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[STATEMENT]
lemma OT_14_correct: "OT_14.correctness M C"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. OT_14.correctness M C
[PROOF STEP]
unfolding OT_14.correctness_def
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. protocol_14_OT M C = funct_OT_14 M C
[PROOF STEP]
using correctness_OT_14
[PROOF STATE]
proof (prove)
using this:
funct_OT_14 ?M ?C = protocol_14_OT ?M ?C
goal (1 subgoal):
1. protocol_14_OT M C = funct_OT_14 M C
[PROOF STEP]
by auto
|
{"llama_tokens": 221, "file": "Multi_Party_Computation_OT14", "length": 3}
|
#Script to plot Rydberg radial wave functions
#23/07/2017
using Plots, JLD, LaTeXStrings
pyplot()
include("functions.jl")
PyPlot.close("all")
#Input information
atom = "87Rb"
nn = 50
ll = 0
jj = 0.5
#Calculate wave function
normY_sol, rr = numerovfunc(atom,nn,ll,jj)
#Rescale for plotting
plotscale = sqrt(rr)
probamp = (normY_sol.*plotscale).^2
alpha_c = getalpha(atom)
PyPlot.figure()
if nn>20
plot(plotscale[plotscale .> sqrt(alpha_c^(1/3))],normY_sol[plotscale .> sqrt(alpha_c^(1/3))],linewidth=2)
else
plot(plotscale,normY_sol,linewidth=2)
end
plot!(xlabel=L"(r/a_0)^{1/2}")
plot!(ylabel=L"r^{1/2}R(r) \,(a_0^{-1})")
plot!(title=string(atom," radial wavefunction |n,l,j⟩ = |",string(nn),",",string(ll),",",string(jj),"⟩"))
plot!(leg=false)
gui()
PyPlot.figure()
if nn>20
plot(rr[rr .> alpha_c^(1/3)],probamp[rr .> alpha_c^(1/3)],linewidth=2)
else
plot(rr,probamp,linewidth=2)
end
plot!(xlabel=L"r/a_0")
plot!(ylabel=L"|rR(r)|^2 \,(a_0^{-1})")
plot!(title=string(atom," radial probability density |n,l,j⟩ = |",string(nn),",",string(ll),",",string(jj),"⟩"))
plot!(leg=false)
gui()
|
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|
!
! Copyright 2013 Guy Munhoven
!
! This file is part of SolveSAPHE.
! SolveSAPHE is free software: you can redistribute it and/or modify
! it under the terms of the GNU Lesser General Public License as published by
! the Free Software Foundation, either version 3 of the License, or
! (at your option) any later version.
!
! SolveSAPHE is distributed in the hope that it will be useful,
! but WITHOUT ANY WARRANTY; without even the implied warranty of
! MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
! GNU Lesser General Public License for more details.
!
! You should have received a copy of the GNU Lesser General Public License
! along with SolveSAPHE. If not, see <http://www.gnu.org/licenses/>.
!
MODULE MOD_CHEMCONST
USE MOD_PRECISION
IMPLICIT NONE
! --------------------------------------------------------
! List of subroutines for the chemical constants (PRIVATE)
! --------------------------------------------------------
PRIVATE AK_CARB_0_WEIS74
PRIVATE AK_CARB_1_MILL95, AK_CARB_2_MILL95
PRIVATE AK_CARB_1_LUEK00, AK_CARB_2_LUEK00
PRIVATE AK_CARB_1_ROYE93, AK_CARB_2_ROYE93
PRIVATE AK_BORA_DICK90
PRIVATE AK_PHOS_1_MILL95, AK_PHOS_2_MILL95, AK_PHOS_3_MILL95
PRIVATE AK_SILI_1_MILL95
PRIVATE AK_H2S_1_MILL95
PRIVATE AK_AMMO_1_YAMI95
PRIVATE AK_W_MILL95
PRIVATE AK_HSO4_DICK90
PRIVATE ABETA_HF_DIRI79
PRIVATE AK_HF_PEFR87
! --------------------------------------
! Parameters for usage within the module
! --------------------------------------
! Gas constant
! ------------
REAL(KIND=wp), PARAMETER, PRIVATE :: gasconst_bar_cm3_o_mol_k = 83.14510_wp ! libthdyct
!REAL(KIND=wp), PARAMETER, PRIVATE :: gasconst_bar_cm3_o_mol_k = 83.14472_wp ! Handbook (2007)
! 0 degrees centigrade in Kelvin
! ------------------------------
REAL(KIND=wp), PARAMETER, PRIVATE :: t_k_zerodegc = 273.15_wp ! Handbook (2007)
! --------------------------------------------------------------
! Chemical constants' products: for usage by users of the module
! --------------------------------------------------------------
! For each acid system A,
! - api1_aaa <-- K_A1
! - api2_aaa <-- K_A1*K_A2
! - api3_aaa <-- K_A1*K_A2*K_A3
! - ...
REAL(KIND=wp) :: api1_dic, api2_dic
REAL(KIND=wp) :: api1_bor
REAL(KIND=wp) :: api1_po4, api2_po4, api3_po4
REAL(KIND=wp) :: api1_sil
REAL(KIND=wp) :: api1_nh4
REAL(KIND=wp) :: api1_h2s
REAL(KIND=wp) :: api1_so4
REAL(KIND=wp) :: api1_flu
REAL(KIND=wp) :: api1_wat, aphscale
!*******************************************************************************
CONTAINS
!*******************************************************************************
!=======================================================================
SUBROUTINE SETUP_API4PHTOT(t_k, s, p_bar)
!=======================================================================
IMPLICIT NONE
! ------------------
! Argument variables
! ------------------
! t_k : temperature in Kelvin
! s : salinity
! p_bar : applied pressure in bar
REAL(KIND=wp), INTENT(IN) :: t_k
REAL(KIND=wp), INTENT(IN) :: s
REAL(KIND=wp), INTENT(IN) :: p_bar
! ---------------
! Local variables
! ---------------
REAL(KIND=wp) :: zcvt_htot_o_hsws, zcvt_htot_o_hfree
zcvt_htot_o_hsws = 1._wp/ACVT_HSWS_O_HTOT(t_k, s, p_bar)
zcvt_htot_o_hfree = ACVT_HTOT_O_HFREE(t_k, s, p_bar)
api1_dic = AK_CARB_1_LUEK00(t_k, s, p_bar)
api2_dic = api1_dic * AK_CARB_2_LUEK00(t_k, s, p_bar)
api1_bor = AK_BORA_DICK90(t_k, s, p_bar)
api1_po4 = AK_PHOS_1_MILL95(t_k, s, p_bar) * zcvt_htot_o_hsws
api2_po4 = api1_po4 * AK_PHOS_2_MILL95(t_k, s, p_bar) * zcvt_htot_o_hsws
api3_po4 = api2_po4 * AK_PHOS_3_MILL95(t_k, s, p_bar) * zcvt_htot_o_hsws
api1_sil = AK_SILI_1_MILL95(t_k, s ) * zcvt_htot_o_hsws
api1_nh4 = AK_AMMO_1_YAMI95(t_k, s, p_bar) * zcvt_htot_o_hsws
api1_h2s = AK_H2S_1_MILL95 (t_k, s, p_bar) * zcvt_htot_o_hsws
api1_so4 = AK_HSO4_DICK90(t_k, s, p_bar) * zcvt_htot_o_hfree
api1_flu = AK_HF_PEFR87(t_k, s, p_bar)
api1_wat = AK_W_MILL95(t_k, s, p_bar) * zcvt_htot_o_hsws
aphscale = zcvt_htot_o_hfree
!=======================================================================
END SUBROUTINE SETUP_API4PHTOT
!=======================================================================
!=======================================================================
SUBROUTINE SETUP_API4PHSWS(t_k, s, p_bar)
!=======================================================================
! ------------------
! Argument variables
! ------------------
! t_k : temperature in Kelvin
! s : salinity
! p_bar : applied pressure in bar
REAL(KIND=wp), INTENT(IN) :: t_k
REAL(KIND=wp), INTENT(IN) :: s
REAL(KIND=wp), INTENT(IN) :: p_bar
! ---------------
! Local variables
! ---------------
REAL(KIND=wp) :: zcvt_hsws_o_htot, zcvt_hsws_o_hfree
zcvt_hsws_o_htot = ACVT_HSWS_O_HTOT(t_k, s, p_bar)
zcvt_hsws_o_hfree = ACVT_HSWS_O_HFREE(t_k, s, p_bar)
api1_dic = AK_CARB_1_MILL95(t_k, s, p_bar)
api2_dic = api1_dic * AK_CARB_2_MILL95(t_k, s, p_bar)
api1_bor = AK_BORA_DICK90(t_k, s, p_bar) * zcvt_hsws_o_htot
api1_po4 = AK_PHOS_1_MILL95(t_k, s, p_bar)
api2_po4 = api1_po4 * AK_PHOS_2_MILL95(t_k, s, p_bar)
api3_po4 = api2_po4 * AK_PHOS_3_MILL95(t_k, s, p_bar)
api1_sil = AK_SILI_1_MILL95(t_k, s )
api1_nh4 = AK_AMMO_1_YAMI95(t_k, s, p_bar)
api1_h2s = AK_H2S_1_MILL95 (t_k, s, p_bar)
api1_so4 = AK_HSO4_DICK90(t_k, s, p_bar) * zcvt_hsws_o_hfree
api1_flu = zcvt_hsws_o_hfree/ABETA_HF_DIRI79(t_k, s, p_bar)
api1_wat = AK_W_MILL95(t_k, s, p_bar)
aphscale = zcvt_hsws_o_hfree
!=======================================================================
END SUBROUTINE SETUP_API4PHSWS
!=======================================================================
!=======================================================================
FUNCTION AK_CARB_0_WEIS74(t_k, s)
!=======================================================================
! Function calculates K0 in (mol/kg-SW)/atmosphere
! References: Weiss (1979) [(mol/kg-SW)/atm]
! pH scale : N/A
! Note : currently no pressure correction
IMPLICIT NONE
REAL(KIND=wp) :: AK_CARB_0_WEIS74
! ------------------
! Argument variables
! ------------------
! s : salinity
! t_k : temperature in K
REAL(KIND=wp), INTENT(IN) :: t_k
REAL(KIND=wp), INTENT(IN) :: s
! ---------------
! Local variables
! ---------------
! zt_k_o_100 : zt_k/100
REAL(KIND=wp) :: zt_k_o_100
zt_k_o_100 = t_k/100._wp
AK_CARB_0_WEIS74 &
= EXP( -60.2409_wp + 93.4517_wp/zt_k_o_100 &
+ 23.3585_wp*LOG(zt_k_o_100) &
+ ( 0.023517_wp - 0.023656_wp*zt_k_o_100 &
+ 0.0047036_wp*zt_k_o_100*zt_k_o_100)*s )
RETURN
!=======================================================================
END FUNCTION AK_CARB_0_WEIS74
!=======================================================================
!=======================================================================
FUNCTION AK_CARB_1_MILL95(t_k, s, p_bar)
!=======================================================================
! Function calculates first dissociation constant of carbonic acid
! in mol/kg-SW on the SWS pH-scale.
! References: Millero (1995, eq 50 -- ln K1(COM))
! Millero (1982) pressure correction
! pH scale: SWS
IMPLICIT NONE
REAL(KIND=wp) :: AK_CARB_1_MILL95
! ------------------
! Argument variables
! ------------------
! t_k : temperature in Kelvin
! s : salinity
! p_bar : applied pressure in bar
REAL(KIND=wp), INTENT(IN) :: t_k
REAL(KIND=wp), INTENT(IN) :: s
REAL(KIND=wp), INTENT(IN) :: p_bar
! ---------------
! Local variables
! ---------------
! zrt : R*t_k, R in bar*cm3/(mol*K)
! zt_degc : temperature in degrees Celsius
! zdvi : volume change for ionization
! zdki : compressibility change for ionization
! zsqrts : square root of salinity
! zds : salinity-34.8
! zln_kc1_p0 : ln(K_C1) at p_bar = 0
! zln_kc1_pp : pressure correction for p_bar /= 0
REAL(KIND=wp) :: zrt, zt_degc, zdvi, zdki, zds, zsqrts
REAL(KIND=wp) :: zln_kc1_p0, zln_kc1_pp
! ln(K_C1) value at p_bar = 0
zsqrts = SQRT(s)
zln_kc1_p0 = 2.18867_wp &
- 2275.0360_wp/t_k &
- 1.468591_wp*LOG(t_k) &
+ ( -0.138681_wp - 9.33291_wp/t_k)*zsqrts &
+ 0.0726483_wp*s - 0.00574938_wp*s*zsqrts
! Pressure correction
zt_degc = t_k - t_k_zerodegc
zds = s - 34.8_wp
zrt = gasconst_bar_cm3_o_mol_k * t_k
zdvi = -25.50_wp - 0.151_wp*zds + 0.1271_wp*zt_degc
zdki = ( -3.08_wp - 0.578_wp*zds + 0.0877_wp*zt_degc)*1.0E-03_wp
zln_kc1_pp = (-zdvi + zdki*p_bar/2._wp)*p_bar/zrt
! Final K_C1 value
AK_CARB_1_MILL95 = EXP( zln_kc1_p0 + zln_kc1_pp )
RETURN
!=======================================================================
END FUNCTION AK_CARB_1_MILL95
!=======================================================================
!=======================================================================
FUNCTION AK_CARB_2_MILL95(t_k, s, p_bar)
!=======================================================================
! Function calculates second dissociation constant K1
! in mol/kg-SW on the SWS pH-scale.
! References: Millero (1995, eq 51 -- ln K2(COM))
! Millero (1979) pressure correction
! pH scale: SWS
IMPLICIT NONE
REAL(KIND=wp) :: AK_CARB_2_MILL95
! Argument variables
! ------------------
! t_k : temperature in Kelvin
! s : salinity
! p_bar : applied pressure in bar
REAL(KIND=wp), INTENT(IN) :: t_k
REAL(KIND=wp), INTENT(IN) :: s
REAL(KIND=wp), INTENT(IN) :: p_bar
! Local variables
! ---------------
! zrt : R*t_k, R in bar*cm3/(mol*K)
! zt_degc : temperature in degrees Celsius
! zdvi : volume change for ionization
! zdki : compressibility change for ionization
! zsqrts : square root of salinity
! zds : salinity-34.8
! zln_kc2_p0 : ln(K_C2) at p_bar = 0
! zln_kc2_pp : pressure correction for p_bar /= 0
REAL(KIND=wp) :: zrt, zt_degc, zdvi, zdki, zds, zsqrts
REAL(KIND=wp) :: zln_kc2_p0, zln_kc2_pp
! ln(K_C2) value at p_bar = 0
zsqrts = SQRT(s)
zln_kc2_p0 = -0.84226_wp &
- 3741.1288_wp/t_k &
- 1.437139_wp*LOG(t_k) &
+ (-0.128417_wp - 24.41239_wp/t_k)*zsqrts &
+ 0.1195308_wp*s &
- 0.00912840_wp*s*zsqrts
! Pressure correction
zt_degc = t_k - t_k_zerodegc
zds = s - 34.8_wp
zrt = gasconst_bar_cm3_o_mol_k * t_k
zdvi = -15.82_wp + 0.321_wp*zds - 0.0219_wp*zt_degc
zdki = ( 1.13_wp - 0.314_wp*zds - 0.1475_wp*zt_degc)*1.0E-03_wp
zln_kc2_pp = (-zdvi + zdki*p_bar/2._wp)*p_bar/zrt
! Final K_C2 value
AK_CARB_2_MILL95 = EXP( zln_kc2_p0 + zln_kc2_pp )
RETURN
!=======================================================================
END FUNCTION AK_CARB_2_MILL95
!=======================================================================
!=======================================================================
FUNCTION AK_CARB_1_LUEK00(t_k, s, p_bar)
!=======================================================================
! Function calculates first dissociation constant of carbonic acid
! in mol/kg-SW on the Total pH-scale.
! References: Luecker et al. (2000) -- also Handbook (2007)
! Millero (1979) pressure correction
! pH scale: Total
IMPLICIT NONE
REAL(KIND=wp) :: AK_CARB_1_LUEK00
! Argument variables
! ------------------
! t_k : temperature in Kelvin
! s : salinity
! p_bar : applied pressure in bar
REAL(KIND=wp), INTENT(IN) :: t_k
REAL(KIND=wp), INTENT(IN) :: s
REAL(KIND=wp), INTENT(IN) :: p_bar
! Local variables
! ---------------
! zrt : R*t_k, R in bar*cm3/(mol*K)
! zt_degc : temperature in degrees Celsius
! zdvi : volume change for ionization
! zdki : compressibility change for ionization
! zds : salinity-34.8
! zlog10_kc1_p0 : log_10(k_C1) at p_bar = 0
! zln_kc1_pp : pressure correction for p_bar /= 0
REAL(KIND=wp) :: zrt, zt_degc, zdvi, zdki, zds, zsqrts
REAL(KIND=wp) :: zlog10_kc1_p0, zln_kc1_pp
! log_10(K_C1) value at p_bar = 0
zlog10_kc1_p0 = 61.2172_wp &
- 3633.86_wp/t_k &
- 9.67770_wp*LOG(t_k) &
+ s*(0.011555 - s*0.0001152_wp)
! Pressure correction
zt_degc = t_k - t_k_zerodegc
zds = s - 34.8_wp
zrt = gasconst_bar_cm3_o_mol_k * t_k
zdvi = -25.50_wp - 0.151_wp*zds + 0.1271_wp*zt_degc
zdki = ( -3.08_wp - 0.578_wp*zds + 0.0877_wp*zt_degc)*1.0E-03_wp
zln_kc1_pp = (-zdvi + zdki*p_bar/2._wp)*p_bar/zrt
! Final K_C1 value
AK_CARB_1_LUEK00 = 10._wp**zlog10_kc1_p0 * EXP(zln_kc1_pp)
RETURN
!=======================================================================
END FUNCTION AK_CARB_1_LUEK00
!=======================================================================
!=======================================================================
FUNCTION AK_CARB_2_LUEK00(t_k, s, p_bar)
!=======================================================================
! Function calculates second dissociation constant K1
! in mol/kg-SW on the Total pH-scale.
! References: Luecker et al. (2000) -- also Handbook (2007)
! Millero (1979) pressure correction
! pH scale: Total
IMPLICIT NONE
REAL(KIND=wp) :: AK_CARB_2_LUEK00
! Argument variables
! ------------------
! t_k : temperature in Kelvin
! s : salinity
! p_bar : applied pressure in bar
REAL(KIND=wp), INTENT(IN) :: t_k
REAL(KIND=wp), INTENT(IN) :: s
REAL(KIND=wp), INTENT(IN) :: p_bar
! Local variables
! ---------------
! zrt : R*t_k, R in bar*cm3/(mol*K)
! zt_degc : temperature in degrees Celsius
! zdvi : volume change for ionization
! zdki : compressibility change for ionization
! zsqrts : square root of salinity
! zds : salinity-34.8
! zlog10_kc2_p0 : log_10(K_C2) at p_bar = 0
! zln_kc2_pp : pressure correction for p_bar /= 0
REAL(KIND=wp) :: zrt, zt_degc, zdvi, zdki, zds, zsqrts
REAL(KIND=wp) :: zlog10_kc2_p0, zln_kc2_pp
! log_10(K_C2) value at p_bar = 0
zlog10_kc2_p0 = -25.9290_wp &
- 471.78_wp/t_k + 3.16967_wp*LOG(t_k) &
+ s*(0.01781_wp - s*0.0001122_wp)
! Pressure correction
zt_degc = t_k - t_k_zerodegc
zds = s - 34.8_wp
zrt = gasconst_bar_cm3_o_mol_k * t_k
zdvi = -15.82_wp + 0.321_wp*zds - 0.0219_wp*zt_degc
zdki = ( 1.13_wp - 0.314_wp*zds - 0.1475_wp*zt_degc)*1.0E-03_wp
zln_kc2_pp = (-zdvi + zdki*p_bar/2._wp)*p_bar/zrt
! Final K_C2 value
AK_CARB_2_LUEK00 = 10._wp**zlog10_kc2_p0 *EXP(zln_kc2_pp)
RETURN
!=======================================================================
END FUNCTION AK_CARB_2_LUEK00
!=======================================================================
!=======================================================================
FUNCTION AK_CARB_1_ROYE93(t_k, s, p_bar)
!=======================================================================
! Function calculates first dissociation constant of carbonic acid
! in mol/kg-SW on the Total pH-scale.
! References: Roy et al. (1993) -- also Handbook (1994)
! Millero (1979) pressure correction
! pH scale : Total
! Note : converted here from mol/kg-H2O to mol/kg-SW
IMPLICIT NONE
REAL(KIND=wp) :: AK_CARB_1_ROYE93
! Argument variables
! ------------------
! t_k : temperature in Kelvin
! s : salinity
! p_bar : applied pressure in bar
REAL(KIND=wp), INTENT(IN) :: t_k
REAL(KIND=wp), INTENT(IN) :: s
REAL(KIND=wp), INTENT(IN) :: p_bar
! Local variables
! ---------------
! zrt : R*t_k, R in bar*cm3/(mol*K)
! zt_degc : temperature in degrees Celsius
! zdvi : volume change for ionization
! zdki : compressibility change for ionization
! zds : salinity-34.8
! zln_kc1_p0 : ln(k_C1) at p_bar = 0
! zln_kc1_pp : pressure correction for p_bar /= 0
REAL(KIND=wp) :: zsqrts, zcvt_to_kgsw
REAL(KIND=wp) :: zrt, zt_degc, zdvi, zdki, zds
REAL(KIND=wp) :: zln_kc1_p0, zln_kc1_pp
! ln(K_C1) value at p_bar = 0
zsqrts = SQRT(s)
zcvt_to_kgsw = ACVT_KGH2O_O_KGSW(s)
zln_kc1_p0 = -2307.1255_wp/t_k + 2.83655_wp - 1.5529413_wp*LOG(t_k) &
+ (-4.0484_wp/t_k - 0.20760841)*zsqrts &
+ 0.08468345*s &
- 0.00654208*zsqrts*s
! Pressure correction
zt_degc = t_k - t_k_zerodegc
zds = s - 34.8_wp
zrt = gasconst_bar_cm3_o_mol_k * t_k
zdvi = -25.50_wp - 0.151_wp*zds + 0.1271_wp*zt_degc
zdki = ( -3.08_wp - 0.578_wp*zds + 0.0877_wp*zt_degc)*1.0E-03_wp
zln_kc1_pp = (-zdvi + zdki*p_bar/2._wp)*p_bar/zrt
! Final K_C1 value
AK_CARB_1_ROYE93 = EXP(zln_kc1_p0 + zln_kc1_pp) * zcvt_to_kgsw
RETURN
!=======================================================================
END FUNCTION AK_CARB_1_ROYE93
!=======================================================================
!=======================================================================
FUNCTION AK_CARB_2_ROYE93(t_k, s, p_bar)
!=======================================================================
! Function calculates second dissociation constant K1
! in mol/kg-SW on the Total pH-scale.
! References: Roy et al. (1993) -- also Handbook (1994)
! Millero (1979) pressure correction
! pH scale : Total
! Note : converted here from mol/kg-H2O to mol/kg-SW
IMPLICIT NONE
REAL(KIND=wp) :: AK_CARB_2_ROYE93
! Argument variables
! ------------------
! t_k : temperature in Kelvin
! s : salinity
! p_bar : applied pressure in bar
REAL(KIND=wp), INTENT(IN) :: t_k
REAL(KIND=wp), INTENT(IN) :: s
REAL(KIND=wp), INTENT(IN) :: p_bar
! Local variables
! ---------------
! zrt : R*t_k, R in bar*cm3/(mol*K)
! zt_degc : temperature in degrees Celsius
! zdvi : volume change for ionization
! zdki : compressibility change for ionization
! zsqrts : square root of salinity
! zds : salinity-34.8
! zln_kc2_p0 : ln(K_C2) at p_bar = 0
! zln_kc2_pp : pressure correction for p_bar /= 0
REAL(KIND=wp) :: zsqrts, zcvt_to_kgsw
REAL(KIND=wp) :: zrt, zt_degc, zdvi, zdki, zds
REAL(KIND=wp) :: zln_kc2_p0, zln_kc2_pp
! ln(K_C2) value at p_bar = 0
zsqrts = SQRT(s)
zcvt_to_kgsw = ACVT_KGH2O_O_KGSW(s)
zln_kc2_p0 = -3351.6106_wp/t_k - 9.226508_wp - 0.2005743_wp*LOG(t_k) &
+ ( -23.9722_wp/t_k - 0.106901773_wp)*zsqrts &
+ 0.1130822*s - 0.00846934_wp*zsqrts*s
! Pressure correction
zt_degc = t_k - t_k_zerodegc
zds = s - 34.8_wp
zrt = gasconst_bar_cm3_o_mol_k * t_k
zdvi = -15.82_wp + 0.321_wp*zds - 0.0219_wp*zt_degc
zdki = ( 1.13_wp - 0.314_wp*zds - 0.1475_wp*zt_degc)*1.0E-03_wp
zln_kc2_pp = (-zdvi + zdki*p_bar/2._wp)*p_bar/zrt
! Final K_C2 value
AK_CARB_2_ROYE93 = EXP(zln_kc2_p0 + zln_kc2_pp) * zcvt_to_kgsw
RETURN
!=======================================================================
END FUNCTION AK_CARB_2_ROYE93
!=======================================================================
!=======================================================================
FUNCTION AK_BORA_DICK90(t_k, s, p_bar)
!=======================================================================
! Function calculates boric acid dissociation constant KB
! in mol/kg-SW on the total pH-scale.
! References: Dickson (1990, eq. 23) -- also Handbook (2007, eq. 37)
! Millero (1979) pressure correction
! pH scale : total
IMPLICIT NONE
REAL(KIND=wp) :: AK_BORA_DICK90
! ------------------
! Argument variables
! ------------------
! t_k : temperature in Kelvin
! s : salinity
! p_bar : applied pressure in bar
REAL(KIND=wp), INTENT(IN) :: t_k
REAL(KIND=wp), INTENT(IN) :: s
REAL(KIND=wp), INTENT(IN) :: p_bar
! ---------------
! Local variables
! ---------------
! zrt : R*t_k, R in bar*cm3/(mol*K)
! zt_degc : temperature in degrees Celsius
! zdvi : volume change for ionization
! zdki : compressibility change for ionization
! zsqrts : square root of salinity
! zds : salinity-34.8
! zln_kb_p0 : K_b at p_bar = 0
! zln_kb_pp : pressure correction for p_bar /= 0
REAL(KIND=wp) :: zrt, zt_degc, zdvi, zdki, zds, zsqrts
REAL(KIND=wp) :: zln_kb_p0, zln_kb_pp
! ln(K_B) value at p_bar = 0
zsqrts = SQRT(s)
zln_kb_p0 = ( -8966.90_wp &
+ zsqrts*( -2890.53_wp &
+ zsqrts*( -77.942_wp &
+ zsqrts*( 1.728_wp - 0.0996_wp*zsqrts)))) / t_k &
+ 148.0248_wp + zsqrts*(137.1942_wp + zsqrts*1.62142_wp) &
+ (-24.4344_wp + zsqrts*(-25.085_wp - zsqrts*0.2474_wp)) * LOG(t_k) &
+ 0.053105_wp*zsqrts*t_k
! Pressure correction
zt_degc = t_k - t_k_zerodegc
zds = s - 34.8_wp
zrt = gasconst_bar_cm3_o_mol_k * t_k
zdvi = -29.48_wp + 0.295_wp*zds + 0.1622_wp*zt_degc - 0.002608_wp*zt_degc*zt_degc
zdki = (-2.84_wp + 0.354_wp*zds)*1.0E-03_wp
zln_kb_pp = (-zdvi + zdki*p_bar/2._wp)*p_bar/zrt
! Final K_B value
AK_BORA_DICK90 = EXP( zln_kb_p0 + zln_kb_pp )
!=======================================================================
END FUNCTION AK_BORA_DICK90
!=======================================================================
!=======================================================================
FUNCTION AK_W_MILL95(t_k, s, p_bar)
!=======================================================================
! Function calculates water dissociation constant Kw in (mol/kg-SW)^2
! References: Millero (1995) for value at p_bar = 0
! Millero (pers. comm. 1996) for pressure correction
! pH scale : SWS
IMPLICIT NONE
REAL(KIND=wp) :: AK_W_MILL95
! ------------------
! Argument variables
! ------------------
! t_k : temperature in K
! s : salinity
! p_bar : applied pressure in bar
REAL(KIND=wp), INTENT(IN) :: t_k, s, p_bar
! ---------------
! Local variables
! ---------------
! zrt : R*t_k
! zt_degc : temperature in degrees Celsius
! zdvi : volume change for ionization
! zdki : compressibility change for ionization
! zln_kw_p0 : ln(K_w) at p_bar = 0
! zln_kw_pp : pressure correction for p_bar /= 0
REAL(KIND=wp) :: zrt, zt_degc, zdvi, zdki, zds, zsqrts
REAL(KIND=wp) :: zln_kw_p0, zln_kw_pp
! ln(K_w) value at p_bar = 0
zln_kw_p0 = 148.9802_wp &
- 13847.26_wp/t_k &
- 23.6521_wp*LOG(t_k) &
+ ( -5.977_wp + 118.67_wp/t_k + 1.0495_wp*LOG(t_k))*SQRT(s) &
- 0.01615_wp*s
! Pressure correction
zt_degc = t_k - t_k_zerodegc
zrt = gasconst_bar_cm3_o_mol_k * t_k
zdvi = -20.02_wp + 0.1119_wp*zt_degc - 0.1409E-02_wp*zt_degc*zt_degc
zdki = ( -5.13_wp + 0.0794_wp*zt_degc)*1.0E-03_wp
zln_kw_pp = (-zdvi + zdki*p_bar/2._wp)*p_bar/zrt
! Final K_w value
AK_W_MILL95 = EXP( zln_kw_p0 + zln_kw_pp )
RETURN
!=======================================================================
END FUNCTION AK_W_MILL95
!=======================================================================
!=======================================================================
FUNCTION AK_PHOS_1_MILL95(t_k, s, p_bar)
!=======================================================================
! Function returns the first dissociation constant
! of phosphoric acid (H3PO4) in seawater
! References: Yao and Millero (1995)
! Millero (1995) for pressure correction
! pH scale : SWS
IMPLICIT NONE
REAL(KIND=wp) :: AK_PHOS_1_MILL95
! ------------------
! Argument variables
! ------------------
! t_k : temperature in K
! s : salinity
! p_bar : applied pressure in bar
REAL(KIND=wp), INTENT(IN) :: t_k, s, p_bar
! ---------------
! Local variables
! ---------------
! zrt : R*t_k, R in bar*cm3/(mol*K)
! zt_degc : temperature in degrees Celsius
! zdvi : volume change for ionization
! zdki : compressibility change for ionization
! zln_kp1_p0 : ln(K_p1) at p_bar = 0
! zln_kp1_pp : pressure correction for p_bar /= 0
REAL(KIND=wp) :: zrt, zt_degc, zdvi, zdki
REAL(KIND=wp) :: zln_kp1_p0, zln_kp1_pp
! ln(K_P1) for p_bar = 0
zln_kp1_p0 = 115.54_wp - 4576.752_wp/t_k - 18.453_wp*LOG(t_k) &
+ ( 0.69171_wp - 106.736_wp/t_k)* SQRT(s) &
+ (-0.01844_wp - 0.65643_wp/t_k)*s
! Pressure correction
zt_degc = t_k - t_k_zerodegc
zrt = gasconst_bar_cm3_o_mol_k * t_k
zdvi = -14.51_wp + 0.1211_wp*zt_degc - 0.321E-03*zt_degc*zt_degc
zdki = ( -2.67_wp + 0.0427_wp*zt_degc)*1.0E-03_wp
zln_kp1_pp = (-zdvi + zdki*p_bar/2._wp)*p_bar/zrt
! Final value of K_P1
AK_PHOS_1_MILL95 = EXP(zln_kp1_p0 + zln_kp1_pp)
RETURN
!=======================================================================
END FUNCTION AK_PHOS_1_MILL95
!=======================================================================
!=======================================================================
FUNCTION AK_PHOS_2_MILL95(t_k, s, p_bar)
!=======================================================================
! Function returns the second dissociation constant
! of phosphoric acid (H3PO4) in seawater
! References: Yao and Millero (1995)
! Millero (1995) for pressure correction
! pH scale : SWS
IMPLICIT NONE
REAL(KIND=wp) :: AK_PHOS_2_MILL95
! ------------------
! Argument variables
! ------------------
! t_k : temperature in K
! s : salinity
! p_bar : applied pressure in bar
REAL(KIND=wp), INTENT(IN) :: t_k, s, p_bar
! ---------------
! Local variables
! ---------------
! zrt : R*t_k, R in bar*cm3/(mol*K)
! zt_degc : temperature in degrees Celsius
! zdvi : volume change for ionization
! zdki : compressibility change for ionization
! zln_kp2_p0 : ln(K_P2) at p_bar = 0
! zln_kp2_pp : pressure correction for p_bar /= 0
REAL(KIND=wp) :: zrt, zt_degc, zdvi, zdki
REAL(KIND=wp) :: zln_kp2_p0, zln_kp2_pp
! ln(K_P2) for p_bar = 0
zln_kp2_p0 = 172.1033_wp &
- 8814.715_wp/t_k &
- 27.927_wp*LOG(t_k) &
+ ( 1.3566_wp - 160.340_wp/t_k)*SQRT(s) &
+ (-0.05778_wp + 0.37335_wp/t_k)*s
! Pressure correction
zt_degc = t_k - t_k_zerodegc
zrt = gasconst_bar_cm3_o_mol_k * t_k
zdvi = -23.12_wp + 0.1758_wp*zt_degc -2.647E-03_wp*zt_degc*zt_degc
zdki = ( -5.15_wp + 0.09_wp*zt_degc)*1.0E-03_wp
zln_kp2_pp = (-zdvi + zdki*p_bar/2._wp)*p_bar/zrt
! Final K_P2 value
AK_PHOS_2_MILL95 = EXP( zln_kp2_p0 + zln_kp2_pp )
RETURN
!=======================================================================
END FUNCTION AK_PHOS_2_MILL95
!=======================================================================
!=======================================================================
FUNCTION AK_PHOS_3_MILL95(t_k, s, p_bar)
!=======================================================================
! Function returns the third dissociation constant
! of phosphoric acid (H3PO4) in seawater
! References: Yao and Millero (1995)
! Millero (1995) for pressure correction
! pH scale : SWS
IMPLICIT NONE
REAL(KIND=wp) :: AK_PHOS_3_MILL95
! ------------------
! Argument variables
! ------------------
! t_k : temperature in K
! s : salinity
! p_bar : applied pressure in bar
REAL(KIND=wp), INTENT(IN) :: t_k, s, p_bar
! ---------------
! Local variables
! ---------------
! zrt : R*t_k, R in bar*cm3/(mol*K)
! zt_degc : temperature in degrees Celsius
! zdvi : volume change for ionization
! zdki : compressibility change for ionization
! zln_kp3_p0 : ln(K_P3) at p_bar = 0
! zln_kp3_pp : pressure correction for p_bar /= 0
REAL(KIND=wp) :: zrt, zt_degc, zdvi, zdki
REAL(KIND=wp) :: zln_kp3_p0, zln_kp3_pp
! ln(K_P3) for p_bar = 0
zln_kp3_p0 = -18.126_wp - 3070.75_wp/t_k &
+ ( 2.81197_wp + 17.27039_wp/t_k)*SQRT(s) &
+ (-0.09984_wp - 44.99486_wp/t_k)*s
! Pressure correction
zt_degc = t_k - t_k_zerodegc
zrt = gasconst_bar_cm3_o_mol_k * t_k
zdvi = -26.57_wp + 0.2020_wp*zt_degc -3.042E-03*zt_degc*zt_degc
zdki = ( -4.08_wp + 0.0714_wp*zt_degc)*1.0E-03_wp
zln_kp3_pp = (-zdvi + zdki*p_bar/2._wp)*p_bar/zrt
! Final K_P3 value
AK_PHOS_3_MILL95 = EXP( zln_kp3_p0 + zln_kp3_pp )
RETURN
!=======================================================================
END FUNCTION AK_PHOS_3_MILL95
!=======================================================================
!=======================================================================
FUNCTION AK_SILI_1_MILL95(t_k, s)
!=======================================================================
! Function returns the first dissociation constant
! of silicic acid (H4SiO4) in seawater
! References: Yao and Millero (1995) cited by Millero (1995)
! pH scale : SWS (according to Dickson et al, 2007)
! Note : No pressure correction available
! Note : converted here from mol/kg-H2O to mol/kg-sw
IMPLICIT NONE
REAL(KIND=wp) :: AK_SILI_1_MILL95
! ------------------
! Argument variables
! ------------------
! t_k : temperature in K
! s : salinity
REAL(KIND=wp), INTENT(IN) :: t_k, s
! ---------------
! Local variables
! ---------------
! zcvt_to_kgsw: fraction of pure water in 1 kg seawater at salinity s
! zionst : ionic strength [mol/kg-H2O]
! zln_ksi1_p0 : ln(K_Si1) at p_bar = 0
! zln_ksi1_pp : pressure correciotn for p_bar /= 0
REAL(KIND=wp) :: zionst, zcvt_to_kgsw
REAL(KIND=wp) :: zln_ksi1_p0, zln_ksi1_pp
! K_Si1 value at p_bar = 0
zcvt_to_kgsw = ACVT_KGH2O_O_KGSW(s)
zionst = A_IONSTRENGTH_SALIN(s)/zcvt_to_kgsw ! mol/kg-H2O !!
zln_ksi1_p0 = 117.40_wp - 8904.2_wp/t_k - 19.334_wp * LOG(t_k) &
+ ( 3.5913_wp - 458.79_wp/t_k) * SQRT(zionst) &
+ (-1.5998_wp + 188.74_wp/t_k) * zionst &
+ (0.07871_wp - 12.1652_wp/t_k) * zionst*zionst
! Pressure correction : currently none
zln_ksi1_pp = 0._wp
! Final value
AK_SILI_1_MILL95 = EXP( zln_ksi1_p0 + zln_ksi1_pp ) * zcvt_to_kgsw
RETURN
!=======================================================================
END FUNCTION AK_SILI_1_MILL95
!=======================================================================
!=======================================================================
FUNCTION AK_H2S_1_MILL95(t_k, s, p_bar)
!=======================================================================
! Function returns the dissociation constant of hydrogen sulfide in sea-water
! References: Millero et al. (1988) (cited by Millero (1995)
! Millero (1995) for pressure correction
! pH scale : - SWS (according to Yao and Millero, 1995, p. 82: "refitted if necessary")
! - Total (according to Lewis and Wallace, 1998)
! Note : we stick to SWS here for the time being
! Note : the fits from Millero (1995) and Yao and Millero (1995)
! derive from Millero et al. (1998), with all the coefficients
! multiplied by -ln(10)
IMPLICIT NONE
REAL(KIND=wp) :: AK_H2S_1_MILL95
! ------------------
! Argument variables
! ------------------
! t_k : temperature in K
! s : salinity
! p_bar : applied pressure in bar
REAL(KIND=wp), INTENT(IN) :: t_k, s, p_bar
! ---------------
! Local variables
! ---------------
! zt_degc : temperature in degrees Celsius
! zrt : R*t_k, R in bar*cm3/(mol*K)
! zdvi : volume change for ionization
! zdki : compressibility change for ionization
! zln_kh2s_p0 : ln(K_H2S) at p_bar = 0
! zln_kh2s_pp : pressure correction for p_bar /= 0
REAL(KIND=wp) :: zrt, zt_degc, zdvi, zdki
REAL(KIND=wp) :: zln_kh2s_p0, zln_kh2s_pp
! K_H2S value at p_bar = 0
! ------------------------
zln_kh2s_p0 = 225.838_wp &
- 13275.3_wp/t_k &
- 34.6435_wp * LOG(t_k) &
+ 0.3449_wp*SQRT(s) &
- 0.0274_wp*s
! Pressure correction
! -------------------
zt_degc = t_k - t_k_zerodegc
zrt = gasconst_bar_cm3_o_mol_k * t_k
zdvi = -14.80_wp + zt_degc*(0.0020_wp - zt_degc*0.400E-03_wp)
zdki = ( 2.89_wp + zt_degc*0.054_wp)*1.0E-03_wp
zln_kh2s_pp = (-zdvi + zdki*p_bar/2._wp)*p_bar/zrt
! Final K_H2S value
! -----------------
AK_H2S_1_MILL95 = EXP( zln_kh2s_p0 + zln_kh2s_pp )
RETURN
!=======================================================================
END FUNCTION AK_H2S_1_MILL95
!=======================================================================
!=======================================================================
FUNCTION AK_AMMO_1_YAMI95(t_k, s, p_bar)
!=======================================================================
! Function returns the dissociation constant
! of ammonium in sea-water [mol/kg-SW]
! References: Yao and Millero (1995)
! Millero (1995) for pressure correction
! pH scale : SWS
IMPLICIT NONE
REAL(KIND=wp) :: AK_AMMO_1_YAMI95
! ------------------
! Argument variables
! ------------------
! t_k : temperature in K
! s : salinity
! p_bar : applied pressure in bar
REAL(KIND=wp), INTENT(IN) :: t_k, s, p_bar
! ---------------
! Local variables
! ---------------
! zt_degc : temperature in degrees Celsius
! zrt : R*t_k, R in bar*cm3/(mol*K)
! zdvi : volume change for ionization
! zdki : compressibility change for ionization
! zln_knh4_p0 : ln(K_NH4) at p_bar = 0
! zln_knh4_pp : pressure correction for p_bar /= 0
REAL(KIND=wp) :: zrt, zt_degc, zdvi, zdki
REAL(KIND=wp) :: zln_knh4_p0, zln_knh4_pp
! K_NH4 value at p_bar = 0
! ------------------------
zln_knh4_p0 = -0.25444_wp - 6285.33_wp/t_k + 0.0001635_wp*t_k &
+ ( 0.46532_wp - 123.7184_wp/t_k) * SQRT(s) &
+ (-0.01992_wp + 3.17556_wp/t_k) * s
! Pressure correction
! -------------------
zt_degc = t_k - t_k_zerodegc
zrt = gasconst_bar_cm3_o_mol_k * t_k
zdvi = -26.43_wp + zt_degc*(0.0889_wp - zt_degc*0.905E-03_wp)
zdki = ( -5.03_wp + zt_degc*0.0814_wp)*1.0E-03_wp
zln_knh4_pp = (-zdvi + zdki*p_bar/2._wp)*p_bar/zrt
! Final K_NH4 value
! -----------------
AK_AMMO_1_YAMI95 = EXP( zln_knh4_p0 + zln_knh4_pp )
RETURN
!=======================================================================
END FUNCTION AK_AMMO_1_YAMI95
!=======================================================================
!=======================================================================
FUNCTION ACVT_KGH2O_O_KGSW(s)
!=======================================================================
! Function returns the mass of pure water in one kg of seawater
! of salinity s
! References: "libthdyct" -- derived by Munhoven (1997) from data by Millero (1982)
! "Handbook (2007)" -- Handbook (2007)
! pH scale: N/A
IMPLICIT NONE
REAL(KIND=wp) :: ACVT_KGH2O_O_KGSW
REAL(KIND=wp), INTENT(IN) :: s
!ACVT_KGH2O_O_KGSW = 1._wp - 0.0010049_wp*s ! libthdyct
ACVT_KGH2O_O_KGSW = 1._wp - 0.001005_wp*s ! Handbook (2007)
RETURN
!=======================================================================
END FUNCTION ACVT_KGH2O_O_KGSW
!=======================================================================
!=======================================================================
FUNCTION A_IONSTRENGTH_SALIN(s)
!=======================================================================
! Function calculates ionic strength in mol/kg-SW, for given salinity.
! References: "libthdyct" -- derived by Munhoven (1997) from data by Millero (1982)
! "Handbook (2007)" -- Handbook (2007)
! pH scale: N/A
IMPLICIT NONE
REAL(KIND=wp) :: A_IONSTRENGTH_SALIN
! ------------------
! Argument variables
! ------------------
REAL(KIND=wp), INTENT(IN) :: s
!A_IONSTRENGTH_SALIN = (0.019920D+00*s) ! libthdyct
A_IONSTRENGTH_SALIN = (0.019924D+00*s) ! Handbook (2007)
RETURN
!=======================================================================
END FUNCTION A_IONSTRENGTH_SALIN
!=======================================================================
!=======================================================================
FUNCTION ABETA_HF_DIRI79(t_k, s, p_bar)
!=======================================================================
! Function calculates association constant \beta_{HF} [(mol/kg-SW)^{-1}]
! in (mol/kg-SW)^{-1}, where
! \beta_{HF} = \frac{ [HF] }{ [H^{+}] [F^{-}] }
! References: Dickson and Riley (1979)
! Millero (1995) for pressure correction
! pH scale : free
! Note : converted here from mol/kg-H2O to mol/kg-SW
IMPLICIT NONE
REAL(KIND=wp) :: ABETA_HF_DIRI79
! ------------------
! Argument variables
! ------------------
! t_k : temperature in K
! s : salinity
! p_bar : applied pressure in bar
REAL(KIND=wp), INTENT(IN) :: t_k, s, p_bar
! ---------------
! Local variables
! ---------------
! zrt : R*t_k, R in bar*cm3/(mol*K)
! zt_degc : temperature in degrees Celsius
! zdvi : volume change for ionization
! zdki : compressibility change for ionization
! zionst : ionic strength [mol/kg-H2O]
! zcvt_to_kgsw : mass of pure water in 1kg of seawater as a fct. of salinity
! zln_bhf_p0 : \beta_HF at p_bar = 0
! zln_khf_pp : pressure correction for k_HF = 1/\beta_HF at p_bar /= 0
REAL(KIND=wp) :: zrt, zt_degc, zdvi, zdki, zds, zsqrts
REAL(KIND=wp) :: zionst, zcvt_to_kgsw
REAL(KIND=wp) :: zln_bhf_p0, zln_khf_pp
! \beta_HF at p_bar = 0
! ---------------------
zcvt_to_kgsw = ACVT_KGH2O_O_KGSW(s)
zionst = A_IONSTRENGTH_SALIN(s)/zcvt_to_kgsw
zln_bhf_p0 = -1590.2_wp/t_k + 12.641_wp - 1.525_wp*SQRT(zionst)
! Pressure correction
! -------------------
zt_degc = t_k - t_k_zerodegc
zrt = gasconst_bar_cm3_o_mol_k * t_k
zdvi = -9.78_wp + zt_degc*(-0.0090_wp - zt_degc*0.942E-03_wp)
zdki = ( -3.91_wp + zt_degc*0.054_wp)*1.0E-03_wp
zln_khf_pp = (-zdvi + zdki*p_bar/2._wp)*p_bar/zrt
! Final \beta_HF value
! --------------------
! notice that ln(k_HF(P)) = ln(k_HF(0)) + zln_khf_pp
! <=> -ln(\beta_HF(P)) = -ln(\beta_HF(0)) + zln_khf_pp
! <=> ln(\beta_HF(P)) = ln(\beta_HF(0)) - zln_khf_pp
ABETA_HF_DIRI79 = EXP(zln_bhf_p0 - zln_khf_pp ) / zcvt_to_kgsw
RETURN
!=======================================================================
END FUNCTION ABETA_HF_DIRI79
!=======================================================================
!=======================================================================
FUNCTION AK_HF_PEFR87(t_k, s, p_bar)
!=======================================================================
! Function calculates dissociation constant for hydrogen fluoride
! in mol/kg-SW
! References: Perez and Fraga (1987)
! Millero (1995) for pressure correction
! pH scale : Total (according to Handbook, 2007)
IMPLICIT NONE
REAL(KIND=wp) :: AK_HF_PEFR87
! ------------------
! Argument variables
! ------------------
! t_k : temperature in K
! s : salinity
! p_bar : applied pressure in bar
REAL(KIND=wp), INTENT(IN) :: t_k, s, p_bar
! ---------------
! Local variables
! ---------------
! zrt : R*t_k, R in bar*cm3/(mol*K)
! zt_degc : temperature in degrees Celsius
! zdvi : volume change for ionization
! zdki : compressibility change for ionization
! zln_khf_p0 : ln(K_HF) at p_bar = 0
! zln_khf_pp : pressure correction for p_bar /= 0
REAL(KIND=wp) :: zrt, zt_degc, zdvi, zdki, zds, zsqrts
REAL(KIND=wp) :: zln_khf_p0, zln_khf_pp
! ln(K_HF) at p_bar = 0
zln_khf_p0 = 874._wp/t_k - 9.68_wp + 0.111_wp*SQRT(s)
! Pressure correction
zt_degc = t_k - t_k_zerodegc
zrt = gasconst_bar_cm3_o_mol_k * t_k
zdvi = -9.78_wp + zt_degc*(-0.0090_wp - zt_degc*0.942E-03_wp)
zdki = ( -3.91_wp + zt_degc*0.054_wp)*1.0E-03_wp
zln_khf_pp = (-zdvi + zdki*p_bar/2._wp)*p_bar/zrt
! Final value of K_HF
AK_HF_PEFR87 = EXP( zln_khf_p0 + zln_khf_pp )
RETURN
!=======================================================================
END FUNCTION AK_HF_PEFR87
!=======================================================================
!=======================================================================
FUNCTION AK_HSO4_DICK90(t_k, s, p_bar)
!=======================================================================
! Function returns the dissociation constant of hydrogen sulfate (bisulfate)
! References: Dickson (1990) -- also Handbook (2007)
! Millero (1995) for pressure correction
! pH scale : free
! Note : converted here from mol/kg-H2O to mol/kg-SW
IMPLICIT NONE
REAL(KIND=wp) :: AK_HSO4_DICK90
! ------------------
! Argument variables
! ------------------
! t_k : temperature in K
! s : salinity
! p_bar : applied pressure in bar
REAL(KIND=wp), INTENT(IN) :: t_k, s, p_bar
! ---------------
! Local variables
! ---------------
! zrt : R*t_k, R in bar*cm3/(mol*K)
! zt_degc : temperature in degrees Celsius
! zdvi : volume change for ionization
! zdki : compressibility change for ionization
! zionst : ionic strength in mol/-kg-H2O
! zsqrti : square root og ion strength
! zcvt_to_kgsw : mass of pure water in 1kg of seawater as a fct. of salinity
! zln_khso4_p0 : K_HSO4 at p_bar = 0
! zln_khso4_pp : pressure correction for p_bar /= 0
REAL(KIND=wp) :: zrt, zt_degc, zdvi, zdki
REAL(KIND=wp) :: zcvt_to_kgsw, zionst, zsqrti
REAL(KIND=wp) :: zln_khso4_p0, zln_khso4_pp
! ln(K_HSO4) at p_bar = 0
zcvt_to_kgsw = ACVT_KGH2O_O_KGSW(s)
zionst = A_IONSTRENGTH_SALIN(s)/zcvt_to_kgsw
zsqrti = SQRT(zionst)
zln_khso4_p0 = -4276.1_wp/t_k + 141.328_wp - 23.093_wp*LOG(t_k) &
+ (-13856._wp/t_k + 324.57_wp - 47.986_wp*LOG(t_k)) * zsqrti &
+ ( 35474._wp/t_k - 771.54_wp + 114.723_wp*LOG(t_k)) * zionst &
- ( 2698._wp/t_k)*zsqrti * zionst &
+ ( 1776._wp/t_k)*zionst*zionst
! Pressure correction
zt_degc = t_k - t_k_zerodegc
zrt = gasconst_bar_cm3_o_mol_k * t_k
zdvi = -18.03_wp + zt_degc*(0.0466_wp + zt_degc*0.316E-03_wp)
zdki = ( -4.53_wp + zt_degc*0.0900_wp)*1.0E-03_wp
zln_khso4_pp = (-zdvi + zdki*p_bar/2._wp)*p_bar/zrt
! ln(K_HSO4) at p_bar = 0
AK_HSO4_DICK90 = zcvt_to_kgsw * EXP( zln_khso4_p0 + zln_khso4_pp )
RETURN
!=======================================================================
END FUNCTION AK_HSO4_DICK90
!=======================================================================
!=======================================================================
FUNCTION ASP_CALC_MUCC83(t_k, s, p_bar)
!=======================================================================
! Function returns stoechiometric solubility product
! of calcite in seawater
! References: Mucci (1983)
! Millero (1995) for pressure correction
! pH scale : N/A
! Units : (mol/kg-SW)^2
IMPLICIT NONE
REAL(KIND=wp) :: ASP_CALC_MUCC83
! ------------------
! Argument variables
! ------------------
! t_k : temperature in K
! s : salinity
! p_bar : applied pressure in bar
REAL(KIND=wp), INTENT(IN) :: t_k, s, p_bar
! ---------------
! Local variables
! ---------------
! zrt : R*t_k, R in bar*cm3/(mol*K)
! zsqrts : square root of salinity
! zt_degc : temperature in degrees Celsius
! zdvi : volume change for ionization
! zdki : compressibility change for ionization
! zln_kp1_p0 : ln(K_p1) at p_bar = 0
! zln_kp1_pp : pressure correction for p_bar /= 0
REAL(KIND=wp) :: zrt, zsqrts, zt_degc, zdvi, zdki
REAL(KIND=wp) :: zlog10_kspcalc_p0, zln_kspcalc_pp
zsqrts = SQRT(s)
! log10(Ksp_Calc) for p_bar = 0
zlog10_kspcalc_p0 = &
-171.9065_wp - 0.077993_wp*t_k &
+ 2839.319_wp/t_k + 71.595_wp*LOG10(t_k) &
+ ( -0.77712_wp + 0.0028426*t_k + 178.34_wp/t_k)*zsqrts &
- 0.07711_wp*s + 0.0041249_wp*s*zsqrts
! Pressure correction
zt_degc = t_k - t_k_zerodegc
zrt = gasconst_bar_cm3_o_mol_k * t_k
zdvi = -48.76_wp + 0.5304_wp*zt_degc
zdki = (-11.76_wp + 0.3692_wp*zt_degc)*1.0E-03_wp
zln_kspcalc_pp = (-zdvi + zdki*p_bar/2._wp)*p_bar/zrt
! Final value of Ksp_Calc
ASP_CALC_MUCC83 = 10._wp**(zlog10_kspcalc_p0) * EXP(zln_kspcalc_pp)
RETURN
!=======================================================================
END FUNCTION ASP_CALC_MUCC83
!=======================================================================
!=======================================================================
FUNCTION ASP_ARAG_MUCC83(t_k, s, p_bar)
!=======================================================================
! Function returns stoechiometric solubility product
! of aragonite in seawater
! References: Mucci (1983)
! Millero (1979) for pressure correction
! pH scale : N/A
! Units : (mol/kg-SW)^2
IMPLICIT NONE
REAL(KIND=wp) :: ASP_ARAG_MUCC83
! ------------------
! Argument variables
! ------------------
! t_k : temperature in K
! s : salinity
! p_bar : applied pressure in bar
REAL(KIND=wp), INTENT(IN) :: t_k, s, p_bar
! ---------------
! Local variables
! ---------------
! zrt : R*t_k, R in bar*cm3/(mol*K)
! zsqrts : square root of salinity
! zt_degc : temperature in degrees Celsius
! zdvi : volume change for ionization
! zdki : compressibility change for ionization
! zln_kp1_p0 : ln(K_p1) at p_bar = 0
! zln_kp1_pp : pressure correction for p_bar /= 0
REAL(KIND=wp) :: zrt, zsqrts, zt_degc, zdvi, zdki
REAL(KIND=wp) :: zlog10_ksparag_p0, zln_ksparag_pp
zsqrts = SQRT(s)
! log10(Ksp_Arag) for p_bar = 0
zlog10_ksparag_p0 = &
-171.945_wp - 0.077993_wp*t_k &
+ 2903.293_wp/t_k + 71.595_wp*LOG10(t_k) &
+ ( -0.068393_wp + 0.0017276_wp*t_k + 88.135_wp/t_k)*zsqrts &
- 0.10018_wp*s + 0.0059415_wp*s*zsqrts
! Pressure correction
zt_degc = t_k - t_k_zerodegc
zrt = gasconst_bar_cm3_o_mol_k * t_k
zdvi = -48.76_wp + 0.5304_wp*zt_degc + 2.8_wp
zdki = (-11.76_wp + 0.3692_wp*zt_degc)*1.0E-03_wp
zln_ksparag_pp = (-zdvi + zdki*p_bar/2._wp)*p_bar/zrt
! Final value of Ksp_Arag
ASP_ARAG_MUCC83 = 10._wp**(zlog10_ksparag_p0) * EXP(zln_ksparag_pp)
RETURN
!=======================================================================
END FUNCTION ASP_ARAG_MUCC83
!=======================================================================
!=======================================================================
FUNCTION A_BTOT_SALIN(s)
!=======================================================================
! Function returns total borate concentration in mol/kg-SW
! given the salinity of a sample
! References: Uppström (1974), cited by Dickson et al. (2007, chapter 5, p 10)
! Millero (1982) cited in Millero (1995)
! pH scale : N/A
IMPLICIT NONE
REAL(KIND=wp) :: A_BTOT_SALIN
! ------------------
! Argument variables
! ------------------
REAL(KIND=wp), INTENT(IN) :: s
A_BTOT_SALIN = 0.000416_wp*(s/35._wp)
RETURN
!=======================================================================
END FUNCTION A_BTOT_SALIN
!=======================================================================
!=======================================================================
FUNCTION A_CATOT_SALIN(s)
!=======================================================================
! Function returns total calcium concentration in mol/kg-SW
! given the salinity of a sample
! References: Culkin (1965)
! pH scale : N/A
IMPLICIT NONE
REAL(KIND=wp) :: A_CATOT_SALIN
! ------------------
! Argument variables
! ------------------
REAL(KIND=wp), INTENT(IN) :: s
A_CATOT_SALIN = 0.010282_wp*(s/35._wp)
RETURN
!=======================================================================
END FUNCTION A_CATOT_SALIN
!=======================================================================
!=======================================================================
FUNCTION A_FTOT_SALIN(s)
!=======================================================================
! Function returns total calcium concentration in mol/kg-SW
! given the salinity of a sample
! References: Culkin (1965) (???)
! pH scale : N/A
IMPLICIT NONE
REAL(KIND=wp) :: A_FTOT_SALIN
! ------------------
! Argument variables
! ------------------
REAL(KIND=wp), INTENT(IN) :: s
A_FTOT_SALIN = 0.000068_wp*(s/35._wp)
RETURN
!=======================================================================
END FUNCTION A_FTOT_SALIN
!=======================================================================
!=======================================================================
FUNCTION A_SO4TOT_SALIN(s)
!=======================================================================
! Function returns total sulfate concentration in mol/kg-SW
! given the salinity of a sample
! References: Morris, A.W. and Riley, J.P. (1966) quoted in Handbook (2007)
! pH scale : N/A
IMPLICIT NONE
REAL(KIND=wp) :: A_SO4TOT_SALIN
! ------------------
! Argument variables
! ------------------
REAL(KIND=wp), INTENT(IN) :: s
!A_SO4TOT_SALIN = 0.028234_wp*(s/35._wp) ! in libthdyct and Thesis
!A_SO4TOT_SALIN = 0.02824_wp*(s/35._wp) ! Handbook (2007, chap 6, p 10, tab 2, col 3)
A_SO4TOT_SALIN = (0.1400_wp/96.062_wp)*(s/1.80655_wp) ! Handbook (2007, chap 6, p 10)
RETURN
!=======================================================================
END FUNCTION A_SO4TOT_SALIN
!=======================================================================
!=======================================================================
FUNCTION ACVT_HSWS_O_HTOT(t_k, s, p_bar)
!=======================================================================
! Function returns the ratio H_SWS/H_Tot as a function of salinity s
! Reference: Munhoven
! pH scale: all
IMPLICIT NONE
REAL(KIND=wp) :: ACVT_HSWS_O_HTOT
! ------------------
! Argument variables
! ------------------
! t_k : temperature in K
! s : salinity
! p_bar : applied pressure in bar
REAL(KIND=wp), INTENT(IN) :: t_k
REAL(KIND=wp), INTENT(IN) :: s
REAL(KIND=wp), INTENT(IN) :: p_bar
! ---------------
! Local variables
! ---------------
! zso4_tot: total sulfate concentration in mol/kg-SW
! zf_tot : total fluoride concentration in mol/kg-SW
REAL(KIND=wp) :: zso4_tot, zf_tot
!-----------------------------------------------------------------------
zso4_tot = A_SO4TOT_SALIN(s)
zf_tot = A_FTOT_SALIN(s)
ACVT_HSWS_O_HTOT = 1._wp + (zf_tot*ABETA_HF_DIRI79(t_k, s, p_bar)) &
/(1._wp + zso4_tot/AK_HSO4_DICK90(t_k,s, p_bar))
RETURN
!=======================================================================
END FUNCTION ACVT_HSWS_O_HTOT
!=======================================================================
!=======================================================================
FUNCTION ACVT_HTOT_O_HFREE(t_k, s, p_bar)
!=======================================================================
! Function returns the ratio H_Tot/H_free as a function of salinity s
! Reference: Munhoven
! pH scale: N/A
IMPLICIT NONE
REAL(KIND=wp) :: ACVT_HTOT_O_HFREE
! ------------------
! Argument variables
! ------------------
! t_k : temperature in K
! s : salinity
! p_bar : applied pressure in bar
REAL(KIND=wp), INTENT(IN) :: t_k
REAL(KIND=wp), INTENT(IN) :: s
REAL(KIND=wp), INTENT(IN) :: p_bar
! ---------------
! Local variables
! ---------------
! zso4_tot: total sulfate concentration in mol/kg-SW
REAL(KIND=wp) :: zso4_tot
!-----------------------------------------------------------------------
zso4_tot = A_SO4TOT_SALIN(s)
ACVT_HTOT_O_HFREE = 1._wp + zso4_tot/AK_HSO4_DICK90(t_k,s, p_bar)
RETURN
!=======================================================================
END FUNCTION ACVT_HTOT_O_HFREE
!=======================================================================
!=======================================================================
FUNCTION ACVT_HSWS_O_HFREE(t_k, s, p_bar)
!=======================================================================
! Function returns the ratio H_SWS/H_free as a function
! of salinity s
! Reference: Munhoven
! pH scale: N/A
IMPLICIT NONE
REAL(KIND=wp) :: ACVT_HSWS_O_HFREE
! ------------------
! Argument variables
! ------------------
! t_k : temperature in K
! s : salinity
! p_bar : applied pressure in bar
REAL(KIND=wp), INTENT(IN) :: t_k
REAL(KIND=wp), INTENT(IN) :: s
REAL(KIND=wp), INTENT(IN) :: p_bar
! ---------------
! Local variables
! ---------------
! zso4_tot: total sulfate concentration in mol/kg-SW
! zf_tot : total fluoride concentration in mol/kg-SW
REAL(KIND=wp) :: zso4_tot, zf_tot
!-----------------------------------------------------------------------
zso4_tot = A_SO4TOT_SALIN(s)
zf_tot = A_FTOT_SALIN(s)
ACVT_HSWS_O_HFREE = 1._wp + zf_tot*ABETA_HF_DIRI79(t_k, s, p_bar) &
+ zso4_tot/AK_HSO4_DICK90(t_k,s, p_bar)
RETURN
!=======================================================================
END FUNCTION ACVT_HSWS_O_HFREE
!=======================================================================
!=======================================================================
FUNCTION A_RHOSW1_MUNH97(t_k, s, p_bar)
!=======================================================================
! Function returns first order approximation of \rho in (kg-SW)/(m^3-SW)
! References: Munhoven (1997)
! after EOS80 (UNESCO, 1981, 1983)
IMPLICIT NONE
REAL(KIND=wp) :: A_RHOSW1_MUNH97
! ------------------
! Argument variables
! ------------------
! s : salinity
! tk : temperature in K
! p_bar : depth in m
REAL(KIND=wp), INTENT(IN) :: t_k, s, p_bar
! ---------------
! Local variables
! ---------------
! s0 : 35.5
! t_k0 : 285.16 K
! p_bar0 : 300 bar
REAL(KIND=wp), PARAMETER :: s0 = 35.5_wp
REAL(KIND=wp), PARAMETER :: t_k0 = 285.16_wp
REAL(KIND=wp), PARAMETER :: p_bar0 = 300.0_wp
A_RHOSW1_MUNH97 = 1039.9044_wp + 0.77629393_wp*(s-s0) &
- 0.19692738_wp*(t_k-t_k0) &
+ 0.044038615_wp*(p_bar-p_bar0)
RETURN
!=======================================================================
END FUNCTION A_RHOSW1_MUNH97
!=======================================================================
!=======================================================================
FUNCTION A_RHOSW2_MUNH97(t_k, s, p_bar)
!=======================================================================
! Function returns first order approximation of \rho in (kg-SW)/(m^3-SW)
! References: Munhoven (1997)
! after EOS80 (UNESCO, 1981, 1983)
IMPLICIT NONE
REAL(KIND=wp) :: A_RHOSW2_MUNH97
! ------------------
! Argument variables
! ------------------
! s : salinity
! tk : temperature in K
! p_bar : depth in m
REAL(KIND=wp), INTENT(IN) :: t_k, s, p_bar
! ---------------
! Local variables
! ---------------
! s0 : 35.5
! t_k0 : 285.16 K
! p_bar0 : 300 bar
REAL(KIND=wp), PARAMETER :: s0 = 35.5_wp
REAL(KIND=wp), PARAMETER :: t_k0 = 285.16_wp
REAL(KIND=wp), PARAMETER :: p_bar0 = 300.0_wp
A_RHOSW2_MUNH97 = 1040.0145_wp &
+ 0.77629393_wp*(s-s0) &
- 0.25013591_wp*(t_k-t_k0) &
+ 4.2026266E-02_wp*(p_bar-p_bar0) &
- 4.7473116E-03_wp*(t_k-t_k0)*(t_k-t_k0) &
- 4.7974224E-06_wp*(p_bar-p_bar0)*(p_bar-p_bar0) &
- 2.1404592E-04_wp*(t_k-t_k0)*(p_bar-p_bar0)
RETURN
!=======================================================================
END FUNCTION A_RHOSW2_MUNH97
!=======================================================================
!=======================================================================
SUBROUTINE CHECKCONSTANTS
!=======================================================================
IMPLICIT NONE
! ------------------
! Argument variables
! ------------------
! N/A
! ---------------
! Local variables
! ---------------
! s : salinity
! tk : temperature in K
! p_bar : applied pressure in bar
REAL(KIND=wp) :: t_k, s, p_bar
REAL(KIND=wp) :: zkc0, zkc1, zkc2
REAL(KIND=wp) :: zkb
REAL(KIND=wp) :: zkhf
REAL(KIND=wp) :: zkhso4
REAL(KIND=wp) :: zkp1, zkp2, zkp3
REAL(KIND=wp) :: zksi1
REAL(KIND=wp) :: zkw
REAL(KIND=wp) :: zknh4
REAL(KIND=wp) :: zkh2s
INTEGER, PARAMETER :: logunit = 1
OPEN(logunit,FILE='checkconst.log')
WRITE(logunit,*) 'Checking constant values generated from MOD_CHEMCONST'
WRITE(logunit,*)
WRITE(logunit,*) ' % indicates checking against the Handbook (1994);'
WRITE(logunit,*) ' $ indicates checking against the Lewis and Wallace (1998);'
WRITE(logunit,*) ' * indicates checking against the Handbook (2007);'
WRITE(logunit,*) ' target values are quoted in brackets'
WRITE(logunit,*)
WRITE(logunit,*)
WRITE(logunit,*) ' For S = 35, P = 0 and T/K = 298.15:'
WRITE(logunit,*)
s = 35._wp
p_bar = 0._wp
t_k = 298.15_wp
zkc0 = AK_CARB_0_WEIS74(t_k, s)
WRITE(logunit,*)
WRITE(logunit,*) 'K_0 -- Weiss (1974)'
WRITE(logunit,*) '==================='
WRITE(logunit,*)
WRITE(logunit,*) ' K_0 :', zkc0
WRITE(logunit,*) ' ln(K_0) :', LOG(zkc0)
WRITE(logunit,*) ' pK_0 :', -LOG10(zkc0)
WRITE(logunit,'(" * ln(K_0) :", F8.4, " (-3.5617)")') LOG(zkc0)
WRITE(logunit,*)
zkhso4 = AK_HSO4_DICK90(t_k, s, p_bar)
WRITE(logunit,*)
WRITE(logunit,*) 'K_HSO4 -- Dickson (1990) -- pH_free'
WRITE(logunit,*) '==================================='
WRITE(logunit,*)
WRITE(logunit,*) ' K_HSO4 :', zkhso4
WRITE(logunit,*) ' ln(K_HSO4) :', LOG(zkhso4)
WRITE(logunit,*) ' pK_HSO4 :', -LOG10(zkhso4)
WRITE(logunit,'(" * ln(K_HSO4) :", F6.2, " (-2.30)")') LOG(zkhso4)
WRITE(logunit,*)
zkb = AK_BORA_DICK90(t_k, s, p_bar)
WRITE(logunit,*)
WRITE(logunit,*) 'K_b -- Dickson (1990) -- pH_tot'
WRITE(logunit,*) '==============================='
WRITE(logunit,*)
WRITE(logunit,*) ' K_b :', zkb
WRITE(logunit,*) ' ln(K_b) :', LOG(zkb)
WRITE(logunit,*) ' pK_b :', -LOG10(zkb)
WRITE(logunit,'(" * ln(K_b) :", F9.4, " (-19.7964)")') LOG(zkb)
WRITE(logunit,*)
zkc1 = AK_CARB_1_LUEK00(t_k, s, p_bar)
WRITE(logunit,*)
WRITE(logunit,*) 'K_1 -- Luecker et al (2000) -- pH_tot'
WRITE(logunit,*) '====================================='
WRITE(logunit,*)
WRITE(logunit,*) ' K_1 :', zkc1
WRITE(logunit,*) ' ln(K_1) :', LOG(zkc1)
WRITE(logunit,*) ' pK_1 :', -LOG10(zkc1)
WRITE(logunit,'(" * log10(K_1) :", F8.4, " (-5.8472)")') LOG10(zkc1)
WRITE(logunit,*)
zkc2 = AK_CARB_2_LUEK00(t_k, s, p_bar)
WRITE(logunit,*)
WRITE(logunit,*) 'K_2 -- Luecker et al (2000) -- pH_tot'
WRITE(logunit,*) '====================================='
WRITE(logunit,*)
WRITE(logunit,*) ' K_2 :', zkc2
WRITE(logunit,*) ' ln(K_2) :', LOG(zkc2)
WRITE(logunit,*) ' pK_2 :', -LOG10(zkc2)
WRITE(logunit,'(" * log10(K_2) :", F8.4, " (-8.9660)")') LOG10(zkc2)
WRITE(logunit,*)
zkc1 = AK_CARB_1_ROYE93(t_k, s, p_bar)
WRITE(logunit,*)
WRITE(logunit,*) 'K_1 -- Roy et al (1993) -- pH_tot'
WRITE(logunit,*) '================================='
WRITE(logunit,*)
WRITE(logunit,*) ' K_1 :', zkc1
WRITE(logunit,*) ' ln(K_1) :', LOG(zkc1)
WRITE(logunit,*) ' pK_1 :', -LOG10(zkc1)
WRITE(logunit,'(" % ln(K_1) :", F9.4, " (-13.4847)")') LOG(zkc1)
WRITE(logunit,*)
zkc2 = AK_CARB_2_ROYE93(t_k, s, p_bar)
WRITE(logunit,*)
WRITE(logunit,*) 'K_2 -- Roy et al (1993) -- pH_tot'
WRITE(logunit,*) '================================='
WRITE(logunit,*)
WRITE(logunit,*) ' K_2 :', zkc2
WRITE(logunit,*) ' ln(K_2) :', LOG(zkc2)
WRITE(logunit,*) ' pK_2 :', -LOG10(zkc2)
WRITE(logunit,'(" % ln(K_2) :", F9.4, " (-20.5504)")') LOG(zkc2)
WRITE(logunit,*)
zkhf = AK_HF_PEFR87(t_k, s, p_bar)
WRITE(logunit,*)
WRITE(logunit,*) 'K_HF -- Perez and Fraga (1987) -- pH_tot'
WRITE(logunit,*) '========================================'
WRITE(logunit,*)
WRITE(logunit,*) ' K_HF :', zkhf
WRITE(logunit,*) ' ln(K_HF) :', LOG(zkhf)
WRITE(logunit,*) ' pK_HF :', -LOG10(zkhf)
WRITE(logunit,'(" * ln(K_HF) :", F6.2, " (-6.09)")') LOG(zkhf)
WRITE(logunit,*)
zkp1 = AK_PHOS_1_MILL95(t_k, s, p_bar)
WRITE(logunit,*)
WRITE(logunit,*) 'K_P1 -- Millero (1995) -- pH_SWS'
WRITE(logunit,*) '================================'
WRITE(logunit,*)
WRITE(logunit,*) ' K_P1 :', zkp1
WRITE(logunit,*) ' ln(K_P1) :', LOG(zkp1)
WRITE(logunit,*) ' pK_1 :', -LOG10(zkp1)
WRITE(logunit,'(" * ln(K_P1)-0.015 :", F6.2, " (-3.71)")') LOG(zkp1)-0.015_wp
WRITE(logunit,*)
zkp2 = AK_PHOS_2_MILL95(t_k, s, p_bar)
WRITE(logunit,*)
WRITE(logunit,*) 'K_P2 -- Millero (1995) -- pH_SWS'
WRITE(logunit,*) '================================'
WRITE(logunit,*)
WRITE(logunit,*) ' K_2 :', zkp2
WRITE(logunit,*) ' ln(K_P2) :', LOG(zkp2)
WRITE(logunit,*) ' pK_2 :', -LOG10(zkp2)
WRITE(logunit,'(" * ln(K_P2)-0.015 :", F8.3, " (-13.727)")') LOG(zkp2)-0.015_wp
WRITE(logunit,*)
zkp3 = AK_PHOS_3_MILL95(t_k, s, p_bar)
WRITE(logunit,*)
WRITE(logunit,*) 'K_P3 -- Millero (1995) -- pH_SWS'
WRITE(logunit,*) '================================'
WRITE(logunit,*)
WRITE(logunit,*) ' K_P3 :', zkp3
WRITE(logunit,*) ' ln(K_P3) :', LOG(zkp3)
WRITE(logunit,*) ' pK_P3 :', -LOG10(zkp3)
WRITE(logunit,'(" * ln(K_P3)-0.015 :", F7.2, " (-20.24)")') LOG(zkp3)-0.015_wp
WRITE(logunit,*)
zksi1 = AK_SILI_1_MILL95(t_k, s)
WRITE(logunit,*)
WRITE(logunit,*) 'K_Si1 -- Millero (1995) -- pH_SWS'
WRITE(logunit,*) '================================='
WRITE(logunit,*)
WRITE(logunit,*) ' K_Si1 :', zksi1
WRITE(logunit,*) ' ln(K_Si1) :', LOG(zksi1)
WRITE(logunit,*) ' pK_Si1 :', -LOG10(zksi1)
WRITE(logunit,'(" * ln(K_Si1)-0.015:", F7.2, " (-21.61)")') LOG(zksi1)-0.015_wp
WRITE(logunit,*)
zkw = AK_W_MILL95(t_k, s, p_bar)
WRITE(logunit,*)
WRITE(logunit,*) 'K_w -- Millero (1995) -- pH_SWS'
WRITE(logunit,*) '==============================='
WRITE(logunit,*)
WRITE(logunit,*) ' K_w :', zkw
WRITE(logunit,*) ' ln(K_w) :', LOG(zkw)
WRITE(logunit,*) ' pK_w :', -LOG10(zkw)
WRITE(logunit,'(" * ln(K_w)-0.015 :", F8.3, " (-30.434)")') LOG(zkw)-0.015_wp
WRITE(logunit,*)
zkh2s = AK_H2S_1_MILL95(t_k, s, p_bar)
WRITE(logunit,*)
WRITE(logunit,*) 'K_H2S -- Millero (1995) -- pH_SWS'
WRITE(logunit,*) '================================='
WRITE(logunit,*)
WRITE(logunit,*) ' K_H2S :', zkh2s
WRITE(logunit,*) ' ln(K_H2S) :', LOG(zkh2s)
WRITE(logunit,*) ' pK_H2S :', -LOG10(zkh2s)
WRITE(logunit,'(" $ pK_H2S :", F5.2, " (6.51)")') -LOG10(zkh2s)
WRITE(logunit,*)
zknh4 = AK_AMMO_1_YAMI95(t_k, s, p_bar)
WRITE(logunit,*)
WRITE(logunit,*) 'K_NH4 -- Yao and Millero (1995) -- pH_SWS'
WRITE(logunit,*) '========================================='
WRITE(logunit,*)
WRITE(logunit,*) ' K_NH4 :', zknh4
WRITE(logunit,*) ' ln(K_NH4) :', LOG(zknh4)
WRITE(logunit,*) ' pK_NH4 :', -LOG10(zknh4)
WRITE(logunit,'(" $ pK_NH4 :", F5.2, " (9.26)")') -LOG10(zknh4)
WRITE(logunit,*)
CLOSE(logunit)
RETURN
!=======================================================================
END SUBROUTINE CHECKCONSTANTS
!=======================================================================
END MODULE MOD_CHEMCONST
|
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|
import math
import torch
import paddle
import pgl
import numpy as np
import paddle.fluid as F
import paddle.fluid.layers as L
import copy
from pgl.contrib.ogb.nodeproppred.dataset_pgl import PglNodePropPredDataset
from ogb.nodeproppred import Evaluator
from utils import to_undirected, add_self_loop, linear_warmup_decay
from model import Products_label_embedding_model
from dataloader.ogb_products_dataloader import SampleDataGenerator
import paddle.fluid.profiler as profiler
from pgl.utils import paddle_helper
import argparse
from tqdm import tqdm
evaluator = Evaluator(name='ogbn-products')
def get_config():
parser = argparse.ArgumentParser()
## data_sampling_arg
data_group= parser.add_argument_group('data_arg')
data_group.add_argument('--batch_size', default=1500, type=int)
data_group.add_argument('--num_workers', default=12, type=int)
data_group.add_argument('--sizes', default=[10, 10, 10], type=int, nargs='+' )
data_group.add_argument('--buf_size', default=1000, type=int)
## model_arg
model_group=parser.add_argument_group('model_base_arg')
model_group.add_argument('--num_layers', default=3, type=int)
model_group.add_argument('--hidden_size', default=128, type=int)
model_group.add_argument('--num_heads', default=4, type=int)
model_group.add_argument('--dropout', default=0.3, type=float)
model_group.add_argument('--attn_dropout', default=0, type=float)
## label_embed_arg
embed_group=parser.add_argument_group('embed_arg')
embed_group.add_argument('--use_label_e', action='store_true')
embed_group.add_argument('--label_rate', default=0.625, type=float)
## train_arg
train_group=parser.add_argument_group('train_arg')
train_group.add_argument('--runs', default=10, type=int )
train_group.add_argument('--epochs', default=100, type=int )
train_group.add_argument('--lr', default=0.001, type=float)
train_group.add_argument('--place', default=-1, type=int)
train_group.add_argument('--log_file', default='result_products.txt', type=str)
return parser.parse_args()
def optimizer_func(lr):
return F.optimizer.AdamOptimizer(learning_rate=lr)
def eval_test(parser, test_p_list, model, test_exe, dataset, split_idx):
eval_gg=SampleDataGenerator(graph_wrappers=[model.gw_list[0]], buf_size=parser.buf_size,
batch_size=parser.batch_size , num_workers=1,
sizes=[-1,], shuffle=False,
dataset=dataset,
nodes_idx=None)
out_r_temp=[]
test_p, out=test_p_list[0]
pbar = tqdm(total=eval_gg.num_nodes* model.num_layers)
pbar.set_description('Evaluating')
for feed_batch in tqdm(eval_gg.generator()):
feed_batch['label_idx']=split_idx['train']
feat_batch= test_exe.run(test_p,
feed=feed_batch,
fetch_list=out)
out_r_temp.append(feat_batch[0])
pbar.update(feed_batch['label'].shape[0])
our_r=np.concatenate(out_r_temp, axis=0)
for test_p, out in test_p_list[1:]: #np.concatenate
out_r_temp=[]
for feed_batch in tqdm(eval_gg.generator()):
feed_batch['hidden_node_feat'] = our_r[feed_batch['batch_nodes_0']]
feat_batch= test_exe.run(test_p,
feed=feed_batch,
fetch_list=out)
out_r_temp.append(feat_batch[0])
pbar.update(feed_batch['label'].shape[0])
our_r=np.concatenate(out_r_temp, axis=0)
pbar.close()
y_pred=our_r.argmax(axis=-1)
y_pred=np.expand_dims(y_pred, 1)
y_true=eval_gg.labels
train_acc = evaluator.eval({
'y_true': y_true[split_idx['train']],
'y_pred': y_pred[split_idx['train']],
})['acc']
val_acc = evaluator.eval({
'y_true': y_true[split_idx['valid']],
'y_pred': y_pred[split_idx['valid']],
})['acc']
test_acc = evaluator.eval({
'y_true': y_true[split_idx['test']],
'y_pred': y_pred[split_idx['test']],
})['acc']
return train_acc, val_acc, test_acc
def train_loop(parser, start_program, main_program, test_p_list,
model, feat_init, place, dataset, split_idx, exe, run_id, wf=None):
#build up training program
exe.run(start_program)
feat_init(place)
max_acc=0 # best test_acc
max_step=0 # step for best test_acc
max_val_acc=0 # best val_acc
max_cor_acc=0 # test_acc for best val_acc
max_cor_step=0 # step for best val_acc
#training loop
for epoch_id in range(parser.epochs):
#start training
if parser.use_label_e:
train_idx_temp=copy.deepcopy(split_idx['train'])
np.random.shuffle(train_idx_temp)
label_idx=train_idx_temp[ :int(parser.label_rate*len(train_idx_temp))]
unlabel_idx=train_idx_temp[int(parser.label_rate*len(train_idx_temp)):]
train_gg=SampleDataGenerator(graph_wrappers=model.gw_list, buf_size=parser.buf_size,
batch_size=parser.batch_size , num_workers=parser.num_workers,
sizes=parser.sizes, shuffle=True,
dataset=dataset,
nodes_idx=unlabel_idx)
pbar = tqdm(total=unlabel_idx.shape[0])
pbar.set_description(f'Epoch {epoch_id:02d}')
total=0.0
acc_num=0.0
for batch_feed in tqdm(train_gg.generator()):
batch_feed['label_idx']=label_idx
loss = exe.run(main_program,
feed=batch_feed,
fetch_list=[model.avg_cost, model.out_feat])
total+=loss[0][0]
acc_num=(loss[1].argmax(axis=-1)==batch_feed['label'].reshape(-1)).sum()+acc_num
pbar.update(batch_feed['label'].shape[0])
pbar.close()
print(total/(len(train_gg)/parser.batch_size))
print('acc: ', (acc_num/unlabel_idx.shape[0])*100)
#eval result
if (epoch_id+1)>=50 and (epoch_id+1)%10==0:
result = eval_test(parser, test_p_list, model, exe, dataset, split_idx)
train_acc, valid_acc, test_acc = result
max_acc = max(test_acc, max_acc)
if max_acc == test_acc:
max_step=epoch_id
max_val_acc=max(valid_acc, max_val_acc)
if max_val_acc==valid_acc:
max_cor_acc=test_acc
max_cor_step=epoch_id
max_acc=max(result[2], max_acc)
if max_acc==result[2]:
max_step=epoch_id
result_t=(f'Run: {run_id:02d}, '
f'Epoch: {epoch_id:02d}, '
f'Loss: {total:.4f}, '
f'Train: {100 * train_acc:.2f}%, '
f'Valid: {100 * valid_acc:.2f}%, '
f'Test: {100 * test_acc:.2f}% \n'
f'max_Test: {100 * max_acc:.2f}%, '
f'max_step: {max_step}\n'
f'max_val: {100 * max_val_acc:.2f}%, '
f'max_val_Test: {100 * max_cor_acc:.2f}%, '
f'max_val_step: {max_cor_step}\n'
)
# if (epoch_id+1)%50==0:
print(result_t)
wf.write(result_t)
wf.write('\n')
wf.flush()
return max_cor_acc
if __name__ == '__main__':
parser = get_config()
print('===========args==============')
print(parser)
print('=============================')
startup_prog = F.default_startup_program()
train_prog = F.default_main_program()
place=F.CPUPlace() if parser.place <0 else F.CUDAPlace(parser.place)
dataset = PglNodePropPredDataset(name="ogbn-products")
# dataset = PglNodePropPredDataset(name="ogbn-arxiv")
split_idx=dataset.get_idx_split()
graph, label = dataset[0]
print(label.shape)
with F.program_guard(train_prog, startup_prog):
with F.unique_name.guard():
gw_list=[]
for i in range(len(parser.sizes)):
gw_list.append(pgl.graph_wrapper.GraphWrapper(
name="product_"+str(i)))
feature_input, feat_init=paddle_helper.constant(
name='node_feat_input',
dtype='float32',
value=graph.node_feat['feat'])
if parser.use_label_e:
model=Products_label_embedding_model(feature_input, gw_list,
parser.hidden_size, parser.num_heads,
parser.dropout, parser.num_layers)
else:
model=Arxiv_baseline_model(gw, parser.hidden_size, parser.num_heads,
parser.dropout, parser.num_layers)
# test_prog=train_prog.clone(for_test=True)
model.train_program()
adam_optimizer = optimizer_func(parser.lr)#optimizer
adam_optimizer.minimize(model.avg_cost)
test_p_list=[]
with F.unique_name.guard():
## build up eval program
test_p=F.Program()
with F.program_guard(test_p, ):
gw_test=pgl.graph_wrapper.GraphWrapper(
name="product_"+str(0))
feature_input, feat_init__=paddle_helper.constant(
name='node_feat_input',
dtype='float32',
value=graph.node_feat['feat'])
label_feature=model.label_embed_input(model.feature_input)
feature_batch=model.get_batch_feature(label_feature) # 把batch_feat打出来
feature_batch=model.get_gat_layer(0, gw_test, feature_batch,
hidden_size=model.hidden_size,
num_heads=model.num_heads,
concat=True,
layer_norm=True, relu=True)
sub_node_index=F.data(name='sub_node_index_0', shape=[None],
dtype="int64")
feature_batch=L.gather(feature_batch, sub_node_index, overwrite=False)
# test_p=test_p.clone(for_test=True)
test_p_list.append((test_p, feature_batch))
for i in range(1,model.num_layers-1):
test_p=F.Program()
with F.program_guard(test_p, ):
gw_test=pgl.graph_wrapper.GraphWrapper(
name="product_"+str(0))
# feature_batch=model.get_batch_feature(label_feature, test=True)
feature_batch = F.data( 'hidden_node_feat',
shape=[None, model.num_heads*model.hidden_size],
dtype='float32')
feature_batch=model.get_gat_layer(i, gw_test, feature_batch,
hidden_size=model.hidden_size,
num_heads=model.num_heads,
concat=True,
layer_norm=True, relu=True)
sub_node_index=F.data(name='sub_node_index_0', shape=[None],
dtype="int64")
feature_batch=L.gather(feature_batch, sub_node_index, overwrite=False)
# test_p=test_p.clone(for_test=True)
test_p_list.append((test_p, feature_batch))
test_p=F.Program()
with F.program_guard(test_p, ):
gw_test=pgl.graph_wrapper.GraphWrapper(
name="product_"+str(0))
# feature_batch=model.get_batch_feature(label_feature, test=True)
feature_batch = F.data( 'hidden_node_feat',
shape=[None, model.num_heads*model.hidden_size ],
dtype='float32')
feature_batch = model.get_gat_layer(model.num_layers-1, gw_test, feature_batch,
hidden_size=model.out_size, num_heads=model.num_heads,
concat=False, layer_norm=False, relu=False, gate=True)
sub_node_index=F.data(name='sub_node_index_0', shape=[None],
dtype="int64")
feature_batch=L.gather(feature_batch, sub_node_index, overwrite=False)
# test_p=test_p.clone(for_test=True)
test_p_list.append((test_p, feature_batch))
exe = F.Executor(place)
wf = open(parser.log_file, 'w', encoding='utf-8')
total_test_acc=0.0
for run_i in range(parser.runs):
total_test_acc+=train_loop(parser, startup_prog, train_prog, test_p_list, model, feat_init,
place, dataset, split_idx, exe, run_i, wf)
wf.write(f'average: {100 * (total_test_acc/parser.runs):.2f}%')
wf.close()
|
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|
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from caffe2.python import core
from hypothesis import given
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import numpy as np
# Reference implementation from detectron/lib/utils/boxes.py
def bbox_transform(boxes, deltas, weights=(1.0, 1.0, 1.0, 1.0)):
"""Forward transform that maps proposal boxes to predicted ground-truth
boxes using bounding-box regression deltas. See bbox_transform_inv for a
description of the weights argument.
"""
if boxes.shape[0] == 0:
return np.zeros((0, deltas.shape[1]), dtype=deltas.dtype)
boxes = boxes.astype(deltas.dtype, copy=False)
widths = boxes[:, 2] - boxes[:, 0] + 1.0
heights = boxes[:, 3] - boxes[:, 1] + 1.0
ctr_x = boxes[:, 0] + 0.5 * widths
ctr_y = boxes[:, 1] + 0.5 * heights
wx, wy, ww, wh = weights
dx = deltas[:, 0::4] / wx
dy = deltas[:, 1::4] / wy
dw = deltas[:, 2::4] / ww
dh = deltas[:, 3::4] / wh
# Prevent sending too large values into np.exp()
BBOX_XFORM_CLIP = np.log(1000. / 16.)
dw = np.minimum(dw, BBOX_XFORM_CLIP)
dh = np.minimum(dh, BBOX_XFORM_CLIP)
pred_ctr_x = dx * widths[:, np.newaxis] + ctr_x[:, np.newaxis]
pred_ctr_y = dy * heights[:, np.newaxis] + ctr_y[:, np.newaxis]
pred_w = np.exp(dw) * widths[:, np.newaxis]
pred_h = np.exp(dh) * heights[:, np.newaxis]
pred_boxes = np.zeros(deltas.shape, dtype=deltas.dtype)
# x1
pred_boxes[:, 0::4] = pred_ctr_x - 0.5 * pred_w
# y1
pred_boxes[:, 1::4] = pred_ctr_y - 0.5 * pred_h
# x2 (note: "- 1" is correct; don't be fooled by the asymmetry)
pred_boxes[:, 2::4] = pred_ctr_x + 0.5 * pred_w - 1
# y2 (note: "- 1" is correct; don't be fooled by the asymmetry)
pred_boxes[:, 3::4] = pred_ctr_y + 0.5 * pred_h - 1
return pred_boxes
# Reference implementation from detectron/lib/utils/boxes.py
def clip_tiled_boxes(boxes, im_shape):
"""Clip boxes to image boundaries. im_shape is [height, width] and boxes
has shape (N, 4 * num_tiled_boxes)."""
assert boxes.shape[1] % 4 == 0, \
'boxes.shape[1] is {:d}, but must be divisible by 4.'.format(
boxes.shape[1]
)
# x1 >= 0
boxes[:, 0::4] = np.maximum(np.minimum(boxes[:, 0::4], im_shape[1] - 1), 0)
# y1 >= 0
boxes[:, 1::4] = np.maximum(np.minimum(boxes[:, 1::4], im_shape[0] - 1), 0)
# x2 < im_shape[1]
boxes[:, 2::4] = np.maximum(np.minimum(boxes[:, 2::4], im_shape[1] - 1), 0)
# y2 < im_shape[0]
boxes[:, 3::4] = np.maximum(np.minimum(boxes[:, 3::4], im_shape[0] - 1), 0)
return boxes
def generate_rois(roi_counts, im_dims):
assert len(roi_counts) == len(im_dims)
all_rois = []
for i, num_rois in enumerate(roi_counts):
if num_rois == 0:
continue
# [batch_idx, x1, y1, x2, y2]
rois = np.random.uniform(
0, im_dims[i], size=(roi_counts[i], 5)
).astype(np.float32)
rois[:, 0] = i # batch_idx
# Swap (x1, x2) if x1 > x2
rois[:, 1], rois[:, 3] = np.minimum(rois[:, 1], rois[:, 3]), \
np.maximum(rois[:, 1], rois[:, 3])
# Swap (y1, y2) if y1 > y2
rois[:, 2], rois[:, 4] = np.minimum(rois[:, 2], rois[:, 4]), \
np.maximum(rois[:, 2], rois[:, 4])
all_rois.append(rois)
if len(all_rois) > 0:
return np.vstack(all_rois)
return np.empty((0, 5)).astype(np.float32)
class TestBBoxTransformOp(hu.HypothesisTestCase):
@given(
num_rois=st.integers(1, 10),
num_classes=st.integers(1, 10),
im_dim=st.integers(100, 600),
skip_batch_id=st.booleans(),
**hu.gcs_cpu_only
)
def test_bbox_transform(
self, num_rois, num_classes, im_dim, skip_batch_id, gc, dc
):
"""
Test with all rois belonging to a single image per run.
"""
rois = generate_rois([num_rois], [im_dim])
if skip_batch_id:
rois = rois[:, 1:5]
deltas = np.random.randn(num_rois, 4 * num_classes).astype(np.float32)
im_info = np.array([im_dim, im_dim,
1.0]).astype(np.float32).reshape(1, 3)
def bbox_transform_ref(rois, deltas, im_info):
boxes = rois if rois.shape[1] == 4 else rois[:, 1:5]
box_out = bbox_transform(boxes, deltas)
im_shape = im_info[0, 0:2]
box_out = clip_tiled_boxes(box_out, im_shape)
return [box_out]
op = core.CreateOperator(
"BBoxTransform",
["rois", "deltas", "im_info"],
["box_out"],
apply_scale=False,
correct_transform_coords=True,
)
self.assertReferenceChecks(
device_option=gc,
op=op,
inputs=[rois, deltas, im_info],
reference=bbox_transform_ref,
)
@given(
roi_counts=st.lists(st.integers(0, 5), min_size=1, max_size=10),
num_classes=st.integers(1, 10),
**hu.gcs_cpu_only
)
def test_bbox_transform_batch(self, roi_counts, num_classes, gc, dc):
"""
Test with rois for multiple images in a batch
"""
batch_size = len(roi_counts)
total_rois = sum(roi_counts)
im_dims = np.random.randint(100, 600, batch_size)
rois = generate_rois(roi_counts, im_dims)
deltas = np.random.randn(total_rois, 4 * num_classes).astype(np.float32)
im_info = np.zeros((batch_size, 3)).astype(np.float32)
im_info[:, 0] = im_dims
im_info[:, 1] = im_dims
im_info[:, 2] = 1.0
def bbox_transform_ref(rois, deltas, im_info):
box_out = []
offset = 0
for i, num_rois in enumerate(roi_counts):
if num_rois == 0:
continue
cur_boxes = rois[offset:offset + num_rois, 1:5]
cur_deltas = deltas[offset:offset + num_rois]
cur_box_out = bbox_transform(cur_boxes, cur_deltas)
im_shape = im_info[i, 0:2]
cur_box_out = clip_tiled_boxes(cur_box_out, im_shape)
box_out.append(cur_box_out)
offset += num_rois
if len(box_out) > 0:
box_out = np.vstack(box_out)
else:
box_out = np.empty(deltas.shape).astype(np.float32)
return [box_out, roi_counts]
op = core.CreateOperator(
"BBoxTransform",
["rois", "deltas", "im_info"],
["box_out", "roi_batch_splits"],
apply_scale=False,
correct_transform_coords=True,
)
self.assertReferenceChecks(
device_option=gc,
op=op,
inputs=[rois, deltas, im_info],
reference=bbox_transform_ref,
)
|
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|
from __future__ import print_function
import torch
import torch.nn as nn
import pickle
import data_prep as prep
from torchvision import transforms, utils
import torch.nn.parallel
import numpy as np
from torch.utils.data import DataLoader
from generator import Generator
from discriminator import Discriminator
from torch.utils.data import DataLoader
import matplotlib.pyplot as plt
import cv2
__author__ = 'JosueCom'
__date__ = '5/8/2020'
__email__ = "josue.n.rivera@outlook.com"
#image_size = 500
nc = 3
ngf = 25
batch_size = 4
image_size = 500
beta1 = 0.5
ngpu = torch.cuda.device_count()
lf_to_rg_ratio = 0.5
diff_pickle = open("planet_earth_diff.pickle","rb")
print("loading dataset")
dataset = prep.PIFDataset(
path='data_prepocessing/PlanetEarth',
diff = pickle.load(diff_pickle),
transform=transforms.Compose([
transforms.ToPILImage(),
#transforms.Resize(image_size),
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
]))
dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)
print("Done loading dataset")
device = torch.device("cuda:0" if (torch.cuda.is_available() and ngpu > 0) else "cpu")
#device = torch.device("cpu")
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
nn.init.normal_(m.weight.data, 0.0, 0.02)
elif classname.find('BatchNorm') != -1:
nn.init.normal_(m.weight.data, 1.0, 0.02)
nn.init.constant_(m.bias.data, 0)
## generator
#netG = Generator(ngpu, nc, ngf).to(device)
"""if (device.type == 'cuda') and (ngpu > 1):
netG = nn.DataParallel(netG, list(range(ngpu)))"""
#netG.apply(weights_init)
netG = torch.load("nice/generator6.bin")
netG.eval()
print("running model")
batch = next(iter(dataloader))
torch.cuda.empty_cache()
out = netG(batch["prev"][0].to(device).unsqueeze(0), batch["next"][0].to(device).unsqueeze(0))
print("done running model")
#plt.figure(figsize=(2,2))
#plt.axis("off")
#plt.title("Previous Training Images")
print(batch["prev"].size())
#plt.imshow(np.transpose(utils.make_grid(batch["prev"].to(device)[:batch_size], padding=2, normalize=True).cpu().detach(),(1,2,0)))
#plt.show()
#img = np.transpose(utils.make_grid(batch["prev"].to(device)[:batch_size], padding=2, normalize=True).cpu().detach(),(1,2,0)).numpy()
#plt.imsave('prev.jpg', img)
utils.save_image(batch["prev"][0], 'cool/img1.png', normalize=True, padding=0)
#plt.axis("off")
#plt.title("Next Training Images")
print(batch["next"].size())
#plt.imshow(np.transpose(utils.make_grid(batch["next"].to(device)[:batch_size], padding=2, normalize=True).cpu().detach(),(1,2,0)))
#plt.show()
#img = np.transpose(utils.make_grid(batch["next"].to(device)[:batch_size], padding=2, normalize=True).cpu().detach(),(1,2,0)).numpy()
#plt.imsave('next.jpg', img)
utils.save_image(batch["next"][0], 'cool/img3.png', normalize=True, padding=0)
#plt.axis("off")
#plt.title("Infered Images")
print(out.size())
#plt.imshow(np.transpose(utils.make_grid(out.to(device)[:batch_size], padding=2, normalize=True).cpu().detach(),(1,2,0)))
#plt.show()
#img = np.transpose(utils.make_grid(out.to(device)[:batch_size], padding=2, normalize=True).cpu().detach(),(1,2,0)).numpy()
#plt.imsave('infered.jpg', img)
utils.save_image(out, 'cool/img2.png', normalize=True, padding=0)
|
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|
using Lindenmayer, Luxor, Colors, ColorSchemes
crystal = LSystem(Dict(
"F" => "9F[F-]+*",
),
"F")
plant = LSystem(Dict(
"A" => "UBB8D", # initialize
"X" => "*[-F*X*]+F*X"),
"AX")
global x = 0
function f(t::Turtle)
pos = Point(t.xpos, t.ypos)
if x == 0
# we'll just do this at the very start
sethue("black")
circle(O, 245, :clip)
paint()
end
d = distance(pos, boxbottomcenter(BoundingBox()))
setcolor([Luxor.julia_purple, Luxor.julia_red, Luxor.julia_green][rand(1:end)])
circle(pos, 10, :fill)
global x += 1
end
drawLSystem(plant,
forward = 70,
turn = 17,
iterations=4,
#startingx=-250,
#startingy=250,
startingorientation = -π/2,
startingpen = (1, 1, 1),
width=500,
height=500,
asteriskfunction = f,
filename="docs/src/assets/logo.png",
backgroundcolor = RGBA(1, 1, 1, 0))
|
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|
import numpy as np
from tensorflow.contrib.graph_editor import Transformer
def crop(image, bbox, x, y, length):
x, y, bbox = x.astype(np.int), y.astype(np.int), bbox.astype(np.int)
x_min, y_min, x_max, y_max = bbox
w, h = x_max - x_min, y_max - y_min
# Crop image to bbox
image = image[y_min:y_min + h, x_min:x_min + w, :]
# Crop joints and bbox
x -= x_min
y -= y_min
bbox = np.array([0, 0, x_max - x_min, y_max - y_min])
# Scale to desired size
side_length = max(w, h)
f_xy = float(length) / float(side_length)
image, bbox, x, y = Transformer.scale(image, bbox, x, y, f_xy)
# Pad
new_w, new_h = image.shape[1], image.shape[0]
cropped = np.zeros((length, length, image.shape[2]))
dx = length - new_w
dy = length - new_h
x_min, y_min = int(dx / 2.), int(dy / 2.)
x_max, y_max = x_min + new_w, y_min + new_h
cropped[y_min:y_max, x_min:x_max, :] = image
x += x_min
y += y_min
x = np.clip(x, x_min, x_max)
y = np.clip(y, y_min, y_max)
bbox += np.array([x_min, y_min, x_min, y_min])
return cropped, bbox, x.astype(np.int), y.astype(np.int)
def scale(image, bbox, x, y, f_xy):
(h, w, _) = image.shape
h, w = int(h * f_xy), int(w * f_xy)
from numpy import resize
image = resize(image, (h, w), preserve_range=True, anti_aliasing=True, mode='constant').astype(np.uint8)
x = x * f_xy
y = y * f_xy
bbox = bbox * f_xy
x = np.clip(x, 0, w)
y = np.clip(y, 0, h)
return image, bbox, x, y
def flip(image, bbox, x, y):
image = np.fliplr(image).copy()
w = image.shape[1]
x_min, y_min, x_max, y_max = bbox
bbox = np.array([w - x_max, y_min, w - x_min, y_max])
x = w - x
x, y = Transformer.swap_joints(x, y)
return image, bbox, x, y
def rotate(image, bbox, x, y, angle):
# image - -(256, 256, 3)
# bbox - -(4,)
# x - -[126 129 124 117 107 99 128 107 108 105 137 155 122 99]
# y - -[209 176 136 123 178 225 65 47 46 24 44 64 49 54]
# angle - --8.165648811999333
# center of image [128,128]
o_x, o_y = (np.array(image.shape[:2][::-1]) - 1) / 2.
width, height = image.shape[0], image.shape[1]
x1 = x
y1 = height - y
o_x = o_x
o_y = height - o_y
image = rotate(image, angle, preserve_range=True).astype(np.uint8)
r_x, r_y = o_x, o_y
angle_rad = (np.pi * angle) / 180.0
x = r_x + np.cos(angle_rad) * (x1 - o_x) - np.sin(angle_rad) * (y1 - o_y)
y = r_y + np.sin(angle_rad) * (x1 - o_x) + np.cos(angle_rad) * (y1 - o_y)
x = x
y = height - y
bbox[0] = r_x + np.cos(angle_rad) * (bbox[0] - o_x) + np.sin(angle_rad) * (bbox[1] - o_y)
bbox[1] = r_y + -np.sin(angle_rad) * (bbox[0] - o_x) + np.cos(angle_rad) * (bbox[1] - o_y)
bbox[2] = r_x + np.cos(angle_rad) * (bbox[2] - o_x) + np.sin(angle_rad) * (bbox[3] - o_y)
bbox[3] = r_y + -np.sin(angle_rad) * (bbox[2] - o_x) + np.cos(angle_rad) * (bbox[3] - o_y)
return image, bbox, x.astype(np.int), y.astype(np.int)
|
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|
#include <cstdlib>
#include <iostream>
#include <fstream>
#include <exception>
#include <ctime>
#include <boost/program_options.hpp>
#include <boost/random.hpp>
#include "scene.h"
#include "../../src/parameters/ParamParser_getopt.hpp"
#include "../../src/pointsets/Pointset.hpp"
#include "../../src/io/fileIO.hpp"
double random01()
{
static boost::mt19937 rng(time(NULL));
static boost::uniform_01<boost::mt19937&> zeroone(rng);
return zeroone();
}
int main(int argc, char** argv)
{
/* ARG PARSER *****************************************************/
bool help=false;
int nReal=1;
int nPts=1024;
std::string fn_output;
utk::ParamParser_getopt parser;
parser.addShortOption('h', &help, 0, utk::assignBoolTrue, utk::displayBool, "\tDisplays this help message", "");
parser.addShortOption('n', &nPts, 1, utk::assignInt, utk::displayInt, "[int=1024] \tThe number of samples", "");
parser.addShortOption('o', &fn_output, 1, utk::assignString, utk::displayString, "[string]\tThe output file", "");
parser.addShortOption('m', &nReal, 1, utk::assignInt, utk::displayInt, "[int=1]\tThe number of realisations", "Realisations:");
parser.parse(argc, argv);
if(fn_output.empty())
{
ERROR("Parameter -o mandatory");
std::cout << parser.getHelp() << std::endl;
return 0;
}
if(help)
{
std::cout << parser.getHelp() << std::endl;
return 0;
}
int current_real=0;
while(current_real<nReal)
{
/* PROG ***********************************************************/
try
{
Scene bnot_scene;
std::vector<Point> in_points;
/* GENERATE ***************************************************/
std::cout << "Intializing w " << nPts << " random points" << std::endl;
for(int i=0; i<nPts; i++)
in_points.push_back(Point(random01(), random01()));
std::cout << "Done" << std::endl;
/* OPTIMIZE ***************************************************/
std::vector<FT> noise(in_points.size(), 0.0);
std::vector<FT> weights(in_points.size(), 0.0);
std::cout << "Construct Tglation" << std::endl;
bnot_scene.construct_triangulation(in_points, weights, noise);
std::cout << "Done" << std::endl;
std::cout << "Optimizing" << std::endl;
//max newton iter, epsilon, maxiter, ...
//high number of points
if(nPts > 150)
bnot_scene.optimize_all(0.0, 0.0, 500, 0.2, 500, std::cout, true);
else
//low number of points
bnot_scene.optimize_all(0.0, 0.0, 20, 0.2, 20, std::cout, true);
std::cout << "Done" << std::endl;
std::vector<Point> out_points;
bnot_scene.collect_visible_points(out_points);
/* WRITE ******************************************************/
{
utk::Pointset<2, double, utk::Point<2, double> > pts;
pts.resize(out_points.size());
for(unsigned int i=0; i<out_points.size(); i++)
{
pts[i].pos()[0] = out_points.at(i).x();
pts[i].pos()[1] = out_points.at(i).y();
}
utk::PointsetWriter<2, double, utk::Point<2, double> > writer;
writer.open(fn_output);
writer.writePointset(pts);
writer.close();
}
}
catch(const std::exception& e)
{
std::cerr << "Error : " << e.what() << std::endl;
exit(EXIT_FAILURE);
}
current_real++;
}
exit(EXIT_SUCCESS);
}
|
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|
import gym
import numpy as np
from abc import abstractmethod
from fault_tolerant_flight_control_drl.agent import SAC
from fault_tolerant_flight_control_drl.tools import AltitudeTask, AttitudeTask, BodyRateTask
from fault_tolerant_flight_control_drl.tools import ReliabilityTask, DisturbanceRejectionAtt
from fault_tolerant_flight_control_drl.tools import plot_response
import importlib
from fault_tolerant_flight_control_drl.tools.math_util import unscale_action, d2r, r2d
from fault_tolerant_flight_control_drl.tools import get_ID
from alive_progress import alive_bar
class Citation(gym.Env):
"""
Citation environment that follows the gym.env interface
Developed to be interfaced with a modified version of the CitAST environment, built with the DASMAT model and owned
by the Delft University of Technology. Follow the 'CitAST for Python' instructions at
https://github.com/kdally/fault-tolerant-flight-control-drl/blob/master/docs/CitAST_for_Python.pdf for installation.
Author: Killian Dally
:param evaluation: (bool) If False, the environment will be given training-specific shorter tasks.
If True, the environment is given longer and unseen tasks as part of the evaluation.
:param FDD: (bool) If True, the Fault Detection and Diagnosis module is added which switches from robust to
adaptive control at self.FDD_switch_time.
:param task: (Task) one of AltitudeTask, AttitudeTask, BodyRateTask, ReliabilityTask, DisturbanceRejection
:param disturbance: (bool) If True, disturbance forces are added in the environment. Normal disturbance values from
https://doi.org/10.2514/6.2018-1127.
:param sensor_noise: (bool) If True, sensor noise is added to the environment observations based on the sensor noise
estimates of the Cessna Citation 550 given in https://doi.org/10.2514/6.2018-1127.
:param low_pass: (bool) It True, control inputs are filtered with a first-order low-pass filter.
:param init_alt: (float) Initial flight altitude. One of 2000 or 5000.
:param init_speed: (float) Initial speed. One of 90 or 140.
"""
def __init__(self, evaluation=False, FDD=False, task=AttitudeTask,
disturbance=False, sensor_noise=False, low_pass=False,
init_alt=2000, init_speed=90):
super(Citation, self).__init__()
assert bool((FDD and init_alt == 2000 and init_speed == 90) or not FDD), \
'Failure cases only implemented for initial conditions init_alt == 2000 & init_speed == 90'
self.rate_limits = self.ActionLimits(np.array([[-20, -40, -20], [20, 40, 20]]))
self.deflection_limits = self.ActionLimits(np.array([[-20.05, -37.24, -21.77], [14.9, 37.24, 21.77]]))
self.placeholder_cond = False
self.C_MODEL, self.failure_input = self.get_plant()
self.FDD_switch_time = 60
self.failure_time = 10
self.task = task()
self.task_fun, self.evaluation, self.FDD = self.task.choose_task(evaluation, self.failure_input, FDD)
self.has_sensor_noise = sensor_noise
self.has_disturbance = disturbance
self.enable_low_pass = low_pass
self.time = self.task_fun()[3]
self.dt = self.time[1] - self.time[0]
self.ref_signal = self.task_fun(init_alt=init_alt)[0]
self.track_indices = self.task_fun()[1]
self.obs_indices = self.task_fun()[2]
self.sideslip_factor, self.pitch_factor, self.roll_factor, self.alt_factor = self.adapt_to_failure()
self.observation_space = gym.spaces.Box(-100, 100, shape=(len(self.obs_indices) + 3,), dtype=np.float64)
self.action_space = gym.spaces.Box(-1., 1., shape=(3,), dtype=np.float64)
self.current_deflection = np.zeros(3)
self.agent_path = 'fault_tolerant_flight_control_drl/agent/trained'
self.agents, self.agentID = self.load_agent(FDD) # type: SAC
# self.agents, self.agentID = None, None
self.state = None
self.state_deg = None
self.scale_s = None
self.state_history = None
self.action_history = None
self.error = None
self.step_count = None
self.external_ref_signal = None
def step(self, action_rates: np.ndarray):
self.current_deflection = self.current_deflection + self.scale_a(action_rates) * self.dt
if self.sideslip_factor[self.step_count - 1] == 0.0: self.current_deflection[2] = 0.0
filtered_deflection = self.filter_control_input(self.current_deflection)
if self.time[self.step_count] < self.failure_time and self.evaluation:
self.state = self.C_MODEL.step(
np.hstack([d2r(filtered_deflection + self.add_disturbance()[:, self.step_count]), 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
self.failure_input[1]]))
else:
self.state = self.C_MODEL.step(
np.hstack([d2r(filtered_deflection + self.add_disturbance()[:, self.step_count]), 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
self.failure_input[2]]))
self.state_deg = self.state * self.scale_s
self.error = d2r(self.ref_signal[:, self.step_count] -
self.state_deg[self.track_indices] + self.get_sensor_noise()[self.track_indices]) \
* self.scale_error(self.step_count)
self.state_history[:, self.step_count] = self.state_deg
self.action_history[:, self.step_count] = filtered_deflection
self.step_count += 1
done = bool(self.step_count >= self.time.shape[0])
if np.isnan(self.state).sum() > 0:
self.stop_NaNs()
return self.get_obs(), self.get_reward(), done, {'is_success': True}
def reset(self):
self.reset_soft()
self.ref_signal = self.task_fun()[0]
return np.zeros(self.observation_space.shape)
def reset_soft(self):
self.C_MODEL.initialize()
action_trim = np.array(
[0, 0, 0, 0., 0., 0., 0., 0.,
0, 0, self.failure_input[1]])
self.state = self.C_MODEL.step(action_trim)
self.scale_s = np.ones(self.state.shape)
self.scale_s[[0, 1, 2, 4, 5, 6, 7, 8]] = 180 / np.pi
self.state_deg = self.state * self.scale_s
self.state_history = np.zeros((self.state.shape[0], self.time.shape[0]))
self.action_history = np.zeros((self.action_space.shape[0], self.time.shape[0]))
self.error = np.zeros(len(self.track_indices))
self.step_count = 0
self.current_deflection = np.zeros(3)
return np.zeros(self.observation_space.shape)
def get_reward(self):
max_bound = np.ones(self.error.shape)
# reward_vec = np.abs(np.maximum(np.minimum(r2d(self.error / 30)**2, max_bound), -max_bound)) # square function
reward_vec = np.abs(np.maximum(np.minimum(r2d(self.error / 30), max_bound), -max_bound)) # rational function
# reward_vec = - np.maximum(np.minimum(1 / (np.abs(self.error) * 10 + 1), max_bound),
# - max_bound) # abs. linear function
reward = -reward_vec.sum() / self.error.shape[0]
return reward
def get_obs(self):
untracked_obs_index = np.setdiff1d(self.obs_indices, self.track_indices)
return np.hstack([self.error, self.state[untracked_obs_index], self.current_deflection])
def get_RMSE(self):
assert bool(self.step_count >= self.time.shape[0]), \
f'Error: cannot obtain RMSE before episode is completed. Current time is {self.time[self.step_count]}s.'
y_ref = self.ref_signal.copy()
y_ref2 = self.ref_signal.copy()
y_meas = self.state_history[self.track_indices, :].copy()
y_ref2[-1, 0] = 5
y_ref2[-1, 1] = -5
RMSE = np.sqrt(np.mean(np.square((y_ref - y_meas)), axis=1)) / (y_ref2.max(axis=1) - y_ref2.min(axis=1))
return RMSE
def get_MAE(self):
assert bool(self.step_count >= self.time.shape[0]), \
f'Error: cannot obtain MAE before episode is completed. Current time is {self.time[self.step_count]}s.'
y_ref = self.ref_signal.copy()
y_ref2 = self.ref_signal.copy()
y_meas = self.state_history[self.track_indices, :].copy()
y_ref2[-1, 0] = 5
y_ref2[-1, 1] = -5
MAE = np.mean(np.absolute(y_ref - y_meas), axis=1) / (y_ref2.max(axis=1) - y_ref2.min(axis=1))
return MAE
def stop_NaNs(self):
print('Encountered crash. Episode terminated early.')
if not self.evaluation:
ID = get_ID(6)
agent = SAC.load("fault_tolerant_flight_control_drl/agent/trained/tmp/best_model.zip", env=self)
agent.ID = ID
agent.save(f'{self.agent_path}/{self.task_fun()[4]}_{agent.ID}.zip')
print('Training is corrupt because of NaN values, terminated early. '
'So-far best trained agent may show good performance.')
plot_response('before_crash', self, self.task_fun(), 100, during_training=False,
failure=self.failure_input[0], FDD=self.FDD, broken=True)
exit()
def filter_control_input(self, deflection):
w_0 = 2 * 2 * np.pi # rad/s
filtered_deflection = deflection.copy()
if self.step_count > 1 and self.enable_low_pass:
filtered_deflection = self.action_history[:, self.step_count - 1] / (1 + w_0 * self.dt) + \
deflection * (w_0 * self.dt) / (1 + w_0 * self.dt)
return filtered_deflection
def get_sensor_noise(self):
# values in degrees, SSD
sensor_noise = np.zeros(self.state.shape)
if self.has_sensor_noise:
# p, q, r measurement from https://doi.org/10.2514/6.2018-0385
sensor_noise[0:3] += r2d(np.random.normal(scale=np.sqrt(4.0e-7), size=3)+3.0e-5)
# sideslip, estimate from https://doi.org/10.2514/6.2018-0385
sensor_noise[5] += r2d(np.random.normal(scale=np.sqrt(7.5e-8))+1.8e-3)
# phi, theta measurement from https://doi.org/10.2514/6.2018-0385
sensor_noise[6:8] += r2d(np.random.normal(scale=np.sqrt(1e-9), size=2)+4.0e-3)
# h estimate from https://doi.org/10.2514/6.2018-0385
sensor_noise[9] += np.random.normal(scale=np.sqrt(4.5e-3))+8.0e-3
return sensor_noise
def add_disturbance(self):
disturbance = np.zeros((self.action_space.shape[0], self.time.shape[0]))
if self.has_disturbance: # 3211 input in deg
disturbance[0, np.argwhere(self.time == 1)[0, 0]:np.argwhere(self.time == 4)[0, 0]] = 0.5
disturbance[0, np.argwhere(self.time == 4)[0, 0]:np.argwhere(self.time == 6)[0, 0]] = -0.9
disturbance[0, np.argwhere(self.time == 6)[0, 0]:np.argwhere(self.time == 7)[0, 0]] = 1.2
disturbance[0, np.argwhere(self.time == 7)[0, 0]:np.argwhere(self.time == 8)[0, 0]] = -1.2
disturbance[1, np.argwhere(self.time == 10)[0, 0]:np.argwhere(self.time == 13)[0, 0]] = -0.5
disturbance[1, np.argwhere(self.time == 13)[0, 0]:np.argwhere(self.time == 15)[0, 0]] = 0.9
disturbance[1, np.argwhere(self.time == 15)[0, 0]:np.argwhere(self.time == 16)[0, 0]] = -1.2
disturbance[1, np.argwhere(self.time == 16)[0, 0]:np.argwhere(self.time == 17)[0, 0]] = 1.2
return disturbance
def scale_error(self, step_count):
if 7 in self.track_indices:
return np.array([self.pitch_factor[step_count],
self.roll_factor[step_count], self.sideslip_factor[step_count]])
else:
return np.array([self.alt_factor[step_count],
self.roll_factor[step_count], self.sideslip_factor[step_count]])
def scale_a(self, action_unscaled: np.ndarray) -> np.ndarray:
"""Min-max un-normalization from [-1, 1] action space to actuator limits"""
return unscale_action(self.rate_limits, action_unscaled)
def bound_a(self, action):
return np.minimum(np.maximum(action, self.deflection_limits.low), self.deflection_limits.high)
@abstractmethod
def get_plant(self):
pass
@abstractmethod
def load_agent(self, FDD):
pass
def adapt_to_failure(self):
pitch_factor = np.ones(self.time.shape[0])
roll_factor = np.ones(self.time.shape[0])
alt_factor = np.ones(self.time.shape[0])
if self.evaluation:
sideslip_factor = 4.0 * np.ones(self.time.shape[0])
if self.task_fun()[4] == 'altitude_2attitude':
roll_factor *= 2
else:
sideslip_factor = 10.0 * np.ones(self.time.shape[0])
return sideslip_factor, pitch_factor, roll_factor, alt_factor
def FFD_change(self):
pass
def render(self, ext_agent=None, verbose=1):
during_training = False
if ext_agent is not None:
self.agents = [ext_agent]
# self.agents.save(f'agent/trained/{self.task_fun()[4]}_last.zip')
self.agentID = 'last'
verbose = 0
during_training = True
if self.FDD:
self.reset()
agent_robust = self.agents[0]
agent_adaptive = self.agents[1]
else:
agent_robust = self.agents[0]
agent_adaptive = None
obs = self.reset_soft()
return_a = 0
done = False
items = range(self.time.shape[0])
with alive_bar(len(items)) as bar:
while not done:
if self.time[self.step_count] < self.FDD_switch_time or not self.FDD:
action, _ = agent_robust.predict(obs, deterministic=True)
else:
self.FFD_change()
action, _ = agent_adaptive.predict(obs, deterministic=True)
obs, reward, done, info = self.step(action)
return_a += reward
bar()
plot_response(self.agentID, self, self.task_fun(), return_a, during_training,
self.failure_input[0], FDD=self.FDD)
if verbose > 0:
# print(f'Goal reached! Return = {return_a:.2f}')
np.set_printoptions(formatter={'float': lambda x: "{0:0.3f}".format(x)})
print(f'nRMSE% avg: {(self.get_RMSE().sum()) / 3 * 100:.2f}%')
print(f'nMAE% avg: {(self.get_MAE().sum()) / 3 * 100:.2f}%')
print('')
def close(self):
self.C_MODEL.terminate()
return
class ActionLimits:
def __init__(self, limits):
self.low, self.high = limits[0, :], limits[1, :]
class CitationNormal(Citation):
"""
Normal Citation Dynamics class, a sub-class of the Citation class.
Author: Killian Dally
:param evaluation: (bool) If False, the environment will be given training-specific shorter tasks.
If True, the environment is given longer and unseen tasks as part of the evaluation.
:param FDD: (bool) If True, the Fault Detection and Diagnosis module is added which switches from robust to
adaptive control at self.FDD_switch_time.
:param task: (Task) one of AltitudeTask, AttitudeTask, BodyRateTask, ReliabilityTask
:param disturbance: (bool) If True, disturbance forces are added in the environment. Normal disturbance values from
https://doi.org/10.2514/6.2018-1127.
:param sensor_noise: (bool) If True, sensor noise is added to the environment observations based on the sensor noise
estimates of the Cessna Citation 550 given in https://doi.org/10.2514/6.2018-1127.
:param low_pass: (bool) It True, control inputs are filtered with a first-order low-pass filter.
:param init_alt: (float) Initial flight altitude. One of 2000 or 5000.
:param init_speed: (float) Initial speed. One of 90 or 140.
"""
def __init__(self, init_alt=2000, init_speed=90, evaluation=False, FDD=False, task=AttitudeTask,
disturbance=False, sensor_noise=False, low_pass=False):
self.init_alt = init_alt
self.init_speed = init_speed
super(CitationNormal, self).__init__(evaluation=evaluation, FDD=FDD, task=task,
disturbance=disturbance, sensor_noise=sensor_noise, low_pass=low_pass)
self.ref_signal = self.task_fun(init_alt=init_alt)[0]
def get_plant(self):
path = 'fault_tolerant_flight_control_drl.envs.citation'
if self.init_alt == 2000 and self.init_speed == 90:
plant = importlib.import_module(f'{path}.normal_2000_90._citation', package=None)
elif self.init_alt == 2000 and self.init_speed == 140:
plant = importlib.import_module(f'{path}.normal_2000_140._citation', package=None)
self.placeholder_cond = True
elif self.init_alt == 5000 and self.init_speed == 90:
plant = importlib.import_module(f'{path}.normal_5000_90._citation', package=None)
elif self.init_alt == 5000 and self.init_speed == 140:
plant = importlib.import_module(f'{path}.normal_5000_140._citation', package=None)
else:
raise NotImplementedError('No model with the specified initial conditions is present. ' \
'Choose within init_alt={2000, 5000} and init_speed={90, 120}.')
return plant, ['normal', 1.0, 1.0]
def load_agent(self, FDD=False):
if FDD:
raise NotImplementedError('No fault detection and diagnosis on the non-failed system.')
return [SAC.load(f"{self.agent_path}/{self.task.agent_catalog['normal']}.zip",
env=self)], self.task.agent_catalog['normal']
def reset(self):
super(CitationNormal, self).reset()
self.ref_signal = self.task_fun(init_alt=self.init_alt)[0]
return np.zeros(self.observation_space.shape)
def reset_soft(self):
super(CitationNormal, self).reset_soft()
self.ref_signal = self.task_fun(init_alt=self.init_alt)[0]
return np.zeros(self.observation_space.shape)
class CitationRudderStuck(Citation):
"""
Citation Dynamics class with rudder failure, a sub-class of the Citation class.
The rudder is stuck at -15deg starting from self.failure_time.
Author: Killian Dally
"""
def get_plant(self):
plant = importlib.import_module(f'fault_tolerant_flight_control_drl.envs.citation.dr._citation', package=None)
return plant, ['dr', 0.0, -15.0]
def load_agent(self, FDD):
if FDD:
return [SAC.load(f"{self.agent_path}/{self.task.agent_catalog['normal']}.zip", env=self),
SAC.load(f"{self.agent_path}/{self.task.agent_catalog['rudder_stuck']}.zip", env=self)], \
self.task.agent_catalog['rudder_stuck']
return CitationNormal().load_agent()
def adapt_to_failure(self):
sideslip_factor, pitch_factor, roll_factor, alt_factor = super(CitationRudderStuck, self).adapt_to_failure()
if self.FDD:
sideslip_factor[np.argwhere(self.time == self.FDD_switch_time)[0, 0]:] *= 0.0
roll_factor[np.argwhere(self.time == self.FDD_switch_time)[0, 0]:] *= 0.5
return sideslip_factor, pitch_factor, roll_factor, alt_factor
class CitationAileronEff(Citation):
"""
Citation Dynamics class with aileron failure, a sub-class of the Citation class.
The aileron effectiveness is reduced by 70% from self.failure_time.
Author: Killian Dally
"""
def get_plant(self):
plant = importlib.import_module(f'fault_tolerant_flight_control_drl.envs.citation.da._citation', package=None)
return plant, ['da', 1.0, 0.3]
def load_agent(self, FDD):
if FDD:
return [SAC.load(f"{self.agent_path}/{self.task.agent_catalog['normal']}.zip", env=self),
SAC.load(f"{self.agent_path}/{self.task.agent_catalog['aileron_eff']}.zip", env=self)], \
self.task.agent_catalog['aileron_eff']
return CitationNormal().load_agent()
def adapt_to_failure(self):
sideslip_factor, pitch_factor, roll_factor, alt_factor = super(CitationAileronEff, self).adapt_to_failure()
if self.FDD:
pitch_factor[np.argwhere(self.time == self.FDD_switch_time)[0, 0]:] *= 1.5
return sideslip_factor, pitch_factor, roll_factor, alt_factor
class CitationElevRange(Citation):
"""
Citation Dynamics class with elevator failure, a sub-class of the Citation class.
The elevator operating range is reduced to [-3 deg, 3 deg] from self.failure_time.
Author: Killian Dally
"""
def get_plant(self):
plant = importlib.import_module(f'fault_tolerant_flight_control_drl.envs.citation.de._citation', package=None)
return plant, ['de', 20.05, 2.5]
def load_agent(self, FDD):
if FDD:
return [SAC.load(f"{self.agent_path}/{self.task.agent_catalog['normal']}.zip", env=self),
SAC.load(f"{self.agent_path}/{self.task.agent_catalog['elev_range']}.zip", env=self)], \
self.task.agent_catalog['elev_range']
return CitationNormal().load_agent()
def FFD_change(self):
self.deflection_limits = self.ActionLimits(np.array([[-3.0, -37.24, -21.77], [3.0, 37.24, 21.77]]))
self.rate_limits = self.ActionLimits(np.array([[-7, -40, -20], [7, 40, 20]]))
class CitationCgShift(Citation):
"""
Citation Dynamics class with backwards c.g. shift, a sub-class of the Citation class.
A 300kg payload moving from the from the front to the back of the passenger cabin is simulated,
which translates to a backwards c.g. shift of 0.25m from self.failure_time.
Author: Killian Dally
"""
def get_plant(self):
plant = importlib.import_module(f'fault_tolerant_flight_control_drl.envs.citation.cg._citation', package=None)
return plant, ['cg', 1.0, 1.04]
def load_agent(self, FDD):
if FDD:
return [SAC.load(f"{self.agent_path}/{self.task.agent_catalog['normal']}.zip", env=self),
SAC.load(f"{self.agent_path}/{self.task.agent_catalog['cg_shift']}.zip", env=self)], \
self.task.agent_catalog['cg_shift']
return CitationNormal().load_agent()
def adapt_to_failure(self):
sideslip_factor, pitch_factor, roll_factor, alt_factor = super(CitationCgShift, self).adapt_to_failure()
if self.FDD:
alt_factor[np.argwhere(self.time == self.FDD_switch_time)[0, 0]:] *= 0.5
return sideslip_factor, pitch_factor, roll_factor, alt_factor
class CitationIcing(Citation):
"""
Citation Dynamics class with icing, a sub-class of the Citation class.
A large accumulation of ice on the wing is simulated according to the measurements made
in https://doi.org/10.1016/S0376-0421(01)00018-5 from self.failure_time. In practice, C_L_max and alpha_stall are
reduced by 30% and C_D increased by 0.06.
Author: Killian Dally
"""
def get_plant(self):
plant = importlib.import_module(f'fault_tolerant_flight_control_drl.envs.citation.ice._citation', package=None)
return plant, ['ice', 1.0, 0.7] # https://doi.org/10.1016/S0376-0421(01)00018-5
def load_agent(self, FDD):
if FDD:
return [SAC.load(f"{self.agent_path}/{self.task.agent_catalog['normal']}.zip", env=self),
SAC.load(f"{self.agent_path}/{self.task.agent_catalog['icing']}.zip", env=self,
policy_kwargs=dict(layers=[32, 32]))], \
self.task.agent_catalog['icing']
return CitationNormal().load_agent()
def reset(self):
super(CitationIcing, self).reset()
self.ref_signal = self.task_fun()[0]
return np.zeros(self.observation_space.shape)
def adapt_to_failure(self):
sideslip_factor, pitch_factor, roll_factor, alt_factor = super(CitationIcing, self).adapt_to_failure()
if self.FDD:
alt_factor[np.argwhere(self.time == self.FDD_switch_time)[0, 0]:] *= 0.25
return sideslip_factor, pitch_factor, roll_factor, alt_factor
class CitationHorzTail(Citation):
"""
Citation Dynamics class with partial horizontal tail loss, a sub-class of the Citation class.
Author: Killian Dally
"""
def get_plant(self):
plant = importlib.import_module(f'fault_tolerant_flight_control_drl.envs.citation.ht._citation', package=None)
return plant, ['ht', 1.0, 0.3]
def load_agent(self, FDD):
if FDD:
return [SAC.load(f"{self.agent_path}/{self.task.agent_catalog['normal']}.zip", env=self),
SAC.load(f"{self.agent_path}/{self.task.agent_catalog['horz_tail']}.zip", env=self,
policy_kwargs=dict(layers=[32, 32]))], \
self.task.agent_catalog['horz_tail']
return CitationNormal().load_agent()
def adapt_to_failure(self):
sideslip_factor, pitch_factor, roll_factor, alt_factor = super(CitationHorzTail, self).adapt_to_failure()
if self.FDD:
alt_factor[np.argwhere(self.time == self.FDD_switch_time)[0, 0]:] *= 0.01
return sideslip_factor, pitch_factor, roll_factor, alt_factor
class CitationVertTail(Citation):
"""
Citation Dynamics class with partial vertical tail loss, a sub-class of the Citation class.
Author: Killian Dally
"""
def get_plant(self):
plant = importlib.import_module(f'fault_tolerant_flight_control_drl.envs.citation.vt._citation', package=None)
return plant, ['vt', 1.0, 0.3]
def load_agent(self, FDD):
if FDD:
return [SAC.load(f"{self.agent_path}/{self.task.agent_catalog['normal']}.zip", env=self),
SAC.load(f"{self.agent_path}/{self.task.agent_catalog['normal']}.zip", env=self)], \
self.task.agent_catalog['vert_tail']
return CitationNormal().load_agent()
def adapt_to_failure(self):
sideslip_factor, pitch_factor, roll_factor, alt_factor = super(CitationVertTail, self).adapt_to_failure()
if self.FDD:
sideslip_factor[np.argwhere(self.time == self.FDD_switch_time)[0, 0]:] *= 0.25
return sideslip_factor, pitch_factor, roll_factor, alt_factor
class CitationDistAlpha(CitationNormal):
"""
CitationNormal Dynamics class with atmospheric disturbances as verital .
The rudder is stuck at -15deg starting from self.failure_time.
Author: Killian Dally
"""
def get_plant(self):
path = 'fault_tolerant_flight_control_drl.envs.citation'
if self.init_alt == 2000 and self.init_speed == 90:
plant = importlib.import_module(f'{path}.normal_2000_90_dist._citation', package=None)
else:
raise NotImplementedError('No model with the specified initial conditions is present.')
return plant, ['normal', 1.0, 1.0]
class CitationVerif(CitationNormal):
"""
Normal Citation Dynamics class for verification, a sub-class of the Citation class.
It emulates MATLAB from Python to compare the response of the compiled model and that of the Simulink model.
Author: Killian Dally
"""
def step(self, actions: np.ndarray):
self.current_deflection = actions
if self.sideslip_factor[self.step_count - 1] == 0.0: self.current_deflection[2] = 0.0
if self.time[self.step_count] < 5.0 and self.evaluation:
self.state = self.C_MODEL.step(
np.hstack([d2r(self.current_deflection), 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, self.failure_input[1]]))
else:
self.state = self.C_MODEL.step(
np.hstack([d2r(self.current_deflection), 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, self.failure_input[2]]))
self.state_deg = self.state * self.scale_s
self.error = d2r(self.ref_signal[:, self.step_count] - self.state_deg[self.track_indices])
self.error[self.track_indices.index(5)] *= self.sideslip_factor[self.step_count]
self.error[self.track_indices.index(6)] *= self.roll_factor[self.step_count]
if 7 in self.track_indices:
self.error[self.track_indices.index(7)] *= self.pitch_factor[self.step_count]
if 9 in self.track_indices:
self.error[self.track_indices.index(9)] *= 1.0
self.state_history[:, self.step_count] = self.state_deg
self.action_history[:, self.step_count] = self.current_deflection
self.step_count += 1
done = bool(self.step_count >= self.time.shape[0])
if np.isnan(self.state).sum() > 0:
print(self.state_history[:, self.step_count - 2], self.time[self.step_count - 1])
plot_response('before_crash', self, self.task_fun(), 100, during_training=False,
failure=self.failure_input[0], FDD=self.FDD, broken=True)
exit()
return self.get_obs(), self.get_reward(), done, {'is_success': True}
#
# import os
# print(os.getcwd())
# from stable_baselines.common.env_checker import check_env
# envs = CitationNormal()
# print("Observation space:", envs.observation_space.shape)
# print("Action space:", envs.action_space.shape)
# check_env(envs, warn=True)
#
|
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|
SUBROUTINE MA_CGRM (fldnin, prsdon, rmkdon, fldnou, ier)
C************************************************************************
C* MA_CGRM *
C* *
C* This subroutine decodes the character remarks fields in a single *
C* Coast Guard report. Parameters used which are not in the calling *
C* sequence are found in macmn.cmn. *
C* *
C* MA_CGRM ( FLDNIN, PRSDON, RMKDON, FLDNOU, IER ) *
C* *
C* Input parameters: *
C* FIELDS CHAR*(*) Array of fields found in input *
C* string *
C* LENSF INTEGER Array of lengths of fields *
C* FLDNIN INTEGER Number of field to work on. *
C* *
C* Input and Output parameters: *
C* PRSDON LOGICAL if true, pressure field has been*
C* decoded. *
C* RMKDON LOGICAL if true, remarks fields have *
C* *
C* Output parameters: *
C* RIVALS(IRTERC) REAL tidal elev. relative to local *
C* chart (inches) *
C* RIVALS(IRHOCB) REAL height of cloud base (meters) *
C* RIVALS(IRGUST) REAL max. wind speed (gust) (kts) *
C* RIVALS(IRMXWH) REAL maximum wave height (ft) *
C* RIVALS(IRCORN) REAL correction indicator *
C* FLDNOU INTEGER Number of next field to work on *
C* IER INTEGER Return code *
C* 0 = Normal return *
C* non-zero = Problem *
C** *
C* Log: *
C* C. Caruso Magee/NCEP 4/01 Original Author *
C* F. J. Yen/NCEP 4/01 Cleaned up, reformatted and renamed from*
C* CG_RMKA. Added additional check for *
C* another numeric field. Added RAIN, HAZE *
C* and FOG. Kept value of fldnin unchanged.*
C* Changed prologue and sequence order of *
C* parameters. Added more comments. *
C************************************************************************
INCLUDE 'macmn.cmn'
C*
INTEGER fldnin, fldnou
LOGICAL prsdon, rmkdon, MA_FIND
C*
CHARACTER*5 swls (3)
CHARACTER*3 dirtn (16)
C*
DATA swls / 'SWELL', 'SWL', 'SWEL' /
DATA dirtn / 'SSW', 'WSW', 'WNW', 'NNW',
+ 'NNE', 'ENE', 'ESE', 'SSE',
+ 'SE', 'SW', 'NW', 'NE',
+ 'S', 'W', 'N', 'E' /
C------------------------------------------------------------------------
ier = 0
i = fldnin
fldnou = fldnin
IF ( lensf(i) .eq. 5 ) THEN
IF ( fields(i) .eq. 'GUSTY' ) THEN
fldnou = fldnin + 1
rmkdon = .true.
RETURN
ELSE IF ( fields(i) .eq. 'MINUS' .and.
+ i + 1 .le. nflds) THEN
C
C* Decode tidal elevation
C
CALL ST_INTG ( fields(i+1)(1:lensf(i+1)), ist1, ier )
IF ( ier .eq. 0 .and. ist1 .lt. 1000 ) THEN
rivals ( irterc ) = FLOAT( -ist1 )
ELSE
WRITE ( UNIT = logmsg, FMT = '( A )' )
+ ' Invalid group/format error in char remarks'
CALL DC_WLOG ( 2, 'MA', 1, logmsg, ierwlg )
END IF
fldnou = fldnin + 2
END IF
ELSE IF ( lensf(i) .eq. 4 ) THEN
IF ( fields(i) .eq. 'CEIL' .and.
+ i + 1 .le. nflds ) THEN
C
C* Decode ceiling (height of cloud base)
C
IF ( itypsf(i+1) .eq. ALPHA ) THEN
IF ( fields(i+1) .eq. 'UNL' ) THEN
fldnou = fldnin + 2
ELSE
fldnou = fldnin + 1
END IF
ELSE IF ( itypsf(i+1) .eq. NMR ) THEN
IF ( lensf(i+1) .eq. 3 ) THEN
C
C* Ceiling is in hundreds of feet, so convert in
C* feet, then to meters before saving into irhocb.
C
CALL ST_INTG ( fields(i+1)(1:lensf(i+1)),
+ ist1, ier )
IF ( ier .eq. 0 ) THEN
rivals ( irhocb(1) ) =
+ FLOAT( ist1 ) * 100./3.28
fldnou = fldnin + 2
END IF
ELSE
fldnou = fldnin + 2
END IF
ELSE
fldnou = fldnin + 2
END IF
ELSE IF ( fields(i) .eq. 'GUST' .and.
+ i + 1 .le. nflds ) THEN
IF ( itypsf(i+1) .eq. NMR ) THEN
C
C* Decode maximum wind speed
C
CALL ST_INTG ( fields(i+1)(1:lensf(i+1)),
+ ist1, ier )
IF ( ier .eq. 0 .and. ist1 .lt. 300 ) THEN
rivals ( irgust ) = FLOAT ( ist1 )
END IF
fldnou = fldnin + 2
ELSE
WRITE ( UNIT = logmsg, FMT = '( A )' )
+ ' Invalid group/format error in char remarks'
CALL DC_WLOG ( 2, 'MA', 1, logmsg, ierwlg )
fldnou = fldnin + 1
END IF
ELSE IF ( fields(i) .eq. 'PLUS' .and.
+ i + 1 .le. nflds ) THEN
C
C* Decode tidal elevation
C
CALL ST_INTG ( fields(i+1)(1:lensf(i+1)), ist1, ier )
IF ( ier .eq. 0 .and. ist1 .lt. 1000 ) THEN
rivals ( irterc ) = FLOAT ( ist1 )
ELSE
WRITE ( UNIT = logmsg, FMT = '( A )' )
+ ' Invalid group/format error in char remarks'
CALL DC_WLOG ( 2, 'MA', 1, logmsg, ierwlg )
END IF
fldnou = fldnin + 2
ELSE IF ( fields(i) .eq. 'HAZE' .or.
+ fields(i) .eq. 'RAIN' ) THEN
IF ( iwxvln .lt. 20 ) THEN
C
C* Append haze or rain to the weather visibility
C* text string and redecode the weather phenomenon
C
wxvsav = wxvsav(1:iwxvln) // fields (i) (1:1)
iwxvln = iwxvln + 1
CALL MA_CGWX ( wxvsav(1:iwxvln), ier )
END IF
fldnou = fldnin + 1
END IF
ELSE IF ( lensf(i) .eq. 3 ) THEN
IF ( fields(i) .eq. 'SCA' .or.
+ fields(i) .eq. 'UNL') THEN
fldnou = fldnin + 1
rmkdon = .true.
RETURN
ELSE IF ( fields(i) .eq. 'COR' ) THEN
rivals ( ircorn ) = 1.
fldnou = fldnin + 1
ELSE IF ( fields(i) .eq. 'MAX' .and.
+ i + 1 .le. nflds ) THEN
IF ( itypsf(i+1) .eq. NMR ) THEN
C
C* Decode maximum wave height
C
CALL ST_INTG ( fields(i+1)(1:lensf(i+1)),
+ ist1, ier )
IF ( ier .eq. 0 .and. ist1 .lt. 200 ) THEN
rivals ( irmxwh ) = FLOAT ( ist1 )
END IF
fldnou = fldnin + 3
ELSE
fldnou = fldnin + 1
END IF
C
C* Test for presence of direction text strings and for
C* possible swell text strings
C
ELSE IF ( MA_FIND ( fields(i), dirtn, 8 ) .and.
+ i + 1 .le. nflds ) THEN
IF ( MA_FIND ( fields(i+1), swls, 3 ) ) THEN
C
C* Decode the swell direction
C
CALL MA_CGWD ( fields(i), swls(1), iret )
fldnou = fldnin + 2
ELSE
fldnou = fldnin + 1
END IF
ELSE IF ( fields(i) .eq. 'FOG' ) THEN
IF ( iwxvln .lt. 20 ) THEN
C
C* Append fog to the weather visibility
C* text string and redecode the weather phenomenon
C
wxvsav = wxvsav(1:iwxvln) // fields (i) (1:1)
iwxvln = iwxvln + 1
CALL MA_CGWX ( wxvsav(1:iwxvln), ier )
END IF
fldnou = fldnin + 1
END IF
ELSE IF ( lensf(i) .eq. 2 ) THEN
C
C* Test for presence of direction text strings and for
C* possible swell text strings
C
IF ( MA_FIND ( fields(i), dirtn(9), 4 ) .and.
+ i + 1 .le. nflds ) THEN
IF ( MA_FIND ( fields(i+1), swls, 3 ) ) THEN
C
C* Decode the swell direction
C
CALL MA_CGWD ( fields(i), swls(1), iret )
fldnou = fldnin + 2
ELSE
fldnou = fldnin + 1
END IF
ELSE IF ( fields(i) .eq. 'MX' .and.
+ i + 1 .le. nflds ) THEN
C
C* Decode maximum wave height
C
IF ( itypsf(i+1) .eq. NMR ) THEN
CALL ST_INTG ( fields(i+1)(1:lensf(i+1)),
+ ist1, ier )
IF ( ier .eq. 0 .and. ist1 .lt. 200 ) THEN
rivals ( irmxwh ) = FLOAT ( ist1 )
fldnou = fldnin + 3
ELSE
fldnou = fldnin + 3
END IF
ELSE
fldnou = fldnin + 1
END IF
END IF
C
C* Test for presence of direction text strings and for
C* possible swell text strings
C
ELSE IF ( lensf(i) .eq. 1 .and.
+ MA_FIND ( fields(i), dirtn(13), 4 ) .and.
+ i + 1 .le. nflds ) THEN
IF ( MA_FIND ( fields(i+1), swls, 3) ) THEN
C
C* Decode the swell direction
C
CALL MA_CGWD ( fields(i), swls(1), iret )
fldnou = fldnin + 2
ELSE
fldnou = fldnin + 1
END IF
ELSE IF ( lensf(i) .eq. 1 .and.
+ ( fields(i) .eq. 'G' ) .and.
+ i + 1 .le. nflds ) THEN
IF ( itypsf(i+1) .eq. NMR ) THEN
C
C* Decode maximum wind speed
C
CALL ST_INTG ( fields(i+1)(1:lensf(i+1)), ist1, ier )
IF ( ier .eq. 0 .and. ist1 .lt. 300 ) THEN
rivals ( irgust ) = FLOAT ( ist1 )
END IF
fldnou = fldnin + 2
ELSE
fldnou = fldnin + 1
END IF
END IF
IF ( .not. rmkdon ) THEN
C
C* Check to see if next field (or initial field if no match
C* found) is alpha or numeric. If numeric, it's more remarks,
C* else it's station name or a typo.
C
IF ( itypsf(fldnou) .eq. NMR ) THEN
IF ( fldnou .le. nflds ) THEN
infld = fldnou
CALL MA_CGPT ( infld, prsdon, rmkdon, fldnou, ier )
ELSE
rmkdon = .true.
END IF
ELSE
fldnou = fldnin
rmkdon = .true.
END IF
END IF
C*
RETURN
END
|
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|
"""Unittests for rasterio.plot"""
import numpy as np
import pytest
try:
import matplotlib as mpl
mpl.use('agg')
import matplotlib.pyplot as plt
plt.show = lambda :None
except ImportError:
plt = None
import rasterio
from rasterio.plot import (show, show_hist, get_plt,
plotting_extent, adjust_band)
from rasterio.enums import ColorInterp
def test_show_raster_band():
"""Test plotting a single raster band."""
matplotlib = pytest.importorskip('matplotlib')
with rasterio.open('tests/data/RGB.byte.tif') as src:
show((src, 1))
fig = plt.gcf()
plt.close(fig)
def test_show_raster_mult_bands():
"""Test multiple bands plotting."""
matplotlib = pytest.importorskip('matplotlib')
with rasterio.open('tests/data/RGB.byte.tif') as src:
show((src, (1, 2, 3)))
fig = plt.gcf()
plt.close(fig)
def test_show_raster_object():
"""Test plotting a raster object."""
matplotlib = pytest.importorskip('matplotlib')
with rasterio.open('tests/data/RGB.byte.tif') as src:
show(src)
fig = plt.gcf()
plt.close(fig)
def test_show_raster_float():
"""Test plotting a raster object with float data."""
matplotlib = pytest.importorskip('matplotlib')
with rasterio.open('tests/data/float.tif') as src:
show(src)
fig = plt.gcf()
plt.close(fig)
def test_show_cmyk_interp(tmpdir):
"""A CMYK TIFF has cyan, magenta, yellow, black bands."""
matplotlib = pytest.importorskip('matplotlib')
with rasterio.open('tests/data/RGB.byte.tif') as src:
meta = src.meta
meta['photometric'] = 'CMYK'
meta['count'] = 4
tiffname = str(tmpdir.join('foo.tif'))
with rasterio.open(tiffname, 'w', **meta) as dst:
assert dst.profile['photometric'] == 'cmyk'
assert dst.colorinterp == (
ColorInterp.cyan,
ColorInterp.magenta,
ColorInterp.yellow,
ColorInterp.black)
with rasterio.open(tiffname) as src:
try:
show(src)
fig = plt.gcf()
plt.close(fig)
except ImportError:
pass
def test_show_raster_no_bounds():
"""
This test only verifies that code up to the point of plotting with
matplotlib works correctly. Tests do not exercise matplotlib.
"""
matplotlib = pytest.importorskip('matplotlib')
with rasterio.open('tests/data/RGB.byte.tif') as src:
try:
show((src, 1), with_bounds=False)
fig = plt.gcf()
plt.close(fig)
except ImportError:
pass
def test_show_raster_title():
"""
This test only verifies that code up to the point of plotting with
matplotlib works correctly. Tests do not exercise matplotlib.
"""
matplotlib = pytest.importorskip('matplotlib')
with rasterio.open('tests/data/RGB.byte.tif') as src:
try:
show((src, 1), title="insert title here")
fig = plt.gcf()
plt.close(fig)
except ImportError:
pass
def test_show_hist_large():
"""
This test only verifies that code up to the point of plotting with
matplotlib works correctly. Tests do not exercise matplotlib.
"""
matplotlib = pytest.importorskip('matplotlib')
try:
rand_arr = np.random.randn(10, 718, 791)
show_hist(rand_arr)
fig = plt.gcf()
plt.close(fig)
except ImportError:
pass
def test_show_raster_cmap():
"""
This test only verifies that code up to the point of plotting with
matplotlib works correctly. Tests do not exercise matplotlib.
"""
matplotlib = pytest.importorskip('matplotlib')
with rasterio.open('tests/data/RGB.byte.tif') as src:
try:
show((src, 1), cmap='jet')
fig = plt.gcf()
plt.close(fig)
except ImportError:
pass
def test_show_raster_ax():
"""
This test only verifies that code up to the point of plotting with
matplotlib works correctly. Tests do not exercise matplotlib.
"""
matplotlib = pytest.importorskip('matplotlib')
with rasterio.open('tests/data/RGB.byte.tif') as src:
try:
fig, ax = plt.subplots(1)
show((src, 1), ax=ax)
fig = plt.gcf()
plt.close(fig)
except ImportError:
pass
def test_show_array():
"""
This test only verifies that code up to the point of plotting with
matplotlib works correctly. Tests do not exercise matplotlib.
"""
matplotlib = pytest.importorskip('matplotlib')
with rasterio.open('tests/data/RGB.byte.tif') as src:
try:
show(src.read(1))
fig = plt.gcf()
plt.close(fig)
except ImportError:
pass
def test_show_array3D():
"""
This test only verifies that code up to the point of plotting with
matplotlib works correctly. Tests do not exercise matplotlib.
"""
matplotlib = pytest.importorskip('matplotlib')
with rasterio.open('tests/data/RGB.byte.tif') as src:
try:
show(src.read((1, 2, 3)))
fig = plt.gcf()
plt.close(fig)
except ImportError:
pass
def test_show_hist():
"""
This test only verifies that code up to the point of plotting with
matplotlib works correctly. Tests do not exercise matplotlib.
"""
matplotlib = pytest.importorskip('matplotlib')
with rasterio.open('tests/data/RGB.byte.tif') as src:
try:
show_hist((src, 1), bins=256)
fig = plt.gcf()
plt.close(fig)
except ImportError:
pass
try:
show_hist(src.read(), bins=256)
fig = plt.gcf()
plt.close(fig)
except ImportError:
pass
try:
fig, ax = plt.subplots(1)
show_hist(src.read(), bins=256, ax=ax)
fig = plt.gcf()
plt.close(fig)
except ImportError:
pass
def test_show_hist_mplargs():
"""
This test only verifies that code up to the point of plotting with
matplotlib works correctly. Tests do not exercise matplotlib.
"""
matplotlib = pytest.importorskip('matplotlib')
with rasterio.open('tests/data/RGB.byte.tif') as src:
try:
show_hist(src, bins=50, lw=0.0, stacked=False, alpha=0.3,
histtype='stepfilled', title="World Histogram overlaid")
fig = plt.gcf()
plt.close(fig)
except ImportError:
pass
def test_show_contour():
"""
This test only verifies that code up to the point of plotting with
matplotlib works correctly. Tests do not exercise matplotlib.
"""
matplotlib = pytest.importorskip('matplotlib')
with rasterio.open('tests/data/RGB.byte.tif') as src:
try:
show((src, 1), contour=True)
fig = plt.gcf()
plt.close(fig)
except ImportError:
pass
def test_show_contour_mplargs():
"""
This test only verifies that code up to the point of plotting with
matplotlib works correctly. Tests do not exercise matplotlib.
"""
matplotlib = pytest.importorskip('matplotlib')
with rasterio.open('tests/data/RGB.byte.tif') as src:
try:
show((src, 1), contour=True,
levels=[25, 125], colors=['white', 'red'], linewidths=4,
contour_label_kws=dict(fontsize=18, fmt="%1.0f", inline_spacing=15, use_clabeltext=True))
fig = plt.gcf()
plt.close(fig)
except ImportError:
pass
def test_get_plt():
"""
This test only verifies that code up to the point of plotting with
matplotlib works correctly. Tests do not exercise matplotlib.
"""
matplotlib = pytest.importorskip('matplotlib')
with rasterio.open('tests/data/RGB.byte.tif'):
try:
assert plt == get_plt()
except ImportError:
pass
def test_plt_transform():
matplotlib = pytest.importorskip('matplotlib')
with rasterio.open('tests/data/RGB.byte.tif') as src:
show(src.read(), transform=src.transform)
show(src.read(1), transform=src.transform)
def test_plotting_extent():
from rasterio.plot import reshape_as_image
expected = (101985.0, 339315.0, 2611485.0, 2826915.0)
with rasterio.open('tests/data/RGB.byte.tif') as src:
assert plotting_extent(src) == expected
assert plotting_extent(
reshape_as_image(src.read()), transform=src.transform) == expected
assert plotting_extent(
src.read(1), transform=src.transform) == expected
# array requires a transform
with pytest.raises(ValueError):
plotting_extent(src.read(1))
def test_plot_normalize():
a = np.linspace(1, 6, 10)
b = adjust_band(a, 'linear')
np.testing.assert_array_almost_equal(np.linspace(0, 1, 10), b)
|
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|
import numpy as np
import pprint
import cta_fspecial
import cta_chog
class Cta_products():
def __init__(self, Fourier_coefficients, product_options, output_order=0):
self.monoms = product_options['monoms']
self.feature_order=[0,5];
self.angular_power=[self.feature_order[0],self.feature_order[1]];
self.angular_cross=[1,2];
self.Fourier_coefficients = Fourier_coefficients
self.num_features=len(self.Fourier_coefficients) # window の数
self.feature_order[0]=max([self.feature_order[0], output_order])
def in_interval(self, value, interval): #value が1つの値の場合でもリストで与える。
ok=True
if len(interval)<2:
ok=False
for v in range(len(value)):
if (value[v]<interval[0] or value[v]>interval[1]):
ok=False
return ok
### l=0 がなかったのはここだーーーーー！！！！！
# if exist('output_order'),
# feature_order(1)=max(feature_order(1),output_order);
# end;
# assert(max(cellfun(@numel,monoms))<4);
# assert(min(cellfun(@numel,monoms))>0);
###
def cta_products(self):
product_mat=[]
test = []
for m in range(len(self.monoms)):
morder=len(self.monoms[m]) #monoms{m} = '01' なら morder = 2
#print(morder)
monom=self.monoms[m]
#print('monom: ' + str(monom))
for a in range(1, self.num_features+1): # a は window function の番号を表す
#print('a: ' + str(a))
for la in range(self.Fourier_coefficients[a-1]["L"]+1): # Fourier_coefficients[a][1] には L が入っている
# print('la: ' +str(la))
if morder==1: #window function を1種類のみ使う場合
assert float(monom[0])==0, 'ERROR: conj is not necessary' #条件式が False の場合にエラーを投げる
new_product=[a,la,float(monom[0]),
-1,-1,-1,
-1,-1,-1,
la,0] # +１で本当に合ってるか確認する
product_mat.append(new_product)
# #print(product_mat)
else:
for b in range(a,self.num_features+1):
# print('b: ' + str(b))
start_bl=0;
if a==b and monom[0]==monom[1]:
start_bl=la;
for lb in range(start_bl, self.Fourier_coefficients[b-1]["L"]+1):
# print('lb: ' + str(lb))
if morder==2: ## window function を２種類使う場合
l=((-1)**float(monom[0]))*la + ((-1)**float(monom[1]))*lb
#print('-- l: ' + str(l))
# test = (in_interval([l],feature_order) and
# ((a==b and in_interval([la,lb],angular_power)) or
# (a!=b and in_interval([la,lb],angular_cross))))
# #print(test)
if (Cta_products(self.Fourier_coefficients, product_options).in_interval([l],self.feature_order) and ((a==b and Cta_products(self.Fourier_coefficients, product_options).in_interval([la,lb],self.angular_power)) or (a!=b and Cta_products(self.Fourier_coefficients, product_options).in_interval([la,lb],self.angular_cross)))):
new_product=[a,la,float(monom[0]),
b,lb,float(monom[1]),
-1,-1,-1,
l,0];
product_mat.append(new_product)
# print(product_mat)
### 20201002 for debug
elif morder==3: ## window function を3種類使う場合
# print('morder==3')
# print('self.Fourier_coefficients.shape')
# print(len(self.Fourier_coefficients))
for c in range(b, self.num_features+1):
# print('c: ' + str(c))
# print('self.Fourier_coefficients[c]')
# print(self.Fourier_coefficients[c])
start_cl=0;
if c==b and monom[1]==monom[2]:
start_cl=lb
if c==a and monom[0]==monom[2]:
start_cl=np.max(la,start_cl)
# print('start_cl: ' + str(start_cl))
# print('self.Fourier_coefficients[c][1]+1: ' + str(self.Fourier_coefficients[c][1]+1))
for lc in range(start_cl,self.Fourier_coefficients[c-1]["L"]+1):
#print('lc: ' + str(lc))
# if np.min([la,lb,lc])>0
if np.min([lb,lc])>0:
# #if 1
l=(((-1)**float(monom[0]))*la
+((-1)**float(monom[1]))*lb
+((-1)**float(monom[2]))*lc)
#print('---l: ' + str(l))
# print('self.feature_order')
# print(self.feature_order)
if (Cta_products(self.Fourier_coefficients,product_options).in_interval([l],self.feature_order) and (((a==b and b==c and a==c) and Cta_products(self.Fourier_coefficients, product_options).in_interval([la,lb,lc],self.angular_power)) or ((a!=b or b!=c or a!=c) and Cta_products(self.Fourier_coefficients, product_options).in_interval([la,lb,lc],self.angular_cross)))):
new_product=[a,la,float(monom[0]),
b,lb,float(monom[1]),
c,lc,float(monom[2]),
l,0];
product_mat.append(new_product)
product_mat = np.array(product_mat)
# product_mat = product_mat[product_mat[:,-2]!=0] # コメントアウト
# v = np.sort(product_mat[:,-2])[::-1]
indx = np.argsort(product_mat[:,-2])
product_mat=product_mat[indx]
# # -2 列目を sort された状態を保ちつつ、さらに 1 列目で sort
# for i in range(self.feature_order[1]+1):
# i_indx = np.argsort(product_mat[product_mat[:,-2]==i][:,0])
# if i == 0:
# i_product_mat=product_mat[product_mat[:,-2]==i][i_indx]
# else:
# i_product_mat = np.concatenate([i_product_mat, product_mat[product_mat[:,-2]==i][i_indx]], axis=0)
# # print(i_product_mat)
# product_mat = i_product_mat
# # （列, 規則）: (-2,降） → (0,昇) → (1,昇) → (3, 昇) → （4, 昇） の順に sort
# 20201005 この部分は octave と出力を揃えるため
print('self.feature_order[1]')
print(self.feature_order[1])
for i in range(self.feature_order[1],-1,-1):#-2
tmp_i = product_mat[product_mat[:,-2]==i]
# print('i: ' + str(i))
for j in range(1, self.feature_order[1]+1):#0
# print('j: ' + str(j))
tmp_j = tmp_i[tmp_i[:,0]==j]
# print(tmp_j)
for k in range(self.feature_order[1]+1):#1
# print('k: ' + str(k))
tmp_k = tmp_j[tmp_j[:,1]==k]
# print(tmp_k)
for l in range(self.feature_order[1]+1):#3
tmp_l = tmp_k[tmp_k[:,3]==l]
for m in range(self.feature_order[1]+1):#4
tmp_m = tmp_l[tmp_l[:,4]==m]
ijkl_indx = np.argsort(tmp_m[:,6])#6
# print('ijk_indx' + str(len(ijk_indx)))
if i == self.feature_order[1] and j ==1 and k ==0: # flag に書き換え
ijkl_product_mat=tmp_m[ijkl_indx]
# print(ijkl_product_mat)
else:
ijkl_product_mat = np.concatenate([ijkl_product_mat, tmp_m[ijkl_indx]], axis=0)
# print(ijk_product_mat)
product_mat = ijkl_product_mat
return product_mat
|
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|
[STATEMENT]
lemma lran_bwd_simp: "lran a l h = (if l<h then lran a l (h-1)@[a (h-1)] else [])"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. lran a l h = (if l < h then lran a l (h - 1) @ [a (h - 1)] else [])
[PROOF STEP]
apply (induction a l h rule: lran.induct)
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<And>a l h. (l < h \<Longrightarrow> lran a (l + 1) h = (if l + 1 < h then lran a (l + 1) (h - 1) @ [a (h - 1)] else [])) \<Longrightarrow> lran a l h = (if l < h then lran a l (h - 1) @ [a (h - 1)] else [])
[PROOF STEP]
apply (rewrite in "\<hole> = _" lran.simps)
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<And>a l h. (l < h \<Longrightarrow> lran a (l + 1) h = (if l + 1 < h then lran a (l + 1) (h - 1) @ [a (h - 1)] else [])) \<Longrightarrow> (if l < h then a l # lran a (l + 1) h else []) = (if l < h then lran a l (h - 1) @ [a (h - 1)] else [])
[PROOF STEP]
apply (rewrite in "_ = \<hole>" lran.simps)
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<And>a l h. (l < h \<Longrightarrow> lran a (l + 1) h = (if l + 1 < h then lran a (l + 1) (h - 1) @ [a (h - 1)] else [])) \<Longrightarrow> (if l < h then a l # lran a (l + 1) h else []) = (if l < h then (if l < h - 1 then a l # lran a (l + 1) (h - 1) else []) @ [a (h - 1)] else [])
[PROOF STEP]
by (auto simp: less_le)
|
{"llama_tokens": 617, "file": "IMP2_lib_IMP2_Aux_Lemmas", "length": 4}
|
import geopandas as gpd
import pandas as pd
from shapely.geometry import Polygon,Point
from .grids import GPS_to_grids,grids_centre
import math
import numpy as np
from .preprocess import *
def busgps_arriveinfo(data,line,stop,col = ['VehicleId','GPSDateTime','lon','lat','stopname'],
stopbuffer = 200,mintime = 300,project_epsg = 2416,timegap = 1800,method = 'project',projectoutput = False):
'''
输入公交GPS数据、公交线路与站点的GeoDataFrame，该方法能够识别公交的到离站信息
输入
-------
data : DataFrame
公交GPS数据，单一公交线路，且需要含有车辆ID、GPS时间、经纬度（wgs84）
line : GeoDataFrame
公交线型的GeoDataFrame数据，单一公交线路
stop : GeoDataFrame
公交站点的GeoDataFrame数据
col : List
列名，按[车辆ID,时间,经度,纬度，站点名称字段]的顺序
stopbuffer : number
米，站点的一定距离范围，车辆进入这一范围视为到站，离开则视为离站
mintime : number
秒，短时间内公交再次到站则需要与前一次的到站数据结合一起计算到离站时间，该参数设置阈值
project_epsg : number
匹配时会将数据转换为投影坐标系以计算距离，这里需要给定投影坐标系的epsg代号
timegap : number
秒，清洗数据用，多长时间车辆不出现，就视为新的车辆
method : str
公交运行图匹配方法，可选'project'或'dislimit'；
project为直接匹配线路上最近点，匹配速度快；
dislimit则需要考虑前面点位置，加上距离限制，匹配速度慢。
projectoutput : bool
是否输出投影后的数据
输出
-------
arrive_info : DataFrame
公交到离站信息
'''
VehicleId,GPSDateTime,lon,lat,stopcol = col
#数据清洗
print('数据清洗中',end = '')
line.set_crs(crs='epsg:4326',allow_override=True,inplace=True)
line = line.to_crs(epsg = project_epsg)
line_buffer = line.copy()
line_buffer['geometry'] = line_buffer.buffer(200)
line_buffer = line_buffer.to_crs(epsg = 4326)
print('.',end = '')
data = clean_same(data,col=[VehicleId,GPSDateTime,lon,lat])
print('.',end = '')
data = clean_outofshape(data,line_buffer,col = [lon,lat],accuracy = 500)
print('.')
data = id_reindex(data,VehicleId,timegap = timegap,timecol = GPSDateTime,suffix='')
print('运行位置匹配中',end = '')
#利用project方法，将数据点投影至公交线路上
lineshp = line['geometry'].iloc[0]
print('.',end = '')
data['geometry'] = gpd.points_from_xy(data[lon],data[lat])
data = gpd.GeoDataFrame(data)
data.set_crs(crs='epsg:4326',allow_override=True,inplace=True)
print('.',end = '')
data = data.to_crs(epsg = project_epsg)
print('.',end = '')
if method == 'project':
data['project'] = data['geometry'].apply(lambda r:lineshp.project(r))
elif method == 'dislimit':
tmps = []
#改进的匹配方法
for vid in data[VehicleId].drop_duplicates():
print('.',end = '')
tmp = data[data[VehicleId]==vid].copy()
gap = 30
i = 0
tmp = tmp.sort_values(by = [VehicleId,GPSDateTime]).reset_index(drop=True)
tmp['project'] = 0
from shapely.geometry import LineString
for i in range(len(tmp)-1):
if i == 0:
proj = lineshp.project(tmp.iloc[i]['geometry'])
tmp.loc[i,'project'] = proj
else:
proj = tmp['project'].iloc[i]
dis = tmp.iloc[i+1]['geometry'].distance(tmp.iloc[i]['geometry'])
if dis == 0:
proj1 = proj
else:
proj2 = lineshp.project(tmp.iloc[i+1]['geometry'])
if abs(proj2-proj)>dis:
proj1 = np.sign(proj2-proj)*dis+proj
else:
proj1 = proj2
tmp.loc[i+1,'project'] = proj1
tmps.append(tmp)
data = pd.concat(tmps)
print('.',end = '')
#公交站点也进行project
stop = stop.to_crs(epsg = project_epsg)
stop['project'] = stop['geometry'].apply(lambda r:lineshp.project(r))
print('.',end = '')
#标准化时间
starttime = data[GPSDateTime].min()
data['time_st'] = (data[GPSDateTime]-starttime).dt.total_seconds()
BUS_project = data
print('.')
from shapely.geometry import LineString,Polygon
import shapely
#定义一个空的list存储识别结果
ls = []
print('匹配到离站信息...',end = '')
#对每一辆车遍历
for car in BUS_project[VehicleId].drop_duplicates():
print('.',end = '')
#提取车辆轨迹
tmp = BUS_project[BUS_project[VehicleId] == car]
#如果车辆数据点少于1个，则无法构成轨迹
if len(tmp)>1:
#对每一个站点识别
for stopname in stop[stopcol].drop_duplicates():
#提取站点位置
position = stop[stop[stopcol] == stopname]['project'].iloc[0]
#通过缓冲区与线段交集识别到离站轨迹
buffer_polygon = LineString([[0,position],
[data['time_st'].max(),position]]).buffer(stopbuffer)
bus_linestring = LineString(tmp[['time_st','project']].values)
line_intersection = bus_linestring.intersection(buffer_polygon)
#整理轨迹，提取到离站时间
if line_intersection.is_empty:
#如果为空，说明车辆没有到站信息
continue
else:
if type(line_intersection) == shapely.geometry.linestring.LineString:
arrive = [line_intersection]
else:
arrive = list(line_intersection)
arrive = pd.DataFrame(arrive)
arrive['arrivetime']= arrive[0].apply(lambda r:r.coords[0][0])
arrive['leavetime']= arrive[0].apply(lambda r:r.coords[-1][0])
#通过时间阈值筛选到离站信息
a = arrive[['arrivetime']].copy()
a.columns = ['time']
a['flag'] = 1
b = arrive[['leavetime']].copy()
b.columns = ['time']
b['flag'] = 0
c = pd.concat([a,b]).sort_values(by = 'time')
c['time1'] = c['time'].shift(-1)
c['flag_1'] = ((c['time1']-c['time'])<mintime)&(c['flag']==0)
c['flag_2'] = c['flag_1'].shift().fillna(False)
c['flag_3'] = c['flag_1']|c['flag_2']
c = c[-c['flag_3']]
arrive_new = c[c['flag'] == 1][['time']].copy()
arrive_new.columns = ['arrivetime']
arrive_new['leavetime'] = list(c[c['flag'] == 0]['time'])
arrive_new[stopcol] = stopname
arrive_new[VehicleId] = car
#合并数据
ls.append(arrive_new)
#合成一个大表
arrive_info = pd.concat(ls)
arrive_info['arrivetime'] = starttime+arrive_info['arrivetime'].apply(lambda r:pd.Timedelta(int(r),unit = 's'))
arrive_info['leavetime'] = starttime+arrive_info['leavetime'].apply(lambda r:pd.Timedelta(int(r),unit = 's'))
if projectoutput:
return arrive_info,data
else:
return arrive_info
def busgps_onewaytime(arrive_info,start,end,col = ['VehicleId','stopname']):
'''
输入到离站信息表arrive_info与起终点名称，计算单程耗时
输入
-------
arrive_info : DataFrame
公交到离站数据
start : Str
起点站名字
end : Str
终点站名字
col : List
字段列名[车辆ID,站点名称]
输出
-------
onewaytime : DataFrame
公交单程耗时
'''
#上行
#将起终点的信息提取后合并到一起
#终点站的到达时间
[VehicleId,stopname] = col
a = arrive_info[arrive_info[stopname] == end][['arrivetime',stopname,VehicleId]]
#起点站的离开时间
b = arrive_info[arrive_info[stopname] == start][['leavetime',stopname,VehicleId]]
a.columns = ['time',stopname,VehicleId]
b.columns = ['time',stopname,VehicleId]
#合并信息
c = pd.concat([a,b])
#排序后提取每一单程的出行时间
c = c.sort_values(by = [VehicleId,'time'])
for i in c.columns:
c[i+'1'] = c[i].shift(-1)
#提取以申昆路枢纽站为起点，延安东路外滩为终点的趟次
c = c[(c[VehicleId] == c[VehicleId+'1'])&
(c[stopname]==start)&
(c[stopname+'1']==end)]
#计算该趟出行的持续时间
c['duration'] = (c['time1'] - c['time']).dt.total_seconds()
#标识该趟出行的时间中点在哪一个小时
c['shour'] = c['time'].dt.hour
c['方向'] = start+'-'+end
#储存为c1变量
c1 = c.copy()
#下行
a = arrive_info[arrive_info[stopname] == start][['arrivetime',stopname,VehicleId]]
b = arrive_info[arrive_info[stopname] == end][['leavetime',stopname,VehicleId]]
a.columns = ['time',stopname,VehicleId]
b.columns = ['time',stopname,VehicleId]
c = pd.concat([a,b])
c = c.sort_values(by = [VehicleId,'time'])
for i in c.columns:
c[i+'1'] = c[i].shift(-1)
c = c[(c[VehicleId] == c[VehicleId+'1'])&(c[stopname]==end)&(c[stopname+'1']==start)]
c['duration'] = (c['time1'] - c['time']).dt.total_seconds()
c['shour'] = c['time'].dt.hour
c['方向'] = end+'-'+start
c2 = c.copy()
onewaytime = pd.concat([c1,c2])
return onewaytime
|
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|
import sys
import os
sys.path.insert(1, os.path.join(sys.path[0], '..'))
from vaccine_alloc_instance import *
import numpy as np
import random
class RandomInstanceGenerator:
def __init__(self,number_of_instances, n,c,d,q, Q_d_min, Q_d_max, Q_c_min, Q_c_max, p_availability=0.6 ):
self.number_of_instances = number_of_instances
self.Q_d_min = Q_d_min
self.Q_d_max = Q_d_max
self.Q_c_min = Q_c_min
self.Q_c_max =Q_c_max
self.p_availability = p_availability
self.n = n
self.c = c
self.d = d
self.q = q
def generate(self):
random_instances = []
for i in range(self.number_of_instances):
availability = [[np.random.choice([0,1], p=[1-self.p_availability, self.p_availability]) for j in range(self.d) ] for i in range(self.n)]
belongsToCatagory = [[random.randint(0,1) for j in range(self.c)] for i in range(self.n)]
Q_d = [random.randint(self.Q_d_min,self.Q_d_max) for i in range(self.d)]
Q_c = [random.randint(self.Q_c_min,self.Q_c_max) for i in range(self.c)]
Q_cxd = [[random.randint(0,min(Q_d[j],Q_c[i])) for j in range(self.d)] for i in range(self.c)]
#Setting Utility values
U_nxd = []
for i in range(self.n):
delta=random.random()
U_nxd.append([1*(delta**i) for i in range(self.d)])
new_instance = VaccineAllocInstance(self.n,self.c,self.d,self.q,availability,belongsToCatagory,Q_d,Q_c,Q_cxd,U_nxd)
random_instances.append(new_instance)
return random_instances
|
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|
#!/usr/bin/env python
"""
Use forced alignments to separate digit sequences into individual digits.
Author: Herman Kamper
Contact: kamperh@gmail.com
Date: 2018
Edited: Ryan Eloff
Date: June 2018
"""
from __future__ import absolute_import, division, print_function
from os import path
import argparse
import sys
import numpy as np
#-----------------------------------------------------------------------------#
# UTILITY FUNCTIONS #
#-----------------------------------------------------------------------------#
def check_argv():
"""Check the command line arguments."""
parser = argparse.ArgumentParser(
description=__doc__.strip().split("\n")[0], add_help=False
)
parser.add_argument(
"fadir", type=str,
help="Directory containing forced alignments."
)
parser.add_argument(
"outdir", type=str,
help="Diretory to write output individual segment files"
)
parser.add_argument(
"dataset", type=str, choices={"train", "test"},
help="Dataset to obtain segments for."
)
if len(sys.argv) == 1:
parser.print_help()
sys.exit(1)
return parser.parse_args()
#-----------------------------------------------------------------------------#
# MAIN FUNCTION #
#-----------------------------------------------------------------------------#
def main():
args = check_argv()
segments = {}
# Read forced alignment
fa_dir = args.fadir
fa_fn = path.join(fa_dir, args.dataset + "_word_align.ctm")
print("Reading:", fa_fn)
with open(fa_fn) as f:
# Keep track of the number of each digit key added per utterance sequence (in case of duplicates with the same key)
utt_digit_keys = {}
# For each entry in the forced alignments
for line in f:
# Create a segments entry
line = line.split()
utt_key = line[0]
digit_start = float(line[2])
digit_duration = float(line[3])
digit_key = line[4]
# Keep track of the number of each digit key per utterance key (in case of duplicate digit keys in same sequence)
if not utt_key in utt_digit_keys:
utt_digit_keys[utt_key] = {}
if not digit_key in utt_digit_keys[utt_key]:
utt_digit_keys[utt_key][digit_key] = 0
else:
utt_digit_keys[utt_key][digit_key] += 1
# Change digit key to be '<digit_key>a' for first occurence, '<digit_key>b' for second occurence, etc.
digit_key = digit_key + chr(utt_digit_keys[utt_key][digit_key] + ord('a'))
segments[utt_key + "_" + digit_key] = (
# NOTE: Previously used `extract-rows` Kaldi module to extract individual digit features with segment start/end specified in frames (time/[10 ms frame-shift] -> time * 100).
# Kaldi now uses `extract-feature-segments` instead, with segment start/end specified in seconds.
# Integer floor of (time*100) maintained to generate same feature segments as previously.
utt_key, int(np.floor(digit_start*100))/100.0,
int(np.floor((digit_start + digit_duration)*100))/100.0
)
# Write segments
segments_fn = path.join(args.outdir, "segments_indiv")
print("Writing:", segments_fn)
with open(segments_fn, "w") as f:
for segment_key in sorted(segments):
utt_key, digit_start, digit_end = segments[segment_key]
f.write(
"{} {} {} {}\n".format(segment_key, utt_key, digit_start,
digit_end)
)
if __name__ == "__main__":
main()
|
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|
import json
import logging
import os
import shutil
import numpy as np
import torch
from datetime import datetime, timedelta
from torch import nn, optim
from torch.nn import functional as F
from models.fc_model import FCModel
from sklearn.preprocessing import label_binarize
_RNG_SEED = None
def split(a, n):
k, m = divmod(len(a), n)
return (a[i * k + min(i, m):(i + 1) * k + min(i + 1, m)] for i in range(n))
def fix_rng_seed(seed):
"""
Call this function at the beginning of program to fix rng seed.
Args:
seed (int):
Note:
See https://github.com/tensorpack/tensorpack/issues/196.
Example:
Fix random seed in both tensorpack and tensorflow.
.. code-block:: python
import utils
seed = 42
utils.fix_rng_seed(seed)
torch.manual_seed(seed)
if config.cuda: torch.cuda.manual_seed(seed)
# run trainer
"""
global _RNG_SEED
_RNG_SEED = int(seed)
def get_rng(obj=None):
"""
Get a good RNG seeded with time, pid and the object.
Args:
obj: some object to use to generate random seed.
Returns:
np.random.RandomState: the RNG.
"""
seed = (id(obj) + os.getpid() +
int(datetime.now().strftime("%Y%m%d%H%M%S%f"))) % 4294967295
if _RNG_SEED is not None:
seed = _RNG_SEED
return np.random.RandomState(seed)
class RunningAverage():
"""A simple class that maintains the running average of a quantity
Example:
```
loss_avg = RunningAverage()
loss_avg.update(2)
loss_avg.update(4)
loss_avg() = 3
```
"""
def __init__(self):
self.steps = 0
self.total = 0
def update(self, val):
self.total += val
self.steps += 1
def __call__(self):
return self.total/float(self.steps)
def set_logger(log_path):
"""Set the logger to log info in terminal and file `log_path`.
In general, it is useful to have a logger so that every output to the terminal is saved
in a permanent file. Here we save it to `model_dir/train.log`.
Example:
```
logging.info("Starting training...")
```
Args:
log_path: (string) where to log
"""
logger = logging.getLogger()
logger.setLevel(logging.INFO)
if not logger.handlers:
# Logging to a file
file_handler = logging.FileHandler(log_path)
file_handler.setFormatter(logging.Formatter('%(asctime)s:%(levelname)s: %(message)s'))
logger.addHandler(file_handler)
# Logging to console
stream_handler = logging.StreamHandler()
stream_handler.setFormatter(logging.Formatter('%(message)s'))
logger.addHandler(stream_handler)
def save_dict_to_json(d, json_path):
"""Saves dict of floats in json file
Args:
d: (dict) of float-castable values (np.float, int, float, etc.)
json_path: (string) path to json file
"""
def to_float(d):
for k,v in d.items():
if type(v) is dict:
d[k] = to_float(v)
elif isinstance(v, np.float32) or isinstance(v, np.float64):
d[k] = float(v)
elif isinstance(v, list):
d[k] = [float(x) for x in v]
return d
with open(json_path, 'w') as f:
# We need to convert the values to float for json (it doesn't accept np.array, np.float, )
d = to_float(d) #{k: float(v) for k, v in d.items()}
json.dump(d, f, indent=2)
def save_checkpoint(state, is_best, checkpoint, name=None):
"""Saves model and training parameters at checkpoint + 'last.pth.tar'. If is_best==True, also saves
checkpoint + 'best.pth.tar'
Args:
state: (dict) contains model's state_dict, may contain other keys such as epoch, optimizer state_dict
is_best: (bool) True if it is the best model seen till now
checkpoint: (string) folder where parameters are to be saved
"""
filepath = os.path.join(checkpoint, 'last.pth.tar')
if not os.path.exists(checkpoint):
print("Checkpoint Directory does not exist! Making directory {}".format(checkpoint))
os.mkdir(checkpoint)
#else:
#print("Checkpoint Directory exists! ")
torch.save(state, filepath)
if is_best:
save_name = 'best.pth.tar' if name is None else 'best_{}.pth.tar'.format(name)
shutil.copyfile(filepath, os.path.join(checkpoint, save_name))
def load_checkpoint(checkpoint, model, optimizer=None):
"""Loads model parameters (state_dict) from file_path. If optimizer is provided, loads state_dict of
optimizer assuming it is present in checkpoint.
Args:
checkpoint: (string) filename which needs to be loaded
model: (torch.nn.Module) model for which the parameters are loaded
optimizer: (torch.optim) optional: resume optimizer from checkpoint
"""
if not os.path.exists(checkpoint):
raise ValueError("File doesn't exist {}".format(checkpoint))
checkpoint = torch.load(checkpoint)
model.load_state_dict(checkpoint['state_dict'])
if optimizer:
optimizer.load_state_dict(checkpoint['optim_dict'])
return checkpoint
class _ECELoss(nn.Module):
"""
Calculates the Expected Calibration Error of a model.
The input to this loss is the logits of a model, NOT the softmax scores.
This divides the confidence outputs into equally-sized interval bins.
In each bin, we compute the confidence gap:
bin_gap = | avg_confidence_in_bin - accuracy_in_bin |
We then return a weighted average of the gaps, based on the number
of samples in each bin
See: Naeini, Mahdi Pakdaman, Gregory F. Cooper, and Milos Hauskrecht.
"Obtaining Well Calibrated Probabilities Using Bayesian Binning." AAAI.
2015.
"""
def __init__(self, n_bins=15, save_bins=False, save_path=None):
"""
n_bins (int): number of confidence interval bins
"""
super(_ECELoss, self).__init__()
bin_boundaries = torch.linspace(0, 1, n_bins + 1)
self.bin_lowers = bin_boundaries[:-1]
self.bin_uppers = bin_boundaries[1:]
self.save_bins = save_bins
self.save_path = save_path
def forward(self, logits, labels):
softmaxes = F.softmax(logits, dim=1)
confidences, predictions = torch.max(softmaxes, 1)
accuracies = predictions.eq(labels)
ece = torch.zeros(1, device=logits.device)
if self.save_bins:
bin_data = {'bin_lowers': [], 'bin_uppers': [], 'props': [], 'accs': [], 'confs': []}
for bin_lower, bin_upper in zip(self.bin_lowers, self.bin_uppers):
# Calculated |confidence - accuracy| in each bin
in_bin = confidences.gt(bin_lower.item()) * confidences.le(bin_upper.item())
prop_in_bin = in_bin.float().mean()
if prop_in_bin.item() > 0:
accuracy_in_bin = accuracies[in_bin].float().mean()
avg_confidence_in_bin = confidences[in_bin].mean()
ece += torch.abs(avg_confidence_in_bin - accuracy_in_bin) * prop_in_bin
if self.save_bins:
bin_data['bin_lowers'].append(bin_lower.item())
bin_data['bin_uppers'].append(bin_upper.item())
bin_data['props'].append(prop_in_bin.item())
bin_data['accs'].append(accuracy_in_bin.item())
bin_data['confs'].append(avg_confidence_in_bin.item())
if self.save_bins:
save_dict_to_json(bin_data, self.save_path)
return ece
class _CwECELoss(nn.Module):
"""
Calculates the Class-wise Expected Calibration Error of a model.
The input to this loss is the logits of a model, NOT the softmax scores.
This divides the confidence outputs of each class j into equally-sized
interval bins. In each bin, we compute the confidence gap:
bin_gap = | avg_confidence_in_bin_j - accuracy_in_bin_j |
We then return a weighted average of the gaps, based on the number
of samples in each bin
"""
def __init__(self, n_bins=15, avg=True):
"""
n_bins (int): number of confidence interval bins
"""
super(_CwECELoss, self).__init__()
bin_boundaries = torch.linspace(0, 1, n_bins + 1)
self.bin_lowers = bin_boundaries[:-1]
self.bin_uppers = bin_boundaries[1:]
self.avg = avg
def forward(self, logits, labels):
softmaxes = F.softmax(logits, dim=1)
num_classes = logits.shape[1]
cw_ece = torch.zeros(1, device=logits.device)
for j in range(num_classes):
confidences_j = softmaxes[:,j]
ece_j = torch.zeros(1, device=logits.device)
for bin_lower, bin_upper in zip(self.bin_lowers, self.bin_uppers):
in_bin = confidences_j.gt(bin_lower.item()) * confidences_j.le(bin_upper.item())
prop_in_bin = in_bin.float().mean()
if prop_in_bin.item() > 0:
accuracy_j_in_bin = labels[in_bin].eq(j).float().mean()
avg_confidence_j_in_bin = confidences_j[in_bin].mean()
ece_j += torch.abs(avg_confidence_j_in_bin - accuracy_j_in_bin) * prop_in_bin
cw_ece += ece_j
if self.avg:
return cw_ece/num_classes
else:
return cw_ece
# The next two functions are copied from Kull etal implementation
# for testing
def binary_ECE(probs, y_true, power = 1, bins = 15):
idx = np.digitize(probs, np.linspace(0, 1, bins)) - 1
bin_func = lambda p, y, idx: (np.abs(np.mean(p[idx]) - np.mean(y[idx])) ** power) * np.sum(idx) / len(probs)
ece = 0
for i in np.unique(idx):
ece += bin_func(probs, y_true, idx == i)
return ece
def classwise_ECE(probs, y_true, power = 1, bins = 15):
probs = np.array(probs)
if not np.array_equal(probs.shape, y_true.shape):
y_true = label_binarize(np.array(y_true), classes=range(probs.shape[1]))
n_classes = probs.shape[1]
return np.sum(
[
binary_ECE(
probs[:, c], y_true[:, c].astype(float), power = power, bins = bins
) for c in range(n_classes)
]
)
class _CwECELossDir(nn.Module):
"""
Calculates the Class-wise Expected Calibration Error of a model.
The input to this loss is the logits of a model, NOT the softmax scores.
This divides the confidence outputs of each class j into equally-sized
interval bins. In each bin, we compute the confidence gap:
bin_gap = | avg_confidence_in_bin_j - accuracy_in_bin_j |
We then return a weighted average of the gaps, based on the number
of samples in each bin
"""
def __init__(self, n_bins=15):
"""
n_bins (int): number of confidence interval bins
"""
super(_CwECELossDir, self).__init__()
self.n_bins = n_bins
def forward(self, logits, labels):
probs = F.softmax(logits, dim=1).detach().cpu().numpy()
y_true = labels.detach().cpu().numpy()
cwece = classwise_ECE(probs, y_true, bins=self.n_bins)
return torch.tensor(cwece, device=logits.device, dtype=logits.dtype)
class _MCELoss(nn.Module):
"""
Calculates the Maximum Calibration Error of a model.
The input to this loss is the logits of a model, NOT the softmax scores.
This divides the confidence outputs into equally-sized interval bins.
In each bin, we compute the confidence gap:
bin_gap = | avg_confidence_in_bin - accuracy_in_bin |
We then return a maximum of the gaps
See: Naeini, Mahdi Pakdaman, Gregory F. Cooper, and Milos Hauskrecht.
"Obtaining Well Calibrated Probabilities Using Bayesian Binning." AAAI.
2015.
"""
def __init__(self, n_bins=15):
"""
n_bins (int): number of confidence interval bins
"""
super(_MCELoss, self).__init__()
bin_boundaries = torch.linspace(0, 1, n_bins + 1)
self.bin_lowers = bin_boundaries[:-1]
self.bin_uppers = bin_boundaries[1:]
def forward(self, logits, labels):
softmaxes = F.softmax(logits, dim=1)
confidences, predictions = torch.max(softmaxes, 1)
accuracies = predictions.eq(labels)
cal_errors = []
for bin_lower, bin_upper in zip(self.bin_lowers, self.bin_uppers):
# Calculated |confidence - accuracy| in each bin
in_bin = confidences.gt(bin_lower.item()) * confidences.le(bin_upper.item())
prop_in_bin = in_bin.float().mean()
if prop_in_bin.item() > 0:
accuracy_in_bin = accuracies[in_bin].float().mean()
avg_confidence_in_bin = confidences[in_bin].mean()
cal_errors.append(torch.abs(avg_confidence_in_bin - accuracy_in_bin))
return torch.max(torch.stack(cal_errors))
class _BrierLoss(nn.Module):
"""
Calculates the Brier Error of a model.
The input to this loss is the logits of a model, NOT the softmax scores.
We then return a mean square of the gaps between one-hot labels and the
predicted scores.
"""
def __init__(self):
"""
"""
super(_BrierLoss, self).__init__()
def forward(self, logits, labels):
softmaxes = F.softmax(logits, dim=1)
labels_onehot = torch.zeros_like(softmaxes)
labels_onehot.scatter_(1, labels[...,None], 1)
diff = (labels_onehot - softmaxes)
diff = diff *diff
return diff.mean()
class _MSODIRLoss(object):
"""
Calculates the nll + Matrix scaling off-diagonal andbias regularization
of a model.
The input to this loss is the logits of a model, NOT the softmax scores.
NOTE: This loss only works with FCModel with zero hidden
layers (i.e., num_hiddens is [])
"""
def __init__(self, model, weight_lambda=5e-4, bias_mu=5e-4):
"""
Args:
model: (models.fc_model.FCModel object)
weight_lambda: regularization weight for weights of fc in model
bias_mu: regularization weights for bias of fc in model
"""
super(_MSODIRLoss, self).__init__()
assert(isinstance(model, FCModel))
assert(len(model.num_hiddens) == 0)
self.model = model
self.ce = nn.CrossEntropyLoss()
self.weight_lambda = weight_lambda
self.bias_mu = bias_mu
def __call__(self, logits, labels):
ce_part = self.ce(logits, labels)
weight_part = (1 -
torch.eye(self.model.fc.weight.shape[0])
).to(logits.device)*self.model.fc.weight
weight_part = torch.sum(weight_part*weight_part)
if self.model.fc.bias is not None:
bias_part = torch.sum(self.model.fc.bias * self.model.fc.bias)
else:
bias_part=0
return ce_part + self.weight_lambda * weight_part + self.bias_mu * bias_part
class EarlyStopping:
"""Early stops the training if validation loss doesn't improve after a given patience."""
def __init__(self, patience=7, verbose=False, delta=0):
"""
Args:
patience (int): How long to wait after last time validation loss improved.
Default: 7
verbose (bool): If True, prints a message for each validation loss improvement.
Default: False
delta (float): Minimum change in the monitored quantity to qualify as an improvement.
Default: 0
"""
self.patience = patience
self.verbose = verbose
self.counter = 0
self.best_score = None
self.early_stop = False
self.val_loss_min = np.Inf
self.delta = delta
def __call__(self, val_loss, model):
score = -val_loss
if self.best_score is None:
self.best_score = score
self.save_checkpoint(val_loss, model)
elif score < self.best_score + self.delta:
self.counter += 1
print('EarlyStopping counter: {} out of {}'.format(self.counter, self.patience))
if self.counter >= self.patience:
self.early_stop = True
else:
self.best_score = score
self.save_checkpoint(val_loss, model)
self.counter = 0
def save_checkpoint(self, val_loss, model):
'''Saves model when validation loss decrease.'''
if self.verbose:
print('Validation loss decreased ({:.6f} --> {:.6f}). Saving model ...'.format(self.val_loss_min, self.val_loss_min))
torch.save(model.state_dict(), 'checkpoint.pt')
self.val_loss_min = val_loss
|
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|
import numpy as np
import scipy.optimize
import warnings
def calc_weights(cov, x0=None, options=None, scale_factor=10000,
pcr_tolerance=0.001, ignore_objective=False):
"""
Calculate the weights associated with the equal risk contribution
portfolio. Refer to "On the Properties of Equally-Weighted Risk
Contributions Portfolios" by Maillard, Roncalli, and Teiletche for
definitions.
Parameters
----------
cov: numpy.ndarray
(N, N) covariance matrix of assets, must be positive definite
x0: numpy.ndarray
(N,) initial solution guess. If None is given uses inverse of standard
deviation regularized to be between 0 and 1.
options: dictionary
A dictionary of solver options. See scipy.optimize.minimize.
scale_factor: float
Number to scale the optimization function by, can be helpful for
convergence
pcr_tolerance: float
The max allowable tolerance for differences in the PCR coming from
different assets in decimal terms, e.g. 1% would be 0.01
ignore_objective: False
Provided the max difference in PCR satifies pcr_tolerance, ignore
whether the objective function has converged. See Notes below.
Returns
-------
w: numpy.ndarray
(N,) array of asset weights
Notes:
------
The objective function from the paper embodies but is not exactly
the same as the desired result, which is to have equal risk contributions
in terms of PCR for each asset. As a result, there are scenarios where the
maxiter will be exceeded (i.e. non convergence) when in fact the goal of
having equal risk contributions within some acceptable tolerance has been
achieved. In these scenaries playing around with 'ftol' and 'maxiter' in
'options' and 'scale_factor' is helpful. The objective function can also
be ignored using ignore_objective=True, meaning the weights will be
returned provided the max PCR tolerance is satiesfied even if the objective
has not converged. See https://github.com/matthewgilbert/erc/issues/1
"""
# check matrix is PD
np.linalg.cholesky(cov)
if not options:
options = {'ftol': 1e-20, 'maxiter': 800}
def fun(x):
# these are non normalized risk contributions, i.e. not regularized
# by total risk, seems to help numerically
risk_contributions = x.dot(cov) * x
a = np.reshape(risk_contributions, (len(risk_contributions), 1))
# broadcasts so you get pairwise differences in risk contributions
risk_diffs = a - a.transpose()
sum_risk_diffs_squared = np.sum(np.square(np.ravel(risk_diffs)))
# https://stackoverflow.com/a/36685019/1451311
return sum_risk_diffs_squared / scale_factor
N = cov.shape[0]
if x0 is None:
x0 = 1 / np.sqrt(np.diag(cov))
x0 = x0 / x0.sum()
bounds = [(0, 1) for i in range(N)]
constraints = {'type': 'eq', 'fun': lambda x: np.sum(x) - 1}
res = scipy.optimize.minimize(fun, x0, method='SLSQP', bounds=bounds,
constraints=constraints,
options=options)
weights = res.x
risk_squared = weights.dot(cov).dot(weights)
pcrs = weights.dot(cov) * weights / risk_squared
pcrs = np.reshape(pcrs, (len(pcrs), 1))
pcr_max_diff = np.max(np.abs(pcrs - pcrs.transpose()))
if not res.success:
if ignore_objective and (pcr_max_diff < pcr_tolerance):
return weights
else:
msg = ("Max difference in percentage contribution to risk "
"in decimals is {0:.2E}, "
"tolerance is {1:.2E}".format(pcr_max_diff, pcr_tolerance))
warnings.warn(msg)
raise RuntimeError(res)
if pcr_max_diff > pcr_tolerance:
raise RuntimeError("Max difference in percentage contribution to risk "
"in decimals is %s which exceeds tolerance of %s." %
(pcr_max_diff, pcr_tolerance))
return weights
|
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|
\section{Croissant}
\label{croissant}
\setcounter{secnumdepth}{0}
Time: 9 hours (30 minutes prep, 7+ hours inactive rising and resting, 18 minutes baking)
Serves: 12 pastries, 6-12 people, depending on generosity
\begin{multicols}{2}
\subsection*{Ingredients}
\begin{itemize}
\item 1 recipe of \nameref{viennoiserie}
\item Flour to roll out dough
\item Butter for cooking sheet
\item 1 egg
\item 1 teaspoon water
\end{itemize}
\subsection*{Hardware}
\begin{itemize}
\item Large surface for rolling
\item Knife with which to cut pastry
\item Baking sheet
\end{itemize}
\clearpage
\subsection*{Instructions}
\begin{enumerate}
\item Make the \nameref{viennoiserie} dough if you have not yet, this will take roughly 8 hours.
\item Take the recently chilled and turned dough from the fridge.
\item Lightly flour your surface.
\item Lightly butter your baking sheet.
\item Roll out the dough into a rectangle, about 24x6 inches.
\item Cut the rectangle in half, so you have two 12x6 inch rectanlges.
\item Cover and chill one rectangle while working with the other.
\item Roll the remaining rectangle into a longer rectangle, about 15x7 inches.
\item Cut the rectangle into three rectangles, each about 5x7 inches.
\item Take one rectangle, roll it into a square, about 6x6 inches.
\item Cut the rectangle along the diagonal.
\item Take one of these right triangles and flare out the two shorter corners to match.
\item Take the two shorter corners and roll the pastry up towards the long point.
\item Tuck the end of the long point under the now-rolled pastry and bend the ends to shape into a crescent.
\item Place the pastry on a baking sheet, do not crowd the pastries.
\item Repeat with each triangle and rectangle to get 12 croissants.
\item Make an egg wash by mixing 1 egg and 1 teaspoon water in a small bowl.
\item Once all croissants are on baking sheet, cover lightly in egg wash.
\item Pre-heat oven to 455F.
\item Allow croissants to rise (double in size) on the baking sheet.
\item Place baking sheet in oven, and allow to bake until golden brown, about 18-20 minutes.
\item Allow croissants to cool 10-15 minutes before consuming.
\end{enumerate}
\subsection*{Notes}
\begin{itemize}
\item This is based on the Croissant recipe of Julia Child and Simone Beck, as seen in Mastering the Art of French Cooking, Volume 2, page 96 and on "The French Chef", episode 1.
\begin{itemize}
\item Main difference is in my \nameref{viennoiserie} dough, I have different pastry flour, and use of weights over volumetric measurements.
\end{itemize}
\item While the book gives a lot of detail, watching someone shape the dough is the best way to learn. I recommend watching the episode, which can be seen here: \url{https://www.youtube.com/watch?v=uZmrvEfhfsg}.
\item This recipe, while French in origin, is based on American ingredients and ovens.
\item While croissants are best served fresh, they can be frozen after cooling completely. Seal in an air-tight container in the freezer.
\item To reheat, place on a lightly buttered baking sheet into a 400F pre-heated oven for about 10 minutes.
\item I often make by dough then make half \nameref{painAuChocolat} and half croissant.
\item A huge thanks to Nicolas Bidron and Nicolas Guigo for inspiring my French baking.
\end{itemize}
\end{multicols}
\clearpage
|
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|
# -*- coding: utf-8 -*-
"""
Created on Thu Jun 3 17:29:01 2021
@author: Luigi
"""
import allMethods as fz
#import funzioni_zeri as fz
import numpy as np
import sympy as sym
import sympy.utilities.lambdify
x = sym.symbols("x")
fx = x**3 + x**2 - 33*x + 63
dfx = sym.diff(fx, x, 1)
f = sym.lambdify(x, fx, np)
df = sym.lambdify(x, dfx, np)
x0 = 1
tolx = 1e-12
tolf = tolx
xkNew, itNew, xksNew = fz.newton(f, df, x0, tolx, tolf, 500)
xkNewM, itNewM, xksNewM = fz.newtonModificato(f, df, x0, tolx, tolf, 500, 2)
convNewton = fz.stimaOrdine(xksNew, itNew-1)
convMod = fz.stimaOrdine(xksNewM, itNewM-1)
print(f"Newton normale -> {convNewton}")
print(f"Newton modificato -> {convMod}")
|
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|
__doc__ = """Timoshenko beam validation case, for detailed explanation refer to
Gazzola et. al. R. Soc. 2018 section 3.4.3 """
import numpy as np
import sys
# FIXME without appending sys.path make it more generic
sys.path.append("../../")
from elastica import *
from examples.TimoshenkoBeamCase.timoshenko_postprocessing import plot_timoshenko
class TimoshenkoBeamSimulator(BaseSystemCollection, Constraints, Forcing):
pass
timoshenko_sim = TimoshenkoBeamSimulator()
final_time = 5000
# Options
PLOT_FIGURE = True
SAVE_FIGURE = False
SAVE_RESULTS = False
ADD_UNSHEARABLE_ROD = False
# setting up test params
n_elem = 100
start = np.zeros((3,))
direction = np.array([0.0, 0.0, 1.0])
normal = np.array([0.0, 1.0, 0.0])
base_length = 3.0
base_radius = 0.25
base_area = np.pi * base_radius ** 2
density = 5000
nu = 0.1
E = 1e6
# For shear modulus of 1e4, nu is 99!
poisson_ratio = 99
shearable_rod = CosseratRod.straight_rod(
n_elem,
start,
direction,
normal,
base_length,
base_radius,
density,
nu,
E,
poisson_ratio,
)
timoshenko_sim.append(shearable_rod)
timoshenko_sim.constrain(shearable_rod).using(
OneEndFixedRod, constrained_position_idx=(0,), constrained_director_idx=(0,)
)
end_force = np.array([-15.0, 0.0, 0.0])
timoshenko_sim.add_forcing_to(shearable_rod).using(
EndpointForces, 0.0 * end_force, end_force, ramp_up_time=final_time / 2.0
)
if ADD_UNSHEARABLE_ROD:
# Start into the plane
unshearable_start = np.array([0.0, -1.0, 0.0])
unshearable_rod = CosseratRod.straight_rod(
n_elem,
unshearable_start,
direction,
normal,
base_length,
base_radius,
density,
nu,
E,
# Unshearable rod needs G -> inf, which is achievable with -ve poisson ratio
poisson_ratio=-0.7,
)
timoshenko_sim.append(unshearable_rod)
timoshenko_sim.constrain(unshearable_rod).using(
OneEndFixedRod, constrained_position_idx=(0,), constrained_director_idx=(0,)
)
timoshenko_sim.add_forcing_to(unshearable_rod).using(
EndpointForces, 0.0 * end_force, end_force, ramp_up_time=final_time / 2.0
)
timoshenko_sim.finalize()
timestepper = PositionVerlet()
# timestepper = PEFRL()
dl = base_length / n_elem
dt = 0.01 * dl
total_steps = int(final_time / dt)
print("Total steps", total_steps)
integrate(timestepper, timoshenko_sim, final_time, total_steps)
if PLOT_FIGURE:
plot_timoshenko(shearable_rod, end_force, SAVE_FIGURE, ADD_UNSHEARABLE_ROD)
if SAVE_RESULTS:
import pickle
filename = "Timoshenko_beam_data.dat"
file = open(filename, "wb")
pickle.dump(shearable_rod, file)
file.close()
|
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|
Require Import Crypto.Arithmetic.PrimeFieldTheorems.
Require Import Crypto.Specific.montgomery64_2e416m2e208m1_7limbs.Synthesis.
(* TODO : change this to field once field isomorphism happens *)
Definition add :
{ add : feBW_small -> feBW_small -> feBW_small
| forall a b, phiM_small (add a b) = F.add (phiM_small a) (phiM_small b) }.
Proof.
Set Ltac Profiling.
Time synthesize_add ().
Show Ltac Profile.
Time Defined.
Print Assumptions add.
|
{"author": "anonymous-code-submission-01", "repo": "sp2019-54-code", "sha": "8867f5bed0821415ec99f593b1d61f715ed4f789", "save_path": "github-repos/coq/anonymous-code-submission-01-sp2019-54-code", "path": "github-repos/coq/anonymous-code-submission-01-sp2019-54-code/sp2019-54-code-8867f5bed0821415ec99f593b1d61f715ed4f789/src/Specific/montgomery64_2e416m2e208m1_7limbs/feadd.v"}
|
#!/usr/bin/env python
# -*- coding: latin1 -*-
import scipy as sp
import matplotlib.pyplot as plt
# Get data from external file
file = "./data/web_traffic.tsv"
data = sp.genfromtxt(file, delimiter="\t")
# all examples will have three classes in this file
colors = ['g', 'k', 'b', 'm', 'r']
linestyles = ['-', '-.', '--', ':', '-']
# Divide into two lists
x = data[:, 0]
y = data[:, 1]
# Remove NAN
x = x[~sp.isnan(y)]
y = y[~sp.isnan(y)]
fx = sp.linspace(0, x[-1], 1000)
# Plot data in graphics
def plot_data(x, y, models, mx=None, ymax=None, xmin=None):
plt.clf()
plt.scatter(x, y, s=10)
plt.title("Web traffic over the last month")
plt.xlabel("Time")
plt.ylabel("Hits/hour")
plt.xticks([w * 7 * 24 for w in range(10)], ['week %i' % w for w in range(10)])
if models:
if mx is None:
mx = sp.linspace(0, x[-1], 1000)
for model, style, color in zip(models, linestyles, colors):
plt.plot(mx, model(mx), linestyle=style, linewidth=2, c=color)
plt.legend(["d=%i" % m.order for m in models], loc="upper left")
plt.autoscale(tight=True)
plt.ylim(ymin=0)
if ymax:
plt.ylim(ymax=ymax)
if xmin:
plt.xlim(xmin=xmin)
plt.grid(True, linestyle='-', color='0.75')
plt.show()
# Divide data into two blocks, separated at 3.5 weeks
inflection = 3.5 * 7 * 24
xa = x[:inflection] # before the inflection point
ya = y[:inflection]
xb = x[inflection:] # after the inflection point
yb = y[inflection:]
fa = sp.poly1d(sp.polyfit(xa, ya, 1))
fb = sp.poly1d(sp.polyfit(xb, yb, 1))
plot_data(x, y, [fa, fb])
|
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|
# -*- coding: utf-8 -*-
""" TODO:
"""
import numpy as np
from scipy import interpolate
def t_list(mb_solve, speed_of_light):
""" Return the time points shifted to the fixed (lab) frame of
reference given a speed-of-light.
Args:
mb_solve: An MBSolve object
speed_of_light: The speed of light in the system.
Returns:
Array of time values in the fixed frame of reference.
"""
t_scale = 1.0 + mb_solve.z_max/(speed_of_light * mb_solve.t_max)
return mb_solve.tlist*t_scale
def rabi_freq(mb_solve, field_idx, speed_of_light, part='real',
interp_kind='linear'):
""" Return the field results shifted to the fixed (lab) frame of reference
given a speed-of-light by interpolation. Can return the real part or
abs value.
Args:
mb_solve: An MBSolve object
field_idx: The field to return
speed_of_light: The speed of light in the system
part: Which part of the complex field ('real' or 'abs')
interp_kind: The kind of spline interpolation to use ('linear',
'cubic' or 'quintic')
Returns:
Array[num_fields, num_space_points, num_time_points] of field
values in the fixed frame of reference.
"""
if part == 'abs':
rabi_freq_zt = np.abs(mb_solve.Omegas_zt[field_idx])
elif part == 'real':
rabi_freq_zt = np.real(mb_solve.Omegas_zt[field_idx])
else:
raise ValueError('Invalid part. Try "abs" or "real"')
rabi_freq_intp = interpolate.interp2d(mb_solve.tlist, mb_solve.zlist,
rabi_freq_zt, bounds_error=False, fill_value=0., kind=interp_kind)
rabi_freq_fixed = np.zeros(mb_solve.Omegas_zt[field_idx].shape,
dtype=np.float)
for i, z_i in enumerate(mb_solve.zlist):
rabi_freq_fixed[i] = rabi_freq_intp(t_list(mb_solve, speed_of_light) -
z_i / speed_of_light, z_i)
return rabi_freq_fixed
def rabi_freq_abs(mb_solve, field_idx, speed_of_light, interp_kind='linear'):
""" DEPRECATED. Use rabi_freq with part='abs'. Return the absolute value of
the complex solved field results shifted to the fixed (lab) frame of
reference given a speed-of-light by interpolation.
Args:
mb_solve: An MBSolve object
field_idx: The field to return
speed_of_light: The speed of light in the system
interp_kind: The kind of spline interpolation to use ('linear',
'cubic' or 'quintic')
Returns:
Array[num_fields, num_space_points, num_time_points] of field
values in the fixed frame of reference.
"""
return rabi_freq(mb_solve, field_idx, speed_of_light, part='abs',
interp_kind=interp_kind)
|
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|
# Created by Dennis Willsch (d.willsch@fz-juelich.de)
# Modified by Gabriele Cavallaro (g.cavallaro@fz-juelich.de)
import os
import sys
import re
import numpy as np
import numpy.lib.recfunctions as rfn
import matplotlib.pyplot as plt
from utils import *
import shutil
import pickle
import numpy.lib.recfunctions as rfn
from dwave.system.samplers import DWaveSampler
from dwave.system.composites import EmbeddingComposite
from dimod import BinaryQuadraticModel
def gen_svm_qubos(B,K,xi,gamma,path_data_key,data_key,path_out):
data,label = loaddataset(path_data_key+data_key)
N = len(data)
Q = np.zeros((K*N,K*N))
print(f'Creating the QUBO of size {Q.shape}')
for n in range(N): # not optimized: size will not be so large and this way its more easily verifyable
for m in range(N):
for k in range(K):
for j in range(K):
Q[K*n+k,K*m+j] = .5 * B**(k+j) * label[n] * label[m] * (kernel(data[n], data[m], gamma) + xi)
if n == m and k == j:
Q[K*n+k,K*m+j] += - B**k
print(f'Extracting nodes and couplers')
Q = np.triu(Q) + np.tril(Q,-1).T # turn the symmetric matrix into upper triangular
qubo_nodes = np.asarray([[n, n, Q[n,n]] for n in range(len(Q))]) # if not np.isclose(Q[n,n],0)]) NOTE: removed due to variable order!
qubo_couplers = np.asarray([[n, m, Q[n,m]] for n in range(len(Q)) for m in range(n+1,len(Q)) if not np.isclose(Q[n,m],0)])
qubo_couplers = qubo_couplers[np.argsort(-np.abs(qubo_couplers[:,2]))]
#path = f'runs/run{data_key}_B={B}_K={K}_xi={xi}_gamma={gamma}/'
path = f'{path_out}run{data_key}_B={B}_K={K}_xi={xi}_gamma={gamma}/'
print(f'Saving {len(qubo_nodes)} nodes and {len(qubo_couplers)} couplers for {path}')
os.makedirs(path, exist_ok=True)
np.save(path+'Q.npy', Q)
np.savetxt(path+'qubo_nodes.dat', qubo_nodes, fmt='%g', delimiter='\t')
np.savetxt(path+'qubo_couplers.dat', qubo_couplers, fmt='%g', delimiter='\t')
return path
def dwave_run(path_data_key,path_in):
MAXRESULTS = 20 # NOTE: to save space only 20 best results
match = re.search('run([^/]*)_B=(.*)_K=(.*)_xi=(.*)_gamma=([^/]*)', path_in)
data_key = match.group(1)
B = int(match.group(2))
K = int(match.group(3))
xi = float(match.group(4))
gamma = float(match.group(5))
data,label = loaddataset(path_data_key+data_key)
path = path_in+ ('/' if path_in[-1] != '/' else '')
qubo_couplers = np.loadtxt(path+'qubo_couplers.dat')
qubo_nodes = np.loadtxt(path+'qubo_nodes.dat')
qubo_nodes = np.array([[i,i,(qubo_nodes[qubo_nodes[:,0]==i,2][0] if i in qubo_nodes[:,0] else 0.)] for i in np.arange(np.concatenate((qubo_nodes,qubo_couplers))[:,[0,1]].max()+1)]) # to make sure every (i,i) occurs in the qubo in increasing order such that the variable order in BinaryQuadraticModel is consistent (see locate wrongenergies-* github issue)
maxcouplers = len(qubo_couplers) ## POSSIBLE INPUT if len(sys.argv) <= 2 else int(sys.argv[2])
if not 'train' in data_key:
raise Exception(f'careful: datakey={data_key} => youre trying to train on a validation / test set!')
couplerslist = [maxcouplers]
for trycouplers in [2500, 2000, 1800, 1600, 1400, 1200, 1000, 500]:
if maxcouplers > trycouplers:
couplerslist += [trycouplers]
sampler = EmbeddingComposite(DWaveSampler())
for couplers in couplerslist: # try to reduce as little couplers as necessary to find an embedding
Q = { (q[0], q[1]): q[2] for q in np.vstack((qubo_nodes, qubo_couplers[:couplers])) }
pathsub = path + f'result_couplers={couplers}/'
os.makedirs(pathsub, exist_ok=True)
print(f'running {pathsub} with {len(qubo_nodes)} nodes and {couplers} couplers')
ordering = np.array(list(BinaryQuadraticModel.from_qubo(Q)))
if not (ordering == np.arange(len(ordering),dtype=ordering.dtype)).all():
print(f'WARNING: variables are not correctly ordered! path={path} ordering={ordering}')
try:
response = sampler.sample_qubo(Q, num_reads=10000) # NOTE: it may be worth trying to pass additional parameters such as
# annealing_time and chain_strength here. Regarding the latter,
# if the scale of the Qij is very different from 1, one should not use
# the default chain_strength=1 for the embedding here because the
# embedding would not use properly scaled strengths to tie physical qubits together
# (This will show up in a large chain_break_fraction)
# Instead, a useful approach is to set
# chain_strength = r * max(abs(Qij))
# for r = 3.0, 2.5, 2.0, 1.5, 1.0, 0.9, 0.8, ...
# until the best chain_strength is found.
except ValueError as v:
print(f' -- no embedding found, removing {pathsub} and trying less couplers')
shutil.rmtree(pathsub)
continue
break
save_json(pathsub+'info.json', response.info) # contains response.info
#NOTE left out: pickle.dump(response, open(pathsub+'response.pkl','wb')) # contains full response incl. response.record etc; can be loaded with pickle.load(open('response.pkl','rb'))
samples = np.array([''.join(map(str,sample)) for sample in response.record['sample']]) # NOTE: it would be safer to use the labeling from record.data() for the qubit variable order
unique_samples, unique_idx, unique_counts = np.unique(samples, return_index=True, return_counts=True) # unfortunately, num_occurrences seems not to be added up after unembedding
unique_records = response.record[unique_idx]
result = rfn.merge_arrays((unique_samples, unique_records['energy'], unique_counts, unique_records['chain_break_fraction'])) # see comment on chain_strength above
result = result[np.argsort(result['f1'])]
np.savetxt(pathsub+'result.dat', result[:MAXRESULTS], fmt='%s', delimiter='\t', header='\t'.join(response.record.dtype.names), comments='') # load with np.genfromtxt(..., dtype=['<U2000',float,int,float], names=True, encoding=None)
alphas = np.array([decode(sample,B,K) for sample in result['f0'][:MAXRESULTS]])
np.save(pathsub+f'alphas{data_key}_gamma={gamma}.npy', alphas)
return pathsub
def eval_run_trainaccuracy(path_in):
regex = 'run([^/]*)_B=(.*)_K=(.*)_xi=(.*)_gamma=([^/]*)/result_couplers.*/?$'
match = re.search(regex, path_in)
path = path_in + ('/' if path_in[-1] != '/' else '')
data_key = match.group(1)
B = int(match.group(2))
K = int(match.group(3))
xi = float(match.group(4))
gamma = float(match.group(5))
data,label = loaddataset(data_key)
alphas_file = path+f'alphas{data_key}_gamma={gamma}.npy'
if not os.path.isfile(alphas_file):
print('result '+alphas_file+' doesnt exist, exiting')
sys.exit(-1)
alphas = np.atleast_2d(np.load(alphas_file))
nalphas = len(alphas)
assert len(data) == alphas.shape[1], "alphas do not seem to be for the right data set?)"
result = np.genfromtxt(path+'result.dat', dtype=['<U2000',float,int,float], names=True, encoding=None, max_rows=nalphas)
Cs = [100, 10, (B**np.arange(K)).sum(), 1.5]
evaluation = np.zeros(nalphas, dtype=[('sum_antn',float)]+[(f'acc(C={C})',float) for C in Cs])
for n,alphas_n in enumerate(alphas):
evaluation[n]['sum_antn'] = (label * alphas_n).sum()
for j,field in enumerate(evaluation.dtype.names[1:]):
b = eval_offset_avg(alphas_n, data, label, gamma, Cs[j]) # NOTE: this is NAN if no support vectors were found, see TODO file
label_predicted = np.sign(eval_classifier(data, alphas_n, data, label, gamma, b)) # NOTE: this is only train accuracy! (see eval_result_roc*)
evaluation[n][field] = (label == label_predicted).sum() / len(label)
result_evaluated = rfn.merge_arrays((result,evaluation), flatten=True)
fmt = '%s\t%.3f\t%d\t%.3f' + '\t%.3f'*len(evaluation.dtype.names)
#NOTE: left out
# np.savetxt(path+'result_evaluated.dat', result_evaluated, fmt=fmt, delimiter='\t', header='\t'.join(result_evaluated.dtype.names), comments='') # load with np.genfromtxt(..., dtype=['<U2000',float,int,float,float,float,float,float], names=True, encoding=None)
print(result_evaluated.dtype.names)
print(result_evaluated)
def eval_run_rocpr_curves(path_data_key,path_in,plotoption):
regex = 'run([^/]*)_B=(.*)_K=(.*)_xi=(.*)_gamma=([^/]*)/result_couplers.*/?$'
match = re.search(regex, path_in)
path = path_in + ('/' if path_in[-1] != '/' else '')
data_key = match.group(1)
B = int(match.group(2))
K = int(match.group(3))
xi = float(match.group(4))
gamma = float(match.group(5))
data,label = loaddataset(path_data_key+data_key)
dwavesolutionidx=0
C=(B**np.arange(K)).sum()
if 'calibtrain' in data_key:
testname = 'Validation'
datatest,labeltest = loaddataset(path_data_key+data_key.replace('calibtrain','calibval'))
else:
print('be careful: this does not use the aggregated bagging classifier but only the simple one as in calibration')
testname = 'Test'
datatest,labeltest = loaddataset(re.sub('train(?:set)?[0-9]*(?:bag)[0-9]*','test',data_key))
alphas_file = path+f'alphas{data_key}_gamma={gamma}.npy'
if not os.path.isfile(alphas_file):
print('result '+alphas_file+' doesnt exist, exiting')
sys.exit(-1)
alphas = np.atleast_2d(np.load(alphas_file))
nalphas = len(alphas)
assert len(data) == alphas.shape[1], "alphas do not seem to be for the right data set?)"
print('idx \tsum_antn\ttrainacc\ttrainauroc\ttrainauprc\ttestacc \ttestauroc\ttestauprc')
trainacc_all=np.zeros([nalphas])
trainauroc_all=np.zeros([nalphas])
trainauprc_all=np.zeros([nalphas])
testacc_all=np.zeros([nalphas])
testauroc_all=np.zeros([nalphas])
testauprc_all=np.zeros([nalphas])
for i in range(nalphas):
alphas_n = alphas[i]
b = eval_offset_avg(alphas_n, data, label, gamma, C) # NOTE: this is NAN if no support vectors were found, see TODO file
score = eval_classifier(data, alphas_n, data, label, gamma, b)
scoretest = eval_classifier(datatest, alphas_n, data, label, gamma, b)
trainacc,trainauroc,trainauprc = eval_acc_auroc_auprc(label,score)
testacc,testauroc,testauprc = eval_acc_auroc_auprc(labeltest,scoretest)
trainacc_all[i]=trainacc
trainauroc_all[i]=trainauroc
trainauprc_all[i]=trainauprc
testacc_all[i]=testacc
testauroc_all[i]=testauroc
testauprc_all[i]=testauprc
print(f'{i}\t{(label*alphas_n).sum():8.4f}\t{trainacc:8.4f}\t{trainauroc:8.4f}\t{trainauprc:8.4f}\t{testacc:8.4f}\t{testauroc:8.4f}\t{testauprc:8.4f}')
# plot code starts here
if plotoption != 'noplotsave':
alphas_n = alphas[dwavesolutionidx] # plot only the requested
b = eval_offset_avg(alphas_n, data, label, gamma, C) # NOTE: this is NAN if no support vectors were found, see TODO file
score = eval_classifier(data, alphas_n, data, label, gamma, b)
scoretest = eval_classifier(datatest, alphas_n, data, label, gamma, b)
# roc curve
plt.figure(figsize=(6.4,3.2))
plt.subplot(1,2,1)
plt.subplots_adjust(top=.95, right=.95, bottom=.15, wspace=.3)
fpr, tpr, thresholds = roc_curve(labeltest, scoretest)
auroc = roc_auc_score(labeltest, scoretest)
plt.plot(fpr, tpr, label='AUROC=%0.3f' % auroc, color='g')
plt.fill_between(fpr, tpr, alpha=0.2, color='g', step='post')
plt.plot([0, 1], [0, 1], 'k--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.0])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
#plt.title('Receiver Operating Curve')
plt.legend(loc="lower right")
# pr curve
plt.subplot(1,2,2)
precision, recall, _ = precision_recall_curve(labeltest, scoretest)
auprc = auc(recall, precision)
plt.step(recall, precision, color='g', where='post',
label='AUPRC=%0.3f' % auprc)
plt.fill_between(recall, precision, alpha=0.2, color='g', step='post')
plt.xlabel('Recall')
plt.ylabel('Precision')
plt.ylim([0.0, 1.0])
plt.xlim([0.0, 1.0])
#plt.title('PR curve')
plt.legend(loc="lower right")
# save the data for gnuplot
savename = f'{path.replace("/","_")}{dwavesolutionidx}'
#with open('results/rocpr_curves/'+savename,'w') as out:
with open(path_in+savename,'w') as out:
out.write(f'AUROC\t{auroc:0.3f}\t# ROC:FPR,TPR\n')
assert len(fpr) == len(tpr)
for i in range(len(fpr)):
out.write(f'{fpr[i]}\t{tpr[i]}\n')
out.write(f'\n\nAUPRC\t{auprc:0.3f}\t# PRC:Recall,Precision\n')
assert len(recall) == len(precision)
for i in range(len(recall)):
out.write(f'{recall[i]}\t{precision[i]}\n')
print(f'saved data for {savename}')
if plotoption == 'saveplot':
savefigname = path_in+savename+'.pdf'
plt.savefig(savefigname)
print(f'saved as {savefigname}')
else:
plt.show()
return np.average(trainacc_all), np.average(trainauroc_all), np.average(trainauprc_all) ,np.average(testacc_all), np.average(testauroc_all), np.average(testauprc_all)
def predict(path_data_key,path_in,datatest):
regex = 'run([^/]*)_B=(.*)_K=(.*)_xi=(.*)_gamma=([^/]*)/result_couplers.*/?$'
match = re.search(regex, path_in)
path = path_in + ('/' if path_in[-1] != '/' else '')
data_key = match.group(1)
B = int(match.group(2))
K = int(match.group(3))
xi = float(match.group(4))
gamma = float(match.group(5))
data,label = loaddataset(path_data_key+data_key)
C=(B**np.arange(K)).sum()
# Load the alphas (20xnumber of samples)
#alphas=np.load(path_files[y]+f'alphas{data_key}{i}_{y}_gamma={gamma}.npy')
alphas = np.atleast_2d(np.load(path_in+f'alphas{data_key}_gamma={gamma}.npy'))
nalphas = len(alphas)
#print(nalphas)
# Compute the mean of the alphas
alphas_avg=np.mean(alphas,axis=0)
b = eval_offset_avg(alphas_avg, data, label, gamma, C) # NOTE: this is NAN if no support vectors were found, see TODO file
scoretest = eval_classifier(datatest, alphas_avg, data, label, gamma, b)
return scoretest
|
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|
"""
MonteCarloModel(core, dates, paths)
A `MonteCarloModel` is the result of a simulation of a series of asset prices.
* `core`: a reference `CoreModel`
* `dates`: an `AbstractVector{Date}`
* `paths`: a matrix of the scenario paths: the rows are the scenarios, and the columns are the values at each date in `dates`.
"""
struct MonteCarloModel{C,D,T} <: AbstractModel
core::C
dates::D
paths::Matrix{T}
end
"""
MonteCarloScenario(core, dates, path)
A `MonteCarloScenario` is a single simulation scenario of a `MonteCarloModel`.
* `core`: a reference `CoreModel`
* `dates`: an `AbstractVector{Date}`
* `paths`: an `AbstractVector` of the values at each date in `dates`.
"""
struct MonteCarloScenario{C,D,S} <: AbstractModel
core::C
dates::D
path::S
end
numeraire(m::Union{MonteCarloModel, MonteCarloScenario}) = numeraire(m.core)
startdate(m::Union{MonteCarloModel, MonteCarloScenario}) = startdate(m.core)
yearfractionto(m::Union{MonteCarloModel, MonteCarloScenario}, dt::Date) =
yearfractionto(m.core, dt)
# the value of currency is the same under every scenario
value(m::MonteCarloModel, c::WhenAt{Receive{T}}) where {T} =
value(m.core,c)
value(m::MonteCarloScenario, c::WhenAt{Receive{T}}) where {T} =
value(m.core,c)
struct ScenarioIterator{M<:MonteCarloModel}
m::M
n::Int
end
"""
scenarios(m::MonteCarloModel)
Returns an iterator over each `MonteCarloScenario` in `m`.
"""
scenarios(m::MonteCarloModel) = ScenarioIterator(m, size(m.paths, 1))
Base.length(sc::ScenarioIterator) = sc.n
Base.iterate(sc::ScenarioIterator, i::Int=1) = i > sc.n ? nothing :
(MonteCarloScenario(sc.m.core, sc.m.dates, view(sc.m.paths, i, :)), i+1)
"""
date2index(m::Union{MonteCarloScenario, MonteCarloModel}, dt::Date)
Returns the index of `dt` in the path(s) of `m`.
"""
function date2index(m::Union{MonteCarloScenario, MonteCarloModel}, dt::Date)
ii = searchsorted(m.dates, dt)
isempty(ii) && throw(DomainError(dt))
return ii[1]
end
index2date(m::Union{MonteCarloScenario, MonteCarloModel}, i) = m.dates[i]
function forwardprice(m::MonteCarloScenario, s::SingleStock, dt::Date)
valueat(m,s,date2index(m,dt))
end
function forwardprice(m::MonteCarloModel, s::SingleStock, dt::Date)
mean(forwardprice(ms, s, dt) for ms in scenarios(m))
end
valueat(m::MonteCarloScenario, ::SingleStock, i::Int) = m.path[i]
valueat(m::MonteCarloScenario, ::SingleStock, i::Int, ::Type{Dual}) =
Dual(m.path[i], 1)
# fallback
function value(m::MonteCarloModel, c::WhenAt)
N = date2index(m, maturitydate(c))
discount(m.core.yieldcurve, maturitydate(c)) * mean(valueat(ms, c.c, N) for ms in scenarios(m))
end
"""
montecarlo(m::GeomBMModel, dates, npaths)
Sample `npaths` Monte Carlo paths of the model `m`, at time `dates`.
"""
function montecarlo(m::GeomBMModel{CoreModel{T,R,Q}, V}, dates::StepRange{Date}, npaths::Integer) where {T,R,Q,V}
σ = m.volatility
S = typeof(m.core.yieldcurve.rate)
Xt = Array{promote_type(T,V,S)}(undef, length(dates), npaths)
Δt = yearfraction(daycount(m.core.yieldcurve), step(dates))
df = discount(m.core.carrycurve, Δt) / discount(m.core.yieldcurve, Δt)
for i = 1:npaths
x = value(m, SingleStock())
for (j, dt) in enumerate(dates)
if j == 1
Δt1 = yearfraction(daycount(m.core.yieldcurve), startdate(m), first(dates))
df1 = Δt1 == 0 ? 1.0 : discount(m.core.carrycurve, Δt1) / discount(m.core.yieldcurve, Δt1)
x *= df1 * exp(-σ^2*Δt1/2 + σ*sqrt(Δt1)*randn())
else
x *= df * exp(-σ^2*Δt/2 + σ*sqrt(Δt)*randn())
end
Xt[j,i] = x
end
end
MonteCarloModel(m.core, dates, copy(transpose(Xt)))
end
function value(m::GeomBMModel, c::Contract, ::Type{MonteCarloModel}, dates::StepRange{Date}, npaths::Integer)
mcm = montecarlo(m, dates, npaths)
value(mcm, c)
end
value(m::GeomBMModel, c::Contract, ::Type{MonteCarloModel}, npaths::Integer) =
value(m, c, MonteCarloModel, startdate(m):Day(1):maturitydate(c), npaths)
|
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|
from tkinter import *
from tkinter import messagebox
import numpy as np
import pandas as pd
l1=['itching','skin_rash','nodal_skin_eruptions','continuous_sneezing','shivering','chills','joint_pain',
'stomach_pain','acidity','ulcers_on_tongue','muscle_wasting','vomiting','burning_micturition','spotting_ urination','fatigue',
'weight_gain','anxiety','cold_hands_and_feets','mood_swings','weight_loss','restlessness','lethargy','patches_in_throat',
'irregular_sugar_level','cough','high_fever','sunken_eyes','breathlessness','sweating','dehydration','indigestion',
'headache','yellowish_skin','dark_urine','nausea','loss_of_appetite','pain_behind_the_eyes','back_pain','constipation',
'abdominal_pain','diarrhoea','mild_fever','yellow_urine','yellowing_of_eyes','acute_liver_failure','fluid_overload',
'swelling_of_stomach','swelled_lymph_nodes','malaise','blurred_and_distorted_vision','phlegm','throat_irritation',
'redness_of_eyes','sinus_pressure','runny_nose','congestion','chest_pain','weakness_in_limbs','fast_heart_rate',
'pain_during_bowel_movements','pain_in_anal_region','bloody_stool','irritation_in_anus','neck_pain','dizziness','cramps',
'bruising','obesity','swollen_legs','swollen_blood_vessels','puffy_face_and_eyes','enlarged_thyroid','brittle_nails',
'swollen_extremeties','excessive_hunger','extra_marital_contacts','drying_and_tingling_lips','slurred_speech','knee_pain','hip_joint_pain',
'muscle_weakness','stiff_neck','swelling_joints','movement_stiffness','spinning_movements','loss_of_balance','unsteadiness','weakness_of_one_body_side',
'loss_of_smell','bladder_discomfort','foul_smell_of urine','continuous_feel_of_urine','passage_of_gases','internal_itching','toxic_look_(typhos)',
'depression','irritability','muscle_pain','altered_sensorium','red_spots_over_body','belly_pain','abnormal_menstruation','dischromic _patches',
'watering_from_eyes','increased_appetite','polyuria','family_history','mucoid_sputum','rusty_sputum','lack_of_concentration','visual_disturbances',
'receiving_blood_transfusion','receiving_unsterile_injections','coma','stomach_bleeding','distention_of_abdomen','history_of_alcohol_consumption',
'fluid_overload','blood_in_sputum','prominent_veins_on_calf','palpitations','painful_walking','pus_filled_pimples','blackheads','scurring','skin_peeling',
'silver_like_dusting','small_dents_in_nails','inflammatory_nails','blister','red_sore_around_nose','yellow_crust_ooze']
disease=['Fungal infection','Allergy','GERD','Chronic cholestasis','Drug Reaction',
'Peptic ulcer diseae','AIDS','Diabetes','Gastroenteritis','Bronchial Asthma','Hypertension',
' Migraine','Cervical spondylosis',
'Paralysis (brain hemorrhage)','Jaundice','Malaria','Chicken pox','Dengue','Typhoid','hepatitis A',
'Hepatitis B','Hepatitis C','Hepatitis D','Hepatitis E','Alcoholic hepatitis','Tuberculosis',
'Common Cold','Pneumonia','Dimorphic hemmorhoids(piles)',
'Heartattack','Varicoseveins','Hypothyroidism','Hyperthyroidism','Hypoglycemia','Osteoarthristis',
'Arthritis','(vertigo) Paroymsal Positional Vertigo','Acne','Urinary tract infection','Psoriasis',
'Impetigo']
l2=[]
for x in range(0,len(l1)):
l2.append(0)
# TESTING DATA
tr=pd.read_csv("Testing.csv")
tr.replace({'prognosis':{'Fungal infection':0,'Allergy':1,'GERD':2,'Chronic cholestasis':3,'Drug Reaction':4,
'Peptic ulcer diseae':5,'AIDS':6,'Diabetes ':7,'Gastroenteritis':8,'Bronchial Asthma':9,'Hypertension ':10,
'Migraine':11,'Cervical spondylosis':12,
'Paralysis (brain hemorrhage)':13,'Jaundice':14,'Malaria':15,'Chicken pox':16,'Dengue':17,'Typhoid':18,'hepatitis A':19,
'Hepatitis B':20,'Hepatitis C':21,'Hepatitis D':22,'Hepatitis E':23,'Alcoholic hepatitis':24,'Tuberculosis':25,
'Common Cold':26,'Pneumonia':27,'Dimorphic hemmorhoids(piles)':28,'Heart attack':29,'Varicose veins':30,'Hypothyroidism':31,
'Hyperthyroidism':32,'Hypoglycemia':33,'Osteoarthristis':34,'Arthritis':35,
'(vertigo) Paroymsal Positional Vertigo':36,'Acne':37,'Urinary tract infection':38,'Psoriasis':39,
'Impetigo':40}},inplace=True)
X_test= tr[l1]
y_test = tr[["prognosis"]]
np.ravel(y_test)
# TRAINING DATA
df=pd.read_csv("Training.csv")
df.replace({'prognosis':{'Fungal infection':0,'Allergy':1,'GERD':2,'Chronic cholestasis':3,'Drug Reaction':4,
'Peptic ulcer diseae':5,'AIDS':6,'Diabetes ':7,'Gastroenteritis':8,'Bronchial Asthma':9,'Hypertension ':10,
'Migraine':11,'Cervical spondylosis':12,
'Paralysis (brain hemorrhage)':13,'Jaundice':14,'Malaria':15,'Chicken pox':16,'Dengue':17,'Typhoid':18,'hepatitis A':19,
'Hepatitis B':20,'Hepatitis C':21,'Hepatitis D':22,'Hepatitis E':23,'Alcoholic hepatitis':24,'Tuberculosis':25,
'Common Cold':26,'Pneumonia':27,'Dimorphic hemmorhoids(piles)':28,'Heart attack':29,'Varicose veins':30,'Hypothyroidism':31,
'Hyperthyroidism':32,'Hypoglycemia':33,'Osteoarthristis':34,'Arthritis':35,
'(vertigo) Paroymsal Positional Vertigo':36,'Acne':37,'Urinary tract infection':38,'Psoriasis':39,
'Impetigo':40}},inplace=True)
X= df[l1]
y = df[["prognosis"]]
np.ravel(y)
def message():
if (Symptom1.get() == "None" and Symptom2.get() == "None" and Symptom3.get() == "None" and Symptom4.get() == "None" and Symptom5.get() == "None"):
messagebox.showinfo("OPPS!!", "ENTER SYMPTOMS PLEASE")
else :
NaiveBayes()
def NaiveBayes():
from sklearn.naive_bayes import MultinomialNB
gnb = MultinomialNB()
gnb=gnb.fit(X,np.ravel(y))
from sklearn.metrics import accuracy_score
y_pred = gnb.predict(X_test)
print(accuracy_score(y_test, y_pred))
print(accuracy_score(y_test, y_pred, normalize=False))
psymptoms = [Symptom1.get(),Symptom2.get(),Symptom3.get(),Symptom4.get(),Symptom5.get()]
for k in range(0,len(l1)):
for z in psymptoms:
if(z==l1[k]):
l2[k]=1
inputtest = [l2]
predict = gnb.predict(inputtest)
predicted=predict[0]
h='no'
for a in range(0,len(disease)):
if(disease[predicted] == disease[a]):
h='yes'
break
if (h=='yes'):
t3.delete("1.0", END)
t3.insert(END, disease[a])
else:
t3.delete("1.0", END)
t3.insert(END, "No Disease")
root = Tk()
root.title(" Disease Prediction From Symptoms")
root.configure()
Symptom1 = StringVar()
Symptom1.set(None)
Symptom2 = StringVar()
Symptom2.set(None)
Symptom3 = StringVar()
Symptom3.set(None)
Symptom4 = StringVar()
Symptom4.set(None)
Symptom5 = StringVar()
Symptom5.set(None)
w2 = Label(root, justify=LEFT, text=" Disease Prediction From Symptoms ")
w2.config(font=("Elephant", 30))
w2.grid(row=1, column=0, columnspan=2, padx=100)
NameLb1 = Label(root, text="")
NameLb1.config(font=("Elephant", 20))
NameLb1.grid(row=5, column=1, pady=10, sticky=W)
S1Lb = Label(root, text="Symptom 1")
S1Lb.config(font=("Elephant", 15))
S1Lb.grid(row=7, column=1, pady=10 , sticky=W)
S2Lb = Label(root, text="Symptom 2")
S2Lb.config(font=("Elephant", 15))
S2Lb.grid(row=8, column=1, pady=10, sticky=W)
S3Lb = Label(root, text="Symptom 3")
S3Lb.config(font=("Elephant", 15))
S3Lb.grid(row=9, column=1, pady=10, sticky=W)
S4Lb = Label(root, text="Symptom 4")
S4Lb.config(font=("Elephant", 15))
S4Lb.grid(row=10, column=1, pady=10, sticky=W)
S5Lb = Label(root, text="Symptom 5")
S5Lb.config(font=("Elephant", 15))
S5Lb.grid(row=11, column=1, pady=10, sticky=W)
lr = Button(root, text="Predict",height=2, width=20, command=message)
lr.config(font=("Elephant", 15))
lr.grid(row=15, column=1,pady=20)
OPTIONS = sorted(l1)
S1En = OptionMenu(root, Symptom1,*OPTIONS)
S1En.grid(row=7, column=2)
S2En = OptionMenu(root, Symptom2,*OPTIONS)
S2En.grid(row=8, column=2)
S3En = OptionMenu(root, Symptom3,*OPTIONS)
S3En.grid(row=9, column=2)
S4En = OptionMenu(root, Symptom4,*OPTIONS)
S4En.grid(row=10, column=2)
S5En = OptionMenu(root, Symptom5,*OPTIONS)
S5En.grid(row=11, column=2)
NameLb = Label(root, text="")
NameLb.config(font=("Elephant", 20))
NameLb.grid(row=13, column=1, pady=10, sticky=W)
NameLb = Label(root, text="")
NameLb.config(font=("Elephant", 15))
NameLb.grid(row=18, column=1, pady=10, sticky=W)
t3 = Text(root, height=2, width=30)
t3.config(font=("Elephant", 20))
t3.grid(row=20, column=1 , padx=10)
root.mainloop()
|
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|
const GWPos = SVector{2,Int}
const TwoAgentPos = SVector{4,Int}
const dir = Dict(:up=>GWPos(0,1), :down=>GWPos(0,-1), :left=>GWPos(-1,0), :right=>GWPos(1,0), :stay=>GWPos(0,0),
:upleft=>GWPos(-1,1), :upright=>GWPos(1,1), :downright=>GWPos(1,-1), :downleft=>GWPos(-1,-1))
const aind = Dict(:up=>1, :down=>2, :left=>3, :right=>4, :stay=>5, :upleft=>6, :upright=>7, :downright=>8, :downleft=>9)
const syma = [:up, :down, :left, :right, :stay, :upleft, :upright, :downleft, :downright]
const S = TwoAgentPos
const A = Symbol
# Gridworld with adversary
@with_kw mutable struct AdversarialGridworldMDP <:MDP{S, A}
size::Tuple{Int, Int} = (10,10)
rewards::Dict{GWPos, Float64} = Dict()
walls::Vector{GWPos} = []
tprob::Float64 = 0.7
discount::Float64 = 0.95
agent_gets_action = :ego # :ego or :adversary
ego_policy = (s, rng::AbstractRNG = Random.GLOBAL_RNG) -> rand(rng, syma)
adversary_policy = (s, rng::AbstractRNG = Random.GLOBAL_RNG) -> rand(rng, syma)
failure_penalty = 5
end
valid_pos(mdp::AdversarialGridworldMDP, pos::GWPos) = !(pos in mdp.walls || any((pos .> mdp.size) .| (pos .< GWPos(1,1))))
function random_valid_pos(mdp::AdversarialGridworldMDP, rng::AbstractRNG = Random.GLOBAL_RNG, exclude = [], max_trials = 1000)
trial = 0
while trial < max_trials
pos = GWPos(rand(rng, 1:mdp.size[1]), rand(rng, 1:mdp.size[2]))
if valid_pos(mdp, pos) && !(haskey(mdp.rewards, pos) || pos in exclude)
return pos
end
trial += 1
end
end
function POMDPs.initialstate(mdp::AdversarialGridworldMDP, rng::AbstractRNG = Random.GLOBAL_RNG)
ego = random_valid_pos(mdp, rng)
adversary = random_valid_pos(mdp, rng, [ego])
Deterministic(S(ego..., adversary...))
end
function POMDPs.states(mdp::AdversarialGridworldMDP)
lengths = (mdp.size[1], mdp.size[2], mdp.size[1], mdp.size[2])
ss = S[]
for ijk in CartesianIndices(lengths)
s = S(ijk.I...)
valid_pos(mdp, ego_pos(s)) && valid_pos(mdp, adversary_pos(s)) && push!(ss, s)
end
push!(ss, S(-1,-1,-1,-1))
ss
end
POMDPs.actions(mdp::AdversarialGridworldMDP) = syma
POMDPs.actionindex(mdp::AdversarialGridworldMDP, a::A) = aind[a]
ego_pos(s::S) = GWPos(s[1], s[2])
adversary_pos(s::S) = GWPos(s[3], s[4])
agents_overlap(s::S) = ego_pos(s) == adversary_pos(s)
POMDPs.isterminal(mdp::AdversarialGridworldMDP, s::S) = any(s .< 0)
POMDPs.discount(mdp::AdversarialGridworldMDP) = mdp.discount
# Returns a sample next state and reward
function POMDPs.gen(mdp::AdversarialGridworldMDP, s::S, a::A, rng::AbstractRNG = Random.GLOBAL_RNG)
if haskey(mdp.rewards, ego_pos(s)) || agents_overlap(s) || isterminal(mdp, s)
return (sp = S(-1,-1,-1,-1), r = reward(mdp, s))
else
# Compute the direction based on the provided action
rdir = (rand(rng) < mdp.tprob) ? dir[a] : dir[rand(rng, syma[a .!= syma])]
# If this MDP controls the agent then use the adversary policy for the adversary
# Do the opposite if the adversary is being controlled by the MDP action
if mdp.agent_gets_action == :ego
new_ego = ego_pos(s) + rdir
new_adv = adversary_pos(s) + dir[mdp.adversary_policy(s, rng)]
else
new_ego = ego_pos(s) + dir[mdp.ego_policy(s, rng)]
new_adv = adversary_pos(s) + rdir
end
# Make sure the moves are in bound and not hitting a wall
new_ego = valid_pos(mdp, new_ego) ? new_ego : ego_pos(s)
new_adv = valid_pos(mdp, new_adv) ? new_adv : adversary_pos(s)
return (sp = S(new_ego..., new_adv...), r = reward(mdp, s))
end
end
# Returns the reward for the provided state
function POMDPs.reward(mdp::AdversarialGridworldMDP, s::S)
isterminal(mdp, s) && return 0
r = (get(mdp.rewards, ego_pos(s), 0.0) - mdp.failure_penalty*agents_overlap(s)) / mdp.failure_penalty
mdp.agent_gets_action == :ego ? r : -r
end
function tocolor(mdp::AdversarialGridworldMDP, r::Float64)
maxr = maximum(values(mdp.rewards))
minr = -maxr
frac = (r-minr)/(maxr-minr)
return get(ColorSchemes.redgreensplit, frac)
end
# Renders the mdp
function POMDPModelTools.render(mdp::AdversarialGridworldMDP, s::S)
nx, ny = mdp.size
cells = []
for x in 1:nx, y in 1:ny
pos = GWPos(x,y)
reward_index = findfirst([pos] .== keys(mdp.rewards))
wall_index = findfirst([pos] .== mdp.walls)
ctx = context((x-1)/nx, (ny-y)/ny, 1/nx, 1/ny)
color = "white"
if !isnothing(reward_index)
color = tocolor(mdp, get(mdp.rewards, pos, 0))
elseif !isnothing(wall_index)
color = "black"
end
cell = compose(ctx, Compose.rectangle(), fill(color))
push!(cells, cell)
end
grid = compose(context(), Compose.stroke("gray"), cells...)
outline = compose(context(), Compose.rectangle())
if all(s .> 0)
x,y = ego_pos(s)
agent_ctx = context((x-1)/nx, (ny-y)/ny, 1/nx, 1/ny)
ego = compose(agent_ctx, Compose.circle(0.5, 0.5, 0.4), Compose.stroke("black"), fill("blue"))
x,y = adversary_pos(s)
agent_ctx = context((x-1)/nx, (ny-y)/ny, 1/nx, 1/ny)
adversary = compose(agent_ctx, Compose.circle(0.5, 0.5, 0.4), Compose.stroke("black"), fill("orange"))
agents_comp = compose(context(), ego, adversary)
sz = min(w, h)
return compose(context((w-sz)/2, (h-sz)/2, sz, sz), agents_comp, grid, outline)
else
sz = min(w, h)
return compose(context((w-sz)/2, (h-sz)/2, sz, sz), grid, outline)
end
end
|
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|
#!/usr/bin/env python
"""
Test module for TwoPhaseFlow
"""
import pytest
import tables
import numpy as np
import proteus.defaults
from proteus import Context
from proteus import default_so
from proteus.iproteus import *
import os
import sys
Profiling.logLevel=1
Profiling.verbose=True
class TestTwoPhaseFlow(object):
def setup_method(self,method):
self._scriptdir = os.path.dirname(__file__)
self.path = proteus.__path__[0]+"/tests/TwoPhaseFlow/"
def teardown_method(self, method):
""" Tear down function """
FileList = ['marin.h5','marin.xmf'
'moses.h5','moses.xmf'
'damBreak.h5','damBreak.xmf'
'damBreak_solver_options.h5','damBreak_solver_options.xmf'
'TwoDimBucklingFlow.h5','TwoDimBucklingFlow.xmf'
'filling.h5','filling.xmf'
]
for file in FileList:
if os.path.isfile(file):
os.remove(file)
else:
pass
def compare_vs_saved_files(self,name):
actual = tables.open_file(name+'.h5','r')
expected_path = 'comparison_files/' + 'comparison_' + name + '_phi_t2.csv'
#write comparison file
#np.array(actual.root.phi_t2).tofile(os.path.join(self._scriptdir, expected_path),sep=",")
np.testing.assert_almost_equal(np.fromfile(os.path.join(self._scriptdir, expected_path),sep=","),np.array(actual.root.phi_t2).flatten(),decimal=6)
expected_path = 'comparison_files/' + 'comparison_' + name + '_velocity_t2.csv'
#write comparison file
#np.array(actual.root.velocity_t2).tofile(os.path.join(self._scriptdir, expected_path),sep=",")
np.testing.assert_almost_equal(np.fromfile(os.path.join(self._scriptdir, expected_path),sep=","),np.array(actual.root.velocity_t2).flatten(),decimal=6)
actual.close()
# *** 2D tests *** #
def test_risingBubble(self): #uses structured triangle mesh
os.system("parun --TwoPhaseFlow --path " + self.path + " "
"risingBubble.py -l5 -v -C 'final_time=0.1 dt_output=0.1 refinement=1'")
self.compare_vs_saved_files("risingBubble")
def test_damBreak(self):
os.system("parun --TwoPhaseFlow --path " + self.path + " "
"damBreak.py -l5 -v -C 'final_time=0.1 dt_output=0.1 he=0.1'")
self.compare_vs_saved_files("damBreak")
@pytest.mark.skip(reason="numerics are very sensitive, hashdist build doesn't pass but conda does")
def test_damBreak_solver_options(self):
os.system("parun --TwoPhaseFlow --path " + self.path + " "
"damBreak_solver_options.py -l5 -v -C 'final_time=0.1 dt_output=0.1 he=0.1'")
self.compare_vs_saved_files("damBreak_solver_options")
# @pytest.mark.skip(reason="long test")
def test_TwoDimBucklingFlow(self):
os.system("parun --TwoPhaseFlow --path " + self.path + " "
"TwoDimBucklingFlow.py -l5 -v -C 'final_time=0.1 dt_output=0.1 he=0.09'")
self.compare_vs_saved_files("TwoDimBucklingFlow")
# @pytest.mark.skip(reason="long test")
@pytest.mark.skip(reason="need to redo after history revision")
def test_fillingTank(self):
os.system("parun --TwoPhaseFlow --path " + self.path + " "
"fillingTank.py -l5 -v -C 'final_time=0.02 dt_output=0.02 he=0.01'")
self.compare_vs_saved_files("fillingTank")
# *** 3D tests *** #
def test_marin(self):
os.system("parun --TwoPhaseFlow --path " + self.path + " "
"marin.py -l5 -v -C 'final_time=0.1 dt_output=0.1 he=0.5'")
self.compare_vs_saved_files("marin")
def test_moses(self):
os.system("parun --TwoPhaseFlow --path " + self.path + " "
"moses.py -l5 -v -C 'final_time=0.1 dt_output=0.1 he=0.5'")
self.compare_vs_saved_files("moses")
|
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|
using GeoFormatTypes, Test
using GeoFormatTypes: Geom, CRS, Extended, Unknown
@testset "Test construcors" begin
@test_throws ArgumentError ProjString("+lat_ts=56.5 +ellps=GRS80")
@test_throws ArgumentError ProjJSON(Dict("fype" => 1))
@test_throws ArgumentError ProjJSON("fype")
@test_throws ArgumentError EPSG("ERROR:4326")
@test EPSG("EPSG:4326") == EPSG(4326)
end
@testset "Test constructors" begin
@test ProjString("+proj=test") isa ProjString
@test ProjJSON(Dict("type" => "GeographicCRS")) isa ProjJSON
@test ProjJSON("type: GeographicCRS") isa ProjJSON
@test EPSG(4326) isa EPSG
@test WellKnownText("test") isa WellKnownText{Unknown}
@test WellKnownBinary([1, 2, 3, 4]) isa WellKnownBinary{Unknown}
@test WellKnownText2("test") isa WellKnownText2{Unknown}
@test ESRIWellKnownText("test") isa ESRIWellKnownText{Unknown}
@test WellKnownText(Extended(), "test") isa WellKnownText{Extended}
@test WellKnownBinary(Extended(), [1, 2, 3, 4]) isa WellKnownBinary{Extended}
@test WellKnownText2(CRS(), "test") isa WellKnownText2{CRS}
@test ESRIWellKnownText(Geom(), "test") isa ESRIWellKnownText{Geom}
@test GML("test") isa GML{Unknown}
@test GML(Geom(), "test") isa GML{Geom}
@test GML(CRS(), "test") isa GML{CRS} # Probably doesn't actually exist
@test KML("test") isa KML
@test GeoJSON("test") isa GeoJSON
end
@testset "Test conversion to string or int" begin
@test convert(String, ProjString("+proj=test")) == "+proj=test"
@test convert(String, EPSG(4326)) == "EPSG:4326"
@test convert(Int, EPSG(4326)) == 4326
@test convert(String, WellKnownText("test")) == "test"
@test convert(String, WellKnownText2("test")) == "test"
@test convert(String, ESRIWellKnownText("test")) == "test"
@test convert(String, GML("test")) == "test"
@test convert(String, KML("test")) == "test"
@test convert(String, GeoJSON("test")) == "test"
end
# `convert` placeholder methods
Base.convert(target::Type{<:GeoFormat}, mode::Union{Geom,Type{Geom}}, source::GeoFormat; kwargs...) =
(:geom, kwargs...)
Base.convert(target::Type{<:GeoFormat}, mode::Union{CRS,Type{CRS}}, source::GeoFormat; kwargs...) =
(:crs, kwargs...)
@testset "Test convert mode allocation" begin
@testset "Test identical type is passed through unchanged" begin
@test convert(WellKnownText, WellKnownText(Extended(), "test")) == WellKnownText(Extended(), "test")
@test convert(ProjString, ProjString("+proj=test")) == ProjString("+proj=test")
end
@testset "Test conversions are assigned to crs or geom correctly" begin
@test convert(WellKnownText, WellKnownText2(CRS(), "test")) == (:crs,)
@test convert(WellKnownText2, WellKnownText(CRS(), "test")) == (:crs,)
@test convert(WellKnownBinary, WellKnownText(CRS(), "test")) == (:crs,)
@test convert(ProjString, WellKnownText(CRS(), "test")) == (:crs,)
@test convert(EPSG, ProjString("+proj=test")) == (:crs,)
@test convert(CoordSys, ProjString("+proj=test")) == (:crs,)
@test convert(GeoJSON, WellKnownText(Geom(), "test")) == (:geom,)
@test convert(KML, WellKnownText(Geom(), "test")) == (:geom,)
@test convert(GML, WellKnownText(Geom(), "test")) == (:geom,)
@test convert(ESRIWellKnownText, WellKnownText(Geom(), "test")) == (:geom,)
@test convert(WellKnownBinary, WellKnownText(Geom(), "test")) == (:geom,)
@test convert(WellKnownText2, WellKnownText(Geom(), "test")) == (:geom,)
@test convert(WellKnownText2, WellKnownText(Geom(), "test")) == (:geom,)
@test convert(WellKnownText, WellKnownText2(Geom(), "test")) == (:geom,)
@test convert(GeoJSON, WellKnownText(Extended(), "test")) == (:geom,)
@test convert(KML, WellKnownText(Extended(), "test")) == (:geom,)
@test convert(GML, WellKnownText(Extended(), "test")) == (:geom,)
@test convert(ESRIWellKnownText, WellKnownText(Extended(), "test")) == (:geom,)
@test convert(WellKnownBinary, WellKnownText(Extended(), "test")) == (:geom,)
@test convert(WellKnownText2, WellKnownText(Extended(), "test")) == (:geom,)
@test convert(WellKnownText2, WellKnownText(Extended(), "test")) == (:geom,)
@test convert(WellKnownText, WellKnownText2(Extended(), "test")) == (:geom,)
@test convert(GeoJSON, WellKnownText(Unknown(), "test")) == (:geom,)
@test convert(KML, WellKnownText(Unknown(), "test")) == (:geom,)
@test convert(GML, WellKnownText(Unknown(), "test")) == (:geom,)
@test convert(ESRIWellKnownText, WellKnownText(Unknown(), "test")) == (:geom,)
@test convert(WellKnownBinary, WellKnownText(Unknown(), "test")) == (:geom,)
@test convert(WellKnownText2, WellKnownText(Unknown(), "test")) == (:geom,)
@test convert(WellKnownText2, WellKnownText(Unknown(), "test")) == (:geom,)
@test convert(WellKnownText, WellKnownText2(Unknown(), "test")) == (:geom,)
end
@testset "Test kargs pass through convert" begin
@test convert(WellKnownText, WellKnownText2(CRS(), "test"); order=:trad) == (:crs, :order => :trad,)
@test convert(GML, WellKnownText(Extended(), "test"); order=:custom) == (:geom, :order => :custom)
end
@testset "Test conversions that are not possible throw an error" begin
@test_throws ArgumentError convert(KML, ProjString("+proj=test"))
@test_throws ArgumentError convert(GeoJSON, ProjString("+proj=test"))
@test_throws ArgumentError convert(ProjString, WellKnownText(Geom(), "test"))
@test_throws ArgumentError convert(CoordSys, WellKnownText(Geom(), "test"))
@test_throws ArgumentError convert(EPSG, WellKnownText(Geom(), "test"))
end
end
|
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|
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import scipy.sparse
def rcm(g):
'''Compute the reverse Cuthill-Mckee permutation of a graph. Note that the
method does NOT modify the graph, but rather just returns a permutation
vector that can be used by Graph.permute to achieve the actual reordering.
Parameters
----------
g: Graph
The graph to be reordered.
Returns
-------
perm: numpy.ndarray
Array of permuted node indices.
'''
return scipy.sparse.csgraph.reverse_cuthill_mckee(
g.adjacency_matrix, symmetric_mode=True
)
|
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|
"""
Cart pole swing-up: Original version from:
https://github.com/zuoxingdong/DeepPILCO/blob/master/cartpole_swingup.py
Modified so that done=True when x is outside of -2.4 to 2.4
Reward is also reshaped to be similar to PyBullet/roboschool version
More difficult, since dt is 0.05 (not 0.01), and only 200 timesteps
"""
import logging
import math
import gym
from gym import spaces
from gym.utils import seeding
import numpy as np
logger = logging.getLogger(__name__)
class CartPoleSwingUpEnv(gym.Env):
metadata = {
'render.modes': ['human', 'rgb_array'],
'video.frames_per_second': 100
}
def __init__(self, masscart=0.5, masspole=0.5, polelength=0.5):
self.g = 9.82 # gravity
self.m_c = masscart # cart mass, default 0.5
self.m_p = masspole # pendulum mass, default 0.5
self.total_m = (self.m_p + self.m_c)
self.l = polelength # pole's length, default 0.6
self.m_p_l = (self.m_p * self.l)
self.force_mag = 20.0
self.dt = 0.04 # seconds between state updates
self.tau = 0.02
self.b = 0.1 # friction coefficient, default 0.1
self.bouncing = False
self.t = 0 # timestep
self.t_limit = 200 # todo: tlimit originally 1000
# Angle at which to fail the episode
self.theta_threshold_radians = 12 * 2 * math.pi / 360
self.x_threshold = 2.4
self.kinematics_integrator = 'euler'
high = np.array([
np.finfo(np.float32).max,
np.finfo(np.float32).max,
np.finfo(np.float32).max,
np.finfo(np.float32).max,
np.finfo(np.float32).max])
self.action_space = spaces.Box(-1.0, 1.0, shape=(1,))
self.observation_space = spaces.Box(-high, high)
self.seed()
self.viewer = None
self.state = None
self.constraint = True #safe constraint
self.constraint_reward = -100
self.coslimit = 0.98
def seed(self, seed=None):
self.np_random, seed = seeding.np_random(seed)
return [seed]
def step(self, action):
# Valid action
action = np.clip(action, -1.0, 1.0)[0]
action *= self.force_mag
state = self.state
x, x_dot, theta, theta_dot = state
s = math.sin(theta)
c = math.cos(theta)
xdot_update = (-2 * self.m_p_l * (
theta_dot ** 2) * s + 3 * self.m_p * self.g * s * c + 4 * action - 4 * self.b * x_dot) / (
4 * self.total_m - 3 * self.m_p * c ** 2)
thetadot_update = (-3 * self.m_p_l * (theta_dot ** 2) * s * c + 6 * self.total_m * self.g * s + 6 * (
action - self.b * x_dot) * c) / (4 * self.l * self.total_m - 3 * self.m_p_l * c ** 2)
if self.bouncing:
if x < -self.x_threshold:
x = -self.x_threshold
x_dot = -0.1*x_dot
elif x > self.x_threshold:
x = self.x_threshold
x_dot = -0.1*x_dot
else:
x = x + x_dot * self.dt
x_dot = x_dot + xdot_update * self.dt
else:
x = x + x_dot * self.dt
x_dot = x_dot + xdot_update * self.dt
theta = theta + theta_dot * self.dt
theta_dot = theta_dot + thetadot_update * self.dt
self.state = (x, x_dot, theta, theta_dot)
done = False
if not self.bouncing:
if x < -self.x_threshold or x > self.x_threshold:
done = True
self.t += 1
if self.t >= self.t_limit:
done = True
self.t = 0
obs = np.array([x, x_dot, np.cos(theta), np.sin(theta), theta_dot])
reward = self.get_reward_mujoco()
violation = self.constraint_violated()
return obs, reward, done, violation
def constraint_violated(self):
state = self.state
x, x_dot, theta, theta_dot = state
if np.cos(theta) > 0 and np.cos(theta) < self.coslimit and np.sin(theta) > 0:
return 1
elif x < -self.x_threshold or x > self.x_threshold:
return 1
return 0
def get_reward(self):
state = self.state
x, x_dot, theta, theta_dot = state
reward_theta = (np.cos(theta) + 1.0) / 2.0
reward_x = np.cos((x / self.x_threshold) * (np.pi / 2.0))
reward = reward_theta * reward_x
reward = np.max((np.min((reward, 1)), 0))
return reward
def get_reward_mujoco(self):
# mujoco env reward
state = self.state
x, x_dot, theta, theta_dot = state
length = self.l # pole length
x_tip_error = x - length * np.sin(theta)
y_tip_error = length - length * np.cos(theta)
reward = np.exp(-(x_tip_error ** 2 + y_tip_error ** 2) / length ** 2)
# print('theta ', theta)
# print('x ', x)
# print('x_tip_error ', x_tip_error, 'y_tip_error ', y_tip_error)
return reward
def reset(self):
# self.state = self.np_random.normal(loc=np.array([0.0, 0.0, 30*(2*np.pi)/360, 0.0]), scale=np.array([0.0, 0.0, 0.0, 0.0]))
# self.state = np.random.normal(loc=np.array([0.0, 0.0, np.pi, 0.0]), scale=np.array([0.2, 0.2, 0.2, 0.2]))
self.state = (0.0, 0.0, np.pi, 0.0)
self.steps_beyond_done = None
self.t = 0 # timestep
x, x_dot, theta, theta_dot = self.state
obs = np.array([x, x_dot, np.cos(theta), np.sin(theta), theta_dot])
# return obs, np.array(self.state)
return obs
# def set_state(self, obs):
# # state = (x, x_dot, theta, theta_dot)
# # obs = [x, x_dot, np.cos(theta), np.sin(theta), theta_dot]
# self.state = (obs[0], obs[1], obs[2], obs[3]) #wrong!
# # theta = np.arctan(obs[3]/obs[2])
# # self.t = 0
# # self.state = (obs[0], obs[1], theta, obs[4])
def render(self, mode='human', close=False):
if close:
if self.viewer is not None:
self.viewer.close()
self.viewer = None
return
screen_width = 600
screen_height = 600 # before was 400
world_width = 5 # max visible position of cart
scale = screen_width / world_width
carty = screen_height / 2 # TOP OF CART
polewidth = 6.0
polelen = scale * self.l # 0.6 or self.l
cartwidth = 40.0
cartheight = 20.0
if self.viewer is None:
from gym.envs.classic_control import rendering
self.viewer = rendering.Viewer(screen_width, screen_height)
l, r, t, b = -cartwidth / 2, cartwidth / 2, cartheight / 2, -cartheight / 2
cart = rendering.FilledPolygon([(l, b), (l, t), (r, t), (r, b)])
self.carttrans = rendering.Transform()
cart.add_attr(self.carttrans)
cart.set_color(1, 0, 0)
self.viewer.add_geom(cart)
l, r, t, b = -polewidth / 2, polewidth / 2, polelen - polewidth / 2, -polewidth / 2
pole = rendering.FilledPolygon([(l, b), (l, t), (r, t), (r, b)])
pole.set_color(0, 0, 1)
self.poletrans = rendering.Transform(translation=(0, 0))
pole.add_attr(self.poletrans)
pole.add_attr(self.carttrans)
self.viewer.add_geom(pole)
self.axle = rendering.make_circle(polewidth / 2)
self.axle.add_attr(self.poletrans)
self.axle.add_attr(self.carttrans)
self.axle.set_color(0.1, 1, 1)
self.viewer.add_geom(self.axle)
# Make another circle on the top of the pole
self.pole_bob = rendering.make_circle(polewidth / 2)
self.pole_bob_trans = rendering.Transform()
self.pole_bob.add_attr(self.pole_bob_trans)
self.pole_bob.add_attr(self.poletrans)
self.pole_bob.add_attr(self.carttrans)
self.pole_bob.set_color(0, 0, 0)
self.viewer.add_geom(self.pole_bob)
self.wheel_l = rendering.make_circle(cartheight / 4)
self.wheel_r = rendering.make_circle(cartheight / 4)
self.wheeltrans_l = rendering.Transform(translation=(-cartwidth / 2, -cartheight / 2))
self.wheeltrans_r = rendering.Transform(translation=(cartwidth / 2, -cartheight / 2))
self.wheel_l.add_attr(self.wheeltrans_l)
self.wheel_l.add_attr(self.carttrans)
self.wheel_r.add_attr(self.wheeltrans_r)
self.wheel_r.add_attr(self.carttrans)
self.wheel_l.set_color(0, 0, 0) # Black, (B, G, R)
self.wheel_r.set_color(0, 0, 0) # Black, (B, G, R)
self.viewer.add_geom(self.wheel_l)
self.viewer.add_geom(self.wheel_r)
self.track = rendering.Line(
(screen_width / 2 - self.x_threshold * scale, carty - cartheight / 2 - cartheight / 4),
(screen_width / 2 + self.x_threshold * scale, carty - cartheight / 2 - cartheight / 4))
self.track.set_color(0, 0, 0)
self.viewer.add_geom(self.track)
if self.state is None: return None
x = self.state
cartx = x[0] * scale + screen_width / 2.0 # MIDDLE OF CART
self.carttrans.set_translation(cartx, carty)
self.poletrans.set_rotation(x[2])
self.pole_bob_trans.set_translation(-self.l * np.sin(x[2]), self.l * np.cos(x[2]))
return self.viewer.render(return_rgb_array=mode == 'rgb_array')
|
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|
# Copyright 2018 The Cirq Developers
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math
import random
from typing import Sequence
import numpy as np
import pytest
import sympy
import cirq
def assert_gates_implement_unitary(gates: Sequence[cirq.SingleQubitGate],
intended_effect: np.ndarray,
atol: float):
actual_effect = cirq.dot(*[cirq.unitary(g) for g in reversed(gates)])
cirq.testing.assert_allclose_up_to_global_phase(actual_effect,
intended_effect,
atol=atol)
def test_is_negligible_turn():
assert cirq.is_negligible_turn(0, 1e-5)
assert cirq.is_negligible_turn(1e-6, 1e-5)
assert cirq.is_negligible_turn(1, 1e-5)
assert cirq.is_negligible_turn(1 + 1e-6, 1e-5)
assert cirq.is_negligible_turn(1 - 1e-6, 1e-5)
assert cirq.is_negligible_turn(-1, 1e-5)
assert cirq.is_negligible_turn(-1 + 1e-6, 1e-5)
assert cirq.is_negligible_turn(-1 - 1e-6, 1e-5)
assert cirq.is_negligible_turn(3, 1e-5)
assert cirq.is_negligible_turn(3 + 1e-6, 1e-5)
assert not cirq.is_negligible_turn(1e-4, 1e-5)
assert not cirq.is_negligible_turn(-1e-4, 1e-5)
assert not cirq.is_negligible_turn(0.5, 1e-5)
assert not cirq.is_negligible_turn(-0.5, 1e-5)
assert not cirq.is_negligible_turn(0.5, 1e-5)
assert not cirq.is_negligible_turn(4.5, 1e-5)
# Variable sympy expression
assert not cirq.is_negligible_turn(sympy.Symbol('a'), 1e-5)
assert not cirq.is_negligible_turn(sympy.Symbol('a') + 1, 1e-5)
assert not cirq.is_negligible_turn(sympy.Symbol('a') * 1e-10, 1e-5)
# Constant sympy expression
assert cirq.is_negligible_turn(sympy.Symbol('a') * 0 + 3 + 1e-6, 1e-5)
assert not cirq.is_negligible_turn(sympy.Symbol('a') * 0 + 1.5 - 1e-6, 1e-5)
def test_single_qubit_matrix_to_gates_known_x():
actual = cirq.single_qubit_matrix_to_gates(
np.array([[0, 1], [1, 0]]), tolerance=0.01)
assert cirq.approx_eq(actual, [cirq.X], atol=1e-9)
def test_single_qubit_matrix_to_gates_known_y():
actual = cirq.single_qubit_matrix_to_gates(
np.array([[0, -1j], [1j, 0]]), tolerance=0.01)
assert cirq.approx_eq(actual, [cirq.Y], atol=1e-9)
def test_single_qubit_matrix_to_gates_known_z():
actual = cirq.single_qubit_matrix_to_gates(
np.array([[1, 0], [0, -1]]), tolerance=0.01)
assert cirq.approx_eq(actual, [cirq.Z], atol=1e-9)
def test_single_qubit_matrix_to_gates_known_s():
actual = cirq.single_qubit_matrix_to_gates(
np.array([[1, 0], [0, 1j]]), tolerance=0.01)
assert cirq.approx_eq(actual, [cirq.Z**0.5], atol=1e-9)
def test_known_s_dag():
actual = cirq.single_qubit_matrix_to_gates(
np.array([[1, 0], [0, -1j]]), tolerance=0.01)
assert cirq.approx_eq(actual, [cirq.Z**-0.5], atol=1e-9)
def test_known_h():
actual = cirq.single_qubit_matrix_to_gates(
np.array([[1, 1], [1, -1]]) * np.sqrt(0.5), tolerance=0.001)
assert cirq.approx_eq(actual, [cirq.Y**-0.5, cirq.Z], atol=1e-9)
@pytest.mark.parametrize('intended_effect', [
np.array([[0, 1j], [1, 0]]),
# Historical failure:
np.array([[-0.10313355-0.62283483j, 0.76512225-0.1266025j],
[-0.72184177+0.28352196j, 0.23073193+0.5876415j]]),
] + [
cirq.testing.random_unitary(2) for _ in range(10)
])
def test_single_qubit_matrix_to_gates_cases(intended_effect):
for atol in [1e-1, 1e-8]:
gates = cirq.single_qubit_matrix_to_gates(
intended_effect, tolerance=atol / 10)
assert len(gates) <= 3
assert sum(1 for g in gates if not isinstance(g, cirq.ZPowGate)) <= 1
assert_gates_implement_unitary(gates, intended_effect, atol=atol)
@pytest.mark.parametrize('pre_turns,post_turns',
[(random.random(), random.random())
for _ in range(10)])
def test_single_qubit_matrix_to_gates_fuzz_half_turns_merge_z_gates(
pre_turns, post_turns):
intended_effect = cirq.dot(
cirq.unitary(cirq.Z**(2 * pre_turns)),
cirq.unitary(cirq.X),
cirq.unitary(cirq.Z**(2 * post_turns)))
gates = cirq.single_qubit_matrix_to_gates(
intended_effect, tolerance=1e-7)
assert len(gates) <= 2
assert_gates_implement_unitary(gates, intended_effect, atol=1e-6)
def test_single_qubit_matrix_to_gates_tolerance_z():
z = np.diag([1, np.exp(1j * 0.01)])
optimized_away = cirq.single_qubit_matrix_to_gates(
z, tolerance=0.1)
assert len(optimized_away) == 0
kept = cirq.single_qubit_matrix_to_gates(z, tolerance=0.0001)
assert len(kept) == 1
def test_single_qubit_matrix_to_gates_tolerance_xy():
c, s = np.cos(0.01), np.sin(0.01)
xy = np.array([[c, -s], [s, c]])
optimized_away = cirq.single_qubit_matrix_to_gates(
xy, tolerance=0.1)
assert len(optimized_away) == 0
kept = cirq.single_qubit_matrix_to_gates(xy, tolerance=0.0001)
assert len(kept) == 1
def test_single_qubit_matrix_to_gates_tolerance_half_turn_phasing():
a = np.pi / 2 + 0.01
c, s = np.cos(a), np.sin(a)
nearly_x = np.array([[c, -s], [s, c]])
z1 = np.diag([1, np.exp(1j * 1.2)])
z2 = np.diag([1, np.exp(1j * 1.6)])
phased_nearly_x = z1.dot(nearly_x).dot(z2)
optimized_away = cirq.single_qubit_matrix_to_gates(
phased_nearly_x, tolerance=0.1)
assert len(optimized_away) == 2
kept = cirq.single_qubit_matrix_to_gates(
phased_nearly_x, tolerance=0.0001)
assert len(kept) == 3
def test_single_qubit_op_to_framed_phase_form_output_on_example_case():
u, t, g = cirq.single_qubit_op_to_framed_phase_form(
cirq.unitary(cirq.Y**0.25))
assert cirq.allclose_up_to_global_phase(u, cirq.unitary(cirq.X**0.5))
assert abs(t - (1 + 1j) * math.sqrt(0.5)) < 0.00001
assert abs(g - 1) < 0.00001
@pytest.mark.parametrize('mat', [
np.eye(2),
cirq.unitary(cirq.H),
cirq.unitary(cirq.X),
cirq.unitary(cirq.X**0.5),
cirq.unitary(cirq.Y),
cirq.unitary(cirq.Z),
cirq.unitary(cirq.Z**0.5),
] + [cirq.testing.random_unitary(2)
for _ in range(10)])
def test_single_qubit_op_to_framed_phase_form_equivalent_on_known_and_random(
mat):
u, t, g = cirq.single_qubit_op_to_framed_phase_form(mat)
z = np.diag([g, g * t])
assert np.allclose(mat, np.conj(u.T).dot(z).dot(u))
def test_single_qubit_matrix_to_native_gates_known():
actual = cirq.single_qubit_matrix_to_phased_x_z(
np.array([[0, 1], [1, 0]]), atol=0.01)
assert cirq.approx_eq(actual, [cirq.PhasedXPowGate(phase_exponent=1.0)],
atol=1e-9)
actual = cirq.single_qubit_matrix_to_phased_x_z(
np.array([[0, -1j], [1j, 0]]), atol=0.01)
assert cirq.approx_eq(actual, [cirq.Y], atol=1e-9)
actual = cirq.single_qubit_matrix_to_phased_x_z(
np.array([[1, 0], [0, -1]]), atol=0.01)
assert cirq.approx_eq(actual, [cirq.Z], atol=1e-9)
actual = cirq.single_qubit_matrix_to_phased_x_z(
np.array([[1, 0], [0, 1j]]), atol=0.01)
assert cirq.approx_eq(actual, [cirq.Z**0.5], atol=1e-9)
actual = cirq.single_qubit_matrix_to_phased_x_z(
np.array([[1, 0], [0, -1j]]), atol=0.01)
assert cirq.approx_eq(actual, [cirq.Z**-0.5], atol=1e-9)
actual = cirq.single_qubit_matrix_to_phased_x_z(
np.array([[1, 1], [1, -1]]) * np.sqrt(0.5), atol=0.001)
assert cirq.approx_eq(
actual,
[cirq.PhasedXPowGate(phase_exponent=-0.5, exponent=0.5), cirq.Z**-1],
atol=1e-9)
@pytest.mark.parametrize('intended_effect', [
np.array([[0, 1j], [1, 0]]),
] + [
cirq.testing.random_unitary(2) for _ in range(10)
])
def test_single_qubit_matrix_to_native_gates_cases(intended_effect):
gates = cirq.single_qubit_matrix_to_phased_x_z(intended_effect, atol=1e-6)
assert len(gates) <= 2
assert_gates_implement_unitary(gates, intended_effect, atol=1e-5)
@pytest.mark.parametrize('pre_turns,post_turns',
[(random.random(), random.random())
for _ in range(10)])
def test_single_qubit_matrix_to_native_gates_fuzz_half_turns_always_one_gate(
pre_turns, post_turns):
atol = 1e-6
aggr_atol = atol * 10.0
intended_effect = cirq.dot(
cirq.unitary(cirq.Z**(2 * pre_turns)),
cirq.unitary(cirq.X),
cirq.unitary(cirq.Z**(2 * post_turns)))
gates = cirq.single_qubit_matrix_to_phased_x_z(
intended_effect, atol=atol)
assert len(gates) == 1
assert_gates_implement_unitary(gates, intended_effect, atol=aggr_atol)
def test_single_qubit_matrix_to_native_gates_tolerance_z():
z = np.diag([1, np.exp(1j * 0.01)])
optimized_away = cirq.single_qubit_matrix_to_phased_x_z(
z, atol=0.1)
assert len(optimized_away) == 0
kept = cirq.single_qubit_matrix_to_phased_x_z(z, atol=0.0001)
assert len(kept) == 1
def test_single_qubit_matrix_to_native_gates_tolerance_xy():
c, s = np.cos(0.01), np.sin(0.01)
xy = np.array([[c, -s], [s, c]])
optimized_away = cirq.single_qubit_matrix_to_phased_x_z(
xy, atol=0.1)
assert len(optimized_away) == 0
kept = cirq.single_qubit_matrix_to_phased_x_z(xy, atol=0.0001)
assert len(kept) == 1
def test_single_qubit_matrix_to_native_gates_tolerance_half_turn_phasing():
a = np.pi / 2 + 0.01
c, s = np.cos(a), np.sin(a)
nearly_x = np.array([[c, -s], [s, c]])
z1 = np.diag([1, np.exp(1j * 1.2)])
z2 = np.diag([1, np.exp(1j * 1.6)])
phased_nearly_x = z1.dot(nearly_x).dot(z2)
optimized_away = cirq.single_qubit_matrix_to_phased_x_z(
phased_nearly_x, atol=0.1)
assert len(optimized_away) == 1
kept = cirq.single_qubit_matrix_to_phased_x_z(
phased_nearly_x, atol=0.0001)
assert len(kept) == 2
|
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|
#include <iostream>
#include <vector>
#include <map>
#include <string>
#include <exception>
#include <cstring>
#include <boost/algorithm/string.hpp>
#include <tao/pegtl.hpp>
#include "cli.h"
#include "../engine/engine.h"
#include "grammar_cli.h"
using std::endl;
using std::cin;
using std::cout;
using std::istream;
using std::ostream;
using std::vector;
using std::string;
using std::map;
int CliParser::run(istream& in, ostream& out){
string current_line;
map<string, string> vars;
size_t linecount = 0;
try {
if (!in)
return 2;
while(in) {
out << prompt << " ";
getline(in, current_line);
if (current_line.empty())
continue;
tao::pegtl::memory_input<> p_in(current_line, "STDIN");
tao::pegtl::parse< cli::grammar, cli::impl >(p_in, vars);
log_command(out, vars);
int rc = handle_command(vars);
++linecount;
vars.clear();
if (rc == 1)
break;
}
return 0;
} catch (std::exception e){
// This will hande EngineExceptions
out << "Command failed: " << e.what() << endl;
return 1;
}
}
int CliParser::handle_command(map<string, string> vars) {
// static Engine engine;
string action = vars["action"];
if (action == "open") {
engine->open_file(vars["open_path"], vars["open_name"], vars["open_type"]);
return RC_CONTINUE;
} else if ( action == "list") {
cout << "list(): " << endl;
engine->list_file();
return RC_CONTINUE;
} else if ( action == "exit") {
cout << "Bye!" << endl;
return RC_BREAK;
} else {
cout << "Command not found" << endl;
return RC_BAD_CMD;
}
}
void CliParser::log_command(ostream& out, map<string, string> vars){
typedef map<string, string>::const_iterator iter;
out << "DEBUG: " << vars["action"] << ": ";
for(iter it = vars.begin(); it != vars.end(); ++it){
out << it->first << "=" << it->second << " ";
}
out << endl;
}
void CliParser::set_prompt(string s) {
prompt = s;
}
|
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|
simplifyProject(P::Project) = map(P) do branch
map(branch) do solution
solution.data
end
end
#TODO actually convert(T, Project)
function complicateProject(V) #::Vector{Vector{Vector{Float64}}}
P = Project()
branches = map(V) do bData
branch = Branch(P)
solutions::Vector{Solution} = map(bData) do sData
Solution(sData, branch)
end
branch.solutions = solutions
return branch
end
P.branches = branches
return P
end
"""
create(homotopy, jacobian, projection)
"""
create(v...) = begin
ses = Session()
ses.P = Project()
ses.core = Galerkin(ses, v...)
ses.cont = PC(ses)
ses.viz = GalerkinViz(ses)
show(ses.cont); show(ses.core); show(ses.viz)
return ses
end
"""
save(filename, session[, overwrite])
"""
save(filename, S::Session; overwrite=false) = begin
if !isfile("save/$(filename)") || overwrite; open("save/$(filename)", "w") do f serialize(f, simplifyProject(S.P)) end
else error("File already exists. Use overwrite=true .")end
return Void
end
#TODO restore non-serializable stuff (figures, observer)
"""
load(filename, homotopy, jacobian, projection)
"""
load(filename, v...) = begin
V = open(deserialize, "save/$(filename)")
ses = Session()
ses.P = complicateProject(V)
ses.core = Galerkin(ses, v...)
ses.cont = PC(ses)
ses.viz = GalerkinViz(ses)
show(ses.cont); show(ses.core); show(ses.viz)
return ses
end
|
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|
# assume this is run after detect.py has been run, this means that the images in data/images
# have corresponding data in labels
from PIL import Image
import numpy as np
import pandas as pd
import os
import random
import sklearn
import skimage
import skimage.io
import matplotlib.pyplot as plt
import pathlib
PROJECT_DIR = str(pathlib.Path(__file__).parent.absolute()) +'/'
IMAGES_DIR = PROJECT_DIR + 'data/images'
LABELS_DIR = PROJECT_DIR + 'data/cropped/labels'
CROPPED_DIR = PROJECT_DIR + 'data/cropped/'
files = []
for (dirpath, dirnames, filenames) in os.walk(IMAGES_DIR):
files.extend(filenames)
break
def map_files_to_labels(image):
text_path = image + ".txt"
return text_path
labels = [map_files_to_labels(file) for file in files]
def crop_image(file,label, output_dir):
width, height = image.size
# find the largest of the squares here
# but for now just get the first one
class_id, center_x, center_y, box_width, box_height = [float(x) for x in list(label.iloc[0])[0].split(' ')]
pixel_center_x = width*center_x
pixel_center_y = height*center_y
pixel_box_width = width*box_width
pixel_box_height = height*box_height
box_top_left_x = pixel_center_x - pixel_box_width/2
box_top_left_y = pixel_center_y - pixel_box_height/2
left = box_top_left_x
top= box_top_left_y
right = (box_top_left_x + pixel_box_width )
bottom = (box_top_left_y + pixel_box_height )
cropped_image = image.crop((left, top, right, bottom))
#image.show()
cropped_image.save(output_dir + files[i])
for i in range(len(files)):
image = Image.open(IMAGES_DIR + '/' + files[i])
if( not os.path.exists(LABELS_DIR + "/" +labels[i])):
continue
label = pd.read_csv(LABELS_DIR + "/" +labels[i], header=None)
crop_image(image,label,CROPPED_DIR)
|
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|
import numpy as np
import pandas as pd
from glob import glob
dfs = list()
directoryPath = 'data/raw/data_for_november_2019_evaluation/south_sudan_data/IMF/'
filenames = glob(directoryPath + 'imf*.xlsx')
for filename in filenames:
df = pd.read_excel(filename)
df = df.transpose()
index_val = df.index.values
indicator = index_val[0]
year_val = index_val[1:]
colnames = df.iloc[0,:]
df = df.iloc[1:, :]
col_dict = dict(zip(list(range(df.shape[1])), colnames))
df.rename(col_dict, axis=1, inplace=True)
df.dropna(axis=1, how='all', inplace=True)
df.rename({'South Sudan, Republic of' : 'Value'}, axis=1, inplace=True)
df['Value'].replace('no data', np.nan, inplace=True)
df['Variable'] = indicator
df['Year'] = df.index
df['Country'] = 'South Sudan'
for col in colnames:
if 'Ethiopia' == col:
ethiopia_ind_val = df[col].values
df1 = pd.DataFrame({'Variable':indicator, 'Year':df.index, 'Value': ethiopia_ind_val, 'Country':'Ethiopia'})
df = pd.concat([df, df1], sort=False, ignore_index=True)
df = df[['Year', 'Variable', 'Value', 'Country']]
dfs.append(df)
big_frame = pd.concat(dfs, sort=False, ignore_index=False)
big_frame.index = list(range(big_frame.shape[0]))
big_frame['Unit'] = big_frame['Variable'].apply(lambda st: st[st.find('(') + 1:st.find(')')])
big_frame['Source'], big_frame['Month'], big_frame['County'], big_frame['State'] = 'IMF', None, None, None
big_frame.dropna(subset=['Value'], inplace=True)
big_frame['Variable'] = big_frame['Variable'].str.replace(r'\(.*?\)', '').str.strip()
big_frame.to_csv('data/IMF-data.csv', index=False)
|
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|
"""
Test to exercise Small File Workload
Note:
This test is using the benchmark-operator and the elastic search, so it start
process with port forwarding on port 9200 from the host that run the test (localhost)
to the elastic-search within the open-shift cluster, so, if you host is listen to
port 9200, this test can not be running in your host.
"""
# Builtin modules
import json
import logging
# 3ed party modules
import os.path
from elasticsearch import Elasticsearch, exceptions as ESExp
import numpy as np
import pytest
import time
# Local modules
from ocs_ci.framework import config
from ocs_ci.framework.testlib import performance
from ocs_ci.helpers.helpers import get_full_test_logs_path
from ocs_ci.ocs import benchmark_operator, constants, exceptions
from ocs_ci.ocs.elasticsearch import ElasticSearch
from ocs_ci.ocs.perfresult import PerfResult
from ocs_ci.ocs.perftests import PASTest
from ocs_ci.ocs.utils import get_pod_name_by_pattern
from ocs_ci.utility import templating
from ocs_ci.utility.utils import ceph_health_check
log = logging.getLogger(__name__)
class SmallFileResultsAnalyse(PerfResult):
"""
This class is reading all test results from elasticsearch server (which the
benchmark-operator running of the benchmark is generate), aggregate them by :
test operation (e.g. create / delete etc.)
sample (for test to be valid it need to run with more the one sample)
host (test can be run on more then one pod {called host})
it generates results for all tests as one unit which will be valid only
if the deviation between samples is less the 5%
"""
managed_keys = {
"IOPS": {"name": "iops", "op": np.sum},
"MiBps": {"name": "mbps", "op": np.sum},
"elapsed": {"name": "elapsed-time", "op": np.average},
"files": {"name": "files_per_thread", "op": np.sum},
"filesPerSec": {"name": "Files-Sec", "op": np.sum},
"records": {"name": "Rec-per-thread", "op": np.sum},
}
def __init__(self, uuid, crd, full_log_path, es_con):
"""
Initialize the object by reading some of the data from the CRD file and
by connecting to the ES server and read all results from it.
Args:
uuid (str): the unique uid of the test
crd (dict): dictionary with test parameters - the test yaml file
that modify it in the test itself.
full_log_path (str): the path of the results files to be found
es_con (elasticsearch): an elasticsearch connection
"""
super(SmallFileResultsAnalyse, self).__init__(uuid, crd)
self.index = crd["spec"]["es_index"] + "-results"
self.new_index = crd["spec"]["es_index"] + "-fullres"
self.full_log_path = full_log_path
# make sure we have connection to the elastic search server
self.es = es_con
# WA for Cloud environment where pod can not send results to ES
self.dont_check = False
# make sure we have connection to the elastic search server
# self.es_connect()
# Creating full results dictionary
self.add_key("clients", crd["spec"]["workload"]["args"]["clients"])
self.add_key("samples", crd["spec"]["workload"]["args"]["samples"])
self.add_key("threads", crd["spec"]["workload"]["args"]["threads"])
self.add_key("operations", crd["spec"]["workload"]["args"]["operation"])
self.add_key("full-res", {})
# Calculate the number of records for the test
# Total threads for one sample - one operation
self.records = self.results["clients"] * self.results["threads"]
# Number of threads for all samples
self.records *= self.results["samples"]
# Number of records for all operation - cleanup does not count
numofops = len(self.results["operations"])
if "cleanup" in self.results["operations"]:
numofops -= 1
self.records *= numofops
def read(self):
"""
Reading all test records from the elasticsearch server into dictionary
inside this object
"""
query = {"query": {"match": {"uuid": f'"{self.uuid}"'}}}
log.info("Reading all data from ES server")
try:
# Initialize the scroll
page = self.es.search(index=self.index, scroll="2m", size=1000, body=query)
sid = page["_scroll_id"]
scroll_size = page["hits"]["total"]["value"]
log.info(
f"Looking for {self.records} records and found {scroll_size} records."
)
self.all_results = page["hits"]["hits"]
# Start scrolling
while scroll_size > 0:
page = self.es.scroll(scroll_id=sid, scroll="2m")
# Update the scroll ID
sid = page["_scroll_id"]
self.all_results += page["hits"]["hits"]
# Get the number of results that we returned in the last scroll
scroll_size = len(page["hits"]["hits"])
log.debug(f"{scroll_size} records was read")
log.info(f"The total record that was read : {len(self.all_results)}")
log.debug(self.all_results)
total_rec_found = len(self.all_results)
if total_rec_found < 1:
log.warning("No data in ES server, disabling results calculation")
self.dont_check = True
if total_rec_found < self.records:
log.error("Not all data read from ES server")
self.dont_check = True
if total_rec_found > self.records:
log.warning("More records then expected was read, check the results!")
except ESExp.NotFoundError:
log.warning("No data in ES server, disabling results calculation")
self.dont_check = True
def thread_read(self, host, op, snum):
"""
This method read all threads record of one host / operation and sample
Args:
host (str): the name of the pod that ran the test
op (str): the operation that is tested
snum (int): sample of test as string
Returns:
dict : dictionary of results records
"""
res = {}
log.debug(f"Reading all threads for {op} / {snum} / {host}")
for hit in self.all_results:
if (
hit["_source"]["host"] == host
and hit["_source"]["optype"] == op
and hit["_source"]["sample"] == snum
):
for key in self.managed_keys.keys():
# not all operation have all values, so i am using try
try:
val = float("{:.2f}".format(hit["_source"][key]))
if self.managed_keys[key]["name"] in res.keys():
res[self.managed_keys[key]["name"]].append(val)
else:
res[self.managed_keys[key]["name"]] = [val]
except Exception:
pass
res = self.aggregate_threads_results(res)
return res
def aggregate_threads_results(self, res):
"""
Aggregation of one section of results, this can be threads in host,
hosts in sample, samples in test
Args:
res (dict) : dictionary of results
Returns:
dict : dictionary with the aggregate results.
"""
results = {}
for key in self.managed_keys.keys():
if self.managed_keys[key]["name"] in res.keys():
results[key] = self.managed_keys[key]["op"](
res[self.managed_keys[key]["name"]]
)
# This is the place to check in host (treads) deviation.
return results
def combine_results(self, results, clear):
"""
Combine 2 or more results (hosts in sample / samples in test)
to one result.
Args:
results (dict): dictionary of results to combine
clear (bool): return only combined results or not.
True - return only combined results
False - add the combine results to originals results
Returns:
dict : dictionary of results records
"""
res = {}
log.debug(f"The results to combine {json.dumps(results, indent=2)}")
for rec in results.keys():
record = results[rec]
for key in self.managed_keys.keys():
# not all operation have all values, so i am using try
try:
val = float("{:.2f}".format(record[key]))
if self.managed_keys[key]["name"] in res.keys():
res[self.managed_keys[key]["name"]].append(val)
else:
res[self.managed_keys[key]["name"]] = [val]
except Exception:
pass
if not clear:
res.update(self.aggregate_threads_results(res))
else:
res = self.aggregate_threads_results(res)
log.debug(f"The combines results are : {json.dumps(res, indent=2)}")
return res
def aggregate_host_results(self):
"""
Aggregation results from all hosts in single sample
"""
results = {}
for op in self.results["operations"]:
for smp in range(self.results["samples"]):
sample = smp + 1
if op in self.results["full-res"].keys():
self.results["full-res"][op][sample] = self.combine_results(
self.results["full-res"][op][sample], True
)
return results
def aggregate_samples_results(self):
"""
Aggregation results from all hosts in single sample, and compare
between samples.
Returns:
bool: True if results deviation (between samples) is les or equal
to 20%, otherwise False
"""
test_pass = True
for op in self.results["operations"]:
log.debug(
f'Aggregating {op} - {json.dumps(self.results["full-res"][op], indent=3)}'
)
results = self.combine_results(self.results["full-res"][op], False)
log.info(f"Check IOPS {op} samples deviation")
for key in self.managed_keys.keys():
if self.managed_keys[key]["name"] in results.keys():
results[key] = self.managed_keys[key]["op"](
results[self.managed_keys[key]["name"]]
)
if isinstance(results[self.managed_keys[key]["name"]], list):
results[key] = np.average(
results[self.managed_keys[key]["name"]]
)
results[key] = float("{:.2f}".format(results[key]))
if key == "IOPS":
st_deviation = np.std(results[self.managed_keys[key]["name"]])
mean = np.mean(results[self.managed_keys[key]["name"]])
pct_dev = (st_deviation / mean) * 100
if pct_dev > 20:
log.error(
f"Deviation for {op} IOPS is more the 20% ({pct_dev})"
)
# TODO: unmarked next line after implementing data cleansing
# test_pass = False
self.results["full-res"][op] = results
return test_pass
def get_clients_list(self):
"""
Finding and creating a list of all hosts that was used in this test
Returns:
list: a list of pods name
"""
res = []
for hit in self.all_results:
host = hit["_source"]["host"]
if host not in res:
res.append(host)
log.info(f"The pods names used in this test are {res}")
return res
def init_full_results(self):
"""
Initialize the full results Internal DB as dictionary.
"""
log.info("Initialising results DB")
# High level of internal results DB is operation
for op in self.results["operations"]:
self.results["full-res"][op] = {}
# second level is sample
for smp in range(self.results["samples"]):
sample = smp + 1
self.results["full-res"][op][sample] = {}
# last level is host (all threads will be in the host)
for host in self.results["hosts"]:
self.results["full-res"][op][sample][host] = self.thread_read(
host, op, sample
)
log.debug(f"The Initial DB is : {self.results['full-res']}")
@performance
class TestSmallFileWorkload(PASTest):
"""
Deploy benchmark operator and run SmallFile workload
SmallFile workload using https://github.com/distributed-system-analysis/smallfile
smallfile is a python-based distributed POSIX workload generator which can be
used to quickly measure performance for a variety of metadata-intensive
workloads
"""
def setup(self):
"""
Setting up test parameters
"""
log.info("Starting the test setup")
self.benchmark_name = "SmallFiles"
self.client_pod_name = "smallfile-client"
if config.PERF.get("deploy_internal_es"):
self.es = ElasticSearch()
else:
if config.PERF.get("internal_es_server") == "":
self.es = None
return
else:
self.es = {
"server": config.PERF.get("internal_es_server"),
"port": config.PERF.get("internal_es_port"),
"url": f"http://{config.PERF.get('internal_es_server')}:{config.PERF.get('internal_es_port')}",
}
# verify that the connection to the elasticsearch server is OK
if not super(TestSmallFileWorkload, self).es_connect():
self.es = None
return
super(TestSmallFileWorkload, self).setup()
# deploy the benchmark-operator
self.deploy_benchmark_operator()
def setting_storage_usage(self, file_size, files, threads, samples, clients):
"""
Getting the storage capacity, calculate the usage of the storage and
setting the workload CR rile parameters.
Args:
file_size (int) : the size of the file to be used
files (int) : number of files to use
threads (int) : number of threads to be use in the test
samples (int) : how meany samples to run for each test
clients (int) : number of clients (pods) to use in the test
"""
self.crd_data["spec"]["workload"]["args"]["file_size"] = file_size
self.crd_data["spec"]["workload"]["args"]["files"] = files
self.crd_data["spec"]["workload"]["args"]["threads"] = threads
self.crd_data["spec"]["workload"]["args"]["samples"] = samples
self.crd_data["spec"]["workload"]["args"]["clients"] = clients
# Calculating the size of the volume that need to be test, it should
# be at least twice in the size then the size of the files, and at
# least 100Gi.
# Since the file_size is in Kb and the vol_size need to be in Gb, more
# calculation is needed.
vol_size = int(files * threads * file_size * 3)
vol_size = int(vol_size / constants.GB2KB)
if vol_size < 100:
vol_size = 100
self.crd_data["spec"]["workload"]["args"]["storagesize"] = f"{vol_size}Gi"
def init_full_results(self, full_results):
"""
Initialize the full results object which will send to the ES server
Args:
full_results (obj): an empty SmallFileResultsAnalyse object
Returns:
SmallFileResultsAnalyse (obj): the input object fill with data
"""
for key in self.environment:
full_results.add_key(key, self.environment[key])
# Calculating the total size of the working data set - in GB
full_results.add_key(
"dataset",
self.crd_data["spec"]["workload"]["args"]["file_size"]
* self.crd_data["spec"]["workload"]["args"]["files"]
* self.crd_data["spec"]["workload"]["args"]["threads"]
* full_results.results["clients"]
/ constants.GB2KB,
)
full_results.add_key(
"global_options",
{
"files": self.crd_data["spec"]["workload"]["args"]["files"],
"file_size": self.crd_data["spec"]["workload"]["args"]["file_size"],
"storageclass": self.crd_data["spec"]["workload"]["args"][
"storageclass"
],
"vol_size": self.crd_data["spec"]["workload"]["args"]["storagesize"],
},
)
return full_results
def generate_kibana_link(self, index, columns):
"""
Generating full link to the Kibana server with full test results information
Args:
index (str): the kibana index name (results, response time, etc.)
columns (str): list of all columns to display
Return:
str : an http link to the appropriate kibana report
"""
stime = self.start_time.replace("GMT", ".000Z")
etime = self.end_time.replace("GMT", ".000Z")
log.info(json.dumps(self.crd_data.get("spec").get("elasticsearch"), indent=2))
host = self.crd_data.get("spec").get("elasticsearch").get("url")
try:
host = host.split(":")[1].replace("//", "")
except Exception:
log.error("No ES configuretion")
return ""
kibana_id = self.get_kibana_indexid(host, index)
app = "app/kibana#/discover"
if self.dev_mode:
app = "app/discover#/"
result = (
f"http://{host}:5601/{app}"
f"?_a=(columns:!({columns}),filters:!(),index:'{kibana_id}',interval:auto,"
f"query:(language:kuery,query:'uuid:{self.uuid}'),sort:!())"
f"&_g=(filters:!(),refreshInterval:(pause:!t,value:0),time:(from:'{stime}',to:'{etime}'))"
)
return result
def collect_benchmark_logs(self):
"""
Collecting the test log from all benchmark pods
"""
# Getting full list of benchmark clients
self.full_client_list = get_pod_name_by_pattern(
self.client_pod_name, benchmark_operator.BMO_NAME
)
# Collecting logs from each pod
for clpod in self.full_client_list:
test_logs = self.pod_obj.exec_oc_cmd(f"logs {clpod}", out_yaml_format=False)
log_file_name = f"{self.full_log_path}/{clpod}-pod.log"
try:
with open(log_file_name, "w") as f:
f.write(test_logs)
log.info(f"The Test log can be found at : {log_file_name}")
except Exception:
log.warning(f"Cannot write the log to the file {log_file_name}")
log.info("Logs from all client pods got successfully")
def run(self):
log.info("Running SmallFile bench")
self.deploy_and_wait_for_wl_to_start(timeout=240, sleep=10)
# Getting the UUID from inside the benchmark pod
self.uuid = self.operator.get_uuid(self.client_pod)
self.wait_for_wl_to_finish(sleep=30)
self.collect_benchmark_logs()
try:
if "RUN STATUS DONE" in self.test_logs:
log.info("SmallFiles has completed successfully")
return True
except IOError:
log.warning("SmallFiles failed to complete")
return False
def teardown(self):
"""
The teardown of the test environment in the end.
"""
log.info("cleanup the environment")
if isinstance(self.es, ElasticSearch):
self.es.cleanup()
self.operator.cleanup()
# wait up to 45 min for the ceph cluster be health OK after backend
# operation completed.
log.info("Verify (and wait if needed) that ceph health is OK")
ceph_health_check(tries=45, delay=60)
# Let the background operation (delete backed images) to finish
time.sleep(120)
@pytest.mark.parametrize(
argnames=["file_size", "files", "threads", "samples", "clients", "interface"],
argvalues=[
pytest.param(*[4, 5000, 22, 5, 33, constants.CEPHBLOCKPOOL]),
pytest.param(*[16, 5000, 8, 5, 21, constants.CEPHBLOCKPOOL]),
pytest.param(*[4, 2500, 4, 5, 9, constants.CEPHFILESYSTEM]),
pytest.param(*[16, 1500, 4, 5, 9, constants.CEPHFILESYSTEM]),
],
)
@pytest.mark.polarion_id("OCS-1295")
def test_smallfile_workload(
self, file_size, files, threads, samples, clients, interface
):
"""
Run SmallFile Workload
Args:
file_size (int) : the size of the file to be used
files (int) : number of files to use
threads (int) : number of threads to be use in the test
samples (int) : how meany samples to run for each test
interface (str) : the volume type (rbd / cephfs)
"""
# verify that there is an elasticsearch server for the benchmark
if not self.es:
log.error("This test must have an Elasticsearch server")
return False
# Getting the full path for the test logs
self.full_log_path = get_full_test_logs_path(cname=self)
self.results_path = get_full_test_logs_path(cname=self)
self.full_log_path += (
f"-{file_size}-{files}-{threads}-{samples}-{clients}-{interface}"
)
log.info(f"Logs file path name is : {self.full_log_path}")
# Loading the main template yaml file for the benchmark
log.info("Create resource file for small_files workload")
self.crd_data = templating.load_yaml(constants.SMALLFILE_BENCHMARK_YAML)
# Saving the Original elastic-search IP and PORT - if defined in yaml
self.es_info_backup(self.es)
self.set_storageclass(interface=interface)
# Setting the data set to 40% of the total storage capacity
self.setting_storage_usage(file_size, files, threads, samples, clients)
self.get_env_info()
if not self.run():
log.error("The benchmark failed to run !")
return
# Setting back the original elastic-search information
if self.backup_es:
self.crd_data["spec"]["elasticsearch"] = self.backup_es
# Initialize the results doc file.
full_results = self.init_full_results(
SmallFileResultsAnalyse(
self.uuid, self.crd_data, self.full_log_path, self.main_es
)
)
log.info(f"Full results is : {full_results.results}")
if isinstance(self.es, ElasticSearch):
# Using internal deployed elasticsearch
log.info("Getting data from internal ES")
if self.main_es:
self.copy_es_data(self.es)
full_results.read()
else:
log.info("Dumping data from the Internal ES to tar ball file")
self.es.dumping_all_data(self.full_log_path)
else:
log.info(self.es)
self.es = Elasticsearch(
hosts=[{"host": self.es["server"], "port": self.es["port"]}]
)
full_results.read()
full_results.add_key(
"test_time", {"start": self.start_time, "end": self.end_time}
)
if self.main_es:
full_results.es = self.main_es
if not full_results.dont_check:
full_results.add_key("hosts", full_results.get_clients_list())
full_results.init_full_results()
full_results.aggregate_host_results()
test_status = full_results.aggregate_samples_results()
# Generate link for the all data in the kibana
columens = "optype,files,filesPerSec,elapsed,sample,tid"
klink = self.generate_kibana_link("ripsaw-smallfile-results", columens)
# Generate link for the all response-time data in the kibana
columens = "optype,sample,iops,max,min,mean,'90%25','95%25','99%25'"
rtlink = self.generate_kibana_link("ripsaw-smallfile-rsptimes", columens)
full_results.all_results = {"kibana_all": klink, "kibana_rsptime": rtlink}
if full_results.es_write():
res_link = full_results.results_link()
log.info(f"The Result can be found at : {res_link}")
# Create text file with results of all subtest (4 - according to the parameters)
self.write_result_to_file(res_link)
else:
test_status = True
assert test_status, "Test Failed !"
def test_smallfile_results(self):
"""
This is not a test - it is only check that previous test ran and finish as expected
and reporting the full results (links in the ES) of previous tests (4)
"""
# TODO : This function will push the results (if exists) to the performance dashboard.
self.results_path = get_full_test_logs_path(
cname=self, fname="test_smallfile_workload"
)
self.results_file = os.path.join(self.results_path, "all_results.txt")
log.info(f"Check results in {self.results_file}")
try:
input_file = open(self.results_file, "r")
data = input_file.read().split("\n")
data.pop() # remove the last empty element
input_file.close()
if len(data) != 4:
log.error("Not all tests finished")
raise exceptions.BenchmarkTestFailed()
else:
log.info("All test finished OK, and the results can be found at :")
for res in data:
log.info(res)
except OSError as err:
log.error(f"OS error: {err}")
raise err
|
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|
import pandas as pd
import numpy as np
import random
import sys
import pathlib
import string
from datetime import datetime
# TODO:
# Ensure generated company names are unique
# OverflowError: int too large to convert to float
test_data = pd.DataFrame()
def string_generator(size):
chars = string.ascii_uppercase + string.ascii_lowercase
return ''.join(random.choice(chars) for _ in range(size))
def word_generator(num, max_size=10):
"""Return a string of random length (1-10) num times"""
words = []
for num in range(num):
r_number = random.randint(1,max_size)
words.append(string_generator(r_number))
return words
def save_file(file_name, file_type='xlsx'):
"""Save xlsx file as file_name"""
if file_type is 'xlsx':
save_name = (sys.path[0] + '\\'+ file_name + '.xlsx')
save_name_path = pathlib.Path(save_name)
if save_name_path.is_file():
print('File already exists.')
else:
test_data.to_excel(save_name)
elif file_type is 'csv':
save_name = (sys.path[0] + '\\'+ file_name + '.csv')
save_name_path = pathlib.Path(save_name)
if save_name_path.is_file():
print('File already exists.')
else:
test_data.to_csv(save_name)
else:
print('File not saved. Invalid filetype.')
def linear_graph(size, positive=True):
"""Take a size and return either a positive or negative linear set, adjusted by a random number"""
graph_frame = []
if positive:
for i in range(size):
graph_frame.append(i + 1 + np.round(np.random.uniform(low=0.5, high=5), decimals=2))
else:
for i in range(size):
graph_frame.append(i - 1 - np.round(np.random.uniform(low=0.5, high=5), decimals=2))
return graph_frame
def exponential_graph(size, positive=True):
"""Take a size and return either a positive or negative exponential set, adjusted by a random number"""
graph_frame = []
if positive:
for i in range(size):
graph_frame.append(i*i + np.round(np.random.uniform(low=0.5, high=5), decimals=2))
else:
for i in range(size):
graph_frame.append(-i*i - np.round(np.random.uniform(low=0.5, high=5), decimals=2))
return graph_frame
def cubic_graph(size, positive=True):
"""Take a size and return either a positive or negative cubic set, adjusted by a random number"""
graph_frame = []
if positive:
for i in range(size):
graph_frame.append(i*i*i + np.round(np.random.uniform(low=0.5, high=5), decimals=2))
else:
for i in range(size):
graph_frame.append(-i*i*i - np.round(np.random.uniform(low=0.5, high=5), decimals=2))
return graph_frame
def expo_graph(size, positive=True):
"""Take a size and return either a positive or negative expo set, adjusted by a random number"""
graph_frame = []
if positive:
for i in range(size):
graph_frame.append(5**i + np.round(np.random.uniform(low=0.0, high=0.5), decimals=2))
else:
for i in range(size):
graph_frame.append(5**(-i) - np.round(np.random.uniform(low=0.0, high=0.5), decimals=2))
return graph_frame
def random_dates(n, unit='D', seed=None):
"""Return random dates between a year n times"""
start_time = pd.to_datetime('2019-01-01', infer_datetime_format=True )
end_time = pd.to_datetime('2019-12-31', infer_datetime_format=True )
time_frame = []
if not seed:
np.random.seed(0)
ndays = (end_time - start_time).days + 1
time_frame.append(start_time + pd.to_timedelta(
np.random.randint(0, ndays, n), unit=unit
))
returned_time = pd.DataFrame(time_frame).transpose()
returned_time.columns = ['Dates']
ordered_time = returned_time.sort_values('Dates').reset_index(drop=True)
return ordered_time
list_of_graphs = [cubic_graph, exponential_graph, linear_graph, expo_graph]
list_of_companies = word_generator(500)
def build_dataframe():
"""Return a dataframe made of several companies with random data"""
my_frame_data = pd.DataFrame()
for company in list_of_companies:
mini_data = pd.DataFrame()
mini_data['Data'] = random.choice(list_of_graphs)(200, positive=bool(random.choice([True, False])))
mini_data['Company'] = company
mini_data['Dates'] = random_dates(200)
my_frame_data = my_frame_data.append(mini_data, ignore_index=True) # Append doesn't happen in-place, so we have to store it..
my_frame_data = my_frame_data[['Dates', 'Company', 'Data']] # Reorder the columns
return my_frame_data
example = build_dataframe()
#build_dataframe().to_csv('savederp3.csv')
print(example)
|
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|
[STATEMENT]
lemma list_member_conv_member [simp]:
"equal_base.list_member (=) = List.member"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. equal_base.list_member (=) = List.member
[PROOF STEP]
proof(intro ext)
[PROOF STATE]
proof (state)
goal (1 subgoal):
1. \<And>x xa. equal_base.list_member (=) x xa = List.member x xa
[PROOF STEP]
fix xs and x :: 'a
[PROOF STATE]
proof (state)
goal (1 subgoal):
1. \<And>x xa. equal_base.list_member (=) x xa = List.member x xa
[PROOF STEP]
show "equal_base.list_member (=) xs x = List.member xs x"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. equal_base.list_member (=) xs x = List.member xs x
[PROOF STEP]
by(induct xs)(auto simp add: List.member_def)
[PROOF STATE]
proof (state)
this:
equal_base.list_member (=) xs x = List.member xs x
goal:
No subgoals!
[PROOF STEP]
qed
|
{"llama_tokens": 348, "file": "Containers_DList_Set", "length": 5}
|
import sys
import json
import time
import array
import struct
import logging
import numpy as np
from copy import deepcopy
from pybleno import *
import wasatch
from wasatch.WasatchDevice import WasatchDevice
from wasatch.WasatchBus import WasatchBus
from wasatch import applog
logger = logging.getLogger(__name__)
################################################################################
# #
# Characteristics #
# #
################################################################################
class Battery_Status(Characteristic):
def __init__(self, uuid, device, msg_queue, msg_func):
Characteristic.__init__(self, {'uuid': uuid, 'properties': [ 'read', 'notify'], 'value': None})
self._value = array.array('B', [0] * 0)
self.page = None
self.subpage = None
self.device = device
self.guid = deepcopy(uuid)
self.msg_num = 0
self.msg_queue = msg_queue
self.msg_func = msg_func
def onReadRequest(self, offset, callback):
logger.debug("Bluetooth: Central requested battery status.")
msg_id = self.guid + str(self.msg_num)
msg = {'Command': 'HAS_BATTERY', 'Value': None}
has_battery = self.msg_func(msg_id, msg ,5)["Res_Value"]
if has_battery:
self.msg_num += 1
msg_id = self.guid + str(self.msg_num)
msg = {'Command': 'BATTERY', 'Value': None}
dev_battery = self.msg_func(msg_id, msg , 5)["Res_Value"]
logger.debug(f"Bluetooth: Device has battery. Returning state of {dev_battery}%.")
self.msg_num += 1
self.msg_num %= 8000
dev_battery = int(dev_battery)
callback(Characteristic.RESULT_SUCCESS, dev_battery.to_bytes(2,"big"))
else:
logger.debug("Bluetooth: Device does not have battery. Returning 100.%")
full_battery = 100
callback(Characteristic.RESULT_SUCCESS,full_battery.to_bytes(2,"big"))
class Acquire_Spectrum(Characteristic):
def __init__(self, uuid, device, msg_queue, msg_func):
Characteristic.__init__(self, {'uuid': uuid, 'properties': ['write'], 'value': None})
self._value = array.array('B',[0] * 0)
self.current_spec = None
self.device = device
self.guid = deepcopy(uuid)
self.msg_num = 0
self.msg_queue = msg_queue
self.msg_func = msg_func
def onWriteRequest(self,data,offset,withoutResponse,callback):
logger.debug("Bluetooth: Received command to acquire spectrum. Acquiring spectrum...")
msg_id = self.guid + str(self.msg_num)
msg = {"Command": "GET_SPECTRA", "Value": None}
res = self.msg_func(msg_id, msg, 5)["Res_Value"]
if res is not None:
self.current_spec = res
self.msg_num += 1
self.msg_num %= 8000
callback(Characteristic.RESULT_SUCCESS)
def get_current_spectra(self):
return self.current_spec
def reset_current_spectra(self):
self.current_spec = None
class Spectrum_Request(Characteristic):
def __init__(self, uuid, device):
Characteristic.__init__(self, {'uuid': uuid, 'properties': [ 'write'], 'value': None})
self._value = array.array('B',[0] * 0)
self.pixel_offset = None
self.device = device
def onWriteRequest(self, data, offset, withoutResponse, callback):
pixel_start_value = int.from_bytes(data, "big")
logger.debug(f"Bluetooth: Received request to set pixel offset for spectra {pixel_start_value}.")
self.pixel_offset = pixel_start_value
callback(Characteristic.RESULT_SUCCESS)
def get_current_offset(self):
return self.pixel_offset
def reset_current_offset(self):
self.pixel_offset = None
class EEPROM_Cmd(Characteristic):
def __init__(self, uuid, device, msg_queue, msg_func):
Characteristic.__init__(self, {'uuid': uuid, 'properties': [ 'write'], 'value': None})
self._value = array.array('B', [0] * 0)
self.page = None
self.subpage = None
self.device = device
self.msg_queue = msg_queue
self.write_buffers = None
self.msg_num = 0
self.guid = deepcopy(uuid)
self.msg_func = msg_func
def onWriteRequest(self, data, offset, withoutResponse, callback):
# data comes in as a byte array so it is easy to manipulate
page = int(data[0])
subpage = int(data[1])
if page == 0 and subpage == 0:
msg_id = self.guid + str(self.msg_num)
msg = {'Command': 'EEPROM', 'Value': None}
self.write_buffers = self.msg_func(msg_id, msg, 5)["Res_Value"]
self.msg_num += 1
self.msg_num %= 8000
self.page = page
self.subpage = subpage
callback(Characteristic.RESULT_SUCCESS)
def get_page(self):
return self.page
def get_subpage(self):
return self.subpage
class EEPROM_Data(Characteristic):
def __init__(self, uuid, cmd_status, device):
Characteristic.__init__(self, {'uuid': uuid, 'properties': ['read', 'notify'], 'value': None})
self.eeprom_cmd = cmd_status
self._value = array.array('B', [0] * 0)
self._updateValueCallback = None
self.device = device
def onReadRequest(self, offset, callback):
page = self.eeprom_cmd.get_page()
subpage = self.eeprom_cmd.get_subpage()
logger.debug(f"Bluetooth: Central requested EEPROM read of page {page} and subpage {subpage}")
self._value = bytearray(self.eeprom_cmd.write_buffers[page])[(0+16*subpage):(16+16*subpage)]
callback(Characteristic.RESULT_SUCCESS, self._value)
def onSubscribe(self, maxValueSize, updateValueCallback):
logger.debug('Bluetooth: EEPROM Data subscribed to.')
slef._updateValueCallback = updateValueCallback
class IntegrationTime(Characteristic):
def __init__(self, uuid, device, msg_func):
Characteristic.__init__(self, {'uuid': uuid, 'properties': ['read', 'write'], 'value': None})
self._value = array.array('B', [0] * 0)
self._updateValueCallback = None
self.device = device
self.msg_num = 0
self.guid = deepcopy(uuid)
self.msg_func = msg_func
def onReadRequest(self, offset, callback):
#logger.debug(offset, callback, self._value)
msg_id = self.guid + str(self.msg_num)
msg = {"Command": "GET_INT_TIME", "Value": None}
self._value = self.msg_func(msg_id, msg ,5)["Res_Value"]
self.msg_num += 1
self.msg_num %= 8000
logger.debug(f"Bluetooth: Got integration time of {self._value}")
callback(Characteristic.RESULT_SUCCESS, self._value.to_bytes(2, "big"))
def onWriteRequest(self, data, offset, withoutResponse, callback):
self._value = int.from_bytes(data,"big")
msg_id = self.guid + str(self.msg_num)
int_value = {"Command": "SET_INT_TIME", "Value": f"{self._value}"}
self.msg_func(msg_id, int_value, 5)
self.msg_num += 1
self.msg_num %= 8000
logger.debug("Integration time changed to %d ms" % self._value)
if self._updateValueCallback:
self._updateValueCallback(self._value)
callback(Characteristic.RESULT_SUCCESS)
def onSubscribe(self, maxValueSize, updateValueCallback):
logger.debug("onSubscribe")
self._updateValueCallback = updatevalueCallback
def onUnsubscribe(self):
logger.debug("on unsubscribe")
self._updateValueCallback = None
class Scans_to_average(Characteristic):
def __init__(self, uuid, device):
Characteristic.__init__(self, {'uuid': uuid, 'properties': ['read', 'write'], 'value': None})
self._value = array.array('B', [0] * 0)
self._updateValueCallback = None
self.device = device
def onReadRequest(self, offset, callback):
logger.debug("Scans to average read called")
callback(Characteristic.RESULT_SUCCESS, self._value)
def onWriteRequest(self, data, offset, withoutResponse, callback):
self._value = data
device.change_setting("scans_to_average", data)
logger.debug("Scans average changed to %d" %int(data))
if self._updateValueCallback:
self._updateValueCallback(self._value)
callback(Characteristic.RESULT_SUCCESS)
def onSubscribe(self, maxValueSize, updateValueCallback):
logger.debug("onSubscribe")
self._updateValueCallback = updatevalueCallback
def onUnsubscribe(self):
logger.debug("on unsubscribe")
self._updateValueCallback = None
class Read_Spectrum(Characteristic):
def __init__(self, uuid, spec_acquire, spec_cmd, device, laser_state):
Characteristic.__init__(self, {'uuid': uuid, 'properties': ['read'], 'value': None})
self._value = array.array('B', [0] * 0)
self._updateValueCallback = None
self.spec_acquire = spec_acquire
self.spec_cmd = spec_cmd
self.device = device
def onReadRequest(self, offset, callback):
#logger.debug(self._value, self.value, callback, offset)
logger.debug("Bluetooth: Received request to return spectrum that has been taken.")
spec_read = self.spec_acquire.get_current_spectra()
pixel_offset = self.spec_cmd.get_current_offset()
reading = spec_read
logger.debug(f"Creating return bytes from reading. Starting at pixel {pixel_offset}.")
return_bytes = bytes()
if reading is not None:
while len(return_bytes) < 180 and pixel_offset < len(reading):
pixel_byte_value = int(reading[pixel_offset]).to_bytes(2,"little")
return_bytes += pixel_byte_value
pixel_offset += 1
return_bytes = pixel_offset.to_bytes(2,"big") + return_bytes
else:
return_bytes = pixel_offset.to_bytes(2,"big") + return_bytes
logger.error(f"Reading was None, so returning null bytes value")
logger.debug(f"Finished building return bytes of length {len(return_bytes)} containing up to pixel {pixel_offset}.")
callback(Characteristic.RESULT_SUCCESS, return_bytes)
def onSubscribe(self, maxValueSize, updateValueCallback):
logger.debug("onSubscribe")
self._updateValueCallback = updatevalueCallback
def onUnsubscribe(self):
logger.debug("on unsubscribe")
self._updateValueCallback = None
class Gain(Characteristic):
def __init__(self, uuid, device, msg_func):
Characteristic.__init__(self, {'uuid': uuid, 'properties': ['read', 'write'], 'value': None})
self._value = array.array('B', [0] * 0)
self._updateValueCallback = None
self.device = device
self.msg_func = msg_func
self.guid = deepcopy(uuid)
self.msg_num = 0
def onReadRequest(self, offset, callback):
msg_id = self.guid + str(self.msg_num)
msg = {"Command": "GET_GAIN", "Value": None}
gain = self.msg_func(msg_id, msg, 5)["Res_Value"]
self.msg_num += 1
self.msg_num %= 8000
gain = int(gain)
logger.debug("Bluetooth: Received device response for gain of {gain}")
callback(Characteristic.RESULT_SUCCESS, gain.to_bytes(2, "big"))
def onWriteRequest(self, data, offset, withoutResponse, callback):
data = bytearray(data)
lsb = data[1]
msb = data[0]
gain = msb + lsb / 256.0
msg_id = self.guid + str(self.msg_num)
logger.debug(f"Bluetooth: Updating gain value to {gain}")
msg = {"Command": "SET_GAIN", "Value": f"{gain}"}
self.msg_func(msg_id, msg, 5)
self.msg_num += 1
self.msg_num %= 8000
callback(Characteristic.RESULT_SUCCESS)
class Laser_State(Characteristic):
def __init__(self, uuid, device, msg_queue, msg_func):
Characteristic.__init__(self, {'uuid': uuid, 'properties': ['read', 'write', 'notify'], 'value': None})
self._value = array.array('B', [0] * 0)
self._updateValueCallback = None
self.raman_mode = False
self.device = device
self.laser_type = 0
self.laser_enable = False
self.laser_watchdog = False
self.watchdog_time = 5
self.laser_delay = 300
self.msg_func = msg_func
self.guid = deepcopy(uuid)
self.msg_num = 0
def disable_laser_error_byte(self):
msg_id = self.guid + str(self.msg_num)
self.device.hardware.set_laser_enable(False)
msg = {"Command": "SET_LASER", "Value": "0"}
self.msg_func(msg_id, msg, 0)
self.msg_num += 1
self.msg_num %= 8000
logger.warn("Bluetooth: Received an incorrect byte that triggered a laser shut off.")
def onReadRequest(self, offset, callback):
logger.debug("Bluetooth: Received laser read request.")
msg_id = self.guid + str(self.msg_num)
msg = {"Command": "GET_RAMAN_MODE", "Value": None}
raman_mode = self.msg_func(msg_id, msg, 2)["Res_Value"]
if raman_mode == None:
logger.error("Got a none value for raman_mode, returning 0")
raman_mode = 0
laser_type = 0
msg = {"Command": "GET_LASER_STATE", "Value": None}
laser_enable = self.msg_func(msg_id, msg, 2)["Res_Value"]
if laser_enable == None:
logger.error("Got a none value for laser_enable, returning 0")
laser_enable = 0
msg = {"Command": "GET_WATCHDOG_DELAY", "Value": None}
laser_watchdog = self.msg_func(msg_id, msg, 2)["Res_Value"]
if laser_watchdog == None:
logger.error("Got a none value for laser_watchdog, returning 0")
laser_watchdog = 0
msg = {"Command": "GET_RAMAN_DELAY", "Value": None}
laser_delay = self.msg_func(msg_id, msg, 2)["Res_Value"]
if laser_delay == None:
logger.error("Got a none value for laser_delay, returning 0")
laser_delay = 0
return_bytes = raman_mode.to_bytes(2, "big") + laser_type.to_bytes(2, "big") + laser_enable.to_bytes(2, "big")
return_bytes += laser_watchdog.to_bytes(2, "big") + laser_delay.to_bytes(2, "big")
self.msg_num += 1
self.msg_num %= 8000
callback(Characteristic.RESULT_SUCCESS, return_bytes)
def onWriteRequest(self, data, offset, withoutResponse, callback):
logger.debug(f"Bluetooth: Received laser write request with data {data}")
msg_id = self.guid + str(self.msg_num)
msg_raman = int(data[0])
msg_laser_type = int(data[1])
msg_laser_enable = int(data[2])
msg_laser_watch = int(data[3])
msg_laser_delay = int.from_bytes(data[4:6], "big")
logger.debug(f"Bluetooth: Laser message values were Raman mode {msg_raman}, Laser type {msg_laser_type}, Laser enable {msg_laser_enable}, Laser watchdog {msg_laser_watch}, and Laser delay {msg_laser_delay}.")
if msg_raman == 0:
self.raman_mode = False
elif msg_raman == 1:
self.raman_mode = True
elif msg_raman != 255:
self.disable_laser_error_byte()
if msg_laser_type == 0:
self.laser_type = 0
elif msg_laser_type != 255:
self.disable_laser_error_byte()
if msg_laser_enable == 0:
msg = {"Command": "SET_LASER", "Value": "0"}
self.msg_func(msg_id, msg, 0)
elif msg_laser_enable == 1:
msg = {"Command": "SET_LASER", "Value": "1"}
self.msg_func(msg_id, msg, 0)
elif msg_laser_enable != 255:
self.diable_laser_error_byte()
if msg_laser_watch != 255:
msg = {"Command": "SET_WATCHDOG", "Value": f"{msg_laser_watch}"}
self.msg_func(msg_id, msg, 1)
msg = {"Command": "SET_RAMAN_DELAY", "Value": f"{msg_laser_delay}"}
self.msg_func(msg_id, msg, 1)
self.msg_num += 1
self.msg_num %= 8000
callback(Characteristic.RESULT_SUCCESS)
def onSubscribe(self, maxValueSize, updateValueCallback):
logger.debug("onSubscribe")
self._updateValueCallback = updatevalueCallback
def onUnsubscribe(self):
logger.debug("on unsubscribe")
self._updateValueCallback = None
class Detector_ROI(Characteristic):
def __init__(self, uuid, device, msg_queue, msg_func):
Characteristic.__init__(self, {'uuid': uuid, 'properties': ['read', 'write'], 'value': None})
self._value = array.array('B', [0] * 0)
self._updateValueCallback = None
self.device = device
self.guid = deepcopy(uuid)
self.msg_num = 0
self.msg_queue = msg_queue
self.msg_func = msg_func
def onReadRequest(self, offset, callback):
logger.debug("Bluetooth: Received request for detector roi")
msg_id = self.guid + str(self.msg_num)
msg = {"Command": "GET_ROI", "Value": None}
start_roi, end_roi = self.msg_func(msg_id, msg, 5)["Res_Value"]
self.msg_num += 1
self.msg_num %= 8000
return_bytes = start_roi.to_bytes(2, "big") + end_roi.to_bytes(2, "big")
logger.debug("Bluetooth: returning roi values of {start_roi} and {end_roi}")
callback(Characteristic.RESULT_SUCCESS, return_bytes)
def onWriteRequest(self, data, offset, withoutResponse, callback):
# For enlighten mobile the bytes are coming in cropped
# This pads the bytes to the ENG-120 specific 4 in order to get the correct value
msg_id = self.guid + str(self.msg_num)
while len(data) < 4:
data += bytes([0])
start_roi = int.from_bytes(data[0:2], "big")
end_roi = int.from_bytes(data[2:4], "big")
logger.debug(f"Bluetooth: Received command of data {data} to set roi to {start_roi} and {end_roi}")
msg = {"Command": "SET_ROI", "Value": f"{start_roi},{end_roi}"}
self.msg_func(msg_id, msg, 5)
self.msg_num += 1
self.msg_num %= 8000
callback(Characteristic.RESULT_SUCCESS)
|
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|
#!/usr/bin/python
# -*- coding: utf-8 -*-
from PIL import Image
import numpy as np
#Returns numpy image at size imageSize*imageSize
def getProcessedData(img,imageSize):
img = img.resize((imageSize,imageSize), resample=Image.ANTIALIAS)
imgData = np.asarray(img, dtype=np.uint8).reshape(imageSize,imageSize,1)
imgData = imgData/255.
return imgData
#Returns numpy image at size imageSize*imageSize
def getImageData(filename,imageSize):
img = Image.open(filename)
imgData = getProcessedData(img, imageSize)
return imgData
|
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|
[STATEMENT]
lemma reach_reach\<^sub>t_fst:
"reach \<Sigma> \<delta> q\<^sub>0 = fst ` reach\<^sub>t \<Sigma> \<delta> q\<^sub>0"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. reach \<Sigma> \<delta> q\<^sub>0 = fst ` reach\<^sub>t \<Sigma> \<delta> q\<^sub>0
[PROOF STEP]
unfolding reach\<^sub>t_def reach_def image_def
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. {run \<delta> q\<^sub>0 w n |w n. {y. \<exists>x\<in>UNIV. y = w x} \<subseteq> \<Sigma>} = {y. \<exists>x\<in>{run\<^sub>t \<delta> q\<^sub>0 w n |w n. {y. \<exists>x\<in>UNIV. y = w x} \<subseteq> \<Sigma>}. y = fst x}
[PROOF STEP]
by fastforce
|
{"llama_tokens": 274, "file": "LTL_to_DRA_DTS", "length": 2}
|
import pandas as pd
import numpy as np
from pandas import Series
from pandas import DataFrame
from statsmodels import regression
def init(context):
context.hs300 = "000300.XSHG"
# window must larger than 64
context.WINDOW = 400
def handle_bar(context, bar_dict):
time_series = history_bars(context.hs300, context.WINDOW, '1d', 'close')
hurstex = hurst(time_series)
#hurstex = Hurst(time_series, 318)
plot("hurst", hurstex)
'''
if abs(hurstex-0.5)<0.05:
order_target_value(context.s,0)
elif hurstex>0.5 and :
order_target_percent(context.s,1)
elif hurst.ex<0.5:
'''
'''
# 买入卖出条件需要再次调试
curPosition = context.portfolio.positions[context.hs300].quantity
if hurstex > 0.55:
if curPosition == 0:
order_target_percent(context.hs300, 1)
elif hurstex < 0.45:
if curPosition > 0:
order_target_value(context.hs300, 0)
'''
# def hurst(ts):
#
# if not isinstance(ts, Iterable):
# print 'error'
# return
#
# n_min, n_max = 2, len(ts) // 3
# RSlist = []
# for cut in range(n_min, n_max):
# children = len(ts) // cut
# children_list = [ts[i * children:(i + 1) * children] for i in range(cut)]
# L = []
# for a_children in children_list:
# Ma = np.mean(a_children)
# Xta = Series(map(lambda x: x - Ma, a_children)).cumsum()
# Ra = max(Xta) - min(Xta)
# Sa = np.std(a_children)
# rs = Ra / Sa
# L.append(rs)
# RS = np.mean(L)
# RSlist.append(RS)
# return np.polyfit(np.log(range(2 + len(RSlist), 2, -1)), np.log(RSlist), 1)[0]
def hurst(history):
daily_return = list(Series(history).pct_change())[1:]
ranges = ['1', '2', '4', '8', '16', '32']
lag = Series(index=ranges)
for i in range(len(ranges)):
if i == 0:
lag[i] = len(daily_return)
else:
lag[i] = lag[0] // (2 ** i)
ARS = Series(index=ranges)
for r in ranges:
# RS用来存储每一种分割方式中各个片段的R/S值
RS = list()
# 第i个片段
for i in range(int(r)):
# 用Range存储每一个片段数据
Range = daily_return[int(i * lag[r]):int((i + 1) * lag[r])]
mean = np.mean(Range)
Deviation = np.cumsum(Range - mean,axis=0)
#Deviation = Range - mean
maxi = max(Deviation)
mini = min(Deviation)
RS.append(maxi - mini)
sigma = np.std(Range)
RS[i] = RS[i] / sigma
ARS[r] = np.mean(RS)
lag = np.log10(lag)
ARS = np.log10(ARS)
hurst_exponent = np.polyfit(lag, ARS, 1)
hurst = hurst_exponent[0]
return hurst
# def Hurst(XX,T):
# XX = np.array(XX) #读入时间序列,一维矩阵
# Lenth = XX.shape[0] #读取时间序列的总长度，
# hurst = np.zeros(Lenth)
# for i in xrange(T,Lenth):
# X=XX[i-T:i+1]
# RS = np.zeros(T) #array
# logRS = np.zeros(T) #array
# logn = np.zeros(T) #array
# for n in xrange(10,T): #每个长度计算一次
# a = int(T/n)
# x1 = X[0:n*a].reshape(n,a) #正向分段,n行a列，每一列为一个子序列
# x2 = X[(T - a * n): T].reshape(n,a) #反向分段,n行a列，每一列为一个子序列
# m1 = np.mean(x1,axis=0) #按列取均值，1行a列
# m2 = np.mean(x2,axis=0) #按列取均值，1行a列
# p = np.ones((n,1)) #n行，1列
# y1 = x1 - p * m1 #n行，a列，对每一列求离差
# y2 = x2 - p * m2 #n行，a列，对每一列求离差
# sig1 = np.std(x1,axis=0) #1行，a列，对每一列求标准差
# sig2 = np.std(x2,axis=0) #1行，a列，对每一列求标准差
# sum1 = np.cumsum(y1,axis=0) #n行，a列，求累计离差
# sum2 = np.cumsum(y2,axis=0) #n行，a列，求累计离差
# r1 = np.max(sum1,axis=0) - np.min(sum1,axis=0) #%1行，a列
# r2 = np.max(sum2,axis=0) - np.min(sum2,axis=0) #%1行，a列
# RS1[n] = np.mean(r1 / sig1,axis=0) #1行，1列
# RS2[n] = np.mean(r2 / sig2,axis=0) #1行，1列
# RS[n] = 0.5*RS1[n] + 0.5*RS2[n]
# logRS[n] = np.log(RS[n])
# logn[n] = np.log(n)
# R = regression.linear_model.OLS(logRS[10:T],logn[10:T]).fit()
# hurst[i] = R.params[0]
# return hurst
|
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|
"""
MinOver algorithm to find a point inside a polytope.
Francesc Font-Clos
Oct 2018
"""
import numpy as np
class MinOver(object):
"""MinOver solver."""
def __init__(self, polytope, ):
"""
Create a MinOver solver.
Parameters
----------
polytope: hitandrun.polytope
Polytope in H-representation
"""
self.polytope = polytope
def run(self, speed=1, starting_point=None, max_iters=100, verbose=False):
"""
Run the MinOver algorithm.
Parameters
----------
speed: float
Distance moved at each learning step
max_iters: int
Maximum number of iterations (per hyperplan).
starting_poin: np.array
Initial condition.
Returns
-------
current: np.array
The final point.
convergence: bool
True if the algorithm converged, False, otherwise.
"""
self.max_iters = max_iters * self.polytope.nplanes
self.speed = speed
if starting_point is None:
self.current = np.zeros(self.polytope.dim)
else:
self.current = starting_point
# compute step 0 worst planes
# this is a trick to handle first steps
self.worst_indexes = [-1, -2]
self.worst_distances = [-1, -2]
self._set_worst_constraint()
for i in range(self.max_iters):
convergence = self._step()
self.iter = i
self._check_speed()
if verbose:
self._print_worst()
if convergence:
break
return self.current, convergence
def _step(self):
self._move_towards_worst_plane()
self._set_worst_constraint()
return np.all(self.distances < 0)
def _check_speed(self):
i0, i1, i2 = self.worst_indexes[::-1][:3]
d0, d1, d2 = self.worst_distances[::-1][:3]
if i0 != i1 and i0 == i2 and d0 >= d2:
self.speed *= 0.9
def _set_worst_constraint(self):
self.distances = self.polytope.A @ self.current - self.polytope.b
self.worst = np.argmax(self.distances)
self.worst_indexes.append(self.worst)
self.worst_distances.append(self.distances[self.worst])
def _move_towards_worst_plane(self):
self.current = self.current - self.speed * self.polytope.A[self.worst]
def _print_worst(self):
worst_distance = self.distances[self.worst]
print("iter", self.iter,
"index:", self.worst,
"distance:", worst_distance,
"speed:", self.speed)
|
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|
###################################
# Script :
# 1) Contains class to generate XL-MS
# plots
# 2) Inherits from CX class
#
# ganesans - Salilab - UCSF
# ganesans@salilab.org
###################################
import pandas as pd
import glob
import sys,os,math,itertools
import numpy as np
import pandas as pd
from validation import sas, get_input_information,cx
from bokeh.io import output_file, show, curdoc, export_png, export_svgs
from bokeh.models import Span,ColumnDataSource, LinearAxis, Legend, FactorRange
from bokeh.palettes import GnBu3, RdBu,OrRd3,Blues,YlOrBr, Spectral6, Set1
from bokeh.plotting import figure, output_file, save
from bokeh.models.widgets import Tabs, Panel
from bokeh.layouts import row,column,gridplot
class cx_validation_plots(cx.cx_validation):
def __init__(self,mmcif_file):
super().__init__(mmcif_file)
self.ID=str(get_input_information.get_id(self))
self.xl_df=cx.cx_validation.get_xl_data(self)
self.model_df=cx.cx_validation.get_df_for_models(self)
self.filename = os.path.join('Output/images//')
self.filename_add = os.path.join('static/images//')
def plot_linker_dist_I(self,df,intra=1,key='Intra'):
'''
plot distance distribution per linker
based on inter and intra links
'''
for i in df['Linker'].unique():
df_c=df[df['Linker']==i]
if i=='DSS':
loc=30
elif i=='EDC':
loc=20
else:
loc=30
output_file(self.ID+i+"linker.html",mode="inline")
measured=df_c[df_c['Intra']==intra]['dist']
hist, edges = np.histogram(measured, density=False, bins=50)
#hist_l, edges_l = np.histogram(measured, density=False, bins=25)
p = figure(title=key+'-molecular distances/Linker '+i,
plot_height=400, plot_width=400)
p.quad(top=hist, bottom=0, left=edges[:-1], right=edges[1:],
fill_color="navy", line_color="white", alpha=0.3)
#p.line(edges, hist, line_color="navy", line_width=4, alpha=0.7, legend_label=key+"/"+i)
vline = Span(location=loc, dimension='height', line_color='red', line_width=3,line_dash='dashed')
p.renderers.extend([vline])
p.xaxis.major_label_text_font_size="14pt"
p.yaxis.major_label_text_font_size="14pt"
p.title.text_font_size='12pt'
p.title.align="center"
p.title.vertical_align='top'
p.xaxis.axis_label = 'Distance \u212B'
p.xaxis.axis_label_text_font_size='14pt'
p.yaxis.axis_label = 'Number of cross-links'
p.yaxis.axis_label_text_font_size='14pt'
p.output_backend="svg"
save(p,filename=self.filename+'/'+self.ID+i+key+"linker.html")
export_svgs(p,filename=self.filename+'/'+self.ID+i+key+"linker.svg")
save(p,filename=self.filename_add+'/'+self.ID+i+key+"linker.html")
export_svgs(p,filename=self.filename_add+'/'+self.ID+i+key+"linker.svg")
def plot_linker_dist_S(self,df,struc=1,key='Structured'):
'''
plot distance distribution per linker
based on structured/unstrcutured/between struc&unstruc
'''
for i in df['Linker'].unique():
df_c=df[df['Linker']==i]
if i=='DSS':
loc=30
elif i=='EDC':
loc=20
else:
loc=30
output_file(self.ID+i+"linker.html",mode="inline")
measured=df_c[df_c['Structured']==struc]['dist']
hist, edges = np.histogram(measured, density=False, bins=50)
#hist_l, edges_l = np.histogram(measured, density=False, bins=25)
p = figure(title=key+ ' regions/Linker '+i,
plot_height=350, plot_width=350)
p.quad(top=hist, bottom=0, left=edges[:-1], right=edges[1:],
fill_color="navy", line_color="white", alpha=0.3)
#p.line(edges, hist, line_color="navy", line_width=4, alpha=0.7, legend_label=key+"/"+i)
vline = Span(location=loc, dimension='height', line_color='red', line_width=3,line_dash='dashed')
p.renderers.extend([vline])
p.xaxis.major_label_text_font_size="14pt"
p.yaxis.major_label_text_font_size="14pt"
p.title.text_font_size='12pt'
p.title.align="center"
p.title.vertical_align='top'
p.xaxis.axis_label = 'Distance \u212B'
p.xaxis.axis_label_text_font_size='14pt'
p.yaxis.axis_label = 'Number of cross-links'
p.yaxis.axis_label_text_font_size='14pt'
p.output_backend="svg"
save(p,filename=self.filename+'/'+self.ID+i+key+"linker.html")
export_svgs(p,filename=self.filename+'/'+self.ID+i+key+"linker.svg")
save(p,filename=self.filename_add+'/'+self.ID+i+key+"linker.html")
export_svgs(p,filename=self.filename_add+'/'+self.ID+i+key+"linker.svg")
def plot_intra_summary_deprecated(self):
'''
plot summary of intra/inter xl-ms satisfaction
'''
for model_id,df in self.model_df.items():
factors=self.get_factors(df)
regions = ['Satisfied', 'Violated']
source = ColumnDataSource(data=dict(
x=factors,
Satisfied=self.get_satisfied(df,factors),
Violated=self.get_violated(df,factors), ))
fig_id = figure(x_range=FactorRange(*factors), plot_height=400,plot_width=500,
title='CX-MS Satisfaction for model:'+str(model_id))
fig_id.vbar_stack(regions, x='x', width=0.9, alpha=0.5, color=["blue", "red"], source=source,
legend_label=regions)
fig_id.xaxis.major_label_text_font_size="14pt"
fig_id.yaxis.major_label_text_font_size="14pt"
fig_id.yaxis.axis_label_text_font_size='14pt'
fig_id.title.text_font_size='12pt'
fig_id.title.align="center"
fig_id.title.vertical_align='top'
fig_id.yaxis.axis_label = 'Number of cross-links'
fig_id.y_range.start = 0
fig_id.y_range.end = df.shape[0]
fig_id.x_range.range_padding = 0.1
fig_id.xaxis.major_label_orientation = 1
fig_id.xgrid.grid_line_color = None
fig_id.legend.location = "top_center"
fig_id.legend.orientation = "horizontal"
fig_id.output_backend="svg"
save(fig_id,filename=self.filename+'/'+self.ID+str(model_id)+"IS.html")
export_svgs(fig_id,filename=self.filename+'/'+self.ID+str(model_id)+"IS.svg")
save(fig_id,filename=self.filename_add+'/'+self.ID+str(model_id)+"IS.html")
export_svgs(fig_id,filename=self.filename_add+'/'+self.ID+str(model_id)+"IS.svg")
def plot_distributions(self):
'''
plot inter and intra distance distributions
'''
for model_id,df in self.model_df.items():
self.plot_linker_dist_I(df,intra=1,key='Intra')
self.plot_linker_dist_I(df,intra=0,key='Inter')
self.plot_linker_dist_S(df,struc=1,key='Structured')
self.plot_linker_dist_S(df,struc=0,key='Unstructured')
self.plot_linker_dist_S(df,struc=2,key='Intermediate')
def get_factors(self,df):
'''
get grouped inter/intra factors for stacked bar plot
'''
link=df['Linker'].unique()
xl=['Inter','Intra']
factors=list(itertools.product(link, xl))
return factors
def get_factors_struc(self,df):
'''
get grouped struc factors for stacked bar plot
'''
link=df['Linker'].unique()
xl=['Structured','Unstructured','Intermediate']
factors=list(itertools.product(link, xl))
return factors
def get_satisfied(self,df,factors):
'''
get satisfied list for stacked bar plot;inter/intra info
'''
Satisfied=[]
for i in factors:
df_1=df[df['Linker']==i[0]]
df_2=df_1[df_1[i[1]]==1]
Satisfied.append(df_2[df_2['Satisfied']==1].shape[0])
return Satisfied
def get_satisfied_struc(self,df,factors):
'''
get satisfied list for stacked bar plot;struc info
'''
Satisfied=[]
struc_dict={'Structured':1,'Unstructured':0,'Intermediate':2}
for i in factors:
df_1=df[df['Linker']==i[0]]
df_2=df_1[df_1['Structured']==struc_dict[i[1]]]
Satisfied.append(df_2[df_2['Satisfied']==1].shape[0])
return Satisfied
def get_violated(self,df,factors):
'''
get violated list for stacked bar plot;inter/intra info
'''
Violated=[]
for i in factors:
df_1=df[df['Linker']==i[0]]
df_2=df_1[df_1[i[1]]==1]
Violated.append(df_2[df_2['Satisfied']==0].shape[0])
return Violated
def get_violated_struc(self,df,factors):
'''
get violated list for stacked bar plot;struc info
'''
struc_dict={'Structured':1,'Unstructured':0,'Intermediate':2}
Violated=[]
for i in factors:
df_1=df[df['Linker']==i[0]]
df_2=df_1[df_1['Structured']==struc_dict[i[1]]]
Violated.append(df_2[df_2['Satisfied']==0].shape[0])
return Violated
def make_gridplot_intra(self):
'''
make gridplot;inter/intra info
'''
grid=[]
for model_id,df in self.model_df.items():
grid.append(self.plot_intra_summary(df,model_id))
gridP=gridplot(grid, ncols=len(grid))
save(gridP,filename=self.filename+'/'+self.ID+"IS.html")
export_png(gridP,filename=self.filename+'/'+self.ID+"IS.png")
save(gridP,filename=self.filename_add+'/'+self.ID+"IS.html")
export_png(gridP,filename=self.filename_add+'/'+self.ID+"IS.png")
def plot_intra_summary(self,df,model_id):
'''
plot summary stats for inter/intra data
'''
factors=self.get_factors(df)
regions = ['Satisfied', 'Violated']
source = ColumnDataSource(data=dict(
x=factors,
Satisfied=self.get_satisfied(df,factors),
Violated=self.get_violated(df,factors), ))
fig_id = figure(x_range=FactorRange(*factors), plot_height=300,plot_width=350,
title='Model:'+str(model_id))
fig_id.vbar_stack(regions, x='x', width=0.9, alpha=0.5, color=["blue", "red"], source=source,
legend_label=regions)
fig_id.xaxis.major_label_text_font_size="12pt"
fig_id.yaxis.major_label_text_font_size="12pt"
fig_id.yaxis.axis_label_text_font_size='12pt'
fig_id.title.text_font_size='12pt'
fig_id.title.align="center"
fig_id.title.vertical_align='top'
fig_id.yaxis.axis_label = 'Number of cross-links'
#fig_id.y_range.start = 0
#fig_id.y_range.end = df.shape[0]
fig_id.x_range.range_padding = 0.1
fig_id.xaxis.major_label_orientation = 1
fig_id.xgrid.grid_line_color = None
fig_id.legend.location = "top_center"
fig_id.legend.orientation = "horizontal"
return fig_id
def make_gridplot_struc(self):
'''
plot grid plot for struc info
'''
grid=[]
for model_id,df in self.model_df.items():
grid.append(self.plot_struc_summary(df,model_id))
gridP=gridplot(grid, ncols=len(grid))
save(gridP,filename=self.filename+'/'+self.ID+"SS.html")
export_png(gridP,filename=self.filename+'/'+self.ID+"SS.png")
save(gridP,filename=self.filename_add+'/'+self.ID+"SS.html")
export_png(gridP,filename=self.filename_add+'/'+self.ID+"SS.png")
def plot_struc_summary(self,df,model_id):
'''
plot summary stats: struc/unstruc/intermediate
'''
factors=self.get_factors_struc(df)
regions = ['Satisfied', 'Violated']
source = ColumnDataSource(data=dict(
x=factors,
Satisfied=self.get_satisfied_struc(df,factors),
Violated=self.get_violated_struc(df,factors), ))
fig_id = figure(x_range=FactorRange(*factors), plot_height=350, plot_width=400,
title='Model:'+str(model_id))
fig_id.vbar_stack(regions, x='x', width=0.9, alpha=0.5, color=["blue", "red"], source=source,
legend_label=regions)
fig_id.xaxis.major_label_text_font_size="12pt"
fig_id.yaxis.major_label_text_font_size="12pt"
fig_id.yaxis.axis_label_text_font_size='12pt'
fig_id.title.text_font_size='12pt'
fig_id.title.align="center"
fig_id.title.vertical_align='top'
fig_id.yaxis.axis_label = 'Number of cross-links'
#fig_id.y_range.start = 0
#fig_id.y_range.end = df.shape[0]
fig_id.x_range.range_padding = 0.1
fig_id.xaxis.major_label_orientation = 1
fig_id.xgrid.grid_line_color = None
fig_id.legend.location = "top_center"
fig_id.legend.orientation = "horizontal"
return fig_id
|
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import os,sys,glob,time
import obspy
import scipy
import pycwt
import pyasdf
import datetime
import numpy as np
import pandas as pd
from obspy.signal.invsim import cosine_taper
from obspy.signal.regression import linear_regression
from scipy.fftpack import fft,ifft,next_fast_len
from seisgo import stacking as stack
from seisgo.types import CorrData, FFTData
from seisgo import utils
#####
########################################################
################ CROSS-CORRELATE FUNCTIONS ##################
########################################################
def cc_memory(inc_hours,sps,nsta,ncomp,cc_len,cc_step):
"""
Estimates the memory usage with given correlation parameters, assuming float 32.
"""
nseg_chunk = int(np.floor((3600*inc_hours-cc_len)/cc_step))+1
npts_chunk = int(nseg_chunk*cc_len*sps)
memory_size = nsta*npts_chunk*4/1024/1024/1024**ncomp
return memory_size
def compute_fft(trace,win_len,step,stainv=None,
freqmin=None,freqmax=None,time_norm='no',freq_norm='no',
smooth=20,smooth_spec=None,misc=dict(),taper_frac=0.05,df=None):
"""
Call FFTData to build the object. This is an alternative of directly call FFTData().
The motivation of this function is to provide an user interface to build FFTData object.
"""
return FFTData(trace=trace,win_len=win_len,step=step,
stainv=stainv,freqmin=freqmin,freqmax=freqmax,time_norm=time_norm,
freq_norm=freq_norm,smooth=smooth,smooth_spec=smooth_spec,misc=misc,
taper_frac=taper_frac,df=df)
#assemble FFT with given asdf file name
def assemble_fft(sfile,win_len,step,freqmin=None,freqmax=None,
time_norm='no',freq_norm='no',smooth=20,smooth_spec=20,
taper_frac=0.05,df=None,exclude_chan=[None],v=True):
#only deal with ASDF format for now.
# retrive station information
ds=pyasdf.ASDFDataSet(sfile,mpi=False,mode='r')
sta_list = ds.waveforms.list()
nsta=len(sta_list)
print('found %d stations in total'%nsta)
fftdata_all=[]
if nsta==0:
print('no data in %s'%sfile);
return fftdata_all
# loop through all stations
print('working on file: '+sfile.split('/')[-1])
for ista in sta_list:
# get station and inventory
try:
inv1 = ds.waveforms[ista]['StationXML']
except Exception as e:
print('abort! no stationxml for %s in file %s'%(ista,sfile))
continue
# get days information: works better than just list the tags
all_tags = ds.waveforms[ista].get_waveform_tags()
if len(all_tags)==0:continue
#----loop through each stream----
for itag in all_tags:
if v:print("FFT for station %s and trace %s" % (ista,itag))
# read waveform data
source = ds.waveforms[ista][itag]
if len(source)==0:continue
# channel info
comp = source[0].stats.channel
if comp[-1] =='U': comp.replace('U','Z')
#exclude some channels in the exclude_chan list.
if comp in exclude_chan:
print(comp+" is in the exclude_chan list. Skip it!")
continue
fftdata=FFTData(source,win_len,step,stainv=inv1,
time_norm=time_norm,freq_norm=freq_norm,
smooth=smooth,freqmin=freqmin,freqmax=freqmax,
smooth_spec=smooth_spec,taper_frac=taper_frac,df=df)
if fftdata.data is not None:
fftdata_all.append(fftdata)
####
return fftdata_all
def smooth_source_spect(fft1,cc_method,sn):
'''
this function smoothes amplitude spectrum of the 2D spectral matrix. (used in S1)
PARAMETERS:
---------------------
cc_para: dictionary containing useful cc parameters
fft1: source spectrum matrix
RETURNS:
---------------------
sfft1: complex numpy array with normalized spectrum
'''
smoothspect_N = sn #cc_para['smoothspect_N']
N=fft1.shape[0]
Nfft2=fft1.shape[1]
fft1=fft1.reshape(fft1.size)
if cc_method == 'deconv':
#-----normalize single-station cc to z component-----
temp = utils.moving_ave(np.abs(fft1),smoothspect_N)
try:
sfft1 = fft1/temp**2
except Exception:
raise ValueError('smoothed spectrum has zero values')
elif cc_method == 'coherency':
temp = utils.moving_ave(np.abs(fft1),smoothspect_N)
try:
sfft1 = fft1/temp
except Exception:
raise ValueError('smoothed spectrum has zero values')
elif cc_method == 'xcorr':
sfft1 = fft1
else:
raise ValueError('no correction correlation method is selected at L59')
return sfft1.reshape(N,Nfft2)
#
def do_correlation(sfile,win_len,step,maxlag,cc_method='xcorr',acorr_only=False,
xcorr_only=False,substack=False,substack_len=None,smoothspect_N=20,
maxstd=10,freqmin=None,freqmax=None,time_norm='no',freq_norm='no',
smooth_N=20,exclude_chan=[None],outdir='.',v=True):
"""
Wrapper for computing correlation functions. It includes two key steps: 1) compute and assemble
the FFT of all data in the sfile, into a list of FFTData objects; 2) loop through the FFTData object
list and do correlation (auto or xcorr) for each source-receiver pair.
====RETURNS====
ndata: the number of station-component pairs in the sfile, that have been processed.
"""
if win_len in [1,2,3]:
print("!!!WARNING: you may call do_correlation() in the old way with the 2nd argument as the ncomp info.")
print(" This may cause errors with arguments getting the wrong values. In this version and later,")
print(" ncomp is deprecated. No change for other arguments. This warning will be removed in")
print(" versions v0.7.x and later.")
if acorr_only and xcorr_only:
raise ValueError('acorr_only and xcorr_only CAN NOT all be True.')
tname = sfile.split('/')[-1]
tmpfile = os.path.join(outdir,tname.split('.')[0]+'.tmp')
if not os.path.isdir(outdir):os.makedirs(outdir)
#file to store CC results.
outfile=os.path.join(outdir,tname)
# check whether time chunk been processed or not
if os.path.isfile(tmpfile):
ftemp = open(tmpfile,'r')
alines = ftemp.readlines()
if len(alines) and alines[-1] == 'done':
return 0
else:
ftemp.close()
os.remove(tmpfile)
if os.path.isfile(outfile): os.remove(outfile)
ftmp = open(tmpfile,'w')
##############compute FFT#############
fftdata=assemble_fft(sfile,win_len,step,freqmin=freqmin,freqmax=freqmax,
time_norm=time_norm,freq_norm=freq_norm,smooth=smooth_N,exclude_chan=exclude_chan)
ndata=len(fftdata)
#############PERFORM CROSS-CORRELATION##################
if v: print(tname)
iend=ndata
for iiS in range(ndata):
# get index right for auto/cross correlation
istart=iiS;
src=fftdata[iiS].net+"."+fftdata[iiS].sta
# if acorr_only:iend=np.minimum(iiS+ncomp,ndata)
# if xcorr_only:istart=np.minimum(iiS+ncomp,ndata)
#-----------now loop III for each receiver B----------
for iiR in range(istart,iend):
# if v:print('receiver: %s %s' % (fftdata[iiR].net,fftdata[iiR].sta))
rcv=fftdata[iiR].net+"."+fftdata[iiR].sta
if (acorr_only and src==rcv) or (xcorr_only and src != rcv) or (not acorr_only and not xcorr_only):
if fftdata[iiS].data is not None and fftdata[iiR].data is not None:
if v:print('receiver: %s %s' % (fftdata[iiR].net,fftdata[iiR].sta))
corrdata=correlate(fftdata[iiS],fftdata[iiR],maxlag,method=cc_method,substack=substack,
smoothspect_N=smoothspect_N,substack_len=substack_len,
maxstd=maxstd)
if corrdata.data is not None: corrdata.to_asdf(file=outfile)
# create a stamp to show time chunk being done
ftmp.write('done')
ftmp.close()
return ndata
def correlate(fftdata1,fftdata2,maxlag,method='xcorr',substack=False,
substack_len=None,smoothspect_N=20,maxstd=10,terror=0.01):
'''
this function does the cross-correlation in freq domain and has the option to keep sub-stacks of
the cross-correlation if needed. it takes advantage of the linear relationship of ifft, so that
stacking is performed in spectrum domain first to reduce the total number of ifft.
PARAMETERS:
---------------------
fftdata1: FFTData for the source station
fftdata2: FFTData of the receiver station
maxlag: maximum lags to keep in the cross correlation
method: cross-correlation methods selected by the user
terror: 0-1 fraction of timing error in searching for overlapping. The timing error =
terror*dt
RETURNS:
---------------------
corrdata: CorrData object of cross-correlation functions in time domain
'''
corrdata=CorrData()
#check overlapping timestamps before any other processing
#this step is required when there are gaps in the data.
ind1,ind2=utils.check_overlap(fftdata1.time,fftdata2.time,error=terror*fftdata1.dt)
if not len(ind1):
print('no overlapped timestamps in the data.')
return corrdata
#---------- check the existence of earthquakes by std of the data.----------
source_std = fftdata1.std[ind1]
sou_ind = np.where((source_std<maxstd)&(source_std>0)&(np.isnan(source_std)==0))[0]
if not len(sou_ind): return corrdata
receiver_std = fftdata2.std[ind2]
rec_ind = np.where((receiver_std<maxstd)&(receiver_std>0)&(np.isnan(receiver_std)==0))[0]
if not len(rec_ind): return corrdata
bb=np.intersect1d(sou_ind,rec_ind)
if len(bb)==0:return corrdata
bb_data1=[ind1[i] for i in bb]
bb_data2=[ind2[i] for i in bb]
#----load paramters----
dt = fftdata1.dt
cc_len = fftdata1.win_len
cc_step = fftdata1.step
if substack_len is None: substack_len=cc_len
Nfft = fftdata1.Nfft
Nfft2 = Nfft//2
fft1=np.conj(fftdata1.data[bb_data1,:Nfft2]) #get the conjugate of fft1
nwin = fft1.shape[0]
fft2=fftdata2.data[bb_data2,:Nfft2]
timestamp=fftdata1.time[bb_data1]
if method != "xcorr":
fft1 = smooth_source_spect(fft1,method,smoothspect_N)
#------convert all 2D arrays into 1D to speed up--------
corr = np.zeros(nwin*Nfft2,dtype=np.complex64)
corr = fft1.reshape(fft1.size,)*fft2.reshape(fft2.size,)
if method == "coherency":
temp = utils.moving_ave(np.abs(fft2.reshape(fft2.size,)),smoothspect_N)
corr /= temp
corr = corr.reshape(nwin,Nfft2)
if substack:
if substack_len == cc_len:
# choose to keep all fft data for a day
s_corr = np.zeros(shape=(nwin,Nfft),dtype=np.float32) # stacked correlation
ampmax = np.zeros(nwin,dtype=np.float32)
n_corr = np.zeros(nwin,dtype=np.int16) # number of correlations for each substack
t_corr = timestamp # timestamp
crap = np.zeros(Nfft,dtype=np.complex64)
for i in range(nwin):
n_corr[i]= 1
crap[:Nfft2] = corr[i,:]
crap[:Nfft2] = crap[:Nfft2]-np.mean(crap[:Nfft2]) # remove the mean in freq domain (spike at t=0)
crap[-(Nfft2)+1:] = np.flip(np.conj(crap[1:(Nfft2)]),axis=0)
crap[0]=complex(0,0)
s_corr[i,:] = np.real(np.fft.ifftshift(scipy.fftpack.ifft(crap, Nfft, axis=0)))
# remove abnormal data
ampmax = np.max(s_corr,axis=1)
tindx = np.where( (ampmax<20*np.median(ampmax)) & (ampmax>0))[0]
s_corr = s_corr[tindx,:]
t_corr = t_corr[tindx]
n_corr = n_corr[tindx]
else:
# get time information
Ttotal = timestamp[-1]-timestamp[0] # total duration of what we have now
tstart = timestamp[0]
nstack = int(np.round(Ttotal/substack_len))
ampmax = np.zeros(nstack,dtype=np.float32)
s_corr = np.zeros(shape=(nstack,Nfft),dtype=np.float32)
n_corr = np.zeros(nstack,dtype=np.int)
t_corr = np.zeros(nstack,dtype=np.float)
crap = np.zeros(Nfft,dtype=np.complex64)
for istack in range(nstack):
# find the indexes of all of the windows that start or end within
itime = np.where( (timestamp >= tstart) & (timestamp < tstart+substack_len) )[0]
if len(itime)==0:tstart+=substack_len;continue
crap[:Nfft2] = np.mean(corr[itime,:],axis=0) # linear average of the correlation
crap[:Nfft2] = crap[:Nfft2]-np.mean(crap[:Nfft2]) # remove the mean in freq domain (spike at t=0)
crap[-(Nfft2)+1:]=np.flip(np.conj(crap[1:(Nfft2)]),axis=0)
crap[0]=complex(0,0)
s_corr[istack,:] = np.real(np.fft.ifftshift(scipy.fftpack.ifft(crap, Nfft, axis=0)))
n_corr[istack] = len(itime) # number of windows stacks
t_corr[istack] = tstart # save the time stamps
tstart += substack_len
#print('correlation done and stacked at time %s' % str(t_corr[istack]))
# remove abnormal data
ampmax = np.max(s_corr,axis=1)
tindx = np.where( (ampmax<20*np.median(ampmax)) & (ampmax>0))[0]
s_corr = s_corr[tindx,:]
t_corr = t_corr[tindx]
n_corr = n_corr[tindx]
else:
# average daily cross correlation functions
ampmax = np.max(corr,axis=1)
tindx = np.where( (ampmax<20*np.median(ampmax)) & (ampmax>0))[0]
n_corr = nwin
s_corr = np.zeros(Nfft,dtype=np.float32)
t_corr = timestamp[0]
crap = np.zeros(Nfft,dtype=np.complex64)
crap[:Nfft2] = np.mean(corr[tindx],axis=0)
crap[:Nfft2] = crap[:Nfft2]-np.mean(crap[:Nfft2],axis=0)
crap[-(Nfft2)+1:]=np.flip(np.conj(crap[1:(Nfft2)]),axis=0)
s_corr = np.real(np.fft.ifftshift(scipy.fftpack.ifft(crap, Nfft, axis=0)))
# trim the CCFs in [-maxlag maxlag]
t = np.arange(-Nfft2+1, Nfft2)*dt
ind = np.where(np.abs(t) <= maxlag)[0]
if s_corr.ndim==1:
s_corr = s_corr[ind]
elif s_corr.ndim==2:
s_corr = s_corr[:,ind]
### call CorrData to build the object
cc_comp= fftdata1.chan[-1]+fftdata2.chan[-1]
dist,azi,baz = obspy.geodetics.base.gps2dist_azimuth(fftdata1.lat,fftdata1.lon,fftdata2.lat,fftdata2.lon)
corrdata=CorrData(net=[fftdata1.net,fftdata2.net],sta=[fftdata1.sta,fftdata2.sta],\
loc=[fftdata1.loc,fftdata2.loc],chan=[fftdata1.chan,fftdata2.chan],\
lon=[fftdata1.lon,fftdata2.lon],lat=[fftdata1.lat,fftdata2.lat],\
ele=[fftdata1.ele,fftdata2.ele],cc_comp=cc_comp,lag=maxlag,\
dt=fftdata1.dt,cc_len=cc_len,cc_step=cc_step,dist=dist/1000,az=azi,\
baz=baz,time=t_corr,data=s_corr,substack=substack,\
side="A",misc={"cc_method":method,"dist_unit":"km"})
return corrdata
def do_stacking(ccfiles,pairlist=None,outdir='./STACK',method=['linear'],
rotation=False,correctionfile=None,flag=False,keep_substack=False,
to_egf=False):
# source folder
if pairlist is None:
pairlist,netsta_all=get_stationpairs(ccfiles,False)
if len(ccfiles)==0:
raise IOError('Abort! no available CCF data for stacking')
for s in netsta_all:
tmp = os.path.join(outdir,s)
if not os.path.isdir(tmp):os.mkdir(tmp)
if isinstance(pairlist,str):pairlist=[pairlist]
if not os.path.isdir(outdir):os.makedirs(outdir)
if rotation:
enz_system = ['EE','EN','EZ','NE','NN','NZ','ZE','ZN','ZZ']
rtz_components = ['ZR','ZT','ZZ','RR','RT','RZ','TR','TT','TZ']
for pair in pairlist:
ttr = pair.split('_')
snet,ssta = ttr[0].split('.')
rnet,rsta = ttr[1].split('.')
idir = ttr[0]
# continue when file is done
toutfn = os.path.join(outdir,idir+'/'+pair+'.tmp')
if os.path.isfile(toutfn):continue
if flag:print('assembling all corrdata ...')
t0=time.time()
corrdict_all=dict() #all components for the single station pair
txtract=np.zeros(len(ccfiles),dtype=np.float32)
tmerge=np.zeros(len(ccfiles),dtype=np.float32)
tparameters=None
for i,ifile in enumerate(ccfiles):
# tt00=time.time()
corrdict=extract_corrdata(ifile,pair=pair)
# txtract[i]=time.time()-tt00
if len(list(corrdict.keys()))>0:
comp_list=list(corrdict[pair].keys())
if len(comp_list)==0:
continue
elif len(comp_list) >9:
print(comp_list)
raise ValueError('more than 9 cross-component exists for %s %s! please double check'%(ifile,pair))
### merge same component corrdata.
# tt11=time.time()
for c in comp_list:
#convert corrdata to empirical Green's functions by
#taking the negative time derivative. See types.CorrData.to_egf() for details.
if to_egf:
corrdict[pair][c].to_egf()
if tparameters is None:tparameters=corrdict[pair][c].misc
if c in list(corrdict_all.keys()):
corrdict_all[c].merge(corrdict[pair][c])
else:corrdict_all[c]=corrdict[pair][c]
# tmerge[i]=time.time()-tt11
#
# if flag:print('extract time:'+str(np.sum(txtract)))
# if flag:print('merge time:'+str(np.sum(tmerge)))
t1=time.time()
if flag:print('finished assembling in %6.2fs ...'%(t1-t0))
#get length info from anyone of the corrdata, assuming all corrdata having the same length.
cc_comp=list(corrdict_all.keys()) #final check on number of keys after merging all data.
if len(cc_comp)==0:
if flag:print('continue! no cross components for %s'%(pair))
continue
elif len(cc_comp)<9 and rotation:
if flag:print('continue! not enough cross components for %s to do rotation'%(pair))
continue
elif len(cc_comp) >9:
print(cc_comp)
raise ValueError('more than 9 cross-component exists for %s! please double check'%(pair))
#save data.
outfn = pair+'.h5'
if flag:print('ready to output to %s'%(outfn))
t2=time.time()
# loop through cross-component for stacking
if isinstance(method,str):method=[method]
tparameters['station_source']=ssta
tparameters['station_receiver']=rsta
if rotation: #need to order the components according to enz_system list.
if corrdict_all[cc_comp[0]].substack:
npts_segmt = corrdict_all[cc_comp[0]].data.shape[1]
else:
npts_segmt = corrdict_all[cc_comp[0]].data.shape[0]
bigstack=np.zeros(shape=(9,npts_segmt),dtype=np.float32)
if flag:print('applying stacking and rotation ...')
stack_h5 = os.path.join(outdir,idir+'/'+outfn)
ds=pyasdf.ASDFDataSet(stack_h5,mpi=False)
#codes for ratation option.
for m in method:
data_type = 'Allstack_'+m
bigstack=np.zeros(shape=(9,npts_segmt),dtype=np.float32)
for icomp in range(9):
comp = enz_system[icomp]
indx = np.where(cc_comp==comp)[0]
# jump if there are not enough data
dstack,stamps_final=stacking(corrdict_all[cc_comp[indx[0]]],method=m)
bigstack[icomp]=dstack
tparameters['time'] = stamps_final[0]
ds.add_auxiliary_data(data=dstack, data_type=data_type, path=comp,
parameters=tparameters)
# start rotation
if np.all(bigstack==0):continue
bigstack_rotated = rotation(bigstack,tparameters,correctionfile,flag)
# write to file
data_type = 'Allstack_'+m
for icomp2 in range(9):
rcomp = rtz_components[icomp2]
if rcomp != 'ZZ':
ds.add_auxiliary_data(data=bigstack_rotated[icomp2], data_type=data_type,
path=rcomp, parameters=tparameters)
if keep_substack:
for ic in cc_comp:
for ii in range(corrdict_all[ic].data.shape[0]):
tparameters2=tparameters
tparameters2['time'] = corrdict_all[ic].time[ii]
data_type = 'T'+str(int(corrdict_all[ic].time[ii]))
ds.add_auxiliary_data(data=corrdict_all[ic].data[ii], data_type=data_type,
path=ic, parameters=tparameters2)
else: #no need to care about the order of components.
stack_h5 = os.path.join(outdir,idir+'/'+outfn)
ds=pyasdf.ASDFDataSet(stack_h5,mpi=False)
if flag:print('applying stacking ...')
for ic in cc_comp:
# write stacked data into ASDF file
dstack,stamps_final=stacking(corrdict_all[ic],method=method)
tparameters['time'] = stamps_final[0]
for i in range(len(method)):
m=method[i]
ds.add_auxiliary_data(data=dstack[i,:], data_type='Allstack_'+m, path=ic,
parameters=tparameters)
if keep_substack:
for ii in range(corrdict_all[ic].data.shape[0]):
tparameters2=tparameters
tparameters2['time'] = corrdict_all[ic].time[ii]
data_type = 'T'+str(int(corrdict_all[ic].time[ii]))
ds.add_auxiliary_data(data=corrdict_all[ic].data[ii], data_type=data_type,
path=ic, parameters=tparameters2)
#
if flag: print('stacking and saving took %6.2fs'%(time.time()-t2))
# write file stamps
ftmp = open(toutfn,'w');ftmp.write('done');ftmp.close()
del corrdict_all
####
def stacking(corrdata,method='linear',par=None):
'''
this function stacks the cross correlation data
PARAMETERS:
----------------------
corrdata: CorrData object.
method: stacking method, could be: linear, robust, pws, acf, or nroot.
par: stacking parameters in a dictionary. See stacking.seisstack() for details.
RETURNS:
----------------------
dstack: 1D matrix of stacked cross-correlation functions over all the segments
cc_time: timestamps of the traces for the stack
'''
if isinstance(method,str):method=[method]
# remove abnormal data
if corrdata.data.ndim==1:
cc_time = [corrdata.time]
# do stacking
dstack = np.zeros((len(method),corrdata.data.shape[0]),dtype=np.float32)
for i in range(len(method)):
m =method[i]
dstack[i,:]=corrdata.data[:]
else:
ampmax = np.max(corrdata.data,axis=1)
tindx = np.where( (ampmax<20*np.median(ampmax)) & (ampmax>0))[0]
nstacks=len(tindx)
dstack=[]
cc_time=[]
if nstacks >0:
# remove ones with bad amplitude
cc_array = corrdata.data[tindx,:]
cc_time = corrdata.time[tindx]
# do stacking
dstack = np.zeros((len(method),corrdata.data.shape[1]),dtype=np.float32)
for i in range(len(method)):
m =method[i]
if nstacks==1: dstack[i,:]=cc_array
else:
dstack[i,:] = stack.seisstack(cc_array,method=method,par=par)
# good to return
return dstack,cc_time
def rotation(bigstack,parameters,locs,flag):
'''
this function transfers the Green's tensor from a E-N-Z system into a R-T-Z one
PARAMETERS:
-------------------
bigstack: 9 component Green's tensor in E-N-Z system
parameters: dict containing all parameters saved in ASDF file
locs: dict containing station angle info for correction purpose
RETURNS:
-------------------
tcorr: 9 component Green's tensor in R-T-Z system
'''
# load parameter dic
pi = np.pi
azi = parameters['azi']
baz = parameters['baz']
ncomp,npts = bigstack.shape
if ncomp<9:
print('crap did not get enough components')
tcorr=[]
return tcorr
staS = parameters['station_source']
staR = parameters['station_receiver']
if locs is not None:
sta_list = list(locs['station'])
angles = list(locs['angle'])
# get station info from the name of ASDF file
ind = sta_list.index(staS)
acorr = angles[ind]
ind = sta_list.index(staR)
bcorr = angles[ind]
#---angles to be corrected----
cosa = np.cos((azi+acorr)*pi/180)
sina = np.sin((azi+acorr)*pi/180)
cosb = np.cos((baz+bcorr)*pi/180)
sinb = np.sin((baz+bcorr)*pi/180)
else:
cosa = np.cos(azi*pi/180)
sina = np.sin(azi*pi/180)
cosb = np.cos(baz*pi/180)
sinb = np.sin(baz*pi/180)
# rtz_components = ['ZR','ZT','ZZ','RR','RT','RZ','TR','TT','TZ']
tcorr = np.zeros(shape=(9,npts),dtype=np.float32)
tcorr[0] = -cosb*bigstack[7]-sinb*bigstack[6]
tcorr[1] = sinb*bigstack[7]-cosb*bigstack[6]
tcorr[2] = bigstack[8]
tcorr[3] = -cosa*cosb*bigstack[4]-cosa*sinb*bigstack[3]-sina*cosb*bigstack[1]-sina*sinb*bigstack[0]
tcorr[4] = cosa*sinb*bigstack[4]-cosa*cosb*bigstack[3]+sina*sinb*bigstack[1]-sina*cosb*bigstack[0]
tcorr[5] = cosa*bigstack[5]+sina*bigstack[2]
tcorr[6] = sina*cosb*bigstack[4]+sina*sinb*bigstack[3]-cosa*cosb*bigstack[1]-cosa*sinb*bigstack[0]
tcorr[7] = -sina*sinb*bigstack[4]+sina*cosb*bigstack[3]+cosa*sinb*bigstack[1]-cosa*cosb*bigstack[0]
tcorr[8] = -sina*bigstack[5]+cosa*bigstack[2]
return tcorr
####
def merging(ccfiles,pairlist=None,outdir='./MERGED_PAIRS',verbose=False,to_egf=False,
stack=False,stack_method='linear',stack_win_len=None):
print("WARNING: Old function call, will be deprecated in v0.7.x. Function has been renamed to: merge_pairs() with the same options.")
merge_pairs(ccfiles,pairlist=pairlist,outdir=outdir,verbose=verbose,to_egf=to_egf,
stack=stack,stack_method=stack_method,stack_win_len=stack_win_len)
###
def merge_pairs(ccfiles,pairlist=None,outdir='./MERGED_PAIRS',verbose=False,to_egf=False,
stack=False,stack_method='linear',stack_win_len=None):
"""
This is a wrapper function that merges all data for the same station pair
to a single CorrData object. It calls CorrData.merge() to assemble all CorrData.
PARAMETERS
----------------------
ccfiles: a list of correlation functions in ASDF format, saved to *.h5 file.
pairlist: a list of station pairs to merge. If None (default), it will merge all
station pairs.
outdir: directory to save the data. Defautl is ./MERGED_PAIRS.
verbose: verbose flag. Default is False.
to_egf: whether to convert the data to empirical Green's functions (EGF) before
saving. Default is False.
stack: whether to stack all merged data before saving. Default: False.
stack_method: when stack is True, this is the method for stacking.
stack_win_len: window length in seconds for stacking, only used when stack is True.
When stack_win_len is not None, the stacking will be done over the specified
windown lengths, instead of the entire data set.
"""
# source folder
if pairlist is None:
pairlist,netsta_all=get_stationpairs(ccfiles,False)
if len(ccfiles)==0:
raise IOError('Abort! no available CCF data for merging')
for s in netsta_all:
tmp = os.path.join(outdir,s)
if not os.path.isdir(tmp):os.mkdir(tmp)
if isinstance(pairlist,str):pairlist=[pairlist]
if not os.path.isdir(outdir):os.makedirs(outdir)
for pair in pairlist:
ttr = pair.split('_')
snet,ssta = ttr[0].split('.')
rnet,rsta = ttr[1].split('.')
idir = ttr[0]
# continue when file is done
ioutdir=os.path.join(outdir,idir)
if not os.path.isdir(ioutdir):os.makedirs(ioutdir)
if verbose:print('assembling all corrdata ...')
t0=time.time()
corrdict_all=dict() #all components for the single station pair
# txtract=np.zeros(len(ccfiles),dtype=np.float32)
# tmerge=np.zeros(len(ccfiles),dtype=np.float32)
tparameters=None
for i,ifile in enumerate(ccfiles):
# tt00=time.time()
corrdict=extract_corrdata(ifile,pair=pair)
# txtract[i]=time.time()-tt00
if len(list(corrdict.keys()))>0:
comp_list=list(corrdict[pair].keys())
if len(comp_list)==0:
continue
### merge same component corrdata.
# tt11=time.time()
for c in comp_list:
if c in list(corrdict_all.keys()):
corrdict_all[c].merge(corrdict[pair][c])
else:corrdict_all[c]=corrdict[pair][c]
del corrdict
# tmerge[i]=time.time()-tt11
#
# if flag:print('extract time:'+str(np.sum(txtract)))
# if flag:print('merge time:'+str(np.sum(tmerge)))
t1=time.time()
if verbose:print('finished assembling in %6.2fs ...'%(t1-t0))
#get length info from anyone of the corrdata, assuming all corrdata having the same length.
cc_comp=list(corrdict_all.keys()) #final check on number of keys after merging all data.
if len(cc_comp)==0:
if verbose:print('continue! no cross components for %s'%(pair))
continue
#save data.
outfn = pair+'.h5'
if verbose:print('save to %s'%(outfn))
merged_h5 = os.path.join(ioutdir,outfn)
for ic in cc_comp:
#save components.
#convert corrdata to empirical Green's functions by
#taking the negative time derivative. See types.CorrData.to_egf() for details.
try:
if stack:
corrdict_all[ic].stack(method=stack_method,win_len=stack_win_len)
if to_egf:
corrdict_all[ic].to_egf()
corrdict_all[ic].to_asdf(file=merged_h5)
except Exception as e:
print(str(e)+"--> skipped: "+corrdict_all[ic].id)
del corrdict_all
###
def merge_chunks(ccfiles,outdir='./MERGED_CHUNKS',verbose=False,to_egf=False,
stack=False,stack_method='linear',stack_win_len=None):
"""
This is a wrapper function that merges all data in the given list of correlation files.
It calls CorrData.merge() to assemble all CorrData for the same station and component pairs.
The functionality is similar with noise.merge_pairs(). This is particularly useful when the
number of chunks is too large to be handled. At the same time, it provides the option to further
reduce the data size by stacking. Please note that the stacking here works for the given
list of files.
PARAMETERS
----------------------
ccfiles: a list of correlation functions in ASDF format, saved to *.h5 file.
outdir: directory to save the data. Defautl is ./MERGED_PAIRS.
verbose: verbose flag. Default is False.
to_egf: whether to convert the data to empirical Green's functions (EGF) before
saving. Default is False.
stack: whether to stack all merged data before saving. Default: False.
stack_method: when stack is True, this is the method for stacking.
stack_win_len: window length in seconds for stacking, only used when stack is True.
When stack_win_len is not None, the stacking will be done over the specified
windown lengths, instead of the entire data set. The function stacks all data if "stack_win_len"
> the time duration of the whole list of correlation files.
"""
pairs_all,netsta,trange=get_stationpairs(ccfiles,getcclist=False,gettimerange=True)
ts,te=trange
outfile = os.path.join(outdir,str(obspy.UTCDateTime(ts)).replace(':', '-') + \
'T' + str(obspy.UTCDateTime(te)).replace(':', '-') + '.h5')
for p in pairs_all:
corrdict_all=dict()
for f in ccfiles:
# print("---> "+ifile)
corrdict=extract_corrdata(f,pair=p)
# txtract[i]=time.time()-tt00
if len(list(corrdict.keys()))>0:
comp_list=list(corrdict[p].keys())
if len(comp_list)==0:
continue
### merge same pair and component corrdata.
# tt11=time.time()
if p not in list(corrdict_all.keys()):
corrdict_all[p]=corrdict[p]
for c in comp_list:
if c in list(corrdict_all[p].keys()):
corrdict_all[p][c].merge(corrdict[p][c])
else:
corrdict_all[p][c]=corrdict[p][c]
del corrdict
#
if p in list(corrdict_all.keys()):
comp_list=list(corrdict_all[p].keys())
if len(comp_list)>0:
for c in comp_list:
if corrdict_all[p][c].data is not None:
if stack:
corrdict_all[p][c].stack(method=stack_method,win_len=stack_win_len)
if to_egf:
corrdict_all[p][c].to_egf()
corrdict_all[p][c].to_asdf(file=outfile,v=False)
del corrdict_all
########################################################
################ XCORR ANALYSIS FUNCTIONS ##################
########################################################
def save_xcorr_amplitudes(dict_in,filenamebase=None):
"""
This function saves the amplitude data for both negative and positive lags,
for each xcorr component pair, to csv files.
PARAMETERS:
----------------------------
dict_in: dictionary containing peak amplitude information from one virtual source to all other receivers.
This can be the output of get_xcorr_peakamplitudes().
filenamebase: file name base of the csv file, default is source_component_peakamp.txt in the current dir.
"""
source=dict_in['source']['name']
lonS0,latS0,eleS0=dict_in['source']['location']
#
if filenamebase is None:
filenamebase = source
cc_comp=list(dict_in['cc_comp'].keys())
for ic in range(len(cc_comp)):
comp = cc_comp[ic]
receivers=list(dict_in['cc_comp'][comp].keys())
lonS=lonS0*np.ones((len(receivers),))
latS=latS0*np.ones((len(receivers),))
eleS=eleS0*np.ones((len(receivers),))
comp_out=len(receivers)*[comp]
source_out=len(receivers)*[source]
lonR=[]
latR=[]
eleR=[]
dist=[]
peakamp_neg=[]
peakamp_pos=[]
peaktt_neg=[]
peaktt_pos=[]
az=[]
baz=[]
for ir in range(len(receivers)):
receiver=receivers[ir]
dist0=dict_in['cc_comp'][comp][receiver]['dist']
dist.append(dist0)
lonR.append(dict_in['cc_comp'][comp][receiver]['location'][0])
latR.append(dict_in['cc_comp'][comp][receiver]['location'][1])
eleR.append(0.0)
az.append(dict_in['cc_comp'][comp][receiver]['az'])
baz.append(dict_in['cc_comp'][comp][receiver]['baz'])
peakamp_neg.append(np.array(dict_in['cc_comp'][comp][receiver]['peak_amplitude'])[0])
peakamp_pos.append(np.array(dict_in['cc_comp'][comp][receiver]['peak_amplitude'])[1])
peaktt_neg.append(np.array(dict_in['cc_comp'][comp][receiver]['peak_amplitude_time'])[0])
peaktt_pos.append(np.array(dict_in['cc_comp'][comp][receiver]['peak_amplitude_time'])[1])
outDF=pd.DataFrame({'source':source_out,'lonS':lonS,'latS':latS,'eleS':eleS,
'receiver':receivers,'lonR':lonR,'latR':latR,'eleR':eleR,
'az':az,'baz':baz,'dist':dist,'peakamp_neg':peakamp_neg,
'peakamp_pos':peakamp_pos,'peaktt_neg':peaktt_neg,
'peaktt_pos':peaktt_pos,'comp':comp_out})
fname=filenamebase+'_'+comp+'_peakamp.txt'
outDF.to_csv(fname,index=False)
print('data was saved to: '+fname)
def get_stationpairs(ccfiles,getcclist=False,verbose=False,gettimerange=False):
"""
Extract unique station pairs from all cc files in ASDF format.
====PARAMETERS===
ccfiles: a list of cc files.
getcclist: get cc component list. default False.
verbose: verbose flag; default False.
====RETURNS===
pairs_all: all netstaion pairs in the format of NET1.STA1_NET2.STA2
netsta_all: all net.sta (unique list)
ccomp_all: all unique list of cc components.
"""
if isinstance(ccfiles,str):ccfiles=[ccfiles]
pairs_all = []
ccomp_all=[]
if gettimerange:
ts=[]
te=[]
for f in ccfiles:
# load the data from daily compilation
try:
ds=pyasdf.ASDFDataSet(f,mpi=False,mode='r')
except Exception as e:
raise IOError("error openning "+f+":"+str(e))
try:
pairlist = ds.auxiliary_data.list()
if getcclist:
for p in pairlist:
chanlist=ds.auxiliary_data[p].list()
for c in chanlist:
if gettimerange:
para=ds.auxiliary_data[p][c].parameters
ttime=para['time']
if 'time_mean' in list(para.keys()):
ttime += para['time_mean']
ts.append(np.min(ttime))
te.append(np.max(ttime))
c1,c2=c.split('_')
ccomp_all.extend(c1[-1]+c2[-1])
ccomp_all=sorted(set(ccomp_all))
elif gettimerange:
for p in pairlist:
chanlist=ds.auxiliary_data[p].list()
for c in chanlist:
para=ds.auxiliary_data[p][c].parameters
ttime=para['time']
if 'time_mean' in list(para.keys()):
ttime += para['time_mean']
ts.append(np.min(ttime))
te.append(np.max(ttime))
pairs_all.extend(pairlist)
pairs_all=sorted(set(pairs_all))
except Exception:
if verbose:print('continue! no data in %s'%(f))
continue
netsta_all=[]
for p in pairs_all:
netsta=p.split('_')
netsta_all.extend(netsta)
netsta_all=sorted(set(netsta_all))
if getcclist:
if gettimerange:
trange=[np.min(ts),np.max(te)]
return pairs_all,netsta_all,ccomp_all,trange
else:
return pairs_all,netsta_all,ccomp_all
else:
if gettimerange:
trange=[np.min(ts),np.max(te)]
return pairs_all,netsta_all,trange
else:
return pairs_all,netsta_all
def get_cctimerange(ccfiles,verbose=False):
"""
Extract time range from all cc files in ASDF format.
====PARAMETERS===
ccfiles: a list of cc files.
verbose: verbose flag; default False.
====RETURNS===
ts,te: start and end time of all ccdata.
"""
if isinstance(ccfiles,str):ccfiles=[ccfiles]
ts_all = []
te_all = []
for f in ccfiles:
# load the data from daily compilation
corrdata=extract_corrdata(f,dataless=True)
plist=list(corrdata.keys())
for p in plist:
clist=list(corrdata[p].keys())
c=clist[0]
if corrdata[p][c].substack:
ts_all.append(corrdata[p][c].time[0])
te_all.append(corrdata[p][c].time[-1])
else:
ts_all.append(corrdata[p][c].time)
te_all.append(corrdata[p][c].time)
del corrdata
ts=np.array(ts_all).min()
te=np.array(te_all).max()
return ts,te
def extract_corrdata(sfile,pair=None,comp=['all'],dataless=False):
'''
extract the 2D matrix of the cross-correlation functions and the metadata for a certain time-chunck.
PARAMETERS:
--------------------------
sfile: cross-correlation functions outputed by SeisGo cross-correlation workflow
pair: net1.sta1-net2.sta2 pair to extract, default is to extract all pairs.
comp: cross-correlation component or a list of components to extract, default is all components.
RETURN:
--------------------------
corrdict: a dictionary that contains all extracted correlations, which each key as the station pair name.
for each station pair, the correlaitons are saved as a list of CorrData objects.
USAGE:
--------------------------
extract_corrdata('temp.h5',comp='ZZ')
'''
#check help or not at the very beginning
# open data for read
if isinstance(pair,str): pair=[pair]
if isinstance(comp,str): comp=[comp]
corrdict=dict()
try:
ds = pyasdf.ASDFDataSet(sfile,mpi=False,mode='r')
# extract common variables
spairs_all = ds.auxiliary_data.list()
except Exception:
print("exit! cannot open %s to read"%sfile);sys.exit()
if pair is None: pair=spairs_all
overlap_pair=list(set(pair) & set(spairs_all))
if len(overlap_pair)<1:
print(str(pair)+" not found. Return empty.")
return corrdict
for spair in overlap_pair:
ttr = spair.split('_')
snet,ssta = ttr[0].split('.')
rnet,rsta = ttr[1].split('.')
path_lists = ds.auxiliary_data[spair].list()
corrdict[spair]=dict()
for ipath in path_lists:
schan,rchan = ipath.split('_')
cc_comp=schan[-1]+rchan[-1]
if cc_comp in comp or comp == ['all'] or comp ==['ALL']:
try:
para=ds.auxiliary_data[spair][ipath].parameters
substack,ttime,dt,maxlag,az,baz,cc_method,dist,slat,slon,rlat,rlon = \
[para['substack'],para['time'],\
para['dt'],para['maxlag'],para['azi'],para['baz'],\
para['cc_method'],para['dist'],para['latS'],para['lonS'],\
para['latR'],para['lonR']]
if "eleS" in list(para.keys()):
sele = para['eleS']
else:
sele = 0.0
if "eleR" in list(para.keys()):
rele = para['eleR']
else:
rele = 0.0
if "cc_len" in list(para.keys()):
cc_len = para['cc_len']
else:
cc_len = None
if "cc_step" in list(para.keys()):
cc_step = para['cc_step']
else:
cc_step = None
if "side" in list(para.keys()):
side = para['side']
else:
side = "A"
##special handling of time, in case time_mean is saved to reduce the attribute memory_size
if "time_mean" in list(para.keys()):
tmean=para["time_mean"]
ttime = np.float64(ttime) + tmean
if not dataless: data = np.array(ds.auxiliary_data[spair][ipath].data)
else: data = None
except Exception:
print('continue! something wrong with %s %s'%(spair,ipath))
continue
corrdict[spair][cc_comp]=CorrData(net=[snet,rnet],sta=[ssta,rsta],loc=['',''],\
chan=[schan,rchan],lon=[slon,rlon],lat=[slat,rlat],
ele=[sele,rele],cc_comp=cc_comp,dt=dt,lag=maxlag,
cc_len=cc_len,cc_step=cc_step,dist=dist,az=az,
baz=baz,time=ttime,data=data,
substack=substack,side=side,misc=para)
if "type" in list(para.keys()): corrdict[spair][cc_comp].type=para['type']
return corrdict
def save_corrfile_to_sac(cfile,rootdir='.',pair=None,comp=['all'],v=True):
"""
Save correlation files in ASDF to sac files.
=== PARAMETERS ===
cfile: correlation file from SeisGo workflow. It could be a list of files.
rootdir: folder to save the converted sac files. this is the root folder, not
the folder for individual sources/receivers, which will be created
by this function. Default is the current directory.
pair: net1.sta1_net2.sta2 pair to extract, default is to extract all pairs.
comp: cross-correlation component or a list of components to extract, default is 'all'.
v: verbose or not, default is True.
"""
if isinstance(cfile,str):cfile=[cfile]
if isinstance(pair,str): pair=[pair]
nfile=len(cfile)
for cf in cfile:
if v: print('working on file: '+cf.split('/')[-1])
corrdict=extract_corrdata(cf)
pairs_all=list(corrdict.keys())
if pair is None:
extract_pair=pairs_all
else:
extract_pair=pair
for p in extract_pair:
if p in pairs_all:
netsta1,netsta2=p.split('_')
outdir=os.path.join(rootdir,netsta1,netsta2)
comp_all=list(corrdict[p].keys())
for c in comp_all:
if c in comp or comp == ['all'] or comp ==['ALL']:
corrdict[p][c].to_sac(outdir=outdir)
else:
print('Pair %s not found. Skip.'%(p))
continue
|
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|
#include <boost/make_shared.hpp>
#include <boost/thread/locks.hpp>
#include <boost/thread/mutex.hpp>
#include <string>
#include <vector>
#include "caffe/array/array.hpp"
#include "caffe/array/math.hpp"
namespace caffe {
template<typename T>
Array<T>::Array(const Array & o) : ArrayMemory(o), ArrayBase<T>(o) { }
template<typename T>
Array<T>::Array(ArrayMode mode) : ArrayMemory(), ArrayBase<T>(mode) { }
template<typename T>
Array<T>::Array(const ArrayShape &shape, ArrayMode mode):
ArrayMemory(count(shape)*sizeof(T)), ArrayBase<T>(shape, mode) { }
template<typename T>
Array<T>::Array(SyncedMemory *memory, const ArrayShape &shape,
ArrayMode mode):ArrayMemory(memory, count(shape)), ArrayBase<T>(shape, mode) {
CHECK_GE(memory->size(), count(shape) *sizeof(T)) << "SyncedMemory size '"
<< memory->size() << "' is smaller than shape " << shapeToString(shape)
<< " with element size " << sizeof(T);
}
template<typename T>
Array<T>::Array(shared_ptr<SyncedMemory> memory, const ArrayShape &shape,
ArrayMode mode):ArrayMemory(memory, count(shape)), ArrayBase<T>(shape, mode) {
CHECK_GE(memory->size(), count(shape)*sizeof(T)) << "SyncedMemory size '"
<< memory->size() << "' is smaller than shape " << shapeToString(shape)
<< " with element size " << sizeof(T);
}
template<typename T>
Array<T>::Array(shared_ptr<SyncedMemory> m, size_t o, const ArrayShape &s,
ArrayMode mode):ArrayMemory(m, o*sizeof(T), count(s)*sizeof(T)),
ArrayBase<T>(s, mode) {
CHECK_GE(m->size(), (o+count(s))*sizeof(T)) << "SyncedMemory size '"
<< m->size() << "' is smaller than shape " << shapeToString(s)
<< " with element size " << sizeof(T) << " and offset " << o;
}
template<typename T>
Array<T>::~Array() {}
template<typename T>
void Array<T>::initialize(const ArrayShape &shape) {
CHECK_EQ(count(this->shape_), 0) << "Array already initialized!";
this->shape_ = shape;
ArrayMemory::initializeMemory(count(shape) * sizeof(T));
}
template<typename T>
void Array<T>::setMode(ArrayMode mode) {
this->mode_ = mode;
}
template <typename T>
void Array<T>::FromProto(const BlobProto& proto, bool reshape) {
ArrayShape shape;
if (proto.has_num() || proto.has_channels() ||
proto.has_height() || proto.has_width()) {
// Using deprecated 4D Blob dimensions --
// shape is (num, channels, height, width).
shape.resize(4);
shape[0] = proto.num();
shape[1] = proto.channels();
shape[2] = proto.height();
shape[3] = proto.width();
} else {
shape.resize(proto.shape().dim_size());
for (int i = 0; i < proto.shape().dim_size(); ++i) {
shape[i] = proto.shape().dim(i);
}
}
if (reshape)
initialize(shape);
else
CHECK_EQ(shape, this->shape_) << "shape mismatch (reshape not set)";
// copy data
T* data_vec = mutable_cpu_data();
for (int i = 0; i < count(this->shape_); i++)
data_vec[i] = proto.data(i);
CHECK_EQ(proto.diff_size(), 0) << "Cannot read BlobProto diff";
}
template <typename T>
void Array<T>::ToProto(BlobProto* proto) const {
proto->clear_shape();
for (int i = 0; i < this->shape_.size(); i++) {
proto->mutable_shape()->add_dim(this->shape_[i]);
}
proto->clear_data();
proto->clear_diff();
const T* data_vec = cpu_data();
for (int i = 0; i < count(this->shape_); i++)
proto->add_data(data_vec[i]);
}
template<typename T>
Array<T> Array<T>::eval() const {
return *this;
}
template<typename T>
shared_ptr<SyncedMemory> Array<T>::memory() const {
return memory_;
}
template<typename T>
const T *Array<T>::cpu_data() const {
return static_cast<const T *>(ArrayMemory::cpu_data_());
}
template<typename T>
const T *Array<T>::gpu_data() const {
return static_cast<const T *>(ArrayMemory::gpu_data_());
}
template<typename T>
T *Array<T>::mutable_cpu_data() {
return static_cast<T *>(ArrayMemory::mutable_cpu_data_());
}
template<typename T>
T *Array<T>::mutable_gpu_data() {
return static_cast<T *>(ArrayMemory::mutable_gpu_data_());
}
template<typename T>
Array<T> &Array<T>::operator=(const Expression<T> & other) {
if (!memory_) {
initialize(other.shape());
setMode(other.mode());
}
CHECK_EQ(this->shape(), other.shape()) << "Array shape missmatches";
other.evaluate(this);
return *this;
}
template<typename T>
Array<T> &Array<T>::operator=(const T &v) {
CHECK(memory_) << "Array not initialized";
#ifndef CPU_ONLY
if (this->effectiveMode() == AR_GPU)
caffe_gpu_set(count(this->shape()), v, this->mutable_gpu_data());
else
#endif
caffe_set(count(this->shape()), v, this->mutable_cpu_data());
return *this;
}
template<typename T>
Array<T> &Array<T>::operator=(const Array<T> &other) {
if (!memory_) {
initialize(other.shape());
setMode(other.mode());
}
CHECK_EQ(this->shape(), other.shape()) << "Array shape missmatches";
#ifndef CPU_ONLY
if (this->effectiveMode() == AR_GPU)
// NOLINT_NEXT_LINE(caffe/alt_fn)
CUDA_CHECK(cudaMemcpy(this->mutable_gpu_data(), other.gpu_data(),
sizeof(T) * count(this->shape()), cudaMemcpyDefault));
else
#endif
// NOLINT_NEXT_LINE(caffe/alt_fn)
memcpy(this->mutable_cpu_data(), other.cpu_data(),
sizeof(T) * count(this->shape()));
return *this;
}
template<typename T>
Array<T> Array<T>::reshape(ArrayShape shape) const {
size_t p = 1;
int md = -1;
for (int d = 0; d < shape.size(); d++)
if (shape[d] == -1) {
CHECK_EQ(md, -1) << "Only one missing dimension supported";
md = d;
} else {
p *= shape[d];
}
if (md >= 0) shape[md] = count(this->shape()) / p;
CHECK_EQ(count(this->shape()), count(shape)) <<
"reshape cannot change array size";
return Array<T>(memory_, offset_/sizeof(T), shape, this->mode());
}
template<typename T>
Array<T> Array<T>::operator[](size_t d) {
CHECK_GT(this->shape().size(), 0) << "At least one dimension required";
CHECK_LT(d, this->shape()[0]) << "Index out of range";
ArrayShape s(this->shape().begin()+1, this->shape().end());
return Array<T>(memory_, d*count(s), s, this->mode());
}
template<typename T>
const Array<T> Array<T>::operator[](size_t d) const {
CHECK_GT(this->shape().size(), 0) << "At least one dimension required";
CHECK_LT(d, this->shape()[0]) << "Index out of range";
ArrayShape s(this->shape().begin()+1, this->shape().end());
return Array<T>(memory_, d*count(s), s, this->mode());
}
INSTANTIATE_CLASS(Array);
} // namespace caffe
|
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|
# This file is part of the Open Data Cube, see https://opendatacube.org for more information
#
# Copyright (c) 2015-2020 ODC Contributors
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import toolz
from ..model import Dataset
from ..storage import reproject_and_fuse, BandInfo
from ..storage._rio import RasterioDataSource, RasterDatasetDataSource
from ..utils.geometry._warp import resampling_s2rio
from ..storage._read import rdr_geobox
from ..utils.geometry import GeoBox
from ..utils.geometry import gbox as gbx
from ..index.eo3 import is_doc_eo3, _norm_grid # type: ignore[attr-defined]
from types import SimpleNamespace
class RasterFileDataSource(RasterioDataSource):
""" This is only used in test code
"""
def __init__(self, filename, bandnumber, nodata=None, crs=None, transform=None, lock=None):
super(RasterFileDataSource, self).__init__(filename, nodata, lock=lock)
self.bandnumber = bandnumber
self.crs = crs
self.transform = transform
def get_bandnumber(self, src):
return self.bandnumber
def get_transform(self, shape):
if self.transform is None:
raise RuntimeError('No transform in the data and no fallback')
return self.transform
def get_crs(self):
if self.crs is None:
raise RuntimeError('No CRS in the data and no fallback')
return self.crs
def _raster_metadata(band):
source = RasterDatasetDataSource(band)
with source.open() as rdr:
return SimpleNamespace(dtype=rdr.dtype.name,
nodata=rdr.nodata,
geobox=rdr_geobox(rdr))
def get_raster_info(ds: Dataset, measurements=None):
"""
:param ds: Dataset
:param measurements: List of band names to load
"""
if measurements is None:
measurements = list(ds.type.measurements)
return {n: _raster_metadata(BandInfo(ds, n))
for n in measurements}
def eo3_geobox(ds: Dataset, band: str) -> GeoBox:
mm = ds.measurements.get(ds.type.canonical_measurement(band),
None)
if mm is None:
raise ValueError(f"No such band: {band}")
crs = ds.crs
doc_path = ('grids', mm.get('grid', 'default'))
grid = toolz.get_in(doc_path, ds.metadata_doc)
if crs is None or grid is None:
raise ValueError('Not a valid EO3 dataset')
grid = _norm_grid(grid)
h, w = grid.shape
return GeoBox(w, h, grid.transform, crs)
def native_geobox(ds, measurements=None, basis=None):
"""Compute native GeoBox for a set of bands for a given dataset
:param ds: Dataset
:param measurements: List of band names to consider
:param basis: Name of the band to use for computing reference frame, other
bands might be reprojected if they use different pixel grid
:return: GeoBox describing native storage coordinates.
"""
gs = ds.type.grid_spec
if gs is not None:
# Dataset is from ingested product, figure out GeoBox of the tile this dataset covers
bb = [gbox for _, gbox in gs.tiles(ds.bounds)]
if len(bb) != 1:
# Ingested product but dataset overlaps several/none tiles -- no good
raise ValueError('Broken GridSpec detected')
return bb[0]
if measurements is None and basis is None:
measurements = list(ds.type.measurements)
if is_doc_eo3(ds.metadata_doc):
if basis is not None:
return eo3_geobox(ds, basis)
gboxes = [eo3_geobox(ds, band) for band in measurements]
else:
if basis is not None:
return get_raster_info(ds, [basis])[basis].geobox
ii = get_raster_info(ds, measurements)
gboxes = [info.geobox for info in ii.values()]
geobox = gboxes[0]
consistent = all(geobox == gbox for gbox in gboxes)
if not consistent:
raise ValueError('Not all bands share the same pixel grid')
return geobox
def native_load(ds, measurements=None, basis=None, **kw):
"""Load single dataset in native resolution.
:param ds: Dataset
:param measurements: List of band names to load
:param basis: Name of the band to use for computing reference frame, other
bands might be reprojected if they use different pixel grid
:param **kw: Any other parameter load_data accepts
:return: Xarray dataset
"""
from datacube import Datacube
geobox = native_geobox(ds, measurements, basis) # early exit via exception if no compatible grid exists
if measurements is not None:
mm = ds.type.lookup_measurements(measurements)
else:
mm = ds.type.measurements
return Datacube.load_data(Datacube.group_datasets([ds], 'time'),
geobox,
measurements=mm, **kw)
def dc_read(path,
band=1,
gbox=None,
resampling='nearest',
dtype=None,
dst_nodata=None,
fallback_nodata=None):
"""
Use default io driver to read file without constructing Dataset object.
"""
source = RasterFileDataSource(path, band, nodata=fallback_nodata)
with source.open() as rdr:
dtype = rdr.dtype if dtype is None else dtype
if gbox is None:
gbox = rdr_geobox(rdr)
if dst_nodata is None:
dst_nodata = rdr.nodata
# currently dst_nodata = None case is not supported. So if fallback_nodata
# was None and file had none set, then use 0 as default output fill value
if dst_nodata is None:
dst_nodata = 0
im = np.full(gbox.shape, dst_nodata, dtype=dtype)
reproject_and_fuse([source], im, gbox, dst_nodata, resampling=resampling)
return im
def write_gtiff(fname,
pix,
crs='epsg:3857',
resolution=(10, -10),
offset=(0.0, 0.0),
nodata=None,
overwrite=False,
blocksize=None,
gbox=None,
**extra_rio_opts):
""" Write ndarray to GeoTiff file.
Geospatial info can be supplied either via
- resolution, offset, crs
or
- gbox (takes precedence if supplied)
"""
# pylint: disable=too-many-locals
from affine import Affine
import rasterio
from pathlib import Path
if pix.ndim == 2:
h, w = pix.shape
nbands = 1
band = 1
elif pix.ndim == 3:
nbands, h, w = pix.shape
band = tuple(i for i in range(1, nbands+1))
else:
raise ValueError('Need 2d or 3d ndarray on input')
if not isinstance(fname, Path):
fname = Path(fname)
if fname.exists():
if overwrite:
fname.unlink()
else:
raise IOError("File exists")
if gbox is not None:
assert gbox.shape == (h, w)
A = gbox.transform
crs = str(gbox.crs)
else:
sx, sy = resolution
tx, ty = offset
A = Affine(sx, 0, tx,
0, sy, ty)
rio_opts = dict(width=w,
height=h,
count=nbands,
dtype=pix.dtype.name,
crs=crs,
transform=A,
predictor=2,
compress='DEFLATE')
if blocksize is not None:
rio_opts.update(tiled=True,
blockxsize=min(blocksize, w),
blockysize=min(blocksize, h))
if nodata is not None:
rio_opts.update(nodata=nodata)
rio_opts.update(extra_rio_opts)
with rasterio.open(str(fname), 'w', driver='GTiff', **rio_opts) as dst:
dst.write(pix, band)
meta = dst.meta
meta['gbox'] = gbox if gbox is not None else rio_geobox(meta)
meta['path'] = fname
return SimpleNamespace(**meta)
def dc_crs_from_rio(crs):
from datacube.utils.geometry import CRS
if crs.is_epsg_code:
return CRS('EPSG:{}'.format(crs.to_epsg()))
return CRS(crs.wkt)
def rio_geobox(meta):
""" Construct geobox from src.meta of opened rasterio dataset
"""
if 'crs' not in meta or 'transform' not in meta:
return None
h, w = (meta['height'], meta['width'])
crs = dc_crs_from_rio(meta['crs'])
transform = meta['transform']
return GeoBox(w, h, transform, crs)
def _fix_resampling(kw):
r = kw.get('resampling', None)
if isinstance(r, str):
kw['resampling'] = resampling_s2rio(r)
def rio_slurp_reproject(fname, gbox, dtype=None, dst_nodata=None, **kw):
"""
Read image with reprojection
"""
import rasterio
from rasterio.warp import reproject
_fix_resampling(kw)
with rasterio.open(str(fname), 'r') as src:
if src.count == 1:
shape = gbox.shape
src_band = rasterio.band(src, 1)
else:
shape = (src.count, *gbox.shape)
src_band = rasterio.band(src, tuple(range(1, src.count+1)))
if dtype is None:
dtype = src.dtypes[0]
if dst_nodata is None:
dst_nodata = src.nodata
if dst_nodata is None:
dst_nodata = 0
pix = np.full(shape, dst_nodata, dtype=dtype)
reproject(src_band, pix,
dst_nodata=dst_nodata,
dst_transform=gbox.transform,
dst_crs=str(gbox.crs),
**kw)
meta = src.meta
meta['src_gbox'] = rio_geobox(meta)
meta['path'] = fname
meta['gbox'] = gbox
return pix, SimpleNamespace(**meta)
def rio_slurp_read(fname, out_shape=None, **kw):
"""
Read whole image file using rasterio.
:returns: ndarray (2d or 3d if multi-band), dict (rasterio meta)
"""
import rasterio
_fix_resampling(kw)
if out_shape is not None:
kw.update(out_shape=out_shape)
with rasterio.open(str(fname), 'r') as src:
data = src.read(1, **kw) if src.count == 1 else src.read(**kw)
meta = src.meta
src_gbox = rio_geobox(meta)
same_gbox = out_shape is None or out_shape == src_gbox.shape
gbox = src_gbox if same_gbox else gbx.zoom_to(src_gbox, out_shape)
meta['src_gbox'] = src_gbox
meta['gbox'] = gbox
meta['path'] = fname
return data, SimpleNamespace(**meta)
def rio_slurp(fname, *args, **kw):
"""
Dispatches to either:
rio_slurp_read(fname, out_shape, ..)
rio_slurp_reproject(fname, gbox, ...)
"""
if len(args) == 0:
if 'gbox' in kw:
return rio_slurp_reproject(fname, **kw)
else:
return rio_slurp_read(fname, **kw)
if isinstance(args[0], GeoBox):
return rio_slurp_reproject(fname, *args, **kw)
else:
return rio_slurp_read(fname, *args, **kw)
def rio_slurp_xarray(fname, *args, rgb='auto', **kw):
"""
Dispatches to either:
rio_slurp_read(fname, out_shape, ..)
rio_slurp_reproject(fname, gbox, ...)
then wraps it all in xarray.DataArray with .crs,.nodata etc.
"""
from xarray import DataArray
if len(args) == 0:
if 'gbox' in kw:
im, mm = rio_slurp_reproject(fname, **kw)
else:
im, mm = rio_slurp_read(fname, **kw)
else:
if isinstance(args[0], GeoBox):
im, mm = rio_slurp_reproject(fname, *args, **kw)
else:
im, mm = rio_slurp_read(fname, *args, **kw)
if im.ndim == 3:
dims = ('band', *mm.gbox.dims)
if rgb and im.shape[0] in (3, 4):
im = im.transpose([1, 2, 0])
dims = tuple(dims[i] for i in [1, 2, 0])
else:
dims = mm.gbox.dims
return DataArray(im,
dims=dims,
coords=mm.gbox.xr_coords(with_crs=True),
attrs=dict(
nodata=mm.nodata))
|
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|
"""
parse_options(kwargs)
Internal function. Takes the keyword arguments from the main function and parses it into a
usable Dict object
# Examples
```julia-repl
julia> parse_options(ex::Expr)
Dict{String,Any} with 2 entries:
"screen_name" => "jack"
...
```
"""
function parse_options(kwargs)
options = Dict{String, Any}()
for arg in kwargs
options[string(arg[1])] = arg[2]
end
options
end
"""
parse_results(cursorable, newdata::Dict, api_options, data_holder, cur_count)
Internal function. parses Twitter API results by determining the type of data and organizing
for cursorized processing.
There are two methods, this one takes a DICT object, indicating a set of user IDs or search results.
returns cursorable, newdata, api_options, cur_count.
# Examples
```julia-repl
julia> parse_results(cursorable, newdata::Dict, api_options, data_holder, cur_count)
...
```
"""
function parse_results(cursorable, newdata::Dict, api_options, data_holder, cur_count)
# Handle data type to cursor
if haskey(newdata, "ids")
newdata["ids"] = vcat(data_holder, newdata["ids"])
cur_count += length(newdata["ids"])
cursorable = (newdata["next_cursor"] != 0) & (cur_count < api_options["count"] )
api_options["cursor"] = newdata["next_cursor"]
elseif haskey(newdata, "statuses")
out = [Tweets(x) for x in newdata["statuses"]]
newdata["statuses"] = vcat(data_holder, out)
cur_count += length(newdata["statuses"])
cursorable = cur_count < api_options["count"]
api_options["max_id"] = minimum(x.id for x in newdata["statuses"])
else
cur_count += length(newdata)
cursorable = cur_count < api_options["count"]
end
cursorable, newdata, api_options, cur_count
end
"""
parse_results(cursorable, newdata::Array, api_options, data_holder, cur_count)
Internal function. parses Twitter API results by determining the type of data and organizing appropriately.
There are two methods, this one takes an ARRAY object, indicating a set of Tweets.
returns cursorable, newdata, api_options, cur_count
# Examples
```julia-repl
julia> parse_results(cursorable, newdata::Array, api_options, data_holder, cur_count)
...
```
"""
function parse_results(cursorable, newdata::Array, api_options, data_holder, cur_count)
newdata = Tweets[Tweets(x) for x in newdata]
length(newdata) == 0 && return false, data_holder, api_options, cur_count
# tree of options for max_id or since id
cur_count += length(newdata)
cursorable = cur_count < api_options["count"]
api_options["max_id"] = minimum([x.id for x in newdata])-1 # get min id
newdata = vcat(data_holder, newdata)
cursorable, newdata, api_options, cur_count
end
"""
cursor(cursorable::Bool, newdata::Dict, options::Dict, endp::String, cur_count::Integer)
Internal function method for gathering IDS. Takes a tuple and returns a tuple of equal size, calls the Twitter API
until the desired count of records is recovered or until the API exhausts its limits.
Note: when a DICT object is provided as the data, this function assumes you are gathering
follower or friends ids.
# Examples
```julia-repl
julia> while cursorable & (length(newdata["ids"]) < min_records)
cursorable, newdata, options, kwargs, endp = cursor(cursorable, newdata, options, kwargs, endp)
end
```
"""
################# cursor when new data is a Dict object - like followers or friends IDS
function cursor(cursorable::Bool, newdata::Dict, options::Dict, endp::String, cur_count::Integer)
cursorable == false && return cursorable, newdata, options, endp, cur_count
data_holder = haskey(newdata, "ids") ? newdata["ids"] : haskey(newdata, "statuses") ? newdata["statuses"] : [] # save existing ids
api_options = copy(options) # the get_oauth overwrites options, so store the correct data here
cur_alloc = reconnect("$endp") # start reconnect loop
remaining_calls = cur_alloc["remaining"]
@debug "$remaining_calls calls left on this endpoint."
r = get_oauth("https://api.twitter.com/1.1/$endp", options)
if r.status == 200
newdata = JSON.parse(String(r.body))
cursorable, newdata, api_options, cur_count = parse_results(cursorable, newdata, api_options, data_holder, cur_count)
cursorable, newdata, api_options, endp, cur_count
else
error("Twitter API returned $(r.status) status")
end
end
"""
cursor(cursorable::Bool, newdata::Dict, options::Dict, endp::String, cur_count::Integer)
Internal function for gathering . Takes a tuple and returns a tuple of equal size, calls the Twitter API
until the desired count of records is recovered or until the API exhausts its limits.
Note: when an ARRAY object is provided as the data, this function assumes you are gathering
a tweet timeline.
Note: when retrieving tweets, the API always starts with the most recent. Therefore,
if you want a chunk of older tweets, you must specify both since_id, and max_id when cursoring.
# Examples
```julia-repl
julia> while cursorable & (length(newdata["ids"]) < count)
cursorable, newdata, options, endp = cursor(cursorable, newdata, options, endp)
end
```
"""
function cursor(cursorable::Bool, newdata::Array, options::Dict, endp::String, cur_count::Integer)
cursorable == false && return cursorable, newdata, options, endp, cur_count
data_holder = copy(newdata) # save existing ids
api_options = copy(options) # the get_oauth overwrites options, so store the correct data here
cur_alloc = reconnect("$endp") # start reconnect loop
remaining_calls = cur_alloc["remaining"]
@debug "$remaining_calls calls left on this endpoint."
r = get_oauth("https://api.twitter.com/1.1/$endp", options)
if r.status == 200
# parse and put into proper type form
newdata = JSON.parse(String(r.body))
cursorable, newdata, api_options, cur_count = parse_results(cursorable, newdata, api_options, data_holder, cur_count)
cursorable, newdata, api_options, endp, cur_count
else
error("Twitter API returned $(r.status) status")
end
end
########### EXPORTED FUNCTIONS.......
"""
get_followers_ids(; kwargs...)
Get a Dict object of follower ids from a particular Twitter user. This function will call the API as
many times as allowed or until the desired `max_records` is reached, whichever comes first.
# Examples
```julia-repl
julia> get_followers_ids(screen_name = "jack", count = 10_000)
Dict{String,Any} with 6 entries:
"previous_cursor_str" => "0"
...
```
"""
function get_followers_ids(; kwargs...)
# Could be doing some pre-allocation here to optimize performance,
# but since this is an API function that only deals with 25K records at most...
endp = "followers/ids.json"
options = parse_options(kwargs)
if "count" ∈ keys(options)
count = options["count"]
else
options["count"] = 1
count = 1 # default to one record
end
cur_count = 0
cursorable = true
newdata = Dict{String,Any}()
newdata["ids"] = [] #Array{String,1}[]
while cursorable & (length(newdata["ids"]) < count)
cursorable, newdata, options, endp, cur_count = cursor(cursorable, newdata, options, endp, cur_count)
end
newdata
end
"""
get_friends_ids(; kwargs...)
Get a Dict object of follower ids from a particular Twitter user. This function will call the API as
until the desired `count` is reached or the API runs out, whichever comes first.
# Examples
```julia-repl
julia> get_friends_ids(screen_name = "barackobama", count = 1000)
```
"""
function get_friends_ids(; kwargs...)
# Could be doing some pre-allocation here to optimize performance,
# but since this is an API function that only deals with 25K records at most...
endp = "friends/ids.json"
options = parse_options(kwargs)
if "count" ∈ keys(options)
count = options["count"]
else
options["count"] = 1
count = 1 # default to one record
end
cur_count = 0
cursorable = true
newdata = Dict{String,Any}()
newdata["ids"] = [] #Array{String,1}[]
while cursorable & (length(newdata["ids"]) < count)
cursorable, newdata, options, endp, cur_count = cursor(cursorable, newdata, options, endp, cur_count)
end
newdata
end
########################## OTHER TYPE:
"""
get_mentions_timeline(; kwargs...)
Get an array object of mentions for a particular Twitter user. This function will call the API until the
desired `count` is reached or the API runs out, whichever comes first.
# Examples
```julia-repl
julia> get_mentions_timeline(screen_name = "twitter", count = 1000)
```
"""
function get_mentions_timeline(; kwargs...)
# Could be doing some pre-allocation here to optimize performance,
# but since this is an API function that only deals with 25K records at most...
endp = "statuses/mentions_timeline.json"
options = parse_options(kwargs)
if "count" ∈ keys(options)
count = options["count"]
else
options["count"] = 1
count = 1
end
cur_count = 0
# make the first call to the API
cursorable = true
newdata = Tweets[]
while cursorable & (length(newdata) < count)
cursorable, newdata, options, endp, cur_count = cursor(cursorable, newdata, options, endp, cur_count)
end
newdata
end
"""
get_user_timeline(; kwargs...)
Get an array object of timeline tweets from a particular Twitter user. This function will call the API until the
desired `count` is reached or the API runs out, whichever comes first.
# Examples
```julia-repl
julia> get_user_timeline(screen_name = "twitter", count = 1000)
```
"""
function get_user_timeline(; kwargs...)
# Could be doing some pre-allocation here to optimize performance,
# but since this is an API function that only deals with 25K records at most...
endp = "statuses/user_timeline.json"
options = parse_options(kwargs)
if "count" ∈ keys(options)
count = options["count"]
else
options["count"] = 1
count = 1
end
cur_count = 0
# make the first call to the API
cursorable = true
newdata = Tweets[]
while cursorable & (length(newdata) < count)
cursorable, newdata, options, endp, cur_count = cursor(cursorable, newdata, options, endp, cur_count)
end
newdata
end
"""
get_home_timeline(; kwargs...)
Get an array object of timeline tweets from the owning user. This function will call the API until the
desired `count` is reached or the API runs out, whichever comes first.
# Examples
```julia-repl
julia> get_home_timeline(count = 1000)
```
"""
function get_home_timeline(; kwargs...)
# Could be doing some pre-allocation here to optimize performance,
# but since this is an API function that only deals with 25K records at most...
endp = "statuses/home_timeline.json"
options = parse_options(kwargs)
if "count" ∈ keys(options)
count = options["count"]
else
options["count"] = 1
count = 1
end
cur_count = 0
# make the first call to the API
cursorable = true
newdata = Tweets[]
while cursorable & (length(newdata) < count)
cursorable, newdata, options, endp, cur_count = cursor(cursorable, newdata, options, endp, cur_count)
end
newdata
end
"""
get_retweets_of_me(; kwargs...)
Get an array object of retweets from the owning user. This function will call the API until the
desired `count` is reached or the API runs out, whichever comes first.
# Examples
```julia-repl
julia> get_retweets_of_me(count = 1000)
```
"""
function get_retweets_of_me(; kwargs...)
# Could be doing some pre-allocation here to optimize performance,
# but since this is an API function that only deals with 25K records at most...
endp = "statuses/retweets_of_me.json"
options = parse_options(kwargs)
if "count" ∈ keys(options)
count = options["count"]
else
options["count"] = 1
count = 1
end
cur_count = 0
# make the first call to the API
cursorable = true
newdata = Tweets[]
while cursorable & (length(newdata) < count)
cursorable, newdata, options, endp, cur_count = cursor(cursorable, newdata, options, endp, cur_count)
end
newdata
end
"""
get_retweets_of_me(; kwargs...)
Get an array object of retweets from the owning user. This function will call the API until the
desired `count` is reached or the API runs out, whichever comes first.
# Examples
```julia-repl
julia> get_retweets_of_me(count = 1000)
```
"""
function get_search_tweets(; kwargs...)
# Could be doing some pre-allocation here to optimize performance,
# but since this is an API function that only deals with 25K records at most...
endp = "search/tweets.json"
options = parse_options(kwargs)
if "count" ∈ keys(options)
count = options["count"]
else
options["count"] = 1
count = 1
end
cur_count = 0
# make the first call to the API
cursorable = true
newdata = Tweets[]
while cursorable & (length(newdata) < count)
cursorable, newdata, options, endp, cur_count = cursor(cursorable, newdata, options, endp, cur_count)
end
newdata
end
|
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|
# In Pandas which is an open source BSD-licensed python library, easy to use data structures and data
# analysis tools for the python PL
# Pandas delase with three DS, Panel, Dataframe, series
# In Pandas DataFrame, .head(n=5) return the first n rows
# In Pandas DataFrame, .describe() generates descriptive statistics that summarize the central tendency,
# dispersion, shape of a dataset's distribution, exluding NaN (Not a number) values.
import numpy as np
from keras.layers import Dense
from keras.models import Sequential
target = np.loadtxt('Datasets/hourly_wages.csv', dtype=float, delimiter=',', skiprows=1, usecols=0)
predictors = np.loadtxt('Datasets/hourly_wages.csv',
dtype=float,
delimiter=',', skiprows=1,
usecols=(1, 2, 3, 4, 5, 6, 7, 8, 9))
n_cols = predictors.shape[1]
model = Sequential()
# Add the first layer
model.add(Dense(50, activation="relu", input_shape=(n_cols,)))
# Add the second layer
model.add(Dense(32, activation="relu"))
# Add the output layer
model.add(Dense(1))
# Compile the model
model.compile(optimizer='adam', loss='mean_squared_error')
# What is loss function of the method?
# print("Loss Function: "+ model.loss)
# By Printing model.loss u can access its loss function
# Fitting the model
model.fit(predictors, target, epochs=10)
|
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|
import numpy as np
from sitator.dynamics import JumpAnalysis
from sitator.util import PBCCalculator
from sitator.network.merging import MergeSites
from sitator.util.mcl import markov_clustering
import logging
logger = logging.getLogger(__name__)
class MergeSitesByDynamics(MergeSites):
"""Merges sites using dynamical data.
Given a SiteTrajectory, merges sites using Markov Clustering.
:param float distance_threshold: Don't merge sites further than this
in real space. Zeros out the connectivity_matrix at distances greater than
this; a hard, step function style cutoff. For a more gentle cutoff, try
changing `connectivity_matrix_generator` to incorporate distance.
:param float post_check_thresh_factor: Throw an error if proposed merge sites
are further than this * distance_threshold away. Only a sanity check; not
a hard guerantee. Can be `None`; defaults to `1.5`. Can be loosely
thought of as how "normally distributed" the merge sites need to be, with
larger values allowing more and more oblong point clouds.
:param bool check_types: If True, only sites of the same type are candidates to
be merged; if false, type information is ignored. Merged sites will only
be assigned types if this is True.
:param int iterlimit: Maximum number of Markov Clustering iterations to run
before throwing an error.
:param dict markov_parameters: Parameters for underlying Markov Clustering.
Valid keys are ``'inflation'``, ``'expansion'``, and ``'pruning_threshold'``.
"""
def __init__(self,
connectivity_matrix_generator = None,
distance_threshold = 1.0,
post_check_thresh_factor = 1.5,
check_types = True,
iterlimit = 100,
markov_parameters = {}):
super().__init__(
maximum_merge_distance = post_check_thresh_factor * distance_threshold,
check_types = check_types
)
if connectivity_matrix_generator is None:
connectivity_matrix_generator = MergeSitesByDynamics.connectivity_n_ij
assert callable(connectivity_matrix_generator)
self.connectivity_matrix_generator = connectivity_matrix_generator
self.distance_threshold = distance_threshold
self.post_check_thresh_factor = post_check_thresh_factor
self.check_types = check_types
self.iterlimit = iterlimit
self.markov_parameters = markov_parameters
# Connectivity Matrix Generation Schemes:
@staticmethod
def connectivity_n_ij(sn):
"""Basic default connectivity scheme: uses n_ij directly as connectivity matrix.
Works well for systems with sufficient statistics.
"""
return sn.n_ij
@staticmethod
def connectivity_jump_lag_biased(jump_lag_coeff = 1.0,
jump_lag_sigma = 20.0,
jump_lag_cutoff = np.inf,
distance_coeff = 0.5,
distance_sigma = 1.0):
"""Bias the typical connectivity matrix p_ij with jump lag and distance contributions.
The jump lag and distance are processed through Gaussian functions with
the given sigmas (i.e. higher jump lag/larger distance => lower
connectivity value). These matrixes are then added to p_ij, with a prefactor
of ``jump_lag_coeff`` and ``distance_coeff``.
Site pairs with jump lags greater than ``jump_lag_cutoff`` have their bias
set to zero regardless of ``jump_lag_sigma``. Defaults to ``inf``.
"""
def cfunc(sn):
jl = sn.jump_lag.copy()
jl -= 1.0 # Center it around 1 since that's the minimum lag, 1 frame
jl /= jump_lag_sigma
np.square(jl, out = jl)
jl *= -0.5
np.exp(jl, out = jl) # exp correctly takes the -infs to 0
jl[sn.jump_lag > jump_lag_cutoff] = 0.
# Distance term
pbccalc = PBCCalculator(sn.structure.cell)
dists = pbccalc.pairwise_distances(sn.centers)
dmat = dists.copy()
# We want to strongly boost the similarity of *very* close sites
dmat /= distance_sigma
np.square(dmat, out = dmat)
dmat *= -0.5
np.exp(dmat, out = dmat)
return (sn.p_ij + jump_lag_coeff * jl) * (distance_coeff * dmat + (1 - distance_coeff))
return cfunc
# Real methods
def _get_sites_to_merge(self, st):
# -- Compute jump statistics
if not st.site_network.has_attribute('n_ij'):
ja = JumpAnalysis()
ja.run(st)
pbcc = PBCCalculator(st.site_network.structure.cell)
site_centers = st.site_network.centers
# -- Build connectivity_matrix
connectivity_matrix = self.connectivity_matrix_generator(st.site_network).copy()
n_sites_before = st.site_network.n_sites
assert n_sites_before == connectivity_matrix.shape[0]
centers_before = st.site_network.centers
# For diagnostic purposes
no_diag_graph = connectivity_matrix.astype(dtype = np.float, copy = True)
np.fill_diagonal(no_diag_graph, np.nan)
# Rather arbitrary, but this is really just an alarm for if things
# are really, really wrong
edge_threshold = np.nanmean(no_diag_graph) + 3 * np.nanstd(no_diag_graph)
n_alarming_ignored_edges = 0
# Apply distance threshold
for i in range(n_sites_before):
dists = pbcc.distances(centers_before[i], centers_before[i + 1:])
js_too_far = np.where(dists > self.distance_threshold)[0]
js_too_far += i + 1
if np.any(connectivity_matrix[i, js_too_far] > edge_threshold) or \
np.any(connectivity_matrix[js_too_far, i] > edge_threshold):
n_alarming_ignored_edges += 1
connectivity_matrix[i, js_too_far] = 0
connectivity_matrix[js_too_far, i] = 0 # Symmetry
if n_alarming_ignored_edges > 0:
logger.warning(" At least %i site pairs with high (z-score > 3) fluxes were over the given distance cutoff.\n"
" This may or may not be a problem; but if `distance_threshold` is low, consider raising it." % n_alarming_ignored_edges)
# -- Do Markov Clustering
clusters = markov_clustering(connectivity_matrix, **self.markov_parameters)
return clusters
|
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|
#!/usr/bin/env python
# coding: utf-8
from keras.models import load_model
from keras.preprocessing.image import img_to_array, load_img
import sys
from urllib.request import urlopen
import numpy as np
# Base values
target_height = 180
target_width = 320
channels = 3
model = load_model('../models/human_not_human.h5')
url = sys.argv[1]
print(url)
img = load_img(urlopen(url), target_size=(target_height, target_width))
x = img_to_array(img)
x = x / 255.0
size = img.size
channels=3
dataset = np.ndarray(shape=(1, size[1], size[0], channels),dtype=np.float32)
dataset[0] = x
result = model.predict(dataset)
print(result[0][0])
|
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|
subroutine UpdateBladeVel(IFLG)
use configr
use blade
use wake
use wallsoln
integer :: i,ygcErr
real :: Point(3), dVel(3), dUdX
! Calculate the velocity induced on the blades by wake, wall, and freestream
if (iflg .eq. 0) then
! re-initialize uiwake viwake wiwake as we are beginning a new time step
uiwake(:)=0.0
viwake(:)=0.0
wiwake(:)=0.0
end if
do I=1,NE
! If flag is set, just recompute the velocity contiribution due to the bound vorticies on the blades.
! Otherwise, calculate all wake, wall and freestream induced velocity.
if (IFLG .eq. 0) then
! Calculate freestream velocity at blade elements
CALL CalcFreestream(X(NT,I),Y(NT,I),Z(NT,I),UFSB(I),VFSB(I),WFSB(I),ygcErr)
! Set freestream velocity of next shed wake elements to that calculated on the blade
UFS(NT,I)=UFSB(I)
VFS(NT,I)=VFSB(I)
WFS(NT,I)=WFSB(I)
USUM=0.0
VSUM=0.0
WSUM=0.0
if (NT > 1) then
! Calculate wake velocity at blade elements (excluding bound vorticity component)
Call BladeIndVel(NT,ntTerm,NBE,NB,NE,X(NT,I),Y(NT,I),Z(NT,I),USUM,VSUM,WSUM,dUdX,2,0)
! Calculate wall induced velocities at blade locations
Point=[X(NT,I),Y(NT,I),Z(NT,I)]
Call WallIndVel(Point,dVel)
USUM=USUM+dVel(1)
VSUM=VSUM+dVel(2)
WSUM=WSUM+dVel(3)
end if
uiwake(I)=USUM
viwake(I)=VSUM
wiwake(I)=WSUM
else
USUM=uiwake(I)
VSUM=viwake(I)
WSUM=wiwake(I)
end if
! CALCULATE THE VELOCITY CONTRIBUTIONS DUE TO JUST THE BOUND VORTICIES ON THE BLADES ( GS(NT,:) )
Call BladeIndVel(NT,ntTerm,NBE,NB,NE,X(NT,I),Y(NT,I),Z(NT,I),UP,VP,WP,dUdX,1,0)
! Set wake and wall velocities on blade
UB(I)=USUM+UP
VB(I)=VSUM+VP
WB(I)=WSUM+WP
! Set induced velocity of next shed wake elements
if (iut .eq. -2) then
! Fix wake velocities at freestream velocity
U(NT,I)=0.0
V(NT,I)=0.0
W(NT,I)=0.0
else
U(NT,I)=UB(I)
V(NT,I)=VB(I)
W(NT,I)=WB(I)
end if
end do
return
end subroutine UpdateBladeVel
|
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|
/*
* ====================================================================
* Licensed to the Apache Software Foundation (ASF) under one
* or more contributor license agreements. See the NOTICE file
* distributed with this work for additional information
* regarding copyright ownership. The ASF licenses this file
* to you under the Apache License, Version 2.0 (the
* "License"); you may not use this file except in compliance
* with the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing,
* software distributed under the License is distributed on an
* "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
* KIND, either express or implied. See the License for the
* specific language governing permissions and limitations
* under the License.
* ====================================================================
*/
#include <boost/test/unit_test.hpp>
#include <codecvt>
#include <cstdint>
#include <locale>
#include <random>
#include <stdexcept>
#include <string>
#include <vector>
#include "private/svn_utf_private.h"
#include "../src/aprwrap.hpp"
namespace {
std::string to_utf8(const std::u32string& str)
{
static const int32_t endiancheck = 0xa5cbbc5a;
static const bool arch_big_endian =
(reinterpret_cast<const char*>(&endiancheck)[sizeof(endiancheck) - 1] == '\x5a');
apr::pool scratch_pool;
const svn_string_t* utf8_string;
auto err = svn_utf__utf32_to_utf8(
&utf8_string,
reinterpret_cast<const apr_int32_t*>(str.c_str()),
str.size(), arch_big_endian, scratch_pool.get(), scratch_pool.get());
if (err)
{
svn_error_clear(err);
throw std::range_error("bad unicode code point");
}
return std::string(utf8_string->data, utf8_string->len);
}
template<typename C> struct codepoint;
template<> struct codepoint<void>
{
using src_type = char32_t;
static constexpr std::uint_least32_t min = 0;
static constexpr std::uint_least32_t max = 0x10ffff;
static constexpr std::uint_least32_t surrogate_min = 0xd800;
static constexpr std::uint_least32_t surrogate_max = 0xdfff;
};
template<> struct codepoint<char32_t> : public codepoint<void>
{
using dst_type = char32_t;
static std::u32string convert(const std::u32string& str)
{
return str;
};
};
template<> struct codepoint<char16_t> : public codepoint<void>
{
using dst_type = char16_t;
static std::u16string convert(const std::u32string& str)
{
std::wstring_convert<std::codecvt_utf8_utf16<dst_type>, dst_type> u;
return u.from_bytes(to_utf8(str));
}
};
template<> struct codepoint<wchar_t> : public codepoint<void>
{
using dst_type = wchar_t;
#ifdef WIN32
// Be conservative, use UCS-2 for wchar_t on Windows
static_assert(sizeof(wchar_t) == sizeof(char16_t),
"I thought we had 2-byte wide chars on Windows");
static constexpr std::uint_least32_t max = 0xffff;
#endif
static std::wstring convert(const std::u32string& str)
{
#ifdef WIN32
const auto from_utf8 =
[](const std::string& sstr)
{
apr::pool scratch_pool;
const wchar_t* result;
auto err = svn_utf__win32_utf8_to_utf16(
&result, sstr.c_str(), nullptr, scratch_pool.get());
if (err)
{
svn_error_clear(err);
throw std::range_error("bad conversion to utf16");
}
return std::wstring(result);
}
#else
std::wstring_convert<std::codecvt_utf8<dst_type>, dst_type> u;
const auto from_utf8 = [&u](const std::string& sstr)
{
return u.from_bytes(sstr);
};
#endif
return from_utf8(to_utf8(str));
}
};
// Generate random strings.
template<typename C>
inline std::vector<std::basic_string<C>> generate_string_data(int count)
{
using cp = codepoint<C>;
std::mt19937 mt{std::random_device()()};
std::uniform_int_distribution<> cgen{typename cp::src_type(cp::min),
typename cp::src_type(cp::max)};
std::uniform_int_distribution<> lgen{7U, 31U};
std::vector<std::basic_string<C>> result;
result.reserve(count);
for (int i = 0; i < count; ++i)
{
const unsigned len = lgen(mt);
std::u32string val;
val.reserve(len);
for (unsigned j = 0; j < len; ++j)
{
repeat:
auto c = cgen(mt);
if (uint_least32_t(c) >= cp::surrogate_min
&& uint_least32_t(c) <= cp::surrogate_max)
goto repeat;
val.push_back(c);
}
result.emplace_back(cp::convert(val));
}
return result;
}
} // anonymous namespace
#include "../src/private/strings_private.hpp"
#include "fixture_init.hpp"
namespace svn = ::apache::subversion::svnxx;
namespace impl = ::apache::subversion::svnxx::impl;
BOOST_AUTO_TEST_SUITE(strings,
* boost::unit_test::fixture<init>());
BOOST_AUTO_TEST_CASE(wstring_conversion_roundtrip)
{
for (const auto& sample : generate_string_data<wchar_t>(100))
BOOST_TEST((sample == impl::convert<wchar_t>(impl::convert(sample))));
}
BOOST_AUTO_TEST_CASE(u16string_conversion_roundtrip)
{
for (const auto& sample : generate_string_data<char16_t>(100))
BOOST_TEST((sample == impl::convert<char16_t>(impl::convert(sample))));
}
BOOST_AUTO_TEST_CASE(u32string_conversion_roundtrip)
{
for (const auto& sample : generate_string_data<char32_t>(100))
BOOST_TEST((sample == impl::convert<char32_t>(impl::convert(sample))));
}
BOOST_AUTO_TEST_CASE(nulchar)
{
const std::string nulstr("\0", 1);
const std::wstring wnulstr(L"\0", 1);
const std::u16string u16nulstr(u"\0", 1);
const std::u32string u32nulstr(U"\0", 1);
BOOST_TEST(nulstr.size() == 1);
BOOST_TEST(wnulstr.size() == 1);
BOOST_TEST(u16nulstr.size() == 1);
BOOST_TEST(u32nulstr.size() == 1);
BOOST_TEST(impl::convert<wchar_t>(nulstr).size() == 1);
BOOST_TEST(impl::convert<char16_t>(nulstr).size() == 1);
BOOST_TEST(impl::convert<char32_t>(nulstr).size() == 1);
BOOST_TEST((impl::convert<wchar_t>(nulstr) == wnulstr));
BOOST_TEST((impl::convert<char16_t>(nulstr) == u16nulstr));
BOOST_TEST((impl::convert<char32_t>(nulstr) == u32nulstr));
BOOST_TEST(impl::convert(wnulstr).size() == 1);
BOOST_TEST(impl::convert(u16nulstr).size() == 1);
BOOST_TEST(impl::convert(u32nulstr).size() == 1);
BOOST_TEST((impl::convert(wnulstr) == nulstr));
BOOST_TEST((impl::convert(u16nulstr) == nulstr));
BOOST_TEST((impl::convert(u32nulstr) == nulstr));
}
BOOST_AUTO_TEST_SUITE_END();
|
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|
import unittest
import numpy as np
import pandas as pd
from apollon.tools import time_stamp
from comsar.tracks import TimbreTrack
class TestTimbreTrack(unittest.TestCase):
def setUp(self):
self.track = TimbreTrack()
def test_nfeatures(self):
self.assertIsInstance(self.track.n_features, int)
|
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|
import numpy as np
from spn.algorithms.Inference import EPSILON, add_node_likelihood
from spn.structure.leaves.spmnLeaves.SPMNLeaf import Utility
from spn.structure.leaves.histogram.Inference import histogram_likelihood
def utility_value(node, data=None, dtype=np.float64):
uVal = np.ones((data.shape[0], 1), dtype=dtype)
nd = data[:, node.scope[0]]
marg_ids = np.isnan(nd)
uVal[~marg_ids] = nd[~marg_ids].reshape((-1,1))
uVal[uVal < EPSILON] = EPSILON
return uVal
def add_utility_inference_support():
add_node_likelihood(Utility, histogram_likelihood)
|
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|
[STATEMENT]
lemma rev_nth_snoc: \<open>(xs @ [x]) !. Suc v = Some y \<Longrightarrow> xs !. v = Some y\<close>
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (xs @ [x]) !. Suc v = Some y \<Longrightarrow> xs !. v = Some y
[PROOF STEP]
by (induct xs) auto
|
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|
/* ****************************************************************** **
** OpenSees - Open System for Earthquake Engineering Simulation **
** Pacific Earthquake Engineering Research Center **
** **
** **
** (C) Copyright 1999, The Regents of the University of California **
** All Rights Reserved. **
** **
** Commercial use of this program without express permission of the **
** University of California, Berkeley, is strictly prohibited. See **
** file 'COPYRIGHT' in main directory for information on usage and **
** redistribution, and for a DISCLAIMER OF ALL WARRANTIES. **
** **
** Developed by: **
** Frank McKenna (fmckenna@ce.berkeley.edu) **
** Gregory L. Fenves (fenves@ce.berkeley.edu) **
** Filip C. Filippou (filippou@ce.berkeley.edu) **
** **
** ****************************************************************** */
// $Revision: 1.1.1.1 $
// $Date: 2000-09-15 08:23:16 $
// $Source: /usr/local/cvs/OpenSees/SRC/analysis/algorithm/eigenAlgo/EigenAlgorithm.cpp,v $
// File: ~/analysis/algorithm/eigenAlgo/EigenAlgorithm.C
//
// Written: Jun Peng
// Created: Wed Jan 27, 1999
// Revision: A
//
// Description: This file contains the class definition of EigenAlgorithm.
// EigenAlgorithm is a class which performs a eigen solution algorithm
// to solve the equations.
//
// This class is inheritanted from the base class of SolutionAlgorithm
// which was created by fmk (Frank).
#include <EigenAlgorithm.h>
#include <AnalysisModel.h>
#include <EigenIntegrator.h>
#include <EigenSOE.h>
EigenAlgorithm::EigenAlgorithm(int classTag)
:SolutionAlgorithm(classTag),
theModel(0), theIntegrator(0), theSOE(0)
{
// need do nothing here.
}
EigenAlgorithm::~EigenAlgorithm()
{
// do nothing here.
}
void
EigenAlgorithm::setLinks(AnalysisModel &theNewModel,
EigenIntegrator &theNewIntegrator,
EigenSOE &theNewSOE)
{
theModel = &theNewModel;
theIntegrator = &theNewIntegrator;
theSOE = &theNewSOE;
}
AnalysisModel *
EigenAlgorithm::getAnalysisModelPtr() const
{
return theModel;
}
EigenIntegrator *
EigenAlgorithm::getEigenIntegratorPtr() const
{
return theIntegrator;
}
EigenSOE *
EigenAlgorithm::getEigenSOEptr() const
{
return theSOE;
}
|
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|
import pandas as pd
import numpy as np
from tqdm import tqdm
import argparse
from datetime import datetime
parser = argparse.ArgumentParser()
parser.add_argument("--data", default='../data_cleaned/time_evolution_10_levels_natural.csv', \
help="filename.", type=str)
parser.add_argument("--maxlevel", default=10, help="Maximum level of the book to study", type=int)
parser.add_argument("--time_delta", default=1, help="Time delta in minutes", type=float)
parser.add_argument("--acquisition_day", default='2020-04-06', \
help="First day of data acquisition in format YYYY-MM-DD", type=str)
def main(data, maxlevel, time_delta, acquisition_day, tick_size=1e-4):
"""
Computes the order flow imbalance for each level of the book and
computes the corresponding evolution of mid price.
args:
data: path to the dataframe containing the data, obtained by running 'deltas_computation.py'
maxlevel: maximum level of the book to study
time_delta: time delta in seconds to discretize time
acquisition_day: first day of data acquisition in format YYYY-MM-DD
"""
date_acquisition = int(datetime.fromisoformat(acquisition_day).timestamp())
df = pd.read_csv(data)
df = df.groupby('time', sort=False).agg(np.mean)
df['time'] = df.index
df.index = range(len(df))
#clean datetime according to the date of acquisition
df.drop (df[df['time']<date_acquisition].index, axis=0, inplace=True)
df = df.sort_values(['time'], ignore_index=True)
# clean meaningless datetime, being sure that the last elment has the right length
df.drop (df[[len(str((df['time'][i])))<len(str(list(df['time'])[-1]))\
for i in range(len(df))]].index, axis=0, inplace=True)
conversion = 1e9
df['time_isoformat'] = df['time'].apply(lambda x: datetime.fromtimestamp(x/conversion))
#rescaling prices
for i in range(0, maxlevel):
df['ask_price_{}'.format(i)] = df['ask_price_{}'.format(i)]*tick_size
df['bid_price_{}'.format(i)] = df['bid_price_{}'.format(i)]*tick_size
df['mid_price'] = df['mid_price']*tick_size
#computing the quantities delta V delta D
for i in range(maxlevel):
print('level {}'.format(i))
check_bid_prices = np.diff (df['bid_price_{}'.format(i)])
check_ask_prices = np.diff (df['ask_price_{}'.format(i)])
delta_W = [0]
delta_V = [0]
j = 1
# for future improvement: this process can be optimized by employing .diff and masks
with tqdm(total=len(df)) as pbar:
for bcheck, acheck in zip(check_bid_prices, check_ask_prices):
if bcheck > 0:
delta_W.append(np.array(df['bid_volume_{}'.format(i)])[j])
elif bcheck == 0:
delta_W.append(np.array(df['bid_volume_{}'.format(i)])[j] - \
np.array(df['bid_volume_{}'.format(i)])[j-1])
else:
delta_W.append(- np.array(df['bid_volume_{}'.format(i)])[j-1])
if acheck < 0:
delta_V.append(np.array(df['ask_volume_{}'.format(i)])[j])
elif acheck == 0:
delta_V.append(np.array(df['ask_volume_{}'.format(i)])[j] - \
np.array(df['ask_volume_{}'.format(i)])[j-1])
else:
delta_V.append(np.array(-df['ask_volume_{}'.format(i)])[j-1])
j+=1
pbar.update(1)
df['delta_W_{}'.format(i)] = delta_W
df['delta_V_{}'.format(i)] = delta_V
df['e_{}'.format(i)] = df['delta_W_{}'.format(i)]-df['delta_V_{}'.format(i)]
#drop row without a previous item, (does not make sense in .diff)
df.index = range(len(df))
df.drop ([0], axis=0, inplace=True)
df.to_csv('../data_cleaned/time_evolution_{}_levels_processed.csv'.format(maxlevel),index=False)
# discretizig time
bin_edges = []
df = df.sort_values(['time_isoformat'], ignore_index=True)
t = df['time_isoformat'].iloc[0]
from datetime import timedelta
time_delta = timedelta(minutes=time_delta)
while t <= df['time_isoformat'].iloc[-1] + time_delta:
bin_edges.append(t)
t = t + time_delta
df['time_bin'] = pd.cut(df['time_isoformat'], bin_edges)
df = df.dropna()
df.index = range(len(df))
# computing mid_price_delta
bins = df['time_bin'].unique()
mid_price_delta = []
ofi = pd.DataFrame(bins, columns=['time_bin'])
ofi['bin_label'] = np.arange(len(bins))
for l in range(maxlevel): ofi['OFI_{}'.format(l)] = np.zeros(len(ofi))
index = 0
for b in bins:
grouped = df[df['time_bin']==b]
mid_price_delta.append(grouped['mid_price'].iloc[-1] - grouped['mid_price'].iloc[0])
for l in range(maxlevel):
ofi.iloc[index, l+2]=grouped['e_{}'.format(l)].sum()
index += 1
ofi['mid_price_delta'] = mid_price_delta
ofi.to_csv('../data_cleaned/ofi_{}_levels.csv'.format(maxlevel), index=False)
if __name__ == "__main__":
args = vars(parser.parse_args())
main(**args)
|
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|
import speech_recognition as sr
from tkinter import *
from tkinter import ttk
from tkinter import filedialog
import threading
import time
import os
import numpy as np
import librosa.display
import copy
from sklearn.externals import joblib
from winsound import *
from numpy import array, zeros, argmin, inf, ndim
from scipy.spatial.distance import cdist
import json
import sounddevice as sd
import soundfile as sf
from pydub import AudioSegment
from pydub.silence import split_on_silence
import os
from os import listdir
from os.path import isfile, join
ed = []
with open('eng_dict.json') as data_file:
eng_dict = json.load(data_file)
for i in eng_dict:
ed.append(i)
filename = 'hin_dict'
hin_dict = joblib.load(filename)
###DTW
def dtw(x, y, dist, warp=1):
"""
Computes Dynamic Time Warping (DTW) of two sequences.
:param array x: N1*M array
:param array y: N2*M array
:param func dist: distance used as cost measure
:param int warp: how many shifts are computed.
Returns the minimum distance, the cost matrix, the accumulated cost matrix, and the wrap path.
"""
assert len(x)
assert len(y)
r, c = len(x), len(y)
D0 = zeros((r + 1, c + 1))
D0[0, 1:] = inf
D0[1:, 0] = inf
D1 = D0[1:, 1:] # view
for i in range(r):
for j in range(c):
D1[i, j] = dist(x[i], y[j])
C = D1.copy()
for i in range(r):
for j in range(c):
min_list = [D0[i, j]]
for k in range(1, warp + 1):
i_k = min(i + k, r - 1)
j_k = min(j + k, c - 1)
min_list += [D0[i_k, j], D0[i, j_k]]
D1[i, j] += min(min_list)
if len(x)==1:
path = zeros(len(y)), range(len(y))
elif len(y) == 1:
path = range(len(x)), zeros(len(x))
else:
path = _traceback(D0)
return D1[-1, -1] / sum(D1.shape), C, D1, path
def accelerated_dtw(x, y, dist, warp=1):
"""
Computes Dynamic Time Warping (DTW) of two sequences in a faster way.
Instead of iterating through each element and calculating each distance,
this uses the cdist function from scipy (https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.cdist.html)
:param array x: N1*M array
:param array y: N2*M array
:param string or func dist: distance parameter for cdist. When string is given, cdist uses optimized functions for the distance metrics.
If a string is passed, the distance function can be 'braycurtis', 'canberra', 'chebyshev', 'cityblock', 'correlation', 'cosine', 'dice', 'euclidean', 'hamming', 'jaccard', 'kulsinski', 'mahalanobis', 'matching', 'minkowski', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'wminkowski', 'yule'.
:param int warp: how many shifts are computed.
Returns the minimum distance, the cost matrix, the accumulated cost matrix, and the wrap path.
"""
assert len(x)
assert len(y)
if ndim(x) == 1:
x = x.reshape(-1, 1)
if ndim(y) == 1:
y = y.reshape(-1, 1)
r, c = len(x), len(y)
D0 = zeros((r + 1, c + 1))
D0[0, 1:] = inf
D0[1:, 0] = inf
D1 = D0[1:, 1:]
D0[1:, 1:] = cdist(x, y, dist)
C = D1.copy()
for i in range(r):
for j in range(c):
min_list = [D0[i, j]]
for k in range(1, warp + 1):
min_list += [D0[min(i + k, r - 1), j],
D0[i, min(j + k, c - 1)]]
D1[i, j] += min(min_list)
if len(x) == 1:
path = zeros(len(y)), range(len(y))
elif len(y) == 1:
path = range(len(x)), zeros(len(x))
else:
path = _traceback(D0)
return D1[-1, -1] / sum(D1.shape), C, D1, path
def _traceback(D):
i, j = array(D.shape) - 2
p, q = [i], [j]
while (i > 0) or (j > 0):
tb = argmin((D[i, j], D[i, j+1], D[i+1, j]))
if tb == 0:
i -= 1
j -= 1
elif tb == 1:
i -= 1
else: # (tb == 2):
j -= 1
p.insert(0, i)
q.insert(0, j)
return array(p), array(q)
###DTW-End
reply = 0
entries = {}
textbs = {}
textpops = ""
test_files = []
current_test = ""
cg_dirname = ""
flag_audio_pop = 0
def language_selection_window(a):
global reply
e = entries["mic"]
reply = int(e.get())
# print(reply)
def enghin(a):
# global entries
# print(entries)
# e = entries["mic"]
# rep = int(e.get())
# print(rep)
global reply
rep = reply
# print(rep)
mic_list = sr.Microphone.list_microphone_names()
j = 1
mic_name = ""
sample_rate = 48000
chunk_size = 2048
for i, microphone_name in enumerate(mic_list):
# print(j,microphone_name)
if j == rep:
mic_name = microphone_name
# print("MIC",mic_name)
j += 1
r = sr.Recognizer()
mic_list = sr.Microphone.list_microphone_names()
for i, microphone_name in enumerate(mic_list):
if microphone_name == mic_name:
device_id = i
# print("HELL",device_id)
def exitf(a):
root.destroy()
def status_popup():
global textpops
savp = Tk()
savp.iconbitmap('wait.ico')
savp.wm_title("Recognition in progress...")
# Label(savp, text="Please wait...").grid(row=1, column=0, sticky="ew")
prog = Text(savp, height=10, width=40, bd=5, font=("Times", 20))
prog.grid(row=2, columnspan=3, sticky="ew")
# print("txtpps - ", textpops)
prog.insert(INSERT, " Recognition in progress, Please wait! \n")
prog.insert(INSERT, " Loading! \n")
start = time.time()
while not textpops:
if (time.time() - start) > 5:
break
prog.insert(INSERT, ".")
savp.update_idletasks()
savp.update()
textpops = ""
def speakeng(a):
with sr.Microphone(device_index=device_id, sample_rate=sample_rate, chunk_size=chunk_size) as source:
# Adjusting noise level
r.adjust_for_ambient_noise(source)
audio = r.listen(source)
global textpops
t1 = threading.Thread(target=status_popup)
t1.start()
try:
text = r.recognize_google(audio, language='en-IN')
textpops = text
# print("Speakeng - ",textpops)
text = text + "\n"
eng.insert(INSERT, text)
except sr.UnknownValueError:
text = "\n---\nGoogle Speech Recognition could not understand audio\n---\n"
eng.insert(INSERT, text)
except sr.RequestError as e:
eng.insert(INSERT, "---")
eng.insert(INSERT,
"Could not request results from Google Speech Recognition service; {0}".format(e))
eng.insert(INSERT, "---")
t1.join()
# print("\nt1 still alive - ", t1.is_alive())
def speakhin(a):
with sr.Microphone(device_index=device_id, sample_rate=sample_rate, chunk_size=chunk_size) as source:
# Adjusting noise level
r.adjust_for_ambient_noise(source)
audio = r.listen(source)
global textpops
t1 = threading.Thread(target=status_popup)
t1.start()
try:
text = r.recognize_google(audio, language='hi-IN')
textpops = text
# print("Speakhin - ", textpops)
text = text + "\n"
hin.insert(INSERT, text)
except sr.UnknownValueError:
text = "\n---\nGoogle Speech Recognition could not understand audio\n---\n"
hin.insert(INSERT, text)
except sr.RequestError as e:
hin.insert(INSERT, "---")
hin.insert(INSERT,
"Could not request results from Google Speech Recognition service; {0}".format(e))
hin.insert(INSERT, "---")
t1.join()
# print("\nt1 still alive - ", t1.is_alive())
def cleareng(a):
eng.delete(1.0, END)
def clearhin(a):
hin.delete(1.0, END)
def saveeng(a):
location = ""
def browse(a):
x = filedialog.askdirectory()
e = entries["save_file_location"]
e.insert(0, x)
location = x
def savv(a):
e = entries["save_file_location"]
location = str(e.get())
e = entries["save_file_name"]
name = str(e.get())
input = eng.get("1.0", 'end-1c')
# print(name)
loc = location + "/" + name + ".txt"
# print("\nFinal loc\n", loc)
f = open(loc, 'w')
f.write(input)
f.close()
sav.destroy()
sav = Tk()
sav.iconbitmap('save.ico')
sav.wm_title("Save English Transcript")
Label(sav, text="Enter the file name you want: ").grid(row=0, column=0, sticky=W)
e = Entry(sav, width=50)
e.grid(row=1, columnspan=2, sticky="ew")
entries["save_file_name"] = e
Label(sav, text="Choose the location to save at: ").grid(row=2, column=0, sticky=W)
folentry = Entry(sav, width=77)
folentry.grid(row=3, column=0, sticky="ew")
entries["save_file_location"] = folentry
ch = Button(sav, text="Browse")
ch.bind("<Button-1>", browse)
ch.grid(row=3, column=1, sticky="ew")
ttk.Separator(sav).grid(row=4, pady=2, padx=2, columnspan=3, sticky="ew")
ent = Button(sav, text="Save", width=11)
ent.bind("<Button-1>", savv)
ent.grid(row=5, column=1, sticky="ew")
sav.mainloop()
def savehin(a):
location = ""
def browse(a):
x = filedialog.askdirectory()
e = entries["save_file_location"]
e.insert(0, x)
location = x
def savv(a):
e = entries["save_file_location"]
location = str(e.get())
e = entries["save_file_name"]
name = str(e.get())
input = hin.get("1.0", 'end-1c')
# print(name)
loc = location + "/" + name + ".txt"
# print("\nFinal loc\n", loc)
f = open(loc, 'w', encoding="utf-8")
f.write(input)
f.close()
sav.destroy()
sav = Tk()
sav.iconbitmap('save.ico')
sav.wm_title("Save Hindi Transcript")
Label(sav, text="Enter the file name you want: ").grid(row=0, column=0, sticky=W)
e = Entry(sav, width=50)
e.grid(row=1, columnspan=2, sticky="ew")
entries["save_file_name"] = e
Label(sav, text="Choose the location to save at: ").grid(row=2, column=0, sticky=W)
folentry = Entry(sav, width=77)
folentry.grid(row=3, column=0, sticky="ew")
entries["save_file_location"] = folentry
ch = Button(sav, text="Browse")
ch.bind("<Button-1>", browse)
ch.grid(row=3, column=1, sticky="ew")
ttk.Separator(sav).grid(row=4, pady=2, padx=2, columnspan=3, sticky="ew")
ent = Button(sav, text="Save", width=11)
ent.bind("<Button-1>", savv)
ent.grid(row=5, column=1, sticky="ew")
sav.mainloop()
win.destroy()
root = Tk()
root.iconbitmap('icon.ico')
root.title("English and Hindi Voice Typing Editor")
Label(root, text="English Speech to text:").grid(row=0, column=0, sticky=W)
eng = Text(root, height=12, width=72, bd=5, font=("Times", 12))
eng.grid(row=3, columnspan=3)
se = Button(root, text="Speak English", width=11)
se.bind("<Button-1>", speakeng)
se.grid(row=6, column=0)
es = Button(root, text="Clear English", width=11)
es.bind("<Button-1>", cleareng)
es.grid(row=6, column=1)
ce = Button(root, text="Save English", width=11)
ce.bind("<Button-1>", saveeng)
ce.grid(row=6, column=2)
Label(root, text="Hindi Speech to text:").grid(row=7, column=0, sticky=W)
hin = Text(root, height=12, width=72, bd=5, font=("Times", 12))
hin.grid(row=10, columnspan=3)
sh = Button(root, text="Speak Hindi", width=11)
sh.bind("<Button-1>", speakhin)
sh.grid(row=13, column=0)
hs = Button(root, text="Clear Hindi", width=11)
hs.bind("<Button-1>", clearhin)
hs.grid(row=13, column=1)
ch = Button(root, text="Save Hindi", width=11)
ch.bind("<Button-1>", savehin)
ch.grid(row=13, column=2)
ttk.Separator(root).grid(row=14, pady=2, padx=2, columnspan=3, sticky="ew")
ex = Button(root, text="Exit", width=11)
ex.bind("<Button-1>", exitf)
ex.grid(row=16, columnspan=3, sticky="ew")
root.mainloop()
def exitwin(a):
win.destroy()
def test_folder(a):
def cg(a):
mfcc_arr = joblib.load('Training_mfcc_arr.pkl')
y = joblib.load('Training_y.pkl')
def exitcg(a):
cgroot.destroy()
def preprocess_mfcc(mfcc):
mfcc_cp = copy.deepcopy(mfcc)
for i in range(mfcc.shape[1]):
mfcc_cp[:, i] = mfcc[:, i] - np.mean(mfcc[:, i])
mfcc_cp[:, i] = mfcc_cp[:, i] / np.max(np.abs(mfcc_cp[:, i]))
return mfcc_cp
def audio_popup():
def play(a):
file = cg_dirname + "/" + current_test
PlaySound(file, SND_FILENAME | SND_ASYNC)
def exi(a):
global flag_audio_pop
flag_audio_pop = 1
savp.destroy()
savp = Tk()
savp.iconbitmap('audio.ico')
savp.wm_title("Audio Player")
Label(savp,
text="Click on Play to play the following Audio file:\n" + current_test + "\nClick on Exit to close this window.").grid(
row=1, column=0, sticky="ew")
se = Button(savp, text="Play", width=11)
se.bind("<Button-1>", play)
se.grid(row=2, column=0)
es = Button(savp, text="Exit", width=11)
es.bind("<Button-1>", exi)
es.grid(row=2, column=1)
savp.mainloop()
def recognize_mic(a):
fs = 44100
duration = 5 # seconds
myrecording = sd.rec(duration * fs, samplerate=fs, channels=2, dtype='float64')
print("Recording Audio")
sd.wait()
print("Audio recording complete , Play Audio")
sf.write("temp.wav", myrecording, fs)
sd.wait()
print("Play Audio Complete")
AudioSegment.ffmpeg = "C://ffmpeg//bin"
cwd = os.getcwd()
loc = cwd + "\\" + "temp.wav"
sound_file = AudioSegment.from_wav(loc)
audio_chunks = split_on_silence(sound_file,
# must be silent for at least half a second
min_silence_len=250,
# consider it silent if quieter than -16 dBF
silence_thresh=-38
)
print("Hello")
for i, chunk in enumerate(audio_chunks):
out_file = cwd + "\\" + "temp\\temp_{0}.wav".format(i)
print(i)
if i < 10:
out_file = cwd + "\\" + "temp\\temp_0{0}.wav".format(i)
print("exporting", out_file)
chunk.export(out_file, format="wav")
foldname = cwd + "\\" + "temp"
onlyfiles = [f for f in listdir(foldname) if isfile(join(foldname, f))]
answer = ""
for i in onlyfiles:
# start = time.perf_counter()
yTest, srTest = librosa.load(foldname + "/" + i)
mfccTest = librosa.feature.mfcc(yTest, srTest)
mfccTest = preprocess_mfcc(mfccTest)
dists = []
for i in range(len(mfcc_arr)):
mfcci = mfcc_arr[i]
disti = dtw(mfcci.T, mfccTest.T, dist=lambda x, y: np.exp(np.linalg.norm(x - y, ord=1)))[0]
dists.append(disti)
# plt.plot(dists)
min_dist = min(dists)
min_dist_index = dists.index(min_dist)
pre = int(y[min_dist_index])
output = hin_dict[pre]
answer = answer + " " + output
mi.insert(INSERT, answer)
def recognize_all(a):
start = time.perf_counter()
dirname = cg_dirname
files = test_files
Test_Result = []
Reult_indices = []
for j in range(len(files)):
start1 = time.perf_counter()
yTest, srTest = librosa.load(dirname + "/" + files[j])
mfccTest = librosa.feature.mfcc(yTest, srTest)
mfccTest = preprocess_mfcc(mfccTest)
dists = []
for i in range(len(mfcc_arr)):
mfcci = mfcc_arr[i]
disti = dtw(mfcci.T, mfccTest.T, dist=lambda x, y: np.exp(np.linalg.norm(x - y, ord=1)))[0]
dists.append(disti)
min_dist = min(dists)
min_dist_index = dists.index(min_dist)
pre = int(y[min_dist_index])
output = hin_dict[pre]
tt = time.perf_counter() - start1
output = "Input File : " + current_test + ".\nThe spoken word is : " + output + ".\nTime taken for Recognition : " + str(tt) + "\n"
micl.insert(INSERT, output)
Test_Result.append(hin_dict[pre])
Reult_indices.append(pre)
# print(hin_dict[pre])
tt = time.perf_counter() - start
output = "\nTotal Time taken for Recognizing "+str(len(test_files))+" Testing files : " +str(tt) + "\n"
micl.insert(INSERT, output)
#Accuracy
j=0
correct = 0
total_files = len(test_files)
#Precision
precisions = np.array([0]*58)
num = [0] * 58
den = [0] * 58
for i in range(len(Test_Result)):
den[Reult_indices[i]] += 1
lis = list(files[i].split('_'))
# print(eng_dict)
index = ed.index(str(lis[0]))
# print(index)
if Reult_indices[i] == index:
num[Reult_indices[i]] += 1
# print("Precisions word-wise:")
for i in range(58):
try:
precisions[i] = (num[i] / den[i]) * 100
except:
precisions[i] = -1
pass
prc = np.array(precisions)
np.save("precisions",prc)
for i in test_files:
lis = list(i.split('_'))
index = ed.index(str(lis[0]))
true_value = hin_dict[index]
if Test_Result[j]==true_value:
correct+=1
j+=1
accuracy = (correct/total_files)*100
output = "\nAccuracy of the complete Recognition : " + str(correct) + " out of " + str(total_files) + ".\nAccuracy percentage : "+str(accuracy)+"\n"
anarray = [0,0]
anarray = np.array(anarray)
np.save("accuracy",anarray)
micl.insert(INSERT, output)
def selected_from_dd(*args):
global current_test
current_test = tkvar.get()
t1 = threading.Thread(target=audio_popup)
t1.start()
start = time.perf_counter()
yTest, srTest = librosa.load(cg_dirname + "/" + current_test)
mfccTest = librosa.feature.mfcc(yTest, srTest)
mfccTest = preprocess_mfcc(mfccTest)
dists = []
for i in range(len(mfcc_arr)):
mfcci = mfcc_arr[i]
disti = dtw(mfcci.T, mfccTest.T, dist=lambda x, y: np.exp(np.linalg.norm(x - y, ord=1)))[0]
dists.append(disti)
# plt.plot(dists)
min_dist = min(dists)
min_dist_index = dists.index(min_dist)
pre = int(y[min_dist_index])
output = hin_dict[pre]
tt = time.perf_counter()-start
output = "Input File : "+str(current_test)+".\nThe spoken word is : "+str(output)+".\nTime taken for Recognition : "+str(tt)+"\n"
sop.insert(INSERT, output)
global flag_audio_pop
if flag_audio_pop == 1:
t1.join()
flag_audio_pop = 0
fol.destroy()
cgroot = Tk()
cgroot.iconbitmap('icon.ico')
tkvar = StringVar(cgroot)
cgroot.title("Chhattisgarhi Small Vocabulary Speech Recognition")
drop_down_menu = OptionMenu(cgroot, tkvar, *test_files)
Label(cgroot, text="Recognize a single file, Choose from below: ").grid(row=0, columnspan=2, sticky="w")
drop_down_menu.grid(row=2, column=1, sticky="ew")
tkvar.trace('w', selected_from_dd)
sop = Text(cgroot, height=6, width=60, bd=5, font=("Times", 12))
sop.grid(row=2, column=0)
ttk.Separator(cgroot).grid(row=3, pady=2, padx=2, columnspan=3, sticky="ew")
Label(cgroot, text="Recognize all the Audio files of Test folder: ").grid(row=4, columnspan=2, sticky="w")
micl = Text(cgroot, height=6, width=60, bd=5, font=("Times", 12))
micl.grid(row=5, column=0, sticky="w")
reczall = Button(cgroot, text="Recognize All", width=11)
reczall.bind("<Button-1>", recognize_all)
reczall.grid(row=5, column=1, sticky="ew")
ttk.Separator(cgroot).grid(row=6, pady=2, padx=2, columnspan=3, sticky="ew")
Label(cgroot, text="Recognize through Mic (5-second recording): ").grid(row=7, columnspan=2, sticky="w")
mi = Text(cgroot, height=6, width=60, bd=5, font=("Times", 12))
mi.grid(row=8, column=0, sticky="w")
recmic = Button(cgroot, text="Recognize", width=11)
recmic.bind("<Button-1>", recognize_mic)
recmic.grid(row=9, column=1, sticky="ew")
ttk.Separator(cgroot).grid(row=10, pady=2, padx=2, columnspan=3, sticky="ew")
ex = Button(cgroot, text="Exit", width=11)
ex.bind("<Button-1>", exitcg)
ex.grid(row=11, columnspan=3, sticky="ew")
cgroot.mainloop()
def askfolder(a):
global cg_dirname
cg_dirname = filedialog.askdirectory()
folentry.insert(0, cg_dirname)
global test_files
test_files = [f for f in os.listdir(cg_dirname) if os.path.isfile(os.path.join(cg_dirname,f))]
if "desktop.ini" in test_files:
test_files.remove("desktop.ini")
# print(test_files)
win.destroy()
fol = Tk()
fol.iconbitmap('save.ico')
fol.title("Testing Folder Selection")
Label(fol, text="Choose the folder containing Testing Audio files:").grid(row=0, column=0, sticky=W)
folentry = Entry(fol, width=77)
folentry.grid(row=1, sticky=W, column=0)
ch = Button(fol, text="Browse")
ch.bind("<Button-1>", askfolder)
ch.grid(row=1, column=1, sticky=E)
ch = Button(fol, text="Next")
ch.bind("<Button-1>", cg)
ch.grid(row=2, columnspan=2, sticky="ew")
fol.mainloop()
popup.destroy()
win = Tk()
win.iconbitmap('icon.ico')
win.title("Select the language for Recognition")
Label(win, text="English/Hindi Speech to text:").grid(row=0, column=0, sticky=W)
se = Button(win, text="English/Hindi", width=11)
se.bind("<Button-1>", enghin)
se.grid(row=0, column=1, sticky="ew")
ttk.Separator(win).grid(row=2, pady=2, padx=2, columnspan=3, sticky="ew")
Label(win, text="Chhattisgarhi Small Vocabulary Recognition(Words listed below):").grid(row=4, column=0, sticky=W)
words = Text(win, height=4, width=60, bd=5, font=("Times", 12))
words.grid(row=6, column=0)
words.insert(INSERT, "'आबे', 'बईठ', 'बेरा', 'एती', 'गोड़', 'हमर', 'हे', 'जाहूँ', 'काबर', 'कहत', 'करत', 'खाबे', 'कोति', 'लइका','मोर', 'पीरात', 'रेंगत', 'टेरत', 'टूरा', 'तुमन'")
sh = Button(win, text="Chhattisgarhi", width=11)
sh.bind("<Button-1>", test_folder)
sh.grid(row=6, column=1, sticky="ew")
ttk.Separator(win).grid(row=10, pady=2, padx=2, columnspan=3, sticky="ew")
exx = Button(win, text="Exit", width=11)
exx.bind("<Button-1>", exitwin)
exx.grid(row=12, columnspan=3, sticky="ew")
win.mainloop()
def genlist(a):
mic_list = sr.Microphone.list_microphone_names()
j = 1
li = ""
for i, microphone_name in enumerate(mic_list):
temp = str(j)
temp = temp + " - " + microphone_name + "\n"
li = li + temp
j += 1
# print("\ngenlist's --\n",li)
e = textbs["miclist"]
# print("\ninslist's --\n", li)
e.insert(INSERT, li)
popup = Tk()
popup.iconbitmap('mic.ico')
popup.wm_title("Microphone Confirmation")
Label(popup, text="Enter the serial number of the appropriate mic from the following list").grid(row=0,column=0,sticky=W)
micl = Text(popup, height=6, width=30, bd=9, font=("Times", 12))
micl.grid(row=1, columnspan=1, sticky = "ew")
textbs["miclist"] = micl
gl = Button(popup, text="Generate list", width=11)
gl.bind("<Button-1>",genlist)
gl.grid(row=1, column=1, sticky = "ew")
e = Entry(popup,width = 50)
e.grid(row=7,sticky = "ew")
entries["mic"] = e
ent = Button(popup, text="Submit", width=11)
ent.bind("<Button-1>", language_selection_window)
ent.grid(row=7, column=1, sticky = "ew")
popup.mainloop()
|
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|
import os
import argparse
from DFLIMG import DFLIMG, DFLPNG
from pathlib import Path
from PIL import Image
import numpy as np
parser = argparse.ArgumentParser()
parser.add_argument('--upscale_factor', type=int, default=1)
parser.add_argument('--model_path', type=str, default='experiments/pretrained_models/GFPGANv1.pth')
parser.add_argument('--input_dir', type=str, default='')
parser.add_argument('--output_dir', type=str, default='')
parser.add_argument('--suffix', type=str, default=None, help='Suffix of the restored faces')
parser.add_argument('--only_center_face', action='store_true')
parser.add_argument('--aligned', action='store_true')
parser.add_argument('--paste_back', action='store_true')
parser.add_argument("--gpu_id", dest='gpu_id', default=0, type=int)
parser.add_argument('--data_type', type=str, dest="data_type", default='dfl', choices=['dfl', 'raw'],
help='Input image type. raw input image does not have meta data for face attributes')
parser.add_argument('--randomize_noise', action='store_true')
args = parser.parse_args()
if args.input_dir.endswith('/'):
args.input_dir = args.input_dir[:-1]
save_root = args.output_dir
os.makedirs(save_root, exist_ok=True)
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"]=str(args.gpu_id)
import cv2
import glob
import numpy as np
import torch
from facexlib.utils.face_restoration_helper import FaceRestoreHelper
from torchvision.transforms.functional import normalize
from archs.gfpganv1_arch import GFPGANv1
from basicsr.utils import img2tensor, imwrite, tensor2img
def restoration(gfpgan,
face_helper,
img_path,
save_root,
has_aligned=False,
only_center_face=True,
suffix=None,
paste_back=False):
# read image
img_name = os.path.basename(img_path)
print(f'Processing {img_name} ...')
basename, _ = os.path.splitext(img_name)
input_img = cv2.imread(img_path, cv2.IMREAD_COLOR)
face_helper.clean_all()
if has_aligned:
input_img = cv2.resize(input_img, (512, 512))
face_helper.cropped_faces = [input_img]
else:
face_helper.read_image(input_img)
# get face landmarks for each face
face_helper.get_face_landmarks_5(only_center_face=only_center_face, pad_blur=False)
# align and warp each face
face_helper.align_warp_face()
# face restoration
for idx, cropped_face in enumerate(face_helper.cropped_faces):
# prepare data
cropped_face_t = img2tensor(cropped_face / 255., bgr2rgb=True, float32=True)
normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
cropped_face_t = cropped_face_t.unsqueeze(0).to('cuda')
try:
with torch.no_grad():
output = gfpgan(cropped_face_t, return_rgb=False, randomize_noise=args.randomize_noise)[0]
# convert to image
restored_face = tensor2img(output.squeeze(0), rgb2bgr=True, min_max=(-1, 1))
except RuntimeError as error:
print(f'\tFailed inference for GFPGAN: {error}.')
restored_face = cropped_face
restored_face = restored_face.astype('uint8')
face_helper.add_restored_face(restored_face)
if not has_aligned and paste_back:
face_helper.get_inverse_affine(None)
save_restore_path = os.path.join(save_root, img_name)
# paste each restored face to the input image
face_helper.paste_faces_to_input_image(save_restore_path)
# Add DFL meta data to output image
if args.data_type == 'dfl':
dfl_img1 = DFLIMG.load(Path(img_path))
if dfl_img1:
if save_restore_path.split('.')[-1] == 'jpg':
dfl_img2 = DFLIMG.load(Path(save_restore_path))
# Add meta data to output image
dfl_img2.set_face_type(dfl_img1.get_face_type())
dfl_img2.set_landmarks(dfl_img1.get_landmarks())
dfl_img2.set_source_rect(dfl_img1.get_source_rect())
dfl_img2.set_source_filename(dfl_img1.get_source_filename())
dfl_img2.set_source_landmarks(dfl_img1.get_source_landmarks())
dfl_img2.set_image_to_face_mat(dfl_img1.get_image_to_face_mat())
dfl_img2.save()
elif save_restore_path.split('.')[-1] == 'png':
DFLPNG.DFLPNG.embed_data(
filename = save_restore_path,
face_type = dfl_img1.get_face_type(),
landmarks = dfl_img1.get_landmarks(),
source_filename = dfl_img1.get_source_filename(),
source_rect = dfl_img1.get_source_rect(),
source_landmarks = dfl_img1.get_source_landmarks(),
image_to_face_mat = dfl_img1.get_image_to_face_mat(),
pitch_yaw_roll = None,
eyebrows_expand_mod = dfl_img1.get_eyebrows_expand_mod(),
cfg = None,
model_data = None
)
else:
print('unknown output format: ' + save_restore_path.split('.')[-1])
if __name__ == '__main__':
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# initialize the GFP-GAN
gfpgan = GFPGANv1(
out_size=512,
num_style_feat=512,
channel_multiplier=1,
decoder_load_path=None,
fix_decoder=True,
# for stylegan decoder
num_mlp=8,
input_is_latent=True,
different_w=True,
narrow=1,
sft_half=True)
gfpgan.to(device)
checkpoint = torch.load(args.model_path, map_location=lambda storage, loc: storage)
gfpgan.load_state_dict(checkpoint['params_ema'])
gfpgan.eval()
types = ('*.png', '*.jpg', '*.jpeg')
files_grabbed = []
for files in types:
files_grabbed.extend(glob.glob(os.path.join(args.input_dir, files)))
img_list = sorted(files_grabbed)
# initialize face helper
face_helper = FaceRestoreHelper(
upscale_factor=args.upscale_factor, face_size=512, crop_ratio=(1, 1), det_model='retinaface_resnet50', save_ext=img_list[0].split('.')[-1])
for img_path in img_list:
restoration(
gfpgan,
face_helper,
img_path,
save_root,
has_aligned=args.aligned,
only_center_face=args.only_center_face,
suffix=args.suffix,
paste_back=args.paste_back)
print('Results are in the ' + args.output_dir + ' folder.')
|
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|
"""
Luis Eduardo Sánchez González
Universidad Autonoma de Coahuila
Facultad de Ciencias Físico Matemáticas
mié 03 feb 2021 13:10:46 CST
"""
import numpy as np
class Difference:
def __init__(self, f):
if callable(f):
self.f = f
else:
raise ValueError("La derivada es igual a cero.")
def InitialConditions(self, x0, h):
if isinstance(x0, (int, float)):
self.x0 = x0
else:
self.x0 = np.asarray(x0)
self.h = h
class Forward(Difference):
def Solve(self):
f, x0, h = self.f, self.x0, self.h
return (f(x0 + h) - f(x0))/h
class Central(Difference):
def Solve(self):
f, x0, h = self.f, self.x0, self.h
return (f(x0 + h) - f(x0 - h))/(2*h)
class Backward(Difference):
def Solve(self):
f, x0, h = self.f, self.x0, self.h
return (f(x0) - f(x0 - h))/h
|
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|
#pragma once
#include <boost/filesystem.hpp>
namespace rai
{
boost::filesystem::path AppPath();
void SetStdinEcho(bool);
std::string PemPath();
}
|
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|
"""
Simulated devices for documentation and testing
"""
import collections
import itertools
import os
import tempfile
import threading
import time
from bluesky.utils import short_uid
import numpy as np
from ophyd import Signal, Device, Component, DeviceStatus, Staged
from ophyd.sim import new_uid
import scipy.special
x, y = np.mgrid[-100:100, -100:100] * 1/200
r = np.hypot(x, y)
r *= 20
r -= 15
diffraction_pattern = scipy.special.airy(r)[0]
diffraction_pattern -= diffraction_pattern.min()
diffraction_pattern *= np.ptp(diffraction_pattern) * 0.5 * (2 ** 16)
diffraction_pattern = diffraction_pattern.astype('uint16')
shutter_state = {'state': 'open'}
class Shutter(Signal):
def put(self, value):
shutter_state['state'] = value
super().put(value)
def generate_dark_frame():
values = (np.random.RandomState(0).randint(0, 2**16, 10) * 0.2).astype('uint16')
# Tile values into bands.
return np.broadcast_to(np.repeat(values, 20), (200, 200)).copy()
def generate_image(dark=False):
# TODO Add noise, zingers, and other nondeterministic things.
output = generate_dark_frame()
if not dark:
output += diffraction_pattern
return output
class TimerStatus(DeviceStatus):
"""Simulate the time it takes for a detector to acquire an image."""
def __init__(self, device, delay):
super().__init__(device)
self.delay = delay # for introspection purposes
threading.Timer(delay, self._finished).start()
class DiffractionDetector(Device):
exposure_time = Component(Signal, value=1)
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self._resource_uid = None
self._datum_counter = None
self._asset_docs_cache = collections.deque()
self.save_path = tempfile.mkdtemp()
self._path_stem = None
self._stashed_image_reading = None
self._stashed_image_data_key = None
def stage(self):
file_stem = short_uid()
self._datum_counter = itertools.count()
self._path_stem = os.path.join(self.save_path, file_stem)
self._resource_uid = new_uid()
resource = {'spec': 'NPY_SEQ',
'root': self.save_path,
'resource_path': file_stem,
'resource_kwargs': {},
'uid': self._resource_uid,
'path_semantics': {'posix': 'posix', 'nt': 'windows'}[os.name]}
self._asset_docs_cache.append(('resource', resource))
return super().stage()
def trigger(self):
if not self._staged == Staged.yes:
raise RuntimeError("Device must be staged before it is triggered.")
image = generate_image(dark=shutter_state['state'] == 'closed')
# Save the actual reading['value'] to disk. For a real detector,
# this part would be done by the detector IOC, not by ophyd.
data_counter = next(self._datum_counter)
np.save(f'{self._path_stem}_{data_counter}.npy', image,
allow_pickle=False)
# Generate a stash and Datum document.
datum_id = '{}/{}'.format(self._resource_uid, data_counter)
datum = {'resource': self._resource_uid,
'datum_kwargs': dict(index=data_counter),
'datum_id': datum_id}
self._asset_docs_cache.append(('datum', datum))
self._stashed_image_reading = {'value': datum_id,
'timestamp': time.time()}
self._stashed_image_data_key = {'source': 'SIM:image',
'shape': image.shape,
'dtype': 'array',
'external': 'FILESTORE'}
return TimerStatus(self, self.exposure_time.get())
def read(self):
ret = super().read()
ret[f'{self.name}_image'] = self._stashed_image_reading
return ret
def describe(self):
ret = super().describe()
ret[f'{self.name}_image'] = self._stashed_image_data_key
return ret
def collect_asset_docs(self):
items = list(self._asset_docs_cache)
self._asset_docs_cache.clear()
for item in items:
yield item
def unstage(self):
self._resource_uid = None
self._datum_counter = None
self._asset_docs_cache.clear()
self._path_stem = None
return super().unstage()
|
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|
'''
Record Linkage Testing Script using Logistic Regression Method over Graph Embeddings generated using TransH
'''
import numpy as np
import pandas as pd
import random
import re
import recordlinkage
import unittest
import xml.etree.ElementTree
from common import get_logger, log_quality_results, InformationRetrievalMetrics, export_embeddings, export_result_prob
from data.cora import Cora
from data.febrl import FEBRL
from data.census import Census
from ER.transh import TransH
class TestLogisticTransH(unittest.TestCase):
def _test_logistic_transh_erer(self, dataset, params):
model = dataset()
logger = get_logger('RL.Test.erer.LogisticTransH.ERER.' + str(model))
entA, entB, relA, relB, triA, triB, entity_pairs, prior_pairs, true_pairs = model.get_erer_model()
self.assertTrue(all([(tp in entity_pairs) for tp in true_pairs]))
#Generate embeddings for datasetA
transh = TransH(entA, relA, triA, prior_pairs,
dimension=params['dimension'],
learning_rate=params['learning_rate'],
margin=params['margin'],
regularizer_scale=params['regularizer_scale'],
batchSize=params['batchSize'])
loss = transh.train(max_epochs=params['epochs'])
logger.info("Training Complete with loss: %f", loss)
ent_embeddingsA = transh.get_ent_embeddings()
transh.close_tf_session()
del transh
#Generate embeddings for datasetB
transh = TransH(entB, relB, triB, entity_pairs,
dimension=params['dimension'],
learning_rate=params['learning_rate'],
margin=params['margin'],
regularizer_scale=params['regularizer_scale'],
batchSize=params['batchSize'])
loss = transh.train(max_epochs=params['epochs'])
logger.info("Training Complete with loss: %f", loss)
ent_embeddingsB = transh.get_ent_embeddings()
transh.close_tf_session()
ent_embeddingsA = [np.array(ent_embeddingsA[i]) for i in range(ent_embeddingsA.shape[0])]
ent_embeddingsB = [np.array(ent_embeddingsB[i]) for i in range(ent_embeddingsB.shape[0])]
trainDataA = pd.DataFrame(data=ent_embeddingsA)
trainDataB = pd.DataFrame(data=ent_embeddingsB)
#Define comparision Class
compare_cl = recordlinkage.Compare()
for i in range(0, params['dimension']):
compare_cl.numeric(i, i, label=str(i)) #method='exp')
#sample negative pairs
train_pairs = []
tuple_pp = set(map(tuple, prior_pairs))
logger.info("Number of prior_pairs: %d", len(prior_pairs))
for e1, e2 in prior_pairs:
train_pairs.append((e1, e2))
while True:
neg_e2 = random.choice(xrange(0, len(entB)))
if neg_e2 == e2 or (e1, neg_e2) in tuple_pp:
continue
else:
train_pairs.append((e1, neg_e2))
break
logger.info("Number of Train Pairs: %d", len(train_pairs))
candidate_links = pd.MultiIndex.from_tuples(train_pairs)
features = compare_cl.compute(candidate_links, trainDataA, trainDataB)
logger.info("Train Features %s", str(features.describe()))
#Train Logistic Regression Model
logrg = recordlinkage.LogisticRegressionClassifier()
candidate_links = pd.MultiIndex.from_tuples(prior_pairs)
logrg.fit(features, candidate_links)
#Test Classifier
compare_cl = recordlinkage.Compare()
for i in range(0, params['dimension']):
compare_cl.numeric(i, i, label=str(i))
candidate_links = pd.MultiIndex.from_tuples(entity_pairs)
features = compare_cl.compute(candidate_links, trainDataA, trainDataB)
logger.info("Test Features %s", str(features.describe()))
result = logrg.predict(features)
log_quality_results(logger, result, true_pairs, len(entity_pairs))
prob_series = logrg.prob(features)
prob = [(1 - p) for p in prob_series.tolist()]
result_prob = [(entity_pairs[i][0], entity_pairs[i][1], prob[i]) for i in range(0, len(prob))]
ir_metrics = InformationRetrievalMetrics(result_prob, true_pairs)
ir_metrics.log_metrics(logger, params, params)
#Export results
export_embeddings('erer', str(model), 'LogTransH', entA, ent_embeddingsA)
export_embeddings('erer', str(model), 'LogTransH', entB, ent_embeddingsB)
export_result_prob(dataset, 'erer', str(model), 'LogTransH', entA, result_prob, true_pairs, entB)
def _test_logistic_transh(self, dataset, params):
"""Note: Zero aligned pairs are returned, require fixation."""
model = dataset()
logger = get_logger('RL.Test.LogisticTransH.' + str(model))
entity, relation, triples, entity_pairs, true_pairs = model.get_er_model()
transh = TransH(entity, relation, triples, entity_pairs,
dimension=params['dimension'],
learning_rate=params['learning_rate'],
margin=params['margin'],
regularizer_scale=params['regularizer_scale'],
batchSize=params['batchSize'])
loss = transh.train(max_epochs=params['epochs'])
logger.info("Training Complete with loss: %f", loss)
ent_embeddings = transh.get_ent_embeddings()
ent_embeddings = [np.array(ent_embeddings[i]) for i in range(ent_embeddings.shape[0])]
trainDataA = pd.DataFrame(data=ent_embeddings)
trainDataB = pd.DataFrame(data=ent_embeddings)
compare_cl = recordlinkage.Compare()
for i in range(0, params['dimension']):
compare_cl.numeric(i, i, label=str(i), method='gauss')
candidate_links = pd.MultiIndex.from_tuples(entity_pairs)
features = compare_cl.compute(candidate_links, trainDataA, trainDataB)
logger.info("Features %s", str(features.describe()))
logrg = recordlinkage.LogisticRegressionClassifier()
logrg.fit(features, true_pairs)
result = logrg.predict(features)
log_quality_results(logger, result, true_pairs, len(entity_pairs))
prob_series = logrg.prob(features)
prob = [(1 - p) for p in prob_series.tolist()]
result_prob = [(entity_pairs[i][0], entity_pairs[i][1], prob[i]) for i in range(0, len(prob))]
ir_metrics = InformationRetrievalMetrics(result_prob, true_pairs)
ir_metrics.log_metrics(logger, params)
def get_default_params(self):
return {'learning_rate': 0.1, 'margin': 1, 'dimension': 80, 'epochs': 100,
'regularizer_scale' : 0.1, 'batchSize' : 100}
def test_cora(self):
self._test_logistic_transh(Cora, self.get_default_params())
def test_febrl(self):
self._test_logistic_transh(FEBRL, self.get_default_params())
def test_census(self):
self._test_logistic_transh(Census, self.get_default_params())
def test_cora_erer(self):
self._test_logistic_transh_erer(Cora, self.get_default_params())
def test_febrl_erer(self):
self._test_logistic_transh_erer(FEBRL, self.get_default_params())
def test_census_erer(self):
self._test_logistic_transh_erer(Census, self.get_default_params())
|
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|
[STATEMENT]
lemma list_rel_induct[induct set,consumes 1, case_names Nil Cons]:
assumes "(l,l')\<in>\<langle>R\<rangle> list_rel"
assumes "P [] []"
assumes "\<And>x x' l l'. \<lbrakk> (x,x')\<in>R; (l,l')\<in>\<langle>R\<rangle>list_rel; P l l' \<rbrakk>
\<Longrightarrow> P (x#l) (x'#l')"
shows "P l l'"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. P l l'
[PROOF STEP]
using assms
[PROOF STATE]
proof (prove)
using this:
(l, l') \<in> \<langle>R\<rangle>list_rel
P [] []
\<lbrakk>(?x, ?x') \<in> R; (?l, ?l') \<in> \<langle>R\<rangle>list_rel; P ?l ?l'\<rbrakk> \<Longrightarrow> P (?x # ?l) (?x' # ?l')
goal (1 subgoal):
1. P l l'
[PROOF STEP]
unfolding list_rel_def
[PROOF STATE]
proof (prove)
using this:
(l, l') \<in> {(l, l'). list_all2 (\<lambda>x x'. (x, x') \<in> R) l l'}
P [] []
\<lbrakk>(?x, ?x') \<in> R; (?l, ?l') \<in> {(l, l'). list_all2 (\<lambda>x x'. (x, x') \<in> R) l l'}; P ?l ?l'\<rbrakk> \<Longrightarrow> P (?x # ?l) (?x' # ?l')
goal (1 subgoal):
1. P l l'
[PROOF STEP]
apply simp
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>list_all2 (\<lambda>x x'. (x, x') \<in> R) l l'; P [] []; \<And>x x' l l'. \<lbrakk>(x, x') \<in> R; list_all2 (\<lambda>x x'. (x, x') \<in> R) l l'; P l l'\<rbrakk> \<Longrightarrow> P (x # l) (x' # l')\<rbrakk> \<Longrightarrow> P l l'
[PROOF STEP]
by (rule list_all2_induct)
|
{"llama_tokens": 643, "file": "Automatic_Refinement_Parametricity_Relators", "length": 4}
|
[STATEMENT]
lemma observable_io_target_unique_target :
assumes "observable M"
and "io_targets M q1 io = {q2}"
and "path M (io || tr) q1"
and "length io = length tr"
shows "target (io || tr) q1 = q2"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. target (io || tr) q1 = q2
[PROOF STEP]
using assms
[PROOF STATE]
proof (prove)
using this:
observable M
io_targets M q1 io = {q2}
path M (io || tr) q1
length io = length tr
goal (1 subgoal):
1. target (io || tr) q1 = q2
[PROOF STEP]
by auto
|
{"llama_tokens": 216, "file": "Adaptive_State_Counting_FSM_FSM", "length": 2}
|
df = DataFrame()
df[:A] = 1:numData
lamb_grid = [10. .^(-7:1)]
c_grid = linspace(1, 5, 6) # This choice of c_grid yields no distinguishable difference. Try: c_grid = 2. .^(1:5)
deg_grid = [2:6] #2 is a pretty meaningless choice. drop to 3.
N = length(lamb_grid) * length(c_grid) * length(deg_grid)
res = Array(Float64, N, 5)
ix = 1
for l in lamb_grid
for c in c_grid
for d in deg_grid
train(df) = train(df[:A], l, d, c, demand_data, flow_data, arcs)
test(df, fit) = test(fit, df[:A], demand_data, flow_data, arcs, g, vArcs)
rtrain, rtest = kfold_crossvalidate(df, train, test, 5)
res[ix, 1] = l
res[ix, 2] = c
res[ix, 3] = float(d)
res[ix, 4] = mean(rtest) / 1e6
res[ix, 5] = std(rtest) / 1e6
ix +=1
show(res)
end
end
end
writetable("trafficCVal.csv", DataFrame(res))
|
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|
import geometry.tarski_2
open classical set
namespace Euclidean_plane
variables {point : Type} [Euclidean_plane point]
local attribute [instance, priority 0] prop_decidable
-- Right Angles
def R (a b c : point) : Prop := eqd a c a (S b c)
theorem R.symm {a b c : point} : R a b c → R c b a :=
begin
intro h,
have h1 := seven13 b a (S b c),
simp at h1,
unfold R at h,
exact (eqd.trans h h1).flip
end
theorem eight3 {a b c a' : point} : R a b c → a ≠ b → col b a a' → R a' b c :=
begin
intros h h1 h2,
unfold R at *,
exact four17 h1.symm h2 (seven5 b c).2 h
end
theorem R.flip {a b c : point} : R a b c → R a b (S b c) :=
begin
intro h,
unfold R at *,
simpa using h.symm
end
@[simp] theorem eight4 (a b : point) : R a a b := (seven5 a b).2
@[simp] theorem eight5a (a b : point) : R a b b := (eight4 b a).symm
theorem eight6 {a b c a' : point} : R a b c → R a' b c → B a c a' → b = c :=
begin
intros h h1 h2,
unfold R at *,
generalize h3 : S b c = c',
rw h3 at *,
have : c = c',
exact four19 h2 h h1.flip,
rw ←this at *,
exact (seven10.1 h3)
end
theorem eight7 {a b c : point} : R a b c → R a c b → b = c :=
begin
intros h h1,
have h_1 : eqd a c a (S b c),
unfold R at h,
exact h,
have h_2 : eqd a b a (S c b),
unfold R at h1,
exact h1,
have h2 := seven5 b c,
generalize h3 : S b c = c',
rw h3 at *,
generalize h4 : S c a = a',
by_contradiction h5,
have h6 : col c b c',
left,
exact h2.1,
have h7 := eight3 h1.symm h5 h6,
unfold R at h7,
rw h4 at h7,
have h8 := seven5 c a,
rw h4 at h8,
have h9 := h8.2,
have h10 : R a' b c,
unfold R,
rw h3,
exact eqd.trans h9.symm.flip (eqd.trans h_1 h7.flip),
exact h5 (eight6 h h10 h8.1)
end
theorem eight8 {a b : point} : R a b a → a = b :=
begin
intro h,
exact eight7 (eight5a b a).symm h
end
theorem eight9 {a b c : point} : R a b c → col a b c → a = b ∨ c = b :=
begin
intros h h1,
cases em (a = b),
simp *,
right,
have h2 := eight3 h h_1 (four11 h1).2.1,
exact eight8 h2
end
theorem eight10 {a b c a' b' c' : point} : R a b c → cong a b c a' b' c' → R a' b' c' :=
begin
intros h h1,
cases em (b = c),
rw h_1 at *,
have h2 : b' = c',
exact id_eqd h1.2.1.symm,
rw h2,
exact eight5a a' c',
unfold R at *,
generalize h2 : S b c = d,
generalize h3 : S b' c' = d',
have h4 := seven5 b c,
have h5 := seven5 b' c',
rw h2 at *,
rw h3 at *,
have h6 : afs c b d a c' b' d' a',
repeat {split},
exact h4.1,
exact h5.1,
exact h1.2.1.flip,
exact eqd.trans h4.2.symm (eqd.trans h1.2.1 h5.2),
exact h1.2.2.flip,
exact h1.1.flip,
have h7 := afive_seg h6 (ne.symm h_1),
exact eqd.trans h1.2.2.symm (eqd.trans h h7.flip)
end
def xperp (x : point) (A A' : set point) : Prop := line A ∧ line A' ∧ x ∈ A ∧ x ∈ A' ∧
∀ {u v}, u ∈ A → v ∈ A' → R u x v
def perp (A A' : set point) : Prop := ∃ x, xperp x A A'
notation A ` ⊥ ` B := perp A B
theorem xperp.symm {x : point} {A A' : set point} : xperp x A A' → xperp x A' A :=
begin
intro h,
unfold xperp at *,
split,
exact h.2.1,
split,
exact h.1,
split,
exact h.2.2.2.1,
split,
exact h.2.2.1,
intros u v hu hv,
exact (h.2.2.2.2 hv hu).symm
end
theorem perp.symm {A A' : set point} : perp A A' → perp A' A :=
begin
intro h,
cases h with x hx,
constructor,
exact hx.symm
end
theorem eight14a {A A' : set point} : perp A A' → A ≠ A' :=
begin
intros h h1,
subst A',
rcases h with ⟨x, h1, h2, h3, h4, h5⟩,
cases six22 h1 h3 with y hy,
rw hy.2 at h5,
exact hy.1.symm (eight8 (h5 (six17b x y) (six17b x y)))
end
theorem eight14b {x : point} {A A' : set point} : xperp x A A' → A ≠ A' :=
λ h, eight14a ⟨x, h⟩
theorem eight14c {x : point} {A A' : set point} : xperp x A A' ↔ perp A A' ∧ is x A A' :=
begin
split,
intro h,
split,
constructor,
exact h,
have h1 : perp A A',
constructor,
exact h,
unfold xperp at h,
unfold is,
split,
exact h.1,
split,
exact h.2.1,
split,
exact eight14a h1,
split,
exact h.2.2.1,
exact h.2.2.2.1,
intro h,
cases h with h h1,
cases h with y hy,
unfold is at h1,
suffices : x = y,
rwa ←this at hy,
by_contradiction,
suffices : A = A',
exact h1.2.2.1 this,
apply six21 a h1.1 h1.2.1 h1.2.2.2.1 h1.2.2.2.2,
unfold xperp at hy,
exact hy.2.2.1,
exact hy.2.2.2.1
end
theorem eight14d {x y : point} {A A' : set point} : xperp x A A' → xperp y A A' → x = y :=
begin
intros hx hy,
by_contradiction,
have h : perp A A',
constructor,
exact hx,
have h1 := eight14a h,
suffices : A = A',
exact h1 this,
unfold xperp at *,
exact six21 a hx.1 hx.2.1 hx.2.2.1 hx.2.2.2.1 hy.2.2.1 hy.2.2.2.1
end
theorem eight14e {A A' : set point} : perp A A' → line A ∧ line A' :=
begin
intro h,
cases h with x hx,
split,
exact hx.1,
exact hx.2.1
end
theorem eight14f {a b c : point} {A : set point} : perp (l a b) A → col a b c → a ≠ c → perp (l a c) A :=
begin
intros h h1 h2,
suffices : l a b = l a c,
rwa ←this,
exact six18 (eight14e h).1 h2 (six17a a b) h1
end
theorem eight13 {x : point} {A A' : set point} : xperp x A A' ↔ line A ∧ line A' ∧ x ∈ A ∧ x ∈ A' ∧
∃ u v, u ∈ A ∧ v ∈ A' ∧ u ≠ x ∧ v ≠ x ∧ R u x v :=
begin
split,
intro h,
split,
exact h.1,
split,
exact h.2.1,
split,
exact h.2.2.1,
split,
exact h.2.2.2.1,
unfold xperp at h,
cases six22 h.1 h.2.2.1 with u hu,
cases six22 h.2.1 h.2.2.2.1 with v hv,
existsi u,
existsi v,
have h1 : u ∈ A,
rw hu.2,
simp,
have h2 : v ∈ A',
rw hv.2,
simp,
split,
exact h1,
split,
exact h2,
split,
exact hu.1.symm,
split,
exact hv.1.symm,
exact h.2.2.2.2 h1 h2,
intro h,
unfold xperp,
split,
exact h.1,
split,
exact h.2.1,
split,
exact h.2.2.1,
split,
exact h.2.2.2.1,
intros a b ha hb,
cases h.2.2.2.2 with u hu,
cases hu with v hv,
have h1 : R a x v,
apply eight3 hv.2.2.2.2 hv.2.2.1,
have h_1 : A = l x u,
exact six18 h.1 hv.2.2.1.symm h.2.2.1 hv.1,
rw h_1 at ha,
exact ha,
apply R.symm,
apply eight3 h1.symm hv.2.2.2.1,
have h_2 : A' = l x v,
exact six18 h.2.1 hv.2.2.2.1.symm h.2.2.2.1 hv.2.1,
rw h_2 at hb,
exact hb
end
theorem perp_of_R {a b c : point} : a ≠ b → c ≠ b → R a b c → perp (l a b) (l c (S b c)) :=
λ h h1 h2, ⟨b, eight13.2 ⟨six14 h, six14 (seven12b h1).symm, six17b a b,
or.inr (or.inl (seven5 b c).1.symm), a, c, six17a a b, six17a c (S b c), h, h1, h2⟩⟩
theorem xperp_of_R {a b c : point} : a ≠ b → c ≠ b → R a b c → xperp b (l a b) (l c b) :=
λ h h1 h2, eight13.2 ⟨six14 h, six14 h1, six17b a b,
six17b c b, a, c, six17a a b, six17a c b, h, h1, h2⟩
theorem eight15 {x : point} {A B : set point} : perp A B → x ∈ A → x ∈ B → xperp x A B :=
begin
intros h h1 h2,
cases h with y hy,
suffices : x = y,
subst x,
exact hy,
by_contradiction h_1,
apply eight14b hy,
apply six21 h_1,
exact hy.1,
exact hy.2.1,
exact h1,
exact h2,
exact hy.2.2.1,
exact hy.2.2.2.1
end
theorem eight16 {a b c u x : point} : col a b x → col a b u → u ≠ x →
(c ≠ x ∧ perp (l a b) (l c x) ↔ ¬col a b c ∧ R c x u) :=
begin
intros h1 h2 h3,
split,
intro h4,
have h5 : xperp x (l a b) (l c x),
exact eight15 h4.2 h1 (six17b c x),
split,
intro h_1,
apply eight14b h5,
exact six18 h5.1 h4.1 h_1 h1,
apply R.symm,
exact h5.2.2.2.2 h2 (six17a c x),
intro h4,
cases h4 with h4 h5,
have h_1 : c ≠ x,
intro h_1,
rw h_1 at h4,
exact h4 h1,
split,
exact h_1,
existsi x,
apply eight13.2,
split,
exact six14 (six26 h4).1,
split,
exact six14 h_1,
split,
exact h1,
split,
simp,
existsi u,
existsi c,
split,
exact h2,
split,
exact (six17a c x),
split,
exact h3,
split,
exact h_1,
exact h5.symm
end
theorem eight18 {a b c : point} : ¬col a b c → ∃! x, col a b x ∧ perp (l a b) (l c x) :=
begin
intros h,
cases seg_cons a a c b with y hy,
cases seven25 hy.2.symm with p hp,
have h1: R a p y,
unfold R,
suffices : S p y = c,
rw this,
exact hy.2,
exact (seven6 hp.symm).symm,
cases seg_cons y y p a with z hz,
cases seg_cons y y a p with q hq,
generalize hq' : S z q = q',
cases seg_cons y y c q' with c' hc',
have h2 : afs a y z q q y p a,
focus {repeat {split}},
exact hz.1,
exact hq.1.symm,
exact hq.2.symm.flip,
exact hz.2,
exact two5 (eqd.refl a q),
exact hq.2,
have h3 : a ≠ y,
intro h_1,
rw h_1 at *,
have : y = c,
exact id_eqd hy.2.symm,
exact (six26 h).2.2 this,
have h4 := afive_seg h2 h3,
have h5 : cong a p y q z y,
split,
exact h4.symm.flip,
split,
exact hz.2.symm.flip,
exact hq.2.symm.flip,
have h6 := (eight10 h1 h5).symm,
have h7 : eqd y q y q',
unfold R at h6,
rwa hq' at h6,
cases seven25 hc'.2 with x hx,
existsi x,
have h8 : R y x c,
unfold R,
suffices : S x c = c',
rw this,
exact hc'.2.symm,
exact (seven6 hx.symm).symm,
have h9 : c ≠ y,
intro h_1,
rw ←h_1 at hy,
apply h,
right, right,
exact hy.1.symm,
have h10 : y ≠ p,
intro h_1,
rw ←h_1 at hp,
unfold M at hp,
apply h9,
exact id_eqd hp.2.flip,
have h11 : hourglass q q' y c c' z x,
have h_1 := seven5 z q,
rw hq' at h_1,
focus {repeat {split}},
exact (three7a hp.1 hq.1 h10.symm).symm,
exact hc'.1,
exact h7,
exact hc'.2.symm,
exact h_1.1,
exact h_1.2,
exact hx.1.symm,
exact hx.2.symm,
have h12 := seven22 h11,
have h13 : y ≠ z,
intro h_1,
rw ←h_1 at hz,
exact h10 (id_eqd hz.2.symm),
have h14 : a ≠ y,
intro h_1,
rw ←h_1 at hy,
apply (six26 h).2.2,
exact id_eqd hy.2.symm,
have h15 : l y z = l a b,
apply six18 (six14 h13) (six26 h).1,
right, right,
exact hz.1,
right, right,
exact three7a hy.1 hz.1 h14,
have h16 : c ≠ x,
intro h_1,
rw h_1 at *,
apply h,
have h_2 : x ∈ l a b,
rw ←h15,
right, right,
exact h12.symm,
exact h_2,
have h17 : q ≠ z,
intro h_1,
rw h_1 at *,
have h_2 : B z y c,
exact three7b hq.1.symm hp.1.symm h10,
apply h,
suffices : c ∈ l a b,
exact this,
rw ←h15,
right, right,
exact h_2.symm,
have h18 : xperp x (l y z) (l c x),
apply eight13.2,
split,
exact six14 h13,
split,
exact (six14 h16),
split,
right, right,
exact h12.symm,
split,
simp,
existsi y,
existsi c,
simp,
split,
intro h_1,
rw h_1 at *,
have h_1 : q ∈ l c x,
left,
exact three7a hp.1 hq.1 h10.symm,
have h_2 : q' ∈ l c x,
suffices : c' ≠ x,
cases five2 this hx.1 hc'.1.symm,
right, right,
exact h_2.symm,
right, left,
exact h_2,
intro h_2,
rw h_2 at *,
apply h9,
exact id_eqd hx.2.symm.flip,
have h_3 := seven5 z q,
rw hq' at h_3,
have h_4 : q ≠ q',
intro h_4,
rw ←h_4 at *,
apply h17,
exact seven3.1 h_3,
have h_5 : l c x = l q q',
exact six18 (six14 h9) h_4 h_1 h_2,
have h_6 : z ∈ l c x,
rw h_5,
right, left,
exact h_3.1.symm,
have h_7 := (four11 h_6).2.2.1,
have h_8 : c ∈ l a b,
rw ←h15,
exact h_7,
exact h h_8,
split,
exact h16,
exact h8,
rw h15 at h18,
have h19 : x ∈ l y z,
right, right,
exact h12.symm,
split,
split,
rw h15 at h19,
exact h19,
constructor,
exact h18,
intros x' hx',
have h20 : c ≠ x',
intro h_1,
apply h,
rw ←h_1 at hx',
exact hx'.1,
have h21 : xperp x' (l a b) (l c x'),
exact eight15 hx'.2 hx'.1 (six17b c x'),
have h22 : R c x x',
apply (h18.symm).2.2.2.2,
simp,
exact hx'.1,
have h23 : R c x' x,
apply (h21.symm).2.2.2.2,
simp,
rw ←h15,
exact h19,
exact eight7 h23 h22
end
theorem eight17 {a : point} {A : set point} : line A → a ∉ A → ∃! x, xperp x A (l a x) :=
begin
intros h h1,
rcases h with ⟨p, q, hq, h2⟩,
subst h2,
have h3 : ¬col p q a,
intro h_1,
exact h1 h_1,
cases eight18 h3 with x hx,
refine ⟨x, eight15 hx.1.2 hx.1.1 (six17b a x), _⟩,
intros y hy,
exact hx.2 y ⟨hy.2.2.1, y, hy⟩
end
theorem eight19 {p q r : point} (a : point) : R p q r ↔ R (S a p) (S a q) (S a r) :=
begin
unfold R,
split,
intro h,
suffices : (S (S a q) (S a r)) = (S a (S q r)),
rw this,
exact (seven16 a).1 h,
have h1 := seven5 (S a q) (S a r),
suffices : M (S a r) (S a q) (S a (S q r)),
exact seven4 h1 this,
apply (seven14 a).1,
exact seven5 q r,
intro h,
suffices : S a ((S (S a q) (S a r))) = S q r,
rw ←this,
apply (seven16 a).2,
simp,
exact h,
suffices : S a (S a (S (S a q) (S a r))) = (S a (S q r)),
exact seven9 this,
simp,
have h1 := seven5 (S a q) (S a r),
suffices : M (S a r) (S a q) (S a (S q r)),
exact seven4 h1 this,
apply (seven14 a).1,
exact seven5 q r
end
theorem eight20 {a b c p : point} : R a b c → M (S a c) p (S b c) → R b a p ∧ (b ≠ c → a ≠ p) :=
begin
intros h h1,
have h2 := seven5 b c,
have h3 := seven5 a b,
have h4 := seven5 a c,
have h5 := seven5 a (S b c),
have h6 := seven5 a p,
have h7 : R (S a b) b c,
cases em (a = b),
rw h_1 at *,
simp,
apply eight3 h h_1,
left,
exact h3.1,
have h8 := (eight19 a).1 h7,
unfold R at h7,
have h9 := (seven16 a).1 h7,
simp at *,
have h10 : ifs (S a c) p (S b c) b (S a (S b c)) (S a p) c b,
focus {repeat {split}},
exact h1.1,
have h_1 := (seven15 a).1 h1.1,
simp at h_1,
exact h_1.symm,
apply two5,
have h_2 := seven13 a (S a c) (S b c),
simp at h_2,
exact h_2,
apply eqd.trans h1.2.symm,
have h_3 := seven13 a p (S a c),
simp at h_3,
exact h_3,
exact h9.flip,
exact h2.2.symm.flip,
have h11 := four2 h10,
split,
unfold R,
exact h11.flip,
intros hbc hap,
apply hbc,
have h12 := seven7 a c,
rw hap at *,
have h13 := seven5 p (S p c),
simp at h13,
have h14 := seven4 h13 h1,
rw ←h14 at h2,
exact (seven3.1 h2).symm
end
theorem eight21 {a b : point} (hab : a ≠ b) (c : point) : ∃ p t, perp (l a b) (l p a) ∧ col a b t ∧ B c t p :=
begin
cases em (col a b c) with habc h,
cases six25 hab with c' h,
cases eight18 h with x hx,
have h1 : c' ≠ x,
intro h_1,
rw h_1 at *,
exact h hx.1.1,
have h2 : xperp x (l a b) (l c' x),
exact eight15 hx.1.2 hx.1.1 (six17b c' x),
unfold xperp at h2,
have h3 := h2.2.2.2.2 (six17a a b) (six17a c' x),
unfold R at h3,
have h4 := seven5 a c',
cases seven25 (eqd.trans h4.2.symm h3) with p hp,
have h5 := eight20 (h2.2.2.2.2 (six17a a b) (six17a c' x)) hp,
have h6 := h5.2 h1.symm,
existsi p,
existsi c,
cases em (x = a),
rw h_1 at hx,
rw h_1 at hp,
have h_2 : S a c' = p,
exact seven3.1 hp,
rw h_2 at h4,
have h_3 : col c' a p,
left,
exact h4.1,
have h_4 : l c' a = l p a,
apply six18 (six14 (six26 h).2.2.symm) h6.symm h_3 (six17b c' a),
have h_5 := hx.1,
rw h_4 at h_5,
split,
exact h_5.2,
split,
exact habc,
exact three3 c p,
split,
existsi a,
apply eight13.2,
split,
exact six14 (six26 h).1,
split,
exact six14 h6.symm,
split,
simp,
split,
simp,
existsi x,
existsi p,
split,
exact hx.1.1,
simp,
split,
exact h_1,
split,
exact h6.symm,
exact h5.1,
split,
exact habc,
exact three3 c p,
cases eight18 h with x hx,
have h1 : c ≠ x,
intro h_1,
rw h_1 at *,
exact h hx.1.1,
have h2 : xperp x (l a b) (l c x),
exact eight15 hx.1.2 hx.1.1 (six17b c x),
unfold xperp at h2,
have h3 := h2.2.2.2.2 (six17a a b) (six17a c x),
unfold R at h3,
have h4 := seven5 a c,
cases seven25 (eqd.trans h4.2.symm h3) with p hp,
have h5 := eight20 (h2.2.2.2.2 (six17a a b) (six17a c x)) hp,
have h6 := h5.2 h1.symm,
cases three17 (seven5 x c).1.symm h4.1.symm hp.1.symm with t ht,
cases em (x = a),
rw h_1 at ht,
existsi p,
existsi a,
have h_2 : t = a,
exact (bet_same ht.2).symm,
rw h_2 at *,
rw h_1 at hx,
have h_3 := hx.1,
have h_4 : l c a = l p a,
apply six18 (six14 (six26 h).2.2.symm) h6.symm,
left,
exact ht.1.symm,
simp,
rw h_4 at h_3,
split,
exact h_3.2,
split,
exact h_3.1,
exact ht.1.symm,
existsi p,
existsi t,
have h7 : col a b t,
have h_2 : col a x t,
right, left,
exact ht.2,
exact five4 (ne.symm h_1) (four11 hx.1.1).1 h_2,
split,
existsi a,
apply eight13.2,
split,
exact six14 (six26 h).1,
split,
exact six14 h6.symm,
split,
simp,
split,
simp,
existsi x,
existsi p,
split,
exact hx.1.1,
split,
simp,
split,
exact h_1,
split,
exact h6.symm,
exact h5.1,
split,
exact h7,
exact ht.1.symm
end
lemma eight23 {a b p q t t' r : point} (hp : ((l a b) ⊥ l p a) ∧ col a b t' ∧ B a t' p)
(ht : ((l b a) ⊥ l q b) ∧ col b a t ∧ B p t q) (hr : B b r q ∧ eqd a p b r): ∃ x, M a x b ∧ M p x r :=
begin
have h : a ≠ b,
exact six13 (eight14e hp.1).1,
cases pasch ht.2.2 hr.1 with x hx,
have h1 : col a b x,
have h_1 : col b t x,
right, left,
exact hx.1,
cases em (b = t),
rw ←h_2 at *,
have h_3 : x = b,
exact (bet_same hx.1).symm,
rw h_3,
left,
exact three1 a b,
exact (four11 (five4 h_2 (four11 ht.2.1).1 h_1)).2.1,
have h2 : xperp a (l a b) (l p a),
exact eight15 hp.1 (four11 (four12 a b)).1 (six17b p a),
have h3 : xperp b (l b a) (l q b),
exact eight15 ht.1 (four11 (four12 b a)).1 (six17b q b),
have h4 := h2.2.2.2.2 (six17b a b) (six17a p a),
have h5 := h3.2.2.2.2 (six17b b a) (six17a q b),
have h6 : R a b r,
have : col b q r,
right, left,
exact hr.1.symm,
exact (eight3 h5.symm (six13 h3.2.1) this).symm,
have h7 : ¬col a p b,
intro h_1,
cases eight9 h4 (four11 h_1).2.2.2.1,
exact h h_2.symm,
exact (six13 h2.2.1) h_2,
have h8 : ¬col a b r,
intro h_1,
cases eight9 h6 h_1,
exact h h_2,
rw h_2 at *,
exact (six13 h2.2.1).symm (id_eqd hr.2),
suffices : eqd b p a r,
have h_1 : p ≠ r,
intro h_1,
rw h_1 at hx,
have h_2 : r = x,
exact bet_same hx.2,
rw h_2 at h8,
exact h8 h1,
constructor,
apply seven21 h7 h_1 hr.2 this.flip (four11 h1).1,
left,
exact hx.2.symm,
have h9 : x ≠ a,
intro h_1,
rw h_1 at *,
have h_2 : col a p r,
right, right,
exact hx.2,
have h_3 : R r a b,
exact eight3 h4.symm (six13 h2.2.1) h_2,
apply h,
exact eight7 h_3 h6.symm,
have h10 := seven5 a p,
cases seg_cons x x r (S a p) with r' hr',
cases seven25 hr'.2 with m hm,
have h11 := seven5 m r,
have h12 := seven4 h11 hm.symm,
have h13 : R x m r,
unfold R,
rw ←h12 at hr',
exact hr'.2.symm,
have h14 : R x a p,
exact eight3 h4 (ne.symm h) h1,
have h15 : ¬col x p (S a p),
intro h_1,
have h_2 : col p a (S a p),
left,
exact (seven5 a p).1,
have h_3 : p ≠ (S a p),
intro h_3,
exact (six13 h2.2.1) (seven10.1 h_3.symm).symm,
have h_4 : col p a x,
exact five4 h_3 (four11 h_2).1 (four11 h_1).2.2.1,
cases eight9 h14.symm h_4,
exact (six13 h2.2.1) h_5,
exact h9 h_5,
have h16 : hourglass p (S a p) x r r' a m,
focus {repeat {split}},
exact hx.2.symm,
exact hr'.1,
unfold R at h14,
exact h14,
exact hr'.2.symm,
exact h10.1,
exact h10.2,
rw ←h12,
exact h11.1,
rw ←h12,
exact h11.2,
have h17 := seven22 h16,
have h18 : r ≠ m,
intro h_1,
rw ←h_1 at h17,
have h_2 : col a x r,
left,
exact h17,
apply h8,
exact five4 h9.symm (four11 h1).1 h_2,
have h19 : x ≠ m,
intro h_1,
have h_2 : col r x p,
left,
exact hx.2,
have h_3 : x ≠ r',
intro h_3,
rw [←h_1, ←h_3] at h12,
rw ←h_1 at h18,
apply h18,
exact seven9 (eq.trans h12 (seven11 x).symm),
have h_4 : col r x (S a p),
have h_4 : col x r' (S a p),
right, right,
exact hr'.1,
have h_5 : col x r' r,
rw h_1,
right, right,
exact hm.1.symm,
exact (four11 (five4 h_3 h_5 h_4)).2.1,
have h_5 : x ≠ r,
intro h_5,
rw h_5 at h_1,
exact h18 h_1,
apply h15,
exact five4 h_5 (four11 h_2).2.1 (four11 h_4).2.1,
have h20 : col a b m,
have h_1 : col a x m,
left,
exact h17,
exact five4 h9.symm (four11 h1).1 h_1,
have h21 : xperp b (l a b) (l r b),
apply eight13.2,
split,
exact six14 h,
split,
exact six14 (six26 h8).2.1.symm,
split,
simp,
split,
simp,
existsi a,
existsi r,
simp,
split,
exact h,
split,
exact (six26 h8).2.1.symm,
exact h6,
have h22 : xperp m (l a b) (l r m),
apply eight13.2,
split,
exact six14 h,
split,
exact six14 h18,
split,
exact h20,
split,
simp,
existsi x,
existsi r,
split,
exact h1,
simp,
split,
exact h19,
split,
exact h18,
exact h13,
have h23 : perp (l a b) (l r b),
constructor,
exact h21,
have h24 : perp (l a b) (l r m),
constructor,
exact h22,
have h25 : m = b,
apply unique_of_exists_unique (eight18 h8),
split,
exact h20,
exact h24,
split,
left,
exact three1 a b,
exact h23,
subst m,
have h26 : ifs (S a p) a p r r b r' (S a p),
focus {repeat {split}},
exact h10.1.symm,
exact hm.1.symm,
apply two4,
apply two11 h10.1 hm.1.symm,
exact hr.2.flip,
exact eqd.trans h10.2.symm (eqd.trans hr.2 hm.2.symm),
exact eqd.trans hr.2 hm.2.symm,
exact two4 (eqd.refl r (S a p)),
apply two5,
apply two11 hx.2.symm hr'.1,
unfold R at h14,
exact h14.flip,
exact hr'.2.symm,
have h27 := four2 h26,
unfold R at h4,
exact eqd.trans h4 h27.symm
end
theorem eight22 (a b : point) : ∃! x, M a x b :=
begin
cases em (a = b),
rw h,
existsi b,
split,
apply seven3.2,
refl,
intros y hy,
exact (seven3.1 hy).symm,
apply exists_unique_of_exists_of_unique,
cases eight21 h a with p hp,
cases eight21 (ne.symm h) p with q hq,
cases hp with t' hp,
cases hq with t ht,
cases five10 a p b q,
cases h_1 with r hr,
cases eight23 hp ht hr with x hx,
constructor,
exact hx.1,
suffices : ∃ x, M b x a,
cases this with x hx,
constructor,
exact hx.symm,
cases h_1 with r hr,
have : ∃ x, M b x a ∧ M q x r,
apply eight23,
split,
exact ht.1,
split,
exact (four11 (four12 b a)).1,
exact three3 b q,
split,
exact hp.1,
split,
exact (four11 ht.2.1).2.1,
exact ht.2.2.symm,
exact hr,
cases this with x hx,
constructor,
exact hx.1,
intros x y hx hy,
exact seven17 hx hy
end
theorem eight24 {a b p q r t : point} : perp (l p a) (l a b) → perp (l q b) (l a b) →
col a b t → B p t q → B b r q → eqd a p b r → ∃ x, M a x b ∧ M p x r :=
begin
intros g3 g4 g5 g6 g7 g8,
have g9 := (four11 (four12 a b)).1,
have g10 := three3 a p,
have g11 := six17 a b,
rw g11 at g4,
have hp : ((l a b) ⊥ l p a) ∧ col a b a ∧ B a a p,
exact ⟨g3.symm, ⟨g9, g10⟩⟩,
have ht : ((l b a) ⊥ l q b) ∧ col b a t ∧ B p t q,
exact ⟨g4.symm, ⟨(four11 g5).2.1, g6⟩⟩,
have hr : B b r q ∧ eqd a p b r,
exact ⟨g7, g8⟩,
exact eight23 hp ht hr
end
theorem eight25 {a b : point} : a ≠ b → ∃ c, R a b c ∧ c ≠ b :=
begin
intro h,
rcases eight21 h.symm a with ⟨c, p, h1⟩,
refine ⟨c, (eight15 h1.1 (six17a b a) (six17b c b)).2.2.2.2 (six17b b a) (six17a c b), _⟩,
exact six13 (eight14e h1.1).2
end
end Euclidean_plane
|
{"author": "ImperialCollegeLondon", "repo": "xena-UROP-2018", "sha": "b111fb87f343cf79eca3b886f99ee15c1dd9884b", "save_path": "github-repos/lean/ImperialCollegeLondon-xena-UROP-2018", "path": "github-repos/lean/ImperialCollegeLondon-xena-UROP-2018/xena-UROP-2018-b111fb87f343cf79eca3b886f99ee15c1dd9884b/src/Geometry/tarski_3.lean"}
|
using HarwellRutherfordBoeing
using Krylov
using LinearOperators
# using ProfileView
# M = HarwellBoeingMatrix("data/illc1033.rra");
M = HarwellBoeingMatrix("data/illc1850.rra");
A = M.matrix;
(m, n) = size(A);
@printf("System size: %d rows and %d columns\n", m, n);
# Define a linear operator with preallocation.
Ap = zeros(m);
Atq = zeros(n);
op = LinearOperator(m, n, false, false,
p -> A_mul_B!(1.0, A, p, 0.0, Ap),
q -> At_mul_B!(1.0, A, q, 0.0, Atq),
q -> At_mul_B!(1.0, A, q, 0.0, Atq));
λ = 1.0e-3;
λ > 0.0 && (N = 1./λ * opEye(n))
for nrhs = 1 : size(M.rhs, 2)
b = M.rhs[:,nrhs];
(x, stats) = lsqr(op, b, λ=λ, sqd=λ > 0, atol=0.0, btol=0.0, N=N);
# @profile (x, stats) = lsqr(op, b, λ=λ, sqd=λ > 0, atol=0.0, btol=0.0, N=N);
@time (x, stats) = lsqr(op, b, λ=λ, sqd=λ > 0, atol=0.0, btol=0.0, N=N);
show(stats);
resid = norm(A' * (A * x - b) + λ * x) / norm(b);
@printf("LSQR: Relative residual: %8.1e\n", resid);
@printf("LSQR: ‖x‖: %8.1e\n", norm(x));
end
# ProfileView.view()
|
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|
#!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
Principal components analysis (PCA)
=========================================================
These figures aid in illustrating how a point cloud
can be very flat in one direction--which is where PCA
comes in to choose a direction that is not flat.
"""
from __future__ import print_function
# Authors: Gael Varoquaux
# Jaques Grobler
# Kevin Hughes
# License: BSD 3 clause
#from scipy import stats
import vtk
import os
import argparse
import timeit
import pickle as pickle
import random
from imblearn.over_sampling import SMOTE
#import matplotlib.pyplot as plt
import pprint
import inputData
#from sklearn.decomposition import PCA
import math
import inputData
import glob
import numpy as np
import collections
from sklearn import svm
from sklearn.metrics import accuracy_score
#from matplotlib.colors import ListedColormap
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.datasets import make_moons, make_circles
from sklearn.neural_network import MLPClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.gaussian_process import GaussianProcessClassifier
from sklearn.gaussian_process.kernels import RBF
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
from sklearn.svm import LinearSVC
from sklearn.metrics import confusion_matrix, roc_curve, auc
import itertools
from sklearn import preprocessing
# #############################################################################
# Generate data
parser = argparse.ArgumentParser(description='Shape Variation Analyzer', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
#parser.add_argument('--model', type=str, help='pickle file with the pca decomposition', required=True)
#parser.add_argument('--shapeDir', type=str, help='Directory with vtk files .vtk', required=True)
parser.add_argument('--picklefile',dest='picklefile',help='picklefile with the dataset',required=True)
#parser.add_argument('--dataPathtrain', action='store', dest='dirwithSubtrain', help='folder with subclasses', required=True)
#parser.add_argument('--dataPathtest', action='store', dest='dirwithSubtest', help='folder with subclasses', required=True)
#parser.add_argument('--train_size', help='train ratio', type=float, default=0.8)
#parser.add_argument('--validation_size', help='validation ratio from test data', default=0.5, type=float)
#parser.add_argument('--feature_names', help='Extract the following features from the polydatas', nargs='+', default=["Normals", "Mean_Curvature", "distanceGroup"], type=str)
#parser.add_argument('--out', dest="pickle_file_new", help='Pickle file output', default="new_dataset.pickle", type=str)
#parser.add_argument('-outputdataPath', action='store', dest='dirwithSubGenerated', help='folder with subclasses after generation of data', required=True)
#parser.add_argument('--outputGenerated', help='output folder for shapes', default='./out')
#parser.add_argument('--num_shapes', type=int, help='number shapes to be generated', default=10)
#parser.add_argument('--meanShape',help='mean shape', required=True)
def readData(shapedir):
#Read data from vtk files
print("loading data ......")
print("+++++++Read the surface shape data+++++++")
vtkdirshapes = os.listdir(shapedir)
y_design = []
numpoints = -1
nshape = 0
firstshapedata = 0
for vtkfilename in vtkdirshapes:
if vtkfilename.endswith((".vtk")):
print("Reading", vtkfilename)
reader = vtk.vtkPolyDataReader()
reader.SetFileName(os.path.join(shapedir, vtkfilename))
reader.Update()
shapedata = reader.GetOutput()
shapedatapoints = shapedata.GetPoints()
if firstshapedata == 0:
firstshapedata = shapedata
y_design.append([])
if numpoints == -1:
numpoints = shapedatapoints.GetNumberOfPoints()
if numpoints != shapedatapoints.GetNumberOfPoints():
print("WARNING! The number of points is not the same for the shape:", vtkfilename)
for i in range(shapedatapoints.GetNumberOfPoints()):
p = shapedatapoints.GetPoint(i)
y_design[nshape].append(p)
nshape+=1
y_design = np.array(y_design)
return y_design.reshape(y_design.shape[0], -1), firstshapedata
def writeData(data_for_training,outputdataPath):
#write data in a vtk file
vtkdirshapes = os.listdir(outputdataPath)
for vtkfilename in vtkdirshapes:
if vtkfilename.endswith((".vtk")):
print("Writing", vtkfilename)
writer = vtk.vtkPolyDataWriter()
writer.SetInput(data_for_training)
writer.SetFileName(os.path.join(outputdataPath),vtkfilename)
writer.Write()
def get_labels(pickle_file):
#get labels of a dataset and returns the labels array and the dataset with features
#num_classes=len(pickle_file)
#num_shapes = 268 #should be changed!!
labels = []
shape =[]
dataset_concatenated =[]
for label, pickle_file in enumerate(pickle_file):
try:
with open(pickle_file,'rb') as f:
dataset=pickle.load(f)
shape_dataset = np.shape(dataset)
num_shapes_per_group = shape_dataset[0]
print('num shapes per group',label,num_shapes_per_group)
l=[label]*num_shapes_per_group
labels.extend(l)
dataset_concatenated.extend(dataset)
except Exception as e:
print('Unable to process', pickle_file,':',e)
raise
features=np.array(dataset_concatenated)
shape_features=np.shape(features)
return features.reshape(-1,shape_features[1]*shape_features[2]), np.array(labels)
def generate_data(pca_model):
#generate data thanks to pca decomposition (not used)
print("Generating data ...")
pca = pca_model["pca"]
X_ = pca_model["X_"]
X_pca_ = pca_model["X_pca_"]
X_pca_var = pca_model["X_pca_var"]
print('Variance',X_pca_var)
print('Mean',X_pca_)
#between -1 and 1
alpha = 2.0*(np.random.random_sample(np.size(X_pca_))) - 1.0
print('alpha', alpha)
data_compressed = 1.5*X_pca_var * alpha + X_pca_
print('data compressed',data_compressed)
data_generated = pca.inverse_transform(data_compressed) + X_
return data_generated
def generate_with_SMOTE(dataset,labels):
#generate data thanks to SMOTE algorithm, it balances different groups
sm=SMOTE(random_state=42,kind='borderline1')
print('shape dataset',dataset.shape)
print('shape labels',labels.shape)
dataset_res, labels_res = sm.fit_sample(dataset,labels)
print('shape dataset resampled',np.shape(dataset_res),'shape lables resampled',np.shape(labels_res))
return dataset_res,labels_res
# def PCA_plot(dataset,labels,dataset_res,labels_res):
# #plot original dat and data resampled after a PCA decomposition
# pca = PCA(n_components=200)
# pca.fit(dataset)
# dataset_pca=pca.transform(dataset)
# print('original shape: ',dataset.shape)
# print('transformed shape:',dataset_pca.shape)
# #print('Ratio variance',pca.explained_variance_ratio_)
# #plt.scatter(dataset[:,0],dataset[:,1],alpha=0.2)
# #dataset_new = pca.inverse_transform(dataset_pca)
# plt.figure(2)
# plt.subplot(121)
# plt.scatter(dataset_pca[:,0],dataset_pca[:,1],edgecolor='none',alpha=0.5,c=labels,cmap=plt.cm.get_cmap('nipy_spectral',np.shape(np.unique(labels))[0]))
# plt.title('Original data with pca (' + str(dataset.shape[0]) + ' samples)')
# #pca.fit(dataset_res)
# dataset_res_pca=pca.transform(dataset_res)
# plt.subplot(122)
# plt.scatter(dataset_res_pca[:,0],dataset_res_pca[:,1],edgecolor='none',alpha=0.5,c=labels_res,cmap=plt.cm.get_cmap('nipy_spectral',np.shape(np.unique(labels_res))[0]))
# plt.title('Resampled data with pca (' + str(dataset_res_pca.shape[0]) + ' samples)')
# for i in range(1,3):
# plt.subplot(1,2,i)
# plt.xlabel('component 1')
# plt.ylabel('component 2')
# plt.colorbar()
# cumsum = np.cumsum(pca.explained_variance_ratio_)
# plt.figure(1)
# plt.plot(cumsum)
# plt.xlabel('nb of components')
# plt.ylabel('cumulative explained variance')
# plt.axhline(y=0.95, linestyle=':', label='.95 explained', color="#f23e3e")
# numcomponents = len(np.where(cumsum < 0.95)[0])
# plt.axvline(x=numcomponents, linestyle=':', label=(str(numcomponents) + ' components'), color="#31f9ad")
# plt.legend(loc=0)
# histo = np.bincount(labels)
# histo_range = np.array(range(histo.shape[0]))
# plt.figure(3)
# plt.bar(histo_range, histo)
# plt.xlabel('Groups')
# plt.ylabel('Number of samples')
# for xy in zip(histo_range, histo):
# plt.annotate(xy[1], xy=xy, ha="center", color="#4286f4")
# plt.show()
# def plot_confusion_matrix(cm, classes,
# normalize=False,
# title='Confusion matrix',
# cmap=plt.cm.Blues):
# """
# This function prints and plots the confusion matrix.
# Normalization can be applied by setting `normalize=True`.
# """
# if normalize:
# cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
# print("Normalized confusion matrix")
# else:
# print('Confusion matrix, without normalization')
# print(cm)
# plt.figure()
# plt.imshow(cm, interpolation='nearest', cmap=cmap)
# plt.title(title)
# plt.colorbar()
# tick_marks = np.arange(len(classes))
# plt.xticks(tick_marks, classes, rotation=45)
# plt.yticks(tick_marks, classes)
# fmt = '.2f' if normalize else 'd'
# thresh = cm.max() / 2.
# for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
# plt.text(j, i, format(cm[i, j], fmt),
# horizontalalignment="center",
# color="white" if cm[i, j] > thresh else "black")
# #plt.tight_layout()
# plt.ylabel('True label')
# plt.xlabel('Predicted label')
def training_acc(X_train,y_train,X_test,y_test,classifiers):
tab_score=[]
for clf in classifiers:
clf.fit(X_train, y_train)
score = clf.score(X_test, y_test)
print('score',score)
tab_score.append(score)
print('TAB score',tab_score)
return tab_score
def SVM_classification(X_dataset,y_labels,dataset_test,labels_test):
# model = svm.SVC(decision_function_shape='ovr',kernel='rbf',C=100,gamma=10)
# model.fit(X_dataset,y_labels)
# model.score(X_dataset,y_labels)
print('data shape',X_dataset.shape)
# predicted_labels = model.predict(test_dataset)
# acc = accuracy_score(test_labels,predicted_labels,normalize=True)
# print('accuracy',acc)
h = .02 # step size in the mesh
names = ["Nearest Neighbors", "Linear SVM", "RBF SVM", "Gaussian Process",
"Decision Tree", "Random Forest", "MLP Classifier", "AdaBoost",
"Naive Bayes", "QDA"]
classifiers = [
KNeighborsClassifier(3),
SVC(kernel="linear", C=0.025),
SVC(gamma=2, C=1),
GaussianProcessClassifier(1.0 * RBF(1.0)),
DecisionTreeClassifier(max_depth=5),
RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1),
MLPClassifier(alpha=1),
AdaBoostClassifier(),
GaussianNB(),
QuadraticDiscriminantAnalysis()]
#linearly_separable = (X_dataset,y_labels)
#datasets = [linearly_separable]
i=1
#standardizing features
#X_stand = StandardScaler().fit_transform(X)
X_train,X_test, y_train, y_test = train_test_split(X_dataset,y_labels, test_size =0.4)
# just plot the dataset first
cm = plt.cm.RdBu
#cm_bright = ListedColormap(['#FF0000', '#0000FF','#48FF00'])
#fig1,ax1=plt.subplots(3,4)
#fig2,ax2=plt.subplots(3,4)
#if ds_cnt == 0:
# ax.set_title("Input data")
# Plot the training points
#score=training_acc(X_train,y_train,X_test,y_test,classifiers)
# iterate over classifiers
#for name, clf in zip(names, classifiers):
# ax = plt.subplot(3, 4, i)
# clf.fit(X_train, y_train)
#score = clf.score(X_test[:,2:], y_test)
#print('score 2 features',score)
#make meshgrid
# x_min, x_max = X_train[:, 0].min()-1, X_train[:, 0].max()+1
# y_min, y_max = X_train[:, 1].min()-1, X_train[:, 1].max()+1
# xx, yy= np.meshgrid(np.arange(x_min, x_max,1) ,np.arange(y_min, y_max, 1))
# print('shape xx',xx.shape,'shape yy',yy.shape)
# print('shape ravel',np.c_[xx.ravel(),yy.ravel()].shape)
# if hasattr(clf, "decision_function"):
# Z = clf.decision_function(np.c_[xx.ravel(),yy.ravel()])
# print('xx shape',xx.shape,'yy shape',yy.shape,'Z shape',Z.shape)
# Z=Z[:,1]
# else:
# Z = clf.predict_proba(np.c_[xx.ravel(),yy.ravel()])[:,1]
# print('Z shape',Z.shape)
# Z = Z.reshape(xx.shape)
# ax.contourf(xx,yy,Z, cmap=plt.cm.get_cmap('nipy_spectral',np.shape(np.unique(y_test))[0]), alpha=.4)
# Plot also the training points
# CS=ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=plt.cm.get_cmap('nipy_spectral',np.shape(np.unique(y_train))[0]))
# and testing points
# ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=plt.cm.get_cmap('nipy_spectral',np.shape(np.unique(y_test))[0]),edgecolors='k', alpha=0.6)
# ax.set_xticks(())
# ax.set_yticks(())
# ax.set_title(name)
#plt.suptitle(score,y=1.05,fontsize=18)
#a=0.98
#if name=="Current neural network":
# ax.text(240, -40, a,size=15, horizontalalignment='right')
#else:
#ax.text(240, -40, ('%.2f' % score).lstrip('0'),size=15, horizontalalignment='right')
#clf.fit(X_train, y_train)
#score_training = clf.score(X_train, y_train)
#print('score training',score_training)
#ax.text(x_max-0.5, y_min+0.5, ('%.2f' % score).lstrip('0'),size=15, horizontalalignment='right')
for name,clf in zip(names,classifiers):
clf.fit(X_train, y_train)
y_prediction = clf.predict(dataset_test)
print('y_prediction',y_prediction,'labels_test',labels_test)
test_score = accuracy_score(labels_test,y_prediction)
confusion = confusion_matrix(labels_test,y_prediction)
print('The accuracy of ',name,'is',test_score)
name_labels=["group0","group1","group2","group3","group4","group5"]
plot_confusion_matrix(confusion,name_labels,title=name)
if hasattr(clf, "decision_function"):
#binarize labels
lb = preprocessing.LabelBinarizer()
lb.fit([0,1,2,3,4,5])
print('y_test',y_test)
y_test_bin=lb.transform(y_test)
fpr=dict()
tpr=dict()
roc_auc=dict()
y_score = clf.fit(X_train,y_train).decision_function(X_test)
print('y_score',y_score)
print('y_score shape',y_score.shape,'y_test shape',y_test.shape)
#compute ROC curve and ROC area for each class
for j in range(6):
print(j)
fpr[j], tpr[j], _ = roc_curve(y_test_bin[:,j],y_score[:,j])
roc_auc[j]=auc(fpr[j],tpr[j])
plt.figure()
lw=2
plt.plot(fpr[2],tpr[2],color='darkorange',lw=lw,label='ROC curve (area = %0.2f)'%roc_auc[2])
plt.show
#ax.text(x_max-1,y_min+0.1,('%.3f' % score_training),size=10,horizontalalignment='right')
#score=trainin_acc(X_train,y_train,X_test,y_test,classifiers)
#ax.text(x_max-0.5,y_min+0.1,('%.3f' % score[i-1]),size=8,horizontalalignment='right')
print('score testing',test_score)
#plt.colorbar(CS)
i += 1
#printing our neural network accuracy
# ax=plt.subplot(3,4,11)
# ax.scatter(X_dataset[:, 0], X_dataset[:, 1], c=y_labels, cmap=plt.cm.get_cmap('nipy_spectral',np.shape(np.unique(y_labels))[0]))
# ax.scatter(dataset_test[:, 0], dataset_test[:, 1], c=labels_test, cmap=plt.cm.get_cmap('nipy_spectral',np.shape(np.unique(labels_test))[0]),edgecolors='k', alpha=0.6)
# ax.set_xticks(())
# ax.set_yticks(())
# ax.set_title("5-layers neural network")
# score_fake=0.971
# maxi=X_dataset[:,0].max()
# mini=X_dataset[:,0].min()
# maxi1=X_dataset[:,1].max()
# mini1=X_dataset[:,1].min()
# print('x_max',maxi,'x_min',mini,'y_max',maxi1,'y_min',mini1)
# #plt.axis([0,1,-1,1])
# ax.text(maxi,mini1,('%.3f' % score_fake),size=8,horizontalalignment='right')
plt.tight_layout()
plt.show()
#def generate(args):
if __name__ == '__main__':
np.set_printoptions(threshold='nan')
args = parser.parse_args()
pickle_file = args.picklefile
#pickle_file_output= args.pickle_file_new
# Get the data from the folders with vtk files
inputdata = inputData.inputData()
fi = open(pickle_file,'rb')
dataset=pickle.load(fi)
test_labels =dataset["test_labels"]
train_labels =dataset["train_labels"]
valid_labels =dataset["valid_labels"]
test_dataset =dataset["test_dataset"]
train_dataset =dataset["train_dataset"]
valid_dataset =dataset["valid_dataset"]
print('counter',collections.Counter(train_labels))
#data_folders_train = inputdata.get_folder_classes_list(dataPathtrain)
#data_folders_test = inputdata.get_folder_classes_list(dataPathtest)
#pickled_datasets_train,vtklisttrain = inputdata.maybe_pickle(data_folders_train, 6, feature_points=args.feature_names)
#pickled_datasets_test,vtklisttest = inputdata.maybe_pickle(data_folders_test, 0, feature_points=args.feature_names)
#Create the labels, i.e., enumerate the groups
#dataset_train,labels_train = get_labels(pickled_datasets_train)
#print('pickled_datasets_train',pickled_datasets_train,'pickled_datasets_test',pickled_datasets_test)
#dataset_test,labels_test = get_labels(pickled_datasets_test)
# Compute the total number of shapes and train/test size
total_number_shapes_train=train_dataset.shape[0]
total_number_shapes_test=test_dataset.shape[0]
print('total number of shapes train',np.shape(train_dataset))
print('total number of shapes test', np.shape(test_dataset))
print('labels to train',train_labels,'labels to test',test_labels)
#num_train = int(args.train_size*total_number_shapes_train)
#num_valid = int((total_number_shapes_train - num_train)*args.validation_size)
# Randomize the original dataset
#print('shape before randomize',dataset_train.shape)
shuffled_dataset, shuffled_labels = inputdata.randomize(train_dataset, train_labels)
#print('shape after randomize',shuffled_dataset.shape)
#shuffled_dataset_test,shuffled_labels_test = inputdata.randomize(dataset_test,labels_test)
shuffled_dataset = np.reshape(shuffled_dataset, (total_number_shapes_train, -1))
#print('shape after reshape',shuffled_dataset.shape)
#shuffled_dataset_test = np .reshape(shuffled_dataset_test,(total_number_shapes_test,-1))
# Generate SMOTE with out including the valid/test samples, in some cases, this may raise an error
# as the number of samples in one class is less than 5 and SMOTE cannot continue. Just run it again
dataset_res,labels_res=generate_with_SMOTE(np.nan_to_num(shuffled_dataset),shuffled_labels)
# SANITY CHECKS
print('dataset train',np.shape(train_dataset))
print('labels train',np.shape(train_labels))
#print('dataset_res',np.shape(dataset_res))
#print('labels_res',np.shape(labels_res))
#print('num_train', num_train)
#print('num_valid', num_valid)
print('number of labels',np.shape(np.unique(train_labels)))
#print('number of labels resampled',np.shape(np.unique(labels_res)))
#print('Labels resampled',np.unique(labels_res).tolist())
print('test labels', test_labels)
print('counter after SMOTE',collections.Counter(labels_res))
#SVM_classification(dataset_res,labels_res,dataset_test,labels_test)
#clf=LinearSVC(random_state=0)
#clf=GaussianProcessClassifier(1.0 * RBF(1.0))
#clf.fit(dataset_res,labels_res)
#prediction = clf.predict(dataset_test)
#for i in range(0,total_number_shapes_test):
# head,tail = os.path.split(vtklisttest[i])
# print(tail,prediction[i])
#PCA_plot(dataset,labels,dataset_res,labels_res)
try:
f = open(pickle_file, 'wb')
save = {
'train_dataset': dataset_res,
'train_labels': labels_res,
'valid_dataset': valid_dataset,
'valid_labels': valid_labels,
'test_dataset': test_dataset,
'test_labels': test_labels
}
pickle.dump(save, f, pickle.HIGHEST_PROTOCOL)
except Exception as e:
print('Unable to save data to', pickle_file, ':', e)
raise
|
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|
/**
@file
@author Alexander Sherikov
@copyright 2017 Alexander Sherikov. Licensed under the Apache License,
Version 2.0. (see LICENSE or http://www.apache.org/licenses/LICENSE-2.0)
@brief
*/
#include "utf_common.h"
#include <boost/mpl/vector.hpp>
#include <qpmad/solver.h>
#include <qpmad/testing.h>
//===========================================================================
// ResolveFixture
//===========================================================================
template <class t_Solver>
class ResolveFixture
{
public:
Eigen::VectorXd x;
Eigen::MatrixXd H;
Eigen::MatrixXd H_copy;
Eigen::VectorXd h;
Eigen::MatrixXd A;
Eigen::VectorXd Alb;
Eigen::VectorXd Aub;
Eigen::VectorXd lb;
Eigen::VectorXd ub;
t_Solver solver;
typename t_Solver::ReturnStatus status;
qpmad::SolverParameters param;
public:
ResolveFixture()
{
qpmad::MatrixIndex size = 20;
qpmad::MatrixIndex num_general_ctr = 1;
qpmad_utils::getRandomPositiveDefiniteMatrix(H, size);
H_copy = H;
h.setOnes(size);
A.resize(num_general_ctr, size);
A.setOnes();
Alb.resize(num_general_ctr);
Aub.resize(num_general_ctr);
Alb << -1.5;
Aub << 1.5;
lb.resize(size);
ub.resize(size);
lb << 1, 2, 3, 4, -5, -5, -5, -5, -5, -5, -5, -5, -5, -5, -5, -5, -5, -5, -5, -5;
ub << 1, 2, 3, 4, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5;
}
void solve()
{
status = solver.solve(x, H, h, lb, ub, A, Alb, Aub, param);
BOOST_CHECK_EQUAL(status, qpmad::Solver::OK);
}
};
using TypeListResolve = boost::mpl::vector<qpmad::Solver, qpmad::SolverTemplate<double, 20, 1, 1> >;
BOOST_FIXTURE_TEST_CASE_TEMPLATE(resolve_with_cholesky, t_Solver, TypeListResolve, ResolveFixture<t_Solver>)
{
this->solve();
// Hessian changed;
BOOST_CHECK(not this->H_copy.isApprox(this->H, g_default_tolerance));
// next iteration
this->H_copy = this->H;
Eigen::VectorXd x_copy = this->x;
this->param.hessian_type_ = this->solver.getHessianType();
BOOST_CHECK_EQUAL(this->param.hessian_type_, qpmad::SolverParameters::HESSIAN_CHOLESKY_FACTOR);
this->solve();
// Hessian not changed
BOOST_CHECK(this->H_copy.isApprox(this->H, g_default_tolerance));
// solution is the same
BOOST_CHECK(x_copy.isApprox(this->x, g_default_tolerance));
}
BOOST_FIXTURE_TEST_CASE_TEMPLATE(resolve_with_inverted_cholesky, t_Solver, TypeListResolve, ResolveFixture<t_Solver>)
{
this->param.return_inverted_cholesky_factor_ = true;
this->solve();
// Hessian changed;
BOOST_CHECK(not this->H_copy.isApprox(this->H, g_default_tolerance));
// next iteration
this->H_copy = this->H;
Eigen::VectorXd x_copy = this->x;
this->param.hessian_type_ = this->solver.getHessianType();
BOOST_CHECK_EQUAL(this->param.hessian_type_, qpmad::SolverParameters::HESSIAN_INVERTED_CHOLESKY_FACTOR);
this->solve();
// Hessian not changed
BOOST_CHECK(this->H_copy.isApprox(this->H, g_default_tolerance));
// solution is the same
BOOST_CHECK(x_copy.isApprox(this->x, g_default_tolerance));
}
//===========================================================================
// ResolveUnconstrainedFixture
//===========================================================================
template <class t_Solver>
class ResolveUnconstrainedFixture
{
public:
Eigen::VectorXd x;
Eigen::MatrixXd H;
Eigen::MatrixXd H_copy;
Eigen::VectorXd h;
Eigen::VectorXd lb;
Eigen::VectorXd ub;
t_Solver solver;
typename t_Solver::ReturnStatus status;
qpmad::SolverParameters param;
public:
ResolveUnconstrainedFixture()
{
qpmad::MatrixIndex size = 20;
qpmad_utils::getRandomPositiveDefiniteMatrix(H, size);
H_copy = H;
h.setOnes(size);
lb.setConstant(size, -1e20);
ub.setConstant(size, 1e20);
lb(0) = 1;
ub(0) = 1;
}
void solve()
{
status = solver.solve(x, H, h, lb, ub, param);
BOOST_CHECK_EQUAL(status, qpmad::Solver::OK);
}
};
using TypeListResolveUnconstrained = boost::mpl::vector<qpmad::Solver, qpmad::SolverTemplate<double, 20, 1, 0> >;
BOOST_FIXTURE_TEST_CASE_TEMPLATE(
resolve_unconstrained_with_cholesky,
t_Solver,
TypeListResolveUnconstrained,
ResolveUnconstrainedFixture<t_Solver>)
{
this->solve();
// Hessian changed;
BOOST_CHECK(not this->H_copy.isApprox(this->H, g_default_tolerance));
// next iteration
this->H_copy = this->H;
Eigen::VectorXd x_copy = this->x;
this->param.hessian_type_ = this->solver.getHessianType();
BOOST_CHECK_EQUAL(this->param.hessian_type_, qpmad::SolverParameters::HESSIAN_CHOLESKY_FACTOR);
this->solve();
// Hessian not changed
BOOST_CHECK(this->H_copy.isApprox(this->H, g_default_tolerance));
// solution is the same
BOOST_CHECK(x_copy.isApprox(this->x, g_default_tolerance));
}
BOOST_FIXTURE_TEST_CASE_TEMPLATE(
resolve_unconstrained_with_inverted_cholesky,
t_Solver,
TypeListResolveUnconstrained,
ResolveUnconstrainedFixture<t_Solver>)
{
this->param.return_inverted_cholesky_factor_ = true;
this->solve();
// Hessian changed;
BOOST_CHECK(not this->H_copy.isApprox(this->H, g_default_tolerance));
// next iteration
this->H_copy = this->H;
Eigen::VectorXd x_copy = this->x;
this->param.hessian_type_ = this->solver.getHessianType();
BOOST_CHECK_EQUAL(this->param.hessian_type_, qpmad::SolverParameters::HESSIAN_INVERTED_CHOLESKY_FACTOR);
this->solve();
// Hessian not changed
BOOST_CHECK(this->H_copy.isApprox(this->H, g_default_tolerance));
// solution is the same
BOOST_CHECK(x_copy.isApprox(this->x, g_default_tolerance));
}
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|
from abc import ABC, abstractmethod
from collections import Counter
from functools import reduce
from typing import List, Tuple
import numpy as np
from sklearn.utils.linear_assignment_ import linear_assignment
class Scorer(ABC):
precision: float
recall: float
def get_scores(self, predicted_chains: List[List[int]], label_chains: List[List[int]]) \
-> Tuple[float, float, float]:
self._clear_memo()
precision = self._compute_precision(predicted_chains, label_chains)
recall = self._compute_recall(predicted_chains, label_chains)
f1 = self._compute_f1(predicted_chains, label_chains)
return precision, recall, f1
def _clear_memo(self):
self._precision = None
self._recall = None
def _compute_f1(self, predicted_chains: List[List[int]], label_chains: List[List[int]]) -> float:
precision = self._compute_precision(predicted_chains, label_chains)
recall = self._compute_recall(predicted_chains, label_chains)
if precision + recall == 0:
return 0
return 2 * precision * recall / (precision + recall)
def _compute_precision(self, predicted_chains: List[List[int]], label_chains: List[List[int]]) -> float:
if self._precision is None:
self._precision = self.compute_precision(predicted_chains, label_chains)
return self._precision
def _compute_recall(self, predicted_chains: List[List[int]], label_chains: List[List[int]]) -> float:
if self._recall is None:
self._recall = self.compute_recall(predicted_chains, label_chains)
return self._recall
@abstractmethod
def compute_precision(self, predicted_chains: List[List[int]], label_chains: List[List[int]]) -> float:
pass
@abstractmethod
def compute_recall(self, predicted_chains: List[List[int]], label_chains: List[List[int]]) -> float:
pass
class MUCScorer(Scorer):
def compute_precision(self, predicted_chains: List[List[int]], label_chains: List[List[int]]) -> float:
return self._general_compute(predicted_chains, label_chains)
def compute_recall(self, predicted_chains: List[List[int]], label_chains: List[List[int]]) -> float:
return self._general_compute(label_chains, predicted_chains)
def _general_compute(self, chain1: List[List[int]], chain2: List[List[int]]) -> float:
nominator = 0
denominator = 0
for c1 in chain1:
ki = len(c1)
part_left = ki
partition = 0
for c2 in chain2:
found = False
for s in c2:
if s in c1:
found = True
part_left -= 1
if found:
partition += 1
nominator += (ki - (partition + part_left))
denominator += ki - 1
if denominator == 0:
return 0
return nominator / denominator
class B3Scorer(Scorer):
def compute_precision(self, predicted_chains: List[List[int]], label_chains: List[List[int]]) -> float:
return self._general_compute(predicted_chains, label_chains)
def compute_recall(self, predicted_chains: List[List[int]], label_chains: List[List[int]]) -> float:
return self._general_compute(label_chains, predicted_chains)
def _general_compute(self, chain1: List[List[int]], chain2: List[List[int]]) -> float:
mention_to_gold = {}
for c in chain2:
for m in c:
mention_to_gold[m] = c
num, dem = 0, 0
for c in chain1:
if len(c) == 1:
continue
gold_counts = Counter()
correct = 0
for m in c:
if m in mention_to_gold:
gold_counts[tuple(mention_to_gold[m])] += 1
for c2, count in gold_counts.items():
if len(c2) != 1:
correct += count * count
num += correct / float(len(c))
dem += len(c)
if dem == 0:
return 0
return num / dem
class CEAFeScorer(Scorer):
similarity: int = None
def reset(self) -> None:
self.similarity = None
def compute_precision(self, predicted_chains: List[List[int]], label_chains: List[List[int]]) -> float:
return self._compute_similarity(predicted_chains, label_chains) / len(predicted_chains)
def compute_recall(self, predicted_chains: List[List[int]], label_chains: List[List[int]]) -> float:
return self._compute_similarity(predicted_chains, label_chains) / len(label_chains)
def _compute_similarity(self, predicted_chains: List[List[int]], label_chains: List[List[int]]) -> int:
if self.similarity is not None:
return self.similarity
predicted_chains = [c for c in predicted_chains if len(c) != 1]
label_chains = [c for c in label_chains if len(c) != 1]
scores = np.zeros((len(label_chains), len(predicted_chains)))
for i in range(len(label_chains)):
for j in range(len(predicted_chains)):
scores[i, j] = self._compute_phi4(label_chains[i], predicted_chains[j])
matching = linear_assignment(-scores)
similarity = sum(scores[matching[:, 0], matching[:, 1]])
self.similarity = similarity
return self.similarity
def _compute_phi4(self, c1: List[List[int]], c2: List[List[int]]) -> float:
return 2 * len([m for m in c1 if m in c2]) / float(len(c1) + len(c2))
class AverageScorer(Scorer):
score: float
def __init__(self, scorers: List[Scorer]):
self.scorers = scorers
def get_scores(self, predicted_chains: List[List[int]], label_chains: List[List[int]]) \
-> Tuple[float, float, float]:
self.score = None
return super().get_scores(predicted_chains, label_chains)
def compute_precision(self, predicted_chains: List[List[int]], label_chains: List[List[int]]) -> float:
return self._compute_score(predicted_chains, label_chains)
def compute_recall(self, predicted_chains: List[List[int]], label_chains: List[List[int]]) -> float:
return self._compute_score(predicted_chains, label_chains)
def _compute_score(self, predicted_chains: List[List[int]], label_chains: List[List[int]]) -> float:
if self.score is not None:
return self.score
if len(self.scorers) == 0:
self.score = 0
return self.score
sum_f1 = reduce(lambda prv, scorer: prv + scorer.get_scores(predicted_chains, label_chains)[2], self.scorers, 0)
self.score = sum_f1 / len(self.scorers)
return self.score
|
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|
! This test checks lowering of OpenMP threadprivate Directive.
// RUN: not flang-new -fc1 -emit-fir -fopenmp %s 2>&1 | FileCheck %s
program main
integer, save :: x, y
// CHECK: not yet implemented: OpenMPThreadprivate
!$omp threadprivate(x, y)
end
|
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|
import utils
import sklearn
import tensorflow.compat.v1 as tf
import numpy as np
def tf_dataset(batch_pc_gen):
while True:
yield next(batch_pc_gen)
def get_dataset(batch_pc_gen, batch_size):
with tf.device('/device:CPU:0'):
ds = tf.data.Dataset.from_generator(lambda: tf_dataset(batch_pc_gen), tf.float32, (batch_size, None, 6))
if tf.test.is_gpu_available():
ds = ds.apply(tf.data.experimental.prefetch_to_device('/device:GPU:0', 4))
return ds
def pc_batcher(x):
return np.array(list(y[0][:, :6] for y in x))
class Dataset:
def __init__(self, gen, batch_size):
# Set up data pipelines
self.gen = gen
self.batcher = lambda x: pc_batcher(x)
self.batch_gen = utils.generators.BatchGenerator(self.gen, batch_size, self.batcher)
# TF graph data pipeline
self.batch_ds = get_dataset(self.batch_gen, batch_size)
self.iterator = tf.data.make_one_shot_iterator(self.batch_ds)
self.output_types = tf.data.get_output_types(self.batch_ds)
self.output_shapes = tf.data.get_output_shapes(self.batch_ds)
self.string_handle = self.iterator.string_handle
class InputPipeline:
def __init__(self, files, batch_size, train_test_split, infinite_data, test_size=0.1):
self.files = files
def data_transform(data):
if infinite_data:
return utils.generators.sampling_generator(data)
else:
return iter(data)
if train_test_split:
files_train, files_test = sklearn.model_selection.train_test_split(files, test_size=test_size)
self.len_train, self.len_test = len(files_train) // batch_size, len(files_test) // batch_size
data_train = utils.pc_io.load_points(files_train)
data_test = utils.pc_io.load_points(files_test)
self.pc_ds_test = Dataset(data_transform(data_test), batch_size)
self.pc_ds_train = Dataset(data_transform(data_train), batch_size)
# Train/Test switching
self.handle = tf.placeholder(tf.string, shape=[])
iterator = tf.data.Iterator.from_string_handle(self.handle, self.pc_ds_train.output_types,
self.pc_ds_train.output_shapes)
self.next_element = iterator.get_next()
else:
self.data = utils.pc_io.load_points(files)
self.pc_ds = Dataset(data_transform(self.data), batch_size)
self.next_element = self.pc_ds.iterator.get_next()
|
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|
// Copyright (c) 2009-2010 Satoshi Nakamoto
// Copyright (c) 2009-2017 The Bitcoin Core developers
// Distributed under the MIT software license, see the accompanying
// file COPYING or http://www.opensource.org/licenses/mit-license.php.
#include <pow.h>
#include <arith_uint256.h>
#include <boost/multiprecision/cpp_int.hpp>
#include <chain.h>
#include <primitives/block.h>
#include <uint256.h>
#include <chainparams.h>
#include <crypto/equihash.h>
#include <streams.h>
#include <util.h>
#include <ed25519/ed25519.h>
#include "validation.h"
unsigned int GetNextWorkRequired(const CBlockIndex* pindexLast, const CBlockHeader *pblock, const Consensus::Params& params)
{
assert(pindexLast != nullptr);
// Only change once per difficulty adjustment interval
if ((pindexLast->nHeight+1) % params.DifficultyAdjustmentInterval() != 0)
{
unsigned int nProofOfWorkLimit = UintToArith256(params.powLimit).GetCompact();
if (pindexLast->nHeight < 60500)
{
if (params.fPowAllowMinDifficultyBlocks)
{
// Special difficulty rule for testnet:
// If the new block's timestamp is more than 2* 10 minutes
// then allow mining of a min-difficulty block.
if (pblock->GetBlockTime() > pindexLast->GetBlockTime() + params.nPowTargetSpacing*2)
return nProofOfWorkLimit;
else
{
// Return the last non-special-min-difficulty-rules-block
const CBlockIndex* pindex = pindexLast;
while (pindex->pprev && pindex->nHeight % params.DifficultyAdjustmentInterval() != 0 && pindex->nBits == nProofOfWorkLimit)
pindex = pindex->pprev;
return pindex->nBits;
}
}
return pindexLast->nBits;
} else {
// Return the last non-special-min-difficulty-rules-block
const CBlockIndex* pindex = pindexLast;
while (pindex->pprev && pindex->nHeight % params.DifficultyAdjustmentInterval() != 0 && pindex->nBits == nProofOfWorkLimit)
pindex = pindex->pprev;
return pindex->nBits;
}
}
// Go back by what we want to be 14 days worth of blocks
int nHeightFirst = pindexLast->nHeight - (params.DifficultyAdjustmentInterval()-1);
assert(nHeightFirst >= 0);
const CBlockIndex* pindexFirst = pindexLast->GetAncestor(nHeightFirst);
assert(pindexFirst);
return CalculateNextWorkRequired(pindexLast, pindexFirst->GetBlockTime(), params);
}
static boost::multiprecision::uint512_t UintToCpp512(const uint256 & n)
{
std::string n_string = n.ToString();
return boost::multiprecision::uint512_t("0x" + n_string);
}
unsigned int CalculateNextWorkRequired(const CBlockIndex* pindexLast, int64_t nFirstBlockTime, const Consensus::Params& params)
{
if (params.fPowNoRetargeting)
return pindexLast->nBits;
// Limit adjustment step
int64_t nActualTimespan = pindexLast->GetBlockTime() - nFirstBlockTime;
if (nActualTimespan < params.nPowTargetTimespan/4)
nActualTimespan = params.nPowTargetTimespan/4;
if (nActualTimespan > params.nPowTargetTimespan*4)
nActualTimespan = params.nPowTargetTimespan*4;
// Retarget
auto bnPowLimit = UintToCpp512(params.powLimit);
arith_uint256 bnNewtmp;
unsigned int nProofOfWorkLimit = UintToArith256(params.powLimit).GetCompact();
// Use the last non-special-min-difficulty-rules-block
if (pindexLast->nHeight > 51840)
{
const CBlockIndex* pindex = pindexLast;
while (pindex->pprev && pindex->nHeight % params.DifficultyAdjustmentInterval() != 0 && pindex->nBits == nProofOfWorkLimit)
pindex = pindex->pprev;
bnNewtmp.SetCompact(pindex->nBits);
}
else
{
bnNewtmp.SetCompact(pindexLast->nBits);
}
boost::multiprecision::uint512_t bnNew = UintToCpp512(ArithToUint256(bnNewtmp));
// bnNew.SetCompact(pindexLast->nBits);
bnNew *= nActualTimespan;
bnNew /= params.nPowTargetTimespan;
if (bnNew > bnPowLimit)
bnNew = bnPowLimit;
std::stringstream converted_stream;
converted_stream << std::hex << std::showbase << bnNew;
std::string converted_string = converted_stream.str();
return UintToArith256(uint256S(converted_string)).GetCompact();
}
bool CheckEquihashSolution(const CBlockHeader *pblock, const CChainParams& params)
{
unsigned int n = params.EquihashN();
unsigned int k = params.EquihashK();
// Hash state
blake2b_state state;
EhInitialiseState(n, k, state);
// I = the block header minus nonce and solution.
CEquihashInput I{*pblock};
// I||V
CDataStream ss(SER_NETWORK, PROTOCOL_VERSION);
ss << I;
ss << pblock->nNonce;
// H(I||V||...
blake2b_update(&state, (unsigned char*)&ss[0], ss.size());
bool isValid;
EhIsValidSolution(n, k, state, pblock->nSolution, isValid);
if (!isValid)
return error("CheckEquihashSolution(): invalid solution");
return true;
}
bool CheckProofOfWork(uint256 hash, unsigned int nBits, const Consensus::Params& params)
{
bool fNegative;
bool fOverflow;
arith_uint256 bnTarget;
bnTarget.SetCompact(nBits, &fNegative, &fOverflow);
// Check range
if (fNegative || bnTarget == 0 || fOverflow || bnTarget > UintToArith256(params.powLimit))
return false;
// Check proof of work matches claimed amount
if (UintToArith256(hash) > bnTarget)
return false;
return true;
}
bool CheckAuthorization(const CBlock *pblock, const CChainParams& params)
{
CBlockIndex * chainIndex = chainActive.Tip();
if (chainIndex == nullptr ||
params.GetConsensus().authorizationForkHeight <= 0 ||
chainIndex->nHeight + 1 < params.GetConsensus().authorizationForkHeight) {
return true;
}
if (!params.GetConsensus().authorizationKey.IsFullyValid()) {
return true;
}
if (pblock->vtx.empty() || !pblock->vtx[0]->IsCoinBase()) {
return false;
}
const CTransaction& coinbase = *pblock->vtx[0];
CScript scriptSig = coinbase.vin[0].scriptSig;
// 0x40 + 64个字节的signature
if (scriptSig.size() < 65) {
return false;
}
CScript::const_iterator pc = scriptSig.begin();
// 第一个元素是区块高度
const int nHeight = chainIndex->nHeight + 1;
CScript nHeightScript = CScript() << nHeight;
// 第二个元素是timestamp
CScript nTimeScript = CScript() << pblock->nTime;
pc = pc + nHeightScript.size() + nTimeScript.size();
std::vector<unsigned char> sig;
opcodetype opcode;
if (!scriptSig.GetOp(pc, opcode, sig)) {
return false;
}
// signature长度是64
if (opcode != 64) {
return false;
}
CHashWriter ss(SER_GETHASH, PROTOCOL_VERSION);
for(auto vout : coinbase.vout) {
ss << vout;
}
auto hash = ss.GetHash();
return params.GetConsensus().authorizationKey.Verify(hash, sig);
}
|
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|
import numpy as np
from pyKriging.krige import kriging
class MyKriging(kriging):
def __init__(self,*args,**kwargs):
kriging.__init__(self,*args,**kwargs)
def kdata(self):
# Create a set of data to plot
plotgrid = 61
x = np.linspace(0, 1, num=plotgrid)
y = np.linspace(0, 1, num=plotgrid)
X, Y = np.meshgrid(x, y)
# Predict based on the optimized results
zs = np.array([self.predict([x,y]) for x,y in zip(np.ravel(X), np.ravel(Y))])
Z = zs.reshape(X.shape)
#Calculate errors
zse = np.array([self.predict_var([x,y]) for x,y in zip(np.ravel(X), np.ravel(Y))])
Ze = zse.reshape(X.shape)
#Sample point
spx = (self.X[:,0] * (self.normRange[0][1] - self.normRange[0][0])) + self.normRange[0][0]
spy = (self.X[:,1] * (self.normRange[1][1] - self.normRange[1][0])) + self.normRange[1][0]
return X,Y,Z,Ze,spx,spy
|
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|
import random
import numpy as np
import gym
import imageio # write env render to mp4
import datetime
from collections import deque
import tensorflow as tf
from tensorflow.keras import Sequential
from tensorflow.keras.layers import Dense, Input, Conv2D, Flatten
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.models import Model
'''
Original paper: https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf
- DQN model with Dense layers only
- Model input is changed to take current and n previous states where n = time_steps
- Multiple states are concatenated before given to the model
- Uses target model for more stable training
- More states was shown to have better performance for CartPole env
'''
class DQN:
def __init__(
self,
memory_cap=3000,
time_steps=3,
gamma=0.85,
epsilon=1.0,
epsilon_decay=0.995,
epsilon_min=0.01,
learning_rate=0.005,
batch_size=256,
tau=0.125
):
self.env = EnvDrones(map_size=50, drone_num=1, view_range=10, tree_num=30, human_num=1)
self.full_state_shape = self.env.full_state_shape
self.drones_shape = self.env.drones_shape
self.action_dim = self.env.action_dim
self.memory = deque(maxlen=memory_cap)
self.time_steps = time_steps
#self.stored_states = np.zeros((self.time_steps, self.drones_shape))
self.gamma = gamma # discount factor
self.epsilon = epsilon # amount of randomness in e-greedy policy
self.epsilon_min = epsilon_min
self.epsilon_decay = epsilon_decay # exponential decay
self.learning_rate = learning_rate
self.batch_size = batch_size
self.tau = tau # target model update
self.model = self.create_model()
self.target_model = self.create_model()
self.target_model.trainable = False
self.target_model.set_weights(self.model.get_weights())
self.optimizer = tf.keras.optimizers.Adam(lr=self.learning_rate)
self.summaries = {}
def create_model(self):
input1 = Input(shape=self.drones_shape)
input2 = Input(shape=self.full_state_shape)
conv1 = Conv2D(16, kernel_size=[4, 4], strides=[1, 1], activation='relu', padding="valid")(input1)
conv2 = Conv2D(16, kernel_size=[4, 4], strides=[1, 1], activation='relu', padding="valid")(input2)
f1 = Flatten()(conv1)
f2 = Flatten()(conv2)
f = tf.concat((f1, f2), axis=1)
hidden = Dense(64, activation="relu")(f)
hidden = Dense(32, activation="relu")(hidden)
q = Dense(self.env.action_dim)(hidden)
model = Model(inputs=[input1,input2], outputs=q)
return model
def update_states(self, new_state):
# move the oldest state to the end of array and replace with new state
self.stored_states = np.roll(self.stored_states, -1, axis=0)
self.stored_states[-1] = new_state
def act(self, drone_obs, states, test=False):
self.epsilon *= self.epsilon_decay
self.epsilon = max(self.epsilon_min, self.epsilon)
epsilon = 0.01 if test else self.epsilon # use epsilon = 0.01 when testing
q_values = self.model.predict([drone_obs, states])[0]
self.summaries['q_val'] = max(q_values)
if np.random.random() < epsilon:
return np.random.randint(0, self.action_dim) # sample random action
return np.argmax(q_values)
def remember(self, state, action, reward, new_state, done, all, all_):
self.memory.append([state, action, reward, new_state, done, all, all_])
def replay(self):
if len(self.memory) < self.batch_size:
return
samples = random.sample(self.memory, self.batch_size)
s = []
a = []
r = []
s_ = []
done = []
all_s = []
all_s_ = []
for sample in samples:
states, action, reward, new_states, d, all, all_ = sample
s.append(states)
a.append(action)
r.append(reward)
s_.append(new_states)
all_s.append(all)
all_s_.append(all_)
if d:
done.append([1])
else:
done.append([0])
done = np.asarray(done)
q_next = self.target_model([np.asarray(s_), np.asarray(all_s)])
q_target = r + self.gamma * (1 - done) * tf.reduce_max(q_next, axis=1, keepdims=True)
with tf.GradientTape() as tape:
q = self.model([np.asarray(s), np.asarray(all_s_)]) # (batch_size, s_shape*time_step)
q_eval = tf.gather(params=q, indices=np.asarray(a), axis=1, batch_dims=1)
td_error = q_target - q_eval
q_loss = tf.reduce_mean(tf.square(td_error))
grads = tape.gradient(q_loss, self.model.trainable_variables)
self.optimizer.apply_gradients(zip(grads, self.model.trainable_variables))
self.summaries['loss'] = q_loss
def target_update(self):
weights = self.model.get_weights()
target_weights = self.target_model.get_weights()
for i in range(len(target_weights)): # set tau% of target model to be new weights
target_weights[i] = weights[i] * self.tau + target_weights[i] * (1 - self.tau)
self.target_model.set_weights(target_weights)
def save_model(self, fn):
# save model to file, give file name with .h5 extension
self.model.save(fn)
def load_model(self, fn):
# load model from .h5 file
self.model = tf.keras.models.load_model(fn)
self.target_model = self.create_model()
self.target_model.set_weights(self.model.get_weights())
def train(self, max_episodes=1000, max_steps=100, save_freq=10):
current_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
train_log_dir = 'logs/DQN_basic_time_step{}/'.format(self.time_steps) + current_time
summary_writer = tf.summary.create_file_writer(train_log_dir)
episode = 0
epoch = 0
while episode < max_episodes:
self.env.reset()
done, steps, total_reward = False, 0, 0
cur_states = self.env.get_drones_obs()
all_s = self.env.get_full_obs()
while steps < max_steps:
action = self.act(cur_states, all_s) # model determine action, states taken from self.stored_states
reward, done = self.env.step(human_act_list=[np.random.randint(0,4) for i in range(self.env.human_num)], drone_act_list=[action]) # perform action on env
new_state = self.env.get_drones_obs()
all_s_ = self.env.get_full_obs()
self.remember(cur_states[0], [action], reward, new_state[0], done, all_s[0], all_s_[0]) # add to memory
cur_states = new_state
all_s = all_s_
self.replay() # iterates default (prediction) model through memory replay
if steps%10==0:
self.target_update() # iterates target model
total_reward += reward[0]
steps += 1
epoch += 1
if done:
#if episode % save_freq == 0: # save model every n episodes
#self.save_model("dqn_basic_episode{}_time_step{}.h5".format(episode, self.time_steps))
break
# Tensorboard update
with summary_writer.as_default():
if len(self.memory) > self.batch_size:
tf.summary.scalar('Stats/loss', self.summaries['loss'], step=epoch)
tf.summary.scalar('Stats/q_val', self.summaries['q_val'], step=epoch)
tf.summary.scalar('Main/step_reward', reward[0], step=epoch)
with summary_writer.as_default():
tf.summary.scalar('Main/episode_reward', total_reward, step=episode)
tf.summary.scalar('Main/episode_steps', steps, step=episode)
summary_writer.flush()
print("episode {}: steps:{} {} reward".format(episode, steps, total_reward))
episode += 1
self.save_model("./model/dqn_basic_final_episode{}_time_step{}.h5".format(episode, self.time_steps))
def test(self,max_episodes=300, max_steps=100):
self.load_model(fn="./model/dqn_basic_final_episode{}_time_step{}.h5".format(1,1))
episode = 0
while episode < max_episodes:
self.env.reset()
done, steps, total_reward = False, 0, 0
cur_states = self.env.get_drones_obs()
while steps < max_steps:
action = self.act(states=cur_states) # model determine action, states taken from self.stored_states
reward, done = self.env.drone_step(drone_act_list=[action]) # perform action on env
new_state = self.env.get_drones_obs()
cur_states = new_state
total_reward += reward[0]
steps += 1
if done:
# if episode % save_freq == 0: # save model every n episodes
# self.save_model("dqn_basic_episode{}_time_step{}.h5".format(episode, self.time_steps))
break
print("episode {}: steps:{} {} reward".format(episode, steps, total_reward))
episode += 1
from MAEnv.env_Drones.env_Drones import EnvDrones
if __name__ == "__main__":
dqn_agent = DQN()
# dqn_agent.load_model("basic_models/time_step4/dqn_basic_episode50_time_step4.h5")
# rewards = dqn_agent.test()
# print("Total rewards: ", rewards)
dqn_agent.train()
|
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|
from torchvision import datasets, transforms
from torch.utils.data import Dataset, DataLoader
import torch
import torchvision
import numpy as np
from PIL import ImageFilter, Image
from tqdm import tqdm
import pandas as pd
import random
from typing import Callable, Optional
import os
class ImageNetSubset(datasets.ImageFolder):
def __init__(
self,
root: str,
transform: Optional[Callable] = None,
indices = None
):
super(ImageNetSubset, self).__init__(root, transform=transform)
self.indices = indices
def __getitem__(self, index):
path, target = self.samples[self.indices[index]]
sample = self.loader(path)
if self.transform is not None:
sample = self.transform(sample)
return sample, target, self.indices[index]
def __len__(self):
return len(self.indices)
class CIFAR100Subset(Dataset):
def __init__(self, path, transform, indices):
self.cifar100 = datasets.CIFAR100(root=path,
download=True,
train=True,
transform=transform)
self.indices = indices
def __getitem__(self, index):
data, target = self.cifar100[self.indices[index]]
return data, target, self.indices[index]
def __len__(self):
return len(self.indices)
class CIFAR10Subset(Dataset):
def __init__(self, path, transform, indices):
self.cifar10 = datasets.CIFAR10(root=path,
download=True,
train=True,
transform=transform)
self.indices = indices
def __getitem__(self, index):
data, target = self.cifar10[self.indices[index]]
return data, target, self.indices[index]
def __len__(self):
return len(self.indices)
class LT_Dataset(Dataset):
def __init__(self, root, txt, transform=None, indices=None):
self.img_path = []
self.labels = []
self.transform = transform
self.indices = indices
with open(txt) as f:
for line in f:
self.img_path.append(os.path.join(root, line.split()[0]))
self.labels.append(int(line.split()[1]))
if self.indices is not None:
self.img_path = [self.img_path[i] for i in self.indices]
self.labels = [self.labels[i] for i in self.indices]
def __len__(self):
return len(self.labels)
def __getitem__(self, index):
path = self.img_path[index]
label = self.labels[index]
with open(path, 'rb') as f:
sample = Image.open(f).convert('RGB')
if self.transform is not None:
sample = self.transform(sample)
return sample, label, index
class TwoCropsTransform:
"""Take two random crops of one image as the query and key."""
def __init__(self, base_transform):
self.base_transform = base_transform
def __call__(self, x):
q = self.base_transform(x)
k = self.base_transform(x)
return [q, k]
class GaussianBlur(object):
"""Gaussian blur augmentation in SimCLR https://arxiv.org/abs/2002.05709"""
def __init__(self, sigma=[.1, 2.]):
self.sigma = sigma
def __call__(self, x):
sigma = random.uniform(self.sigma[0], self.sigma[1])
x = x.filter(ImageFilter.GaussianBlur(radius=sigma))
return x
class ImageFolderEx(datasets.ImageFolder) :
def __getitem__(self, index):
sample, target = super(ImageFolderEx, self).__getitem__(index)
return index, sample, target
|
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|
using Weiqi
import Weiqi: empty, black, white, magnitude, cb
# Chinese rules https://www.cs.cmu.edu/~wjh/go/rules/Chinese.html
abstract type Player end
struct Blackplayer <: Player end
struct Whiteplayer <: Player end
mutable struct NewPosition{T<:Player}
player::T
coords::Tuple{Int64, Int64}
stone::Stone
end
bp = Blackplayer()
wp = Whiteplayer()
function nextplayer(newpos)
if newpos.player == bp
nextplayer = wp
elseif newpos.player == wp
nextplayer = bp
else
nextplayer == bp
end
end
# incomplete testing
"If a `player` chooses `coords == [0,0]`, `player` passes and `nextplayer(np)` is called"
function pass(newpos)
if newpos.player == bp && newpos.coords == [0,0]
pass = Black
elseif newpos.player == wp && newpos.coords == [0,0]
pass = White
else
println("No passes")
end
end
"Lists all cardinal directions (empty or not) around a stone"
function neighbors(cb, row::Int64, col::Int64)
neighbor_list = Tuple{Int, Int}[]
if row != 1
push!(neighbor_list, (row-1, col))
end
if row != size(cb, 1)
push!(neighbor_list, (row+1, col))
end
if col != size(cb, 2)
push!(neighbor_list, (row, col+1))
end
if col != 1
push!(neighbor_list, (row, col-1))
end
neighbor_list
end
"Searches for empty cardinal directions (liberties) around a stone or group of stones"
function liberties(cb, row::Int64, col::Int64)
stone = cb.array[row, col]
checked = fill(false, size(cb)) # heap allocation
checked[row, col] = true # mark true for visited (row, col) (loop invariant)
open_set = [] # non-visited nodes
closed_set = [] # visited nodes
for neighbor ∈ neighbors(cb, row, col)
neighbor_row, neighbor_col = neighbor
if !checked[neighbor_row, neighbor_col] # if (row, col) not visited
if cb.array[neighbor_row, neighbor_col] == stone # if i equals arg
push!(open_set, neighbor)
elseif cb.array[neighbor_row, neighbor_col] == empty
push!(closed_set, neighbor) # liberties
end
checked[neighbor_row, neighbor_col] = true # loop invariant for correct termination
end
end
while !isempty(open_set)
coords = shift!(open_set) # check neighbors of this coordinate and do the same as above
end
closed_set # liberties
end
function removal end
function forbidden end
function gameover end
function winner end
|
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|
[STATEMENT]
lemma sig_red_tail_lt_rep_list: "sig_red sing_reg (\<prec>) F p q \<Longrightarrow> punit.lt (rep_list q) = punit.lt (rep_list p)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. sig_red sing_reg (\<prec>) F p q \<Longrightarrow> punit.lt (rep_list q) = punit.lt (rep_list p)
[PROOF STEP]
by (auto simp: sig_red_def intro: sig_red_single_tail_lt_rep_list)
|
{"llama_tokens": 150, "file": "Signature_Groebner_Signature_Groebner", "length": 1}
|
# Copyright 2018 Samuel Payne sam_payne@byu.edu
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import pandas as pd
import numpy as np
import os
import warnings
import datetime
from cptac.dataset import Dataset
from cptac.dataframe_tools import *
from cptac.exceptions import FailedReindexWarning, PublicationEmbargoWarning, ReindexMapError
class Harmonized(Dataset):
def __init__(self, no_internet, version, filter_type):
"""Load all of the mssmclinical dataframes as values in the self._data dict variable, with names as keys, and format them properly.
Parameters:
version (str, optional): The version number to load, or the string "latest" to just load the latest building. Default is "latest".
no_internet (bool, optional): Whether to skip the index update step because it requires an internet connection. This will be skipped automatically if there is no internet at all, but you may want to manually skip it if you have a spotty internet connection. Default is False.
"""
# Set some needed variables, and pass them to the parent Dataset class __init__ function
# This keeps a record of all versions that the code is equipped to handle. That way, if there's a new data release but they didn't update their package, it won't try to parse the new data version it isn't equipped to handle.
valid_versions = ["1.0"]
data_files = {
"1.0": [
"PanCan_Union_Maf_Broad_WashU.maf"
]
}
# Call the parent class __init__ function
super().__init__(cancer_type='harmonized', version=version, valid_versions=valid_versions, data_files=data_files, no_internet=no_internet) # changed 'mssmclinical' to cancer_type
# Load the data into dataframes in the self._data dict
loading_msg = f"Loading {self.get_cancer_type()} v{self.version()}"
for file_path in self._data_files_paths: # Loops through files variable
# Print a loading message. We add a dot every time, so the user knows it's not frozen.
loading_msg = loading_msg + "."
print(loading_msg, end='\r')
path_elements = file_path.split(os.sep) # Get a list of the levels of the path
file_name = path_elements[-1] # The last element will be the name of the file. We'll use this to identify files for parsing in the if/elif statements below
# Get tumor_code
tumor_codes = {'pancanbrca': 'BRCA', 'pancanccrcc':'CCRCC',
'pancanucec':'UCEC','pancangbm':'GBM','pancanhnscc':'HNSCC',
'pancanlscc': 'LSCC','pancanluad':'LUAD', 'pancanpdac':'PDAC',
'pancanhcc':'HCC','pancancoad':'COAD','pancanov':'OV'}
if file_name == "PanCan_Union_Maf_Broad_WashU.maf":
df = pd.read_csv(file_path, sep="\t", low_memory = False)
df = df.loc[df['COHORT'] == tumor_codes[filter_type]]
df['Patient_ID'] = df.loc[:, 'Tumor_Sample_Barcode']
df = df.rename(columns={
"Hugo_Symbol":"Gene",
"Variant_Classification":"Mutation",
"Protein_Change":"Location"})
df = df.set_index("Patient_ID")
df = df[ ['Gene'] + ["Mutation"] + ["Location"] + [ col for col in df.columns if col not in ["Gene","Mutation","Location"] ] ]
df.index = df.index.str.replace(r"_T", "", regex=True) # data based on Tumor and Normal. Remove _T
self._data["somatic_mutation"] = df
print(' ' * len(loading_msg), end='\r') # Erase the loading message
formatting_msg = f"Formatting {self.get_cancer_type()} dataframes..."
print(formatting_msg, end='\r')
self._data = sort_all_rows_pancan(self._data) # Sort IDs (tumor first then normal)
'''
if filter_type == 'pancanucec':
print("True")
mut_df = self._data["somatic_mutation"]
mut_df = mut_df.loc[mut_df.index[~ mut_df.index.str.contains('NX', regex = True)]] # Drop quality control
self._data["somatic_mutation"] = mut_df
'''
print(" " * len(formatting_msg), end='\r') # Erase the formatting message
|
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|
# Mecánica con SymPy
_Si SymPy te ha parecido hasta ahora un CAS decente e incluso interesante (nada como tener los resultados en $\LaTeX$ incrustados en el notebook y la sintaxis de Python para hacer cálculo simbólico) entonces espera a ver el paquete `mechanics`. Con él, podremos manipular velocidades y aceleraciones de sólidos expresadas en distintos sistemas de referencia con una facilidad impresionante._
_Tienes disponible la documentación de `mechanics` en http://docs.sympy.org/0.7.5/modules/physics/mechanics/index.html._
## Sistemas de referencia
El objeto primordial que vamos a manejar van a ser los sistemas de referencia. Podremos definir relaciones geométricas entre ellos y de esta forma las transformaciones de vectores entre un sistema y otro serán triviales.
La manera usual de empezar a trabajar con SymPy es importar la función `init_session`:
```
from sympy import init_session
init_session(use_latex=True)```
Esta función ya se encarga de importar todas las funciones básicas y preparar las salidas gráficas. Sin embargo, en este momento, esta función se encuentra en mantenimiento para su uso dentro de los notebooks por lo que activaremos la salida gráfica e importaremos las funciones de la manera usual. Puedes consultar el estado de la corrección en: https://github.com/sympy/sympy/pull/13300 y https://github.com/sympy/sympy/issues/13319 .
```python
from sympy import *
init_printing(use_latex='mathjax')
```
```python
from sympy import symbols
```
Todo lo que necesitamos está en `sympy.physics.mechanics`, incluyendo la clase `ReferenceFrame`. Nada más crear un sistema de referencia podemos acceder a sus versores unitarios: `x`, `y` y `z`.
http://docs.sympy.org/0.7.5/modules/physics/vector/vectors.html
```python
from sympy.physics.mechanics import ReferenceFrame
```
```python
A = ReferenceFrame("A")
A.x
```
$\displaystyle \mathbf{\hat{a}_x}$
Y para definir vectores solo tenemos que **multiplicar cada componente por su versor**:
```python
2 * A.x - 1 * A.y
```
$\displaystyle 2\mathbf{\hat{a}_x} - \mathbf{\hat{a}_y}$
De ahora en adelante, para trabajar como si nos enfrentáramos a un problema de la escuela, vamos a hacer dos cosas:
* Definir un sistema inercial $1$ del que partir, para así poder referir todos los demás sistemas a él.
* Que los versores de ese sistema sean $i, j, k$.
```python
A = ReferenceFrame("1", latexs=['\mathbf{i}', '\mathbf{j}', '\mathbf{k}'])
A.x + A.y + A.z
```
$\displaystyle \mathbf{i} + \mathbf{j} + \mathbf{k}$
Y para no tener que hacerlo siempre, un pequeño truco de magia:
```python
# Definimos nuestra propia clase para que los versores sean IJK
# aeropython: preserve
class IJKReferenceFrame(ReferenceFrame):
def __init__(self, name):
super().__init__(name, latexs=['\mathbf{%s}_{%s}' % (idx, name) for idx in ("i", "j", "k")])
self.i = self.x
self.j = self.y
self.k = self.z
```
```python
A = IJKReferenceFrame("1")
A.i + A.j + A.k
```
$\displaystyle \mathbf{i}_{1} + \mathbf{j}_{1} + \mathbf{k}_{1}$
### Álgebra vectorial
Nuestros vectores funcionan también con símbolos, y podemos realizar las operaciones de producto escalar y producto vectorial con ellos.
```python
R, V = symbols('R, V', positive=True)
r1 = R * (A.x + A.y + A.z)
v1 = V * (A.x - 2 * A.z)
```
```python
r1
```
$\displaystyle R\mathbf{i}_{1} + R\mathbf{j}_{1} + R\mathbf{k}_{1}$
```python
v1
```
$\displaystyle V\mathbf{i}_{1} - 2 V\mathbf{k}_{1}$
```python
from sympy.physics.mechanics import dot, cross
```
```python
# All these are ways to carry out a dot prduct between two vectors
r1.dot(v1)
dot(r1, v1)
r1 & v1
```
$\displaystyle - R V$
```python
# All these are ways to carry out a cross prduct between two vectors
r1.cross(v1)
cross(r1, v1)
r1 ^ v1
```
$\displaystyle - 2 R V\mathbf{i}_{1} + 3 R V\mathbf{j}_{1} - R V\mathbf{k}_{1}$
Podemos hallar también la norma de los vectores con su método `magnitude` e incluso normalizarlos con `normalize`:
```python
(r1 ^ v1).magnitude()
cross(r1, v1).magnitude()
```
$\displaystyle \sqrt{14} R V$
```python
(r1 ^ v1).normalize()
```
$\displaystyle - \frac{\sqrt{14}}{7}\mathbf{i}_{1} + \frac{3 \sqrt{14}}{14}\mathbf{j}_{1} - \frac{\sqrt{14}}{14}\mathbf{k}_{1}$
##### Ejercicio
Usando directamente la fórmula para la derivada en ejes móviles:
$$\left(\frac{\operatorname{d}\!\mathbf{a}}{\operatorname{d}\!t}\right)_1 = \left(\frac{\operatorname{d}\!\mathbf{a}}{\operatorname{d}\!t}\right)_0 + \mathbf{\omega}_{01}\! \times \mathbf{a}$$
Calcula la derivada del vector de posición $R \mathbf{i}_0$, siendo $A_0$ un sistema de referencia que gira respecto al inercial con velocidad angular $\mathbf{\omega}_{01}=\Omega \mathbf{k}_0$. **¿Cuál es el módulo de la derivada?**
```python
R, Omega = symbols('R, Omega', positive=True)
A0 = IJKReferenceFrame('0')
```
```python
a = R * A0.i
a
```
$\displaystyle R\mathbf{i}_{0}$
```python
omega01 = Omega * A0.k
omega01
```
$\displaystyle \Omega\mathbf{k}_{0}$
```python
da = omega01 ^ a # Cross product between omega01 and a
da
```
$\displaystyle \Omega R\mathbf{j}_{0}$
```python
da.magnitude()
```
$\displaystyle \Omega R$
<div class="alert alert-warning">Si no especificaste `positive=True` vas a ver algo como $\sqrt{\Omega^2 R^2}$. Debería haber una forma de simplificar esta expresión _a posteriori_, pero de momento no funciona del todo bien. Preparando este notebook nos hemos dado cuenta y ya les hemos avisado :) https://github.com/sympy/sympy/issues/8326
</div>
### Movimiento relativo
¿A quién no le gusta multiplicar matrices de rotación? Para esa minoría que lo detesta, existe SymPy. Para ello debemos especificar la orientación de nuestros sistemas de referencia usando el método `orient`, y recuperaremos la matriz de cosenos directores usando el método `dcm`.
```python
A1 = IJKReferenceFrame("1")
A0 = IJKReferenceFrame("0")
```
```python
phi = symbols('phi')
A0.orient(A1, 'Axis', [phi, A1.z]) # Rotación phi alrededor del eje A1.z
A0.dcm(A1) # "Direct Cosine Matrix"
```
$\displaystyle \left[\begin{matrix}\cos{\left(\phi \right)} & \sin{\left(\phi \right)} & 0\\- \sin{\left(\phi \right)} & \cos{\left(\phi \right)} & 0\\0 & 0 & 1\end{matrix}\right]$
Usando el argumento `Axis` hemos especificado que rotamos el sistema un ángulo especificado alrededor de un eje. Otros métodos son:
* `Body`: se especifican los tres ángulos de Euler.
* `Space`: igual que `Body`, pero las rotaciones se aplican en orden inverso.
* `Quaternion`: utilizando cuaternios, rotación alrededor de un vector unitario $\lambda$ una cantidad $\theta$.
<div class="alert alert-success">¿Qué es lo bueno de usar uno de estos métodos? ¡Que **siempre** tenemos la transformación bien definida! Es imposible meter "a capón" una matriz de rotación que sea incorrecta o absurda.</div>
#### Diferente sistema de referencia
Para expresar un vector en otro sistema de referencia, no hay más que usar los métodos `express` o `to_matrix`:
```python
A0.x.express(A1)
```
$\displaystyle \cos{\left(\phi \right)}\mathbf{i}_{1} + \sin{\left(\phi \right)}\mathbf{j}_{1}$
```python
A0.x.to_matrix(A1)
```
$\displaystyle \left[\begin{matrix}\cos{\left(\phi \right)}\\\sin{\left(\phi \right)}\\0\end{matrix}\right]$
```python
Matrix([ [A0.x.to_matrix(A1)], [A0.y.to_matrix(A1)],[A0.z.to_matrix(A1)] ])
```
$\displaystyle \left[\begin{matrix}\cos{\left(\phi \right)}\\\sin{\left(\phi \right)}\\0\\- \sin{\left(\phi \right)}\\\cos{\left(\phi \right)}\\0\\0\\0\\1\end{matrix}\right]$
#### Símbolos dinámicos
Si queremos especificar que un símbolo puede variar con el tiempo, hay que usar la función `dynamicsymbols`:
```python
from sympy.physics.mechanics import dynamicsymbols
```
```python
alpha = dynamicsymbols('alpha')
alpha
```
$\displaystyle \alpha{\left(t \right)}$
Y pedir su derivada con el método `diff`:
```python
alpha.diff()
```
$\displaystyle \frac{d}{d t} \alpha{\left(t \right)}$
##### Ejercicio
(Sacado de Cuerva et al. "Teoría de los Helicópteros")
**Obtener la matriz de rotación de la pala $B$ respecto a los ejes $A1$.**
```python
A = IJKReferenceFrame("A")
```
```python
A1 = IJKReferenceFrame("A1")
psi = dynamicsymbols('psi')
A1.orient(A, 'Axis', [psi, A.z])
A1.dcm(A) # T_{A1A}
```
$\displaystyle \left[\begin{matrix}\cos{\left(\psi{\left(t \right)} \right)} & \sin{\left(\psi{\left(t \right)} \right)} & 0\\- \sin{\left(\psi{\left(t \right)} \right)} & \cos{\left(\psi{\left(t \right)} \right)} & 0\\0 & 0 & 1\end{matrix}\right]$
```python
A2 = IJKReferenceFrame("A2")
beta = dynamicsymbols('beta')
A2.orient(A1, 'Axis', [beta, -A1.y])
A2.dcm(A1) # T_{A2A1}
```
$\displaystyle \left[\begin{matrix}\cos{\left(\beta{\left(t \right)} \right)} & 0 & \sin{\left(\beta{\left(t \right)} \right)}\\0 & 1 & 0\\- \sin{\left(\beta{\left(t \right)} \right)} & 0 & \cos{\left(\beta{\left(t \right)} \right)}\end{matrix}\right]$
```python
A3 = IJKReferenceFrame("A3")
zeta = dynamicsymbols('zeta')
A3.orient(A2, 'Axis', [zeta, A2.z])
A3.dcm(A1) # T_{A3A1}
```
$\displaystyle \left[\begin{matrix}\cos{\left(\beta{\left(t \right)} \right)} \cos{\left(\zeta{\left(t \right)} \right)} & \sin{\left(\zeta{\left(t \right)} \right)} & \sin{\left(\beta{\left(t \right)} \right)} \cos{\left(\zeta{\left(t \right)} \right)}\\- \sin{\left(\zeta{\left(t \right)} \right)} \cos{\left(\beta{\left(t \right)} \right)} & \cos{\left(\zeta{\left(t \right)} \right)} & - \sin{\left(\beta{\left(t \right)} \right)} \sin{\left(\zeta{\left(t \right)} \right)}\\- \sin{\left(\beta{\left(t \right)} \right)} & 0 & \cos{\left(\beta{\left(t \right)} \right)}\end{matrix}\right]$
```python
B = IJKReferenceFrame("B")
theta = dynamicsymbols('theta')
B.orient(A3, 'Axis', [theta, A3.x])
B.dcm(A3) # T_{BA3}
```
$\displaystyle \left[\begin{matrix}1 & 0 & 0\\0 & \cos{\left(\theta{\left(t \right)} \right)} & \sin{\left(\theta{\left(t \right)} \right)}\\0 & - \sin{\left(\theta{\left(t \right)} \right)} & \cos{\left(\theta{\left(t \right)} \right)}\end{matrix}\right]$
```python
B.dcm(A2)
```
$\displaystyle \left[\begin{matrix}\cos{\left(\zeta{\left(t \right)} \right)} & \sin{\left(\zeta{\left(t \right)} \right)} & 0\\- \sin{\left(\zeta{\left(t \right)} \right)} \cos{\left(\theta{\left(t \right)} \right)} & \cos{\left(\theta{\left(t \right)} \right)} \cos{\left(\zeta{\left(t \right)} \right)} & \sin{\left(\theta{\left(t \right)} \right)}\\\sin{\left(\theta{\left(t \right)} \right)} \sin{\left(\zeta{\left(t \right)} \right)} & - \sin{\left(\theta{\left(t \right)} \right)} \cos{\left(\zeta{\left(t \right)} \right)} & \cos{\left(\theta{\left(t \right)} \right)}\end{matrix}\right]$
```python
B.dcm(A1)
```
$\displaystyle \left[\begin{matrix}\cos{\left(\beta{\left(t \right)} \right)} \cos{\left(\zeta{\left(t \right)} \right)} & \sin{\left(\zeta{\left(t \right)} \right)} & \sin{\left(\beta{\left(t \right)} \right)} \cos{\left(\zeta{\left(t \right)} \right)}\\- \sin{\left(\beta{\left(t \right)} \right)} \sin{\left(\theta{\left(t \right)} \right)} - \sin{\left(\zeta{\left(t \right)} \right)} \cos{\left(\beta{\left(t \right)} \right)} \cos{\left(\theta{\left(t \right)} \right)} & \cos{\left(\theta{\left(t \right)} \right)} \cos{\left(\zeta{\left(t \right)} \right)} & - \sin{\left(\beta{\left(t \right)} \right)} \sin{\left(\zeta{\left(t \right)} \right)} \cos{\left(\theta{\left(t \right)} \right)} + \sin{\left(\theta{\left(t \right)} \right)} \cos{\left(\beta{\left(t \right)} \right)}\\- \sin{\left(\beta{\left(t \right)} \right)} \cos{\left(\theta{\left(t \right)} \right)} + \sin{\left(\theta{\left(t \right)} \right)} \sin{\left(\zeta{\left(t \right)} \right)} \cos{\left(\beta{\left(t \right)} \right)} & - \sin{\left(\theta{\left(t \right)} \right)} \cos{\left(\zeta{\left(t \right)} \right)} & \sin{\left(\beta{\left(t \right)} \right)} \sin{\left(\theta{\left(t \right)} \right)} \sin{\left(\zeta{\left(t \right)} \right)} + \cos{\left(\beta{\left(t \right)} \right)} \cos{\left(\theta{\left(t \right)} \right)}\end{matrix}\right]$
#### Velocidad angular
También podemos hallar la velocidad angular de un sistema respecto a otro usando el método `ang_vel_in`:
```python
B.ang_vel_in(A2)
```
$\displaystyle \frac{d}{d t} \theta{\left(t \right)}\mathbf{i}_{A3} + \frac{d}{d t} \zeta{\left(t \right)}\mathbf{k}_{A2}$
```python
B.ang_vel_in(A)
```
$\displaystyle \frac{d}{d t} \theta{\left(t \right)}\mathbf{i}_{A3} + \frac{d}{d t} \zeta{\left(t \right)}\mathbf{k}_{A2} - \frac{d}{d t} \beta{\left(t \right)}\mathbf{j}_{A1} + \frac{d}{d t} \psi{\left(t \right)}\mathbf{k}_{A}$
```python
B.ang_vel_in(A).express(A)
```
$\displaystyle (\left(- \sin{\left(\psi{\left(t \right)} \right)} \sin{\left(\zeta{\left(t \right)} \right)} + \cos{\left(\beta{\left(t \right)} \right)} \cos{\left(\psi{\left(t \right)} \right)} \cos{\left(\zeta{\left(t \right)} \right)}\right) \frac{d}{d t} \theta{\left(t \right)} - \sin{\left(\beta{\left(t \right)} \right)} \cos{\left(\psi{\left(t \right)} \right)} \frac{d}{d t} \zeta{\left(t \right)} + \sin{\left(\psi{\left(t \right)} \right)} \frac{d}{d t} \beta{\left(t \right)})\mathbf{i}_{A} + (\left(\sin{\left(\psi{\left(t \right)} \right)} \cos{\left(\beta{\left(t \right)} \right)} \cos{\left(\zeta{\left(t \right)} \right)} + \sin{\left(\zeta{\left(t \right)} \right)} \cos{\left(\psi{\left(t \right)} \right)}\right) \frac{d}{d t} \theta{\left(t \right)} - \sin{\left(\beta{\left(t \right)} \right)} \sin{\left(\psi{\left(t \right)} \right)} \frac{d}{d t} \zeta{\left(t \right)} - \cos{\left(\psi{\left(t \right)} \right)} \frac{d}{d t} \beta{\left(t \right)})\mathbf{j}_{A} + (\sin{\left(\beta{\left(t \right)} \right)} \cos{\left(\zeta{\left(t \right)} \right)} \frac{d}{d t} \theta{\left(t \right)} + \cos{\left(\beta{\left(t \right)} \right)} \frac{d}{d t} \zeta{\left(t \right)} + \frac{d}{d t} \psi{\left(t \right)})\mathbf{k}_{A}$
En ocasiones, la representación gráfica puede fallar, pero se puede volver a desactivar y activar llamando a la función`init_printing(pretty_print=True)` con diferentes valores (True/False) para `pretty_print`
### Derivada en ejes móviles
Hacer una derivada con la fórmula lo hace cualquiera, pero SymPy puede encargarse automáticamente.
```python
v1 = A1.x
v1
```
$\displaystyle \mathbf{i}_{A1}$
```python
#v1.diff(dynamicsymbols._t, A2)
dv1 = v1.diff(symbols('t'), A)
dv1
```
$\displaystyle (\sin^{2}{\left(\psi{\left(t \right)} \right)} \frac{d}{d t} \psi{\left(t \right)} + \cos^{2}{\left(\psi{\left(t \right)} \right)} \frac{d}{d t} \psi{\left(t \right)})\mathbf{j}_{A1}$
```python
dv1.to_matrix(A1)
```
$\displaystyle \left[\begin{matrix}0\\\sin^{2}{\left(\psi{\left(t \right)} \right)} \frac{d}{d t} \psi{\left(t \right)} + \cos^{2}{\left(\psi{\left(t \right)} \right)} \frac{d}{d t} \psi{\left(t \right)}\\0\end{matrix}\right]$
```python
(dv1 & A1.j).simplify() # dv1 & A1.j is the dot produt
```
$\displaystyle \frac{d}{d t} \psi{\left(t \right)}$
### Puntos, velocidades y la rueda que no desliza
El último paso que nos queda para completar la cinemática es la posibilidad de definir puntos en sólidos y aplicar su campo de velocidades. SymPy también permite esto, y para ello no tenemos más que importar la clase `Point`.
```python
from sympy.physics.mechanics import Point
```
```python
O = Point("O")
```
Para trabajar como lo haríamos en la escuela, vamos a especificar que $O$ es el origen de $A$, y para eso vamos a imponer que su velocidad es cero con el método `set_vel`:
```python
O.set_vel(A, 0)
```
Para definir nuevos puntos, podemos utilizar el método `locate_new`:
```python
e_b = symbols('e_b')
E_b = O.locatenew('E_b', e_b * A1.x)
```
Y para obtener vectores de un punto a otro, el método `pos_from`:
```python
E_b.pos_from(O)
```
$\displaystyle e_{b}\mathbf{i}_{A1}$
<div class="alert alert-info">La notación de este paquete está influenciada por el libro Kane, T. R. & Levinson, D. A. "Dynamics, Theory and Applications". Es ligeramente distinto a como estudiamos nosotros en la escuela, pero ¡están abiertos a que les hagamos cualquier tipo de sugerencia! https://github.com/sympy/sympy/issues/2584#issuecomment-31552654</div>
Por último, el **campo de velocidades de un sólido rígido** se formula usando el método `v2pt_theory`.
$$v^P_A = v^O_A + \omega_{A_1 A} \times \mathbf{OP}$$
Este método pertenece *al punto del cual queremos conocer la velocidad* y recibe tres parámetros:
* `O`, punto de velocidad conocida respecto a A
* `A`, sistema de referencia donde queremos calcular la velocidad
* `A1`, sistema de referencia donde están fijos ambos puntos (_sistema de arrastre_)
Por tanto, para hallar la velocidad del punto que acabamos de crear:
```python
E_b.v2pt_theory(O, A, A1)
```
$\displaystyle e_{b} \frac{d}{d t} \psi{\left(t \right)}\mathbf{j}_{A1}$
##### Ejercicio
(Apuntes de Óscar López Rebollal)
**¡Halla la velocidad y la aceleración de $P$!**
```python
# Creamos nuestros sistemas de referencia
A1 = IJKReferenceFrame('1')
A0 = IJKReferenceFrame('0')
A2 = IJKReferenceFrame('2')
```
```python
# Creamos los símbolos dinámicos necesarios
xi, theta = dynamicsymbols('xi, theta')
xi, theta
```
$\displaystyle \left( \xi{\left(t \right)}, \ \theta{\left(t \right)}\right)$
```python
# Orientamos los sistemas de referencia
A0.orient(A1, 'Axis', [0, A1.k]) # A0 no gira respecto a A1
A2.orient(A0, 'Axis', [theta, A0.k])
```
```python
A2.dcm(A1)
```
$\displaystyle \left[\begin{matrix}\cos{\left(\theta{\left(t \right)} \right)} & \sin{\left(\theta{\left(t \right)} \right)} & 0\\- \sin{\left(\theta{\left(t \right)} \right)} & \cos{\left(\theta{\left(t \right)} \right)} & 0\\0 & 0 & 1\end{matrix}\right]$
```python
# Creamos el punto C, centro del disco, y especificamos su velocidad
# respecto a A1
C = Point('C')
C.set_vel(A1, xi.diff() * A1.x)
```
```python
# Localizamos el punto P, punto fijo del disco, respecto a C, en
# el sistema A2 (que gira solidariamente con el disco)
R = symbols('R')
P = C.locatenew('P', -R * A2.j)
P.pos_from(C)
```
$\displaystyle - R\mathbf{j}_{2}$
```python
# Hallamos la velocidad de P en A1, expresada en A0
# ¡Con esta llamada ya estamos diciendo que C y P son fijos en A2!
P.v2pt_theory(C, A1, A2).express(A0)
```
$\displaystyle (R \cos{\left(\theta{\left(t \right)} \right)} \frac{d}{d t} \theta{\left(t \right)} + \frac{d}{d t} \xi{\left(t \right)})\mathbf{i}_{0} + R \sin{\left(\theta{\left(t \right)} \right)} \frac{d}{d t} \theta{\left(t \right)}\mathbf{j}_{0}$
**Misión cumplida :)**
---
_Hemos hecho un repaso bastante profundo de las posibilidades del paquete `mechanics` de SymPy. Nos hemos dejado algunas cosas en el tintero pero no demasiadas: esta funcionalidad aún se está expandiendo y necesita pulir algunos detalles._
**Referencias**
* Capítulo de **aeromecánica** del libro de Cuerva y otros http://nbviewer.ipython.org/gist/Juanlu001/7711865
* Estabilidad longitudinal de un Boeing 747 http://nbviewer.ipython.org/github/AlexS12/Mecanica_Vuelo/blob/master/MVII_MatrizSistema.ipynb
_¿Serás tú el siguiente que publique un notebook usando SymPy? ;)_
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#### <h4 align="right">¡Síguenos en Twitter!
###### <a href="https://twitter.com/Pybonacci" class="twitter-follow-button" data-show-count="false">Follow @Pybonacci</a> <a href="https://twitter.com/Alex__S12" class="twitter-follow-button" data-show-count="false" align="right";>Follow @Alex__S12</a> <a href="https://twitter.com/newlawrence" class="twitter-follow-button" data-show-count="false" align="right";>Follow @newlawrence</a>
##### <a rel="license" href="http://creativecommons.org/licenses/by/4.0/deed.es"></a><br /><span xmlns:dct="http://purl.org/dc/terms/" property="dct:title">Curso AeroPython</span> por <span xmlns:cc="http://creativecommons.org/ns#" property="cc:attributionName">Juan Luis Cano Rodriguez y Alejandro Sáez Mollejo</span> se distribuye bajo una <a rel="license" href="http://creativecommons.org/licenses/by/4.0/deed.es">Licencia Creative Commons Atribución 4.0 Internacional</a>.
#####
---
_Las siguientes celdas contienen configuración del Notebook_
_Para visualizar y utlizar los enlaces a Twitter el notebook debe ejecutarse como [seguro](http://ipython.org/ipython-doc/dev/notebook/security.html)_
File > Trusted Notebook
```python
%%html
<a href="https://twitter.com/Pybonacci" class="twitter-follow-button" data-show-count="false">Follow @Pybonacci</a>
```
<a href="https://twitter.com/Pybonacci" class="twitter-follow-button" data-show-count="false">Follow @Pybonacci</a>
```python
# Esta celda da el estilo al notebook
from IPython.core.display import HTML
css_file = '../styles/aeropython.css'
HTML(open(css_file, "r").read())
```
/* This template is inspired in the one used by Lorena Barba
in the numerical-mooc repository: https://github.com/numerical-mooc/numerical-mooc
We thank her work and hope you also enjoy the look of the notobooks with this style */
<link href='http://fonts.googleapis.com/css?family=Source+Sans+Pro|Josefin+Sans:400,700,400italic|Ubuntu+Condensed' rel='stylesheet' type='text/css'>
El estilo se ha aplicado =)
<style>
#notebook_panel { /* main background */
background: #f7f7f7;
}
div.cell { /* set cell width */
width: 900px;
}
div #notebook { /* centre the content */
background: #fff; /* white background for content */
width: 950px;
margin: auto;
padding-left: 0em;
}
#notebook li { /* More space between bullet points */
margin-top:0.7em;
}
/* draw border around running cells */
div.cell.border-box-sizing.code_cell.running {
border: 1px solid #111;
}
/* Put a solid color box around each cell and its output, visually linking them*/
div.cell.code_cell {
font-family: 'Source Sans Pro', sans-serif;
background-color: rgb(256,256,256);
font-size: 110%;
border-radius: 0px;
padding: 0.5em;
margin-left:1em;
margin-top: 1em;
}
div.text_cell_render{
font-family: 'Josefin Sans', serif;
line-height: 145%;
font-size: 125%;
font-weight: 500;
width:750px;
margin-left:auto;
margin-right:auto;
}
/* Formatting for header cells */
.text_cell_render h1, .text_cell_render h2, .text_cell_render h3,
.text_cell_render h4, .text_cell_render h5 {
font-family: 'Ubuntu Condensed', sans-serif;
}
/*
.text_cell_render h1 {
font-family: Flux, 'Ubuntu Condensed', serif;
font-style:regular;
font-weight: 400;
font-size: 30pt;
text-align: center;
line-height: 100%;
color: #335082;
margin-bottom: 0.5em;
margin-top: 0.5em;
display: block;
}
*/
.text_cell_render h1 {
font-weight: 600;
font-size: 35pt;
line-height: 100%;
color: #000000;
margin-bottom: 0.1em;
margin-top: 0.3em;
display: block;
}
.text_cell_render h2 {
margin-top:16px;
font-size: 27pt;
font-weight: 550;
margin-bottom: 0.1em;
margin-top: 0.3em;
font-style: regular;
color: #2c6391;
}
.text_cell_render h3 {
font-size: 20pt;
font-weight: 550
text-align: left;
margin-bottom: 0.1em;
margin-top: 0.3em;
font-style: regular;
color: #387eb8;
}
.text_cell_render h4 { /*Use this for captions*/
font-size: 18pt;
font-weight: 450
text-align: left;
margin-bottom: 0.1em;
margin-top: 0.3em;
font-style: regular;
color: #5797cc;
}
.text_cell_render h5 { /*Use this for small titles*/
font-size: 18pt;
font-weight: 550;
color: rgb(163,0,0);
font-style: italic;
margin-bottom: .1em;
margin-top: 0.8em;
display: block;
color: #b21c0d;
}
.text_cell_render h6 { /*use this for copyright note*/
font-family: 'Ubuntu Condensed', sans-serif;
font-weight: 300;
font-size: 14pt;
line-height: 100%;
color: #252525;
text-align: right;
margin-bottom: 1px;
margin-top: 1px;
}
.CodeMirror{
font-family: 'Duru Sans', sans-serif;
font-size: 100%;
}
</style>
|
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|
import pandas as pd
import numpy as np
import os
import datetime
from typing import Any, Dict, Optional, Union, Dict, List, Callable
import warnings
import logging
import copy
from qualipy.backends.pandas_backend.generator import BackendPandas
from qualipy.backends.sql_backend.generator import BackendSQL
from qualipy.exceptions import FailException, NullableError
from qualipy.project import Project
from qualipy.util import setup_logging
from qualipy.backends.base import MetricResult
try:
from qualipy.backends.spark_backend.generator import BackendSpark
except Exception as e:
BackendSpark = None
# supress numpy future warning for now
warnings.simplefilter(action="ignore", category=FutureWarning)
HOME = os.path.expanduser("~")
GENERATORS = {"pandas": BackendPandas, "spark": BackendSpark, "sql": BackendSQL}
# types
Measure = List[Dict[str, Any]]
# TODO: dont really need this method now
def _create_value(
value: Any,
metric: str,
name: str,
date: datetime.datetime,
type: str,
return_format: str,
run_name,
):
metric_res = MetricResult(
value=value,
metric=metric,
date=date,
column_name=name,
return_format=return_format,
type=type,
run_name=run_name,
)
return metric_res
class Qualipy(object):
"""
This is the main entrypoint to Qualipy. This is the object that will actually
execute on your data.
"""
def __init__(
self,
project: Project,
backend: str = "pandas",
time_of_run: Optional[datetime.datetime] = None,
batch_name: str = None,
overwrite_arguments: dict = None,
):
"""
Args:
project: Your defined qualipy.Project
backend: Can be either "pandas", "sql", or "spark" depending on what kind
of data you are tracking
time_of_run: If None, this will be the current datetime. Note, this is very important
for analysis, as time_of_run is essentially your x_axis in all time series analysis.
Being able to set it to a specific date can be useful when generating retrospective
statistics.
batch_name: Useful for comparing specific time points by name during analysis. By default it will
take the time_of_run as batch_name
"""
self.project = project
self.time_of_run = (
datetime.datetime.now() if time_of_run is None else time_of_run
)
self.batch_name = batch_name if batch_name is not None else self.time_of_run
self.current_data = None
self.total_measures = []
self.generator = GENERATORS[backend](project.config_dir)
self.chunk = False
self.run_n = 0
self.schema = {}
self.from_step = None
self.stratify = False
self.backend = backend
self.overwrite_arguments = overwrite_arguments
self._setup_logger()
self.logger = logging.getLogger(__name__)
self.logger.info(f"Working on batch {self.batch_name}")
def run(self, autocommit: bool = False, profile_batch=False) -> None:
"""The method that runs the execution
Note: You must first set a dataset using either ``set_dataset`` or
``set_chunked_dataset``
Args:
autocommit: If set to True, qualipy will automatically write to it's backend. If set
to False, the user will have to manually run the ``commit`` function.
profile_batch: If set to True, Qualipy will generate metadata used to construct
a batch report by using the ``produce_batch_report`` CLI command.
Returns:
None
"""
if not self.chunk:
self._run_with_optional_stratify(autocommit, profile_batch=profile_batch)
self.run_n += 1
else:
if profile_batch:
raise Exception("Can only profile batch without chunking data")
for chunk in self.time_chunks:
self.logger.info(f"Running on chunk: {chunk['batch_name']}")
self.current_data = chunk["chunk"]
if self.current_data.shape[0] == 0:
self.current_data = self.fallback_data
if self.current_data is not None:
self.batch_name = str(chunk["batch_name"])
self.time_of_run = chunk["batch_name"]
self._run_with_optional_stratify(autocommit)
def _run_with_optional_stratify(self, autocommit, profile_batch=False):
if self.stratify:
self.original_data = self.current_data.copy()
self.original_name = self.current_name
for stratify_value in self.stratify_values:
self.current_data = self.stratify_function(
self.current_data, stratify_value
)
self.current_name = f"{self.current_name}_{stratify_value}"
self._generate_metrics(
autocommit=autocommit, profile_batch=profile_batch
)
# turn back name and data
self.current_name = self.original_name
self.current_data = self.original_data
else:
self._generate_metrics(autocommit=autocommit, profile_batch=profile_batch)
def _setup_logger(self):
setup_logging()
def set_dataset(
self, df, columns: Optional[List[str]] = None, run_name: str = None
) -> None:
"""This specified the exact subset of data you want to run on.
Use this method when you don't have all of the data (a live process) and want
to only run on one batch of data.
Args:
df: Can be either PandasData, SQLData, or SparkData
columns: If you don't want to run all mappings on this specific subset
of data, you can specify just the columns you want to run. Note - this
corresponds to the ``name`` argument when adding a column to a project
run_name: If you're running metrics from a project on many different subsets any
iterations of the data, you might want to give each specific subset a
name. This is especially necessary when running aggregates on a column
where the column name itself stays the same, but the meaning changes based
on the subset. By default, this will take the value of '0'
Returns:
None
"""
# NOTE: if sqldata but pandas backend, should pull data and work on that!
# also give option of query or taking last x rows
self._set_data(df, allowed_dataclasses=["SQLData", "PandasData", "SparkData"])
self.current_name = run_name if run_name is not None else self.run_n
self._set_stratification(df)
self.columns = self._set_columns(columns)
self._set_schema(self.current_data)
def set_chunked_dataset(
self,
df,
columns: Optional[List[str]] = None,
run_name: str = None,
time_freq: str = "1D",
time_column=None,
):
"""This specified the exact subset of data you want to run on.
Use this method when you already have all data available, and want to retrospectively
analyze all historical as if it was a live process. Note - There's nothing stopping you
from first running this on the available data and then running on a batch-per-batch basis
afterwards using regular ``set_dataset``.
Args:
df: Can be either PandasData, SQLData, or SparkData
columns: If you don't want to run all mappings on this specific subset
of data, you can specify just the columns you want to run. Note - this
corresponds to the ``name`` argument when adding a column to a project
run_name: If you're running metrics from a project on many different subsets any
iterations of the data, you might want to give each specific subset a
name. This is especially necessary when running aggregates on a column
where the column name itself stays the same, but the meaning changes based
on the subset. By default, this will take the value of '0'
time_freq: A pandas-like timeseries frequency term. Use this page to know what you
can use: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliases (turn to link)
time_column: The time series column qualipy should use to chunk the data
Returns:
None
"""
self._set_data(df, allowed_dataclasses=["SQLData", "PandasData", "SparkData"])
self.current_name = run_name if run_name is not None else self.run_n
self._set_stratification(df)
self.columns = self._set_columns(columns)
self._set_schema(self.current_data)
self.chunk = True
try:
time_column = (
time_column if time_column is not None else self.project.time_column
)
except AttributeError:
raise Exception(
"No time_column specified. Must be given if chunking dataset"
)
self.time_chunks = self.generator.get_chunks(
self.current_data, time_freq, time_column
)
def _set_data(self, df, allowed_dataclasses):
if df.__class__.__name__ in allowed_dataclasses:
self.current_data = df.get_data()
try:
self.fallback_data = df.set_fallback_data()
except:
pass
else:
raise Exception(f"{df.__class__.__name__} is not yet a supported datatype")
def _set_stratification(self, df):
# stratification only implemented in Pandas for now
if self.backend == "pandas":
if df.stratify:
self.stratify = True
self.stratify_values = df.stratify_values
self.stratify_function = df.subset_function()
def _set_schema(self, df):
schema = self.generator.set_schema(df, self.columns, self.current_name)
self.schema = {**self.schema, **schema}
def _set_columns(self, columns: Optional[List[str]]):
if columns is None:
ret_columns = self.project.columns
else:
ret_columns = {}
for col, items in self.project.columns.items():
stage_name = items.get("column_stage_collection_name")
if stage_name in columns:
ret_columns[col] = items
return ret_columns
def commit(self):
with self.project.engine.begin() as conn:
self._write(conn=conn, measures=self.total_measures)
self.project.write_functions_to_config()
self.project.update_config_and_project_files()
def _set_default_view(self):
self.data_view = self.generator.return_data_copy(self.current_data)
self.current_name_view = self.current_name
def _generate_metrics(
self, autocommit: bool = True, profile_batch: bool = False
) -> None:
measures = []
self._set_default_view()
for col, specs in self.project.columns.items():
if col not in self.columns:
continue
self.logger.info(f"Analyzing column: {col}")
if specs["split_on"] is not None:
column_name = specs["name"].split("||")[0]
self.data_view = self.generator.return_split_subset(
self.current_data, specs["split_on"][0], specs["split_on"][1]
)
self.current_name_view = f"{self.current_name}-{specs['split_on'][1]}"
else:
column_name = specs["name"]
self.data_view = self.generator.return_data_copy(self.current_data)
self.current_name_view = self.current_name
# enforce type for function
# TODO: fix types when sql data is converted to pandas data
try:
if specs["type"] is not None:
self.generator.check_type(
data=self.data_view,
column=column_name,
desired_type=specs["type"],
force=specs["force_type"],
)
overwrite_type = specs["overwrite_type"]
if overwrite_type:
self.data_view = self.generator.overwrite_type(
self.data_view, column_name, specs["type"]
)
except AttributeError:
pass
# get default column info
measures = self._get_column_specific_general_info(specs, measures)
for function_name, function in (
specs["functions"] + specs["extra_functions"]
):
should_fail = function.fail
arguments = function.arguments
return_format = function.return_format
# return_format_repr = types[return_format]
viz_type = self._set_viz_type(function, function_name)
# generate result row
result = self.generator.generate_description(
function=function,
data=self.data_view,
column=column_name,
function_name=function_name,
date=self.time_of_run,
viz_type=viz_type,
return_format=return_format,
run_name=self.current_name_view,
kwargs=arguments,
overwrite_kwargs=self.overwrite_arguments,
)
# set value type
result.set_return_value_type()
if should_fail and not result["value"]:
raise FailException(
"Program halted by function '{}' for variable '{}' with "
"parameter 'fail=True'".format(function_name, col)
)
if return_format == "custom":
for sub_value in result.value:
new_result = copy.deepcopy(result)
new_result.update_keys(
value=sub_value["value"], run_name=sub_value["run_name"]
)
if "metric_name" in sub_value:
new_result.update_keys(metric=sub_value["metric_name"])
measures.append(new_result)
else:
measures.append(result)
measures = self._get_general_info(measures)
# measures = [{**m, **{"run_name": self.current_name_view}} for m in measures]
self._add_to_total_measures(measures)
if profile_batch:
self.generator.profile_batch(
self.data_view,
self.batch_name,
self.current_name_view,
self.columns,
self.project.config_dir,
self.project.project_name,
)
if autocommit:
self.commit()
def _add_to_total_measures(self, measures: List[Dict]):
self.total_measures.extend(measures)
def _get_column_specific_general_info(self, specs, measures: Measure):
col_name = specs["name"]
unique, perc_missing, value_props = self.generator.generate_column_general_info(
specs, self.data_view, self.time_of_run, self.current_name_view
)
if unique is not None:
measures.append(unique)
if value_props is not None:
measures.append(value_props)
measures.append(perc_missing)
if perc_missing.value > 0 and specs["force_null"] and not specs["null"]:
raise NullableError(
"Column {} has {} percent missing even"
" though it is not nullable".format(col_name, perc_missing["value"])
)
measures.append(
_create_value(
str(self.generator.get_dtype(self.data_view, col_name)),
"dtype",
col_name,
self.time_of_run,
"data-characteristic",
str,
self.current_name_view,
)
)
return measures
def _get_general_info(self, measures: Measure) -> Measure:
rows, cols = self.generator.get_shape(self.data_view)
measures.append(
_create_value(
rows,
"count",
"rows",
self.time_of_run,
"data-characteristic",
int,
self.current_name,
)
)
measures.append(
_create_value(
cols,
"count",
"columns",
self.time_of_run,
"data-characteristic",
int,
self.current_name,
)
)
return measures
def _set_viz_type(self, function: Callable, function_name: str) -> str:
return_format = function.return_format
if return_format == "custom":
return_format = function.custom_value_return_format
types = {
float: "numerical",
int: "numerical",
bool: "boolean",
dict: "categorical",
str: "not_sure",
}
viz_type = types[return_format]
return viz_type
def _write(self, conn, measures: Measure) -> None:
if self.chunk:
batch_name = "from_chunked"
else:
batch_name = self.batch_name
self.generator.write(
conn, measures, self.project, batch_name, schema=self.project.db_schema
)
|
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|
// This file is part of snark, a generic and flexible library for robotics research
// Copyright (c) 2011 The University of Sydney
// All rights reserved.
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are met:
// 1. Redistributions of source code must retain the above copyright
// notice, this list of conditions and the following disclaimer.
// 2. Redistributions in binary form must reproduce the above copyright
// notice, this list of conditions and the following disclaimer in the
// documentation and/or other materials provided with the distribution.
// 3. Neither the name of the University of Sydney nor the
// names of its contributors may be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// NO EXPRESS OR IMPLIED LICENSES TO ANY PARTY'S PATENT RIGHTS ARE
// GRANTED BY THIS LICENSE. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT
// HOLDERS AND CONTRIBUTORS \"AS IS\" AND ANY EXPRESS OR IMPLIED
// WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
// MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
// DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR
// BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
// WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE
// OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN
// IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#include "battery.h"
#include <boost/static_assert.hpp>
namespace snark { namespace ocean {
std::string battery_t::state_to_string( int st )
{
switch( st )
{
case battery_state::initialised:
return "IN";
break;
case battery_state::uninitialised:
return "UN";
break;
case battery_state::fully_discharged:
return "FD";
break;
case battery_state::fully_charged:
return "FC";
break;
case battery_state::discharging:
return "DC";
break;
default:
return "CH";
break;
}
}
// Removes checksum wrapper, TODO: throws exception on incorrect checksum
std::string& battery_t::strip( std::string& line )
{
/// '$B15,....,FF00%B2' becomes $B15,....,FF00
std::size_t pos = line.find_first_of( '%', line.size() - 4 );
if( pos != std::string::npos ) { line = line.substr( 0, pos); }
return line;
}
void battery_t::operator&(const data_t& data)
{
// std::cerr << " address " << data.address() << std::endl;
switch( data.address() )
{
case address::temperature:
{
static const double unit = 0.1; // Kelvin
temperature = static_cast< celcius_t >( data.value() * unit * kelvin ); // 0.1k unit
// std::cerr << "got temperature: " << temperature.value() << std::endl;
break;
}
case address::voltage:
{
voltage = data.value() / 1000.0 * volt; // millivolts to volts
// std::cerr << "got voltage: " << voltage.value() << std::endl;
break;
}
case address::current:
{
current = data.value.cast() / 1000.0 * ampere; //mAmp to Amps
// std::cerr << "got current: " << current.value() << std::endl;
break;
}
case address::average_current:
{
average_current = data.value.cast() / 1000.0 * ampere; //mAmp to Amps
// std::cerr << "got average_current: " << average_current.value() << std::endl;
break;
}
case address::rel_state_of_charge:
{
charge_pc = data.value(); // percentage, unit is %
break;
}
case address::remaining_capacity:
{
remaining_capacity = data.value.cast() / 100.0 * watt; // eacho unit is 10mWh - to Watts
break;
}
case address::run_time_to_empty:
{
time_to_empty = boost::posix_time::minutes( data.value() );
break;
}
case address::status:
{
if( !(data.value() & battery_state::initialised) )
{
state = battery_state::uninitialised;
return;
}
comma::uint16 val = data.value() & 0x0070; // masks out everything including 'initialised' flag
switch( val )
{
case battery_state::discharging:
state = battery_state::discharging;
break;
case battery_state::fully_charged:
state = battery_state::fully_charged;
break;
case battery_state::fully_discharged:
state = battery_state::fully_discharged;
break;
default:
state = battery_state::charging;
break;
}
// std::cerr << "battery: " << int(id) << " state: " << state << " value: " << data.value() << " val: " << val <<std::endl;
break;
}
default:
{
return;
}
}
}
} } // namespace snark { namespace ocean {
|
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|
from numpy.distutils.core import Extension, setup
ext = Extension(name='finite_diff', sources=['finite_diff.f90'])
setup(
name="kdv",
description="Python version of the KdV solver",
install_requires=['scipy', 'matplotlib'],
ext_modules=[ext],
script_name='setup.py',
script_args=['build_ext', '--inplace']
)
|
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|
[STATEMENT]
lemma ascii_of_idem:
"ascii_of c = c" if "\<not> digit7 c"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. ascii_of c = c
[PROOF STEP]
using that
[PROOF STATE]
proof (prove)
using this:
\<not> digit7 c
goal (1 subgoal):
1. ascii_of c = c
[PROOF STEP]
by (cases c) simp
|
{"llama_tokens": 133, "file": null, "length": 2}
|
# Copyright (c) 2013, Aakvatech and contributors
# For license information, please see license.txt
import frappe
from frappe import msgprint, _
import pandas as pd
import numpy as np
def execute(filters=None):
columns = get_columns(filters)
data = []
lab_details = get_lab_results(filters)
if not lab_details:
msgprint(frappe.bold(
"No Record Found for Filters You Specified, Please Choose Different Filters and Try Again..!! "))
else:
lab_colnames = [key for key in lab_details[0].keys()]
df = pd.DataFrame.from_records(lab_details, columns = lab_colnames)
pvt = pd.pivot_table(
df,
values="result_value",
index="lab_test_name",
columns="result_date",
fill_value=" ",
aggfunc="first"
)
columns += pvt.columns.values.tolist()
data += pvt.reset_index().values.tolist()
return columns, data
def get_columns(filters):
columns = [
{
"fieldname": "lab_test_name",
"fieldtype": "Data",
"label": _("Lab Test Name")
}
]
return columns
def get_lab_results(filters):
conditions = ""
if filters.get("patient"):
conditions += "and lb.patient = %(patient)s"
if filters.get("from_date"):
conditions += "and lb.result_date >= %(from_date)s"
if filters.get("to_date"):
conditions += "and lb.result_date <= %(to_date)s"
if filters.get("department"):
conditions += "and lb.department = %(department)s"
return frappe.db.sql("""
select lb.lab_test_name as lab_test_name, date_format(lb.result_date, '%%Y-%%m-%%d') as result_date, n.result_value as result_value
from `tabLab Test` lb inner join `tabNormal Test Result` n on lb.name = n.parent
where lb.docstatus = 1
and lb.lab_test_name not in (select lbt.lab_test_name from `tabLab Test Template` lbt where lbt.lab_test_template_type="Grouped")
and lb.status = "Completed" {conditions}
union
select lb.lab_test_name as lab_test_name, date_format(lb.result_date, '%%Y-%%m-%%d') as result_date, d.result_value as result_value
from `tabLab Test` lb inner join `tabDescriptive Test Result` d on lb.name = d.parent
where lb.docstatus = 1
and lb.lab_test_name not in (select lbt.lab_test_name from `tabLab Test Template` lbt where lbt.lab_test_template_type="Grouped")
and lb.status = "Completed" {conditions}
union
select lb.lab_test_name as lab_test_name, date_format(lb.result_date, '%%Y-%%m-%%d') as result_date, org.colony_population as result_value
from `tabLab Test` lb inner join `tabOrganism Test Result` org on lb.name = org.parent
where lb.docstatus = 1
and lb.lab_test_name not in (select lbt.lab_test_name from `tabLab Test Template` lbt where lbt.lab_test_template_type="Grouped")
and lb.status = "Completed" {conditions}
union
select lb.lab_test_name as lab_test_name, date_format(lb.result_date, '%%Y-%%m-%%d') as result_date, ss.antibiotic_sensitivity as result_value
from `tabLab Test` lb inner join `tabSensitivity Test Result` ss on lb.name = ss.parent
where lb.docstatus = 1
and lb.lab_test_name not in (select lbt.lab_test_name from `tabLab Test Template` lbt where lbt.lab_test_template_type="Grouped")
and lb.status = "Completed" {conditions}
""".format(conditions=conditions), filters, as_dict=1
)
|
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|
\chapter{Conclusion}
This paper uses the Gibbs sampler with a Metropolis-Hastings step to generate new samples from a NHPP. Test statistics are used to check if the new samples are from the NHPP. The NHPP used has a rate function which is a combination of a log-linear function and a power-law function. By testing the samples for different parameters in the rate function, non of the null hypothesis were rejected. Hence this sampler can be used to generate new samples from a NHPP given a data set.
|
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|
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
import astropy.units as u
from astropy.constants import h, c, k_B
from astropy.visualization import quantity_support
from .chemistry import chemistry
from .opacity import kappa
__all__ = [
'dashboard'
]
def dashboard(
lam, F_2_up, binned_phoenix_spectrum, dtaus,
pressures, temps, temperature_history, opacities
):
"""
Generate a dashboard plot.
Parameters
----------
lam : ~astropy.units.Quantity
Wavelength grid
F_2_up : ~astropy.units.Quantity
Emission spectrum
binned_phoenix_spectrum : ~astropy.units.Quantity
Binned PHOENIX spectrum
dtaus : list of lists, or ~numpy.ndarray
Change in optical depth
pressures : ~astropy.units.Quantity
Pressure grid
temps : ~astropy.units.Quantity
Final temperatures after iteration for radiative equilibrium
temperature_history : ~astropy.units.Quantity
Grid of temperatures for each timestep and pressure
opacities : dict
Opacity dictionary of xarray.DataArray's
Returns
-------
fig, ax : ~matplotlib.axes.Figure, ~matplotlib.axes.Axes
"""
from .chemistry import iso_to_species
flux_unit = u.erg/u.cm**3/u.s
fig = plt.figure(figsize=(12, 7))
gs = GridSpec(2, 4, figure=fig)
ax = [fig.add_subplot(ax)
for ax in [gs[0, :], gs[1, 0], gs[1, 1], gs[1, 2], gs[1, 3]]]
with quantity_support():
if np.any(binned_phoenix_spectrum.value != 0):
ax[0].loglog(
lam, binned_phoenix_spectrum.to(flux_unit), color='C1', label='PHOENIX'
)
ax[0].loglog(lam, F_2_up.to(flux_unit), color='C0', label='frei')
ax[0].legend()
tau = np.cumsum(dtaus[::-1], axis=0)
nus = lam.to(u.cm**-1, u.spectral())
hcperk = h * c / k_B
dlogP = (np.log10(pressures.max().to(u.bar).value) -
np.log10(pressures.min().to(u.bar).value)
) / (len(pressures) - 1)
k = 10 ** -dlogP
dParr = (1 - k) * pressures
cf = (
np.exp(-tau) * np.array(dtaus)[::-1] *
(pressures[::-1, None] / dParr[::-1, None]) *
nus**3 / np.expm1(hcperk * nus /
temps[::-1, None]))
cf /= np.sum(cf, axis=0)
lg, pg = np.meshgrid(lam.value, pressures.to(u.bar).value)
cax = ax[1].pcolormesh(lg, pg, cf[::-1], cmap=plt.cm.Greys, shading='auto')
plt.colorbar(cax, ax=ax[1])
ax[1].set_yscale('log')
ax[1].invert_yaxis()
ax[1].set(
xlabel=r'Wavelength [$\mu$m]', ylabel='Pressure [bar]',
title='Contrib Func',
xlim=[lam.value.min(), lam.value.max()],
ylim=[pressures.to(u.bar).value.max(), pressures.to(u.bar).value.min()]
)
ax[0].set(
xlabel=r'Wavelength [$\mu$m]', title='Emission spectrum',
)
ax[1].set_xscale('log')
cmap = plt.cm.winter_r
for i in range(temperature_history.shape[1]):
color = cmap(i / temperature_history.shape[1])
if np.all(temperature_history[:, i] != 0):
ax[2].semilogy(temperature_history[:, i], pressures[:].to(u.bar),
c=color, alpha=0.3)
ax[2].semilogy(temps[:], pressures[:].to(u.bar), '-', color='k', lw=3)
ax[2].invert_yaxis()
ax[2].annotate("Initial", (0.1, 0.18), color=cmap(0),
xycoords='axes fraction')
ax[2].annotate("Final", (0.1, 0.1), xycoords='axes fraction')
ax[2].set(
xlabel='Temperature [K]', ylabel='Pressure [bar]',
)
fastchem_mmr, fastchem_vmr = chemistry(
temps[:], pressures[:], opacities.keys(), return_vmr=True
)
for isotopologue in fastchem_vmr:
species_name = iso_to_species(isotopologue)
ax[3].semilogy(
np.log10(fastchem_vmr[isotopologue]), pressures.to(u.bar),
label=species_name.replace('2', '$_2$'), lw=2
)
ax[3].legend()
ax[3].invert_yaxis()
ax[3].set(
xlabel='log(VMR)', ylabel='Pressure [bar]',
title='Chemistry (FastChem)',
ylim=ax[1].get_ylim()
)
k, sigma_scattering = kappa(
opacities, np.interp(1 * u.bar, pressures[::-1].to(u.bar), temps[::-1]), 1 * u.bar, lam
)
with quantity_support():
ax[4].loglog(lam, k.to(u.cm ** 2 / u.g).flatten(), label='Total')
ax[4].loglog(lam, sigma_scattering.to(u.cm ** 2 / u.g).flatten(), label='Scattering')
ax[4].set(
xlabel=r'Wavelength [$\mu$m]', ylabel='Opacity [cm$^2$ g$^{-1}$]'
)
ax[4].legend()
for axis in ax:
for sp in ['right', 'top']:
axis.spines[sp].set_visible(False)
fig.tight_layout()
return fig, ax
|
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|
import cv2
import numpy as np
frontal_face = cv2.CascadeClassifier('classifier/haarcascade_frontalface_default.xml')
#eye_cascade = cv2.CascadeClassifier('classifier/eye_pair_big.xml')
#eye_cascade = cv2.CascadeClassifier('classifier/eye_pair_small.xml')
eye_cascade = cv2.CascadeClassifier('classifier/haarcascade_eye.xml')
kernel = np.ones((4,4),np.uint8)
kernel2 = np.ones((8,8),np.uint8)
capture = cv2.VideoCapture(0)
while True:
ret, frame = capture.read()
frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = frontal_face.detectMultiScale(frame_gray)
for face in faces:
x,y,w,h = face
frame = cv2.rectangle(frame, (x,y), (x+w,y+h), (0,255,0), 3)
faceROI = frame_gray[y:y+h,x:x+w]
eyes = eye_cascade.detectMultiScale(faceROI, minNeighbors = 20)
gaze_left = [False,False]
gaze_right = [False,False]
for i,(x2,y2,w2,h2) in enumerate(eyes):
if i == 2:
break
frame = cv2.rectangle(frame, (x+x2,y+y2), (x+x2+h2,y+y2+w2), (255, 0, 0 ), 4)
eyeROI = faceROI[y2:y2+w2,x2:x2+h2]
scale_percent = 500 # percent of original size
width = int(eyeROI.shape[1] * scale_percent / 100)
height = int(eyeROI.shape[0] * scale_percent / 100)
dim = (width, height)
# resize image
resized = cv2.resize(eyeROI, dim, interpolation = cv2.INTER_AREA)
resized = cv2.equalizeHist(resized)
resized = cv2.GaussianBlur(resized,(5,5), cv2.BORDER_DEFAULT)
rows, cols = resized.shape
_, threshold = cv2.threshold(resized, 10, 255, cv2.THRESH_BINARY_INV)
dilation = cv2.dilate(threshold,kernel,iterations = 4)
erosion = cv2.erode(dilation,kernel,iterations = 2)
#_, threshold2 = cv2.threshold(resized,40 , 255, cv2.THRESH_BINARY_INV)
#erosion2 = cv2.erode(threshold2,kernel2,iterations = 3)
#dilation2 = cv2.dilate(erosion2,kernel2,iterations = 1)
contours,_ = cv2.findContours(dilation, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
contours = sorted(contours, key=lambda x: cv2.contourArea(x), reverse=True)
contours = contours[:1]
#contours = sorted(contours, key=lambda ctr: cv2.boundingRect(ctr)[0])
#contours2,_ = cv2.findContours(dilation2, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
#contours2 = sorted(contours2, key=lambda x: cv2.contourArea(x), reverse=True)
#contours2 = contours2[:2]
#contours2 = sorted(contours2, key=lambda ctr: cv2.boundingRect(ctr)[0])
#i = 0
#while i<2:
# try:
# eye = contours2[i]
# pupil = contours[i]
# (xe, ye, we, he) = cv2.boundingRect(eye)
# (xp, yp, wp, hp) = cv2.boundingRect(pupil)
# cv2.rectangle(resized, (xe, ye), (xe + we, ye + he), (255, 0, 0), 2)
# cv2.line(resized, (xe + int(we/2), 0), (xe + int(we/2), rows), (0, 255, 0), 2)
# cv2.line(resized, (0, ye + int(he/2)), (cols, ye + int(he/2)), (0, 255, 0), 2)
# cv2.rectangle(resized, (xp, yp), (xp + wp, yp + hp), (255, 0, 0), 2)
# cv2.line(resized, (xp + int(wp/2), 0), (xp + int(wp/2), rows), (0, 255, 0), 2)
# cv2.line(resized, (0, yp + int(hp/2)), (cols, yp + int(hp/2)), (0, 255, 0), 2)
# if (xp+(xp+wp)/2)-(xe+(xe+we)/2)>17:
# gaze_left[i] = True
# elif (xp+(xp+wp)/2)-(xe+(xe+we)/2)<-17:
# gaze_right[i] = True
# except:
# pass
# i+=1
(xp, yp, wp, hp) = cv2.boundingRect(contours[0])
cv2.rectangle(resized, (xp, yp), (xp + wp, yp + hp), (255, 0, 0), 2)
cv2.line(resized, (xp + int(wp/2), 0), (xp + int(wp/2), rows), (0, 255, 0), 2)
cv2.line(resized, (0, yp + int(hp/2)), (cols, yp + int(hp/2)), (0, 255, 0), 2)
if (xp+int(wp/2))-(cols/2)>18:
gaze_left[i] =True
elif (xp+int(wp/2))-(cols/2)<-18:
gaze_right[i] =True
cv2.imshow("pupil"+str(i), erosion)
cv2.imshow("eye"+str(i), resized)
if gaze_left[0] and gaze_left[1]:
print("looking left")
elif gaze_right[0] and gaze_right[1]:
print("looking right")
else:
print("looking center")
cv2.imshow("webcam", frame)
if cv2.waitKey(1) & 0xFF == ord("q"):
break
|
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|
{- Byzantine Fault Tolerant Consensus Verification in Agda, version 0.9.
Copyright (c) 2021, Oracle and/or its affiliates.
Licensed under the Universal Permissive License v 1.0 as shown at https://opensource.oracle.com/licenses/upl
-}
open import LibraBFT.Impl.OBM.Logging.Logging
open import LibraBFT.ImplShared.Consensus.Types
import LibraBFT.ImplShared.Util.Crypto as Crypto
open import Optics.All
open import Util.Prelude
module LibraBFT.Impl.Types.Ledger2WaypointConverter where
new : LedgerInfo → Ledger2WaypointConverter
new ledgerInfo = mkLedger2WaypointConverter
(ledgerInfo ^∙ liEpoch)
(ledgerInfo ^∙ liTransactionAccumulatorHash)
(ledgerInfo ^∙ liVersion)
--(ledgerInfo ^∙ liTimestamp)
(ledgerInfo ^∙ liNextEpochState)
|
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|
# have all phylogenies in one file
# have a file with the chromosome and window for each tree in the correct order
# have a popmap with the individual names and groupings wished to test
# have the outgroup labeled once in the popmap as "outgroup"
library(ape)
library(phytools)
options(scipen=999)
# read in trees, info, and popmap for 50kbp trees
x_trees <- read.tree("certhia_50kbp.trees")
x_info <- read.table("certhia_50kbp_tree_info.txt", sep="\t", stringsAsFactors=F)
x_popmap <- read.table("gsi_popmap.txt", sep="\t", stringsAsFactors=F)
x_output <- "b_gsi_50kbp_output.txt"
# define outgroup
outgroup <- x_popmap[x_popmap[,2] == "outgroup", 1]
# remove outgroup from popmap
x_popmap <- x_popmap[x_popmap[,2] != "outgroup", ]
# write initial line of output
write(c("chr", "start", "end", "pop", "gsi"), file=x_output, sep="\t", ncolumns=5)
# loop for each tree
for(a in 1:length(x_trees)) {
# select the tree
x <- x_trees[a][[1]]
# reroot
x <- midpoint.root(x)
x <- root(x, outgroup, resolve.root=T)
# define the groups
groups <- unique(x_popmap[,2])
# loop for each group of interest
for(g in 1:length(groups)) {
# define the group of interest
group_of_interest <- x_popmap[x_popmap[,2] == groups[g],1]
# calculate MRCA of group of interest
group_mrca <- getMRCA(x, tip = group_of_interest)
# calculate number of ndoes to reach common ancestor for each individual in group
# and then remove redundant nodes
nodes_needed <- c()
for(b in 1:length(group_of_interest)) {
# what is the number of this individual?
b_number <- match(group_of_interest[b], x$tip.label)
# loop throup edge table until reaching MRCA
while_loop <- 0
while(while_loop != group_mrca) {
# add node
nodes_needed <- c(nodes_needed, x$edge[x$edge[,2]==b_number,1])
# change new number to that node and update the while_loop object to the node
b_number <- x$edge[x$edge[,2]==b_number,1]
while_loop <- b_number
}
# add the MRCA to the nodes_needed object
nodes_needed <- c(nodes_needed, group_mrca)
# only keep unique nodes
nodes_needed <- unique(nodes_needed)
}
# calculate gsi
# gs
gs <- (length(group_of_interest) - 1) / length(nodes_needed)
# max gs = 1
max_gs <- 1
# min gs = minimum number of nodes to connect all individuals (n - 1) / total number of nodes
min_gs <- (length(group_of_interest) - 1) / length(unique(x$edge[,1]))
# equation 4 of Cummings et al. 2008 (GSI paper)
gsi <- (gs - min_gs) / (max_gs - min_gs)
# write output
write(c(x_info[a,1], x_info[a,2], x_info[a,3], groups[g], gsi), file=x_output, sep="\t", ncolumns=5, append=T)
}
}
# read in trees, info, and popmap for 100kbp trees
x_trees <- read.tree("certhia_100kbp.trees")
x_info <- read.table("certhia_100kbp_tree_info.txt", sep="\t", stringsAsFactors=F)
x_popmap <- read.table("gsi_popmap.txt", sep="\t", stringsAsFactors=F)
x_output <- "b_gsi_100kbp_output.txt"
# define outgroup
outgroup <- x_popmap[x_popmap[,2] == "outgroup", 1]
# remove outgroup from popmap
x_popmap <- x_popmap[x_popmap[,2] != "outgroup", ]
# write initial line of output
write(c("chr", "start", "end", "pop", "gsi"), file=x_output, sep="\t", ncolumns=5)
# loop for each tree
for(a in 1:length(x_trees)) {
# select the tree
x <- x_trees[a][[1]]
# reroot
x <- midpoint.root(x)
x <- root(x, outgroup, resolve.root=T)
# define the groups
groups <- unique(x_popmap[,2])
# loop for each group of interest
for(g in 1:length(groups)) {
# define the group of interest
group_of_interest <- x_popmap[x_popmap[,2] == groups[g],1]
# calculate MRCA of group of interest
group_mrca <- getMRCA(x, tip = group_of_interest)
# calculate number of ndoes to reach common ancestor for each individual in group
# and then remove redundant nodes
nodes_needed <- c()
for(b in 1:length(group_of_interest)) {
# what is the number of this individual?
b_number <- match(group_of_interest[b], x$tip.label)
# loop throup edge table until reaching MRCA
while_loop <- 0
while(while_loop != group_mrca) {
# add node
nodes_needed <- c(nodes_needed, x$edge[x$edge[,2]==b_number,1])
# change new number to that node and update the while_loop object to the node
b_number <- x$edge[x$edge[,2]==b_number,1]
while_loop <- b_number
}
# add the MRCA to the nodes_needed object
nodes_needed <- c(nodes_needed, group_mrca)
# only keep unique nodes
nodes_needed <- unique(nodes_needed)
}
# calculate gsi
# gs
gs <- (length(group_of_interest) - 1) / length(nodes_needed)
# max gs = 1
max_gs <- 1
# min gs = minimum number of nodes to connect all individuals (n - 1) / total number of nodes
min_gs <- (length(group_of_interest) - 1) / length(unique(x$edge[,1]))
# equation 4 of Cummings et al. 2008 (GSI paper)
gsi <- (gs - min_gs) / (max_gs - min_gs)
# write output
write(c(x_info[a,1], x_info[a,2], x_info[a,3], groups[g], gsi), file=x_output, sep="\t", ncolumns=5, append=T)
}
}
|
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|
#!/usr/bin/env python
from setuptools import setup
from setuptools.command.build_ext import build_ext as _build_ext
class build_ext(_build_ext):
def finalize_options(self):
_build_ext.finalize_options(self)
# Prevent numpy from thinking it is still in its setup process:
__builtins__.__NUMPY_SETUP__ = False
import numpy
self.include_dirs.append(numpy.get_include())
REQUIRES = ['numpy',
'asteval',
'astropy',
'astroquery',
'batman-package',
'bibtexparser',
'bokeh',
'cython',
'flask',
'h5py',
'lmfit',
'matplotlib',
'numba',
'pandas',
'pysynphot',
'scipy',
'sphinx',
'svo_filters']
SETUP_REQUIRES = ['numpy']
setup(name='exoctk',
version='0.2.2',
description='Observation reduction and planning tools for exoplanet science',
cmdclass={'build_ext': build_ext},
setup_requires=SETUP_REQUIRES,
install_requires=REQUIRES,
author='The ExoCTK Group',
author_email='exoctk@gmail.com',
license='MIT',
url='https://github.com/ExoCTK/exoctk',
long_description='',
zip_safe=True,
use_2to3=False
)
|
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|
\chapter{Related Works} \label{ch:review}
In this chapter, I select the most outstanding studies based on a self-defined criteria (either published in a set of pre-selected venues or performed the highest impact by receiving at least fifty citations). To better introduce these papers in a well-organized manner, I categorize them into five following tracks borrowing ideas from aforementioned survey studies \cite{fortunato2010community, fortunato2016community, coscia2011classification}. In detail, papers in the Graph Type track focus on detecting communities in different types of graphs, such as heterogeneous or sparse graphs. In the Task track, the selected studies aim to solve particular tasks in community detection, such as deciding the correct number of communities. In the Methodological track, the introduced studies solve the general community detection problem via different types of model frameworks such as Modularity or spectral methods. In the Application track, selected studies discuss community detection applications and how to apply them to other disciplines. In the last Evaluation track, it lists papers to summarize the evaluation metrics widely used for model justification and comparison.
\input{chapter2/chapter2.1.tex}
\input{chapter2/chapter2.2.tex}
\input{chapter2/chapter2.3.tex}
\input{chapter2/chapter2.4.tex}
\input{chapter2/chapter2.5.tex}
|
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|
import os
import pickle
import sys
import warnings
from collections import OrderedDict
import biosppy.signals.tools as st
import numpy as np
import wfdb
from biosppy.signals.ecg import correct_rpeaks, hamilton_segmenter
from hrv.classical import frequency_domain, time_domain
from scipy.signal import medfilt
from tqdm import tqdm
warnings.filterwarnings(action="ignore")
base_dir = "dataset"
fs = 100 # ECG sample frequency
hr_min = 20
hr_max = 300
def feature_extraction(recording, signal, labels):
data = []
for i in tqdm(range(len(labels)), desc=recording, file=sys.stdout):
segment = signal[i * fs * 60:(i + 1) * fs * 60]
segment, _, _ = st.filter_signal(segment, ftype='FIR', band='bandpass', order=int(0.3 * fs), frequency=[3, 45],
sampling_rate=fs)
# Finding R peaks
rpeaks, = hamilton_segmenter(segment, sampling_rate=fs)
rpeaks, = correct_rpeaks(segment, rpeaks, sampling_rate=fs, tol=0.1)
# Extracting feature
label = 0 if labels[i] == "N" else 1
if 40 <= len(rpeaks) <= 200: # Remove abnormal R peaks
rri_tm, rri = rpeaks[1:] / float(fs), np.diff(rpeaks, axis=-1) / float(fs)
rri = medfilt(rri, kernel_size=3)
edr_tm, edr = rpeaks / float(fs), segment[rpeaks]
# Remove physiologically impossible HR signal
if np.all(np.logical_and(60 / rri >= hr_min, 60 / rri <= hr_max)):
rri_time_features, rri_frequency_features = time_domain(rri * 1000), frequency_domain(rri, rri_tm)
edr_frequency_features = frequency_domain(edr, edr_tm)
# 6 + 6 + 6 + 1 = 19
data.append([
rri_time_features["rmssd"], rri_time_features["sdnn"], rri_time_features["nn50"],
rri_time_features["pnn50"], rri_time_features["mrri"], rri_time_features["mhr"],
rri_frequency_features["vlf"] / rri_frequency_features["total_power"],
rri_frequency_features["lf"] / rri_frequency_features["total_power"],
rri_frequency_features["hf"] / rri_frequency_features["total_power"],
rri_frequency_features["lf_hf"], rri_frequency_features["lfnu"], rri_frequency_features["hfnu"],
edr_frequency_features["vlf"] / edr_frequency_features["total_power"],
edr_frequency_features["lf"] / edr_frequency_features["total_power"],
edr_frequency_features["hf"] / edr_frequency_features["total_power"],
edr_frequency_features["lf_hf"], edr_frequency_features["lfnu"], edr_frequency_features["hfnu"],
label
])
else:
data.append([np.nan] * 18 + [label])
else:
data.append([np.nan] * 18 + [label])
data = np.array(data, dtype="float")
return data
if __name__ == "__main__":
apnea_ecg = OrderedDict()
# train data
recordings = [
"a01", "a02", "a03", "a04", "a05", "a06", "a07", "a08", "a09", "a10",
"a11", "a12", "a13", "a14", "a15", "a16", "a17", "a18", "a19", "a20",
"b01", "b02", "b03", "b04", "b05",
"c01", "c02", "c03", "c04", "c05", "c06", "c07", "c08", "c09", "c10"
]
for recording in recordings:
signal = wfdb.rdrecord(os.path.join(base_dir, recording), channels=[0]).p_signal[:, 0]
labels = wfdb.rdann(os.path.join(base_dir, recording), extension="apn").symbol
apnea_ecg[recording] = feature_extraction(recording, signal, labels)
print()
# test data
recordings = [
"x01", "x02", "x03", "x04", "x05", "x06", "x07", "x08", "x09", "x10",
"x11", "x12", "x13", "x14", "x15", "x16", "x17", "x18", "x19", "x20",
"x21", "x22", "x23", "x24", "x25", "x26", "x27", "x28", "x29", "x30",
"x31", "x32", "x33", "x34", "x35"
]
answers = {}
filename = os.path.join(base_dir, "event-2-answers")
with open(filename, "r") as f:
for answer in f.read().split("\n\n"):
answers[answer[:3]] = list("".join(answer.split()[2::2]))
for recording in recordings:
signal = wfdb.rdrecord(os.path.join(base_dir, recording), channels=[0]).p_signal[:, 0]
labels = answers[recording]
apnea_ecg[recording] = feature_extraction(recording, signal, labels)
with open(os.path.join(base_dir, "apnea-ecg.pkl"), "wb") as f:
pickle.dump(apnea_ecg, f, protocol=2)
print("ok")
|
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|
import os
import torch
import torch.nn.functional as F
import random
import numpy as np
import pandas as pd
from config import Config
from dataset import THUMOSInferenceDataset, inference_collate_fn
from model import SSAD
from utils import post_process, temporal_nms
os.environ["CUDA_VISIBLE_DEVICES"] = "1"
device = torch.device('cuda')
torch.backends.cudnn.benchmark = True
torch.backends.cudnn.deterministic = True
torch.set_default_tensor_type('torch.FloatTensor')
def inference(config):
# setup data_loader instances
inference_loader = torch.utils.data.DataLoader(THUMOSInferenceDataset(config),
batch_size=config.batch_size, shuffle=False,
num_workers=8, pin_memory=True, drop_last=False,
collate_fn=inference_collate_fn)
# build model architecture and load checkpoint
model = SSAD(config).to(device)
checkpoint = torch.load(config.checkpoint_path + "/model_best.pth.tar")
model.load_state_dict(checkpoint['state_dict'])
model = model.to(device)
model.eval()
'''
['xmin', 'xmax', 'conf', 'score_0', 'score_1', 'score_2',
'score_3', 'score_4', 'score_5', 'score_6', 'score_7', 'score_8',
'score_9', 'score_10', 'score_11', 'score_12', 'score_13', 'score_14',
'score_15', 'score_16', 'score_17', 'score_18', 'score_19', 'score_20']
'''
results = []
results_name = []
with torch.no_grad():
for n_iter, (batch_data, batch_video_names, batch_window_start) in enumerate(inference_loader):
batch_data = batch_data.to(device)
output_x, output_w, output_scores, output_labels = model(batch_data, device)
output_labels = F.softmax(output_labels, dim=1)
output_x = output_x.cpu().detach().numpy()
output_w = output_w.cpu().detach().numpy()
output_scores = output_scores.cpu().detach().numpy()
output_labels = output_labels.cpu().detach().numpy()
output_min = output_x - output_w / 2
output_max = output_x + output_w / 2
for ii in range(len(batch_video_names)):
video_name = batch_video_names[ii]
window_start = batch_window_start[ii]
a_min = output_min[ii, :]
a_max = output_max[ii, :]
a_scores = output_scores[ii, :]
a_labels = output_labels[ii, :, :]
for jj in range(output_min.shape[-1]):
corrected_min = max(a_min[jj] * config.window_size * config.unit_size, 0.) + window_start
corrected_max = min(a_max[jj] * config.window_size * config.unit_size,
config.window_size * config.unit_size) + window_start
results_name.append([video_name])
results.append([corrected_min, corrected_max, a_scores[jj]] + a_labels[:, jj].tolist())
results_name = np.stack(results_name)
results = np.stack(results)
df = pd.DataFrame(results, columns=config.outdf_columns)
df['video_name'] = results_name
result_file = './results.txt'
if os.path.isfile(result_file):
os.remove(result_file)
df = df[df.score_0 < config.filter_neg_threshold]
df = df[df.conf > config.filter_conf_threshold]
video_name_list = list(set(df.video_name.values[:]))
for video_name in video_name_list:
tmpdf = df[df.video_name == video_name]
tmpdf = post_process(tmpdf, config)
temporal_nms(config, tmpdf, result_file, video_name)
if __name__ == '__main__':
config = Config()
random.seed(config.seed)
np.random.seed(config.seed)
torch.manual_seed(config.seed)
torch.cuda.manual_seed(config.seed)
torch.cuda.manual_seed_all(config.seed)
inference(config)
|
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|
(* -------------------------------------------------------------------- *)
From mathcomp Require Import all_ssreflect all_algebra bigenough.
(* ------- *) Require Import finmap boolp reals.
(* ------- *) Require (*--*) Setoid.
(* -------------------------------------------------------------------- *)
Set Implicit Arguments.
Unset Strict Implicit.
Unset Printing Implicit Defensive.
Import GRing.Theory Num.Theory BigEnough.
Local Open Scope ring_scope.
Local Open Scope real_scope.
(* -------------------------------------------------------------------- *)
Section ToBeEventuallyMovedToBoolP.
Context {T : Type} {P Q : T -> Prop}.
Lemma asboolb (b : bool) : `[< b >] = b.
Proof. by apply/asboolP/idP. Qed.
(* TODO : add its friends... *)
Lemma neg_or (A B : Prop) : ~ (A \/ B) <-> ~ A /\ ~ B.
Proof.
split; last by case=> [nA nB]; case.
by move=> nAoB; split => ?; apply: nAoB; [left| right].
Qed.
Lemma existsNP : ~ (exists x, P x) -> forall x, ~ P x.
Proof. by move/asboolPn/forallp_asboolPn. Qed.
Lemma exists2NP : ~ (exists2 x, P x & Q x) -> forall x, ~ P x \/ ~ Q x.
Proof.
apply: contrapR; case/asboolPn/existsp_asboolPn=> [x].
by case/neg_or => /contrapT Px /contrapT Qx; exists x.
Qed.
End ToBeEventuallyMovedToBoolP.
(* -------------------------------------------------------------------- *)
Section ProofIrrelevantChoice.
Context {T : choiceType}.
Lemma existsP (P : T -> Prop) : (exists x, P x) -> {x : T | P x}.
Proof.
move/asboolP/exists_asboolP=> h; have/asboolP hxh := (xchooseP h).
by exists (xchoose h).
Qed.
Lemma existsTP (P : T -> Prop) : { x : T | P x } + (forall x, ~ P x).
Proof.
case: (boolP `[<exists x : T, P x>]) => [/exists_asboolP | /asboolPn] h.
by case/existsP: h => w Pw; left; exists w; apply/asboolP.
by right=> x Px; apply/h; exists x.
Qed.
End ProofIrrelevantChoice.
(* -------------------------------------------------------------------- *)
Section PredSubtype.
Section Def.
Variable T : Type.
Variable E : pred T.
Record pred_sub : Type :=
PSubSub { rsval : T; rsvalP : rsval \in E }.
Coercion rsval : pred_sub >-> T.
Canonical pred_sub_subType := Eval hnf in [subType for rsval].
End Def.
Definition pred_sub_eqMixin (T : eqType) (E : pred T) :=
Eval hnf in [eqMixin of pred_sub E by <:].
Canonical pred_sub_eqType (T : eqType) (E : pred T) :=
Eval hnf in EqType (@pred_sub T E) (pred_sub_eqMixin E).
Definition pred_sub_choiceMixin (T : choiceType) (E : pred T) :=
Eval hnf in [choiceMixin of pred_sub E by <:].
Canonical pred_sub_choiceType (T : choiceType) (E : pred T) :=
Eval hnf in ChoiceType (@pred_sub T E) (pred_sub_choiceMixin E).
Definition pred_sub_countMixin (T : countType) (E : pred T) :=
Eval hnf in [countMixin of pred_sub E by <:].
Canonical pred_sub_countType (T : countType) (E : pred T) :=
Eval hnf in CountType (@pred_sub T E) (pred_sub_countMixin E).
End PredSubtype.
Notation "[ 'psub' E ]" := (@pred_sub _ E)
(format "[ 'psub' E ]").
(* -------------------------------------------------------------------- *)
Section PIncl.
Variables (T : Type) (E F : pred T) (le : {subset E <= F}).
Definition pincl (x : [psub E]) : [psub F] :=
PSubSub (le (valP x)).
End PIncl.
(* -------------------------------------------------------------------- *)
Section Countable.
Variable (T : Type) (E : pred T).
CoInductive countable : Type :=
Countable
(rpickle : [psub E] -> nat)
(runpickle : nat -> option [psub E])
of pcancel rpickle runpickle.
Definition rpickle (c : countable) :=
let: Countable p _ _ := c in p.
Definition runpickle (c : countable) :=
let: Countable _ p _ := c in p.
Lemma rpickleK c: pcancel (rpickle c) (runpickle c).
Proof. by case: c. Qed.
End Countable.
(* -------------------------------------------------------------------- *)
Section CountableTheory.
Lemma countable_countable (T : countType) (E : pred T) : countable E.
Proof. by exists pickle unpickle; apply/pickleK. Qed.
Section CanCountable.
Variables (T : Type) (U : countType) (E : pred T).
Variables (f : [psub E] -> U) (g : U -> [psub E]).
Lemma can_countable : cancel f g -> countable E.
Proof.
pose p := pickle \o f; pose u n := omap g (unpickle n).
move=> can_fg; apply (@Countable _ E p u) => x.
by rewrite {}/u {}/p /= pickleK /= can_fg.
Qed.
End CanCountable.
Section CountType.
Variables (T : eqType) (E : pred T) (c : countable E).
Definition countable_countMixin := CountMixin (rpickleK c).
Definition countable_choiceMixin := CountChoiceMixin countable_countMixin.
Definition countable_choiceType :=
ChoiceType [psub E] countable_choiceMixin.
Definition countable_countType :=
CountType countable_choiceType countable_countMixin.
End CountType.
End CountableTheory.
Notation "[ 'countable' 'of' c ]" := (countable_countType c)
(format "[ 'countable' 'of' c ]").
(* -------------------------------------------------------------------- *)
Section Finite.
Variables (T : eqType).
CoInductive finite (E : pred T) : Type :=
| Finite s of uniq s & {subset E <= s}.
End Finite.
(* -------------------------------------------------------------------- *)
Section FiniteTheory.
Context {T : choiceType}.
Lemma finiteP (E : pred T) : (exists s : seq T, {subset E <= s}) -> finite E.
Proof.
case/existsP=> s sEs; exists (undup s); first by rewrite undup_uniq.
by move=> x; rewrite mem_undup; exact: sEs.
Qed.
Lemma finiteNP (E : pred T): (forall s : seq T, ~ {subset E <= s}) ->
forall n, exists s : seq T, [/\ size s = n, uniq s & {subset s <= E}].
Proof.
move=> finN; elim=> [|n [s] [<- uq_s sE]]; first by exists [::].
have [x sxN xE]: exists2 x, x \notin s & x \in E.
apply: contrapR (finN (filter (mem E) s)) => /exists2NP finE x Ex.
move/or_asboolP: (finE x).
by rewrite !asbool_neg !asboolb negbK Ex mem_filter orbF [(mem E) x]Ex.
exists (x :: s) => /=; rewrite sxN; split=> // y.
by rewrite in_cons => /orP[/eqP->//|/sE].
Qed.
End FiniteTheory.
(* -------------------------------------------------------------------- *)
Section FiniteCountable.
Variables (T : eqType) (E : pred T).
Lemma finite_countable : finite E -> countable E.
Proof.
case=> s uqs Es; pose t := pmap (fun x => (insub x : option [psub E])) s.
pose f x := index x t; pose g i := nth None [seq Some x | x <- t] i.
apply (@Countable _ E f g) => x; rewrite {}/f {}/g /=.
have x_in_t: x \in t; first case: x => x h.
by rewrite {}/t mem_pmap_sub /= Es.
by rewrite (nth_map x) ?index_mem ?nth_index.
Qed.
End FiniteCountable.
(* -------------------------------------------------------------------- *)
Section CountSub.
Variables (T : eqType) (E F : pred T).
Lemma countable_sub: {subset E <= F} -> countable F -> countable E.
Proof.
move=> le_EF [f g fgK]; pose f' (x : [psub E]) := f (pincl le_EF x).
pose g' x := obind (insub (sT := [subType of [psub E]])) (omap val (g x)).
by exists f' g' => x; rewrite /f' /g' fgK /= valK.
Qed.
End CountSub.
(* -------------------------------------------------------------------- *)
Section CountableUnion.
Variables (T : eqType) (E : nat -> pred T).
Hypothesis cE : forall i, countable (E i).
Lemma cunion_countable : countable [pred x | `[exists i, x \in E i]].
Proof.
pose S := { i : nat & [countable of cE i] }; set F := [pred x | _].
have H: forall (x : [psub F]), exists i : nat, val x \in E i.
by case=> x /= /existsbP[i] Eix; exists i.
have G: forall (x : S), val (tagged x) \in F.
by case=> i [x /= Eix]; apply/existsbP; exists i.
pose f (x : [psub F]) : S := Tagged (fun i => [psub E i])
(PSubSub (xchooseP (H x))).
pose g (x : S) := PSubSub (G x).
by have /can_countable: cancel f g by case=> x hx; apply/val_inj.
Qed.
End CountableUnion.
|
{"author": "ejgallego", "repo": "coq-alternate-reals", "sha": "8e1ad799ae9ae80d3c1d97d0a5f5b6d772eb6e01", "save_path": "github-repos/coq/ejgallego-coq-alternate-reals", "path": "github-repos/coq/ejgallego-coq-alternate-reals/coq-alternate-reals-8e1ad799ae9ae80d3c1d97d0a5f5b6d772eb6e01/src/discrete.v"}
|
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