diff --git "a/4315.jsonl" "b/4315.jsonl" new file mode 100644--- /dev/null +++ "b/4315.jsonl" @@ -0,0 +1,856 @@ +{"seq_id":"11724711461","text":"\"\"\"Provides the repository macro to import TFRT.\"\"\"\n\nload(\"//third_party:repo.bzl\", \"tf_http_archive\", \"tf_mirror_urls\")\n\ndef repo():\n \"\"\"Imports TFRT.\"\"\"\n\n # Attention: tools parse and update these lines.\n TFRT_COMMIT = \"c7b49a9da77583bd0d925f3db5799a7ab03f731b\"\n TFRT_SHA256 = \"947c27411727bb01a99102aeea88d310ec68399f77c92fc2e5c9ece16d52da67\"\n\n tf_http_archive(\n name = \"tf_runtime\",\n sha256 = TFRT_SHA256,\n strip_prefix = \"runtime-{commit}\".format(commit = TFRT_COMMIT),\n urls = tf_mirror_urls(\"https://github.com/tensorflow/runtime/archive/{commit}.tar.gz\".format(commit = TFRT_COMMIT)),\n # A patch file can be provided for atomic commits to both TF and TFRT.\n # The job that bumps the TFRT_COMMIT also resets patch_file to 'None'.\n patch_file = None,\n )\n","repo_name":"ReganQing/tensorflow","sub_path":"third_party/tf_runtime/workspace.bzl","file_name":"workspace.bzl","file_ext":"bzl","file_size_in_byte":827,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"85"} +{"seq_id":"29361871384","text":"import json\nimport pandas as pd\nimport numpy as np\nimport logging\n\nlogger = logging.getLogger(__name__)\n\ndef write_json(path: str, data):\n \"\"\"Save data to json\n\n Args:\n path (str): filepath\n data (object): the jsonlike data\n\n Returns:\n bool: true if the save was a success\n \"\"\"\n with open(path, 'w') as f:\n json.dump(data, f)\n logger.info(f\"Saved json file @ path {path}.\")\n\ndef read_json(path: str):\n \"\"\"Save data to json\n\n Args:\n path (str): filepath\n data (object): the jsonlike data\n\n Returns:\n bool: true if the save was a success\n \"\"\"\n with open(path, 'w') as f:\n data = json.load(f)\n logger.info(f\"Read json file @ path {path}.\")\n return data\n\ndef write_dict_as_json(path: str, data: dict, key_type: type, val_type: type):\n \"\"\"Save a dict with tuple keys to json\n\n Args:\n path (str): filepath\n data (object): the jsonlike data\n key_type (type): the type to cast the key to\n val_type (type): the type to case the value to\n\n Returns:\n bool: true if the save was a success\n \"\"\"\n with open(path, 'w') as f:\n json.dump({key_type(k): val_type(v) for k,v in data.items()}, f)\n logger.info(f\"Saved json file @ path {path}.\")\n\ndef read_q_as_dict(path: str) -> dict:\n \"\"\"Read json in as a dict, expecting tuple keys with list values.\n\n Args:\n path (str): filepath\n data (object): the jsonlike data\n key_type (type): the type to cast the key to\n val_type (type): the type to case the value to\n\n Returns:\n dict: the q value read in.\n \"\"\"\n logger.info(f\"Attempting to read in file @ {path}\")\n with open(path, 'r') as f:\n data = json.load(f)\n logger.info(f\"Read json file to dict @ path {path}.\")\n return {eval(k): np.array(v) for k,v in data.items()}\n\ndef write_numpy_as_csv(path: str, data: np.array, header: list):\n pd.DataFrame(data).to_csv(path, header=header)","repo_name":"cboin1996/casino","sub_path":"casino/io/disk.py","file_name":"disk.py","file_ext":"py","file_size_in_byte":1982,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"21821470517","text":"\"\"\"\nMain configuration file\n\nWarning: this feature (i.e., centralized config file) is still in development\nIt is going to be largely extended in future\n\"\"\"\nfrom core.abstract_config import AbstractConfig\nfrom core.collect import get_float_from_string, get_int_from_string, parse_time\nfrom environment import GenericEnvironment, ASanEnvironment, MPXEnvironment, SGXEnvironment\n\n\nclass Config(AbstractConfig):\n \"\"\"\n Example config\n\n Note that Config is a singleton\n \"\"\"\n\n # ========================\n # Run and Build parameters\n # ========================\n\n # default input type\n input_type = \"\"\n\n # list of used environments\n environments = (\n GenericEnvironment,\n ASanEnvironment,\n MPXEnvironment,\n # SGXEnvironment\n )\n\n # measurement tools\n stats_action = {\n \"perf\": \"perf stat \" +\n \"-e cycles,instructions \" +\n \"-e branch-instructions,branch-misses \" +\n \"-e major-faults,minor-faults \" +\n \"-e dTLB-loads,dTLB-load-misses,dTLB-stores,dTLB-store-misses \",\n \"perf_cache\": \"perf stat \" +\n \"-e instructions \" +\n \"-e L1-dcache-loads,L1-dcache-load-misses \" +\n \"-e L1-dcache-stores,L1-dcache-store-misses \" +\n \"-e LLC-loads,LLC-load-misses \" +\n \"-e LLC-store-misses,LLC-stores \",\n \"perf_instr\": \"perf stat \" +\n \"-e instructions \" +\n \"-e instructions:u \" +\n \"-e instructions:k \" +\n \"-e mpx:mpx_new_bounds_table\",\n \"perf_ports\": \"perf stat \" + # ports for Intel Skylake!\n \"-e r02B1 \" + # UOPS_EXECUTED.CORE\n \"-e r01A1,r02A1 \" + # ports 0 and 1 (UOPS_DISPATCHED_PORT.PORT_X)\n \"-e r04A1,r08A1 \" + # ports 2 and 3\n \"-e r10A1,r20A1 \" + # ports 4 and 5\n \"-e r40A1,r80A1 \", # ports 6 and 7\n \"time\": \"/usr/bin/time --verbose\",\n \"mpxcount\": \"bin/pin/pin -t bin/pin/mpxinscount.so -o mpxcount.tmp --\",\n \"none\": \"\",\n }\n\n # ========================\n # Data preparation\n # ========================\n\n # Results processing (how data is gathered from raw logs)\n\n # the format is as follows:\n # name of field in csv file: [ keyword to identify a necessary line in logs, function to process the line]\n parsed_data = {\n \"perf\": {\n \"cycles\": [\"cycles\", lambda l: get_int_from_string(l)],\n \"instructions\": [\" instructions \", lambda l: get_int_from_string(l)], # spaces are added not to confuse with branch-instructions\n\n \"branch_instructions\": [\"branch-instructions\", lambda l: get_int_from_string(l)],\n \"branch_misses\": [\"branch-misses\", lambda l: get_int_from_string(l)],\n\n \"major_faults\": [\"major-faults\", lambda l: get_int_from_string(l)],\n \"minor_faults\": [\"minor-faults\", lambda l: get_int_from_string(l)],\n\n \"dtlb_loads\": [\"dTLB-loads\", lambda l: get_int_from_string(l)],\n \"dtlb_load_misses\": [\"dTLB-load-misses\", lambda l: get_int_from_string(l)],\n \"dtlb_stores\": [\"dTLB-stores\", lambda l: get_int_from_string(l)],\n \"dtlb_store_misses\": [\"dTLB-store-misses\", lambda l: get_int_from_string(l)],\n\n \"time\": [\"seconds time elapsed\", lambda l: get_float_from_string(l)],\n },\n \"perf_cache\": {\n \"l1_dcache_loads\": [\"L1-dcache-loads\", lambda l: get_int_from_string(l)],\n \"l1_dcache_load_misses\": [\"L1-dcache-load-misses\", lambda l: get_int_from_string(l)],\n \"l1_dcache_stores\": [\"L1-dcache-stores\", lambda l: get_int_from_string(l)],\n \"l1_dcache_store_misses\": [\"L1-dcache-store-misses\", lambda l: get_int_from_string(l)],\n\n \"llc_loads\": [\"LLC-loads\", lambda l: get_int_from_string(l)],\n \"llc_load_misses\": [\"LLC-load-misses\", lambda l: get_int_from_string(l)],\n \"llc_store_misses\": [\"LLC-store-misses\", lambda l: get_int_from_string(l)],\n \"llc_stores\": [\"LLC-stores\", lambda l: get_int_from_string(l)],\n\n \"time\": [\"seconds time elapsed\", lambda l: get_float_from_string(l)],\n \"instructions\": [\" instructions \", lambda l: get_int_from_string(l)],\n },\n \"perf_instr\": {\n \"instructions\": [\" instructions \", lambda l: get_int_from_string(l)],\n \"instructions:u\": [\" instructions:u \", lambda l: get_int_from_string(l)],\n \"instructions:k\": [\" instructions:k \", lambda l: get_int_from_string(l)],\n\n \"mpx_new_bounds_table \": [\"mpx:mpx_new_bounds_table \", lambda l: get_int_from_string(l)],\n \"time\": [\"seconds time elapsed\", lambda l: get_float_from_string(l)],\n },\n \"perf_ports\": {\n \"UOPS_EXECUTED.CORE\": [\"r02B1\", lambda l: get_int_from_string(l)],\n \"PORT_0\": [\"r01A1\", lambda l: get_int_from_string(l)],\n \"PORT_1\": [\"r02A1\", lambda l: get_int_from_string(l)],\n \"PORT_2\": [\"r04A1\", lambda l: get_int_from_string(l)],\n \"PORT_3\": [\"r08A1\", lambda l: get_int_from_string(l)],\n \"PORT_4\": [\"r10A1\", lambda l: get_int_from_string(l)],\n \"PORT_5\": [\"r20A1\", lambda l: get_int_from_string(l)],\n \"PORT_6\": [\"r40A1\", lambda l: get_int_from_string(l)],\n \"PORT_7\": [\"r80A1\", lambda l: get_int_from_string(l)],\n },\n \"time\": {\n \"time\": [\"Elapsed (wall clock) time\", lambda l: parse_time(l)],\n \"user_time\": [\"User time (seconds)\", lambda l: get_float_from_string(l)],\n \"sys_time\": [\"System time (seconds)\", lambda l: get_float_from_string(l)],\n\n \"major_faults\": [\"Major (requiring I/O) page faults\", lambda l: get_int_from_string(l)],\n \"minor_faults\": [\"Minor (reclaiming a frame) page faults\", lambda l: get_int_from_string(l)],\n\n \"voluntary_context_switches\": [\"Voluntary context switches\", lambda l: get_int_from_string(l)],\n \"involuntary_context_switches\": [\"Involuntary context switches\", lambda l: get_int_from_string(l)],\n\n \"maxsize\": [\"Maximum resident set size\", lambda l: get_int_from_string(l)],\n },\n \"mpxcount\": {\n \"instructions\": [\"program: total\", lambda l: get_int_from_string(l)],\n\n \"memory_reads\": [\"program: memreads\", lambda l: get_int_from_string(l)],\n \"memory_writes\": [\"program: memwrites\", lambda l: get_int_from_string(l)],\n\n \"bndmk\": [\"program: bndmk\", lambda l: get_int_from_string(l)],\n \"bndcl\": [\"program: bndcl\", lambda l: get_int_from_string(l)],\n \"bndcu\": [\"program: bndcu\", lambda l: get_int_from_string(l)],\n\n \"bndldx\": [\"program: bndldx\", lambda l: get_int_from_string(l)],\n \"bndstx\": [\"program: bndstx\", lambda l: get_int_from_string(l)],\n\n \"bndmovreg\": [\"program: bndmovreg\", lambda l: get_int_from_string(l)],\n \"bndmovmem\": [\"program: bndmovmem\", lambda l: get_int_from_string(l)],\n },\n \"none\": {},\n }\n\n # Results aggregation (what means to calculate)\n\n # the format is as follows:\n # type: (what column to aggregate, what columns to keep untouched)\n aggregated_data = {\n \"perf\": ([\"time\"], [\"compiler\", \"type\", \"name\", \"input\", \"threads\"]),\n \"multi\": ([\"time\"], [\"compiler\", \"type\", \"name\", \"input\", \"threads\"]),\n \"perfstacked\": ([\"time\"], [\"compiler\", \"type\", \"name\", \"input\", \"threads\"]),\n \"mem\": ([\"maxsize\"], [\"compiler\", \"type\", \"name\", \"input\", \"threads\"]),\n \"tput\": ([\"tput\", \"lat\"], [\"compiler\", \"type\", \"name\", \"num_clients\", \"input\", \"threads\"]),\n \"instr\": ([\"instructions\"], [\"compiler\", \"type\", \"name\", \"input\", \"threads\"]),\n \"mpxcount\": (\n [\"bndldx\", \"bndstx\", \"bndmovreg\", \"bndmovmem\", \"bndcu\", \"bndmk\", \"bndcl\", \"instructions\", \"memory_reads\", \"memory_writes\"],\n [\"compiler\", \"type\", \"name\", \"input\", \"threads\"]\n ),\n \"cache\": (\n [\"instructions\", \"l1_dcache_loads\", \"l1_dcache_load_misses\", \"l1_dcache_stores\", \"l1_dcache_store_misses\", \"llc_loads\", \"llc_load_misses\", \"llc_stores\", \"llc_store_misses\"],\n [\"compiler\", \"type\", \"name\", \"input\", \"threads\"]\n ),\n \"misc_stat\": (\n [\"instructions\", \"dtlb_stores\", \"dtlb_store_misses\", \"dtlb_load_misses\", \"dtlb_loads\", \"branch_misses\", \"branch_instructions\"],\n [\"compiler\", \"type\", \"name\", \"input\", \"threads\"]\n ),\n \"ku_instr\": (\n [\"instructions\", \"instructions:u\", \"instructions:k\"],\n [\"compiler\", \"type\", \"name\", \"input\", \"threads\"]\n ),\n \"native_mem_access\": (\n [\"instructions\", \"l1_dcache_loads\", \"l1_dcache_stores\"],\n [\"compiler\", \"type\", \"name\", \"input\", \"threads\"]\n ),\n \"ipc\": (\n [\"instructions\", \"cycles\"],\n [\"compiler\", \"type\", \"name\", \"input\", \"threads\"]\n ),\n \"mpx_feature_perf\": ([\"time\"], [\"compiler\", \"type\", \"name\", \"input\", \"threads\"]),\n \"mpx_feature_mem\": ([\"maxsize\"], [\"compiler\", \"type\", \"name\", \"input\", \"threads\"]),\n }\n\n # ========================\n # Plotting\n # ========================\n build_names = {\n \"long\": {\n \"clang-native\": \"Native (Clang)\",\n \"safecode-enabled\": \"SAFECode (Clang)\",\n \"safecode-native\": \"Native (SAFECode)\",\n \"clang-asan\": \"ASan (Clang)\",\n \"softbound-enabled\": \"SoftBound (Clang)\",\n \"softbound-native\": \"Native (SoftBound)\",\n \"icc-native\": \"Native (ICC)\",\n \"icc-mpx_no_narrow_bounds_only_write\": \"MPX n.n.b. o.w. (ICC)\",\n \"icc-mpx_no_narrow_bounds\": \"MPX n.n.b. (ICC)\",\n \"icc-mpx_only_write\": \"MPX o.w. (ICC)\",\n \"icc-mpx\": \"MPX (ICC)\",\n \"icc-ptr\": \"Pointer Checker (ICC)\",\n\n \"gcc-native\": \"Native (GCC)\",\n \"gcc-mpx_no_narrow_bounds_only_write\": \"MPX n.n.b. o.w. (GCC)\",\n \"gcc-mpx_no_narrow_bounds\": \"MPX n.n.b. (GCC)\",\n \"gcc-mpx_only_write\": \"MPX o.w. (GCC)\",\n \"gcc-mpx\": \"MPX (GCC)\",\n \"gcc-asan\": \"ASan (GCC)\",\n \"gcc-asan_only_write\": \"ASan o.w. (GCC)\",\n },\n \"short\": {\n \"clang-native\": \"Native (Clang)\",\n \"safecode-enabled\": \"SAFECode\",\n \"safecode-native\": \"Native (SAFECode)\",\n \"clang-asan\": \"ASan\",\n \"softbound-enabled\": \"SoftBound\",\n \"softbound-native\": \"Native (SoftBound)\",\n\n \"icc-native\": \"Native (ICC)\",\n \"icc-mpx_no_narrow_bounds_only_write\": \"MPX (ICC)\",\n \"icc-mpx_no_narrow_bounds\": \"MPX (ICC)\",\n \"icc-mpx_only_write\": \"MPX (ICC)\",\n \"icc-mpx\": \"MPX (ICC)\",\n \"icc-ptr\": \"Pointer Checker (ICC)\",\n\n \"gcc-native\": \"Native (GCC)\",\n \"gcc-mpx_no_narrow_bounds_only_write\": \"MPX (GCC)\",\n \"gcc-mpx_no_narrow_bounds\": \"MPX (GCC)\",\n \"gcc-mpx_only_write\": \"MPX (GCC)\",\n \"gcc-mpx\": \"MPX (GCC)\",\n \"gcc-asan\": \"ASan\",\n \"gcc-asan_only_write\": \"ASan\",\n },\n \"tiny\": {\n \"Native (Clang)\": r\"$N$\",\n \"SAFECode (Clang)\": r\"$C$\",\n \"ASan (Clang)\": r\"$A$\",\n \"SoftBound (Clang)\": r\"$B$\",\n\n \"Native (ICC)\": r\"$N$\",\n \"MPX n.n.b. (ICC)\": r\"$I$\",\n \"MPX o.w. (ICC)\": r\"$\\bar{I}$\",\n \"MPX (ICC)\": r\"$I$\",\n\n \"Native (GCC)\": r\"$N$\",\n \"MPX n.n.b. (GCC)\": r\"$G$\",\n \"MPX o.w. (GCC)\": r\"$\\bar{G}$\",\n \"MPX (GCC)\": r\"$G$\",\n \"ASan (GCC)\": r\"$A$\",\n },\n \"empty\": {\n \"clang-native\": \"\",\n \"safecode-enabled\": \"\",\n \"clang-asan\": \"\",\n \"softbound-enabled\": \"\",\n\n \"icc-native\": \"\",\n \"icc-mpx_no_narrow_bounds_only_write\": \"\",\n \"icc-mpx_no_narrow_bounds\": \"\",\n \"icc-mpx_only_write\": \"\",\n \"icc-mpx\": \"\",\n\n \"gcc-native\": \"\",\n \"gcc-mpx_no_narrow_bounds_only_write\": \"\",\n \"gcc-mpx_no_narrow_bounds\": \"\",\n \"gcc-mpx_only_write\": \"\",\n \"gcc-mpx\": \"\",\n \"gcc-asan\": \"\",\n \"gcc-asan_only_write\": \"\",\n },\n \"mpx_feature\": {\n \"icc-native\": \"Native (ICC)\",\n \"icc-mpx_no_narrow_bounds_only_write\": \"No narrow bounds only write (ICC)\",\n \"icc-mpx_no_narrow_bounds\": \"No narrow bounds (ICC)\",\n \"icc-mpx_only_write\": \"Only write (ICC)\",\n \"icc-mpx\": \"Full (ICC)\",\n\n \"gcc-native\": \"Native (GCC)\",\n \"gcc-mpx_no_narrow_bounds_only_write\": \"No narrow bounds only write (GCC)\",\n \"gcc-mpx_no_narrow_bounds\": \"No narrow bounds (GCC)\",\n \"gcc-mpx_only_write\": \"Only write (GCC)\",\n \"gcc-mpx\": \"Full (GCC)\",\n }\n }\n\n input_names = {\n \"long\": {\n 0: \"Small\",\n 1: \"Medium\",\n 2: \"Large\",\n 3: \"Extra Large\",\n 4: \"XXL\"\n },\n \"short\": {\n 0: \"S\",\n 1: \"M\",\n 2: \"L\",\n 3: \"XL\",\n 4: \"XXL\"\n }\n }\n\n default_build_order = (\n \"clang-asan\",\n \"icc-mpx\",\n # \"icc-ptr\",\n \"gcc-mpx\",\n # \"gcc-mpx_disabled\",\n \"safecode-enabled\",\n \"softbound-enabled\",\n )\n other_build_orders = {\n \"mpxcount\": (\n \"icc-mpx\",\n \"icc-mpx_only_write\",\n \"gcc-mpx\",\n \"gcc-mpx_only_write\",\n ),\n \"multi\": (\n \"gcc-native\",\n \"clang-asan\",\n \"icc-mpx\",\n \"gcc-mpx\",\n ),\n \"native_mem_access\": (\n \"clang-native\",\n \"icc-native\",\n \"gcc-native\",\n \"safecode-native\",\n \"softbound-native\",\n ),\n \"ipc\": (\n \"gcc-native\",\n \"clang-asan\",\n \"icc-mpx\",\n \"gcc-mpx\",\n \"safecode-enabled\",\n \"softbound-enabled\",\n ),\n \"cache\": (\n \"gcc-native\",\n \"clang-asan\",\n \"icc-mpx\",\n \"gcc-mpx\",\n \"safecode-enabled\",\n \"softbound-enabled\",\n ),\n \"mpx_feature_perf\": (\n \"icc-mpx\",\n \"icc-mpx_no_narrow_bounds\",\n \"icc-mpx_only_write\",\n \"gcc-mpx\",\n \"gcc-mpx_no_narrow_bounds\",\n \"gcc-mpx_only_write\",\n ),\n \"mpx_feature_mem\": (\n \"icc-mpx\",\n \"icc-mpx_no_narrow_bounds\",\n \"icc-mpx_only_write\",\n \"gcc-mpx\",\n \"gcc-mpx_no_narrow_bounds\",\n \"gcc-mpx_only_write\",\n ),\n \"tput\": (\n \"gcc-native\",\n \"gcc-asan\",\n \"icc-mpx_no_narrow_bounds\",\n \"gcc-mpx_no_narrow_bounds\",\n ),\n }\n","repo_name":"tudinfse/intel_mpx_explained","sub_path":"config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":15049,"program_lang":"python","lang":"en","doc_type":"code","stars":25,"dataset":"github-code","pt":"85"} +{"seq_id":"38973666332","text":"import pygame\r\nimport os\r\nimport pygame\r\nimport sys\r\nfrom pygame import mixer\r\nfrom pygame import mixer\r\npygame.init()\r\n\r\nscreen = pygame.display.set_mode((700, 600))\r\n\r\n# Background\r\nbackground = pygame.image.load('Background_Wallpaper.jpg')\r\nscreen.blit(background, (0, 0))\r\n\r\n\r\n# Background Music\r\nmixer.music.load('Mainpage_Music.mp3')\r\nmixer.music.set_volume(0.05)\r\nmixer.music.play()\r\nfont = pygame.font.Font('freesansbold.ttf', 60)\r\nfont1 = pygame.font.SysFont('Comic Sans Ms', 40)\r\nmain_font = pygame.font.SysFont('Comic Sans Ms', 40)\r\n#Title and Icon\r\npygame.display.set_caption(\"Covid-19 Fighter \")\r\nicon = pygame.image.load('rainbow (1).png')\r\npygame.display.set_icon(icon)\r\n\r\ndef TEXT(x, y ,inputtext):\r\n textout = font.render(inputtext, True, (255, 255, 255))\r\n screen.blit(textout, (x, y))\r\n\r\ndef smallText(x, y ,inputtext):\r\n textout = font1.render(inputtext, True, (127.5, 127.5, 127.5))\r\n screen.blit(textout, (x, y))\r\n\r\nclass Button():\r\n def __init__(self, image, x_pos, y_pos, text_input):\r\n self.image = image\r\n self.x_pos = x_pos\r\n self.y_pos = y_pos\r\n self.rect = self.image.get_rect(center=(self.x_pos, self.y_pos))\r\n self.text_input = text_input\r\n self.text = main_font.render(self.text_input, True, \"white\")\r\n self.text_rect = self.text.get_rect(center=(self.x_pos, self.y_pos))\r\n\r\n def update(self):\r\n screen.blit(self.image, self.rect)\r\n screen.blit(self.text, self.text_rect)\r\n\r\n def checkForInput(self, position,buttontext):\r\n if position[0] in range(self.rect.left, self.rect.right) and position[1] in range(self.rect.top, self.rect.bottom):\r\n if buttontext == \" \":\r\n mixer.music.stop()\r\n os.system('Mainpage.py')\r\n\r\n def changeColor(self, position):\r\n if position[0] in range(self.rect.left, self.rect.right) and position[1] in range(self.rect.top, self.rect.bottom):\r\n self.text = main_font.render(self.text_input, True, \"pink\")\r\n else:\r\n self.text = main_font.render(self.text_input, True, \"white\")\r\n\r\n\r\n\r\n\r\n\r\nrunning = True\r\nsleeptime = 1\r\nwhile running:\r\n TEXTa = \"GAME\"\r\n TEXTb = \"The purpose of this game is for kids\"\r\n TEXTc = \" to develop self-awareness of\"\r\n TEXTd = \"COVID-19and how to act in a secure\"\r\n TEXTe = \"manner. While playing this game,\"\r\n TEXTf = \"you will learn some good practices \"\r\n TEXTg = \" to adapt and follow.\"\r\n\r\n\r\n TEXTh = \"If you want to move to the right\"\r\n TEXTi = \"of the screen, press the right arrow\"\r\n TEXTj = \"key, and if you want to move to the \"\r\n TEXTk = \"left, press the left arrow key.\"\r\n TEXTl = \"CONTROLS\"\r\n TEXTX1 = 260\r\n TEXTX = 10\r\n TEXTY = 1\r\n\r\n TEXT(TEXTX1, TEXTY,TEXTa)\r\n TEXT(TEXTX1-90, TEXTY+320,TEXTl)\r\n smallText(TEXTX, TEXTY+50,TEXTb)\r\n smallText(TEXTX, TEXTY+90,TEXTc)\r\n smallText(TEXTX, TEXTY+130,TEXTd)\r\n smallText(TEXTX, TEXTY+170,TEXTe)\r\n smallText(TEXTX, TEXTY+210,TEXTf)\r\n smallText(TEXTX, TEXTY+250,TEXTg)\r\n smallText(TEXTX, TEXTY+360,TEXTh)\r\n smallText(TEXTX, TEXTY+400,TEXTi)\r\n smallText(TEXTX, TEXTY+440,TEXTj)\r\n smallText(TEXTX, TEXTY+480,TEXTk)\r\n button_surface = pygame.image.load(\"CLOSE (2).png\")\r\n button_surface = pygame.transform.scale(button_surface, (80, 80))\r\n button = Button(button_surface,640, 540, \" \")\r\n button.update()\r\n button.changeColor(pygame.mouse.get_pos())\r\n for event in pygame.event.get():\r\n if event.type == pygame.QUIT:\r\n pygame.quit()\r\n sys.exit()\r\n if event.type == pygame.MOUSEBUTTONDOWN:\r\n button.checkForInput(pygame.mouse.get_pos(), button.text_input)\r\n pygame.display.update()\r\n","repo_name":"pavitralakshmi/covid19game","sub_path":"INTRO.py","file_name":"INTRO.py","file_ext":"py","file_size_in_byte":3747,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"581332740","text":"import torch\r\nimport torch.nn as nn\r\nfrom torch.autograd import Variable\r\nfrom torchvision import transforms, datasets\r\nimport torchvision\r\nfrom torch.utils.data import DataLoader,Dataset\r\nfrom PIL import Image\r\n\r\nimport math\r\nimport time\r\n\r\nclass DarkNet(nn.Module):\r\n def __init__(self):\r\n\r\n super(DarkNet, self).__init__()\r\n \r\n self.conv1 = nn.Sequential(nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3, stride=1, padding=1, bias=False),\\\r\n nn.BatchNorm2d(32),nn.LeakyReLU(0.1))\r\n self.pool1 = nn.MaxPool2d(2)\r\n self.conv2 = nn.Sequential(nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, stride=1, padding=1, bias=False),\\\r\n nn.BatchNorm2d(64),nn.LeakyReLU(0.1))\r\n self.pool2 = nn.MaxPool2d(2)\r\n self.conv3 = nn.Sequential(nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=1, padding=1, bias=False),\\\r\n nn.BatchNorm2d(128),nn.LeakyReLU(0.1))\r\n self.conv4 = nn.Sequential(nn.Conv2d(in_channels=128, out_channels=64, kernel_size=1, stride=1, padding=0, bias=False),\\\r\n nn.BatchNorm2d(64),nn.LeakyReLU(0.1))\r\n self.conv5 = nn.Sequential(nn.Conv2d(in_channels=64, out_channels=128, kernel_size=2, stride=1, padding=1, bias=False),\\\r\n nn.BatchNorm2d(128),nn.LeakyReLU(0.1))\r\n self.pool5 = nn.MaxPool2d(2)\r\n self.conv6 = nn.Sequential(nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, stride=1, padding=1, bias=False),\\\r\n nn.BatchNorm2d(256), nn.LeakyReLU(0.1))\r\n self.conv7= nn.Sequential(nn.Conv2d(in_channels=256, out_channels=128, kernel_size=1, stride=1, padding=0, bias=False),\\\r\n nn.BatchNorm2d(128), nn.LeakyReLU(0.1))\r\n self.conv8 = nn.Sequential(nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, stride=1, padding=1, bias=False),\\\r\n nn.BatchNorm2d(256), nn.LeakyReLU(0.1))\r\n self.pool8 = nn.MaxPool2d(2)\r\n self.conv9 = nn.Sequential(nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, stride=1, padding=1, bias=False),\\\r\n nn.BatchNorm2d(512), nn.LeakyReLU(0.1))\r\n self.conv10 = nn.Sequential(nn.Conv2d(in_channels=512, out_channels=256, kernel_size=1, stride=1, padding=0, bias=False),\\\r\n nn.BatchNorm2d(256), nn.LeakyReLU(0.1))\r\n self.conv11 = nn.Sequential(nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, stride=1, padding=1, bias=False),\\\r\n nn.BatchNorm2d(512), nn.LeakyReLU(0.1))\r\n self.conv12 = nn.Sequential(nn.Conv2d(in_channels=512, out_channels=256, kernel_size=1, stride=1, padding=0, bias=False),\\\r\n nn.BatchNorm2d(256), nn.LeakyReLU(0.1))\r\n self.conv13 = nn.Sequential(nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, stride=1, padding=1, bias=False),\\\r\n nn.BatchNorm2d(512), nn.LeakyReLU(0.1))\r\n self.pool13 = nn.MaxPool2d(2)\r\n self.conv14 = nn.Sequential(nn.Conv2d(in_channels=512, out_channels=1024, kernel_size=3, stride=1, padding=1, bias=False),\\\r\n nn.BatchNorm2d(1024), nn.LeakyReLU(0.1))\r\n self.conv15 = nn.Sequential(nn.Conv2d(in_channels=1024, out_channels=512, kernel_size=1, stride=1, padding=0, bias=False),\\\r\n nn.BatchNorm2d(512), nn.LeakyReLU(0.1))\r\n self.conv16 = nn.Sequential(nn.Conv2d(in_channels=512, out_channels=1024, kernel_size=3, stride=1, padding=1, bias=False),\\\r\n nn.BatchNorm2d(1024), nn.LeakyReLU(0.1))\r\n self.conv17 = nn.Sequential(nn.Conv2d(in_channels=1024, out_channels=512, kernel_size=1, stride=1, padding=0, bias=False),\\\r\n nn.BatchNorm2d(512), nn.LeakyReLU(0.1))\r\n self.conv18 = nn.Conv2d(in_channels=512, out_channels=30, kernel_size=3, stride=1, padding=1, bias=False)\r\n self.pool18 = nn.AvgPool2d(7)\r\n \r\n def forward(self, x):\r\n x = self.conv1(x)\r\n x = self.pool1(x)\r\n x = self.conv2(x)\r\n x = self.pool2(x)\r\n x = self.conv3(x)\r\n x = self.conv4(x)\r\n x = self.conv5(x)\r\n x = self.pool5(x)\r\n x = self.conv6(x)\r\n x = self.conv7(x)\r\n x = self.conv8(x)\r\n x = self.pool8(x)\r\n x = self.conv9(x)\r\n x = self.conv10(x)\r\n x = self.conv11(x)\r\n x = self.conv12(x)\r\n x = self.conv13(x)\r\n x = self.pool13(x)\r\n x = self.conv14(x)\r\n x = self.conv15(x)\r\n x = self.conv16(x)\r\n x = self.conv17(x)\r\n x = self.conv18(x)\r\n x = self.pool18(x)\r\n x = x.view(x.size(0), -1)\r\n return x\r\n\t\t\r\ndata_transform = transforms.Compose([\r\n transforms.RandomResizedCrop (224),\r\n transforms.RandomHorizontalFlip(),\r\n transforms.ToTensor(),\r\n transforms.Normalize(mean=[0.485, 0.456, 0.406],\r\n std=[0.229, 0.224, 0.225])\r\n ])\r\n\r\ntrain_dataset = torchvision.datasets.ImageFolder(root='/media/lulugay/PC/CCCV/',transform=data_transform)\r\ntrain_loader = torch.utils.data.DataLoader(train_dataset, batch_size = 32, shuffle=True, num_workers=12)\r\n \r\nval_dataset = torchvision.datasets.ImageFolder(root='/media/lulugay/PC/CCCV/', transform=data_transform)\r\nval_loader = torch.utils.data.DataLoader(val_dataset, batch_size = 32, shuffle=True, num_workers=12)\r\n\r\nmodel = DarkNet()\r\nmodel.cuda()\r\noptimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.9)\r\nloss_func = nn.CrossEntropyLoss()\r\n\r\nfor epoch in range(1):\r\n\tbatch_size_start = time.time()\r\n\trunning_loss = 0.0\r\n\tfor i, (inputs, labels) in enumerate(train_loader):\r\n\t\tinputs = inputs.cuda()\r\n\t\tlabels = labels.cuda()\r\n\t\tinputs = Variable(inputs)\r\n\t\tlables = Variable(labels)\r\n\t\toptimizer.zero_grad()\r\n\t\toutputs = model(inputs)\r\n\t\tloss = loss_func(outputs, labels) #交叉熵\r\n\t\tloss.backward()\r\n\t\toptimizer.step() #更新权重\r\n\t\trunning_loss += loss.data[0]\r\n \r\nprint('Epoch [%d/%d], Loss: %.4f,need time %.4f' % (epoch + 1, num_epochs, running_loss / (4000 / batch_size), time.time() - batch_size_start))\r\n","repo_name":"jiangwx/PyTorch","sub_path":"DarkNet19/DarkNet19.py","file_name":"DarkNet19.py","file_ext":"py","file_size_in_byte":6430,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"10659307373","text":"import numpy as np\nimport matplotlib.pyplot as plt\ng = 9.81\nfrom IPython import display\n\n\n# Randbedingungen Funktionen\n\ndef periodischer_block(h):\n h[0, :] = h[-2, :]\n h[-1, :] = h[1, :]\n return h\n\ndef reflektierender_block(h):\n h[0, :] = h[1, :]\n h[-1, :] = h[-2, :]\n h[:, 0] = h[:, 1]\n h[:, -1] = h[:, -2]\n return h\n\n#Anfangsbedingungen Funktionen für Aufgaben 3.2 und 3.3\n\ndef anfangsbedingungen32(hh, ht, Nx, Ny):\n x = np.linspace(0, 10, Nx)\n y = np.linspace(0, 10, Ny)\n # Initialisierung der Arrays\n h = np.zeros((Nx, Ny), dtype=np.double)\n hu = np.zeros((Nx, Ny), dtype=np.double)\n hv = np.zeros((Nx, Ny), dtype=np.double)\n\n for j in range(Nx):\n for k in range(Ny):\n if (4 <= x[j] <= 6) and (4 <= y[k] <= 6):\n h[j, k] = hh\n else:\n h[j, k] = ht\n return h, hu, hv\n\ndef anfangsbedingungen33(Nx, Ny, darstellung=1):\n\n # Anfangsparameter\n interval = 100e3\n\n dx = interval\n dy = dx\n\n x = np.arange(0, interval * Nx, interval)\n y = np.arange(0, interval * Ny, interval)\n\n [Y, X] = np.meshgrid(y, x)\n\n # Konstanten\n Ωe = 7.2921e-5 # Drehfrequenz der Erde in 1/s\n Re = 6371e3 # Erdradius in m\n y0 = 3e6 # Breitengrad in Grad\n\n #Berechnung des Coriolisfaktors\n\n latitude = 35\n θ_0 = np.deg2rad(latitude) # Breitengrad in Bogenmaß\n fc_0 = 2 * Ωe * np.sin(θ_0) # Mittlere Zentrifugalkraft in 1/s^2\n f = fc_0 + (2*Ωe/Re)*(Y-y0) # Coriolisfaktor\n\n\n if darstellung == 1:\n #Barotropische Instabilität\n W = 10000 - 500 * np.tanh(3e-6 * (Y - y0))\n R = np.random.randint(-1, 6, size=W.shape)\n W = W+R\n B = np.zeros((Nx, Ny), dtype=np.double)\n\n elif darstellung==2:\n #Rossby Wellen in der nördlichen Hemisphäre\n westwind = 30\n fc_0 = 2 * Ωe * np.sin(latitude)\n W = 10000 + westwind/g * fc_0 * (Y-y0)\n print(f'fc_0: {fc_0}')\n print(f'W: {W}')\n\n\n if Nx>10 and Nx<200:\n sigma_x = 5 * dx\n sigma_y = 7 * dy\n elif(Nx>=200):\n sigma_x = 9 * dx\n sigma_y = 7 * dy\n\n B = 4000 * np.exp(-0.5 * ((X-(Nx//2) * 1e5)/sigma_x)**2-0.5 * ((Y-y0)/sigma_y)**2) #https://en.wikipedia.org/wiki/Gaussian_function\n\n #Berechnung der Gradienten\n\n [dWdx, dWdy] = np.gradient(W, *[dy, dx])\n [dBdx, dBdy] = np.gradient(B, *[dy, dx])\n\n #Berechnung der Geschwindigkeiten\n u = (-g/f)*dWdy\n v = (g/f)*dWdx\n\n # Initialisierung der Arrays\n h = W-B\n hu = h*u\n hv = h*v\n\n if darstellung == 1:\n return h, hu, hv, f\n elif(darstellung == 2):\n return h, hu, hv, f, B, dBdx, dBdy\n\n\ndef erhaltungsschema_2D(h, hu, hv, CFL, Nx, Ny, darstellung):\n\n #Diskretisierung des Gebiets\n x = np.linspace(0, 10, Nx)\n y = np.linspace(0, 10, Ny)\n\n dx = x[1] - x[0]\n dy = y[1] - y[0]\n\n # Zeitparameter\n z = 0\n tmax = 5\n\n # Initialisierung der Matrizen F_j12a, F_j12b, F_j12c und G_k12a, G_k12b, G_k12c\n\n F_j12a = np.zeros((Nx-1, Ny), dtype = np.double)\n F_j12b = np.zeros((Nx-1, Ny), dtype = np.double)\n F_j12c = np.zeros((Nx-1, Ny), dtype = np.double)\n G_k12a = np.zeros((Nx, Ny-1), dtype = np.double)\n G_k12b = np.zeros((Nx, Ny-1), dtype = np.double)\n G_k12c = np.zeros((Nx, Ny-1), dtype = np.double)\n Fa = np.zeros((Nx, Ny), dtype = np.double)\n Fb = np.zeros((Nx, Ny), dtype = np.double)\n Fc = np.zeros((Nx, Ny), dtype = np.double)\n Ga = np.zeros((Nx, Ny), dtype = np.double)\n Gb = np.zeros((Nx, Ny), dtype = np.double)\n Gc = np.zeros((Nx, Ny), dtype = np.double)\n v1 = np.amax(h)\n t1 = np.zeros(1)\n\n\n #Initialisierung des Plots\n if darstellung == 3:\n fig = plt.figure(figsize=(10,10))\n if darstellung == 2:\n fig = plt.figure(figsize=(20,10))\n ax_contour = fig.add_subplot(111,frameon = False)\n plt.show(block= False)\n\n # Lax-Friedrichs-Verfahren\n imageCounter = 0\n while z < tmax:\n # Berechnung der Eigenwerte\n EWX = np.array([hu[0,0]/h[0,0]-np.sqrt(g*h[0,0]), hu[0,0]/h[0,0]+np.sqrt(g*h[0,0])]) # Quelle: S.34 (3.5)\n EWY = np.array([hv[0,0]/h[0,0]-np.sqrt(g*h[0,0]), hv[0,0]/h[0,0]+np.sqrt(g*h[0,0])])\n for j in range(0,Nx):\n for k in range(0,Ny):\n EWX = np.append(EWX,[hu[j,k]/h[j,k]-np.sqrt(g*h[j,k]), hu[j,k]/h[j,k]+np.sqrt(g*h[j,k])])\n EWY = np.append(EWY,[hv[j,k]/h[j,k]-np.sqrt(g*h[j,k]), hv[j,k]/h[j,k]+np.sqrt(g*h[j,k])])\n dt = CFL * min(dx,dy)/(max(np.amax(EWX), np.amax(EWY))) # Quelle: S. 13 (1.58)\n z += dt\n\n # Berechnung von Flussvektoren in der Mitte des Zeitschritts\n #x-Richtung\n Fa[:Nx, :Ny] = hu[:Nx, :Ny]\n Fb[:Nx, :Ny] = (hu[:Nx, :Ny]**2)/(h[:Nx, :Ny]) + 0.5*g*(h[:Nx, :Ny]**2)\n Fc[:Nx, :Ny] = (hu[:Nx, :Ny]*hv[:Nx, :Ny])/(h[:Nx, :Ny])\n\n #y-Richtung\n Ga[:Nx,:Ny] = hv[:Nx,:Ny]\n Gb[:Nx,:Ny] = (hu[:Nx,:Ny]*hv[:Nx,:Ny])/(h[:Nx,:Ny])\n Gc[:Nx,:Ny] = (hv[:Nx,:Ny]**2)/(h[:Nx,:Ny]) + 0.5*g*(h[:Nx,:Ny]**2)\n\n # Berechnung von Flussvektoren an den Randzellen\n #x-Richtung\n F_j12a[:Nx-1, :Ny-1] = 0.25 * (dx/dt)*(h[:Nx-1, :Ny-1] - h[1:Nx, :Ny-1]) + 0.5 * (Fa[:Nx-1, :Ny-1] + Fa[1:Nx, :Ny-1]) # Quelle: S.15 (1.63)\n F_j12b[:Nx-1, :Ny-1] = 0.25 * (dx/dt)*(hu[:Nx-1, :Ny-1] - hu[1:Nx, :Ny-1]) + 0.5 * (Fb[:Nx-1, :Ny-1] + Fb[1:Nx, :Ny-1])\n F_j12c[:Nx-1, :Ny-1] = 0.25 * (dx/dt)*(hv[:Nx-1, :Ny-1] - hv[1:Nx, :Ny-1]) + 0.5 * (Fc[:Nx-1, :Ny-1] + Fc[1:Nx, :Ny-1])\n\n #y-Richtung\n G_k12a[:Nx-1, :Ny-1] = 0.25 * (dy/dt)*(h[:Nx-1, :Ny-1] - h[:Nx-1, 1:Ny]) + 0.5 * (Ga[:Nx-1, :Ny-1] + Ga[:Nx-1, 1:Ny]) # Quelle: S.15 (1.64)\n G_k12b[:Nx-1, :Ny-1] = 0.25 * (dy/dt)*(hu[:Nx-1, :Ny-1] - hu[:Nx-1, 1:Ny]) + 0.5 * (Gb[:Nx-1, :Ny-1] + Gb[:Nx-1, 1:Ny])\n G_k12c[:Nx-1, :Ny-1] = 0.25 * (dy/dt)*(hv[:Nx-1, :Ny-1] - hv[:Nx-1, 1:Ny]) + 0.5 * (Gc[:Nx-1, :Ny-1] + Gc[:Nx-1, 1:Ny])\n\n\n #Berechnung der h, hu und hv\n h[1:Nx-1, 1:Ny-1] = h[1:Nx-1, 1:Ny-1] - (dt/dx) * (F_j12a[1:Nx-1, 1:Ny-1] - F_j12a[0:Nx-2, 1:Ny-1]) - ((dt/dy) * (G_k12a[1:Nx-1, 1:Ny-1] - G_k12a[1:Nx-1, 0:Ny-2])) # Quelle: HA 3 (3.33)\n hu[1:Nx-1, 1:Ny-1] = hu[1:Nx-1, 1:Ny-1] - (dt/dx) * (F_j12b[1:Nx-1, 1:Ny-1] - F_j12b[0:Nx-2, 1:Ny-1]) - ((dt/dy) * (G_k12b[1:Nx-1, 1:Ny-1] - G_k12b[1:Nx-1, 0:Ny-2])) # + dt * S(U) für 3.3\n hv[1:Nx-1, 1:Ny-1] = hv[1:Nx-1, 1:Ny-1] - (dt/dx) * (F_j12c[1:Nx-1, 1:Ny-1] - F_j12c[0:Nx-2, 1:Ny-1]) - ((dt/dy) * (G_k12c[1:Nx-1, 1:Ny-1] - G_k12c[1:Nx-1, 0:Ny-2]))\n\n #Randbedingungen\n h = reflektierender_block(h)\n hu = reflektierender_block(hu)\n hv = reflektierender_block(hv)\n\n # Berechnung der Geschwindigkeiten\n\n u = hu/h\n v = hv/h\n v1 = np.append(v1, [np.amax(h)])\n t1 = np.append(t1, [z])\n\n # Meshgrid für die Darstellung\n X,Y = np.meshgrid(x,y)\n\n #Surface plot in 3D\n if darstellung == 3:\n ax = fig.gca(projection='3d')\n ax.plot_surface(X, Y, h, cmap='cool', linewidth=0, antialiased=False)\n ax.set_title('Lax-Friedrich')\n ax.set_xlabel('x')\n ax.set_ylabel('y')\n ax.set_zlabel('h')\n ax.set_xlim(0,10)\n ax.set_ylim(0,10)\n ax.set_zlim(1.4,2.1)\n\n display.display(plt.gcf())\n display.clear_output(wait=True)\n plt.pause(0.01)\n plt.savefig('pngs/{:03d}'.format(imageCounter))\n plt.clf()\n\n #Contour plot und Quiver plot in 2D\n if darstellung == 2:\n ax_contour.cla()\n ax_contour.set_title('Höhenverlauf')\n contour = ax_contour.contourf(X, Y, h, shading='auto', vmax=2, vmin=1.5, cmap='jet')\n cb = fig.colorbar(contour, ax=ax_contour)\n # ax_cotour.pcolormesh(X, Y, h, shading='auto', vmax=2, vmin=1.5, cmap ='jet')\n\n ax_contour.quiver(X, Y, v, u)\n ax_contour.set_aspect('equal')\n\n plt.draw()\n plt.pause(0.01)\n cb.remove()\n imageCounter += 1\n\n return h, hu, hv, v1, t1\n\ndef maccormack(h, hu, hv, f, CFL, Nx, Ny, B, darstellung, aufgabe,teil,dBdx, dBdy):\n\n #Diskretisierung des Gebietes\n if (aufgabe == 3.2):\n x = np.linspace(0, 10, Nx)\n y = np.linspace(0, 10, Ny)\n elif(aufgabe == 3.3):\n interval = 100e3\n x = np.arange(0, interval * Nx, interval)\n y = np.arange(0, interval * Ny, interval)\n\n dx = x[1] - x[0]\n dy = y[1] - y[0]\n\n # Zeitparameter\n z = 0\n if aufgabe == 3.2:\n tmax = 5\n if aufgabe == 3.3:\n tmax = 20 * 3600\n\n\n # Initialisierung der Matrizen F_j12a, F_j12b, F_j12c und G_k12a, G_k12b, G_k12c\n\n Fa = np.zeros((Nx, Ny), dtype = np.double)\n Fb = np.zeros((Nx, Ny), dtype = np.double)\n Fc = np.zeros((Nx, Ny), dtype = np.double)\n Ga = np.zeros((Nx, Ny), dtype = np.double)\n Gb = np.zeros((Nx, Ny), dtype = np.double)\n Gc = np.zeros((Nx, Ny), dtype = np.double)\n Sb = np.zeros((Nx, Ny), dtype = np.double)\n Sc = np.zeros((Nx, Ny), dtype = np.double)\n h_12 = np.zeros((Nx-1, Ny-1), dtype = np.double)\n hu_12 = np.zeros((Nx-1, Ny-1), dtype = np.double)\n hv_12 = np.zeros((Nx-1, Ny-1), dtype = np.double)\n Fa_12 = np.zeros((Nx-1, Ny-1), dtype = np.double)\n Fb_12 = np.zeros((Nx-1, Ny-1), dtype = np.double)\n Fc_12 = np.zeros((Nx-1, Ny-1), dtype = np.double)\n Ga_12 = np.zeros((Nx-1, Ny-1), dtype = np.double)\n Gb_12 = np.zeros((Nx-1, Ny-1), dtype = np.double)\n Gc_12 = np.zeros((Nx-1, Ny-1), dtype = np.double)\n Sb_12 = np.zeros((Nx-1, Ny-1), dtype = np.double)\n Sc_12 = np.zeros((Nx-1, Ny-1), dtype = np.double)\n v2 = np.amax(h)\n t2 = np.zeros(1)\n\n #Initialisierung des Plots\n\n if darstellung == 3:\n fig = plt.figure(figsize=(10,10))\n if darstellung == 2:\n if teil == 2:\n fig = plt.figure(figsize=(15,6))\n elif teil == 3:\n fig = plt.figure(figsize=(30,5))\n\n ax_contour = fig.add_subplot(111, frameon=False)\n plt.show(block= False)\n\n imageCounter = 0;\n #MacCormack Verfahren\n while z < tmax:\n\n # Berechnung der Eigenwerte\n EWX = np.array([hu[0, 0] / h[0, 0] - np.sqrt(g * h[0, 0]),\n hu[0, 0] / h[0, 0] + np.sqrt(g * h[0, 0])]) # Quelle: S.34 (3.5)\n EWY = np.array([hv[0, 0] / h[0, 0] - np.sqrt(g * h[0, 0]), hv[0, 0] / h[0, 0] + np.sqrt(g * h[0, 0])])\n for j in range(0, Nx):\n for k in range(0, Ny):\n EWX = np.append(EWX,\n [hu[j, k] / h[j, k] - np.sqrt(g * h[j, k]), hu[j, k] / h[j, k] + np.sqrt(g * h[j, k])])\n EWY = np.append(EWY,\n [hv[j, k] / h[j, k] - np.sqrt(g * h[j, k]), hv[j, k] / h[j, k] + np.sqrt(g * h[j, k])])\n dt = CFL * min(dx, dy) / (max(np.amax(EWX), np.amax(EWY))) # Quelle: S. 13 (1.58)\n z += dt\n\n # Berechnung von Flussvektoren in der Mitte des Zeitschritts\n\n #x-Richtung\n Fa[:Nx,:Ny] = hu[:Nx,:Ny]\n Fb[:Nx,:Ny] = (hu[:Nx,:Ny]**2)/(h[:Nx,:Ny]) + 0.5*g*(h[:Nx,:Ny]**2)\n Fc[:Nx,:Ny] = (hu[:Nx,:Ny]*hv[:Nx,:Ny])/(h[:Nx,:Ny])\n\n #y-Richtung\n Ga[:Nx,:Ny] = hv[:Nx,:Ny]\n Gb[:Nx,:Ny] = (hu[:Nx,:Ny]*hv[:Nx,:Ny])/(h[:Nx,:Ny])\n Gc[:Nx,:Ny] = (hv[:Nx,:Ny]**2)/(h[:Nx,:Ny]) + 0.5*g*(h[:Nx,:Ny]**2)\n\n #Quellterm\n if teil == 1:\n Sb = np.zeros((Nx, Ny), dtype = np.double)\n Sc = np.zeros((Nx, Ny), dtype = np.double)\n\n elif teil == 2:\n Sb[:Nx,:Ny] = (f[:Nx,:Ny] * hv[:Nx,:Ny])\n Sc[:Nx,:Ny] = -(f[:Nx,:Ny] * hu[:Nx,:Ny])\n\n elif teil == 3:\n\n Sb[:Nx,:Ny] = -g*(h[:Nx,:Ny])*dBdx[:Nx,:Ny] + (f[:Nx,:Ny] * hv[:Nx,:Ny])\n Sc[:Nx,:Ny] = -g*(h[:Nx,:Ny])*dBdy[:Nx,:Ny] - (f[:Nx,:Ny] * hu[:Nx,:Ny])\n\n\n # Berechnung von Flussvektoren an den Randzellen\n\n h_12[0:Nx-1, 0:Ny-1] = h[0:Nx-1, 0:Ny-1] - (dt/dx) * (Fa[1:Nx, 0:Ny-1] - Fa[0:Nx-1, 0:Ny-1]) - ((dt/dy) * (Ga[0:Nx-1, 1:Ny] - Ga[0:Nx-1, 0:Ny-1])) # Quelle: TUT\n hu_12[0:Nx-1, 0:Ny-1] = hu[0:Nx-1, 0:Ny-1] - (dt/dx) * (Fb[1:Nx, 0:Ny-1] - Fb[0:Nx-1, 0:Ny-1]) - ((dt/dy) * (Gb[0:Nx-1, 1:Ny] - Gb[0:Nx-1, 0:Ny-1])) + (dt * Sb[0:Nx-1, 0:Ny-1])\n hv_12[0:Nx-1, 0:Ny-1] = hv[0:Nx-1, 0:Ny-1] - (dt/dx) * (Fc[1:Nx, 0:Ny-1] - Fc[0:Nx-1, 0:Ny-1]) - ((dt/dy) * (Gc[0:Nx-1, 1:Ny] - Gc[0:Nx-1, 0:Ny-1])) + (dt * Sc[0:Nx-1, 0:Ny-1])\n\n # x-Richtung\n Fa_12[0:Nx-1, 0:Ny-1] = hu_12[0:Nx-1, 0:Ny-1] # Quelle: S.4 (1.1)\n Fb_12[0:Nx-1, 0:Ny-1] = (hu_12[0:Nx-1, 0:Ny-1]**2)/(h_12[0:Nx-1, 0:Ny-1]) + 0.5*g*(h_12[0:Nx-1, 0:Ny-1]**2)\n Fc_12[0:Nx-1, 0:Ny-1] = (hu_12[0:Nx-1, 0:Ny-1]*hv_12[0:Nx-1, 0:Ny-1])/(h_12[0:Nx-1, 0:Ny-1])\n\n # y-Richtung\n Ga_12[0:Nx-1, 0:Ny-1] = hv_12[0:Nx-1, 0:Ny-1] # Quelle: S.4 (1.1)\n Gb_12[0:Nx-1, 0:Ny-1] = (hu_12[0:Nx-1, 0:Ny-1]*hv_12[0:Nx-1, 0:Ny-1])/(h_12[0:Nx-1, 0:Ny-1])\n Gc_12[0:Nx-1, 0:Ny-1] = (hv_12[0:Nx-1, 0:Ny-1]**2)/(h_12[0:Nx-1, 0:Ny-1]) + 0.5*g*(h_12[0:Nx-1, 0:Ny-1]**2)\n\n # Quellterm\n if teil == 1:\n Sb_12 = np.zeros((Nx-1, Ny-1), dtype = np.double)\n Sc_12 = np.zeros((Nx-1, Ny-1), dtype = np.double)\n\n elif (teil == 2):\n Sb_12[0:Nx-1, 0:Ny-1] = (f[0:Nx-1, 0:Ny-1] * hv_12[0:Nx-1, 0:Ny-1])\n Sc_12[0:Nx-1, 0:Ny-1] = -(f[0:Nx-1, 0:Ny-1] * hu_12[0:Nx-1, 0:Ny-1])\n\n elif(teil ==3) :\n Sb_12[0:Nx-1, 0:Ny-1] = -g*(h_12[0:Nx-1, 0:Ny-1])*dBdx[0:Nx-1, 0:Ny-1] + (f[0:Nx-1, 0:Ny-1] * hv_12[0:Nx-1, 0:Ny-1])\n Sc_12[0:Nx-1, 0:Ny-1] = -g*(h_12[0:Nx-1, 0:Ny-1])*dBdy[0:Nx-1, 0:Ny-1] - (f[0:Nx-1, 0:Ny-1] * hu_12[0:Nx-1, 0:Ny-1])\n\n # Berechnung der h, hu und hv\n\n h[1:Nx-1, 1:Ny-1] = 0.5 * (h[1:Nx-1, 1:Ny-1] + h_12[1:Nx-1, 1:Ny-1]) - (0.5 * (dt/dx) * (Fa_12[1:Nx-1, 1:Ny-1] - Fa_12[0:Nx-2, 1:Ny-1])) - (0.5*(dt/dy) * (Ga_12[1:Nx-1, 1:Ny-1] - Ga_12[1:Nx-1, 0:Ny-2])) # Quelle: TUT\n hu[1:Nx-1, 1:Ny-1] = 0.5 * (hu[1:Nx-1, 1:Ny-1] + hu_12[1:Nx-1, 1:Ny-1]) - (0.5 * (dt/dx) * (Fb_12[1:Nx-1, 1:Ny-1] - Fb_12[0:Nx-2, 1:Ny-1])) - (0.5*(dt/dy) * (Gb_12[1:Nx-1, 1:Ny-1] - Gb_12[1:Nx-1, 0:Ny-2])) + (dt*0.5*Sb_12[1:Nx-1, 1:Ny-1])\n hv[1:Nx-1, 1:Ny-1] = 0.5 * (hv[1:Nx-1, 1:Ny-1] + hv_12[1:Nx-1, 1:Ny-1]) - (0.5 * (dt/dx) * (Fc_12[1:Nx-1, 1:Ny-1] - Fc_12[0:Nx-2, 1:Ny-1])) - (0.5*(dt/dy) * (Gc_12[1:Nx-1, 1:Ny-1] - Gc_12[1:Nx-1, 0:Ny-2])) + (dt*0.5*Sc_12[1:Nx-1, 1:Ny-1])\n\n\n #Randbedingungen je nach Aufgabe\n if aufgabe == 3.2:\n h = reflektierender_block(h)\n hu = reflektierender_block(hu)\n hv = reflektierender_block(hv)\n\n if aufgabe == 3.3:\n h = periodischer_block(h)\n hu = periodischer_block(hu)\n hv = periodischer_block(hv)\n\n # Berechnung der Geschwindigkeiten\n u = hu/h\n v = hv/h\n v2 = np.append(v2, [np.amax(h)])\n t2 = np.append(t2, [z])\n\n # Meshgrid für die Darstellung\n if (aufgabe==3.2):\n X,Y = np.meshgrid(x,y)\n elif (aufgabe==3.3):\n Y,X = np.meshgrid(y,x)\n\n #Surface plot in 3D\n if darstellung == 3:\n ax = fig.gca(projection='3d')\n ax.plot_surface(X, Y, h, cmap='cool', linewidth=0, antialiased=False)\n ax.set_title('Mccormack')\n ax.set_xlabel('x')\n ax.set_ylabel('y')\n ax.set_zlabel('h')\n ax.set_xlim(0,10)\n ax.set_ylim(0,10)\n ax.set_zlim(1.4,2.1)\n display.display(plt.gcf())\n display.clear_output(wait=True)\n plt.pause(0.01)\n plt.savefig('pngs/{:03d}'.format(imageCounter))\n plt.clf()\n\n #Contour plot und Quiver plot in 2D\n if darstellung == 2:\n\n ax_contour.cla()\n\n if Nx>=100:\n # ax_contour.set_xlim(0, Nx*1e4)\n ax_contour.set_xticks(np.arange(0, Nx*1e6, 20e5))\n ax_contour.set_xticklabels(np.arange(0, Nx, 2))\n ax_contour.set_xlabel(f' x [10^4 km]')\n ax_contour.set_yticklabels(())\n else:\n ax_contour.set_xlim(0, Nx*1e4)\n ax_contour.set_xticks(np.arange(0, Nx*1e5, 20e4))\n ax_contour.set_xticklabels(np.arange(0, Nx, 2))\n ax_contour.set_xlabel(f' x [{10}\\u00B3 km]')\n ax_contour.set_yticklabels(())\n\n if teil == 2:\n #Barotropische Instabilität / Höhe gleichen Drucks\n ax_contour.set_title(f'Höhe gleichen Drucks [km], t = {z//3600} Stunden')\n contour = ax_contour.pcolormesh(X, Y, h, vmin=9.5e3, vmax=10.5e3 , shading='auto', cmap='jet')\n cb = fig.colorbar(contour, ax=ax_contour)\n #ax_contour.quiver(X, Y, u, v, scale=20000, units='width', width=0.002)\n\n elif teil == 3:\n #Rossby Wellen in der nördlichen Hemisphäre\n\n ax_contour.set_title(f'Höhe gleichen Drucks [km] mit Windgeschwindigkeitsvektoren t = {z//3600} Stunden')\n contour = ax_contour.contourf(X, Y, B+h, vmin=9.5e3, vmax=10.5e3, shading='auto',cmap ='jet')\n cb = fig.colorbar(contour, ax=ax_contour)\n\n ax_contour.contour(X,Y,B, colors='black', linewidths=0.75)\n ax_contour.quiver(X, Y, u, v)\n\n plt.draw()\n plt.pause(0.01)\n plt.savefig('pngs/{:03d}'.format(imageCounter))\n cb.remove()\n imageCounter += 1\n\n return h, hu, hv, v2, t2\n\nif __name__ == \"__main__\":\n\n plt.style.use('seaborn')\n\n #Aufgabe 3.3.1 und 3.3.2 sind kommentiert. Je nach Aufgabe können die entsprechenden Zeilen auskommentiert werden.\n\n ### Aufagabe 3.2 ###\n\n # # Lax-Friedrich\n # h, hu, hv = anfangsbedingungen32(hh=2, ht=1.5, Nx=50, Ny=50)\n # h, hu, hv, v1, t1 = erhaltungsschema_2D(h, hu, hv, CFL=0.4, Nx = 50, Ny = 50, darstellung=3)\n\n # Maccormack\n h, hu, hv = anfangsbedingungen32(hh = 2, ht = 1.5, Nx = 50, Ny = 50)\n f = np.zeros([50, 50])\n h, hu, hv, v2, t2 = maccormack(h, hu, hv, f, CFL=0.4, Nx = 50, Ny = 50, darstellung= 3, aufgabe= 3.2, B=0,teil = 1, dBdx = 0, dBdy = 0)\n\n\n # # Vergleich der Lösungen\n # plt.plot(t2, v2, label='MacCormack')\n # plt.plot(t1, v1, label='Lax-Friedrich')\n # plt.title('MacCormack vs Lax-Friedrich')\n # plt.xlabel('Zeit (s)')\n # plt.ylabel('Höhe (m)')\n # plt.legend()\n # plt.show()\n\n\n ### Aufgabe 3.3 ###\n\n #Die zwei folgenden Zeilen müssen auch auskommentiert werden, wenn die Aufgabe 3.3.1 oder 3.3.2 gelöst werden soll.\n\n # Nx,Ny = 240, 60\n # CFL = 0.45\n\n # # 3.3.1: Barotropische Instabilität\n # h, hu, hv, f = anfangsbedingungen33(Nx,Ny)\n # h, hu, hv, v3, t3 = maccormack(h, hu, hv, f, CFL, Nx, Ny, darstellung= 2, aufgabe= 3.3, teil=2, B=0, dBdx = 0, dBdy = 0)\n\n\n # # 3.3.2: Rossby Wellen in der nördlichen Hemisphäre\n # h, hu, hv, f, B, dBdx, dBdy = anfangsbedingungen33(Nx,Ny,darstellung = 2)\n # h, hu, hv, v4, t4 = maccormack(h, hu, hv, f, CFL, Nx, Ny,B, darstellung = 2, aufgabe = 3.3, teil = 3, dBdx = dBdx, dBdy = dBdy)\n\n","repo_name":"Javiermateor/Flachwassergleichung","sub_path":"HA3.py","file_name":"HA3.py","file_ext":"py","file_size_in_byte":19300,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"85"} +{"seq_id":"7615373308","text":"import numpy as np\n# import os\nimport matplotlib.pyplot as plt\nimport torch\nimport pandas as pd\nfrom matplotlib import cm\n\n\ndef visual_picture(tensor: torch.Tensor, n_pictures, dim=2):\n size = 5\n if dim == 2:\n pictures = tensor[:n_pictures, 0].cpu().numpy()\n else:\n pictures = tensor[0].cpu().numpy()\n X = n_pictures\n fig, axes = plt.subplots(1, X)\n for i in range(X):\n if dim == 2:\n axes[i].imshow(pictures[i])\n fig.colorbar(cm.ScalarMappable(), ax=axes[i])\n else:\n axes[i].plot(pictures[i])\n fig.set_figwidth(size * X)\n fig.set_figheight(size)\n plt.show()\n\n\ndef visual_table_of_pictures(data,\n sample_titles,\n y_titles,\n visual_fun,\n size=(5, 5)):\n '''\n Params:\n data : List[List[np.array 1D or 2D]] - first coord -- sample, second -- different views\n sample_titles: List[str]\n y_titles: List[str] in order top down\n visual_fun: List[plt funcs] for each row\n\n usage example:\n \n data= [[pred_pic[i + 7*j] for i in range(3)] for j in range(4)]\n x_titles = [\"best\", \"random\", \"random\", \"worst\"]\n y_titles = [\"true\", \"predict\", \"signal\"]\n visual_func = [lambda ax, pic: ax.imshow(pic)]*3\n visual_table_of_pictures(data, x_titles, y_titles, visual_func)\n '''\n X = len(data)\n Y = len(data[0])\n fig, axes = plt.subplots(Y, X, squeeze=False, sharex=True, sharey='row')\n for y in range(Y):\n axes[y][0].set_ylabel(y_titles[y], fontsize=20)\n for x in range(X):\n pic = data[x][y]\n visual_fun[y](axes[y][x], pic)\n for x in range(X):\n axes[0][x].set_title(sample_titles[x], fontsize=20)\n fig.set_figwidth(size[1] * X)\n fig.set_figheight(size[0] * Y)\n # plt.show()\n","repo_name":"RQC-Robotics/RQC-Robotics-tactile_sensor","sub_path":"torch_sensor_lib/visual.py","file_name":"visual.py","file_ext":"py","file_size_in_byte":1861,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"45258776","text":"import torch,torchvision\nimport torch.optim\nimport os\nimport argparse\nimport time\nimport dataloader\nimport networks\nfrom SSIM import SSIM\nimport matplotlib.pyplot as plt\nimport torch.nn as nn\nimport numpy as np\n\n\ndef train(config):\n\n dehaze_net = networks.IRDN(config.recurrent_iter).cuda()\n if config.epoched == 0:\n pass\n else:\n dehaze_net.load_state_dict(torch.load('trained_model/i6-outdoor-MSE+SSIM/Epoch%i.pth'%config.epoched))\n \n if config.in_or_out == \"outdoor\":\n train_dataset = dataloader.dehazing_loader(config.orig_images_path,\n config.hazy_images_path)\n else:\n config.orig_images_path = \"dataset/train_data/indoor/clear/\"\n config.hazy_images_path = \"dataset/train_data/indoor/hazy/\"\n train_dataset = dataloader.dehazing_loader(config.orig_images_path,\n config.hazy_images_path)\n val_dataset = dataloader.dehazing_loader(config.orig_images_path,\n config.hazy_images_path, mode=\"val\")\n train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=config.train_batch_size, shuffle=True,\n num_workers=config.num_workers, pin_memory=True)\n val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=config.val_batch_size, shuffle=False,\n num_workers=config.num_workers, pin_memory=True)\n\n if config.lossfunc == \"MSE\":\n criterion = nn.MSELoss().cuda()\n elif config.lossfunc == \"SSIM\":\n criterion = SSIM()\n else:\t\t\t\t\t\t\t\t\t\t\t\t\t#MSE+SSIM Loss\n criterion1 = nn.MSELoss().cuda()\n criterion2 = SSIM()\n comput_ssim = SSIM() \n\n optimizer = torch.optim.Adam(dehaze_net.parameters(), lr=config.lr)\n\n dehaze_net.train()\n zt = 1\n Iters = 0\n indexX = []\n indexY = []\n for epoch in range(config.epoched,config.num_epochs):\n print(\"*\" * 80 + \"第%i轮\" % epoch + \"*\" * 80)\n\n\n for iteration, (img_orig, img_haze) in enumerate(train_loader):\n\n img_orig = img_orig.cuda()\n img_haze = img_haze.cuda()\n\n try:\n clean_image,_ = dehaze_net(img_haze)\n if config.lossfunc == \"MSE\":\n loss = criterion(clean_image,img_orig) \n elif config.lossfunc == \"SSIM\":\n\n loss = criterion(img_orig,clean_image)\n loss = -loss\n else:\t\t\t\t\t\t\t\t\t\t\t\t\t\n ssim = criterion2(img_orig,clean_image)\n mse = criterion1(clean_image,img_orig)\n loss = mse-ssim\n\n\n\n del clean_image, img_orig\n optimizer.zero_grad()\n loss.backward()\n torch.nn.utils.clip_grad_norm(dehaze_net.parameters(), config.grad_clip_norm)\n optimizer.step()\n Iters += 1\n if ((iteration + 1) % config.display_iter) == 0:\n print(\"Loss at iteration\", iteration + 1, \":\", loss.item())\n\n if ((iteration + 1) % config.snapshot_iter) == 0:\n torch.save(dehaze_net.state_dict(), config.snapshots_folder + \"Epoch\" + str(epoch) + '.pth')\n except RuntimeError as e:\n if 'out of memory' in str(e):\n print(e)\n torch.cuda.empty_cache()\n else:\n raise e\n\n\n\n # if zt == 0 and Iters >= 700:\t\t\t\t\t\t\t\t\t#early stop\n # break\n _ssim=[]\n\n\n #Validation Stage\n with torch.no_grad():\n\n for iteration, (clean, haze) in enumerate(val_loader):\n clean = clean.cuda()\n haze = haze.cuda()\n\n clean_,_ = dehaze_net(haze)\n _s = comput_ssim(clean,clean_)#计算ssim值\n _ssim.append(_s.item())\n\n torchvision.utils.save_image(torch.cat((haze,clean_,clean), 0),\n config.sample_output_folder + \"/epoch%s\"%epoch +\"/\"+ str(iteration + 1) + \".jpg\")\n _ssim = np.array(_ssim)\n\n print(\"-----The %i Epoch mean-ssim is :%f-----\" %(epoch,np.mean(_ssim)))\n with open(\"trainlog/indoor/i%i_%s.log\"%(config.recurrent_iter,config.lossfunc), \"a+\", encoding=\"utf-8\") as f:\n s = \"The %i Epoch mean-ssim is :%f\" %(epoch,np.mean(_ssim))+ \"\\n\"\n f.write(s)\n indexX.append(epoch+1)\n indexY.append(np.mean(_ssim))\n print(indexX,indexY)\n plt.plot(indexX,indexY,linewidth=2)\n plt.pause(0.1)\n plt.savefig(\"trainlog/i%i_%s.png\" % (config.recurrent_iter,config.lossfunc))\n torch.save(dehaze_net.state_dict(), config.snapshots_folder + \"IRDN.pth\")\n\n\nif __name__ == \"__main__\":\n\n \n torch.cuda.empty_cache()\n\n parser = argparse.ArgumentParser()\n\n # Input Parameters\n parser.add_argument('--orig_images_path', type=str, default=\"dataset/train_data/outdoor/clear/\")\n parser.add_argument('--hazy_images_path', type=str, default=\"dataset/train_data/outdoor/hazy/\")\n parser.add_argument('--lr', type=float, default=0.0001)\n parser.add_argument('--weight_decay', type=float, default=0.0001)\n parser.add_argument('--grad_clip_norm', type=float, default=0.1)\n parser.add_argument('--num_epochs', type=int, default=20)\n parser.add_argument('--train_batch_size', type=int, default=1)\n parser.add_argument('--val_batch_size', type=int, default=1)\n parser.add_argument('--num_workers', type=int, default=1)\n parser.add_argument('--display_iter', type=int, default=1)\n parser.add_argument('--snapshot_iter', type=int, default=20)\n parser.add_argument('--snapshots_folder', type=str, default=\"trained_model/i1-outdoor-ssim/\")\n parser.add_argument('--sample_output_folder', type=str, default=\"samples/i1-ssim/\")\n parser.add_argument('--recurrent_iter', type=int, default=1)\n parser.add_argument('--in_or_out', type=str, default=\"indoor\")\n parser.add_argument('--lossfunc', type=str, default=\"SSIM\",help=\"choose Loss Function(MSE or -SSIM or MSE+SSIM).\")\n parser.add_argument('--cudaid', type=str, default=\"0\",help=\"choose cuda device id 0-7).\")\n parser.add_argument('--epoched', type=int, default=0,help=\"choose cuda device id 0-7).\")\n\n\n config = parser.parse_args()\n print(config)\n\n os.environ['CUDA_VISIBLE_DEVICES'] = config.cudaid\n\n if not os.path.exists(config.snapshots_folder):\n os.mkdir(config.snapshots_folder)\n if not os.path.exists(config.sample_output_folder):\n os.mkdir(config.sample_output_folder)\n\n for i in range(config.num_epochs):\n path = config.sample_output_folder + \"/epoch%s\" % str(i)\n if not os.path.exists(path):\n os.mkdir(path)\n\n s = time.time()\n\n train(config)\n e = time.time()\n print(str(e-s))","repo_name":"CCECfgd/IRN","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":6938,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"85"} +{"seq_id":"13934482071","text":"class Solution(object):\n def canJump(self, nums):\n \"\"\"\n :type nums: List[int]\n :rtype: bool\n \"\"\"\n maxt=nums[0]\n le=len(nums)-1\n for i in range(len(nums)):\n if(maxt=le):\n return(True)","repo_name":"manojmk29/Leetcode-Problems-DSA","sub_path":"55-jump-game/55-jump-game.py","file_name":"55-jump-game.py","file_ext":"py","file_size_in_byte":352,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"44921264398","text":"def binarySearch(list, search):\n print(list)\n if len(list) == 1 and list[0] == search:\n return True\n if len(list) == 1 and list[0] != search:\n return False\n if len(list) == 0:\n return False\n\n medium = len(list) // 2\n if list[medium] == search:\n return True\n else:\n if list[medium] < search:\n return binarySearch(list[medium + 1:], search)\n else:\n return binarySearch(list[:medium], search)\n\n\ndef sequentialSearch(list, search):\n for index in range(len(list)):\n if list[index] == search:\n return True\n return False\n\n\nimport random\n\nlist = random.sample(range(50), 20)\nlist.sort()\nprint(binarySearch(list, 31))\n\nlist2 = random.sample(range(50), 20)\nprint(list2)\nprint(sequentialSearch(list2, 31))\n","repo_name":"liyusang1/ProblemSolving","sub_path":"ProblemSolving/Binary & Sequenital Search.py","file_name":"Binary & Sequenital Search.py","file_ext":"py","file_size_in_byte":804,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"85"} +{"seq_id":"10281018220","text":"import sqlite3\nimport sys\n\nDBNAME = 'choc.db'\n\n\n#\n# FUNCTION: Performs given sql query and returns the tuple result\n#\ndef perform_query(sql):\n conn = sqlite3.connect(DBNAME)\n cur = conn.cursor()\n results = cur.execute(sql)\n result_list = results.fetchall()\n conn.close()\n return result_list\n\n\n\n#\n# FUNCTION: Prepares a row of data for output to the terminal, given a one\n# dimensional tuple of length 6. The tuple is stransformed into\n# a table like structure, for easy reading by the user. \n#\ndef prepare_bars_output_row(data):\n return (\n ((data[0][:20] + '..').ljust(30) if len(data[0]) > 25 else data[0].ljust(30)) +\n ((data[1][:20] + '..').ljust(25) if len(data[1]) > 20 else data[1].ljust(25)) +\n ((data[2][:20] + '..').ljust(30) if len(data[2]) > 25 else data[2].ljust(30)) + \n (str(data[3]).ljust(5)) +\n ((str(data[4]) + '%').ljust(8)) +\n ((data[5][:20] + '..').ljust(25) if len(data[5]) > 25 else data[5].ljust(25))\n )\n\n\n\n#\n# FUNCTION: Processes and interprets bars command parameters.\n# Handles invalid parameters for Bars command\n#\ndef process_bars_command(params):\n\n #error check, bars command takes at most 3 paramaters\n if len(params) > 3:\n return ('error', 'Command \"Bars\" takes at most 3 parameters.')\n\n # SQL query stub. the following is present in every query for bars\n sql = '''\n SELECT SpecificBeanBarName,\n Company,\n C1.EnglishName AS country,\n Rating, CocoaPercent,\n C2.EnglishName AS origin\n FROM Bars \n JOIN Countries AS C1 ON Bars.CompanyLocationId=C1.Id\n JOIN Countries AS C2 ON Bars.BroadBeanOriginId=C2.Id\n '''\n\n # default values as specified by spec!!\n where = '' # No default - dont require filter\n orderBy = ' ORDER BY RATING' # Default value to order results by\n limit = ' DESC LIMIT 10' # default value for how many results to show\n\n # variables to check for duplicate paramter types\n whereSet = 0\n limitSet = 0\n orderBySet = 0\n\n # loop through and interpret each parameter\n for option in params:\n\n # conditional checks for filters/limits.\n if len(option.split('=')) == 2:\n if option.split('=')[0] == 'sellcountry':\n where += ' WHERE C1.Alpha2=\"' + option.split('=')[1] + '\"'\n whereSet += 1\n\n elif option.split('=')[0] == 'sellregion':\n where += ' WHERE C1.Region=\"' + option.split('=')[1] + '\"'\n whereSet += 1\n\n elif option.split('=')[0] == 'sourcecountry':\n where += ' WHERE C2.Alpha2=\"' + option.split('=')[1] + '\"'\n whereSet += 1\n\n elif option.split('=')[0] == 'sourceregion':\n where += ' WHERE C2.Region=\"' + option.split('=')[1] + '\"'\n whereSet += 1\n \n elif option.split('=')[0] == 'top':\n limit = ' DESC LIMIT ' + option.split('=')[1]\n limitSet += 1\n\n elif option.split('=')[0] == 'bottom':\n limit = ' ASC LIMIT ' + option.split('=')[1]\n limitSet += 1\n\n # filter or limit parameter not recognized - return erropr\n else:\n return('error', 'Parameter with value\"' + option + '\" not recognized. Please try again.')\n \n\n # check for / interpret order by parameters\n elif option == 'ratings':\n orderBy = ' ORDER BY RATING'\n orderBySet += 1\n\n elif option == 'cocoa':\n orderBy = ' ORDER BY CocoaPercent'\n orderBySet += 1\n\n # paramater not recognized as valid bars param - return error\n else:\n return('error', 'Parameter \"' + option + '\" not recognized. Please try again.')\n\n\n # Check for duplicate param types. If duplictes, return error\n if whereSet > 1 or limitSet > 1 or orderBySet > 1:\n return('error', 'Multiple filter options not allowed.')\n\n\n sql += where + orderBy + limit \n return perform_query(sql)\n","repo_name":"LaurenElbaum/F2018-507-Project3","sub_path":"project_scripts/command_bars.py","file_name":"command_bars.py","file_ext":"py","file_size_in_byte":4127,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"85"} +{"seq_id":"25987753368","text":"import asyncio\nimport operator\nimport os\nimport pickle\n\nimport aiomas\nimport numpy as np\nfrom creamas.core.environment import Environment\nfrom creamas.core.simulation import Simulation\nfrom creamas.examples.spiro.spiro_agent_mp import SpiroEnvManager\nfrom creamas.examples.spiro.spiro_agent_mp import SpiroMultiEnvManager\nfrom creamas.util import run\n\nfrom environments.spiro.spr_environment_equal import SprEnvironmentEqual\nfrom utilities.math import gini\nfrom utilities.result_analyzer import analyze\nfrom utilities.serializers import get_spiro_ser_own\n\n\ndef print_and_save_stuff():\n text = ''\n\n chosen_counts = {}\n\n for agent in agents:\n chosen_counts[agent] = 0\n\n # Print who chose who and how many times\n for acquaintance, counts in acquaintance_counts.items():\n most_chosen = max(counts, key=operator.itemgetter(1))\n\n chosen_counts[most_chosen[0]] += 1\n\n text += 'Agent {} chose {} ({} times)\\n'.format(acquaintance, most_chosen[0], most_chosen[1])\n text += str(counts) + '\\n'\n\n text += '\\n'\n\n # Print how many times the spiro were chosen\n for agent, count in chosen_counts.items():\n text += '{} was chosen {} times\\n'.format(agent, count)\n\n text += '\\n'\n\n acquaintance_avgs = {}\n\n # Print how the spiro value other's opinions\n for acquaintance, values in acquaintance_values.items():\n acquaintance_avgs[acquaintance[:22]] = 0\n text += str(acquaintance) + '\\n'\n text += str(values) + '\\n'\n\n text += '\\n'\n\n # Print when spiro had learned their best friend\n for name, last_change in last_changes.items():\n text += '{} learned best friend at iteration: {}\\n'.format(name, last_change)\n\n text += '\\n'\n\n # Calculate and print for each agent how the other spiro value them on average\n for acquaintance, values in acquaintance_values.items():\n for agent, value in values.items():\n acquaintance_avgs[agent] += value\n\n for key, value in acquaintance_avgs.items():\n acquaintance_avgs[key] = value/(len(acquaintance_avgs) - 1)\n text += 'Agent {} avg: {}\\n'.format(key, acquaintance_avgs[key])\n\n text += '\\n'\n text += 'Comparisons: ' + str(total_comparisons) + '\\n'\n text += 'Mean novelty: ' + str(mean) + '\\n'\n text += '\\n'\n\n text += 'Number of accepted artifacts: ' + str(num_of_accepted_artifacts) + '\\n'\n\n # Calculate and print how many times spiro got their artifacts accepted\n creator_counts = {}\n\n for artifact in artifacts:\n if artifact.creator not in creator_counts:\n creator_counts[artifact.creator] = 1\n else:\n creator_counts[artifact.creator] += 1\n\n for creator, count in creator_counts.items():\n text += 'Agent {} created {} accepted artifacts\\n'.format(creator, count)\n\n text += '\\n'\n\n # Print overcoming own thershold counts\n for name, count in overcame_own_threshold_counts.items():\n text += '{} overcame itself {}/{} times\\n'.format(name, count, steps)\n\n text += '\\n'\n\n # Print criticism stats\n for name, c_stats in criticism_stats.items():\n text += '{} rejected {}/{} times\\n'.format(name, c_stats[0], c_stats[1])\n\n text += '\\n'\n\n # Print total rewards\n total_reward = 0\n for agent in agents:\n reward = 0\n if agent in criticism_stats:\n reward += criticism_stats[agent][0]\n if agent in creator_counts:\n reward += creator_counts[agent]\n total_reward += reward\n text += '{} total reward: {}\\n'.format(agent, reward)\n\n text+= 'total total reward: ' + str(total_reward)\n text += '\\n\\n'\n\n # Print memory state times\n for name, thing in memory_state_times.items():\n total_memory_state_times[name] = [sum(x) for x in zip(total_memory_state_times[name], thing)]\n text += name + '\\n'\n text += '{}, total: {}\\n'.format(thing, tuple(total_memory_state_times[name]))\n\n text += '\\n'\n\n # Calculate gini coefficient of accepted artifacts\n gini_coef = gini(np.array(list(creator_counts.values())).astype(float))\n text += '\\nGini coefficient for amount of accepted artifacts: ' + str(gini_coef)\n text += '\\n'\n\n print(text)\n\n with open(save_file, \"a\") as file:\n file.write('\\n\\n***** {} *****\\n\\n'.format(round))\n file.write(text + '\\n\\n\\n\\n')\n\n stats['comps'].append(total_comparisons)\n stats['novelty'].append(mean)\n stats['gini'].append(gini_coef)\n stats['bestie_find_speed'].append(last_changes)\n\n\nif __name__ == \"__main__\":\n directory = 'results_q'\n save_file = \"{}/print.txt\".format(directory)\n\n # remove old save file\n if os.path.exists(save_file):\n os.remove(save_file)\n\n #Create result directory if needed\n if not os.path.exists(directory):\n os.makedirs(directory)\n\n # ALL PARAMETERS\n\n critic_threshold = 0.08\n veto_threshold = 0.08\n ask_passing = True\n random_choosing = False\n memory_states = (10, 60, 120)\n initial_state = 1\n invent_n = 120\n\n discount_factor = 0.99\n learning_factor = 0.9\n\n num_of_agents = 5\n num_of_artifacts = 10\n num_of_simulations = 5\n num_of_steps = 10\n\n use_steps = False # Stop when enough steps or when enough artifacts\n\n # Other stuff\n\n log_folder = 'q_logs'\n logger = None\n\n addr = ('localhost', 5555)\n addrs = [('localhost', 5560),\n ('localhost', 5561),\n ('localhost', 5562),\n ('localhost', 5563)\n ]\n\n env_kwargs = {'extra_serializers': [get_spiro_ser_own], 'codec': aiomas.MsgPack}\n slave_kwargs = [{'extra_serializers': [get_spiro_ser_own], 'codec': aiomas.MsgPack} for _ in range(len(addrs))]\n\n stats = {'comps': [], 'novelty': [], 'gini': [], 'bestie_find_speed': []}\n\n # Run simulation x times and record stats\n for _ in range(num_of_simulations):\n\n env = SprEnvironmentEqual(addr,\n env_cls=Environment,\n mgr_cls=SpiroMultiEnvManager,\n logger=logger,\n **env_kwargs)\n\n loop = asyncio.get_event_loop()\n\n ret = run(env.spawn_slaves(slave_addrs=addrs,\n slave_env_cls=Environment,\n slave_mgr_cls=SpiroEnvManager,\n slave_kwargs=slave_kwargs))\n\n ret = loop.run_until_complete(env.set_host_managers())\n ret = loop.run_until_complete(env.wait_slaves(30, check_ready=True))\n ret = loop.run_until_complete(env.is_ready())\n\n for _ in range(num_of_agents):\n print(aiomas.run(until=env.spawn('agents.spiro.critic_q_agent:CriticQAgent',\n desired_novelty=-1,\n log_folder=log_folder,\n critic_threshold=critic_threshold,\n veto_threshold=veto_threshold,\n ask_passing=ask_passing,\n rand=random_choosing,\n memory_states=memory_states,\n initial_state=initial_state,\n invent_n=invent_n,\n discount_factor=discount_factor,\n learning_factor=learning_factor)))\n\n env.set_agent_acquaintances()\n\n sim = Simulation(env=env, log_folder=log_folder, callback=env.vote_and_save_info)\n\n agents = env.get_agents(addr=True)\n total_memory_state_times = {}\n round = 0\n\n for agent in agents:\n total_memory_state_times[agent] = [0] * len(memory_states)\n\n if use_steps:\n sim.async_steps(num_of_steps)\n else:\n while True:\n while len(env.artifacts) < num_of_artifacts:\n sim.async_step()\n\n # gather info\n env._consistent = False\n acquaintance_counts = env.get_acquaintance_counts()\n acquaintance_values = env.get_acquaintance_values()\n total_comparisons = env.get_comparison_count()\n mean, _, _ = env._calc_distances()\n num_of_accepted_artifacts = len(env.artifacts)\n artifacts = env.artifacts\n last_changes = env.get_last_best_acquaintance_changes()\n overcame_own_threshold_counts = env.get_overcame_own_threshold_counts()\n steps = env.age\n criticism_stats = env.get_criticism_stats()\n memory_state_times = env.get_memory_state_times()\n round += 1\n\n print_and_save_stuff()\n\n # reset stuff\n env._artifacts = []\n env.reset_agents()\n\n sim.end()\n\n\n\n file = \"{}/stats_artifacts{}.p\".format(directory, num_of_artifacts)\n pickle.dump(stats, open(file, \"wb\"))\n\n analyze(file)\n\n # print()\n #\n # for data in stats['bestie_find_speed']:\n # for name, last_change in data.items():\n # print('{} learned best friend at iteration: {}'.format(name, last_change))\n # print()\n\n","repo_name":"Outsou/rlmas_experiments","sub_path":"experiments/spiro/spr_q_experiment.py","file_name":"spr_q_experiment.py","file_ext":"py","file_size_in_byte":9341,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"28722829527","text":"\"\"\"\nClass for creating the menu bar based on the state of the IMU object.\n\"\"\"\nimport constants as c\n\n\nclass Menu:\n def __init__(self):\n # Set initial values\n self.imuConnected = False\n self.imuImenu = None\n # Initial creation of menus.\n self.__generateMenus()\n\n def getMenu(self, imuConnected=False):\n \"\"\"\n Return the current menu bar based on the parameter values given. The local parameter values are updated based on\n the given parameters and the menu(s) are generated. The generated menus are combined into a single menu bar\n layout and returned.\n\n Args:\n imuConnected (bool): True if IMU object is connected, else False.\n\n Returns:\n menuFinal (list): Final menu layout.\n \"\"\"\n # Local variable update.\n self.imuConnected = imuConnected\n # Generate menus.\n self.__generateMenus()\n\n menuFinal = [self.imuImenu]\n # Return menu bar layout.\n return menuFinal\n\n def __generateImuMenu(self):\n \"\"\"\n Function for creating imu menus based on the connection status of the IMU object. For controlling the\n connection to the IMU. if self.imuConnected:\n\n False (initial state): Menu to show when the IMU is not connected. Since the IMU is not connected the\n return rate cannot be changed and the acceleration cannot be calibrated, so these\n options are disabled.\n True (post connection): Menu to show when the IMU has been connected, enabling return rate and acceleration\n calibration.\n \"\"\"\n if not self.imuConnected:\n self.imuImenu = ['IMU', ['Connect::-MENU-IMU-CONNECT-',\n '---',\n '!Set Return Rate',\n '!Set Bandwidth',\n '!Set Algorithm',\n '!Calibrate Acceleration::-MENU-IMU-CALIBRATE-']\n ]\n if self.imuConnected:\n self.imuImenu = ['IMU', ['Disconnect::-MENU-IMU-DISCONNECT-',\n '---',\n 'Set Return Rate', [f'{i}::-MENU-IMU-RATE-' for i in c.IMU_RATE_OPTIONS],\n 'Set Bandwidth', [f'{i}::-MENU-IMU-BANDWIDTH-' for i in c.IMU_BANDWIDTH_OPTIONS],\n 'Set Algorithm', [f'{i}::-MENU-IMU-ALGORITHM-' for i in c.IMU_ALGORITHM_OPTIONS],\n 'Calibrate Acceleration::-MENU-IMU-CALIBRATE-']\n ]\n\n def __generateMenus(self):\n \"\"\"\n Function to call individual menu generating functions. More menus can be added, which now only require a single\n function to create the menu and a single call to this __generateMenus function.\n \"\"\"\n # IMU Menu.\n self.__generateImuMenu()\n","repo_name":"rorybennett/SCG-Sensor-Windows","sub_path":"Menu.py","file_name":"Menu.py","file_ext":"py","file_size_in_byte":3061,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"28319765482","text":"import sys\nimport os\n\n# Add the parent directory to the Python module search path\ntry:\n sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))\nexcept Exception as e:\n print(\"Error setting path: {}\".format(e))\n print(\"Continuing anyway...\")\n\n# Test functions inside ./utils/utils.py\nimport utils.utils as utils\nimport pytest\n\ntest_add_two = [\n (1, 2, 3),\n (2, 3, 5),\n (3, 4, 7),\n (4, 5, 9)\n]\n\ntest_diff_two = [\n (1, 2, -1),\n (2, 3, -1),\n (3, 4, -1),\n (4, 5, -1)\n]\n\ntest_np_add_two = [\n (1, 2, 3),\n (2, 3, 5),\n (3, 4, 7),\n (4, 5, 9)\n]\n\ntest_np_diff_two = [\n (1, 2, -1),\n (2, 3, -1),\n (3, 4, -1),\n (4, 5, -1)\n]\n\n@pytest.mark.parametrize(\"a, b, expected\", test_add_two)\ndef test_add_two(a, b, expected):\n assert utils.add_two(a, b) == expected\n\n@pytest.mark.parametrize(\"a, b, expected\", test_diff_two)\ndef test_diff_two(a, b, expected):\n assert utils.diff_two(a, b) == expected\n\n@pytest.mark.parametrize(\"a, b, expected\", test_np_add_two)\ndef test_np_add_two(a, b, expected):\n assert utils.np_add_two(a, b) == expected\n\n@pytest.mark.parametrize(\"a, b, expected\", test_np_diff_two)\ndef test_np_diff_two(a, b, expected):\n assert utils.np_diff_two(a, b) == expected","repo_name":"MaxSvjatoha/yaml-refresher","sub_path":"tests/test_utils.py","file_name":"test_utils.py","file_ext":"py","file_size_in_byte":1256,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"9536493373","text":"\n\ndef findbackwardmatchingparenthesis(text, index):\n count = 0\n for i in range(index, -1, -1):\n char = text[i]\n if char == ')':\n count += 1\n elif char == '(':\n count -= 1\n if count == 0:\n return i\n\ndef removeparenthesis(text):\n right = text.rfind('|)')\n while right != -1:\n match = findbackwardmatchingparenthesis(text, right + 1)\n text = text[:match] + text[right+2:]\n\n right = text.rfind('|)')\n return text\n'''\nline = '123456(456|)abc(34|)789'\ntext = removeparenthesis(line)\nprint(text)\n'''\nwith open('day20/test6') as f:\n line = f.readline()\n print(len(line))\n text = removeparenthesis(line)\n print(len(text))\n\nwith open('day20/test6.1') as f:\n line = f.readline()\n print(len(line))\n","repo_name":"r-udd/adventofcode2018","sub_path":"day20/testing.py","file_name":"testing.py","file_ext":"py","file_size_in_byte":809,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"73198240598","text":"from django.urls import path\nfrom .views import *\n\napp_name = 'posts'\n\nurlpatterns = [\n path('', post_id_handler, name='uuid'),\n path('', post_list_handler, name='list'),\n path('feedback', feedback_handler, name='feedback'),\n path('flag', flag_handler)\n]\n","repo_name":"ZeddYu/CTFs-2020-Archive-Public","sub_path":"rctf2020/rblog2015-attachment/rblog/posts/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":276,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"85"} +{"seq_id":"43985978303","text":"import matplotlib.pyplot as plt\n\ndef visualize_ruonia(data, count):\n plt.close(\"all\")\n\n font = {'family' : 'monospace',\n 'size' : 8}\n plt.rc('font', **font)\n plt.grid()\n plt.xticks(rotation=90)\n\n colors = [(0,0,1), (1,0,0), (0,1,0)]\n for idx, dataItem in enumerate(data):\n plt.plot(*zip(*dataItem), color=colors[idx])\n\n # Настройки шага по оси X\n plt.gca().margins(x=0)\n plt.gcf().canvas.draw()\n tl = plt.gca().get_xticklabels()\n maxsize = max([t.get_window_extent().width for t in tl])\n m = 0.2 # inch margin\n s = maxsize/plt.gcf().dpi*count+2*m\n margin = m/plt.gcf().get_size_inches()[0]\n\n plt.gcf().subplots_adjust(left=margin, right=1.-margin)\n plt.gcf().set_size_inches(s, plt.gcf().get_size_inches()[1])\n # Конец настройки шага по оси X\n\n plt.show()","repo_name":"Securities-Analysis-Information-System/ruonia_parser","sub_path":"visualize_ruonia.py","file_name":"visualize_ruonia.py","file_ext":"py","file_size_in_byte":873,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"85"} +{"seq_id":"39528416964","text":"import math\nimport json\n\nclass MoodClassifier(object):\n\tdef get_vocab(self, X):\n\t\tvocab = {}\n\t\tdata_lines = X.splitlines()\n\t\tfor row in data_lines:\n\t\t\ttokens = row.split(' ')\n\t\t\tfor word in tokens:\n\t\t\t\tif str(word).isspace():\n\t\t\t\t\tcontinue\n\t\t\t\telif word in vocab:\n\t\t\t\t\tvocab[word] += 1\n\t\t\t\telse:\n\t\t\t\t\tvocab[word] = 1\n\t\treturn vocab\n\n\tdef fit(self, X, truth_labels):\n\t\tdata_len = len(X)\n\t\tself.word_count = {'valencePos': {}, 'valenceNeg': {}}\n\t\tself.mood_class = {\t'valencePos': math.log(sum(1 for label in truth_labels if label == 1) / data_len), \n\t\t\t\t\t\t\t'valenceNeg': math.log(sum(1 for label in truth_labels if label == 0) / data_len)}\n\t\tself.vocab = set()\n\n\t\tfor lyric, truthv in zip(X, truth_labels):\n\t\t\tif truthv == 1:\n\t\t\t\tvalClass = 'valencePos'\n\t\t\telif truthv == 0:\n\t\t\t\tvalClass = 'valenceNeg'\n\t\t\tcounts = self.get_vocab(lyric)\n\t\t\tfor word, count in counts.items():\n\t\t\t\tif len(word) < 1:\n\t\t\t\t\tcontinue\n\t\t\t\tif word not in self.vocab:\n\t\t\t\t\tself.vocab.add(word)\n\t\t\t\tif word not in self.word_count[valClass]:\n\t\t\t\t\tself.word_count[valClass][word] = 0.0\n\n\t\t\t\tself.word_count[valClass][word] += count\n\n\tdef predict(self, X):\n\t\tresultVal = []\n\t\tresultAro = []\n\t\tfor lyric in X:\n\t\t\tcounts = self.get_vocab(lyric)\n\t\t\tvalP_score = 0\n\t\t\tvalN_score = 0\n\t\t\tfor word, count in counts.items():\n\t\t\t\tif word not in self.vocab:\n\t\t\t\t\tcontinue\n\t\t\t\t#laplacing\n\t\t\t\tlog_w_given_posVal = math.log( (self.word_count['valencePos'].get(word, 0.0) + 1) / (sum(self.word_count['valencePos'].values()) + len(self.vocab)) )\n\t\t\t\tlog_w_given_negVal = math.log( (self.word_count['valenceNeg'].get(word, 0.0) + 1) / (sum(self.word_count['valenceNeg'].values()) + len(self.vocab)) )\n\n\t\t\t\tvalP_score += log_w_given_posVal\n\t\t\t\tvalN_score += log_w_given_negVal\n\n\t\t\tvalP_score += self.mood_class['valencePos']\n\t\t\tvalN_score += self.mood_class['valenceNeg']\n\n\t\t\tbiggest = 0\n\t\t\tif (valP_score >= valN_score):\n\t\t\t\tresultVal.append(1)\n\t\t\telif (valP_score < valN_score):\n\t\t\t\tresultVal.append(0)\n\t\treturn resultVal\n\n\tdef writeClf(self, path):#to Json file\n\t\tdict_list = [self.mood_class, self.word_count, dict.fromkeys(self.vocab, 0)]\n\t\twith open(path, 'w') as outfile:\n\t\t\tjson.dump(dict_list, outfile)\n\n\tdef readClf(self, path):\n\t\tself.mood_class = {}\n\t\tself.word_count = {}\n\t\tself.vocab = set()\n\t\twith open(path) as json_file: \n\t\t\tdata = json.load(json_file)\n\t\t\tself.mood_class = dict(data[0])\n\t\t\tself.word_count = dict(data[1])\n\t\t\tself.vocab = set(data[2].keys())\n","repo_name":"TheBeast762/TheBeast762.github.io","sub_path":"lyrics/moodclassifier.py","file_name":"moodclassifier.py","file_ext":"py","file_size_in_byte":2428,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"5669661511","text":"# 카잉 달력\n## 카잉 제국만의 달력을 표시할 필요가 있음\n### 입력 : 첫 줄에 입력 데이터 수가 주어지고, 각 테스트 데이터는 한 줄로 구성되며, 각 줄에는 4개의 정수가 입력\n### 출력 : 유효하지 않으면 -1, 바꿀 수 있다면 현재로 몇년인지를 출력\n\nimport sys\ninput = sys.stdin.readline\n\ncaseSize = int(input()) # 전체 경우의 수를 받기\n\nresult = []\nfor _ in range(caseSize):\n m,n,x,y = tuple(map(int, input().split())) # 각 케이스 별로 달력 입력 받기\n # 달력상 기준과 동일한 값이 나왔다면 나머지 처리를 위해 변경해주기\n if m == x:\n x = 0\n if n == y:\n y = 0\n # 큰 숫자를 기준으로 처리해야 깔끔하게 처리가 되므로 큰 숫자에 따라 경우 나누기\n if m >= n:\n count = 0\n while True:\n temp = count * m + x\n if temp > m*n: # 두 수를 곱한 경우가 최대이므로, 해당 숫자를 초과하면 표현 불가 상태로 처리\n result.append(-1)\n break\n if temp % n == y:\n result.append(temp)\n break\n else:\n count += 1\n else:\n count = 0\n while True:\n temp = count * n + y\n if temp > m*n:\n result.append(-1)\n #print(-1)\n break\n if temp % m == x:\n result.append(temp)\n #print(temp)\n break\n else:\n count += 1\n\nfor i in result:\n print(i)","repo_name":"lungnahahd/Python_Prac","sub_path":"backjoon/BruteForce/KhaingSche.py","file_name":"KhaingSche.py","file_ext":"py","file_size_in_byte":1619,"program_lang":"python","lang":"ko","doc_type":"code","stars":1,"dataset":"github-code","pt":"85"} +{"seq_id":"19573270739","text":"import os\nimport os.path as osp\nobj_cnt={}\nfor i in range(1,1001):\n obj_cnt[i]=0\n \nsheet_location = '/viscam/projects/objectfolder_benchmark/benchmarks/Multi_Sensory_3D_Reconstruction/DATA_new/local_gt_sheets'\nfor root,dirs,files in os.walk(sheet_location):\n for file in files:\n file_path=osp.join(root,file)\n object_index, file_index = int(file_path.split('.')[0].split('/')[-2]), int(file_path.split('.')[0].split('/')[-1])\n obj_cnt[object_index]+=1\nsheet_remain=[str(i)+'\\n' for i in range(1,1001) if obj_cnt[i]<100]\nprint(sheet_remain)\nwith open(\"./sheet_remain.txt\",'w') as f:\n f.writelines(sheet_remain)","repo_name":"objectfolder/shape-reconstruction","sub_path":"models/MDN/get_sheet_remain.py","file_name":"get_sheet_remain.py","file_ext":"py","file_size_in_byte":642,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"85"} +{"seq_id":"3661853489","text":"import argparse\nimport os\n\nimport joblib\nimport pandas as pd\nfrom sklearn import metrics\n\nimport config\nfrom models import models\nfrom prepare import prepare\n\n\ndef train(fold, model, last):\n\n # read the training data with folds\n df = pd.read_csv(config.TRAIN_FILE)\n\n if last:\n # train on whole dataset\n x_train = df.drop([config.TARGET_LABEL, \"FOLD\"], axis=1)\n y_train = df[config.TARGET_LABEL]\n else:\n # training data is where kfold is not equal to provided fold\n df_train = df[df[\"FOLD\"] != fold].reset_index(drop=True)\n\n # validation data is where kfold is equal to provided fold\n df_valid = df[df[\"FOLD\"] == fold].reset_index(drop=True)\n\n # create training samples\n x_train = df_train.drop([config.TARGET_LABEL, \"FOLD\"], axis=1)\n y_train = df_train[config.TARGET_LABEL]\n\n # create validation samples\n x_valid = df_valid.drop([config.TARGET_LABEL, \"FOLD\"], axis=1)\n y_valid = df_valid[config.TARGET_LABEL]\n\n # perform cleaning, feature engineering,\n # categorical variables encoding & scaling\n x_train = prepare(x_train)\n\n # fetch the model from models\n clf = models[model]\n\n # fit the model on training data\n clf.fit(x_train, y_train)\n\n if not last:\n # calculate & print metric\n predictions = clf.predict(prepare(x_valid))\n scorer = metrics.get_scorer(config.METRIC)\n metric = scorer._score_func(y_valid, predictions)\n print(f\"Fold={fold}, {config.METRIC}={metric}\")\n\n model_path = f\"{model}.bin\" if last else f\"{model}_fold{fold}.bin\"\n\n # save the model\n joblib.dump(clf, os.path.join(config.MODEL_OUTPUT, model_path))\n\n if last:\n print(\"Last model saved at: \" + model_path)\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n\n parser.add_argument(*[\"--fold\", \"-f\"], type=str, default=None)\n parser.add_argument(*[\"--model\", \"-m\"], type=str)\n\n args = parser.parse_args()\n\n if args.fold == \"all\":\n for f in range(config.N_FOLDS):\n train(fold=f, model=args.model, last=args.last)\n elif args.fold is None:\n train(fold=args.fold, model=args.model, last=True)\n else:\n train(fold=int(args.fold), model=args.model, last=False)\n","repo_name":"nicohlr/ml-template","sub_path":"{{cookiecutter.project_name}}/src/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":2273,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"8624919629","text":"import os\nimport re\nimport struct\nimport sys\nfrom . import packagequery\n\nfrom .helper import decode_it\n\n\ndef cmp(a, b):\n return (a > b) - (a < b)\n\n\nclass RpmError(packagequery.PackageError):\n pass\n\n\nclass RpmHeaderError(RpmError):\n pass\n\n\nclass RpmHeader:\n \"\"\"corresponds more or less to the indexEntry_s struct\"\"\"\n\n def __init__(self, offset, length):\n self.offset = offset\n # length of the data section (without length of indexEntries)\n self.length = length\n self.entries = []\n\n def append(self, entry):\n self.entries.append(entry)\n\n def gettag(self, tag):\n for i in self.entries:\n if i.tag == tag:\n return i\n return None\n\n def __iter__(self):\n yield from self.entries\n\n def __len__(self):\n return len(self.entries)\n\n\nclass RpmHeaderEntry:\n \"\"\"corresponds to the entryInfo_s struct (except the data attribute)\"\"\"\n\n # each element represents an int\n ENTRY_SIZE = 16\n\n def __init__(self, tag, type, offset, count):\n self.tag = tag\n self.type = type\n self.offset = offset\n self.count = count\n self.data = None\n\n\nclass RpmQuery(packagequery.PackageQuery, packagequery.PackageQueryResult):\n LEAD_SIZE = 96\n LEAD_MAGIC = 0xedabeedb\n HEADER_MAGIC = 0x8eade801\n HEADERSIG_TYPE = 5\n\n LESS = 1 << 1\n GREATER = 1 << 2\n EQUAL = 1 << 3\n\n SENSE_STRONG = 1 << 27\n\n default_tags = (\n 1000, 1001, 1002, 1003, 1004, 1022, 1005, 1020,\n 1047, 1112, 1113, # provides\n 1049, 1048, 1050, # requires\n 1054, 1053, 1055, # conflicts\n 1090, 1114, 1115, # obsoletes\n 1156, 1158, 1157, # oldsuggests\n 5046, 5047, 5048, # recommends\n 5049, 5051, 5050, # suggests\n 5052, 5053, 5054, # supplements\n 5055, 5056, 5057 # enhances\n )\n\n def __init__(self, fh):\n self.__file = fh\n self.__path = os.path.abspath(fh.name)\n self.filename_suffix = 'rpm'\n self.header = None\n\n def read(self, all_tags=False, self_provides=True, *extra_tags, **extra_kw):\n # self_provides is unused because a rpm always has a self provides\n self.__read_lead()\n data = self.__file.read(RpmHeaderEntry.ENTRY_SIZE)\n hdrmgc, reserved, il, dl = struct.unpack('!I3i', data)\n if self.HEADER_MAGIC != hdrmgc:\n raise RpmHeaderError(self.__path, 'invalid headermagic \\'%s\\'' % hdrmgc)\n # skip signature header for now\n size = il * RpmHeaderEntry.ENTRY_SIZE + dl\n # data is 8 byte aligned\n pad = (size + 7) & ~7\n querysig = extra_kw.get('querysig')\n if not querysig:\n self.__file.read(pad)\n data = self.__file.read(RpmHeaderEntry.ENTRY_SIZE)\n hdrmgc, reserved, il, dl = struct.unpack('!I3i', data)\n self.header = RpmHeader(pad, dl)\n if self.HEADER_MAGIC != hdrmgc:\n raise RpmHeaderError(self.__path, 'invalid headermagic \\'%s\\'' % hdrmgc)\n data = self.__file.read(il * RpmHeaderEntry.ENTRY_SIZE)\n while len(data) > 0:\n ei = struct.unpack('!4i', data[:RpmHeaderEntry.ENTRY_SIZE])\n self.header.append(RpmHeaderEntry(*ei))\n data = data[RpmHeaderEntry.ENTRY_SIZE:]\n data = self.__file.read(self.header.length)\n for i in self.header:\n if i.tag in self.default_tags + extra_tags or all_tags:\n try: # this may fail for -debug* packages\n self.__read_data(i, data)\n except:\n pass\n return self\n\n def __read_lead(self):\n data = self.__file.read(self.LEAD_SIZE)\n leadmgc, = struct.unpack('!I', data[:4])\n if leadmgc != self.LEAD_MAGIC:\n raise RpmError(self.__path, 'not a rpm (invalid lead magic \\'%s\\')' % leadmgc)\n sigtype, = struct.unpack('!h', data[78:80])\n if sigtype != self.HEADERSIG_TYPE:\n raise RpmError(self.__path, 'invalid header signature \\'%s\\'' % sigtype)\n\n def __read_data(self, entry, data):\n off = entry.offset\n if entry.type == 2:\n entry.data = struct.unpack('!%dc' % entry.count, data[off:off + 1 * entry.count])\n if entry.type == 3:\n entry.data = struct.unpack('!%dh' % entry.count, data[off:off + 2 * entry.count])\n elif entry.type == 4:\n entry.data = struct.unpack('!%di' % entry.count, data[off:off + 4 * entry.count])\n elif entry.type == 6:\n entry.data = unpack_string(data[off:])\n elif entry.type == 7:\n entry.data = data[off:off + entry.count]\n elif entry.type == 8 or entry.type == 9:\n cnt = entry.count\n entry.data = []\n while cnt > 0:\n cnt -= 1\n s = unpack_string(data[off:])\n # also skip '\\0'\n off += len(s) + 1\n entry.data.append(s)\n if entry.type == 8:\n return\n lang = os.getenv('LANGUAGE') or os.getenv('LC_ALL') \\\n or os.getenv('LC_MESSAGES') or os.getenv('LANG')\n if lang is None:\n entry.data = entry.data[0]\n return\n # get private i18n table\n table = self.header.gettag(100)\n # just care about the country code\n lang = lang.split('_', 1)[0]\n cnt = 0\n for i in table.data:\n if cnt > len(entry.data) - 1:\n break\n if i == lang:\n entry.data = entry.data[cnt]\n return\n cnt += 1\n entry.data = entry.data[0]\n else:\n raise RpmHeaderError(self.__path, 'unsupported tag type \\'%d\\' (tag: \\'%s\\'' % (entry.type, entry.tag))\n\n def __reqprov(self, tag, flags, version, strong=None):\n pnames = self.header.gettag(tag)\n if not pnames:\n return []\n pnames = pnames.data\n pflags = self.header.gettag(flags).data\n pvers = self.header.gettag(version).data\n if not (pnames and pflags and pvers):\n raise RpmError(self.__path, 'cannot get provides/requires, tags are missing')\n res = []\n for name, flags, ver in zip(pnames, pflags, pvers):\n if strong is not None:\n # compat code for the obsolete RPMTAG_OLDSUGGESTSNAME tag\n # strong == 1 => return only \"recommends\"\n # strong == 0 => return only \"suggests\"\n if strong == 1:\n strong = self.SENSE_STRONG\n if (flags & self.SENSE_STRONG) != strong:\n continue\n # RPMSENSE_SENSEMASK = 15 (see rpmlib.h) but ignore RPMSENSE_SERIAL (= 1 << 0) therefore use 14\n if flags & 14:\n name += b' '\n if flags & self.GREATER:\n name += b'>'\n elif flags & self.LESS:\n name += b'<'\n if flags & self.EQUAL:\n name += b'='\n name += b' %s' % ver\n res.append(name)\n return res\n\n def vercmp(self, rpmq):\n res = RpmQuery.rpmvercmp(str(self.epoch()), str(rpmq.epoch()))\n if res != 0:\n return res\n res = RpmQuery.rpmvercmp(self.version(), rpmq.version())\n if res != 0:\n return res\n res = RpmQuery.rpmvercmp(self.release(), rpmq.release())\n return res\n\n # XXX: create dict for the tag => number mapping?!\n def name(self):\n return self.header.gettag(1000).data\n\n def version(self):\n return self.header.gettag(1001).data\n\n def release(self):\n return self.header.gettag(1002).data\n\n def epoch(self):\n epoch = self.header.gettag(1003)\n if epoch is None:\n return 0\n return epoch.data[0]\n\n def arch(self):\n return self.header.gettag(1022).data\n\n def summary(self):\n return self.header.gettag(1004).data\n\n def description(self):\n return self.header.gettag(1005).data\n\n def url(self):\n entry = self.header.gettag(1020)\n if entry is None:\n return None\n return entry.data\n\n def path(self):\n return self.__path\n\n def provides(self):\n return self.__reqprov(1047, 1112, 1113)\n\n def requires(self):\n return self.__reqprov(1049, 1048, 1050)\n\n def conflicts(self):\n return self.__reqprov(1054, 1053, 1055)\n\n def obsoletes(self):\n return self.__reqprov(1090, 1114, 1115)\n\n def recommends(self):\n recommends = self.__reqprov(5046, 5048, 5047)\n if not recommends:\n recommends = self.__reqprov(1156, 1158, 1157, 1)\n return recommends\n\n def suggests(self):\n suggests = self.__reqprov(5049, 5051, 5050)\n if not suggests:\n suggests = self.__reqprov(1156, 1158, 1157, 0)\n return suggests\n\n def supplements(self):\n return self.__reqprov(5052, 5054, 5053)\n\n def enhances(self):\n return self.__reqprov(5055, 5057, 5506)\n\n def is_src(self):\n # SOURCERPM = 1044\n return self.gettag(1044) is None\n\n def is_nosrc(self):\n # NOSOURCE = 1051, NOPATCH = 1052\n return self.is_src() and \\\n (self.gettag(1051) is not None or self.gettag(1052) is not None)\n\n def gettag(self, num):\n return self.header.gettag(num)\n\n def canonname(self):\n if self.is_nosrc():\n arch = b'nosrc'\n elif self.is_src():\n arch = b'src'\n else:\n arch = self.arch()\n return RpmQuery.filename(self.name(), None, self.version(), self.release(), arch)\n\n @staticmethod\n def query(filename):\n f = open(filename, 'rb')\n rpmq = RpmQuery(f)\n rpmq.read()\n f.close()\n return rpmq\n\n @staticmethod\n def queryhdrmd5(filename):\n f = open(filename, 'rb')\n rpmq = RpmQuery(f)\n rpmq.read(1004, querysig=True)\n f.close()\n entry = rpmq.gettag(1004)\n if entry is None:\n return None\n return ''.join([\"%02x\" % x for x in struct.unpack('16B', entry.data)])\n\n @staticmethod\n def rpmvercmp(ver1, ver2):\n \"\"\"\n implementation of RPM's version comparison algorithm\n (as described in lib/rpmvercmp.c)\n \"\"\"\n if ver1 == ver2:\n return 0\n res = 0\n ver1 = decode_it(ver1)\n ver2 = decode_it(ver2)\n while res == 0:\n # remove all leading non alphanumeric or tilde chars\n ver1 = re.sub('^[^a-zA-Z0-9~]*', '', ver1)\n ver2 = re.sub('^[^a-zA-Z0-9~]*', '', ver2)\n if ver1.startswith('~') or ver2.startswith('~'):\n if not ver1.startswith('~'):\n return 1\n elif not ver2.startswith('~'):\n return -1\n ver1 = ver1[1:]\n ver2 = ver2[1:]\n continue\n\n if not (len(ver1) and len(ver2)):\n break\n\n # check if we have a digits segment\n mo1 = re.match(r'(\\d+)', ver1)\n mo2 = re.match(r'(\\d+)', ver2)\n numeric = True\n if mo1 is None:\n mo1 = re.match('([a-zA-Z]+)', ver1)\n mo2 = re.match('([a-zA-Z]+)', ver2)\n numeric = False\n # check for different types: alpha and numeric\n if mo2 is None:\n if numeric:\n return 1\n return -1\n seg1 = mo1.group(0)\n ver1 = ver1[mo1.end(0):]\n seg2 = mo2.group(1)\n ver2 = ver2[mo2.end(1):]\n if numeric:\n # remove leading zeros\n seg1 = re.sub('^0+', '', seg1)\n seg2 = re.sub('^0+', '', seg2)\n # longer digit segment wins - if both have the same length\n # a simple ascii compare decides\n res = len(seg1) - len(seg2) or cmp(seg1, seg2)\n else:\n res = cmp(seg1, seg2)\n if res > 0:\n return 1\n elif res < 0:\n return -1\n return cmp(ver1, ver2)\n\n @staticmethod\n def filename(name, epoch, version, release, arch):\n return b'%s-%s-%s.%s.rpm' % (name, version, release, arch)\n\n\ndef unpack_string(data, encoding=None):\n \"\"\"unpack a '\\\\0' terminated string from data\"\"\"\n idx = data.find(b'\\0')\n if idx == -1:\n raise ValueError('illegal string: not \\\\0 terminated')\n data = data[:idx]\n if encoding is not None:\n data = data.decode(encoding)\n return data\n\n\nif __name__ == '__main__':\n try:\n rpmq = RpmQuery.query(sys.argv[1])\n except RpmError as e:\n print(e.msg)\n sys.exit(2)\n print(rpmq.name(), rpmq.version(), rpmq.release(), rpmq.arch(), rpmq.url())\n print(rpmq.summary())\n print(rpmq.description())\n print('##########')\n print('\\n'.join(rpmq.provides()))\n print('##########')\n print('\\n'.join(rpmq.requires()))\n print('##########')\n print(RpmQuery.queryhdrmd5(sys.argv[1]))\n","repo_name":"openSUSE/osc","sub_path":"osc/util/rpmquery.py","file_name":"rpmquery.py","file_ext":"py","file_size_in_byte":13167,"program_lang":"python","lang":"en","doc_type":"code","stars":151,"dataset":"github-code","pt":"85"} +{"seq_id":"30057002445","text":"from django.core.mail import EmailMessage, EmailMultiAlternatives\nfrom django.conf import settings\nfrom mcfeely.models import Queue\n\ndef default_queue(queue):\n if queue is None:\n default_queue = getattr(\n settings, 'DEFAULT_EMAIL_QUEUE', 'Default'\n )\n return(Queue.objects.get(queue=default_queue))\n if isinstance(queue, str):\n return(Queue.objects.get(queue=queue))\n else:\n return(queue)\n\n\nclass QueueEmailMessage(EmailMessage):\n \"\"\" Override EmailMessage to allow the addition of queues \"\"\"\n def __init__(self, subject='', body='', from_email=None, to=None,\n bcc=None, connection=None, attachments=None, headers=None,\n cc=None, queue=None):\n\n super(QueueEmailMessage, self).__init__(\n subject,\n body,\n from_email,\n to,\n bcc,\n connection,\n attachments,\n headers,\n cc)\n self.queue = default_queue(queue)\n\n\nclass QueueEmailMultiAlternatives(EmailMultiAlternatives):\n \"\"\" Override EmailMultiAlternatives to allow the addition of\n queues \"\"\"\n def __init__(self, subject='', body='', from_email=None, to=None, bcc=None,\n connection=None, attachments=None, headers=None,\n alternatives=None, cc=None, queue=None):\n super(QueueEmailMultiAlternatives, self).__init__(\n subject,\n body,\n from_email,\n to,\n bcc,\n connection,\n attachments,\n headers,\n cc)\n self.queue = default_queue(queue)\n","repo_name":"Khabi/django-mcfeely","sub_path":"mcfeely/engine.py","file_name":"engine.py","file_ext":"py","file_size_in_byte":1638,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"85"} +{"seq_id":"31552792916","text":"from blp import blp\r\n\r\nbbg_gdp_dict = {\r\n \"AL\" : \"RGDPREAL\",\r\n \"AK\" : \"RGDPREAA\",\r\n \"AZ\" : \"RGDPREAZ\",\r\n \"AR\" : \"RGDPREAK\",\r\n \"CA\" : \"RGDPRECA\",\r\n \"CO\" : \"RGDPRECO\",\r\n \"CT\" : \"RGDPRECN\",\r\n \"DE\" : \"RGDPREDE\",\r\n \"FL\" : \"RGDPREFL\",\r\n \"GA\" : \"RGDPREGA\",\r\n \"HI\" : \"RGDPREHA\",\r\n \"ID\" : \"RGDPREID\",\r\n \"IL\" : \"RGDPREIL\",\r\n \"IN\" : \"RGDPREIN\",\r\n \"IA\" : \"RGDPREIW\",\r\n \"KS\" : \"RGDPREKA\",\r\n \"KY\" : \"RGDPREKY\",\r\n \"LA\" : \"RGDPRELA\",\r\n \"ME\" : \"RGDPREMA\",\r\n \"MD\" : \"RGDPREMD\",\r\n \"MA\" : \"RGDPREMT\",\r\n \"MI\" : \"RGDPREMI\",\r\n \"MN\" : \"RGDPREMN\",\r\n \"MS\" : \"RGDPREMS\",\r\n \"MO\" : \"RGDPREMO\",\r\n \"MT\" : \"RGDPREMR\",\r\n \"NE\" : \"RGDPRENB\",\r\n \"NV\" : \"RGDPRENV\",\r\n \"NH\" : \"RGDPRENH\",\r\n \"NJ\" : \"RGDPRENJ\",\r\n \"NM\" : \"RGDPRENM\",\r\n \"NY\" : \"RGDPRENY\",\r\n \"NC\" : \"RGDPRENC\",\r\n \"ND\" : \"RGDPREND\",\r\n \"OH\" : \"RGDPREOH\",\r\n \"OK\" : \"RGDPREOK\",\r\n \"OR\" : \"RGDPREOR\",\r\n \"PA\" : \"RGDPREPN\",\r\n \"RI\" : \"RGDPRERI\",\r\n \"SC\" : \"RGDPRESC\",\r\n \"SD\" : \"RGDPRESD\",\r\n \"TN\" : \"RGDPRETN\",\r\n \"TX\" : \"RGDPRETX\",\r\n \"UT\" : \"RGDPREUT\",\r\n \"VT\" : \"RGDPREVM\",\r\n \"VA\" : \"RGDPREVI\",\r\n \"WA\" : \"RGDPREWA\",\r\n \"WV\" : \"RGDPREWV\",\r\n \"WI\" : \"RGDPREWI\",\r\n \"WY\" : \"RGDPREWY\",\r\n \"DC\" : \"RGDPREDC\"\r\n}\r\n\r\nSTART_DATE = '20170101'\r\nEND_DATE = '20300101'\r\n\r\nSECURITY_SUFFIX = ' Index'\r\n\r\n\r\nquery = blp.BlpQuery().start()\r\n\r\nsecurity_list = []\r\nfor state in bbg_gdp_dict.values():\r\n new_sec=state+SECURITY_SUFFIX\r\n print(new_sec)\r\n security_list.append(new_sec)\r\n\r\nresults = query.bdh(securities=security_list, fields=['Last_Price'], start_date=START_DATE, end_date=END_DATE)\r\nresults.to_csv('bbg_gdp_data.csv')","repo_name":"hyunjimoon/VaccineMisinf","sub_path":"States/bbg_data_getter/bbg_data_getter.py","file_name":"bbg_data_getter.py","file_ext":"py","file_size_in_byte":1682,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"85"} +{"seq_id":"24518171230","text":"# -*- coding=utf-8 -*-\n\nimport torch\n\n\nclass MLP(torch.nn.Sequential):\n\n \"\"\"\n [Source]\n\n ## Description\n\n Implements a simple multi-layer perceptron.\n\n ## Example\n\n ~~~python\n net = MLP(128, 1, [1024, 1024], activation=torch.nn.GELU)\n ~~~\n \"\"\"\n\n def _linear(self, input_size, output_size, bias):\n linear = torch.nn.Linear(input_size, output_size, bias=bias)\n gain = torch.nn.init.calculate_gain('relu')\n torch.nn.init.orthogonal_(linear.weight.data, gain=gain)\n torch.nn.init.constant_(linear.bias.data, 0.0)\n return linear\n\n def __init__(\n self,\n input_size,\n output_size,\n hidden_sizes,\n activation=None,\n bias=True,\n ):\n \"\"\"\n ## Arguments\n\n * `input_size` (int) - Input size of the MLP.\n * `output_size` (int) - Number of output units.\n * `hidden_sizes` (list of int) - Each int is the number of hidden units of a layer.\n * `activation` (callable) - Activation function to use for the MLP.\n * `bias` (bool, *optional*, default=True) - Whether the MLP uses bias terms.\n \"\"\"\n if activation is None:\n activation = torch.nn.ReLU\n if isinstance(hidden_sizes, int):\n hidden_sizes = [hidden_sizes, ]\n\n layers = []\n in_size = input_size\n for out_size in hidden_sizes[1:]:\n layers.append(self._linear(in_size, out_size, bias))\n layers.append(activation())\n layers.append(self._linear(hidden_sizes[-1], output_size, bias))\n super(MLP, self).__init__(*layers)\n","repo_name":"learnables/cherry","sub_path":"cherry/nn/mlp.py","file_name":"mlp.py","file_ext":"py","file_size_in_byte":1649,"program_lang":"python","lang":"en","doc_type":"code","stars":192,"dataset":"github-code","pt":"85"} +{"seq_id":"10171632062","text":"import os,subprocess\nfrom tkinter import * \nfrom tkinter.ttk import *\nimport tkinter as tk\nfrom tkinter import filedialog\nroot = Tk() \nroot.wm_iconbitmap('@/home/s2/Documents/hds/favicon.xbm')\nroot.wm_title(\"X=10Hrs to 22Hrs <::Set Schedule::> Y=0Mins to 59Mins\")\nroot.geometry('1175x650') \nglobal doc\ndoc=[]\nglobal btn\n#f=open(\"/home/s2/Documents/hds/choda.txt\",\"r\")\n#fn=f.read()\n#f.close()\nfpt=\"/home/s2/Documents/hds/\"\nfN=\"schd\"\nfn1=fpt+fN+\".txt\"\nprint(fn1) \nLABEL_BG = \"#ccc\"\nROWS, COLS = 1050, 650\nROWS_DISP = 400\nCOLS_DISP = 650\nmaster_frame = tk.Frame(bg=\"Light Blue\", bd=3, relief=tk.RIDGE)\nmaster_frame.grid(sticky=tk.NSEW)\nmaster_frame.columnconfigure(0, weight=1)\nframe2 = tk.Frame(master_frame)\nframe2.grid(row=3, column=0, sticky=tk.NW)\ncanvas = tk.Canvas(frame2, bg=\"white\")\ncanvas.grid(row=0, column=0)\nvsbar = tk.Scrollbar(frame2, orient=tk.VERTICAL, command=canvas.yview)\nvsbar.grid(row=0, column=1, sticky=tk.NS)\ncanvas.configure(yscrollcommand=vsbar.set)\nbuttons_frame = tk.Frame(canvas, bg=\"gray\", bd=2)\ndef open_file(h,m):\n print(h,m)\n global doc\n tp=0\n doc1=[]\n doc2=[]\n file = filedialog.askopenfile(mode ='r', filetypes =[('Videos & Images', '*.*')])\n if file is not None:\n f=file.name\n if \".mp4\" in f or \".vlc\" in f:\n dur = subprocess.run([\"ffprobe\",\"-v\",\"error\",\"-show_entries\",\"format=duration\",\"-of\",\"default=noprint_wrappers=1:nokey=1\",f],stdout=subprocess.PIPE,stderr=subprocess.STDOUT)\n dr1=dur.stdout.decode('utf-8')\n print(\"dr1\",dr1)\n dr=str(round(float(dr1)))\n else:\n dr=\"60\"\n f1=str(h+10)+\"=\"+str(m)+\"=\"+f+\"=\"+dr\n doc[h*60+m]=f1\n k=round(int(dr)/60)\n while tp<(k-1):\n doc[h*60+m+tp+1]=str(h+10)+\"=\"+str(m+tp+1)+\"=\"+\"n\"+str(h+10)+str(m+tp+1)+\"=\"+\"0\"\n tp+=1 \n print(file,f1)\n print(\"doc[h*m]\",doc[h*m])\n #print(doc)\n cnr0() \n \n \n\ndef clear():\n\tglobal var\n\tlist = buttons_frame.grid_slaves()\n\tfor l in list:\n l.destroy()\n\ndef cnr0():\n with open(fn1, 'w+') as filehandle:\n for listitem in doc:\n filehandle.write('%s\\n' % listitem)\n cnr1()\n \n \t \ndef cnr1():\n da=[]\n dh=[]\n dm=[]\n dp=[]\n dx=[]\n for d in doc:\n da=d.split(\"=\")\n #print(len(da),da[0],da[1],da[2],da[3])\n du=float(da[3])/60\n #print(du,round(du))\n dt=os.path.basename(da[2])\n da0=int(da[0])-10\n #print(da0)\n dh.append(int(da[0])-10)\n dm.append(int(da[1]))\n dp.append(dt)\n dx.append(round(du))\n print(dp)\n clear()\n t=0\n vh=0\n vm=0\n vx=0#print(\"dh\",dh,\"dm\",dm)\n while t<(len(doc)):\n vm=dm[t]\n vh=dh[t]\n print(vh,vm)\n if vx-2>0:\n btn=Button(buttons_frame, text =\"Disable\", state='disabled').grid(row=dm[t],column=dh[t],columnspan=1)\n vx-=1\n else:\n btn=Button(buttons_frame, text =dp[t], command = lambda vh=vh,vm=vm:open_file(vh,vm)).grid(row=dm[t],column=dh[t],columnspan=1)\n vx=dx[t]\n t+=1\n for g in range(10,23,1):\n Label(buttons_frame, text = (str(g)+\"Hrs\"),font =('Times New Roman', 12)).grid(row=60,column=g-10,columnspan=1)\n for h in range(0,60,1):\n Label(buttons_frame, text = (str(h)+\"Mins\"),font =('Times New Roman', 12)).grid(row=h,column=24,columnspan=1) \n \n\n \t\n\t\n\nwith open(fn1,'r+') as filehandle:\n for line in filehandle:\n currentPlace = line[:-1]\n doc.append(currentPlace)\n\n\nif len(doc)>0:\n cnr1()\n\ncanvas.create_window((0,0), window=buttons_frame, anchor=tk.NW)\nbuttons_frame.update_idletasks()\nbbox = canvas.bbox(tk.ALL)\nw, h = bbox[2]-bbox[1], bbox[3]-bbox[1]\ndw, dH = int((w/COLS) * COLS_DISP), int((h/ROWS) * ROWS_DISP)\ncanvas.configure(scrollregion=bbox, width=dw, height=dH) \nmainloop() ","repo_name":"s2et/hdsplayer","sub_path":"schdst.py","file_name":"schdst.py","file_ext":"py","file_size_in_byte":4320,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"10680499268","text":"N = int(input())\nh = [int(i) for i in input().split()]\ncount = 0\n\ns = [] #stack\nfor i in range(0,N):\n while len(s) > 0 and s[-1][0] < h[i]:\n s.pop()\n if len(s) > 0 and s[-1][0] == h[i]:\n count += s[-1][1]\n s[-1][1] += 1\n else:\n s.append([h[i],1])\nprint(2*count)\n\n#\n# Take Out ----\n# \n# when you have increasing and decreasing elements use stack\n# \n","repo_name":"avinashdvv/Hackerrank-Solutions","sub_path":"Trees/Jim and the Skyscrapers.py","file_name":"Jim and the Skyscrapers.py","file_ext":"py","file_size_in_byte":385,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"85"} +{"seq_id":"8244790350","text":"import cv2\nimport time\nimport urllib.request\nimport socketio\nimport numpy as np\n\nfrom threading import Thread\n\nfrom api.models.Cam import Cam\nfrom cap.Cap import Cap\n\nclass Sockets:\n\t\"\"\"docstring for Sockets\"\"\"\n\tdef __init__(self, app):\n\t\tself.App = app\n\t\tself.Client = socketio.Client(reconnection=True)\n\n\t\tself.init_handlers()\n\t\tself.Thread = Thread(target=self.connect).start()\n\n\tdef init_handlers(self):\n\t\tself.Client.on('connect', self.on_connect)\n\t\tself.Client.on('disconnect', self.on_disconnect)\n\t\tself.Client.on('orm', self.on_orm)\n\n\tdef connect(self):\n\t\t# try:\n\t\t# \tself.Client.connect(self.App.Config.sockets_endpoint)\n\t\t# except Exception as e:\n\t\t# \tprint('[Sockets]: Connection refused, retry.. (theard blocked)')\n\t\t# \ttime.sleep(self.App.Config.sockets_reconnect_sleep)\n\t\t# \tself.connect()\n\t\twhile True:\n\t\t\ttry:\n\t\t\t\tself.Client.connect(self.App.Config.sockets_endpoint)\n\n\t\t\t\tif self.Client.eio.state != 'connected':\n\t\t\t\t\tprint('[Sockets][eio-status]: Connection aborted, retry.. (theard blocked)')\n\t\t\t\t\tself.Client.disconnect()\n\t\t\t\t\tself.Client.connect(self.App.Config.sockets_endpoint)\n\t\t\t\t\ttime.sleep(self.App.Config.sockets_reconnect_sleep)\n\t\t\texcept Exception as e:\n\t\t\t\tprint('[Sockets][exception]: Connection refused, retry.. (theard blocked)')\n\t\t\t\ttime.sleep(self.App.Config.sockets_reconnect_sleep)\n\t\t\t\n\t\t\ttime.sleep(self.App.Config.sockets_reconnect_sleep)\n\n\tdef on_connect(self):\n\t\tprint('[Sockets]: Connection established')\n\n\tdef on_disconnect(self):\n\t\tprint('[Sockets]: Disconnected from server')\n\n\tdef on_orm(self, data):\n\t\tif data['type'] == 'Cams':\n\t\t\tif data['operation'] == 'remove':\n\t\t\t\tfor rid in data['ids']:\n\t\t\t\t\tself.App.Caps[rid].is_run = False\n\t\t\t\t\tself.App.Caps[rid].Thread.join()\n\t\t\t\t\tself.App.Caps.pop(rid, None)\n\t\t\t\t\tself.App.Cams.pop(rid, None)\n\t\t\t\t\tself.App.FrameHandler.run_dvr[rid].release()\n\t\t\t\t\tprint('[Sockets][ORM][Cams]: removed %s' % rid)\n\t\t\tif data['operation'] == 'update':\n\t\t\t\tfor rid in data['ids']:\n\t\t\t\t\t# self.App.Caps.pop(rid, None)\n\t\t\t\t\t# self.App.Caps[rid].Thread.join()\n\t\t\t\t\tself.App.Cams[rid].load(data['record'])\n\t\t\t\t\tself.App.Caps[rid] = Cap(self.App, self.App.Cams[rid])\n\t\t\t\t\tprint('[Sockets][ORM][Cams]: updated %s' % rid)\n\t\t\tif data['operation'] == 'create':\n\t\t\t\tfor rid in data['ids']:\n\t\t\t\t\tself.App.Cams[rid] = Cam(rid)\n\t\t\t\t\tself.App.Cams[rid].load(data['record'])\n\t\t\t\t\tself.App.Caps[rid] = Cap(self.App, self.App.Cams[rid])\n\t\t\t\t\tprint('[Sockets][ORM][Cams]: created %s' % rid)\n\t\tif data['type'] == 'Humans':\n\t\t\tif data['operation'] == 'add_media':\n\t\t\t\tfor rid in data['ids']:\n\t\t\t\t\tif 'face_identification' in self.App.Modules:\n\t\t\t\t\t\timage_url = \"/\".join(['http:/', data['record']['cdn_public_url'], data['record']['cdn_id']])\n\n\t\t\t\t\t\tresp = urllib.request.urlopen(image_url)\n\t\t\t\t\t\timage = np.asarray(bytearray(resp.read()), dtype=\"uint8\")\n\t\t\t\t\t\timage = cv2.imdecode(image, cv2.IMREAD_COLOR)\n\n\t\t\t\t\t\tself.App.Modules['face_identification'].add_facemodel(rid, data['record']['id'], image)\n\n\t\t\t\t\t\tprint('[Sockets][ORM][Humans]: add_media %s' % rid)\n\t\t\t\t\t#\n\t\t\t\t#\n\t\t\t#\n\t\t#","repo_name":"Maestrto69/Test","sub_path":"python-va-head/sockets/Sockets.py","file_name":"Sockets.py","file_ext":"py","file_size_in_byte":3035,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"41780824071","text":"from unittest import mock\nfrom pathlib import PosixPath\n\nimport pytest\n\nfrom ..exc import AccessDenied\nfrom .. import QREXEC_CLIENT\nfrom ..tools import qrexec_policy_exec\n\n# Disable warnings that conflict with Pytest's use of fixtures.\n# pylint: disable=redefined-outer-name\n\n\nclass TestPolicy:\n def __init__(self):\n self.resolution_type = None\n self.targets_for_ask = None\n self.default_target = None\n self.target = None\n self.rule = mock.NonCallableMock()\n self.rule.filepath = \"file\"\n self.rule.autostart = True\n self.rulelineno = 42\n\n def set_ask(self, targets_for_ask, default_target=None, notify=False):\n self.resolution_type = \"ask\"\n self.targets_for_ask = targets_for_ask\n self.default_target = default_target\n self.rule.action.notify = notify\n\n def set_allow(self, target, notify=False):\n self.resolution_type = \"allow\"\n self.target = target\n self.rule.action.notify = notify\n\n def set_deny(self, notify=True):\n self.resolution_type = \"deny\"\n self.rule.action.notify = notify\n\n def evaluate(self, request):\n assert self.resolution_type is not None\n\n if self.resolution_type == \"ask\":\n return request.ask_resolution_type(\n self.rule,\n request,\n user=\"user\",\n targets_for_ask=self.targets_for_ask,\n default_target=self.default_target,\n autostart=True,\n )\n\n if self.resolution_type == \"allow\":\n return request.allow_resolution_type(\n self.rule, request, user=\"user\", target=self.target,\n autostart=True,\n )\n\n if self.resolution_type == \"deny\":\n raise AccessDenied(\"denied\", notify=self.rule.action.notify)\n\n assert False, self.resolution_type\n return None\n\n\n@pytest.fixture(autouse=True)\ndef policy():\n \"\"\"\n Mock for FilePolicy object that will evaluate the requests.\n \"\"\"\n\n policy = TestPolicy()\n with mock.patch(\"qrexec.policy.parser.FilePolicy\") as mock_policy:\n mock_policy.return_value = policy\n yield policy\n\n assert mock_policy.mock_calls == [\n mock.call(policy_path=PosixPath(\"/etc/qubes/policy.d\"))\n ]\n\n\n@pytest.fixture(autouse=True)\ndef system_info():\n system_info = {\n \"domains\": {\n \"dom0\": {\n \"icon\": \"black\",\n \"template_for_dispvms\": False,\n \"guivm\": None,\n },\n \"source\": {\n \"icon\": \"red\",\n \"template_for_dispvms\": False,\n \"guivm\": \"gui\",\n },\n \"test-vm1\": {\n \"icon\": \"red\",\n \"template_for_dispvms\": False,\n \"guivm\": None,\n },\n \"test-vm2\": {\n \"icon\": \"red\",\n \"template_for_dispvms\": False,\n \"guivm\": None,\n },\n \"test-vm3\": {\n \"icon\": \"green\",\n \"template_for_dispvms\": True,\n \"guivm\": None,\n },\n \"gui\": {\n \"icon\": \"orange\",\n \"template_for_dispvms\": False,\n \"guivm\": None,\n },\n },\n }\n with mock.patch(\"qrexec.utils.get_system_info\") as mock_system_info:\n mock_system_info.return_value = system_info\n yield system_info\n\n\ndef icons():\n return {\n \"dom0\": \"black\",\n \"source\": \"red\",\n \"test-vm1\": \"red\",\n \"test-vm2\": \"red\",\n \"test-vm3\": \"green\",\n \"gui\": \"orange\",\n \"@dispvm:test-vm3\": \"green\",\n }\n\n\n@pytest.fixture(autouse=True)\ndef agent_service():\n \"\"\"\n Mock for call_socket_service() used to contact the qrexec-policy-agent.\n \"\"\"\n\n with mock.patch(\n \"qrexec.tools.qrexec_policy_exec.call_socket_service\", mock.AsyncMock()\n ) as mock_call_socket_service:\n yield mock_call_socket_service\n\n\ndef notify_call(resolution, *, argument=\"+arg\"):\n \"\"\"\n Mock call() object for policy.Notify.\n \"\"\"\n\n return mock.call(\n \"gui\",\n \"policy.Notify\",\n \"dom0\",\n {\n \"resolution\": resolution,\n \"service\": \"service\",\n \"source\": \"source\",\n \"argument\": argument,\n \"target\": \"test-vm1\",\n },\n )\n\n\ndef ask_call(*, argument=\"+arg\", default_target=\"\"):\n \"\"\"\n Mock call() object for policy.Ask.\n \"\"\"\n\n return mock.call(\n \"gui\",\n \"policy.Ask\",\n \"dom0\",\n {\n \"source\": \"source\",\n \"service\": \"service\",\n \"argument\": argument,\n \"targets\": [\"test-vm1\", \"test-vm2\"],\n \"default_target\": default_target,\n \"icons\": icons(),\n },\n )\n\n\ndef test_000_allow(policy, agent_service):\n policy.set_allow(\"test-vm1\")\n retval = qrexec_policy_exec.get_result(\n [\"source\", \"test-vm1\", \"service+arg\"]\n )\n assert retval == \"user=user\\nresult=allow\\ntarget=test-vm1\\nautostart=True\\nrequested_target=test-vm1\"\n assert agent_service.mock_calls == []\n\n\ndef test_001_allow_notify(policy, agent_service):\n policy.set_allow(\"test-vm1\", notify=True)\n retval = qrexec_policy_exec.get_result(\n [\"source\", \"test-vm1\", \"service+arg\"]\n )\n assert retval == \"user=user\\nresult=allow\\ntarget=test-vm1\\nautostart=True\\nrequested_target=test-vm1\"\n assert agent_service.mock_calls == [\n notify_call(\"allow\"),\n ]\n\ndef test_002_allow_notify_failed(policy, agent_service):\n policy.set_allow(\"test-vm1\", notify=True)\n agent_service.side_effect = Exception(\"calling agent service failed\")\n\n retval = qrexec_policy_exec.get_result(\n [\"source\", \"test-vm1\", \"service+arg\"]\n )\n assert retval == \"user=user\\nresult=allow\\ntarget=test-vm1\\nautostart=True\\nrequested_target=test-vm1\"\n assert agent_service.mock_calls == [\n notify_call(\"allow\"),\n ]\n\n\ndef test_004_allow_no_guivm(policy, system_info, agent_service):\n system_info[\"domains\"][\"source\"][\"guivm\"] = None\n policy.set_allow(\"test-vm1\", notify=True)\n\n retval = qrexec_policy_exec.get_result(\n [\"source\", \"test-vm1\", \"service+arg\"]\n )\n assert retval == \"user=user\\nresult=allow\\ntarget=test-vm1\\nautostart=True\\nrequested_target=test-vm1\"\n assert agent_service.mock_calls == []\n\n\ndef test_010_ask_allow(policy, agent_service):\n policy.set_ask([\"test-vm1\", \"test-vm2\"])\n agent_service.return_value = \"allow:test-vm1\"\n retval = qrexec_policy_exec.get_result(\n [\"source\", \"test-vm1\", \"service+arg\"]\n )\n assert retval == \"user=user\\nresult=allow\\ntarget=test-vm1\\nautostart=True\\nrequested_target=test-vm1\"\n assert agent_service.mock_calls == [\n ask_call(),\n ]\n\n\ndef test_011_ask_allow_notify(policy, agent_service):\n policy.set_ask([\"test-vm1\", \"test-vm2\"], notify=True)\n agent_service.return_value = \"allow:test-vm1\"\n retval = qrexec_policy_exec.get_result(\n [\"source\", \"test-vm1\", \"service+arg\"]\n )\n assert retval == \"user=user\\nresult=allow\\ntarget=test-vm1\\nautostart=True\\nrequested_target=test-vm1\"\n assert agent_service.mock_calls == [\n ask_call(),\n notify_call(\"allow\"),\n ]\n\n\ndef test_012_ask_allow_notify_no_argument(policy, agent_service):\n policy.set_ask([\"test-vm1\", \"test-vm2\"], notify=True)\n agent_service.return_value = \"allow:test-vm1\"\n retval = qrexec_policy_exec.get_result(\n [\"source\", \"test-vm1\", \"service\"]\n )\n assert retval == \"user=user\\nresult=allow\\ntarget=test-vm1\\nautostart=True\\nrequested_target=test-vm1\"\n assert agent_service.mock_calls == [\n ask_call(argument=\"+\"),\n notify_call(\"allow\", argument=\"+\"),\n ]\n\n\ndef test_015_ask_deny(policy, agent_service):\n policy.set_ask([\"test-vm1\", \"test-vm2\"])\n agent_service.return_value = \"deny\"\n retval = qrexec_policy_exec.get_result(\n [\"source\", \"test-vm1\", \"service+arg\"]\n )\n assert retval == \"result=deny\"\n assert agent_service.mock_calls == [\n ask_call(),\n ]\n\n\ndef test_016_ask_deny_notify(policy, agent_service):\n policy.set_ask([\"test-vm1\", \"test-vm2\"], notify=True)\n agent_service.return_value = \"deny\"\n retval = qrexec_policy_exec.get_result(\n [\"source\", \"test-vm1\", \"service+arg\"]\n )\n assert retval == \"result=deny\"\n assert agent_service.mock_calls == [\n ask_call(),\n notify_call(\"deny\"),\n ]\n\n\ndef test_017_ask_default_target(policy, agent_service):\n policy.set_ask([\"test-vm1\", \"test-vm2\"], \"test-vm1\")\n agent_service.return_value = \"allow:test-vm1\"\n retval = qrexec_policy_exec.get_result(\n [\"source\", \"test-vm1\", \"service+arg\"]\n )\n assert retval == \"user=user\\nresult=allow\\ntarget=test-vm1\\nautostart=True\\nrequested_target=test-vm1\"\n assert agent_service.mock_calls == [\n ask_call(default_target=\"test-vm1\"),\n ]\n\n\ndef test_018_ask_invalid_response(policy, agent_service):\n policy.set_ask([\"test-vm1\", \"test-vm2\"])\n agent_service.return_value = \"xxx\"\n retval = qrexec_policy_exec.get_result(\n [\"source\", \"test-vm1\", \"service+arg\"]\n )\n assert retval == \"result=deny\"\n assert agent_service.mock_calls == [\n ask_call(),\n notify_call(\"deny\"),\n ]\n\n\ndef test_013_ask_no_guivm(policy, system_info, agent_service):\n system_info[\"domains\"][\"source\"][\"guivm\"] = None\n policy.set_ask([\"test-vm1\", \"test-vm2\"])\n retval = qrexec_policy_exec.get_result(\n [\"source\", \"test-vm1\", \"service+arg\"]\n )\n assert retval == \"result=deny\"\n assert agent_service.mock_calls == []\n\n\ndef test_020_deny(policy, agent_service):\n policy.set_deny()\n retval = qrexec_policy_exec.get_result(\n [\"source\", \"test-vm1\", \"service+arg\"]\n )\n assert retval == \"result=deny\"\n assert agent_service.mock_calls == [\n notify_call(\"deny\"),\n ]\n\n\ndef test_021_deny_no_notify(policy, agent_service):\n policy.set_deny(notify=False)\n retval = qrexec_policy_exec.get_result(\n [\"source\", \"test-vm1\", \"service+arg\"]\n )\n assert retval == \"result=deny\"\n assert agent_service.mock_calls == []\n\n\ndef test_022_deny_no_guivm(policy, system_info, agent_service):\n system_info[\"domains\"][\"source\"][\"guivm\"] = None\n policy.set_deny()\n retval = qrexec_policy_exec.get_result(\n [\"source\", \"test-vm1\", \"service+arg\"]\n )\n assert retval == \"result=deny\"\n assert agent_service.mock_calls == []\n\n\ndef test_030_just_evaluate_allow(policy, agent_service):\n policy.set_allow(\"test-vm1\")\n retval = qrexec_policy_exec.get_result(\n [\n \"--just-evaluate\",\n \"source\",\n \"test-vm1\",\n \"service+arg\",\n ]\n )\n assert retval == 0\n assert agent_service.mock_calls == []\n\n\ndef test_031_just_evaluate_deny(policy, agent_service):\n policy.set_deny()\n retval = qrexec_policy_exec.get_result(\n [\n \"--just-evaluate\",\n \"source\",\n \"test-vm1\",\n \"service+arg\",\n ]\n )\n assert retval == 1\n assert agent_service.mock_calls == []\n\n\ndef test_032_just_evaluate_ask(policy, agent_service):\n policy.set_ask([\"test-vm1\", \"test-vm2\"])\n retval = qrexec_policy_exec.get_result(\n [\n \"--just-evaluate\",\n \"source\",\n \"test-vm1\",\n \"service+arg\",\n ]\n )\n assert retval == 1\n assert agent_service.mock_calls == []\n\n\ndef test_033_just_evaluate_ask_assume_yes(policy, agent_service):\n policy.set_ask([\"test-vm1\", \"test-vm2\"])\n retval = qrexec_policy_exec.get_result(\n [\n \"--just-evaluate\",\n \"--assume-yes-for-ask\",\n \"source\",\n \"test-vm1\",\n \"service+arg\",\n ]\n )\n assert retval == 0\n assert agent_service.mock_calls == []\n\n\ndef test_034_allow_policy_exec(policy, agent_service):\n policy.set_allow(\"test-vm1\")\n with mock.patch(\"subprocess.call\", return_value=0) as m, \\\n mock.patch(\"qrexec.utils.qubesd_call\") as c:\n retval = qrexec_policy_exec.get_result(\n [\"source-id\", \"source\", \"test-vm1\", \"service+arg\",\n \"process_ident\"]\n )\n assert c.mock_calls == [mock.call(\"test-vm1\", \"admin.vm.Start\")]\n assert agent_service.mock_calls == []\n assert retval == 0\n assert m.mock_calls == [\n mock.call((\n QREXEC_CLIENT,\n \"-Ed\",\n \"test-vm1\",\n \"-c\",\n \"process_ident,source,source-id\",\n \"--\",\n \"user:QUBESRPC service+arg source\",\n )),\n ]\n","repo_name":"QubesOS/qubes-core-qrexec","sub_path":"qrexec/tests/cli.py","file_name":"cli.py","file_ext":"py","file_size_in_byte":12709,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"85"} +{"seq_id":"3204257678","text":"import requests\n# 1. Дан список с визитами по городам и странам. Напишите код,\n# который возвращает отфильтрованный список geo_logs, содержащий только визиты из России.\n\ngeo_logs = [\n {'visit1': ['Москва', 'Россия']},\n {'visit2': ['Дели', 'Индия']},\n {'visit3': ['Владимир', 'Россия']},\n {'visit4': ['Лиссабон', 'Португалия']},\n {'visit5': ['Париж', 'Франция']},\n {'visit6': ['Лиссабон', 'Португалия']},\n {'visit7': ['Тула', 'Россия']},\n {'visit8': ['Тула', 'Россия']},\n {'visit9': ['Курск', 'Россия']},\n {'visit10': ['Архангельск', 'Росси��']}\n]\n\ndef city_search(geo):\n city_city = []\n for visit in geo_logs:\n for country in visit.values():\n if country[1] == \"Россия\":\n city_city.append(country[0])\n return city_city\n\n# Выведите на экран все уникальные гео-ID из значений словаря ids.\n# Т.е. список вида [213, 15, 54, 119, 98, 35]\n\nids = {'user1': [213, 213, 213, 15, 213],\n 'user2': [54, 54, 119, 119, 119],\n 'user3': [213, 98, 98, 35]}\n\ndef unique_number(ids):\n val = list(ids.values())\n unique = []\n for number in val:\n for dubl in number:\n if dubl not in unique:\n unique.append(dubl)\n return sum(unique)\n\n# Дана статистика рекламных каналов по объемам продаж.\n# Напишите скрипт, который возвращает название канала с максимальным объемом\n\nstats = {'facebook': 155, 'yandex': 225, 'vk': 115, 'google': 99, 'email': 42, 'ok': 98}\n\ndef chanell_max(statss):\n maximum = max(list(statss.values()))\n for title_channel in statss.keys():\n if statss[title_channel] == maximum:\n return title_channel\n\n##создание папки на ЯндексДиске\ndef creating_folder(path):\n ''' Создание папки на Яндекс.Диске '''\n token = 'токен'\n upload_url = \"https://cloud-api.yandex.net/v1/disk/resources\"\n params = {\"path\": path}\n headers = {'Content-Type': 'application/json', 'Accept': 'application/json', 'Authorization': f'OAuth {token}'}\n response = requests.put(url=upload_url, headers=headers, params=params)\n return response.status_code\n\n\n\nif __name__ == '__main__':\n result = city_search(geo_logs)\n print(result)\n result_2 = unique_number(ids)\n print(result_2)\n result_3 = chanell_max(stats)\n print(result_3)\n result_4 = creating_folder(\"hello\")\n print(result_4)\n\n\n\n\n\n","repo_name":"ART20230129/hw_7_6","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2820,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"33862182206","text":"# To make print working for Python2/3\nfrom __future__ import print_function\n\nimport datetime\n\n# user\nimport stock_db_mgr as sdm\n\n\nstartdate = datetime.date(2014, 1, 6) # Start of Questrade portfolio\n# startdate = datetime.date(2013, 8, 12) # Start of Questrade portfolio component highest start date (VUN.TO)\ntoday = datetime.date.today()\n\n# Pick one:\nenddate = datetime.date(2018, 1, 1)\n# enddate = today\n\n\ndef indicator_test():\n print('indicator_test')\n\n db = sdm.StockDBMgr('stock_db/test', startdate, enddate)\n df = db.get_all_symbol_single_data_item('Close')\n print(df.describe())\n\n rp = (df.iloc[-1] - df.min()) / (df.max() - df.min())\n rps = 2.0 * rp - 1.0\n rr = (df.max() - df.min()) / df.max()\n\n t = rps * rr.pow(0.1)\n\n # Price in the floor (5% tolerance) & 15% drop\n print(t.loc[(rp < 0.05) & (rr > 0.15)])\n\n\ndef correlation_test():\n print('correlation_test')\n\n db = sdm.StockDBMgr('stock_db/test', startdate, enddate)\n df = db.get_all_symbol_single_data_item('Close')\n df.dropna(how='any', inplace=True)\n\n dfc = df.corr()\n\n print(dfc)\n\n # Find inverse correlation\n print(dfc.min())\n print(dfc.idxmin())\n\n\ndef _main():\n indicator_test()\n correlation_test()\n\n\nif __name__ == '__main__':\n _main()\n","repo_name":"mathieugouin/tradesim","sub_path":"playground.py","file_name":"playground.py","file_ext":"py","file_size_in_byte":1273,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"85"} +{"seq_id":"11866767836","text":"# coding=utf-8\nfrom __future__ import absolute_import, unicode_literals\n\nimport octoprint.plugin\n\nclass RequestSpinnerPlugin(octoprint.plugin.AssetPlugin, octoprint.plugin.TemplatePlugin):\n\n\tdef get_assets(self):\n\t\treturn dict(\n\t\t\tcss=[\"css/requestspinner.css\"],\n\t\t\tjs=[\"js/requestspinner.js\"],\n\t\t\tless=[\"less/requestspinner.less\"]\n\t\t)\n\n\t##~~ Softwareupdate hook\n\n\tdef get_update_information(self):\n\t\treturn dict(\n\t\t\trequestspinner=dict(\n\t\t\t\tdisplayName=\"RequestSpinner Plugin\",\n\t\t\t\tdisplayVersion=self._plugin_version,\n\n\t\t\t\t# version check: github repository\n\t\t\t\ttype=\"github_release\",\n\t\t\t\tuser=\"OctoPrint\",\n\t\t\t\trepo=\"OctoPrint-RequestSpinner\",\n\t\t\t\tcurrent=self._plugin_version,\n\n\t\t\t\t# update method: pip\n\t\t\t\tpip=\"https://github.com/OctoPrint/OctoPrint-RequestSpinner/archive/{target_version}.zip\"\n\t\t\t)\n\t\t)\n\n__plugin_name__ = \"RequestSpinner\"\n__plugin_pythoncompat__ = \">=2.7,<4\"\n\ndef __plugin_load__():\n\tglobal __plugin_implementation__\n\t__plugin_implementation__ = RequestSpinnerPlugin()\n\n\tglobal __plugin_hooks__\n\t__plugin_hooks__ = {\n\t\t\"octoprint.plugin.softwareupdate.check_config\": __plugin_implementation__.get_update_information\n\t}\n","repo_name":"OctoPrint/OctoPrint-RequestSpinner","sub_path":"octoprint_requestspinner/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1141,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"85"} +{"seq_id":"22672279860","text":"from django.http import HttpResponseBadRequest\n\nfrom .models import Analista\nfrom .serializers import AnalistaSerializer, UsuarioSerializer\nfrom .utils.jsonresponse import JSONResponse\n\ndef detalhar_analista(request):\n \"\"\"\n Function that detail a analyst\n \"\"\"\n if request.method == 'GET':\n analystid = request.GET.get('analystid')\n analyst = Analista.objects.get(pk=analystid)\n ser_user = UsuarioSerializer(analyst.user)\n ser_anal = AnalistaSerializer(analyst)\n ser_return = {\n 'user': ser_user.data,\n 'analyst': ser_anal.data\n }\n return JSONResponse(ser_return, status=200)\n return HttpResponseBadRequest()\n","repo_name":"Engenharia-de-Software-UFRPE/lanterna-verde","sub_path":"lanternaverde_web/control_analista.py","file_name":"control_analista.py","file_ext":"py","file_size_in_byte":694,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"85"} +{"seq_id":"8757915918","text":"#print elements occuring more than just one time\n\ns = [int(i) for i in input().split()]\ns.sort()\ncount = 1\nres = \"\"\nfor i in range(len(s)):\n\tif s[i - 1] != s[i]:\n\t\tcount=1\n\t\tcontinue\n\telse:\n\t\tif count == 1:\n\t\t\tres += str(s[i]) + ' '\n\t\t\tcount+=1\n\t\telse:\n\t\t\tcontinue\nif len(s) == 1:\n\tres = ''\nprint(res) \n","repo_name":"nurSaadat/pythonLearning","sub_path":"find_repeating2.py","file_name":"find_repeating2.py","file_ext":"py","file_size_in_byte":303,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"85"} +{"seq_id":"33964130607","text":"import sys\n\nSHAPE_POINTS = {\n \"X\": 1,\n \"Y\": 2,\n \"Z\": 3,\n}\n\nSHAPES_MAP = {\n \"X\": \"A\",\n \"Y\": \"B\",\n \"Z\": \"C\",\n}\n\nWIN_SHAPES = {\n \"A\": \"Y\",\n \"B\": \"Z\",\n \"C\": \"X\",\n}\n\n\ndef main():\n total_score = 0\n\n file_name = sys.argv[1]\n\n print(sys.argv[0])\n with open(file_name, \"r\") as file:\n for line in file:\n opponent_shape, shape_selected = line.strip().split(\" \")\n\n if opponent_shape == SHAPES_MAP[shape_selected]:\n total_score += 3\n\n if WIN_SHAPES[opponent_shape] == shape_selected:\n total_score += 6\n\n total_score += SHAPE_POINTS[shape_selected]\n\n print(total_score)\n return total_score\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"Daniel-Moreira/advent_code","sub_path":"2022/day_2/challenge_1/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":742,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"41895630581","text":"import cv2 as cv\nimport numpy as np\n\nimage = 'assets/photos/marleny3.jpg'\nimg = cv.imread(image)\n\ncv.imshow('wife', img)\n\nresized = cv.resize(img, (500,500), interpolation=cv.INTER_CUBIC)\n\ncv.imshow('translated',resized)\n\n# 0 = vertical flip\n# 1 = horizontal flip\n# -1 = both vertical and horizontal flip\nflip = cv.flip(img, -1)\ncv.imshow('translated', flip)\n\ncropped = img[10:500, 100:500]\ncv.imshow('cropped', cropped)\n\n\ncv.waitKey(0)","repo_name":"waynet022/ImageProcessing","sub_path":"assets/videos/resizing.py","file_name":"resizing.py","file_ext":"py","file_size_in_byte":436,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"21933925969","text":"# This code demonstrates a while loop with a \"counting variable\"\r\ni = 0 # int\r\nwhile i < 99: # jb tk i ki value 99 se chhoti nahi hojati/rehti\r\n print (i);\r\n i = i+1 # order --> pichli i ki value me k i plus hoga\r\n\r\n# This uses a while loop to remove all the spaces from a string of\r\n# text. Can you figure out how it works?\r\n\r\ndef remove_spaces(text): \r\n text_without_spaces = '' #empty string for now\r\n while text != '': # space jo hai wo vvalue ke qual nahi hoajti answer print krna hai \r\n # space to kabhi bhi value ke equal ho hi nai skti.\r\n next_character = text[0]\r\n if next_character != ' ': #that's a single space\r\n text_without_spaces = text_without_spaces + next_character\r\n text = text[1:]\r\n return text_without_spaces\r\nprint (remove_spaces(\"hello my name is Owais how are you?\"));","repo_name":"owais9061/udacity-programming-fundamentals","sub_path":"python-work/concepts/control-flow/10-while-loop-play-Assign.py","file_name":"10-while-loop-play-Assign.py","file_ext":"py","file_size_in_byte":854,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"85"} +{"seq_id":"21727237217","text":"# -*-coding: utf8-*\n\nimport os\nfrom random import choice as randchoice\nfrom glob import glob\nfrom math import trunc\nimport dialog_box as dlb\nimport constantes as cst\nimport time\nimport socket\nfrom niveau import Carte, LANMap, img_\nfrom commerces_p import message_affiche, message_affiche_large, passant_parle, message_affiche_non_bloquant\nimport pygame\nimport pickle\nfrom pygame.locals import *\nfrom items import Conteneur\nimport FPS_regulator\nimport parametrage as prm\nimport bombs_manager as bm\nimport cmd_block_mgr as cb_mgr\n\n\ndef padding_0(liste):\n intermediaire = []\n for i in liste:\n temp = str(int(i[21::].split('.')[0]))\n intermediaire.append('0' * (4 - len(temp)) + str(temp))\n intermediaire.sort()\n return intermediaire\n\n\ndef reseau_speaking(socket, message, params, personnage, carte, blocs_):\n if message[:4] != 'tp->' and message[:6] != \"give->\" and message[:5] != \"ban->\" \\\n and message[:9] != \"upgrade->\" and message[:8] != \"season->\":\n requete = \"set->chat\" + message\n socket.sendto(pickle.dumps(requete), params)\n elif message[:6] == \"give->\":\n temp = message[:6].split(',')\n personne = temp[0]\n bloc = temp[1]\n quantity = temp[2]\n if bloc in blocs_.list():\n if quantity <= blocs_.get(bloc):\n blocs_.use(bloc, nbr=quantity)\n ############################################ non terminée\n elif message[:5] == \"ban->\" or message[:9] == 'upgrade->':\n socket.sendto(pickle.dumps(message), params)\n\n\nclass Game:\n def __init__(self, surface, personnage, en_reseau, inventory, creatif, params_co_network,\n root_surface, carte, rcenter, dust_electricty_driven_manager, network,\n hauteur_fen, light_start=False):\n \"\"\"\n :param surface: a pygame sub-surface\n :param personnage: an instance of the class Personnage\n :param en_reseau: a boolean who say if you are connect to a network or not\n :param inventory: an instance of the class Inventory\n :param creatif: a boolean who say if you are in infinite creation mode or not\n :param params_co_network: the parameters to connect the socket to the network\n :param root_surface: a pygame surface (the window)\n :param dust_electricty_driven_manager: a instance of the class DustElectricityDriven\n :return: nothing\n \"\"\"\n self.light_start = light_start\n self.fenetre = surface\n self.root = root_surface\n self.personnage = personnage\n self.en_reseau = en_reseau\n self.network = network\n self.blocs = inventory\n self.equipement_courant = '0'\n self.numero_niv = 'map'\n self.carte = carte\n self.dust_electricty_driven_manager = dust_electricty_driven_manager\n self.cmd_block_mgr = cb_mgr.CmdBlockManager()\n self.bomb_mgr = bm.BombManager(self.carte)\n self.dust_electricty_driven_manager.set_bomb_mgr(self.bomb_mgr)\n self.teleporteurs = []\n self.normal_gm = creatif\n self.pancartes_lst = []\n self.inventaire = []\n self.testeur = os.path.exists('test.test')\n self.windowed_is = self.testeur\n self.params_co = params_co_network\n self.nb_blocs_large = self.fenetre.get_size()[0] // 30 + 1\n self.rcenter = self.fenetre.get_size()[0] // 2, rcenter[1] # self.fenetre.get_size()[1] // 2\n self.last_music_time = time.time() + 30 # Secondes\n self.FPS = FPS_regulator.IAFPS(100)\n self.tps_tour = time.time() + 1\n self.nom_mechant = \"Gordavus\"\n self.volume_son_j = 50\n self.font = pygame.font.Font(\"..\" + os.sep + \"assets\" + os.sep + \"GUI\" + os.sep + \"Fonts\" + os.sep + \"freesansbold.otf\", 8)\n self.font2 = pygame.font.Font(\"..\" + os.sep + \"assets\" + os.sep + \"GUI\" + os.sep + \"Fonts\" + os.sep + \"freesansbold.otf\", 10)\n self.grd_font = pygame.font.Font(\"..\" + os.sep + \"assets\" + os.sep + \"GUI\" + os.sep + \"Fonts\" + os.sep + \"freesansbold.otf\", 12)\n self.y_ecart = (self.root.get_size()[1] - 600) // 2\n self.obj_courant = '0'\n self.petits_blocs = img_\n self.index_couleur = 0\n self.last_vie = 100\n self.liste_couleur = [\n (23, 220, 189),\n (16, 209, 182),\n (9, 198, 175),\n (2, 187, 168),\n (235, 176, 161),\n (228, 165, 154),\n (221, 154, 147),\n (214, 143, 140),\n (207, 132, 133),\n (200, 121, 126),\n (193, 110, 119)\n ]\n self.temps_saut_attendre = 0.125\n self.liste_hauteur_saut = [\n -1, -1, -1,\n +1, +1, +1, +1,\n ]\n self.hauteur_saut = 0\n self.show_cursor = False\n self.annee = len(glob(\"..\" + os.sep + \"assets\" + os.sep + \"Maps\" + os.sep + \"Olds Maps\" + os.sep + \"*.lvl\")) + 1\n self.music_liste = [\n \"..\" + os.sep + \"assets\" + os.sep + \"Sons\" + os.sep + \"urworld1.wav\",\n \"..\" + os.sep + \"assets\" + os.sep + \"Sons\" + os.sep + \"urworld2.wav\",\n \"..\" + os.sep + \"assets\" + os.sep + \"Sons\" + os.sep + \"Urworld_3.wav\",\n \"..\" + os.sep + \"assets\" + os.sep + \"Sons\" + os.sep + \"Urworld_4.wav\"\n ]\n self.number_of_case = 0\n self.breakListe = []\n self.liste_septre = [\n 'D', 'F',\n 'G', 'H',\n 'J', 'K'\n ]\n self.jump_height = 3\n self.dico_cd = cst.cds\n self.show_stats = True\n self.saut = False\n self.nb_cases_chut = 0\n self.clique_gauche = 0\n self.vu_vip_change = False\n self.var_gravite = 0.142\n self.courir_bool = False\n self.last_cd_used = \"\"\n self.indice_son = 0\n self.prise_de_degats = 0\n self.conteneur = Conteneur()\n self.continuer = 1\n self.temps_avant_fps = time.time()\n self.suiveur = False\n self.surf_debug = pygame.Surface((435, 300))\n self.surf_debug.fill((220, 220, 220))\n self.surf_debug.set_alpha(90)\n self.surf_debug.convert_alpha()\n self.ZQSD = False\n self.play_song = False\n self.hauteur_fenetre = hauteur_fen\n self.max_FPS_atteint = 0\n\n def partial_load(self):\n # Pickling elements\n if os.path.exists(\"..\" + os.sep + \"assets\" + os.sep + \"Save\" + os.sep + \"inventaire.sav\"):\n with open(\"..\" + os.sep + \"assets\" + os.sep + \"Save\" + os.sep + \"inventaire.sav\", \"rb\") as inventory_r:\n self.inventaire = pickle.Unpickler(inventory_r).load()\n else:\n self.inventaire = [\n ['h', 's', 'e', 'a', 'q', 'm', 't', 'd', 'r', 'y', 'u', 'i', 'M', 'v', 'l', 'k'],\n ['/', '.', '?', 'n', 'b', 'x', 'f', 'g', 'A', 'Z', 'E', 'R', 'T', 'Y', 'U', 'I'],\n ['O', 'P', 'Q', 'S', 'D', 'F', 'G', 'H', 'J', 'K', 'W', 'X', 'C', 'V', 'B', 'az'],\n ['ze', 'er', 'rt', 'ty', 'yu', 'ui', 'io', 'op', 'pq', 'qs', 'sd', 'df', 'fg', 'gh', 'hj', 'jk'],\n ['kl', 'lm', 'mw', 'wx', 'xc', 'cv', 'vb', 'bn', 'n?', '?.', './', '%a', '%b', 'aaa', 'bbb', 'ccc'],\n ['ddd', 'eee', 'fff', 'ggg', 'hhh', 'iii', 'jjj', '404', 'ttt', '0', '0', '0', '0', '0', '0', '0'],\n ['0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0'],\n ['pio', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '/§']\n ]\n if os.path.exists(\"..\" + os.sep + \"assets\" + os.sep + \"Save\" + os.sep + \"equipement_en_cours.sav\"):\n with open(\"..\" + os.sep + \"assets\" + os.sep + \"Save\" + os.sep + \"equipement_en_cours.sav\", \"rb\") as lire_equipement:\n self.obj_courant = pickle.Unpickler(lire_equipement).load()\n self.number_of_case = {v: k for k, v in enumerate([elt for line in self.inventaire for elt in line])}[self.obj_courant]\n else:\n self.obj_courant = self.inventaire[0][0]\n self.number_of_case = 0\n if os.path.exists(\"..\" + os.sep + \"assets\" + os.sep + \"Save\" + os.sep + \"niveau.sav\"):\n with open(\"..\" + os.sep + \"assets\" + os.sep + \"Save\" + os.sep + \"niveau.sav\", \"rb\") as niv_lire:\n self.numero_niv = pickle.Unpickler(niv_lire).load()\n else:\n self.numero_niv = \"map\"\n if os.path.exists(\"..\" + os.sep + \"assets\" + os.sep + \"Save\" + os.sep + \"pos.sav\"):\n with open(\"..\" + os.sep + \"assets\" + os.sep + \"Save\" + os.sep + \"pos.sav\", \"rb\") as pos_lire:\n self.personnage.set_pos(pickle.Unpickler(pos_lire).load())\n else:\n self.personnage.set_pos((self.fenetre.get_size()[0] // 2 - 1, 0))\n if os.path.exists(\"..\" + os.sep + \"assets\" + os.sep + \"Save\" + os.sep + \"fov.sav\"):\n with open(\"..\" + os.sep + \"assets\" + os.sep + \"Save\" + os.sep + \"fov.sav\", \"rb\") as fov_lire:\n new = pickle.Unpickler(fov_lire).load()\n self.carte.set_fov(new[0], new[1])\n else:\n self.carte.set_fov(0, self.fenetre.get_size()[0] // 30 + 1)\n if os.path.exists(\"..\" + os.sep + \"assets\" + os.sep + \"Save\" + os.sep + \"mana.sav\"):\n with open(\"..\" + os.sep + \"assets\" + os.sep + \"Save\" + os.sep + \"mana.sav\", \"rb\") as mana_lire:\n self.personnage.set_mana(pickle.Unpickler(mana_lire).load())\n else:\n self.personnage.set_mana(100)\n if os.path.exists(\"..\" + os.sep + \"assets\" + os.sep + \"Save\" + os.sep + \"teleporteurs.sav\"):\n with open(\"..\" + os.sep + \"assets\" + os.sep + \"Save\" + os.sep + \"teleporteurs.sav\", \"rb\") as teleport_lire:\n self.teleporteurs = pickle.Unpickler(teleport_lire).load()\n else:\n self.teleporteurs = []\n if os.path.exists(\"..\" + os.sep + \"assets\" + os.sep + \"Save\" + os.sep + \"gamemode.sav\"):\n with open(\"..\" + os.sep + \"assets\" + os.sep + \"Save\" + os.sep + \"gamemode.sav\", \"rb\") as creatifmode_lire:\n self.normal_gm = pickle.Unpickler(creatifmode_lire).load()\n else:\n self.normal_gm = True\n if os.path.exists(\"..\" + os.sep + \"assets\" + os.sep + \"Save\" + os.sep + \"shader.sav\"):\n with open(\"..\" + os.sep + \"assets\" + os.sep + \"Save\" + os.sep + \"shader.sav\", \"rb\") as shader_lire:\n self.carte.set_current_shader(pickle.Unpickler(shader_lire).load())\n else:\n self.carte.set_current_shader(\"nul\")\n if os.path.exists(\"..\" + os.sep + \"assets\" + os.sep + \"Save\" + os.sep + \"pancartes.sav\"):\n with open(\"..\" + os.sep + \"assets\" + os.sep + \"Save\" + os.sep + \"pancartes.sav\", \"rb\") as lire_pancartes:\n self.pancartes_lst = pickle.Unpickler(lire_pancartes).load()\n else:\n self.pancartes_lst = []\n if os.path.exists(\"..\" + os.sep + \"assets\" + os.sep + \"Save\" + os.sep + \"gamer.gm\"):\n self.ZQSD = True\n\n # Files\n with open(\"..\" + os.sep + \"assets\" + os.sep + \"Save\" + os.sep + \"pseudo.sav\", \"r\") as nom_perso: #pour le pseudo\n self.personnage.set_pseudo(nom_perso.read())\n with open(\"..\" + os.sep + \"assets\" + os.sep + \"Textes\" + os.sep + \"bonjour.txt\", \"r\") as msg_bjr_lire:\n self.grd_msg_bjr = str(msg_bjr_lire.read()).format(self.personnage.get_pseudo(), self.nom_mechant)\n self.grd_msg_bjr += \"\\n\" * 4 + \"Bonne aventure `{0}` !\".format(self.personnage.get_pseudo())\n if os.path.exists(\"..\" + os.sep + \"assets\" + os.sep + \"Personnage\" + os.sep + \"0\" + os.sep + \"vip.file\"):\n with open(\"..\" + os.sep + \"assets\" + os.sep + \"Personnage\" + os.sep + \"0\" + os.sep + \"vip.file\", \"r\") as lire_vip:\n if lire_vip.read() == self.personnage.get_pseudo() + \"::VIP\":\n self.vip_bool = True\n else:\n self.vip_bool = False\n\n # Chargements optionnels\n if os.path.exists(\"..\" + os.sep + \"assets\" + os.sep + \"Save\" + os.sep + 'texture_pack.sav'):\n with open(\"..\" + os.sep + \"assets\" + os.sep + \"Save\" + os.sep + 'texture_pack.sav', 'r') as txtpr:\n self.carte.set_texture_pack(txtpr.read())\n if os.path.exists(\"..\" + os.sep + \"assets\" + os.sep + \"Save\" + os.sep + 'jheight.sav'):\n with open(\"..\" + os.sep + \"assets\" + os.sep + \"Save\" + os.sep + 'jheight.sav', 'r') as jhr:\n self.jump_height = int(jhr.read())\n self.liste_hauteur_saut = [-1 for _ in range(self.jump_height + 1)] + [+1 for _ in range(self.jump_height + 2)]\n if os.path.exists(\"..\" + os.sep + \"assets\" + os.sep + \"Save\" + os.sep + 'jtime.sav'):\n with open(\"..\" + os.sep + \"assets\" + os.sep + \"Save\" + os.sep + 'jtime.sav', 'r') as tjr:\n self.temps_saut_attendre = int(tjr.read()) / 1000\n if not os.path.exists(\"..\" + os.sep + \"assets\" + os.sep + \"Save\" + os.sep + 'pos.sav'):\n #création de nouveau fichiers\n message_affiche_large(self.grd_msg_bjr, self.fenetre, self.rcenter)\n\n def load_coponents(self):\n \"\"\"\n load all the files, the surfaces, the sound ... that the game need to work\n :return: nothing\n \"\"\"\n #Pygame elements\n #surfaces\n self.check = pygame.image.load(\"..\" + os.sep + \"assets\" + os.sep + \"Particules\" + os.sep + \"check_vert.png\").convert_alpha()\n self.arme_h_g = pygame.image.load(\"..\" + os.sep + \"assets\" + os.sep + \"Personnage\" + os.sep + \"Arme\" + os.sep + \"sword_up.png\").convert_alpha()\n self.pioche_h_g = pygame.image.load(\"..\" + os.sep + \"assets\" + os.sep + \"Personnage\" + os.sep + \"Arme\" + os.sep + \"pick_up.png\").convert_alpha()\n\n self.pointeur = self.arme_h_g\n\n #activation de la répétition des touches\n pygame.key.set_repeat(200, self.personnage.get_speed())\n\n #on n'affiche pas le curseur de la souris !\n pygame.mouse.set_visible(False)\n\n #mode VIP\n self.vip_bool = os.path.exists(\"..\" + os.sep + \"assets\" + os.sep + \"Personnage\" + os.sep + \"0\" + os.sep + \"vip.file\")\n if self.vip_bool:\n tmp = open(\"..\" + os.sep + \"assets\" + os.sep + \"Personnage\" + os.sep + \"0\" + os.sep + \"vip.file\", \"r\")\n if tmp.read() != self.personnage.get_pseudo() + \"::VIP\":\n self.vip_bool = False\n tmp.close()\n\n #si on est en créatif, on a tout les blocs en *9999 !\n if not self.normal_gm or self.vip_bool:\n #on est encore et quand même en créatif :D\n for index in self.blocs.list():\n if self.blocs.get(index) < 900 and index not in ('bn', 'n?', '?.', '/§', 'pio'):\n quant = 5000 if not self.normal_gm else self.blocs.get(index) + 150\n self.blocs.set(index, nbr=quant)\n\n # Musics\n self.falling = pygame.mixer.Sound(\"..\" + os.sep + \"assets\" + os.sep + \"Sons\" + os.sep + \"falling.wav\")\n self.explode = pygame.mixer.Sound(\"..\" + os.sep + \"assets\" + os.sep + \"Sons\" + os.sep + \"explode.wav\")\n self.eau_bruit = pygame.mixer.Sound(\"..\" + os.sep + \"assets\" + os.sep + \"Sons\" + os.sep + \"water.wav\")\n self.breaking_bloc = pygame.mixer.Sound(\"..\" + os.sep + \"assets\" + os.sep + \"Sons\" + os.sep + \"wooden.wav\")\n self.volume = pygame.mixer.music.get_volume() # Retourne la valeur du volume, entre 0 et 1\n pygame.mixer.music.set_volume(self.volume_son_j / 4 * 3) # Réglage du volume\n\n # Chargement obligatoire\n self.carte.load_components()\n\n # Personnal elements\n if self.en_reseau:\n self.network.sendto(pickle.dumps(\"get->configuration\"), self.params_co)\n temp = self.network.recv(4096)\n temp = pickle.loads(temp)\n if type(temp) != list and type(temp) != tuple:\n temp = [\n \"Erreur\",\n \"Aucune description n'a été fournie\"\n ]\n data_serv = []\n with open(\"..\" + os.sep + \"assets\" + os.sep + \"Save\" + os.sep + \"serveur.sav\", \"rb+\") as data_serv_wrb:\n data_serv = pickle.Unpickler(data_serv_wrb).load()\n for i in range(len(data_serv)):\n if data_serv[i][0] == str(self.params_co[0]) + ':' + str(self.params_co[1]):\n data_serv[i][1] = temp[0]\n data_serv[i][2] = temp[1]\n break\n pickle.Pickler(data_serv_wrb).dump(data_serv)\n with open(\"..\" + os.sep + \"assets\" + os.sep + \"Save\" + os.sep + \"serveur.sav\", \"wb\") as f:\n pickle.Pickler(f).dump(data_serv)\n self.carte.create_conteneur(self.conteneur)\n self.carte.conteneur_load()\n self.carte.set_cb_mgr(self.cmd_block_mgr)\n self.personnage.change_test(self.testeur)\n\n self.partial_load()\n\n # A lancer apres avoir chargé || initialisé une Carte | LANMap\n self.img_tous_blocs = self.carte.get_img_dict()\n\n def actualise_chat(self):\n \"\"\"\n a function who ask to have the messages of the chat, and who draw it\n :return: nothing\n \"\"\"\n self.network.sendto(pickle.dumps(\"get->chat\"), self.params_co)\n temp = self.network.recv(4096)\n temp = pickle.loads(temp)\n surf = pygame.Surface((420, 22 * 6))\n surf.fill(0x000000)\n surf.set_alpha(60)\n surf.convert_alpha()\n self.fenetre.blit(surf, (self.fenetre.get_size()[0] - 430, self.fenetre.get_size()[1] - surf.get_size()[1] - 10))\n for i in range(len(temp)):\n msg = temp[i][0]\n color = temp[i][1]\n rendu = self.font.render(msg, 1, color)\n self.fenetre.blit(rendu, (self.fenetre.get_size()[0] - 420, i * rendu.get_size()[1] + (self.fenetre.get_size()[1] - surf.get_size()[1] - 10)))\n\n def save(self):\n \"\"\"\n a function to save some dependencies of the game\n :return: nothing\n \"\"\"\n print(\"\\n\\n\" + \"*\" * 34 + \" SAUVEGARDE \" + \"*\" * 34 + \"\\n\")\n self.carte.save()\n #avec Pickle\n with open(\"..\" + os.sep + \"assets\" + os.sep + \"Save\" + os.sep + \"inventaire.sav\", \"wb\") as inventory_w:\n pickle.Pickler(inventory_w).dump(self.inventaire)\n with open(\"..\" + os.sep + \"assets\" + os.sep + \"Save\" + os.sep + \"equipement_en_cours.sav\", \"wb\") as ecrire_equipement:\n pickle.Pickler(ecrire_equipement).dump(self.equipement_courant)\n with open(\"..\" + os.sep + \"assets\" + os.sep + \"Save\" + os.sep + \"niveau.sav\", \"wb\") as niv_ecrire:\n pickle.Pickler(niv_ecrire).dump(self.numero_niv)\n with open(\"..\" + os.sep + \"assets\" + os.sep + \"Save\" + os.sep + \"bloc.sav\", \"wb\") as bloc_save:\n pickle.Pickler(bloc_save).dump(self.blocs)\n with open(\"..\" + os.sep + \"assets\" + os.sep + \"Save\" + os.sep + \"pos.sav\", \"wb\") as pos_save:\n pickle.Pickler(pos_save).dump(self.personnage.get_pos())\n with open(\"..\" + os.sep + \"assets\" + os.sep + \"Save\" + os.sep + \"fov.sav\", \"wb\") as fov_ecrire:\n pickle.Pickler(fov_ecrire).dump(self.carte.get_fov())\n with open(\"..\" + os.sep + \"assets\" + os.sep + \"Save\" + os.sep + \"mana.sav\", \"wb\") as mana_ecrire:\n pickle.Pickler(mana_ecrire).dump(self.personnage.get_mana())\n with open(\"..\" + os.sep + \"assets\" + os.sep + \"Save\" + os.sep + \"teleporteurs.sav\", \"wb\") as teleport_ecrire:\n pickle.Pickler(teleport_ecrire).dump(self.teleporteurs)\n with open(\"..\" + os.sep + \"assets\" + os.sep + \"Save\" + os.sep + \"gamemode.sav\", \"wb\") as creatifmode_ecrire:\n pickle.Pickler(creatifmode_ecrire).dump(self.normal_gm)\n with open(\"..\" + os.sep + \"assets\" + os.sep + \"Save\" + os.sep + \"shader.sav\", \"wb\") as shader_ecrire:\n pickle.Pickler(shader_ecrire).dump(self.carte.get_curent_shader())\n with open(\"..\" + os.sep + \"assets\" + os.sep + \"Save\" + os.sep + \"pancartes.sav\", \"wb\") as ecrire_pancartes:\n pickle.Pickler(ecrire_pancartes).dump(self.pancartes_lst)\n self.carte.conteneur_save()\n print('Sauvegarde réussie !\\n\\n')\n\n def molette_(self, direction):\n \"\"\"\n a function who change the current bloc you are using\n :param direction: the direction of the mouse wheel\n :return: nothing\n \"\"\"\n s = [elt for line in self.inventaire for elt in line]\n if direction == 'bas':\n self.number_of_case = self.number_of_case + 1 if self.number_of_case + 1 <= len(s) - 1 else len(s) - 1\n elif direction == 'haut':\n self.number_of_case = self.number_of_case - 1 if self.number_of_case - 1 >= 0 else 0\n self.obj_courant = s[self.number_of_case]\n\n def print_fps(self):\n \"\"\"\n a function to calculate the FPS and draw it on the screen\n :return: nothing\n \"\"\"\n vrais_fps = trunc(((1000 / (time.time() - self.temps_avant_fps)) / 1000) if time.time() - self.temps_avant_fps else 300)\n self.max_FPS_atteint = vrais_fps if self.max_FPS_atteint < vrais_fps else self.max_FPS_atteint\n titre = \"/* FPS : %5i */\" % vrais_fps\n pygame.draw.rect(self.root, (75, 155, 180), (0, self.rcenter[1] + 360, 115, 20))\n self.root.blit(self.font.render(titre, 1, (10, 10, 10)), (4, self.rcenter[1] + 362))\n\n def s_invent_dd(self, bloc_choisi, tout, obj_survol):\n \"\"\"\n a function to to draw the inventory elements\n :param bloc_choisi: the current bloc\n :param tout: if we want to see all the specifications of the blocs or not\n :param obj_survol: the bloc your mouse if on\n :return: nothing\n \"\"\"\n vide_choisi = False\n for y_, ligne in enumerate(self.inventaire):\n for x_, x_s in enumerate(ligne):\n nom_entite = x_s\n if nom_entite in self.blocs.list():\n self.fenetre.blit(self.img_tous_blocs[nom_entite], (x_ * 31 + 52, y_ * 31 + 52))\n if nom_entite == bloc_choisi and bloc_choisi != '0':\n self.fenetre.blit(self.check, (x_ * 31 + 52, y_ * 31 + 52))\n elif nom_entite == '0' and bloc_choisi == '0' and not vide_choisi:\n self.fenetre.blit(self.check, (x_ * 31 + 52, y_ * 31 + 52))\n vide_choisi = True\n if tout:\n if nom_entite != \"0\" and nom_entite in self.blocs.list() and self.blocs.get(nom_entite) <= 999:\n #sinon on aura des gros trait blancs tout moches :P\n nb = self.font.render(\"%3i\" % self.blocs.get(nom_entite), 1, (240, 240, 240))\n self.fenetre.blit(nb, (52 + x_ * 31, 52 + y_ * 31))\n elif self.blocs.get(nom_entite) > 999 and nom_entite != \"0\" and nom_entite in self.blocs.list():\n nb = self.font.render(\"N/A\", 1, (240, 240, 240))\n self.fenetre.blit(nb, (52 + x_ * 31, 52 + y_ * 31))\n if not tout:\n breaking = False\n for y_, ligne in enumerate(self.inventaire):\n for x_, x_s in enumerate(ligne):\n entite = self.inventaire[y_][x_]\n if entite == obj_survol:\n if self.blocs.get(entite) <= 999:\n nb = self.font.render(self.blocs.get_name(entite) + \" : %3i\" % self.blocs.get(entite), 1, (240, 240, 240))\n pygame.draw.rect(self.fenetre, (150, 150, 150), (50 + x_ * 31 + 30, 50 + y_ * 31 + 30, nb.get_size()[0] + 2, nb.get_size()[1] + 2))\n self.fenetre.blit(nb, (52 + x_ * 31 + 30, 52 + y_ * 31 + 30))\n elif self.blocs.get(entite) > 999:\n nb = self.font.render(self.blocs.get_name(entite) + \" : N/A\", 1, (240, 240, 240))\n pygame.draw.rect(self.fenetre, (150, 150, 150), (50 + x_ * 31 + 30, 50 + y_ * 31 + 30, nb.get_size()[0] + 2, nb.get_size()[1] + 2))\n self.fenetre.blit(nb, (52 + x_ * 31 + 30, 52 + y_ * 31 + 30))\n breaking = True\n break\n if breaking:\n break\n\n def afficher_degats_pris(self):\n \"\"\"\n a function who draw the damage you had took\n :return: nothing\n \"\"\"\n x = self.personnage.get_pos()[0] - 2\n y = self.personnage.get_pos()[1] - 30\n self.fenetre.blit(self.font.render(\"-\" + str(0.5), 1, (208, 6, 6)), (x, y))\n\n def drag_and_drop_invent(self):\n \"\"\"\n a function who draw and managed the events you create when the inventory is open\n :return: nothing\n \"\"\"\n continue3, clic = 1, 0\n obj_pris, obj_avant = \"\", \"\"\n obj_retour_actu = self.obj_courant\n obj_survol = \"\"\n last_x, last_y = 0, 0\n\n tout = False\n\n structure_niveau = self.carte.get_list()\n\n liste_innafichable_dad = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'c', '/§', 'Q', 'S', 'pio']\n\n #carte affichage type bandeau / reader-like\n numero_niv = self.carte.get_fov()[0]\n decalage_x = 0\n slice_ = [0, 54]\n structure = structure_niveau\n center_screen = (600 - 250) // 2\n center_x = self.rcenter[0] - 580 // 2 + 740 // 2 - 10\n inventaire = pygame.image.load(\"..\" + os.sep + \"assets\" + os.sep + \"GUI\" + os.sep + \"Inventory\" + os.sep + \"inventaire.png\").convert_alpha()\n carte_img = pygame.image.load(\"..\" + os.sep + \"assets\" + os.sep + \"GUI\" + os.sep + \"Inventory\" + os.sep + \"carte.png\").convert_alpha()\n\n while continue3:\n self.carte.update(self.personnage.get_case_pos())\n\n #on dessine le fond (et accessoirement on efface ainsi la fenetre) :\n self.fenetre.blit(inventaire, (10, 10))\n\n #carte\n self.fenetre.blit(carte_img, (center_x - 16, center_screen - 8))\n self.fenetre.blit(self.font2.render(\"*-* Carte *-*\", 1, (220, 220, 220)), (center_x + 580 // 2 - 40, center_screen))\n carte_miniature = [line[slice_[0]:slice_[1]] for line in structure]\n for y_ in range(20):\n for x_ in range(len(carte_miniature[0])):\n case = carte_miniature[y_][x_]\n if case not in liste_innafichable_dad:\n self.fenetre.blit(self.petits_blocs[case], ((x_ * 10) + center_x + 10,\n (y_ * 10) + center_screen + 30))\n elif case == '0':\n pygame.draw.rect(self.fenetre, (30, 150, 205), (x_ * 10 + center_x + 10,\n y_ * 10 + center_screen + 30,\n 10, 10))\n else:\n pygame.draw.rect(self.fenetre, (250, 150, 205), (x_ * 10 + center_x + 10,\n y_ * 10 + center_screen + 30,\n 10, 10))\n\n #et on met les icones et leur quantité !\n self.s_invent_dd(obj_retour_actu, tout, obj_survol)\n\n #et enfin on laisse TOUT LE TEMPS le bloc suivre la souris, si y en a un ;)\n x_souris, y_souris = self.souris_ou_t_es()\n\n if obj_pris != \"\":\n #y a un bloc qui doit suivre la souris !\n self.fenetre.blit(self.img_tous_blocs[obj_pris], (x_souris, y_souris))\n\n #gestion des events\n for event in pygame.event.get():\n if event.type == MOUSEBUTTONDOWN:\n if event.button == 1:\n xi = (event.pos[0] - 52) // 31\n yi = (event.pos[1] - 52) // 31\n if event.pos[0] >= 52 and event.pos[0] <= 52 + 31 * 16 and event.pos[1] >= 52 and event.pos[1] <= 52 + 31 * 8:\n obj_pris = self.inventaire[yi][xi]\n last_x, last_y = xi, yi\n self.inventaire[yi][xi] = \"0\" #on vide la case !\n clic = 1 #clic actif\n elif event.button == 3:\n xi = (event.pos[0] - 52) // 31\n yi = (event.pos[1] - 52) // 31\n if event.pos[0] >= 52 and event.pos[0] <= 52 + 31 * 16 and event.pos[1] >= 52 and event.pos[1] <= 52 + 31 * 8:\n obj_retour_actu = self.inventaire[yi][xi]\n elif event.type == MOUSEBUTTONUP:\n if event.button == 1:\n xi = (event.pos[0] - 52) // 31\n yi = (event.pos[1] - 52) // 31\n if event.pos[0] >= 52 and event.pos[0] <= 52 + 31 * 16 and event.pos[1] >= 52 and event.pos[1] <= 52 + 31 * 8 \\\n and obj_pris != \"\":\n obj_avant = self.inventaire[yi][xi]\n self.inventaire[last_y][last_x] = obj_avant\n self.inventaire[yi][xi] = obj_pris\n obj_retour_actu = obj_pris\n obj_pris = \"\"\n clic = 0 #clic non actif\n else:\n self.inventaire[last_y][last_x] = obj_pris\n obj_pris = \"\"\n clic = 0 #clic non actif\n elif event.button == 3:\n xi = (event.pos[0] - 52) // 31\n yi = (event.pos[1] - 52) // 31\n if event.pos[0] >= 52 and event.pos[0] <= 52 + 31 * 16 and event.pos[1] >= 52 and event.pos[1] <= 52 + 31 * 8:\n obj_retour_actu = self.inventaire[yi][xi]\n elif event.type == MOUSEMOTION:\n xi = event.pos[0]\n yi = event.pos[1]\n if clic and obj_pris != \"\":\n #déplacement car clic est ok, bouton gauche enfoncé (ou droit)\n #faut que le bloc suive la souris !\n self.fenetre.blit(self.img_tous_blocs[obj_pris], (xi, yi))\n else:\n if xi >= 52 and xi <= 52 + 31 * 16 and yi >= 52 and yi <= 52 + 31 * 8:\n yi_selec = (yi - 52) // 31\n xi_selc = (xi - 52) // 31\n if 0 <= yi_selec <= len(self.inventaire) - 1 and 0 <= xi_selc <= len(self.inventaire[0]) - 1:\n obj_survol = self.inventaire[yi_selec][xi_selc]\n elif event.type == KEYDOWN:\n if event.key == K_RIGHT:\n if slice_[1] + 5 <= 5000:\n slice_[0] += 5\n slice_[1] += 5\n elif event.key == K_LEFT:\n if slice_[0] - 5 >= 0:\n slice_[0] -= 5\n slice_[1] -= 5\n elif event.key == K_PRINT:\n pass\n else:\n continue3 = 0\n\n message_affiche_non_bloquant(\"Vous êtes actuellement dans la section : \" + str(numero_niv) + \"/\" + str(self.carte.get_max_fov()) + \".\", self.rcenter)\n pygame.display.flip()\n\n if obj_retour_actu != \"\":\n self.obj_courant = obj_retour_actu\n self.number_of_case = {v: k for k, v in enumerate([elt for line in self.inventaire for elt in line])}[self.obj_courant]\n\n def souris_ou_t_es(self):\n \"\"\"\n a function who calculate the position of the mouse and draw the cursor\n :return: nothing\n \"\"\"\n x_souris, y_souris = pygame.mouse.get_pos()\n self.fenetre.blit(self.pointeur, (x_souris, y_souris))\n return x_souris, y_souris\n\n def aff_bloc(self):\n \"\"\"\n a function who draw your unclickable hotbar and the blocs who are near of the current bloc you are using\n :return: nothing\n \"\"\"\n liste_ordre_invent = []\n pos_bloc = 0\n for ligne in self.inventaire:\n for element in ligne:\n liste_ordre_invent.append(element)\n for x, case in enumerate(liste_ordre_invent):\n if case == self.obj_courant:\n pos_bloc = x\n liste_ordre_invent = liste_ordre_invent[pos_bloc:pos_bloc+9]\n centrage = self.rcenter[0] - (9 * (34 + 2)) // 2\n for nombre, bloc in enumerate(liste_ordre_invent):\n if bloc != self.obj_courant:\n pygame.draw.rect(self.root, (140, 140, 140), (centrage + nombre * 36, self.rcenter[1] + 310, 34, 34))\n elif bloc == self.obj_courant:\n pygame.draw.rect(self.root, (0, 0, 0), (centrage + nombre * 36, self.rcenter[1] + 310 + 30 + 4, 300, 30))\n self.root.blit(self.font.render(self.blocs.get_name(bloc) + \" : \" + str(self.blocs.get(bloc)), 1,\n (255, 255, 255), (0, 0, 0)),\n (centrage + nombre * 36, self.rcenter[1] + 310 + 30 + 4))\n pygame.draw.rect(self.root, (41, 235, 20), (centrage + nombre * 36, self.rcenter[1] + 310, 34, 34))\n self.root.blit(self.img_tous_blocs[bloc], (centrage + nombre * 36 + 2, self.rcenter[1] + 310 + 2))\n\n def flash(self):\n \"\"\"\n a function who flash your screen\n :return: nothing\n \"\"\"\n pygame.draw.rect(self.fenetre, (240, 240, 240), (0, 0, self.fenetre.get_size()[0], 600))\n pygame.display.flip()\n pygame.time.wait(0.1)\n self.carte.update(self.personnage.get_case_pos())\n pygame.display.flip()\n\n def time_cruise(self):\n \"\"\"\n a function who draw the GUI, and manage it, who allow you to go on an older map\n :return: nothing\n \"\"\"\n field_of_view_chose = 0\n width_ = 500\n height_ = 450\n continuer = 1\n liste_cartes = [i for i in glob(\"..\" + os.sep + \"assets\" + os.sep + \"Maps\" + os.sep + \"Olds Maps\" + os.sep + \"*.lvl\")]\n liste_cartes_originelles = liste_cartes\n liste_cartes = padding_0(liste_cartes)\n choisi = -1\n\n while continuer:\n self.carte.update(self.personnage.get_case_pos())\n pygame.draw.rect(self.fenetre, (80, 160, 80), ((self.fenetre.get_size()[0] - width_) // 2,\n 300 - (height_ // 2),\n width_,\n height_))\n for i in range(len(liste_cartes)):\n if 300 - (height_ // 2) <= 310 - (height_ // 2) + i * 32 + field_of_view_chose <= height_ + 268 - (height_ // 2):\n if choisi == i:\n pygame.draw.rect(self.fenetre, (85, 215, 45), ((self.fenetre.get_size()[0] - width_) // 2 + 10,\n 310 - (height_ // 2) + i * 32 + field_of_view_chose,\n width_ - 10 * 2, 30))\n else:\n pygame.draw.rect(self.fenetre, (45, 175, 190), ((self.fenetre.get_size()[0] - width_) // 2 + 10,\n 310 - (height_ // 2) + i * 32 + field_of_view_chose,\n width_ - 10 * 2, 30))\n for i, j in enumerate(liste_cartes):\n nom_destination = ('Année ' + j).replace(' 0', ' ').replace(' 0', ' ').replace(' 0', ' ')\n if 300 - (height_ // 2) <= 310 - (height_ // 2) + i * 32 + field_of_view_chose <= height_ + 268 - (height_ // 2):\n self.fenetre.blit(self.font.render(nom_destination, 1, (10, 10, 10)),\n ((self.fenetre.get_size()[0] - width_) // 2 + 10,\n 310 - (height_ // 2) + i * 32 + field_of_view_chose))\n\n pygame.draw.rect(self.fenetre, (85, 215, 45), ((self.fenetre.get_size()[0] - width_) // 2 + 10,\n 310 + height_ // 2 - 40,\n 35, 20))\n\n x_s, x_s1 = self.souris_ou_t_es()\n\n for event in pygame.event.get():\n if event.type == KEYDOWN:\n if event.key == K_ESCAPE:\n continuer = 0\n if event.type == MOUSEBUTTONDOWN:\n if event.button == 4:\n #molette UP\n field_of_view_chose += 32\n elif event.button == 5:\n #molette DOWN\n field_of_view_chose -= 32\n elif event.button == 1:\n #clic LEFT\n if (self.fenetre.get_size()[0] - width_) // 2 + 10 <= x_s <= (self.fenetre.get_size()[0] - width_) // 2 + 10 + 35 \\\n and 310 + height_ // 2 - 40 <= x_s1 <= 310 + height_ // 2 - 20:\n continuer = 0\n elif (self.fenetre.get_size()[0] - width_) // 2 <= x_s <= (self.fenetre.get_size()[0] - width_) // 2 + width_:\n choisi = (x_s1 - (310 - (height_ // 2) + field_of_view_chose)) // 32\n pygame.display.flip()\n\n if choisi != -1 and 0 <= choisi <= len(liste_cartes) - 1:\n self.carte.load(liste_cartes_originelles[choisi])\n self.flash()\n\n self.personnage.set_y(0)\n\n self.annee = choisi if choisi != -1 else self.annee\n\n def mettre_eau(self, x_blit, y_blit):\n \"\"\"\n a function who put the water tile on the map\n :param x_blit: the position of the mouse click\n :param y_blit: the second position of the mouse click\n :return: nothing\n \"\"\"\n y_bloque = []\n cpt_blit_eau = 0\n for i in range(y_blit, 20):\n if self.carte.get_tile(x_blit, i) == \"0\":\n if i not in y_bloque:\n self.carte.remove_bloc(x_blit, i, '0')\n else:\n y_bloque.append(i)\n cpt_blit_eau += 1\n for j in range(x_blit - cpt_blit_eau, x_blit + cpt_blit_eau):\n if j >= 0 and j <= self.carte.get_x_len():\n #pour ne pas dépasser\n if self.carte.get_tile(j, i) == \"0\" and i not in y_bloque:\n self.carte.remove_bloc(j, i, 'e')\n self.eau_bruit.play()\n self.eau_bruit.stop()\n\n def mettre_lava(self, x_blit, y_blit):\n \"\"\"\n a function who put the lava tile on the map\n :param x_blit: the position of the mouse click\n :param y_blit: the second position of the mouse click\n :return: nothing\n \"\"\"\n y_bloque = []\n cpt_blit_lav = 0\n for i in range(y_blit, 20):\n if self.carte.get_tile(x_blit, i) == \"0\":\n if i not in y_bloque:\n self.carte.remove_bloc(x_blit, i, '0')\n else:\n y_bloque.append(i)\n cpt_blit_lav += 1\n for j in range(x_blit - cpt_blit_lav, x_blit + cpt_blit_lav):\n if j >= 0 and j <= self.carte.get_x_len():\n #pour ne pas dépasser\n if self.carte.get_tile(j, i) == \"0\" and i not in y_bloque:\n self.carte.remove_bloc(j, i, 'lav')\n \n def custom(self):\n \"\"\"\n a function who draw the border of the game\n :return: nothing\n \"\"\"\n #modification du \"launcher\"\n pygame.draw.rect(self.root, (0, 0, 0), (self.rcenter[0] - 20, 9, 200, 17))\n #textes\n self.root.blit(self.font.render(\"Créatif (Off - On)\", 1, (255, 255, 255)), (self.rcenter[0] - 10, 12))\n #boutons\n pygame.draw.rect(self.root, (140, 140, 140), (self.rcenter[0] + 120, 9, 43, 17))\n if self.normal_gm:\n pygame.draw.rect(self.root, (140, 140, 140), (self.rcenter[0] + 120, 10, 43, 15))\n pygame.draw.rect(self.root, (180, 20, 20), (self.rcenter[0] + 120 + 1, 10, 20, 15))\n elif not self.normal_gm:\n pygame.draw.rect(self.root, (140, 140, 140), (self.rcenter[0] + 120, 10, 43, 15))\n pygame.draw.rect(self.root, (20, 180, 20), (self.rcenter[0] + 120 + 22, 10, 20, 15))\n #actualisation de l'écran pour afficher les changements\n pygame.display.flip()\n\n def poser_teleporteur(self, x, y):\n \"\"\"\n a function who put a teleporteur\n :param x: the position of the block\n :param y: the second position of the block\n :return: nothing\n \"\"\"\n #téléporteur\n if not self.en_reseau:\n self.teleporteurs.append([len(self.teleporteurs), (x, y)])\n else:\n self.network.sendto(pickle.dumps('set->telep' + str(x) + ',' + str(y)), self.params_co)\n\n def poser_pancarte(self, x_blit, y_blit):\n \"\"\"\n a function who put a panneau\n :param x_blit: the position of the block\n :param y_blit: the second position of the block\n :return: nothing\n \"\"\"\n #pancartes\n if not self.en_reseau:\n self.pancartes_lst.append([(x_blit, y_blit), ''])\n else:\n self.network.sendto(pickle.dumps(\"set->pan\" + str(x_blit) + \",\" + str(y_blit)), self.params_co)\n\n def break_pancarte(self, x_blit, y_blit):\n \"\"\"\n a function who destroy a panneau\n :param x_blit the position of the block\n :param y_blit: the second position of the block\n :return: nothing\n \"\"\"\n #on casse une pancarte\n if not self.en_reseau:\n #on doit donc liberer la place pour ne pas perdre en espace disque\n for i in range(len(self.pancartes_lst)):\n if self.pancartes_lst[i][0] == (x_blit, y_blit):\n self.pancartes_lst.pop(i)\n break\n else:\n self.network.sendto(pickle.dumps(\"break->pan\" + str(x_blit) + \",\" + str(y_blit)), self.params_co)\n\n def break_telep(self, x_blit, y_blit):\n \"\"\"\n a function who put a teleporteur\n :param x_blit: the position of the block\n :param y_blit: the second position of the block\n :return: nothing\n \"\"\"\n #on casse un téléporteur\n if not self.en_reseau:\n for i in range(len(self.teleporteurs)):\n if self.teleporteurs[i][1] == (x_blit, y_blit):\n if self.teleporteurs[i][0] % 2:\n #impair\n self.carte.remove_bloc(self.teleporteurs[i][1][0], self.teleporteurs[i][1][1], '0')\n self.carte.remove_bloc(self.teleporteurs[i - 1][1][0], self.teleporteurs[i - 1][1][1], '0')\n self.teleporteurs.pop(i)\n self.teleporteurs.pop(i - 1)\n elif not self.teleporteurs[i][0] % 2:\n #pair\n if not len(self.teleporteurs) % 2:\n #la longueur est paire, alors un autre téléporteur est associé a celui ci\n #et on doit donc aussi le casser\n self.carte.remove_bloc(self.teleporteurs[i + 1][1][0], self.teleporteurs[i + 1][1][1], '0')\n self.carte.remove_bloc(self.teleporteurs[i][1][0], self.teleporteurs[i][1][1], '0')\n self.teleporteurs.pop(i + 1)\n self.teleporteurs.pop(i)\n \n def put_water(self, x, y):\n \"\"\"\n a function who put some water\n :param x: the position of the block\n :param y: the second position of the block\n :return: nothing\n \"\"\"\n #eau\n self.carte.remove_bloc(x, y, \"e\")\n if self.carte.get_tile(x, y) == \"e\":\n self.mettre_eau(x, y)\n\n def put_lava(self, x, y):\n \"\"\"\n a function who put some lava\n :param x: the poition of the block\n :param y: the second position of the block\n :return: nothing\n \"\"\"\n #lava\n self.carte.remove_bloc(x, y, 'lav')\n if self.carte.get_tile(x, y) == 'lav':\n self.mettre_lava(x, y)\n\n def put_blocs(self, x_blit, y_blit):\n \"\"\"\n a function who do all the test and check if we can put a bloc or not\n :param x_blit: the position of the mouse click\n :param y_blit: the second position of the mouse click\n :return: nothing\n \"\"\"\n if self.carte.get_tile(x_blit, y_blit) == '0' and self.obj_courant not in self.blocs.list_unprintable():\n if self.normal_gm:\n #on enlève 1 pour le bloc POSé:\n if self.blocs.use(self.obj_courant):\n #on vient d'enlever un bloc, et cela a fonctionner (renvoit de True)\n #\"temps\" de destruction d'un bloc\n #item: #0 : x #1 : y #2 : nouveau bloc #3 : temps #4 : heure de la pose\n self.breakListe.append([x_blit, y_blit, self.obj_courant, self.blocs.get_time(self.carte.get_tile(x_blit, y_blit)[2::]) // 100, time.time()])\n self.blocs.set(self.carte.get_tile(x_blit, y_blit), nbr=self.blocs.get(self.carte.get_tile(x_blit, y_blit))+1)\n self.carte.remove_bloc(x_blit, y_blit, self.obj_courant)\n if self.obj_courant == 'vb':\n self.poser_teleporteur(x_blit, y_blit)\n if self.obj_courant == '%a':\n self.poser_pancarte(x_blit, y_blit)\n if self.carte.get_tile(x_blit, y_blit) == '%a' and self.obj_courant != '%a':\n self.break_pancarte(x_blit, y_blit)\n if self.carte.get_tile(x_blit, y_blit) == 'vb' and self.obj_courant != 'vb':\n self.break_telep(x_blit, y_blit)\n if self.obj_courant == 'e':\n self.put_water(x_blit, y_blit)\n if self.obj_courant == 'lav':\n self.put_lava(x_blit, y_blit)\n if self.obj_courant == 'pio':\n #on a la pioche, on peut donc casser des blocs :D\n if self.carte.get_tile(x_blit, y_blit) not in self.blocs.list_unprintable():\n if self.normal_gm:\n #on \"gagne\" un bloc :D\n self.blocs.set(self.carte.get_tile(x_blit, y_blit), nbr=self.blocs.get(self.carte.get_tile(x_blit, y_blit))+1)\n self.carte.remove_bloc(x_blit, y_blit, '0')\n elif self.carte.get_tile(x_blit, y_blit) != '0':\n self.lc(x_blit, y_blit)\n\n def lc(self, x, y):\n if self.carte.get_tile(x, y) == 'ggg':\n self.lc_cmd_block(x, y)\n if self.carte.get_tile(x, y) == '%a':\n self.lc_pancarte(x, y)\n\n def lc_pancarte(self, x, y):\n if not self.en_reseau:\n for i in self.pancartes_lst:\n if i[0] == (x, y):\n i[1] = dlb.DialogBox(self.fenetre, 'Entrez votre texte :', 'Edition d\\'une pancarte',\n self.rcenter, self.grd_font, self.y_ecart, type_btn=2, mouse=True,\n carte=self.carte).render()\n break\n else:\n texte_pan_to_send = dlb.DialogBox(self.fenetre, 'Entrez votre texte :', 'Edition d\\'une pancarte',\n self.rcenter, self.grd_font, self.y_ecart, type_btn=2,\n mouse=True, carte=self.carte).render()\n self.network.sendto(pickle.dumps(\"set->pan\" + str(x) + \",\" + str(y) + \",\" + texte_pan_to_send), self.params_co)\n\n def lc_cmd_block(self, x, y):\n code = dlb.DialogBox(self.fenetre, 'Entrez votre code :', 'Edition d\\'un command block',\n self.rcenter, self.grd_font, self.y_ecart, type_btn=2, mouse=True,\n carte=self.carte).render()\n self.cmd_block_mgr.modify(x, y, code)\n \n def rc_telep(self, x_clic, y_clic):\n #on veut se téléporter\n if not self.en_reseau:\n for i in range(len(self.teleporteurs)):\n if self.teleporteurs[i][1] == (x_clic, y_clic):\n if self.teleporteurs[i][0] % 2:\n #impair\n #passage en cases\n z = self.teleporteurs[i - 1][1][0] - (self.personnage.get_pos()[0] // 30 + self.carte.get_fov()[0])\n if self.carte.get_fov()[0] + z <= self.carte.get_max_fov() - self.carte.get_space():\n self.carte.set_fov(self.carte.get_fov()[0] + z, self.carte.get_fov()[1] + z)\n else:\n self.carte.set_fov(self.carte.get_max_fov() - self.carte.get_space(), self.carte.get_max_fov())\n self.personnage.set_y((self.teleporteurs[i - 1][1][1] - 1 if y_clic - 1 >= 0 else self.teleporteurs[i - 1][1][1] + 1) * 30)\n #pour etre au dessus et pas dedans\n elif not self.teleporteurs[i][0] % 2:\n #pair\n if not len(self.teleporteurs) % 2:\n #passage en cases\n z = self.teleporteurs[i + 1][1][0] - (self.personnage.get_pos()[0] // 30 + self.carte.get_fov()[0])\n if self.carte.get_fov()[0] + z <= self.carte.get_max_fov() - self.carte.get_space():\n self.carte.set_fov(self.carte.get_fov()[0] + z, self.carte.get_fov()[1] + z)\n else:\n self.carte.set_fov(self.carte.get_max_fov() - self.carte.get_space(), self.carte.get_max_fov())\n self.personnage.set_y((self.teleporteurs[i + 1][1][1] - 1 if y_clic - 1 >= 0 else self.teleporteurs[i + 1][1][1] + 1) * 30)\n elif len(self.teleporteurs) % 2 and i == len(self.teleporteurs) - 1:\n message_affiche(\"Aucune cible n'a été définie pour ce téléporteur !\", self.rcenter)\n else:\n self.network.sendto(pickle.dumps('get->telep' + str(x_clic) + ',' + str(y_clic)), self.params_co)\n temp = self.network.recv(4096)\n temp = pickle.loads(temp)\n #passage en cases\n z = temp[0] - (self.personnage.get_pos()[0] // 30 + self.carte.get_fov()[0])\n if self.carte.get_fov()[0] + z <= self.carte.get_max_fov() - self.carte.get_space():\n self.carte.set_fov(self.carte.get_fov()[0] + z, self.carte.get_fov()[1] + z)\n else:\n self.carte.set_fov(self.carte.get_max_fov() - self.carte.get_space(), self.carte.get_max_fov())\n self.personnage.set_y((temp[1] - 1 if y_clic - 1 >= 0 else temp[1] + 1) * 30)\n #pour etre au dessus et pas dedans\n\n def rc_jukebox(self):\n #jukebox\n self.indice_son = 0 if self.obj_courant == 'qs' else 1\n self.indice_son = 1 if self.obj_courant == 'sd' else 2\n self.indice_son = 2 if self.obj_courant == 'df' else 3\n if not pygame.mixer.music.get_busy():\n self.last_cd_used = self.obj_courant\n if pygame.mixer.music.get_busy():\n pygame.mixer.music.stop()\n self.blocs.set(self.last_cd_used, nbr=self.blocs.get(self.last_cd_used)+1)\n pygame.mixer.music.load(self.music_liste[self.indice_son])\n pygame.mixer.music.play()\n\n def rc_pancarte(self, x_clic, y_clic):\n if not self.en_reseau:\n for i in self.pancartes_lst:\n if i[0] == (x_clic, y_clic):\n message_affiche(i[1], self.rcenter)\n break\n else:\n self.network.sendto(pickle.dumps(\"get->pan\" + str(x_clic) + \",\" + str(y_clic)), self.params_co)\n temp = self.network.recv(4096)\n temp = pickle.loads(temp)\n message_affiche(temp, self.rcenter)\n \n def rc_time_telep(self):\n if not self.en_reseau:\n self.time_cruise()\n else:\n message_affiche(\"Vous ne pouvez pas voyager dans le temps en mode réseau\", self.rcenter)\n\n def rc_cmd_block(self, x_clic, y_clic):\n texte = self.cmd_block_mgr.rc(x_clic, y_clic)\n message_affiche(texte, self.rcenter)\n\n def rc(self, x_clic, y_clic):\n self.dust_electricty_driven_manager.right_click(x_clic, y_clic)\n tile = self.carte.conteneur_right_click(x_clic, y_clic)\n if tile != '':\n self.blocs.set(tile, nbr=self.blocs.get(tile)+1)\n if self.carte.get_tile(x_clic, y_clic) == 'cv':\n #bombe atomique\n self.bomb_mgr.add(x_clic, y_clic)\n elif self.carte.get_tile(x_clic, y_clic) == 'vb':\n self.rc_telep(x_clic, y_clic)\n elif self.obj_courant in self.dico_cd.keys() and self.carte.get_tile(x_clic, y_clic) == 'B':\n self.rc_jukebox()\n elif self.carte.get_tile(x_clic, y_clic) == '%a':\n self.rc_pancarte(x_clic, y_clic)\n elif self.carte.get_tile(x_clic, y_clic) == '%b':\n self.rc_time_telep()\n elif self.carte.get_tile(x_clic, y_clic) == 'ggg':\n self.rc_cmd_block(x_clic, y_clic)\n elif self.carte.get_tile(x_clic, y_clic) == '0':\n self.second_layer_put(x_clic, y_clic)\n\n def second_layer_put(self, x, y):\n if self.blocs.get(self.obj_courant) > 0:\n self.blocs.use(self.obj_courant)\n self.carte.put_bloc_snd_lay(x, y, self.obj_courant)\n \n def check_perso(self):\n \"\"\"\n fonction vérifiant que le personnage n'est pas dans un bloc et le déplacant dans ce cas\n a améliorer\n :return: nothing\n \"\"\"\n x, y = self.personnage.get_pos()\n if self.carte.collide(x, y):\n if not self.carte.collide(x, 0):\n self.personnage.move_to_y(0)\n else:\n self.carte.remove_bloc(x, y, '0')\n\n def pause_screen(self):\n \"\"\"\n a function who display the pause screen, with the settings button, and the quit one\n :return: nothing\n \"\"\"\n continuer = 1\n #le fond\n blur_surf = pygame.Surface(self.fenetre.get_size())\n blur_surf.fill((0, 0, 0))\n blur_surf.set_alpha(150)\n blur_surf.convert_alpha()\n self.fenetre.blit(blur_surf, (0, 0))\n #le titre\n titre = self.grd_font.render(\"Pause\", 1, (240, 240, 240))\n #les boutons\n btn_param = (self.fenetre.get_size()[0] // 2 - 110 // 2, 200, 110, 50)\n btn_menu = (self.fenetre.get_size()[0] // 2 - 70 // 2, 300, 70, 50)\n btn_back = (self.fenetre.get_size()[0] // 2 - 70 // 2, 400, 70, 50)\n focus_param = False\n focus_menu = False\n focus_back = False\n menu_txt = self.font2.render(\"Menu\", 1, (10, 10, 10))\n param_txt = self.font2.render(\"Paramètres\", 1, (10, 10, 10))\n back_txt = self.font2.render(\"Jeu\", 1, (10, 10, 10))\n\n pygame.mouse.set_visible(True)\n\n while continuer:\n color_menu = (140, 140, 140) if not focus_menu else (180, 20, 20)\n color_param = (140, 140, 140) if not focus_param else (180, 180, 20)\n color_back = (140, 140, 140) if not focus_back else (20, 20, 180)\n\n pygame.draw.rect(self.fenetre, color_menu, btn_menu)\n pygame.draw.rect(self.fenetre, color_param, btn_param)\n pygame.draw.rect(self.fenetre, color_back, btn_back)\n self.fenetre.blit(menu_txt, (btn_menu[0] + 11, btn_menu[1] + 11))\n self.fenetre.blit(param_txt, (btn_param[0] + 7, btn_param[1] + 11))\n self.fenetre.blit(back_txt, (btn_back[0] + 17, btn_back[1] + 12))\n\n self.fenetre.blit(titre, (self.fenetre.get_size()[0] // 2 - titre.get_size()[0] // 2, 30))\n\n for e in pygame.event.get():\n if e.type == KEYDOWN:\n if e.key == K_ESCAPE:\n continuer = 0\n if e.type == MOUSEBUTTONUP:\n x, y = e.pos\n if btn_menu[0] <= x <= btn_menu[0] + btn_menu[2] and btn_menu[1] + self.hauteur_fenetre <= y <= btn_menu[1] + self.hauteur_fenetre + btn_menu[3]:\n #on quitte le jeu et on retourne au menu\n self.continuer = 0\n continuer = 0\n if btn_param[0] <= x <= btn_param[0] + btn_param[2] and btn_param[1] + self.hauteur_fenetre <= y <= btn_param[1] + self.hauteur_fenetre + btn_param[3]:\n prm.parametres(self.fenetre, self.grd_font, self.hauteur_fenetre, not self.windowed_is, in_game=True)\n if btn_back[0] <= x <= btn_back[0] + btn_back[2] and btn_back[1] + self.hauteur_fenetre <= y <= btn_back[1] + self.hauteur_fenetre + btn_back[3]:\n continuer = 0\n\n #le focus des boutons\n x_s, y_s = pygame.mouse.get_pos()\n if btn_menu[0] <= x_s <= btn_menu[0] + btn_menu[2] and btn_menu[1] + self.hauteur_fenetre <= y_s <= btn_menu[1] + self.hauteur_fenetre + btn_menu[3]:\n focus_menu = True\n else:\n focus_menu = False\n if btn_param[0] <= x_s <= btn_param[0] + btn_param[2] and btn_param[1] + self.hauteur_fenetre <= y_s <= btn_param[1] + self.hauteur_fenetre + btn_param[3]:\n focus_param = True\n else:\n focus_param = False\n if btn_back[0] <= x_s <= btn_back[0] + btn_back[2] and btn_back[1] + self.hauteur_fenetre <= y_s <= btn_back[1] + self.hauteur_fenetre + btn_back[3]:\n focus_back = True\n else:\n focus_back = False\n\n pygame.display.flip()\n pygame.mouse.set_visible(False)\n # on doit recharger tous les paramètres\n self.partial_load()\n \n def get_events(self):\n \"\"\"\n a function who get the pygame events and run the associate action\n :return: nothing\n \"\"\"\n for ev in [pygame.event.poll()]:\n #petite optimisation maison qui attend les evennements, et ne les checks pas tout le temps\n if ev.type == KEYDOWN and (ev.key == K_ESCAPE or ev.key == K_F4):\n self.save()\n self.pause_screen()\n elif ev.type == QUIT and self.windowed_is:\n self.save()\n self.continuer = 0\n #controles a la souris\n elif ev.type == MOUSEBUTTONDOWN:\n if ev.button == 5:\n #la molette descend\n self.molette_('bas')\n elif ev.button == 4:\n #la molette monte\n self.molette_('haut')\n elif ev.button == 1:\n self.clique_gauche = 1\n #clic, donc on pose un bloc là où on a cliqué !\n x_blit = ev.pos[0] // 30 + self.carte.get_fov()[0] + self.carte.get_offset() // 30\n y_blit = ev.pos[1] // 30\n self.souris_ou_t_es()\n if y_blit <= 18 and x_blit <= self.carte.get_max_fov() - 1 and (self.blocs.get(self.obj_courant) > 0 or not self.normal_gm) \\\n and ((x_blit, y_blit) != (self.personnage.get_pos()[0] // 30 + self.carte.get_fov()[0],\n self.personnage.get_pos()[1] // 30) or self.obj_courant not in self.blocs.list_solid()):\n self.put_blocs(x_blit, y_blit)\n elif ev.button == 3:\n x_clic = ev.pos[0] // 30 + self.carte.get_fov()[0]\n y_clic = ev.pos[1] // 30\n self.rc(x_clic, y_clic)\n elif ev.type == MOUSEBUTTONUP:\n if ev.button == 1:\n #clic gauche, on relache la souris donc on met à false le 'booleen' qui dit que l'on peut\n self.clique_gauche = 0\n elif ev.button == 3:\n #clique droit\n if self.obj_courant in self.blocs.list_unprintable():\n #on enlève 1 potion\n if self.obj_courant not in self.liste_septre and self.obj_courant not in self.dico_cd.keys():\n self.blocs.use(self.obj_courant)\n if self.obj_courant == 'Q':\n self.personnage.update_vie(100)\n elif self.obj_courant == 'S':\n self.personnage.update_mana(100)\n elif self.obj_courant in self.liste_septre:\n self.personnage.mana_action(self.normal_gm, self.obj_courant, ev.pos)\n elif ev.button == 2: # bouton du milieu (molette de la souris)\n # on a récupéré le bloc et on l'a affecté comme bloc en cours d'utilisation\n self.obj_courant = self.carte.get_tile((ev.pos[0] // 30) + self.carte.get_fov()[0], (ev.pos[1] // 30))\n elif ev.type == MOUSEMOTION:\n if self.clique_gauche and not self.normal_gm:\n #en fait on est en créatif quand meme dans ce cas ci :)\n x_blit = ev.pos[0] // 30 + self.carte.get_fov()[0] + self.carte.get_offset() // 30\n y_blit = ev.pos[1] // 30\n if y_blit <= 19 and x_blit <= self.carte.get_max_fov() and self.blocs.get(self.obj_courant) > 0 \\\n and ((x_blit, y_blit) != (\n self.personnage.get_pos()[0] // 30 + self.carte.get_fov()[0],\n self.personnage.get_pos()[1] // 30)\n or self.obj_courant not in self.blocs.list_solid()):\n if self.obj_courant not in self.blocs.list_unprintable() and self.carte.get_tile(x_blit, y_blit) == '0':\n if self.normal_gm:\n # donc si on est en créatif, on ajoute pas\n # le bloc existe, on met 1 bloc en plus dans l'inventaire\n self.blocs.set(self.carte.get_tile(x_blit, y_blit), nbr=self.blocs.get(self.carte.get_tile(x_blit, y_blit))+1)\n # on enlève 1 pour le bloc POSé:\n if self.blocs.get(self.obj_courant) - 1 >= 0:\n self.blocs.use(self.obj_courant)\n self.carte.remove_bloc(x_blit, y_blit, self.obj_courant)\n else:\n self.carte.remove_bloc(x_blit, y_blit, self.obj_courant)\n # raffraichir la map pour voir le placement multiple :\n self.carte.update(self.personnage.get_case_pos())\n if self.show_stats:\n self.personnage.afficher_vie()\n self.personnage.afficher_mana()\n if self.obj_courant == 'e':\n self.mettre_eau(x_blit, y_blit)\n if self.obj_courant == 'lav':\n self.mettre_lava(x_blit, y_blit)\n else:\n if self.obj_courant == 'pio' and self.carte.get_tile(x_blit, y_blit) not in self.blocs.list_unprintable():\n #on a la pioche, on peut donc casser des blocs :D\n if self.normal_gm:\n #on \"gagne\" un bloc :D\n self.blocs.set(self.carte.get_tile(x_blit, y_blit), nbr=self.blocs.get(self.carte.get_tile(x_blit, y_blit))+1)\n self.carte.remove_bloc(x_blit, y_blit, '0')\n self.souris_ou_t_es()\n #controles au clavier\n elif ev.type == KEYDOWN:\n #controles de déplacement au clavier\n if not self.ZQSD:\n if ev.key == K_UP:\n #on monte\n self.personnage.move(\"haut\")\n elif ev.key == K_DOWN:\n #on descend mais uniquement si il y a une echelle en dessous de nous\n if self.carte.get_tile(self.personnage.get_pos()[0] // 30 + self.carte.get_fov()[0],\n self. personnage.get_pos()[1] // 30 + 1) == './':\n self.personnage.set_y(self.personnage.get_pos()[1] + 30)\n elif ev.key == K_LEFT:\n #on va à gauche\n self.personnage.move(\"gauche\")\n elif ev.key == K_RIGHT:\n #on va à droite\n self.personnage.move(\"droite\")\n #controle de l'affichage de l'inventaire Drag&Drop\n elif ev.key == K_LSHIFT or ev.key == K_RSHIFT:\n self.drag_and_drop_invent()\n elif self.ZQSD:\n #déplacement avec les touches ZQSD\n if ev.key == K_w:\n #on monte\n self.personnage.move(\"haut\")\n elif ev.key == K_s:\n #on descend mais uniquement si il y a une echelle en dessous de nous\n if self.carte.get_tile(self.personnage.get_pos()[0] // 30 + self.carte.get_fov()[0],\n self. personnage.get_pos()[1] // 30 + 1) == './':\n self.personnage.set_y(self.personnage.get_pos()[1] + 30)\n elif ev.key == K_a:\n #on va à gauche\n self.personnage.move(\"gauche\")\n elif ev.key == K_d:\n #on va à droite\n self.personnage.move(\"droite\")\n #controle de l'affichage de l'inventaire Drag&Drop\n elif ev.key == K_e:\n self.drag_and_drop_invent()\n if ev.key == K_KP9:\n self.show_cursor = not self.show_cursor\n #changement de la taille du FOV\n if ev.key == K_KP7:\n new_size_fov = dlb.DialogBox(self.fenetre, [\"Entrez la nouvelle taille du\", \"FOV (entre 0 et \" + str(self.nb_blocs_large) + \" ) :\"],\n \"Réglage du FOV\", self.rcenter, self.grd_font, self.y_ecart, type_btn=3, carte=self.carte).render()\n if new_size_fov.isdigit():\n self.carte.set_fov(self.carte.get_fov()[0], self.carte.get_fov()[0] + abs(int(new_size_fov)))\n #controles discuter et inventaire\n elif ev.key == K_KP6:\n #on parle à la personne la plus proche de soi\n passant_parle(self.fenetre, self.personnage.get_direction(), self.personnage, self.carte.get_list(),\n self.blocs.get('/§'), self.rcenter, self.carte.get_img_dict(), self.carte.get_fov())\n elif ev.key == K_KP5:\n #la musique en pause ou pas !\n self.play_song = not self.play_song\n if self.play_song:\n pygame.mixer.music.set_volume(0)\n elif not self.play_song:\n pygame.mixer.music.set_volume(self.volume_son_j)\n #on affiche les autres ou pas :D\n elif ev.key == K_KP4:\n if self.en_reseau:\n self.carte.change_oth_visibility()\n dlb.DialogBox(self.fenetre, \"Les autres joueurs ne sont plus visibles\" if not self.carte.get_oth_visibility() else \"Les autres joueurs sont visibles\",\n \"Visiblité des joueurs\", self.rcenter, self.grd_font, self.y_ecart, type_btn=0, carte=self.carte).render()\n elif ev.key == K_KP3:\n self.max_FPS_atteint = 0\n self.carte.switch_shader()\n elif ev.key == K_KP2:\n if self.normal_gm:\n self.normal_gm = False\n pygame.draw.rect(self.root, (140, 140, 140), (self.rcenter[0] + 120, 9, 43, 17))\n pygame.draw.rect(self.root, (20, 180, 20), (self.rcenter[0] + 120 + 1, 10, 20, 15))\n self.personnage.set_vie(self.last_vie)\n elif not self.normal_gm:\n self.normal_gm = True\n pygame.draw.rect(self.root, (140, 140, 140), (self.rcenter[0] + 120, 9, 43, 17))\n pygame.draw.rect(self.root, (180, 20, 20), (self.rcenter[0] + 120 + 22, 10, 20, 15))\n self.last_vie = self.personnage.get_vie()\n self.personnage.set_vie(100)\n #passage fullscreen -> windowed / windowed -> fullscreen\n elif ev.key == K_KP1:\n if self.windowed_is:\n self.root = pygame.display.set_mode((0, 0), FULLSCREEN + HWSURFACE)\n r = pygame.Rect(0, 0, self.fenetre.get_size()[0], 600) # definition de la taille de la fenetre de jeu\n r.center = self.root.get_rect().center # centrage de la fenetre par rapport a l'ecran total\n self.fenetre = self.root.subsurface(r) # definition de la fenetre de jeu\n pygame.display.update(r) # mise a jour de la fenetre seulement\n self.custom()\n self.windowed_is = False\n else:\n self.root = pygame.display.set_mode((0, 0))\n r = pygame.Rect(0, 0, self.fenetre.get_size()[0], 600) # definition de la taille de la fenetre de jeu\n r.center = self.root.get_rect().center # centrage de la fenetre par rapport a l'ecran total\n self.fenetre = self.root.subsurface(r) # definition de la fenetre de jeu\n pygame.display.update(r) # mise a jour de la fenetre seulement\n self.custom()\n pygame.display.set_caption(\"UrWorld\")\n self.windowed_is = True\n #controle du tchat\n elif ev.key == K_KP0:\n self.txt_chat = dlb.DialogBox(self.fenetre, \"Que voulez-vous dire ?\", \"Chat\", self.rcenter, self.grd_font, self.y_ecart, type_btn=2, carte=self.carte).render()\n self.time_blitting_txt_chat = time.time() + 10\n if self.txt_chat[:16] == 'toggledownfalled':\n self.carte.set_meteo('toggledownfalled')\n elif self.txt_chat[:6] == 'invert':\n self.carte.set_meteo('invert')\n elif self.txt_chat[:4] == 'tp->':\n go_to = self.txt_chat[4::].split(',')\n x_ = self.personnage.get_pos()[0] // 30 + self.carte.get_fov()[0] - int(go_to[0])\n new0 = self.carte.get_fov()[0] - x_ if self.carte.get_fov()[0] - x_ >= 0 else 0\n new0 = new0 if new0 <= self.carte.get_max_fov() - (self.carte.get_fov()[1] - self.carte.get_fov()[0]) else self.carte.get_max_fov() - (self.carte.get_fov()[1] - self.carte.get_fov()[0])\n self.carte.set_fov(new0, new0 + (self.carte.get_fov()[1] - self.carte.get_fov()[0]))\n if self.en_reseau:\n reseau_speaking(self.network, self.txt_chat, self.params_co, self.personnage, self.carte, self.blocs)\n #controle pour courir\n elif ev.key == K_RETURN:\n #pour courir\n self.courir_bool = not self.courir_bool\n new_speed = self.personnage.get_speed() - self.personnage.get_speed_decrease() if self.courir_bool else self.personnage.get_speed() + self.personnage.get_speed_decrease()\n self.personnage.set_speed(new_speed)\n elif ev.type == KEYUP:\n #saut\n if not self.ZQSD:\n if ev.key == K_SPACE:\n self.saut = True\n self.time_saut = time.time() + self.temps_saut_attendre\n elif self.ZQSD:\n if ev.key == K_q:\n self.saut = True\n self.time_saut = time.time() + self.temps_saut_attendre\n if ev.key == K_SPACE:\n self.testeur = not self.testeur\n self.personnage.change_test(self.testeur)\n self.carte.set_pixel_offset(0)\n self.personnage.set_x_to_default()\n if ev.key == K_PRINT:\n pygame.image.save(self.root, \"..\" + os.sep + \"assets\" + os.sep + \"Screenchots\" + os.sep +\n str(time.strftime('%Y%m%d')) + \" - \" + str(time.strftime('%H %M %S')) + \".png\")\n\n def thread_destroy_bloc(self):\n \"\"\"\n a function who destroy asynchronicously the blocs\n :return: nothing\n \"\"\"\n iBreakList = 0\n while iBreakList <= len(self.breakListe) - 1:\n item = self.breakListe[iBreakList]\n if time.time() - item[4] > item[3]:\n self.carte.remove_bloc(item[0], item[1], item[2])\n self.breakListe.pop(iBreakList)\n self.breaking_bloc.play()\n iBreakList += 1\n\n def auto_update(self, lite=False):\n \"\"\"\n a function who call all the updater of the dependencies of the game\n :return: nothing\n \"\"\"\n #pour les FPS\n self.temps_avant_fps = time.time()\n\n if not lite:\n if not self.en_reseau:\n self.carte.update(self.personnage.get_case_pos())\n else:\n self.carte.update_([self.personnage.get_pos()[0] // 30 + self.carte.get_fov()[0],\n self.personnage.get_pos()[1] // 30, self.personnage.get_direction()])\n self.pointeur = self.arme_h_g if self.obj_courant != 'pio' else self.pioche_h_g\n pygame.mouse.set_visible(False)\n #pour ne pas se retrouver bloqué\n self.check_perso()\n elif lite:\n self.carte.update(self.personnage.get_case_pos(), lite=True)\n\n #destruction des bombes atomiques non bloquantes\n self.bomb_mgr.check()\n #destruction des blocs non bloquant\n self.thread_destroy_bloc()\n\n #vie & mana\n if self.show_stats:\n self.personnage.afficher_vie()\n self.personnage.afficher_mana()\n\n #régénration de la mana\n self.personnage.regen_mana()\n\n def debug_on_windows(self, xs, ys):\n rel = 30\n #pour le bloc sélectionné\n self.fenetre.blit(self.carte.get_img_dict()['0'], ((xs // 30) * 30 + self.carte.get_offset(), (ys // 30) * 30))\n #logs\n to_print = [\n \"Heure jeu : \" + str(self.carte.get_skybox().get_game_time()),\n \"Couleur RGB : \" + str(self.carte.get_skybox().get_color()),\n \"Mauvais temps : \" + str(self.carte.get_skybox().get_bad_weather()),\n \"Vitesse de répétition des touches : \" + str(self.personnage.get_speed()) + \" ms\",\n \"Shader : \" + self.carte.get_curent_shader(),\n \"Intensité du shader std : \" + str(self.carte.get_std_shader_shade()),\n \"Nombre de chunks : \" + str(self.carte.count_chunks()),\n \"Temps de génération de l'affichage de la carte : \" + str(self.carte.get_generation_time()) + \" ms\",\n \"Position absolue (blocs) : \" + str(self.personnage.get_abs_pos()),\n \"Position relative (pixels) : \" + str(self.personnage.get_rel_pos_px()),\n \"Pixel offset de la carte : \" + str(self.carte.get_pixel_offset()),\n \"FOV : \" + str(self.carte.get_fov()),\n \"Testeur : \" + str(self.testeur),\n \"Gamer : \" + str(self.ZQSD),\n \"Année : \" + str(self.annee + 1),\n \"Taille du bombs manager : \" + str(self.bomb_mgr.size()),\n \"Light start : \" + str(self.light_start),\n \"Nombre de bloc d'arrière plan blitté : \" + str(self.carte.get_count_fnd_blit()),\n \"Max FPS atteint : \" + str(self.max_FPS_atteint)\n ]\n self.fenetre.blit(self.surf_debug, (15, rel))\n self.fenetre.blit(self.grd_font.render(\"Mode debug ON\", 1, (160, 20, 40)), (20, rel + 2))\n for i in range(len(to_print)):\n self.fenetre.blit(self.font.render(to_print[i], 1, (10, 10, 10)), (30, rel + 25 + 15 * i))\n\n def lite_start(self):\n \"\"\"\n a sub main function of this class. run the main thread and check the minimal number of coponents\n use if your configuration is very, very bad\n :return: nothing\n \"\"\"\n self.tps_tour = time.time() + 0.1\n self.load_coponents()\n\n self.txt_chat = \"\"\n self.time_blitting_txt_chat = 0\n self.nb_cases_chut = 0\n pseudo_aff = self.font.render(self.personnage.get_pseudo(), 1, (0, 0, 0))\n\n #le \"tour\" de l'ecran de jeu\n self.custom()\n\n while self.continuer:\n self.auto_update(lite=True)\n\n #pour la souris\n x_souris, y_souris = self.souris_ou_t_es()\n\n #pour les events\n self.get_events()\n\n #fin de boucle => régulation et affichage des FPS\n self.FPS.actualise()\n self.print_fps()\n\n #blit ici de toutes les surfaces\n #on affiche le personnage\n self.personnage.render()\n\n self.fenetre.blit(pseudo_aff, (self.personnage.get_pos()[0] - len(self.personnage.get_pseudo()), self.personnage.get_pos()[1] - 12))\n\n #affichage du personnage en fonction de la souris\n if x_souris < self.personnage.get_pos()[0]:\n #souris à gauche\n self.personnage.change_direction('gauche', mouse=True)\n else:\n #souris à droite\n self.personnage.change_direction('droite', mouse=True)\n\n if self.prise_de_degats > 0:\n self.personnage.encaisser_degats(0.5)\n self.afficher_degats_pris()\n self.falling.play()\n self.falling.stop()\n self.prise_de_degats = 0\n\n self.aff_bloc()\n\n #saut\n if self.saut and self.time_saut <= time.time():\n self.time_saut = time.time() + self.temps_saut_attendre\n if self.personnage.get_pos()[1] + (self.liste_hauteur_saut[self.hauteur_saut % len(self.liste_hauteur_saut)] * 30) >= 0 and \\\n not self.carte.collide(self.personnage.get_pos()[0] // 30 + self.carte.get_fov()[0], self.personnage.get_pos()[1] // 30 +\n (self.liste_hauteur_saut[self.hauteur_saut % len(self.liste_hauteur_saut)])):\n self.personnage.set_y(self.personnage.get_pos()[1] + (self.liste_hauteur_saut[self.hauteur_saut % len(self.liste_hauteur_saut)]) * 30)\n self.hauteur_saut += 1\n if self.hauteur_saut == len(self.liste_hauteur_saut) - 1:\n self.saut = False\n self.hauteur_saut = 0\n\n #gravité active non bloquante\n if self.personnage.get_pos()[1] // 30 + 1 <= 18:\n if self.carte.get_tile(self.personnage.get_pos()[0] // 30 + self.carte.get_fov()[0], self.personnage.get_pos()[1] // 30 + 1) not in self.blocs.list_solid() \\\n and self.carte.get_tile(self.personnage.get_pos()[0] // 30 + self.carte.get_fov()[0], self.personnage.get_pos()[1] // 30 + 1) != './' \\\n and not self.saut:\n self.personnage.set_y(self.personnage.get_pos()[1] + 30)\n self.nb_cases_chut += 1\n if self.nb_cases_chut >= 3:\n self.prise_de_degats = 1\n else:\n self.nb_cases_chut = 0\n\n pygame.display.flip()\n\n self.save()\n\n def start(self):\n if self.light_start:\n self.lite_start()\n else:\n self.start_game()\n\n def start_game(self):\n \"\"\"\n the main function of this class. run the main thread and load the different coponents\n :return: nothing\n \"\"\"\n self.tps_tour = time.time() + 0.1\n self.load_coponents()\n\n self.txt_chat = \"\"\n self.time_blitting_txt_chat = 0\n self.nb_cases_chut = 0\n pseudo_aff = self.font.render(self.personnage.get_pseudo(), 1, (0, 0, 0))\n\n #le \"tour\" de l'ecran de jeu\n self.custom()\n\n while self.continuer:\n self.auto_update()\n\n #pour la souris\n x_souris, y_souris = self.souris_ou_t_es()\n\n #pour le suiveur\n last_pos = (self.personnage.get_pos()[0] - 30, self.personnage.get_pos()[1]) if self.personnage.get_direction() == 'droite' else (self.personnage.get_pos()[0] + 30, self.personnage.get_pos()[1])\n\n #pour les events\n self.get_events()\n\n #gestion du réseau ici\n if self.en_reseau:\n self.actualise_chat()\n\n #fin de boucle => régulation et affichage des FPS\n self.FPS.actualise()\n self.print_fps()\n\n #blit ici de toutes les surfaces\n #on affiche le personnage\n self.personnage.render()\n\n if self.vip_bool:\n if int(time.time() * 10) % 3 == 0 and not self.vu_vip_change:\n self.index_couleur = (self.index_couleur + 1) % (len(self.liste_couleur) - 1)\n pseudo_aff = self.font.render(self.personnage.get_pseudo(), 1, self.liste_couleur[self.index_couleur])\n self.vu_vip_change = True\n elif int(time.time() * 10) % 3:\n self.vu_vip_change = False\n self.fenetre.blit(pseudo_aff, (self.personnage.get_pos()[0] - len(self.personnage.get_pseudo()), self.personnage.get_pos()[1] - 12))\n\n #musique\n if time.time() >= self.last_music_time and not self.play_song:\n pygame.mixer.music.load(randchoice(self.music_liste))\n pygame.mixer.music.play()\n self.last_music_time = time.time() + 360\n\n #affichage du personnage en fonction de la souris\n if x_souris < self.personnage.get_pos()[0]:\n #souris à gauche\n self.personnage.change_direction('gauche', mouse=True)\n else:\n #souris à droite\n self.personnage.change_direction('droite', mouse=True)\n\n if self.prise_de_degats > 0:\n self.personnage.encaisser_degats(0.5)\n self.afficher_degats_pris()\n self.falling.play()\n self.falling.stop()\n self.prise_de_degats = 0\n\n self.aff_bloc()\n\n if self.show_cursor:\n #on affiche l'interface de debug\n self.debug_on_windows(x_souris, y_souris)\n #on affiche les caractéristiques du bloc survolé :)\n if self.carte.get_tile(x_souris // 30 + self.carte.get_fov()[0], y_souris // 30) != 'p' and \\\n 0 <= x_souris // 30 <= self.fenetre.get_size()[0] and 0 <= y_souris // 30 <= self.carte.get_y_len():\n bloc_actuel = self.carte.get_tile(x_souris // 30 + self.carte.get_fov()[0], y_souris // 30)\n bloc_carac = self.font.render(self.blocs.dict_name()[bloc_actuel] + ' : %3i,' % self.blocs.get(bloc_actuel) +\n ' x:{}, y:{}, collide:{}, innafichable:{}'\n .format(str(x_souris // 30 + self.carte.get_fov()[0]),\n str(y_souris // 30),\n str(self.carte.collide(x_souris // 30 + self.carte.get_fov()[0], y_souris // 30)),\n str(self.blocs.get_unprintable(self.carte.get_tile(x_souris // 30 + self.carte.get_fov()[0], y_souris // 30)))),\n 1, (10, 10, 10))\n pygame.draw.rect(self.fenetre, (150, 150, 150), (\n x_souris, y_souris,\n 4 + bloc_carac.get_size()[0],\n 4 + bloc_carac.get_size()[1]))\n self.fenetre.blit(bloc_carac, (x_souris + 2, y_souris + 2))\n\n #saut\n if self.saut and self.time_saut <= time.time():\n self.time_saut = time.time() + self.temps_saut_attendre\n if self.personnage.get_pos()[1] + (self.liste_hauteur_saut[self.hauteur_saut % len(self.liste_hauteur_saut)] * 30) >= 0 and \\\n not self.carte.collide(self.personnage.get_pos()[0] // 30 + self.carte.get_fov()[0], self.personnage.get_pos()[1] // 30 +\n (self.liste_hauteur_saut[self.hauteur_saut % len(self.liste_hauteur_saut)])):\n self.personnage.set_y(self.personnage.get_pos()[1] + (self.liste_hauteur_saut[self.hauteur_saut % len(self.liste_hauteur_saut)]) * 30)\n self.hauteur_saut += 1\n if self.hauteur_saut == len(self.liste_hauteur_saut) - 1:\n self.saut = False\n self.hauteur_saut = 0\n\n #gravité active non bloquante\n if self.personnage.get_pos()[1] // 30 + 1 <= 18:\n if self.carte.get_tile(self.personnage.get_pos()[0] // 30 + self.carte.get_fov()[0], self.personnage.get_pos()[1] // 30 + 1) not in self.blocs.list_solid() \\\n and self.carte.get_tile(self.personnage.get_pos()[0] // 30 + self.carte.get_fov()[0], self.personnage.get_pos()[1] // 30 + 1) != './' \\\n and not self.saut:\n self.personnage.set_y(self.personnage.get_pos()[1] + 30)\n self.nb_cases_chut += 1\n if self.nb_cases_chut >= 3:\n self.prise_de_degats = 1\n else:\n self.nb_cases_chut = 0\n\n #affichage du 'suiveur'\n if self.suiveur:\n pygame.draw.rect(self.fenetre, (180, 25, 150), (last_pos[0], last_pos[1], 30, 30))\n\n #affichage du chat\n if self.txt_chat != \"\" or time.time() <= self.time_blitting_txt_chat:\n txt_afficher_chat = self.font.render(self.txt_chat, 1, (10, 10, 10))\n pygame.draw.rect(self.fenetre, (150, 150, 150), (self.personnage.get_pos()[0] + 30,\n self.personnage.get_pos()[1] - txt_afficher_chat.get_size()[1] - 10,\n txt_afficher_chat.get_size()[0] + 4,\n txt_afficher_chat.get_size()[1] + 1))\n self.fenetre.blit(txt_afficher_chat, (self.personnage.get_pos()[0] + 32, self.personnage.get_pos()[1] - 7 - txt_afficher_chat.get_size()[1]))\n\n pygame.display.flip()\n\n self.save()","repo_name":"SuperFola/UrWorld-Alpha-3.x","sub_path":"src/gamecore.py","file_name":"gamecore.py","file_ext":"py","file_size_in_byte":87839,"program_lang":"python","lang":"fr","doc_type":"code","stars":1,"dataset":"github-code","pt":"85"} +{"seq_id":"19787814305","text":"import cv2\r\nimport os\r\nimport numpy as np\r\n\r\ndataPath = 'C:/Users/FF-admin/Videos/Reconhecimento Facial em video/Data'\r\npeopleList = os.listdir(dataPath)\r\nprint('Lista de pessoas: ', peopleList)\r\n\r\nlabels = []\r\nfacesData = []\r\nlabel = 0\r\n\r\nfor nameDir in peopleList:\r\n personPath = dataPath + '/' + nameDir\r\n print('Lendo as imagens')\r\n\r\n for fileName in os.listdir(personPath):\r\n print('Rostos: ', nameDir + '/' + fileName)\r\n labels.append(label)\r\n facesData.append(cv2.imread(personPath+'/'+fileName,0))\r\n #image = cv2.imread(personPath+'/'+fileName,0)\r\n #cv2.imshow('image',image)\r\n #cv2.waitKey(10)\r\n label = label + 1\r\n\r\n#print('labels= ',labels)\r\n#print('Número de etiquetas 0: ',np.count_nonzero(np.array(labels)==0))\r\n#print('Número de etiquetas 1: ',np.count_nonzero(np.array(labels)==1))\r\n\r\n#face_recognizer = cv2.face.EigenFaceRecognizer_create()\r\n#face_recognizer = cv2.face.FisherFaceRecognizer_create()\r\nface_recognizer = cv2.face.LBPHFaceRecognizer_create()\r\n\r\n#Treiando o reconhecedor de rostos\r\nprint(\"Treinando...\")\r\nface_recognizer.train(facesData, np.array(labels))\r\n\r\n#Salvando o modelo obtido\r\nface_recognizer.write('modeloLBPHFace.xml')\r\nprint(\"Salvando Modelo...\")\r\n","repo_name":"avvnessa/Reconhecimento_facial","sub_path":"TreinandoEigenFaces.py","file_name":"TreinandoEigenFaces.py","file_ext":"py","file_size_in_byte":1270,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"85"} +{"seq_id":"5580813328","text":"# Create by MrZhang on 2019-11-22\n\nimport numpy as np\nimport re\n\n# 对获取到对原始文本进行预处理：\n# 去除标点符号，去除 's 之类对影响，然后对文本进行切分，使输出矩阵对每一行数据仅包含一个句子。\n# 对每一行句子，去掉首尾对空格符号\n# 保存处理后获得的矩阵\ndef preproData(data_path, save_path):\n fr = open(data_path, 'r')\n sentData = []\n for line in fr:\n line = line.strip('\\n')\n line = re.sub(r'[{}]+'.format('“”!,;:?\"-'), '', line)\n line = re.sub(r'\\d+', '', line)\n line = re.sub(r'[{}]+'.format('\\'s'), '', line)\n sentences = line.split('.')\n sentList = [sentence for sentence in sentences if sentence != '']\n for sentence in sentList:\n sentData.append(sentence.strip(' '))\n sentData = np.array(sentData)\n np.save(save_path, sentData)\n\ndef createVocabList(data_path):\n dataSet = np.load(data_path)\n vocabSet = set([])\n for sentence in dataSet:\n vocabSet = vocabSet | set(re.split(r'\\W+', str.lower(sentence)))\n vocab_list = list(vocabSet)\n vocab_list.remove('')\n vocab_list.sort()\n return vocab_list\n\n# 将一个单词转化为独热向量。\ndef wordToOneHotVec(word, vocab_list):\n word_vec = np.zeros(len(vocab_list))\n index = vocab_list.index(word)\n word_vec[index] = 1\n return word_vec\n\n# 输入一个句子，根据窗口的大小，生成多组\"中心词 -> 关联词\"的词对\ndef generateWordCouple(input_sentence, window_size):\n wordList = re.split(r'\\W+', str.lower(input_sentence))\n sentence_len = len(wordList)\n centreWord = []\n contextWord = []\n for i in range(sentence_len):\n for j in range(window_size):\n if i + j + 1 < sentence_len:\n centreWord.append(wordList[i])\n contextWord.append(wordList[i + j + 1])\n if i - j - 1 >= 0:\n centreWord.append(wordList[i])\n contextWord.append(wordList[i - j - 1])\n return centreWord, contextWord\n\n# 将词列表转化为独热向量矩阵\ndef listToOneHotMat(word_list, vocab_list):\n words_one_hot_mat = []\n for word in word_list:\n if word == '': continue\n words_one_hot_mat.append(wordToOneHotVec(word, vocab_list))\n return words_one_hot_mat\n\n# 对于已经预处理过的训练数据，生成若干对\"中心词 -> 关联词\"\ndef createTrainData(train_data_path, window_size, vocab_list):\n train_data = np.load(train_data_path)\n sentNum = np.shape(train_data)[0]\n centreWordList = []\n contextWordList = []\n for i in range(sentNum):\n sentence = train_data[i]\n if len(sentence) < 3: continue\n centreWord, contextWord = generateWordCouple(sentence, window_size)\n centreWordList += centreWord\n contextWordList += contextWord\n centreWordMat = listToOneHotMat(centreWordList, vocab_list)\n contextWordMat = listToOneHotMat(contextWordList, vocab_list)\n return centreWordMat, contextWordMat\n\n\n","repo_name":"xsy1988/learn_skip-gram","sub_path":"toolPre.py","file_name":"toolPre.py","file_ext":"py","file_size_in_byte":3028,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"30376294596","text":"import pandas as pd\nimport sys\n\n\nimport os\n#i=0\ni=int(sys.argv[1])\n\nmanifest_dir='/cellar/users/btsui/Project/METAMAP/notebook/RapMapTest/XGS_WGS/./tcga_lgg_wgs_bams.df.pickle'\ntoken_dir='/cellar/users/ramarty/tokens/gdc-user-token.2018-06-25T22_21_40.089Z.txt'\nbam_read_count_dir='/cellar/users/btsui/Program/bam_read_count/bam-readcount-master/bin/bam-readcount'\n\ngdc_cmd_fmt='gdc-client download -t {token_dir} -d {out_dir} {file_uuid}'\n#the following was just tmp out dir\nout_dir='/nrnb/users/btsui/Data/tcga_raw_lgg/'\nsnp_out_dir='/nrnb/users/btsui/Data/tcga_extracted_lgg_snp/'\n\nmanifest_df=pd.read_pickle(manifest_dir)\nmanifest_S=manifest_df.iloc[i]\n\nfile_uuid=manifest_S['file_id']\nprint ('UUID: ',file_uuid)\n\ngdc_cmd=gdc_cmd_fmt.format(out_dir=out_dir,file_uuid=file_uuid,token_dir=token_dir)\n\n\nresult = os.system(gdc_cmd)\n\nprint ('time for alignent:',total)\n### pipe the data\ntmpDir=out_dir+file_uuid+'/'\nos.chdir(tmpDir)\n\nspecie='Homo_sapiens'\n\nsnpBed='/cellar/users/btsui/Data/dbsnp/snp_beds/'+specie+'.bed.with_chr'\nfa_dir='/cellar/users/btsui/Data/ensembl/snp_masked/'+specie+'.microbe.fa'\n#cmd_bam_read_count=bam_read_count_dir+' -l '+snpBed+' {} |gzip > snp.txt.gz'.format(manifest_S['file_name'])\n\ncmd_bam_read_count=bam_read_count_dir+' {} |gzip > {}'.format(manifest_S['file_name'],snp_out_dir+manifest_S['file_name'].replace('.bam','.snp.gz'))\nos.system(cmd_bam_read_count)\n","repo_name":"brianyiktaktsui/Skymap","sub_path":"XGS_WGS/TestPipeline/tcga_extract_read_count.py","file_name":"tcga_extract_read_count.py","file_ext":"py","file_size_in_byte":1394,"program_lang":"python","lang":"en","doc_type":"code","stars":31,"dataset":"github-code","pt":"85"} +{"seq_id":"11136844238","text":"L784 - Letter_Permutations.py\n\n\nclass Solution:\n def letterCasePermutation(self, s: str) -> List[str]:\n permutations = [\"\"]\n for ch in s:\n if ch.isalpha():\n permutations = [\n perm + letter\n for perm in permutations\n for letter in [ch, ch.swapcase()]\n ]\n else:\n permutations = [perm + ch for perm in permutations]\n return permutations\n\n\nclass Solution:\n def letterCasePermutation(self, s: str) -> List[str]:\n # there will be 2^N permutations where N is the number of letters in s\n permutations = [\"\"]\n for ch in s:\n new_perms = []\n for perm in permutations:\n if ch.isalpha():\n new_perms.append(perm + ch.swapcase())\n new_perms.append(perm + ch)\n permutations = new_perms\n return permutations\n\n\n# time O(2^N)\n# space O(2^N)\n","repo_name":"sterfd/LC-Solutions","sub_path":"L784-Letter_Permutations.py","file_name":"L784-Letter_Permutations.py","file_ext":"py","file_size_in_byte":983,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"73512409557","text":"def digit_counter(number):\n counter = 0\n if number == 0:\n return 1\n\n while number:\n if number % 10 == 0:\n counter += 1\n number /= 10\n\n return counter\n\nvalue = int(input('Enter a value or null: '))\nnr_of_null = 0\nif value == 0:\n print(digit_counter(value))\nelse:\n while(value):\n nr_of_null += digit_counter(value)\n value = int(input('Enter a value or null: '))\n\nprint(nr_of_null)\n\n","repo_name":"DariusBolos/Python","sub_path":"lab1/pb6 a).py","file_name":"pb6 a).py","file_ext":"py","file_size_in_byte":444,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"16237689490","text":"\"\"\"Add Map model\n\nRevision ID: 745d612eb7fb\nRevises: 166ca87ba6cb\nCreate Date: 2022-12-09 20:00:19.182394\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = '745d612eb7fb'\ndown_revision = '166ca87ba6cb'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.create_table('shop_position',\n sa.Column('id', sa.Integer(), nullable=False),\n sa.Column('shop_id', sa.Integer(), nullable=True),\n sa.Column('meridian', sa.REAL(), nullable=True),\n sa.Column('longitude', sa.REAL(), nullable=True),\n sa.PrimaryKeyConstraint('id')\n )\n # ### end Alembic commands ###\n\n\ndef downgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.drop_table('shop_position')\n # ### end Alembic commands ###\n","repo_name":"Tunaxx-New/OnlineShop-Android-Flask","sub_path":"PythonFlaskServer/migrations/versions/745d612eb7fb_add_map_model.py","file_name":"745d612eb7fb_add_map_model.py","file_ext":"py","file_size_in_byte":860,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"73399310678","text":"# Desenvolva um programa que peça ao usuário uma palavra e verifique se ela é um palíndromo. Um palíndromo é uma palavra que é lida da mesma forma tanto da esquerda para a direita quanto da direita para a esquerda.\n# Exemplos:\n# Digite a palavra: radar A palavra \"radar\" é um palíndromo.\n# Digite a palavra: python A palavra \"python\" NÃO é um palíndromo.\n\ndef is_palindrome(palavra):\n palavra = palavra.replace(\" \", \"\").lower()\n \n \n return palavra == palavra[::-1]\n\ndef main():\n user_input = input(\"Digite a palavra: \")\n \n if is_palindrome(user_input):\n print(f'A palavra \"{user_input}\" é um palíndromo.')\n else:\n print(f'A palavra \"{user_input}\" NÃO é um palíndromo.')\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"lauralima6/pc-imd","sub_path":"unidade3/prova/q1.py","file_name":"q1.py","file_ext":"py","file_size_in_byte":763,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"14259054614","text":"#f(n)=f(n-1)+100 when n>0\r\n#and f(0)=0\r\n\r\ndef fac(n):\r\n if n==0:\r\n \r\n return 0\r\n else:\r\n return fac(n-1)+100\r\nn=int(input(\"enter any number\"))\r\nprint(fac(n))\r\n\r\n \r\n","repo_name":"abhinai96/Python_Practise_Programs","sub_path":"55 day15 recursive computation.py","file_name":"55 day15 recursive computation.py","file_ext":"py","file_size_in_byte":230,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"18946051598","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[60]:\n\n\nimport numpy as np\nimport pandas as pd \nimport matplotlib.pyplot as plt\nimport matplotlib.ticker as ticker\nimport csv\nimport os\nimport sys\nimport glob2\nimport os.path\nfrom pathlib import Path\nimport fnmatch\nfrom simple_colors import * # pip install simple-colors\nimport time\nfrom time import sleep\nimport math\nimport random\nimport PyPDF2\nfrom PyPDF2 import PdfFileMerger, PdfFileReader\nfrom itertools import cycle\nfrom datetime import datetime\nfrom matplotlib.backends.backend_pdf import PdfPages\nimport warnings #fixed any warning in terminal\nimport matplotlib.cbook\n# Ignore DtypeWarnings from pandas' read_csv\nwarnings.filterwarnings('ignore', message=\"^Columns.*\")\nwarnings.filterwarnings(\"ignore\",category=matplotlib.cbook.mplDeprecation)\n\n\n# In[ ]:\n\n\ndef merge_VcomPDF():\n merger = PdfFileMerger()\n filenames = glob2.glob(results_dir + 'Vcom*.pdf') \n for filename in filenames:\n merger.append(filename)\n os.remove(filename)\n with open(results_dir + 'Results_VcomChar.pdf', \"ab\") as fout:\n merger.write(fout)\n merger.close()\n\n\n# In[ ]:\n\n\ndef merge_HistogramPDF():\n merger = PdfFileMerger()\n filenames = glob2.glob(results_dir + 'Histogram*.pdf') \n for filename in filenames:\n merger.append(filename)\n os.remove(filename)\n with open(results_dir + 'Results_Histogram.pdf', \"ab\") as fout:\n merger.write(fout)\n merger.close()\n\n\n# In[ ]:\n\n\ndef merge_PowerSpectrumPDF():\n merger = PdfFileMerger()\n filenames = glob2.glob(results_dir + 'PowerSpectrum*.pdf') \n for filename in filenames:\n merger.append(filename)\n os.remove(filename)\n with open(results_dir + 'Results_PowerSpectrum.pdf', \"ab\") as fout:\n merger.write(fout)\n merger.close()\n\n\n# In[61]:\n\n\ndef merge_ChargeSharingShmooPDF():\n merger = PdfFileMerger()\n filenames = glob2.glob(results_dir + 'GateChargeSharing*.pdf') \n for filename in filenames:\n merger.append(filename)\n os.remove(filename)\n with open(results_dir + 'Results_ChargeSharingShmoo.pdf', \"ab\") as fout:\n merger.write(fout)\n merger.close()\n\n\n# In[62]:\n\n\ndef merge_3v3PDF():\n merger = PdfFileMerger()\n filenames = glob2.glob(results_dir + '3v3*.pdf') \n for filename in filenames:\n merger.append(filename)\n os.remove(filename)\n with open(results_dir + 'Results_3v3.pdf', \"ab\") as fout:\n merger.write(fout)\n merger.close()\n\n\n# In[63]:\n\n\ndef merge_ALPMPDF():\n merger = PdfFileMerger()\n filenames = [\"ALPM_30Hz.pdf\", \"ALPM_60Hz.pdf\"]\n for filename in filenames:\n merger.append(results_dir + filename)\n os.remove(results_dir + filename)\n with open(results_dir + 'Results_ALPM.pdf', \"ab\") as fout:\n merger.write(fout)\n merger.close()\n\n\n# In[64]:\n\n\ndef merge_PowerMeasPDF():\n merger = PdfFileMerger()\n filenames = [\"PowerOnTcon.pdf\", \"PowerOffTcon.pdf\", \"PowerOnCDIC.pdf\", \"PowerOffCDIC.pdf\", \"PowerOnGateDriver.pdf\", \"PowerOffGateDriver.pdf\"]\n for filename in filenames:\n merger.append(results_dir + filename)\n os.remove(results_dir + filename)\n with open(results_dir + 'Results_PowerMeas.pdf', \"ab\") as fout:\n merger.write(fout)\n merger.close()\n\n\n# In[65]:\n\n\ndef merge_GateDriverTimingPDF():\n merger = PdfFileMerger()\n filenames = [\"GateDriverTimingFull.pdf\", \"GateDriverTiming.pdf\", \"eof1.pdf\", \"hvlfs.pdf\", \"CDICOutputTiming.pdf\", \"CDICOutputCLK1.pdf\"]\n for filename in filenames:\n merger.append(results_dir + filename)\n os.remove(results_dir + filename)\n with open(results_dir + 'Results_GateDriverTiming.pdf', \"ab\") as fout:\n merger.write(fout)\n merger.close()\n\n\n# In[66]:\n\n\ndef merge_AnalogDigitalPDF():\n merger = PdfFileMerger()\n filenames = [\"AnalogPowerOn.pdf\", \"AnalogPowerOff.pdf\", \"DigitalPowerOn.pdf\", \"DigitalPowerOff.pdf\"]\n for filename in filenames:\n merger.append(results_dir + filename)\n os.remove(results_dir + filename)\n with open(results_dir + 'Results_AnalogDigital.pdf', \"ab\") as fout:\n merger.write(fout)\n merger.close()\n\n\n# In[67]:\n\n\ndef merge_VoltageRippleText():\n filenames = glob2.glob(results_dir + 'VoltageRipple*.txt') # list of all .txt files in the directory\n with open(results_dir + 'Results_VoltageRipple.txt', 'a') as f:\n for file in filenames:\n with open(file) as infile:\n f.write(infile.read()+'\\n')\n os.remove(file)\n\n\n# In[68]:\n\n\ndef merge_PowerMeasText():\n filenames = glob2.glob(results_dir + 'Power*.txt') # list of all .txt files in the directory\n with open(results_dir + 'Results_PowerMeas.txt', 'a') as f:\n for file in filenames:\n with open(file) as infile:\n f.write(infile.read()+'\\n')\n os.remove(file)\n\n\n# In[69]:\n\n\ndef merge_SystemMatchText():\n filenames = glob2.glob(results_dir + 'SystemMatch*.txt') # list of all .txt files in the directory\n with open(results_dir + 'Results_SystemMatch.txt', 'a') as f:\n for file in filenames:\n with open(file) as infile:\n f.write(infile.read()+'\\n')\n os.remove(file)\n\n\n# In[70]:\n\n\ndef merge_VoltageRipplePDF():\n merger = PdfFileMerger()\n filenames = glob2.glob(results_dir + 'VoltageRipple*.pdf') \n for filename in filenames:\n merger.append(filename)\n os.remove(filename)\n with open(results_dir + 'Results_VoltageRipple.pdf', \"ab\") as fout:\n merger.write(fout)\n merger.close()\n\n\n# In[71]:\n\n\ndef merge_SystemMatchPDF():\n #list = os.listdir(results_dir) \n #number_files = len(list)\n merger = PdfFileMerger()\n filenames = glob2.glob(results_dir + 'SystemMatch*.pdf') \n for filename in filenames:\n merger.append(filename)\n os.remove(filename)\n with open(results_dir + 'Results_SystemMatch.pdf', \"ab\") as fout:\n merger.write(fout)\n merger.close()\n\n\n# In[72]:\n\n\n\npath1 = input(\"Enter the path of test items: \").strip()\npath1 = path1 + '/'\nmain_dirs = os.listdir(path1)\nfor folder_name in sorted(main_dirs):\n if folder_name.startswith('.') or folder_name == \"Results\" or folder_name == \"Temp\":\n continue\n path = os.path.join(path1,folder_name)\n path = path + '/'\n dirs = os.listdir(path)\n results_dir = os.path.join(path1, 'Results/')\nlist_of_functions = [merge_SystemMatchPDF, merge_VoltageRipplePDF, merge_SystemMatchText, merge_VoltageRippleText, merge_AnalogDigitalPDF, merge_GateDriverTimingPDF, merge_PowerMeasPDF, merge_3v3PDF, merge_ALPMPDF, merge_PowerMeasText, merge_ChargeSharingShmooPDF, merge_HistogramPDF, merge_PowerSpectrumPDF, merge_VcomPDF]\nfor f in list_of_functions: #call all functions in the list\n try:\n f()\n except:\n pass\ntextfiles = glob2.glob(results_dir + '*.txt')\npdffiles = glob2.glob(results_dir + '*.pdf')\nfor file in textfiles:\n if os.path.getsize(file) < 100:\n os.remove(file)\nfor file in pdffiles:\n if os.path.getsize(file) < 2000:\n os.remove(file) \n\n","repo_name":"hieppham8083/FinalProject","sub_path":"MergeAll.py","file_name":"MergeAll.py","file_ext":"py","file_size_in_byte":7056,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"85"} +{"seq_id":"28381882868","text":"import urllib.request\r\nfrom asyncio import TimeoutError\r\nfrom aiohttp import ClientSession, ClientResponseError,ClientTimeout, ClientConnectionError, ClientPayloadError\r\nfrom aiohttp.client_exceptions import TooManyRedirects\r\n\r\nfrom rotatingProxy.proxy import Proxy \r\nimport rotatingProxy.heap as HA\r\nimport random, time, os\r\n\r\nclass InvalidContentTypeError(Exception):\r\n \"\"\"\r\n This represents an exception when response content type is not in the valid content type set.\r\n \"\"\"\r\n def __init__(self,response):\r\n self.response = response\r\n\r\nclass RotatingProxy():\r\n valid_content_types = set([\r\n 'text/html',\r\n 'text/xhtml',\r\n 'application/xhtml+xml',\r\n 'application/xhtml',\r\n 'application/html'\r\n ])\r\n def __init__(self, timeout=1, proxy_list=None, perserve_state=True):\r\n ###Utilize a heap representation\r\n self.proxy_heap = HA.HeapArr() \r\n self.proxy_size = 0 \r\n\r\n self.proxy_list = proxy_list\r\n if self.proxy_list is not None:\r\n self.generateProxyList(proxy_list)\r\n\r\n ### optional declarations\r\n self.timeout=timeout \r\n\r\n self.session = ClientSession()\r\n\r\n def generateProxyList(self,proxy_list_path):\r\n with open(proxy_list_path,'r') as fobj:\r\n for line in fobj:\r\n self.proxy_size += 1\r\n proxy = Proxy(line)\r\n proxy.count = 1 \r\n self.proxy_heap.pushToHeap(proxy)\r\n\r\n def decrement_and_check(self,index):\r\n self.proxy_heap[index].decrementCount()\r\n if self.proxy_heap[index].count <= 0:\r\n self.proxy_heap.popHeap()\r\n else:\r\n HA._sift_down(self.proxy_heap,index)\r\n if self.proxy_size <= 0:\r\n raise IndexError(\"No ip's work\")\r\n ###\r\n ### Successive calls to get RawHTML will alter the\r\n ### heap, and heapify accordingly\r\n ### \r\n async def _make_request(self,url):\r\n if not self.session:\r\n self.session = ClientSession()\r\n timeout = ClientTimeout(total=self.timeout)\r\n\r\n ### If a proxy list isn't specified, try a simple urlopen.\r\n if self.proxy_list is None:\r\n with urllib.request.urlopen(url) as response:\r\n mybytes = response.read()\r\n\r\n return mybytes\r\n\r\n for index,proxy in self.proxy_heap.heap_gen():\r\n if len(self.proxy_heap)<= 0:\r\n raise IndexError(\"No ip's work\")\r\n\r\n print(index,proxy)\r\n ### Build the proxy headers and http headers\r\n proxy.generateHeader() \r\n\r\n try:\r\n async with self.session.get(\r\n url = url,\r\n headers = proxy.header,\r\n raise_for_status=True,\r\n timeout=timeout,\r\n proxy=f\"http://{proxy.ip}\"\r\n ) as response:\r\n if response.content_type not in self.valid_content_types:\r\n raise InvalidContentTypeError(response)\r\n\r\n html = await response.txt()\r\n \r\n ### Handle the errors that is thrown from session.get()\r\n except ClientConnectionError as e:\r\n self.decrement_and_check(index,)\r\n print(e ,flush=True)\r\n except ClientResponseError as e:\r\n self.decrement_and_check(index)\r\n print(e ,flush=True)\r\n except TimeoutError as e:\r\n self.decrement_and_check(index)\r\n print(e ,flush=True)\r\n except Exception as e:\r\n print(\"ran into exception\")\r\n print(e)\r\n raise e\r\n else:\r\n self.proxy_heap[index].incrementCount()\r\n HA._sift_up(self.proxy_heap, index)\r\n return html\r\n \r\n return None ","repo_name":"mavho/Rotating-Proxy","sub_path":"rotatingProxy/rotatingProxy.py","file_name":"rotatingProxy.py","file_ext":"py","file_size_in_byte":3903,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"28046829836","text":"from prismriver.plugin.common import Plugin\nfrom prismriver.struct import Song\n\n\nclass UtaNetPlugin(Plugin):\n ID = 'utanet'\n RANK = 9\n\n def __init__(self, config):\n super(UtaNetPlugin, self).__init__('Uta-Net', config)\n\n def search_song(self, artist, title):\n link = 'https://www.uta-net.com/search/?Aselect=2&Keyword={}'.format(\n self.prepare_url_parameter(title, delimiter='+'))\n\n page = self.download_webpage(link)\n\n if page:\n soup = self.prepare_soup(page)\n\n search_pane = soup.find('tbody')\n for item in search_pane.findAll('tr', recursive=False):\n tds = item.findAll('td', recursive=False)\n\n song_artist = tds[1].a.text\n song_title = tds[0].a.text\n song_id = tds[0].a['href'].split('/')[2]\n\n if self.compare_strings(artist, song_artist) and self.compare_strings(title, song_title):\n song_link = 'https://www.uta-net.com/song/{}/'.format(song_id)\n song_page = self.download_webpage_text(song_link)\n if song_page:\n soup = self.prepare_soup(song_page)\n lyric_pane = soup.find('div', {'id': 'kashi_area'})\n\n if lyric_pane:\n lyric = self.parse_verse_block(lyric_pane)\n return Song(song_artist, song_title, self.sanitize_lyrics([lyric]))\n","repo_name":"anlar/prismriver-lyrics","sub_path":"prismriver/plugin/utanet.py","file_name":"utanet.py","file_ext":"py","file_size_in_byte":1477,"program_lang":"python","lang":"en","doc_type":"code","stars":18,"dataset":"github-code","pt":"85"} +{"seq_id":"20586892249","text":"from os.path import splitext\nfrom os import listdir\nimport numpy as np\nfrom glob import glob\nimport torch\nfrom torch.utils.data import Dataset\nimport logging\nfrom PIL import Image\nimport torchvision.transforms as transforms\nimport torchvision.transforms.functional as TF\nimport random\nseed = 23\n\nclass BasicDataset(Dataset):\n def __init__(self, imgs_dir, masks_dir, scale=1):\n self.imgs_dir = imgs_dir\n self.masks_dir = masks_dir\n self.scale = scale\n assert 0 < scale <= 1, 'Scale must be between 0 and 1'\n\n self.ids = [splitext(file)[0] for file in listdir(imgs_dir)\n if not file.startswith('.')]\n logging.info(f'Creating dataset with {len(self.ids)} examples')\n\n def __len__(self):\n return len(self.ids)\n\n @classmethod\n def preprocess(cls, pil_img, scale):\n w, h = pil_img.size\n newW, newH = int(scale * w), int(scale * h)\n assert newW > 0 and newH > 0, 'Scale is too small'\n pil_img = pil_img.resize((newW, newH))\n\n img_nd = np.array(pil_img)\n\n if len(img_nd.shape) == 2:\n img_nd = np.expand_dims(img_nd, axis=2)\n\n # HWC to CHW\n img_trans = img_nd.transpose((2, 0, 1))\n if img_trans.max() > 1:\n img_trans = img_trans / 255\n\n return img_trans\n\n def transform(self, image, mask):\n # ToPILImage = transforms.ToPILImage()\n # image = ToPILImage(image_np)\n # mask = ToPILImage(mask_np)\n image = TF.pad(image, padding=20, padding_mode='reflect')\n mask = TF.pad(mask, padding=20, padding_mode='reflect')\n\n angle = random.uniform(-180, 180)\n width, height = image.size\n max_dx = 0.1 * width\n max_dy = 0.1 * height\n translations = (np.round(random.uniform(-max_dx, max_dx)), np.round(random.uniform(-max_dy, max_dy)))\n scale = random.uniform(0.8, 1.2)\n shear = random.uniform(-0.5, 0.5)\n image = TF.affine(image, angle=angle, translate=translations, scale=scale, shear=shear)\n mask = TF.affine(mask, angle=angle, translate=translations, scale=scale, shear=shear)\n\n image = TF.center_crop(image, (256, 256))\n mask = TF.center_crop(mask, (256, 256))\n\n # image = TF.to_tensor(image)\n # mask = TF.to_tensor(mask)\n return image, mask\n\n def __getitem__(self, i):\n idx = self.ids[i]\n mask_file = glob(self.masks_dir + idx + '.*')\n img_file = glob(self.imgs_dir + idx + '.*')\n\n assert len(mask_file) == 1, \\\n f'Either no mask or multiple masks found for the ID {idx}: {mask_file}'\n assert len(img_file) == 1, \\\n f'Either no image or multiple images found for the ID {idx}: {img_file}'\n mask = Image.open(mask_file[0])\n mask = mask.resize((512, 512))\n mask = mask.convert(\"L\")\n img = Image.open(img_file[0])\n img = img.resize((512, 512))\n assert img.size == mask.size, \\\n f'Image and mask {idx} should be the same size, but are {img.size} and {mask.size}'\n img, mask = self.transform(img, mask)\n img = self.preprocess(img, self.scale)\n mask = self.preprocess(mask, self.scale)\n\n\n\n return {\n 'image': torch.from_numpy(img).type(torch.FloatTensor),\n 'mask': torch.from_numpy(mask).type(torch.FloatTensor)\n }\n","repo_name":"ljllili23/unet_baseline","sub_path":"utils/dataset.py","file_name":"dataset.py","file_ext":"py","file_size_in_byte":3367,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"11724810988","text":"import pandas as pd\nimport numpy as np\nfrom sklearn import metrics\nimport os\nimport shutil\nimport matplotlib.pyplot as plt\n\nthreshold = 0.5\n\ndef get_fn_sphere(test_result,saveAddress):\n\t\n\ttest_result = pd.DataFrame.from_csv(test_result,header=0,sep=\",\",index_col=None)\n\t\n\ty = np.asarray(test_result['label'])\n\tscores = np.asarray(test_result['predict'])\n\n\tfpr, tpr, thresholds = metrics.roc_curve(y, scores)\n\t\n\tfor i in xrange(len(thresholds)):\n\t\t#name the doc by index and threshold\n\t\tsaveTo = saveAddress+str(i)+\"_\"+str(thresholds[i])\n\t\t\n\t\tfn_sphereList = np.asarray(test_result['name'][(test_result['predict'] < thresholds[i]) & (test_result['label'] == 1)] )\n\t\t\n\n\t\tif (not os.path.exists(saveTo)) &(len(fn_sphereList)!=0):\n\t\t\tos.mkdir(saveTo)\n\n\t\tfor name in fn_sphereList:\n\t\t\t\n\t\t\timgName = os.path.basename(name)\n\t\t\tscore = np.asarray(test_result['predict'][test_result['name']==name])[0]\n\t\t\tnewname = imgName.split(\".\")[0]+\"_\"+str(score)+\".png\"\n\t\t\tshutil.copy(name,saveTo+\"/\"+ newname)\n\t\n\t\n\n\ndef report(test_result,saveReport):\n\tmethod = os.path.basename(test_result).split(\".\")[0]\n\ttest_result = pd.DataFrame.from_csv(test_result,header=0,sep=\",\",index_col=None)\n\t\n\ty = np.asarray(test_result['label'])\n\tscores = np.asarray(test_result['predict'])\n\n\tfpr, tpr, thresholds = metrics.roc_curve(y, scores)\n\n\tfnNumbers = []\n\tfor i in xrange(len(thresholds)):\n\t\t#name the doc by index and threshold\n\t\tfn_sphereList = np.asarray(test_result['name'][(test_result['predict'] < thresholds[i]) & (test_result['label'] == 1)] )\n\t\tfnNumbers.append(len(fn_sphereList))\n\n\tmaxscore = max(thresholds)\n\tminscore = min(thresholds)\n\tmeanscore = np.mean(thresholds)\n\t\n\t#scoreHistogram = \n\tplt.xlabel(\"threshold\")\n\tplt.ylabel(\"number\")\n\tplt.hist(np.asarray(test_result['predict']), bins='auto')\n\ttitle = method+\t\"_histogram.png \"+\"\\n\"+ \\\n\t\t\t\t\t\t\"maxscore: \"+str(maxscore) +\"\\n\"+ \\\n\t\t\t\t\t\t\"minscore: \"+str(minscore)+\"\\n\"+\\\n\t\t\t\t\t\t\"meanscore: \"+str(meanscore)\n\tplt.title(title)\n\tscoreHistogram = plt.gcf()\n\tpngname = method+\t\"_histogram.png\"\n\tscoreHistogram.savefig(saveReport+pngname)\n\tplt.close()\n\n\tpngname2 = method + \"_fnnumbers\"\n\tplt.title(\"thresholds VS fnnum\")\n\tplt.xlabel(\"threshold\")\n\tplt.ylabel(\"fasle negative number\")\n\tplt.plot(thresholds,fnNumbers,'yo-')\n\tthresholdsVSfnnum = plt.gcf()\n\tthresholdsVSfnnum.savefig(saveReport+pngname2)\n\tplt.close()\n\n\tpngname3 = method + \"precison.png\"\n\tplt.title(\"precision\")\n\tplt.xlabel(\"threshold\")\n\tplt.ylabel(\"precision\")\n\tplt.plot(thresholds[::-1],tpr)\n\tprecision = plt.gcf()\n\tprecision.savefig(saveReport+pngname3)\n\tplt.close()\n\n\n\nif __name__ == '__main__':\n\tcsvname = \"CNNResultPatchCPP.csv\" #\"CNNResultPatchCPP.csv\" \"ResultBoosting.csv\"\n\tdocname = \"CNN\" #\"CNN\" \"ResultBoosting\"\n\tfigname = \"fnNumber.png\"\n\ttest_result =\"/home/henry/projects/sphere_detection/algoopt/testData/\"+csvname\n\tsaveAddress =\"/home/henry/projects/sphere_detection/algoopt/Filter/\"+docname+\"/fnSphere/\"\n\tsaveReport = \"/home/henry/projects/sphere_detection/algoopt/Filter/\"+docname+\"/report/\"\n\t\n\t#get_fn_sphere(test_result,saveAddress)\n\treport(test_result,saveReport)","repo_name":"henrylovedesign/modelAnalysis","sub_path":"get_fn_sphere.py","file_name":"get_fn_sphere.py","file_ext":"py","file_size_in_byte":3073,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"16133742586","text":"'''\nUSACO 2018 December Contest (Gold)\nProblem 2: Cowpatibility\n\nHey, an efficiency problem! I love efficiency problems! Definitely!\nI'm not sure this is going to go well.\n\nGuess what? It didn't go so well.\n'''\n\n\ndef main():\n # Input parsing\n file_in = open(\"cowpatibility.in\", mode='r')\n input = file_in.readlines()\n file_in.close()\n\n # Everything is squeezed here to try and iterate over the list of\n # cows as little as possible to save time\n num_cows = int(input[0])\n cows = {}\n ftocow = {}\n ftocount = {}\n for i in range(1, num_cows + 1):\n flavors = list(map(int, input[i].split()))\n cows[i] = flavors\n # Keeping track of which flavors correspond to which cows and\n # the count of total # of flavors\n # To try and help optimize the later code\n for flavor in flavors:\n if flavor not in ftocow:\n ftocow[flavor] = []\n ftocow[flavor].append(i)\n if flavor not in ftocount:\n ftocount[flavor] = 0\n ftocount[flavor] += 1\n result = solve_2(num_cows, cows, ftocow, ftocount)\n\n file_out = open(\"cowpatibility.out\", mode='w')\n file_out.write(str(result))\n file_out.close()\n\n\ndef solve_2(num_cows, cows, ftocow, ftocount):\n '''\n Method 2: Make a list of already found pairs using the ftocow dict.\n Then, subtract that list's length from the total # of possible pairs.\n Time complexity: n^2\n Result: Still too slow. I think I might have to do all my stuff inside\n of the input parsing part of this.\n '''\n found_pairs = {}\n compatible_pairs = 0\n for cows in ftocow.values():\n for i, cow in enumerate(cows):\n if cow not in found_pairs:\n found_pairs[cow] = []\n found_pairs[cow] += cows[i+1:]\n for cow in found_pairs:\n found_pairs[cow] = set(found_pairs[cow])\n compatible_pairs += len(found_pairs[cow])\n # print(found_pairs)\n return int(round(num_cows / 2 * (num_cows - 1), 5) - compatible_pairs)\n\n\ndef solve_1(num_cows, cows, ftocow, ftocount):\n '''\n Ah, optimization problems. The most annoying type of problem there\n is. Let me think a bit...\n Method 1: Make a dictionary with all the possible pairs of cows\n (key is cow, value is list of pair cows). Use the ftocow dict to\n find compatible pairs. Finally, figure out the number of pairs still\n left in the dictionary. Complexity: n^3\n Result: Well I mean I didn't expect that to work so yeah. n^3 is way\n too long.\n '''\n # Make all possible pairs\n all_pairs = {}\n for c in range(1, num_cows):\n all_pairs[c] = []\n for d in range(c + 1, num_cows + 1):\n all_pairs[c].append(d)\n # Look for pairs in the flavor to cows dictionary\n for cows in ftocow.values():\n for ci, c in enumerate(cows[:-1]):\n for d in cows[ci+1:]:\n if d in all_pairs[c]:\n all_pairs[c].remove(d)\n # Count up total number of incompatible pairs\n incompatible_pairs = 0\n for pairs in all_pairs.values():\n incompatible_pairs += len(pairs)\n return incompatible_pairs\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"Giantpizzahead/comp-programming","sub_path":"USACO/Practice Problems/December 2018/Gold/cowpatibility.py","file_name":"cowpatibility.py","file_ext":"py","file_size_in_byte":3207,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"85"} +{"seq_id":"17267534467","text":"# rantai penyebaran\r\ndef rantai_penyebaran(nama_penular): # return string\r\n # base case: print nama orang sekarang\r\n hasil = f\"- {nama_penular}\\n\"\r\n for nama in daftar_terinfeksi[nama_penular]:\r\n hasil += rantai_penyebaran(nama)\r\n\r\n return hasil\r\n \r\n\r\n\r\n# cek penyebaran\r\ndef cek_penyebaran(tertular, penular): # cek apakar penular menulari tertular, return value boolean\r\n # base case: jika tertular == penular, maka return true\r\n if (tertular == penular):\r\n return True\r\n \r\n hasil = False\r\n for nama in daftar_terinfeksi[penular]:\r\n hasil = hasil or cek_penyebaran(tertular, nama)\r\n\r\n return hasil\r\n\r\n\r\n# main\r\ndaftar_terinfeksi = {} # key: penular, value: list orang2 yang tertular secara langsung oleh penular\r\n\r\nprint(\"Masukkan rantai penyebaran:\")\r\nwhile True:\r\n nama = input().split()\r\n if len(nama) == 0: # Handle input string kosong\r\n continue\r\n elif nama[0] == \"selesai\":\r\n break\r\n elif nama[0] in daftar_terinfeksi:\r\n daftar_terinfeksi[nama[0]].extend(nama[1:]) # Gabung ke list yang sudah ada pada dictionary\r\n else:\r\n daftar_terinfeksi[nama[0]] = nama[1:] # Tambahkan ke dictionary\r\n\r\n# for i in daftar_terinfeksi:\r\n # print(i, daftar_terinfeksi[i])\r\n\r\n\r\nprint(\"\\nList perintah:\\n1. RANTAI_PENYEBARAN\\n2. CEK_PENULARAN\\n3. EXIT\\n\")\r\nwhile True:\r\n perintah = input(\"Masukkan perintah: \").split()\r\n\r\n if len(perintah) == 2 and perintah[0] == \"RANTAI_PENYEBARAN\":\r\n if (perintah[1] in daftar_terinfeksi.keys()):\r\n print(f\"Rantai penyebaran {perintah[1]}:\")\r\n print(rantai_penyebaran(perintah[1]))\r\n \r\n else:\r\n print(f\"Maaf, nama {perintah[1]} tidak terdapat dalam rantai penyebaran.\")\r\n elif len(perintah) == 3 and perintah[0] == \"CEK_PENULARAN\":\r\n if (perintah[2]) in daftar_terinfeksi.keys():\r\n if perintah[1] in daftar_terinfeksi.keys():\r\n if perintah[1] == perintah[2] or cek_penyebaran(perintah[1], perintah[2]):\r\n print(\"YA\")\r\n else:\r\n print(\"TIDAK\")\r\n else:\r\n print(f\"Maaf, nama {perintah[1]} tidak terdapat dalam rantai penyebaran.\")\r\n else:\r\n if perintah[1] in daftar_terinfeksi.keys():\r\n print(f\"Maaf, nama {perintah[2]} tidak terdapat dalam rantai penyebaran.\")\r\n else:\r\n print(f\"Maaf, nama {perintah[1]} dan {perintah[2]} tidak terdapat dalam rantai penyebaran.\")\r\n elif len(perintah) == 1 and perintah[0] == \"EXIT\":\r\n print(\"Goodbye~ Semoga virus KOPIT cepat berakhir.\", end=\"\")\r\n break\r\n else:\r\n print(\"Maaf perintah tidak dkenali. Masukkan perintah yang valid.\")\r\n print()","repo_name":"SyahrulApr86/DDP1-2021-2022-Ganjil","sub_path":"Solusi Lab/Lab 08/Solusi Lab08.py","file_name":"Solusi Lab08.py","file_ext":"py","file_size_in_byte":2765,"program_lang":"python","lang":"id","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"28046751826","text":"from prismriver.plugin.common import Plugin\nfrom prismriver.struct import Song\n\n\nclass LyricsFreakPlugin(Plugin):\n ID = 'lyricsfreak'\n\n def __init__(self, config):\n super(LyricsFreakPlugin, self).__init__('LyricsFreak', config)\n\n def search_song(self, artist, title):\n link = 'http://www.lyricsfreak.com/search.php?a=search&type=song&q={}'.format(\n self.prepare_url_parameter(title))\n\n page = self.download_webpage(link)\n\n if page:\n soup = self.prepare_soup(page)\n\n search_result = self.parse_search_page(soup, artist, title)\n\n if search_result:\n page = self.download_webpage_text(search_result[2])\n\n if page:\n soup = self.prepare_soup(page)\n\n lyric_pane = soup.find('div', {'id': 'content_h'})\n lyric = self.parse_verse_block(lyric_pane)\n\n return Song(search_result[0], search_result[1],\n self.sanitize_lyrics([lyric], remove_duplicate_spaces=True))\n\n def parse_search_page(self, soup, artist, title):\n pane = soup.find('div', {'class': 'colortable green'})\n if pane:\n pane = pane.table.tbody\n\n for elem in pane.findAll('tr', recursive=False):\n item_artist = elem.find('td', {'class': 'colfirst'}, recursive=False).a.text[1:].strip()\n\n title_pane = elem.find('a', {'class': 'song'})\n item_title = title_pane.text[:-7]\n item_link = title_pane['href']\n\n if self.compare_strings(artist, item_artist) and self.compare_strings(title, item_title):\n return [item_artist, item_title, 'http://www.lyricsfreak.com' + item_link]\n","repo_name":"anlar/prismriver-lyrics","sub_path":"prismriver/plugin/lyricsfreak.py","file_name":"lyricsfreak.py","file_ext":"py","file_size_in_byte":1774,"program_lang":"python","lang":"en","doc_type":"code","stars":18,"dataset":"github-code","pt":"85"} +{"seq_id":"41707462391","text":"\"\"\"\nCreated on 12/12/2019\n@author: Sunny Raj\n\"\"\"\n\"\"\"\nProblem Statement:\nWrite a Python program to find files and skip directories of a given directory\n\"\"\"\n#importing os module\nimport os\n\n# This is to get the directory of the folder/file\ndir_path = os.getcwd()\n\nfor fname in os.listdir(dir_path):\n path = os.path.join(dir_path, fname)\n if os.path.isfile(path):\n print(dir_path + '/' + fname)","repo_name":"SunnyRaj94/Basic-Python-And-Data-Structures","sub_path":"Basic/39_skip_directories.py","file_name":"39_skip_directories.py","file_ext":"py","file_size_in_byte":403,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"1455208918","text":"from google.auth.transport.requests import Request\nfrom google.oauth2.credentials import Credentials\nfrom google_auth_oauthlib.flow import InstalledAppFlow\nfrom googleapiclient.discovery import build\nimport datetime as dt\nimport os\n\n\nclass GoogleCalendar:\n def __init__(self):\n self.scopes = ['https://www.googleapis.com/auth/calendar.readonly']\n self.creds = None\n self.service = None\n self.__token_path__ = 'token.json'\n self.__credentials_path__ = 'credentials.json'\n\n def login(self):\n if os.path.exists(self.__token_path__):\n self.creds = Credentials.from_authorized_user_file(self.__token_path__, self.scopes)\n # If there are no (valid) credentials available, let the user log in.\n if not self.creds or not self.creds.valid:\n if self.creds and self.creds.expired and self.creds.refresh_token:\n self.creds.refresh(Request())\n else:\n flow = InstalledAppFlow.from_client_secrets_file(\n self.__credentials_path__, self.scopes)\n self.creds = flow.run_local_server(port=0)\n # Save the credentials for the next run\n with open(self.__token_path__, 'w') as token:\n token.write(self.creds.to_json())\n self.service = build('calendar', 'v3', credentials=self.creds)\n\n def get_next_event(self):\n now = dt.datetime.utcnow().isoformat() + 'Z'\n events_result = self.service.events().list(calendarId='primary', timeMin=now,\n maxResults=1, singleEvents=True,\n orderBy='startTime').execute()\n events = events_result.get('items', [])\n if not events:\n return None\n else:\n return events\n","repo_name":"paulgr98/sekretarka","sub_path":"components/google_calendar.py","file_name":"google_calendar.py","file_ext":"py","file_size_in_byte":1834,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"24663555050","text":"import logging\nfrom typing import List, Union\n\nfrom fastapi import APIRouter, Depends, HTTPException, Query\n\nfrom src.services.sumo_access._helpers import SumoCase\nfrom src.services.smda_access.stratigraphy_access import StratigraphyAccess\nfrom src.services.sumo_access.polygons_access import PolygonsAccess\nfrom src.services.smda_access.stratigraphy_utils import sort_stratigraphic_names_by_hierarchy\nfrom src.services.smda_access.mocked_drogon_smda_access import _mocked_stratigraphy_access\nfrom src.services.utils.authenticated_user import AuthenticatedUser\nfrom src.services.utils.perf_timer import PerfTimer\nfrom src.backend.auth.auth_helper import AuthHelper\n\n\nfrom . import schemas\nfrom . import converters\n\nLOGGER = logging.getLogger(__name__)\n\nrouter = APIRouter()\n\n\n@router.get(\"/polygons_directory/\")\nasync def get_polygons_directory(\n authenticated_user: AuthenticatedUser = Depends(AuthHelper.get_authenticated_user),\n case_uuid: str = Query(description=\"Sumo case uuid\"),\n ensemble_name: str = Query(description=\"Ensemble name\"),\n) -> List[schemas.PolygonsMeta]:\n \"\"\"\n Get a directory of polygons in a Sumo ensemble\n \"\"\"\n access = await PolygonsAccess.from_case_uuid(authenticated_user.get_sumo_access_token(), case_uuid, ensemble_name)\n polygons_dir = await access.get_polygons_directory_async()\n\n case_inspector = await SumoCase.from_case_uuid(authenticated_user.get_sumo_access_token(), case_uuid)\n strat_column_identifier = await case_inspector.get_stratigraphic_column_identifier()\n strat_access: Union[StratigraphyAccess, _mocked_stratigraphy_access.StratigraphyAccess]\n\n if strat_column_identifier == \"DROGON_HAS_NO_STRATCOLUMN\":\n strat_access = _mocked_stratigraphy_access.StratigraphyAccess(authenticated_user.get_smda_access_token())\n else:\n strat_access = StratigraphyAccess(authenticated_user.get_smda_access_token())\n strat_units = await strat_access.get_stratigraphic_units(strat_column_identifier)\n sorted_stratigraphic_surfaces = sort_stratigraphic_names_by_hierarchy(strat_units)\n\n return converters.to_api_polygons_directory(polygons_dir, sorted_stratigraphic_surfaces)\n\n\n@router.get(\"/polygons_data/\")\nasync def get_polygons_data(\n authenticated_user: AuthenticatedUser = Depends(AuthHelper.get_authenticated_user),\n case_uuid: str = Query(description=\"Sumo case uuid\"),\n ensemble_name: str = Query(description=\"Ensemble name\"),\n realization_num: int = Query(description=\"Realization number\"),\n name: str = Query(description=\"Surface name\"),\n attribute: str = Query(description=\"Surface attribute\"),\n) -> List[schemas.PolygonData]:\n timer = PerfTimer()\n\n access = await PolygonsAccess.from_case_uuid(authenticated_user.get_sumo_access_token(), case_uuid, ensemble_name)\n xtgeo_poly = await access.get_polygons_async(real_num=realization_num, name=name, attribute=attribute)\n\n if not xtgeo_poly:\n raise HTTPException(status_code=404, detail=\"Polygons not found\")\n\n poly_data_response = converters.to_api_polygons_data(xtgeo_poly)\n\n LOGGER.debug(f\"Loaded polygons and created response, total time: {timer.elapsed_ms()}ms\")\n return poly_data_response\n","repo_name":"HansKallekleiv/webviz","sub_path":"backend/src/backend/primary/routers/polygons/router.py","file_name":"router.py","file_ext":"py","file_size_in_byte":3189,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"85"} +{"seq_id":"17670550438","text":"from django.conf import settings\nfrom django.conf.urls.static import static\nfrom django.urls import path\nfrom .views import security, index, users, courses, students, promotions, calendar\n\nurlpatterns = [\n path('login', security.login, name='login'),\n path('logout', security.logout, name='logout'),\n path('profile', security.profile, name='profile'),\n #path('password_change', auth_views.PasswordChangeView.as_view(), name='password_change'),\n #path('password_change_done', auth_views.PasswordChangeDoneView.as_view(), name='password_change_done'),\n\n path('', index.home, name='index'),\n\n path('courses', courses.courses_list, name='courses'),\n path('courses/add', courses.courses_add, name='courses_add'),\n path('courses/notation/', courses.courses_notation, name='courses_notation'),\n path('courses/', courses.courses_show, name='courses_show'),\n path('courses/course-supports', courses.courses_supports, name='courses_supports'),\n path('courses/course-supports/add', courses.courses_add_supports, name='courses_add_supports'),\n path('courses//edit', courses.courses_edit, name='courses_edit'),\n path('courses//delete', courses.courses_delete, name='courses_delete'),\n\n path('users', users.users_list, name='users'),\n path('users/add', users.users_add, name='users_add'),\n path('users/import', users.users_import, name='users_import'),\n path('users/', users.users_edit, name='users_edit'),\n path('users//delete', users.users_delete, name='users_delete'),\n\n path('grades', students.grades, name='grades'),\n path('grades/', students.student_grades_show, name='grades_show'),\n\n path('promotions', promotions.promotions_list, name='promotions'),\n path('promotions/add', promotions.promotions_add, name='promotions_add'),\n path('promotions//edit', promotions.promotions_edit, name='promotions_edit'),\n path('promotions/', promotions.promotions_show, name='promotions_show'),\n path('promotions//delete', promotions.promotions_delete, name='promotions_delete'),\n path('promotions//courses', promotions.promotion_courses, name='promotion_courses'),\n\n path('promotions/students', promotions.promotions_students, name='promotions_students'),\n\n path('calendar', calendar.calendar, name='calendar'),\n\n] + static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)\n","repo_name":"elie91/ESGI-Projects","sub_path":"MyGesLowCost-Django/web/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":2519,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"85"} +{"seq_id":"39029957267","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Mon May 3 13:52:38 2021\r\n\r\n@author: Song Yifan\r\n\"\"\"\r\n\r\n'''\r\nGiven an array and a value, remove all instances of that value in place and return the new length.\r\nInput: nums = [0,1,2,2,3,0,4,2], val = 2\r\nOutput: 5, nums = [0,1,4,0,3]\r\nExplanation: Your function should return length = 5, with the first five elements of nums \r\ncontaining 0, 1, 3, 0, and 4. Note that the order of those five elements can be arbitrary. \r\nIt doesn't matter what values are set beyond the returned length.\r\n'''\r\n\r\ndef removeElement(nums, val):\r\n index = 0\r\n for i in range(len(nums)):\r\n if nums[i] != val:\r\n nums[index] = nums[i]\r\n index += 1\r\n return index\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"Teodora-tart/LeetCode-exercise","sub_path":"Array/removeElements.py","file_name":"removeElements.py","file_ext":"py","file_size_in_byte":771,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"15048523955","text":"import psycopg2\nimport psycopg2.extras\nimport base64\nimport json\n\nfrom email.message import EmailMessage\nimport ssl\nimport smtplib\n\nhostname = 'localhost'\ndatabase = 'harvesthaven'\nusername = 'postgres'\npwd = 'Hardikts@563'\nport_id = 5432\n\nconn = None\ncur = None\n\nconn = psycopg2.connect(\n host = hostname,\n dbname = database,\n user = username,\n password = pwd,\n port = port_id\n)\n\ncur = conn.cursor(cursor_factory = psycopg2.extras.DictCursor)\n\nallProductsInTheStore = {}\n\n\ncreate_script = '''\n -- USERS TABLE --\n create table if not exists users (\n u_id integer not null, \n username varchar(100) not null, \n gender boolean not null, \n email varchar(100) not null, \n passcode varchar(200), \n isAdmin boolean not null default false, \n PRIMARY KEY (u_id)\n );\n\n -- CATEGORY TABLE --\n create table if not exists category (\n c_id integer not null, \n c_name varchar(100) not null, \n PRIMARY KEY (c_id)\n );\n\n -- PRODUCTS TABLE --\n create table if not exists products (\n p_id integer not null, \n p_name varchar(100) not null, \n p_qty varchar(50) not null, \n p_price float not null, \n p_stock_qty integer not null, \n p_img bytea not null, \n c_id integer not null, \n stock_available integer, \n PRIMARY KEY (p_id), \n FOREIGN KEY (c_id) REFERENCES category (c_id)\n );\n\n -- ORDERS TABLE --\n create table if not exists orders (\n o_id integer not null, \n u_id integer not null, \n username varchar(100) not null, \n email varchar(100) not null, \n addr varchar(200) not null, \n city varchar(50) not null, \n state_province_ut varchar(50) not null, \n zip integer not null, \n order_total float not null, \n purchase jsonb not null, \n total_order_qty integer not null, \n PRIMARY KEY (o_id), \n FOREIGN KEY (u_id) REFERENCES users (u_id)\n );\n\n -- USER_SEQ_NO SEQUENCE --\n create sequence if not exists public.user_seq_no\n increment 1\n start 1\n minvalue 1\n maxvalue 99999\n owned by users.u_id;\n\n alter sequence public.user_seq_no\n owner to postgres;\n\n -- CATEGORY_SEQ_NO SEQUENCE --\n create sequence if not exists public.category_seq_no\n increment 1\n start 1\n minvalue 1\n maxvalue 99999\n owned by category.c_id;\n\n alter sequence public.category_seq_no\n owner to postgres;\n\n -- PRODUCT_SEQ_NO SEQUENCE --\n create sequence if not exists public.product_seq_no\n increment 1\n start 1\n minvalue 1\n maxvalue 99999\n owned by products.p_id;\n\n alter sequence public.product_seq_no\n owner to postgres;\n\n -- ORDER_SEQ_NO SEQUENCE --\n create sequence if not exists public.order_seq_no\n increment 1\n start 1\n minvalue 1\n maxvalue 99999\n owned by booking.booking_id;\n\n alter sequence public.order_seq_no\n owner to postgres;\n'''\n# # gender - true is male and false is female\n# # card details wont be save therefore did not make columns for that\n# cur.execute(create_script)\n# conn.commit()\n\n# -------------------------------------------------------\n\ndef registerAccount(name, gender, email, password): \n insert_script = '''\n select email from users where email = %s\n '''\n insert_values = ([email])\n cur.execute(insert_script, insert_values)\n conn.commit()\n if(cur.fetchall()):\n return False\n\n insert_script = '''\n insert into users (u_id, username, gender, email, passcode) \n values (NEXTVAL('user_seq_no'), %s, %s, %s, %s)\n '''\n insert_values = (name, gender, email, password)\n cur.execute(insert_script, insert_values)\n if(conn.commit()): \n return True\n\n# -------------------------------------------------------\n\ndef loginAccount(email, password): \n insert_script = '''\n select passcode from users where email = %s\n '''\n insert_values = ([email])\n cur.execute(insert_script, insert_values)\n conn.commit()\n data = cur.fetchall()\n if(not data):\n return False\n if(data[0][0] == password): \n return True\n else: \n return False\n\n# -------------------------------------------------------\n\ndef getUID(email): \n insert_script = '''\n select u_id from users where email = %s\n '''\n insert_values = ([email])\n cur.execute(insert_script, insert_values)\n conn.commit()\n data = cur.fetchone()\n return data[0]\n\n# -------------------------------------------------------\n\ndef getName(email): \n insert_script = '''\n select username from users where email = %s\n '''\n insert_values = ([email])\n cur.execute(insert_script, insert_values)\n conn.commit()\n data = cur.fetchone()\n return data[0]\n\n# -------------------------------------------------------\n\ndef adminCheck(email): \n insert_script = '''\n select isadmin from users where email = %s\n '''\n insert_values = ([email])\n cur.execute(insert_script, insert_values)\n conn.commit()\n data = cur.fetchone()\n return data[0]\n\n# -------------------------------------------------------\n\ndef getCategories(): \n get_script = '''\n select c_name from category\n '''\n cur.execute(get_script)\n conn.commit()\n data = cur.fetchall()\n categories = []\n for category in data: \n categories.append(category[0])\n return categories\n \n# -------------------------------------------------------\n\ndef getCategoryID(): \n get_script = '''\n select c_id from category\n '''\n cur.execute(get_script)\n conn.commit()\n data = cur.fetchall()\n categoryIDs = []\n for id in data: \n categoryIDs.append(id[0])\n return categoryIDs\n\n# -------------------------------------------------------\n\ndef getCategoryById(c_id): \n get_script = '''\n select c_name from category where c_id = %s\n '''\n get_values = ([c_id])\n cur.execute(get_script, get_values)\n conn.commit()\n data = cur.fetchall()\n return data[0]\n\n# -------------------------------------------------------\n\ndef getCategoryIdFromName(c_name): \n get_script = '''\n select c_id from category where c_name = %s\n '''\n get_values = ([c_name])\n cur.execute(get_script, get_values)\n conn.commit()\n data = cur.fetchall()\n return data\n\n# -------------------------------------------------------\n\ndef addNewCategory(c_name):\n add_script = '''\n insert into category\n values (NEXTVAL('category_seq_no'), %s)\n '''\n add_values = ([c_name])\n cur.execute(add_script, add_values)\n if (conn.commit()):\n return True\n\n# -------------------------------------------------------\n\ndef editCategoryName(old_id, new_name):\n edit_script = '''\n update category\n set c_name = %s\n where c_id = %s\n '''\n edit_values = (new_name, old_id)\n cur.execute(edit_script, edit_values)\n if (conn.commit()):\n return True\n\n# -------------------------------------------------------\n\ndef deleteCategoryCompletely(c_id):\n delete_script = '''\n delete from products where c_id = %s;\n delete from category where c_id = %s;\n '''\n delete_values = (c_id, c_id)\n cur.execute(delete_script, delete_values)\n if (conn.commit()): \n return True\n\n# -------------------------------------------------------\n\ndef getAllItemsFromDB():\n allProductsInTheStore = {}\n\n categoryList = getCategories()\n for category in categoryList: \n allProductsInTheStore[category] = []\n\n get_script = '''\n select * from products\n '''\n cur.execute(get_script)\n conn.commit()\n data = cur.fetchall()\n\n for product in data: \n c_name = getCategoryById(product[6])[0]\n allProductsInTheStore[c_name].append(product)\n\n return allProductsInTheStore\n\n# -------------------------------------------------------\n\ndef getAllItems():\n return allProductsInTheStore\n\n# -------------------------------------------------------\n\ndef getAllItemNamesAndIDs(): \n items = []\n get_script = '''\n select p_id, p_name from products\n '''\n cur.execute(get_script)\n conn.commit()\n data = cur.fetchall()\n return data\n\n# -------------------------------------------------------\n\ndef putItems(pName, pQty, pPrice, pStockQty, pImg, cID): \n insert_script = '''\n insert into products (p_id, p_name, p_qty, p_price, p_stock_qty, p_img, c_id)\n values (NEXTVAL('product_seq_no'), %s, %s, %s, %s, %s, %s)\n '''\n insert_values = (pName, pQty, pPrice, pStockQty, pImg, cID)\n cur.execute(insert_script, insert_values)\n conn.commit()\n get_script = '''\n select * from products where p_name = %s and p_qty = %s\n '''\n get_values = (pName, pQty)\n cur.execute(get_script, get_values)\n if(conn.commit()): \n return True\n\n# -------------------------------------------------------\n\ndef reduceStock(cart):\n for keys in cart:\n get_script = '''\n select stock_available \n from products \n where p_id = %s\n '''\n get_values = ([keys[0]])\n cur.execute(get_script, get_values)\n conn.commit()\n data = cur.fetchone()[0]\n data = data - keys[8]\n update_script = '''\n update products\n set stock_available = %s\n where p_id = %s\n '''\n update_values = (data, keys[0])\n cur.execute(update_script, update_values)\n conn.commit()\n\n# -------------------------------------------------------\n\ndef getProductFromID(p_id):\n get_script = '''\n select * from products where p_id = %s\n '''\n get_values = ([p_id])\n cur.execute(get_script, get_values)\n conn.commit()\n data = cur.fetchall()[0]\n return data\n\n# -------------------------------------------------------\n\ndef editItemDetails(c_id, p_id, new_name, p_qty, p_price, p_stock_qty):\n edit_script = '''\n update products \n set p_name = %s, p_qty = %s, p_price = %s, p_stock_qty = %s, c_id = %s where p_id = %s\n '''\n edit_values = (new_name, p_qty, p_price, p_stock_qty, c_id, p_id)\n cur.execute(edit_script, edit_values)\n if (conn.commit()):\n return True\n\n# -------------------------------------------------------\n\ndef deleteProduct(p_id):\n delete_script = '''\n delete from products where p_id = %s\n '''\n delete_value = ([p_id])\n cur.execute(delete_script, delete_value)\n if conn.commit(): \n return True\n\n# -------------------------------------------------------\n\ndef initializeCart(): \n cart = {}\n allItems = getAllItemNamesAndIDs()\n for item in allItems: \n cart[item[0]] = 0\n return cart\n\n# -------------------------------------------------------\n\ndef recalculateDisplayCart(cart):\n display_cart = []\n allItems = getAllItemsFromDB()\n for item in allItems:\n for i in allItems[item]:\n if cart[i[0]] > 0: \n list = i\n list.append(cart[i[0]])\n data = base64.b64encode(i[5])\n i[5] = data.decode()\n display_cart.append(list)\n return display_cart\n\n# -------------------------------------------------------\n\ndef createPurchaseJSON(cart):\n purchase = {}\n for item in cart:\n if (cart[item] > 0):\n purchase[item] = cart[item]\n \n return json.dumps(purchase)\n\n# -------------------------------------------------------\n\ndef totalOrderCount(cart):\n count = 0\n for item in cart: \n count += cart[item]\n return count\n\ndef checkoutPurchase(u_id, fullName, email, address, city, state, zip, total, cart): \n purchase = createPurchaseJSON(cart)\n total_order_qty = totalOrderCount(cart)\n insert_script = '''\n insert into orders (o_id, u_id, username, email, addr, city, state_province_ut, zip, order_total, purchase, total_order_qty)\n values (NEXTVAL('order_seq_no'), %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)\n '''\n insert_values = (u_id, fullName, email, address, city, state, zip, total, purchase, total_order_qty)\n cur.execute(insert_script, insert_values)\n if (conn.commit()):\n return True\n\n# -------------------------------------------------------\n\ndef calcTotal(cart): \n total = 0\n for cart_item in cart: \n total = total + (cart_item[3] * cart_item[8])\n return total\n\n# -------------------------------------------------------\n\ndef calcGST(total):\n return 0.09 * total\n\n# -------------------------------------------------------\n\ndef setShopItemsAndCategories():\n getCategories()\n getCategoryID()\n getAllItemsFromDB()\n# -------------------------------------------------------\n\ndef sendEmail(email, title, message):\n emailSender = 'hardikts@gmail.com'\n emailPassword = 'iughuynszadhvwrl'\n emailReceiver = email\n\n subject = \"Message Sent to Havest Haven\"\n body = \"Hello, \\nGreetings from Harvest Haven!\\nThis is an auto generated email that was sent to Harvest Haven\" + \"\\nFrom: \" + email + \"\\nTitle: \" + title + \"\\nMessage: \" + message\n\n em = EmailMessage()\n em['From'] = emailSender\n em['To'] = emailReceiver\n em['Subject'] = subject\n em.set_content(body)\n\n context = ssl.create_default_context()\n\n with smtplib.SMTP_SSL('smtp.gmail.com', 465, context=context) as smtp: \n smtp.login(emailSender, emailPassword)\n smtp.sendmail(emailSender, emailReceiver, em.as_string())\n\n# -------------------------------------------------------\n# -------------------------------------------------------\n# COSTOMER DEMOGRAPHICS:\ndef totalUsers():\n get_script = '''\n select count(distinct u_id)\n from users\n '''\n cur.execute(get_script)\n conn.commit()\n data = cur.fetchone()[0]\n if data == None:\n return 0\n else:\n return data\n\n# -------------------------------------------------------\n\ndef totalMaleUsers():\n get_script = '''\n select count(distinct u_id)\n from users\n where gender = True\n '''\n cur.execute(get_script)\n conn.commit()\n data = cur.fetchone()[0]\n if data == None:\n return 0\n else:\n return data\n\n# -------------------------------------------------------\n# -------------------------------------------------------\n# SALES AND REVENUE METRICS: \ndef totalSales():\n sales = 0\n get_script = '''\n select sum(order_total)\n from orders\n '''\n cur.execute(get_script)\n conn.commit()\n sales = cur.fetchone()[0]\n if sales == None:\n return 0.0\n else:\n return sales\n\n# -------------------------------------------------------\n\ndef totalSalesRevenue():\n sales = totalSales()\n if sales == None:\n return 0\n else:\n return 0.2 * sales\n\n# -------------------------------------------------------\n\ndef averageOrderValue():\n get_script = '''\n select avg(order_total)\n from orders\n '''\n cur.execute(get_script)\n conn.commit()\n value = cur.fetchone()[0]\n if value == None:\n return 0.0\n else:\n return value\n\n# -------------------------------------------------------\n\ndef repeatPurchaseRate():\n get_script = '''\n select count(distinct u_id)\n from orders\n '''\n cur.execute(get_script)\n conn.commit()\n allBuyers = cur.fetchone()[0]\n if allBuyers == 0:\n return 0\n\n get_script = '''\n select count(*) as repeat_order_count\n from (\n select u_id\n from orders\n group by u_id\n having count(*) > 1\n ) as repeat_orders\n '''\n cur.execute(get_script)\n conn.commit()\n repeatingBuyers = cur.fetchone()[0]\n return int((repeatingBuyers / allBuyers) * 100)\n# -------------------------------------------------------\n# -------------------------------------------------------\n# INVENTORY AND PRODUCT METRICS: \ndef stockLevels():\n get_script = '''\n select p_name, stock_available\n from products\n '''\n cur.execute(get_script)\n conn.commit()\n stock = cur.fetchall()\n # returns a list of list\n # print it in the form of table\n return stock\n\n# -------------------------------------------------------\n\ndef bestSellingProducts():\n get_script = '''\n select p_id\n from products\n where (p_stock_qty - stock_available) = (\n select max(p_stock_qty - stock_available)\n from products\n )\n '''\n cur.execute(get_script)\n conn.commit()\n p_id = cur.fetchone()[0]\n product = getProductFromID(p_id)\n data = base64.b64encode(product[5])\n product[5] = data.decode()\n return product\n\n# -------------------------------------------------------\n\ndef slowMovingProduct():\n get_script = '''\n select p_id\n from products\n where (p_stock_qty - stock_available) = (\n select min(p_stock_qty - stock_available)\n from products\n )\n '''\n cur.execute(get_script)\n conn.commit()\n p_id = cur.fetchone()[0]\n product = getProductFromID(p_id)\n data = base64.b64encode(product[5])\n product[5] = data.decode()\n return product\n\n# -------------------------------------------------------\n\ndef totalOrders():\n get_script = '''\n select count(distinct o_id)\n from orders\n '''\n cur.execute(get_script)\n conn.commit()\n data = cur.fetchone()[0]\n if data == None:\n return 0\n else:\n return data\n\n# -------------------------------------------------------\n\n# INCOMPLETE FUNCTION\ndef customerLifeTimeValue(): \n get_script = '''\n select u_id, order_total \n from orders\n '''\n cur.execute(get_script)\n conn.commit()\n data = cur.fetchall()\n value = 0\n return value\n","repo_name":"HardikShah563/harvest-haven-flask","sub_path":"database.py","file_name":"database.py","file_ext":"py","file_size_in_byte":17854,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"42412196655","text":"# Programa de control de Stock para el negocio de EL NOBLE\n# Desarrollado por Federico Lopez y Agustin Gutierrez\n\n# Estructura de producto (idProducto, nombre, stockCritico, cajas, cantidadPorCaja, precioXUnidad):\n\n\nfrom srcModelo.ClaseProducto import Producto\n\nproducto = Producto(1, \"Jamon y Queso\", 0, 0, 0, 0)\ndicEmpanadas = {'jq' : producto}\n\nproducto = Producto(2, \"EQ\", 0, 0, 0, 0) #????????\ndicEmpanadas['eq'] = producto\n\nproducto = Producto(3, \"Carne Suave\", 0, 0, 0, 0)\ndicEmpanadas['cs'] = producto\n\nproducto = Producto(4, \"Carne a Cuchillo\", 0, 0, 0, 0)\ndicEmpanadas['cc'] = producto\n\nproducto = Producto(5, \"Carne Picante\", 0, 0, 0, 0)\ndicEmpanadas['cp'] = producto\n\nproducto = Producto(6, \"Humita\", 0, 0, 0, 0)\ndicEmpanadas['h'] = producto\n\nproducto = Producto(7, \"Pollo\", 0, 0, 0, 0)\ndicEmpanadas['p'] = producto\n\nproducto = Producto(8, \"CAD\", 0, 0, 0, 0)\ndicEmpanadas['cad'] = producto","repo_name":"AgusGuti/Control-de-Stock-Python","sub_path":"Codigo/Control de Stock V1.6 (Obsoleta)/Control de Stock/srcModelo/dicEmpanadas.py","file_name":"dicEmpanadas.py","file_ext":"py","file_size_in_byte":908,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"10210354606","text":"def yummy(a):\r\n sr = str(a)\r\n if len(sr):\r\n return False\r\n pre = \"\"\r\n suf = \"\"\r\n if len(sr) % 2 == 1:\r\n pre = sr[:(len(s)//2)]\r\n suf = sr[(len(s)//2)+1:]\r\n if len(sr) % 2 == 0:\r\n pre = sr[:(len(s)//2)]\r\n suf = sr[len(sr) // 2+1:]\r\n return pre is suf\r\n\r\ncases = int(input())\r\nfor i in range(cases):\r\n num = int(input())\r\n c = 0\r\n for s in range(num):\r\n if yummy(s+1):\r\n c += 1\r\n print(\"Number #\" + str(i + 1) + \": There are \" + str(c) + \" sandwich numbers that meet our criteria\")\r\n","repo_name":"sebboj/kattis","sub_path":"python/sandwich.py","file_name":"sandwich.py","file_ext":"py","file_size_in_byte":567,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"39948304513","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nfrom PyQt5 import QtCore, QtWidgets\nfrom PyQt5.QtWidgets import QGraphicsScene, QGraphicsView, QGraphicsItem, QGraphicsItemGroup, QGraphicsPixmapItem, QGraphicsEllipseItem, QFrame, QFileDialog, QPushButton, QGraphicsObject, QMenu, QAction\nfrom PyQt5.QtGui import QPixmap, QImage, QPainter, QPolygonF, QColor, QCursor\nfrom PyQt5.QtCore import QPoint, QPointF, QRectF, pyqtSlot, QObject\n\nimport numpy as np\nimport pandas as pd\n\nimport scipy.interpolate as interpolate\nfrom functools import cmp_to_key\nfrom .tracking_path import TrackingPath\nfrom .rotatable_ellipse import RotatableEllipseData,RotatableEllipse\nfrom scipy import interpolate\n\nclass HandInputSystem(QGraphicsObject):\n def __init__(self,parent = None):\n super(HandInputSystem,self).__init__(parent)\n self.setZValue(1000)\n self.rect = QRectF()\n \"\"\"\n self.df = pd.DataFrame({\"x0\":[],\"y0\":[],\n \"VX0_0\":[],\"VY0_0\":[],\n \"VX1_0\":[],\"VY1_0\":[],\n \"depth_0\":[]})\n \"\"\"\n self.df = pd.DataFrame()\n self.dataFrameNo = -1\n self.editingNo = 0\n self.lastInputedFrameIndex = {}\n self.currentFrameNo = 0\n self.itemList = []\n self.drawItemFlag = True\n self.selectedItemList = []\n self.overlayFrameNo = 1\n \"\"\"\n df = pd.DataFrame([[100,200]],columns = [\"x0\",\"y0\"],index = [10])\n self.df = df.combine_first(self.df)\n df = pd.DataFrame([[100,200]],columns = [\"x0\",\"y0\"],index = [10])\n\n self.df = df.combine_first(self.df)\n self.df.loc[100] = [200,100]\n #print(self.df.loc[100]['x0'])\n \"\"\"\n \"\"\"\n print(self.df.loc[100:100,('x0','y0')])\n self.df.loc[100:100,('x0','y0')] = [500,200]\n print(self.df.loc[100:100,('x0','y0')])\n \"\"\"\n\n \"\"\"\n self.workingNo += 1\n self.addNewDataFrame(self.workingNo)\n \"\"\"\n\n #print(self.df.iloc[0])\n #self.df.concat([[100,100],[\"x0\",\"y0\"]])\n #data = data.append(pd.dataFrame([1,2,3,4,5],columns=[\"A\",\"B\",\"C\",\"D\",\"E\"],index=data[-1:].index+1))\n self.positionStack = {}\n \n\n def inputMouseMoveEvent(self,mousePosition,currentFrameNo):\n mapper = self.generateIndexMapper(self.editingNo)\n #self.positionStack[currentFrameNo] = mousePosition\n #print(currentFrameNo)\n #self.positionStack.append(mousePosition)\n \"\"\"\n df = pd.DataFrame([mousePosition],columns = [mapper['x'],mapper['y']],index = [currentFrameNo])\n self.df = df.combine_first(self.df)\n print(currentFrameNo)\n \"\"\"\n\n def inputMouseReleaseEvent(self):\n mapper = self.generateIndexMapper(self.editingNo)\n mouses = np.array(list(self.positionStack.values()))\n if len(mouses) is 0:\n return\n data = {\n mapper['x']:mouses[:,0],\n mapper['y']:mouses[:,1]\n }\n dataIndex = list(self.positionStack.keys())\n df = pd.DataFrame(data,index = dataIndex)\n self.df = df.combine_first(self.df)\n #self.df.insert(df)\n print(self.df)\n lastInputedFrameNum = self.lastInputedFrameIndex[self.editingNo]\n print(\"BB\",self.lastInputedFrameIndex[self.editingNo],min(dataIndex))\n if self.lastInputedFrameIndex[self.editingNo] == min(dataIndex)-1:\n print(\"AA\",self.lastInputedFrameIndex[self.editingNo])\n self.lastInputedFrameIndex[self.editingNo] = max(dataIndex)\n self.positionStack = {} \n return False\n else:\n newDataIndex = range(lastInputedFrameNum,min(dataIndex))\n A = self.df.ix[lastInputedFrameNum+1:min(dataIndex)-1]\n X_new,Y_new = [],[]\n time_new = []\n if A.empty:\n ## 補完\n \n if lastInputedFrameNum == 0:\n time_old = range(min(dataIndex),min(dataIndex)+6)\n time_new = list(range(lastInputedFrameNum,min(dataIndex)))\n data = self.df.ix[min(dataIndex):min(dataIndex)+5].as_matrix()\n X,Y = data[:,0],data[:,1]\n A = np.array([time_old,np.ones(len(time_old))])\n A = A.T\n # X\n a,b = np.linalg.lstsq(A,X)[0]\n time_new = np.array(time_new)\n X_new = a*time_new+b\n # Y\n a,b = np.linalg.lstsq(A,Y)[0]\n time_new = np.array(time_new)\n Y_new = a*time_new+b\n tmp_new = np.dstack([X_new,Y_new])[0]\n \n else:\n time_old = list(range(min(dataIndex),min(dataIndex)+6))+\\\n list(range(lastInputedFrameNum-6,lastInputedFrameNum))\n time_new = list(range(lastInputedFrameNum+1,min(dataIndex)))\n data0 = self.df.ix[min(dataIndex):min(dataIndex)+5].as_matrix()\n data1 = self.df.ix[lastInputedFrameNum-5:lastInputedFrameNum].as_matrix()\n \n data = np.r_[data0,data1]\n X,Y = data[:,0],data[:,1]\n mode = 'slinear'\n X_new = interpolate.interp1d(time_old,X,kind = mode)(time_new)\n Y_new = interpolate.interp1d(time_old,Y,kind = mode)(time_new)\n self.lastInputedFrameIndex[self.editingNo] = max(dataIndex)\n data = {\n mapper['x']:X_new,\n mapper['y']:Y_new\n }\n df = pd.DataFrame(data,index = time_new)\n self.df = df.combine_first(self.df)\n print(self.df)\n \n self.positionStack = {}\n #self.addNewDataFrame()\n return True\n def setPoints(self,frameNo = None):\n if frameNo is not None:\n self.currentFrameNo = frameNo\n \n min_value = max(self.currentFrameNo - self.overlayFrameNo, 0)\n max_value = self.currentFrameNo + self.overlayFrameNo\n pos = self.currentFrameNo - min_value\n \n for i, item in enumerate(self.itemList):\n mapper = self.generateIndexMapper(i)\n firstValidIndex = self.df[mapper['x']].last_valid_index()\n if firstValidIndex is None:\n firstValidIndex = 0\n max_value = self.currentFrameNo# + self.overlayFrameNo\n max_value = np.min([max_value,firstValidIndex])\n array = self.df.loc[min_value:max_value, (mapper['x'],mapper['y'])].as_matrix()\n if len(array) is 0:\n continue\n #array = [item for item in array if item[0]!=float(\"NaN\")]\n \n flags = np.full(len(array), False, dtype=np.bool)\n if self.drawItemFlag and pos < len(array):\n flags[pos] = True\n\n item.setPoints(array, flags)\n\n def nextDataFrame(self):\n if self.editingNo <= self.dataFrameNo-1:\n self.editingNo+=1\n else:\n self.addNewDataFrame()\n self.editingNo+=1\n #print(\"NextDataFrame\",self.editingNo)\n self.setEditingLastValidFrameNo()\n \n def setEditingLastValidFrameNo(self):\n #\n mapper = self.generateIndexMapper(self.editingNo)\n firstValidIndex = self.df[mapper['x']].last_valid_index()\n if firstValidIndex is None:\n firstValidIndex = 0\n self.lastInputedFrameIndex[self.editingNo] = firstValidIndex\n \n def getLastInputedFrameIndex(self):\n return self.lastInputedFrameIndex[self.editingNo]\n\n def previousDataFrame(self):\n if self.editingNo > 0:\n self.editingNo-=1\n print(\"Previous Frame\",self.editingNo)\n #print(self.df,self.editingNo)\n self.setEditingLastValidFrameNo()\n\n def setEditingNo(self,editingNo):\n self.editingNo = editingNo\n\n def addNewDataFrame(self):\n workingNo = self.dataFrameNo+1\n mapper = self.generateIndexMapper(workingNo)\n df = pd.DataFrame(dict(zip(mapper.values(),[[] for i in range(len(mapper.values()))])))\n self.df = self.df.append(df)\n self.dataFrameNo+=1\n self.lastInputedFrameIndex[self.dataFrameNo] = 0\n\n #\n scene = self.scene()\n \"\"\"\n if scene is not None:\n for item in self.itemList:\n scene.removeItem(item)\n del item\n self.itemList.clear()\n \"\"\"\n rgb = (255,0,0)\n rgb = np.random.randint(0, 255, (1, 3)).tolist()[0]\n trackingPath = TrackingPath(self)\n trackingPath.setRect(scene.sceneRect())\n trackingPath.setColor(rgb)\n trackingPath.setLineWidth(14)\n #trackingPath.setRadius(10)\n trackingPath.itemSelected.connect(self.itemSelected)\n self.itemList.append(trackingPath)\n\n def appendPosition(self,mousePosition,frameNo = None):\n if frameNo is not None:\n self.currentFrameNo = frameNo\n self.positionStack[self.currentFrameNo] = mousePosition\n\n def generateIndexMapper(self,dataNumber):\n dataIndex = dataNumber\n indexMapper = {'x':'x{0}'.format(dataIndex),'y':'y{0}'.format(dataIndex),\n }\n \"\"\"\n 'VX0':'VX0_{0}'.format(dataIndex), #major\n 'VY0':'VY0_{0}'.format(dataIndex),\n 'VX1':'VX1_{0}'.format(dataIndex), #minor\n 'VY1':'VY1_{0}'.format(dataIndex),\n 'depth':'depth_{0}'.format(dataIndex)}\n \"\"\"\n return indexMapper\n\n def saveCSV(self,filePath):\n df = self.df.copy()\n N = len(self.generateIndexMapper(0).keys())\n col_n = df.as_matrix().shape[1]/N\n col_names = np.array([('x{0}'.format(i),\n 'y{0}'.format(i)) for i in range(int(round(col_n)))]).flatten()\n #df.columns = pd.Index(col_names)\n df.to_csv(filePath)\n\n def setDataFrame(self,df):\n mapper = self.generateIndexMapper(0)\n columnNum = len(mapper.values())\n shape = df.shape\n self.dataFrameNo = int(shape[1]/columnNum)-1\n self.editingNo = 0\n self.df = df\n self.setEditingLastValidFrameNo()\n\n scene = self.scene()\n \n if scene is not None:\n for item in self.itemList:\n scene.removeItem(item)\n del item\n self.itemList.clear()\n\n for i in range(self.dataFrameNo+1):\n rgb = np.random.randint(0, 255, (1, 3)).tolist()[0]\n trackingPath = TrackingPath(self)\n trackingPath.setRect(scene.sceneRect())\n trackingPath.setColor(rgb)\n trackingPath.setLineWidth(14)\n trackingPath.itemSelected.connect(self.itemSelected)\n self.itemList.append(trackingPath)\n\n @pyqtSlot(object)\n def itemSelected(self, item):\n if item.selected:\n self.selectedItemList.append(item)\n if len(self.selectedItemList)>2:\n removedItem = self.selectedItemList.pop(0)\n removedItem.selected = False\n removedItem.itemType = QGraphicsEllipseItem\n removedItem.setPoints()\n else:\n try:\n self.selectedItemList.remove(item)\n except ValueError:\n pass\n\n def contextMenuEvent(self, event):\n if len(self.selectedItemList) == 2:\n widget = self.parentWidget()\n menu = QMenu(widget)\n\n swapAction = QAction(\"Swap\", widget)\n swapAction.triggered.connect(self.swap)\n menu.addAction(swapAction)\n\n menu.exec(event.screenPos())\n\n def swap(self):\n pos0, pos1 = [self.itemList.index(item) for item in self.selectedItemList]\n mapper0 = self.generateIndexMapper(pos0)\n mapper1 = self.generateIndexMapper(pos1)\n arrayX0 = self.df.loc[self.currentFrameNo:, mapper0['x']].as_matrix()\n arrayY0 = self.df.loc[self.currentFrameNo:, mapper0['y']].as_matrix()\n arrayX1 = self.df.loc[self.currentFrameNo:, mapper1['x']].as_matrix()\n arrayY1 = self.df.loc[self.currentFrameNo:, mapper1['y']].as_matrix()\n tmpX = arrayX0.copy()\n arrayX0[:] = arrayX1\n arrayX1[:] = tmpX\n\n tmpY = arrayY0.copy()\n arrayY0[:] = arrayY1\n arrayY1[:] = tmpY\n\n for item in self.selectedItemList:\n item.setPoints()\n\n return\n\n def isDataFrame(self):\n return (self.df is not None)\n\n def setColor(self,rgb):\n self.itemList[self.editingNo].setColor(rgb)\n\n def setDrawItem(self, pos, flag):\n self.drawItemFlag = flag\n for item in self.itemList:\n item.setDrawItem(pos, flag)\n\n def setDrawLine(self, flag):\n self.drawLineFlag = flag\n for item in self.itemList:\n item.setDrawLine(flag)\n\n def setOverlayFrameNo(self, n):\n self.overlayFrameNo = n\n self.setPoints()\n def setRadius(self,r):\n self.radius = r\n for item in self.itemList:\n item.setRadius(self.radius)\n def setRect(self, rect):\n self.rect = rect\n def boundingRect(self):\n return self.rect\n def paint(self, painter, option, widget):\n pass\n \n \n\n\ndef main():\n pass\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"ymnk13/UMATracker-Commando","sub_path":"lib/python/ui/hand_input_system.py","file_name":"hand_input_system.py","file_ext":"py","file_size_in_byte":13468,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"85"} +{"seq_id":"37548036341","text":"#lista 6\n#Faça um Programa que peça o raio de um círculo, calcule e mostre sua área.\n\nprint(\"Programa que calcula a área de um círculo\")\nr= float(input(\"Qual é o raio do círculo? \"))\n\npi = 3.14\n\narea = pi*r*r\n\nprint(\"A área do círculo é: \",area)\n\n","repo_name":"michaelsss16/EstudosPython","sub_path":"ListasDeExercicios/Lista 1/e6l1.py","file_name":"e6l1.py","file_ext":"py","file_size_in_byte":258,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"74986815956","text":"#!/usr/bin/python3.6\n# -*- coding: utf-8 -*-\n\"\"\"\n@Time : 2022/3/31 21:36\n@Author : estelle.ji\n@Email : mingshu.ji@amh-group.com\n@File : leet_5_longestPalindrome.py\n@Url : https://leetcode-cn.com/problems/longest-palindromic-substring/\n@Software: PyCharm\n\"\"\"\nimport time\nimport numpy as np\ns = \"babad\"\n\n# 判断当前字符串是否为回文串\ndef isPalindromeStr(s):\n if len(s) < 2: return True\n if len(s) == 2: return s[0] == s[1]\n\n # 从中间位置对半分，比较数值是否一致,不一致则返回不是回文串\n for i in range(len(s)//2):\n if s[i] != s[len(s)-i-1]:\n return False\n return True\n\n\n# 1. 暴力求解\n# def longestPalindrome(s):\n# max_string_len = 0\n# result_string = \"\"\n# # 特殊情况处理：\n# # 字符串长度为1，一定为回文字串；\n# # 长度为2，如果两个字符一致则为回文子串\n# n = len(s)\n# if n < 2: return s\n# if n == 2 and s[0] == s[1]: return s\n# for i in range(len(s)):\n# for j in range(i+1, len(s)):\n# current_str = s[i:j + 1]\n# if isPalindromeStr(current_str) and len(current_str)>max_string_len:\n# max_string_len = len(current_str)\n# result_string = current_str\n# return result_string\n\n# 2. 优化版暴力求解——动态规划\ndef longestPalindrome(s):\n start = time.clock()\n if len(s) < 2: return s\n if len(s) == 2 and s[0] == s[1]: return s\n max_string_len = 0\n result_string = \"\"\n # 动态规划，先初始化dp矩阵,dp[i,j]表示字符串s[i:j]是回文子串\n dp = [[False] * len(s) for _ in range(len(s))]\n # 遍历所有可能回文串的长度\n for i in range(1,len(s)+1):\n for left in range(len(s)):\n right = left+i-1\n if (right >= len(s)): break\n dp[left][right] = (i==1) or (i==2 and s[left]==s[right]) or (dp[left+1][right-1] and s[left]==s[right])\n if (dp[left][right] and i>max_string_len):\n max_string_len = i\n result_string = s[left:right+1]\n end = time.clock()\n print(\"运行耗时(ms)\", (end - start)*1000)\n return result_string\n\ns=\"reifadyqgztixemwswtccodfnchcovrmiooffbbijkecuvlvukecutasfxqcqygltrogrdxlrslbnzktlanycgtniprjlospzhhgdrqcwlukbpsrumxguskubokxcmswjnssbkutdhppsdckuckcbwbxpmcmdicfjxaanoxndlfpqwneytatcbyjmimyawevmgirunvmdvxwdjbiqszwhfhjmrpexfwrbzkipxfowcbqjckaotmmgkrbjvhihgwuszdrdiijkgjoljjdubcbowvxslctleblfmdzmvdkqdxtiylabrwaccikkpnpsgcotxoggdydqnuogmxttcycjorzrtwtcchxrbbknfmxnonbhgbjjypqhbftceduxgrnaswtbytrhuiqnxkivevhprcvhggugrmmxolvfzwadlnzdwbtqbaveoongezoymdrhywxcxvggsewsxckucmncbrljskgsgtehortuvbtrsfisyewchxlmxqccoplhlzwutoqoctgfnrzhqctxaqacmirrqdwsbdpqttmyrmxxawgtjzqjgffqwlxqxwxrkgtzqkgdulbxmfcvxcwoswystiyittdjaqvaijwscqobqlhskhvoktksvmguzfankdigqlegrxxqpoitdtykfltohnzrcgmlnhddcfmawiriiiblwrttveedkxzzagdzpwvriuctvtrvdpqzcdnrkgcnpwjlraaaaskgguxzljktqvzzmruqqslutiipladbcxdwxhmvevsjrdkhdpxcyjkidkoznuagshnvccnkyeflpyjzlcbmhbytxnfzcrnmkyknbmtzwtaceajmnuyjblmdlbjdjxctvqcoqkbaszvrqvjgzdqpvmucerumskjrwhywjkwgligkectzboqbanrsvynxscpxqxtqhthdytfvhzjdcxgckvgfbldsfzxqdozxicrwqyprgnadfxsionkzzegmeynye\"\n\n\n# 3. 最长公共子串法【连续】\ndef longestPalindrome2(s):\n # 特殊情况处理：\n if s == '': return s\n n = len(s)\n dp = [[0] * n for _ in range(n)]\n s2 = s[::-1]\n max_len = 0\n max_end = 0\n for i in range(n):\n for j in range(n):\n if s[i] == s2[j]:\n if (i == 0) or (j == 0):\n dp[i][j] = 1\n else:\n dp[i][j] = dp[i - 1][j - 1] + 1\n\n if dp[i][j] > max_len:\n max_len = dp[i][j]\n max_end = i\n return s[max_end - max_len + 1:max_end + 1]\n\nprint(longestPalindrome2(\"aacabdkacaa\"))\n\n","repo_name":"estelle18/Baldy","sub_path":"leetcode/top100/leet_5_longestPalindrome.py","file_name":"leet_5_longestPalindrome.py","file_ext":"py","file_size_in_byte":3824,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"69799130517","text":"import json\nimport plotly\nimport pandas as pd\nimport re\nimport pandas as pd\n\nfrom nltk.stem import WordNetLemmatizer\nfrom nltk.tokenize import word_tokenize, sent_tokenize\nfrom nltk.tag import pos_tag\n\nimport nltk\nnltk.download(['punkt', 'wordnet', 'averaged_perceptron_tagger', 'stopwords'])\n\nfrom sklearn.base import TransformerMixin, BaseEstimator\n\nfrom flask import Flask\nfrom flask import render_template, request, jsonify\nfrom plotly.graph_objs import Bar, Pie\nfrom sklearn.externals import joblib\nfrom sqlalchemy import create_engine\n\n\napp = Flask(__name__)\n\ndef tokenize(text):\n \"\"\"\n Normalize and tokenize a piece of text\n Input:\n text (str): text from all messages\n \n Returns:\n clean_tokens (list): list of words into numbers of same meaning\n \"\"\" \n url_regex = 'http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\(\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+'\n \n detected_urls = re.findall(url_regex, text)\n \n for url in detected_urls:\n text = text.replace(url, \"urlplaceholder\") \n \n text = re.sub(r\"[^a-zA-Z0-9]\", \" \", text)\n\n text = re.sub(r'[0-9]', \" \", text)\n \n tokens = word_tokenize(text)\n\n #tokens = [word for word in tokens if not word in stopwords.words('english')]\n\n clean_tokens = []\n \n lemmatizer = WordNetLemmatizer()\n \n for tok in tokens:\n clean_tok = lemmatizer.lemmatize(tok).lower().strip()\n clean_tokens.append(clean_tok)\n \n return clean_tokens\n\nclass StartingVerbExtractor(BaseEstimator, TransformerMixin):\n\n def starting_verb(self, text):\n # tokenize by sentences\n sentence_list = sent_tokenize(text)\n \n for sentence in sentence_list:\n # tokenize each sentence into words and tag part of speech\n pos_tags = pos_tag(tokenize(sentence))\n\n # index pos_tags to get the first word and part of speech tag\n \n if not len(pos_tags): \n return False\n first_word, first_tag = pos_tags[0]\n \n # return true if the first word is an appropriate verb or RT for retweet\n if first_tag in ['VB', 'VBP'] or first_word == 'RT':\n return True\n\n return False\n\n def fit(self, x, y=None):\n return self\n\n def transform(self, X):\n # apply starting_verb function to all values in X\n X_tagged = pd.Series(X).apply(self.starting_verb)\n return pd.DataFrame(X_tagged)\n\n# load data\nengine = create_engine('sqlite:///data/DisasterResponseDB.db')\ndf = pd.read_sql_table('dr_messages_tbl', engine)\n\n# load model\nmodel = joblib.load(\"models/classifier.pkl\")\n\n# index webpage displays cool visuals and receives user input text for model\n@app.route('/')\n@app.route('/index')\ndef index():\n \n # extract data needed for visuals\n \n most_common_sum = df.drop(['id', 'related', 'message'], axis=1).sum().sort_values(ascending=False).nlargest()\n most_common_cols = list(most_common_sum.index)\n \n least_common_sum = df.drop(['id', 'related', 'message'], axis=1).sum().sort_values(ascending=False).nsmallest()\n least_common_cols = list(least_common_sum.index) \n \n percent_cols_pie = df.drop(['id', 'related', 'message'], axis=1).sum().sort_values(ascending=False)/len(df)\n all_cols_pie = list(df.drop(['id', 'related', 'message'], axis=1).columns)\n \n # create visuals\n \n graphs = [\n #Graph 1: Most common categories in training set\n {\n 'data': [\n Bar(\n x=most_common_cols,\n y=most_common_sum\n )\n ],\n\n 'layout': {\n 'title': 'Most common categories in training set',\n 'yaxis': {\n 'title': \"Count\"\n },\n 'xaxis': {\n 'title': \"All categories\"\n }\n }\n },\n #Graph 2: Least common categories in training set\n {\n 'data': [\n Bar(\n x=least_common_cols,\n y=least_common_sum\n )\n ],\n\n 'layout': {\n 'title': 'Least common categories in training set',\n 'yaxis': {\n 'title': \"Count\"\n },\n 'xaxis': {\n 'title': \"All categories\"\n }\n }\n },\n #Graph 3: Pie chart showing distribution of training data set\n {\n 'data': [\n Pie(\n labels=all_cols_pie,\n values=percent_cols_pie\n )\n ],\n\n 'layout': {\n 'title': 'Pie chart showing distribution of training data set',\n 'yaxis': {\n 'title': \"Count\"\n },\n 'xaxis': {\n 'title': \"All categories\"\n }\n }\n } \n ]\n \n # encode plotly graphs in JSON\n ids = [\"graph-{}\".format(i) for i, _ in enumerate(graphs)]\n graphJSON = json.dumps(graphs, cls=plotly.utils.PlotlyJSONEncoder)\n \n # render web page with plotly graphs\n return render_template('master.html', ids=ids, graphJSON=graphJSON)\n\n\n# web page that handles user query and displays model results\n@app.route('/go')\ndef go():\n # save user input in query\n query = request.args.get('query', '') \n\n # use model to predict classification for query\n classification_labels = model.predict([query])[0]\n classification_results = dict(zip(df.columns[4:], classification_labels))\n\n # This will render the go.html Please see that file. \n return render_template(\n 'go.html',\n query=query,\n classification_result=classification_results\n )\n\n\ndef main():\n app.run(host='0.0.0.0', port=3001, debug=True)\n\n\nif __name__ == '__main__':\n main()","repo_name":"dirklambrechts/DisasterMessageResponsePipeline","sub_path":"app/run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":5926,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"13045372510","text":"import datetime\nfrom msnoise.api import *\n\nfrom .default import default\nfrom .sara_table_def import SaraConfig, SaraStation\n\ndef main():\n engine = get_engine()\n Session = sessionmaker(bind=engine)\n session = Session()\n\n SaraConfig.__table__.create(bind=engine, checkfirst=True)\n for name in default.keys():\n session.add(SaraConfig(name=name,value=default[name][-1]))\n try:\n session.commit()\n except:\n session.rollback()\n\n SaraStation.__table__.create(bind=engine, checkfirst=True)\n\n db = connect()\n for station in get_stations(db):\n session.add(SaraStation(net=station.net, sta=station.sta,\n sensitivity=1, site_effect=1 ))\n\n session.commit()\n\n ccjobs = get_jobs_by_lastmod(session, \"CC\",\n lastmod=datetime.datetime(1970, 1, 1))\n \n for job in ccjobs:\n update_job(session, job.day, job.pair, \"SARA_ENV\", \"T\")\n session.commit()\n session.close()\n","repo_name":"ThomasLecocq/msnoise-sara","sub_path":"msnoise_sara/install.py","file_name":"install.py","file_ext":"py","file_size_in_byte":1005,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"85"} +{"seq_id":"30704410128","text":"# https://www.acmicpc.net/problem/16991\nimport math\nimport sys\nfrom collections import defaultdict\n\nread = sys.stdin.readline\nn = int(read().strip())\nvillages = [list(map(int, read().strip().split())) for _ in range(n)]\nall = (1 << n) - 1\nadj = defaultdict(lambda: defaultdict(lambda: float('inf')))\ndp = [[[-1 for _ in range(1 << n)] for _ in range(n)] for _ in range(n)]\n\n\ndef getDistance(dept, arrival):\n return math.sqrt(pow(dept[0] - arrival[0], 2) + pow(dept[1] - arrival[1], 2))\n\n\nfor i, village in enumerate(villages):\n for j, e in enumerate(villages):\n if i != j:\n adj[i][j] = getDistance(village, e)\n\n\ndef go(start, cur, state):\n global all\n if state == all:\n return adj[cur][start]\n if dp[start][cur][state] != -1:\n return dp[start][cur][state]\n\n ret = float('inf')\n for nxt, nw in adj[cur].items():\n if state & (1 << nxt) == 0:\n ret = min(ret, go(start, nxt, state | (1 << nxt)) + adj[cur][nxt])\n dp[start][cur][state] = ret\n return ret\n\n\nans = float('inf')\nfor i in range(n):\n ans = min(ans, go(i, i, 1 << i))\n\nprint(ans)\n","repo_name":"naemoo/Algorithm-Python","sub_path":"DP/Problem55.py","file_name":"Problem55.py","file_ext":"py","file_size_in_byte":1115,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"85"} +{"seq_id":"16341687749","text":"def sorting(array):\n\tzeros = []\n\tones = []\n\ttwos = []\n\tfor ele in array:\n\t\tif ele == 0:\n\t\t\tzeros.append(ele)\n\t\telif ele == 1:\n\t\t\tones.append(ele)\n\t\telif ele == 2:\n\t\t\ttwos.append(ele)\n\treturn zeros + ones + twos\n\nsize = int(input())\nnum_list = list(map(int, input().split()))\nsorted_list = sorting(num_list)\nfor ele in sorted_list:\n\tprint(ele, end=' ')","repo_name":"jinarma/Python_General","sub_path":"General/sort0s1s2s.py","file_name":"sort0s1s2s.py","file_ext":"py","file_size_in_byte":351,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"20188729214","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import unicode_literals\n\nfrom googlecloudsdk.api_lib.api_gateway import apis\nfrom googlecloudsdk.api_lib.util import common_args\nfrom googlecloudsdk.calliope import base\nfrom googlecloudsdk.command_lib.api_gateway import resource_args\n\n\n@base.ReleaseTracks(base.ReleaseTrack.ALPHA, base.ReleaseTrack.BETA,\n base.ReleaseTrack.GA)\nclass List(base.ListCommand):\n \"\"\"List APIs.\"\"\"\n\n detailed_help = {\n 'DESCRIPTION':\n '{description}',\n 'EXAMPLES':\n \"\"\"\\\n To list all apis, run:\n\n $ {command}\n \"\"\",\n }\n\n LIST_FORMAT = \"\"\"\n table(\n name.segment(5):label=API_ID,\n displayName,\n managedService,\n state,\n createTime.date()\n )\n \"\"\"\n\n @staticmethod\n def Args(parser):\n resource_args.AddLocationResourceArg(parser,\n 'apis will be listed from',\n default='global')\n\n # Remove unneeded list-related flags from parser\n base.URI_FLAG.RemoveFromParser(parser)\n parser.display_info.AddFormat(List.LIST_FORMAT)\n\n def Run(self, args):\n parent_ref = args.CONCEPTS.location.Parse()\n sort_by = common_args.ParseSortByArg(args.sort_by)\n\n return apis.ApiClient().List(parent_ref.RelativeName(),\n filters=args.filter,\n limit=args.limit,\n page_size=args.page_size,\n sort_by=sort_by)\n","repo_name":"google-cloud-sdk-unofficial/google-cloud-sdk","sub_path":"lib/surface/api_gateway/apis/list.py","file_name":"list.py","file_ext":"py","file_size_in_byte":1588,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"85"} +{"seq_id":"34639339442","text":"#--------- QFCNN stuff -----------\n\nimport torch\nfrom torch.utils.data import Dataset\nfrom torchvision.io import read_image\nfrom torchvision import transforms\nimport pandas as pd\nimport os\nimport numpy as np\nfrom tqdm import tqdm\nimport matplotlib.pyplot as plt\nimport qiskit\nfrom qiskit.circuit.library import QFT\nfrom qiskit_textbook.widgets import scalable_circuit\n\nclass CustomImageDataset(Dataset):\n\n def __init__(self, annotations_file, img_dir, transform = None, target_transform = None) -> None:\n self.img_labels = pd.read_csv(annotations_file, index_col = [0])\n self.img_dir = img_dir\n self.transform = transform\n self.target_transform = target_transform\n\n def __len__(self):\n return len(self.img_labels)\n\n def __getitem__(self, idx):\n img_path = os.path.join(self.img_dir, self.img_labels.iloc[idx,0])\n image = read_image(img_path)\n label = self.img_labels.iloc[idx,1]\n if self.transform:\n image = self.transform(image)\n if self.target_transform:\n label = self.target_transform(label)\n return image, label\n\nx_train = CustomImageDataset(\n annotations_file = 'brain_cancer_output/val.csv',\n img_dir = 'brain_cancer_output/val/Brain Tumor/',\n transform = transforms.Compose([transforms.ToPILImage(), transforms.Grayscale(), transforms.ToTensor()])\n)\n\nx_test = CustomImageDataset(\n annotations_file = 'brain_cancer_output/test.csv',\n img_dir = 'brain_cancer_output/test/Brain Tumor/',\n transform = transforms.Compose([transforms.ToPILImage(), transforms.Grayscale(), transforms.ToTensor()])\n)\n\ntrain_loader = torch.utils.data.DataLoader(x_train, batch_size = 1, shuffle = True)\ntest_loader = torch.utils.data.DataLoader(x_test, batch_size = 1, shuffle = True)\n\nsample_image = torch.squeeze(x_train[0][0]).cpu().detach().numpy()\n\nimage_shape = sample_image[0].shape #(28,28)\n\n# QFT code: https://qiskit.org/textbook/ch-algorithms/quantum-fourier-transform.html\n\n# Quantum circuit for rotation blocks in QFT\ndef qft_rotations(circuit, n):\n # No qubits => no circuit\n if n == 0:\n return circuit\n # Reduce by 1 because of Python indexing\n n -= 1\n # Superpose last qubit\n circuit.h(n)\n for qubit in range(n):\n # Rotate by pi/(2^i) for i=1 to n\n circuit.cp(np.pi/2**(n-qubit), qubit, n)\n # Recursive function\n qft_rotations(circuit, n)\n\nscalable_circuit(qft_rotations)\n\n# Quantum circuit for swap operations in QFT\ndef swap_registers(circuit, n):\n # Swaps qubits outside to inside\n for qubit in range(n//2):\n circuit.swap(qubit, n-qubit-1)\n return circuit\n\n# Function that performs QFT given an initial circuit and n qubits\ndef qft(circuit, n):\n qft_rotations(circuit, n)\n swap_registers(circuit, n)\n return circuit\n\nscalable_circuit(qft)\n\n# Function that performs inverse QFT given an initial circuit and n qubits\ndef inverse_qft(circuit, n):\n qft_circuit = qft(qiskit.QuantumCircuit(n), n)\n invqft_circuit = qft_circuit.inverse()\n circuit.append(invqft_circuit, circuit.qubits[:n])\n return circuit.decompose()\n\ndef prep_image(image):\n # Flatten image matrix (M,L) into a vector (length = ML)\n flat_image = image.flatten()\n # Find norm of flattened image\n mag = np.linalg.norm(flat_image)\n # Find values of c_k in FRQI representation\n thetas = flat_image/mag\n n_qubits = 11\n circuit = qiskit.QuantumCircuit(n_qubits)\n for qubit in range(n_qubits-1):\n circuit.h(qubit)\n\ndef frqi(image):\n flat_image = image.flatten()\n mag = np.linalg.norm(flat_image)\n thetas = flat_image*(np.pi/2)/mag\n n_qubits = 3\n circuit = qiskit.QuantumCircuit(n_qubits)\n circuit.h(range(n_qubits-1))\n circuit.barrier()\n circuit.x(n_qubits-2)\n n = len(thetas)\n circuit.mcry(thetas[0], q_controls = list(range(n_qubits-1)), q_target = n_qubits-1)\n circuit.barrier()\n for i in range(1,n):\n circuit.x(n_qubits-2)\n for j in range(1, n_qubits-1):\n if i % 2**j:\n circuit.x(n_qubits-2-j)\n circuit.mcry(thetas[i], q_controls = list(range(n_qubits-1)), q_target = n_qubits-1)\n circuit.barrier()\n return circuit\n\n\nimg = np.array([[1,2],[2,1]])\ncircuit = frqi(img)\ncircuit.draw('mpl')\nplt.show()\n\n''' \ncircuit = frqi(sample_image)\ncircuit = qft(circuit, 17)\nprint('Circuit completed')\ncircuit.measure_all()\nprint('Measurement taken')\nbackend = qiskit.BasicAer.get_backend('qasm_simulator')\nt = qiskit.transpile(circuit, backend)\nprint('Transpiled')\nqobj = qiskit.assemble(t, shots = 4096)\nprint('Assembled')\nresult = backend.run(qobj).result()\nprint('Run completed')\ncounts = result.get_counts(circuit)\nqiskit.visualization.plot_histogram(counts)\nplt.show()\n'''\n#---------------------------------","repo_name":"virajbpatel/MSci_Project","sub_path":"frqi_encoding.py","file_name":"frqi_encoding.py","file_ext":"py","file_size_in_byte":4798,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"72252521557","text":"#!/usr/bin/python\n# coding: utf-8\n\nr\"\"\"\n\"\"\"\n\nimport sys\n\nfrom OCC.Display.SimpleGui import init_display\nfrom OCC.BRepPrimAPI import BRepPrimAPI_MakeBox\n\ndisplay, start_display, add_menu, add_function_to_menu = init_display('wx')\nmy_box = BRepPrimAPI_MakeBox(10., 20., 30.).Shape()\n\ndisplay.DisplayShape(my_box, update=True)\n\n# in order to be able to export to usual file formats\n# oce must have been compiled with FreeImage\n# otherwise, images will be exported to PPM format\n\n\ndef export_to_BMP(event=None):\n display.View.Dump('./capture_bmp.bmp')\n\n\ndef export_to_PNG(event=None):\n display.View.Dump('./capture_png.png')\n\n\ndef export_to_JPEG(event=None):\n display.View.Dump('./capture_jpeg.jpeg')\n\n\ndef export_to_TIFF(event=None):\n display.View.Dump('./capture_tiff.tiff')\n\n\ndef exit(event=None):\n sys.exit()\n\nif __name__ == '__main__':\n add_menu('screencapture')\n add_function_to_menu('screencapture', export_to_BMP)\n add_function_to_menu('screencapture', export_to_PNG)\n add_function_to_menu('screencapture', export_to_JPEG)\n add_function_to_menu('screencapture', export_to_TIFF)\n add_function_to_menu('screencapture', exit)\n start_display()\n","repo_name":"guillaume-florent/aoc-examples","sub_path":"occexamples/core_display_export_to_image.py","file_name":"core_display_export_to_image.py","file_ext":"py","file_size_in_byte":1180,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"13973282456","text":"import requests\nimport dataset\nimport os\nimport sys\nimport re\nimport datetime as dt\nimport time\nimport traceback\nfrom tqdm import tqdm\nfrom selenium import webdriver\nfrom selenium.common.exceptions import NoSuchElementException, ElementNotVisibleException, TimeoutException\nfrom dateutil.parser import parse as dtparse\n\n# Number of seconds to sleep between crawls\nCRAWL_TIMEOUT = 10\n# Number of timeouts from steam after which we take a long nap (crawl_timeout * 100)\nSTEAM_TIMEOUT_THRESHOLD = 5\n\nTHIRTY_DAY_REVIEW_REGEX = re.compile(r'^([0-9]+)% of the ([,0-9]+) user reviews in the last 30 days')\nALL_TIME_REVIEW_REGEX = re.compile(r'^([0-9]+)% of the ([,0-9]+) user reviews for this game')\nDETAILS_BOX_REGEX = re.compile(r'^Title: ([^\\n]+)'\n r'(?:\\nGenre: ([^\\n]+))?'\n r'(?:\\nDeveloper: ([^\\n]+))?'\n r'(?:\\nPublisher: ([^\\n]+))?')\nNUM_ACHIEVEMENTS_REGEX = re.compile(r'Includes ([,[0-9]+) Steam Achievements')\n\nFREE_TO_PLAY_PHRASES = frozenset(('free to play', 'free', 'play for free!', 'free demo', 'play for free',\n 'free mod', 'play now', 'install theme'))\nFREE_TO_PLAY_REGEXES = frozenset((re.compile('Play .* Demo'),))\n\n# NOTE: Release dates will have punctuation removed before checking\n# against these phrases\nCOMING_SOON_PHRASES = frozenset(('coming soon', 'to be announced',\n 'tbd', 'when you least expect it', 'tba',\n 'скоро', 'not yet available', 'early access soon',\n 'eventually', 'coming this year', 'alpha now available',\n 'soon', 'early access', 'tba - add to your wishlist',\n 'sign up for the close alpha', 'early access coming soon',\n 'when its ready', 'free alpha', 'before the second apocalypse',\n 'soon™', 'when it is finished', 'when the time comes', 'when its done',\n 'wishlist to get notified', ))\n\n# Some release dates are vague ex. \"Summer 2017\" or \"Q2 2016\"; map a season/quarter to a month so Python\n# can parse the date\nSEASON_MONTH_MAPPING = {\n \"summer\": \"july\",\n \"spring\": \"april\",\n \"winter\": \"january\",\n \"fall\": \"october\",\n \"q1\": \"february\",\n \"q2\": \"may\",\n \"q3\": \"august\",\n \"q4\": \"november\",\n}\n\nYEAR_REGEX = re.compile(r'(\\d{4})')\n\n\ndef upsert_all_apps(db):\n '''\n Get the full list of steam apps and upsert them in our database\n on the basis of steam's app ID.\n '''\n json = requests.get('http://api.steampowered.com/ISteamApps/GetAppList/v0001/').json()\n\n apps = json['applist']['apps']['app']\n\n db.begin()\n\n for app in tqdm(apps):\n db['game'].upsert({\n 'steam_app_id': app['appid'],\n 'game_name': app['name'],\n }, keys=['app_id'])\n\n db.commit()\n\n\ndef clean_release_str(str_):\n '''\n Apply some cleaning to a string which we've already determined isn't a date in order\n to more conveniently match it to a list of known phrases\n '''\n return (str_.lower().strip()\n .replace('!', '').replace('.', '').replace('?', '')\n .replace(\"'\", ''))\n\n\ndef pass_through_age_gate(driver):\n '''\n Click through steam's age gate (asking when our birthday is)\n\n Return value: whether we found an age gate\n '''\n try:\n # If this succeeds, we need to pass through the age gate.\n driver.find_element_by_id('agegate_box')\n\n select_element = driver.find_element_by_id('ageYear')\n # open year dialog\n select_element.click()\n # select correct year\n select_element.find_element_by_css_selector('option[value=\"1993\"]').click()\n # close year dialog\n select_element.click()\n # submit the form\n driver.find_element_by_id('agecheck_form').submit()\n return True\n except NoSuchElementException:\n # No age gate; we're good to continue\n return False\n\n\ndef pass_through_nsfw_gate(driver):\n '''\n Click through steam's nsfw gate (\"content may be inappropriate for viewing at work\")\n\n Return value: whether we found an NSFW gate\n '''\n try:\n # If this succeeds, we need to click on the \"continue\" button to tell\n # steam we're okay with seeing NSFW content.\n driver.find_element_by_class_name('agegate_tags')\n\n # Click the \"Continue\" button\n (driver.find_element_by_css_selector(\n '.agegate_text_container.btns > a.btn_grey_white_innerfade:first-child'\n )\n .click())\n\n return True\n except NoSuchElementException:\n # No NSFW gate; we're good to continue\n return False\n\n\ndef scrape_store_page(driver, app_id):\n '''\n Extract all the information we can from the store page for a given app ID.\n\n Use the given driver so we don't have to worry about closing it when we exit.\n '''\n # TODO (maybe): add \"ignore_reason\" field to track why we skipped an app\n results = {'steam_app_id': app_id}\n store_base_url = \"http://store.steampowered.com\"\n app_url = \"{}/app/{}\".format(store_base_url, app_id)\n driver.get(app_url)\n\n # We may have to pass through multiple gates; keep going until\n # the page doesn't match either gate\n while True:\n age_gate_found = pass_through_age_gate(driver)\n nsfw_gate_found = pass_through_nsfw_gate(driver)\n\n if not (age_gate_found or nsfw_gate_found):\n break\n\n if driver.current_url in (store_base_url, '{}/'.format(store_base_url)):\n # We were redirected; the app doesn't have a store page.\n return results\n elif 'store.steampowered.com/video' in driver.current_url:\n # This is a trailer for something else; we'll get the actual app later.\n return results\n elif 'store.steampowered.com/sale' in driver.current_url:\n # This redirects to a store sale page for some reason; ignore it.\n return results\n\n try:\n # If this succeeds, the app has no store page; its store page\n # redirects to its community hub instead. Skip it.\n driver.find_element_by_id('AppHubCards')\n return results\n except NoSuchElementException:\n pass\n\n try:\n # If this succeeds, we've got a steam store error\n error_element = driver.find_element_by_id('error_box')\n error_text = error_element.find_element_by_class_name('error')\n if error_text.text == 'This item is currently unavailable in your region':\n # We can't see this app; ignore it\n return results\n except NoSuchElementException:\n pass\n\n try:\n # If this succeeds, Chrome is showing us an error\n error_element = driver.find_element_by_class_name('error-code')\n if error_element.text == 'ERR_TOO_MANY_REDIRECTS':\n # Something wonky with the server response for this store page;\n # it's redirecting infinitely to itself. Ignore it\n return results\n except NoSuchElementException:\n pass\n\n # Get the description first, since it tells us whether the app is streaming video\n # (which means we don't care about it)\n descriptions = driver.find_elements_by_class_name('game_area_description')\n for description in descriptions:\n if description.text.startswith('ABOUT THIS GAME'):\n results['is_dlc'] = False\n results['long_description'] = description.text\n elif description.text.startswith('ABOUT THIS CONTENT'):\n results['is_dlc'] = True\n results['long_description'] = description.text\n elif description.text.startswith('FEATURE LIST'):\n # This has always been DLC, from what I've seen, but I don't\n # think we can assume that\n results['is_dlc'] = None\n results['long_description'] = description.text\n elif description.text.startswith('ABOUT THIS SERIES'):\n # This is streaming video; we don't care about it, so just return\n return results\n elif description.text.startswith('ABOUT THIS SOFTWARE'):\n # This is computer software; ignore it\n return results\n elif description.text.startswith('ABOUT THIS VIDEO'):\n # Video content; ignore it\n return results\n elif description.text.startswith('ABOUT THIS HARDWARE'):\n # Steam hardware; ignore it\n return results\n if 'long_description' not in results and len(descriptions) > 0:\n raise RuntimeError('Unable to parse description for app_id {}'.format(app_id))\n\n results['game_name'] = (driver\n .find_element_by_class_name('apphub_AppName')\n .text)\n\n try:\n results['short_description'] = (driver\n .find_element_by_class_name('game_description_snippet')\n .text)\n except NoSuchElementException:\n # DLC doesn't have a short description\n pass\n\n reviews_texts = [element.get_attribute('data-store-tooltip')\n for element in (driver\n .find_elements_by_class_name('user_reviews_summary_row'))]\n\n for text in reviews_texts:\n thirty_day_match = THIRTY_DAY_REVIEW_REGEX.match(text)\n\n if thirty_day_match:\n results['pct_positive_reviews_last_30_days'] = int(thirty_day_match.group(1))\n results['reviews_last_30_days'] = int(thirty_day_match.group(2).replace(',', ''))\n else:\n all_time_match = ALL_TIME_REVIEW_REGEX.match(text)\n\n if all_time_match:\n results['pct_positive_reviews_all_time'] = int(all_time_match.group(1))\n results['reviews_all_time'] = int(all_time_match.group(2).replace(',', ''))\n\n try:\n raw_date = driver.find_element_by_css_selector('.release_date .date').text\n\n # Replace seasons with months if needed\n for season, month in SEASON_MONTH_MAPPING.items():\n if season in raw_date.lower():\n raw_date = raw_date.lower().replace(season, month)\n\n try:\n results['release_date'] = dtparse(raw_date)\n except ValueError:\n # Failed to parse the date; match it or raise an error\n if clean_release_str(raw_date) in COMING_SOON_PHRASES:\n # Don't really have a better way to represent a missing\n # release date than None\n results['release_date'] = None\n # Failing everything else, try to just parse a year out and use that\n elif YEAR_REGEX.search(raw_date):\n results['release_date'] = dtparse(YEAR_REGEX.search(raw_date).group(1))\n elif raw_date.lower().startswith('this'):\n results['release_date'] = dtparse((raw_date.lower().replace('this', '')\n .strip() + ' {}'.format(dt.datetime.now().year)))\n else:\n raise ValueError('Unable to parse release date for app {}: {}'.format(\n app_id, raw_date))\n except NoSuchElementException:\n # This app doesn't have a release date for some reason\n pass\n\n # There's additional detail about VR stuff here, but we're not worried about that for now\n details_text = driver.find_elements_by_css_selector('.details_block:not(.vrsupport)')[0].text\n details_match = DETAILS_BOX_REGEX.match(details_text)\n results['title'] = details_match.group(1)\n raw_genre = details_match.group(2)\n if raw_genre is not None:\n results['genres'] = raw_genre.split(', ')\n results['developer'] = details_match.group(3)\n results['publisher'] = details_match.group(4)\n\n block_titles_texts = [element.text\n for element in (driver\n .find_elements_by_class_name('block_title'))]\n\n for text in block_titles_texts:\n num_achievements_match = NUM_ACHIEVEMENTS_REGEX.match(text)\n\n if num_achievements_match:\n results['num_achievements'] = int(num_achievements_match.group(1))\n\n try:\n raw_metacritic_score = driver.find_element_by_class_name('score').text\n if raw_metacritic_score != 'NA':\n results['metacritic_score'] = int(raw_metacritic_score)\n except NoSuchElementException:\n # Some games don't have metascores\n pass\n\n try:\n # Look for prices\n # NOTE: we'll take the first price available on the page (since\n # it's impossible to tell which one is for the actual game), so\n # if a game is only available in a package, we'll record its price\n # as the price of the package\n game_area = driver.find_element_by_class_name(\n 'game_area_purchase_game'\n )\n except NoSuchElementException:\n # No price on the page\n game_area = None\n\n if game_area is not None:\n try:\n # Check within the \"game_area\" to avoid getting a DLC price\n raw_price = game_area.find_element_by_class_name('game_purchase_price').text\n if raw_price.lower() in FREE_TO_PLAY_PHRASES:\n price = 0\n elif any(regex.match(raw_price) for regex in FREE_TO_PLAY_REGEXES):\n price = 0\n elif raw_price == 'Third-party':\n # For all examples thus far, this has meant \"free\", but I don't think\n # we can assume that if the source is a 3rd party\n price = None\n else:\n price = float(raw_price.replace('$', ''))\n results['full_price'] = price\n except NoSuchElementException:\n # On sale\n try:\n raw_price = game_area.find_element_by_class_name('discount_original_price').text\n results['full_price'] = float(raw_price.replace('$', ''))\n except NoSuchElementException:\n # There's a \"game area\" block, but it doesn't have a price\n # in it (the game is free)\n pass\n\n results['game_details'] = []\n game_details_elements = driver.find_elements_by_class_name('game_area_details_specs')\n for element in game_details_elements:\n results['game_details'].append(element.find_element_by_css_selector('.name').text)\n\n results['tags'] = []\n try:\n # Try to get the big list of tags if it's there\n driver.find_element_by_css_selector('.app_tag.add_button').click()\n tag_elements = driver.find_elements_by_css_selector('#app_tagging_modal a.app_tag')\n except (NoSuchElementException, ElementNotVisibleException):\n # Settle for the short list if not\n tag_elements = driver.find_elements_by_css_selector('a.app_tag')\n for element in tag_elements:\n results['tags'].append(element.text)\n\n return results\n\n\ndef insert_with_mapping(*, db, descrs, entity_table, pk_name, join_table, mapping, app_id, crawl_time):\n '''\n Given a db connection, some list of description data, a table of entities, the name of the PK,\n a many-to-many join table for the entities to game crawls,\n a mapping from descrs to entity table IDs, and an app_id/crawl_time,\n update the entity table/mapping if necessary and insert\n the entity in the many-to-many join table.\n\n Mutates the mapping.\n '''\n for descr in descrs:\n try:\n entity_id = mapping[descr]\n except KeyError:\n entity_id = db[entity_table].insert({'descr': descr})\n mapping[descr] = entity_id\n\n db[join_table].insert({\n 'steam_app_id': app_id,\n 'crawl_time': crawl_time,\n pk_name: entity_id,\n })\n\n\ndef do_crawl(app_ids, db):\n '''\n Given a list of steam app IDs and a db connection, do a crawl for the app IDs\n and append the results to our list of crawls in the database.\n '''\n tag_mapping = {r['descr']: r['tag_id'] for r in db['steam_tag'].find()}\n detail_mapping = {r['descr']: r['detail_id'] for r in db['steam_game_detail'].find()}\n genre_mapping = {r['descr']: r['genre_id'] for r in db['steam_genre'].find()}\n\n # Add a handler here to allow us to gracefully save our work and quit\n # if the user halts a crawl early via Ctrl + C.\n # NOTE: since background jobs ignore SIGINT, this has no effect if\n # the script is launched in the background; see\n # http://stackoverflow.com/questions/1112343/how-do-i-capture-sigint-in-python#comment68802096_1112357\n should_quit = False\n\n # Set up a driver and re-use it so we don't have to worry about\n # closing it for each app\n driver = webdriver.Chrome()\n\n # Keep track of the number of times steam has timed out our request\n steam_timeouts = 0\n\n for app_id in tqdm(app_ids):\n try:\n db.begin()\n if should_quit:\n break\n\n time.sleep(CRAWL_TIMEOUT)\n try:\n results = scrape_store_page(driver, app_id)\n except TimeoutException:\n steam_timeouts += 1\n if steam_timeouts >= STEAM_TIMEOUT_THRESHOLD:\n print('Reached timeout threshold of {}; taking a long nap.'.format(\n STEAM_TIMEOUT_THRESHOLD))\n time.sleep(CRAWL_TIMEOUT * 100)\n continue\n\n crawl_time = dt.datetime.now()\n results['crawl_time'] = crawl_time\n\n # Default to empty list if the results don't contain any of these\n tags, details, genres = [], [], []\n\n # Pull the lists off before we insert the main crawl record.\n # Use sets so we don't try to insert duplicates, if there are any.\n if 'tags' in results:\n tags = set(results['tags'])\n del results['tags']\n if 'game_details' in results:\n details = set(results['game_details'])\n del results['game_details']\n if 'genres' in results:\n genres = set(results['genres'])\n del results['genres']\n\n db['game_crawl'].insert(results)\n\n if len(tags) > 0:\n insert_with_mapping(\n db=db,\n descrs=tags,\n entity_table='steam_tag',\n pk_name='tag_id',\n join_table='game_crawl_tag',\n mapping=tag_mapping,\n app_id=app_id,\n crawl_time=crawl_time\n )\n\n if len(details) > 0:\n insert_with_mapping(\n db=db,\n descrs=details,\n entity_table='steam_game_detail',\n pk_name='detail_id',\n join_table='game_crawl_detail',\n mapping=detail_mapping,\n app_id=app_id,\n crawl_time=crawl_time\n )\n\n if len(genres) > 0:\n insert_with_mapping(\n db=db,\n descrs=genres,\n entity_table='steam_genre',\n pk_name='genre_id',\n join_table='game_crawl_genre',\n mapping=genre_mapping,\n app_id=app_id,\n crawl_time=crawl_time\n )\n db.commit()\n except Exception as e:\n # Problem app; pass along our failure and continue to the next one\n print('Failed to load app ID {}; continuing'.format(app_id), file=sys.stderr)\n traceback.print_exc()\n\n # Ensure Postgres lets us continue by rolling back the current transaction\n db.rollback()\n driver.close()\n\n\ndef run():\n db = dataset.connect(os.environ['POSTGRES_URI'], ensure_schema=False)\n\n # Since we're not super worried about having an up-to-date list of apps,\n # run this only if the table is empty\n if db['game'].count() == 0:\n upsert_all_apps(db)\n\n # For now, just crawl the apps we don't already have\n missing_crawl_query = '''\n SELECT g.steam_app_id\n FROM game g\n LEFT JOIN game_crawl gc\n USING (steam_app_id)\n WHERE gc.steam_app_id IS NULL;\n '''\n\n missing_app_ids = [r['steam_app_id'] for r in db.query(missing_crawl_query)]\n\n do_crawl(missing_app_ids, db)\n\n\nif __name__ == '__main__':\n run()\n","repo_name":"jasonnance/steam-store-analysis","sub_path":"scrape.py","file_name":"scrape.py","file_ext":"py","file_size_in_byte":20556,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"72192061719","text":"'''\nQ:Write a program that takes an Integer (0 < x <= 10) then takes X integers one\nafter another, after inputting all those numbers, you should print 'N is odd' or 'N\nis even' depending on each N that was received. Your whole program should be\nimplemented using a single function.\n! You don't have to check for cases that breaks your code.\n\n'''\n\n\ndef odd_even():\n value = int(input(\"enter X value:\"))\n numbers = []\n for i in range(value):\n num = int(input())\n numbers.append(num)\n\n for x in numbers:\n if x % 2 == 0:\n print(f'{x} is even ')\n elif x % 2 != 0:\n print(f'{x} is odd')\n\n\nodd_even()\n","repo_name":"douhahasoon/python-dart-pass","sub_path":"python_pass/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":656,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"26613218720","text":"# Basic Class for MainNN\nimport sys\nimport numpy as np\nimport tensorflow.compat.v1 as tf\ntf.disable_v2_behavior()\n\nsys.path.append('../Lib_Utils')\nimport Utils as utils\n\n\nclass NeuralNetwork(object):\n def __init__(self):\n self.name_tensor_input = \"nn_input\"\n self.name_tensor_output = \"nn_output\"\n self.name_tensor_batch_size = \"nn_batch_size\"\n self.name_tensor_keep_prob = \"nn_keep_prob\"\n # for reproducibility\n self.rng = np.random.RandomState(1234)\n tf.set_random_seed(1234)\n\n def ProcessData(self, load_path, save_path, type_normalize=0):\n \"\"\"\n :param load_path: path of data\n :param save_path: path for saving\n :param type_normalize: how to normalize the data\n 0. (default normalization) load mean and std from txt and (data-mean)/std\n 1. coming soon\n\n :return: a. build savepath\n b. laod data\n c. get the input dim, output dim and data size\n \"\"\"\n self.save_path = save_path\n utils.build_path([save_path])\n if (type_normalize == 0):\n self.input_data, self.input_mean, self.input_std = utils.Normalize(\n np.float32(np.loadtxt(load_path + '/Input.txt')),\n np.float32(np.loadtxt(load_path + '/InputNorm.txt')),\n savefile=save_path + '/X')\n self.output_data, self.output_mean, self.output_std = utils.Normalize(\n np.float32(np.loadtxt(load_path + '/Output.txt')),\n np.float32(np.loadtxt(load_path + '/OutputNorm.txt')),\n savefile=save_path + '/Y')\n else:\n print(\"Coming Soon!!!\")\n self.input_dim = self.input_data.shape[1]\n self.output_dim = self.output_data.shape[1]\n self.data_size = self.input_data.shape[0]\n print(\"Data is Processed\")\n\n def LoadTestData(self, load_path, type_normalize=0):\n if (type_normalize == 0):\n self.input_test = (np.float32(np.loadtxt(load_path + '/Input.txt')) - self.input_mean) / self.input_std\n self.output_test = (np.float32(np.loadtxt(load_path + '/Output.txt')) - self.output_mean) / self.output_std\n else:\n print(\"Coming Soon!!!\")\n self.data_size_test = self.input_test.shape[0]\n print(\"TestData is Processed\")\n\n def BuildConstantPlaceHolder(self):\n self.nn_batch_size = tf.placeholder(tf.int32, name=self.name_tensor_batch_size)\n self.nn_X = tf.placeholder(tf.float32, [None, self.input_dim], name=self.name_tensor_input)\n self.nn_Y = tf.placeholder(tf.float32, [None, self.output_dim], name=self.name_tensor_output)\n self.nn_keep_prob = tf.placeholder(tf.float32, name=self.name_tensor_keep_prob)\n","repo_name":"sebastianstarke/AI4Animation","sub_path":"AI4Animation/SIGGRAPH_Asia_2019/TensorFlow/NSM/Lib_NSM/NeuralNetwork.py","file_name":"NeuralNetwork.py","file_ext":"py","file_size_in_byte":2769,"program_lang":"python","lang":"en","doc_type":"code","stars":7032,"dataset":"github-code","pt":"85"} +{"seq_id":"35742379321","text":"from os.path import join\nimport numpy as np\nfrom torch.utils.data import DataLoader, Dataset\nfrom torch.autograd import Variable\nimport torch\ntrain_dir = './dataset/spectrogram/Training'\n\"\"\"\nclass checkdata(Dataset):\n\n _X = []\n def __init__(self, dir):\n with open(dir) as f:\n for line in f:\n fn = line.split()\n feature_path = join(dir, fn[0].split(\"|\")[0])\n self._X.append(feature_path)\n\n\n def __len__(self):\n return len(self._X)\n\n def __getitem__(self, idx):\n feature = np.load(self._X[idx])\n emotion = feature[:, -1] # last\n spectrum = feature[:, :-1] # except last\n\n sample = {'Spectrum': spectrum, 'Emotion': emotion}\n\n return sample\n\nclass checkdata2(Dataset):\n\n def __init__(self, dir, list_name):\n self._X = []\n with open(join(dir,list_name)) as f:\n for line in f:\n fn = line.split()\n feature_path = join(dir, fn[0].split(\"|\")[0])\n\n self._X.append(feature_path)\n\n\n\n def __len__(self):\n return len(self._X)\n\n def __getitem__(self, idx):\n feature = np.load(self._X[idx])\n #emotion = feature[:, -1] # last\n #spectrum = feature[:, :-1] # except last\n #frame_len = self._frame_len[idx]\n #sample = {'Spectrum': spectrum, 'Emotion': emotion, \"Length\" : frame_len}\n\n return feature\n\ndef collate_fn(batch):\n lengths = [len(x) for x in batch]\n total_lengths = np.sum(lengths)\n emotion = [x[:,-1] for x in batch]\n spectrum = [x[:,:-1] for x in batch]\n data = {'Spectrum': spectrum, 'Emotion': emotion, 'Length' : lengths}\n print(total_lengths)\n return data, total_lengths\n\n\n\ndef concat_batch(data):\n batch_length = data[1]\n X = data[0]['Spectrum']\n y = data[0]['Emotion']\n frm = data[0]['Length']\n data_X = np.zeros((batch_length, 513), dtype=np.float32)\n data_y = np.zeros((batch_length, 1), dtype=np.float32)\n idx = 0\n for i in range(X.__len__()):\n data_X[idx:idx+frm[i],:] = X[i]\n data_y[idx:idx+frm[i],0] = y[i]\n idx += frm[i]\n\n return data_X, data_y\n\n\n\n #sample = {'Spectrum': spectrum, 'Emotion': emotion}\ntrain_ratio = 0.8\n\n\ntrain_data = checkdata2('./dataset/spectrogram/Training', 'train.txt')\nvalid_data = checkdata2('./dataset/spectrogram/Training', 'valid.txt')\n\ntrain_loader = DataLoader(train_data, num_workers=8, collate_fn=collate_fn, batch_size=4)\nvalid_loader = DataLoader(valid_data, num_workers=8, collate_fn=collate_fn, batch_size=2)\n\nfor i, data in enumerate(train_loader):\n print(data)\n batch_X, batch_y = concat_batch(data)\n device = 'cuda:0'\n\n batch_X = Variable(torch.from_numpy(batch_X)).to(device)\n batch_y = Variable(torch.from_numpy(batch_y)).to(device)\n\"\"\"\n\n\n\"\"\"\nz_range = 2.0\nn_img_y = 4\nn_img_x = 11\nv = z_range * 0.7\nz = [[v, v], [-v, v], [v, -v], [-v, -v]]\nz2 = 1.4 * np.ones((513,1))\nrepeat_shape = list(np.int32(np.ones(n_img_y) * n_img_x))\nz = np.repeat(z, repeat_shape, axis=0)\nz = np.clip(z, -z_range, z_range)\nfake_id_PARR = np.zeros(shape=[z.shape[0], 10])\nfor i in range(z.shape[0]):\n if i % 11 == 0: # template\n label = 3 # let's fix label for template as 3 for better style-comparison.\n else:\n label = (i % 11) - 1\n fake_id_PARR[i, label] = 1.0\nprint(z)\n\"\"\"\nn=100\nneu = np.load('result2/ema00096-S_Neutral.npy').T[n]\nang = np.load('result2/ema00096-T_Angry.npy').T[n]\nhap = np.load('result2/ema00096-T_Happy.npy').T[n]\nsad = np.load('result2/ema00096-T_Sad.npy').T[n]\n\nimport matplotlib.pyplot as plt\nplt.subplot(411)\nplt.plot(neu)\nplt.subplot(412)\nplt.plot(ang)\nplt.subplot(413)\nplt.plot(hap)\nplt.subplot(414)\nplt.plot(sad)\nplt.show()\n\ndd = neu - ang\n\nprint(dd)\n","repo_name":"chj1330/homework","sub_path":"final_project/vae/testest.py","file_name":"testest.py","file_ext":"py","file_size_in_byte":3761,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"71158316758","text":"'''\n剑指 Offer 35. 复杂链表的复制\n请实现 copyRandomList 函数，复制一个复杂链表\n。在复杂链表中，每个节点除了有一个 next 指针指向下一个节点\n还有一个 random 指针指向链表中的任意节点或者 null。\n\n本题与主站 138 题相同\n'''\n\n\n# Definition for a Node.\n# class Node:\n# def __init__(self, x: int, next: 'Node' = None, random: 'Node' = None):\n# self.val = int(x)\n# self.next = next\n# self.random = random\n\n# Key: how to know the new node address?\n\n# Method 1: map\n# record a map of old node to new node, then rebuild the link\n# how to know the new node address? -> use map\n\n# Method 2: follow the old node\n# for each old node, create a new node and insert it after the old node, then split the two list\n# how to know the new node address? -> just follow the old node\n\nclass Solution:\n def copyRandomList(self, head):\n old_to_new = {}\n curr = head\n while curr:\n new_node = Node(curr.val)\n old_to_new[curr] = new_node\n curr = curr.next\n\n curr = head\n while curr:\n old_to_new[curr].next = old_to_new[curr.next] if curr.next else None\n old_to_new[curr].random = old_to_new[curr.random] if curr.random else None\n curr = curr.next\n\n # print(old_to_new)\n # while head:\n # print(head.val, head.next.val if head.next else None, head.random.val if head.random else None)\n # head = head.next\n\n return old_to_new[head] if head else None\n","repo_name":"lphlch/Other-Codes","sub_path":"LeetCode/JZ0035.py","file_name":"JZ0035.py","file_ext":"py","file_size_in_byte":1565,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"11675035234","text":"import numpy as np\r\nimport pandas as pd\r\nfrom sklearn.neighbors import NearestNeighbors\r\nfrom collections import Counter\r\nfrom sklearn.metrics.pairwise import cosine_similarity\r\nimport matplotlib.pyplot as plt\r\nimport seaborn as sns\r\nfrom scipy.sparse import csr_matrix\r\nfrom fuzzywuzzy import process\r\nimport warnings\r\nwarnings.simplefilter(action='ignore', category=FutureWarning)\r\n\r\nmovies = pd.read_csv('Data\\\\movies.csv')\r\nratings = pd.read_csv('Data\\\\ratings.csv')\r\n#cold_start_experiment_ratings = pd.read_csv('Data\\\\experiment_data.xlsx')\r\nmovie_titles = dict(zip(movies['movieId'], movies['title']))\r\n\r\ndef main():\r\n\r\n \"\"\" Baseline method \"\"\"\r\n\r\n movie_id = 114074\r\n\r\n movie_recommendation = collaborative_filter(\"The Skeleton Twins (2014)\", k=5)\r\n movie_title = movie_titles[movie_id]\r\n\r\n print(f\"Because you watched {movie_title}\")\r\n for i in movie_recommendation:\r\n print(movie_titles[i])\r\n\r\n \"\"\" Refined design \"\"\"\r\n\r\n data_cleanup(movies)\r\n get_movie_features(movies)\r\n title = 'The Skeleton Twins'\r\n movie_recommendation = content_based_filter(title, recommendations=5)\r\n\r\n print(f\"Recommendations for {title}:\")\r\n print(movies['title'].iloc[movie_recommendation])\r\n\r\n\r\ndef create_user_item(df):\r\n \"\"\"\r\n Generates a sparse matrix.\r\n\r\n Args:\r\n df: pandas dataframe\r\n\r\n Returns:\r\n user_item: sparse matrix\r\n user_mapper: dict that maps user id's to user indices\r\n user_inv_mapper: dict that maps user indices to user id's\r\n movie_mapper: dict that maps movie id's to movie indices\r\n movie_inv_mapper: dict that maps movie indices to movie id's\r\n \"\"\"\r\n N = df['userId'].nunique()\r\n M = df['movieId'].nunique()\r\n\r\n user_mapper = dict(zip(np.unique(df[\"userId\"]), list(range(N))))\r\n movie_mapper = dict(zip(np.unique(df[\"movieId\"]), list(range(M))))\r\n\r\n user_inv_mapper = dict(zip(list(range(N)), np.unique(df[\"userId\"])))\r\n movie_inv_mapper = dict(zip(list(range(M)), np.unique(df[\"movieId\"])))\r\n\r\n user_index = [user_mapper[i] for i in df['userId']]\r\n movie_index = [movie_mapper[i] for i in df['movieId']]\r\n\r\n user_item = csr_matrix((df[\"rating\"], (movie_index, user_index)), shape=(M, N))\r\n\r\n return user_item, user_mapper, movie_mapper, user_inv_mapper, movie_inv_mapper\r\n\r\n\r\ndef collaborative_filter(movie_name, k=5, metric='cosine', show_distance=False):\r\n\r\n \"\"\"\r\n Finds k-nearest neighbours for a given movie id.\r\n\r\n Args:\r\n ratings: pandas dataframe\r\n movie_id: id of the movie of interest\r\n k: number of similar movies to retrieve\r\n metric: distance metric for kNN calculations\r\n\r\n Returns:\r\n list of k similar movie ID's\r\n \"\"\"\r\n\r\n user_item, user_mapper, movie_mapper, user_inv_mapper, movie_inv_mapper = create_user_item(ratings)\r\n neighbour_ids = []\r\n movie_id = get_movie_id(movie_name)\r\n movie_ind = movie_mapper[movie_id]\r\n movie_vec = user_item[movie_ind]\r\n k += 1\r\n kNN = NearestNeighbors(n_neighbors=k, algorithm=\"brute\", metric=metric)\r\n kNN.fit(user_item)\r\n if isinstance(movie_vec, (np.ndarray)):\r\n movie_vec = movie_vec.reshape(1, -1)\r\n neighbour = kNN.kneighbors(movie_vec, return_distance=show_distance)\r\n for i in range(0, k):\r\n n = neighbour.item(i)\r\n neighbour_ids.append(movie_inv_mapper[n])\r\n neighbour_ids.pop(0)\r\n return neighbour_ids\r\n\r\ndef get_year(movie):\r\n year = 2000\r\n if \"(\" in movie:\r\n year = movie.replace(\")\", \"\").split(\"(\")[-1]\r\n if \"–\" in year:\r\n year = year.split(\"–\")[0]\r\n return int(year)\r\n\r\ndef data_cleanup(movie):\r\n movie['genres'] = movie['genres'].apply(lambda x: x.split(\"|\"))\r\n movie['year'] = movie['title'].apply(lambda x: get_year(x))\r\n return movie\r\n\r\ndef round_down(year):\r\n return year - (year % 10)\r\n\r\ndef get_movie_features(movie):\r\n df_genres_count = Counter(genre for genres in movie['genres'] for genre in genres)\r\n # print(f\"There are {len(df_genres_count)} genre labels.\")\r\n \"\"\" deleting no genres listed \"\"\"\r\n del df_genres_count['(no genres listed)']\r\n genres = list(df_genres_count.keys())\r\n for genre in genres:\r\n movie[genre] = movie['genres'].transform(lambda x: int(genre in x))\r\n movie['decade'] = movie['year'].apply(round_down)\r\n movie_decades = pd.get_dummies(movie['decade'])\r\n df_movie_features = pd.concat([movie[genres], movie_decades], axis=1)\r\n #print(df_movie_features.head())\r\n return df_movie_features\r\n\r\n\r\ndef cosine_similiarity(df_movie_features):\r\n cosine_score = cosine_similarity(df_movie_features, df_movie_features)\r\n return cosine_score\r\n\r\ndef get_movie_title(movie,movie_title):\r\n all_movie_titles = movie['title'].tolist()\r\n movie_match = process.extractOne(movie_title, all_movie_titles)\r\n return movie_match[0]\r\n\r\ndef similar_movies_content(movie):\r\n title = get_movie_title(movie,'juminji')\r\n movie_index = dict(zip(movie['title'],list(movie.index)))\r\n movie_features = get_movie_features(movie)\r\n cosine_score = cosine_similiarity(movie_features)\r\n index = movie_index[title]\r\n recommendations = 10\r\n similarity_scores = list(enumerate(cosine_score[index]))\r\n similarity_scores = sorted(similarity_scores, key=lambda x: x[1], reverse=True)\r\n similarity_scores = similarity_scores[1:(recommendations + 1)]\r\n movie_recommendation = [i[0] for i in similarity_scores]\r\n return movie_recommendation\r\n\r\ndef content_based_filter(title_string, recommendations=5):\r\n title = get_movie_title(movies, title_string)\r\n movie_index = dict(zip(movies['title'], list(movies.index)))\r\n index = movie_index[title]\r\n movie_features = get_movie_features(movies)\r\n cosine_score = cosine_similiarity(movie_features)\r\n similarity_scores = list(enumerate(cosine_score[index]))\r\n similarity_scores = sorted(similarity_scores, key=lambda x: x[1], reverse=True)\r\n similarity_scores = similarity_scores[1:(recommendations+1)]\r\n movie_recommendation = [i[0] for i in similarity_scores]\r\n return movie_recommendation\r\n\r\ndef evaluate_contentbased(ratings):\r\n mean = ratings.groupby(['movieId']).mean()\r\n print(mean)\r\n\r\n\r\ndef evaluation_data(ratings):\r\n experiment_data = ratings.sample(frac=.2)\r\n return None\r\n\r\ndef hybrid_model(user_id, liked_movie_name):\r\n num_movies = len(ratings.loc[ratings['userId'] == user_id].index)\r\n if num_movies >= 50:\r\n movie_recommendations = collaborative_filter(liked_movie_name)\r\n print(f\"Because user_id : {user_id} liked {liked_movie_name}\")\r\n print(\"Recommending below movies through collaborative filtering\")\r\n for i in movie_recommendations:\r\n print(f\"{i} {movie_titles[i]}\")\r\n else:\r\n data_cleanup(movies)\r\n get_movie_features(movies)\r\n movie_recommendations = content_based_filter(liked_movie_name)\r\n print(f\"Because user_id : {user_id} liked {liked_movie_name}\")\r\n print(\"Recommending below movies through content based filtering\")\r\n print(movies['title'].iloc[movie_recommendations])\r\n\r\ndef get_movie_id(movie_name):\r\n movie_id = movies.loc[movies[\"title\"] == movie_name][\"movieId\"]\r\n return movie_id.values[0]\r\n\r\n\r\nif __name__ == '__main__':\r\n # hybrid_model(1, \"Toy Story (1995)\")\r\n print()\r\n hybrid_model(2, \"Toy Story (1995)\")\r\n # main()\r\n\r\n\r\n\r\n","repo_name":"IshaSharmaGtech/MovieRecommendation","sub_path":"movie_recommendation.py","file_name":"movie_recommendation.py","file_ext":"py","file_size_in_byte":7414,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"85"} +{"seq_id":"74392109717","text":"\"\"\"These curves are not the same mod Diff+\"\"\"\nimport torch\nfrom math import pi, sqrt\nfrom neural_reparam.plotting import plot_curve, plot_curve_1d\nfrom neural_reparam.utils import stack_last_dim, use_torch_with_numpy\n\n\n# parametrization\n@use_torch_with_numpy\ndef ksi(t):\n return t - torch.sin(2 * pi * t) / (2 * pi)\n\n\n@use_torch_with_numpy\ndef d_ksi_dt(t):\n return 1 - torch.cos(2 * pi * t)\n\n\n# cure 1\n@use_torch_with_numpy\ndef c_1(t):\n return pi ** (-1 / 3) * stack_last_dim(torch.cos(pi * t), torch.sin(pi * t))\n\n\n@use_torch_with_numpy\ndef q(t):\n q_x = torch.cos(pi * t)\n q_y = torch.sin(pi * t)\n return stack_last_dim(q_x, q_y)\n\n\n# curve 2 reparameterized\n@use_torch_with_numpy\ndef c_2(t):\n # c_1 = c_2 o ksi\n c_x = torch.zeros_like(t)\n c_y = torch.pow(3 * t + 1, 1 / 3)\n return stack_last_dim(c_x, c_y)\n\n\n@use_torch_with_numpy\ndef r(t):\n # r = Q(c_2)\n r_x = torch.zeros_like(t)\n r_y = torch.ones_like(t)\n out = stack_last_dim(r_x, r_y)\n return out\n\n\nDIST_R_Q = 2 - sqrt(2)\n\n# run this to show the curves\nif __name__ == \"__main__\":\n # Data frame with dat\n plot_curve(c_1, name=\"../figures/curve_2/curve_c_1.pdf\")\n plot_curve(c_2, name=\"../figures/curve_2/curve_c_2.pdf\")\n plot_curve(q, name=\"../figures/curve_2/curve_q.pdf\")\n plot_curve(r, name=\"../figures/curve_2/curve_r.pdf\")\n plot_curve_1d(ksi)\n","repo_name":"alexarntzen/neural-reparam","sub_path":"experiments/curves_2.py","file_name":"curves_2.py","file_ext":"py","file_size_in_byte":1365,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"74426781077","text":"import http.server\nimport socketserver\nimport logging\nfrom urllib.parse import urlparse\nfrom urllib.parse import parse_qs\nfrom jaeger_client import Config\nimport time\nfrom opentracing.ext import tags\nfrom opentracing.propagation import Format\nfrom random import randrange\n\n# Tracer init and config\ndef init_tracer(service):\n logging.getLogger('').handlers = []\n logging.basicConfig(format='%(message)s', level=logging.DEBUG)\n\n config = Config(\n config={\n 'sampler': {\n 'type': 'const',\n 'param': 1,\n },\n 'local_agent': {\n 'reporting_host': \"my-jaeger-agent.observability.svc.cluster.local\",\n },\n 'logging': True,\n 'reporter_batch_size': 1,\n },\n service_name= service\n )\n\n # this call also sets opentracing.tracer\n return config.initialize_tracer() \n\nclass MyHttpRequestHandler(http.server.SimpleHTTPRequestHandler):\n def do_GET(self):\n # Extract informations for Jaeger\n span_ctx = tracer.extract(Format.HTTP_HEADERS, self.headers)\n span_tags = {tags.SPAN_KIND: tags.SPAN_KIND_RPC_SERVER}\n with tracer.start_span('format', child_of=span_ctx, tags=span_tags) as span:\n\n # Sending an '200 OK' response\n self.send_response(200)\n\n # Setting the header\n self.send_header(\"Content-type\", \"text/html\")\n\n # Whenever using 'send_header', you also have to call 'end_headers'\n self.end_headers()\n\n # Forge html response\n html = f\"

Hello

\"\n\n span.log_kv({'event': 'this is a span'})\n\n # Add some random latency\n time.sleep(randrange(5))\n\n # Writing the HTML contents with UTF-8\n self.wfile.write(bytes(html, \"utf8\"))\n return\n\n# Init tracer\ntracer = init_tracer('server')\n\n# Create an object of the above class\nhandler_object = MyHttpRequestHandler\n\nPORT = 80\n\n# Configure the server\nmy_server = socketserver.TCPServer((\"0.0.0.0\", PORT), handler_object)\n\nprint('Backend server is running')\n\n# Start the server\nmy_server.serve_forever()\n","repo_name":"baalooos/jaeger-example","sub_path":"docker/server/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":2206,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"74631995798","text":"\"\"\"The functions and procedures of this file are responsible\nfor editing the teams and the games.\n\n\"\"\"\n\nfrom _utils import (\n check,\n get_amount, get_option,\n get_non_empty_group,\n get_team, get_two_different_teams,\n get_date, get_place,\n );\n\ndef edit_team(cup : dict, games : dict) -> None:\n \"\"\"This function replaces a team registered in the group\n by an unregistered team.\n\n \"\"\"\n\n group : str = get_non_empty_group(cup);\n\n team : str = get_team(cup, group, False, 'Type the team you want to edit: ');\n new_team : str = get_team(cup, group, message='New team: ');\n \n message : str = f'Do you really want to replace {team} by {new_team}';\n\n if check(message):\n\n # Chain Replacement #\n # The foreign key needs to be changed #\n # because the primary key has been #\n # changed #\n\n for game in games[group]:\n\n if game[0] == team:\n game[0] = new_team;\n elif game[1] == team:\n game[1] = new_team;\n\n # Replacing the team name #\n\n index = cup[group].index(team);\n cup[group][index] = new_team;\n\ndef edit_game(cup : dict, games : dict) -> None:\n \"\"\"The procedure allows that the user to change\n attributes of a match.\n\n \"\"\"\n\n group : str = get_non_empty_group(cup);\n first_team, second_team = get_two_different_teams(cup, group, False);\n\n for game in games[group]:\n \n if (\n (game[0] == first_team and game[1] == second_team) or\n (game[0] == second_team and game[1] == first_team)\n ):\n \n message : str = f'Do you really want to modify the game {first_team} vs {second_team}';\n\n if check(message):\n\n print(f'1 → The number of goals for team {first_team} is wrong');\n print(f'2 → The number of goals for team {second_team} is wrong');\n print ('3 → The time is wrong' );\n print ('4 → The place is wrong');\n\n option : int = get_option(\n [1, 2, 3, 4]\n );\n\n if option == 1:\n \n number_of_goals : int = get_amount(f'Number of goals for {first_team}: ');\n\n if game[0] == first_team:\n game[2] = number_of_goals;\n else:\n game[3] = number_of_goals;\n\n elif option == 2:\n \n number_of_goals : int = get_amount(f'Number of goals for {second_team}: ');\n\n if game[1] == second_team:\n game[3] = number_of_goals;\n else:\n game[2] = number_of_goals;\n\n elif option == 3:\n game[4] = get_date();\n elif option == 4:\n game[5] = get_place();\n\n break;\n else:\n print('The game that you\\'re trying to edit hasn\\'t been registered yet');\n\n","repo_name":"josevaltersilvacarneiro/world-cup","sub_path":"_edit.py","file_name":"_edit.py","file_ext":"py","file_size_in_byte":3163,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"40880717693","text":"import json\nimport logging\nimport urllib.error\nimport urllib.request\n\nimport gi\n\ngi.require_version('Notify', '0.7')\nfrom gi.repository import Notify\n\n\n# noinspection SpellCheckingInspection\nclass NotificationService:\n\tdef __init__ (self):\n\t\tself.down_hosts = []\n\n\tdef notify (self):\n\t\tif self.get_down_hosts():\n\t\t\tif self.down_hosts and len(self.down_hosts) > 0:\n\t\t\t\tself._notify_error()\n\t\t\telse:\n\t\t\t\tself._notify_success()\n\t\telse:\n\t\t\tself._notify_network_failure()\n\n\tdef get_down_hosts (self):\n\t\ttry:\n\t\t\tresponse = urllib.request.urlopen(\"http://localhost:3000/notify\").read().decode('UTF-8')\n\t\t\tself.down_hosts = json.loads(response)[\"failed_hosts\"]\n\t\t\treturn True\n\t\texcept urllib.error.URLError as msg:\n\t\t\t# noinspection PyAttributeOutsideInit\n\t\t\tself.failure_msg = msg.reason\n\t\t\tlogging.error(msg.reason)\n\t\t\treturn False\n\n\tdef _notify_error (self):\n\t\ttitle = \"[Pinger] \" + str(len(self.down_hosts)) + \" hosts are down\"\n\t\tsubtitle = \"Hosts: \"\n\t\tfor host in self.down_hosts:\n\t\t\tsubtitle += host + \", \"\n\t\tsubtitle += \"are down!\"\n\t\tNotify.init(title)\n\t\tnotification = Notify.Notification.new(title, subtitle, \"dialog-error\")\n\t\tnotification.show()\n\n\t# noinspection PyMethodMayBeStatic\n\tdef _notify_success (self):\n\t\ttitle = \"[Pinger] Success\"\n\t\tsubtitle = \"All sites are working.\"\n\t\tNotify.init(title)\n\t\tnotification = Notify.Notification.new(title, subtitle, \"face-smile\")\n\t\tnotification.show()\n\n\tdef _notify_network_failure (self):\n\t\ttitle = \"[Pinger] Error occured\"\n\t\tsubtitle = str(self.failure_msg)\n\t\tNotify.init(title)\n\t\tnotification = Notify.Notification.new(title, subtitle, \"dialog-error\")\n\t\tnotification.show()\n","repo_name":"mszynka/SmallPinger","sub_path":"Notification/NotificationService.py","file_name":"NotificationService.py","file_ext":"py","file_size_in_byte":1621,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"32228163229","text":"#!/usr/bin/python3\n\"\"\"Script that starts a Flask web application\"\"\"\nfrom flask import Flask, render_template\nfrom models import storage\nfrom models.state import State\napp = Flask(__name__)\n\n@app.route('/hbnb_filters', strict_slashes=False)\ndef filters():\n \"\"\"Display a HTML page like 6-index.html\"\"\"\n states = storage.all(State).values()\n return render_template('10-hbnb_filters.html', states=states)\n\n\n@app.teardown_appcontext\ndef teardown_session(Exception=None):\n \"\"\"Tear down sqlalchemy session\"\"\"\n storage.close()\n\nif __name__ == \"__main__\":\n app.run(host='0.0.0.0', port='5000')\n","repo_name":"TobyMike-max/AirBnB_clone_v2","sub_path":"web_flask/10-hbnb_filters.py","file_name":"10-hbnb_filters.py","file_ext":"py","file_size_in_byte":603,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"19865915837","text":"#-*-coding: utf-8 -*-\r\n\r\nfrom datetime import datetime\r\nimport math\r\nimport collections\r\nfrom PyQt5.QAxContainer import *\r\nimport time\r\nimport threading\r\nimport copy\r\nimport Data_Processing.Preprocessing_to_TF as Preprocessing_to_TF\r\nimport Pyro4\r\nimport log\r\nimport sys\r\n\r\nclass Singleton:\r\n __instance = None\r\n\r\n @classmethod\r\n def __get_instance(cls):\r\n return cls.__instance\r\n\r\n @classmethod\r\n def instance(cls, *args, **kargs):\r\n cls.__instance = cls(*args, **kargs)\r\n cls.instance = cls.__get_instance\r\n return cls.__instance\r\n\r\n\r\nclass My_Kiwoom(Singleton):\r\n callback = None\r\n def __init__(self):\r\n self.uri = input(\"Pyro uri : \").strip()\r\n self.transmitter = Pyro4.Proxy(self.uri)\r\n self.predict = 0\r\n\r\n self.kiwoom = QAxWidget(\"KHOPENAPI.KHOpenAPICtrl.1\")\r\n self.kiwoom.OnEventConnect.connect(self.OnEventConnect)\r\n self.kiwoom.OnReceiveTrData.connect(self.OnReceiveTrData)\r\n self.kiwoom.OnReceiveRealData.connect(self.OnReceiveRealData)\r\n self.kiwoom.OnReceiveChejanData.connect(self.OnReceiveChejanData)\r\n self.kiwoom.OnReceiveMsg.connect(self.OnReceiveMsg)\r\n\r\n self.today = datetime.now().strftime(\"%Y%m%d\") # 실현 손익 조회에 사용됨\r\n\r\n self.cur_account = str()\r\n self.cur_code = str()\r\n\r\n self.output_file_name = str()\r\n\r\n self.market = str() # 코스피 or 코스닥\r\n\r\n self.now = 0\r\n self.std_price = 0 # 기준가\r\n self.opening_price = 0 # 시가\r\n self.quotes = [] # 모든 가능한 호가들을 GUI에 넘겨준다.\r\n\r\n self.quote_list = []\r\n self.pre_dict_data = collections.OrderedDict()\r\n self.real_data = collections.OrderedDict()\r\n\r\n self.stop = True\r\n\r\n self.call_price = 200000 # thread에서 매수하는 기본 단위\r\n self.purchase_list = dict()\r\n self.order_cnt = 0 # order 화면 번호의 중복을 방지. 필요한지는 의문임.\r\n\r\n sys.excepthook = self.excepthook\r\n self.btn_login()\r\n\r\n @staticmethod\r\n def excepthook(type, value, traceback):\r\n print(\"Unhandled Error : \", type, value, traceback)\r\n\r\n def set_callback(self, the_callback):\r\n self.callback = the_callback\r\n\r\n def reset_datas(self):\r\n self.pre_dict_data = collections.OrderedDict()\r\n self.quotes.clear()\r\n self.real_data.clear()\r\n\r\n def btn_login(self):\r\n self.kiwoom.dynamicCall(\"CommConnect()\")\r\n\r\n def btn_search_basic(self):\r\n if self.status_check() == 0:\r\n return\r\n if self.cur_code == \"\":\r\n self.callback.show_log(\"Select Code\")\r\n return\r\n log.log_info(\"BTN_search_basic\")\r\n self.kiwoom.dynamicCall('SetInputValue(QString, QString)', \"종목코드\", self.cur_code)\r\n self.kiwoom.dynamicCall('CommRqData(QString, QString, int, QString)', \"주식기본정보\", \"OPT10001\", 0, \"0101\")\r\n\r\n def btn_trading_start(self):\r\n if self.status_check() == 0:\r\n return\r\n if self.cur_code == \"\":\r\n self.callback.show_log(\"Select Code\")\r\n return\r\n print(\"btn trading start\")\r\n log.log_info(\"BTN_trading_start\")\r\n\r\n self.stop = False\r\n\r\n self.thr_trading = threading.Thread(target=self.thread_trading, args=())\r\n self.thr_trading.daemon = True\r\n self.thr_acc = threading.Thread(target = self.thread_refresh_acc, args=())\r\n self.thr_acc.daemon = True\r\n self.thr_trading.start()\r\n self.thr_acc.start()\r\n\r\n self.callback.ui.btn_trading.setEnabled(False)\r\n self.callback.ui.btn_stop.setEnabled(True)\r\n\r\n self.callback.show_log(\"※ 실시간 데이터 수신 시작\")\r\n self.kiwoom.dynamicCall('SetInputValue(QString, QString)', \"종목코드\", self.cur_code)\r\n self.kiwoom.dynamicCall('SetRealReg(QString, QString, QString, QString)', \"0102\", self.cur_code, \"\", \"0\")\r\n self.kiwoom.dynamicCall('CommRqData(QString, QString, int, QString)', \"주식호가요청\", \"OPT10004\", 0, \"0102\")\r\n # 이유는 모르겠으나 주식호가요청tr을 해야 실시간 이벤트가 들어옴\r\n\r\n def btn_real_stop(self):\r\n self.stop = True # thread를 종료시키는 역할\r\n print(\"btn real stop\")\r\n log.log_info(\"BTN_stop\")\r\n if self.status_check() == 0:\r\n self.kiwoom.dynamicCall('SetRealRemove(\"All\", \"All\")')\r\n return\r\n\r\n self.callback.ui.btn_trading.setEnabled(True)\r\n self.callback.ui.btn_stop.setEnabled(False)\r\n\r\n self.callback.show_log(\"※ 실시간 데이터 수신 종료\", t=True)\r\n self.kiwoom.dynamicCall('SetRealRemove(\"All\", \"All\")')\r\n\r\n def btn_call(self, quote, vol, method=\"시장가\"):\r\n print(\"BTN_call\")\r\n log.log_info(\"BTN_call\")\r\n if self.status_check() == 0 or quote == 0 or vol == 0:\r\n return\r\n\r\n self.order_cnt+=1\r\n sRQName = \"매수\" # 사용자 구분 요청 명\r\n sScreenNo = str(self.order_cnt) # 화면 번호\r\n sAccNo = str(self.cur_account) # 계좌 번호\r\n nOrderType = 1 # 주문 유형 (1:신규매수, 2:신규매도, 3:매수취소, 4:매도취소, 5:매수정정, 6:매도정정)\r\n sCode = str(self.cur_code) # 주식 종목 코드\r\n nQty = vol # 주문 수량\r\n nPrice = quote # 주문 단가\r\n\r\n if method == \"지정가\":\r\n sHogaGb = \"00\"\r\n elif method == \"시장가\":\r\n sHogaGb = \"03\"\r\n else:\r\n print(\"btn_call_return\")\r\n return\r\n sOrgOrderNo = \"\" # 원 주문 번호 (취소, 정정 시)\r\n\r\n print(quote, vol)\r\n print(sAccNo, sCode, nQty, nPrice)\r\n\r\n log.log_order(\"<<매수>> [\"+str(nPrice)+\"] 원 [\"+str(nQty)+\"] 주 - \"+str(sCode)+\" \"+str(sAccNo))\r\n self.callback.show_log(\"매수 주문 전송\")\r\n self.kiwoom.dynamicCall('SendOrder(QString, QString, QString, int, QString, int, int, QString, QString)',\r\n [sRQName, sScreenNo, sAccNo, nOrderType, sCode, nQty, nPrice, sHogaGb, sOrgOrderNo])\r\n\r\n def btn_put(self, quote, vol, method=\"시장가\"):\r\n print(\"BTN_put\")\r\n log.log_info(\"BTN_put\")\r\n if self.status_check() == 0 or quote == 0 or vol == 0:\r\n return\r\n\r\n self.order_cnt+=1\r\n sRQName = \"매도\" # 사용자 구분 요청 명\r\n sScreenNo = str(self.order_cnt) # 화면 번호\r\n sAccNo = str(self.cur_account) # 계좌 번호\r\n nOrderType = 2 # 주문 유형 (1:신규매수, 2:신규매도, 3:매수취소, 4:매도취소, 5:매수정정, 6:매도정정)\r\n sCode = str(self.cur_code) # 주식 종목 코드\r\n nQty = vol # 주문 수량\r\n nPrice = quote # 주문 단가\r\n if method == \"지정가\":\r\n sHogaGb = \"00\"\r\n elif method == \"시장가\":\r\n sHogaGb = \"03\"\r\n else:\r\n print(\"btn_put_return\")\r\n return\r\n sOrgOrderNo = \"\" # 원 주문 번호 (취소, 정정 시)\r\n\r\n print(quote, vol)\r\n print(sAccNo, sCode, nQty, nPrice)\r\n\r\n log.log_order(\"<매도> [\"+str(nPrice)+\"] 원 [\"+str(nQty)+\"] 주 - \"+str(sCode)+\" \"+str(sAccNo))\r\n self.callback.show_log(\"매도 주문 전송\")\r\n self.kiwoom.dynamicCall('SendOrder(QString, QString, QString, int, QString, int, int, QString, QString)',\r\n [sRQName, sScreenNo, sAccNo, nOrderType, sCode, nQty, nPrice, sHogaGb, sOrgOrderNo])\r\n\r\n def refresh_acc(self):\r\n log.log_acc(\"Refresh account============================================================\")\r\n self.kiwoom.dynamicCall(\"SetInputValue(QString, Qstring)\", \"계좌번호\", self.cur_account)\r\n self.kiwoom.dynamicCall(\"SetInputValue(QString, Qstring)\", \"비밀번호\", \"0000\")\r\n self.kiwoom.dynamicCall(\"SetInputValue(QString, Qstring)\", \"상장폐지조회구분\", \"0\")\r\n self.kiwoom.dynamicCall(\"SetInputValue(QString, Qstring)\", \"비밀번호입력매체구분\", \"00\")\r\n self.kiwoom.dynamicCall(\"CommRqData(QString, QString, QString, QString)\", \"계좌조회\", \"OPW00004\", \"0\", \"0001\")\r\n # TODO 실현손익 조회에서 문제 발생\r\n self.kiwoom.dynamicCall(\"SetInputValue(QString, Qstring)\", \"계좌번호\", self.cur_account)\r\n self.kiwoom.dynamicCall(\"SetInputValue(QString, Qstring)\", \"비밀번호\", \"0000\")\r\n self.kiwoom.dynamicCall(\"SetInputValue(QString, Qstring)\", \"시작일자\", self.today)\r\n self.kiwoom.dynamicCall(\"SetInputValue(QString, Qstring)\", \"종료일자\", self.today)\r\n self.kiwoom.dynamicCall(\"CommRqData(QString, QString, QString, QString)\", \"실현손익조회\", \"OPT10074\", \"0\", \"0002\")\r\n\r\n def status_check(self):\r\n if self.kiwoom.dynamicCall('GetConnectState()') == 0:\r\n self.callback.show_log(\"Not Connected\", t=True)\r\n return 0\r\n else:\r\n return 1\r\n\r\n def show_order_log(self, signal_, code_, name_, price_, vol_):\r\n # 주문이 체결 된 후 호출됨.\r\n signal = signal_\r\n code = code_\r\n name = name_\r\n price = price_\r\n vol = vol_\r\n if signal == \"+매수\":\r\n self.callback.show_order_log(\"\", t=True)\r\n self.callback.show_order_log(\"======== 매수 체결 ========\", t=False, pre=\" * \", color=\"red\")\r\n self.callback.show_order_log(code + \" \" + name, t=False, pre=\" * \")\r\n self.callback.show_order_log(str(price) + \"] 원 x [\" + str(vol) + \"] 개 체결 완료\", t=False,\r\n pre=\" * [\")\r\n elif signal == \"-매도\":\r\n self.callback.show_order_log(\"\", t=True)\r\n self.callback.show_order_log(\"======== 매도 체결 ========\", t=False, pre=\" * \", color=\"blue\")\r\n self.callback.show_order_log(code + \" \" + name, t=False, pre=\" * \")\r\n self.callback.show_order_log(str(price) + \"] 원 x [\" + str(vol) + \"] 개 체결 완료\", t=False,\r\n pre=\" * [\")\r\n\r\n def set_acc(self, cur_acc): # self.cur_account 변수에, 선택한 계좌번호 입력\r\n self.cur_account = cur_acc\r\n self.callback.show_log(self.cur_account+\" 선택\", t=True)\r\n\r\n if '5073' in self.cur_account or '5134' in self.cur_account:\r\n self.callback.show_log(\" **** WARNING **** 실제 투자 접속\", t=False, color=\"red\")\r\n else:\r\n self.callback.show_log(\" * 모의 투자 *\", t=False, color=\"blue\")\r\n self.refresh_acc()\r\n\r\n def set_cur_code(self, cur_code):\r\n self.reset_datas()\r\n\r\n self.kiwoom.dynamicCall('SetRealRemove(\"All\", \"All\")')\r\n if self.status_check() == 0:\r\n return\r\n self.cur_code = cur_code\r\n self.purchase_list[\"\"] = [0, 0, 0]\r\n if self.cur_code not in self.purchase_list.keys():\r\n self.purchase_list[self.cur_code] = [0, 0, 0] # 손익 금액, 총 매입 가격, 수량\r\n self.output_file_name = \"{}{}{}\".format(datetime.now().strftime(\"%m.%d \"), self.cur_code, \" - output.csv\")\r\n self.kiwoom.dynamicCall('SetInputValue(QString, QString)', \"종목코드\", self.cur_code)\r\n self.kiwoom.dynamicCall('CommRqData(QString, QString, int, QString)', \"기준가_시가\", \"OPT10001\", 0, \"0103\")\r\n\r\n foo = self.kiwoom.GetMarketType(self.cur_code)\r\n if foo == 0:\r\n self.market = \"코스피\"\r\n elif foo == 10:\r\n self.market = \"코스닥\"\r\n log.log_info(\"code selected : \" + str(self.cur_code))\r\n\r\n def set_quote(self):\r\n self.std_price = int(self.std_price)\r\n self.opening_price = abs(int(self.opening_price))\r\n\r\n lower_limit = math.ceil(self.std_price * 0.7)\r\n upper_limit = math.floor(self.std_price * 1.3)\r\n i = self.set_unit(lower_limit)\r\n j = self.set_unit(upper_limit)\r\n\r\n while lower_limit % i != 0:\r\n lower_limit += 1\r\n while upper_limit % j != 0:\r\n upper_limit -= 1\r\n\r\n q = lower_limit\r\n self.real_data[0] = [0,0] # 상한가/하한가 초과 호가는 0으로 들어옴.\r\n self.real_data['time'] = 0\r\n self.real_data['now'] = 0\r\n while upper_limit not in self.quotes:\r\n self.real_data[q] = [0,0] # [0] : 잔량, [1] : 주문량\r\n self.quotes.append(q) # 매수, 매도 시 선택할 수 있는 호가\r\n i = self.set_unit(q)\r\n q += i\r\n\r\n self.callback.show_quote(self.quotes, self.opening_price)\r\n\r\n def set_unit(self, num):\r\n if self.market == \"코스피\":\r\n if num < 1000:\r\n i = 1\r\n elif num < 5000:\r\n i = 5\r\n elif num < 10000:\r\n i = 10\r\n elif num < 50000:\r\n i = 50\r\n elif num < 100000:\r\n i = 100\r\n elif num < 500000:\r\n i = 500\r\n else:\r\n i = 1000\r\n return i\r\n elif self.market == \"코스닥\":\r\n if num < 1000:\r\n i = 1\r\n elif num < 5000:\r\n i = 5\r\n elif num < 10000:\r\n i = 10\r\n elif num < 50000:\r\n i = 50\r\n else:\r\n i = 100\r\n return i\r\n else :\r\n print(\"시장 구분 에러.\")\r\n\r\n def my_OnReceiveRealData_new(self, sJongmokCode, sRealType, sRealData):\r\n data_seq = [58, 52, 46, 40, 34, 28, 22, 16, 10, 4, 1, 7, 13, 19, 25, 31, 37, 43, 49, 55]\r\n\r\n data = sRealData.split('\\t')[:65]\r\n data = list(map(int, data))\r\n data = list(map(abs, data))\r\n\r\n self.real_data['time'] = data[0]\r\n self.real_data['now'] = data[4]\r\n self.callback.show_price(abs(int(data[4]))) # GUI에 매수1 가격 보여줌\r\n\r\n self.quote_list = [data[k] for k in data_seq] # 이벤트로 들어온 호가 리스트를 저장한다.\r\n for k in range(0,10): # 해당 호가에 주문 잔량을 넣어준다.\r\n idx = data_seq[k]\r\n self.real_data[data[idx]][0] = data[idx+1]\r\n\r\n for k in range(10,20): # 매도 호가의 주문 잔량은 기본적으로 (-)이다.\r\n idx = data_seq[k]\r\n self.real_data[data[idx]][0] = -1 * data[idx+1]\r\n\r\n if len(self.pre_dict_data) == 0 or self.pre_dict_data['time'] == 0: # 초기 실행일 때\r\n print(\"초기 실행 : {}\")\r\n self.pre_dict_data = copy.deepcopy(self.real_data)\r\n return\r\n\r\n def thread_refresh_acc(self):\r\n while True:\r\n if self.stop == True:\r\n break\r\n t = datetime.now().strftime(\"%S\")\r\n if int(t) % 5 == 0: # 5초마다 계좌 조회\r\n print(\"5초\")\r\n self.callback.ui.btn_ref_acc.click() # refresh_acc를 직접 호출하면 꺼지기도 한다.\r\n time.sleep(1)\r\n\r\n def thread_trading(self):\r\n \"\"\"\r\n btn_trading_start를 누르면\r\n 주문이 안들어와도 계속 실행된다.\r\n btn_real_stop을 누르면 종료.\r\n \"\"\"\r\n\r\n \"\"\"\r\n 오른다는 예측이 연달아 나타남.\r\n \"\"\"\r\n while True:\r\n try:\r\n if self.real_data['time'] == 0:\r\n print(\"신호 없음.\")\r\n if self.stop == True:\r\n print(\"Stoped 0\")\r\n break\r\n time.sleep(1)\r\n continue\r\n if self.stop == True:\r\n print(\"Stoped 1\")\r\n break\r\n print(\"Thread {}\".format(datetime.now().strftime(\"%H:%M:%S\")))\r\n for q in self.quote_list: # 추가 주문량 계산\r\n if self.pre_dict_data[\"now\"] == self.real_data[\"now\"]: # 현재가 변화 없음\r\n self.real_data[q][1] = self.real_data[q][0] - self.pre_dict_data[q][0] # 호가 잔량을 제대로 넣어준다.\r\n else: # 현재가 변화\r\n self.real_data[q][1] = 0\r\n\r\n processed_data = Preprocessing_to_TF.process(copy.deepcopy(self.real_data), copy.deepcopy(self.quote_list))\r\n self.predict = self.transmitter.wrapper(processed_data[2:]) # np.array 에 대해 argmax한 값. 0, 1, 2\r\n\r\n if self.predict != 0:\r\n print(\"** predict value : \", self.predict)\r\n log.log_trading(\"predict value : \"+str(self.predict))\r\n # Call trading function. Then should I reinitialize self.predict??\r\n if self.predict == 0: # 주가 유지 예상 TODO 일단 binary Positive만 예측\r\n pass\r\n elif self.predict == 1: # 주가 상승 예상\r\n # TODO 지정가로 거래가 효율적으로 되는지 확인\r\n quote = self.real_data[\"now\"]\r\n vol = math.floor(self.call_price / self.real_data[\"now\"])\r\n method = \"시장가\"\r\n self.btn_call(quote=quote, vol=vol, method=method)\r\n self.purchase_list[self.cur_code][1] += (quote * vol)\r\n self.purchase_list[self.cur_code][2] = self.purchase_list[self.cur_code][1] + vol\r\n else:\r\n pass\r\n\r\n #한 프로그램에서 여러개를 거래할 떄에는 list 원소들에 대해 thread를 돌려서 현재가vs매입가 비교가 필요.\r\n #이 때, 제한 횟수를 안넘도록 주기 조정\r\n\r\n if self.purchase_list[self.cur_code][1] == 0: # 총 매수 금액이 0일 때\r\n profit = 0\r\n else:\r\n profit = (self.purchase_list[self.cur_code][0] / self.purchase_list[self.cur_code][1]) * 100\r\n #print(self.purchase_list)\r\n #print(self.cur_code, \"현재 수익률 : \", profit)\r\n if profit < -3: # 수익률 #TODO interval을 고려해야함 interval이 100인데 1초밖에 안되고 팔면 안됨\r\n quote = -1\r\n vol = self.purchase_list[self.cur_code][2]\r\n method = \"시장가\"\r\n self.btn_put(quote=quote, vol=vol, method=method) # (일단) 다 팔아버린다.\r\n self.purchase_list[self.cur_code][1] = 0\r\n self.purchase_list[self.cur_code][2] -= vol\r\n elif profit > 3: # 수익률\r\n quote = -1\r\n vol = self.purchase_list[self.cur_code][2]\r\n method = \"시장가\"\r\n self.btn_put(quote=quote, vol=vol, method=method) # (일단) 다 팔아버린다.\r\n self.purchase_list[self.cur_code][1] = 0\r\n self.purchase_list[self.cur_code][2] -= vol\r\n\r\n self.pre_dict_data = copy.deepcopy(self.real_data)\r\n time.sleep(1) # 1초 간격으로 재실행된다.\r\n except Exception as e:\r\n print(e)\r\n\r\n print(\"Thread-trading Stop\")\r\n\r\n def OnEventConnect(self, nErrCode):\r\n if nErrCode == 0:\r\n log.log_info(\"로그인 성공\")\r\n self.callback.show_log(\"로그인 성공\")\r\n else:\r\n log.log_info(\"로그인 해제\")\r\n self.callback.show_log(\"로그인 해제\")\r\n acc_all = self.kiwoom.dynamicCall('GetLoginInfo(QString)', [\"ACCNO\"]) # str type\r\n account = acc_all[:-1].split(';') # 계좌 리스트 마지막에 공백 문자 있음\r\n self.callback.status_changed(nErrCode) # 상태바 메시지 변경\r\n self.callback.refresh_account(account, nErrCode) # combo box 에 계좌 입력\r\n\r\n def OnReceiveRealData(self, sJongmokCode, sRealType, sRealData):\r\n # sJongmokCode 종목코드\r\n # sRealType 리얼타입 ex.'주식호가요청'\r\n # sRealData 리얼데이터\r\n #print(\"sREALDATA : \", sRealType)\r\n if sRealType == \"주식호가잔량\":\r\n self.my_OnReceiveRealData_new(sJongmokCode, sRealType, sRealData)\r\n\r\n def OnReceiveTrData(self, sScrNo, sRQName, sTRCode, sRecordName, sPreNext):\r\n # sScrNo - 화면 번호 ex.0101\r\n # sRQName - 사용자 구분 명 ex.주식기본정보\r\n # sTRCode - Tran 명 ex.OPT10001\r\n # sRecordName - Record 명 ex.\r\n # sPreNext - 연속 조회 유무 ex.0\r\n print(\"{} ※Tr Data Event※\".format(datetime.now().strftime(\"%H:%M:%S \")))\r\n print(\" ※\", sRQName, sTRCode, sRecordName, sPreNext)\r\n\r\n if sRQName == \"실현손익조회\":\r\n try:\r\n print(\"TR 실현 손익 조회\")\r\n self.total_call = int(self.kiwoom.dynamicCall(\"GetCommData(QString, QString, int, QString\", sTRCode, sRQName, 0, \"총매수금액\").strip()) # str type\r\n self.total_put = int(self.kiwoom.dynamicCall(\"GetCommData(QString, QString, int, QString\", sTRCode, sRQName, 0, \"총매도금액\").strip())\r\n self.commission = int(self.kiwoom.dynamicCall(\"GetCommData(QString, QString, int, QString\", sTRCode, sRQName, 0, \"매매수수료\").strip())\r\n self.tax = int(self.kiwoom.dynamicCall(\"GetCommData(QString, QString, int, QString\", sTRCode, sRQName, 0,\"매매세금\").strip())\r\n self.profit = int(self.kiwoom.dynamicCall(\"GetCommData(QString, QString, int, QString\", sTRCode, sRQName, 0, \"실현손익\").strip()) # 이 자체가 실현손익\r\n\r\n if self.total_call ==\"\" or self.total_put ==\"\" or self.commission ==\"\" or self.tax ==\"\" or self.profit==\"\":\r\n print(\"실현손익 데이터 오류\")\r\n log.log_acc(\"계좌 조회 실패 (실현 손익 조회)\")\r\n return\r\n self.commission = self.commission + self.tax\r\n print(\"acc_table2()\")\r\n acc_info_profit = list(map(str, [self.total_call, self.total_put, self.commission, self.profit]))\r\n self.callback.refresh_acc_table2(acc_info_profit)\r\n log.log_acc(\",\".join(acc_info_profit))\r\n except :\r\n print(\"error 발생1\")\r\n return\r\n\r\n if sRQName == \"현재���\":\r\n self.now = self.kiwoom.dynamicCall(\"GetCommData(QString, QString, int, QString\", sTRCode, sRQName, 0, \"현재가\")\r\n\r\n if sRQName == \"계좌조회\":\r\n print(\"TR 계좌조회\")\r\n #멀티데이터\r\n try:\r\n num_code = self.kiwoom.dynamicCall('GetRepeatCnt(QString, QString)', sTRCode, sRQName)\r\n acc_info_detail = list() # 여러 개의 종목 정보를 저장\r\n profit_loss = 0\r\n\r\n for cnt in range(num_code):\r\n list_ = [] # 한 개의 종목 정보를 저장\r\n code = self.kiwoom.dynamicCall(\"GetCommData(QString, QString, int, QString\", sTRCode, sRQName, cnt, \"종목코드\").strip() #\r\n list_.append(code)\r\n self.purchase_list[code[1:]] = [0,0,0] # 보유 종목 정보를 추가.\r\n t = self.kiwoom.dynamicCall(\"GetCommData(QString, QString, int, QString\", sTRCode, sRQName, cnt, \"종목명\").strip() #\r\n list_.append(t)\r\n t = self.kiwoom.dynamicCall(\"GetCommData(QString, QString, int, QString\", sTRCode, sRQName, cnt, \"손익금액\") #\r\n list_.append(int(t))\r\n self.purchase_list[code[1:]][0] = int(t) # 보유 종목 정보에 손익 금액 추가.\r\n profit_loss += int(t)\r\n t = self.kiwoom.dynamicCall(\"GetCommData(QString, QString, int, QString\", sTRCode, sRQName, cnt, \"손익율\") # %값으로 출력됨.\r\n list_.append(round(float(t), 2))\r\n t = self.kiwoom.dynamicCall(\"GetCommData(QString, QString, int, QString\", sTRCode, sRQName, cnt, \"평균단가\") # 매입 평균 단가\r\n list_.append(round(float(t), 2))\r\n t = self.kiwoom.dynamicCall(\"GetCommData(QString, QString, int, QString\", sTRCode, sRQName, cnt, \"현재가\") #\r\n list_.append(int(t))\r\n t = self.kiwoom.dynamicCall(\"GetCommData(QString, QString, int, QString\", sTRCode, sRQName, cnt, \"매입금액\") # (평균단가 * 보유 수량)\r\n list_.append(int(t))\r\n self.purchase_list[code[1:]][1] = int(t) # 보유 종목 정보에 총 매입 금액 추가.\r\n t = self.kiwoom.dynamicCall(\"GetCommData(QString, QString, int, QString\", sTRCode, sRQName, cnt, \"평가금액\") # (현재가 * 보유 수량)\r\n list_.append(int(t))\r\n t = self.kiwoom.dynamicCall(\"GetCommData(QString, QString, int, QString\", sTRCode, sRQName, cnt, \"보유수량\") #\r\n list_.append(int(t))\r\n self.purchase_list[code[1:]][2] = int(t) # 보유 종목 정보에 보유 수량 추가.\r\n\r\n acc_info_detail.append(list_)\r\n log.log_acc(\",\".join(list(map(str, list_))))\r\n\r\n if len(acc_info_detail) != 0:\r\n for acc in acc_info_detail:\r\n if \"\" in acc:\r\n print(\"계좌 조회 데이터 오류.\")\r\n return\r\n\r\n print(\"acc_table_detail\")\r\n self.callback.refresh_acc_table_detail(acc_info_detail)\r\n elif len(acc_info_detail) == 0:\r\n print(\"계좌 데이터가 없습니다.\")\r\n self.callback.ui.tb_acc_detail.setRowCount(0)\r\n\r\n except Exception as e:\r\n print(\"계좌 조회 데이터 오류 : \", e)\r\n log.log_acc(\"계좌 조회 실패 (데이터 조회)\")\r\n\r\n #싱글데이터\r\n try:\r\n acc_info = list()\r\n t = self.kiwoom.dynamicCall(\"GetCommData(QString, QString, int, QString\", sTRCode, sRQName, 0, \"총매입금액\") # 총 매입\r\n acc_info.append(int(t))\r\n t = self.kiwoom.dynamicCall(\"GetCommData(QString, QString, int, QString\", sTRCode, sRQName, 0, \"유가잔고평가액\") # 총 평가\r\n acc_info.append(int(t))\r\n acc_info.append(profit_loss) # 멀티 데이터에서 계산한 profit_loss\r\n if float(acc_info[0] == 0.0): # 매입이 0일 때\r\n 현재수익률 = 0\r\n else:\r\n 현재수익률 = str(round(float(profit_loss) / float(acc_info[0]), 2) * 100)\r\n acc_info.append(현재수익률)\r\n acc_info = list(map(str, acc_info))\r\n log.log_acc(\",\".join(acc_info))\r\n\r\n if \"\" in acc_info:\r\n print(\"계좌 싱글 데이터 오류.\")\r\n return\r\n\r\n print(\"acc_table(acc_info)\")\r\n self.callback.refresh_acc_table(acc_info)\r\n except :\r\n print(\"계좌 실글 데���터 오류\")\r\n log.log_acc(\"계좌 조회 실패 (데이터 조회)\")\r\n\r\n if sRQName == \"매수\":\r\n print(\"TR - 매수\")\r\n print(sTRCode) # KOA_NORMAL_BUY_KP_ORD\r\n pass\r\n\r\n if sRQName == \"매도\":\r\n print(\"TR - 매도\")\r\n print(sTRCode) # KOA_NORMAL_SELL_KP_ORD\r\n pass\r\n\r\n if sRQName == \"기준가_시가\":\r\n self.std_price = self.kiwoom.dynamicCall('GetCommData(QString, QString, int, QString)', sTRCode, sRQName, 0, \"기준가\").strip()\r\n self.opening_price = self.kiwoom.dynamicCall('GetCommData(QString, QString, int, QString)', sTRCode, sRQName, 0, \"시가\").strip()\r\n self.kiwoom.dynamicCall('SetRealRemove(\"All\", \"All\")')\r\n self.set_quote()\r\n\r\n if sRQName == \"주식기본정보\":\r\n name = self.kiwoom.dynamicCall('GetCommData(QString, QString, int, QString)', sTRCode, sRQName, 0, \"종목명\")\r\n cord = self.kiwoom.dynamicCall('GetCommData(QString, QString, int, QString)', sTRCode, sRQName, 0, \"종목코드\")\r\n self.opening_price = self.kiwoom.dynamicCall('GetCommData(QString, QString, int, QString)', sTRCode, sRQName, 0, \"시가\")\r\n self.std_price = self.kiwoom.dynamicCall('GetCommData(QString, QString, int, QString)', sTRCode, sRQName, 0, \"기준가\")\r\n cur_price = self.kiwoom.dynamicCall('GetCommData(QString, QString, int, QString)', sTRCode, sRQName, 0, \"현재가\")\r\n self.kiwoom.dynamicCall('SetRealRemove(\"All\", \"All\")')\r\n\r\n info_name = [\"종목명\",\"종목코드\", \"시장\", \"시가\",\"기준가\",\"현재가\"]\r\n info = [name, cord, self.market, self.opening_price, self.std_price, cur_price]\r\n\r\n self.callback.show_log(\"\", t=True)\r\n for i in range(len(info)):\r\n info[i] = info[i].strip()\r\n self.callback.show_log(info_name[i]+\" : \"+info[i], t=False, pre=\" * \")\r\n\r\n def OnReceiveChejanData(self, sGubun, nItemCnt, sFidList):\r\n # sGubun – 체결 구분 - '0': 주문체결통보, '1': 잔고통보, '3': 특이신호\r\n # nItemCnt - 아이템 갯수\r\n # sFidList – 데이터 리스트 - 데이터 구분은 ‘;’ 이다.\r\n #print(sGubun, type(sGubun))\r\n #print(nItemCnt)\r\n #print(sFidList)\r\n\r\n fid_list = sFidList.split(';')\r\n #for fid in fid_list:\r\n # result = self.kiwoom.dynamicCall(\"GetChejanData(int)\", int(fid))\r\n # print(\"fid : {}, result : {}\".format(fid, result))\r\n\r\n # 주문 -> 체결\r\n if sGubun == '0':\r\n \"\"\"\r\n [9201] = 계좌번호\r\n [9203] = 주문번호\r\n [9205] = 관리자사번\r\n [9001] = 종목코드,업종코드\r\n [912] = 주문업무분류\r\n [913] = 주문상태\r\n [302] = 종목명\r\n [900] = 주문수량\r\n [901] = 주문가격 # 시장가 주문일 경우 0\r\n [902] = 미체결수량\r\n [903] = 체결누계금액 # \"체결\"시에만 체결누계(?)금액 출력.\r\n [904] = 원주문번호\r\n [905] = 주문구분\r\n [906] = 매매구분\r\n [907] = 매도수구분\r\n [908] = 주문/체결시간\r\n [909] = 체결번호\r\n [910] = 체결가 ####\r\n [911] = 체결량 ####\r\n [10] = 현재가\r\n [27] = (최우선)매도호가\r\n [28] = (최우선)매수호가\r\n [914] = 단위체결가\r\n [915] = 단위체결량\r\n [938] = 당일매매수수료\r\n [939] = 당일매매세금\r\n [919] = 거부사유\r\n [920] = 화면번호\r\n [921] = 터미널번호\r\n [922] = 신용구분(실시간 체결용)\r\n [923] = 대출일(실시간 체결용)\r\n \"\"\"\r\n signal = self.kiwoom.dynamicCall(\"GetChejanData(int)\", 905) # \"+매수\" or \"-매도\"\r\n c = self.kiwoom.dynamicCall(\"GetChejanData(int)\", 9001)\r\n n = self.kiwoom.dynamicCall(\"GetChejanData(int)\", 302).strip()\r\n p = self.kiwoom.dynamicCall(\"GetChejanData(int)\", 901)\r\n v = self.kiwoom.dynamicCall(\"GetChejanData(int)\", 900)\r\n\r\n if self.kiwoom.dynamicCall(\"GetChejanData(int)\", 913) == '접수':\r\n if signal == \"+매수\":\r\n color = \"red\"\r\n else:\r\n color = \"blue\"\r\n self.callback.show_order_log(\"\", t=True)\r\n self.callback.show_order_log(n + \" [\" + p + \"]원 [\" + v + \"]주 \" + signal, pre=\"[접수] \", t=False, color=color)\r\n elif self.kiwoom.dynamicCall(\"GetChejanData(int)\", 913) == '체결':\r\n self.show_order_log(signal, c, n, p, v)\r\n #self.refresh_acc()\r\n\r\n L = [9203,9205,9001,912,913,302,900,901,902,903,904,905,906,907,908,909,910,911,10,27,28,914,915,938,939,919,920,921,922,923]\r\n #for i in L:\r\n # print(\"{} : {}\".format(i, self.kiwoom.dynamicCall(\"GetChejanData(int)\", i)))\r\n # 잔고 통보\r\n elif sGubun == '1':\r\n \"\"\"\r\n [9201] = 계좌번호\r\n [9001] = 종목코드,업종코드\r\n [917] = 신용구분\r\n [916] = 대출일\r\n [302] = 종목명\r\n [10] = 현재가\r\n [930] = 보유수량\r\n [931] = 매입단가\r\n [932] = 총매입가\r\n [933] = 주문가능수량\r\n [945] = 당일순매수량\r\n [946] = 매도/매수구분\r\n [950] = 당일총매도손일\r\n [951] = 예수금\r\n [27] = (최우선)매도호가\r\n [28] = (최우선)매수호가\r\n [307] = 기준가\r\n [8019] = 손익율\r\n [957] = 신용금액\r\n [958] = 신용이자\r\n [918] = 만기일\r\n [990] = 당일실현손익(유가)\r\n [991] = 당일실현손익률(유가)\r\n [992] = 당일실현손익(신용)\r\n [993] = 당일실현손익률(신용)\r\n [959] = 담보대출수량\r\n [924] = Extra Item\r\n \"\"\"\r\n print(\"잔고 통보\")\r\n L = [9001,917,916,302,10,930,931,932,933,945,946,950,951,27,28,307,8019,957,958,918,990,991,992,993,959,924]\r\n #for i in L:\r\n # print(\"{} : {}\".format(i, self.kiwoom.dynamicCall(\"GetChejanData(int)\", i)))\r\n else:\r\n print(\"기타 케이스\")\r\n\r\n def OnReceiveMsg(self, sScrNo, sRQName, sTrCode, sMsg):\r\n if sScrNo == \"777\": # 매수\r\n self.callback.show_log(sMsg, pre=\"[M] \", color=\"purple\")\r\n elif sScrNo == \"778\": # 매도\r\n self.callback.show_log(sMsg, pre=\"[M] \", color=\"purple\")\r\n else:\r\n self.callback.show_log(sMsg, pre=\"[M] \", color=\"purple\")\r\n","repo_name":"a2tt/system-trading","sub_path":"Kiwoom_stock.py","file_name":"Kiwoom_stock.py","file_ext":"py","file_size_in_byte":34358,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"12311588926","text":"# input = 1 liter / second coming from the reactor\n# condition = needs to rest for 5 seconds\n\nimport json\nimport socket\nimport random\nimport time\nimport platform\nfrom multiclient_functions.multiclient_functions import send\n\nHEADER = 1024\nPORT = 5050\nFORMAT = 'utf-8'\nDISCONNECT_MESSAGE = \"!DISCONNECT\"\nSERVER_WIN = \"192.168.0.6\"\nSERVER_LINUX = \"127.0.1.1\"\n\ncur_os = platform.system()\n\nif cur_os == \"Windows\":\n ADDR = (SERVER_WIN, PORT)\nelse:\n ADDR = (SERVER_LINUX, PORT)\n\nclient = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\nclient.connect(ADDR)\n\ndef main():\n max_capacity = 10\n decanter_time = 5\n\n while True:\n decanter_out_dict = {}\n\n decanter_data = send(\"[DECANTER-GET]\", client)\n decanter_dict = json.loads(decanter_data.replace(\"\\'\", \"\\\"\"))\n print(f'[DECANTER-GET] CAPACITY: {decanter_dict[\"capacity\"]} | STATUS: {decanter_dict[\"status\"]}')\n\n if decanter_dict['status'] == \"processing\":\n print(f\"waiting {decanter_time} to process\")\n time.sleep(decanter_time)\n decanter_out_dict[\"glycerine\"] = decanter_dict[\"capacity\"] * 0.01\n decanter_out_dict[\"EtOH\"] = decanter_dict[\"capacity\"] * 0.03\n decanter_out_dict[\"solution\"] = decanter_dict[\"capacity\"] * 0.96\n\n decanter_out_dict[\"status\"] = \"waiting\"\n \n send(f\"[DECANTER-OUT]_{decanter_out_dict}\", client)\n\n time.sleep(1)\n\nmain()","repo_name":"MathPelicer/CC7261_Project","sub_path":"elements/decanter_client.py","file_name":"decanter_client.py","file_ext":"py","file_size_in_byte":1432,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"25929982763","text":"from nltk.tokenize import word_tokenize\r\nfrom nltk.tokenize import sent_tokenize\r\nimport gensim\r\nfrom gensim.parsing.preprocessing import remove_stopwords\r\nimport numpy as np\r\nimport os\r\nfrom azure_api import read_results\r\nimport csv\r\n\r\ndef get_textfiles(input_path, output_folder):\r\n file_path = input_path # source path for the pdf\r\n folder_name = file_path.split(\"/\")[-1]\r\n out_path = output_folder + folder_name + \"/\" # destination path for output html files\r\n if not os.path.exists(out_path): # Check if the directory exists\r\n os.makedirs(out_path) # Create directory with the output path\r\n print(folder_name)\r\n if not len(os.listdir(os.path.join(output_folder,folder_name))):\r\n text_list = []\r\n print(os.listdir(input_path))\r\n for pdf_name in sorted(os.listdir(input_path), key=len):\r\n print(pdf_name)\r\n pdf_name = pdf_name.split(\".pdf\")[0]\r\n words, sentences = read_results(pdf_name)\r\n number = len(sentences)\r\n print(number)\r\n for i in range(number):\r\n text = \" \"\r\n for idx in range(len(sentences[i][1])):\r\n text = text + \" \" + sentences[i][1][idx][0]\r\n with open(out_path + \"{}.txt\".format(pdf_name),\r\n \"a+\") as file: # If already a sentences.txt file exists, replace it by the new predictions\r\n file.write(\r\n text) # Write the output sentences into a txt file in the directories that they belong to\r\n file.write(\"\\n\")\r\n text_list.append([text])\r\n\r\ndef build_similarity(input_path, folder_name, document_name):\r\n file_docs = []\r\n files = []\r\n for i,file_name in enumerate(sorted(os.listdir(os.path.join(input_path, folder_name)), key=len)):\r\n if file_name.split(\".txt\")[0] == document_name: continue\r\n files.append(file_name.split(\".txt\")[0])\r\n with open(os.path.join(input_path,folder_name +\"/\" + file_name)) as f:\r\n text = f.read()\r\n filtered_text = remove_stopwords(text)\r\n tokens = word_tokenize(filtered_text)\r\n file_docs.append(tokens)\r\n # print(file_docs)\r\n # print(len(file_docs))\r\n gen_docs = file_docs\r\n dictionary = gensim.corpora.Dictionary(gen_docs)\r\n corpus = [dictionary.doc2bow(gen_doc) for gen_doc in gen_docs]\r\n # print(corpus)\r\n tf_idf = gensim.models.TfidfModel(corpus)\r\n # for doc in tf_idf[corpus]:\r\n # print([[dictionary[id], np.around(freq, decimals=2)] for id, freq in doc])\r\n sims = gensim.similarities.Similarity('./workdir/', tf_idf[corpus], num_features=len(dictionary))\r\n return sims, dictionary, tf_idf, files\r\n\r\ndef compare_docs(input_path, folder_name, document_name, output_path):\r\n sims, dictionary, tf_idf, files = build_similarity(input_path, folder_name, document_name)\r\n with open(os.path.join(input_path, folder_name + \"/\" + document_name + \".txt\")) as f:\r\n text = f.read()\r\n filtered_text = remove_stopwords(text)\r\n query_doc = [w.lower() for w in word_tokenize(filtered_text)]\r\n query_doc_bow = dictionary.doc2bow(query_doc)\r\n query_doc_tf_idf = tf_idf[query_doc_bow]\r\n # print('Comparing Result:', sims[query_doc_tf_idf]*100)\r\n values = sims[query_doc_tf_idf]\r\n result = {files[i]: round(values[i]*100,2) for i in range(len(values))}\r\n if not os.path.exists(os.path.join(output_path, folder_name)):\r\n os.makedirs(os.path.join(output_path, folder_name))\r\n output_path = os.path.join(output_path, folder_name)\r\n with open(output_path+\"/\"+document_name+\".csv\", 'w+',newline=\"\") as f:\r\n field_names = ['Document Name', '% Similarity']\r\n writer = csv.DictWriter(f, fieldnames=field_names)\r\n writer.writeheader()\r\n for key in result.keys():\r\n writer.writerow({'Document Name': key, '% Similarity': result[key]})\r\n\r\n\r\nif __name__ == \"__main__\":\r\n compare_docs(\"../Data/Texts\", \"Covid_medicine\", \"Covid_Article_23\", \"../Output/Document_Similarity\")\r\n # compare_docs()\r\n # get_textfiles(\"../Data/Pdfs/Covid_medicine\", \"../Data/Texts/\")","repo_name":"swpnk/LSA_Document_Similiarity","sub_path":"Doc_Similarity.py","file_name":"Doc_Similarity.py","file_ext":"py","file_size_in_byte":4166,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"11452397382","text":"#!/usr/bin/env python3\nimport pysam\nimport sys\nimport gfapy\nimport networkx as nx\nfrom pysam import VariantFile\nimport json\nimport numpy\n\nCONTIG_MARGIN = 200\nMIN_SINGLECOPY_LENGTH = 20000\ntarget_left = 0\ntarget_right = 500000000\n\ngfa = gfapy.Gfa.from_file(sys.argv[1])\nvcf_in = pysam.VariantFile(sys.argv[2])\nbam = pysam.AlignmentFile(sys.argv[3], \"rb\" )\nid_map_file = open(sys.argv[4], 'r')\ntarget_contig_id = sys.argv[5]\n\n# find the target contig\nid_map_json = json.load(id_map_file)\nfor contig in id_map_json:\n if id_map_json[contig] == target_contig_id:\n target_contig = contig\n\n# get the reference sequences\nref_seqs = {}\nfor seg in gfa.segments:\n ref_seqs[seg.name] = seg.sequence\n\n# get the variant sites and assign numeric IDs for use as array indices later\nvariants = {}\nvariant_IDs = {}\nvID_lookup = []\nvid = 0\nfor rec in vcf_in.fetch():\n if target_contig is not None and rec.contig != target_contig: continue\n# if rec.pos > 300: continue\n if rec.pos < target_left or rec.pos > target_right: continue\n variants[(rec.contig,rec.pos)] = [rec.ref, rec.alts]\n variant_IDs[(rec.contig,rec.pos)] = vid\n vID_lookup.append((rec.contig,rec.pos))\n vid += 1\n\nprint(\"found \"+str(len(variants))+\" variants\")\n\nlocal_var_counts = {}\nhic_var_counts = {}\nHIC_MIN_DIST = 800 # reads separated by more than this many bp are treated as Hi-C pairs\nread_iter = bam.fetch(until_eof=True)\nnext_read = next(read_iter)\nhic_pairs = 0\nlocal_pairs = 0\nsingle_reads = 0\ntry:\n#if True:\n while True:\n # get a pair of aligned reads\n cur_read_1 = next_read\n next_read = next(read_iter)\n if cur_read_1.reference_name is None: continue\n if target_contig is not None and cur_read_1.reference_name != target_contig: continue\n if next_read.query_name == cur_read_1.query_name:\n cur_read_2 = next_read\n next_read = next(read_iter)\n if abs(cur_read_2.reference_start - cur_read_1.reference_start) > HIC_MIN_DIST:\n hic_pairs+=1\n else:\n local_pairs+=1\n else:\n cur_read_2 = None\n single_reads += 1\n\n # identify all the variant sites covered by the aligned reads\n var_sites = []\n for pair in cur_read_1.get_aligned_pairs(matches_only=True):\n if (cur_read_1.reference_name,pair[1]+1) in variants:\n refalt = 0\n if ref_seqs[cur_read_1.reference_name][pair[1]] != cur_read_1.query_sequence[pair[0]]:\n refalt = 1\n var_sites.append((cur_read_1.reference_name,pair[1],refalt))\n if cur_read_2 is not None:\n for pair in cur_read_2.get_aligned_pairs(matches_only=True):\n if (cur_read_2.reference_name,pair[1]+1) in variants:\n refalt = 0\n if ref_seqs[cur_read_2.reference_name][pair[1]] != cur_read_2.query_sequence[pair[0]]:\n refalt = 1\n var_sites.append((cur_read_2.reference_name,pair[1],refalt))\n\n # tally up a co-observation for each pair of variant sites\n for p_i in range(len(var_sites)):\n for p_j in range(p_i+1,len(var_sites)):\n var_pair_ij = (var_sites[p_i][0],var_sites[p_i][1],var_sites[p_j][0],var_sites[p_j][1])\n indy = var_sites[p_i][2] + 2*var_sites[p_j][2]\n if var_pair_ij[0] == var_pair_ij[2] and abs(var_pair_ij[3]-var_pair_ij[1]) < HIC_MIN_DIST:\n if not var_pair_ij in local_var_counts:\n local_var_counts[var_pair_ij] = [0,0,0,0]\n local_var_counts[var_pair_ij][indy] += 1\n else:\n if not var_pair_ij in hic_var_counts:\n hic_var_counts[var_pair_ij] = [0,0,0,0]\n hic_var_counts[var_pair_ij][indy] += 1\nexcept:\n pass\nprint(\"found \"+str(len(local_var_counts))+\" local variant site pairs in mapped reads\")\nprint(\"found \"+str(len(hic_var_counts))+\" long range (HiC) variant site pairs in mapped reads\")\n\nprint(\"hic pairs: \"+str(hic_pairs))\nprint(\"local pairs: \"+str(local_pairs))\nprint(\"single reads (no pair on target contig): \"+str(single_reads))\ndat = {\n 'K': 0,\n 'V': vid,\n 'L_hic': len(hic_var_counts),\n 'L_local': len(local_var_counts),\n 'hic_linkcounts': [[0] * 4 for i in range(len(hic_var_counts))],\n 'hic_linksites': [[0] * 2 for i in range(len(hic_var_counts))],\n 'local_linkcounts': [[0] * 4 for i in range(len(local_var_counts))],\n 'local_linksites': [[0] * 2 for i in range(len(local_var_counts))],\n 'site_map': [[0] * 2 for i in range(len(vID_lookup))],\n 'abundance_prior': 1000,\n 'subsample': 10\n}\n\nl = 0\nhic_minor = 0\nfor var in hic_var_counts:\n for i in range(4): dat['hic_linkcounts'][l][i] = hic_var_counts[var][i]\n gt1 = 0\n for i in range(1,4):\n if dat['hic_linkcounts'][l][i] > 0: gt1+=1\n if gt1 > 0:\n hic_minor += 1\n dat['hic_linksites'][l][0] = variant_IDs[(var[0],var[1]+1)]+1\n dat['hic_linksites'][l][1] = variant_IDs[(var[2],var[3]+1)]+1\n l+=1\nprint(\"hic_minor allele links: \"+str(hic_minor))\nl = 0\nlocal_minor = 0\nfor var in local_var_counts:\n for i in range(4): dat['local_linkcounts'][l][i] = local_var_counts[var][i]\n gt1 = 0\n for i in range(4):\n if dat['local_linkcounts'][l][i] > 1: gt1+=1\n if gt1 > 1:\n local_minor += 1\n dat['local_linksites'][l][0] = variant_IDs[(var[0],var[1]+1)]+1\n dat['local_linksites'][l][1] = variant_IDs[(var[2],var[3]+1)]+1\n l+=1\nprint(\"local_minor allele links: \"+str(local_minor))\n\ndat['subsample'] = 10\n\nwith open(target_contig_id+'.standat.json', 'w') as json_file:\n json.dump(dat, json_file)\n\n#l = 0\n#for vID in vID_lookup:\n# dat['site_map'][l][0] = vID_lookup[l][0]\n# dat['site_map'][l][1] = vID_lookup[l][1]\n# l+=1\n\nexit(0)\n\n# find all contig ends that have two outgoing edges\nsegmap = {}\nsegseqlens = {}\nrev = {'+':'-','-':'+'}\nfor seg in gfa.segments:\n segmap[seg.name+'+'] = set()\n segmap[seg.name+'-'] = set()\n segseqlens[seg.name]=len(seg.sequence)\n\n\nfor edge in gfa.edges:\n# segmap[(edge.from_segment.name,edge.from_orient)].append((edge.to_segment.name,edge.to_orient))\n# segmap[(edge.to_segment.name,edge.to_orient)].append((edge.from_segment.name,edge.from_orient))\n if segseqlens[edge.from_segment.name] < MIN_SINGLECOPY_LENGTH and segseqlens[edge.to_segment.name] < MIN_SINGLECOPY_LENGTH: continue\n segmap[edge.from_segment.name+edge.from_orient].add(edge.to_segment.name+edge.to_orient)\n segmap[edge.to_segment.name+rev[edge.to_orient]].add(edge.from_segment.name+rev[edge.from_orient])\n\n# print(segmap)\n\nG = nx.Graph()\nfor end in segmap:\n for dest_i in segmap[end]:\n for dest_j in segmap[end]:\n G.add_edge(dest_i, dest_j)\n if dest_i == ('edge_2668-') or dest_j == ('edge_2668-') or \\\n dest_i == ('edge_2669-') or dest_j == ('edge_2669-') or \\\n dest_i == ('edge_3789-') or dest_j == ('edge_3789-'):\n print(str(dest_i) + \" linked to \" + str(dest_j) + ' source end: '+ str(end))\n\ncomponents = nx.connected_components(G)\nfor comp in components:\n if len(comp)>5:\n print(comp)\n incoming = 0\n for seg in segmap:\n if len(segmap[seg] & comp) > 0:\n incoming += 1\n print(\"Incoming: \"+str(incoming))\n\n\n\nexit(0)\n\nbi_seg_map = {}\nbi_seg_counts = {}\nfor seg in gfa.segments:\n for o in ['-','+']:\n if len(segmap[(seg.name,o)]) == 2:\n if segmap[(seg.name,o)][0] in bi_seg_map or segmap[(seg.name,o)][1] in bi_seg_map:\n print(\"by golly this graph is complicated\")\n print(segmap[(seg.name,o)][0])\n print(segmap[(seg.name,o)][1])\n bi_seg_map[segmap[(seg.name,o)][0]] = segmap[(seg.name,o)][1]\n bi_seg_map[segmap[(seg.name,o)][1]] = segmap[(seg.name,o)][0]\n if len(segmap[(seg.name,o)]) > 2:\n print(\"Holy mackerel: \"+seg.name)\n print(segmap[(seg.name,o)])\n\n\nref_lens = {}\nread_links = {}\nread_link_diffs = {}\nread_refs = {}\nbam = pysam.AlignmentFile(sys.argv[3], \"rb\" )\nfor ref in bam.references:\n ref_lens[ref] = bam.get_reference_length(ref)\n\nfor read in bam.fetch():\n orient = ''\n if read.reference_pos < CONTIG_MARGIN:\n orient = '-'\n if read.reference_pos > ref_lens[read.reference_name] - CONTIG_MARGIN:\n orient = '+'\n if orient == '': continue\n if (read.reference_name,orient) not in bi_seg_map: continue\n","repo_name":"koadman/strain3C","sub_path":"bin/get_arcs.py","file_name":"get_arcs.py","file_ext":"py","file_size_in_byte":8552,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"85"} +{"seq_id":"71158326038","text":"'''\n面试题61. 扑克牌中的顺子\n从若干副扑克牌中随机抽 5 张牌，判断是不是一个顺子，即这5张牌是不是连续的。2～10为数字本身，A为1，J为11，Q为12，K为13，而大、小王为 0 ，可以看成任意数字。A 不能视为 14。\n\n'''\n\n\nclass Solution:\n def isStraight(self, nums) -> bool:\n def trans(x):\n if x == 'A':\n return 1\n elif x == 'J':\n return 11\n elif x == 'Q':\n return 12\n elif x == 'K':\n return 13\n else:\n return int(x)\n\n s = sorted(nums, key=trans)\n nums.sort(key=trans)\n print(s)\n\n sum = 0\n for i in range(4):\n if s[i] == 0:\n sum += 1\n elif s[i] == s[i+1]:\n return False\n else:\n sum -= s[i+1] - s[i] - 1\n\n if sum >= 0:\n return True\n else:\n return False\n\n\na = Solution()\nprint(a.isStraight([0, 0, 1, 2, 5]))\n","repo_name":"lphlch/Other-Codes","sub_path":"LeetCode/JZ0061.py","file_name":"JZ0061.py","file_ext":"py","file_size_in_byte":1059,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"14947969157","text":"from django.shortcuts import render, get_object_or_404, redirect\nfrom dropkickApp.models import MyFile, CustomParam, Contact\nfrom django.views import generic\nfrom .forms import UploadFileForm, CheckboxForm, CustomForm, ScoreForm, ContactForm\nfrom django.http import HttpResponse, StreamingHttpResponse, FileResponse\nfrom django.core.files.storage import FileSystemStorage\nimport csv\nimport os\nimport zipfile\nimport datetime\nfrom io import BytesIO\nfrom django.core.exceptions import ValidationError\nfrom django.contrib import messages\n\nimport scanpy as sc; sc.set_figure_params(color_map=\"viridis\", frameon=False)\nimport dropkick as dk\nimport matplotlib.pyplot as plt; plt.switch_backend(\"Agg\")\nimport io, base64, urllib\nimport numpy as np\nimport pandas as pd\n\n\nSESSION_EXPIRE_AT_BROWSER_CLOSE = True\n# SESSION_COOKIE_AGE = 10 # set just 10 seconds to test\n# SESSION_SAVE_EVERY_REQUEST = True\n\n\ndef qc_plot(adata, instance):\n # plot QC metrics\n adata = dk.recipe_dropkick(adata, n_hvgs=None, X_final=\"raw_counts\")\n qc_plt = dk.qc_summary(adata)\n \n # display chart\n buf = io.BytesIO()\n qc_plt.savefig(buf, format = 'png')\n qc_plt.savefig('media/' + instance.name + '_' + str(instance.id) + '_qcplot.png')\n buf.seek(0)\n string = base64.b64encode(buf.read())\n uri = urllib.parse.quote(string)\n return uri\n\ndef labels(adata, min_genes, mito_names, n_ambient, n_hvgs, thresh_methods, alphas, max_iter, seed, instance):\n adata_model = dk.dropkick(\n adata, \n min_genes=min_genes, \n mito_names=mito_names, \n n_ambient=n_ambient,\n n_hvgs=n_hvgs,\n thresh_methods=thresh_methods,\n alphas=alphas,\n max_iter=max_iter,\n n_jobs=5,\n seed=seed)\n \n # display coefficient plot\n coef_plt = dk.coef_plot(adata)\n buf_coef = io.BytesIO()\n coef_plt.savefig(buf_coef, format = 'png')\n coef_plt.savefig('media/' + instance.name + '_' + str(instance.id) + '_coefplot.png')\n buf_coef.seek(0)\n string_coef = base64.b64encode(buf_coef.read())\n uri_coef = urllib.parse.quote(string_coef)\n \n # display score plot\n adata_score = dk.recipe_dropkick(adata, n_hvgs=None, verbose=False, filter=True, min_genes=50)\n score_plt = dk.score_plot(adata_score)\n buf_score = io.BytesIO()\n score_plt.savefig(buf_score, format = 'png')\n score_plt.savefig('media/' + instance.name + '_' + str(instance.id) + '_scoreplot.png')\n buf_score.seek(0)\n string_score = base64.b64encode(buf_score.read())\n uri_score = urllib.parse.quote(string_score)\n \n return uri_score, uri_coef\n\ndef param_assignment(instance):\n if not instance.min_genes:\n instance.min_genes = 50\n instance.save()\n if not instance.mito_names:\n instance.mito_names = '^mt-|^MT-'\n instance.save()\n if not instance.n_ambient:\n instance.n_ambient = 10\n instance.save()\n if not instance.n_hvgs:\n instance.n_hvgs = 2000\n instance.save()\n if not instance.score_thresh:\n instance.score_thresh = 0.5\n instance.save()\n if not instance.alphas:\n instance.alphas = '0.1'\n instance.save()\n if not instance.max_iter:\n instance.max_iter = 2000\n instance.save()\n if not instance.seed:\n instance.seed = 18\n instance.save()\n \ndef remove_suffix(input_string, suffix):\n if suffix and input_string.endswith(suffix):\n return input_string[:-len(suffix)]\n return input_string\n\ndef index(request):\n \"\"\"View function for home page of site.\"\"\"\n form = CustomForm(request.POST or None, initial = {'min_genes': 50, 'mito_names': 'mt'})\n model = CustomParam\n # upload file\n if request.method == 'POST' and 'document' in request.POST:\n if 'document' in request.FILES:\n if form.is_valid():\n instance = form.save()\n request.session['id'] = instance.id\n# global getID\n# def getID():\n# return instance.id\n\n if instance.qc_plot or instance.dropkick:\n uploaded_file = request.FILES['document']\n if uploaded_file.name.endswith('.csv'):\n fs = FileSystemStorage()\n fs.save(uploaded_file.name, uploaded_file)\n instance.name = remove_suffix(uploaded_file.name, '.csv')\n os.rename('media/' + uploaded_file.name, 'media/' + instance.name + '_' + str(instance.id) + '.csv')\n instance.csv_bool = True\n instance.save()\n param_assignment(instance)\n return redirect(process)\n elif uploaded_file.name.endswith('.h5ad'):\n fs = FileSystemStorage()\n fs.save(uploaded_file.name, uploaded_file)\n instance.name = remove_suffix(uploaded_file.name, '.h5ad')\n os.rename('media/' + uploaded_file.name, 'media/' + instance.name + '_' + str(instance.id) + '.h5ad')\n instance.h5ad_bool = True\n instance.save()\n param_assignment(instance)\n return redirect(process)\n elif uploaded_file.name.endswith('.tsv'):\n fs = FileSystemStorage()\n fs.save(uploaded_file.name, uploaded_file)\n instance.name = remove_suffix(uploaded_file.name, '.tsv')\n os.rename('media/' + uploaded_file.name, 'media/' + instance.name + '_' + str(instance.id) + '.tsv')\n instance.tsv_bool = True\n instance.save()\n param_assignment(instance)\n return redirect(process)\n else:\n messages.error(request,'Please upload a file of CSV, H5AD, or TSV type', extra_tags='document')\n else:\n messages.error(request, 'Please select an action to run.', extra_tags='document')\n \n else:\n form = CustomForm(request.POST or None)\n else:\n messages.error(request,'Please select a file.', extra_tags='document')\n# else:\n# form = CheckboxForm()\n form2 = ContactForm(request.POST or None)\n if request.method=='POST' and 'contact' in request.POST:\n if form2.is_valid():\n contact = form2.save()\n if not contact.name:\n contact.name = \"Anonymous\"\n contact.save()\n messages.success(request,'Your comments have been recorded.', extra_tags='contact')\n else:\n form2 = ContactForm(request.POST or None)\n \n return render(request,'index.html', context = {\n 'form': form,\n 'form2': form2,\n })\n\ndef process(request):\n context = {\n 'title': None, 'counts_text': None, 'counts_false': None, 'counts_true': None, \n 'qc_text': None, 'score_text': None, 'coef_text': None, 'labels_text': None,\n 'qc_plot': None, 'score_plot': None, 'coef_plot': None, 'labels': None,\n }\n \n model = CustomParam\n cur_id = request.session['id']\n instance = model.objects.filter(id=cur_id)[0]\n if instance.csv_bool:\n adata = sc.read('media/' + instance.name + '_' + str(instance.id) + '.csv')\n elif instance.h5ad_bool:\n adata = sc.read('media/' + instance.name + '_' + str(instance.id) + '.h5ad')\n elif instance.tsv_bool:\n adata = sc.read('media/' + instance.name + '_' + str(instance.id) + '.tsv')\n \n # label data results\n context['title'] = 'Your Results'\n if request.method == 'POST':\n form = ScoreForm(request.POST or None)\n model = CustomParam\n if form.is_valid():\n instance.score_thresh = form.cleaned_data.get('score_thresh')\n instance.save()\n return redirect(calc_score_thresh)\n \n else:\n form = ScoreForm()\n if instance.qc_plot:\n # qc_plot checkbox was checked\n context['qc_text'] = 'QC Plot'\n context['qc_plot'] = qc_plot(adata, instance)\n request.session['qc_uri'] = context['qc_plot']\n \n if instance.dropkick:\n # filter checkbox was checked\n\n # run dropkick\n context['counts_text'] = 'Droplets Inventory'\n context['score_text'] = 'Score Plot'\n context['coef_text'] = 'Coefficient Plot'\n context['labels_text'] = 'Dropkick Labels'\n \n alphas_list = instance.alphas.split(',')\n alphas = [float(x) for x in alphas_list]\n\n\n context['score_plot'], context['coef_plot'] = labels(\n adata, instance.min_genes, instance.mito_names, instance.n_ambient, instance.n_hvgs, instance.thresh_methods, alphas,\n instance.max_iter, instance.seed, instance)\n request.session['score_uri'] = context['score_plot']\n request.session['coef_uri'] = context['coef_plot']\n \n context['score_thresh'] = instance.score_thresh\n\n adata.obs['dropkick_label'] = adata.obs['dropkick_score'] > instance.score_thresh\n\n context['counts_false'] = adata.obs['dropkick_label'].value_counts()[0]\n context['counts_true'] = adata.obs['dropkick_label'].value_counts()[1]\n\n # convert dataframe to csv\n adata.obs[[\"dropkick_score\",\"dropkick_label\"]].to_csv('media/' + instance.name + '_' + str(instance.id) + '_dropkicklabels.csv')\n\n # convert to h5ad file\n adata.write('media/' + instance.name + '_' + str(instance.id) + '_dropkickfilter.h5ad', compression='gzip')\n\n # output counts and genes matrices\n # output counts csv\n #data_out = adata[df.obs['dropkick_label']==True]\n #data = pd.DataFrame(data_out.X.toarray())\n #data.to_csv('media/dropkick_counts_' + instance.name + '_' + str(instance.id) + '.csv', header=False, index=False)\n \n # output genes csv\n adata.var[[\"pct_dropout_by_counts\",\"ambient\",\"dropkick_coef\"]].to_csv('media/' + instance.name + '_' + str(instance.id) + '_dropkickgenes.csv', header=True, index=True)\n\n return render(request, 'process.html', context)\n\n\ndef calc_score_thresh(request):\n context = {\n 'score_thresh': None, 'title': None, 'qc_text': None, 'counts_text': None, 'counts_false': None, 'counts_true': None,\n }\n form = ScoreForm(request.POST or None)\n model = CustomParam\n cur_id = request.session['id']\n instance = model.objects.filter(id=cur_id)[0]\n context['title'] = 'Your Results'\n if request.method == 'POST':\n if form.is_valid():\n instance.score_thresh = form.cleaned_data.get('score_thresh')\n instance.save()\n return redirect(calc_score_thresh)\n \n else:\n form = ScoreForm()\n if instance.qc_plot:\n # qc_plot checkbox was checked\n context['qc_text'] = 'QC Plot'\n context['qc_plot'] = request.session['qc_uri']\n \n if instance.dropkick:\n # filter checkbox was checked\n\n # run dropkick\n context['counts_text'] = 'Droplets Inventory'\n context['score_text'] = 'Score Plot'\n context['score_plot'] = request.session['score_uri']\n context['coef_text'] = 'Coefficient Plot'\n context['coef_plot'] = request.session['coef_uri']\n context['labels_text'] = 'Dropkick Labels'\n \n score_thresh = instance.score_thresh\n context['score_thresh'] = score_thresh\n \n adata = sc.read('media/' + instance.name + '_' + str(instance.id) + '_dropkickfilter.h5ad')\n \n adata.obs['dropkick_label'] = adata.obs['dropkick_score'] > score_thresh\n\n context['counts_false'] = adata.obs['dropkick_label'].value_counts()[0]\n context['counts_true'] = adata.obs['dropkick_label'].value_counts()[1]\n \n\n # convert dataframe to csv\n adata.obs[[\"dropkick_score\",\"dropkick_label\"]].to_csv('media/' + instance.name + '_' + str(instance.id) + '_dropkicklabels.csv')\n\n # convert to h5ad file\n adata.write('media/' + instance.name + '_' + str(instance.id) + '_dropkickfilter.h5ad', compression='gzip')\n return render(request, 'score_thresh.html', context)\n\ndef download_csv(request):\n cur_id = request.session['id']\n instance = CustomParam.objects.filter(id=cur_id)[0]\n file = open('media/' + instance.name + '_' + str(instance.id) + '_dropkicklabels.csv', 'rb') # Read the file in binary mode, this file must exist\n response = FileResponse(file)\n\n # decide the file name\n new_filename = instance.name + '_' + str(instance.id)+ '_dropkicklabels.csv';\n response['Content-Disposition'] = 'attachment; filename=%s' % new_filename\n return response\n\ndef download_h5ad(request):\n cur_id = request.session['id']\n instance = CustomParam.objects.filter(id=cur_id)[0]\n file = open('media/' + instance.name + '_' + str(instance.id) + '_dropkickfilter.h5ad', 'rb') # Read the file in binary mode, this file must exist\n response = FileResponse(file)\n\n # decide the file name\n new_filename = instance.name + '_' + str(instance.id) + '_dropkickfilter.h5ad';\n response['Content-Disposition'] = 'attachment; filename=%s' % new_filename\n return response\n\ndef download_counts(request):\n cur_id = request.session['id']\n instance = CustomParam.objects.filter(id=cur_id)[0]\n file = open('media/' + instance.name + '_' + str(instance.id) + '_dropkickcounts.csv', 'rb')\n response = FileResponse(file)\n \n new_filename = instance.name + '_' + str(instance.id) + '_dropkickcounts.csv';\n response['Content-Disposition'] = 'attachment; filename=%s' % new_filename\n return response\n\ndef download_genes(request):\n cur_id = request.session['id']\n instance = CustomParam.objects.filter(id=cur_id)[0]\n file = open('media/' + instance.name + '_' + str(instance.id) + '_dropkickgenes.csv', 'rb')\n response = FileResponse(file)\n \n new_filename = instance.name + '_' + str(instance.id) + '_dropkickgenes.csv';\n response['Content-Disposition'] = 'attachment; filename=%s' % new_filename\n return response\n\ndef download_sample(request):\n cur_id = request.session['id']\n instance = CustomParam.objects.filter(id=cur_id)[0]\n file = open('dropkickApp/static/t_4k_small_dropkick_scores.csv', 'rb') # Read the file in binary mode, this file must exist\n response = FileResponse(file)\n \n response['Content-Disposition'] = 'attachment; filename=\"sample_dropkick_scores.csv\"'\n return response\n\ndef download_qc(request):\n cur_id = request.session['id']\n instance = CustomParam.objects.filter(id=cur_id)[0]\n file = open('media/' + instance.name + '_' + str(instance.id) + '_qcplot.png', 'rb') # Read the file in binary mode, this file must exist\n response = FileResponse(file)\n \n new_filename = instance.name + '_' + str(instance.id) + '_qcplot.png';\n response['Content-Disposition'] = 'attachment; filename=%s' % new_filename\n return response\n\ndef download_coef(request):\n cur_id = request.session['id']\n instance = CustomParam.objects.filter(id=cur_id)[0]\n file = open('media/' + instance.name + '_' + str(instance.id) + '_coefplot.png', 'rb') # Read the file in binary mode, this file must exist\n response = FileResponse(file)\n \n new_filename = instance.name + '_' + str(instance.id) + '_coefplot.png';\n response['Content-Disposition'] = 'attachment; filename=%s' % new_filename\n return response\n\ndef download_score(request):\n cur_id = request.session['id']\n instance = CustomParam.objects.filter(id=cur_id)[0]\n file = open('media/' + instance.name + '_' + str(instance.id) + '_scoreplot.png', 'rb') # Read the file in binary mode, this file must exist\n response = FileResponse(file)\n \n new_filename = instance.name + '_' + str(instance.id) + '_scoreplot.png';\n response['Content-Disposition'] = 'attachment; filename=%s' % new_filename\n return response\n\ndef download_all_no_qc(request):\n # Files (local path) to put in the .zip\n # FIXME: Change this (get paths from DB etc)\n cur_id = request.session['id']\n instance = CustomParam.objects.filter(id=cur_id)[0]\n filenames = ['media/' + instance.name + '_' + str(instance.id) + '_dropkicklabels.csv', 'media/' + instance.name + '_' + str(instance.id) + '_dropkickgenes.csv', 'media/' + instance.name + '_' + str(instance.id) + '_dropkickfilter.h5ad', 'media/' + instance.name + '_' + str(instance.id) + '_coefplot.png', 'media/' + instance.name + '_' + str(instance.id) + '_scoreplot.png']\n \n # Folder name in ZIP archive which contains the above files\n # E.g [thearchive.zip]/somefiles/file2.txt\n # FIXME: Set this to something better\n zip_subdir = instance.name + '_' + str(instance.id) + '_dropkick'\n zip_filename = \"%s.zip\" % zip_subdir\n \n # Open StringIO to grab in-memory ZIP contents\n s = BytesIO()\n\n # The zip compressor\n zf = zipfile.ZipFile(s, \"w\")\n\n for fpath in filenames:\n # Calculate path for file in zip\n fdir, fname = os.path.split(fpath)\n zip_path = os.path.join(zip_subdir, fname)\n\n # Add file, at correct path\n zf.write(fpath, zip_path)\n\n # Must close zip for all contents to be written\n zf.close()\n\n # Grab ZIP file from in-memory, make response with correct MIME-type\n response = HttpResponse(s.getvalue(), content_type = \"application/x-zip-compressed\")\n\n # ..and correct content-disposition\n response['Content-Disposition'] = 'attachment; filename=%s' % zip_filename\n\n return response\n\ndef download_all(request):\n # Files (local path) to put in the .zip\n # FIXME: Change this (get paths from DB etc)\n cur_id = request.session['id']\n instance = CustomParam.objects.filter(id=cur_id)[0]\n filenames = ['media/' + instance.name + '_' + str(instance.id) + '_dropkicklabels.csv', 'media/' + instance.name + '_' + str(instance.id) + '_dropkickgenes.csv', 'media/' + instance.name + '_' + str(instance.id) + '_dropkickfilter.h5ad', 'media/' + instance.name + '_' + str(instance.id) + '_qcplot.png', 'media/' + instance.name + '_' + str(instance.id) + '_coefplot.png', 'media/' + instance.name + '_' + str(instance.id) + '_scoreplot.png']\n \n # Folder name in ZIP archive which contains the above files\n # E.g [thearchive.zip]/somefiles/file2.txt\n # FIXME: Set this to something better\n zip_subdir = instance.name + '_' + str(instance.id) + '_dropkick'\n zip_filename = \"%s.zip\" % zip_subdir\n \n # Open StringIO to grab in-memory ZIP contents\n s = BytesIO()\n\n # The zip compressor\n zf = zipfile.ZipFile(s, \"w\")\n\n for fpath in filenames:\n # Calculate path for file in zip\n fdir, fname = os.path.split(fpath)\n zip_path = os.path.join(zip_subdir, fname)\n\n # Add file, at correct path\n zf.write(fpath, zip_path)\n\n # Must close zip for all contents to be written\n zf.close()\n\n # Grab ZIP file from in-memory, make response with correct MIME-type\n response = HttpResponse(s.getvalue(), content_type = \"application/x-zip-compressed\")\n\n # ..and correct content-disposition\n response['Content-Disposition'] = 'attachment; filename=%s' % zip_filename\n\n return response\n","repo_name":"wangvictoria/dropkick","sub_path":"dropkickApp/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":19393,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"24576891411","text":"from typing import Dict, List, Optional, Tuple\nfrom itertools import chain, repeat\nfrom numpy.lib.twodim_base import tri\nimport yaml\n\nimport pandas as pd\nimport numpy as np\n\nfrom tshistory.api import timeseries\nfrom tqdm.contrib.concurrent import thread_map\n\nfrom dw_squared.plot import _DWSquared, DWSquared\nfrom dw_squared.area import Area\nfrom dw_squared.bar import StackedBar\nfrom dw_squared.line import Lines\nfrom dw_squared.seasonal import Seasonal\nfrom dw_squared.moments import evaluate_not_none\nfrom dw_squared.table import Table, triple_loop_list\n\n\nPLOT_TYPE = {\n 'area': Area,\n 'stacked': StackedBar,\n 'line': Lines,\n 'seasonal': Seasonal,\n 'table': Table,\n 'undefined': _DWSquared,\n 'table': Table,\n}\n\nTSAResult = Dict[Tuple[str, Optional[pd.Timestamp]], Optional[pd.Series]]\n\n\ndef safe_dt_none(x, return_type=None):\n if isinstance(x, pd.Timestamp):\n return x\n else:\n return return_type\n\n\nclass PlotConfig():\n\n def __init__(self, path) -> None:\n self.path = path\n with open(self.path, 'r') as stream:\n self.config = yaml.load(stream, Loader=yaml.FullLoader)\n self.df_config = pd.json_normalize(self.config)\n\n def single_config(self, title):\n config = list(filter(lambda x: x['title'] == title, self.config))[0]\n if 'graph_end' in config.keys():\n config['graph_end'] = evaluate_not_none(config['graph_end'])\n if 'graph_start' in config.keys():\n config['graph_start'] = evaluate_not_none(config['graph_start'])\n del config['series']\n return config\n\n def order_series(self, title: str):\n conf = list(filter(lambda x: title == x['title'], self.config))[0]\n return [s['legend'] for s in conf['series']]\n\n def series_queries(self, titles: List[str] = list()):\n mask = self.df_config.title.isin(titles)\n t, s = self.df_config[mask]['title'], self.df_config[mask]['series']\n dfs = {title: pd.DataFrame.from_records(x)\n for title, x in zip(t, s)}\n df = pd.concat(dfs, ignore_index=False)\n dates = df[['start', 'end', 'revision']].applymap(evaluate_not_none)\n return pd.concat((df[['series_id', 'legend']], dates), axis=1).replace({np.nan: None})\n\n def series_bounds(self, titles: List[str] = list()):\n return (self.series_queries(titles)\n .groupby(['series_id', 'revision'], dropna=False)\n .agg({'start': 'min', 'end': 'max'}).replace({np.nan: None})\n .to_dict('index')\n )\n\n\nclass TableConfig():\n\n def __init__(self, path) -> None:\n self.path = path\n with open(self.path, 'r') as stream:\n self.config = yaml.load(stream, Loader=yaml.FullLoader)\n self.df_config = pd.json_normalize(self.config)\n\n def single_config(self, title):\n config = list(filter(lambda x: x['title'] == title, self.config))[0]\n if 'graph_end' in config.keys():\n config['graph_end'] = evaluate_not_none(config['graph_end'])\n if 'graph_start' in config.keys():\n config['graph_start'] = evaluate_not_none(config['graph_start'])\n return config\n\n def order_series(self, title: str):\n conf = list(filter(lambda x: title == x['title'], self.config))[0]\n return triple_loop_list(conf, 'legend')\n\n def series_queries(self, titles: List[str] = list()):\n mask = self.df_config.title.isin(titles)\n t = self.df_config[mask]['title']\n start = self.df_config[mask]['graph_start'].apply(evaluate_not_none)\n end = self.df_config[mask]['graph_end'].apply(evaluate_not_none)\n frames = []\n for x in self.config:\n if x['title'] in t.values:\n series_id = triple_loop_list(x, 'series_id')\n legend = triple_loop_list(x, 'legend')\n _dataframe = pd.DataFrame(np.array([series_id, legend]).T,\n columns=['series_id', 'legend'])\n _dataframe['start'] = start[mask].values[0]\n _dataframe['end'] = end[mask].values[0]\n _dataframe['revision'] = np.nan\n _dataframe['title'] = x['title']\n frames.append(\n _dataframe.reset_index().set_index(['title', 'index']))\n return pd.concat(frames, axis=0)\n\n def series_bounds(self, titles: List[str] = list()):\n return (self.series_queries(titles)\n .groupby(['series_id', 'revision'], dropna=False)\n .agg({'start': 'min', 'end': 'max'}).replace({np.nan: None})\n .to_dict('index')\n )\n\n\ndef get_data(tsa: timeseries, queries: Dict = None):\n def body(item):\n key, val = item\n kwargs = dict(name=key[0],\n from_value_date=safe_dt_none(val['start']),\n to_value_date=safe_dt_none(val['end']),\n revision_date=safe_dt_none(key[-1]))\n return key, tsa.get(**kwargs)\n\n return dict(thread_map(body, queries.items()))\n\n\ndef saturn_to_frame(data: TSAResult,\n config: PlotConfig,\n title: str):\n series = (config\n .series_queries([title])\n .xs(title, level=0)\n .set_index(['series_id', 'revision']))\n\n def key(name, rev):\n return series.xs([name, rev])['legend']\n\n def slice_range(name, rev, serie):\n bounds = series.xs([name, rev])\n start, end = bounds['start'], bounds['end']\n return serie[start: end]\n\n filtered = {key(n, r): slice_range(n, r, v) for (n, r), v in data.items()}\n return pd.concat(filtered, axis=1)\n\n\ndef create_single_plot(data: pd.DataFrame,\n config: PlotConfig,\n title: dict,\n token: str):\n cols = config.order_series(title)\n kwargs = {**config.single_config(title),\n 'data': data[cols],\n 'token': token}\n plot = PLOT_TYPE[kwargs['chart_type']](**kwargs)\n return plot.publish()\n\n\ndef create_single_table(data: pd.DataFrame,\n config: TableConfig,\n title: dict,\n token: str):\n cols = config.order_series(title)\n _config = config.single_config(title)\n kwargs = {**_config,\n 'data': data[cols],\n 'table_config': _config,\n 'token': token}\n plot = PLOT_TYPE[kwargs['chart_type']](**kwargs)\n return plot.publish()\n\n\ndef update_single_plot(data: pd.DataFrame,\n config: PlotConfig,\n title: dict,\n token: str):\n cols = config.order_series(title)\n kwargs = {**config.single_config(title),\n 'data': data[cols],\n 'token': token}\n plot = PLOT_TYPE[kwargs['chart_type']](**kwargs)\n return plot.update_data(data)\n","repo_name":"lofriedman/dw-squared","sub_path":"dw_squared/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":6920,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"22872674025","text":"from selenium import webdriver\nfrom bs4 import BeautifulSoup\nfrom datetime import datetime\nimport requests\nimport time\nimport smtplib\nimport random\nimport json\nimport os\n\ndef readJson(path):\n with open(path, \"r\") as f:\n return json.loads(f.read())\n\ndef sendEmail(sentFrom, to, emailText, cred):\n server = smtplib.SMTP_SSL(cred['smtp'], cred['smtpPort'])\n server.ehlo()\n server.login(cred['login'], cred['password'])\n server.sendmail(sentFrom, to, emailText)\n server.close()\n\nconfig = readJson(os.path.join(\"./config.json\"))\n\ndriver = webdriver.Chrome(config['chromeDriverPath'])\ntime.sleep(5)\n\nf = open(config['logFile'], \"a\")\nf.write(\"============================================================= \\r\\n\")\nf.write(\"Starting to monitor UPS devices on %s\\r\\n\" % datetime.now())\n\nupsIps = config['upsIps']\n\nfor upsIp in upsIps:\n f.write(\"Trying to scrub data for UPS with IP: %s\\r\\n\" % upsIp)\n try:\n driver.implicitly_wait(10)\n\n driver.get(\"http://\" + upsIp + \"/logon.htm\")\n driver.find_element_by_name(\"login_username\").send_keys(config['upsLogin'])\n driver.find_element_by_name(\"login_password\").send_keys(config['upsPassword'])\n driver.find_element_by_name(\"submit\").click()\n\n driver.find_element_by_link_text(\"Home\").click()\n content = driver.page_source\n soup = BeautifulSoup(content)\n\n # Get Ups ip\n upsScrubedIp = soup.findAll('td', {\"class\": \"update\"})[0].text\n f.write(\"Logged in on UPS with IP: %s\\r\\n\" % upsScrubedIp)\n except Exception as e:\n upsScrubedIp = 'Not Set'\n f.write(\"Could not logged in on UPS with error: %s\\r\\n\" % str(e))\n\n # Generate event logs for understanding what is going on in UPS env\n try:\n envLog = []\n for parentTd in soup.findAll('td', id=\"env\"):\n for td in parentTd.findAll('td'):\n if td.text[0:5] != 'Smart' and td.text != 'Environment' and len(td.text) > 1:\n envLog.append(td.text.replace(\"\\xa0\", \"\").strip())\n # print((td.text.replace(\"\\xa0\", \"\")).strip())\n f.write(\"Event logs are generated: %s\\r\\n\" % str(envLog))\n except Exception as e:\n envLog = []\n f.write(\"Could not get event logs with error: %s\\r\\n\" % str(e))\n\n # Get Load in Watts in percents\n time.sleep(5)\n try:\n elem = driver.find_element_by_link_text(\"UPS\")\n elem.click()\n\n content = driver.page_source\n soup = BeautifulSoup(content)\n x = soup.findAll('tbody')\n\n loadInWatts = x[10].findAll('td')[-1].contents[0].strip('%').strip()\n\n f.write(\"Load in watts: %s\\r\\n\" % str(loadInWatts))\n except Exception as e:\n loadInWatts = 'Not Set'\n f.write(\"Could not get load in watts with error: %s\\r\\n\" % str(e))\n\n # Get Battery Capacity in percents\n try:\n batteryCapacity = x[12].findAll('td')[-1].contents[0].strip('%').strip()\n\n f.write(\"Battery capacity: %s\\r\\n\" % str(batteryCapacity))\n except Exception as e:\n batteryCapacity = 'Not Set'\n f.write(\"Could not get battery capacity with error: %s\\r\\n\" % str(e))\n\n # Get Input Voltage\n try:\n inputVoltage = x[8].findAll('tr')[20].findAll('td')[-1].contents[0].strip()\n\n f.write(\"Input Voltage: %s\\r\\n\" % str(inputVoltage))\n except Exception as e:\n inputVoltage = 'Not Set'\n f.write(\"Could not get input voltage with error: %s\\r\\n\" % str(e))\n\n # Get Output Voltage\n try:\n outputVoltage = x[8].findAll('tr')[21].findAll('td')[-1].contents[0].strip()\n\n f.write(\"Output Voltage: %s\\r\\n\" % str(outputVoltage))\n except Exception as e:\n outputVoltage = 'Not Set'\n f.write(\"Could not get output voltage with error: %s\\r\\n\" % str(e))\n\n # Get Runtime Remaining\n try:\n runtimeRemaining = x[8].findAll('tr')[23].findAll('td')[-1].contents[0].strip()\n\n f.write(\"Runtime Remaining: %s\\r\\n\" % str(runtimeRemaining))\n except Exception as e:\n runtimeRemaining = 'Not Set'\n f.write(\"Could not get runtime remaining with error: %s\\r\\n\" % str(e))\n\n # Get Temperature in C\n try:\n time.sleep(5)\n elem = driver.find_element_by_link_text(\"Environment\")\n elem.click()\n time.sleep(5)\n content = driver.page_source\n soup = BeautifulSoup(content)\n x = soup.findAll('tbody')\n\n temperatureInC = x[7].findAll('td')[-2].contents[0].strip('C').strip().strip('°')\n\n f.write(\"Temperature In C: %s\\r\\n\" % str(temperatureInC))\n except Exception as e:\n temperatureInC = 'Not Set'\n f.write(\"Could not get temperature in C with error: %s\\r\\n\" % str(e))\n\n # Get Humidity info\n try:\n humidity = x[7].findAll('td')[-1].contents[0]\n\n f.write(\"Humidity: %s\\r\\n\" % str(humidity))\n except Exception as e:\n humidity = 'Not Set'\n f.write(\"Could not get humidity with error: %s\\r\\n\" % str(e))\n\n allIsOkCheckOne = [i for i, x in enumerate(envLog) if x == 'No Alarms Present']\n allIsOkCheckTwo = [i for i, x in enumerate(envLog) if x == 'UPS is online.']\n\n subject = 'UPS ' + upsScrubedIp\n body = \"Logs: \" + \", \".join(envLog) + \"\\n\"\n body = body + \"Load in Watts: \" + loadInWatts + '%' + \"\\n\"\n body = body + 'Battery Capacity: ' + batteryCapacity + '%' + \"\\n\"\n body = body + 'Input Voltage: ' + inputVoltage + \"\\n\"\n body = body + 'Output Voltage: ' + outputVoltage + \"\\n\"\n body = body + 'Runtime Remaining: ' + runtimeRemaining + \"\\n\"\n body = body + 'Temperature of server Room: ' + temperatureInC + \" C\" + \"\\n\"\n body = body + 'Humidity: ' + humidity + \"\\n\"\n\n sentFrom = config['email']['sentFrom']\n\n if (not (len(allIsOkCheckOne) == 2 and len(allIsOkCheckTwo) == 1)):\n to = config['email']['criticalReceivers']\n emailText = \"\"\"\\\nFrom: %s\nTo: %s\nSubject: %s\n\nAlert: Something went wrong with UPS. \\n\n%s\n \"\"\" % (sentFrom, \", \".join(to), subject, body)\n\n try:\n sendEmail(sentFrom, to, emailText, config['email'])\n\n f.write(\"An error ALERT email was sent from \" + sentFrom + \" to \" + str(to) + \" with subject \" + str(subject) + \"\\r\\n\")\n except Exception as e:\n f.write(\"Could not send an email concerning to ups bad log with error: %s\\r\\n\" % str(e))\n elif (str(temperatureInC) != 'Not Set' and int(float(temperatureInC)) > 23):\n to = config['email']['warningReceivers']\n emailText = \"\"\"\\\nFrom: %s\nTo: %s\nSubject: %s\n\nWarning: The temperature of the server room exceeds 23 C! \\n\n%s\n \"\"\" % (sentFrom, \", \".join(to), subject, body)\n\n try:\n sendEmail(sentFrom, to, emailText, config['email'])\n\n f.write(\"A temperature WARNING email was sent from \" + sentFrom + \" to \" + str(to) + \" with subject \" + str(subject) + \"\\r\\n\")\n except Exception as e:\n f.write(\"Could not send an email concerning to server room tempreture with error: %s\\r\\n\" % str(e))\n else:\n to = config['email']['infoReceivers']\n emailText = \"\"\"\\\nFrom: %s\nTo: %s\nSubject: %s\n\nThis is an information message about the state of UPS, which is sent randomly.\\n\n%s\n \"\"\" % (sentFrom, \", \".join(to), subject, body)\n try:\n if random.randint(1, 500) == 168:\n\n sendEmail(sentFrom, to, emailText, config['email'])\n\n f.write(\"A random INFO email was sent from \" + sentFrom + \" to \" + str(to) + \" with subject \" + str(subject) + \"\\r\\n\")\n except Exception as e:\n f.write(\"Could not send an email concerning to server room envoirenment with error: %s\\r\\n\" % str(e))\n\n time.sleep(5)\n try:\n elem = driver.find_element_by_link_text(\"Log Off\")\n elem.click()\n\n f.write(\"The Ups is logged off\\r\\n\\n\\n\\n\\n\")\n time.sleep(5)\n except Exception as e:\n f.write(\"Could not log off with error: %s\\r\\n\" % str(e))\n\n\ndriver.quit()\n\nf.write(\"============================================================= \\r\\n\")\nf.close()\n","repo_name":"araGasparyan/scraping-apc-ups","sub_path":"scrub_ups_data.py","file_name":"scrub_ups_data.py","file_ext":"py","file_size_in_byte":8047,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"7875687471","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Jan 3 00:26:51 2020\n\n@author: Username\n\"\"\"\n\nimport sys\n\nfor t in range(sys.stdin):\n a,b,c=list(map(int, input().replace('\\n','').split(' ')))\n tes=False\n for i in range(a+1,b):\n if i%c:\n tes=True\n print(i,end=' ')\n if tes: print()\n else: print('No free parking spaces.')","repo_name":"scorpio-su/zerojudge","sub_path":"python/e621.py","file_name":"e621.py","file_ext":"py","file_size_in_byte":358,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"26780506959","text":"class MyLinkedList:\r\n\r\n def __init__(self):\r\n self.head=ListNode(0)\r\n # p = self.head\r\n\r\n # 获取链表中第 index 个节点的值\r\n def get(self, index: int) -> int:\r\n # 索引为负无效\r\n if index < 0:\r\n return -1\r\n p = self.head\r\n for i in range(index+1):\r\n # 超出链表长度无效\r\n if p.next is None:\r\n return -1\r\n else:\r\n p = p.next\r\n return p.val\r\n\r\n # 在链表的第一个元素之前添加一个值为 val 的节点。插入后，新节点将成为链表的第一个节点。\r\n def addAtHead(self, val: int) -> None:\r\n node = ListNode(val)\r\n node.next = self.head.next\r\n self.head.next = node\r\n\r\n # 将值为 val 的节点追加到链表的最后一个元素。\r\n def addAtTail(self, val: int) -> None:\r\n node = ListNode(val)\r\n p = self.head\r\n while p.next is not None:\r\n p = p.next\r\n p.next = node\r\n\r\n\r\n def addAtIndex(self, index: int, val: int) -> None:\r\n node = ListNode(val)\r\n p = self.head\r\n if index < 0:\r\n node.next = self.head.next\r\n self.head.next = node\r\n else:\r\n for i in range(0,index):\r\n if p.next is None:\r\n p.next = node\r\n p=p.next\r\n node.next=p.next\r\n p.next=node\r\n\r\n def deleteAtIndex(self, index: int) -> None:\r\n p=self.head\r\n if index<0:\r\n return\r\n for i in range(index): \r\n p=p.next\r\n if p.next == None and i < index-1:\r\n return\r\n if p.next is not None:\r\n p.next=p.next.next\r\n \r\nclass ListNode(object):\r\n def __init__(self, val=None, next=None):\r\n self.val = val\r\n self.next = next\r\n\r\n\r\n# Your MyLinkedList object will be instantiated and called as such:\r\n# obj = MyLinkedList()\r\n# param_1 = obj.get(index)\r\n# obj.addAtHead(val)\r\n# obj.addAtTail(val)\r\n# obj.addAtIndex(index,val)\r\n# obj.deleteAtIndex(index)\r\n","repo_name":"BigCatHu/algorithm-learning-2023-winter","sub_path":"CodeEveryday/20221213_707设计链表_1.py","file_name":"20221213_707设计链表_1.py","file_ext":"py","file_size_in_byte":2114,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"12243397333","text":"import zutil\nimport sys\nimport getopt\nfrom datetime import datetime, timedelta\nimport zutc\n\ndef discretizer(time):\n\t# convierte la hora en las regiones establecidas:\n\t# t- primera transición, 5-7\n\t# m- mañana, 8-13\n\t# tt- segunda transición, 14-17\n\t# n- noche, 18-4\n\t# w- weekend, whole sat and sun\n\tif time.weekday() > 4:\n\t\tdiscrete_time = 'w'\n\telse:\n\t\tif time.hour >= 5 and time.hour <= 7:\n\t\t\tdiscrete_time = 't'\n\t\telif time.hour >= 8 and time.hour <= 13:\n\t\t\tdiscrete_time = 'm'\n\t\telif time.hour >= 14 and time.hour <= 17:\n\t\t\tdiscrete_time = 'tt'\n\t\telse:\n\t\t\tdiscrete_time = 'n'\n\treturn discrete_time\n\ndef sincronizer(user_texts, user_times):\n\t# genera representación de usuario en base a tokens con temporalidad\n\tnew_user = []\n\tfor post in zip(user_texts, user_times):\n\t\tnew_user.append(getToken(post[0], post[1]))\n\treturn new_user\n\ndef getToken(post, time):\n\t# genera el token con temporalidad para cada post\n\ttoken = discretizer(time) + str(post)\n\t# token = discretizer(time)\n\treturn token\n\ndef getTimeStats(user_texts, user_times, histogram):\n\t# saca estadísticas de los tokens de cada usuario\n\tvalid_tokens = ['t0', 'm0', 'tt0', 'n0', 'w0', 't1', 'm1', 'tt1', 'n1', 'w1']\n\t# valid_tokens = ['t', 'm', 'tt', 'n', 'w']\n\tnew_user = sincronizer(user_texts, user_times)\n\tuser_size = len(new_user)\n\t# process used in contest...\n\t#new_user = [round(new_user.count(token) / user_size * 100, 4) for token in histogram]\n\t# fixed contest\n\tnew_user = [round(new_user.count(token) / user_size * 100, 4) for token in histogram if token in valid_tokens]\n\treturn new_user\n\n# las siguientes librerías son importadas de zutc\n# getBaseStats\n# getStats\n\ndef main(argv):\n\t# init\n\tdescription = 'ztrans.py -m -t -i '\n\n\t# get input argument\n\ttry:\n\t\topts, args = getopt.getopt(argv, \"hm:t:i:\")\n\texcept getopt.GetoptError:\n\t\tprint(description)\n\t\tsys.exit(2)\n\tfor opt, arg in opts:\n\t\tif opt == '-h':\n\t\t\tprint(description)\n\t\t\tsys.exit()\n\t\telif opt == '-m':\n\t\t\tif arg == 'full' or arg == 'ig' or arg == 'stats':\n\t\t\t\tmode = arg\n\t\t\telse:\n\t\t\t\tprint('Error: invalid mode or not specified.')\n\t\t\t\tsys.exit(2)\n\t\telif opt == '-t':\n\t\t\thistogram_file = arg\n\t\telif opt == '-i':\n\t\t\tlist_file = arg\n\n\t# initialize\n\tinput_files = zutil.getUserList(list_file)\n\tif mode == 'full':\n\t\thistogram = ['t0', 'm0', 'tt0', 'n0', 'w0', 't1', 'm1', 'tt1', 'n1', 'w1']\n\t\t# histogram = ['t', 'm', 'tt', 'n', 'w']\n\t\tzutil.saveUser(['pos', 'neg', 'negpos', 'posneg', 'negneg', 'pospos'] + histogram, histogram_file) #lol hax\n\telif mode == 'ig' or mode == 'stats':\n\t\thistogram = zutil.loadUser(histogram_file) #lol hax\n\n\t# create new user representation\n\tfor file in input_files:\n\t\tuser = zutil.loadUser(file)\n\t\tuser_texts = zutil.getUserTexts(user)\n\t\tuser_times = zutil.getUserTimes(user)\n\t\tuser_time_stats = getTimeStats(user_texts, user_times, histogram)\n\t\tuser_stats = zutc.getStats(user_texts)\n\t\tif mode == 'stats':\n\t\t\tuser_base_stats = zutc.getBaseStats(user_times)\n\t\t\tnew_user_texts = [user_stats + user_time_stats + user_base_stats]\n\t\telse:\n\t\t\tnew_user_texts = [user_stats + user_time_stats]\n\t\tnew_user = zutil.saveUserNewRepresentation(user, new_user_texts)\n\t\tzutil.saveUser(new_user, file)\n\nif __name__ == \"__main__\":\n\tmain(sys.argv[1:])","repo_name":"dxzander/MastersThesis2017","sub_path":"Experiments/ztrans.py","file_name":"ztrans.py","file_ext":"py","file_size_in_byte":3251,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"85"} +{"seq_id":"41099151000","text":"from sklearn import svm, metrics\nfrom sklearn.model_selection import train_test_split\nimport matplotlib.pyplot as plt\nimport pandas as pd \n\ncsvData = pd.read_csv('bmi.csv')\nlabel = csvData['label']\nh = csvData['height']\nw = csvData['weight']\n# axis사용하면 인식되어지는 값이 위에 붙는다. \nwh = pd.concat([w,h],axis=1)\n\ntrainData, testData, trainLabel, testLabel = train_test_split(wh,label)\n\nclf = svm.SVC()\nclf.fit(trainData,trainLabel)\npre = clf.predict(testData)\nscore = metrics.accuracy_score(pre,testLabel)\nreport = metrics.classification_report(pre,testLabel)\nprint(score)\nprint(report)\n","repo_name":"dl57934/python-learning","sub_path":"ch4/ch4-5/bmi-test.py","file_name":"bmi-test.py","file_ext":"py","file_size_in_byte":610,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"22894998496","text":"import re\r\nimport os\r\nimport sys\r\nfrom typing import List, Dict\r\n\r\n\"\"\" \r\nThe program assumes the following file format\r\n- values are either numbers or strings only\r\n- value/key pairs are defined on a new line\r\n- value and key are separated by a single space\r\n- input files need to have .dict extension\r\n\"\"\"\r\n\r\n# This function reads a file and\r\n# returns the string representation \r\n# of the data contained in the file\r\ndef read_file(file_path: str) -> str:\r\n # make sure to open file in read-only mode\r\n try:\r\n with open(file_path, 'r') as f:\r\n return f.read()\r\n except FileNotFoundError:\r\n print(\"The provided file does not exist\")\r\n except OSError:\r\n print(\"There was an error in reading the file\")\r\n\r\n\r\n# This function converts string\r\n# read from a file\r\n# into a list containing \r\n# key-value pairs\r\ndef get_key_value_pairs(file_data:str) -> List[str]:\r\n str_data = file_data.split('\\n')\r\n key_value_array = []\r\n for line in str_data:\r\n key_value_array.append(line.split(\" \"))\r\n return key_value_array\r\n\r\n# This function converts the\r\n# list of key-value pairs\r\n# to list of formatted strings \r\n# with the tokesn in reverse order\r\ndef reverse_order_of_pairs(pairs:List[str]) -> List[str]:\r\n inverted = []\r\n for pair in pairs:\r\n inverted.append(f\"{pair[1]} {pair[0]}\\n\")\r\n return inverted\r\n\r\n# This function will write the \r\n# inverted dict to a new file\r\ndef write_to_file(inverted_list, filepath) -> None:\r\n if os.path.exists(filepath):\r\n raise FileExistsError(f\"There is already an inverted dict file. Aborting...\")\r\n else:\r\n try:\r\n file = open(filepath, 'w')\r\n for reversed_pair in inverted_list:\r\n file.write(reversed_pair)\r\n except OSError:\r\n print(\"Could not write to file\")\r\n\r\nif __name__==\"__main__\":\r\n \r\n if len(sys.argv) != 2:\r\n raise ValueError(\"Please provide the filepath of the dictionary file\")\r\n\r\n input_file_path = sys.argv[1]\r\n \r\n if input_file_path[-4:] != \"dict\":\r\n raise ValueError(\"Files must have .dict extension\")\r\n \r\n output_file_path = input_file_path[:-4]+\"_inverted.dict\"\r\n\r\n # do the processing\r\n input_data_str = read_file(input_file_path)\r\n key_val_pairs = get_key_value_pairs(input_data_str)\r\n inverted = reverse_order_of_pairs(key_val_pairs)\r\n # write the inverted dict to file\r\n write_to_file(inverted, output_file_path)\r\n \r\n# no other prints provided, \r\n# no output in Unix typically means\r\n# everything is OK \r\n#########################################\r\n\r\n\r\n ","repo_name":"Bitcents/cs_1101","sub_path":"learning_journal_8.py","file_name":"learning_journal_8.py","file_ext":"py","file_size_in_byte":2613,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"85"} +{"seq_id":"33842149702","text":"from math import e\nimport jwt\nimport sys\nfrom cryptography.x509 import load_pem_x509_certificate\nfrom pathlib import Path\n\nprint(\"Token validation Use: python3 jwt_decode public_key.pem access_token\")\n\npublic_key_text = Path(\"public_key.pem\").read_text()\npublic_key = load_pem_x509_certificate(public_key_text.encode(\"utf-8\")).public_key()\naccess_token = \"eyJhbGciOiJSUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJodHRwczovL2F1dGguY29mZmVlbWVzaC5pby8iLCJzdWIiOiJlYzdiYmNjZi1jYTg5LTRhZjMtODJhYy1iNDFlNDgzMWE5NjIiLCJhdWQiOiJodHRwOi8vMTI3LjAuMC4xOjgwMDAvb3JkZXJzIiwiaWF0IjoxNjk5NDQzMTY1LjcyNjMxNywiZXhwIjoxNjk5NTI5NTY1LjcyNjMxNywic2NvcGUiOiJvcGVuaWQifQ.hpfxFqDtFz3KG0RQEoA0hBNyPbegnwKL76ZGuaGeLmdi7l61-MOfasQZzKTp6blYAspjF_E7N4nzd3al2RFMHQH9PGZznAD9_llKaSq3NRzNgOvabMOgCLxEaWKHcNAyiyo3vvlpHVsQjkhi-dH3V1mpiBxu_jA8EqvdU2w76_7YKxZowa38UddTi6UCXSdx6Psg8k_EIQRNklorDU1YLzPUHctdsbhtbNecstlmCWHwLYV_yc-KrlnH62c_4r1RpIBijtR1GW_nEW_nPQ_JE5iOzudZE78wbb3O6-XMWZzbvIfz03sCA1OwPhWnOhXqxdNLZVkHYJVIulkP-bgx9A\"\n\nif len(sys.argv) == 1:\n print(\"No params passed, using defaults\")\n print(\"public_key.pem file : public_key.pem\")\n print(\"access_token : \", access_token)\nelif len(sys.argv) == 3:\n public_key_text = Path(sys.argv[1]).read_text()\n public_key = load_pem_x509_certificate(public_key_text.encode(\"utf-8\")).public_key()\n access_token = sys.argv[2]\n\ntry:\n decode = jwt.decode(\n access_token,\n key=public_key,\n algorithms=[\"RS256\"],\n audience=[\"http://127.0.0.1:8000/orders\"],\n )\n print(decode)\nexcept Exception as error:\n print(\"Decoding error : \", error)\n","repo_name":"Santiagus/Python","sub_path":"APIs_and_Microservices/06_deployment/jwt_decode.py","file_name":"jwt_decode.py","file_ext":"py","file_size_in_byte":1591,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"21539157797","text":"import sys\nimport cv2\nfrom PyQt5 import QtCore, QtWidgets\nfrom PyQt5.QtCore import *\nfrom PyQt5.QtGui import *\nfrom PyQt5.QtWidgets import *\nfrom GUI import Ui_MainWindow\nimport numpy as np\nimport math\nfrom matplotlib import pyplot as plt\n\nclass ShowImage(QMainWindow, Ui_MainWindow):\n def __init__(self):\n super(ShowImage, self).__init__()\n self.setupUi(self) # Set up the UI elements\n # Rest of your code...\n self.cap = cv2.VideoCapture(0)\n\n self.actionGrayscale.triggered.connect(self.gray)\n self.actionCalculate_Gradient.triggered.connect(self.gradient)\n self.actionBlur_Image.triggered.connect(self.blur)\n self.actionBinary_Tresholding_2.triggered.connect(self.binarythresh)\n self.actionMorphology_3.triggered.connect(self.morphology)\n self.actionErode_and_Dilate.triggered.connect(self.erode_dilate)\n self.actionDetect_Barcode.triggered.connect(self.find_barcode)\n\n\n def gray(self):\n while True:\n _, frame = self.cap.read() # Read camera frame\n\n gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n\n cv2.imshow('Grayscale', gray) # Display grayscale frame\n\n key = cv2.waitKey(1) # Prevent window from closing\n if key == 27: # Press ESC to stop\n break\n\n self.cap.release()\n cv2.destroyAllWindows()\n\n def gradient(self):\n while True:\n _, frame = self.cap.read() # Read camera frame\n\n # Grayscale conversion\n gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n\n # Calculate gradient using Sobel kernel\n gradient_x = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=3)\n gradient_y = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=3)\n\n # Combine x and y gradients to get magnitude and angle\n gradient = cv2.subtract(gradient_x, gradient_y)\n gradient = cv2.convertScaleAbs(gradient)\n\n # Display gradient magnitude\n cv2.imshow('Gradient', gradient)\n\n key = cv2.waitKey(1) # Prevent window from closing\n if key == 27: # Press ESC to stop\n break\n\n self.cap.release()\n cv2.destroyAllWindows()\n\n def blur(self):\n while True:\n _, frame = self.cap.read() # Read camera frame\n\n # Grayscale conversion\n gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n\n # Calculate gradient using Sobel kernel\n gradient_x = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=3)\n gradient_y = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=3)\n\n # Combine x and y gradients to get magnitude and angle\n gradient = cv2.subtract(gradient_x, gradient_y)\n gradient = cv2.convertScaleAbs(gradient)\n\n # Apply blurring to the gradient image\n blurred = cv2.blur(gradient, (3, 3))\n\n # Display the blurred frame\n cv2.imshow('Blurred', blurred)\n\n key = cv2.waitKey(1) # Prevent window from closing\n if key == 27: # Press ESC to stop\n break\n\n self.cap.release()\n cv2.destroyAllWindows()\n\n def binarythresh(self):\n while True:\n _, frame = self.cap.read() # Read camera frame\n\n gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n blurred = cv2.blur(gray, (3, 3))\n\n # Apply binary thresholding with intensity threshold of 225\n _, binary = cv2.threshold(blurred, 225, 255, cv2.THRESH_BINARY)\n\n # Display the binary thresholded frame\n cv2.imshow('Binary Threshold', binary)\n\n key = cv2.waitKey(1) # Prevent window from closing\n if key == 27: # Press ESC to stop\n break\n\n self.cap.release()\n cv2.destroyAllWindows()\n\n def morphology(self):\n while True:\n _, frame = self.cap.read() # Read camera frame\n\n gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n blurred = cv2.blur(gray, (3, 3))\n\n _, thresh = cv2.threshold(blurred, 225, 255, cv2.THRESH_BINARY)\n\n kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (21, 7))\n closed = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)\n\n # Display the morphology result\n cv2.imshow('Morphology', closed)\n\n key = cv2.waitKey(1) # Prevent window from closing\n if key == 27: # Press ESC to stop\n break\n\n self.cap.release()\n cv2.destroyAllWindows()\n\n def erode_dilate(self):\n while True:\n _, frame = self.cap.read() # Read camera frame\n\n gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n blurred = cv2.blur(gray, (3, 3))\n\n _, thresh = cv2.threshold(blurred, 225, 255, cv2.THRESH_BINARY)\n\n kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (21, 7))\n closed = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)\n\n # Apply erosion and dilation operations\n eroded = cv2.erode(closed, None, iterations=4)\n dilated = cv2.dilate(eroded, None, iterations=4)\n\n # Display the eroded and dilated result\n cv2.imshow('Erosion and Dilation', dilated)\n\n key = cv2.waitKey(1) # Prevent window from closing\n if key == 27: # Press ESC to stop\n break\n\n self.cap.release()\n cv2.destroyAllWindows()\n\n def find_barcode(self):\n while True:\n # Read frame from the camera\n ret, frame = self.cap.read()\n if not ret:\n break\n\n # Convert frame to grayscale\n gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n\n # Calculate x and y gradient\n gradX = cv2.Sobel(gray, ddepth=cv2.CV_32F, dx=1, dy=0, ksize=-1)\n gradY = cv2.Sobel(gray, ddepth=cv2.CV_32F, dx=0, dy=1, ksize=-1)\n\n # Subtract the y-gradient from the x-gradient\n gradient = cv2.subtract(gradX, gradY)\n gradient = cv2.convertScaleAbs(gradient)\n\n # Blur the image\n blurred = cv2.blur(gradient, (3, 3))\n\n # Threshold the image\n _, thresh = cv2.threshold(blurred, 225, 255, cv2.THRESH_BINARY)\n\n # Perform morphology operations\n kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (21, 7))\n closed = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)\n\n # Erosion and dilation\n for i in range(4):\n closed = cv2.erode(closed, None, iterations=4)\n closed = cv2.dilate(closed, None, iterations=4)\n\n # Find contours in the thresholded image\n cnts, hierarchy = cv2.findContours(closed.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n if len(cnts) > 0:\n c = sorted(cnts, key=cv2.contourArea, reverse=True)[0]\n\n # Compute the rotated bounding box of the largest contour\n rect = cv2.minAreaRect(c)\n box = np.int0(cv2.boxPoints(rect))\n\n # Draw a bounding box around the detected barcode\n cv2.drawContours(frame, [box], -1, (0, 255, 0), 3)\n\n # Display the frame with the bounding box\n cv2.imshow(\"Camera\", frame)\n\n # Check for key press\n key = cv2.waitKey(1) & 0xFF\n if key == ord('q'):\n break\n\n # Release the video capture object and close all windows\n self.cap.release()\n cv2.destroyAllWindows()\n\n\napp = QtWidgets.QApplication(sys.argv)\nwindow = ShowImage()\nwindow.setWindowTitle('Project Akhir')\nwindow.show()\nsys.exit(app.exec_())\n","repo_name":"readdoc-png/Barcode-Detection","sub_path":"Barcode.py","file_name":"Barcode.py","file_ext":"py","file_size_in_byte":7725,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"73981581716","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# The [data](https://www.kaggle.com/henriqueyamahata/bank-marketing) is related with direct marketing campaigns of a Portuguese banking institution. \n# The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, \n# in order to access if the product (bank term deposit) would be (or not) subscribed. \n# \n# > Number of Instances: 45211\n# \n# > Number of Attributes: 16 + output attribute.\n# \n# \n# The classification goal is to predict if the client will subscribe a term deposit (variable y).\n# \n\n# # Attribute information:\n# Input variables:\n# \n# ## bank client data:\n# \n# 1 - age (numeric)\n# \n# 2 - job : type of job (categorical: \"admin.\",\"unknown\",\"unemployed\",\"management\",\"housemaid\",\"entrepreneur\",\"student\",\n# \"blue-collar\",\"self-employed\",\"retired\",\"technician\",\"services\") \n# \n# 3 - marital : marital status (categorical: \"married\",\"divorced\",\"single\"; note: \"divorced\" means divorced or widowed)\n# \n# 4 - education (categorical: \"unknown\",\"secondary\",\"primary\",\"tertiary\")\n# \n# 5 - default: has credit in default? (binary: \"yes\",\"no\")\n# \n# 6 - balance: average yearly balance, in euros (numeric) \n# \n# 7 - housing: has housing loan? (binary: \"yes\",\"no\")\n# \n# 8 - loan: has personal loan? (binary: \"yes\",\"no\")\n# \n# ## related with the last contact of the current campaign:\n# \n# 9 - contact: contact communication type (categorical: \"unknown\",\"telephone\",\"cellular\") \n# \n# 10 - day: last contact day of the month (numeric)\n# \n# 11 - month: last contact month of year (categorical: \"jan\", \"feb\", \"mar\", ..., \"nov\", \"dec\")\n# \n# 12 - duration: last contact duration, in seconds (numeric)\n# \n# ## other attributes:\n# \n# 13 - campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact)\n# \n# 14 - pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric, -1 means client was not previously contacted)\n# \n# 15 - previous: number of contacts performed before this campaign and for this client (numeric)\n# \n# 16 - poutcome: outcome of the previous marketing campaign (categorical: \"unknown\",\"other\",\"failure\",\"success\")\n# \n# # Output variable (desired target):\n# \n# 17 - y - has the client subscribed a term deposit? (binary: \"yes\",\"no\")\n\n# In[ ]:\n\n\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nget_ipython().run_line_magic('matplotlib', 'inline')\nimport seaborn as sns\n\nimport warnings\nwarnings.filterwarnings('ignore')\n\n\n# In[ ]:\n\n\npath = '../input/bank-marketing/bank-additional-full.csv'\ndf = pd.read_csv(path,sep=';')\n\n\n# In[ ]:\n\n\ndf.head()\n\n\n# In[ ]:\n\n\ndf.info()\n\n\n# In[ ]:\n\n\ndf.y.value_counts()\n\n\n# #EDA \n\n# In[ ]:\n\n\nint_column = df.dtypes[df.dtypes == 'int64'].index | df.dtypes[df.dtypes == 'float64'].index\n\n\n# In[ ]:\n\n\n\nfor column in int_column:\n plt.figure(figsize=(16,4))\n\n plt.subplot(1,3,1)\n sns.distplot(df[column])\n plt.xlabel(column)\n plt.ylabel('Density')\n plt.title(f'{column} Distribution')\n\n plt.subplot(1,3,2)\n sns.boxplot(x='y', y=column, data =df, showmeans=True )\n plt.xlabel('Target')\n plt.ylabel(column)\n plt.title(f'{column} Distribution')\n\n plt.subplot(1,3,3)\n counts, bins = np.histogram(df[column], bins=20, normed=True)\n cdf = np.cumsum (counts)\n plt.plot (bins[1:], cdf/cdf[-1])\n #plt.xticks(range(15,100,5))\n plt.yticks(np.arange(0,1.1,.1))\n plt.title(f'{column} cdf')\n plt.show()\n print()\n\n\n# In[ ]:\n\n\n# Quantiles\nfor column in int_column:\n print(f'For {column}:')\n\n print('Min:', df[column].quantile(q = 0))\n print('1º Quartile:', df[column].quantile(q = 0.25))\n print('2º Quartile:', df[column].quantile(q = 0.50))\n print('3º Quartile:', df[column].quantile(q = 0.75))\n print('Max:', df[column].quantile(q = 1.00),'\\n')\n\n\n# In[ ]:\n\n\ndf.drop(df[df.age>60].index, inplace=True)\ndf.drop(df[df.campaign>10].index, inplace=True)\ndf.drop(df[df.duration>1000].index, inplace=True)\ndf.drop('pdays', axis=1, inplace=True)\n\n\n# ##For object type\n\n# In[ ]:\n\n\ndfgrouped = df.groupby('y')\n\n\n# In[ ]:\n\n\ndef plot_barh(array,incrementer, bias, text_color ='blue', palette_style = 'darkgrid',palette_color = 'RdBu'):\n\n sns.set_style(palette_style)\n sns.set_palette(palette_color)\n\n plt.barh(array.index, width = array.values, height = .5)\n plt.yticks(np.arange(len(array)))\n plt.xticks( range(0, round(max(array)) +bias, incrementer ))\n\n for index, value in enumerate(array.values):\n plt.text(value +.5, index, s= '{:.1f}%'.format(value), color = text_color)\n\n #plt.show()\n return plt\n\n\n# In[ ]:\n\n\ndef feature_perc(feature,groupby= 'yes'):\n\n count = dfgrouped.get_group(groupby)[feature].value_counts()\n total_count = df[feature].value_counts()[count.index]\n\n perc = (count/total_count)*100\n return perc \n\n\n# In[ ]:\n\n\nobj_column = df.dtypes[df.dtypes == 'object'].index\nobj_column\n\n\n# In[ ]:\n\n\nfor column in obj_column[:-1]:\n\n yes_perc = feature_perc(column, groupby='yes')\n no_perc = feature_perc(column, groupby='no')\n\n plt.figure(figsize=(16,6))\n\n plt.subplot(1,2,1)\n plt.title(f'Success rate by {column}')\n plot_barh(yes_perc.sort_values(),5,10)\n\n plt.subplot(1,2,2)\n plt.title(f'Failure rate by {column}')\n plot_barh(no_perc.sort_values(),5,10)\n plt.show()\n print()\n\n\n# ##Modeling\n\n# In[ ]:\n\n\ndf1 = df.copy()\ndf1['y'] = df1.y.apply(lambda x:0 if x=='no' else 1)\n\n\n# In[ ]:\n\n\ndf1.y.value_counts()\n\n\n# In[ ]:\n\n\nfrom sklearn.utils import resample\n\n# Separate majority and minority classes\ndf1_majority = df1[df1.y==0]\ndf1_minority = df1[df1.y==1]\n \n# Upsample minority class\ndf1_minority_upsampled = resample(df1_minority, \n replace=True, # sample with replacement\n n_samples=36962, # to match majority class\n random_state=42) # reproducible results\n \n# Combine majority class with upsampled minority class\ndf = pd.concat([df1_majority, df1_minority_upsampled])\n \n# Display new class counts\ndf.y.value_counts()\n\n\n# In[ ]:\n\n\ndf\n\n\n# In[ ]:\n\n\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.ensemble import RandomForestClassifier\nfrom xgboost import XGBClassifier\nfrom sklearn.preprocessing import LabelEncoder\n\nfrom sklearn.preprocessing import MinMaxScaler\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import classification_report, confusion_matrix\n\n\n# In[ ]:\n\n\nobj_column = df.dtypes[df.dtypes == 'object'].index\nmapingdf = pd.DataFrame()\n\nfor column in obj_column:\n labelencoder = LabelEncoder()\n df[column] = labelencoder.fit_transform(df[column])\n mapingdf[column] = df[column]\n mapingdf['_'+column] = labelencoder.inverse_transform(df[column])\n\n\n# In[ ]:\n\n\n#for reference\nmapingdf\n\n\n# In[ ]:\n\n\ndf.head()\n\n\n# In[ ]:\n\n\ndf.corr().y.sort_values()\n\n\n# In[ ]:\n\n\nX_train, X_test, y_train, y_test = train_test_split(df.drop('y',axis=1),\n df['y'],\n test_size=.3, random_state = 42,\n stratify= df['y'])\n\n\n# In[ ]:\n\n\nscaler = MinMaxScaler()\nX_train = scaler.fit_transform(X_train)\nX_test = scaler.transform(X_test)\n\n\n# In[ ]:\n\n\nmodels = [DecisionTreeClassifier(),\n RandomForestClassifier(),\n XGBClassifier()]\n\nnames = [ 'DecisionTreeClassifier',\n 'RandomForestClassifier',\n 'XGBClassifier']\n\nfor model,name in zip(models,names):\n m = model.fit(X_train,y_train)\n print(name, 'report:')\n print('Train score',model.score(X_train,y_train))\n print('Test score',model.score(X_test,y_test))\n print()\n print(\"Train confusion matrix:\\n\",confusion_matrix(y_train, model.predict(X_train)),'\\n')\n print(\"Test confusion matrix:\\n\",confusion_matrix(y_test, model.predict(X_test)))\n print('*'*50)\n\n\n# In[ ]:\n\n\nmodel = DecisionTreeClassifier(max_depth=3)\nmodel.fit(X_train, y_train)\n\nfrom sklearn.tree import plot_tree\nplt.figure(figsize=(20,15))\nplot_tree(model,\n feature_names= df1.drop('y', axis=1).columns, \n class_names= ['yes','no'],\n filled=True)\nplt.show()\n\n","repo_name":"UsmanGohar/FairEnsemble","sub_path":"BankMarketingNoteBook/Kernels/Stacking/5-marketing-strategy-eda-97.py","file_name":"5-marketing-strategy-eda-97.py","file_ext":"py","file_size_in_byte":8369,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"3238598217","text":"import setuptools\n\nwith open('README.md', 'r') as fh:\n long_description = fh.read()\n\nsetuptools.setup(\n name='makefilemenu',\n version='0.4.2',\n python_requires='~=3.5',\n author='Isaac To',\n author_email='isaac.to@gmail.com',\n description='Console menu for Makefiles',\n long_description=long_description,\n long_description_content_type='text/markdown',\n url='https://github.com/isaacto/makefilemenu',\n packages=setuptools.find_packages(),\n entry_points={\n \"console_scripts\": [\n \"makefilemenu=makefilemenu.__main__:main\",\n ]\n },\n package_data={'makefilemenu': ['py.typed']},\n install_requires=[\n 'attrs',\n 'calf',\n ],\n classifiers=[\n 'Development Status :: 4 - Beta',\n 'Programming Language :: Python :: 3.5',\n 'Programming Language :: Python :: 3.6',\n 'Programming Language :: Python :: 3.7',\n 'Programming Language :: Python :: 3.8',\n 'Topic :: Software Development :: Libraries',\n 'License :: OSI Approved :: MIT License',\n 'Operating System :: POSIX :: Linux',\n ],\n)\n","repo_name":"isaacto/makefilemenu","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1118,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"85"} +{"seq_id":"7314862727","text":"# -*- coding: UTF-8 -*-\n'''\n@Project ：week01 \n@File ：1978.py\n@IDE ：PyCharm \n@Author ： Hwang\n@Date ：2021-11-05 오후 2:09 \n'''\n\n# 에라토스테네스의 체\n# prime = list(range(2,1001))\n# for i in prime:\n# if i != -1:\n# for j in prime[prime.index(i)+1:]:\n# if j % i == 0 :\n# prime[prime.index(j)] = -1\n#\n# print(prime)\nimport sys\n\nnum = int(sys.stdin.readline().split()[0])\ndata = list(map(int, sys.stdin.readline().split()))\ndata.sort()\n\nmaxNum = data[-1]\n\nprime = list(range(2,maxNum+1))\nfor i in prime:\n if i != -1:\n for j in prime[prime.index(i)+1:]:\n if j % i == 0 :\n prime[prime.index(j)] = -1\n\ncnt = 0\nfor i in data:\n if i in prime:\n cnt=cnt+1\n\nprint(cnt)","repo_name":"DongGeun974/computingThinking","sub_path":"week01/1978.py","file_name":"1978.py","file_ext":"py","file_size_in_byte":762,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"9718931795","text":"from django.conf.urls import patterns, url\nfrom ski_app import views\n\nurlpatterns = patterns('',\n\turl(r'^$', views.index, name='index'),\n\turl(r'^about/', views.about, name='about'),\n\turl(r'^add_ski/$', views.add_ski, name ='add_ski'),\n\turl(r'^register/$',views.register, name='register'),\n\turl(r'^login/$',views.user_login, name='login'),\n\turl(r'^logout/$',views.user_logout, name='logout'),\n\turl(r'^search/$', views.search, name='search'),\n\turl(r'^(?P\\w+)/$',views.category, name='category'),\n\turl(r'^Skis/(?P\\w+)/$',views.item,name='item'),\n\t\n\t)","repo_name":"karenski/Django_Project","sub_path":"ski_app/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":581,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"18784517007","text":"import os \nimport sys \nimport requests \nfrom flask import Flask, request, jsonify \nimport time\nos.system(\"clear||cls\")\n\napi_key = \"cap key\"\n\napp = Flask(__name__)\n\ndef solve(site, key):\n print(\"attempting to solve\")\n session = requests.Session()\n json = {\"clientKey\": api_key, \"task\": {\"type\": \"HCaptchaTaskProxyLess\", \"websiteURL\": site, \"websiteKey\": key}}\n headers = {\"Content-Type\": \"application/json\"}\n resp = session.post(\"https://api.capsolver.com/createTask\", headers=headers, json=json)\n try:\n tid = resp.json()['taskId']\n except:\n print(\"failed to create task\")\n return solve(site, key)\n print(\"task created\")\n pay = {\"clientKey\": api_key, \"taskId\": tid}\n while True:\n time.sleep(1)\n result = session.post(\"https://api.capsolver.com/getTaskResult\", headers=headers, json=pay)\n print(result.json())\n if result.json()[\"status\"] == \"ready\":\n capkey = result.json()['solution']['gRecaptchaResponse']\n print(\"Captcha Solved!\")\n return capkey \n else:\n continue\n\n@app.route(\"/\")\ndef index():\n return \"sure\"\n\n@app.route(\"/solve\")\ndef indextwo():\n sitekey = request.args.get(\"sk\")\n siteurl = request.args.get(\"su\")\n if not request.headers.get(\"Authorization\") == \"enter api pass\":\n return jsonify({\"Message\": \"invalid auth\"}), 401\n key = solve(siteurl, sitekey)\n return {\"key\": key, 'time': \"n/a\"}\n\napp.run(host=\"0.0.0.0\", port=8080)\n","repo_name":"idkconsole/CapSolver","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1398,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"85"} +{"seq_id":"20172395534","text":"import pdb\nimport netlink\nimport pprint\nimport cStringIO\n\n\ndef get_family():\n con = netlink.new_generic()\n hdr = netlink.new_genlmsg(\n {\n \"cmd\": netlink.CTRL_CMD_GETFAMILY,\n \"version\": 0,\n \"reserved\": 0\n }\n ) \n payload = netlink.generic_id_ctrl(hdr,0x12345) \n con.send(payload)\n msgs = []\n goout = False \n while True: \n d = con.recv(65533)\n b = cStringIO.StringIO(d)\n while True:\n if b.tell() >= len(d):\n break\n msg = netlink.parse_nlmsg(b) \n if msg[\"type\"] == netlink.DONE:\n goout = True\n break\n elif msg[\"type\"] == netlink.ERROR:\n raise ValueError(msg)\n mlen = b.tell() - 16 + msg[\"len\"] \n payload = netlink.parse_genlmsg(b) \n attrs = netlink.parse_attrs(b, mlen) \n msgs.append({\n \"msg\": msg,\n \"payload\": payload,\n \"attrs\": attrs\n })\n if goout:\n break\n b.close() \n return msgs \n\ndef get_iface():\n con = netlink.new_generic()\n hdr = netlink.new_genlmsg(\n {\n \"cmd\": netlink.NL80211_CMD_GET_SCAN,\n \"version\": 0,\n \"reserved\": 0\n }\n ) \n attr = netlink.newnlattr(netlink.NL80211_ATTR_IFINDEX, netlink.new_policy_u32(3)) \n payload = netlink.generic_wireless(hdr+attr, 0x12345) \n con.send(payload) \n d = con.recv(4096)\n b = cStringIO.StringIO(d)\n msg = netlink.parse_nlmsg(b)\n mlen = b.tell() - 16 + msg[\"len\"]\n payload = netlink.parse_genlmsg(b) \n attrs = netlink.parse_attrs(b, mlen) \n return {\n \"msg\": msg,\n \"payload\": payload,\n \"attrs\": attrs\n }\n \n\npprint.pprint(get_iface())\n\n\n\n","repo_name":"maliubiao/python_netlink","sub_path":"getfamily.py","file_name":"getfamily.py","file_ext":"py","file_size_in_byte":1900,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"85"} +{"seq_id":"19688975710","text":"from typing import Callable, List\n\nimport evaluate\nimport torch\nfrom torch import nn\nfrom transformers import Trainer\n\n\ndef compute_metrics(eval_preds):\n mse_metric = evaluate.load(\"mse\", \"multilist\")\n logits, labels = (torch.from_numpy(p) for p in eval_preds)\n sigmoid = nn.Sigmoid()\n preds = sigmoid(logits)\n\n return mse_metric.compute(predictions=preds, references=labels)\n\n\nclass MultiClassRegressionTrainer(Trainer):\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n\n def compute_loss(self, model, inputs, return_outputs=False):\n labels = inputs.get(\"labels\")\n\n # forward pass\n outputs = model(**inputs)\n logits = outputs.get(\"logits\")\n\n sigmoid = nn.Sigmoid()\n normalised_logits = sigmoid(logits).to(torch.float64)\n\n loss_fct = nn.MSELoss()\n loss = loss_fct(normalised_logits.view(-1), labels.view(-1))\n return (loss, outputs) if return_outputs else loss\n\n\nclass ColorPredictionDataset(torch.utils.data.Dataset):\n def __init__(\n self,\n texts: List[str],\n labels: List[List[int]],\n tokenize_fn: Callable\n ):\n self.texts = texts\n self.labels = labels\n self.tokenize_fn = tokenize_fn \n\n def __getitem__(self, idx):\n item = self.tokenize_fn(self.texts[idx])\n item['labels'] = torch.tensor(self.labels[idx])\n return item\n\n def __len__(self):\n return len(self.labels)","repo_name":"BarneyMurray/xkcd-color-prediction","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1484,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"71697166997","text":"'''\nreader.py script\nversion 2013.06.27\n\n@author: Bing\nreads the directions.txt and tells the processor.py how to treat the file\nnew: change name of functions\n\n'''\nimport processor\nimport os\n\ninput_directory = os.path.join(os.path.dirname(__file__), 'Input/')\noutput_directory = os.path.join(os.path.dirname(__file__), 'Output')\n\nold_dir = os.getcwd()\nos.chdir(os.path.dirname(__file__))\n\nf = open('directions.txt', 'r')\n\nf.readline()\n\nf.readline()\nf.readline()\ncompare_type = f.readline().rstrip()\n\nf.readline()\nf.readline()\nfile_first = f.readline().rstrip()\n\nf.readline()\nf.readline()\nfile_second = f.readline().rstrip()\n\nf.readline()\nf.readline()\nfile_output = f.readline().rstrip()\n\nf.readline()\nf.readline()\ntable = f.readline().rstrip()\ntable = [s.strip().split(', ') for s in table.splitlines()]\nfile_directory = table[0] ## should make cleaner\n\nstring = f.readline()\nstring = f.readline()\nstring = f.readline()\nlines_skip = int(string)\n\nif compare_type == 'Mutect':\n\tfor x in range(0, len(file_directory)):\n\t\tprocessor.processMutect(file_first, file_second, input_directory + file_directory[x], file_output + file_directory[x] + '.xls', output_directory, lines_skip)\nelif compare_type == 'Nucleotide':\n\tfor x in range(0, len(file_directory)):\n\t\tprocessor.processNucleotides(file_first, file_second, input_directory + file_directory[x], file_output + file_directory[x] + '.xls', output_directory, lines_skip)\nelif compare_type == 'Somatic':\n\tfor x in range(0, len(file_directory)):\n\t\tprocessor.processSomatic(file_first, file_second, input_directory + file_directory[x], file_output + file_directory[x] + '.xls', output_directory, lines_skip)\nelse:\n\tprint('Unknown type of comparison')\n\nf.close()","repo_name":"kotoroshinoto/Cluster_SimpleJob_Generator","sub_path":"pybin/MutectAnalysis/reader.py","file_name":"reader.py","file_ext":"py","file_size_in_byte":1704,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"11619158554","text":"from time import sleep\nfrom drivers.init_drivers import init_driver_my\nfrom time import sleep\n\n\ndef get_set_prof(driver, url_http:str) -> set:\n driver.get(url_http)\n key_go = True\n set_prof = set()\n k = 0\n while key_go :\n l_tr = driver.find_elements_by_xpath('//table//tr')\n for row in l_tr[1:]:\n l_col = row.find_elements_by_xpath('.//*')\n print(l_col[2].text)\n set_prof.add(l_col[2].text.strip())\n try:\n k +=1\n print('page = ', str(k))\n butt = driver.find_element_by_xpath('//div[@id = \"pager-nav\"]//li[@class=\"next\"]/a')\n butt.click()\n sleep(7)\n \n except Exception:\n key_go = False\n return set_prof\n\n\ndef write_result(set_prof:set):\n f_r = open('list_slug.csv', 'w')\n for line in set_prof:\n f_r.write(line+'\\n')\n \n \nif __name__ == \"__main__\":\n \n driver = init_driver_my()\n set_prof = get_set_prof(driver, 'https://etks.info/okpdtr/office')\n write_result(set_prof)","repo_name":"gansiorag/NLP-russian-language","sub_path":"NLP_gansior/professions/get_information/get_from_web.py","file_name":"get_from_web.py","file_ext":"py","file_size_in_byte":1061,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"85"} +{"seq_id":"10665486303","text":"import os\nimport argparse\nimport csv\nimport pandas as pd\nimport spacy\nfrom spacy.lang.en import English\nfrom spacy.pipeline import Sentencizer\nfrom sentence_transformers import SentenceTransformer\n\nparser = argparse.ArgumentParser(description=\"what\")\nparser.add_argument('--path', type=str, help='path to the folder with files')\nparser.add_argument('--flatten', type=int, help='return a list of sentences(1) or a list of paragraphs(0)', default=1)\nargs = parser.parse_args()\n\ndef load_corpus(DIR_NAME):\n '''Loads all articles and into a list.'''\n articles = []\n for folder in os.listdir(DIR_NAME):\n if not folder.startswith('.'):\n folder = os.path.join(DIR_NAME, folder)\n for article in os.listdir(folder):\n articles.append(open(os.path.join(folder,article), mode='r', encoding='utf-8-sig').readlines())\n return articles\n\ndef preprocess(corpus):\n \"\"\"Preprocesses the corpus and flattens it.\"\"\"\n corpus = [item.rstrip() for article in corpus for item in article]\n corpus = [item for item in corpus if item != \"\"]\n return corpus\n\ndef flatten(corpus):\n \"\"\"Flattens the corpus, one sentence per list.\"\"\"\n nlp = English()\n sentencizer = Sentencizer()\n temp = []\n for index, element in enumerate(corpus):\n try:\n sentences = list(sentencizer(nlp(element)).sents)\n except ValueError:\n pass\n for sent in sentences:\n temp.append(sent)\n return temp\n\nif __name__ == '__main__':\n print(\"Loading BERT model\")\n model = SentenceTransformer('bert-base-nli-cls-token')\n path_name = args.path\n documents = load_corpus(path_name)\n documents = preprocess(documents)\n if args.flatten == 1:\n documents = flatten(documents)\n print(\"Number of sentences:\", len(documents))\n print(\"Calculating embeddings...\")\n embedding = pd.Series([model.encode(str(document)) for document in documents])\n text = pd.Series(str(document) for document in documents)\n df = pd.DataFrame({\"text\":text, \"embedding\":embedding})\n df.to_csv('corpus_sentences.csv')\n else:\n print(\"Number of paragraphs:\", len(documents))\n print(\"Calculating embeddings...\")\n embedding = pd.Series([model.encode(str(document)) for document in documents])\n text = pd.Series(str(document) for document in documents)\n df = pd.DataFrame({\"text\":text, \"embedding\":embedding})\n df.to_csv('corpus_paragraphs.csv')\n print(\"Done!\")\n","repo_name":"garneele/Sentiment-and-argument-mining-1","sub_path":"scripts/load_corpus.py","file_name":"load_corpus.py","file_ext":"py","file_size_in_byte":2508,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"74061151957","text":"#!/usr/bin/env python\n# -*- coding: UTF-8 -*-\nimport os\nfrom tools.tools import read_text, write_csv\n\nbase_dir = os.path.dirname(__file__)\n\n\nclass DataProcessor(object):\n def __init__(self):\n self.bio_list = list()\n self.all_bio_list = list()\n self.csv_path = os.path.join(base_dir, \"../demo_data.csv\")\n self.head_list = [\"id\", \"input_text\", \"target_text\"]\n self.demo_file_path = os.path.join(base_dir, \"demo_data.txt\")\n\n def get_bio_list(self):\n ann_bio_list = read_text(self.demo_file_path)\n for bio_data in ann_bio_list:\n if bio_data:\n self.bio_list.append(bio_data)\n else:\n self.all_bio_list.append(self.bio_list)\n self.bio_list = list()\n return self.all_bio_list\n\n @staticmethod\n def get_ann_words(bio_data):\n \"\"\"\n 将标注数据标注的词语拿出来，并返回\n :param bio_data:\n :return keyword\n \"\"\"\n entities, entity_tags, words, targets = list(), list(), list(), list()\n start, end, start_flag = 0, 0, False\n for line in bio_data:\n if line != \"\\n\":\n words.append(line.split(\"\\t\")[0])\n targets.append(line.split(\"\\t\")[1])\n for idx, tag in enumerate(targets):\n if tag.startswith('B-'): # 一个实体开头 另一个实体（I-）结束\n end = idx\n if start_flag: # 另一个实体以I-结束，紧接着当前实体B-出现\n entities.append(\"\".join(words[start:end]))\n entity_tags.append(targets[start][2:].lower())\n start_flag = False\n start = idx\n start_flag = True\n elif tag.startswith('I-'): # 实体中间，不是开头也不是结束，end+1即可\n end = idx\n elif tag.startswith('O'): # 无实体，可能是上一个实体的结束\n end = idx\n if start_flag: # 上一个实体结束\n entities.append(\"\".join(words[start:end]))\n entity_tags.append(targets[start][2:].lower())\n start_flag = False\n if start_flag: # 句子以实体I-结束，未被添加\n entities.append(\"\".join(words[start:end + 1]))\n entity_tags.append(targets[start][2:].lower())\n start_flag = False\n return entities, entity_tags\n\n @classmethod\n def get_bio_content(cls, bio_data):\n word_list = [data.split(\"\\t\")[0] for data in bio_data]\n return \"\".join(word_list)\n\n def __call__(self):\n\n content_keyword_list = list()\n ner_data = self.get_bio_list()\n for num, data in enumerate(ner_data):\n temp_dict = dict()\n temp_dict['id'] = num+1\n # temp_dict['prefix'] = \"webNLG\"\n entities_list, _ = DataProcessor.get_ann_words(data)\n temp_dict['input_text'] = \" | \".join(entities_list)\n content = DataProcessor.get_bio_content(data)\n temp_dict['target_text'] = content\n if \" | \".join(entities_list) and content:\n content_keyword_list.append(temp_dict)\n write_csv(self.csv_path, self.head_list, content_keyword_list)\n return content_keyword_list\n\n\nif __name__ == \"__main__\":\n data_processor = DataProcessor()\n data_processor()\n","repo_name":"zhangnn520/keyword2textgenration","sub_path":"data/enoch_data/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":3417,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"85"} +{"seq_id":"29198951626","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[ ]:\n\n\ndef general_pool():\n \n import sqlite3\n import pandas as pd\n import ast\n from pandas import ExcelWriter\n from io import BytesIO\n \n conn = sqlite3.connect('D:/Oybek/Python/Bot/Real sector/data/real_sector.db', check_same_thread=False)\n cursor = conn.cursor()\n \n def into_int(x):\n try:\n value = int(x)\n return value\n except:\n return x\n \n gen_cols = pd.Series(['user', 'user_name', 'language', 'time'])\n \n pool_list = ['manufacturing_pool', 'construction_pool', 'service_pool', 'retail_pool']\n \n df_list = []\n for i in pool_list:\n df = pd.read_sql_query(f'SELECT * FROM {i}', conn)\n df = df.fillna(0).applymap(into_int)\n questions_df = pd.read_csv(f'D:/Oybek/Python/Bot/Real sector/csv_files/{i[:i.index(\"_\")]}_questions_uz.csv')\n df.columns = pd.concat([gen_cols, questions_df.question.drop_duplicates()])\n answer_df = questions_df[['answer_id', 'answer']].set_index('answer_id')\n \n def convert_answer(x):\n try:\n value = answer_df.loc[x, 'answer']\n return value\n except:\n try:\n answer_ids = ast.literal_eval(x)\n value = ', '.join(answer_df.loc[answer_ids, 'answer'].to_list())\n return value\n except:\n return x\n \n df = df.applymap(convert_answer)\n \n df_list.append(df)\n \n manufacturing = df_list[0]\n final_q = manufacturing.columns[-2]\n region_q = 'Фаолият юритаётган ҳудуд (вилоят):'\n manufacturing = manufacturing[manufacturing[final_q] != 0]\n \n construction = df_list[1]\n final_q = construction.columns[-2]\n region_q = 'Фаолият юритаётган ҳудуд (вилоят):'\n construction = construction[construction[final_q] != 0]\n \n service = df_list[2]\n final_q = service.columns[-2]\n region_q = 'Фаолият юритаётган ҳудуд (вилоят):'\n service = service[service[final_q] != 0]\n \n retail = df_list[3]\n final_q = retail.columns[-2]\n region_q = 'Фаолият юритаётган ҳудуд (вилоят):'\n retail = retail[retail[final_q] != 0]\n \n list_dfs = [manufacturing, construction, service, retail]\n \n sheet_names = ['manufacturing', 'construction', 'service', 'retail']\n \n def save_xls(list_dfs, xls_path):\n with ExcelWriter(xls_path) as writer:\n for n, df in enumerate(list_dfs):\n df.to_excel(writer, sheet_names[n], index=False)\n writer.save()\n \n plot_file = BytesIO()\n save_xls(list_dfs, plot_file)\n plot_file.seek(0)\n return plot_file","repo_name":"OybekAIcomXuniverse/Survey_bot_onTelegram","sub_path":"general_pool.py","file_name":"general_pool.py","file_ext":"py","file_size_in_byte":2842,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"71549624599","text":"from ..models.tickets import Ticket\nfrom ..models.user import User\nfrom ..models.db import db\nfrom flask import Blueprint, request, jsonify\nfrom flask_login import current_user, login_required\nfrom datetime import datetime, time\nfrom .auth_routes import validation_errors_to_error_messages\nfrom ..forms.ticket_form import TicketForm\n\n\nticket_routes = Blueprint('tickets', __name__)\n\n\n@ticket_routes.route('/', methods=['GET'])\n@login_required\ndef get_tickets():\n tickets = Ticket.query.all()\n return {ticket.id: ticket.to_dict() for ticket in tickets}\n\n\n@ticket_routes.route('/', methods=['GET'])\n@login_required\ndef get_user_tickets(userId):\n tickets = Ticket.query.all()\n return {ticket.id: ticket.to_dict() for ticket in tickets}\n\n\n@ticket_routes.route('/', methods=['POST'])\n@login_required\ndef create_ticket():\n user = current_user\n form = TicketForm()\n form['csrf_token'].data = request.cookies['csrf_token']\n if form.validate_on_submit():\n data = form.data\n\n new_ticket = Ticket(\n user_id=user.id,\n event_id=data['event_id'],\n num_ticket=data['num_ticket'],\n )\n\n db.session.add(new_ticket)\n db.session.commit()\n return new_ticket.to_dict()\n\n return {'errors': validation_errors_to_error_messages(form.errors)}, 401\n\n\n@ticket_routes.route('/', methods=['DELETE'])\n@login_required\ndef delete_ticket(id):\n ticket = Ticket.query.get(id)\n if ticket:\n db.session.delete(ticket)\n db.session.commit()\n else:\n return \"The ticket you are trying to delete doesn't exist\", 400\n\n return jsonify(\"Delete Ticket Success\")\n","repo_name":"KyleHere/Gamernite","sub_path":"app/api/ticket_routes.py","file_name":"ticket_routes.py","file_ext":"py","file_size_in_byte":1669,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"85"} +{"seq_id":"5752559239","text":"from flask import jsonify\nfrom flask_restful import abort, reqparse, Resource\n\nfrom . import db_session\nfrom .jobs import Jobs\n\nparser = reqparse.RequestParser()\nparser.add_argument('team_leader', required=True, type=int)\nparser.add_argument('job', required=True)\nparser.add_argument('work_size', required=False, type=int)\nparser.add_argument('collaborators', required=True)\nparser.add_argument('start_date', required=False)\nparser.add_argument('end_date', required=False)\nparser.add_argument('is_finished', required=False, type=bool)\n\n\nclass JobsListResource(Resource):\n def get(self):\n session = db_session.create_session()\n jobs = session.query(Jobs).all()\n return jsonify({'jobs':\n [item.to_dict(rules=('-user', '-user.jobs')) for item in jobs]})\n\n def post(self):\n args = parser.parse_args()\n session = db_session.create_session()\n job = Jobs(\n team_leader=args['team_leader'],\n job=args['job'],\n work_size=args['work_size'],\n collaborators=args['collaborators'],\n start_date=args['start_date'],\n end_date=args['end_date'],\n is_finished=args['is_finished'],\n )\n session.add(job)\n session.commit()\n return jsonify({'success': 'OK'})\n\n\nclass JobResource(Resource):\n def get(self, job_id):\n abort_if_job_not_found(job_id)\n session = db_session.create_session()\n user = session.query(Jobs).get(job_id)\n return jsonify(\n {'jobs': user.to_dict(rules=('-user', '-user.jobs'))})\n\n def delete(self, job_id):\n abort_if_job_not_found(job_id)\n session = db_session.create_session()\n job = session.query(Jobs).get(job_id)\n session.delete(job)\n session.commit()\n return jsonify({'success': 'OK'})\n\n\ndef abort_if_job_not_found(job_id):\n session = db_session.create_session()\n job = session.query(Jobs).get(job_id)\n if not job:\n abort(404, message=f\"Job {job_id} not found\")\n","repo_name":"sersad/web-mars","sub_path":"data/jobs_resource.py","file_name":"jobs_resource.py","file_ext":"py","file_size_in_byte":2047,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"7546910122","text":"'''\nYour ferry made decent progress toward the island, but the storm came in faster than anyone expected. The ferry needs to take evasive actions!\n\nUnfortunately, the ship's navigation computer seems to be malfunctioning; rather than giving a route directly to safety, it produced extremely circuitous instructions. When the captain uses the PA system to ask if anyone can help, you quickly volunteer.\n\nThe navigation instructions (your puzzle input) consists of a sequence of single-character actions paired with integer input values. After staring at them for a few minutes, you work out what they probably mean:\n\nAction N means to move north by the given value.\nAction S means to move south by the given value.\nAction E means to move east by the given value.\nAction W means to move west by the given value.\nAction L means to turn left the given number of degrees.\nAction R means to turn right the given number of degrees.\nAction F means to move forward by the given value in the direction the ship is currently facing.\nThe ship starts by facing east. Only the L and R actions change the direction the ship is facing. (That is, if the ship is facing east and the next instruction is N10, the ship would move north 10 units, but would still move east if the following action were F.)\n\nFor example:\n\nF10\nN3\nF7\nR90\nF11\nThese instructions would be handled as follows:\n\nF10 would move the ship 10 units east (because the ship starts by facing east) to east 10, north 0.\nN3 would move the ship 3 units north to east 10, north 3.\nF7 would move the ship another 7 units east (because the ship is still facing east) to east 17, north 3.\nR90 would cause the ship to turn right by 90 degrees and face south; it remains at east 17, north 3.\nF11 would move the ship 11 units south to east 17, south 8.\nAt the end of these instructions, the ship's Manhattan distance (sum of the absolute values of its east/west position and its north/south position) from its starting position is 17 + 8 = 25.\n\nFigure out where the navigation instructions lead. What is the Manhattan distance between that location and the ship's starting position?\n'''\nimport re\nimport math\n\ndef addTuple(first, second):\n firstList = list(first)\n secondList = list(second)\n firstIndex = 0\n secondIndex = 0\n resultList = []\n while firstIndex < len(firstList) and secondIndex < len(secondList):\n resultList.append(firstList[firstIndex] + secondList[secondIndex])\n firstIndex += 1\n secondIndex += 1\n return tuple(resultList)\n\ndef multiplyTuple(tup, scale):\n result = []\n for num in tup:\n result.append(num * scale)\n return tuple(result)\n\ndef degreesToRadians(degrees):\n return (degrees * math.pi) / 180.0\n\ndef convertAngleToVector(angle):\n return (math.cos(degreesToRadians(angle)), math.sin(degreesToRadians(angle)))\n\ndef convertCardinalDirectionToAngle(direction):\n dirMap = {\"N\":90, \"E\":0, \"S\":270, \"W\":180}\n return dirMap[direction]\n\nclass Ship:\n def __init__(self):\n self.coords = (0,0)\n self.orientation = 0\n\n def moveForward(self, distance):\n moveVector = multiplyTuple(convertAngleToVector(self.orientation), distance)\n self.coords = addTuple(self.coords, moveVector)\n\n def move(self, direction, distance):\n moveVector = multiplyTuple(convertAngleToVector(direction), distance)\n self.coords = addTuple(self.coords, moveVector)\n\n def rotate(self, angle):\n self.orientation += angle\n\n def __repr__(self):\n return \"{} -> {}\".format(self.coords, self.orientation)\n\nship = Ship()\ninputRegex = re.compile(\"(?P\\w)(?P\\d+)\")\n\nwith open(\"Data\\input.txt\", \"r\") as inputFile:\n for line in inputFile:\n line = line.strip()\n command = inputRegex.match(line).groupdict()\n if command[\"Direction\"] == \"F\":\n ship.moveForward(int(command[\"Amount\"]))\n elif command[\"Direction\"] == \"R\":\n ship.rotate(-1*int(command[\"Amount\"]))\n elif command[\"Direction\"] == \"L\":\n ship.rotate(int(command[\"Amount\"]))\n else:\n ship.move(convertCardinalDirectionToAngle(command[\"Direction\"]), int(command[\"Amount\"]))\n\nprint(abs(ship.coords[0]) + abs(ship.coords[1]))\n\n","repo_name":"olber027/AdventOfCode2020","sub_path":"Day_12/Part1.py","file_name":"Part1.py","file_ext":"py","file_size_in_byte":4235,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"70034296597","text":"#!/usr/bin/env python3\n\n\n# ME499-S20 Python Lab 2 Problem 2\n# Programmer: Jacob Gray\n# Last Edit: 4/21/2020\n\n\nfrom random import choice\nfrom random import randint\nfrom sum import compare_test\nfrom string import ascii_letters\n\n\ndef reverse_i(character_list):\n \"\"\"\n This function takes the input character list and returns a new list with all the elements in reverse order by\n use of a for loop.\n :param character_list: List of numbers or characters.\n :return: New list containing all elements of input character list, but in reverse order.\n \"\"\"\n\n new_list = []\n\n for i in range(1, len(character_list) + 1):\n new_list.append(character_list[-i])\n\n return new_list\n\n\ndef reverse_r(character_list):\n \"\"\"\n This function takes the input character list and returns a new list with all the elements in reverse order by\n use of recursion.\n :param character_list:\n :return:\n \"\"\"\n\n if len(character_list) == 0:\n return []\n else:\n return [character_list[-1]] + reverse_r(character_list[:-1])\n\n\ndef random_string(lower_bound, upper_bound):\n \"\"\"\n Generates a random string of upper and lowercase letters of random length between two limits.\n :param lower_bound: Lower letter limit length.\n :param upper_bound: Upper letter limit length.\n :return: A random string of letters.\n \"\"\"\n rand_length = randint(lower_bound, upper_bound)\n return ''.join(choice(ascii_letters) for i in range(rand_length))\n\n\nif __name__ == '__main__':\n for i in range(0, 999):\n test_string = random_string(5, 15)\n compare_test(reverse_i(test_string), reverse_r(test_string))\n else:\n print('No errors found')\n","repo_name":"grayjac/Filters","sub_path":"reverse.py","file_name":"reverse.py","file_ext":"py","file_size_in_byte":1689,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"24268073859","text":"import sqlite3\r\n\r\nclass transact:\r\n\r\n\tdef get_connection(self):\r\n\t\tconnection = sqlite3.connect(\"database.db\")\r\n\t\treturn connection\r\n\r\n\tdef update_items(self,transaction_details,des,pot_id):\r\n\t\tconn = self.get_connection()\r\n\t\tcurr = conn.cursor()\r\n\t\titem_id = 0\r\n\t\tfor detail in transaction_details:\r\n\t\t\tif detail[0] == \"ADD\":\r\n\t\t\t\tcurr.execute(\" INSERT INTO transactions (pot_id,description,amount,paidby) VALUES(?,?,?,?)\",(pot_id,des,detail[2],detail[1]))\r\n\t\t\t\titem_id = curr.execute(\"SELECT t_id FROM transactions WHERE description = ? and pot_id = ?\",(des,pot_id,)).fetchone()\r\n\t\t\telif detail[0] == \"DEDUCT\":\r\n\t\t\t\tcurr.execute(\"INSERT INTO consumers (transaction_id,consumer_name,amount) VALUES(?,?,?)\",(item_id[0],detail[1],detail[2]))\r\n\t\tconn.commit()\r\n\t\tconn.close()\r\n\r\n","repo_name":"suchith989/Divideal","sub_path":"transactions.py","file_name":"transactions.py","file_ext":"py","file_size_in_byte":777,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"85"} +{"seq_id":"33954252925","text":"import json\n\nimport websocket\nimport _thread\nimport time\nimport rel\n#\n# config_logging(logging, logging.DEBUG)\n#\n# cm_futures_client = CMFutures()\n# get_request = (cm_futures_client.index_price_klines(pair[:-1], time,\n# **{\"limit\": 999}))\nfrom binance.um_futures import UMFutures\n\n\ndef on_message(ws, message):\n # print(message, type(message))\n source = (json.loads(message))[\"message\"]\n while True:\n cm_futures_client = UMFutures()\n get_request = cm_futures_client.mark_price(\"BTCUSDT\")\n actual_price = get_request['markPrice']\n print(actual_price)\n src = []\n for i in source:\n if i['type_of_pos'] == \"LONG\":\n if float(i['price']) >= float(actual_price):\n src += [i['id']]\n elif i['type_of_pos'] == \"SHORT\":\n if float(i['price']) <= float(actual_price):\n src += [i['id']]\n\n data = {'message': ''}\n if src:\n source = filter(lambda x: float(x['id']) not in src, source)\n # print(source, list(source))\n data = {\n \"message\": \"open_pos\",\n \"data\": src\n }\n msg = json.dumps(\n data\n )\n ws.send(msg)\n # print(get_request)\n # for i in data:\n # print(i)\n\n\ndef on_error(ws, error):\n print(error)\n\n\ndef on_close(ws, close_status_code, close_msg):\n print(\"### closed ###\")\n\n\ndef on_open(ws):\n print(\"Opened connection\")\n\n\nif __name__ == \"__main__\":\n websocket.enableTrace(True)\n ws = websocket.WebSocketApp(\"ws://127.0.0.1:8000/ws/orders/\",\n on_open=on_open,\n on_message=on_message,\n on_error=on_error,\n on_close=on_close)\n # ws.send()\n\n ws.run_forever(dispatcher=rel,\n reconnect=5) # Set dispatcher to automatic reconnection, 5 second reconnect delay if connection closed unexpectedly\n rel.signal(2, rel.abort) # Keyboard Interrupt\n rel.dispatch()\n\n\n","repo_name":"bekov001/byfalio","sub_path":"ws_server/main1.py","file_name":"main1.py","file_ext":"py","file_size_in_byte":2158,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"85"} +{"seq_id":"71256900119","text":"import sqlite3\r\n\r\ncon = sqlite3.connect('info.db')\r\ncur = con.cursor()\r\n\r\n# cur.execute(\"INSERT INTO students VALUES ('Matin',17,'Mehregan','Riazi');\")\r\n# cur.execute(\"INSERT INTO students VALUES ('Arman',14,'Pishgaman','Riazi');\")\r\n# cur.execute(\"DELETE FROM students WHERE name='Arman';\")\r\ncur.execute(\"SELECT * FROM students;\")\r\nrecords = cur.fetchall()\r\nprint(records)\r\n\r\ncon.commit()\r\ncon.close()\r\nprint(\"Done\")\r\n","repo_name":"ThePythonist/Pishgaman-401-F","sub_path":"114.py","file_name":"114.py","file_ext":"py","file_size_in_byte":418,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"19336174132","text":"'''Tic Tac Toe Game...'''\n#Import Modules:\nimport pyttsx3 # --> Text to speech.\n\n# Voice Initialization:\nengine = pyttsx3.init('sapi5')\nvoice = engine.getProperty('voices')\nengine.setProperty('voice', voice[1].id)\n\n# voice[0].id --> Male Voice.\n# voice[1].id --> Female Voice.\n\n# Functions:\n\ndef speak(audio):\n '''This Function speaks the input value.'''\n engine.say(audio)\n engine.runAndWait()\n\ndef do_sum (first,second,third):\n ''' This Function sums the input values..'''\n return first+second+third\n\ndef print_board(state_x,state_tick):\n '''Board Function'''\n # Variable Declaration.\n zero = 'X' if state_x[0] else ('✓' if state_tick[0] else 0)\n one = 'X' if state_x[1] else ('✓' if state_tick[1] else 1)\n two = 'X' if state_x[2] else ('✓' if state_tick[2] else 2)\n three = 'X' if state_x[3] else ('✓' if state_tick[3] else 3)\n four = 'X' if state_x[4] else ('✓' if state_tick[4] else 4)\n five = 'X' if state_x[5] else ('✓' if state_tick[5] else 5)\n six = 'X' if state_x[6] else ('✓' if state_tick[6] else 6)\n seven = 'X' if state_x[7] else ('✓' if state_tick[7] else 7)\n eight = 'X' if state_x[8] else ('✓' if state_tick[8] else 8)\n\n # Print Values in the Board.\n print (f\"{zero} |{one} |{two}\")\n print(\"--|--|--\")\n print (f\"{three} |{four} |{five}\")\n print(\"--|--|--\")\n print (f\"{six} |{seven} |{eight}\")\n print(\"--|--|--\")\n\ndef check_string():\n '''This functions check can the input value changes into int or not.'''\n while True:\n input_value = input(\"Please enter a value: \")\n if input_value.isnumeric():\n input_value = int(input_value)\n return input_value\n else:\n print('OOps! Invalid Input. Try between 0 - 9.')\n speak('OOps! Invalid Input. Try between 0 - 9.')\n\ndef check_winner(state_x,state_tick):\n '''This function checks who is the winner.'''\n\n win_condition = [[0, 1, 2], [3, 4, 5], [6, 7, 8],\n [0, 3, 6], [1, 4, 7], [2, 5, 8], [0, 4, 8], [2, 4, 6]]\n\n for win in win_condition:\n\n if do_sum(state_x[win[0]], state_x[win[1]], state_x[win[2]]) == 3:\n print(f\"Congratulations! {player1} You have won the match\")\n speak(f\"Congratulations! {player1} You have won the match\")\n return 1\n\n if do_sum(state_tick[win[0]], state_tick[win[1]], state_tick[win[2]]) == 3:\n print(f\"Congratulations! {player2} You have won the match\")\n speak(f\"Congratulations! {player2} You have won the match\")\n return 0\n\n return -1\n\n\nif __name__ == \"__main__\":\n\n cross = [0,0,0,0,0,0,0,0,0]\n tick = [0,0,0,0,0,0,0,0,0]\n\n # Welcome Message.\n print ('Welcome to Tic Tac Toe Game..')\n speak ('Welcome to Tic Tac Toe Game..')\n\n # Player Names.\n player1 = input ('Please Enter Player 1 name: ')\n player2 = input ('Please Enter Player 2 name: ')\n\n # Logic\n\n TURN = 1 # --> 1 for X 0 for ✓\n REPLAY = 'valid'\n\n while REPLAY == 'valid':\n\n while True:\n print_board(cross,tick)\n\n if TURN == 1:\n print(f\"{player1}'s Chance\")\n value = check_string()\n cross[value] = 1\n\n else:\n print(f\"{player2}'s Chance\")\n value = check_string()\n tick[value] = 1\n\n CHECK_WIN = check_winner(cross, tick)\n\n if CHECK_WIN != -1 :\n print(\"Match over\")\n break\n\n TURN = 1 - TURN\n\n CHECK_REPLAY = 'valid'\n\n while CHECK_REPLAY == 'valid':\n speak('Do You wanna Play the Match Again')\n ask_replay = input (\"Do You wanna Play the Match Again (y or n): \")\n\n if ask_replay == 'y':\n CHECK_REPLAY = \"invalid\"\n\n elif ask_replay == 'n':\n REPLAY = \"invalid\"\n CHECK_REPLAY = \"invalid\"\n print (\"ThankYou! For playing this game..\")\n speak (\"ThankYou! For playing this game..\")\n\n else:\n print(\"You Have Entered invalid value Please Try Again..\")\n speak(\"You Have Entered invalid value Please Try Again..\")\n","repo_name":"SyedHussainAhmad/Tic_Tac_toe-Game-Python-","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4196,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"24936899032","text":"from Blockchain import Blockchain\nimport time\nimport json\nfrom Block import Block\nimport requests\nimport random\nimport hashlib\n\n\ndef new_users(users: Blockchain, request):\n user_data = request.get_json()\n required_fields = [\"access_key\", \"name\", \"description\"]\n\n for field in required_fields:\n if not user_data.get(field):\n return \"Invalid User Info\", 404\n\n # check access key\n if user_data['access_key'] != \"PASSWORD\":\n return \"Forbidden\", 403\n del user_data[\"access_key\"]\n\n # generate key pair\n res = [random.randrange(1, 16, 1) for i in range(64)]\n\n # private key\n private_key = hashlib.sha256(str(res).encode('utf-8')).hexdigest()\n # public key\n public_key = hashlib.sha256(private_key.encode('utf-8')).hexdigest()\n\n response = {}\n response['public_key'] = str(public_key)\n response['private_key'] = str(private_key)\n\n user_data['public_key'] = str(public_key)\n user_data[\"timestamp\"] = time.time()\n users.add_new_info(user_data)\n\n return response, 201\n\n\ndef get_users(users: Blockchain):\n chain_data = []\n length = 0\n for block in users.chain:\n chain_data.append(block.__dict__)\n length = block.index\n return json.dumps({\"length\": length,\n \"chain\": chain_data})\n\n\ndef consensus(users: Blockchain, peers):\n longest_chain = None\n current_len = len(users.chain)\n\n for node in peers:\n response = requests.get('{}/users/chain'.format(node))\n length = response.json()['length']\n chain = response.json()['chain']\n if length > current_len and users.check_chain_validity(chain):\n # Longer valid chain found!\n current_len = length\n longest_chain = chain\n\n if longest_chain:\n blockchain = longest_chain # 选择最长链\n return True\n return False\n\n\ndef announce_new_block(block, peers):\n headers = {'Content-Type': \"application/json\"}\n for peer in peers:\n url = \"{}/users/add_block\".format(peer)\n requests.post(url, data=json.dumps(block.__dict__, sort_keys=True), headers=headers)\n\n\ndef mine_unconfirmed_users(users: Blockchain, peers):\n result = users.mine()\n if not result:\n return \"No New Users\", 400\n else:\n # Making sure we have the longest chain before announcing to the network\n chain_length = len(users.chain)\n consensus(users, peers)\n\n if chain_length == len(users.chain):\n # announce the recently mined block to the network\n announce_new_block(users.last_block, peers)\n return \"User Block #{} is mined.\".format(users.last_block.index), 201\n\n return \"Current Chain is not the latest version. New chain is updated.\", 200\n\n\ndef users_add_block(users: Blockchain, request):\n block_data = request.get_json()\n block = Block(block_data[\"_Block__index\"],\n block_data[\"_Block__info\"],\n block_data[\"_Block__timestamp\"],\n block_data[\"_Block__previous_hash\"])\n block.nonce = block_data[\"nonce\"]\n\n proof = block.compute_hash()\n added = users.add_block(block, proof)\n\n if not added:\n return \"The block was discarded by the node\", 400\n\n return \"User Block added to the chain\", 201\n","repo_name":"xyzhang7/blockchain","sub_path":"bc-f/Users.py","file_name":"Users.py","file_ext":"py","file_size_in_byte":3276,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"73"} +{"seq_id":"3490841829","text":"# 用于多线程执行爬虫程序\n# python多线程教程：https://www.cnblogs.com/yuanwt93/p/15886333.html\n\nfrom threading import Thread, Lock, Event\nimport time\nfrom queue import Queue\n\nclass MultiThreadQueueWorker:\n def __init__(self, threadNum = 1, minQueueSize = 500, crawlFunc = None, createJobFunc = None):\n '''\n :param threadNum: 线程个数\n :param minQueueSize: 队列少于多少个item就调用createJobFunc来填充\n :param crawlFunc: 用于实际爬取的function, return crawl status\n :param createJobFunc: 用于往Queue中加入item的function. 返回False or None就意味着没有可以爬取的item了\n '''\n self.itemQueue = Queue()\n self.threadList = []\n self.threadNum = threadNum\n self.minQueueSize = minQueueSize\n self.crawlFunc = crawlFunc\n self.createJobFunc = createJobFunc\n\n def startAllThreads(self):\n threadList = []\n for i in range(self.threadNum):\n event = Event()\n thread = Thread(target=self.worker, args=[i])\n self.threadList.append(thread)\n thread.start()\n threadList.append(thread)\n time.sleep(1)\n\n def start(self):\n # 使用一个单独的thread来逐步启动所有线程\n startThread = Thread(target=self.startAllThreads)\n startThread.start()\n\n if self.crawlFunc is None or self.createJobFunc is None:\n print(\"please provide the crawlFunc and createJobFunc\")\n return\n\n while True:\n if self.itemQueue.qsize() >= self.minQueueSize:\n time.sleep(1)\n continue\n\n preLen = self.itemQueue.qsize()\n self.createJobFunc(self.itemQueue)\n addedItemLen = self.itemQueue.qsize() - preLen\n print(f\"add {addedItemLen} items\")\n if addedItemLen <= 0:\n break\n\n def worker(self, threadId):\n errorCount = 0\n sleepSeconds = 0\n while True:\n try:\n item = self.itemQueue.get()\n if item is None:\n time.sleep(1)\n sleepSeconds += 1\n if sleepSeconds >= 60:\n break\n else:\n print(f\"thread {threadId} sleeps {sleepSeconds} seconds\")\n continue\n sleepSeconds = 0\n\n succ = self.crawlFunc(threadId, item)\n if succ is False:\n errorCount += 1\n if errorCount >= 5:\n time.sleep(10)\n errorCount = 0\n else:\n time.sleep(1)\n\n except Exception as ex:\n print(ex)\n print(f\"error on thread {threadId}\")\n errorCount += 1\n if errorCount >= 5:\n time.sleep(10)\n errorCount = 0\n else:\n time.sleep(1)\n\ndef tryworker():\n def createJobWorker(itemList:list):\n print(\"main thread create job\")\n itemList.append(\"abc\")\n\n def crawlWorker(threadId:int, item):\n print(f\"working on {threadId} with {item}\")\n\n worker = MultiThreadQueueWorker(threadNum=2, minQueueSize=10, crawlFunc=crawlWorker, createJobFunc=createJobWorker)\n worker.start()\n\nif __name__ == \"__main__\":\n tryworker()","repo_name":"shengcanxu/crawler","sub_path":"utils/multiThreadQueue.py","file_name":"multiThreadQueue.py","file_ext":"py","file_size_in_byte":3461,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"73"} +{"seq_id":"71129248236","text":"import cv2\nfrom random import randrange\n\n# Load pre-trained data on face frontals from opencv (haar cascade algorithm)\ntrained_face_data = cv2.CascadeClassifier(\"trains/haarcascade_frontalface_default.xml\")\n# trained_face_data = cv2.CascadeClassifier(cv2.data.haarcascades + \"haarcascade_frontalface_default.xml\")\n\n# image to detect faces in\nimg = cv2.imread('images/faces.png')\n\n# covert image to gray scale\ngray_scaled_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n\n# detect faces\n# (98, 30) top left corner coordinates, (86, 86) bottom right coordinates\nface_coordinates = trained_face_data.detectMultiScale(gray_scaled_img)\n\n# Draw rectangles around the faces\nfor (x, y, a, b) in face_coordinates:\n cv2.rectangle(img, (x, y), (x + a, y + b), (randrange(256), randrange(256), randrange(256)), 2)\n\ncv2.imshow('Face Detector Application', img)\n# closes window till a key is pressed\ncv2.waitKey()\n","repo_name":"Emmanuel-Dominic/ai-face-detection","sub_path":"faces_image_detector.py","file_name":"faces_image_detector.py","file_ext":"py","file_size_in_byte":898,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"73"} +{"seq_id":"22884353789","text":"import gspread\nfrom google.oauth2.service_account import Credentials\n\nSCOPE = [\n \"https://www.googleapis.com/auth/spreadsheets\",\n \"https://www.googleapis.com/auth/drive.file\",\n \"https://www.googleapis.com/auth/drive\"\n ]\n\nCREDS = Credentials.from_service_account_file('creds.json')\nSCOPED_CREDS = CREDS.with_scopes(SCOPE)\nGSPREAD_CLIENT = gspread.authorize(SCOPED_CREDS)\nSHEET = GSPREAD_CLIENT.open('love_sandwiches')\n\ndef get_sales_data():\n \"\"\"\n Get sales figures input from the user.\n Run a while loop to collect a valid string of data from the user\n via the terminal, which must be a string of 6 numbers separated\n by commas. The loop will repeatedly request data, until it is valid.\n \"\"\"\n while True:\n print(\"Please enter sales data from the last market.\")\n print(\"Data should be six numbers, separated by commas.\")\n print(\"Example: 10,20,30,40,50,60\\n\")\n\n\n data_str = input(\"Enter your data here:\\n\")\n \n sales_data = data_str.split(\",\") # Split() method returns the broken up values as a list\n\n if validate_data(sales_data):\n print(\"Data is valid!\")\n break\n\n return sales_data\n\n\ndef validate_data(values):\n \"\"\"\n Inside the try, converts all string values into integers.\n Raises ValueError if strings cannot be converted into int,\n or if there aren't exactly 6 values.\n \"\"\"\n try:\n [int(value) for value in values]\n if len(values) != 6:\n raise ValueError(\n f\"Exactly 6 values required, you provided {len(values)}\"\n )\n except ValueError as e:\n print(f\"Invalid data: {e}, please try again.\\n\")\n return False\n\n return True\n\n\ndef update_worksheet(data, worksheet):\n \"\"\"\n Receives a list of integers to be inserted into a worksheet\n Update the relevant worksheet with the data provided\n \"\"\"\n print(f\"Updating {worksheet} worksheet...\\n\")\n # Uses 'SHEET' variable assigned above with the gspread worksheet() method to access worksheet.\n # Value passed to worksheet() method relates to name of the current worksheet page.\n worksheet_to_update = SHEET.worksheet(worksheet)\n # Uses gspread append_row() method to pass our data to the spreadsheet.\n # The append_row method adds a new row to the end of our data in the worksheet selected.\n worksheet_to_update.append_row(data)\n print(f\"{worksheet} worksheet updated successfully.\\n\")\n\ndef calculate_surplus_data(sales_row):\n \"\"\"\n Compare sales with stock and calculate the surplus for each item type.\n\n The surplus is defined as the sales figure subtracted from the stock:\n - Positive surplus indicates waste\n - Negative surplus indicates extra made when stock was sold out.\n \"\"\"\n print(\"Calculating surplus data...\\n\")\n # gspread get_all_values() method gets all of the cells from the 'stock' worksheet\n stock = SHEET.worksheet(\"stock\").get_all_values()\n stock_row = stock[-1] # Slice the final item of the list and return it to the stock_row variable\n \n \"\"\"\n When used with a for loop, the zip() method allows us to iterate through two or more iterable data \n structures in a single loop. In this case, our iterable data structures, are lists.\n \"\"\"\n surplus_data = []\n for stock, sales in zip(stock_row, sales_row):\n surplus = int(stock) - sales\n surplus_data.append(surplus)\n \n return surplus_data\n\n\ndef get_last_5_entries_sales():\n \"\"\"\n Collects columns of data from sales worksheet, collecting\n the last 5 entries for each sandwich and returns the data\n as a list of lists.\n \"\"\"\n sales = SHEET.worksheet(\"sales\")\n\n columns = []\n for ind in range(1, 7):\n # gspread col_values() method used to get columns from worksheet. \n # Number in method specifies column number.\n column = sales.col_values(ind)\n # List is sliced to get the last 5 values. Colon used for multiple values.\n columns.append(column[-5:])\n \n return columns\n\n\ndef calculate_stock_data(data):\n \"\"\"\n Calculate the average stock for each item type, adding 10%\n \"\"\"\n print(\"Calculating stock data...\\n\")\n new_stock_data = []\n\n for column in data:\n int_column = [int(num) for num in column] # Converting column values into integers\n # Calculating average by dividing sum of list by length or the number of elements in the list\n average = sum(int_column) / len(int_column)\n # Adding 10% to new stock to account for additional sales\n stock_num = average * 1.1\n # Round() method used to round stock numbers to whole numbers\n new_stock_data.append(round(stock_num))\n\n return new_stock_data\n\n\ndef main():\n \"\"\"\n Run all program functions\n \"\"\"\n data = get_sales_data()\n sales_data = [int(num) for num in data] # List comprehension used to convert entered values to integers\n update_worksheet(sales_data, \"sales\")\n\n new_surplus_row = calculate_surplus_data(sales_data)\n update_worksheet(new_surplus_row, \"surplus\")\n\n sales_columns = get_last_5_entries_sales()\n stock_data = calculate_stock_data(sales_columns)\n update_worksheet(stock_data, \"stock\")\n return stock_data\n\n\nprint(\"Welcome to Love Sandwiches data automation.\\n\")\nstock_data = main()\n\n\ndef get_stock_values(data):\n \"\"\"\n Gets sandwhich heading types and stock data, \n returns sandwhiches to be made for next market day.\n \"\"\"\n headings = SHEET.worksheet(\"stock\").row_values(1) # gspread row_values() method used to get the first row values of the stock sheet\n stock_dict = dict(zip(headings, data)) # Zip() method used to convert both lists to a dictionary\n return stock_dict\n\nprint(\"Make the following numbers of sandwiches for the next market:\\n\")\nstock_values = get_stock_values(stock_data)\nprint(stock_values)","repo_name":"MarkD117/love-sandwiches","sub_path":"run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":5865,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"73"} +{"seq_id":"74154098797","text":"\nclass Node:\n '''\n A class to create a node\n Input: the value of the data for the node.\n constructor: vlaue, next (pointer to the next)\n '''\n def __init__(self,value):\n self.value = value\n self.next = None\n\n\nclass Queue:\n '''\n A class to creat a queue\n Input: no input\n constructor: front node, rear node\n '''\n\n def __init__(self):\n self.front = None\n self.rear = None\n\n \n def enqueue(self, new_value):\n '''\n A method to add a node to the queue (to the rear)\n Input: new value\n '''\n if type(new_value) is Node:\n return 'Please enter a value and it will converted to Node automaticly'\n else:\n new_node = Node(new_value)\n if not self.front:\n self.front = new_node\n self.rear = new_node\n else:\n self.rear.next = new_node\n self.rear = new_node\n\n\n def dequeue(self):\n '''\n A method to remove a node from the queue (from front)\n Input: nothing\n '''\n if self.is_empty():\n return 'The queue is empty'\n else:\n old_front = self.front\n self.front = self.front.next\n old_front.next = None\n return old_front.value\n\n\n \n def peek(self):\n '''\n A method to show the front of the queue\n '''\n if self.is_empty():\n return 'The queue is empty'\n else:\n return self.front.value\n \n\n def is_empty(self):\n '''\n A method to check if the queue is empty or not\n Input: nothing\n Output: boolian (True if the queue is empty)\n '''\n if self.front is None:\n return True\n else:\n return False\n\n\nclass Stack:\n '''\n A class to creat a stack\n Input: no input\n constructor: top node\n '''\n\n def __init__(self):\n self.top = None\n\n\n def push(self, new_value):\n '''\n A method to add a node to the stack\n Input: new_value\n '''\n if type(new_value) is Node:\n return 'Please enter a value and it will converted to Node automaticly'\n else:\n new_node = Node(new_value)\n new_node.next = self.top\n self.top = new_node\n \n\n def pop(self):\n '''\n A method to remove a node from the stack\n Input: nothing\n '''\n if self.is_empty():\n return 'The stack is empty'\n old_top = self.top\n self.top = self.top.next\n old_top.next = None\n return old_top.value\n\n\n def peek(self):\n '''\n A method to show the top of the stack\n '''\n if self.is_empty():\n return 'The stack is empty'\n else:\n return self.top.value\n \n\n def is_empty(self):\n '''\n A method to check if the stack is empty or not\n Input: nothing\n Output: boolian (True if the stack is empty)\n '''\n if self.top is None:\n return True\n else:\n return False\n\n\nclass Vertex:\n '''\n A class to creat a vertex\n Input: no input\n constructor: value, edges (list of edges)\n '''\n def __init__(self, value):\n self.value = value\n self.edges = []\n\n\nclass Graph:\n def __init__(self):\n self.adjacency_list = {}\n\n\n def add_node(self, value):\n '''\n Add a node (vertex) to the graph\n Input: value\n output: the added node (vertex)\n '''\n new_vertex = Vertex(value)\n self.adjacency_list[value] = new_vertex\n return new_vertex\n\n\n def add_edge(self, vertex1, vertex2, weight=None):\n '''\n Adds a new edge between two nodes (vertecies) in the graph\n Input: vertex1, vertex2, weight\n output: nothing\n '''\n if vertex1 not in self.adjacency_list:\n self.add_node(vertex1)\n if vertex2 not in self.adjacency_list:\n self.add_node(vertex2)\n if weight is None:\n self.adjacency_list[vertex1].edges.append(vertex2)\n self.adjacency_list[vertex2].edges.append(vertex1)\n else:\n self.adjacency_list[vertex1].edges.append((vertex2, weight))\n self.adjacency_list[vertex2].edges.append((vertex1, weight))\n\n \n def get_nodes(self):\n '''\n Returns all of the nodes (verticies) in the graph as a collection\n Input: nothing\n output: set of nodes (verticies)\n '''\n verticies = set()\n for key in self.adjacency_list:\n verticies.add(key)\n return verticies\n\n\n \n def get_neighbors(self, vertex):\n '''\n Returns a collection of edges connected to the given node (vertex) including the weight of the connection\n Input: node (vertex)\n output: collection of edges\n '''\n return self.adjacency_list[vertex].edges\n\n\n def size(self):\n '''\n Returns the total number of nodes (verticies) in the graph\n Input: nothing\n output: int (number of nodes/verticies)\n '''\n return len(self.adjacency_list)\n\n\n def breadth_first(self, vertex):\n '''\n Returns a collection of nodes in the graph starting from the given node in breadth first order\n Input: vertex\n output: collection of nodes (verticies)\n '''\n verticies = []\n breadth = Queue()\n visited = set()\n breadth.enqueue(vertex)\n visited.add(vertex)\n while not breadth.is_empty():\n front = breadth.dequeue()\n verticies.append(front)\n for neighbor in self.get_neighbors(front):\n if neighbor not in visited:\n breadth.enqueue(neighbor)\n visited.add(neighbor)\n return verticies\n\n\n def depth_first(self, vertex):\n '''\n Returns a collection of nodes in the graph starting from the given node in depth first order\n Input: vertex\n output: collection of nodes (verticies)\n '''\n verticies = []\n depth = Stack()\n visited = set()\n depth.push(vertex)\n visited.add(vertex)\n while depth.is_empty() is not True:\n top = depth.pop()\n verticies.append(top)\n for neighbor in self.get_neighbors(top):\n if neighbor not in visited:\n depth.push(neighbor)\n visited.add(neighbor)\n return verticies\n\n\n def __str__(self):\n '''\n print the adjacency list of the graph\n '''\n if self.adjacency_list == {}:\n return 'Null'\n output = 'vertex : edges \\n'\n for key in self.adjacency_list:\n output += f'{key} : {self.adjacency_list[key].edges} \\n'\n return output\n\n\nif __name__ == '__main__':\n graph = Graph()\n graph.add_node(0)\n graph.add_node('1')\n graph.add_node(2)\n graph.add_node(3)\n graph.add_edge(0, '1')\n graph.add_edge(0, 2)\n graph.add_edge('1', 2)\n # graph.add_edge(2, 0, 5)\n graph.add_edge(2, 0)\n graph.add_edge(2, 3)\n graph.add_edge(3, 3)\n print(graph)\n print('get_nodes: ',graph.get_nodes())\n print('get_neighbors: ',graph.get_neighbors(2))\n print('size: ',graph.size())\n print('breadth_first: ',graph.breadth_first(2))\n print('depth_first: ',graph.depth_first(2))\n\n graph2 = Graph()\n print(graph2)","repo_name":"emad-almajdalawi/data-structures-and-algorithms","sub_path":"class35_graphs/class35_graphs/graphs.py","file_name":"graphs.py","file_ext":"py","file_size_in_byte":7469,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"73"} +{"seq_id":"28751372833","text":"import animeClass\r\nfrom animeClass import *\r\nimport listImport\r\nfrom listImport import *\r\nfrom operator import attrgetter\r\n\r\n#tkinter for GUI\r\nimport tkinter as tk\r\nimport tkinter.font as tkFont\r\nfrom tkinter import ttk\r\nfrom tkinter import *\r\n\r\n\r\nclass App:\r\n def __init__(self, root):\r\n #setting title\r\n root.title(\"aniSearch\")\r\n #setting window size\r\n root.geometry(\"1300x706\")\r\n root.configure(bg = '#efecec')\r\n root.resizable(width=False, height=False)\r\n\r\n self.epTTK=ttk.Combobox(root)\r\n self.epTTK[\"values\"]=[\"Any\", \"13 and Below\", \"14 up to 26\", \"27 and up\" ]\r\n self.epTTK.current(0)\r\n self.epTTK[\"justify\"] = \"center\"\r\n self.epTTK.place(x=15,y=180,width=90,height=30)\r\n\r\n self.labelRecommendedAnimeVar = StringVar()\r\n self.labelRecommendedAnime = tk.Label(root, textvariable = self.labelRecommendedAnimeVar)\r\n ft = tkFont.Font(family='Times',size=14)\r\n self.labelRecommendedAnime[\"font\"] = ft\r\n self.labelRecommendedAnime[\"justify\"] = \"center\"\r\n self.labelRecommendedAnime.place(x = 640, y = 50, width = 660, height = 656)\r\n\r\n\r\n labelRec=tk.Label(root)\r\n labelRec[\"bg\"] = \"#e38383\"\r\n ft = tkFont.Font(family='Times',size=26)\r\n labelRec[\"font\"] = ft\r\n labelRec[\"text\"] = \"Recommended Anime\"\r\n labelRec[\"justify\"] = \"center\"\r\n labelRec.place(x=640,y=0,width=660,height=50)\r\n\r\n labelSep=tk.Label(root)\r\n labelSep[\"bg\"] = \"black\"\r\n labelSep.place(x=635,y=0,width=5,height=706)\r\n\r\n labelEpisodes=tk.Label(root)\r\n labelEpisodes[\"bg\"] = \"#e38383\"\r\n ft = tkFont.Font(family='Times',size=22)\r\n labelEpisodes[\"font\"] = ft\r\n labelEpisodes[\"fg\"] = \"#000000\"\r\n labelEpisodes[\"justify\"] = \"center\"\r\n labelEpisodes[\"text\"] = \"Episodes\"\r\n labelEpisodes.place(x=0,y=140,width=140,height=30)\r\n\r\n labelRatings=tk.Label(root)\r\n labelRatings[\"bg\"] = \"#e38383\"\r\n ft = tkFont.Font(family='Times',size=15)\r\n labelRatings[\"font\"] = ft\r\n labelRatings[\"fg\"] = \"#000000\"\r\n labelRatings[\"justify\"] = \"center\"\r\n labelRatings[\"text\"] = \"Ratings/Rankings\"\r\n labelRatings.place(x=250,y=140,width=140,height=30)\r\n\r\n typeLabel=tk.Label(root)\r\n typeLabel[\"bg\"] = \"#e38383\"\r\n ft = tkFont.Font(family='Times',size=22)\r\n typeLabel[\"font\"] = ft\r\n typeLabel[\"fg\"] = \"#333333\"\r\n typeLabel[\"justify\"] = \"center\"\r\n typeLabel[\"text\"] = \"Type\"\r\n typeLabel.place(x=490,y=140,width=140,height=30)\r\n\r\n self.ratingsTTK=ttk.Combobox(root)\r\n self.ratingsTTK[\"values\"]=[\"No order\", \"Highest Rated\", \"Worst Rated\"]\r\n self.ratingsTTK.current(0)\r\n self.ratingsTTK[\"justify\"] = \"center\"\r\n self.ratingsTTK.place(x=275,y=180,width=90,height=30) \r\n\r\n self.typeTTK=ttk.Combobox(root)\r\n self.typeTTK[\"values\"]=[\"Any\",\"Movie\", \"TV\", \"OVA\", \"Special\", \"Music\", \"ONA\"]\r\n self.typeTTK.current(0)\r\n self.typeTTK[\"justify\"] = \"center\"\r\n self.typeTTK.place(x=520,y=180,width=90,height=30) \r\n\r\n\r\n logoLabel=tk.Label(root)\r\n logoLabel[\"bg\"] = \"#e38383\"\r\n ft = tkFont.Font(family='Times',size=98)\r\n logoLabel[\"font\"] = ft\r\n logoLabel[\"fg\"] = \"#333333\"\r\n logoLabel[\"justify\"] = \"center\"\r\n logoLabel[\"text\"] = \"AniSearch\"\r\n logoLabel.place(x=0,y=0,width=635,height=115)\r\n\r\n genreLabel=tk.Label(root)\r\n genreLabel[\"bg\"] = \"#e38383\"\r\n ft = tkFont.Font(family='Times',size=22)\r\n genreLabel[\"font\"] = ft\r\n genreLabel[\"fg\"] = \"#333333\"\r\n genreLabel[\"justify\"] = \"center\"\r\n genreLabel[\"text\"] = \"Genre\"\r\n genreLabel.place(x=0,y=340,width=635,height=30)\r\n\r\n #Int variables\r\n #the assign to object\r\n self.actionVar = IntVar()\r\n action=tk.Checkbutton(root)\r\n action[\"bg\"] = \"#e38383\"\r\n ft = tkFont.Font(family='Times',size=10)\r\n action[\"font\"] = ft\r\n action[\"fg\"] = \"#333333\"\r\n action[\"justify\"] = \"center\"\r\n action[\"text\"] = \"Action\"\r\n action.place(x=0,y=380,width=70,height=25)\r\n action[\"offvalue\"] = 0\r\n action[\"onvalue\"] = 1\r\n action[\"variable\"] = self.actionVar\r\n\r\n self.adventureVar = IntVar()\r\n adventure=tk.Checkbutton(root)\r\n adventure[\"bg\"] = \"#e38383\"\r\n ft = tkFont.Font(family='Times',size=10)\r\n adventure[\"font\"] = ft\r\n adventure[\"fg\"] = \"#333333\"\r\n adventure[\"justify\"] = \"center\"\r\n adventure[\"text\"] = \"Adventure\"\r\n adventure.place(x=0,y=410,width=70,height=25)\r\n adventure[\"offvalue\"] = 0\r\n adventure[\"onvalue\"] = 1\r\n adventure[\"variable\"] = self.adventureVar\r\n\r\n self.carsVar = IntVar()\r\n cars=tk.Checkbutton(root)\r\n cars[\"bg\"] = \"#e38383\"\r\n ft = tkFont.Font(family='Times',size=10)\r\n cars[\"font\"] = ft\r\n cars[\"fg\"] = \"#333333\"\r\n cars[\"justify\"] = \"center\"\r\n cars[\"text\"] = \"Cars\"\r\n cars.place(x=0,y=440,width=70,height=25)\r\n cars[\"offvalue\"] = 0\r\n cars[\"onvalue\"] = 1\r\n cars[\"variable\"] = self.carsVar\r\n\r\n self.comedyVar = IntVar()\r\n comedy=tk.Checkbutton(root)\r\n comedy[\"bg\"] = \"#e38383\"\r\n ft = tkFont.Font(family='Times',size=10)\r\n comedy[\"font\"] = ft\r\n comedy[\"fg\"] = \"#333333\"\r\n comedy[\"justify\"] = \"center\"\r\n comedy[\"text\"] = \"Comedy\"\r\n comedy.place(x=0,y=470,width=70,height=25)\r\n comedy[\"offvalue\"] = 0\r\n comedy[\"onvalue\"] = 1\r\n comedy[\"variable\"] = self.comedyVar\r\n\r\n self.dementiaVar = IntVar()\r\n dementia=tk.Checkbutton(root)\r\n dementia[\"bg\"] = \"#e38383\"\r\n ft = tkFont.Font(family='Times',size=10)\r\n dementia[\"font\"] = ft\r\n dementia[\"fg\"] = \"#333333\"\r\n dementia[\"justify\"] = \"center\"\r\n dementia[\"text\"] = \"Dementia\"\r\n dementia.place(x=0,y=500,width=70,height=25)\r\n dementia[\"offvalue\"] = 0\r\n dementia[\"onvalue\"] = 1\r\n dementia[\"variable\"] = self.dementiaVar\r\n\r\n self.ecchiVar = IntVar()\r\n ecchi=tk.Checkbutton(root)\r\n ecchi[\"bg\"] = \"#e38383\"\r\n ft = tkFont.Font(family='Times',size=10)\r\n ecchi[\"font\"] = ft\r\n ecchi[\"fg\"] = \"#333333\"\r\n ecchi[\"justify\"] = \"center\"\r\n ecchi[\"text\"] = \"Ecchi\"\r\n ecchi.place(x=0,y=590,width=70,height=25)\r\n ecchi[\"offvalue\"] = 0\r\n ecchi[\"onvalue\"] = 1\r\n ecchi[\"variable\"] = self.ecchiVar\r\n\r\n self.fantasyVar = IntVar()\r\n fantasy=tk.Checkbutton(root)\r\n fantasy[\"bg\"] = \"#e38383\"\r\n ft = tkFont.Font(family='Times',size=10)\r\n fantasy[\"font\"] = ft\r\n fantasy[\"fg\"] = \"#333333\"\r\n fantasy[\"justify\"] = \"center\"\r\n fantasy[\"text\"] = \"Fantasy\"\r\n fantasy.place(x=100,y=380,width=70,height=25)\r\n fantasy[\"offvalue\"] = 0\r\n fantasy[\"onvalue\"] = 1\r\n fantasy[\"variable\"] = self.fantasyVar\r\n\r\n self.gameVar = IntVar()\r\n game=tk.Checkbutton(root)\r\n game[\"bg\"] = \"#e38383\"\r\n ft = tkFont.Font(family='Times',size=10)\r\n game[\"font\"] = ft\r\n game[\"fg\"] = \"#333333\"\r\n game[\"justify\"] = \"center\"\r\n game[\"text\"] = \"Game\"\r\n game.place(x=100,y=410,width=70,height=25)\r\n game[\"offvalue\"] = 0\r\n game[\"onvalue\"] = 1\r\n game[\"variable\"] = self.gameVar\r\n\r\n self.psychologicalVar = IntVar()\r\n psychological=tk.Checkbutton(root)\r\n psychological[\"bg\"] = \"#e38383\"\r\n ft = tkFont.Font(family='Times',size=7)\r\n psychological[\"font\"] = ft\r\n psychological[\"fg\"] = \"#333333\"\r\n psychological[\"justify\"] = \"center\"\r\n psychological[\"text\"] = \"Psychological\"\r\n psychological.place(x=320,y=440,width=70,height=25)\r\n psychological[\"offvalue\"] = 0\r\n psychological[\"onvalue\"] = 1\r\n psychological[\"variable\"] = self.psychologicalVar\r\n\r\n self.romanceVar = IntVar()\r\n romance=tk.Checkbutton(root)\r\n romance[\"bg\"] = \"#e38383\"\r\n ft = tkFont.Font(family='Times',size=10)\r\n romance[\"font\"] = ft\r\n romance[\"fg\"] = \"#333333\"\r\n romance[\"justify\"] = \"center\"\r\n romance[\"text\"] = \"Romance\"\r\n romance.place(x=320,y=470,width=70,height=25)\r\n romance[\"offvalue\"] = 0\r\n romance[\"onvalue\"] = 1\r\n romance[\"variable\"] = self.romanceVar\r\n\r\n self.samuraiVar = IntVar()\r\n samurai=tk.Checkbutton(root)\r\n samurai[\"bg\"] = \"#e38383\"\r\n ft = tkFont.Font(family='Times',size=10)\r\n samurai[\"font\"] = ft\r\n samurai[\"fg\"] = \"#333333\"\r\n samurai[\"justify\"] = \"center\"\r\n samurai[\"text\"] = \"Samurai\"\r\n samurai.place(x=320,y=500,width=70,height=25)\r\n samurai[\"offvalue\"] = 0\r\n samurai[\"onvalue\"] = 1\r\n samurai[\"variable\"] = self.samuraiVar\r\n\r\n self.schoolVar = IntVar()\r\n school=tk.Checkbutton(root)\r\n school[\"bg\"] = \"#e38383\"\r\n ft = tkFont.Font(family='Times',size=10)\r\n school[\"font\"] = ft\r\n school[\"fg\"] = \"#333333\"\r\n school[\"justify\"] = \"center\"\r\n school[\"text\"] = \"School\"\r\n school.place(x=320,y=530,width=70,height=25)\r\n school[\"offvalue\"] = 0\r\n school[\"onvalue\"] = 1\r\n school[\"variable\"] = self.schoolVar\r\n\r\n self.haremVar = IntVar()\r\n harem=tk.Checkbutton(root)\r\n harem[\"bg\"] = \"#e38383\"\r\n ft = tkFont.Font(family='Times',size=10)\r\n harem[\"font\"] = ft\r\n harem[\"fg\"] = \"#333333\"\r\n harem[\"justify\"] = \"center\"\r\n harem[\"text\"] = \"Harem\"\r\n harem.place(x=100,y=440,width=70,height=25)\r\n harem[\"offvalue\"] = 0\r\n harem[\"onvalue\"] = 1\r\n harem[\"variable\"] = self.haremVar\r\n\r\n self.hentaiVar = IntVar()\r\n hentai=tk.Checkbutton(root)\r\n hentai[\"bg\"] = \"#e38383\"\r\n ft = tkFont.Font(family='Times',size=10)\r\n hentai[\"font\"] = ft\r\n hentai[\"fg\"] = \"#333333\"\r\n hentai[\"justify\"] = \"center\"\r\n hentai[\"text\"] = \"Hentai\"\r\n hentai.place(x=100,y=470,width=70,height=25)\r\n hentai[\"offvalue\"] = 0\r\n hentai[\"onvalue\"] = 1\r\n hentai[\"variable\"] = self.hentaiVar\r\n\r\n self.historicalVar = IntVar()\r\n historical=tk.Checkbutton(root)\r\n historical[\"bg\"] = \"#e38383\"\r\n ft = tkFont.Font(family='Times',size=10)\r\n historical[\"font\"] = ft\r\n historical[\"fg\"] = \"#333333\"\r\n historical[\"justify\"] = \"center\"\r\n historical[\"text\"] = \"Historical\"\r\n historical.place(x=100,y=500,width=70,height=25)\r\n historical[\"offvalue\"] = 0\r\n historical[\"onvalue\"] = 1\r\n historical[\"variable\"] = self.historicalVar\r\n\r\n self.horrorVar = IntVar()\r\n horror=tk.Checkbutton(root)\r\n horror[\"bg\"] = \"#e38383\"\r\n ft = tkFont.Font(family='Times',size=10)\r\n horror[\"font\"] = ft\r\n horror[\"fg\"] = \"#333333\"\r\n horror[\"justify\"] = \"center\"\r\n horror[\"text\"] = \"Horror\"\r\n horror.place(x=100,y=530,width=70,height=25)\r\n horror[\"offvalue\"] = 0\r\n horror[\"onvalue\"] = 1\r\n horror[\"variable\"] = self.horrorVar\r\n\r\n self.joseiVar = IntVar()\r\n josei=tk.Checkbutton(root)\r\n josei[\"bg\"] = \"#e38383\"\r\n ft = tkFont.Font(family='Times',size=10)\r\n josei[\"font\"] = ft\r\n josei[\"fg\"] = \"#333333\"\r\n josei[\"justify\"] = \"center\"\r\n josei[\"text\"] = \"Josei\"\r\n josei.place(x=100,y=560,width=70,height=25)\r\n josei[\"offvalue\"] = 0\r\n josei[\"onvalue\"] = 1\r\n josei[\"variable\"] = self.joseiVar\r\n\r\n self.kidsVar = IntVar()\r\n kids=tk.Checkbutton(root)\r\n kids[\"bg\"] = \"#e38383\"\r\n ft = tkFont.Font(family='Times',size=10)\r\n kids[\"font\"] = ft\r\n kids[\"fg\"] = \"#333333\"\r\n kids[\"justify\"] = \"center\"\r\n kids[\"text\"] = \"Kids\"\r\n kids.place(x=210,y=380,width=70,height=25)\r\n kids[\"offvalue\"] = 0\r\n kids[\"onvalue\"] = 1\r\n kids[\"variable\"] = self.kidsVar\r\n\r\n self.demonsVar = IntVar()\r\n demons=tk.Checkbutton(root)\r\n demons[\"bg\"] = \"#e38383\"\r\n ft = tkFont.Font(family='Times',size=10)\r\n demons[\"font\"] = ft\r\n demons[\"fg\"] = \"#333333\"\r\n demons[\"justify\"] = \"center\"\r\n demons[\"text\"] = \"Demons\"\r\n demons.place(x=0,y=530,width=70,height=25)\r\n demons[\"offvalue\"] = 0\r\n demons[\"onvalue\"] = 1\r\n demons[\"variable\"] = self.demonsVar\r\n\r\n self.mechaVar = IntVar()\r\n mecha=tk.Checkbutton(root)\r\n mecha[\"bg\"] = \"#e38383\"\r\n ft = tkFont.Font(family='Times',size=10)\r\n mecha[\"font\"] = ft\r\n mecha[\"fg\"] = \"#333333\"\r\n mecha[\"justify\"] = \"center\"\r\n mecha[\"text\"] = \"Mecha\"\r\n mecha.place(x=210,y=470,width=70,height=25)\r\n mecha[\"offvalue\"] = 0\r\n mecha[\"onvalue\"] = 1\r\n mecha[\"variable\"] = self.mechaVar\r\n\r\n self.militaryVar = IntVar(0)\r\n military=tk.Checkbutton(root)\r\n military[\"bg\"] = \"#e38383\"\r\n ft = tkFont.Font(family='Times',size=10)\r\n military[\"font\"] = ft\r\n military[\"fg\"] = \"#333333\"\r\n military[\"justify\"] = \"center\"\r\n military[\"text\"] = \"Military\"\r\n military.place(x=210,y=500,width=70,height=25)\r\n military[\"offvalue\"] = 0\r\n military[\"onvalue\"] = 1\r\n military[\"variable\"] = self.militaryVar\r\n\r\n self.martialArtsVar = IntVar()\r\n martialArts=tk.Checkbutton(root)\r\n martialArts[\"bg\"] = \"#e38383\"\r\n ft = tkFont.Font(family='Times',size=8)\r\n martialArts[\"font\"] = ft\r\n martialArts[\"fg\"] = \"#333333\"\r\n martialArts[\"justify\"] = \"center\"\r\n martialArts[\"text\"] = \"Martial Arts\"\r\n martialArts.place(x=210,y=440,width=70,height=25)\r\n martialArts[\"offvalue\"] = 0\r\n martialArts[\"onvalue\"] = 1\r\n martialArts[\"variable\"] = self.martialArtsVar\r\n\r\n self.mysteryVar = IntVar()\r\n mystery=tk.Checkbutton(root)\r\n mystery[\"bg\"] = \"#e38383\"\r\n ft = tkFont.Font(family='Times',size=10)\r\n mystery[\"font\"] = ft\r\n mystery[\"fg\"] = \"#333333\"\r\n mystery[\"justify\"] = \"center\"\r\n mystery[\"text\"] = \"Mystery\"\r\n mystery.place(x=210,y=560,width=70,height=25)\r\n mystery[\"offvalue\"] = 0\r\n mystery[\"onvalue\"] = 1\r\n mystery[\"variable\"] = self.mysteryVar\r\n\r\n self.parodyVar = IntVar()\r\n parody=tk.Checkbutton(root)\r\n parody[\"bg\"] = \"#e38383\"\r\n ft = tkFont.Font(family='Times',size=10)\r\n parody[\"font\"] = ft\r\n parody[\"fg\"] = \"#333333\"\r\n parody[\"justify\"] = \"center\"\r\n parody[\"text\"] = \"Parody\"\r\n parody.place(x=320,y=380,width=70,height=25)\r\n parody[\"offvalue\"] = 0\r\n parody[\"onvalue\"] = 1\r\n parody[\"variable\"] = self.parodyVar\r\n\r\n self.policeVar = IntVar()\r\n police=tk.Checkbutton(root)\r\n police[\"bg\"] = \"#e38383\"\r\n ft = tkFont.Font(family='Times',size=10)\r\n police[\"font\"] = ft\r\n police[\"fg\"] = \"#333333\"\r\n police[\"justify\"] = \"center\"\r\n police[\"text\"] = \"Police\"\r\n police.place(x=320,y=410,width=70,height=25)\r\n police[\"offvalue\"] = 0\r\n police[\"onvalue\"] = 1\r\n police[\"variable\"] = self.policeVar\r\n\r\n self.magicVar = IntVar()\r\n magic=tk.Checkbutton(root)\r\n magic[\"bg\"] = \"#e38383\"\r\n ft = tkFont.Font(family='Times',size=10)\r\n magic[\"font\"] = ft\r\n magic[\"fg\"] = \"#333333\"\r\n magic[\"justify\"] = \"center\"\r\n magic[\"text\"] = \"Magic\"\r\n magic.place(x=210,y=410,width=70,height=25)\r\n magic[\"offvalue\"] = 0\r\n magic[\"onvalue\"] = 1\r\n magic[\"variable\"] = self.magicVar\r\n\r\n self.musicVar = IntVar()\r\n music=tk.Checkbutton(root)\r\n music[\"bg\"] = \"#e38383\"\r\n ft = tkFont.Font(family='Times',size=10)\r\n music[\"font\"] = ft\r\n music[\"fg\"] = \"#333333\"\r\n music[\"justify\"] = \"center\"\r\n music[\"text\"] = \"Music\"\r\n music.place(x=210,y=530,width=70,height=25)\r\n music[\"offvalue\"] = 0\r\n music[\"onvalue\"] = 1\r\n music[\"variable\"] = self.musicVar\r\n\r\n self.seinenVar = IntVar()\r\n seinen=tk.Checkbutton(root)\r\n seinen[\"bg\"] = \"#e38383\"\r\n ft = tkFont.Font(family='Times',size=10)\r\n seinen[\"font\"] = ft\r\n seinen[\"fg\"] = \"#333333\"\r\n seinen[\"justify\"] = \"center\"\r\n seinen[\"text\"] = \"Seinen\"\r\n seinen.place(x=440,y=380,width=70,height=25)\r\n seinen[\"offvalue\"] = 0\r\n seinen[\"onvalue\"] = 1\r\n seinen[\"variable\"] = self.seinenVar\r\n\r\n self.shoujoVar = IntVar()\r\n shoujo=tk.Checkbutton(root)\r\n shoujo[\"bg\"] = \"#e38383\"\r\n ft = tkFont.Font(family='Times',size=10)\r\n shoujo[\"font\"] = ft\r\n shoujo[\"fg\"] = \"#333333\"\r\n shoujo[\"justify\"] = \"center\"\r\n shoujo[\"text\"] = \"Shoujo\"\r\n shoujo.place(x=440,y=410,width=70,height=25)\r\n shoujo[\"offvalue\"] = 0\r\n shoujo[\"onvalue\"] = 1\r\n shoujo[\"variable\"] = self.shoujoVar\r\n\r\n self.shoujoAiVar = IntVar()\r\n shoujoAi=tk.Checkbutton(root)\r\n shoujoAi[\"bg\"] = \"#e38383\"\r\n ft = tkFont.Font(family='Times',size=10)\r\n shoujoAi[\"font\"] = ft\r\n shoujoAi[\"fg\"] = \"#333333\"\r\n shoujoAi[\"justify\"] = \"center\"\r\n shoujoAi[\"text\"] = \"Shoujo-ai\"\r\n shoujoAi.place(x=440,y=440,width=70,height=25)\r\n shoujoAi[\"offvalue\"] = 0\r\n shoujoAi[\"onvalue\"] = 1\r\n shoujoAi[\"variable\"] = self.shoujoAiVar\r\n\r\n self.shounenVar = IntVar()\r\n shounen=tk.Checkbutton(root)\r\n shounen[\"bg\"] = \"#e38383\"\r\n ft = tkFont.Font(family='Times',size=10)\r\n shounen[\"font\"] = ft\r\n shounen[\"fg\"] = \"#333333\"\r\n shounen[\"justify\"] = \"center\"\r\n shounen[\"text\"] = \"Shounen\"\r\n shounen.place(x=440,y=470,width=70,height=25)\r\n shounen[\"offvalue\"] = 0\r\n shounen[\"onvalue\"] = 1\r\n shounen[\"variable\"] = self.shounenVar\r\n\r\n self.shounenAiVar = IntVar()\r\n shounenAi=tk.Checkbutton(root)\r\n shounenAi[\"bg\"] = \"#e38383\"\r\n ft = tkFont.Font(family='Times',size=8)\r\n shounenAi[\"font\"] = ft\r\n shounenAi[\"fg\"] = \"#333333\"\r\n shounenAi[\"justify\"] = \"center\"\r\n shounenAi[\"text\"] = \"Shounen-ai\"\r\n shounenAi.place(x=440,y=500,width=70,height=25)\r\n shounenAi[\"offvalue\"] = 0\r\n shounenAi[\"onvalue\"] = 1\r\n shounenAi[\"variable\"] = self.shounenAiVar\r\n\r\n self.sliceOfLifeVar = IntVar()\r\n sliceOfLife=tk.Checkbutton(root)\r\n sliceOfLife[\"bg\"] = \"#e38383\"\r\n ft = tkFont.Font(family='Times',size=8)\r\n sliceOfLife[\"font\"] = ft\r\n sliceOfLife[\"fg\"] = \"#333333\"\r\n sliceOfLife[\"justify\"] = \"center\"\r\n sliceOfLife[\"text\"] = \"Slice of Life\"\r\n sliceOfLife.place(x=440,y=530,width=70,height=25)\r\n sliceOfLife[\"offvalue\"] = 0\r\n sliceOfLife[\"onvalue\"] = 1\r\n sliceOfLife[\"variable\"] = self.sliceOfLifeVar\r\n\r\n self.spaceVar = IntVar()\r\n space=tk.Checkbutton(root)\r\n space[\"bg\"] = \"#e38383\"\r\n ft = tkFont.Font(family='Times',size=10)\r\n space[\"font\"] = ft\r\n space[\"fg\"] = \"#333333\"\r\n space[\"justify\"] = \"center\"\r\n space[\"text\"] = \"Space\"\r\n space.place(x=440,y=560,width=70,height=25)\r\n space[\"offvalue\"] = 0\r\n space[\"onvalue\"] = 1\r\n space[\"variable\"] = self.spaceVar\r\n\r\n self.sportsVar = IntVar()\r\n sports=tk.Checkbutton(root)\r\n sports[\"bg\"] = \"#e38383\"\r\n ft = tkFont.Font(family='Times',size=10)\r\n sports[\"font\"] = ft\r\n sports[\"fg\"] = \"#333333\"\r\n sports[\"justify\"] = \"center\"\r\n sports[\"text\"] = \"Sports\"\r\n sports.place(x=560,y=380,width=70,height=25)\r\n sports[\"offvalue\"] = 0\r\n sports[\"onvalue\"] = 1\r\n sports[\"variable\"] = self.sportsVar\r\n\r\n self.superpowerVar = IntVar()\r\n superpower=tk.Checkbutton(root)\r\n superpower[\"bg\"] = \"#e38383\"\r\n ft = tkFont.Font(family='Times',size=8)\r\n superpower[\"font\"] = ft\r\n superpower[\"fg\"] = \"#333333\"\r\n superpower[\"justify\"] = \"center\"\r\n superpower[\"text\"] = \"Superpower\"\r\n superpower.place(x=560,y=410,width=70,height=25)\r\n superpower[\"offvalue\"] = 0\r\n superpower[\"onvalue\"] = 1\r\n superpower[\"variable\"] = self.superpowerVar\r\n\r\n self.supernaturalVar = IntVar()\r\n supernatural=tk.Checkbutton(root)\r\n supernatural[\"bg\"] = \"#e38383\"\r\n ft = tkFont.Font(family='Times',size=7)\r\n supernatural[\"font\"] = ft\r\n supernatural[\"fg\"] = \"#333333\"\r\n supernatural[\"justify\"] = \"center\"\r\n supernatural[\"text\"] = \"Supernatural\"\r\n supernatural.place(x=560,y=440,width=70,height=25)\r\n supernatural[\"offvalue\"] = 0\r\n supernatural[\"onvalue\"] = 1\r\n supernatural[\"variable\"] = self.supernaturalVar\r\n\r\n self.thrillerVar = IntVar()\r\n thriller=tk.Checkbutton(root)\r\n thriller[\"bg\"] = \"#e38383\"\r\n ft = tkFont.Font(family='Times',size=10)\r\n thriller[\"font\"] = ft\r\n thriller[\"fg\"] = \"#333333\"\r\n thriller[\"justify\"] = \"center\"\r\n thriller[\"text\"] = \"Thriller\"\r\n thriller.place(x=560,y=470,width=70,height=25)\r\n thriller[\"offvalue\"] = 0\r\n thriller[\"onvalue\"] = 1\r\n thriller[\"variable\"] = self.thrillerVar\r\n\r\n self.vampireVar = IntVar()\r\n vampire=tk.Checkbutton(root)\r\n vampire[\"bg\"] = \"#e38383\"\r\n ft = tkFont.Font(family='Times',size=10)\r\n vampire[\"font\"] = ft\r\n vampire[\"fg\"] = \"#333333\"\r\n vampire[\"justify\"] = \"center\"\r\n vampire[\"text\"] = \"Vampire\"\r\n vampire.place(x=560,y=500,width=70,height=25)\r\n vampire[\"offvalue\"] = 0\r\n vampire[\"onvalue\"] = 1\r\n vampire[\"variable\"] = self.vampireVar\r\n\r\n self.yaoiVar = IntVar()\r\n yaoi=tk.Checkbutton(root)\r\n yaoi[\"bg\"] = \"#e38383\"\r\n ft = tkFont.Font(family='Times',size=10)\r\n yaoi[\"font\"] = ft\r\n yaoi[\"fg\"] = \"#333333\"\r\n yaoi[\"justify\"] = \"center\"\r\n yaoi[\"text\"] = \"Yaoi\"\r\n yaoi.place(x=560,y=530,width=70,height=25)\r\n yaoi[\"offvalue\"] = 0\r\n yaoi[\"onvalue\"] = 1\r\n yaoi[\"variable\"] = self.yaoiVar\r\n\r\n self.sciFiVar = IntVar()\r\n sciFi=tk.Checkbutton(root)\r\n sciFi[\"bg\"] = \"#e38383\"\r\n ft = tkFont.Font(family='Times',size=10)\r\n sciFi[\"font\"] = ft\r\n sciFi[\"fg\"] = \"#333333\"\r\n sciFi[\"justify\"] = \"center\"\r\n sciFi[\"text\"] = \"Sci-Fi\"\r\n sciFi.place(x=320,y=560,width=70,height=25)\r\n sciFi[\"offvalue\"] = 0\r\n sciFi[\"onvalue\"] = 1\r\n sciFi[\"variable\"] = self.sciFiVar\r\n\r\n self.yuriVar = IntVar()\r\n yuri=tk.Checkbutton(root)\r\n yuri[\"bg\"] = \"#e38383\"\r\n ft = tkFont.Font(family='Times',size=10)\r\n yuri[\"font\"] = ft\r\n yuri[\"fg\"] = \"#333333\"\r\n yuri[\"justify\"] = \"center\"\r\n yuri[\"text\"] = \"Yuri\"\r\n yuri.place(x=560,y=560,width=70,height=25)\r\n yuri[\"offvalue\"] = 0\r\n yuri[\"onvalue\"] = 1\r\n yuri[\"variable\"] = self.yuriVar\r\n\r\n self.dramaVar = IntVar()\r\n drama=tk.Checkbutton(root)\r\n drama[\"bg\"] = \"#e38383\"\r\n ft = tkFont.Font(family='Times',size=10)\r\n drama[\"font\"] = ft\r\n drama[\"fg\"] = \"#333333\"\r\n drama[\"justify\"] = \"center\"\r\n drama[\"text\"] = \"Drama\"\r\n drama.place(x=0,y=560,width=70,height=25)\r\n drama[\"offvalue\"] = 0\r\n drama[\"onvalue\"] = 1\r\n drama[\"variable\"] = self.dramaVar\r\n\r\n searchButton=tk.Button(root)\r\n searchButton[\"bg\"] = \"#e38383\"\r\n ft = tkFont.Font(family='Times',size=10)\r\n searchButton[\"font\"] = ft\r\n searchButton[\"fg\"] = \"#000000\"\r\n searchButton[\"justify\"] = \"center\"\r\n searchButton[\"text\"] = \"Search\"\r\n searchButton.place(x=220,y=620,width=183,height=60)\r\n searchButton[\"command\"] = self.searchButton_command\r\n #variables\r\n #list for the checkboxes' vars of genre\r\n self.genreVarListTTK = [self.actionVar, self.adventureVar, self.carsVar, self.comedyVar, self.dementiaVar, self.demonsVar, self.dramaVar, self.ecchiVar, self.fantasyVar,\r\n self.gameVar, self.haremVar, self.hentaiVar, self.historicalVar, self.horrorVar, self.joseiVar, self.kidsVar, self.magicVar, self.martialArtsVar, self.mechaVar, \r\n self.militaryVar, self.musicVar, self.mysteryVar, self.parodyVar, self.policeVar, self.psychologicalVar, self.romanceVar, self.samuraiVar, self.schoolVar, \r\n self.sciFiVar, self.seinenVar, self.shoujoVar, self.shoujoAiVar, self.shounenVar, self.shounenAiVar, self.sliceOfLifeVar, self.spaceVar, self.sportsVar, \r\n self.superpowerVar, self.supernaturalVar, self.thrillerVar, self.vampireVar, self.yaoiVar, self.yuriVar]\r\n\r\n self.genreListTTK = [action, adventure, cars, comedy, dementia, demons, drama, ecchi, fantasy, game, harem, hentai, historical, horror, josei, \r\n kids, magic, martialArts, mecha, military, music, mystery, parody, police, psychological, romance, samurai, school, sciFi, seinen, shoujo, \r\n shoujoAi, shounen, shounenAi, sliceOfLife, space, sports, superpower, supernatural, thriller, vampire, yaoi, yuri]\r\n\r\n\r\n \r\n #functions\r\n def searchButton_command(self):\r\n genreList = []\r\n counter = 0\r\n #checks if the checkboxes are checked\r\n for self.genreTTK in self.genreVarListTTK:\r\n if self.genreTTK.get() == 1:\r\n genreList.append(self.genreListTTK[counter].cget(\"text\"))\r\n counter += 1\r\n\r\n #assigns the amount of genres to this var\r\n genreListNum = len(genreList)\r\n\r\n #turns the list into a string in one var\r\n genreFinal = \"\"\r\n for genre in genreList[:-1]:\r\n genreFinal = genreFinal + genre + \", \"\r\n genreFinal = genreFinal + genreList[genreListNum-1]\r\n #GENRE USED FOR FILTERING\r\n\r\n #Filter Type first\r\n animeListCheckType = []\r\n animeListCheckGenre = []\r\n animeListCheckEpisode = []\r\n animeListCheckRating = []\r\n animeListGenre = []\r\n animeListNoneType = []\r\n animeListFinal = []\r\n\r\n###FOR TYPES\r\n if self.typeTTK.get() == \"Any\":\r\n animeListCheckType = aniList\r\n else:\r\n for sampleType in aniList:\r\n if sampleType.getType() == self.typeTTK.get():\r\n animeListCheckType.append(sampleType)\r\n\r\n\r\n###FOR EPISODES\r\n if self.epTTK.get() == \"Any\":\r\n animeListCheckEpisode = animeListCheckType\r\n else:\r\n if self.epTTK.get() == \"13 and Below\":\r\n for sampleEpisode in animeListCheckType:\r\n if sampleEpisode.getEpisodes() == \"Unknown\":\r\n continue\r\n else:\r\n if int(sampleEpisode.getEpisodes()) <= 13:\r\n animeListCheckEpisode.append(sampleEpisode)\r\n\r\n if self.epTTK.get() == \"14 up to 26\":\r\n for sampleEpisode in animeListCheckType:\r\n if sampleEpisode.getEpisodes() == \"Unknown\":\r\n continue\r\n else:\r\n if int(sampleEpisode.getEpisodes()) >= 14 and int(sampleEpisode.getEpisodes()) <= 26:\r\n animeListCheckEpisode.append(sampleEpisode)\r\n\r\n if self.epTTK.get() == \"27 and up\":\r\n for sampleEpisode in animeListCheckType:\r\n if sampleEpisode.getEpisodes() == \"Unknown\":\r\n continue\r\n else:\r\n if int(sampleEpisode.getEpisodes()) >= 27:\r\n animeListCheckEpisode.append(sampleEpisode)\r\n \r\n###FOR GENRES \r\n for sampleGenre in animeListCheckEpisode:\r\n if sampleGenre.getGenre() == genreFinal:\r\n animeListGenre.append(sampleGenre)\r\n\r\n###FOR RATINGS\r\n if self.ratingsTTK.get() == \"No order\":\r\n animeListFinal = animeListGenre\r\n else:\r\n if self.ratingsTTK.get() == \"Highest Rated\":\r\n for sampleRating in animeListGenre:\r\n if sampleRating.rating is None:\r\n animeListNoneType.append(sampleRating)\r\n else:\r\n animeListFinal.append(sampleRating)\r\n animeListFinal.sort(key=attrgetter('rating'), reverse=True)\r\n animeListFinal += animeListNoneType\r\n \r\n elif self.ratingsTTK.get() == \"Worst Rated\":\r\n for sampleRating in animeListGenre:\r\n if sampleRating.rating is None:\r\n animeListNoneType.append(sampleRating)\r\n else:\r\n animeListFinal.append(sampleRating)\r\n animeListFinal.sort(key=attrgetter('rating'))\r\n animeListFinal += animeListNoneType\r\n\r\n textAcc = \"\"\r\n for anime in animeListFinal:\r\n text = (str(anime.getName()) + \" \" + str(anime.getType()) + \" \" + str(anime.getGenre()) + \" \" + str(anime.getEpisodes()) + \" \" + str(anime.getRating()) + '\\n')\r\n textAcc += text\r\n \r\n self.labelRecommendedAnimeVar.set(textAcc)\r\n \r\nif __name__ == \"__main__\":\r\n root = tk.Tk()\r\n app = App(root)\r\n root.mainloop()","repo_name":"CkyleCasumpang/Sprint","sub_path":"alternateGUI.py","file_name":"alternateGUI.py","file_ext":"py","file_size_in_byte":29810,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"73"} +{"seq_id":"26456485565","text":"# user, date, in/out, type\n\nimport csv\nimport numpy as np\nfrom datetime import date, timedelta\n\n\nusers = ['James']\n\n\nincome_type = [\"Salary\", \"Investment\", \"Gift\", \"Other\", \"Youtube\"]\nexpend_type = [\"Food\", \"Shopping\", \"Transport\",\n \"Fitness\", \"Entertainment\", \"Social\", \"Travel\"]\n\n\ndata = []\n\nwith open('data2.csv', 'w', newline='') as csvfile:\n moneywriter = csv.writer(csvfile, delimiter=',',\n quotechar='|', quoting=csv.QUOTE_MINIMAL)\n moneywriter.writerow(['date', 'money', 'money_typ'])\n\n start_date = date(2017, 1, 1) # start date\n end_date = date(2019, 12, 31) # end date\n\n delta = end_date - start_date # as timedelta\n\n for i in range(delta.days + 1):\n day = start_date + timedelta(days=i)\n\n money = np.random.normal(0,100)\n\n money_typ = None\n if (money > 0):\n money_typ = np.random.choice(income_type)\n elif (money < 0):\n money_typ = np.random.choice(expend_type)\n\n moneywriter.writerow([day, money, money_typ])\n","repo_name":"XiaoLuoLYG/Hackathon","sub_path":"data_gen.py","file_name":"data_gen.py","file_ext":"py","file_size_in_byte":1055,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"73"} +{"seq_id":"7547595936","text":"class Employee:\n pass\n\nclass emp:\n empName = \"Peter\"\n empAge = 25\n empDesignation = \"Manager\"\n\nobj = emp()\nprint(obj.empAge)\n\n######################################\n# Dynamic\n\nclass dynamic_emp():\n totalEmp = 0\n\n def __init__(self, empName, empAge, empDesignation):\n self.empName = empName\n self.empAge = empAge\n self.empDesignation = empDesignation\n \n dynamic_emp.totalEmp += 1\n \n def getEmpDetails (self):\n return self.empName, self.empAge, self.empDesignation\n \n def updateDesignation(self, newDesig):\n self.empDesignation = newDesig\n print(\"Designation updated\")\n return self.empDesignation\n\nclass intern (dynamic_emp):\n def __init__(self, empName, empAge, empDesignation, internPeriod):\n self.internPeriod = internPeriod\n dynamic_emp.__init__(self, empName, empAge, empDesignation)\n \n def getPeriod(self):\n return \"Intern Period in months: \", self.internPeriod\n\nemp1 = dynamic_emp(\"Eva\", 35, \"Dev\")\nprint(\"Name of the 1st emp: \", emp1.empName)\nprint(\"Total emps: \", dynamic_emp.totalEmp)\n\nprint(\"Details: \", emp1.getEmpDetails())\nemp1.updateDesignation(\"DevOp\")\nprint(\"Details: \", emp1.getEmpDetails())\n\nintern1 = intern(\"John\", 21, \"intern\", 5)\nprint(\"Intern details: \", intern1.getEmpDetails())\nprint(intern1.getPeriod())\n\n#########################################\n# polymorphism\n\nclass base:\n def name():\n pass\nclass derived:\n def name():\n print(\"EOF\")\nobj = derived\nobj.name()","repo_name":"carleoner/python_learning","sub_path":"17_objects_classes_OOP.py","file_name":"17_objects_classes_OOP.py","file_ext":"py","file_size_in_byte":1528,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"73"} +{"seq_id":"5696128032","text":"from Quoridor import *\n\n\nclass Point(object):\n def __init__(self, owner, location):\n self.owner = owner\n self.location = location\n if self.owner is 0:\n self.destination = 6\n self.h = self.destination - location[0]\n else:\n self.destination = 0\n self.h = location[0]\n self.g = 0\n self.f = 0\n\n\nclass SearchRouteBoard(object):\n def __init__(self):\n self.close_list = []\n self.open_list = []\n\n\ndef search_path(owner, origin, testBoard):\n \"\"\"\n search path length with AStar\n :param owner: whose's turn\n :param origin: start location\n :param testBoard: board now\n :return: length of route\n \"\"\"\n temp = 1\n sonPointInOpenlistFlag = 0\n searchPoint = Point(owner, origin)\n neighbor = testBoard.possible_piece_location(searchPoint.location)\n for i in neighbor:\n sonPointInOpenlistFlag = 0\n sonPoint = Point(owner, i)\n sonPoint.fatherPoint = searchPoint.location\n sonPoint.g = searchPoint.g + 1\n sonPoint.f = sonPoint.g + sonPoint.h\n\n if sonPoint.location[0] == searchPoint.destination:\n temp = 0\n break\n for searchRepeat in testBoard.openList:\n if sonPoint.location == searchRepeat.location:\n sonPointInOpenlistFlag = 1\n if sonPoint.g < searchRepeat.g:\n searchRepeat.fatherPoint = searchPoint.location\n searchRepeat.g = searchPoint.g + 1\n searchRepeat.f = searchRepeat.g + searchRepeat.h\n break\n if not sonPointInOpenlistFlag:\n testBoard.openList.append(sonPoint)\n\n minimalSearch = 0x3f3f3f3f\n for tempPoint in testBoard.openList:\n if tempPoint.f <= minimalSearch:\n searchPoint = tempPoint\n minimalSearch = tempPoint.f\n testBoard.openList.remove(searchPoint)\n testBoard.closeList.append(searchPoint)\n tempPoint = testBoard.closeList[-1]\n result = [tempPoint.location]\n\n while not (tempPoint.location == origin):\n result.append(tempPoint.fatherPoint)\n for i in reversed(testBoard.closeList):\n if i.location == tempPoint.fatherPoint:\n tempPoint = i\n testBoard.closeList.remove(i)\n break\n\n testBoard.openList = []\n testBoard.closeList = []\n return len(result) - 1 # return distance\n\n\nif __name__ == \"__main__\":\n test_board = SearchRouteBoard()\n l = search_path(0, (0, 0), test_board)","repo_name":"Martha-Zhao/Smartcar","sub_path":"imx6_camera/AStar.py","file_name":"AStar.py","file_ext":"py","file_size_in_byte":2584,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"73"} +{"seq_id":"10267818009","text":"# Set Okada Parameters here to run in model \nxcen = 0 # [m] NS offset of source from center of grid \ndef okada(example='strike slip'):\n\t'''\n\texamples = ['strike slip', 'thrust', normal', 'finite sill', 'point sill', 'dyke']\n\t\n\tycen = # [m] EW offset of source from center of grid\n\tU = # [m] U is slip\n\td = # [m] depth (positive down)\n\tnu = # [unitless] Poisson ratio\n\tdelta = # [degrees] delta is dip angle, 90.0 exactly might cause numerical issues?\n\tstrike = # [degrees] counter clockwise from north\n\tlength = # [m] # len,W are the fault length and width, resp.\n\twidth = # [m]\n\tfault_type = # fault_type is 1 2 3 for strike, dip, and opening\n\t\n\tUsage:\n\tparams = rp.models.examples.okada('point sill')\n\t\n\t'''\n\tif example == 'strike slip':\n\t\tprint('1 m of left-lateral slip on NS-striking vertical fault (70km down to 14km) that ruptures surface')\n\t\txcen=0\n\t\tycen=0\n\t\tU = 1.0 \n\t\td = 1e-3 \n\t\tnu = 0.27 \n\t\tdelta = 90.0 \n\t\tstrike = 90.0 \n\t\tlength = 70e3 \n\t\twidth = 15e3\n\t\tfault_type = 1 \n\t\t\n\tif example == 'thrust':\n\t\tprint('1m of slip, 70x30km fault, 30-dipping to W with top at 1km below surface')\n\t\txcen = 0 \n\t\tycen = 0 \n\t\tU = 1.0 \n\t\td = 1e3 \n\t\tnu = 0.25 \n\t\tdelta = 30.0 \n\t\tstrike = 180.0 \n\t\tlength = 70e3 \n\t\twidth = 30e3 \n\t\tfault_type = 2 \n\t\t\n\t\n\tif example == 'finite sill':\n\t\tprint('1m of opening, 50x50km sill at 10km depth') \n\t\txcen = 0 \n\t\tycen = 0 \n\t\tU = -1.0 \n\t\td = 10e3 \n\t\tnu = 0.25 \n\t\tdelta = 0.0 \n\t\tstrike = 0.0 \n\t\tlength = 50e3 \n\t\twidth = 50e3 \n\t\tfault_type = 3 \n\t\n\t\n\tif example == 'point sill':\n\t\tprint('Sill dimensions (3x3km) much less than depth (20km)')\n\t\txcen = 0 \n\t\tycen = 0 \n\t\tU = -1.0 \n\t\td = 20e3 \n\t\tnu = 0.25 \n\t\tdelta = 0.0 \n\t\tstrike = 0.0 \n\t\tlength = 3e3 \n\t\twidth = 3e3 \n\t\tfault_type = 3 \n\t\n\t\n\tif example == 'normal fault':\n\t\tprint('1m of slip, 70x30km fault, 60-dipping to E with top at 1km below surface')\n\t\txcen = 0 \n\t\tycen = 0 \n\t\tU = -1.0 \n\t\td = 1e3 \n\t\tnu = 0.25 \n\t\tdelta = 60.0 \n\t\tstrike = 0.0 \n\t\tlength = 70e3 \n\t\twidth = 30e3 \n\t\tfault_type = 2 \n\t\n\t\n\tif example == 'dyke':\n\t\tprint('1m of opening on vertical NS-striking dyke w/ top at 5km depth') \n\t\txcen = 0 \n\t\tycen = 0 \n\t\tU = -1.0 \n\t\td = 5e3 \n\t\tnu = 0.25 \n\t\tdelta = 90.0 \n\t\tstrike = 0.0 \n\t\tlength = 70e3 \n\t\twidth = 30e3 \n\t\tfault_type = 3 \n\t\t\n\t\n\treturn xcen,ycen,U,d,nu,delta,strike,length,width,fault_type\n","repo_name":"scottyhq/roipy","sub_path":"models/examples.py","file_name":"examples.py","file_ext":"py","file_size_in_byte":2677,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"73"} +{"seq_id":"35911050086","text":"\"\"\"\nGiven a singly linked list L: L0 -> L1 -> .. -> Ln-1 -> Ln,\nreorder it to: L0 -> Ln -> L1 -> Ln-1 -> L2 -> Ln-2 ->...\n\n\"\"\"\nclass Solution:\n # @param head, a ListNode\n # @return nothing\n def reorderList(self, head):\n if not head or not head.next:\n return head\n # go to the mid of listnode\n slow = head\n fast = head\n while True:\n fast = fast.next\n if not fast:\n break\n fast = fast.next\n if not fast:\n break\n slow = slow.next\n \n tail = self.reverse(slow)\n slow.next = None\n self.merge(head, tail)\n\n def reverse(self, head):\n cur = head\n pre = head.next\n while pre:\n tmp = pre.next\n pre.next = cur\n cur = pre\n pre = tmp\n return cur\n\n def merge(self, head1, head2):\n while head1 and head2 and head1 != head2:\n tmp = head2.next\n head2.next = head1.next\n head1.next = head2\n head1 = head2.next\n head2 = tmp\n","repo_name":"linyaoli/acm","sub_path":"linked_list/intermediate/reorder_list.py","file_name":"reorder_list.py","file_ext":"py","file_size_in_byte":1112,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"73"} +{"seq_id":"3523442017","text":"import pathlib\nimport tempfile\nimport unittest\n\nimport svgwrite\nfrom svgwrite import mm, percent, shapes\n\nfrom qr_payment_slip import SVGPrinter\nfrom qr_payment_slip.errors import ConversionError\n\n\nclass SVGPrinterTest(unittest.TestCase):\n\n @classmethod\n def setUpClass(cls) -> None:\n cls.temp_dir = tempfile.TemporaryDirectory()\n\n @classmethod\n def tearDownClass(cls) -> None:\n cls.temp_dir.cleanup()\n\n def test_init(self):\n printer = SVGPrinter()\n\n self.assertEqual(repr(printer), f\"<{printer.__class__.__name__}>\")\n\n def test_white_cross(self):\n height = SVGPrinter.convert_to_pixel(32 * mm)\n\n file_path = pathlib.Path(self.temp_dir.name) / \"cross.svg\"\n\n dwg = svgwrite.Drawing(size=(height, height), filename=file_path)\n cross = SVGPrinter._draw_white_cross()\n\n self.assertEqual(len(cross.elements), 4)\n\n self.assertEqual(cross.elements[1].tostring(),\n '')\n\n self.assertEqual(cross.elements[2].tostring(),\n '')\n\n self.assertEqual(cross.elements[3].tostring(),\n '')\n\n def test_convert_to_pixel(self):\n MM_CONST = 3.543307\n\n value = SVGPrinter.convert_to_pixel(1) # pixel value (unitless)\n self.assertEqual(value, 1)\n\n value = SVGPrinter.convert_to_pixel(1 * mm) # millimeter value\n self.assertEqual(value, MM_CONST)\n\n value = SVGPrinter.convert_to_pixel(1.05 * mm) # millimeter value with decimal point\n self.assertEqual(value, 1.05 * MM_CONST)\n\n with self.assertRaises(ConversionError):\n SVGPrinter.convert_to_pixel(1 * percent) # percentage (cannot convert)\n","repo_name":"molitoris/qr_payment_slip","sub_path":"tests/test_printer.py","file_name":"test_printer.py","file_ext":"py","file_size_in_byte":1919,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"73"} +{"seq_id":"74965554156","text":"\"\"\"\nFormat for entry:\npython3 filename filepath bins Etcut legend_adjustment_x legend_adjustment_y (both of these for only 2 histograms) TPave_textsize legend_start legendfont TPave_start --test --log \n\npython3 Singlefile_Eta_distributions.py /Users/sergeyscoville/Desktop/Projects/ROOT_Github/ROOTwork/Data/16eta_bins/hist_Et0p0.root 0 0.0 0.0 0.0 0.04 .7 0.03 0.19 --test\n\n\"\"\"\n\nimport ROOT\nimport sys\nfrom array import array\nfrom helper_functions import *\n\nstylistic = apply_atlas_style()\nROOT.gROOT.SetStyle(\"AtlasStyle\")\n\nbins = int(sys.argv[2])\n\nsoftie = False\ntesting = False\nlogging = False\ncompare = False\nif \"--sk\" in sys.argv:\n softie = True\nif \"--log\" in sys.argv:\n logging = True\nif \"--test\" in sys.argv:\n testing = True\nif \"--onvoff\" in sys.argv:\n compare = True\n\nfile = ROOT.TFile(sys.argv[1])\nplotting = sys.argv[2]\nif \"--sk\" in sys.argv:\n hist1 = file.Get(\"h_Calo422SKclusters_et\")\n hist2 = file.Get(\"h_Calo420SKclusters_et\")\n hist3 = file.Get(\"h_CaloCalSKclusters_et\")\nelif \"--onvoff\" in sys.argv:\n hist1 = file.Get(\"h_Calo\"+sys.argv[-1]+\"SKclusters_et\")\n hist2 = file.Get(\"h_Calo\"+sys.argv[-1]+\"SKTopoClusters_et\")\n hist3 = file.Get(\"h_CaloCalSKclusters_et\")\nelse:\n hist1 = file.Get(\"h_Calo422TopoClusters_et\") # Change for pre versus post sk: Calo422TopoClusters_N -> Calo422SKclusters_N\n hist2 = file.Get(\"h_Calo420TopoClusters_et\") # Change for pre versus post sk: Calo420TopoClusters_N -> Calo420SKclusters_N\n hist3 = file.Get(\"h_CaloCalTopoClusters_et\") # Change for pre versus post sk: CaloCalTopoClusters_N -> CaloCalSKclusters_N\n\n\nif \"--onvoff\" in sys.argv:\n hist_legend_names = [sys.argv[-1]+\"-Global SK\", sys.argv[-1]+\"-Offline SK\", \"Calo Cal\"]\nelse:\n hist_legend_names = [\"Calo 422\", \"Calo 420\", \"Calo Cal\"]\n\nhist1.SetName(hist_legend_names[0])\nhist2.SetName(hist_legend_names[1])\nhist3.SetName(hist_legend_names[2])\n\ncanvas = ROOT.TCanvas(\"canvas\", \"Histograms\", 1200, 800)\nif \"--log\" in sys.argv:\n canvas.SetLogy()\ncanvas.Update()\n\nbin_options = find_divisors(hist1.GetNbinsX())\nif bins == 0 and \"--suppressbins\" not in sys.argv:\n print(\"The options available to rebin your dataset are\", str(bin_options), \". To rebin your dataset, add the integer you wish to end of command line prompt and it will divide the number of bins by this integer to give new binning.\")\n\n\nhistogram_total = []\nfor i in [hist1, hist2, hist3]:\n histogram_total.append(i.GetEntries())\n\n\nhist_titles = [\"E_{t} distribution\", \"E_{t} distribution post SK\"]\nif softie:\n hist1.SetTitle(hist_titles[1])\nelse:\n hist1.SetTitle(hist_titles[0])\n\ncut = sys.argv[3]\nset_y_axis_to_bin_ratio([hist1, hist2, hist3])\n\nx_max = get_histograms_xmax([hist1, hist2, hist3])\ny_max = get_histograms_ymax([hist1, hist2, hist3], bins)\n\nET_dist_axis_set([hist1, hist2, hist3], x_max, y_max, logging)\n#histogram_modifiers([hist1, hist2, hist3], x_max, y_max, bins, cut)\n\noverflow_bin_set([hist1, hist2, hist3])\n\nhist1.GetXaxis().SetTitle(\"E_{t} [MeV]\")\nhist1.GetYaxis().SetTitle(\"Fraction of Topoclusters\")\nhist1.GetXaxis().SetRange(1, hist1.GetNbinsX())\n\nhist1.SetFillColorAlpha(ROOT.kBlue, 0.1)\nhist1.SetFillStyle(3144)\nhist1.SetLineWidth(2)\nhist1.Sumw2()\n\nhist1.Draw(\"hist\")\ncanvas.Update()\n\n\nstarting = float(sys.argv[9])\n\natlas, sim_internal, hl, min_bia = write_all_but_ETC(starting, sys.argv[6])\natlas.Draw()\nsim_internal.Draw()\nhl.Draw()\nmin_bia.Draw()\ncanvas.Update()\n\nif \"NoCut\" not in get_save_file_name(sys.argv[1], bins, \"N\"):\n etcut = write_ET_cut(starting, sys.argv[6], cut)\n etcut.Draw()\n canvas.Update()\n\n\nhist2.Sumw2()\nhist2.SetLineColor(ROOT.kRed)\n#hist2.SetFillColorAlpha(ROOT.kRed, 0.1)\n#hist2.SetFillStyle(3490)\nhist2.SetLineWidth(2)\nhist2.SetEntries(histogram_total[1])\nhist2.Draw(\"hist same\")\ncanvas.Update()\n\nhist3.SetLineColor(ROOT.kGreen+2)\n\nhist3.Sumw2()\nthird_histogram = False\nif \"NoCut\" in get_save_file_name(sys.argv[1], bins, plotting) and \"--onvoff\" not in sys.argv:\n third_histogram = True\n hist3.SetEntries(histogram_total[2])\n hist3.Draw(\"hist SAME\")\n hist3.SetLineWidth(2)\n #hist3.SetFillColorAlpha(ROOT.kGreen-2, 0.1)\n canvas.Update()\n\ncanvas.SetName(\"All_GEP_Algo\")\ncanvas.Update()\n\nlegend_font = sys.argv[8]\nlegend_start = float(sys.argv[7])\nlegend_adjustment_x = float(sys.argv[4])\nlegend_adjustment_y = float(sys.argv[5])\nlegend_sizes_third = {\"0.03\": ROOT.TLegend(legend_start, 0.5, legend_start + 0.12, 0.62), \"0.04\": ROOT.TLegend(legend_start, 0.5, legend_start + 0.15, 0.62)}\nlegend_sizes = {\"0.03\": ROOT.TLegend(legend_adjustment_x + 0.2, legend_adjustment_y + 0.3,legend_adjustment_x + 0.3, legend_adjustment_y + 0.42), \"0.04\": ROOT.TLegend(legend_adjustment_x + 0.2, legend_adjustment_y + 0.3,legend_adjustment_x + 0.33, legend_adjustment_y + 0.42)}\nif third_histogram:\n legend = legend_sizes_third[legend_font]\nelse:\n legend = legend_sizes[legend_font]\n\n\nlegend.AddEntry(hist1,hist_legend_names[0].replace(\"_\", \" \")) \nlegend.AddEntry(hist2,hist_legend_names[1].replace(\"_\", \" \"), \"l\") \n#legend.SetFillColor(ROOT.kRed)\nif third_histogram:\n legend.AddEntry(hist3, hist_legend_names[2].replace(\"_\", \" \"), \"l\") \nhist1.GetXaxis().SetTitleSize(0.04)\nhist1.GetYaxis().SetTitleSize(0.04)\nlegend.SetTextSize(float(sys.argv[8]))\n#legend.SetLineWidth(0) # Remove the boundary on the legend\nlegend.Draw(\"same\") \n\ncanvas.Update()\n\nfilepaths = \"/Users/sergeyscoville/Desktop/Projects/ROOT_Github/ROOTwork/Doc/\"\nextensions = \"\"\nif softie:\n extensions += get_save_file_name(sys.argv[1], bins, \"Et\")+\"_SK\"\nelse:\n extensions += get_save_file_name(sys.argv[1], bins, \"Et\")\nif logging:\n extensions += \"_LOG\"\nif compare:\n extensions += \"_ONVOFF\"\nif testing:\n extensions += \"_TESTING\"\n\nif testing:\n if \"--sk\" in sys.argv:\n canvas.SaveAs(filepaths+\"Plots_PDFs/Plots_PDFs_w_SK/\"+extensions+\".pdf\")\n else:\n canvas.SaveAs(filepaths+\"Plots_PDFs/Plots_PDFs_No_SK/\"+extensions+\".pdf\")\nelif \"--sk\" in sys.argv:\n canvas.SaveAs(filepaths+\"Plots/Plots_w_SK/\"+extensions+\".png\")\n canvas.SaveAs(filepaths+\"Plots_PDFs/Plots_PDFs_w_SK/\"+extensions+\".pdf\")\n canvas.SaveSource(filepaths+\"Source_Files/Source_Files_w_SK/\"+extensions+\".C\")\n output_file = ROOT.TFile(filepaths+\"ROOT_files/ROOT_files_w_SK/\"+extensions+\".root\", \"RECREATE\")\n canvas.Write()\nelse:\n canvas.SaveAs(filepaths+\"Plots/Plots_No_SK/\"+extensions+\".png\")\n canvas.SaveAs(filepaths+\"Plots_PDFs/Plots_PDFs_No_SK/\"+extensions+\".pdf\")\n canvas.SaveSource(filepaths+\"Source_Files/Source_Files_No_SK/\"+extensions+\".C\")\n output_file = ROOT.TFile(filepaths+\"ROOT_files/ROOT_files_No_SK/\"+extensions+\".root\", \"RECREATE\")\n canvas.Write()\n\nif not testing:\n output_file.Close()\n\n","repo_name":"SergeyScoville/ROOTwork","sub_path":"bin/Offline_GEP_Testing_Python_Scripts/Singlefile_Et_distributions.py","file_name":"Singlefile_Et_distributions.py","file_ext":"py","file_size_in_byte":6709,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"73"} +{"seq_id":"25895200845","text":"from tkinter import *\nfrom tkinter import filedialog\nimport os\nimport logging\nimport tkinter\n\n__author__ = \"Davide Micieli\"\n__all__ = ['get_image_gui',\n\t 'save_filename_gui',\n\t\t'get_folder_gui',\n\t\t'get_filename_gui',\n\t\t'get_screen_resolution']\n\n#logging.basicConfig(level=logging.WARNING)\nlogs = logging.getLogger(__name__)\n\n\ndef get_image_gui(initialdir='', message='Select image...'):\n\t\"\"\"\n\tThis function opens a dialog box to select an image (TIFF or FITS) and to get\n\tits file path.\n\n\tParameters\n\t----------\n\tinitialdir : str, optional\n\t\tString defining the path of the initial folder to open in the dialog box.\n\n\tmessage : str, optional\n\t\tString defining the dialog box title.\n\n\tReturns\n\t-------\n\tfname : str\n\t\tString defining the file path selected using the dialog box.\n\t\"\"\"\n\n\tif not (initialdir):\n\t\tinitialdir = os.path.abspath(os.sep)\n\n\troot = Tk()\n\troot.withdraw()\n\troot.wm_attributes('-topmost', 1)\n\n\twhile True:\n\t\tfname = filedialog.askopenfilename(initialdir=initialdir, title=message,\n\t\t\t\t\t\tfiletypes = ((\"Image files\", \"*.tif *.tiff *.fits\"),\n\t\t\t\t\t\t(\"TIFF files\",\"*.tif *.tiff\"),(\"FITS files\",\"*.fits\")))\n\n\t\tif(fname):\n\t\t\tbreak\n\n\treturn fname\n\n\ndef save_filename_gui(initialdir='', message='Select folder and the name of the file to save...' ):\n\t\"\"\"\n\tThis function opens a dialog box to select a file to save and get its file path.\n\n\tParameters\n\t----------\n\tinitialdir : str, optional\n\t\tString defining the path of the initial folder to open in the dialog box.\n\n\tmessage : str, optional\n\t\tString defining the dialog box title.\n\n\tReturns\n\t-------\n\tfname : str\n\t\tString defining the file path selected using the dialog box.\n\t\"\"\"\n\n\tif not (initialdir):\n\t\tinitialdir = os.path.abspath(os.sep)\n\n\troot = Tk()\n\troot.withdraw()\n\troot.wm_attributes('-topmost', 1)\n\n\twhile True:\n\t\tfname = filedialog.asksaveasfilename(initialdir = initialdir, title = message)\n\n\t\tif(fname):\n\t\t\tbreak\n\n\n\treturn fname\n\n\ndef get_folder_gui(initialdir='', message='Select folder...'):\n\t\"\"\"\n\tThis function opens a dialog box to select a folder and get its file path.\n\n\tParameters\n\t----------\n\tinitialdir : str, optional\n\t\tString defining the path of the initial folder to open in the dialog box.\n\n\tmessage : str, optional\n\t\tString defining the dialog box title.\n\n\tReturns\n\t-------\n\tfname : str\n\t\tString defining the folder path selected using the dialog box.\n\t\"\"\"\n\n\tif not (initialdir):\n\t\tinitialdir = os.path.abspath(os.sep)\n\n\troot = Tk()\n\troot.withdraw()\n\troot.wm_attributes('-topmost', 1)\n\n\n\twhile True:\n\t\tfname = filedialog.askdirectory(initialdir=initialdir, title=message)\n\n\t\tif(fname):\n\t\t\tbreak\n\n\treturn fname\n\n\ndef get_filename_gui(initialdir='', message='Select file...', ext=None ):\n\t\"\"\"\n\tThis function opens a dialog box to select a file and get its file path.\n\n\tParameters\n\t----------\n\tinitialdir : str, optional\n\t\tString defining the path of the initial folder to open in the dialog box.\n\n\tmessage : str, optional\n\t\tString defining the dialog box title.\n\n\text : tuple, optional\n\t\tTuple defining the file types to show. It includes the description and a\n\t\tshell-style wildcards defining the extension of the files.\n\t\tE.g. to filter TIFF images: (('Tiff iamges', '*.tiff *.tif'))\n\n\tReturns\n\t-------\n\tfname : str\n\t\tString defining the file path selected using the dialog box.\n\t\"\"\"\n\n\tif not (initialdir):\n\t\tinitialdir = os.path.abspath(os.sep)\n\n\troot = Tk()\n\troot.withdraw()\n\troot.wm_attributes('-topmost', 1)\n\n\twhile True:\n\n\t\tif ext is None:\n\t\t\tfname = filedialog.askopenfilename(initialdir=initialdir, title=message)\n\t\telse:\n\t\t\text = ( ext, (\"All files\", \"*.*\") )\n\t\t\tfname = filedialog.askopenfilename(initialdir=initialdir, title=message,\n\t\t\t \t\t\t\t filetypes = ext)\n\n\t\tif(fname):\n\t\t\tbreak\n\n\treturn fname\n\n\ndef get_screen_resolution():\n\t\"\"\"\n\tThis function returns the screen resolution as tuple.\n\n\tExample\n\t-------\n\t>>> width, height = ntp.get_screen_resolution()\n\t\"\"\"\n\troot = tkinter.Tk()\n\troot.withdraw()\n\twidth = root.winfo_screenwidth()\n\theight = root.winfo_screenheight()\n\treturn (width, height)\n","repo_name":"dmici/NeuTomPy-toolbox","sub_path":"neutompy/misc/uitools.py","file_name":"uitools.py","file_ext":"py","file_size_in_byte":4008,"program_lang":"python","lang":"en","doc_type":"code","stars":29,"dataset":"github-code","pt":"73"} +{"seq_id":"40117637039","text":"\"\"\"\nFunction to lineraize stuff\n\"\"\"\nimport numpy as np\nimport scipy as sc\nimport scipy.spatial.distance as sc\n\n\ndef linspace_seg(point1, point2, length, end=False):\n \"\"\"\n Discretize segment into length points\n\n @param point1 (numpy array) first point of the segment\n @param point2 (numpy array) second point of the segment\n @param length (int) number of discretized points on the segment\n @param end (optional logical) include or not the last point\n\n @returns (numpy.array)\n \"\"\"\n v_1 = np.linspace(point1[0], point2[0], length, endpoint=end)\n v_2 = np.linspace(point1[1], point2[1], length, endpoint=end)\n line = np.zeros(shape=[length, 2])\n line[:, 0] = v_1\n line[:, 1] = v_2\n return line\n\n\ndef linspace_poly(poly_points, poly_number):\n \"\"\"\n Discretize polyline\n\n @param poly_points (list of numpy array) list of polyline points\n @param poly_number (list of int) number of discretized points for each\n polyline segments\n\n @returns (numpy.array)\n \"\"\"\n list_seg = []\n for i in range(len(poly_points)-1):\n lin = linspace_seg(poly_points[i], poly_points[i+1], poly_number[i],\n end=False)\n list_seg.append(lin)\n length = sum(poly_number)+1\n poly = np.zeros(shape=[length, 2])\n poly[0:poly_number[0], :] = list_seg[0]\n deb = 0\n for i in range(0, len(poly_number)-1):\n deb = deb + poly_number[i]\n poly[deb:(poly_number[i+1]+deb), :] = list_seg[i+1][:]\n poly[-1, :] = poly_points[-1]\n return poly\n\n\ndef curvilinear_abscissa(coord_poly):\n \"\"\"\n Compute curvilinear abscissa of a polyline\n\n @param coord_poly (list) coordinates of polyline (list of 2-uple)\n\n @return curv_absc (list) list of curvilinear abscissa (list of float)\n \"\"\"\n curv_absc = np.zeros(len(coord_poly))\n\n for i in range(len(coord_poly)-1):\n coord_point_1 = coord_poly[i, :]\n coord_point_2 = coord_poly[i+1, :]\n curv_absc[i+1] = curv_absc[i] + sc.euclidean(coord_point_1,\n coord_point_2)\n\n return curv_absc\n","repo_name":"DassHydro-dev/dassflow2d","sub_path":"Tools/1_pre-treatment/1_Telemac-to-DassFlow/pytel/data_manip/extraction/linspace.py","file_name":"linspace.py","file_ext":"py","file_size_in_byte":2107,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"73"} +{"seq_id":"71501997997","text":"from os import name\nfrom django.urls import path\nfrom Apprestapp1.views import *\nfrom django.contrib.auth.views import LogoutView\n\nurlpatterns = [\n path('',inicio, name=\"inicio\"),\n path('error/',error, name=\"error\"),\n path('contacto/', contacto, name='contacto'),\n \n path('listarestaurants/', RestaurantList.as_view(),name='List'),\n path('detallerestaurants//', RestaurantDetail.as_view(),name='Detail'),\n path('crearrestaurants/', RestaurantCreate.as_view(),name='New'),\n path('actualizarestaurants//', RestaurantUpdate.as_view(),name='Edit'),\n path('eliminarestaurants//', RestaurantDelate.as_view(),name='Delete'),\n \n path('buscar/',buscar, name=\"buscar\"),\n \n path('login/',login_request, name='Login'),\n path('register/',register,name='Register'),\n path('logout/', LogoutView.as_view(template_name='Apprestapp1/logout.html'), name = 'Logout'),\n path('editarPerfil/', editarPerfil, name = \"EditarPerfil\"),\n]","repo_name":"francooforniz58/Proyecto-restapp-coderhouse","sub_path":"Apprestapp1/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":970,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"73"} +{"seq_id":"35588812140","text":"import torch\nimport torchvision.transforms as transforms\nimport torch.utils.data as data\nfrom datasets import SubDataset, AbstractDomainInterface, ExpandRGBChannels\nimport os\nimport os.path as osp\nimport csv\nimport numpy as np\nimport subprocess\nfrom PIL import Image\n\nLABELS = [\"ELBOW\", \"FINGER\", \"FOREARM\", \"HAND\", \"HUMERUS\", \"SHOULDER\", \"WRIST\"]\nN_CLASS = 7\n\ndef to_tensor(crops):\n return torch.stack([transforms.ToTensor()(crop) for crop in crops])\n\ndef group_normalize(crops):\n return torch.stack([transforms.Normalize([0.485, 0.456, 0.406],\n [0.229, 0.224, 0.225])(crop) for crop in crops])\n\nclass MURABase(data.Dataset):\n def __init__(self, source_dir, split, index_file=\"train_image_paths.csv\",\n image_dir=\"train\", imsize=224, transforms=None, to_rgb=False, download=False, extract=True):\n super(MURABase,self).__init__()\n self.source_dir = source_dir\n self.split = split\n self.index_file = index_file\n self.image_dir = image_dir\n\n self.imsize = imsize\n self.to_rgb = to_rgb\n if transforms is None:\n self.transforms = transforms.Compose([transforms.Resize((imsize, imsize)),\n transforms.ToTensor()])\n else:\n self.transforms = transforms\n assert split in [\"train\", \"val\"]\n if extract:\n self.extract()\n cache_file = self.generate_index()\n self.img_list = cache_file['img_list']\n self.label_tensors = cache_file['label_tensors']\n self.split_inds = cache_file[\"split_inds\"]\n\n def __len__(self):\n return len(self.split_inds)\n\n def __getitem__(self, item):\n index = self.split_inds[item]\n img_name = self.img_list[index]\n label = self.label_tensors[index]\n\n imp = osp.join(self.source_dir, self.image_dir, img_name)\n with open(imp, 'rb') as f:\n with Image.open(f) as img:\n if self.to_rgb:\n img = self.transforms(img.convert('RGB'))\n else:\n img = self.transforms(img.convert('L'))\n return img, label\n\n def extract(self):\n if os.path.exists(os.path.join(self.source_dir, self.image_dir)):\n return\n import tarfile\n with tarfile.open(os.path.join(self.source_dir, \"images.tar.gz\")) as tar:\n tar.extractall(os.path.join(self.source_dir, self.image_dir))\n return\n\n def generate_index(self):\n \"\"\"\n Scan index file to create list of images and labels for each image\n :return:\n \"\"\"\n img_list = []\n label_list = []\n print(\"Reading %s\"%self.index_file)\n with open(osp.join(self.source_dir, self.index_file), 'r') as fp:\n csvf = csv.DictReader(fp, ['Image Path', ])\n for row in csvf:\n raw_path = row['Image Path'].split('/')[2:]\n imp = osp.join(self.source_dir, self.image_dir, *raw_path)\n\n if osp.exists(imp):\n #add subpath after 'train' or 'valid' and image name to img_list\n img_list.append(osp.join(*row['Image Path'].split('/')[2:]))\n label = np.zeros(N_CLASS)\n for i, l in enumerate(LABELS):\n if l in row['Image Path']:\n label[i] = 1\n break\n if label.sum() < 1:\n print(\"AHH\")\n #label = [0, 1] if 'positive' in row['Image Path'] else [1, 0]\n label_list.append(label)\n label_tensors = torch.LongTensor(label_list)\n return {'img_list': img_list, 'label_tensors': label_tensors, 'label_list': label_list,\n 'split_inds': torch.arange(len(img_list))\n }\n\n\nclass MURA(AbstractDomainInterface):\n dataset_path = \"MURA\"\n\n def __init__(self, root_path=\"./workspace/datasets/MURA\", keep_class=None,downsample=None, expand_channels=False,\n test_length=None, download=False, extract=True, doubledownsample=None):\n self.name = \"MURA\"\n super(MURA, self).__init__()\n self.downsample = downsample\n self.keep_in_classes = keep_class\n self.expand_channels=expand_channels\n self.max_l = test_length\n cache_path = root_path\n source_path = root_path\n if doubledownsample is not None:\n transform_list = [transforms.Resize(doubledownsample),]\n else:\n transform_list = []\n if downsample is not None:\n print(\"downsampling to\", downsample)\n transform = transforms.Compose(transform_list +\n [transforms.Resize((downsample, downsample)),\n transforms.ToTensor()])\n self.image_size = (downsample, downsample)\n else:\n transform = transforms.Compose(transform_list +\n [transforms.Resize((224, 224)),\n transforms.ToTensor()])\n self.image_size = (224, 224)\n\n self.ds_train = MURABase(source_path, \"train\", transforms=transform,index_file=\"train_image_paths.csv\",\n image_dir=\"images_224\", to_rgb=expand_channels, download=download, extract=extract)\n self.ds_valid = MURABase(source_path, \"val\", transforms=transform,index_file=\"valid_image_paths.csv\",\n image_dir=\"images_224\", to_rgb=expand_channels, download=download, extract=extract)\n\n if extract:\n self.D1_train_ind = self.get_filtered_inds(self.ds_train, shuffle=True)\n self.D1_valid_ind = self.get_filtered_inds(self.ds_valid, shuffle=True, max_l=self.max_l)\n self.D1_test_ind = self.get_filtered_inds(self.ds_valid, shuffle=True)\n\n self.D2_valid_ind = self.get_filtered_inds(self.ds_train, shuffle=True)\n self.D2_test_ind = self.get_filtered_inds(self.ds_valid)\n\n def get_filtered_inds(self, basedata: MURABase, shuffle=False, max_l=None):\n if not self.keep_in_classes is None:\n #print(basedata.__dict__)\n keep_in_mask_label = torch.zeros(N_CLASS).int()\n for cla in self.keep_in_classes:\n ii = LABELS.index(cla)\n keep_in_mask_label[ii] = 1\n keep_inds = []\n for seq_ind, base_ind in enumerate(basedata.split_inds):\n label = basedata.label_tensors[base_ind].int()\n if torch.sum(label * keep_in_mask_label) > 0:\n keep_inds.append(seq_ind)\n else:\n pass\n output_inds = torch.Tensor(keep_inds).int()\n else:\n output_inds = torch.arange(0, len(basedata)).int()\n #output_inds = torch.arange(0, len(basedata)).int()\n if shuffle:\n output_inds = output_inds[torch.randperm(len(output_inds))]\n if max_l is not None:\n if len(output_inds) >max_l:\n output_inds = output_inds[:max_l]\n return output_inds\n\n def get_D1_train(self):\n return SubDataset(self.name, self.ds_train, self.D1_train_ind)\n def get_D1_valid(self):\n return SubDataset(self.name, self.ds_valid, self.D1_valid_ind, label=0)\n def get_D1_test(self):\n return SubDataset(self.name, self.ds_valid, self.D1_test_ind, label=0)\n\n def get_D2_valid(self, D1):\n assert self.is_compatible(D1)\n target_indices = self.D2_valid_ind\n return SubDataset(self.name, self.ds_train, target_indices, label=1, transform=D1.conformity_transform())\n\n def get_D2_test(self, D1):\n assert self.is_compatible(D1)\n target_indices = self.D2_test_ind\n return SubDataset(self.name, self.ds_valid, target_indices, label=1, transform=D1.conformity_transform())\n\n def conformity_transform(self):\n target = 224\n if self.downsample is not None:\n target = self.downsample\n if self.expand_channels:\n return transforms.Compose([ExpandRGBChannels(),\n transforms.ToPILImage(),\n transforms.Resize((target, target)),\n transforms.ToTensor()\n ])\n else:\n return transforms.Compose([\n transforms.ToPILImage(),\n transforms.Grayscale(),\n transforms.Resize((target, target)),\n transforms.ToTensor()\n ])\n\nclass MURAHAND(MURA):\n dataset_path = \"MURA\"\n def __init__(self, root_path=\"./workspace/datasets/MURA\", downsample=None, expand_channels=False,\n test_length=None, download=False, extract=True, doubledownsample=None):\n super(MURAHAND, self).__init__(root_path, [\"HAND\", ], downsample, expand_channels,\n test_length, download, extract, doubledownsample)\n\nclass MURAWRIST(MURA):\n dataset_path = \"MURA\"\n def __init__(self, root_path=\"./workspace/datasets/MURA\", downsample=None, expand_channels=False,\n test_length=None, download=False, extract=True, doubledownsample=None):\n super(MURAWRIST, self).__init__(root_path, [\"WRIST\", ], downsample, expand_channels,\n test_length, download, extract, doubledownsample)\n\nclass MURASHOULDER(MURA):\n dataset_path = \"MURA\"\n def __init__(self, root_path=\"./workspace/datasets/MURA\", downsample=None, expand_channels=False,\n test_length=None, download=False, extract=True, doubledownsample=None):\n super(MURASHOULDER, self).__init__(root_path, [\"SHOULDER\", ], downsample, expand_channels,\n test_length, download, extract, doubledownsample)\n #[\"ELBOW\", \"FINGER\", \"FOREARM\", \"HAND\", \"HUMERUS\", \"SHOULDER\", \"WRIST\"]\nclass MURAFOREARM(MURA):\n dataset_path = \"MURA\"\n def __init__(self, root_path=\"./workspace/datasets/MURA\", downsample=None, expand_channels=False,\n test_length=None, download=False, extract=True, doubledownsample=None):\n super(MURAFOREARM, self).__init__(root_path, [\"FOREARM\", ], downsample, expand_channels,\n test_length, download, extract, doubledownsample)\nclass MURAFINGER(MURA):\n dataset_path = \"MURA\"\n def __init__(self, root_path=\"./workspace/datasets/MURA\", downsample=None, expand_channels=False,\n test_length=None, download=False, extract=True, doubledownsample=None):\n super(MURAFINGER, self).__init__(root_path, [\"FINGER\", ], downsample, expand_channels,\n test_length, download, extract, doubledownsample)\n\nclass MURAELBOW(MURA):\n dataset_path = \"MURA\"\n def __init__(self, root_path=\"./workspace/datasets/MURA\", downsample=None, expand_channels=False,\n test_length=None, download=False, extract=True, doubledownsample=None):\n super(MURAELBOW, self).__init__(root_path, [\"ELBOW\", ], downsample, expand_channels,\n test_length, download, extract, doubledownsample)\n\nclass MURAHUMERUS(MURA):\n dataset_path = \"MURA\"\n def __init__(self, root_path=\"./workspace/datasets/MURA\", downsample=None, expand_channels=False,\n test_length=None, download=False, extract=True, doubledownsample=None):\n super(MURAHUMERUS, self).__init__(root_path, [\"HUMERUS\", ], downsample, expand_channels,\n test_length, download, extract, doubledownsample)\n\nif __name__ == \"__main__\":\n dataset = MURAHand()\n d1_train = dataset.get_D1_train()\n print(len(d1_train))\n loader = data.DataLoader(d1_train, batch_size=1, shuffle=True)\n import matplotlib.pyplot as plt\n for batch, batch_ind in zip(loader, range(10)):\n print(batch_ind)\n x, y = batch\n plt.imshow(x.numpy().reshape(dataset.image_size), cmap='gray')\n\n\n","repo_name":"caotians1/OD-test-master","sub_path":"datasets/MURA.py","file_name":"MURA.py","file_ext":"py","file_size_in_byte":12288,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"73"} +{"seq_id":"33748701505","text":"import mysql.connector as conn\nfrom flask import Flask, request, jsonify\n\napp = Flask(__name__)\n\n\n@app.route('/sql', methods = ['GET','POST'])\ndef fetchsql():\n mydb = conn.connect(host='localhost', user='root')\n cursor = mydb.cursor()\n cursor.execute(\"select * from cardataset.car\")\n x = cursor.fetchall()\n return (jsonify(x))\n","repo_name":"rahulsm27/Flask","sub_path":"Task/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":342,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"73"} +{"seq_id":"6834000727","text":"import os.path\nimport sublime\nimport sublime_plugin\n\nclass ShowFileStatus(sublime_plugin.EventListener):\n\n def update_file_status(self, view):\n\n if not view.file_name():\n status = \"NONE\"\n elif not os.path.exists(view.file_name()):\n status = \"DEL\"\n elif view.is_dirty():\n status = \"UNSAVED\"\n else:\n status = \"SAVED\"\n\n window = sublime.active_window()\n status = str(len(window.views())) + \" ~ \" + status\n\n view.set_status(self.KEY_SIZE, \"[%s]\" % status)\n\n on_post_save_async = update_file_status\n on_modified_async = update_file_status\n on_activated_async = update_file_status\n","repo_name":"void285/sublime-text-small-plugins","sub_path":"show_file_status.py","file_name":"show_file_status.py","file_ext":"py","file_size_in_byte":680,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"73"} +{"seq_id":"25076445474","text":"#-*-coding:utf-8-*-\nimport os\nimport sys\nfrom PyQt4.QtCore import *\nfrom PyQt4.QtGui import *\n\nclass BaseDialog(QDialog):\n #def __init__(self, parent): #不知道为什么这里的init不会被执行, 待解决\n # super(QDialog, self).__init__(parent)\n def _initUI(self, parent):\n self.parent = parent\n main_layout = QVBoxLayout(self)\n self.setLayout(main_layout)\n self.initUI()\n self.on_submit = lambda *args: None\n self.on_cancel = lambda *args: None\n\n def add_ok_cancel_buttons(self, buttons='', text_ok='确定', text_cancel='取消'):\n '''添加确认取消按钮\n buttons: 默认都需要, 1代表只需要确认, 0代表只需要取消'''\n if buttons == 0:\n _buttons = QDialogButtonBox.Cancel\n elif buttons == 1:\n _buttons = QDialogButtonBox.Ok\n else:\n _buttons = QDialogButtonBox.Ok | QDialogButtonBox.Cancel\n buttonBox = QDialogButtonBox(_buttons, Qt.Horizontal, parent=self)\n self.layout().addWidget(buttonBox)\n if buttonBox.button(QDialogButtonBox.Ok):\n buttonBox.button(QDialogButtonBox.Ok).setText(text_ok)\n if buttonBox.button(QDialogButtonBox.Cancel):\n buttonBox.button(QDialogButtonBox.Cancel).setText(text_cancel)\n buttonBox.accepted.connect(self._on_submit)\n buttonBox.rejected.connect(self._on_cancel)\n\n @pyqtSlot()\n def _on_submit(self):\n self.on_submit()\n self.close()\n\n @pyqtSlot()\n def _on_cancel(self):\n self.on_cancel()\n self.close()\n \ndef ShowMessageDialog(parent, title, message):\n return QMessageBox.information(parent, title, message)\n\nclass ShowCustomMessageDialog(BaseDialog):\n def __init__(self, parent, message, title='提醒'):\n super(BaseDialog, self).__init__(parent)\n self.message = message\n self.title = title\n self._initUI(parent)\n\n def initUI(self):\n self.setWindowTitle(self.title)\n self.layout().addWidget(QLabel(self.message, self))\n self.add_ok_cancel_buttons(0, text_cancel='确定')\n\nclass HomePageDialog(BaseDialog):\n def __init__(self, parent):\n super(BaseDialog, self).__init__(parent)\n self._initUI(parent)\n\n def initUI(self):\n self.setWindowTitle('设置主页')\n self.resize(600, 100)\n self.setWindowModality(Qt.ApplicationModal)\n widget = QWidget(self)\n grid_layout = QGridLayout()\n widget.setLayout(grid_layout)\n label_current_homepage = QLabel('当前主页: ', widget)\n grid_layout.addWidget(label_current_homepage, 0, 0)\n text_current_homepage = QLabel(self.parent.webview.url().url(), widget)\n grid_layout.addWidget(text_current_homepage, 0, 1)\n label_new_homepage = QLabel('新的主页: ', widget)\n grid_layout.addWidget(label_new_homepage, 1, 0)\n text_new_homepage = QLineEdit('https://', widget)\n text_new_homepage.setFixedWidth(500)\n text_new_homepage.setStyleSheet('line-height:20px;font-size:18px')\n text_new_homepage.selectAll()\n grid_layout.addWidget(text_new_homepage, 1, 1)\n self.layout().addWidget(widget)\n self.add_ok_cancel_buttons()\n \n","repo_name":"suzj801/BrowserPlus","sub_path":"dialogs.py","file_name":"dialogs.py","file_ext":"py","file_size_in_byte":3258,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"73"} +{"seq_id":"25943749965","text":"import torch\r\nfrom torch import nn\r\nimport torchvision\r\nfrom torch.autograd import Variable\r\nfrom torch.nn.init import kaiming_normal_\r\nfrom tqdm import tqdm\r\nfrom sklearn.metrics import f1_score\r\nimport numpy as np\r\n\r\nclass BasicBlock1d(nn.Module):\r\n expansion = 1\r\n\r\n def __init__(self, inplanes, planes, stride=1, downsample=None,kernel_size=3,dilation=1):\r\n super(BasicBlock1d, self).__init__()\r\n self.conv1 = nn.Conv1d(inplanes, planes, kernel_size=kernel_size,padding=dilation*(kernel_size-1)/2,stride=stride,dilation=dilation)\r\n self.bn1 = nn.BatchNorm1d(planes)\r\n self.relu = nn.ReLU(inplace=True)\r\n self.conv2 = nn.Conv1d(planes, planes,kernel_size=kernel_size,padding=dilation*(kernel_size-1)/2,dilation=dilation)\r\n self.bn2 = nn.BatchNorm1d(planes)\r\n self.downsample = downsample\r\n self.stride = stride\r\n\r\n def forward(self, x):\r\n residual = x\r\n\r\n out = self.conv1(x)\r\n out = self.bn1(out)\r\n out = self.relu(out)\r\n\r\n out = self.conv2(out)\r\n out = self.bn2(out)\r\n\r\n if self.downsample is not None:\r\n residual = self.downsample(x)\r\n\r\n out += residual\r\n out = self.relu(out)\r\n\r\n return out\r\n\r\nclass Bottleneck1D(nn.Module):\r\n expansion = 4\r\n\r\n def __init__(self, inplanes, planes, stride=1, downsample=None,kernel_size=3):\r\n super(Bottleneck1D, self).__init__()\r\n self.conv1 = nn.Conv1d(inplanes, planes, kernel_size=1)\r\n self.bn1 = nn.BatchNorm1d(inplanes)\r\n self.conv2 = nn.Conv1d(planes, planes, kernel_size=kernel_size, stride=stride,\r\n padding=(kernel_size-1)/2)\r\n self.bn2 = nn.BatchNorm1d(planes)\r\n self.conv3 = nn.Conv1d(planes, planes * 4, kernel_size=1)\r\n self.bn3 = nn.BatchNorm1d(planes)\r\n self.relu = nn.ReLU(inplace=True)\r\n self.downsample = downsample\r\n self.stride = stride\r\n\r\n def forward(self, x):\r\n residual = x\r\n out = self.relu(self.bn1(x))\r\n out = self.conv1(out)\r\n\r\n out = self.relu(self.bn2(out))\r\n out = self.conv2(out)\r\n\r\n out = self.relu(self.bn3(out))\r\n out = self.conv3(out)\r\n\r\n if self.downsample is not None:\r\n residual = self.downsample(x)\r\n\r\n out += residual\r\n return out\r\n\r\nclass ResNet1D(nn.Module):\r\n\r\n def __init__(self, block, layers, kernel_size=3,input_channels=1,return_multiple_outputs=False,first_channels=64):\r\n self.inplanes = first_channels\r\n self.expansion = block.expansion\r\n self.return_multiple_outputs = return_multiple_outputs\r\n self.kernel_size = kernel_size\r\n super(ResNet1D, self).__init__()\r\n self.conv1 = nn.Conv1d(input_channels, 64, kernel_size=self.kernel_size, stride=1, padding=(self.kernel_size-1)/2)\r\n self.bn1 = nn.BatchNorm1d(64)\r\n self.relu = nn.ReLU(inplace=True)\r\n mid_layers = []\r\n mid_layers += self._make_layer(block, 64, layers[0]) #32\r\n channels = first_channels\r\n for layer in layers[1::]:\r\n channels *= 2\r\n mid_layers += self._make_layer(block, channels, layer, stride=2)#16\r\n self.layers = nn.ModuleList(mid_layers)\r\n self.init_weights()\r\n\r\n def init_weights(self):\r\n for m in self.modules():\r\n if isinstance(m, nn.Conv1d) or isinstance(m, nn.Linear):\r\n kaiming_normal_(m.weight.data)\r\n elif isinstance(m, nn.BatchNorm1d):\r\n m.weight.data.fill_(1)\r\n m.bias.data.zero_()\r\n\r\n def _make_layer(self, block, planes, blocks, stride=1):\r\n downsample = None\r\n if stride != 1 or self.inplanes != planes * block.expansion:\r\n downsample = nn.Conv1d(self.inplanes, planes * block.expansion,\r\n kernel_size=1, stride=stride)\r\n layers = []\r\n layers.append(block(self.inplanes, planes, stride, downsample,kernel_size=self.kernel_size))\r\n self.inplanes = planes * block.expansion\r\n for i in range(1, blocks):\r\n layers.append(block(self.inplanes, planes,kernel_size=self.kernel_size))\r\n\r\n return nn.Sequential(*layers)\r\n\r\n def forward(self, x):\r\n x = self.conv1(x)\r\n outputs = []\r\n for layer in self.layers:\r\n outputs += [layer(x)]\r\n x = outputs[-1]\r\n if self.return_multiple_outputs:\r\n return outputs\r\n else:\r\n return outputs[-1]\r\n\r\n\r\nclass Dilated_ResNet1D(nn.Module):\r\n def __init__(self, block, layers, kernel_size=3,input_channels=1,return_multiple_outputs=False,first_channels=64):\r\n self.inplanes = first_channels\r\n channels = 64\r\n self.expansion = block.expansion\r\n self.return_multiple_outputs = return_multiple_outputs\r\n self.kernel_size = kernel_size\r\n super(Dilated_ResNet1D, self).__init__()\r\n self.conv1 = nn.Conv1d(input_channels, channels, kernel_size=self.kernel_size, stride=1, padding=(self.kernel_size-1)/2)\r\n self.bn1 = nn.BatchNorm1d(channels)\r\n self.relu = nn.ReLU(inplace=True)\r\n mid_layers = []\r\n mid_layers += self._make_layer(block, channels, layers[0],dilation=1) #32\r\n dilation = 1\r\n for layer in layers[1::]:\r\n mid_layers += self._make_layer(block, channels, layer,dilation=dilation)#16\r\n channels *= 2\r\n dilation *= 2\r\n self.layers = nn.ModuleList(mid_layers)\r\n self.init_weights()\r\n\r\n def init_weights(self):\r\n for m in self.modules():\r\n if isinstance(m, nn.Conv1d) or isinstance(m, nn.Linear):\r\n kaiming_normal_(m.weight.data)\r\n elif isinstance(m, nn.BatchNorm1d):\r\n m.weight.data.fill_(1)\r\n m.bias.data.zero_()\r\n\r\n def _make_layer(self, block, planes, blocks, stride=1,dilation=1):\r\n layers = []\r\n downsample = nn.Conv1d(self.inplanes,planes,kernel_size=1)\r\n layers.append(block(self.inplanes, planes,kernel_size=self.kernel_size,dilation=dilation,downsample=downsample))\r\n for i in range(1, blocks):\r\n layers.append(block(planes, planes,kernel_size=self.kernel_size,dilation=dilation))\r\n self.inplanes = planes\r\n return nn.Sequential(*layers)\r\n\r\n def forward(self, x):\r\n x = self.conv1(x)\r\n outputs = []\r\n for layer in self.layers:\r\n outputs += [layer(x)]\r\n x = outputs[-1]\r\n if self.return_multiple_outputs:\r\n return outputs\r\n else:\r\n return outputs[-1]\r\n\r\ndef resnet_encoder(depth,block=BasicBlock1d,kernel_size=3,input_channels=1,return_multiple_outputs=False,dilated=False):\r\n if not dilated:\r\n model = ResNet1D(block, depth,kernel_size=kernel_size,input_channels=input_channels,return_multiple_outputs=return_multiple_outputs)\r\n else:\r\n model = Dilated_ResNet1D(block, depth,kernel_size=kernel_size,input_channels=input_channels,return_multiple_outputs=return_multiple_outputs)\r\n return model\r\n\r\nclass HAR_ResNet1D_AuxOuts(nn.Module):\r\n def __init__(self,depth=56,kernel_size=3,input_channels=30,nb_classes=18,outputs=3):\r\n super(HAR_ResNet1D_AuxOuts,self).__init__()\r\n self.encoder = resnet_encoder(depth,kernel_size=kernel_size,input_channels=input_channels,return_multiple_outputs=True)\r\n self.last_compressions = [nn.Sequential(nn.BatchNorm1d(int(256*self.encoder.expansion/2**i)),nn.ReLU())]\r\n self.last_compressions = nn.ModuleList(self.last_compressions)\r\n self.nb_classes = nb_classes\r\n def init_weights(self):\r\n self.encoder.init_weights()\r\n for compression_container,classifier in zip(self.last_compressions,self.classifiers):\r\n for m in compression_container.modules():\r\n if isinstance(m, nn.Conv1d):\r\n kaiming_normal_(m.weight.data)\r\n elif isinstance(m, nn.BatchNorm1d):\r\n m.weight.data.fill_(1)\r\n m.bias.data.zero_()\r\n if isinstance(classifier, nn.Linear):\r\n kaiming_normal_(classifier.weight.data)\r\n def build_classifier(self,x):\r\n x = self.encoder(x)\r\n self.classifiers = []\r\n for compression_layer,xx in zip(self.last_compressions,x):\r\n xx = compression_layer(xx)\r\n xx = xx.view(xx.size(0),-1)\r\n self.classifiers += [nn.Linear(xx.size(1),self.nb_classes)]\r\n self.classifiers = nn.ModuleList(self.classifiers)\r\n self.init_weights()\r\n def forward(self,x):\r\n x = self.encoder(x)\r\n outputs = []\r\n for xx,compression,classifier in zip(x,self.last_compressions,self.classifiers):\r\n xx = compression(xx)\r\n xx = xx.view(xx.size(0),-1)\r\n outputs += [classifier(xx)]\r\n return outputs\r\n\r\n\r\nclass HAR_ResNet1D(nn.Module):\r\n def __init__(self,depth=[5,5,5,5],kernel_size=5,input_channels=30,nb_classes=18,dilated=False):\r\n super(HAR_ResNet1D,self).__init__()\r\n self.encoder = resnet_encoder(depth,kernel_size=kernel_size,input_channels=input_channels,dilated=dilated)\r\n self.last_compression = nn.Sequential(nn.BatchNorm1d(int(self.encoder.layers[-1].conv2.out_channels)),nn.ReLU())\r\n self.nb_classes = nb_classes\r\n def init_weights(self):\r\n self.encoder.init_weights()\r\n for m in self.last_compression.modules():\r\n if isinstance(m, nn.Conv1d):\r\n kaiming_normal_(m.weight.data)\r\n elif isinstance(m, nn.BatchNorm1d):\r\n m.weight.data.fill_(1)\r\n m.bias.data.zero_()\r\n kaiming_normal_(self.classifier.weight.data)\r\n def build_classifier(self,x):\r\n x = self.encoder(x)\r\n x = self.last_compression(x)\r\n x = x.view(x.size(0),-1)\r\n self.classifier = nn.Linear(x.size(1),self.nb_classes)\r\n self.init_weights()\r\n def forward(self,x):\r\n x = self.encoder(x)\r\n x = self.last_compression(x)\r\n x = x.view(x.size(0),-1)\r\n x = self.classifier(x)\r\n return [x]\r\n","repo_name":"EnriqueSMarquez/HumanActivityRecognition","sub_path":"models/resnets.py","file_name":"resnets.py","file_ext":"py","file_size_in_byte":10165,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"73"} +{"seq_id":"12268166650","text":"import math\nimport numpy as np\nfrom numba import jit, njit\n\n@njit\ndef SinalBasico(Tempo, Frequencia, Amplitude):\n return Amplitude*np.sin(Frequencia*2*np.pi*Tempo)\n\n@jit\ndef EnergiaWale(arrayWavelet):\n return np.trapz((arrayWavelet)**2)\n\n@jit\ndef WaleNormaliza(funcWavelet,tempo,dilat,posic):\n wavelet = funcWavelet(tempo,dilat,posic)\n Energia = EnergiaWale(wavelet)\n return 1/(Energia**(1/2))*(wavelet)\n\n@njit\ndef WaleNotNormaliza(funcWavelet,tempo,dilat,posic):\n wavelet = funcWavelet(tempo,dilat,posic)\n return wavelet\n\n@jit\ndef HermitianHat (tempo,dilat,posic ):\n \"\"\"Retornar um array das posições de uma OndaLet(Chápeu de Hermitian) em relação ao tempo\n \n Argumentos:\n tempo: um Numpy Array com os pontos do sinal que serão exibido\n dilat: O valor de dilatação da Wavelet\n posic: O valor da posição em relação ao tempo da Wavelet\n\n Retorno:\n Retorna um Array 2D com os valores de Amplitude em relação ao tempo.\"\"\"\n\n t = ((tempo-posic)/dilat)\n return 2/(5**(1/2))*math.pi**-(1/4)*t*(1+t)*math.e**((-1/2)*t**2)\n\n@njit\ndef MexicanHat (tempo,dilat,posic):\n \"\"\"Retornar um array das posições de uma OndaLet(Chápeu de mexicano) em relação ao tempo\n \n Argumentos:\n tempo: um Numpy Array com os pontos do sinal que serão exibido\n dilat: O valor de dilatação da Wavelet\n posic: O valor da posição em relação ao tempo da Wavelet\n\n Retorno:\n Retorna um Array 2D com os valores de Amplitude em relação ao tempo.\"\"\"\n t = ((tempo-posic)/dilat)\n return (1-t**2)*math.e**((t**2/2)*-1)\n\n@njit \ndef MeyerWale (tempo,dilat,posic ):\n \"\"\"Retornar um array das posições de uma OndaLet(Meyer) em relação ao tempo\n \n Argumentos:\n tempo: um Numpy Array com os pontos do sinal que serão exibido\n dilat: O valor de dilatação da Wavelet\n posic: O valor da posição em relação ao tempo da Wavelet\n\n Retorno:\n Retorna um Array 2D com os valores de Amplitude em relação ao tempo.\n \n Formando a imagem correta(provavelmente), mas com problemas no deslocamento do eixo do tempo, indo da direita pra esquerda\"\"\"\n\n t = ((tempo-posic)/dilat)\n outputArray = np.array([])\n for x in t:\n if x == 0:\n output = 2/3 + 4/(3*math.pi)\n else: \n output = ((np.sin((2*math.pi)/(3)*x)+(4/3)*x*np.cos(((4*math.pi)/3)*x))/(math.pi*x-(16*math.pi/9)*(x**3)))\n output = output \n outputArray = np.append(output, outputArray)\n return outputArray\n\n@njit\ndef HermitianWale1(tempo,dilat,posic):\n\n t = ((tempo-posic)/dilat)\n return (2**(1/2))*math.pi**(-1/4)*t*math.e**((-t**2)/2)\n \n@njit\ndef MorletWavelet(tempo,dilat,posic):\n\n t = ((tempo-posic)/dilat)\n return math.e**((-t**2)/2)*math.e**(1j*6*t)","repo_name":"matiasfreitas/PyLets","sub_path":"PyLets/Wavelets.py","file_name":"Wavelets.py","file_ext":"py","file_size_in_byte":2833,"program_lang":"python","lang":"pt","doc_type":"code","stars":1,"dataset":"github-code","pt":"73"} +{"seq_id":"70665438636","text":"import math\nimport random\nfrom typing import List, Dict, Tuple\n\nimport numpy as np\nfrom scipy.spatial import Delaunay\n\nfrom src.Graph.CandidateGraph import CandidateGraph\n\n\nclass GraphGenerator:\n \"\"\"Class that generates pseudorandom candidate graphs from clustered sources/sinks and their Delaunay triangulation\"\"\"\n\n def __init__(self, name: str, numNodes: int, numSources: int, minSourceClusters: int, sourcesPerClusterRange: tuple,\n numSinks: int, minSinkClusters: int, sinksPerClusterRange: tuple, clusterRadiusRange: tuple):\n \"\"\"Constructor of a GraphGenerator instance\"\"\"\n # Hyperparameters for candidate graph generation/computing pseudo-random costs\n self.embeddingSize: float = 100.0 # The n x n size in R^2 that the candidate graph is embedded in\n self.arcCostLookupTable: List[List[float, float, float]] = self.getArcCostLookupTable() # List of all possible arc capacities, their fixed cost scalar, and their variable cost scalar\n self.edgePenaltyRange: List[float, float] = [0.95, 1.50] # Range that is uniformly, randomly sampled for assigning edge penalties\n self.randomEdgePenalties: Dict[Tuple[int, int], float] = {} # Dictionary mapping edgeID keys, given as (fromNode, toNode), to values of the edge penalty assigned\n self.isSourceSinkCapacitated: bool = True # Boolean flag indicating if sources and sinks are capacitated\n self.sourceCapRange: List[float, float] = [1, 20] # Range that is uniformly, randomly sampled for assigning source capacities\n self.sinkCapRange: List[float, float] = [1, 20] # Range that is uniformly, randomly sampled for assigning sink capacities\n self.isSourceSinkCharged: bool = False # Boolean flag indicating if sources and sinks are charged\n self.sourceChargeRange: List[float, float] = [1, 20] # Range that is uniformly, randomly sampled for assigning source variable costs\n self.sinkChargeRange: List[float, float] = [1, 20] # Range that is uniformly, randomly sampled for assigning sink variable costs\n # Cluster specific hyperparameters\n self.minSourceClusters: int = minSourceClusters # Minimum number of source clusters that are attempted\n self.sourcesPerClusterRange: Tuple[int, int] = sourcesPerClusterRange # Range that is uniformly, randomly sampled to determine the sources in each cluster\n self.minSinkClusters: int = minSinkClusters # Minimum number of sink clusters that are attempted\n self.sinksPerClusterRange: Tuple[int, int] = sinksPerClusterRange # Range that is uniformly, randomly sampled to determine the sinks in each cluster\n self.clusterRadiusRange: Tuple[int, int] = clusterRadiusRange # Range that is uniformly, randomly sampled to determine the maximum radius of each cluster\n self.tempNodeIDs: List[Tuple[int, float, float]] = [] # Temporary data structure to hold node IDs before casting to a numpy array\n self.tempPoints: List[Tuple[float, float]] = [] # Temporary data structure to hold node (x-position, y-position) before casting to a numpy array\n\n # Output candidate graph to be built\n self.newGraph: CandidateGraph = CandidateGraph() # Candidate graph object that the graph generator object constructs\n self.newGraph.name = name\n self.newGraph.numTotalNodes = numNodes\n self.newGraph.numSources = numSources\n self.newGraph.numSinks = numSinks\n\n def generateGraph(self) -> CandidateGraph:\n \"\"\"Constructs a pseudo-random candidate graph embedded in a 2D plane\"\"\"\n self.embedRandomIntermediatePoints()\n self.assignClusteredSourceSinks()\n self.buildEdgesFromTriangulation()\n self.computeEdgeDistances()\n self.randomEdgePenalties = self.initializeRandomEdgePenalties()\n self.assignInOutEdgesToNodes()\n self.setPossibleArcCapacities()\n self.buildArcsDictAndMatrix()\n self.assignSourceSinkCapAndCharge()\n return self.newGraph\n\n def embedRandomIntermediatePoints(self) -> None:\n \"\"\"Randomly embeds the intermediate points (i.e. n - (s + t)) in a 2D plane\"\"\"\n random.seed()\n tempInterNodes = []\n for n in range(self.newGraph.numTotalNodes - (self.newGraph.numSources + self.newGraph.numSinks)):\n xPos = random.random() * self.embeddingSize\n yPos = random.random() * self.embeddingSize\n self.tempNodeIDs.append((n, xPos, yPos))\n self.newGraph.addNodeToDict(n, xPos, yPos)\n self.tempPoints.append((xPos, yPos))\n tempInterNodes.append(n)\n self.newGraph.interNodesArray = np.array(tempInterNodes, dtype='i')\n self.newGraph.numInterNodes = len(self.newGraph.interNodesArray)\n\n def assignClusteredSourceSinks(self) -> None:\n \"\"\"Randomly embeds source and sink using a pseudo-random clustered strategy\"\"\"\n random.seed()\n # Create all sources in clusters\n tempSrcIDs = []\n remainingSources = self.newGraph.numSources\n # Create each cluster\n for srcCluster in range(self.minSourceClusters):\n # Randomly generate a number of sources for this cluster within the bounds\n thisClusterDensity = random.randint(self.sourcesPerClusterRange[0], self.sourcesPerClusterRange[1])\n # Check if a cluster can be filled; otherwise, give it remaining sources\n if remainingSources < thisClusterDensity:\n thisClusterOfSources = self.buildClusteredPoints(remainingSources)\n else:\n thisClusterOfSources = self.buildClusteredPoints(thisClusterDensity)\n # Decrement sources left to assign\n remainingSources -= thisClusterDensity\n # Create each source\n for sourcePos in thisClusterOfSources:\n nodeID = len(self.tempNodeIDs)\n tempSrcIDs.append(nodeID)\n self.tempNodeIDs.append((nodeID, sourcePos[0], sourcePos[1]))\n self.tempPoints.append((sourcePos[0], sourcePos[1]))\n self.newGraph.addNodeToDict(nodeID, sourcePos[0], sourcePos[1])\n self.newGraph.setNodeType(nodeID, 0)\n # Ensure all sources were assigned; otherwise assign randomly across R^2\n while len(tempSrcIDs) < self.newGraph.numSources:\n xPos = random.random() * self.embeddingSize\n yPos = random.random() * self.embeddingSize\n nodeID = len(self.tempNodeIDs)\n tempSrcIDs.append(nodeID)\n self.tempNodeIDs.append((nodeID, xPos, yPos))\n self.tempPoints.append((xPos, yPos))\n self.newGraph.addNodeToDict(nodeID, xPos, yPos)\n self.newGraph.setNodeType(nodeID, 0)\n # Build sources array\n self.newGraph.sourcesArray = np.array(tempSrcIDs, dtype='i')\n # Create all sinks in clusters\n tempSinkIDs = []\n remainingSinks = self.newGraph.numSinks\n # Create each cluster\n for sinkCluster in range(self.minSinkClusters):\n # Randomly generate a number of sources for this cluster within the bounds\n thisClusterDensity = random.randint(self.sinksPerClusterRange[0], self.sinksPerClusterRange[1])\n # Check if a cluster can be filled; otherwise, give it remaining sources\n if remainingSinks < thisClusterDensity:\n thisClusterOfSinks = self.buildClusteredPoints(remainingSinks)\n else:\n thisClusterOfSinks = self.buildClusteredPoints(thisClusterDensity)\n # Decrement sources left to assign\n remainingSinks -= thisClusterDensity\n # Create each source\n for sinkPos in thisClusterOfSinks:\n nodeID = len(self.tempNodeIDs)\n tempSinkIDs.append(nodeID)\n self.tempNodeIDs.append((nodeID, sinkPos[0], sinkPos[1]))\n self.tempPoints.append((sinkPos[0], sinkPos[1]))\n self.newGraph.addNodeToDict(nodeID, sinkPos[0], sinkPos[1])\n self.newGraph.setNodeType(nodeID, 1)\n # Ensure all sinks were assigned; otherwise assign randomly across R^2\n while len(tempSinkIDs) < self.newGraph.numSinks:\n xPos = random.random() * self.embeddingSize\n yPos = random.random() * self.embeddingSize\n nodeID = len(self.tempNodeIDs)\n tempSinkIDs.append(nodeID)\n self.tempNodeIDs.append((nodeID, xPos, yPos))\n self.tempPoints.append((xPos, yPos))\n self.newGraph.addNodeToDict(nodeID, xPos, yPos)\n self.newGraph.setNodeType(nodeID, 1)\n # Build sources array\n self.newGraph.sinksArray = np.array(tempSinkIDs, dtype='i')\n # Cast temp points list to ndArrays in the newGraph object\n self.newGraph.points = np.array(self.tempPoints, dtype='f')\n\n def buildClusteredPoints(self, numNodesInCluster: int) -> list:\n \"\"\"Builds a cluster of points for source/sink generation sources or sinks\"\"\"\n random.seed()\n clusteredPoints = []\n # Randomly choose cluster radius\n thisClusterRadius = random.randint(self.clusterRadiusRange[0], self.clusterRadiusRange[1])\n # Build cluster center\n xClusterCenter = random.random() * self.embeddingSize\n yClusterCenter = random.random() * self.embeddingSize\n # Generate each point around center\n for thisPoint in range(numNodesInCluster):\n distFromCenter = thisClusterRadius * math.sqrt(random.random())\n angle = 2 * math.pi * random.random()\n xPos = xClusterCenter + distFromCenter * math.cos(angle)\n yPos = yClusterCenter + distFromCenter * math.sin(angle)\n clusteredPoints.append((xPos, yPos))\n return clusteredPoints\n\n def buildEdgesFromTriangulation(self) -> None:\n \"\"\"Builds a Delaunay triangulation to determine edges\"\"\"\n triangulation = Delaunay(self.newGraph.points) # Compute the Delaunay triangulation\n edgeSet = set() # Declare a new set for the edges\n for simplex in range(triangulation.nsimplex): # Iterate over each simplex\n firstNode = -1 # To complete the third edge of the simplex\n for vertex in range(3): # Iterate over the three points in a simplex\n if vertex == 0:\n firstNode = triangulation.simplices[simplex, vertex] # Store the first vertex in the simplex\n if vertex != 2:\n edgeList = [triangulation.simplices[simplex, vertex],\n triangulation.simplices[simplex, vertex + 1]] # Makes bi-directional edge\n forwardEdge = tuple(sorted(edgeList, reverse=False)) # Makes unidirectional forward edge\n edgeSet.add(forwardEdge) # Adds to set for deduplication\n backwardEdge = tuple(sorted(edgeList, reverse=True)) # Makes unidirectional backward edge\n edgeSet.add(backwardEdge) # Adds to set for deduplication\n else: # Logic for the edge connecting the last point to the first\n edgeList = (triangulation.simplices[simplex, vertex], firstNode)\n forwardEdge = tuple(sorted(edgeList, reverse=False))\n edgeSet.add(forwardEdge)\n backwardEdge = tuple(sorted(edgeList, reverse=True))\n edgeSet.add(backwardEdge)\n self.newGraph.edgesArray = np.array(list(edgeSet), dtype='i')\n self.newGraph.numEdges = len(self.newGraph.edgesArray)\n # Build edge dict\n for i in range(self.newGraph.numEdges):\n fromNode = self.newGraph.edgesArray[i][0]\n toNode = self.newGraph.edgesArray[i][1]\n self.newGraph.addEdgeToDict((fromNode, toNode), i)\n\n def computeEdgeDistances(self) -> None:\n \"\"\"Calculates the Euclidean distance of each edge\"\"\"\n tempDistances = []\n for edge in self.newGraph.edgesArray:\n pointOne = self.newGraph.getNodeCoordinates(edge[0])\n pointTwo = self.newGraph.getNodeCoordinates(edge[1])\n distance = math.sqrt((pointTwo[0] - pointOne[0]) ** 2 + (pointTwo[1] - pointOne[1]) ** 2)\n tempDistances.append(distance)\n self.newGraph.distancesArray = np.array(tempDistances, dtype='f')\n\n def assignInOutEdgesToNodes(self) -> None:\n \"\"\"Updates the topology of the graph at the node level by assigning ingoing and outgoing edges\"\"\"\n for edge in self.newGraph.edgesArray:\n thisEdge = (edge[0], edge[1])\n self.newGraph.addOutgoingEdgeToNode(edge[0], thisEdge)\n self.newGraph.addIncomingEdgeToNode(edge[1], thisEdge)\n\n def initializeRandomEdgePenalties(self) -> dict:\n \"\"\"Initializes a random penalties for each opposing edge pair to mimic spatial variability in costs (i.e. a cost raster)\"\"\"\n random.seed()\n edgePenalties = {}\n for edge in self.newGraph.edgesArray:\n thisEdge = (edge[0], edge[1])\n backEdge = (edge[1], edge[0])\n thisPenalty = random.uniform(self.edgePenaltyRange[0], self.edgePenaltyRange[1])\n edgePenalties[thisEdge] = thisPenalty\n edgePenalties[backEdge] = thisPenalty\n return edgePenalties\n\n def setPossibleArcCapacities(self) -> None:\n \"\"\"Sets the possible arc capacities for the parallel edges\"\"\"\n tempArcCaps = []\n for cap in self.arcCostLookupTable:\n tempArcCaps.append(cap[0])\n self.newGraph.possibleArcCapsArray = np.array(tempArcCaps, dtype='f')\n self.newGraph.numArcsPerEdge = len(self.newGraph.possibleArcCapsArray)\n\n def buildArcsDictAndMatrix(self) -> None:\n \"\"\"Builds the dictionary of arcs\"\"\"\n tempArcs = []\n numID = 0\n for edgeID in range(self.newGraph.numEdges):\n fromNode = self.newGraph.edgesArray[edgeID][0]\n toNode = self.newGraph.edgesArray[edgeID][1]\n distance = self.newGraph.distancesArray[edgeID]\n for capID in range(self.newGraph.numArcsPerEdge):\n cap = self.newGraph.possibleArcCapsArray[capID]\n fixedCost = self.calculateArcFixedCost(edgeID, capID)\n variableCost = self.calculateArcVariableCost(edgeID, capID)\n self.newGraph.addArcToDict(numID, (fromNode, toNode, cap), distance, fixedCost, variableCost)\n thisArc = [numID, fromNode, toNode, cap, distance, fixedCost, variableCost]\n tempArcs.append(thisArc)\n numID += 1\n self.newGraph.arcsMatrix = np.array(tempArcs, dtype='f')\n self.newGraph.numTotalArcs = len(self.newGraph.arcsMatrix)\n\n def calculateArcFixedCost(self, edgeID: int, capID: int) -> float:\n \"\"\"Calculates the fixed cost of the arc in a pseudorandom manner\"\"\"\n distance = self.newGraph.distancesArray[edgeID]\n thisEdge = self.newGraph.edgesArray[edgeID]\n penalty = self.randomEdgePenalties[(thisEdge[0], thisEdge[1])]\n fixedCostScalar = self.arcCostLookupTable[capID][1]\n fixedCost = distance * penalty * fixedCostScalar\n return fixedCost\n\n def calculateArcVariableCost(self, edgeID: int, capID: int) -> float:\n \"\"\"Calculates the variable cost of the arc in a pseudorandom manner\"\"\"\n distance = self.newGraph.distancesArray[edgeID]\n thisEdge = self.newGraph.edgesArray[edgeID]\n penalty = self.randomEdgePenalties[(thisEdge[0], thisEdge[1])]\n variableCostScalar = self.arcCostLookupTable[capID][2]\n variableCost = distance * penalty * variableCostScalar\n return variableCost\n\n def assignSourceSinkCapAndCharge(self) -> None:\n \"\"\"Assigns source/sink capacities and/or charges if requested\"\"\"\n random.seed()\n if self.isSourceSinkCapacitated is True:\n self.newGraph.isSourceSinkCapacitated = True\n tempSrcCaps = []\n for source in range(self.newGraph.numSources):\n thisSrcCap = random.uniform(self.sourceCapRange[0], self.sourceCapRange[1])\n tempSrcCaps.append(thisSrcCap)\n self.newGraph.sourceCapsArray = np.array(tempSrcCaps, dtype='f')\n tempSinkCaps = []\n for sink in range(self.newGraph.numSinks):\n thisSinkCap = random.uniform(self.sinkCapRange[0], self.sinkCapRange[1])\n tempSinkCaps.append(thisSinkCap)\n self.newGraph.sinkCapsArray = np.array(tempSinkCaps, dtype='f')\n if self.isSourceSinkCharged is True:\n self.newGraph.isSourceSinkCharged = True\n tempSrcCosts = []\n for source in range(self.newGraph.numSources):\n thisSrcCost = random.uniform(self.sourceChargeRange[0], self.sourceChargeRange[1])\n tempSrcCosts.append(thisSrcCost)\n self.newGraph.sourceVariableCostsArray = np.array(tempSrcCosts, dtype='f')\n tempSinkCosts = []\n for sink in range(self.newGraph.numSinks):\n thisSinkCost = random.uniform(self.sinkChargeRange[0], self.sinkChargeRange[1])\n tempSinkCosts.append(thisSinkCost)\n self.newGraph.sinkVariableCostsArray = np.array(tempSinkCosts, dtype='f')\n self.newGraph.totalPossibleDemand = self.newGraph.calculateTotalPossibleDemand()\n\n def setArcCostLookupTable(self, embeddingSize=100.0, edgePenaltyRange=(0.95, 1.50),\n arcCostLookupTable=(\n [0.19, 12.38148027, 10.68565602],\n [0.54, 14.09940018, 4.500588868],\n [1.13, 16.21640614, 2.747501568],\n [3.25, 21.69529036, 1.700860451],\n [6.86, 30.97486282, 1.407282483],\n [12.26, 41.79573329, 1.2908691],\n [19.69, 55.47324885, 1.235063683],\n [35.13, 77.6425424, 1.194592382],\n [56.46, 104.7159663, 1.175094135],\n [83.95, 136.7519562, 1.164578466],\n [119.16, 172.7476864, 1.09878848],\n )) -> None:\n \"\"\"Allows the hyperparameters that calculate the pseudorandom cost to be tuned\"\"\"\n self.embeddingSize = embeddingSize\n self.edgePenaltyRange = edgePenaltyRange\n self.arcCostLookupTable = arcCostLookupTable\n\n def setSourceSinkGeneralizations(self, isCapacitated=True, isCharged=False, srcCapRange=(1, 20),\n sinkCapRange=(1, 20),\n srcChargeRange=(1, 20), sinkChargeRange=(1, 20)) -> None:\n \"\"\"Allows the capacitated/charged source/sink generalizations to be turned on and tuned\"\"\"\n self.isSourceSinkCapacitated = isCapacitated\n self.sourceCapRange = srcCapRange\n self.sinkCapRange = sinkCapRange\n self.isSourceSinkCharged = isCharged\n self.sourceChargeRange = srcChargeRange\n self.sinkChargeRange = sinkChargeRange\n\n @staticmethod\n def getArcCostLookupTable() -> list:\n \"\"\"Initializes the cost scalar lookup table (loosely based on the SimCCS model formulation- See data dir)\"\"\"\n # Columns: capacity, fixed cost, variable cost\n # DIRECTLY FROM THE SIMCCS PIPELINE MODEL (SCALED BY 1000)\n costLookupTable = [\n [0.19, 12.38148027, 10.68565602],\n [0.54, 14.09940018, 4.500588868],\n [1.13, 16.21640614, 2.747501568],\n [3.25, 21.69529036, 1.700860451],\n [6.86, 30.97486282, 1.407282483],\n [12.26, 41.79573329, 1.2908691],\n [19.69, 55.47324885, 1.235063683],\n [35.13, 77.6425424, 1.194592382],\n [56.46, 104.7159663, 1.175094135],\n [83.95, 136.7519562, 1.164578466],\n [119.16, 172.7476864, 1.09878848],\n ]\n return costLookupTable\n","repo_name":"DaltonGomez/FCNF-Solver","sub_path":"src/Graph/GraphGenerator.py","file_name":"GraphGenerator.py","file_ext":"py","file_size_in_byte":20102,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"73"} +{"seq_id":"74650632235","text":"# Author: Frederico Lacs\nimport csv, click\nimport matplotlib.pyplot as plt\nfrom fee_simulator.simulator import simulate_auction\n\n@click.command()\n@click.option(\"--outputfile\", default=\"results.csv\", help=\"Simulation output filename\", type=str)\n@click.option('--graph_each', is_flag=True, help=\"Plot graph with simulation results labeling each agent\")\n@click.option('--graph_avg', is_flag=True, help=\"Plot graph with simulation results and avg bid price\")\n@click.argument(\"iterations\", type=int)\ndef run(outputfile, graph_each, graph_avg, iterations):\n \"\"\"\n A wrapper around the auction simulation to allow it to be called as a CLI application.\n Exports data into a csv file, and allows for visualisation.\n \"\"\"\n # runs auction the requested number of times\n bids = simulate_auction(iterations)\n \n # export results to csv format\n csvfile = open(outputfile, 'w', newline='')\n writer = csv.writer(csvfile, dialect='excel')\n writer.writerow([\"Bidder\", \"BidValue\", \"BidWeight\", \"CreationTimestep\", \"TimestepPaid\"])\n\n for bid in bids:\n entry = [bid.bidder, bid.value, bid.weight, bid.creation_timestep, bid.payment_timestep]\n writer.writerow(entry)\n\n # display data visualisation if --graphEach flag is used on cli\n if graph_each:\n plt.title('Auction simulation results')\n plt.xlabel('Simulation Timestep')\n plt.ylabel('Price Paid per Transaction by Agent')\n\n # for each unique bidder\n for bidder in list(set([bid.bidder for bid in bids])):\n x = []\n y = []\n\n for bid in bids:\n if bidder == bid.bidder:\n x.append(bid.payment_timestep)\n y.append(bid.value)\n \n # add results for current bidder to graph plot\n plt.scatter(x, y, label=bidder, alpha=0.5, s=2)\n\n plt.legend(loc='best')\n plt.show()\n\n # display data visualisation if --graphAvg flag is used on cli\n if graph_avg:\n plt.title('Auction simulation results')\n plt.xlabel('Simulation Timestep')\n plt.ylabel('Average Price Paid per Transaction by Block')\n\n # unique timesteps in the history\n x = list(set(bid.creation_timestep for bid in bids))\n y = []\n\n import statistics\n for timestep in x:\n bids_accepted = [bid.value for bid in bids if bid.creation_timestep == timestep]\n y.append(statistics.mean(bids_accepted))\n\n plt.plot(x, y, label=\"Avg Gas Price Paid\")\n plt.legend(loc='best')\n plt.show()\n","repo_name":"fredlacs/fee-model-simulator","sub_path":"fee_simulator/cli.py","file_name":"cli.py","file_ext":"py","file_size_in_byte":2571,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"73"} +{"seq_id":"36746157741","text":"#!/usr/bin/python3\n\nimport math\nimport pygame\nimport random \nimport time\n\ndef main():\n pygame.init()\n \n pygame.display.set_caption(\"BingBong\")\n\n WIDTH = 400\n HEIGHT = 400\n screen = pygame.display.set_mode((WIDTH, HEIGHT))\n \n background = pygame.image.load(\"../assets/bg.png\")\n img = pygame.image.load(\"../assets/Ball.png\")\n\n angle = 20\n xStep = 5\n yStep = 5\n x = 0\n y = 0\n \n running = True\n\n while running:\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n running = False\n\n x += math.sin(math.radians(angle)) * xStep\n y += math.cos(math.radians(angle)) * yStep \n\n if x >= HEIGHT or x <= 0:\n xStep = -xStep\n if y >= WIDTH or y <= 0:\n yStep = -yStep\n\n screen.blit(background, (0, 0))\n screen.blit(img, (x, y))\n print(x, y)\n pygame.display.update()\n time.sleep(0.001)\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"Pippadi/GesturePong","sub_path":"fooling-around/ballTest.py","file_name":"ballTest.py","file_ext":"py","file_size_in_byte":991,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"73"} +{"seq_id":"72697565355","text":"'''\nDaniel Flores Rodríguez\nA01734184\nLast modify: September 07 2022\n\nMachine learning Linear regresion using a framework\n'''\n\n\n#we import libryries\nimport sre_compile\nimport pandas as pd #we use pandas to manage dataframes\nimport matplotlib.pyplot as plt # and matplotlib to show predictions\nimport numpy as np\nfrom sklearn.metrics import mean_squared_error\nfrom sklearn.model_selection import train_test_split, cross_val_score\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.linear_model import Ridge\nfrom sklearn.metrics import r2_score\n\n\n\n\n'''\nwe are using a dataset that contains two columns. \nthat is the number of hours student studied and the marks they got. \n\n'''\n\ndata = pd.read_csv('./score.csv')\n\n#we asigned our x and y values, being x our independent variable (hours of study) and y our dependent variable (marks) \n#we use reshape function in order to redimention our y and x variable arrays\n\nX = np.array(data.iloc[:,0]).reshape(-1,1)\nY = np.array(data.iloc[:,1]).reshape(-1,1)\n\n# we can take a look to our data distribution with scatter plot\n# plt.scatter(X,Y)\n# plt.show()\n\n#building the model\n\n# in order to calculate linear regresion, we need to split the data into trining and testing data\nx_train, x_test, y_train, y_test = train_test_split(X,Y, test_size= 0.25, random_state = 45)\n\nregr = LinearRegression()\nregr.fit(x_train,y_train)\n\n#exploring our results\n\nprint(\"Score: \", regr.score(x_test,y_test))\nprint(\"coefficient: \", regr.coef_)\nprint(\"Intercept: \", regr.intercept_)\n\n#we make some predictions\n\nprint(\"\")\nprint(\"\")\n\n\nprint(\"Predictions: \")\npredict_regr = [[10],[8.5],[6],[4]]\n\nfor i in predict_regr:\n print(f\"Horas estudiadas {i} puntaje estimado {regr.predict([i])}\")\n\n\nprint(\"\")\nprint(\"\")\n\n#we calculate the prediction error in test and train\n\ny_pred = regr.predict(x_test)\ny_pred_train = regr.predict(x_train)\ncrossValidaton = abs(cross_val_score(regr, x_train, y_train, cv=5).mean())\n\nprint(\"Model score test: \", r2_score(y_test, y_pred))\nprint(\"Model score train: \", r2_score(y_train, y_pred_train\t))\n\nprint(\"MSE test: \", mean_squared_error(y_test,y_pred))\nprint(\"MSE train: \",mean_squared_error(y_train, y_pred_train))\n\nprint(\"\")\n\nprint(\"Cross validaton: \", crossValidaton)\n\n#IMPROVING MODEL WITH RIDGE\n\nclf = Ridge(alpha=1.0)\nclf.fit(x_train, y_train)\n\nprint(\"\")\nprint(\"\")\n\ntrain_ridge_pred = clf.predict(x_train)\ntest_ridge_pred = clf.predict(x_test)\n\n\nprint(\"MSE in ridge test: \", mean_squared_error(y_test, test_ridge_pred))\nprint(\"MSE in ridge train: \", mean_squared_error(y_train, train_ridge_pred))\n \nprint(\"ridge model score train: \", r2_score(y_train, train_ridge_pred))\nprint(\"ridge model score test: \", r2_score(y_test, test_ridge_pred))\n\n\n\n#plotting...\n\nfigure, axis = plt.subplots(2,2)\n\n#using test\n\n#Lineal regression\naxis[0,0].scatter(x_test, y_test)\naxis[0,0].plot(x_test, y_pred, color='red')\naxis[0,0].set_title(\"study hours vs score (test data)\")\naxis[0,0].set(xlabel = 'study hours', ylabel = 'score')\n\n\n#Var\naxis[0,1].scatter(range(len(x_test)), y_test, alpha = 0.9, label = 'Real data')\naxis[0,1].scatter(range(len(x_test)), y_pred, color='red',alpha = 0.9, label = 'Predicted data')\naxis[0,1].set_title(\"Real test data vs Predicted test data\")\naxis[0,1].set(xlabel = 'study hours', ylabel = 'score')\naxis[0,1].legend()\n\n#using train\n\n#regresion\naxis[1,0].scatter(x_train, y_train)\naxis[1,0].plot(x_train, y_pred_train, color='red')\naxis[1,0].set_title(\"study hours vs score\")\naxis[1,0].set(xlabel = 'study hours', ylabel = 'score')\n\n#var\naxis[1,1].scatter(range(len(x_train)), y_train, alpha = 0.9, label = 'Real data')\naxis[1,1].scatter(range(len(x_train)), y_pred_train, color='red',alpha = 0.9, label = 'Predicted data')\naxis[1,1].set_title(\"Real train data vs Predicted train data\")\naxis[1,1].set(xlabel = 'study hours', ylabel = 'score')\naxis[1,1].legend()\nplt.show()","repo_name":"danoman17/RegresionLinearGradienteDescendiente","sub_path":"linear_reg_framework.py","file_name":"linear_reg_framework.py","file_ext":"py","file_size_in_byte":3852,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"73"} +{"seq_id":"32824791563","text":"point = []\nn, m = int(input()), int(input())\nfor i in range(0, m):\n s = input().split()\n coords = [*map(int, s[1:])]\n point.append((s[0], coords))\nall = []\nfor i in range(0, m):\n for j in range(0, m):\n for z in range(0, m):\n if i != j and j != z and i != z:\n a = point[i][1]\n b = point[j][1]\n c = point[z][1]\n vector1 = [*map(lambda x: x[1] - x[0], zip(a, b))]\n vector2 = [*map(lambda x: x[1] - x[0], zip(b, c))]\n if sum(map(lambda y: y[0] * y[1], zip(vector1, vector2))) == 0:\n pointSet = {point[i][0], point[j][0], point[z][0]}\n if pointSet not in all:\n all.append(pointSet)\n print(point[i][0], point[j][0], point[z][0])\n","repo_name":"PankillerG/Public_Projects","sub_path":"Programming/PycharmProjects/untitled/Python_Start/EXAM/B.py","file_name":"B.py","file_ext":"py","file_size_in_byte":827,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"73"} +{"seq_id":"22071876943","text":"from selenium.webdriver import Chrome\nfrom selenium.webdriver.common.keys import Keys\nfrom selenium.webdriver.common.action_chains import ActionChains\nimport time\ndef test_01():\n driver = Chrome()\n driver.get('https://google.com')\n search_field_locator = \"//textarea[@type='search']\"\n element = driver.find_element(by='xpath', value=search_field_locator)\n element.send_keys('how to use webdriver')\n element.send_keys(Keys.ENTER)\n time.sleep(5)\n #time.sleep(5)\n #python_search_result_locator = '//div[@aria-label=\"how to use webdriver manager in python\"]'\n #python_search_element = driver.find_element(by='xpath', value=python_search_result_locator)\n #python_search_element.click()\n second_page = driver.find_element(by='xpath', value = search_field_locator)\n assert second_page.text == 'how to use webdriver'\n\ndef test_02():\n driver = Chrome()\n driver.get('https://google.com')\n search_field_locator = \"//textarea[@type='search']\"\n element = driver.find_element(by='xpath', value=search_field_locator)\n element.send_keys('how to use webdriver')\n element.send_keys(Keys.ENTER)\n time.sleep(5)\n first_link_locator = \"//h3[text()='Selenium Webdriver Tutorial with Examples - BrowserStack']\"\n first_link = driver.find_element(by='xpath', value=first_link_locator)\n first_link.click()\n\n time.sleep(5)\n desired_text_locator = '//p[@class=\"guide-banner-section__heading\"]'\n desired_text = driver.find_element(by='xpath', value=desired_text_locator)\n assert desired_text.text == 'Run Selenium Tests on Cloud'\n\ndef test_03():\n driver = Chrome()\n driver.get('https://google.com')\n search_field_locator = \"//textarea[@type='search']\"\n element = driver.find_element(by='xpath', value=search_field_locator)\n element.send_keys('how to use webdriver')\n action = ActionChains(driver)\n action.key_down(Keys.CONTROL).send_keys('a').key_up(Keys.CONTROL).perform()\n time.sleep(3)\n action.key_down(Keys.CONTROL).send_keys('x').key_up(Keys.CONTROL).perform()\n time.sleep(5)\n","repo_name":"WhoisGoodBoy/PythonAQAVershynin2106","sub_path":"lesson19/test_01.py","file_name":"test_01.py","file_ext":"py","file_size_in_byte":2062,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"73"} +{"seq_id":"38808136892","text":"_auther_ = 'Harry'\n_date_ = '2/6/2018 9:25 PM'\n\nfrom django import forms\nfrom operation.models import UserAsk\nimport re #正则表达式\n\n\n#用户咨询表单\n# class UserAskForm(forms.Form):\n# name = forms.CharField(required=True,min_length=2,max_length=20)\n# phone = forms.CharField(required=True,min_length=10,max_length=14)\n# course_name = forms.CharField(required=True,min_length=5,max_length=50)\n#\n# #不难发现这个class和我们在operation中定义的非常相似，再重写一遍是非常浪费时间的，所以才有了 modelform\n\n\nclass UserAskForm(forms.ModelForm):\n # 继承之余还可以新增字段\n\n class Meta:\n model = UserAsk #指明来自于哪个model\n fields=['name','mobile','course_name'] #挑出自己需要的字段\\\n\n def clean_mobile(self): #验证手机号码是否合法\n mobile=self.cleaned_data['mobile'] #当提交一个form以后，将被清除的数据放到了 cleaned_data里面\n REGER_MOBILE =\"^1[358]\\d{9}$|^147\\d{8}$|^176\\d{8}|^226\\d{7}$\"\n p = re.compile(REGER_MOBILE)\n if p.match(mobile):\n return mobile\n else:\n raise forms.ValidationError(\"your phone number invalid\",code=\"mobile_invalidation\")\n\n\n\n\n","repo_name":"Harrymissi/mxonline","sub_path":"untitled1/apps/organization/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":1247,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"73"} +{"seq_id":"28609195647","text":"from util import read_into\n\narr = []\nread_into(arr)\n\nprev = None\nincreases = 0\nfor num in arr:\n if prev and prev < num:\n increases += 1\n\n prev = num\n\nprint(increases)\n# answer is 1477","repo_name":"Jandhi/AdventOfCode2021","sub_path":"day 1/problem1.py","file_name":"problem1.py","file_ext":"py","file_size_in_byte":196,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"73"} +{"seq_id":"37021616501","text":"import torch\nimport torchvision\nimport torchvision.transforms as transforms\nimport numpy as np\nfrom PIL import Image\n\ndata_transform = transforms.Compose([\n transforms.Resize(32),\n transforms.CenterCrop(32),\n transforms.ToTensor(),\n transforms.Normalize(\n mean=[0.485, 0.456, 0.406],\n std=[0.229, 0.224, 0.225])\n ])\n\n\ndef pil_loader(path: str) -> Image.Image:\n # open path as file to avoid ResourceWarning (https://github.com/python-pillow/Pillow/issues/835)\n with open(path, \"rb\") as f:\n img = Image.open(f)\n return img.convert(\"RGB\")\n\n\nclass ImbSmallImageNet127(torchvision.datasets.ImageFolder):\n def __init__(self, root, rand_number=0, transform=None, target_transform=None, loader=pil_loader, train=True):\n super(ImbSmallImageNet127, self).__init__(root=root, transform=transform, target_transform=target_transform, loader=loader)\n self.root = root\n np.random.seed(rand_number)\n self.train = train\n \n def __getitem__(self, index):\n path, target = self.samples[index]\n img = self.loader(path)\n if self.train:\n return {'image1':self.transform(img), 'image2': self.transform(img), 'target':target, 'index':index}\n \n return {'image':self.transform(img), 'target': target, 'index':index}","repo_name":"xyk0058/3LPR","sub_path":"Stage1/datasets/imbSmallImageNet127.py","file_name":"imbSmallImageNet127.py","file_ext":"py","file_size_in_byte":1347,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"73"} +{"seq_id":"11924094050","text":"# -*- coding: utf-8 -*-\n\nfrom tasklist.models import TaskList, User\n\n\ndef total_tasklist_context(request):\n context = dict()\n context['user_total_nmb'] = User.objects.count()\n\n site_tasklists = TaskList.objects.all()\n\n total_tasklist_number = site_tasklists.count()\n complete_tasklist_number = site_tasklists.filter(status=True).count()\n earliest_created_tasklist = site_tasklists.earliest('created')\n latest_created_tasklist = site_tasklists.latest('created')\n\n context['ttn'] = total_tasklist_number\n context['ctn'] = complete_tasklist_number\n context['ect'] = earliest_created_tasklist\n context['lct'] = latest_created_tasklist\n\n return context\n\n\ndef user_tasklist_context(request):\n context = dict()\n user = request.user\n\n if user.is_authenticated:\n user_tasklists = TaskList.objects.filter(user=user)\n if user_tasklists.count() != 0:\n u_total_tasklist_number = user_tasklists.count()\n u_complete_tasklist_number = user_tasklists.filter(status=True).count()\n u_earliest_created_tasklist = user_tasklists.earliest('created')\n u_latest_created_tasklist = user_tasklists.latest('created')\n\n context['u_ttn'] = u_total_tasklist_number\n context['u_ctn'] = u_complete_tasklist_number\n context['u_ect'] = u_earliest_created_tasklist\n context['u_lct'] = u_latest_created_tasklist\n\n return context\n","repo_name":"BakinSergey/todolist","sub_path":"tasklist/context_processors.py","file_name":"context_processors.py","file_ext":"py","file_size_in_byte":1443,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"73"} +{"seq_id":"42896880477","text":"import pytorch_lightning as pl\nimport torch\nfrom src.model.nets import Seq2Seq\nfrom src.metrics.loss import custom_metric, uncertainty_metric\n\n\nclass RNNModel(pl.LightningModule):\n\n def __init__(self, input_dim, hidden_dim, num_layers, lr=1e-2):\n super().__init__()\n self.lr = lr\n\n self.save_hyperparameters()\n\n self.model = Seq2Seq(input_dim, hidden_dim, num_layers)\n\n self.prediction_loss_fc = custom_metric\n self.confidence_loss_fc = uncertainty_metric\n\n def forward(self, temp_features, num_features, cat_features, y):\n return self.model(temp_features, num_features, cat_features, y)\n\n def _base_run(self, batch):\n # Unpack batch\n encoder_temp_features = batch[\"encoder_temp_features\"]\n encoder_num_features = batch[\"encoder_num_features\"]\n encoder_cat_features = batch[\"encoder_cat_features\"]\n decoder_temp_features = batch[\"decoder_temp_features\"]\n y = batch[\"y_norm\"]\n avg_12_volume = batch[\"avg_12_volume\"]\n max_volume = batch[\"max_volume\"]\n\n # Permute arrays\n encoder_temp_features = encoder_temp_features.permute(1, 0, 2)\n y = y.permute(1, 0, 2)\n\n # encoder_num_features = encoder_num_features.permute(1, 0)\n encoder_cat_features = encoder_cat_features.permute(1, 0)\n\n # Predict\n y_hat = self(encoder_temp_features,\n encoder_num_features,\n encoder_cat_features,\n y)\n\n # Loss\n pred_loss = self.prediction_loss_fc(actuals=y,\n forecast=y_hat[\"prediction\"],\n max_volume=max_volume,\n avg_volume=avg_12_volume)\n confidence_loss = self.confidence_loss_fc(actuals=y,\n upper_bound=y_hat[\"upper_bound\"],\n lower_bound=y_hat[\"lower_bound\"],\n max_volume=max_volume,\n avg_volume=avg_12_volume)\n\n loss = (0.5 * pred_loss + (1 - 0.5) * confidence_loss)\n\n return loss, pred_loss, confidence_loss\n\n def training_step(self, batch, batch_idx):\n loss, pred_loss, confidence_loss = self._base_run(batch)\n self.log('train/loss', loss)\n self.log('train/prediction_loss', pred_loss)\n self.log('train/confidence_loss', confidence_loss)\n return loss\n\n def validation_step(self, batch, batch_idx):\n loss, pred_loss, confidence_loss = self._base_run(batch)\n self.log('val/loss', loss)\n self.log('val/prediction_loss', pred_loss)\n self.log('val/confidence_loss', confidence_loss)\n return loss\n\n def configure_optimizers(self):\n optimizer = torch.optim.Adam(self.parameters(), lr=self.lr)\n\n lambda1 = lambda epoch: 0.9 ** epoch\n scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer,\n lr_lambda=lambda1)\n\n return [optimizer], [scheduler]\n","repo_name":"dacfortuny/godatathon_2020","sub_path":"src/model/trainer.py","file_name":"trainer.py","file_ext":"py","file_size_in_byte":3162,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"73"} +{"seq_id":"26542261596","text":"\"\"\"Tests for the forms of the ``subscriptions`` app.\"\"\"\nfrom django.test import TestCase\n\nfrom mixer.backend.django import mixer\n\nfrom ..forms import SubscriptionCreateForm, SubscriptionDeleteForm\nfrom ..models import Subscription\n\n\nclass SubscriptionCreateFormTestCase(TestCase):\n \"\"\"Tests for the ``SubscriptionCreateForm`` form class.\"\"\"\n longMessage = True\n\n def test_save(self):\n \"\"\"Should create a new subscription.\"\"\"\n user = mixer.blend('auth.User')\n dummy = mixer.blend('test_app.DummyModel')\n form = SubscriptionCreateForm(user=user, content_object=dummy, data={})\n self.assertTrue(form.is_valid(), msg=(\n 'Errors: {0}'.format(form.errors.items())))\n instance = form.save()\n self.assertTrue(instance.pk)\n\n\nclass SubscriptionDeleteFormTestCase(TestCase):\n \"\"\"Tests for the ``SubscriptionDeleteForm`` form class.\"\"\"\n longMessage = True\n\n def test_save(self):\n \"\"\"Should delete the subscription.\"\"\"\n sub = mixer.blend('subscribe.Subscription',\n content_object=mixer.blend('test_app.DummyModel'))\n form = SubscriptionDeleteForm(\n user=sub.user, content_object=sub.content_object, data={})\n self.assertTrue(form.is_valid(), msg=(\n 'Errors: {0}'.format(form.errors.items())))\n form.save()\n self.assertEqual(Subscription.objects.all().count(), 0)\n\n def test_no_subscription(self):\n \"\"\"\n Should fail graciously if trying to delete a non existant subscription.\n\n \"\"\"\n sub = mixer.blend('subscribe.Subscription',\n content_object=mixer.blend('test_app.DummyModel'))\n form = SubscriptionDeleteForm(\n user=sub.user, content_object=sub.content_object, data={})\n self.assertTrue(form.is_valid(), msg=(\n 'Errors: {0}'.format(form.errors.items())))\n sub.delete()\n form.save()\n self.assertEqual(Subscription.objects.all().count(), 0)\n","repo_name":"bitlabstudio/django-subscribe","sub_path":"subscribe/tests/forms_tests.py","file_name":"forms_tests.py","file_ext":"py","file_size_in_byte":2006,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"73"} +{"seq_id":"13154053430","text":"import logging\nimport os\n\n\nclass QMVerifier(object):\n \"\"\"\n Verifies whether a QM job (externalized) was succesfully completed by \n * searching for specific keywords in the output files, \n * located in a specific directory (e.g. \"QMFiles\")\n \"\"\"\n\n def __init__(self, molfile):\n self.molfile = molfile\n self.gaussianResultExists = False\n self.mopacResultExists = False\n self.mm4ResultExists = False\n\n self.outputExtension = '.out'\n self.inputExtension = '.mop'\n\n def check_for_inchi_key_collision(self, log_file_inchi):\n \"\"\"\n This method is designed in the case a MOPAC output file was found but the InChI found in the file did not\n correspond to the InChI of the given molecule.\n \n This could mean two things:\n 1) that the InChI Key hash does not correspond to the InChI it is hashed from. This is the rarest case of them all\n 2) the complete InChI did not fit onto just one line in the MOPAC output file. Therefore it was continued on the\n second line and only a part of the InChI was actually taken as the 'whole' InChI.\n \n This method reads in the MOPAC input file and compares the found InChI in there to the InChI of the given molecule.\n \"\"\"\n # if InChIPartialMatch == 1:#case where the InChI in memory begins with the InChI in the log file we will continue and check the input file, pring a warning if there is no match\n # look in the input file if the InChI doesn't match (apparently, certain characters can be deleted in MOPAC output file for long InChIs)\n input_file = os.path.join(self.molfile.directory, self.molfile.name + self.inputExtension)\n\n assert os.path.exists(input_file)\n\n with open(input_file) as input_file: # read the MOPAC input_file\n lineI = input_file.readline()\n for line in input_file:\n if line.startswith(\"InChI=\"):\n input_file_inchi = lineI.rstrip()\n break\n\n if input_file_inchi == self.molfile.InChIAug:\n logging.info('An InChI match could be found in the input file, but not in the output file. Anywho, a\\\n pre-existing successful MOPAC result exists.')\n return True\n\n else:\n logging.info(\"*****Warning: potential InChIKey collision: InChIKey(augmented) = \" +\n self.molfile.name + \" RMG Augmented InChI = \" + self.molfile.InChIAug +\n \" Log file Augmented InChI = \" + log_file_inchi +\n \" . InChI could not be found in the MOPAC input file. You should manually check that the output file contains the ended species.\")\n return False\n\n # returns True if an MM4 output file for the given name and directory (.mm4out suffix) exists and indicates successful completion (same criteria as used after calculation runs) terminates if the InChI doesn't match the InChI in the file or if there is no InChI in the file returns False otherwise\n\n def succesful_job_exists(self):\n \"\"\"\n checks whether one of the flags is true.\n If so, it returns true.\n \"\"\"\n return self.gaussianResultExists or self.mopacResultExists or self.mm4ResultExists\n","repo_name":"ReactionMechanismGenerator/RMG-Py","sub_path":"rmgpy/qm/qmverifier.py","file_name":"qmverifier.py","file_ext":"py","file_size_in_byte":3336,"program_lang":"python","lang":"en","doc_type":"code","stars":348,"dataset":"github-code","pt":"73"} +{"seq_id":"19906903157","text":"from distutils.core import setup\nfrom pathlib import Path\n\nthis_directory = Path(__file__).parent\nlong_description = (this_directory / \"README.md\").read_text()\n\nsetup(\n name = 'gold_python',\n packages = ['gold_python'],\n version = '0.1.3',\n long_description=long_description,\n long_description_content_type='text/markdown',\n license='BSD 3-Clause',\n description = 'Library for the developement of finite automata',\n author = 'Nicolas Saavedra Gonzalez',\n author_email = 'personal@nicolassaavedra.com',\n url = 'https://github.com/GOLD-Python/GOLD-Python',\n download_url = 'https://github.com/GOLD-Python/GOLD-Python/archive/refs/tags/v012-BETA.zip',\n keywords = ['automata', 'gold', 'transducer', 'pushdown', 'deterministic'],\n install_requires=[\n 'networkx',\n 'anytree',\n 'graphviz'\n ],\n classifiers=[\n 'Development Status :: 4 - Beta',\n 'Intended Audience :: Developers',\n 'Topic :: Software Development :: Build Tools',\n 'License :: OSI Approved :: BSD License',\n 'Programming Language :: Python :: 3.10',\n 'Programming Language :: Python :: 3.11',\n ],\n)\n","repo_name":"GOLD-Python/GOLD-Python","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1122,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"73"} +{"seq_id":"6586789726","text":"# coding: utf-8\n\nfrom django import template\n\n\nregister = template.Library()\n\n\n@register.simple_tag\ndef url_replace(request, field, value):\n dict_ = request.GET.copy()\n dict_[field] = value\n return '%s?%s' % (request.META['PATH_INFO'], dict_.urlencode())\n","repo_name":"AnYeMoWang/bnu","sub_path":"apps/trade/templatetags/url_replace.py","file_name":"url_replace.py","file_ext":"py","file_size_in_byte":264,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"73"} +{"seq_id":"16628904128","text":"# 1.4 Übung Taxifahrt\n# Schreiben Sie eine Funktion die den Preis für eine Taxifahrt berechnet:\n# \n# Pro Fahrt wird ein Startpreis von 8.-CHF am Tag und 10.-CHF in der Nacht berechnet.\n# Pro gefahrener Kilometer werden 3.-CHF am Tag und 4.-CHF in der Nacht berechnet.\n# Die ersten 2 Kilometer sind im Startpreis inbegriffen.\n# \n# Die Funktion soll dabei folgende Argumente entgegen nehmen:\n# Tageszeit (“Tag” oder “Nacht”)\n# Die Strecke in Kilometer\n# \n# Erweitern Sie die Funktion sodass sie den Benutzer nach den Daten abfragt,\n# also ohne Argumente aufgerufen werden muss.\n\n\ndef taxifahrt(tageszeit,strecke):\n if tageszeit == \"Tag\":\n preis = 8 + (strecke - 2)* 3\n if tageszeit == \"Nacht\":\n preis = 10 + (strecke - 2)* 4 \n return preis\n\ntageszeit = input(\"War die Fahrt am Tag oder in der Nacht? \")\nstrecke = int(input(\"Wie lang war die Strecke (in km)? \"))\nprint(\"Kosten in CHF:\", taxifahrt(tageszeit,strecke))\n\n","repo_name":"iorala/PYTH1","sub_path":"1.4_Folien_Uebung Taxifahrt.py","file_name":"1.4_Folien_Uebung Taxifahrt.py","file_ext":"py","file_size_in_byte":948,"program_lang":"python","lang":"de","doc_type":"code","stars":1,"dataset":"github-code","pt":"73"} +{"seq_id":"14363703395","text":"import numpy as np\nimport cv2\nfrom GMM import GMM\n\n'''\n@brief \n'''\nclass BuoyDetector:\n yellowGMM = GMM()\n orangeGMM = GMM()\n greenGMM = GMM()\n \n\n def __init__(self, yellowGMMParams, orangeGMMParams, greenGMMParams):\n self.yellowGMM.load(yellowGMMParams)\n self.orangeGMM.load(orangeGMMParams)\n self.greenGMM.load(greenGMMParams)\n \n\n def detectBuoys(self, frame):\n image = frame.copy()\n\n height = image.shape[0]\n width = image.shape[1]\n\n #print('BP1')\n\n yellowMask = np.zeros((height, width)).astype('uint8')\n orangeMask = np.zeros((height, width)).astype('uint8')\n greenMask = np.zeros((height, width)).astype('uint8')\n #print('BP2')\n\n yellowErrors = self.yellowGMM.getLogLikelihoodError(np.reshape(image, (height*width, 3)))\n orangeErrors = self.orangeGMM.getLogLikelihoodError(np.reshape(image, (height*width, 3)))\n greenErrors = self.greenGMM.getLogLikelihoodError(np.reshape(image, (height*width, 3)))\n\n yellowErrors = np.reshape(yellowErrors, (height, width))\n orangeErrors = np.reshape(orangeErrors, (height, width))\n greenErrors = np.reshape(greenErrors, (height, width))\n\n for i in range(height):\n for j in range(width):\n yellowError = yellowErrors[i,j]\n orangeError = orangeErrors[i,j]\n greenError = greenErrors[i,j]\n\n if (yellowError > 11 and orangeError > 14 and greenError > 12.5):\n continue\n elif (yellowError == min(yellowError, orangeError, greenError)):\n yellowMask[i,j] = 255\n elif (orangeError == min(yellowError, orangeError, greenError)):\n orangeMask[i,j] = 255\n elif (greenError == min(yellowError, orangeError, greenError)):\n greenMask[i,j] = 255\n\n yellowMask = cv2.erode(yellowMask, None, iterations=1)\n yellowMask = cv2.dilate(yellowMask, None, iterations=2)\n\n orangeMask = cv2.erode(orangeMask, None, iterations=1)\n orangeMask = cv2.dilate(orangeMask, None, iterations=2)\n\n greenMask = cv2.erode(greenMask, None, iterations=1)\n greenMask = cv2.dilate(greenMask, None, iterations=2)\n\n yellowContours, hierarchy = cv2.findContours(yellowMask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n orangeContours, hierarchy = cv2.findContours(orangeMask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n greenContours, hierarchy = cv2.findContours(greenMask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n\n if (len(yellowContours) != 0):\n maxContour = max(yellowContours, key = cv2.contourArea)\n center, radius = cv2.minEnclosingCircle(maxContour)\n cv2.circle(image, (int(center[0]), int(center[1])), int(radius), \\\n color=(0, 255, 255), thickness=2)\n cv2.circle(image, (int(center[0]), int(center[1])), 1, \\\n color=(0, 0, 255), thickness=1)\n #cv2.drawContours(image, [maxContour], contourIdx=-1, color=(0, 255, 255), thickness=2)\n\n if (len(orangeContours) != 0):\n maxContour = max(orangeContours, key = cv2.contourArea)\n center, radius = cv2.minEnclosingCircle(maxContour)\n cv2.circle(image, (int(center[0]), int(center[1])), int(radius), \\\n color=(0, 125, 255), thickness=2)\n cv2.circle(image, (int(center[0]), int(center[1])), 1, \\\n color=(0, 0, 255), thickness=1)\n #cv2.drawContours(image, [maxContour], contourIdx=-1, color=(0, 125, 255), thickness=2)\n\n if (len(greenContours) != 0):\n maxContour = max(greenContours, key = cv2.contourArea)\n center, radius = cv2.minEnclosingCircle(maxContour)\n cv2.circle(image, (int(center[0]), int(center[1])), int(radius), \\\n color=(0, 255, 0), thickness=2)\n cv2.circle(image, (int(center[0]), int(center[1])), 1, \\\n color=(0, 0, 255), thickness=1)\n #cv2.drawContours(image, [maxContour], contourIdx=-1, color=(0, 255, 0), thickness=2)\n \n return image\n \n\n def runApplication(self, videoFile, saveVideo=False):\n # Create video stream object\n videoCapture = cv2.VideoCapture(videoFile)\n \n # Define video codec and output file if video needs to be saved\n if (saveVideo == True):\n fourcc = cv2.VideoWriter_fourcc(*'mp4v')\n # 720p 30fps video\n out = cv2.VideoWriter('BuoyDetection.mp4', fourcc, 30, (1280, 720))\n\n # Continue to process frames if the video stream object is open\n while(videoCapture.isOpened()):\n ret, frame = videoCapture.read()\n\n # Continue processing if a valid frame is received\n if ret == True:\n newFrame = self.detectBuoys(frame)\n\n # Save video if desired, resizing frame to 720p\n if (saveVideo == True):\n out.write(cv2.resize(newFrame, (1280, 720)))\n \n # Display frame to the screen in a video preview\n cv2.imshow(\"Frame\", cv2.resize(newFrame, (1280, 720)))\n\n # Exit if the user presses 'q'\n if cv2.waitKey(1) & 0xFF == ord('q'):\n break\n \n # If the end of the video is reached, wait for final user keypress and exit\n else:\n cv2.waitKey(0)\n break\n \n # Release video and file object handles\n videoCapture.release()\n if (saveVideo == True):\n out.release()\n \n print('Video and file handles closed')\n\n\n\nif __name__ == '__main__':\n yellowGMMParams = 'trained_parameters/yellowGMM.npz'\n orangeGMMParams = 'trained_parameters/orangeGMM.npz'\n greenGMMParams = 'trained_parameters/greenGMM.npz'\n\n # Select video file and ID of the desired tag to overlay cube on\n videoFile = 'test_set/testVideo.avi'\n\n # Choose whether or not to save the output video\n saveVideo = False\n\n # Run application\n buoyDetector = BuoyDetector(yellowGMMParams, orangeGMMParams, greenGMMParams)\n buoyDetector.runApplication(videoFile, saveVideo)\n\n '''\n image = cv2.imread('test_set/buoys.png')\n output = buoyDetector.detectBuoys(image)\n cv2.imshow('image', output)\n cv2.waitKey(0)\n '''\n","repo_name":"kdglider/buoy_detection","sub_path":"BuoyDetector.py","file_name":"BuoyDetector.py","file_ext":"py","file_size_in_byte":6443,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"73"} +{"seq_id":"35910251159","text":"\"\"\"Provides a class that represents an iTerm2 tab.\"\"\"\nimport abc\nimport enum\nimport json\nimport typing\n\nimport iterm2.api_pb2\nimport iterm2.capabilities\nimport iterm2.rpc\nimport iterm2.session\nimport iterm2.util\n\n\nclass NavigationDirection(enum.Enum):\n \"\"\"Cardinal directions.\"\"\"\n LEFT = \"left\"\n RIGHT = \"right\"\n ABOVE = \"above\"\n BELOW = \"below\"\n\n\nclass Tab:\n \"\"\"Represents a tab.\n\n Don't create this yourself. Instead, use :class:`~iterm2.App`.\"\"\"\n\n # pylint: disable=too-few-public-methods\n class Delegate:\n \"\"\"Delegate for Tab.\"\"\"\n @abc.abstractmethod\n def tab_delegate_get_window(\n self, tab: 'Tab') -> typing.Optional['iterm2.window.Window']:\n \"\"\"Returns the Window for a Tab.\"\"\"\n\n @abc.abstractmethod\n async def tab_delegate_get_window_by_id(\n self,\n window_id: str) -> typing.Optional['iterm2.window.Window']:\n \"\"\"Returns the Window with the given ID.\"\"\"\n # pylint: enable=too-few-public-methods\n\n delegate: typing.Optional[Delegate] = None\n\n # pylint: disable=too-many-arguments\n def __init__(\n self,\n connection,\n tab_id,\n root,\n tmux_window_id=None,\n tmux_connection_id=None,\n minimized_sessions=[]):\n self.connection = connection\n self.__tab_id = tab_id\n self.__root = root\n self.__active_session_id = None\n self.__tmux_window_id = tmux_window_id\n self.__tmux_connection_id = tmux_connection_id\n self.__minimized_sessions = list(minimized_sessions)\n # pylint: enable=too-many-arguments\n\n def __repr__(self):\n return \"\" % (self.__tab_id, self.sessions)\n\n @property\n def active_session_id(self):\n return self.__active_session_id\n\n @active_session_id.setter\n def active_session_id(self, active_session_id):\n self.__active_session_id = active_session_id\n\n def update_from(self, other):\n \"\"\"Copies state from another tab into this one.\"\"\"\n self.__root = other.root\n self.__minimized_sessions = list(other.minimized_sessions)\n\n def update_session(self, session):\n \"\"\"Replaces references to a session.\"\"\"\n if self.__root.update_session(session):\n return\n indexes = [idx for idx, candidate in enumerate(self.__minimized_sessions) if candidate.session_id == session.session_id]\n if len(indexes) == 0:\n return\n i = indexes[0]\n self.__minimized_sessions[i] = session\n\n @property\n def window(self) -> typing.Optional['iterm2.window.Window']:\n \"\"\"Returns the window this tab belongs to.\"\"\"\n assert self.__class__.delegate\n return self.__class__.delegate.tab_delegate_get_window(self)\n\n @property\n def tmux_connection_id(self):\n \"\"\"Returns the connection ID.\"\"\"\n return self.__tmux_connection_id\n\n @property\n def tab_id(self) -> str:\n \"\"\"\n Each tab has a globally unique identifier.\n\n :returns: The tab's identifier, a string.\n \"\"\"\n return self.__tab_id\n\n @property\n def sessions(self) -> typing.List['iterm2.session.Session']:\n \"\"\"\n A tab contains a list of sessions, which are its split panes.\n\n This excludes minimized sessions. Use `all_sessions` to get both visible\n and minimized sessions in this tab.\n\n :returns: The sessions belonging to this tab, in no particular order.\n \"\"\"\n return self.__root.sessions\n\n @property\n def all_sessions(self) -> typing.List['iterm2.session.Session']:\n \"\"\"\n Returns both visible and minimized sessions in this tab.\n\n A session would be minimized if another session in the tab is maximized.\n\n :returns: All sessions in this tab, including minimized sessions, in no\n particular order.\n \"\"\"\n return self.sessions + self.minimized_sessions\n\n @property\n def root(self) -> iterm2.session.Splitter:\n \"\"\"\n A tab's sessions are stored in a tree. This returns the root of that\n tree.\n\n An interior node of the tree is a Splitter. That corresponds to a\n collection of adjacent sessions with split pane dividers that are all\n either vertical or horizontal.\n\n Leaf nodes are Sessions.\n\n :returns: The root of the session tree.\n \"\"\"\n return self.__root\n\n @property\n def current_session(self) -> typing.Union[None, iterm2.session.Session]:\n \"\"\"\n :returns: The active session in this tab or `None` if it could not be\n determined.\n \"\"\"\n for session in self.sessions:\n if session.session_id == self.active_session_id:\n return session\n return None\n\n @property\n def minimized_sessions(self) -> typing.List[iterm2.session.Session]:\n \"\"\"\n :returns: Minimized sessions in this tab. Empty array if none.\n \"\"\"\n return list(self.__minimized_sessions)\n\n def pretty_str(self, indent: str = \"\") -> str:\n \"\"\"\n :returns: A human readable description of the tab and its sessions.\n \"\"\"\n session = indent + \"Tab id=%s\\n\" % self.tab_id\n session += self.__root.pretty_str(indent=indent + \" \")\n return session\n\n async def async_select(self, order_window_front: bool = True) -> None:\n \"\"\"Deprecated in favor of `async_activate`.\"\"\"\n await self.async_activate(order_window_front)\n\n async def async_activate(self, order_window_front: bool = True) -> None:\n \"\"\"\n Selects this tab.\n\n :param order_window_front: Whether the window this session is in should\n be brought to the front and given keyboard focus.\n\n .. seealso:: Example \":ref:`function_key_tabs_example`\"\n \"\"\"\n await iterm2.rpc.async_activate(\n self.connection,\n False,\n True,\n order_window_front,\n tab_id=self.__tab_id)\n\n async def async_select_pane_in_direction(\n self, direction: NavigationDirection) -> typing.Optional[str]:\n \"\"\"\n Activates a split pane adjacent to the currently selected pane.\n Requires iTerm2 version 3.3.2.\n\n :param direction: Specifies the direction to move. For example, LEFT\n will cause the pane to the left of the currently active one.\n :returns: The ID of the newly selected session ID, or None if there was\n no session in that direction.\n\n :throws: :class:`~iterm2.rpc.RPCException` if something goes wrong.\n \"\"\"\n if not iterm2.capabilities.supports_select_pane_in_direction(\n self.connection):\n raise iterm2.capabilities.AppVersionTooOld()\n\n invocation = iterm2.util.invocation_string(\n \"iterm2.select_pane_in_direction\",\n {\"direction\": direction.value})\n return await iterm2.rpc.async_invoke_method(\n self.connection, self.tab_id, invocation, -1)\n\n async def async_update_layout(self) -> None:\n \"\"\"Adjusts the layout of the sessions in this tab.\n\n Change the `Session.preferred_size` of any sessions you wish to adjust\n before calling this.\n\n :throws: :class:`~iterm2.rpc.RPCException` if something goes wrong.\n \"\"\"\n response = await iterm2.rpc.async_set_tab_layout(\n self.connection, self.tab_id, self.__root.to_protobuf())\n status = response.set_tab_layout_response.status\n # pylint: disable=no-member\n if status == iterm2.api_pb2.SetTabLayoutResponse.Status.Value(\"OK\"):\n return response.set_tab_layout_response\n raise iterm2.rpc.RPCException(\n iterm2.api_pb2.SetTabLayoutResponse.Status.Name(status))\n\n @property\n def tmux_window_id(self) -> typing.Union[None, str]:\n \"\"\"Returns this tab's tmux window id or None.\n\n :returns: A tmux window id or `None` if this is not a tmux integration\n window.\n \"\"\"\n return self.__tmux_window_id\n\n async def async_set_variable(self, name: str, value: typing.Any) -> None:\n \"\"\"\n Sets a user-defined variable in the tab.\n\n See the Scripting Fundamentals documentation for more information on\n user-defined variables.\n\n :param name: The variable's name. Must begin with `user.`.\n :param value: The new value to assign.\n\n :throws: :class:`RPCException` if something goes wrong.\n \"\"\"\n result = await iterm2.rpc.async_variable(\n self.connection,\n sets=[(name, json.dumps(value))],\n tab_id=self.__tab_id)\n status = result.variable_response.status\n # pylint: disable=no-member\n if status != iterm2.api_pb2.VariableResponse.Status.Value(\"OK\"):\n raise iterm2.rpc.RPCException(\n iterm2.api_pb2.VariableResponse.Status.Name(status))\n\n async def async_get_variable(self, name: str) -> typing.Any:\n \"\"\"\n Fetches a tab variable.\n\n See Badges documentation for more information on variables.\n\n :param name: The variable's name.\n\n :returns: The variable's value or `None` if it is undefined.\n\n :throws: :class:`RPCException` if something goes wrong.\n\n .. seealso:: Example \":ref:`sorttabs_example`\"\n \"\"\"\n result = await iterm2.rpc.async_variable(\n self.connection, gets=[name], tab_id=self.__tab_id)\n status = result.variable_response.status\n # pylint: disable=no-member\n if status != iterm2.api_pb2.VariableResponse.Status.Value(\"OK\"):\n raise iterm2.rpc.RPCException(\n iterm2.api_pb2.VariableResponse.Status.Name(status))\n return json.loads(result.variable_response.values[0])\n\n async def async_close(self, force: bool = False) -> None:\n \"\"\"\n Closes the tab.\n\n :param force: If True, the user will not be prompted for a\n confirmation.\n\n :throws: :class:`RPCException` if something goes wrong.\n\n .. seealso:: Example \":ref:`close_to_the_right_example`\"\n \"\"\"\n result = await iterm2.rpc.async_close(\n self.connection, tabs=[self.__tab_id], force=force)\n status = result.close_response.statuses[0]\n # pylint: disable=no-member\n if status != iterm2.api_pb2.CloseResponse.Status.Value(\"OK\"):\n raise iterm2.rpc.RPCException(\n iterm2.api_pb2.CloseResponse.Status.Name(status))\n\n async def async_set_title(self, title: str):\n \"\"\"Changes the tab's title.\n\n This is equivalent to editing the tab's title with the menu item Edit\n Tab Title. The title is an interpolated string.\n\n :param title: The new title. Set it to an empty string to use the\n default value (the current session's title).\n\n :throws: :class:`~iterm2.rpc.RPCException` if something goes wrong.\n \"\"\"\n invocation = iterm2.util.invocation_string(\n \"iterm2.set_title\",\n {\"title\": title})\n await iterm2.rpc.async_invoke_method(\n self.connection, self.tab_id, invocation, -1)\n\n async def async_invoke_function(\n self, invocation: str, timeout: float = -1):\n \"\"\"\n Invoke an RPC. Could be a registered function by this or another script\n of a built-in function.\n\n This invokes the RPC in the context of this tab. Note that most\n user-defined RPCs expect to be invoked in the context of a session.\n Default variables will be pulled from that scope. If you call a\n function from the wrong context it may fail because its defaults will\n not be set properly.\n\n :param invocation: A function invocation string.\n :param timeout: Max number of secondsto wait. Negative values mean to\n use the system default timeout.\n\n :returns: The result of the invocation if successful.\n\n :throws: :class:`~iterm2.rpc.RPCException` if something goes wrong.\n \"\"\"\n response = await iterm2.rpc.async_invoke_function(\n self.connection,\n invocation,\n tab_id=self.tab_id,\n timeout=timeout)\n which = response.invoke_function_response.WhichOneof('disposition')\n if which == 'error':\n # pylint: disable=no-member\n if (response.invoke_function_response.error.status ==\n iterm2.api_pb2.InvokeFunctionResponse.Status.\n Value(\"TIMEOUT\")):\n raise iterm2.rpc.RPCException(\"Timeout\")\n raise iterm2.rpc.RPCException(\"{}: {}\".format(\n iterm2.api_pb2.InvokeFunctionResponse.Status.Name(\n response.invoke_function_response.error.status),\n response.invoke_function_response.error.error_reason))\n return json.loads(\n response.invoke_function_response.success.json_result)\n\n async def async_move_to_window(self) -> 'iterm2.window.Window':\n \"\"\"\n Moves this tab to its own window, provided there are multiple tabs in\n the window it belongs to.\n\n :returns: The new window ID.\n\n :throws: :class:`~iterm2.rpc.RPCException` if something goes wrong.\n \"\"\"\n window_id = await self.async_invoke_function(\n \"iterm2.move_tab_to_window()\")\n assert self.__class__.delegate\n window = await self.__class__.delegate.tab_delegate_get_window_by_id(\n window_id)\n if not window:\n raise iterm2.rpc.RPCException(\n \"No such window {}\".format(window_id))\n return window\n","repo_name":"gnachman/iTerm2","sub_path":"api/library/python/iterm2/iterm2/tab.py","file_name":"tab.py","file_ext":"py","file_size_in_byte":13723,"program_lang":"python","lang":"en","doc_type":"code","stars":14081,"dataset":"github-code","pt":"73"} +{"seq_id":"75047277996","text":"from time import sleep\nimport RPi.GPIO as GPIO\n\ndef TurnServoMotor(open):\n try:\n GPIO.setwarnings(False)\n GPIO.setmode(GPIO.BOARD)\n pin = 33\n GPIO.setup(pin, GPIO.OUT)\n #pulse width moderation, set the frequency\n pwm = GPIO.PWM(pin, 50)\n if (open):\n #Servo motor angle will always be initialise at 90 degree\n pwm.start(11.5)\n sleep(0.2)\n #Turn from 90 degree to 0 degree\n pwm.ChangeDutyCycle(2.0)\n sleep(4)\n #To prevent any jaggering\n pwm.ChangeDutyCycle(0)\n sleep(0.5)\n #Stop sending any pulse width\n pwm.stop()\n #Clean up used pin back to input pin (protection)\n GPIO.cleanup()\n return True\n else:\n pwm.start(11.5)\n sleep(1)\n pwm.stop()\n GPIO.cleanup()\n return True\n return False\n except:\n return False\n","repo_name":"clifL/SmartBinOneMap","sub_path":"ServoMotor.py","file_name":"ServoMotor.py","file_ext":"py","file_size_in_byte":990,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"73"} +{"seq_id":"7099611404","text":"def flip_case(phrase, to_swap):\n \"\"\"Flip [to_swap] case each time it appears in phrase.\n\n >>> flip_case('Aaaahhh', 'a')\n 'aAAAhhh'\n\n >>> flip_case('Aaaahhh', 'A')\n 'aAAAhhh'\n\n >>> flip_case('Aaaahhh', 'h')\n 'AaaaHHH'\n\n \"\"\"\n\n # Converting the to_swap to lowercase and then iterating through the phrase and swapping the case\n # of the letter if it is equal to the to_swap.\n to_swap = to_swap.lower()\n out = \"\"\n\n for ltr in phrase:\n if ltr.lower() == to_swap:\n ltr = ltr.swapcase()\n out += ltr\n\n return out\n","repo_name":"AnkIsAlive/python-ds-practice","sub_path":"11_flip_case/flip_case.py","file_name":"flip_case.py","file_ext":"py","file_size_in_byte":593,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"73"} +{"seq_id":"13974070412","text":"from statemachine import StateMachine, State\nfrom pyeda import inter as eda\n\n\nclass DTrigger(StateMachine):\n locked = State('Locked', initial=True)\n unlocked = State('Unlocked')\n result_Q = 0\n rule = {}\n\n lock = unlocked.to(locked)\n unlock = locked.to(unlocked)\n\n # function for adding rules from pyeda expression\n def add_rule_from_expr(self, pyeda_expr):\n self.rule = {}\n rule_tt = eda.expr2truthtable(pyeda_expr)\n for line in str(rule_tt).splitlines():\n if ':' in line:\n q = \"\".join(line.split(\":\")[-1].strip())\n x2 = \"\".join(line.split()[0].strip())\n x1 = \"\".join(line.split()[1].strip())\n self.rule[x2 + x1] = q\n\n # function for recording new data\n def change_last_value(self, data):\n if self.current_state == self.unlocked:\n self.result_Q = data\n\n # function for checking whether values match a rule\n def check_values(self, values):\n if values in self.rule:\n if self.rule[values] == '1':\n return True\n return False\n\n # function for change state trigger\n def change_state(self, values):\n if self.check_values(values) == True or self.rule == {}:\n if self.current_state != self.unlocked:\n self.unlock()\n else:\n if self.current_state != self.locked:\n self.lock()\n\n # function for working with a trigger\n def run_trigger(self, data, values):\n self.change_state(values)\n self.change_last_value(data)\n return self.result_Q\n\nd_tr = DTrigger()\nresult = d_tr.run_trigger('aaaa', '0') # write data without rules\nprint('result_without_rule=', result) # search result\nX = eda.exprvars('X', 2) # create a rule truthtable\nrule_tt = eda.truthtable(X, '0100')\nprint('rule_table:\\n', rule_tt) # print a rule truthtable\nrule_expr = eda.truthtable2expr(rule_tt) # create a rule expression\n\nd_tr.add_rule_from_expr(rule_expr)\nresult = d_tr.run_trigger('bbbb', '11') # write data with rule and wrong values\nprint('result_with_rule_and_wrong_key=', result) # data not write\nresult = d_tr.run_trigger('bbbb', '01') # write data with rule and good values\nprint('result_with_rule_and_good_key=', result) # data changed\n\n","repo_name":"EnikeevAI/create_state_machine","sub_path":"CreateTrigger.py","file_name":"CreateTrigger.py","file_ext":"py","file_size_in_byte":2345,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"73"} +{"seq_id":"21498021865","text":"#!/usr/bin/python\n\n# autor: Ramon dos Reis Fontes\n# livro: Emulando Redes sem Fio com Mininet-WiFi\n# github: https://github.com/ramonfontes/mn-wifi-book-pt\n\nimport os\n\nfrom mininet.log import setLogLevel, info\nfrom mininet.node import Controller\nfrom mn_wifi.net import Mininet_wifi\nfrom mn_wifi.cli import CLI\nfrom mn_wifi.replaying import ReplayingNetworkConditions\nfrom sys import version_info as py_version_info\n\n\ndef topology():\n \"Create a network.\"\n net = Mininet_wifi( controller=Controller )\n\n info(\"*** Creating nodes\")\n sta1 = net.addStation( 'sta1', mac='00:00:00:00:00:01',\n ip='192.168.0.1/24',\n position='47.28,50,0' )\n sta2 = net.addStation( 'sta2', mac='00:00:00:00:00:02',\n ip='192.168.0.2/24',\n position='54.08,50,0' )\n ap3 = net.addAccessPoint( 'ap3', ssid='ap-ssid3', mode='g',\n channel='1', position='50,50,0' )\n c0 = net.addController('c0', controller=Controller, port=6653)\n\n info(\"*** Configuring wifi nodes\")\n net.configureWifiNodes()\n\n info(\"*** Starting network\")\n net.build()\n c0.start()\n ap3.start( [c0] )\n\n sta1.cmd('iw dev sta1-wlan0 interface add mon0 type monitor &')\n sta1.cmd('ifconfig mon0 up &')\n sta2.cmd('iw dev sta2-wlan0 interface add mon0 type monitor &')\n sta2.cmd('ifconfig mon0 up &')\n if py_version_info < (3, 0):\n sta2.cmd('pushd ~/Downloads; python -m SimpleHTTPServer 80 &')\n else:\n sta2.cmd('pushd ~/Downloads; python -m http.server 80 &')\n\n path = os.path.dirname(os.path.abspath(__file__))\n getTrace(sta1, '%s/replayingNetworkConditions/clientTrace.txt' % path)\n getTrace(sta2, '%s/replayingNetworkConditions/serverTrace.txt' % path)\n\n replayingNetworkConditions.addNode(sta1)\n replayingNetworkConditions.addNode(sta2)\n replayingNetworkConditions(net)\n\n info(\"*** Running CLI\")\n CLI( net )\n\n info(\"*** Stopping network\")\n net.stop()\n\ndef getTrace(sta, file):\n\n file = open(file, 'r')\n raw_data = file.readlines()\n file.close()\n\n sta.time = []\n sta.bw = []\n sta.loss = []\n sta.delay = []\n sta.latency = []\n\n for data in raw_data:\n line = data.split()\n sta.time.append(float(line[0])) #First Column = Time\n sta.bw.append(((float(line[1]))/1000000)/2) #Second Column = BW\n sta.loss.append(float(line[2])) #second Column = LOSS\n sta.latency.append(float(line[3])) #Second Column = LATENCY\n\nif __name__ == '__main__':\n setLogLevel( 'info' )\n topology()\n","repo_name":"ramonfontes/mn-wifi-book-pt","sub_path":"codigos/cap4/replayingNetworkConditions.py","file_name":"replayingNetworkConditions.py","file_ext":"py","file_size_in_byte":2602,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"73"} +{"seq_id":"16961256317","text":"# Example of getting information about the weather of\r\n# a location\r\nimport http.client\r\nimport json\r\nimport sys\r\n\r\n\r\n\r\ncapital_city=input(\"Put a capital city that you would like to know the weather:\")\r\nHOSTNAME = \"www.metaweather.com\"\r\nENDPOINT = \"/api/location/search/?query=\"+capital_city\r\nMETHOD = \"GET\"\r\nheaders = {'User-Agent': 'http-client'}\r\nconn = http.client.HTTPSConnection(HOSTNAME)\r\nconn.request(METHOD, ENDPOINT, None, headers)\r\n# -- Wait for the server's response\r\nr1 = conn.getresponse()\r\n\r\n# -- Print the status\r\n\r\n\r\n# -- Read the response's body and close\r\n# -- the connection\r\nraw = r1.read().decode(\"utf-8\")\r\nconn.close()\r\ninfo = json.loads(raw)\r\nif len(info)==0:\r\n print(\"Sorry but this city doesn´t exist or the web not have information\")\r\n sys.exit()\r\n#print(info)\r\nwoeid=info[0]['woeid']\r\n\r\nENDPOINT = \"/api/location/\"\r\nconn.request(METHOD, ENDPOINT + str(woeid) + '/', None, headers)\r\n\r\n# -- Wait for the server's response\r\nr1 = conn.getresponse()\r\n\r\n# -- Print the status\r\n\r\n# -- Read the response's body and close\r\n# -- the connection\r\ntext_json = r1.read().decode(\"utf-8\")\r\nconn.close()\r\n\r\n# -- Optionally you can print the\r\n# -- received json file for testing\r\n# print(text_json)\r\n\r\n# -- Generate the object from the json file\r\nweather = json.loads(text_json)\r\n\r\n# -- Get the data\r\ntime = weather['time']\r\ntemp0 = weather['consolidated_weather'][0]\r\n\r\ndescription = temp0['weather_state_name']\r\ntemp = temp0['the_temp']\r\nplace = weather['title']\r\nsun_set=weather['sun_set']\r\n\r\nprint()\r\nprint(\"Place: {}\".format(place))\r\nprint(\"Time: {}\".format(time))\r\nprint(\"Sun set time: {}\".format(sun_set))\r\nprint(\"Weather description: {}\".format(description))\r\nprint(\"Current temp: {} degrees\".format(temp))\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"emiliopf17/2018-19-PNE-practices","sub_path":"Session-19/Excercise-2/Excercise-2.py","file_name":"Excercise-2.py","file_ext":"py","file_size_in_byte":1761,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"73"} +{"seq_id":"73384071916","text":"#!/usr/bin/python3\n'''script that finds the X-Request-Id header for URL'''\n\nimport urllib.request\nimport sys\nimport urllib.parse\n\nif __name__ == \"__main__\":\n url = sys.argv[1]\n values = {'email': sys.argv[2]}\n data = urllib.parse.urlencode(values)\n data = data.encode('ascii')\n req = urllib.request.Request(url, data)\n with urllib.request.urlopen(req) as res:\n mess = res.read()\n print(mess.decode())\n","repo_name":"EbubeCode/alx-higher_level_programming","sub_path":"0x11-python-network_1/2-post_email.py","file_name":"2-post_email.py","file_ext":"py","file_size_in_byte":433,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"73"} +{"seq_id":"19490477554","text":"# problem link : https://www.codetree.ai/missions/2/problems/select-segments-without-overlap-2?&utm_source=clipboard&utm_medium=text\n\nn = int(input())\nsegments = [tuple(map(int, input().split())) for _ in range(n)]\ndp = [0] * n\n\nsegments.sort()\n\nfor i in range(n):\n dp[i] = 1\n\n for j in range(i):\n x1_i, _ = segments[i]\n _, x2_j = segments[j]\n\n if x2_j < x1_i:\n dp[i] = max(dp[i], dp[j] + 1)\n\nprint(max(dp))","repo_name":"jaesukpark77/Coding_Test_Study","sub_path":"codetree/INTERMEDIATE LOW/05. DP I/조건에 맞게 선택적으로 전진하는 DP/겹치지 않게 선분 고르기 2.py","file_name":"겹치지 않게 선분 고르기 2.py","file_ext":"py","file_size_in_byte":445,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"73"} +{"seq_id":"37821308155","text":"#! /usr/bin/env\n# Find TCP service for given socket\n\nimport socket\n\ndef find_service_name():\n protocol_name = 'tcp'\n for port in [80, 25]:\n print(\"Port: %s => service name: %s\" %(port, socket.getservbyport(port, protocol_name)))\n\nif __name__ == '__main__':\n find_service_name()","repo_name":"norman-ricky-bennett/python_networking","sub_path":"find_service_by_port.py","file_name":"find_service_by_port.py","file_ext":"py","file_size_in_byte":293,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"29483316818","text":"## https://programmers.co.kr/learn/courses/30/lessons/60057\n\nfrom collections import defaultdict\ndef solution(s):\n if len(s) == 1 : return 1\n answer = len(s)\n for n in range(1, len(s)):\n L = len(s)\n \n key = list()\n for i in range(len(s)):\n if not s[n*i:n*(i+1)]:\n break\n key.append(s[n*i:n*(i+1)])\n dup = [1]\n dup_key = list()\n for idx in range(1,len(key)):\n if key[idx] == key[idx-1] :\n dup[-1] += 1\n else :\n dup.append(1)\n dup_key.append(key[idx-1])\n dup_key.append(key[idx])\n \n tmp = 0\n for word, cnt in zip(dup_key, dup):\n if cnt == 1 :\n tmp += len(word)\n else :\n tmp += (len(str(cnt)) + len(word))\n \n if tmp < answer :\n answer = tmp\n print(dup, dup_key)\n return answer","repo_name":"GyuhoonK/algorithm","sub_path":"kakao/kakao-blind-2020/1st/문자열압축.py","file_name":"문자열압축.py","file_ext":"py","file_size_in_byte":954,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"7510342261","text":"from sqlalchemy.orm import Mapped, mapped_column\nfrom models.base import Base\nfrom models.enums.attribute import Attribute\nfrom models.enums.equipment_slot import EquipmentSlot\n\n\nclass EquipmentStats(Base):\n __tablename__ = \"equipment_stats\"\n\n id: Mapped[int] = mapped_column(primary_key=True)\n power: Mapped[int]\n precision: Mapped[int]\n toughness: Mapped[int]\n vitality: Mapped[int]\n\n concentration: Mapped[int]\n condition_damage: Mapped[int]\n expertise: Mapped[int]\n ferocity: Mapped[int]\n healing_power: Mapped[int]\n\n def __init__(self):\n super().__init__()\n self.power = 1000\n self.precision = 1000\n self.toughness = 1000\n self.vitality = 1000\n\n self.concentration = 0\n self.condition_damage = 0\n self.expertise = 0\n self.ferocity = 0\n self.healing_power = 0\n\n @property\n def boon_duration(self):\n return round(self.concentration / 1500, 4)\n\n @property\n def critical_chance(self):\n return round((self.precision - 1000) / 2100 + 0.05, 4)\n\n @property\n def critical_damage(self):\n return round(self.ferocity / 1500 + 1.5, 4)\n\n @property\n def condition_duration(self):\n return round(self.expertise / 1500, 4)\n\n def add_attribute(self, attribute: str, value: int) -> None:\n try:\n attribute = Attribute[attribute]\n setattr(self, attribute.value, getattr(self, attribute.value) + value)\n except KeyError:\n pass\n\n def add_attributes(self, slot: EquipmentSlot, *, stats: dict = None, infix_upgrade: dict = None, multiplier: int = 1) -> None:\n # Skip weapons in second weapon set to prevent duplicate stats\n if slot in EquipmentSlot.get_weapon_slots()[2:]:\n return\n\n if stats:\n if \"attributes\" not in stats:\n raise Exception(\"No attributes in stats\")\n for attribute, value in stats[\"attributes\"].items():\n self.add_attribute(attribute, int(value) * multiplier)\n\n if infix_upgrade:\n if \"attributes\" not in infix_upgrade:\n raise Exception(\"No attributes in infix_upgrade\")\n for attribute in infix_upgrade[\"attributes\"]:\n self.add_attribute(attribute[\"attribute\"], attribute[\"modifier\"] * multiplier)\n\n def calculate_attributes(self, slot: EquipmentSlot, attributes: list, attribute_adjustment: int = None):\n # Skip weapons in second weapon set to prevent duplicate stats\n if slot in EquipmentSlot.get_weapon_slots()[2:]:\n return\n\n for attribute in attributes:\n self.add_attribute(attribute[\"attribute\"],\n attribute[\"value\"] + round(attribute[\"multiplier\"] * attribute_adjustment))\n\n def to_dict(self):\n return {\"Power\": self.power, \"Precision\": self.precision, \"Toughness\": self.toughness,\n \"Vitality\": self.vitality, \"Concentration\": self.concentration,\n \"Condition Damage\": self.condition_damage, \"Expertise\": self.expertise, \"Ferocity\": self.ferocity,\n \"Healing Power\": self.healing_power, \"Boon Duration\": self.boon_duration,\n \"Critical Chance\": self.critical_chance, \"Critical Damage\": self.critical_damage,\n \"Condition Duration\": self.condition_duration}\n\n def __str__(self):\n return f\"Power: {self.power}\\nPrecision: {self.precision}\\nToughness: {self.toughness}\\n\" \\\n f\"Vitality: {self.vitality}\\nConcentration: {self.concentration}\\n\" \\\n f\"Condition Damage: {self.condition_damage}\\nExpertise: {self.expertise}\\n\" \\\n f\"Ferocity: {self.ferocity}\\nHealing Power: {self.healing_power}\\n\" \\\n f\"Boon Duration: {self.boon_duration}\\nCritical Chance: {self.critical_chance}\\n\" \\\n f\"Critical Damage: {self.critical_damage}\\nCondition Duration: {self.condition_duration}\"\n","repo_name":"Taflaxx/crossroads-inn-bot","sub_path":"src/models/stats.py","file_name":"stats.py","file_ext":"py","file_size_in_byte":3960,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"32713624838","text":"daily_calory = 1700\nrunning_distance = 3\nwalking_steps = 4000\n\nis_calory = daily_calory < 1500\nis_runnig = running_distance >= 4\nis_steps = walking_steps >=10000\n\ncan_loose_weight = (is_calory or is_runnig) and is_steps\nprint(can_loose_weight)","repo_name":"sayinfatih/devopspython","sub_path":"Boolean Tyoe/ex.3.py","file_name":"ex.3.py","file_ext":"py","file_size_in_byte":243,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"38191785625","text":"from loader import load_config, load_data\nimport numpy as np\nimport os\nclass CF_Dataset(object):\n def __init__(self, train_file, test_file, addnoise, noise_scale = 0.0):\n if (not (os.path.isfile(train_file))):\n raise Exception('No Such Training File')\n if (not (os.path.isfile(test_file))):\n raise Exception('No Such Test File')\n self.train_x, self.train_t, self.train_y = load_data(train_file)\n if (addnoise):\n self.train_y += np.random.normal(0, noise_scale, size = self.train_y.shape)\n self.test_x, self.test_t, self.test_y = load_data(test_file)\n self.x = self.train_x\n self.t = self.train_t\n self.y = self.train_y\n def getBasicInfo(self):\n return self.x.shape[0], self.t.shape[1], self.x.shape[1]\n def switch(self, mode):\n mode = mode.lower()\n if (mode[:5] == 'train'):\n self.x = self.train_x\n self.t = self.train_t\n self.y = self.train_y\n elif (mode[:4] == 'test'):\n self.x = self.test_x\n self.t = self.test_t\n self.y = self.test_y\n def getData(self):\n return self.x, self.t, self.y","repo_name":"moonwalker300/vsr","sub_path":"data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":1190,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"85"} +{"seq_id":"18447061814","text":"import sqlite3\r\n \r\n# Connecting to sqlite\r\n# connection object\r\nconnection_obj = sqlite3.connect('IoT.db')\r\n \r\n# cursor object\r\ncursor_obj = connection_obj.cursor()\r\n \r\n# Drop the GEEK table if already exists.\r\ncursor_obj.execute(\"DROP TABLE IF EXISTS charger_status\")\r\ncursor_obj.execute(\"DROP TABLE IF EXISTS charger_enable\")\r\n \r\n# Creating table\r\ntable = \"\"\" CREATE TABLE charger_enable (\r\n id integer primary key autoincrement,\r\n productId INTEGER,\r\n productEnable INTEGER\r\n ); \"\"\"\r\n \r\ncursor_obj.execute(table)\r\n \r\nprint(\"Table is Ready\")\r\n \r\n# Close the connection\r\nconnection_obj.close()","repo_name":"danuihzap/coba-flask","sub_path":"addTable.py","file_name":"addTable.py","file_ext":"py","file_size_in_byte":634,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"36288710156","text":"temp = []\nnumeros = [[], []]\n\nfor c in range(1,8):\n while True:\n try:\n n = int(input(f\"Digite o {c} valor: \"))\n break\n except ValueError:\n print(\"Valor invalido. Tente novamente\")\n if n % 2 == 0:\n numeros[0].append(n)\n elif n % 2 == 1:\n numeros[1].append(n)\nprint(numeros)\nnumeros[0].sort()\nnumeros[1].sort()\n\nprint(f\"Os numeros pares foram:\")# {numeros[0]}\")\nfiltro = []\nfor c in numeros[0]:\n if c not in filtro:\n filtro.append(c)\n print(c, end=\" \")\ndel filtro[0:] \nprint(f\"\\nOs números impares foram:\")# {numeros[1]}\")\nfor c in numeros[1]:\n if c not in filtro:\n filtro.append(c)\n print(c, end=\" \")\n","repo_name":"JoaoPa33/curso_python_guanabara","sub_path":"python_guanabara/exercicio85.py","file_name":"exercicio85.py","file_ext":"py","file_size_in_byte":709,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"14209472699","text":"import numpy as np\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\nclass Rule(nn.Module):\n def __init__(self, HK_MAX, RADIUS=2):\n super().__init__()\n self.radius = RADIUS\n self.Hk_max = HK_MAX\n Rk = 2*RADIUS + 1\n\n nearest_neighbours = torch.zeros(1, 1, Rk, Rk).cuda()\n nearest_neighbours[:, :, RADIUS, :] = 1.\n nearest_neighbours[:, :, :, RADIUS] = 1.\n nearest_neighbours[:, :, RADIUS, RADIUS] = 0\n\n self.nearest_neighbours = nn.Parameter(nearest_neighbours, requires_grad=False)\n\n def forward(self, x):\n\n Rk = self.radius\n s = x[:, [0], ...]\n s_pad = F.pad(s, (Rk, Rk, Rk, Rk), mode='circular')\n momentum = x[:, [1], ...]\n Js = F.conv2d(s_pad, self.nearest_neighbours, padding=0)\n delta_e = 2 * s * Js\n\n Hk_new = momentum - delta_e\n conserve_flag = torch.logical_and(Hk_new >= 0., Hk_new <= self.Hk_max)\n dropout_mask = (torch.rand_like(x[0, 0]) > 0.75).unsqueeze(0).unsqueeze(0)\n\n flip_flag = torch.logical_and(conserve_flag, dropout_mask)\n flip_spin = -2. * flip_flag + 1\n\n s_new = x[:, [0], ...] * flip_spin\n momentum = torch.where(flip_flag, Hk_new, momentum)\n\n return torch.cat([s_new, momentum], axis=1)\n\nclass isingCA(nn.Module):\n def __init__(self, HK_MAX, RADIUS=1):\n super().__init__()\n self.radius = RADIUS\n\n self.rule = Rule(HK_MAX, RADIUS)\n\n def initGrid(self, shape, init_order=None, init_energy=None):\n if init_energy is None:\n init_energy = self.rule.Hk_max\n if init_order is None:\n init_order = 0.5\n\n rand_spin = (torch.rand(1, 1, shape[0], shape[1]) > init_order) * 2. - 1.\n rand_momentum = torch.rand_like(rand_spin) * np.clip(init_energy, None, self.rule.Hk_max)\n\n return torch.cat([rand_spin, rand_momentum], axis=1).cuda()\n\n def forward(self, x):\n return self.rule(x)\n","repo_name":"heysoos/ising-CA","sub_path":"isingCreutzCA.py","file_name":"isingCreutzCA.py","file_ext":"py","file_size_in_byte":1974,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"40800986020","text":"\"\"\"Computes the following matrix operations:\n addition: __add__\n subtraction: __sub__\n multiplication: __mul__\n exponentiation: __pow__\n find the inverse: takes a matrix object and returns its inverse\n ALSO:\n Matrices() is a collection of matrices.\n AddMatrix: adds a matrix to the collection-object. \n \"\"\"\n\nclass Matrix(object):\n\n def __init__(self, rows):\n \"\"\" Creates a matrix object. Rows is a list L containing lists li. Each li represents a row in a matrix, represented by L.\"\"\"\n length = len(rows[0])\n for item in rows[1:]:\n if len(item) != length:\n print(\"Your rows are of unequal length. Not a real matrix!\")\n return None\n self.matrix = rows\n\n def PrintMatrix(self):\n \"\"\"returns a string for printing; prints out the matrix object with one row on top of another so that it LOOKS LIKE a matrix\n pre: Matrix object\n post: returns a string representation of self\"\"\"\n matrix = self.matrix\n for row in matrix:\n print(row)\n\n def FindEntry(self, t):\n \"\"\" t is a tuple. Finds the element (row, column) in a matrix object. \"\"\"\n r, c = t\n return self.matrix[r-1][c-1]\n\n def __add__(self, other):\n\n m1Rows = len(self.matrix)\n m1Cols = len(self.matrix[0])\n m2Rows = len(other.matrix)\n m2Cols = len(other.matrix[0])\n\n m3 = []\n\n if m1Rows != m2Rows or m1Cols != m2Cols:\n print(\"Your matrices aren't the same size.\")\n return None\n else:\n i = 0\n while i < m1Rows:\n r = []\n j = 0\n while j < m1Cols:\n r.append(self.matrix[i][j] + other.matrix[i][j])\n j += 1\n m3.append(r)\n i += 1\n \n return Matrix(m3)\n \n def __sub__(self, other):\n \"\"\" self - other (self and other are matrix objects) \"\"\"\n m1Rows = len(self.matrix) # number of rows in self.matrix (i.e., its column length)\n m1Cols = len(self.matrix[0]) # length of rows in self.matrix (i.e., its number of columns)\n m2Rows = len(other.matrix) # number of rows in other.matrix (i.e., its column length)\n m2Cols = len(other.matrix[0]) # length of rows in other.matrix (i.e., its number of columns)\n\n m3 = []\n\n if m1Rows != m2Rows or m1Cols != m2Cols:\n print(\"Your matrices aren't the same size.\")\n return None\n else:\n i = 0\n while i < m1Rows:\n r = []\n j = 0\n while j < m1Cols:\n r.append(self.matrix[i][j] - other.matrix[i][j])\n j += 1\n m3.append(r)\n i += 1\n \n return Matrix(m3) \n\n def __mul__(self, other):\n \"\"\" self * other (self and other are either matrix objects or scalars) [THAT'S WHAT IT WAS SUPPOSED TO DO; ACTUALLY ONLY WORKS IF SELF AND OTHER ARE BOTH MATRICES] \"\"\"\n m1Rows = len(self.matrix) # number of rows in self.matrix (i.e., its column length)\n m1Cols = len(self.matrix[0]) # length of rows in self.matrix (i.e., its number of columns)\n m2Rows = len(other.matrix) # number of rows in other.matrix (i.e., its column length)\n m2Cols = len(other.matrix[0]) # length of rows in other.matrix (i.e., its number of columns)\n\n m3 = [] # this list will eventually be returned as the product of self.matrix and other.matrix\n \n if m1Cols != m2Rows:\n print(\"The length of the rows of your first matrix doesn't equal the number of rows in your second.\")\n print(\"Therefore, the mutliplication is undefined.\")\n return None\n elif isinstance(self.matrix, int) or isinstance(self.matrix, float): # in these 2 elif-clauses I try to enable the function to handle multiplying a matrix by a number. \n for row in other.matrix: # However, the test said that * was unsupported for combining an int and a matrix object.\n r = []\n for item in row:\n r.append(self.matrix * item)\n m3.append(r)\n m = Matrix(m3)\n return m\n elif isinstance(other.matrix, int) or isinstance(other.matrix, float):\n for row in self.matrix:\n r = []\n for item in row:\n r.append(item * other.matrix)\n m3.append(r)\n m = Matrix(m3)\n return m\n else: # above we computed the answer if self or other was a scalar. Now we compute the answer when they're both matrices.\n j = 0 # marks the row number we're on\n while j < m1Rows:\n r = [] # we're gonna create rows (m1Rows of them) & add them to m3. r is a row we're gonna add.\n c = 0 # c marks the column we're on & also the number of entry in r we're calculating\n while c < m2Cols: # we're calculating m2Cols entries in r\n i = 0\n entry = []\n while i < m1Cols: # we have to multiply m1Cols numbers and then add them up to calculate one entry\n entry.append(self.matrix[j][i] * other.matrix[i][c])\n i += 1\n r.append(sum(entry))\n c += 1\n m3.append(r)\n j += 1\n m = Matrix(m3)\n return m\n\n def __str__(self):\n return str(self.matrix)\n\n \n\n def __pow__(self, other):\n \"\"\" pow(self, other), where self is a matrix object and other is the exponent (>0) we're taking self to).\n WEIRD: PASSES THE UNITTEST, BUT WON'T WORK IN IDLE. \"\"\"\n ans = self.matrix\n i = 1\n while i < n:\n ans = ans * self.matrix\n i += 1\n answ = Matrix(ans)\n return answ\n\n \n\n''' \n\n #def inverse(self):\n \"\"\" Self is a matrix object. Returns the inverse matrix of self. \"\"\"\n'''\n \n","repo_name":"d-dodd/Fibonacci-Matrices","sub_path":"MatrixOperations.py","file_name":"MatrixOperations.py","file_ext":"py","file_size_in_byte":6300,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"42256407834","text":"import torch\nfrom torch.autograd import Function\nfrom torch.autograd import Variable\n\nclass EBLinear(Function):\n # Note that both forward and backward are @staticmethods\n @staticmethod\n # bias is an optional argument\n def forward(ctx, input, weight, bias=None):\n ctx.save_for_backward(input, weight, bias)\n output = input.mm(weight.t())\n if bias is not None:\n output += bias.unsqueeze(0).expand_as(output)\n return output\n\n # This function has only a single output, so it gets only one gradient\n @staticmethod\n def backward(ctx, grad_output):\n input, weight, bias = ctx.saved_variables\n\n ### start EB-SPECIFIC CODE ###\n # Grad output is gradient of output\n\n # print(\"this is a {} linear layer ({})\"\n # .format('pos' if torch.use_pos_weights else 'neg', grad_output.sum().data[0]))\n\n # Enforce that weights are non-negative\n weight = weight.clamp(min=0) if torch.use_pos_weights else weight.clamp(max=0).abs()\n\n input.data = input.data - input.data.min() if input.data.min() < 0 else input.data\n grad_output /= input.mm(weight.t()).abs() + 1e-10 # normalize\n ### stop EB-SPECIFIC CODE ###\n\n grad_input = grad_weight = grad_bias = None\n\n # These needs_input_grad checks are optional and there only to\n # improve efficiency. If you want to make your code simpler, you can\n # skip them. Returning gradients for inputs that don't require it is\n # not an error.\n if ctx.needs_input_grad[0]:\n grad_input = grad_output.mm(weight)\n ### start EB-SPECIFIC CODE ###\n grad_input *= input\n ### stop EB-SPECIFIC CODE ###\n\n if ctx.needs_input_grad[1]:\n grad_weight = grad_output.t().mm(input)\n if bias is not None and ctx.needs_input_grad[2]:\n grad_bias = grad_output.sum(0).squeeze(0)\n\n\n return grad_input, grad_weight, grad_bias","repo_name":"mims-harvard/GraphXAI","sub_path":"graphxai/explainers/utils/eb_linear.py","file_name":"eb_linear.py","file_ext":"py","file_size_in_byte":1982,"program_lang":"python","lang":"en","doc_type":"code","stars":121,"dataset":"github-code","pt":"85"} +{"seq_id":"7077448704","text":"import pandas as pd\nfrom sklearn.metrics import mean_absolute_error\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.tree import DecisionTreeRegressor\nfrom sklearn.ensemble import RandomForestRegressor\n\n\ndef train_decision_tree_regressor(X, y):\n train_X, val_X, train_y, val_y = train_test_split(X, y, random_state=0)\n\n max_leaf_nodes_candidates = [10, 20, 50, 100, 200]\n max_leaf_nodes_mae = dict()\n for max_leaf_nodes in max_leaf_nodes_candidates:\n model = DecisionTreeRegressor(max_leaf_nodes=max_leaf_nodes, random_state=0)\n model.fit(train_X, train_y)\n preds_val = model.predict(val_X)\n mae = mean_absolute_error(val_y, preds_val)\n max_leaf_nodes_mae[max_leaf_nodes] = mae\n\n best_max_leaf_nodes = sorted(max_leaf_nodes_mae.items(), key=lambda _item: _item[1])[0][0]\n model = DecisionTreeRegressor(max_leaf_nodes=best_max_leaf_nodes, random_state=0)\n model.fit(X, y)\n predicted = model.predict(X)\n mae = mean_absolute_error(y, predicted)\n return mae\n\n\ndef train_random_forest(X, y):\n model = RandomForestRegressor(random_state=0)\n model.fit(X, y)\n predicted = model.predict(X)\n mae = mean_absolute_error(y, predicted)\n return mae\n\n\ndef main():\n melbourne_file_path = '../../../input/melbourne-housing-snapshot/melb_data.csv'\n melbourne_data = pd.read_csv(melbourne_file_path)\n melbourne_data.describe()\n\n melbourne_data = melbourne_data.dropna(axis=0)\n\n y = melbourne_data.Price\n melbourne_features = ['Rooms', 'Bathroom', 'Landsize', 'BuildingArea', 'YearBuilt', 'Lattitude', 'Longtitude']\n X = melbourne_data[melbourne_features]\n\n mae = train_decision_tree_regressor(X, y)\n print(\"decision tree: {}\".format(mae))\n\n mae = train_random_forest(X, y)\n print(\"random forest: {}\".format(mae))\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"liangxh/kaggle-courses","sub_path":"src/draft/intro/melborne.py","file_name":"melborne.py","file_ext":"py","file_size_in_byte":1863,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"26973627515","text":"from enum import Enum\nfrom fastapi import APIRouter, Depends, Request\nfrom pydantic import BaseModel\nfrom src.api import auth\nimport sqlalchemy\nfrom src import database as db\n\nrouter = APIRouter(\n prefix=\"/carts\",\n tags=[\"cart\"],\n dependencies=[Depends(auth.get_api_key)],\n)\n\nclass search_sort_options(str, Enum):\n customer_name = \"customer_name\"\n item_sku = \"item_sku\"\n line_item_total = \"line_item_total\"\n timestamp = \"timestamp\"\n\nclass search_sort_order(str, Enum):\n asc = \"asc\"\n desc = \"desc\" \n\n@router.get(\"/search/\", tags=[\"search\"])\ndef search_orders(\n customer_name: str = \"\",\n potion_sku: str = \"\",\n search_page: str = \"\",\n sort_col: search_sort_options = search_sort_options.timestamp,\n sort_order: search_sort_order = search_sort_order.desc,\n):\n \"\"\"\n Search for cart line items by customer name and/or potion sku.\n\n Customer name and potion sku filter to orders that contain the \n string (case insensitive). If the filters aren't provided, no\n filtering occurs on the respective search term.\n\n Search page is a cursor for pagination. The response to this\n search endpoint will return previous or next if there is a\n previous or next page of results available. The token passed\n in that search response can be passed in the next search request\n as search page to get that page of results.\n\n Sort col is which column to sort by and sort order is the direction\n of the search. They default to searching by timestamp of the order\n in descending order.\n\n The response itself contains a previous and next page token (if\n such pages exist) and the results as an array of line items. Each\n line item contains the line item id (must be unique), item sku, \n customer name, line item total (in gold), and timestamp of the order.\n Your results must be paginated, the max results you can return at any\n time is 5 total line items.\n \"\"\"\n\n sort_column_str = {\n search_sort_options.customer_name: \"carts.customer_name\",\n search_sort_options.item_sku: \"potion_inventory.sku\",\n search_sort_options.line_item_total: \"(cart_items.quantity * potion_inventory.price)\",\n search_sort_options.timestamp: \"ledger_all.created_at\",\n }.get(sort_col)\n \n if not sort_column_str:\n raise ValueError(\"Invalid sorting column provided.\")\n\n order_direction = \"ASC\" if sort_order == search_sort_order.asc else \"DESC\"\n \n current_page_number = int(search_page) if search_page else 0\n records_offset = current_page_number * 5\n \n sql_query = f\"\"\"\n SELECT\n cart_items.id AS line_item_id,\n potion_inventory.sku AS item_sku,\n carts.customer_name AS customer_name,\n (cart_items.quantity * potion_inventory.price) AS order_total_price,\n ledger_all.created_at AS order_timestamp\n FROM cart_items\n JOIN carts ON carts.cart_id = cart_items.cart_id\n JOIN potion_inventory ON potion_inventory.id = cart_items.potion_id\n LEFT JOIN ledger_all ON ledger_all.potion_id = potion_inventory.id\n WHERE carts.customer_name LIKE %s\n AND potion_inventory.sku LIKE %s\n ORDER BY {sort_column_str} {order_direction}\n LIMIT 5 OFFSET {records_offset}\n \"\"\"\n \n customer_name_filter = f\"%{customer_name}%\" if customer_name else \"%\"\n potion_sku_filter = f\"%{potion_sku}%\" if potion_sku else \"%\"\n\n with db.engine.connect() as connection:\n query_results = connection.execute(sql_query, (customer_name_filter, potion_sku_filter)).fetchall()\n\n formatted_output = [\n {\n \"line_item_id\": row[0],\n \"item_sku\": row[1],\n \"customer_name\": row[2],\n \"line_item_total\": row[3],\n \"timestamp\": row[4].isoformat() if row[4] else None\n }\n for row in query_results\n ]\n\n count_query = f\"\"\"\n SELECT COUNT(*)\n FROM cart_items\n JOIN carts ON carts.cart_id = cart_items.cart_id\n JOIN potion_inventory ON potion_inventory.id = cart_items.potion_id\n WHERE carts.customer_name LIKE %s\n AND potion_inventory.sku LIKE %s\n \"\"\"\n with db.engine.connect() as connection:\n total_record_count = connection.execute(count_query, (customer_name_filter, potion_sku_filter)).scalar()\n\n previous_page_token = str(current_page_number - 1) if current_page_number > 0 else \"\"\n next_page_token = str(current_page_number + 1) if (current_page_number + 1) * 5 < total_record_count else \"\"\n\n return {\n \"previous\": previous_page_token,\n \"next\": next_page_token,\n \"results\": formatted_output\n }\n\n\nclass NewCart(BaseModel):\n customer: str\n\n\n@router.post(\"/\")\ndef create_cart(new_cart: NewCart):\n \"\"\" \"\"\"\n with db.engine.begin() as connection:\n cart_id = connection.execute(sqlalchemy.text(\"\"\"\n INSERT INTO carts (customer)\n VALUES (:customer)\n RETURNING cart_id\n \"\"\"), [{\"customer\": new_cart.customer}]).scalar_one()\n return {'cart_id': cart_id}\n\n\n@router.get(\"/{cart_id}\")\ndef get_cart(cart_id: int):\n \"\"\" \"\"\"\n with db.engine.begin() as connection:\n cart = connection.execute(sqlalchemy.text(\"\"\"\n SELECT * FROM cart_items\n WHERE cart_id = :cart_id\n \"\"\"), [{\"cart_id\": cart_id}])\n return cart\n\n\nclass CartItem(BaseModel):\n quantity: int\n\n\n@router.post(\"/{cart_id}/items/{item_sku}\")\ndef set_item_quantity(cart_id: int, item_sku: str, cart_item: CartItem):\n \"\"\" \"\"\"\n with db.engine.begin() as connection:\n connection.execute(sqlalchemy.text(\"\"\"\n INSERT INTO cart_items (cart_id, quantity, potion_id)\n SELECT :cart_id, :quantity, potion_inventory.id\n FROM potion_inventory WHERE potion_inventory.sku = :item_sku\n \"\"\"), [{\"cart_id\": cart_id, \"quantity\": cart_item.quantity, \"item_sku\": item_sku}])\n \n return \"OK\"\n\n\nclass CartCheckout(BaseModel):\n payment: str\n\n@router.post(\"/{cart_id}/checkout\")\ndef checkout(cart_id: int, cart_checkout: CartCheckout):\n \"\"\" \"\"\"\n with db.engine.begin() as connection:\n total_potions_bought = connection.execute(sqlalchemy.text(\"\"\"\n SELECT SUM(cart_items.quantity)\n FROM cart_items\n JOIN potion_inventory ON potion_inventory.id = cart_items.potion_id\n WHERE cart_id = :cart_id\n \"\"\"), [{\"cart_id\": cart_id}]).scalar_one()\n \n potion_id = connection.execute(sqlalchemy.text(\"\"\"\n SELECT potion_id\n FROM cart_items\n WHERE cart_id = :cart_id\n \"\"\"), [{\"cart_id\": cart_id}]).scalar_one()\n \n curr_num = connection.execute(sqlalchemy.text(\"\"\"\n SELECT SUM(potion_quantity)\n FROM ledger_all\n WHERE potion_id = :potion_id\n \"\"\"), [{\"potion_id\": potion_id}]).scalar_one()\n\n if curr_num < total_potions_bought:\n return {\"total_potions_bought\": 0, \"total_gold_paid\": 0}\n\n\n total_gold_paid = connection.execute(sqlalchemy.text(\"\"\"\n SELECT SUM(cart_items.quantity * potion_inventory.price)\n FROM cart_items\n JOIN potion_inventory ON potion_inventory.id = cart_items.potion_id\n WHERE cart_id = :cart_id\n \"\"\"), [{\"cart_id\": cart_id}]).scalar_one()\n\n connection.execute(sqlalchemy.text(\"\"\"\n INSERT INTO ledger_all(gold_change, potion_id, potion_quantity)\n SELECT :gold, cart_items.potion_id, -:potion_quantity\n FROM cart_items\n WHERE cart_id = :cart_id;\n \"\"\"), [{\"cart_id\": cart_id, \"gold\": total_gold_paid, \"potion_quantity\": total_potions_bought}])\n\n return {\"total_potions_bought\": total_potions_bought, \"total_gold_paid\": total_gold_paid}","repo_name":"Rotih/RoPotions","sub_path":"src/api/carts.py","file_name":"carts.py","file_ext":"py","file_size_in_byte":7806,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"85"} +{"seq_id":"20091111878","text":"# SHETTY_ENDPOINT = \"https://api.sheety.co/3f42fccabe16677986b39a272775566e/flightDeals/prices\"\nimport httplib2\nimport apiclient.discovery\nfrom oauth2client.service_account import ServiceAccountCredentials\n\nCREDENTIALS_FILE = 'mypython.json' # имя файла с закрытым ключом\n\ncredentials = ServiceAccountCredentials.from_json_keyfile_name(CREDENTIALS_FILE,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t ['https://www.googleapis.com/auth/spreadsheets',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t'https://www.googleapis.com/auth/drive'])\nhttpAuth = credentials.authorize(httplib2.Http())\nservice = apiclient.discovery.build('sheets', 'v4', http=httpAuth)\n\n\nclass DataManager:\n\tdef __init__(self):\n\t\tself.credentials = ServiceAccountCredentials.from_json_keyfile_name(CREDENTIALS_FILE,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t ['https://www.googleapis.com/auth/spreadsheets',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t'https://www.googleapis.com/auth/drive'])\n\t\tself.httpAuth = credentials.authorize(httplib2.Http())\n\t\tself.service = apiclient.discovery.build('sheets', 'v4', http=httpAuth)\n\t\tself.spreadsheetId = '1DBpCp6UyPTOx2oQhc7A734u9Y9Wm8vW1VM0yuIN-dXg'\n\t\tself.sheet_update = []\n\n\tdef receive_data(self):\n\t\t\"\"\"Read the data from the sheet\"\"\"\n\t\trange_name = 'prices!A1:C'\n\t\ttable = service.spreadsheets().values().get(spreadsheetId=self.spreadsheetId, range=range_name).execute()\n\t\tself.destination_data = table['values'][1:]\n\t\treturn self.destination_data\n\n\n\tdef update_iatacodes(self):\n\t\tfor element in self.destination_data:\n\t\t\tself.sheet_update.append(element[1])\n\t\trange_update = f'prices!B2:B{len(self.sheet_update)+1}'\n\t\tself.service.spreadsheets().values().batchUpdate(spreadsheetId=self.spreadsheetId,\n\t\t\t\t\t\t\t\t\t\t\t\t\tbody={\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"valueInputOption\": \"USER_ENTERED\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"data\": [\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t{\"range\": range_update,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t \"majorDimension\": \"COLUMNS\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t \"values\": [self.sheet_update]}\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t]\n\t\t\t\t\t\t\t\t\t\t\t\t\t}).execute()\n\n\tdef send_list(self):\n\t\trange_name = 'users!A1:C'\n\t\ttable = service.spreadsheets().values().get(spreadsheetId=self.spreadsheetId, range=range_name).execute()\n\t\tusers = table['values'][1:]\n\t\tusers_list = [element[2] for element in users]\n\t\treturn users_list\n# responce = requests.get(\"https://api.sheety.co/3f42fccabe16677986b39a272775566e/flightDeals/prices\")\n# result = responce.json()\n# responce.raise_for_status()\n# print(responce.text)\n","repo_name":"Alex-Tret/Cheap-flights-finder","sub_path":"data_manager.py","file_name":"data_manager.py","file_ext":"py","file_size_in_byte":2341,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"16576809718","text":"import os\nfrom datetime import datetime, date, timedelta\nfrom tinydb import TinyDB, Query\nsportscribe = __import__(\"sportscribe-python\")\nfrom sportscribe import SportScribe\nfrom dotenv import load_dotenv\n\n\nif not load_dotenv():\n print('cannot find .env file')\n exit()\n\n\n# Setup TinyDB object\ndb = TinyDB(os.path.dirname(os.path.realpath(__file__)) + '/sportscribe.db.json')\n\n# Get number of days to pull data from .env\ntry:\n pull_days = int(os.getenv('SPORTSCRIBE_PULL_DAYS'))\n pull_days = min(pull_days,10)\nexcept e:\n pull_days = 3\n\n# Setup SportScribe object\nss = SportScribe(os.getenv('SPORTSCRIBE_API_KEY'))\nss.setEndpoint(os.getenv('SPORTSCRIBE_ENDPOINT'))\n\n# For each day in range, pull that day's match previews, and store in the fixtures table\nfor i in range(0,pull_days):\n\n pull_time = date.today() + timedelta(days=i)\n pull_date = pull_time.strftime('%Y-%m-%d')\n print('Pulling ', pull_date)\n result = ss.getMatchPreviewByDate(pull_date)\n\n if result:\n for r in result.data:\n try:\n id = r['fixture_id']\n Data = Query()\n if not db.search(Data.id == id):\n print(\"Inserting \" , id)\n start = datetime.strptime(r['start_utc_timestamp'],'%Y-%m-%d %H:%M:%S')\n db.insert({'id':id,'posted':False,'start_time_utc':r['start_utc_timestamp'],'start_timestamp_utc':start.timestamp(),'data':r})\n except:\n print('Error, skipping')\n else:\n print('Error pulling ' , pull_date)\n\n","repo_name":"sportscribe/sportscribe-twitter","sub_path":"sportscribe-twitter-poll.py","file_name":"sportscribe-twitter-poll.py","file_ext":"py","file_size_in_byte":1451,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"21095310395","text":"#! /usr/bin/python\nimport socket\nfrom argparse import ArgumentParser\n\nparser = ArgumentParser(description='tcp scanner, looks for open ports')\nparser.add_argument('--addr', type=str, help='ip или доменное имя', default='localhost')\nparser.add_argument('--bottom', type=int, help='нижнее значение портов', default=1)\nparser.add_argument('--top', type=int, help='верхнее значение портов', default=65535)\n\nargs = parser.parse_args().__dict__\n\naddr = socket.gethostbyname(args['addr'])\nports_range = (args['bottom'], args['top'])\n\nfor port in range(ports_range[0], ports_range[1]):\n sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n socket.setdefaulttimeout(10)\n res = sock.connect_ex((addr, port))\n if res == 0:\n print(f'Доступен порт: {port}')\n sock.close()\n","repo_name":"ut1Puti/inet-protocol","sub_path":"tcp-scanner/tcp-scanner.py","file_name":"tcp-scanner.py","file_ext":"py","file_size_in_byte":853,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"16356668265","text":"from aiogram import types\nfrom keyboards.inline import kb_menu_panel, kb_back\nfrom loader import dp\nfrom aiogram.dispatcher import FSMContext\nfrom time import sleep\nfrom youtubesearchpython import CustomSearch, VideoUploadDateFilter\nfrom utils.misc import rate_limit\n\n\n@dp.callback_query_handler(text='back', state='youtube')\nasync def quit(call: types.CallbackQuery, state: FSMContext):\n await call.answer(cache_time=5)\n await state.finish()\n await call.message.edit_text('Для продолжения воспользуйтесь кнопками меню', reply_markup=kb_menu_panel)\n\n\n@rate_limit(limit=5)\n@dp.callback_query_handler(text='searchyoutube')\nasync def search_youtube(call: types.CallbackQuery, state: FSMContext):\n await call.answer(cache_time=5)\n global msg_youtube\n msg_youtube = await call.message.edit_text(f'Пожалуйста, введите название видео(Выдаст 5 ссылок): ', reply_markup=kb_back)\n await state.set_state(\"youtube\") # --- назначем состояние\n\n\n@dp.message_handler(state=\"youtube\") # ---- получаем стейт youtube (который назаначен выше)\nasync def get_youtube(message: types.Message, state: FSMContext):\n await msg_youtube.delete()\n try:\n customSearch = CustomSearch(f'{message.text.lower()}', VideoUploadDateFilter.thisYear, limit=20)\n for i in range(5):\n search = customSearch.result()['result'][i]['link']\n print(search)\n sleep(1.5)\n await message.answer(f'{search}')\n await state.finish() # ------ Сбрасываем состояние\n except Exception:\n await message.reply(\"Проверьте правильно ли написано название\")\n await state.finish() # ------ Сбрасываем состояние\n\n await message.answer('Поиск видео завершен!\\n'\n 'Воспользуйтесь кнопками меню', reply_markup=kb_menu_panel)\n","repo_name":"archiewh1te/ToolsBot_v2.0","sub_path":"handlers/users/search_youtube.py","file_name":"search_youtube.py","file_ext":"py","file_size_in_byte":2048,"program_lang":"python","lang":"ru","doc_type":"code","stars":2,"dataset":"github-code","pt":"85"} +{"seq_id":"70600619478","text":"import os\nimport re\nimport sys\n\nfrom setuptools import setup, find_packages\n\nhere = os.path.abspath(os.path.dirname(__file__))\n\n__name__ = \"drf-serialization-magic\"\n__description__ = (\n \"Open library for Django Rest Framework to reduce the duplication of code when working with Serializer\"\n)\n__url__ = \"https://github.com/tkppro/drf-serialization-magic\"\n__author__ = \"Thang Dang Minh\"\n__author_email__ = \"thangdangdev@gmail.com\"\n__license__ = \"BSD\"\n\n\ndef get_version(package):\n \"\"\"\n Return package version as listed in `__version__` in `init.py`.\n \"\"\"\n package_dir = os.path.join(here, package)\n init_py = open(os.path.join(package_dir, \"__init__.py\")).read()\n return re.search(\"^__version__ = ['\\\"]([^'\\\"]+)['\\\"]\", init_py, re.MULTILINE).group(\n 1\n )\n\n\ndef get_package():\n return find_packages(exclude=[\"tests*\"])\n\n\ndef read_file(f):\n with open(f, \"r\", encoding=\"utf-8\") as fh:\n long_description = fh.read()\n return long_description\n\n\npackage = get_package()\nversion = get_version(package[0])\n\nif sys.argv[-1] == \"publish\":\n if os.system(\"pip freeze | grep wheel\"):\n print(\"wheel not installed.\\nUse `pip install wheel`.\\nExiting.\")\n sys.exit()\n if os.system(\"pip freeze | grep twine\"):\n print(\"twine not installed.\\nUse `pip install twine`.\\nExiting.\")\n sys.exit()\n os.system(\"python -m build\")\n os.system(\"twine check dist/*\")\n os.system(\"twine upload dist/*\")\n print(\"You probably want to also tag the version now:\")\n print(\" git tag -a %s -m 'version %s'\" % (version, version))\n print(\" git push --tags\")\n sys.exit()\n\n\nsetup(\n name=__name__,\n version=version,\n packages=package,\n description=__description__,\n long_description=read_file(\"README.md\"),\n long_description_content_type=\"text/markdown\",\n author=__author__,\n author_email=__author_email__,\n url=__url__,\n license=__license__,\n python_requires=\">=3.7\",\n install_requires=[\n \"Django>=3.0\",\n ],\n classifiers=[\n \"Development Status :: 5 - Production/Stable\",\n \"Environment :: Web Environment\",\n \"Framework :: Django\",\n \"Intended Audience :: Developers\",\n \"License :: OSI Approved :: BSD License\",\n \"Operating System :: OS Independent\",\n \"Natural Language :: English\",\n \"Programming Language :: Python :: 3\",\n \"Programming Language :: Python :: 3.6\",\n \"Programming Language :: Python :: 3.7\",\n \"Programming Language :: Python :: 3.8\",\n \"Topic :: Internet :: WWW/HTTP\",\n ],\n)\n","repo_name":"tkppro/drf-serialization-magic","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":2580,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"85"} +{"seq_id":"70002619797","text":"from tracemalloc import start\r\nimport pygame\r\nimport math\r\nimport time\r\n\r\n\r\nscreen = pygame.display.set_mode((500, 600))\r\n\r\nrunning = 1\r\n\r\nWHITE = (255, 255, 255)\r\nBLACK = (0, 0, 0)\r\n\r\nposX = 250\r\nposY = 165\r\n\r\nsecs = 0\r\nclockwiseLength = 300 - posY\r\ntotalTime = 0\r\n\r\nwhile running:\r\n startTime = time.time()\r\n screen\r\n pygame.draw.circle(screen, WHITE, (250, 300), 150)\r\n pygame.draw.circle(screen, BLACK, (250, 300), 150 - 2)\r\n pygame.draw.line(screen, WHITE, (250, 300), (posX, posY))\r\n pygame.display.flip()\r\n for event in pygame.event.get():\r\n if event.type == pygame.QUIT:\r\n running = 0\r\n period = time.time() - startTime\r\n totalTime += period\r\n if totalTime >= 1:\r\n secs += 1\r\n alpha = 6 * secs * math.pi / 180\r\n posX = 250 + clockwiseLength * math.sin(alpha)\r\n posY = 300 - clockwiseLength * math.cos(alpha)\r\n totalTime = 0\r\n","repo_name":"FunGi2410/Alarm","sub_path":"program.py","file_name":"program.py","file_ext":"py","file_size_in_byte":916,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"34155731238","text":"from collections import deque\r\n\r\n# Vertex, Edge 입력 받기\r\nv, e = map(int, input().split())\r\n# 모든 노드에 대한 indegree 0으로 초기화\r\nindegree = [0] * (v + 1)\r\n# 각 노드에 연결된 간선 정보를 담기 위한 연결 리스트\r\ngraph = [[] for i in range(v + 1)]\r\n\r\n# 방향 그래프의 모든 간선 정보 입력받기\r\nfor _ in range(e):\r\n a, b = map(int, input().split())\r\n graph[a].append(b) # A -> B 로 이동 가능\r\n indegree[b] += 1 # 진입 차수 올려주기\r\n\r\n\r\ndef topology_sort():\r\n result = []\r\n q = deque()\r\n \r\n # 처음 시작할 때 진입차수가 0인 노드 큐에 삽입\r\n for i in range(1, v + 1):\r\n if indegree[i] == 0:\r\n q.append(i)\r\n\r\n # 큐가 빌 때까지 반복\r\n while q:\r\n # 큐에서 원소 꺼내기\r\n now = q.popleft()\r\n result.append(now)\r\n # 해당 원소와 연결된 노드들의 진입차수에서 1 빼기\r\n for i in graph[now]:\r\n indegree[i] -= 1\r\n # 새롭게 진입차수가 0이 되는 노드를 큐에 삽입\r\n if indegree[i] == 0:\r\n q.append(i)\r\n\r\n for i in result:\r\n print(i, end=' ')\r\n\r\ntopology_sort()","repo_name":"hyukjinKimm/CodingTest-template","sub_path":"위상정렬.py","file_name":"위상정렬.py","file_ext":"py","file_size_in_byte":1222,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"71714740118","text":"class InterfacePortChannelApi():\n def __init__(self):\n self.interfaces_pc_mo = {}\n\n def get_interface_port_channels_mo(self, pod_id, node_id):\n key = '%s.%s' % (pod_id, node_id)\n if key in self.interfaces_pc_mo:\n return self.interfaces_pc_mo[key]\n\n cache = self.get_object_cache(\n 'pcAggrIf',\n object_selector=key\n )\n if cache is not None:\n self.interfaces_pc_mo[key] = cache\n self.log.apic_mo(\n 'pcAggrIf.%s' % (key),\n self.interfaces_pc_mo[key]\n )\n return self.interfaces_pc_mo[key]\n\n class_name = 'topology/pod-%s/node-%s/pcAggrIf' % (pod_id, node_id)\n query = 'rsp-subtree=children&rsp-subtree-include=health,fault-count,required'\n managed_objects = self.get_class(\n class_name,\n query=query\n )\n\n if managed_objects is None:\n self.log.error(\n 'get_interface_port_channels_mo',\n 'API failed'\n )\n return None\n\n self.interfaces_pc_mo[key] = []\n for managed_object in managed_objects['imdata']:\n attributes = managed_object['pcAggrIf']['attributes']\n attributes['ethpmAggrIf'] = self.get_mo_child_attributes(\n 'pcAggrIf',\n managed_object,\n 'ethpmAggrIf',\n include_grandchildren=True\n )\n attributes['rmonIfOut'] = self.get_mo_child_attributes(\n 'pcAggrIf',\n managed_object,\n 'rmonIfOut'\n )\n attributes['rmonIfIn'] = self.get_mo_child_attributes(\n 'pcAggrIf',\n managed_object,\n 'rmonIfIn'\n )\n attributes['rmonEtherStats'] = self.get_mo_child_attributes(\n 'pcAggrIf',\n managed_object,\n 'rmonEtherStats'\n )\n attributes['pcRsMbrIfs'] = self.get_mo_children_attributes(\n 'pcAggrIf',\n managed_object,\n 'pcRsMbrIfs',\n include_grandchildren=True\n )\n attributes['healthInst'] = self.get_mo_child_attributes(\n 'pcAggrIf',\n managed_object,\n 'healthInst'\n )\n attributes['faultCounts'] = self.get_mo_child_attributes(\n 'pcAggrIf',\n managed_object,\n 'faultCounts'\n )\n\n self.interfaces_pc_mo[key].append(\n attributes\n )\n\n self.log.apic_mo(\n 'pcAggrIf.%s' % (key),\n self.interfaces_pc_mo[key]\n )\n\n self.set_object_cache(\n 'pcAggrIf',\n self.interfaces_pc_mo[key],\n object_selector=key\n )\n\n return self.interfaces_pc_mo[key]\n","repo_name":"datacenter/iserver","sub_path":"lib/aci/intf/port_channel/api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":2932,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"85"} +{"seq_id":"75230053076","text":"from flask_wtf import FlaskForm\nfrom wtforms import StringField, SubmitField\nfrom wtforms.validators import DataRequired\n\nclass soilparameters(FlaskForm):\n e = FloatField('e')\n Gs = FloatField('Gs')\n w = FloatField('w')\n S = FloatField('S')\n gammad = FloatField('gammad')\n gammam = FloatField('gammam')\n \n\n\n\n\n\n\n#'e', 'Gs', 'w', 'S', 'gammas', 'gammad', 'gammam'","repo_name":"JoeHu1997/consolidation","sub_path":"flask/effectivestress/form.py","file_name":"form.py","file_ext":"py","file_size_in_byte":382,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"24547564863","text":"from github import Github\nimport os\nfrom pprint import pprint\nfrom github import InputGitTreeElement\n\ntoken = os.getenv('GITHUB_TOKEN', 'f753eb7a2b07d0723e5311c0b814d5fdb8e1f2f4')\ng = Github(token)\n\nrepo = g.get_repo(\"PiyaRathi/myApp\")\nprint(repo)\n\nmaster_ref = repo.get_git_ref('heads/master')\n\nprint(master_ref)\n\nfile_list = [\n 'CrudDemo.py',\n 'Hello.py'\n]\nmaster_sha = master_ref.object.sha\n\nprint(master_sha)\nbase_tree = repo.get_git_tree(master_sha)\nelement_list = list()\nfor entry in file_list:\n with open(entry, 'r') as input_file:\n data = input_file.read()\n element = InputGitTreeElement(entry, '100644', 'blob', data)\n element_list.append(element)\ntree = repo.create_git_tree(element_list, base_tree)\nparent = repo.get_git_commit(master_sha)\ncommit = repo.create_git_commit(\"Test commit\", tree, [parent])\nmaster_ref.edit(commit.sha)\n\n\nprint(\"--File pushed to git repo--\")\n","repo_name":"PiyaRathi/PythonGithubCommitScript","sub_path":"GitPushDemo.py","file_name":"GitPushDemo.py","file_ext":"py","file_size_in_byte":903,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"3991967687","text":"st = ''\n\n\ndef func():\n m1, m2 = n, 0\n c = i = 1\n while i < n:\n if l[i] - l[i - 1] < 3:\n c += 1\n else:\n m1, m2 = min(m1, c), max(m2, c)\n c = 1\n i += 1\n m1, m2 = min(m1, c), max(m2, c)\n return (str(m1) + ' ' + str(m2))\n\n\nfor _ in range(int(input())):\n n = int(input())\n l = list(map(int, input().split()))\n # print(l)\n st += str(func()) + '\\n'\nprint(st)\n","repo_name":"arin17bishwa/myCP_sols","sub_path":"CC/COVID19.py","file_name":"COVID19.py","file_ext":"py","file_size_in_byte":433,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"85"} +{"seq_id":"36011739681","text":"import pickle\nimport string\nfrom sys import argv\nimport sys\nimport xml.etree.ElementTree as et\nfrom pprint import pprint\n\n\nif len(sys.argv) == 1:\n exit\n\nwn_tree = et.parse(sys.argv[1])\nsynsets = {}\ntypes = set()\n\nfor ss_el in wn_tree.getroot().findall(\"SYNSET\"):\n ss_id = ss_el.findtext(\"ID\")\n ss_set = [\n (ss_lit.findtext(\"VAL\"), ss_lit.findtext(\"SENSE\")) for ss_lit in ss_el.find(\"SYNONYM\").findall(\"LITERAL\")\n ]\n ss_hypernyms = [\n ilr_el.findtext(\"VAL\") for ilr_el in ss_el.findall(\"ILR\") if ilr_el.findtext(\"TYPE\") == \"hypernym\"\n ]\n ss_relations = {\n ilr_el.findtext(\"VAL\"):ilr_el.findtext(\"TYPE\") for ilr_el in ss_el.findall(\"ILR\") if ilr_el.findtext(\"TYPE\") == \"hypernym\"\n }\n ss_cpa = [\n cpa_el.text for cpa_el in ss_el.findall(\"CPA\")\n ]\n ss_def = ss_el.findtext(\"DEF\")\n synsets[ss_id] = {\n \"id\": ss_id,\n \"set\": ss_set,\n \"hypernyms\": ss_hypernyms,\n \"cpa\": ss_cpa,\n \"hyponyms\": [],\n \"def\": ss_def,\n \"relations\": ss_relations,\n \"print\": len(ss_hypernyms) > 1\n }\n\nfor synset in synsets.values():\n for hypernym in synset[\"hypernyms\"]:\n if hypernym in synsets:\n synsets[hypernym][\"hyponyms\"].append(synset[\"id\"])\n else:\n print(f\"Hypernym {hypernym} not in set\")\n\nif len(sys.argv) > 2:\n with open(sys.argv[2], \"wb\") as picklejar:\n pickle.dump(synsets, picklejar)\n\npprint(synsets)\n","repo_name":"DCL-IBL/SemNet","sub_path":"Scripts/construct-tree.py","file_name":"construct-tree.py","file_ext":"py","file_size_in_byte":1452,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"4419050823","text":"#! /usr/bin/python\n# -*- coding: utf-8 -*-\n\nimport torch.optim.lr_scheduler as lr_scheduler\n\nfrom . import BaseLRScheduler, register_lr_scheduler\n\n__author__ = 'fyabc'\n\n\n@register_lr_scheduler('reduce_lr_on_plateau')\nclass ReduceLROnPlateau(BaseLRScheduler):\n \"\"\"Decay the LR by a factor every time the validation loss plateaus.\"\"\"\n\n def __init__(self, hparams, optimizer):\n super().__init__(hparams, optimizer)\n if len(hparams.lr) > 1:\n raise ValueError(\n 'Cannot use a fixed learning rate schedule with reduce_lr_on_plateau.'\n ' Consider --lr-scheduler=fixed instead.'\n )\n self.lr_scheduler = lr_scheduler.ReduceLROnPlateau(\n self.optimizer.optimizer, patience=0, factor=hparams.lr_shrink)\n\n def state_dict(self):\n \"\"\"Return the LR scheduler state dict.\"\"\"\n return {\n 'best': self.lr_scheduler.best,\n 'last_epoch': self.lr_scheduler.last_epoch,\n }\n\n def load_state_dict(self, state_dict):\n \"\"\"Load an LR scheduler state dict.\"\"\"\n self.lr_scheduler.best = state_dict['best']\n if 'last_epoch' in state_dict:\n self.lr_scheduler.last_epoch = state_dict['last_epoch']\n\n def step(self, epoch, val_loss=None):\n \"\"\"Update the learning rate at the end of the given epoch.\"\"\"\n if val_loss is not None:\n self.lr_scheduler.step(val_loss, epoch)\n else:\n self.lr_scheduler.last_epoch = epoch\n return self.optimizer.get_lr()\n","repo_name":"renqianluo/NAS4Text","sub_path":"libs/optimizers/lr_schedulers/reduce_lr_on_plateau.py","file_name":"reduce_lr_on_plateau.py","file_ext":"py","file_size_in_byte":1536,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"1986938526","text":"from turtle import *\nimport math\n\ndef makeTriangle(l):\n penup()\n backward(l)\n pendown()\n l = math.sqrt((l/2)**2 + (l/2)**2)\n right(45)\n forward(l)\n left(90)\n forward(l)\n\n\ndef levy(l):\n if l < 1:\n return l\n makeTriangle(l)\n l = math.sqrt((l/2)**2 + (l/2)**2)\n levy(l)\n \n\nforward(100)\nlevy(100)\n\n","repo_name":"Lowity/PAM","sub_path":"levy-c-kurve/levy copy.py","file_name":"levy copy.py","file_ext":"py","file_size_in_byte":340,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"4893166020","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# Examples require an initialized GsSession and relevant entitlements. External clients need to substitute thier own client id and client secret below. Please refer to [Authentication](https://developer.gs.com/p/docs/institutional/platform/authentication/) for details.\n\n# In[1]:\n\n\nfrom gs_quant.session import GsSession\nGsSession.use(client_id=None, client_secret=None, scopes=('read_product_data',))\n\n# ## How to query data \n# The Data APIs support many ways to query datasets to intuitively fetch only the data users need.\n# More details on [Querying Data](https://developer.gs.com/p/docs/services/data/data-access/query-data/) can be found in the documentation\n\n# In[2]:\n\n\nfrom datetime import date, timedelta, datetime\nfrom gs_quant.data import Dataset\nimport pydash\n\n# Data in Marquee is available in the form of Datasets (collections of homogenous data). Each Dataset has a set of entitlements, a fixed schema, and assets in coverage.\n\n# In[3]:\n\n\ndataset_id = 'FXIVOL_STANDARD' # https://marquee.gs.com/s/developer/datasets/FXIVOL_STANDARD\nds = Dataset(dataset_id)\n\n# Data for limited number of assets or spanning a small time frame can be queried in one go by specifying the assets to query and date/time range.\n\n# In[4]:\n\n\nstart_date = date(2019, 1, 15)\nend_date = date(2019, 1, 18)\n\ndata = ds.get_data(start_date, end_date, bbid=['EURCAD'])\ndata.head()\n\n# Instead of a range, one can also specify a set of date/times to get data for just those specific date/times\n\n# In[5]:\n\n\ndata = ds.get_data(dates=[date(2019, 1, 15), date(2019, 1, 18)],\n bbid=['EURCAD'])\ndata.head()\n\n# For larger number of assets or for longer time ranges, \n# we recommend iterating over assets and time to avoid hitting API query limits. \n\n# In[6]:\n\n\n# loop over assets\ndef iterate_over_assets(dataset, coverage, start, end, batch_size=5, query_dimension='assetId', delta=timedelta(days=6)):\n for ids in pydash.chunk(coverage[query_dimension].tolist(), size=batch_size):\n print('iterate over assets', ids)\n iterate_over_time(start, end, ids, dataset, delta=delta, query_dimension=query_dimension)\n\n# loop over time\ndef iterate_over_time(start, end, ids, dataset, delta=timedelta(days=6), query_dimension='assetId'):\n iter_start = start\n while iter_start < end:\n iter_end = min(iter_start + delta, end)\n print('time iteration since', iter_start, 'until', iter_end)\n data = dataset.get_data(iter_start, iter_end, **{query_dimension: ids})\n # Add your code here to make use of fetched data\n \n iter_start = iter_end\n\n# In[7]:\n\n\ndataset_id = 'EDRVOL_PERCENT_V1_STANDARD' # https://marquee.gs.com/s/developer/datasets/EDRVOL_PERCENT_V1_STANDARD \nds = Dataset(dataset_id)\n\ncoverage = ds.get_coverage()\n\niterate_over_assets(ds, coverage, date(2021, 5, 1), date(2021, 5, 31), batch_size=5)\n\n# Similar approach can be used to download all data of a dataset\n\n# In[ ]:\n\n\ncoverage = ds.get_coverage(include_history=True)\ncoverage = coverage.sort_values(by='historyStartDate', axis=0)\nstart_date = datetime.strptime(coverage['historyStartDate'].values[0], '%Y-%m-%d').date()\n\n# warning: long running operation\niterate_over_assets(ds, coverage, start_date, date.today())\n\n","repo_name":"RIMEL-UCA/RIMEL-UCA.github.io","sub_path":"chapters/2023/Qualité logicielle dans les notebooks Jupyter/assets/python-scripts/0000_query_dataset.py","file_name":"0000_query_dataset.py","file_ext":"py","file_size_in_byte":3263,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"85"} +{"seq_id":"41466610451","text":"class Weapon:\n def __init__(self, nm, type):\n self.wname = nm\n self.type = type\n\nclass Role:\n def __init__(self, nm, wp):\n self.name = nm\n self.weapon = wp\n\n def show_me(self):\n print('我是: %s，擅用%s' % (self.name, self.weapon))\n\nif __name__ == '__main__':\n ji = Weapon('方天画戟', '物理攻击')\n lb = Role('吕布', ji)\n print(ji.wname, ji.type)\n print(lb.weapon.wname, lb.weapon.type)\n","repo_name":"MrZhangzhg/nsd2019","sub_path":"nsd1905/py02/day03/myclass2.py","file_name":"myclass2.py","file_ext":"py","file_size_in_byte":456,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"85"} +{"seq_id":"11494229647","text":"import comms\nfrom comms import twoWayConnection\n\nconn = comms.twoWayConnection(comms.twoWayConnection.HostMode.SERVER, \"127.0.0.1::65489\")\nprint(\"hi\")\nngrokKey = conn.get_ngrok_key()\nwhile (ngrokKey is None):\n ngrokKey = conn.get_ngrok_key()\nprint(ngrokKey)\n\ntry:\n while conn.get_state() > twoWayConnection.State.ERROR:\n message = conn.readMessage()\n if message: \n conn.send(message)\n print(message)\nexcept KeyboardInterrupt:\n conn.terminate()\n print(\"Shutting down server. Goodbye!\")\n","repo_name":"taldabba/Remauto","sub_path":"src/communications/testServ.py","file_name":"testServ.py","file_ext":"py","file_size_in_byte":536,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"33178604784","text":"import numpy as np\nimport pickle\nimport sys\nfrom read_CSIRO import *\nfrom Smooth import *\nfrom readMarzeion import *\n\n''' DriftCorr.py\n\nThis script applies a drift correction to ZOSTOGA\n\nParameters: \nZOSTOGA = Time series of thermosteric sea-level change for each included model as derived\n\t\t by the IncludeModels.py or the SmoothZOSTOGA.py script (years)\nyears = Years of interest\nbaseyear = Year that is used to center the ZOSTOGA and global sea-level values\nscenario = RCP scenario (e.g \"rcp85\")\ngslfile = Full path to Global sea-level rise data\n\n\nReturn: \ndcZOSTOGA = Drift-corrected global average thermosteric sea-level change (years)\nCWdrift = Drift in global sea-level change\nhistGICrate = Historical GIC rate\nselectyears = Years that coincide with years and the GIC records\n\n'''\n\ndef DriftCorr(ZOSTOGA, years, baseyear, scenario, gslfile):\n\t\n\t# Read in the global SLR data\n\t(GSLx,GSLy,_,_,_,_,_,_,_) = read_CSIRO(gslfile)\n\t\n\t# Smooth the sea-level signal and convert to meters\n\tGSLy = Smooth(GSLy, 19)/1000\n\t\n\t# Calculate the CWDrift value\n\tinds = np.nonzero(GSLx[:,2] <= 1900)[0]\n\tCWdrift = (GSLy[inds.max()] - GSLy[inds[0]]) / (GSLx[inds.max(),2]-GSLx[inds[0],2])\n\t\n\t# Center the SLR on baseyear\n\tbaseyear_ind = np.nonzero(np.floor(GSLx[:,2])==baseyear)[0]\n\tGSLy = GSLy - np.mean(GSLy[baseyear_ind])\n\t\n\t# Determine the years over which the corrections are applied\n\tselectyears = np.array([1861, np.floor(GSLx[inds.max(),2])])\n\t\n\t# Load the glacier data\n\tglacdir = os.path.join(os.path.dirname(__file__), \"Marzeion2012supplement\")\n\tfpmap = os.path.join(os.path.dirname(__file__), \"fingerprint_region_map.csv\")\n\t(projGIC85, projGIC85se, projGIC85yrs, projGIC85model,_,_,_) = readMarzeion(scenario, glacdir, fpmap, discardAntarctica=True)\n\t\n\t# Find which indices in the glacier data correspond to the start and end years in selectyears\n\tstartyear_ind = np.nonzero(projGIC85yrs[:,0] == selectyears[0])[0]\n\tendyear_ind = np.nonzero(projGIC85yrs[:,0] == selectyears[1])[0]\n\t\n\t# Sum the contributions across regions\n\thistGIC = np.sum(projGIC85, axis=1)\n\t\n\t# Calculate the historic rate of change of GIC contributions and convert to meters\n\thistGICrate = (histGIC[endyear_ind,:] - histGIC[startyear_ind,:]) / np.diff(selectyears) / 1000\n\t\n\t# Find the difference between the CWdrift and the mean historical GIC rate\n\t# Note: Units are in meters\n\thistresrate = CWdrift - np.mean(histGICrate)\n\t\n\t# Find where in the ZOSTOGA data the years that match selectyears\n\tZOSTOGA_startyear_ind = np.nonzero(years == selectyears[0])[0]\n\tZOSTOGA_endyear_ind = np.nonzero(years == selectyears[1])[0]\n\t\n\t# Calculate the ZOSTOGA drift\n\tdrift = (ZOSTOGA[ZOSTOGA_endyear_ind,:] - ZOSTOGA[ZOSTOGA_startyear_ind,:]) / np.diff(selectyears)\n\t\n\t# Apply the pure ZOSTOGA drift correction\n\tZOSTOGA_tmp = ZOSTOGA - np.outer(years - selectyears[0], drift)\n\t\n\t# Apply the drift correction from the GSL and glacier data\n\tdcZOSTOGA = ZOSTOGA_tmp + (histresrate * (years - selectyears[0]))[:,np.newaxis]\n\t\n\t# Center the drift-corrected ZOSTOGA to the baseyear\n\tZOSTOGA_baseyear_ind = np.nonzero(years == baseyear)[0]\n\tdcZOSTOGA = dcZOSTOGA - dcZOSTOGA[ZOSTOGA_baseyear_ind,:]\n\t\n\t# Return variables\n\t# Note: dcZOSTOGA, CWdrift, and histGICrate are all in meters\n\treturn(dcZOSTOGA, CWdrift, histGICrate, selectyears)\n\nif __name__ == '__main__':\n\t\n\tgslfile = os.path.join(os.path.dirname(__file__), \"CSIRO_Recons_gmsl_yr_2011.csv\")\n\t\n\t# Load the ZOSTOGA file\n\tzostogafile = os.path.join(os.path.dirname(__file__), \"kopp14_thermalexp_ZOSTOGA.pkl\")\n\ttry:\n\t\tf = open(zostogafile, 'rb')\n\texcept:\n\t\tprint(\"Cannot open ZOSTOGA file\\n\")\n\t\n\t# Extract the configuration variables\n\tmy_zostoga = pickle.load(f)\n\tf.close()\n\n\tZOSTOGA = my_zostoga[\"ZOSTOGA\"]\n\t\n\tx = DriftCorr(ZOSTOGA,np.arange(1861,2300),2000, \"rcp85\", gslfile)\n\t#print(x)\n\t\n\texit()","repo_name":"radical-collaboration/facts","sub_path":"modules/kopp14/sterodynamics/DriftCorr.py","file_name":"DriftCorr.py","file_ext":"py","file_size_in_byte":3804,"program_lang":"python","lang":"en","doc_type":"code","stars":18,"dataset":"github-code","pt":"85"} +{"seq_id":"22299244104","text":"import bpy\n\n\ndef main():\n def get_spine_list():\n spine_bone_list = [\n 'c_head.x'\n ,'c_neck.x'\n ,'c_spine_03.x'#\n , 'c_spine_02.x'#\n , 'c_spine_01.x'#\n , 'c_root_master.x'#\n # 'neck_ref.x'\n # , 'spine_03_ref.x'#\n # , 'spine_02_ref.x'#\n # , 'spine_01_ref.x'#\n # , 'root_ref.x'#\n ]\n a = []\n for bn in spine_bone_list:\n if bpy.context.active_object.pose.bones.get(bn):\n a.append(bn)\n return a\n pass\n def add_constraint(const_type, const_entity, target_obj, sub_target_name,head_tail=0.0):\n constraint = const_entity.constraints.new(const_type)\n constraint.target = target_obj\n constraint.subtarget = sub_target_name\n constraint.head_tail = head_tail\n return constraint\n\n bpy.ops.object.mode_set(mode='EDIT')\n bones = bpy.context.active_object.data.bones\n rig_obj = bpy.context.active_object\n\n spine_bone_list = get_spine_list()\n #\n edit_bones = rig_obj.data.edit_bones\n pose_bones = rig_obj.pose.bones\n # 创建neck root\n head_root = bpy.context.active_object.data.edit_bones.new('head_root[S]')\n head_root.head = bones['c_head.x'].head_local\n # head_root.head = (head_root.head[0],\n # head_root.head[1]+0.2, head_root.head[2])\n head_root.tail = (head_root.head[0],\n head_root.head[1]+0.2, head_root.head[2])\n # edit_bones['c_head.x'].parent = neck_root\n #\n # create rev bone\n last_rev_bone_name = None\n sun_bone_list = []\n for bone_name in spine_bone_list:\n bpy.ops.object.mode_set(mode='EDIT')\n spine_bone = bones[bone_name]\n rev_bone = bpy.context.active_object.data.edit_bones.new(\n bone_name+'[R]')\n rev_bone.tail = spine_bone.head_local\n rev_bone.head = spine_bone.tail_local\n \n rev_bone_name = rev_bone.name\n \n sun_bone = bpy.context.active_object.data.edit_bones.new(\n bone_name+'[S]')\n sun_bone.head = spine_bone.head_local\n sun_bone.tail = (sun_bone.head[0],\n sun_bone.head[1]+0.5, sun_bone.head[2])\n sun_bone_name = sun_bone.name\n sun_bone_list.append(sun_bone_name)\n # edit_bones[sun_bone_name].parent = head_root\n # set parent\n if last_rev_bone_name:\n edit_bones[rev_bone.name].parent = edit_bones[last_rev_bone_name]\n pass\n last_rev_bone_name = rev_bone.name\n # set constraint\n bpy.ops.object.mode_set(mode='POSE')\n add_constraint('COPY_LOCATION', pose_bones[bone_name], rig_obj, rev_bone_name,1)\n add_constraint('DAMPED_TRACK', pose_bones[rev_bone_name], rig_obj, sun_bone_name)\n pose_bones[sun_bone_name].custom_shape=pose_bones['c_arms_pole.r'].custom_shape\n pose_bones[sun_bone_name].custom_shape_translation[1] = 0.5\n pose_bones[sun_bone_name].custom_shape_scale_xyz[0] = 0.2\n pose_bones[sun_bone_name].custom_shape_scale_xyz[1] = 0.2\n pose_bones[sun_bone_name].custom_shape_scale_xyz[2] = 0.2\n # lock x y rotation\n pose_bones[bone_name].lock_rotation[0] = True\n pose_bones[bone_name].lock_rotation[2] = True\n # set pole parent\n bpy.ops.object.mode_set(mode='EDIT')\n bpy.ops.armature.select_all(action='DESELECT')\n head_root.select = True\n sun_bone_list.append('c_head.x[R]')\n for bone_name in sun_bone_list:\n edit_bones[bone_name].parent = edit_bones['head_root[S]']\n pass\n bpy.ops.object.mode_set(mode='POSE')\nmain()\n","repo_name":"solpie/BlendExec","sub_path":"bpy_scripts/rig_tools/Re224_doll_rig.py","file_name":"Re224_doll_rig.py","file_ext":"py","file_size_in_byte":3680,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"74875275157","text":"import os\nfrom prediction_service import prediction\nfrom flask import Flask, request, render_template, jsonify\n\nwebapp_root = \"webapp\"\n\nstatic_dir = os.path.join(webapp_root, \"static\")\ntemplate_dir = os.path.join(webapp_root, \"templates\")\n\n\napp = Flask(__name__, static_folder=static_dir, template_folder=template_dir)\n\n\n@app.route('/', methods=['GET', 'POST'])\ndef index():\n if request.method == 'POST':\n try:\n if request.form:\n data = dict(request.form).values()\n data = [list(map(float, data))]\n response = prediction.predict(data)\n return render_template(\"index.html\", response=response)\n elif request.json:\n reponse = prediction.api_response(request)\n return jsonify(reponse)\n except Exception as e:\n print(e)\n error = {'error': \"Somthing went wrong!! Try again\"}\n return render_template('404.html', error=error)\n else:\n return render_template('index.html')\n\n\nif __name__ == '__main__':\n app.run(host='0.0.0.0', port=5000, debug=True)\n","repo_name":"bala-dg/mlops_demo","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1115,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"2028504315","text":"for tc in range(1, int(input()) + 1):\n n = int(input())\n\n # (0,0)점 한개 + 각 꼭짓점 4개 + x축 y축에 걸쳐있는 (n-1)개의 점 * 4(4분면)\n points = (n - 1) * 4 + 5\n cnt = 0\n\n for i in range(1, n):\n for j in range(1, n):\n if (i ** 2) + (j ** 2) <= n ** 2:\n cnt += 1\n\n points += (cnt * 4)\n print(f'#{tc} {points}')","repo_name":"dydgla36/Algorithm","sub_path":"SWEA/D3/16910. 원 안의 점/원 안의 점.py","file_name":"원 안의 점.py","file_ext":"py","file_size_in_byte":382,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"40733440191","text":"import os\nimport sys\nimport time\nimport math\n\nimport torch.nn as nn\nimport torch.nn.init as init\n\n\ndef get_mean_and_std(dataset):\n '''Compute the mean and std value of dataset.'''\n dataloader = torch.utils.data.DataLoader(dataset, batch_size=1, shuffle=True, num_workers=2)\n mean = torch.zeros(3)\n std = torch.zeros(3)\n print('==> Computing mean and std..')\n for inputs, targets in dataloader:\n for i in range(3):\n mean[i] += inputs[:,i,:,:].mean()\n std[i] += inputs[:,i,:,:].std()\n mean.div_(len(dataset))\n std.div_(len(dataset))\n return mean, std\n\ndef init_params(net):\n '''Init layer parameters.'''\n for m in net.modules():\n if isinstance(m, nn.Conv2d):\n init.kaiming_normal(m.weight, mode='fan_out')\n if m.bias:\n init.constant(m.bias, 0)\n elif isinstance(m, nn.BatchNorm2d):\n init.constant(m.weight, 1)\n init.constant(m.bias, 0)\n elif isinstance(m, nn.Linear):\n init.normal(m.weight, std=1e-3)\n if m.bias:\n init.constant(m.bias, 0)\n\n\n\n\nTOTAL_BAR_LENGTH = 65.\nlast_time = time.time()\nbegin_time = last_time\ndef progress_bar(current, total, msg=None):\n _, term_width = os.popen('stty size', 'r').read().split()\n term_width = int(term_width)\n\n global last_time, begin_time\n if current == 0:\n begin_time = time.time() # Reset for new bar.\n\n cur_len = int(TOTAL_BAR_LENGTH*current/total)\n rest_len = int(TOTAL_BAR_LENGTH - cur_len) - 1\n\n sys.stdout.write(' [')\n for i in range(cur_len):\n sys.stdout.write('=')\n sys.stdout.write('>')\n for i in range(rest_len):\n sys.stdout.write('.')\n sys.stdout.write(']')\n\n cur_time = time.time()\n step_time = cur_time - last_time\n last_time = cur_time\n tot_time = cur_time - begin_time\n\n L = []\n L.append(' Step: %s' % format_time(step_time))\n L.append(' | Tot: %s' % format_time(tot_time))\n if msg:\n L.append(' | ' + msg)\n\n msg = ''.join(L)\n sys.stdout.write(msg)\n for i in range(term_width-int(TOTAL_BAR_LENGTH)-len(msg)-3):\n sys.stdout.write(' ')\n\n # Go back to the center of the bar.\n for i in range(term_width-int(TOTAL_BAR_LENGTH/2)+2):\n sys.stdout.write('\\b')\n sys.stdout.write(' %d/%d ' % (current+1, total))\n\n if current < total-1:\n sys.stdout.write('\\r')\n else:\n sys.stdout.write('\\n')\n sys.stdout.flush()\n\ndef format_time(seconds):\n days = int(seconds / 3600/24)\n seconds = seconds - days*3600*24\n hours = int(seconds / 3600)\n seconds = seconds - hours*3600\n minutes = int(seconds / 60)\n seconds = seconds - minutes*60\n secondsf = int(seconds)\n seconds = seconds - secondsf\n millis = int(seconds*1000)\n\n f = ''\n i = 1\n if days > 0:\n f += str(days) + 'D'\n i += 1\n if hours > 0 and i <= 2:\n f += str(hours) + 'h'\n i += 1\n if minutes > 0 and i <= 2:\n f += str(minutes) + 'm'\n i += 1\n if secondsf > 0 and i <= 2:\n f += str(secondsf) + 's'\n i += 1\n if millis > 0 and i <= 2:\n f += str(millis) + 'ms'\n i += 1\n if f == '':\n f = '0ms'\n return f\n","repo_name":"HazyResearch/torchhalp","sub_path":"examples/cifar10/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":3234,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"85"} +{"seq_id":"3886352813","text":"import os\nimport numpy as np\nfrom io import BytesIO\nfrom struct import pack, unpack\n\n#from shakelab.signals.base import Record\n#from shakelab.signals.libio import mseed, sac, smdb\n\nfrom shakelab.libutils.time import Date\nfrom shakelab.libutils.time import days_to_month\n\n\nclass ByteStream(object):\n \"\"\"\n This class allows reading binary data from a file or from a\n byte buffer using the same format.\n \"\"\"\n def __init__(self, byte_stream=None, byte_order='be'):\n\n self.buffer = b''\n self.length = 0\n\n if byte_stream is not None:\n self.open(byte_stream)\n\n self.byte_order = byte_order\n\n def open(self, byte_stream):\n \"\"\"\n \"\"\"\n if isinstance(byte_stream, bytes):\n self.buffer = BytesIO(byte_stream)\n else:\n self.buffer = open(byte_stream, 'rb')\n\n self.goto(0, 2)\n self.length = self.offset\n self.goto(0)\n\n def goto(self, offset, whence=0):\n \"\"\"\n offset − position of the read/write pointer within the file.\n whence − 0 for absolute file positioning, 1 for relative to\n the current position and 2 seek relative to the file's end.\n \"\"\"\n self.buffer.seek(offset, whence)\n\n def shift(self, places):\n \"\"\"\n \"\"\"\n self.goto(places, 1)\n\n @property\n def offset(self):\n \"\"\"\n \"\"\"\n return self.buffer.tell()\n\n def get(self, byte_format, byte_num=1, offset=None):\n \"\"\"\n \"\"\"\n if offset is not None:\n self.goto(offset)\n\n byte_buffer = self.buffer.read(byte_num)\n\n if byte_format == 's':\n byte_format = str(byte_num) + byte_format\n\n byte_map = {'be': '>', 'le': '<'}\n byte_format = byte_map[self.byte_order] + byte_format\n\n value = unpack(byte_format, byte_buffer)[0]\n\n if isinstance(value, bytes):\n value = value.decode()\n\n return value\n\n def close(self):\n \"\"\"\n \"\"\"\n self.buffer.close()\n\n\ndef reader(file, ftype=None, byte_order='be'):\n \"\"\"\n \"\"\"\n\n if ftype is None:\n # Try to identify file from extension\n fext = os.path.splitext(file)[1]\n\n if fext in ['.ms', '.mseed', '.miniseed', '.seed']:\n ftype = 'mseed'\n\n elif fext in ['.sac', '.SAC']:\n ftype = 'sac'\n\n else:\n raise NotImplementedError('format not recognized')\n\n # Import recordings\n\n if ftype == 'mseed':\n sc = mseed.read(file, byte_order=byte_order)\n\n elif ftype == 'sac':\n\n sc = sac.Sac(file, byte_order=byte_order)\n record = Record()\n record.head.delta= sc.delta\n record.head.time = Date(sc.time, format='julian')\n record.data = np.array(sc.data[0])\n rec_list.append(record)\n\n elif ftype == 'itaca':\n\n it = smdb.Itaca(file)\n record = Record()\n record.head.rate = it.sampling_rate()\n record.head.time = Date(it.time)\n record.data = np.array(it.data)\n rec_list.append(record)\n\n elif ftype == 'ascii':\n raise NotImplementedError('format not yet implemented')\n\n elif ftype == 'seisan':\n raise NotImplementedError('format not yet implemented')\n\n elif ftype == 'seg2':\n raise NotImplementedError('format not yet implemented')\n\n elif ftype == 'dat':\n raise NotImplementedError('format not yet implemented')\n\n elif ftype == 'gse':\n raise NotImplementedError('format not yet implemented')\n\n elif ftype == 'reftek':\n raise NotImplementedError('format not yet implemented')\n\n else:\n pass\n\n return rec_list\n\n","repo_name":"shakelab/shakelab","sub_path":"shakelab/signals/io.py","file_name":"io.py","file_ext":"py","file_size_in_byte":3649,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"85"} +{"seq_id":"22399755880","text":"import logging\nimport socket\n\nfrom ops.charm import CharmBase\nfrom ops.framework import StoredState\nfrom ops.main import main\nfrom ops.model import ActiveStatus, BlockedStatus, MaintenanceStatus, ModelError, Relation\n\nlogger = logging.getLogger(__name__)\n\n\nclass JaegerCharm(CharmBase):\n \"\"\"Charm the service.\"\"\"\n\n _stored = StoredState()\n\n def __init__(self, *args):\n super().__init__(*args)\n self.framework.observe(self.on.agent_pebble_ready, self._on_agent_pebble_ready)\n self.framework.observe(self.on.collector_pebble_ready, self._on_collector_pebble_ready)\n self.framework.observe(self.on.query_pebble_ready, self._on_query_pebble_ready)\n\n self.framework.observe(self.on.config_changed, self._on_config_changed)\n\n self.framework.observe(self.on[\"datastore\"].relation_changed,\n self._update_datastore_relation)\n self.framework.observe(self.on[\"datastore\"].relation_broken,\n self._update_datastore_relation)\n\n self.framework.observe(self.on[\"distributed-tracing\"].relation_joined,\n self._update_distributed_tracing_relation)\n self.framework.observe(self.on[\"distributed-tracing\"].relation_changed,\n self._update_distributed_tracing_relation)\n\n self.framework.observe(self.on.restart_action, self._on_restart_action)\n\n @property\n def datastore_relation(self) -> Relation:\n # only one relation to datastore is needed/supported\n for datastore_relation in self.framework.model.relations[\"datastore\"]:\n return datastore_relation\n\n @property\n def datastore_provider_unit(self) -> Relation:\n # only one relation to datastore is needed/supported\n for datastore_provider in self.datastore_relation.units:\n return datastore_provider\n\n @property\n def datastore_endpoint(self) -> str:\n try:\n rel_data = self.datastore_relation.data[self.datastore_provider_unit]\n hostname = rel_data.get(\"ingress-address\")\n port = str(rel_data.get(\"port\"))\n return \"http://{}:{}\".format(hostname, port)\n except (AttributeError, KeyError):\n logger.debug(\"no datastore endpoint present\")\n return None\n\n def _on_agent_pebble_ready(self, event):\n self._update_agent_and_run()\n\n def _update_agent_and_run(self):\n self.unit.status = MaintenanceStatus('Configuring jaeger-agent')\n\n pebble_layer = {\n \"summary\": \"jaeger-agent layer\",\n \"description\": \"pebble config layer for jaeger-agent\",\n \"services\": {\n \"agent\": {\n \"override\": \"replace\",\n \"summary\": \"jaeger-agent\",\n \"command\": \"/go/bin/agent-linux --reporter.grpc.host-port=127.0.0.1:14250\"\n \" --processor.jaeger-compact.server-host-port={}\"\n \" --processor.jaeger-binary.server-host-port={}\"\n .format(str(self.model.config['agent-port']),\n str(self.model.config['agent-port-binary'])),\n \"startup\": \"enabled\",\n \"environment\": {},\n }\n },\n }\n\n container = self.unit.get_container(\"agent\")\n container.add_layer(\"agent\", pebble_layer, combine=True)\n\n if container.get_service(\"agent\").is_running():\n container.stop(\"agent\")\n container.start(\"agent\")\n\n self.unit.status = ActiveStatus()\n\n def _update_collector_and_run(self):\n self.unit.status = MaintenanceStatus('Configuring jaeger-collector')\n\n pebble_layer = {\n \"summary\": \"jaeger-collector layer\",\n \"description\": \"pebble config layer for jaeger-collector\",\n \"services\": {\n \"collector\": {\n \"override\": \"replace\",\n \"summary\": \"jaeger-collector\",\n \"command\": \"/go/bin/collector-linux\",\n \"startup\": \"enabled\",\n \"environment\": {\n \"SPAN_STORAGE_TYPE\": self.model.config[\"span-storage-type\"],\n \"ES_SERVER_URLS\": self.datastore_endpoint,\n },\n }\n },\n }\n\n container = self.unit.get_container(\"collector\")\n container.add_layer(\"collector\", pebble_layer, combine=True)\n\n if container.get_service(\"collector\").is_running():\n container.stop(\"collector\")\n container.start(\"collector\")\n\n if not self.datastore_endpoint:\n self.unit.status = BlockedStatus(\"Datastore endpoint missing, check relations\")\n else:\n self.unit.status = ActiveStatus()\n\n def _update_query_service_and_run(self):\n self.unit.status = MaintenanceStatus('Configuring jaeger-query')\n\n pebble_layer = {\n \"summary\": \"jaeger-query layer\",\n \"description\": \"pebble config layer for jaeger-query\",\n \"services\": {\n \"query\": {\n \"override\": \"replace\",\n \"summary\": \"jaeger-query\",\n \"command\": \"/go/bin/query-linux\",\n \"startup\": \"enabled\",\n \"environment\": {\n \"SPAN_STORAGE_TYPE\": self.model.config[\"span-storage-type\"],\n \"ES_SERVER_URLS\": self.datastore_endpoint,\n },\n }\n },\n }\n\n container = self.unit.get_container(\"query\")\n container.add_layer(\"query\", pebble_layer, combine=True)\n\n if container.get_service(\"query\").is_running():\n container.stop(\"query\")\n container.start(\"query\")\n\n if not self.datastore_endpoint:\n self.unit.status = BlockedStatus(\"Datastore endpoint missing, check relations\")\n else:\n self.unit.status = ActiveStatus()\n\n def _on_collector_pebble_ready(self, event):\n self._update_collector_and_run()\n\n def _on_query_pebble_ready(self, event):\n self._update_query_service_and_run()\n\n def _on_config_changed(self, _):\n self.unit.status = MaintenanceStatus('Updating configuration')\n\n # update config and restart everything\n self._update_collector_and_run()\n self._update_query_service_and_run()\n self._update_agent_and_run()\n\n self.unit.status = ActiveStatus()\n\n def _update_datastore_relation(self, event):\n self.unit.status = MaintenanceStatus(\n \"Updating datastore endpoint\"\n )\n\n self._update_collector_and_run()\n self._update_query_service_and_run()\n\n def _get_app_fqdn(self, relation):\n try:\n pod_addr = self.model.get_binding(relation).network.bind_address\n addr = socket.getnameinfo((str(pod_addr), 0), socket.NI_NAMEREQD)[0]\n return addr\n except Exception:\n return\n\n def _update_distributed_tracing_relation(self, event):\n if self.unit.is_leader():\n address = self._get_app_fqdn(event.relation)\n if not address:\n address = self.model.get_binding(event.relation).network.ingress_address\n if not address:\n address = self.model.get_binding(event.relation).network.bind_address\n\n event.relation.data[self.app]['agent-address'] = str(address)\n\n event.relation.data[self.app]['port'] = \\\n str(self.model.config['agent-port'])\n event.relation.data[self.app]['port_binary'] = \\\n str(self.model.config['agent-port-binary'])\n\n logger.debug(\"Published relation data: %s\", str(event.relation.data))\n\n def _on_restart_action(self, event):\n name = event.params[\"service\"]\n event.log(\"Restarting service {}\".format(name))\n # note: containers and services use the same names, so it's safe to do that\n try:\n self._restart_container_service(name, name)\n except ModelError as e:\n event.fail(message=str(e))\n\n # workaround for https://github.com/canonical/operator/issues/491\n def _restart_container_service(self, container_name, svc_name):\n container = self.unit.get_container(container_name)\n if not container:\n msg = \"Container {} not found\".format(container_name)\n logger.error(msg)\n return\n\n if container.get_service(svc_name).is_running():\n container.stop(svc_name)\n container.start(svc_name)\n\n\nif __name__ == \"__main__\":\n main(JaegerCharm)\n","repo_name":"przemeklal/charm-jaeger","sub_path":"src/charm.py","file_name":"charm.py","file_ext":"py","file_size_in_byte":8714,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"27087592148","text":"from typing import Text, Any, Dict, List, Union, Tuple\nimport requests\nfrom sagas.conf.conf import cf\nfrom sagas.nlu.inspector_common import Context\nfrom sagas.nlu.patterns import Patterns\nfrom sagas.nlu.rules_meta import build_meta\nimport sagas.tracker_fn as tc\nfrom sagas.nlu.utils import join_text\nimport sagas\nfrom dataclasses import dataclass\nfrom dataclasses_json import dataclass_json, LetterCase\nfrom pprint import pprint\nimport logging\n\nlogger = logging.getLogger(__name__)\n\n# def fix_data(data):\n# if 'engine' not in data:\n# data['engine'] = cf.engine(data['lang'])\n# data['sents']=fix_sents(data['sents'], data['lang'])\n# return data\n\ndef parse(data):\n if 'engine' not in data:\n data['engine']=cf.engine(data['lang'])\n engine=data['engine']\n response = requests.post(f'{cf.servant(engine)}/verb_domains', json=data)\n if response.status_code == 200:\n return response.json()\n return None\n\ndef norm_arg(arg):\n return arg.replace(':', '_')\n\nclass InferExtensionPoints(object):\n def __init__(self):\n self.exts={}\n self.exists=lambda key: key in self.exts\n\n @property\n def extensions(self) -> Dict[Text, List[Any]]:\n return self.exts\n def register(self, key, val):\n if key in self.exts:\n self.exts[key].append(val)\n else:\n self.exts[key]=[val]\n def value(self, key):\n if key in self.exts:\n return self.exts[key][0]\n return None\n def values(self, key):\n return self.exts[key]\n\n def register_parts(self, lang, fn_map):\n for k,f in fn_map.items():\n ext_point = f\"part.{lang}.{k}\"\n self.register(ext_point, f)\n\n def register_domains(self, lang, fn_map):\n for k,f in fn_map.items():\n ext_point = f\"domain.{lang}.{k}\"\n self.register(ext_point, f)\n\nextensions=InferExtensionPoints()\n\nclass DomainToken(object):\n def __init__(self, **kwargs):\n self.props=kwargs\n\n @property\n def type_name(self):\n return self.props['type']\n\n @property\n def type(self):\n return self.props['type']\n\n @property\n def lang(self):\n return self.props['lang']\n @property\n def text(self):\n return self.props['text']\n\n @property\n def lemma(self):\n return self.props['lemma']\n @property\n def index(self):\n return self.props['index']\n\n @property\n def head(self):\n return self.props['head']\n\n @property\n def head_trans(self):\n return self.props['head_trans']\n\n @property\n def translit(self):\n return self.props['translit']\n\n @property\n def translate(self):\n return self.props['translate']\n\n @property\n def rels(self) -> List[Text]:\n return self.props['rels']\n\n @property\n def ctx(self) -> Context:\n return self.props['ctx']\n\n @property\n def stems(self) -> List[Tuple[Text,Text]]:\n return self.props['stems']\n\n def pattern(self, dominator='verb', priority=5, name='_noname_'):\n \"\"\"\n >>> from sagas.nlu.rules_header import *\n >>> pat=token.pattern('verb')\n >>> r=pat(behaveof('eat', 'v'))\n >>> assert r[1]\n >>> r=pat(obl=kindof('building', 'n'))\n >>> assert r[1]\n :param dominator:\n :param priority:\n :param name:\n :return:\n \"\"\"\n ctx=self.ctx\n domain=dominator.split('_')[0]\n pat = Patterns(ctx.domains, ctx.meta, priority=priority, name=name)\n serv = pat.prepare(domain)\n return serv\n\n@dataclass_json(letter_case=LetterCase.CAMEL) # all fields are encoded/decoded from camelCase\n@dataclass\nclass InferPart:\n name: str\n chunk: str\n text: str\n lemma: str\n translit: str\n translate: str\n index: int\n domain: DomainToken=None\n\n @property\n def word(self) -> str:\n return f\"{self.text}/{self.lemma}\"\n\nclass Inferencer(object):\n def __init__(self, lang):\n self.lang=lang\n\n def proc_word(self, type_name:Text, word:Text, head:Text, index:int, r, lang:Text) -> Dict[Text, Any]:\n from sagas.tool.misc import translit_chunk, display_synsets, target_lang\n from sagas.nlu.translator import translate\n res, _ = translate(word, source=lang, target=target_lang(lang),\n trans_verbose=False)\n\n # result=f\"[{type_name}]({word}{translit_chunk(word, lang)}) {res}{target}\"\n result = {'type': type_name,\n 'text': word,\n 'lemma': r['lemma'],\n 'translit': translit_chunk(word, lang),\n 'translate': res,\n 'lang': lang,\n 'index': index,\n }\n if head != '':\n res_t, _ = translate(head, source=lang, target=target_lang(lang),\n trans_verbose=False, options={'disable_correct'})\n # target=f\" ⊙︿⊙ {res_t}({head})\"\n result['head'] = head\n result['head_trans'] = res_t\n # tc.emp('magenta', result)\n return result\n\n # def proc_children_column(partcol, textcol, idxcol, lang):\n def proc_children_column(self, df, lang:Text) -> List[InferPart]:\n from sagas.nlu.translator import translate\n from sagas.tool.misc import translit_chunk, display_synsets, target_lang\n result = []\n # for id, (name, r) in enumerate(zip(partcol, textcol)):\n rels = []\n for id, row in df.iterrows():\n # df['rel'], df['children'], df['index']\n name, r, idx = row['rel'], row['children'], row['index']\n if name in rels:\n continue\n else:\n rels.append(name)\n if name not in ('punct', 'head_root'):\n sent = join_text(r, lang)\n res, _ = translate(sent, source=lang, target=target_lang(lang),\n trans_verbose=False, options={'disable_correct'})\n # chunk=f\"{indent}[{name}]({sent}{translit_chunk(sent, lang)}) {res}\"\n chunk = InferPart(name= name,\n chunk= sent,\n text= row['text'],\n lemma= row['lemma'],\n translit= translit_chunk(sent, lang),\n translate= res,\n index= idx,\n )\n result.append(chunk)\n # tc.emp('cyan', chunk)\n return result\n\n def induce_spec(self, el, pats:List[Tuple[int, Text]], type_name:Text) -> None:\n # print(f\"{el.indicator} {el.word}: {el.spec}\")\n if el.indicator == '[verb]':\n pats.append((3, f\"behaveof('{el.spec}', 'v')\"))\n elif el.indicator in ('[aux]', '[subj]'):\n # pats.append((3, f\"behaveof('{el.spec}', '*')\"))\n pass\n elif el.indicator == '[root]':\n pats.append((3, f\"behaveof('{el.spec}', 'n')\"))\n elif el.indicator == '[predicate]':\n pats.append((3, f\"specsof('*', '{el.spec}')\"))\n else:\n if type_name in ('aux_domains', 'subj_domains') and el.indicator == 'head':\n pats.append((3, f\"behaveof('{el.spec}', '*')\"))\n else:\n pats.append((1, f\"{norm_arg(el.indicator)}=kindof('{el.spec}', '*')\"))\n\n def induce_part(self, chunk:InferPart, pats:List[Tuple[int, Text]],\n type_name:Text, enable_verbose=False):\n if enable_verbose:\n tc.emp('cyan', chunk)\n\n\n ext_point=f\"part.{self.lang}.{chunk.name}\"\n global_point=f\"part.*.{chunk.name}\"\n fn=extensions.value(ext_point) or extensions.value(global_point)\n logger.debug(f\".. get extension from {ext_point}: {fn}\")\n fnr=None\n if fn:\n fnr=fn(chunk, type_name)\n # if fnr is None and chunk.name in gen_map:\n # fnr=gen_map[chunk.name]()\n\n if fnr:\n if isinstance(fnr, list):\n pats.extend(fnr)\n else:\n pats.append(fnr)\n\n def induce_domain_from_exts(self, chunk:DomainToken,\n domain: Text, pats:List[Text]):\n ext_point = f\"domain.{self.lang}.{domain}\"\n fn = extensions.value(ext_point)\n logger.debug(f\".. get extension from {ext_point}: {fn}\")\n if fn:\n fnr = fn(chunk, domain)\n if fnr:\n if isinstance(fnr, list):\n pats.extend(fnr)\n else:\n pats.append(fnr)\n\n def induce_pattern(self, pat:DomainToken, ds, enable_verbose=False) -> Text:\n if enable_verbose:\n tc.emp('magenta', pat)\n\n def gen_verb(ind='verb', prefix='behave'):\n spec = [d for d in ds if d['indicator'] == f'[{ind}]']\n if spec:\n ref = spec[0]['spec']\n else:\n ref = pat.translate.replace(' ', '_')\n return f\"pat(5, name='{prefix}_{ref}').verb\"\n\n def gen_root():\n spec = [d for d in ds if d['indicator'] == '[root]']\n if spec:\n ref = spec[0]['spec']\n else:\n ref = pat.translate.replace(' ', '_')\n return f\"pat(5, name='ana_{ref}').root\"\n\n def gen_cop():\n spec = [d for d in ds if d['indicator'] == 'head']\n if spec:\n ref = spec[0]['spec']\n else:\n ref = pat.head_trans.replace(' ', '_') if pat.lang != 'en' else pat.head.replace(' ', '_')\n return f\"pat(5, name='desc_{ref}').cop\"\n\n domap = {'verb_domains': gen_verb,\n 'aux_domains': gen_cop,\n 'subj_domains': gen_cop,\n 'root_domains': gen_root,\n 'predicate': lambda: gen_verb('predicate', 'predict'),\n }\n return domap[pat.type]().lower()\n\n def stem_chunks(self, r):\n tuple_list=[] # 使用tuple-list是因为一个句子会有重复的成分\n for stem in r['stems']:\n # if stem[0] in stem_filters:\n # stem[1]是一个列表, 包含了所有的word-lemmas\n if stem[1]:\n value = ' '.join(stem[1])\n # stem[0]是成分名称, 比如obj/obl/nsubj/...\n tuple_list.append((stem[0], value))\n return tuple_list\n\n def infer(self, sents, verbose=False) -> List[Text]:\n from sagas.tool.misc import translit_chunk, display_synsets, target_lang\n data = {'lang': self.lang, \"sents\": sents}\n rs = parse(data)\n result_pats=[]\n # sinkers = Sinkers()\n for serial, r in enumerate(rs):\n type_name = r['type']\n theme = type_name.split('_')[0]\n # print(type_name)\n meta = build_meta(r, data)\n # print(f\"meta keys {meta.keys()}\")\n\n # mod_rs = langspecs.check_langspec(data['lang'], meta, r['domains'], type_name)\n # sinkers.add_module_results(mod_rs)\n\n # infers\n pats = [] # tuples list\n\n def do_infers(ctx:Context, ds, filters):\n from sagas.nlu.inspectors_dataset import get_interrogative\n if 'head' in r:\n # $ se 'you are dead' # true\n # $ spt 'Com licença, onde é o banheiro?' # false\n logger.debug(f\"head: {r['head']}, filter: {'head' in filters}\")\n rep = get_interrogative(r['head'], self.lang)\n if rep:\n pats.append((5, f\"interr_root('{rep}')\"))\n\n df = sagas.to_df(r['domains'], ['rel', 'index',\n 'text', 'lemma',\n 'children', 'features'])\n pat = self.proc_word(type_name, r['word'],\n r['head'] if 'head' in r else '',\n r['index'], r,\n self.lang)\n pat['rels']=[sub[0] for sub in r['domains']]\n pat['stems']=self.stem_chunks(r)\n pat['ctx']=ctx\n\n domain=DomainToken(**pat)\n logger.debug(f\".. proc word {r['word']}, \"\n f\"verb in filter ({'[verb]' in filters}), \"\n f\"predicate in filter ({'[predicate]' in filters}), \"\n f\"stems: {domain.stems}\")\n if '[verb]' not in filters and '[predicate]' not in filters:\n self.induce_domain_from_exts(domain, 'verb', pats)\n\n pat_r = self.induce_pattern(domain, ds, verbose)\n parts = self.proc_children_column(df, self.lang)\n for part in parts:\n # logger.debug(f\"{part.name}: {part.word}\")\n if part.name not in filters:\n part.domain=domain\n self.induce_part(part, pats, type_name, verbose)\n # display_synsets(f\"[{theme}]\", meta, r, data['lang'])\n return pat_r\n\n # induce with wordnet\n ds = display_synsets(f\"[{theme}]\", meta, r, self.lang, collect=True)\n domains = r['domains']\n ctx = Context(meta, domains, name='_test_')\n pat_r = do_infers(ctx, ds, [el['indicator'] for el in ds])\n indicators = []\n for el in ds:\n if el['indicator'] not in indicators:\n self.induce_spec(sagas.to_obj(el), pats, type_name)\n indicators.append(el['indicator'])\n\n pats = sorted(pats, key=lambda pat: -pat[0])\n paras = ', '.join(p[1] for p in pats)\n if paras:\n result_pats.append(f\"{pat_r}({paras}),\")\n\n # debug\n if verbose:\n print('*', '-' * 45)\n pprint(ds)\n\n # sinkers.process_with_sinkers()\n return result_pats\n\n# infers=Inferencer()\n\ndef do_infers(text:Text, source:Text) -> (Text, List[Text]):\n infers = Inferencer(source)\n pats = infers.infer(text)\n\n # generate cli command\n shortcuts = {'ja': 'sj', 'zh': 'sz'}\n cli_head = shortcuts[source] if source in shortcuts else f\"s{source}\"\n cli_cmd = f\"# $ {cli_head} '{text}'\"\n tc.emp('white', cli_cmd)\n\n for pat in pats:\n tc.emp('yellow', pat)\n return cli_cmd, pats\n\nclass InferencerCli(object):\n def infer(self, sents, lang='en', verbose=False):\n \"\"\"\n $ python -m sagas.nlu.inferencer infer '水としょうゆを混ぜた。' ja\n pat(5, name='predict_mix').verb(specsof('*', 'mix'), ヲ=kindof('water', '*')),\n $ python -m sagas.nlu.inferencer infer 'Ellos ya leyeron ese libro en la escuela.' es\n pat(5, name='behave_read').verb(extract_for('plain', 'advmod'), behaveof('read', 'v'), nsubj=agency, obl=kindof('school', '*'), obj=kindof('book', '*')),\n $ python -m sagas.nlu.inferencer infer 'you are dead' en\n pat(5, name='desc_dead').cop(behaveof('dead', '*'), nsubj=agency, cop='c_aux'),\n $ python -m sagas.nlu.inferencer infer '予約を火曜日から木曜日に変えてもらった。' ja\n $ python -m sagas.nlu.inferencer infer ''太陽は月に比べて大きいです。' ja\n $ python -m sagas.nlu.inferencer infer 'Berapa umur kamu?' id # (en=\"How old are you?\")\n pat(5, name='ana_age').root(behaveof('age', 'n')),\n $ python -m sagas.nlu.inferencer infer 'Apa yang lebih murah?' id # (What is cheaper?)\n pat(5, name='desc_abundant').cop(extract_for('plain', 'advmod'), behaveof('abundant', '*'), nsubj=agency, head_amod=interr('what')),\n\n :param sents:\n :param lang:\n :return:\n \"\"\"\n infers = Inferencer(lang)\n return infers.infer(sents, verbose=verbose)\n\nif __name__ == '__main__':\n\n import fire\n\n # startup.start()\n fire.Fire(InferencerCli)\n\n","repo_name":"samlet/stack","sub_path":"sagas/nlu/inferencer.py","file_name":"inferencer.py","file_ext":"py","file_size_in_byte":16006,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"85"} +{"seq_id":"2694408437","text":"from tkinter import *\r\nfrom tkinter import messagebox\r\n\r\n\r\ndef new_game_btn():\r\n root.destroy()\r\n game()\r\n\r\n\r\ndef exit():\r\n root.destroy()\r\n\r\n\r\n# Проверка наличия победителя\r\ndef check_winner():\r\n # Проверка строк\r\n for row in board:\r\n if row[0] == row[1] == row[2] != ' ':\r\n return row[0]\r\n\r\n # Проверка столбцов\r\n for col in range(3):\r\n if board[0][col] == board[1][col] == board[2][col] != ' ':\r\n return board[0][col]\r\n\r\n # Проверка диагоналей\r\n if board[0][0] == board[1][1] == board[2][2] != ' ':\r\n return board[0][0]\r\n if board[0][2] == board[1][1] == board[2][0] != ' ':\r\n return board[0][2]\r\n\r\n # Проверка ничьей\r\n if all(board[i][j] != ' ' for i in range(3) for j in range(3)):\r\n return 'Ничья'\r\n\r\n return None\r\n\r\n\r\ndef ai_move():\r\n best_score = float('-inf')\r\n best_move = None\r\n\r\n # Перебор всех возможных ходов\r\n for i in range(3):\r\n for j in range(3):\r\n if board[i][j] == ' ':\r\n board[i][j] = 'O'\r\n score = minimax(board, 0, False)\r\n board[i][j] = ' '\r\n\r\n # Выбор лучшего хода\r\n if score > best_score:\r\n best_score = score\r\n best_move = (i, j)\r\n\r\n # Выполнение хода компьютера\r\n if best_move:\r\n board[best_move[0]][best_move[1]] = 'O'\r\n buttons[best_move[0]][best_move[1]].config(text='о', font=(\"Arial 36 bold\"), state='disabled')\r\n winner = check_winner()\r\n if winner:\r\n end_game(winner)\r\n\r\n\r\n# Алгоритм минимакс\r\ndef minimax(board, depth, is_maximizing):\r\n global winner\r\n winner = check_winner()\r\n\r\n if winner:\r\n if winner == 'O':\r\n return 1\r\n elif winner == 'X':\r\n return -1\r\n else:\r\n return 0\r\n\r\n if is_maximizing:\r\n best_score = float('-inf')\r\n for i in range(3):\r\n for j in range(3):\r\n if board[i][j] == ' ':\r\n board[i][j] = 'O'\r\n score = minimax(board, depth + 1, False)\r\n board[i][j] = ' '\r\n best_score = max(score, best_score)\r\n return best_score\r\n else:\r\n best_score = float('inf')\r\n for i in range(3):\r\n for j in range(3):\r\n if board[i][j] == ' ':\r\n board[i][j] = 'X'\r\n score = minimax(board, depth + 1, True)\r\n board[i][j] = ' '\r\n best_score = min(score, best_score)\r\n return best_score\r\n\r\n\r\n# Завершение игры\r\ndef end_game(winner):\r\n for row in buttons:\r\n for button in row:\r\n button.config(state='disabled')\r\n if winner == 'Ничья':\r\n messagebox.showinfo('Конец игры', 'Ничья!')\r\n new_game()\r\n else:\r\n messagebox.showinfo('Конец игры', f'Победитель: {winner}!')\r\n new_game()\r\n\r\n\r\ndef dismiss(win):\r\n win.grab_release()\r\n win.destroy()\r\n\r\n\r\ndef new_game():\r\n win = Toplevel(root)\r\n w = root.winfo_screenwidth() // 2 - 150\r\n h = root.winfo_screenheight() // 2 - 50\r\n win.title('Конец игры')\r\n win.geometry(f'300x100+{w}+{h}')\r\n win.resizable(False, False)\r\n win.protocol(\"VM_DELETE_WINDOW\", lambda: dismiss(win))\r\n win.grab_set()\r\n\r\n main_label = Label(win, text=\"Выберите нужный вариант.\", font=(\"Arial 10 bold\"), justify=CENTER, )\r\n main_label.pack()\r\n\r\n new_game_btn1 = Button(win, text='Новая Игра', command=new_game_btn)\r\n new_game_btn1.pack()\r\n\r\n exit_btn = Button(win, text='Выход', command=exit)\r\n exit_btn.pack()\r\n\r\n\r\n# Обработчик клика по кнопке\r\ndef button_click(i, j):\r\n if board[i][j] == ' ':\r\n buttons[i][j].config(text='x', font=(\"Arial 36 bold\"), state='disabled')\r\n board[i][j] = 'X'\r\n winner = check_winner()\r\n if not winner:\r\n ai_move()\r\n else:\r\n end_game(winner)\r\n\r\n\r\ndef game():\r\n global board\r\n board = [[' ' for _ in range(3)] for _ in range(3)]\r\n\r\n global root\r\n root = Tk()\r\n root.title('Крестики-нолики')\r\n\r\n w = root.winfo_screenwidth() // 2 - 225\r\n h = root.winfo_screenheight() // 2 - 225\r\n\r\n root.geometry(f'450x450+{w}+{h}')\r\n root.resizable(False, False)\r\n\r\n # создание и размещение кнопок\r\n for c in range(3): root.columnconfigure(index=c, weight=1)\r\n for r in range(3): root.rowconfigure(index=r, weight=1)\r\n global buttons\r\n buttons = []\r\n for i in range(3):\r\n row = []\r\n for j in range(3):\r\n button = Button(root, text=' ', font=(\"Arial 36 bold\"), command=lambda x=i, y=j: button_click(x, y))\r\n button.grid(row=i, column=j, sticky=NSEW)\r\n row.append(button)\r\n\r\n buttons.append(row)\r\n\r\n root.mainloop()\r\n\r\n\r\ngame()\r\n","repo_name":"Loki2942/Laba-AiSD-10","sub_path":"lab10.py","file_name":"lab10.py","file_ext":"py","file_size_in_byte":5173,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"4356661750","text":"import asyncio\nimport logging\nfrom enum import Enum\nfrom typing import Self\n\nimport serial_asyncio\n\nLINTRONIC_ADDRESS = b\"01\"\nOUR_ADDRESS = b\"00\"\n\nSTART_OF_TRANSMISSION = b\"<\"\nEND_OF_TRANSMISSION = b\">\"\n\nMAGIC_SUFFIX = b\"024\"\n\n\nlogger = logging.getLogger(__name__)\n\n\nclass BeoCommand(Enum):\n VOLUME_DOWN = b\"040255010010701001100000000000000\"\n VOLUME_UP = b\"040255010010701000096000000000000\"\n AUDIO_AUX = b\"040255010010701001131000000000000\"\n AUDIO_NEXT = b\"040255010010701001052000000000000\"\n AUDIO_PREV = b\"040255010010701001050000000000000\"\n AUDIO_PAUSE = b\"040255010010701001054000000000000\"\n AUDIO_PLAY = b\"040255010010701001053000000000000\"\n A_TAPE2 = b\"040255010010701001148000000000000\"\n AUDIO_POWER_OFF = b\"040255010010701001012000000000000\"\n\n\nclass LinTronicConnection:\n def __init__(\n self, reader: asyncio.StreamReader, writer: asyncio.StreamWriter\n ) -> None:\n self.reader = reader\n self.writer = writer\n\n @classmethod\n async def create(cls, url: str) -> Self:\n reader, writer = await serial_asyncio.open_serial_connection(\n url=url, baudrate=19200\n )\n return cls(reader, writer)\n\n async def listen_for_incoming_messages(self) -> None:\n while True:\n await self.reader.readuntil(START_OF_TRANSMISSION)\n await self.handle_incoming_message()\n\n async def handle_incoming_message(self) -> None:\n logger.debug(\"Got incoming message from Lintronic\")\n to_address = await self.reader.readexactly(2)\n if to_address != OUR_ADDRESS:\n logger.warning(\"Received msg not addressed to us, but %s\", to_address)\n return\n\n from_address = await self.reader.readexactly(2)\n if from_address != LINTRONIC_ADDRESS:\n logger.warning(\n \"Received msg not sent from Lintronic, but: %s\", from_address\n )\n return\n\n cmd = await self.reader.readexactly(3)\n data_and_checksum = await self.reader.readuntil(END_OF_TRANSMISSION)\n\n data = data_and_checksum[:-4]\n checksum = data_and_checksum[-3:-1]\n\n msg = to_address + from_address + cmd + data\n\n expected_checksum = bytes(f\"{sum(c for c in msg) % 256:03}\", \"ascii\")\n if checksum != expected_checksum:\n logger.warning(\n \"Got invalid checksum in command %s vs %s\", checksum, expected_checksum\n )\n return\n\n logger.info(\"Got cmd: %s and data: %s\", cmd, data)\n\n async def write_message_to_lintronic(\n self, beo_command: BeoCommand, repeat_count=1\n ) -> None:\n binary_repeat_count = bytes(f\"{repeat_count:03}\", \"ascii\")\n msg = (\n LINTRONIC_ADDRESS\n + OUR_ADDRESS\n + beo_command.value\n + binary_repeat_count\n + MAGIC_SUFFIX\n )\n\n checksum = bytes(f\"{sum(c for c in msg) % 256:03}\", \"ascii\")\n message = START_OF_TRANSMISSION + msg + checksum + END_OF_TRANSMISSION\n\n logger.debug(\"Sending message to LinTronic: %s\", message)\n self.writer.write(message)\n await self.writer.drain()\n","repo_name":"magnuswatn/raspessence","sub_path":"raspessence/lintronic.py","file_name":"lintronic.py","file_ext":"py","file_size_in_byte":3158,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"1311990465","text":"\nclass Solution:\n def maxOperations(self, nums: list[int], k: int) -> int:\n lookup = {}\n pair = 0\n for x in nums:\n search = k - x\n if search in lookup and lookup[search] > 0:\n lookup[search] -= 1\n pair += 1\n continue\n\n if x in lookup:\n lookup[x] += 1\n else:\n lookup[x] = 1\n return pair\n\n\ns = Solution()\n\nprint(s.maxOperations([1, 2, 3, 4], 5))\n\n","repo_name":"zenubis/LeetCodeAnswers","sub_path":"04-Daily/LeeCodeDaily_20210118/LeetCodeDaily_20210118.py","file_name":"LeetCodeDaily_20210118.py","file_ext":"py","file_size_in_byte":495,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"35852626187","text":"import math\r\nimport random\r\nfrom math import *\r\nimport ValidacionDeDatos\r\n\r\n\"\"\"1. Simular el lamzamiento de un dado\"\"\"\r\n\r\n# Crear una variable donde almacenar el resultado del lanzamiento.\r\nvalorDado = 0\r\nnumeroIntentos = 0\r\n\r\nwhile valorDado != 3:\r\n valorDado = random.randint(1, 6)\r\n numeroIntentos += 1\r\n\r\nprint(valorDado, \"Número de intentos:\", numeroIntentos)\r\n\r\n\r\n\"\"\"2. Suma de los cuadrados de los n números naturales\"\"\"\r\n\r\n# Solicitar el número de naturales a sumar.\r\nwhile True:\r\n cantidadNumeros = ValidacionDeDatos.ValidarEntero(\"¿Cúantos números naturales desea sumar? \")\r\n if cantidadNumeros > 0:\r\n break;\r\n\r\n# Crear la variable contadora.\r\ncontador1 = 0\r\n\r\n# Crear las variables para almacenar cada uno de los números enteros y la suma de sus cuadrados.\r\nnumeroEntero = 1\r\nsumaNumerosEnteros = 0\r\n\r\nwhile contador1 < cantidadNumeros:\r\n cuadrado = math.pow(numeroEntero, 2)\r\n sumaNumerosEnteros += cuadrado\r\n\r\n numeroEntero += 1\r\n contador1 += 1\r\n\r\nprint(\"Suma = \", sumaNumerosEnteros)\r\n\r\n\r\n\"\"\"3. Conteo de bacterias.\"\"\"\r\n\r\n# Cantidad inicial de bacterias.\r\ncantidadInicial = ValidacionDeDatos.ValidarEntero(\"Cantidad inicial de bacterias: \")\r\ncantidadMaxima = ValidacionDeDatos.ValidarEntero(\"Cantidad máxima de bacterias: \")\r\n\r\n# Contador de días\r\ndia = 0\r\n\r\ncantidadBacterias = cantidadInicial\r\nwhile cantidadBacterias <= cantidadMaxima:\r\n dia += 1\r\n cantidadBacterias *= 2\r\n\r\nprint(\"la cantidad excedió el máximo en el día: \", dia)\r\n\r\n# En Python, no existe la estructura Do - while, pero se puede implementar a través de un while True en combinación\r\n# con una instrucción break.\r\n\r\n","repo_name":"amarulandap/Python_programacion_3","sub_path":"EjerciciosCicloWhile1.py","file_name":"EjerciciosCicloWhile1.py","file_ext":"py","file_size_in_byte":1653,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"12307720545","text":"import pandas\n\nbuildings = pandas.read_csv('buildings.csv', index_col='name')\nenergy = pandas.read_csv('energy.csv', parse_dates=['date'])\n\n# filter out missing energy rows\nenergy = energy[energy['kBtu'] > 0]\n\n# pivot the building data to get a dataframe where the index \n# is the time and the columns are all the individual buildings\nenergy = energy.pivot(index='date', columns='building', values='kBtu')\n\n# calculate the average energy used by building, and assign\nbuildings['kBtu'] = energy.mean()\n\n# calculate the average energy used per square foot\nbuildings['kBtu/sqft'] = buildings['kBtu'] / buildings['area']\n\n# sort the buildings j\nbuildings = buildings.sort_values('kBtu/sqft', ascending=False)\nprint(\"Top 10\")\n\nbuildings = buildings.sort_values('kBtu/sqft', ascending=False)\nprint(buildings.head(10))\n\nbuildings.to_csv('buildings_energy.csv')\n","repo_name":"edsu/inst126","sub_path":"project/example/analyze.py","file_name":"analyze.py","file_ext":"py","file_size_in_byte":854,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"85"} +{"seq_id":"8474158578","text":"import sys, pygame;\nfrom pygame.locals import *;\n\nimport game;\n\nscreenW = 800;\nscreenH = 600;\n\npygame.init();\n\nclock = pygame.time.Clock();\n\neventList = [];\nkeyboardState = [];\nkeyboardOldState = [];\n\ngameRunning = True;\n\ndef updateEvents():\n\tglobal eventList, gameRunning;\n\tpygame.event.pump();\n\teventList = pygame.event.get();\n\n\tfor event in eventList:\n\t\tif event.type == QUIT:\n\t\t\tgameRunning = False;\n\n\tupdateKeyboard();\n\ndef updateKeyboard():\n\tglobal keyboardState, keyboardOldState;\n\n\tkeyboardOldState = keyboardState;\n\tkeyboardState = pygame.key.get_pressed();\n\ndef isKeyDown(key):\n\treturn keyboardState[key];\ndef isKeyPressed(key):\n\treturn keyboardState[key] and (not keyboardOldState[key]);\ndef isKeyReleased(key):\n\treturn (not keyboardState[key]) and keyboardOldState[key];\n\n\nwindowSurface = pygame.display.set_mode( (screenW, screenH) );\nfont = pygame.font.Font('freesansbold.ttf', 20);\n\ngame.startGame();\n\ngameRunning = True;\nwhile gameRunning == True:\n\n\tupdateEvents();\n\n\tgame.updateGame();\n\tgame.drawGame();\n\tpygame.display.update();\n\n\tclock.tick(60);\t\n\npygame.quit();\nsys.exit();\n","repo_name":"praashie/pikselipeli-python","sub_path":"__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":1094,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"6434452718","text":"cont = 0\nmaior = 0\nmenor = 0\n\nwhile cont >= 0:\n n = int(input(\"Digite uma sequencia de numeros: \"))\n cont = cont + 1\n if n < 0:\n break\n \n if cont == 1:\n maior = n\n menor = n\n else:\n if n > maior:\n maior = n\n if n < menor:\n menor = n\n \nprint(\"O maior numero é:\", maior)\nprint(\"O menor numero é:\", menor)","repo_name":"NTNGLTT/python","sub_path":"Testes/ATV05/ex2maiormenor.py","file_name":"ex2maiormenor.py","file_ext":"py","file_size_in_byte":342,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"27818222952","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nRRNPP_detector: a tool to detect RRNPP-type quorum sensing systems in chromosomes, plasmids and phages of Firmicutes\n@author: Bernard Charles\nTeam AIRE (Adaptation, Integration, Reticulation, Evolution): http://www.evol-net.fr/\n\"\"\"\n\nimport os\nimport pandas\nimport re\nimport shutil\nimport subprocess\n\n\n#######################################\n# Prodigal\n#######################################\ndef prodigal(fna, out_dir):\n # produce a faa and a gff from a fna\n gff = os.path.join(out_dir, 'predicted_ORFs.gff')\n faa = os.path.join(out_dir, 'predicted_proteins.faa')\n prodigal_args = ['prodigal', '-i', fna, '-f', 'gff', '-o', gff, '-a', faa]\n subprocess.run(prodigal_args, check = True)\n print(' Prodigal done!')\n return gff, faa\n\n\n#######################################\n# hmmsearch\n#######################################\n# because the tblout output of hmmsearch is not a tsv but a messy ' '-separated file\ndef tblout_to_tsv(tblout, tsv, header, nb_field_separators):\n with open(tsv, mode='w') as outfile:\n if header:\n outfile.write(header) \n with open(tblout, mode='r') as infile:\n for line in infile:\n if not line.startswith('#'):\n newline = re.sub(' *', '\\t', line, count = nb_field_separators)\n outfile.write(newline)\n infile.close()\n outfile.close()\n\n\n# for each protein matched (evalue <= max_evalue), keep only the info of the hmm that gave rise to the best evalue\ndef filter_hmmsearch_results(tsv, max_evalue, min_pcover):\n useful_column_names = ['target_name', 'hmm_name', 'hmm_accession', 'hmm_len', 'E-value', 'score', 'hmm_start', 'hmm_end']\n column_types = {'target_name': str, 'hmm_name': str, 'hmm_accession': str, 'hmm_len': int, 'E-value': float, 'score': float, 'hmm_start': int, 'hmm_end': int}\n df = pandas.read_csv(tsv, sep='\\t', usecols=useful_column_names, dtype=column_types)\n df['hmm_coverage'] = round((df['hmm_end'] - df['hmm_start'] + 1) / df['hmm_len'] * 100, 1)\n df = df.loc[(df['E-value'] <= max_evalue) & (df['hmm_coverage'] >= min_pcover)]\n df = df.sort_values(by='E-value', ascending=True, kind='mergesort').drop_duplicates(subset=['target_name']) \n return df \n\n\ndef hmmsearch(faa, hmm_library, stdout, tblout, domtblout, cpu, max_evalue, min_pcover):\n hmmsearch_args = ['hmmsearch', '--cpu', cpu, '--tblout', tblout, \n '--domtblout', domtblout,'-o', stdout, hmm_library, faa]\n subprocess.run(hmmsearch_args, check = True)\n \n header = ('target_name\\ttgt_accession\\thmm_name\\thmm_accession\\t'\n 'E-value\\tscore\\tbias\\t'\n 'E-value_best_domain\\tscore_best_domain\\tbias_best_domain'\n '\\texp\\treg\\tclu\\tov\\tenv\\tdom\\trep\\tinc\\tdescription_of_target\\n')\n \n nb_tabs = len(header.split('\\t'))-1\n tblout_to_tsv(tblout, tblout[:-4] + '.tsv', header, nb_tabs)\n \n header = ('target_name\\ttgt_accession\\ttgt_len\\thmm_name\\thmm_accession\\thmm_len\\t'\n 'E-value\\tscore\\tfull_seq_bias\\t'\n 'domain_nb\\ttotal_nb_domains\\tc-Evalue\\ti-Evalue\\tdomain_score\\tdomain_bias\\t'\n 'hmm_start\\thmm_end\\ttgt_start\\ttgt_end\\tenv_start\\tenv_end\\tacc\\tdescription_of_target\\n')\n nb_tabs = len(header.split('\\t'))-1\n tblout_to_tsv(domtblout, domtblout[:-4] + '.tsv', header, nb_tabs)\n \n results_df = filter_hmmsearch_results(domtblout[:-4] + '.tsv', max_evalue, min_pcover)\n\n # return results_df[['target_name', 'hmm_name', 'hmm_accession', 'E-value', 'score']]\n return results_df[['target_name', 'hmm_name', 'hmm_accession', 'hmm_coverage', 'E-value', 'score']]\n\n\n#######################################\n# tprpred\n#######################################\ndef filter_tprpred_results(tsv, min_proba):\n useful_column_names = ['Sequence', 'Score', 'P-value', 'Probab']\n column_types = {'Sequence': str, 'Score': float, 'P-value': float, 'Probab': str}\n df = pandas.read_csv(tsv, sep='\\t', usecols=useful_column_names, dtype=column_types)\n df['Probab'] = df['Probab'].str[:-1].astype('float')\n df = df.loc[df['Probab'] >= min_proba]\n return df\n\n\ndef tprpred_to_hmmsearch_df_format(df):\n df = df[['Sequence', 'P-value', 'Score']]\n df.columns = ['target_name', 'E-value', 'score']\n # df['hmm_name'] = 'tpr2.8'\n # df['hmm_accession'] = 'Tprpred'\n # return df[['target_name', 'hmm_name', 'hmm_accession', 'E-value', 'score']]\n df['hmm_name'] = 'tprpred'\n return df[['target_name', 'hmm_name', 'E-value', 'score']]\n \n \ndef tprpred(faa, outfile, rrnpp_detector_dir, current_dir, min_proba):\n tprpred_dir = os.path.join(rrnpp_detector_dir, 'tprpred')\n os.chdir(tprpred_dir)\n tprpred_args = ['perl', 'tprpred.pl', faa, '-r', 'tpr2.8.pp']\n with open(outfile, mode = 'w') as f:\n subprocess.run(tprpred_args, check = True, stdout = f)\n f.close()\n os.chdir(current_dir)\n \n df = filter_tprpred_results(outfile, min_proba)\n df = tprpred_to_hmmsearch_df_format(df)\n return df\n\n\n#######################################\n# orfipy\n#######################################\n# def orfipy(fna, flanking_dict, parameters, cpu, working_dir):\n# min_len = parameters['propeptide_len_boundaries'][0] * 3\n# max_len = parameters['propeptide_len_boundaries'][1] * 3\n# orfipy_args = ['orfipy', '--outdir', working_dir, '--procs', cpu, '--ignore-case', '--dna', 'orfipy_micropeptides.fna', \n# '--pep', 'orfipy_micropeptides.faa', '--bed', 'orfipy_micropeptides.bed', '--strand', 'f', \n# '--min', str(min_len), '--max', str(max_len), '--start', ','.join(parameters['orfipy_start_codons']), fna]\n# subprocess.run(orfipy_args, check = True)\n \n \n#######################################\n# BlastP\n#######################################\n# apply thresholds and keep only the best homolog for each protein \ndef filter_blastp_results(blastp_results, blastp_min_pident, blastp_min_pcover, blastp_max_evalue):\n if os.stat(blastp_results).st_size == 0:\n filtered_df = pandas.DataFrame(columns=['target_name', 'query_name', 'blastp_evalue', 'perc_identity', 'perc_cover'])\n else:\n df = pandas.read_csv(blastp_results, sep='\\t', header=None)\n df.columns = ['query_name', 'target_name', 'qlen', 'slen', 'perc_identity', 'blastp_evalue', 'bitscore', 'qstart', 'qend', 'sstart', 'send']\n query_coverage = round((df['qend'] - df['qstart'] + 1) / df['qlen'] * 100, 1)\n target_coverage = round((df['send'] - df['sstart'] + 1) / df['slen'] * 100, 1)\n df['perc_cover'] = pandas.concat([query_coverage, target_coverage], axis=1).apply(min, axis=1)\n filtered_df = df.loc[(df['perc_identity'] >= blastp_min_pident) & (df['perc_cover'] >= blastp_min_pcover) & (df['blastp_evalue'] <= blastp_max_evalue)]\n filtered_df = filtered_df.sort_values(by = 'blastp_evalue', ascending = True, kind = 'mergesort').drop_duplicates(subset=['target_name'])\n return filtered_df[['target_name', 'query_name', 'blastp_evalue', 'perc_identity', 'perc_cover']]\n\n\ndef blastp(query_faa, target_faa, out_dir, cpu, blastp_min_pident, blastp_min_pcover, blastp_max_evalue):\n basename_target = os.path.basename(target_faa).split('.fa')[0]\n basename_query = os.path.basename(query_faa).split('.fa')[0]\n \n # makeblastdb\n database = os.path.join(out_dir, basename_target + '_blastpDB')\n makeblastdb_args = ['makeblastdb', '-dbtype', 'prot', '-in', target_faa, '-out', database]\n subprocess.run(makeblastdb_args, check = True)\n \n # blastp\n blastp_results = os.path.join(out_dir, basename_query + '_vs_' + basename_target + '_blastp_results.tsv')\n blastp_args = ['blastp', '-query', query_faa, '-db', database, '-out', blastp_results, '-num_threads', cpu, \n '-max_target_seqs', '1000000', '-outfmt', '6 qseqid sseqid qlen slen pident evalue bitscore qstart qend sstart send']\n subprocess.run(blastp_args, check = True)\n\n # clean blast\n df = filter_blastp_results(blastp_results, blastp_min_pident, blastp_min_pcover, blastp_max_evalue)\n return df\n \n\n#######################################\n# Signalp\n#######################################\ndef signalp(faa, out_dir, rrnpp_detector_dir, current_dir):\n try:\n signalp_dir = os.path.dirname(shutil.which('signalp'))\n except:\n print('warning: the signalp binary is not found in your $PATH. RRNPP_detector will try to run built-in signalp version 5.0b Linux x86_64')\n signalp_dir = os.path.join(rrnpp_detector_dir, 'signalp-5.0b', 'bin')\n # signalp must be run from the directory where the binary is stored\n os.chdir(signalp_dir)\n signalp_args = ['./signalp', '-org', 'gram+', '-format', 'short', '-fasta', faa, '-prefix', os.path.join(out_dir, 'signalp')]\n subprocess.run(signalp_args, check = True)\n os.chdir(current_dir)\n df = pandas.read_csv(os.path.join(out_dir, 'signalp_summary.signalp5'), comment='#', sep='\\t', header=None)\n df.columns = ['protein_id', 'Prediction', 'SP(Sec/SPI)', 'TAT(Tat/SPI)', 'LIPO(Sec/SPII)', 'OTHER', 'CS_Position']\n df['propeptide_score'] = df['SP(Sec/SPI)']\n return df[['protein_id', 'Prediction', 'propeptide_score', 'SP(Sec/SPI)', 'TAT(Tat/SPI)', 'LIPO(Sec/SPII)', 'OTHER', 'CS_Position']]\n\n#######################################\n# PrediSi\n#######################################\ndef predisi(faa, out_dir, rrnpp_detector_dir, current_dir, min_likelihood):\n predisi_dir = os.path.join(rrnpp_detector_dir, 'predisi')\n os.chdir(predisi_dir)\n try:\n subprocess.call(['java', 'JSPP',\n os.path.join(predisi_dir, 'matrices(SearchMatrix-objects)', 'gramp.smx'),\n faa, os.path.join(out_dir, 'predisi_output.tsv')])\n df = pandas.read_csv(os.path.join(out_dir, 'predisi_output.tsv'), sep = \"\\t\", header=None)\n df.columns = ['protein_id', 'CS_Position', 'Prediction', 'propeptide_score']\n df = df.loc[df['propeptide_score'] >= min_likelihood]\n # df = df.loc[df['Prediction'] == 'Y']\n df['Prediction'] = df['Prediction'].map({'Y': 'PrediSi_pos', 'N': 'PrediSi_neg_(but_high_score)'})\n df['protein_id'] = df['protein_id'].apply(lambda x: x.split(' ')[0])\n df['CS_Position'] = df['CS_Position'].apply(lambda x: \"CS pos: \" + str(x) + ':' + str(x + 1))\n return df\n except:\n print('Warning: Predisi exited with an error (only signalp results will be considered)')\n return None\n ","repo_name":"TeamAIRE/RRNPP_detector","sub_path":"rrnpp_detector/wrappers.py","file_name":"wrappers.py","file_ext":"py","file_size_in_byte":10559,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"85"} +{"seq_id":"5527056537","text":"pregunta = bool(int(input(\"1 = True, 0 = False: \")))\nprint(pregunta)\n\nvalor = input(\"¿Es de noche? (True/False): \")\nnoche = False\n\nif valor == \"True\":\n noche = True\nelse:\n noche = False\n\n#(no)(noche)\n#(0)(1) = 0\n#True = 1 y False = 0\n#Si (no)(noche) es verdadero\n\nif not noche is True:\n luz = True\n print(\"Ufas, ando prendido\")\nelse:\n luz = False\n print(\"Estoy apagado\")","repo_name":"axelFrias1998/CursoPython","sub_path":"Estructuras de control/condicionesParteUno.py","file_name":"condicionesParteUno.py","file_ext":"py","file_size_in_byte":389,"program_lang":"python","lang":"es","doc_type":"code","stars":2,"dataset":"github-code","pt":"85"} +{"seq_id":"70534041238","text":"#encoding=utf-8\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\nimport matplotlib as mpl\nmpl.rcParams.update({'font.size':24, 'figure.figsize': [10, 6], 'figure.autolayout': True})\n\nfiltro_3=396.45\nfiltro_2=21.38\nfiltro_1=8.1\n\nespectro=\"./espectro/084.txt\"\nespectro1=\"./espectro/077.txt\"\nx,y = np.loadtxt(espectro, unpack=True)\nx1,y1=np.loadtxt(espectro1, unpack=True)\ny=(y-875)*filtro_2\ny1=(y1 -875)*10\n\n\ny1=y1/y.max()\ny=y/y.max()\n\nplt.plot(1239.84/x1,y1, marker=\"s\" , label=\"8.3 mW (x10)\")#\\n$1.6\\\\times1.6\\\\,\\\\mu$m\")\nplt.plot(1239.84/x,y, marker=\"^\" , label=\"15.4 mW\")#\\n$1.6\\\\times1.6\\\\,\\\\mu$m\")\n\nplt.xlabel(u\"Energía [eV]\")\nplt.ylabel(u\"Intensidad [u.a.]\")\nplt.grid(alpha=0.4)\nplt.legend()\nplt.xlim(1.531,1.537)\nplt.show()","repo_name":"astrocronopio/Lab_Avanzado","sub_path":"Condensacion/19-05-09/03/plot_espectro.py","file_name":"plot_espectro.py","file_ext":"py","file_size_in_byte":739,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"7871429105","text":"from pykickstart.base import BaseData, KickstartCommand\nfrom pykickstart.constants import BOOTPROTO_BOOTP, BOOTPROTO_DHCP, BOOTPROTO_IBFT, BOOTPROTO_QUERY, BOOTPROTO_STATIC, BIND_TO_MAC\nfrom pykickstart.options import KSOptionParser\nfrom pykickstart.errors import KickstartValueError, formatErrorMsg\n\nimport gettext\nimport warnings\n_ = lambda x: gettext.ldgettext(\"pykickstart\", x)\n\nMIN_VLAN_ID = 0\nMAX_VLAN_ID = 4095\n\nclass FC3_NetworkData(BaseData):\n removedKeywords = BaseData.removedKeywords\n removedAttrs = BaseData.removedAttrs\n\n def __init__(self, *args, **kwargs):\n BaseData.__init__(self, *args, **kwargs)\n self.bootProto = kwargs.get(\"bootProto\", BOOTPROTO_DHCP)\n self.dhcpclass = kwargs.get(\"dhcpclass\", \"\")\n self.device = kwargs.get(\"device\", \"\")\n self.essid = kwargs.get(\"essid\", \"\")\n self.ethtool = kwargs.get(\"ethtool\", \"\")\n self.gateway = kwargs.get(\"gateway\", \"\")\n self.hostname = kwargs.get(\"hostname\", \"\")\n self.ip = kwargs.get(\"ip\", \"\")\n self.mtu = kwargs.get(\"mtu\", \"\")\n self.nameserver = kwargs.get(\"nameserver\", \"\")\n self.netmask = kwargs.get(\"netmask\", \"\")\n self.nodns = kwargs.get(\"nodns\", False)\n self.onboot = kwargs.get(\"onboot\", True)\n self.wepkey = kwargs.get(\"wepkey\", \"\")\n\n def __eq__(self, y):\n if not y:\n return False\n\n return self.device and self.device == y.device\n\n def __ne__(self, y):\n return not self == y\n\n def _getArgsAsStr(self):\n retval = \"\"\n\n if self.bootProto != \"\":\n retval += \" --bootproto=%s\" % self.bootProto\n if self.dhcpclass != \"\":\n retval += \" --dhcpclass=%s\" % self.dhcpclass\n if self.device != \"\":\n retval += \" --device=%s\" % self.device\n if self.essid != \"\":\n retval += \" --essid=\\\"%s\\\"\" % self.essid\n if self.ethtool != \"\":\n retval += \" --ethtool=\\\"%s\\\"\" % self.ethtool\n if self.gateway != \"\":\n retval += \" --gateway=%s\" % self.gateway\n if self.hostname != \"\":\n retval += \" --hostname=%s\" % self.hostname\n if self.ip != \"\":\n retval += \" --ip=%s\" % self.ip\n if self.mtu != \"\":\n retval += \" --mtu=%s\" % self.mtu\n if self.nameserver != \"\":\n retval += \" --nameserver=%s\" % self.nameserver\n if self.netmask != \"\":\n retval += \" --netmask=%s\" % self.netmask\n if self.nodns:\n retval += \" --nodns\"\n if not self.onboot:\n retval += \" --onboot=off\"\n if self.wepkey != \"\":\n retval += \" --wepkey=%s\" % self.wepkey\n\n return retval\n\n def __str__(self):\n retval = BaseData.__str__(self)\n retval += \"network %s\\n\" % self._getArgsAsStr()\n return retval\n\nclass FC4_NetworkData(FC3_NetworkData):\n removedKeywords = FC3_NetworkData.removedKeywords\n removedAttrs = FC3_NetworkData.removedAttrs\n\n def __init__(self, *args, **kwargs):\n FC3_NetworkData.__init__(self, *args, **kwargs)\n self.notksdevice = kwargs.get(\"notksdevice\", False)\n\n def _getArgsAsStr(self):\n retval = FC3_NetworkData._getArgsAsStr(self)\n\n if self.notksdevice:\n retval += \" --notksdevice\"\n\n return retval\n\nclass FC6_NetworkData(FC4_NetworkData):\n removedKeywords = FC4_NetworkData.removedKeywords\n removedAttrs = FC4_NetworkData.removedAttrs\n\n def __init__(self, *args, **kwargs):\n FC4_NetworkData.__init__(self, *args, **kwargs)\n self.noipv4 = kwargs.get(\"noipv4\", False)\n self.noipv6 = kwargs.get(\"noipv6\", False)\n\n def _getArgsAsStr(self):\n retval = FC4_NetworkData._getArgsAsStr(self)\n\n if self.noipv4:\n retval += \" --noipv4\"\n if self.noipv6:\n retval += \" --noipv6\"\n\n return retval\n\nclass F8_NetworkData(FC6_NetworkData):\n removedKeywords = FC6_NetworkData.removedKeywords\n removedAttrs = FC6_NetworkData.removedAttrs\n\n def __init__(self, *args, **kwargs):\n FC6_NetworkData.__init__(self, *args, **kwargs)\n self.ipv6 = kwargs.get(\"ipv6\", \"\")\n\n def _getArgsAsStr(self):\n retval = FC6_NetworkData._getArgsAsStr(self)\n\n if self.ipv6 != \"\":\n retval += \" --ipv6=%s\" % self.ipv6\n\n return retval\n\nclass F16_NetworkData(F8_NetworkData):\n removedKeywords = F8_NetworkData.removedKeywords\n removedAttrs = F8_NetworkData.removedAttrs\n\n def __init__(self, *args, **kwargs):\n F8_NetworkData.__init__(self, *args, **kwargs)\n self.activate = kwargs.get(\"activate\", None)\n self.nodefroute = kwargs.get(\"nodefroute\", False)\n self.wpakey = kwargs.get(\"wpakey\", \"\")\n\n def _getArgsAsStr(self):\n retval = F8_NetworkData._getArgsAsStr(self)\n\n if self.activate:\n retval += \" --activate\"\n if self.nodefroute:\n retval += \" --nodefroute\"\n if self.wpakey != \"\":\n retval += \" --wpakey=%s\" % self.wpakey\n\n return retval\n\nclass F19_NetworkData(F16_NetworkData):\n removedKeywords = F16_NetworkData.removedKeywords\n removedAttrs = F16_NetworkData.removedAttrs\n\n def __init__(self, *args, **kwargs):\n F16_NetworkData.__init__(self, *args, **kwargs)\n self.bondslaves = kwargs.get(\"bondslaves\", \"\")\n self.bondopts = kwargs.get(\"bondopts\", \"\")\n self.vlanid = kwargs.get(\"vlanid\", \"\")\n self.ipv6gateway = kwargs.get(\"ipv6gateway\", \"\")\n\n def _getArgsAsStr(self):\n retval = F16_NetworkData._getArgsAsStr(self)\n\n if self.bondslaves != \"\":\n retval += \" --bondslaves=%s\" % self.bondslaves\n if self.bondopts != \"\":\n retval += \" --bondopts=%s\" % self.bondopts\n if self.vlanid:\n retval += \" --vlanid=%s\" % self.vlanid\n if self.ipv6gateway:\n retval += \" --ipv6gateway=%s\" % self.ipv6gateway\n\n return retval\n\nclass F20_NetworkData(F19_NetworkData):\n removedKeywords = F19_NetworkData.removedKeywords\n removedAttrs = F19_NetworkData.removedAttrs\n\n def __init__(self, *args, **kwargs):\n F19_NetworkData.__init__(self, *args, **kwargs)\n self.teamslaves = kwargs.get(\"teamslaves\", [])\n self.teamconfig = kwargs.get(\"teamconfig\", \"\")\n\n def _getArgsAsStr(self):\n retval = F19_NetworkData._getArgsAsStr(self)\n\n # see the tests for format description\n if self.teamslaves:\n slavecfgs = []\n for slave, config in self.teamslaves:\n if config:\n config = \"'\" + config + \"'\"\n slavecfgs.append(slave+config)\n slavecfgs = \",\".join(slavecfgs).replace('\"', r'\\\"')\n retval += ' --teamslaves=\"%s\"' % slavecfgs\n if self.teamconfig:\n retval += ' --teamconfig=\"%s\"' % self.teamconfig.replace('\"', r'\\\"')\n return retval\n\nclass F21_NetworkData(F20_NetworkData):\n removedKeywords = F20_NetworkData.removedKeywords\n removedAttrs = F20_NetworkData.removedAttrs\n\n def __init__(self, *args, **kwargs):\n F20_NetworkData.__init__(self, *args, **kwargs)\n self.interfacename = kwargs.get(\"interfacename\", \"\")\n\n def _getArgsAsStr(self):\n retval = F20_NetworkData._getArgsAsStr(self)\n if self.interfacename:\n retval += \" --interfacename=%s\" % self.interfacename\n\n return retval\n\nclass F22_NetworkData(F21_NetworkData):\n removedKeywords = F21_NetworkData.removedKeywords\n removedAttrs = F21_NetworkData.removedAttrs\n\n def __init__(self, *args, **kwargs):\n F21_NetworkData.__init__(self, *args, **kwargs)\n self.bridgeslaves = kwargs.get(\"bridgeslaves\", \"\")\n self.bridgeopts = kwargs.get(\"bridgeopts\", \"\")\n\n def _getArgsAsStr(self):\n retval = F21_NetworkData._getArgsAsStr(self)\n if self.bridgeslaves != \"\":\n retval += \" --bridgeslaves=%s\" % self.bridgeslaves\n if self.bridgeopts != \"\":\n retval += \" --bridgeopts=%s\" % self.bridgeopts\n\n return retval\n\nclass RHEL4_NetworkData(FC3_NetworkData):\n removedKeywords = FC3_NetworkData.removedKeywords\n removedAttrs = FC3_NetworkData.removedAttrs\n\n def __init__(self, *args, **kwargs):\n FC3_NetworkData.__init__(self, *args, **kwargs)\n self.notksdevice = kwargs.get(\"notksdevice\", False)\n\n def _getArgsAsStr(self):\n retval = FC3_NetworkData._getArgsAsStr(self)\n\n if self.notksdevice:\n retval += \" --notksdevice\"\n\n return retval\n\nclass RHEL6_NetworkData(F8_NetworkData):\n removedKeywords = F8_NetworkData.removedKeywords\n removedAttrs = F8_NetworkData.removedAttrs\n\n def __init__(self, *args, **kwargs):\n F8_NetworkData.__init__(self, *args, **kwargs)\n self.activate = kwargs.get(\"activate\", None)\n self.nodefroute = kwargs.get(\"nodefroute\", False)\n self.vlanid = kwargs.get(\"vlanid\", \"\")\n self.bondslaves = kwargs.get(\"bondslaves\", \"\")\n self.bondopts = kwargs.get(\"bondopts\", \"\")\n\n def _getArgsAsStr(self):\n retval = F8_NetworkData._getArgsAsStr(self)\n\n if self.activate:\n retval += \" --activate\"\n if self.nodefroute:\n retval += \" --nodefroute\"\n if self.vlanid:\n retval += \" --vlanid=%s\" % self.vlanid\n if self.bondslaves:\n retval += \" --bondslaves=%s\" % self.bondslaves\n if self.bondopts:\n retval += \" --bondopts=%s\" % self.bondopts\n\n\n return retval\n\nclass RHEL7_NetworkData(F21_NetworkData):\n removedKeywords = F21_NetworkData.removedKeywords\n removedAttrs = F21_NetworkData.removedAttrs\n\n def __init__(self, *args, **kwargs):\n F21_NetworkData.__init__(self, *args, **kwargs)\n self.bridgeslaves = kwargs.get(\"bridgeslaves\", \"\")\n self.bridgeopts = kwargs.get(\"bridgeopts\", \"\")\n self.bindto = kwargs.get(\"bindto\", None)\n\n def _getArgsAsStr(self):\n retval = F21_NetworkData._getArgsAsStr(self)\n if self.bridgeslaves != \"\":\n retval += \" --bridgeslaves=%s\" % self.bridgeslaves\n if self.bridgeopts != \"\":\n retval += \" --bridgeopts=%s\" % self.bridgeopts\n if self.activate == False:\n retval += \" --no-activate\"\n if self.bindto == BIND_TO_MAC:\n retval += \" --bindto=%s\" % self.bindto\n\n return retval\n\nclass FC3_Network(KickstartCommand):\n removedKeywords = KickstartCommand.removedKeywords\n removedAttrs = KickstartCommand.removedAttrs\n\n def __init__(self, writePriority=0, *args, **kwargs):\n KickstartCommand.__init__(self, writePriority, *args, **kwargs)\n self.bootprotoList = [BOOTPROTO_DHCP, BOOTPROTO_BOOTP,\n BOOTPROTO_STATIC]\n\n self.op = self._getParser()\n\n self.network = kwargs.get(\"network\", [])\n\n def __str__(self):\n retval = \"\"\n\n for nic in self.network:\n retval += nic.__str__()\n\n if retval != \"\":\n return \"# Network information\\n\" + retval\n else:\n return \"\"\n\n def _getParser(self):\n op = KSOptionParser()\n op.add_option(\"--bootproto\", dest=\"bootProto\",\n default=BOOTPROTO_DHCP,\n choices=self.bootprotoList)\n op.add_option(\"--dhcpclass\", dest=\"dhcpclass\")\n op.add_option(\"--device\", dest=\"device\")\n op.add_option(\"--essid\", dest=\"essid\")\n op.add_option(\"--ethtool\", dest=\"ethtool\")\n op.add_option(\"--gateway\", dest=\"gateway\")\n op.add_option(\"--hostname\", dest=\"hostname\")\n op.add_option(\"--ip\", dest=\"ip\")\n op.add_option(\"--mtu\", dest=\"mtu\")\n op.add_option(\"--nameserver\", dest=\"nameserver\")\n op.add_option(\"--netmask\", dest=\"netmask\")\n op.add_option(\"--nodns\", dest=\"nodns\", action=\"store_true\",\n default=False)\n op.add_option(\"--onboot\", dest=\"onboot\", action=\"store\",\n type=\"ksboolean\")\n op.add_option(\"--wepkey\", dest=\"wepkey\")\n return op\n\n def parse(self, args):\n (opts, _extra) = self.op.parse_args(args=args, lineno=self.lineno)\n nd = self.handler.NetworkData()\n self._setToObj(self.op, opts, nd)\n nd.lineno = self.lineno\n\n # Check for duplicates in the data list.\n if nd in self.dataList():\n warnings.warn(_(\"A network device with the name %s has already been defined.\") % nd.device)\n\n return nd\n\n def dataList(self):\n return self.network\n\nclass FC4_Network(FC3_Network):\n removedKeywords = FC3_Network.removedKeywords\n removedAttrs = FC3_Network.removedAttrs\n\n def _getParser(self):\n op = FC3_Network._getParser(self)\n op.add_option(\"--notksdevice\", dest=\"notksdevice\", action=\"store_true\",\n default=False)\n return op\n\nclass FC6_Network(FC4_Network):\n removedKeywords = FC4_Network.removedKeywords\n removedAttrs = FC4_Network.removedAttrs\n\n def _getParser(self):\n op = FC4_Network._getParser(self)\n op.add_option(\"--noipv4\", dest=\"noipv4\", action=\"store_true\",\n default=False)\n op.add_option(\"--noipv6\", dest=\"noipv6\", action=\"store_true\",\n default=False)\n return op\n\nclass F8_Network(FC6_Network):\n removedKeywords = FC6_Network.removedKeywords\n removedAttrs = FC6_Network.removedAttrs\n\n def _getParser(self):\n op = FC6_Network._getParser(self)\n op.add_option(\"--ipv6\", dest=\"ipv6\")\n return op\n\nclass F9_Network(F8_Network):\n removedKeywords = F8_Network.removedKeywords\n removedAttrs = F8_Network.removedAttrs\n\n def __init__(self, writePriority=0, *args, **kwargs):\n F8_Network.__init__(self, writePriority, *args, **kwargs)\n self.bootprotoList.append(BOOTPROTO_QUERY)\n\n def _getParser(self):\n op = F8_Network._getParser(self)\n op.add_option(\"--bootproto\", dest=\"bootProto\",\n default=BOOTPROTO_DHCP,\n choices=self.bootprotoList)\n return op\n\nclass F16_Network(F9_Network):\n removedKeywords = F9_Network.removedKeywords\n removedAttrs = F9_Network.removedAttrs\n\n def __init__(self, writePriority=0, *args, **kwargs):\n F9_Network.__init__(self, writePriority, *args, **kwargs)\n self.bootprotoList.append(BOOTPROTO_IBFT)\n\n def _getParser(self):\n op = F9_Network._getParser(self)\n op.add_option(\"--activate\", dest=\"activate\", action=\"store_true\",\n default=None)\n op.add_option(\"--nodefroute\", dest=\"nodefroute\", action=\"store_true\",\n default=False)\n op.add_option(\"--wpakey\", dest=\"wpakey\", action=\"store\", default=\"\")\n return op\n\nclass F18_Network(F16_Network):\n\n @property\n def hostname(self):\n for nd in self.dataList():\n if nd.hostname:\n return nd.hostname\n return None\n\nclass F19_Network(F18_Network):\n\n def _getParser(self):\n op = F18_Network._getParser(self)\n op.add_option(\"--bondslaves\", dest=\"bondslaves\", action=\"store\",\n default=\"\")\n op.add_option(\"--bondopts\", dest=\"bondopts\", action=\"store\",\n default=\"\")\n op.add_option(\"--vlanid\", dest=\"vlanid\")\n op.add_option(\"--ipv6gateway\", dest=\"ipv6gateway\", action=\"store\",\n default=\"\")\n return op\n\nclass F20_Network(F19_Network):\n\n def _getParser(self):\n # see the tests for teamslaves option\n def teamslaves_cb(option, opt_str, value, parser):\n # value is of: \"[''],[''],...\"\n # for example: \"eth1,eth2'{\"prio\": 100}',eth3\"\n teamslaves = []\n if value:\n # Although slaves, having optional config, are separated by \",\"\n # first extract json configs because they can contain the \",\"\n parts = value.split(\"'\")\n # parts == ['eth1,eth2', '{\"prio\": 100}', ',eth3']\n # ensure the list has even number of items for further zipping,\n # for odd number of items\n if len(parts) % 2 == 1:\n # if the list ends with an empty string which must be a leftover\n # from splitting string not ending with device eg\n # \"eth1,eth2'{\"prio\":100\"}'\"\n if not parts[-1]:\n # just remove it\n parts = parts[:-1]\n # if not (our example), add empty config for the last device\n else:\n parts.append('')\n # parts == ['eth1,eth2', '{\"prio\": 100}', ',eth3', '']\n # zip devices with their configs\n it = iter(parts)\n for devs, cfg in zip(it,it):\n # first loop:\n # devs == \"eth1,eth2\", cfg == '{\"prio\": 100}'\n devs = devs.strip(',').split(',')\n # devs == [\"eth1\", \"eth2\"]\n # initialize config of all devs but the last one to empty\n for d in devs[:-1]:\n teamslaves.append((d, ''))\n # teamslaves == [(\"eth1\", '')]\n # and set config of the last device\n teamslaves.append((devs[-1], cfg))\n # teamslaves == [('eth1', ''), ('eth2', '{\"prio\": 100}']\n parser.values.teamslaves = teamslaves\n\n op = F19_Network._getParser(self)\n op.add_option(\"--teamslaves\", dest=\"teamslaves\", action=\"callback\",\n callback=teamslaves_cb, nargs=1, type=\"string\")\n op.add_option(\"--teamconfig\", dest=\"teamconfig\", action=\"store\",\n default=\"\")\n return op\n\nclass F21_Network(F20_Network):\n def _getParser(self):\n op = F20_Network._getParser(self)\n op.add_option(\"--interfacename\", dest=\"interfacename\", action=\"store\",\n default=\"\")\n return op\n\nclass F22_Network(F21_Network):\n def _getParser(self):\n op = F21_Network._getParser(self)\n op.add_option(\"--bridgeslaves\", dest=\"bridgeslaves\", action=\"store\",\n default=\"\")\n op.add_option(\"--bridgeopts\", dest=\"bridgeopts\", action=\"store\",\n default=\"\")\n return op\n\n def parse(self, args):\n # call the overridden command to do it's job first\n retval = F21_Network.parse(self, args)\n\n if retval.bridgeopts:\n if not retval.bridgeslaves:\n msg = formatErrorMsg(self.lineno, msg=_(\"Option --bridgeopts requires \"\\\n \"--bridgeslaves to be specified\"))\n raise KickstartValueError(msg)\n opts = retval.bridgeopts.split(\",\")\n for opt in opts:\n _key, _sep, value = opt.partition(\"=\")\n if not value or \"=\" in value:\n msg = formatErrorMsg(self.lineno, msg=_(\"Bad format of --bridgeopts, expecting key=value options separated by ','\"))\n raise KickstartValueError(msg)\n\n return retval\n\nclass RHEL4_Network(FC3_Network):\n removedKeywords = FC3_Network.removedKeywords\n removedAttrs = FC3_Network.removedAttrs\n\n def _getParser(self):\n op = FC3_Network._getParser(self)\n op.add_option(\"--notksdevice\", dest=\"notksdevice\", action=\"store_true\",\n default=False)\n return op\n\nclass RHEL5_Network(FC6_Network):\n removedKeywords = FC6_Network.removedKeywords\n removedAttrs = FC6_Network.removedAttrs\n\n def __init__(self, writePriority=0, *args, **kwargs):\n FC6_Network.__init__(self, writePriority, *args, **kwargs)\n self.bootprotoList.append(BOOTPROTO_QUERY)\n\n def _getParser(self):\n op = FC6_Network._getParser(self)\n op.add_option(\"--bootproto\", dest=\"bootProto\",\n default=BOOTPROTO_DHCP,\n choices=self.bootprotoList)\n return op\n\nclass RHEL6_Network(F9_Network):\n removedKeywords = F9_Network.removedKeywords\n removedAttrs = F9_Network.removedAttrs\n\n def __init__(self, writePriority=0, *args, **kwargs):\n F9_Network.__init__(self, writePriority, *args, **kwargs)\n self.bootprotoList.append(BOOTPROTO_IBFT)\n\n def _getParser(self):\n op = F9_Network._getParser(self)\n op.add_option(\"--activate\", dest=\"activate\", action=\"store_true\",\n default=None)\n op.add_option(\"--nodefroute\", dest=\"nodefroute\", action=\"store_true\",\n default=False)\n op.add_option(\"--vlanid\", dest=\"vlanid\")\n op.add_option(\"--bondslaves\", dest=\"bondslaves\")\n op.add_option(\"--bondopts\", dest=\"bondopts\")\n return op\n\ndef validate_network_interface_name(name):\n \"\"\"Check if the given network interface name is valid, return an error message\n if an error is found or None if no errors are found\n\n :param str name: name to validate\n :returns: error message or None if no error is found\n :rtype: str or NoneType\n \"\"\"\n # (for reference see the NetworkManager source code:\n # NetworkManager/src/settings/plugins/ifcfg-rh/reader.c\n # and the make_vlan_setting function)\n\n vlan_id = None\n\n # if it contains '.', vlan id should follow (eg 'ens7.171', 'mydev.171')\n (vlan, dot, id_candidate) = name.partition(\".\")\n if dot:\n # 'vlan' can't be followed by a '.'\n if vlan == \"vlan\":\n return _(\"When using the . interface name notation, can't be equal to 'vlan'.\")\n try:\n vlan_id = int(id_candidate)\n except ValueError:\n return _(\"If network --interfacename contains a '.', valid vlan id should follow.\")\n\n # if it starts with 'vlan', vlan id should follow ('vlan171')\n (empty, sep, id_candidate) = name.partition(\"vlan\")\n if sep and empty == \"\":\n # if we checked only for empty == \"\", we would evaluate missing interface name as an error\n try:\n vlan_id = int(id_candidate)\n except ValueError:\n return _(\"If network --interfacename starts with 'vlan', valid vlan id should follow.\")\n\n # check if the vlan id is in range\n if vlan_id is not None:\n if not(MIN_VLAN_ID <= vlan_id <= MAX_VLAN_ID):\n return _(\"The vlan id out of the %d-%d vlan id range.\") % (MIN_VLAN_ID, MAX_VLAN_ID)\n\n # network interface name seems to be valid (no error found)\n return None\n\nclass RHEL7_Network(F21_Network):\n def __init__(self, writePriority=0, *args, **kwargs):\n self.bind_to_choices = [BIND_TO_MAC]\n F21_Network.__init__(self, writePriority, *args, **kwargs)\n\n def _getParser(self):\n op = F21_Network._getParser(self)\n op.add_option(\"--bridgeslaves\", dest=\"bridgeslaves\", action=\"store\",\n default=\"\")\n op.add_option(\"--bridgeopts\", dest=\"bridgeopts\", action=\"store\",\n default=\"\")\n op.add_option(\"--no-activate\", dest=\"activate\", action=\"store_false\",\n default=None)\n op.add_option(\"--bindto\", dest=\"bindto\", default=None,\n choices=self.bind_to_choices)\n return op\n\n def parse(self, args):\n # call the overridden command to do it's job first\n retval = F21_Network.parse(self, args)\n\n # validate the network interface name\n error_message = validate_network_interface_name(retval.interfacename)\n # something is wrong with the interface name\n if error_message is not None:\n raise KickstartValueError(formatErrorMsg(self.lineno,msg=error_message))\n\n if retval.bridgeopts:\n if not retval.bridgeslaves:\n msg = formatErrorMsg(self.lineno, msg=_(\"Option --bridgeopts requires \"\\\n \"--bridgeslaves to be specified\"))\n raise KickstartValueError(msg)\n opts = retval.bridgeopts.split(\",\")\n for opt in opts:\n _key, _sep, value = opt.partition(\"=\")\n if not value or \"=\" in value:\n msg = formatErrorMsg(self.lineno, msg=_(\"Bad format of --bridgeopts, expecting key=value options separated by ','\"))\n raise KickstartValueError(msg)\n\n if retval.bindto == BIND_TO_MAC:\n if retval.vlanid and not retval.bondopts:\n msg = formatErrorMsg(self.lineno, msg=_(\"--bindto=%s is not supported for this type of device\") % BIND_TO_MAC)\n raise KickstartValueError(msg)\n\n return retval\n","repo_name":"wzzalx/wzz","sub_path":"python2.7/site-packages/pykickstart/commands/network.py","file_name":"network.py","file_ext":"py","file_size_in_byte":24726,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"85"} +{"seq_id":"17726491587","text":"#! python3\n# showFilesGreaterThan.py - shows files whose size is greater than a desired value\n\nimport os\n\ndef showFilesWithSizeGreaterThan(folder,size):\n\n for aFolder, subfolders, filenames in os.walk(folder):\n for filename in filenames:\n if os.path.getsize(os.path.join(aFolder,filename))/1000000 > size:\n print(filename)\n\nshowFilesWithSizeGreaterThan('C:\\Lucas', 20)","repo_name":"LucasAstol/automatetheboring-python","sub_path":"chapter 10/showFilesGreaterThan.py","file_name":"showFilesGreaterThan.py","file_ext":"py","file_size_in_byte":404,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"13288074978","text":"from flask import Flask\n\napp = Flask(__name__)\n\n@app.route(\"/\")\ndef home():\n html = f\"

Udacity Capstone Project-Cloud DevOps Engineer

\"\n return html.format(format)\n\nif __name__ == \"__main__\":\n # load pretrained model as clf\n app.run(host='0.0.0.0', port=80, debug=True) # specify port=80\n","repo_name":"tungthanh97/udacity-cloud-devops-capstone","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":307,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"85"} +{"seq_id":"75019599958","text":"from sys import argv\nfrom collections import defaultdict\n\n# test: \n# input: 3065\n\nlines = open(argv[1], 'r').read().splitlines()\nhot_gas = [int(i.replace(\"<\", \"-1\").replace(\">\", \"1\")) for i in [c for c in lines[0]]]\n\n\ndef print_cave(cave, floor_y):\n for y in range(floor_y, 0, -1):\n line_vals = []\n line_vals.append(\"|\")\n for x in range(1, 8):\n line_vals.append(cave[(x, y)])\n line_vals.append(f\"| {y} {'--- !!!' if all([cave[(x, y)] == '#' for x in range(1, 8)]) else ''}\")\n print(\"\".join(line_vals))\n print(\"+\" + \"-\"*7 + \"+\")\n \n\ndef next_rock(floor_y, rock_i):\n rocks_ascii = [\"-\", \"+\", \"_|\", \"|\", \"o\"]\n y = floor_y + 4\n # ----\n if rock_i == 0:\n return [(3, y), (4, y), (5, y), (6, y)]\n # +\n elif rock_i == 1:\n return [(4, y), (3, y+1), (4, y+1), (5, y+1), (4, y+2)]\n # _|\n elif rock_i == 2:\n return [(3, y), (4, y), (5, y), (5, y+1), (5, y+2)]\n # |\n elif rock_i == 3:\n return [(3, y), (3, y+1), (3, y+2), (3, y+3)]\n # o\n elif rock_i == 4:\n return [(3, y), (4, y), (3, y+1), (4, y+1)] \n\n\ndef colision_detected(cave, rock):\n for (x, y) in rock:\n if x < 1 or x > 7:\n return True\n if y < 1:\n return True\n if cave[(x, y)] == \"#\":\n return True\n return False\n\ndef park_rock(cave, rock): \n global hot_gas, gas_i\n\n while True:\n # hot gas \n gas_x = hot_gas[gas_i]\n gas_i = (gas_i + 1) % len(hot_gas)\n rock_candidate = [(x + gas_x, y) for (x, y) in rock]\n if not colision_detected(cave, rock_candidate):\n rock = rock_candidate\n \n # gravitation\n rock_candidate = [(x, y - 1) for (x, y) in rock]\n if not colision_detected(cave, rock_candidate):\n rock = rock_candidate\n continue\n else:\n return rock\n \n\ndef add_rock(cave, rock):\n for r in rock:\n cave[r] = \"#\"\n\n\ncave = defaultdict(lambda: '.')\nfloor_y = 0\nrock_i = 0\ngas_i = 0\n\nfor _ in range(2022):\n rock = next_rock(floor_y, rock_i)\n parked_rock = park_rock(cave, rock)\n floor_y = max(floor_y, max([c[1] for c in parked_rock]))\n add_rock(cave, parked_rock)\n rock_i = (rock_i + 1) % 5\n\nprint_cave(cave, floor_y+3)\nprint(floor_y)","repo_name":"lenrok258/advent-of-code-2022","sub_path":"017/script1.py","file_name":"script1.py","file_ext":"py","file_size_in_byte":2320,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"85"} +{"seq_id":"6748717252","text":"def isgroup(str):\n id = {}\n for i in range(len(str)):\n if str[i] not in id.keys():\n id[str[i]] = list()\n id[str[i]].append(i)\n for i in id.keys():\n if len(id[i]) == 1:\n continue\n for j in range(len(id[i])-1):\n if id[i][j] != id[i][j+1] - 1:\n return 0\n return 1\n\nres = 0\nfor _ in range(int(input())):\n res += isgroup(input())\nprint(res)","repo_name":"rdgldkthu/baekjoon_python","sub_path":"level_06/1316.py","file_name":"1316.py","file_ext":"py","file_size_in_byte":426,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"739397058","text":"from collections import deque\n\ndx=[-1,1,0,0]\ndy=[0,0,-1,1]\ndef bfs(x,y,maps,n,m):\n q=deque([(x,y)])\n maps[x][y]=1\n while q:\n x,y=q.popleft()\n for i in range(4):\n nx=x+dx[i]\n ny=y+dy[i]\n if 0<=nx\" + title + \"\" + html_blob\n else:\n title = \"No Tab Title\"\n\n page = '''\n\n\n \n \n '''\n page += \"\" + title + \"\"\n page += '''\n'''\n\n page += '

'\n page += '''''' + navigation.course_name + ''' | '''\n page += '''Course Outline'''\n page += '

'\n page += html_blob\n\n page += '''\n\n'''\n\n page = page.replace(\"/static\", \"../static\")\n return page","repo_name":"jonnyBohnanny/open_edx_to_static_pages","sub_path":"ocx_to_html/writer/TabWriter.py","file_name":"TabWriter.py","file_ext":"py","file_size_in_byte":903,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"6897237460","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\n\r\nRichard Paredes\r\nAssignment #2\r\nCOSC1306\r\n\r\n\"\"\"\r\n# instead of using math.ceil()\r\ndef roundUp(number):\r\n return int(number+0.999999999999)\r\n\r\n# determines area of the floor\r\ndef getRoomArea(length, width):\r\n area = length*width\r\n return roundUp(area)\r\n\r\n# determines number of liters needed to cover area\r\ndef getLitersNeeded(area):\r\n SQM_PER_LITER = 6\r\n liters = area / SQM_PER_LITER\r\n return roundUp(liters)\r\n\r\n# determines number of bottles needed for given liters\r\ndef getBottlesNeeded(liters):\r\n LITERS_PER_BOTTLE = 2\r\n bottles = liters / LITERS_PER_BOTTLE\r\n return roundUp(bottles)\r\n\r\n# determines total cost of bottles needed\r\ndef getCost(bottles, costPerBottle):\r\n totalCost = bottles * costPerBottle\r\n return roundUp(totalCost)\r\n\r\ndef main():\r\n # get input from user\r\n length = float(input(\"Enter the length of the room (m): \"))\r\n width = float(input(\"Enter the width of the room (m): \"))\r\n cost = float(input(\"Enter the cost per bottle ($): \"))\r\n \r\n # get calculated values\r\n area = getRoomArea(length, width)\r\n liters = getLitersNeeded(area)\r\n bottles = getBottlesNeeded(liters)\r\n totalCost = getCost(bottles, cost)\r\n \r\n # prints the output\r\n print()\r\n print('='*40)\r\n print('Room area (m2) =', area)\r\n print('Sealant needed (L) =', liters)\r\n print('Bottles needed =', bottles)\r\n print('Total Cost ($) =', totalCost)\r\n print('='*40)\r\n \r\nmain()\r\n ","repo_name":"richard-paredes/CS-Fundamentals","sub_path":"IntroToProgramming/Week4/assignment2.py","file_name":"assignment2.py","file_ext":"py","file_size_in_byte":1497,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"34560449638","text":"from __future__ import print_function\nimport pickle\nimport os.path\nfrom outputpdf import html2pdf\nfrom googleapiclient.discovery import build\nfrom google_auth_oauthlib.flow import InstalledAppFlow\nfrom google.auth.transport.requests import Request\nimport tkinter as tk # 使用Tkinter前需要先匯入\n\n# If modifying these scopes, delete the file token.pickle.\nSCOPES = ['https://www.googleapis.com/auth/spreadsheets.readonly']\n\n# The ID and range of a sample spreadsheet.\nSAMPLE_SPREADSHEET_ID = '1p8Zpw7hLjbO3ECJqbYz3Zg2LN-CfMEHlqCxDxdFcylA'\n\n# 第1步，例項化object，建立視窗window\nwindow = tk.Tk()\n\n# 第2步，給視窗的視覺化起名字\nwindow.title('產生訂單明細')\n\n# 第3步，設定視窗的大小(長 * 寬)\nwindow.geometry('600x500') # 這裡的乘是小x\n\n# 第4步，在圖形介面上設定輸入框控制元件entry框並放置\ne = tk.Entry(window, show=None, width=10, font=('Arial', 14)) # 顯示成明文形式\ne.pack()\n\n\n# 第5步，定義兩個觸發事件時的函式insert_point和insert_end（注意：因為Python的執行順序是從上往下，所以函式一定要放在按鈕的上面）\n\ndef appendtext(row): # 將資料列為文件\n\n number = '** 輸入編號對應列表左邊的數字 **\\n\\n歡迎使用大玩家包車旅遊服務，您的訂單資訊如下：\\n\\n訂單編號:' + \\\n row[0]\n\n return number\n\n\ndef insert_point(): # 在滑鼠焦點處插入輸入內容\n SAMPLE_RANGE_NAME = '2:2'\n var = e.get()\n if not var:\n print('列出第一筆')\n else:\n SAMPLE_RANGE_NAME = var + ':' + var\n\n \"\"\"Shows basic usage of the Sheets API.\n Prints values from a sample spreadsheet.\n \"\"\"\n creds = None\n # The file token.pickle stores the user's access and refresh tokens, and is\n # created automatically when the authorization flow completes for the first\n # time.\n if os.path.exists('./data/token/token.pickle'):\n with open('./data/token/token.pickle', 'rb') as token:\n creds = pickle.load(token)\n # If there are no (valid) credentials available, let the user log in.\n if not creds or not creds.valid:\n if creds and creds.expired and creds.refresh_token:\n creds.refresh(Request())\n else:\n flow = InstalledAppFlow.from_client_secrets_file(\n './data/token/credentials.json', SCOPES)\n creds = flow.run_local_server(port=0)\n # Save the credentials for the next run\n with open('./data/token/token.pickle', 'wb') as token:\n pickle.dump(creds, token)\n\n service = build('sheets', 'v4', credentials=creds)\n\n # Call the Sheets API\n sheet = service.spreadsheets()\n result = sheet.values().get(spreadsheetId=SAMPLE_SPREADSHEET_ID,\n range=SAMPLE_RANGE_NAME).execute()\n values = result.get('values', [])\n\n if not values:\n print('找不到資料')\n else:\n for row in values:\n # Print columns A and E, which correspond to indices 0 and 4.\n print(row)\n row[0] = row[0].replace('-', '').replace(':', '').replace(' ', '')\n if len(row[0]) < 14:\n row[0] = row[0][:8] + '0' + row[0][8:]\n totaltext = appendtext(row)\n t.insert('end', totaltext)\n html2pdf(row, optionmenu_event())\n print(totaltext)\ndef optionmenu_event(*args):\n from_text = var.get()\n mylabel['text'] = '來自 ' + from_text\n return from_text\noptionList = [\"夢玩家包車旅遊\", \"九賓商務租車\", \"天地玩家包車旅遊\", \"海山林玩家包車旅遊\", \"天地遊覽車\"]\nvar = tk.StringVar()\nvar.set(optionList[0])\nc1 = tk.OptionMenu(window, var, *optionList)\nc1.pack()\n\nmylabel = tk.Label(window, text='來自', height=2)\nmylabel.pack()\n\n#var.trace('w', lambda *args: print(var.get()))\nvar.trace(\"w\", optionmenu_event)\n\n# 第6步，建立並放置按鈕分別觸發兩種情況\nb1 = tk.Button(window, text='產生明細', width=20,\n height=3, command=insert_point)\nb1.pack()\n\n# 第7步，建立並放置一個多行文字框text用以顯示，指定height=3為文字框是三個字元高度\nt = tk.Text(window, height=30)\nt.pack()\n\n# 第8步，主視窗迴圈顯示\nwindow.mainloop()\n","repo_name":"waley168/detail","sub_path":"createdetail.py","file_name":"createdetail.py","file_ext":"py","file_size_in_byte":4234,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"3129709615","text":"import unittest\nfrom queue import Queue\nfrom threading import Event\n\nfrom unitem_task.components.producer import Producer\n\n\nclass ProducerTestCase(unittest.TestCase):\n def setUp(self) -> None:\n self.source_shape = (2, 4, 6)\n self.queue: Queue = Queue()\n self.stop_event = Event()\n self.data_limit = 10\n self.time_period = 1 # ms\n\n def test_number_of_data_put_to_queue(self):\n data_limit = 10\n producer = Producer(\n source_shape=self.source_shape,\n queue=self.queue,\n stop_event=self.stop_event,\n data_limit=data_limit,\n time_period=self.time_period,\n )\n\n producer.start()\n producer.join()\n\n data_put_into_queue_counter = 0\n while not self.queue.empty():\n self.queue.get()\n data_put_into_queue_counter += 1\n\n self.assertEqual(data_put_into_queue_counter, self.data_limit)\n\n def test_shape_of_producer_data(self):\n data_limit = 1\n producer = Producer(\n source_shape=self.source_shape,\n queue=self.queue,\n stop_event=self.stop_event,\n data_limit=data_limit,\n time_period=self.time_period,\n )\n\n producer.start()\n producer.join()\n\n while not self.queue.empty():\n data_item = self.queue.get()\n self.assertEqual(data_item.shape, self.source_shape)\n","repo_name":"wojtek11530/UnitemTask","sub_path":"tests/test_producer.py","file_name":"test_producer.py","file_ext":"py","file_size_in_byte":1438,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"8752355614","text":"from db.add_contact import Add_contact\nfrom components.get_all_contacts import Get_all_contacts\nfrom db.select_contact_view import Select_contact_view\nfrom db.delete_contact_select import Delete_contact_select\nfrom db.delete_contact_view import Delete_contact_view\nfrom db.search_contact import Search_contact\nfrom components.clear_view import Clear_view\nfrom screen.Edit_windows import Edit_window\nfrom screen.Edit_window_selection_checker import Selection_checker\nimport tkinter as tk\nfrom tkinter import StringVar, ttk\nfrom style import style\nfrom PIL import ImageTk, Image\n\nclass Main_windows(tk.Frame):\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.init_widgets()\n self.window_settings()\n self.grid(row=0, column=0, sticky=tk.NSEW)\n\n\n def init_widgets(self):\n self.logo_img_search = ImageTk.PhotoImage(Image.open(\"img/search.jpg\").resize((25, 25), Image.ANTIALIAS))\n self.logo_img_delete = ImageTk.PhotoImage(Image.open(\"img/delete.jpg\").resize((25, 25), Image.ANTIALIAS))\n self.logo_img_edit = ImageTk.PhotoImage(Image.open(\"img/edit.png\").resize((25, 25), Image.ANTIALIAS))\n\n self.frame_contact = tk.Frame(self)\n self.frame_contact.columnconfigure(0, weight=1)\n \n # - Inputs -\n self.container_inputs = tk.LabelFrame(self.frame_contact, text='Nuevo contacto')\n self.container_inputs.columnconfigure(1, weight=1)\n\n # Text inputs\n name_label = tk.Label(self.container_inputs, text='Nombre: ')\n name_label.grid(row=0, column=0, sticky=tk.NSEW)\n self.new_name = tk.Entry(self.container_inputs, width=20)\n self.new_name.focus()\n self.new_name.grid(row=0, column=1, padx=5, sticky=tk.NSEW)\n\n phone_label = tk.Label(self.container_inputs, text='Telefono: ')\n phone_label.grid(row=1, column=0, sticky=tk.NSEW, padx=5)\n self.phone = tk.Entry(self.container_inputs, width=20)\n self.phone.grid(row=1, column=1, padx=5, pady=5, sticky=tk.NSEW)\n\n tk.Label(self.container_inputs, text='Email: ').grid(row=2, column=0, sticky=tk.NSEW)\n self.email = tk.Entry(self.container_inputs, width=20)\n self.email.grid(row=2, column=1, padx=5, sticky=tk.NSEW)\n\n tk.Button(self.container_inputs, text='Guardar', **style.button_style_save, relief=tk.FLAT, overrelief=tk.RAISED, command=lambda:Add_contact(self.new_name, self.phone, self.email, name_label, phone_label, self.tree)).grid(row=3, column=0, columnspan=2, padx=5, pady=5, sticky=tk.NSEW)\n\n self.container_inputs.grid(row=0, column=0, pady=10, padx=10, sticky=tk.NSEW)\n # ------------------------------------------------------------------------------\n # - Separator -\n ttk.Separator(self.frame_contact, orient='horizontal').grid(row=2, column=0, pady=10, sticky=tk.NSEW)\n\n # - Contact display -\n self.container_contact = tk.LabelFrame(self.frame_contact, text='Contacto')\n self.container_contact.columnconfigure(0, weight=1)\n\n name = StringVar()\n phone = StringVar()\n email = StringVar()\n \n self.container_name = tk.LabelFrame(self.container_contact, text='Nombre: ')\n self.container_name.columnconfigure(0, weight=1)\n tk.Label(self.container_name, textvariable=name).grid(row=0, column=0, sticky=tk.NSEW)\n self.container_name.grid(row=0, column=0, padx=10, columnspan=2, sticky=tk.NSEW)\n\n tk.Button(self.container_name, image=self.logo_img_delete, bd=0, command=lambda:Clear_view(name, phone,email, self.button_delete, self.button_edit)).grid(row=0, column=1, sticky=tk.E)\n \n tk.Label(self.container_contact, text='Número: ').grid(row=1, column=0, padx=20, sticky=tk.W)\n tk.Label(self.container_contact, textvariable=phone).grid(row=1, column=1, padx=20, sticky=tk.E)\n tk.Label(self.container_contact, text='Email: ').grid(row=2, column=0, padx=20, sticky=tk.W)\n tk.Label(self.container_contact, textvariable=email).grid(row=2, column=1, padx=20, sticky=tk.E)\n\n self.container_buttons = tk.Frame(self.container_contact)\n self.container_buttons.columnconfigure(0, weight=1)\n self.container_buttons.columnconfigure(1, weight=1)\n self.container_buttons.columnconfigure(2, weight=1)\n\n tk.Button(self.container_buttons, text='Ver', **style.button_style_save, relief=tk.FLAT, overrelief=tk.RAISED, command=lambda:Select_contact_view(name, phone, email, self.button_edit, self.button_delete, self.tree), width=10).grid(row=0, column=0, padx=5, sticky=tk.NSEW)\n\n self.button_edit = tk.Button(self.container_buttons, text='Editar', **style.button_style_edit, relief=tk.FLAT, overrelief=tk.RAISED, state=tk.DISABLED, command=lambda:Selection_checker(self.tree, 'view', name, phone, email, self.button_delete, self.button_edit), width=10)\n self.button_edit.grid(row=0, column=1, padx=5, sticky=tk.NSEW)\n\n self.button_delete = tk.Button(self.container_buttons, text='Eliminar', **style.button_style_delete, relief=tk.FLAT, overrelief=tk.RAISED, state=tk.DISABLED, command=lambda:Delete_contact_view(name, phone, email, self.button_edit, self.button_delete, self.tree), width=10)\n self.button_delete.grid(row=0, column=2, padx=5, sticky=tk.NSEW)\n \n self.container_buttons.grid(row=4, column=0, columnspan=2, pady=5, sticky=tk.NSEW)\n self.container_contact.grid(row=3, column=0, sticky=tk.NSEW, padx=10)\n # ----------------------------------------------------------------------\n\n\n # - Search -\n self.container_search = tk.Frame(self.frame_contact)\n self.container_search.columnconfigure(0, weight=1)\n \n self.search = tk.Entry(self.container_search)\n self.search.grid(row=0, column=0, padx=5, sticky=tk.NSEW)\n tk.Button(self.container_search, image=self.logo_img_search, bd=0, command=lambda:Search_contact(self.search, name, phone, email, self.button_edit, self.button_delete)).grid(row=0, column=1, padx=5, sticky=tk.NSEW)\n\n self.container_search.grid(row=4, column=0, pady=15, padx=10, sticky=tk.NSEW)\n\n self.frame_contact.grid(row=0, column=0, padx=10, sticky=tk.NSEW )\n # ------------------------------------------------------------------------------\n \n # - Separator -\n ttk.Separator(self, orient='vertical').grid(row=0, column=1, pady=10, sticky=tk.NSEW)\n\n\n # - Table -\n self.table = tk.Frame(self)\n self.table.rowconfigure(0, weight=1)\n self.table.columnconfigure(0, weight=1) \n\n self.tree = ttk.Treeview(self.table, columns=2)\n self.tree.heading('#0', text='Nombre', anchor='center')\n self.tree.heading('#1', text='Número', anchor='center')\n self.tree.column('#1', anchor='center', stretch=0)\n\n self.tree.grid(row=0, column=0, pady=10, padx=10, sticky=tk.NSEW)\n \n self.table.grid(row=0, column=2, sticky=tk.NSEW)\n\n Get_all_contacts(self.tree)\n\n self.button_table = tk.Frame(self)\n tk.Button(self.button_table, image=self.logo_img_delete, bd=0, command=lambda:Delete_contact_select(name, phone, email, self.button_edit, self.button_delete, self.tree)).grid(row=0, column=0, pady=5)\n\n tk.Button(self.button_table, image=self.logo_img_edit, bd=0, command=lambda:Selection_checker(self.tree, 'selection', name, phone, email, self.button_delete, self.button_edit)).grid(row=1, column=0, pady=5)\n self.button_table.grid(row=0, column=3, padx=10, pady=10, sticky=tk.NSEW)\n \n\n \n def window_settings(self):\n self.rowconfigure(0, weight=1)\n self.columnconfigure(0, minsize=300)\n self.columnconfigure(1, weight=1)\n self.columnconfigure(3, weight=1)\n\n","repo_name":"AdelSuarez/Contact","sub_path":"screen/Main_window.py","file_name":"Main_window.py","file_ext":"py","file_size_in_byte":7778,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"35873584069","text":"def makeStringFromList(strList):\n\treturnStr = \"\"\n\t\n\tfor x in strList:\n\t\treturnStr += x + \" \"\n\t\n\treturn returnStr\n\t\nfile = open(\"output.txt\", \"w\")\n\ncases = int(input())\n\nfor case in range(0, cases):\n\tinStrList = input().split(' ')\n\t\n\tfor xPos in range(0, int(len(inStrList) / 2)):\n\t\ttemp = inStrList[xPos]\n\t\tinStrList[xPos] = inStrList[len(inStrList) - 1 - xPos]\n\t\tinStrList[len(inStrList) - 1 - xPos] = temp\n\t\n\tprint(\"Case #\" + str(case + 1) + \": \" + makeStringFromList(inStrList))\n\tfile.write(\"Case #\" + str(case + 1) + \": \" + makeStringFromList(inStrList) + \"\\n\")\n\t\nfile.close()","repo_name":"newyork167/Interview-Questions","sub_path":"Google Code Jam/ReverseWords.py","file_name":"ReverseWords.py","file_ext":"py","file_size_in_byte":580,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"74411155157","text":"import subprocess\nimport csv\nimport filecmp\nimport os\nimport platform\nimport sys\nfinal_report = []\n\nenable_debug = False\n\nref_outputs_base_dir = 'test_data' \nrt_base_dir_py = 'examples/osrt_python/'\nrt_base_dir_bash = 'scripts'\n\ntry:\n SOC = os.environ['SOC']\nexcept:\n print('SOC env variable not found')\n exit(-1)\n\nglobal test_configs\nif SOC == \"am62\":\n device = 'am62'\n test_configs = [\n {'script_name':'tflrt_delegate.py', 'script_dir':'tfl','lang':'py', 'rt_type':'tfl-py'},\n {'script_name':'onnxrt_ep.py', 'script_dir':'ort','lang':'py', 'rt_type':'ort-py'},\n #{'script_name':'dlr_inference_example.py', 'script_dir':'tvm_dlr','lang':'py', 'rt_type':'dlr-py'},\n {'script_name':'run_tfl_models.sh', 'script_dir':'osrt_cpp_scripts/','lang':'bash','rt_type':'tfl-cpp'},\n {'script_name':'run_onnx_models.sh', 'script_dir':'osrt_cpp_scripts/','lang':'bash','rt_type':'ort-cpp'},\n #{'script_name':'run_dlr_models.sh', 'script_dir':'osrt_cpp_scripts/','lang':'bash','rt_type':'dlr-cpp'},\n ]\nelif SOC == \"am68pa\" :\n device = 'am68pa'\n test_configs = [\n {'script_name':'tflrt_delegate.py', 'script_dir':'tfl','lang':'py', 'rt_type':'tfl-py'},\n {'script_name':'onnxrt_ep.py', 'script_dir':'ort','lang':'py', 'rt_type':'ort-py'},\n {'script_name':'dlr_inference_example.py', 'script_dir':'tvm_dlr','lang':'py', 'rt_type':'dlr-py'},\n {'script_name':'run_tfl_models.sh', 'script_dir':'osrt_cpp_scripts/','lang':'bash','rt_type':'tfl-cpp'},\n {'script_name':'run_onnx_models.sh', 'script_dir':'osrt_cpp_scripts/','lang':'bash','rt_type':'ort-cpp'},\n {'script_name':'run_dlr_models.sh', 'script_dir':'osrt_cpp_scripts/','lang':'bash','rt_type':'dlr-cpp'},\n ]\nelif SOC == \"am68a\" :\n device = 'am68a'\n test_configs = [\n {'script_name':'tflrt_delegate.py', 'script_dir':'tfl','lang':'py', 'rt_type':'tfl-py'},\n {'script_name':'onnxrt_ep.py', 'script_dir':'ort','lang':'py', 'rt_type':'ort-py'},\n {'script_name':'dlr_inference_example.py', 'script_dir':'tvm_dlr','lang':'py', 'rt_type':'dlr-py'},\n {'script_name':'run_tfl_models.sh', 'script_dir':'osrt_cpp_scripts/','lang':'bash','rt_type':'tfl-cpp'},\n {'script_name':'run_onnx_models.sh', 'script_dir':'osrt_cpp_scripts/','lang':'bash','rt_type':'ort-cpp'},\n {'script_name':'run_dlr_models.sh', 'script_dir':'osrt_cpp_scripts/','lang':'bash','rt_type':'dlr-cpp'},\n ]\nelif SOC == \"am69a\" :\n device = 'am69a'\n test_configs = [\n {'script_name':'tflrt_delegate.py', 'script_dir':'tfl','lang':'py', 'rt_type':'tfl-py'},\n {'script_name':'onnxrt_ep.py', 'script_dir':'ort','lang':'py', 'rt_type':'ort-py'},\n {'script_name':'dlr_inference_example.py', 'script_dir':'tvm_dlr','lang':'py', 'rt_type':'dlr-py'},\n {'script_name':'run_tfl_models.sh', 'script_dir':'osrt_cpp_scripts/','lang':'bash','rt_type':'tfl-cpp'},\n {'script_name':'run_onnx_models.sh', 'script_dir':'osrt_cpp_scripts/','lang':'bash','rt_type':'ort-cpp'},\n {'script_name':'run_dlr_models.sh', 'script_dir':'osrt_cpp_scripts/','lang':'bash','rt_type':'dlr-cpp'},\n ] \nelif SOC == \"am62a\" :\n device = 'am62a'\n test_configs = [\n {'script_name':'tflrt_delegate.py', 'script_dir':'tfl','lang':'py', 'rt_type':'tfl-py'},\n {'script_name':'onnxrt_ep.py', 'script_dir':'ort','lang':'py', 'rt_type':'ort-py'},\n {'script_name':'dlr_inference_example.py', 'script_dir':'tvm_dlr','lang':'py', 'rt_type':'dlr-py'},\n {'script_name':'run_tfl_models.sh', 'script_dir':'osrt_cpp_scripts/','lang':'bash','rt_type':'tfl-cpp'},\n {'script_name':'run_onnx_models.sh', 'script_dir':'osrt_cpp_scripts/','lang':'bash','rt_type':'ort-cpp'},\n {'script_name':'run_dlr_models.sh', 'script_dir':'osrt_cpp_scripts/','lang':'bash','rt_type':'dlr-cpp'},\n ] \nelse:\n print( \"Set SOC variable in your shell\")\n exit(-1)\n\ncurrIdx = 0\nif platform.machine() != 'aarch64':\n device = 'pc'\n\n\ndef run_cmd(cmd, dir):\n try:\n msg = f'Command : {cmd} in Dir : {dir} Started'\n print(msg)\n lines = []\n process = subprocess.Popen([cmd], shell=True, cwd=dir, stdout=subprocess.PIPE, universal_newlines=True, stderr=subprocess.STDOUT)\n while True:\n b = process.stdout.readline()\n if not len(b):\n break\n sys.stdout.write(b)\n sys.stdout.flush()\n lines.append(b.rstrip())\n\n if process.returncode is not None and process.returncode != 0 :\n msg = f'Command : {cmd} in Dir : {dir} Failed with Error code {process.returncode}'\n print(msg)\n exit(-1)\n return lines\n except Exception as e:\n raise e\n\nfor test_config in test_configs:\n script_name = test_config['script_name']\n rt_type = test_config['rt_type']\n if(test_config['lang'] == 'bash'):\n rt_base_dir = rt_base_dir_bash\n curr_rt_base_dir= os.path.join(rt_base_dir,test_config['script_dir'])\n cmd = ('bash '+ script_name)\n\n elif(test_config['lang'] == 'py'):\n rt_base_dir = rt_base_dir_py\n curr_rt_base_dir= os.path.join(rt_base_dir,test_config['script_dir'])\n cmd = ('python3 '+ script_name)\n\n if device != 'pc':\n curr_ref_outputs_base_dir = ref_outputs_base_dir+'/refs-'+device+'/'\n else:\n curr_ref_outputs_base_dir = ref_outputs_base_dir+'/refs-'+device+'-'+SOC+'/'\n\n #result = subprocess.run(cmd, cwd=curr_rt_base_dir, shell=True, stdout=subprocess.PIPE, check=True, universal_newlines=True)\n #lines = result.stdout.splitlines()\n \n lines = run_cmd(cmd=cmd, dir=curr_rt_base_dir)\n\n if enable_debug:\n for i in lines:\n print(i)\n\n rt_report = []\n golden_ref_file= \"\"\n if device != 'pc':\n golden_ref_file = ref_outputs_base_dir+'/golden_ref_'+device+'.csv'\n else:\n golden_ref_file = ref_outputs_base_dir+'/golden_ref_'+device+'_'+SOC+'.csv'\n with open(golden_ref_file, 'r') as f:\n ref_report = [{k:v for k, v in row.items()} for row in csv.DictReader(f, skipinitialspace=True)]\n if enable_debug:\n print(ref_report)\n\n for i in lines:\n if i.startswith('Completed_Model : '):\n curr = i.split(',')\n tc_dict = {}\n for pair in curr:\n pair = pair.strip()\n pair = pair.split(':')\n tc_dict[pair[0].strip()] = pair[1].strip()\n rt_report.append(tc_dict)\n if enable_debug:\n print(rt_report)\n for r in ref_report:\n curr = [item for item in rt_report if ((item[\"Name\"] == r['Name']) and (rt_type == r['rt type']) )]\n if enable_debug:\n print(curr)\n if (len(curr) == 0 and rt_type == r['rt type']):\n r['Offload Time'] = '0'\n r['Functional'] = 'FAIL'\n r['info'] = 'op not detected'\n final_report.append(r)\n final_report[-1]['Completed_Model'] = currIdx\n final_report[-1]['rt type'] = rt_type\n currIdx+= 1\n elif(len(curr) != 0 and rt_type == r['rt type']):\n final_report.append(curr[0])\n out_file_name = os.path.join('./output_images',final_report[-1]['Output File'])\n ref_file_name = os.path.join(curr_ref_outputs_base_dir,final_report[-1]['Output File'])\n if filecmp.cmp(out_file_name, ref_file_name) == True:\n final_report[-1]['Functional'] = 'PASS'\n final_report[-1]['info'] = ''\n else:\n final_report[-1]['Functional'] = 'FAIL'\n final_report[-1]['info'] = 'output file mismatch'\n if platform.machine() == 'aarch64':\n final_report[-1]['Ref Total Time'] = r['Total time']\n final_report[-1]['Ref Offload Time'] = r['Offload Time']\n diff_in_total_time = float(final_report[-1]['Total time']) - float(final_report[-1]['Ref Total Time'])\n diff_in_total_time = (diff_in_total_time/float(final_report[-1]['Ref Total Time']))*100.0\n final_report[-1]['Diff in Total Time %'] = f'{diff_in_total_time:5.2f}'\n if(diff_in_total_time > 2.0):\n final_report[-1]['Performance Status'] = \"FAIL\"\n final_report[-1]['info'] =\"Failed : diff in total time > 2.\"\n else :\n final_report[-1]['Performance Status'] = \"PASS\"\n final_report[-1]['Completed_Model'] = currIdx\n final_report[-1]['rt type'] = rt_type\n currIdx+= 1\n \nprint(final_report)\nif(len(final_report) > 0):\n keys =final_report[0].keys()\n if device == 'pc':\n for i in range(len(final_report)):\n final_report[i].pop('Total time',None)\n final_report[i].pop('Offload Time',None)\n final_report[i].pop('DDR RW MBs',None)\n if 'Output File' not in final_report[0]:\n final_report[0]['Output File'] = ''\n\n if device != 'pc':\n report_file = 'test_report_'+device+'.csv'\n else:\n report_file = 'test_report_'+device+'_'+ SOC+ '.csv'\n with open(report_file, 'w', newline='') as output_file:\n dict_writer = csv.DictWriter(output_file, keys)\n dict_writer.writeheader()\n dict_writer.writerows(final_report)\nelse:\n print(\"no test_report generated \")","repo_name":"Arrowes/DMS-YOLOv8","sub_path":"TI/tidl_tools/scripts/gen_test_report.py","file_name":"gen_test_report.py","file_ext":"py","file_size_in_byte":9811,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"14703534242","text":"import unittest\nimport sys\nsys.path.insert(0,'../../../')\nfrom ds.node import data\n\n\nclass DataTest(unittest.TestCase):\n \n\n def test_setKey(self):\n dataTest = data.Data()\n idx = 1\n dataTest.setKey(idx)\n\n self.assertEqual(dataTest.getKey(),idx)\n\n def test_setVersion(self):\n dataTest = data.Data()\n version = 1\n dataTest.setVersion(version)\n\n self.assertEqual(dataTest.getVersion(),version)\n\nif __name__ == '__main__':\n unittest.main()\n","repo_name":"pphhss/DistributedSystemProject","sub_path":"ds/test/node/dataTest.py","file_name":"dataTest.py","file_ext":"py","file_size_in_byte":503,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"39054463248","text":"#/usr/bin/python\r\n# coding=utf-8\r\n#-------------------------------\r\n#\tAuthor: LSK\r\n#\tFilename: wechat-socket.py\r\n#\tDatetime: 2018-7-5\r\n#-------------------------------\r\nimport threading\r\nimport socket\r\nimport urllib\r\nimport re\r\nimport wechat\r\nimport logging\r\nimport ConfigParser\r\nimport json\r\nencoding = 'utf-8'\r\nBUFSIZE = 1024\r\nlogging.basicConfig(level=logging.DEBUG,filename='./logs/wechat.log',\r\n format='%(asctime)s - %(levelname)s: %(message)s')\r\nclass Reader(threading.Thread):\r\n def __init__(self,client):\r\n threading.Thread.__init__(self)\r\n self.client = client\r\n def run(self):\r\n while True:\r\n data = self.client.recv(BUFSIZE)\r\n if(data):\r\n string = urllib.unquote(data)\r\n self.handle_String(string)\r\n #print string\r\n else:\r\n break\r\n def readline(self):\r\n rec = self.inputs.readline()\r\n if rec:\r\n string = urllib.unquote(rec)\r\n if len(string) >2:\r\n string = string[0:-2]\r\n else:\r\n string =''\r\n else:\r\n string = False\r\n return string\r\n#接收4567端口的数据，截取分析字符串\r\n def handle_String(self,s):\r\n a = s.split('&')\r\n dict = {}\r\n for i in range(len(a)):\r\n if len(re.split('\\[',a[i]))>1:\r\n dict['content']=re.split('content=',a[i])[1]\r\n elif len(re.split('tos=',a[i]))>1:\r\n dict['tos']=re.split('tos=',a[i])[1]\r\n flag = re.split('\\[',dict['content'])[5].split('*')[0]\r\n touser = dict['tos']\r\n content = dict['content']\r\n obj = wechat.Weixin(flag, content)\r\n obj.message(touser)\r\n logging.debug('Send to %s Wechat %s %s ' % (touser,flag,content))\r\n #print dict['content'],dict['tos'],flag\r\n\r\nclass Listener(threading.Thread):\r\n def __init__(self,port):\r\n threading.Thread.__init__(self)\r\n self.port = port\r\n self.sock = socket.socket(socket.AF_INET,socket.SOCK_STREAM)\r\n self.sock.setsockopt(socket.SOL_SOCKET,socket.SO_REUSEADDR,1)\r\n self.sock.bind(('0.0.0.0',port))\r\n self.sock.listen(0)\r\n def run(self):\r\n print(\"listener started\")\r\n while True:\r\n client,cltadd = self.sock.accept()\r\n Reader(client).start()\r\n #cltadd = cltadd\r\n #print(\"accept a connect\")\r\n#定时获取access_token\r\ndef get_accesstoken():\r\n url = 'https://qyapi.weixin.qq.com/'\r\n ap = ConfigParser.SafeConfigParser()\r\n cp = ConfigParser.SafeConfigParser()\r\n ap.read('./config/wechat.conf')\r\n config = ap.sections()\r\n for i in config:\r\n if i != 'http' and i != 'weixin':\r\n agentid = ap.get(i, 'AgentId')\r\n corpid = ap.get(i, 'CorpID')\r\n corpsecret = ap.get(i, 'Secret')\r\n token_url = '%s/cgi-bin/gettoken?corpid=%s&corpsecret=%s' % (url, corpid, corpsecret)\r\n access_token = json.loads(urllib.urlopen(token_url).read().decode())['access_token']\r\n cp.add_section(i)\r\n cp.set(i,\"AgentId\",agentid)\r\n cp.set(i, \"access_token\", access_token)\r\n cp.write(open('./config/access_token.conf','w'))\r\n global timer\r\n timer = threading.Timer(3600,get_accesstoken)\r\n timer.start()\r\nif __name__ == '__main__':\r\n lst = Listener(4567)\r\n timer = threading.Timer(1,get_accesstoken)\r\n timer.start()\r\n lst.start()\r\n","repo_name":"libuliduobuqiuqiu/openfalcon-wechat","sub_path":"wechat-socket.py","file_name":"wechat-socket.py","file_ext":"py","file_size_in_byte":3509,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"85"} +{"seq_id":"43310070148","text":"\n'''\n@author LeslieZhao\n@date 20220721\n'''\nimport torch \n\nimport sys\nsys.path.append('/workspace/Lab/DCTNet/')\n\nfrom data.CCNLoader import CCNData\nfrom data.TTNLoader import TTNData\n\ndef requires_grad(model, flag=True):\n if model is None:\n return \n for p in model.parameters():\n p.requires_grad = flag\ndef need_grad(x):\n x = x.detach()\n x.requires_grad_()\n return x\n\ndef init_weights(m,init_type='normal', gain=0.02):\n \n classname = m.__class__.__name__\n if classname.find('BatchNorm2d') != -1:\n if hasattr(m, 'weight') and m.weight is not None:\n torch.nn.init.normal_(m.weight.data, 1.0, gain)\n if hasattr(m, 'bias') and m.bias is not None:\n torch.nn.init.constant_(m.bias.data, 0.0)\n elif hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1):\n if init_type == 'normal':\n torch.nn.init.normal_(m.weight.data, 0.0, gain)\n elif init_type == 'xavier':\n torch.nn.init.xavier_normal_(m.weight.data, gain=gain)\n elif init_type == 'xavier_uniform':\n torch.nn.init.xavier_uniform_(m.weight.data, gain=1.0)\n elif init_type == 'kaiming':\n torch.nn.init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')\n elif init_type == 'orthogonal':\n torch.nn.init.orthogonal_(m.weight.data, gain=gain)\n elif init_type == 'none': # uses pytorch's default init method\n m.reset_parameters()\n else:\n raise NotImplementedError('initialization method [%s] is not implemented' % init_type)\n if hasattr(m, 'bias') and m.bias is not None:\n torch.nn.init.constant_(m.bias.data, 0.0)\ndef accumulate(model1, model2, decay=0.999):\n par1 = dict(model1.named_parameters())\n par2 = dict(model2.named_parameters())\n\n for k in par1.keys():\n par1[k].data.mul_(decay).add_(par2[k].data, alpha=1 - decay)\ndef setup_seed(seed):\n torch.manual_seed(seed)\n if torch.cuda.is_available():\n torch.cuda.manual_seed_all(seed)\n torch.backends.cudnn.deterministic = True\n\ndef get_data_loader(args):\n if args.model == 'ccn':\n train_data = CCNData(root=args.root,dist=args.dist)\n test_data = None \n if args.model == 'ttn':\n train_data = TTNData(dist=args.dist,eval=False,\n src_root=args.train_src_root,\n tgt_root=args.train_tgt_root,\n score_info=args.score_info)\n test_data = TTNData(dist=args.dist,eval=True,\n src_root=args.val_src_root,\n tgt_root=args.val_tgt_root,\n score_info=args.score_info)\n\n train_loader = torch.utils.data.DataLoader(\n train_data,\n batch_size=args.batch_size,\n num_workers=args.nDataLoaderThread,\n pin_memory=False,\n drop_last=True\n )\n test_loader = None if test_data is None else \\\n torch.utils.data.DataLoader(\n test_data,\n batch_size=args.batch_size,\n num_workers=args.nDataLoaderThread,\n pin_memory=False,\n drop_last=True\n )\n return train_loader,test_loader,len(train_data) \n\n\n\ndef merge_args(args,params):\n for k,v in vars(params).items():\n setattr(args,k,v)\n return args\n\ndef convert_img(img,unit=False):\n \n img = (img + 1) * 0.5\n if unit:\n return torch.clamp(img*255+0.5,0,255)\n \n return torch.clamp(img,0,1)","repo_name":"SShowbiz/DCT-Net","sub_path":"DCTNet/utils/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":3696,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"31854659339","text":"from datetime import datetime, timedelta\n\nfrom django.conf import settings\nfrom django.db.models import FloatField, F, Sum, Q\nfrom django.db.models.functions import Cast\nfrom django.utils import timezone\nfrom rest_framework.exceptions import APIException\nfrom rest_framework.exceptions import PermissionDenied\n\nfrom .models import Box\n\n\nclass BoxService:\n\n @classmethod\n def __validate_create_request(cls, length, breadth, height):\n if length is None or breadth is None or height is None:\n raise APIException(\"Missing Dimensions\")\n if length < 0 or breadth < 0 or height < 0:\n raise APIException(\"Invalid Dimensions\")\n else:\n pass\n\n @classmethod\n def __check_average_area(cls, length, breadth):\n sum_of_areas = Box.objects.aggregate(sum_of_areas=Sum(F('length') * F('breadth')))['sum_of_areas']\n if sum_of_areas is None:\n sum_of_areas = 0\n current_area = length * breadth\n average_area = (sum_of_areas + current_area) / (Box.objects.count() + 1)\n\n if average_area is not None and average_area > settings.A1:\n print(\"something\", average_area, settings.A1)\n raise APIException(\"Average area exceeded.\")\n\n @classmethod\n def __check_average_volume(cls, length, breadth, height, user):\n box_created_by_user = Box.objects.filter(created_by=user)\n sum_of_volume = box_created_by_user.aggregate(\n sum_of_vol=Sum(F('length') * F('breadth') * F('height')))['sum_of_vol']\n if sum_of_volume is None:\n sum_of_volume = 0\n current_volume = length * breadth * height\n average_volume = (sum_of_volume + current_volume) / (box_created_by_user.count() + 1)\n\n if average_volume is not None and average_volume > settings.V1:\n raise APIException(\"Average volume exceeded for the current user.\")\n\n @classmethod\n def __check_total_boxes_added_in_week(cls):\n week_start = timezone.now().date() - timedelta(days=7)\n week_box_count = Box.objects.filter(created_at__gte=week_start).count()\n\n if week_box_count >= settings.L1:\n raise APIException(\"Total boxes added in a week exceeded.\")\n\n @classmethod\n def __check_total_boxes_added_in_week_for_user(cls, user):\n week_start = timezone.now().date() - timedelta(days=7)\n week_user_box_count = Box.objects.filter(created_by=user, created_at__gte=week_start).count()\n\n if week_user_box_count >= settings.L2:\n raise APIException(\"Total boxes added in a week exceeded for the current user.\")\n\n @classmethod\n def filter_boxes(cls, queryset, params):\n length_more_than = params.get('length_more_than')\n length_less_than = params.get('length_less_than')\n breadth_more_than = params.get('breadth_more_than')\n breadth_less_than = params.get('breadth_less_than')\n height_more_than = params.get('height_more_than')\n height_less_than = params.get('height_less_than')\n area_more_than = params.get('area_more_than')\n area_less_than = params.get('area_less_than')\n volume_more_than = params.get('volume_more_than')\n volume_less_than = params.get('volume_less_than')\n\n if length_more_than:\n queryset = queryset.filter(length__gt=length_more_than)\n if length_less_than:\n queryset = queryset.filter(length__lt=length_less_than)\n if breadth_more_than:\n queryset = queryset.filter(breadth__gt=breadth_more_than)\n if breadth_less_than:\n queryset = queryset.filter(breadth__lt=breadth_less_than)\n if height_more_than:\n queryset = queryset.filter(height__gt=height_more_than)\n if height_less_than:\n queryset = queryset.filter(height__lt=height_less_than)\n if area_more_than:\n queryset = queryset.filter(Q(length__gt=area_more_than/F('breadth')) | Q(breadth__gt=area_more_than/F('length')))\n if area_less_than:\n queryset = queryset.filter(Q(length__lt=area_less_than/F('breadth')) | Q(breadth__lt=area_less_than/F('length')))\n if volume_more_than:\n queryset = queryset.filter(Q(length__gt=volume_more_than / (F('breadth') * F('height'))) | Q(breadth__gt=volume_more_than / (F('length') * F('height'))) | Q(height__gt=volume_more_than / (F('length') * F('breadth'))))\n if volume_less_than:\n queryset = queryset.filter(Q(length__lt=volume_less_than / (F('breadth') * F('height'))) | Q(breadth__lt=volume_less_than / (F('length') * F('height'))) | Q(height__lt=volume_less_than / (F('length') * F('breadth'))))\n return queryset\n\n @classmethod\n def staff_member_filter(cls, queryset, params):\n filter_user = params.get('user')\n if filter_user:\n queryset = queryset.filter(created_by__username=filter_user)\n # Filter by date created before a specific date\n date_filter_before = params.get('date_filter_before')\n if date_filter_before:\n try:\n date_filter_before = datetime.strptime(date_filter_before, '%Y-%m-%d').date()\n queryset = queryset.filter(created_at__lt=date_filter_before)\n except ValueError:\n pass\n\n # Filter by date created after a specific date\n date_filter_after = params.get('date_filter_after')\n if date_filter_after:\n try:\n date_filter_after = datetime.strptime(date_filter_after, '%Y-%m-%d').date()\n queryset = queryset.filter(created_at__gt=date_filter_after)\n except ValueError:\n pass\n return queryset\n\n @classmethod\n def create_box(cls, request):\n\n length = request.data.get(\"length\")\n breadth = request.data.get(\"breadth\")\n height = request.data.get(\"height\")\n user = request.user\n\n cls.__validate_create_request(length, breadth, height)\n cls.__check_average_area(length, breadth)\n cls.__check_average_volume(length, breadth, height, user)\n cls.__check_total_boxes_added_in_week()\n cls.__check_total_boxes_added_in_week_for_user(user)\n\n Box.objects.create(length=length, breadth=breadth, height=height, created_by=user)\n\n @classmethod\n def delete_box(cls, request, *args, **kwargs):\n box_id = kwargs.get('pk')\n box = Box.objects.get(id=box_id)\n # Check if the user is the creator of the instance\n if box.created_by != request.user:\n raise PermissionDenied(\"You do not have permission to delete this object.\")\n Box.objects.filter(id=box_id).delete()\n\n @classmethod\n def update_box(cls, request, *args, **kwargs):\n box_id = kwargs.get('pk')\n box = Box.objects.get(id=box_id)\n if request.user.is_staff:\n if request.data.get('length'):\n box.length = request.data.get('length')\n if request.data.get('breadth'):\n box.breadth = request.data.get('breadth')\n if request.data.get('height'):\n box.height = request.data.get('height')\n\n length = box.length\n breadth = box.breadth\n height = box.height\n user = request.user\n\n cls.__validate_create_request(length, breadth, height)\n cls.__check_average_area(length, breadth)\n cls.__check_average_volume(length, breadth, height, user)\n\n box.save()\n\n else:\n # If the user is not a staff user, return a 403 Forbidden response\n raise APIException('You are not authorized to perform this action.')\n","repo_name":"diwanshi07/DjangoProject","sub_path":"CRUD_app/services.py","file_name":"services.py","file_ext":"py","file_size_in_byte":7662,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"30290776806","text":"# Реализовать функцию psort, которая принимает на вход набор заранее неизвестных поименованных параметров.\n# Функция возвращает список имен параметров, отсортированный по значениям параметров.\n# Пример: psort(c=21, a=22, ac=17, b=16) -> [b, ac, c, a]\n\ndef psort(**p):\n lst = sorted(p.items(), key=lambda tup: tup[1])\n lst = [i[0] for i in lst]\n return lst\n\n\nprint(psort(c=21, a=22, ac=17, b=16))\n","repo_name":"Redegit/Python","sub_path":"Python exam/20.py","file_name":"20.py","file_ext":"py","file_size_in_byte":570,"program_lang":"python","lang":"ru","doc_type":"code","stars":1,"dataset":"github-code","pt":"85"} +{"seq_id":"23216181638","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import migrations, models\nimport django.db.models.deletion\n\n\ndef migrate_volume_instances(apps, schema_editor):\n Volume = apps.get_model('openstack', 'Volume')\n\n for volume in Volume.objects.all():\n instance = volume.instances.all().first()\n if instance:\n volume.instance = instance\n volume.save()\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('openstack', '0020_tenant_extra_configuration'),\n ]\n\n operations = [\n migrations.AddField(\n model_name='volume',\n name='instance',\n field=models.ForeignKey(related_name='+', blank=True, to='openstack.Instance', null=True, on_delete=django.db.models.deletion.SET_NULL),\n ),\n migrations.RunPython(migrate_volume_instances),\n migrations.RemoveField(\n model_name='instance',\n name='volumes',\n ),\n migrations.AlterField(\n model_name='volume',\n name='instance',\n field=models.ForeignKey(related_name='volumes', blank=True, to='openstack.Instance', null=True, on_delete=django.db.models.deletion.SET_NULL),\n ),\n ]\n","repo_name":"opennode/waldur-openstack","sub_path":"src/waldur_openstack/openstack/migrations/0021_volume_instance.py","file_name":"0021_volume_instance.py","file_ext":"py","file_size_in_byte":1245,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"85"} +{"seq_id":"27872275737","text":"#백준 11286번 절댓값 힙\nimport sys\nimport heapq\ninput = sys.stdin.readline\n\nn = int(input())\nabs_heap = []\n\nfor _ in range(n):\n num = int(input())\n if num == 0:\n if abs_heap:\n print(heapq.heappop(abs_heap)[1])\n else:\n print(0)\n else:\n heapq.heappush(abs_heap, (abs(num), num))","repo_name":"RE-Heat/CodingTest","sub_path":"백준/baekjoon11286.py","file_name":"baekjoon11286.py","file_ext":"py","file_size_in_byte":305,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"74396435797","text":"import sys\n\nsys.path.append(\"../../\")\n\nfrom animation.animation_manager import fetch_animations, unpack\nimport so3.curves as multi\nfrom so3 import animation_to_SO3\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom math import floor\n\n\ndef divisorGenerator(n):\n for i in range(1, n / 4 + 1):\n if n % i == 0:\n yield i\n\n\ndef rounddown(n):\n return int(floor(n / 100) * 100)\n\n\nprint(\"Load data\")\ndata = fetch_animations(1, description=\"run\")\nif not data:\n print(\"No animations found.\")\n sys.exit()\n\n\nprint(\"Parse data\")\ns, a, d = unpack(data[0])\nc0d = animation_to_SO3(s, a)\n\nprint(\"Calculate distances: \")\n\nfig = plt.figure()\nax = fig.add_subplot(111)\ncolors = [\"r\", \"b\", \"y\", \"g\", \"m\"]\n\nN = c0d.shape[0]\ncrop = rounddown(c0d.shape[1])\nfor i in range(N):\n for j in range(N):\n if i <= j:\n continue\n step_arr = []\n dist_arr = []\n\n for step in divisorGenerator(crop):\n dist = multi.distance(c0d[i], c0d[j], step=step, stop=crop)\n if not np.isnan(dist):\n step_arr.append(step)\n dist_arr.append(dist)\n else:\n ax.plot(\n [step, step], [0, 10 + (float(i) / crop)], color=\"k\", marker=\"x\"\n )\n break\n\n ax.plot(step_arr, dist_arr, color=colors[i % 5], marker=\"o\")\n\nplt.show()\n","repo_name":"alexarntzen/signatureshape","sub_path":"signatureshape/so3/experiments/downsampling_1.py","file_name":"downsampling_1.py","file_ext":"py","file_size_in_byte":1371,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"17428724817","text":"from odoo import models, fields, api\nfrom odoo.tools.misc import DEFAULT_SERVER_DATETIME_FORMAT\n\n\nclass SalesTeamXlsx(models.AbstractModel):\n _name = 'report.zt_sales_report.sale_order_xlsx'\n _inherit = 'report.report_xlsx.abstract'\n\n def generate_xlsx_report(self, workbook, data, partners):\n start_date = data['date_start']\n end_date = data['date_end']\n sales_team_id = data['team_sales_id']\n crm_team_obj = self.env['crm.team'].browse(sales_team_id)\n domain=[]\n if start_date:\n domain.append(('date_order', '>=', start_date))\n if end_date:\n domain.append(('date_order', '<=', end_date))\n if sales_team_id:\n domain.append(('team_id','=',sales_team_id))\n sale_orders = self.env['sale.order'].sudo().search(domain)\n sheet = workbook.add_worksheet()\n format1 = workbook.add_format({'font_size': 16, 'align': 'vcenter', 'bg_color': '#D3D3D3', 'bold': True})\n format1.set_align('center')\n cell_format = workbook.add_format({'font_size': '12px'})\n head = workbook.add_format({'align': 'center', 'bold': True, 'font_size': '20px'})\n table_head = workbook.add_format({'align': 'center', 'bold': True, 'font_size': '10px'})\n txt = workbook.add_format({'font_size': '10px'})\n irow = 5\n icol = 0\n sheet.merge_range(irow, icol, irow + 2, icol + 13, 'SALES ORDER REPORT DETAILS', format1)\n sheet.write('B2', 'From:', cell_format)\n sheet.merge_range('C2:D2', data['date_start'], txt)\n sheet.write('E2', 'To:', cell_format)\n sheet.merge_range('F2:G2', data['date_end'], txt)\n sheet.write('H2', 'Sales By:', cell_format)\n sheet.merge_range('I2:J2', crm_team_obj.name, txt)\n sl = 1\n xls_date_format = workbook.add_format({'num_format': 'dd-mm-yy hh:mm'})\n sheet.write('B9', 'Number', table_head)\n sheet.write('C9', 'Order ID', table_head)\n sheet.write('D9', 'Order Date', table_head)\n sheet.write('E9', 'Customer Email ', table_head)\n sheet.write('F9', 'Customer Name', table_head)\n sheet.write('G9', 'Product', table_head)\n sheet.write('H9', 'Quantity', table_head)\n sheet.write('I9', 'Product Type', table_head)\n sheet.write('J9', 'Gross Amount', table_head)\n sheet.write('K9', 'Fees', table_head)\n sheet.write('L9', 'Total Amount', table_head)\n sheet.write('M9', 'Currency', table_head)\n sheet.write('N9', 'Origin of Customer', table_head)\n sheet.write('O9', 'Payment Delay', table_head)\n sheet.write('P9', 'Order Status', table_head)\n num = 10\n sl = 1\n for order in sale_orders:\n sheet.write('B' + str(num), sl, table_head)\n sheet.write('C' + str(num), order.name, cell_format)\n sheet.write('D' + str(num), order.date_order.strftime(DEFAULT_SERVER_DATETIME_FORMAT) if order.date_order else ' ', cell_format)\n sheet.write('E' + str(num), order.partner_id.email if order.partner_id.email else ' ', cell_format)\n sheet.write('F' + str(num), order.partner_id.name if order.partner_id else ' ', cell_format)\n sheet.write('P' + str(num),\n dict(order._fields['state'].selection).get(order.state) if order.state else ' ',\n cell_format)\n sheet.write('O' + str(num), order.payment_term_id.name if order.payment_term_id else ' ', cell_format)\n\n pro_payment_cur = ''\n for pro in order.order_line:\n sheet.write('G' + str(num), pro.product_id.product_tmpl_id.name, cell_format)\n sheet.write('H' + str(num), pro.product_uom_qty, cell_format)\n sheet.write('I' + str(num), pro.product_id.categ_id.name, cell_format)\n sheet.write('J' + str(num), pro.price_unit, cell_format)\n sheet.write('K' + str(num), pro.tax_id.name, cell_format)\n num = num + 1\n sheet.write('M' + str(num), order.currency_id.name, cell_format)\n sheet.write('N' + str(num), order.partner_id.comment if order.partner_id.comment else ' ', cell_format)\n sheet.write('L' + str(num), order.amount_total, cell_format)\n #\n num = num + 2\n sl = sl + 1\n workbook.close()\n","repo_name":"dbreugne/maison21g","sub_path":"zt_sales_report/wizard/sale_report.py","file_name":"sale_report.py","file_ext":"py","file_size_in_byte":4367,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"11619201594","text":"'''\n This module make \n \nAthor: Gansior Alexander, gansior@gansior.ru, +79173383804\nStarting 2022//\nEnding 2022//\n \n'''\n \nfrom termcolor import cprint\nimport inspect\n'''\nText colors: grey red green yellow blue magenta cyan white\nText highlights: on_grey on_red on_green on_yellow on_blue on_magenta on_cyan on_white\nAttributes: bold dark underline blink reverse concealed\ntemplate --> cprint(f'{}' , 'red', attrs=['bold'])\n \n \nShows which module is currently running\ncprint('='*20 + ' >> ' + inspect.stack()[0][0].f_code.co_name + ' << '+'='*20, 'red', attrs=['bold'])\n'''\n \nimport os\nimport sys\n\nnameProjectStart = 'NLP-russian-language'\nnameProject = 'NLP-russian-language/NLP_gansior/work_with_error'\ncprint(os.getcwd(), 'green')\nPathPrj = os.getcwd().split(nameProjectStart)[0] + nameProject + '/'\ncprint(PathPrj, 'blue')\nsys.path.append(PathPrj)\n\ngeomPar = {'rootGeometry':\"1750x700\",\n 'widthLabe':500,\n 'widthBt' : 300,\n 'heighY' : 10,\n 'P2widthW' : 560\n }","repo_name":"gansiorag/NLP-russian-language","sub_path":"NLP_gansior/work_with_error/dividing_continuous_stream_characters_into_words/ModulesInterface/baseModul.py","file_name":"baseModul.py","file_ext":"py","file_size_in_byte":1033,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"85"} +{"seq_id":"9350108749","text":"import socket\r\nimport json\r\n\r\nip = \"localhost\"\r\nport = 2802\r\nBUFFER_SIZE = 2048\r\n\r\n# Open and bind to socket 2802\r\ns = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\r\ns.bind((ip, port))\r\n\r\n# Wait for a connection from SSF2\r\ns.listen(1)\r\nconn, addr = s.accept()\r\nprint(\"Connected\")\r\n\r\nattempts = 0\r\nwhile attempts < 10:\r\n\tdata = conn.recv(BUFFER_SIZE)\r\n\t# If data not received, try again for 10 attempts\r\n\tif not data:\r\n\t\tattempts += 1\r\n\t\tcontinue\r\n\tdata = str(data.decode()) \t# Decode data\r\n\tif data[0] != \"#\": continue\t# If start symbol not the first character, discard\r\n\tdata = data.split('#')[1]\t# Only get the first packet (In case 2 packets have been read simultaneously)\r\n\tprint(\"Received Data: \" + data)\r\n\ttry:\r\n\t\tdataObj = json.loads(data)\r\n\texcept ValueError:\r\n\t\tprint(\"JSON invalid, discarding packet\")\r\n\t\tcontinue\t\t# If invalid JSON, discard it\r\n\r\n\tprint(dataObj[\"platforms\"],dataObj[\"player\"][\"x\"],dataObj[\"player\"][\"y\"])\r\n\tattempts = 0\r\ns.close()\r\n","repo_name":"aaronchn99/SmashNNAI","sub_path":"listener.py","file_name":"listener.py","file_ext":"py","file_size_in_byte":964,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"85"} +{"seq_id":"29356257352","text":"from selenium import webdriver\nfrom selenium.webdriver.support.select import Select\n\nfrom auth_data import *\nimport time\nimport pickle\n\ndriver = webdriver.Safari(executable_path=\"/usr/bin/safaridriver\")\nurl = \"https://tilda.cc/login/\"\ncompany_list = ['maxon', 'максим', 'ozon']\n\n\n\ndef set_viewport_size(driver, width, height):\n window_size = driver.execute_script(\"\"\"\n return [window.outerWidth - window.innerWidth + arguments[0],\n window.outerHeight - window.innerHeight + arguments[1]];\n \"\"\", width, height)\n driver.set_window_size(*window_size)\n\n\ndef automation():\n i = 1\n try:\n # Авторизация по куки\n driver.get(\"https://tilda.cc/login/\")\n time.sleep(3)\n\n for cookie in pickle.load(open(f\"{site_log}_cookies\", \"rb\")):\n driver.add_cookie(cookie)\n\n time.sleep(5)\n driver.refresh()\n time.sleep(10)\n\n # Авторизация по логину и паролю\n # driver.get(url=url)\n # time.sleep(3)\n #\n # log_input = driver.find_element_by_id('email')\n # log_input.clear()\n # log_input.send_keys(site_log)\n # time.sleep(3)\n #\n # pass_input = driver.find_element_by_id('password')\n # pass_input.clear()\n # pass_input.send_keys(site_pass)\n # time.sleep(3)\n #\n # log_button = driver.find_element_by_id('send').click()\n # time.sleep(4)\n #\n # pickle.dump(driver.get_cookies(), open(f\"{site_log}_cookies\", \"wb\"))\n\n # Навигация по сайту\n close_button = driver.find_element_by_xpath('//*[@id=\"myModalContent\"]/div[1]/button').click()\n time.sleep(2)\n main_project = driver.find_element_by_xpath('//*[@id=\"project5141784\"]/div/a/div[1]').click()\n time.sleep(3)\n settings_btn = driver.find_element_by_xpath('/html/body/div[7]/div[2]/div/div[1]/div[2]/div/div[2]/div[1]').click()\n time.sleep(3)\n personal_cabinet = driver.find_element_by_xpath('//*[@id=\"formss\"]/div[3]/div[1]/ul/li[13]/a').click()\n time.sleep(3)\n user_control = driver.find_element_by_xpath('//*[@id=\"ss_menu_members\"]/div[2]/div/a').click()\n driver.close()\n time.sleep(4)\n driver.switch_to.window(driver.window_handles[0])\n time.sleep(4)\n driver.refresh()\n print(driver.window_handles)\n time.sleep(5)\n open_for_group = driver.find_element_by_xpath('//*[@id=\"left_column\"]/div[1]/div[2]/a[1]/span[1]').click()\n time.sleep(3)\n count = driver.find_elements_by_xpath('//*[@id=\"member_to_group\"]/div/div[2]/div[1]/div[2]/div[2]/div/div[1]/span[1]')\n\n print(len(count))\n time.sleep(4)\n\n while i <= len(count):\n # Открыть форму\n all_application = driver.find_element_by_xpath('//*[@id=\"member_to_group\"]/div/div[2]/div[1]/div[2]/div[2]/div[' + str(i) + ']/div[1]/span[1]')\n company_title = str(all_application.text).lower()\n if company_title in company_list:\n copy_email = driver.find_element_by_xpath(\n '//*[@id=\"member_to_group\"]/div/div[2]/div[1]/div[2]/div[2]/div[' + str(i) + ']/div[2]/span')\n email = copy_email.text\n print(email)\n # all_application.click()\n time.sleep(2)\n add_btn = driver.find_element_by_xpath('//*[@id=\"member_to_group\"]/div/div[2]/div[1]/div[2]/div[2]/div[' + str(i) + ']/div[6]/div/label/span[1]').click()\n time.sleep(2)\n # driver.refresh()\n\n else:\n print('Пользователь не найден')\n\n i = i + 1\n\n except Exception as ex:\n print(ex)\n","repo_name":"MaxSvt/TildaProject","sub_path":"SeleniumProject — Tilda/scrypt.py","file_name":"scrypt.py","file_ext":"py","file_size_in_byte":3791,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"44328340057","text":"#!/usr/bin/env python3\n\nimport sys\nimport textwrap\nfrom io import BytesIO\nfrom urllib.request import urlopen\nimport configparser\n\nfrom PIL import Image, ImageDraw, ImageFont, ImageColor\n\n# read config\nconfig = configparser.ConfigParser()\nconfig.read('config.ini')\n\n# handle user input\ntheme = sys.argv[1]\ntext = sys.argv[2].upper()\n\n# split text into lines\nwrapper = textwrap.TextWrapper(width=config.getint('other', 'text_width'), break_long_words=False)\ntext_on_image = wrapper.fill(text=text)\n\n# download image and create corresponding object\nbackground_image_url = f\"https://source.unsplash.com/\" \\\n f\"{config.getint('background', 'width')}x{config.getint('background', 'height')}/?{theme}\"\nimage_data = BytesIO(urlopen(background_image_url).read())\nimage = Image.open(image_data).convert(\"RGBA\")\n\n# create background object\nbackground_shape = [(config.getint('background', 'margin'), config.getint('background', 'margin')),\n (config.getint('background', 'width') - config.getint('background', 'margin'),\n config.getint('background', 'height') - config.getint('background', 'margin'))]\nbackground = Image.new('RGBA', image.size, (0, 0, 0, 0))\n\n# create font object\nfont = ImageFont.truetype(config['font']['path'], config.getint('font', 'size'))\n\n# draw and save images\nfor font_color in config['font']['colors'].split(\",\"):\n draw = ImageDraw.Draw(background)\n draw.rectangle(background_shape,\n fill=ImageColor.getrgb(config['background']['color']) + (config.getint('background', 'opacity'),))\n font_width, font_height = draw.textsize(text_on_image, font=font)\n draw.multiline_text(((config.getint('background', 'width') - font_width) / 2,\n (config.getint('background', 'height') - font_height) / 2), text_on_image,\n font=font, fill=ImageColor.getrgb(font_color), align=\"center\")\n\n final_image = Image.alpha_composite(image, background)\n final_image.save(f\"{config['other']['destination_folder']}{font_color}_\" + theme + \".png\", 'PNG')\n","repo_name":"digitalstudium/youtube-thumbnail-generator","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2094,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"34427881134","text":"#####Usado para extrair os ids (hashes) das transações\n#####facilita as análises e o upload no database\nimport pandas as pd\n\n\ntransactions_ids = []\nyear = 2020\nmonth = 1\nday = 1\n\n\nwhile True:\n try:\n data_file_name = \"file_\" + str(year) + \"-\" + str(month) + \"-\" + str(day) + \".tsv\"\n #tentativa de abertura do arquivo tsv originado do Blockchair.com\n outputs_data = pd.read_csv(f'path/to/file/{year}/{data_file_name}', compression='gzip', delimiter=\"\\t\")\n except:\n if month != 12:\n print(f\"Month {month} completed!!!\")\n month += 1\n day = 1\n data_file_name = \"file_\" + str(year) + \"-\" + str(month) + \"-\" + str(day) + \".tsv\"\n outputs_data = pd.read_csv(f'path/to/file/{year}/{data_file_name}', compression='gzip', delimiter=\"\\t\")\n else:\n exit()\n\n file_name_ids = f'ids_{data_file_name[5:len(data_file_name)-4]}'\n\n for line in outputs_data.iterrows():\n transaction_data = line[1]\n if transaction_data['script_hex'][:2] == '6a' and transaction_data['transaction_hash'] not in transactions_ids:\n try:\n with open(f'transactions_ids/{year}/{file_name_ids}', 'a') as file:\n file.write(f\"{transaction_data['transaction_hash']}\\n\")\n except Exception as error:\n print(f'ERROR: {error}')\n print(f'\\n\\ntransactions failed to be saved:')\n print(transactions_ids)\n exit()\n transactions_ids.append(transaction_data['transaction_hash'])\n transactions_ids = []\n day += 1\n","repo_name":"benitoSan/codigos-tcc","sub_path":"txs-ids-extractor.py","file_name":"txs-ids-extractor.py","file_ext":"py","file_size_in_byte":1614,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"21854385450","text":"\"\"\"\nJSON API for the Order app\n\"\"\"\n\n# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django_filters.rest_framework import DjangoFilterBackend\nfrom rest_framework import generics, permissions\nfrom rest_framework import filters\n\nfrom django.conf.urls import url, include\n\nfrom InvenTree.helpers import str2bool\nfrom InvenTree.api import AttachmentMixin\nfrom InvenTree.status_codes import PurchaseOrderStatus, SalesOrderStatus\n\nfrom part.models import Part\nfrom company.models import SupplierPart\n\nfrom .models import PurchaseOrder, PurchaseOrderLineItem\nfrom .models import PurchaseOrderAttachment\nfrom .serializers import POSerializer, POLineItemSerializer, POAttachmentSerializer\n\nfrom .models import SalesOrder, SalesOrderLineItem\nfrom .models import SalesOrderAttachment\nfrom .serializers import SalesOrderSerializer, SOLineItemSerializer, SOAttachmentSerializer\n\n\nclass POList(generics.ListCreateAPIView):\n \"\"\" API endpoint for accessing a list of PurchaseOrder objects\n\n - GET: Return list of PO objects (with filters)\n - POST: Create a new PurchaseOrder object\n \"\"\"\n\n queryset = PurchaseOrder.objects.all()\n serializer_class = POSerializer\n\n def get_serializer(self, *args, **kwargs):\n\n try:\n kwargs['supplier_detail'] = str2bool(self.request.query_params.get('supplier_detail', False))\n except AttributeError:\n pass\n\n # Ensure the request context is passed through\n kwargs['context'] = self.get_serializer_context()\n\n return self.serializer_class(*args, **kwargs)\n\n def get_queryset(self, *args, **kwargs):\n\n queryset = super().get_queryset(*args, **kwargs)\n\n queryset = queryset.prefetch_related(\n 'supplier',\n 'lines',\n )\n\n queryset = POSerializer.annotate_queryset(queryset)\n\n return queryset\n\n def filter_queryset(self, queryset):\n\n # Perform basic filtering\n queryset = super().filter_queryset(queryset)\n\n params = self.request.query_params\n\n # Filter by 'outstanding' status\n outstanding = params.get('outstanding', None)\n\n if outstanding is not None:\n outstanding = str2bool(outstanding)\n\n if outstanding:\n queryset = queryset.filter(status__in=PurchaseOrderStatus.OPEN)\n else:\n queryset = queryset.exclude(status__in=PurchaseOrderStatus.OPEN)\n\n # Special filtering for 'status' field\n status = params.get('status', None)\n\n if status is not None:\n # First attempt to filter by integer value\n queryset = queryset.filter(status=status)\n\n # Attempt to filter by part\n part = params.get('part', None)\n\n if part is not None:\n try:\n part = Part.objects.get(pk=part)\n queryset = queryset.filter(id__in=[p.id for p in part.purchase_orders()])\n except (Part.DoesNotExist, ValueError):\n pass\n\n # Attempt to filter by supplier part\n supplier_part = params.get('supplier_part', None)\n\n if supplier_part is not None:\n try:\n supplier_part = SupplierPart.objects.get(pk=supplier_part)\n queryset = queryset.filter(id__in=[p.id for p in supplier_part.purchase_orders()])\n except (ValueError, SupplierPart.DoesNotExist):\n pass\n\n return queryset\n\n permission_classes = [\n permissions.IsAuthenticated,\n ]\n\n filter_backends = [\n DjangoFilterBackend,\n filters.SearchFilter,\n filters.OrderingFilter,\n ]\n\n filter_fields = [\n 'supplier',\n ]\n\n ordering_fields = [\n 'creation_date',\n 'reference',\n ]\n\n ordering = '-creation_date'\n\n\nclass PODetail(generics.RetrieveUpdateAPIView):\n \"\"\" API endpoint for detail view of a PurchaseOrder object \"\"\"\n\n queryset = PurchaseOrder.objects.all()\n serializer_class = POSerializer\n\n def get_serializer(self, *args, **kwargs):\n\n try:\n kwargs['supplier_detail'] = str2bool(self.request.query_params.get('supplier_detail', False))\n except AttributeError:\n pass\n\n # Ensure the request context is passed through\n kwargs['context'] = self.get_serializer_context()\n\n return self.serializer_class(*args, **kwargs)\n\n def get_queryset(self, *args, **kwargs):\n\n queryset = super().get_queryset(*args, **kwargs)\n\n queryset = queryset.prefetch_related(\n 'supplier',\n 'lines',\n )\n\n queryset = POSerializer.annotate_queryset(queryset)\n\n return queryset\n\n permission_classes = [\n permissions.IsAuthenticated\n ]\n\n\nclass POLineItemList(generics.ListCreateAPIView):\n \"\"\" API endpoint for accessing a list of POLineItem objects\n\n - GET: Return a list of PO Line Item objects\n - POST: Create a new PurchaseOrderLineItem object\n \"\"\"\n\n queryset = PurchaseOrderLineItem.objects.all()\n serializer_class = POLineItemSerializer\n\n def get_serializer(self, *args, **kwargs):\n\n try:\n kwargs['part_detail'] = str2bool(self.request.query_params.get('part_detail', False))\n except AttributeError:\n pass\n\n kwargs['context'] = self.get_serializer_context()\n\n return self.serializer_class(*args, **kwargs)\n\n permission_classes = [\n permissions.IsAuthenticated,\n ]\n\n filter_backends = [\n DjangoFilterBackend,\n ]\n\n filter_fields = [\n 'order',\n 'part'\n ]\n\n\nclass POLineItemDetail(generics.RetrieveUpdateAPIView):\n \"\"\" API endpoint for detail view of a PurchaseOrderLineItem object \"\"\"\n\n queryset = PurchaseOrderLineItem\n serializer_class = POLineItemSerializer\n\n permission_classes = [\n permissions.IsAuthenticated,\n ]\n\n\nclass SOAttachmentList(generics.ListCreateAPIView, AttachmentMixin):\n \"\"\"\n API endpoint for listing (and creating) a SalesOrderAttachment (file upload)\n \"\"\"\n\n queryset = SalesOrderAttachment.objects.all()\n serializer_class = SOAttachmentSerializer\n\n filter_fields = [\n 'order',\n ]\n\n\nclass SOList(generics.ListCreateAPIView):\n \"\"\"\n API endpoint for accessing a list of SalesOrder objects.\n\n - GET: Return list of SO objects (with filters)\n - POST: Create a new SalesOrder\n \"\"\"\n\n queryset = SalesOrder.objects.all()\n serializer_class = SalesOrderSerializer\n\n def get_serializer(self, *args, **kwargs):\n\n try:\n kwargs['customer_detail'] = str2bool(self.request.query_params.get('customer_detail', False))\n except AttributeError:\n pass\n\n # Ensure the context is passed through to the serializer\n kwargs['context'] = self.get_serializer_context()\n\n return self.serializer_class(*args, **kwargs)\n\n def get_queryset(self, *args, **kwargs):\n\n queryset = super().get_queryset(*args, **kwargs)\n\n queryset = queryset.prefetch_related(\n 'customer',\n 'lines'\n )\n\n queryset = SalesOrderSerializer.annotate_queryset(queryset)\n\n return queryset\n\n def filter_queryset(self, queryset):\n \"\"\"\n Perform custom filtering operations on the SalesOrder queryset.\n \"\"\"\n\n queryset = super().filter_queryset(queryset)\n\n params = self.request.query_params\n\n # Filter by 'outstanding' status\n outstanding = params.get('outstanding', None)\n\n if outstanding is not None:\n outstanding = str2bool(outstanding)\n\n if outstanding:\n queryset = queryset.filter(status__in=SalesOrderStatus.OPEN)\n else:\n queryset = queryset.exclude(status__in=SalesOrderStatus.OPEN)\n\n status = params.get('status', None)\n\n if status is not None:\n queryset = queryset.filter(status=status)\n\n # Filter by \"Part\"\n # Only return SalesOrder which have LineItem referencing the part\n part = params.get('part', None)\n\n if part is not None:\n try:\n part = Part.objects.get(pk=part)\n queryset = queryset.filter(id__in=[so.id for so in part.sales_orders()])\n except (Part.DoesNotExist, ValueError):\n pass\n\n return queryset\n\n permission_classes = [\n permissions.IsAuthenticated\n ]\n\n filter_backends = [\n DjangoFilterBackend,\n filters.SearchFilter,\n filters.OrderingFilter,\n ]\n\n filter_fields = [\n 'customer',\n ]\n\n ordering_fields = [\n 'creation_date',\n 'reference'\n ]\n\n ordering = '-creation_date'\n\n\nclass SODetail(generics.RetrieveUpdateAPIView):\n \"\"\"\n API endpoint for detail view of a SalesOrder object.\n \"\"\"\n\n queryset = SalesOrder.objects.all()\n serializer_class = SalesOrderSerializer\n\n def get_serializer(self, *args, **kwargs):\n\n try:\n kwargs['customer_detail'] = str2bool(self.request.query_params.get('customer_detail', False))\n except AttributeError:\n pass\n\n kwargs['context'] = self.get_serializer_context()\n\n return self.serializer_class(*args, **kwargs)\n\n def get_queryset(self, *args, **kwargs):\n\n queryset = super().get_queryset(*args, **kwargs)\n\n queryset = queryset.prefetch_related('customer', 'lines')\n\n queryset = SalesOrderSerializer.annotate_queryset(queryset)\n\n return queryset\n\n permission_classes = [permissions.IsAuthenticated]\n\n\nclass SOLineItemList(generics.ListCreateAPIView):\n \"\"\"\n API endpoint for accessing a list of SalesOrderLineItem objects.\n \"\"\"\n\n queryset = SalesOrderLineItem.objects.all()\n serializer_class = SOLineItemSerializer\n\n def get_serializer(self, *args, **kwargs):\n\n try:\n kwargs['part_detail'] = str2bool(self.request.query_params.get('part_detail', False))\n except AttributeError:\n pass\n\n try:\n kwargs['order_detail'] = str2bool(self.request.query_params.get('order_detail', False))\n except AttributeError:\n pass\n\n try:\n kwargs['allocations'] = str2bool(self.request.query_params.get('allocations', False))\n except AttributeError:\n pass\n\n kwargs['context'] = self.get_serializer_context()\n\n return self.serializer_class(*args, **kwargs)\n\n def get_queryset(self, *args, **kwargs):\n\n queryset = super().get_queryset(*args, **kwargs)\n\n queryset = queryset.prefetch_related(\n 'part',\n 'part__stock_items',\n 'allocations',\n 'allocations__item__location',\n 'order',\n 'order__stock_items',\n )\n\n return queryset\n\n permission_classes = [permissions.IsAuthenticated]\n\n filter_backends = [DjangoFilterBackend]\n\n filter_fields = [\n 'order',\n 'part',\n ]\n\n\nclass SOLineItemDetail(generics.RetrieveUpdateAPIView):\n \"\"\" API endpoint for detail view of a SalesOrderLineItem object \"\"\"\n\n queryset = SalesOrderLineItem.objects.all()\n serializer_class = SOLineItemSerializer\n\n permission_classes = [permissions.IsAuthenticated]\n\n\nclass POAttachmentList(generics.ListCreateAPIView, AttachmentMixin):\n \"\"\"\n API endpoint for listing (and creating) a PurchaseOrderAttachment (file upload)\n \"\"\"\n\n queryset = PurchaseOrderAttachment.objects.all()\n serializer_class = POAttachmentSerializer\n\n filter_fields = [\n 'order',\n ]\n\n\norder_api_urls = [\n # API endpoints for purchase orders\n url(r'^po/(?P\\d+)/$', PODetail.as_view(), name='api-po-detail'),\n url(r'po/attachment/', include([\n url(r'^.*$', POAttachmentList.as_view(), name='api-po-attachment-list'),\n ])),\n url(r'^po/.*$', POList.as_view(), name='api-po-list'),\n\n # API endpoints for purchase order line items\n url(r'^po-line/(?P\\d+)/$', POLineItemDetail.as_view(), name='api-po-line-detail'),\n url(r'^po-line/$', POLineItemList.as_view(), name='api-po-line-list'),\n\n # API endpoints for sales ordesr\n url(r'^so/(?P\\d+)/$', SODetail.as_view(), name='api-so-detail'),\n url(r'so/attachment/', include([\n url(r'^.*$', SOAttachmentList.as_view(), name='api-so-attachment-list'),\n ])),\n\n url(r'^so/.*$', SOList.as_view(), name='api-so-list'),\n\n # API endpoints for sales order line items\n url(r'^so-line/(?P\\d+)/$', SOLineItemDetail.as_view(), name='api-so-line-detail'),\n url(r'^so-line/$', SOLineItemList.as_view(), name='api-so-line-list'),\n]\n","repo_name":"ikassim9/InvenTree","sub_path":"InvenTree/order/api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":12585,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"85"} +{"seq_id":"39600985260","text":"import cv2\nimport numpy as np\n\nsamples = np.loadtxt('generalsamples.data', np.float32)\nresponses = np.loadtxt('generalresponses.data', np.float32)\nresponses = responses.reshape((responses.size, 1))\n\nmodel = cv2.ml.KNearest_create()\nmodel.train(samples, cv2.ml.ROW_SAMPLE, responses)\n\n#im = cv2.imread('train.png')\n#out = np.zeros(im.shape, np.uint8)\n#gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)\n#thresh = cv2.threshold(gray, 100, 255, cv2.THRESH_OTSU | cv2.THRESH_BINARY)[1]\n\n#contours, hierarchy = cv2.findContours(thresh, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)\n\ndef ocr(im,thresh ,cont):\n for cnt in cont:\n if 850 < cv2.contourArea(cnt) < 4000:\n [x, y, w, h] = cv2.boundingRect(cnt)\n if h > 28:\n cv2.rectangle(im, (x, y), (x + w, y + h), (0, 255, 0), 2)\n roi = thresh[y:y + h, x:x + w]\n roismall = cv2.resize(roi, (10, 10))\n roismall = roismall.reshape((1, 100))\n roismall = np.float32(roismall)\n retval, results, neigh_resp, dists = model.findNearest(roismall, k=1)\n string = int((results[0][0]))\n return string\n #cv2.putText(out, string, (x, y + h), 0, 1, (0, 255, 0))\n\n#cv2.imshow('im', im)\n#cv2.imshow('out', out)\n#cv2.waitKey(0)\n","repo_name":"NabeelUppel/Android-Sudoku-Solver","sub_path":"Tester.py","file_name":"Tester.py","file_ext":"py","file_size_in_byte":1304,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"21762591797","text":"# app.py\n\n# -*- coding: utf-8 -*-\n\nimport sys \nimport json\n\nfrom flask import Flask, jsonify,request\nfrom flask.ext.cors import CORS, cross_origin\nfrom pymongo import MongoClient\nfrom bson.json_util import dumps\nfrom bson import json_util\n\napp = Flask(__name__)\ncors = CORS(app)\napp.config['CORS_HEADERS'] = 'Content-Type'\n\n@app.route('/')\ndef index():\n\treturn '

Hackaton REP ULima 2016

'\n\n\n@app.route('/listar/', methods=['GET'])\ndef listar_ubicaciones(coleccion):\n\tclient = MongoClient('mongodb://localhost:27017/')\n\tdb = client.db_rep\n\n\trpta = []\n\t\n\tif coleccion == \"mantenimientos\":\n\t\trpta = list(db.mantenimientos.find())\n\n\tif coleccion == \"ubicaciones\":\n\t\trpta = list(db.ubicaciones.find())\n\n\tif coleccion == \"equipos\":\n\t\trpta = list(db.equipos.find())\n\n\tif coleccion == \"cpmms\":\n\t\trpta = list(db.cpmms.find())\n\n\treturn json.dumps(rpta, default=json_util.default)\n\nif __name__ == '__main__':\n\tapp.run(debug=True, host='0.0.0.0', port=5001)","repo_name":"pepevaldivia/hackaton","sub_path":"python/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":964,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"5726649159","text":"from typing import List\n\nimport pytest\n\nfrom src.jptranstokenizer.mainword.spacy_luw import SpacyluwTokenizer\n\nsentence_1: str = \"未来科学部でコンビニ店員になりきってお釣りを返していこう！\"\nsentence_2: str = \"外国人参政権\"\nsentence_3: str = \"魔法少女リリカルなのは\"\n\n\ndef test_spacyluw() -> None:\n tokenizer: SpacyluwTokenizer = SpacyluwTokenizer()\n lst_tokens_1: List[str] = [\n \"未来科学部\",\n \"で\",\n \"コンビニ店員\",\n \"に\",\n \"なりきっ\",\n \"て\",\n \"お釣り\",\n \"を\",\n \"返し\",\n \"ていこう\",\n \"!\",\n ]\n assert tokenizer.tokenize(sentence_1) == lst_tokens_1\n lst_tokens_2: List[str] = [\"外国人参政権\"]\n assert tokenizer.tokenize(sentence_2) == lst_tokens_2\n lst_tokens_3: List[str] = [\"魔法少女リリカル\", \"な\", \"の\", \"は\"]\n assert tokenizer.tokenize(sentence_3) == lst_tokens_3\n\n\n@pytest.mark.parametrize(\n \"do_lower_case, normalize_text, expected\",\n [\n (False, False, [\"Example\", \": ① is\", \"1\", \"．\"]),\n (False, True, [\"Example\", \": 1 is\", \"1\", \".\"]),\n (True, False, [\"example\", \": ① is\", \"1\", \"．\"]),\n (True, True, [\"example\", \": 1 is\", \"1\", \".\"]),\n ],\n)\ndef test_sudachi_lower_and_normalize(\n do_lower_case: bool, normalize_text: bool, expected: List[str]\n) -> None:\n tokenizer: SpacyluwTokenizer = SpacyluwTokenizer(\n do_lower_case=do_lower_case, normalize_text=normalize_text\n )\n text: str = \"Example: ① is 1．\"\n assert tokenizer.tokenize(text) == expected\n","repo_name":"retarfi/jptranstokenizer","sub_path":"tests/mainword/test_spacy_luw.py","file_name":"test_spacy_luw.py","file_ext":"py","file_size_in_byte":1612,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"85"} +{"seq_id":"32414814183","text":"import rospy\nimport path_planner\nfrom geometry_msgs.msg import Twist\nfrom nav_msgs.msg import Path\nfrom sensor_msgs.msg import LaserScan\nfrom math import atan2, pi\nfrom numpy import nanmin\n\n\nmove_tolerance = 0.1\nscan_tolerance_front = 0.4\nscan_tolerance_side = 0.3\nrotate_tolerance = 0.004\nlinear_velocity = 0.25\nangular_velocity = 1.5\n\n\nclass GotoMover:\n def __init__(self, planner):\n self.robot_position = None # Set from planner\n self.path_deviation = 0.0 # Used to avoid obstacles\n\n self.planner = planner\n\n self.is_shutdown_initiated = False\n self.is_moving = False\n self.is_obstacle_ahead = False\n\n self.mover_subscriber = rospy.Subscriber(\"/move\", Path, self.move_callback)\n self.scan_subscriber = rospy.Subscriber(\"/scan\", LaserScan, self.scan_callback)\n\n self.velocity_publisher = rospy.Publisher(\"/cmd_vel\", Twist, queue_size=10)\n\n def move_callback(self, path: Path):\n node_path = []\n\n for pose in path.poses:\n node_path.append(path_planner.Node.from_pose(pose.pose))\n\n self.follow_path(node_path)\n\n def scan_callback(self, scan_data: LaserScan):\n if nanmin(scan_data.ranges[0:10] + scan_data.ranges[350:360]) < scan_tolerance_front:\n self.is_obstacle_ahead = True\n elif nanmin(scan_data.ranges[11:165]) < scan_tolerance_side:\n self.path_deviation = -0.6\n elif nanmin(scan_data.ranges[195:349]) < scan_tolerance_side:\n self.path_deviation = 0.6\n else:\n self.path_deviation = 0\n\n def initialize_stop(self):\n self.is_shutdown_initiated = True\n\n def stop_moving(self):\n self.is_shutdown_initiated = False\n self.is_moving = False\n self.velocity_publisher.publish(Twist())\n\n def follow_path(self, path: list):\n self.is_moving = True\n\n for node in path:\n if self.is_shutdown_initiated:\n self.stop_moving()\n return\n if self.is_obstacle_ahead:\n self.velocity_publisher.publish(Twist())\n self.go_back()\n self.velocity_publisher.publish(Twist())\n self.planner.calculate_path() # Recalculating path\n return\n\n self.move_to_point(node)\n\n self.velocity_publisher.publish(Twist()) # Stopping robot\n self.rotate_to_goal(self.planner.goal)\n self.velocity_publisher.publish(Twist())\n\n self.planner.is_goal_reached = True\n self.is_moving = False\n\n def go_back(self):\n current_distance = 0.0\n vel_msg = Twist()\n vel_msg.linear.x = -linear_velocity\n t0 = rospy.Time().now().to_sec()\n loop_rate = rospy.Rate(1000)\n\n while current_distance < 0.4:\n if self.is_shutdown_initiated:\n self.is_obstacle_ahead = False\n return\n\n self.velocity_publisher.publish(vel_msg)\n t1 = rospy.Time().now().to_sec()\n current_distance = linear_velocity * (t1 - t0)\n loop_rate.sleep()\n\n self.is_obstacle_ahead = False\n\n def move_to_point(self, point: path_planner.Node):\n vel_msg = Twist()\n vel_msg.linear.x = linear_velocity\n loop_rate = rospy.Rate(1000)\n\n while self.robot_position.calculate_distance(point) > move_tolerance:\n if self.is_shutdown_initiated or self.is_obstacle_ahead:\n return\n\n speed = angular_velocity * self.angular_difference(point)\n vel_msg.angular.z = min(angular_velocity, speed) + self.path_deviation\n\n self.velocity_publisher.publish(vel_msg)\n loop_rate.sleep()\n\n def rotate_to_goal(self, goal: path_planner.Node):\n vel_msg = Twist()\n loop_rate = rospy.Rate(1000)\n\n while abs(goal.theta - self.robot_position.theta) > rotate_tolerance:\n if self.is_shutdown_initiated:\n self.stop_moving()\n return\n\n speed = angular_velocity * (goal.theta - self.robot_position.theta)\n vel_msg.angular.z = min(angular_velocity, speed)\n self.velocity_publisher.publish(vel_msg)\n loop_rate.sleep()\n\n self.velocity_publisher.publish(Twist())\n\n def angular_difference(self, point: path_planner.Node) -> float:\n angle = atan2(point.y - self.robot_position.y, point.x - self.robot_position.x) \\\n - self.robot_position.theta\n\n if angle <= -pi: # Normalizing angle\n angle += 2 * pi\n elif angle > pi:\n angle -= 2 * pi\n\n return angle\n\n","repo_name":"4uf04eG/ROS-AStar","sub_path":"src/path_planner/mover.py","file_name":"mover.py","file_ext":"py","file_size_in_byte":4626,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"85"} +{"seq_id":"7802032412","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport itertools\n\nfrom absl.testing import parameterized\nfrom graph_nets import utils_np\nfrom graph_nets.tests import test_utils\nimport networkx as nx\nimport numpy as np\nfrom six.moves import range\nimport tensorflow as tf\n\n\nclass ConcatenationTest(test_utils.GraphsTest, parameterized.TestCase):\n\n def test_compute_stacked_offsets(self):\n sizes = np.array([5, 4, 3, 1, 2, 0, 3, 0, 4, 7])\n repeats = [2, 2, 0, 2, 1, 3, 2, 0, 3, 2]\n offsets0 = utils_np._compute_stacked_offsets(sizes, repeats)\n offsets1 = utils_np._compute_stacked_offsets(sizes, np.array(repeats))\n expected_offsets = [\n 0, 0, 5, 5, 12, 12, 13, 15, 15, 15, 15, 15, 18, 18, 18, 22, 22\n ]\n self.assertAllEqual(expected_offsets, offsets0.tolist())\n self.assertAllEqual(expected_offsets, offsets1.tolist())\n\n def test_concatenate_data_dicts(self):\n cat = utils_np._concatenate_data_dicts(self.graphs_dicts_in)\n for k, v in cat.items():\n self.assertAllEqual(getattr(self.reference_graph, k), v)\n\n\nclass DataDictsConversionTest(test_utils.GraphsTest, parameterized.TestCase):\n\n @parameterized.parameters(([],),\n ([\"edges\"],),\n ([\"globals\"],),\n ([\"edges\", \"receivers\", \"senders\"],))\n def test_data_dicts_to_graphs_tuple(self, none_fields):\n \"\"\"Fields in `none_fields` will be cleared out.\"\"\"\n for field in none_fields:\n for graph_dict in self.graphs_dicts_in:\n if field in graph_dict:\n if field == \"nodes\":\n graph_dict[\"n_node\"] = graph_dict[\"nodes\"].shape[0]\n graph_dict[field] = None\n self.reference_graph = self.reference_graph._replace(**{field: None})\n if field == \"senders\":\n self.reference_graph = self.reference_graph._replace(\n n_edge=np.zeros_like(self.reference_graph.n_edge))\n graphs = utils_np.data_dicts_to_graphs_tuple(self.graphs_dicts_in)\n for field in none_fields:\n self.assertEqual(None, getattr(graphs, field))\n self._assert_graph_equals_np(self.reference_graph, graphs)\n\n @parameterized.parameters((\"receivers\",), (\"senders\",))\n def test_data_dicts_to_graphs_tuple_missing_field_raises(self, none_field):\n \"\"\"Fields that cannot be missing.\"\"\"\n for graph_dict in self.graphs_dicts_in:\n graph_dict[none_field] = None\n with self.assertRaisesRegexp(ValueError, none_field):\n utils_np.data_dicts_to_graphs_tuple(self.graphs_dicts_in)\n\n def test_data_dicts_to_graphs_tuple_infer_n_node(self):\n \"\"\"Not having nodes is fine if providing the number of nodes.\"\"\"\n for graph_dict in self.graphs_dicts_in:\n graph_dict[\"n_node\"] = graph_dict[\"nodes\"].shape[0]\n graph_dict[\"nodes\"] = None\n out = utils_np.data_dicts_to_graphs_tuple(self.graphs_dicts_in)\n self.assertAllEqual([0, 1, 1, 1, 2, 2, 2], out.n_node)\n\n def test_data_dicts_to_graphs_tuple_cast_types(self):\n \"\"\"Index and number fields should be cast to numpy arrays.\"\"\"\n for graph_dict in self.graphs_dicts_in:\n graph_dict[\"n_node\"] = np.array(\n graph_dict[\"nodes\"].shape[0], dtype=np.int64)\n graph_dict[\"receivers\"] = graph_dict[\"receivers\"].astype(np.int16)\n graph_dict[\"senders\"] = graph_dict[\"senders\"].astype(np.float64)\n graph_dict[\"nodes\"] = graph_dict[\"nodes\"].astype(np.float64)\n graph_dict[\"edges\"] = graph_dict[\"edges\"].astype(np.float64)\n out = utils_np.data_dicts_to_graphs_tuple(self.graphs_dicts_in)\n for key in [\"n_node\", \"n_edge\", \"receivers\", \"senders\"]:\n self.assertEqual(np.int32, getattr(out, key).dtype)\n for key in [\"nodes\", \"edges\"]:\n self.assertEqual(np.float64, getattr(out, key).dtype)\n\n def test_data_dicts_to_graphs_tuple_from_lists(self):\n \"\"\"Tests creatings a GraphsTuple from python lists.\"\"\"\n for graph_dict in self.graphs_dicts_in:\n graph_dict[\"receivers\"] = graph_dict[\"receivers\"].tolist()\n graph_dict[\"senders\"] = graph_dict[\"senders\"].tolist()\n graphs = utils_np.data_dicts_to_graphs_tuple(self.graphs_dicts_in)\n self._assert_graph_equals_np(self.reference_graph, graphs)\n\n @parameterized.named_parameters(\n (\"all_fields\", []),\n (\"no_data\", [\"nodes\", \"edges\", \"globals\"]),\n (\"no_edges\", [\"edges\", \"receivers\", \"senders\"]))\n def test_graphs_tuple_to_data_dicts(self, none_fields):\n graphs_tuple = utils_np.data_dicts_to_graphs_tuple(self.graphs_dicts_in)\n graphs_tuple = graphs_tuple.map(lambda _: None, none_fields)\n data_dicts = utils_np.graphs_tuple_to_data_dicts(graphs_tuple)\n for none_field, data_dict in itertools.product(none_fields, data_dicts):\n self.assertEqual(None, data_dict[none_field])\n for expected_data_dict, data_dict in zip(self.graphs_dicts_out, data_dicts):\n for k, v in expected_data_dict.items():\n if k not in none_fields:\n self.assertAllClose(v, data_dict[k])\n\n\ndef _single_data_dict_to_networkx(data_dict):\n graph_nx = nx.OrderedMultiDiGraph()\n if data_dict[\"nodes\"].size > 0:\n for i, x in enumerate(data_dict[\"nodes\"]):\n graph_nx.add_node(i, features=x)\n\n if data_dict[\"edges\"].size > 0:\n edge_data = zip(data_dict[\"senders\"], data_dict[\"receivers\"], [{\n \"features\": x\n } for x in data_dict[\"edges\"]])\n graph_nx.add_edges_from(edge_data)\n graph_nx.graph[\"features\"] = data_dict[\"globals\"]\n\n return graph_nx\n\n\nclass NetworkxConversionTest(test_utils.GraphsTest, parameterized.TestCase):\n\n def test_order_preserving(self):\n \"\"\"Tests that edges order can be preserved when importing from networks.\"\"\"\n graph = nx.DiGraph()\n for node_index in range(4):\n graph.add_node(node_index, features=np.array([node_index]))\n receivers = [0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3]\n senders = [1, 2, 3, 0, 2, 3, 0, 1, 3, 0, 1, 2]\n for edge_index, (receiver, sender) in enumerate(zip(receivers, senders)):\n # Removing the \"index\" key makes this test fail 100%.\n edge_data = {\"features\": np.array([edge_index]), \"index\": edge_index}\n graph.add_edge(sender, receiver, **edge_data)\n graph.graph[\"features\"] = np.array([0.])\n graphs_graph = utils_np.networkx_to_data_dict(graph)\n self.assertAllEqual(receivers, graphs_graph[\"receivers\"])\n self.assertAllEqual(senders, graphs_graph[\"senders\"])\n self.assertAllEqual([[x] for x in range(4)], graphs_graph[\"nodes\"])\n self.assertAllEqual([[x] for x in range(12)], graphs_graph[\"edges\"])\n\n def test_networkxs_to_graphs_tuple_with_none_fields(self):\n graph_nx = nx.OrderedMultiDiGraph()\n data_dict = utils_np.networkx_to_data_dict(\n graph_nx,\n node_shape_hint=None,\n edge_shape_hint=None)\n self.assertEqual(None, data_dict[\"edges\"])\n self.assertEqual(None, data_dict[\"globals\"])\n self.assertEqual(None, data_dict[\"nodes\"])\n graph_nx.add_node(0, features=None)\n data_dict = utils_np.networkx_to_data_dict(\n graph_nx,\n node_shape_hint=1,\n edge_shape_hint=None)\n self.assertEqual(None, data_dict[\"nodes\"])\n graph_nx.add_edge(0, 0, features=None)\n data_dict = utils_np.networkx_to_data_dict(\n graph_nx,\n node_shape_hint=[1],\n edge_shape_hint=[1])\n self.assertEqual(None, data_dict[\"edges\"])\n graph_nx.graph[\"features\"] = None\n utils_np.networkx_to_data_dict(graph_nx)\n self.assertEqual(None, data_dict[\"globals\"])\n\n def test_networkxs_to_graphs_tuple(self):\n graph0 = utils_np.data_dicts_to_graphs_tuple(self.graphs_dicts_in)\n graph_nxs = []\n for data_dict in self.graphs_dicts_in:\n graph_nxs.append(_single_data_dict_to_networkx(data_dict))\n hints = {\n \"edge_shape_hint\": data_dict[\"edges\"].shape[1:],\n \"node_shape_hint\": data_dict[\"nodes\"].shape[1:],\n \"data_type_hint\": data_dict[\"nodes\"].dtype,\n }\n graph = utils_np.networkxs_to_graphs_tuple(graph_nxs, **hints)\n self._assert_graph_equals_np(graph0, graph, force_edges_ordering=True)\n\n def test_networkxs_to_data_dict_raises_node_key_error(self):\n \"\"\"If the nodes have keys not consistent with the order they were added.\"\"\"\n graph_nx = nx.OrderedMultiDiGraph()\n graph_nx.add_node(0, features=None)\n graph_nx.add_node(1, features=None)\n graph_nx.add_node(3, features=None)\n\n with self.assertRaisesRegexp(\n ValueError, \"found node with index 2 and key 3\"):\n utils_np.networkx_to_data_dict(graph_nx)\n\n # Check that it is still raised even if there is a node with each key,\n # and only the order is wrong.\n graph_nx.add_node(2, features=None)\n with self.assertRaisesRegexp(\n ValueError, \"found node with index 2 and key 3\"):\n utils_np.networkx_to_data_dict(graph_nx)\n\n def test_networkxs_to_graphs_tuple_raises_key_error(self):\n \"\"\"If the \"features\" field is not present in the nodes or edges.\"\"\"\n graph_nx = _single_data_dict_to_networkx(self.graphs_dicts_in[-1])\n first_node = list(graph_nx.nodes(data=True))[0]\n del first_node[1][\"features\"]\n with self.assertRaisesRegexp(\n KeyError, \"This could be due to the node having been silently added\"):\n utils_np.networkxs_to_graphs_tuple([graph_nx])\n graph_nx = _single_data_dict_to_networkx(self.graphs_dicts_in[-1])\n first_edge = list(graph_nx.edges(data=True))[0]\n del first_edge[2][\"features\"]\n with self.assertRaises(KeyError):\n utils_np.networkxs_to_graphs_tuple([graph_nx])\n\n def test_networkxs_to_graphs_tuple_raises_assertion_error(self):\n \"\"\"Either all nodes (resp. edges) should have features, or none of them.\"\"\"\n graph_nx = _single_data_dict_to_networkx(self.graphs_dicts_in[-1])\n first_node = list(graph_nx.nodes(data=True))[0]\n first_node[1][\"features\"] = None\n with self.assertRaisesRegexp(\n ValueError, \"Either all the nodes should have features\"):\n utils_np.networkxs_to_graphs_tuple([graph_nx])\n graph_nx = _single_data_dict_to_networkx(self.graphs_dicts_in[-1])\n first_edge = list(graph_nx.edges(data=True))[0]\n first_edge[2][\"features\"] = None\n with self.assertRaisesRegexp(\n ValueError, \"Either all the edges should have features\"):\n utils_np.networkxs_to_graphs_tuple([graph_nx])\n\n @parameterized.named_parameters(\n (\"all fields defined\", []),\n (\"stateless\", [\"nodes\", \"edges\", \"globals\"]))\n def test_graphs_tuple_to_networkxs(self, none_fields):\n if \"nodes\" in none_fields:\n for graph in self.graphs_dicts_in:\n graph[\"n_node\"] = graph[\"nodes\"].shape[0]\n graphs = utils_np.data_dicts_to_graphs_tuple(self.graphs_dicts_in)\n graphs = graphs.map(lambda _: None, none_fields)\n graph_nxs = utils_np.graphs_tuple_to_networkxs(graphs)\n for data_dict, graph_nx in zip(self.graphs_dicts_out, graph_nxs):\n if \"globals\" in none_fields:\n self.assertEqual(None, graph_nx.graph[\"features\"])\n else:\n self.assertAllClose(data_dict[\"globals\"], graph_nx.graph[\"features\"])\n nodes_data = graph_nx.nodes(data=True)\n for i, (v, (j, n)) in enumerate(zip(data_dict[\"nodes\"], nodes_data)):\n self.assertEqual(i, j)\n if \"nodes\" in none_fields:\n self.assertEqual(None, n[\"features\"])\n else:\n self.assertAllClose(v, n[\"features\"])\n edges_data = sorted(\n graph_nx.edges(data=True), key=lambda x: x[2][\"index\"])\n for v, (_, _, e) in zip(data_dict[\"edges\"], edges_data):\n if \"edges\" in none_fields:\n self.assertEqual(None, e[\"features\"])\n else:\n self.assertAllClose(v, e[\"features\"])\n for r, s, (i, j, _) in zip(\n data_dict[\"receivers\"], data_dict[\"senders\"], edges_data):\n self.assertEqual(s, i)\n self.assertEqual(r, j)\n\n\nclass GetItemTest(test_utils.GraphsTest, parameterized.TestCase):\n\n def test_get_single_item(self):\n index = 2\n expected = self.graphs_dicts_out[index]\n\n graphs = utils_np.data_dicts_to_graphs_tuple(self.graphs_dicts_in)\n graph = utils_np.get_graph(graphs, index)\n actual, = utils_np.graphs_tuple_to_data_dicts(graph)\n\n for k, v in expected.items():\n self.assertAllClose(v, actual[k])\n\n def test_get_many_items(self):\n index = slice(1, 3)\n expected = self.graphs_dicts_out[index]\n\n graphs = utils_np.data_dicts_to_graphs_tuple(self.graphs_dicts_in)\n graphs2 = utils_np.get_graph(graphs, index)\n actual = utils_np.graphs_tuple_to_data_dicts(graphs2)\n\n for ex, ac in zip(expected, actual):\n for k, v in ex.items():\n self.assertAllClose(v, ac[k])\n\nif __name__ == \"__main__\":\n tf.test.main()\n","repo_name":"deepmind/graph_nets","sub_path":"graph_nets/tests/utils_np_test.py","file_name":"utils_np_test.py","file_ext":"py","file_size_in_byte":12569,"program_lang":"python","lang":"en","doc_type":"code","stars":5300,"dataset":"github-code","pt":"85"} +{"seq_id":"30979056342","text":"import random\nfrom collections import defaultdict\nfrom torch.utils.data import Dataset, DataLoader\nfrom model.utils import PAD_ID, UNK_ID, SOS_ID, EOS_ID\nimport numpy as np\n\nempty_sentence = ['', '', '', '', '', '', '', '', '', '', '',\n '', '', '', '', '', '', '', '', '', '', '',\n '', '', '', '', '', '', '', '']\ndefault_max_convo_length = 10\n\n\nclass DialogDataset(Dataset):\n def __init__(self, sentences, conversation_length, sentence_length, vocab,\n data=None, emojis=None, infersent=None):\n\n # [total_data_size, max_conversation_length, max_sentence_length]\n # tokenized raw text of sentences\n self.sentences = sentences\n self.vocab = vocab\n\n # conversation length of each batch\n # [total_data_size]\n self.conversation_length = conversation_length\n\n # list of length of sentences\n # [total_data_size, max_conversation_length]\n self.sentence_length = sentence_length\n self.data = data\n\n # Emoji vector for each sentence\n self.emojis = emojis\n\n # Infersent embedding vector for each sentence\n self.infersent = infersent\n\n # There is no emoji annotation for blank sentences,\n # so need to remove these\n if emojis is not None or infersent is not None:\n if emojis is not None: assert len(self.emojis) == len(self.sentences)\n if infersent is not None: assert len(self.infersent) == len(self.sentences)\n\n if self.data is not None:\n print('Warning! Unpredictable behavior when input needs filtering and data variable present')\n\n to_delete = []\n for i in range(len(self.sentences)):\n if ((emojis and len(self.emojis[i]) != len(self.sentences[i])) or\n (infersent and len(self.infersent[i]) != len(self.sentences[i]))):\n non_blanks = [s for s in self.sentences[i] if s != empty_sentence]\n if len(non_blanks) < 2:\n # Need to totally discard this row\n to_delete.append(i)\n else:\n keep_indices = [j for j, s in enumerate(sentences[i]) if s != empty_sentence]\n self.sentence_length[i] = [self.sentence_length[i][x] for x in keep_indices]\n self.conversation_length[i] = len(non_blanks)\n self.sentences[i] = non_blanks\n\n if emojis is not None and len(emojis[i]) != len(non_blanks):\n if len(emojis[i]) > default_max_convo_length:\n self.emojis[i] = self.emojis[i][:len(non_blanks)]\n else:\n print('Number of emoji sentences', len(emojis[i]),\n 'differs from length of sentences', len(non_blanks),\n 'even with blank sentences removed and emojis trimmed.')\n\n if infersent is not None and len(infersent[i]) != len(non_blanks):\n self.infersent[i] = [self.infersent[i][x] for x in keep_indices]\n\n if len(self.infersent[i]) != len(non_blanks):\n print('Number of infersent sentences', len(infersent[i]),\n 'differs from length of sentences', len(non_blanks),\n 'even with blank sentences removed and infersent trimmed.')\n\n # Delete the necessary indices\n print('Removing', len(to_delete), \"conversations with too many blank lines from the dataset.\")\n for idx in to_delete:\n del self.sentences[idx]\n del self.emojis[idx]\n del self.conversation_length[idx]\n del self.sentence_length[idx]\n if infersent is not None:\n del self.infersent[idx]\n\n self.len = len(self.sentences)\n assert self.len == len(self.conversation_length) == len(self.sentence_length)\n\n if emojis is not None:\n assert self.len == len(self.emojis)\n if infersent is not None:\n assert self.len == len(self.infersent)\n\n def __getitem__(self, index):\n \"\"\"Return Single data sentence\"\"\"\n # [max_conversation_length, max_sentence_length]\n sentence = self.sentences[index]\n conversation_length = self.conversation_length[index]\n sentence_length = self.sentence_length[index]\n\n # word => word_ids\n sentence = self.sent2id(sentence)\n\n emojis = None\n infersent = None\n if self.emojis:\n emojis = self.emojis[index]\n if self.infersent:\n infersent = self.infersent[index]\n\n return sentence, conversation_length, sentence_length, emojis, infersent\n\n def __len__(self):\n return self.len\n\n def sent2id(self, sentences):\n \"\"\"word => word id\"\"\"\n # [max_conversation_length, max_sentence_length]\n return [self.vocab.sent2id(sentence) for sentence in sentences]\n\n\ndef get_loader(sentences, conversation_length, sentence_length, vocab, batch_size=100, data=None,\n shuffle=True, emojis=None, infersent=None):\n \"\"\"Load DataLoader of given DialogDataset\"\"\"\n\n def collate_fn(data):\n \"\"\"\n Collate list of data in to batch\n Args:\n data: list of tuple(source, target, conversation_length, source_length, target_length)\n Return:\n Batch of each feature\n - source (LongTensor): [batch_size, max_conversation_length, max_source_length]\n - target (LongTensor): [batch_size, max_conversation_length, max_source_length]\n - conversation_length (np.array): [batch_size]\n - source_length (LongTensor): [batch_size, max_conversation_length]\n \"\"\"\n # Sort by conversation length (descending order) to use 'pack_padded_sequence'\n data.sort(key=lambda x: x[1], reverse=True)\n\n # Separate\n sentences, conversation_length, sentence_length, emojis, infersent = zip(*data)\n\n # return sentences, conversation_length, sentence_length.tolist()\n return sentences, conversation_length, sentence_length, emojis, infersent\n\n dataset = DialogDataset(sentences, conversation_length,\n sentence_length, vocab, data=data,\n emojis=emojis, infersent=infersent)\n\n data_loader = DataLoader(\n dataset=dataset,\n batch_size=batch_size,\n shuffle=shuffle,\n collate_fn=collate_fn)\n\n return data_loader","repo_name":"natashamjaques/neural_chat","sub_path":"model/data_loader.py","file_name":"data_loader.py","file_ext":"py","file_size_in_byte":6871,"program_lang":"python","lang":"en","doc_type":"code","stars":172,"dataset":"github-code","pt":"85"} +{"seq_id":"30360746400","text":"from turtle import Turtle\r\n\r\n\r\nclass Rockets(Turtle):\r\n def __init__(self, x_position, y_position, color_type):\r\n super().__init__()\r\n self.shape(\"square\")\r\n self.resizemode(\"user\")\r\n self.shapesize(stretch_wid=7, stretch_len=1, outline=1)\r\n self.color(color_type)\r\n self.penup()\r\n self.setposition(x_position, y_position)\r\n\r\n def move_up(self):\r\n new_position = self.ycor() + 20\r\n self.goto(self.xcor(), new_position)\r\n\r\n def move_down(self):\r\n new_position = self.ycor() - 20\r\n self.goto(self.xcor(), new_position)\r\n\r\n","repo_name":"Nika-Chinchaladze/Ping_Pong_Game","sub_path":"Rockets_Section.py","file_name":"Rockets_Section.py","file_ext":"py","file_size_in_byte":606,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"71226200278","text":"from flask import Blueprint, render_template, redirect, url_for\nfrom ..models.shipping_form import ShippingForm\nfrom ..models.models import db, Package\n\nbp = Blueprint(\"home\", __name__, url_prefix=\"\")\n\nshipping_request = Blueprint(\"new_package\", __name__, url_prefix=\"\")\n\n@bp.route(\"/\")\ndef root_endpoint():\n packages = Package.query.all()\n return render_template('package_status.html', packages=packages)\n\n\n@shipping_request.route('/new_package', methods=['GET', 'POST'])\ndef new_package():\n form = ShippingForm()\n if form.validate_on_submit():\n # print('FORM DATA -->', form.data)\n # FORM DATA --> {'sender_name': 'cxczc', 'recipient_name': 'zxczc', 'origin': 'Seattle', 'destination': 'Seattle', 'express_shipping': False, 'csrf_token': 'IjkzZjg4NzM2NTdlMGZlZTY4NjIyNzBhNzBiMzczY2Q4ZWQxMTM2MTMi.ZFw4MA.dL0lu-d9Y-8fMm0Hhny5RcFI0hw'}\n data = form.data\n new_package = Package(sender=data[\"sender_name\"],\n recipient=data[\"recipient_name\"],\n origin=data[\"origin\"],\n destination=data[\"destination\"],\n location=data[\"origin\"])\n\n\n db.session.add(new_package)\n db.session.commit()\n return redirect('/')\n\n return render_template('shipping_request.html', form=form)\n","repo_name":"athena-codes/mod6-practices","sub_path":"week-35/practice-for-sprint-18-python-package-tracker-long-practice-main/app/routes/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":1326,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"37149642055","text":"from pytumblr import TumblrRestClient\nfrom pprint import pformat\nfrom operator import itemgetter\n\nfrom . import log\n\nlogger = log.getChild(__name__)\n\nDEBUG = True\nif DEBUG:\n log.setDebugLevel(log.DEBUG)\nelse:\n log.setDebugLevel(log.INFO)\n\ndef get_client(appinfo, authinfo):\n client = TumblrRestClient(\n appinfo.consumer_key, appinfo.consumer_secret,\n authinfo.token, authinfo.secret\n )\n user = client.info().get('user')\n logger.debug(\n 'connect to account %s with %d followings and %d likes',\n user.get('name'), user.get('following'), user.get('likes')\n )\n return client\n\ndef make_it_do(appinfo, authinfo, method_name, data):\n client = get_client(appinfo, authinfo)\n username = client.info()['user']['name']\n completed = list()\n uncompleted = list()\n\n logger.debug('performing %s on data set %s', method_name, pformat(data))\n for f in data:\n logger.info('make %s to %s %s', username, method_name, f)\n ret = getattr(client, method_name)(*f)\n logger.debug('%s request returns %s', method_name, ret)\n if 'meta' in ret and int(ret['meta'].get('status', 0) / 100) != 2:\n uncompleted.append(f)\n logger.error('cannot %s the data %s', method_name, f)\n logger.error('server returns %s', ret)\n else:\n completed.append(f)\n logger.info('%d of %d %sed', len(completed), len(data), method_name)\n if uncompleted:\n logger.warn(\n 'following items are not %sed by %s: %s',\n method_name, username, pformat(uncompleted)\n )\n return completed, uncompleted\n\ndef make_it_follow(appinfo, authinfo, followings):\n ans = make_it_do(\n appinfo,\n authinfo,\n 'follow',\n list((f.get('uuid'),) for f in followings.get('blogs', []))\n )\n return ans\n\ndef make_it_like(appinfo, authinfo, likes):\n ans = make_it_do(\n appinfo,\n authinfo,\n 'like',\n list((l.get('id'), l.get('reblog_key')) for l in likes.get('liked_posts', []))\n )\n return ans\n\ndef collect_all_pages(collector, data_getter, initial=None):\n ans = list()\n if initial:\n ans.extend(initial)\n offset = len(ans)\n while True:\n page = collector(offset=offset)\n logger.debug('new page received at offset %d: %s', offset, pformat(page))\n\n data = data_getter(page)\n logger.info('%d data retrieved from source', len(data))\n if not data:\n break\n ans.extend(data)\n offset = len(ans)\n return ans\n\ndef migrate(config):\n likes = None\n followings = None\n source_client = get_client(config.appinfo, config.source)\n\n logger.info('retrieving followings...')\n followings = source_client.following()\n logger.info(\n 'found %d followings, grab the whole list...', \n followings.get('total_blogs', -1)\n )\n followings['blogs'] = collect_all_pages(source_client.following, itemgetter('blogs'), followings['blogs'])\n logger.info(\n '%d of %d followings retrieved',\n len(followings.get('blogs', [])),\n followings.get('total_blogs', -1)\n )\n logger.debug('followings: %s', pformat(followings))\n\n logger.info('retrieving likes...')\n likes = source_client.likes()\n logger.info(\n 'found %d likes, grab the whole list...', \n likes.get('liked_count', -1)\n )\n likes['liked_posts'] = collect_all_pages(source_client.likes, itemgetter('liked_posts'), likes['liked_posts'])\n logger.info('%d likes retrieved', len(likes))\n logger.debug('likes: %s', pformat(likes))\n\n for authinfo in config.targets:\n if followings:\n completed, uncompleted = make_it_follow(config.appinfo, authinfo, followings)\n\n if likes:\n completed, uncompleted = make_it_like(config.appinfo, authinfo, likes)\n\n logger.info('all done.')\n\n\n\nif __name__ == '__main__':\n from .config import load\n config = load('config.json')\n migrate(config)\n","repo_name":"genzj/tumblr-migrate","sub_path":"tumblrmigrate/migrate.py","file_name":"migrate.py","file_ext":"py","file_size_in_byte":4001,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"85"} +{"seq_id":"40346599368","text":"\"\"\"\nConcatenate some strings into a longer\nstring and print out the result\n\"\"\"\n\nword1 = \"tree\"\nword2 = \"branch\"\nword3 = \"leaf\"\n\nsentence = word1 + Word2 + word3\n\nprint(sentence)\n\n# Error was with Word2 being capital W\n# Concatenation is a bit fishy without spaces but what are you going to do :/","repo_name":"callous4567/UoE-Projects","sub_path":"2nd Year Miscellanies/Unedited Errors/error1.py","file_name":"error1.py","file_ext":"py","file_size_in_byte":295,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"85"} +{"seq_id":"1478878230","text":"# Dictionary and for loop\napples = {'cox': 17, 'braeburn': 21, 'pink lady': 7, 'royal gala': 3, 'fuji': 1}\ntotal_items = 0\nfor key, value in apples.items():\n total_items += value\n print(str(key), str(value))\nprint('total_items: ', total_items)\nprint()\n\nallGuests = {'Alice': {'apples': 5, 'pretzels': 12},\n 'Bob': {'ham sandwiches': 3, 'apples': 2},\n 'Sam': {'apples': 3, 'apple pies': 2},\n 'Carol': {'cups': 3, 'apple pies': 1}}\n\n\ndef total_bought(guests, items):\n num_brought = 0\n for key, value in guests.items():\n num_brought = num_brought + value.get(items, 0)\n return num_brought\n\n\nprint('Number of things being brought:')\nprint(' - Apples ' + str(total_bought(allGuests, 'apples')))\nprint(' - Cups ' + str(total_bought(allGuests, 'cups')))\nprint(' - Ham Sandwiches ' + str(total_bought(allGuests, 'ham sandwiches')))\n\n# Looping / Iterate Methods\ndiction = {'userid': 3203, 'message': 'hello world!', 'language': 'English'}\nprint('Diction loop example 1')\nfor key in diction.keys():\n value = diction[key]\n print(key, '=', value)\n\nprint('\\nDiction loop example 2')\nfor key, value in diction.items():\n print(key, '=', value)\n\n# The setdefault() method call ensures that the key is in the count dictionary (with a default value of 0)\nprint()\nimport pprint\n\nmessage = 'It was a bright cold day in April, and the clocks were striking thirteen.'\ncount = {}\nfor character in message:\n count.setdefault(character, 0)\n count[character] = count[character] + 1\npprint.pprint(count)\n\nprint()\n\nprint()\n# inventory.py\nstuff = {'rope': 1, 'torch': 6, 'gold coin': 42, 'dagger': 1, 'arrow': 12}\n\n\ndef displayInventory(inventory):\n print(\"Inventory:\")\n item_total = 0\n for key, value in inventory.items():\n item_total += value\n print(str(value), str(key))\n print('Total number of items: ', item_total)\n\n\n# displayInventory(stuff)\n\ndef addToInventory(inventory, addeditems):\n # uni_dragon_loot = {i:addeditems.count(i) for i in addeditems}\n \"\"\" Add Items to inventory\n Args:\n inventory (dict): Inventory containing items and their counts\n addedItems (list): Items to add to inventory\n Returns:\n updatedInventory (dict): Inventory containing updated items and their counts\n \"\"\"\n\n for i in addeditems: # a list and looping through ['gold coin', 'dagger', 'gold coin', 'gold coin', 'ruby']\n print('Item in list:', i, ', item in Dictionary', inventory, end='')\n inventory.setdefault(i, 0) # if no value in dic; then assign 0 else return its current value\n inventory[i] = inventory[i] + 1\n print(' , newly updated dictionary: ', inventory)\n\n return inventory\n\n\ninv = {'gold coin': 42, 'rope': 1}\ndragonLoot = ['gold coin', 'dagger', 'gold coin', 'gold coin', 'ruby']\ninv = addToInventory(inv, dragonLoot)\ndisplayInventory(inv)\n","repo_name":"UncleBob2/MyPythonCookBook","sub_path":"for loop in dictionaries.py","file_name":"for loop in dictionaries.py","file_ext":"py","file_size_in_byte":2924,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"30438543882","text":"import time\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.common.exceptions import NoSuchElementException\nfrom functions import *\n\nPATH = \"/Users/josepachecosanchez/Documents/chromedriver\"\ndriver = webdriver.Chrome(PATH)\n\nf = open(\"log-\" + str(get_date()) + \".txt\", \"a\")\nf.write(\"******* LOG \" + str(get_date()) + \" - \" + str(get_time()) + \" *******\\n\")\n\nf.write(\"Waiting for the page to load...\\n\")\nf.close()\nprint(\"Waiting for the page to load...\")\ndriver.get('https://cotps.com/#/pages/transaction/transaction')\ntry:\n print(\"Login initialization...\")\n id_box = driver.find_element(By.XPATH, \"//*[contains(text(), 'United States +1')]\")\n id_box.click()\n time.sleep(2)\n id_box = driver.find_element(By.XPATH, \"//input[@type='number']\")\n id_box.click()\n id_box.send_keys(\"52\")\n time.sleep(2)\n id_box = driver.find_element(By.XPATH, \"//*[contains(text(), 'Confirmar')]\")\n id_box.click()\n time.sleep(2)\n\n id_box = driver.find_element(By.XPATH, \"//input[@type='number']\")\n id_box.click()\n id_box.send_keys(\"phone\")\n\n id_box = driver.find_element(By.XPATH, \"//input[@type='password']\")\n id_box.click()\n id_box.send_keys(\"pawssword\")\n time.sleep(2)\n id_box = driver.find_element(By.CLASS_NAME, \"login\")\n id_box.click()\n print(\"Starting transactions...\")\n time.sleep(10)\n\n while True:\n try:\n driver.get('https://cotps.com/#/pages/transaction/transaction')\n print(\"Trying to sell...\")\n time.sleep(10)\n classNames = driver.find_element(By.XPATH, \"(//uni-view[@class='division-num'])[2]\").text\n value = float(classNames)\n if value >= 5:\n time.sleep(2)\n id_box = driver.find_element(By.CLASS_NAME, \"orderBtn\")\n id_box.click()\n time.sleep(15)\n id_box = driver.find_element(By.XPATH, \"//*[contains(text(), 'Vendido')]\")\n id_box.click()\n print(get_time())\n time.sleep(15)\n else:\n print(get_time())\n f = open(\"log-\" + str(get_date()) + \".txt\", \"a\")\n processing = driver.find_element(By.XPATH, \"(//uni-view[@class='division-num'])[1]\").text\n f.write(\"You don't have enough money to sell, we will try again in 15 mins: \\n\")\n f.write(\"TIME: \" + str(get_time()) + \"\\nCurrent PROCESSING: \" + processing + \"\\n\")\n f.close()\n print(\"You don't have enough money to sell, we will try again in 15 mins , in process:\", processing)\n time.sleep(900)\n\n except NoSuchElementException:\n f = open(\"log-\" + str(get_date()) + \".txt\", \"a\")\n f.write(\"There was an error TIME: \" + str(get_time()) + \" DATE: \" + str(get_date()) + \"\\n\")\n f.close()\n print(\"There was an error, trying again...\")\n get_time()\n time.sleep(2)\n\nexcept:\n restart()\n","repo_name":"JoseCarlosPa/cotps-bot","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3014,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"1202959551","text":"import sys\nsys.stdin = open('swea_5248_그룹나누기_solution.txt')\n\ndef make_set(x):\n p[x] = x\n\n\ndef find_set(x):\n if p[x] != x:\n p[x] = find_set(p[x])\n return p[x]\n\n\ndef union(x, y):\n p[find_set(y)] = find_set(x)\n\n\nT = int(input())\nfor tc in range(1, T + 1):\n V, E = map(int, input().split())\n team = [False] * (V + 1)\n edge = list(map(int, input().split()))\n\n p = [0] * (V + 1)\n\n for i in range(V + 1):\n make_set(i)\n\n for i in range(E):\n A = edge[2 * i]\n B = edge[2 * i + 1]\n union(A, B)\n\n for i in range(1, V + 1):\n find_set(i)\n\n print('#{} {}'.format(tc, len(set(p)) - 1))\n","repo_name":"Anseik/algorithm","sub_path":"1106_문제풀이/swea_5248_그룹나누기_solution(union).py","file_name":"swea_5248_그룹나누기_solution(union).py","file_ext":"py","file_size_in_byte":658,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"30923467494","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport json\nimport logging\nfrom pathlib import Path\nfrom typing import NamedTuple, Optional, Tuple, Union\n\nimport aicsimageio\nimport dask.dataframe as dd\nimport pandas as pd\nfrom aics_dask_utils import DistributedHandler\nfrom aicsimageio import transforms\nfrom aicsimageio.writers import OmeTiffWriter\nfrom datastep import Step, log_run_params\n\nfrom ...constants import DatasetFields\nfrom ...utils import dataset_utils, image_utils\n\n###############################################################################\n\nlog = logging.getLogger(__name__)\n\n###############################################################################\n\nREQUIRED_DATASET_FIELDS = [\n DatasetFields.FOVId,\n DatasetFields.SourceReadPath,\n DatasetFields.NucleusSegmentationReadPath,\n DatasetFields.MembraneSegmentationReadPath,\n DatasetFields.ChannelIndexDNA,\n DatasetFields.ChannelIndexMembrane,\n DatasetFields.ChannelIndexStructure,\n DatasetFields.ChannelIndexBrightfield,\n DatasetFields.ChannelIndexNucleusSegmentation,\n DatasetFields.ChannelIndexMembraneSegmentation,\n]\n\n\nclass StandardizeFOVArrayResult(NamedTuple):\n fov_id: Union[int, str]\n path: Path\n\n\nclass StandardizeFOVArrayError(NamedTuple):\n fov_id: int\n error: str\n\n\n###############################################################################\n\n\nclass StandardizeFOVArray(Step):\n def __init__(self, filepath_columns=[DatasetFields.StandardizedFOVPath], **kwargs):\n super().__init__(filepath_columns=filepath_columns, **kwargs)\n\n @staticmethod\n def _generate_standardized_fov_array(\n row_index: int,\n row: pd.Series,\n current_pixel_sizes: Optional[Tuple[float]],\n desired_pixel_sizes: Optional[Tuple[float]],\n save_dir: Path,\n overwrite: bool,\n ) -> Union[StandardizeFOVArrayResult, StandardizeFOVArrayError]:\n # Don't use dask for image reading\n aicsimageio.use_dask(False)\n\n # Get the ultimate end save path for this cell\n save_path = save_dir / f\"{row.FOVId}.ome.tiff\"\n\n # Check skip\n if not overwrite and save_path.is_file():\n log.info(f\"Skipping standardized FOV generation for FOVId: {row.FOVId}\")\n return StandardizeFOVArrayResult(row.FOVId, save_path)\n\n # Overwrite or didn't exist\n log.info(f\"Beginning standardized FOV generation for FOVId: {row.FOVId}\")\n\n # Wrap errors for debugging later\n try:\n # Get normalized image array\n normalized_img, channels, pixel_sizes = image_utils.get_normed_image_array(\n raw_image=row.SourceReadPath,\n nucleus_seg_image=row.NucleusSegmentationReadPath,\n membrane_seg_image=row.MembraneSegmentationReadPath,\n dna_channel_index=row.ChannelIndexDNA,\n membrane_channel_index=row.ChannelIndexMembrane,\n structure_channel_index=row.ChannelIndexStructure,\n brightfield_channel_index=row.ChannelIndexBrightfield,\n nucleus_seg_channel_index=row.ChannelIndexNucleusSegmentation,\n membrane_seg_channel_index=row.ChannelIndexMembraneSegmentation,\n current_pixel_sizes=current_pixel_sizes,\n desired_pixel_sizes=desired_pixel_sizes,\n )\n\n # Reshape data for serialization\n reshaped = transforms.transpose_to_dims(normalized_img, \"CYXZ\", \"CZYX\")\n\n # Save array as OME Tiff\n with OmeTiffWriter(save_path, overwrite_file=True) as writer:\n writer.save(\n data=reshaped,\n dimension_order=\"CZYX\",\n channel_names=channels,\n pixels_physical_size=pixel_sizes,\n )\n\n log.info(f\"Completed standardized FOV generation for FOVId: {row.FOVId}\")\n return StandardizeFOVArrayResult(row.FOVId, save_path)\n\n # Catch and return error\n except Exception as e:\n log.info(\n f\"Failed standardized FOV generation for FOVId: {row.FOVId}. Error: {e}\"\n )\n return StandardizeFOVArrayError(row.FOVId, str(e))\n\n @log_run_params\n def run(\n self,\n dataset: Union[str, Path, pd.DataFrame, dd.DataFrame],\n current_pixel_sizes: Optional[Tuple[float]] = (\n 0.10833333333333332,\n 0.10833333333333332,\n 0.29,\n ),\n desired_pixel_sizes: Tuple[float] = (0.29, 0.29, 0.29),\n distributed_executor_address: Optional[str] = None,\n batch_size: Optional[int] = None,\n overwrite: bool = False,\n **kwargs,\n ) -> Path:\n \"\"\"\n Convert a dataset of raw FOV images and their nucleus and membrane\n segmentations, into a single, standard order and shape, and normalized image.\n\n Parameters\n ----------\n dataset: Union[str, Path, pd.DataFrame, dd.DataFrame]\n The dataset to use for generating standard order, normalized, image arrays.\n\n **Required dataset columns:** *[\"FOVId\", \"SourceReadPath\",\n \"NucleusSegmentationReadPath\", \"MembraneSegmentationReadPath\",\n \"ChannelIndexDNA\", \"ChannelIndexMembrane\", \"ChannelIndexStructure\",\n \"ChannelIndexBrightfield\"]*\n\n\n current_pixel_sizes: Optional[Tuple[float]]\n The current physical pixel sizes as a tuple of the raw image.\n Default: (0.10833333333333332, 0.10833333333333332, 0.29), though if None,\n uses (`aicsimageio.AICSImage.get_physical_pixel_size` on the raw image)\n\n\n desired_pixel_sizes: Tuple[float]\n The desired pixel size for to resize each image to in XYZ order.\n Default: (0.29, 0.29, 0.29)\n\n distributed_executor_address: Optional[str]\n An optional executor address to pass to some computation engine.\n Default: None\n\n batch_size: Optional[int]\n An optional batch size to process n features at a time.\n Default: None (Process all at once)\n\n overwrite: bool\n If this step has already partially or completely run, should it overwrite\n the previous files or not.\n Default: False (Do not overwrite or regenerate files)\n\n Returns\n -------\n manifest_save_path: Path\n Path to the produced manifest with the StandardizedFOVPath column added.\n \"\"\"\n # Handle dataset provided as string or path\n if isinstance(dataset, (str, Path)):\n dataset = Path(dataset).expanduser().resolve(strict=True)\n\n # Read dataset\n dataset = pd.read_csv(dataset)\n\n # Check the dataset for the required columns\n dataset_utils.check_required_fields(\n dataset=dataset,\n required_fields=REQUIRED_DATASET_FIELDS,\n )\n\n # Log original length of cell dataset\n log.info(f\"Original dataset length: {len(dataset)}\")\n\n # Check assumption: all fields per FOV are constant\n # except CellID and CellIndex\n const_cols_per_fov = [\n c for c in dataset.columns if c not in [\"CellId\", \"CellIndex\"]\n ]\n df_const_cols = (\n dataset.groupby(\"FOVId\")[const_cols_per_fov].nunique(dropna=False).eq(1)\n )\n\n for col_name, is_const in df_const_cols.all().iteritems():\n try:\n assert is_const\n except AssertionError:\n example = df_const_cols[~df_const_cols[col_name]].sample()\n raise ValueError(\n f\"{col_name} has multiple values per FOV. \"\n f\"Example: FOV {example.index.item()}\"\n )\n\n # As there is an assumption that this dataset is for cells,\n # generate the FOV dataset by selecting unique FOV Ids\n fov_dataset = dataset.drop_duplicates(DatasetFields.FOVId)\n\n # Log produced FOV dataset length\n log.info(f\"Unique FOV's found in dataset: {len(fov_dataset)}\")\n\n # Create standardized fovs directory\n fovs_dir = self.step_local_staging_dir / \"standardized_fovs\"\n fovs_dir.mkdir(exist_ok=True)\n\n # Process each row\n with DistributedHandler(distributed_executor_address) as handler:\n # Start processing\n results = handler.batched_map(\n self._generate_standardized_fov_array,\n # Convert dataframe iterrows into two lists of items to iterate over\n # One list will be row index\n # One list will be the pandas series of every row\n *zip(*list(fov_dataset.iterrows())),\n # Pass the other parameters as list of the same thing for each\n # mapped function call\n [current_pixel_sizes for i in range(len(fov_dataset))],\n [desired_pixel_sizes for i in range(len(fov_dataset))],\n [fovs_dir for i in range(len(fov_dataset))],\n [overwrite for i in range(len(dataset))],\n batch_size=batch_size,\n )\n\n # Generate fov paths rows\n standardized_fov_paths_dataset = []\n errors = []\n for result in results:\n if isinstance(result, StandardizeFOVArrayResult):\n standardized_fov_paths_dataset.append(\n {\n DatasetFields.FOVId: result.fov_id,\n DatasetFields.StandardizedFOVPath: result.path,\n }\n )\n else:\n errors.append(\n {DatasetFields.FOVId: result.fov_id, \"Error\": result.error}\n )\n\n # Convert fov paths to dataframe\n standardized_fov_paths_dataset = pd.DataFrame(standardized_fov_paths_dataset)\n\n # Drop StandardizedFOVPath column if it already exists\n if DatasetFields.StandardizedFOVPath in dataset.columns:\n dataset = dataset.drop(columns=[DatasetFields.StandardizedFOVPath])\n\n # Join original dataset to the fov paths\n self.manifest = dataset.merge(\n standardized_fov_paths_dataset, on=DatasetFields.FOVId\n )\n\n # Save manifest to CSV\n manifest_save_path = self.step_local_staging_dir / \"manifest.csv\"\n self.manifest.to_csv(manifest_save_path, index=False)\n\n # Save errored FOVs to JSON\n with open(self.step_local_staging_dir / \"errors.json\", \"w\") as write_out:\n json.dump(errors, write_out)\n\n return manifest_save_path\n","repo_name":"AllenCellModeling/actk","sub_path":"actk/steps/standardize_fov_array/standardize_fov_array.py","file_name":"standardize_fov_array.py","file_ext":"py","file_size_in_byte":10595,"program_lang":"python","lang":"en","doc_type":"code","stars":21,"dataset":"github-code","pt":"85"} +{"seq_id":"10921224765","text":"# author ：feng\n# time ：2018/1/25\n# function : 错误类型定义\n\n# 异常描述\ndef error_0(self=None):\n print('0错误')\ndef error_600(self=None):\n print('600错误')\nErrorCode={'0':{'method':error_0,\"description\":\"发生了某些错误\"},\n '600':{'method':error_600,\"description\":\"发生了某些错误\"},\n '601': {\"description\": \"账户参数个数不对\"},\n '602': {\"description\": \"未设置账户名\"},\n '603': {\"description\": \"未设置密码\"},\n '604': {\"description\": \"该用户已经存在\"},\n '605': {\"description\": \"两次密码不一致\"},\n '606': {\"description\": \"账号密码不一致\"},\n\n '701': {\"description\": \"领域个数应大于等于3\"},\n '702': {\"description\": \"领域个数应小于等于3\"},\n }\n\nclass MyError(Exception):\n \"\"\"自定义错误类\"\"\"\n def __init__(self,code=None):\n if code is not None :\n code=str(code)\n self.code=code\n if code in ErrorCode.keys():\n self.description=ErrorCode[code]['description']\n if 'method' in ErrorCode[code].keys():\n self.method=ErrorCode[code]['method']\n def __str__(self):\n s='code:%r\\ndescription:%r'%(self.code,self.description)\n return s\n\n code=0\n description='发生了某些错误'\n method=None\n\n","repo_name":"fengges/eds","sub_path":"eds/error.py","file_name":"error.py","file_ext":"py","file_size_in_byte":1406,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"27224429756","text":"import asyncio\n\nfrom ..workers.elster_candump_reader import read_elster_candump\nfrom ..utils.publish_subscribe_topic import PublishSubscribeTopic\nfrom ..elster_protocol.elster_frame import ElsterFrame\n\n\nasync def run_parse_candump():\n elster_frames = PublishSubscribeTopic() # type: PublishSubscribeTopic[ElsterFrame]\n\n print_task = asyncio.create_task(print_frames(elster_frames))\n\n await asyncio.gather(\n # print_frames(elster_frames),\n elster_frames.join(),\n read_elster_candump(elster_frames),\n )\n\n print_task.cancel()\n\n\nasync def print_frames(topic: PublishSubscribeTopic[ElsterFrame]):\n async for frame in topic.items():\n print(frame)\n","repo_name":"weltenwort/py-hpsu-monitor","sub_path":"py_hpsu_monitor/commands/parse_candump.py","file_name":"parse_candump.py","file_ext":"py","file_size_in_byte":688,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"85"} +{"seq_id":"8232954273","text":"#usr/bin/python/\r\nimport random\r\nimport sys\r\nimport keyboard\r\n\r\ndeck = [\"A♣\", \"2♣\", \"3♣\", \"4♣\", \"5♣\", \"6♣\", \"7♣\", \"8♣\", \"9♣\", \"10♣\", \"J♣\", \"Q♣\", \"K♣\", \"A♦\", \"2♦\", \"3♦\", \"4♦\", \"5♦\", \"6♦\", \"7♦\", \"8♦\", \"9♦\", \"10♦\", \"J♦\", \"Q♦\", \"K♦\", \"A♥\", \"2♥\", \"3♥\", \"4♥\", \"5♥\", \"6♥\", \"7♥\", \"8♥\", \"9♥\", \"10♥\", \"J♥\", \"Q♥\", \"K♥\", \"A♠\", \"2♠\", \"3♠\", \"4♠\", \"5♠\", \"6♠\", \"7♠\", \"8♠\", \"9♠\", \"10♠\", \"J♠\", \"Q♠\", \"K♠\"]\r\ndef shuffle_deck(deck_tbs):\r\n random.shuffle(deck_tbs)\r\ndef print_deck(deck_tbp):\r\n print(\"Current Order of Deck:\")\r\n print(deck_tbp)\r\n\r\nprint_deck(deck) \r\nprint (\"Press ENTER key to shuffle deck, ctrl+c to exit\")\r\nwhile True:\r\n try:\r\n if keyboard.is_pressed('ENTER'):\r\n shuffle_deck(deck)\r\n print_deck(deck)\r\n print (\"Press ENTER key to shuffle deck again, ctrl+c to exit\")\r\n keyboard.wait('ENTER')\r\n except:\r\n break\r\n","repo_name":"lvxwrkr777/cardshuffler.py","sub_path":"card_shuffler.py","file_name":"card_shuffler.py","file_ext":"py","file_size_in_byte":988,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"15978677459","text":"from typing import *\n\nfrom string import Template\n\nfrom invoice.utils.pretty_dict import PrettyDict\nfrom invoice.utils.ui import err\nfrom invoice.domain.product import Product\n\n\nclass ProductTemplate(PrettyDict):\n\n def __init__(self, json: Dict[str, str]) -> None:\n self.description = json['description']\n\n def create_product(\n self,\n replacements: Dict[str, str],\n price=None) -> Product:\n\n try:\n description = Template(self.description).substitute(replacements)\n except KeyError:\n raise KeyError('The replacements for the product template are not valid. \"{}\", repls: {}'.format(self.description, replacements))\n\n return Product(description, price or self.price)\n\n\n\n\n\n\n\n\n","repo_name":"jonathanglasmeyer/invoice","sub_path":"invoice/domain/product_template.py","file_name":"product_template.py","file_ext":"py","file_size_in_byte":761,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"32951614606","text":"a = int(input())\ndef gcd(a,b):\n\twhile b!=0:\n\t\tbb = b\n\t\tb = a%b\n\t\ta = bb\n\treturn a\n\t#if b==0:\n\t#\treturn a\n\t#return gcd(b,a%b)\nadj = [[False for x in range(1001)] for y in range(1001)]\nfor x in range(1,1001):\n\tfor y in range(x,1001):\n\t\tadj[x][y]=adj[y][x]=(gcd(x,y)==1)\nfor i in range(a):\n\tb = int(input())\n\tc = map(int,raw_input().split(\" \"))\n\tmaxi = -1\n\tult = [-1 for i in range(1001)]\n\tfor i in range(b-1,-1,-1):\n\t\tif ult[c[i]]==-1:\n\t\t\tult[c[i]]=i\n\tfor i in range(1,1001):\n\t\tif ult[i]==-1:\n\t\t\tcontinue\n\t\tfor j in range(i,1001):\n\t\t\tif adj[i][j] and ult[i]>-1 and ult[j]>-1:\n\t\t\t\tmaxi = max(maxi,ult[i]+ult[j]+2)\n\tprint(maxi)\n","repo_name":"Phicar/semillero","sub_path":"copr.py","file_name":"copr.py","file_ext":"py","file_size_in_byte":624,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"17560583070","text":"# This file will handle the plugin system and serve as a middle-man pseudo-library for all calls to said plugins\nimport collections\nimport os\nfrom integrations import assetpanda # Replace with plugin!\n\n# Database info\nos.environ.setdefault(\"DJANGO_SETTINGS_MODULE\", \"settings\")\nfrom django.core.wsgi import get_wsgi_application\n\napplication = get_wsgi_application()\n\n# DB models and exceptions\nfrom data import models\n\n\ndef getAuth(credentials):\n \"\"\"Given dictionary credentials, get the auth token from plugin\"\"\"\n return assetpanda.getToken(credentials) # Replace with plugin!\n\n\ndef makeAllAssets(auth):\n \"\"\"Takes all Machine objects without cloudIDs and makes the asset remotely. Aught to return a list of IDs\"\"\"\n for machine in models.Machine.objects.all().filter(cloudID=None):\n print(machine.name + \" will now be added to the asset tracker\")\n assetReply = assetpanda.makeAsset(machine, auth) # Replace with plugin!\n if assetReply:\n machine.cloudID = assetReply\n machine.save()\n\n\ndef updateCloudID(auth, machines=models.Machine.objects.all()):\n \"\"\"Given list or single Machine object, update cloudID\"\"\"\n if isinstance(machines, collections.Iterable):\n for machine in machines: # This needs to modified as it explicitly calls for models.\n machine.cloudID = assetpanda.getMachineAssetID(machine.network_set.first().mac, auth) # Replace w/ plugin!\n machine.save()\n else:\n machines.cloudID = assetpanda.getMachineAssetID(machines.network_set.first().mac, auth) # Replace with plugin!\n machines.save()\n","repo_name":"crutchcorn/WMIControl","sub_path":"lib/pluginHelper.py","file_name":"pluginHelper.py","file_ext":"py","file_size_in_byte":1614,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"85"} +{"seq_id":"17933239609","text":"from pydub import AudioSegment\nimport os\nimport wave\nimport contextlib\nfrom math import ceil\n\n\nclass AudioSplit():\n def split(fname,captionTitle):\n if not os.path.exists('../Datas/Splits/'+captionTitle+'/'):\n os.mkdir('../Datas/Splits/'+captionTitle+'/')\n\n with contextlib.closing(wave.open(fname, 'r')) as f:\n frames = f.getnframes()\n rate = f.getframerate()\n duration = frames / float(rate)\n duration *= 1000\n duration = int(ceil(duration))\n print(duration)\n YOUR_AUDIO_FILE = fname\n t1 = 0 # Works in milliseconds\n t2 = 5000\n newAudio = AudioSegment.from_wav(YOUR_AUDIO_FILE)\n i = 1\n while (1):\n if (t1 > duration):\n break\n newAudio2 = newAudio[t1:t2]\n newAudio2.export('../Datas/Splits/'+captionTitle+'/' + str(i) + '.wav', format=\"wav\")\n t1 += 5000\n t2 += 5000\n i += 1\n\nif __name__ == '__main__':\n AudioSplit.split('https://www.youtube.com/watch?v=9No-FiEInLA','dsadsadsdasdasssdsa')\n","repo_name":"Thinupavi/Research","sub_path":"subtitleGenarator1/scripts/audiosplit.py","file_name":"audiosplit.py","file_ext":"py","file_size_in_byte":1110,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"13269761545","text":"import os\nimport csv\nfrom pathlib import Path\nimport random\nimport time\n\nif os.path.exists('./dataset') == False:\n raise Exception('Download Dataset')\n\nseed_numbers = range(1, 10)\n\nids = [f for f in os.listdir('./dataset')]\n\nwith open(\"submissions/submission.{}.csv\".format(int(time.time())), 'w', newline='') as csvfile:\n fieldnames = ['id', 'team_a', 'team_b', 'time (ms)']\n writer = csv.DictWriter(csvfile, fieldnames=fieldnames)\n writer.writeheader()\n\n for id in ids:\n numbers = ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10']\n random.shuffle(numbers)\n aNumber = random.choice(seed_numbers)\n bNumber = 10 - aNumber\n team_a = []\n team_b = []\n\n images = [f for f in os.listdir('./dataset/{}'.format(id)) if f.endswith('.jpg')]\n\n if len(images) != 10:\n raise Exception(\"Invalid Dataset {}\".format(id))\n\n ms = int(round(time.time() * 1000))\n\n # Execute your code to sort players here\n\n ms = int(round(time.time() * 1000)) - ms\n\n writer.writerow({'id': id, 'team_a': \",\".join(\n team_a), 'team_b': \",\".join(team_b), 'time (ms)': str(ms)})\n","repo_name":"hacinc/team-classification","sub_path":"run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":1174,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"26170622258","text":"import warnings\nwarnings.filterwarnings('ignore')\nimport ujson as json\nimport numpy as np\nfrom jax import vmap\nimport matplotlib\nmatplotlib.rc('font', family='Times New Roman')\nmatplotlib.rc('text')\nmatplotlib.rcParams['lines.linewidth'] = 0.5\nmatplotlib.rcParams['lines.markersize'] = 0.5\nmatplotlib.rcParams['axes.xmargin'] = 0\nimport matplotlib.pyplot as plt\nfrom SIMMBA import BaseModel\nfrom SIMMBA.experiments.HeySnipsDEMAND import HeySnipsDEMAND\nfrom rockpool.timeseries import TSContinuous\nfrom rockpool import layers\nfrom rockpool.layers import RecRateEulerJax_IO, H_tanh, JaxADS\nimport os\nimport sys\nif not sys.warnoptions:\n import warnings\n warnings.simplefilter(\"ignore\")\nimport argparse\nfrom copy import deepcopy\n\ndef filter_1d(data, alpha = 0.9):\n last = data[0]\n out = np.zeros((len(data),))\n out[0] = last\n for i in range(1,len(data)):\n out[i] = alpha*out[i-1] + (1-alpha)*data[i]\n last = data[i]\n return out\n\nclass HeySnipsNetworkADS(BaseModel):\n def __init__(self,\n noise_std,\n labels,\n fs=16000.,\n verbose=0,\n network_idx=\"\",\n use_ebn=False,\n use_batching=False,\n name=\"Snips ADS\",\n version=\"1.0\"):\n \n super(HeySnipsNetworkADS, self).__init__(name,version)\n\n self.verbose = verbose\n self.fs = fs\n self.dt = 0.001\n self.noise_std = noise_std\n self.noise_gain = 1.0\n\n self.num_rate_neurons = 128 \n self.num_targets = len(labels)\n self.time_base = np.arange(0.0,5.0,self.dt)\n home = os.path.expanduser('~')\n self.base_path = f\"{home}/Documents/RobustClassificationWithEBNs/membranePotentialNoise\"\n\n rate_net_path = os.path.join(self.base_path, \"Resources/rate_heysnips_tanh_0_16.model\")\n with open(rate_net_path, \"r\") as f:\n config = json.load(f)\n\n self.w_in = np.array(config['w_in'])\n self.w_rec = np.array(config['w_recurrent'])\n self.w_out = np.array(config['w_out'])\n self.bias = config['bias']\n self.tau_rate = config['tau']\n\n self.rate_layer = RecRateEulerJax_IO(w_in=self.w_in,\n w_recurrent=self.w_rec,\n w_out=self.w_out,\n tau=self.tau_rate,\n bias=self.bias,\n activation_func=H_tanh,\n dt=self.dt,\n noise_std=0.0,\n name=\"hidden\")\n \n self.N_out = self.w_out.shape[1]\n\n # - Create NetworkADS\n postfix = \"\"\n if(use_batching):\n postfix += \"_batched\"\n if(use_ebn):\n postfix += \"_ebn\"\n network_name = f\"Resources/jax_ads{network_idx}{postfix}.json\"\n home = os.path.expanduser('~')\n self.model_path_ads_net = f\"{home}/Documents/RobustClassificationWithEBNs/mismatch/{network_name}\"\n\n if(os.path.exists(self.model_path_ads_net)):\n print(\"Loading networks...\")\n self.ads_layer = self.load(self.model_path_ads_net)\n self.tau_mem = self.ads_layer.tau_mem[0]\n self.Nc = self.ads_layer.weights_in.shape[0]\n if(postfix == \"\"):\n self.ads_layer.weights_out = self.ads_layer.weights_in.T\n # - Set the noise level\n self.ads_layer.noise_std = self.noise_std\n\n if(use_ebn):\n scale = 1.0\n if(self.noise_std == 2.5):\n scale = 0.8\n elif(self.noise_std == 5.0):\n scale = 0.6\n self.ads_layer.weights_slow *= scale\n self.ads_layer.weights_fast *= scale\n\n self.amplitude = 50 / self.tau_mem\n else:\n assert(False), \"Some network file was not found\"\n\n def load(self, fn):\n with open(fn, \"r\") as f:\n loaddict = json.load(f)\n self.threshold0 = loaddict.pop(\"threshold0\")\n self.best_val_acc = loaddict.pop(\"best_val_acc\")\n self.best_boundary = loaddict.pop(\"best_boundary\")\n return JaxADS.load_from_dict(loaddict)\n\n def save(self, fn):\n return\n\n def get_data(self, filtered_batch):\n \"\"\"\n :brief Evolves filtered audio samples in the batch through the rate network to obtain target dynamics\n :params filtered_batch : Shape: [batch_size,T,num_channels], e.g. [100,5000,16]\n :returns batched_spiking_net_input [batch_size,T,Nc], batched_rate_net_dynamics [batch_size,T,self.Nc], batched_rate_output [batch_size,T,N_out] [Batch size is always first dimensions]\n \"\"\"\n num_batches = filtered_batch.shape[0]\n T = filtered_batch.shape[1]\n time_base = np.arange(0,int(T * self.dt),self.dt)\n batched_spiking_in = np.empty(shape=(batch_size,T,self.Nc))\n batched_rate_net_dynamics = np.empty(shape=(batch_size,T,self.Nc))\n batched_rate_output = np.empty(shape=(batch_size,T,self.N_out))\n # - This can be parallelized\n for batch_id in range(num_batches):\n # - Pass through the rate network\n ts_filt = TSContinuous(time_base, filtered_batch[batch_id])\n batched_rate_output[batch_id] = self.rate_layer.evolve(ts_filt).samples\n self.rate_layer.reset_all()\n # - Get the target dynamics\n batched_rate_net_dynamics[batch_id] = self.rate_layer.res_acts_last_evolution.samples\n # - Calculate the input to the spiking network\n batched_spiking_in[batch_id] = self.amplitude * (ts_filt(time_base) @ self.w_in)\n\n return (batched_spiking_in, batched_rate_net_dynamics, batched_rate_output)\n\n def find_gain(self, target_labels, output_new):\n gains = np.linspace(0.5,5.5,100)\n best_gain=1.0; best_acc=0.5\n for gain in gains:\n correct = 0\n for idx_b in range(output_new.shape[0]):\n predicted_label = self.get_prediction(gain*output_new[idx_b])\n if(target_labels[idx_b] == predicted_label):\n correct += 1\n if(correct/len(target_labels) > best_acc):\n best_acc=correct/len(target_labels)\n best_gain=gain\n print(f\"Noise {self.noise_std * self.dt / self.ads_layer.tau_mem[0]} gain {best_gain} val acc {best_acc} \")\n return best_gain\n\n def perform_validation_set(self, data_loader, fn_metrics):\n num_batches = 5\n bs = data_loader.batch_size\n outputs_new = np.zeros((num_batches*bs,5000,1))\n true_labels = []\n\n for batch_id, [batch, _] in enumerate(data_loader.val_set()):\n if (batch_id >= num_batches):\n break\n filtered = np.stack([s[0][1] for s in batch])\n target_labels = [s[1] for s in batch]\n (batched_spiking_in, _, _) = self.get_data(filtered_batch=filtered)\n _, _, states_t = vmap(self.ads_layer._evolve_functional, in_axes=(None, None, 0))(self.ads_layer._pack(), False, batched_spiking_in)\n batched_output = np.squeeze(np.array(states_t[\"output_ts\"]), axis=-1) @ self.w_out\n outputs_new[int(batch_id*bs):int(batch_id*bs+bs),:,:] = batched_output\n for bi in range(batched_output.shape[0]):\n true_labels.append(target_labels[bi])\n\n self.noise_gain = self.find_gain(true_labels, outputs_new)\n\n def train(self, data_loader, fn_metrics):\n yield {\"train_loss\": 0.0}\n\n def get_prediction(self, final_out):\n integral_final_out = np.copy(final_out)\n integral_final_out[integral_final_out < self.threshold0] = 0.0\n for t,val in enumerate(integral_final_out):\n if(val > 0.0):\n integral_final_out[t] = val + integral_final_out[t-1]\n\n # - Get final prediction using the integrated response\n predicted_label = 0\n if(np.max(integral_final_out) > self.best_boundary):\n predicted_label = 1\n return predicted_label\n\n def get_mfr(self, spikes):\n # - Mean firing rate of each neuron in Hz\n return np.sum(spikes) / (768 * 5.0)\n\n def test(self, data_loader, fn_metrics):\n\n correct_rate = correct = counter = 0\n\n final_out_power = []\n final_out_mse = []\n final_out_mse_tgt = []\n mfr = []\n dynamics_power = []\n dynamics_mse = []\n\n for batch_id, [batch, _] in enumerate(data_loader.test_set()):\n\n if (batch_id * data_loader.batch_size >= 1000):\n break\n\n # - Get input\n filtered = np.stack([s[0][1] for s in batch])\n target_labels = [s[1] for s in batch]\n tgt_signals = np.stack([s[2] for s in batch])\n (batched_spiking_in, batched_rate_net_dynamics, batched_rate_output) = self.get_data(filtered_batch=filtered)\n\n spikes_ts, _, states_t = vmap(self.ads_layer._evolve_functional, in_axes=(None, None, 0))(self.ads_layer._pack(), False, batched_spiking_in)\n batched_output = np.squeeze(np.array(states_t[\"output_ts\"]), axis=-1)\n\n for idx in range(len(batch)):\n \n final_out = self.noise_gain * (batched_output[idx] @ self.w_out)\n \n final_out_power.append( np.var(final_out-batched_rate_output[idx]) / np.var(batched_rate_output[idx]) )\n final_out_mse.append( np.mean( (final_out-batched_rate_output[idx])**2 ) )\n final_out_mse_tgt.append( np.mean( (final_out-tgt_signals[idx])**2 ) )\n mfr.append(self.get_mfr(np.array(spikes_ts[idx])))\n dynamics_power.append( np.mean(np.var(batched_output[idx]-batched_rate_net_dynamics[idx], axis=0)) / (np.sum(np.var(batched_rate_net_dynamics[idx], axis=0))) )\n dynamics_mse.append( np.mean(np.mean((batched_output[idx]-batched_rate_net_dynamics[idx])**2, axis=0)) )\n \n final_out = filter_1d(final_out, alpha=0.95)\n\n # - Some plotting\n if(self.verbose > 0):\n target = tgt_signals[idx]\n plt.clf()\n plt.subplot(211)\n plt.plot(self.time_base, final_out, label=\"Spiking\")\n plt.plot(self.time_base, target, label=\"Target\")\n plt.plot(self.time_base, batched_rate_output[idx], label=\"Rate\")\n plt.ylim([-0.5,1.0])\n plt.legend()\n plt.subplot(212)\n spikes_ind = np.nonzero(spikes_ts[idx])\n plt.scatter(self.dt * spikes_ind[0], spikes_ind[1], color=\"k\", linewidths=0.0)\n plt.xlim([0.0,5.0])\n plt.draw()\n plt.pause(0.001)\n\n predicted_label = self.get_prediction(final_out)\n\n predicted_label_rate = 0\n if((batched_rate_output[idx] > 0.7).any()):\n predicted_label_rate = 1\n\n if(predicted_label == target_labels[idx]):\n correct += 1\n if(predicted_label_rate == target_labels[idx]):\n correct_rate += 1\n counter += 1\n\n print(f\"Noise: {self.noise_std} True label {target_labels[idx]} Noisy {predicted_label}\")\n\n # - End batch for loop\n # - End testing loop\n\n test_acc = correct / counter\n test_acc_rate = correct_rate / counter\n print(f\"Test accuracy: Full: {test_acc} Rate: {test_acc_rate}\")\n\n out_dict = {}\n out_dict[\"test_acc\"] = [test_acc,test_acc_rate]\n out_dict[\"final_out_power\"] = [np.mean(final_out_power).item()]\n out_dict[\"final_out_mse\"] = [np.mean(final_out_mse).item()]\n out_dict[\"final_out_mse_tgt\"] = [np.mean(final_out_mse_tgt).item()]\n out_dict[\"mfr\"] = [np.mean(mfr).item()]\n out_dict[\"dynamics_power\"] = [np.mean(dynamics_power).item()]\n out_dict[\"dynamics_mse\"] = [np.mean(dynamics_mse).item()]\n\n print(out_dict)\n self.out_dict = out_dict\n\nif __name__ == \"__main__\":\n\n parser = argparse.ArgumentParser(description='Learn classifier using pre-trained rate network')\n parser.add_argument('--verbose', default=0, type=int, help=\"Level of verbosity. Default=0. Range: 0 to 2\")\n parser.add_argument('--network-idx', default=\"\", type=str, help=\"Index of the network to be analyzed\")\n parser.add_argument('--use-batching', default=False, action=\"store_true\", help=\"Use the networks trained in batched mode\")\n parser.add_argument('--use-ebn', default=False, action=\"store_true\", help=\"Use the networks trained with EBNs\")\n\n args = vars(parser.parse_args())\n verbose = args['verbose']\n network_idx = args['network_idx']\n use_batching = args['use_batching']\n use_ebn = args['use_ebn']\n\n postfix = \"\"\n if(use_batching):\n postfix += \"_batched\"\n if(use_ebn):\n postfix += \"_ebn\"\n\n home = os.path.expanduser('~')\n ads_orig_final_path = f'{home}/Documents/RobustClassificationWithEBNs/membranePotentialNoise/Resources/Plotting/ads_jax{postfix}{network_idx}_noise_analysis_output.json'\n\n if(os.path.exists(ads_orig_final_path)):\n print(\"Exiting because data was already generated. Uncomment this line to reproduce the results.\")\n sys.exit(0)\n\n np.random.seed(42)\n\n batch_size = 100\n balance_ratio = 1.0\n snr = 10.\n output_dict = {}\n\n # - These are the target std's of the noise we want to investigate. This is Gaussian noise with zero mean.\n # - For BPTT one can use these values. For FORCE and Reservoir one has to consider that the difference between\n # - V_reset and V_thresh is different so the std should corresponds to X*difference. Example: V_reset: -65 V_thresh: -55 -> std for 0.1 = abs(-65)-abs(-55)*0.1 = 1.0 \n noise_stds_untransformed = [0.0, 0.01, 0.05, 0.1]\n # - These values then need to be transformed using x = noise_std * tau_mem / dt\n # - NOTE This assumes that the noise is injected as current which needs to be checked.\n noise_stds = [0.0, 0.5, 2.5, 5.0]\n\n for noise_idx,noise_std in enumerate(noise_stds):\n\n noise_gain = 1.0\n experiment = HeySnipsDEMAND(batch_size=batch_size,\n percentage=1.0,\n snr=snr,\n randomize_after_epoch=True,\n downsample=1000,\n is_tracking=False,\n cache_folder=None,\n one_hot=False)\n\n num_train_batches = int(np.ceil(experiment.num_train_samples / batch_size))\n num_val_batches = int(np.ceil(experiment.num_val_samples / batch_size))\n num_test_batches = int(np.ceil(experiment.num_test_samples / batch_size))\n\n model = HeySnipsNetworkADS(labels=experiment._data_loader.used_labels,\n noise_std=noise_std,\n verbose=verbose,\n network_idx=network_idx,\n use_batching=use_batching,\n use_ebn=use_ebn)\n\n # - Compute the optimal gain for the current level of noise using the validation set\n model.perform_validation_set(experiment._data_loader, 0.0)\n\n experiment.set_model(model)\n experiment.set_config({'num_train_batches': num_train_batches,\n 'num_val_batches': num_val_batches,\n 'num_test_batches': num_test_batches,\n 'batch size': batch_size,\n 'percentage data': 1.0,\n 'snr': snr,\n 'balance_ratio': balance_ratio})\n experiment.start()\n output_dict[str(noise_stds_untransformed[noise_idx])] = model.out_dict\n\n # - End outer loop\n print(output_dict[\"0.0\"])\n print(output_dict[\"0.01\"])\n print(output_dict[\"0.05\"])\n print(output_dict[\"0.1\"])\n\n # - Save\n with open(ads_orig_final_path, 'w') as f:\n json.dump(output_dict, f)\n","repo_name":"synsense/Robust-Classification-EBN","sub_path":"membranePotentialNoise/NetworkADS/analyse.py","file_name":"analyse.py","file_ext":"py","file_size_in_byte":16426,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"85"} +{"seq_id":"13503032802","text":"from options.test_options import TestOptions\nfrom data import create_dataset\nfrom models import create_model\nfrom util.visualizer import Visualizer, save_images\nfrom util.html import HTML\nimport os\nfrom util.util import AverageMeter, set_seed\n\nif __name__ == '__main__':\n\n opt = TestOptions().parse() # get test options\n # hard-code some parameters for test\n opt.num_threads = 0 # test code only supports num_threads = 1\n opt.batch_size = 1 # test code only supports batch_size = 1\n opt.serial_batches = True # disable data shuffling; comment this line if results on randomly chosen images are needed.\n dataset = create_dataset(opt) # create a dataset given opt.dataset_mode and other options\n model = create_model(opt) # create a model given opt.model and other options\n model.setup(opt) # regular setup: load and print networks; create schedulers\n visualizer = Visualizer(opt) # create a visualizer that display/save images and plots\n meters_tst = {stat: AverageMeter() for stat in model.loss_names}\n\n set_seed(opt.seed)\n\n web_dir = os.path.join(opt.results_dir, opt.name,\n '{}_{}'.format(opt.testset_name, opt.epoch)) # define the website directory\n print('creating web directory', web_dir)\n webpage = HTML(web_dir, 'Experiment = %s, Phase = %s, Epoch = %s' % (opt.name, opt.phase, opt.epoch))\n\n for i, data in enumerate(dataset):\n visualizer.reset()\n model.set_input(data) # unpack data from data loader\n model.test() # run inference: forward + compute_visuals\n\n losses = model.get_current_losses()\n visualizer.print_test_losses(i, losses)\n for loss_name in model.loss_names:\n meters_tst[loss_name].update(float(losses[loss_name]))\n\n visuals = model.get_current_visuals()\n visualizer.display_current_results(visuals, epoch=None, save_result=False)\n img_path = model.get_image_paths()\n save_images(webpage, visuals, img_path, aspect_ratio=opt.aspect_ratio, width=opt.load_size)\n losses = {}\n for loss_name in model.loss_names:\n losses[loss_name] = meters_tst[loss_name].avg\n visualizer.print_test_losses('average', losses)\n\n webpage.save()","repo_name":"KovenYu/uORF","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":2269,"program_lang":"python","lang":"en","doc_type":"code","stars":164,"dataset":"github-code","pt":"85"} +{"seq_id":"32294692401","text":"import s3fs\nimport datetime as dt\nimport os\n\ntoday = dt.date.today()\n\ndag_path = os.getcwd()\n\nwith open(dag_path + '/credentials.txt', 'r') as f:\n credentials = f.read()\n aws_access_key, aws_secret_key = credentials.split(',')\n\ns3_uri = f's3://viagens-a-servico-gov/{today.year}/{str(today.month).zfill(2)}/processed-data/'\n\nlocal_file = 'viagens-processed-data.csv'\n\ndef run_load_processed_data():\n\n fs = s3fs.S3FileSystem(key=aws_access_key, secret=aws_secret_key)\n\n with fs.open(s3_uri + local_file, 'wb') as s3_file:\n with open(f'{dag_path}/processed-data/{local_file}', 'rb') as file:\n s3_file.write(file.read())","repo_name":"elvinmatheus/Viagens-Dashboard","sub_path":"src/load_processed_data_to_s3.py","file_name":"load_processed_data_to_s3.py","file_ext":"py","file_size_in_byte":647,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"85"} +{"seq_id":"38661995028","text":"from bs4 import BeautifulSoup\r\nimport requests\r\nimport pandas as pd\r\n\r\n#Write your code here\r\nresponse = requests.get(\"https://en.wikipedia.org/wiki/List_of_largest_banks\")\r\nhtml_data = response.content\r\n\r\n\r\nsoup = BeautifulSoup(html_data, \"html5lib\")\r\n\r\n\r\ndata = pd.DataFrame(columns=[\"Name\", \"Market Cap (US$ Billion)\"])\r\nfor row in soup.find_all('tbody')[3].find_all('tr'):\r\n col = row.find_all('td')\r\n if col:\r\n bank_name = col[1].find_all(\"a\")[1].text\r\n market_cap = float(col[2].text)\r\n data = data.append({\"Name\": bank_name,\r\n \"Market Cap (US$ Billion)\": market_cap},\r\n ignore_index=True)\r\n\r\nprint(data.head())\r\n\r\ndata.to_json(\"bank_market_cap_1.json\")\r\n","repo_name":"Nova-T1/ibm-python-project-for-data-engineering","sub_path":"webscrapping.py","file_name":"webscrapping.py","file_ext":"py","file_size_in_byte":741,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"29047173667","text":"from django.http import HttpResponse\nfrom django.shortcuts import render\nfrom recetasapp.models import Receta\nfrom django.core.mail import send_mail\n\ndef home(request):\n\n #return HttpResponse('

Hello World

')\n recetas = Receta.objects.all()\n\n contexto = {\n 'recetas' : recetas\n }\n\n return render(request,'home.html', contexto)\n\ndef contacto(request):\n\n contexto = {}\n if request.method == 'POST':\n print('Enviar email')\n\n email = request.POST.get('email')\n mensaje = request.POST.get('mensaje') \n\n #print('{} {}'.format(email, mensaje))\n mensaje_html = 'email {}
mensaje: {}'.format(email, mensaje)\n send_mail(\"Contacto de recetas\", mensaje_html,\n 'info@recetas.com',['destinatario@recetas.com'])\n\n contexto ['mensaje'] = 'Petición enviada correctamente'\n return render (request,'contacto.html', contexto)\n\n","repo_name":"luism240174/recetas_curso","sub_path":"recetas/recetasapp/views/home_views.py","file_name":"home_views.py","file_ext":"py","file_size_in_byte":920,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"33858553490","text":"import os\nimport sys\n\n\nattackDirs = ['/home/victim/.etc', '/home/victim/.var']\nattackFiles = ['/Launch_Attack.py', '/SetUp_Attack.py', '/TA_Flood_Attack']\nattackCommand = '* * * * * root ( cd /home/victim/.etc/.module && python Launch_Attack.py ) || ( cd /home/victim/.var/.module && python Launch_Attack.py )'\n\n\ndef set_up_crontab():\n # if is_set_up_crontab():\n # print('Already set up crontab')\n # return\n\n # Write Crontab\n os.system('sudo chmod +w /etc/crontab || ( test )')\n\n crontab = open('/etc/crontab', 'a')\n\n crontab.write(attackCommand)\n crontab.write('\\n')\n crontab.close()\n\n\ndef set_up_attack():\n if is_set_up_attack():\n print('Already set up attack module')\n return\n\n # Make directories\n for attackDir in attackDirs:\n os.system('sudo chmod +x TA_Flood_Attack')\n os.system('mkdir {0}'.format(attackDir))\n os.system('mkdir {0}/.module'.format(attackDir))\n os.system('cp Launch_Attack.py {0}/.module/'.format(attackDir))\n os.system('cp SetUp_Attack.py {0}/.module/'.format(attackDir))\n os.system('cp TA_Flood_Attack {0}/.module/'.format(attackDir))\n\n\ndef is_set_up_attack():\n for attackDir in attackDirs:\n for attackFile in attackFiles:\n if not os.path.isfile(attackDir + '/.module' + attackFile):\n return False\n\n return True\n\n\ndef is_set_up_crontab():\n # Read Crontab\n os.system('sudo chmod +r /etc/crontab || ( test )')\n\n crontab = open('/etc/crontab', 'r')\n\n result = False\n\n for line in crontab:\n if line.find('Launch_Attack.py') > -1:\n result = True\n\n crontab.close()\n\n return result\n\n\ndef is_root():\n return os.geteuid() == 0\n\n\nif __name__ == '__main__':\n\n if not is_root():\n sys.exit('You must run the script with \\'sudo\\'')\n\n set_up_attack()\n set_up_crontab()\n\n print('Set up worm success!.')\n","repo_name":"yuliangkuocs/Worm","sub_path":"Worm.py","file_name":"Worm.py","file_ext":"py","file_size_in_byte":1911,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"85"} +{"seq_id":"31965447064","text":"import logging\nfrom typing import Optional\n\nfrom fastapi import APIRouter\nfrom pydantic import BaseModel\n\nfrom services.yeet import models\nfrom services import BaseSerializer\n\nlogger = logging.getLogger(__name__)\nrouter = APIRouter(\n prefix=\"/yeets\",\n tags=[\"yeets\"],\n dependencies=[],\n)\n\nclass CreateValidator(BaseModel):\n content: str\n title: str\n snippet: str\n slug: str\n yeep_id: str\n\nclass Serializer(BaseSerializer):\n data: list[models.YeetData]\n\n\n@router.get(\"/\", response_model=Serializer)\nasync def yeets(yeet_id: Optional[str] = None):\n data = await models.listing(yeet_id=yeet_id)\n return Serializer(data=data)\n\n\n@router.post(\"/\", response_model=Serializer)\nasync def create_yeet(item: CreateValidator):\n data = await models.create(\n content=item.content,\n snippet=item.snippet,\n slug=item.slug,\n title=item.title,\n yeep_id=item.yeep_id,\n )\n return Serializer(data=[data])\n","repo_name":"padrepitufo/yeets-api","sub_path":"app/services/yeet/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":961,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"2575292640","text":"from process.db.structure import engine, Base, Corpus, Document, Term, TermDocument, TermCorpus\nfrom sqlalchemy.orm import sessionmaker\nfrom sqlalchemy import func\nfrom math import floor\n\n\nclass Interaction:\n def __init__(self, corpus_name):\n self.corpus_name = corpus_name\n self.db_engine = engine\n self.Base = Base\n self.Base.metadata.bind = self.db_engine\n self.DBSession = sessionmaker(bind=self.db_engine)\n self.session = self.DBSession()\n\n def create_corpus_entry(self):\n \"\"\"\n creates an instance of the corpus in database\n insert new row in table 'corpus' if doesn't exist\n \"\"\"\n self.get_or_create(Corpus, corpus_name=self.corpus_name)\n self.session.commit()\n\n def create_document_entry(self, document_list):\n \"\"\"\n creates instances of the document in database\n insert new row in table 'document' if doesn't exist\n \"\"\"\n parent_corpus = self.get(Corpus, corpus_name=self.corpus_name)\n for each_document in document_list:\n self.get_or_create(Document, corpus_id=parent_corpus.corpus_id, document_name=each_document)\n self.session.commit()\n\n def populate_term_frequency(self, my_dictionary, eval_document_name):\n \"\"\"\n insert new row in table 'term' if doesn't exist\n populate the table \"term_document\" with each term and it's term frequency for\n corresponding document\n \"\"\"\n this_corpus_id = self.get_corpus_id\n for key in my_dictionary:\n term_frequency = my_dictionary[key]\n if any(c.isalpha() for c in key):\n try:\n term_object = self.get_or_create(Term, term_name=key)\n document_object = self.get(Document, document_name=eval_document_name)\n self.get_or_create(\n TermDocument,\n term_id=term_object.term_id,\n document_id=document_object.document_id,\n term_frequency=term_frequency\n )\n self.get_or_create(TermCorpus, term_id=term_object.term_id, corpus_id=this_corpus_id)\n except UnicodeEncodeError:\n continue\n\n self.session.commit()\n\n def insert_document_frequency(self):\n \"\"\"\n this method inserts document frequency for all the terms in a specific corpus in term_corpus table\n \"\"\"\n term_list = self.get_corpus_term_id_list\n document_list = self.get_corpus_document_id_list\n this_corpus_id = self.get_corpus_id\n for each_term_id in term_list:\n document_frequency = self.session.query(TermDocument).filter(\n TermDocument.term_id == each_term_id,\n TermDocument.document_id.in_( document_list)\n ).count()\n term_corpus_object = self.get(TermCorpus, term_id=each_term_id, corpus_id=this_corpus_id)\n if term_corpus_object is not None:\n term_corpus_object.document_frequency = document_frequency\n self.session.commit()\n\n def insert_weightij(self, huge_dictionary):\n \"\"\"\n this method inserts weight for each term with respect to each document\n in term_document table for the whole corpus a time\n \"\"\"\n doc_list = huge_dictionary.keys()\n counter = 0\n for doc_id in doc_list:\n term_weight_dictionary = huge_dictionary[doc_id]\n for id in term_weight_dictionary:\n term_document_object = self.get(TermDocument, term_id=id, document_id=doc_id)\n term_document_object.weightij = term_weight_dictionary[id]\n counter += 1\n print(counter, end='\\r')\n self.session.commit()\n\n def insert_weighti(self):\n \"\"\"inserts average weight for each term in the corpus in term_corpus table \"\"\"\n term_id_list = self.get_corpus_term_id_list\n document_id_list = self.get_corpus_document_id_list\n N = len(document_id_list)\n c_id = self.get_corpus_id\n counter = 0\n for t_id in term_id_list:\n counter += 1\n summation = self.session.query(func.sum(TermDocument.weightij)).filter(\n TermDocument.term_id == t_id,\n TermDocument.document_id.in_(document_id_list)\n )\n term_corpus_object = self.get(TermCorpus, term_id=t_id, corpus_id=c_id)\n term_corpus_object.weighti = (1.0 * summation[0][0]) / N\n print(counter, end='\\r')\n self.session.commit()\n\n def insert_si(self, si_dictionary):\n \"\"\"\n inserts the standard variance of each term of the corpus in table 'term_corpus'\n \"\"\"\n keys = si_dictionary.keys()\n c_id = self.get_corpus_id\n for key in keys:\n self.get(TermCorpus, term_id=key, corpus_id=c_id).si = si_dictionary[key]\n self.session.commit()\n\n def insert_dispi(self):\n \"\"\"\n inserts the dispersion of each term of the corpus in table 'term_corpus'\n \"\"\"\n c_id = self.get_corpus_id\n object_list = self.session.query(TermCorpus.term_id, TermCorpus.weighti, TermCorpus.si).filter(\n TermCorpus.corpus_id == c_id)\n dictionary = {obj[0]: obj[1] / obj[2] for obj in object_list}\n for key in dictionary:\n self.get(TermCorpus, term_id=key, corpus_id=c_id).dispi = dictionary[key]\n self.session.commit()\n\n def insert_weightj(self):\n \"\"\"\n inserts the average terms weight of document in table 'document'\n \"\"\"\n documents = self.get_corpus_document_id_list\n temp_list = self.session.query(\n TermDocument.document_id,\n func.count(TermDocument.term_id),\n func.sum(TermDocument.weightij)\n ).group_by(TermDocument.document_id).filter(\n TermDocument.document_id.in_(documents)\n )\n dictionary = {temp[0]: temp[2] / (1.0 * temp[1]) for temp in temp_list}\n for key in dictionary:\n self.get(Document, document_id=key).weightj = dictionary[key]\n self.session.commit()\n\n def insert_deviation(self, deviation_dictionary):\n \"\"\"\n this method inserts deviation for each term in term_document table\n \"\"\"\n counter = 0\n for key in deviation_dictionary:\n self.get(TermDocument, term_id=key[0], document_id=key[1]).deviation = deviation_dictionary[key]\n counter += 1\n print(counter, end='\\r')\n self.session.commit()\n\n def insert_domain_relevance(self, dri_dic):\n \"\"\"\n inserts domain-relevance for each term in TermCorpus table\n \"\"\"\n for key in dri_dic:\n c_id = self.get_corpus_id\n self.get(TermCorpus, term_id=key, corpus_id=c_id).domain_relevance = dri_dic[key]\n self.session.commit()\n\n @property\n def get_wij(self):\n \"\"\"\n returns the collection of weight for each term with respect to each document\n of the corpus as a dictionary in the format -> { (term_id, document_id) : weight }\n \"\"\"\n huge_list = self.session.query(TermDocument.term_id, TermDocument.document_id, TermDocument.weightij).filter(\n TermDocument.document_id.in_(self.get_corpus_document_id_list))\n return {(list_item[0], list_item[1]): list_item[2] for list_item in huge_list}\n\n @property\n def get_wi(self):\n \"\"\"\n returns the collection of average weight for each term of the corpus as a\n dictionary in the format -> { term_id : average_weight }\n \"\"\"\n temp = self.session.query(TermCorpus.term_id, TermCorpus.weighti).filter(\n TermCorpus.corpus_id == self.get_corpus_id)\n return {t[0]: t[1] for t in temp}\n\n @property\n def get_wj(self):\n \"\"\"\n returns the collection of average term weight for each document of the corpus as a\n dictionary in the format -> { document_id : average_weight }\n \"\"\"\n c_id = self.get_corpus_id\n temp = self.session.query(Document.document_id, Document.weightj).filter(Document.corpus_id == c_id)\n return {t[0]: t[1] for t in temp}\n\n @property\n def get_dispi(self):\n \"\"\"\n returns the collection of dispersion for each term of the corpus as a\n dictionary in the format -> { term_id : dispersion }\n \"\"\"\n c_id = self.get_corpus_id\n temp = self.session.query(TermCorpus.term_id, TermCorpus.dispi).filter(TermCorpus.corpus_id == c_id)\n return {t[0]: t[1] for t in temp}\n\n @property\n def get_devij(self):\n \"\"\"\n returns the collection of deviation for each term with respect to corpus documents as a\n dictionary in the format -> { (term_id, document_id) : deviation }\n \"\"\"\n documents = self.get_corpus_document_id_list\n temp = self.session.query(TermDocument.term_id, TermDocument.document_id, TermDocument.deviation).filter(\n TermDocument.document_id.in_(documents))\n return {(t[0], t[1]): t[2] for t in temp}\n\n @property\n def get_corpus_term_id_list(self):\n \"\"\"\n returns the collection of term_id of current corpus as a list\n \"\"\"\n id = self.get_corpus_id\n result_list = self.session.query(TermCorpus.term_id).filter(TermCorpus.corpus_id == id).all()\n term_id_list = [term[0] for term in result_list]\n return term_id_list\n\n @property\n def get_corpus_document_id_list(self):\n \"\"\"\n returns the collection of document_id of current corpus as a list\n \"\"\"\n c_id = self.get_corpus_id\n result_list = self.session.query(Document.document_id).filter(Document.corpus_id == c_id).all()\n document_id_list = [result[0] for result in result_list]\n return document_id_list\n\n @property\n def get_corpus_id(self):\n \"\"\"\n returns current corpus id\n \"\"\"\n corpus = self.session.query(Corpus).filter(Corpus.corpus_name == self.corpus_name).first()\n if corpus is not None:\n return corpus.corpus_id\n return None\n\n @property\n def get_document_frequency(self):\n \"\"\"\n This method gets all the term's document frequency from the table \"term_corpus\"\n and returns a dictionary in the format : {term_id:document_frequency}\n \"\"\"\n temp_list = self.session.query(TermCorpus.term_id, TermCorpus.document_frequency).filter(\n TermCorpus.corpus_id == self.get_corpus_id).all()\n dictionary = {each_tuple[0]: each_tuple[1] for each_tuple in temp_list}\n return dictionary\n\n @property\n def get_term_frequency(self):\n \"\"\"\n This method gets all the term's term frequency from the table \"term_document\"\n and returns a dictionary in the format -> {document_id:{term_id:term_frequency}}\n \"\"\"\n doc_list = self.get_corpus_document_id_list\n huge_dictionary = {}\n for doc_id in doc_list:\n temp_list = self.session.query(TermDocument.term_id, TermDocument.term_frequency).filter(\n TermDocument.document_id == doc_id)\n dictionary = {each_tuple[0]: each_tuple[1] for each_tuple in temp_list}\n huge_dictionary[doc_id] = dictionary\n return huge_dictionary\n\n def get_or_create(self, model, **kwargs):\n \"\"\"\n Creates an object or returns the object of argument 'model' if exists\n filtered or created by given argument as **kwargs\n \"\"\"\n instance = self.session.query(model).filter_by(**kwargs).first()\n if instance:\n return instance\n else:\n instance = model(**kwargs)\n self.session.add(instance)\n return instance\n\n def get(self, model, **kwargs):\n \"\"\"\n return an object of 'model' if exists filtered by given arguments as **kwargs\n \"\"\"\n instance = self.session.query(model).filter_by(**kwargs).first()\n return instance\n\n def create(self, model, **kwargs):\n \"\"\"\n creates on object of 'model' with given arguments as **kwargs\n \"\"\"\n instance = model(**kwargs)\n self.session.add(instance)\n\n\nclass PerformanceInteraction:\n def __init__(self, dependent_corpus_name, independent_corpus_name):\n self.db_engine = engine\n self.Base = Base\n self.Base.metadata.bind = self.db_engine\n self.DBSession = sessionmaker(bind=self.db_engine)\n self.session = self.DBSession()\n self.dependent_corpus_name = dependent_corpus_name\n self.independent_corpus_name = independent_corpus_name\n self.dependant_corpus_id = self.session.query(Corpus.corpus_id).filter(\n Corpus.corpus_name == self.dependent_corpus_name).first()[0]\n self.independent_corpus_id = self.session.query(Corpus.corpus_id).filter(\n Corpus.corpus_name == self.independent_corpus_name).first()[0]\n\n def get_all_domain_relevance(self, dependent=True, rounded_up_to=3):\n \"\"\"\n returns domain relevance of all features of either dependent or independent corpus\n as a list, based on the value of input parameter 'dependent'.\n \"\"\"\n if dependent:\n corpus_id = self.dependant_corpus_id\n else:\n corpus_id = self.independent_corpus_id\n temp_list = self.session.query(TermCorpus.domain_relevance).filter(TermCorpus.corpus_id == corpus_id).all()\n temp_list = [round(each_tuple[0], rounded_up_to) for each_tuple in temp_list]\n relevance_list = list(set(temp_list))\n return relevance_list\n\n def get_max_dr(self, dependent=True):\n \"\"\"\n returns the highest value from domain relevance among all features of either dependent\n or independent corpus, based on the value of input parameter 'dependent'.\n \"\"\"\n if dependent:\n corpus_id = self.dependant_corpus_id\n else:\n corpus_id = self.independent_corpus_id\n return self.session.query(func.max(TermCorpus.domain_relevance).label(\"max\")).filter(\n TermCorpus.corpus_id == corpus_id).one().max\n\n def get_min_dr(self, dependent=True):\n \"\"\"\n returns the lowest value of domain relevance among all features of either dependent\n or independent corpus, based on the value of input parameter 'dependent'.\n \"\"\"\n if dependent:\n corpus_id = self.dependant_corpus_id\n else:\n corpus_id = self.independent_corpus_id\n return self.session.query(func.min(TermCorpus.domain_relevance).label(\"min\")).filter(\n TermCorpus.corpus_id == corpus_id).one().min\n\n def get_count(self, dependent=True):\n \"\"\"\n returns the count of candidate features of either dependent\n or independent corpus, based on the value of input parameter 'dependent'.\n \"\"\"\n if dependent:\n corpus_id = self.dependant_corpus_id\n else:\n corpus_id = self.independent_corpus_id\n return self.session.query(TermCorpus.term_id).filter(TermCorpus.corpus_id == corpus_id).count()\n\n def get_median_threshold(self, dependent=True):\n \"\"\"\n returns a threshold (median, measurement of central tendency) domain relevance value\n of either dependent or independent corpus, based on the value of input parameter 'dependent'.\n \"\"\"\n if dependent:\n corpus_id = self.dependant_corpus_id\n else:\n corpus_id = self.independent_corpus_id\n\n temp_list = self.session.query(TermCorpus.domain_relevance).filter(TermCorpus.corpus_id == corpus_id).all()\n relevance_list = [each_tuple[0] for each_tuple in temp_list]\n count = self.get_count(dependent=dependent)\n\n if not count % 2 == 0:\n threshold = relevance_list[floor(count / 2)]\n else:\n i = int(count / 2)\n threshold = (relevance_list[i] + relevance_list[i - 1]) / 2\n return threshold\n\n @property\n def get_domain_relevance_dictionary(self):\n \"\"\"\n returns one list, two dictionaries:\n 1. candidate_feature_id_list : Contains ids of all the candidate features ( of dependent corpus )\n 2. idr_score_dictionary : Contains id (of candidate features) as key and their intrinsic domain\n relevance as value.\n 3. edr_score_dictionary : Contains id (of candidate features) as key and their extrinsic domain\n relevance as value.\n \"\"\"\n temp_list = self.session.query(TermCorpus.term_id, TermCorpus.domain_relevance).filter(\n TermCorpus.corpus_id == self.dependant_corpus_id).all()\n\n candidate_feature_id_list = [each_tuple[0] for each_tuple in temp_list]\n idr_score_dictionary = {each_tuple[0]: each_tuple[1] for each_tuple in temp_list}\n\n temp_list = self.session.query(TermCorpus.term_id, TermCorpus.domain_relevance).filter(\n TermCorpus.corpus_id == self.independent_corpus_id,\n TermCorpus.term_id.in_(candidate_feature_id_list)).all()\n\n edr_score_dictionary = {each_tuple[0]: each_tuple[1] for each_tuple in temp_list}\n return candidate_feature_id_list, idr_score_dictionary, edr_score_dictionary\n\n def get_final_features(self, idr_threshold, edr_threshold):\n \"\"\"\n returns all the resultant features of this IEDR method for feature extraction as a list,\n based on two input parameter idr_threshold, edr_threshold\n \"\"\"\n candidate_feature_id_list, idr_dictionary, edr_dictionary = self.get_domain_relevance_dictionary\n for feature_id in candidate_feature_id_list:\n if feature_id not in edr_dictionary:\n edr_dictionary[feature_id] = float(0)\n\n feature_id_list = [feature_id for feature_id in candidate_feature_id_list if\n idr_dictionary[feature_id] >= idr_threshold and edr_dictionary[feature_id] <= edr_threshold]\n if len(feature_id_list) > 0:\n temp_list = self.session.query(Term.term_name).filter(Term.term_id.in_(feature_id_list)).all()\n final_features = [each_tuple[0] for each_tuple in temp_list]\n return final_features\n return []\n","repo_name":"zubairazami/Optimization-of-IEDR-for-Feature-Extraction-in-Opinion-Mining","sub_path":"process/db/interaction.py","file_name":"interaction.py","file_ext":"py","file_size_in_byte":18318,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"34934481","text":"import openai\nimport os\nimport json\nimport time\nfrom tqdm import tqdm\nfrom tenacity import(\n retry,\n stop_after_attempt,\n wait_random_exponential\n)\nimport evaluate\nimport numpy as np\nfrom transformers import AutoTokenizer\n\nfrom dataConfig.chemdner import chemdner\n\nopenai.api_key_path = \"./openai_api_key\"\nrpm = 60\ndata_dir = \"\"\noutput_file = \"./results/chatgpt_chemdner.json\"\ntemplate = [\n \"Description: In this task, you are given a small paragraph of a PubMed article, and your task is to identify all the named entities (particular chemical related entity) from the given input and also provide type of the each entity according to structure-associated chemical entity mention classes (ABBREVIATION, IDENTIFIER, FORMULA, SYSTEMATIC, MULTIPLE, TRIVIAL, FAMILY). Specifically, the paragraph are given with seperate tokens and you need to list all the chemical named entities in order and also tag their types. Generate the output in this format: entity1 , entity2 .\",\n \"Examples:\",\n \"Input: In situ C-C bond cleavage of vicinal diol following by the lactolisation resulted from separated treatment of Arjunolic acid ( 1 ) , 24-hydroxytormentic acid ( 2 ) and 3-O-β-D-glucopyranosylsitosterol ( 3 ) with sodium periodate and silica gel in dried THF according to the strategic position of hydroxyl functions in the molecule .\",\n \"Output: C-C , vicinal diol , Arjunolic acid , 24-hydroxytormentic acid , 3-O-β-D-glucopyranosylsitosterol , sodium periodate , silica gel , THF , hydroxyl \",\n \"Input: Structural studies using LC/MS/MS analysis and ( 1 ) H NMR spectroscopy showed the formation of a glycosidic bond between the primary hydroxyl group of RVX-208 and glucuronic acid .\",\n \"Output: ( 1 ) H , primary hydroxyl , RVX-208 , glucuronic acid \",\n \"Input: The lystabactins are composed of serine ( Ser ) , asparagine ( Asn ) , two formylated/hydroxylated ornithines ( FOHOrn ) , dihydroxy benzoic acid ( Dhb ) , and a very unusual nonproteinogenic amino acid , 4,8-diamino-3-hydroxyoctanoic acid ( LySta ) .\",\n \"Output: lystabactins , serine , Ser , asparagine , Asn , formylated/hydroxylated ornithines , FOHOrn , dihydroxy benzoic acid , Dhb , 4,8-diamino-3-hydroxyoctanoic acid , LySta \",\n \"Please continue:\",\n \"Input: %s\",\n \"Output: \"\n]\n# template = [\n# \"Given tokens of a paragraph of PubMed article, please tag each chemical named entity token according to structure-associated chemical entity mention classes:\",\n# \"- ABBREVIATION\",\n# \"- IDENTIFIER\",\n# \"- FORMULA\",\n# \"- SYSTEMATIC\",\n# \"- MULTIPLE\",\n# \"- TRIVIAL\",\n# \"- FAMILY\",\n# \"Please format the output as list of pairs of tokens and tags (O, B-ABBREVIATION, I-ABBREVIATION, B-IDENTIFIER, I-IDENTIFIER, B-FORMULA, I-FORMULA, B-SYSTEMATIC, I-SYSTEMATIC, B-MULTIPLE, I-MULTIPLE, B-TRIVIAL, I-TRIVIAL, B-FAMILY, I-FAMILY):\",\n# \"```\",\n# \"tags = [(token0, tag0), (token1, tag1), ...]\",\n# \"```\",\n# \"Paragraph tokens:\",\n# \"%s\"\n# ]\n\n# @retry(wait=wait_random_exponential(min=max(1, 60//rpm), max=60), stop=stop_after_attempt(3))\ndef completion_with_backoff(**kwargs):\n return openai.ChatCompletion.create(**kwargs)\n\ndef load_file():\n preds = {}\n if os.path.exists(output_file):\n with open(output_file, \"r\", encoding=\"utf-8\") as f:\n preds = json.load(f)\n return preds\n\ndef save_file(preds):\n with open(output_file, \"w\", encoding=\"utf-8\") as f:\n json.dump(preds, f, indent=4, ensure_ascii=False)\n\ndef convert_ans(content, response):\n tags = []\n pt = 0\n for line in response.split('\\n'):\n if \">, \" in line:\n for ctx in line.split(\">, \"):\n if \" <\" in ctx:\n token, tag = ctx.split(\" <\")[:2]\n token = token.split(' ')\n tag = tag.lstrip('<').rstrip('>')\n while pt < len(content) and content[pt:pt+len(token)] != token:\n tags.append('O')\n pt += 1\n if pt < len(content):\n tags += [\"B-\" + tag] + [\"I-\" + tag] * (len(token) - 1)\n pt += len(token)\n while pt < len(content):\n tags.append('O')\n pt += 1\n return tags\n\ndef convert_ans_fillall(content, response):\n tags = ['O'] * len(content)\n for line in response.split('\\n'):\n if \">, \" in line:\n for ctx in line.split(\">, \"):\n if \" <\" in ctx:\n token, tag = ctx.split(\" <\")[:2]\n token = token.split(' ')\n tag = tag.lstrip('<').rstrip('>')\n for pt in range(0, len(content) - len(token) + 1):\n if content[pt:pt+len(token)] == token:\n tags[pt:pt+len(token)] = [\"B-\" + tag] + [\"I-\" + tag] * (len(token) - 1)\n return tags\n\ndef requestAPI(idx, content, preds):\n prompts = [\n {\n \"role\": \"system\",\n \"content\": \"You are an expert of chemical named entity recognition tasks\"\n },\n {\n \"role\": \"user\",\n \"content\": '\\n'.join(template) % ' '.join(content)\n }\n ]\n response = completion_with_backoff(\n model=\"gpt-3.5-turbo\",\n messages=prompts\n )['choices'][0]['message']['content']\n\n preds[str(idx)] = [response, convert_ans(content, response)]\n save_file(preds)\n\ndef trying(dataset, test_ids, preds):\n for idx in tqdm(test_ids):\n if str(idx) in preds:\n continue\n input_tokens = dataset[idx][\"tokens\"]\n requestAPI(idx, input_tokens, preds)\n time.sleep(80 / rpm)\n\n dead = []\n for idx in test_ids:\n if not str(idx) in preds:\n dead.append(idx)\n return dead\n\ndef chatgpt_chemdner(dataset):\n if not os.path.exists(os.path.dirname(output_file)):\n os.makedirs(os.path.dirname(output_file))\n num_test = len(dataset)\n preds = load_file()\n remaining_ids = []\n for idx in range(num_test):\n if not str(idx) in preds:\n remaining_ids.append(idx)\n if len(remaining_ids) == 0:\n print(f\"Completed, skip\")\n return\n if len(remaining_ids) < num_test:\n print(f\"Detect {num_test - len(remaining_ids)} completed, test the rest {len(remaining_ids)} only...\")\n\n while True:\n remaining_ids = trying(dataset, remaining_ids, preds)\n if len(remaining_ids) > 0:\n print(f\"remaining ids: {remaining_ids}\")\n print(\"retrying all remaining ids...\")\n else:\n print(f\"All completed\")\n break\n\ndef evalution(dataConfig, dataset):\n preds = load_file()\n predictions = []\n for key, pred in preds.items():\n predictions.append([dataConfig.label2id.get(label, 0) for label in pred[1]])\n references = [batch['ner_tags'] for batch in dataset]\n\n assert len(predictions) == len(references), f\"{len(predictions)} != {len(references)}\"\n for idx, (pred, ref) in enumerate(zip(predictions, references)):\n assert len(pred) == len(ref), f\"{idx}: {len(pred)} != {len(ref)}\"\n predictions[idx] = [dataConfig.id2label[p] for p in pred]\n references[idx] = [dataConfig.id2label[r] for r in ref]\n\n seqeval = evaluate.load('evaluate-metric/seqeval')\n results = seqeval.compute(predictions=predictions, references=references)\n print(results)\n\nif __name__ == '__main__':\n dataConfig = chemdner(data_dir)\n dataset = dataConfig.load_dataset()['evaluation']\n chatgpt_chemdner(dataset)\n\n # preds = load_file()\n # for idx, batch in enumerate(dataset):\n # pred = convert_ans_fillall(batch['tokens'], preds[str(idx)][0])\n # preds[str(idx)][1] = pred\n # save_file(preds)\n \n evalution(dataConfig, dataset)","repo_name":"Lhtie/Bio-Domain-Transfer","sub_path":"LLM/chatgpt_chemdner.py","file_name":"chatgpt_chemdner.py","file_ext":"py","file_size_in_byte":8048,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"25118391095","text":"import logging\nimport cv2\nimport torch\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom torch.utils.data import DataLoader\nfrom torchvision.transforms import Compose, Resize, ToTensor\nfrom dataset_modified import MaskDataset, get_img_files\nfrom nets.MobileNetV2_unet import MobileNetV2_unet\n\nimport glob\nfrom pylab import close, colorbar, figure, gray, hist, imshow, plot, savefig, show\nimport scipy\nfrom math import sin, cos, tan, atan, pi\nimport statistics\n\nIMG_SIZE = 224\n\n\ndef get_data_loaders(val_files):\n val_transform = Compose([Resize((224, 224)), ToTensor(), ])\n val_loader = DataLoader(MaskDataset(val_files, val_transform), batch_size=1, shuffle=False,\n pin_memory=True, num_workers=4)\n return val_loader\n\n\n# References:\n# Arganda - Carreras I, Turaga SC, Berger DR, et al.(2015) Crowdsourcing the creation of image\n# segmentation algorithms for connectomics.Front.Neuroanat. 9: 142. DOI: 10.3389/ fnana .2015 .00142\n# def precision_recall(im_true, im_test):\n# error, precision, recall = adapted_rand_error(im_true, im_test)\n#\n# # Intersection over union\n# intersection = np.logical_and(im_true, im_test)\n# union = np.logical_or(im_true, im_test)\n# iou_score = np.sum(intersection) / np.sum(union)\n#\n# # print(f\"\\n## Method: unet\")\n# # print(f\"error: {error}\")\n# # print(f\"precision: {precision}\")\n# # print(f\"recall: {recall}\")\n# #\n# # fig, axes = plt.subplots(1, 3, figsize=(12, 6), constrained_layout=True)\n# # ax = axes.ravel()\n# # ax[0].imshow(im_true)\n# # ax[0].set_title('Hand Segmentation')\n# # ax[0].set_axis_off()\n# #\n# # ax[1].imshow(im_test)\n# # ax[1].set_title('Predicted Segmentation')\n# # ax[1].set_axis_off()\n# #\n# # ax[2].imshow(image)\n# # ax[2].set_title('Predicted Segmentation')\n# # ax[2].set_axis_off()\n# # plt.show()\n# return precision, recall, error, iou_score\n\n\ndef Gio_precision_recall(M, P):\n M = 1.0 * (M > 127)\n P = 1.0 * (P > 127)\n TP = len(np.where(P * M > 0)[0]) # AND between prediction (P) and ground truth (M)\n FP = len(np.where(cv2.subtract(P, M) > 0)[0]) # white pixels in P that don't have corresponding white pixels in M\n FN = len(np.where(cv2.subtract(M, P) > 0)[0]) # white pixels in M that don't have correspondence in P\n return TP, FP, FN\n\n\ndef calc_range3(f, h0, vlo, dv, gamma): # this works!\n Z = h0 * (f * cos(gamma) - vlo * sin(gamma)) / dv\n return Z\n\n\ndef calc_range2(h0, gamma, alpha, delta):\n return h0 / (tan(gamma + alpha + delta) - tan(gamma + alpha))\n\n\ndef angle_from_center(f, p): # pixel p is 0 at center of 1D image\n return atan(p / f)\n\n\ndef distanceMeasuring(apparent_height, bottom_row, gamma):\n # gamma = gamma * pi / 180\n h0 = .20 # meters\n f = 1602\n #f = (112 / 1440) * f\n #h, w = 149.34, 112\n h, w = 1920, 1440\n alpha = angle_from_center(f, h / 2 - bottom_row)\n delta = angle_from_center(f, h / 2 - (bottom_row - apparent_height)) - angle_from_center(f, h / 2 - bottom_row)\n Z_Cal3 = calc_range3(f, h0, h / 2 - bottom_row, apparent_height, gamma)\n Z_Cal2 = calc_range2(h0, gamma, alpha, delta)\n return Z_Cal2\n\n\ndef evaluate():\n model_path = \"/home/skeri/tentorch/unet/trained_models/ali_best/trained/\"\n mask_type = 'png'\n image_type = 'jpg'\n dataset_path = '/home/skeri/Squared_Unrolled_Exit_Signs_by_Distance_and_Their_UNET_Masks/'\n list_of_models = glob.glob(model_path + \"*.pth\")\n models_numbers = []\n\n for items in list_of_models:\n p1 = items.split('/')\n model_no = p1[len(p1) - 1].split('-')\n models_numbers.append(int(model_no[0]))\n\n sorted_items = sorted(models_numbers)\n sorted_items.append(sorted_items.pop(0))\n\n list_of_models = []\n for items in sorted_items:\n list_of_models.append(model_path + str(items) + '-best.pth')\n # precision_list = []\n # recall_list = []\n # error_list = []\n # iou_list = []\n\n # print(' FP ', ' TP ', ' FN ', ' Recall ', ' Precision ', ' Model ')\n\n for loading_model in list_of_models:\n print(loading_model)\n list_of_distances = []\n lm = loading_model.split('/')\n for meters in range(2, 7):\n distances = []\n img_size = (IMG_SIZE, IMG_SIZE)\n\n image_files = get_img_files(dataset_path + str(meters) + '/masks',\n dataset_path + str(meters) + '/imgs',\n mask_type, image_type)\n\n device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n data_loader = get_data_loaders(image_files)\n model = MobileNetV2_unet()\n model.load_state_dict(torch.load(loading_model))\n model.to(device)\n model.eval()\n\n # TP = 0\n # FP = 0\n # FN = 0\n counter = 0\n\n with torch.no_grad():\n for inputs, labels in data_loader:\n inputs = inputs.to(device)\n labels = labels.to(device)\n outputs = model(inputs)\n\n for image, label, output in zip(inputs, labels, outputs):\n image = image.cpu().numpy().transpose((1, 2, 0)) * 255\n image = cv2.resize(image.astype(np.uint8), img_size)\n\n label = label.cpu().numpy().reshape(*img_size) * 255\n label = cv2.resize(label.astype(np.uint8), img_size)\n\n output = output.cpu().numpy().reshape(int(IMG_SIZE / 2), int(IMG_SIZE / 2)) * 255\n\n # print(output.shape)\n output = cv2.resize(output.astype(np.uint8), img_size)\n output[output>0] = 255\n\n #\n\n # print('\\r', FP, TP, FN, end='')\n # # time.sleep(0.1)\n # r1, r2, r3 = Gio_precision_recall(label, output)\n # TP = r1 + TP\n # FP = r2 + FP\n # FN = r3 + FN\n\n if len(np.where(output > 0)[0]) != 0:\n\n output1 = cv2.connectedComponentsWithStats(output, 4, cv2.CV_32S)\n num_labels = output1[0]\n labels = output1[1]\n while num_labels>2:\n small_component = 0\n component_size = labels[labels == 0].shape[0]\n for i in range(1, num_labels):\n if component_size > labels[labels == i].shape[0]:\n small_component = i\n component_size = labels[labels == i].shape[0]\n labels[labels==small_component]=0\n num_labels-=1\n output[labels==0]=0\n # print(labels)\n dim = (1440, 1440)\n # resize image\n output = cv2.resize(output, dim, interpolation=cv2.INTER_AREA)\n # fig, axes = plt.subplots(1, 3, figsize=(12, 6), constrained_layout=True)\n # ax = axes.ravel()\n # ax[0].imshow(label)\n # ax[0].set_title('Hand Segmentation')\n # ax[0].set_axis_off()\n minY = np.amin(np.where(output > 0)[0])\n minX = np.amin(np.where(output > 0)[1])\n maxY = np.amax(np.where(output > 0)[0])\n maxX = np.amax(np.where(output > 0)[1])\n output1 = output\n output1[output > 128] = 255\n output1[output <= 128] = 0\n\n nz = np.where(output1 != 0)\n if len(nz[0]) != 0 and len(nz[1] != 0):\n height = []\n for iteration in range(np.amin(nz[1]), np.amax(nz[1])):\n height.append(np.where(output1[:, iteration] != 0))\n\n av_height = []\n for iterations in height:\n if (len(iterations[0])) != 0:\n mx = np.amax(iterations)\n mn = np.amin(iterations)\n av_height.append(mx - mn)\n\n # output = cv2.rectangle(output, (minX, minY), (maxX, maxY), (255, 255, 255), -1)\n\n sp0 = image_files.item(counter).split('/')\n sp1 = sp0[len(sp0) - 1].split('_')\n # print(image_files.item(counter), sp1[1])\n #distances.append(distanceMeasuring(output, maxY - minY, maxY, float(sp1[1])))\n distace_par = distanceMeasuring(statistics.median(av_height),\n maxY,\n float(sp1[1]))\n distances.append(distace_par)\n # print(image_files.item(counter), distace_par, statistics.mean(av_height), maxY, float(sp1[1]))\n # ax[1].imshow(output)\n # ax[1].set_title('Predicted Segmentation')\n # ax[1].set_axis_off()\n # ax[2].imshow(image)\n # ax[2].set_title('Predicted Segmentation')\n # ax[2].set_axis_off()\n # plt.show()\n # else:\n # distances.append(0)\n # # fig, axes = plt.subplots(1, 3, figsize=(12, 6), constrained_layout=True)\n # # ax = axes.ravel()\n # # ax[0].imshow(label)\n # # ax[0].set_title('Hand Segmentation')\n # # ax[0].set_axis_off()\n # # ax[1].imshow(output)\n # # ax[1].set_title('Predicted Segmentation')\n # # ax[1].set_axis_off()\n # # ax[2].imshow(image)\n # # ax[2].set_title('Predicted Segmentation')\n # # ax[2].set_axis_off()\n # # plt.show()\n counter += 1\n print(distances, meters)\n\n list_of_distances.append(distances)\n # print(list_of_distances)\n # print(\"*********************\")\n # print(list_of_distances)\n counter = 2\n for items in list_of_distances:\n print(\"Distance of \", counter, \" for loading model \", loading_model)\n if len(items)>2:\n print(\"Standard Deviation of sample is % s \"\n % (statistics.stdev(items)))\n print(\"Median of sample is % s \"\n % (statistics.median(items)))\n print(\"Mean of sample is % s \"\n % (statistics.mean(items)))\n print(\"Max of sample is % s \"\n % (np.amax(items)))\n print(\"Min of sample is % s \"\n % (np.amin(items)))\n axes = plt.gca()\n axes.set_xlim([0, 160])\n axes.set_ylim([1, 9])\n axes.plot(items, label=str(counter)+\" M\")\n counter += 1\n\n # if (TP + FN) != 0 and (TP + FP) != 0:\n# recall_list.append(TP / (TP + FN))\n# precision_list.append(TP / (TP + FP))\n# print(\" --> \", round(TP / (TP + FN), 2), \" \", round(TP / (TP + FP), 2), \"----->\", loading_model)\n# else:\n# recall_list.append(0)\n# precision_list.append(0)\n# print(\" --> \", 0, \" \", 0, \"----->\", loading_model)\n #\n # plt.plot(list_of_distances, label='Precision')\n # plt.plot(recall_list, label='Recall')\n # plt.plot(error_list, label='Error')\n # plt.plot(iou_list, label='IOU')\n # plt.legend()\n #\n plt.legend()\n plt.savefig('Squared_Unrolled_Parches_From_Top_Left_Z_Cal2_' + lm[len(lm)-1] +'.png')\n plt.cla()\n # plt.show()\n\n\nif __name__ == '__main__':\n\n logger = logging.getLogger(\"logger\")\n logger.setLevel(logging.DEBUG)\n if not logger.hasHandlers():\n logger.addHandler(logging.FileHandler(filename=\"outputs/{}.log\".format('train_unet')))\n\n evaluate()\n","repo_name":"seyedalicheraghi/UNET","sub_path":"eval_modified.py","file_name":"eval_modified.py","file_ext":"py","file_size_in_byte":12920,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"22171539044","text":"class BalancePattern(object):\n\n def soft_balance(self, step_pace=1):\n subsuccers = self._helper.only_subdirect_successors()\n succers = self._helper.only_direct_not_drawed()\n succers_size = len(succers + subsuccers)\n\n start_y = self._default_y()\n\n nstep = self._step + step_pace\n nposy = self._max_empty_y(nstep)\n\n if (succers_size > 0) and (start_y > nposy):\n diff = start_y - nposy\n for i in range(diff):\n posy = nposy + i\n self._grid.create_dummy((nstep, posy))\n\n def self_balance(self, step_pace=0):\n subsuccers = self._helper.only_subdirect_successors()\n succers = self._helper.only_direct_not_drawed()\n succers_size = len(succers + subsuccers)\n\n start_y = self._default_y()\n\n nstep = self._step + step_pace\n nposy = self._max_empty_y(nstep)\n\n if (start_y < nposy):\n diff = nposy - start_y\n for i in range(diff):\n posy = start_y + i\n self._grid.create_dummy((self._step, posy))\n\n self.balance_nodes(diff)\n\n def predecessors_balance(self, step_pace=1):\n preds = self._helper.grab_drawed_predecers()\n succers_size = len(preds)\n\n if succers_size > 0:\n pred = self._grid.get_index(preds[0])\n nposy = pred[1]\n start_y = self._default_y()\n\n if (start_y < nposy):\n diff = nposy - start_y\n for i in range(diff):\n posy = start_y + i\n self._grid.create_dummy((self._step, posy))\n\n def child_balance(self):\n succers = self._helper.only_direct_not_drawed()\n succers_size = len(succers)\n\n if succers_size >= 2:\n self.balance_nodes(succers_size - 1)\n\n def balance_nodes(self, qtd):\n for nl in range(self._step):\n last = self._max_empty_y(nl)\n for np in range(qtd):\n posy = last + np\n self._grid.create_dummy((nl, posy), self._options.get('grow_mark'))\n\n anode = self.find_next_node(0, last + 1, nl)\n if anode:\n self._grid.inc_size_index(anode, qtd)","repo_name":"maestro-server/analytics-maestro","sub_path":"app/libs/patterns/balance.py","file_name":"balance.py","file_ext":"py","file_size_in_byte":2227,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"85"} +{"seq_id":"10442388631","text":"import math\n\ndef f(x):\n return eval(fx.replace(\"x\",str(x)))\n\ndef secant(x0, x1, e, N):\n print('\\n\\n*** SECANT METHOD IMPLEMENTATION ***')\n step = 1\n condition = True\n while condition:\n if f(x0) == f(x1):\n print('Divide by zero error!')\n break\n\n x2 = x0 - (x1 - x0) * f(x0) / (f(x1) - f(x0))\n print('Iteration-%d, x2 = %0.6f and f(x2) = %0.6f' % (step, x2, f(x2)))\n x0 = x1\n x1 = x2\n step = step + 1\n\n if step > N:\n print('Not Convergent!')\n break\n\n condition = abs(f(x2)) > e\n print('\\n Required root is: %0.8f' % x2)\n\n\nfx = input(\"enter f(x) : \")\nx0 = float(input('Enter Guess: '))\nx1 = float(input('Enter Guess: '))\ne = float(input('Tolerable Error: '))\nN = int(input('Maximum Step: '))\n\nsecant(x0, x1, e, N)","repo_name":"yacinebensaid/secant","sub_path":"secant.py","file_name":"secant.py","file_ext":"py","file_size_in_byte":828,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"22960029247","text":"# Author: Christoph Lassner.\n\n_dtype_str_translation = { 'int': 'i',\n 'i': 'i',\n 'float': 'f',\n 'f': 'f',\n 'double': 'd',\n 'd': 'd',\n 'ui': 'uint',\n 'uint': 'uint',\n 'unsigned int': 'uint',\n 'ui8': 'uint8',\n 'uint8': 'uint8',\n 'uint8_t': 'uint8',\n 'unsigned char': 'uint8',\n 'uchar': 'uint8',\n 'uc': 'uint8',\n 'char': 'int8',\n 'c': 'int8',\n 'int16': 'int16',\n 'int16_t': 'int16',\n 'i16': 'int16',\n 'uint16': 'uint16',\n 'uint16_t': 'uint16',\n 'ui16': 'uint16',\n 'std::vector': 'fv',\n 'std::vector': 'dv',\n 'std::pair>>': 'hp',\n 'std::vector>>>': 'vhp',\n 'std::pair>,std::shared_ptr>>': 'rpf',\n 'std::vector>,std::shared_ptr>>,float>>': 'vprpff',\n 'std::pair>,std::shared_ptr>>': 'rpd',\n 'std::vector>,std::shared_ptr>>,float>>': 'vprpfd' }\n\n# Translations from C++ to C types.\n# #include is required!\n_dtype_c_translation = {'int': 'int',\n 'void': 'void',\n 'float': 'float',\n 'double': 'double',\n 'uint': 'unsigned int',\n 'fertilized::uint': 'unsigned int',\n 'unsigned int': 'unsigned int',\n 'uint8': 'uint8_t',\n 'uint8_t': 'uint8_t',\n 'int16_t': 'int16_t',\n 'size_t': 'size_t', # otherwise unsigned long long int\n 'bool': 'int',\n 'std::string': 'char*'}\n\n# See http://www.mathworks.de/help/matlab/apiref/mxcreatenumericarray.html.\n_matlab_cpp_translation = {\"mxDOUBLE_CLASS\":\"double\",\n \"mxSINGLE_CLASS\":\"float\",\n \"mxUINT64_CLASS\":\"uint64_t\",\n \"mxINT64_CLASS\":\"int64_t\",\n \"mxUINT32_CLASS\":\"uint32_t\",\n \"mxINT32_CLASS\":\"int32_t\",\n \"mxUINT16_CLASS\":\"uint16_t\",\n \"mxINT16_CLASS\":\"int16_t\",\n \"mxUINT8_CLASS\":\"uint8_t\",\n \"mxINT8_CLASS\":\"int8_t\"};\n_cpp_matlab_translation = {}\nfor _key, _val in list(_matlab_cpp_translation.items()):\n _cpp_matlab_translation[_val] = _key\n if not _val in ['float', 'double']:\n _cpp_matlab_translation['std::'+_val] = _key\n_cpp_matlab_translation['long'] = 'mxINT64_CLASS'\n_cpp_matlab_translation['ulong'] = 'mxUINT64_CLASS'\n_cpp_matlab_translation['int'] = 'mxINT32_CLASS'\n_cpp_matlab_translation['uint'] = 'mxUINT32_CLASS'\n_cpp_matlab_translation['fertilized::uint'] = 'mxUINT32_CLASS'\n_cpp_matlab_translation['short'] = 'mxINT16_CLASS'\n_cpp_matlab_translation['ushort'] = 'mxUINT16_CLASS'\n_cpp_matlab_translation['char'] = 'mxINT8_CLASS'\n_cpp_matlab_translation['uchar'] = 'mxUINT8_CLASS'\n_cpp_matlab_translation['size_t'] = 'mxUINT64_CLASS'\n","repo_name":"classner/fertilized-forests","sub_path":"bindings/python/fertilized/TypeTranslations.py","file_name":"TypeTranslations.py","file_ext":"py","file_size_in_byte":3948,"program_lang":"python","lang":"en","doc_type":"code","stars":35,"dataset":"github-code","pt":"85"} +{"seq_id":"70896016599","text":"\"\"\"\n @Author: Mayank Anand\n @Date: 2022-03-11\n @Last Modified by: Mayank Anand\n @Last Modified time: 2022-03-11\n @Title : Basic Python Data Structure Programs - File Exist Check\n \"\"\"\nimport os\n\n\ndef main():\n try:\n file_name = input(\"Enter file name to check whether it exists or not?: \")\n if os.path.exists(file_name):\n print(\"File exists.\")\n else:\n print(\"File doesn't exist.\")\n except Exception as e:\n print(\"{} is raised.\".format(e))\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"mayankan/data-structures","sub_path":"basic_python/11_file_exists.py","file_name":"11_file_exists.py","file_ext":"py","file_size_in_byte":551,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"38239008671","text":"\"\"\"\nTests for the lms_result_processor\n\"\"\"\nfrom xmodule.modulestore.tests.factories import CourseFactory, ItemFactory\nfrom xmodule.modulestore.tests.django_utils import ModuleStoreTestCase\n\nfrom courseware.tests.factories import UserFactory\n\nfrom lms.lib.courseware_search.lms_result_processor import LmsSearchResultProcessor\n\n\nclass LmsSearchResultProcessorTestCase(ModuleStoreTestCase):\n \"\"\" Test case class to test search result processor \"\"\"\n\n def build_course(self):\n \"\"\"\n Build up a course tree with an html control\n \"\"\"\n self.global_staff = UserFactory(is_staff=True)\n\n self.course = CourseFactory.create(\n org='Elasticsearch',\n course='ES101',\n run='test_run',\n display_name='Elasticsearch test course',\n )\n self.section = ItemFactory.create(\n parent=self.course,\n category='chapter',\n display_name='Test Section',\n )\n self.subsection = ItemFactory.create(\n parent=self.section,\n category='sequential',\n display_name='Test Subsection',\n )\n self.vertical = ItemFactory.create(\n parent=self.subsection,\n category='vertical',\n display_name='Test Unit',\n )\n self.html = ItemFactory.create(\n parent=self.vertical,\n category='html',\n display_name='Test Html control',\n )\n self.ghost_subsection = ItemFactory.create(\n parent=self.section,\n category='sequential',\n display_name=None,\n )\n self.ghost_vertical = ItemFactory.create(\n parent=self.ghost_subsection,\n category='vertical',\n display_name=None,\n )\n self.ghost_html = ItemFactory.create(\n parent=self.ghost_vertical,\n category='html',\n display_name='Ghost Html control',\n )\n\n def setUp(self):\n # from nose.tools import set_trace\n # set_trace()\n super(LmsSearchResultProcessorTestCase, self).setUp()\n self.build_course()\n\n def test_url_parameter(self):\n fake_url = \"\"\n srp = LmsSearchResultProcessor({}, \"test\")\n with self.assertRaises(ValueError):\n fake_url = srp.url\n self.assertEqual(fake_url, \"\")\n\n srp = LmsSearchResultProcessor(\n {\n \"course\": unicode(self.course.id),\n \"id\": unicode(self.html.scope_ids.usage_id),\n \"content\": {\"text\": \"This is the html text\"}\n },\n \"test\"\n )\n\n self.assertEqual(\n srp.url, \"/courses/{}/jump_to/{}\".format(unicode(self.course.id), unicode(self.html.scope_ids.usage_id)))\n\n def test_should_remove(self):\n \"\"\"\n Tests that \"visible_to_staff_only\" overrides start date.\n \"\"\"\n srp = LmsSearchResultProcessor(\n {\n \"course\": unicode(self.course.id),\n \"id\": unicode(self.html.scope_ids.usage_id),\n \"content\": {\"text\": \"This is html test text\"}\n },\n \"test\"\n )\n\n self.assertEqual(srp.should_remove(self.global_staff), False)\n","repo_name":"Edraak/edx-platform","sub_path":"lms/lib/courseware_search/test/test_lms_result_processor.py","file_name":"test_lms_result_processor.py","file_ext":"py","file_size_in_byte":3235,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"85"} +{"seq_id":"70388163157","text":"conjunto_elementos = {\n 'A',\n 'B',\n 'C',\n 'D'\n}\n\ndef Conjunto_Potencia_Set(conjunto_elementos):\n '''Función para calcular el conjunto potencia de una estructura de datos tipo conjunto\n \n \n Parámetros\n -----------------\n conjunto_elementos : set\n \n Ejemplo de conjunto:\n conjunto_elementos = {\n 'A',\n 'B',\n 'C',\n 'D'\n }\n ''' \n \n #Si se recibe como parámetro el conjunto vacío, se retorna el conjunto vacio\n if len(conjunto_elementos) == 0:\n return [set()]\n #En caso contrario, en primer lugar se realiza la copia del conjunto de entrada\n copia_conjunto = conjunto_elementos.copy()\n #Para cada elemento del conjunto se crea una copia del elemento en una variable\n for item in conjunto_elementos:\n it1 = item\n #Y posteriormente se elimina el primer elemento del conjunto del conjunto copia\n copia_conjunto.discard(it1)\n #Para volver a llamar recursivamente a la función con los elementos del conjunto restantes\n r = Conjunto_Potencia_Set(copia_conjunto)\n #Luego del último llamado se empieza a recorrer la lista retornda en cada llamado y se suma\n #cada uno de los elementos cuardados en la cariable de copia\n Lista = r + [s | {it1} for s in r] \n return Lista ","repo_name":"lemmhub/MCCRB","sub_path":"Conjunto_potencia_set.py","file_name":"Conjunto_potencia_set.py","file_ext":"py","file_size_in_byte":1377,"program_lang":"python","lang":"es","doc_type":"code","stars":1,"dataset":"github-code","pt":"85"} +{"seq_id":"10211863698","text":"import RPi.GPIO as GPIO\nimport time\n\n\n# Pin tanımlamaları\nPWM_PIN = 33 \n\n# GPIO ayarları\n\nGPIO.setup(PWM_PIN, GPIO.OUT)\npwm = GPIO.PWM(PWM_PIN, 100) # old freq = 50\n # PWM nesnesi olu��turma, frekans=50\n \ndef open_servo():\n # Motoru 90 derece pozisyonda başlatma\n global pwm\n pwm.start(5) #old: 7.5 1.5ms duty cycle\n time.sleep(3)\n pwm.ChangeDutyCycle(0)\n\ndef close_servo():\n global pwm\n # Motoru -90 derece pozisyonda hareket ettirme\n pwm.start(15) #old: 12.5 2.5ms duty cycle\n time.sleep(3)\n pwm.ChangeDutyCycle(0)\n\n# Test\n#while True:\n# open_servo()\n # time.sleep(2)\n # close_servo()\n # time.sleep(1)\n# Motoru durdurma\n#pwm.stop()\n\n# GPIO ayarlarını temizleme\n#GPIO.cleanup()","repo_name":"akinemreyazici/Web_Based_Smart_Garage","sub_path":"python/s90.py","file_name":"s90.py","file_ext":"py","file_size_in_byte":730,"program_lang":"python","lang":"tr","doc_type":"code","stars":2,"dataset":"github-code","pt":"85"} +{"seq_id":"25091552878","text":"import os\nimport csv\nimport argparse\nimport datetime\nimport torch\nimport numpy as np\nfrom tqdm import tqdm\nfrom PIL import Image\nimport torch.nn as nn\nfrom datetime import datetime\nfrom torch.utils.data import DataLoader\nfrom torch.optim import lr_scheduler\nfrom src.dataset import get_digits_dataset\nfrom src.diffusion import *\nfrom src.models import *\nfrom src.utils import *\n\nparser = argparse.ArgumentParser()\n\nparser.add_argument('-bs',type=int,default=100,help='batch size')\nparser.add_argument('-ep',type=int,default=300,help='epoch')\nparser.add_argument('-lr',type=float,default=3e-4,help='learning rate')\nparser.add_argument('-dp','--dropout',type=float,default=0.2,help='dropout')\nparser.add_argument('--size',type=int,default=28,help='image size')\nparser.add_argument('--in_channels',type=int,default=3,help='input channels')\nparser.add_argument('--num_classes',type=int,default=10,help='number of class')\nparser.add_argument('--topk',type=int,default=5,help='top k checkpoint')\nparser.add_argument('--step_size',type=int,default=5,help='step size')\nparser.add_argument('--save_dir',type=str,default='./checkpoint')\nparser.add_argument('--data_root',type=str,default='./mnistm')\nparser.add_argument('--num_workers',type=int,default=4,help='num_workers')\n\nopt = parser.parse_args()\n\ndef train():\n\tckpt_loc = os.path.join(opt.save_dir,f'{datetime.today().strftime(\"%m-%d-%H-%M-%S\")}_DDPM')\n\tmod_loc = os.path.join(ckpt_loc,'model')\n\timg_loc = os.path.join(ckpt_loc,'generate')\n\tos.makedirs(ckpt_loc,exist_ok=True)\n\tos.makedirs(mod_loc,exist_ok=True)\n\tos.makedirs(img_loc,exist_ok=True)\n\tcsv_file = open(os.path.join(ckpt_loc,'result.csv'),mode='w',newline='')\n\twriter = csv.writer(csv_file)\n\twriter.writerow(['epoch','Loss'])\n\ttorch.manual_seed(1)\n\tdevice = torch.device(\"cuda:0\" if torch.cuda.is_available() else 'cpu')\n\n\t#model\n\tmodel = UNet_conditional(num_classes=opt.num_classes).to(device)\n\toptimizer = torch.optim.AdamW(model.parameters(), lr=opt.lr)\n\tcriterion = nn.MSELoss()\n\tdiffusion = Diffusion(img_size=opt.size, device=device)\n\t\n\ttrain_set = get_digits_dataset(opt,'train') ##need to modify\n\n\ttrain_loader = DataLoader(\n\t\ttrain_set,\n\t\tbatch_size=opt.bs,\n\t\tnum_workers=opt.num_workers,\n\t\tshuffle=True\n\t \t)\n \n\n\tfor epoch in range(1,opt.ep+1):\n\t\ttrain_bar = tqdm(train_loader)\n\t\tfor i, data in enumerate(train_bar):\n\t\t\timages = data['image'].to(device)\n\t\t\tlabels = data['label'].to(device)\n\t\t\tt = diffusion.sample_timesteps(images.shape[0]).to(device)\n\t\t\tx_t, noise = diffusion.noise_images(images, t)\n\t\t\tif np.random.random() < 0.1:\n\t\t\t\tlabels = None\n\t\t\tpredicted_noise = model(x_t, t, labels)\n\t\t\tloss = criterion(noise, predicted_noise)\n\n\t\t\toptimizer.zero_grad()\n\t\t\tloss.backward()\n\t\t\toptimizer.step()\n\t\t\t\n\t\t\ttrain_bar.set_postfix(MSE=loss.item())\n\t\twriter.writerow([f'Training {epoch}',loss.item()\n\t\t])\n\n\t\tif epoch % 5 == 0:\n\t\t\tlabels = torch.arange(10).long().to(device)\n\t\t\tsampled_images = diffusion.sample(model, n=len(labels), labels=labels)\n\t\t\tsave_images(sampled_images, os.path.join(img_loc, f\"{epoch}.png\"),nrow=10)\n\t\t\ttorch.save(model, os.path.join(mod_loc, f\"{epoch:0>4}ckpt.pt\"))\n\nif __name__ == '__main__':\n train()","repo_name":"ShangYenLee/Conditional-Diffusion-models","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":3156,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"11841107765","text":"import requests\nimport pandas as pd\nimport datetime\n\nfrom stockstats import StockDataFrame\n\n\ndef fetch(pair, time_period=None, interval=None):\n \"\"\"\n Fetch data from Plato-microservice by last 30 min\n :param time_period:\n\n :return: json\n \"\"\"\n\n if time_period is None:\n url = f'http://platotradeinfo.silencatech.com/main/dashboard/ajaxgetetradedata'\n response = requests.get(url, params={'pair': pair})\n return response.json()['result']\n elif time_period is not None and interval is not None:\n url = 'http://platotradeinfo.silencatech.com/main/dashboard/ajaxgetetradedataforperiod'\n response = requests.get(url, params={'pair': pair,\n 'from': time_period['from'],\n 'to': time_period['to'],\n 'period': interval})\n return response.json()['data']\n\n\ndef parse_date_period(data):\n df = pd.DataFrame(data)\n df = df[['minute_ts', 'o', 'h', 'l', 'c', 'vo']]\n df = df.sort_values(by=['minute_ts'])\n df = df.rename(columns={'vo': 'volume',\n 'o': 'open',\n 'h': 'high',\n 'l': 'low',\n 'c': 'close'})\n\n date = pd.to_datetime(df['minute_ts'], unit='s')\n df.insert(0, 'date', date)\n df['date'] = df['date'].dt.strftime('%Y-%m-%d %H:%M:%S')\n\n return StockDataFrame.retype(df)\n\ndef parse_data(data):\n \"\"\"\n Parse the response and retype the DataFrame object to StockDataFrame\n :param data: response from microservice\n\n :return: StockDataFrame\n \"\"\"\n\n d = dict()\n for key in data.keys():\n d[int(key)] = parse_date_period(data[key])\n\n return d\n\n\ndef get_macd_by_id(id, items):\n \"\"\"\n Get item from list by id\n \n :param id:\n :param items: list of items\n\n :return MACD or None\n \"\"\"\n\n for x in items:\n if x.plato_ids == id:\n return x\n \n return None\n\n\ndef is_macd_object_exists(id, items):\n \"\"\"\n Check if the object exists\n \n :param id:\n :param items: list of items\n\n :return: bool\n \"\"\"\n macd = get_macd_by_id(id, items)\n return True if macd != None else False","repo_name":"silenca/platotrade_backtester","sub_path":"app/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":2269,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"7048878145","text":"import sys\r\nsys.path.append('/project2/tas1/miyawaki/projects/003/scripts')\r\nfrom misc.dirnames import get_datadir, get_plotdir\r\nfrom misc.filenames import remove_repdots\r\nfrom proc.ga import make_ga_dev_vint\r\nfrom plot.titles import make_title_sim_time\r\nimport os\r\nimport pickle\r\nimport numpy as np\r\nfrom scipy.ndimage import uniform_filter\r\nimport matplotlib.pyplot as plt\r\nfrom matplotlib.ticker import (MultipleLocator, AutoMinorLocator)\r\n# import tikzplotlib\r\n\r\ndef ga_dev_mon_lat(sim, **kwargs):\r\n\r\n categ = 'mon_lat'\r\n\r\n zonmean = kwargs.get('zonmean', 'zonmean') # zonal mean?\r\n timemean = kwargs.get('timemean', '') # type of time mean (yearmean, jjamean, djfmean, ymonmean-30)\r\n vertcoord = kwargs.get('vertcoord', 'si') # vertical coordinate (si for sigma, pa for pressure, z for height)\r\n try_load = kwargs.get('try_load', 1) # try to load data if available; otherwise, compute R1\r\n vertbnd = kwargs.get('vertbnd', (0.7, 0.3)) # sigma bounds of vertical integral\r\n\r\n if sim == 'longrun':\r\n model = kwargs.get('model', 'MPIESM12_abrupt4x')\r\n yr_span = kwargs.get('yr_span', '1000')\r\n yr_base = 0\r\n elif sim == 'rcp85':\r\n model = kwargs.get('model', 'MPI-ESM-LR')\r\n yr_span = kwargs.get('yr_span', '200601-230012')\r\n yr_base = 0 if 'ymonmean' in timemean else 2006\r\n elif sim == 'echam':\r\n model = kwargs.get('model', 'rp000140')\r\n yr_span = kwargs.get('yr_span', '0001_0039')\r\n yr_base = 0\r\n elif sim == 'era5':\r\n model = None\r\n yr_span = kwargs.get('yr_span', '1980_2005')\r\n yr_base = 0 if 'ymonmean' in timemean else 1980\r\n\r\n if vertbnd[0] == 1:\r\n vmin = -100\r\n vmax = 100\r\n vint = 20\r\n else:\r\n vmin = -50\r\n vmax = 50\r\n vint = 5\r\n\r\n # load data and plot directories\r\n datadir = get_datadir(sim, model=model, yr_span=yr_span)\r\n plotdir = get_plotdir(sim, model=model, yr_span=yr_span, categ=categ)\r\n\r\n ga_dev_vint = make_ga_dev_vint(sim, vertbnd, model=model, vertcoord = vertcoord, zonmean=zonmean, timemean=timemean, yr_span=yr_span, try_load=try_load)\r\n\r\n # print(np.reshape(ga_dev, (-1,96,12)).shape)\r\n if timemean == '':\r\n ga_dev_vint['ga_dev_vint'] = np.mean(np.reshape(ga_dev_vint['ga_dev_vint'], (-1,12,ga_dev_vint['ga_dev_vint'].shape[1])),1)\r\n\r\n rolling_mean = 0; # smooth data using a rolling mean? (units: yr)\r\n ga_dev_vint_filt = uniform_filter(ga_dev_vint['ga_dev_vint'], [rolling_mean,0]) # apply rolling mean\r\n\r\n [mesh_lat, mesh_time] = np.meshgrid(ga_dev_vint['grid']['lat'], yr_base + np.arange(ga_dev_vint['ga_dev_vint'].shape[0])) # create mesh\r\n\r\n ##################################\r\n # PLOT\r\n ##################################\r\n plotname = '%s/ga_dev_mon_lat.%g.%g.%s' % (plotdir, vertbnd[0], vertbnd[1], timemean)\r\n fig, ax = plt.subplots()\r\n csf = ax.contourf(mesh_time, mesh_lat, ga_dev_vint['ga_dev_vint'], np.arange(vmin,vmax,vint), cmap='RdBu', vmin=vmin, vmax=vmax, extend='both')\r\n if vertbnd[0] == 1:\r\n cs_inv = ax.contour(mesh_time, mesh_lat, ga_dev_vint['ga_dev_vint'], levels=[100], colors='royalblue', linewidths=3)\r\n else:\r\n cs_ma = ax.contour(mesh_time, mesh_lat, ga_dev_vint['ga_dev_vint'], levels=[13], colors='sandybrown', linewidths=3)\r\n ax.tick_params(which='both', bottom=True, top=True, left=True, right=True)\r\n if 'ymonmean' in timemean:\r\n ax.set_xticks(np.arange(0,12,1))\r\n ax.set_xticklabels(['J','F','M','A','M','J','J','A','S','O','N','D'])\r\n else:\r\n ax.set_xlabel('Year')\r\n ax.xaxis.set_minor_locator(AutoMinorLocator())\r\n make_title_sim_time(ax, sim, model=model, timemean=timemean)\r\n ax.set_ylabel('Latitude (deg)')\r\n ax.set_yticks(np.arange(-90,91,30))\r\n ax.yaxis.set_minor_locator(MultipleLocator(10))\r\n cbar = plt.colorbar(csf)\r\n cbar.set_label(r'$\\langle(\\Gamma_m - \\Gamma)/\\Gamma_m\\rangle_{%0.1f}^{%0.1f}$ (%%)' % (vertbnd[0], vertbnd[1]))\r\n plt.savefig(remove_repdots('%s.pdf' % (plotname)), format='pdf', dpi=300)\r\n plt.show()\r\n","repo_name":"omiyawaki/phd_projects","sub_path":"003/scripts/plot/mon_lat/ga_dev_mon_lat.py","file_name":"ga_dev_mon_lat.py","file_ext":"py","file_size_in_byte":4095,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"7865565003","text":"'''\r\nimplement queue using class\r\n'''\r\nclass Queue:\r\n\tdef __init__(self, size = 5):\r\n\t\tself.size = size\r\n\t\tself.lst = [] \r\n\t\t\r\n\tdef add(self, value):\r\n\t\tif False == self.isFull():\r\n\t\t\tself.lst.append(value)\r\n\t\telse:\r\n\t\t\tprint(\"QUEUE IS FULL!!!!\")\r\n\t\t\r\n\tdef remove(self):\r\n\t\tif False == self.isEmpty():\r\n\t\t\tprint(\"Element is:{}\".format(self.lst.pop(0)))\r\n\t\telse:\r\n\t\t\tprint(\"QUEUE IS EMPTY!!!!\")\r\n\t\t\t\r\n\tdef isFull(self):\r\n\t\tif self.size == len(self.lst):\r\n\t\t\treturn True\r\n\t\treturn False\r\n\t\t\r\n\tdef isEmpty(self):\r\n\t\tif 0 == len(self.lst):\r\n\t\t\treturn True\r\n\t\treturn False\r\n\t\t\r\ndef main():\r\n\tqueobj = Queue()\r\n\tprint(\"1.Add element to queue\")\r\n\tprint(\"2.Remove element to queue\")\r\n\tprint(\"3.Check empty queue\")\r\n\tprint(\"4.Check full queue\")\r\n\tprint(\"5.EXIT\")\r\n\tch = input(\"Enter choice:\")\r\n\twhile 5 != ch:\r\n\t\tif 1 == ch:\r\n\t\t\tval = input(\"Enter element to add:\")\r\n\t\t\tqueobj.add(val)\r\n\t\telif 2 == ch:\r\n\t\t\tqueobj.remove()\r\n\t\telif 3 == ch:\r\n\t\t\tprint(\"Queue empty : {}\".format(queobj.isEmpty()))\r\n\t\telif 4 == ch:\r\n\t\t\tprint(\"Queue full : {}\".format(queobj.isFull()))\r\n\t\telif 5 == ch:\r\n\t\t\tbreak\r\n\t\tch = input(\"Enter choice:\")\r\n\t\r\nif __name__ == '__main__':\r\n\tmain()\r\n","repo_name":"palandesupriya/python","sub_path":"OOP/que_class.py","file_name":"que_class.py","file_ext":"py","file_size_in_byte":1155,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"2140801054","text":"import argparse\nimport os\nimport shutil\nimport time\n\nfrom omegaconf import OmegaConf\n\nfrom train.encode import encode\nfrom train.sample import sample\nfrom train.train_ae import train_ae\nfrom train.train_ddpm import train_ddpm\nfrom train.train_ncsnpp import train_ncsnpp\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser(\"DPLDM\")\n # 添加子命令\n subparsers = parser.add_subparsers(dest='subcommand', help='Available subcommands')\n\n # 子命令 train1\n train1_parser = subparsers.add_parser('train1', help='train first stage')\n train1_parser.add_argument('--config', type=str, help='location of config file')\n\n # 子命令 encode\n encode_parser = subparsers.add_parser('encode', help='generate encoded dataset')\n encode_parser.add_argument('--config', type=str, help='location of the config file used to train first stage model')\n encode_parser.add_argument('--path', type=str, help='state dict files of first stage model')\n encode_parser.add_argument('--target-path', type=str, help='target path of encoding dataset')\n encode_parser.add_argument('--labeled', type=str, help='if labeled')\n # 子命令 train2\n train2_parser = subparsers.add_parser('train2', help='Description for train2 subcommand')\n train2_parser.add_argument('--path', type=str, help='state dict files of first stage model')\n train2_parser.add_argument('--config-stage1', type=str, help='location of the config file used to train first stage'\n 'model')\n train2_parser.add_argument('--config', type=str, help='location of the config file used to train diffusion model')\n\n # 子命令 sample\n sample_parser = subparsers.add_parser('sample', help='Description for sample subcommand')\n sample_parser.add_argument('--path', type=str, help='state dict files of first stage model')\n sample_parser.add_argument('--config-stage1', type=str, help='location of the config file used to train first stage'\n 'model')\n sample_parser.add_argument('--denoisez-path', type=str, help='denoisez pkl file path')\n sample_parser.add_argument('--output', type=str, help='sample results path')\n\n parser.add_argument('--config', help='配置文件名称')\n args = parser.parse_args()\n\n if args.subcommand == 'train1':\n print('Running train1 with argument:', args.config)\n conf = OmegaConf.load(f'{args.config}')\n config_file = f'{args.config}'\n destination_folder = f'{conf.output_dir}'\n if not os.path.exists(destination_folder):\n os.makedirs(destination_folder)\n\n ts = time.time()\n timeStr = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime(ts))\n output_dir = f'{destination_folder}/{timeStr}'\n if not os.path.exists(output_dir):\n os.makedirs(output_dir)\n shutil.copy(config_file, output_dir)\n train_ae(conf, output_dir)\n\n elif args.subcommand == 'encode':\n print('Running encode with argument:', args.config)\n conf = OmegaConf.load(f'{args.config}')\n path = args.path\n target_path = args.target_path\n labeled = args.labeled\n encode(conf, path, target_path, labeled)\n elif args.subcommand == 'train2':\n print('Running train2 with argument:', args.config)\n conf_stage1 = OmegaConf.load(f'{args.config_stage1}')\n conf = OmegaConf.load(f'{args.config}')\n path = args.path\n train_ncsnpp(conf_stage1, conf, path)\n elif args.subcommand == 'sample':\n print('Running sample with argument:', args.config)\n conf_stage1 = OmegaConf.load(f'{args.config_stage1}')\n path = args.path\n output = args.output\n denoisez_path = args.denoisez_path\n sample(denoisez_path, path, conf_stage1, output)\n\n\n else:\n print('Invalid subcommand')\n","repo_name":"avocado8370/dpldm","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3918,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"38324682309","text":"#!/usr/bin/env python\n\"\"\"\nManages Route53 domains and DNS records\n\nUsage:\n disco_route53.py [--debug] list-zones\n disco_route53.py [--debug] list-records [--zone ]\n disco_route53.py [--debug] create-record \n disco_route53.py [--debug] delete-record \n disco_route53.py (-h | --help)\n\nCommands:\n list-zones List all Hosted Zones\n list-records List all DNS records\n create-record Create a new DNS record\n delete-record Delete a DNS record\n\nOptions:\n -h --help Show this screen\n --zone Show records for a specific Hosted Zone\n --debug Log in debug level\n\"\"\"\n\nfrom __future__ import print_function\nfrom docopt import docopt\n\nfrom disco_aws_automation import DiscoRoute53\nfrom disco_aws_automation.disco_aws_util import run_gracefully\nfrom disco_aws_automation.disco_logging import configure_logging\n\n\ndef run():\n \"\"\"Parses command line and dispatches the commands\"\"\"\n args = docopt(__doc__)\n\n configure_logging(args[\"--debug\"])\n\n disco_route53 = DiscoRoute53()\n\n if args['list-zones']:\n for hosted_zone in disco_route53.list_zones():\n is_private_zone = hosted_zone.config['PrivateZone']\n print(\"{0:<20} {1:10} {2:5}\".format(hosted_zone.name, hosted_zone.id, is_private_zone))\n elif args['list-records']:\n for hosted_zone in disco_route53.list_zones():\n # the Hosted Zone name is the domain name with a period appended to it\n # allow searching by either with or without the period\n if not args['--zone'] or hosted_zone.name in (args['--zone'], args['--zone'] + '.'):\n for record in disco_route53.list_records(hosted_zone.name):\n values = ','.join(record.resource_records)\n print(\"{0:<5} {1:20} {2:50}\".format(record.type, record.name, values))\n elif args['create-record']:\n disco_route53.create_record(args[''],\n args[''],\n args[''],\n args[''])\n elif args['delete-record']:\n record_name = args['']\n # AWS appends a . to the end of the record name.\n # Add it here as a convenience if the argument is missing it\n if not record_name.endswith('.'):\n record_name += '.'\n disco_route53.delete_record(args[''], record_name, args[''])\n\n\nif __name__ == \"__main__\":\n run_gracefully(run)\n","repo_name":"amplify-education/asiaq","sub_path":"bin/disco_route53.py","file_name":"disco_route53.py","file_ext":"py","file_size_in_byte":2611,"program_lang":"python","lang":"en","doc_type":"code","stars":25,"dataset":"github-code","pt":"85"} +{"seq_id":"31081714736","text":"#!/usr/bin/env python3\n\nimport contextlib\nimport os\n\n\n@contextlib.contextmanager\ndef working_directory(path):\n \"\"\"A context manager which changes the working directory to the given\n path, and then changes it back to its previous value on exit.\n\n \"\"\"\n prev_cwd = os.getcwd()\n os.chdir(path)\n try:\n yield\n finally:\n os.chdir(prev_cwd)\n\n\ndef prep_path(path):\n script_abs_path = os.path.abspath(os.path.dirname(__file__))\n return os.path.normpath(os.path.join(script_abs_path, path))\n\n\ndef log_msg(msg):\n sep = len(msg) * '-'\n print(sep)\n print(msg)\n print(sep)\n","repo_name":"mwu-tow/luna-4390132f7b193ddc","sub_path":"luna/config/build/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":611,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"85"} +{"seq_id":"41347984066","text":"\"\"\"\nCP1401 2021-1 Assignment 2\nMarket Garden Simulator\nStudent Name: Rajkumar Senthilraj Ragulraj\nDate started: 06/09/2021\n\nPseudocode:\n\"\"\"\nimport random\n\nmenu_items = [\"(W)ait\", \"(D)isplay plants\", \"(A)dd new plant\", \"(Q)uit\"]\nplants = [\"Parsley\", \"Sage\", \"Rosemary\", \"Thyme\"]\nfood_generated = []\nnum_days = []\n\n\ndef main():\n starter_message()\n choice = ''\n while choice != \"q\":\n menu()\n choice = input(\"Choose: \").lower()\n get_choice(choice)\n quit_program()\n\n\ndef starter_message():\n print(\"Welcome to the Market Garden Simulator \\n\"\n \"Plants cost and generate food according to their name length (e.g., Sage plants cost 4). \\n\"\n \"You can buy new plants with the food your garden generates. \\n\"\n \"You get up to 100 mm of rain per day. Not all plants can survive with less than 30. \\n\"\n \"Let's hope it rains... a lot! \\n\"\n \"You start with these plants:\")\n display_plants()\n\n\ndef display_plants():\n for i in plants:\n print(i, end=\", \")\n\n\ndef menu():\n print(\"\\n\")\n display_stats()\n for option in menu_items:\n print(option)\n\n\ndef get_choice(choice):\n '''\n gets user choice and calls the corresponding function\n '''\n if choice == 'w':\n wait()\n elif choice == 'd':\n display_plants()\n elif choice == 'a':\n add_new_plant()\n elif choice == 'q':\n print(\"You finished with these plants:\")\n display_plants()\n else:\n print(\"Invalid choice\")\n\n\ndef quit_program():\n '''\n displays the exit message\n '''\n print(\"\\n\")\n display_stats()\n print(\"Thank you for simulating. Now go and enjoy a real garden.\")\n\n\ndef display_stats():\n '''\n displays the number of days passed, number of plants and food available\n '''\n total_food = get_total_food()\n print(f\"After {len(num_days)} days, you have {len(plants)} plants and your total food is {total_food}.\")\n\n\ndef wait():\n '''\n simulates rainfall and generates food according to the simulated rainfall\n '''\n rainfall = random.randint(0, 100)\n print(f\"Rainfall: {rainfall}mm\")\n MIN_RAIN = 30\n food_generated_temp = []\n if rainfall < MIN_RAIN:\n remove_plant()\n for i in range(len(plants)):\n multiplier = random.randint(int(rainfall / 2), int(rainfall))\n final_multiplier = multiplier / 100\n gen_food = int(final_multiplier * len(plants[i]))\n food_generated.append(gen_food)\n food_generated_temp.append(gen_food)\n for i in range(len(plants)):\n print(f\"{plants[i]} produced {food_generated_temp[i]}\", end=\", \")\n num_days.append(1)\n\n\ndef remove_plant():\n '''\n removes a plant in case of low rainfall\n '''\n delete_plant = random.randint(0, len(plants) - 1)\n print(f\"Sadly your {plants[delete_plant]} has died.\")\n plants.pop(delete_plant)\n\n\ndef add_new_plant():\n '''\n allows the user to add a new plant\n '''\n new_plant = input(\"Enter plant name: \")\n total_food = get_total_food()\n if len(new_plant) > total_food:\n print(f\"{new_plant} would cost {len(new_plant)} food. With only {total_food}, you cannot afford it\")\n elif new_plant in plants:\n print(f\"You already have a {new_plant}.\")\n else:\n plants.append(new_plant)\n food_generated.append(0 - len(new_plant))\n\n\ndef get_total_food():\n '''\n calculates the total food available\n '''\n total_food = 0\n for i in range(len(food_generated)):\n total_food += (food_generated[i])\n return total_food\n\n\nmain()\n","repo_name":"Rockhp/CP1401_Assignment-2","sub_path":"a2_garden First Draft.py","file_name":"a2_garden First Draft.py","file_ext":"py","file_size_in_byte":3550,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"6389169260","text":"# Import all of the required packages\r\nimport time\r\nimport sys, getopt\r\nimport datetime\r\nfrom poloniex import poloniex\r\n\r\ndef main(argv):\r\n period = 10 ## Run the script once every 10 seconds\r\n pair = \"BTC_XRP\" # \r\n \r\n # Get the arguments/options from the command line\r\n try:\r\n opts, args = getopt.getopt(argv,\"hp:\",[\"period=\"])\r\n except getopt.GetoptError:\r\n print('Please try\\t\\t trading-bot.py -p ')\r\n sys.exit(2)\r\n\r\n # Handle the various options from the command line of the program\r\n for opt, arg in opts: \r\n # Command for getting the help options\r\n if opt == '-h': \r\n print('trading-bot.py -p ')\r\n sys.exit()\r\n \r\n # Command for getting the period option\r\n elif opt in (\"-p\", \"--period\"):\r\n if (int(arg) in [300,900,1800,7200,14400,86400]):\r\n period = arg\r\n else:\r\n print('Poloniex requires periods in 300,900,1800,7200,14400, or 86400 second increments')\r\n sys.exit(2)\r\n \r\n elif opt in (\"-c\", \"--currency\"):\r\n pair = arg\r\n \r\n conn = poloniex('keys go here', 'keys go here')\r\n \r\n\r\n\r\n while True:\r\n currVals = conn.api_query(\"returnTicker\")\r\n \r\n lastPrice = currVals[pair][\"last\"]\r\n\r\n print()\r\n \r\n print(\"{:%Y-%m-%d %H:%M:%S}\".format(datetime.datetime.now()) + \" Period:%ss %s: %s\" % (period, pair, lastPrice))\r\n time.sleep(int(period))\r\n \r\n \r\nif __name__ == \"__main__\": \r\n main(sys.argv[1:])","repo_name":"rudyg-123/Trading-Bot-2020","sub_path":"2020 - Trading Bot/Crypto Trading/CryptoTradingBot_MA.py","file_name":"CryptoTradingBot_MA.py","file_ext":"py","file_size_in_byte":1623,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"42508232253","text":"import numpy as np\n\n\nclass Statistic(object):\n _context = {}\n\n _current = 0\n _total = 0\n _success = 0\n _failure = 0\n\n def __str__(self):\n return f\"[{self._current}/{self._total}] ({self._success}/{self._failure})\"\n\n @property\n def context(self):\n return self._context\n\n @context.setter\n def context(self, value):\n self._context = value\n\n @property\n def progress_percent(self):\n if self._total == 0:\n return 0\n return self._current * 100 // self._total\n\n @property\n def progress_percent_float(self):\n if self._total == 0:\n return 0\n return self._current * 100 / self._total\n\n @property\n def total(self):\n return self._total\n\n @total.setter\n def total(self, value):\n self._total = value\n\n @property\n def current(self):\n return self._current\n\n @property\n def success(self):\n return self._success\n\n @property\n def failure(self):\n return self._failure\n\n def increase_current(self):\n self._current += 1\n\n def increase_success(self):\n self._success += 1\n\n def increase_failure(self):\n self._failure += 1\n\n def reset(self):\n self._total = 0\n self._current = 0\n self._success = 0\n self._failure = 0\n\ndef reduce_mem_usage(df):\n \"\"\" iterate through all the columns of a dataframe and modify the data type\n to reduce memory usage.\n \"\"\"\n start_mem = df.memory_usage().sum() / 1024\n # print('Memory usage of dataframe is {:.2f} KB'.format(start_mem))\n\n for col in df.columns:\n col_type = df[col].dtype\n\n if col_type != object and col_type.name != 'category' and 'datetime' not in col_type.name:\n c_min = df[col].min()\n c_max = df[col].max()\n if str(col_type)[:3] == 'int':\n if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max:\n df[col] = df[col].astype(np.int8)\n elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max:\n df[col] = df[col].astype(np.int16)\n elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max:\n df[col] = df[col].astype(np.int32)\n elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max:\n df[col] = df[col].astype(np.int64)\n else:\n if c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max:\n df[col] = df[col].astype(np.float16)\n elif c_min > np.finfo(np.float32).min and c_max < np.finfo(np.float32).max:\n df[col] = df[col].astype(np.float32)\n else:\n df[col] = df[col].astype(np.float64)\n elif 'datetime' not in col_type.name:\n df[col] = df[col].astype('category')\n\n end_mem = df.memory_usage().sum() / 1024\n # print('Memory usage after optimization is: {:.2f} KB'.format(end_mem))\n # print('Decreased by {:.1f}%'.format(100 * (start_mem - end_mem) / start_mem))\n\n return df\n","repo_name":"kolodyadanil/appsurify-testbrain-core","sub_path":"src/applications/ml/utils/functional.py","file_name":"functional.py","file_ext":"py","file_size_in_byte":3144,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"19945020667","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Jul 15 17:05:36 2018\n\n@author: Ahmet\n\"\"\"\n\n#import numpy as np\n#import matplotlib.pyplot as plt\nimport pandas as pd\n\n# load data\ndatas = pd.read_csv(\"eksikveriler.csv\")\n\n# fill missing values\nfrom sklearn.preprocessing import Imputer\nimputer = Imputer(missing_values=\"NaN\", strategy=\"mean\", axis=0)\nage = datas.iloc[:,1:4].values\nimputer = imputer.fit(age[:,1:4])\nage[:,1:4] = imputer.transform(age[:,1:4])\n\n# encode categoric to numeric\nfrom sklearn.preprocessing import LabelEncoder, OneHotEncoder\ncountry = datas.iloc[:,0:1].values\nle = LabelEncoder()\ncountry[:,0] = le.fit_transform(country[:,0])\nohe = OneHotEncoder(categorical_features=\"all\")\ncountry = ohe.fit_transform(country).toarray()\n\n# reunion\nresult = pd.DataFrame(data = country, index = range(22), columns = ['fr', 'tr', 'us'])\nresult2 = pd.DataFrame(data = age, index = range(22), columns = ['boy', 'kilo', 'yas'])\ngender = datas.iloc[:,-1:].values\nresult3 = pd.DataFrame(data = gender, index = range(22), columns=['cinsiyet'])\nreunion = pd.concat([result, result2], axis=1)\n#reunion2 = pd.concat([reunion, result3], axis=1)\n\n# train and test\nfrom sklearn.cross_validation import train_test_split\nx_train, x_test, y_train, y_test = train_test_split(reunion, result3, test_size=0.33, random_state=0)\n\n# scaling\nfrom sklearn.preprocessing import StandardScaler\nsc = StandardScaler()\nX_train = sc.fit_transform(x_train)\nX_test = sc.fit_transform(x_test)","repo_name":"ahmsay/Machine-Learning","sub_path":"00 - Preprocessing/scaling.py","file_name":"scaling.py","file_ext":"py","file_size_in_byte":1459,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"1578894955","text":"from flask import jsonify, request, Blueprint\nimport requests\nimport json\nimport jwt\nfrom api.model import UserTable\nfrom api.config import (URL_PREFIX, GOOGLE_CLIENT_ID,\n GOOGLE_CLIENT_SECRET, GOOGLE_TOKEN_ENDPOINT,\n TABLE_TYPE, S3_USER_KEY, S3_MEDIA_KEY, CF_HOST_NAME)\nfrom api.util import (Util, catch_exception, LaneInvalidGrantException,\n LaneResourceNotFoundException, _info, authorize, logger)\nimport base64\n\nbp_user = Blueprint('bp_user', __name__, url_prefix=URL_PREFIX+'/user')\n\n\n@bp_user.route('/user/ok', methods=['GET'])\ndef ok():\n return jsonify({'message': 'ok, it works'})\n\n\n@bp_user.route('/', methods=['GET'])\n@catch_exception\ndef get(user_id):\n user_exists = True if len(UserTable().get(user_id)) > 0 else False\n return jsonify({'user_exists': user_exists}), 200\n\n\n@bp_user.route('/info', methods=['GET'])\n@authorize\n@catch_exception\ndef get_info():\n data = request.headers.get('Authorization')\n _, id_token = data.split()\n\n sub = Util().get_user_sub_without_verification(id_token)['sub']\n hashed_user_sub = Util().hash_user_sub(sub)\n res = UserTable().get_sub(hashed_user_sub)\n return jsonify({'user': res}), 200\n\n\n@bp_user.route('', methods=['POST'])\n@catch_exception\ndef post():\n data = request.form.to_dict()\n code = data['serverAuthCode']\n init_id_token = data['idToken']\n user_id = data['user_id']\n ua = request.headers.get('User-Agent')\n try:\n if 'Android' not in ua:\n res = Util().validate_id_token(init_id_token, 'accounts.google.com')\n else:\n res = Util().validate_id_token(init_id_token)\n\n except StopIteration as e:\n raise LaneInvalidGrantException(str(e))\n\n user_sub = res['sub']\n hashed_user_sub = Util().hash_user_sub(user_sub)\n\n headers = {'Content-Type': 'application/x-www-form-urlencoded'}\n data = {\n 'code': code,\n 'client_id': GOOGLE_CLIENT_ID,\n 'client_secret': GOOGLE_CLIENT_SECRET,\n 'redirect_uri': 'http://localhost:8081/callback',\n 'grant_type': 'authorization_code',\n 'access_type': 'offline',\n }\n\n res = requests.post(GOOGLE_TOKEN_ENDPOINT, data=data, headers=headers)\n if('error' in json.loads(res.text)):\n raise LaneInvalidGrantException(json.loads(res.text)['error'])\n\n new_id_token = json.loads(res.text)['id_token']\n refresh_token = json.loads(res.text)['refresh_token']\n\n data = {\n 'lane_type': TABLE_TYPE.USER.value,\n 'user_id': user_id,\n 'user_sub': hashed_user_sub,\n 'refresh_token': refresh_token,\n 'created_at': Util().get_current_time(),\n 'avator_url': CF_HOST_NAME+S3_USER_KEY+hashed_user_sub+'.jpeg'\n }\n\n res = UserTable().post(data)\n\n return jsonify({'id_token': new_id_token,\n 'avator_url': data['avator_url']\n })\n\n\n@bp_user.route('', methods=['PUT'])\n@authorize\n@catch_exception\ndef put():\n data = request.form.to_dict()\n file_name = data['avator_url'].split('/')[-1]\n print(file_name)\n if 'upfile' in data:\n decoded_image = base64.b64decode(data['upfile'])\n Util().upload_to_s3(decoded_image, S3_USER_KEY+file_name)\n\n data['lane_type'] = 'user001'\n UserTable().put(data)\n return jsonify({'message': 'ok'})\n\n\n@bp_user.route('/update', methods=['POST'])\n@catch_exception\ndef update_token():\n data = request.form\n id_token = data['idToken']\n\n try:\n Util().validate_id_token(id_token)\n return jsonify({'id_token': ''}), 200\n except jwt.InvalidIssuerError:\n Util().validate_id_token(id_token, 'accounts.google.com')\n return jsonify({'id_token': ''}), 200\n except jwt.ExpiredSignatureError:\n sub = Util().get_user_sub_without_verification(id_token)['sub']\n hashed_user_sub = Util().hash_user_sub(sub)\n\n res = UserTable().get_sub(hashed_user_sub)\n if(len(res) == 0):\n raise LaneResourceNotFoundException\n\n headers = {'Content-Type': 'application/x-www-form-urlencoded'}\n refresh_token = res[0]['refresh_token']\n data = {\n 'client_id': GOOGLE_CLIENT_ID,\n 'client_secret': GOOGLE_CLIENT_SECRET,\n 'refresh_token': refresh_token,\n 'grant_type': 'refresh_token'\n }\n res = requests.post(GOOGLE_TOKEN_ENDPOINT, data=data, headers=headers)\n if('error' in json.loads(res.text)):\n raise LaneInvalidGrantException(json.loads(res.text)['error'])\n\n return jsonify({'id_token': json.loads(res.text)['id_token']})\n except Exception:\n raise LaneInvalidGrantException\n\n\n@bp_user.route('/login', methods=['POST'])\n@catch_exception\ndef login():\n data = request.form\n id_token = data['idToken']\n ua = request.headers.get('User-Agent')\n\n try:\n if 'Android' not in ua:\n res = Util().validate_id_token(id_token, 'accounts.google.com')\n else:\n res = Util().validate_id_token(id_token)\n\n except StopIteration as e:\n raise LaneInvalidGrantException(str(e))\n\n sub = res['sub']\n\n hashed_user_sub = Util().hash_user_sub(sub)\n\n res = UserTable().get_sub(hashed_user_sub)\n\n if(len(res) == 0):\n raise LaneResourceNotFoundException\n\n return jsonify({'message': 'ok'})\n","repo_name":"wf001/blog-reference","sub_path":"lane-backend/lane-api/api/user.py","file_name":"user.py","file_ext":"py","file_size_in_byte":5339,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"34868925387","text":"def find_longest_words(file2):\r\n with open(file2, 'r') as file:\r\n # read the file content\r\n text = file.read()\r\n # split the text into words\r\n words = text.split()\r\n # find the longest word\r\n longest_word = max(words, key=len)\r\n # find all words with the same length as the longest word\r\n longest_words = [word for word in words if len(word) == len(longest_word)]\r\n return longest_words\r\n\r\n# test the function\r\nprint(find_longest_words(\"file2.txt\"))\r\n","repo_name":"akashjthomas/labs1","sub_path":"AKASH/practice question/p8.py","file_name":"p8.py","file_ext":"py","file_size_in_byte":517,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"5390985490","text":"import sys\nimport math\n\n# w: width of the building.\n# h: height of the building.\nw, h = [int(i) for i in input().split()]\nn = int(input()) # maximum number of turns before game over.\nx0, y0 = [int(i) for i in input().split()] # my initial position.\n\nmin_x, min_y = 0, 0\nmid_x, mid_y = x0, y0\nmax_x, max_y = w - 1, h - 1\n\n# game loop\nwhile True:\n # the direction of the bombs from batman's current location\n # (U, UR, R, DR, D, DL, L or UL)\n bomb_dir = input()\n\n # print(bomb_dir, min_x, mid_x, max_x, min_y,\n # mid_y, max_y, file=sys.stderr, flush=True)\n\n if 'L' in bomb_dir:\n max_x = mid_x\n mid_x = int(math.floor((min_x + mid_x) / 2))\n elif 'R' in bomb_dir:\n min_x = mid_x\n mid_x = int(math.ceil((max_x + mid_x) / 2))\n\n if 'U' in bomb_dir:\n max_y = mid_y\n mid_y = int(math.floor((min_y + mid_y) / 2))\n elif 'D' in bomb_dir:\n min_y = mid_y\n mid_y = int(math.ceil((max_y + mid_y) / 2))\n\n # To debug: print(\"Debug messages...\", file=sys.stderr, flush=True)\n print(f'{mid_x} {mid_y}')\n","repo_name":"gsmrana/CodinGame","sub_path":"puzzle/shadows-of-the-knight-episode-1.py","file_name":"shadows-of-the-knight-episode-1.py","file_ext":"py","file_size_in_byte":1080,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"73286587156","text":"import gym\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nenv = gym.make('Humanoid-v2')\n\ndef run_episode(env, parameters):\n observation = env.reset()\n totalreward = 0\n for _ in range(200):\n env.render()\n action = 0 if np.matmul(parameters,observation) < 0 else 1\n observation, reward, done, info = env.step(action)\n totalreward += reward\n if done:\n break\n \n return totalreward\n\ndef train():\n counter = 0\n bestparams = None\n bestreward = 0\n\n for _ in range(10000):\n counter += 1\n parameters = np.random.rand(4) * 2 - 1\n reward = run_episode(env,parameters)\n if reward > bestreward:\n bestreward = reward\n bestparams = parameters\n if reward == 400:\n break\n \n \n return counter, bestparams\n\nCounter, finalParameters = train()\nprint(Counter,finalParameters)","repo_name":"femtomc/scratch-code","sub_path":"ReinforcementPoleGame.py","file_name":"ReinforcementPoleGame.py","file_ext":"py","file_size_in_byte":946,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"38663407275","text":"from django.test import TestCase\nfrom django.test.client import RequestFactory\n\nfrom mysqlapi.api.models import Instance\nfrom mysqlapi.api.tests import mocks\nfrom mysqlapi.api.views import Healthcheck\n\nimport mock\n\n\nclass HealthcheckTestCase(TestCase):\n\n def setUp(self):\n self.instance = Instance.objects.create(name=\"g8mysql\",\n state=\"running\")\n\n def tearDown(self):\n self.instance.delete()\n\n def test_healthcheck_returns_204_if_the_mysql_server_is_on(self):\n request = RequestFactory().get(\"/resources/g8mysql/status/\")\n with mock.patch(\"mysqlapi.api.models.DatabaseManager.is_up\") as is_up:\n is_up.return_value = True\n view = Healthcheck()\n fake = mocks.FakeEC2Client()\n view._client = fake\n response = view.get(request, \"g8mysql\")\n self.assertEqual(204, response.status_code)\n\n def test_healthcheck_returns_500_if_the_mysql_server_is_off(self):\n request = RequestFactory().get(\"/resources/g8mysql/status/\")\n with mock.patch(\"mysqlapi.api.models.DatabaseManager.is_up\") as is_up:\n is_up.return_value = False\n view = Healthcheck()\n fake = mocks.FakeEC2Client()\n view._client = fake\n response = view.get(request, \"g8mysql\")\n self.assertEqual(500, response.status_code)\n\n def test_healthcheck_returns_201_when_ec2_instance_is_running(self):\n request = RequestFactory().get(\"/resources/g8mysql/status/\")\n with mock.patch(\"mysqlapi.api.models.DatabaseManager.is_up\") as is_up:\n is_up.return_value = True\n view = Healthcheck()\n fake = mocks.FakeEC2Client()\n view._client = fake\n response = view.get(request, \"g8mysql\")\n self.assertEqual(204, response.status_code)\n\n def test_healthcheck_doesnt_touch_ec2_when_instance_is_pending(self):\n self.instance.state = \"pending\"\n self.instance.save()\n request = RequestFactory().get(\"/resources/g8mysql/status/\")\n view = Healthcheck()\n fake = mocks.FakeEC2ClientPendingInstance()\n view._client = fake\n\n response = view.get(request, \"g8mysql\")\n self.assertEqual(202, response.status_code)\n self.assertEqual([], fake.actions)\n","repo_name":"tsuru/mysqlapi","sub_path":"mysqlapi/api/tests/test_healthcheck.py","file_name":"test_healthcheck.py","file_ext":"py","file_size_in_byte":2332,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"85"} +{"seq_id":"15044580415","text":"# related to open_sbt\nfrom problem.adas_problem import ADASProblem\nfrom evaluation.fitness import *\nfrom evaluation.critical import *\nfrom algorithm.nsga2_optimizer import NsgaIIOptimizer\nfrom experiment.search_configuration import DefaultSearchConfiguration\nfrom utils import log_utils\n\n# Related to example\nfrom udacity_simulator import UdacitySimulator\nfrom Criticality import UdacityFitnessFunction, UdacityCriticality\n\nif __name__ == \"__main__\":\n # Define search problem\n problem = ADASProblem(\n problem_name=\"UdacityRoadGenerationProblem\",\n scenario_path=\"\",\n xl=[-10, -10, -10, -10, -10, -10, -10, -10, 0, 1],\n xu=[10, 10, 10, 10, 10, 10, 10, 10, 4, 6],\n simulation_variables=[\n \"angle1\",\n \"angle2\",\n \"angle3\",\n \"angle4\",\n \"angle5\",\n \"angle6\",\n \"angle7\",\n \"angle8\",\n \"perturbation_scale\",\n \"perturbation_function\",\n ],\n fitness_function=UdacityFitnessFunction(),\n critical_function=UdacityCriticality(),\n simulate_function=UdacitySimulator.simulate,\n simulation_time=30,\n sampling_time=0.25,\n )\n\n log_utils.setup_logging(\"./log.txt\")\n\n # Set search configuration\n config = DefaultSearchConfiguration()\n config.n_generations = 10\n config.population_size = 20\n\n # Instantiate search algorithm\n optimizer = NsgaIIOptimizer(problem=problem, config=config)\n\n # Run search\n res = optimizer.run()\n\n # Write results\n res.write_results(params=optimizer.parameters)\n","repo_name":"HannesLeonhard/PerturbationDrive","sub_path":"examples/udacity/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1592,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"33604808386","text":"from itertools import permutations\n\ndef factorial(n):\n if(n<=1):\n return 1\n else:\n return n*factorial(n-1)\n \ndef rearrangements(n):\n num_set=range(1,n+1)\n permutation_list = list(permutations(num_set))\n \n return permutation_list\n\ndef main():\n \n with open('datasets/rosalind_perm.txt') as input_file:\n n=int(input_file.read())\n \n list_perm=rearrangements(n)\n \n print(factorial(n))\n for idx in range(len(list_perm)):\n print(' '.join(map(str, (list_perm[idx]))))\n \n with open('solutions/rosalind_perm.txt', 'w') as output_file:\n output_file.write(str(factorial(n))+'\\n')\n for idx in range(len(list_perm)):\n output_file.write(' '.join(map(str, (list_perm[idx])))+'\\n')\nif(__name__=='__main__'):\n main()\n","repo_name":"ChaoticMarauder/Project_Rosalind","sub_path":"Algorithms/018_PERM.py","file_name":"018_PERM.py","file_ext":"py","file_size_in_byte":808,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"85"} +{"seq_id":"10519798666","text":"import nuke\nimport os\n\nclass DA_WriteMovieSlices:\n\n @staticmethod\n def onUserCreateCallback():\n \"\"\"\n Populate defaults on creation of a DA_WriteMovieSlices node.\n \"\"\"\n if (nuke.thisNode().Class().endswith('DA_WriteMovieSlices')):\n nuke.thisNode().knob('first').setValue(nuke.root().knob('first_frame').value())\n nuke.thisNode().knob('last').setValue(nuke.root().knob('last_frame').value())\n\n\n @staticmethod\n def beforeRenderCallback():\n \"\"\"\n Prepare to render from a DA_WriteMovieSlices node.\n \"\"\"\n slice_number = nuke.thisNode().knob('slice').value()\n output_fpath = nuke.thisParent().knob('file').value()\n\n if not len(output_fpath) > 0:\n raise RuntimeError('No output filepath has been specified!')\n\n base = os.path.basename(output_fpath)\n\n prefix = base[:base.index('.')]\n suffix = base[base.index('.'):]\n\n slice_name = 'slice_%s' % int(slice_number)\n\n destdir = os.path.join(os.path.dirname(output_fpath), prefix, slice_name)\n destfile = prefix + '_' + slice_name + suffix\n\n if not os.path.exists(destdir):\n os.makedirs(destdir)\n\n nuke.thisNode().knob('file').setValue(os.path.join(destdir, destfile))\n nuke.thisNode().knob('first').setValue(nuke.thisParent().knob('first').value())\n nuke.thisNode().knob('last').setValue(nuke.thisParent().knob('last').value())\n","repo_name":"UTSDataArena/examples","sub_path":"nuke/nodes/DA_WriteMovieSlices.py","file_name":"DA_WriteMovieSlices.py","file_ext":"py","file_size_in_byte":1462,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"85"} +{"seq_id":"4264082707","text":"import os\n\nimport requests\n\nfrom absortium.auth import HMACAuth\nfrom absortium.compat import imap, urljoin, quote\nfrom absortium.error import build_api_error\nfrom absortium.services import Account, Withdrawal, Order, Deposit\nfrom absortium.util import encode_params\nfrom core.utils.logging import getPrettyLogger\n\n__author__ = 'andrew.shvv@gmail.com'\n\nABSORTIUM_CRT_PATH = os.path.join(\n os.path.dirname(os.path.realpath(__file__)), 'ca-absortium.crt')\n\nABSORTIUM_CALLBACK_PUBLIC_KEY_PATH = os.path.join(\n os.path.dirname(os.path.realpath(__file__)), 'absortium-callback.pub')\n\nlogger = getPrettyLogger(__name__)\n\n_client = None\n\n\ndef get_absortium_client(*args, **kwargs):\n global _client\n if _client is None:\n _client = Client(*args, **kwargs)\n return _client\n\n\nclass Client():\n \"\"\" API Client for the Absortium API.\n\n Entry point for making requests to the Absortium API. Provides helper methods\n for common API endpoints, as well as niceties around response verification\n and formatting.\n\n Any errors will be raised as exceptions. These exceptions will always be\n subclasses of `absortium.error.APIError`. HTTP-related errors will also be\n subclasses of `requests.HTTPError`.\n \"\"\"\n VERIFY_SSL = False\n API_VERSION = '2016-07-17'\n\n cached_callback_public_key = None\n\n def __init__(self, api_key, api_secret, base_api_uri, api_version=None):\n if not api_key:\n raise ValueError('Missing `api_key`.')\n if not api_secret:\n raise ValueError('Missing `api_secret`.')\n\n # Allow passing in a different API base.\n self.BASE_API_URI = base_api_uri\n\n self.API_VERSION = api_version or self.API_VERSION\n\n # Set up a requests session for interacting with the API.\n self.session = self._build_session(HMACAuth, api_key, api_secret, self.API_VERSION)\n\n self.orders = Order(self)\n self.withdrawals = Withdrawal(self)\n self.deposits = Deposit(self)\n self.accounts = Account(self)\n\n def _build_session(self, auth_class, *args, **kwargs):\n \"\"\"Internal helper for creating a requests `session` with the correct\n authentication handling.\"\"\"\n session = requests.session()\n session.auth = auth_class(*args, **kwargs)\n session.headers.update({'Accept': 'application/json',\n 'Content-Type': 'application/json',\n 'User-Agent': 'absortium/python/3.0'})\n return session\n\n def _create_api_uri(self, *parts):\n \"\"\"Internal helper for creating fully qualified endpoint URIs.\"\"\"\n\n parts = [str(part) for part in parts]\n return urljoin(self.BASE_API_URI, '/'.join(imap(quote, parts)) + '/')\n\n def _request(self, method, *relative_path_parts, **kwargs):\n \"\"\"Internal helper for creating HTTP requests to the ethwallet API.\n\n Raises an APIError if the response is not 20X. Otherwise, returns the\n response object. Not intended for direct use by API consumers.\n \"\"\"\n uri = self._create_api_uri(*relative_path_parts)\n data = kwargs.get('data', None)\n if data and isinstance(data, dict):\n kwargs['data'] = encode_params(data)\n if self.VERIFY_SSL:\n kwargs.setdefault('verify', ABSORTIUM_CRT_PATH)\n else:\n kwargs.setdefault('verify', False)\n kwargs.update(verify=self.VERIFY_SSL)\n response = getattr(self.session, method)(uri, **kwargs)\n return self._handle_response(response)\n\n def _handle_response(self, response):\n if not str(response.status_code).startswith('2'):\n raise build_api_error(response)\n return response.json()\n\n def get(self, *args, **kwargs):\n return self._request('get', *args, **kwargs)\n\n def post(self, *args, **kwargs):\n return self._request('post', *args, **kwargs)\n\n def put(self, *args, **kwargs):\n return self._request('put', *args, **kwargs)\n\n def delete(self, *args, **kwargs):\n return self._request('delete', *args, **kwargs)\n","repo_name":"absortium/absortium-api","sub_path":"absortium/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":4091,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"19814504478","text":"from recidiviz.big_query.big_query_view import SimpleBigQueryViewBuilder\nfrom recidiviz.calculator.query.state import dataset_config\nfrom recidiviz.case_triage.views.dataset_config import CASE_TRIAGE_FEDERATED_DATASET\nfrom recidiviz.utils.environment import GCP_PROJECT_STAGING\nfrom recidiviz.utils.metadata import local_project_id_override\n\nPRODUCT_ROSTER_VIEW_NAME = \"product_roster\"\n\nPRODUCT_ROSTER_DESCRIPTION = \"\"\"View of all users that may have access to Polaris products.\nPulls data from roster Cloud SQL tables. Should only be used for Polaris product-related views.\"\"\"\n\nPRODUCT_ROSTER_QUERY_TEMPLATE = \"\"\"\n WITH product_roster_permissions AS (\n SELECT\n {columns_query}\n FROM\n `{project_id}.{case_triage_federated_dataset_id}.roster` roster\n FULL OUTER JOIN\n `{project_id}.{case_triage_federated_dataset_id}.user_override` user_override\n USING (email_address)\n FULL OUTER JOIN\n `{project_id}.{case_triage_federated_dataset_id}.state_role_permissions` state_role\n ON\n COALESCE(user_override.state_code, roster.state_code) = state_role.state_code\n AND COALESCE(user_override.role, roster.role) = state_role.role\n FULL OUTER JOIN\n `{project_id}.{case_triage_federated_dataset_id}.permissions_override` permissions_override\n USING(email_address)\n )\n SELECT\n {joined_columns},\n {expanded_routes}\n FROM product_roster_permissions\n\"\"\"\n\nROSTER_COLUMNS = [\n \"state_code\",\n \"external_id\",\n \"email_address\",\n \"role\",\n \"district\",\n \"user_hash\",\n \"first_name\",\n \"last_name\",\n]\n\nPERMISSIONS_COLUMNS = [\n \"routes\",\n \"feature_variants\",\n]\n\nROUTES = [\n \"system_libertyToPrison\",\n \"system_prison\",\n \"system_prisonToSupervision\",\n \"system_supervision\",\n \"system_supervisionToPrison\",\n \"system_supervisionToLiberty\",\n \"operations\",\n \"workflows\",\n]\n\nPRODUCT_ROSTER_VIEW_BUILDER = SimpleBigQueryViewBuilder(\n dataset_id=dataset_config.REFERENCE_VIEWS_DATASET,\n case_triage_federated_dataset_id=CASE_TRIAGE_FEDERATED_DATASET,\n view_id=PRODUCT_ROSTER_VIEW_NAME,\n view_query_template=PRODUCT_ROSTER_QUERY_TEMPLATE,\n description=PRODUCT_ROSTER_DESCRIPTION,\n should_materialize=True,\n columns_query=\"\\n \".join(\n [\n f\"COALESCE(user_override.{col}, roster.{col}) AS {col},\"\n for col in ROSTER_COLUMNS\n ]\n + [\n f\"COALESCE(permissions_override.{col}, state_role.{col}) AS {col},\"\n for col in PERMISSIONS_COLUMNS\n ]\n ),\n joined_columns=\",\\n \".join(ROSTER_COLUMNS + PERMISSIONS_COLUMNS),\n expanded_routes=\"\\n \".join(\n [\n f\"IFNULL(CAST(JSON_VALUE(routes, '$.{route}') AS BOOL), FALSE) AS routes_{route},\"\n for route in ROUTES\n ]\n ),\n)\n\nif __name__ == \"__main__\":\n with local_project_id_override(GCP_PROJECT_STAGING):\n PRODUCT_ROSTER_VIEW_BUILDER.build_and_print()\n","repo_name":"Recidiviz/pulse-data","sub_path":"recidiviz/calculator/query/state/views/reference/product_roster.py","file_name":"product_roster.py","file_ext":"py","file_size_in_byte":3027,"program_lang":"python","lang":"en","doc_type":"code","stars":35,"dataset":"github-code","pt":"85"} +{"seq_id":"37346872860","text":"class Filtros:\r\n\r\n def __init__(self, campo = None, tipo = None, valor = None):\r\n self.str = \"\"\r\n if(type(valor) == int or type(valor) == float):\r\n valor = str(valor)\r\n if(tipo == \"igual\"):\r\n self.str += campo + \"=\" + valor\r\n\r\n if(tipo == \"menor\"):\r\n self.str += campo + \"<\" + valor\r\n\r\n if(tipo == \"maior\"):\r\n self.str += campo + \">\" + valor\r\n\r\n if(tipo == \"maiorigual\"):\r\n self.str += campo + \">=\" + valor\r\n\r\n if(tipo == \"menorigual\"):\r\n self.str += campo + \"<=\" + valor\r\n\r\n if(tipo == \"diferente\"):\r\n self.str += campo + \"<>\" + valor\r\n\r\n if(tipo == \"intervalofechado\"):\r\n self.str += campo + \">=\" + str(valor[0]) + \" and \" + campo + \"<=\" + str(valor[1])\r\n\r\n if(tipo == \"intervaloaberto\"):\r\n self.str += campo + \">\" + str(valor[0]) + \" and \" + campo + \"<\" + str(valor[1])\r\n\r\n if(tipo == \"intervaloiniciofechado\"):\r\n self.str += campo + \">=\" + str(valor[0]) + \" and \" + campo + \"<\" + str(valor[1])\r\n\r\n if(tipo == \"intervalofimfechado\"):\r\n self.str += campo + \">\" + str(valor[0]) + \" and \" + campo + \"<=\" + str(valor[1])\r\n\r\n def __mul__(self, outro):\r\n return Filtros.filtro_customizado(self.str + \" and \" + outro.str)\r\n\r\n def __add__(self, outro):\r\n return Filtros.filtro_customizado(self.str + \" or \" + outro.str)\r\n\r\n def __repr__(self):\r\n return self.str\r\n\r\n\r\n def filtracao_customizada(self, filtro):\r\n self.str = filtro\r\n\r\n @classmethod\r\n def filtro_customizado(cls, filtro):\r\n retorno = cls()\r\n retorno.filtracao_customizada(filtro)\r\n return retorno\r\n","repo_name":"danilodcn/PET-Engenharia-Eletrica","sub_path":"filtros.py","file_name":"filtros.py","file_ext":"py","file_size_in_byte":1731,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"20816855045","text":"#####autosmelt#####\r\n\r\n'''\r\nSimple auto click macro that clicks on a furnace and smelts item.\r\nNOTE: do not move screen after starting program.\r\n'''\r\n\r\nimport pyautogui\r\nimport time\r\nimport random\r\n\r\n\r\nduration_m = input(\"How long do you want to run this program for (minutes)? \")\r\n\r\nprint(\"\\nProgram will start in: \")\r\nfor x in range(3,0,-1):\r\n print(x)\r\n time.sleep(1)\r\n \r\nx1 = 100\r\ny1 = 40\r\n\r\nmid_x1 = 950\r\nmid_y1 = 700\r\n\r\nprint(\"\\nChecking the four corners of furnace to confirm valid location...\")\r\n\r\n#furnace\r\npyautogui.moveTo(mid_x1-x1, mid_y1-y1, 0.5)\r\npyautogui.click()\r\n\r\npyautogui.moveTo(mid_x1+x1, mid_y1-y1, 0.5)\r\npyautogui.click()\r\n\r\npyautogui.moveTo(mid_x1+x1, mid_y1+y1, 0.5)\r\npyautogui.click()\r\n\r\npyautogui.moveTo(mid_x1-x1, mid_y1+y1, 0.5)\r\npyautogui.click()\r\ntime.sleep(1)\r\n\r\nduration_s = float(duration_m)*60\r\n\r\nt_current = 0\r\nt_start = time.time() # because time.time() is current time, not 0\r\n\r\nrandom_cis = random.randint(98,102)\r\nt_cis = 0\r\n\r\ndrag_time1 = float(random.randint(200,300))/1000\r\n\r\nt_tick = float(random.randint(1000,1200))/1000\r\n\r\nwhile t_current < t_start + duration_s:\r\n time.sleep(0.1) # drastically decrease cpu usage, sleep for 100ms\r\n\r\n if t_current >= t_start + t_cis + random_cis:\r\n random_cis = random.randint(88,94)\r\n t_cis += random_cis\r\n \r\n # move cursor to forge (location 1)\r\n x1_offset = random.randint(-x1,x1)\r\n y1_offset = random.randint(-y1,y1)\r\n pyautogui.moveTo(mid_x1 + x1_offset, mid_y1 + y1_offset, drag_time1)\r\n pyautogui.click() #click the forge\r\n\r\n time.sleep(t_tick) # wait for tick to process action\r\n\r\n # press space to confirm smelting the ores\r\n # space seems bugged, sometimes does not press, have multiple to ensure\r\n pyautogui.keyDown('space')\r\n pyautogui.keyUp('space')\r\n pyautogui.keyDown('space')\r\n pyautogui.keyUp('space')\r\n pyautogui.keyDown('space')\r\n pyautogui.keyUp('space')\r\n\r\n time.sleep(t_tick)\r\n\r\n # use prayer regen on slot 9\r\n pyautogui.keyDown('9')\r\n pyautogui.keyUp('9')\r\n \r\n t_current = time.time()\r\n","repo_name":"jonathanlu02/rs3_scripts","sub_path":"autosmelt.py","file_name":"autosmelt.py","file_ext":"py","file_size_in_byte":2165,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"72787701398","text":"#CalThreeKingdomsV2.py\nimport jieba\ntxt = open(\"CalThreeKingdomsV2.txt\",\"r\",encoding=\"utf-8\").read()\nexcludes = {\"将军\", \"却说\", \"荆州\", \"二人\", \"不可\", \"军士\", \"不能\",\\\n \"如此\", \"商议\", \"如何\", \"主公\", \"左右\", \"军马\", \"次日\",\\\n \"大喜\", \"引兵\", \"天下\", \"东吴\", \"于是\", \"不敢\", \"今日\",\\\n \"魏兵\", \"陛下\", \"人马\", \"不知\", \"一人\", \"都督\", \"汉中\",\\\n \"众将\", \"只见\", \"后主\", \"蜀兵\", \"大叫\", \"上马\", \"此人\",\\\n \"先主\", \"太守\", \"天子\", \"后人\", \"背后\", \"城中\", \"一面\",\\\n \"何不\", \"忽报\", \"大军\", \"先生\", \"何故\", \"然后\", \"先锋\",\\\n \"夫人\", \"不如\", \"赶来\", \"原来\", \"令人\", \"江东\", \"徐州\"}\nwords = jieba.lcut(txt)\ncounts = {}\nfor word in words:\n if len(word) == 1:\n continue\n elif word == \"丞相\" or word == \"孟德\":\n rword = \"曹操\"\n elif word == \"诸葛亮\" or word == \"孔明曰\":\n rword = \"孔明\"\n elif word == \"玄德\" or word == \"玄德曰\":\n rword = \"刘备\"\n elif word == \"关公\" or word == \"云长\":\n rword = \"关羽\"\n else:\n rword = word\n counts[rword] = counts.get(rword,0) + 1\nfor word in excludes:\n del counts[word]\nitems = list(counts.items())\nitems.sort(key=lambda x:x[1], reverse=True)\nfor i in range(20):\n word, count = items[i]\n print(\"{:<10}{:>5}\".format(word, count))","repo_name":"ZYM1111/Pycharm_Python","sub_path":"CalThreeKingdomsV2.py","file_name":"CalThreeKingdomsV2.py","file_ext":"py","file_size_in_byte":1438,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"20353298202","text":"from utils.auth import verify_token\n\ndef AttachUserMiddleware(get_response):\n # One-time configuration and initialization.\n\n def middleware(request):\n token = request.COOKIES.get('token')\n user = verify_token(token)\n if user: request.decoded_user = user\n else : request.decoded_user = False\n\n response = get_response(request)\n\n return response\n return middleware","repo_name":"2110521-2564-1-SW-ARCH/Merchy","sub_path":"gateway/backend/middleware.py","file_name":"middleware.py","file_ext":"py","file_size_in_byte":413,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"32779322802","text":"# Given a string and a pattern, find out if the string contains any permutation of the pattern.\n\ninput = [\n {\n \"string\": \"oidbcaf\",\n \"pattern\": \"abc\",\n \"output\": True,\n },\n {\n \"string\": \"odicf\",\n \"pattern\": \"dc\",\n \"output\": False,\n },\n {\n \"string\": \"bcdxabcdy\",\n \"pattern\": \"bcdyabcdx\",\n \"output\": True,\n },\n {\n \"string\": \"aaacb\", # { 'a': 1, 'b': 1, 'c': 1}\n \"pattern\": \"abc\",\n \"output\": True,\n },\n]\n\ndef findPermutation(string, pattern):\n # trying to match all characters of sliding window with pattern string\n # for every end ptr char, check if its in the pattern\n # if so, subtract 1 to frequency hashmap and check for match\n # return true if num of matches == length of frequency hashmap\n # move start pointer whenever size of sliding window is larger than pattern string length\n # check if startptr in pattern string\n # if so, add 1 to frequency hashmap and check for match\n\n windowStart, matched = 0, 0\n charFreq = {}\n\n for char in pattern:\n if char not in charFreq:\n charFreq[char] = 1\n else:\n charFreq[char] += 1\n \n for windowEnd in range(len(string)):\n endChar = string[windowEnd]\n if endChar in charFreq:\n charFreq[endChar] -= 1\n if charFreq[endChar] == 0:\n matched += 1\n \n if matched == len(charFreq):\n return True\n\n # starts moving pointer once it reaches the length of pattern\n if windowEnd >= len(pattern) - 1:\n startChar = string[windowStart]\n windowStart += 1\n if startChar in charFreq:\n if charFreq[startChar] == 0:\n matched -= 1\n charFreq[startChar] += 1\n return False\n\nfor i in range(len(input)):\n print(\"input \" + str(i + 1) + \": \", end=\"\")\n\n if findPermutation(input[i][\"string\"], input[i][\"pattern\"]) == input[i][\"output\"]:\n print(\"Correct! ✅\")\n else:\n print(\"Not Quite! ❌\")\n","repo_name":"Pochetes/DS-Practice","sub_path":"Problems/Arrays/findPermutation.py","file_name":"findPermutation.py","file_ext":"py","file_size_in_byte":2081,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"33823629053","text":"# -*- coding: utf-8 -*-\nimport time\nimport traceback\n\nfrom auto_start import auto_start\n\n__author__ = 'wangyongjun'\n\n\nif __name__ == '__main__':\n try:\n auto_start()\n except Exception as err:\n traceback.print_exc()\n print(err)\n time.sleep(3)\n input('ERROR:发生错误, 按【回车键】退出...')\n\n\n","repo_name":"WangYongjun1990/Python-Project","sub_path":"Vinsmoke/run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":342,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"21144881720","text":"import numpy as np\nimport pandas as pd\nimport geopandas\n\nfrom ...InspectionRBD import InspectionRBD\n\n\nclass GroupHousing(InspectionRBD):\n def __init__(self, data):\n super().__init__(data)\n self._inspectionDataModification()\n self._licensesDataModification()\n\n self.featuresColumns = [ 'AREA', 'ESTIMATED_CAPACITY',\n 'Accomodation Type', 'HR_Path_Type',\n 'License Active Duration days', 'Persons_PER_Room', 'Persons_PER_Toilet','LICENSE Inspection No',\n 'PERCENTAGE of Beneficiary NonCompliance_last5', 'PERCENTAGE of LICENSE NonCompliance_last5','InsPostExpiry days',\n 'License Prev Violation'\n ]\n self.labelsColumn = ['Violation Event']\n\n print('self.featuresColumns')\n print(self.featuresColumns)\n \n print('self.labelsColumn')\n print(self.labelsColumn)\n \n self.methods = {'trainTestModel' : 'trainTestModelGroupHousing', 'processModel' : 'processModelExcavation', 'getModelResults' : 'getModelResultsGroupHousing'}\n\n def _inspectionDataModification(self):\n Status = ['Resolved-Completed', 'Under Review and Approval', 'Resolved-Withdrawn','Resolved-NoViolations']\n \n self.data.inspectionsDf = self.data.inspectionsDf.loc[\n (self.data.inspectionsDf['INSPECTYPE ID'] == '6') \n & (self.data.inspectionsDf['INSPECTION ID'].str.startswith('VIO') == False) \n & (self.data.inspectionsDf['INSPECTION ID'].str.startswith('OBJ') == False)\n & self.data.inspectionsDf['Status of Work'].isin(Status)\n ].copy()\n\n self.data.inspectionsDf.rename(columns={'INSPECTION ID' : 'INSEPECTION ID', 'INSPECTYPE ID' : \"INSPECTYPE TYPE ID\",\n 'compliant_clauses_number' : \"Number of compliant clauses\"}, inplace=True)\n \n self.data.inspectionsDf = self.data.inspectionsDf[['LICENSE NUMBER', 'INSEPECTION ID', \n 'Status of Work', 'Business Activity Weight',\n 'Inspection Date', 'Number of clauses',\n 'Number of compliant clauses', 'Number of non-compliant clauses', \n 'Issued fine amount', 'Fine payment status']]\n self.data.inspectionsDf.drop_duplicates(inplace=True)\n\n self.data.inspectionsDf['Violation Event'] = self.data.inspectionsDf['Number of non-compliant clauses'].apply(lambda x: '1' if x > 0 else '0').astype(int)\n\n\n def _licensesDataModification(self):\n #add column renaming \n self.data.groupHousingLicenses['ISSUE_DATE_new'].fillna('0', inplace = True)\n self.data.groupHousingLicenses['ISSUE_DATE_new'] = self.data.groupHousingLicenses['ISSUE_DATE_new'].apply(lambda x:pd.to_datetime(x,format=\"%Y-%m-%d\").date() if x!='0' else np.datetime64('nat'))\n \n ##1) License active duration\n self.data.groupHousingLicenses['License_end_date_new'].fillna('0', inplace = True)\n self.data.groupHousingLicenses['License_end_date_new'] = self.data.groupHousingLicenses['License_end_date_new'].apply(lambda x:pd.to_datetime(x) if x!='0' else None)\n self.data.groupHousingLicenses['Application Date'] = self.data.groupHousingLicenses['Application Date'].apply(lambda x:pd.to_datetime(x) if x!='0' else None)\n \n self.data.groupHousingLicenses['LicenseExpired'] = self.data.groupHousingLicenses['License_end_date_new'].apply(lambda x: 1 if (x<=pd.Timestamp('now'))&(~pd.isnull(x)) else 0)\n \n\n def getInspectionPostExpiryFeature(self):\n self.resultDf['InsPostExpiry days'] = self.resultDf['Inspection Date']-self.resultDf['License_end_date_new']\n self.resultDf['InsPostExpiry days'] = self.resultDf['InsPostExpiry days'].apply(lambda x: x.days if x!=0 else 0)\n\n def getLicenseActiveDurationfeature(self):\n print(\"Issue_Date_new Dataype check\")\n print(self.resultDf['ISSUE_DATE_new'].dtypes)\n #print(pd.Timestamp('now').date().dtypes)\n if 'datetime' in str(self.resultDf['ISSUE_DATE_new'].dtypes):\n print(\"conversion time\")\n self.resultDf['ISSUE_DATE_new'] = pd.to_datetime(self.resultDf['ISSUE_DATE_new'])\n print(self.resultDf['ISSUE_DATE_new'].dtypes)\n self.resultDf['License Active Duration days'] = self.resultDf['ISSUE_DATE_new'].apply(lambda x: pd.to_datetime(pd.Timestamp('now').date()) - x if x!=pd.to_datetime(\"1970-01-01\").date() else 0)\n \n else:\n self.resultDf['License Active Duration days'] = self.resultDf['ISSUE_DATE_new'].apply(lambda x: pd.Timestamp('now') - pd.to_datetime(x) if x!=pd.to_datetime(\"1970-01-01\").date() else 0)\n self.resultDf['License Active Duration days'] = self.resultDf['License Active Duration days'].apply(lambda x: x.days if x!=0 else 0)\n \n def getPersonsPerRoomFeature(self):\n self.resultDf['Persons_PER_Room'] = self.resultDf['ESTIMATED_CAPACITY']/self.resultDf['ROOMS_COUNT']\n\n def getPersonsPerToiletFeature(self):\n self.resultDf['Persons_PER_Toilet'] = self.resultDf['ESTIMATED_CAPACITY']/self.resultDf['TOILETS_COUNT']\n\n def getBeneficiaryNonCompliancePercentageInLast5InspectionFeature(self):\n self.resultDf.sort_values(by=['Beneficiary ID','Inspection Date'],ascending=True, inplace=True)\n\n self.resultDf['Beneficiary_Cumsum5'] = self.resultDf.groupby('Beneficiary ID')['Violation Event'].rolling(5,min_periods=1).sum().reset_index(0,drop=True)\n self.resultDf['Beneficiary_Cumsum5'] =self.resultDf.groupby('Beneficiary ID')['Beneficiary_Cumsum5'].shift()\n self.resultDf['Beneficiary_Cumcount5'] = self.resultDf.groupby('Beneficiary ID')['Violation Event'].rolling(5,min_periods=1).count().reset_index(0,drop=True)\n self.resultDf['Beneficiary_Cumcount5'] =self.resultDf.groupby('Beneficiary ID')['Beneficiary_Cumcount5'].shift()\n self.resultDf['PERCENTAGE of Beneficiary NonCompliance_last5'] = (self.resultDf['Beneficiary_Cumsum5']/self.resultDf['Beneficiary_Cumcount5'])\n\n ##Treat missing values for first inspection as separate category\n self.resultDf['PERCENTAGE of Beneficiary NonCompliance_last5'].fillna(-1,inplace=True)\n self.resultDf.drop(columns=['Beneficiary_Cumcount5', 'Beneficiary_Cumsum5'], axis=1, inplace =True)\n \n def getBeneficiaryNonCompliancePercentageInLast5InspectionArtificialFeature(self):\n df1 = self.resultDf[self.resultDf['INSPECTION ID'] != 'NEWINS-' + self.resultDf['LICENSE_ID'].astype(str)]\n df2 = self.resultDf[self.resultDf['INSPECTION ID'] == 'NEWINS-' + self.resultDf['LICENSE_ID'].astype(str)]\n\n df3 = df2.sort_values(by=['Violation Event'], ascending=True)\n df3.drop_duplicates(['Beneficiary ID','Inspection Date'], keep='last', inplace=True)\n\n df4 = pd.concat([df1, df3])\n df4.sort_values(by=['Beneficiary ID','Inspection Date'], ascending=True, inplace=True)\n\n df4['Beneficiary Inspection#'] = df4.groupby(['Beneficiary ID'])['Inspection Date'].cumcount()+1\n df4['Beneficiary_Cumsum5'] = df4.groupby('Beneficiary ID')['Violation Event'].rolling(5,min_periods=1).sum().reset_index(0,drop=True)\n df4['Beneficiary_Cumsum5'] =df4.groupby('Beneficiary ID')['Beneficiary_Cumsum5'].shift()\n df4['Beneficiary_Cumcount5'] = df4.groupby('Beneficiary ID')['Violation Event'].rolling(5,min_periods=1).count().reset_index(0,drop=True)\n df4['Beneficiary_Cumcount5'] =df4.groupby('Beneficiary ID')['Beneficiary_Cumcount5'].shift()\n df4['PERCENTAGE of Beneficiary NonCompliance_last5'] = (df4['Beneficiary_Cumsum5']/df4['Beneficiary_Cumcount5'])\n df4['PERCENTAGE of Beneficiary NonCompliance_last5'].fillna(-1,inplace=True)\n\n LatestInspection = df4.drop_duplicates(keep='last')\n LatestInspection = LatestInspection[['Beneficiary ID','Inspection Date','Beneficiary Inspection#','Beneficiary_Cumsum5','Beneficiary_Cumcount5','PERCENTAGE of Beneficiary NonCompliance_last5']]\n LatestInspection.drop_duplicates(inplace=True)\n\n df2=pd.merge(df2,LatestInspection,how='left',left_on=['Beneficiary ID','Inspection Date'],right_on=['Beneficiary ID','Inspection Date'])\n\n self.resultDf = pd.concat([df4, df2])\n self.resultDf.drop_duplicates(inplace = True)\n self.resultDf.drop(columns=['Beneficiary_Cumcount5', 'Beneficiary_Cumsum5'], axis=1, inplace =True)\n\n def getLicensesNonCompliancePercentageInLast5InspectionFeature(self):\n self.resultDf.sort_values(by=['LICENSE_ID','Inspection Date'],ascending=True, inplace=True)\n\n self.resultDf['License_Cumsum5'] = self.resultDf.groupby('LICENSE_ID')['Violation Event'].rolling(5,min_periods=1).sum().reset_index(0,drop=True)\n self.resultDf['License_Cumsum5'] =self.resultDf.groupby('LICENSE_ID')['License_Cumsum5'].shift()\n self.resultDf['License_Cumcount5'] = self.resultDf.groupby('LICENSE_ID')['Violation Event'].rolling(5,min_periods=1).count().reset_index(0,drop=True)\n self.resultDf['License_Cumcount5'] =self.resultDf.groupby('LICENSE_ID')['License_Cumcount5'].shift()\n self.resultDf['PERCENTAGE of LICENSE NonCompliance_last5'] = (self.resultDf['License_Cumsum5']/self.resultDf['License_Cumcount5'])\n self.resultDf['PERCENTAGE of LICENSE NonCompliance_last5'].fillna(-1, inplace=True)\n self.resultDf.drop(columns=['License_Cumcount5', 'License_Cumsum5'], inplace =True, axis =1 )\n\n def getLicenseNumberFeature(self):\n self.resultDf['LICENSE Inspection No'] = self.resultDf.groupby('LICENSE_ID')['Inspection Date'].cumcount()+1\n \n def getLicensePreviousViolationFeature(self):\n self.resultDf['License CumSum-1'] = self.resultDf.groupby('LICENSE_ID')['Violation Event'].transform(lambda x: x.shift().cumsum())\n self.resultDf['License Prev Violation'] = self.resultDf['License CumSum-1'].apply(lambda x: 1 if x>0 else 0)\n\n def buildFeatureDf(self):\n self.resultDf = pd.merge(self.data.groupHousingLicenses, self.data.inspectionsDf,how='inner',left_on='LICENSE_ID',right_on='LICENSE NUMBER')\n\n self.getInspectionPostExpiryFeature()\n self.getLicenseActiveDurationfeature()\n self.getPersonsPerRoomFeature()\n self.getPersonsPerToiletFeature()\n self.getBeneficiaryNonCompliancePercentageInLast5InspectionFeature()\n self.getLicensesNonCompliancePercentageInLast5InspectionFeature()\n self.getLicensePreviousViolationFeature()\n self.getLicenseNumberFeature() \n\n return self.resultDf[['LATITUDE', 'LONGITUDE', 'LICENSE_ID'] + self.featuresColumns + self.labelsColumn]\n \n def buildArtificialDf(self):\n self.resultDf = None\n\n artificial_date = pd.to_datetime(\"today\").date()\n artificial_id = 'NEWINS-'\n\n licenses = self.data.groupHousingLicenses[self.data.groupHousingLicenses['LicenseExpired']==0].copy()\n \n licenses = licenses[~pd.isnull(licenses['ISSUE_DATE_new'])]\n self.resultDf = pd.merge(licenses, self.data.inspectionsDf, how='left',left_on='LICENSE_ID',right_on='LICENSE NUMBER')\n self.resultDf_gpd = geopandas.GeoDataFrame(\n self.resultDf, geometry = geopandas.points_from_xy( self.resultDf['LATITUDE'], self.resultDf['LONGITUDE'],), crs=\"EPSG:4326\")\n Amana_Municipality = self.data.AmanaMunicipality[['AMANACODE','MUNICIPALI','geometry']]\n Amana_Municipality = Amana_Municipality.drop_duplicates()\n self.resultDf = geopandas.sjoin(self.resultDf_gpd,Amana_Municipality,how='left', predicate='intersects')\n self.resultDf = self.resultDf.drop(['index_right','geometry'],axis=1)\n\n\n\n self.resultDf.sort_values(['LICENSE_ID','Inspection Date'], ascending=True, inplace=True)\n new_inspection_by_license = self.resultDf.drop_duplicates('LICENSE_ID', keep='last', inplace=False)\n new_inspection_by_license['Inspection Date'] = artificial_date\n new_inspection_by_license['INSPECTION ID'] = artificial_id + new_inspection_by_license['LICENSE_ID'].astype(str)\n \n self.resultDf = pd.concat([self.resultDf, new_inspection_by_license])\n self.resultDf['Inspection Date'] = self.resultDf['Inspection Date'].apply(lambda x: pd.to_datetime(x))\n self.resultDf.dropna(subset=['Inspection Date'], inplace=True)\n self.resultDf['Violation Event'].fillna(0,inplace=True)\n self.resultDf['Violation Event'] = self.resultDf['Violation Event'].astype('int')\n\n self.getInspectionPostExpiryFeature()\n self.getLicenseActiveDurationfeature()\n self.getPersonsPerRoomFeature()\n self.getPersonsPerToiletFeature()\n\n self.getBeneficiaryNonCompliancePercentageInLast5InspectionArtificialFeature()\n self.getLicensesNonCompliancePercentageInLast5InspectionFeature()\n self.getLicensePreviousViolationFeature()\n self.getLicenseNumberFeature() \n\n self.resultDf.sort_values(['LICENSE_ID','Inspection Date'], ascending=True, inplace=True)\n self.resultDf.drop_duplicates('LICENSE_ID', keep='last', inplace=True)\n\n return self.resultDf[['LATITUDE', 'LONGITUDE', 'LICENSE_ID', 'AMANA','AMANACODE','MUNICIPALI'] + self.featuresColumns + self.labelsColumn]","repo_name":"addarshh/Inspection_Dispatch_AI","sub_path":"scripts/GroupHousing_RBD/classes/types/RBD/GroupHousing.py","file_name":"GroupHousing.py","file_ext":"py","file_size_in_byte":13138,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"85"} +{"seq_id":"4893808930","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# ## Hedging using Machine Learning Techniques\n\n# ### Summary\n\n# As systematic and macro factors dominate the investment landscape, we see equity investors move away from one-size-fits all hedging strategies to more precise ways to separate intended and unintended risks and isolate alpha. \n# \n# If you are a fundamental equity investor, take concentrated positions in single names, or have significant idiosyncratic risk that is otherwise difficult to hedge, this highly customizable correlation-based approach is for you. Traditional factor based hedges can fare poorly when most of the risk cannot be explained by factors – in those instances this approach can allow for much better correlation to offset risk as well as a number of ways to guide which names get included while ensuring a high level of tradability by controlling liquidity and borrow costs.\n# \n# In this notebook, we will showcase how to leverage this approach through one of our most popular tools, the [marquee performance hedger](https://marquee.gs.com/s/hedging/performance).\n# \n# Additionally, based on feedback we have received from top users, we are adding the ability to easily run and compare hedges in python through [gs quant](https://developer.gs.com/docs/gsquant/) as well as new modeling techniques, two of whose key advantages are:\n# \n# * **Increased accuracy** through reduced overfitting\n# * **More control** by allowing the user to specify how concentrated or diversified the hedge portfolio is\n# \n# \n# The contents of this notebook are as follows:\n# * [1 - Let's get started with gs quant](#1---Let's-get-started-with-gs-quant)\n# * [2 - Calculate a hedge](#2---Calculate-a-hedge)\n# * [3 - Increased accuracy](#3---Increased-accuracy)\n# * [4 - More control](#4---More-control)\n# * [5 - Customizing your optimization](#5---Customizing-your-optimization)\n\n# ### 1 - Let's get started with gs quant\n# Start every session with authenticating with your unique client id and secret. If you don't have a registered app, create one [here](https://marquee.gs.com/s/developer/myapps/register). `run_analytics` scope is required for the functionality covered in this example.\n\n# In[1]:\n\n\nfrom gs_quant.session import GsSession, Environment\nGsSession.use(client_id=None, client_secret=None, scopes=('run_analytics',))\n\n# Below, you can set the logging level of the notebook. By default, we set the level to INFO to show informative statements about the hedging functions.\n\n# In[2]:\n\n\nimport logging\n\nlogging.basicConfig(level=logging.INFO)\n\n# ### 2 - Calculate a Hedge\n\n# Let's start by calculating a hedge for Amazon with some starting parameters.\n# \n# To leverage the (machine learning-based) techniques in the enhanced performance hedger, please ensure:\n# * `use_machine_learning` parameter is true\n# \n# Note: the hedge result utilizes optimal values found (using grid search) for the ML parameters, which are known as Concentration (`lasso_weight`) and Diversity (`ridge_weight`)\n\n# In[3]:\n\n\nfrom gs_quant.api.gs.hedges import GsHedgeApi\nfrom gs_quant.markets.securities import SecurityMaster, AssetIdentifier\nfrom datetime import date\nimport pandas as pd\n\ntarget_asset = SecurityMaster.get_asset('AMZN UW', AssetIdentifier.BLOOMBERG_ID).get_marquee_id()\nuniverse = SecurityMaster.get_asset('SPX', AssetIdentifier.BLOOMBERG_ID).get_marquee_id()\nhedge_query_example = GsHedgeApi.construct_performance_hedge_query(hedge_target=target_asset, \n universe=(universe, ),\n notional=10e6,\n observation_start_date=date(2019, 3, 25), \n observation_end_date=date(2020, 3, 24), \n backtest_start_date=date(2020, 3, 25), \n backtest_end_date=date(2020, 4, 24),\n use_machine_learning=True)\n\n# Calculate the hedge using the hedge_query_example as input\nresults = GsHedgeApi.calculate_hedge(hedge_query_example)\npd.DataFrame(results['result']['hedgedTarget']['constituents']).head(3)\n\n# ### 3 - Increased Accuracy\n\n# Now let's compare results with and without using the new machine learning parameters and examine the first advantage - improved accuracy.\n# \n# We can do this by looking at the out-of-sample differences in cumulative returns of the two hedges over a hedge backtest period. Note here that we fit the hedge to the observation window and use the backtest window to see how both hedges perform. \n# \n# Note for this example: the ML parameters, Concentration (lasso_weight) and Diversity (ridge_weight), are being manually passed in, which is an alternative way to run the new performance hedger (compared to running grid search to find the optimal pair)\n\n# In[4]:\n\n\nobservation_start = date(2019, 3, 25)\nobservation_end = date(2020, 3, 24)\nbacktest_start = observation_end\nbacktest_end = date(2020, 4, 24)\n\nstandard_hedger_query = GsHedgeApi.construct_performance_hedge_query(hedge_target=target_asset, \n universe=(universe, ), \n notional=10e6,\n observation_start_date=observation_start, \n observation_end_date=observation_end, \n backtest_start_date=backtest_start, \n backtest_end_date=backtest_end,\n max_return_deviation=20)\n\nnew_hedger_query = GsHedgeApi.construct_performance_hedge_query(hedge_target=target_asset, \n universe=(universe, ), \n notional=10e6,\n observation_start_date=observation_start,\n observation_end_date=observation_end,\n backtest_start_date=backtest_start,\n backtest_end_date=backtest_end,\n use_machine_learning=True,\n lasso_weight=5.0,\n ridge_weight=5.0,\n max_return_deviation=20)\n\nstandard_results = GsHedgeApi.calculate_hedge(standard_hedger_query)\nnew_hedger_results = GsHedgeApi.calculate_hedge(new_hedger_query)\n\n# In[5]:\n\n\nimport matplotlib.pyplot as plt\ndef compare_backtests_against_target_asset(new_results, standard_results, figsize=(10, 6)):\n dates, target_returns = zip(*new_results['result']['target']['backtestPerformance'])\n _, new_hedge_returns = zip(*new_results['result']['hedge']['backtestPerformance'])\n _, standard_hedge_returns = zip(*standard_results['result']['hedge']['backtestPerformance'])\n \n results = pd.DataFrame([pd.Series(target_returns, dates, name='Target'), \n pd.Series(new_hedge_returns, dates, name='New Hedger'),\n pd.Series(standard_hedge_returns, dates, name='Standard Hedger')]).T\n \n results.plot(figsize=(10, 6))\n plt.legend(fontsize=18)\n plt.xlabel('Backtest Period', size=13)\n plt.ylabel('Cumulative Returns (% change)', size=13)\n plt.title('Hedge Performance Against Asset', size=22)\n\n# Now, we plot the cumulative returns of the target asset against the cumulative returns of each hedge.\n\n# In[6]:\n\n\ncompare_backtests_against_target_asset(new_hedger_results, standard_results)\n\n# As you can see above, the New Performance Hedger more closely tracks the target asset in the backtest, or \"simulated future\", period (and thus is more accurate).\n\n# ### 4 - More Control\n\n# Now, let's demonstrate the second advantage, namely the enhanced control we have over the performance hedger results.\n# \n# Below, you can plot the effects of varying Concentration and Diversity on your hedge query from section 2 above. \n# \n# In this example, if the values for Concentration are 'None' and the values for Diversity are [10, 20], then the plotting function would run the hedge query passed in for Diversity values of 10% and 20% and then plot the weight/number of asset distributions on the y-axis and x-axis, respectively. \n# \n\n# In[7]:\n\n\n# For now, use ONLY Concentration or Diversity when plotting to view the effects of this hyperparameter value changing on a hedge (set the hyperparam you aren't \n# using to None)\nhyperparams = {'Concentration': None, 'Diversity': [10, 20]}\n\n# In[8]:\n\n\nfrom gs_quant.markets.hedge import Hedge\n\n# Plot results of the hedge - with emphasis on the effects that either the Concentration or Diversity hyperparameter has on the hedge\nhedge_plot = Hedge.plot_weights_against_number_of_assets(hedge_query_example, hyperparams, figsize=(10, 6))\n\n# As you can see above, varying the value of a single hyperparameter has significant effects on the weights & total number of assets of the hedge portfolio that is constructed to hedge the single target asset.\n# \n# For this particular example, the effect is that the weights become more balanced the more the \"Diversity\" hyperparameter increases.\n\n# ### 5 - Customizing your Optimization\n\n# Since we know how to run a basic hedge, let's experiment with the concentration and diversity metrics to find an optimal hedge.\n# \n# Note, the hedger is flexible and allows you to choose which metric to optimize - for example, you may want to run to optimize correlation (in which case you would maximize r-squared) or to optimize transaction costs (in which case you would minimize total transaction costs).\n\n# In[9]:\n\n\n# Modify hyperparameter grid to the values you want to use to find the optimal hedge . As mentioned before, the terminology used for Concentration and Diversity\n# seen below relates to the terminology used for each term in the machine learning literature.\n# Concentration = Lasso (as a percentage)\n# Diversity = Ridge (as a percentage)\nhyperparams = {'Concentration': [0, 20, 40], 'Diversity': [10, 20]}\n\n# Modify this to optimize a metric (maximize or minimize depending on the metric. \n# See the create_optimization_mappings function for how metrics are optimized\nmetric_to_optimize = 'rSquared'\n\n# Let's now run the optimization through the custom grid of concentration/diversity values we specified, and optimize for the specified metric. In this case, we will look for the combination of concentration and diversity values that maximizes correlation, or r-squared.\n\n# In[10]:\n\n\nfrom gs_quant.markets.hedge import Hedge\n\nopt_hedge, opt_metric_val, opt_hyperparams = Hedge.find_optimal_hedge(hedge_query_example, hyperparams, metric_to_optimize)\nprint(f'The optimal pair of hyperparameters was {opt_hyperparams}, achieving a value for {metric_to_optimize} '\n f'of {opt_metric_val*100:.3}% during the out of sample period.')\n\n# Stay tuned for additional ways to use the New Performance Hedger using `gs-quant`. For any questions/comments, feel free to reach out to the email distro **gs-data-ml**!\n","repo_name":"RIMEL-UCA/RIMEL-UCA.github.io","sub_path":"chapters/2023/Qualité logicielle dans les notebooks Jupyter/assets/python-scripts/9-Hedging using Machine Learning Techniques.py","file_name":"9-Hedging using Machine Learning Techniques.py","file_ext":"py","file_size_in_byte":11727,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"85"} +{"seq_id":"23965553846","text":"import sys\nimport random\nimport os\n\nfor x in range(0,10):\n print(x,' ',end=\"\")\n\nprint('\\n')\n\ngrocery_list = ['juice', 'Tomatoes','Potatoes','Bannanas']\n\nfor y in grocery_list:\n print(y)\n\n\n for x in [2,4,6,8,10]:\n print(x)\n\n\n num_list = [[1,2,3],[10,20,30],[100,200,300]]\n\nfor x in range(0,3):\n for y in range(0,3):\n print(num_list[x][y])\n\nprint('----while loop------')\nrandom_num = random.randrange(0,100)\n\nwhile(random_num != 15):\n print(random_num)\n random_num = random.randrange(0,100)\n\ni =0\nwhile i <=20:\n if(i%2==0):\n print(i)\n elif i==9:\n break\n else:\n i+=1\n continue\n \n i+=1\nprint('\\n')\nfor x in range(0,50,5):\n print(x)\n\n\n","repo_name":"turcuciprian/pythonBasics","sub_path":"looping.py","file_name":"looping.py","file_ext":"py","file_size_in_byte":715,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"29590785010","text":"import cv2\nimport random\nimport numpy as np\n\ndef random_flip(image, mask, random_rate=0.5):\n if random.random() < random_rate:\n flip_code = random.randint(-1, 1)\n image = cv2.flip(image, flip_code)\n mask = cv2.flip(mask, flip_code)\n return image, mask\n\ndef random_rotate(image, mask, random_rate=0.5):\n if random.random() < random_rate:\n rows, cols = image.shape[:2]\n angle = random.randint(-10, 10)\n M = cv2.getRotationMatrix2D((rows/2, cols/2), angle, 1)\n image = cv2.warpAffine(image, M, (rows, cols))\n mask = cv2.warpAffine(mask, M, (rows, cols))\n return image, mask\n\n\ndef random_scale_and_crop(image, mask, random_rate=0.5):\n #### It is assumed here that the length and width of the image are equal. E.g. [512, 512]\n if random.random() < random_rate:\n base_size = image.shape[0]\n scale_size = random.randint(int(base_size * 0.9), int(base_size * 1.1))\n image = cv2.resize(image, (scale_size, scale_size), cv2.INTER_LINEAR)\n mask = cv2.resize(mask, (scale_size, scale_size), cv2.INTER_NEAREST)\n if scale_size < base_size:\n pad = base_size - scale_size\n image = cv2.copyMakeBorder(image, 0, pad, 0, pad, cv2.BORDER_CONSTANT, 0)\n mask = cv2.copyMakeBorder(mask, 0, pad, 0, pad, cv2.BORDER_CONSTANT, 0)\n else:\n crop_location = random.randint(0, scale_size - base_size)\n image = image[crop_location: crop_location+base_size, crop_location: crop_location+base_size]\n mask = mask[crop_location: crop_location+base_size, crop_location: crop_location+base_size]\n return image, mask\n\n\n\ndef random_data_augmentation(image_and_mask, random_rate=0.5):\n image_and_mask = np.squeeze(image_and_mask, axis=2)\n for i, current_image_and_mask in enumerate(image_and_mask):\n current_image = current_image_and_mask[0]\n current_mask = current_image_and_mask[1]\n # current_image, current_mask = random_scale_and_crop(current_image, current_mask, random_rate)\n # current_image, current_mask = random_flip(current_image, current_mask, random_rate)\n current_image, current_mask = random_rotate(current_image, current_mask, random_rate)\n\n image_and_mask[i][0] = current_image\n image_and_mask[i][1] = current_mask\n image_and_mask = np.expand_dims(image_and_mask, axis=2)\n return image_and_mask\n\n\n","repo_name":"liut969/CHD-Seg","sub_path":"CHD-Seg/data/custom_transforms.py","file_name":"custom_transforms.py","file_ext":"py","file_size_in_byte":2418,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"85"} +{"seq_id":"924364148","text":"NumOfLine=0\ndef FileReader(NumOfLine):\n NumOfLine = 0\n i = 0\n\n file = open(\"QuestionAnswerFileData.txt\")\n\n # opening file\n\n for line in file.readlines():\n NumOfLine = NumOfLine + 1\n\n # counting number of lines in text document\n\n Section = []\n Question = []\n Answer = []\n\n file = open(\"QuestionAnswerFileData.txt\")\n\n for line in file.readlines():\n # going through each line in txt file\n line = line.strip()\n # stripping each line of un-needed whitespace\n Section.append(0)\n Question.append(0)\n Answer.append(0)\n # adding a new value to each array\n\n str.replace(\"\\t\", \"\", \"\")\n # replacing the \\t with spaces (temp method)\n Linearray = line.split('&')\n # the line is split into three sections seperated by the & sign\n\n Section[i] = Linearray[0]\n Question[i] = Linearray[1]\n Answer[i] = Linearray[2]\n i = i + 1\n\n return\n\n # each array is assigned its value of the fragmented string\n\n print(Section)\n print(Question)\n print(Answer)\n\n # Array values are printed out\nFileReader()\nprint(NumOfLine)","repo_name":"colesamson16/BramptonCompWork","sub_path":"testingfinn.py","file_name":"testingfinn.py","file_ext":"py","file_size_in_byte":1156,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"5942804110","text":"from rx.disposable import Disposable, CompositeDisposable, RefCountDisposable, SingleAssignmentDisposable, SerialDisposable\nfrom rx.internal import Struct\nfrom rx.observable import Producer\nfrom rx.subject import Subject\nfrom .addRef import AddRef\nimport rx.linq.sink\nfrom collections import deque\nfrom threading import RLock\n\n\nclass Window(Producer):\n def __init__(self, source, count=0, skip=0, timeSpan=0, timeShift=0, scheduler=None):\n if skip == 0:\n skip = count\n\n self.source = source\n self.count = count # length of each window\n self.skip = skip # number of elements to skip between creation of windows\n self.timeShift = timeShift\n self.timeSpan = timeSpan\n self.scheduler = scheduler\n\n def run(self, observer, cancel, setSink):\n if self.scheduler == None:\n sink = self.SinkWithCount(self, observer, cancel)\n setSink(sink)\n return sink.run()\n elif self.count > 0:\n sink = self.SinkWithCountAndTimeSpan(self, observer, cancel)\n setSink(sink)\n return sink.run()\n else:\n if self.timeSpan == self.timeShift:\n sink = self.SinkWithTimeSpan(self, observer, cancel)\n setSink(sink)\n return sink.run()\n else:\n sink = self.SinkWithTimerAndTimeSpan(self, observer, cancel)\n setSink(sink)\n return sink.run()\n\n class SinkWithCount(rx.linq.sink.Sink):\n def __init__(self, parent, observer, cancel):\n super(Window.SinkWithCount, self).__init__(observer, cancel)\n self.parent = parent\n\n def run(self):\n self.queue = deque()\n self.n = 0\n self.m = SingleAssignmentDisposable()\n self.refCountDisposable = RefCountDisposable(self.m)\n\n firstWindow = self.createWindow()\n self.observer.onNext(firstWindow)\n\n self.m.disposable = self.parent.source.subscribeSafe(self)\n\n return self.refCountDisposable\n\n def createWindow(self):\n s = Subject()\n self.queue.append(s)\n # AddRef was originally WindowObservable but this is just an alias for AddRef\n return AddRef(s, self.refCountDisposable)\n\n def onNext(self, value):\n for s in self.queue:\n s.onNext(value)\n\n c = self.n - self.parent.count + 1\n\n if c >= 0 and c % self.parent.skip == 0:\n s = self.queue.popleft()\n s.onCompleted()\n\n self.n += 1\n\n if self.n % self.parent.skip == 0:\n newWindow = self.createWindow()\n self.observer.onNext(newWindow)\n\n def onError(self, exception):\n while len(self.queue) > 0:\n self.queue.popleft.onError(exception)\n\n self.observer.onError(exception)\n self.dispose()\n\n def onCompleted(self):\n while len(self.queue) > 0:\n self.queue.popleft().onCompleted()\n\n self.observer.onCompleted()\n self.dispose()\n\n class SinkWithTimeSpan(rx.linq.sink.Sink):\n def __init__(self, parent, observer, cancel):\n super(Window.SinkWithTimeSpan, self).__init__(observer, cancel)\n self.parent = parent\n\n def run(self):\n self.gate = RLock()\n\n groupDisposable = CompositeDisposable()\n self.refCountDisposable = RefCountDisposable(groupDisposable)\n\n self.createWindow()\n\n groupDisposable.add(self.parent.scheduler.schedulePeriodic(self.parent.timeSpan, self.tick))\n groupDisposable.add(self.parent.source.subscribeSafe(self))\n\n return self.refCountDisposable\n\n def tick(self):\n with self.gate:\n self.subject.onCompleted()\n self.createWindow()\n\n def createWindow(self):\n self.subject = Subject()\n self.observer.onNext(AddRef(self.subject, self.refCountDisposable))\n\n def onNext(self, value):\n with self.gate:\n self.list.append(value)\n\n def onError(self, exception):\n with self.gate:\n self.subject.onError(exception)\n\n self.observer.onError(exception)\n self.dispose()\n\n def onCompleted(self):\n with self.gate:\n self.subject.onCompleted()\n\n self.observer.onCompleted()\n self.dispose()\n\n\n class SinkWithTimerAndTimeSpan(rx.linq.sink.Sink):\n def __init__(self, parent, observer, cancel):\n super(Window.SinkWithTimerAndTimeSpan, self).__init__(observer, cancel)\n self.parent = parent\n\n def run(self):\n self.totalTime = 0\n self.nextShift = self.parent.timeShift\n self.nextSpan = self.parent.timeSpan\n\n self.gate = RLock()\n self.queue = deque()\n\n self.timerDisposable = SerialDisposable()\n\n groupDisposable = CompositeDisposable(self.timerDisposable)\n self.refCountDisposable = RefCountDisposable(groupDisposable)\n\n self.createWindow()\n self.createTimer()\n\n groupDisposable.add(self.parent.source.subscribeSafe(self))\n\n return self.refCountDisposable\n\n def createWindow(self):\n s = Subject()\n self.queue.append(s)\n self.observer.onNext(AddRef(s, self.refCountDisposable))\n\n def createTimer(self):\n m = SingleAssignmentDisposable()\n self.timerDisposable.disposable = m\n\n isSpan = False\n isShift = False\n\n if self.nextSpan == self.nextShift:\n isSpan = True\n isShift = True\n elif self.nextShift < self.nextShift:\n isSpan = True\n else:\n isShift = True\n\n newTotalTime = self.nextSpan if isSpan else self.nextShift\n ts = newTotalTime - self.totalTime\n self.totalTime = newTotalTime\n\n if isSpan:\n self.nextSpan += self.parent.timeShift\n if isShift:\n self.nextShift += self.parent.timeShift\n\n m.disposable = self.parent.scheduler.scheduleWithRelativeAndState(\n Struct(isSpan=isSpan, isShift=isShift),\n ts,\n self.tick\n )\n\n def tick(self, scheduler, state):\n with self.gate:\n if state.isSpan:\n s = self.queue.popleft()\n s.onCompleted()\n\n if state.isShift:\n self.createWindow()\n\n self.createTimer()\n\n return Disposable.empty()\n\n def onNext(self, value):\n with self.gate:\n for s in self.queue:\n s.onNext(value)\n\n def onError(self, exception):\n with self.gate:\n for o in self.queue:\n o.onError(exception)\n\n self.observer.onError(exception)\n self.dispose()\n\n def onCompleted(self):\n with self.gate:\n for o in self.queue:\n o.onCompleted()\n\n self.observer.onCompleted()\n self.dispose()\n\n class SinkWithCountAndTimeSpan(rx.linq.sink.Sink):\n def __init__(self, parent, observer, cancel):\n super(Window.SinkWithCountAndTimeSpan, self).__init__(observer, cancel)\n self.parent = parent\n\n def run(self):\n self.gate = RLock()\n self.s = Subject()\n self.n = 0\n self.windowId = 0\n\n self.timerDisposable = SerialDisposable()\n groupDisposable = CompositeDisposable(self.timerDisposable)\n self.refCountDisposable = RefCountDisposable(groupDisposable)\n\n # AddRef was originally WindowObservable but this is just an alias for AddRef\n self.observer.onNext(AddRef(self.s, self.refCountDisposable))\n self.createTimer(0)\n\n groupDisposable.add(self.parent.source.subscribeSafe(self))\n\n return self.refCountDisposable\n\n def createTimer(self, wId):\n m = SingleAssignmentDisposable()\n self.timerDisposable.disposable = m\n\n m.disposable = self.parent.scheduler.scheduleWithRelativeAndState(\n wId,\n self.parent.timeSpan,\n self.tick\n )\n\n def tick(self, scheduler, wId):\n d = Disposable.empty()\n\n newId = 0\n\n with self.gate:\n if wId != self.windowId:\n return d\n\n self.n = 0\n self.windowId += 1\n newId = self.windowId\n\n self.s.onCompleted()\n self.s = Subject()\n self.observer.onNext(AddRef(self.s, self.refCountDisposable))\n\n self.createTimer(newId)\n\n return d\n\n def onNext(self, value):\n newWindow = False\n newId = 0\n\n with self.gate:\n self.s.onNext(value)\n self.n += 1\n\n if self.n == self.parent.count:\n newWindow = True\n self.n = 0\n self.windowId += 1\n newId = self.windowId\n\n self.s.onCompleted()\n self.s = Subject()\n self.observer.onNext(AddRef(self.s, self.refCountDisposable))\n\n if newWindow:\n self.createTimer(newId)\n\n def onError(self, exception):\n with self.gate:\n self.s.onError(exception)\n self.observer.onError(exception)\n self.dispose()\n\n def onCompleted(self):\n with self.gate:\n self.s.onCompleted()\n self.observer.onCompleted()\n self.dispose()","repo_name":"akuendig/RxPython","sub_path":"rx/linq/window.py","file_name":"window.py","file_ext":"py","file_size_in_byte":8582,"program_lang":"python","lang":"en","doc_type":"code","stars":27,"dataset":"github-code","pt":"85"} +{"seq_id":"32051290342","text":"\r\n\r\ndef longestCommonPrefix(strs):\r\n\r\n if strs is None or len(strs) < 0: return \"\"\r\n\r\n maxLength = len(strs[0])\r\n prevString = strs[0]\r\n for i in range(1, len(strs)):\r\n j = 0\r\n while j < maxLength and j < len(strs[i]) and j < len(prevString):\r\n if prevString[j] != strs[i][j]:\r\n break\r\n j += 1\r\n maxLength = j\r\n prevString = strs[i]\r\n if maxLength <= 0: return \"\"\r\n\r\n\r\n return strs[0][:maxLength]\r\n\r\nstrs = [\"flower\",\"flow\",\"flight\"]\r\n#strs = [\"flower\",\"flow\",\"light\"]\r\n#strs = [\"aaa\",\"aa\",\"aaa\"]\r\nstrs = [\"a\",\"aa\",\"aaa\", \"\"]\r\nprint(longestCommonPrefix(strs))\r\n","repo_name":"nikhillahoti/LeetCode_Solutions","sub_path":"LongestCommonPrefix.py","file_name":"LongestCommonPrefix.py","file_ext":"py","file_size_in_byte":648,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"3562977085","text":"from os import listdir, mkdir\nfrom os.path import isfile, join, exists\nimport simplejson as json\nimport sys\n\n\nclass ProjectManager():\n\n\n CONFIG_FILE = 'config/config.json'\n PROJECT_FILE = 'config/project.json'\n\n\n # def __init__(self):\n # # Don't need to instantiate for now: static access only.\n # return\n\n\n @staticmethod\n def _exists(path, name):\n return exists(join(path, name))\n\n\n @staticmethod\n def _mkdir(path, name):\n mkdir(join(path, name))\n\n\n @staticmethod\n def getConfFile(path):\n try:\n with open(path, 'r') as f:\n return json.load(f)\n except FileNotFoundError:\n print('Cannot find the file: %s.' % path)\n sys.exit(1)\n\n\n @staticmethod\n def setConfFile(path, key, val):\n confFile = ProjectManager.getConfFile(path)\n confFile[key] = val\n with open(path, 'w') as f:\n return json.dump(confFile, f, indent=' ')\n\n\n @staticmethod\n def getCurrentProject():\n projectSettings = ProjectManager.getConfFile(ProjectManager.PROJECT_FILE)\n return projectSettings['current']\n\n\n @staticmethod\n def setCurrentProject(projectName):\n ProjectManager.setConfFile(ProjectManager.PROJECT_FILE, 'current', projectName)\n return ProjectManager.getCurrentProject()\n\n\n @staticmethod\n def showProjects(projectRoot):\n currentProject = ProjectManager.getCurrentProject()\n pRootDir = str(projectRoot)\n dirs = [d for d in listdir(pRootDir) if not isfile(join(pRootDir, d))]\n for d in dirs:\n if d == currentProject:\n print('* %s' % d)\n else:\n print(' %s' % d)\n print()\n if len(dirs) == 0:\n print('No project exists.')\n print('Use create-project command to create a new project.')\n elif len(dirs) == 1:\n print('%d project exists.' % len(dirs))\n print('Use select-project command to select a project to work on.')\n else:\n print('%d projects exist.' % len(dirs))\n print('Use select-project command to select a project to work on.')\n\n\n @staticmethod\n def createProject(dataRoot, outputRoot, projectName):\n dRootDir = str(dataRoot)\n oRootDir = str(outputRoot)\n dExists = ProjectManager._exists(dRootDir, projectName)\n if dExists:\n # The project path exists. Failed to create a project.\n print('Couldn\\'t create a new project as a project with the same name already exists.')\n print('Use show-projects command to see existing projects.')\n else:\n # It doesn't exist. Safely create a new project.\n ProjectManager._mkdir(dRootDir, projectName)\n # TODO: clear the output path?\n oExists = ProjectManager._exists(oRootDir, projectName)\n if not oExists:\n ProjectManager._mkdir(oRootDir, projectName)\n print('Your project \\'%s\\' has been created.' % projectName)\n ProjectManager.selectProject(dataRoot, outputRoot, projectName)\n\n\n @staticmethod\n def selectProject(dataRoot, outputRoot, projectName):\n # Make sure the required paths exist\n dRootDir = str(dataRoot)\n oRootDir = str(outputRoot)\n dExists = ProjectManager._exists(dRootDir, projectName)\n oExists = ProjectManager._exists(oRootDir, projectName)\n if dExists:# and oExists:\n # Safely select the project.\n currentProject = ProjectManager.setCurrentProject(projectName)\n print('Now you\\'re working on project \\'%s\\'.' % currentProject)\n else:\n # Something's wrong.\n print('Couldn\\'t select the project you specified.')\n print('Use show-projects command to see existing projects.')\n\n\n @staticmethod\n def showThemes():\n pass\n\n\n @staticmethod\n def showLayouts(themeName):\n pass\n\n\n @staticmethod\n def showPages(dataRoot):\n # Make sure the required paths exists\n dRootDir = str(dataRoot)\n currentProject = ProjectManager.getCurrentProject()\n dExists = ProjectManager._exists(dRootDir, currentProject)\n if dExists:\n # Show existing data files\n files = [f for f in listdir(join(dRootDir, currentProject)) if f.endswith('.glide')]\n for f in files:\n print(' %s' % f)\n print()\n if len(files) == 0:\n print('Project \\'%s\\' doens\\'t have a page.' % currentProject)\n elif len(files) == 1:\n print('Project \\'%s\\' has %d page.' % (currentProject, len(files)))\n else:\n print('Project \\'%s\\' has %d pages.' % (currentProject, len(files)))\n print('Use create-page command to create a new page.')\n else:\n print('Couldn\\'t find pages in the current project.')\n print('Use show-projects command to see existing projects.')\n print('Use select-project command to select a project to work on.')\n\n\n @staticmethod\n def createPage(dataRoot, pageName, themeName, layoutName):\n dRootDir = str(dataRoot)\n dExists = ProjectManager._exists(dRootDir, pageName)\n if dExists:\n # The project path exists. Failed to create a project.\n print('Couldn\\'t create a new page as a page with the same name already exists.')\n print('Use show-pages command to see existing pages.')\n else:\n # It doesn't exist. Safely create a new page.\n # TODO: Create a data template from the theme and layout specified\n pass\n\n\n @staticmethod\n def build():\n pass\n\n\n @staticmethod\n def test():\n pass\n\n\n @staticmethod\n def launch():\n pass\n\n","repo_name":"stlim0730/glide-cmd","sub_path":"projectmanager/projectmanager.py","file_name":"projectmanager.py","file_ext":"py","file_size_in_byte":5253,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"7065152812","text":"from __future__ import unicode_literals\nimport frappe\nfrom frappe import _\nimport requests\nimport json\n\nfrom frappe.model.document import Document\nfrom frappe.utils import get_url, call_hook_method, cint, getdate\nfrom frappe.integrations.utils import create_payment_gateway, create_request_log\nfrom six.moves.urllib.parse import urlencode\n\nfrom requests.auth import HTTPBasicAuth\nfrom erpnext.accounts.doctype.payment_entry.payment_entry import get_payment_entry\n\nclass AffirmSettings(Document):\n\tsupported_currencies = [\n\t\t\"USD\"\n\t]\n\n\tservice_name = \"Affirm\"\n\tis_embedable = True\n\n\tdef validate(self):\n\t\tcreate_payment_gateway(\"Affirm\")\n\t\tcall_hook_method('payment_gateway_enabled', gateway=self.service_name)\n\n\tdef get_payment_url(self, **kwargs):\n\t\treturn get_url(\"./integrations/affirm_checkout?{0}\".format(urlencode(kwargs)))\n\n\n\tdef validate_transaction_currency(self, currency):\n\t\tif currency not in self.supported_currencies:\n\t\t\tfrappe.throw(_(\"Please select another payment method. Affirm does not support transactions in currency '{0}'\").format(currency))\n\ndef create_order(**kwargs):\n\t\"\"\"\n\t\tRead the checkout data and current checkout status for a specific checkout attempt.\n\t\"\"\"\n\tfull_name = kwargs.get(\"payer_name\")\n\n\t# Affirm needs Full Name to have atleast 2 words\n\tif len(full_name.split()) == 1:\n\t\tfull_name = full_name + \" .\"\n\n\tref_doc = frappe.get_doc(kwargs['reference_doctype'], kwargs['reference_docname'])\n\n\t# fetch the actual doctype use for this transaction. Could be Quotation, Sales Order or Invoice\n\torder_doc = frappe.get_doc(ref_doc.reference_doctype, ref_doc.reference_name)\n\n\titems = []\n\tdiscounts = {}\n\tbilling_address = None\n\tshipping_address = None\n\n\tif order_doc.get(\"customer_address\"):\n\t\tbilling_address = frappe.get_doc(\"Address\", order_doc.customer_address)\n\n\tif order_doc.get(\"shipping_address_name\"):\n\t\tshipping_address = frappe.get_doc(\"Address\", order_doc.shipping_address_name)\n\n\tif not shipping_address:\n\t\tshipping_address = frappe.get_doc(\"Address\", order_doc.customer_address)\n\n\t# deduce shipping from taxes table\n\tshipping_fee = 0\n\tfor tax in order_doc.taxes:\n\t\tif 'shipping ' in tax.description.lower():\n\t\t\tshipping_fee = tax.tax_amount\n\n\tfor idx, item in enumerate(order_doc.items):\n\t\titem_discount = item.price_list_rate - item.rate\n\n\t\tif item_discount > 0:\n\t\t\tdiscount_percent = 100 - (item.rate * 100 / item.price_list_rate)\n\t\t\tdiscount_code = _(\"LINE {0} | {1} | {2}% DISCOUNT ($ -{3:,.2f})\").format(idx, item.item_code, discount_percent, item_discount)\n\t\t\tdiscounts[discount_code] = {\n\t\t\t\t\"discount_amount\": convert_to_cents(item_discount),\n\t\t\t\t\"discount_display_name\": discount_code\n\t\t\t}\n\n\t\titems.append({\n\t\t\t\"display_name\": item.item_name,\n\t\t\t\"sku\": item.item_code,\n\t\t\t\"unit_price\": convert_to_cents(item.price_list_rate),\n\t\t\t\"qty\": item.qty,\n\t\t\t\"item_image_url\": get_url(item.get(\"image\", \"\")),\n\t\t\t\"item_url\": get_url()\n\t\t})\n\n\tcheckout_data = {\n\t\t\"merchant\": {\n\t\t\t\"user_confirmation_url\": get_url(\n\t\t\t\t(\n\t\t\t\t\t\"/api/method/erpnext.erpnext_integrations\"\n\t\t\t\t\t\".doctype.affirm_settings.affirm_settings.process_payment_callback\"\n\t\t\t\t\t\"?reference_doctype={0}&reference_docname={1}\"\n\t\t\t\t).format(ref_doc.doctype, ref_doc.name)\n\t\t\t),\n\t\t\t\"user_cancel_url\": get_url(\"/cart\"),\n\t\t\t\"user_confirmation_url_action\": \"GET\",\n\t\t\t\"name\": frappe.defaults.get_user_default(\"company\")\n\t\t},\n\t\t\"items\": items,\n\t\t\"discounts\": discounts,\n\t\t\"order_id\": order_doc.name,\n\t\t\"shipping_amount\": convert_to_cents(shipping_fee),\n\t\t\"tax_amount\": convert_to_cents(order_doc.total_taxes_and_charges - shipping_fee),\n\t\t\"total\": convert_to_cents(order_doc.grand_total)\n\t}\n\n\tif billing_address:\n\t\tcheckout_data['billing'] = {\n\t\t\t\"name\": {\n\t\t\t\t\"full\": full_name\n\t\t\t},\n\t\t\t\"address\": {\n\t\t\t\t\"line1\": billing_address.get(\"address_line1\"),\n\t\t\t\t\"line2\": billing_address.get(\"address_line2\"),\n\t\t\t\t\"city\": billing_address.get(\"city\"),\n\t\t\t\t\"state\": billing_address.get(\"state\"),\n\t\t\t\t\"zipcode\": billing_address.get(\"pincode\"),\n\t\t\t\t\"country\": billing_address.get(\"country\")\n\t\t\t}\n\t\t}\n\n\tif shipping_address:\n\t\tcheckout_data['shipping'] = {\n\t\t\t\"name\": {\n\t\t\t\t\"full\": full_name\n\t\t\t},\n\t\t\t\"address\": {\n\t\t\t\t\"line1\": shipping_address.get(\"address_line1\"),\n\t\t\t\t\"line2\": shipping_address.get(\"address_line2\"),\n\t\t\t\t\"city\": shipping_address.get(\"city\"),\n\t\t\t\t\"state\": shipping_address.get(\"state\"),\n\t\t\t\t\"zipcode\": shipping_address.get(\"pincode\"),\n\t\t\t\t\"country\": shipping_address.get(\"country\")\n\t\t\t}\n\t\t}\n\n\tcreate_request_log(checkout_data, \"Host\", \"Affirm\")\n\treturn checkout_data\n\n@frappe.whitelist(allow_guest=1)\ndef process_payment_callback(checkout_token, reference_doctype, reference_docname):\n\t\"\"\"'\n\t\tCharge authorization occurs after a user completes the Affirm checkout flow and returns to the merchant site.\n\t\tAuthorizing the charge generates a charge ID that will be used to reference it moving forward.\n\t\tYou must authorize a charge to fully create it. A charge is not visible in the Read charge response,\n\t\tnor in the merchant dashboard until you authorize it.\n\t\"\"\"\n\tdata= {\n\t\t\"checkout_token\":checkout_token,\n\t\t\"reference_doctype\":reference_doctype,\n\t\t\"reference_docname\":reference_docname\n\t}\n\tintegration_request = create_request_log(data, \"Host\", \"Affirm\")\n\tredirect_url = \"/integrations/payment-failed\"\n\taffirm_settings = get_api_config()\n\tauthorization_response = requests.post(\n\t\t\"{api_url}/charges\".format(**affirm_settings),\n\t\tauth=HTTPBasicAuth(\n\t\t\taffirm_settings.get('public_api_key'),\n\t\t\taffirm_settings.get('private_api_key')),\n\t\tjson={\"checkout_token\": checkout_token})\n\n\taffirm_data = authorization_response.json()\n\tintegration_request = frappe.get_doc(\"Integration Request\", integration_request.name)\n\tintegration_request.update_status(affirm_data, \"Authorized\")\n\t# frappe.log(\"Response: {}\".format(json.dumps(affirm_data)))\n\n\tif affirm_data:\n\t\tauthorize_payment(affirm_data, reference_doctype, reference_docname, integration_request)\n\ndef authorize_payment(affirm_data, reference_doctype, reference_docname, integration_request):\n\t\"\"\"\n\t\tonce callback return checkout token it will authroized payment status as failed or sucessful\n\t\"\"\"\n\tredirect_url = \"/integrations/payment-failed\"\n\n\t# check if callback already happened\n\tif affirm_data.get(\"status_code\") == 400 and affirm_data.get(\"code\") == \"checkout-token-used\":\n\t\tintegration_request.update_status(affirm_data, \"Completed\")\n\t\tredirect_url = '/integrations/payment-success'\n\telif affirm_data.get(\"status_code\") == 400 and affirm_data.get(\"type\") == \"invalid_request\":\n\t\tintegration_request.update_status(affirm_data, \"Failed\")\n\t\tfrappe.msgprint(affirm_data.get(\"message\"))\n\t\tredirect_url = \"/cart\"\n\telse:\n\t\tpayment_successful(affirm_data, reference_doctype, reference_docname, integration_request)\n\t\tredirect_url = '/integrations/payment-success'\n\n\tfrappe.local.response[\"type\"] = \"redirect\"\n\tfrappe.local.response[\"location\"] = get_url(redirect_url)\n\treturn \"\"\n\ndef payment_successful(affirm_data, reference_doctype, reference_docname, integration_request):\n\t\"\"\"\n\t\ton sucessful payment response it will create payment entry for refernce docname and\n\t\tupdate Affirm ID and Affirm status in refrence docname\n\t\"\"\"\n\tcharge_id = affirm_data.get('id')\n\taffirm_capture_status = affirm_data.get('status')\n\tpayment_request = frappe.get_doc(reference_doctype, reference_docname)\n\torder_doc = frappe.get_doc(payment_request.reference_doctype, affirm_data.get('order_id'))\n\torder_doc.affirm_id = charge_id\n\torder_doc.affirm_status = affirm_capture_status\n\torder_doc.flags.ignore_permissions = 1\n\torder_doc.save()\n\tfrappe.db.commit()\n\tintegration_request.update_status(affirm_data, \"Completed\")\n\n@frappe.whitelist()\ndef capture_payment(affirm_id, sales_order):\n\t\"\"\"\n\t\tCapture the funds of an authorized charge, similar to capturing a credit card transaction.\n\t\"\"\"\n\taffirm_data ={\n\t\t\"affirm_id\":affirm_id,\n\t\t\"sales_order\":sales_order\n\t}\n\tintegration_request = create_request_log(affirm_data, \"Host\", \"Affirm\")\n\taffirm_settings = get_api_config()\n\tauthorization_response = requests.post(\n\t\t\"{0}/charges/{1}/capture\".format(affirm_settings.get(\"api_url\"), affirm_id),\n\t\tauth=HTTPBasicAuth(\n\t\t\taffirm_settings.get('public_api_key'),\n\t\t\taffirm_settings.get('private_api_key')),\n\t\t)\n\tif authorization_response.status_code==200:\n\t\taffirm_data = authorization_response.json()\n\t\t#make payment entry agianst Sales Order\n\t\tintegration_request.update_status(affirm_data, \"Authorized\")\n\t\tmake_so_payment_entry(affirm_data, sales_order, integration_request)\n\t\treturn affirm_data\n\telse:\n\t\tintegration_request.update_status(affirm_data, \"Failed\")\n\t\tfrappe.throw(\"Something went wrong.\")\n\ndef make_so_payment_entry(affirm_data, sales_order, integration_request):\n\tpayment_entry = get_payment_entry(dt=\"Sales Order\", dn=sales_order, bank_amount=affirm_data.get(\"amount\"))\n\tpayment_entry.reference_no = affirm_data.get(\"transaction_id\")\n\tpayment_entry.reference_date = getdate(affirm_data.get(\"created\"))\n\tpayment_entry.submit()\n\tintegration_request.update_status(affirm_data, \"Completed\")\n\n@frappe.whitelist(allow_guest=1)\ndef get_public_config():\n\tconfig = get_api_config()\n\tdel config['private_api_key'];\n\n\treturn config\n\ndef get_api_config():\n\tsettings = frappe.get_doc(\"Affirm Settings\", \"Affirm Settings\")\n\n\tif settings.is_sandbox:\n\t\tvalues = dict(\n\t\t\tpublic_api_key = settings.public_sandbox_api_key,\n\t\t\tprivate_api_key = settings.get_password(\"private_sandbox_api_key\"),\n\t\t\tcheckout_url = settings.sandbox_checkout_url,\n\t\t\tapi_url = settings.sandbox_api_url\n\t\t)\n\t\treturn values\n\telse:\n\t\tvalues = dict(\n\t\t\tpublic_api_key = settings.public_api_key,\n\t\t\tprivate_api_key = settings.get_password(\"private_api_key\"),\n\t\t\tcheckout_url = settings.live_checkout_url,\n\t\t\tapi_url = settings.live_api_url\n\t\t)\n\t\treturn values\n\ndef convert_to_cents(amount):\n\treturn cint(amount * 100)\n","repo_name":"meshramsaroj/erpnext","sub_path":"erpnext/erpnext_integrations/doctype/affirm_settings/affirm_settings.py","file_name":"affirm_settings.py","file_ext":"py","file_size_in_byte":9719,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"85"} +{"seq_id":"8336858283","text":"from operator import itemgetter\nfrom functools import lru_cache, reduce\n\n\nclass PointsInArea(object):\n def __init__(self, points):\n # points = [(x, y), (x, y), ...]\n self.sorted_x = sorted([point for point in points], key=itemgetter(0))\n self.sorted_y = sorted([point for point in points], key=itemgetter(1))\n\n @lru_cache\n def get_points(self, x, y, r):\n\n points_x = set()\n points_y = set()\n\n for p in self.sorted_x:\n if p[0] < x - r:\n continue\n if p[0] > x + r:\n break\n points_x.add(p)\n\n for p in self.sorted_y:\n if p[1] < y - r:\n continue\n if p[1] > y + r:\n break\n points_y.add(p)\n\n return points_x.intersection(points_y)\n\n def has_points(self, x, y, r):\n return len(self.get_points(x, y, r)) > 0\n\n def get_center_of_mass(self, x, y, r):\n points_in_area = self.get_points(x, y, r)\n sum_x, sum_y = reduce(\n lambda acc, item: (acc[0] + item[0], acc[1] + item[1]),\n points_in_area,\n (0, 0),\n )\n\n l = len(points_in_area)\n return sum_x / l, sum_y / l, points_in_area\n","repo_name":"risboo6909/chesscake","sub_path":"src/points_area/points.py","file_name":"points.py","file_ext":"py","file_size_in_byte":1236,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"85"} +{"seq_id":"19524054425","text":"import OOMP\nimport OOMP_projects_BASE\n\ndef createProjects():\n projects = []\n \n count = 1\n base = {}\n base[\"oompSize\"] = \"OSOB\"\n base[\"format\"] = \"eagle\"\n base[\"github\"] = \"https://github.com/joeycastillo/\"\n base[\"oompIndex\"] = \"01\" ###### default to rev 01\n\n #############################################################\n ############################################################# \n\n projectStrings = []\n projectStrings.append([\"Sensor-Watch\",\"PCB/ Main Boards/OSO-SWAT-A1-05\",\"Sensor Watch\"])\n projectStrings.append([\"LCD-FeatherWing\",\"OSO-WILD-A3/OSO-WILD-A3\",\"LCD FeatherWing\"])\n \n\n #############################################################\n for item in projectStrings:\n if isinstance(item, list):\n repo = item[0]\n file = item[1]\n if len(item) > 2:\n name = item[2]\n else:\n name = item[1]\n if len(item) > 3:\n index = item[3]\n else:\n index = \"01\" \n else:\n repo = item\n file = item\n name = item\n index = \"01\"\n if repo != \"\":\n d = base.copy() \n d[\"repo\"] = repo\n d[\"file\"] = file.replace(\"_hw\",\"\") \n d[\"name\"] = name.replace(\"_hw\",\"\").replace(\"_\",\" \").capitalize()\n d[\"count\"] = count ; count = count + 1 \n if index != \"01\":\n d[\"oompIndex\"] = index\n ###### company specific checks\n #############\n projects.append(d)\n\n for d in projects:\n OOMP_projects_BASE.makeProjectNew(d)\n\n","repo_name":"oomlout/oomlout_OOMP_V2","sub_path":"OOMP_projects_OSOB.py","file_name":"OOMP_projects_OSOB.py","file_ext":"py","file_size_in_byte":1668,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"15478208418","text":"import argparse\nimport os\nimport pathlib\nfrom typing import Generator, Optional, Tuple\n\nimport structlog\nfrom dotenv import load_dotenv\n\nfrom wafp.__main__ import main as run\nfrom wafp.fuzzers import loader as fuzzers_loader\nfrom wafp.targets import loader as targets_loader\n\nlogger = structlog.get_logger()\nload_dotenv()\n\nBASIC_SCHEMATHESIS = (\n \"schemathesis:AllChecks\",\n \"schemathesis:Default\",\n \"schemathesis:LessPreProcessing\",\n \"schemathesis:Negative\",\n \"schemathesis:NegativeNoSwarm\",\n \"schemathesis:NoFormats\",\n \"schemathesis:NoMutations\",\n)\n\nCOMBINATIONS = {\n \"age_of_empires_2_api:Default\": {\n \"fuzzers\": [\n \"api_fuzzer\",\n \"got_swag\",\n \"restler\",\n *BASIC_SCHEMATHESIS,\n ],\n },\n # \"age_of_empires_2_api:Linked\": {\n # \"fuzzers\": [\"schemathesis:StatefulNew\", \"schemathesis:StatefulOld\"],\n # },\n \"cccatalog_api:Default\": {\n \"fuzzers\": [\"got_swag\", \"restler\", *BASIC_SCHEMATHESIS],\n },\n # \"cccatalog_api:Linked\": {\n # \"fuzzers\": [\"schemathesis:StatefulNew\"],\n # },\n \"covid19_japan_web_api:Default\": {\n \"fuzzers\": [\n \"api_fuzzer\",\n \"cats\",\n \"restler\",\n *BASIC_SCHEMATHESIS,\n ],\n },\n # \"covid19_japan_web_api:Linked\": {\n # \"fuzzers\": [\"schemathesis:StatefulNew\", \"schemathesis:StatefulOld\"],\n # },\n \"disease_sh:Default\": {\"fuzzers\": [\"api_fuzzer\", \"cats\", *BASIC_SCHEMATHESIS]},\n # \"disease_sh:Linked\": {\"fuzzers\": [\"schemathesis:StatefulNew\", \"schemathesis:StatefulOld\"]},\n \"httpbin\": {\n \"fuzzers\": [\"api_fuzzer\", *BASIC_SCHEMATHESIS],\n },\n \"jupyter_server:Default\": {\n \"fuzzers\": [\"cats\", \"restler\", *BASIC_SCHEMATHESIS],\n },\n # \"jupyter_server:Linked\": {\n # \"fuzzers\": [\"schemathesis:StatefulNew\", \"schemathesis:StatefulOld\"],\n # },\n \"jupyterhub:Default\": {\n \"fuzzers\": [*BASIC_SCHEMATHESIS],\n },\n # \"jupyterhub:Linked\": {\n # \"fuzzers\": [\"schemathesis:StatefulNew\", \"schemathesis:StatefulOld\"],\n # },\n \"mailhog\": {\"fuzzers\": [\"api_fuzzer\", \"cats\", \"restler\", *BASIC_SCHEMATHESIS]},\n \"open_fec:Default\": {\n \"fuzzers\": [\n \"api_fuzzer\",\n \"cats\",\n \"fuzz_lightyear\",\n \"got_swag\",\n \"restler\",\n *BASIC_SCHEMATHESIS,\n \"swagger_fuzzer\",\n ],\n },\n # \"open_fec:Linked\": {\n # \"fuzzers\": [\"schemathesis:StatefulNew\", \"schemathesis:StatefulOld\"],\n # },\n \"opentopodata\": {\n \"fuzzers\": [\"api_fuzzer\", \"cats\", \"restler\", *BASIC_SCHEMATHESIS],\n },\n \"otto_parser\": {\"fuzzers\": [*BASIC_SCHEMATHESIS]},\n \"pslab_webapp\": {\n \"fuzzers\": [\"api_fuzzer\", \"cats\", \"fuzz_lightyear\", \"restler\", *BASIC_SCHEMATHESIS],\n },\n \"pulpcore\": {\n \"fuzzers\": [\n \"api_fuzzer\",\n \"cats\",\n \"got_swag\",\n *BASIC_SCHEMATHESIS,\n \"tnt_fuzzer\",\n ],\n },\n \"request_baskets:Default\": {\"fuzzers\": [\"api_fuzzer\", \"cats\", \"restler\", *BASIC_SCHEMATHESIS]},\n # \"request_baskets:Linked\": {\"fuzzers\": [\"schemathesis:StatefulNew\", \"schemathesis:StatefulOld\"]},\n \"restler_demo:Default\": {\n \"fuzzers\": [\"got_swag\", \"restler\", *BASIC_SCHEMATHESIS],\n },\n # \"restler_demo:Linked\": {\n # \"fuzzers\": [\"schemathesis:StatefulNew\", \"schemathesis:StatefulOld\"],\n # },\n \"worklog:Default\": {\n \"fuzzers\": [\"api_fuzzer\", \"restler\", *BASIC_SCHEMATHESIS],\n },\n # \"worklog:Linked\": {\n # \"fuzzers\": [\"schemathesis:StatefulNew\", \"schemathesis:StatefulOld\"],\n # },\n # \"gitlab\": {\"fuzzers\": [\"schemathesis:Default\"]},\n}\n\n\ndef split_name(name: str) -> Tuple[str, Optional[str]]:\n return tuple(name.split(\":\", 1) + [None])[:2] # type:ignore\n\n\ndef get_env_var_prefix(target: str) -> str:\n name, _ = split_name(target)\n return name.upper()\n\n\ndef get_sentry_dsn_env_var_name(target: str) -> str:\n prefix = get_env_var_prefix(target)\n return f\"{prefix}_SENTRY_DSN\"\n\n\ndef get_sentry_dsn(target: str) -> Optional[str]:\n env_var_name = get_sentry_dsn_env_var_name(target)\n return os.getenv(env_var_name)\n\n\ndef is_match(value: str, expected: str) -> bool:\n name, variant = split_name(value)\n expected_name, expected_variant = split_name(expected)\n return name == expected_name and (expected_variant is None or expected_variant == variant)\n\n\ndef expand_options(options: Generator[str, None, None]) -> list[str]:\n output = []\n for option in options:\n output.append(option)\n name, variant = split_name(option)\n if variant is not None:\n output.append(name)\n return output\n\n\ndef parse_args() -> argparse.Namespace:\n parser = argparse.ArgumentParser()\n parser.add_argument(\n \"--output-dir\",\n action=\"store\",\n required=True,\n type=str,\n )\n parser.add_argument(\n \"--iterations\",\n action=\"store\",\n default=30,\n type=int,\n )\n parser.add_argument(\"--fuzzer\", choices=expand_options(fuzzers_loader.get_all_variants()), help=\"Fuzzer to run\")\n parser.add_argument(\"--target\", choices=expand_options(targets_loader.get_all_variants()), help=\"Target to run\")\n return parser.parse_args()\n\n\ndef run_single(fuzzer: str, target: str, iteration: int, output_dir: pathlib.Path, sentry_dsn: Optional[str]) -> None:\n final_dir = output_dir / f\"{fuzzer}-{target}-{iteration}\"\n if final_dir.exists():\n print(\"The output directory exists! Skipping\", final_dir)\n return\n args = [fuzzer, target, \"--build\", f\"--output-dir={final_dir}\"]\n if sentry_dsn is not None:\n args.append(f\"--sentry-dsn={sentry_dsn}\")\n run(args)\n\n\ndef main() -> None:\n args = parse_args()\n assert args.iterations >= 0, \"The number of iterations should be a positive integer\"\n output_dir = pathlib.Path(args.output_dir).absolute()\n for target, data in COMBINATIONS.items():\n if args.target and not is_match(target, args.target):\n continue\n sentry_dsn = get_sentry_dsn(target)\n for fuzzer in data.get(\"fuzzers\", ()):\n if args.fuzzer and not is_match(fuzzer, args.fuzzer):\n continue\n if sentry_dsn:\n logger.info(\"Sentry is installed\")\n else:\n logger.warn(\"Sentry is not installed\")\n for iteration in range(1, args.iterations + 1):\n run_single(fuzzer, target, iteration, output_dir, sentry_dsn)\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"schemathesis/web-api-fuzzing-project","sub_path":"run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":6570,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"85"} +{"seq_id":"16010508301","text":"import torchvision\r\nfrom torch.utils.data import DataLoader\r\nfrom torch.utils.tensorboard import SummaryWriter\r\n\r\ndataset_test=torchvision.datasets.CIFAR10(\"train_test_datasets\",train=False,transform=torchvision.transforms.ToTensor())\r\n\r\ndataset_loader=DataLoader(dataset_test,batch_size=64,shuffle=True,num_workers=0,drop_last=False)\r\n\r\n\r\n\r\nwriter=SummaryWriter(\"Dataload_test\")\r\nfor epcho in range(2):\r\n step=0\r\n for data in dataset_loader:\r\n imgs,targets=data\r\n writer.add_images(\"{}\".format(epcho),imgs,step)\r\n step+=1\r\nwriter.close()\r\n","repo_name":"Rookie764/machine_learning","sub_path":"DataLoader.py","file_name":"DataLoader.py","file_ext":"py","file_size_in_byte":567,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"85"} +{"seq_id":"17873486635","text":"#refer notes to understand this logic\r\ndef partition(list,l,h):\r\n pivot=list[h]\r\n i=l-1\r\n for j in range(l,h):\r\n if list[j]