diff options
Diffstat (limited to 'receive_tcp.ipynb')
| -rw-r--r-- | receive_tcp.ipynb | 324 |
1 files changed, 300 insertions, 24 deletions
diff --git a/receive_tcp.ipynb b/receive_tcp.ipynb index 8c2cb79..5352b50 100644 --- a/receive_tcp.ipynb +++ b/receive_tcp.ipynb @@ -20,6 +20,8 @@ "import src.signal_gen as sg\n", "import src.tcp_sync as ts\n", "import src.dab_tuning_lib as dt\n", + "import pandas as pd\n", + "from tqdm import tqdm\n", "reload(sg)\n", "reload(du)\n", "reload(dt)" @@ -34,7 +36,20 @@ }, "outputs": [], "source": [ - "import src.SendDictTcp as sdt" + "use_fft=False" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "if use_fft: t1 = ts.UhdSyncMsg(port=47009, packet_size=4*16384, packet_type=\"f\"*16384)\n", + "else: t1 = ts.UhdSyncMsg(port=47009, packet_size=4*1, packet_type=\"f\")" ] }, { @@ -46,7 +61,7 @@ }, "outputs": [], "source": [ - "t1 = ts.UhdSyncMsg(port=47009, packet_size=4*16384, packet_type=\"f\"*16384)" + "import src.SendDictTcp as sdt" ] }, { @@ -69,7 +84,35 @@ "editable": true }, "outputs": [], - "source": [] + "source": [ + "sender.send({\"txgain\":80})\n", + "sender.send({\"rxgain\":15})\n", + "sender.send({\"a1\":0.8})\n", + "sender.send({\"a2\":0.0})\n", + "sender.send({\"a3\":0.0})\n", + "sender.send({\"a4\":0.0})\n", + "sender.send({\"a5\":0.0})\n", + "sender.send({\"a6\":0.0})\n", + "sender.send({\"a7\":0.0})\n", + "sender.send({\"a8\":0.0})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "t1.has_msg()\n", + "t = time.time()\n", + "t1.get_msgs(10000)\n", + "print( time.time() - t)\n", + "t = time.time()\n", + "t1.get_msgs(10000)" + ] }, { "cell_type": "code", @@ -80,7 +123,19 @@ }, "outputs": [], "source": [ - "sender.send({\"txgain\":60})" + "reload(dt)\n", + "sender.send({\"txgain\":81})\n", + "sender.send({\"rxgain\":15})\n", + "sender.send({\"a1\":0.8})\n", + "sender.send({\"a2\":0.0})\n", + "sender.send({\"a3\":0.0})\n", + "sender.send({\"a4\":0.0})\n", + "sender.send({\"a5\":0.0})\n", + "sender.send({\"a6\":0.0})\n", + "sender.send({\"a7\":0.0})\n", + "sender.send({\"a8\":0.0})\n", + "t1.has_msg()\n", + "np.mean(t1.get_msgs(10000))" ] }, { @@ -92,7 +147,7 @@ }, "outputs": [], "source": [ - "sender.send({\"a1\":1})" + "reload(dt)" ] }, { @@ -103,6 +158,50 @@ "editable": true }, "outputs": [], + "source": [ + "def measure_sholders(verbose = False, raw_data=False, std_max=0.025):\n", + " for i in range(20):\n", + " try:\n", + " if verbose: print(\"%d measurement\" % i)\n", + " t1.has_msg()\n", + " msgs = t1.get_msgs_fft(20000)\n", + " #sholders = [dt.calc_signal_sholder_ratio(np.array(msg), 8000000) for msg in msgs]\n", + " #sholders = [dt.calc_max_in_freq_range(np.array(msg), 8000000, 1000000, 1900000, debug=False) for msg in msgs]\n", + " #sholders = [dt.calc_mean_in_freq_range(np.array(msg), 8000000, 1000000, 1900000, debug=False) for msg in msgs]\n", + " sholders = np.array(msgs)\n", + " std = np.std(sholders)\n", + " mean = np.mean(sholders)\n", + " std_perc = std/np.abs(mean)\n", + " if verbose == 2:\n", + " print( {\"mean\":mean, \"std\":std, \"std_perc\":std_perc})\n", + " if std_perc > std_max:\n", + " if verbose: print(\"%.4f std/mean\" % (std_perc))\n", + " continue\n", + " else:\n", + " if raw_data: return sholders\n", + " else: return np.mean(sholders)\n", + " except Exception as e:\n", + " print e\n", + " raise Exception(\"Variance of measurement to high\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "a = np.array(measure_sholders(verbose=2, std_max=100, raw_data=True)).flatten()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [] }, { @@ -114,18 +213,8 @@ }, "outputs": [], "source": [ - "def measure_sholders():\n", - " for i in range(10):\n", - " t1.has_msg()\n", - " msgs = t1.get_msgs(20)\n", - " sholders = [dt.calc_signal_sholder_ratio(np.array(msg), 8000000) for msg in msgs]\n", - " std = np.std(sholders)\n", - " mean = np.mean(sholders)\n", - " if std/mean < 0.05:\n", - " continue\n", - " else:\n", - " return np.mean(sholders)\n", - " raise Exception(\"Variance of measurement to high\")" + "b = a[a<0.04]\n", + "plt.hist(b, bins=100);" ] }, { @@ -137,7 +226,33 @@ }, "outputs": [], "source": [ - "measure_sholders()" + "from tqdm import tqdm_notebook\n", + "\n", + "sender.send({\"txgain\":83})\n", + "sender.send({\"rxgain\":15})\n", + "sender.send({\"a1\":0.8})\n", + "\n", + "res = []\n", + "err = []\n", + "t = time.time()\n", + "for a2 in tqdm_notebook(np.linspace(-0.5, 0.5, num=10)):\n", + " for a3 in tqdm_notebook([0]):\n", + " for a4 in tqdm_notebook([0]):\n", + "#for a2 in tqdm_notebook(np.linspace(-0.5, 0.5, num=21)):\n", + "# for a3 in tqdm_notebook(np.linspace(-0.2, 0.2, num=5)):\n", + "# for a4 in tqdm_notebook(np.linspace(-0.2, 0.2, num=5)):\n", + " try:\n", + " sender.send({\"a2\":a2})\n", + " sender.send({\"a3\":a3})\n", + " sender.send({\"a4\":a4})\n", + " losses = measure_sholders(verbose=0, std_max=100, raw_data=True)\n", + " for i, loss in enumerate(losses):\n", + " res.append({\"a2\":a2, \"a3\":a3, \"a4\":a4, \"loss\":loss, \"run\":i})\n", + " except Exception as e:\n", + " err.append(e)\n", + " df = pd.DataFrame(res)\n", + " df.to_csv(\"/tmp/sholder_a2.csv\")\n", + "print(time.time() - t)" ] }, { @@ -148,7 +263,26 @@ "editable": true }, "outputs": [], - "source": [] + "source": [ + "res = []\n", + "err = []\n", + "t = time.time()\n", + "for a2 in tqdm_notebook(np.linspace(-0.2, 0.2, num=5)):\n", + " for a3 in tqdm_notebook(np.linspace(-0.5, 0.5, num=21)):\n", + " for a4 in tqdm_notebook(np.linspace(-0.2, 0.2, num=5)):\n", + " try:\n", + " sender.send({\"a2\":a2})\n", + " sender.send({\"a3\":a3})\n", + " sender.send({\"a4\":a4})\n", + " losses = measure_sholders(raw_data=True, std=0.03)\n", + " for i, loss in enumerate(losses):\n", + " res.append({\"a2\":a2, \"a3\":a3, \"a4\":a4, \"loss\":loss, \"run\":i})\n", + " except Exception as e:\n", + " err.append(e)\n", + " df = pd.DataFrame(res)\n", + " df.to_csv(\"/tmp/sholder_a3.csv\")\n", + "print(time.time() - t)" + ] }, { "cell_type": "code", @@ -159,7 +293,24 @@ }, "outputs": [], "source": [ - "t1.has_msg()" + "res = []\n", + "err = []\n", + "t = time.time()\n", + "for a2 in tqdm_notebook(np.linspace(-0.2, 0.2, num=5)):\n", + " for a3 in tqdm_notebook(np.linspace(-0.2, 0.2, num=5)):\n", + " for a4 in tqdm_notebook(np.linspace(-0.5, 0.5, num=21)):\n", + " try:\n", + " sender.send({\"a2\":a2})\n", + " sender.send({\"a3\":a3})\n", + " sender.send({\"a4\":a4})\n", + " losses = measure_sholders(raw_data=True, std=0.03)\n", + " for i, loss in enumerate(losses):\n", + " res.append({\"a2\":a2, \"a3\":a3, \"a4\":a4, \"loss\":loss, \"run\":i})\n", + " except Exception as e:\n", + " err.append(e)\n", + " df = pd.DataFrame(res)\n", + " df.to_csv(\"/tmp/sholder_a4.csv\")\n", + "print(time.time() - t)" ] }, { @@ -180,7 +331,69 @@ "editable": true }, "outputs": [], - "source": [] + "source": [ + "from tqdm import tqdm_notebook\n", + "\n", + "sender.send({\"txgain\":81})\n", + "sender.send({\"rxgain\":15})\n", + "sender.send({\"a1\":0.8})\n", + "\n", + "res = []\n", + "err = []\n", + "t = time.time()\n", + "for a2 in tqdm_notebook(np.linspace(-0.5, 0.5, num=21)):\n", + " for a3 in tqdm_notebook(np.linspace(-0.2, 0.2, num=5)):\n", + " for a4 in tqdm_notebook(np.linspace(-0.2, 0.2, num=5)):\n", + " try:\n", + " sender.send({\"a2\":a2})\n", + " sender.send({\"a3\":a3})\n", + " sender.send({\"a4\":a4})\n", + " losses = measure_sholders(raw_data=True, std=0.03)\n", + " for i, loss in enumerate(losses):\n", + " res.append({\"a2\":a2, \"a3\":a3, \"a4\":a4, \"loss\":loss, \"run\":i})\n", + " except Exception as e:\n", + " err.append(e)\n", + " df = pd.DataFrame(res)\n", + " df.to_csv(\"/tmp/sholder_a2_81.csv\")\n", + "print(time.time() - t)\n", + "\n", + "res = []\n", + "err = []\n", + "t = time.time()\n", + "for a2 in tqdm_notebook(np.linspace(-0.2, 0.2, num=5)):\n", + " for a3 in tqdm_notebook(np.linspace(-0.5, 0.5, num=21)):\n", + " for a4 in tqdm_notebook(np.linspace(-0.2, 0.2, num=5)):\n", + " try:\n", + " sender.send({\"a2\":a2})\n", + " sender.send({\"a3\":a3})\n", + " sender.send({\"a4\":a4})\n", + " losses = measure_sholders(raw_data=True, std=0.03)\n", + " for i, loss in enumerate(losses):\n", + " res.append({\"a2\":a2, \"a3\":a3, \"a4\":a4, \"loss\":loss, \"run\":i})\n", + " except Exception as e:\n", + " err.append(e)\n", + " df = pd.DataFrame(res)\n", + " df.to_csv(\"/tmp/sholder_a3_81.csv\")\n", + "print(time.time() - t)\n", + "res = []\n", + "err = []\n", + "t = time.time()\n", + "for a2 in tqdm_notebook(np.linspace(-0.2, 0.2, num=5)):\n", + " for a3 in tqdm_notebook(np.linspace(-0.2, 0.2, num=5)):\n", + " for a4 in tqdm_notebook(np.linspace(-0.5, 0.5, num=21)):\n", + " try:\n", + " sender.send({\"a2\":a2})\n", + " sender.send({\"a3\":a3})\n", + " sender.send({\"a4\":a4})\n", + " losses = measure_sholders(raw_data=True, std=0.03)\n", + " for i, loss in enumerate(losses):\n", + " res.append({\"a2\":a2, \"a3\":a3, \"a4\":a4, \"loss\":loss, \"run\":i})\n", + " except Exception as e:\n", + " err.append(e)\n", + " df = pd.DataFrame(res)\n", + " df.to_csv(\"/tmp/sholder_a4_81.csv\")\n", + "print(time.time() - t)" + ] }, { "cell_type": "code", @@ -190,7 +403,69 @@ "editable": true }, "outputs": [], - "source": [] + "source": [ + "from tqdm import tqdm_notebook\n", + "\n", + "sender.send({\"txgain\":85})\n", + "sender.send({\"rxgain\":15})\n", + "sender.send({\"a1\":0.8})\n", + "\n", + "res = []\n", + "err = []\n", + "t = time.time()\n", + "for a2 in tqdm_notebook(np.linspace(-0.5, 0.5, num=21)):\n", + " for a3 in tqdm_notebook(np.linspace(-0.2, 0.2, num=5)):\n", + " for a4 in tqdm_notebook(np.linspace(-0.2, 0.2, num=5)):\n", + " try:\n", + " sender.send({\"a2\":a2})\n", + " sender.send({\"a3\":a3})\n", + " sender.send({\"a4\":a4})\n", + " losses = measure_sholders(raw_data=True, std=0.03)\n", + " for i, loss in enumerate(losses):\n", + " res.append({\"a2\":a2, \"a3\":a3, \"a4\":a4, \"loss\":loss, \"run\":i})\n", + " except Exception as e:\n", + " err.append(e)\n", + " df = pd.DataFrame(res)\n", + " df.to_csv(\"/tmp/sholder_a2_85.csv\")\n", + "print(time.time() - t)\n", + "\n", + "res = []\n", + "err = []\n", + "t = time.time()\n", + "for a2 in tqdm_notebook(np.linspace(-0.2, 0.2, num=5)):\n", + " for a3 in tqdm_notebook(np.linspace(-0.5, 0.5, num=21)):\n", + " for a4 in tqdm_notebook(np.linspace(-0.2, 0.2, num=5)):\n", + " try:\n", + " sender.send({\"a2\":a2})\n", + " sender.send({\"a3\":a3})\n", + " sender.send({\"a4\":a4})\n", + " losses = measure_sholders(raw_data=True, std=0.03)\n", + " for i, loss in enumerate(losses):\n", + " res.append({\"a2\":a2, \"a3\":a3, \"a4\":a4, \"loss\":loss, \"run\":i})\n", + " except Exception as e:\n", + " err.append(e)\n", + " df = pd.DataFrame(res)\n", + " df.to_csv(\"/tmp/sholder_a3_85.csv\")\n", + "print(time.time() - t)\n", + "res = []\n", + "err = []\n", + "t = time.time()\n", + "for a2 in tqdm_notebook(np.linspace(-0.2, 0.2, num=5)):\n", + " for a3 in tqdm_notebook(np.linspace(-0.2, 0.2, num=5)):\n", + " for a4 in tqdm_notebook(np.linspace(-0.5, 0.5, num=21)):\n", + " try:\n", + " sender.send({\"a2\":a2})\n", + " sender.send({\"a3\":a3})\n", + " sender.send({\"a4\":a4})\n", + " losses = measure_sholders(raw_data=True, std=0.03)\n", + " for i, loss in enumerate(losses):\n", + " res.append({\"a2\":a2, \"a3\":a3, \"a4\":a4, \"loss\":loss, \"run\":i})\n", + " except Exception as e:\n", + " err.append(e)\n", + " df = pd.DataFrame(res)\n", + " df.to_csv(\"/tmp/sholder_a4_85.csv\")\n", + "print(time.time() - t)" + ] }, { "cell_type": "code", @@ -201,8 +476,9 @@ }, "outputs": [], "source": [ - "#top.stop()\n", - "#top.wait()" + "sender.send({\"txgain\":20})\n", + "sender.send({\"rxgain\":15})\n", + "sender.send({\"a1\":0.1})" ] }, { |