Datasets:

ytzi

/

the-stack-dedup-python-scored

like 0

e468b2b5e8f04b80c414c4137b991f429ffae653

py

Python

kedro/extras/logging/color_logger.py

daniel-falk/kedro

19187199339ddc4a757aaaa328f319ec4c1e452a

2022-01-10T15:22:12.000Z

2022-03-31T13:38:56.000Z

kedro/extras/logging/color_logger.py

daniel-falk/kedro

19187199339ddc4a757aaaa328f319ec4c1e452a

2022-01-10T12:44:31.000Z

2022-03-31T17:01:24.000Z

kedro/extras/logging/color_logger.py

daniel-falk/kedro

19187199339ddc4a757aaaa328f319ec4c1e452a

2022-01-10T19:15:24.000Z

2022-03-30T11:20:52.000Z

"""A logging handler class which produces coloured logs.""" import logging import click class ColorHandler(logging.StreamHandler): """A color log handler. You can use this handler by incorporating the example below into your logging configuration: ``conf/project/logging.yml``: :: formatters: simple: format: "%(asctime)s - %(name)s - %(levelname)s - %(message)s" handlers: console: class: kedro.extras.logging.ColorHandler level: INFO formatter: simple stream: ext://sys.stdout # defining colors is optional colors: debug: white info: magenta warning: yellow root: level: INFO handlers: [console] The ``colors`` parameter is optional, and you can use any ANSI color. * Black * Red * Green * Yellow * Blue * Magenta * Cyan * White The default colors are: * debug: magenta * info: cyan * warning: yellow * error: red * critical: red """ def __init__(self, stream=None, colors=None): logging.StreamHandler.__init__(self, stream) colors = colors or {} self.colors = { "critical": colors.get("critical", "red"), "error": colors.get("error", "red"), "warning": colors.get("warning", "yellow"), "info": colors.get("info", "cyan"), "debug": colors.get("debug", "magenta"), } def _get_color(self, level): if level >= logging.CRITICAL: return self.colors["critical"] # pragma: no cover if level >= logging.ERROR: return self.colors["error"] # pragma: no cover if level >= logging.WARNING: return self.colors["warning"] # pragma: no cover if level >= logging.INFO: return self.colors["info"] if level >= logging.DEBUG: # pragma: no cover return self.colors["debug"] # pragma: no cover return None # pragma: no cover def format(self, record: logging.LogRecord) -> str: """The handler formatter. Args: record: The record to format. Returns: The record formatted as a string. """ text = logging.StreamHandler.format(self, record) color = self._get_color(record.levelno) return click.style(text, color)

e46b6c69ae3a4c3f1fee528d4d729291bff4cf8d

py

Python

qt_figure.py

liwenlongonly/LogAnalyzer

4981c0673cf0d1a52ad76e473ffc1c30bb6bf22b

qt_figure.py

liwenlongonly/LogAnalyzer

4981c0673cf0d1a52ad76e473ffc1c30bb6bf22b

qt_figure.py

liwenlongonly/LogAnalyzer

4981c0673cf0d1a52ad76e473ffc1c30bb6bf22b

# -*- coding: utf-8 -*- from PyQt5 import QtCore import numpy as np from matplotlib.figure import Figure import time import matplotlib matplotlib.use("Qt5Agg") # 声明使用QT5 from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas class QtFigure(FigureCanvas): def __init__(self, width=5, height=4, dpi=100): # 第一步：创建一个创建Figure self.fig = Figure(figsize=(width, height), dpi=dpi) # 第二步：在父类中激活Figure窗口 super(QtFigure, self).__init__(self.fig) # 此句必不可少，否则不能显示图形 # 第三步：创建一个子图，用于绘制图形用，111表示子图编号，如matlab的subplot(1,1,1) self.axes = self.fig.add_subplot(111) self.timer = QtCore.QTimer(self) self.timer.timeout.connect(self._update_figure) self.timer.start(1000) self.date_list = None def plot(self): self.axes.grid(True, linestyle='--') self.date_list = [0, 0, 0, 0] h, = self.axes.plot(self.date_list, [1, 2, 0, 4], color='r', linewidth=0.5) def _update_figure(self): # 构建4个随机整数，位于闭区间[0, 10] self.axes.cla() l = [np.random.randint(0, 10) for i in range(4)] self.date_list.pop(0) self.date_list.append(QtFigure.date_format()) self.axes.grid(True, linestyle='--') self.axes.plot(self.date_list, l, color='r', linewidth=0.5) # self.h.set_ydata(l) self.draw() @staticmethod def date_format(): return time.strftime("%H:%M:%S", time.localtime())

e46c1072625294f177cc250fe85584da3ad9a985

py

Python

python/target_selection/cartons/bhm_spiders_agn.py

sdss/target_selection

7196bf1491c4e9c18140301c7001e503f391a8e1

2020-07-07T01:38:59.000Z

2020-11-24T21:46:58.000Z

python/target_selection/cartons/bhm_spiders_agn.py

sdss/target_selection

7196bf1491c4e9c18140301c7001e503f391a8e1

2020-05-28T07:18:54.000Z

2021-11-30T18:36:10.000Z

python/target_selection/cartons/bhm_spiders_agn.py

sdss/target_selection

7196bf1491c4e9c18140301c7001e503f391a8e1

#!/usr/bin/env python # -*- coding: utf-8 -*- # # @Author: Tom Dwelly # @Date: 2020-03-03 # @Filename: bhm_spiders_agn.py # @License: BSD 3-clause (http://www.opensource.org/licenses/BSD-3-Clause) # derived from guide.py # ### flake8: noqa # isort: skip_file import peewee from peewee import JOIN from peewee import fn from target_selection.cartons.base import BaseCarton # general catalogdb imports from sdssdb.peewee.sdss5db.catalogdb import ( Catalog, EROSITASupersetAGN, ) # imports of existing spectro catalogues from sdssdb.peewee.sdss5db.catalogdb import ( CatalogToSDSS_DR16_SpecObj, SDSS_DR16_SpecObj, CatalogToBHM_eFEDS_Veto, BHM_eFEDS_Veto, SDSSV_BOSS_SPALL, SDSSV_BOSS_Conflist, SDSSV_Plateholes, SDSSV_Plateholes_Meta, ) # additional imports required by bhm_spiders_agn_lsdr8 from sdssdb.peewee.sdss5db.catalogdb import ( CatalogToLegacy_Survey_DR8, Legacy_Survey_DR8, ) # additional imports required by bhm_spiders_agn_gaiadr2 from sdssdb.peewee.sdss5db.catalogdb import ( CatalogToTIC_v8, TIC_v8, ) # additional imports required by bhm_spiders_agn_ps1dr2 from sdssdb.peewee.sdss5db.catalogdb import ( Panstarrs1, CatalogToPanstarrs1, ) # additional imports required by bhm_spiders_agn_skymapperdr2 from sdssdb.peewee.sdss5db.catalogdb import ( SkyMapper_DR2, CatalogToSkyMapper_DR2, ) # additional imports required by bhm_spiders_agn_supercosmos from sdssdb.peewee.sdss5db.catalogdb import ( SuperCosmos, CatalogToSuperCosmos, CatalogToCatWISE2020, ) from target_selection.mag_flux import AB2nMgy, AB2Jy # used by cartons that need to compute Galactic latitude: north_gal_pole_ra = 192.85948 # deg, J2000 north_gal_pole_dec = +27.12825 # deg, J2000 # ############################################ # ############################################ # ############################################ # ############################################ # This provides the following BHM SPIDERS AGN cartons in v0.5: # * bhm_spiders_agn_lsdr8 # * bhm_spiders_agn_efeds_stragglers # * bhm_spiders_agn_gaiadr2 # * bhm_spiders_agn_sep # * bhm_spiders_agn_ps1dr2 # * bhm_spiders_agn_skymapperdr2 # bhm_spiders_agn_supercosmos # ############################################ # ############################################ # ############################################ # ############################################ # some reference points for AB->nMgy conversions # 30.0 AB = 1e-3 nMgy # 22.5 AB = 1.0 nMgy # 22.0 AB = 1.58489 nMgy # 21.5 AB = 2.51189 nMgy # 21.0 AB = 3.98107 nMgy # 20.0 AB = 10.0 nMgy # 18.5 AB = 39.8107 nMgy # 16.5 AB = 251.189 nMgy # 14.0 AB = 2511.89 nMgy # 13.5 AB = 3981.07 nMgy # some reference points for AB->Jy conversions (for ps1_dr2 fluxes) # 30.0 AB = 3.631e-9 Jy # 22.5 AB = 3.631e-6 Jy # 22.0 AB = 5.754e-6 Jy # 21.5 AB = 9.120e-6 Jy # 21.0 AB = 1.445e-5 Jy # 20.5 AB = 2.291e-5 Jy # 18.5 AB = 1.445e-4 Jy # 16.5 AB = 9.120e-4 Jy # 14.0 AB = 9.120e-3 Jy # 13.5 AB = 1.445e-2 Jy # Notes on catalogdb.panstarrs1.flags aka objInfoFlag from ObjectThin # https://outerspace.stsci.edu/display/PANSTARRS/PS1+ObjectThin+table+fields # https://outerspace.stsci.edu/display/PANSTARRS/PS1+Object+Flags # select objects that have the GOOD_STACK flag set: # Flag name value decimal Notes # GOOD_STACK 0x08000000 134217728 good-quality object in the stack (> 1 good stack measurement) # Use these two flags to decide whether to use aper mags or not # Flag name value decimal Notes # EXT 0x00800000 8388608 extended in our data (eg, PS) # EXT_ALT 0x01000000 16777216 extended in external data (eg, 2MASS) # Notes on how many targets to expect: # sdss5db=> SELECT ero_version,xmatch_method,xmatch_version,opt_cat,count(*) # FROM erosita_superset_agn GROUP BY ero_version,xmatch_method,xmatch_version,opt_cat; # ero_version | xmatch_method | xmatch_version | opt_cat | count # --------------------------+----------------+--------------------------+--------------+-------- # eFEDS_c001_V18C_V3_ext | XPS/MLR | Merged_03DEC2020 | lsdr8 | 14 # eFEDS_c001_V18C_V3_ext | XPS/NWAY | Merged_03DEC2020 | lsdr8 | 248 # eFEDS_c001_V18C_V3_main | XPS/MLR | Merged_03DEC2020 | lsdr8 | 794 # eFEDS_c001_V18C_V3_main | XPS/NWAY | Merged_03DEC2020 | lsdr8 | 26575 # em01_c946_201008_poscorr | XPS/NWAY | JWMS_CW2_v_03_TDopt | gaiadr2 | 441175 # em01_c946_201008_poscorr | XPS/NWAY | JWMS_CW2_v_03_TDopt | lsdr8 | 305076 # em01_c946_201008_poscorr | XPS/NWAY | JWMS_CW2_v_03_TDopt | ps1dr2 | 241150 # em01_c946_201008_poscorr | XPS/NWAY | JWMS_CW2_v_03_TDopt | skymapperdr2 | 312372 # em01_c946_201008_poscorr | XPS/NWAY | JWMS_v_03 | catwise2020 | 740691 # em01_c946_201008_poscorr | XPS/NWAY | JWMS_v_40 | lsdr8 | 345189 # em01_SEP_c946 | XPS/NWAY | SEP_CW2_07DEC2020 | catwise2020 | 32268 # em01_SEP_c946 | XPS/NWAY | SEP_CW2_07DEC2020_TDopt | gaiadr2 | 309 # em01_SEP_c946 | XPS/NWAY | SEP_CW2_NOV2020_MSopt | gaiadr2 | 740 # (13 rows) # Notes on avoiding saturated legacysurvey sources # https://www.legacysurvey.org/dr8/bitmasks/ # Bit Name Description # 0 NPRIMARY touches a pixel that is outside the BRICK_PRIMARY region of a brick # 1 BRIGHT touches a pixel within the locus of a radius-magnitude relation for # Tycho-2 stars or one for Gaia DR2 stars to G < 13 # 2 SATUR_G touches a pixel that was saturated in at least one g-band image # 3 SATUR_R touches a pixel that was saturated in at least one r-band image # 4 SATUR_Z touches a pixel that was saturated in at least one z-band image # 5 ALLMASK_G touches a pixel that has any of the ALLMASK_G bits set # 6 ALLMASK_R touches a pixel that has any of the ALLMASK_R bits set # 7 ALLMASK_Z touches a pixel that has any of the ALLMASK_Z bits set # 8 WISEM1 touches a pixel in a WISEMASK_W1 bright star mask # 9 WISEM2 touches a pixel in a WISEMASK_W2 bright star mask # 10 BAILOUT touches a pixel in a blob where we "bailed out" of source fitting # 11 MEDIUM touches a pixel within the locus of a radius-magnitude relation # for Gaia DR2 stars to G < 16 # 12 GALAXY touches a pixel in an SGA large galaxy # 13 CLUSTER touches a pixel in a globular cluster # # so, mask to avoid saturated targets is 2**2 + 2**3 + 2**4 = 4+8+16 = 28 # # END PREAMBLE # ################################################################################## class BhmSpidersAgnLsdr8Carton(BaseCarton): name = 'bhm_spiders_agn_lsdr8' category = 'science' mapper = 'BHM' program = 'bhm_spiders' tile = False instrument = 'BOSS' def build_query(self, version_id, query_region=None): c = Catalog.alias() x = EROSITASupersetAGN.alias() ls = Legacy_Survey_DR8.alias() c2ls = CatalogToLegacy_Survey_DR8.alias() instrument = peewee.Value(self.instrument) fibertotflux_r_max = AB2nMgy(self.parameters['fibertotmag_r_min']) fibertotflux_r_min = AB2nMgy(self.parameters['fibertotmag_r_max']) fibertotflux_z_max = AB2nMgy(self.parameters['fibertotmag_z_min']) fibertotflux_z_min = AB2nMgy(self.parameters['fibertotmag_z_max']) fibertotflux_r_min_for_cadence1 = AB2nMgy(self.parameters['fibertotmag_r_for_cadence1']) fibertotflux_r_min_for_cadence2 = AB2nMgy(self.parameters['fibertotmag_r_for_cadence2']) gaia_g_max_for_cadence1 = self.parameters['gaia_g_max_for_cadence1'] gaia_rp_max_for_cadence1 = self.parameters['gaia_rp_max_for_cadence1'] # flux30 = AB2nMgy(30.00) value = peewee.Value(self.parameters.get('value', 1.0)).cast('float') # ######################################################################### # prepare the spectroscopy catalogues match_radius_spectro = self.parameters['spec_join_radius'] / 3600.0 spec_sn_thresh = self.parameters['spec_sn_thresh'] spec_z_err_thresh = self.parameters['spec_z_err_thresh'] # SDSS DR16 c2s16 = CatalogToSDSS_DR16_SpecObj.alias() ss16 = SDSS_DR16_SpecObj.alias() s16 = ( ss16.select( ss16.specobjid.alias('specobjid'), ) .where( ss16.snmedian >= spec_sn_thresh, ss16.zwarning == 0, ss16.zerr <= spec_z_err_thresh, ss16.zerr > 0.0, ss16.scienceprimary > 0, ) .alias('s16') ) # SDSS-IV/eFEDS March2020 c2s2020 = CatalogToBHM_eFEDS_Veto.alias() ss2020 = BHM_eFEDS_Veto.alias() s2020 = ( ss2020.select( ss2020.pk.alias('pk'), ) .where( ss2020.sn_median_all >= spec_sn_thresh, ss2020.zwarning == 0, ss2020.z_err <= spec_z_err_thresh, ss2020.z_err > 0.0, ) .alias('s2020') ) # SDSS-V spAll ssV = SDSSV_BOSS_SPALL.alias() sV = ( ssV.select( ssV.specobjid.alias('specobjid'), ssV.plug_ra.alias('plug_ra'), ssV.plug_dec.alias('plug_dec'), ) .where( ssV.sn_median_all >= spec_sn_thresh, ssV.zwarning == 0, ssV.z_err <= spec_z_err_thresh, ssV.z_err > 0.0, ssV.specprimary > 0, ) .alias('sV') ) # SDSS-V plateholes - only consider plateholes that # were drilled+shipped but that were not yet observed ssph = SDSSV_Plateholes.alias() ssphm = SDSSV_Plateholes_Meta.alias() ssconf = SDSSV_BOSS_Conflist.alias() sph = ( ssph.select( ssph.pkey.alias('pkey'), ssph.target_ra.alias('target_ra'), ssph.target_dec.alias('target_dec'), ) .join( ssphm, on=(ssph.yanny_uid == ssphm.yanny_uid) ) .join( ssconf, JOIN.LEFT_OUTER, on=(ssphm.plateid == ssconf.plate) ) .where( (ssph.holetype == 'BOSS_SHARED'), (ssph.sourcetype == 'SCI') | (ssph.sourcetype == 'STA'), ssphm.isvalid > 0, ssconf.plate.is_null(), ) .alias('sph') ) # priority is determined by target properties # start with a priority floor value (per carton) # then increment if any conditions are met: # add +dpriority_match_flags if target is a secondary cross-match (match_flag > 1) # add +dpriority_det_like if target has a low value of ero_det_like # add +dpriority_has_spec if target has existing good SDSS spectroscopy priority_1 = peewee.Case( None, ((x.xmatch_flags > 1, 1), ), 0) priority_2 = peewee.Case( None, ((x.ero_det_like < self.parameters['det_like_for_priority'], 1), ), 0) priority_3 = peewee.Case( None, ( (s16.c.specobjid.is_null(False), 1), # any of these can be satisfied (s2020.c.pk.is_null(False), 1), (sV.c.specobjid.is_null(False), 1), (sph.c.pkey.is_null(False), 1), ), 0) priority = fn.max( self.parameters['priority_floor'] + priority_1 * self.parameters['dpriority_match_flags'] + priority_2 * self.parameters['dpriority_det_like'] + priority_3 * self.parameters['dpriority_has_spec'] ) # choose cadence based on fiber magnitude in r-band cadence1 = self.parameters['cadence1'] cadence2 = self.parameters['cadence2'] cadence3 = self.parameters['cadence3'] cadence4 = 'unknown_cadence' cadence = peewee.Case( None, ( ( ((ls.fibertotflux_r > fibertotflux_r_min_for_cadence1) | (ls.gaia_phot_g_mean_mag.between(0.1, gaia_g_max_for_cadence1)) | (ls.gaia_phot_rp_mean_mag.between(0.1, gaia_rp_max_for_cadence1))), cadence1), (ls.fibertotflux_r > fibertotflux_r_min_for_cadence2, cadence2), (ls.fibertotflux_r <= fibertotflux_r_min_for_cadence2, cadence3), ), cadence4) # compute transformed SDSS mags for pointlike and extended sources separately # transform the legacysurvey grz into sdss psfmag griz # extract coeffs from fit logs via: # awk 'BEGIN {print("coeffs = {")} /POLYFIT/{ if($3~/sdss_psfmag/){pe="p"} else if ($3~/sdss_fiber2mag/){pe="e"} else{pe="error"}; printf("\"%s%d_%s\": %s,\n", substr($3,length($3)), $8, pe, $10)} END {print("}")}' bhm_spiders_agn_lsdr8_*/lsdr8_*mag_to_sdss_*mag_?_results.log # noqa coeffs = { "g2_e": -0.113816, "g1_e": 0.317176, "g0_e": 0.094145, "i2_e": -0.415858, "i1_e": 0.168922, "i0_e": -0.010771, "r2_e": 0.029398, "r1_e": -0.019938, "r0_e": 0.354042, "z2_e": -0.111262, "z1_e": 0.237656, "z0_e": 0.148923, "g2_p": 0.187193, "g1_p": -0.184362, "g0_p": 0.049492, "i2_p": -0.098979, "i1_p": -0.405518, "i0_p": 0.009688, "r2_p": -0.001935, "r1_p": 0.098201, "r0_p": 0.050321, "z2_p": -0.034163, "z1_p": 0.109878, "z0_p": -0.030167, } nMgy_min = 1e-3 # equiv to AB=30 # pointlike - start from ls8 (psf)fluxes g0_p = (22.5 - 2.5 * peewee.fn.log(peewee.fn.greatest(nMgy_min, ls.flux_g))) r0_p = (22.5 - 2.5 * peewee.fn.log(peewee.fn.greatest(nMgy_min, ls.flux_r))) z0_p = (22.5 - 2.5 * peewee.fn.log(peewee.fn.greatest(nMgy_min, ls.flux_z))) g_r_p = (-2.5 * peewee.fn.log(peewee.fn.greatest(nMgy_min, ls.flux_g) / peewee.fn.greatest(nMgy_min, ls.flux_r))) r_z_p = (-2.5 * peewee.fn.log(peewee.fn.greatest(nMgy_min, ls.flux_r) / peewee.fn.greatest(nMgy_min, ls.flux_z))) # extended - start from ls8 fiberfluxes g0_e = (22.5 - 2.5 * peewee.fn.log(peewee.fn.greatest(nMgy_min, ls.fiberflux_g))) r0_e = (22.5 - 2.5 * peewee.fn.log(peewee.fn.greatest(nMgy_min, ls.fiberflux_r))) z0_e = (22.5 - 2.5 * peewee.fn.log(peewee.fn.greatest(nMgy_min, ls.fiberflux_z))) g_r_e = (-2.5 * peewee.fn.log(peewee.fn.greatest(nMgy_min, ls.fiberflux_g) / peewee.fn.greatest(nMgy_min, ls.fiberflux_r))) r_z_e = (-2.5 * peewee.fn.log(peewee.fn.greatest(nMgy_min, ls.fiberflux_r) / peewee.fn.greatest(nMgy_min, ls.fiberflux_z))) g_p = (g0_p + coeffs['g0_p'] + coeffs['g1_p'] * g_r_p + coeffs['g2_p'] * g_r_p * g_r_p) r_p = (r0_p + coeffs['r0_p'] + coeffs['r1_p'] * g_r_p + coeffs['r2_p'] * g_r_p * g_r_p) i_p = (r0_p + coeffs['i0_p'] + coeffs['i1_p'] * r_z_p + coeffs['i2_p'] * r_z_p * r_z_p) z_p = (z0_p + coeffs['z0_p'] + coeffs['z1_p'] * r_z_p + coeffs['z2_p'] * r_z_p * r_z_p) g_e = (g0_e + coeffs['g0_e'] + coeffs['g1_e'] * g_r_e + coeffs['g2_e'] * g_r_e * g_r_e) r_e = (r0_e + coeffs['r0_e'] + coeffs['r1_e'] * g_r_e + coeffs['r2_e'] * g_r_e * g_r_e) i_e = (r0_e + coeffs['i0_e'] + coeffs['i1_e'] * r_z_e + coeffs['i2_e'] * r_z_e * r_z_e) z_e = (z0_e + coeffs['z0_e'] + coeffs['z1_e'] * r_z_e + coeffs['z2_e'] * r_z_e * r_z_e) # validity checks - set limits semi-manually g_r_p_min = -0.25 g_r_p_max = 1.75 r_z_p_min = -0.5 r_z_p_max = 2.5 g_r_e_min = 0.0 g_r_e_max = 1.75 r_z_e_min = 0.2 r_z_e_max = 1.6 valid_p = (g0_p.between(0.1, 29.9) & r0_p.between(0.1, 29.9) & z0_p.between(0.1, 29.9) & g_r_p.between(g_r_p_min, g_r_p_max) & r_z_p.between(r_z_p_min, r_z_p_max)) valid_e = (g0_e.between(0.1, 29.9) & r0_e.between(0.1, 29.9) & z0_e.between(0.1, 29.9) & g_r_e.between(g_r_e_min, g_r_e_max) & r_z_e.between(r_z_e_min, r_z_e_max)) # We want to switch between psfmags and fibertotmags depending on # ls.type parameter (PSF or extended) # For 'PSF' targets, we use psfmags, but for extended sources use fiber2mags opt_prov = peewee.Case( None, ( ((ls.type == 'PSF') & valid_p, 'sdss_psfmag_from_lsdr8'), ((ls.type != 'PSF') & valid_e, 'sdss_fiber2mag_from_lsdr8'), ), 'undefined') magnitude_g = peewee.Case( None, ( ((ls.type == 'PSF') & valid_p, g_p.cast('float')), ((ls.type != 'PSF') & valid_e, g_e.cast('float')), ), 'NaN') magnitude_r = peewee.Case( None, ( ((ls.type == 'PSF') & valid_p, r_p.cast('float')), ((ls.type != 'PSF') & valid_e, r_e.cast('float')), ), 'NaN') magnitude_i = peewee.Case( None, ( ((ls.type == 'PSF') & valid_p, i_p.cast('float')), ((ls.type != 'PSF') & valid_e, i_e.cast('float')), ), 'NaN') magnitude_z = peewee.Case( None, ( ((ls.type == 'PSF') & valid_p, z_p.cast('float')), ((ls.type != 'PSF') & valid_e, z_e.cast('float')), ), 'NaN') magnitude_gaia_g = peewee.Case( None, ((ls.gaia_phot_g_mean_mag.between(0.1, 29.9), ls.gaia_phot_g_mean_mag),), 'NaN') magnitude_gaia_bp = peewee.Case( None, ((ls.gaia_phot_bp_mean_mag.between(0.1, 29.9), ls.gaia_phot_bp_mean_mag),), 'NaN') magnitude_gaia_rp = peewee.Case( None, ((ls.gaia_phot_rp_mean_mag.between(0.1, 29.9), ls.gaia_phot_rp_mean_mag),), 'NaN') query = ( c.select( fn.min(c.catalogid).alias('catalogid'), fn.min(ls.ls_id).alias('ls_id'), # extra fn.min(x.ero_detuid).alias('ero_detuid'), # extra fn.min(c.ra).alias('ra'), # extra fn.min(c.dec).alias('dec'), # extra priority.alias("priority"), fn.min(value).alias('value'), fn.min(cadence).alias('cadence'), fn.min(instrument).alias('instrument'), fn.min(opt_prov).alias('optical_prov'), fn.min(magnitude_g).alias('g'), fn.min(magnitude_r).alias('r'), fn.min(magnitude_i).alias('i'), fn.min(magnitude_z).alias('z'), fn.min(magnitude_gaia_g).alias('gaia_g'), fn.min(magnitude_gaia_bp).alias('bp'), fn.min(magnitude_gaia_rp).alias('rp'), fn.min(g0_p).alias("ls8_mag_g"), # extra fn.min(r0_p).alias("ls8_mag_r"), # extra fn.min(z0_p).alias("ls8_mag_z"), # extra fn.min(g0_e).alias("ls8_fibermag_g"), # extra fn.min(r0_e).alias("ls8_fibermag_r"), # extra fn.min(z0_e).alias("ls8_fibermag_z"), # extra fn.min(ls.type).alias("ls8_type"), # extra ) .join(c2ls) .where( c.version_id == version_id, c2ls.version_id == version_id, c2ls.best >> True, ) .join(ls) .join(x, on=(ls.ls_id == x.ls_id)) # start joining the spectroscopy .switch(c) .join(c2s16, JOIN.LEFT_OUTER) .join( s16, JOIN.LEFT_OUTER, on=( (c2s16.target_id == s16.c.specobjid) & (c2s16.version_id == version_id) ) ) .switch(c) .join(c2s2020, JOIN.LEFT_OUTER) .join( s2020, JOIN.LEFT_OUTER, on=( (c2s2020.target_id == s2020.c.pk) & (c2s2020.version_id == version_id) ) ) .join( sV, JOIN.LEFT_OUTER, on=( fn.q3c_join(sV.c.plug_ra, sV.c.plug_dec, c.ra, c.dec, match_radius_spectro) ) ) .join( sph, JOIN.LEFT_OUTER, on=( fn.q3c_join(sph.c.target_ra, sph.c.target_dec, c.ra, c.dec, match_radius_spectro) ) ) # finished joining the spectroscopy .where( (x.ero_version == self.parameters['ero_version']), (x.xmatch_method == self.parameters['xmatch_method']), (x.xmatch_version == self.parameters['xmatch_version']), (x.opt_cat == self.parameters['opt_cat']), (x.xmatch_metric >= self.parameters['p_any_min']), ( (ls.fibertotflux_r.between(fibertotflux_r_min, fibertotflux_r_max)) | (ls.fibertotflux_z.between(fibertotflux_z_min, fibertotflux_z_max)) ), (x.ero_det_like > self.parameters['det_like_min']), (ls.maskbits.bin_and(2**2 + 2**3 + 2**4) == 0), # avoid saturated sources (ls.nobs_r > 0), # always require r-band coverage ((ls.nobs_g > 0) | (ls.nobs_z > 0)), # plus at least one other optical band # gaia safety checks to avoid bad ls photometry ~(ls.gaia_phot_g_mean_mag.between(0.1, self.parameters['gaia_g_mag_limit'])), ~(ls.gaia_phot_rp_mean_mag.between(0.1, self.parameters['gaia_rp_mag_limit'])), ) .group_by(ls) # avoid duplicates - we trust the legacy survey entries ) if query_region: query = query.where(peewee.fn.q3c_radial_query(c.ra, c.dec, query_region[0], query_region[1], query_region[2])) return query # # END BhmSpidersAgnLsdr8Carton # ################################################################################## class BhmSpidersAgnEfedsStragglersCarton(BaseCarton): name = 'bhm_spiders_agn_efeds_stragglers' category = 'science' mapper = 'BHM' program = 'bhm_spiders' tile = False instrument = 'BOSS' cadence = None def build_query(self, version_id, query_region=None): c = Catalog.alias() x = EROSITASupersetAGN.alias() ls = Legacy_Survey_DR8.alias() c2ls = CatalogToLegacy_Survey_DR8.alias() instrument = peewee.Value(self.instrument) fibertotflux_r_max = AB2nMgy(self.parameters['fibertotmag_r_min']) fibertotflux_r_min = AB2nMgy(self.parameters['fibertotmag_r_max']) fibertotflux_z_max = AB2nMgy(self.parameters['fibertotmag_z_min']) fibertotflux_z_min = AB2nMgy(self.parameters['fibertotmag_z_max']) fibertotflux_r_min_for_cadence1 = AB2nMgy(self.parameters['fibertotmag_r_for_cadence1']) fibertotflux_r_min_for_cadence2 = AB2nMgy(self.parameters['fibertotmag_r_for_cadence2']) gaia_g_max_for_cadence1 = self.parameters['gaia_g_max_for_cadence1'] gaia_rp_max_for_cadence1 = self.parameters['gaia_rp_max_for_cadence1'] # flux30 = AB2nMgy(30.00) value = peewee.Value(self.parameters.get('value', 1.0)).cast('float') match_radius_spectro = self.parameters['spec_join_radius'] / 3600.0 spec_sn_thresh = self.parameters['spec_sn_thresh'] spec_z_err_thresh = self.parameters['spec_z_err_thresh'] # ########################################################################## # prepare the spectroscopy catalogues # SDSS DR16 c2s16 = CatalogToSDSS_DR16_SpecObj.alias() ss16 = SDSS_DR16_SpecObj.alias() s16 = ( ss16.select( ss16.specobjid.alias('specobjid'), ) .where( ss16.snmedian >= spec_sn_thresh, ss16.zwarning == 0, ss16.zerr <= spec_z_err_thresh, ss16.zerr > 0.0, ss16.scienceprimary > 0, ) .alias('s16') ) # SDSS-IV/eFEDS March2020 c2s2020 = CatalogToBHM_eFEDS_Veto.alias() ss2020 = BHM_eFEDS_Veto.alias() s2020 = ( ss2020.select( ss2020.pk.alias('pk'), ) .where( ss2020.sn_median_all >= spec_sn_thresh, ss2020.zwarning == 0, ss2020.z_err <= spec_z_err_thresh, ss2020.z_err > 0.0, ) .alias('s2020') ) # SDSS-V spAll ssV = SDSSV_BOSS_SPALL.alias() sV = ( ssV.select( ssV.specobjid.alias('specobjid'), ssV.plug_ra.alias('plug_ra'), ssV.plug_dec.alias('plug_dec'), ) .where( ssV.sn_median_all >= spec_sn_thresh, ssV.zwarning == 0, ssV.z_err <= spec_z_err_thresh, ssV.z_err > 0.0, ssV.specprimary > 0, ) .alias('sV') ) # All eFEDS plates have been observed so ignore plateholes now # redundant # # SDSS-V plateholes - only consider plateholes that # redundant # # were drilled+shipped but that were not yet observed # redundant # ssph = SDSSV_Plateholes.alias() # redundant # ssphm = SDSSV_Plateholes_Meta.alias() # redundant # ssconf = SDSSV_BOSS_Conflist.alias() # redundant # sph = ( # redundant # ssph.select( # redundant # ssph.pkey.alias('pkey'), # redundant # ssph.target_ra.alias('target_ra'), # redundant # ssph.target_dec.alias('target_dec'), # redundant # ) # redundant # .join( # redundant # ssphm, # redundant # on=(ssph.yanny_uid == ssphm.yanny_uid) # redundant # ) # redundant # .join( # redundant # ssconf, JOIN.LEFT_OUTER, # redundant # on=(ssphm.plateid == ssconf.plate) # redundant # ) # redundant # .where( # redundant # (ssph.holetype == 'BOSS_SHARED'), # redundant # (ssph.sourcetype == 'SCI') | (ssph.sourcetype == 'STA'), # redundant # ssphm.isvalid > 0, # redundant # ssconf.plate.is_null(), # redundant # ) # redundant # .alias('sph') # redundant # ) # priority is determined by target properties # start with a priority floor value (per carton) # then increment if any conditions are met: # add +dpriority_match_flags if target is a secondary cross-match (match_flag > 1) # add +dpriority_det_like if target has a low value of ero_det_like # add +dpriority_ero_version if target is from the secondary eFEDS catalogue # add +dpriority_has_spec if target has existing good SDSS spectroscopy priority_1 = peewee.Case( None, ((x.xmatch_flags > 1, 1), ), 0) priority_2 = peewee.Case( None, ((x.ero_det_like < self.parameters['det_like_for_priority'], 1), ), 0) # add a step down in priority for anything only selected by the secondary xmatch_version priority_3 = peewee.Case( None, ((x.ero_version == self.parameters['ero_version2'], 1), ), 0) # no need for the step below because we just reject everything with a spectrum. priority_4 = peewee.Case( None, ( (s16.c.specobjid.is_null(False), 1), # any of these can be satisfied (s2020.c.pk.is_null(False), 1), (sV.c.specobjid.is_null(False), 1), # redundant # (sph.c.pkey.is_null(False), 1), ), 0) priority = ( self.parameters['priority_floor'] + fn.min(priority_1) * self.parameters['dpriority_match_flags'] + fn.min(priority_2) * self.parameters['dpriority_det_like'] + fn.min(priority_3) * self.parameters['dpriority_ero_version'] + fn.max(priority_4) * self.parameters['dpriority_has_spec'] ) # choose cadence based on fiber magnitude in r-band cadence1 = self.parameters['cadence1'] cadence2 = self.parameters['cadence2'] cadence3 = self.parameters['cadence3'] cadence4 = 'unknown_cadence' cadence = peewee.Case( None, ( ( ((ls.fibertotflux_r > fibertotflux_r_min_for_cadence1) | (ls.gaia_phot_g_mean_mag.between(0.1, gaia_g_max_for_cadence1)) | (ls.gaia_phot_rp_mean_mag.between(0.1, gaia_rp_max_for_cadence1))), cadence1), (ls.fibertotflux_r > fibertotflux_r_min_for_cadence2, cadence2), (ls.fibertotflux_r <= fibertotflux_r_min_for_cadence2, cadence3), ), cadence4) # compute transformed SDSS mags for pointlike and extended sources separately # transform the legacysurvey grz into sdss psfmag griz # extract coeffs from fit logs via: # awk 'BEGIN {print("coeffs = {")} /POLYFIT/{ if($3~/sdss_psfmag/){pe="p"} else if ($3~/sdss_fiber2mag/){pe="e"} else{pe="error"}; printf("\"%s%d_%s\": %s,\n", substr($3,length($3)), $8, pe, $10)} END {print("}")}' bhm_spiders_agn_lsdr8_*/lsdr8_*mag_to_sdss_*mag_?_results.log # noqa coeffs = { "g2_e": -0.113816, "g1_e": 0.317176, "g0_e": 0.094145, "i2_e": -0.415858, "i1_e": 0.168922, "i0_e": -0.010771, "r2_e": 0.029398, "r1_e": -0.019938, "r0_e": 0.354042, "z2_e": -0.111262, "z1_e": 0.237656, "z0_e": 0.148923, "g2_p": 0.187193, "g1_p": -0.184362, "g0_p": 0.049492, "i2_p": -0.098979, "i1_p": -0.405518, "i0_p": 0.009688, "r2_p": -0.001935, "r1_p": 0.098201, "r0_p": 0.050321, "z2_p": -0.034163, "z1_p": 0.109878, "z0_p": -0.030167, } nMgy_min = 1e-3 # equiv to AB=30 # pointlike - start from ls8 (psf)fluxes g0_p = (22.5 - 2.5 * peewee.fn.log(peewee.fn.greatest(nMgy_min, ls.flux_g))) r0_p = (22.5 - 2.5 * peewee.fn.log(peewee.fn.greatest(nMgy_min, ls.flux_r))) z0_p = (22.5 - 2.5 * peewee.fn.log(peewee.fn.greatest(nMgy_min, ls.flux_z))) g_r_p = (-2.5 * peewee.fn.log(peewee.fn.greatest(nMgy_min, ls.flux_g) / peewee.fn.greatest(nMgy_min, ls.flux_r))) r_z_p = (-2.5 * peewee.fn.log(peewee.fn.greatest(nMgy_min, ls.flux_r) / peewee.fn.greatest(nMgy_min, ls.flux_z))) # extended - start from ls8 fiberfluxes g0_e = (22.5 - 2.5 * peewee.fn.log(peewee.fn.greatest(nMgy_min, ls.fiberflux_g))) r0_e = (22.5 - 2.5 * peewee.fn.log(peewee.fn.greatest(nMgy_min, ls.fiberflux_r))) z0_e = (22.5 - 2.5 * peewee.fn.log(peewee.fn.greatest(nMgy_min, ls.fiberflux_z))) g_r_e = (-2.5 * peewee.fn.log(peewee.fn.greatest(nMgy_min, ls.fiberflux_g) / peewee.fn.greatest(nMgy_min, ls.fiberflux_r))) r_z_e = (-2.5 * peewee.fn.log(peewee.fn.greatest(nMgy_min, ls.fiberflux_r) / peewee.fn.greatest(nMgy_min, ls.fiberflux_z))) g_p = (g0_p + coeffs['g0_p'] + coeffs['g1_p'] * g_r_p + coeffs['g2_p'] * g_r_p * g_r_p) r_p = (r0_p + coeffs['r0_p'] + coeffs['r1_p'] * g_r_p + coeffs['r2_p'] * g_r_p * g_r_p) i_p = (r0_p + coeffs['i0_p'] + coeffs['i1_p'] * r_z_p + coeffs['i2_p'] * r_z_p * r_z_p) z_p = (z0_p + coeffs['z0_p'] + coeffs['z1_p'] * r_z_p + coeffs['z2_p'] * r_z_p * r_z_p) g_e = (g0_e + coeffs['g0_e'] + coeffs['g1_e'] * g_r_e + coeffs['g2_e'] * g_r_e * g_r_e) r_e = (r0_e + coeffs['r0_e'] + coeffs['r1_e'] * g_r_e + coeffs['r2_e'] * g_r_e * g_r_e) i_e = (r0_e + coeffs['i0_e'] + coeffs['i1_e'] * r_z_e + coeffs['i2_e'] * r_z_e * r_z_e) z_e = (z0_e + coeffs['z0_e'] + coeffs['z1_e'] * r_z_e + coeffs['z2_e'] * r_z_e * r_z_e) # validity checks - set limits semi-manually g_r_p_min = -0.25 g_r_p_max = 1.75 r_z_p_min = -0.5 r_z_p_max = 2.5 g_r_e_min = 0.0 g_r_e_max = 1.75 r_z_e_min = 0.2 r_z_e_max = 1.6 valid_p = (g0_p.between(0.1, 29.9) & r0_p.between(0.1, 29.9) & z0_p.between(0.1, 29.9) & g_r_p.between(g_r_p_min, g_r_p_max) & r_z_p.between(r_z_p_min, r_z_p_max)) valid_e = (g0_e.between(0.1, 29.9) & r0_e.between(0.1, 29.9) & z0_e.between(0.1, 29.9) & g_r_e.between(g_r_e_min, g_r_e_max) & r_z_e.between(r_z_e_min, r_z_e_max)) # We want to switch between psfmags and fibertotmags depending on # ls.type parameter (PSF or extended) # For 'PSF' targets, we use psfmags, but for extended sources use fiber2mags opt_prov = peewee.Case( None, ( ((ls.type == 'PSF') & valid_p, 'sdss_psfmag_from_lsdr8'), ((ls.type != 'PSF') & valid_e, 'sdss_fiber2mag_from_lsdr8'), ), 'undefined') magnitude_g = peewee.Case( None, ( ((ls.type == 'PSF') & valid_p, g_p.cast('float')), ((ls.type != 'PSF') & valid_e, g_e.cast('float')), ), 'NaN') magnitude_r = peewee.Case( None, ( ((ls.type == 'PSF') & valid_p, r_p.cast('float')), ((ls.type != 'PSF') & valid_e, r_e.cast('float')), ), 'NaN') magnitude_i = peewee.Case( None, ( ((ls.type == 'PSF') & valid_p, i_p.cast('float')), ((ls.type != 'PSF') & valid_e, i_e.cast('float')), ), 'NaN') magnitude_z = peewee.Case( None, ( ((ls.type == 'PSF') & valid_p, z_p.cast('float')), ((ls.type != 'PSF') & valid_e, z_e.cast('float')), ), 'NaN') magnitude_gaia_g = peewee.Case( None, ((ls.gaia_phot_g_mean_mag.between(0.1, 29.9), ls.gaia_phot_g_mean_mag),), 'NaN') magnitude_gaia_bp = peewee.Case( None, ((ls.gaia_phot_bp_mean_mag.between(0.1, 29.9), ls.gaia_phot_bp_mean_mag),), 'NaN') magnitude_gaia_rp = peewee.Case( None, ((ls.gaia_phot_rp_mean_mag.between(0.1, 29.9), ls.gaia_phot_rp_mean_mag),), 'NaN') query = ( c.select( fn.min(c.catalogid).alias('catalogid'), fn.min(ls.ls_id).alias('ls_id'), fn.min(x.ero_detuid).alias('ero_detuid'), fn.min(c.ra).alias('ra'), fn.min(c.dec).alias('dec'), priority.alias("priority"), fn.min(value).alias('value'), fn.min(cadence).alias('cadence'), fn.min(instrument).alias('instrument'), fn.min(opt_prov).alias('optical_prov'), fn.min(magnitude_g).alias('g'), fn.min(magnitude_r).alias('r'), fn.min(magnitude_i).alias('i'), fn.min(magnitude_z).alias('z'), fn.min(magnitude_gaia_g).alias('gaia_g'), fn.min(magnitude_gaia_bp).alias('bp'), fn.min(magnitude_gaia_rp).alias('rp'), fn.min(g0_p).alias("ls8_mag_g"), # extra fn.min(r0_p).alias("ls8_mag_r"), # extra fn.min(z0_p).alias("ls8_mag_z"), # extra fn.min(g0_e).alias("ls8_fibermag_g"), # extra fn.min(r0_e).alias("ls8_fibermag_r"), # extra fn.min(z0_e).alias("ls8_fibermag_z"), # extra fn.min(ls.type).alias("ls8_type"), # extra ) .join(c2ls) .where( c.version_id == version_id, c2ls.version_id == version_id, c2ls.best >> True, ) .join(ls) .join(x, on=(ls.ls_id == x.ls_id)) # start joining the spectroscopy .switch(c) .join(c2s16, JOIN.LEFT_OUTER) .join( s16, JOIN.LEFT_OUTER, on=( (c2s16.target_id == s16.c.specobjid) & (c2s16.version_id == version_id) ) ) .switch(c) .join(c2s2020, JOIN.LEFT_OUTER) .join( s2020, JOIN.LEFT_OUTER, on=( (c2s2020.target_id == s2020.c.pk) & (c2s2020.version_id == version_id) ) ) .join( sV, JOIN.LEFT_OUTER, on=( fn.q3c_join(sV.c.plug_ra, sV.c.plug_dec, c.ra, c.dec, match_radius_spectro) ) ) # .join( # sph, JOIN.LEFT_OUTER, # on=( # fn.q3c_join(sph.c.target_ra, sph.c.target_dec, # c.ra, c.dec, # match_radius_spectro) # ) # ) # finished joining the spectroscopy .where( ( (x.ero_version == self.parameters['ero_version1']) & (x.xmatch_method == self.parameters['xmatch_method1']) & (x.xmatch_version == self.parameters['xmatch_version1']) ) | ( (x.ero_version == self.parameters['ero_version2']) & (x.xmatch_method == self.parameters['xmatch_method2']) & (x.xmatch_version == self.parameters['xmatch_version2']) ), x.opt_cat == self.parameters['opt_cat'], x.xmatch_metric >= self.parameters['p_any_min'], (ls.fibertotflux_r.between(fibertotflux_r_min, fibertotflux_r_max) | ls.fibertotflux_z.between(fibertotflux_z_min, fibertotflux_z_max)), x.ero_det_like > self.parameters['det_like_min'], ls.maskbits.bin_and(2**2 + 2**3 + 2**4) == 0, # avoid saturated sources ls.nobs_r > 0, # always require r-band coverage (ls.nobs_g > 0) | (ls.nobs_z > 0), # plus at least one other optical band # gaia safety checks to avoid bad ls photometry ~(ls.gaia_phot_g_mean_mag.between(0.1, self.parameters['gaia_g_mag_limit'])), ~(ls.gaia_phot_rp_mean_mag.between(0.1, self.parameters['gaia_rp_mag_limit'])), ) .group_by(x.ls_id) # avoid duplicates - we trust the legacy survey entries ) if query_region: query = query.where(peewee.fn.q3c_radial_query(c.ra, c.dec, query_region[0], query_region[1], query_region[2])) return query # # END BhmSpidersAgnEfedsStragglersCarton # ################################################################################## class BhmSpidersAgnGaiadr2Carton(BaseCarton): name = 'bhm_spiders_agn_gaiadr2' category = 'science' mapper = 'BHM' program = 'bhm_spiders' tile = False instrument = 'BOSS' def build_query(self, version_id, query_region=None): c = Catalog.alias() x = EROSITASupersetAGN.alias() tic = TIC_v8.alias() c2tic = CatalogToTIC_v8.alias() instrument = peewee.Value(self.instrument) gaia_g_max_for_cadence1 = self.parameters['gaia_g_max_for_cadence1'] gaia_rp_max_for_cadence1 = self.parameters['gaia_rp_max_for_cadence1'] gaia_g_max_for_cadence2 = self.parameters['gaia_g_max_for_cadence2'] gaia_rp_max_for_cadence2 = self.parameters['gaia_rp_max_for_cadence2'] # these control matching to spectroscopy match_radius_spectro = self.parameters['spec_join_radius'] / 3600.0 spec_sn_thresh = self.parameters['spec_sn_thresh'] spec_z_err_thresh = self.parameters['spec_z_err_thresh'] # ######################################################################### # prepare the spectroscopy catalogues # SDSS DR16 c2s16 = CatalogToSDSS_DR16_SpecObj.alias() ss16 = SDSS_DR16_SpecObj.alias() s16 = ( ss16.select( ss16.specobjid.alias('specobjid'), ) .where( ss16.snmedian >= spec_sn_thresh, ss16.zwarning == 0, ss16.zerr <= spec_z_err_thresh, ss16.zerr > 0.0, ss16.scienceprimary > 0, ) .alias('s16') ) # SDSS-IV/eFEDS March2020 c2s2020 = CatalogToBHM_eFEDS_Veto.alias() ss2020 = BHM_eFEDS_Veto.alias() s2020 = ( ss2020.select( ss2020.pk.alias('pk'), ) .where( ss2020.sn_median_all >= spec_sn_thresh, ss2020.zwarning == 0, ss2020.z_err <= spec_z_err_thresh, ss2020.z_err > 0.0, ) .alias('s2020') ) # SDSS-V spAll ssV = SDSSV_BOSS_SPALL.alias() sV = ( ssV.select( ssV.specobjid.alias('specobjid'), ssV.plug_ra.alias('plug_ra'), ssV.plug_dec.alias('plug_dec'), ) .where( ssV.sn_median_all >= spec_sn_thresh, ssV.zwarning == 0, ssV.z_err <= spec_z_err_thresh, ssV.z_err > 0.0, ssV.specprimary > 0, ) .alias('sV') ) # SDSS-V plateholes - only consider plateholes that # were drilled+shipped but that were not yet observed ssph = SDSSV_Plateholes.alias() ssphm = SDSSV_Plateholes_Meta.alias() ssconf = SDSSV_BOSS_Conflist.alias() sph = ( ssph.select( ssph.pkey.alias('pkey'), ssph.target_ra.alias('target_ra'), ssph.target_dec.alias('target_dec'), ) .join( ssphm, on=(ssph.yanny_uid == ssphm.yanny_uid) ) .join( ssconf, JOIN.LEFT_OUTER, on=(ssphm.plateid == ssconf.plate) ) .where( (ssph.holetype == 'BOSS_SHARED'), (ssph.sourcetype == 'SCI') | (ssph.sourcetype == 'STA'), ssphm.isvalid > 0, ssconf.plate.is_null(), ) .alias('sph') ) # ######################################################################### # compute the abs(Galactic latitude): gal_lat = peewee.fn.abs(90.0 - peewee.fn.q3c_dist(north_gal_pole_ra, north_gal_pole_dec, c.ra, c.dec)) # value = peewee.Value(self.parameters.get('value', 1.0)).cast('float') value = peewee.Case( None, ((gal_lat > self.parameters['in_plane_lat_cut'], self.parameters.get('value', 1.0)),), 0.0 ).cast('float') # priority is determined by target properties # start with a priority floor value (per carton) # then increment if any conditions are met: # add +dpriority_match_flags if target is a secondary cross-match (match_flag > 1) # add +dpriority_det_like if target has a low value of ero_det_like # add +dpriority_has_spec if target has existing good SDSS spectroscopy priority_1 = peewee.Case( None, ((x.xmatch_flags > 1, 1), ), 0) priority_2 = peewee.Case( None, ((x.ero_det_like < self.parameters['det_like_for_priority'], 1), ), 0) priority_3 = peewee.Case( None, ( (s16.c.specobjid.is_null(False), 1), # any of these can be satisfied (s2020.c.pk.is_null(False), 1), (sV.c.specobjid.is_null(False), 1), (sph.c.pkey.is_null(False), 1), ), 0) priority_4 = peewee.Case( None, ((gal_lat > self.parameters['in_plane_lat_cut'], 0),), 1 ) priority = fn.max( self.parameters['priority_floor'] + priority_1 * self.parameters['dpriority_match_flags'] + priority_2 * self.parameters['dpriority_det_like'] + priority_3 * self.parameters['dpriority_has_spec'] + priority_4 * self.parameters['dpriority_in_plane'] ) # choose cadence based on magnitude in Gaia G and RP-bands cadence1 = self.parameters['cadence1'] cadence2 = self.parameters['cadence2'] cadence3 = self.parameters['cadence3'] cadence4 = 'unknown_cadence' cadence = peewee.Case( None, ( ((tic.gaiamag < gaia_g_max_for_cadence1) | (tic.gaiarp < gaia_rp_max_for_cadence1), cadence1), ((tic.gaiamag < gaia_g_max_for_cadence2) | (tic.gaiarp < gaia_rp_max_for_cadence2), cadence2), ((tic.gaiamag >= gaia_g_max_for_cadence2) & (tic.gaiarp >= gaia_rp_max_for_cadence2), cadence3), ), cadence4) # compute transformed SDSS mags # transform the Gaia dr2 G,BP,RP into sdss psfmag griz # piecewise transformation either side of BP-RP=1.8 # fit to blue end is cubic, fit to red end is quadratic # awk 'BEGIN {print("coeffs = {")} /POLYFIT/{ if(FILENAME~/_red/){pe="red"} else if (FILENAME~/_blue/){pe="blue"} else{pe="error"}; printf("\"%s%d_%s\": %s,\n", substr($3,length($3)), $8, pe, $10)} END {print("}")}' bhm_spiders_agn_gaiadr2_red/gdr2_*mag_to_sdss_*mag_?_results.log bhm_spiders_agn_gaiadr2_blue/gdr2_*mag_to_sdss_*mag_?_results.log # noqa coeffs = { "g2_red": 0.081178, "g1_red": 0.355677, "g0_red": 0.510306, "i2_red": 0.048864, "i1_red": -0.287475, "i0_red": -0.336712, "r2_red": 0.028080, "r1_red": 0.542331, "r0_red": -1.055168, "z2_red": -0.131385, "z1_red": 0.302555, "z0_red": -1.381648, "g3_blue": 0.639054, "g2_blue": -1.739187, "g1_blue": 1.420330, "g0_blue": -0.194071, "i3_blue": 0.780585, "i2_blue": -2.549848, "i1_blue": 1.489880, "i0_blue": -0.241381, "r3_blue": 0.575494, "r2_blue": -2.077000, "r1_blue": 1.573302, "r0_blue": -0.295026, "z3_blue": 1.064986, "z2_blue": -3.162969, "z1_blue": 1.493750, "z0_blue": -0.199582, } # # Old method - only use a single transformation for all colour ranges # # extract coeffs from fit logs via: # # awk 'BEGIN {print("coeffs = {")} /POLYFIT/{ if($3~/sdss_psfmag/){pe="p"} else if ($3~/sdss_fiber2mag/){pe="e"} else{pe="error"}; printf("\"%s%d_%s\": %s,\n", substr($3,length($3)), $8, pe, $10)} END {print("}")}' bhm_spiders_agn_gaiadr2_pontlike/gdr2_*mag_to_sdss_*mag_?_results.log # noqa # coeffs = { # "g2_p": 0.236233, # "g1_p": 0.154277, # "g0_p": -0.066625, # "i2_p": 0.340616, # "i1_p": -1.395607, # "i0_p": 0.555709, # "r2_p": 0.410346, # "r1_p": -1.065556, # "r0_p": 0.441098, # "z2_p": 0.512729, # "z1_p": -2.214448, # "z0_p": 0.865291, # } # bp_rp_p = tic.gaiabp - tic.gaiarp # g_p = (tic.gaiamag + coeffs['g0_p'] + coeffs['g1_p'] * bp_rp_p + # coeffs['g2_p'] * bp_rp_p * bp_rp_p) # r_p = (tic.gaiamag + coeffs['r0_p'] + coeffs['r1_p'] * bp_rp_p + # coeffs['r2_p'] * bp_rp_p * bp_rp_p) # i_p = (tic.gaiamag + coeffs['i0_p'] + coeffs['i1_p'] * bp_rp_p + # coeffs['i2_p'] * bp_rp_p * bp_rp_p) # z_p = (tic.gaiamag + coeffs['z0_p'] + coeffs['z1_p'] * bp_rp_p + # coeffs['z2_p'] * bp_rp_p * bp_rp_p) bp_rp = tic.gaiabp - tic.gaiarp g_blue = (tic.gaiamag + coeffs['g0_blue'] + coeffs['g1_blue'] * bp_rp + coeffs['g2_blue'] * bp_rp * bp_rp + coeffs['g3_blue'] * bp_rp * bp_rp * bp_rp) r_blue = (tic.gaiamag + coeffs['r0_blue'] + coeffs['r1_blue'] * bp_rp + coeffs['r2_blue'] * bp_rp * bp_rp + coeffs['r3_blue'] * bp_rp * bp_rp * bp_rp) i_blue = (tic.gaiamag + coeffs['i0_blue'] + coeffs['i1_blue'] * bp_rp + coeffs['i2_blue'] * bp_rp * bp_rp + coeffs['i3_blue'] * bp_rp * bp_rp * bp_rp) z_blue = (tic.gaiamag + coeffs['z0_blue'] + coeffs['z1_blue'] * bp_rp + coeffs['z2_blue'] * bp_rp * bp_rp + coeffs['z3_blue'] * bp_rp * bp_rp * bp_rp) g_red = (tic.gaiamag + coeffs['g0_red'] + coeffs['g1_red'] * bp_rp + coeffs['g2_red'] * bp_rp * bp_rp) r_red = (tic.gaiamag + coeffs['r0_red'] + coeffs['r1_red'] * bp_rp + coeffs['r2_red'] * bp_rp * bp_rp) i_red = (tic.gaiamag + coeffs['i0_red'] + coeffs['i1_red'] * bp_rp + coeffs['i2_red'] * bp_rp * bp_rp) z_red = (tic.gaiamag + coeffs['z0_red'] + coeffs['z1_red'] * bp_rp + coeffs['z2_red'] * bp_rp * bp_rp) # validity checks - set limits semi-manually bp_rp_min = 0.0 bp_rp_max = 3.0 valid = (tic.gaiamag.between(0.1, 29.9) & tic.gaiabp.between(0.1, 29.9) & tic.gaiarp.between(0.1, 29.9) & bp_rp.between(bp_rp_min, bp_rp_max)) opt_prov = peewee.Case(None, ((valid, 'sdss_psfmag_from_gdr2'),), 'undefined') magnitude_g = peewee.Case(None, ( (valid & (bp_rp < 1.8), g_blue), (valid & (bp_rp > 1.8), g_red), ), 'NaN') magnitude_r = peewee.Case(None, ( (valid & (bp_rp < 1.8), r_blue), (valid & (bp_rp > 1.8), r_red), ), 'NaN') magnitude_i = peewee.Case(None, ( (valid & (bp_rp < 1.8), i_blue), (valid & (bp_rp > 1.8), i_red), ), 'NaN') magnitude_z = peewee.Case(None, ( (valid & (bp_rp < 1.8), z_blue), (valid & (bp_rp > 1.8), z_red), ), 'NaN') query = ( c.select( fn.min(c.catalogid).alias('catalogid'), fn.min(tic.gaia_int).alias('gaia_source'), # extra fn.min(x.ero_detuid).alias('ero_detuid'), # extra fn.min(c.ra).alias('ra'), # extra fn.min(c.dec).alias('dec'), # extra priority.alias("priority"), fn.min(value).alias('value'), fn.min(cadence).alias('cadence'), fn.min(instrument).alias('instrument'), fn.min(opt_prov).alias('optical_prov'), fn.min(magnitude_g).alias('g'), fn.min(magnitude_r).alias('r'), fn.min(magnitude_i).alias('i'), fn.min(magnitude_z).alias('z'), fn.min(tic.gaiamag).alias('gaia_g'), fn.min(tic.gaiabp).alias('bp'), fn.min(tic.gaiarp).alias('rp'), ) .join(c2tic) .where( c.version_id == version_id, c2tic.version_id == version_id, c2tic.best >> True, ) .join(tic) .join(x, on=(tic.gaia_int == x.gaia_dr2_id)) .switch(c) # start joining the spectroscopy .switch(c) .join(c2s16, JOIN.LEFT_OUTER) .join( s16, JOIN.LEFT_OUTER, on=( (c2s16.target_id == s16.c.specobjid) & (c2s16.version_id == version_id) ) ) .switch(c) .join(c2s2020, JOIN.LEFT_OUTER) .join( s2020, JOIN.LEFT_OUTER, on=( (c2s2020.target_id == s2020.c.pk) & (c2s2020.version_id == version_id) ) ) .join( sV, JOIN.LEFT_OUTER, on=( fn.q3c_join(sV.c.plug_ra, sV.c.plug_dec, c.ra, c.dec, match_radius_spectro) ) ) .join( sph, JOIN.LEFT_OUTER, on=( fn.q3c_join(sph.c.target_ra, sph.c.target_dec, c.ra, c.dec, match_radius_spectro) ) ) # finished joining the spectroscopy .where( (x.ero_version == self.parameters['ero_version']), (x.xmatch_method == self.parameters['xmatch_method']), (x.xmatch_version == self.parameters['xmatch_version']), (x.opt_cat == self.parameters['opt_cat']), (x.xmatch_metric >= self.parameters['p_any_min']), (tic.gaiamag > self.parameters['gaia_g_mag_limit']), (tic.gaiarp > self.parameters['gaia_rp_mag_limit']), (x.ero_det_like > self.parameters['det_like_min']), ) .group_by(tic.gaia_int) # avoid duplicates - we trust the gaia ids ) if query_region: query = query.where(peewee.fn.q3c_radial_query(c.ra, c.dec, query_region[0], query_region[1], query_region[2])) return query # # END BhmSpidersAgnGaiadr2Carton # ################################################################################## class BhmSpidersAgnSepCarton(BaseCarton): name = 'bhm_spiders_agn_sep' category = 'science' mapper = 'BHM' program = 'bhm_spiders' tile = False instrument = 'BOSS' def build_query(self, version_id, query_region=None): c = Catalog.alias() x = EROSITASupersetAGN.alias() tic = TIC_v8.alias() c2tic = CatalogToTIC_v8.alias() instrument = peewee.Value(self.instrument) gaia_g_max_for_cadence1 = self.parameters['gaia_g_max_for_cadence1'] gaia_rp_max_for_cadence1 = self.parameters['gaia_rp_max_for_cadence1'] gaia_g_max_for_cadence2 = self.parameters['gaia_g_max_for_cadence2'] gaia_rp_max_for_cadence2 = self.parameters['gaia_rp_max_for_cadence2'] value = peewee.Value(self.parameters.get('value', 1.0)).cast('float') # priority is determined by target properties # start with a priority floor value (per carton) # then increment if any conditions are met: # add +dpriority_match_flags if target is a secondary cross-match (match_flag > 1) # add +dpriority_det_like if target has a low value of ero_det_like # add +dpriority_has_spec if target has existing good SDSS spectroscopy priority_1 = peewee.Case( None, ((x.xmatch_flags > 1, 1), ), 0) priority_2 = peewee.Case( None, ((x.ero_det_like < self.parameters['det_like_for_priority'], 1), ), 0) # add a step down in priority for anything selected by the secondary xmatch_version priority_3 = peewee.Case( None, ((x.xmatch_method == self.parameters['xmatch_version2'], 1), ), 0) # choose the maximum priority option for all combinations of this target priority = fn.max( self.parameters['priority_floor'] + priority_1 * self.parameters['dpriority_match_flags'] + priority_2 * self.parameters['dpriority_det_like'] + priority_3 * self.parameters['dpriority_match_method'] ) # choose cadence based on magnitude in Gaia G and RP-bands cadence1 = self.parameters['cadence1'] cadence2 = self.parameters['cadence2'] cadence3 = self.parameters['cadence3'] cadence4 = 'unknown_cadence' cadence = peewee.Case( None, ( ((tic.gaiamag < gaia_g_max_for_cadence1) | (tic.gaiarp < gaia_rp_max_for_cadence1), cadence1), ((tic.gaiamag < gaia_g_max_for_cadence2) | (tic.gaiarp < gaia_rp_max_for_cadence2), cadence2), ((tic.gaiamag >= gaia_g_max_for_cadence2) & (tic.gaiarp >= gaia_rp_max_for_cadence2), cadence3), ), cadence4) # compute transformed SDSS mags # transform the Gaia dr2 G,BP,RP into sdss psfmag griz # direct copy of method for bhm_spiders_agn_gaiadr2 coeffs = { "g2_red": 0.081178, "g1_red": 0.355677, "g0_red": 0.510306, "i2_red": 0.048864, "i1_red": -0.287475, "i0_red": -0.336712, "r2_red": 0.028080, "r1_red": 0.542331, "r0_red": -1.055168, "z2_red": -0.131385, "z1_red": 0.302555, "z0_red": -1.381648, "g3_blue": 0.639054, "g2_blue": -1.739187, "g1_blue": 1.420330, "g0_blue": -0.194071, "i3_blue": 0.780585, "i2_blue": -2.549848, "i1_blue": 1.489880, "i0_blue": -0.241381, "r3_blue": 0.575494, "r2_blue": -2.077000, "r1_blue": 1.573302, "r0_blue": -0.295026, "z3_blue": 1.064986, "z2_blue": -3.162969, "z1_blue": 1.493750, "z0_blue": -0.199582, } bp_rp = tic.gaiabp - tic.gaiarp g_blue = (tic.gaiamag + coeffs['g0_blue'] + coeffs['g1_blue'] * bp_rp + coeffs['g2_blue'] * bp_rp * bp_rp + coeffs['g3_blue'] * bp_rp * bp_rp * bp_rp) r_blue = (tic.gaiamag + coeffs['r0_blue'] + coeffs['r1_blue'] * bp_rp + coeffs['r2_blue'] * bp_rp * bp_rp + coeffs['r3_blue'] * bp_rp * bp_rp * bp_rp) i_blue = (tic.gaiamag + coeffs['i0_blue'] + coeffs['i1_blue'] * bp_rp + coeffs['i2_blue'] * bp_rp * bp_rp + coeffs['i3_blue'] * bp_rp * bp_rp * bp_rp) z_blue = (tic.gaiamag + coeffs['z0_blue'] + coeffs['z1_blue'] * bp_rp + coeffs['z2_blue'] * bp_rp * bp_rp + coeffs['z3_blue'] * bp_rp * bp_rp * bp_rp) g_red = (tic.gaiamag + coeffs['g0_red'] + coeffs['g1_red'] * bp_rp + coeffs['g2_red'] * bp_rp * bp_rp) r_red = (tic.gaiamag + coeffs['r0_red'] + coeffs['r1_red'] * bp_rp + coeffs['r2_red'] * bp_rp * bp_rp) i_red = (tic.gaiamag + coeffs['i0_red'] + coeffs['i1_red'] * bp_rp + coeffs['i2_red'] * bp_rp * bp_rp) z_red = (tic.gaiamag + coeffs['z0_red'] + coeffs['z1_red'] * bp_rp + coeffs['z2_red'] * bp_rp * bp_rp) # validity checks - set limits semi-manually bp_rp_min = 0.0 bp_rp_max = 3.0 valid = (tic.gaiamag.between(0.1, 29.9) & tic.gaiabp.between(0.1, 29.9) & tic.gaiarp.between(0.1, 29.9) & bp_rp.between(bp_rp_min, bp_rp_max)) opt_prov = peewee.Case(None, ((valid, 'sdss_psfmag_from_gdr2'),), 'undefined') magnitude_g = peewee.Case(None, ( (valid & (bp_rp < 1.8), g_blue), (valid & (bp_rp > 1.8), g_red), ), 'NaN') magnitude_r = peewee.Case(None, ( (valid & (bp_rp < 1.8), r_blue), (valid & (bp_rp > 1.8), r_red), ), 'NaN') magnitude_i = peewee.Case(None, ( (valid & (bp_rp < 1.8), i_blue), (valid & (bp_rp > 1.8), i_red), ), 'NaN') magnitude_z = peewee.Case(None, ( (valid & (bp_rp < 1.8), z_blue), (valid & (bp_rp > 1.8), z_red), ), 'NaN') query = ( c.select( fn.min(c.catalogid).alias('catalogid'), fn.min(tic.gaia_int).alias('gaia_source'), # extra fn.min(x.ero_detuid).alias('ero_detuid'), # extra fn.min(c.ra).alias('ra'), # extra fn.min(c.dec).alias('dec'), # extra priority.alias("priority"), fn.min(value).alias('value'), fn.min(cadence).alias('cadence'), fn.min(instrument).alias('instrument'), fn.min(opt_prov).alias('optical_prov'), fn.min(magnitude_g).alias('g'), fn.min(magnitude_r).alias('r'), fn.min(magnitude_i).alias('i'), fn.min(magnitude_z).alias('z'), fn.min(tic.gaiamag).alias('gaia_g'), fn.min(tic.gaiabp).alias('bp'), fn.min(tic.gaiarp).alias('rp'), ) .join(c2tic) .where( c.version_id == version_id, c2tic.version_id == version_id, c2tic.best >> True, ) .join(tic) .join(x, on=(tic.gaia_int == x.gaia_dr2_id)) .switch(c) .where( x.ero_version == self.parameters['ero_version'], x.xmatch_method == self.parameters['xmatch_method'], ( (x.xmatch_version == self.parameters['xmatch_version1']) | (x.xmatch_version == self.parameters['xmatch_version2']) ), x.opt_cat == self.parameters['opt_cat'], x.xmatch_metric >= self.parameters['p_any_min'], tic.gaiamag > self.parameters['gaia_g_mag_limit'], tic.gaiarp > self.parameters['gaia_rp_mag_limit'], x.ero_det_like > self.parameters['det_like_min'], ) .group_by(tic.gaia_int) # avoid duplicates - we trust the gaia ids ) if query_region: query = query.where(peewee.fn.q3c_radial_query(c.ra, c.dec, query_region[0], query_region[1], query_region[2])) return query # # END BhmSpidersAgnSepCarton # ################################################################################## class BhmSpidersAgnPs1dr2Carton(BaseCarton): name = 'bhm_spiders_agn_ps1dr2' category = 'science' mapper = 'BHM' program = 'bhm_spiders' tile = False instrument = 'BOSS' def build_query(self, version_id, query_region=None): c = Catalog.alias() x = EROSITASupersetAGN.alias() ps = Panstarrs1.alias() c2ps = CatalogToPanstarrs1.alias() tic = TIC_v8.alias() c2tic = CatalogToTIC_v8.alias() instrument = peewee.Value(self.instrument) g_psf_flux_max = AB2Jy(self.parameters['g_psf_mag_min']) r_psf_flux_max = AB2Jy(self.parameters['r_psf_mag_min']) i_psf_flux_max = AB2Jy(self.parameters['i_psf_mag_min']) z_psf_flux_max = AB2Jy(self.parameters['z_psf_mag_min']) g_psf_flux_min = AB2Jy(self.parameters['g_psf_mag_max']) r_psf_flux_min = AB2Jy(self.parameters['r_psf_mag_max']) i_psf_flux_min = AB2Jy(self.parameters['i_psf_mag_max']) z_psf_flux_min = AB2Jy(self.parameters['z_psf_mag_max']) g_psf_flux_min_for_cadence1 = AB2Jy(self.parameters['g_psf_mag_max_for_cadence1']) r_psf_flux_min_for_cadence1 = AB2Jy(self.parameters['r_psf_mag_max_for_cadence1']) i_psf_flux_min_for_cadence1 = AB2Jy(self.parameters['i_psf_mag_max_for_cadence1']) g_psf_flux_min_for_cadence2 = AB2Jy(self.parameters['g_psf_mag_max_for_cadence2']) r_psf_flux_min_for_cadence2 = AB2Jy(self.parameters['r_psf_mag_max_for_cadence2']) i_psf_flux_min_for_cadence2 = AB2Jy(self.parameters['i_psf_mag_max_for_cadence2']) # value = peewee.Value(self.parameters.get('value', 1.0)).cast('float') # these control matching to spectroscopy match_radius_spectro = self.parameters['spec_join_radius'] / 3600.0 spec_sn_thresh = self.parameters['spec_sn_thresh'] spec_z_err_thresh = self.parameters['spec_z_err_thresh'] # ######################################################################### # prepare the spectroscopy catalogues # SDSS DR16 c2s16 = CatalogToSDSS_DR16_SpecObj.alias() ss16 = SDSS_DR16_SpecObj.alias() s16 = ( ss16.select( ss16.specobjid.alias('specobjid'), ) .where( ss16.snmedian >= spec_sn_thresh, ss16.zwarning == 0, ss16.zerr <= spec_z_err_thresh, ss16.zerr > 0.0, ss16.scienceprimary > 0, ) .alias('s16') ) # SDSS-IV/eFEDS March2020 c2s2020 = CatalogToBHM_eFEDS_Veto.alias() ss2020 = BHM_eFEDS_Veto.alias() s2020 = ( ss2020.select( ss2020.pk.alias('pk'), ) .where( ss2020.sn_median_all >= spec_sn_thresh, ss2020.zwarning == 0, ss2020.z_err <= spec_z_err_thresh, ss2020.z_err > 0.0, ) .alias('s2020') ) # SDSS-V spAll ssV = SDSSV_BOSS_SPALL.alias() sV = ( ssV.select( ssV.specobjid.alias('specobjid'), ssV.plug_ra.alias('plug_ra'), ssV.plug_dec.alias('plug_dec'), ) .where( ssV.sn_median_all >= spec_sn_thresh, ssV.zwarning == 0, ssV.z_err <= spec_z_err_thresh, ssV.z_err > 0.0, ssV.specprimary > 0, ) .alias('sV') ) # SDSS-V plateholes - only consider plateholes that # were drilled+shipped but that were not yet observed ssph = SDSSV_Plateholes.alias() ssphm = SDSSV_Plateholes_Meta.alias() ssconf = SDSSV_BOSS_Conflist.alias() sph = ( ssph.select( ssph.pkey.alias('pkey'), ssph.target_ra.alias('target_ra'), ssph.target_dec.alias('target_dec'), ) .join( ssphm, on=(ssph.yanny_uid == ssphm.yanny_uid) ) .join( ssconf, JOIN.LEFT_OUTER, on=(ssphm.plateid == ssconf.plate) ) .where( (ssph.holetype == 'BOSS_SHARED'), (ssph.sourcetype == 'SCI') | (ssph.sourcetype == 'STA'), ssphm.isvalid > 0, ssconf.plate.is_null(), ) .alias('sph') ) # ######################################################################### # compute the abs(Galactic latitude): gal_lat = peewee.fn.abs(90.0 - peewee.fn.q3c_dist(north_gal_pole_ra, north_gal_pole_dec, c.ra, c.dec)) value = peewee.Case( None, ((gal_lat > self.parameters['in_plane_lat_cut'], self.parameters.get('value', 1.0)),), 0.0 ).cast('float') # priority is determined by target properties # start with a priority floor value (per carton) # then increment if any conditions are met: # add +dpriority_match_flags if target is a secondary cross-match (match_flag > 1) # add +dpriority_det_like if target has a low value of ero_det_like # add +dpriority_has_spec if target has existing good SDSS spectroscopy # add +dpriority_in_plane if target lies at |b| < in_plane_lat_cut priority_1 = peewee.Case( None, ((x.xmatch_flags > 1, 1), ), 0) priority_2 = peewee.Case( None, ((x.ero_det_like < self.parameters['det_like_for_priority'], 1), ), 0) priority_3 = peewee.Case( None, ( (s16.c.specobjid.is_null(False), 1), # any of these can be satisfied (s2020.c.pk.is_null(False), 1), (sV.c.specobjid.is_null(False), 1), (sph.c.pkey.is_null(False), 1), ), 0) priority_4 = peewee.Case( None, ((gal_lat > self.parameters['in_plane_lat_cut'], 0),), 1 ) priority = fn.max( self.parameters['priority_floor'] + priority_1 * self.parameters['dpriority_match_flags'] + priority_2 * self.parameters['dpriority_det_like'] + priority_3 * self.parameters['dpriority_has_spec'] + priority_4 * self.parameters['dpriority_in_plane'] ) # choose cadence based on psf_flux magnitude in panstarrs1 g,r,i-bands cadence1 = self.parameters['cadence1'] cadence2 = self.parameters['cadence2'] cadence3 = self.parameters['cadence3'] cadence4 = 'unknown_cadence' cadence = peewee.Case( None, ( ((ps.g_stk_psf_flux > g_psf_flux_min_for_cadence1) | (ps.r_stk_psf_flux > r_psf_flux_min_for_cadence1) | (ps.i_stk_psf_flux > i_psf_flux_min_for_cadence1), cadence1), ((ps.g_stk_psf_flux > g_psf_flux_min_for_cadence2) | (ps.r_stk_psf_flux > r_psf_flux_min_for_cadence2) | (ps.i_stk_psf_flux > i_psf_flux_min_for_cadence2), cadence2), ((ps.g_stk_psf_flux <= g_psf_flux_min_for_cadence2) & (ps.r_stk_psf_flux <= r_psf_flux_min_for_cadence2) & (ps.i_stk_psf_flux <= i_psf_flux_min_for_cadence2), cadence3), ), cadence4) # compute transformed SDSS mags for pointlike and extended sources separately # transform the panstarrs1-dr2 griz into sdss psfmag griz # extract coeffs from fit logs via: # awk 'BEGIN {print("coeffs = {")} /POLYFIT/{ if($3~/sdss_psfmag/){pe="p"} else if ($3~/sdss_fiber2mag/){pe="e"} else{pe="error"}; printf("\"%s%d_%s\": %s,\n", substr($3,length($3)), $8, pe, $10)} END {print("}")}' bhm_spiders_agn_ps1dr2_pointlike/ps1dr2_stk_psf_to_sdss_psfmag_?_results.log bhm_spiders_agn_ps1dr2_extended/ps1dr2_stk_psf_to_sdss_fiber2mag_?_results.log # noqa coeffs = { "g2_p": 0.275586, "g1_p": -0.178727, "g0_p": 0.024900, "i2_p": -0.051817, "i1_p": 0.098077, "i0_p": -0.028243, "r2_p": -0.031567, "r1_p": 0.056499, "r0_p": -0.013487, "z2_p": -0.290196, "z1_p": 0.156009, "z0_p": -0.079393, "g2_e": 0.084856, "g1_e": -0.076550, "g0_e": 0.841168, "i2_e": 0.048106, "i1_e": 0.025289, "i0_e": 0.652371, "r2_e": 0.066827, "r1_e": -0.118807, "r0_e": 0.752550, "z2_e": 0.558727, "z1_e": -0.006461, "z0_e": 0.512403, } Jy_min = AB2Jy(30.00) # pointlike and extended - both start from ps1dr2 stk psf fluxes g0 = (8.9 - 2.5 * peewee.fn.log(peewee.fn.greatest(Jy_min, ps.g_stk_psf_flux))) r0 = (8.9 - 2.5 * peewee.fn.log(peewee.fn.greatest(Jy_min, ps.r_stk_psf_flux))) i0 = (8.9 - 2.5 * peewee.fn.log(peewee.fn.greatest(Jy_min, ps.i_stk_psf_flux))) z0 = (8.9 - 2.5 * peewee.fn.log(peewee.fn.greatest(Jy_min, ps.z_stk_psf_flux))) g_r = g0 - r0 r_i = r0 - i0 i_z = i0 - z0 # use different transform coeffs for pointlike and extended sources g_p = (g0 + coeffs['g0_p'] + coeffs['g1_p'] * g_r + coeffs['g2_p'] * g_r * g_r) r_p = (r0 + coeffs['r0_p'] + coeffs['r1_p'] * g_r + coeffs['r2_p'] * g_r * g_r) i_p = (i0 + coeffs['i0_p'] + coeffs['i1_p'] * r_i + coeffs['i2_p'] * r_i * r_i) z_p = (z0 + coeffs['z0_p'] + coeffs['z1_p'] * i_z + coeffs['z2_p'] * i_z * i_z) g_e = (g0 + coeffs['g0_e'] + coeffs['g1_e'] * g_r + coeffs['g2_e'] * g_r * g_r) r_e = (r0 + coeffs['r0_e'] + coeffs['r1_e'] * g_r + coeffs['r2_e'] * g_r * g_r) i_e = (i0 + coeffs['i0_e'] + coeffs['i1_e'] * r_i + coeffs['i2_e'] * r_i * r_i) z_e = (z0 + coeffs['z0_e'] + coeffs['z1_e'] * i_z + coeffs['z2_e'] * i_z * i_z) # validity checks - set limits semi-manually g_r_p_min = -0.5 g_r_p_max = 1.4 r_i_p_min = -0.5 r_i_p_max = 2.0 i_z_p_min = -0.5 i_z_p_max = 1.0 g_r_e_min = -0.5 g_r_e_max = 1.5 r_i_e_min = -0.2 r_i_e_max = 1.3 i_z_e_min = -0.3 i_z_e_max = 0.8 # valid = (g0.between(0.1, 29.9) & # r0.between(0.1, 29.9) & # i0.between(0.1, 29.9) & # z0.between(0.1, 29.9)) valid_p = (g0.between(0.1, 29.9) & r0.between(0.1, 29.9) & z0.between(0.1, 29.9) & g_r.between(g_r_p_min, g_r_p_max) & r_i.between(r_i_p_min, r_i_p_max) & i_z.between(i_z_p_min, i_z_p_max)) valid_e = (g0.between(0.1, 29.9) & r0.between(0.1, 29.9) & z0.between(0.1, 29.9) & g_r.between(g_r_e_min, g_r_e_max) & r_i.between(r_i_e_min, r_i_e_max) & i_z.between(i_z_e_min, i_z_e_max)) # We want to switch between psfmags and fibertotmags depending on # ps.flags EXT+EXT_ALT (i.e. extended sources) ext_flags = 8388608 + 16777216 good_stack_flag = 134217728 opt_prov = peewee.Case( None, ( ((ps.flags.bin_and(ext_flags) == 0) & valid_p, 'sdss_psfmag_from_ps1dr2'), ((ps.flags.bin_and(ext_flags) > 0) & valid_e, 'sdss_fiber2mag_from_ps1dr2'), ), 'undefined') magnitude_g = peewee.Case( None, ( ((ps.flags.bin_and(ext_flags) == 0) & valid_p, g_p.cast('float')), ((ps.flags.bin_and(ext_flags) > 0) & valid_e, g_e.cast('float')), ), 'NaN') magnitude_r = peewee.Case( None, ( ((ps.flags.bin_and(ext_flags) == 0) & valid_p, r_p.cast('float')), ((ps.flags.bin_and(ext_flags) > 0) & valid_e, r_e.cast('float')), ), 'NaN') magnitude_i = peewee.Case( None, ( ((ps.flags.bin_and(ext_flags) == 0) & valid_p, i_p.cast('float')), ((ps.flags.bin_and(ext_flags) > 0) & valid_e, i_e.cast('float')), ), 'NaN') magnitude_z = peewee.Case( None, ( ((ps.flags.bin_and(ext_flags) == 0) & valid_p, z_p.cast('float')), ((ps.flags.bin_and(ext_flags) > 0) & valid_e, z_e.cast('float')), ), 'NaN') ############################## # # We want to switch between psfmags and fibertotmags depending on # # ps.flags EXT+EXT_ALT (i.e. extended sources) # # For non-extended targets, we use psfmags, but for extended sources use apermag # flux30 = AB2Jy(30.00) # ext_flags = 8388608 + 16777216 # good_stack_flag = 134217728 # opt_prov = peewee.Case( # ps.flags.bin_and(ext_flags), # ((0, 'ps_psfmag'),), # 'ps_apermag') # # magnitude_g = peewee.Case( # ps.flags.bin_and(ext_flags), # ((0, (8.9 - 2.5 * fn.log10(fn.greatest(flux30, ps.g_stk_psf_flux))).cast('float')),), # (8.9 - 2.5 * fn.log10(fn.greatest(flux30, ps.g_stk_aper_flux))).cast('float')) # # magnitude_r = peewee.Case( # ps.flags.bin_and(ext_flags), # ((0, (8.9 - 2.5 * fn.log10(fn.greatest(flux30, ps.r_stk_psf_flux))).cast('float')),), # (8.9 - 2.5 * fn.log10(fn.greatest(flux30, ps.r_stk_aper_flux))).cast('float')) # # magnitude_i = peewee.Case( # ps.flags.bin_and(ext_flags), # ((0, (8.9 - 2.5 * fn.log10(fn.greatest(flux30, ps.i_stk_psf_flux))).cast('float')),), # (8.9 - 2.5 * fn.log10(fn.greatest(flux30, ps.i_stk_aper_flux))).cast('float')) # # magnitude_z = peewee.Case( # ps.flags.bin_and(ext_flags), # ((0, (8.9 - 2.5 * fn.log10(fn.greatest(flux30, ps.z_stk_psf_flux))).cast('float')),), # (8.9 - 2.5 * fn.log10(fn.greatest(flux30, ps.z_stk_aper_flux))).cast('float')) query = ( c.select( fn.min(c.catalogid).alias('catalogid'), fn.min(ps.catid_objid).alias('ps1_catid_objid'), # extra fn.min(tic.gaia_int).alias('gaia_source'), # extra fn.min(x.ero_detuid).alias('ero_detuid'), # extra fn.min(c.ra).alias('ra'), # extra fn.min(c.dec).alias('dec'), # extra priority.alias("priority"), fn.min(value).alias('value'), fn.min(cadence).alias('cadence'), fn.min(instrument).alias('instrument'), fn.min(magnitude_g).alias('g'), fn.min(magnitude_r).alias('r'), fn.min(magnitude_i).alias('i'), fn.min(magnitude_z).alias('z'), fn.min(tic.gaiamag).alias('gaia_g'), fn.min(tic.gaiabp).alias('bp'), fn.min(tic.gaiarp).alias('rp'), fn.min(opt_prov).alias('optical_prov'), fn.min(g0).alias("ps1dr2_stk_psf_mag_g"), # extra fn.min(r0).alias("ps1dr2_stk_psf_mag_r"), # extra fn.min(i0).alias("ps1dr2_stk_psf_mag_i"), # extra fn.min(z0).alias("ps1dr2_stk_psf_mag_z"), # extra fn.min(ps.flags).alias("ps1dr2_flags"), # extra ) .join(c2ps) .where( c.version_id == version_id, c2ps.version_id == version_id, c2ps.best >> True, ) .join(ps) .join(x, on=(ps.catid_objid == x.ps1_dr2_id)) .switch(c) .join( c2tic, JOIN.LEFT_OUTER, on=( (c.catalogid == c2tic.catalogid) & (c2tic.version_id == version_id) # explicitly do not want to take only c2tic.best entries ) ) .join(tic, JOIN.LEFT_OUTER) .switch(c) # start joining the spectroscopy .switch(c) .join(c2s16, JOIN.LEFT_OUTER) .join( s16, JOIN.LEFT_OUTER, on=( (c2s16.target_id == s16.c.specobjid) & (c2s16.version_id == version_id) ) ) .switch(c) .join(c2s2020, JOIN.LEFT_OUTER) .join( s2020, JOIN.LEFT_OUTER, on=( (c2s2020.target_id == s2020.c.pk) & (c2s2020.version_id == version_id) ) ) .join( sV, JOIN.LEFT_OUTER, on=( fn.q3c_join(sV.c.plug_ra, sV.c.plug_dec, c.ra, c.dec, match_radius_spectro) ) ) .join( sph, JOIN.LEFT_OUTER, on=( fn.q3c_join(sph.c.target_ra, sph.c.target_dec, c.ra, c.dec, match_radius_spectro) ) ) # finished joining the spectroscopy .where( (x.ero_version == self.parameters['ero_version']), (x.xmatch_method == self.parameters['xmatch_method']), (x.xmatch_version == self.parameters['xmatch_version']), (x.opt_cat == self.parameters['opt_cat']), (x.xmatch_metric >= self.parameters['p_any_min']), (x.ero_det_like > self.parameters['det_like_min']), (ps.g_stk_psf_flux < g_psf_flux_max), (ps.r_stk_psf_flux < r_psf_flux_max), (ps.i_stk_psf_flux < i_psf_flux_max), (ps.z_stk_psf_flux < z_psf_flux_max), (ps.g_stk_psf_flux != 'NaN'), # these NaNs are NOT represented as nulls (ps.r_stk_psf_flux != 'NaN'), # these NaNs are NOT represented as nulls (ps.i_stk_psf_flux != 'NaN'), # these NaNs are NOT represented as nulls ( (ps.g_stk_psf_flux > g_psf_flux_min) | (ps.r_stk_psf_flux > r_psf_flux_min) | (ps.i_stk_psf_flux > i_psf_flux_min) | (ps.z_stk_psf_flux > z_psf_flux_min) ), (ps.flags.bin_and(good_stack_flag) > 0), ) .group_by(ps.catid_objid) # avoid duplicates - we trust the ps1 ids .having( # each and every match to the tic must satisfy the bright star rejection criteria fn.sum( peewee.Case( None, ( (tic.gaiamag < self.parameters['gaia_g_mag_limit'], 1), (tic.gaiarp < self.parameters['gaia_rp_mag_limit'], 1), (tic.tmag < self.parameters['tic_t_mag_limit'], 1), ), 0) ) == 0 ) ) if query_region: query = query.where(peewee.fn.q3c_radial_query(c.ra, c.dec, query_region[0], query_region[1], query_region[2])) return query # # END BhmSpidersAgnPs1dr2Carton # ################################################################################## class BhmSpidersAgnSkyMapperDr2Carton(BaseCarton): name = 'bhm_spiders_agn_skymapperdr2' category = 'science' mapper = 'BHM' program = 'bhm_spiders' tile = False instrument = 'BOSS' def build_query(self, version_id, query_region=None): c = Catalog.alias() x = EROSITASupersetAGN.alias() sm = SkyMapper_DR2.alias() c2sm = CatalogToSkyMapper_DR2.alias() tic = TIC_v8.alias() c2tic = CatalogToTIC_v8.alias() instrument = peewee.Value(self.instrument) g_psf_mag_min = self.parameters['g_psf_mag_min'] r_psf_mag_min = self.parameters['r_psf_mag_min'] i_psf_mag_min = self.parameters['i_psf_mag_min'] z_psf_mag_min = self.parameters['z_psf_mag_min'] g_psf_mag_max = self.parameters['g_psf_mag_max'] r_psf_mag_max = self.parameters['r_psf_mag_max'] i_psf_mag_max = self.parameters['i_psf_mag_max'] z_psf_mag_max = self.parameters['z_psf_mag_max'] g_psf_mag_max_for_cadence1 = self.parameters['g_psf_mag_max_for_cadence1'] r_psf_mag_max_for_cadence1 = self.parameters['r_psf_mag_max_for_cadence1'] i_psf_mag_max_for_cadence1 = self.parameters['i_psf_mag_max_for_cadence1'] g_psf_mag_max_for_cadence2 = self.parameters['g_psf_mag_max_for_cadence2'] r_psf_mag_max_for_cadence2 = self.parameters['r_psf_mag_max_for_cadence2'] i_psf_mag_max_for_cadence2 = self.parameters['i_psf_mag_max_for_cadence2'] # value = peewee.Value(self.parameters.get('value', 1.0)).cast('float') # these control matching to spectroscopy match_radius_spectro = self.parameters['spec_join_radius'] / 3600.0 spec_sn_thresh = self.parameters['spec_sn_thresh'] spec_z_err_thresh = self.parameters['spec_z_err_thresh'] # ######################################################################### # prepare the spectroscopy catalogues # SDSS DR16 c2s16 = CatalogToSDSS_DR16_SpecObj.alias() ss16 = SDSS_DR16_SpecObj.alias() s16 = ( ss16.select( ss16.specobjid.alias('specobjid'), ) .where( ss16.snmedian >= spec_sn_thresh, ss16.zwarning == 0, ss16.zerr <= spec_z_err_thresh, ss16.zerr > 0.0, ss16.scienceprimary > 0, ) .alias('s16') ) # SDSS-IV/eFEDS March2020 c2s2020 = CatalogToBHM_eFEDS_Veto.alias() ss2020 = BHM_eFEDS_Veto.alias() s2020 = ( ss2020.select( ss2020.pk.alias('pk'), ) .where( ss2020.sn_median_all >= spec_sn_thresh, ss2020.zwarning == 0, ss2020.z_err <= spec_z_err_thresh, ss2020.z_err > 0.0, ) .alias('s2020') ) # SDSS-V spAll ssV = SDSSV_BOSS_SPALL.alias() sV = ( ssV.select( ssV.specobjid.alias('specobjid'), ssV.plug_ra.alias('plug_ra'), ssV.plug_dec.alias('plug_dec'), ) .where( ssV.sn_median_all >= spec_sn_thresh, ssV.zwarning == 0, ssV.z_err <= spec_z_err_thresh, ssV.z_err > 0.0, ssV.specprimary > 0, ) .alias('sV') ) # SDSS-V plateholes - only consider plateholes that # were drilled+shipped but that were not yet observed ssph = SDSSV_Plateholes.alias() ssphm = SDSSV_Plateholes_Meta.alias() ssconf = SDSSV_BOSS_Conflist.alias() sph = ( ssph.select( ssph.pkey.alias('pkey'), ssph.target_ra.alias('target_ra'), ssph.target_dec.alias('target_dec'), ) .join( ssphm, on=(ssph.yanny_uid == ssphm.yanny_uid) ) .join( ssconf, JOIN.LEFT_OUTER, on=(ssphm.plateid == ssconf.plate) ) .where( (ssph.holetype == 'BOSS_SHARED'), (ssph.sourcetype == 'SCI') | (ssph.sourcetype == 'STA'), ssphm.isvalid > 0, ssconf.plate.is_null(), ) .alias('sph') ) # ######################################################################### # compute the abs(Galactic latitude): gal_lat = peewee.fn.abs(90.0 - peewee.fn.q3c_dist(north_gal_pole_ra, north_gal_pole_dec, c.ra, c.dec)) value = peewee.Case( None, ((gal_lat > self.parameters['in_plane_lat_cut'], self.parameters.get('value', 1.0)),), 0.0 ).cast('float') # priority is determined by target properties # start with a priority floor value (per carton) # then increment if any conditions are met: # add +dpriority_match_flags if target is a secondary cross-match (match_flag > 1) # add +dpriority_det_like if target has a low value of ero_det_like # add +dpriority_has_spec if target has existing good SDSS spectroscopy # add +dpriority_in_plane if target lies at |b| < in_plane_lat_cut priority_1 = peewee.Case( None, ((x.xmatch_flags > 1, 1), ), 0) priority_2 = peewee.Case( None, ((x.ero_det_like < self.parameters['det_like_for_priority'], 1), ), 0) priority_3 = peewee.Case( None, ( (s16.c.specobjid.is_null(False), 1), # any of these can be satisfied (s2020.c.pk.is_null(False), 1), (sV.c.specobjid.is_null(False), 1), (sph.c.pkey.is_null(False), 1), ), 0) priority_4 = peewee.Case( None, ((gal_lat > self.parameters['in_plane_lat_cut'], 0),), 1 ) priority = fn.max( self.parameters['priority_floor'] + priority_1 * self.parameters['dpriority_match_flags'] + priority_2 * self.parameters['dpriority_det_like'] + priority_3 * self.parameters['dpriority_has_spec'] + priority_4 * self.parameters['dpriority_in_plane'] ) # choose cadence based on psf_flux magnitude in skymapper g,r,i-bands cadence1 = self.parameters['cadence1'] cadence2 = self.parameters['cadence2'] cadence3 = self.parameters['cadence3'] cadence4 = 'unknown_cadence' cadence = peewee.Case( None, ( ((sm.g_psf < g_psf_mag_max_for_cadence1) | (sm.r_psf < r_psf_mag_max_for_cadence1) | (sm.i_psf < i_psf_mag_max_for_cadence1), cadence1), ((sm.g_psf < g_psf_mag_max_for_cadence2) | (sm.r_psf < r_psf_mag_max_for_cadence2) | (sm.i_psf < i_psf_mag_max_for_cadence2), cadence2), ((sm.g_psf >= g_psf_mag_max_for_cadence2) & (sm.r_psf >= r_psf_mag_max_for_cadence2) & (sm.i_psf >= i_psf_mag_max_for_cadence2), cadence3), ), cadence4) # compute transformed SDSS mags for pointlike and extended sources separately # transform the SkyMapper dr2 griz into sdss psfmag griz # extract coeffs from fit logs via: # awk 'BEGIN {print("coeffs = {")} /POLYFIT/{ if($3~/sdss_psfmag/){pe="p"} else if ($3~/sdss_fiber2mag/){pe="e"} else{pe="error"}; printf("\"%s%d_%s\": %s,\n", substr($3,length($3)), $8, pe, $10)} END {print("}")}' bhm_spiders_agn_skymapperdr2_pointlike/sm2_psfmag_to_sdss_psfmag_?_results.log # noqa coeffs = { "g2_p": -0.393954, "g1_p": 0.817394, "g0_p": 0.048210, "i2_p": -0.041044, "i1_p": 0.086928, "i0_p": 0.119810, "r2_p": -0.850989, "r1_p": 0.784271, "r0_p": 0.020350, "z2_p": -1.044019, "z1_p": 0.686397, "z0_p": 0.069980, } g_r = sm.g_psf - sm.r_psf r_i = sm.r_psf - sm.i_psf i_z = sm.i_psf - sm.z_psf g_p = (sm.g_psf + coeffs['g0_p'] + coeffs['g1_p'] * g_r + coeffs['g2_p'] * g_r * g_r) r_p = (sm.r_psf + coeffs['r0_p'] + coeffs['r1_p'] * g_r + coeffs['r2_p'] * g_r * g_r) i_p = (sm.i_psf + coeffs['i0_p'] + coeffs['i1_p'] * r_i + coeffs['i2_p'] * r_i * r_i) z_p = (sm.z_psf + coeffs['z0_p'] + coeffs['z1_p'] * i_z + coeffs['z2_p'] * i_z * i_z) # validity checks - set limits semi-manually g_r_p_min = -0.2 g_r_p_max = 1.2 r_i_p_min = -0.2 r_i_p_max = 2.0 i_z_p_min = -0.3 i_z_p_max = 0.8 valid = (sm.g_psf.between(0.1, 29.9) & sm.r_psf.between(0.1, 29.9) & sm.i_psf.between(0.1, 29.9) & sm.z_psf.between(0.1, 29.9) & g_r.between(g_r_p_min, g_r_p_max) & r_i.between(r_i_p_min, r_i_p_max) & i_z.between(i_z_p_min, i_z_p_max)) # opt_prov = peewee.Value('sdss_psfmag_from_sm2') opt_prov = peewee.Case(None, ((valid, 'sdss_psfmag_from_sm2'),), 'undefined') magnitude_g = peewee.Case(None, ((valid, g_p),), 'NaN') magnitude_r = peewee.Case(None, ((valid, r_p),), 'NaN') magnitude_i = peewee.Case(None, ((valid, i_p),), 'NaN') magnitude_z = peewee.Case(None, ((valid, z_p),), 'NaN') query = ( c.select( fn.min(c.catalogid).alias('catalogid'), fn.min(sm.object_id).alias('sm2_object_id'), # extra fn.min(tic.gaia_int).alias('gaia_source'), # extra fn.min(x.ero_detuid).alias('ero_detuid'), # extra fn.min(c.ra).alias('ra'), # extra fn.min(c.dec).alias('dec'), # extra priority.alias("priority"), fn.min(value).alias('value'), fn.min(cadence).alias('cadence'), fn.min(instrument).alias('instrument'), fn.min(magnitude_g).alias('g'), fn.min(magnitude_r).alias('r'), fn.min(magnitude_i).alias('i'), fn.min(magnitude_z).alias('z'), fn.min(tic.gaiamag).alias('gaia_g'), fn.min(tic.gaiabp).alias('bp'), fn.min(tic.gaiarp).alias('rp'), fn.min(opt_prov).alias('optical_prov'), # extra fn.min(sm.g_psf).alias('sm2_psfmag_g'), # extra fn.min(sm.r_psf).alias('sm2_psfmag_r'), # extra fn.min(sm.i_psf).alias('sm2_psfmag_i'), # extra fn.min(sm.z_psf).alias('sm2_psfmag_z'), # extra ) .join(c2sm) .where( c.version_id == version_id, c2sm.version_id == version_id, c2sm.best >> True, ) .join(sm) .join(x, on=(sm.object_id == x.skymapper_dr2_id)) .switch(c) .join( c2tic, JOIN.LEFT_OUTER, on=( (c.catalogid == c2tic.catalogid) & (c2tic.version_id == version_id) # & (c2tic.best >> True) # No, we want all possible matches ) ) .join(tic, JOIN.LEFT_OUTER) .switch(c) # start joining the spectroscopy .switch(c) .join(c2s16, JOIN.LEFT_OUTER) .join( s16, JOIN.LEFT_OUTER, on=( (c2s16.target_id == s16.c.specobjid) & (c2s16.version_id == version_id) ) ) .switch(c) .join(c2s2020, JOIN.LEFT_OUTER) .join( s2020, JOIN.LEFT_OUTER, on=( (c2s2020.target_id == s2020.c.pk) & (c2s2020.version_id == version_id) ) ) .join( sV, JOIN.LEFT_OUTER, on=( fn.q3c_join(sV.c.plug_ra, sV.c.plug_dec, c.ra, c.dec, match_radius_spectro) ) ) .join( sph, JOIN.LEFT_OUTER, on=( fn.q3c_join(sph.c.target_ra, sph.c.target_dec, c.ra, c.dec, match_radius_spectro) ) ) # finished joining the spectroscopy .where( (x.ero_version == self.parameters['ero_version']), (x.xmatch_method == self.parameters['xmatch_method']), (x.xmatch_version == self.parameters['xmatch_version']), (x.opt_cat == self.parameters['opt_cat']), (x.xmatch_metric >= self.parameters['p_any_min']), (x.ero_det_like > self.parameters['det_like_min']), (sm.g_psf > g_psf_mag_min), (sm.r_psf > r_psf_mag_min), (sm.i_psf > i_psf_mag_min), (sm.z_psf > z_psf_mag_min), (sm.g_psf.is_null(False) | sm.r_psf.is_null(False) | sm.i_psf.is_null(False)), ( (sm.g_psf < g_psf_mag_max) | (sm.r_psf < r_psf_mag_max) | (sm.i_psf < i_psf_mag_max) | (sm.z_psf < z_psf_mag_max) ), # see description of skymapper flags here: # http://skymapper.anu.edu.au/table-browser/dr2/ # "flags - Bitwise OR of Source Extractor flags across all observations" # Sextractor flags described here: https://sextractor.readthedocs.io/en/latest/Flagging.html # noqa # everything beyond flag>=4 sounds pretty bad (sm.flags < 4), ) .group_by(sm.object_id) # avoid duplicates - we trust the sm2 ids .having( # each and every match to the tic must satisfy the bright star rejection criteria fn.sum( peewee.Case( None, ( (tic.gaiamag < self.parameters['gaia_g_mag_limit'], 1), (tic.gaiarp < self.parameters['gaia_rp_mag_limit'], 1), (tic.tmag < self.parameters['tic_t_mag_limit'], 1), ), 0) ) == 0 ) ) if query_region: query = query.where(peewee.fn.q3c_radial_query(c.ra, c.dec, query_region[0], query_region[1], query_region[2])) return query # # END BhmSpidersAgnSkyMapperDr2Carton # ################################################################################## class BhmSpidersAgnSuperCosmosCarton(BaseCarton): name = 'bhm_spiders_agn_supercosmos' category = 'science' mapper = 'BHM' program = 'bhm_spiders' tile = False instrument = 'BOSS' def build_query(self, version_id, query_region=None): c = Catalog.alias() x = EROSITASupersetAGN.alias() sc = SuperCosmos.alias() c2sc = CatalogToSuperCosmos.alias() c2cw = CatalogToCatWISE2020.alias() tic = TIC_v8.alias() c2tic = CatalogToTIC_v8.alias() instrument = peewee.Value(self.instrument) b_psf_mag_min = self.parameters['b_psf_mag_min'] r_psf_mag_min = self.parameters['r_psf_mag_min'] i_psf_mag_min = self.parameters['i_psf_mag_min'] b_psf_mag_max = self.parameters['b_psf_mag_max'] r_psf_mag_max = self.parameters['r_psf_mag_max'] i_psf_mag_max = self.parameters['i_psf_mag_max'] b_psf_mag_max_for_cadence1 = self.parameters['b_psf_mag_max_for_cadence1'] r_psf_mag_max_for_cadence1 = self.parameters['r_psf_mag_max_for_cadence1'] i_psf_mag_max_for_cadence1 = self.parameters['i_psf_mag_max_for_cadence1'] b_psf_mag_max_for_cadence2 = self.parameters['b_psf_mag_max_for_cadence2'] r_psf_mag_max_for_cadence2 = self.parameters['r_psf_mag_max_for_cadence2'] i_psf_mag_max_for_cadence2 = self.parameters['i_psf_mag_max_for_cadence2'] # value = peewee.Value(self.parameters.get('value', 1.0)).cast('float') # these control matching to spectroscopy match_radius_spectro = self.parameters['spec_join_radius'] / 3600.0 spec_sn_thresh = self.parameters['spec_sn_thresh'] spec_z_err_thresh = self.parameters['spec_z_err_thresh'] # ######################################################################### # prepare the spectroscopy catalogues # SDSS DR16 c2s16 = CatalogToSDSS_DR16_SpecObj.alias() ss16 = SDSS_DR16_SpecObj.alias() s16 = ( ss16.select( ss16.specobjid.alias('specobjid'), ) .where( ss16.snmedian >= spec_sn_thresh, ss16.zwarning == 0, ss16.zerr <= spec_z_err_thresh, ss16.zerr > 0.0, ss16.scienceprimary > 0, ) .alias('s16') ) # SDSS-IV/eFEDS March2020 c2s2020 = CatalogToBHM_eFEDS_Veto.alias() ss2020 = BHM_eFEDS_Veto.alias() s2020 = ( ss2020.select( ss2020.pk.alias('pk'), ) .where( ss2020.sn_median_all >= spec_sn_thresh, ss2020.zwarning == 0, ss2020.z_err <= spec_z_err_thresh, ss2020.z_err > 0.0, ) .alias('s2020') ) # SDSS-V spAll ssV = SDSSV_BOSS_SPALL.alias() sV = ( ssV.select( ssV.specobjid.alias('specobjid'), ssV.plug_ra.alias('plug_ra'), ssV.plug_dec.alias('plug_dec'), ) .where( ssV.sn_median_all >= spec_sn_thresh, ssV.zwarning == 0, ssV.z_err <= spec_z_err_thresh, ssV.z_err > 0.0, ssV.specprimary > 0, ) .alias('sV') ) # SDSS-V plateholes - only consider plateholes that # were drilled+shipped but that were not yet observed ssph = SDSSV_Plateholes.alias() ssphm = SDSSV_Plateholes_Meta.alias() ssconf = SDSSV_BOSS_Conflist.alias() sph = ( ssph.select( ssph.pkey.alias('pkey'), ssph.target_ra.alias('target_ra'), ssph.target_dec.alias('target_dec'), ) .join( ssphm, on=(ssph.yanny_uid == ssphm.yanny_uid) ) .join( ssconf, JOIN.LEFT_OUTER, on=(ssphm.plateid == ssconf.plate) ) .where( (ssph.holetype == 'BOSS_SHARED'), (ssph.sourcetype == 'SCI') | (ssph.sourcetype == 'STA'), ssphm.isvalid > 0, ssconf.plate.is_null(), ) .alias('sph') ) # ######################################################################### # compute the abs(Galactic latitude): gal_lat = peewee.fn.abs(90.0 - peewee.fn.q3c_dist(north_gal_pole_ra, north_gal_pole_dec, c.ra, c.dec)) value = peewee.Case( None, ((gal_lat > self.parameters['in_plane_lat_cut'], self.parameters.get('value', 1.0)),), 0.0 ).cast('float') # priority is determined by target properties # start with a priority floor value (per carton) # then increment if any conditions are met: # add +dpriority_match_flags if target is a secondary cross-match (match_flag > 1) # add +dpriority_det_like if target has a low value of ero_det_like # add +dpriority_has_spec if target has existing good SDSS spectroscopy # add +dpriority_in_plane if target lies at |b| < in_plane_lat_cut priority_1 = peewee.Case( None, ((x.xmatch_flags > 1, 1), ), 0) priority_2 = peewee.Case( None, ((x.ero_det_like < self.parameters['det_like_for_priority'], 1), ), 0) priority_3 = peewee.Case( None, ( (s16.c.specobjid.is_null(False), 1), # any of these can be satisfied (s2020.c.pk.is_null(False), 1), # (sV.c.specobjid.is_null(False), 1), # (sph.c.pkey.is_null(False), 1), ), 0) priority_4 = peewee.Case( None, ((gal_lat > self.parameters['in_plane_lat_cut'], 0),), 1 ) priority = fn.max( self.parameters['priority_floor'] + priority_1 * self.parameters['dpriority_match_flags'] + priority_2 * self.parameters['dpriority_det_like'] + priority_3 * self.parameters['dpriority_has_spec'] + priority_4 * self.parameters['dpriority_in_plane'] ) # choose cadence based on psf-like magnitude in supercosmos b,r2,i-bands cadence1 = self.parameters['cadence1'] cadence2 = self.parameters['cadence2'] cadence3 = self.parameters['cadence3'] cadence = peewee.Case( None, ( ((sc.scormagb.between(b_psf_mag_min, b_psf_mag_max_for_cadence1)) | (sc.scormagr2.between(r_psf_mag_min, r_psf_mag_max_for_cadence1)) | (sc.scormagi.between(i_psf_mag_min, i_psf_mag_max_for_cadence1)), cadence1), ((sc.scormagb.between(b_psf_mag_max_for_cadence1, b_psf_mag_max_for_cadence2)) | (sc.scormagr2.between(r_psf_mag_max_for_cadence1, r_psf_mag_max_for_cadence2)) | (sc.scormagi.between(i_psf_mag_max_for_cadence1, i_psf_mag_max_for_cadence2)), cadence2), ), cadence3) # compute transformed SDSS mags for pointlike and extended sources separately # transform the supercosmos B,R2,I mags into sdss psfmag griz # extract coeffs from fit logs via: # awk 'BEGIN {print("coeffs = {")} /POLYFIT/{ if(FILENAME~/extended/){pe="e"} else if (FILENAME~/pointlike/){pe="p"} else{pe="error"}; printf("\"%s%d_%s\": %s,\n", substr($3,length($3)), $8, pe, $10)} END {print("}")}' bhm_spiders_agn_supercosmos_*/sc_scormag_to_sdss_psfmag_?_results.log # noqa coeffs = { "g2_e": 0.274247, "g1_e": -0.257719, "g0_e": 0.249223, "i2_e": 0.190439, "i1_e": -1.086073, "i0_e": 1.163261, "r2_e": -0.098811, "r1_e": 0.799153, "r0_e": 0.191604, "z2_e": 0.460228, "z1_e": -0.382180, "z0_e": 0.978730, "g2_p": 0.180404, "g1_p": -0.581735, "g0_p": 0.150270, "i2_p": -0.211221, "i1_p": -0.357351, "i0_p": 0.344712, "r2_p": -0.157387, "r1_p": 0.479759, "r0_p": 0.089783, "z2_p": -0.425527, "z1_p": 0.663737, "z0_p": 0.239992, } b_r = sc.scormagb - sc.scormagr2 r_i = sc.scormagr2 - sc.scormagi g_p = (sc.scormagb + coeffs['g0_p'] + coeffs['g1_p'] * b_r + coeffs['g2_p'] * b_r * b_r) r_p = (sc.scormagr2 + coeffs['r0_p'] + coeffs['r1_p'] * b_r + coeffs['r2_p'] * b_r * b_r) i_p = (sc.scormagr2 + coeffs['i0_p'] + coeffs['i1_p'] * r_i + coeffs['i2_p'] * r_i * r_i) z_p = (sc.scormagi + coeffs['z0_p'] + coeffs['z1_p'] * r_i + coeffs['z2_p'] * r_i * r_i) g_e = (sc.scormagb + coeffs['g0_e'] + coeffs['g1_e'] * b_r + coeffs['g2_e'] * b_r * b_r) r_e = (sc.scormagr2 + coeffs['r0_e'] + coeffs['r1_e'] * b_r + coeffs['r2_e'] * b_r * b_r) i_e = (sc.scormagr2 + coeffs['i0_e'] + coeffs['i1_e'] * r_i + coeffs['i2_e'] * r_i * r_i) z_e = (sc.scormagi + coeffs['z0_e'] + coeffs['z1_e'] * r_i + coeffs['z2_e'] * r_i * r_i) # validity checks - set limits semi-manually b_r_p_min = -0.5 b_r_p_max = 2.5 r_i_p_min = -0.6 r_i_p_max = 2.0 b_r_e_min = -0.5 b_r_e_max = 2.5 r_i_e_min = -0.3 r_i_e_max = 1.2 # validity checks valid_p = (sc.scormagb.between(0.1, 29.9) & sc.scormagr2.between(0.1, 29.9) & sc.scormagi.between(0.1, 29.9) & b_r.between(b_r_p_min, b_r_p_max) & r_i.between(r_i_p_min, r_i_p_max)) valid_e = (sc.scormagb.between(0.1, 29.9) & sc.scormagr2.between(0.1, 29.9) & sc.scormagi.between(0.1, 29.9) & b_r.between(b_r_e_min, b_r_e_max) & r_i.between(r_i_e_min, r_i_e_max)) # We map to SDSS psfmags for both pointlike and extended taegets # - this was found to be the solution with the least scatter opt_prov = peewee.Case( None, ( ((sc.meanclass == 1) & valid_e, 'sdss_psfmag_from_sc'), # galaxies ((sc.meanclass == 2) & valid_p, 'sdss_psfmag_from_sc'), # stars ), 'undefined') magnitude_g = peewee.Case( None, ( ((sc.meanclass == 1) & valid_e, g_e.cast('float')), # galaxies ((sc.meanclass == 2) & valid_p, g_p.cast('float')), # stars ), 'NaN') magnitude_r = peewee.Case( None, ( ((sc.meanclass == 1) & valid_e, r_e.cast('float')), # galaxies ((sc.meanclass == 2) & valid_p, r_p.cast('float')), # stars ), 'NaN') magnitude_i = peewee.Case( None, ( ((sc.meanclass == 1) & valid_e, i_e.cast('float')), # galaxies ((sc.meanclass == 2) & valid_p, i_p.cast('float')), # stars ), 'NaN') magnitude_z = peewee.Case( None, ( ((sc.meanclass == 1) & valid_e, z_e.cast('float')), # galaxies ((sc.meanclass == 2) & valid_p, z_p.cast('float')), # stars ), 'NaN') # # We only use pseudo psfmags for SuperCosmos # opt_prov = peewee.Value('sc_psfmag') # # - transform the photographic B,R,I -> to griz # # some very crude by-eye fits to SPIDERS AGN targets matched to SC and SDSSdr9 # # completely ignore differences between psfmags and total mags # # Check for out-of-range errors via case statements (fall back to single band # # estimates and typical colours when secondary mag is missing) # # assume a typical scormagb-scormagr2 = 0.8 mag in these cases # magnitude_g = peewee.Case( # None, # ( # ((sc.scormagb > -90.) & (sc.scormagr2 > -90.), # (sc.scormagb + 0.1 + (sc.scormagb - sc.scormagr2) * -0.23).cast('float')), # ((sc.scormagb > -90.), # (sc.scormagb + 0.1 + (0.8 * -0.23)).cast('float')), # ), # None) # magnitude_r = peewee.Case( # None, # ( # ((sc.scormagb > -90.) & (sc.scormagr2 > -90.), # (sc.scormagr2 + 0.25 + (sc.scormagb - sc.scormagr2) * 0.12).cast('float')), # ((sc.scormagr2 > -90.), # (sc.scormagr2 + 0.25 + (0.8 * 0.12)).cast('float')), # ), # None) # magnitude_i = peewee.Case( # None, # ( # ((sc.scormagr2 > -90.) | (sc.scormagi > -90.), # (fn.greatest(sc.scormagr2, sc.scormagi) + 0.1).cast('float')), # ), # None) # magnitude_z = peewee.Case( # None, # ( # ( # (sc.scormagb > -90.) & (sc.scormagr2 > -90.), # ( # fn.greatest(sc.scormagr2, sc.scormagi) + 0.2 + # -0.2 * (sc.scormagb - sc.scormagr2) + # -0.28 * (sc.scormagb - sc.scormagr2) * (sc.scormagb - sc.scormagr2) # ).cast('float') # ), # ( # (sc.scormagr2 > -90.), # ( # fn.greatest(sc.scormagr2, sc.scormagi) + 0.2 + # (-0.2 * 0.8) + (-0.28 * 0.8 * 0.8) # ).cast('float') # ), # ), # None) bquery = ( c.select( fn.min(c.catalogid).alias('catalogid'), x.catwise2020_id.alias('cw2020_source_id'), # extra fn.min(tic.gaia_int).alias('gaia_source'), # extra fn.min(x.ero_detuid).alias('ero_detuid'), # extra fn.min(c.ra).alias('ra'), fn.min(c.dec).alias('dec'), priority.alias("priority"), fn.min(value).alias('value'), fn.min(cadence).alias('cadence'), fn.min(instrument).alias('instrument'), fn.min(magnitude_g).alias('g'), fn.min(magnitude_r).alias('r'), fn.min(magnitude_i).alias('i'), fn.min(magnitude_z).alias('z'), fn.min(tic.gaiamag).alias('gaia_g'), fn.min(tic.gaiabp).alias('bp'), fn.min(tic.gaiarp).alias('rp'), fn.min(opt_prov).alias('optical_prov'), fn.min(sc.scormagb).alias('scormagb'), # extra fn.min(sc.scormagr2).alias('scormagr2'), # extra fn.min(sc.scormagi).alias('scormagi'), # extra fn.min(sc.classmagb).alias('classmagb'), # extra fn.min(sc.classmagr2).alias('classmagr2'), # extra fn.min(sc.classmagi).alias('classmagi'), # extra fn.min(sc.meanclass).alias('meanclass'), # extra ) .join(c2cw) .join(x, on=(c2cw.target_id == x.catwise2020_id)) .switch(c) .join(c2sc) .join(sc) .where( c.version_id == version_id, c2cw.version_id == version_id, c2sc.version_id == version_id, c2cw.best >> True, c2sc.best >> True, ) .switch(c) .join( c2tic, JOIN.LEFT_OUTER, on=( (c.catalogid == c2tic.catalogid) & (c2tic.version_id == version_id) ) ) .join(tic, JOIN.LEFT_OUTER) .switch(c) # start joining the spectroscopy .switch(c) .join(c2s16, JOIN.LEFT_OUTER) .join( s16, JOIN.LEFT_OUTER, on=( (c2s16.target_id == s16.c.specobjid) & (c2s16.version_id == version_id) ) ) .switch(c) .join(c2s2020, JOIN.LEFT_OUTER) .join( s2020, JOIN.LEFT_OUTER, on=( (c2s2020.target_id == s2020.c.pk) & (c2s2020.version_id == version_id) ) ) # .join( # sV, JOIN.LEFT_OUTER, # on=( # fn.q3c_join(sV.c.plug_ra, sV.c.plug_dec, # c.ra, c.dec, # match_radius_spectro) # ) # ) # .join( # sph, JOIN.LEFT_OUTER, # on=( # fn.q3c_join(sph.c.target_ra, sph.c.target_dec, # c.ra, c.dec, # match_radius_spectro) # ) # ) # finished joining the spectroscopy .where( (x.ero_version == self.parameters['ero_version']), (x.xmatch_method == self.parameters['xmatch_method']), (x.xmatch_version == self.parameters['xmatch_version']), (x.opt_cat == self.parameters['opt_cat']), (x.xmatch_metric >= self.parameters['p_any_min']), (x.ero_det_like > self.parameters['det_like_min']), ( # include anything that falls in suitable mag range sc.scormagb.between(b_psf_mag_min, b_psf_mag_max) | sc.scormagr2.between(r_psf_mag_min, r_psf_mag_max) | sc.scormagi.between(i_psf_mag_min, i_psf_mag_max) ), # exclude anything that strays too bright: ~sc.scormagb.between(-99.0, b_psf_mag_min), ~sc.scormagr2.between(-99.0, r_psf_mag_min), ~sc.scormagi.between(-99.0, i_psf_mag_min), ) .group_by(x.catwise2020_id) # avoid duplicates - we trust the CatWISE2020 ids .having( # each and every match to the tic must satisfy the bright star rejection criteria fn.sum( peewee.Case( None, ( (tic.gaiamag < self.parameters['gaia_g_mag_limit'], 1), (tic.gaiarp < self.parameters['gaia_rp_mag_limit'], 1), (tic.tmag < self.parameters['tic_t_mag_limit'], 1), ), 0) ) == 0 ) ) # Below ra, dec and radius are in degrees # query_region[0] is ra of center of the region # query_region[1] is dec of center of the region # query_region[2] is radius of the region if query_region: bquery = (bquery .where(peewee.fn.q3c_radial_query(c.ra, c.dec, query_region[0], query_region[1], query_region[2]))) self.log.debug('Creating temporary table for base query ...') bquery.create_table(self.name + '_bquery', temporary=True) self.database.execute_sql(f'CREATE INDEX ON {self.name}_bquery (ra, dec)') self.database.execute_sql(f'ANALYZE {self.name}_bquery') sph.create_table(self.name + '_sph', temporary=True) self.database.execute_sql(f'CREATE INDEX ON {self.name}_sph (target_ra, target_dec)') self.database.execute_sql(f'ANALYZE {self.name}_sph') sV.create_table(self.name + '_sv', temporary=True) self.database.execute_sql(f'CREATE INDEX ON {self.name}_sv (plug_ra, plug_dec)') self.database.execute_sql(f'ANALYZE {self.name}_sv') bquery_table = peewee.Table(f'{self.name}_bquery', alias='bquery') sph_table = peewee.Table(f'{self.name}_sph') sV_table = peewee.Table(f'{self.name}_sv') query = ( bquery_table .select(peewee.SQL('bquery.*')) .join( sV_table, JOIN.LEFT_OUTER, on=( fn.q3c_join(bquery_table.c.ra, bquery_table.c.dec, sV_table.c.plug_ra, sV_table.c.plug_dec, match_radius_spectro) ) ) .join( sph_table, JOIN.LEFT_OUTER, on=( fn.q3c_join(bquery_table.c.ra, bquery_table.c.dec, sph_table.c.target_ra, sph_table.c.target_dec, match_radius_spectro) ) ) # then reject any targets with existing good SDSS-V spectroscopy or a platehole .where( sV_table.c.specobjid.is_null(True), sph_table.c.pkey.is_null(True), ) ) # if query_region: # query = query.where(peewee.fn.q3c_radial_query(c.ra, c.dec, # query_region[0], # query_region[1], # query_region[2])) return query # # END BhmSpidersAgnSuperCosmosCarton # ##################################################################################

e47016cbd72e7098c0941f4e47d79ce1b7c698d1

py

Python

back-end/erasmail/emails/migrations/0038_auto_20210422_0059.py

SamirM-BE/ErasMail

88602a039ae731ca8566c96c7c4d2635f82a07a5

2021-02-06T21:06:23.000Z

2022-01-31T09:33:26.000Z

back-end/erasmail/emails/migrations/0038_auto_20210422_0059.py

SamirM-BE/ErasMail

88602a039ae731ca8566c96c7c4d2635f82a07a5

back-end/erasmail/emails/migrations/0038_auto_20210422_0059.py

SamirM-BE/ErasMail

88602a039ae731ca8566c96c7c4d2635f82a07a5

2021-05-07T15:35:25.000Z

2022-03-21T09:11:24.000Z

# Generated by Django 3.1.6 on 2021-04-22 00:59 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('emails', '0037_remove_emailstats_connected_count'), ] operations = [ migrations.AddField( model_name='emailstats', name='stats_shared', field=models.PositiveIntegerField(default=0, help_text='The user has shared his stats on social media'), ), migrations.AlterField( model_name='emailstats', name='badges_shared', field=models.PositiveIntegerField(default=0, help_text='The user has shared his badges on social media'), ), migrations.DeleteModel( name='Success', ), ]

e4705f3acb58336e0e7ad1a046d3910433815d04

py

Python

worldmap/src/worldmap/model/dtm.py

expertanalytics/fagkveld

96e16b9610e8b60d36425e7bc5435d266de1f8bf

worldmap/src/worldmap/model/dtm.py

expertanalytics/fagkveld

96e16b9610e8b60d36425e7bc5435d266de1f8bf

worldmap/src/worldmap/model/dtm.py

expertanalytics/fagkveld

96e16b9610e8b60d36425e7bc5435d266de1f8bf

""" Data terrain model (DTM). Examples:: >>> from worldmap import DTM >>> dtm = DTM() >>> print(dtm["NOR"]) Location('Norway') """ from typing import Dict, List, Tuple, Set, Optional from bokeh.models import Model from bokeh.models import ColumnDataSource, Patches, LabelSet import logging import numpy as np from .location import Location from .coloring import set_location_colors from ..utils.data_fetcher import get_world_topology, get_country_polygon class DTM: locations: Location = {} data = None def __init__(self): # add countries: logging.info("Fetching topological data") countries = get_world_topology() for name, country in countries.items(): self.locations[name] = Location( name=name, long_name=country["name"]) # add country neighbors: for name, country in countries.items(): self.locations[name].neighbors = { neighbor: self.locations[neighbor] for neighbor in country["borders"] } # add country colors self.set_location_colors() logging.info("Finshed __init__") def __getitem__(self, item): return self.locations[item] def set_location_colors(self): """Set color values on all locations and all location children.""" for location in self.locations.values(): if not location.color: set_location_colors(location)

e4710ca29b9a5a6a2747143e02042c64942aa376

py

Python

src/blockchain/crypto_tools/__init__.py

ParisNeo/blockchain

8bc2768a3e89088e202ea89e5f301576f6f9d95c

src/blockchain/crypto_tools/__init__.py

ParisNeo/blockchain

8bc2768a3e89088e202ea89e5f301576f6f9d95c

src/blockchain/crypto_tools/__init__.py

ParisNeo/blockchain

8bc2768a3e89088e202ea89e5f301576f6f9d95c

""" """ from Crypto.PublicKey import RSA from Crypto.Hash import SHA256 from Crypto.Signature import PKCS1_v1_5 from base58 import b58encode, b58decode # ================= cryptography helpers ====================================== def hash(data): """Hash some data """ # Hash the stuff we need to hash digest = SHA256.new() digest.update(data) hash= digest.hexdigest() return hash def generateKeys(): private_key = RSA.generate(1024) public_key = private_key.public_key() return private_key, public_key def sign(private_key:RSA.RsaKey, message): """Sign a message Parameters ---------- private_key (RSAPublicKey) : The private key to sign the message with message (str) : The message to be signed """ hasher = SHA256.new(message) signer = PKCS1_v1_5.new(private_key) signature = signer.sign(hasher) return signature def verify(public_key, message, signature): """Verify the message signature Parameters ---------- public_key (RSAPublicKey) : The public key to verify that the sender is the right one message (str) : The signed message (used for verification) signature (str) : The signature """ hasher = SHA256.new(message) verifier = PKCS1_v1_5.new(public_key) return verifier.verify(hasher, signature) def privateKey2Text(key:RSA.RsaKey): """Converts a private key to text """ return b58encode(key.exportKey('DER')) def publicKey2Text(key:RSA.RsaKey): """Converts a public key to text """ return b58encode(key.exportKey('DER')) def text2PrivateKey(text:str): """Convert a text to a private key """ return RSA.importKey(b58decode(text)) def text2PublicKey(text:str): """Convert a text to a key """ return RSA.importKey(b58decode(text))

e472ad25bd9133e0e1fe623219e0826f24f2f7ef

py

Python

Mandelbrot fractal/visualize.py

TTimerkhanov/parallel_computing

75c79a4e50ac2f5f9fab90cd79560cd8e848228e

2018-03-21T12:26:44.000Z

2019-10-05T08:46:20.000Z

Mandelbrot fractal/visualize.py

TTimerkhanov/parallel_computing

75c79a4e50ac2f5f9fab90cd79560cd8e848228e

Mandelbrot fractal/visualize.py

TTimerkhanov/parallel_computing

75c79a4e50ac2f5f9fab90cd79560cd8e848228e

import numpy as np import matplotlib.pyplot as plt def mandelbrot(threshold, density): atlas = np.empty((density, density)) with open('set', 'r') as f: for line in f.readlines(): i, j, val = line.split(",") atlas[int(i), int(j)] = val plt.imshow(atlas.T, interpolation="nearest") plt.show() mandelbrot(120, 6000)

e472f3485c0e5680d2198a1ae932c5d7712b5057

py

Python

tools/unit_tests/test_config_transforms.py

dice-project/DICE-deployment-service

e209c6a061a78f170e81cfc03d2959af0283ed15

2018-04-03T20:45:26.000Z

2022-02-07T19:53:42.000Z

tools/unit_tests/test_config_transforms.py

dice-project/DICE-deployment-service

e209c6a061a78f170e81cfc03d2959af0283ed15

2016-11-15T10:41:43.000Z

2020-03-16T07:49:03.000Z

tools/unit_tests/test_config_transforms.py

dice-project/DICE-deployment-service

e209c6a061a78f170e81cfc03d2959af0283ed15

2018-07-04T11:37:12.000Z

2022-02-07T19:53:43.000Z

import unittest import copy import os import tempfile from config_tool.utils import * class TestConfigurationTransformation(unittest.TestCase): @classmethod def setUpClass(cls): base_path = os.path.dirname(os.path.realpath(__file__)) file_path = os.path.join(base_path, 'files') def fpath(*names): return os.path.join(file_path, *names) cls.blueprints = { 'single-node': fpath('single-node-blueprint.yaml'), 'full': fpath('full-blueprint.yaml'), 'mixed': fpath('full-blueprint-mixed.yaml'), 'conflicting': fpath('full-blueprint-conflicting.yaml'), } cls.options = { 'normal': fpath('expconfig.yaml'), 'multinode': fpath('expconfig-multinode.yaml'), 'bad-var-info': fpath('expconfig-bad-var-info.yaml'), } cls.config = { 'matlab': fpath('config-matlab.txt'), 'json': fpath('config-matlab.json'), } def test_load_blueprint(self): """ Load the blueprint from a yaml file and check its contents """ blueprint = load_blueprint(self.blueprints['single-node']) self.assertIsNotNone(blueprint) self.assertTrue('tosca_definitions_version' in blueprint) self.assertTrue('imports' in blueprint) self.assertTrue('node_templates' in blueprint) self.assertTrue('outputs' in blueprint) node_templates = blueprint['node_templates'] self.assertTrue('storm_vm' in node_templates) self.assertTrue('storm' in node_templates) storm = node_templates['storm'] self.assertTrue('properties' in storm) storm_config = storm['properties']['configuration'] expected_storm_config = { "component.count_bolt_num": 1, "component.split_bolt_num": 1, "component.spout_num": 3, "storm.messaging.netty.min_wait_ms": 100, "topology.max.spout.pending": 100, "topology.sleep.spout.wait.strategy.time.ms": 1 } self.assertEqual(expected_storm_config, storm_config) def test_load_options(self): """ Load the Configuration Optimization options and check their contents. """ options = load_options(self.options['normal']) expected_options = [ { 'node': 'storm', 'paramname': 'component.spout_num' }, { 'node': 'storm', 'paramname': 'topology.max.spout.pending' }, { 'node': 'storm', 'paramname': 'topology.sleep.spout.wait.strategy.time.ms' }, { 'node': 'storm', 'paramname': 'component.split_bolt_num' }, { 'node': 'storm', 'paramname': 'component.count_bolt_num' }, { 'node': 'storm', 'paramname': 'storm.messaging.netty.min_wait_ms' }, ] self.assertEqual(expected_options, options) def test_load_options_multiple_nodes(self): """ Load the Configuration Optimization options containing several nodes and check their contents. """ options = load_options(self.options['multinode']) expected_options = [ { 'node': ['storm', 'storm_nimbus'], 'paramname': 'component.spout_num' }, { 'node': ['storm', 'storm_nimbus'], 'paramname': 'topology.max.spout.pending' }, { 'node': ['storm', 'storm_nimbus'], 'paramname': 'topology.sleep.spout.wait.strategy.time.ms' }, { 'node': ['storm', 'storm_nimbus'], 'paramname': 'component.split_bolt_num' }, { 'node': ['storm', 'storm_nimbus'], 'paramname': 'component.count_bolt_num' }, { 'node': ['storm', 'storm_nimbus'], 'paramname': 'storm.messaging.netty.min_wait_ms' }, { 'node': 'zookeeper', 'paramname': 'tickTime' }, { 'node': 'zookeeper', 'paramname': 'initLimit' }, { 'node': 'zookeeper', 'paramname': 'syncLimit' }, ] self.assertEqual(expected_options, options) def test_load_options_bad_var_info(self): """ Loading of variables where not all fields are present is not supported """ with self.assertRaises(KeyError): load_options(self.options['bad-var-info']) def test_load_config_matlab(self): """ Test loading the configuration data. We assume that this is a dump from Matlab. """ expected_config = [2, 4, 10, 15000, 20, 2.400003] config = load_configuration_matlab(self.config['matlab']) self.assertEqual(expected_config, config) def test_load_config_json(self): """ Test loading the configuration data. We assume that this is a json file containing an array. """ expected_config = [2, 4, 10, 15000, 20, 2.400003] config = load_configuration_json(self.config['json']) self.assertEqual(expected_config, config) def test_export_config(self): """ Confirm that the configuration values exported to string (for text file outputs) can be read back again properly. """ # example configurations configurations = [ [ 3, 100, 1, 2, 3, 110 ], [ 3, 100, None, 2, 3, 110, None ], [ ], ] for configuration in configurations: with tempfile.NamedTemporaryFile('w+') as f: fname = f.name # dump json save_configuration_json(configuration, fname) # read json back configuration_imported_json = load_configuration_json(fname) # check the contents self.assertEqual(configuration, configuration_imported_json) # dump matlab save_configuration_matlab(configuration, fname) # read matlab back configuration_imported_matlab = load_configuration_matlab(fname) # check the contents self.assertEqual(configuration, configuration_imported_matlab) def test_single_node_update(self): """ Load a blueprint with a single node (one VM, one Storm service on top of it). Update the blueprint with new configurations. """ # Load and set the input parameters blueprint = load_blueprint(self.blueprints['single-node']) options = load_options(self.options['normal']) config = [ 2, 4, 10, 15, 20, 2 ] # routine check of the blueprint representation self.assertIsNotNone(blueprint) self.assertTrue('node_templates' in blueprint) node_templates = blueprint['node_templates'] self.assertTrue('storm' in node_templates) # run the update updated_blueprint = update_blueprint(blueprint, options, config) # prepare the expected values configuration = { "component.spout_num": 2, "topology.max.spout.pending": 4, "topology.sleep.spout.wait.strategy.time.ms": 10, "component.split_bolt_num": 15, "component.count_bolt_num": 20, "storm.messaging.netty.min_wait_ms": 2 } blueprint['node_templates']['storm']['properties']['configuration'] = \ configuration # verify the outcome self.maxDiff = None self.assertEqual(blueprint, updated_blueprint) def test_single_node_shorter_config(self): """ Test updating a blueprint where the blueprint has a longer list of properties than the configuration to be updated. """ # Load and set the input parameters: truncated options and config values blueprint = load_blueprint(self.blueprints['single-node']) options = load_options(self.options['normal']) options = options[0:4] config = [ 2, 4, 10, 15 ] self.assertEqual(len(config), len(options)) # routine check of the blueprint representation self.assertIsNotNone(blueprint) self.assertTrue('node_templates' in blueprint) node_templates = blueprint['node_templates'] self.assertTrue('storm' in node_templates) # run the update updated_blueprint = update_blueprint(blueprint, options, config) # prepare the expected values configuration = { "component.spout_num": 2, "topology.max.spout.pending": 4, "topology.sleep.spout.wait.strategy.time.ms": 10, "component.split_bolt_num": 15, "component.count_bolt_num": 1, "storm.messaging.netty.min_wait_ms": 100 } blueprint['node_templates']['storm']['properties']['configuration'] = \ configuration # verify the outcome self.assertEqual(blueprint, updated_blueprint) def test_single_node_longer_config(self): """ Test updating a blueprint where the configuration to be updated has a longer list of properties than the configuration to be updated. """ # Load and set the input parameters blueprint = load_blueprint(self.blueprints['single-node']) options = load_options(self.options['normal']) config = [ 2, 4, 10, 15, 20, 2 ] # routine check of the blueprint representation self.assertIsNotNone(blueprint) self.assertTrue('node_templates' in blueprint) node_templates = blueprint['node_templates'] self.assertTrue('storm' in node_templates) # truncate the blueprint's parameter list blueprint_storm_config = \ blueprint['node_templates']['storm']['properties']['configuration'] del blueprint_storm_config['component.split_bolt_num'] del blueprint_storm_config['component.spout_num'] del blueprint_storm_config['storm.messaging.netty.min_wait_ms'] self.assertEqual(3, len(blueprint_storm_config)) # run the update updated_blueprint = update_blueprint(blueprint, options, config) # prepare the expected values configuration = { "component.spout_num": 2, "topology.max.spout.pending": 4, "topology.sleep.spout.wait.strategy.time.ms": 10, "component.split_bolt_num": 15, "component.count_bolt_num": 20, "storm.messaging.netty.min_wait_ms": 2 } blueprint['node_templates']['storm']['properties']['configuration'] = \ configuration # verify the outcome self.maxDiff = None self.assertEqual(blueprint, updated_blueprint) def test_multiple_node_update(self): """ Load a blueprint with multiple nodes. Update the blueprint with new configurations. """ # Load and set the input parameters blueprint = load_blueprint(self.blueprints['full']) options = load_options(self.options['multinode']) config = [ 2, 4, 10, 15, 20, 2, 3000, 21, 7 ] # routine check of the blueprint representation self.assertIsNotNone(blueprint) self.assertTrue('node_templates' in blueprint) node_templates = blueprint['node_templates'] self.assertTrue('storm' in node_templates) self.assertTrue('storm_nimbus' in node_templates) self.assertTrue('zookeeper' in node_templates) # run the update updated_blueprint = update_blueprint(blueprint, options, config) # prepare the expected values expected_storm_nimbus_config = { "component.spout_num": 2, "topology.max.spout.pending": 4, "topology.sleep.spout.wait.strategy.time.ms": 10, "component.split_bolt_num": 15, "component.count_bolt_num": 20, "storm.messaging.netty.min_wait_ms": 2 } expected_storm_config = { "component.spout_num": 2, "topology.max.spout.pending": 4, "topology.sleep.spout.wait.strategy.time.ms": 10, "component.split_bolt_num": 15, "component.count_bolt_num": 20, "storm.messaging.netty.min_wait_ms": 2 } expected_zookeeper_config = { "tickTime": 3000, "initLimit": 21, "syncLimit": 7 } blueprint['node_templates']['storm']['properties']\ ['configuration'] = expected_storm_config blueprint['node_templates']['storm_nimbus']['properties'] = \ {'configuration': expected_storm_nimbus_config} blueprint['node_templates']['zookeeper']['properties']\ ['configuration'] = expected_zookeeper_config # verify the outcome self.maxDiff = None self.assertEqual(blueprint, updated_blueprint) def test_multiple_node_partial_config_update(self): """ In the input configuration, some of the values may be missing. In the Python array representation, the missing values are marked with None. The corresponding parameters then have to be absent from the blueprint (effectively setting them to the default value). """ # Load and set the input parameters blueprint = load_blueprint(self.blueprints['mixed']) options = load_options(self.options['multinode']) config = [ None, 4, 10, 15, None, 2, 3000, 21, 7 ] # routine check of the blueprint representation self.assertIsNotNone(blueprint) self.assertTrue('node_templates' in blueprint) node_templates = blueprint['node_templates'] self.assertTrue('storm' in node_templates) self.assertTrue('storm_nimbus' in node_templates) self.assertTrue('zookeeper' in node_templates) # run the update updated_blueprint = update_blueprint(blueprint, options, config) # prepare the expected values expected_storm_nimbus_config = { #"component.spout_num": 2, # omitted - missing in config "topology.max.spout.pending": 4, "topology.sleep.spout.wait.strategy.time.ms": 10, "component.split_bolt_num": 15, "component.spout_num": 3, #"component.count_bolt_num": 20,# missing in config and blueprint "storm.messaging.netty.min_wait_ms": 2, "nimbus.thrift.max_buffer_size": 1048576, # present in blueprint } expected_storm_config = { #"component.spout_num": 2, # omitted - missing in config "topology.max.spout.pending": 4, "topology.sleep.spout.wait.strategy.time.ms": 10, "component.split_bolt_num": 15, "component.spout_num": 3, #"component.count_bolt_num": 20,# missing in config and blueprint "storm.messaging.netty.min_wait_ms": 2, "nimbus.thrift.max_buffer_size": 1048576, # present in blueprint } expected_zookeeper_config = { "tickTime": 3000, "initLimit": 21, "syncLimit": 7, "preAllocSize": 64, # present in blueprint } blueprint['node_templates']['storm']['properties']\ ['configuration'] = expected_storm_config blueprint['node_templates']['storm_nimbus']['properties'] = \ {'configuration': expected_storm_nimbus_config} blueprint['node_templates']['zookeeper']['properties']\ ['configuration'] = expected_zookeeper_config # verify the outcome self.maxDiff = None self.assertEqual(blueprint, updated_blueprint) def test_single_node_config_extract_full(self): """ Load a blueprint with a single node (one VM, one Storm service on top of it). This blueprint has the complete set of propertes needed for the configuration. """ # Load and set the input parameters blueprint = load_blueprint(self.blueprints['single-node']) options = load_options(self.options['normal']) # run the extraction extracted_config = extract_blueprint_config(blueprint, options) # prepare the expected values config = [ 3, 100, 1, 1, 1, 100 ] # verify the outcome self.assertEqual(config, extracted_config) def test_multiple_node_config_extract_full(self): """ Load a blueprint with multiple nodes, which already has the set of properties matching the ones in the configuration. """ # Load and set the input parameters blueprint = load_blueprint(self.blueprints['full']) options = load_options(self.options['multinode']) # run the extraction extracted_config = extract_blueprint_config(blueprint, options) # prepare the expected values config = [ 3, 100, 1, 1, 1, 100, 1500, 10, 5 ] # verify the outcome self.assertEqual(config, extracted_config) def test_multiple_node_config_extract_mixed(self): """ Load a blueprint with multiple nodes, which has a mixed set of properties in comparison with the options: some of the properties are present, some are missing, and there are a few in the blueprint that are not declared in the options. The extraction needs to pick up the ones that are present, use a dedicated value for the missing ones and ignore the extra ones in the blueprint. """ # Load and set the input parameters blueprint = load_blueprint(self.blueprints['mixed']) options = load_options(self.options['multinode']) # run the extraction extracted_config = extract_blueprint_config(blueprint, options) # prepare the expected values config = [ 3, 100, 1, None, None, 100, None, 10, 5 ] # verify the outcome self.assertEqual(config, extracted_config) def test_multiple_node_config_extract_conflicting(self): """ Load a blueprint with multiple nodes, which already has the set of properties matching the ones in the configuration. The properties appearing in multiple nodes have conflicting values, which the tool has to reject with an error. """ # Load and set the input parameters blueprint = load_blueprint(self.blueprints['conflicting']) options = load_options(self.options['multinode']) # run the extraction with self.assertRaises(Exception): extracted_config = extract_blueprint_config(blueprint, options) if __name__ == '__main__': unittest.main()

e47311462a03c6a7eb9b40addcc16befdf99f631

py

Python

code/venv/lib/python3.8/site-packages/datadog_api_client/v2/model/permission_attributes.py

Valisback/hiring-engineers

7196915dd5a429ae27c21fa43d527f0332e662ed

code/venv/lib/python3.8/site-packages/datadog_api_client/v2/model/permission_attributes.py

Valisback/hiring-engineers

7196915dd5a429ae27c21fa43d527f0332e662ed

code/venv/lib/python3.8/site-packages/datadog_api_client/v2/model/permission_attributes.py

Valisback/hiring-engineers

7196915dd5a429ae27c21fa43d527f0332e662ed

# Unless explicitly stated otherwise all files in this repository are licensed under the Apache-2.0 License. # This product includes software developed at Datadog (https://www.datadoghq.com/). # Copyright 2019-Present Datadog, Inc. from datadog_api_client.v2.model_utils import ( ModelNormal, cached_property, datetime, ) class PermissionAttributes(ModelNormal): """NOTE: This class is auto generated by OpenAPI Generator. Ref: https://openapi-generator.tech Do not edit the class manually. """ validations = {} @cached_property def openapi_types(): return { "created": (datetime,), "description": (str,), "display_name": (str,), "display_type": (str,), "group_name": (str,), "name": (str,), "restricted": (bool,), } attribute_map = { "created": "created", "description": "description", "display_name": "display_name", "display_type": "display_type", "group_name": "group_name", "name": "name", "restricted": "restricted", } read_only_vars = {} def __init__(self, *args, **kwargs): """PermissionAttributes - a model defined in OpenAPI Keyword Args: created (datetime): [optional] Creation time of the permission. description (str): [optional] Description of the permission. display_name (str): [optional] Displayed name for the permission. display_type (str): [optional] Display type. group_name (str): [optional] Name of the permission group. name (str): [optional] Name of the permission. restricted (bool): [optional] Whether or not the permission is restricted. """ super().__init__(kwargs) self._check_pos_args(args) @classmethod def _from_openapi_data(cls, *args, **kwargs): """Helper creating a new instance from a response.""" self = super(PermissionAttributes, cls)._from_openapi_data(kwargs) self._check_pos_args(args) return self

e4751dd89498b1da7109ee5f07994f5fbd04447a

py

Python

vulture/whitelists/logging_whitelist.py

kianmeng/vulture

b8cbc44dac89b2a96f6da7033424f52525d6f574

2017-03-06T14:45:21.000Z

2022-03-31T13:29:34.000Z

vulture/whitelists/logging_whitelist.py

kianmeng/vulture

b8cbc44dac89b2a96f6da7033424f52525d6f574

2017-03-06T12:13:37.000Z

2022-03-15T11:21:27.000Z

vulture/whitelists/logging_whitelist.py

kianmeng/vulture

b8cbc44dac89b2a96f6da7033424f52525d6f574

2017-03-06T20:48:04.000Z

2022-03-17T09:49:32.000Z

import logging logging.Filter.filter logging.getLogger().propagate logging.StreamHandler.emit

e479315b2fec6b1b30374526a8f3ec4a57556364

py

Python

tests/test_set_key.py

GustavoKatel/pushbullet-cli

e5102772752a97db539594b0d50b5effb36a22e2

2017-01-30T16:21:48.000Z

2022-02-10T05:32:57.000Z

tests/test_set_key.py

GustavoKatel/pushbullet-cli

e5102772752a97db539594b0d50b5effb36a22e2

2017-01-21T20:27:03.000Z

2022-01-16T02:57:51.000Z

tests/test_set_key.py

GustavoKatel/pushbullet-cli

e5102772752a97db539594b0d50b5effb36a22e2

2017-01-26T06:08:54.000Z

2022-01-04T19:53:25.000Z

import platform import pytest @pytest.mark.skipif( platform.system() == "Windows", reason="cannot test this on windows" ) def test_set_key(set_key, pb_api, mocker): import keyring import getpass import six prev_token = six.text_type(keyring.get_password("pushbullet", "cli")) try: mocker.patch.object(getpass, "getpass", return_value="abc") set_key() assert keyring.get_password("pushbullet", "cli") == "abc" finally: keyring.set_password("pushbullet", "cli", prev_token)

e4797fdc0550e8c83ce7e94b28483dfdbf77d5d3

py

Python

02. Programacion estructurada/05. datos tupla/e1.py

Cidryl/python-desde-cero

fade09d13ab0ed0cbb4f45a49a4ad9e3980f3276

02. Programacion estructurada/05. datos tupla/e1.py

Cidryl/python-desde-cero

fade09d13ab0ed0cbb4f45a49a4ad9e3980f3276

02. Programacion estructurada/05. datos tupla/e1.py

Cidryl/python-desde-cero

fade09d13ab0ed0cbb4f45a49a4ad9e3980f3276

def cargar_fecha(): dd=int(input("Ingrese numero de dia:")) mm=int(input("Ingrese numero de mes:")) aa=int(input("Ingrese numero de año:")) tupla = (dd,mm,aa) return tupla def imprimir_fecha(fecha): print(fecha[0],fecha[1],fecha[2],sep="/") # bloque principal fecha=cargar_fecha() imprimir_fecha(fecha)

e47c17dddcd00889d223b3d8fce4a9d9c3d285a3

py

Python

doingmathwithpython/ch03.py

andyliumathematics/mlnotes

7b7a1c37d7660bdf9144c59693719033080d654b

doingmathwithpython/ch03.py

andyliumathematics/mlnotes

7b7a1c37d7660bdf9144c59693719033080d654b

doingmathwithpython/ch03.py

andyliumathematics/mlnotes

7b7a1c37d7660bdf9144c59693719033080d654b

# %% l = [38,32,49,15,806,806] sum(l) # %% len(l) # %% sum(l)//len(l) # %% l.sort() # %% l # %% # %% l.most_common(1) # %% ''' 求众数：出现频率最高的数据 ''' from collections import Counter l = ['38','32','49','15','806','806'] c = Counter(l) print(c.most_common()[0][0]) print(c.most_common(1)) print(c.most_common(2)) # %% c.most_common()[0] # %% print(33) # %%

e4813380bf2daa72d111c3321e1a0890661d1b92

py

Python

CodedCaching/Network.py

qizhu8/CodedCachingSim

84e8f1e58e1c431ee4916525487d4b28f92e629b

CodedCaching/Network.py

qizhu8/CodedCachingSim

84e8f1e58e1c431ee4916525487d4b28f92e629b

CodedCaching/Network.py

qizhu8/CodedCachingSim

84e8f1e58e1c431ee4916525487d4b28f92e629b

""" Network class is in charge of: 1. Storing M - User Cache Size, N - Number of Files, K - Number of Users 2. Storing User instances, Server instance, and attacker instance """ import numpy as np from scipy import special import itertools from Server import Server from User import User from tabulate import tabulate T_BE_INTEGER = True class Network(): def __init__(self, M, N, K, t=None, fileId2Alphabet=False): self.M = M self.N = int(N) self.K = int(K) if t is None: self.t = self.M * self.K / self.N else: self.t = t self.M = self.t * self.N/self.K # make up the missing block of M if T_BE_INTEGER and self.t != int(self.t): raise Exception("Make sure t = M*K/N is an integer") self.numOfSubfile = int(special.comb(self.K, self.t)) self.numOfCodedSubfiles = int(special.comb(self.K, self.t+1)) self.fileId2Alphabet = fileId2Alphabet self.server = Server(self.M, self.N, self.K, self.t, self.fileId2Alphabet) self.userset = [User(id, self.M, self.N, self.K, self.t, fileId2Alphabet=fileId2Alphabet) for id in range(self.K)] self.placementDone = False def placement(self, isRandom=False, verboseForUser=False, verboseForCache=False): Z = self.server.generateZ(isRandom=isRandom) for userId in range(self.K): self.userset[userId].setZ(Z[userId, :]) if verboseForUser: # self.userset[userId].printUserDetail(fileId2Alphabet=fileId2Alphabet) print(self.userset[userId]) if verboseForCache: self.printCacheContent(Z) self.placementDone = True def delivery(self, D=None, verbose=False): if not self.placementDone: self.placement(verbose=verbose) if D is None: D = [self.userset[id].genD() for id in range(self.K)] if verbose: print("D:", D) X, groupList = self.server.generateX(D) if verbose: print("Server Transmission is:") print(self.printableServerTransmission(X)) return D, X, groupList def printableServerTransmission(self, X, inList=False, fileId2Alphabet=False): printoutList = [] totalRow, totalCol = X.shape for row in range(totalRow): subfileList = [] for col in range(totalCol): if X[row][col]: fileId = int(col / self.numOfSubfile) subfileId = int(col % self.numOfSubfile) if fileId2Alphabet: subfileList.append("{fileIdChr}{subfileId}".format(fileIdChr=chr(65+fileId), subfileId=subfileId+1)) else: subfileList.append("{fileId}-{subfileId}".format(fileId=fileId, subfileId=subfileId)) if len(subfileList): printoutList.append(" + ".join(subfileList)) # printoutList.append("{id}:{subfileInfo}".format(id=row, subfileInfo=" + ".join(subfileList))) if not inList: return " || ".join(printoutList) else: return printoutList def printCacheContent(self, Z): if self.fileId2Alphabet: header = ["UserId"] + [chr(65+fileId)+""+str(subfileId+1) for fileId in range(self.N) for subfileId in range(self.numOfSubfile)] else: header = ["UserId"] + [str(fileId)+"-"+str(subfileId) for fileId in range(self.N) for subfileId in range(self.numOfSubfile)] UserId = np.asarray([range(self.K)]).T print(tabulate(np.hstack([UserId, Z]), headers=header)) def allD(self): curD = [0] * self.K while curD != [self.N-1]*self.K: yield curD for checkPos in range(self.K-1, -1, -1): if curD[checkPos] < self.N-1: curD[checkPos] += 1 break else: curD[checkPos] = 0 yield curD def __str__(self): print_template = """M:{M}\nN:{N}\nK:{K}\nt:{t}""" return print_template.format(M=self.M, N=self.N, K=self.K, t=self.t) if __name__ == "__main__": # if t is specified, M is not needed. Currently, I only consider t to be a positive integer. # M: unified cache size per user (if t is specified, M is not useful anymore) # N: number of files in the network # K: number of users in the network # t: M*K/N, # M, N, K, t = -1, 3, 3, 1 M, N, K, t = -1, 3, 5, 3 # M, N, K, t = -1, 4, 5, 2 codedCachingNetwork = Network(M=M, N=N, K=K, t=t, fileId2Alphabet=True) print(codedCachingNetwork) # codedCachingNetwork.placement(verboseForCache=True, verboseForUser=True, isRandom=True) codedCachingNetwork.placement(verboseForCache=True, verboseForUser=True, isRandom=False) X_D_table = [] # for D in itertools.combinations_with_replacement(range(N), K): for D in codedCachingNetwork.allD(): D, X, groupList = codedCachingNetwork.delivery(verbose=False, D=D) # generate X based on D groupList D_str = ",".join(list(map(lambda d: chr(65+ d), D))) X_D_table.append(["["+D_str+"]"] + codedCachingNetwork.printableServerTransmission(X, inList=True, fileId2Alphabet=True)) # header = ["D", "X"] header = ["D"] + groupList print(tabulate(X_D_table, headers=header))

e4832a86e7db8f21257aa59834d215a8144ccb1f

py

Python

protonets/data/__init__.py

sripathisridhar/prototypical-networks

02a1379dceea896e23ecf21384d4a6ee2393f38c

2017-11-12T22:04:25.000Z

2022-03-31T09:42:13.000Z

protonets/data/__init__.py

Harzva/prototypical-networks

c9bb4d258267c11cb6e23f0a19242d24ca98ad8a

2017-12-06T19:28:23.000Z

2021-11-27T11:35:53.000Z

protonets/data/__init__.py

Harzva/prototypical-networks

c9bb4d258267c11cb6e23f0a19242d24ca98ad8a

2017-11-12T22:04:28.000Z

2022-03-26T09:25:42.000Z

from . import omniglot

e483e0a6252d9a8ff4f77f42bb3708e55acf3498

py

Python

flask/part3_templates/ex3-app/students2.py

macloo/python-beginners

1124922cd57d3f647eacafa0b82948587514d4bd

2018-03-25T22:41:57.000Z

2022-01-08T21:23:02.000Z

flask/part3_templates/ex3-app/students2.py

pavanpatil45/python-beginners

1124922cd57d3f647eacafa0b82948587514d4bd

flask/part3_templates/ex3-app/students2.py

pavanpatil45/python-beginners

1124922cd57d3f647eacafa0b82948587514d4bd

2018-03-20T00:56:57.000Z

2022-01-12T06:36:18.000Z

# two templates are used in this app from flask import Flask, render_template app = Flask(__name__) @app.route('/') def index(): return render_template('index.html') @app.route('/student/<idnum>') def student(idnum): return render_template('student.html', id=idnum) if __name__ == '__main__': app.run(debug=True)

e4884423f1f3c28f1f01d03c9e676127547b57c0

py

Python

docs/autodoc_example.py

aio-libs/sphinxcontrib-asyncio

dbfa79e29980e73ad2dd9dec59f1238b1a8a7cd7

2016-02-21T13:27:43.000Z

2020-02-19T17:22:38.000Z

docs/autodoc_example.py

aio-libs/sphinxcontrib-asyncio

dbfa79e29980e73ad2dd9dec59f1238b1a8a7cd7

2016-04-15T08:43:39.000Z

2022-01-06T10:43:08.000Z

docs/autodoc_example.py

aio-libs/sphinxcontrib-asyncio

dbfa79e29980e73ad2dd9dec59f1238b1a8a7cd7

2016-04-11T07:32:41.000Z

2019-09-28T10:59:51.000Z

import asyncio class MyClass: def my_func(self): """ Normal function """ @asyncio.coroutine def my_coro(self): """ This is my coroutine """ @asyncio.coroutine def coro(param): """ Module level async function """

e488e12d7b940dac3d9db3e9a5b2cb31258d0310

py

Python

test/recipe/test_edit_recipe.py

fredsonchaves07/foodfy

5bff04434749f369f982090b00590cca31fce186

test/recipe/test_edit_recipe.py

fredsonchaves07/foodfy

5bff04434749f369f982090b00590cca31fce186

2021-03-03T01:38:10.000Z

2022-01-16T15:42:02.000Z

test/recipe/test_edit_recipe.py

fredsonchaves07/foodfy

5bff04434749f369f982090b00590cca31fce186

import json from io import BytesIO from app.ext.api.exceptions import ( ChefNotFound, InvalidToken, MaximumImageCapacityError, OperationNotAllowed, RecipeWithoutIngredient, RecipeWithoutPreparationMode, ) from app.ext.api.services import token_services def test_edit_recipe(client, admin_user): new_user1 = { "name": "Usuário teste", "email": "email@email.com", "password": "123456", "admin": False, } new_chef1 = {"name": "chef test", "avatar": (BytesIO(b"avatar"), "test.jpg")} new_chef2 = {"name": "chef test", "avatar": (BytesIO(b"avatar"), "test.jpg")} headers = { "Authorization": admin_user.get("token"), "content-type": "application/json", } user1 = client.post( "/api/v1/user", data=json.dumps(new_user1), headers=headers, ) chef1 = client.post( "/api/v1/chef", data=new_chef1, headers=headers, follow_redirects=True, content_type="multipart/form-data", ) chef2 = client.post( "/api/v1/chef", data=new_chef2, headers=headers, follow_redirects=True, content_type="multipart/form-data", ) token = token_services.generate_token(user1.json["id"], user1.json["email"]) headers["Authorization"] = token new_recipe = { "name": "recipe test", "ingredients": ["Ovo", "Carne de Hamburguer"], "preparation_mode": ["Bata um ovo na frigideira", "Frite a carne"], "additional_information": "", "chef_id": chef1.json["id"], "recipe_imgs": [ (BytesIO(b"recipe_imgs"), "test1.jpg"), (BytesIO(b"recipe_imgs"), "test2.jpg"), ], } recipe = client.post( "/api/v1/recipe", data=new_recipe, headers=headers, follow_redirects=True, content_type="multipart/form-data", ) recipe_id = recipe.json["id"] old_imgs = [recipe_img.get("file_id") for recipe_img in recipe.json["recipe_imgs"]] update_recipe = { "name": "recipe updated", "chef_id": chef2.json["id"], "ingredients": ["Ovo", "Carne de Hamburguer", "Salada"], "preparation_mode": ["Bata um ovo na frigideira", "Coloque a salada"], "delete_imgs": old_imgs, "recipe_imgs": [ (BytesIO(b"recipe_imgs"), "test-alterado.jpg"), ], } response = client.patch( f"/api/v1/recipe/{recipe_id}", data=update_recipe, headers=headers, content_type="multipart/form-data", ) assert response.content_type == "application/json" assert response.status_code == 200 assert response.json.get("id") == recipe_id assert response.json.get("name") == update_recipe.get("name") assert response.json.get("chef").get("id") == update_recipe.get("chef_id") assert len(response.json.get("recipe_imgs")) == len( update_recipe.get("recipe_imgs") ) for ingredient in response.json.get("ingredients"): assert ingredient in update_recipe.get("ingredients") for preparation_mode in response.json.get("preparation_mode"): assert preparation_mode in update_recipe.get("preparation_mode") def test_edit_recipe_other_users_if_user_is_admin(client, admin_user): headers = { "Authorization": admin_user.get("token"), "content-type": "application/json", } users = [ { "name": "Usuário teste", "email": "email@email.com", "password": "123456", "admin": False, }, { "name": "Usuário teste", "email": "email1@email.com", "password": "123456", "admin": False, }, ] users_created = [] for user in users: user_info = client.post( "/api/v1/user", data=json.dumps(user), headers=headers, ) users_created.append(user_info) new_chef1 = {"name": "chef test", "avatar": (BytesIO(b"avatar"), "test.jpg")} chef1 = client.post( "/api/v1/chef", data=new_chef1, headers=headers, follow_redirects=True, content_type="multipart/form-data", ) ids_recipe_list = [] for user in users_created: token = token_services.generate_token(user.json["id"], user.json["email"]) headers["Authorization"] = token new_recipe = { "name": "recipe test", "ingredients": ["Ovo", "Carne de Hamburguer"], "preparation_mode": ["Bata um ovo na frigideira", "Frite a carne"], "additional_information": "", "chef_id": chef1.json["id"], "recipe_imgs": [ (BytesIO(b"recipe_imgs"), "test1.jpg"), (BytesIO(b"recipe_imgs"), "test2.jpg"), ], } recipe = client.post( "/api/v1/recipe", data=new_recipe, headers=headers, follow_redirects=True, content_type="multipart/form-data", ) ids_recipe_list.append(recipe.json["id"]) update_recipe = { "name": "recipe updated", "chef_id": chef1.json["id"], "ingredients": ["Ovo", "Carne de Hamburguer", "Salada"], "preparation_mode": ["Bata um ovo na frigideira", "Coloque a salada"], } headers["Authorization"] = admin_user.get("token") response = client.patch( f"/api/v1/recipe/{ids_recipe_list[0]}", data=update_recipe, headers=headers, content_type="multipart/form-data", ) assert response.content_type == "application/json" assert response.status_code == 200 assert response.json.get("name") == update_recipe.get("name") assert response.json.get("chef").get("id") == update_recipe.get("chef_id") for ingredient in response.json.get("ingredients"): assert ingredient in update_recipe.get("ingredients") for preparation_mode in response.json.get("preparation_mode"): assert preparation_mode in update_recipe.get("preparation_mode") def test_no_edit_recipe_if_user_not_authenticated(client, admin_user): headers = { "Authorization": admin_user.get("token"), "content-type": "application/json", } new_chef1 = {"name": "chef test", "avatar": (BytesIO(b"avatar"), "test.jpg")} chef1 = client.post( "/api/v1/chef", data=new_chef1, headers=headers, follow_redirects=True, content_type="multipart/form-data", ) new_recipe = { "name": "recipe test", "ingredients": ["Ovo", "Carne de Hamburguer"], "preparation_mode": ["Bata um ovo na frigideira", "Frite a carne"], "additional_information": "", "chef_id": chef1.json["id"], "recipe_imgs": [ (BytesIO(b"recipe_imgs"), "test1.jpg"), (BytesIO(b"recipe_imgs"), "test2.jpg"), ], } recipe = client.post( "/api/v1/recipe", data=new_recipe, headers=headers, follow_redirects=True, content_type="multipart/form-data", ) recipe_id = recipe.json["id"] update_recipe = { "name": "recipe updated", "ingredients": ["Ovo", "Carne de Hamburguer", "Salada"], "preparation_mode": ["Bata um ovo na frigideira", "Coloque a salada"], "recipe_imgs": [ (BytesIO(b"recipe_imgs"), "test-alterado.jpg"), ], } response = client.patch( f"/api/v1/recipe/{recipe_id}", data=update_recipe, content_type="multipart/form-data", ) assert response.status_code == InvalidToken.code assert response.json["message"] == InvalidToken.message def test_no_edit_recipe_if_recipe_is_other_user(client, admin_user): headers = { "Authorization": admin_user.get("token"), "content-type": "application/json", } users = [ { "name": "Usuário teste", "email": "email@email.com", "password": "123456", "admin": False, }, { "name": "Usuário teste", "email": "email1@email.com", "password": "123456", "admin": False, }, ] users_created = [] for user in users: user_info = client.post( "/api/v1/user", data=json.dumps(user), headers=headers, ) users_created.append(user_info) new_chef1 = {"name": "chef test", "avatar": (BytesIO(b"avatar"), "test.jpg")} chef1 = client.post( "/api/v1/chef", data=new_chef1, headers=headers, follow_redirects=True, content_type="multipart/form-data", ) ids_recipe_list = [] for user in users_created: token = token_services.generate_token(user.json["id"], user.json["email"]) headers["Authorization"] = token new_recipe = { "name": "recipe test", "ingredients": ["Ovo", "Carne de Hamburguer"], "preparation_mode": ["Bata um ovo na frigideira", "Frite a carne"], "additional_information": "", "chef_id": chef1.json["id"], "recipe_imgs": [ (BytesIO(b"recipe_imgs"), "test1.jpg"), (BytesIO(b"recipe_imgs"), "test2.jpg"), ], } recipe = client.post( "/api/v1/recipe", data=new_recipe, headers=headers, follow_redirects=True, content_type="multipart/form-data", ) ids_recipe_list.append(recipe.json["id"]) update_recipe = { "name": "recipe updated", "chef_id": chef1.json["id"], "ingredients": ["Ovo", "Carne de Hamburguer", "Salada"], "preparation_mode": ["Bata um ovo na frigideira", "Coloque a salada"], } response = client.patch( f"/api/v1/recipe/{ids_recipe_list[0]}", data=update_recipe, headers=headers, content_type="multipart/form-data", ) assert response.status_code == OperationNotAllowed.code assert response.json["message"] == OperationNotAllowed.message def test_no_edit_recipe_if_have_maximum_capacity_images(client, admin_user): new_user1 = { "name": "Usuário teste", "email": "email@email.com", "password": "123456", "admin": False, } new_chef1 = {"name": "chef test", "avatar": (BytesIO(b"avatar"), "test.jpg")} headers = { "Authorization": admin_user.get("token"), "content-type": "application/json", } user1 = client.post( "/api/v1/user", data=json.dumps(new_user1), headers=headers, ) chef1 = client.post( "/api/v1/chef", data=new_chef1, headers=headers, follow_redirects=True, content_type="multipart/form-data", ) token = token_services.generate_token(user1.json["id"], user1.json["email"]) headers["Authorization"] = token new_recipe = { "name": "recipe test", "ingredients": ["Ovo", "Carne de Hamburguer"], "preparation_mode": ["Bata um ovo na frigideira", "Frite a carne"], "additional_information": "", "chef_id": chef1.json["id"], "recipe_imgs": [ (BytesIO(b"recipe_imgs"), "test1.jpg"), ], } recipe = client.post( "/api/v1/recipe", data=new_recipe, headers=headers, follow_redirects=True, content_type="multipart/form-data", ) recipe_id = recipe.json["id"] update_recipe = { "name": "recipe updated", "chef_id": chef1.json["id"], "ingredients": ["Ovo", "Carne de Hamburguer", "Salada"], "preparation_mode": ["Bata um ovo na frigideira", "Coloque a salada"], "recipe_imgs": [ (BytesIO(b"recipe_imgs"), "test-alterado.jpg"), (BytesIO(b"recipe_imgs"), "test-alterado.jpg"), (BytesIO(b"recipe_imgs"), "test-alterado.jpg"), (BytesIO(b"recipe_imgs"), "test-alterado.jpg"), (BytesIO(b"recipe_imgs"), "test-alterado.jpg"), (BytesIO(b"recipe_imgs"), "test-alterado.jpg"), ], } response = client.patch( f"/api/v1/recipe/{recipe_id}", data=update_recipe, headers=headers, content_type="multipart/form-data", ) assert response.content_type == "application/json" assert response.status_code == MaximumImageCapacityError.code assert response.json["message"] == MaximumImageCapacityError.message def test_no_edit_recipe_if_chef_not_already_exist(client, admin_user): new_user1 = { "name": "Usuário teste", "email": "email@email.com", "password": "123456", "admin": False, } new_chef1 = {"name": "chef test", "avatar": (BytesIO(b"avatar"), "test.jpg")} headers = { "Authorization": admin_user.get("token"), "content-type": "application/json", } user1 = client.post( "/api/v1/user", data=json.dumps(new_user1), headers=headers, ) chef1 = client.post( "/api/v1/chef", data=new_chef1, headers=headers, follow_redirects=True, content_type="multipart/form-data", ) token = token_services.generate_token(user1.json["id"], user1.json["email"]) headers["Authorization"] = token new_recipe = { "name": "recipe test", "ingredients": ["Ovo", "Carne de Hamburguer"], "preparation_mode": ["Bata um ovo na frigideira", "Frite a carne"], "additional_information": "", "chef_id": chef1.json["id"], "recipe_imgs": [ (BytesIO(b"recipe_imgs"), "test1.jpg"), ], } recipe = client.post( "/api/v1/recipe", data=new_recipe, headers=headers, follow_redirects=True, content_type="multipart/form-data", ) recipe_id = recipe.json["id"] update_recipe = { "name": "recipe updated", "chef_id": 10, "ingredients": ["Ovo", "Carne de Hamburguer", "Salada"], "preparation_mode": ["Bata um ovo na frigideira", "Coloque a salada"], "recipe_imgs": [ (BytesIO(b"recipe_imgs"), "test-alterado.jpg"), (BytesIO(b"recipe_imgs"), "test-alterado.jpg"), (BytesIO(b"recipe_imgs"), "test-alterado.jpg"), ], } response = client.patch( f"/api/v1/recipe/{recipe_id}", data=update_recipe, headers=headers, content_type="multipart/form-data", ) assert response.content_type == "application/json" assert response.status_code == ChefNotFound.code assert response.json["message"] == ChefNotFound.message def test_no_edit_recipe_if_recipe_without_ingredient(client, admin_user): new_user1 = { "name": "Usuário teste", "email": "email@email.com", "password": "123456", "admin": False, } new_chef1 = {"name": "chef test", "avatar": (BytesIO(b"avatar"), "test.jpg")} headers = { "Authorization": admin_user.get("token"), "content-type": "application/json", } user1 = client.post( "/api/v1/user", data=json.dumps(new_user1), headers=headers, ) chef1 = client.post( "/api/v1/chef", data=new_chef1, headers=headers, follow_redirects=True, content_type="multipart/form-data", ) token = token_services.generate_token(user1.json["id"], user1.json["email"]) headers["Authorization"] = token new_recipe = { "name": "recipe test", "ingredients": ["Ovo", "Carne de Hamburguer"], "preparation_mode": ["Bata um ovo na frigideira", "Frite a carne"], "additional_information": "", "chef_id": chef1.json["id"], "recipe_imgs": [ (BytesIO(b"recipe_imgs"), "test1.jpg"), ], } recipe = client.post( "/api/v1/recipe", data=new_recipe, headers=headers, follow_redirects=True, content_type="multipart/form-data", ) recipe_id = recipe.json["id"] update_recipe = { "name": "recipe updated", "chef_id": chef1.json["id"], "ingredients": [], "preparation_mode": ["Bata um ovo na frigideira", "Coloque a salada"], "recipe_imgs": [ (BytesIO(b"recipe_imgs"), "test-alterado.jpg"), (BytesIO(b"recipe_imgs"), "test-alterado.jpg"), (BytesIO(b"recipe_imgs"), "test-alterado.jpg"), ], } response = client.patch( f"/api/v1/recipe/{recipe_id}", data=update_recipe, headers=headers, content_type="multipart/form-data", ) assert response.content_type == "application/json" assert response.status_code == RecipeWithoutIngredient.code assert response.json["message"] == RecipeWithoutIngredient.message def test_no_edit_recipe_if_recipe_without_preparation_mode(client, admin_user): new_user1 = { "name": "Usuário teste", "email": "email@email.com", "password": "123456", "admin": False, } new_chef1 = {"name": "chef test", "avatar": (BytesIO(b"avatar"), "test.jpg")} headers = { "Authorization": admin_user.get("token"), "content-type": "application/json", } user1 = client.post( "/api/v1/user", data=json.dumps(new_user1), headers=headers, ) chef1 = client.post( "/api/v1/chef", data=new_chef1, headers=headers, follow_redirects=True, content_type="multipart/form-data", ) token = token_services.generate_token(user1.json["id"], user1.json["email"]) headers["Authorization"] = token new_recipe = { "name": "recipe test", "ingredients": ["Ovo", "Carne de Hamburguer"], "preparation_mode": ["Bata um ovo na frigideira", "Frite a carne"], "additional_information": "", "chef_id": chef1.json["id"], "recipe_imgs": [ (BytesIO(b"recipe_imgs"), "test1.jpg"), ], } recipe = client.post( "/api/v1/recipe", data=new_recipe, headers=headers, follow_redirects=True, content_type="multipart/form-data", ) recipe_id = recipe.json["id"] update_recipe = { "name": "recipe updated", "chef_id": chef1.json["id"], "ingredients": ["Ovo", "Carne de Hamburguer"], "preparation_mode": [], "recipe_imgs": [ (BytesIO(b"recipe_imgs"), "test-alterado.jpg"), (BytesIO(b"recipe_imgs"), "test-alterado.jpg"), (BytesIO(b"recipe_imgs"), "test-alterado.jpg"), ], } response = client.patch( f"/api/v1/recipe/{recipe_id}", data=update_recipe, headers=headers, content_type="multipart/form-data", ) assert response.content_type == "application/json" assert response.status_code == RecipeWithoutPreparationMode.code assert response.json["message"] == RecipeWithoutPreparationMode.message def test_delete_recipe(client, admin_user): new_user1 = { "name": "Usuário teste", "email": "email@email.com", "password": "123456", "admin": False, } new_chef1 = {"name": "chef test", "avatar": (BytesIO(b"avatar"), "test.jpg")} headers = { "Authorization": admin_user.get("token"), "content-type": "application/json", } user1 = client.post( "/api/v1/user", data=json.dumps(new_user1), headers=headers, ) chef1 = client.post( "/api/v1/chef", data=new_chef1, headers=headers, follow_redirects=True, content_type="multipart/form-data", ) token = token_services.generate_token(user1.json["id"], user1.json["email"]) headers["Authorization"] = token new_recipe = { "name": "recipe test", "ingredients": ["Ovo", "Carne de Hamburguer"], "preparation_mode": ["Bata um ovo na frigideira", "Frite a carne"], "additional_information": "", "chef_id": chef1.json["id"], "recipe_imgs": [ (BytesIO(b"recipe_imgs"), "test1.jpg"), ], } recipe = client.post( "/api/v1/recipe", data=new_recipe, headers=headers, follow_redirects=True, content_type="multipart/form-data", ) recipe_id = recipe.json["id"] response = client.delete( f"/api/v1/recipe/{recipe_id}", headers=headers, content_type="multipart/form-data", ) assert response.content_type == "application/json" assert response.status_code == 204 def test_delete_recipe_other_users_if_user_is_admin(client, admin_user): headers = { "Authorization": admin_user.get("token"), "content-type": "application/json", } users = [ { "name": "Usuário teste", "email": "email@email.com", "password": "123456", "admin": False, }, { "name": "Usuário teste", "email": "email1@email.com", "password": "123456", "admin": False, }, ] users_created = [] for user in users: user_info = client.post( "/api/v1/user", data=json.dumps(user), headers=headers, ) users_created.append(user_info) new_chef1 = {"name": "chef test", "avatar": (BytesIO(b"avatar"), "test.jpg")} chef1 = client.post( "/api/v1/chef", data=new_chef1, headers=headers, follow_redirects=True, content_type="multipart/form-data", ) ids_recipe_list = [] for user in users_created: token = token_services.generate_token(user.json["id"], user.json["email"]) headers["Authorization"] = token new_recipe = { "name": "recipe test", "ingredients": ["Ovo", "Carne de Hamburguer"], "preparation_mode": ["Bata um ovo na frigideira", "Frite a carne"], "additional_information": "", "chef_id": chef1.json["id"], "recipe_imgs": [ (BytesIO(b"recipe_imgs"), "test1.jpg"), (BytesIO(b"recipe_imgs"), "test2.jpg"), ], } recipe = client.post( "/api/v1/recipe", data=new_recipe, headers=headers, follow_redirects=True, content_type="multipart/form-data", ) ids_recipe_list.append(recipe.json["id"]) headers["Authorization"] = admin_user.get("token") response = client.delete( f"/api/v1/recipe/{ids_recipe_list[0]}", headers=headers, content_type="multipart/form-data", ) assert response.content_type == "application/json" assert response.status_code == 204 def test_no_delete_recipe_if_recipe_is_other_user(client, admin_user): headers = { "Authorization": admin_user.get("token"), "content-type": "application/json", } users = [ { "name": "Usuário teste", "email": "email@email.com", "password": "123456", "admin": False, }, { "name": "Usuário teste", "email": "email1@email.com", "password": "123456", "admin": False, }, ] users_created = [] for user in users: user_info = client.post( "/api/v1/user", data=json.dumps(user), headers=headers, ) users_created.append(user_info) new_chef1 = {"name": "chef test", "avatar": (BytesIO(b"avatar"), "test.jpg")} chef1 = client.post( "/api/v1/chef", data=new_chef1, headers=headers, follow_redirects=True, content_type="multipart/form-data", ) ids_recipe_list = [] for user in users_created: token = token_services.generate_token(user.json["id"], user.json["email"]) headers["Authorization"] = token new_recipe = { "name": "recipe test", "ingredients": ["Ovo", "Carne de Hamburguer"], "preparation_mode": ["Bata um ovo na frigideira", "Frite a carne"], "additional_information": "", "chef_id": chef1.json["id"], "recipe_imgs": [ (BytesIO(b"recipe_imgs"), "test1.jpg"), (BytesIO(b"recipe_imgs"), "test2.jpg"), ], } recipe = client.post( "/api/v1/recipe", data=new_recipe, headers=headers, follow_redirects=True, content_type="multipart/form-data", ) ids_recipe_list.append(recipe.json["id"]) response = client.delete( f"/api/v1/recipe/{ids_recipe_list[0]}", headers=headers, content_type="multipart/form-data", ) assert response.status_code == OperationNotAllowed.code assert response.json["message"] == OperationNotAllowed.message

e4892ccb409c7b541cbd948a9e9898c388a282c5

py

Python

survey/migrations/0018_auto_20161128_0936.py

watchdogpolska/ankieta-rodzic-po-ludzku-nfz

68b1d1ccac969ca51416761d1168678effb1e6c6

survey/migrations/0018_auto_20161128_0936.py

watchdogpolska/ankieta-rodzic-po-ludzku-nfz

68b1d1ccac969ca51416761d1168678effb1e6c6

survey/migrations/0018_auto_20161128_0936.py

watchdogpolska/ankieta-rodzic-po-ludzku-nfz

68b1d1ccac969ca51416761d1168678effb1e6c6

# -*- coding: utf-8 -*- # Generated by Django 1.10.3 on 2016-11-28 09:36 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('survey', '0017_auto_20161128_0242'), ] operations = [ migrations.AlterField( model_name='survey', name='style', field=models.IntegerField(choices=[(0, 'Total view'), (1, 'Hospital view'), (2, 'Question view')], default=2), ), ]

e48a824fae829e3008efd9a9fbcb0f03d3adc45f

py

Python

tests/__init__.py

slinksoft/PathExactDelayPrototype

633576cfe031e8ee884daaa453a3e5d206eaeaa9

tests/__init__.py

slinksoft/PathExactDelayPrototype

633576cfe031e8ee884daaa453a3e5d206eaeaa9

tests/__init__.py

slinksoft/PathExactDelayPrototype

633576cfe031e8ee884daaa453a3e5d206eaeaa9

import os import sys from pathlib import Path sys.path.insert(0, 'exactdelaypathfinder')

e48ba9fc67c09776260edc71cd67600e98eb63a9

py

Python

2017/day07/code.py

Fadi88/AoC

8b24f2f2cc7b4e1c63758e81e63d8670a261cc7c

2019-12-15T21:53:19.000Z

2021-12-24T17:03:41.000Z

2017/day07/code.py

Fadi88/adventofcode19

dd2456bdd163beb02dbfe9dcea2b021061c7671e

2021-12-15T20:40:51.000Z

2021-12-15T22:19:48.000Z

2017/day07/code.py

Fadi88/adventofcode19

dd2456bdd163beb02dbfe9dcea2b021061c7671e

2020-12-11T06:00:24.000Z

2021-12-20T21:37:46.000Z

import time from collections import defaultdict def profiler(method): def wrapper_method(*arg, **kw): t = time.time() method(*arg, **kw) print('Method ' + method.__name__ + ' took : ' + "{:2.5f}".format(time.time()-t) + ' sec') return wrapper_method @profiler def part1(): towers = defaultdict(list) for l in open('input.txt'): l = l.strip() p = l.split() if '->' in l: towers[p[0]] = l[l.find('->') + 3:].split(', ') else: towers[p[0]] = [] leaves = [] for ele in towers: leaves += towers[ele] for ele in towers: if ele not in leaves: print(ele) break def get_weight(towers,weights,prog): if len(towers[prog]) == 0: return weights[prog] else : w = weights[prog] for sub in towers[prog]: w += get_weight(towers,weights,sub) return w @profiler def part2(): towers = defaultdict(list) weights = {} for l in open('input.txt'): l = l.strip() p = l.split() weights[p[0]] = int(p[1][1:-1]) if '->' in l: towers[p[0]] = l[l.find('->') + 3:].split(', ') else: towers[p[0]] = [] leaves = [] for ele in towers: leaves += towers[ele] for ele in towers: if ele not in leaves: root = ele break diff = 0 while True: tmp = [get_weight(towers,weights,p) for p in towers[root]] same = [tmp.count(tmp[i]) > 1 for i in range(len(tmp))] if not all(same): root = towers[root][same.index(False)] diff = tmp[same.index(False)] - tmp[(same.index(False) + 1)% len(tmp)] else: print(weights[root] - diff) break if __name__ == "__main__": part1() part2()

e48e53ba04ff99bdd6227e182235f811ae1dc4ee

py

Python

src/microbit/spi-tof-master.py

romilly/multi-VL53L0X

80cf0d82d93ceae9c54acb967c24a1bf8deb5e3a

src/microbit/spi-tof-master.py

romilly/multi-VL53L0X

80cf0d82d93ceae9c54acb967c24a1bf8deb5e3a

src/microbit/spi-tof-master.py

romilly/multi-VL53L0X

80cf0d82d93ceae9c54acb967c24a1bf8deb5e3a

from microbit import * import struct from time import sleep SENSORS = 2 def spi_read(sensor): pin16.write_digital(0) # Chip select ibuffer = struct.pack('<B', sensor) spi.write(ibuffer) result = spi.read(1) pin16.write_digital(1) # Chip select off return result spi.init(baudrate=100000) while True: for i in [0, 1]: print(i, ord(spi_read(i))) sleep(0.1)

e48f98c85bda6baa0cc86d71b689b55e8122a390

py

Python

hasher-matcher-actioner/hmalib/models.py

isabella232/ThreatExchange

0d07a800bbf25d8541f40b828e2dfd377395af9b

hasher-matcher-actioner/hmalib/models.py

isabella232/ThreatExchange

0d07a800bbf25d8541f40b828e2dfd377395af9b

2021-04-19T10:20:43.000Z

2021-04-19T10:20:43.000Z

hasher-matcher-actioner/hmalib/models.py

isabella232/ThreatExchange

0d07a800bbf25d8541f40b828e2dfd377395af9b

# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved import datetime import typing as t import json from dataclasses import dataclass, field from mypy_boto3_dynamodb.service_resource import Table from boto3.dynamodb.conditions import Attr, Key """ Data transfer object classes to be used with dynamodbstore Classes in this module should implement methods `to_dynamodb_item(self)` and `to_sqs_message(self)` """ class DynamoDBItem: CONTENT_KEY_PREFIX = "c#" SIGNAL_KEY_PREFIX = "s#" TYPE_PREFIX = "type#" def write_to_table(self, table: Table): table.put_item(Item=self.to_dynamodb_item()) def to_dynamodb_item(self) -> t.Dict: raise NotImplementedError @staticmethod def get_dynamodb_content_key(c_id: str) -> str: return f"{DynamoDBItem.CONTENT_KEY_PREFIX}{c_id}" @staticmethod def get_dynamodb_signal_key(source: str, s_id: t.Union[str, int]) -> str: return f"{DynamoDBItem.SIGNAL_KEY_PREFIX}{source}#{s_id}" @staticmethod def remove_signal_key_prefix(key: str, source: str) -> str: return key[len(DynamoDBItem.SIGNAL_KEY_PREFIX) + len(source) + 1 :] @staticmethod def get_dynamodb_type_key(type: str) -> str: return f"{DynamoDBItem.TYPE_PREFIX}{type}" @staticmethod def remove_content_key_prefix(key: str) -> str: return key[len(DynamoDBItem.CONTENT_KEY_PREFIX) :] class SNSMessage: def to_sns_message(self) -> str: raise NotImplementedError @classmethod def from_sns_message(cls, message: str) -> "SNSMessage": raise NotImplementedError @dataclass class SignalMetadataBase(DynamoDBItem): """ Base for signal metadata. 'ds' refers to dataset which for the time being is quivalent to collab or privacy group (and in the long term could map to bank) """ DATASET_PREFIX = "ds#" signal_id: t.Union[str, int] ds_id: str updated_at: datetime.datetime signal_source: str signal_hash: str # duplicated field with PDQMatchRecord having both for now to help with debuging/testing tags: t.List[str] = field(default_factory=list) @staticmethod def get_dynamodb_ds_key(ds_id: str) -> str: return f"{SignalMetadataBase.DATASET_PREFIX}{ds_id}" @dataclass class PDQSignalMetadata(SignalMetadataBase): """ PDQ Signal metadata. This object is designed to be an ~lookaside on some of the values used by PDQMatchRecord for easier and more consistent updating by the syncer and UI. Otherwise updates on a signals metadata would require updating all PDQMatchRecord associated; TODO: For now there will be some overlap between this object and PDQMatchRecord. """ SIGNAL_TYPE = "pdq" def to_dynamodb_item(self) -> dict: return { "PK": self.get_dynamodb_signal_key(self.signal_source, self.signal_id), "SK": self.get_dynamodb_ds_key(self.ds_id), "SignalHash": self.signal_hash, "SignalSource": self.signal_source, # defaults to 'te' in the current pipeline "UpdatedAt": self.updated_at.isoformat(), "HashType": self.SIGNAL_TYPE, "Tags": self.tags, } @classmethod def get_from_signal( cls, table: Table, signal_id: t.Union[str, int], signal_source: str, ) -> t.List["PDQSignalMetadata"]: items = table.query( KeyConditionExpression=Key("PK").eq( cls.get_dynamodb_signal_key(signal_source, signal_id) ) & Key("SK").begins_with(cls.DATASET_PREFIX), ProjectionExpression="PK, ContentHash, UpdatedAt, SK, SignalSource, SignalHash, Tags", FilterExpression=Attr("HashType").eq(cls.SIGNAL_TYPE), ).get("Items", []) return cls._result_items_to_metadata(items) @classmethod def _result_items_to_metadata( cls, items: t.List[t.Dict], ) -> t.List["PDQSignalMetadata"]: return [ PDQSignalMetadata( signal_id=cls.remove_signal_key_prefix( item["PK"], item["SignalSource"] ), ds_id=item["SK"][len(cls.DATASET_PREFIX) :], updated_at=datetime.datetime.fromisoformat(item["UpdatedAt"]), signal_source=item["SignalSource"], signal_hash=item["SignalHash"], tags=item["Tags"], ) for item in items ] @dataclass class Label: key: str value: str def to_dynamodb_dict(self) -> dict: return {"K": self.key, "V": self.value} @classmethod def from_dynamodb_dict(cls, d: dict) -> "Label": return cls(d["K"], d["V"]) def __eq__(self, another_label: object) -> bool: if not isinstance(another_label, Label): return NotImplemented return self.key == another_label.key and self.value == another_label.value @dataclass class PDQRecordBase(DynamoDBItem): """ Abstract Base Record for PDQ releated items. """ SIGNAL_TYPE = "pdq" content_id: str content_hash: str updated_at: datetime.datetime def to_dynamodb_item(self) -> dict: raise NotImplementedError def to_sqs_message(self) -> dict: raise NotImplementedError @classmethod def get_from_time_range( cls, table: Table, start_time: str = None, end_time: str = None ) -> t.List: raise NotImplementedError @dataclass class PipelinePDQHashRecord(PDQRecordBase): """ Successful execution at the hasher produces this record. """ quality: int def to_dynamodb_item(self) -> dict: return { "PK": self.get_dynamodb_content_key(self.content_id), "SK": self.get_dynamodb_type_key(self.SIGNAL_TYPE), "ContentHash": self.content_hash, "Quality": self.quality, "UpdatedAt": self.updated_at.isoformat(), "HashType": self.SIGNAL_TYPE, } def to_sqs_message(self) -> dict: return { "hash": self.content_hash, "type": self.SIGNAL_TYPE, "key": self.content_id, } @classmethod def get_from_content_id( cls, table: Table, content_key: str ) -> t.Optional["PipelinePDQHashRecord"]: items = HashRecordQuery.from_content_key( table, cls.get_dynamodb_content_key(content_key), cls.get_dynamodb_type_key(cls.SIGNAL_TYPE), ) records = cls._result_items_to_records(items) return None if not records else records[0] @classmethod def get_from_time_range( cls, table: Table, start_time: str = None, end_time: str = None ) -> t.List["PipelinePDQHashRecord"]: items = HashRecordQuery.from_time_range( table, cls.get_dynamodb_type_key(cls.SIGNAL_TYPE), start_time, end_time ) return cls._result_items_to_records(items) @classmethod def _result_items_to_records( cls, items: t.List[t.Dict], ) -> t.List["PipelinePDQHashRecord"]: return [ PipelinePDQHashRecord( content_id=item["PK"][len(cls.CONTENT_KEY_PREFIX) :], content_hash=item["ContentHash"], updated_at=datetime.datetime.fromisoformat(item["UpdatedAt"]), quality=item["Quality"], ) for item in items ] @dataclass class PDQMatchRecord(PDQRecordBase): """ Successful execution at the matcher produces this record. """ signal_id: t.Union[str, int] signal_source: str signal_hash: str labels: t.List[Label] = field(default_factory=list) def to_dynamodb_item(self) -> dict: return { "PK": self.get_dynamodb_content_key(self.content_id), "SK": self.get_dynamodb_signal_key(self.signal_source, self.signal_id), "ContentHash": self.content_hash, "UpdatedAt": self.updated_at.isoformat(), "SignalHash": self.signal_hash, "SignalSource": self.signal_source, "GSI1-PK": self.get_dynamodb_signal_key(self.signal_source, self.signal_id), "GSI1-SK": self.get_dynamodb_content_key(self.content_id), "HashType": self.SIGNAL_TYPE, "GSI2-PK": self.get_dynamodb_type_key(self.SIGNAL_TYPE), "Labels": [x.to_dynamodb_dict() for x in self.labels], } def to_sqs_message(self) -> dict: # TODO add method for when matches are added to a sqs raise NotImplementedError @classmethod def get_from_content_id( cls, table: Table, content_id: str ) -> t.List["PDQMatchRecord"]: items = MatchRecordQuery.from_content_key( table, cls.get_dynamodb_content_key(content_id), cls.SIGNAL_KEY_PREFIX, cls.SIGNAL_TYPE, ) return cls._result_items_to_records(items) @classmethod def get_from_signal( cls, table: Table, signal_id: t.Union[str, int], signal_source: str ) -> t.List["PDQMatchRecord"]: items = MatchRecordQuery.from_signal_key( table, cls.get_dynamodb_signal_key(signal_source, signal_id), cls.SIGNAL_TYPE, ) return cls._result_items_to_records(items) @classmethod def get_from_time_range( cls, table: Table, start_time: str = None, end_time: str = None ) -> t.List["PDQMatchRecord"]: items = MatchRecordQuery.from_time_range( table, cls.get_dynamodb_type_key(cls.SIGNAL_TYPE), start_time, end_time ) return cls._result_items_to_records(items) @classmethod def _result_items_to_records( cls, items: t.List[t.Dict], ) -> t.List["PDQMatchRecord"]: return [ PDQMatchRecord( content_id=cls.remove_content_key_prefix(item["PK"]), content_hash=item["ContentHash"], updated_at=datetime.datetime.fromisoformat(item["UpdatedAt"]), signal_id=cls.remove_signal_key_prefix( item["SK"], item["SignalSource"] ), signal_source=item["SignalSource"], signal_hash=item["SignalHash"], labels=[Label.from_dynamodb_dict(x) for x in item["Labels"]], ) for item in items ] class HashRecordQuery: DEFAULT_PROJ_EXP = "PK, ContentHash, UpdatedAt, Quality" @classmethod def from_content_key( cls, table: Table, content_key: str, hash_type_key: str = None ) -> t.List[t.Dict]: """ Given a content key (and optional hash type), return its content hash (for that type). Written to be agnostic to hash type so it can be reused by other types of 'HashRecord's. """ if hash_type_key is None: key_con_exp = Key("PK").eq(content_key) & Key("SK").begins_with( DynamoDBItem.SIGNAL_KEY_PREFIX ) else: key_con_exp = Key("PK").eq(content_key) & Key("SK").eq(hash_type_key) return table.query( KeyConditionExpression=key_con_exp, ProjectionExpression=cls.DEFAULT_PROJ_EXP, ).get("Items", []) @classmethod def from_time_range( cls, table: Table, hash_type: str, start_time: str = None, end_time: str = None ) -> t.List[t.Dict]: """ Given a hash type and time range, give me all the hashes found for that type and time range """ if start_time is None: start_time = datetime.datetime.min.isoformat() if end_time is None: end_time = datetime.datetime.max.isoformat() return table.scan( FilterExpression=Key("SK").eq(hash_type) & Key("UpdatedAt").between(start_time, end_time), ProjectionExpression=cls.DEFAULT_PROJ_EXP, ).get("Items", []) class MatchRecordQuery: """ Written to be agnostic to hash type so it can be reused by other types of 'MatchRecord's. """ DEFAULT_PROJ_EXP = ( "PK, ContentHash, UpdatedAt, SK, SignalSource, SignalHash, Labels" ) @classmethod def from_content_key( cls, table: Table, content_key: str, source_prefix: str = DynamoDBItem.SIGNAL_KEY_PREFIX, hash_type: str = None, ) -> t.List[t.Dict]: """ Given a content key (and optional hash type), give me its content hash (for that type). """ filter_exp = None if not hash_type is None: filter_exp = Attr("HashType").eq(hash_type) return table.query( KeyConditionExpression=Key("PK").eq(content_key) & Key("SK").begins_with(source_prefix), ProjectionExpression=cls.DEFAULT_PROJ_EXP, FilterExpression=filter_exp, ).get("Items", []) @classmethod def from_signal_key( cls, table: Table, signal_key: str, hash_type: str = None, ) -> t.List[t.Dict]: """ Given a Signal ID/Key (and optional hash type), give me any content matches found """ filter_exp = None if not hash_type is None: filter_exp = Attr("HashType").eq(hash_type) return table.query( IndexName="GSI-1", KeyConditionExpression=Key("GSI1-PK").eq(signal_key), ProjectionExpression=cls.DEFAULT_PROJ_EXP, FilterExpression=filter_exp, ).get("Items", []) @classmethod def from_time_range( cls, table: Table, hash_type: str, start_time: str = None, end_time: str = None ) -> t.List[t.Dict]: """ Given a hash type and time range, give me all the matches found for that type and time range """ if start_time is None: start_time = datetime.datetime.min.isoformat() if end_time is None: end_time = datetime.datetime.max.isoformat() return table.query( IndexName="GSI-2", KeyConditionExpression=Key("GSI2-PK").eq(hash_type) & Key("UpdatedAt").between(start_time, end_time), ProjectionExpression=cls.DEFAULT_PROJ_EXP, ).get("Items", []) @dataclass class MatchMessage(SNSMessage): """ Captures a set of matches that will need to be processed. We create one match message for a single content key. It is possible that a single content hash matches multiple datasets. When it does, the entire set of matches are forwarded together so that *one* appropriate action can be taken. - `content_key`: A way for partners to refer uniquely to content on their site - `content_hash`: The hash generated for the content_key """ content_key: str content_hash: str match_details: t.List["DatasetMatchDetails"] = field(default_factory=list) def to_sns_message(self) -> str: return json.dumps( { "ContentKey": self.content_key, "ContentHash": self.content_hash, "MatchDetails": [x.to_dict() for x in self.match_details], } ) @classmethod def from_sns_message(cls, message: str) -> "MatchMessage": parsed = json.loads(message) return cls( parsed["ContentKey"], parsed["ContentHash"], [DatasetMatchDetails.from_dict(d) for d in parsed["MatchDetails"]], ) @dataclass class DatasetMatchDetails: """ Dataset fields: - `banked_content_id`: Inside the bank, what's a unique way to refer to what was matched against? - `bank_id`: [optional][Defaults to 'threatexchange_all_collabs'] Which bank did we fetch this banked_content from? - `bank_source`: [optional][Defaults to 'api/threatexchange'] This is forward looking, but potentially, we could have this be 'local', or 'api/some-other-api' """ banked_indicator_id: str # source information, for now, it's okay to be hardcoded # to threatexchange bank_id: str = "threatexchange_all_collabs" bank_source: str = "api/threatexchange" def to_dict(self) -> dict: return { "BankedIndicatorId": self.banked_indicator_id, "BankId": self.bank_id, "BankSource": self.bank_source, } @classmethod def from_dict(cls, d: dict) -> "DatasetMatchDetails": return cls( d["BankedIndicatorId"], d["BankId"], d["BankSource"], )

e48fad25c05c483e3b144a00ff76a128d96f4a18

py

Python

colossalai/utils/commons/__init__.py

mrriteshranjan/ColossalAI

0d057a1bae67b915a385be7edab7da83413cb645

colossalai/utils/commons/__init__.py

mrriteshranjan/ColossalAI

0d057a1bae67b915a385be7edab7da83413cb645

colossalai/utils/commons/__init__.py

mrriteshranjan/ColossalAI

0d057a1bae67b915a385be7edab7da83413cb645

from .bucket_tensor_copy import BucketizedTensorCopy __all__ = ['BucketizedTensorCopy']

e49334330d41dc2dca73dcd98740a04934ce3d79

py

Python

DRF-React/appy/apps.py

Preet538-neitzen/LOC-Hackathon

e7bad458ef0069becdba42576f5fe1bfd736678b

DRF-React/appy/apps.py

Preet538-neitzen/LOC-Hackathon

e7bad458ef0069becdba42576f5fe1bfd736678b

2021-03-19T13:44:46.000Z

2022-03-12T00:55:03.000Z

DRF-React/appy/apps.py

Preet538-neitzen/LOC-Hackathon

e7bad458ef0069becdba42576f5fe1bfd736678b

2021-02-13T00:16:36.000Z

2021-02-13T00:16:36.000Z

from django.apps import AppConfig class AppyConfig(AppConfig): name = 'appy'

e494747ad6589e1234241f26ac62dacfe6cecd8c

py

Python

test/test_truss.py

deeepeshthakur/ddtruss

86aa945d577c6efe752099eee579386762942289

2020-01-27T12:03:47.000Z

2020-01-27T12:03:47.000Z

test/test_truss.py

deeepeshthakur/ddtruss

86aa945d577c6efe752099eee579386762942289

test/test_truss.py

deeepeshthakur/ddtruss

86aa945d577c6efe752099eee579386762942289

import numpy as np import pytest from ddtruss import Truss, DataDrivenSolver points = np.array([[0, 0], [1, 0], [0.5, 0.5], [2, 1]]) lines = np.array([[0, 2], [1, 2], [1, 3], [2, 3]], dtype=int) truss = Truss(points, lines) E = 1.962e11 A = [2e-4, 2e-4, 1e-4, 1e-4] U_dict = {0: [0, 0], 1: [0, 0]} F_dict = {3: [0, -9.81e3]} u_ref = np.array( [0, 0, 0, 0, 2.65165043e-4, 8.83883476e-5, 3.47902545e-3, -5.60034579e-3] ) def test_truss(): u, *_ = truss.solve(A=A, E=E, U_dict=U_dict, F_dict=F_dict) assert np.allclose(u, u_ref) @pytest.mark.parametrize( "n_data", [5000, 10000] ) def test_data_driven_solver(n_data): ddsolver = DataDrivenSolver(truss) eps_max = 1.1e-3 eps = np.linspace(-eps_max, eps_max, n_data) sig = E * eps material_data = np.hstack([eps.reshape((-1, 1)), sig.reshape((-1, 1))]) ddsolver.load_material_data(material_data) u, *_ = ddsolver.solve(A=A, U_dict=U_dict, F_dict=F_dict) assert np.allclose(u, u_ref, rtol=1e-2)

e494dbf6ede35cd65a3c40c381a319f33cf3e78d

py

Python

app/models.py

MilanMathew/machine_test_focaloid

fa179e655c531825167e97aed4e2d6affea9c736

app/models.py

MilanMathew/machine_test_focaloid

fa179e655c531825167e97aed4e2d6affea9c736

app/models.py

MilanMathew/machine_test_focaloid

fa179e655c531825167e97aed4e2d6affea9c736

from datetime import datetime from app import db class City(db.Model): __tablename__ = 'Cities' __table_args__ = {'sqlite_autoincrement': True} id = db.Column(db.Integer, primary_key=True) name = db.Column(db.String(50), index=True) class Venue(db.Model): __tablename__ = 'Venues' __table_args__ = {'sqlite_autoincrement': True} id = db.Column(db.Integer, primary_key=True) city_id = db.Column(db.Integer, db.ForeignKey('Cities.id')) name = db.Column(db.String(100), index=True) class Team(db.Model): __tablename__ = 'Teams' __table_args__ = {'sqlite_autoincrement': True} id = db.Column(db.Integer, primary_key=True) # venue_id = db.Column(db.Integer, db.ForeignKey('Venues.id')) name = db.Column(db.String(100), index=True, unique=True) # squad = db.relationship('Player', backref='team', lazy='dynamic') def __repr__(self): return '<Team {}>'.format(self.name) class Player(db.Model): __tablename__ = 'Players' __table_args__ = {'sqlite_autoincrement': True} id = db.Column(db.Integer, primary_key=True) team_id = db.Column(db.Integer, db.ForeignKey('Teams.id')) name = db.Column(db.String(100), index=True) def __repr__(self): return '<Player {}>'.format(self.name) class Official(db.Model): __tablename__ = 'Officials' __table_args__ = {'sqlite_autoincrement': True} id = db.Column(db.Integer, primary_key=True) name = db.Column(db.String(100), index=True) class Match(db.Model): __tablename__ = 'Matches' __table_args__ = {'sqlite_autoincrement': True} id = db.Column(db.Integer, primary_key=True) city = db.Column(db.Integer, db.ForeignKey('Cities.id')) venue = db.Column(db.Integer, db.ForeignKey('Venues.id')) umpire1 = db.Column(db.Integer, db.ForeignKey('Officials.id')) umpire2 = db.Column(db.Integer, db.ForeignKey('Officials.id')) team1 = db.Column(db.Integer, db.ForeignKey('Teams.id')) team2 = db.Column(db.Integer, db.ForeignKey('Teams.id')) toss_winner = db.Column(db.Integer, db.ForeignKey('Teams.id')) winner = db.Column(db.Integer, db.ForeignKey('Teams.id')) player_of_match = db.Column(db.String(100), index=True) toss_decision = db.Column(db.String(10), index=True) result = db.Column(db.String(10), index=True) win_by_runs = db.Column(db.Integer, index=True) win_by_wickets = db.Column(db.Integer, index=True) season = db.Column(db.Integer, index=True) date = db.Column(db.DateTime) dl_applied = db.Column(db.Integer, index=True)

e49516ca8ad700f85017d9325736d77d5ccd8a3d

py

Python

PTO-yelp/Modules/attention_classifier.py

LegendTianjin/Point-Then-Operate

a6b0818343bc34c468738ab91ecea89dd03a9535

2019-06-06T05:30:32.000Z

2021-11-18T07:24:36.000Z

PTO-yelp/Modules/attention_classifier.py

lancopku/Point-Then-Operate

1c04ec326b52fc65f97f5610a6f16f6e938d583e

2019-08-30T09:49:26.000Z

2020-01-17T04:20:53.000Z

PTO-yelp/Modules/attention_classifier.py

ChenWu98/Point-Then-Operate

a6b0818343bc34c468738ab91ecea89dd03a9535

2019-06-17T06:20:47.000Z

2020-10-26T03:19:44.000Z

import os import numpy as np import torch import torch.nn as nn import torch.nn.functional as F from torch.autograd import Variable from utils.utils import gpu_wrapper from Modules.subModules.attention import AttentionUnit from torch.nn.utils.rnn import pack_padded_sequence as pack from torch.nn.utils.rnn import pad_packed_sequence as unpack class AttenClassifier(nn.Module): def __init__(self, emb_dim, dim_h, n_layers, dropout, bi): super(AttenClassifier, self).__init__() self.emb_dim = emb_dim self.n_layers = n_layers self.dim_h = dim_h self.dropout = dropout self.n_dir = 2 if bi else 1 self.Encoder = nn.GRU(input_size=self.emb_dim, hidden_size=self.dim_h, num_layers=self.n_layers, dropout=self.dropout, bidirectional=bi) self.Attention = AttentionUnit(query_dim=self.dim_h * self.n_dir, key_dim=self.dim_h * self.n_dir, atten_dim=self.dim_h) self.MLP = nn.Sequential(nn.Linear(self.dim_h * self.n_dir, 1), nn.Sigmoid()) def forward(self, inp, l, null_mask): """ :param inp: shape = (B, T, emb_dim) :param null_mask: shape = (B, T) :return: """ B = inp.shape[0] T = inp.shape[1] inp = inp.transpose(0, 1) # shape = (20, n_batch, emb_dim) packed_emb = pack(inp, l) outputs, h_n = self.Encoder(packed_emb) # h_n.shape = (n_layers * n_dir, n_batch, dim_h) outputs = unpack(outputs, total_length=T)[0] # shape = (20, n_batch, dim_h * n_dir) h_n = h_n.view(self.n_layers, self.n_dir, B, self.dim_h).transpose(1, 2).transpose(2, 3).contiguous().view(self.n_layers, B, -1) # shape = (n_layers, n_batch, dim_h * n_dir) h_n = h_n[-1, :, :] # shape = (n_batch, dim_h * n_dir) context, att_weight = self.Attention(h_n, outputs.transpose(0, 1), null_mask) # (n_batch, dim_h * n_dir), (n_batch, 20) cls = self.MLP(context).squeeze(1) # shape = (n_batch, ) return cls, att_weight

e4954716a9c58078b2bd1d9c30cda00cbdaf306f

py

Python

training/continueLearning.py

dwright37/generative-concatenative-image-captioner

2bb257d4791e362e42a30bf0e4ca32e84f80d942

training/continueLearning.py

dwright37/generative-concatenative-image-captioner

2bb257d4791e362e42a30bf0e4ca32e84f80d942

training/continueLearning.py

dwright37/generative-concatenative-image-captioner

2bb257d4791e362e42a30bf0e4ca32e84f80d942

def continueLearning(losses): improving = losses[0] < losses[1] or losses[1] < losses[2] return improving

e4954d56f09841ccf54e7784967df8b418345b0e

py

Python

minion/parser.py

timofurrer/minion-ci

411d0ea6638fb37d7e170cc8c8c5815304cc9f5c

2016-03-07T06:42:40.000Z

2021-03-06T02:43:02.000Z

minion/parser.py

timofurrer/minion-ci

411d0ea6638fb37d7e170cc8c8c5815304cc9f5c

2016-03-08T07:20:52.000Z

2017-04-21T18:15:12.000Z

minion/parser.py

timofurrer/minion-ci

411d0ea6638fb37d7e170cc8c8c5815304cc9f5c

2016-03-29T22:08:52.000Z

2021-06-16T16:29:30.000Z

""" `minion-ci` is a minimalist, decentralized, flexible Continuous Integration Server for hackers. This module contains the parser to parse the `minion.yml` file. :copyright: (c) by Timo Furrer :license: MIT, see LICENSE for details """ import yaml from .errors import MinionError def parse(path): """Parse the given minion.yml file""" try: with open(path) as minion_file: config = yaml.load(minion_file) except OSError: raise MinionError("No minion.yml config file found in repository") return config

e4966893077ea1b5b172e99b11516e9d34d31ec5

py

Python

Python/problem0852.py

1050669722/LeetCode-Answers

c8f4d1ccaac09cda63b60d75144335347b06dc81

Python/problem0852.py

1050669722/LeetCode-Answers

c8f4d1ccaac09cda63b60d75144335347b06dc81

Python/problem0852.py

1050669722/LeetCode-Answers

c8f4d1ccaac09cda63b60d75144335347b06dc81

class Solution: def peakIndexInMountainArray(self, A: List[int]) -> int: if not A: return None #前向差分 for k in range(1, len(A)-1): if A[k-1]<A[k] and A[k]>A[k+1]: return k return None #最大值 return A.index(max(A)) #二分查找 p, q = 0, len(A)-1 while p < q: k = (p+q) // 2 if A[k-1]<A[k]<A[k+1]: p = k+1 elif A[k-1]>A[k]>A[k+1]: q = k-1 else: return k return p

e49a3917364c39b81a8dd470087dc69990edf5b7

py

Python

finace/utils/rong_city.py

pythonyhd/finace

614d98ad92e1bbaa6cf7dc1d6dfaba4f24431688

2020-08-18T01:55:14.000Z

2020-08-18T01:55:14.000Z

finace/utils/rong_city.py

pythonyhd/finace

614d98ad92e1bbaa6cf7dc1d6dfaba4f24431688

finace/utils/rong_city.py

pythonyhd/finace

614d98ad92e1bbaa6cf7dc1d6dfaba4f24431688

# -*- coding: utf-8 -*- from pymongo import MongoClient from finace import settings class SpiderCity(object): """获取所有城市列表""" def __init__(self, ip=settings.MONGODB_HOST, port=settings.MONGODB_PORT, db_name=settings.MONGO_DATA_BASE, user=settings.MONGO_USER, password=settings.MONGO_PWD): self.db_name = db_name self.connection = MongoClient(ip, port) db_auth = self.connection.admin db_auth.authenticate(user, password) self.db = self.connection[db_name] self.table = self.db[settings.MONGO_CITY] def find(self, *args): """ 指定查找，默认是pymongo.cursor.Cursor，需要遍历获取单条 :param args: 字典 :return:mongo的cursor对象 """ return self.table.find(*args) def find_all(self): """ 返回所有，默认是pymongo.cursor.Cursor，需要遍历获取单条 :return: mongo的cursor对象 """ return self.table.find({}) def count(self): """ 返回数据总量 :return: """ return self.table.find({}).count() def get(self): start_urls = [] datas = self.find_all() for data in datas: url = data.get('url') name = data.get('name') item = dict(name=name, url=url) start_urls.append(item) return start_urls if __name__ == '__main__': spider = SpiderCity() url_list = spider.get() print(url_list)

e49b044e4f3bdfef09e6426d0ff3c5f755aa63ae

py

Python

bufflog/bufflog.py

bufferapp/python-bufflog

12d218dfb917419789c720fb1851a35708909810

bufflog/bufflog.py

bufferapp/python-bufflog

12d218dfb917419789c720fb1851a35708909810

bufflog/bufflog.py

bufferapp/python-bufflog

12d218dfb917419789c720fb1851a35708909810

2021-02-08T12:53:43.000Z

2021-02-08T12:53:43.000Z

import structlog import logging import sys import os from structlog.processors import JSONRenderer from structlog.stdlib import filter_by_level from structlog.stdlib import add_log_level_number from .datadog import tracer_injection def rename_message_key(_, __, event_dict): event_dict["message"] = event_dict["event"] event_dict.pop("event", None) return event_dict def increase_level_numbers(_, __, event_dict): event_dict["level"] = event_dict["level_number"] * 10 event_dict.pop("level_number", None) return event_dict LOG_LEVEL = os.getenv("LOG_LEVEL", "INFO") def get_logger(name=None, datadog=False): logging.basicConfig( format="%(message)s", stream=sys.stdout, level=LOG_LEVEL, ) processors = [ filter_by_level, rename_message_key, add_log_level_number, increase_level_numbers, structlog.stdlib.PositionalArgumentsFormatter(), structlog.processors.StackInfoRenderer(), structlog.processors.format_exc_info, structlog.processors.UnicodeDecoder(), JSONRenderer(), ] if datadog: processors.insert(0, tracer_injection) structlog.configure( context_class=dict, logger_factory=structlog.stdlib.LoggerFactory(), wrapper_class=structlog.stdlib.BoundLogger, cache_logger_on_first_use=True, processors=processors, ) return structlog.get_logger(name)

e49cb572bd1c712b03397fca3826c3ed98801ce6

py

Python

templator.py

daren-thomas/template-system-example

248d2f78392be826f3223ee27e90c82feb70a17a

templator.py

daren-thomas/template-system-example

248d2f78392be826f3223ee27e90c82feb70a17a

templator.py

daren-thomas/template-system-example

248d2f78392be826f3223ee27e90c82feb70a17a

""" templator.py reads in an excel file and a template and outputs a file for each row in the excel file, by substituting the template variables with the values in the columns. This technique uses pandas to read the excel file into a DataFrame and the python format operator ``%``` to apply the values. """ import sys import os import pandas as pd def main(template_file, excel_file): # read in the template and the excel file template = open(template_file, 'r').read() variables = pd.read_excel(excel_file) # loop over each row (note, one of the columns must be called "filename") for row_number, values in variables.iterrows(): filename = values['filename'] with open(filename, 'w') as f: f.write(template % values) if __name__ == '__main__': template_file = os.path.join(os.path.dirname(__file__), 'template.txt') excel_file = os.path.join(os.path.dirname(__file__), 'variables.xls') main(template_file, excel_file)

e4a25083351a643d4c8f2b90bb9ef5552f4ba55d

py

Python

src/stackoverflow/56339991/tests.py

mrdulin/python-codelab

3d960a14a96b3a673b7dc2277d202069b1f8e778

src/stackoverflow/56339991/tests.py

mrdulin/python-codelab

3d960a14a96b3a673b7dc2277d202069b1f8e778

src/stackoverflow/56339991/tests.py

mrdulin/python-codelab

3d960a14a96b3a673b7dc2277d202069b1f8e778

2020-02-19T08:02:04.000Z

2021-06-08T13:27:51.000Z

import unittest from test_base import TestBaseImporter from test_child import TestChildImporter if __name__ == '__main__': test_loader = unittest.TestLoader() test_classes_to_run = [TestBaseImporter, TestChildImporter] suites_list = [] for test_class in test_classes_to_run: suite = test_loader.loadTestsFromTestCase(test_class) suites_list.append(suite) big_suite = unittest.TestSuite(suites_list) unittest.TextTestRunner().run(big_suite)

e4a318b6cae44face6ae5927fa38829adbecfa61

py

Python

src/mains/test_tf.py

JungleEngine/Intelligent_Frame_Skipping_Network

8178acfe06e112f5d33acbd17ad33239a6c4afc2

src/mains/test_tf.py

JungleEngine/Intelligent_Frame_Skipping_Network

8178acfe06e112f5d33acbd17ad33239a6c4afc2

src/mains/test_tf.py

JungleEngine/Intelligent_Frame_Skipping_Network

8178acfe06e112f5d33acbd17ad33239a6c4afc2

# import tensorflow as tf # print(tf.__version__) # # # with tf.name_scope('scalar_set_one') as scope: # tf_constant_one = tf.constant(10, name="ten") # tf_constant_two = tf.constant(20, name="twenty") # scalar_sum_one = tf.add(tf_constant_one, tf_constant_two, name="scalar_ten_plus_twenty") # # # # with tf.name_scope('scalar_set_two') as scope: # tf_constant_three = tf.constant(30, name="thirty") # tf_constant_four = tf.constant(40, name="fourty") # scalar_sum_two = tf.add(tf_constant_three, tf_constant_four, name="scalar_thirty_plus_fourty") # # # scalar_sum_sum = tf.add(scalar_sum_one, scalar_sum_two) # # # sess = tf.Session() # sess.run(tf.global_variables_initializer()) # # tf_tensorboard_writer = tf.summary.FileWriter('./graphs', sess.graph) # tf_tensorboard_writer.close() # sess.close() import scipy import _pickle as cPickle def unpickle(file): with open(file, 'rb') as fo: dict = cPickle.load(fo) return dict data = unpickle("model_svm_relations.pkl")

e4a3bd3abdfaed582c987ca4af954c061d659067

py

Python

src/menus/user/Menu.py

stregea/TransactionTrackr

c38b99d56816becaa47a21400fb20c615d3483ef

2021-07-02T19:49:24.000Z

2021-07-08T02:59:25.000Z

src/menus/user/Menu.py

stregea/TransactionTrackr

c38b99d56816becaa47a21400fb20c615d3483ef

src/menus/user/Menu.py

stregea/TransactionTrackr

c38b99d56816becaa47a21400fb20c615d3483ef

from objects.user.User import User from objects.interface.dbconn import DB from objects.user.Currency import get_currency_symbol from objects.threads.UploadThread import UploadThread import utils.globals as _globals from utils.print import print_message, print_error from utils.enums import Months, SettingsSelection, is_valid_month, month_string_to_enum from utils.visualizer import visualizer, visualizer_helper from utils.builders.folderbuilder import create_user_folder from utils.exceptions import NoDataFound, NoTotalFound, InvalidMonth, InvalidYear, UserNotFound from utils.dates.dates import get_dates, subtract_days from utils.averager.averager import calculate_average from utils.formatting.formatter import format_date_pretty, format_month_enum_to_string from utils.generators.csv_generator import generate_transaction_files from menus.user.Settings import Settings def user_has_data(user: User) -> bool: """ Test to determine if a user has any data :param user: The user to check. :return: True if the user has data. False otherwise. """ # Determine if the user has any available data. try: user.get_earliest_transaction_date() except Exception: # excepting NoDataFound here does not work for some reason? print_error("No data is currently available.") return False return True def is_valid_year(year_to_check: str) -> bool: """ Determine if the passed in year currently exists within the database. :param year_to_check: The year to check. :return: True if the year exists, false otherwise. """ year_is_valid = False db = DB(_globals.DATABASE) years = db.fetchall(f"SELECT DISTINCT strftime('%Y', Date) from Transactions;") db.close() # search through all the years. If the year that was specified exists, set the flag to true. for year in years: if year_to_check == year[0]: year_is_valid = True break return year_is_valid def get_month_and_year() -> (Months, str): """ Prompt a user to enter a month and a year. :raises InvalidMonth: Exception that is to be raised when user enters an invalid month. :raises InvalidYear: Exception that is to be raised when user enters an invalid year. :return: A Month enum and the year the user selected. """ month = input("Enter a month:\t") month_enum = month_string_to_enum(month) if is_valid_month(month_enum): year = input("Enter a year:\t") if is_valid_year(year): return month_enum, year else: raise InvalidYear(year) else: raise InvalidMonth(month) def get_year(): """ Prompt a user to enter a year. :raises InvalidYear: Exception that is to be raised if a user enters an invalid year. :return: The year the user enters. """ year = input("Enter a year:\t") if is_valid_year(year): return year raise InvalidYear(year) def display_monthly_information(user: User, month: Months, year: str, show_console: bool = False, show_visual: bool = False) -> None: """ Display information regarding the total money spent within a month. :param user: The current user. :param month: The month to get the information regarding how much was spent. :param year: The year corresponding to the month. :param show_console: Boolean to determine whether or not to display the information of the total spent in a month to the console. :param show_visual: Boolean to determine whether or not to display a visualization of the total spent in the month. """ try: # Dictionary that contains the information about all of the transactions in a given month. # The key is the day, the value is the total spent on that day. transactions_dictionary = visualizer_helper.get_transactions_by_month(month, year, user.id) # The total amount of money spent during the specified month. total = visualizer_helper.get_monthly_total(month, year, user.id) except (NoDataFound, NoTotalFound) as n: print_error(n.message) return # List to hold the dollar values for each day. dollars = [] # List to hold the labels that correspond to each day in the month that had a transaction. day_labels = [] # List of hold the labels that correspond to the dollar values for the transactions. dollars_labels = [] # The type of currency the current user is using. currency_symbol = get_currency_symbol(user.currency_id) # The title to be displayed on the console and/or the visualization title = f"Total spent in {format_month_enum_to_string(month)} {year}: {currency_symbol}{total:,}" for date_key in transactions_dictionary: day_labels.append(date_key) # Sort the labels (YYYY-MM-DD - End of Month) day_labels.sort() # Add the dollar amount to the corresponding day index, then create a label for that day. for day in day_labels: value = round(float(transactions_dictionary[day]), 2) dollars.append(value) dollars_labels.append(f"{currency_symbol}{value:,}") # Display each day and then display the total spent for the month if show_console: # TODO: change to function to prevent duplicated code. for i, day in enumerate(day_labels): print_message(f"{day}:\t{dollars_labels[i]}") print_message(f"{title}") # Display a visualization of the money spent in the month specified if show_visual: visualizer.display_bar_chart(title=title, list_of_values=dollars, list_of_labels=day_labels, currency_labels=dollars_labels) def display_yearly_information(user: User, year: str, show_console: bool = False, show_visual: bool = False) -> None: """ Display information regarding the total money spent within a certain year. :param user: The current user. :param year: The year to gather information from. :param show_console: Boolean to determine whether or not to display the information of the total spent in a year to the console. :param show_visual: Boolean to determine whether or not to display a visualization of the total spent in the month. """ try: # Dictionary to contain the total transaction values per month given the year transactions_dictionary = visualizer_helper.get_transactions_by_year(year, user.id) # The total amount of money spent during the specified year. total = visualizer_helper.get_yearly_total(year, user.id) except (NoDataFound, NoTotalFound) as n: print_error(n.message) return # List to hold the dollar values for each month. dollars = [] # List to hold the labels that correspond to the total number of transactions in each month. month_labels = [] # List of hold the labels that correspond to the dollar values for the transactions. dollars_labels = [] # The type of currency the current user is using. currency_symbol = get_currency_symbol(user.currency_id) # The title to be displayed on the console and/or the visualization title = f"Total Spent in {year}: {currency_symbol}{total:,}" for month_name in transactions_dictionary: value = round(float(transactions_dictionary[month_name]), 2) dollars.append(value) dollars_labels.append(f"{currency_symbol}{value:,}") # Not formatting month name here since the string is already in the key format for the months dictionary. month_labels.append(_globals.months[month_name]) if show_console: for i, month in enumerate(month_labels): print_message(f"{month}: {dollars_labels[i]}") print_message(f"{title}") if show_visual: visualizer.display_bar_chart(title=title, list_of_values=dollars, list_of_labels=month_labels, currency_labels=dollars_labels) def display_information_all_time(user: User, show_console: bool = False, show_visual: bool = False) -> None: """ Display information regarding the total money spent all time. :param user: The current user. :param show_console: Boolean to determine whether or not to display the information of the total spent in a year to the console. :param show_visual: Boolean to determine whether or not to display a visualization of the total spent in the month. """ try: transactions_dictionary = visualizer_helper.get_transactions_all_time(user.id) total = visualizer_helper.get_total_all_time(user.id) except NoDataFound as ndf: print_error(ndf.message) return # List to hold the total dollar values for each available year. dollars = [] # List to hold the labels that correspond to the total number of transactions in each year. year_labels = [] # List of hold the labels that correspond to the dollar values for the transactions. dollars_labels = [] # The type of currency the current user is using. currency_symbol = get_currency_symbol(user.currency_id) # The title to be displayed on the console and/or the visualization title = f"Total Spent All Time: {currency_symbol}{total:,}" for year in transactions_dictionary: value = round(float(transactions_dictionary[year]), 2) dollars.append(value) dollars_labels.append(f"{currency_symbol}{value:,}") year_labels.append(year) if show_console: for i, year in enumerate(year_labels): print_message(f"{year}: {dollars_labels[i]}") print_message(f"{title}") if show_visual: visualizer.display_bar_chart(title=title, list_of_values=dollars, list_of_labels=year_labels, currency_labels=dollars_labels) class Menu: """ This class serves as the main menu for the user. """ def __init__(self, user: User, show_console=False, show_visual=False) -> None: self.user = user self.show_console = show_console self.show_visual = show_visual def display_money_spent_per_month(self) -> None: """ Menu option that will display information based on a specified month. (Menu option #1). """ # Determine if the user has any available data. if not user_has_data(self.user): return try: month, year = get_month_and_year() except (InvalidMonth, InvalidYear) as e: print_error(e.message) return display_monthly_information(user=self.user, month=month, year=year, show_console=self.show_console, show_visual=self.show_visual) def display_money_spent_per_year(self) -> None: """ Menu option that will display yearly information. (Menu option #2). """ # Determine if the user has any available data. if not user_has_data(self.user): return try: year = get_year() except InvalidYear as iy: print_error(iy.message) return display_yearly_information(self.user, year, show_console=self.show_console, show_visual=self.show_visual) def display_money_spent_all_time(self) -> None: """ Menu option that will display the total money spent all time. (Menu option #2). """ # Determine if the user has any available data. if not user_has_data(self.user): return display_information_all_time(self.user, show_console=self.show_console, show_visual=self.show_visual) def display_daily_average_spending_per_month(self) -> None: """ Menu option that will display the user's daily average spending over a specified month as well as a Pie Chart to show where most of the money was spent. """ # Determine if the user has any available data. if not user_has_data(self.user): return try: month, year = get_month_and_year() except (InvalidMonth, InvalidYear) as e: print_error(e.message) return # Get the starting and end dates for the user-specified month. dates = get_dates(month, year) # Get the user's specified currency currency_symbol = get_currency_symbol(self.user.currency_id) try: # get the monthly average spent per day. monthly_average = calculate_average(start_date=dates[0], end_date=dates[1], user_id=self.user.id, exception_type="monthly average") except NoDataFound as ndf: print_message(ndf.message) return print_message( f"Your daily average spending for {format_month_enum_to_string(month)} {year} is: {currency_symbol}{monthly_average:,}") def display_daily_average_spending_per_year(self) -> None: """ Menu option that will display the user's daily average spending over a specified year. """ # Determine if the user has any available data. if not user_has_data(self.user): return try: year = get_year() except InvalidYear as iy: print_error(iy.message) return # Get the user's specified currency currency_symbol = get_currency_symbol(self.user.currency_id) start_date = f"{year}-01-01" end_date = f"{year}-12-31" dates = (start_date, end_date) try: yearly_average = calculate_average(start_date=dates[0], end_date=dates[1], user_id=self.user.id, exception_type="yearly average") except NoDataFound as ndf: print_error(ndf.message) return print_message(f"Your daily average spending for {year} is: {currency_symbol}{yearly_average:,}") def display_daily_average_spending_all_time(self) -> None: """ Menu option that will display the user's all-time daily average spending. """ # Determine if the user has any available data. if not user_has_data(self.user): return try: start_date = self.user.get_earliest_transaction_date() end_date = self.user.get_latest_transaction_date() all_time_average = calculate_average(start_date=start_date, end_date=end_date, user_id=self.user.id, exception_type="all time average") except NoDataFound as ndf: print_error(ndf.message) return currency_symbol = get_currency_symbol(self.user.currency_id) print_message(f"Your all-time daily average spending is: {currency_symbol}{all_time_average:,}") def display_daily_average_over_n_days(self) -> None: """ Menu option that will allow a user to determine the user's daily spending average over a certain number of days. """ # Determine if the user has any available data. if not user_has_data(self.user): return number_of_days = input("How many days would you like to go back?\t") if number_of_days.isdigit(): try: earliest_date = self.user.get_earliest_transaction_date() end_date = self.user.get_latest_transaction_date() start_date = subtract_days(starting_date=end_date, days=int(number_of_days)) # if a user selects a day that is older than their oldest transaction, # select the date associated with their oldest transaction. if start_date < earliest_date: formatted_start_date = format_date_pretty(start_date) formatted_earliest_date = format_date_pretty(earliest_date) print_message( f"The selected date is older than {formatted_earliest_date}, which is the oldest known transaction date.") print_error( f"Using '{formatted_earliest_date}' to calculate the average instead of '{formatted_start_date}'...") start_date = earliest_date daily_average = calculate_average(start_date=start_date, end_date=end_date, user_id=self.user.id, exception_type="all time average") except NoDataFound as ndf: print_error(ndf.message) return currency_symbol = get_currency_symbol(self.user.currency_id) formatted_start_date = format_date_pretty(start_date) formatted_end_date = format_date_pretty(end_date) print_message(f"Using the latest data from: {formatted_end_date}...") print_message( f"Your daily average spending between {formatted_start_date} - {formatted_end_date} is: {currency_symbol}{daily_average:,}") elif number_of_days.startswith("-") and number_of_days[1:].isdigit(): # if a negative number was entered. print_error("Please enter a positive integer.") else: print_error("Invalid number of days to go back.") def display_daily_average_over_period(self): # Determine if the user has any available data. if not user_has_data(self.user): return try: earliest_date = self.user.get_earliest_transaction_date() latest_date = self.user.get_latest_transaction_date() except NoDataFound as ndf: print_error(ndf.message) return try: print_message("Select your first month/year:") month1, year1 = get_month_and_year() print_message("Select your second month/year:") month2, year2 = get_month_and_year() except (InvalidMonth, InvalidYear) as e: print_error(e.message) return starting_date = get_dates(month1, year1)[0] ending_date = get_dates(month2, year2)[1] # If the starting date is greater than the ending date, swap the dates by: # changing the starting_date to the beginning of the second selected month, # then change the ending_date to the end of the first month. if starting_date > ending_date: starting_date = get_dates(month2, year2)[0] ending_date = get_dates(month1, year1)[1] # If starting or ending day < earliest date if starting_date < earliest_date: starting_date = earliest_date if ending_date < earliest_date: ending_date = earliest_date # If starting date or ending date > latest date, use the latest date and the option if starting_date > latest_date: starting_date = latest_date if ending_date > latest_date: ending_date = latest_date try: average_over_period = calculate_average(start_date=starting_date, end_date=ending_date, user_id=self.user.id) except NoDataFound as ndf: print(ndf.message) return formatted_start_date = format_date_pretty(starting_date) formatted_end_date = format_date_pretty(ending_date) currency_symbol = get_currency_symbol(self.user.currency_id) print_message( f"Your daily average spending between {formatted_start_date} - {formatted_end_date} is: {currency_symbol}{average_over_period:,}") def display_total_number_of_transactions(self): # Determine if the user has any available data. if not user_has_data(self.user): return try: earliest_date = format_date_pretty(self.user.get_earliest_transaction_date()) latest_date = format_date_pretty(self.user.get_latest_transaction_date()) print_message( f"You have made {self.user.get_total_transactions()} total transactions between {earliest_date} and {latest_date}") except NoDataFound as ndf: print_error(ndf.message) return def run(self) -> bool: """ Run the driver to this object. """ # show where money is most spent + include all time total (merchant) -- maybe implement as pie chart # include option to perform query's -- make it dynamic print_message(f"--- Welcome, {self.user.username}! ---") print_message("Enter a number for the following prompt:") running = True while running: print_message("1.\tShow money spent per month") print_message("2.\tShow money spent per year") print_message("3.\tShow money spent all time") print_message("4.\tTotal daily average spending per month") print_message("5.\tTotal daily average spending per year") print_message("6.\tTotal daily average spending all time") print_message("7.\tTotal daily average spending over number of days.") print_message("8.\tTotal daily average spending over period.") print_message("9.\tDisplay total number of transactions") print_message("10.\tUpload Data") print_message("11.\tSettings") print_message("12.\tSign out") print_message("13.\tQuit") response = input() if response == '1': # display a chart of the total money spent in a given month. self.display_money_spent_per_month() elif response == '2': # display a chart of the total money spent in a given year. self.display_money_spent_per_year() elif response == '3': # display a chart of all of the money spent. self.display_money_spent_all_time() elif response == '4': # display the daily average spending that a user has made. self.display_daily_average_spending_per_month() elif response == '5': # display the daily average spending that a user has made. self.display_daily_average_spending_per_year() elif response == '6': # display the daily average spending that a user has made. self.display_daily_average_spending_all_time() elif response == '7': # display the daily average spending that a user has made. self.display_daily_average_over_n_days() elif response == '8': # display the daily average spending that a user has made. self.display_daily_average_over_period() elif response == '9': # display the daily average spending that a user has made. self.display_total_number_of_transactions() elif response == '10' or response.lower() == 'upload': # upload data to the database from the 'upload' directory. # UserNotFound Exception should never be caught here, but it is still being handled. try: create_user_folder(user=self.user) UploadThread(self.user).run() except UserNotFound as unf: print_error(unf.message) elif response == '11': # User settings selection = Settings(self.user).run() # immediately exit the user menu if the user had selected to delete their account. if selection.value == SettingsSelection.DELETE_ACCOUNT.value: running = False elif response == '12' or response.lower() == 'signout': self.user.sign_out() running = False # set running to false, then return to sign in screen. elif response == '13' or response.lower() == 'quit': self.user.sign_out() exit(0) # exit the main program successfully. else: print_error("Unknown Command.") return not running

e4a69e3428e588c7d00739ddb17751edb51f6451

py

Python

website/CookieHelper.py

sousic/flask.huny.kr

53a8f5af1fa63b290a4e97278a86328758e97d43

website/CookieHelper.py

sousic/flask.huny.kr

53a8f5af1fa63b290a4e97278a86328758e97d43

website/CookieHelper.py

sousic/flask.huny.kr

53a8f5af1fa63b290a4e97278a86328758e97d43

# -*- coding: UTF-8 -*- import base64 from functools import wraps import pyaes from flask import request from werkzeug.utils import redirect from website.domain.UserVO import UserVO class CookieHelper(object): def init_app(self, app): self.app = app self.app.cookie_helper = self def SetCookies(self, response, user): aes = pyaes.AESModeOfOperationCTR(self.app.config['SECRET_KEY']) chiphertext = aes.encrypt(user.to_json()) response.set_cookie(self.app.config['COOKIE_NAME'], base64.b16encode(chiphertext)) def GetCookies(self, request): if request is not None: decrypted = request.cookies.get(self.app.config['COOKIE_NAME']) if decrypted is not None: cookies = base64.b16decode(decrypted) aes = pyaes.AESModeOfOperationCTR(self.app.config['SECRET_KEY']) decrpyted = aes.decrypt(cookies) return decrpyted else: return None else: return None # 쿠키 내역 아이디만 추출 def GetUserID(self, request): c = self.GetCookies(request) user = UserVO() if c is not None: user.to_object(c) return user.user_id # 쿠키 체크 decorator def CheckCookie(self, f): @wraps(f) def _check_cookie_(*arg, **kwargs): cookie = self.GetCookies(request) if cookie is None: return redirect('/') return f(*arg, **kwargs) return _check_cookie_ #로그인 체크 def IsLogin(self, request): cookie = self.GetCookies(request) if cookie is None: return False else: return True

e4a6e1bb797c7875ed388c77bf15d0c26b3189cb

py

Python

export_resized_ios_assets.py

Tubbebubbe/gimp-plugins

11221ded072d8d3001202f30fda266e0cccd3a36

2016-08-03T18:20:59.000Z

2020-05-24T04:38:47.000Z

export_resized_ios_assets.py

Tubbebubbe/gimp-plugins

11221ded072d8d3001202f30fda266e0cccd3a36

export_resized_ios_assets.py

Tubbebubbe/gimp-plugins

11221ded072d8d3001202f30fda266e0cccd3a36

2017-10-23T08:23:36.000Z

2020-05-24T04:38:57.000Z

#!/usr/bin/env python """ export_resized_ios_images Gimp plugin to export image to icon files usable on iOS. Author: ------- Tobias Blom, Techne Development AB <tobias.blom@techne-dev.se> Installation: ------------- Under Mac OS X, copy this file to ~/Library/Application Support/GIMP/x.x/plug-ins and make it executable (chmod 755) Usage: ------ 1. Create your image at a resolution four times what you want on a standard iOS device, twice the size on a retina device. GIMP image Plug-in output ------------------------------------------------- 80 x 80 @ 144 dpi | Icon 20 x 20 @ 72 dpi | Icon 40 x 40 @ 144 dpi | Icon 60 x 60 @ 144 dpi ------------------------------------------------- 120 x 120 @ 144 dpi | Icon 30 x 30 @ 72 dpi | Icon 60 x 60 @ 144 dpi | Icon 90 x 90 @ 144 dpi ------------------------------------------------- 2. Run the plug-in (from the File menu) and select the output directory. License: -------- Released under the MIT License Copyright (c) 2013-2017 Techne Development AB Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. """ from gimpfu import * import os def gprint(text): pdb.gimp_message(text) return def resize_and_save_image(timg, tdrawable, scale_factor, dpi, directory, filename): img = timg.duplicate() img.merge_visible_layers(0) width = timg.width * scale_factor height = timg.height * scale_factor fullpath = os.path.join(directory, filename) pdb.gimp_image_merge_visible_layers(img, CLIP_TO_IMAGE) pdb.gimp_image_scale(img, width, height) pdb.gimp_image_set_resolution(img, dpi, dpi) pdb.file_png_save(img, img.layers[0], fullpath, filename, 0, 9, 1, 1, 1, 1, 1) def plugin_main(img, drawable, directory): basename = os.path.basename(img.filename[0:-4]) resize_and_save_image(img, drawable, 0.25, 72, directory, basename + ".png") resize_and_save_image(img, drawable, 0.5, 144, directory, basename + "@2x.png") resize_and_save_image(img, drawable, 0.75, 144, directory, basename + "@3x.png") register( "export_resized_ios_assets", "Exports iOS assets at 50% and 75% (144 dpi) and 25% (72 dpi) size", "Exports iOS assets at 50% and 75% (144 dpi) and 25% (72 dpi) size", "Techne Development AB", "Copyright (c) 2013-2017 Techne Development AB. Released under MIT License.", "2017", "<Image>/File/Export as iOS assets...", "RGB*, GRAY*", [ (PF_DIRNAME, "directory", "Output directory", os.path.expanduser("~")), ], [], plugin_main) main()

e4a86bcd74faf3a16d79362c4832a1d23917c50f

py

Python

SortingComparison.py

kogol99/MSiD

bbe0ee535f785476a3fe75f0654f496c185565e4

SortingComparison.py

kogol99/MSiD

bbe0ee535f785476a3fe75f0654f496c185565e4

2020-03-15T20:17:04.000Z

2020-06-05T01:58:35.000Z

SortingComparison.py

kogol99/MSiD

bbe0ee535f785476a3fe75f0654f496c185565e4

2020-03-15T17:30:40.000Z

2020-04-11T20:16:28.000Z

from timeit import default_timer as timer import random def insertion_sort(list_to_sort): for step in range(1, len(list_to_sort)): key = list_to_sort[step] j = step - 1 while j >= 0 and list_to_sort[j] > key: list_to_sort[j + 1] = list_to_sort[j] j -= 1 list_to_sort[j + 1] = key def quick_sort(list_to_sort): def partition(sub_list, low, hi): pivot = sub_list[(low + hi) // 2] left = low right = hi while left <= right: while sub_list[left] < pivot: left += 1 while sub_list[right] > pivot: right -= 1 if left <= right: sub_list[left], sub_list[right] = sub_list[right], sub_list[left] left += 1 right -= 1 return left, right def quick_sort_fun(list_to_sort, low, hi): if low < hi: left, right = partition(list_to_sort, low, hi) quick_sort_fun(list_to_sort, low, right) quick_sort_fun(list_to_sort, left, hi) quick_sort_fun(list_to_sort, 0, len(list_to_sort) - 1) # shell sort using Knuth's sequence def shell_sort(list_to_sort): def sublist_sort(list_to_sort, start_index, gap): for i in range(start_index + gap, len(list_to_sort), gap): current_val = list_to_sort[i] index = i while index >= gap and list_to_sort[index - gap] > current_val: list_to_sort[index] = list_to_sort[index - gap] index -= gap list_to_sort[index] = current_val n = len(list_to_sort) gap = 1 while gap < n // 3: gap = 3 * gap + 1 while gap > 0: for i in range(gap): sublist_sort(list_to_sort, i, gap) gap //= 3 def heap_sort(list_to_sort): def heapify(list_to_sort, n, i): largest_index = i left_index = 2 * i + 1 right_index = 2 * i + 2 if left_index < n and list_to_sort[i] < list_to_sort[left_index]: largest_index = left_index if right_index < n and list_to_sort[largest_index] < list_to_sort[right_index]: largest_index = right_index if largest_index != i: list_to_sort[i], list_to_sort[largest_index] = list_to_sort[largest_index], list_to_sort[i] heapify(list_to_sort, n, largest_index) n = len(list_to_sort) for i in range(n, -1, -1): heapify(list_to_sort, n, i) for i in range(n - 1, 0, -1): list_to_sort[i], list_to_sort[0] = list_to_sort[0], list_to_sort[i] heapify(list_to_sort, i, 0) def benchmark_sorting_algorithms(functions_list, list_to_sort): print("BENCHMARK START (Times in ms)") def benchmark_one_function(function_to_benchmark, *arguments): start = timer() function_to_benchmark(*arguments) end = timer() print("{0}: {1}".format(function_to_benchmark.__name__, (end - start) * 1000)) for function in functions_list: benchmark_one_function(function, list_to_sort.copy()) def main(): list_to_sort_1 = [random.randint(-10000, 10000) for _ in range(10000)] list_to_sort_2 = list(range(10000)) list_to_sort_3 = list(range(10000, 0, -1)) print("Random list") benchmark_sorting_algorithms([quick_sort, heap_sort, shell_sort, insertion_sort], list_to_sort_1) print("Increasing list") benchmark_sorting_algorithms([quick_sort, heap_sort, shell_sort, insertion_sort], list_to_sort_2) print("Decreasing list") benchmark_sorting_algorithms([quick_sort, heap_sort, shell_sort, insertion_sort], list_to_sort_3) if __name__ == "__main__": main()

e4a93421928eb84ea60e2492daf9f320c6c9d564

py

Python

site/office/compline.py

scottBowles/dailyoffice2019

ca750ac77316d247ca7a7a820e085f9968fbc8ff

2020-01-12T23:57:22.000Z

2022-03-30T16:35:17.000Z

site/office/compline.py

scottBowles/dailyoffice2019

ca750ac77316d247ca7a7a820e085f9968fbc8ff

2020-01-13T00:45:27.000Z

2022-02-20T04:10:05.000Z

site/office/compline.py

scottBowles/dailyoffice2019

ca750ac77316d247ca7a7a820e085f9968fbc8ff

2020-01-21T21:12:03.000Z

2021-10-24T01:15:50.000Z

import datetime from django.utils.functional import cached_property from django.utils.safestring import mark_safe from office.offices import Office, OfficeSection from psalter.utils import get_psalms class Compline(Office): name = "Compline" office = "compline" def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.description = "Office: {}, Date: {}, Commemoration: {}, Prayer Book: {}".format( "Compline (Bedtime Prayer)", self.get_formatted_date_string(), self.date.primary_evening.name, "The Book of Common Prayer (2019), Anglican Church in North America", ) self.start_time = datetime.datetime.combine(self.date.date, datetime.time()) self.start_time = self.start_time.replace(minute=0, hour=20, second=0) self.end_time = self.start_time.replace(minute=59, hour=23, second=59) @cached_property def modules(self): return [ (ComplineHeading(self.date, self.office_readings), "office/heading.html"), (ComplineCommemorationListing(self.date, self.office_readings), "office/commemoration_listing.html"), (ComplineOpening(self.date, self.office_readings), "office/compline_opening.html"), (ComplineConfession(self.date, self.office_readings), "office/compline_confession.html"), (ComplineInvitatory(self.date, self.office_readings), "office/compline_invitatory.html"), (ComplinePsalms(self.date, self.office_readings), "office/minor_office_psalms.html"), (ComplineScripture(self.date, self.office_readings), "office/minor_office_scripture.html"), (ComplinePrayers(self.date, self.office_readings), "office/compline_prayers.html"), (ComplineCanticle(self.date, self.office_readings), "office/compline_canticle.html"), (ComplineConclusion(self.date, self.office_readings), "office/compline_conclusion.html"), ] class ComplineHeading(OfficeSection): @cached_property def data(self): return {"heading": mark_safe("Compline"), "calendar_date": self.date} class ComplineCommemorationListing(OfficeSection): @cached_property def data(self): return { "day": self.date, "evening": True, "heading": "This Nights's Commemoration{}".format("s" if len(self.date.all) > 1 else ""), "commemorations": self.date.all_evening, } class ComplineOpening(OfficeSection): @cached_property def data(self): return {} class ComplineConfession(OfficeSection): @cached_property def data(self): return {"heading": "Confession of Sin"} class ComplineInvitatory(OfficeSection): @cached_property def data(self): return { "alleluia": self.date.evening_season.name != "Lent" and self.date.evening_season.name != "Holy Week", "heading": "Invitatory", } class ComplinePsalms(OfficeSection): @cached_property def data(self): return {"heading": "The Psalms", "psalms": get_psalms("4,31:1-6,91,134")} class ComplineScripture(OfficeSection): def get_scripture(self): if self.date.date.weekday() in [0, 4]: return { "sentence": "You, O Lord, are in the midst of us, and we are called by your name; do not leave us.", "citation": "JEREMIAH 14:9", } if self.date.date.weekday() in [1, 5]: return { "sentence": "Come to me, all who labor and are heavy laden, and I will give you rest. Take my yoke upon you, and learn from me, for I am gentle and lowly in heart, and you will find rest for your souls. For my yoke is easy, and my burden is light.", "citation": "MATTHEW 11:28-30", } if self.date.date.weekday() in [2, 6]: return { "sentence": "Now may the God of peace who brought again from the dead our Lord Jesus, the great shepherd of the sheep, by the blood of the eternal covenant, equip you with everything good that you may do his will, working in us that which is pleasing in his sight, through Jesus Christ, to whom be glory forever and ever. Amen.", "citation": "HEBREWS 13:20-21", } if self.date.date.weekday() in [3]: return { "sentence": "Be sober-minded; be watchful. Your adversary the devil prowls around like a roaring lion, seeking someone to devour. Resist him, firm in your faith.", "citation": "1 PETER 5:8-9", } @cached_property def data(self): return {"heading": "The Reading", "sentence": self.get_scripture()} class ComplinePrayers(OfficeSection): collects = [ ( "A Collect for Evening", "Visit this place, O Lord, and drive far from it all snares of the enemy; let your holy angels dwell with us to preserve us in peace; and let your blessing be upon us always; through Jesus Christ our Lord.", ), ( "A Collect for Aid Against Peril", "Lighten our darkness, we beseech you, O Lord; and by your great mercy defend us from all perils and dangers of this night; for the love of your only Son, our Savior Jesus Christ.", ), ( "A Collect for Evening", "Be present, O merciful God, and protect us through the hours of this night, so that we who are wearied by the changes and chances of this life may rest in your eternal changelessness; through Jesus Christ our Lord.", ), ( "A Collect for Evening", "Look down, O Lord, from your heavenly throne, illumine this night with your celestial brightness, and from the children of light banish the deeds of darkness; through Jesus Christ our Lord.", ), ( "A Collect for Saturdays", "We give you thanks, O God, for revealing your Son Jesus Christ to us by the light of his resurrection: Grant that as we sing your glory at the close of this day, our joy may abound in the morning as we celebrate the Paschal mystery; through Jesus Christ our Lord.", ), ( "A Collect for Mission", "Keep watch, dear Lord, with those who work, or watch, or weep this night, and give your angels charge over those who sleep. Tend the sick, Lord Christ; give rest to the weary, bless the dying, soothe the suffering, pity the afflicted, shield the joyous; and all for your love’s sake.", ), ( "A Collect for Evening", "O God, your unfailing providence sustains the world we live in and the life we live: Watch over those, both night and day, who work while others sleep, and grant that we may never forget that our common life depends upon each other’s toil; through Jesus Christ our Lord.", ), ] def get_collects(self): if self.date.date.weekday() in [6]: # Sunday return self.collects[0], self.collects[1], self.collects[5] if self.date.date.weekday() in [0]: # Monday return self.collects[2], self.collects[3], self.collects[5] if self.date.date.weekday() in [1]: # Tuesday return self.collects[0], self.collects[2], self.collects[5] if self.date.date.weekday() in [2]: # Wednesday return self.collects[1], self.collects[3], self.collects[6] if self.date.date.weekday() in [3]: # Thursday return self.collects[0], self.collects[3], self.collects[5] if self.date.date.weekday() in [4]: # Friday return self.collects[1], self.collects[2], self.collects[6] if self.date.date.weekday() in [5]: # Saturday return self.collects[2], self.collects[4], self.collects[5] @cached_property def data(self): return {"heading": "The Prayers", "collects": self.get_collects()} class ComplineCanticle(OfficeSection): @cached_property def data(self): return { "heading": "Nunc Dimittis", "subheading": "The Song of Simeon", "alleluia": self.date.evening_season.name == "Eastertide", } class ComplineConclusion(OfficeSection): @cached_property def data(self): return {"alleluia": self.date.evening_season.name == "Eastertide"}

e4a98810c99783995caf35d9ff70ccf375552008

py

Python

src/tide_constituents/water_level_prediction.py

slawler/SI_2019_Coastal

4064d323bc62ce2f47a7af41b9a11ea5538ad181

2020-03-13T07:51:44.000Z

2020-03-13T07:51:44.000Z

src/tide_constituents/water_level_prediction.py

cheginit/SI_2019_Coastal

4064d323bc62ce2f47a7af41b9a11ea5538ad181

src/tide_constituents/water_level_prediction.py

cheginit/SI_2019_Coastal

4064d323bc62ce2f47a7af41b9a11ea5538ad181

2020-03-13T14:44:57.000Z

2020-03-13T14:44:57.000Z

import tide_constituents as tc from py_noaa import coops import pandas as pd import numpy as np import tappy start = '20180201' end = '20180228' interval = 1 start = pd.to_datetime(start) end = pd.to_datetime(end) d = start w, t, p, r = [], [], [], [] while d < end: start_ = d end_ = start_ + pd.DateOffset(interval) end_ = end_ if end_ < end else end water_level, tide = tc.get_water_levels(start_.strftime('%Y%m%d'), end_.strftime('%Y%m%d'), -88.2, 30.4) water_level = water_level.water_level.astype('float') prediction = 0.0 if 'Z0' not in list(tide.speed_dict.keys()) else tide.speed_dict['Z0'] prediction += sum_signals(tide.key_list, tide.dates, tide.speed_dict, tide.r, tide.phase) residual = water_level - prediction w.append(water_level) p.append(prediction) d = end_ water_level = pd.concat(w).to_frame() water_level.columns = ['observation'] water_level['prediction'] = np.hstack(p) data = tc.get_tides('20180101', '20181231', -88.2, 30.4) wl = data.predicted_wl.copy() grouped = wl.groupby(pd.Grouper(freq='M')) def f(group): return pd.DataFrame({'original': group, 'demeaned': group - group.mean()}) wl_demeaned = grouped.apply(f) min_month = wl_demeaned.rolling(30).min().groupby(pd.Grouper(freq='M')).last() max_month = wl_demeaned.rolling(30).max().groupby(pd.Grouper(freq='M')).last() monthly_minmax = min_month.copy() monthly_minmax['high'] = max_month['demeaned'] monthly_minmax = monthly_minmax[['demeaned', 'high']] monthly_minmax.columns = ['low', 'high'] monthly_minmax['range'] = monthly_minmax.high - monthly_minmax.low monthly_minmax.sort_values('range')

e4ab8e400d3a8428f396d10f517cf745bdb624df

py

Python

sdk/python/pulumi_azure/cosmosdb/cassandra_table.py

henriktao/pulumi-azure

f1cbcf100b42b916da36d8fe28be3a159abaf022

2018-06-18T00:19:44.000Z

2022-02-20T05:32:57.000Z

sdk/python/pulumi_azure/cosmosdb/cassandra_table.py

henriktao/pulumi-azure

f1cbcf100b42b916da36d8fe28be3a159abaf022

2018-06-18T21:08:46.000Z

2022-03-31T20:10:11.000Z

sdk/python/pulumi_azure/cosmosdb/cassandra_table.py

henriktao/pulumi-azure

f1cbcf100b42b916da36d8fe28be3a159abaf022

2018-07-19T22:37:38.000Z

2022-03-14T10:56:26.000Z

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union, overload from .. import _utilities from . import outputs from ._inputs import * __all__ = ['CassandraTableArgs', 'CassandraTable'] @pulumi.input_type class CassandraTableArgs: def __init__(__self__, *, cassandra_keyspace_id: pulumi.Input[str], schema: pulumi.Input['CassandraTableSchemaArgs'], analytical_storage_ttl: Optional[pulumi.Input[int]] = None, autoscale_settings: Optional[pulumi.Input['CassandraTableAutoscaleSettingsArgs']] = None, default_ttl: Optional[pulumi.Input[int]] = None, name: Optional[pulumi.Input[str]] = None, throughput: Optional[pulumi.Input[int]] = None): """ The set of arguments for constructing a CassandraTable resource. :param pulumi.Input[str] cassandra_keyspace_id: The ID of the Cosmos DB Cassandra Keyspace to create the table within. Changing this forces a new resource to be created. :param pulumi.Input['CassandraTableSchemaArgs'] schema: A `schema` block as defined below. Changing this forces a new resource to be created. :param pulumi.Input[int] analytical_storage_ttl: Time to live of the Analytical Storage. Possible values are at least `-1`. `-1` means the Analytical Storage never expires. Changing this forces a new resource to be created. :param pulumi.Input[int] default_ttl: Time to live of the Cosmos DB Cassandra table. Possible values are at least `-1`. `-1` means the Cassandra table never expires. :param pulumi.Input[str] name: Specifies the name of the Cosmos DB Cassandra Table. Changing this forces a new resource to be created. """ pulumi.set(__self__, "cassandra_keyspace_id", cassandra_keyspace_id) pulumi.set(__self__, "schema", schema) if analytical_storage_ttl is not None: pulumi.set(__self__, "analytical_storage_ttl", analytical_storage_ttl) if autoscale_settings is not None: pulumi.set(__self__, "autoscale_settings", autoscale_settings) if default_ttl is not None: pulumi.set(__self__, "default_ttl", default_ttl) if name is not None: pulumi.set(__self__, "name", name) if throughput is not None: pulumi.set(__self__, "throughput", throughput) @property @pulumi.getter(name="cassandraKeyspaceId") def cassandra_keyspace_id(self) -> pulumi.Input[str]: """ The ID of the Cosmos DB Cassandra Keyspace to create the table within. Changing this forces a new resource to be created. """ return pulumi.get(self, "cassandra_keyspace_id") @cassandra_keyspace_id.setter def cassandra_keyspace_id(self, value: pulumi.Input[str]): pulumi.set(self, "cassandra_keyspace_id", value) @property @pulumi.getter def schema(self) -> pulumi.Input['CassandraTableSchemaArgs']: """ A `schema` block as defined below. Changing this forces a new resource to be created. """ return pulumi.get(self, "schema") @schema.setter def schema(self, value: pulumi.Input['CassandraTableSchemaArgs']): pulumi.set(self, "schema", value) @property @pulumi.getter(name="analyticalStorageTtl") def analytical_storage_ttl(self) -> Optional[pulumi.Input[int]]: """ Time to live of the Analytical Storage. Possible values are at least `-1`. `-1` means the Analytical Storage never expires. Changing this forces a new resource to be created. """ return pulumi.get(self, "analytical_storage_ttl") @analytical_storage_ttl.setter def analytical_storage_ttl(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "analytical_storage_ttl", value) @property @pulumi.getter(name="autoscaleSettings") def autoscale_settings(self) -> Optional[pulumi.Input['CassandraTableAutoscaleSettingsArgs']]: return pulumi.get(self, "autoscale_settings") @autoscale_settings.setter def autoscale_settings(self, value: Optional[pulumi.Input['CassandraTableAutoscaleSettingsArgs']]): pulumi.set(self, "autoscale_settings", value) @property @pulumi.getter(name="defaultTtl") def default_ttl(self) -> Optional[pulumi.Input[int]]: """ Time to live of the Cosmos DB Cassandra table. Possible values are at least `-1`. `-1` means the Cassandra table never expires. """ return pulumi.get(self, "default_ttl") @default_ttl.setter def default_ttl(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "default_ttl", value) @property @pulumi.getter def name(self) -> Optional[pulumi.Input[str]]: """ Specifies the name of the Cosmos DB Cassandra Table. Changing this forces a new resource to be created. """ return pulumi.get(self, "name") @name.setter def name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "name", value) @property @pulumi.getter def throughput(self) -> Optional[pulumi.Input[int]]: return pulumi.get(self, "throughput") @throughput.setter def throughput(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "throughput", value) @pulumi.input_type class _CassandraTableState: def __init__(__self__, *, analytical_storage_ttl: Optional[pulumi.Input[int]] = None, autoscale_settings: Optional[pulumi.Input['CassandraTableAutoscaleSettingsArgs']] = None, cassandra_keyspace_id: Optional[pulumi.Input[str]] = None, default_ttl: Optional[pulumi.Input[int]] = None, name: Optional[pulumi.Input[str]] = None, schema: Optional[pulumi.Input['CassandraTableSchemaArgs']] = None, throughput: Optional[pulumi.Input[int]] = None): """ Input properties used for looking up and filtering CassandraTable resources. :param pulumi.Input[int] analytical_storage_ttl: Time to live of the Analytical Storage. Possible values are at least `-1`. `-1` means the Analytical Storage never expires. Changing this forces a new resource to be created. :param pulumi.Input[str] cassandra_keyspace_id: The ID of the Cosmos DB Cassandra Keyspace to create the table within. Changing this forces a new resource to be created. :param pulumi.Input[int] default_ttl: Time to live of the Cosmos DB Cassandra table. Possible values are at least `-1`. `-1` means the Cassandra table never expires. :param pulumi.Input[str] name: Specifies the name of the Cosmos DB Cassandra Table. Changing this forces a new resource to be created. :param pulumi.Input['CassandraTableSchemaArgs'] schema: A `schema` block as defined below. Changing this forces a new resource to be created. """ if analytical_storage_ttl is not None: pulumi.set(__self__, "analytical_storage_ttl", analytical_storage_ttl) if autoscale_settings is not None: pulumi.set(__self__, "autoscale_settings", autoscale_settings) if cassandra_keyspace_id is not None: pulumi.set(__self__, "cassandra_keyspace_id", cassandra_keyspace_id) if default_ttl is not None: pulumi.set(__self__, "default_ttl", default_ttl) if name is not None: pulumi.set(__self__, "name", name) if schema is not None: pulumi.set(__self__, "schema", schema) if throughput is not None: pulumi.set(__self__, "throughput", throughput) @property @pulumi.getter(name="analyticalStorageTtl") def analytical_storage_ttl(self) -> Optional[pulumi.Input[int]]: """ Time to live of the Analytical Storage. Possible values are at least `-1`. `-1` means the Analytical Storage never expires. Changing this forces a new resource to be created. """ return pulumi.get(self, "analytical_storage_ttl") @analytical_storage_ttl.setter def analytical_storage_ttl(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "analytical_storage_ttl", value) @property @pulumi.getter(name="autoscaleSettings") def autoscale_settings(self) -> Optional[pulumi.Input['CassandraTableAutoscaleSettingsArgs']]: return pulumi.get(self, "autoscale_settings") @autoscale_settings.setter def autoscale_settings(self, value: Optional[pulumi.Input['CassandraTableAutoscaleSettingsArgs']]): pulumi.set(self, "autoscale_settings", value) @property @pulumi.getter(name="cassandraKeyspaceId") def cassandra_keyspace_id(self) -> Optional[pulumi.Input[str]]: """ The ID of the Cosmos DB Cassandra Keyspace to create the table within. Changing this forces a new resource to be created. """ return pulumi.get(self, "cassandra_keyspace_id") @cassandra_keyspace_id.setter def cassandra_keyspace_id(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "cassandra_keyspace_id", value) @property @pulumi.getter(name="defaultTtl") def default_ttl(self) -> Optional[pulumi.Input[int]]: """ Time to live of the Cosmos DB Cassandra table. Possible values are at least `-1`. `-1` means the Cassandra table never expires. """ return pulumi.get(self, "default_ttl") @default_ttl.setter def default_ttl(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "default_ttl", value) @property @pulumi.getter def name(self) -> Optional[pulumi.Input[str]]: """ Specifies the name of the Cosmos DB Cassandra Table. Changing this forces a new resource to be created. """ return pulumi.get(self, "name") @name.setter def name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "name", value) @property @pulumi.getter def schema(self) -> Optional[pulumi.Input['CassandraTableSchemaArgs']]: """ A `schema` block as defined below. Changing this forces a new resource to be created. """ return pulumi.get(self, "schema") @schema.setter def schema(self, value: Optional[pulumi.Input['CassandraTableSchemaArgs']]): pulumi.set(self, "schema", value) @property @pulumi.getter def throughput(self) -> Optional[pulumi.Input[int]]: return pulumi.get(self, "throughput") @throughput.setter def throughput(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "throughput", value) class CassandraTable(pulumi.CustomResource): @overload def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, analytical_storage_ttl: Optional[pulumi.Input[int]] = None, autoscale_settings: Optional[pulumi.Input[pulumi.InputType['CassandraTableAutoscaleSettingsArgs']]] = None, cassandra_keyspace_id: Optional[pulumi.Input[str]] = None, default_ttl: Optional[pulumi.Input[int]] = None, name: Optional[pulumi.Input[str]] = None, schema: Optional[pulumi.Input[pulumi.InputType['CassandraTableSchemaArgs']]] = None, throughput: Optional[pulumi.Input[int]] = None, __props__=None): """ Manages a Cassandra Table within a Cosmos DB Cassandra Keyspace. ## Example Usage ```python import pulumi import pulumi_azure as azure example_resource_group = azure.core.get_resource_group(name="tflex-cosmosdb-account-rg") example_account = azure.cosmosdb.Account("exampleAccount", resource_group_name=example_resource_group.name, location=example_resource_group.location, offer_type="Standard", capabilities=[azure.cosmosdb.AccountCapabilityArgs( name="EnableCassandra", )], consistency_policy=azure.cosmosdb.AccountConsistencyPolicyArgs( consistency_level="Strong", ), geo_locations=[azure.cosmosdb.AccountGeoLocationArgs( location="West US", failover_priority=0, )]) example_cassandra_keyspace = azure.cosmosdb.CassandraKeyspace("exampleCassandraKeyspace", resource_group_name=example_account.resource_group_name, account_name=example_account.name, throughput=400) example_cassandra_table = azure.cosmosdb.CassandraTable("exampleCassandraTable", cassandra_keyspace_id=example_cassandra_keyspace.id, schema=azure.cosmosdb.CassandraTableSchemaArgs( columns=[ azure.cosmosdb.CassandraTableSchemaColumnArgs( name="test1", type="ascii", ), azure.cosmosdb.CassandraTableSchemaColumnArgs( name="test2", type="int", ), ], partition_keys=[azure.cosmosdb.CassandraTableSchemaPartitionKeyArgs( name="test1", )], )) ``` ## Import Cosmos Cassandra Table can be imported using the `resource id`, e.g. ```sh $ pulumi import azure:cosmosdb/cassandraTable:CassandraTable ks1 /subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/rg1/providers/Microsoft.DocumentDB/databaseAccounts/account1/cassandraKeyspaces/ks1/tables/table1 ``` :param str resource_name: The name of the resource. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[int] analytical_storage_ttl: Time to live of the Analytical Storage. Possible values are at least `-1`. `-1` means the Analytical Storage never expires. Changing this forces a new resource to be created. :param pulumi.Input[str] cassandra_keyspace_id: The ID of the Cosmos DB Cassandra Keyspace to create the table within. Changing this forces a new resource to be created. :param pulumi.Input[int] default_ttl: Time to live of the Cosmos DB Cassandra table. Possible values are at least `-1`. `-1` means the Cassandra table never expires. :param pulumi.Input[str] name: Specifies the name of the Cosmos DB Cassandra Table. Changing this forces a new resource to be created. :param pulumi.Input[pulumi.InputType['CassandraTableSchemaArgs']] schema: A `schema` block as defined below. Changing this forces a new resource to be created. """ ... @overload def __init__(__self__, resource_name: str, args: CassandraTableArgs, opts: Optional[pulumi.ResourceOptions] = None): """ Manages a Cassandra Table within a Cosmos DB Cassandra Keyspace. ## Example Usage ```python import pulumi import pulumi_azure as azure example_resource_group = azure.core.get_resource_group(name="tflex-cosmosdb-account-rg") example_account = azure.cosmosdb.Account("exampleAccount", resource_group_name=example_resource_group.name, location=example_resource_group.location, offer_type="Standard", capabilities=[azure.cosmosdb.AccountCapabilityArgs( name="EnableCassandra", )], consistency_policy=azure.cosmosdb.AccountConsistencyPolicyArgs( consistency_level="Strong", ), geo_locations=[azure.cosmosdb.AccountGeoLocationArgs( location="West US", failover_priority=0, )]) example_cassandra_keyspace = azure.cosmosdb.CassandraKeyspace("exampleCassandraKeyspace", resource_group_name=example_account.resource_group_name, account_name=example_account.name, throughput=400) example_cassandra_table = azure.cosmosdb.CassandraTable("exampleCassandraTable", cassandra_keyspace_id=example_cassandra_keyspace.id, schema=azure.cosmosdb.CassandraTableSchemaArgs( columns=[ azure.cosmosdb.CassandraTableSchemaColumnArgs( name="test1", type="ascii", ), azure.cosmosdb.CassandraTableSchemaColumnArgs( name="test2", type="int", ), ], partition_keys=[azure.cosmosdb.CassandraTableSchemaPartitionKeyArgs( name="test1", )], )) ``` ## Import Cosmos Cassandra Table can be imported using the `resource id`, e.g. ```sh $ pulumi import azure:cosmosdb/cassandraTable:CassandraTable ks1 /subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/rg1/providers/Microsoft.DocumentDB/databaseAccounts/account1/cassandraKeyspaces/ks1/tables/table1 ``` :param str resource_name: The name of the resource. :param CassandraTableArgs args: The arguments to use to populate this resource's properties. :param pulumi.ResourceOptions opts: Options for the resource. """ ... def __init__(__self__, resource_name: str, *args, **kwargs): resource_args, opts = _utilities.get_resource_args_opts(CassandraTableArgs, pulumi.ResourceOptions, *args, **kwargs) if resource_args is not None: __self__._internal_init(resource_name, opts, **resource_args.__dict__) else: __self__._internal_init(resource_name, *args, **kwargs) def _internal_init(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, analytical_storage_ttl: Optional[pulumi.Input[int]] = None, autoscale_settings: Optional[pulumi.Input[pulumi.InputType['CassandraTableAutoscaleSettingsArgs']]] = None, cassandra_keyspace_id: Optional[pulumi.Input[str]] = None, default_ttl: Optional[pulumi.Input[int]] = None, name: Optional[pulumi.Input[str]] = None, schema: Optional[pulumi.Input[pulumi.InputType['CassandraTableSchemaArgs']]] = None, throughput: Optional[pulumi.Input[int]] = None, __props__=None): if opts is None: opts = pulumi.ResourceOptions() if not isinstance(opts, pulumi.ResourceOptions): raise TypeError('Expected resource options to be a ResourceOptions instance') if opts.version is None: opts.version = _utilities.get_version() if opts.id is None: if __props__ is not None: raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource') __props__ = CassandraTableArgs.__new__(CassandraTableArgs) __props__.__dict__["analytical_storage_ttl"] = analytical_storage_ttl __props__.__dict__["autoscale_settings"] = autoscale_settings if cassandra_keyspace_id is None and not opts.urn: raise TypeError("Missing required property 'cassandra_keyspace_id'") __props__.__dict__["cassandra_keyspace_id"] = cassandra_keyspace_id __props__.__dict__["default_ttl"] = default_ttl __props__.__dict__["name"] = name if schema is None and not opts.urn: raise TypeError("Missing required property 'schema'") __props__.__dict__["schema"] = schema __props__.__dict__["throughput"] = throughput super(CassandraTable, __self__).__init__( 'azure:cosmosdb/cassandraTable:CassandraTable', resource_name, __props__, opts) @staticmethod def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions] = None, analytical_storage_ttl: Optional[pulumi.Input[int]] = None, autoscale_settings: Optional[pulumi.Input[pulumi.InputType['CassandraTableAutoscaleSettingsArgs']]] = None, cassandra_keyspace_id: Optional[pulumi.Input[str]] = None, default_ttl: Optional[pulumi.Input[int]] = None, name: Optional[pulumi.Input[str]] = None, schema: Optional[pulumi.Input[pulumi.InputType['CassandraTableSchemaArgs']]] = None, throughput: Optional[pulumi.Input[int]] = None) -> 'CassandraTable': """ Get an existing CassandraTable resource's state with the given name, id, and optional extra properties used to qualify the lookup. :param str resource_name: The unique name of the resulting resource. :param pulumi.Input[str] id: The unique provider ID of the resource to lookup. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[int] analytical_storage_ttl: Time to live of the Analytical Storage. Possible values are at least `-1`. `-1` means the Analytical Storage never expires. Changing this forces a new resource to be created. :param pulumi.Input[str] cassandra_keyspace_id: The ID of the Cosmos DB Cassandra Keyspace to create the table within. Changing this forces a new resource to be created. :param pulumi.Input[int] default_ttl: Time to live of the Cosmos DB Cassandra table. Possible values are at least `-1`. `-1` means the Cassandra table never expires. :param pulumi.Input[str] name: Specifies the name of the Cosmos DB Cassandra Table. Changing this forces a new resource to be created. :param pulumi.Input[pulumi.InputType['CassandraTableSchemaArgs']] schema: A `schema` block as defined below. Changing this forces a new resource to be created. """ opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id)) __props__ = _CassandraTableState.__new__(_CassandraTableState) __props__.__dict__["analytical_storage_ttl"] = analytical_storage_ttl __props__.__dict__["autoscale_settings"] = autoscale_settings __props__.__dict__["cassandra_keyspace_id"] = cassandra_keyspace_id __props__.__dict__["default_ttl"] = default_ttl __props__.__dict__["name"] = name __props__.__dict__["schema"] = schema __props__.__dict__["throughput"] = throughput return CassandraTable(resource_name, opts=opts, __props__=__props__) @property @pulumi.getter(name="analyticalStorageTtl") def analytical_storage_ttl(self) -> pulumi.Output[Optional[int]]: """ Time to live of the Analytical Storage. Possible values are at least `-1`. `-1` means the Analytical Storage never expires. Changing this forces a new resource to be created. """ return pulumi.get(self, "analytical_storage_ttl") @property @pulumi.getter(name="autoscaleSettings") def autoscale_settings(self) -> pulumi.Output[Optional['outputs.CassandraTableAutoscaleSettings']]: return pulumi.get(self, "autoscale_settings") @property @pulumi.getter(name="cassandraKeyspaceId") def cassandra_keyspace_id(self) -> pulumi.Output[str]: """ The ID of the Cosmos DB Cassandra Keyspace to create the table within. Changing this forces a new resource to be created. """ return pulumi.get(self, "cassandra_keyspace_id") @property @pulumi.getter(name="defaultTtl") def default_ttl(self) -> pulumi.Output[int]: """ Time to live of the Cosmos DB Cassandra table. Possible values are at least `-1`. `-1` means the Cassandra table never expires. """ return pulumi.get(self, "default_ttl") @property @pulumi.getter def name(self) -> pulumi.Output[str]: """ Specifies the name of the Cosmos DB Cassandra Table. Changing this forces a new resource to be created. """ return pulumi.get(self, "name") @property @pulumi.getter def schema(self) -> pulumi.Output['outputs.CassandraTableSchema']: """ A `schema` block as defined below. Changing this forces a new resource to be created. """ return pulumi.get(self, "schema") @property @pulumi.getter def throughput(self) -> pulumi.Output[int]: return pulumi.get(self, "throughput")

e4ad4d1b1a19faa8dce0b003b788008a58802470

py

Python

HW10/b06502027_hw10.py

Pyrojewel-zard/ML

d8a11d893eed3e889b9af0d6aeb3ab08cd60d997

2021-11-26T10:05:03.000Z

2022-03-17T11:45:46.000Z

HW10/b06502027_hw10.py

Pyrojewel-zard/ML

d8a11d893eed3e889b9af0d6aeb3ab08cd60d997

HW10/b06502027_hw10.py

Pyrojewel-zard/ML

d8a11d893eed3e889b9af0d6aeb3ab08cd60d997

2022-01-09T02:17:19.000Z

2022-01-09T02:17:19.000Z

# -*- coding: utf-8 -*- """「hw10_adversarial_attack.ipynb」的副本 Automatically generated by Colaboratory. Original file is located at https://colab.research.google.com/drive/1yPa2ushzqw8FNobfonL79PHzudn0vjrN # **Homework 10 - Adversarial Attack** Slides: https://reurl.cc/v5kXkk Videos: TA: ntu-ml-2021spring-ta@googlegroups.com ## Enviroment & Download We make use of [pytorchcv](https://pypi.org/project/pytorchcv/) to obtain CIFAR-10 pretrained model, so we need to set up the enviroment first. We also need to download the data (200 images) which we want to attack. """ !nvidia-smi # set up environment !pip install pytorchcv # download !gdown --id 1fHi1ko7wr80wXkXpqpqpOxuYH1mClXoX -O data.zip # unzip !unzip ./data.zip !rm ./data.zip """## Global Settings * $\epsilon$ is fixed to be 8. But on **Data section**, we will first apply transforms on raw pixel value (0-255 scale) **by ToTensor (to 0-1 scale)** and then **Normalize (subtract mean divide std)**. $\epsilon$ should be set to $\frac{8}{255 * std}$ during attack. * Explaination (optional) * Denote the first pixel of original image as $p$, and the first pixel of adversarial image as $a$. * The $\epsilon$ constraints tell us $\left| p-a \right| <= 8$. * ToTensor() can be seen as a function where $T(x) = x/255$. * Normalize() can be seen as a function where $N(x) = (x-mean)/std$ where $mean$ and $std$ are constants. * After applying ToTensor() and Normalize() on $p$ and $a$, the constraint becomes $\left| N(T(p))-N(T(a)) \right| = \left| \frac{\frac{p}{255}-mean}{std}-\frac{\frac{a}{255}-mean}{std} \right| = \frac{1}{255 * std} \left| p-a \right| <= \frac{8}{255 * std}.$ * So, we should set $\epsilon$ to be $\frac{8}{255 * std}$ after ToTensor() and Normalize(). """ import torch import torch.nn as nn device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') batch_size = 8 # the mean and std are the calculated statistics from cifar_10 dataset cifar_10_mean = (0.491, 0.482, 0.447) # mean for the three channels of cifar_10 images cifar_10_std = (0.202, 0.199, 0.201) # std for the three channels of cifar_10 images # convert mean and std to 3-dimensional tensors for future operations mean = torch.tensor(cifar_10_mean).to(device).view(3, 1, 1) std = torch.tensor(cifar_10_std).to(device).view(3, 1, 1) epsilon = 8/255/std # TODO: iterative fgsm attack # alpha (step size) can be decided by yourself alpha = 0.01/255/std root = './data' # directory for storing benign images # benign images: images which do not contain adversarial perturbations # adversarial images: images which include adversarial perturbations """## Data Construct dataset and dataloader from root directory. Note that we store the filename of each image for future usage. """ import os import glob import shutil import numpy as np from PIL import Image from torchvision.transforms import transforms from torch.utils.data import Dataset, DataLoader transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize(cifar_10_mean, cifar_10_std) ]) class AdvDataset(Dataset): def __init__(self, data_dir, transform): self.images = [] self.labels = [] self.names = [] ''' data_dir ├── class_dir │   ├── class1.png │   ├── ... │   ├── class20.png ''' for i, class_dir in enumerate(sorted(glob.glob(f'{data_dir}/*'))): images = sorted(glob.glob(f'{class_dir}/*')) self.images += images self.labels += ([i] * len(images)) self.names += [os.path.relpath(imgs, data_dir) for imgs in images] self.transform = transform def __getitem__(self, idx): image = self.transform(Image.open(self.images[idx])) label = self.labels[idx] return image, label def __getname__(self): return self.names def __len__(self): return len(self.images) adv_set = AdvDataset(root, transform=transform) adv_names = adv_set.__getname__() adv_loader = DataLoader(adv_set, batch_size=batch_size, shuffle=False) print(f'number of images = {adv_set.__len__()}') """## Utils -- Benign Images Evaluation""" # to evaluate the performance of model on benign images def epoch_benign(model, loader, loss_fn): model.eval() train_acc, train_loss = 0.0, 0.0 for x, y in loader: x, y = x.to(device), y.to(device) yp = model(x) loss = loss_fn(yp, y) train_acc += (yp.argmax(dim=1) == y).sum().item() train_loss += loss.item() * x.shape[0] return train_acc / len(loader.dataset), train_loss / len(loader.dataset) """## Utils -- Attack Algorithm""" # perform fgsm attack def fgsm(model, x, y, loss_fn, epsilon=epsilon): x_adv = x.detach().clone() # initialize x_adv as original benign image x x_adv.requires_grad = True # need to obtain gradient of x_adv, thus set required grad loss = loss_fn(model(x_adv), y) # calculate loss loss.backward() # calculate gradient # fgsm: use gradient ascent on x_adv to maximize loss x_adv = x_adv + epsilon * x_adv.grad.detach().sign() return x_adv # TODO: perform iterative fgsm attack # set alpha as the step size in Global Settings section # alpha and num_iter can be decided by yourself def ifgsm(model, x, y, loss_fn, epsilon=epsilon, alpha=alpha, num_iter=1600): # initialize x_adv as original benign image x x_adv = x.detach().clone() # write a loop of num_iter to represent the iterative times # for each loop for i in range(num_iter): # call fgsm with (epsilon = alpha) to obtain new x_adv x_adv = fgsm(model, x_adv, y, loss_fn, alpha) # clip new x_adv back to [x-epsilon, x+epsilon] x_adv = torch.max(torch.min(x_adv,x+epsilon), x-epsilon) return x_adv """## Utils -- Attack * Recall * ToTensor() can be seen as a function where $T(x) = x/255$. * Normalize() can be seen as a function where $N(x) = (x-mean)/std$ where $mean$ and $std$ are constants. * Inverse function * Inverse Normalize() can be seen as a function where $N^{-1}(x) = x*std+mean$ where $mean$ and $std$ are constants. * Inverse ToTensor() can be seen as a function where $T^{-1}(x) = x*255$. * Special Noted * ToTensor() will also convert the image from shape (height, width, channel) to shape (channel, height, width), so we also need to transpose the shape back to original shape. * Since our dataloader samples a batch of data, what we need here is to transpose **(batch_size, channel, height, width)** back to **(batch_size, height, width, channel)** using np.transpose. """ # perform adversarial attack and generate adversarial examples def gen_adv_examples(model, loader, attack, loss_fn): model.eval() adv_names = [] train_acc, train_loss = 0.0, 0.0 for i, (x, y) in enumerate(loader): x, y = x.to(device), y.to(device) x_adv = attack(model, x, y, loss_fn) # obtain adversarial examples yp = model(x_adv) loss = loss_fn(yp, y) train_acc += (yp.argmax(dim=1) == y).sum().item() train_loss += loss.item() * x.shape[0] # store adversarial examples adv_ex = ((x_adv) * std + mean).clamp(0, 1) # to 0-1 scale adv_ex = (adv_ex * 255).clamp(0, 255) # 0-255 scale adv_ex = adv_ex.detach().cpu().data.numpy().round() # round to remove decimal part adv_ex = adv_ex.transpose((0, 2, 3, 1)) # transpose (bs, C, H, W) back to (bs, H, W, C) adv_examples = adv_ex if i == 0 else np.r_[adv_examples, adv_ex] return adv_examples, train_acc / len(loader.dataset), train_loss / len(loader.dataset) # create directory which stores adversarial examples def create_dir(data_dir, adv_dir, adv_examples, adv_names): if os.path.exists(adv_dir) is not True: _ = shutil.copytree(data_dir, adv_dir) for example, name in zip(adv_examples, adv_names): im = Image.fromarray(example.astype(np.uint8)) # image pixel value should be unsigned int im.save(os.path.join(adv_dir, name)) """## Model / Loss Function Model list is available [here](https://github.com/osmr/imgclsmob/blob/master/pytorch/pytorchcv/model_provider.py). Please select models which has _cifar10 suffix. Some of the models cannot be accessed/loaded. You can safely skip them since TA's model will not use those kinds of models. """ from pytorchcv.model_provider import get_model as ptcv_get_model model = ptcv_get_model('preresnet110_cifar10', pretrained=True).to(device) loss_fn = nn.CrossEntropyLoss() benign_acc, benign_loss = epoch_benign(model, adv_loader, loss_fn) print(f'benign_acc = {benign_acc:.5f}, benign_loss = {benign_loss:.5f}') """## FGSM""" adv_examples, fgsm_acc, fgsm_loss = gen_adv_examples(model, adv_loader, fgsm, loss_fn) print(f'fgsm_acc = {fgsm_acc:.5f}, fgsm_loss = {fgsm_loss:.5f}') create_dir(root, 'fgsm', adv_examples, adv_names) """## I-FGSM""" # TODO: iterative fgsm attack adv_examples, ifgsm_acc, ifgsm_loss = gen_adv_examples(model, adv_loader, ifgsm, loss_fn) print(f'ifgsm_acc = {ifgsm_acc:.5f}, ifgsm_loss = {ifgsm_loss:.5f}') create_dir(root, 'ifgsm', adv_examples, adv_names) """## Compress the images""" # Commented out IPython magic to ensure Python compatibility. # %cd fgsm # !tar zcvf ../fgsm.tgz * # %cd .. # %cd ifgsm !tar zcvf ../ifgsm_preresnet110_1600.tgz * # %cd .. """## Visualization""" import matplotlib.pyplot as plt classes = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck'] plt.figure(figsize=(10, 20)) cnt = 0 for i, cls_name in enumerate(classes): path = f'{cls_name}/{cls_name}1.png' # benign image cnt += 1 plt.subplot(len(classes), 4, cnt) im = Image.open(f'./data/{path}') logit = model(transform(im).unsqueeze(0).to(device))[0] predict = logit.argmax(-1).item() prob = logit.softmax(-1)[predict].item() plt.title(f'benign: {cls_name}1.png\n{classes[predict]}: {prob:.2%}') plt.axis('off') plt.imshow(np.array(im)) # adversarial image cnt += 1 plt.subplot(len(classes), 4, cnt) im = Image.open(f'./fgsm/{path}') logit = model(transform(im).unsqueeze(0).to(device))[0] predict = logit.argmax(-1).item() prob = logit.softmax(-1)[predict].item() plt.title(f'adversarial: {cls_name}1.png\n{classes[predict]}: {prob:.2%}') plt.axis('off') plt.imshow(np.array(im)) plt.tight_layout() plt.show()

e4ae21080507e35b553b7b372118c5c586495e00

py

Python

main/make_gradsamplingbasedexact_mesh.py

tttor/nbwpg

271718362cf0cd810c7ea0cd9726e77276947e58

main/make_gradsamplingbasedexact_mesh.py

tttor/nbwpg

271718362cf0cd810c7ea0cd9726e77276947e58

main/make_gradsamplingbasedexact_mesh.py

tttor/nbwpg

271718362cf0cd810c7ea0cd9726e77276947e58

#!/usr/bin/env python3 import argparse, os, sys, pickle import numpy as np, pathos.multiprocessing as mp, torch import gym_util.common_util as cou, polnet as pn, util_bwopt as u from collections import defaultdict from poleval_pytorch import get_rpi_s, get_Ppi_ss, get_ppisteady_s, get_Qsa def main(): arg = parse_arg() cfg = u.load_cfg(arg.cfg, arg) wxmat, wymat = u.get_wxwymesh(cfg) print(cfg); print('wxmat.shape', wxmat.shape) # print('sorted(np.unique(wxmat).tolist())', sorted(np.unique(wxmat).tolist())) print('making arg_generator...') nrow, ncol = wxmat.shape arg_generator = ({**cfg, **{'init_param_x': wxmat[i, j], \ 'init_param_y': wymat[i, j], 'ij': (i, j)}} \ for i in range(nrow) for j in range(ncol)) print('making grad_b samplingbased expression exactly mesh...') log_list = [] if arg.ncpu==1: for i, cfg_i in enumerate(arg_generator): # if not(cfg_i['init_param_x']==0. and cfg_i['init_param_y']==5.): # continue print('i {}/{} ij {} wx {} wy {}'.format(i+1, nrow*ncol, cfg_i['ij'], cfg_i['init_param_x'], cfg_i['init_param_y'])) finalinfo = get_gradbias_samplingbased_exactly(cfg_i) log_list.append(finalinfo) else: pool = mp.ProcessingPool(ncpus=cfg['ncpu']) log_list = pool.map(get_gradbias_samplingbased_exactly, arg_generator) print('meshdata...') meshdata = defaultdict(dict) for log in log_list: for k,v in log.items(): if k=='ij': continue meshdata[k][log['ij']] = v meshdata['wxmat'] = wxmat; meshdata['wymat'] = wymat; meshdata['cfg'] = cfg meshdata = dict(meshdata) print('writing...') envid_short = u.get_shortenvid(cfg['envid']) datadir = os.path.join(log_list[0]['assetdir'], envid_short, 'data') os.makedirs(datadir, exist_ok=True) tag = ['meshdata_gradbias_samplingbased_exactly', 'res{:.2f}'.format(cfg['resolution']), cfg['polnet']['mode']] fname = u.make_stamp(tag, cfg['timestamp']) + '.pkl' with open(os.path.join(datadir, fname), 'wb') as f: pickle.dump(meshdata, f) def get_gradbias_samplingbased_exactly(arg): log = defaultdict(list); log['ij'] = arg['ij'] sfx = arg['polnet']['state_feature_extractor_id'] env = cou.make_single_env(arg['envid'], arg['seed']) nS, nA = env.nS, env.nA; nA_list = env.nA_list Psas = torch.tensor(env.get_Psas()).double() Rsa = torch.tensor(env.get_Rsa()).double() s0 = env.reset() # assume to be deterministic allstatefeature = torch.from_numpy(env.get_allstatefeature(sfx)) log['assetdir'] = os.path.join(env.dpath, 'asset') PolicyNetwork = pn.policynetclass_dict[arg['polnet']['mode']] pi_net = PolicyNetwork(nA_list); pi_net.double() n_param = sum([i.numel() for i in pi_net.parameters()]) init_param = {pi_net.weight_x_name: arg['init_param_x'], pi_net.weight_y_name: arg['init_param_y']} for n, p in pi_net.named_parameters(): p.data.fill_(init_param[n]) p.data = p.data.double() # policy evaluation PI = pn.policy_net2tabular(allstatefeature, pi_net, requires_grad=True) rpi = get_rpi_s(Rsa, PI); Ppi = get_Ppi_ss(Psas, PI) ppi_steady = get_ppisteady_s(Ppi, PI) Ppi_steady = torch.vstack([ppi_steady]*nS) # unichain: same rows Zpi = torch.inverse(torch.eye(nS) - Ppi + Ppi_steady) # fundamental matrix Hpi = torch.matmul(Zpi, torch.eye(nS) - Ppi_steady) # deviation matrix g = torch.dot(ppi_steady, rpi) # gain b = torch.matmul(Hpi, rpi) # bias Q = get_Qsa(g, b, Psas, Rsa, nA_list) # action value # Exact via autograd grad_b = torch.autograd.grad(b[s0], pi_net.parameters(), allow_unused=False, create_graph=True, retain_graph=True) grad_b = torch.vstack(grad_b).squeeze() grad_b_np = grad_b.detach().numpy() grad_g = torch.autograd.grad(g, pi_net.parameters(), allow_unused=False, create_graph=False, retain_graph=True) grad_g = torch.vstack(grad_g).squeeze() # premix part tmix = None; tmix_rtol = env.tmix_cfg['rtol']; tmix_atol = env.tmix_cfg['atol'] premix = torch.zeros(n_param).double() # accumulator for premix terms for t in range(env.tmax_xep): Ppi_pwr = torch.matrix_power(Ppi, t) premix_t = torch.zeros(n_param).double() for s in range(nS): for a in range(nA_list[s]): pi = pi_net(torch.from_numpy(env.get_statefeature([s], sfx))) logprob_a = pi.log_prob(torch.tensor([a])) grad_logpi = torch.autograd.grad(logprob_a, pi_net.parameters(), allow_unused=False, create_graph=False, retain_graph=True) grad_logpi = torch.vstack(grad_logpi).squeeze(dim=u.feature_dimth) premix_t += Ppi_pwr[s0, s]*PI[s, a]*Q[s, a]*grad_logpi premix_t -= grad_g # substract grad_g at each timestep term! premix_t_norm = torch.linalg.norm(premix_t, ord=None) # check here so that we can assert `premix_t_norm` if torch.allclose(Ppi_steady[s0, :], Ppi_pwr[s0, :], rtol=tmix_rtol, atol=tmix_atol): tmix = t; log['tmix'] = tmix # specific to s0 # premix diminishing check at mixing assert torch.allclose(premix_t_norm, torch.zeros(1).double(), rtol=float(arg['premix_diminishing_rtol']), \ atol=float(arg['premix_diminishing_atol'])), \ premix_t_norm.item() break else: premix += premix_t # premix so far log['premix_angerr'].append(u.get_angular_err(premix.detach().numpy(), grad_b_np)) log['premix_normerr'].append(torch.linalg.norm(premix - grad_b, ord=None).item()) # print(t, 'premix', premix.data) assert tmix is not None # ensure env.tmax_xep is long enough, bigger than tmix # print('tmix', tmix) # postmix part postmix = torch.zeros(n_param).double() for s in range(nS): for a in range(nA_list[s]): grad_qsa = torch.autograd.grad(Q[s, a], pi_net.parameters(), allow_unused=False, create_graph=False, retain_graph=True) grad_qsa = torch.vstack(grad_qsa).squeeze() postmix += Ppi_steady[s0, s]*PI[s, a]*grad_qsa postmix += grad_g # involving: plus grad g! log['postmix_normerr'] = torch.linalg.norm(postmix - grad_b, ord=None).item() log['postmix_angerr'] = u.get_angular_err(postmix.detach().numpy(), grad_b_np) # total prepostmix = premix + postmix prepostmix_normerr = torch.linalg.norm(prepostmix - grad_b, ord=None) prepostmix_angerr = u.get_angular_err(prepostmix.detach().numpy(), grad_b_np) log['prepostmix_angerr'] = prepostmix_angerr log['prepostmix_normerr'] = prepostmix_normerr.item() assert torch.isfinite(prepostmix).all() # print('postmix', postmix.data) # print('prepostmix', prepostmix.data) # print('grad_b', grad_b.data) assert torch.allclose(grad_b, prepostmix, rtol=float(arg['gradbias_cmp_rtol']), atol=float(arg['gradbias_cmp_atol'])), \ prepostmix_normerr.data return dict(log) def parse_arg(): parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) parser.add_argument('--cfg', help='cfg filepath', type=str, default=None, required=True) parser.add_argument('--ncpu', help='number of cpu', type=int, default=None, required=True) parser.add_argument('--repo', help='repo dirpath list', type=str, nargs='+', default=None, required=True) parser.add_argument('--custom', help='list of (key: value) items', type=str, nargs='+', default=[]) arg = parser.parse_args() arg.cfg = arg.cfg.replace('file://','') return arg if __name__ == '__main__': main()

e4b424d5ad2b323394201895d8483eb6857e159f

py

Python

Python/tdw/FBOutput/StaticSpring.py

ricklentz/tdw

da40eec151acae20b28d6486defb4358d96adb0e

Python/tdw/FBOutput/StaticSpring.py

ricklentz/tdw

da40eec151acae20b28d6486defb4358d96adb0e

Python/tdw/FBOutput/StaticSpring.py

ricklentz/tdw

da40eec151acae20b28d6486defb4358d96adb0e

# automatically generated by the FlatBuffers compiler, do not modify # namespace: FBOutput import tdw.flatbuffers class StaticSpring(object): __slots__ = ['_tab'] @classmethod def GetRootAsStaticSpring(cls, buf, offset): n = tdw.flatbuffers.encode.Get(tdw.flatbuffers.packer.uoffset, buf, offset) x = StaticSpring() x.Init(buf, n + offset) return x # StaticSpring def Init(self, buf, pos): self._tab = tdw.flatbuffers.table.Table(buf, pos) # StaticSpring def Id(self): o = tdw.flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) if o != 0: return self._tab.Get(tdw.flatbuffers.number_types.Int32Flags, o + self._tab.Pos) return 0 # StaticSpring def HasLimits(self): o = tdw.flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) if o != 0: return bool(self._tab.Get(tdw.flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) return False # StaticSpring def MinLimit(self): o = tdw.flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) if o != 0: return self._tab.Get(tdw.flatbuffers.number_types.Float32Flags, o + self._tab.Pos) return 0.0 # StaticSpring def MaxLimit(self): o = tdw.flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) if o != 0: return self._tab.Get(tdw.flatbuffers.number_types.Float32Flags, o + self._tab.Pos) return 0.0 # StaticSpring def Axis(self): o = tdw.flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12)) if o != 0: x = o + self._tab.Pos from .Vector3 import Vector3 obj = Vector3() obj.Init(self._tab.Bytes, x) return obj return None # StaticSpring def Force(self): o = tdw.flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14)) if o != 0: return self._tab.Get(tdw.flatbuffers.number_types.Float32Flags, o + self._tab.Pos) return 0.0 # StaticSpring def Damper(self): o = tdw.flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(16)) if o != 0: return self._tab.Get(tdw.flatbuffers.number_types.Float32Flags, o + self._tab.Pos) return 0.0 def StaticSpringStart(builder): builder.StartObject(7) def StaticSpringAddId(builder, id): builder.PrependInt32Slot(0, id, 0) def StaticSpringAddHasLimits(builder, hasLimits): builder.PrependBoolSlot(1, hasLimits, 0) def StaticSpringAddMinLimit(builder, minLimit): builder.PrependFloat32Slot(2, minLimit, 0.0) def StaticSpringAddMaxLimit(builder, maxLimit): builder.PrependFloat32Slot(3, maxLimit, 0.0) def StaticSpringAddAxis(builder, axis): builder.PrependStructSlot(4, tdw.flatbuffers.number_types.UOffsetTFlags.py_type(axis), 0) def StaticSpringAddForce(builder, force): builder.PrependFloat32Slot(5, force, 0.0) def StaticSpringAddDamper(builder, damper): builder.PrependFloat32Slot(6, damper, 0.0) def StaticSpringEnd(builder): return builder.EndObject()

e4b481ea04167900e771c376b8996b0f7e02b22f

py

Python

models/locobuyticketresponse.py

jujinesy/Empier_PythonKakaoBot

80d2951955002b1a0b5d77b5c2830bc8def63ea3

2017-03-30T15:20:18.000Z

2018-01-04T12:46:05.000Z

models/locobuyticketresponse.py

skdltmxn/kakaobot

e738b4a8d994fc4125bbd471bd48378a11a8d371

2020-08-06T08:13:22.000Z

2020-08-06T08:13:22.000Z

models/locobuyticketresponse.py

skdltmxn/kakaobot

e738b4a8d994fc4125bbd471bd48378a11a8d371

2020-08-06T08:18:02.000Z

2021-02-28T03:59:45.000Z

# -*- coding: utf-8 -*- from locoresponse import LocoResponse class LocoBuyTicketResponse(LocoResponse): def host(self): return self._data["host"] def port(self): return self._data["port"]

e4b4e1f9c8eb01d9ce9d5ca44f6c9f1bce4a4c9a

py

Python

travian4api/resources.py

ihoromi4/travian4api

1fa9023d62d8dfca00f5276eff13868ddc057811

2022-03-08T20:50:08.000Z

2022-03-08T20:50:13.000Z

travian4api/resources.py

ihoromi4/travian4api

1fa9023d62d8dfca00f5276eff13868ddc057811

travian4api/resources.py

ihoromi4/travian4api

1fa9023d62d8dfca00f5276eff13868ddc057811

2021-03-10T18:43:53.000Z

2021-12-18T13:31:22.000Z

RESOURCE_TYPES = ['lumber', 'clay', 'iron', 'crop'] LUMBER = 0 CLAY = 1 IRON = 2 CROP = 3

e4b5033ef04e9a7be53412dd0c2573434a49130e

py

Python

pikapy/interpreter.py

DanyGLewin/pykachu

d9faeb3e938e8f8da3250e432a9dd70487291627

pikapy/interpreter.py

DanyGLewin/pykachu

d9faeb3e938e8f8da3250e432a9dd70487291627

pikapy/interpreter.py

DanyGLewin/pykachu

d9faeb3e938e8f8da3250e432a9dd70487291627

"""Check the syntax and execute Pikachu commands. Methods: run -- The main context for the pikachu vm. """ from pikapy.utils import pika_error, pika_print from pikapy.reader import PikaReader from pikapy.stack import PikaStack def run(file_name, args, debug): """ Run a specified Pikachu file in a virtual environment. Arguments: file_name -- the name and path of a file containing a pikachu program. args -- the command line arguments specified when the pikachu interpreter was run. """ pi_stack = PikaStack() pika_stack = PikaStack() stacks_dict = { "pi pikachu": pi_stack, "pika pikachu": pika_stack } for a in args: pi_stack.PUSH(a) reader = PikaReader(file_name) while True: try: if debug: try: print "\nline {}: {}\npi {}\npika {}".format(reader.line_num, reader.lines[reader.line_num], pi_stack.elements, pika_stack.elements) except KeyError: pass command = next(reader) except StopIteration: print '' break command = command.split(' chu')[0] terms = command.split() if len(terms) == 0: continue if len(terms) == 1: pika_error(reader.line_num, 'unknown command "{}"'.format(terms[0])) elif len(terms) < 3: command = " ".join(terms) if command == "pi pikachu": pi_stack.POP() elif command == "pika pikachu": pika_stack.POP() elif command == "pi pika": if not pi_stack.EMPTY(): pika_stack.PUSH(pi_stack.PEEK()) elif command == "pika pi": if not pika_stack.EMPTY(): pi_stack.PUSH(pika_stack.PEEK()) elif command == "pi pi": if not pika_stack.EMPTY(): pika_stack.RAND() elif command == "pikachu pikachu": try: line_num = len(next(reader).split()) except StopIteration: pika_error(reader.line_num - 1, "unexpected EoF, expected new line") if pi_stack.PEEK() != pika_stack.PEEK(): continue reader.goto(line_num) elif command == "pika pika": try: line_num = len(next(reader).split()) except StopIteration: pika_error(reader.line_num - 1, "unexpected EoF, expected new line") if pi_stack.PEEK() == pika_stack.PEEK(): continue reader.goto(line_num) else: pika_error(reader.line_num, 'unknown command "{}"'.format(reader.lines[reader.line_num])) elif len(terms) < 4: try: current_stack = stacks_dict[" ".join(terms[-2:])] except KeyError: pika_error(reader.line_num, 'unknown pikachu "{}"'.format(" ".join(terms[-2:]))) command = terms[0] if command == "pikachu": current_stack.DIV() if current_stack.PEEK() == float('NaN'): pika_error(reader.line_num, 'cannot divide by 0') else: current_stack.PUSH(1) elif len(terms) < 5: try: current_stack = stacks_dict[" ".join(terms[-2:])] except KeyError: pika_error(reader.line_num, 'unknown pikachu "{}"'.format(" ".join(terms[-2:]))) command = " ".join(terms[:-2]) if command == "pi pika": current_stack.ADD() elif command == "pika pi": current_stack.SUB() elif command == "pi pikachu": current_stack.MULT() elif command == "pika pikachu": if not current_stack.EMPTY(): pika_print(current_stack.POP()) else: pika_print("undefined") elif command == "pikachu pikachu": n = current_stack.POP() if n and type(n) == int: pika_print(chr(n)) else: pika_print("undefined") else: current_stack.PUSH(2) else: try: current_stack = stacks_dict[" ".join(terms[-2:])] except KeyError: pika_error(reader.line_num, 'unknown pikachu "{}"'.format(" ".join(terms[-2:]))) current_stack.PUSH(len(terms) - 2)

e4b53b56c59f025bc7d30fa6a90cb388b81c2484

py

Python

app/accounts/views/vendor_profile.py

phessabi/eshop

6a5352753a0c27f9c3f0eda6eec696f49ef4a8eb

2020-02-04T21:18:31.000Z

2020-02-04T21:18:31.000Z

app/accounts/views/vendor_profile.py

phessabi/eshop

6a5352753a0c27f9c3f0eda6eec696f49ef4a8eb

2020-01-01T11:46:33.000Z

2022-03-12T00:10:01.000Z

app/accounts/views/vendor_profile.py

phessabi/eshop

6a5352753a0c27f9c3f0eda6eec696f49ef4a8eb

2020-02-18T11:12:48.000Z

2020-02-18T11:12:48.000Z

from rest_framework import status from rest_framework.generics import ListAPIView, RetrieveAPIView, CreateAPIView, UpdateAPIView from rest_framework.permissions import AllowAny, IsAuthenticated from rest_framework.response import Response from rest_framework.viewsets import GenericViewSet from _helpers.permissions import IsVendor from _helpers.throttles import SustainedAnonRateThrottle, BurstAnonRateThrottle from accounts.models import Vendor from accounts.serializers import UserSerializer, VendorProfileSerializer from accounts.serializers import VendorSerializer class CreateVendorViewSet(GenericViewSet, CreateAPIView): permission_classes = (AllowAny,) throttle_classes = (BurstAnonRateThrottle, SustainedAnonRateThrottle,) queryset = Vendor.objects.all() serializer_class = UserSerializer def create(self, request, *args, **kwargs): data = request.data serializer = self.get_serializer(data=data) serializer.is_valid(raise_exception=True) user = serializer.save() Vendor.objects.create(user=user, name=data['name']) headers = self.get_success_headers(serializer.data) return Response(serializer.data, status=status.HTTP_201_CREATED, headers=headers) class ListRetrieveVendorViewSet(GenericViewSet, ListAPIView, RetrieveAPIView): permission_classes = (AllowAny,) throttle_classes = (BurstAnonRateThrottle, SustainedAnonRateThrottle,) queryset = Vendor.objects.all() serializer_class = VendorSerializer class UpdateRetrieveVendorViewSet(GenericViewSet, UpdateAPIView, RetrieveAPIView, ListAPIView): permission_classes = (IsAuthenticated, IsVendor) queryset = Vendor.objects.all() serializer_class = VendorProfileSerializer def get_queryset(self): vendor = self.request.user.vendor return Vendor.objects.filter(id=vendor.id)

e4b72c3c2f5a5bbfee4b0bb9f47cf02969cbd82b

py

Python

plotoptix/tkoptix.py

robertsulej/plotoptix

628694351791c7fb8cd631a6efe6cc0fd7d9f4f8

2019-04-03T10:51:41.000Z

2022-03-28T05:35:09.000Z

plotoptix/tkoptix.py

robertsulej/plotoptix

628694351791c7fb8cd631a6efe6cc0fd7d9f4f8

2019-05-11T08:53:32.000Z

2022-02-07T22:43:21.000Z

plotoptix/tkoptix.py

robertsulej/plotoptix

628694351791c7fb8cd631a6efe6cc0fd7d9f4f8

2019-08-29T21:50:23.000Z

2022-03-03T05:21:15.000Z

""" Tkinter UI for PlotOptiX raytracer. https://github.com/rnd-team-dev/plotoptix/blob/master/LICENSE.txt Have a look at examples on GitHub: https://github.com/rnd-team-dev/plotoptix. """ import logging import numpy as np import tkinter as tk from PIL import Image, ImageTk from ctypes import byref, c_float, c_uint from typing import List, Tuple, Optional, Union from plotoptix.enums import * from plotoptix._load_lib import PLATFORM from plotoptix.npoptix import NpOptiX class TkOptiX(NpOptiX): """Tkinter based UI for PlotOptiX. Derived from :class:`plotoptix.NpOptiX`. Summary of mouse and keys actions: - rotate camera eye around the target: hold and drag left mouse button - rotate camera target around the eye: hold and drag right mouse button - zoom out/in (change camera field of view): hold shift + left mouse button and drag up/down - move camera eye backward/forward: hold shift + right mouse button and drag up/down - change focus distance in "depth of field" cameras: hold ctrl + left mouse button and drag up/down - change aperture radius in "depth of field" cameras: hold ctrl + right mouse button and drag up/down - focus at an object: hold ctrl + double-click left mouse button - select an object: double-click left mouse button (info on terminal output) Note: functions with the names ``_gui_*`` can be used from the GUI thread (Tk event loop) only. Parameters ---------- src : string or dict, optional Scene description, file name or dictionary. Empty scene is prepared if the default ``None`` value is used. on_initialization : callable or list, optional Callable or list of callables to execute upon starting the raytracing thread. These callbacks are executed on the main thread. on_scene_compute : callable or list, optional Callable or list of callables to execute upon starting the new frame. Callbacks are executed in a thread parallel to the raytracing. on_rt_completed : callable or list, optional Callable or list of callables to execute when the frame raytracing is completed (execution may be paused with pause_compute() method). Callbacks are executed in a thread parallel to the raytracing. on_launch_finished : callable or list, optional Callable or list of callables to execute when the frame raytracing is completed. These callbacks are executed on the raytracing thread. on_rt_accum_done : callable or list, optional Callable or list of callables to execute when the last accumulation frame is finished. These callbacks are executed on the raytracing thread. width : int, optional Pixel width of the raytracing output. Default value is half of the screen width. height : int, optional Pixel height of the raytracing output. Default value is half of the screen height. start_now : bool, optional Open the GUI window and start raytracing thread immediately. If set to ``False``, then user should call ``start()`` or ``show()`` method. Default is ``False``. devices : list, optional List of selected devices, with the primary device at index 0. Empty list is default, resulting with all compatible devices selected for processing. log_level : int or string, optional Log output level. Default is ``WARN``. """ def __init__(self, src: Optional[Union[str, dict]] = None, on_initialization = None, on_scene_compute = None, on_rt_completed = None, on_launch_finished = None, on_rt_accum_done = None, width: int = -1, height: int = -1, start_now: bool = False, devices: List = [], log_level: Union[int, str] = logging.WARN) -> None: """TkOptiX constructor """ # pass all arguments, except start_now - we'll do that later super().__init__( src=src, on_initialization=on_initialization, on_scene_compute=on_scene_compute, on_rt_completed=on_rt_completed, on_launch_finished=on_launch_finished, on_rt_accum_done=on_rt_accum_done, width=width, height=height, start_now=False, # do not start yet devices=devices, log_level=log_level) # save initial values to set size of Tk window on startup self._ini_width = width self._ini_height = height self._dummy_rgba = np.ascontiguousarray(np.zeros((8, 8, 4), dtype=np.uint8)) if PLATFORM == "Windows": dpi_scale = self._optix.get_display_scaling() self._logger.info("DPI scaling: %d", dpi_scale) if dpi_scale != 1: self._logger.warn("DPI setting may cause blurred raytracing output, see this answer") self._logger.warn("for the solution https://stackoverflow.com/a/52599951/10037996:") self._logger.warn("set python.exe and pythonw.exe files Properties -> Compatibility") self._logger.warn("-> Change high DPI settings -> check Override high DPI scaling") self._logger.warn("behaviour, select Application in the drop-down menu.") if self._is_scene_created and start_now: self._logger.info("Starting TkOptiX window and raytracing thread.") self.start() ############################################################### # For matplotlib users convenience. def show(self) -> None: """Start raytracing thread and open the GUI window. Convenience method to call :meth:`plotoptix.NpOptiX.start`. Actions provided with ``on_initialization`` parameter of TkOptiX constructor are executed by this method on the main thread, before the ratracing thread is started and GUI window open. """ self.start() def _run_event_loop(self): """Override NpOptiX's method for running the UI event loop. Configure the GUI window properties and events, prepare image to display raytracing output. """ # setup Tk window ############################################# self._root = tk.Tk() screen_width = self._root.winfo_screenwidth() screen_height = self._root.winfo_screenheight() if self._ini_width <= 0: self._ini_width = int(screen_width / 2) else: self._ini_width = None if self._ini_height <= 0: self._ini_height = int(screen_height / 2) else: self._ini_height = None self.resize(self._ini_width, self._ini_height) self._mouse_from_x = 0 self._mouse_from_y = 0 self._mouse_to_x = 0 self._mouse_to_y = 0 self._left_mouse = False self._right_mouse = False self._any_mouse = False self._ctrl_key = False self._shift_key = False self._any_key = False self._selection_handle = -1 self._selection_index = -1 self._fixed_size = None self._image_scale = 1.0 self._image_at = (0, 0) self._root.title("R&D PlotOptiX") self._root.protocol("WM_DELETE_WINDOW", self._gui_quit_callback) self._canvas = tk.Canvas(self._root, width=self._width, height=self._height) self._canvas.grid(column=0, row=0, columnspan=3, sticky="nsew") self._canvas.pack_propagate(0) self._canvas.bind("<Configure>", self._gui_configure) self._canvas.bind('<Motion>', self._gui_motion) self._canvas.bind('<B1-Motion>', self._gui_motion_pressed) self._canvas.bind('<B3-Motion>', self._gui_motion_pressed) self._canvas.bind("<Button-1>", self._gui_pressed_left) self._canvas.bind("<Button-3>", self._gui_pressed_right) self._canvas.bind("<ButtonRelease-1>", self._gui_released_left) self._canvas.bind("<ButtonRelease-3>", self._gui_released_right) self._canvas.bind("<Double-Button-1>", self._gui_doubleclick_left) self._canvas.bind("<Double-Button-3>", self._gui_doubleclick_right) self._root.bind_all("<KeyPress>", self._gui_key_pressed) self._root.bind_all("<KeyRelease>", self._gui_key_released) self._canvas.bind("<<LaunchFinished>>", self._gui_update_content) self._canvas.bind("<<ApplyUiEdits>>", self._gui_apply_scene_edits) self._canvas.bind("<<CloseScene>>", self._gui_quit_callback) self._status_main_text = tk.StringVar(value="Selection: camera") self._status_main = tk.Label(self._root, textvariable=self._status_main_text, bd=1, relief=tk.SUNKEN, anchor=tk.W) self._status_main.grid(column=0, row=1, sticky="ew") self._status_action_text = tk.StringVar(value="") self._status_action = tk.Label(self._root, textvariable=self._status_action_text, width=70, bd=1, relief=tk.SUNKEN, anchor=tk.W) self._status_action.grid(column=1, row=1, sticky="ew") self._status_fps_text = tk.StringVar(value="FPS") self._status_fps = tk.Label(self._root, textvariable=self._status_fps_text, width=16, bd=1, relief=tk.SUNKEN, anchor=tk.W) self._status_fps.grid(column=2, row=1, sticky="ew") self._root.rowconfigure(0, weight=1) self._root.columnconfigure(0, weight=1) self._logger.info("Tkinter widgets ready.") self._logger.info("Couple scene to the output window...") with self._padlock: if self._img_rgba is not None: pil_img = Image.fromarray(self._img_rgba, mode="RGBX") else: pil_img = Image.fromarray(self._dummy_rgba, mode="RGBX") self._tk_img = ImageTk.PhotoImage(pil_img) self._img_id = self._canvas.create_image(0, 0, image=self._tk_img, anchor=tk.NW) ############################################################### # start event loop ############################################ self._logger.info("Start UI event loop...") self._is_started = True self._update_req = False self._root.mainloop() ############################################################### def close(self) -> None: """Stop the raytracing thread, release resources. Raytracing cannot be restarted after this method is called. See Also -------- :meth:`plotoptix.NpOptiX.close` """ if not self._is_closed: self._optix.break_launch() self._canvas.event_generate("<<CloseScene>>", when="head") else: self._logger.warn("UI already closed.") def _gui_quit_callback(self, *args): super().close() self._root.quit() def _get_image_xy(self, wnd_x, wnd_y): if self._fixed_size is None: return wnd_x, wnd_y else: x = int((wnd_x - self._image_at[0]) / self._image_scale) y = int((wnd_y - self._image_at[1]) / self._image_scale) return x, y def _get_hit_at(self, x, y): c_x = c_float() c_y = c_float() c_z = c_float() c_d = c_float() if self._optix.get_hit_at(x, y, byref(c_x), byref(c_y), byref(c_z), byref(c_d)): return c_x.value, c_y.value, c_z.value, c_d.value else: return 0, 0, 0, 0 def _gui_get_object_at(self, x, y): c_handle = c_uint() c_index = c_uint() c_prim = c_uint() if self._optix.get_object_at(x, y, byref(c_handle), byref(c_index), byref(c_prim)): return c_handle.value, c_index.value, c_prim.value else: return None, None, None def _gui_motion(self, event): if not (self._any_mouse or self._any_key): x, y = self._get_image_xy(event.x, event.y) handle, index, prim = self._gui_get_object_at(x, y) if (handle != 0x3FFFFFFF): hx, hy, hz, hd = self._get_hit_at(x, y) if handle in self.geometry_names: if (prim != 0xFFFFFFFF): self._status_action_text.set("%s[prim:%d; vtx:%d]: 2D (%d %d), 3D (%f %f %f), at dist.: %f" % (self.geometry_names[handle], prim, index, x, y, hx, hy, hz, hd)) else: self._status_action_text.set("%s[%d]: 2D (%d %d), 3D (%f %f %f), at dist.: %f" % (self.geometry_names[handle], index, x, y, hx, hy, hz, hd)) else: lh = self._optix.get_light_handle(handle, index) if lh in self.light_names: self._status_action_text.set("%s: 2D (%d %d), 3D (%f %f %f), at dist.: %f" % (self.light_names[lh], x, y, hx, hy, hz, hd)) else: self._status_action_text.set("unknown: 2D (%d %d), 3D (%f %f %f), at dist.: %f" % (x, y, hx, hy, hz, hd)) else: self._status_action_text.set("empty area") def _gui_motion_pressed(self, event): self._mouse_to_x, self._mouse_to_y = self._get_image_xy(event.x, event.y) self._optix.break_launch() def _gui_pressed_left(self, event): self._mouse_from_x, self._mouse_from_y = self._get_image_xy(event.x, event.y) self._mouse_to_x = self._mouse_from_x self._mouse_to_y = self._mouse_from_y self._left_mouse = True self._any_mouse = True def _gui_pressed_right(self, event): self._mouse_from_x, self._mouse_from_y = self._get_image_xy(event.x, event.y) self._mouse_to_x = self._mouse_from_x self._mouse_to_y = self._mouse_from_y self._right_mouse = True self._any_mouse = True def _gui_released_left(self, event): self._mouse_to_x, self._mouse_to_y = self._get_image_xy(event.x, event.y) self._mouse_from_x = self._mouse_to_x self._mouse_from_y = self._mouse_to_y self._left_mouse = False self._any_mouse = False def _gui_released_right(self, event): self._mouse_to_x, self._mouse_to_y = self._get_image_xy(event.x, event.y) self._mouse_from_x = self._mouse_to_x self._mouse_from_y = self._mouse_to_y self._right_mouse = False self._any_mouse = False def _gui_doubleclick_left(self, event): assert self._is_started, "Raytracing thread not running." x, y = self._get_image_xy(event.x, event.y) handle, index, _ = self._gui_get_object_at(x, y) if (handle != 0xFFFFFFFF): if handle in self.geometry_names: # switch selection: primitive / whole geom if self._ctrl_key or (self._selection_handle == handle and self._selection_index == -1): self._status_main_text.set("Selection: %s[%d]" % (self.geometry_names[handle], index)) self._selection_index = index else: self._status_main_text.set("Selection: %s" % self.geometry_names[handle]) self._selection_handle = handle self._selection_index = -1 if self._ctrl_key: hx, hy, hz, hd = self._get_hit_at(x, y) if hd > 0: self._status_action_text.set("Focused at (%f %f %f), distance %f" % (hx, hy, hz, hd)) cam_info = self.get_camera() if "fisheye" in cam_info["RayGeneration"]: w = np.array([hx, hy, hz], dtype=np.float32) - np.array(cam_info["Eye"], dtype=np.float32) _ = self._optix.set_camera_focal_length(np.linalg.norm(w)) else: _ = self._optix.set_camera_focal_length(hd) return else: lh = self._optix.get_light_handle(handle, index) if lh in self.light_names: self._status_main_text.set("Selection: %s" % self.light_names[lh]) self._selection_handle = -2 self._selection_index = lh return self._status_main_text.set("Selection: camera") self._selection_handle = -1 self._selection_index = -1 def select(self, name: Optional[str] = None, index: int = -1): """Select geometry, light or camera. Select object for manual manipulations (rotations, shifts, etc). Geometry or light is selected by its name. If ``name`` is not provided, then active camera is selected. Optional ``index`` allows selection of a primitive within the geometry. Parameters ---------- name : string, optional Name of the geometry or light to select. If ``None`` then active camera is selected. index : int, optional Primitive index to select. Entire geometry is selected if ``index`` is out of primitives range. """ if name is None: self._status_main_text.set("Selection: camera") self._selection_handle = -1 self._selection_index = -1 return if name in self.geometry_data: self._selection_handle = self.geometry_data[name]._handle if index >= 0 and index < self.geometry_data[name]._size: self._status_main_text.set("Selection: %s[%d]" % (name, index)) self._selection_index = index else: self._status_main_text.set("Selection: %s" % name) self._selection_index = -1 return if name in self.light_handles: self._status_main_text.set("Selection: %s" % name) self._selection_handle = -2 self._selection_index = self.light_handles[name] return def _gui_doubleclick_right(self, event): self._status_main_text.set("Selection: camera") self._selection_handle = -1 self._selection_index = -1 def _gui_key_pressed(self, event): if event.keysym == "Control_L": self._ctrl_key = True self._any_key = True elif event.keysym == "Shift_L": self._shift_key = True self._any_key = True self._any_key = True else: self._any_key = False def _gui_key_released(self, event): if event.keysym == "Control_L": self._ctrl_key = False elif event.keysym == "Shift_L": self._shift_key = False self._any_key = False def get_rt_size(self) -> Tuple[int, int]: """Get size of ray-tracing output image. Get fixed dimensions of the output image or ``None`` if the image is fit to the GUI window size. Returns ------- out : tuple (int, int) Output image size or ``None`` if set auto-fit mode. """ return self._fixed_size def set_rt_size(self, size: Tuple[int, int]) -> None: """Set fixed / free size of ray-tracing output image. Set fixed dimensions of the output image or allow automatic fit to the GUI window size. Fixed size image updates are slower, but allow ray tracing of any size. Default mode is fit to the GUI window size. Parameters ---------- size : tuple (int, int) Output image size or ``None`` to set auto-fit mode. """ assert self._is_started, "Raytracing thread not running." if self._fixed_size == size: return self._fixed_size = size with self._padlock: if self._fixed_size is None: w, h = self._canvas.winfo_width(), self._canvas.winfo_height() else: w, h = self._fixed_size self.resize(width=w, height=h) def _gui_internal_image_update(self): if self._img_rgba is not None: pil_img = Image.fromarray(self._img_rgba, mode="RGBX") else: pil_img = Image.fromarray(self._dummy_rgba, mode="RGBX") move_to = (0, 0) self._image_scale = 1.0 if self._fixed_size is not None: wc, hc = self._canvas.winfo_width(), self._canvas.winfo_height() if self._width / wc > self._height / hc: self._image_scale = wc / self._width hnew = int(self._height * self._image_scale) pil_img = pil_img.resize((wc, hnew), Image.ANTIALIAS) move_to = (0, (hc - hnew) // 2) else: self._image_scale = hc / self._height wnew = int(self._width * self._image_scale) pil_img = pil_img.resize((wnew, hc), Image.ANTIALIAS) move_to = ((wc - wnew) // 2, 0) tk_img = ImageTk.PhotoImage(pil_img) # update image on canvas self._canvas.itemconfig(self._img_id, image=tk_img) if self._image_at != move_to: self._canvas.move(self._img_id, -self._image_at[0], -self._image_at[1]) self._canvas.move(self._img_id, move_to[0], move_to[1]) self._image_at = move_to # swap reference stored in the window instance self._tk_img = tk_img # no redraws until the next launch self._update_req = False def _gui_configure(self, event): assert self._is_started, "Raytracing thread not running." if not self._started_event.is_set(): self._started_event.set() with self._padlock: if self._fixed_size is None: w, h = self._canvas.winfo_width(), self._canvas.winfo_height() if (w == self._width) and (h == self._height): return self._logger.info("Resize to: %d x %d", w, h) self.resize(width=w, height=h) self._gui_internal_image_update() ########################################################################### # update raytraced image in Tk window ######## def _gui_update_content(self, *args): assert self._is_started, "Raytracing thread not running." if self._update_req: self._status_fps_text.set("FPS: %.3f" % self._optix.get_fps()) with self._padlock: self._gui_internal_image_update() def _launch_finished_callback(self, rt_result: int): super()._launch_finished_callback(rt_result) if self._is_started and rt_result < RtResult.NoUpdates.value: self._update_req = True self._canvas.event_generate("<<LaunchFinished>>", when="now") ########################################################################### ########################################################################### # apply manual scene edits made in ui ######## def _gui_apply_scene_edits(self, *args): if (self._mouse_from_x == self._mouse_to_x) and (self._mouse_from_y == self._mouse_to_y): return if self._selection_handle == -1: # manipulate camera: if self._left_mouse: if not self._any_key: self._status_action_text.set("rotate camera eye XZ") self._optix.rotate_camera_eye( self._mouse_from_x, self._mouse_from_y, self._mouse_to_x, self._mouse_to_y) elif self._ctrl_key: self._status_action_text.set("change camera focus") df = 1 + 0.01 * (self._mouse_from_y - self._mouse_to_y) f = self._optix.get_camera_focal_scale(0) # 0 is current cam self._optix.set_camera_focal_scale(df * f) elif self._shift_key: self._status_action_text.set("change camera FoV") df = 1 + 0.005 * (self._mouse_from_y - self._mouse_to_y) f = self._optix.get_camera_fov(0) # 0 is current cam self._optix.set_camera_fov(df * f) elif self._right_mouse: if not self._any_key: self._status_action_text.set("camera pan/tilt") self._optix.rotate_camera_tgt( self._mouse_from_x, self._mouse_from_y, self._mouse_to_x, self._mouse_to_y) elif self._ctrl_key: self._status_action_text.set("change camera aperture") da = 1 + 0.01 * (self._mouse_from_y - self._mouse_to_y) a = self._optix.get_camera_aperture(0) # 0 is current cam self._optix.set_camera_aperture(da * a) elif self._shift_key: self._status_action_text.set("camera dolly") target = np.ascontiguousarray([0, 0, 0], dtype=np.float32) self._optix.get_camera_target(0, target.ctypes.data) # 0 is current cam eye = np.ascontiguousarray([0, 0, 0], dtype=np.float32) self._optix.get_camera_eye(0, eye.ctypes.data) # 0 is current cam dl = 0.01 * (self._mouse_from_y - self._mouse_to_y) eye = eye - dl * (target - eye) self._optix.set_camera_eye(eye.ctypes.data) elif self._selection_handle == -2: # manipulate light: if self._selection_index in self.light_names: name = self.light_names[self._selection_index] if self._left_mouse: if not self._any_key: rx = np.pi * (self._mouse_to_y - self._mouse_from_y) / self._height ry = np.pi * (self._mouse_to_x - self._mouse_from_x) / self._width self._status_action_text.set("rotate light in camera XY") self._optix.rotate_light_in_view(name, rx, ry, 0) elif self._ctrl_key and self._shift_key: s = 1 - (self._mouse_to_y - self._mouse_from_y) / self._height self._status_action_text.set("scale light") self._optix.scale_light(name, s) elif self._ctrl_key: rx = np.pi * (self._mouse_to_y - self._mouse_from_y) / self._height rz = np.pi * (self._mouse_from_x - self._mouse_to_x) / self._width self._status_action_text.set("rotate light in camera XZ") self._optix.rotate_light_in_view(name, rx, 0, rz) elif self._shift_key: dx = (self._mouse_to_x - self._mouse_from_x) / self._width dy = (self._mouse_from_y - self._mouse_to_y) / self._height self._status_action_text.set("move light in camera XY") self._optix.move_light_in_view(name, dx, dy, 0) elif self._right_mouse: if not self._any_key: dx = (self._mouse_to_x - self._mouse_from_x) / self._width dz = (self._mouse_to_y - self._mouse_from_y) / self._height self._status_action_text.set("move light in camera XZ") self._optix.move_light_in_view(name, dx, 0, dz) elif self._shift_key: dx = (self._mouse_from_y - self._mouse_to_y) / self._height self._status_action_text.set("move light in normal direction") self._optix.dolly_light(name, dx) else: # manipulate selected ogject name = self.geometry_names[self._selection_handle] if self._left_mouse: if not self._any_key: rx = np.pi * (self._mouse_to_y - self._mouse_from_y) / self._height ry = np.pi * (self._mouse_to_x - self._mouse_from_x) / self._width if self._selection_index == -1: self._status_action_text.set("rotate geometry in camera XY") self._optix.rotate_geometry_in_view(name, rx, ry, 0, True) else: self._status_action_text.set("rotate primitive in camera XY") self._optix.rotate_primitive_in_view(name, self._selection_index, rx, ry, 0, True) elif self._ctrl_key and self._shift_key: s = 1 - (self._mouse_to_y - self._mouse_from_y) / self._height if self._selection_index == -1: self._status_action_text.set("scale geometry") self._optix.scale_geometry(name, s, True) else: self._status_action_text.set("scale primitive") self._optix.scale_primitive(name, self._selection_index, s, True) elif self._ctrl_key: rx = np.pi * (self._mouse_to_y - self._mouse_from_y) / self._height rz = np.pi * (self._mouse_from_x - self._mouse_to_x) / self._width if self._selection_index == -1: self._status_action_text.set("rotate geometry in camera XZ") self._optix.rotate_geometry_in_view(name, rx, 0, rz, True) else: self._status_action_text.set("rotate primitive in camera XY") self._optix.rotate_primitive_in_view(name, self._selection_index, rx, 0, rz, True) elif self._shift_key: dx = (self._mouse_to_x - self._mouse_from_x) / self._width dy = (self._mouse_from_y - self._mouse_to_y) / self._height if self._selection_index == -1: self._status_action_text.set("move geometry in camera XY") self._optix.move_geometry_in_view(name, dx, dy, 0, True) else: self._status_action_text.set("move primitive in camera XY") self._optix.move_primitive_in_view(name, self._selection_index, dx, dy, 0, True) elif self._right_mouse: if not self._any_key: dx = (self._mouse_to_x - self._mouse_from_x) / self._width dz = (self._mouse_to_y - self._mouse_from_y) / self._height if self._selection_index == -1: self._status_action_text.set("move geometry in camera XZ") self._optix.move_geometry_in_view(name, dx, 0, dz, True) else: self._status_action_text.set("move primitive in camera XZ") self._optix.move_primitive_in_view(name, self._selection_index, dx, 0, dz, True) self._mouse_from_x = self._mouse_to_x self._mouse_from_y = self._mouse_to_y def _scene_rt_starting_callback(self): if self._is_started: super()._scene_rt_starting_callback() self._canvas.event_generate("<<ApplyUiEdits>>", when="now") ###########################################################################

e4b892cce045d4a84ff88607bc919a58e081ea7c

py

Python

tools/ckssh.py

luisxue/TreesShell

cd35826ca495264afa1e30f9b4f06eadd13ecb48

tools/ckssh.py