From 23af9637757e5cc3ee95d91b9d57f0d9277b142b Mon Sep 17 00:00:00 2001 From: Gao Date: Sun, 23 Jul 2023 17:12:41 +0200 Subject: [PATCH] regular backup --- backupScript/2D-MOT power.ipynb | 146 ++++++- backupScript/figS1_v4.pdf | Bin 0 -> 23891 bytes test.ipynb | 700 +++++++++++++++++++++++++++++--- 3 files changed, 789 insertions(+), 57 deletions(-) create mode 100644 backupScript/figS1_v4.pdf diff --git a/backupScript/2D-MOT power.ipynb b/backupScript/2D-MOT power.ipynb index cdb9ca8..3560270 100644 --- a/backupScript/2D-MOT power.ipynb +++ b/backupScript/2D-MOT power.ipynb @@ -10,7 +10,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -51,7 +51,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -65,7 +65,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -75,7 +75,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -101,7 +101,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -3307,7 +3307,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -3317,7 +3317,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -3326,7 +3326,7 @@ "2492.803132748206" ] }, - "execution_count": 39, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -3337,7 +3337,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -3357,7 +3357,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -3371,7 +3371,7 @@ " 6.97112396e+07, 8.07563517e+07]])" ] }, - "execution_count": 29, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -3880,6 +3880,130 @@ "sat_ncount_withpush_errors/1e8" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## V3" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "def curve(A, P0, Psat, x):\n", + " return A*(1-np.exp(-(x-P0)/Psat))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=(8, 6), dpi=120)\n", + "\n", + "plot_axes = fig.gca()\n", + "\n", + "plot_kwarg = {\n", + " # 'fmt': 'o',\n", + " \n", + " # Style of line\n", + " 'linestyle': 'None',\n", + " 'linewidth': 2,\n", + " \n", + " # Style of markder\n", + " # 'marker': '.',\n", + " 'markersize': 5,\n", + " # 'markerfacecolor': plot_blue,\n", + " # 'markeredgecolor': plot_blue,\n", + " 'markeredgewidth': 4,\n", + " \n", + " # Style of errorbar\n", + " 'capsize': 0,\n", + " # 'ecolor': plot_blue, # color of errorbar line\n", + " 'capthick': 1.5,\n", + " 'elinewidth': 3,\n", + " \n", + " # text for legend\n", + " 'label': 'Experiment',\n", + " }\n", + "\n", + "\n", + "# 1.2157134383410748+/-0.02622973148346536\n", + "# 0.2556310933363963+/-0.009332131323758952\n", + "\n", + "Nmax = np.max(ncounts_withpush)\n", + "plot_axes.errorbar(powers, ncounts_withpush / Nmax, yerr=ncount_withpush_errors / Nmax, color=plot_red, marker='^', **plot_kwarg)\n", + "\n", + "Nmax = np.max(sat_ncount_withpush)\n", + "plot_axes.errorbar(powers, sat_ncount_withpush / Nmax, yerr=sat_ncount_withpush_errors / Nmax, color=plot_blue, marker='o', **plot_kwarg)\n", + "\n", + "x = np.linspace(100, 450, 1000)\n", + "fit_line = curve(1.422, 105.9, 234.2, x)\n", + "plot_axes.errorbar(x, fit_line, color=plot_blue, fmt='--')\n", + "\n", + "x = np.linspace(100, 450, 1000)\n", + "fit_line = curve(3.843, 117.4, 923.4, x)\n", + "plot_axes.errorbar(x, fit_line, color=plot_red, fmt='--')\n", + "\n", + "plot_axes.set_xlim([100, 450])\n", + "plot_axes.set_ylim([-0, 1.2])\n", + "\n", + "plot_axes.set_xlabel(\"2D-MOT power $P_\\mathrm{2D}$ (mW)\", fontsize=20)\n", + "plot_axes.set_ylabel(\"Relative performance\", fontsize=20)\n", + "\n", + "plot_axes.tick_params(axis='both', which='major', labelsize=20)\n", + "plot_axes.tick_params(axis='both', which='minor', labelsize=16)\n", + "plot_axes.xaxis.offsetText.set_fontsize(20)\n", + "plot_axes.yaxis.offsetText.set_fontsize(20)\n", + "\n", + "plt.setp(plot_axes.spines.values(), linewidth=3)\n", + "plot_axes.xaxis.set_tick_params(width=3)\n", + "plot_axes.yaxis.set_tick_params(width=3)\n", + "plot_axes.tick_params(direction='in', length=10)\n", + "\n", + "if np.max(plot_axes.get_xticks()) < 1000:\n", + " plot_axes.ticklabel_format(scilimits=(0, 0), axis='x', style='plain', useMathText=True)\n", + "else:\n", + " plot_axes.ticklabel_format(scilimits=(0, 0), axis='x', style='sci', useMathText=True)\n", + "if np.max(plot_axes.get_yticks()) < 1000:\n", + " plot_axes.ticklabel_format(scilimits=(0, 0), axis='y', style='plain', useMathText=True)\n", + "else:\n", + " plot_axes.ticklabel_format(scilimits=(0, 0), axis='y', style='sci', useMathText=True)\n", + "\n", + "plt.setp(plot_axes.get_xticklabels()[0], visible=False)\n", + "plt.setp(plot_axes.get_yticklabels()[0], visible=False)\n", + "\n", + "plot_axes.legend( [\"$\\Phi_\\mathrm{3D}/\\Phi_\\mathrm{3D}^{400}$\", \"$N_\\mathrm{sat}/N_\\mathrm{sat}^{400}$\"],\n", + " fontsize=20, loc = 'lower right', bbox_to_anchor=(-0.07, 0.7, 0.5, 0.5), shadow=False, \n", + " facecolor='white', framealpha=1, edgecolor='gray')\n", + "\n", + "fig.savefig('figS1_v4.pdf', bbox_inches = \"tight\")\n", + "\n", + "plt.show()" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/backupScript/figS1_v4.pdf b/backupScript/figS1_v4.pdf new file mode 100644 index 0000000000000000000000000000000000000000..4b16226ac4fdd4e1d1d7248b6c8f9661ab674f8d GIT binary patch literal 23891 zcmeIa2{@L)*EpO|JV~;(*dCQN+q2;jvPbqUyX;$blC6k{EC~tO*QBy!$ySlIY>8yg z7E+=_S^slCQnvT^_rBkIUEg(m*H_n==bn4!%$YN1X3jbHIm4tRA;pQ{Lc^FI^}@4D zVF)-6+|I}fCMXE!Rx&gvb*eP z49EhF`!87_N^PJC1b>}~QkK?W*l=zsYd{wXQ)4?5Kt?%JTXSa%IAV>q0DC8AM^i%^ zm}}hQ82Jc_bL9TtmQODP9D1jE_vckzYH@>7*7LqlE1Q zUgeZ=@t-jv9ZF%JS{_`P_t zY0CHdJL@AMo}ZPsHTr@`InUhx5f)y|%qfwORVS(7GW1mcwcCj|4h;%H97M-GiMWbR z@U^F&I3RXG7?Zy!b*H5;|Ea@A)f78P+Xr=$%qOLU8PUxYU01X3>hbzEbWs(Pxn;wzRT1F5T*(jwX!rR+ZK z*BHh(DTwUb*ar-q^qyMp23>4wmJLTQNR)cA)0|n!e_Ja-QA@|&3&-%Y zXHHHWds_2DNsc_QiBXYP44XV_nlr>z^g+O;No>{4S#zn3YG6sA?MIj@hoRNd&TL9oer`7n-CqG%0x#mo=1D4Y)j=!nupddjs*G{;M4>_5RqP7?Xp&1!e_@Wjm`Mp(60QP zwd@&rjPKWDui5W&%RevLtv>&{T3+F`JhL*j@ao{@-!GOsepn7%d-27UVSiF_1fG(M zSI=s<*U_7J`8>ysIZP86)JjyWsRssM8_X)^r+fW=G;r*u+4#j=Q^vu2_@XktG7Ys4 zn6GsV*~&%+;HfV%(HGZ8%~~A0hkn*cebywAR(i=+M$tFTD4qrsskZXHRjlToc|f2` zo|-1e8RL)$mnDK_Qrjyj)^= zCsiB=6IE{~DP5*wor*p;oBe}r-;3G6Ym%99CIf6EQqscd`NNT?e2dv?%f~3=`KHsK zo>5SCmt!6+{}vfcCCAG+#n1F6Og3_SE^bK9u$a6mR3i?9uf{#)t@GO8VAuGSzN(4j zUt|oQqke>sT=P8h@wSQjiwN~Tx8QlZj0H8r&$Y?JXuVvcjOrxJjEhs}#TAnU7KY+4 z1;c6LUtH#RWu8`zeNl8gch=kEyz>o5{swuon7wH6`w`^{K9=O-Y6txzR5Ft_8yY^) z%D!425)~F?c|4tbHr#jFmb56ik9`=iI?-YwZuBwF1(`2o#DAGDq&6BpcZ%;tIbPK} zuO&;{xzNhJuh{fiVwg_15;%Aaf0wb%y%0*uWGazKGj-E74CZ&~+GJtoTqKh~!i3ki z=DCi1{bsUCrw6X30b&HH_$`zqeOF!}0TTpIWh6<=07I3V4UMFR9h(rnY9$QdE zkj?@V5I&t(gr5|URb>*=8R=)6^ph`Et<2r#HP>R2GlzkFTw6_UlQYM1*J4WHL4j0` zyEO2g)3RL8AE=xt)Jw|du|w|tl#*$6giW|C+Wk0NRC5V?`c)z3KtfH4_?E7P2fSfn zAL3!j2Z`Wc^s?x5jHDKeisdfAI<@^pvCPRL7DIdK|v(JxF z;P%hTt~7%NF18m>Nn%9{($5cFVx&=%^V{dvek%fYDX#k^miE3=Wo~#^t=v#9it&?* z$}`bM77dSrAQ6S^K>TAAV@BeA@u>wr7sR~UeSO&X>Nz|<;wxEjmYFiARzeqsHTgAB zS~;z8vXa7yv6T-gA3?0^t6MW8t$2}i4-Gl3F$|IJe9rltd`@g^yWPpl405bXGuZhhRPfg2d+Rnk_@_2O^H^Eu$ z!|WEpTpAW%C^J6Crq)rt1+ zMfg8IH}~jdj=9U!o?}#3VwE{LV_SM=l8F6&{PQySkEidA>6mo8Qmr`JvOJZ8&qNT~ zzc4gCz0ZREf-DEcusokC=GG^@Vzs@A3##*pw^QovLc7mn-r1TFnw@z0@*STMXM&EE zu)RY6y#Ts-Kd%AuVyouN#yNh27MhBEp5+epbFk@2_Bf+R856r=(n}8+l}2-ZS+%6-W5$72>vb3^TDp z;L5Q2yiDIos?T=MN!|t76V5C4)kO4M>0DhI$euiu$bIeiFsJ4Vue{>(i*H;@>}!V> zjJ_>3SYsUJ1u09&@7?ieW@GWM>1)Wp)bdpR1N-;+*p7o;+SAfz`bsKFdybuc%zCQB ze;;dRPs9ozrbNdHqoLR4brS_y{?)StzF z-lwB=K!s4RK_fsm&;3Dqz;Bk zexAPDJ@)z@`aqD~x4b8rFfx_Ap!+#5GJ=rej}u?_=XF!X1poNuIzRMntf}w#_`MQX zS>`wWk9(4-UA4mJdWx#9J|6nHybv_CDj_Y|3kl|b#K}J*I!?@lyPu0#jS03ySPRQcUUAA z(rxXfO)bqWoWXl;2!H4eClbYrQ{K#h+Q_@>;s0MShjGgQJ=)S()YjbE6zWdi&=rR- z9D%}sM^$H2n+s6++H7ngD7WTcJU0vr8t2-2=H=mm|1Tui{}cuaa2*=VE*kIzKZ+mD zi{gc0!LaxMlOcgtjl%Na5(eN2y@%?fcoAzkiVv#)pAu~SJqpx?5(HyC0VN*rAw&Rv zG)MptmLJZ?2cA(#I0keMsr7sqBpivwz(9iYVtC;g3;@E5g7cw4U4DQ75BLq`(0dd} zz^cFp+Cwn0{8*5$l|eafFepA4ltV~BgMtz-0)=af0Rvxy0qTN&alcV$KnQ+Z!th~n zIhGIL3(9#BNHD;)e$k*`3=$9q3$;VTF(Bs$#Da2M0=5c>7!VPl9O@r8X%GPc4uG&Q zs80-NgCoj1a!`)rG^i&u^uPm=2tpeg8kB>Cn-{3NwFJX}A>vv>^!@h(Ah-qsxz;-d z!U|x%&Iaq#wVnfPVe5$CutM`8K!de;g!o_+s?eBl%mf$;YV{WigcJ`C4EOF204y&S zEYh_EF~S-TKzy({;b;x`0^*4c3C9l*Ppl^#ul(T&h#xj42rt|-fC2HwdV)sI%fk=m z5a0^&zzzuu`oQr8*qnGbq;;NH&jBw0{LnC54*lMcaC5!(9;)*j{ z&^4hmn9n+MJxuu~>XrFWgYUaMw7nfw@Fk+0le0wfdm~;}SH`-M9Kg#oykpEYpdO{3 zdG|?dNJ3|Yv1o_oJ09Zz6LO1t=gXrl z4hE_-B(1XZr18mD#*s#rA72d53RShf9g;d)Q{4PmGZ&bSmwcXmrb>uchw+Wyxx)2M z&5-2b33dF*P+`&3dznTr@BR9{m!`n5eFxfalxqGHqoH{CHZxkdtSvqQM?NmHmkV79 zKhjuBqx-#jSePbm(RNWwT1A!0X{pkOp(Ad1O-$S8#eY9z-C}0;Q9I>c;ArGnE0xhy zif31&l%5t#&F33t0>$IX*dzOS#2W5c4v6X&Zev;*#5k;E6B`d;wb zgLtZ^->N=;e^zw!88)-rNoN%<+aos_>Fd8gm>y02a7nD?SMF)1S?Le{C`Xnli`q}^ z^+RDR{UHRhx1U%r{`i<<`<~07Tvb5Q$V9>Mk!6s&!n_jYyOLKGBxFCWCB@mC?sR;< z_^SE9r6_A^*YgX6^r?N!vZNQfdLAfzdo0-1I?tfp>CCVL?Kf@*|B3d1|2J`NNaf*| za3X?LHBn+1tXq*e;^y3H2pQ7TV2pI?2H}AsHz@|fyUhAjn)}Zi_bS~MsTg$RZ0IFC zWY6N&-KrKB_3n!09T|1D-ZXNzRZ?^2um^5$jpOGJ7%!d_+yTqoY9lDH{cS{(Q$^OX z22RrB1k+1G{n|ewIN@CCTCrLb9w8}3YA}l!^$$GnsRb`ieP3N1(kS9Ln3LV;*CO3h zFA(r1Ea+J{r=N!e#|WZYu-fNa_6$A!??;UdPxseycU?JjdanJ3DdTX>v2H`EIzy@v z&D_h34?SE}(_Uqr`Y;@{DqQ2lNxzVeZL?!zyKaDg8=N%}#@??=DCcP`mI-Tj^Tcg)VEJZ}qd)mAZ8@ zEHUeZU{YE`BQtF1b{xGUJHFxP*eA}8H0lnS35}ThLB%K;v&rYuc`;RPdi+mUNL0-V z-B?;2Q`)nLzU7(AwzguQBveFcSvcRPC{Mf5Qmde-@LU5m$%v&Q&N9;0sI;VvF(U3E zzo@FL8tZ;+W%2ve%$aK|cpOfiPTQvioNK!~AuyX~1;xv&!Lvt9Pxd6CF}$a@UWKtH z>`i?;L1W{n5Ai2^t)sr`qxQ!{ETdCspO8F3?^(P-x&!NiQ+;>6Bfz%3ktrl^%aRg+ z^JzXVCG=$-!jK_**inF3ZCAEar+*K|d zYWR5bQSBSX-XxA`9~pLkMx~(W<99esZHv8ocn{|1-&IWy%?(QBxjk24O+|k(Md!rj z@UyrF8Y_@5}mAf}VS&;US23pe6JHXk8ciPYV%+=GinFj0)D406{Xj47&>V zw(a@W1(&|F^x|Mv7bU)fvr6O{Z8euSH~rKKzc~LcxZ6$6H*&?)ubQ|aoBXSazx0Fh z3ydA#Vvsq-7;}PpmGr8Sv=N5}pR=#hGY{1LF3QUpt*5(9uSs<>S>h$Krh5E%RP$7> zna;H+%a^xZZ^WH`&snEQs6x}n|Mk{qkC|__b7LPdC zgs5ZuL7MRD81eZ9LCP1mnJDNY>jy{=CVO3^PYsUa?FZ)Cvo=)wNr#@k(l5DAb^geu z@Oiu}x$)~0t2r};&pSIA=)QW_?l3vfhs)he4iYH98(EE0S;i4xl&Gxb^z03@%mL$j zr*Dp|b`rhbzwcm>h_m)kQg9-iL!*53PPWCX0TjY#fbizE)15Y&BO+>W8Dk|(vdfJ7 zEd2+EXyV3*_qilyj}9#bv-OVG3?h!17k8SrlYP<45sDFQc%)m+L~%r}!|Dd@0 zSAmldAAC$o^PXXt?S(a!$l#Xq8K`RKcd^vh-2LqDDJBL-Y$$C|0%4SO^- ze6Lb7sJY*1E5h3`G*BKeycnD_Huif7|Du6Tzz#HoJ_PTIhCmG7NJES=mK2|nEhz&NTZ>>k4vSK8azXoVh&D&TbTepXb@ z{W$z(pjm)#rDgOpyjVdz@y@pol*NzGx3;INvgY;@2;VBUJ=AAhc>P`x<5(cvi1iqU zXj7IX1+`5JOB&{deh^Lny{~=ss_C^+N3ZY0yTDJ|Io>&aS4ffhtuqy}Jsb1pC^3cP z3Bli?866ap+UPbUd3){QM^^adXO?*s_Uxf>c(d0e@qFadk!riV*{Az77FO^QUi zrw?)?kNvalh{-D}Z?f%dwCNymAJ;e8bYNR-I%qyPx0AJ@lLegH-PF+zC`xc{=PP#L zkAHThuvv&n}anBgE(;0ALK^n2aZv!4I2nS5}k#QI(*0ms>OfWML<=n=Oo zuAKqEWQTKO0&ZS^Y(i_t22RKa)q(dGZWD$5IXQr#3s)aHOn`um`;9wcfC{#GuSM-v zdjod7jR!vpS**n4{fbf?r~vc=_!`^KOdtyHPNqNr)8|Iel8RW;S3D#=%dl&3@5^t# z$sBf;UVU-FQOTKHJ|>OzM6`WXrPFl{IR8vaWy+!XW0C&8Xi;rlf&ijhPLy(X2*RHR zIyKZ}4mU(BjhN6S7DL`8?=Sjir{W7t(!OZ-$R+GG|Jt2HVp*x8T>+DcSK`(y&{HeU zVLBC*_j!W#Ub{OEUb(}2&6jGES1*VbGF)B}k zQ#5A+$AnbXg$h<*M5VnUCa4Sx@HXTp?Iup3i1545XMU|fW3i;=Q-%HP9-FEVnl43M zo4h0$gao#HA5t&0MdCeiXBj;{ebta7f4g;Q8lhvy)7LqD716NM2uE)>EvVLIc`Xi6+}V3k4t>)R>2sC>lD9R|2twP(xtfC_nmM~R15 zg$IdN=i%kqYh>(4DbDpknkxX0{Pm!ZjF=|8fj0W#ngY2ZLrSa|DRa; z+7cp&dRo{#ub*F%Y5wsr)7E{}M!t=uiHJNGpPTh!E>{5K$arK!54l99-nC9u#lC!rL>IxegfBZ`6mG+pP41+y;h}EDbJ!9C2 z4Xlg(tm)KK7t2VL;BP-f$ET(A+u1}{?nxzyjMkvNR3Bq7?bJ0p3)y3dA&OCB#F0^B zT@gh^AD_N{^m7G2R@{tp2ior1U=6-QZlbLM&@eqyykpQ|Ng;QJqX++y z>h)u~3o@1FeuMeSjGv$FuL?};U~q(m?^CBAY%q}GW*NU;Qh7^LDBB~rk6uW3aE!#C z9Yx{rq%VlS+L3FZZ}^BMX@r=&*reoCp6jYLIg@FY#MHZ9AEQIWI*jG|y-#oAX$Wz$ ze@@Y1)syCSGPxt+kaR0pqg%3?S%m_-pUJY6q7FXxFpaR{1iypQea~|WXO@`bq_9F) zf*7kBpX8;=JEmKlHEyHJqi+6sy-2n06ehMsyRRUj(2*ynun5}!S#rQNE#gE7nZaKV( zbrb%>%K74PiW-@9DaF}0+A`-fb&5}Zt+@+t2~~Ys!!p!(dEv}41^=&|NKdKhp(FQ= z;7*AFOce)4c;7jaJ)a9@GVgSK!nv1q@aSQo!^f4&X|1L%y(=>lwC78e*rOQHmL-~P z70JV4p%b6iIOBRT>>+A)Dd%muv*?}iC#)nz?cxNlCrFpt8u%qA4ilPvrPh37f_hjp zS*X>a>D?%$9i~5i{K4gmGZSrTchcGzJ<=ap5=MR}XN=ztFn1IxBh|9LGU5}7y=pCG!f}z}(=+#F;L_Y{Fy}co)~vE9z4*L) zuCK#2Hh1{c{evxWC+;14+V%97iP;Kj4V&OuB&Ox*v_E-fD-N($Mfzm@y2JX0!I z$+Q&wuzK4;#y#s!_u*x6PRP=hw!D*wF~)Rm_~>Aa{W!8e$F8GY!8K*zgB- z+J|!Zo=atk3me5$iNU`cV2tQK^a?6IuenPQPDY(Cnc>Q8L0Wn;JZDm69mnffRXfgH zVocp@9*jCM^steyCVzUVtn=5gZ)aPJv@Et$5Xiy1li*O8%?ngWW$ip1qts1O`4#a) zPW|%RE2}mKCnjG91R{8c=?i3ngrghWRKF)H>buWo>?D)JM&HT!WwxlQiY z)p4!Q=aa{YT7A-*KR!M6%LP51BK?7F`m>g9(}f|T?*#6Xu3pz;d7O_WKTL=Sna#52)40oa@7?^0F%`KDb5 z1W7){9%#s`EV^Qs!NPWil;Ci~Q})nSS$a+JxNi}{lQf4D^A<{}NPBqQnw3<=QHBW= zqLkPB70xqT#qr3{4j2wxmEz?m!B6;o3Q^=Bqa@eb5OgBKRHN(2L6zsUg&wF^ z1AeY)Pp@ClyYX!KvP;u}m9gA6a>vBAW{WwwsA_-x{9t^XdG^Dz9;*Z0jn?T+RRnvB7{PY>z%%RoLz&4tb`=FWo_1~*j|Xs5 zso%?GVqrQldYSR$XQFEkwQ2jGOneFNQ*n{tOusdu%@rcxFJNsHZ+!8d$L+dn6|GG$ zJI&M67mJvRQZ0Jlha7fwAe%T!d1v`0?5r;;;K=flp-;fff#1i5vRs4e;nL0+c*Y*; zv7(@kam(Bz(KOmTfrU{A$j*h>zc0S3=P9XZeDcY!*x7uyrp2VZ4vxJnl|)Tsk_Y+n z{bLi@K9fFUT2TBr-Z@CDI-M)$VReOzAqO}lhsum0AnU+-dH+ zy-E35Utl&ojwgV!9%gGv`ISIPic*O)i8vbcDUv+Fh#@BV@k!W6qi$Xak%QSklBG+} zdd0p=58h#}cPpABw|w5gC^a|`h!QO-Qo^p^pK^Ua`rSBwwR3N$u$csYx-YM9()||? zgWgx&6JA`Qi>_UC2vY1=l{Gw%mLN}`b{{(anY!(G$Nm_<*B?*WB|Si0q?hzN(Z`=v z9$UAfL4?Vr##>U?A#(gC2b-YFVJU%SQiA?|5F!Q)#%vpZb3TZ~6F4i{%u_0;a zgsM%*dx-nAu=$q3o|EpEbq1We`S$4pE_u9(l-5H}?W+MSUN)1rz z!o7bM3bhd4L<2w2EOD6l4UV^`D&{N2=sjZ$&lxP%6bpTk&|iX?*v0vMCVV5YSUEX! zxwZJ?l_mw<`wQ;*b4mHNMIZYm8HVM{QX9JI#?^cI)U3>VB=s2xWv+F=57`AQor)pt zt#&s2YIHh9QmEXL{PKx*ZXLL6jcYhpM<>O~1pmcMTlopR>A>KFNyUbbL=8y}-ZW~zCDhsALO1>}_PF+Ytj zvdZ*?SGm_63AXHfI87iFU8*iO^>b3I?IvBMe70@MQw&Ob?c7)->{5X^78P zsAA0W5x!G*G)Zf`e$jfw`-DoP^jo91alXed5#A2^N?ylnUFycf?0aI6YpL9yD73QD zMOUyvf zbK{qpJtH)u^@G*R5 zDmeK#sSm;UI<-kD-1SXsiQhod^Sj3NU+!mdJm@r&GCpD_F<>SBsrr%ybsD*L+~*ry z*JB-rRIVsQ_i{8D4&Ij(e|M*FIYd{{Xl^JpX}DYKc&oHRlu)4Js&ORKBWVKvpcDJwKS#2QC$!ej(ARO z>`=i<(_=xV?vhi}eret&kE^84Xh0z(m0*nW-k5KrPTF9^x&7WF9}3bqf4luoIr)8j zsryIwvDK3|?>k+W>Q~6-ztw1%UOcX(%9_GyC6aIF&mo5QJU@@K+}kj@FTP1|>?+fp zf`LPsdsA;6zHyFI=F_lda-w-REx8wpfid`c?9BJ}f|L%`&}R*@==LB9+ostcM6XQJ z2**l2DZDO)OZJibA3V+ep7C>){!Kok8?PVxSi4}#MOwbi39=xVFX*`w8&|4N?-M^B zVu-%=GMw$0fm+2kXfty;K6$?tLWt7R8BbC9UA1?aP8;tHG zN}5XNt+@Mg?y~oJg3mTjokp@?mo5)hEhI#F@v+J~!$h_043#9tJSgSS{qyFS59Ph= z{z6v$6eS|wVF6g04uS!jh2({UUk6tA)J|sd?Z5%MSCFLD!_YgaM(@7BMCE%?M+Q!YvX=$il|J_&tI}MlkFYJa z+6g24ilr?71JBKgXQ5N`YIK-OUEN$_Z*^M($R=IBNVvJyY6YBlaDmzSZc9quNv$pi zELSI!E-UkMC#7%qET3D-o=o{Nx%h*jF1<)cs8X^f%{8mDaa@Lr@v&RY$$bj)?6<~j z)YPOjn9UFOS600any6N*!;IO@_R*;q4!Ii^20ylQ$uA2l^ZlxAK`E^-PnGtbo2=ny zsqmMtO5KH@S1-;7=gce+dPiuT-A+K@H@!Rk2#MJ&a04OC2DpVRmce+*L^5x?Gi#3I zO{~(K?>7)6Gn7rMZzR6X?U{7~bBF8K`@;WRTgNS1&u2CPK$uLP8>mBRYDz1oW`V+!Bmk zbcf?cmtLvIbG#1~G}E|`dB^I@hc|4b$0kpw6Vflg3dXgjsczPA&KT zEt}tI#@D6}qKH0Ec02c93m>Exa6rDDd0~|GVr5Tlw_d^ywA`)of&d<$8&3myMfrRm zV0)dX#O_tP_S(&v_K5rH;|Yzi-_-Fp_L1jmlc5LN1M~gLLe(D)&C!7FW%h>@4#Pr<4{pZqzX9rQ26Bj8O-YkpUCr=c53xb?`4q_|)S1cx+*XQ0VHV!#hxSw-PJ@1QTqWSa~HxIHcSF zle+<#>mcwEe^S5vA_N{GcdL*9ng1c+q>chZ!aahb-plgK17zCYbwAI%ieu0cGs-^d zSexWAUG#BbO7~0jfrnQ`YMS_soparqeQh(fu?^>IxLqDU_+C4A0Z9drp z=kt(TZ1~l<{}2b=9ZXzT_dP9v`#NXD;%Jma?V^T14jP&I7MluY8VfWOYF=s?PsTr; z4$BdJ(_f%v9((q;uGi$uUS>^+9X>kn?pDG?g2;@G6u>BnIsy}5=_1vFh&cyw!;D8; z*F^g23+lL8g1{SlGz#V46mVZTJ#Yd0wP*jTgv}yV1N+Hy1tpn=>3y#*ISgHUbso;Y`12}djUo}ud|d#cig5ff@t(-gX& zsywPaAMzW>eJXWpNUBdxX})Av=CMR_s9e6r(}|J$b)eiQF4TbOB`p)pvu2z*CgkBG z6ppbP6_fUH_(V}3_>a=XhMA5ZK59B>!E;y9j)YoHm>^n{nZIF_J%g-U>Cw5=q>}O1 zACDbc>0DUCtGubPLm(TFZ+CgDfk2^+RFelD^I%m<%nK88lZh;y7f`&F6g0BZBg`)U z)=%DDqKTv~hDrS4NQQ@umCQmIcZG0kc3C@Ti|nx1k*q#-=|;9vkvYnDm5pJGru5;T zj7)F7o4dbw%`N!d@7_i(;>%OiEQcw0pJ&M2@Ie;xv0yV?n}=IF6bio!PdrgS7nnkI zfbYG4vTQDOJt5b*0IfIp;z8H%vlsPKz4{qsuoPwgP|m>S`N5y$r06d8aoNW#_^B@v$hmq}8 zq(ty-`R1<#K8Z7ul>LBt8JseT3Y(Wx|Jk~-s(mvnwcI(0DB%1OIf{*Mu zTtBL+pvn&{zI-cEB&6k0rPUZyT{_%qkgb_@+$r5s10^A@c==#ssHDV*r7s)#RW(A3 z73NoB67S_R_L?+(Amnz5>6&f1c6wwbrteC~=W%yix~k9*rhSLo?xy$`sWq#F5@R3v z;IWI^Q#(?)RQ){E$rnGWo^bKWQK7}{g9hs9?$!&4!`!r9Fd+z1V0Xp$&>v*+rpO-= zD%Q5Sv*_g!8hLupo0t53g8>=uv%?S$HxWgPnBP5G)CuPhT&a(Um%nxJDa zjtavg)g#eUdv~bN@+s+9B6D+E)q7&Sx$YnM0;II+J7BwOK@0e;ZCu@$P+8!$KqNjN z=X0`pDLgJyy`yG!r3a5$51wEhEGX%k`$_KT8QvG70pwn3oVo>H_n76P8=`~rVyW?C z-Pwz@+QZ{7rUgViEf+3${W#9p z3$YPt3fzldo*h*JMzra@dHbm0|`Nl zXvkd?z=8&|jwCK7c#R0Sy6fdg2nUd$_fQV?|L5J`0pj35?*VMwJMbIA5eF0ehH@xO z3<*ptP!16Qnlz{`lmoapSP-1G@Z&#-{4Ib+^7jF1f(r`&!$0dNLKQ#)LDy@baiIRT zT0;T_hX{k~93TaOg^-3Stq*uT2gv?KiVqx4C?F8v-T~yH`cN4LxX3}#*BIby0F|vL z3?B$6j7dB3smzcY^E{mTuT-~htx*at*32rkI~#!2H2 z^mV7%zefW&wsGF9z#sNsnGtl9L;K{0GH`-JLJ*J${?-H6tN@?u&Gn$e{EzSKzj{D4 zz`4+FY>S2@f-P-9ygu$o*w8Q7dqKd*mVWslfniHquurL7SN`QK6x zhyZ}oHn#<000uXX4LAc)0DKtUQV(Lgtz`h-pQG(B4N!*QhK4Qm08xMs$Yywe0Ko3K z8D}t9oYi#W*l;tywXHE6XC>P>M?hQvw!h7NKnr~9H~>+=3fkN+APUYHu(4l26tGyf zzytg2A8GOLc)?)7Hn^o87LF79H^DQ61A`q@4~Wg{hZuNXXH+S0Q2gmn3JIdt*FZ{v zV`6Po|5|M{i-{s|m%)gOg6iubKY#D<;O8MLk12E-r&{Xy(s zzZ(RIe}xd9e}{nL+;EYNl7%JMsn>=f58YJp=O?%tVr@?T0l~dnhq4}&ghX-y(F*tX z-ycY}`VTSuC~)7*u0!}yO7%lXrRlv!Cxuk~bh-4%2$>z9g+(9J)m6~lQ}Av7>~`e> zhxz{oq6lyy#O^V*2@@jqhLiZsC-@u?R1v+VOIP6^gqT^$hHFboOIykgoa+(&T5XkV zVo;#EKX0xdSx0`TTh74|%k#dBKSOSsk&bZYLb!&Y4~KuQW3`4}=|CDhNXnbx`L zuSRQ)hhAbQ#SVpJ;HfPSP8|QqW05I&<+Mfd&9ii--XRtl6mA)^JGdBZv{Q@QT?Y5; z0OpdlG=c6T0=px`>R`IoxNYqg+J7?+6#RdDBMz=jb+)v#m4GgtWt9*>@*u%|0Z1Mo zO#?ce=0URX@UXDKxN-HsT|}%vhPJo1bGEiLg1ex&FkA=@xP`N`y^{bpx6R*ITy~D; zpb@x2$>g#zxV&{^D|-_&xRIgpC9n?vKw#Uz{jQFtW-uUnpkO$C5BCRb8W~>uExA%(%bGD@|4~ie!2R6auL7{1Z86)+kU%-!(g!c!w)q8@Z*AijaH|g_(rm_=4-K8}TgsrTZZ?-8KuFcrei2wS zxK?IMJ$^pGQJc$ICKLIAef)(4V*8y|q#g(9vt;{wF; zZEaEfkc7Co9`LT*%HzO}3rTrf>hXemnzoiv9p7YD4eLKYUyqYHh5^$cXBp#bjEG@NF=xg55~kK IsVD{eKmVdd-v9sr literal 0 HcmV?d00001 diff --git a/test.ipynb b/test.ipynb index f46ff34..49da503 100644 --- a/test.ipynb +++ b/test.ipynb @@ -211,7 +211,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -248,9 +248,19 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 3, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\data\\AppData\\Roaming\\Python\\Python39\\site-packages\\distributed\\node.py:182: UserWarning: Port 8787 is already in use.\n", + "Perhaps you already have a cluster running?\n", + "Hosting the HTTP server on port 52475 instead\n", + " warnings.warn(\n" + ] + }, { "data": { "text/html": [ @@ -258,7 +268,7 @@ "
\n", "
\n", "

Client

\n", - "

Client-f3431762-0b91-11ee-bc80-80e82ce2fa8e

\n", + "

Client-5ac7f000-261b-11ee-9f6c-80e82ce2fa8e

\n", " \n", "\n", " \n", @@ -271,7 +281,7 @@ " \n", " \n", " \n", " \n", " \n", @@ -289,11 +299,11 @@ " \n", "
\n", "

LocalCluster

\n", - "

6e648e73

\n", + "

c80cced4

\n", "
\n", - " Dashboard: http://127.0.0.1:8787/status\n", + " Dashboard: http://127.0.0.1:52475/status\n", "
\n", " \n", " \n", "
\n", - " Dashboard: http://127.0.0.1:8787/status\n", + " Dashboard: http://127.0.0.1:52475/status\n", " \n", " Workers: 6\n", @@ -326,11 +336,11 @@ "
\n", "
\n", "

Scheduler

\n", - "

Scheduler-669a9b65-bae9-4798-96b7-f5d552eb72f9

\n", + "

Scheduler-0363aa80-9ca7-4bd2-8096-e563f44991c3

\n", " \n", " \n", " \n", " \n", " \n", " \n", "
\n", - " Comm: tcp://127.0.0.1:51057\n", + " Comm: tcp://127.0.0.1:52478\n", " \n", " Workers: 6\n", @@ -338,7 +348,7 @@ "
\n", - " Dashboard: http://127.0.0.1:8787/status\n", + " Dashboard: http://127.0.0.1:52475/status\n", " \n", " Total threads: 60\n", @@ -372,7 +382,7 @@ " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -417,7 +427,7 @@ "
\n", - " Comm: tcp://127.0.0.1:51088\n", + " Comm: tcp://127.0.0.1:52506\n", " \n", " Total threads: 10\n", @@ -380,7 +390,7 @@ "
\n", - " Dashboard: http://127.0.0.1:51093/status\n", + " Dashboard: http://127.0.0.1:52509/status\n", " \n", " Memory: 9.31 GiB\n", @@ -388,13 +398,13 @@ "
\n", - " Nanny: tcp://127.0.0.1:51060\n", + " Nanny: tcp://127.0.0.1:52481\n", "
\n", - " Local directory: C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-9s507mc2\n", + " Local directory: C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-bwtzfry2\n", "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -462,7 +472,7 @@ "
\n", - " Comm: tcp://127.0.0.1:51084\n", + " Comm: tcp://127.0.0.1:52512\n", " \n", " Total threads: 10\n", @@ -425,7 +435,7 @@ "
\n", - " Dashboard: http://127.0.0.1:51085/status\n", + " Dashboard: http://127.0.0.1:52513/status\n", " \n", " Memory: 9.31 GiB\n", @@ -433,13 +443,13 @@ "
\n", - " Nanny: tcp://127.0.0.1:51061\n", + " Nanny: tcp://127.0.0.1:52482\n", "
\n", - " Local directory: C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-y5skkt4c\n", + " Local directory: C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-mp7n6m76\n", "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -507,7 +517,7 @@ "
\n", - " Comm: tcp://127.0.0.1:51098\n", + " Comm: tcp://127.0.0.1:52515\n", " \n", " Total threads: 10\n", @@ -470,7 +480,7 @@ "
\n", - " Dashboard: http://127.0.0.1:51100/status\n", + " Dashboard: http://127.0.0.1:52516/status\n", " \n", " Memory: 9.31 GiB\n", @@ -478,13 +488,13 @@ "
\n", - " Nanny: tcp://127.0.0.1:51062\n", + " Nanny: tcp://127.0.0.1:52483\n", "
\n", - " Local directory: C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-gmddkxg0\n", + " Local directory: C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-co90qahq\n", "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -552,7 +562,7 @@ "
\n", - " Comm: tcp://127.0.0.1:51095\n", + " Comm: tcp://127.0.0.1:52518\n", " \n", " Total threads: 10\n", @@ -515,7 +525,7 @@ "
\n", - " Dashboard: http://127.0.0.1:51096/status\n", + " Dashboard: http://127.0.0.1:52519/status\n", " \n", " Memory: 9.31 GiB\n", @@ -523,13 +533,13 @@ "
\n", - " Nanny: tcp://127.0.0.1:51063\n", + " Nanny: tcp://127.0.0.1:52484\n", "
\n", - " Local directory: C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-oycines6\n", + " Local directory: C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-4p3qclmm\n", "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -597,7 +607,7 @@ "
\n", - " Comm: tcp://127.0.0.1:51087\n", + " Comm: tcp://127.0.0.1:52521\n", " \n", " Total threads: 10\n", @@ -560,7 +570,7 @@ "
\n", - " Dashboard: http://127.0.0.1:51091/status\n", + " Dashboard: http://127.0.0.1:52522/status\n", " \n", " Memory: 9.31 GiB\n", @@ -568,13 +578,13 @@ "
\n", - " Nanny: tcp://127.0.0.1:51064\n", + " Nanny: tcp://127.0.0.1:52485\n", "
\n", - " Local directory: C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-a8kpxp6o\n", + " Local directory: C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-jl88pafx\n", "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -646,10 +656,10 @@ "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 45, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -670,7 +680,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -694,7 +704,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -713,7 +723,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -740,14 +750,14 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "f:\\Jianshun\\analyseScript\\DataContainer\\ReadData.py:178: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", + "f:\\Jianshun\\analyseScript\\DataContainer\\ReadData.py:234: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", " if not key in datesetOfGlobal.scanAxis\n" ] }, @@ -1139,7 +1149,7 @@ " z_offset: 0.189\n", " z_offset_img: 0.189\n", " scanAxis: []\n", - " scanAxisLength: []
\n", - " Comm: tcp://127.0.0.1:51099\n", + " Comm: tcp://127.0.0.1:52507\n", " \n", " Total threads: 10\n", @@ -605,7 +615,7 @@ "
\n", - " Dashboard: http://127.0.0.1:51101/status\n", + " Dashboard: http://127.0.0.1:52508/status\n", " \n", " Memory: 9.31 GiB\n", @@ -613,13 +623,13 @@ "
\n", - " Nanny: tcp://127.0.0.1:51065\n", + " Nanny: tcp://127.0.0.1:52486\n", "
\n", - " Local directory: C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-thoxr07z\n", + " Local directory: C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-lczd_jce\n", "
\n", + " scanAxisLength: []
\n", " \n", "
\n", " \n", @@ -1194,7 +1204,7 @@ "\n", " \n", " \n", - "
  • background
    (y, x)
    uint16
    dask.array<chunksize=(1200, 1920), meta=np.ndarray>
    IMAGE_SUBCLASS :
    IMAGE_GRAYSCALE
    IMAGE_VERSION :
    1.2
    IMAGE_WHITE_IS_ZERO :
    0
    \n", + "
  • background
    (y, x)
    uint16
    dask.array<chunksize=(1200, 1920), meta=np.ndarray>
    IMAGE_SUBCLASS :
    IMAGE_GRAYSCALE
    IMAGE_VERSION :
    1.2
    IMAGE_WHITE_IS_ZERO :
    0
    \n", " \n", "
    \n", " \n", @@ -1249,7 +1259,7 @@ "\n", " \n", " \n", - "
  • dark
    (y, x)
    uint16
    dask.array<chunksize=(1200, 1920), meta=np.ndarray>
    IMAGE_SUBCLASS :
    IMAGE_GRAYSCALE
    IMAGE_VERSION :
    1.2
    IMAGE_WHITE_IS_ZERO :
    0
    \n", + "
  • dark
    (y, x)
    uint16
    dask.array<chunksize=(1200, 1920), meta=np.ndarray>
    IMAGE_SUBCLASS :
    IMAGE_GRAYSCALE
    IMAGE_VERSION :
    1.2
    IMAGE_WHITE_IS_ZERO :
    0
    \n", " \n", "
    \n", " \n", @@ -1304,7 +1314,7 @@ "\n", " \n", " \n", - "
  • shotNum
    ()
    <U2
    '11'
    array('11', dtype='<U2')
  • OD
    (y, x)
    float64
    dask.array<chunksize=(1200, 1920), meta=np.ndarray>
    IMAGE_SUBCLASS :
    IMAGE_GRAYSCALE
    IMAGE_VERSION :
    1.2
    IMAGE_WHITE_IS_ZERO :
    0
    \n", + "
  • shotNum
    ()
    <U2
    '11'
    array('11', dtype='<U2')
  • OD
    (y, x)
    float64
    dask.array<chunksize=(1200, 1920), meta=np.ndarray>
    IMAGE_SUBCLASS :
    IMAGE_GRAYSCALE
    IMAGE_VERSION :
    1.2
    IMAGE_WHITE_IS_ZERO :
    0
    \n", " \n", "
    \n", " \n", @@ -1359,7 +1369,7 @@ "\n", " \n", " \n", - "
    • TOF_free :
      0.02
      abs_img_freq :
      110.858
      absorption_imaging_flag :
      True
      backup_data :
      True
      blink_off_time :
      nan
      blink_on_time :
      nan
      c_duration :
      0.2
      cmot_final_current :
      0.65
      cmot_hold :
      0.06
      cmot_initial_current :
      0.18
      compX_current :
      0.005
      compX_current_sg :
      0
      compX_final_current :
      0.005
      compX_initial_current :
      0.005
      compY_current :
      0
      compY_current_sg :
      0
      compY_final_current :
      0.0
      compY_initial_current :
      0
      compZ_current :
      0
      compZ_current_sg :
      0.189
      compZ_final_current :
      0.2812
      compZ_initial_current :
      0
      default_camera :
      0
      evap_1_arm_1_final_pow :
      0.35
      evap_1_arm_1_mod_depth_final :
      0
      evap_1_arm_1_mod_depth_initial :
      1.0
      evap_1_arm_1_mod_ramp_duration :
      1.15
      evap_1_arm_1_pow_ramp_duration :
      1.65
      evap_1_arm_1_start_pow :
      7
      evap_1_arm_2_final_pow :
      5
      evap_1_arm_2_ramp_duration :
      0.5
      evap_1_arm_2_start_pow :
      0
      evap_1_mod_ramp_trunc_value :
      1
      evap_1_pow_ramp_trunc_value :
      1.0
      evap_1_rate_constant_1 :
      0.525
      evap_1_rate_constant_2 :
      0.51
      evap_2_arm_1_final_pow :
      0.037
      evap_2_arm_1_start_pow :
      0.35
      evap_2_arm_2_final_pow :
      0.09
      evap_2_arm_2_start_pow :
      5
      evap_2_ramp_duration :
      1.0
      evap_2_ramp_trunc_value :
      1
      evap_2_rate_constant_1 :
      0.37
      evap_2_rate_constant_2 :
      0.71
      evap_3_arm_1_final_pow :
      0.1038
      evap_3_arm_1_mod_depth_final :
      0.43
      evap_3_arm_1_mod_depth_initial :
      0
      evap_3_arm_1_start_pow :
      0.037
      evap_3_ramp_duration :
      0.1
      evap_3_ramp_trunc_value :
      1
      evap_3_rate_constant_1 :
      -0.879
      evap_3_rate_constant_2 :
      -0.297
      final_amp :
      8e-05
      final_freq :
      104.0
      gradCoil_current :
      0.18
      gradCoil_current_sg :
      0
      imaging_method :
      in_situ_absorption
      imaging_pulse_duration :
      2.5e-05
      imaging_wavelength :
      4.21291e-07
      initial_amp :
      0.62
      initial_freq :
      102.13
      mod_depth_initial :
      1.0
      mot_3d_amp :
      0.62
      mot_3d_camera_exposure_time :
      0.002
      mot_3d_camera_trigger_duration :
      0.00025
      mot_3d_freq :
      102.13
      mot_load_duration :
      4
      odt_axis_camera_trigger_duration :
      0.002
      odt_hold_time_1 :
      0.01
      odt_hold_time_2 :
      0.1
      odt_hold_time_3 :
      0.1
      odt_hold_time_4 :
      1
      pow_arm_1 :
      7
      pow_arm_2 :
      0
      pulse_delay :
      8e-05
      push_amp :
      0.16
      push_freq :
      102.25
      ramp_duration :
      1
      recomp_ramp_duration :
      0.5
      recomp_ramp_pow_fin_arm_1 :
      0.1038
      recomp_ramp_pow_fin_arm_2 :
      0.09
      recomp_ramp_pow_ini_arm_1 :
      0.1038
      recomp_ramp_pow_ini_arm_2 :
      0.09
      runs :
      1
      save_results :
      False
      stern_gerlach_duration :
      0.001
      wait_after_2dmot_off :
      0
      wait_time_between_images :
      0.22
      x_offset :
      0
      x_offset_img :
      0
      y_offset :
      0
      y_offset_img :
      0
      z_offset :
      0.189
      z_offset_img :
      0.189
      scanAxis :
      []
      scanAxisLength :
      []
      • " + "
      • TOF_free :
        0.02
        abs_img_freq :
        110.858
        absorption_imaging_flag :
        True
        backup_data :
        True
        blink_off_time :
        nan
        blink_on_time :
        nan
        c_duration :
        0.2
        cmot_final_current :
        0.65
        cmot_hold :
        0.06
        cmot_initial_current :
        0.18
        compX_current :
        0.005
        compX_current_sg :
        0
        compX_final_current :
        0.005
        compX_initial_current :
        0.005
        compY_current :
        0
        compY_current_sg :
        0
        compY_final_current :
        0.0
        compY_initial_current :
        0
        compZ_current :
        0
        compZ_current_sg :
        0.189
        compZ_final_current :
        0.2812
        compZ_initial_current :
        0
        default_camera :
        0
        evap_1_arm_1_final_pow :
        0.35
        evap_1_arm_1_mod_depth_final :
        0
        evap_1_arm_1_mod_depth_initial :
        1.0
        evap_1_arm_1_mod_ramp_duration :
        1.15
        evap_1_arm_1_pow_ramp_duration :
        1.65
        evap_1_arm_1_start_pow :
        7
        evap_1_arm_2_final_pow :
        5
        evap_1_arm_2_ramp_duration :
        0.5
        evap_1_arm_2_start_pow :
        0
        evap_1_mod_ramp_trunc_value :
        1
        evap_1_pow_ramp_trunc_value :
        1.0
        evap_1_rate_constant_1 :
        0.525
        evap_1_rate_constant_2 :
        0.51
        evap_2_arm_1_final_pow :
        0.037
        evap_2_arm_1_start_pow :
        0.35
        evap_2_arm_2_final_pow :
        0.09
        evap_2_arm_2_start_pow :
        5
        evap_2_ramp_duration :
        1.0
        evap_2_ramp_trunc_value :
        1
        evap_2_rate_constant_1 :
        0.37
        evap_2_rate_constant_2 :
        0.71
        evap_3_arm_1_final_pow :
        0.1038
        evap_3_arm_1_mod_depth_final :
        0.43
        evap_3_arm_1_mod_depth_initial :
        0
        evap_3_arm_1_start_pow :
        0.037
        evap_3_ramp_duration :
        0.1
        evap_3_ramp_trunc_value :
        1
        evap_3_rate_constant_1 :
        -0.879
        evap_3_rate_constant_2 :
        -0.297
        final_amp :
        8e-05
        final_freq :
        104.0
        gradCoil_current :
        0.18
        gradCoil_current_sg :
        0
        imaging_method :
        in_situ_absorption
        imaging_pulse_duration :
        2.5e-05
        imaging_wavelength :
        4.21291e-07
        initial_amp :
        0.62
        initial_freq :
        102.13
        mod_depth_initial :
        1.0
        mot_3d_amp :
        0.62
        mot_3d_camera_exposure_time :
        0.002
        mot_3d_camera_trigger_duration :
        0.00025
        mot_3d_freq :
        102.13
        mot_load_duration :
        4
        odt_axis_camera_trigger_duration :
        0.002
        odt_hold_time_1 :
        0.01
        odt_hold_time_2 :
        0.1
        odt_hold_time_3 :
        0.1
        odt_hold_time_4 :
        1
        pow_arm_1 :
        7
        pow_arm_2 :
        0
        pulse_delay :
        8e-05
        push_amp :
        0.16
        push_freq :
        102.25
        ramp_duration :
        1
        recomp_ramp_duration :
        0.5
        recomp_ramp_pow_fin_arm_1 :
        0.1038
        recomp_ramp_pow_fin_arm_2 :
        0.09
        recomp_ramp_pow_ini_arm_1 :
        0.1038
        recomp_ramp_pow_ini_arm_2 :
        0.09
        runs :
        1
        save_results :
        False
        stern_gerlach_duration :
        0.001
        wait_after_2dmot_off :
        0
        wait_time_between_images :
        0.22
        x_offset :
        0
        x_offset_img :
        0
        y_offset :
        0
        y_offset_img :
        0
        z_offset :
        0.189
        z_offset_img :
        0.189
        scanAxis :
        []
        scanAxisLength :
        []
        • " ], "text/plain": [ "\n", @@ -1387,7 +1397,7 @@ " scanAxisLength: []" ] }, - "execution_count": 75, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -1415,6 +1425,604 @@ "dataSet" ] }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "selected = np.zeros((2, 2))\n", + "a = xr.DataArray(\n", + " data=selected,\n", + " dims=[\"x\", \"y\"],\n", + " coords=dict(\n", + " x=np.arange(np.shape(selected)[0]),\n", + " y=np.arange(np.shape(selected)[1]),\n", + " )\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
        \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
        <xarray.Dataset>\n",
+       "Dimensions:           (fileNum: 1, phony_dim_0: 59, phony_dim_1: 0)\n",
+       "Dimensions without coordinates: fileNum, phony_dim_0, phony_dim_1\n",
+       "Data variables:\n",
+       "    connection table  (fileNum, phony_dim_0) [('name', 'S256'), ('class', 'S256'), ('parent', 'S256'), ('parent port', 'S256'), ('unit conversion class', 'S256'), ('unit conversion params', 'O'), ('BLACS_connection', 'S9'), ('properties', 'O')] dask.array<chunksize=(1, 59), meta=np.ndarray>\n",
+       "    script            (fileNum) <U7544 'from labscript import *\\nfrom labscri...\n",
+       "    time_markers      (fileNum, phony_dim_1) [('label', 'S256'), ('time', '<f8'), ('color', '<i4', (1, 3))] dask.array<chunksize=(1, 0), meta=np.ndarray>\n",
+       "    waits             (fileNum, phony_dim_1) [('label', 'S256'), ('time', '<f8'), ('timeout', '<f8')] dask.array<chunksize=(1, 0), meta=np.ndarray>\n",
+       "Attributes:\n",
+       "    n_runs:           12\n",
+       "    run number:       11\n",
+       "    run time:         20230424T183333\n",
+       "    script_basename:  Evaporative_Cooling\n",
+       "    sequence_date:    2023-04-24\n",
+       "    sequence_id:      20230424T183145_Evaporative_Cooling\n",
+       "    sequence_index:   13
        " + ], + "text/plain": [ + "\n", + "Dimensions: (fileNum: 1, phony_dim_0: 59, phony_dim_1: 0)\n", + "Dimensions without coordinates: fileNum, phony_dim_0, phony_dim_1\n", + "Data variables:\n", + " connection table (fileNum, phony_dim_0) [('name', 'S256'), ('class', 'S256'), ('parent', 'S256'), ('parent port', 'S256'), ('unit conversion class', 'S256'), ('unit conversion params', 'O'), ('BLACS_connection', 'S9'), ('properties', 'O')] dask.array\n", + " script (fileNum) \n", + " waits (fileNum, phony_dim_1) [('label', 'S256'), ('time', '\n", + "Attributes:\n", + " n_runs: 12\n", + " run number: 11\n", + " run time: 20230424T183333\n", + " script_basename: Evaporative_Cooling\n", + " sequence_date: 2023-04-24\n", + " sequence_id: 20230424T183145_Evaporative_Cooling\n", + " sequence_index: 13" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "xr.open_mfdataset(filePath, \n", + " concat_dim=\"fileNum\", \n", + " combine=\"nested\", \n", + " engine=\"h5netcdf\", \n", + " phony_dims=\"access\", \n", + " parallel=True, )" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['F:',\n", + " 'Jianshun',\n", + " 'analyseScript',\n", + " 'testData',\n", + " '0002',\n", + " '2023-04-21_0002_Evaporative_Cooling_0.h5']" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a = 'F:\\\\Jianshun/analyseScript/testData/0002/2023-04-21_0002_Evaporative_Cooling_0.h5'\n", + "a.replace('\\\\', '/').split('/')[-1].split('_')" + ] + }, { "attachments": {}, "cell_type": "markdown",