Data loading is in progress
Data loading is in progress
import cv2 import time import datetime cap = cv2.VideoCapture(0) face_cascade = cv2.CascadeClassifier( cv2.data.haarcascades + "haarcascade_frontalface_default.xml") body_cascade = cv2.CascadeClassifier( cv2.data.haarcascades + "haarcascade_fullbody.xml") detection = False detection_stopped_time = None timer_started = False SECONDS_TO_RECORD_AFTER_DETECTION = 5 frame_size = (int(cap.get(3)), int(cap.get(4))) fourcc = cv2.VideoWriter_fourcc(*"mp4v") while True: _, frame = cap.read() gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) faces = face_cascade.detectMultiScale(gray, 1.3, 5) bodies = face_cascade.detectMultiScale(gray, 1.3, 5) if len(faces) + len(bodies) > 0: if detection: timer_started = False else: detection = True current_time = datetime.datetime.now().strftime("%d-%m-%Y-%H-%M-%S") out = cv2.VideoWriter( f"{current_time}.mp4", fourcc, 20, frame_size) print("Started Recording!") elif detection: if timer_started: if time.time() - detection_stopped_time >= SECONDS_TO_RECORD_AFTER_DETECTION: detection = False timer_started = False out.release() print('Stop Recording!') else: timer_started = True detection_stopped_time = time.time() if detection: out.write(frame) # for (x, y, width, height) in faces: # cv2.rectangle(frame, (x, y), (x + width, y + height), (255, 0, 0), 3) cv2.imshow("Camera", frame) if cv2.waitKey(1) == ord('q'): break out.release() cap.release() cv2.destroyAllWindows() @pythonl
%matplotlib inline import quantstats as qs # extend pandas functionality with metrics, etc. qs.extend_pandas() # fetch the daily returns for a stock stock = qs.utils.download_returns('META') # show sharpe ratio qs.stats.sharpe(stock) # or using extend_pandas() :) stock.sharpe()βͺ Github @pythonl
SQLModel 0.0.14Ρ ΠΏΠΎΠ΄Π΄Π΅ΡΠΆΠΊΠΎΠΉ pydantic v2 π Π£Π²Π΅ΡΠ΅Π½, ΡΡΠΎ ΡΡΠΎ ΡΠ°ΠΌΡΠΉ Π±ΠΎΠ»ΡΡΠΎΠΉ ΡΠ΅Π»ΠΈΠ· Π·Π° Π²ΡΠ΅ Π²ΡΠ΅ΠΌΡ π€.
SQLModel- ΡΡΠΎ Π±ΠΈΠ±Π»ΠΈΠΎΡΠ΅ΠΊΠ° Π΄Π»Ρ Π²Π·Π°ΠΈΠΌΠΎΠ΄Π΅ΠΉΡΡΠ²ΠΈΡ Ρ Π±Π°Π·Π°ΠΌΠΈ Π΄Π°Π½Π½ΡΡ SQL ΠΈΠ· ΠΊΠΎΠ΄Π° Python, Ρ ΠΎΠ±ΡΠ΅ΠΊΡΠ°ΠΌΠΈ Python. ΠΠ½Π° ΡΠΎΠ·Π΄Π°Π½Π° Π΄Π»Ρ ΡΠΎΠ³ΠΎ, ΡΡΠΎΠ±Ρ Π±ΡΡΡ ΠΈΠ½ΡΡΠΈΡΠΈΠ²Π½ΠΎ ΠΏΠΎΠ½ΡΡΠ½ΠΎΠΉ, ΠΏΡΠΎΡΡΠΎΠΉ Π² ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠΈ, Ρ ΠΎΡΠΎΡΠΎ ΡΠΎΠ²ΠΌΠ΅ΡΡΠΈΠΌΠΎΠΉ ΠΈ Π½Π°Π΄Π΅ΠΆΠ½ΠΎΠΉ.
$ pip install sqlmodelβͺ Github @pythonl
pip install alpha_vantage from alpha_vantage.timeseries import TimeSeries import matplotlib.pyplot as plt ts = TimeSeries(key='YOUR_API_KEY', output_format='pandas') data, meta_data = ts.get_intraday(symbol='MSFT',interval='1min', outputsize='full') data['4. close'].plot() plt.title('Intraday Times Series for the MSFT stock (1 min)') plt.show()βͺGithub @pythonl
wget https://gitlab.com/api/v4/projects/33695681/packages/generic/nrich/latest/nrich_latest_x86_64.deb $ sudo dpkg -i nrich_latest_x86_64.debβͺ GIthub @pythonl
Cometml,Ρ ΠΏΠΎΠΌΠΎΡΡΡ ΠΏΠ°ΡΡ ΡΡΡΠΎΠΊ ΠΊΠΎΠ΄Π°. ΠΠΎΡΠΌΠΎΡΡΠΈΡΠ΅ Π½Π° ΠΏΡΠΈΠ»Π°Π³Π°Π΅ΠΌΡΠΉ ΡΠΊΡΠΈΠ½ΡΠΎΡ: ΠΠ½ΡΠ΅Π³ΡΠ°ΡΠΈΡ
Comet + OpenAIΠ±ΡΠ΄Π΅Ρ ΠΎΡΡΠ»Π΅ΠΆΠΈΠ²Π°ΡΡ Π²ΡΠ΅ Π°Π²ΡΠΎΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΈ: - ΡΠΎΠΎΠ±ΡΠ΅Π½ΠΈΡ ΠΈ function_call ΠΊΠ°ΠΊ Π²Ρ ΠΎΠ΄Ρ - Π²Π°ΡΠΈΠ°Π½ΡΡ ΠΊΠ°ΠΊ Π²ΡΡ ΠΎΠ΄Ρ - ΡΠΎΠΊΠ΅Π½ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡ ΠΊΠ°ΠΊ ΠΌΠ΅ΡΠ°Π΄Π°Π½Π½ΡΠ΅ - ΡΠ°Π±ΠΎΡΠΌΠΈ Ρ ΠΌΠ΅ΡΠ°Π΄Π°Π½Π½ΡΠΌΠΈ
pip install comet_llmΠΡΠΎΡ Π±Π»ΠΎΠΊΠ½ΠΎΡ Colab ΠΏΡΠΎΠ΄Π΅ΠΌΠΎΠ½ΡΡΡΠΈΡΡΠ΅Ρ Π²Π°ΠΌ ΠΏΡΠΈΠΌΠ΅Ρ ΡΠ°Π±ΠΎΡΡ
Cometml: https://colab.research.google.com/github/comet-ml/comet-examples/blob/master/integrations/llm/openai/notebooks/Comet_and_OpenAI.ipynb#scrollTo=A0-thQauBRRL βͺ Github @pythonl