diff --git a/Spring2024/EDA_spring2024.ipynb b/Spring2024/EDA_spring2024.ipynb index ace2d73..078f06f 100644 --- a/Spring2024/EDA_spring2024.ipynb +++ b/Spring2024/EDA_spring2024.ipynb @@ -926,10 +926,10 @@ "The article is written in Chinese, and below is the summary:\n", "\n", "\n", - "\"截止目前，资源库在线共享数字化标本数据共计843余万份，标本照片669余万份。标本记录来自中科院系统等国内118家重要植物标本馆，覆盖中国83.52%的植物，共计492科3879属36209种。\"\r\n", + "\"截止目前，资源库在线共享数字化标本数据共计843余万份，标本照片669余万份。标本记录来自中科院系统等国内118家重要植物标本馆，覆盖中国83.52%的植物，共计492科3879属36209种。\"\n", "\n", "\"So far (Dec. 2023), the resource library has shared more than 8.43 million digital specimen data and more than 6.69 million specimen photos online. Specimen records come from 118 important domestic herbaria including the Chinese Academy of Sciences system, covering 83.52% of China's plants, totaling 492 families and 3879 genera. 36209 species.\"\n", - "\r\n" + "\n" ] }, { @@ -976,7 +976,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 1, "id": "1f8cf5fa-9408-452e-8da4-cb9973814495", "metadata": {}, "outputs": [], @@ -984,38 +984,175 @@ "!pip install selenium webdriver_manager -q" ] }, + { + "cell_type": "markdown", + "id": "5936dc30", + "metadata": {}, + "source": [ + "On my end, this step usually takes 1.5 minutes to run." + ] + }, + { + "cell_type": "markdown", + "id": "75881743", + "metadata": {}, + "source": [ + "## Use Selenium to fetch the catalogue\n", + "The CVH page currently shows a list of 30 items so it will be offsetted by 30/60/90\n", + "\n", + "Should modify the logic to support concurrencies" + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "920bb3cb-66b4-4b0e-a5b1-b7f068977ea8", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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Data Collection ID
0e6e73365
1e6e73489
2e6e73514
3e6e7359d
4e6e73625
......
295e6e7d3cc
296e6e7d455
297e6e7d4de
298e6e7d567
299e6e7d5ef
\n", + "

300 rows × 1 columns

\n", + "
" + ], + "text/plain": [ + " Data Collection ID\n", + "0 e6e73365\n", + "1 e6e73489\n", + "2 e6e73514\n", + "3 e6e7359d\n", + "4 e6e73625\n", + ".. ...\n", + "295 e6e7d3cc\n", + "296 e6e7d455\n", + "297 e6e7d4de\n", + "298 e6e7d567\n", + "299 e6e7d5ef\n", + "\n", + "[300 rows x 1 columns]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from selenium import webdriver\n", "from selenium.webdriver.chrome.service import Service\n", + "from selenium.webdriver.chrome.options import Options\n", "from selenium.webdriver.common.by import By\n", "from webdriver_manager.chrome import ChromeDriverManager\n", "import pandas as pd\n", "import time\n", "\n", "# Set up Selenium WebDriver\n", - "service = Service(ChromeDriverManager().install())\n", - "driver = webdriver.Chrome(service=service)\n", + "# service = Service(ChromeDriverManager().install())\n", + "# driver = webdriver.Chrome(service=service)\n", + "\n", + "# Alternative\n", + "# Set up Chrome options for headless execution\n", + "chrome_options = Options()\n", + "chrome_options.add_argument('--headless')\n", + "chrome_options.add_argument('--no-sandbox')\n", + "chrome_options.add_argument('--disable-dev-shm-usage')\n", + "\n", + "# Set up Selenium WebDriver with headless Chrome\n", + "driver = webdriver.Chrome(options=chrome_options)\n", "\n", "# URL to scrape\n", - "url = \"https://www.cvh.ac.cn/spms/list.php?&offset=0\"\n", + "# The CVH page shows a list of 30 items so it will be offsetted by 30/60/90\n", + "# url = \"https://www.cvh.ac.cn/spms/list.php?&offset=0\"\n", + "\n", + "base_url = \"https://www.cvh.ac.cn/spms/list.php?&offset=\"\n", + "\n", + "data_collection_ids = []\n", "\n", - "# Open the URL\n", - "driver.get(url)\n", + "# Iterate through the pages to fetch the top 300 responses (10 pages x 30 items per page)\n", + "for offset in range(0, 300, 30):\n", + " # Modify the URL to include the current offset\n", + " url = base_url + str(offset)\n", + " \n", + " # Open the URL\n", + " driver.get(url)\n", "\n", - "# Wait for the dynamic content to load\n", - "time.sleep(5) # Adjust the sleep time according to your internet speed and website response\n", + " # Wait for the dynamic content to load\n", + " time.sleep(5) # Adjust the sleep time according to your internet speed and website response\n", "\n", - "# Find all elements with the specified class\n", - "rows = driver.find_elements(By.CLASS_NAME, 'spms-row')\n", + " # Find all elements with the specified class\n", + " rows = driver.find_elements(By.CLASS_NAME, 'spms-row')\n", "\n", - "# Extract data-collection-id from each row\n", - "data_collection_ids = [row.get_attribute('data-collection-id') for row in rows]\n", + " # Extract data-collection-id from each row and append to the list\n", + " for row in rows:\n", + " data_collection_ids.append(row.get_attribute('data-collection-id'))\n", "\n", "# Close the WebDriver\n", "driver.quit()\n", @@ -1032,7 +1169,9 @@ "id": "bc84faf8-1c50-41c7-b71c-9e84410164b6", "metadata": {}, "source": [ - "## Sample Dataframe" + "## Sample Dataframe\n", + "\n", + "The sample dataframe is used for a placeholder before we setup a headless driver to run on a virtual machine. " ] }, { @@ -1042,24 +1181,67 @@ "metadata": {}, "outputs": [], "source": [ - "data = {\n", - " \"Data Collection ID\": [\n", - " \"e6e73365\", \"e6e73489\", \"e6e73514\", \"e6e7359d\", \"e6e73625\",\n", - " \"e6e736ae\", \"e6e73738\", \"e6e737c0\", \"e6e73847\", \"e6e738ce\",\n", - " \"e6e7395a\", \"e6e739e8\", \"e6e73a6f\", \"e6e73af6\", \"e6e73b7e\",\n", - " \"e6e73c07\", \"e6e73c8f\", \"e6e73d18\", \"e6e73da0\", \"e6e73e2b\",\n", - " \"e6e73eb9\", \"e6e73f42\", \"e6e73fc9\", \"e6e74052\", \"e6e740da\",\n", - " \"e6e74162\", \"e6e741eb\", \"e6e74274\", \"e6e742fc\", \"e6e74389\"\n", - " ]\n", - "}\n", - "\n", - "# Convert the dictionary to a DataFrame\n", - "data_collection_ids = pd.DataFrame(data)" + "# data = {\n", + "# \"Data Collection ID\": [\n", + "# \"e6e73365\", \"e6e73489\", \"e6e73514\", \"e6e7359d\", \"e6e73625\",\n", + "# \"e6e736ae\", \"e6e73738\", \"e6e737c0\", \"e6e73847\", \"e6e738ce\",\n", + "# \"e6e7395a\", \"e6e739e8\", \"e6e73a6f\", \"e6e73af6\", \"e6e73b7e\",\n", + "# \"e6e73c07\", \"e6e73c8f\", \"e6e73d18\", \"e6e73da0\", \"e6e73e2b\",\n", + "# \"e6e73eb9\", \"e6e73f42\", \"e6e73fc9\", \"e6e74052\", \"e6e740da\",\n", + "# \"e6e74162\", \"e6e741eb\", \"e6e74274\", \"e6e742fc\", \"e6e74389\"\n", + "# ]\n", + "# }\n", + "\n", + "# # Convert the dictionary to a DataFrame\n", + "# data_collection_ids = pd.DataFrame(data)" + ] + }, + { + "cell_type": "markdown", + "id": "8dfc5ea6", + "metadata": {}, + "source": [ + "## Use Selenium to Fetch Hebaria Data" + ] + }, + { + "cell_type": "markdown", + "id": "651200ad", + "metadata": {}, + "source": [ + "### Scraped Info\n", + "\n", + "Given a page's id, we could scrape the image link and the corresponding label/ground truth value.\n", + "See the red-rectangle for the corresponding elements from the screen shot of this page: https://www.cvh.ac.cn/spms/detail.php?id=e6e73365\n", + "\n", + "Input:\n", + "* page_id (e.g. e6e73365)\n", + "\n", + "Output:\n", + "* Image link\n", + "* Label data on right\n", + " * Taxon classification tree (Phylum/Order/Family/Genus)\n", + " * Identification Information\n", + " * Scientific Name\n", + " * Chinese Name\n", + " * Identifier\n", + " * Identification Time\n", + " * Collection Information\n", + " * Collector\n", + " * Collection Number\n", + " * Collection Time\n", + " * Collection Place\n", + " * Altitude (meter)\n", + " * Habitat\n", + " * Habits\n", + " * Phenological Stage\n", + "\n", + "" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 3, "id": "7a4ca53c-66da-499d-9c92-457ba72dd005", "metadata": {}, "outputs": [], @@ -1072,10 +1254,12 @@ "from selenium.webdriver.support import expected_conditions as EC\n", "from selenium.common.exceptions import TimeoutException\n", "from webdriver_manager.chrome import ChromeDriverManager\n", + "import concurrent\n", "from concurrent.futures import ThreadPoolExecutor, as_completed\n", "from tqdm.auto import tqdm\n", "import pandas as pd\n", "import time\n", + "import os\n", "\n", "def setup_driver(headless=True):\n", " chrome_options = Options()\n", @@ -1094,11 +1278,16 @@ " print(f\"Retry {retries+1}/{max_retries} for element {value} after timeout.\")\n", " time.sleep(retry_interval)\n", " retries += 1\n", - " raise TimeoutException(f\"Element {value} not found after {max_retries} retries.\")\n", + " finally:\n", + " print(f\"Retry {retries+1}/{max_retries} for element {value} after timeout.\")\n", + " time.sleep(retry_interval)\n", + " # raise TimeoutException(f\"Element {value} not found after {max_retries} retries.\")\n", + " return \"Missing Value or Timeout\" # Return None if element is not found\n", "\n", "def fetch_data(collection_id):\n", " driver = setup_driver()\n", " try:\n", + " print(f\"Fetching data for collection: {collection_id}\\n.\")\n", " driver.get(f\"https://www.cvh.ac.cn/spms/detail.php?id={collection_id}\")\n", " \n", " # Fetch specific details as per your logic\n", @@ -1125,12 +1314,27 @@ " finally:\n", " driver.quit()\n", "\n", - "def sample_run(collection_ids, max_workers=5):\n", + "def fetch_collection_data_concurrently(collection_ids, max_workers=5):\n", " results = []\n", + " \n", + " # Initialize an empty DataFrame to hold column names. This is useful if you expect your first few calls might fail and return None.\n", + " temp_path = \"./scraper_results/temp_results.csv\"\n", + " pd.DataFrame(columns=['Collection ID', 'Image Link', 'Phylum (门)', 'Order (目)', 'Family (科)', \n", + " 'Genus (属)', 'Scientific Name', 'Chinese Name', 'Identified By', \n", + " 'Date Identified', 'Collector', 'Collection Number', 'Collection Date', \n", + " 'Collection Location', 'Altitude', 'Habitat', 'Phenology']).to_csv(temp_path, mode='w', index=False, encoding='utf-8-sig')\n", + "\n", " with ThreadPoolExecutor(max_workers=max_workers) as executor:\n", " futures = [executor.submit(fetch_data, cid) for cid in collection_ids]\n", " for future in tqdm(as_completed(futures), total=len(collection_ids)):\n", " results.append(future.result())\n", + " result = future.result()\n", + " if result:\n", + " # Append the result to the CSV file\n", + " try:\n", + " pd.DataFrame([result]).to_csv(temp_path, mode='a', header=False, index=False, encoding='utf-8-sig')\n", + " except PermissionError:\n", + " continue\n", "\n", " df = pd.DataFrame(results)\n", " return df" @@ -1141,7 +1345,7 @@ "id": "bcd8b481-4f12-4b4a-a572-3122d6129ac7", "metadata": {}, "source": [ - "## Sample Scrapper Run with first 5 Collection_ids" + "## Web Scraper Results" ] }, { @@ -1164,15 +1368,5419 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 4, "id": "e37cc10d-38ee-4cc2-b6cb-119fd88892f6", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "6be73efd69114fe6a49f7f87341825ee", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/300 [00:00 4\u001b[0m results \u001b[38;5;241m=\u001b[39m fetch_collection_data_concurrently(sample_collections, \u001b[38;5;241m30\u001b[39m) \n\u001b[0;32m 5\u001b[0m results_path \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m./scraper_results/results.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 6\u001b[0m results\u001b[38;5;241m.\u001b[39mto_csv(results_path, header\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, index\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, encoding\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mutf-8-sig\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", + "Cell \u001b[1;32mIn[3], line 82\u001b[0m, in \u001b[0;36mfetch_collection_data_concurrently\u001b[1;34m(collection_ids, max_workers)\u001b[0m\n\u001b[0;32m 80\u001b[0m futures \u001b[38;5;241m=\u001b[39m [executor\u001b[38;5;241m.\u001b[39msubmit(fetch_data, cid) \u001b[38;5;28;01mfor\u001b[39;00m cid \u001b[38;5;129;01min\u001b[39;00m collection_ids]\n\u001b[0;32m 81\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m future \u001b[38;5;129;01min\u001b[39;00m tqdm(as_completed(futures), total\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mlen\u001b[39m(collection_ids)):\n\u001b[1;32m---> 82\u001b[0m results\u001b[38;5;241m.\u001b[39mappend(future\u001b[38;5;241m.\u001b[39mresult())\n\u001b[0;32m 83\u001b[0m result \u001b[38;5;241m=\u001b[39m future\u001b[38;5;241m.\u001b[39mresult()\n\u001b[0;32m 84\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m result:\n\u001b[0;32m 85\u001b[0m \u001b[38;5;66;03m# Append the result to the CSV file\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\xieha\\anaconda3\\Lib\\concurrent\\futures\\_base.py:449\u001b[0m, in \u001b[0;36mFuture.result\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m 447\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m CancelledError()\n\u001b[0;32m 448\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state \u001b[38;5;241m==\u001b[39m FINISHED:\n\u001b[1;32m--> 449\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__get_result()\n\u001b[0;32m 451\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_condition\u001b[38;5;241m.\u001b[39mwait(timeout)\n\u001b[0;32m 453\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state \u001b[38;5;129;01min\u001b[39;00m [CANCELLED, CANCELLED_AND_NOTIFIED]:\n", + "File \u001b[1;32mc:\\Users\\xieha\\anaconda3\\Lib\\concurrent\\futures\\_base.py:401\u001b[0m, in \u001b[0;36mFuture.__get_result\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 399\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception:\n\u001b[0;32m 400\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 401\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception\n\u001b[0;32m 402\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m 403\u001b[0m \u001b[38;5;66;03m# Break a reference cycle with the exception in self._exception\u001b[39;00m\n\u001b[0;32m 404\u001b[0m \u001b[38;5;28mself\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\xieha\\anaconda3\\Lib\\concurrent\\futures\\thread.py:58\u001b[0m, in \u001b[0;36m_WorkItem.run\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 55\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[0;32m 57\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m---> 58\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfn(\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkwargs)\n\u001b[0;32m 59\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[0;32m 60\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfuture\u001b[38;5;241m.\u001b[39mset_exception(exc)\n", + "Cell \u001b[1;32mIn[3], line 49\u001b[0m, in \u001b[0;36mfetch_data\u001b[1;34m(collection_id)\u001b[0m\n\u001b[0;32m 43\u001b[0m driver\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhttps://www.cvh.ac.cn/spms/detail.php?id=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcollection_id\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 45\u001b[0m \u001b[38;5;66;03m# Fetch specific details as per your logic\u001b[39;00m\n\u001b[0;32m 46\u001b[0m data \u001b[38;5;241m=\u001b[39m {\n\u001b[0;32m 47\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCollection ID\u001b[39m\u001b[38;5;124m'\u001b[39m: collection_id,\n\u001b[0;32m 48\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mImage Link\u001b[39m\u001b[38;5;124m'\u001b[39m: wait_for_element(driver, By\u001b[38;5;241m.\u001b[39mID, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mspm_image\u001b[39m\u001b[38;5;124m\"\u001b[39m, max_retries\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m)\u001b[38;5;241m.\u001b[39mget_attribute(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msrc\u001b[39m\u001b[38;5;124m'\u001b[39m),\n\u001b[1;32m---> 49\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPhylum (门)\u001b[39m\u001b[38;5;124m'\u001b[39m: wait_for_element(driver, By\u001b[38;5;241m.\u001b[39mID, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtaxon_phy_c\u001b[39m\u001b[38;5;124m\"\u001b[39m, max_retries\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m)\u001b[38;5;241m.\u001b[39mtext,\n\u001b[0;32m 50\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mOrder (目)\u001b[39m\u001b[38;5;124m'\u001b[39m: wait_for_element(driver, By\u001b[38;5;241m.\u001b[39mID, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtaxon_ord_c\u001b[39m\u001b[38;5;124m\"\u001b[39m, max_retries\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m)\u001b[38;5;241m.\u001b[39mtext,\n\u001b[0;32m 51\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFamily (科)\u001b[39m\u001b[38;5;124m'\u001b[39m: wait_for_element(driver, By\u001b[38;5;241m.\u001b[39mID, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtaxon_fam_c\u001b[39m\u001b[38;5;124m\"\u001b[39m, max_retries\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m)\u001b[38;5;241m.\u001b[39mtext,\n\u001b[0;32m 52\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mGenus (属)\u001b[39m\u001b[38;5;124m'\u001b[39m: wait_for_element(driver, By\u001b[38;5;241m.\u001b[39mID, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtaxon_gen_c\u001b[39m\u001b[38;5;124m\"\u001b[39m, max_retries\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m)\u001b[38;5;241m.\u001b[39mtext,\n\u001b[0;32m 53\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mScientific Name\u001b[39m\u001b[38;5;124m'\u001b[39m: wait_for_element(driver, By\u001b[38;5;241m.\u001b[39mID, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mformattedName\u001b[39m\u001b[38;5;124m\"\u001b[39m, max_retries\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m)\u001b[38;5;241m.\u001b[39mtext,\n\u001b[0;32m 54\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mChinese Name\u001b[39m\u001b[38;5;124m'\u001b[39m: wait_for_element(driver, By\u001b[38;5;241m.\u001b[39mID, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mchineseName\u001b[39m\u001b[38;5;124m\"\u001b[39m, max_retries\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m)\u001b[38;5;241m.\u001b[39mtext,\n\u001b[0;32m 55\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mIdentified By\u001b[39m\u001b[38;5;124m'\u001b[39m: wait_for_element(driver, By\u001b[38;5;241m.\u001b[39mID, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124midentifiedBy\u001b[39m\u001b[38;5;124m\"\u001b[39m, max_retries\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m)\u001b[38;5;241m.\u001b[39mtext,\n\u001b[0;32m 56\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDate Identified\u001b[39m\u001b[38;5;124m'\u001b[39m: wait_for_element(driver, By\u001b[38;5;241m.\u001b[39mID, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdateIdentified\u001b[39m\u001b[38;5;124m\"\u001b[39m, max_retries\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m)\u001b[38;5;241m.\u001b[39mtext,\n\u001b[0;32m 57\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCollector\u001b[39m\u001b[38;5;124m'\u001b[39m: wait_for_element(driver, By\u001b[38;5;241m.\u001b[39mID, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrecordedBy\u001b[39m\u001b[38;5;124m\"\u001b[39m, max_retries\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m)\u001b[38;5;241m.\u001b[39mtext,\n\u001b[0;32m 58\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCollection Number\u001b[39m\u001b[38;5;124m'\u001b[39m: wait_for_element(driver, By\u001b[38;5;241m.\u001b[39mID, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrecordNumber\u001b[39m\u001b[38;5;124m\"\u001b[39m, max_retries\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m)\u001b[38;5;241m.\u001b[39mtext,\n\u001b[0;32m 59\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCollection Date\u001b[39m\u001b[38;5;124m'\u001b[39m: wait_for_element(driver, By\u001b[38;5;241m.\u001b[39mID, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mverbatimEventDate\u001b[39m\u001b[38;5;124m\"\u001b[39m, max_retries\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m)\u001b[38;5;241m.\u001b[39mtext,\n\u001b[0;32m 60\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCollection Location\u001b[39m\u001b[38;5;124m'\u001b[39m: wait_for_element(driver, By\u001b[38;5;241m.\u001b[39mID, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlocality\u001b[39m\u001b[38;5;124m\"\u001b[39m, max_retries\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m)\u001b[38;5;241m.\u001b[39mtext,\n\u001b[0;32m 61\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mAltitude\u001b[39m\u001b[38;5;124m'\u001b[39m: wait_for_element(driver, By\u001b[38;5;241m.\u001b[39mID, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124melevation\u001b[39m\u001b[38;5;124m\"\u001b[39m, max_retries\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m)\u001b[38;5;241m.\u001b[39mtext,\n\u001b[0;32m 62\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mHabitat\u001b[39m\u001b[38;5;124m'\u001b[39m: wait_for_element(driver, By\u001b[38;5;241m.\u001b[39mID, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhabitat\u001b[39m\u001b[38;5;124m\"\u001b[39m, max_retries\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m)\u001b[38;5;241m.\u001b[39mtext,\n\u001b[0;32m 63\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPhenology\u001b[39m\u001b[38;5;124m'\u001b[39m: wait_for_element(driver, By\u001b[38;5;241m.\u001b[39mID, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mreproductiveCondition\u001b[39m\u001b[38;5;124m\"\u001b[39m, max_retries\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m)\u001b[38;5;241m.\u001b[39mtext\n\u001b[0;32m 64\u001b[0m }\n\u001b[0;32m 65\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m data\n\u001b[0;32m 66\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n", + "\u001b[1;31mAttributeError\u001b[0m: 'str' object has no attribute 'text'" + ] + } + ], "source": [ "pd.set_option('display.max_columns', None)\n", "\n", - "sample_collections = list(data_collection_ids['Data Collection ID'].head(5))\n", - "resutls = sample_run(sample_collections, 5) \n", + "sample_collections = list(data_collection_ids['Data Collection ID'])\n", + "results = fetch_collection_data_concurrently(sample_collections, 30) \n", + "results_path = \"./scraper_results/results.csv\"\n", + "results.to_csv(results_path, header=True, index=False, encoding='utf-8-sig')\n", + "results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c9f45965", + "metadata": {}, + "outputs": [], + "source": [ "results" ] }, @@ -1185,15 +6793,14 @@ "\n", "Here is the screenshot from my local environment:\n", "\n", - "\n", - "\n", - "\n", - "### Scrapped Info (In red)\n", - "* Image link\n", - "* Taxon data on right\n", - "\n", - "" + "" ] + }, + { + "cell_type": "markdown", + "id": "ed991ec1", + "metadata": {}, + "source": [] } ], "metadata": { @@ -1212,7 +6819,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.6" + "version": "3.11.5" } }, "nbformat": 4, diff --git a/Spring2024/scraper_results/temp_results.csv b/Spring2024/scraper_results/temp_results.csv new file mode 100644 index 0000000..3141ff7 --- /dev/null +++ b/Spring2024/scraper_results/temp_results.csv @@ -0,0 +1,176 @@ +Collection ID,Image Link,Phylum (门),Order (目),Family (科),Genus (属),Scientific Name,Chinese Name,Identified By,Date Identified,Collector,Collection Number,Collection Date,Collection Location,Altitude,Habitat,Phenology +e6e74052,https://www.cvh.ac.cn/controller/spms/image.php?institutionCode=PE&catalogNumber=02334108,维管植物门,菊目,菊科,香青属,Anaphalis virens Chang,黄绿香青,张国进,20180517,PE西藏队,PE6547,20170912,中国 西藏自治区,3980,山坡,有花无果 +e6e736ae,https://www.cvh.ac.cn/controller/spms/image.php?institutionCode=PE&catalogNumber=02334125,维管植物门,菊目,菊科,紫菀属,Aster albescens (DC.) 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Wall.,小舌紫菀,张国进,20180401,PE西藏队,PE5910,20170905,中国 西藏自治区,3020~3460,林缘,有花无果 +e6e75ccd,https://www.cvh.ac.cn/controller/spms/image.php?institutionCode=PE&catalogNumber=02334056,维管植物门,菊目,菊科,紫菀属,Aster asteroides MacMillan,星舌紫菀,张国进,20180401,PE西藏队,PE5972,20170905,中国 西藏自治区,4780,山坡,有花无果 +e6e75996,https://www.cvh.ac.cn/controller/spms/image.php?institutionCode=PE&catalogNumber=02334062,维管植物门,菊目,菊科,黏冠草属,Myriactis nepalensis Less.,圆舌粘冠草,张国进,20180401,PE西藏队,PE5976,20170906,中国 西藏自治区,2935,山谷林缘,有花无果 +e6e75f7e,https://www.cvh.ac.cn/controller/spms/image.php?institutionCode=PE&catalogNumber=02334050,维管植物门,菊目,菊科,紫菀属,Aster albescens (DC.) Wall.,小舌紫菀,张国进,20180517,PE西藏队,PE5881,20170905,中国 西藏自治区,3020~3480,林缘,有花无果 +e6e75e6e,https://www.cvh.ac.cn/controller/spms/image.php?institutionCode=PE&catalogNumber=02334053,维管植物门,菊目,菊科,紫菀属,Aster albescens (DC.) Wall.,小舌紫菀,张国进,20180401,PE西藏队,PE5910,20170905,中国 西藏自治区,3020~3460,林缘,有花无果 +e6e7608c,https://www.cvh.ac.cn/controller/spms/image.php?institutionCode=PE&catalogNumber=02334047,维管植物门,菊目,菊科,香青属,Anaphalis contorta Hook.f.,旋叶香青,张国进,20180517,PE西藏队,PE5879,20170905,中国 西藏自治区,3020~3460,林缘,有花无果 +e6e76114,https://www.cvh.ac.cn/controller/spms/image.php?institutionCode=PE&catalogNumber=02334048,维管植物门,菊目,菊科,紫菀属,Aster albescens (DC.) Wall.,小舌紫菀,张国进,20180517,PE西藏队,PE5881,20170905,中国 西藏自治区,3020~3480,林缘,有花无果 +e6e76005,https://www.cvh.ac.cn/controller/spms/image.php?institutionCode=PE&catalogNumber=02334049,维管植物门,菊目,菊科,紫菀属,Aster albescens (DC.) Wall.,小舌紫菀,张国进,20180517,PE西藏队,PE5881,20170905,中国 西藏自治区,3020~3480,林缘,有花无果 +e6e75ef6,https://www.cvh.ac.cn/controller/spms/image.php?institutionCode=PE&catalogNumber=02334051,维管植物门,菊目,菊科,紫菀属,Aster albescens (DC.) Wall.,小舌紫菀,张国进,20180401,PE西藏队,PE5910,20170905,中国 西藏自治区,3020~3460,林缘,有花无果 +e6e7619f,https://www.cvh.ac.cn/controller/spms/image.php?institutionCode=PE&catalogNumber=02334045,维管植物门,菊目,菊科,香青属,Anaphalis contorta Hook.f.,旋叶香青,张国进,20180517,PE西藏队,PE5879,20170905,中国 西藏自治区,3020~3460,林缘,有花无果 +e6e7622d,https://www.cvh.ac.cn/controller/spms/image.php?institutionCode=PE&catalogNumber=02334046,维管植物门,菊目,菊科,香青属,Anaphalis contorta Hook.f.,旋叶香青,张国进,20180517,PE西藏队,PE5879,20170905,中国 西藏自治区,3020~3460,林缘,有花无果 +e6e762b5,https://www.cvh.ac.cn/controller/spms/image.php?institutionCode=PE&catalogNumber=02334044,维管植物门,菊目,菊科,千里光属,Senecio graciliflorus DC.,纤花千里光,张国进,20180517,PE西藏队,PE5875,20170905,中国 西藏自治区,3020~3460,林缘,有花无果 +e6e7633e,https://www.cvh.ac.cn/controller/spms/image.php?institutionCode=PE&catalogNumber=02334042,维管植物门,菊目,菊科,千里光属,Senecio graciliflorus DC.,纤花千里光,张国进,20180517,PE西藏队,PE5875,20170905,中国 西藏自治区,3020~3460,林缘,有花无果 +e6e763c6,https://www.cvh.ac.cn/controller/spms/image.php?institutionCode=PE&catalogNumber=02334043,维管植物门,菊目,菊科,千里光属,Senecio graciliflorus DC.,纤花千里光,张国进,20180517,PE西藏队,PE5875,20170905,中国 西藏自治区,3020~3460,林缘,有花无果 +e6e7644f,https://www.cvh.ac.cn/controller/spms/image.php?institutionCode=PE&catalogNumber=02334040,维管植物门,菊目,菊科,黏冠草属,Myriactis nepalensis Less.,圆舌粘冠草,张国进,20180401,PE西藏队,PE6192,20170910,中国 西藏自治区,2488~2994,山坡林下,有花无果 +e6e76561,https://www.cvh.ac.cn/controller/spms/image.php?institutionCode=PE&catalogNumber=02334038,维管植物门,菊目,菊科,黏冠草属,Myriactis nepalensis Less.,圆舌粘冠草,张国进,20180401,PE西藏队,PE6192,20170910,中国 西藏自治区,2488~2994,山坡林下,有花无果 +e6e764d8,https://www.cvh.ac.cn/controller/spms/image.php?institutionCode=PE&catalogNumber=02334041,维管植物门,菊目,菊科,飞蓬属,Erigeron multiradiatus Benth. & Hook.f.,多舌飞蓬,张国进,20180401,PE西藏队,PE5856,20170903,中国 西藏自治区,3401,山坡,有花无果 +e6e765ea,https://www.cvh.ac.cn/controller/spms/image.php?institutionCode=PE&catalogNumber=02334039,维管植物门,菊目,菊科,黏冠草属,Myriactis nepalensis Less.,圆舌粘冠草,张国进,20180401,PE西藏队,PE6192,20170910,中国 西藏自治区,2488~2994,山坡林下,有花无果 +e6e7678e,https://www.cvh.ac.cn/controller/spms/image.php?institutionCode=PE&catalogNumber=02334036,维管植物门,菊目,菊科,紫菀属,Aster ageratoides Turcz.,三脉紫菀,张国进,20180401,PE西藏队,PE6187,20170910,中国 西藏自治区,2488~2994,山坡林下,有花无果 +e6e76816,https://www.cvh.ac.cn/controller/spms/image.php?institutionCode=PE&catalogNumber=02334033,维管植物门,菊目,菊科,香青属,Anaphalis contorta Hook.f.,旋叶香青,张国进,20180517,PE西藏队,PE6139,20170907,中国 西藏自治区,4474,山坡,有花无果 +e6e76706,https://www.cvh.ac.cn/controller/spms/image.php?institutionCode=PE&catalogNumber=02334035,维管植物门,菊目,菊科,紫菀属,Aster ageratoides Turcz.,三脉紫菀,张国进,20180401,PE西藏队,PE6187,20170910,中国 西藏自治区,2488~2994,山坡林下,有花无果 +e6e76928,https://www.cvh.ac.cn/controller/spms/image.php?institutionCode=PE&catalogNumber=02334031,维管植物门,菊目,菊科,香青属,Anaphalis contorta Hook.f.,旋叶香青,张国进,20180517,PE西藏队,PE6139,20170907,中国 西藏自治区,4474,山坡,有花无果 +e6e768a0,https://www.cvh.ac.cn/controller/spms/image.php?institutionCode=PE&catalogNumber=02334034,维管植物门,菊目,菊科,香青属,Anaphalis contorta Hook.f.,旋叶香青,张国进,20180517,PE西藏队,PE6139,20170907,中国 西藏自治区,4474,山坡,有花无果 +e6e769af,https://www.cvh.ac.cn/controller/spms/image.php?institutionCode=PE&catalogNumber=02334032,维管植物门,菊目,菊科,香青属,Anaphalis contorta Hook.f.,旋叶香青,张国进,20180507,PE西藏队,PE6139,20170907,中国 西藏自治区,4474,山坡,有花无果 +e6e76a38,https://www.cvh.ac.cn/controller/spms/image.php?institutionCode=PE&catalogNumber=02334030,维管植物门,菊目,菊科,香青属,Anaphalis nepalensis (Spreng.) Hand.-Mazz. var. monocephala (DC.) 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Hand.-Mazz. var. monocephala (DC.) Hand.-Mazz.,单头尼泊尔香青,张国进,20180517,PE西藏队,PE6120,20170907,中国 西藏自治区,4474,山坡,有花无果 +e6e76ac2,https://www.cvh.ac.cn/controller/spms/image.php?institutionCode=PE&catalogNumber=02334028,维管植物门,菊目,菊科,蒲公英属,Taraxacum calanthodium Dahlst.,大头蒲公英,张国进,20180517,PE西藏队,PE6106,20170907,中国 西藏自治区,4398,草甸,有花无果 +e6e76cf4,https://www.cvh.ac.cn/controller/spms/image.php?institutionCode=PE&catalogNumber=02334026,维管植物门,菊目,菊科,飞蓬属,Erigeron multiradiatus Benth. & Hook.f.,多舌飞蓬,张国进,20180401,PE西藏队,PE6091,20170907,中国 西藏自治区,4398,草甸,有花无果 +e6e77023,https://www.cvh.ac.cn/controller/spms/image.php?institutionCode=PE&catalogNumber=02334018,维管植物门,菊目,菊科,合耳菊属,Synotis tetrantha (DC.) 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