![](\"http://img.youtube.com/vi/ooOLl4_H-IE/0.jpg\")
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[Contents](#Contents)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Add webpages as Interactive Frames\n",
"Embed an entire page from another site in an iframe; for example this is the PYNQ documentation page on readthedocs"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" Starting a code cell with a bang character, e.g. `!`, instructs jupyter to treat the code on that line as an OS shell command
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### System Information "
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"processor\t: 0\r\n",
"model name\t: ARMv7 Processor rev 0 (v7l)\r\n",
"BogoMIPS\t: 650.00\r\n",
"Features\t: half thumb fastmult vfp edsp neon vfpv3 tls vfpd32 \r\n",
"CPU implementer\t: 0x41\r\n",
"CPU architecture: 7\r\n",
"CPU variant\t: 0x3\r\n",
"CPU part\t: 0xc09\r\n",
"CPU revision\t: 0\r\n",
"\r\n",
"processor\t: 1\r\n",
"model name\t: ARMv7 Processor rev 0 (v7l)\r\n",
"BogoMIPS\t: 650.00\r\n",
"Features\t: half thumb fastmult vfp edsp neon vfpv3 tls vfpd32 \r\n",
"CPU implementer\t: 0x41\r\n",
"CPU architecture: 7\r\n",
"CPU variant\t: 0x3\r\n",
"CPU part\t: 0xc09\r\n",
"CPU revision\t: 0\r\n",
"\r\n",
"Hardware\t: Xilinx Zynq Platform\r\n",
"Revision\t: 0003\r\n",
"Serial\t\t: 0000000000000000\r\n"
]
}
],
"source": [
"!cat /proc/cpuinfo"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Verify Linux Version "
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"VERSION=\"16.04 LTS (Xenial Xerus)\"\r\n",
"VERSION_ID=\"16.04\"\r\n"
]
}
],
"source": [
"!cat /etc/os-release | grep VERSION"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### CPU speed calculation made by the Linux kernel "
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"BogoMIPS\t: 650.00\r\n"
]
}
],
"source": [
"!head -5 /proc/cpuinfo | grep \"BogoMIPS\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Available DRAM"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"MemTotal: 507892 kB\r\n",
"MemFree: 101684 kB\r\n",
"MemAvailable: 357020 kB\r\n"
]
}
],
"source": [
"!cat /proc/meminfo | grep 'Mem*'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Network connection"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"eth0 Link encap:Ethernet HWaddr 00:18:3e:02:6d:cb \r\n",
" inet addr:172.19.73.161 Bcast:172.19.75.255 Mask:255.255.252.0\r\n",
" inet6 addr: fe80::218:3eff:fe02:6dcb/64 Scope:Link\r\n",
" UP BROADCAST RUNNING MULTICAST MTU:1500 Metric:1\r\n",
" RX packets:22262 errors:0 dropped:0 overruns:0 frame:0\r\n",
" TX packets:9274 errors:0 dropped:0 overruns:0 carrier:0\r\n",
" collisions:0 txqueuelen:1000 \r\n",
" RX bytes:3972408 (3.9 MB) TX bytes:11258468 (11.2 MB)\r\n",
" Interrupt:27 Base address:0xb000 \r\n",
"\r\n",
"eth0:1 Link encap:Ethernet HWaddr 00:18:3e:02:6d:cb \r\n",
" inet addr:192.168.2.99 Bcast:192.168.2.255 Mask:255.255.255.0\r\n",
" UP BROADCAST RUNNING MULTICAST MTU:1500 Metric:1\r\n",
" Interrupt:27 Base address:0xb000 \r\n",
"\r\n",
"lo Link encap:Local Loopback \r\n",
" inet addr:127.0.0.1 Mask:255.0.0.0\r\n",
" inet6 addr: ::1/128 Scope:Host\r\n",
" UP LOOPBACK RUNNING MTU:65536 Metric:1\r\n",
" RX packets:4558 errors:0 dropped:0 overruns:0 frame:0\r\n",
" TX packets:4558 errors:0 dropped:0 overruns:0 carrier:0\r\n",
" collisions:0 txqueuelen:1 \r\n",
" RX bytes:3876932 (3.8 MB) TX bytes:3876932 (3.8 MB)\r\n",
"\r\n"
]
}
],
"source": [
"!ifconfig"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Directory Information"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/home/xilinx/jupyter_notebooks/getting_started\n",
"--------------------------------------------\n",
"\u001b[0m\u001b[01;32m1_jupyter_notebooks.ipynb\u001b[0m \u001b[01;32m4_base_overlay_iop.ipynb\u001b[0m \u001b[34;42mimages\u001b[0m\n",
"\u001b[01;32m2_python_environment.ipynb\u001b[0m \u001b[01;32m5_base_overlay_video.ipynb\u001b[0m\n",
"3_jupyter_notebooks_advanced_features.ipynb \u001b[01;32m6_base_overlay_audio.ipynb\u001b[0m\n"
]
}
],
"source": [
"!pwd\n",
"!echo --------------------------------------------\n",
"!ls -C --color "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[Contents](#Contents)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Shell commands in python code "
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['1_jupyter_notebooks.ipynb', '2_python_environment.ipynb', '3_jupyter_notebooks_advanced_features.ipynb']\n"
]
}
],
"source": [
"files = !ls | head -3\n",
"\n",
"print(files)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Python variables in shell commands \n",
"By enclosing a Python expression within `{}`, i.e. curly braces, we can substitute it into shell commands"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1_jupyter_notebooks.ipynb\r\n",
"2_python_environment.ipynb\r\n",
"3_jupyter_notebooks_advanced_features.ipynb\r\n",
"4_base_overlay_iop.ipynb\r\n",
"5_base_overlay_video.ipynb\r\n",
"6_base_overlay_audio.ipynb\r\n"
]
}
],
"source": [
"shell_nbs = '*.ipynb | grep \"ipynb\"'\n",
"\n",
"!ls {shell_nbs}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[Contents](#Contents)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Magics\n",
"> IPython has a set of predefined ‘magic functions’ that you can call with a command line style syntax. There are two kinds of magics, line-oriented and cell-oriented. Line magics are prefixed with the % character and work much like OS command-line calls: they get as an argument the rest of the line, where arguments are passed without parentheses or quotes. Cell magics are prefixed with a double %%, and they are functions that get as an argument not only the rest of the line, but also the lines below it in a separate argument.\n",
"\n",
"To learn more about the IPython magics, simple type %magic in a separate cell \n",
"Below is a list of available magics"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"application/json": {
"cell": {
"!": "OSMagics",
"HTML": "Other",
"SVG": "Other",
"bash": "Other",
"capture": "ExecutionMagics",
"debug": "ExecutionMagics",
"file": "Other",
"html": "DisplayMagics",
"javascript": "DisplayMagics",
"js": "DisplayMagics",
"latex": "DisplayMagics",
"markdown": "DisplayMagics",
"perl": "Other",
"prun": "ExecutionMagics",
"pypy": "Other",
"python": "Other",
"python2": "Other",
"python3": "Other",
"ruby": "Other",
"script": "ScriptMagics",
"sh": "Other",
"svg": "DisplayMagics",
"sx": "OSMagics",
"system": "OSMagics",
"time": "ExecutionMagics",
"timeit": "ExecutionMagics",
"writefile": "OSMagics"
},
"line": {
"alias": "OSMagics",
"alias_magic": "BasicMagics",
"autocall": "AutoMagics",
"automagic": "AutoMagics",
"autosave": "KernelMagics",
"bookmark": "OSMagics",
"cat": "Other",
"cd": "OSMagics",
"clear": "KernelMagics",
"colors": "BasicMagics",
"config": "ConfigMagics",
"connect_info": "KernelMagics",
"cp": "Other",
"debug": "ExecutionMagics",
"dhist": "OSMagics",
"dirs": "OSMagics",
"doctest_mode": "BasicMagics",
"ed": "Other",
"edit": "KernelMagics",
"env": "OSMagics",
"gui": "BasicMagics",
"hist": "Other",
"history": "HistoryMagics",
"killbgscripts": "ScriptMagics",
"ldir": "Other",
"less": "KernelMagics",
"lf": "Other",
"lk": "Other",
"ll": "Other",
"load": "CodeMagics",
"load_ext": "ExtensionMagics",
"loadpy": "CodeMagics",
"logoff": "LoggingMagics",
"logon": "LoggingMagics",
"logstart": "LoggingMagics",
"logstate": "LoggingMagics",
"logstop": "LoggingMagics",
"ls": "Other",
"lsmagic": "BasicMagics",
"lx": "Other",
"macro": "ExecutionMagics",
"magic": "BasicMagics",
"man": "KernelMagics",
"matplotlib": "PylabMagics",
"mkdir": "Other",
"more": "KernelMagics",
"mv": "Other",
"notebook": "BasicMagics",
"page": "BasicMagics",
"pastebin": "CodeMagics",
"pdb": "ExecutionMagics",
"pdef": "NamespaceMagics",
"pdoc": "NamespaceMagics",
"pfile": "NamespaceMagics",
"pinfo": "NamespaceMagics",
"pinfo2": "NamespaceMagics",
"pip": "BasicMagics",
"popd": "OSMagics",
"pprint": "BasicMagics",
"precision": "BasicMagics",
"profile": "BasicMagics",
"prun": "ExecutionMagics",
"psearch": "NamespaceMagics",
"psource": "NamespaceMagics",
"pushd": "OSMagics",
"pwd": "OSMagics",
"pycat": "OSMagics",
"pylab": "PylabMagics",
"qtconsole": "KernelMagics",
"quickref": "BasicMagics",
"recall": "HistoryMagics",
"rehashx": "OSMagics",
"reload_ext": "ExtensionMagics",
"rep": "Other",
"rerun": "HistoryMagics",
"reset": "NamespaceMagics",
"reset_selective": "NamespaceMagics",
"rm": "Other",
"rmdir": "Other",
"run": "ExecutionMagics",
"save": "CodeMagics",
"sc": "OSMagics",
"set_env": "OSMagics",
"store": "StoreMagics",
"sx": "OSMagics",
"system": "OSMagics",
"tb": "ExecutionMagics",
"time": "ExecutionMagics",
"timeit": "ExecutionMagics",
"unalias": "OSMagics",
"unload_ext": "ExtensionMagics",
"who": "NamespaceMagics",
"who_ls": "NamespaceMagics",
"whos": "NamespaceMagics",
"xdel": "NamespaceMagics",
"xmode": "BasicMagics"
}
},
"text/plain": [
"Available line magics:\n",
"%alias %alias_magic %autocall %automagic %autosave %bookmark %cat %cd %clear %colors %config %connect_info %cp %debug %dhist %dirs %doctest_mode %ed %edit %env %gui %hist %history %killbgscripts %ldir %less %lf %lk %ll %load %load_ext %loadpy %logoff %logon %logstart %logstate %logstop %ls %lsmagic %lx %macro %magic %man %matplotlib %mkdir %more %mv %notebook %page %pastebin %pdb %pdef %pdoc %pfile %pinfo %pinfo2 %popd %pprint %precision %profile %prun %psearch %psource %pushd %pwd %pycat %pylab %qtconsole %quickref %recall %rehashx %reload_ext %rep %rerun %reset %reset_selective %rm %rmdir %run %save %sc %set_env %store %sx %system %tb %time %timeit %unalias %unload_ext %who %who_ls %whos %xdel %xmode\n",
"\n",
"Available cell magics:\n",
"%%! %%HTML %%SVG %%bash %%capture %%debug %%file %%html %%javascript %%js %%latex %%markdown %%perl %%prun %%pypy %%python %%python2 %%python3 %%ruby %%script %%sh %%svg %%sx %%system %%time %%timeit %%writefile\n",
"\n",
"Automagic is ON, % prefix IS NOT needed for line magics."
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%lsmagic"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[Contents](#Contents)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Timing code using magics\n",
"The following examples show how to call the built-in` %time` magic \n",
"`%time` times the execution of a single statement \n",
"\n",
"**Reference: The next two code cells are excerpted from the Python Data Science Handbook by Jake VanderPlas** \n",
"[Link to full handbook](https://jakevdp.github.io/PythonDataScienceHandbook/01.07-timing-and-profiling.html)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Time the sorting on an unsorted list\n",
"A list of 100000 random numbers is sorted and stored in a variable 'L'"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 550 ms, sys: 0 ns, total: 550 ms\n",
"Wall time: 550 ms\n"
]
}
],
"source": [
"import random\n",
"\n",
"L = [random.random() for _ in range(100000)]\n",
"%time L.sort()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Time the sorting of a pre-sorted list\n",
"The list 'L' which was sorted in previous cell is re-sorted to observe execution time, it is much less as expected "
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 40 ms, sys: 0 ns, total: 40 ms\n",
"Wall time: 45 ms\n"
]
}
],
"source": [
"%time L.sort()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[Contents](#Contents)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Coding other languages"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you want to, you can combine code from multiple kernels into one notebook.\n",
"\n",
"Just use IPython Magics with the name of your kernel at the start of each cell that you want to use that Kernel for:\n",
"\n",
"%%bash \n",
"%%HTML \n",
"%%python2 \n",
"%%python3 \n",
"%%ruby \n",
"%%perl "
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Factorial of 5 is : 120\n"
]
}
],
"source": [
"%%bash\n",
"\n",
"factorial()\n",
"{\n",
" if [ \"$1\" -gt \"1\" ]\n",
" then\n",
" i=`expr $1 - 1`\n",
" j=`factorial $i`\n",
" k=`expr $1 \\* $j`\n",
" echo $k\n",
" else\n",
" echo 1\n",
" fi\n",
"}\n",
"\n",
"input=5\n",
"val=$(factorial $input)\n",
"echo \"Factorial of $input is : \"$val"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[Contents](#Contents)"
]
}
],
"metadata": {
"anaconda-cloud": {},
"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": "3.6.0"
},
"nbpresent": {
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