partial_token": partial_token.token}) return self.redirect(self.build_absolute_uri(f"{url}?{qs}")) def is_api_enabled(self): """Returns True if the social auth api is enabled""" return features.is_enabled(features.SOCIAL_AUTH_API) def is_api_request(self): """Returns True if the request is being executed in an API context""" raise NotImplementedError("is_api_request must be implemented") class DefaultStrategy(BootcampDjangoStrategy): """Default strategy for standard social auth requests""" def is_api_request(self): """Returns True if the request is being executed in an API context""" return False def create_user(self, *args, **kwargs): with transaction.atomic(): user = super().create_user(*args, **kwargs) LegalAddress.objects.create(user=user) Profile.objects.create(user=user) return user class DjangoRestFrameworkStrategy(BootcampDjangoStrategy): """Strategy specific to handling DRF requests""" def __init__(self, storage, drf_request=None, tpl=None): self.drf_request = drf_request # pass the original django request to DjangoStrategy request = drf_request._request # pylint: disable=protected-access super().__init__(storage, request=request, tpl=tpl) def is_api_request(self): """Returns True if the request is being executed in an API context""" return True def request_data(self, merge=True): """Returns the request data""" if not self.drf_request: return {} # DRF stores json payload data here, not in request.POST or request.GET like PSA expects return self.drf_request.data

35.428571

96

0.696237

aec73f93f4747f28f93931758f77133311e3036f

83

py

Python

dynasty/__init__.py

LeMinaw/Dynasty

458685df8051cd11f497222e0cd7b672515cd6aa

[ "MIT" ]

2

2021-04-04T19:31:32.000Z

2022-02-06T13:38:09.000Z

dynasty/__init__.py

LeMinaw/Dynasty

458685df8051cd11f497222e0cd7b672515cd6aa

[ "MIT" ]

null

dynasty/__init__.py

LeMinaw/Dynasty

458685df8051cd11f497222e0cd7b672515cd6aa

[ "MIT" ]

null

__version__ = '0.0.2' from pathlib import Path APP_DIR = Path(__file__).parent

10.375

31

0.722892

aec8d771cb10d5ba72ec5fd7b9d6f0e927a8f333

580

py

Python

addons/swift/tests/utils.py

tsukaeru/RDM-osf.io

2dc3e539322b6110e51772f8bd25ebdeb8e12d0e

[ "Apache-2.0" ]

11

2018-12-11T16:39:40.000Z

2022-02-26T09:51:32.000Z

addons/swift/tests/utils.py

tsukaeru/RDM-osf.io

2dc3e539322b6110e51772f8bd25ebdeb8e12d0e

[ "Apache-2.0" ]

52

2018-04-13T05:03:21.000Z

2022-03-22T02:56:19.000Z

addons/swift/tests/utils.py

tsukaeru/RDM-osf.io

2dc3e539322b6110e51772f8bd25ebdeb8e12d0e

[ "Apache-2.0" ]

16

2018-07-09T01:44:51.000Z

2021-06-30T01:57:16.000Z

# -*- coding: utf-8 -*- from addons.base.tests.base import OAuthAddonTestCaseMixin, AddonTestCase from addons.swift.provider import SwiftProvider from addons.swift.serializer import SwiftSerializer from addons.swift.tests.factories import SwiftAccountFactory class SwiftAddonTestCase(OAuthAddonTestCaseMixin, AddonTestCase): ADDON_SHORT_NAME = 'swift' ExternalAccountFactory = SwiftAccountFactory Provider = SwiftProvider Serializer = SwiftSerializer client = None folder = { 'path': 'container', 'name': 'container', 'id': 'container' }

30.526316

73

0.753448

aec9ee64277ff4b007aad939e4ac17372b6cdf19

1,311

py

Python

_grains/mac_sp.py

mosen/salt-osx

818d4ae89bb2853b28999a8ddb883c0fe1b1a657

[ "MIT" ]

68

2015-02-11T00:53:54.000Z

2021-11-06T16:07:17.000Z

_grains/mac_sp.py

Jaharmi/salt-osx

f6db606f04846d45935f3ed729e6243441cee360

[ "MIT" ]

13

2016-01-05T00:01:34.000Z

2022-03-18T23:44:21.000Z

_grains/mac_sp.py

Jaharmi/salt-osx

f6db606f04846d45935f3ed729e6243441cee360

[ "MIT" ]

19

2015-04-09T20:58:55.000Z

2020-11-04T06:39:06.000Z

# -*- coding: utf-8 -*- ''' Mac hardware information, generated by system_profiler. This is a separate grains module because it has a dependency on plistlib. ''' import logging import salt.utils import salt.modules.cmdmod log = logging.getLogger(__name__) __virtualname__ = 'mac_sp' try: import plistlib has_libs = True except ImportError: has_libs = False def __virtual__(): if salt.utils.platform.is_darwin() and has_libs: return __virtualname__ else: return False # Chicken and egg problem, SaltStack style # __salt__ is already populated with grains by this stage. cmdmod = { 'cmd.run': salt.modules.cmdmod._run_quiet, # 'cmd.retcode': salt.modules.cmdmod._retcode_quiet, 'cmd.run_all': salt.modules.cmdmod._run_all_quiet } def _get_spdatatype(sp_data_type): ''' Run system_profiler with a specific data type. Running with all types slows down execution a bit, so be picky about what you need. ''' output_plist = cmdmod['cmd.run']('system_profiler {0} -xml'.format(sp_data_type)) return output_plist def hardware(): ''' Get general hardware information. Provided by SPHardwareDataType (/System/Library/SystemProfiler/SPPlatformReporter.spreporter) ''' sp_hardware = _get_spdatatype('SPHardwareDataType')

25.211538

97

0.723112

aeca5f1921632d7c1be82445181945bbbb66f42a

901

py

Python

cogs/insult.py

nikhilvayeda/Bhendi-Bot-3

2268e4310b91d6f3d62fe7bb642c3a57f623e215

[ "MIT" ]

8

2020-10-02T04:35:01.000Z

2021-11-08T10:38:32.000Z

cogs/insult.py

nikhilvayeda/Bhendi-Bot-3

2268e4310b91d6f3d62fe7bb642c3a57f623e215

[ "MIT" ]

8

2020-10-05T07:45:38.000Z

2021-03-13T22:02:28.000Z

cogs/insult.py

nikhilvayeda/Bhendi-Bot-3

2268e4310b91d6f3d62fe7bb642c3a57f623e215

[ "MIT" ]

2

2020-10-15T05:38:13.000Z

2020-10-29T11:41:16.000Z

import json import requests import discord from discord.ext import commands class Fun_insult(commands.Cog): def __init__(self, client): self.client = client @commands.command() async def insult(self, ctx): '''insult command''' _res = requests.get(url='https://evilinsult.com/generate_insult.php?lang=en&type=json') if _res.status_code == 200: try: _data = _res.json() _insult = _data['insult'] except: return False _embed = discord.Embed(color=0x42c42b, title=_insult) _embed.set_author(name='Bhendi Bot') _embed.set_thumbnail(url='https://media.giphy.com/media/2pjspMQCi70k/giphy.gif') await ctx.send(ctx.author.mention, embed=_embed) def setup(client): client.add_cog(Fun_insult(client))

26.5

96

0.593785

aecc522dc3defa35037e11a7262e7658ad5e8c35

1,873

py

Python

util/monitor.py

reservoirlabs/G2-Mininet

4e0dd6b5367a1d51ee65310e59fc3b0ba55575b8

[ "BSD-3-Clause" ]

2

2021-08-20T08:29:49.000Z

2022-02-25T02:08:26.000Z

util/monitor.py

reservoirlabs/G2-Mininet

4e0dd6b5367a1d51ee65310e59fc3b0ba55575b8

[ "BSD-3-Clause" ]

2

2019-11-27T09:54:35.000Z

2019-12-05T15:52:03.000Z

util/monitor.py

reservoirlabs/G2-Mininet

4e0dd6b5367a1d51ee65310e59fc3b0ba55575b8

[ "BSD-3-Clause" ]

2

2020-03-12T14:38:06.000Z

2022-03-20T10:15:13.000Z

""" G2_RIGHTS. This module defines Monitor class to monitor CPU and memory utilization. Pre-requisite non-standard Python module(s): psutil """ import time import threading import psutil class Monitor(): def __init__(self, interval=1): """ Monitor constructor. Args: interval (int): the polling interval. Attributes: interval (int): The polling interval. readings (list): Observations. _running (bool): State of monitor. """ self.interval = interval self.readings = [] self._running = False def start(self): """Start the monitor. """ self._running = True def monitor(self): """Monitor CPU and memory usage. """ if self._running: # Current time ctime = time.time() # A float representing the current system-wide CPU utilization as a percentage. cpu = psutil.cpu_percent() # System memory usage percent = (total - available) * 100.0 / total memData = dict(psutil.virtual_memory()._asdict()) vmem = memData['percent'] self.readings.append((ctime, cpu, vmem)) t = threading.Timer(self.interval, self.monitor) t.start() def stop(self): """Stop a monitor. """ self._running = False def writeReadings(self, filepath): """Write CPU and memory usage to a CSV file; header row is also written.. Args: filepath (str): Path to output file. """ with open(filepath, "w") as fs: header = "timestamp,cpuPercent,memoryPercent" fs.write(header + '\n') for t, c, m in self.readings: line = str(t)+','+str(c)+','+str(m) fs.write(line + '\n')

25.310811

91

0.549386

aecd3d71119eb36cd9ff199e7ad910beb0a003d1

2,888

py

Python

IO_supervisor.py

dwalley/SICXE

f3d019301e82712a1e94c6936b667bb5c93bbce0

[ "MIT" ]

null

IO_supervisor.py

dwalley/SICXE

f3d019301e82712a1e94c6936b667bb5c93bbce0

[ "MIT" ]

null

IO_supervisor.py

dwalley/SICXE

f3d019301e82712a1e94c6936b667bb5c93bbce0

[ "MIT" ]

null

# Module which defines how the I/O system works import sys, os from Registers import * from random import * from binascii import * class IO_supervisor(Registers): def __init__(self,my_registers): self.i_o = 'stuff' self.num_devices = 2 # native OS files simulate devices working_directory = os.getcwd() device00 = os.path.join(working_directory,'device00') device01 = os.path.join(working_directory,'device01') self.device_names = [device00, device01] self.files = [open(self.device_names[0],'r+'),\ open(self.device_names[1],'r+')] self.device_statuses = [1,1] self.num_channels = 16 self.devices_per_channel = 16 self.my_registers = my_registers self.hex_digits = "0123456789ABCDEF" def read_character(self,device): # low level direct fetch of the next byte from device # device # device is between 0 and 255 # NOTE: files are sequences of 2 character hexidecimal numbers if device < 0 or device >255: raise address_out_of_range my_buffer = bytearray(1) hex_string = self.files[device].read(2) if len(hex_string) <> 2: raise IO_fault # convert two hex digits to a byte my_buffer[0] = unhexlify(hex_string) return my_buffer[0] def test_device(self,device): a = randint(-2,1) self.device_statuses[device] = a result = bytearray(1) if a == -1: result[0] = self.my_registers.CC_less_than elif a == 0: result[0] = self.my_registers.CC_equal_to else: result[0] = self.my_registers.CC_greater_than return result[0] def write_character(self,device,char): # low level direct write of the next byte to device # device # device is between 0 and 255 # NOTE: files are sequences of 2 character hexidecimal numbers if device < 0 or device >255: raise address_out_of_range # break char into low and high half-bytes ## print("in write_character, char is",char) ## print("((char/16)*16) is",((char/16)*16)) low_4_bits = char - ((char/16)*16) ## print("low 4 bits is ",low_4_bits) high_4_bits = (char - low_4_bits)/16 ## print("high 4 bits is ",high_4_bits) # convert to 2 character hex string hex_string = self.hex_digits[high_4_bits] + self.hex_digits[low_4_bits] ## print("hex string is ",hex_string) self.files[device].write(hex_string) def shutdown(self): for i in self.files: i.close() ## raise io_shutdown return def restart(self): self.files = [open(self.device_names[0],'r+'),\ open(self.device_names[1],'r+')] return

30.723404

79

0.602147