diff --git a/CHANGELOG b/CHANGELOG
index f6e346cea7556fe4bc4e5dd4c5ab0932d66b3b8a..99d9c6eb984a6a2e7274f95096db2cb1ca8e5c27 100644
--- a/CHANGELOG
+++ b/CHANGELOG
@@ -1,19 +1,40 @@
<<<<<<< HEAD
+XX.06.2020: Update to V3.5.0
+ - add download maximum heat flux and wetted area
+16.06.2020: Update to V3.4.1
+ - updated and improved documentation
+ - include chache function and a request for all ports for heat flux and divertor load
08.02.2020: Update to V3.4.0
- Bugfix: download_images_by_times produzierte doppelte Werte im Zeit und Bildvektor
- -
+ - Bugfix: download divertor load gave print back that request was made, although no request was made (fixed)
+ - Bugfix: correction of version bug from last commit and change of default value for the profile extraction functions to None
+ - Readme added
Update to V3.3.3:
- -
+ - added function for peaking factor and strike line width calculation in tools
+ - bugfix: bug fix get_trigger_from_PID which caused an error in get_latest_version if it was used for non IR data
Update to V3.3.2:
- -
+ - extended feedback for download_heatflux
+ - changes of wetted area calculation
+ - changes of wetted area calculation
+ - bugfix: get_trigger_PID, get_finger and during import (see issues 6,7)
Update to V3.3.1:
- -
+ - bugfix: FOV for AEF50 was not working correctly
Update to V3.3.0:
- -
+ - IR_config_constants: added path to Test archive and use this variable in downloadversionIRdata when building an archive path
+ - downloadversionIRdata: consistent use of testmode parameter
+ - downloadversionIRdata: added testmode to all reading routines allowing reading from Test archive
+ - IR_image_tools: no longer sets working directory to script file location
+ - IR_config_constants: sets parameter_file_path as absolute path
+ - downloadversionIRdata: new function get_trigger_from_PID() wraps get_program_from_PID() and allows getting timestamps for lab data
+ - downloadversionIRdata: download_images_by_time_via_png() now uses nanosecond timestamps to call AKF_2.get_time_intervals()
+ - downloadversionIRdata: disable DeprecationWarning in import of archivedb (timezone issue on Windows)
+ - downloadversionIRdata: get_NUC_by_times() uses the reference cold frame in case no cold frame (NUC frame) was found for this time
+ - downloadversionIRdata: unified use of stoptime instead of endtime
+ - downloadversionIRdata: expanded testing sections (profile, coldframe, timestamps and scene model)
Update to V3.2.5:
-
+ - remove of old, not needed function and dependencies, update of download_background function
Update to V3.2.4:
-
+ - smaller bugfixes and setup.py got added
Update to V3.2.3:
- bugfix: finding right version for meta informations
Update to V3.2.2:
@@ -84,7 +105,8 @@ bugfixes in downloadversionIRdata:
- fixed: wrong time intervall for TC divertor data
Versions:
-V3.4.0: chaching functionality included, request for all cameras included + Bugfixes
+V3.5.0: download of wetted area, peak loads for different targets and
+V3.4.1: chaching functionality included, request for all cameras included + Bugfixes
V3.3.0: code-cleaning, unification of variable names, new functions: get_trigger_from_PID,
V3.2.0: download of scene models possible, temperature profiles can be extracted, downsampled temperature images available
V3.1.0: add of divertor loads in the upload and the download
diff --git a/IR_image_tools.py b/IR_image_tools.py
index ea65f9c1feb0d1959099936f209a57c990aaf0f4..ce8eefcb760143f010e964aa7f919405a9524f6a 100644
--- a/IR_image_tools.py
+++ b/IR_image_tools.py
@@ -25,14 +25,16 @@ def get_OP_by_time(time_ns=None, shot_no=None, program_str=None):
'''Derives operation phase (OP) of W7-X based on either:
a nanosacond time stamp, a MDSplus style shot no. or a program ID.
- IN:
- time_ns - integer of nanosecond time stamp,
+ INPUT
+ -----
+ time_ns: - integer of nanosecond time stamp,
e.g. 1511972727249834301 (OPTIONAL)
shot_no - integer of MDSplus style shot number,
e.g. 171207022 (OPTIONAL)
program_str - string of CoDaQ ArchiveDB style prgram number or date,
e.g. '20171207.022' or '20171207' (OPTIONAL)
- RETURN:
+ RESULT
+ ------
conn - MDSplus connection object, to be used in e.g. 1511972727249834301
read_MDSplus_image_simple(), read_MDSplus_metadata()
'''
@@ -639,7 +641,13 @@ def reconstruct_coldframe (exposuretime, sT, a, bnew, coldref):
"""
INPUT
------
-
+ exposuretime: integer
+ the exposure time
+ sT:
+ a:
+ bnew:
+ coldref: numpy array
+ the reference cold frame as the base for the reconstruction
RESULT
------
@@ -689,17 +697,27 @@ def get_work_list(pipepath,typ="q"):
"""
INPUT
------
-
+ pipepath: string
+ the path to the folder where the files are located
+ typ: string
+ the typ of data which is requested in the working list\n
+ possiblities: q, Aw, qpeak, width, load\n
+ or anything else for the problematic programs
RESULT
------
-
+ cam_programs: list
+ a list containing two coloumns, cameras and programs
+ reasons: list, optional, only for problematic programs
+ a list showing the reasons, why data are not processed
NOTE
------
"""
today=datetime.datetime.now()
cam_programs=[]
- if typ in ('q','load'):
+ if typ in ('q_old','load_old'):
f=open(pipepath+str(today.year)+str(today.month)+"_"+typ+"_requests.txt")
+ elif typ in ('q','load','qpeak','Aw','width'):
+ f=open(pipepath+"Auto_"+typ+"_requests.txt")
else:
reasons=[]
f = open(pipepath+"problematic_programs.txt")
diff --git a/ToDO.txt b/ToDO.txt
index ccd2421383ddca6edd9d3b785efa1c2fb28a18a3..d03c20de5d2fff827541f84e955a1af730b55179 100644
--- a/ToDO.txt
+++ b/ToDO.txt
@@ -6,3 +6,6 @@ download:
- implement download of the stored temperature data (After the upload)
- implement download of the stored heat flux data --> done in V3.0.0
- implement download of FLIR data --> Done in V2.8.0, in testing phase
+- implement caching
+- implement download of wetted area, peak heat flux and average strike line width
+- implement request of all ports
diff --git a/downloadversionIRdata.py b/downloadversionIRdata.py
index f564fa4ca0713f487d61a14fc46e1cb68135904a..be2437c8cefd5c2a849d882842cef992348140df 100644
--- a/downloadversionIRdata.py
+++ b/downloadversionIRdata.py
@@ -4,7 +4,7 @@ Created on Wed Oct 25 15:51:46 2017
updated on Tue Aug 21 10:20:00 2018
last update on Fr Nov 23 15:37:00 2018
-Version: 3.4.0
+Version: 3.4.1
(Numbering: #of big changes(OP1.2a download V1, OP1.2b download V2, heatflux V3)
.
#of updates to add functionalities
@@ -12,7 +12,7 @@ Version: 3.4.0
#number of updates for bug fixes )
@author: holn
"""
-version="V3.4.0"
+version = "V3.4.1"
import numpy as np
import IR_image_tools as IR_tools
@@ -28,7 +28,7 @@ from IR_config_constants import archivepath, testarchivepath, portcamdict, camli
import datetime
import urllib.request
import urllib.error
-
+import logging
import json
from PIL import Image
from io import BytesIO
@@ -36,7 +36,7 @@ from os.path import join
import matplotlib.pyplot as plt
try:
import threading
- import archivedb as AKF_2
+ import w7xarchive as AKF_2
# disbale deprecation warnings, because of timezone warning spam
import warnings
warnings.filterwarnings("ignore", category=DeprecationWarning)
@@ -81,12 +81,12 @@ def get_latest_version(stream, project="W7X", testmode=False, t_from=None,
return int of version number of None if non-versioned or non-existing stream
author: G. Schlisio, edit by holn
"""
- if testmode:
+ if testmode:#check which archive should be used
base = testarchivepath
else:
base = archivepath
- if program == None and t_from == None and t_to == None:
+ if program == None and t_from == None and t_to == None:# check wheather any time information was given
request = urllib.request.Request(base +project+"/" + stream + "/_versions.json", headers={"Accept": "application/json"})
else:
if program is not None:
@@ -192,42 +192,56 @@ def download_LUT(port, time, exposure=0, emissivity=0, camera_filter=0, version=
testmode=False, verbose=0):
"""
download_LUT(camera, port, time, exposure=0, emissivity=0, camera_filter=0, version=1):
- time in ns
+ download of the look up table for the infrared cameras from the database for OP1.2(a+b)
Have to swap 11, 21 until correction in the database
INPUT
------
- port:
- time:
- exposure:
- emissivity:
- camera_filter:
- version:
- testmode:
- verbose:
-
+ port: interger
+ number of the AEF camera port
+ time: integer
+ nanosecond timestamp for the LUT
+ exposure: integer
+ requested exposure time in microseconds
+ emissivity: float, string or integer
+ emissivity value to identify which LUT is requested
+ camera_filter: inter or string, only needed for port 50
+ filter number of the Infratec camera in port AEF50
+ version: integer, optional, default 0
+ Version of the data in the archiveDB, in case of 0 the highest version will be used
+ testmode: boolean, optional, default False
+ if True, the data will be loaded from the test archive
+ verbose: integer, optional, default 0
+ feedback level (details of print messages)
RESULT
------
-
+ exist: boolean
+ True if Look up table was found, False if not
+ LUT: list
+ contains the calibration information: [digital level, temperature, temperature error]
NOTE
------
+ function need to be adapted for OP2, if AEK ports come into opperation
"""
- OP = IR_tools.get_OP_by_time(time_ns=time)
- if port == 21:
+ OP = IR_tools.get_OP_by_time(time_ns=time)#getting operation phase
+ if port == 21:# swapping both ports (21 and 11)
port = 11
elif port == 11:
port = 21
- if testmode:
+ if testmode: #check which archive should be used
larchivepath = testarchivepath+project+"/"+portpathdict[OP]["AEF"+str(port)]+"LUT_"
else:
larchivepath = archivepath+project+"/"+portpathdict[OP]["AEF"+str(port)]+"LUT_"
+ #camera dependend request string generation
if port == 50 and OP == "OP1.2a":# camera=="INFRATEC" or camera=="infratec" or camera=="Infratec":
query = "Filter_"+str(camera_filter)+"_Texp_"+str(int(exposure))+"us_e_"+str(float(emissivity))
elif port in [10, 11, 20, 21, 30, 31, 40, 41, 51] or OP == "OP1.2b":#camera=="IRCAM" or camera=="IRcam" or camera=="ircam":
query = "Texp_"+str(int(exposure))+"us_e_"+str(float(emissivity))
else:
- print("download_LUT: Error! Camera unknown, stopping here.")
- raise Exception
- if version == 0:
+# print("download_LUT: Error! Camera unknown, stopping here.")
+ logging.warning("download_LUT: Error! Camera unknown, stopping here.")
+ return False, 0
+# raise Exception
+ if version == 0:#version check, if 0
version = get_latest_version(portpathdict[OP]["AEF"+str(port)]+"LUT_DATASTREAM", t_from=time, testmode=testmode)
if verbose > 0:
print("download_LUT: LUT V"+str(version)+" is used")
@@ -259,12 +273,27 @@ def read_LUT_from_file(port, this_time, t_exp, emissivity, cfilter, verbose=0):#
Read LUT V3 from local files for testing new calibration.
INPUT
------
-
+ port: integer or string
+ the port number of the AEF port
+ this_time: integer
+ nanosecond timestamp for the LUT
+ t_exp: integer
+ requested exposure time in microseconds
+ emissivity: float, string or integer
+ emissivity value to identify which LUT is requested
+ cfilter: inter or string, only needed for port 50
+ filter number of the Infratec camera in port AEF50
+ verbose: integer, optional, default 0
+ feedback level (details of print messages)
RESULT
------
-
+ exist: boolean
+ True if Look up table was found, False if not
+ LUT: list
+ contains the calibration information: [digital level, temperature, temperature error]
NOTE
------
+ function need to be adapted for OP2, if AEK ports come into opperation
"""
LUT_dir = '\\\\sv-e4-fs-1\\E4-Mitarbeiter\\E4 Diagnostics\\QIR\\testingLUT'
OP = IR_tools.get_OP_by_time(time_ns=this_time)
@@ -284,14 +313,32 @@ def read_LUT_from_file(port, this_time, t_exp, emissivity, cfilter, verbose=0):#
def download_NUC_by_program(port, program, exposure, version=0, testmode=False, verbose=0):
'''
+ download the NUC data for a given port, program and exposure time of the camera
INPUT
------
-
+ port: integer or string
+ port number of the AEF port of the camera
+ program: string
+ program id in the form of 'YYYYMMDD.XXX', e.g. '20181016.016'
+ exposure: integer
+ exposure time in microseconds
+ version: integer, optional, default 0
+ Version of the data in the archiveDB, in case of 0 the highest version will be used
+ testmode: boolean, optional, default False
+ if True, the data will be loaded from the test archive
+ verbose: integer, optional, default 0
+ feedback level (details of print messages)
RESULT
------
-
+ exist: boolean
+ True if NUC data was found, False if not
+ NUC: list
+ a list vontaining images of gain, offset, cold, badpixels, gain_error and offset_error
+ NUC_describtion: list
+ a list which entry in NUC is what, looks like ['gain', 'offset', 'cold', 'badpixels', 'gain_error', 'offset_error']
NOTE
------
+ function need to be adapted for OP2, if AEK ports come into opperation
'''
# prog=get_program_from_PID(program)
# try:
@@ -310,14 +357,34 @@ def download_NUC_by_program(port, program, exposure, version=0, testmode=False,
def download_NUC_by_times(port, starttime, stoptime, exposure, testmode=False, version=0, verbose=0):
'''
+ download the NUC data for a given port, start and endtime in ns and exposure time of the camera
INPUT
------
-
+ port: integer or string
+ port number of the AEF port of the camera
+ starttime: integer
+ time in ns where the search should start
+ stoptime: integer
+ time in sn where the search should end
+ exposure: integer
+ exposure time in microseconds
+ version: integer, optional
+ Version of the data in the archiveDB, in case of 0 the highest version will be used
+ testmode: boolean, optional
+ if True, the data will be loaded from the test archive
+ verbose: integer, optional
+ feedback level (details of print messages)
RESULT
------
-
+ exist: boolean
+ True if NUC data was found, False if not
+ NUC: list
+ a list vontaining images of gain, offset, cold, badpixels, gain_error and offset_error
+ NUC_describtion: list
+ a list which entry in NUC is what, looks like ['gain', 'offset', 'cold', 'badpixels', 'gain_error', 'offset_error']
NOTE
------
+ function need to be adapted for OP2, if AEK ports come into opperation
'''
OP = IR_tools.get_OP_by_time(time_ns=starttime)
if testmode:
@@ -370,12 +437,29 @@ def get_NUC_by_program(port, program, exposure, version=0, testmode=False, verbo
the cold frame and/or compute the gain, offset from the hot and cold frame.
INPUT
------
-
+ port: integer or string
+ port number of the AEF port of the camera
+ program: string
+ program id in the form of 'YYYYMMDD.XXX', e.g. '20181016.016'
+ exposure: integer
+ exposure time in microseconds
+ version: integer, optional
+ Version of the data in the archiveDB, in case of 0 the highest version will be used
+ testmode: boolean, optional
+ if True, the data will be loaded from the test archive
+ verbose: integer, optional
+ feedback level (details of print messages)
RESULT
------
-
+ exist: boolean
+ True if NUC data was found, False if not
+ NUC: list
+ a list vontaining images of gain, offset, cold, badpixels, gain_error and offset_error
+ NUC_describtion: list
+ a list which entry in NUC is what, looks like ['gain', 'offset', 'cold', 'badpixels', 'gain_error', 'offset_error']
NOTE
------
+ function need to be adapted for OP2, if AEK ports come into opperation
'''
# prog=get_program_from_PID(program)
# try:
@@ -399,23 +483,46 @@ def get_NUC_by_times(port, starttime, stoptime, t_exp, version=0, testmode=False
Loads NUC elements (gain, offset cold image, bad pixels) for an IR camera
in a given port and time interval. Depending on time and camera, reconstruct
the cold frame and/or compute the gain, offset from the hot and cold frame.
+ the result is operation phase dependent, due to changes in the aquisition and saving programs
+
INPUT
------
-
+ port: integer or string
+ port number of the AEF port of the camera
+ starttime: integer
+ time in ns where the search should start
+ stoptime: integer
+ time in sn where the search should end
+ exposure: integer
+ exposure time in microseconds
+ version: integer, optional
+ Version of the data in the archiveDB, in case of 0 the highest version will be used
+ testmode: boolean, optional
+ if True, the data will be loaded from the test archive
+ OP: string, optional, default None
+ name of operation phase
+ verbose: integer, optional
+ feedback level (details of print messages)
RESULT
------
-
+ exist: boolean
+ True if NUC data was found, False if not
+ NUC: list
+ a list vontaining images of gain, offset, cold, badpixels, gain_error and offset_error
+ NUC_describtion: list
+ a list which entry in NUC is what, looks like ['gain', 'offset', 'cold', 'badpixels', 'gain_error', 'offset_error']
NOTE
------
+ function need to be adapted for OP2, if AEK ports come into opperation
"""
- if OP is None:
+ if OP is None: #get operation phase if not given
OP = IR_tools.get_OP_by_time(time_ns=stoptime)
- if OP == "OP1.2a":
+ if OP == "OP1.2a": # check for operation phase
t1 = stoptime
t0 = starttime
prog = read_program(t1)
program = prog[1]['id']
- if (port == 31) or (port == 21 and float(program[4:]) > 1110):
+ if (port == 31) or (port == 21 and float(program[4:]) > 1110):#camera 31 and 21 need after 20171110 new coldframes due to a open shutter during the recording of the coldframes
if verbose > 0:
print("get_NUC_by_times: rebuilding coldframe")
#use any way the rebuilt coldframe.
@@ -427,7 +534,9 @@ def get_NUC_by_times(port, starttime, stoptime, t_exp, version=0, testmode=False
coldref = hotcold[1]
hotref = hotcold[0]
else:
- raise Exception("get_NUC_by_times: unable to download reference frames")
+# raise Exception("get_NUC_by_times: unable to download reference frames")
+ logging.warning("get_NUC_by_times: unable to download reference frames")
+ return False, 0, 0
filestring = 'AEF' + str(port) + '_et' + str(int(t_exp))
amap = np.load(join(IRCamColdframes_fittingpath, filestring + '_a.npy'))
bmap = np.load(join(IRCamColdframes_fittingpath, filestring + '_b.npy'))
@@ -441,7 +550,7 @@ def get_NUC_by_times(port, starttime, stoptime, t_exp, version=0, testmode=False
gain_error = 0
offset_error = 0
return True, [gain, offset, cirebuild, badpixels, gain_error, offset_error], ['gain', 'offset', 'cold', 'badpixels', 'gain_error', 'offset_error']
- elif (port == 11) or (port == 21):
+ elif (port == 11) or (port == 21): # case of the swaped cameras
if verbose > 0:
print('get_NUC_by_times: downloading NUC')
# coldref, hotref = IR_tools.load_ref_images('AEF' + str(port), t_exp)
@@ -450,10 +559,14 @@ def get_NUC_by_times(port, starttime, stoptime, t_exp, version=0, testmode=False
coldref = hotcold[1]
hotref = hotcold[0]
else:
- raise Exception("get_NUC_by_times: unable to download reference frames")
+# raise Exception("get_NUC_by_times: unable to download reference frames")
+ logging.warning("get_NUC_by_times: unable to download reference frames")
+ return False, 0, 0
NUC_DL = download_NUC_by_times(port, t0, t1, t_exp, version, testmode)
if NUC_DL[0] == False:
- raise Exception("get_NUC_by_times: NUC was not found")
+# raise Exception("get_NUC_by_times: NUC was not found")
+ logging.warning("get_NUC_by_times: NUC was not found")
+ return False, 0, 0
else:
ci = NUC_DL[1][2]
gain, offset = IR_tools.calculate_gain_offset_image(ci, None, coldref, hotref, verbose=verbose-1)
@@ -463,7 +576,7 @@ def get_NUC_by_times(port, starttime, stoptime, t_exp, version=0, testmode=False
gain_error = 0
offset_error = 0
return True, [gain, offset, ci, badpixels, gain_error, offset_error], ['gain', 'offset', 'cold', 'badpixels', 'gain_error', 'offset_error']
- else:
+ else: #all other cases, the "normal" case
if verbose > 0:
print('get_NUC_by_times: downloading NUC')
NUC_DL = download_NUC_by_times(port, starttime, stoptime, t_exp, testmode=testmode)
@@ -480,13 +593,15 @@ def get_NUC_by_times(port, starttime, stoptime, t_exp, version=0, testmode=False
coldref = hotcold[1]
hotref = hotcold[0]
else:
- raise Exception("get_NUC_by_times: unable to download reference frames")
+# raise Exception("get_NUC_by_times: unable to download reference frames")
+ logging.warning("get_NUC_by_times: unable to download reference frames")
+ return False, 0, 0
### get the cold frame: 1. get the metastream frametype and exposuretime and check in it for the positions, if -1 type is unknown, when if needs to be identified in a different way
try:
exist, _, coldframes = download_calibration_raw_files_by_time(port, t_exp, starttime, stoptime, frametype=0, version=0, testmode=testmode, verbose=verbose-1)
except:
exist = False
- if exist:
+ if exist:#okay it exist, average the cold frames
cold = np.zeros(np.shape(coldframes[0]), dtype=np.uint64)
for ele in coldframes:
cold += ele
@@ -499,30 +614,54 @@ def get_NUC_by_times(port, starttime, stoptime, t_exp, version=0, testmode=False
badpixels = find_badpixels(port, gain, offset, niterations=10, tolerance=10, verbose=verbose-1)
return True, [gain, offset, cold, badpixels, gain_error, offset_error], ['gain', 'offset', 'cold', 'badpixels', 'gain_error', 'offset_error']
else:
- raise Exception("get_NUC_by_times: unknown Operation phase or NUC method not implemented for this OP")
+# raise Exception("get_NUC_by_times: unknown Operation phase or NUC method not implemented for this OP")
+ logging.warning("get_NUC_by_times: unknown Operation phase or NUC method not implemented for this OP")
+ return False, 0, 0
def download_calibration_raw_files_by_time(port, t_exp, starttime, stoptime, frametype=0, version=0, testmode=False, verbose=0):
"""
- download_calibration_raw_files_by_time(port, t_exp, starttime, stoptime, frametype=0, version=0)
+ download of the calibration raw files which are recorded between t0 and t1 trigger
frametype: 0 for closed shutter frames (cold), 1 for open shutter frames (background)
+
INPUT
------
-
+ port: integer
+ port number of the AEF port of the camera
+ t_exp: integer
+ exposure time in microseconds
+ starttime: integer
+ time in ns where the search should start
+ stoptime: integer
+ time in sn where the search should end
+ frametype: integer, default 0
+ defines which type of frames should be returned, 0 for cold frames, 1 for background frames
+ version: integer, optional
+ Version of the data in the archiveDB, in case of 0 the highest version will be used
+ testmode: boolean, optional
+ if True, the data will be loaded from the test archive
+ verbose: integer, optional
+ feedback level (details of print messages)
RESULT
------
-
+ exist: boolean
+ True if data was found, False if not
+ time: list
+ a list containing the time stamp of each frame
+ frames: list
+ a list of frames, 2D numpy arrays
NOTE
------
+ function need to be adapted for OP2, if AEK ports come into opperation
"""
gotit, time_t, texp_t = get_exposure_by_times(port, int(starttime-100), int(stoptime-20e6), testmode=testmode)
OP = IR_tools.get_OP_by_time(time_ns=stoptime)
if gotit:
expinds = np.where(texp_t == t_exp)[0]
if len(expinds) == 0:
- print("download_calibration_raw_files_by_time: Error! cannot find the exposure time in the given data")
+ logging.warning("download_calibration_raw_files_by_time: Error! cannot find the exposure time in the given data")
return False, [0], [0]
else:
- print("download_calibration_raw_files_by_time: Error! exposure time not found")
+ logging.warning("download_calibration_raw_files_by_time: Error! exposure time not found")
return False, [0], [0]
gotitf, timef, values_f = get_frametype_by_times(port, int(starttime-100), int(stoptime-20e6), testmode=testmode)
if gotitf:
@@ -545,7 +684,9 @@ def download_calibration_raw_files_by_time(port, t_exp, starttime, stoptime, fra
ref_t = [np.min(frametimes[turnpoint+1:]), np.max(frametimes[turnpoint+1:])]
# print((ref_t[1]-ref_t[0])/1e9)
else:
- raise Exception("download_calibration_raw_files_by_time: requested Frametype unknown and not implemented!")
+# raise Exception("download_calibration_raw_files_by_time: requested Frametype unknown and not implemented!")
+ logging.warning("download_calibration_raw_files_by_time: requested Frametype unknown and not implemented!")
+ return False, [0], [0]
else:
print("download_calibration_raw_files_by_time: Error! frametype not found")
frametimes = time_t[expinds]
@@ -559,7 +700,9 @@ def download_calibration_raw_files_by_time(port, t_exp, starttime, stoptime, fra
ref_t = [np.min(frametimes[turnpoint+1:]), np.max(frametimes[turnpoint+1:])]
print((ref_t[1]-ref_t[0])/1e9)
else:
- raise Exception("download_calibration_raw_files_by_time: requested Frametype unknown and not implemented!")
+# raise Exception("download_calibration_raw_files_by_time: requested Frametype unknown and not implemented!")
+ logging.warning("download_calibration_raw_files_by_time: requested Frametype unknown and not implemented!")
+ return False, [0], [0]
# return False, [0], [0]
t1date = datetime.datetime.utcfromtimestamp((stoptime-100)/1e9)
t1date = t1date.isoformat()
@@ -588,12 +731,29 @@ def download_hot_cold_reference_by_times(port, exposure, starttime=1503907200000
Uses first calibration frames if time is not defined.
INPUT
------
-
+ port: integer or string
+ port number of the AEF port of the camera, or string containing the full port name
+ exposure: integer
+ exposure time in microseconds
+ starttime: integer
+ time in ns where the search should start
+ version: integer, optional
+ Version of the data in the archiveDB, in case of 0 the highest version will be used
+ testmode: boolean, optional
+ if True, the data will be loaded from the test archive
+ verbose: integer, optional
+ feedback level (details of print messages)
RESULT
------
-
+ exist: boolean
+ True if data was found, False if not
+ images: list
+ a list vontaining images of gain, offset, cold, badpixels, gain_error and offset_error
+ image_describtion: list
+ a list which entry in NUC is what, looks like ['hot','cold']
NOTE
------
+ function need to be adapted for OP2, if AEK ports come into opperation
"""
OP = IR_tools.get_OP_by_time(time_ns=starttime)
if isinstance(port, int):
@@ -648,15 +808,37 @@ def download_hot_cold_reference_by_times(port, exposure, starttime=1503907200000
def download_background_by_program(port, program, exposure, camera_filter=0, version=0, testmode=False, verbose=0):
'''
+ download the background from the AEF cameras in OP1.2a, which are recorded between t0 and t1 trigger
returned exist, time, frame
INPUT
------
-
+ port: integer
+ port number of the AEF port of the camera
+ program: string
+ program id in the form of 'YYYYMMDD.XXX', e.g. '20181016.016'
+ exposure: integer
+ exposure time in microseconds
+ camera_filter: integer or string, only needed for port 50
+ filter number of the Infratec camera in port AEF50
+ version: integer, optional
+ Version of the data in the archiveDB, in case of 0 the highest version will be used
+ testmode: boolean, optional
+ if True, the data will be loaded from the test archive
+ verbose: integer, optional
+ feedback level (details of print messages)
+ OP: string, optional, default None
+ name of operation phase
RESULT
------
-
+ exist: boolean
+ True if data was found, False if not
+ time: list
+ a list containing the time stamp of each frame
+ frames: list
+ a list of frames, 2D numpy arrays
NOTE
------
+ function need to be adapted for OP2, if AEK ports come into opperation
'''
# prog=get_program_from_PID(program)
# try:
@@ -674,6 +856,40 @@ def download_background_by_program(port, program, exposure, camera_filter=0, ver
def download_background_by_times(port, starttime, stoptime, exposure, camera_filter=0,
version=0, testmode=False, verbose=0):
+ """
+ download the background frames for a given time intervall in nanoseconds, defined by start and stop time
+
+ INPUT
+ ------
+ port: integer
+ port number of the AEF port of the camera
+ starttime: integer
+ time in ns where the search should start
+ stoptime: integer
+ time in sn where the search should end
+ exposure: integer
+ exposure time in microseconds
+ camera_filter: integer or string, only needed for port 50
+ filter number of the Infratec camera in port AEF50
+ version: integer, optional
+ Version of the data in the archiveDB, in case of 0 the highest version will be used
+ testmode: boolean, optional
+ if True, the data will be loaded from the test archive
+ verbose: integer, optional
+ feedback level (details of print messages)
+
+ RESULT
+ ------
+ exist: boolean
+ True if data was found, False if not
+ time: list
+ a list containing the time stamp of each frame
+ frames: list
+ a list of frames, 2D numpy arrays
+ NOTE
+ ------
+ function need to be adapted for OP2, if AEK ports come into opperation
+ """
OP = IR_tools.get_OP_by_time(time_ns=starttime)
if OP == "OP1.2a":
stream = portpathdict[OP]["AEF"+str(port)]+"background_"
@@ -720,8 +936,9 @@ def download_background_by_times(port, starttime, stoptime, exposure, camera_fil
elif OP == "OP1.2b":
NUC_DL = get_NUC_by_times(port, starttime, stoptime, t_exp=exposure, version=version, testmode=testmode, verbose=verbose-1)#download_NUC_by_times(port, t0, t1, t_exp, version)
if NUC_DL[0] == False:
- raise Warning("download_background_by_times: NUC was not found")
-# return False, 0, 0
+# raise Warning("download_background_by_times: NUC was not found")
+ logging.warning("download_background_by_times: NUC was not found")
+ return False, 0, 0
else:
# extract NUC elements
gain = []
@@ -737,14 +954,42 @@ def download_background_by_times(port, starttime, stoptime, exposure, camera_fil
def get_NUCed_background_by_times(port, t0, t1, t_exp, cfilter, gain, offset, version=0, testmode=False, plot_it=False, verbose=0):
"""
OP1.2b function
+ download the background frame and applies the NUC onto the background, given by gain und offset
+ it is an average over 30 frames.
+
INPUT
------
-
+ port: integer
+ port number of the AEF port of the camera
+ t0: integer
+ nanoseconds timestamp of the t0 trigger, start of the program
+ t1: integer
+ nanoseconds timestamp of the t1 trigger, start of the heating
+ t_exp: integer
+ exposure time in microseconds
+ gain: numpy array
+ the gain image from the calibration to convert the counts into digital level
+ offset: numpy array
+ the offset image from the calibration to convert the counts into digital level
+ version: integer, optional
+ Version of the data in the archiveDB, in case of 0 the highest version will be used
+ testmode: boolean, optional
+ if True, the data will be loaded from the test archive
+ plot_it: boolean, optional, default False
+ if True, the data will be plotted directly
+ verbose: integer, optional
+ feedback level (details of print messages)
RESULT
------
-
+ exist: boolean
+ True if data was found, False if not
+ time: integer
+ the timestamp of the background frame (firest timestamp of the 30 frames)
+ background_frame: numpy array
+ the background frame
NOTE
------
+ function need to be adapted for OP2, if AEK ports come into opperation
"""
exist, btime, backgroundframes = download_calibration_raw_files_by_time(port, t_exp, t0, t1, frametype=1, version=version, testmode=testmode, verbose=verbose-1)
camera = portcamdict["OP1.2b"]["AEF"+str(port)]
@@ -856,14 +1101,35 @@ def get_NUCed_coldframe_by_program(port, program, exposure=None, version=0,
def download_raw_images_by_program(port, program, time_window=0, version=0, testmode=False, verbose=0):
"""
+ download the raw frames (counts) from the infrared cameras for a given program
+
INPUT
------
-
+ port: integer
+ port number of the AEF port of the camera
+ program: string
+ program id in the form of 'YYYYMMDD.XXX', e.g. '20181016.016'
+ time_window: list, array, integer or float, optional, default 0
+ if integer or float: size of the window in seconds from t1
+ if list or array: start and endpoint of the window in seconds from t1
+ if 0 the full program will be downloaded (t1 until t6)
+ version: integer, optional, default 0
+ Version of the data in the archiveDB, in case of 0 the highest version will be used
+ testmode: boolean, optional, default False
+ if True, the data will be loaded from the test archive
+ verbose: integer, optional, default 0
+ feedback level (details of print messages)
RESULT
------
-
+ exist: boolean
+ True if data was found, False if not
+ time: list
+ a list containing the time stamp of each frame
+ frames: list
+ a list of frames, 2D numpy arrays
NOTE
------
+ the timewindow will be substructured into intervalls, and each intervall will be downloaded one after the other to avoid a overfilling of the RAM. A to large intervall can still overflow the RAM
"""
exist, _, starttime, stoptime = get_trigger_from_PID(program, port, testmode, verbose=verbose-1)
# prog=AKF_1.get_program_from_PID(program)
@@ -876,11 +1142,15 @@ def download_raw_images_by_program(port, program, time_window=0, version=0, test
if exist:
if type(time_window) == list or type(time_window) == np.ndarray:
if len(time_window) > 2 or time_window[0] > 1000:
- raise Exception("download_raw_images_by_program: invalid input for the time_window!")
+# raise Exception("download_raw_images_by_program: invalid input for the time_window!")
+ logging.warning("download_raw_images_by_program: invalid input for the time_window!")
+ return False, 0, 0
tstart = time_window[0]
tstop = time_window[1]
if tstop <tstart:
- raise Exception("download_raw_images_by_program: t_stop before t_start")
+# raise Exception("download_raw_images_by_program: t_stop before t_start")
+ logging.warning("download_raw_images_by_program: t_stop before t_start")
+ return False, 0, 0
return download_raw_images_by_times(port, int(starttime+tstart*1e9), int(starttime+tstop*1e9), version, testmode=testmode, verbose=verbose)
else:
if time_window == 0:
@@ -893,14 +1163,35 @@ def download_raw_images_by_program(port, program, time_window=0, version=0, test
def download_raw_images_by_times(port, starttime, stoptime, version=0, intervalSize=1E9, testmode=False, verbose=0):
"""
+ download the raw frames (counts) from the infrared cameras for a given time interval in nanoseconds
+
INPUT
------
-
+ port: integer
+ port number of the AEF port of the camera
+ starttime: integer
+ time in ns where the search should start
+ stoptime: integer
+ time in sn where the search should end
+ version: integer, optional, default 0
+ Version of the data in the archiveDB, in case of 0 the highest version will be used
+ intervalSize: integer, optional, default 1e9 (1 second)
+ size of the intervall to be downloaded in nanoseconds
+ testmode: boolean, optional, default False
+ if True, the data will be loaded from the test archive
+ verbose: integer, optional, default 0
+ feedback level (details of print messages)
RESULT
------
-
+ exist: boolean
+ True if data was found, False if not
+ time: list
+ a list containing the time stamp of each frame
+ frames: list
+ a list of frames, 2D numpy arrays
NOTE
------
+ the timewindow will be substructured into intervalls, and each intervall will be downloaded one after the other to avoid a overfilling of the RAM. A to large intervall can still overflow the RAM
"""
OP = IR_tools.get_OP_by_time(time_ns=starttime)
if version == 0:
@@ -915,12 +1206,27 @@ def download_images_by_times(larchivepath, starttime, stoptime, intervalSize=1E9
"""
INPUT
------
-
+ larchivepath: string
+ local archivepath, the URL to download the images
+ starttime: integer
+ time in ns where the search should start
+ stoptime: integer
+ time in sn where the search should end
+ intervalSize: integer, optional, default 1e9 (1 second)
+ size of the intervall to be downloaded in nanoseconds
+ verbose: integer, optional, default 0
+ feedback level (details of print messages)
RESULT
------
-
+ exist: boolean
+ True if data was found, False if not
+ time: list
+ a list containing the time stamp of each frame
+ frames: list
+ a list of frames, 2D numpy arrays
NOTE
------
+ the timewindow will be substructured into intervalls, and each intervall will be downloaded one after the other to avoid a overfilling of the RAM. A to large intervall can still overflow the RAM
"""
if '_raw' in larchivepath:
typo = np.uint16
@@ -985,12 +1291,36 @@ if fastDL:
def download_raw_images_by_program_via_png(port, program, time_window=0, version=0,
threads=1, testmode=False, verbose=0):
"""
+ download of the raw images in png format instead of json for a given program.
+ number of threads can be defined to download several pngs in parallel
+
INPUT
------
-
+ port: integer
+ port number of the AEF port of the camera
+ program: string
+ program id in the form of 'YYYYMMDD.XXX', e.g. '20181016.016'
+ time_window: list, array, integer or float, optional, default 0
+ if integer or float: size of the window in seconds from t1
+ if list or array: start and endpoint of the window in seconds from t1
+ if 0 the full program will be downloaded (t1 until t6)
+ version: integer, optional, default 0
+ Version of the data in the archiveDB, in case of 0 the highest version will be used
+ threads: integer, default 1
+ number of parallel threads to download the images
+ (approx. max. 2x number of cores and max. timeintervall/0.5 for best performance)
+ testmode: boolean, optional, default False
+ if True, the data will be loaded from the test archive
+ verbose: integer, optional, default 0
+ feedback level (details of print messages)
RESULT
------
-
+ exist: boolean
+ True if data was found, False if not
+ time: numpa array
+ an array containing the time stamp of each frame
+ frames: list
+ a list of frames, 2D numpy arrays
NOTE
------
"""
@@ -1025,12 +1355,38 @@ if fastDL:
def download_images_by_time_via_png(larchivepath, starttime, stoptime, time_window=0,
threads=1, version=0, verbose=0, framerate='max'):
"""
+ download of the images in png format instead of json for a given timeintervall defined by start and stop time.
+ number of threads can be defined to download several pngs in parallel
+
INPUT
------
-
+ larchivepath: string
+ local archivepath, the URL to download the images
+ starttime: integer
+ time in ns where the search should start
+ stoptime: integer
+ time in sn where the search should end
+ time_window: list, array, integer or float, optional, default 0
+ if integer or float: size of the window in seconds from t1
+ if list or array: start and endpoint of the window in seconds from t1
+ if 0 the full program will be downloaded (t1 until t6)
+ threads: integer, default 1
+ number of parallel threads to download the images
+ (approx. max. 2x number of cores and max. timeintervall/0.5 for best performance)
+ version: integer, optional, default 0
+ Version of the data in the archiveDB, in case of 0 the highest version will be used
+ verbose: integer, optional, default 0
+ feedback level (details of print messages)
+ framerate: string or integer, optional, default 'max'
+ sets the framerate to downsample the data. normal rate is 100.
RESULT
------
-
+ exist: boolean
+ True if data was found, False if not
+ time: numpa array
+ an array containing the time stamp of each frame
+ frames: list
+ a list of frames, 2D numpy arrays
NOTE
------
"""
@@ -1039,10 +1395,14 @@ if fastDL:
tstart = time_window[0]
tstop = time_window[1]
if tstop < tstart:
- raise Exception("download_images_by_time_via_png: t_stop before t_start")
+# raise Exception("download_images_by_time_via_png: t_stop before t_start")
+ logging.warning("download_images_by_time_via_png: t_stop before t_start")
+ return False, [0], [0]
time_from = (starttime-100)+int(tstart*1E9)
if stoptime < time_from:
- raise Exception("download_images_by_time_via_png: time_window after stoptime")
+# raise Exception("download_images_by_time_via_png: time_window after stoptime")
+ logging.warning("download_images_by_time_via_png: time_window after stoptime")
+ return False, [0], [0]
time_to = starttime+int(tstop*1E9)
if stoptime < time_to:
time_to = stoptime
@@ -1070,8 +1430,9 @@ if fastDL:
except Exception as E:
# print('download_images_by_time_via_png: Error loading times from ', larchivepath, stdate.replace("T", " "), enddate.replace("T", " "))
print('download_images_by_time_via_png: Error loading times from {0} between [{1}, {2}]'.format(larchivepath, time_from, time_to))
- raise Warning(E)
-# return False, [0], [0]
+# raise Warning(E)
+ logging.warning(E)
+ return False, [0], [0]
if framerate != 'max':
if verbose > 5:
@@ -1136,7 +1497,31 @@ if fastDL:
class download_images_thread(threading.Thread):
+ """
+ class for parallel download of images.\n
+ contains an init function and a run function
+ """
def __init__(self, threadID, larchivepath, times, resultlist, version=0):
+ """
+ INPUT
+ ------
+ theadID: integer
+ identification number of the thread
+ larchivepath: string
+ local archivepath, the URL to download the images
+ times: list
+ the timestamps of each frames for the download
+ resultlist: list
+ list which is returned after the thread is done
+ version: integer, optional, default 0
+ Version of the data in the archiveDB, in case of 0 the highest version will be used
+ RESULT
+ ------
+ resultlist: list
+ list containing the ID, exist, time and the images
+ NOTE
+ ------
+ """
threading.Thread.__init__(self)
self.threadID = threadID
self.times = times
@@ -1158,16 +1543,52 @@ if fastDL:
self.resultlist.append([self.threadID, successes, time, images])
class convert_raw_to_temp_thread(threading.Thread):
+ """
+ class for parallel download of images and parallel application the NUC and temperature conversion.\n
+ contains an init function and a run function
+ """
def __init__(self, larchivepath, times,resultlist, threadID, version=0, background=0,
LUT=[[], [], []],refT=28.5, gain=0, offset=0, gain_error=0, offset_error=0,
give_ERROR=False, FOV=0, badpixels=[], verbose=0):
"""
INPUT
------
-
+ larchivepath: string
+ local archivepath, the URL to download the images
+ times: list
+ the timestamps of each frames for the download
+ resultlist: list
+ list which is returned after the thread is done
+ theadID: integer
+ identification number of the thread
+ version: integer, optional, default 0
+ Version of the data in the archiveDB, in case of 0 the highest version will be used
+ background: numpy array
+ background image
+ LUT: list
+ the look up table, containing the digital level, the temperature and the temperature error
+ refT: float
+ reference temperature for the calibration
+ gain: numpy array
+ the gain image from the calibration to convert the counts into digital level
+ offset: numpy array
+ the offset image from the calibration to convert the counts into digital level
+ gain_error: numpy array, optional
+ the gain error image from the calibration to calculate the error, needed if give_ERROR=True
+ offset_error: numpy array, optional
+ the offset error image from the calibration to calculate the error, needed if give_ERROR=True
+ give_ERROR: boolean
+ switch to activate the error calculation and returning of the error
+ FOV: numpy array
+ field of view image, with 1 in the visible region and 0 in the dark region
+ badpixels: list
+ list of the bad pixels for the corrections
+ verbose: integer, optional, default 0
+ feedback level (details of print messages)
RESULT
------
-
+ resultlist: list
+ list containing the ID, exist, time and the images
NOTE
------
"""
@@ -1192,10 +1613,11 @@ if fastDL:
"""
INPUT
------
-
+ same as init
RESULT
------
-
+ resultlist: list
+ list containing the ID, exist, time and the images
NOTE
------
"""
@@ -1223,11 +1645,53 @@ if fastDL:
self.resultlist.append([self.threadID, successes, time, images])
class NUC_raw_thread(threading.Thread):
+ """
+ class for parallel calculation of nuced raw images
+ """
def __init__ (self, larchivepath, times,resultlist, threadID, version=0, background=0,
LUT=[[], [], []],refT=28.5, gain=0, offset=0, gain_error=0, offset_error=0,
give_ERROR=False, FOV=0, badpixels=[], verbose=0):
"""
-
+ INPUT
+ ------
+ larchivepath: string
+ local archivepath, the URL to download the images
+ times: list
+ the timestamps of each frames for the download
+ resultlist: list
+ list which is returned after the thread is done
+ theadID: integer
+ identification number of the thread
+ version: integer, optional, default 0
+ Version of the data in the archiveDB, in case of 0 the highest version will be used
+ background: numpy array
+ background image
+ LUT: list
+ the look up table, containing the digital level, the temperature and the temperature error
+ refT: float
+ reference temperature for the calibration
+ gain: numpy array
+ the gain image from the calibration to convert the counts into digital level
+ offset: numpy array
+ the offset image from the calibration to convert the counts into digital level
+ gain_error: numpy array, optional
+ the gain error image from the calibration to calculate the error, needed if give_ERROR=True
+ offset_error: numpy array, optional
+ the offset error image from the calibration to calculate the error, needed if give_ERROR=True
+ give_ERROR: boolean
+ switch to activate the error calculation and returning of the error
+ FOV: numpy array
+ field of view image, with 1 in the visible region and 0 in the dark region
+ badpixels: list
+ list of the bad pixels for the corrections
+ verbose: integer, optional, default 0
+ feedback level (details of print messages)
+ RESULT
+ ------
+ resultlist: list
+ list containing the ID, exist, time and the images
+ NOTE
+ ------
"""
threading.Thread.__init__(self)
self.threadID = threadID
@@ -1279,12 +1743,40 @@ def download_raw_FLIR_images_via_png(t1, t6, time_window=0, version=0, threads=1
"""
INPUT
------
-
+ t1: integer
+ nanoseconds timestamp of the t1 trigger, start of the heating
+ t6: integer
+ nanoseconds timestamp of the t6 trigger, end of the program
+ time_window: list, array, integer or float, optional, default 0
+ if integer or float: size of the window in seconds from t1
+ if list or array: start and endpoint of the window in seconds from t1
+ if 0 the full program will be downloaded (t1 until t6)
+ version: integer, optional, default 0
+ Version of the data in the archiveDB, in case of 0 the highest version will be used
+ threads: integer, default 1
+ number of parallel threads to download the images
+ (approx. max. 2x number of cores and max. timeintervall/0.5 for best performance)
+ verbose: integer, optional, default 0
+ feedback level (details of print messages)
+ testmode: boolean, optional, default True
+ if True, the data will be loaded from the test archive
RESULT
------
-
+ exist: boolean
+ True if data was found, False if not
+ time: list
+ a list containing the time stamp of each frame
+ frames: list
+ a list of frames, 2D numpy arrays
+ mode: integer
+ kind of data structure,\n 1= pre-sets stored in different lists\n; 2=presets stored interlaced in one list
+ tsets: list
+ contains the information which frame belongs to which pre-set
+ versions: list
+ contains the informations of the version of each pre-set
NOTE
------
+ the FLIR camera is capable to run in superframing mode, recording frames with different exposure times in an interlaced mode. each exposure time frame is stored in a different file named after the pre-set
"""
if testmode:
larchivepath=testarchivepath+project+"/QSR07_FLIR/AEK51_raw"
@@ -1294,7 +1786,9 @@ def download_raw_FLIR_images_via_png(t1, t6, time_window=0, version=0, threads=1
tstart=time_window[0]
tstop=time_window[1]
if tstop<tstart:
- raise Exception("download_raw_FLIR_images_via_png: t_stop before t_start")
+# raise Exception("download_raw_FLIR_images_via_png: t_stop before t_start")
+ logging.warning("download_raw_FLIR_images_via_png: t_stop before t_start")
+ return False, [0], [0], 0, [0], [0]
starttime=int(t1-100+tstart*1e9)
stoptime=int(t1+tstop*1e9)
# stdate=datetime.datetime.utcfromtimestamp((t1-100)/1e9+tstart)
@@ -1330,7 +1824,9 @@ def download_raw_FLIR_images_via_png(t1, t6, time_window=0, version=0, threads=1
### we not know the times for all presets for the program, now we have to download them, if times is not empty
exist, time, frames, tsets=download_FLIR_Psets(larchivepath, timess, threads, versions, verbose=verbose-1)
elif mode==2:##the data is stored only in one datastream, at least this datastream exist
- raise Exception("download_raw_FLIR_images_via_png: not implemented")
+# raise Exception("download_raw_FLIR_images_via_png: not implemented")
+ logging.warning("download_raw_FLIR_images_via_png: not implemented")
+ return False, [0], [0], 0, [0], [0]
#download data and downlaod LUT
# time=[]
@@ -1347,18 +1843,34 @@ def download_raw_FLIR_images_via_png(t1, t6, time_window=0, version=0, threads=1
# success=False
# return success, np.array(time), images
else:
- raise Exception("download_raw_FLIR_images_via_png: cannot identifie the way the searched data is stored!")
+# raise Exception("download_raw_FLIR_images_via_png: cannot identifie the way the searched data is stored!")
+ logging.warning("download_raw_FLIR_images_via_png: cannot identifie the way the searched data is stored!")
+ return False, [0], [0], 0, [0], [0]
return exist, time, frames,mode, tsets, versions
def get_FLIR_Pset_times(stdate, enddate, version=0, verbose=0, testmode=True):
"""
+ Download of the timevector for the different pre-sets of the recorded FLIR images
+
INPUT
------
-
+ stdate: integer
+ time in ns where the search should start
+ enddate: integer
+ time in sn where the search should end
+ version: integer, optional, default 0
+ Version of the data in the archiveDB, in case of 0 the highest version will be used
+ verbose: integer, optional, default 0
+ feedback level (details of print messages)
+ testmode: boolean, optional, default True
+ if True, the data will be loaded from the test archive
RESULT
------
-
+ timess: list
+ timevectors of each pre-set
+ versions: list
+ list of the versions of each pre-set
NOTE
------
"""
@@ -1417,12 +1929,30 @@ def get_FLIR_Pset_times(stdate, enddate, version=0, verbose=0, testmode=True):
def download_FLIR_Psets(larchivepath, timess, threads, versions, verbose=0):
"""
+ Download of the FLIR datasets, which are stored as different datasets. Each dataset has a different exposure time
+
INPUT
------
-
+ larchivepath: string
+ local archivepath, the URL to download the images
+ timess: list
+ list of the timevectors for each pre-set (pset)
+ threads: integer, default 1
+ number of parallel threads to download the images, one or two per pre-set
+ version: integer, optional, default 0
+ Version of the data in the archiveDB, in case of 0 the highest version will be used
+ verbose: integer, optional, default 0
+ feedback level (details of print messages)
RESULT
------
-
+ exist: boolean
+ True if data was found, False if not
+ time: list
+ a list containing the time stamp of each frame
+ frames: list
+ a list of frames, 2D numpy arrays
+ tsets: list
+ contains the information which frames belong to which pre-set
NOTE
------
"""
@@ -1526,12 +2056,22 @@ def download_FLIR_Psets(larchivepath, timess, threads, versions, verbose=0):
def download_last_raw_image_by_time(larchivepath, starttime, stoptime):#, version=0
"""
+ downloads for a given time intervall the image as png. it if always the last image in the intervall
+
INPUT
------
-
+ larchivepath: string
+ local archivepath, the URL to download the images
+ starttime: integer
+ time in ns where the search should start
+ stoptime: integer
+ time in sn where the search should end
RESULT
------
-
+ exist: boolean
+ True if data was found, False if not
+ frame: list
+ a list of frame, 2D numpy array
NOTE
------
"""
@@ -1552,12 +2092,29 @@ def download_last_raw_image_by_time(larchivepath, starttime, stoptime):#, versio
def download_raw_parlog_by_program(port, program, version=0, testmode=False, verbose=0):
"""
+ download of the additional information for the raw data of the AEF cameras for a given program, which are stored in the parlog. contains information as frame width, height bitdepth.
+
INPUT
------
+ port: integer
+ port number of the AEF port of the camera
+ program: string
+ program id in the form of 'YYYYMMDD.XXX', e.g. '20181016.016'
+ version: integer, optional, default 0
+ Version of the data in the archiveDB, in case of 0 the highest version will be used
+ testmode: boolean, optional, default False
+ if True, the data will be loaded from the test archive
+ verbose: integer, optional, default 0
+ feedback level (details of print messages)
RESULT
------
-
+ exist: boolean
+ True if data was found, False if not
+ time: list
+ a list containing the time stamp of the parlog (from to)
+ meta-data: dictionary/list
+ the meta-data from the parlog converted from JSON
NOTE
------
"""
@@ -1577,12 +2134,31 @@ def download_raw_parlog_by_program(port, program, version=0, testmode=False, ver
def download_raw_parlog_by_times(port, starttime, stoptime, version=0, testmode=False):
"""
+ download of the additional information for the raw data of the AEF cameras for a given time intervall, which are stored in the parlog. contains information as frame width, height bitdepth.
+
INPUT
------
+ port: integer
+ port number of the AEF port of the camera
+ starttime: integer
+ time in ns where the search should start
+ stoptime: integer
+ time in sn where the search should end
+ version: integer, optional, default 0
+ Version of the data in the archiveDB, in case of 0 the highest version will be used
+ testmode: boolean, optional, default False
+ if True, the data will be loaded from the test archive
+ verbose: integer, optional, default 0
+ feedback level (details of print messages)
RESULT
------
-
+ exist: boolean
+ True if data was found, False if not
+ time: list
+ a list containing the time stamp of the parlog (from to)
+ meta-data: dictionary/list
+ the meta-data from the parlog converted from JSON
NOTE
------
"""
@@ -1612,12 +2188,28 @@ def download_raw_parlog_by_times(port, starttime, stoptime, version=0, testmode=
def get_INFRATEC_filter_by_program(program, port=50, version=0, testmode=False, verbose=0):
"""
+ downlods the information of the used filter of the INFRATEC camera for a given program
+
INPUT
------
-
+ program: string
+ program id in the form of 'YYYYMMDD.XXX', e.g. '20181016.016'
+ port: integer, optional, default 50
+ port number of the AEF port of the camera
+ version: integer, optional, default 0
+ Version of the data in the archiveDB, in case of 0 the highest version will be used
+ testmode: boolean, optional, default False
+ if True, the data will be loaded from the test archive
+ verbose: integer, optional, default 0
+ feedback level (details of print messages)
RESULT
------
-
+ exist: boolean
+ True if data was found, False if not
+ time: list
+ a list containing the time stamp of the parlog (from to)
+ filter: integer
+ the number of the used filter
NOTE
------
"""
@@ -1637,12 +2229,30 @@ def get_INFRATEC_filter_by_program(program, port=50, version=0, testmode=False,
def get_INFRATEC_filter_by_times(starttime, stoptime, port=50, version=0, testmode=False):
"""
+ Idownlods the information of the used filter of the INFRATEC camera for a given time intervall
+
INPUT
------
-
+ starttime: integer
+ time in ns where the search should start
+ stoptime: integer
+ time in sn where the search should end
+ port: integer, optional, default 50
+ port number of the AEF port of the camera
+ version: integer, optional, default 0
+ Version of the data in the archiveDB, in case of 0 the highest version will be used
+ testmode: boolean, optional, default False
+ if True, the data will be loaded from the test archive
+ verbose: integer, optional, default 0
+ feedback level (details of print messages)
RESULT
------
-
+ exist: boolean
+ True if data was found, False if not
+ time: list
+ a list containing the time stamp of the parlog (from to)
+ filter: integer
+ the number of the used filter
NOTE
------
"""
@@ -1672,12 +2282,29 @@ def get_INFRATEC_filter_by_times(starttime, stoptime, port=50, version=0, testmo
def get_exposure_by_program(port, program, version=0, testmode=False, verbose=0):
"""
+ download the exposure time for a given camera and program for each frame
+
INPUT
------
+ port: integer
+ port number of the AEF port of the camera
+ program: string
+ program id in the form of 'YYYYMMDD.XXX', e.g. '20181016.016'
+ version: integer, optional, default 0
+ Version of the data in the archiveDB, in case of 0 the highest version will be used
+ testmode: boolean, optional, default False
+ if True, the data will be loaded from the test archive
+ verbose: integer, optional, default 0
+ feedback level (details of print messages)
RESULT
------
-
+ exist: boolean
+ True if data was found, False if not
+ time: list
+ a list containing the time stamp of each frame
+ exposure time: list
+ the exposure time of the camera for each frame
NOTE
------
"""
@@ -1698,12 +2325,28 @@ def get_exposure_by_program(port, program, version=0, testmode=False, verbose=0)
def get_exposure_by_times(port, starttime, stoptime, version=0, testmode=False):
"""
+ download the exposure time for a given camera and each frame in a given time intervall
+
INPUT
------
-
+ port: integer
+ port number of the AEF port of the camera
+ starttime: integer
+ time in ns where the search should start
+ stoptime: integer
+ time in sn where the search should end
+ version: integer, optional, default 0
+ Version of the data in the archiveDB, in case of 0 the highest version will be used
+ testmode: boolean, optional, default False
+ if True, the data will be loaded from the test archive
RESULT
------
-
+ exist: boolean
+ True if data was found, False if not
+ time: list
+ a list containing the time stamp of each frame
+ exposure time: list
+ the exposure time of the camera for each frame
NOTE
------
"""
@@ -1719,12 +2362,29 @@ def get_exposure_by_times(port, starttime, stoptime, version=0, testmode=False):
def get_camera_temp_by_program(port, program, version=0, testmode=False, verbose=0):
"""
+ download the camera temperature for a given camera and program for each frame
+
INPUT
------
+ port: integer
+ port number of the AEF port of the camera
+ program: string
+ program id in the form of 'YYYYMMDD.XXX', e.g. '20181016.016'
+ version: integer, optional, default 0
+ Version of the data in the archiveDB, in case of 0 the highest version will be used
+ testmode: boolean, optional, default False
+ if True, the data will be loaded from the test archive
+ verbose: integer, optional, default 0
+ feedback level (details of print messages)
RESULT
------
-
+ exist: boolean
+ True if data was found, False if not
+ time: list
+ a list containing the time stamp of each frame
+ temperature: list
+ the temperature of the camera for each frame
NOTE
------
"""
@@ -1744,14 +2404,30 @@ def get_camera_temp_by_program(port, program, version=0, testmode=False, verbose
def get_camera_temp_by_times(port, starttime, stoptime, version=0, testmode=False):
"""
+ download the camera temperature for a given camera and each frame in a given time intervall
+
INPUT
------
-
+ port: integer
+ port number of the AEF port of the camera
+ starttime: integer
+ time in ns where the search should start
+ stoptime: integer
+ time in sn where the search should end
+ version: integer, optional, default 0
+ Version of the data in the archiveDB, in case of 0 the highest version will be used
+ testmode: boolean, optional, default False
+ if True, the data will be loaded from the test archive
RESULT
------
-
+ exist: boolean
+ True if data was found, False if not
+ time: list
+ a list containing the time stamp of each frame
+ temperature: list
+ the temperature of the camera for each frame
NOTE
- ------
+ -----
"""
OP = IR_tools.get_OP_by_time(time_ns=stoptime)
stream=portpathdict[OP]["AEF"+str(port)]+"meta_"
@@ -1769,12 +2445,29 @@ def get_camera_temp_by_times(port, starttime, stoptime, version=0, testmode=Fals
def get_sensor_temp_by_program(port, program, version=0, testmode=False, verbose=0):
"""
+ download the sensor temperature for a given camera and program for each frame
+
INPUT
------
+ port: integer
+ port number of the AEF port of the camera
+ program: string
+ program id in the form of 'YYYYMMDD.XXX', e.g. '20181016.016'
+ version: integer, optional, default 0
+ Version of the data in the archiveDB, in case of 0 the highest version will be used
+ testmode: boolean, optional, default False
+ if True, the data will be loaded from the test archive
+ verbose: integer, optional, default 0
+ feedback level (details of print messages)
RESULT
------
-
+ exist: boolean
+ True if data was found, False if not
+ time: list
+ a list containing the time stamp of each frame
+ temperature: list
+ the temperature of the camera sensor for each frame
NOTE
------
"""
@@ -1794,14 +2487,30 @@ def get_sensor_temp_by_program(port, program, version=0, testmode=False, verbose
def get_sensor_temp_by_times(port, starttime, stoptime, version=0, testmode=False):
"""
+ download the sensor temperature for a given camera and each frame in a given time intervall
+
INPUT
------
-
+ port: integer
+ port number of the AEF port of the camera
+ starttime: integer
+ time in ns where the search should start
+ stoptime: integer
+ time in sn where the search should end
+ version: integer, optional, default 0
+ Version of the data in the archiveDB, in case of 0 the highest version will be used
+ testmode: boolean, optional, default False
+ if True, the data will be loaded from the test archive
RESULT
------
-
+ exist: boolean
+ True if data was found, False if not
+ time: list
+ a list containing the time stamp of each frame
+ temperature: list
+ the temperature of the camera sensor for each frame
NOTE
- ------
+ -----
"""
OP = IR_tools.get_OP_by_time(time_ns=stoptime)
stream=portpathdict[OP]["AEF"+str(port)]+"meta_"
@@ -1815,12 +2524,29 @@ def get_sensor_temp_by_times(port, starttime, stoptime, version=0, testmode=Fals
def get_camera_framerate_by_program(port, program, version=0, testmode=False, verbose=0):
"""
+ download the framerate for a given camera and program for each frame
+
INPUT
------
+ port: integer
+ port number of the AEF port of the camera
+ program: string
+ program id in the form of 'YYYYMMDD.XXX', e.g. '20181016.016'
+ version: integer, optional, default 0
+ Version of the data in the archiveDB, in case of 0 the highest version will be used
+ testmode: boolean, optional, default False
+ if True, the data will be loaded from the test archive
+ verbose: integer, optional, default 0
+ feedback level (details of print messages)
RESULT
------
-
+ exist: boolean
+ True if data was found, False if not
+ time: list
+ a list containing the time stamp of each frame
+ framerate: list
+ the framerate of the camera for each frame
NOTE
------
"""
@@ -1841,14 +2567,30 @@ def get_camera_framerate_by_program(port, program, version=0, testmode=False, ve
def get_camera_framerate_by_times(port, starttime, stoptime, version=0, testmode=False):
"""
+ download the framerate for a given camera and each frame in a given time intervall
+
INPUT
------
-
+ port: integer
+ port number of the AEF port of the camera
+ starttime: integer
+ time in ns where the search should start
+ stoptime: integer
+ time in sn where the search should end
+ version: integer, optional, default 0
+ Version of the data in the archiveDB, in case of 0 the highest version will be used
+ testmode: boolean, optional, default False
+ if True, the data will be loaded from the test archive
RESULT
------
-
+ exist: boolean
+ True if data was found, False if not
+ time: list
+ a list containing the time stamp of each frame
+ framerate: list
+ the framerate of the camera for each frame
NOTE
- ------
+ -----
"""
OP = IR_tools.get_OP_by_time(time_ns=stoptime)
stream=portpathdict[OP]["AEF"+str(port)]+"meta_"
@@ -1863,12 +2605,29 @@ def get_camera_framerate_by_times(port, starttime, stoptime, version=0, testmode
def get_frametype_by_program(port, program, version=0, testmode=False, verbose=0):
"""
+ download the frametype for a given camera and program for each frame
+
INPUT
------
+ port: integer
+ port number of the AEF port of the camera
+ program: string
+ program id in the form of 'YYYYMMDD.XXX', e.g. '20181016.016'
+ version: integer, optional, default 0
+ Version of the data in the archiveDB, in case of 0 the highest version will be used
+ testmode: boolean, optional, default False
+ if True, the data will be loaded from the test archive
+ verbose: integer, optional, default 0
+ feedback level (details of print messages)
RESULT
------
-
+ exist: boolean
+ True if data was found, False if not
+ time: list
+ a list containing the time stamp of each frame
+ frametype: list
+ the frametype of the camera for each frame
NOTE
------
"""
@@ -1888,14 +2647,30 @@ def get_frametype_by_program(port, program, version=0, testmode=False, verbose=0
def get_frametype_by_times(port, starttime, stoptime, version=0, testmode=False):
"""
+ download the frametyp for a given camera and each frame in a given time intervall
+
INPUT
------
-
+ port: integer
+ port number of the AEF port of the camera
+ starttime: integer
+ time in ns where the search should start
+ stoptime: integer
+ time in sn where the search should end
+ version: integer, optional, default 0
+ Version of the data in the archiveDB, in case of 0 the highest version will be used
+ testmode: boolean, optional, default False
+ if True, the data will be loaded from the test archive
RESULT
------
-
+ exist: boolean
+ True if data was found, False if not
+ time: list
+ a list containing the time stamp of each frame
+ frametype: list
+ the frametype of the camera for each frame
NOTE
- ------
+ -----
"""
OP = IR_tools.get_OP_by_time(time_ns=starttime)
stream=portpathdict[OP]["AEF"+str(port)]+"meta_"
@@ -1909,12 +2684,29 @@ def get_frametype_by_times(port, starttime, stoptime, version=0, testmode=False)
def get_framenumber_by_program(port, program, version=0, testmode=False, verbose=0):
"""
+ download the framenumber for a given camera and program for each frame
+
INPUT
------
+ port: integer
+ port number of the AEF port of the camera
+ program: string
+ program id in the form of 'YYYYMMDD.XXX', e.g. '20181016.016'
+ version: integer, optional, default 0
+ Version of the data in the archiveDB, in case of 0 the highest version will be used
+ testmode: boolean, optional, default False
+ if True, the data will be loaded from the test archive
+ verbose: integer, optional, default 0
+ feedback level (details of print messages)
RESULT
------
-
+ exist: boolean
+ True if data was found, False if not
+ time: list
+ a list containing the time stamp of each frame
+ framenumber: list
+ the framenumber of the camera for each frame
NOTE
------
"""
@@ -1934,14 +2726,30 @@ def get_framenumber_by_program(port, program, version=0, testmode=False, verbose
def get_framenumber_by_times(port, starttime, stoptime, version=0, testmode=False):
"""
+ download the framenumber for a given camera and each frame in a given time intervall
+
INPUT
------
-
+ port: integer
+ port number of the AEF port of the camera
+ starttime: integer
+ time in ns where the search should start
+ stoptime: integer
+ time in sn where the search should end
+ version: integer, optional, default 0
+ Version of the data in the archiveDB, in case of 0 the highest version will be used
+ testmode: boolean, optional, default False
+ if True, the data will be loaded from the test archive
RESULT
------
-
+ exist: boolean
+ True if data was found, False if not
+ time: list
+ a list containing the time stamp of each frame
+ framenumber: list
+ the framenumber of the camera for each frame
NOTE
- ------
+ -----
"""
OP = IR_tools.get_OP_by_time(time_ns=starttime)
stream=portpathdict[OP]["AEF"+str(port)]+"meta_"
@@ -1955,12 +2763,30 @@ def get_framenumber_by_times(port, starttime, stoptime, version=0, testmode=Fals
def get_average_divertor_TC_for_camtemp(port, starttime, stoptime, part="all", TC=0):
"""
+ download of an mean divertor temperature measured by the thermocouples in the divertor with respect to a camera observing this divertor
+
INPUT
------
-
+ port: integer
+ port number of the AEF port of the camera
+ starttime: integer
+ time in ns where the search should start
+ stoptime: integer
+ time in sn where the search should end
+ part: string or integer, optional, default "all"
+ defines the divertor region for the evaluation of the thermocouples (TC)\n
+ options are 'h' (horizontal), 'v' (vertical), 'hli' (horizontal low iota), 'hhi' (horizontal high iota) 's' or 'single' for only one TC\n
+ if it is a number, one thermocouple will be used, as counted from low to high iota region(7), going from 0 to 10, 8-10 vor vertical
+ TC: integer, optional, only for part 's'
+ TC number if part is 'single' or 's'
RESULT
------
-
+ exist: boolean
+ True if data was found, False if not
+ time: list
+ a list containing the time stamp of each temperature
+ temperature: list
+ averaged TC temperature for each timestamp in time
NOTE
------
"""
@@ -1976,8 +2802,10 @@ def get_average_divertor_TC_for_camtemp(port, starttime, stoptime, part="all", T
portnr=port+1
offset=(port//10-1)*32+16
else:
- print("get_average_divertor_TC_for_camtemp: Error! unknown port")
- raise Exception
+ #print("get_average_divertor_TC_for_camtemp: Error! unknown port")
+ #raise Exception
+ logging.warning("get_average_divertor_TC_for_camtemp: Error! unknown port")
+ return False, [0], [0]
if portnr==50:
offset=0
elif portnr==51:
@@ -2045,12 +2873,18 @@ def get_average_divertor_TC_for_camtemp(port, starttime, stoptime, part="all", T
def get_divertor_TC_coordinate(cameraport, TC=0):
"""
+ returns the information of the thermocouple
+
INPUT
------
-
+ cameraport: integer
+ port number of the AEF port of the camera
+ TC: integer, default is 0
+ TC number going from 0 to 10
RESULT
------
-
+ TC_info: dictionary
+ contains x,y,z information and in which target it is located
NOTE
------
"""
@@ -2065,13 +2899,28 @@ def get_divertor_TC_coordinate(cameraport, TC=0):
def get_TC_data_for_port_by_program(port, program, part=0, version=0, verbose=0):
"""
+ download the thermocouple (TC) data for a thermocouple in the port (immersion tube or endoscope)
+
INPUT
------
+ port: integer
+ port number of the AEF port of the camera
+ program: string
+ program id in the form of 'YYYYMMDD.XXX', e.g. '20181016.016'
+ part: integer, default 0
+ 0 or the number of the TC in the port
+ version: integer, optional, default 0
+ Version of the data in the archiveDB, in case of 0 the highest version will be used
verbose: integer, optional, default 0
feedback level (details of print messages)
RESULT
------
-
+ exist: boolean
+ True if data was found, False if not
+ time: list
+ a list containing the time stamp of each frame
+ temperature: list
+ temperature from the TC for each time stamp in time
NOTE
------
"""
@@ -2091,11 +2940,30 @@ def get_TC_data_for_port_by_program(port, program, part=0, version=0, verbose=0)
def get_TC_data_for_port(port, starttime, stoptime, part=0):#, version=0
"""
+ download the thermocouple (TC) data for a thermocouple in the port (immersion tube or endoscope)
+
INPUT
------
-
+ port: integer
+ port number of the AEF port of the camera
+ starttime: integer
+ time in ns where the search should start
+ stoptime: integer
+ time in sn where the search should end
+ part: integer, default 0
+ 0 or the number of the TC in the port
+ version: integer, optional, default 0
+ Version of the data in the archiveDB, in case of 0 the highest version will be used
+ verbose: integer, optional, default 0
+ feedback level (details of print messages)
RESULT
------
+ exist: boolean
+ True if data was found, False if not
+ time: list
+ a list containing the time stamp of each frame
+ temperature: list
+ temperature from the TC for each time stamp in time
NOTE
------
@@ -2159,12 +3027,18 @@ def get_TC_data_for_port(port, starttime, stoptime, part=0):#, version=0
def temp_from_LUT(LUT, images):
"""
+ apply the look up table(LUT) onto images to get temperature images
+
INPUT
------
-
+ LUT: list
+ list in the form [Temperaure,temperature_error]
+ images: list
+ the frames as a list of 2D numpy arrays
RESULT
------
-
+ images: list
+ list of numpy arrays
NOTE
------
"""
@@ -2172,12 +3046,18 @@ def temp_from_LUT(LUT, images):
def terror_from_LUT(LUT, images):
"""
+ apply the look up table(LUT) onto images to get temperature error images
+
INPUT
------
-
+ LUT: list
+ list in the form [Temperaure,temperature_error]
+ images: list
+ the frames as a list of 2D numpy arrays
RESULT
------
-
+ images: list
+ list of numpy arrays
NOTE
------
"""
@@ -2187,12 +3067,25 @@ def check_temperature_range(time,maxvalue_C,minvalue_C, port, exposure, cfilter=
"""
INPUT
------
-
+ time: integer
+ nanosecond time stamp to identify the right camera
+ maxvalue_C: float or integer
+ the maximum value in the data
+ minvalue_C: float or integer
+ the minimum value in the data
+ port: integer
+ port number of the AEF port of the camera
+ exposure: integer
+ exposure time in microseconds
+ cfilter: integer, optional, default 0
+ the filter for the infratec camera
+
RESULT
------
-
+ no return
NOTE
------
+ not fully functional and slow!
"""
try:
cam=portcamdict[IR_tools.get_OP_by_time(time_ns=time)]['AEF'+str(port)]
@@ -2218,13 +3111,43 @@ def check_temperature_range(time,maxvalue_C,minvalue_C, port, exposure, cfilter=
def get_temp_from_raw_by_program_V1(portnr, program, time_window=0, emi=0.8,divertorpart="all",
version=0, intervalSize=1E9, give_ERROR=False, testmode=False, verbose=0):
"""
+ downloads the raw data of the cameras, the calibration and applies it to the data to deliver the temperature data
+ This function is outdated
+
INPUT
------
+ portnr: integer
+ port number of the AEF port of the camera
+ program: string
+ program id in the form of 'YYYYMMDD.XXX', e.g. '20181016.016'
+ time_window: list, array, integer or float, optional, default 0
+ if integer or float: size of the window in seconds from t1
+ if list or array: start and endpoint of the window in seconds from t1
+ if 0 the full program will be downloaded (t1 until t6)
+ emi: float, optional, default 0.8
+ the emissivity value for the calibration
+ divertorpart, optional, default "all"
+ the region of the divertor, which should be used as the reference temperature
+ version: integer, optional, default 0
+ Version of the data in the archiveDB, in case of 0 the highest version will be used
+ intervalSize: integer, optional, default 1e9 (1 second)
+ size of the intervall to be downloaded in nanoseconds
+ give_ERROR: boolean, optional default False
+ switch to turn on the return of error images
+ testmode: boolean, optional, default False
+ if True, the data will be loaded from the test archive
verbose: integer, optional, default 0
feedback level (details of print messages)
RESULT
------
-
+ exist: boolean
+ True if data was found, False if not
+ time: list
+ a list containing the time stamp of each frame
+ frames: list
+ a list of frames, 2D numpy arrays
+ optional: error_frames: list
+ a list of frames, 2D numpy arrays
NOTE
------
"""
@@ -2248,7 +3171,9 @@ def get_temp_from_raw_by_program_V1(portnr, program, time_window=0, emi=0.8,dive
tstart=time_window[0]
tstop=time_window[1]
if tstop<tstart:
- raise Exception("get_temp_from_raw_by_program_V1: t_stop before t_start")
+# raise Exception("get_temp_from_raw_by_program_V1: t_stop before t_start")
+ logging.warning("get_temp_from_raw_by_program_V1: t_stop before t_start")
+ return False, [0], [0]
stoptime=int(starttime+tstop*1e9)
starttime=int(starttime+tstart*1e9)
else:
@@ -2283,7 +3208,9 @@ def get_temp_from_raw_by_program_V1(portnr, program, time_window=0, emi=0.8,dive
times=times+time
del time, images
else:
- raise Exception("get_temp_from_raw_by_program_V1: cannot download the raw images")
+# raise Exception("get_temp_from_raw_by_program_V1: cannot download the raw images")
+ logging.warning("get_temp_from_raw_by_program_V1: cannot download the raw images")
+ return False, [0], [0]
else:
raw_dl=download_raw_images_by_times(portnr, starttime, stoptime, version, intervalSize, testmode=testmode, verbose=verbose-1)
if raw_dl[0]:
@@ -2316,13 +3243,41 @@ def get_temp_from_raw_by_program_V2(portnr, program, time_window=0, emi=0.8, ver
threads=1, give_ERROR=False,use_firstframe_as_background=False,
verbose=0):
"""
+ downloads the raw data of the cameras, the calibration and applies it to the data to deliver the temperature data
+
INPUT
------
+ portnr: integer
+ port number of the AEF port of the camera
+ program: string
+ program id in the form of 'YYYYMMDD.XXX', e.g. '20181016.016'
+ time_window: list, array, integer or float, optional, default 0
+ if integer or float: size of the window in seconds from t1
+ if list or array: start and endpoint of the window in seconds from t1
+ if 0 the full program will be downloaded (t1 until t6)
+ emi: float, optional, default 0.8
+ the emissivity value for the calibration
+ version: integer, optional, default 0
+ Version of the data in the archiveDB, in case of 0 the highest version will be used
+ threads: integer, optional, default 1
+ number of parallel threads to download the images
+ (approx. max. 2x number of cores and max. timeintervall/0.5 for best performance)
+ give_ERROR: boolean, optional default False
+ switch to turn on the return of error images
+ use_firstframe_as_background: boolean
+ in case the background frames are not available, the first frame can be used
verbose: integer, optional, default 0
feedback level (details of print messages)
RESULT
------
-
+ exist: boolean
+ True if data was found, False if not
+ time: list
+ a list containing the time stamp of each frame
+ frames: list
+ a list of frames, 2D numpy arrays
+ optional: error_frames: list
+ a list of frames, 2D numpy arrays
NOTE
------
"""
@@ -2337,20 +3292,59 @@ def get_temp_from_raw_by_program(portnr, program, time_window=0, emi=0.8, T_vers
use_firstframe_as_background=False, back_emissivity=0.8,
verbose=0, testmode=False, framerate='max'):
"""
+ downloads the raw data of the cameras, the calibration and applies it to the data to deliver the temperature data
+ several threads can be used to download in parallel the data
+
INPUT
------
+ portnr: integer
+ port number of the AEF port of the camera
+ program: string
+ program id in the form of 'YYYYMMDD.XXX', e.g. '20181016.016'
+ time_window: list, array, integer or float, optional, default 0
+ if integer or float: size of the window in seconds from t1
+ if list or array: start and endpoint of the window in seconds from t1
+ if 0 the full program will be downloaded (t1 until t6)
+ emi: float, optional, default 0.8
+ the emissivity value for the calibration
+ T_version: integer, default 2
+ set the version of the calibration method, see more on the QRT wiki
+ version: integer, optional, default 0
+ Version of the data in the archiveDB, in case of 0 the highest version will be used
+ threads: integer, optional, default 1
+ number of parallel threads to download the images
+ (approx. max. 2x number of cores and max. timeintervall/0.5 for best performance)
+ give_ERROR: boolean, optional default False
+ switch to turn on the return of error images
+ use_firstframe_as_background: boolean
+ in case the background frames are not available, the first frame can be used
+ back_emissivity: float, optional, default 0.8
+ set the emissivity value for the background region for the calibration
verbose: integer, optional, default 0
feedback level (details of print messages)
+ testmode: boolean, optional, default False
+ if True, the data will be loaded from the test archive
+ framerate: string or integer, optional, default 'max'
+ sets the framerate to downsample the data. normal rate is 100.
RESULT
------
-
+ exist: boolean
+ True if data was found, False if not
+ time: list
+ a list containing the time stamp of each frame
+ frames: list
+ a list of frames, 2D numpy arrays, values are in Kelvin
+ optional: error_frames: list
+ a list of frames, 2D numpy arrays
NOTE
------
"""
FLIR=False
trigger_exist, t0, t1, t6 = get_trigger_from_PID(program, portnr, testmode, verbose=verbose-1)
if not trigger_exist:
- raise Exception("get_temp_from_raw_by_program: unknown Program")
+# raise Exception("get_temp_from_raw_by_program: unknown Program")
+ logging.warning("get_temp_from_raw_by_program: unknown Program")
+ return False, [0], [0]
success=True
OP = IR_tools.get_OP_by_time(time_ns=t1)
@@ -2372,8 +3366,9 @@ def get_temp_from_raw_by_program(portnr, program, time_window=0, emi=0.8, T_vers
FLIR=False
portnr=int(portnr)
else:
- raise Exception("get_temp_from_raw_by_program: the given port is neither a number or a valid String!")
-
+# raise Exception("get_temp_from_raw_by_program: the given port is neither a number or a valid String!")
+ logging.warning("get_temp_from_raw_by_program: the given port is neither a number or a valid String!")
+ return False, [0], [0]
if FLIR:
# --- Glens FLIR camera ---
#
@@ -2479,7 +3474,8 @@ def get_temp_from_raw_by_program(portnr, program, time_window=0, emi=0.8, T_vers
if exist:
frames = [im.swapaxes(0,1) for im in frames]
if not exist:
- raise Warning("get_temp_from_raw_by_program: data not found in database!")
+# raise Warning("get_temp_from_raw_by_program: data not found in database!")
+ logging.warning("get_temp_from_raw_by_program: data not found in database!")
return False, [0], [0], False
if verbose > 0:
print(datetime.datetime.now(), "get_temp_from_raw_by_program: raw download finished")
@@ -2537,13 +3533,45 @@ def get_temp_from_raw_by_program_fullthreads(portnr, program, time_window=0, emi
give_ERROR=False, check_range=True,
testmode=False, verbose=0):
"""
+ downloads the raw data and converting it to temeprature images
+
INPUT
------
+ portnr: integer
+ port number of the AEF port of the camera
+ program: string
+ program id in the form of 'YYYYMMDD.XXX', e.g. '20181016.016'
+ time_window: list, array, integer or float, optional, default 0
+ if integer or float: size of the window in seconds from t1
+ if list or array: start and endpoint of the window in seconds from t1
+ if 0 the full program will be downloaded (t1 until t6)
+ emi: float, optional, default 0.8
+ the emissivity value for the calibration
+ T_version: integer, default 2
+ set the version of the calibration method, see more on the QRT wiki
+ version: integer, optional, default 0
+ Version of the data in the archiveDB, in case of 0 the highest version will be used
+ threads: integer, optional, default 1
+ number of parallel threads to download the images
+ (approx. max. 2x number of cores and max. timeintervall/0.5 for best performance)
+ give_ERROR: boolean, optional default False
+ switch to turn on the return of error images
+ check_range: boolean, optional, default True
+ switch to turn the temperature range check on or off
+ testmode: boolean, optional, default False
+ if True, the data will be loaded from the test archive
verbose: integer, optional, default 0
- feedback level (details of print messages)
+ feedback level (details of print messages)
RESULT
------
-
+ exist: boolean
+ True if data was found, False if not
+ time: list
+ a list containing the time stamp of each frame
+ frames: list
+ a list of frames, 2D numpy arrays, values are in Kelvin
+ optional: error_frames: list
+ a list of frames, 2D numpy arrays
NOTE
------
"""
@@ -2563,7 +3591,10 @@ def get_temp_from_raw_by_program_fullthreads(portnr, program, time_window=0, emi
portnr=int(portnr.split("AEF")[1])
goon=True
except Exception as E:
- raise Exception("get_temp_from_raw_by_program_fullthreads: unknown Port!"+E)
+# raise Exception("get_temp_from_raw_by_program_fullthreads: unknown Port!"+E)
+ logging.warning("get_temp_from_raw_by_program_fullthreads: unknown Port!"+E)
+ return False, [0], [0]
+
elif type(portnr) is int:
FLIR = False
goon = True
@@ -2591,7 +3622,9 @@ def get_temp_from_raw_by_program_fullthreads(portnr, program, time_window=0, emi
tstart = time_window[0]
tstop = time_window[1]
if tstop < tstart:
- raise Exception("get_temp_from_raw_by_program_fullthreads: t_stop before t_start")
+# raise Exception("get_temp_from_raw_by_program_fullthreads: t_stop before t_start")
+ logging.warning("get_temp_from_raw_by_program_fullthreads: t_stop before t_start")
+ return False, [0], [0]
stdate = datetime.datetime.utcfromtimestamp((starttime-100)/1e9+tstart)
stdate = stdate.isoformat()
enddate = datetime.datetime.utcfromtimestamp((starttime)/1e9+tstop)
@@ -2672,13 +3705,45 @@ def get_nuced_raw_by_program(portnr, program, time_window=0,version=0,
emi=1, T_version=2, threads=1,
give_ERROR=False, testmode=False, verbose=0):
"""
+ download the raw data and applies the non uniformity correction to make the image understandable
+
INPUT
------
+ portnr: integer
+ port number of the AEF port of the camera
+ program: string
+ program id in the form of 'YYYYMMDD.XXX', e.g. '20181016.016'
+ time_window: list, array, integer or float, optional, default 0
+ if integer or float: size of the window in seconds from t1
+ if list or array: start and endpoint of the window in seconds from t1
+ if 0 the full program will be downloaded (t1 until t6)
+ version: integer, optional, default 0
+ Version of the data in the archiveDB, in case of 0 the highest version will be used
+ emi: float, optional, default 0.8
+ the emissivity value for the calibration
+ T_version: integer, default 2
+ set the version of the calibration method, see more on the QRT wiki
+ threads: integer, default 1
+ number of parallel threads to download the images
+ (approx. max. 2x number of cores and max. timeintervall/0.5 for best performance)
+ give_ERROR: boolean, optional default False
+ switch to turn on the return of error images
+ testmode: boolean, optional, default False
+ if True, the data will be loaded from the test archive
verbose: integer, optional, default 0
feedback level (details of print messages)
RESULT
------
-
+ exist: boolean
+ True if data was found, False if not
+ time: list
+ a list containing the time stamp of each frame
+ frames: list
+ a list of frames, 2D numpy arrays
+ valid: boolean
+ status wheather the data can be used
+ optional frames_error: list
+ a list of frames, 2D numpy arrays
NOTE
------
"""
@@ -2694,7 +3759,9 @@ def get_nuced_raw_by_program(portnr, program, time_window=0,version=0,
if exist:
frames=[im.swapaxes(0,1) for im in frames]
else:
- raise Exception("get_nuced_raw_by_program: no data found")
+# raise Exception("get_nuced_raw_by_program: no data found")
+ logging.warning("get_nuced_raw_by_program: no data found")
+ return False, [0], [0], False
if verbose > 0:
print(datetime.datetime.now(), "get_nuced_raw_by_program: raw download finished")
FOV=get_FOV_mask(portnr)
@@ -2722,13 +3789,45 @@ def get_nuced_raw_by_program_fullthreads(portnr, program, time_window=0, emi=0.8
give_ERROR=False, check_range=True,
testmode=False, verbose=0):
"""
+ download the raw data and applies the non uniformity correction to make the image understandable by using threads for all steps
+
INPUT
------
+ portnr: integer
+ port number of the AEF port of the camera
+ program: string
+ program id in the form of 'YYYYMMDD.XXX', e.g. '20181016.016'
+ time_window: list, array, integer or float, optional, default 0
+ if integer or float: size of the window in seconds from t1
+ if list or array: start and endpoint of the window in seconds from t1
+ if 0 the full program will be downloaded (t1 until t6)
+ emi: float, optional, default 0.8
+ the emissivity value for the calibration
+ T_version: integer, default 2
+ set the version of the calibration method, see more on the QRT wiki
+ version: integer, optional, default 0
+ Version of the data in the archiveDB, in case of 0 the highest version will be used
+ threads: integer, default 1
+ number of parallel threads to download the images
+ (approx. max. 2x number of cores and max. timeintervall/0.5 for best performance)
+ give_ERROR: boolean, optional default False
+ switch to turn on the return of error images
+ testmode: boolean, optional, default False
+ if True, the data will be loaded from the test archive
verbose: integer, optional, default 0
feedback level (details of print messages)
RESULT
------
-
+ exist: boolean
+ True if data was found, False if not
+ time: list
+ a list containing the time stamp of each frame
+ frames: list
+ a list of frames, 2D numpy arrays
+ valid: boolean
+ status wheather the data can be used
+ optional frames_error: list
+ a list of frames, 2D numpy arrays
NOTE
------
"""
@@ -2764,7 +3863,9 @@ def get_nuced_raw_by_program_fullthreads(portnr, program, time_window=0, emi=0.8
tstart = time_window[0]
tstop = time_window[1]
if tstop < tstart:
- raise Exception("get_nuced_raw_by_program_fullthreads: t_stop before t_start")
+# raise Exception("get_nuced_raw_by_program_fullthreads: t_stop before t_start")
+ logging.warning("get_nuced_raw_by_program_fullthreads: t_stop before t_start")
+ return False, [0], [0], False
stdate = datetime.datetime.utcfromtimestamp((starttime-100)/1e9+tstart)
stdate = stdate.isoformat()
enddate = datetime.datetime.utcfromtimestamp((starttime)/1e9+tstop)
@@ -2832,16 +3933,41 @@ def get_nuced_raw_by_program_fullthreads(portnr, program, time_window=0, emi=0.8
def apply_calib_on_raw(images, background, LUT,refT=28.5, gain=0, offset=0, gain_error=0,
offset_error=0, fullbackground=False, give_ERROR=False, verbose=0):
"""
- apply_calib_on_raw
- return success, images and if give_Error: errorimages
+
+ applies the calibration onto the given raw images and return the temperature images
INPUT
------
+ images: list
+ a list of frames, 2D numpy arrays
+ background: integer or 2D array
+ the value of the background for calibration, which get subtracted from the images
+ LUT: list
+ list or array containing the look up table in the form of [[DL],[temperature],[error]]
+ refT: float, default 28.5
+ reference temperature in degree celcius of the background
+ gain: numpy array
+ the gain image from the calibration to convert the counts into digital level
+ offset: numpy array
+ the offset image from the calibration to convert the counts into digital level
+ gain_error: numpy array
+ the gain error image from the calibration to convert the counts into digital level
+ offset_error: numpy array
+ the offset error image from the calibration to convert the counts into digital level
+ fullbackground: boolean, default False
+ switch to set the treatment of the backgroun input
+ give_ERROR: boolean, optional default False
+ switch to turn on the return of error images
verbose: integer, optional, default 0
feedback level (details of print messages)
RESULT
------
-
+ exist: boolean
+ True if data was found, False if not
+ frames: list
+ a list of frames, 2D numpy arrays, values are in Kelvin
+ optional frames_error: list
+ a list of frames, 2D numpy arrays
NOTE
------
"""
@@ -2894,20 +4020,37 @@ def apply_calib_on_raw(images, background, LUT,refT=28.5, gain=0, offset=0, gain
images[i]=(images[i]+(refT+273.15)).astype(np.float32)
return True, images
except Exception as E:
- raise Warning('apply_calib_on_raw: '+E)
-# return False, [0]
+# raise Warning('apply_calib_on_raw: '+E)
+ logging.warning('apply_calib_on_raw: '+E)
+ return False, [0]
def apply_NUC(images, gain, offset, gain_error=0, offset_error=0, give_error=False):
"""
apply_NUC(images=numpy array(time,width,height), gain, offset =numpy array(width,height) )
+ applies the non-uniformity correction onto the given raw images
+
INPUT
------
+ frames: list
+ a list of frames, 2D numpy arrays
+ gain: numpy array
+ the gain image from the calibration to convert the counts into digital level
+ offset: numpy array
+ the offset image from the calibration to convert the counts into digital level
+ gain_error: numpy array
+ the gain error image from the calibration to convert the counts into digital level
+ offset_error: numpy array
+ the offset error image from the calibration to convert the counts into digital level
+ give_error
RESULT
------
-
+ frames: list
+ a list of frames, 2D numpy arrays
+ optional frames_error: list
+ a list of frames, 2D numpy arrays
NOTE
------
"""
@@ -2930,12 +4073,20 @@ def apply_NUC(images, gain, offset, gain_error=0, offset_error=0, give_error=Fal
def get_cooling_water_temperature_by_time(time_ns):
"""
+ downloads the information of the water temperture in the cooling cycle of W7-X
+
INPUT
------
-
+ time_ns: integer
+ nanosecond time stamp to identify the time window
RESULT
------
-
+ exist: boolean
+ True if data was found, False if not
+ inlet: float
+ the water temperature of the inflow water
+ outlet: float
+ the water temperature of the outgoing water
NOTE
------
"""
@@ -2971,16 +4122,57 @@ def get_cooling_water_temperature_by_time(time_ns):
def get_calib_data(port, program=None, t0=None, t1=None, t6=None, emissivity=0.8,
T_version=2, version=0, back_emissivity=0.82, testmode=False, verbose=0):
"""
- get_calib_data
- return exist, background, LUT,refT, gain, offset, badpixels, t_exp, cfilter, gain_error, offset_error
+ get_calib_data downloads the calibration data for the application onto the images
+
INPUT
------
+ port: integer
+ port number of the AEF port of the camera
+ program: string, default None
+ program id in the form of 'YYYYMMDD.XXX', e.g. '20181016.016'
+ t0: integer, default None
+ nanoseconds timestamp of the t0 trigger, start of the program
+ t1: integer, default None
+ nanoseconds timestamp of the t1 trigger, start of the heating
+ t6: integer, default None
+ nanoseconds timestamp of the t6 trigger, end of the program
+ emissivity: float, optional, default 0.8
+ the emissivity value for the calibration
+ T_version: intger, default 2,
+ set the calibration version, in version 1 the full background image is subtracted\n in version 2 the background value is subtracted, version 3
+ version: integer, optional, default 0
+ Version of the data in the archiveDB, in case of 0 the highest version will be used
+ back_emissivity: float, optional, default 0.82, experimental
+ emissivity value for the background treatment, only needed for T_version 3
+ testmode: boolean, optional, default False
+ if True, the data will be loaded from the test archive
verbose: integer, optional, default 0
feedback level (details of print messages)
RESULT
------
-
+ exist: boolean
+ True if data was found, False if not
+ background: integer or numpy array
+ background image for calibration method 1 and a value for method 2
+ LUT: list
+ the look up table for the temperature calibration, form [[Digital level],[temperature],[error]]
+ refT: float
+ reference temperature of the background for the calibration
+ gain: numpy array
+ the gain image from the calibration to convert the counts into digital level
+ offset: numpy array
+ the offset image from the calibration to convert the counts into digital level
+ badpixels: list
+ list of badpixels
+ t_exp: integer
+ exposure time in microseconds
+ cfilter: integer or string, only needed for port 50
+ filter number of the Infratec camera in port AEF50
+ gain_error: numpy array
+ the gain error image from the calibration to convert the counts into digital level for the error
+ offset_error: numpy array
+ the offset error image from the calibration to convert the counts into digital level for the error
NOTE
------
"""
@@ -2991,12 +4183,16 @@ def get_calib_data(port, program=None, t0=None, t1=None, t6=None, emissivity=0.8
elif t1 is not None:
program = AKF_2.get_program_id(t1)
else:
- raise Exception('get_calib_data: ERROR! Missing time definition!')
+# raise Exception('get_calib_data: ERROR! Missing time definition!')
# print(' function requires either a program ID or the trigger times t0, t1 and t6')
+ logging.warning('get_calib_data: ERROR! Missing time definition!')
+ return False, 0, [], 0, [], [], [], 0, 0, [], []
# get start and end times from program ID
exist, t0, t1, t6 = get_trigger_from_PID(program, port, testmode, verbose=verbose-1)
if not exist:
- raise Exception('get_calib_data: ERROR! program {0} not found!'.format(program))
+# raise Exception('get_calib_data: ERROR! program {0} not found!'.format(program))
+ logging.warning(f'get_calib_data: ERROR! program {program} not found!')
+ return False, 0, [], 0, [], [], [], 0, 0, [], []
# determin camera in this port and OP
OP = IR_tools.get_OP_by_time(time_ns=t0)
@@ -3011,8 +4207,9 @@ def get_calib_data(port, program=None, t0=None, t1=None, t6=None, emissivity=0.8
cfilter = cf[2]
else:
cfilter = -1
- raise Warning("get_calib_data: Filter not found for INFRATEC Camera for Program "+AKF_2.get_program_id(t0))
-# return False, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
+# raise Warning("get_calib_data: Filter not found for INFRATEC Camera for Program "+AKF_2.get_program_id(t0))
+ logging.warning(f"get_calib_data: Filter not found for INFRATEC Camera for Program {AKF_2.get_program_id(t0)}")
+ return False, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
else:
cfilter=0
@@ -3022,8 +4219,9 @@ def get_calib_data(port, program=None, t0=None, t1=None, t6=None, emissivity=0.8
t_exp = int(expo_DL[2][0])
del expo_DL
else:
- raise Warning("get_calib_data: no exposure time found")
-# return False, 0, 0, 0, 0, 0, 0, 0, cfilter, 0, 0
+# raise Warning("get_calib_data: no exposure time found")
+ logging.warning("get_calib_data: no exposure time found")
+ return False, 0, 0, 0, 0, 0, 0, 0, cfilter, 0, 0
# get Look-Up-Table (LUT)
time = int(TimeToNs([2017,9,26], [8, 0, 0, 0]))
@@ -3032,8 +4230,9 @@ def get_calib_data(port, program=None, t0=None, t1=None, t6=None, emissivity=0.8
LUT=LUT_DL[1]
del LUT_DL
else:
- raise Warning("get_calib_data: no LUT found")
-# return False, 0, 0, 0, 0, 0, 0, t_exp, cfilter, 0, 0
+# raise Warning("get_calib_data: no LUT found")
+ logging.warning("get_calib_data: no LUT found")
+ return False, 0, 0, 0, 0, 0, 0, t_exp, cfilter, 0, 0
# treat cameras with and w/o filters differently
if cfilter==0 and not Camera.split("_")[0] == "Infratec":
@@ -3042,8 +4241,9 @@ def get_calib_data(port, program=None, t0=None, t1=None, t6=None, emissivity=0.8
print('get_calib_data: getting NUC arrays')
NUC_DL=get_NUC_by_times(port, t0, t1, t_exp, version, testmode, verbose=verbose-1)#download_NUC_by_times(port, t0, t1, t_exp, version)
if NUC_DL[0]==False:
- raise Warning("get_calib_data: NUC was not found")
-# return False, 0, LUT, 0, 0, 0, 0, t_exp, cfilter, 0, 0
+# raise Warning("get_calib_data: NUC was not found")
+ logging.warning("get_calib_data: NUC was not found")
+ return False, 0, LUT, 0, 0, 0, 0, t_exp, cfilter, 0, 0
# extract NUC elements
gain = []
offset = []
@@ -3081,8 +4281,9 @@ def get_calib_data(port, program=None, t0=None, t1=None, t6=None, emissivity=0.8
bim = apply_NUC([frames[0]], gain, offset)
background = get_average_background_recangle(port, bim[0])
else:
- raise Warning("get_calib_data: cannot find the first frame of the discharge, reconstruction failed")
-# return False, 0, LUT, 0, gain, offset, badpixels, t_exp, cfilter, gain_error, offset_error
+# raise Warning("get_calib_data: cannot find the first frame of the discharge, reconstruction failed")
+ logging.warning("get_calib_data: cannot find the first frame of the discharge, reconstruction failed")
+ return False, 0, LUT, 0, gain, offset, badpixels, t_exp, cfilter, gain_error, offset_error
else:
# --- Infratec Camera case ---
gain = 0
@@ -3097,9 +4298,13 @@ def get_calib_data(port, program=None, t0=None, t1=None, t6=None, emissivity=0.8
elif OP == "OP1.2b":
back_DL = get_NUCed_background_by_times(port, t0, t1, t_exp, cfilter, gain, offset, version, testmode, verbose=verbose-1)
else:
- raise Exception("get_calib_data: unknown Operation Phase or calibration method not implemented for this campaign")
+# raise Exception("get_calib_data: unknown Operation Phase or calibration method not implemented for this campaign")
+ logging.warning("get_calib_data: unknown Operation Phase or calibration method not implemented for this campaign")
+ return False, 0, [], 0, [], [], [], 0, 0, [], []
if not back_DL[0]:
- raise Warning("get_calib_data: no background image found")
+# raise Warning("get_calib_data: no background image found")
+ logging.warning("get_calib_data: no background image found")
+ return False, 0, [], 0, [], [], [], 0, 0, [], []
# return False, 0, LUT, 0, gain, offset, badpixels, t_exp, cfilter, gain_error, offset_error
background = back_DL[2]
@@ -3118,7 +4323,9 @@ def get_calib_data(port, program=None, t0=None, t1=None, t6=None, emissivity=0.8
if TCT_dl[0]:
refT = np.average(TCT_dl[2])
else:
- raise Exception("get_calib_data: Unable to find thermocouple data")
+# raise Exception("get_calib_data: Unable to find thermocouple data")
+ logging.warning("get_calib_data: Unable to find thermocouple data")
+ return
elif T_version == 2:
if verbose > 0:
print('get_calib_data: use temperature calibration version 2')
@@ -3154,7 +4361,9 @@ def get_calib_data(port, program=None, t0=None, t1=None, t6=None, emissivity=0.8
# back_off=estimate_offset(port, program)
# background=(background-back_off)/(back_emissivity)+back_off
else:
- raise Warning("get_calib_data: Unknown temperature calibration method")
+# raise Warning("get_calib_data: Unknown temperature calibration method")
+ logging.warning("get_calib_data: Unknown temperature calibration method")
+ return False, 0, [], 0, [], [], [], 0, 0, [], []
# return False, background, LUT, 0, gain, offset, badpixels, t_exp, cfilter, gain_error, offset_error
# get bad pixels
@@ -3178,13 +4387,30 @@ def get_calib_data(port, program=None, t0=None, t1=None, t6=None, emissivity=0.8
def find_badpixels(port, gain, offset, init_bp_list=None, niterations=3,
tolerance=10, plot_it=False, verbose=0):
"""
+ find the bad pixels by searching outliers in the gain and offset image
+
INPUT
------
+ port: integer
+ port number of the AEF port of the camera
+ gain: numpy array
+ the gain image from the calibration to convert the counts into digital level
+ offset: numpy array
+ the offset image from the calibration to convert the counts into digital level
+ init_bp_list: list, default None
+ initial bad pixel list
+ niterations: intger, default 3
+ number of interation in the process of finding bad pixels
+ tolerance: integer, default 10
+ tolerance value for the idenfication of outlier in the images to identify the bad pixels
+ plot_it: boolean, default False,
+ if True the result of the bad pixel finding will be plotted
verbose: integer, optional, default 0
feedback level (details of print messages)
RESULT
------
-
+ badpixels: numpy array
+ list of the bad pixels
NOTE
------
"""
@@ -3229,12 +4455,15 @@ def find_badpixels(port, gain, offset, init_bp_list=None, niterations=3,
def make_FOV_mask(port):
"""
+ creates the field of view image for the requested port and saves it locally
+
INPUT
------
-
+ port: integer
+ port number of the AEF port of the camera
RESULT
------
-
+ no return
NOTE
------
"""
@@ -3281,12 +4510,16 @@ def make_FOV_mask(port):
def get_FOV_mask(port):
"""
+ loads the locally saved field of view images for the requested port, see also make_FOV_mask
+
INPUT
------
-
+ port: integer
+ port number of the AEF port of the camera
RESULT
------
-
+ FOV: numpy array
+ the image showing the field of view with 1 and 0 for the region of the image which is outside the field of view
NOTE
------
"""
@@ -3306,12 +4539,18 @@ def get_FOV_mask(port):
def get_background(port, image):
"""
+ extracts from a given background image the minimum in the field of view
+
INPUT
------
-
+ port: integer
+ port number of the AEF port of the camera
+ image: numpy array
+ 2D numpy arrays
RESULT
------
-
+ background: integer (or float if image contains float values)
+ returns the background value, extracted from the given image
NOTE
------
"""
@@ -3326,12 +4565,18 @@ def get_background(port, image):
def get_average_background_recangle(port, image):
"""
+ extracts from a given background image the minimum in a defined rectangular region
+
INPUT
------
-
+ port: integer
+ port number of the AEF port of the camera
+ image: numpy array
+ 2D numpy arrays
RESULT
------
-
+ background: integer (or float if image contains float values)
+ returns the background value, extracted from the given image
NOTE
------
"""
@@ -3346,13 +4591,22 @@ def get_average_background_recangle(port, image):
def estimate_offset(port, program, plot_it=False, verbose=0):
"""
+ estimates the offset value based on the background values for different exposure times
+
INPUT
------
+ port: integer
+ port number of the AEF port of the camera
+ program: string
+ program id in the form of 'YYYYMMDD.XXX', e.g. '20181016.016'
+ plot_it: boolean, default False
+ if True the result of the bad pixel finding will be plotted
verbose: integer, optional, default 0
feedback level (details of print messages)
RESULT
------
-
+ Offset: integer
+ estimated offset value
NOTE
------
"""
@@ -3382,24 +4636,44 @@ def estimate_offset(port, program, plot_it=False, verbose=0):
return -1
def download_heatflux_by_program(port, program, time_window=0, testmode=False,
- version=0, verbose=0, givealpha=False,request=True,moffset=0):#threads=1,
- """
- port: string or integer
- program: string
- time_window: integer, float or list/numpy array of interger/float, single value: t1 until so many seconds, two values, t1+ first value until t1+ second value
- threads: integer, number of threads for parallel download
- testmode: True to load data from testarchive
- version: integer, version number for 0 the highest version will be used
- verbose: interger, level of outputs
- returns downlad_heatflux_by_times
+ version=0, verbose=0, givealpha=False,request=True):#threads=1,
+ """
+ download the heatflux for a given program and port, if the data is not available a request is made for the automated evaluation of the heat flux\n
+ if the data is not available or already requested a message will be returned in the console\n
+ For extended use of the heat flux data see also the following functions:\n
+ download_heatflux_mapping_reference --> for a better interpretation of the data with respect to the used coordinate system
+ download_heatflux_scene_model_reference --> for remapping of the data into W7-X coordinates and pixel coordiantes
+ extract_heatflux_profile_from_DL --> to extract a profile out of the data returned by this funciton
+
+
INPUT
------
+ port: string or integer
+ the camera port, for which the data is requested
+ program: string
+ program id in the form of 'YYYYMMDD.XXX', e.g. '20181016.016'
+ time_window: integer, float or list/numpy array of interger/float,
+ time window for the heat flux, single value: t1 until so many seconds, two values, t1+ first value until t1+ second value
+ testmode: boolean, default False
+ True to load data from testarchive
+ version: integer, default 0
+ version number for 0 the highest version will be used
verbose: integer, optional, default 0
feedback level (details of print messages)
+ givealpha: boolean, default False
+ switch to turn on or off the return of the alpha image from the THEODOR calculation, it wil be returned as first image
+ request: boolean, default True
+ switch to turn on or off the request of data if the data is not available in the database
RESULT
------
-
+ (returns downlad_heatflux_by_times)
+ exist: boolean
+ True if data was found, False if not
+ time: list
+ a list containing the time stamp of each frame
+ frames: list
+ a list of frames, 2D numpy arrays, first frame is the alpha frame if it is requested
NOTE
------
"""
@@ -3424,39 +4698,59 @@ def download_heatflux_by_program(port, program, time_window=0, testmode=False,
tstart=int(t1)
tend=int(t1+time_window[0]*1e9)
except Exception as E:
- raise Exception("download_heatflux_by_program: an Exception occured interpretating the time_window: ", E)
+# raise Exception("download_heatflux_by_program: an Exception occured interpretating the time_window: ", E)
+ logging.warning("download_heatflux_by_program: an Exception occured interpretating the time_window: "+E)
+ return False, [0], [0]
elif type(time_window)==int or type(time_window)==float:
tstart=int(t1)
tend=int(t1+time_window*1e9)
else:
- raise Exception("download_heatflux_by_program: given time_window type is not supported")
+# raise Exception("download_heatflux_by_program: given time_window type is not supported")
+ logging.warning("download_heatflux_by_program: given time_window type is not supported")
+ return False, [0], [0]
if givealpha:
tstart=tstart-1.1e9
return download_heatflux_by_times(port=port, tstart=tstart, tend=tend, time_window=0,
testmode=testmode, version=version, verbose=verbose,
- request=request,moffset=moffset)
+ request=request)
-def download_heatflux_by_times(port, tstart, tend, time_window=0, threads=1, testmode=False,
- version=0, verbose=0,request=True,moffset=0):
- """
- port: string or integer
- tstart: int64 timestamp in ns from which point the download should start
- tend: int64 timestamp in ns
- threads: integer, number of threads for parallel download
- testmode: True to load data from testarchive
- version: integer, version number for 0 the highest version will be used
- verbose: interger, level of outputs
- returns found, time, images
+def download_heatflux_by_times(port, tstart, tend, time_window=0, testmode=False,
+ version=0, verbose=0,request=True,request_ALL=False):#threads=1, ,moffset=0
+ """
+
+ download the heatflux for a given start, endtime and port, if the data is not available a request is made for the automated evaluation of the heat flux\n
+ if the data is not available or already requested a message will be returned in the console
INPUT
------
+ port: string or integer
+ the camera port, for which the data is requested
+ tstart: integer
+ time in ns where the search should start
+ tend: integer
+ time in sn where the search should end
+ time_window: integer, float or list/numpy array of interger/float,
+ time window for the heat flux, single value: t1 until so many seconds, two values, t1+ first value until t1+ second value
+ testmode: boolean, default False
+ True to load data from testarchive
+ version: integer, default 0
+ version number for 0 the highest version will be used
verbose: integer, optional, default 0
feedback level (details of print messages)
+ request: boolean, default True
+ switch to turn on or off the request of data if the data is not available in the database
+ request_ALL: boolean, optional, default False
+ switch to turn on the request of data for all AEF ports
RESULT
------
-
+ exist: boolean
+ True if data was found, False if not
+ time: list
+ a list containing the time stamp of each frame, time is in nanoseconds UTC
+ frames: list
+ a list of frames, 2D numpy arrays, values in the images are in W/m2
NOTE
------
"""
@@ -3475,7 +4769,9 @@ def download_heatflux_by_times(port, tstart, tend, time_window=0, threads=1, tes
port=int(port.split("AEF")[1])
goon=True
except Exception as E:
- raise Exception("download_heatflux_by_times: unknown Port!"+E)
+# raise Exception("download_heatflux_by_times: unknown Port!"+E)
+ logging.warning("download_heatflux_by_times: unknown Port!"+E)
+ return False, [0], [0]
elif isinstance(port, (int, np.integer, float, np.float)):
FLIR=False
goon=True
@@ -3501,6 +4797,7 @@ def download_heatflux_by_times(port, tstart, tend, time_window=0, threads=1, tes
if verbose > 0:
print(datetime.datetime.now(), "download_heatflux_by_times: heat flux download started")
if False: ## does not work, the png pictures are interpretated as colors, values are lost
+ threads = 1
exist, time, frames=download_images_by_time_via_png(larchivepath, starttime=tstart, stoptime=tend, time_window=time_window, version=version, threads=threads, verbose=verbose-1)
else:
exist, time, frames=download_images_by_times(larchivepath, starttime=tstart, stoptime=tend, verbose=verbose-1)
@@ -3523,9 +4820,13 @@ def download_heatflux_by_times(port, tstart, tend, time_window=0, threads=1, tes
cam_progs_ig,reasons=IR_tools.get_work_list(heatflux_requestlist_path, typ='ignore')
except:
cam_progs_ig=[]
- if (programid, "AEF"+str(port)) not in cam_progs and (programid, "AEF"+str(port)) not in cam_progs_ig:
- f=open(heatflux_requestlist_path+str(now.year)+str(now.month+moffset)+"_q_requests.txt", 'a')
- f.write(programid+"\tAEF"+str(port)+"\n")
+ cam_p = cam_progs + cam_progs_ig
+ if (programid, "AEF"+str(port)) not in cam_p and (programid, "ALL") not in cam_p:
+ f=open(heatflux_requestlist_path+"Auto_q_requests.txt", 'a')
+ if request_ALL:
+ f.write(programid+"\tALL\n")
+ else:
+ f.write(programid+"\tAEF"+str(port)+"\n")
f.close()
if verbose > 0:
print("download_heatflux_by_times: heat flux calculation request logged for automatic processing (within ca. 1 day)")
@@ -3540,15 +4841,26 @@ def download_heatflux_by_times(port, tstart, tend, time_window=0, threads=1, tes
def download_heatflux_mapping_reference(timepoint=None, version=0, testmode=False,
get_thickness=False, verbose=0):
"""
- return exist(boolean) and dictonary of the mappings informations
+ returns the mapping informations for the heatflux to relate each pixel in the heat flux image to different additional parameters. e.g., X and Y coordiante of the divertor mapping
INPUT
------
+ timepoint: integer, optional, default None
+ set the timepoint in nanoseconds where/when to search for the mapping in the database
+ version: integer, optional, default 0
+ Version of the data in the archiveDB, in case of 0 the highest version will be used
+ testmode: boolean, optional, default False
+ if True, the data will be loaded from the test archive
+ get_thickness: boolean, optional, default False
+ turns on the return of the thickness
verbose: integer, optional, default 0
feedback level (details of print messages)
RESULT
------
-
+ exist: boolean
+ True if data was found, False if not
+ mappings: dictionary
+ the mapping informations containing: surface coordinate s, divertor coordinate system X,Y, Finger coordiante system X, Y, Finger Id, Thickness, target, Target_Module\n all coordinates in meter
NOTE
------
"""
@@ -3682,15 +4994,28 @@ def download_heatflux_mapping_reference(timepoint=None, version=0, testmode=Fals
def download_heatflux_scene_model_reference(port, timepoint=None, program=None,
version=0, testmode=False, verbose=0):
"""
- return exist(boolean) and dictonary of the mappings informations
+ returns the mapping informations based on the scene model for the heatflux to relate each pixel in the heat flux image to different additional parameters. e.g., X and Y coordiante in the original image or the CAD geometry in X,Y,Z
INPUT
------
+ port: integer or string
+ port of the AEF port of the camera
+ timepoint: integer, optional, default None
+ set the timepoint in nanoseconds where/when to search for the mapping in the database
+ version: integer, optional, default 0
+ Version of the data in the archiveDB, in case of 0 the highest version will be used
+ testmode: boolean, optional, default False
+ if True, the data will be loaded from the test archive
+ get_thickness: boolean, optional, default False
+ turns on the return of the thickness
verbose: integer, optional, default 0
feedback level (details of print messages)
RESULT
------
-
+ exist: boolean
+ True if data was found, False if not
+ mappings: dictionary
+ the mapping informations containing: most likely Pixel coordiante X,Y, CAD X,Y,Z and the component id as PFC, \n all coordinates in meter
NOTE
------
"""
@@ -3755,16 +5080,22 @@ def download_heatflux_scene_model_reference(port, timepoint=None, program=None,
def give_finger_ID(profile, finger=None, verbose=0):
"""
- profile: string, integer or float, string: "TM3h_5_5" or "1605" or "16.05" or "5", similar for float and integer. single number only if the finger is given!
- finger: string or integer, string "TM3h_5" or "16" or 16 or 1600 or None
+ returns for a given profile (and finger) information the finger ID which is internally used in the mappings
INPUT
------
+ profile: string, integer or float,
+ string: "TM3h_5_5" or "1605" or "16.05" or "5", similar for float and integer. single number only if the finger is given!
+ finger: string or integer, optional, default None
+ string "TM3h_5" or "16" or 16 or 1600 or None
verbose: integer, optional, default 0
feedback level (details of print messages)
RESULT
------
-
+ success: boolean
+ gives the information whether the idenfication of the fingerID went well or not
+ fingerID: integer
+ the finger idendifikation in the form of XXXYY with XXX the fingernumber *100 and YY the profile number, starting with 0
NOTE
------
"""
@@ -3853,20 +5184,46 @@ def give_finger_ID(profile, finger=None, verbose=0):
def get_heatflux_profile(port, profile, finger=None, timepoint=None, program=None, tstart=None,
tend=None, testmode=False, version=0, verbose=0):
"""
- returns exist, time, s in m, heat flux in W/m2
+ returns a heat flux profile for a given port, profile (and finger) and timepoint in a program/time intervall defined by tstart and tend
+ \n it is a combination of the following functions:\n
+ download_heatflux_by_prrogram or download_heatflux_by_times\n
+ extract_heatflux_profile_from_DL, since it is called in the end\n
+ and also give_finger_ID for the interpretation of the profile/finger input\n
- parameters:
- port: string or integer
- profile: string, integer or float, string: "TM3h_5_5" or "1605" or "16.05" or "5", similar for float and integer. single number only if the finger is given!
- finger: string or integer, string "TM3h_5" or "16"
+ in case you want several profiles, please use the function extract_heatflux_profile_from_DL to lower the amount of downloaded data (speed things up)
INPUT
------
+ port: string or integer
+ the port for which the data is requested, normally a AEF port
+ profile: string, integer or float,
+ string: "TM3h_5_5" or "1605" or "16.05" or "5", similar for float and integer. single number only if the finger is given!
+ finger: string or integer,
+ string "TM3h_5" or "16"
+ timepoint: integer, default None
+ the timepoint for the profile in seconds from T1 trigger
+ program: string
+ program id in the form of 'YYYYMMDD.XXX', e.g. '20181016.016'
+ tstart: integer
+ time in ns where the search should start
+ tend: integer
+ time in ns where the search should end
+ testmode: boolean, default False
+ True to load data from testarchive
+ version: integer, optional, default 0
+ Version of the data in the archiveDB, in case of 0 the highest version will be used
verbose: integer, optional, default 0
feedback level (details of print messages)
RESULT
------
-
+ exist: boolean
+ True if data was found, False if not
+ time: list
+ timestamp of the profile in seconds
+ s: list of float
+ surface coordinate in meter for each profile position
+ heatflux: list of float
+ the heat flux in in W/m2 for each profile position
NOTE
------
"""
@@ -3923,15 +5280,18 @@ def get_heatflux_profile(port, profile, finger=None, timepoint=None, program=Non
port = int(port.split("AEF")[1])
goon = True
except Exception as E:
- raise Warning("get_heatflux_profile: unknown Port!"+E)
-# return False, 0, 0, 0
+# raise Warning("get_heatflux_profile: unknown Port!"+E)
+ logging.warning("get_heatflux_profile: unknown Port!"+E)
+ return False, 0, 0, 0
elif isinstance(port, (int, np.integer, float, np.float)):
FLIR = False
goon = True
else:
goon = False
if not goon or FLIR:
- raise Exception("get_heatflux_profile: Unknown port, FLIR data is unsupported at the moment")
+# raise Exception("get_heatflux_profile: Unknown port, FLIR data is unsupported at the moment")
+ logging.warning("get_heatflux_profile: Unknown port, FLIR data is unsupported at the moment")
+ return False, 0, 0, 0
else:
### if the program goes up to here, time okay, finger okay, port okay, lets download it
exist, timo, frames=download_heatflux_by_times(port, tstart, tend, time_window=0, threads=1, testmode=testmode, version=version, verbose=verbose-1)
@@ -3945,30 +5305,39 @@ def get_heatflux_profile(port, profile, finger=None, timepoint=None, program=Non
def extract_heatflux_profile_from_DL(time, images, profile, finger=None, time_window=None,
inputchecked=False, verbose=0, testmode=False):
"""
- return exist, time, s in m, heat flux in W/m2
- time_point default (None) returns entire shot.
+ extracts from given heat flux images the requested profile for the given time window
INPUT
------
- time:
-
- images:
-
- profile:
-
- finger:
-
- time_window:
-
- inputchecked:
-
+ time: list
+ the timevector of the data in seconds
+ images: list
+ list of frames in form of numpy arrays with the heat flux data
+ profile: string, integer or float,
+ string: "TM3h_5_5" or "1605" or "16.05" or "5", similar for float and integer. single number only if the finger is given!
+ finger: string or integer,
+ string "TM3h_5" or "16"
+ time_window: integer, float or list/numpy array
+ defines the timewindow, if only a number is give or an array/list with only one number, it searches one profile for the given timevector, \n
+ if a list or array is given, several profiles will be returned, in case of None, the full program will be returned
+ inputchecked: boolean, optional, default False
+ defines whether the input parameters have been checked for correct format or not (profile and finger)
verbose: integer, optional, default 0
feedback level (details of print messages)
testmode: boolean, optional, default 0
if True, the data will be loaded from the test archive
RESULT
------
-
+ exist: boolean
+ indicates whether data was found (True) or not (False)
+ time: list
+ the timestamps of the profile
+ s: list
+ the surface coordinates of the profile in meter
+ heatflux: list
+ the heat flux for the profile in W/m2
+
+
NOTE
------
"""
@@ -4027,7 +5396,7 @@ def extract_temperature_profile_from_DL(port, time, images, profile, finger=None
time_window=None, inputchecked=False,
verbose=0, reference_time=0):
"""
-
+ for a given heatflux image and profile and finger information, a profile can be extracted.
INPUT
------
port: int or str
@@ -4036,14 +5405,15 @@ def extract_temperature_profile_from_DL(port, time, images, profile, finger=None
the time vector for the given images
images: numpy array
temperature flux images, from which the profile should be extracted
- profile: float
- ABC
- finger:
-
- time_window:
-
- inputchecked:
-
+ profile: string, integer or float,
+ string: "TM3h_5_5" or "1605" or "16.05" or "5", similar for float and integer. single number only if the finger is given!
+ finger: string or integer,
+ string "TM3h_5" or "16"
+ time_window: integer, float or list/numpy array
+ defines the timewindow, if only a number is give or an array/list with only one number, it searches one profile for the given timevector, \n
+ if a list or array is given, several profiles will be returned, in case of None, the full program will be returned
+ inputchecked: boolean, optional, default False
+ defines whether the input parameters have been checked for correct format or not (profile and finger)
verbose: integer, optional
feedback level (details of print messages)
reference_time : float, optional, default is 0
@@ -4051,15 +5421,13 @@ def extract_temperature_profile_from_DL(port, time, images, profile, finger=None
RESULT
------
exist: boolean
- indicates whether data was found (True) or not (False)
-
- time
- s in m
- heat flux in W/m2
-
-
-
-
+ indicates whether data was found (True) or not (False)
+ time: list
+ the timestamps of the profiles
+ s: list
+ the surface coordinates of the profiles in meter
+ heatflux: list
+ the heat flux for each profile in W/m2
NOTES
-----
"""
@@ -4121,8 +5489,15 @@ def extract_temperature_profile_from_DL(port, time, images, profile, finger=None
def download_divertor_load(port, targetmodule=None, program=None, tstart=None,
tend=None, version=0, verbose=0, testmode=False,
- request=True):
+ request=True, request_ALL=False):
"""
+ downloads the integrated divertor load, based on the heat flux evaluation of the temperature data from the IR cameras.\n
+ The data is structed in total load per divertor, identified by the port, and also in target-modules of the divertor.\n
+ An estiamted error for the data is given back.
+ To get the total divertor load, please download the data of each of the 10 AEF ports.\n
+ In case one or more ports are missing, request can be created (console will give feedback on the request).\n
+ The data will be evaluated by a server over time.
+
INPUT
------
port: int or str
@@ -4131,21 +5506,30 @@ def download_divertor_load(port, targetmodule=None, program=None, tstart=None,
name or description of the target module, nothing wil return total load
program: str
program id as str in the format yyyymmdd.pid
- tstart: uint64
-
- tend: uint64
-
- version: int
-
+ tstart: integer
+ time in ns where the search should start
+ tend: integer
+ time in ns where the search should end
+ version: integer, optional, default 0
+ Version of the data in the archiveDB, in case of 0 the highest version will be used
verbose: integer, optional
feedback level (details of print messages)
- testmode:
-
+ testmode: boolean, default False
+ True to load data from testarchive
request: boolean, optional, default True
+ switch to turn on or off the request of data if the data is not available in the database
+ request_ALL: boolean, optional, default False
+ switch to turn on the request of data for all AEF ports
RESULT
------
exist: boolean
-
+ True if data was found, False if not
+ time: list
+ a list containing the time stamp of each frame, time is in nanoseconds UTC
+ load: list
+ a list of values, values in the images are in W
+ error: list
+ a list of values, the error of each timepoint, values are in W
"""
TMchadict={
'ALL':(0,1, 'divertor_total_load'), 'FULL':(0,1, 'divertor_total_load'),
@@ -4274,9 +5658,13 @@ def download_divertor_load(port, targetmodule=None, program=None, tstart=None,
programid=str(tstart)
cam_progs=IR_tools.get_work_list(heatflux_requestlist_path, typ='load')
cam_progs_ig,reasons=IR_tools.get_work_list(heatflux_requestlist_path, typ='ignore')
- if (programid, str(port)) not in cam_progs and (programid, str(port)) not in cam_progs_ig:
- f = open(heatflux_requestlist_path+str(now.year)+str(now.month)+"_load_requests.txt", 'a')
- f.write(programid+"\t"+str(port)+"\n")
+ cam_p = cam_progs+cam_progs_ig
+ if (programid, str(port)) not in cam_p and (programid, "ALL") not in cam_p:
+ f = open(heatflux_requestlist_path+"Auto_load_requests.txt", 'a')
+ if request_ALL:
+ f.write(programid+"\tALL\n")
+ else:
+ f.write(programid+"\t"+str(port)+"\n")
f.close()
if verbose > 0:
print(now, "download_divertor_load: request created")
@@ -4330,7 +5718,9 @@ def download_scene_model(port, program=None, timepoint=None, version=0, testmode
# else:
exist, _, timepoint, _ = get_trigger_from_PID(program, port, testmode, verbose=verbose-1)
if not exist:
- raise Exception("download_scene_model: Error! Program "+program+" not found!")
+# raise Exception("download_scene_model: Error! Program "+program+" not found!")
+ logging.warning("download_scene_model: Error! Program "+program+" not found!")
+ return False, 0
if testmode:
base=testarchivepath
else:
@@ -4371,6 +5761,59 @@ def download_scene_model(port, program=None, timepoint=None, version=0, testmode
return exist, scene_model
else:
return False, [0]
+#%% caching, requested by Lukas R, sample function supplied by Lukas R.
+import os
+def extract_heatflux_profile_from_cache(port, program, time_window=0, profile="TM3h_5_5", **kwargs):
+ """Returns exists, time, position, heat_flux_profiles
+ """
+ kwargs.setdefault('verbose',0)
+ kwargs.setdefault('testmode',False)
+ data_folder = os.path.join(os.path.dirname(__file__), 'cache')
+ save_path = data_folder + '/'.join(["", program, str(port), ""])
+ start = time_window[0] if hasattr(time_window, '__iter__') else 0.0
+ end = time_window[-1] if hasattr(time_window, '__iter__') else time_window
+ path = save_path + "heatflux_images_[{s:.2f}-{e:.2f}].npz".format(s=start, e=end)
+ if not os.path.isfile(path):
+ if kwargs['verbose'] > 0:
+ print("Generating cache for {program} port {port} timewindow [{s:.2f}-{e:.2f}]".format(
+ port=port, program=program, s=start, e=end
+ ))
+ cache_heatflux_by_program(port, program, time_window=time_window, **kwargs)
+ cache_file = np.load(path)
+ found,time,images = [cache_file[kw] for kw in ['found','time','images']]
+ if not found:
+ raise ValueError("Download was invalid")
+ exist, _, t1, t6 = get_trigger_from_PID(
+ program, port, kwargs['testmode'], verbose=kwargs['verbose']-1)
+ return extract_heatflux_profile_from_DL((np.asarray(time)-t1)/1e9, images, profile, time_window=time_window, **kwargs)
+
+def cache_heatflux_by_program(port, program, time_window=0, **kwargs):
+ """
+ """
+ data_folder = os.path.join(os.path.dirname(__file__), 'cache')
+ save_path = data_folder + '/'.join(["", program, str(port), ""])
+ check_path_and_make_folders(True, save_path)
+ found, time, images = download_heatflux_by_program(port, program,
+ time_window=time_window, **kwargs)
+ start = time_window[0] if hasattr(time_window, '__iter__') else 0.0
+ end = time_window[-1] if hasattr(time_window, '__iter__') else time_window
+ np.savez(save_path + "heatflux_images_[{s:.2f}-{e:.2f}]".format(s=start, e=end),
+ found=found, time=time, images=images)
+
+def check_path_and_make_folders(activate, path, sep='', folder=''):
+ '''Does nothing if file structure already exists.
+ Otherwise recursively goes up layers until existing file is found, then creates subfolders.
+ This kind of thing should be in an independent & general caching handler
+ '''
+ if not activate:
+ return
+ if not os.path.exists(path):
+ check_path_and_make_folders(activate, *path.rpartition('/'))
+ try:
+ os.mkdir(path+sep+folder)
+ except FileExistsError:
+ pass
+
#%% general download functions
"""
@@ -4383,7 +5826,8 @@ def read_restdb_old(request_url):
Reads JSON data from W7-X REST API
INPUT
------
-
+ request_url: string
+ the url from there the data should be loaded
Returns
------
valid: bolean
@@ -4414,7 +5858,10 @@ def read_restdb_old(request_url):
def read_restdb(request_url):
"""
Reads JSON data from W7-X REST API. Signals are converted into double
-
+ INPUT
+ ------
+ request_url: string
+ the url from there the data should be loaded
Returns:
------
valid: bolean
diff --git a/plot_heatflux_example.py b/plot_heatflux_example.py
index 88c6a82619ae708bcc851f82b42e10474327e308..f86f3047ae7902995b77d5b9253d06a2e7d94831 100644
--- a/plot_heatflux_example.py
+++ b/plot_heatflux_example.py
@@ -1,7 +1,7 @@
# -*- coding: utf-8 -*-
"""
Created on Thu Nov 29 17:41:40 2018
-V3.2.0
+V3.4.1
@author: holn
"""
import numpy as np
@@ -18,7 +18,7 @@ if __name__=='__main__':
#%% loading data
print(datetime.datetime.now(),"start")
- status,times,images=IR.download_heatflux_by_program(port,program,time_window=[0,2],version=2,threads=1,verbose=5,testmode=False)
+ status,times,images=IR.download_heatflux_by_program(port,program,time_window=[0,2],version=2,verbose=5,testmode=False)
print('done')
#%% plotting data
diff --git a/setup.py b/setup.py
index f38725a255ef74d0410898445f738df17c2965b6..9bde9f6074bfb0fb77ff2a31a305fc5fb95e7ef5 100644
--- a/setup.py
+++ b/setup.py
@@ -16,7 +16,7 @@ setup(
'plot_heatflux_example'
],
data_files=[
- ('',['upload_config','CHANGELOG','ToDo.txt']),
+ ('',['upload_config','CHANGELOG','ToDo.txt','README.md']),
('data',['data/AEF10_coldframes_background_fails_real.txt',
'data/AEF11_coldframes_background_fails_real.txt',
'data/AEF20_coldframes_background_fails_real.txt',
@@ -30,7 +30,7 @@ setup(
'data/finger_info_TDU.csv'])
],
install_requires = [
- 'archivedb>=0.2.0'
+ 'w7xarchive>=0.11.21'
]
);