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satnogs-wut
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Code Issues 6 Releases Activity

wut-web-dev to fn

master
ml server 2020-01-24 19:54:03 -07:00
parent e78275f0c6
commit b950af525c
1 changed files with 98 additions and 47 deletions
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145
notebooks/wut-web-dev.ipynb
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@ -106,10 +106,22 @@
" directory=test_dir,\n",
" target_size=(IMG_HEIGHT, IMG_WIDTH),\n",
" shuffle=True,\n",
" class_mode='binary');\n",
" class_mode='binary')\n",
" return test_data_gen"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%capture\n",
"def rm_image_tmp(test_dir):\n",
" #print('Not removed:', test_dir)\n",
" shutil.rmtree(test_dir)"
]
},
{
"cell_type": "code",
"execution_count": null,
@ -117,18 +129,15 @@
"outputs": [],
"source": [
"%%capture --no-stderr --no-stdout\n",
"def gen_image_tmp(obs_waterfall_path):\n",
"def gen_image_tmp(obs_waterfalltmp):\n",
" tmp_dir = tempfile.mkdtemp()\n",
" test_dir = os.path.join(tmp_dir)\n",
" os.makedirs(test_dir + '/unvetted', exist_ok=True)\n",
" shutil.copy(obs_waterfall_path, test_dir + '/unvetted/') \n",
" shutil.copy(obs_waterfalltmp, test_dir + '/unvetted/') \n",
" \n",
" img = im.open(obs_waterfall_path).resize( (100,200))\n",
" img = im.open(obs_waterfalltmp).resize( (100,200))\n",
" display(img)\n",
"\n",
" # XXX delete tmp dir down below\n",
" #print(test_dir)\n",
" #shutil.rmtree(test_dir) \n",
" return test_dir"
]
},
@ -156,47 +165,16 @@
"metadata": {},
"outputs": [],
"source": [
"def wutObs(datObs):\n",
" if int(datObs) > ( minobsid - 1 ) and int(datObs) < ( maxobsid + 1):\n",
" obsjsonfile=('/srv/satnogs/download/' + format(datObs) + '/' + format(datObs) + '.json')\n",
" with open(obsjsonfile) as f:\n",
" content = f.read()\n",
" data = json.loads(content)\n",
"def get_obs_dict(datObs):\n",
" obsjsonfile=('/srv/satnogs/download/' + format(datObs) + '/' + format(datObs) + '.json')\n",
" with open(obsjsonfile) as f:\n",
" content = f.read()\n",
" data = json.loads(content)\n",
" res = {x : data[x] for x in range(len(data))}\n",
" res2 = dict(enumerate(data))\n",
" obs_dict=(res2[0])\n",
" obs_id=(obs_dict['id'])\n",
" \n",
" obs_vetted_status=(obs_dict['vetted_status'])\n",
" obs_waterfallurl=(obs_dict['waterfall'])\n",
" obs_transmitter=(obs_dict['transmitter'])\n",
" obs_station_name=(obs_dict['station_name'])\n",
" obs_transmitter_mode=(obs_dict['transmitter_mode'])\n",
" \n",
" obs_waterfall=os.path.basename(obs_waterfallurl)\n",
" obs_waterfall_path = os.path.join('/srv/satnogs/download', str(obs_id), obs_waterfall)\n",
" \n",
" test_dir=gen_image_tmp(obs_waterfall_path)\n",
" test_data_gen=gen_image(obs_waterfall_path, test_dir)\n",
" \n",
" prediction_bool=obs_wutsay(test_data_gen);\n",
"\n",
" print()\n",
" print('Observation ID: ', obs_id)\n",
" print('Encoding: ', obs_transmitter_mode)\n",
" print('Human rating: ', obs_vetted_status)\n",
" if prediction_bool[0] == False:\n",
" rating = 'bad'\n",
" else:\n",
" rating = 'good'\n",
" print('wut AI rating: %s' % (rating)) \n",
" print()\n",
" if obs_transmitter_mode == 'DUV':\n",
" print(\"Using DUV training model.\")\n",
" else:\n",
" print(\"NOTE: wut has not been trained on\", obs_transmitter_mode, \"encodings.\")\n",
" print('https://network.satnogs.org/observations/' + str(obs_id))\n",
" #!cat $obsjsonfile"
" return obs_dict"
]
},
{
@ -205,9 +183,82 @@
"metadata": {},
"outputs": [],
"source": [
"print('Enter an Observation ID between', minobsid, 'and', maxobsid);\n",
"wutObs_slide = wg.IntText(value='1292461');\n",
"wg.interact(wutObs, datObs=wutObs_slide);"
"def get_obs_var(var, datObs):\n",
" obs_dict=get_obs_dict(datObs);\n",
" obs_var=(obs_dict[(var)])\n",
" \n",
" return obs_var"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%capture\n",
"def doallthethings(datObs):\n",
"\n",
" obs_waterfall=get_obs_var('waterfall', datObs) \n",
" obs_waterfallpic=os.path.basename(obs_waterfall)\n",
" obs_waterfalltmp = os.path.join('/srv/satnogs/download', str(get_obs_var('id', datObs)), obs_waterfallpic)\n",
"\n",
" test_dir=gen_image_tmp(obs_waterfalltmp);\n",
" test_data_gen=gen_image(obs_waterfalltmp, test_dir);\n",
" \n",
" prediction_bool=obs_wutsay(test_data_gen);\n",
"\n",
" print()\n",
" print('Observation ID: ', get_obs_var('id', datObs))\n",
" print('Encoding: ', get_obs_var('transmitter_mode', datObs))\n",
" print('Human rating: ', get_obs_var('vetted_status', datObs))\n",
" if prediction_bool[0] == False:\n",
" rating = 'bad'\n",
" else:\n",
" rating = 'good'\n",
" print('wut AI rating: %s' % (rating)) \n",
" print()\n",
" if get_obs_var('transmitter_mode', datObs) == 'DUV':\n",
" print(\"Using DUV training model.\")\n",
" else:\n",
" print(\"NOTE: wut has not been trained on\", get_obs_var('transmitter_mode', datObs), \"encodings.\")\n",
" print('https://network.satnogs.org/observations/' + str(get_obs_var('id', datObs)))\n",
" #!cat $obsjsonfile\n",
" rm_image_tmp(test_dir)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%capture\n",
"def wutObs(datObs):\n",
" if int(datObs) > ( minobsid - 1 ) and int(datObs) < ( maxobsid + 1):\n",
" doallthethings(datObs)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%capture\n",
"def display_results():\n",
" print('Enter an Observation ID between', minobsid, 'and', maxobsid)\n",
" wutObs_slide = wg.IntText(value='1292461')\n",
" wg.interact(wutObs, datObs=wutObs_slide)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"display_results()"
]
}
],