added Fakedata_tiny; cleaned up

.gitignore

@@ -1,3 +1,16 @@
 FakeData/IDLoutput/*
 FakeData/Processed_Data/*
+FakeData_tiny/IDLoutput/*
+FakeData_tiny/Processed_Data/*
+AS_Till_2004/IDL_Output
+AS_Till_2004/Processed Data
+Bente_Test/06_OUTPUT
+Bente_Test/Processed Data
+HS_Till/IDLoutput
+IP_Fure/IDLoutput
+LM_Till_only_FID/output
+MR_Till/IDLoutput
+MS_LSM/IDLoutput
+Or47a_test/IDLoutput
+SS_LSM/IDLoutput
 .*\._.*

AS_Till_2004/usage_till.yml

@@ -28,7 +28,6 @@ LELog_InitialFactor: 3
 LE_PrestimEndBackground: 0
 LE_ShrinkFaktor: 1
 LE_StartBackground: 4
-STG_MotherOfAllFolders: /home/aj/UKN_network_drives/ag_galizia/AjayramaKumaraswamy/Ana_RNPN_SampleSet
 PTA_PlotMeanValue: false
 PTA_PlotTimeRange: false
 RM_FotoOk: 0

Bente_Test/Bente_Test_2021.yml

@@ -35,7 +35,6 @@ LE_ScatteredLightRadius: 50.0
 LE_ShrinkFaktor: 1
 LE_StartBackground: 4
 LE_StimulusBasedBackground: true
-STG_MotherOfAllFolders: /Users/galizia/Documents/DATA/HS_210521_test
 PTA_PlotMeanValue: false
 PTA_PlotTimeRange: false
 RM_FotoOk: 0

FakeData/IDLprogs/tapestry_configs/custom_csv_linux.yml

@@ -4,7 +4,7 @@ row1:
     SO_individualScale: 2
     SO_Method: 0
     CTV_Method: ctv_35_2
-    CTV_MethodFile: /home/aj/SharedWithWindows/FakeData/IDLprogs/custom_csv.py
+    CTV_MethodFile: IDLprogs/custom_csv.py
   measus: [1, 2, 3]
   extra_formats: [tif]
 

FakeData/IDLprogs/tapestry_configs/custom_csv_windows.yml

@@ -4,7 +4,7 @@ row1:
     SO_individualScale: 2
     SO_Method: 0
     CTV_Method: ctv_35_2
-    CTV_MethodFile: E:\FakeData\IDLprogs\custom_csv.py
+    CTV_MethodFile: IDLprogs\custom_csv.py
   measus: [1, 2, 3]
   extra_formats: [tif]
 

FakeData_tiny/Areas/FakeData.area.tif.roi

@@ -0,0 +1 @@
+polygon	1	14.00	39.00	26.00	47.00	40.00	37.00	40.00	20.00	19.00	20.00

FakeData_tiny/Coor/1.roi

@@ -0,0 +1,8 @@
+circle	1	62.66	71.61	8.0	
+circle	2	73.2	88.34	8.0	
+circle	3	117.87	86.5	8.0	
+circle	4	114.69	58.06	8.0	
+circle	5	75.71	50.36	8.0	
+polygon	6	92.14	103.09	108.14	103.09	108.14	119.09	92.14	119.09	
+polygon	7	41.95	79.84	57.95	79.84	57.95	95.84	41.95	95.84	
+polygon	8	61.02	24.63	77.02	24.63	77.02	40.63	61.02	40.63	

FakeData_tiny/Coor/FakeData.coor

@@ -0,0 +1,6 @@
+5		
+42	51	1
+71	53	2
+67	35	3
+33	42	7
+31	54	8

FakeData_tiny/Coor/FakeData.roi

@@ -0,0 +1,5 @@
+circle	goglo	62.10	34.79	8.00
+circle	myBestGlo	66.69	57.34	8.00
+circle	3	62.87	67.02	8.00
+circle	4	85.29	66.51	8.00
+polygon	5	74.61	30.61	77.39	20.77	93.19	17.71	99.40	33.41	101.85	46.12	90.61	46.61	74.61	46.61

FakeData_tiny/Coor/FakeData_raw666_07.roi

@@ -0,0 +1,5 @@
+circle	1	62.10	34.79	8.00
+circle	2	66.69	57.34	8.00
+circle	3	62.87	67.02	8.00
+circle	4	85.29	66.51	8.00
+polygon	5	74.61	30.61	77.39	20.77	93.19	17.71	99.40	33.41	101.85	46.12	90.61	46.61	74.61	46.61

FakeData_tiny/Coor/FakeData_repeated_labels.roi

@@ -0,0 +1,5 @@
+circle	goglo	62.10	34.79	8.00
+circle	myBestGlo	66.69	57.34	8.00
+circle	3	62.87	67.02	8.00
+circle	3	85.29	66.51	8.00
+polygon	5	74.61	30.61	77.39	20.77	93.19	17.71	99.40	33.41	101.85	46.12	90.61	46.61	74.61	46.61

FakeData_tiny/Coor/FakeData_with_tab_endings.roi

@@ -0,0 +1,4 @@
+circle	roi2	48.60	53.13	8.00	
+circle	roi3	75.48	43.83	8.00	
+polygon	roi6	54.23	16.72	70.23	16.72	70.23	32.72	54.23	32.72	
+polygon	roi8	80.09	58.64	96.09	58.64	96.09	74.64	80.09	74.64	

FakeData_tiny/Coor/FakeData_without_tabs_endings.roi

@@ -0,0 +1,9 @@
+circle	1	58.28	58.99	8.00
+circle	2	60.06	67.53	8.00
+circle	3	83.89	67.78	8.00
+circle	4	75.35	49.31	8.00
+polygon	5	62.13	66.41	78.13	66.41	78.13	82.41	62.13	82.41
+polygon	6	50.15	71.12	66.15	71.12	66.15	87.12	50.15	87.12
+polygon	7	47.22	31.12	63.22	31.12	63.22	47.12	47.22	47.12
+polygon	8	65.06	24.24	81.06	24.24	81.06	40.24	65.06	40.24
+polygon	9	82.26	40.80	98.26	40.80	98.26	56.80	82.26	56.80

FakeData_tiny/Coor/animal.coor.roi

@@ -0,0 +1,9 @@
+circle	glo33	51.18	65.76	13.83	
+polygon	area	48.2	78.59	35.83	61.41	38.37	43.79	59.43	19.04	90.12	24.76	96.08	50.87	92.85	71.05	79.8	85.22	63.8	85.22	
+circle	3	99.74	44.46	8.0	
+circle	4	114.63	85.23	15.13	
+circle	mybestglo	114.4	65.76	8.0	
+circle	6	115.32	58.89	8.0	
+circle	7	15.91	5.75	8.0	
+circle	goglolgo	4.68	57.06	8.0	
+circle	9	3.31	96.91	8.0	

FakeData_tiny/IDLprogs/Analysis_concentrations.yml

@@ -0,0 +1,94 @@
+CSM_Datashift: 0
+Data_Median_Filter: 0
+Data_Median_Filter_space: 3
+Data_Median_Filter_time: 3
+CSM_Movement: 0
+CSM_SkipFrmAtBack: 0
+CSM_SkipFrmUpFront: 0
+CTVM_Method: 0
+CTV_Method: 22
+CTV_firstframe: 20
+CTV_lastframe: 30
+CTV_scalebar: false
+VIEW_CorrSignals: true
+FT_AllOdors: true
+FT_Radius: 5
+FT_SelectFrame: true
+FT_Subset: true
+FT_TimeXAxis: true
+Signal_FilterSpaceFlag: false
+Signal_FilterSpaceSize: 3
+Signal_FilterTimeFlag: false
+Signal_FilterTimeSize: 3
+LE_AskForAir: true
+LE_BleachStartFrame: 2
+LE_ClipPixels: 0
+LE_FirstBuffer: 1
+LELog_ExcludeSeconds: 0
+LELog_InitialFactor: 1
+LE_PrestimEndBackground: 1
+LE_ShrinkFaktor: 0
+LE_StartBackground: 4
+STG_MotherOfAllFolders: /Users/galizia/Documents/Code/VIEWsampletrees/FakeData
+PTA_PlotMeanValue: true
+PTA_PlotTimeRange: true
+RM_FotoOk: 0
+RM_NewColumn: true
+RM_NextPosition: (0, 0)
+RM_PlotTrace: true
+RM_PrintAscii: true
+RM_PrintLine: true
+RM_ROITrace: true
+RM_differentViews: true
+RM_separateLayers: 0
+RM_unsharpmask: false
+SO_Method: 10
+SO_indiScale: 3
+SO_morphoBackgr: 0
+SO_morphoBackgrNeg: 0
+SO_withinmask: 0
+STG_Datapath: data
+STG_Measu: 8
+STG_Missing: none
+STG_OdorInfoPath: Lists
+STG_OdorReportFile: FakeData.lst
+STG_OdorReportPath: IDLoutput
+STG_OdormaskPath: Coor
+STG_ReportTag: raw666_00
+VIEW_DeleteRawData: 0
+VIEW_InitCorr: 0
+VIEW_MultiExp: 0
+VIEW_No4Darray: 0
+VIEW_ReportMethod: 10
+VIEW_ScatterLightFactor: 0.0
+VIEW_batchmode: false
+SO_MV_colortable: 12
+mv_FirstFrame: 0
+mv_LastFrame: -1
+mv_SpeedFactor: 1.0
+mv_bgColor: b
+mv_bitrate: 1024k
+mv_correctStimulusOnset: 0
+mv_cutborder: 0.0
+mv_displayTime: false
+mv_exportFormat: single_tif
+mv_fgColor: w
+mv_indiScale3factor: 0.0
+mv_individualScale: 2
+mv_markStimulus: 0
+mv_minimumBrightness: 0.0
+mv_morphoThreshold: 0.0
+mv_percentileScale: false
+mv_percentileValue: 0.0
+mv_realTime: 1
+mv_reverseIt: true
+mv_rotateImage: 0
+mv_sdSignificanceCut: 0.0
+mv_suppressMilliseconds: true
+mv_withinMask: true
+mv_xgap: 0
+mv_ygap: 0
+SO_MV_scalemax: 0.3
+SO_MV_scalemin: -0.08
+LE_CalcMethod: 3900
+LE_loadExp: 666

FakeData_tiny/IDLprogs/Apis2018_summer.pro

@@ -0,0 +1,169 @@
+
+Pro Apis2018_summer
+
+
+common data
+common vars
+common CFD
+common CFDconst
+
+
+
+	SO_MV_scalemax =3.000
+	SO_MV_scalemin =  -2.000
+flag[stg_reporttag] ='HS_bee_PELM_180406a'
+gr_HS_bee_PELM_180406a
+;gr_takefromlist, 'HS_bee_PELM_180406a', 2
+
+	SO_MV_scalemax =3.000
+	SO_MV_scalemin =  -2.000
+flag[stg_reporttag] ='HS_bee_OXON_PELM_180409'
+gr_HS_bee_OXON_PELM_180409
+;gr_takefromlist, 'HS_bee_OXON_PELM_180409', 2
+
+
+
+
+	SO_MV_scalemax =3.000
+	SO_MV_scalemin =  -2.000
+flag[stg_reporttag] ='HS_bee_OXON_PELM_180411'
+gr_HS_bee_OXON_PELM_180411
+;gr_takefromlist, 'HS_bee_OXON_PELM_180411', 2
+
+
+	SO_MV_scalemax =3.000
+	SO_MV_scalemin =  -2.000
+flag[stg_reporttag] ='HS_bee_PELM_180416b'
+gr_HS_bee_PELM_180416b
+;gr_takefromlist, 'HS_bee_PELM_180416b', 2
+
+	SO_MV_scalemax =3.000
+	SO_MV_scalemin =  -2.000
+flag[stg_reporttag] ='HS_bee_OXON_PELM_180417'
+gr_HS_bee_OXON_PELM_180417
+;gr_takefromlist, 'HS_bee_OXON_PELM_180417', 2
+
+	SO_MV_scalemax =3.000
+	SO_MV_scalemin =  -2.000
+flag[stg_reporttag] ='HS_bee_PELM_180418'
+gr_HS_bee_PELM_180418
+;gr_takefromlist, 'HS_bee_PELM_180418', 2
+
+
+
+	SO_MV_scalemax =3.000
+	SO_MV_scalemin =  -2.000
+flag[stg_reporttag] ='HS_bee_PELM_180424b'
+gr_HS_bee_PELM_180424b
+;gr_takefromlist, 'HS_bee_PELM_180424b', 2
+
+	SO_MV_scalemax =3.000
+	SO_MV_scalemin =  -2.000
+flag[stg_reporttag] ='HS_bee_OXON_PELM_180420'
+gr_HS_bee_OXON_PELM_180420
+;gr_takefromlist, 'HS_bee_OXON_PELM_180420', 2
+
+	SO_MV_scalemax =3.000
+	SO_MV_scalemin =  -2.000
+flag[stg_reporttag] ='HS_bee_OXON_PELM_180427'
+gr_HS_bee_OXON_PELM_180427
+;gr_takefromlist, 'HS_bee_OXON_PELM_180427', 2
+
+
+
+
+
+	SO_MV_scalemax =3.000
+	SO_MV_scalemin =  -2.000
+flag[stg_reporttag] ='HS_bee_OXON_PELM_180503'
+gr_HS_bee_OXON_PELM_180503
+;gr_takefromlist, 'HS_bee_OXON_PELM_180503', 2
+
+
+
+
+	SO_MV_scalemax =3.000
+	SO_MV_scalemin =  -2.000
+flag[stg_reporttag] ='HS_bee_OXON_PELM_180507'
+gr_HS_bee_OXON_PELM_180507
+;gr_takefromlist, 'HS_bee_OXON_PELM_180507', 2
+
+	SO_MV_scalemax =3.000
+	SO_MV_scalemin =  -2.000
+flag[stg_reporttag] ='HS_bee_OXON_PELM_180509'
+gr_HS_bee_OXON_PELM_180509
+;gr_takefromlist, 'HS_bee_OXON_PELM_180509', 2
+
+
+	SO_MV_scalemax =3.000
+	SO_MV_scalemin =  -2.000
+flag[stg_reporttag] ='HS_bee_OXON_PELM_180718'
+gr_HS_bee_OXON_PELM_180718
+;gr_takefromlist, 'HS_bee_OXON_PELM_180718', 2
+
+	SO_MV_scalemax =3.000
+	SO_MV_scalemin =  -2.000
+flag[stg_reporttag] ='HS_bee_OXON_180716'
+gr_HS_bee_OXON_180716
+;gr_takefromlist, 'HS_bee_OXON_180716', 2
+
+	SO_MV_scalemax =3.000
+	SO_MV_scalemin =  -2.000
+flag[stg_reporttag] ='HS_bee_OXON_180727'
+gr_HS_bee_OXON_180727
+;gr_takefromlist, 'HS_bee_OXON_180727', 2
+
+
+
+
+
+
+
+
+
+
+;keine Signale:
+;
+;	SO_MV_scalemax =3.000
+;	SO_MV_scalemin =  -2.000
+;flag[stg_reporttag] ='HS_bee_PELM_180413'
+;gr_HS_bee_PELM_180413
+;;gr_takefromlist, 'HS_bee_PELM_180413', 2
+
+
+;	SO_MV_scalemax =3.000
+;	SO_MV_scalemin =  -2.000
+;flag[stg_reporttag] ='HS_bee_OXON_PELM_180410'
+;gr_HS_bee_OXON_PELM_180410
+;;gr_takefromlist, 'HS_bee_OXON_PELM_180410', 2
+
+
+;	SO_MV_scalemax =3.000
+;	SO_MV_scalemin =  -2.000
+;flag[stg_reporttag] ='HS_bee_OXON_180416a'
+;gr_HS_bee_OXON_180416a
+;;gr_takefromlist, 'HS_bee_OXON_180416a', 2
+;
+;
+;	SO_MV_scalemax =3.000
+;	SO_MV_scalemin =  -2.000
+;flag[stg_reporttag] ='HS_bee_PELM_180424a'
+;gr_HS_bee_PELM_180424a
+;;gr_takefromlist, 'HS_bee_PELM_180424a', 2
+
+;	SO_MV_scalemax =3.000
+;	SO_MV_scalemin =  -2.000
+;flag[stg_reporttag] ='HS_bee_OXON_180426'
+;gr_HS_bee_OXON_180426
+;;gr_takefromlist, 'HS_bee_OXON_180426', 2
+;
+;	SO_MV_scalemax =3.000
+;	SO_MV_scalemin =  -2.000
+;flag[stg_reporttag] ='HS_bee_OXON_180502'
+;gr_HS_bee_OXON_180502
+;;gr_takefromlist, 'HS_bee_OXON_180502', 2
+
+
+;
+
+end

FakeData_tiny/IDLprogs/Master_Fake_190822.py

@@ -0,0 +1,212 @@
+# -*- coding: utf-8 -*-
+"""
+
+@author: Giovanni Galizia
+
+
+
+"""
+
+### gio 25.5.2019: "from view.idl_translation_core import View_gr_reports " does not work,
+#                    direct import does: import View_gr_reports as...
+#                  in spyder, anaconca env idl_py, mac. 
+# next line works in Spyder IF it has been called in the right directory.
+from view.idl_translation_core import View_gr_reports as View_gr_reports, IDL_flags as IDL_flags
+#import View_gr_reports as View_gr_reports, IDL_flags as IDL_flags
+
+from view.python_core.flags import FlagsManager
+#import View_gr_reports as View_gr_reports, IDL_flags as IDL_flags
+
+import pathlib as pl
+#import pandas as pd
+import os
+from sys import platform
+#show images inline:
+#%matplotlib inline
+#show images in extra window
+#%matplotlib qt
+import shutil
+
+
+def Set_my_flags(flag):
+    # use this to change any flags that are important locally
+    #flag.STG_reporttag		= '030725bR'
+    
+    #chage to indexing in the future, e.g.
+    #flag["LE_loadExp"] = 666
+    
+    flag.LE_loadExp = 666
+    flag.CSM_Movement = 0 # 2 for movement correction on the spot - is slow!
+    flag.VIEW_batchmode    = 1 # fo
+    
+    flag.LE_CalcMethod = 3900 # calculates raw data
+    
+    flag.VIEW_ReportMethod = 12 #10 for overviews, 11 for Glodatamix, 12 for movies
+    flag.SO_Method    = 10
+    flag.SO_indiScale = 0
+    flag.SO_MV_scalemax     =  0.30#18.0
+    flag.SO_MV_scalemin     = -0.08#-2.0
+    flag.SO_withinmask= 0
+    flag.CTV_firstframe   = 20
+    flag.CTV_lastframe    = 30
+    flag.CTV_Method   = 22  #22 , 12 takes average from firstframe to lastframe
+    flag.RM_FotoOk    = 0
+    flag.RM_NextPosition = (0,0)
+    flag.CTV_scalebar = 0
+    
+    flag.Signal_FilterSpaceFlag = 0
+    flag.mv_individualScale = 2
+    
+    flag.FT_radius = 5
+    flag.SO_MV_colortable = 12
+
+    
+    return flag
+
+
+def ChooseFileFolder():  
+    import tkinter as tk
+    from tkinter.filedialog import askopenfilenames
+
+    # Choose raw files
+    root = tk.Tk()
+    root.withdraw() # so that windows closes after file chosen 
+    root.attributes('-topmost', True)
+    # the mac system does not accept filetypes, therefore ask for system
+    if platform == 'darwin':
+        filenames = askopenfilenames(
+                        parent=root,
+                        title='Select one or more settings files (*.settings.xls)',
+                        ) # ask user to choose file
+    else:
+        filenames = askopenfilenames(
+                    parent=root,
+                    title='Select one or more settings files',
+                    filetypes=[('settings files', '*.settings.xls'), ('all files', '*')]
+                    ) # ask user to choose file
+    return filenames
+
+
+def RunThroughAnimals(flag, animallist):
+    #list all animals to be processed
+#list animals to work with here
+#    animallist = ['190607_locust_ip32', '190702_locust_ip33']
+# animal='pixel_calibration'
+    for i,animal in enumerate(animallist):
+        print(i, animal)
+        flag.STG_ReportTag =animal
+    # run gr_190227_locust_ip14 to select also the order of how measurements are evaluated
+    #(p1, flag) = gr_190227_locust_ip14(flag)
+    # run gr_takefromlist to use the 'analyze' column
+#list what to do with each animal here
+        print('in Master/RunThroughAnimals: now running animal: ', animal)
+        (p1, flag) = View_gr_reports.gr_takefromlist(flag.STG_ReportTag, 1, flag, selectformat ='analyze')
+    
+    return p1, flag
+# end of Inga_2019_Fura_test
+
+
+def gr_190227_locust_ip14(flag):
+    #single animal selection of order how measurements are evaluated
+    flag.RM_NewColumn = 1
+    ### IDL commant is this
+    # subloop, '14';  12_AIR
+    ### converts into Python like this
+    (p1,flag) = View_gr_reports.gr_takefromlist(flag.STG_ReportTag, 1, flag, selectformat ='subloop')
+    flag.RM_NewColumn = 0
+    (p1,flag) = View_gr_reports.gr_takefromlist(flag.STG_ReportTag, 3, flag, selectformat ='subloop') #  01_LINT-4
+    (p1,flag) = View_gr_reports.gr_takefromlist(flag.STG_ReportTag, 5, flag, selectformat ='subloop') #  02_LINT-3
+    (p1,flag) = View_gr_reports.gr_takefromlist(flag.STG_ReportTag, 7, flag, selectformat ='subloop') #  14_LINT-4
+    (p1,flag) = View_gr_reports.gr_takefromlist(flag.STG_ReportTag, 9, flag, selectformat ='subloop') #  00_MOL
+    (p1,flag) = View_gr_reports.gr_takefromlist(flag.STG_ReportTag, 11, flag, selectformat ='subloop') #  13_MOL
+    (p1,flag) = View_gr_reports.gr_takefromlist(flag.STG_ReportTag, 13, flag, selectformat ='subloop') #  15_NONL-3
+    
+    return p1, flag
+
+def gr_190227_locust_ip16(flag):
+    #single animal selection of order how measurements are evaluated
+    flag.RM_NewColumn = 1
+    ### IDL commant is this
+    # subloop, '14';  12_AIR
+    ### converts into Python like this
+    (p1,flag) = View_gr_reports.gr_takefromlist(flag.STG_ReportTag, 1, flag, selectformat ='subloop')
+    flag.RM_NewColumn = 0
+    (p1,flag) = View_gr_reports.gr_takefromlist(flag.STG_ReportTag, 3, flag, selectformat ='subloop') #  01_LINT-4
+    (p1,flag) = View_gr_reports.gr_takefromlist(flag.STG_ReportTag, 5, flag, selectformat ='subloop') #  02_LINT-3
+    (p1,flag) = View_gr_reports.gr_takefromlist(flag.STG_ReportTag, 7, flag, selectformat ='subloop') #  14_LINT-4
+    (p1,flag) = View_gr_reports.gr_takefromlist(flag.STG_ReportTag, 9, flag, selectformat ='subloop') #  00_MOL
+    (p1,flag) = View_gr_reports.gr_takefromlist(flag.STG_ReportTag, 11, flag, selectformat ='subloop') #  13_MOL
+    (p1,flag) = View_gr_reports.gr_takefromlist(flag.STG_ReportTag, 13, flag, selectformat ='subloop') #  15_NONL-3
+    
+    return p1, flag
+
+#########################################################
+########## Main starts here
+#########################################################
+
+# define flags
+    #IDL.flags.IDL_default_flags sets the general rules
+    #Set_my_flags modifies these
+#on my windows system
+#STG_MotherOfAllFolders = 'C:\\Users\\Giovanni Galizia\\Documents\\Code\\ShareWinXP\\WindowsExchange\\IDL_Data\\spont_activity' #if empty, working directory is used
+#if platform == 'darwin':
+#    STG_MotherOfAllFolders = '/Users/galizia/Documents/Code/ShareWinXP/WindowsExchange/IDL_Data/spont_activity'
+
+print()
+print('Running this master file: ', __file__)
+print()
+
+flags_from_yml = True
+
+if flags_from_yml: 
+    #default are settings in yml file, ising same procedure as VIEWgui
+    #modified by Set_my_flags above
+
+    # yml_filename = '/Users/galizia/Documents/Code/VIEWsampletrees/FakeData/view_fake_data.yml'
+    # yml_filename = 'E:\\FakeData\\view_fake_data.yml'
+    yml_filename = '/home/aj/SharedWithWindows/FakeData/view_fake_data.yml'
+    flags = FlagsManager()
+    flags.read_flags_from_yml(yml_filename)
+# convert flags to pd.Series because that is what Set_my_flags is expecting. 
+# this will be obsolete when Set_my_flags will use indexing instead of attribure
+    flag_series = Set_my_flags(flags.to_series()) 
+    
+# or take flags exactly as in .yml file
+#    flag = pd.Series(pc_flags.read_yml_flags(yml_filename))
+else: #alternative: defaults are in IDL_falgs.IDL_default_flags
+    # STG_MotherOfAllFolders = '/Users/galizia/Documents/Code/VIEWsampletrees/FakeData'
+    # STG_MotherOfAllFolders = 'E:\\FakeData'
+    STG_MotherOfAllFolders = '/home/aj/SharedWithWindows/FakeData'
+    flag_series = Set_my_flags(IDL_flags.IDL_default_flags(STG_MotherOfAllFolders))
+
+
+
+#flag.STG_OdorReportPath		= os.path.join(STG_MotherOfAllFolders + 'IDL_output\\movies\\'
+
+# call program that opens .lst file and performs analysis
+# the command in spont_activity_master.pro was "subloop, '24'
+#(p1,flag) = View_gr_reports.gr_takefromlist('030725bR', 3, flag)
+
+
+####make sure all settings/flags are set, then call this animal
+RunThroughAnimals(flag_series, 
+                  ['FakeData'])
+
+#document how this analysis was done, by copying this file into the output folder
+shutil.copy(__file__, flag_series.STG_OdorReportPath)
+if flags_from_yml:
+    # initialize the path of the current script into a pathlib.Path object
+    current_file_path = pl.Path(__file__)
+    # use the stem of the path of the current string to form yml file name in STG_OdorReportPath
+    yml_outfilename_path = pl.Path(flags["STG_OdorReportPath"]) / f"{current_file_path.stem}.yml"
+    yml_outfilename = str(yml_outfilename_path)
+
+    flags.write_flags_to_yml(yml_outfilename)
+
+
+
+
+
+
+
+

FakeData_tiny/IDLprogs/custom_csv.py

@@ -0,0 +1,70 @@
+import numpy as np
+import scipy
+
+
+def ctv_35_2(
+        time_trace, sampling_period,
+        first_frame, last_frame,
+        stimulus_number, stim_on_times, stim_off_times,
+        flags, p1):
+    """
+    fancy new ctv
+
+    :param time_trace: iterable of numbers
+    :param sampling_period: float, sampling period of <time_trace>, in ms
+    :param first_frame: int, interpreted as a frame number, where frames are numbered 0, 1, 2...
+    :param last_frame: int, interpreted as a frame number, where frames are numbered 0, 1, 2...
+    :param stimulus_number: int, indicates the which stimulus to use, use 1 for first stimulus
+    :param stim_on_times: list of floats, stimulus onset times, in ms
+    :param stim_off_times: list of floats, stimulus offset times, in ms
+    :param flags: FlagsManager object, is a mapping of flag names to flags values with additional functions
+    :param p1: pandas.Series object, internal representation of data
+    :rtype: list
+    :return: one member, float
+    """
+
+    stim2_on_frame_ind = int(stim_on_times[1] / sampling_period) + 1
+    frame_ind_after_3s_stim2_onset = int((stim_on_times[1] + 3000) / sampling_period) + 1
+
+    # reset to end of trace if stimulus onset is less than 3 seconds before the end of trace
+    frame_ind_after_3s_stim2_onset = min(frame_ind_after_3s_stim2_onset, len(time_trace) - 1)
+
+    argmax_in_3s_after_stim2_onset \
+        = np.argmax(time_trace[stim2_on_frame_ind: frame_ind_after_3s_stim2_onset + 1]) + stim2_on_frame_ind
+
+    # make sure there are three frames around <argmax_in_3s_after_stim2_onset>. This will only happen if the
+    # <argmax_in_3s_after_stim2_onset> happens to be the first or last frame
+    argmax_in_3s_after_stim2_onset = max(1, argmax_in_3s_after_stim2_onset)
+    argmax_in_3s_after_stim2_onset = min(len(time_trace) - 2, argmax_in_3s_after_stim2_onset)
+
+    A = np.mean(time_trace[argmax_in_3s_after_stim2_onset - 1: argmax_in_3s_after_stim2_onset + 2])
+
+    # make sure there are three frames around <stim2_on_frame_ind>. This will only happen if the
+    # <stim1_on_frame_ind> happens to be the first or last frame
+    stim2_on_frame_ind2use = min(max(1, stim2_on_frame_ind), len(time_trace) - 2)
+
+    B = np.mean(time_trace[stim2_on_frame_ind2use - 1: stim2_on_frame_ind2use + 2])
+
+    return [A - B]
+
+def null(
+        time_trace, sampling_period,
+        first_frame, last_frame,
+        stimulus_number, stim_on_times, stim_off_times,
+        flags, p1):
+    """
+    always returns 0
+    :param time_trace: iterable of numbers
+    :param sampling_period: float, sampling period of <time_trace>, in ms
+    :param first_frame: int, interpreted as a frame number, where frames are numbered 0, 1, 2...
+    :param last_frame: int, interpreted as a frame number, where frames are numbered 0, 1, 2...
+    :param stimulus_number: int, indicates the which stimulus to use, use 1 for first stimulus
+    :param stim_on_times: list of floats, stimulus onset times, in ms
+    :param stim_off_times: list of floats, stimulus offset times, in ms
+    :param flags: FlagsManager object, is a mapping of flag names to flags values with additional functions
+    :param p1: pandas.Series object, internal representation of data
+    :rtype: list
+    :return: one member, float
+    """
+
+    return [0]

FakeData_tiny/IDLprogs/gr_HS_bee_OXON_180416a.pro

@@ -0,0 +1,39 @@
+pro gr_HS_bee_OXON_180416a
+
+common CFD
+common CFDconst
+common Vars
+
+
+flag[rm_printline] = 1
+
+
+flag[rm_newcolumn] = 1
+subloop,'14';  12_AIR
+flag[rm_newcolumn] = 0
+subloop,'3';  01_LINT-4
+subloop,'4';  02_LINT-3
+subloop,'16';  14_LINT-4
+subloop,'2';  00_MOL
+subloop,'15';  13_MOL
+subloop,'17';  15_NONL-3
+
+
+
+
+flag[rm_newcolumn] = 1
+subloop,'5';  03_OXON-10
+flag[rm_newcolumn] = 0
+subloop,'6';  04_OXON-9
+subloop,'7';  05_OXON-8
+subloop,'8';  06_OXON-7
+subloop,'9';  07_OXON-6
+subloop,'10';  08_OXON-5
+subloop,'11';  09_OXON-4
+subloop,'12';  10_OXON-3
+subloop,'13';  11_OXON-2
+
+
+
+
+end

FakeData_tiny/IDLprogs/hannah_master_tiff_neu.pro

@@ -0,0 +1,645 @@
+
+pro hannah_master_tiff_neu
+
+; sample master file
+; containing all available flags as of september 2008
+; sorted in logical groups
+; note, however, that not all flag settings are mentioned
+; you still need to know what you can do and what the program does
+; and you still need to run the necessary constrols
+
+
+;set common blocks to get access to the variables
+common data ;contains all data variables
+common vars ;contains those flags that are not "flag[xxx]"
+common CFD  ;contains the flag-variables
+common CFDconst  ;contains the names of the flag-variables
+common ExportMovieFlags ;contains the flags for movie control
+
+; general system settings in VARS
+	;colortable (for byte values 0-255)
+	colortable 	= 12
+	;often used color tables are: (some defined in DefineExplicitCt.pro)
+	; 0 for black and white
+	; 11, 12, 13 for a smooth rainbow. They differ in BW colors for color 0 and 255
+	;     optimized for printout or for screen
+	; 14 has 128 as grey, lower range cyan to blue, upper range yellow to red
+	; 36: cyan-blue-*black*-red-yellow-white
+	; 37: cyan-blue-*black*-red-yellow
+	; 38: black-red-yellow-white table
+
+	;internal variable for mac compatibitity, not used any more
+	flag[MacSystem] 	= (!version.os_family eq 'MacOS')
+
+	;setting for interactive mode ("view"), or master_mode
+	flag[BatchMode]		= 1
+	;graphics flag
+	flag[TrueColour]	= 0
+;end of general settings
+
+;*****************************
+;flags related to LOADING DATA
+;*****************************
+
+	;format of data files (used in LoadDataMaster.pro)
+	flag[view_loadExp]      = 3;3;4 	; 0 for old setup, to 1 for Visicam, 2 for confocal
+	;TILL photonics single wavelength: 3
+	;TILL photonics dual wavelength (FURA): 4
+	;ZEISS multiphoton data: 20
+
+	;should the raw data be median filtered? (used in ViewLoadData\MedianCorrection.pro)
+	flag[CSM_Median]			= 3
+	;do a median correction when loading the data
+	;0: no median
+	;1: median in space fixed values
+	;2: median in time fixed values
+	;3: median in space and time, using flag values (CSM_Median_space)(CSM_Median_time)
+	flag[CSM_Median_space]		= 5
+	; used with CSM_Median eq 3
+	flag[CSM_Median_time]		= 0
+	; used with CSM_Median eq 3
+
+	;apply an off-line binning (shrinkFaktor).
+	flag[le_shrinkFaktor]	   	= 1     ;no shrinkfaktor with 1
+
+	;are there different focal depth to be split?
+	flag[RM_separateLayers] 	= 0
+	;separates the layers in exportGlomeruli and SingleOverviews
+
+	;how to make movement correction based on .moveList file. (used in ViewLoadData\MovementCorrectionMaster.pro)
+	flag[CSM_Movement]			= 0
+	;set to 0: no movement correction (BUT shifts from .lst file are USED!)
+	;set to 1: on the spot movement correction
+	;set to 2: as 1, but calculated movements are saved in the moveList file
+	;set to 3: movement values are read from the moveList file
+	;set to 5: no movement correction, but shifts are taken from the movement file
+	;values above 10: mathiasCorrection.
+	flag[CSM_DataShift]				= 1
+	;set to 0: data is NOT shifted, coordinates ARE shifted
+	;set to 1: data IS shifted, coordinate are NOT shifted
+	;set to 2: NONE is shifted
+
+	;trim the data frames to be loaded
+	;remove n frames at the beginning of each measurement
+	flag[CSM_SkipFrmUpFront]		= 0
+	;remove n frames at the end of each measurement
+	flag[CSM_SkipFrmAtBack]				= 0
+
+	;settings to reduce memory usage
+	flag[VIEW_No4Darray]			= 0
+	;this reduces the sig1 array to a 3D array instead of a 4D array; not all routines work with this setting
+	;set to 1: odor 0 is cut off memory after loading the data, p1.odors is reduced by 1
+	flag[VIEW_DeleteRawData]		= 1
+	;this removes the raw data from memory after loading
+
+	;correct for scattered light, improving spatial resolution	(used in CalcSigAll3000.pro)
+	flag[VIEW_ScatterLightFactor]	= 1
+	;for the 3xyy and 4xyy family with scattered light correction, this factor gives the strength of the unsharp mask. Default: 1
+	;only used if (yy ne 00)
+
+	;load more than one experiment at the time
+	flag[view_MultiExp]     		= 0 	; 0 for single experiment
+	;load AIR trial alongside the odor response (air trial is given in control column in the .lst file)
+	flag[LE_AskForAir]	     		= 0 	; 0 for not loading air
+
+;END of flags related to loading data
+
+
+;************************************
+;flags related to CALCULATING SIGNALS
+;************************************
+
+	;how to calculate the data (used in ViewCalculateData\CalcSigMaster.pro)
+	flag[view_CalcMethod]   = 3550; see calcsigmaster3000
+	;family 3xyy (deltaF/f) and 4xyy (ratio) uses x for bleach correction settings, and yy for scattered light correction
+	;see  CalcSigAll3000.pro for detailed settings. Example are:
+			; setting:    0 1 2 3 4 5 6 7 8 9
+			;alPerimeter  + + + + - - - - C - ; with 8 bleaching is in coordinates only (variable: CoorPerimeter)
+			;excludeStim  - + - + - + - + - - ; exclude stimulus is obsolete - controled by LE_BleachStartFrame group
+			;addNoise     - - + + - - + + - -
+			;no bleach    - - - - - - - - - +
+			;air bleach   - - - - - - - - + - ;correct with bleach parameters taken from air trial
+	;set to 3 for no calculation (original data).
+
+	;which frames to use for calculating F in deltaF/F
+	flag[LE_StartBackground]   		= 4; for deltaF/F calculations, or bleach corrections
+	;set to -1 not to subtract background in data calculation, to frame for background start. Default: 4
+	flag[LE_PrestimEndBackground]   = 0; for deltaF/F calculations, or bleach corrections
+	;How many frames before stimulus to stop with background. Default: 2
+	;so: background is calculated from LE_StartBackground to StimulusOn - LE_PrestimEndBackground
+	;that means that StimulusOn is an important parameter and needs to be set correctly for deltaF/F
+
+	;settings for bleach correction when using CalcSigAll3000
+	flag[LE_BleachStartFrame]	= 1; start bleaching correction here
+	;for logarithmic bleach correction, all frames smaller are excluded in the fit function
+	;used in CalcSigAll3000; default 2
+	flag[LE_LogInitialFactor]	= 3; weigh the frames before stimulus more than those after stimulus
+	;for logarithmic bleach correction, all frames before stimulus onset are more important by this factor
+	;used in CalcSigAll3000; default 1 for maximum compatibility
+	flag[LE_LogExcludeSeconds]	= 12; 15 für vaga; 12 für vaga2016; 12 für Apis; how many seconds should be excluded during stimulation for bleach log fitting?
+	;for logarithmic bleach correction, how many seconds after stimulus onset to exclude
+	;used in CalcSigAll3000; default 0 for maximum compatibility
+	;this uses the time information - therefore make sure that is correct
+	;graphic display of weights can be switched of and on in the program CalcSigAll3000
+
+;END of flags related to calculating signals
+
+
+;************************************
+;flags related to SIGNAL CORRECTIONS
+;************************************
+;note: this is for the extra set of corrected signals, not for corrections done during loading or calculation
+
+;ask for air needs to be set to 1 to use this
+	flag[VIEW_InitCorr]			= -1	; set to -1 in order not to create a corrected data set
+	CorrectFlag					= 0 	; set to 1 to access corrected dataset, to 0 to access original data set
+
+
+
+;*************************************
+;flags related to data analysis OUTPUT
+;*************************************
+
+	flag[VIEW_ReportMethod]		= 119	; which output do you want? This is one of the main flags
+										; all settings are in View_gr_reports\subloop_report.pro; the most often used are
+										; 10: false-color coded pictures (calls reportTIFF)
+										; 11, 111: glodatamix (without, with tags)
+										; 12: movies
+										; 19, 119: glodatamix with CTV (without, with tags)
+
+	flag[CTV_Method]			= 22	; curve-to-value function for single number output or still images
+										; 22 gives
+										; 22 is the difference between two fixed points
+										; 35 relates to the maximum within 3 secs after stimulus onset
+										; all values in ViewOverview\CurveToValue
+										; values below 0 go to personal program in ImageALlocal folder: CurveToValueLocal.pro
+	flag[CTVM_Method]			= 0		; for multiple CTV values at once, not safely implemented yet
+
+	firstframe 					= 60   	; 70; vaga2016 60; many CTVs use fixed frames. These use the variables firstFrame and lastFrame
+	lastframe  					= 30	; 35; vaga2016 30; for example, CTV 22 calculates the difference between lastframe (3 frames) and firstframe (3 frames)
+
+	flag[LE_FirstBuffer]		= 1		; sets which buffer to start with in output routines
+										; standard is 1. Set to 0 to start with 0 (generally 0 is empty)
+										; instead of LE_firstbuffer you can use LE_usefirstbuffer (synonymous)
+
+	;old flags, currently out of fashion
+	flag[PTA_PlotTimeRange]		= 1		;
+	flag[PTA_PlotMeanValue]		= 1		;
+
+	;flags specifically for graphical output (TIFF files and the like, i.e. "overviews")
+	flag[SO_Method]				= 10	; used in ViewOverview\Overview.pro. Only values 0 or 10 are used now
+										;  0 for calculations pixel by pixel (i.e. on the time-course in each pixel)
+										; 10 for calculations frame by frame (much faster, but not all functions are possible)
+										; for 10, the CTV value is applied within ViewOverview\overview10ctv.pro
+	flag[SO_indiScale]			= 3		; what scaling to use? 0 for fixed values, else individual scale for each frame
+										; 3 for scaling within center of each frame
+										; can be used in quite sophisticated ways, see ViewOverview\SingleOverviews.pro
+										; also explained in the documentation
+	scalemax 				=  3.000	; value to scale maximum to with SO_indiScale equals 0
+	scalemin 				= -1.000	; corresponding value for minimum
+
+	flag[SO_morphoBackgr]		= 0		; Used to show an anatomical picture with an overlay of only the strongest POSITIVE responses
+	flag[SO_morphoBackgrNeg]	= 0		; Used to show an anatomical picture with an overlay of only the strongest NEGATIVE responses
+	flag[SO_withinmask]			= 0		; False-color output limited to the mask in the .area file
+
+	flag[CTV_scalebar]			= 0		; some output options allow to print out the color scalebar (SingleOverviews.pro)
+	flag[RM_FotoOk]				= 1 	; Overlay other information to overview output, (SingleOverviews.pro)
+										; 1: puts squares in the coordinate positions (from .coor file)
+										; 5: shows the perimeter of the .area file
+	flag[RM_differentViews]		= 0		; Change view, e.g. mirror flip right ALs   (SingleOverviews.pro)
+	flag[RM_unsharpmask]		= 0		; Post-hoc filter on false-color images     (SingleOverviews.pro)
+
+	flag[RM_NewColumn]			= 0		; Start a new column in "tapeten" output    (SingleOverviews.pro)
+										; this flag is generally set in the gr_XXX file
+
+	;old flags
+	flag[RM_PlotTrace]			= 0		;
+	flag[RM_PrintAscii]			= 0		;
+	flag[RM_PrintLine]			= 0		;
+	flag[RM_ROItrace]			= 0		;
+
+
+	;flags for filters
+	flag[FT_radius]				= 5	    ; 5 vaga; 10 Apis; For calculating traces (glodatamix), or for some spatial filters
+	FilterSpaceFlag 			= 0 	; If set to 1, the signal is calculated on the time trace at each pixel
+										; after taking the mean of the pixels around it (FT_radius). Therefor this is a slow filter
+										; If set to 0, this filter is switched off
+	FilterSpaceSize 			= 3 	; Filter size applied after overview calculation, always.
+										; set to 0 when FilterSpaceFlag is on, to avoid double filtering.
+	FilterTimeFlag  			= 0		; Switch for the temporal filter
+	FilterTimeSize  			= 0		; Size of the temporal filter
+
+
+	;Linestiles in IDL are (for fast traces):
+	; 0: solid; 1: dotted; 2: dashed; 3: dash-dot; 4: dash-dot-dot; 5: long dashes
+
+
+;*******************************************
+;flags to control PATHS for input and output
+;*******************************************
+
+	; this is one standard arrangement of folders
+	; just adapt the MotherOfAllFolders, but all subfolders must exist
+	; place where all files are
+	; MotherOfAllFolders				= 'C:\Hannah\PhD\Konstanz08\hannah08\'
+	MotherOfAllFolders				= 'E:\HannahData\Konstanz\IDL\'
+
+
+	flag[stg_ReportTag]				= 'XXX' ; this contains the animal flag; set this for each round and animal,
+											; this is set within the gr_file
+
+	; folder for the data files
+	;flag[STG_Datapath]				= MotherOfAllFolders + 'data\'
+	flag[STG_Datapath]				= 'E:\HannahData\Konstanz\Data\2018\Hanna_OXON-PELM_summer2018\'
+	; folder for the .inf files
+	flag[STG_OdorInfoPath]			= MotherOfAllFolders + 'IDL\lists\'
+	;flag[STG_OdorInfoPath]			= 'E:\HannahData\Konstanz\Data\Hannah16\'
+	; folder for the .coor and the .area file
+	flag[STG_OdormaskPath]			= MotherOfAllFolders + 'IDL\coor\'
+	; folder for the all OUTPUT files
+	flag[STG_OdorReportPath]		= MotherOfAllFolders + 'IDL\IDLoutput\'
+	; this is not used any more
+	flag[STG_OdorReportFile]			= ''
+	; I don't know where this is used any more
+	flag[STG_Missing]				= '999'
+
+
+;************************************
+;flags used for MOVIE output
+;************************************
+
+; in interactive mode, these flags are overwritten by the values in
+; imageALlocal\SetExportMovieFlags.pro
+; the variables are defined in view.pro
+
+		mv_exportFormat			= 6		; 1 for TIFF (single files), 2 for PICT (premiere), 3 for MPEG, 4 for multilayer GIF, 5: multilayer TIF
+										; 6 for uncompressed AVI (generally the best)
+		mv_realTime				= 0		; insert frames per second, 0 for no realTime, 24 for MPEG, 15 for GIF->QuickTime
+										; AVI can work with any time, therefore 0 IS realTIME. In the other formats, additional frames are invented to create real time
+										; if a negative number is given, that many frames are removed
+		mv_SpeedFactor			= 1		; for exportFormat 6, increase or decrease speed of movie
+		mv_reverseIt			= 1		; turn it upside down
+		mv_rotateImage			= 0		; rotate only image,  ; 0 for no action, 2 for 180 degrees
+		mv_cutborder			= 0		; how many pixels to cut from each side (border, to hide filter artefacts)
+		mv_morphoThreshold		= 0		; substitutes lower range with morphological image to be taken from file
+		mv_withinMask			= 0		; limits output to within the mask in xxx.area
+		mv_sdSignificanceCut	= 0		; cuts everything below that significance level. Stimulus is included in calculation, ignored if below 0.1.
+		;								; Not implemented yet
+		mv_markStimulus			= 1		; marks stimulus application with a red box
+		mv_percentileScale		= 0		; scaling to a percentaje of noise. don´t use
+		mv_individualScale		= 3		; follows a similar logic to (flag[so_indiScale])
+										;0, 1: Pixmin and Pixmax are taken
+										;2 : min and maximum of sequence is taken
+										;3 : min and max of central region is taken
+										;4 : max of sequence is taken, min is Pixmin
+										;5 : min and max from area region
+										;6 : min from pixmin, max from area
+										;7 : min from pixmin, max from area but only stimulus + 2*stimulus length
+
+		mv_indiScale3factor		= 0.2 	; set to 0.2 for 20 % border to be ignored when scaling usind idividualscale eq 3
+										; for mv_individualScale above 100
+		mv_percentileValue		= 0		; = float(individualScale MOD 100)/100.0
+		mv_xgap					= 30	; vertical + horizontal gaps, only even numbers!
+		mv_ygap					= 50  	; 6, 10
+		mv_correctStimulusOnset	= 0 	; value to be added to stimulus onset (in frames)
+		mv_displayTime			= 1		; time in ss:ms as figures
+		mv_minimumBrightness	= 0		; creates a mask that depends on the brightnes of the foto
+		;these flags are for interactive use in view (fasttraces)
+		;but are also used in movies to select a frame range for movie output
+		;set both to -1 to calculate the entire movie, else to fixed frame numbers
+		flag[FT_FirstFrame]				= -1		; show trace subsed in fast traces in VIEW
+		flag[FT_LastFrame]				= -1		; show trace subsed in fast traces in VIEW
+
+		;flags that are also relevant for movies; do NOT change them here to avoid confusion, change them above
+		;check program viewoverview\Exportmovie
+		;flag[ctv_scalebar]
+		;scaleMin
+		;scaleMax
+		;filterSpaceFlag
+		;filterSpaceSize
+		;colortable
+		;FILENAME defined in localOdorText
+
+;end of movie settings
+
+
+
+;************************************
+;flags NOT USED for off-line analysis
+;************************************
+
+	flag[LE_ShowBox]				= 0		;
+	flag[FT_AllOdors]				= 0		; show all odor traces in fast traces in VIEW
+	flag[FT_TimeXAxis]				= 0		; show time as x-axis in fast traces in VIEW
+	flag[FT_Subset]					= 0		; show trace subsed in fast traces in VIEW
+	flag[FT_SelectFrame]			= 0		; show trace subsed in fast traces in VIEW
+
+;**************************************************************************************************************
+
+
+
+;		calling via a gr file needs a line as this one:
+;flag[stg_reporttag] = '060503a_22a' & gr_060503a_22a
+
+; 		calling without a fixed order can be done with
+; gr_takefromlist, '060503a_22a', 2
+; 		here the number 2 relates to the corresponding column in the .lst file;
+; 		which measurements are picked depends on this number
+
+
+;animallist
+
+
+
+flag[so_indiScale]          = 3; 0
+
+
+;call another program that contains the individual animal's lists
+
+;tapeten_list ;Apis
+
+;GC_list
+
+;GC_2013_list
+
+;Vespula
+
+;vaga2016
+
+;Apis2018_winter
+
+Apis2018_summer
+
+;
+; 	scalemax =3.000
+;	scalemin =  -2.000
+;flag[stg_reporttag] ='HS_bee_OXON_PELM_180420'
+;gr_HS_bee_OXON_PELM_180420
+;;gr_takefromlist, 'HS_bee_OXON_PELM_180420', 2
+
+
+
+
+
+
+;	scalemax =3.000
+;	scalemin =  -2.000
+;flag[stg_reporttag] ='HS_bee_OXON_180727'
+;gr_HS_bee_OXON_180727
+;;gr_takefromlist, 'HS_bee_OXON_180727', 2
+;
+
+
+;	scalemax =3.000
+;	scalemin =  -2.000
+;flag[stg_reporttag] ='HS_bee_OXON_180416a'
+;gr_HS_bee_OXON_180416a
+;;gr_takefromlist, 'HS_bee_OXON_180416a', 2
+;
+;	scalemax =3.000
+;	scalemin =  -2.000
+;flag[stg_reporttag] ='HS_bee_PELM_180416b'
+;gr_HS_bee_PELM_180416b
+;gr_takefromlist, 'HS_bee_PELM_180416b', 2
+;
+;	scalemax =3.000
+;	scalemin =  -2.000
+;flag[stg_reporttag] ='HS_bee_OXON_PELM_180417'
+;;gr_HS_bee_OXON_PELM_180417
+;gr_takefromlist, 'HS_bee_OXON_PELM_180417', 2
+
+
+
+
+
+
+;	scalemax =3.000
+;	scalemin =  -2.000
+;flag[stg_reporttag] ='vaga160425a'
+;;gr_vaga160425a
+;gr_takefromlist, 'vaga160425a', 2
+
+;
+;
+;	scalemax =3.000
+;	scalemin =  -2.000
+;flag[stg_reporttag] ='vaga160407b'
+;gr_vaga160407b
+
+;Unterschied Andrena vaga - Apis: Radius, LogExcludeSeconds, CTV first frame last frame
+
+
+;flag[stg_reporttag] = 'vaga9414_1'
+;	scalemax =  3.500
+;    scalemin = -1.000
+;;gr_vaga9414_1
+;
+;flag[stg_reporttag] = 'vaga9414_2'
+;	scalemax =  3.500
+;    scalemin = -1.000
+;;gr_vaga9414_2
+;
+;flag[stg_reporttag] = 'vaga9415_2'
+;	scalemax =  3.500
+;    scalemin = -1.000
+;;gr_vaga9415_2
+;
+;flag[stg_reporttag] = 'vaga9415_3'
+;	scalemax =  3.500
+;    scalemin = -1.000
+;;gr_vaga9415_3
+;
+;flag[stg_reporttag] = 'vaga9416_1'
+;	scalemax =  3.500
+;    scalemin = -1.000
+;;gr_vaga9416_1
+;
+;flag[stg_reporttag] = 'vaga9416_2'
+;	scalemax =  3.500
+;    scalemin = -1.000
+;;gr_vaga9416_2
+;
+;flag[stg_reporttag] = 'vaga9417_1'
+;	scalemax =  3.500
+;    scalemin = -1.000
+;;gr_vaga9417_1
+;
+;flag[stg_reporttag] = 'vaga9417_2'
+;	scalemax =  3.500
+;    scalemin = -1.000
+;;gr_vaga9417_2
+;
+;
+;flag[stg_reporttag] = 'vaga9418_1'
+;	scalemax =  3.500
+;    scalemin = -1.000
+;;gr_vaga9418_1
+;
+;
+;
+;
+;
+;
+;
+;flag[stg_reporttag] = 'vaga8405a'
+;	scalemax =  3.500
+;    scalemin = -1.000
+;;gr_vaga8405a
+;;gr_takefromlist,'vaga8405a',2
+;
+;flag[stg_reporttag] = 'vaga8406b'
+;	scalemax =  4.000
+;    scalemin = -1.000
+;;gr_vaga8406b
+;;gr_takefromlist,'vaga8406b',2
+;
+;flag[stg_reporttag] = 'vaga8407a'
+;;gr_vaga8407a
+;;gr_takefromlist,'vaga8407a',2
+;
+;flag[stg_reporttag] = 'vaga8407b_2'
+;;gr_vaga8407b_2
+;;gr_takefromlist,'vaga8407b_2',2
+;
+;flag[stg_reporttag] = 'vaga8408a'
+;;gr_vaga8408a
+;;gr_takefromlist,'vaga8408a',2
+;
+;flag[stg_reporttag] = 'vaga8409a'
+;    scalemax =  4.500
+;    scalemin = -1.000
+;;gr_vaga8409a
+;;gr_takefromlist,'vaga8409a',2
+;
+;flag[stg_reporttag] = 'vaga8410b'
+;    scalemax =  4.000
+;    scalemin = -1.000
+;;gr_vaga8410b
+;;gr_takefromlist,'vaga8410b',2
+
+
+
+
+
+;
+;flag[stg_reporttag] = 'vaga8414d'
+;    scalemax =  3.000
+;    scalemin = -1.000
+;gr_vaga8414d
+;;gr_takefromlist,'vaga8414d',2
+;
+;flag[stg_reporttag] = 'vaga8415a'
+;gr_vaga8415a
+;;gr_takefromlist,'vaga8415a',2
+;
+;flag[stg_reporttag] = 'vaga8415b'
+;gr_vaga8415b
+;;gr_takefromlist,'vaga8415b',2
+;
+;flag[stg_reporttag] = 'vaga8415c'
+;gr_vaga8415c
+;;gr_takefromlist,'vaga8415c',2
+;
+;flag[stg_reporttag] = 'vaga8416b'
+;gr_vaga8416b
+;;gr_takefromlist,'vaga8416b',2
+;
+;flag[stg_reporttag] = 'vaga8416c'
+;    scalemax =  2.500
+;    scalemin =  0.000
+;gr_vaga8416c
+;gr_takefromlist,'vaga8416c',2
+;
+;flag[stg_reporttag] = 'vaga8417a'
+;gr_vaga8417a
+;;gr_takefromlist,'vaga8417a',2
+;
+;flag[stg_reporttag] = 'vaga8418a'
+;gr_vaga8418a
+;;gr_takefromlist,'vaga8418a',2
+;
+;flag[stg_reporttag] = 'vaga8418b'
+;;kein IDL/Knime
+;;gr_vaga8418b
+;;gr_takefromlist,'vaga8418b',2
+;
+;flag[stg_reporttag] = 'vaga8419a'
+;gr_vaga8419a
+;;gr_takefromlist,'vaga8419a',2
+;
+;flag[stg_reporttag] = 'vaga8420a'
+;;neue lst und gr erstellt, bringt auch nichts
+;;gr_vaga8420a
+;;gr_takefromlist,'vaga8420a',2
+;
+;flag[stg_reporttag] = 'vaga8421a'
+;gr_vaga8421a
+;;gr_takefromlist,'vaga8421a',2
+;
+;flag[stg_reporttag] = 'vaga8422a'
+;gr_vaga8422a
+;;gr_takefromlist,'vaga8422a',2
+;
+;flag[stg_reporttag] = 'vaga8423a'
+;gr_vaga8423a
+;;gr_takefromlist,'vaga8423a',2
+;
+;flag[stg_reporttag] = 'vaga8423b'
+;gr_vaga8423b
+;;gr_takefromlist,'vaga8423b',2
+;
+;flag[stg_reporttag] = 'vaga8424a'
+;gr_vaga8424a
+;;gr_takefromlist,'vaga8424a',2
+;
+;flag[stg_reporttag] = 'vaga8424b'
+;gr_vaga8424b
+;;gr_takefromlist,'vaga8424b',2
+;
+;flag[stg_reporttag] = 'vaga8425a'
+;gr_vaga8425a
+;;gr_takefromlist,'vaga8425a',2
+;
+;flag[stg_reporttag] = 'vaga8428a'
+;gr_vaga8428a
+;;gr_takefromlist,'vaga8428a',2
+;
+;flag[stg_reporttag] = 'vaga8429a'
+;;keine lst-Datei vorhanden; Stromausfall
+;;gr_vaga8429a
+;;gr_takefromlist,'vaga8429a',2
+;
+;flag[stg_reporttag] = 'vaga8429b'
+;gr_vaga8429b
+;;gr_takefromlist,'vaga8429b',2
+;
+;flag[stg_reporttag] = 'vaga8430a'
+;gr_vaga8430a
+;;gr_takefromlist,'vaga8430a',2
+;
+;flag[stg_reporttag] = 'vaga8430b'
+;gr_vaga8430b
+;;gr_takefromlist,'vaga8430b',2
+;
+;flag[stg_reporttag] = 'vaga8501a'
+;gr_vaga8501a
+;;gr_takefromlist,'vaga8501a',2
+;
+;flag[stg_reporttag] = 'vaga8501b'
+;gr_vaga8501b
+;;gr_takefromlist,'vaga8501b',2
+;
+;flag[stg_reporttag] = 'vaga8501c'
+;gr_vaga8501c
+;;gr_takefromlist,'vaga8501c',2
+;
+;flag[stg_reporttag] = 'vaga8504a'
+;gr_vaga8504a
+;;gr_takefromlist,'vaga8504a',2
+;
+;flag[stg_reporttag] = 'vaga8507a'
+;gr_vaga8507a
+;;gr_takefromlist,'vaga8507a',2
+
+end;
+

FakeData_tiny/IDLprogs/tapestry_configs/custom_csv.yml

@@ -0,0 +1,18 @@
+row1:
+  animal: FakeData
+  flags:
+    SO_individualScale: 2
+    SO_Method: 0
+    CTV_Method: ctv_35_2
+    CTV_MethodFile: IDLprogs/custom_csv.py
+  measus: [1, 2, 3]
+  extra_formats: [tif]
+
+row2:
+  measus: [4, 5, 6]
+  flags:
+    CTV_Method: "null"
+  
+row3:
+  measus: [7, 8, 9]
+    

FakeData_tiny/IDLprogs/tapestry_configs/default.yml

@@ -0,0 +1,16 @@
+row1:
+  animal: FakeData
+  flags:
+    SO_individualScale: 2
+    SO_Method: 0
+    CTV_Method: 22
+    CTV_firstframe: 22
+    CTV_lastframe: 35
+  measus: [1, 2, 3]
+  extra_formats: [tif]
+row2:
+  measus: [4, 5, 6]
+  
+row3:
+  measus: [7, 8, 9]
+    

FakeData_tiny/IDLprogs/tapestry_configs/different_animals.yml

@@ -0,0 +1,12 @@
+row1:
+  animal: FakeData
+  flags:
+    SO_individualScale: 2
+    SO_Method: 0
+    CTV_Method: 22
+    CTV_firstframe: 22
+    CTV_lastframe: 35
+  measus: [1, 3, 5, 7, 9]
+row2:
+  animal: FakeData_inverted_order
+    

FakeData_tiny/IDLprogs/tapestry_configs/different_animals_flags.yml

@@ -0,0 +1,28 @@
+row1:
+  animal: FakeData
+  flags:
+    SO_individualScale: 2
+    SO_Method: 0
+    CTV_Method: 22
+    CTV_firstframe: 22
+    CTV_lastframe: 35
+  measus: [1, 3, 5, 7, 9]
+row2:
+  flags:
+    SO_individualScale: 1
+    SO_MV_scalemin: -0.25
+    SO_MV_scalemax: 0.4
+row3:
+  animal: FakeData_inverted_order
+  flags:
+    SO_individualScale: 2
+    SO_Method: 0
+    CTV_Method: 22
+    CTV_firstframe: 22
+    CTV_lastframe: 35
+row4:
+  flags:
+    SO_individualScale: 1
+    SO_MV_scalemin: -0.25
+    SO_MV_scalemax: 0.4
+    

FakeData_tiny/IDLprogs/tapestry_configs/different_animals_measus.yml

@@ -0,0 +1,12 @@
+row1:
+  animal: FakeData
+  flags:
+    SO_individualScale: 2
+    SO_Method: 0
+    CTV_Method: 22
+    CTV_firstframe: 22
+    CTV_lastframe: 35
+  measus: [1, 3, 5, 7, 9]
+row2:
+  animal: FakeData_inverted_order
+  measus: [2, 4, 6, 8]  

FakeData_tiny/IDLprogs/tapestry_configs/different_flags.yml

@@ -0,0 +1,23 @@
+row1:
+  animal: FakeData
+  flags:
+    SO_individualScale: 2
+    SO_Method: 0
+    CTV_Method: 22
+    CTV_firstframe: 22
+    CTV_lastframe: 35
+  measus: [1, 3, 5, 7, 9]
+row2:
+  flags:
+    SO_individualScale: 1
+    SO_MV_scalemin: -0.25
+    SO_MV_scalemax: 0.4
+row3:
+  flags:
+    SO_showROIs: 10
+row4:
+  flags:
+    SO_showROIs: 13
+row5:
+  flags:
+    SO_showROIs: 14

FakeData_tiny/IDLprogs/tapestry_configs/incomplete_matrix.yml

@@ -0,0 +1,16 @@
+row1:
+  animal: FakeData
+  flags:
+    SO_individualScale: 2
+    SO_Method: 0
+    CTV_Method: 22
+    CTV_firstframe: 22
+    CTV_lastframe: 35
+  measus: [1, 2, 3]
+  extra_formats: [tif]
+row2:
+  measus: [4, 1000, 6]
+  
+row3:
+  measus: [7, 8, 9]
+    

FakeData_tiny/IDLprogs/tapestry_configs/no_extra_formats.yml

@@ -0,0 +1,15 @@
+row1:
+  animal: FakeData
+  flags:
+    SO_individualScale: 2
+    SO_Method: 0
+    CTV_Method: 22
+    CTV_firstframe: 22
+    CTV_lastframe: 35
+  measus: [1, 2, 3]
+row2:
+  measus: [4, 5, 6]
+  
+row3:
+  measus: [7, 8, 9]
+    

FakeData_tiny/IDLprogs/tapestry_configs/redTextOnYellowBG.yml

@@ -0,0 +1,18 @@
+row1:
+  animal: FakeData
+  flags:
+    SO_individualScale: 2
+    SO_Method: 0
+    CTV_Method: 22
+    CTV_firstframe: 22
+    CTV_lastframe: 35
+    SO_fgColor: r
+    SO_bgColor: y
+  measus: [1, 2, 3]
+  extra_formats: [tif]
+row2:
+  measus: [4, 5, 6]
+  
+row3:
+  measus: [7, 8, 9]
+    

FakeData_tiny/IDLprogs/tapestry_configs/small.yml

@@ -0,0 +1,12 @@
+row1:
+  animal: FakeData
+  flags:
+    SO_individualScale: 2
+    SO_Method: 0
+    CTV_Method: 22
+    CTV_firstframe: 22
+    CTV_lastframe: 35
+  measus: [1, 2]
+
+
+    

FakeData_tiny/IDLprogs/tapestry_configs/with_movies_libx264.yml

@@ -0,0 +1,22 @@
+row1:
+  animal: FakeData
+  flags:
+    SO_individualScale: 2
+    SO_Method: 0
+    CTV_Method: 22
+    CTV_firstframe: 22
+    CTV_lastframe: 35
+  measus: [1, 2, 3]
+  extra_formats: [tif]
+  corresponding_movies: True
+  extra_movie_flags:
+    mv_ygap: 30
+    mv_xgap: 30
+    mv_exportFormat: libx264
+    
+row2:
+  measus: [4, 5, 6]
+  
+row3:
+  measus: [7, 8, 9]
+    

FakeData_tiny/IDLprogs/tapestry_configs/with_movies_stack_tif.yml

@@ -0,0 +1,22 @@
+row1:
+  animal: FakeData
+  flags:
+    SO_individualScale: 2
+    SO_Method: 0
+    CTV_Method: 22
+    CTV_firstframe: 22
+    CTV_lastframe: 35
+  measus: [1, 2, 3]
+  extra_formats: [tif]
+  corresponding_movies: True
+  extra_movie_flags:
+    mv_ygap: 30
+    mv_xgap: 30
+    mv_exportFormat: stack_tif
+    
+row2:
+  measus: [4, 5, 6]
+  
+row3:
+  measus: [7, 8, 9]
+    

FakeData/internal_defaults.yml

@@ -110,4 +110,4 @@ mv_ygap: 0
 SO_MV_scalemax: 1.0
 SO_MV_scalemin: 0.0
 LE_CalcMethod: 3900
-LE_loadExp: 666
+LE_loadExp: 676

FakeData_tiny/test_defaults.yml

@@ -0,0 +1,93 @@
+CSM_Datashift: 0
+Data_Median_Filter: 0
+Data_Median_Filter_space: 3
+Data_Median_Filter_time: 3
+CSM_Movement: 0
+CSM_SkipFrmAtBack: 0
+CSM_SkipFrmUpFront: 0
+CTVM_Method: 0
+CTV_Method: 22
+CTV_firstframe: 22
+CTV_lastframe: 36
+CTV_scalebar: true
+VIEW_CorrSignals: false
+FT_AllOdors: false
+FT_Radius: 5
+FT_SelectFrame: false
+FT_Subset: false
+FT_TimeXAxis: false
+Signal_FilterSpaceFlag: false
+Signal_FilterSpaceSize: 3
+Signal_FilterTimeFlag: false
+Signal_FilterTimeSize: 3
+LE_AskForAir: false
+LE_BleachStartFrame: 4
+LE_ClipPixels: 0
+LE_FirstBuffer: 1
+LELog_ExcludeSeconds: 0
+LELog_InitialFactor: 1
+LE_PrestimEndBackground: 2
+LE_ShrinkFaktor: 0
+LE_StartBackground: 4
+PTA_PlotMeanValue: false
+PTA_PlotTimeRange: false
+RM_FotoOk: 0
+RM_NewColumn: false
+RM_NextPosition: (0, 0)
+RM_PlotTrace: false
+RM_PrintAscii: false
+RM_PrintLine: false
+RM_ROITrace: false
+RM_differentViews: false
+RM_separateLayers: 0
+RM_unsharpmask: false
+SO_Method: 0
+SO_indiScale: 0
+SO_morphoBackgr: 0
+SO_morphoBackgrNeg: 0
+SO_withinmask: 0
+STG_Datapath: data
+STG_Measu: 7
+STG_OdorInfoPath: Lists
+STG_OdorReportFile: FakeData.lst
+STG_OdorReportPath: IDLoutput
+STG_ProcessedDataPath: Processed_Data
+STG_OdormaskPath: Coor
+STG_ReportTag: FakeData
+VIEW_DeleteRawData: 0
+VIEW_InitCorr: 0
+VIEW_MultiExp: 0
+VIEW_No4Darray: 0
+VIEW_ReportMethod: 10
+VIEW_ScatterLightFactor: 0.0
+VIEW_batchmode: false
+SO_MV_colortable: 13
+mv_FirstFrame: 0
+mv_LastFrame: -1
+mv_SpeedFactor: 1.0
+mv_bgColor: k
+mv_bitrate: 1024k
+mv_correctStimulusOnset: 0
+mv_cutborder: 0
+mv_displayTime: 0.5
+mv_exportFormat: stack_tif
+mv_fgColor: w
+mv_indiScale3factor: 0.2
+mv_individualScale: 0
+mv_markStimulus: 0
+mv_minimumBrightness: 0.0
+mv_morphoThreshold: 0.0
+mv_percentileScale: false
+mv_percentileValue: 0.0
+mv_realTime: 0
+mv_reverseIt: false
+mv_rotateImage: 0
+mv_sdSignificanceCut: 0.0
+mv_suppressMilliseconds: false
+mv_withinMask: false
+mv_xgap: 30
+mv_ygap: 30
+SO_MV_scalemax: 1.0
+SO_MV_scalemin: 0.0
+LE_CalcMethod: 3900
+LE_loadExp: 676

FakeData/view_fake_data.yml

@@ -92,4 +92,4 @@ mv_ygap: 50
 SO_MV_scalemax: 0.3
 SO_MV_scalemin: -0.08
 LE_CalcMethod: 3900
-LE_loadExp: 666
+LE_loadExp: 676

FakeData/win_test.yml

@@ -115,4 +115,4 @@ mv_ygap: 50
 SO_MV_scalemax: 0.3
 SO_MV_scalemin: -0.08
 LE_CalcMethod: 3900
-LE_loadExp: 666
+LE_loadExp: 676

HS_Till/usage_till.yml

@@ -52,7 +52,7 @@ STG_Measu: 16
 STG_Missing: none
 STG_OdorInfoPath: IDLlist
 STG_OdorReportFile: HS_bee_OXON_180716
-STG_OdorReportPath: ../../HS_Till/IDLoutput
+STG_OdorReportPath: IDLoutput
 STG_OdormaskPath: IDLcoor
 STG_ReportTag: 02_LINT-3
 VIEW_DeleteRawData: 0

IP_Fura/usage_till.yml

@@ -52,7 +52,7 @@ STG_Measu: 16
 STG_Missing: none
 STG_OdorInfoPath: IDLlist
 STG_OdorReportFile: HS_bee_OXON_180716
-STG_OdorReportPath: ../../IP_Fura/IDLoutput
+STG_OdorReportPath: IDLoutput
 STG_OdormaskPath: IDLcoor
 STG_ReportTag: 02_LINT-3
 VIEW_DeleteRawData: 0

LM_Till_only_FID/usage_till.yml

@@ -29,7 +29,6 @@ LELog_InitialFactor: 3
 LE_PrestimEndBackground: 1
 LE_ShrinkFaktor: 0
 LE_StartBackground: 4
-STG_MotherOfAllFolders: /home/aj/SharedWithWindows/LM_Till_only_FID
 PTA_PlotMeanValue: true
 PTA_PlotTimeRange: true
 RM_FotoOk: 0
@@ -51,7 +50,7 @@ STG_Datapath: data
 STG_Measu: 16
 STG_OdorInfoPath: lists
 STG_OdorReportFile: '''empty'
-STG_OdorReportPath: ../../LM_Till_only_FID/output
+STG_OdorReportPath: output
 STG_OdormaskPath: coor
 STG_ReportTag: not set yet
 VIEW_DeleteRawData: 0

MR_Till/usage_till.yml

@@ -52,7 +52,7 @@ STG_Measu: 16
 STG_Missing: none
 STG_OdorInfoPath: IDLlist
 STG_OdorReportFile: HS_bee_OXON_180716
-STG_OdorReportPath: ../../MR_Till/IDLoutput
+STG_OdorReportPath: IDLoutput
 STG_OdormaskPath: IDLcoor
 STG_ReportTag: 02_LINT-3
 VIEW_DeleteRawData: 0
@@ -86,4 +86,4 @@ SO_MV_scalemax: 1.0
 SO_MV_scalemin: 0.0
 LE_CalcMethod: 3900
 LE_loadExp: 3
-STG_TempArchivePath: /home/aj/tmp
+STG_TempArchivePath: /home/aj/tmp

MS_LSM/usage_lsm.yml

@@ -30,7 +30,6 @@ LELog_InitialFactor: 3
 LE_PrestimEndBackground: 1
 LE_ShrinkFaktor: 0
 LE_StartBackground: 10
-STG_MotherOfAllFolders: /Users/MARCO/Desktop/KiaKaha/testview
 PTA_PlotMeanValue: true
 PTA_PlotTimeRange: true
 RM_FotoOk: 0
@@ -71,7 +70,7 @@ STG_Datapath: Data
 STG_Measu: 1
 STG_OdorInfoPath: lists
 STG_OdorReportFile: HS_bee_OXON_180716
-STG_OdorReportPath: ../../MS_LSM/IDLoutput
+STG_OdorReportPath: IDLoutput
 STG_OdormaskPath: coor
 STG_ReportTag: '20200116'
 VIEW_DeleteRawData: 0
@@ -120,4 +119,3 @@ SO_MV_scalemin: 0.0
 LE_CalcMethod: 3900
 LE_labelColumns: ('Measu',)
 LE_loadExp: 20
-STG_TempArchivePath: /home/aj/tmp

Or47a_test/01_DATA/MR_190510b/MR_190510b_or47a.pst/dbb1618.tif

@@ -1 +0,0 @@
-/annex/objects/MD5-s11748146--5b554957a3be07fa78f87f26b77059a0

Or47a_test/01_DATA/MR_190510b/MR_190510b_or47a.pst/dbb161A.tif

@@ -1 +0,0 @@
-/annex/objects/MD5-s11748146--5184f19c819ccbda19570bcacfd95ecb

Or47a_test/01_DATA/MR_190510b/MR_190510b_or47a.pst/dbb161C.tif

@@ -1 +0,0 @@
-/annex/objects/MD5-s11748146--a584c254cb8daea9528e9ffeb1f145ab

Or47a_test/01_DATA/MR_190510b/MR_190510b_or47a.pst/dbb161E.tif

@@ -1 +0,0 @@
-/annex/objects/MD5-s11748146--8751234fcc3565e402b39ae4a80b4768

Or47a_test/01_DATA/MR_190510b/MR_190510b_or47a.pst/dbb1620.tif

@@ -1 +0,0 @@
-/annex/objects/MD5-s11748146--12b00891a9879ca5d124eda031c07037

Or47a_test/01_DATA/MR_190510b/MR_190510b_or47a.pst/dbb1622.tif

@@ -1 +0,0 @@
-/annex/objects/MD5-s11748146--f281495b07b6a5a80b83047ea17c1161

Or47a_test/usage_till_linux.yml

@@ -48,7 +48,7 @@ STG_Measu: 16
 STG_Missing: none
 STG_OdorInfoPath: 02_SETTINGS
 STG_OdorReportFile: HS_bee_OXON_180716
-STG_OdorReportPath: ../../Or47a_test/IDLoutput
+STG_OdorReportPath: IDLoutput
 STG_OdormaskPath: IDLcoor
 STG_ReportTag: 02_LINT-3
 VIEW_DeleteRawData: 0
@@ -79,4 +79,4 @@ SO_MV_scalemax: 1.0
 SO_MV_scalemin: 0.0
 LE_CalcMethod: 3900
 LE_loadExp: 3
-STG_TempArchivePath: /home/aj/tmp
+STG_TempArchivePath: /home/aj/tmp

Or47a_test/usage_till_test.yml

@@ -52,7 +52,7 @@ STG_Measu: 16
 STG_Missing: none
 STG_OdorInfoPath: 02_SETTINGS
 STG_OdorReportFile: HS_bee_OXON_180716
-STG_OdorReportPath: ../../Or47a_test/IDLoutput
+STG_OdorReportPath: IDLoutput
 STG_OdormaskPath: IDLcoor
 STG_ReportTag: 02_LINT-3
 VIEW_DeleteRawData: 0
@@ -85,4 +85,4 @@ mv_ygap: 0
 SO_MV_scalemax: 1.0
 SO_MV_scalemin: 0.0
 LE_CalcMethod: 3900
-LE_loadExp: 3
+LE_loadExp: 3

Or47a_test/usage_till_windows.yml

@@ -48,7 +48,7 @@ STG_Measu: 16
 STG_Missing: none
 STG_OdorInfoPath: 02_SETTINGS
 STG_OdorReportFile: HS_bee_OXON_180716
-STG_OdorReportPath: ../../Or47a_test/IDLoutput
+STG_OdorReportPath: IDLoutput
 STG_OdormaskPath: IDLcoor
 STG_ReportTag: 02_LINT-3
 VIEW_DeleteRawData: 0
@@ -79,4 +79,4 @@ SO_MV_scalemax: 1.0
 SO_MV_scalemin: 0.0
 LE_CalcMethod: 3900
 LE_loadExp: 3
-STG_TempArchivePath: E:/temp
+STG_TempArchivePath: E:/temp

Readme.md

@@ -2,16 +2,17 @@ This repository contains datasets for testing pyview (https://github.com/galizia
 
 Each folder contains a dataset. A brief description of the datasets follow.
 
-|Name|Experimenter|Lab|Setup|Description|LE_loadExp|LE_CalcMethod|
-|----|------------|---|-----|-----------|------------|---------------|
-|FakeData|Giovanni Galizia|Galizia Lab, University of Konstanz, Germany|-|Artificial data simulating single wavelength calcium imaging with pixel shot noise and movement artifacts.|666|3|
-|AS_Till_2004|Ana Silberling|Galizia Lab, University of Konstanz, Germany|Till Photonics|Calcium measurements in the Drosophila Antennal Lobe using GCamp. Short measurements, single wavelength.|3|3|
-|HS_Till|Hanna Schnell|Galizia Lab, University of Konstanz, Germany|Till Photonics|Calcium measurements in the Honeybee Antennal Lobe using GCamp. Short measurements, single wavelength.|3|3|
-|IP_Fura|Inga Petelski|Galizia Lab, University of Konstanz, Germany|Till Photonics|Calcium measurements in the Locust Antennal Lobe using Fura. Short measurements, dual wavelength.|4|4|
-|LM_Till_only_FID|Latha Mukunda|Galizia Lab, University of Konstanz, Germany|Till Photonics|Calcium measurements in the Drosophila Antenna using GCamp. Long measurements, single wavelength.|3|3|
-|MR_Till|Marylin Rowswell|Galizia Lab, University of Konstanz, Germany|Till Photonics|Calcium measurements in the Drosophila Antenna using GCamp. Long measurements, single wavelength.|3|3|
-|MS_LSM|Marco Stucchi|Galizia Lab, University of Konstanz, Germany|Zeiss LSM510|Calcium measurements in the Drosophila Mushroom Body using GCamp. Very Long measurements, single wavelength.|20|3|
-|SS_LSM|Sercan Sayin|Galizia Lab, University of Konstanz, Germany|Zeiss LSM510|Calcium measurements in the Locust Antennal lobe using Oregon green. Short measurements, single wavelength.|20|3|
-|SS_LSM_Pixel_Calibration|Sercan Sayin|Galizia Lab, University of Konstanz, Germany|Zeiss LSM510|Calibration measurements using a very small scale. Short measurements, single wavelength.|20|3|
-|Or47a_test|Alja Luedke, Marylin Rowswell, Paavan Gouniyal|Galizia Lab, University of Konstanz, Germany|Till Photonics|Calcium measurements in the Drosophila Antenna using GCamp. Long measurements, single wavelength.|3|3|
-
+| Folder Name              |Experimenter|Lab|Setup| Description                                                                                                                                            | LE_loadExp |LE_CalcMethod|
+|--------------------------|------------|---|-----|--------------------------------------------------------------------------------------------------------------------------------------------------------|------------|---------------|
+| FakeData                 |Giovanni Galizia|Galizia Lab, University of Konstanz, Germany|-| Artificial data simulating single wavelength calcium imaging with pixel shot noise and movement artifacts. Used only for testing                       | 666        |3|
+| FakeData_tiny            |Giovanni Galizia|Galizia Lab, University of Konstanz, Germany|-| Simulated data as in FakeData above, but smaller (50x50x50). Used only for testing.                                                | 676        |3|
+| AS_Till_2004             |Ana Silberling|Galizia Lab, University of Konstanz, Germany|Till Photonics| Calcium measurements in the Drosophila Antennal Lobe using GCamp. Short measurements, single wavelength.                                               | 3          |3|
+| HS_Till                  |Hanna Schnell|Galizia Lab, University of Konstanz, Germany|Till Photonics| Calcium measurements in the Honeybee Antennal Lobe using GCamp. Short measurements, single wavelength.                                                 | 3          |3|
+| IP_Fura                  |Inga Petelski|Galizia Lab, University of Konstanz, Germany|Till Photonics| Calcium measurements in the Locust Antennal Lobe using Fura. Short measurements, dual wavelength.                                                      | 4          |4|
+| LM_Till_only_FID         |Latha Mukunda|Galizia Lab, University of Konstanz, Germany|Till Photonics| Calcium measurements in the Drosophila Antenna using GCamp. Long measurements, single wavelength.                                                      | 3          |3|
+| MR_Till                  |Marylin Rowswell|Galizia Lab, University of Konstanz, Germany|Till Photonics| Calcium measurements in the Drosophila Antenna using GCamp. Long measurements, single wavelength.                                                      | 3          |3|
+| MS_LSM                   |Marco Stucchi|Galizia Lab, University of Konstanz, Germany|Zeiss LSM510| Calcium measurements in the Drosophila Mushroom Body using GCamp. Very Long measurements, single wavelength.                                           | 20         |3|
+| SS_LSM                   |Sercan Sayin|Galizia Lab, University of Konstanz, Germany|Zeiss LSM510| Calcium measurements in the Locust Antennal lobe using Oregon green. Short measurements, single wavelength.                                            | 20         |3|
+| SS_LSM_Pixel_Calibration |Sercan Sayin|Galizia Lab, University of Konstanz, Germany|Zeiss LSM510| Calibration measurements using a very small scale. Short measurements, single wavelength.                                                              | 20         |3|
+| Or47a_test               |Alja Luedke, Marylin Rowswell, Paavan Gouniyal|Galizia Lab, University of Konstanz, Germany|Till Photonics| Calcium measurements in the Drosophila Antenna using GCamp. Long measurements, single wavelength.                                                      | 3          |3|
+| Bente_test               |Elena Ian|Bente Lab, NTNU, Trondheim, Norway|epifluorescent microscope (Olympus BX51WI) equipped with a 20x (NA 1.00) water immersion objective (Olympus XLUMPlanFLN)| Calcium measurements in the moth antennal lobe using fura-2 dextran dye (10,000 MW, in 2% BSA; Molecular Probes). Short measurements, dual wavelength. | 34         |4|

SS_LSM/usage_lsm.yml

@@ -51,7 +51,7 @@ STG_Measu: 8
 STG_Missing: none
 STG_OdorInfoPath: Lists
 STG_OdorReportFile: 20190809_SS_locust004_CaGreen.lst
-STG_OdorReportPath: ../../SS_LSM/IDLoutput
+STG_OdorReportPath: IDLoutput
 STG_OdormaskPath: Coor
 STG_ReportTag: 07_IsoE_-2_CaG_a
 VIEW_DeleteRawData: 0