From 70baaeb4e78d12c329a03e929fd30d41730ed2b1 Mon Sep 17 00:00:00 2001 From: Mitja Felicijan Date: Sun, 28 Aug 2022 05:39:44 +0200 Subject: Moved statis assets and converted all CSS to Tailwind --- static/assets/algae-sava/dji-algae-0.jpg | Bin 0 -> 145615 bytes static/assets/algae-sava/dji-algae-1.jpg | Bin 0 -> 154416 bytes static/assets/algae-sava/dji-algae-2.jpg | Bin 0 -> 114347 bytes static/assets/algae-sava/dji-algae-3.jpg | Bin 0 -> 128019 bytes static/assets/algae-sava/dji-algae-4.jpg | Bin 0 -> 217747 bytes static/assets/algae-sava/dji-algae-5.jpg | Bin 0 -> 264884 bytes static/assets/cv/avatar.gif | Bin 0 -> 2174 bytes static/assets/dfd-rice/desktop.png | Bin 0 -> 329498 bytes static/assets/dfd-rice/install-00.png | Bin 0 -> 35695 bytes static/assets/dfd-rice/install-01.png | Bin 0 -> 28042 bytes static/assets/dfd-rice/install-02.png | Bin 0 -> 21638 bytes static/assets/dfd-rice/install-03.png | Bin 0 -> 34698 bytes static/assets/dfd-rice/install-04.png | Bin 0 -> 28346 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Database
Database
Web application


[Not supported by viewer]
Write
datapoint
[Not supported by viewer]
Read
datapoints
[Not supported by viewer]
Arduino
MKR1000
[Not supported by viewer]
Web browser
Web browser
Route: /
[Not supported by viewer]
Route: /api
[Not supported by viewer] \ No newline at end of file diff --git a/static/assets/iot-application/simple-iot-application.zip b/static/assets/iot-application/simple-iot-application.zip new file mode 100644 index 0000000..46d3205 Binary files /dev/null and b/static/assets/iot-application/simple-iot-application.zip differ diff --git a/static/assets/iot-application/snakeviz.png b/static/assets/iot-application/snakeviz.png new file mode 100644 index 0000000..5bab395 Binary files /dev/null and b/static/assets/iot-application/snakeviz.png differ diff --git a/static/assets/pid1/boxes.mp4 b/static/assets/pid1/boxes.mp4 new file mode 100644 index 0000000..eb647ff Binary files /dev/null and b/static/assets/pid1/boxes.mp4 differ diff --git a/static/assets/pid1/qemu.log b/static/assets/pid1/qemu.log new file mode 100644 index 0000000..11be312 --- /dev/null +++ b/static/assets/pid1/qemu.log @@ -0,0 +1,320 @@ +[ 0.000000] Linux version 5.15.7 (m@khan) (gcc (GCC) 11.2.1 20211203 (Red Hat 11.2.1-7), GNU ld version 2.37-10.fc35) #7 SMP Mon Dec 13 10:23:25 CET 2021 +[ 0.000000] Command line: console=ttyS0 +[ 0.000000] x86/fpu: x87 FPU will use FXSAVE +[ 0.000000] signal: max sigframe size: 1440 +[ 0.000000] BIOS-provided physical RAM map: +[ 0.000000] BIOS-e820: [mem 0x0000000000000000-0x000000000009fbff] usable +[ 0.000000] BIOS-e820: [mem 0x000000000009fc00-0x000000000009ffff] reserved +[ 0.000000] BIOS-e820: [mem 0x00000000000f0000-0x00000000000fffff] reserved +[ 0.000000] BIOS-e820: [mem 0x0000000000100000-0x0000000007fdffff] usable +[ 0.000000] BIOS-e820: [mem 0x0000000007fe0000-0x0000000007ffffff] reserved +[ 0.000000] BIOS-e820: [mem 0x00000000fffc0000-0x00000000ffffffff] reserved +[ 0.000000] NX (Execute Disable) protection: active +[ 0.000000] SMBIOS 2.8 present. +[ 0.000000] DMI: QEMU Standard PC (i440FX + PIIX, 1996), BIOS 1.14.0-6.fc35 04/01/2014 +[ 0.000000] tsc: Fast TSC calibration failed +[ 0.000000] last_pfn = 0x7fe0 max_arch_pfn = 0x400000000 +[ 0.000000] x86/PAT: Configuration [0-7]: WB WC UC- UC WB WP UC- WT +[ 0.000000] found SMP MP-table at [mem 0x000f5c40-0x000f5c4f] +[ 0.000000] RAMDISK: [mem 0x07e06000-0x07fdffff] +[ 0.000000] ACPI: Early table checksum verification disabled +[ 0.000000] ACPI: RSDP 0x00000000000F5A80 000014 (v00 BOCHS ) +[ 0.000000] ACPI: RSDT 0x0000000007FE1905 000034 (v01 BOCHS BXPC 00000001 BXPC 00000001) +[ 0.000000] ACPI: FACP 0x0000000007FE17B9 000074 (v01 BOCHS BXPC 00000001 BXPC 00000001) +[ 0.000000] ACPI: DSDT 0x0000000007FE0040 001779 (v01 BOCHS BXPC 00000001 BXPC 00000001) +[ 0.000000] ACPI: FACS 0x0000000007FE0000 000040 +[ 0.000000] ACPI: APIC 0x0000000007FE182D 000078 (v01 BOCHS BXPC 00000001 BXPC 00000001) +[ 0.000000] ACPI: HPET 0x0000000007FE18A5 000038 (v01 BOCHS BXPC 00000001 BXPC 00000001) +[ 0.000000] ACPI: WAET 0x0000000007FE18DD 000028 (v01 BOCHS BXPC 00000001 BXPC 00000001) +[ 0.000000] ACPI: Reserving FACP table memory at [mem 0x7fe17b9-0x7fe182c] +[ 0.000000] ACPI: Reserving DSDT table memory at [mem 0x7fe0040-0x7fe17b8] +[ 0.000000] ACPI: Reserving FACS table memory at [mem 0x7fe0000-0x7fe003f] +[ 0.000000] ACPI: Reserving APIC table memory at [mem 0x7fe182d-0x7fe18a4] +[ 0.000000] ACPI: Reserving HPET table memory at [mem 0x7fe18a5-0x7fe18dc] +[ 0.000000] ACPI: Reserving WAET table memory at [mem 0x7fe18dd-0x7fe1904] +[ 0.000000] No NUMA configuration found +[ 0.000000] Faking a node at [mem 0x0000000000000000-0x0000000007fdffff] +[ 0.000000] NODE_DATA(0) allocated [mem 0x07e02000-0x07e05fff] +[ 0.000000] Zone ranges: +[ 0.000000] DMA [mem 0x0000000000001000-0x0000000000ffffff] +[ 0.000000] DMA32 [mem 0x0000000001000000-0x0000000007fdffff] +[ 0.000000] Normal empty +[ 0.000000] Movable zone start for each node +[ 0.000000] Early memory node ranges +[ 0.000000] node 0: [mem 0x0000000000001000-0x000000000009efff] +[ 0.000000] node 0: [mem 0x0000000000100000-0x0000000007fdffff] +[ 0.000000] Initmem setup node 0 [mem 0x0000000000001000-0x0000000007fdffff] +[ 0.000000] On node 0, zone DMA: 1 pages in unavailable ranges +[ 0.000000] On node 0, zone DMA: 97 pages in unavailable ranges +[ 0.000000] On node 0, zone DMA32: 32 pages in unavailable ranges +[ 0.000000] ACPI: PM-Timer IO Port: 0x608 +[ 0.000000] ACPI: LAPIC_NMI (acpi_id[0xff] dfl dfl lint[0x1]) +[ 0.000000] IOAPIC[0]: apic_id 0, version 32, address 0xfec00000, GSI 0-23 +[ 0.000000] ACPI: INT_SRC_OVR (bus 0 bus_irq 0 global_irq 2 dfl dfl) +[ 0.000000] ACPI: INT_SRC_OVR (bus 0 bus_irq 5 global_irq 5 high level) +[ 0.000000] ACPI: INT_SRC_OVR (bus 0 bus_irq 9 global_irq 9 high level) +[ 0.000000] ACPI: INT_SRC_OVR (bus 0 bus_irq 10 global_irq 10 high level) +[ 0.000000] ACPI: INT_SRC_OVR (bus 0 bus_irq 11 global_irq 11 high level) +[ 0.000000] ACPI: Using ACPI (MADT) for SMP configuration information +[ 0.000000] ACPI: HPET id: 0x8086a201 base: 0xfed00000 +[ 0.000000] smpboot: Allowing 1 CPUs, 0 hotplug CPUs +[ 0.000000] PM: hibernation: Registered nosave memory: [mem 0x00000000-0x00000fff] +[ 0.000000] PM: hibernation: Registered nosave memory: [mem 0x0009f000-0x0009ffff] +[ 0.000000] PM: hibernation: Registered nosave memory: [mem 0x000a0000-0x000effff] +[ 0.000000] PM: hibernation: Registered nosave memory: [mem 0x000f0000-0x000fffff] +[ 0.000000] [mem 0x08000000-0xfffbffff] available for PCI devices +[ 0.000000] clocksource: refined-jiffies: mask: 0xffffffff max_cycles: 0xffffffff, max_idle_ns: 1910969940391419 ns +[ 0.000000] setup_percpu: NR_CPUS:64 nr_cpumask_bits:64 nr_cpu_ids:1 nr_node_ids:1 +[ 0.000000] percpu: Embedded 52 pages/cpu s174360 r8192 d30440 u2097152 +[ 0.000000] Built 1 zonelists, mobility grouping on. Total pages: 31968 +[ 0.000000] Policy zone: DMA32 +[ 0.000000] Kernel command line: console=ttyS0 +[ 0.000000] Dentry cache hash table entries: 16384 (order: 5, 131072 bytes, linear) +[ 0.000000] Inode-cache hash table entries: 8192 (order: 4, 65536 bytes, linear) +[ 0.000000] mem auto-init: stack:off, heap alloc:off, heap free:off +[ 0.000000] Memory: 94464K/130552K available (14350K kernel code, 2582K rwdata, 3596K rodata, 1368K init, 1488K bss, 35828K reserved, 0K cma-reserved) +[ 0.000000] SLUB: HWalign=64, Order=0-3, MinObjects=0, CPUs=1, Nodes=1 +[ 0.000000] rcu: Hierarchical RCU implementation. +[ 0.000000] rcu: RCU event tracing is enabled. +[ 0.000000] rcu: RCU restricting CPUs from NR_CPUS=64 to nr_cpu_ids=1. +[ 0.000000] rcu: RCU calculated value of scheduler-enlistment delay is 100 jiffies. +[ 0.000000] rcu: Adjusting geometry for rcu_fanout_leaf=16, nr_cpu_ids=1 +[ 0.000000] NR_IRQS: 4352, nr_irqs: 256, preallocated irqs: 16 +[ 0.000000] random: get_random_bytes called from start_kernel+0x492/0x65f with crng_init=0 +[ 0.000000] Console: colour VGA+ 80x25 +[ 0.000000] printk: console [ttyS0] enabled +[ 0.000000] ACPI: Core revision 20210730 +[ 0.000000] clocksource: hpet: mask: 0xffffffff max_cycles: 0xffffffff, max_idle_ns: 19112604467 ns +[ 0.002000] APIC: Switch to symmetric I/O mode setup +[ 0.005000] ..TIMER: vector=0x30 apic1=0 pin1=2 apic2=-1 pin2=-1 +[ 0.013000] tsc: Unable to calibrate against PIT +[ 0.014000] tsc: using HPET reference calibration +[ 0.014000] tsc: Detected 3189.099 MHz processor +[ 0.001005] clocksource: tsc-early: mask: 0xffffffffffffffff max_cycles: 0x2df8103a89b, max_idle_ns: 440795220785 ns +[ 0.002672] Calibrating delay loop (skipped), value calculated using timer frequency.. 6378.19 BogoMIPS (lpj=3189099) +[ 0.002960] pid_max: default: 32768 minimum: 301 +[ 0.003627] LSM: Security Framework initializing +[ 0.004329] SELinux: Initializing. +[ 0.005051] Mount-cache hash table entries: 512 (order: 0, 4096 bytes, linear) +[ 0.005202] Mountpoint-cache hash table entries: 512 (order: 0, 4096 bytes, linear) +[ 0.020479] process: using AMD E400 aware idle routine +[ 0.020699] Last level iTLB entries: 4KB 512, 2MB 255, 4MB 127 +[ 0.020832] Last level dTLB entries: 4KB 512, 2MB 255, 4MB 127, 1GB 0 +[ 0.021165] Spectre V1 : Mitigation: usercopy/swapgs barriers and __user pointer sanitization +[ 0.021438] Spectre V2 : Mitigation: Full AMD retpoline +[ 0.021586] Spectre V2 : Spectre v2 / SpectreRSB mitigation: Filling RSB on context switch +[ 0.238228] Freeing SMP alternatives memory: 44K +[ 0.242641] random: fast init done +[ 0.350203] smpboot: CPU0: AMD QEMU Virtual CPU version 2.5+ (family: 0xf, model: 0x6b, stepping: 0x1) +[ 0.355136] Performance Events: PMU not available due to virtualization, using software events only. +[ 0.356607] rcu: Hierarchical SRCU implementation. +[ 0.360890] smp: Bringing up secondary CPUs ... +[ 0.361082] smp: Brought up 1 node, 1 CPU +[ 0.361253] smpboot: Max logical packages: 1 +[ 0.361394] smpboot: Total of 1 processors activated (6378.19 BogoMIPS) +[ 0.371481] devtmpfs: initialized +[ 0.378162] clocksource: jiffies: mask: 0xffffffff max_cycles: 0xffffffff, max_idle_ns: 1911260446275000 ns +[ 0.378478] futex hash table entries: 256 (order: 2, 16384 bytes, linear) +[ 0.381522] PM: RTC time: 00:19:47, date: 2021-12-27 +[ 0.384915] NET: Registered PF_NETLINK/PF_ROUTE protocol family +[ 0.387403] audit: initializing netlink subsys (disabled) +[ 0.391765] audit: type=2000 audit(1640564386.402:1): state=initialized audit_enabled=0 res=1 +[ 0.392916] thermal_sys: Registered thermal governor 'step_wise' +[ 0.392950] thermal_sys: Registered thermal governor 'user_space' +[ 0.393202] cpuidle: using governor menu +[ 0.394085] ACPI: bus type PCI registered +[ 0.396583] PCI: Using configuration type 1 for base access +[ 0.415012] Kprobes globally optimized +[ 0.416844] HugeTLB registered 2.00 MiB page size, pre-allocated 0 pages +[ 0.420649] cryptomgr_test (20) used greatest stack depth: 15680 bytes left +[ 0.426071] ACPI: Added _OSI(Module Device) +[ 0.426182] ACPI: Added _OSI(Processor Device) +[ 0.426279] ACPI: Added _OSI(3.0 _SCP Extensions) +[ 0.426376] ACPI: Added _OSI(Processor Aggregator Device) +[ 0.426606] ACPI: Added _OSI(Linux-Dell-Video) +[ 0.426709] ACPI: Added _OSI(Linux-Lenovo-NV-HDMI-Audio) +[ 0.426821] ACPI: Added _OSI(Linux-HPI-Hybrid-Graphics) +[ 0.439511] ACPI: 1 ACPI AML tables successfully acquired and loaded +[ 0.452709] ACPI: Interpreter enabled +[ 0.453468] ACPI: PM: (supports S0 S3 S4 S5) +[ 0.453603] ACPI: Using IOAPIC for interrupt routing +[ 0.454022] PCI: Using host bridge windows from ACPI; if necessary, use "pci=nocrs" and report a bug +[ 0.455266] ACPI: Enabled 2 GPEs in block 00 to 0F +[ 0.480013] ACPI: PCI Root Bridge [PCI0] (domain 0000 [bus 00-ff]) +[ 0.480702] acpi PNP0A03:00: _OSC: OS supports [ASPM ClockPM Segments MSI HPX-Type3] +[ 0.481425] acpi PNP0A03:00: fail to add MMCONFIG information, can't access extended PCI configuration space under this bridge. +[ 0.483666] PCI host bridge to bus 0000:00 +[ 0.483848] pci_bus 0000:00: root bus resource [io 0x0000-0x0cf7 window] +[ 0.484096] pci_bus 0000:00: root bus resource [io 0x0d00-0xffff window] +[ 0.484237] pci_bus 0000:00: root bus resource [mem 0x000a0000-0x000bffff window] +[ 0.484480] pci_bus 0000:00: root bus resource [mem 0x08000000-0xfebfffff window] +[ 0.484578] pci_bus 0000:00: root bus resource [mem 0x100000000-0x17fffffff window] +[ 0.484870] pci_bus 0000:00: root bus resource [bus 00-ff] +[ 0.486588] pci 0000:00:00.0: [8086:1237] type 00 class 0x060000 +[ 0.492625] pci 0000:00:01.0: [8086:7000] type 00 class 0x060100 +[ 0.493621] pci 0000:00:01.1: [8086:7010] type 00 class 0x010180 +[ 0.495015] pci 0000:00:01.1: reg 0x20: [io 0xc040-0xc04f] +[ 0.495760] pci 0000:00:01.1: legacy IDE quirk: reg 0x10: [io 0x01f0-0x01f7] +[ 0.495936] pci 0000:00:01.1: legacy IDE quirk: reg 0x14: [io 0x03f6] +[ 0.496095] pci 0000:00:01.1: legacy IDE quirk: reg 0x18: [io 0x0170-0x0177] +[ 0.496598] pci 0000:00:01.1: legacy IDE quirk: reg 0x1c: [io 0x0376] +[ 0.497793] pci 0000:00:01.3: [8086:7113] type 00 class 0x068000 +[ 0.498219] pci 0000:00:01.3: quirk: [io 0x0600-0x063f] claimed by PIIX4 ACPI +[ 0.498384] pci 0000:00:01.3: quirk: [io 0x0700-0x070f] claimed by PIIX4 SMB +[ 0.499487] pci 0000:00:02.0: [1234:1111] type 00 class 0x030000 +[ 0.500186] pci 0000:00:02.0: reg 0x10: [mem 0xfd000000-0xfdffffff pref] +[ 0.500569] pci 0000:00:02.0: reg 0x18: [mem 0xfebf0000-0xfebf0fff] +[ 0.502569] pci 0000:00:02.0: reg 0x30: [mem 0xfebe0000-0xfebeffff pref] +[ 0.508052] pci 0000:00:03.0: [8086:100e] type 00 class 0x020000 +[ 0.508590] pci 0000:00:03.0: reg 0x10: [mem 0xfebc0000-0xfebdffff] +[ 0.509075] pci 0000:00:03.0: reg 0x14: [io 0xc000-0xc03f] +[ 0.511015] pci 0000:00:03.0: reg 0x30: [mem 0xfeb80000-0xfebbffff pref] +[ 0.517286] ACPI: PCI: Interrupt link LNKA configured for IRQ 10 +[ 0.518032] ACPI: PCI: Interrupt link LNKB configured for IRQ 10 +[ 0.518504] ACPI: PCI: Interrupt link LNKC configured for IRQ 11 +[ 0.518920] ACPI: PCI: Interrupt link LNKD configured for IRQ 11 +[ 0.519208] ACPI: PCI: Interrupt link LNKS configured for IRQ 9 +[ 0.521412] iommu: Default domain type: Translated +[ 0.521589] iommu: DMA domain TLB invalidation policy: lazy mode +[ 0.524448] pci 0000:00:02.0: vgaarb: setting as boot VGA device +[ 0.524569] pci 0000:00:02.0: vgaarb: VGA device added: decodes=io+mem,owns=io+mem,locks=none +[ 0.524633] pci 0000:00:02.0: vgaarb: bridge control possible +[ 0.524846] vgaarb: loaded +[ 0.526151] SCSI subsystem initialized +[ 0.528124] ACPI: bus type USB registered +[ 0.528600] usbcore: registered new interface driver usbfs +[ 0.528917] usbcore: registered new interface driver hub +[ 0.529156] usbcore: registered new device driver usb +[ 0.529593] pps_core: LinuxPPS API ver. 1 registered +[ 0.529693] pps_core: Software ver. 5.3.6 - Copyright 2005-2007 Rodolfo Giometti +[ 0.529916] PTP clock support registered +[ 0.531428] Advanced Linux Sound Architecture Driver Initialized. +[ 0.538313] NetLabel: Initializing +[ 0.538413] NetLabel: domain hash size = 128 +[ 0.538513] NetLabel: protocols = UNLABELED CIPSOv4 CALIPSO +[ 0.539300] NetLabel: unlabeled traffic allowed by default +[ 0.540192] PCI: Using ACPI for IRQ routing +[ 0.541336] hpet: 3 channels of 0 reserved for per-cpu timers +[ 0.541742] hpet0: at MMIO 0xfed00000, IRQs 2, 8, 0 +[ 0.541934] hpet0: 3 comparators, 64-bit 100.000000 MHz counter +[ 0.547124] clocksource: Switched to clocksource tsc-early +[ 0.589778] VFS: Disk quotas dquot_6.6.0 +[ 0.590116] VFS: Dquot-cache hash table entries: 512 (order 0, 4096 bytes) +[ 0.591999] pnp: PnP ACPI init +[ 1.348853] pnp: PnP ACPI: found 6 devices +[ 1.363393] clocksource: acpi_pm: mask: 0xffffff max_cycles: 0xffffff, max_idle_ns: 2085701024 ns +[ 1.364026] NET: Registered PF_INET protocol family +[ 1.364871] IP idents hash table entries: 2048 (order: 2, 16384 bytes, linear) +[ 1.369722] tcp_listen_portaddr_hash hash table entries: 256 (order: 0, 4096 bytes, linear) +[ 1.369973] TCP established hash table entries: 1024 (order: 1, 8192 bytes, linear) +[ 1.370241] TCP bind hash table entries: 1024 (order: 2, 16384 bytes, linear) +[ 1.370483] TCP: Hash tables configured (established 1024 bind 1024) +[ 1.371348] UDP hash table entries: 256 (order: 1, 8192 bytes, linear) +[ 1.371835] UDP-Lite hash table entries: 256 (order: 1, 8192 bytes, linear) +[ 1.373053] NET: Registered PF_UNIX/PF_LOCAL protocol family +[ 1.374701] RPC: Registered named UNIX socket transport module. +[ 1.375153] RPC: Registered udp transport module. +[ 1.375280] RPC: Registered tcp transport module. +[ 1.375386] RPC: Registered tcp NFSv4.1 backchannel transport module. +[ 1.377429] pci_bus 0000:00: resource 4 [io 0x0000-0x0cf7 window] +[ 1.377567] pci_bus 0000:00: resource 5 [io 0x0d00-0xffff window] +[ 1.377738] pci_bus 0000:00: resource 6 [mem 0x000a0000-0x000bffff window] +[ 1.377893] pci_bus 0000:00: resource 7 [mem 0x08000000-0xfebfffff window] +[ 1.378032] pci_bus 0000:00: resource 8 [mem 0x100000000-0x17fffffff window] +[ 1.378574] pci 0000:00:01.0: PIIX3: Enabling Passive Release +[ 1.378817] pci 0000:00:00.0: Limiting direct PCI/PCI transfers +[ 1.378993] pci 0000:00:01.0: Activating ISA DMA hang workarounds +[ 1.379296] pci 0000:00:02.0: Video device with shadowed ROM at [mem 0x000c0000-0x000dffff] +[ 1.379537] PCI: CLS 0 bytes, default 64 +[ 1.385473] Unpacking initramfs... +[ 1.394653] Initialise system trusted keyrings +[ 1.395898] workingset: timestamp_bits=56 max_order=15 bucket_order=0 +[ 1.400517] Freeing initrd memory: 1896K +[ 1.409899] NFS: Registering the id_resolver key type +[ 1.410240] Key type id_resolver registered +[ 1.410358] Key type id_legacy registered +[ 1.436299] Key type asymmetric registered +[ 1.436505] Asymmetric key parser 'x509' registered +[ 1.436899] Block layer SCSI generic (bsg) driver version 0.4 loaded (major 251) +[ 1.437334] io scheduler mq-deadline registered +[ 1.437848] io scheduler kyber registered +[ 1.440723] input: Power Button as /devices/LNXSYSTM:00/LNXPWRBN:00/input/input0 +[ 1.443386] ACPI: button: Power Button [PWRF] +[ 1.445654] Serial: 8250/16550 driver, 4 ports, IRQ sharing enabled +[ 1.447264] 00:04: ttyS0 at I/O 0x3f8 (irq = 4, base_baud = 115200) is a 16550A +[ 1.450740] Non-volatile memory driver v1.3 +[ 1.451106] Linux agpgart interface v0.103 +[ 1.467087] loop: module loaded +[ 1.474468] scsi host0: ata_piix +[ 1.476252] scsi host1: ata_piix +[ 1.476701] ata1: PATA max MWDMA2 cmd 0x1f0 ctl 0x3f6 bmdma 0xc040 irq 14 +[ 1.476882] ata2: PATA max MWDMA2 cmd 0x170 ctl 0x376 bmdma 0xc048 irq 15 +[ 1.481539] libphy: Fixed MDIO Bus: probed +[ 1.482188] e100: Intel(R) PRO/100 Network Driver +[ 1.482313] e100: Copyright(c) 1999-2006 Intel Corporation +[ 1.482507] e1000: Intel(R) PRO/1000 Network Driver +[ 1.482702] e1000: Copyright (c) 1999-2006 Intel Corporation. +[ 1.616439] ACPI: \_SB_.LNKC: Enabled at IRQ 11 +[ 1.649465] ata2.00: ATAPI: QEMU DVD-ROM, 2.5+, max UDMA/100 +[ 1.664135] scsi 1:0:0:0: CD-ROM QEMU QEMU DVD-ROM 2.5+ PQ: 0 ANSI: 5 +[ 1.693021] sr 1:0:0:0: [sr0] scsi3-mmc drive: 4x/4x cd/rw xa/form2 tray +[ 1.693338] cdrom: Uniform CD-ROM driver Revision: 3.20 +[ 1.723925] sr 1:0:0:0: Attached scsi generic sg0 type 5 +[ 1.946674] e1000 0000:00:03.0 eth0: (PCI:33MHz:32-bit) 52:54:00:12:34:56 +[ 1.947107] e1000 0000:00:03.0 eth0: Intel(R) PRO/1000 Network Connection +[ 1.947650] e1000e: Intel(R) PRO/1000 Network Driver +[ 1.947749] e1000e: Copyright(c) 1999 - 2015 Intel Corporation. +[ 1.947947] sky2: driver version 1.30 +[ 1.948805] ehci_hcd: USB 2.0 'Enhanced' Host Controller (EHCI) Driver +[ 1.948993] ehci-pci: EHCI PCI platform driver +[ 1.949218] ohci_hcd: USB 1.1 'Open' Host Controller (OHCI) Driver +[ 1.949394] ohci-pci: OHCI PCI platform driver +[ 1.949636] uhci_hcd: USB Universal Host Controller Interface driver +[ 1.950082] usbcore: registered new interface driver usblp +[ 1.950302] usbcore: registered new interface driver usb-storage +[ 1.951012] i8042: PNP: PS/2 Controller [PNP0303:KBD,PNP0f13:MOU] at 0x60,0x64 irq 1,12 +[ 1.954333] serio: i8042 KBD port at 0x60,0x64 irq 1 +[ 1.954634] serio: i8042 AUX port at 0x60,0x64 irq 12 +[ 1.957984] input: AT Translated Set 2 keyboard as /devices/platform/i8042/serio0/input/input1 +[ 1.960071] rtc_cmos 00:05: RTC can wake from S4 +[ 1.964738] rtc_cmos 00:05: registered as rtc0 +[ 1.965357] rtc_cmos 00:05: alarms up to one day, y3k, 242 bytes nvram, hpet irqs +[ 1.966676] device-mapper: ioctl: 4.45.0-ioctl (2021-03-22) initialised: dm-devel@redhat.com +[ 1.967364] hid: raw HID events driver (C) Jiri Kosina +[ 1.968571] usbcore: registered new interface driver usbhid +[ 1.968750] usbhid: USB HID core driver +[ 1.974818] Initializing XFRM netlink socket +[ 1.975673] NET: Registered PF_INET6 protocol family +[ 1.981212] Segment Routing with IPv6 +[ 1.981421] In-situ OAM (IOAM) with IPv6 +[ 1.982292] sit: IPv6, IPv4 and MPLS over IPv4 tunneling driver +[ 1.984278] NET: Registered PF_PACKET protocol family +[ 1.984857] Key type dns_resolver registered +[ 1.985989] IPI shorthand broadcast: enabled +[ 1.986261] sched_clock: Marking stable (1999028700, -13430834)->(1985937339, -339473) +[ 1.987965] registered taskstats version 1 +[ 1.988095] Loading compiled-in X.509 certificates +[ 1.991283] PM: Magic number: 1:335:305 +[ 1.991523] tty tty34: hash matches +[ 1.991951] printk: console [netcon0] enabled +[ 1.992067] netconsole: network logging started +[ 1.994549] cfg80211: Loading compiled-in X.509 certificates for regulatory database +[ 2.004972] kworker/u2:2 (64) used greatest stack depth: 14856 bytes left +[ 2.012521] cfg80211: Loaded X.509 cert 'sforshee: 00b28ddf47aef9cea7' +[ 2.013924] platform regulatory.0: Direct firmware load for regulatory.db failed with error -2 +[ 2.014318] cfg80211: failed to load regulatory.db +[ 2.016106] ALSA device list: +[ 2.016329] No soundcards found. +[ 2.053176] Freeing unused kernel image (initmem) memory: 1368K +[ 2.056095] Write protecting the kernel read-only data: 20480k +[ 2.058248] Freeing unused kernel image (text/rodata gap) memory: 2032K +[ 2.058811] Freeing unused kernel image (rodata/data gap) memory: 500K +[ 2.059164] Run /init as init process +Hello from Golang +[ 2.386879] tsc: Refined TSC clocksource calibration: 3192.032 MHz +[ 2.387114] clocksource: tsc: mask: 0xffffffffffffffff max_cycles: 0x2e02e31fa14, max_idle_ns: 440795264947 ns +[ 2.387380] clocksource: Switched to clocksource tsc +[ 2.587895] input: ImExPS/2 Generic Explorer Mouse as /devices/platform/i8042/serio1/input/input3 +Hello from Golang +Hello from Golang +Hello from Golang diff --git a/static/assets/pid1/unikernels.png b/static/assets/pid1/unikernels.png new file mode 100644 index 0000000..16026ed Binary files /dev/null and b/static/assets/pid1/unikernels.png differ diff --git a/static/assets/pid1/unikernels.svg b/static/assets/pid1/unikernels.svg new file mode 100644 index 0000000..460fb94 --- /dev/null +++ b/static/assets/pid1/unikernels.svg @@ -0,0 +1,128 @@ + + + + + + + + + + + + + VIRTUAL MACHINE + + + + + HOST OS / HYPERVISOR + + + + + VIRTUAL + MACHINE + + + GUEST OS + + + TARGET + SOFTWARE + + + + + HARDWARE + + + + + + VIRTUAL + MACHINE + + + GUEST OS + + + TARGET + SOFTWARE + + + + CONTAINERS + + + + + HOST OS + + + + + + CONTAINER + + + PROGRAMS & + LIBRARIES + + + TARGET + SOFTWARE + + + + + + HARDWARE + + + + + + CONTAINER + + + PROGRAMS & + LIBRARIES + + + TARGET + SOFTWARE + + + + UNIKERNELS + + + + + HYPERVISOR + + + + + + UNIKERNEL & + APPLICATION + + + + + + HARDWARE + + + + + + UNIKERNEL & + APPLICATION + + + + + + diff --git a/static/assets/profile-bind-error/error.jpg b/static/assets/profile-bind-error/error.jpg new file mode 100644 index 0000000..c2e4e8f Binary files /dev/null and b/static/assets/profile-bind-error/error.jpg differ diff --git a/static/assets/python-profiling/kcachegrind.png b/static/assets/python-profiling/kcachegrind.png new file mode 100644 index 0000000..0dc48ab Binary files /dev/null and b/static/assets/python-profiling/kcachegrind.png differ diff --git a/static/assets/python-profiling/profiling-viewer.png b/static/assets/python-profiling/profiling-viewer.png new file mode 100644 index 0000000..a450513 Binary files /dev/null and b/static/assets/python-profiling/profiling-viewer.png differ diff --git a/static/assets/python-profiling/snakeviz.png b/static/assets/python-profiling/snakeviz.png new file mode 100644 index 0000000..5bab395 Binary files /dev/null and b/static/assets/python-profiling/snakeviz.png differ diff --git a/static/assets/sentiment-analysis/.ipynb_checkpoints/TF Test-checkpoint.ipynb b/static/assets/sentiment-analysis/.ipynb_checkpoints/TF Test-checkpoint.ipynb new file mode 100644 index 0000000..e2a85c4 --- /dev/null +++ b/static/assets/sentiment-analysis/.ipynb_checkpoints/TF Test-checkpoint.ipynb @@ -0,0 +1,588 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/m/.local/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n", + "/home/m/.local/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n", + "/home/m/.local/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n", + "/home/m/.local/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n", + "/home/m/.local/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n", + "/home/m/.local/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.0.0-beta1\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/m/.local/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n", + "/home/m/.local/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n", + "/home/m/.local/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n", + "/home/m/.local/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n", + "/home/m/.local/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n", + "/home/m/.local/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n" + ] + } + ], + "source": [ + "import tensorflow as tf\n", + "from tensorflow import keras\n", + "\n", + "# Helper libraries\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "print(tf.__version__)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from numpy import genfromtxt\n", + "data = genfromtxt('data.csv', delimiter=',')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "data_input = data[:,0:3]\n", + "data_labels = data[:,3]\n", + "\n", + "#data_input = np.transpose(data_input)\n", + "#data_labels = np.transpose(data_labels)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(600, 3)\n", + "[1.e-01 1.e+00 3.e+02]\n" + ] + } + ], + "source": [ + "print(np.shape(data_input))\n", + "print(data_input[2])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "print(len(data_input))\n", + "print(len(data_labels))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(500, 3)\n", + "(100, 3)\n", + "(500,)\n", + "(100,)\n" + ] + } + ], + "source": [ + "data_input_train = data_input[0:500,:]\n", + "data_input_test = data_input[500:,:]\n", + "\n", + "data_labels_train = data_labels[0:500]\n", + "data_labels_test = data_labels[500:]\n", + "\n", + "print(np.shape(data_input_train))\n", + "print(np.shape(data_input_test))\n", + "\n", + "print(np.shape(data_labels_train))\n", + "print(np.shape(data_labels_test))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "model = keras.Sequential([\n", + " keras.layers.Dense(128, activation='relu', input_shape=[3]),\n", + " keras.layers.Dense(512, activation='relu'),\n", + " keras.layers.Dense(512, activation='relu'),\n", + " keras.layers.Dense(512, activation='relu'),\n", + " keras.layers.Dense(128, activation='relu'),\n", + " keras.layers.Dense(1)\n", + "])" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "optimizer = tf.keras.optimizers.RMSprop(0.001)\n", + "model.compile(loss='mse',\n", + " optimizer=optimizer,\n", + " metrics=['accuracy'])" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train on 500 samples\n", + "Epoch 1/100\n", + "500/500 [==============================] - 0s 399us/sample - loss: 247.2794 - accuracy: 0.0040\n", + "Epoch 2/100\n", + "500/500 [==============================] - 0s 121us/sample - loss: 4.2495 - accuracy: 0.0060\n", + "Epoch 3/100\n", + "500/500 [==============================] - 0s 131us/sample - loss: 1.8787 - accuracy: 0.0040\n", + "Epoch 4/100\n", + "500/500 [==============================] - 0s 121us/sample - loss: 0.4284 - accuracy: 0.0060\n", + "Epoch 5/100\n", + "500/500 [==============================] - 0s 107us/sample - loss: 4.7904 - accuracy: 0.0080\n", + "Epoch 6/100\n", + "500/500 [==============================] - 0s 113us/sample - loss: 0.0819 - accuracy: 0.0040\n", + "Epoch 7/100\n", + "500/500 [==============================] - 0s 108us/sample - loss: 1.6904 - accuracy: 0.0040\n", + "Epoch 8/100\n", + "500/500 [==============================] - 0s 116us/sample - loss: 0.1761 - accuracy: 0.0040\n", + "Epoch 9/100\n", + "500/500 [==============================] - 0s 142us/sample - loss: 0.1135 - accuracy: 0.0040\n", + "Epoch 10/100\n", + "500/500 [==============================] - 0s 124us/sample - loss: 0.4387 - accuracy: 0.0040\n", + "Epoch 11/100\n", + "500/500 [==============================] - 0s 112us/sample - loss: 0.0815 - accuracy: 0.0040\n", + "Epoch 12/100\n", + "500/500 [==============================] - 0s 117us/sample - loss: 0.1725 - accuracy: 0.0040\n", + "Epoch 13/100\n", + "500/500 [==============================] - 0s 119us/sample - loss: 0.1487 - accuracy: 0.0040\n", + "Epoch 14/100\n", + "500/500 [==============================] - 0s 111us/sample - loss: 0.0720 - accuracy: 0.0040\n", + "Epoch 15/100\n", + "500/500 [==============================] - 0s 111us/sample - loss: 0.3110 - accuracy: 0.0040\n", + "Epoch 16/100\n", + "500/500 [==============================] - 0s 128us/sample - loss: 0.0947 - accuracy: 0.0040\n", + "Epoch 17/100\n", + "500/500 [==============================] - 0s 133us/sample - loss: 0.0739 - accuracy: 0.0040\n", + "Epoch 18/100\n", + "500/500 [==============================] - 0s 131us/sample - loss: 0.1353 - accuracy: 0.0060\n", + "Epoch 19/100\n", + "500/500 [==============================] - 0s 135us/sample - loss: 0.0837 - accuracy: 0.0040\n", + "Epoch 20/100\n", + "500/500 [==============================] - 0s 130us/sample - loss: 0.0754 - accuracy: 0.0040\n", + "Epoch 21/100\n", + "500/500 [==============================] - 0s 118us/sample - loss: 0.0840 - accuracy: 0.0040\n", + "Epoch 22/100\n", + "500/500 [==============================] - 0s 115us/sample - loss: 0.1105 - accuracy: 0.0040\n", + "Epoch 23/100\n", + "500/500 [==============================] - 0s 116us/sample - loss: 0.0651 - accuracy: 0.0040\n", + "Epoch 24/100\n", + "500/500 [==============================] - 0s 109us/sample - loss: 0.0615 - accuracy: 0.0040\n", + "Epoch 25/100\n", + "500/500 [==============================] - 0s 118us/sample - loss: 0.0656 - accuracy: 0.0040\n", + "Epoch 26/100\n", + "500/500 [==============================] - 0s 113us/sample - loss: 0.0695 - accuracy: 0.0040\n", + "Epoch 27/100\n", + "500/500 [==============================] - 0s 116us/sample - loss: 0.0585 - accuracy: 0.0040\n", + "Epoch 28/100\n", + "500/500 [==============================] - 0s 118us/sample - loss: 0.1300 - accuracy: 0.0040\n", + "Epoch 29/100\n", + "500/500 [==============================] - 0s 112us/sample - loss: 0.0567 - accuracy: 0.0040\n", + "Epoch 30/100\n", + "500/500 [==============================] - 0s 137us/sample - loss: 0.0647 - accuracy: 0.0040\n", + "Epoch 31/100\n", + "500/500 [==============================] - 0s 130us/sample - loss: 0.0559 - accuracy: 0.0040\n", + "Epoch 32/100\n", + "500/500 [==============================] - 0s 130us/sample - loss: 0.0576 - accuracy: 0.0040\n", + "Epoch 33/100\n", + "500/500 [==============================] - 0s 128us/sample - loss: 0.0578 - accuracy: 0.0040\n", + "Epoch 34/100\n", + "500/500 [==============================] - 0s 130us/sample - loss: 0.0512 - accuracy: 0.0040\n", + "Epoch 35/100\n", + "500/500 [==============================] - 0s 114us/sample - loss: 0.0601 - accuracy: 0.0040\n", + "Epoch 36/100\n", + "500/500 [==============================] - 0s 111us/sample - loss: 0.0531 - accuracy: 0.0040\n", + "Epoch 37/100\n", + "500/500 [==============================] - 0s 130us/sample - loss: 0.0532 - accuracy: 0.0040\n", + "Epoch 38/100\n", + "500/500 [==============================] - 0s 131us/sample - loss: 0.0480 - accuracy: 0.0040\n", + "Epoch 39/100\n", + "500/500 [==============================] - 0s 136us/sample - loss: 0.0503 - accuracy: 0.0040\n", + "Epoch 40/100\n", + "500/500 [==============================] - 0s 134us/sample - loss: 0.0468 - accuracy: 0.0040\n", + "Epoch 41/100\n", + "500/500 [==============================] - 0s 115us/sample - loss: 0.0509 - accuracy: 0.0040\n", + "Epoch 42/100\n", + "500/500 [==============================] - 0s 109us/sample - loss: 0.0453 - accuracy: 0.0040\n", + "Epoch 43/100\n", + "500/500 [==============================] - 0s 111us/sample - loss: 0.0484 - accuracy: 0.0040\n", + "Epoch 44/100\n", + "500/500 [==============================] - 0s 104us/sample - loss: 0.0458 - accuracy: 0.0040\n", + "Epoch 45/100\n", + "500/500 [==============================] - 0s 110us/sample - loss: 0.0481 - accuracy: 0.0040\n", + "Epoch 46/100\n", + "500/500 [==============================] - 0s 114us/sample - loss: 0.0468 - accuracy: 0.0060\n", + "Epoch 47/100\n", + "500/500 [==============================] - 0s 124us/sample - loss: 0.0473 - accuracy: 0.0060\n", + "Epoch 48/100\n", + "500/500 [==============================] - 0s 137us/sample - loss: 0.0455 - accuracy: 0.0040\n", + "Epoch 49/100\n", + "500/500 [==============================] - 0s 125us/sample - loss: 0.0431 - accuracy: 0.0060\n", + "Epoch 50/100\n", + "500/500 [==============================] - 0s 132us/sample - loss: 0.0432 - accuracy: 0.0060\n", + "Epoch 51/100\n", + "500/500 [==============================] - 0s 116us/sample - loss: 0.0484 - accuracy: 0.0060\n", + "Epoch 52/100\n", + "500/500 [==============================] - 0s 112us/sample - loss: 0.0482 - accuracy: 0.0040\n", + "Epoch 53/100\n", + "500/500 [==============================] - 0s 117us/sample - loss: 0.0444 - accuracy: 0.0060\n", + "Epoch 54/100\n", + "500/500 [==============================] - 0s 109us/sample - loss: 0.0469 - accuracy: 0.0060\n", + "Epoch 55/100\n", + "500/500 [==============================] - 0s 106us/sample - loss: 0.0427 - accuracy: 0.0040\n", + "Epoch 56/100\n", + "500/500 [==============================] - 0s 110us/sample - loss: 0.0433 - accuracy: 0.0040\n", + "Epoch 57/100\n", + "500/500 [==============================] - 0s 102us/sample - loss: 0.0437 - accuracy: 0.0060\n", + "Epoch 58/100\n", + "500/500 [==============================] - 0s 117us/sample - loss: 0.0425 - accuracy: 0.0040\n", + "Epoch 59/100\n", + "500/500 [==============================] - 0s 105us/sample - loss: 0.0418 - accuracy: 0.0040\n", + "Epoch 60/100\n", + "500/500 [==============================] - 0s 109us/sample - loss: 0.0397 - accuracy: 0.0040\n", + "Epoch 61/100\n", + "500/500 [==============================] - 0s 119us/sample - loss: 0.0507 - accuracy: 0.0040\n", + "Epoch 62/100\n", + "500/500 [==============================] - 0s 112us/sample - loss: 0.0402 - accuracy: 0.0060\n", + "Epoch 63/100\n", + "500/500 [==============================] - 0s 133us/sample - loss: 0.0397 - accuracy: 0.0040\n", + "Epoch 64/100\n", + "500/500 [==============================] - 0s 132us/sample - loss: 0.0427 - accuracy: 0.0060\n", + "Epoch 65/100\n", + "500/500 [==============================] - 0s 138us/sample - loss: 0.0398 - accuracy: 0.0040\n", + "Epoch 66/100\n", + "500/500 [==============================] - 0s 145us/sample - loss: 0.0375 - accuracy: 0.0060\n", + "Epoch 67/100\n", + "500/500 [==============================] - 0s 138us/sample - loss: 0.0402 - accuracy: 0.0060\n", + "Epoch 68/100\n", + "500/500 [==============================] - 0s 132us/sample - loss: 0.0388 - accuracy: 0.0080\n", + "Epoch 69/100\n", + "500/500 [==============================] - 0s 115us/sample - loss: 0.0375 - accuracy: 0.0080\n", + "Epoch 70/100\n", + "500/500 [==============================] - 0s 113us/sample - loss: 0.0384 - accuracy: 0.0040\n", + "Epoch 71/100\n", + "500/500 [==============================] - 0s 109us/sample - loss: 0.0360 - accuracy: 0.0080\n", + "Epoch 72/100\n", + "500/500 [==============================] - 0s 111us/sample - loss: 0.0350 - accuracy: 0.0080\n", + "Epoch 73/100\n", + "500/500 [==============================] - 0s 118us/sample - loss: 0.0370 - accuracy: 0.0060\n", + "Epoch 74/100\n", + "500/500 [==============================] - 0s 95us/sample - loss: 0.0354 - accuracy: 0.0080\n", + "Epoch 75/100\n", + "500/500 [==============================] - 0s 102us/sample - loss: 0.0376 - accuracy: 0.0060\n", + "Epoch 76/100\n", + "500/500 [==============================] - 0s 106us/sample - loss: 0.0371 - accuracy: 0.0080\n", + "Epoch 77/100\n", + "500/500 [==============================] - 0s 100us/sample - loss: 0.0369 - accuracy: 0.0060\n", + "Epoch 78/100\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "500/500 [==============================] - 0s 98us/sample - loss: 0.0315 - accuracy: 0.0060\n", + "Epoch 79/100\n", + "500/500 [==============================] - 0s 97us/sample - loss: 0.0355 - accuracy: 0.0060\n", + "Epoch 80/100\n", + "500/500 [==============================] - 0s 100us/sample - loss: 0.0278 - accuracy: 0.0080\n", + "Epoch 81/100\n", + "500/500 [==============================] - 0s 99us/sample - loss: 0.0320 - accuracy: 0.0080\n", + "Epoch 82/100\n", + "500/500 [==============================] - 0s 99us/sample - loss: 0.0321 - accuracy: 0.0080\n", + "Epoch 83/100\n", + "500/500 [==============================] - 0s 94us/sample - loss: 0.0332 - accuracy: 0.0060\n", + "Epoch 84/100\n", + "500/500 [==============================] - 0s 106us/sample - loss: 0.0317 - accuracy: 0.0060\n", + "Epoch 85/100\n", + "500/500 [==============================] - 0s 103us/sample - loss: 0.0293 - accuracy: 0.0080\n", + "Epoch 86/100\n", + "500/500 [==============================] - 0s 107us/sample - loss: 0.0304 - accuracy: 0.0060\n", + "Epoch 87/100\n", + "500/500 [==============================] - 0s 101us/sample - loss: 0.0327 - accuracy: 0.0040\n", + "Epoch 88/100\n", + "500/500 [==============================] - 0s 100us/sample - loss: 0.0290 - accuracy: 0.0080\n", + "Epoch 89/100\n", + "500/500 [==============================] - 0s 123us/sample - loss: 0.0293 - accuracy: 0.0060\n", + "Epoch 90/100\n", + "500/500 [==============================] - 0s 104us/sample - loss: 0.0246 - accuracy: 0.0060\n", + "Epoch 91/100\n", + "500/500 [==============================] - 0s 124us/sample - loss: 0.0303 - accuracy: 0.0060\n", + "Epoch 92/100\n", + "500/500 [==============================] - 0s 129us/sample - loss: 0.0376 - accuracy: 0.0080\n", + "Epoch 93/100\n", + "500/500 [==============================] - 0s 122us/sample - loss: 0.0264 - accuracy: 0.0080\n", + "Epoch 94/100\n", + "500/500 [==============================] - 0s 102us/sample - loss: 0.0265 - accuracy: 0.0080\n", + "Epoch 95/100\n", + "500/500 [==============================] - 0s 108us/sample - loss: 0.0291 - accuracy: 0.0080\n", + "Epoch 96/100\n", + "500/500 [==============================] - 0s 101us/sample - loss: 0.0314 - accuracy: 0.0080\n", + "Epoch 97/100\n", + "500/500 [==============================] - 0s 95us/sample - loss: 0.0257 - accuracy: 0.0060\n", + "Epoch 98/100\n", + "500/500 [==============================] - 0s 100us/sample - loss: 0.0248 - accuracy: 0.0080\n", + "Epoch 99/100\n", + "500/500 [==============================] - 0s 94us/sample - loss: 0.0250 - accuracy: 0.0040\n", + "Epoch 100/100\n", + "500/500 [==============================] - 0s 106us/sample - loss: 0.0312 - accuracy: 0.0060\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#model.fit(data_input_train, data_labels_train, validation_data=(data_input_test, data_labels_test), epochs=100)\n", + "model.fit(data_input_train, data_labels_train, epochs=100)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "100/100 - 0s - loss: 0.0470 - accuracy: 0.0100\n", + "\n", + "Test accuracy: 0.01\n" + ] + } + ], + "source": [ + "test_loss, test_acc = model.evaluate(data_input_test, data_labels_test, verbose=2)\n", + "\n", + "print('\\nTest accuracy:', test_acc)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0.3141548]]\n" + ] + } + ], + "source": [ + "input = np.array([0.46,2,136])\n", + "input.shape = (1,3)\n", + "\n", + "prediction = model.predict(input)\n", + "print(prediction)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "predictions = model.predict(data_input_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib qt \n", + "plt.plot(predictions)\n", + "plt.plot(data_labels_test, 'r')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 204, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib qt\n", + "a = data_labels_test - predictions\n", + "plt.plot(a[0])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 207, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib qt\n", + "a = data_labels_test - predictions\n", + "plt.plot(predictions)\n", + "plt.plot(data_labels_test, 'r')\n", + "plt.plot(a[0], 'g')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 180, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "-0.08489150602276586" + ] + }, + "execution_count": 180, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.average(a[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 182, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_13\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "dense_38 (Dense) (None, 128) 512 \n", + "_________________________________________________________________\n", + "dense_39 (Dense) (None, 512) 66048 \n", + "_________________________________________________________________\n", + "dense_40 (Dense) (None, 512) 262656 \n", + "_________________________________________________________________\n", + "dense_41 (Dense) (None, 512) 262656 \n", + "_________________________________________________________________\n", + "dense_42 (Dense) (None, 128) 65664 \n", + "_________________________________________________________________\n", + "dense_43 (Dense) (None, 1) 129 \n", + "=================================================================\n", + "Total params: 657,665\n", + "Trainable params: 657,665\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 183, + "metadata": {}, + "outputs": [], + "source": [ + "model.save('my_model.h5')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/static/assets/sentiment-analysis/.ipynb_checkpoints/sentiment-analysis-checkpoint.ipynb b/static/assets/sentiment-analysis/.ipynb_checkpoints/sentiment-analysis-checkpoint.ipynb new file mode 100644 index 0000000..2c0934c --- /dev/null +++ b/static/assets/sentiment-analysis/.ipynb_checkpoints/sentiment-analysis-checkpoint.ipynb @@ -0,0 +1,170 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Sentiment analysis of Guardian World News articles" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Get articles from a website" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Install rss parser dependency" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip3 install feedparser" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Parsing RSS feed for world news" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import feedparser\n", + "feed_url = \"https://www.theguardian.com/world/rss\"\n", + "feed = feedparser.parse(feed_url)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import re\n", + "for item in feed.entries:\n", + " # sanitize html\n", + " item.description = re.sub('<[^<]+?>', '', item.description)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Install Vader Sentiment library and perform sentiment analysis" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip3 install vaderSentiment" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\n", + "analyser = SentimentIntensityAnalyzer()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sentiment_results = []\n", + "for item in feed.entries:\n", + " sentiment_title = analyser.polarity_scores(item.title)\n", + " sentiment_description = analyser.polarity_scores(item.description)\n", + " sentiment_results.append([sentiment_title['compound'], sentiment_description['compound']])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Install Matplotlib and visualize compound score" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip3 install matplotlib" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "plt.rcParams['figure.figsize'] = (15, 3)\n", + "plt.plot(sentiment_results, drawstyle='steps')\n", + "plt.title('Sentiment analysis relationship between title and description (Guardian World News)')\n", + "plt.legend(['title', 'description'])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/static/assets/sentiment-analysis/guardian-sa-title-desc-relationship.png b/static/assets/sentiment-analysis/guardian-sa-title-desc-relationship.png new file mode 100644 index 0000000..7195bbf Binary files /dev/null and b/static/assets/sentiment-analysis/guardian-sa-title-desc-relationship.png differ diff --git a/static/assets/sentiment-analysis/sentiment-analysis.ipynb b/static/assets/sentiment-analysis/sentiment-analysis.ipynb new file mode 100644 index 0000000..2c0934c --- /dev/null +++ b/static/assets/sentiment-analysis/sentiment-analysis.ipynb @@ -0,0 +1,170 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Sentiment analysis of Guardian World News articles" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Get articles from a website" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Install rss parser dependency" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip3 install feedparser" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Parsing RSS feed for world news" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import feedparser\n", + "feed_url = \"https://www.theguardian.com/world/rss\"\n", + "feed = feedparser.parse(feed_url)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import re\n", + "for item in feed.entries:\n", + " # sanitize html\n", + " item.description = re.sub('<[^<]+?>', '', item.description)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Install Vader Sentiment library and perform sentiment analysis" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip3 install vaderSentiment" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\n", + "analyser = SentimentIntensityAnalyzer()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sentiment_results = []\n", + "for item in feed.entries:\n", + " sentiment_title = analyser.polarity_scores(item.title)\n", + " sentiment_description = analyser.polarity_scores(item.description)\n", + " sentiment_results.append([sentiment_title['compound'], sentiment_description['compound']])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Install Matplotlib and visualize compound score" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip3 install matplotlib" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "plt.rcParams['figure.figsize'] = (15, 3)\n", + "plt.plot(sentiment_results, drawstyle='steps')\n", + "plt.title('Sentiment analysis relationship between title and description (Guardian World News)')\n", + "plt.legend(['title', 'description'])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git 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