Datasets:

ytzi

/

the-stack-dedup-python-filtered-docstrings

like 0

6776cf0bd080254a676d541e382d4eb2f31a05f5

py

Python

pymatgen/analysis/tests/test_diffusion_analyzer.py

rajeshprasanth/pymatgen

eb6cd95230c11ac761a96ebf82b98f71177bb71f

pymatgen/analysis/tests/test_diffusion_analyzer.py

rajeshprasanth/pymatgen

eb6cd95230c11ac761a96ebf82b98f71177bb71f

pymatgen/analysis/tests/test_diffusion_analyzer.py

rajeshprasanth/pymatgen

eb6cd95230c11ac761a96ebf82b98f71177bb71f

2018-10-28T01:41:38.000Z

2018-10-28T01:41:38.000Z

# coding: utf-8 # Copyright (c) Pymatgen Development Team. # Distributed under the terms of the MIT License. from __future__ import division, unicode_literals import unittest2 as unittest import os import json import random import numpy as np import csv import scipy.constants as const from pymatgen.analysis.diffusion_analyzer import DiffusionAnalyzer,\ get_conversion_factor, fit_arrhenius from pymatgen.core.structure import Structure from pymatgen.util.testing import PymatgenTest from monty.tempfile import ScratchDir """ TODO: Change the module doc. """ __author__ = "shyuepingong" __version__ = "0.1" __maintainer__ = "Shyue Ping Ong" __email__ = "shyuep@gmail.com" __status__ = "Beta" __date__ = "5/2/13" test_dir = os.path.join(os.path.dirname(__file__), "..", "..", "..", 'test_files') if __name__ == '__main__': unittest.main()

# coding: utf-8 # Copyright (c) Pymatgen Development Team. # Distributed under the terms of the MIT License. from __future__ import division, unicode_literals import unittest2 as unittest import os import json import random import numpy as np import csv import scipy.constants as const from pymatgen.analysis.diffusion_analyzer import DiffusionAnalyzer,\ get_conversion_factor, fit_arrhenius from pymatgen.core.structure import Structure from pymatgen.util.testing import PymatgenTest from monty.tempfile import ScratchDir """ TODO: Change the module doc. """ __author__ = "shyuepingong" __version__ = "0.1" __maintainer__ = "Shyue Ping Ong" __email__ = "shyuep@gmail.com" __status__ = "Beta" __date__ = "5/2/13" test_dir = os.path.join(os.path.dirname(__file__), "..", "..", "..", 'test_files') class FuncTest(unittest.TestCase): def test_get_conversion_factor(self): filepath = os.path.join(test_dir, 'LiFePO4.cif') s = Structure.from_file(filepath) # large tolerance because scipy constants changed between 0.16.1 and 0.17 self.assertAlmostEqual(41370704.343540139, get_conversion_factor(s, "Li", 600), delta=20) def test_fit_arrhenius(self): Ea = 0.5 k = const.k / const.e c = 12 temps = np.array([300, 1000, 500]) diffusivities = c * np.exp(-Ea/(k * temps)) diffusivities *= np.array([1.00601834013, 1.00803236262, 0.98609720824]) r = fit_arrhenius(temps, diffusivities) self.assertAlmostEqual(r[0], Ea) self.assertAlmostEqual(r[1], c) self.assertAlmostEqual(r[2], 0.000895566) # when not enough values for error estimate r2 = fit_arrhenius([1, 2], [10, 10]) self.assertAlmostEqual(r2[0], 0) self.assertAlmostEqual(r2[1], 10) self.assertEqual(r2[2], None) class DiffusionAnalyzerTest(PymatgenTest): def test_init(self): # Diffusion vasprun.xmls are rather large. We are only going to use a # very small preprocessed run for testing. Note that the results are # unreliable for short runs. with open(os.path.join(test_dir, "DiffusionAnalyzer.json")) as f: dd = json.load(f) d = DiffusionAnalyzer.from_dict(dd) # large tolerance because scipy constants changed between 0.16.1 and 0.17 self.assertAlmostEqual(d.conductivity, 74.165372613735684, 4) self.assertAlmostEqual(d.diffusivity, 1.16083658794e-06, 7) self.assertAlmostEqual(d.conductivity_std_dev, 0.0097244677795984488, 7) self.assertAlmostEqual(d.diffusivity_std_dev, 9.1013023085561779e-09, 7) self.assertArrayAlmostEqual( d.conductivity_components, [45.9109703, 26.2856302, 150.5405727], 3) self.assertArrayAlmostEqual( d.diffusivity_components, [7.49601236e-07, 4.90254273e-07, 2.24649255e-06]) self.assertArrayAlmostEqual( d.conductivity_components_std_dev, [0.0063579, 0.0180862, 0.0217917] ) self.assertArrayAlmostEqual( d.diffusivity_components_std_dev, [8.9465670e-09, 2.4931224e-08, 2.2636384e-08] ) self.assertArrayAlmostEqual( d.max_ion_displacements, [1.4620659693989553, 1.2787303484445025, 3.419618540097756, 2.340104469126246, 2.6080973517594233, 1.3928579365672844, 1.3561505956708932, 1.6699242923686253, 1.0352389639563648, 1.1662520093955808, 1.2322019205885841, 0.8094210554832534, 1.9917808504954169, 1.2684148391206396, 2.392633794162402, 2.566313049232671, 1.3175030435622759, 1.4628945430952793, 1.0984921286753002, 1.2864482076554093, 0.655567027815413, 0.5986961164605746, 0.5639091444309045, 0.6166004192954059, 0.5997911580422605, 0.4374606277579815, 1.1865683960470783, 0.9017064371676591, 0.6644840367853767, 1.0346375380664645, 0.6177630142863979, 0.7952002051914302, 0.7342686123054011, 0.7858047956905577, 0.5570732369065661, 1.0942937746885417, 0.6509372395308788, 1.0876687380413455, 0.7058162184725, 0.8298306317598585, 0.7813913747621343, 0.7337655232056153, 0.9057161616236746, 0.5979093093186919, 0.6830333586985015, 0.7926500894084628, 0.6765180009988608, 0.8555866032968998, 0.713087091642237, 0.7621007695790749]) self.assertEqual(d.sq_disp_ions.shape, (50, 206)) self.assertAlmostEqual(d.max_framework_displacement, 1.18656839605) ss = list(d.get_drift_corrected_structures(10, 1000, 20)) self.assertEqual(len(ss), 50) n = random.randint(0, 49) n_orig = n * 20 + 10 self.assertArrayAlmostEqual( ss[n].cart_coords - d.structure.cart_coords + d.drift[:, n_orig, :], d.disp[:, n_orig, :]) d = DiffusionAnalyzer.from_dict(d.as_dict()) self.assertIsInstance(d, DiffusionAnalyzer) #Ensure summary dict is json serializable. json.dumps(d.get_summary_dict(include_msd_t=True)) d = DiffusionAnalyzer(d.structure, d.disp, d.specie, d.temperature, d.time_step, d.step_skip, smoothed="max") self.assertAlmostEqual(d.conductivity, 74.165372613735684, 4) self.assertAlmostEqual(d.diffusivity, 1.14606446822e-06, 7) d = DiffusionAnalyzer(d.structure, d.disp, d.specie, d.temperature, d.time_step, d.step_skip, smoothed=False) self.assertAlmostEqual(d.conductivity, 27.20479170406027, 4) self.assertAlmostEqual(d.diffusivity, 4.25976905436e-07, 7) d = DiffusionAnalyzer(d.structure, d.disp, d.specie, d.temperature, d.time_step, d.step_skip, smoothed="constant", avg_nsteps=100) self.assertAlmostEqual(d.conductivity, 47.404056230438741, 4) self.assertAlmostEqual(d.diffusivity, 7.4226016496716148e-07, 7) # Can't average over 2000 steps because this is a 1000-step run. self.assertRaises(ValueError, DiffusionAnalyzer, d.structure, d.disp, d.specie, d.temperature, d.time_step, d.step_skip, smoothed="constant", avg_nsteps=2000) d = DiffusionAnalyzer.from_structures( list(d.get_drift_corrected_structures()), d.specie, d.temperature, d.time_step, d.step_skip, d.smoothed, avg_nsteps=100) self.assertAlmostEqual(d.conductivity, 47.404056230438741, 4) self.assertAlmostEqual(d.diffusivity, 7.4226016496716148e-07, 7) d.export_msdt("test.csv") with open("test.csv") as f: data = [] for row in csv.reader(f): if row: data.append(row) data.pop(0) data = np.array(data, dtype=np.float64) self.assertArrayAlmostEqual(data[:, 1], d.msd) os.remove("test.csv") if __name__ == '__main__': unittest.main()

d94e383e015801e9caaa411360f5d5c3c970581d

py

Python

checker/logic.py

ucam-cl-dtg/equality_checker

6a31d3dd360f821e36c4742e1d5139d7292f8319

2020-07-18T08:04:27.000Z

2022-03-07T06:46:17.000Z

checker/logic.py

ucam-cl-dtg/equality_checker

6a31d3dd360f821e36c4742e1d5139d7292f8319

2022-03-18T17:05:54.000Z

2022-03-18T17:05:54.000Z

checker/logic.py

ucam-cl-dtg/equality_checker

6a31d3dd360f821e36c4742e1d5139d7292f8319

2020-07-18T08:04:28.000Z

2020-07-18T08:04:28.000Z

import sympy from .utils import known_equal_pair, contains_incorrect_symbols from .utils import EqualityType from .parsing import logic_parser, UnsafeInputException __all__ = ["check"] KNOWN_PAIRS = dict() def parse_expression(expression_str, *, local_dict=None): """Take a string containing a mathematical expression and return a sympy expression. Wrap the parsing class function parse_expr(...) and catch any exceptions that occur. - 'local_dict' can be a dictionary of (name, sympy.Symbol(...)) pairs, where the string 'name' will not be split up and will be turned into the symbol specified. It may be None. """ try: return logic_parser.parse_expr(expression_str, local_dict=local_dict) except logic_parser.ParsingException: print("Incorrectly formatted expression.") print("Fail: '{}'.".format(expression_str)) return None def exact_match(test_expr, target_expr): """Test if the entered expression exactly matches the known expression. This performs as little simplification of the boolean expression as possible, allowing only the commutativity or AND and OR. Returns True if the sympy expressions have the same internal structure, and False if not. - 'test_expr' should be the untrusted sympy expression to check. - 'target_expr' should be the trusted sympy expression to match against. """ print("[EXACT TEST]") if test_expr == target_expr: print("Exact Match (with '==')") return True elif sympy.srepr(test_expr) == sympy.srepr(target_expr): # This is a (perfectly acceptable) hack for ordering the atoms of each # term, but a more explicit method may be preferable in the future. print("Exact Match (with 'srepr')") return True else: return False def symbolic_equality(test_expr, target_expr): """Test if two expressions are symbolically equivalent. Use the sympy 'simplify_logic' function to simplify the two boolean expressions as much as possible. Two equilvalent expressions MUST simplify to the same thing, and then they can be tested for equivalence again. Returns True if sympy can determine that the two expressions are equal, and returns False if they are not equal. - 'test_expr' should be the untrusted sympy expression to check. - 'target_expr' should be the trusted sympy expression to match against. """ print("[SYMBOLIC TEST]") try: simplified_target = sympy.simplify_logic(target_expr) simplified_test = sympy.simplify_logic(test_expr) if simplified_target == simplified_test or sympy.srepr(simplified_target) == sympy.srepr(simplified_test): print("Symbolic match.") print("INFO: Adding known pair ({0}, {1})".format(target_expr, test_expr)) KNOWN_PAIRS[(target_expr, test_expr)] = EqualityType.SYMBOLIC return True else: return False except NotImplementedError as e: print("{0}: {1} - Can't check symbolic equality!".format(type(e).__name__, str(e).capitalize())) return False def expr_equality(test_expr, target_expr): """Given two sympy expressions: test for exact, symbolic and numeric equality. Check two sympy expressions for equality, throwing a TypeError if either of the provided sympy objects is not an expression. - 'test_expr' should be the untrusted sympy expression to check. - 'target_expr' should be the trusted sympy expression to match against. """ equality_type = EqualityType.EXACT equal = exact_match(test_expr, target_expr) if not equal: # Then try checking for symbolic equality: equality_type = EqualityType.SYMBOLIC equal = symbolic_equality(test_expr, target_expr) return equal, equality_type def general_equality(test_expr, target_expr): """Given two general sympy objects: test for exact, symbolic and numeric equality. - 'test_expr' should be the untrusted sympy object to check. - 'target_expr' should be the trusted sympy object to match against. """ equal, equality_type = known_equal_pair(KNOWN_PAIRS, test_expr, target_expr) # If this is a known pair: return immediately: if equal: return equal, equality_type else: print("[[EXPRESSION CHECK]]") return expr_equality(test_expr, target_expr) def check(test_str, target_str, *, symbols=None, check_symbols=True, description=None, _quiet=False): """The main checking function, calls each of the equality checking functions as required. Returns a dict describing the equality; with important keys being 'equal', and 'equality_type'. The key 'error' is added if something went wrong, and this should always be checked for first. - 'test_str' should be the untrusted string for sympy to parse. - 'target_str' should be the trusted string to parse and match against. - 'symbols' should be a string list or comma separated string of symbols not to split during parsing. - 'check_symbols' indicates whether to verfiy the symbols used in each expression are exactly the same or not; setting this to False will allow symbols which cancel out to be included (probably don't want this in questions). - 'description' is an optional description to print before the checker's output to stdout which can be used to improve logging. - '_quiet' is an internal argument used to suppress some output when this function is called from plus_minus_checker(). """ # Suppress this output if necessary: if not _quiet: print("=" * 50) # For logging purposes, if we have a description: print it! if description is not None: print(description) print("=" * 50) print("[LOGIC]") # If nothing to parse, fail. On server, this will be caught in check_endpoint() if (target_str == "") or (test_str == ""): print("ERROR: No input provided!") if not _quiet: print("=" * 50) return dict(error="Empty string as argument.") # Cleanup the strings before anything is done to them: error_is_test = False try: target_str = logic_parser.cleanup_string(target_str, reject_unsafe_input=True) error_is_test = True test_str = logic_parser.cleanup_string(test_str, reject_unsafe_input=True) except UnsafeInputException: print("ERROR: Input contained non-whitelisted characters!") result = dict(error="Bad input provided!") if error_is_test: print("Test string: '{}'".format(test_str)) result["syntax_error"] = str(True).lower() if not _quiet: print("=" * 50) return result print("Target string: '{}'".format(target_str)) print("Test string: '{}'".format(test_str)) print("[[PARSE EXPRESSIONS]]") # Parse the trusted target expression: target_expr = parse_expression(target_str) # Parse the untrusted test expression: test_expr = parse_expression(test_str) result = dict(target=target_str, test=test_str) if target_expr is None: print("ERROR: TRUSTED EXPRESSION CANNOT BE PARSED!") if not _quiet: print("=" * 50) result["error"] = "Parsing TARGET Expression Failed!" result["code"] = 400 # This is fatal! return result if test_expr is None: print("Incorrectly formatted ToCheck expression.") if not _quiet: print("=" * 50) result["error"] = "Parsing Test Expression Failed!" result["syntax_error"] = str(True).lower() return result result["parsed_target"] = str(target_expr) result["parsed_test"] = str(test_expr) # Now check for symbol match and equality: try: print("Parsed Target: {0}\nParsed ToCheck: {1}".format(target_expr, test_expr)) if check_symbols: # Do we have same set of symbols in each? incorrect_symbols = contains_incorrect_symbols(test_expr, target_expr) if incorrect_symbols is not None: print("[[RESULT]]\nEquality: False") if not _quiet: print("=" * 50) result["equal"] = str(False).lower() result["equality_type"] = EqualityType.SYMBOLIC.value result["incorrect_symbols"] = incorrect_symbols return result # Then check for equality proper: equal, equality_type = general_equality(test_expr, target_expr) except (SyntaxError, TypeError, AttributeError) as e: print("Error when comparing expressions: '{}'.".format(e)) if not _quiet: print("=" * 50) result["error"] = "Comparison of expressions failed: '{}'".format(e) return result print("[[RESULT]]") if equal and (equality_type is not EqualityType.EXACT) and ((target_expr, test_expr) not in KNOWN_PAIRS): print("INFO: Adding known pair ({0}, {1})".format(target_expr, test_expr)) KNOWN_PAIRS[(target_expr, test_expr)] = equality_type print("Equality: {}".format(equal)) if not _quiet: print("=" * 50) result["equal"] = str(equal).lower() result["equality_type"] = equality_type.value return result

import sympy from .utils import known_equal_pair, contains_incorrect_symbols from .utils import EqualityType from .parsing import logic_parser, UnsafeInputException __all__ = ["check"] KNOWN_PAIRS = dict() def parse_expression(expression_str, *, local_dict=None): """Take a string containing a mathematical expression and return a sympy expression. Wrap the parsing class function parse_expr(...) and catch any exceptions that occur. - 'local_dict' can be a dictionary of (name, sympy.Symbol(...)) pairs, where the string 'name' will not be split up and will be turned into the symbol specified. It may be None. """ try: return logic_parser.parse_expr(expression_str, local_dict=local_dict) except logic_parser.ParsingException: print("Incorrectly formatted expression.") print("Fail: '{}'.".format(expression_str)) return None def exact_match(test_expr, target_expr): """Test if the entered expression exactly matches the known expression. This performs as little simplification of the boolean expression as possible, allowing only the commutativity or AND and OR. Returns True if the sympy expressions have the same internal structure, and False if not. - 'test_expr' should be the untrusted sympy expression to check. - 'target_expr' should be the trusted sympy expression to match against. """ print("[EXACT TEST]") if test_expr == target_expr: print("Exact Match (with '==')") return True elif sympy.srepr(test_expr) == sympy.srepr(target_expr): # This is a (perfectly acceptable) hack for ordering the atoms of each # term, but a more explicit method may be preferable in the future. print("Exact Match (with 'srepr')") return True else: return False def symbolic_equality(test_expr, target_expr): """Test if two expressions are symbolically equivalent. Use the sympy 'simplify_logic' function to simplify the two boolean expressions as much as possible. Two equilvalent expressions MUST simplify to the same thing, and then they can be tested for equivalence again. Returns True if sympy can determine that the two expressions are equal, and returns False if they are not equal. - 'test_expr' should be the untrusted sympy expression to check. - 'target_expr' should be the trusted sympy expression to match against. """ print("[SYMBOLIC TEST]") try: simplified_target = sympy.simplify_logic(target_expr) simplified_test = sympy.simplify_logic(test_expr) if simplified_target == simplified_test or sympy.srepr(simplified_target) == sympy.srepr(simplified_test): print("Symbolic match.") print("INFO: Adding known pair ({0}, {1})".format(target_expr, test_expr)) KNOWN_PAIRS[(target_expr, test_expr)] = EqualityType.SYMBOLIC return True else: return False except NotImplementedError as e: print("{0}: {1} - Can't check symbolic equality!".format(type(e).__name__, str(e).capitalize())) return False def expr_equality(test_expr, target_expr): """Given two sympy expressions: test for exact, symbolic and numeric equality. Check two sympy expressions for equality, throwing a TypeError if either of the provided sympy objects is not an expression. - 'test_expr' should be the untrusted sympy expression to check. - 'target_expr' should be the trusted sympy expression to match against. """ equality_type = EqualityType.EXACT equal = exact_match(test_expr, target_expr) if not equal: # Then try checking for symbolic equality: equality_type = EqualityType.SYMBOLIC equal = symbolic_equality(test_expr, target_expr) return equal, equality_type def general_equality(test_expr, target_expr): """Given two general sympy objects: test for exact, symbolic and numeric equality. - 'test_expr' should be the untrusted sympy object to check. - 'target_expr' should be the trusted sympy object to match against. """ equal, equality_type = known_equal_pair(KNOWN_PAIRS, test_expr, target_expr) # If this is a known pair: return immediately: if equal: return equal, equality_type else: print("[[EXPRESSION CHECK]]") return expr_equality(test_expr, target_expr) def check(test_str, target_str, *, symbols=None, check_symbols=True, description=None, _quiet=False): """The main checking function, calls each of the equality checking functions as required. Returns a dict describing the equality; with important keys being 'equal', and 'equality_type'. The key 'error' is added if something went wrong, and this should always be checked for first. - 'test_str' should be the untrusted string for sympy to parse. - 'target_str' should be the trusted string to parse and match against. - 'symbols' should be a string list or comma separated string of symbols not to split during parsing. - 'check_symbols' indicates whether to verfiy the symbols used in each expression are exactly the same or not; setting this to False will allow symbols which cancel out to be included (probably don't want this in questions). - 'description' is an optional description to print before the checker's output to stdout which can be used to improve logging. - '_quiet' is an internal argument used to suppress some output when this function is called from plus_minus_checker(). """ # Suppress this output if necessary: if not _quiet: print("=" * 50) # For logging purposes, if we have a description: print it! if description is not None: print(description) print("=" * 50) print("[LOGIC]") # If nothing to parse, fail. On server, this will be caught in check_endpoint() if (target_str == "") or (test_str == ""): print("ERROR: No input provided!") if not _quiet: print("=" * 50) return dict(error="Empty string as argument.") # Cleanup the strings before anything is done to them: error_is_test = False try: target_str = logic_parser.cleanup_string(target_str, reject_unsafe_input=True) error_is_test = True test_str = logic_parser.cleanup_string(test_str, reject_unsafe_input=True) except UnsafeInputException: print("ERROR: Input contained non-whitelisted characters!") result = dict(error="Bad input provided!") if error_is_test: print("Test string: '{}'".format(test_str)) result["syntax_error"] = str(True).lower() if not _quiet: print("=" * 50) return result print("Target string: '{}'".format(target_str)) print("Test string: '{}'".format(test_str)) print("[[PARSE EXPRESSIONS]]") # Parse the trusted target expression: target_expr = parse_expression(target_str) # Parse the untrusted test expression: test_expr = parse_expression(test_str) result = dict(target=target_str, test=test_str) if target_expr is None: print("ERROR: TRUSTED EXPRESSION CANNOT BE PARSED!") if not _quiet: print("=" * 50) result["error"] = "Parsing TARGET Expression Failed!" result["code"] = 400 # This is fatal! return result if test_expr is None: print("Incorrectly formatted ToCheck expression.") if not _quiet: print("=" * 50) result["error"] = "Parsing Test Expression Failed!" result["syntax_error"] = str(True).lower() return result result["parsed_target"] = str(target_expr) result["parsed_test"] = str(test_expr) # Now check for symbol match and equality: try: print("Parsed Target: {0}\nParsed ToCheck: {1}".format(target_expr, test_expr)) if check_symbols: # Do we have same set of symbols in each? incorrect_symbols = contains_incorrect_symbols(test_expr, target_expr) if incorrect_symbols is not None: print("[[RESULT]]\nEquality: False") if not _quiet: print("=" * 50) result["equal"] = str(False).lower() result["equality_type"] = EqualityType.SYMBOLIC.value result["incorrect_symbols"] = incorrect_symbols return result # Then check for equality proper: equal, equality_type = general_equality(test_expr, target_expr) except (SyntaxError, TypeError, AttributeError) as e: print("Error when comparing expressions: '{}'.".format(e)) if not _quiet: print("=" * 50) result["error"] = "Comparison of expressions failed: '{}'".format(e) return result print("[[RESULT]]") if equal and (equality_type is not EqualityType.EXACT) and ((target_expr, test_expr) not in KNOWN_PAIRS): print("INFO: Adding known pair ({0}, {1})".format(target_expr, test_expr)) KNOWN_PAIRS[(target_expr, test_expr)] = equality_type print("Equality: {}".format(equal)) if not _quiet: print("=" * 50) result["equal"] = str(equal).lower() result["equality_type"] = equality_type.value return result

559890790ae2649fffcbdb6731e70ef5186638d7

py

Python

Kattis/How Many Digits/howmanydigits.py

DeepSpace2/Comptitive-Programming

13212d9dbc73ab87519b0596fdb0147d40c7eaa8

2021-11-12T16:39:40.000Z

2021-11-12T16:39:40.000Z

Kattis/How Many Digits/howmanydigits.py

DeepSpace2/Comptitive-Programming

13212d9dbc73ab87519b0596fdb0147d40c7eaa8

Kattis/How Many Digits/howmanydigits.py

DeepSpace2/Comptitive-Programming

13212d9dbc73ab87519b0596fdb0147d40c7eaa8

2021-07-01T11:46:19.000Z

2021-09-12T13:49:04.000Z

from math import e, log10, pi while True: try: n = int(input()) except EOFError: break if n == 0: print(1) else: print(int(n * log10(n / e) + log10(2 * pi * n) / 2) + 1)

from math import e, log10, pi while True: try: n = int(input()) except EOFError: break if n == 0: print(1) else: print(int(n * log10(n / e) + log10(2 * pi * n) / 2) + 1)

72eb43c27020f9c97d40a6a12b90946e9a888bc7

py

Python

game24/gameconsole.py

Adoyan-Grigor/game24

4619e953ed94248669759850b9efb812ecf54786

game24/gameconsole.py

Adoyan-Grigor/game24

4619e953ed94248669759850b9efb812ecf54786

game24/gameconsole.py

Adoyan-Grigor/game24

4619e953ed94248669759850b9efb812ecf54786

#!/usr/bin/python # -*- coding: utf-8 -*- from __future__ import absolute_import, print_function, division import sys import argparse import readline import random import traceback try: import builtins raw_input = getattr(builtins, 'input') except ImportError: pass from game24 import calc, game MSG_MENU_MAIN = '''1. Play (p) 2. Check answer (c) 3. Quit (q)''' MSG_MENU_PLAY = '''1. Definitely no solutions (n) 2. Give me a hint (h) 3. I gave up, show me the answer (s) 4. Back to the main menu (b) 5. Quit the game (q)''' MSG_MENU_SET_END = '''1. One more set (n) 2. Back to the main menu (b) 3. Quit the game (q)''' MSG_MENU_PLAY_RIGHT = '''1. Try other solutions (t) 2. Next hand (n) 3. Show me the answers (s) 4. Quit the game (q)''' MSG_SELECT = 'Your choice: ' MSG_INVALID_INPUT = 'Invalid input!' MSG_MENU_HAND_END = '''1. One more hand (n) 2. Quit this set, back to the main menu (b) 3. Quit the game (q)''' MSG_SELECT = 'Your choice: ' MSG_INVALID_INPUT = 'Invalid input!' MSG_INVALID_INTEGER = 'Invalid integer: %s' MSG_PLAY_NEW_SET = 'Set %d' MSG_PLAY_NEW_HAND = 'Hand %d: %s' MSG_PLAY_INPUT_EXPR = 'Input your solution or one of the above choices: ' MSG_PLAY_RIGHT = 'Good Job!' MSG_PLAY_FIND_BUG = '''Great Job! You not only solved the problem, but also found a bug! Please report to me with the cards and your solution if you don't mind.''' MSG_PLAY_WRONG = "Sorry! It's not correct!" MSG_PLAY_NO_ANSWER = 'Seems no solutions' MSG_PLAY_NO_CARDS = 'Set end, your result' MSG_INPUT_NUMBERS = 'Please input %d integers: ' INPUT_EOF = '\x00' if __name__ == '__main__': main()

#!/usr/bin/python # -*- coding: utf-8 -*- from __future__ import absolute_import, print_function, division import sys import argparse import readline import random import traceback try: import builtins raw_input = getattr(builtins, 'input') except ImportError: pass from game24 import calc, game MSG_MENU_MAIN = '''1. Play (p) 2. Check answer (c) 3. Quit (q)''' MSG_MENU_PLAY = '''1. Definitely no solutions (n) 2. Give me a hint (h) 3. I gave up, show me the answer (s) 4. Back to the main menu (b) 5. Quit the game (q)''' MSG_MENU_SET_END = '''1. One more set (n) 2. Back to the main menu (b) 3. Quit the game (q)''' MSG_MENU_PLAY_RIGHT = '''1. Try other solutions (t) 2. Next hand (n) 3. Show me the answers (s) 4. Quit the game (q)''' MSG_SELECT = 'Your choice: ' MSG_INVALID_INPUT = 'Invalid input!' MSG_MENU_HAND_END = '''1. One more hand (n) 2. Quit this set, back to the main menu (b) 3. Quit the game (q)''' MSG_SELECT = 'Your choice: ' MSG_INVALID_INPUT = 'Invalid input!' MSG_INVALID_INTEGER = 'Invalid integer: %s' MSG_PLAY_NEW_SET = 'Set %d' MSG_PLAY_NEW_HAND = 'Hand %d: %s' MSG_PLAY_INPUT_EXPR = 'Input your solution or one of the above choices: ' MSG_PLAY_RIGHT = 'Good Job!' MSG_PLAY_FIND_BUG = '''Great Job! You not only solved the problem, but also found a bug! Please report to me with the cards and your solution if you don't mind.''' MSG_PLAY_WRONG = "Sorry! It's not correct!" MSG_PLAY_NO_ANSWER = 'Seems no solutions' MSG_PLAY_NO_CARDS = 'Set end, your result' MSG_INPUT_NUMBERS = 'Please input %d integers: ' INPUT_EOF = '\x00' class GameConsole(game.Game): def __init__(self, target=24, count=4, face2ten=False, showcard=False): super(GameConsole, self).__init__(target, count, face2ten) self.showcard = showcard @staticmethod def raw_input_ex(prompt='', default=''): '''enhance raw_input to support default input and also flat EOF''' try: readline.set_startup_hook(lambda: readline.insert_text(default)) try: return input(prompt) finally: readline.set_startup_hook() except EOFError: return INPUT_EOF @staticmethod def print_title(title, dechar='', delen=50): print(dechar * delen) print(title) print(dechar * delen) @staticmethod def ui_menu(menu, choices, eof=True): '''show a menu, and return the selection''' GameConsole.print_title(menu, dechar='-') while True: r = GameConsole.raw_input_ex(MSG_SELECT).strip() if r == '' or (r in choices and len(r) > 1): print(MSG_INVALID_INPUT) continue elif r in choices or (eof and r == INPUT_EOF): print() return r print(MSG_INVALID_INPUT) def ui_check_answer(self): '''show answers for user provided integers''' while True: r = self.raw_input_ex(MSG_INPUT_NUMBERS % self.count).strip() try: integers = [int(s) for s in r.strip().split()] except ValueError: print(MSG_INVALID_INPUT) continue if len(integers) != self.count: print(MSG_INVALID_INPUT) continue break answers = calc.solve(integers, self.target) if answers: s = '\n'.join([str(expr) for expr in answers]) else: s = MSG_PLAY_NO_ANSWER self.print_title(s) def main(self): '''the main entry of the game console''' choices = '1p2c3q' while True: r = self.ui_menu(MSG_MENU_MAIN, choices) if r in '1p': self.play() elif r in '2c': self.ui_check_answer() elif r in ('3q' + INPUT_EOF): return def print_result(self): solved = 0 failed = 0 hinted = 0 for hand in self.hands: if hand.result == game.HAND_RESULT_SOLVED: solved += 1 elif hand.result == game.HAND_RESULT_HINTED: hinted += 1 elif hand.result == game.HAND_RESULT_FAILED: failed += 1 print() print('Total %d hands solved' % solved) print('Total %d hands solved with hint' % hinted) print('Total %d hands failed to solve' % failed) print() def ui_menu_and_expr(self, menu, choices, eof=True): hand_ints = self.hands[-1].integers self.print_title(menu, dechar='-') while True: r = self.raw_input_ex(MSG_PLAY_INPUT_EXPR).strip() if r == '' or (r in choices and len(r) > 1): print(MSG_INVALID_INPUT) continue elif r in choices or (eof and r == INPUT_EOF): print() return r try: expr = calc.parse(r) except ValueError as e: print(str(e)) continue integers = expr.get_integers() for i in integers: if i not in hand_ints: print(MSG_INVALID_INTEGER % i) break else: return expr def play(self): while True: if not self.hands: self.print_title(MSG_PLAY_NEW_SET % self.seti, dechar='*') hand = self.new_hand() if not hand: # no enough cards for a new hand self.print_title(MSG_PLAY_NO_CARDS, dechar='*') self.print_result() choices = '1n2b3q' r = self.ui_menu(MSG_MENU_SET_END, choices) if r in '1n': # renew the set self.reset() continue elif r in ('2b' + INPUT_EOF): # back to the main menu return elif r in '3q': sys.exit(0) print() if self.showcard: sc = hand.str_cards() else: sc = ' '.join([str(i) for i in hand.integers]) self.print_title(MSG_PLAY_NEW_HAND % (len(self.hands), sc), dechar='+') print() while True: choices = '1n2h3s4b5q' r = self.ui_menu_and_expr(MSG_MENU_PLAY, choices) if isinstance(r, calc.Expr): expr = r check_r = str(r) if expr.value == self.target: hand.solved() if not self.calculating_the_number_of_numbers(check_r, sc): print(MSG_INVALID_INPUT) continue s = MSG_PLAY_RIGHT self.print_title(s) choices = '1t2n3s4q' r = self.ui_menu(MSG_MENU_PLAY_RIGHT, choices, eof=False) if r in '1t': continue elif r in '2n': break elif r in '3s': self.print_title(hand.str_answer()) elif r in '4q': sys.exit(0) else: self.print_title(MSG_PLAY_WRONG) continue elif r in '1n': # no answer if hand.answers: self.print_title(MSG_PLAY_WRONG) continue else: hand.solved() self.print_title(MSG_PLAY_RIGHT) elif r in '2h': # show a hint if hand.answers: hand.hinted() self.print_title(hand.str_hint()) continue else: self.print_title(MSG_PLAY_NO_ANSWER) elif r in '3s': # show the answer if hand.answers: s = hand.str_answer() else: s = MSG_PLAY_NO_ANSWER self.print_title(s) elif r in ('4b' + INPUT_EOF): # back to the main menu return elif r in '5q': sys.exit(0) # this hand is end break def calculating_the_number_of_numbers(self, r, sc): """calculates how many numbers are in the user input""" numb = '' check_list = [] choices = '1234567890' r = r.split() sc = sc.split() for i in r: if i in '+-*×/÷': r.remove(i) if len(r) != len(sc): return False return True def arg_parse(): parser = argparse.ArgumentParser(description='Play the 24 Game') parser.add_argument('-c', type=int, default=4, dest='count', help='the number of integers to play with, default=4') parser.add_argument('-C', action='store_true', dest='showcard', help='show cards instead of integers under interactive mode') parser.add_argument('-d', action='store_true', dest='debug', help='enable debug output') parser.add_argument('-i', action='store_true', dest='interactive', help='interactive mode, all positional integers arguments omitted') parser.add_argument('-N', action='store_true', dest='face2ten', help='under interactive mode, set J Q K to 10, default=11,12,13') parser.add_argument('-t', type=int, default=24, dest='target', help='the game target, default=24') parser.add_argument('integers', nargs='*') r = parser.parse_args() if not r.interactive and len(r.integers) == 0: r.interactive = True return r def main(): args = arg_parse() try: if args.interactive: gc = GameConsole(args.target, args.count, args.face2ten, args.showcard) gc.main() sys.exit(0) except KeyboardInterrupt: sys.exit(1) except Exception as e: if args.debug: traceback.print_exc() else: print(str(e), file=sys.stderr) sys.exit(3) if __name__ == '__main__': main()

ce74a3c506ce96d7da83678be3ac5f3605bd112f

py

Python

dynamodb-serverless/functions/put/handler.py

koki-nakamura22/serverless-framework-practice

b6fb96cc97ecb7a1fa167c7cccb143510466d350

dynamodb-serverless/functions/put/handler.py

koki-nakamura22/serverless-framework-practice

b6fb96cc97ecb7a1fa167c7cccb143510466d350

dynamodb-serverless/functions/put/handler.py

koki-nakamura22/serverless-framework-practice

b6fb96cc97ecb7a1fa167c7cccb143510466d350

import json import os import boto3 from faker import Faker # DynamoDB object dynamodb = boto3.resource('dynamodb') table = dynamodb.Table(f"TestUsersTable-{os.environ['STAGE']}")

import json import os import boto3 from faker import Faker # DynamoDB object dynamodb = boto3.resource('dynamodb') table = dynamodb.Table(f"TestUsersTable-{os.environ['STAGE']}") def __truncate(): response = table.scan() key_names = [ x["AttributeName"] for x in table.key_schema ] delete_keys = [ { k:v for k,v in x.items() if k in key_names } for x in response["Items"] ] with table.batch_writer() as batch: for key in delete_keys: batch.delete_item(Key = key) def __put(id, name): table.put_item( Item = { "id" : id, "name" : name, } ) def put(event, context): __truncate() fake = Faker() for n in range(1, 10 + 1): __put(str(n).zfill(3), fake.name()) response = { "statusCode": 200, } return response

8411765f0f08514141bd4c621bce5644bb0156cd

py

Python

Chapter07/4985_07_01-logs.py

mapenthusiast/QGIS-Python-Programming-Cookbook-Second-Edition

1b2fefdb09f614a2005976a451f882a198c6c9c5

2017-03-27T18:58:26.000Z

2022-03-25T15:29:45.000Z

Chapter07/4985_07_01-logs.py

mapenthusiast/QGIS-Python-Programming-Cookbook-Second-Edition

1b2fefdb09f614a2005976a451f882a198c6c9c5

2018-07-02T09:23:47.000Z

2018-08-23T13:57:41.000Z

Chapter07/4985_07_01-logs.py

mapenthusiast/QGIS-Python-Programming-Cookbook-Second-Edition

1b2fefdb09f614a2005976a451f882a198c6c9c5

2017-03-08T06:37:22.000Z

2021-12-17T21:51:30.000Z

# Using Log Files # Settings/Options/System/Environment (use custom variables) # QGIS_LOG_FILE=/qgis_data/log.txt # Restart QGIS # Message to log file: QgsLogger.logMessageToFile("This is a message to a log file.") # Message to QGIS Log Window ( yellow triangle icon in the lower right) QgsMessageLog.logMessage("This is a message from the Python Console", "Python Console", QgsMessageLog.INFO)

# Using Log Files # Settings/Options/System/Environment (use custom variables) # QGIS_LOG_FILE=/qgis_data/log.txt # Restart QGIS # Message to log file: QgsLogger.logMessageToFile("This is a message to a log file.") # Message to QGIS Log Window ( yellow triangle icon in the lower right) QgsMessageLog.logMessage("This is a message from the Python Console", "Python Console", QgsMessageLog.INFO)

fad016a754f61df9c72c04956901d978db0b6df6

py

Python

paddleslim/nas/ofa/utils/utils.py

zhuguiqian/PaddleSlim

c363c91c36bb9ada41f755c0ec4df3282ccdd6f0

paddleslim/nas/ofa/utils/utils.py

zhuguiqian/PaddleSlim

c363c91c36bb9ada41f755c0ec4df3282ccdd6f0

paddleslim/nas/ofa/utils/utils.py

zhuguiqian/PaddleSlim

c363c91c36bb9ada41f755c0ec4df3282ccdd6f0

# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License.

# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. def compute_start_end(kernel_size, sub_kernel_size): center = kernel_size // 2 sub_center = sub_kernel_size // 2 start = center - sub_center end = center + sub_center + 1 assert end - start == sub_kernel_size return start, end def get_same_padding(kernel_size): assert isinstance(kernel_size, int) assert kernel_size % 2 > 0, "kernel size must be odd number" return kernel_size // 2 def convert_to_list(value, n): return [value, ] * n def search_idx(num, sorted_nestlist): max_num = -1 max_idx = -1 for idx in range(len(sorted_nestlist)): task_ = sorted_nestlist[idx] max_num = task_[-1] max_idx = len(task_) - 1 for phase_idx in range(len(task_)): if num <= task_[phase_idx]: return idx, phase_idx assert num > max_num return len(sorted_nestlist) - 1, max_idx

9e42668859a3e942dd8cf341d42cb36a048ac54f

py

Python

backend/openbd.py

n-yU/shisho

dc99a2d90dde3599af62a6a59a4aabf6b5a72011

2021-08-20T05:34:31.000Z

2021-08-20T05:34:31.000Z

backend/openbd.py

n-yU/shisho

dc99a2d90dde3599af62a6a59a4aabf6b5a72011

backend/openbd.py

n-yU/shisho

dc99a2d90dde3599af62a6a59a4aabf6b5a72011

from logging import getLogger, StreamHandler, DEBUG, Formatter from typing import Dict, Union import re import json import requests import MeCab from neologdn import normalize # ロガー設定 logger = getLogger(__name__) handler = StreamHandler() handler.setLevel(DEBUG) logger.setLevel(DEBUG) logger.addHandler(handler) logger.propagate = False handler.setFormatter(Formatter('[openBD] %(asctime)s - %(message)s'))

from logging import getLogger, StreamHandler, DEBUG, Formatter from typing import Dict, Union import re import json import requests import MeCab from neologdn import normalize # ロガー設定 logger = getLogger(__name__) handler = StreamHandler() handler.setLevel(DEBUG) logger.setLevel(DEBUG) logger.addHandler(handler) logger.propagate = False handler.setFormatter(Formatter('[openBD] %(asctime)s - %(message)s')) class OpenBD: # openBD: https://openbd.jp/ def __init__(self, isbn10: int, mecab: MeCab.Tagger): """"インスタンス生成時の初期化処理 Args: isbn10 (int): OpenBDへリクエストする書籍のISBN-10 mecab (MeCab.Tagger): MeCab設定（辞書等） """ self.isbn10 = isbn10 # 書籍のISBN-10 self.result = self.get_json_from_openbd() # openBDへのリクエスト結果 self.mecab = mecab # MeCab設定 def get_json_from_openbd(self) -> str: """openBDから書籍情報取得 Returns: str: openBDリクエスト結果 """ # 指定ISBN-10の書籍情報を取得する, openBDエンドポイント openbd_endpoint = 'https://api.openbd.jp/v1/get?isbn={0}'.format(self.isbn10) try: response = requests.get(openbd_endpoint) response.raise_for_status() except requests.RequestException as e: # ステータスコード200番台以外 -> エラーログ出力 logger.debug(e) return 'FAILED' openbd = json.loads(response.text)[0] # 書籍情報 from openBD # openBDで書籍情報が見つからないケース if openbd is None: return 'NOT FOUND' self.openbd = openbd return 'OK' def get_std_info(self) -> Union[Dict[str, str], bool]: if self.result != 'OK': logger.debug('openBDからの書籍情報取得に失敗しているため基本情報を取得できません') return False # 基本情報取得 title = self.openbd['summary']['title'] # タイトル publisher = self.openbd['summary']['publisher'] # 出版社 authors = self.openbd['summary']['author'] # 著者 cover = self.openbd['summary']['cover'] # 表紙画像URL # ISBN-10ベース情報 isbn10 = self.isbn10 amazon = 'https://www.amazon.co.jp/dp/{0}'.format(isbn10) # 出版日: 形式が異なるため一時変数に代入後処理 tmp_pubdate = self.openbd['summary']['pubdate'] if len(tmp_pubdate) == 8: # pubdate: yyyyMMdd pubdate = '{0}-{1}-{2}'.format(tmp_pubdate[:4], tmp_pubdate[4:6], tmp_pubdate[6:]) else: # pubdare: yyyy-MM pubdate = '{0}-01'.format(tmp_pubdate) # 書籍詳細（目次や概要など）の取得 if self.openbd['onix']['CollateralDetail'].get('TextContent'): # 複数ある場合は連結 detail = ' '.join([text_content['Text'].replace('\n', ' ') for text_content in self.openbd['onix']['CollateralDetail']['TextContent']]) else: # 詳細が存在しない場合 -> 未登録とする detail = '未登録' # 書籍説明（タイトル，出版社，著者，詳細を連結した文章）テキスト（処理前） # neologdnによる正規化 -> 数字削除（目次対策） tmp_description = re.sub(r'[0-9]+', ' ', normalize('{0} {1} {2} {3}'.format(title, publisher, authors, detail))) # 書籍説明テキストの分かち書きと品詞フィルタリング description_word_list = [] # 書籍説明テキスト対象単語 for line in self.mecab.parse(tmp_description).splitlines(): chunks = line.split('\t') if len(chunks) > 3 and (chunks[3].startswith('動詞') or chunks[3].startswith('形容詞') or chunks[3].startswith('名詞')): # 動詞or形容詞or名詞 -> 訓練対象 description_word_list.append(chunks[0]) # 書籍説明テキスト（処理後）: Doc2Vec訓練時に書籍を表す文章として使用 description = ' '.join(description_word_list) info = dict(amazon=amazon, isbn10=isbn10, cover=cover, title=title, publisher=publisher, authors=authors, pubdate=pubdate, description=description) return info

f7fe38c9a4b8c5796670a8aa33b5cb1b8bbd7c39

py

Python

src/jetson/Sensors/sensors_simple.py

ichalkiad/VW_challenge

333222010ecf3d1ca4a0e181239f761c975453e9

2017-08-16T08:42:49.000Z

2017-08-16T08:42:49.000Z

src/jetson/Sensors/sensors_simple.py

ichalkiad/VW_challenge

333222010ecf3d1ca4a0e181239f761c975453e9

2017-08-09T23:01:30.000Z

2017-08-24T16:44:13.000Z

src/jetson/Sensors/sensors_simple.py

yhalk/vw_challenge_ECR

c1ff50070d0f7367ccfbf473c69e90fd2be5e85e

import paho.mqtt.client as mqtt import ev3dev.ev3 as ev3 import ctypes import numpy as np import sys import cv2 from Sensors.mpu6050.mpu6050 import MPU6050 import smbus from Sensors.odometry import Odometry import sys, serial from serial.tools import list_ports #Create camera sensor object camera = OnBoardCamera()

import paho.mqtt.client as mqtt import ev3dev.ev3 as ev3 import ctypes import numpy as np import sys import cv2 from Sensors.mpu6050.mpu6050 import MPU6050 import smbus from Sensors.odometry import Odometry import sys, serial from serial.tools import list_ports class Sensor(object): def __init__(self, *args, **kwargs): pass def read(self): raise ValueError('This function must be implemented by ') class IR_teensy(Sensor): def __init__(self): self.ports = list(list_ports.comports()) # get all the connected serial devices self.serial_port = serial.Serial('/dev/'+self.ports[0].name) # connect to the first def debug(self): ''' Use if cannot connect to the port This function will print all found serial devices and prints the name and index of the port ''' for i, item in enumerate(self.ports): print(i + ' : ' + item.name) def read(self): ''' Reads the current value from the teensy Returns: Distance in cm ''' measurement = self.serial_port.readline() # read the measurement measurement = measurement.decode('utf-8').split('\r') # change it to utf and split it on funny characters return measurement[0] # only return the actual measurment class IMU2(Sensor): def __init__(self, bus='/dev/i2c-1', address=0x68): self.bus = smbus.SMBus(1) self.address = address self.mpu = MPU6050(self.bus,self.address, 'IMU') def read(self): ''' Reads the current values from the IMU using the mpu library Returns: tuple containing: pitch, roll, gyro x,y,z, accel x,y,z these values are scaled and NOT raw ''' return self.mpu.read_all() class IMU(Sensor): def __init__(self, path_to_shared_lib_mpu='/home/nvidia/jetson-robot/IOInterface/jetson/Sensors/mpu/libmpu.so', bus_filename='/dev/i2c-1', bus_adresses=[0x68, 0x69]): bus_filename = bus_filename.encode('ascii') self.libmpu = ctypes.cdll.LoadLibrary(path_to_shared_lib_mpu) self.file_descriptors = [self.libmpu.initIMU(bus_filename, bus_adress) for bus_adress in bus_adresses] self.data_c_arrays = [(ctypes.c_int16*7)() for _ in range(len(bus_adresses))] self.name = 'imu' self.data_sources = ["temperature", "acceleration", "gyro"] def read(self): data_dict = {} for idx, (file_descriptor, data_c_array) in enumerate(zip(self.file_descriptors, self.data_c_arrays)): self.libmpu.readIMU(file_descriptor, data_c_array) data_np_array = np.array(data_c_array) data_dict['temperature_{}'.format(idx)] = data_np_array[0] / 340.0 + 36.53 data_dict['acceleration_{}'.format(idx)] = np.array([int(data_np_array[1]), int(data_np_array[2]), int(data_np_array[3]), ]) data_dict['gyro_{}'.format(idx)] = np.array([int(data_np_array[4]), int(data_np_array[5]), int(data_np_array[6]), ]) return data_dict def read_sensor_nr(self, sensor_nr): # TODO: Ask Max, if the magic values for temperature conversion are correct. data_dict = {} self.libmpu.readIMU(self.file_descriptors[sensor_nr], self.data_c_arrays[sensor_nr]) data_np_array = np.array(self.data_c_arrays[sensor_nr]) data_dict['temperature'] = data_np_array[0] / 340.0 + 36.53 data_dict['acceleration'] = np.array([int(data_np_array[1]), int(data_np_array[2]), int(data_np_array[3])]) data_dict['gyro'] = np.array([int(data_np_array[4]), int(data_np_array[5]), int(data_np_array[6])]) return data_dict def get_data_sources(self): return self.data_sources class OnBoardCamera(Sensor): def __init__(self): self.name = 'onBoardCamera' self.cap = cv2.VideoCapture("nvcamerasrc ! video/x-raw(memory:NVMM), width=(int)160, height=(int)120, format=(string)I420, framerate=(fraction)30/1 ! nvvidconv flip-method=2 ! video/x-raw, format=(string)I420 ! videoconvert ! video/x-raw, format=(string)BGR ! appsink") #self.cap = cv2.VideoCapture("nvcamerasrc ! video/x-raw(memory:NVMM), width=(int)(160), height=(int)(120),format=(string)I420, framerate=(fraction)2/1 ! nvvidconv flip-method=0 ! video/x-raw, format=(string)BGRx ! videoconvert ! video/x-raw, format=(string)BGR ! appsink") def read(self): if self.cap.isOpened(): ret_val, frame = self.cap.read(); frame = cv2.flip(frame,0) frame = cv2.flip(frame,1) else: raise ValueError('Camera not opened. Sorry this message is not really helpful, blame openCV :-) ') return {'onBoardCamera':frame} def clean_buf(self): for i in range(5): self.cap.grab() #Create camera sensor object camera = OnBoardCamera()

790f64346cac505157953135acdaf67a66ffe6fe

py

Python

mphys/integrated_forces.py

timryanb/mphys

74560a163034a0006a17811ba1206bab00f1f775

2022-02-22T18:08:56.000Z

2022-03-14T13:32:46.000Z

mphys/integrated_forces.py

timryanb/mphys

74560a163034a0006a17811ba1206bab00f1f775

2022-02-22T15:10:15.000Z

2022-03-23T16:15:09.000Z

mphys/integrated_forces.py

timryanb/mphys

74560a163034a0006a17811ba1206bab00f1f775

2022-02-22T18:08:35.000Z

2022-03-17T16:21:08.000Z

import numpy as np import openmdao.api as om if __name__ == '__main__': check_integrated_surface_force_partials()

import numpy as np import openmdao.api as om class IntegratedSurfaceForces(om.ExplicitComponent): def setup(self): self.add_input('aoa',desc = 'angle of attack', units='rad',tags=['mphys_input']) self.add_input('yaw',desc = 'yaw angle',units='rad',tags=['mphys_input']) self.add_input('ref_area', val = 1.0,tags=['mphys_input']) self.add_input('moment_center',shape=3,tags=['mphys_input']) self.add_input('ref_length', val = 1.0,tags=['mphys_input']) self.add_input('q_inf', val = 1.0,tags=['mphys_input']) self.add_input('x_aero', shape_by_conn=True, distributed=True, desc = 'surface coordinates', tags=['mphys_coupling']) self.add_input('f_aero', shape_by_conn=True, distributed=True, desc = 'dimensional forces at nodes', tags=['mphys_coupling']) self.add_output('C_L', desc = 'Lift coefficient', tags=['mphys_result']) self.add_output('C_D', desc = 'Drag coefficient', tags=['mphys_result']) self.add_output('C_X', desc = 'X Force coefficient', tags=['mphys_result']) self.add_output('C_Y', desc = 'Y Force coefficient', tags=['mphys_result']) self.add_output('C_Z', desc = 'Z Force coefficient', tags=['mphys_result']) self.add_output('CM_X', desc = 'X Moment coefficient', tags=['mphys_result']) self.add_output('CM_Y', desc = 'Y Moment coefficient', tags=['mphys_result']) self.add_output('CM_Z', desc = 'Z Moment coefficient', tags=['mphys_result']) self.add_output('Lift', desc = 'Total Lift', tags=['mphys_result']) self.add_output('Drag', desc = 'Total Drag', tags=['mphys_result']) self.add_output('F_X', desc = 'Total X Force', tags=['mphys_result']) self.add_output('F_Y', desc = 'Total Y Force', tags=['mphys_result']) self.add_output('F_Z', desc = 'Total Z Force', tags=['mphys_result']) self.add_output('M_X', desc = 'Total X Moment', tags=['mphys_result']) self.add_output('M_Y', desc = 'Total Y Moment', tags=['mphys_result']) self.add_output('M_Z', desc = 'Total Z Moment', tags=['mphys_result']) def compute(self,inputs,outputs): aoa = inputs['aoa'] yaw = inputs['yaw'] area = inputs['ref_area'] q_inf = inputs['q_inf'] xc = inputs['moment_center'][0] yc = inputs['moment_center'][1] zc = inputs['moment_center'][2] c = inputs['ref_length'] x = inputs['x_aero'][0::3] y = inputs['x_aero'][1::3] z = inputs['x_aero'][2::3] fx = inputs['f_aero'][0::3] fy = inputs['f_aero'][1::3] fz = inputs['f_aero'][2::3] fx_total = self.comm.allreduce(np.sum(fx)) fy_total = self.comm.allreduce(np.sum(fy)) fz_total = self.comm.allreduce(np.sum(fz)) outputs['F_X'] = fx_total outputs['F_Y'] = fy_total outputs['F_Z'] = fz_total outputs['C_X'] = fx_total / (q_inf * area) outputs['C_Y'] = fy_total / (q_inf * area) outputs['C_Z'] = fz_total / (q_inf * area) outputs['Lift'] = -fx_total * np.sin(aoa) + fz_total * np.cos(aoa) outputs['Drag'] = ( fx_total * np.cos(aoa) * np.cos(yaw) - fy_total * np.sin(yaw) + fz_total * np.sin(aoa) * np.cos(yaw) ) outputs['C_L'] = outputs['Lift'] / (q_inf * area) outputs['C_D'] = outputs['Drag'] / (q_inf * area) m_x = self.comm.allreduce( np.dot(fz,(y-yc)) - np.dot(fy,(z-zc))) m_y = self.comm.allreduce(-np.dot(fz,(x-xc)) + np.dot(fx,(z-zc))) m_z = self.comm.allreduce( np.dot(fy,(x-xc)) - np.dot(fx,(y-yc))) outputs['M_X'] = m_x outputs['M_Y'] = m_y outputs['M_Z'] = m_z outputs['CM_X'] = m_x / (q_inf * area * c) outputs['CM_Y'] = m_y / (q_inf * area * c) outputs['CM_Z'] = m_z / (q_inf * area * c) def compute_jacvec_product(self, inputs, d_inputs, d_outputs, mode): aoa = inputs['aoa'] yaw = inputs['yaw'] area = inputs['ref_area'] q_inf = inputs['q_inf'] xc = inputs['moment_center'][0] yc = inputs['moment_center'][1] zc = inputs['moment_center'][2] c = inputs['ref_length'] x = inputs['x_aero'][0::3] y = inputs['x_aero'][1::3] z = inputs['x_aero'][2::3] fx = inputs['f_aero'][0::3] fy = inputs['f_aero'][1::3] fz = inputs['f_aero'][2::3] fx_total = self.comm.allreduce(np.sum(fx)) fy_total = self.comm.allreduce(np.sum(fy)) fz_total = self.comm.allreduce(np.sum(fz)) lift = -fx_total * np.sin(aoa) + fz_total * np.cos(aoa) drag = ( fx_total * np.cos(aoa) * np.cos(yaw) - fy_total * np.sin(yaw) + fz_total * np.sin(aoa) * np.cos(yaw) ) m_x = self.comm.allreduce( np.dot(fz,(y-yc)) - np.dot(fy,(z-zc))) m_y = self.comm.allreduce(-np.dot(fz,(x-xc)) + np.dot(fx,(z-zc))) m_z = self.comm.allreduce( np.dot(fy,(x-xc)) - np.dot(fx,(y-yc))) if mode == 'fwd': if 'aoa' in d_inputs: daoa_rad = d_inputs['aoa'] if 'Lift' in d_outputs or 'C_L' in d_outputs: d_lift_d_aoa = ( - fx_total * np.cos(aoa) * daoa_rad - fz_total * np.sin(aoa) * daoa_rad ) if 'Lift' in d_outputs: d_outputs['Lift'] += d_lift_d_aoa if 'C_L' in d_outputs: d_outputs['C_L'] += d_lift_d_aoa / (q_inf * area) if 'Drag' in d_outputs or 'C_D' in d_outputs: d_drag_d_aoa = ( fx_total * (-np.sin(aoa) * daoa_rad) * np.cos(yaw) + fz_total * ( np.cos(aoa) * daoa_rad) * np.cos(yaw)) if 'Drag' in d_outputs: d_outputs['Drag'] += d_drag_d_aoa if 'C_D' in d_outputs: d_outputs['C_D'] += d_drag_d_aoa / (q_inf * area) if 'yaw' in d_inputs: dyaw_rad = d_inputs['yaw'] if 'Drag' in d_outputs or 'C_D' in d_outputs: d_drag_d_yaw = ( fx_total * np.cos(aoa) * (-np.sin(yaw) * dyaw_rad) - fy_total * np.cos(yaw) * dyaw_rad + fz_total * np.sin(aoa) * (-np.sin(yaw) * dyaw_rad) ) if 'Drag' in d_outputs: d_outputs['Drag'] += d_drag_d_yaw if 'C_D' in d_outputs: d_outputs['C_D'] += d_drag_d_yaw / (q_inf * area) if 'ref_area' in d_inputs: d_nondim = - d_inputs['ref_area'] / (q_inf * area**2.0) if 'C_X' in d_outputs: d_outputs['C_X'] += fx_total * d_nondim if 'C_Y' in d_outputs: d_outputs['C_Y'] += fy_total * d_nondim if 'C_Z' in d_outputs: d_outputs['C_Z'] += fz_total * d_nondim if 'C_L' in d_outputs: d_outputs['C_L'] += lift * d_nondim if 'C_D' in d_outputs: d_outputs['C_D'] += drag * d_nondim if 'CM_X' in d_outputs: d_outputs['CM_X'] += m_x * d_nondim / c if 'CM_X' in d_outputs: d_outputs['CM_Y'] += m_y * d_nondim / c if 'CM_Z' in d_outputs: d_outputs['CM_Z'] += m_z * d_nondim / c if 'moment_center' in d_inputs: dxc = d_inputs['moment_center'][0] dyc = d_inputs['moment_center'][1] dzc = d_inputs['moment_center'][2] if 'M_X' in d_outputs: d_outputs['M_X'] += -fz_total * dyc + fy_total * dzc if 'M_Y' in d_outputs: d_outputs['M_Y'] += fz_total * dxc - fx_total * dzc if 'M_Z' in d_outputs: d_outputs['M_Z'] += -fy_total * dxc + fx_total * dyc if 'CM_X' in d_outputs: d_outputs['CM_X'] += (-fz_total * dyc + fy_total * dzc) / (q_inf * area * c) if 'CM_Y' in d_outputs: d_outputs['CM_Y'] += ( fz_total * dxc - fx_total * dzc) / (q_inf * area * c) if 'CM_Z' in d_outputs: d_outputs['CM_Z'] += (-fy_total * dxc + fx_total * dyc) / (q_inf * area * c) if 'ref_length' in d_inputs: d_nondim = - d_inputs['ref_length'] / (q_inf * area * c**2.0) if 'CM_X' in d_outputs: d_outputs['CM_X'] += m_x * d_nondim if 'CM_X' in d_outputs: d_outputs['CM_Y'] += m_y * d_nondim if 'CM_Z' in d_outputs: d_outputs['CM_Z'] += m_z * d_nondim if 'q_inf' in d_inputs: d_nondim = - d_inputs['q_inf'] / (q_inf**2.0 * area) if 'C_X' in d_outputs: d_outputs['C_X'] += fx_total * d_nondim if 'C_Y' in d_outputs: d_outputs['C_Y'] += fy_total * d_nondim if 'C_Z' in d_outputs: d_outputs['C_Z'] += fz_total * d_nondim if 'C_L' in d_outputs: d_outputs['C_L'] += lift * d_nondim if 'C_D' in d_outputs: d_outputs['C_D'] += drag * d_nondim if 'CM_X' in d_outputs: d_outputs['CM_X'] += m_x * d_nondim / c if 'CM_X' in d_outputs: d_outputs['CM_Y'] += m_y * d_nondim / c if 'CM_Z' in d_outputs: d_outputs['CM_Z'] += m_z * d_nondim / c if 'x_aero' in d_inputs: dx = d_inputs['x_aero'][0::3] dy = d_inputs['x_aero'][1::3] dz = d_inputs['x_aero'][2::3] if 'M_X' in d_outputs: d_outputs['M_X'] += np.dot(fz,dy) - np.dot(fy,dz) if 'M_Y' in d_outputs: d_outputs['M_Y'] += -np.dot(fz,dx) + np.dot(fx,dz) if 'M_Z' in d_outputs: d_outputs['M_Z'] += np.dot(fy,dx) - np.dot(fx,dy) if 'CM_X' in d_outputs: d_outputs['CM_X'] += ( np.dot(fz,dy) - np.dot(fy,dz)) / (q_inf * area * c) if 'CM_Y' in d_outputs: d_outputs['CM_Y'] += (-np.dot(fz,dx) + np.dot(fx,dz)) / (q_inf * area * c) if 'CM_Z' in d_outputs: d_outputs['CM_Z'] += ( np.dot(fy,dx) - np.dot(fx,dy)) / (q_inf * area * c) if 'f_aero' in d_inputs: dfx = d_inputs['f_aero'][0::3] dfy = d_inputs['f_aero'][1::3] dfz = d_inputs['f_aero'][2::3] dfx_total = np.sum(dfx) dfy_total = np.sum(dfy) dfz_total = np.sum(dfz) if 'F_X' in d_outputs: d_outputs['F_X'] += dfx_total if 'F_Y' in d_outputs: d_outputs['F_Y'] += dfy_total if 'F_Z' in d_outputs: d_outputs['F_Z'] += dfz_total if 'C_X' in d_outputs: d_outputs['C_X'] += dfx_total / (q_inf * area) if 'C_Y' in d_outputs: d_outputs['C_Y'] += dfy_total / (q_inf * area) if 'C_Z' in d_outputs: d_outputs['C_Z'] += dfz_total / (q_inf * area) if 'Lift' in d_outputs: d_outputs['Lift'] += -dfx_total * np.sin(aoa) + dfz_total * np.cos(aoa) if 'Drag' in d_outputs: d_outputs['Drag'] += ( dfx_total * np.cos(aoa) * np.cos(yaw) - dfy_total * np.sin(yaw) + dfz_total * np.sin(aoa) * np.cos(yaw) ) if 'C_L' in d_outputs: d_outputs['C_L'] += (-dfx_total * np.sin(aoa) + dfz_total * np.cos(aoa)) / (q_inf * area) if 'C_D' in d_outputs: d_outputs['C_D'] += ( dfx_total * np.cos(aoa) * np.cos(yaw) - dfy_total * np.sin(yaw) + dfz_total * np.sin(aoa) * np.cos(yaw) ) / (q_inf * area) if 'M_X' in d_outputs: d_outputs['M_X'] += np.dot(dfz,(y-yc)) - np.dot(dfy,(z-zc)) if 'M_Y' in d_outputs: d_outputs['M_Y'] += -np.dot(dfz,(x-xc)) + np.dot(dfx,(z-zc)) if 'M_Z' in d_outputs: d_outputs['M_Z'] += np.dot(dfy,(x-xc)) - np.dot(dfx,(y-yc)) if 'CM_X' in d_outputs: d_outputs['CM_X'] += ( np.dot(dfz,(y-yc)) - np.dot(dfy,(z-zc))) / (q_inf * area * c) if 'CM_Y' in d_outputs: d_outputs['CM_Y'] += (-np.dot(dfz,(x-xc)) + np.dot(dfx,(z-zc))) / (q_inf * area * c) if 'CM_Z' in d_outputs: d_outputs['CM_Z'] += ( np.dot(dfy,(x-xc)) - np.dot(dfx,(y-yc))) / (q_inf * area * c) elif mode == 'rev': if 'aoa' in d_inputs: if 'Lift' in d_outputs or 'C_L' in d_outputs: d_lift = d_outputs['Lift'] if 'Lift' in d_outputs else 0.0 d_cl = d_outputs['C_L'] if 'C_L' in d_outputs else 0.0 d_inputs['aoa'] += ( - fx_total * np.cos(aoa) - fz_total * np.sin(aoa) ) * (d_lift + d_cl / (q_inf * area)) if 'Drag' in d_outputs or 'C_D' in d_outputs: d_drag = d_outputs['Drag'] if 'Drag' in d_outputs else 0.0 d_cd = d_outputs['C_D'] if 'C_D' in d_outputs else 0.0 d_inputs['aoa'] += ( fx_total * (-np.sin(aoa)) * np.cos(yaw) + fz_total * ( np.cos(aoa)) * np.cos(yaw) ) * (d_drag + d_cd / (q_inf * area)) if 'yaw' in d_inputs: if 'Drag' in d_outputs or 'C_D' in d_outputs: d_drag = d_outputs['Drag'] if 'Drag' in d_outputs else 0.0 d_cd = d_outputs['C_D'] if 'C_D' in d_outputs else 0.0 d_inputs['yaw'] += ( fx_total * np.cos(aoa) * (-np.sin(yaw)) - fy_total * np.cos(yaw) + fz_total * np.sin(aoa) * (-np.sin(yaw)) ) * (d_drag + d_cd / (q_inf * area)) if 'ref_area' in d_inputs: d_nondim = - 1.0 / (q_inf * area**2.0) if 'C_X' in d_outputs: d_inputs['ref_area'] += d_outputs['C_X'] * fx_total * d_nondim if 'C_Y' in d_outputs: d_inputs['ref_area'] += d_outputs['C_Y'] * fy_total * d_nondim if 'C_Z' in d_outputs: d_inputs['ref_area'] += d_outputs['C_Z'] * fz_total * d_nondim if 'C_L' in d_outputs: d_inputs['ref_area'] += d_outputs['C_L'] * lift * d_nondim if 'C_D' in d_outputs: d_inputs['ref_area'] += d_outputs['C_D'] * drag * d_nondim if 'CM_X' in d_outputs: d_inputs['ref_area'] += d_outputs['CM_X'] * m_x * d_nondim / c if 'CM_X' in d_outputs: d_inputs['ref_area'] += d_outputs['CM_Y'] * m_y * d_nondim / c if 'CM_Z' in d_outputs: d_inputs['ref_area'] += d_outputs['CM_Z'] * m_z * d_nondim / c if 'moment_center' in d_inputs: if 'M_X' in d_outputs: d_inputs['moment_center'][1] += -fz_total * d_outputs['M_X'] d_inputs['moment_center'][2] += fy_total * d_outputs['M_X'] if 'M_Y' in d_outputs: d_inputs['moment_center'][0] += fz_total * d_outputs['M_Y'] d_inputs['moment_center'][2] += -fx_total * d_outputs['M_Y'] if 'M_Z' in d_outputs: d_inputs['moment_center'][0] += -fy_total * d_outputs['M_Z'] d_inputs['moment_center'][1] += fx_total * d_outputs['M_Z'] if 'CM_X' in d_outputs: d_inputs['moment_center'][1] += -fz_total * d_outputs['CM_X'] / (q_inf * area * c) d_inputs['moment_center'][2] += fy_total * d_outputs['CM_X'] / (q_inf * area * c) if 'CM_Y' in d_outputs: d_inputs['moment_center'][0] += fz_total * d_outputs['CM_Y'] / (q_inf * area * c) d_inputs['moment_center'][2] += -fx_total * d_outputs['CM_Y'] / (q_inf * area * c) if 'CM_Z' in d_outputs: d_inputs['moment_center'][0] += -fy_total * d_outputs['CM_Z'] / (q_inf * area * c) d_inputs['moment_center'][1] += fx_total * d_outputs['CM_Z'] / (q_inf * area * c) if 'ref_length' in d_inputs: d_nondim = - 1.0 / (q_inf * area * c**2.0) if 'CM_X' in d_outputs: d_inputs['ref_length'] += m_x * d_nondim * d_outputs['CM_X'] if 'CM_X' in d_outputs: d_inputs['ref_length'] += m_y * d_nondim * d_outputs['CM_Y'] if 'CM_Z' in d_outputs: d_inputs['ref_length'] += m_z * d_nondim * d_outputs['CM_Z'] if 'q_inf' in d_inputs: d_nondim = - 1.0 / (q_inf**2.0 * area) if 'C_X' in d_outputs: d_inputs['q_inf'] += d_outputs['C_X'] * fx_total * d_nondim if 'C_Y' in d_outputs: d_inputs['q_inf'] += d_outputs['C_Y'] * fy_total * d_nondim if 'C_Z' in d_outputs: d_inputs['q_inf'] += d_outputs['C_Z'] * fz_total * d_nondim if 'C_L' in d_outputs: d_inputs['q_inf'] += d_outputs['C_L'] * lift * d_nondim if 'C_D' in d_outputs: d_inputs['q_inf'] += d_outputs['C_D'] * drag * d_nondim if 'CM_X' in d_outputs: d_inputs['q_inf'] += d_outputs['CM_X'] * m_x * d_nondim / c if 'CM_X' in d_outputs: d_inputs['q_inf'] += d_outputs['CM_Y'] * m_y * d_nondim / c if 'CM_Z' in d_outputs: d_inputs['q_inf'] += d_outputs['CM_Z'] * m_z * d_nondim / c if 'x_aero' in d_inputs: nondim = 1.0 / (q_inf * area * c) dm_x = d_outputs['M_X'] if 'M_X' in d_outputs else 0.0 dm_y = d_outputs['M_Y'] if 'M_Y' in d_outputs else 0.0 dm_z = d_outputs['M_Z'] if 'M_Z' in d_outputs else 0.0 dcm_x = d_outputs['CM_X']*nondim if 'CM_X' in d_outputs else 0.0 dcm_y = d_outputs['CM_Y']*nondim if 'CM_Y' in d_outputs else 0.0 dcm_z = d_outputs['CM_Z']*nondim if 'CM_Z' in d_outputs else 0.0 d_inputs['x_aero'][0::3] += -fz * (dm_y + dcm_y) + fy * (dm_z + dcm_z) d_inputs['x_aero'][1::3] += fz * (dm_x + dcm_x) - fx * (dm_z + dcm_z) d_inputs['x_aero'][2::3] += -fy * (dm_x + dcm_x) + fx * (dm_y + dcm_y) if 'f_aero' in d_inputs: if 'F_X' in d_outputs: d_inputs['f_aero'][0::3] += d_outputs['F_X'] if 'F_Y' in d_outputs: d_inputs['f_aero'][1::3] += d_outputs['F_Y'] if 'F_Z' in d_outputs: d_inputs['f_aero'][2::3] += d_outputs['F_Z'] if 'C_X' in d_outputs: d_inputs['f_aero'][0::3] += d_outputs['C_X'] / (q_inf * area) if 'C_Y' in d_outputs: d_inputs['f_aero'][1::3] += d_outputs['C_Y'] / (q_inf * area) if 'C_Z' in d_outputs: d_inputs['f_aero'][2::3] += d_outputs['C_Z'] / (q_inf * area) if 'Lift' in d_outputs: d_inputs['f_aero'][0::3] += -np.sin(aoa) * d_outputs['Lift'] d_inputs['f_aero'][2::3] += np.cos(aoa) * d_outputs['Lift'] if 'Drag' in d_outputs: d_inputs['f_aero'][0::3] += np.cos(aoa) * np.cos(yaw) * d_outputs['Drag'] d_inputs['f_aero'][1::3] += -np.sin(yaw) * d_outputs['Drag'] d_inputs['f_aero'][2::3] += np.sin(aoa) * np.cos(yaw) * d_outputs['Drag'] if 'C_L' in d_outputs: d_inputs['f_aero'][0::3] += -np.sin(aoa) * d_outputs['C_L'] / (q_inf * area) d_inputs['f_aero'][2::3] += np.cos(aoa) * d_outputs['C_L'] / (q_inf * area) if 'C_D' in d_outputs: d_inputs['f_aero'][0::3] += np.cos(aoa) * np.cos(yaw) * d_outputs['C_D'] / (q_inf * area) d_inputs['f_aero'][1::3] += -np.sin(yaw) * d_outputs['C_D'] / (q_inf * area) d_inputs['f_aero'][2::3] += np.sin(aoa) * np.cos(yaw) * d_outputs['C_D'] / (q_inf * area) if 'M_X' in d_outputs: d_inputs['f_aero'][1::3] += -(z-zc) * d_outputs['M_X'] d_inputs['f_aero'][2::3] += (y-yc) * d_outputs['M_X'] if 'M_Y' in d_outputs: d_inputs['f_aero'][0::3] += (z-zc) * d_outputs['M_Y'] d_inputs['f_aero'][2::3] += -(x-xc) * d_outputs['M_Y'] if 'M_Z' in d_outputs: d_inputs['f_aero'][0::3] += -(y-yc) * d_outputs['M_Z'] d_inputs['f_aero'][1::3] += (x-xc) * d_outputs['M_Z'] if 'CM_X' in d_outputs: d_inputs['f_aero'][1::3] += -(z-zc) * d_outputs['CM_X'] / (q_inf * area * c) d_inputs['f_aero'][2::3] += (y-yc) * d_outputs['CM_X'] / (q_inf * area * c) if 'CM_Y' in d_outputs: d_inputs['f_aero'][0::3] += (z-zc) * d_outputs['CM_Y'] / (q_inf * area * c) d_inputs['f_aero'][2::3] += -(x-xc) * d_outputs['CM_Y'] / (q_inf * area * c) if 'CM_Z' in d_outputs: d_inputs['f_aero'][0::3] += -(y-yc) * d_outputs['CM_Z'] / (q_inf * area * c) d_inputs['f_aero'][1::3] += (x-xc) * d_outputs['CM_Z'] / (q_inf * area * c) def check_integrated_surface_force_partials(): nnodes = 3 prob = om.Problem() ivc = om.IndepVarComp() ivc.add_output('aoa',val=45.0, units='deg') ivc.add_output('yaw',val=135.0, units='deg') ivc.add_output('ref_area',val=0.2) ivc.add_output('moment_center',shape=3,val=np.zeros(3)) ivc.add_output('ref_length', val = 3.0) ivc.add_output('q_inf',val=10.0) ivc.add_output('x_aero',shape=3*nnodes,val=np.random.rand(3*nnodes),distributed=True) ivc.add_output('f_aero',shape=3*nnodes,val=np.random.rand(3*nnodes),distributed=True) prob.model.add_subsystem('ivc',ivc,promotes_outputs=['*']) prob.model.add_subsystem('forces',IntegratedSurfaceForces(), promotes_inputs=['*']) prob.setup(force_alloc_complex=True) prob.run_model() prob.check_partials(compact_print=True, method='cs') if __name__ == '__main__': check_integrated_surface_force_partials()

7c3b77cba219a97b12762ac1a37f632c5f68d380

py

Python

platformio/project/commands/init.py

ufo2011/platformio-core

0ceae62701731f8b32c34d7993a34dea34aea59c

platformio/project/commands/init.py

ufo2011/platformio-core

0ceae62701731f8b32c34d7993a34dea34aea59c

platformio/project/commands/init.py

ufo2011/platformio-core

0ceae62701731f8b32c34d7993a34dea34aea59c

# Copyright (c) 2014-present PlatformIO <contact@platformio.org> # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # pylint: disable=line-too-long,too-many-arguments,too-many-locals import json import os import click from platformio import fs from platformio.package.commands.install import install_project_dependencies from platformio.package.manager.platform import PlatformPackageManager from platformio.platform.exception import UnknownBoard from platformio.project.config import ProjectConfig from platformio.project.generator import ProjectGenerator from platformio.project.helpers import is_platformio_project @click.command("init", short_help="Initialize a project or update existing") @click.option( "--project-dir", "-d", default=os.getcwd, type=click.Path( exists=True, file_okay=False, dir_okay=True, writable=True, resolve_path=True ), ) @click.option("-b", "--board", multiple=True, metavar="ID", callback=validate_boards) @click.option("--ide", type=click.Choice(ProjectGenerator.get_supported_ides())) @click.option("-e", "--environment", help="Update existing environment") @click.option("-O", "--project-option", multiple=True) @click.option("--env-prefix", default="") @click.option("--no-install-dependencies", is_flag=True) @click.option("-s", "--silent", is_flag=True)

# Copyright (c) 2014-present PlatformIO <contact@platformio.org> # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # pylint: disable=line-too-long,too-many-arguments,too-many-locals import json import os import click from platformio import fs from platformio.package.commands.install import install_project_dependencies from platformio.package.manager.platform import PlatformPackageManager from platformio.platform.exception import UnknownBoard from platformio.project.config import ProjectConfig from platformio.project.generator import ProjectGenerator from platformio.project.helpers import is_platformio_project def validate_boards(ctx, param, value): # pylint: disable=W0613 pm = PlatformPackageManager() for id_ in value: try: pm.board_config(id_) except UnknownBoard: raise click.BadParameter( "`%s`. Please search for board ID using `platformio boards` " "command" % id_ ) return value @click.command("init", short_help="Initialize a project or update existing") @click.option( "--project-dir", "-d", default=os.getcwd, type=click.Path( exists=True, file_okay=False, dir_okay=True, writable=True, resolve_path=True ), ) @click.option("-b", "--board", multiple=True, metavar="ID", callback=validate_boards) @click.option("--ide", type=click.Choice(ProjectGenerator.get_supported_ides())) @click.option("-e", "--environment", help="Update existing environment") @click.option("-O", "--project-option", multiple=True) @click.option("--env-prefix", default="") @click.option("--no-install-dependencies", is_flag=True) @click.option("-s", "--silent", is_flag=True) def project_init_cmd( project_dir, board, ide, environment, project_option, env_prefix, no_install_dependencies, silent, ): is_new_project = not is_platformio_project(project_dir) if is_new_project: if not silent: print_header(project_dir) init_base_project(project_dir) if environment: update_project_env(project_dir, environment, project_option) elif board: update_board_envs(project_dir, board, project_option, env_prefix) # resolve project dependencies if not no_install_dependencies and (environment or board): install_project_dependencies( options=dict( project_dir=project_dir, environments=[environment] if environment else [], silent=silent, ) ) if ide: if not silent: click.echo( "Updating metadata for the %s IDE..." % click.style(ide, fg="cyan") ) with fs.cd(project_dir): config = ProjectConfig.get_instance( os.path.join(project_dir, "platformio.ini") ) config.validate() ProjectGenerator(config, environment, ide, board).generate() if is_new_project: init_cvs_ignore(project_dir) if not silent: print_footer(is_new_project) def print_header(project_dir): if project_dir == os.getcwd(): click.secho("\nThe current working directory ", fg="yellow", nl=False) try: click.secho(project_dir, fg="cyan", nl=False) except UnicodeEncodeError: click.secho(json.dumps(project_dir), fg="cyan", nl=False) click.secho(" will be used for the project.", fg="yellow") click.echo("") click.echo("The next files/directories have been created in ", nl=False) try: click.secho(project_dir, fg="cyan") except UnicodeEncodeError: click.secho(json.dumps(project_dir), fg="cyan") click.echo("%s - Put project header files here" % click.style("include", fg="cyan")) click.echo( "%s - Put here project specific (private) libraries" % click.style("lib", fg="cyan") ) click.echo("%s - Put project source files here" % click.style("src", fg="cyan")) click.echo( "%s - Project Configuration File" % click.style("platformio.ini", fg="cyan") ) def print_footer(is_new_project): if is_new_project: return click.secho( "\nProject has been successfully initialized! Useful commands:\n" "`pio run` - process/build project from the current directory\n" "`pio run --target upload` or `pio run -t upload` " "- upload firmware to a target\n" "`pio run --target clean` - clean project (remove compiled files)" "\n`pio run --help` - additional information", fg="green", ) return click.secho( "Project has been successfully updated!", fg="green", ) def init_base_project(project_dir): with fs.cd(project_dir): config = ProjectConfig() config.save() dir_to_readme = [ (config.get("platformio", "src_dir"), None), (config.get("platformio", "include_dir"), init_include_readme), (config.get("platformio", "lib_dir"), init_lib_readme), (config.get("platformio", "test_dir"), init_test_readme), ] for (path, cb) in dir_to_readme: if os.path.isdir(path): continue os.makedirs(path) if cb: cb(path) def init_include_readme(include_dir): with open(os.path.join(include_dir, "README"), mode="w", encoding="utf8") as fp: fp.write( """ This directory is intended for project header files. A header file is a file containing C declarations and macro definitions to be shared between several project source files. You request the use of a header file in your project source file (C, C++, etc) located in `src` folder by including it, with the C preprocessing directive `#include'. ```src/main.c #include "header.h" int main (void) { ... } ``` Including a header file produces the same results as copying the header file into each source file that needs it. Such copying would be time-consuming and error-prone. With a header file, the related declarations appear in only one place. If they need to be changed, they can be changed in one place, and programs that include the header file will automatically use the new version when next recompiled. The header file eliminates the labor of finding and changing all the copies as well as the risk that a failure to find one copy will result in inconsistencies within a program. In C, the usual convention is to give header files names that end with `.h'. It is most portable to use only letters, digits, dashes, and underscores in header file names, and at most one dot. Read more about using header files in official GCC documentation: * Include Syntax * Include Operation * Once-Only Headers * Computed Includes https://gcc.gnu.org/onlinedocs/cpp/Header-Files.html """, ) def init_lib_readme(lib_dir): with open(os.path.join(lib_dir, "README"), mode="w", encoding="utf8") as fp: fp.write( """ This directory is intended for project specific (private) libraries. PlatformIO will compile them to static libraries and link into executable file. The source code of each library should be placed in a an own separate directory ("lib/your_library_name/[here are source files]"). For example, see a structure of the following two libraries `Foo` and `Bar`: |--lib | | | |--Bar | | |--docs | | |--examples | | |--src | | |- Bar.c | | |- Bar.h | | |- library.json (optional, custom build options, etc) https://docs.platformio.org/page/librarymanager/config.html | | | |--Foo | | |- Foo.c | | |- Foo.h | | | |- README --> THIS FILE | |- platformio.ini |--src |- main.c and a contents of `src/main.c`: ``` #include <Foo.h> #include <Bar.h> int main (void) { ... } ``` PlatformIO Library Dependency Finder will find automatically dependent libraries scanning project source files. More information about PlatformIO Library Dependency Finder - https://docs.platformio.org/page/librarymanager/ldf.html """, ) def init_test_readme(test_dir): with open(os.path.join(test_dir, "README"), mode="w", encoding="utf8") as fp: fp.write( """ This directory is intended for PlatformIO Test Runner and project tests. Unit Testing is a software testing method by which individual units of source code, sets of one or more MCU program modules together with associated control data, usage procedures, and operating procedures, are tested to determine whether they are fit for use. Unit testing finds problems early in the development cycle. More information about PlatformIO Unit Testing: - https://docs.platformio.org/en/latest/advanced/unit-testing/index.html """, ) def init_cvs_ignore(project_dir): conf_path = os.path.join(project_dir, ".gitignore") if os.path.isfile(conf_path): return with open(conf_path, mode="w", encoding="utf8") as fp: fp.write(".pio\n") def update_board_envs(project_dir, board_ids, project_option, env_prefix): config = ProjectConfig( os.path.join(project_dir, "platformio.ini"), parse_extra=False ) used_boards = [] for section in config.sections(): cond = [section.startswith("env:"), config.has_option(section, "board")] if all(cond): used_boards.append(config.get(section, "board")) pm = PlatformPackageManager() modified = False for id_ in board_ids: board_config = pm.board_config(id_) if id_ in used_boards: continue used_boards.append(id_) modified = True envopts = {"platform": board_config["platform"], "board": id_} # find default framework for board frameworks = board_config.get("frameworks") if frameworks: envopts["framework"] = frameworks[0] for item in project_option: if "=" not in item: continue _name, _value = item.split("=", 1) envopts[_name.strip()] = _value.strip() section = "env:%s%s" % (env_prefix, id_) config.add_section(section) for option, value in envopts.items(): config.set(section, option, value) if modified: config.save() def update_project_env(project_dir, environment, project_option): if not project_option: return config = ProjectConfig( os.path.join(project_dir, "platformio.ini"), parse_extra=False ) section = "env:%s" % environment if not config.has_section(section): config.add_section(section) for item in project_option: if "=" not in item: continue _name, _value = item.split("=", 1) config.set(section, _name.strip(), _value.strip()) config.save()

c91b624711d1778d78556d13356f05fa1dcaaef7

py

Python

exercise_monitoring_camera.py

Guvalif/aidor-acceleration-02

afa7aa45bf26f1c2b7f189b6320599357f1e17d3

2018-08-20T02:14:24.000Z

2018-08-20T02:14:24.000Z

exercise_monitoring_camera.py

Guvalif/imedio_0801

afa7aa45bf26f1c2b7f189b6320599357f1e17d3

exercise_monitoring_camera.py

Guvalif/imedio_0801

afa7aa45bf26f1c2b7f189b6320599357f1e17d3

# -*- coding: utf-8 -*- __author__ = 'Kazuyuki TAKASE' __copyright__ = 'PLEN Project Company Inc, and all authors.' __license__ = 'The MIT License (http://opensource.org/licenses/mit-license.php)' # 外部プログラムの読み込み # ============================================================================= from time import sleep from cv2 import VideoCapture, imwrite from wiringpi import * # 定数定義・初期化処理 # ============================================================================= CAMERA_INDEX = 0 MOTION_PIN = 26 camera = VideoCapture(CAMERA_INDEX) wiringPiSetupGpio() # メインループ # ============================================================================= while True: # これ以降を自分で作成 pass

# -*- coding: utf-8 -*- __author__ = 'Kazuyuki TAKASE' __copyright__ = 'PLEN Project Company Inc, and all authors.' __license__ = 'The MIT License (http://opensource.org/licenses/mit-license.php)' # 外部プログラムの読み込み # ============================================================================= from time import sleep from cv2 import VideoCapture, imwrite from wiringpi import * # 定数定義・初期化処理 # ============================================================================= CAMERA_INDEX = 0 MOTION_PIN = 26 camera = VideoCapture(CAMERA_INDEX) wiringPiSetupGpio() # メインループ # ============================================================================= while True: # これ以降を自分で作成 pass

4cc967e9e9d1ac88abda9c1076b57abe84fc47bc

py

Python

src/timeseries.py

AmberCrafter/pythonlib_statistic

0fd49283c8dd75c5d1ade064be3318eabf74bdfe

src/timeseries.py

AmberCrafter/pythonlib_statistic

0fd49283c8dd75c5d1ade064be3318eabf74bdfe

src/timeseries.py

AmberCrafter/pythonlib_statistic

0fd49283c8dd75c5d1ade064be3318eabf74bdfe

#!/bin/python3 # if used ubuntu 20.10 or later, interpreter set as #!/bin/python and use pip instead of pip3 # =================================================================== # # platfrom check # dateutil check and import try: from dateutil.relativedelta import relativedelta except: import os,sys,subprocess if os.name=='nt': subprocess.check_call([sys.executable, "-m", "pip", "install", "dateutil"]) elif os.name=='posix': subprocess.check_call([sys.executable, "-m", "pip3", "install", "dateutil"]) else: raise "Unknow platform, please install 'dateutil' by yourself." from dateutil.relativedelta import relativedelta # =================================================================== # # platfrom check # numpy check and import try: import numpy as np except: import os,sys,subprocess if os.name=='nt': subprocess.check_call([sys.executable, "-m", "pip", "install", "numpy"]) elif os.name=='posix': subprocess.check_call([sys.executable, "-m", "pip3", "install", "numpy"]) else: raise "Unknow platform, please install 'numpy' by yourself." import numpy as np # =================================================================== # import datetime from typing import Union class Time(object): ''' storageForward: True -> storage value in starttime <br> storageForward: False -> storage value in endtime ''' @staticmethod @staticmethod def _set_header(data,header=None): ''' only used to format output data ''' # ----------------------------------------------------------- # # here i'm not sure what data type i need to use. # thus, if data=np.array(obj(dict)), then we need # to use data.item() to get the data try: data=data.item() except: pass # ----------------------------------------------------------- # if header!=None: dummy={} for i,head in enumerate(header): if isinstance(data,dict): for key in data.keys(): if i==0: dummy[key]={} dummy[key][head]=data[key][:,i] else: dummy[head]=data[:,i] return dummy return data @staticmethod def _fixTime(time,data,timeStep:dict,zeroPara:dict,storageForward:bool,outputPara_list:list, starttime:datetime.datetime=None,endtime:datetime.datetime=None): # def _fixTime(time,data,timeStep:dict,ratio:int,zeroPara:dict,storageForward:bool,starttime:datetime.datetime=None,endtime:datetime.datetime=None): ''' zeroPara: set start datetime para season enum: 1: spring 2: summer 3: autumn 4: winter ''' minTime = np.nanmin(time) if starttime==None else starttime maxTime = np.nanmax(time) if endtime==None else endtime # get data_value if isinstance(data,dict): if 'mean' in data.keys(): data=data['mean'] if 'season' in timeStep.keys(): dt = relativedelta(months=3) if not storageForward: time+=dt; time+=datetime.timedelta(microseconds=-1) maxTime+=dt if zeroPara!=None: minTime=minTime.replace(**zeroPara) dummy={} for para in outputPara_list: if para=='quartile': dummy['lower']=[] dummy['median']=[] dummy['upper']=[] else: dummy[para]=[] tummy = [] count = [] # deal with perfix date before a new start i = Time._get_season(minTime.month) year = minTime.year if minTime.month!=12 else minTime.year+1 mask=np.where(time<datetime.datetime(year,3*i,1))[0] t,d,c = Time._nofixTime(time[mask],data[mask],parameter='season',outputPara_list=outputPara_list) tummy+=list(t); count+=list(c) for key in dummy.keys(): dummy[key]+=list(d[key]) minTime=datetime.datetime(year,3*i,1) while minTime<=maxTime: if minTime>max(time): break mask=np.where((time>=minTime) & (time<minTime+dt))[0] t,d,c = Time._nofixTime(time[mask],data[mask],parameter='season',outputPara_list=outputPara_list) tummy+=list(t); count+=list(c) for key in dummy.keys(): dummy[key]+=list(d[key]) minTime+=dt else: dt = relativedelta(**timeStep) if not storageForward: time+=dt; time+=datetime.timedelta(microseconds=-1) maxTime+=dt if zeroPara!=None: minTime=minTime.replace(**zeroPara) # if ratio==None: ratio=0 dummy = {} for para in outputPara_list: if para=='quartile': dummy['lower']=[] dummy['median']=[] dummy['upper']=[] else: dummy[para]=[] tummy = [] count = [] while minTime<=maxTime: mask = np.where((time>=minTime) & (time<minTime+dt))[0] if mask.size==0: minTime+=dt; continue tummy.append(minTime) count.append(np.sum(np.isfinite(data[mask]))) if 'mean' in outputPara_list: dummy['mean'].append(np.nanmean(data[mask],axis=0)) if 'std' in outputPara_list: dummy['std'].append(np.nanstd(data[mask],axis=0)) if 'max' in outputPara_list: dummy['max'].append(np.nanmax(data[mask],axis=0)) if 'min' in outputPara_list: dummy['min'].append(np.nanmin(data[mask],axis=0)) if 'maxTime' in outputPara_list: dummy['maxTime'].append(time[mask][np.argmax(data[mask],axis=0)]) if 'maxTime' in outputPara_list: dummy['minTime'].append(time[mask][np.argmin(data[mask],axis=0)]) if 'quartile' in outputPara_list: dummy['lower'].append(np.nanpercentile(data[mask],25,axis=0)) if ('quartile' in outputPara_list) | ('median' in outputPara_list): dummy['median'].append(np.nanpercentile(data[mask],50,axis=0)) if 'quartile' in outputPara_list: dummy['upper'].append(np.nanpercentile(data[mask],75,axis=0)) # dummy.append(np.nanmean(data[mask],axis=0) if count[-1]>=ratio else np.array([np.nan]*len(data[0]))) minTime+=dt dummy = Time._set_ndarray(dummy) return tummy,dummy,count @staticmethod def _nofixTime(time,data,parameter:str,outputPara_list:list): # def _nofixTime(time,data,parameter:str,ratio:int): ''' parameter: set the datetime parameter (second, minute ...etc) will be used to calculate season enum: 1: winter 2: spring 3: summer 4: autumn ''' season_dict = { 1: 'Winter', 2: 'Spring', 3: 'Summer', 4: 'Autumn', } if parameter.lower()=='season': time_para_list = [Time._get_season(val.month) for val in time] else: time_para_list = [eval(f"val.{parameter}") for val in time] time_para_list = np.array(time_para_list) if time_para_list.size==0: return np.array(np.nan),np.array(np.nan),np.array(np.nan) minTime = np.nanmin(time_para_list) maxTime = np.nanmax(time_para_list) # if ratio==None: ratio=0 # get data_value if isinstance(data,dict): if 'mean' in data.keys(): data=data['mean'] dummy = {} for para in outputPara_list: if para=='quartile': dummy['lower']=[] dummy['median']=[] dummy['upper']=[] else: dummy[para]=[] tummy = [] count = [] for i in range(minTime,maxTime+1): mask = np.where(time_para_list==i)[0] tummy.append(i if parameter.lower()!='season' else [time[mask[0]].year,season_dict[i]]) count.append(np.sum(np.isfinite(data[mask]))) if 'mean' in outputPara_list: dummy['mean'].append(np.nanmean(data[mask],axis=0)) if 'std' in outputPara_list: dummy['std'].append(np.nanstd(data[mask],axis=0)) if 'max' in outputPara_list: dummy['max'].append(np.nanmax(data[mask],axis=0)) if 'min' in outputPara_list: dummy['min'].append(np.nanmin(data[mask],axis=0)) if 'maxTime' in outputPara_list: dummy['maxTime'].append(time[mask][np.argmax(data[mask],axis=0)]) if 'maxTime' in outputPara_list: dummy['minTime'].append(time[mask][np.argmin(data[mask],axis=0)]) if 'quartile' in outputPara_list: dummy['lower'].append(np.nanpercentile(data[mask],25,axis=0)) if ('quartile' in outputPara_list) | ('median' in outputPara_list): dummy['median'].append(np.nanpercentile(data[mask],50,axis=0)) if 'quartile' in outputPara_list: dummy['upper'].append(np.nanpercentile(data[mask],75,axis=0)) # dummy.append(np.nanmean(data[mask],axis=0) if count[-1]>=ratio else np.array([np.nan]*len(data[0]))) dummy = Time._set_ndarray(dummy) return tummy,dummy,count @staticmethod def _get_season(month): ''' enum: 1: winter 2: spring 3: summer 4: autumn ''' return (month%12+3)//3 @staticmethod def set_config(self,init:bool=False,**kwargs) -> None: ''' config['storageForward']: save the value at the start time or not<br> config['outputPara_list]: select output parameter [mean,std,max,min] Arguments: init: Is the initialize status? Default is False If set True, will using the init state. **kwargs: Optional, this work only init set false. config: { asDict: bool, storage: bool, fixTime: bool, zeroStart: bool, selfUpdate: bool, outputPara_list: list = [ mean, std, max, min, maxTime, minTime, quartile, median ] } ''' if init==True: self.config = dict( asDict=False, storageForward=True, fixTime=True, zeroStart=True, selfUpdate=True, outputPara_list=['mean','std','mean'] # ['mean','std','max','min','maxTime','minTime','quartile','median'], ) else: for key in kwargs.keys(): self.config[key] = kwargs[key] def input(self,time: Union[list, np.ndarray],data: Union[list, np.ndarray],dtype:object =float, ratio: Union[int, float]=None,header: list=None,starttime:datetime.datetime=None,endtime:datetime.datetime=None) -> str: ''' time <datetime> : input timelist of data <br> data <numerical>: input data array Arguments: time: list of time series data: list of data set depend on time series dtype: convert type of data elements ratio: require of the data numbers(int) or ratio(float) header: export tag of data header starttime: start of the time endtime: end of the time Returns: return 'Successfully' when process success. ''' self.time = np.array(time) self.data = np.array(data,dtype=dtype) self.ratio = ratio self.header = header self.starttime = starttime self.endtime = endtime self.counts = [] return "Successfully" def isrepeat(self) -> bool: ''' Check weather data repeat depend on time. Returns: check there has repeat datetime in the data set. ''' if len(self.time.reshape(-1))==len(set(self.time)): return False else: return True def second(self,ratio: Union[int, float]=None,base: int=1000) -> Union[None, tuple, list, dict]: ''' Do statistic method base on config setting. Arguments: ratio: require of the data numbers(int) or ratio(float) base: base number of required data, use on ratio<=1 Returns: structure of return data None: if config.selfUpdate==True, then export data by self.get() tuple or list: if config.selfUpdate==False & config.asDict==False, then return the data as tuple. ( time, data, count ) dict: if config.selfUpdate==False & config.asDict==True, then return the data as dictionary. { time: np.ndarray, data: np.ndarray, count: np.ndarray } ''' if ratio!=None: ratio=int(base*ratio) if ratio<=1 else int(ratio) else: if self.ratio!=None: ratio=int(base*self.ratio) if self.ratio<=1 else int(self.ratio) if self.config['fixTime']: if self.config['zeroStart']: tummy,dummy,count = self._fixTime(self.time,self.data,timeStep=dict(seconds=1), zeroPara=dict(microsecond=0),storageForward=self.config['storageForward'], outputPara_list=self.config['outputPara_list'],starttime=self.starttime,endtime=self.endtime) else: tummy,dummy,count = self._fixTime(self.time,self.data,timeStep=dict(seconds=1), outputPara_list=self.config['outputPara_list'],starttime=self.starttime,endtime=self.endtime) else: # self.config['fixTime']==False tummy,dummy,count = self._nofixTime(self.time,self.data,parameter='second',outputPara_list=self.config['outputPara_list']) dummy = self._QC_numbers(dummy,count,ratio) if self.config['selfUpdate']: self.data = np.array(dummy) self.time = np.array(tummy) self.counts = np.array(count) else: print("This is not object standard operation!") print("You need to set config[selfUpdate]=True and use get method to get the result.") dummy = Time._set_header(dummy,header=self.header) if self.config['asDict']: return dict(time=tummy,data=dummy,counts=count) else: return tummy,dummy,count def minute(self,ratio: Union[int, float]=None,base: int=60) -> Union[None, tuple, list, dict]: ''' Do statistic method base on config setting. Arguments: ratio: require of the data numbers(int) or ratio(float) base: base number of required data, use on ratio<=1 Returns: structure of return data None: if config.selfUpdate==True, then export data by self.get() tuple or list: if config.selfUpdate==False & config.asDict==False, then return the data as tuple. ( time, data, count ) dict: if config.selfUpdate==False & config.asDict==True, then return the data as dictionary. { time: np.ndarray, data: np.ndarray, count: np.ndarray } ''' if ratio!=None: ratio=int(base*ratio) if ratio<=1 else int(ratio) else: if self.ratio!=None: ratio=int(base*self.ratio) if self.ratio<=1 else int(self.ratio) if self.config['fixTime']: if self.config['zeroStart']: tummy,dummy,count = self._fixTime(self.time,self.data,timeStep=dict(minutes=1), zeroPara=dict(second=0,microsecond=0),storageForward=self.config['storageForward'], outputPara_list=self.config['outputPara_list'],starttime=self.starttime,endtime=self.endtime) else: tummy,dummy,count = self._fixTime(self.time,self.data,timeStep=dict(minutes=1), outputPara_list=self.config['outputPara_list'],starttime=self.starttime,endtime=self.endtime) else: # self.config['fixTime']==False tummy,dummy,count = self._nofixTime(self.time,self.data,parameter='minute',outputPara_list=self.config['outputPara_list']) dummy = self._QC_numbers(dummy,count,ratio) if self.config['selfUpdate']: self.data = np.array(dummy) self.time = np.array(tummy) self.counts = np.array(count) else: print("This is not object standard operation!") print("You need to set config[selfUpdate]=True and use get method to get the result.") dummy = Time._set_header(dummy,header=self.header) if self.config['asDict']: return dict(time=tummy,data=dummy,counts=count) else: return tummy,dummy,count def hour(self,ratio: Union[int, float]=None,base: int=60) -> Union[None, tuple, list, dict]: ''' Do statistic method base on config setting. Arguments: ratio: require of the data numbers(int) or ratio(float) base: base number of required data, use on ratio<=1 Returns: structure of return data None: if config.selfUpdate==True, then export data by self.get() tuple or list: if config.selfUpdate==False & config.asDict==False, then return the data as tuple. ( time, data, count ) dict: if config.selfUpdate==False & config.asDict==True, then return the data as dictionary. { time: np.ndarray, data: np.ndarray, count: np.ndarray } ''' if ratio!=None: ratio=int(base*ratio) if ratio<=1 else int(ratio) else: if self.ratio!=None: ratio=int(base*self.ratio) if self.ratio<=1 else int(self.ratio) if self.config['fixTime']: if self.config['zeroStart']: tummy,dummy,count = self._fixTime(self.time,self.data,timeStep=dict(hours=1) ,zeroPara=dict(minute=0,second=0,microsecond=0),storageForward=self.config['storageForward'], outputPara_list=self.config['outputPara_list'],starttime=self.starttime,endtime=self.endtime) else: tummy,dummy,count = self._fixTime(self.time,self.data,timeStep=dict(hours=1), outputPara_list=self.config['outputPara_list'],starttime=self.starttime,endtime=self.endtime) else: # self.config['fixTime']==False tummy,dummy,count = self._nofixTime(self.time,self.data,parameter='hour',outputPara_list=self.config['outputPara_list']) dummy = self._QC_numbers(dummy,count,ratio) if self.config['selfUpdate']: self.data = np.array(dummy) self.time = np.array(tummy) self.counts = np.array(count) else: print("This is not object standard operation!") print("You need to set config[selfUpdate]=True and use get method to get the result.") dummy = Time._set_header(dummy,header=self.header) if self.config['asDict']: return dict(time=tummy,data=dummy,counts=count) else: return tummy,dummy,count def day(self,ratio: Union[int, float]=None,base: int=24) -> Union[None, tuple, list, dict]: ''' Do statistic method base on config setting. Arguments: ratio: require of the data numbers(int) or ratio(float) base: base number of required data, use on ratio<=1 Returns: structure of return data None: if config.selfUpdate==True, then export data by self.get() tuple or list: if config.selfUpdate==False & config.asDict==False, then return the data as tuple. ( time, data, count ) dict: if config.selfUpdate==False & config.asDict==True, then return the data as dictionary. { time: np.ndarray, data: np.ndarray, count: np.ndarray } ''' if ratio!=None: ratio=int(base*ratio) if ratio<=1 else int(ratio) else: if self.ratio!=None: ratio=int(base*self.ratio) if self.ratio<=1 else int(self.ratio) if self.config['fixTime']: if self.config['zeroStart']: tummy,dummy,count = self._fixTime(self.time,self.data,timeStep=dict(days=1), zeroPara=dict(hour=0,minute=0,second=0,microsecond=0),storageForward=self.config['storageForward'], outputPara_list=self.config['outputPara_list'],starttime=self.starttime,endtime=self.endtime) else: tummy,dummy,count = self._fixTime(self.time,self.data,timeStep=dict(days=1), outputPara_list=self.config['outputPara_list'],starttime=self.starttime,endtime=self.endtime) else: # self.config['fixTime']==False tummy,dummy,count = self._nofixTime(self.time,self.data,parameter='day',outputPara_list=self.config['outputPara_list']) dummy = self._QC_numbers(dummy,count,ratio) if self.config['selfUpdate']: self.data = np.array(dummy) self.time = np.array(tummy) self.counts = np.array(count) else: print("This is not object standard operation!") print("You need to set config[selfUpdate]=True and use get method to get the result.") dummy = Time._set_header(dummy,header=self.header) if self.config['asDict']: return dict(time=tummy,data=dummy,counts=count) else: return tummy,dummy,count def month(self,ratio: Union[int, float]=None,base: int=30) -> Union[None, tuple, list, dict]: ''' Do statistic method base on config setting. Arguments: ratio: require of the data numbers(int) or ratio(float) base: base number of required data, use on ratio<=1 Returns: structure of return data None: if config.selfUpdate==True, then export data by self.get() tuple or list: if config.selfUpdate==False & config.asDict==False, then return the data as tuple. ( time, data, count ) dict: if config.selfUpdate==False & config.asDict==True, then return the data as dictionary. { time: np.ndarray, data: np.ndarray, count: np.ndarray } ''' if ratio!=None: ratio=int(base*ratio) if ratio<=1 else int(ratio) else: if self.ratio!=None: ratio=int(base*self.ratio) if self.ratio<=1 else int(self.ratio) if self.config['fixTime']: if self.config['zeroStart']: tummy,dummy,count = self._fixTime(self.time,self.data,timeStep=dict(months=1), zeroPara=dict(day=1,hour=0,minute=0,second=0,microsecond=0), outputPara_list=self.config['outputPara_list'],storageForward=self.config['storageForward'], starttime=self.starttime,endtime=self.endtime) else: tummy,dummy,count = self._fixTime(self.time,self.data,timeStep=dict(months=1), outputPara_list=self.config['outputPara_list'],starttime=self.starttime,endtime=self.endtime) else: # self.config['fixTime']==False tummy,dummy,count = self._nofixTime(self.time,self.data,parameter='month',outputPara_list=self.config['outputPara_list']) dummy = self._QC_numbers(dummy,count,ratio) if self.config['selfUpdate']: self.data = np.array(dummy) self.time = np.array(tummy) self.counts = np.array(count) else: print("This is not object standard operation!") print("You need to set config[selfUpdate]=True and use get method to get the result.") dummy = Time._set_header(dummy,header=self.header) if self.config['asDict']: return dict(time=tummy,data=dummy,counts=count) else: return tummy,dummy,count def season(self,ratio: Union[int, float]=None,base: int=3) -> Union[None, tuple, list, dict]: ''' Do statistic method base on config setting. Arguments: ratio: require of the data numbers(int) or ratio(float) base: base number of required data, use on ratio<=1 Returns: structure of return data None: if config.selfUpdate==True, then export data by self.get() tuple or list: if config.selfUpdate==False & config.asDict==False, then return the data as tuple. ( time, data, count ) dict: if config.selfUpdate==False & config.asDict==True, then return the data as dictionary. { time: np.ndarray, data: np.ndarray, count: np.ndarray } ''' ''' Spring: March, April, May <br> Summer: June, July, August <br> Autumn: September, October, November <br> Winter: December, January, February ''' if ratio!=None: ratio=int(base*ratio) if ratio<=1 else int(ratio) else: if self.ratio!=None: ratio=int(base*self.ratio) if self.ratio<=1 else int(self.ratio) if self.config['fixTime']: if self.config['zeroStart']: tummy,dummy,count = self._fixTime(self.time,self.data,timeStep=dict(season=1), zeroPara=dict(day=1,hour=0,minute=0,second=0,microsecond=0), outputPara_list=self.config['outputPara_list'],storageForward=self.config['storageForward'], starttime=self.starttime,endtime=self.endtime) else: tummy,dummy,count = self._fixTime(self.time,self.data,timeStep=dict(season=1), outputPara_list=self.config['outputPara_list'],starttime=self.starttime,endtime=self.endtime) else: # self.config['fixTime']==False tummy,dummy,count = self._nofixTime(self.time,self.data,parameter='season',outputPara_list=self.config['outputPara_list']) dummy = self._QC_numbers(dummy,count,ratio) if self.config['selfUpdate']: self.data = np.array(dummy) self.time = np.array(tummy) self.counts = np.array(count) else: print("This is not object standard operation!") print("You need to set config[selfUpdate]=True and use get method to get the result.") dummy = Time._set_header(dummy,header=self.header) if self.config['asDict']: return dict(time=tummy,data=dummy,counts=count) else: return tummy,dummy,count def year(self,ratio:Union[int, float]=None,base:int=12) -> Union[None, tuple, list, dict]: ''' Do statistic method base on config setting. Arguments: ratio: require of the data numbers(int) or ratio(float) base: base number of required data, use on ratio<=1 Returns: structure of return data None: if config.selfUpdate==True, then export data by self.get() tuple or list: if config.selfUpdate==False & config.asDict==False, then return the data as tuple. ( time, data, count ) dict: if config.selfUpdate==False & config.asDict==True, then return the data as dictionary. { time: np.ndarray, data: np.ndarray, count: np.ndarray } ''' if ratio!=None: ratio=int(base*ratio) if ratio<=1 else int(ratio) else: if self.ratio!=None: ratio=int(base*self.ratio) if self.ratio<=1 else int(self.ratio) if self.config['fixTime']: if self.config['zeroStart']: tummy,dummy,count = self._fixTime(self.time,self.data,timeStep=dict(years=1), zeroPara=dict(month=1,day=1,hour=0,minute=0,second=0,microsecond=0),storageForward=self.config['storageForward'], outputPara_list=self.config['outputPara_list'],starttime=self.starttime,endtime=self.endtime) else: tummy,dummy,count = self._fixTime(self.time,self.data,timeStep=dict(years=1), outputPara_list=self.config['outputPara_list'],starttime=self.starttime,endtime=self.endtime) else: # self.config['fixTime']==False tummy,dummy,count = self._nofixTime(self.time,self.data,parameter='year',outputPara_list=self.config['outputPara_list']) dummy = self._QC_numbers(dummy,count,ratio) if self.config['selfUpdate']: self.data = np.array(dummy) self.time = np.array(tummy) self.counts = np.array(count) else: print("This is not object standard operation!") print("You need to set config[selfUpdate]=True and use get method to get the result.") dummy = Time._set_header(dummy,header=self.header) if self.config['asDict']: return dict(time=tummy,data=dummy,counts=count) else: return tummy,dummy,count def get(self,parameter: str=None) -> Union[list, dict, np.ndarray]: ''' export the data from Time factory. Arguments: parameter: select the return parameter. enum: None: { time, data, counts }, config, time, data, counts Returns: select parameter data set. ''' if parameter=='config': return self.config if (parameter==None) and (self.config['asDict']): return dict(time=self.time, data=Time._set_header(self.data,header=self.header), counts=self.counts) if parameter=='time': return self.time if parameter=='data': return Time._set_header(self.data,header=self.header) if parameter=='counts': return self.counts print("Please select the return parameter or set config['asDict']=True.") if __name__ == "__main__": # Implement the object myobj = Time() # Input data import datetime, random st = datetime.datetime(2020,1,1) number = 50000 time = [st+datetime.timedelta(hours=val) for val in range(number)] data = [[random.gauss(10,5) for _ in range(4)] for _ in range(number)] myobj.input(time,data,header=['a','b','c','d']) # Calculate and Get result # myobj.hour(1,500) myobj.set_config(outputPara_list=['mean','std','max','quartile']) myobj.season() myobj.set_config(asDict=True) result = myobj.get() print(result)

#!/bin/python3 # if used ubuntu 20.10 or later, interpreter set as #!/bin/python and use pip instead of pip3 # =================================================================== # # platfrom check # dateutil check and import try: from dateutil.relativedelta import relativedelta except: import os,sys,subprocess if os.name=='nt': subprocess.check_call([sys.executable, "-m", "pip", "install", "dateutil"]) elif os.name=='posix': subprocess.check_call([sys.executable, "-m", "pip3", "install", "dateutil"]) else: raise "Unknow platform, please install 'dateutil' by yourself." from dateutil.relativedelta import relativedelta # =================================================================== # # platfrom check # numpy check and import try: import numpy as np except: import os,sys,subprocess if os.name=='nt': subprocess.check_call([sys.executable, "-m", "pip", "install", "numpy"]) elif os.name=='posix': subprocess.check_call([sys.executable, "-m", "pip3", "install", "numpy"]) else: raise "Unknow platform, please install 'numpy' by yourself." import numpy as np # =================================================================== # import datetime from typing import Union class Time(object): ''' storageForward: True -> storage value in starttime <br> storageForward: False -> storage value in endtime ''' def __init__(self): self.set_config(init=True) def _check_data(self): if not "self.time" in locals(): raise "Please check input time." if not "self.data" in locals(): raise "Please check input data." return True @staticmethod def _set_ndarray(data): if isinstance(data,dict): for key in data.keys(): data[key]=np.array(data[key]) else: data=np.array(data) return data @staticmethod def _set_header(data,header=None): ''' only used to format output data ''' # ----------------------------------------------------------- # # here i'm not sure what data type i need to use. # thus, if data=np.array(obj(dict)), then we need # to use data.item() to get the data try: data=data.item() except: pass # ----------------------------------------------------------- # if header!=None: dummy={} for i,head in enumerate(header): if isinstance(data,dict): for key in data.keys(): if i==0: dummy[key]={} dummy[key][head]=data[key][:,i] else: dummy[head]=data[:,i] return dummy return data @staticmethod def _fixTime(time,data,timeStep:dict,zeroPara:dict,storageForward:bool,outputPara_list:list, starttime:datetime.datetime=None,endtime:datetime.datetime=None): # def _fixTime(time,data,timeStep:dict,ratio:int,zeroPara:dict,storageForward:bool,starttime:datetime.datetime=None,endtime:datetime.datetime=None): ''' zeroPara: set start datetime para season enum: 1: spring 2: summer 3: autumn 4: winter ''' minTime = np.nanmin(time) if starttime==None else starttime maxTime = np.nanmax(time) if endtime==None else endtime # get data_value if isinstance(data,dict): if 'mean' in data.keys(): data=data['mean'] if 'season' in timeStep.keys(): dt = relativedelta(months=3) if not storageForward: time+=dt; time+=datetime.timedelta(microseconds=-1) maxTime+=dt if zeroPara!=None: minTime=minTime.replace(**zeroPara) dummy={} for para in outputPara_list: if para=='quartile': dummy['lower']=[] dummy['median']=[] dummy['upper']=[] else: dummy[para]=[] tummy = [] count = [] # deal with perfix date before a new start i = Time._get_season(minTime.month) year = minTime.year if minTime.month!=12 else minTime.year+1 mask=np.where(time<datetime.datetime(year,3*i,1))[0] t,d,c = Time._nofixTime(time[mask],data[mask],parameter='season',outputPara_list=outputPara_list) tummy+=list(t); count+=list(c) for key in dummy.keys(): dummy[key]+=list(d[key]) minTime=datetime.datetime(year,3*i,1) while minTime<=maxTime: if minTime>max(time): break mask=np.where((time>=minTime) & (time<minTime+dt))[0] t,d,c = Time._nofixTime(time[mask],data[mask],parameter='season',outputPara_list=outputPara_list) tummy+=list(t); count+=list(c) for key in dummy.keys(): dummy[key]+=list(d[key]) minTime+=dt else: dt = relativedelta(**timeStep) if not storageForward: time+=dt; time+=datetime.timedelta(microseconds=-1) maxTime+=dt if zeroPara!=None: minTime=minTime.replace(**zeroPara) # if ratio==None: ratio=0 dummy = {} for para in outputPara_list: if para=='quartile': dummy['lower']=[] dummy['median']=[] dummy['upper']=[] else: dummy[para]=[] tummy = [] count = [] while minTime<=maxTime: mask = np.where((time>=minTime) & (time<minTime+dt))[0] if mask.size==0: minTime+=dt; continue tummy.append(minTime) count.append(np.sum(np.isfinite(data[mask]))) if 'mean' in outputPara_list: dummy['mean'].append(np.nanmean(data[mask],axis=0)) if 'std' in outputPara_list: dummy['std'].append(np.nanstd(data[mask],axis=0)) if 'max' in outputPara_list: dummy['max'].append(np.nanmax(data[mask],axis=0)) if 'min' in outputPara_list: dummy['min'].append(np.nanmin(data[mask],axis=0)) if 'maxTime' in outputPara_list: dummy['maxTime'].append(time[mask][np.argmax(data[mask],axis=0)]) if 'maxTime' in outputPara_list: dummy['minTime'].append(time[mask][np.argmin(data[mask],axis=0)]) if 'quartile' in outputPara_list: dummy['lower'].append(np.nanpercentile(data[mask],25,axis=0)) if ('quartile' in outputPara_list) | ('median' in outputPara_list): dummy['median'].append(np.nanpercentile(data[mask],50,axis=0)) if 'quartile' in outputPara_list: dummy['upper'].append(np.nanpercentile(data[mask],75,axis=0)) # dummy.append(np.nanmean(data[mask],axis=0) if count[-1]>=ratio else np.array([np.nan]*len(data[0]))) minTime+=dt dummy = Time._set_ndarray(dummy) return tummy,dummy,count @staticmethod def _nofixTime(time,data,parameter:str,outputPara_list:list): # def _nofixTime(time,data,parameter:str,ratio:int): ''' parameter: set the datetime parameter (second, minute ...etc) will be used to calculate season enum: 1: winter 2: spring 3: summer 4: autumn ''' season_dict = { 1: 'Winter', 2: 'Spring', 3: 'Summer', 4: 'Autumn', } if parameter.lower()=='season': time_para_list = [Time._get_season(val.month) for val in time] else: time_para_list = [eval(f"val.{parameter}") for val in time] time_para_list = np.array(time_para_list) if time_para_list.size==0: return np.array(np.nan),np.array(np.nan),np.array(np.nan) minTime = np.nanmin(time_para_list) maxTime = np.nanmax(time_para_list) # if ratio==None: ratio=0 # get data_value if isinstance(data,dict): if 'mean' in data.keys(): data=data['mean'] dummy = {} for para in outputPara_list: if para=='quartile': dummy['lower']=[] dummy['median']=[] dummy['upper']=[] else: dummy[para]=[] tummy = [] count = [] for i in range(minTime,maxTime+1): mask = np.where(time_para_list==i)[0] tummy.append(i if parameter.lower()!='season' else [time[mask[0]].year,season_dict[i]]) count.append(np.sum(np.isfinite(data[mask]))) if 'mean' in outputPara_list: dummy['mean'].append(np.nanmean(data[mask],axis=0)) if 'std' in outputPara_list: dummy['std'].append(np.nanstd(data[mask],axis=0)) if 'max' in outputPara_list: dummy['max'].append(np.nanmax(data[mask],axis=0)) if 'min' in outputPara_list: dummy['min'].append(np.nanmin(data[mask],axis=0)) if 'maxTime' in outputPara_list: dummy['maxTime'].append(time[mask][np.argmax(data[mask],axis=0)]) if 'maxTime' in outputPara_list: dummy['minTime'].append(time[mask][np.argmin(data[mask],axis=0)]) if 'quartile' in outputPara_list: dummy['lower'].append(np.nanpercentile(data[mask],25,axis=0)) if ('quartile' in outputPara_list) | ('median' in outputPara_list): dummy['median'].append(np.nanpercentile(data[mask],50,axis=0)) if 'quartile' in outputPara_list: dummy['upper'].append(np.nanpercentile(data[mask],75,axis=0)) # dummy.append(np.nanmean(data[mask],axis=0) if count[-1]>=ratio else np.array([np.nan]*len(data[0]))) dummy = Time._set_ndarray(dummy) return tummy,dummy,count @staticmethod def _get_season(month): ''' enum: 1: winter 2: spring 3: summer 4: autumn ''' return (month%12+3)//3 @staticmethod def _QC_numbers(data,count,threshold): if threshold==None: return data count = np.array(count) data = np.array(data) mask = np.where(count<threshold)[0] data[mask,:]=np.nan return data def set_config(self,init:bool=False,**kwargs) -> None: ''' config['storageForward']: save the value at the start time or not<br> config['outputPara_list]: select output parameter [mean,std,max,min] Arguments: init: Is the initialize status? Default is False If set True, will using the init state. **kwargs: Optional, this work only init set false. config: { asDict: bool, storage: bool, fixTime: bool, zeroStart: bool, selfUpdate: bool, outputPara_list: list = [ mean, std, max, min, maxTime, minTime, quartile, median ] } ''' if init==True: self.config = dict( asDict=False, storageForward=True, fixTime=True, zeroStart=True, selfUpdate=True, outputPara_list=['mean','std','mean'] # ['mean','std','max','min','maxTime','minTime','quartile','median'], ) else: for key in kwargs.keys(): self.config[key] = kwargs[key] def input(self,time: Union[list, np.ndarray],data: Union[list, np.ndarray],dtype:object =float, ratio: Union[int, float]=None,header: list=None,starttime:datetime.datetime=None,endtime:datetime.datetime=None) -> str: ''' time <datetime> : input timelist of data <br> data <numerical>: input data array Arguments: time: list of time series data: list of data set depend on time series dtype: convert type of data elements ratio: require of the data numbers(int) or ratio(float) header: export tag of data header starttime: start of the time endtime: end of the time Returns: return 'Successfully' when process success. ''' self.time = np.array(time) self.data = np.array(data,dtype=dtype) self.ratio = ratio self.header = header self.starttime = starttime self.endtime = endtime self.counts = [] return "Successfully" def isrepeat(self) -> bool: ''' Check weather data repeat depend on time. Returns: check there has repeat datetime in the data set. ''' if len(self.time.reshape(-1))==len(set(self.time)): return False else: return True def second(self,ratio: Union[int, float]=None,base: int=1000) -> Union[None, tuple, list, dict]: ''' Do statistic method base on config setting. Arguments: ratio: require of the data numbers(int) or ratio(float) base: base number of required data, use on ratio<=1 Returns: structure of return data None: if config.selfUpdate==True, then export data by self.get() tuple or list: if config.selfUpdate==False & config.asDict==False, then return the data as tuple. ( time, data, count ) dict: if config.selfUpdate==False & config.asDict==True, then return the data as dictionary. { time: np.ndarray, data: np.ndarray, count: np.ndarray } ''' if ratio!=None: ratio=int(base*ratio) if ratio<=1 else int(ratio) else: if self.ratio!=None: ratio=int(base*self.ratio) if self.ratio<=1 else int(self.ratio) if self.config['fixTime']: if self.config['zeroStart']: tummy,dummy,count = self._fixTime(self.time,self.data,timeStep=dict(seconds=1), zeroPara=dict(microsecond=0),storageForward=self.config['storageForward'], outputPara_list=self.config['outputPara_list'],starttime=self.starttime,endtime=self.endtime) else: tummy,dummy,count = self._fixTime(self.time,self.data,timeStep=dict(seconds=1), outputPara_list=self.config['outputPara_list'],starttime=self.starttime,endtime=self.endtime) else: # self.config['fixTime']==False tummy,dummy,count = self._nofixTime(self.time,self.data,parameter='second',outputPara_list=self.config['outputPara_list']) dummy = self._QC_numbers(dummy,count,ratio) if self.config['selfUpdate']: self.data = np.array(dummy) self.time = np.array(tummy) self.counts = np.array(count) else: print("This is not object standard operation!") print("You need to set config[selfUpdate]=True and use get method to get the result.") dummy = Time._set_header(dummy,header=self.header) if self.config['asDict']: return dict(time=tummy,data=dummy,counts=count) else: return tummy,dummy,count def minute(self,ratio: Union[int, float]=None,base: int=60) -> Union[None, tuple, list, dict]: ''' Do statistic method base on config setting. Arguments: ratio: require of the data numbers(int) or ratio(float) base: base number of required data, use on ratio<=1 Returns: structure of return data None: if config.selfUpdate==True, then export data by self.get() tuple or list: if config.selfUpdate==False & config.asDict==False, then return the data as tuple. ( time, data, count ) dict: if config.selfUpdate==False & config.asDict==True, then return the data as dictionary. { time: np.ndarray, data: np.ndarray, count: np.ndarray } ''' if ratio!=None: ratio=int(base*ratio) if ratio<=1 else int(ratio) else: if self.ratio!=None: ratio=int(base*self.ratio) if self.ratio<=1 else int(self.ratio) if self.config['fixTime']: if self.config['zeroStart']: tummy,dummy,count = self._fixTime(self.time,self.data,timeStep=dict(minutes=1), zeroPara=dict(second=0,microsecond=0),storageForward=self.config['storageForward'], outputPara_list=self.config['outputPara_list'],starttime=self.starttime,endtime=self.endtime) else: tummy,dummy,count = self._fixTime(self.time,self.data,timeStep=dict(minutes=1), outputPara_list=self.config['outputPara_list'],starttime=self.starttime,endtime=self.endtime) else: # self.config['fixTime']==False tummy,dummy,count = self._nofixTime(self.time,self.data,parameter='minute',outputPara_list=self.config['outputPara_list']) dummy = self._QC_numbers(dummy,count,ratio) if self.config['selfUpdate']: self.data = np.array(dummy) self.time = np.array(tummy) self.counts = np.array(count) else: print("This is not object standard operation!") print("You need to set config[selfUpdate]=True and use get method to get the result.") dummy = Time._set_header(dummy,header=self.header) if self.config['asDict']: return dict(time=tummy,data=dummy,counts=count) else: return tummy,dummy,count def hour(self,ratio: Union[int, float]=None,base: int=60) -> Union[None, tuple, list, dict]: ''' Do statistic method base on config setting. Arguments: ratio: require of the data numbers(int) or ratio(float) base: base number of required data, use on ratio<=1 Returns: structure of return data None: if config.selfUpdate==True, then export data by self.get() tuple or list: if config.selfUpdate==False & config.asDict==False, then return the data as tuple. ( time, data, count ) dict: if config.selfUpdate==False & config.asDict==True, then return the data as dictionary. { time: np.ndarray, data: np.ndarray, count: np.ndarray } ''' if ratio!=None: ratio=int(base*ratio) if ratio<=1 else int(ratio) else: if self.ratio!=None: ratio=int(base*self.ratio) if self.ratio<=1 else int(self.ratio) if self.config['fixTime']: if self.config['zeroStart']: tummy,dummy,count = self._fixTime(self.time,self.data,timeStep=dict(hours=1) ,zeroPara=dict(minute=0,second=0,microsecond=0),storageForward=self.config['storageForward'], outputPara_list=self.config['outputPara_list'],starttime=self.starttime,endtime=self.endtime) else: tummy,dummy,count = self._fixTime(self.time,self.data,timeStep=dict(hours=1), outputPara_list=self.config['outputPara_list'],starttime=self.starttime,endtime=self.endtime) else: # self.config['fixTime']==False tummy,dummy,count = self._nofixTime(self.time,self.data,parameter='hour',outputPara_list=self.config['outputPara_list']) dummy = self._QC_numbers(dummy,count,ratio) if self.config['selfUpdate']: self.data = np.array(dummy) self.time = np.array(tummy) self.counts = np.array(count) else: print("This is not object standard operation!") print("You need to set config[selfUpdate]=True and use get method to get the result.") dummy = Time._set_header(dummy,header=self.header) if self.config['asDict']: return dict(time=tummy,data=dummy,counts=count) else: return tummy,dummy,count def day(self,ratio: Union[int, float]=None,base: int=24) -> Union[None, tuple, list, dict]: ''' Do statistic method base on config setting. Arguments: ratio: require of the data numbers(int) or ratio(float) base: base number of required data, use on ratio<=1 Returns: structure of return data None: if config.selfUpdate==True, then export data by self.get() tuple or list: if config.selfUpdate==False & config.asDict==False, then return the data as tuple. ( time, data, count ) dict: if config.selfUpdate==False & config.asDict==True, then return the data as dictionary. { time: np.ndarray, data: np.ndarray, count: np.ndarray } ''' if ratio!=None: ratio=int(base*ratio) if ratio<=1 else int(ratio) else: if self.ratio!=None: ratio=int(base*self.ratio) if self.ratio<=1 else int(self.ratio) if self.config['fixTime']: if self.config['zeroStart']: tummy,dummy,count = self._fixTime(self.time,self.data,timeStep=dict(days=1), zeroPara=dict(hour=0,minute=0,second=0,microsecond=0),storageForward=self.config['storageForward'], outputPara_list=self.config['outputPara_list'],starttime=self.starttime,endtime=self.endtime) else: tummy,dummy,count = self._fixTime(self.time,self.data,timeStep=dict(days=1), outputPara_list=self.config['outputPara_list'],starttime=self.starttime,endtime=self.endtime) else: # self.config['fixTime']==False tummy,dummy,count = self._nofixTime(self.time,self.data,parameter='day',outputPara_list=self.config['outputPara_list']) dummy = self._QC_numbers(dummy,count,ratio) if self.config['selfUpdate']: self.data = np.array(dummy) self.time = np.array(tummy) self.counts = np.array(count) else: print("This is not object standard operation!") print("You need to set config[selfUpdate]=True and use get method to get the result.") dummy = Time._set_header(dummy,header=self.header) if self.config['asDict']: return dict(time=tummy,data=dummy,counts=count) else: return tummy,dummy,count def month(self,ratio: Union[int, float]=None,base: int=30) -> Union[None, tuple, list, dict]: ''' Do statistic method base on config setting. Arguments: ratio: require of the data numbers(int) or ratio(float) base: base number of required data, use on ratio<=1 Returns: structure of return data None: if config.selfUpdate==True, then export data by self.get() tuple or list: if config.selfUpdate==False & config.asDict==False, then return the data as tuple. ( time, data, count ) dict: if config.selfUpdate==False & config.asDict==True, then return the data as dictionary. { time: np.ndarray, data: np.ndarray, count: np.ndarray } ''' if ratio!=None: ratio=int(base*ratio) if ratio<=1 else int(ratio) else: if self.ratio!=None: ratio=int(base*self.ratio) if self.ratio<=1 else int(self.ratio) if self.config['fixTime']: if self.config['zeroStart']: tummy,dummy,count = self._fixTime(self.time,self.data,timeStep=dict(months=1), zeroPara=dict(day=1,hour=0,minute=0,second=0,microsecond=0), outputPara_list=self.config['outputPara_list'],storageForward=self.config['storageForward'], starttime=self.starttime,endtime=self.endtime) else: tummy,dummy,count = self._fixTime(self.time,self.data,timeStep=dict(months=1), outputPara_list=self.config['outputPara_list'],starttime=self.starttime,endtime=self.endtime) else: # self.config['fixTime']==False tummy,dummy,count = self._nofixTime(self.time,self.data,parameter='month',outputPara_list=self.config['outputPara_list']) dummy = self._QC_numbers(dummy,count,ratio) if self.config['selfUpdate']: self.data = np.array(dummy) self.time = np.array(tummy) self.counts = np.array(count) else: print("This is not object standard operation!") print("You need to set config[selfUpdate]=True and use get method to get the result.") dummy = Time._set_header(dummy,header=self.header) if self.config['asDict']: return dict(time=tummy,data=dummy,counts=count) else: return tummy,dummy,count def season(self,ratio: Union[int, float]=None,base: int=3) -> Union[None, tuple, list, dict]: ''' Do statistic method base on config setting. Arguments: ratio: require of the data numbers(int) or ratio(float) base: base number of required data, use on ratio<=1 Returns: structure of return data None: if config.selfUpdate==True, then export data by self.get() tuple or list: if config.selfUpdate==False & config.asDict==False, then return the data as tuple. ( time, data, count ) dict: if config.selfUpdate==False & config.asDict==True, then return the data as dictionary. { time: np.ndarray, data: np.ndarray, count: np.ndarray } ''' ''' Spring: March, April, May <br> Summer: June, July, August <br> Autumn: September, October, November <br> Winter: December, January, February ''' if ratio!=None: ratio=int(base*ratio) if ratio<=1 else int(ratio) else: if self.ratio!=None: ratio=int(base*self.ratio) if self.ratio<=1 else int(self.ratio) if self.config['fixTime']: if self.config['zeroStart']: tummy,dummy,count = self._fixTime(self.time,self.data,timeStep=dict(season=1), zeroPara=dict(day=1,hour=0,minute=0,second=0,microsecond=0), outputPara_list=self.config['outputPara_list'],storageForward=self.config['storageForward'], starttime=self.starttime,endtime=self.endtime) else: tummy,dummy,count = self._fixTime(self.time,self.data,timeStep=dict(season=1), outputPara_list=self.config['outputPara_list'],starttime=self.starttime,endtime=self.endtime) else: # self.config['fixTime']==False tummy,dummy,count = self._nofixTime(self.time,self.data,parameter='season',outputPara_list=self.config['outputPara_list']) dummy = self._QC_numbers(dummy,count,ratio) if self.config['selfUpdate']: self.data = np.array(dummy) self.time = np.array(tummy) self.counts = np.array(count) else: print("This is not object standard operation!") print("You need to set config[selfUpdate]=True and use get method to get the result.") dummy = Time._set_header(dummy,header=self.header) if self.config['asDict']: return dict(time=tummy,data=dummy,counts=count) else: return tummy,dummy,count def year(self,ratio:Union[int, float]=None,base:int=12) -> Union[None, tuple, list, dict]: ''' Do statistic method base on config setting. Arguments: ratio: require of the data numbers(int) or ratio(float) base: base number of required data, use on ratio<=1 Returns: structure of return data None: if config.selfUpdate==True, then export data by self.get() tuple or list: if config.selfUpdate==False & config.asDict==False, then return the data as tuple. ( time, data, count ) dict: if config.selfUpdate==False & config.asDict==True, then return the data as dictionary. { time: np.ndarray, data: np.ndarray, count: np.ndarray } ''' if ratio!=None: ratio=int(base*ratio) if ratio<=1 else int(ratio) else: if self.ratio!=None: ratio=int(base*self.ratio) if self.ratio<=1 else int(self.ratio) if self.config['fixTime']: if self.config['zeroStart']: tummy,dummy,count = self._fixTime(self.time,self.data,timeStep=dict(years=1), zeroPara=dict(month=1,day=1,hour=0,minute=0,second=0,microsecond=0),storageForward=self.config['storageForward'], outputPara_list=self.config['outputPara_list'],starttime=self.starttime,endtime=self.endtime) else: tummy,dummy,count = self._fixTime(self.time,self.data,timeStep=dict(years=1), outputPara_list=self.config['outputPara_list'],starttime=self.starttime,endtime=self.endtime) else: # self.config['fixTime']==False tummy,dummy,count = self._nofixTime(self.time,self.data,parameter='year',outputPara_list=self.config['outputPara_list']) dummy = self._QC_numbers(dummy,count,ratio) if self.config['selfUpdate']: self.data = np.array(dummy) self.time = np.array(tummy) self.counts = np.array(count) else: print("This is not object standard operation!") print("You need to set config[selfUpdate]=True and use get method to get the result.") dummy = Time._set_header(dummy,header=self.header) if self.config['asDict']: return dict(time=tummy,data=dummy,counts=count) else: return tummy,dummy,count def get(self,parameter: str=None) -> Union[list, dict, np.ndarray]: ''' export the data from Time factory. Arguments: parameter: select the return parameter. enum: None: { time, data, counts }, config, time, data, counts Returns: select parameter data set. ''' if parameter=='config': return self.config if (parameter==None) and (self.config['asDict']): return dict(time=self.time, data=Time._set_header(self.data,header=self.header), counts=self.counts) if parameter=='time': return self.time if parameter=='data': return Time._set_header(self.data,header=self.header) if parameter=='counts': return self.counts print("Please select the return parameter or set config['asDict']=True.") if __name__ == "__main__": # Implement the object myobj = Time() # Input data import datetime, random st = datetime.datetime(2020,1,1) number = 50000 time = [st+datetime.timedelta(hours=val) for val in range(number)] data = [[random.gauss(10,5) for _ in range(4)] for _ in range(number)] myobj.input(time,data,header=['a','b','c','d']) # Calculate and Get result # myobj.hour(1,500) myobj.set_config(outputPara_list=['mean','std','max','quartile']) myobj.season() myobj.set_config(asDict=True) result = myobj.get() print(result)

3d40cc9af82e7caa1ec12b7d4fdc7c7db383ac10

py

Python

Ago-Dic-2021/diaz-delabra-erick/carpeta-practica-3/calculator.py

AnhellO/DAS_Sistemas

07b4eca78357d02d225d570033d05748d91383e3

2017-09-26T09:36:32.000Z

2022-03-19T18:05:25.000Z

Ago-Dic-2021/diaz-delabra-erick/carpeta-practica-3/calculator.py

AnhellO/DAS_Sistemas

07b4eca78357d02d225d570033d05748d91383e3

2017-09-11T05:06:12.000Z

2022-02-14T04:44:04.000Z

Ago-Dic-2021/diaz-delabra-erick/carpeta-practica-3/calculator.py

AnhellO/DAS_Sistemas

07b4eca78357d02d225d570033d05748d91383e3

2017-09-01T00:10:08.000Z

2022-03-19T18:05:12.000Z

import math

import math class Calculator: def __init__(self, a: int, b: int) -> None: self.a = a self.b = b def suma(self) -> int: return self.a + self.b def resta(self) -> int: return self.a - self.b def multiplicacion(self) -> int: return self.a * self.b def division(self) -> int: if self.b == 0: return "No se puede dividir entre 0" else: return (self.a / self.b) def raizNumero(self) -> int: if self.a < 0: return "No se puede raiz de negativos" else: return pow(self.a, (1/(self.b))) def potencia(self) -> int: return pow(self.a, self.b)

f191d89902854c6a45383db6b705fc612cf47791

py

Python

mmdet/models/__init__.py

FelixZhang7/miemiedetection

ca44f33255e0bb9d6150044983a344fb9a288c08

mmdet/models/__init__.py

FelixZhang7/miemiedetection

ca44f33255e0bb9d6150044983a344fb9a288c08

mmdet/models/__init__.py

FelixZhang7/miemiedetection

ca44f33255e0bb9d6150044983a344fb9a288c08

2022-02-16T08:35:00.000Z

2022-02-16T08:35:00.000Z

#!/usr/bin/env python3 # -*- coding:utf-8 -*- # Copyright (c) 2014-2021 Megvii Inc. All rights reserved. from .backbones.darknet import CSPDarknet, Darknet from .backbones.resnet_vd import Resnet18Vd, Resnet50Vd from .backbones.resnet_vb import Resnet50Vb from .losses.yolov3_loss import YOLOv3Loss from .losses.losses import IOUloss from .losses.iou_losses import MyIOUloss, IouLoss, IouAwareLoss from .losses.fcos_loss import FCOSLoss from .heads.yolov3_head import YOLOv3Head from .heads.yolox_head import YOLOXHead from .heads.fcos_head import FCOSHead from .necks.yolo_pafpn import YOLOPAFPN from .necks.yolo_fpn import YOLOFPN from .necks.fpn import FPN from .architectures.yolo import PPYOLO from .architectures.yolox import YOLOX from .architectures.fcos import FCOS

#!/usr/bin/env python3 # -*- coding:utf-8 -*- # Copyright (c) 2014-2021 Megvii Inc. All rights reserved. from .backbones.darknet import CSPDarknet, Darknet from .backbones.resnet_vd import Resnet18Vd, Resnet50Vd from .backbones.resnet_vb import Resnet50Vb from .losses.yolov3_loss import YOLOv3Loss from .losses.losses import IOUloss from .losses.iou_losses import MyIOUloss, IouLoss, IouAwareLoss from .losses.fcos_loss import FCOSLoss from .heads.yolov3_head import YOLOv3Head from .heads.yolox_head import YOLOXHead from .heads.fcos_head import FCOSHead from .necks.yolo_pafpn import YOLOPAFPN from .necks.yolo_fpn import YOLOFPN from .necks.fpn import FPN from .architectures.yolo import PPYOLO from .architectures.yolox import YOLOX from .architectures.fcos import FCOS

0468d6c246b239a4fae46a385cc87c22edb5790e

py

Python

egs/voxceleb/v2.voxceleb1/scp_ark2npy.py

zeek-han/kaldi

e3ed0812db7abd3c266d5616babfd0adff8260ac

egs/voxceleb/v2.voxceleb1/scp_ark2npy.py

zeek-han/kaldi

e3ed0812db7abd3c266d5616babfd0adff8260ac

egs/voxceleb/v2.voxceleb1/scp_ark2npy.py

zeek-han/kaldi

e3ed0812db7abd3c266d5616babfd0adff8260ac

#!/usr/bin/env python import numpy as np import kaldiio id2mfcc = kaldiio.load_scp('/home/sangjik/kaldi/egs/voxceleb/v2.smallest/mfcc/raw_mfcc_train.10.scp') for utt_id, mfcc in id2mfcc.items(): #print(utt_id, mfcc.shape) np.save('./tmp_mfcc/{}.npy'.format(utt_id), mfcc)

#!/usr/bin/env python import numpy as np import kaldiio id2mfcc = kaldiio.load_scp('/home/sangjik/kaldi/egs/voxceleb/v2.smallest/mfcc/raw_mfcc_train.10.scp') for utt_id, mfcc in id2mfcc.items(): #print(utt_id, mfcc.shape) np.save('./tmp_mfcc/{}.npy'.format(utt_id), mfcc)

b75db27a80b0122a92a95241b891c75aed56b87b

py

Python

pipeline/publication.py

Yi-61/map-ephys

8eacd84f67678b05bcc379c7d5a9560ea7a87e46

pipeline/publication.py

Yi-61/map-ephys

8eacd84f67678b05bcc379c7d5a9560ea7a87e46

pipeline/publication.py

Yi-61/map-ephys

8eacd84f67678b05bcc379c7d5a9560ea7a87e46

import logging import pathlib import re import os from fnmatch import fnmatch from textwrap import dedent from collections import defaultdict import datajoint as dj from . import lab from . import experiment from . import ephys from . import tracking from .ingest.tracking import TrackingIngest from pipeline.globus import GlobusStorageManager from . import get_schema_name PUBLICATION_TRANSFER_TIMEOUT = 10000 schema = dj.schema(get_schema_name('publication')) log = logging.getLogger(__name__) __all__ = [experiment, ephys] @schema class GlobusStorageLocation(dj.Lookup): """ globus storage locations """ definition = """ globus_alias: varchar(32) # name for location (e.g. 'raw-ephys') --- globus_endpoint: varchar(255) # globus endpoint (user#endpoint) globus_path: varchar(1024) # unix-style path within endpoint """ @property @classmethod def local_endpoint(cls, globus_alias=None): ''' return local endpoint for globus_alias from dj.config expects: globus.local_endpoints: { globus_alias: { 'endpoint': uuid, # UUID of local endpoint 'endpoint_subdir': str, # unix-style path within endpoint 'endpoint_path': str # corresponding local path } ''' le = dj.config.get('custom', {}).get('globus.local_endpoints', None) if le is None or globus_alias not in le: raise dj.DataJointError( "globus_local_endpoints for {} not configured".format( globus_alias)) return le[globus_alias] @schema @schema @schema @schema @schema @schema @schema class ArchivedTrackingVideo(dj.Imported): ''' ArchivedTrackingVideo storage Note: video_file_name tracked here as trial->file map is non-deterministic Directory locations of the form: {Water restriction number}\{Session Date}\video with file naming convention of the form: {Water restriction number}_{camera-position-string}_NNN-NNNN.avi Where 'NNN' is determined from the 'tracking map file' which maps trials to videos as outlined in tracking.py XXX: Using key-source based loookup as is currently done, may have trials for which there is no tracking, so camera cannot be determined to do file lookup, thus videos are missed. This could be resolved via schema adjustment, or file-traversal based 'opportunistic' registration strategy. ''' definition = """ -> ArchivedSession -> tracking.TrackingDevice --- -> DataSet """ key_source = tracking.TrackingDevice * experiment.Session ingest = None # ingest module reference gsm = None # for GlobusStorageManager @classmethod def get_ingest(cls): ''' return tracking_ingest module not imported globally to prevent ingest schema creation for client case ''' log.debug('ArchivedVideoFile.get_ingest()') if cls.ingest is None: from .ingest import tracking as tracking_ingest cls.ingest = tracking_ingest return cls.ingest @classmethod def discover(cls): """ discover files on globus and attempt to register them """ self = cls() globus_alias = 'raw-video' le = GlobusStorageLocation.local_endpoint(globus_alias) lep, lep_sub, lep_dir = (le['endpoint'], le['endpoint_subdir'], le['endpoint_path']) ra, rep, rep_sub = (GlobusStorageLocation() & {'globus_alias': globus_alias}).fetch1().values() smap = {'{}/{}'.format(s['water_restriction_number'], s['session_date']).replace('-', ''): s for s in (experiment.Session() * (lab.WaterRestriction() * lab.Subject.proj()))} tpos_dev = {s['tracking_position']: s['tracking_device'] for s in tracking.TrackingDevice()} # position:device ftmap = {t['file_type']: t for t in (FileType() & "file_type like 'tracking%%'")} skey = None sskip = set() sfiles = [] # {file_subpath:, trial:, file_type:,} gsm = self.get_gsm() gsm.activate_endpoint(lep) gsm.activate_endpoint(rep) for ep, dirname, node in gsm.fts('{}:{}'.format(rep, rep_sub)): vdir = re.match('([a-z]+[0-9]+)/([0-9]{8})/video', dirname) if not vdir or node['DATA_TYPE'] != 'file': continue h2o, sdate = vdir[1], vdir[2] skey_i = '{}/{}'.format(h2o, sdate) if skey_i != skey: if skey and skey in smap: with dj.conn().transaction: try: commit(skey, sfiles) except Exception as e: log.error( 'Exception {} committing {}. files: {}'.format( repr(e), skey, sfiles)) skey, sfiles = skey_i, [] if skey not in smap: if skey not in sskip: log.debug('session {} not known. skipping'.format(skey)) sskip.add(skey) continue fname = node['name'] log.debug('checking {}/{}'.format(dirname, fname)) if '.' not in fname: log.debug('skipping {} - no dot in fname'.format(fname)) continue froot, fext = fname.split('.', 1) ftype = {g['file_type']: g for g in ftmap.values() if fnmatch(fname, g['file_glob'])} if len(ftype) != 1: log.debug('skipping {} - incorrect type matches: {}'.format( fname, ftype)) continue ftype = next(iter(ftype.values()))['file_type'] log.debug('processing as {}'.format(ftype)) file_subpath = '{}/{}'.format(dirname, fname) if ftype == 'tracking-video-map': # e.g. dl55_20190108_side.txt h2o_f, fdate, pos = froot.split('_') sfiles.append({'water_restriction_number': h2o, 'session_date': '{}-{}-{}'.format( sdate[:4], sdate[4:6], sdate[6:]), 'position': pos, 'file_subpath': file_subpath, 'file_type': ftype}) else: # tracking-video-map # e.g. dl41_side_998-0000.avi or dl41_side_998-0000_00.avi h2o_f, pos, video = froot.replace('-', '_').split('_')[:3] sfiles.append({'water_restriction_number': h2o, 'session_date': '{}-{}-{}'.format( sdate[:4], sdate[4:6], sdate[6:]), 'position': pos, 'video': int(video), 'file_subpath': file_subpath, 'file_type': ftype}) def make(self, key): """ discover files in local endpoint and transfer/register """ log.info('ArchivedVideoFile.make(): {}'.format(key)) # {'tracking_device': 'Camera 0', 'subject_id': 432572, 'session': 1} globus_alias = 'raw-video' le = GlobusStorageLocation.local_endpoint(globus_alias) lep, lep_sub, lep_dir = (le['endpoint'], le['endpoint_subdir'], le['endpoint_path']) re = (GlobusStorageLocation & {'globus_alias': globus_alias}).fetch1() rep, rep_sub = re['globus_endpoint'], re['globus_path'] log.info('local_endpoint: {}:{} -> {}'.format(lep, lep_sub, lep_dir)) log.info('remote_endpoint: {}:{}'.format(rep, rep_sub)) h2o = (lab.WaterRestriction & key).fetch1('water_restriction_number') session = (experiment.Session & key).fetch1() sdate = session['session_date'] sdate_sml = "{}{:02d}{:02d}".format(sdate.year, sdate.month, sdate.day) dev = (tracking.TrackingDevice & key).fetch1() trls = (experiment.SessionTrial & key).fetch( order_by='trial', as_dict=True) tracking_ingest = self.get_ingest() tdev = dev['tracking_device'] # NOQA: notused tpos = dev['tracking_position'] camtrial = '{}_{}_{}.txt'.format(h2o, sdate_sml, tpos) vbase = pathlib.Path(lep_dir, h2o, sdate_sml, 'video') campath = vbase / camtrial if not campath.exists(): # XXX: uses 1st found log.warning('trial map {} n/a! skipping.'.format(campath)) return log.info('loading trial map: {}'.format(campath)) vmap = {v: k for k, v in tracking_ingest.TrackingIngest.load_campath(campath).items()} log.debug('loaded video map: {}'.format(vmap)) # add ArchivedSession as_key = {k: v for k, v in key.items() if k in experiment.Session.primary_key} as_rec = {**as_key, 'globus_alias': globus_alias} ArchivedSession.insert1(as_rec, allow_direct_insert=True, skip_duplicates=True) # add DataSet ds_type = 'tracking-video' ds_name = '{}_{}_{}_{}'.format(h2o, sdate.isoformat(), ds_type, tpos) ds_key = {'globus_alias': globus_alias, 'dataset_name': ds_name} ds_rec = {**ds_key, 'dataset_type': ds_type} DataSet.insert1(ds_rec, allow_direct_insert=True) # add ArchivedVideoTracking vt_key = {**as_key, 'tracking_device': tdev} vt_rec = {**vt_key, 'globus_alias': globus_alias, 'dataset_name': ds_name} self.insert1(vt_rec) filetype = 'tracking-video-trial' for t in trls: trial = t['trial'] log.info('.. tracking trial {} ({})'.format(trial, t)) if t['trial'] not in vmap: log.warning('trial {} not in video map. skipping!'.format(t)) continue vmatch = '{}_{}_{}-*'.format(h2o, tpos, vmap[trial]) log.debug('vbase: {}, vmatch: {}'.format(vbase, vmatch)) vglob = list(vbase.glob(vmatch)) if len(vglob) != 1: emsg = 'incorrect videos found in {}: {}'.format(vbase, vglob) log.warning(emsg) raise dj.DataJointError(emsg) vfile = vglob[0].name gfile = '{}/{}/{}/{}'.format( h2o, sdate_sml, 'video', vfile) # subpath srcp = '{}:{}/{}'.format(lep, lep_sub, gfile) # source path dstp = '{}:{}/{}'.format(rep, rep_sub, gfile) # dest path gsm = self.get_gsm() gsm.activate_endpoint(lep) # XXX: cache / prevent duplicate RPC? gsm.activate_endpoint(rep) # XXX: cache / prevent duplicate RPC? log.info('transferring {} to {}'.format(srcp, dstp)) if not gsm.cp(srcp, dstp): emsg = "couldn't transfer {} to {}".format(srcp, dstp) log.error(emsg) raise dj.DataJointError(emsg) pf_key = {**ds_key, 'file_subpath': vfile} pf_rec = {**pf_key, 'file_type': filetype} DataSet.PhysicalFile.insert1({**pf_rec}, allow_direct_insert=True) trk_key = {k: v for k, v in {**key, 'trial': trial}.items() if k in experiment.SessionTrial.primary_key} tv_rec = {**vt_key, **trk_key, **pf_key} self.TrialVideo.insert1({**tv_rec}) def test_flist(fname='globus-index-full.txt'): ''' spoof tester for discover methods expects: f: ep:/path/to/file d: ep:/path/to/direct etc. (aka: globus-shell 'find' output) replace the line: for ep, dirname, node in gsm.fts('{}:{}'.format(rep, rep_sub)): with: for ep, dirname, node in test_flist('globus-list.txt'): to test against the file 'globus-list.txt' ''' with open(fname, 'r') as infile: for l in infile: try: t, fp = l.split(' ') fp = fp.split(':')[1].lstrip('/').rstrip('\n') dn, bn = os.path.split(fp) if t == 'f:': yield ('ep', dn, {'DATA_TYPE': 'file', 'name': bn}) else: yield ('ep', dn, {'DATA_TYPE': 'dunno', 'path': bn}) except ValueError as e: if 'too many values' in repr(e): pass

import logging import pathlib import re import os from fnmatch import fnmatch from textwrap import dedent from collections import defaultdict import datajoint as dj from . import lab from . import experiment from . import ephys from . import tracking from .ingest.tracking import TrackingIngest from pipeline.globus import GlobusStorageManager from . import get_schema_name PUBLICATION_TRANSFER_TIMEOUT = 10000 schema = dj.schema(get_schema_name('publication')) log = logging.getLogger(__name__) __all__ = [experiment, ephys] @schema class GlobusStorageLocation(dj.Lookup): """ globus storage locations """ definition = """ globus_alias: varchar(32) # name for location (e.g. 'raw-ephys') --- globus_endpoint: varchar(255) # globus endpoint (user#endpoint) globus_path: varchar(1024) # unix-style path within endpoint """ @property def contents(self): custom = dj.config.get('custom', None) if custom and 'globus.storage_locations' in custom: # test config return custom['globus.storage_locations'] return (('raw-ephys', '5b875fda-4185-11e8-bb52-0ac6873fc732', '/'), ('raw-video', '5b875fda-4185-11e8-bb52-0ac6873fc732', '/'),) @classmethod def local_endpoint(cls, globus_alias=None): ''' return local endpoint for globus_alias from dj.config expects: globus.local_endpoints: { globus_alias: { 'endpoint': uuid, # UUID of local endpoint 'endpoint_subdir': str, # unix-style path within endpoint 'endpoint_path': str # corresponding local path } ''' le = dj.config.get('custom', {}).get('globus.local_endpoints', None) if le is None or globus_alias not in le: raise dj.DataJointError( "globus_local_endpoints for {} not configured".format( globus_alias)) return le[globus_alias] @schema class ArchivedSession(dj.Imported): definition = """ -> experiment.Session --- -> GlobusStorageLocation """ @schema class DataSetType(dj.Lookup): definition = """ dataset_type: varchar(64) """ contents = zip(['ephys-raw-trialized', 'ephys-raw-continuous', 'ephys-sorted', 'tracking-video']) @schema class FileType(dj.Lookup): definition = """ file_type: varchar(32) # file type short name --- file_glob: varchar(64) # file match pattern file_descr: varchar(255) # file type long description """ @property def contents(self): data = [('ephys-raw-3a-ap-trial', '*_g0_t[0-9]*.imec.ap.bin', ''' 3A Probe per-trial AP channels high pass filtered at 300Hz and sampled at 30kHz - recording file '''), ('ephys-raw-3a-ap-trial-meta', '*_g0_t[0-9]*.imec.ap.meta', ''' 3A Probe per-trial AP channels high pass filtered at 300Hz and sampled at 30kHz - file metadata '''), ('ephys-raw-3a-lf-trial', '*_g0_t[0-9]*.imec.lf.bin', ''' 3A Probe per-trial AP channels low pass filtered at 300Hz and sampled at 2.5kHz - recording file '''), ('ephys-raw-3a-lf-trial-meta', '*_g0_t[0-9]*.imec.lf.meta', ''' 3A Probe per-trial AP channels low pass filtered at 300Hz and sampled at 2.5kHz - file metadata '''), ('ephys-raw-3b-ap-trial', '*_????????_g?_t[0-9]*.imec.ap.bin', ''' 3B Probe per-trial AP channels high pass filtered at 300Hz and sampled at 30kHz - recording file '''), ('ephys-raw-3b-ap-trial-meta', '*_????????_g?_t[0-9]*.imec.ap.meta', ''' 3B Probe per-trial AP channels high pass filtered at 300Hz and sampled at 30kHz - file metadata '''), ('ephys-raw-3b-lf-trial', '*_????????_g?_t[0-9]*.imec.lf.bin', ''' 3B Probe per-trial AP channels low pass filtered at 300Hz and sampled at 2.5kHz - recording file '''), ('ephys-raw-3b-lf-trial-meta', '*_????????_g?_t[0-9]*.imec.lf.meta', ''' 3B Probe per-trial AP channels low pass filtered at 300Hz and sampled at 2.5kHz - file metadata '''), ('ephys-raw-3b-ap-concat', '*_????????_g?_tcat.imec.ap.bin', ''' 3B Probe concatenated AP channels high pass filtered at 300Hz and sampled at 30kHz - recording file '''), ('ephys-raw-3b-ap-concat-meta', '*_??????_g?_tcat.imec.ap.meta', ''' 3B Probe concatenated AP channels high pass filtered at 300Hz and sampled at 30kHz - file metadata '''), ('ephys-raw-3b-lf-concat', '*_????????_g?_tcat.imec.lf.bin', ''' 3B Probe concatenated AP channels low pass filtered at 300Hz and sampled at 2.5kHz - recording file '''), ('ephys-raw-3b-lf-concat-meta', '*_????????_g?_tcat.imec.lf.meta', ''' 3B Probe concatenated AP channels low pass filtered at 300Hz and sampled at 2.5kHz - file metadata '''), ('tracking-video-trial', '*_*_[0-9]*-*.[am][vp][i4]', ''' Video Tracking per-trial file at 300fps '''), ('tracking-video-map', '*_????????_*.txt', ''' Video Tracking file-to-trial mapping ''')] return [[dedent(i).replace('\n', ' ').strip(' ') for i in r] for r in data] @schema class DataSet(dj.Manual): definition = """ -> GlobusStorageLocation dataset_name: varchar(128) --- -> DataSetType """ class PhysicalFile(dj.Part): definition = """ -> master file_subpath: varchar(128) --- -> FileType """ @schema class ArchivedRawEphys(dj.Imported): definition = """ -> ArchivedSession -> DataSet probe_folder: tinyint """ key_source = experiment.Session gsm = None # for GlobusStorageManager class RawEphysTrial(dj.Part): """ file:trial mapping if applicable """ definition = """ -> master -> experiment.SessionTrial -> DataSet.PhysicalFile """ def get_gsm(self): log.debug('ArchivedRawEphysTrial.get_gsm()') if self.gsm is None: self.gsm = GlobusStorageManager() self.gsm.wait_timeout = PUBLICATION_TRANSFER_TIMEOUT return self.gsm @classmethod def discover(cls): """ Discover files on globus and attempt to register them. """ self = cls() globus_alias = 'raw-ephys' ra, rep, rep_sub = (GlobusStorageLocation() & {'globus_alias': globus_alias}).fetch1().values() smap = {'{}/{}'.format(s['water_restriction_number'], s['session_date']).replace('-', ''): s for s in (experiment.Session() * (lab.WaterRestriction() * lab.Subject.proj()))} ftmap = {t['file_type']: t for t in (FileType() & "file_type like 'ephys%%'")} skey = None sskip = set() sfiles = [] # {file_subpath:, trial:, file_type:,} def commit(skey, sfiles): log.info('commit. skey: {}, sfiles: {}'.format(skey, sfiles)) if not sfiles: log.info('skipping. no files in set') return h2o, sdate, ftypes = set(), set(), set() ptmap = defaultdict(lambda: defaultdict(list)) # probe:trial:file for s in sfiles: ptmap[s['probe']][s['trial']].append(s) h2o.add(s['water_restriction_number']) sdate.add(s['session_date']) ftypes.add(s['file_type']) if len(h2o) != 1 or len(sdate) != 1: log.info('skipping. bad h2o {} or session date {}'.format( h2o, sdate)) return h2o, sdate = next(iter(h2o)), next(iter(sdate)) {k: {kk: vv for kk, vv in v.items()} for k, v in ptmap.items()} if all('trial' in f for f in ftypes): # DataSet ds_type = 'ephys-raw-trialized' ds_name = '{}_{}_{}'.format(h2o, sdate, ds_type) ds_key = {'dataset_name': ds_name, 'globus_alias': globus_alias} if (DataSet & ds_key): log.info('DataSet: {} already exists. Skipping.'.format( ds_key)) return DataSet.insert1({**ds_key, 'dataset_type': ds_type}, allow_direct_insert=True) # ArchivedSession as_key = {k: v for k, v in smap[skey].items() if k in ArchivedSession.primary_key} ArchivedSession.insert1( {**as_key, 'globus_alias': globus_alias}, allow_direct_insert=True, skip_duplicates=True) for p in ptmap: # ArchivedRawEphys ep_key = {**as_key, **ds_key, 'probe_folder': p} ArchivedRawEphys.insert1(ep_key, allow_direct_insert=True) for t in ptmap[p]: for f in ptmap[p][t]: DataSet.PhysicalFile.insert1( {**ds_key, **f}, allow_direct_insert=True, ignore_extra_fields=True) ArchivedRawEphys.RawEphysTrial.insert1( {**ep_key, **ds_key, 'trial': t, 'file_subpath': f['file_subpath']}, allow_direct_insert=True) elif all('concat' in f for f in ftypes): raise NotImplementedError('concatenated not yet implemented') else: log.info('skipping. mixed filetypes detected') return gsm = self.get_gsm() gsm.activate_endpoint(rep) for ep, dirname, node in gsm.fts('{}:{}'.format(rep, rep_sub)): log.debug('checking: {}:{}/{}'.format( ep, dirname, node.get('name', ''))) edir = re.match('([a-z]+[0-9]+)/([0-9]{8})/([0-9]+)', dirname) if not edir or node['DATA_TYPE'] != 'file': continue log.debug('dir match: {}'.format(dirname)) h2o, sdate, probe = edir[1], edir[2], edir[3] skey_i = '{}/{}'.format(h2o, sdate) if skey_i != skey: if skey and skey in smap: with dj.conn().transaction: try: commit(skey, sfiles) except Exception as e: log.error( 'Exception {} committing {}. files: {}'.format( repr(e), skey, sfiles)) skey, sfiles = skey_i, [] if skey not in smap: if skey not in sskip: log.debug('session {} not known. skipping.'.format(skey)) sskip.add(skey) continue fname = node['name'] log.debug('found file {}'.format(fname)) if '.' not in fname: log.debug('skipping {} - no dot in fname'.format(fname)) continue froot, fext = fname.split('.', 1) ftype = {g['file_type']: g for g in ftmap.values() if fnmatch(fname, g['file_glob'])} if len(ftype) != 1: log.debug('skipping {} - incorrect type matches: {}'.format( fname, ftype)) continue ftype = next(iter(ftype.values()))['file_type'] trial = None if 'trial' in ftype: trial = int(froot.split('_t')[1]) file_subpath = '{}/{}'.format(dirname, fname) sfiles.append({'water_restriction_number': h2o, 'session_date': '{}-{}-{}'.format( sdate[:4], sdate[4:6], sdate[6:]), 'probe': int(probe), 'trial': int(trial), 'file_subpath': file_subpath, 'file_type': ftype}) if skey: with dj.conn().transaction: commit(skey, sfiles) def make(self, key): """ discover files in local endpoint and transfer/register """ log.debug(key) globus_alias = 'raw-ephys' le = GlobusStorageLocation.local_endpoint(globus_alias) lep, lep_sub, lep_dir = (le['endpoint'], le['endpoint_subdir'], le['endpoint_path']) re, rep, rep_sub = (GlobusStorageLocation() & {'globus_alias': globus_alias}).fetch1().values() log.info('local_endpoint: {}:{} -> {}'.format(lep, lep_sub, lep_dir)) # Get session related information needed for filenames/records sinfo = (lab.WaterRestriction * lab.Subject.proj() * experiment.Session() & key).fetch1() tinfo = ((lab.WaterRestriction * lab.Subject.proj() * experiment.Session() * experiment.SessionTrial) & key).fetch() h2o = sinfo['water_restriction_number'] sdate = sinfo['session_date'] subdir = pathlib.Path(h2o, str(sdate).replace('-', '')) # + probeno lep_subdir = pathlib.Path(lep_dir, subdir) probechoice = [str(i) for i in range(1, 10)] # XXX: hardcoded file_globs = {i['file_glob']: i['file_type'] for i in FileType & "file_type like 'ephys%%'"} # Process each probe folder for lep_probedir in lep_subdir.glob('*'): lep_probe = str(lep_probedir.relative_to(lep_subdir)) if lep_probe not in probechoice: log.info('skipping lep_probedir: {} - unexpected name'.format( lep_probedir)) continue lep_matchfiles = {} lep_probefiles = lep_probedir.glob('*.*') for pf in lep_probefiles: pfbase = pf.relative_to(lep_probedir) pfmatch = {k: pfbase.match(k) for k in file_globs} if any(pfmatch.values()): log.debug('found valid file: {}'.format(pf)) lep_matchfiles[pf] = tuple(k for k in pfmatch if pfmatch[k]) else: log.debug('skipping non-match file: {}'.format(pf)) continue # Build/Validate file records if not all([len(lep_matchfiles[i]) == 1 for i in lep_matchfiles]): # TODO: handle trial + concatenated match case... log.warning('files matched multiple types'.format( lep_matchfiles)) continue type_to_file = {file_globs[lep_matchfiles[mf][0]]: mf for mf in lep_matchfiles} ds_key, ds_name, ds_files, ds_trials = ( None, None, None, [], []) if all(['trial' in t for t in type_to_file]): dataset_type = 'ephys-raw-trialized' ds_name = '{}_{}_{}'.format(h2o, sdate.isoformat(), dataset_type) ds_key = {'dataset_name': ds_name, 'globus_storage_location': globus_alias} for t in type_to_file: fsp = type_to_file[t].relative_to(lep_dir) dsf = {**ds_key, 'file_subpath': str(fsp)} # e.g : 'tw34_g0_t0.imec.ap.meta' -> *_t(trial).* trial = int(fsp.name.split('_t')[1].split('.')[0]) if trial not in tinfo['trial']: log.warning('unknown trial file: {}. skipping'.format( dsf)) continue ds_trials.append({**dsf, 'trial': trial}) ds_files.append({**dsf, 'file_type': t}) elif all(['concat' in t for t in type_to_file]): dataset_type = 'ephys-raw-continuous' ds_name = '{}_{}_{}'.format(h2o, sdate.isoformat(), dataset_type) ds_key = {'dataset_name': ds_name, 'globus_storage_location': globus_alias} for t in type_to_file: fsp = type_to_file[t].relative_to(lep_dir) ds_files.append({**ds_key, 'file_subpath': str(fsp), 'file_type': t}) else: log.warning("couldn't determine dataset type for {}".format( lep_probedir)) continue # Transfer Files gsm = self.get_gsm() gsm.activate_endpoint(lep) # XXX: cache / prevent duplicate RPC? gsm.activate_endpoint(rep) # XXX: cache / prevent duplicate RPC? DataSet.insert1({**ds_key, 'dataset_type': dataset_type}, allow_direct_insert=True) for f in ds_files: fsp = ds_files[f]['file_subpath'] srcp = '{}:{}/{}'.format(lep, lep_sub, fsp) dstp = '{}:{}/{}'.format(rep, rep_sub, fsp) log.info('transferring {} to {}'.format(srcp, dstp)) # XXX: check if exists 1st? if not gsm.cp(srcp, dstp): emsg = "couldn't transfer {} to {}".format(srcp, dstp) log.error(emsg) raise dj.DataJointError(emsg) DataSet.PhysicalFile.insert1({**ds_key, **ds_files[f]}, allow_direct_insert=True) # Add Records ArchivedSession.insert1( {**key, 'globus_storage_location': globus_alias}, skip_duplicates=True, allow_direct_insert=True) ArchivedRawEphys.insert1( {**key, **ds_key, 'probe_folder': int(str(lep_probe))}, allow_direct_insert=True) if dataset_type == 'ephys-raw-trialized': ArchivedRawEphys.ArchivedTrials.insert( [{**key, **t} for t in ds_trials], allow_direct_insert=True) @classmethod def retrieve(cls): self = cls() for key in self: self.retrieve1(key) @classmethod def retrieve1(cls, key): ''' retrieve related files for a given key ''' self = cls() raise NotImplementedError('retrieve not yet implemented') # Old / to be updated: # >>> list(key.keys()) # ['subject_id', 'session', 'trial', 'electrode_group', 'globus_alia log.debug(key) lep, lep_sub, lep_dir = GlobusStorageLocation().local_endpoint log.info('local_endpoint: {}:{} -> {}'.format(lep, lep_sub, lep_dir)) # get session related information needed for filenames/records sinfo = ((lab.WaterRestriction * lab.Subject.proj() * experiment.Session() * experiment.SessionTrial) & key).fetch1() h2o = sinfo['water_restriction_number'] sdate = sinfo['session_date'] eg = key['electrode_group'] trial = key['trial'] # build file locations: # fpat: base file pattern for this sessions files # gbase: globus-url base path for this sessions files fpat = '{}_{}_{}_g0_t{}'.format(h2o, sdate, eg, trial) gbase = '/'.join((h2o, str(sdate), str(eg), fpat)) repname, rep, rep_sub = (GlobusStorageLocation() & key).fetch()[0] gsm = self.get_gsm() gsm.activate_endpoint(lep) # XXX: cache this / prevent duplicate RPC? gsm.activate_endpoint(rep) # XXX: cache this / prevent duplicate RPC? sfxmap = {'.imec.ap.bin': ArchivedRawEphysTrial.ArchivedApChannel, '.imec.ap.meta': ArchivedRawEphysTrial.ArchivedApMeta, '.imec.lf.bin': ArchivedRawEphysTrial.ArchivedLfChannel, '.imec.lf.meta': ArchivedRawEphysTrial.ArchivedLfMeta} for sfx, cls in sfxmap.items(): if cls & key: log.debug('record found for {} & {}'.format(cls.__name__, key)) gname = '{}{}'.format(gbase, sfx) srcp = '{}:/{}/{}'.format(rep, rep_sub, gname) dstp = '{}:/{}/{}'.format(lep, lep_sub, gname) log.info('transferring {} to {}'.format(srcp, dstp)) # XXX: check if exists 1st? (manually or via API copy-checksum) if not gsm.cp(srcp, dstp): emsg = "couldn't transfer {} to {}".format(srcp, dstp) log.error(emsg) raise dj.DataJointError(emsg) @schema class ArchivedSortedEphys(dj.Imported): definition = """ -> ArchivedSession -> DataSet probe_folder: tinyint --- sorting_time=null: datetime """ key_source = experiment.Session def make(self, key): """ discover files in local endpoint and transfer/register """ raise NotImplementedError('ArchivedSortedEphys.make to be implemented') @schema class ArchivedTrackingVideo(dj.Imported): ''' ArchivedTrackingVideo storage Note: video_file_name tracked here as trial->file map is non-deterministic Directory locations of the form: {Water restriction number}\{Session Date}\video with file naming convention of the form: {Water restriction number}_{camera-position-string}_NNN-NNNN.avi Where 'NNN' is determined from the 'tracking map file' which maps trials to videos as outlined in tracking.py XXX: Using key-source based loookup as is currently done, may have trials for which there is no tracking, so camera cannot be determined to do file lookup, thus videos are missed. This could be resolved via schema adjustment, or file-traversal based 'opportunistic' registration strategy. ''' definition = """ -> ArchivedSession -> tracking.TrackingDevice --- -> DataSet """ key_source = tracking.TrackingDevice * experiment.Session ingest = None # ingest module reference gsm = None # for GlobusStorageManager class TrialVideo(dj.Part): definition = """ -> master -> experiment.SessionTrial --- -> DataSet.PhysicalFile """ @classmethod def get_ingest(cls): ''' return tracking_ingest module not imported globally to prevent ingest schema creation for client case ''' log.debug('ArchivedVideoFile.get_ingest()') if cls.ingest is None: from .ingest import tracking as tracking_ingest cls.ingest = tracking_ingest return cls.ingest def get_gsm(self): log.debug('ArchivedVideoFile.get_gsm()') if self.gsm is None: self.gsm = GlobusStorageManager() self.gsm.wait_timeout = PUBLICATION_TRANSFER_TIMEOUT return self.gsm @classmethod def discover(cls): """ discover files on globus and attempt to register them """ self = cls() globus_alias = 'raw-video' le = GlobusStorageLocation.local_endpoint(globus_alias) lep, lep_sub, lep_dir = (le['endpoint'], le['endpoint_subdir'], le['endpoint_path']) ra, rep, rep_sub = (GlobusStorageLocation() & {'globus_alias': globus_alias}).fetch1().values() smap = {'{}/{}'.format(s['water_restriction_number'], s['session_date']).replace('-', ''): s for s in (experiment.Session() * (lab.WaterRestriction() * lab.Subject.proj()))} tpos_dev = {s['tracking_position']: s['tracking_device'] for s in tracking.TrackingDevice()} # position:device ftmap = {t['file_type']: t for t in (FileType() & "file_type like 'tracking%%'")} skey = None sskip = set() sfiles = [] # {file_subpath:, trial:, file_type:,} gsm = self.get_gsm() gsm.activate_endpoint(lep) gsm.activate_endpoint(rep) def commit(skey, sfiles): log.info('commit. skey: {}'.format(skey)) if not sfiles: log.info('commit skipping {}. no files in set'.format(skey)) # log.debug('sfiles: {}'.format(sfiles)) h2o, sdate, ftypes = set(), set(), set() dftmap = {} # device:file:trial via load_campath mapping files dvfmap = defaultdict(lambda: defaultdict(list)) # device:video:file dtfmap = defaultdict(lambda: defaultdict(list)) # device:trial:file for s in sfiles: if s['file_type'] == 'tracking-video-trial': dvfmap[s['position']][s['video']].append(s) h2o.add(s['water_restriction_number']) sdate.add(s['session_date']) ftypes.add(s['file_type']) if s['file_type'] == 'tracking-video-map': # xfer & load camera:trial map ex: dl55_20190108_side.txtb fsp = s['file_subpath'] lsp = '/tmp/' + s['file_subpath'].split('/')[-1] srcp = '{}:{}/{}'.format(rep, rep_sub, fsp) dstp = '{}:{}/{}'.format(lep, lep_sub, lsp) log.info('transferring {} to {}'.format(srcp, dstp)) if not gsm.cp(srcp, dstp): # XXX: check if exists 1st? emsg = "couldn't transfer {} to {}".format(srcp, dstp) log.error(emsg) raise dj.DataJointError(emsg) lfname = lep_dir + lsp # local filesysem copy location dftmap[s['position']] = TrackingIngest.load_campath(lfname) if len(h2o) != 1 or len(sdate) != 1: log.info('skipping. bad h2o {} or session date {}'.format( h2o, sdate)) return h2o, sdate = next(iter(h2o)), next(iter(sdate)) for d in dvfmap: if d in dftmap: # remap video no -> trial dtfmap[d] = {dftmap[d][v]: dict(dvfmap[d][v], trial=dftmap[d][v]) for v in dvfmap[d]} else: # assign video no -> trial dtfmap[d] = {k: dict(v, trial=v['video']) for k, v in dvfmap[d].items()} # DataSet ds_type = 'tracking-video' ds_name = '{}_{}_{}'.format(h2o, sdate, ds_type) ds_key = {'dataset_name': ds_name, 'globus_alias': globus_alias} if (DataSet & ds_key): log.info('DataSet: {} already exists. Skipping.'.format( ds_key)) return DataSet.insert1({**ds_key, 'dataset_type': ds_type}, allow_direct_insert=True) # ArchivedSession as_key = {k: v for k, v in smap[skey].items() if k in ArchivedSession.primary_key} ArchivedSession.insert1( {**as_key, 'globus_alias': globus_alias}, allow_direct_insert=True, skip_duplicates=True) for d in dtfmap: # ArchivedTrackingVideo atv_key = {**as_key, **ds_key, 'tracking_device': tpos_dev[d]} ArchivedTrackingVideo.insert1( atv_key, allow_direct_insert=True) for t in dtfmap[d]: for f in dtfmap[d][t]: DataSet.PhysicalFile.insert1( {**ds_key, **f}, allow_direct_insert=True, ignore_extra_fields=True) ArchivedTrackingVideo.TrialVideo.insert1( {**atv_key, **ds_key, 'trial': t, 'file_subpath': f['file_subpath']}, allow_direct_insert=True) # end commit() for ep, dirname, node in gsm.fts('{}:{}'.format(rep, rep_sub)): vdir = re.match('([a-z]+[0-9]+)/([0-9]{8})/video', dirname) if not vdir or node['DATA_TYPE'] != 'file': continue h2o, sdate = vdir[1], vdir[2] skey_i = '{}/{}'.format(h2o, sdate) if skey_i != skey: if skey and skey in smap: with dj.conn().transaction: try: commit(skey, sfiles) except Exception as e: log.error( 'Exception {} committing {}. files: {}'.format( repr(e), skey, sfiles)) skey, sfiles = skey_i, [] if skey not in smap: if skey not in sskip: log.debug('session {} not known. skipping'.format(skey)) sskip.add(skey) continue fname = node['name'] log.debug('checking {}/{}'.format(dirname, fname)) if '.' not in fname: log.debug('skipping {} - no dot in fname'.format(fname)) continue froot, fext = fname.split('.', 1) ftype = {g['file_type']: g for g in ftmap.values() if fnmatch(fname, g['file_glob'])} if len(ftype) != 1: log.debug('skipping {} - incorrect type matches: {}'.format( fname, ftype)) continue ftype = next(iter(ftype.values()))['file_type'] log.debug('processing as {}'.format(ftype)) file_subpath = '{}/{}'.format(dirname, fname) if ftype == 'tracking-video-map': # e.g. dl55_20190108_side.txt h2o_f, fdate, pos = froot.split('_') sfiles.append({'water_restriction_number': h2o, 'session_date': '{}-{}-{}'.format( sdate[:4], sdate[4:6], sdate[6:]), 'position': pos, 'file_subpath': file_subpath, 'file_type': ftype}) else: # tracking-video-map # e.g. dl41_side_998-0000.avi or dl41_side_998-0000_00.avi h2o_f, pos, video = froot.replace('-', '_').split('_')[:3] sfiles.append({'water_restriction_number': h2o, 'session_date': '{}-{}-{}'.format( sdate[:4], sdate[4:6], sdate[6:]), 'position': pos, 'video': int(video), 'file_subpath': file_subpath, 'file_type': ftype}) def make(self, key): """ discover files in local endpoint and transfer/register """ log.info('ArchivedVideoFile.make(): {}'.format(key)) # {'tracking_device': 'Camera 0', 'subject_id': 432572, 'session': 1} globus_alias = 'raw-video' le = GlobusStorageLocation.local_endpoint(globus_alias) lep, lep_sub, lep_dir = (le['endpoint'], le['endpoint_subdir'], le['endpoint_path']) re = (GlobusStorageLocation & {'globus_alias': globus_alias}).fetch1() rep, rep_sub = re['globus_endpoint'], re['globus_path'] log.info('local_endpoint: {}:{} -> {}'.format(lep, lep_sub, lep_dir)) log.info('remote_endpoint: {}:{}'.format(rep, rep_sub)) h2o = (lab.WaterRestriction & key).fetch1('water_restriction_number') session = (experiment.Session & key).fetch1() sdate = session['session_date'] sdate_sml = "{}{:02d}{:02d}".format(sdate.year, sdate.month, sdate.day) dev = (tracking.TrackingDevice & key).fetch1() trls = (experiment.SessionTrial & key).fetch( order_by='trial', as_dict=True) tracking_ingest = self.get_ingest() tdev = dev['tracking_device'] # NOQA: notused tpos = dev['tracking_position'] camtrial = '{}_{}_{}.txt'.format(h2o, sdate_sml, tpos) vbase = pathlib.Path(lep_dir, h2o, sdate_sml, 'video') campath = vbase / camtrial if not campath.exists(): # XXX: uses 1st found log.warning('trial map {} n/a! skipping.'.format(campath)) return log.info('loading trial map: {}'.format(campath)) vmap = {v: k for k, v in tracking_ingest.TrackingIngest.load_campath(campath).items()} log.debug('loaded video map: {}'.format(vmap)) # add ArchivedSession as_key = {k: v for k, v in key.items() if k in experiment.Session.primary_key} as_rec = {**as_key, 'globus_alias': globus_alias} ArchivedSession.insert1(as_rec, allow_direct_insert=True, skip_duplicates=True) # add DataSet ds_type = 'tracking-video' ds_name = '{}_{}_{}_{}'.format(h2o, sdate.isoformat(), ds_type, tpos) ds_key = {'globus_alias': globus_alias, 'dataset_name': ds_name} ds_rec = {**ds_key, 'dataset_type': ds_type} DataSet.insert1(ds_rec, allow_direct_insert=True) # add ArchivedVideoTracking vt_key = {**as_key, 'tracking_device': tdev} vt_rec = {**vt_key, 'globus_alias': globus_alias, 'dataset_name': ds_name} self.insert1(vt_rec) filetype = 'tracking-video-trial' for t in trls: trial = t['trial'] log.info('.. tracking trial {} ({})'.format(trial, t)) if t['trial'] not in vmap: log.warning('trial {} not in video map. skipping!'.format(t)) continue vmatch = '{}_{}_{}-*'.format(h2o, tpos, vmap[trial]) log.debug('vbase: {}, vmatch: {}'.format(vbase, vmatch)) vglob = list(vbase.glob(vmatch)) if len(vglob) != 1: emsg = 'incorrect videos found in {}: {}'.format(vbase, vglob) log.warning(emsg) raise dj.DataJointError(emsg) vfile = vglob[0].name gfile = '{}/{}/{}/{}'.format( h2o, sdate_sml, 'video', vfile) # subpath srcp = '{}:{}/{}'.format(lep, lep_sub, gfile) # source path dstp = '{}:{}/{}'.format(rep, rep_sub, gfile) # dest path gsm = self.get_gsm() gsm.activate_endpoint(lep) # XXX: cache / prevent duplicate RPC? gsm.activate_endpoint(rep) # XXX: cache / prevent duplicate RPC? log.info('transferring {} to {}'.format(srcp, dstp)) if not gsm.cp(srcp, dstp): emsg = "couldn't transfer {} to {}".format(srcp, dstp) log.error(emsg) raise dj.DataJointError(emsg) pf_key = {**ds_key, 'file_subpath': vfile} pf_rec = {**pf_key, 'file_type': filetype} DataSet.PhysicalFile.insert1({**pf_rec}, allow_direct_insert=True) trk_key = {k: v for k, v in {**key, 'trial': trial}.items() if k in experiment.SessionTrial.primary_key} tv_rec = {**vt_key, **trk_key, **pf_key} self.TrialVideo.insert1({**tv_rec}) def test_flist(fname='globus-index-full.txt'): ''' spoof tester for discover methods expects: f: ep:/path/to/file d: ep:/path/to/direct etc. (aka: globus-shell 'find' output) replace the line: for ep, dirname, node in gsm.fts('{}:{}'.format(rep, rep_sub)): with: for ep, dirname, node in test_flist('globus-list.txt'): to test against the file 'globus-list.txt' ''' with open(fname, 'r') as infile: for l in infile: try: t, fp = l.split(' ') fp = fp.split(':')[1].lstrip('/').rstrip('\n') dn, bn = os.path.split(fp) if t == 'f:': yield ('ep', dn, {'DATA_TYPE': 'file', 'name': bn}) else: yield ('ep', dn, {'DATA_TYPE': 'dunno', 'path': bn}) except ValueError as e: if 'too many values' in repr(e): pass

4d8532ecf10ba04d9280c47b5810edf61e1c76f0

py

Python

sftp/__init__.py

hiaoxui/span-finder

c5f9886eae12921796b33bdb84ffcb6bfa905cb4

2021-05-08T15:35:21.000Z

2022-01-24T02:52:55.000Z

sftp/__init__.py

hiaoxui/span-finder

c5f9886eae12921796b33bdb84ffcb6bfa905cb4

sftp/__init__.py

hiaoxui/span-finder

c5f9886eae12921796b33bdb84ffcb6bfa905cb4

2021-09-07T22:31:40.000Z

2021-09-07T22:31:40.000Z

from .data_reader import ( BetterDatasetReader, SRLDatasetReader ) from .metrics import SRLMetric, BaseF, ExactMatch, FBetaMixMeasure from .models import SpanModel from .modules import ( MLPSpanTyping, SpanTyping, SpanFinder, BIOSpanFinder ) from .predictor import SpanPredictor from .utils import Span

from .data_reader import ( BetterDatasetReader, SRLDatasetReader ) from .metrics import SRLMetric, BaseF, ExactMatch, FBetaMixMeasure from .models import SpanModel from .modules import ( MLPSpanTyping, SpanTyping, SpanFinder, BIOSpanFinder ) from .predictor import SpanPredictor from .utils import Span

31ae962fdd5c782121ff85fe6854a2b889e2cbfd

py

Python

docs/source/examples/7/sample.py

kumar-pratik/hi-ml

a108cf4ea244a76127adedc0ca60f0a5afdfb3e8

2021-08-18T13:27:36.000Z

2022-03-26T01:25:36.000Z

docs/source/examples/7/sample.py

kumar-pratik/hi-ml

a108cf4ea244a76127adedc0ca60f0a5afdfb3e8

2021-08-18T13:19:46.000Z

2022-03-30T05:57:01.000Z

docs/source/examples/7/sample.py

kumar-pratik/hi-ml

a108cf4ea244a76127adedc0ca60f0a5afdfb3e8

2021-09-13T12:07:58.000Z

2022-03-24T16:31:06.000Z

# ------------------------------------------------------------------------------------------ # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License (MIT). See LICENSE in the repo root for license information. # ------------------------------------------------------------------------------------------ # From: # https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/ml-frameworks/scikit-learn/train-hyperparameter-tune-deploy-with-sklearn/train_iris.py import argparse from pathlib import Path import numpy as np from sklearn.metrics import confusion_matrix from sklearn.model_selection import train_test_split from health_azure import submit_to_azure_if_needed if __name__ == "__main__": main()

# ------------------------------------------------------------------------------------------ # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License (MIT). See LICENSE in the repo root for license information. # ------------------------------------------------------------------------------------------ # From: # https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/ml-frameworks/scikit-learn/train-hyperparameter-tune-deploy-with-sklearn/train_iris.py import argparse from pathlib import Path import numpy as np from sklearn.metrics import confusion_matrix from sklearn.model_selection import train_test_split from health_azure import submit_to_azure_if_needed def main() -> None: run_info = submit_to_azure_if_needed( compute_cluster_name="lite-testing-ds2", default_datastore="himldatasets", input_datasets=["himl_sample7_input"], wait_for_completion=True, wait_for_completion_show_output=True) parser = argparse.ArgumentParser() parser.add_argument('--kernel', type=str, default='linear', help='Kernel type to be used in the algorithm') parser.add_argument('--penalty', type=float, default=1.0, help='Penalty parameter of the error term') args = parser.parse_args() print(f'Kernel type:{args.kernel}') print(f'Penalty: {args.penalty}') # X -> features, y -> label input_folder = run_info.input_datasets[0] or Path("dataset") X = np.loadtxt(fname=input_folder / "X.csv", delimiter=',', skiprows=1) y = np.loadtxt(fname=input_folder / "y.csv", dtype='str', delimiter=',', skiprows=1) # dividing X, y into train and test data X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) # training a linear SVM classifier from sklearn.svm import SVC svm_model_linear = SVC(kernel=args.kernel, C=args.penalty).fit(X_train, y_train) svm_predictions = svm_model_linear.predict(X_test) # model accuracy for X_test accuracy = svm_model_linear.score(X_test, y_test) print('Accuracy of SVM classifier on test set: {:.2f}'.format(accuracy)) # creating a confusion matrix cm = confusion_matrix(y_test, svm_predictions) print(cm) if __name__ == "__main__": main()

6599733b0213579573a907d9fc5ab78c8a716ed8

py

Python

tests/test_cli.py

vfranca/pp

db9e15a490e5b28a177cdcd8f448d21fd5bec8d7

tests/test_cli.py

vfranca/pp

db9e15a490e5b28a177cdcd8f448d21fd5bec8d7

tests/test_cli.py

vfranca/pp

db9e15a490e5b28a177cdcd8f448d21fd5bec8d7

#!/usr/bin/env python # -*- coding: utf-8 -*- import unittest from click.testing import CliRunner from pivotpoint import pp from pivotpoint import cli

#!/usr/bin/env python # -*- coding: utf-8 -*- import unittest from click.testing import CliRunner from pivotpoint import pp from pivotpoint import cli class TestPivotPoint(unittest.TestCase): def setUp(self): self.runner = CliRunner() def tearDown(self): """Tear down test fixtures, if any.""" def test_command_line_interface(self): result = self.runner.invoke(cli.main, ["34.80", "32.80", "33.40"]) assert "35.67\n34.54\n33.67\n32.54\n31.67\n" in result.output

6c59951f383e22b4b6dd672512b717c4ad1ef094

py

Python

rackio/dao/controls.py

crivero7/rackio-framework

d3362041b1fc4c3af7eb51ac06b1f0f1b5aa497c

rackio/dao/controls.py

crivero7/rackio-framework

d3362041b1fc4c3af7eb51ac06b1f0f1b5aa497c

rackio/dao/controls.py

crivero7/rackio-framework

d3362041b1fc4c3af7eb51ac06b1f0f1b5aa497c

# -*- coding: utf-8 -*- """rackio/dao/controls.py This module implements Controls Data Objects Access. """ from .core import RackioDAO

# -*- coding: utf-8 -*- """rackio/dao/controls.py This module implements Controls Data Objects Access. """ from .core import RackioDAO class ControlsDAO(RackioDAO): def get_all(self): app = self.get_app() manager = app.get_manager("control") result = list() for control in manager.get_controls(): result.append(control.serialize()) return result def get(self, name): app = self.get_app() manager = app.get_manager("control") control = manager.get_control(name) if control: return control.serialize() class RulesDAO(RackioDAO): def get_all(self): app = self.get_app() manager = app.get_manager("control") result = list() for rule in manager.get_rules(): result.append(rule.serialize()) return result def get(self, name): app = self.get_app() manager = app.get_manager("control") rule = manager.get_rule(name) if rule: return rule.serialize()

b3ba1dada5fdca8c0505e224c5e297d351338eaf

py

Python

AddPDFBookmarks/handle_pdf.py

wanghuohuo0716/py-project

b771b8005d72843df1653ce68ddb67ccf77a57a8

2018-02-26T07:59:27.000Z

2022-03-31T08:57:51.000Z

AddPDFBookmarks/handle_pdf.py

Linkeer365/py-project

b771b8005d72843df1653ce68ddb67ccf77a57a8

2020-08-19T00:55:52.000Z

2021-03-08T07:37:32.000Z

AddPDFBookmarks/handle_pdf.py

Linkeer365/py-project

b771b8005d72843df1653ce68ddb67ccf77a57a8

2018-09-07T14:26:33.000Z

2022-03-31T08:57:53.000Z

# coding:utf-8 # 添加PDF书签 from pdf_utils import MyPDFHandler,PDFHandleMode as mode import ConfigParser import sys reload(sys) sys.setdefaultencoding('utf-8') if __name__ == '__main__': main()

# coding:utf-8 # 添加PDF书签 from pdf_utils import MyPDFHandler,PDFHandleMode as mode import ConfigParser import sys reload(sys) sys.setdefaultencoding('utf-8') def main(): # 从配置文件中读取配置信息 cf = ConfigParser.SafeConfigParser() cf.read('./info.conf') pdf_path = cf.get('info','pdf_path') bookmark_file_path = cf.get('info','bookmark_file_path') page_offset = cf.getint('info','page_offset') new_pdf_file_name = cf.get('info','new_pdf_file_name') pdf_handler = MyPDFHandler(pdf_path,mode = mode.NEWLY) pdf_handler.add_bookmarks_by_read_txt(bookmark_file_path,page_offset = page_offset) pdf_handler.save2file(new_pdf_file_name) if __name__ == '__main__': main()

28cd58401f73165d35b3541ef644b2fdcb572816

py

Python

openff/utilities/testing.py

openforcefield/openff-utilities

89255d6cc9513df6ad5293841e86ab1f968f7e3b

openff/utilities/testing.py

openforcefield/openff-utilities

89255d6cc9513df6ad5293841e86ab1f968f7e3b

2021-06-09T06:46:20.000Z

2022-03-02T00:30:41.000Z

openff/utilities/testing.py

openforcefield/openff-utilities

89255d6cc9513df6ad5293841e86ab1f968f7e3b

from typing import List, Optional, Union import pytest from openff.utilities.utilities import has_executable, has_package def skip_if_missing(package_name: str, reason: Optional[str] = None): """ Helper function to generate a pytest.mark.skipif decorator for any package. This allows tests to be skipped if some optional dependency is not found. Parameters ---------- package_name : str The name of the package that is required for a test(s) reason : str, optional Explanation of why the skipped it to be tested Returns ------- requires_package : _pytest.mark.structures.MarkDecorator A pytest decorator that will skip tests if the package is not available """ if not reason: reason = f"Package {package_name} is required, but was not found." requires_package = pytest.mark.skipif(not has_package(package_name), reason=reason) return requires_package def skip_if_missing_exec(exec: Union[str, List[str]]): """Helper function to generate a pytest.mark.skipif decorator if an executable(s) is not found.""" if isinstance(exec, str): execs: List = [exec] elif isinstance(exec, list): execs: List = exec # type: ignore[no-redef] else: raise ValueError( "Bad type passed to skip_if_missing_exec. " f"Found type {type(exec)}" ) found_exec = False for exec_ in execs: found_exec = found_exec or has_executable(exec_) reason = f"Package {str(exec)} is required, but was not found." mark = pytest.mark.skipif(not found_exec, reason=reason) return mark

from typing import List, Optional, Union import pytest from openff.utilities.utilities import has_executable, has_package def skip_if_missing(package_name: str, reason: Optional[str] = None): """ Helper function to generate a pytest.mark.skipif decorator for any package. This allows tests to be skipped if some optional dependency is not found. Parameters ---------- package_name : str The name of the package that is required for a test(s) reason : str, optional Explanation of why the skipped it to be tested Returns ------- requires_package : _pytest.mark.structures.MarkDecorator A pytest decorator that will skip tests if the package is not available """ if not reason: reason = f"Package {package_name} is required, but was not found." requires_package = pytest.mark.skipif(not has_package(package_name), reason=reason) return requires_package def skip_if_missing_exec(exec: Union[str, List[str]]): """Helper function to generate a pytest.mark.skipif decorator if an executable(s) is not found.""" if isinstance(exec, str): execs: List = [exec] elif isinstance(exec, list): execs: List = exec # type: ignore[no-redef] else: raise ValueError( "Bad type passed to skip_if_missing_exec. " f"Found type {type(exec)}" ) found_exec = False for exec_ in execs: found_exec = found_exec or has_executable(exec_) reason = f"Package {str(exec)} is required, but was not found." mark = pytest.mark.skipif(not found_exec, reason=reason) return mark

8ef5b889f2b72b0ee8d71a6019e587e98cab7064

py

Python

projects/Show me the Data Structures/problem_1.py

gmendozah/Data-Structures-and-Algorithms

07474db45acfe42855cc0f4cc968c0564b2cb91a

2021-10-08T11:21:08.000Z

2022-01-24T22:40:03.000Z

projects/Show me the Data Structures/problem_1.py

gmendozah/Data-Structures-and-Algorithms

07474db45acfe42855cc0f4cc968c0564b2cb91a

projects/Show me the Data Structures/problem_1.py

gmendozah/Data-Structures-and-Algorithms

07474db45acfe42855cc0f4cc968c0564b2cb91a

2021-12-13T06:50:58.000Z

2022-02-05T03:38:49.000Z

if __name__ == '__main__': test_case_1() test_case_2() test_case_3()

class Node: def __init__(self, key=None, value=None): self.key = key self.value = value self.next = None self.prev = None class LRU_Cache(object): def __init__(self, capacity): if capacity < 1: print('LRUCache should have capacity > 0') return # Initialize class variables self.capacity = capacity self.size = 0 self.map = dict() self.head = None # this node represents the least recently used self.tail = None # this node represents the most recently used def get_capacity(self): return self.capacity def get(self, key): # Retrieve item from provided key. Return -1 if nonexistent. if key is None: return -1 elif key not in self.map: return -1 else: node = self.map[key] self.move_to_front(node) return node.value def set(self, key, value): # Set the value if the key is not present in the cache. If the cache is at capacity remove the oldest item. """ First. we validate the input key Second. we verify if key is already in the map If key in map: We update the node value and move it to the front If not in map: We create a new node and set it in the map Third: we validate if we passed the cache capacity """ if key is None or value is None: return -1 elif key not in self.map: node = Node(key, value) self.map[key] = node self.add(node) else: node = self.map[key] node.value = value self.move_to_front(node) if self.capacity < 0: self.remove_lru() def move_to_front(self, node): self.remove(node) self.add(node) def add(self, node): # add data to the next attribute of the tail (i.e. the end of the queue) # if head and tail have no values if self.head is None or self.tail is None: self.head = node self.tail = node # if the linked list has values already else: node.next = self.head node.prev = None self.head.prev = node self.head = node self.capacity = self.capacity - 1 def remove(self, node): # if the node we want to delete is the head if self.head.key == node.key: next_node = self.head.next self.head = next_node # if the node we want to delete is the tail elif self.tail.key == node.key: prev_node = self.tail.prev self.tail = prev_node # if none of the above happens else: prev_node = node.prev next_node = node.next prev_node.next = next_node next_node.prev = prev_node self.capacity += 1 def remove_lru(self): node = self.tail self.remove(node) del self.map[node.key] if __name__ == '__main__': def test_case_1(): # invalid length cache test case our_cache = LRU_Cache(-1) # should show an invalid capacity value def test_case_2(): # normal length cache test case our_cache = LRU_Cache(5) our_cache.set(1, 11) our_cache.set(2, 22) our_cache.set(3, 33) our_cache.set(4, 44) our_cache.set(5, 55) our_cache.set(6, 66) our_cache.set(7, 77) print(our_cache.get(1)) # returns -1 print(our_cache.get(2)) # returns -1 print(our_cache.get(3)) # returns 33 print(our_cache.get(7)) # returns 77 print(our_cache.get(6)) # returns 66 print(our_cache.get(4)) # returns 44 our_cache.set(8, 88) print(our_cache.get(5)) # returns -1 def test_case_3(): # short cache test case our_cache = LRU_Cache(3) our_cache.set(1, 1) our_cache.set(2, 2) our_cache.set(3, 3) our_cache.set(4, 4) print(our_cache.get(4)) # Expected Value = 4 print(our_cache.get(1)) # Expected Value = -1 our_cache.set(2, 4) print(our_cache.get(2)) # Expected Value = 4 our_cache.set(5, 5) print(our_cache.get(3)) # Expected Value = -1 print(our_cache.get(5)) # Expected Value = 5 our_cache.set(2, 6) print(our_cache.get(2)) # Expected Value = 6 our_cache.set(6, 6) print(our_cache.get(4)) # Expected Value = -1 print(our_cache.get(6)) # Expected Value = 6 our_cache.set(5, 10) our_cache.set(7, 7) print(our_cache.get(2)) # Expected Value = -1 print(our_cache.get(7)) # Expected Value = 7 print(our_cache.get(6)) # Expected Value = 6 print(our_cache.get(5)) # Expected Value = 10 print(our_cache.get(5)) # Expected Value = 10 our_cache.set(8, 8) print(our_cache.get(7)) # Expected Value = -1 test_case_1() test_case_2() test_case_3()

8246e38f21bcb46e020f248157401bcc92b6f2e7

py

Python

hydrus/tests/test_app.py

vcode11/hydrus

4ed8ada7ed8fd7d8897e744bae410b312f4cfb83

2019-12-04T12:54:21.000Z

2019-12-04T12:54:21.000Z

hydrus/tests/test_app.py

vcode11/hydrus

4ed8ada7ed8fd7d8897e744bae410b312f4cfb83

2019-12-21T04:15:23.000Z

2020-04-07T05:11:05.000Z

hydrus/tests/test_app.py

vcode11/hydrus

4ed8ada7ed8fd7d8897e744bae410b312f4cfb83

"""Test for checking if the response format is proper. Run test_crud before running this.""" import unittest import random import string import json import re import uuid from hydrus.app_factory import app_factory from hydrus.socketio_factory import create_socket from hydrus.utils import set_session, set_doc, set_api_name, set_page_size from hydrus.data import doc_parse, crud from hydra_python_core import doc_maker from hydra_python_core.doc_writer import HydraLink from hydrus.samples import doc_writer_sample from sqlalchemy import create_engine from sqlalchemy.orm import sessionmaker, scoped_session from hydrus.data.db_models import Base def gen_dummy_object(class_title, doc): """Create a dummy object based on the definitions in the API Doc. :param class_title: Title of the class whose object is being created. :param doc: ApiDoc. :return: A dummy object of class `class_title`. """ object_ = { "@type": class_title } for class_path in doc.parsed_classes: if class_title == doc.parsed_classes[class_path]["class"].title: for prop in doc.parsed_classes[class_path]["class"].supportedProperty: if isinstance(prop.prop, HydraLink) or prop.write is False: continue if "vocab:" in prop.prop: prop_class = prop.prop.replace("vocab:", "") object_[prop.title] = gen_dummy_object(prop_class, doc) else: object_[prop.title] = ''.join(random.choice( string.ascii_uppercase + string.digits) for _ in range(6)) return object_ class ViewsTestCase(unittest.TestCase): """Test Class for the app.""" @classmethod def setUpClass(self): """Database setup before the tests.""" print("Creating a temporary database...") engine = create_engine('sqlite:///:memory:') Base.metadata.create_all(engine) session = scoped_session(sessionmaker(bind=engine)) self.session = session self.API_NAME = "demoapi" self.page_size = 1 self.HYDRUS_SERVER_URL = "http://hydrus.com/" self.app = app_factory(self.API_NAME) self.socketio = create_socket(self.app, self.session) print("going for create doc") self.doc = doc_maker.create_doc( doc_writer_sample.api_doc.generate(), self.HYDRUS_SERVER_URL, self.API_NAME) test_classes = doc_parse.get_classes(self.doc.generate()) test_properties = doc_parse.get_all_properties(test_classes) doc_parse.insert_classes(test_classes, self.session) doc_parse.insert_properties(test_properties, self.session) print("Classes and properties added successfully.") print("Setting up hydrus utilities... ") self.api_name_util = set_api_name(self.app, self.API_NAME) self.session_util = set_session(self.app, self.session) self.doc_util = set_doc(self.app, self.doc) self.page_size_util = set_page_size(self.app, self.page_size) self.client = self.app.test_client() print("Creating utilities context... ") self.api_name_util.__enter__() self.session_util.__enter__() self.doc_util.__enter__() self.client.__enter__() print("Setup done, running tests...") @classmethod def tearDownClass(self): """Tear down temporary database and exit utilities""" self.client.__exit__(None, None, None) self.doc_util.__exit__(None, None, None) self.session_util.__exit__(None, None, None) self.api_name_util.__exit__(None, None, None) self.session.close() def test_Index(self): """Test for the index.""" response_get = self.client.get("/{}".format(self.API_NAME)) endpoints = json.loads(response_get.data.decode('utf-8')) response_post = self.client.post( "/{}".format(self.API_NAME), data=dict(foo="bar")) response_put = self.client.put( "/{}".format(self.API_NAME), data=dict(foo="bar")) response_delete = self.client.delete("/{}".format(self.API_NAME)) assert "@context" in endpoints assert endpoints["@id"] == "/{}".format(self.API_NAME) assert endpoints["@type"] == "EntryPoint" assert response_get.status_code == 200 assert response_post.status_code == 405 assert response_put.status_code == 405 assert response_delete.status_code == 405 def test_EntryPoint_context(self): """Test for the EntryPoint context.""" response_get = self.client.get( "/{}/contexts/EntryPoint.jsonld".format(self.API_NAME)) response_get_data = json.loads(response_get.data.decode('utf-8')) response_post = self.client.post( "/{}/contexts/EntryPoint.jsonld".format(self.API_NAME), data={}) response_delete = self.client.delete( "/{}/contexts/EntryPoint.jsonld".format(self.API_NAME)) assert response_get.status_code == 200 assert "@context" in response_get_data assert response_post.status_code == 405 assert response_delete.status_code == 405 def test_Vocab(self): """Test the vocab.""" response_get = self.client.get("/{}/vocab#".format(self.API_NAME)) response_get_data = json.loads(response_get.data.decode('utf-8')) assert "@context" in response_get_data assert response_get_data["@type"] == "ApiDocumentation" assert response_get_data["@id"] == "{}{}/vocab".format( self.HYDRUS_SERVER_URL, self.API_NAME) assert response_get.status_code == 200 response_delete = self.client.delete( "/{}/vocab#".format(self.API_NAME)) assert response_delete.status_code == 405 response_put = self.client.put( "/{}/vocab#".format(self.API_NAME), data=json.dumps(dict(foo='bar'))) assert response_put.status_code == 405 response_post = self.client.post( "/{}/vocab#".format(self.API_NAME), data=json.dumps(dict(foo='bar'))) assert response_post.status_code == 405 def test_Collections_GET(self): """Test GET on collection endpoints.""" index = self.client.get("/{}".format(self.API_NAME)) assert index.status_code == 200 endpoints = json.loads(index.data.decode('utf-8')) for endpoint in endpoints: collection_name = "/".join(endpoints[endpoint].split( "/{}/".format(self.API_NAME))[1:]) if collection_name in self.doc.collections: response_get = self.client.get(endpoints[endpoint]) # pdb.set_trace() assert response_get.status_code == 200 response_get_data = json.loads( response_get.data.decode('utf-8')) assert "@context" in response_get_data assert "@id" in response_get_data assert "@type" in response_get_data assert "members" in response_get_data # Check the item URI has the valid format, so it can be dereferenced if len(response_get_data["members"]) > 0: for item in response_get_data["members"]: class_type = item["@type"] if class_type in self.doc.parsed_classes: class_ = self.doc.parsed_classes[class_type]["class"] class_methods = [ x.method for x in class_.supportedOperation] if "GET" in class_methods: item_response = self.client.get( response_get_data["members"][0]["@id"]) assert item_response.status_code == 200 def test_pagination(self): """Test basic pagination""" index = self.client.get("/{}".format(self.API_NAME)) assert index.status_code == 200 endpoints = json.loads(index.data.decode('utf-8')) for endpoint in endpoints: collection_name = "/".join(endpoints[endpoint].split( "/{}/".format(self.API_NAME))[1:]) if collection_name in self.doc.collections: response_get = self.client.get(endpoints[endpoint]) assert response_get.status_code == 200 response_get_data = json.loads( response_get.data.decode('utf-8')) assert "view" in response_get_data assert "first" in response_get_data["view"] assert "last" in response_get_data["view"] if "next" in response_get_data["view"]: response_next = self.client.get(response_get_data["view"]["next"]) assert response_next.status_code == 200 response_next_data = json.loads( response_next.data.decode('utf-8')) assert "previous" in response_next_data["view"] break def test_Collections_PUT(self): """Test insert data to the collection.""" index = self.client.get("/{}".format(self.API_NAME)) assert index.status_code == 200 endpoints = json.loads(index.data.decode('utf-8')) for endpoint in endpoints: collection_name = "/".join(endpoints[endpoint].split( "/{}/".format(self.API_NAME))[1:]) if collection_name in self.doc.collections: collection = self.doc.collections[collection_name]["collection"] dummy_object = gen_dummy_object( collection.class_.title, self.doc) good_response_put = self.client.put( endpoints[endpoint], data=json.dumps(dummy_object)) assert good_response_put.status_code == 201 def test_object_POST(self): """Test replace of a given object using ID.""" index = self.client.get("/{}".format(self.API_NAME)) assert index.status_code == 200 endpoints = json.loads(index.data.decode('utf-8')) for endpoint in endpoints: collection_name = "/".join(endpoints[endpoint].split( "/{}/".format(self.API_NAME))[1:]) if collection_name in self.doc.collections: collection = self.doc.collections[collection_name]["collection"] class_ = self.doc.parsed_classes[collection.class_.title]["class"] class_methods = [x.method for x in class_.supportedOperation] dummy_object = gen_dummy_object( collection.class_.title, self.doc) initial_put_response = self.client.put( endpoints[endpoint], data=json.dumps(dummy_object)) assert initial_put_response.status_code == 201 response = json.loads( initial_put_response.data.decode('utf-8')) regex = r'(.*)ID (.{36})* (.*)' matchObj = re.match(regex, response["description"]) assert matchObj is not None id_ = matchObj.group(2) if "POST" in class_methods: dummy_object = gen_dummy_object( collection.class_.title, self.doc) post_replace_response = self.client.post( '{}/{}'.format(endpoints[endpoint], id_), data=json.dumps(dummy_object)) assert post_replace_response.status_code == 200 def test_object_DELETE(self): """Test DELETE of a given object using ID.""" index = self.client.get("/{}".format(self.API_NAME)) assert index.status_code == 200 endpoints = json.loads(index.data.decode('utf-8')) for endpoint in endpoints: collection_name = "/".join(endpoints[endpoint].split( "/{}/".format(self.API_NAME))[1:]) if collection_name in self.doc.collections: collection = self.doc.collections[collection_name]["collection"] class_ = self.doc.parsed_classes[collection.class_.title]["class"] class_methods = [x.method for x in class_.supportedOperation] dummy_object = gen_dummy_object( collection.class_.title, self.doc) initial_put_response = self.client.put( endpoints[endpoint], data=json.dumps(dummy_object)) assert initial_put_response.status_code == 201 response = json.loads( initial_put_response.data.decode('utf-8')) regex = r'(.*)ID (.{36})* (.*)' matchObj = re.match(regex, response["description"]) assert matchObj is not None id_ = matchObj.group(2) if "DELETE" in class_methods: delete_response = self.client.delete( '{}/{}'.format(endpoints[endpoint], id_)) assert delete_response.status_code == 200 def test_object_PUT_at_id(self): """Create object in collection using PUT at specific ID.""" index = self.client.get("/{}".format(self.API_NAME)) assert index.status_code == 200 endpoints = json.loads(index.data.decode('utf-8')) for endpoint in endpoints: collection_name = "/".join(endpoints[endpoint].split( "/{}/".format(self.API_NAME))[1:]) if collection_name in self.doc.collections: collection = self.doc.collections[collection_name]["collection"] class_ = self.doc.parsed_classes[collection.class_.title]["class"] class_methods = [x.method for x in class_.supportedOperation] dummy_object = gen_dummy_object( collection.class_.title, self.doc) if "PUT" in class_methods: dummy_object = gen_dummy_object( collection.class_.title, self.doc) put_response = self.client.put('{}/{}'.format( endpoints[endpoint], uuid.uuid4()), data=json.dumps(dummy_object)) assert put_response.status_code == 201 def test_endpointClass_PUT(self): """Check non collection Class PUT.""" index = self.client.get("/{}".format(self.API_NAME)) assert index.status_code == 200 endpoints = json.loads(index.data.decode('utf-8')) for endpoint in endpoints: if endpoint not in ["@context", "@id", "@type"]: class_name = "/".join(endpoints[endpoint].split( "/{}/".format(self.API_NAME))[1:]) if class_name not in self.doc.collections: class_ = self.doc.parsed_classes[class_name]["class"] class_methods = [ x.method for x in class_.supportedOperation] if "PUT" in class_methods: dummy_object = gen_dummy_object(class_.title, self.doc) put_response = self.client.put( endpoints[endpoint], data=json.dumps(dummy_object)) assert put_response.status_code == 201 def test_endpointClass_POST(self): """Check non collection Class POST.""" index = self.client.get("/{}".format(self.API_NAME)) assert index.status_code == 200 endpoints = json.loads(index.data.decode('utf-8')) for endpoint in endpoints: if endpoint not in ["@context", "@id", "@type"]: class_name = "/".join(endpoints[endpoint].split( "/{}/".format(self.API_NAME))[1:]) if class_name not in self.doc.collections: class_ = self.doc.parsed_classes[class_name]["class"] class_methods = [ x.method for x in class_.supportedOperation] if "POST" in class_methods: dummy_object = gen_dummy_object(class_.title, self.doc) post_response = self.client.post( endpoints[endpoint], data=json.dumps(dummy_object)) assert post_response.status_code == 200 def test_endpointClass_DELETE(self): """Check non collection Class DELETE.""" index = self.client.get("/{}".format(self.API_NAME)) assert index.status_code == 200 endpoints = json.loads(index.data.decode('utf-8')) for endpoint in endpoints: if endpoint not in ["@context", "@id", "@type"]: class_name = "/".join(endpoints[endpoint].split( "/{}/".format(self.API_NAME))[1:]) if class_name not in self.doc.collections: class_ = self.doc.parsed_classes[class_name]["class"] class_methods = [ x.method for x in class_.supportedOperation] if "DELETE" in class_methods: delete_response = self.client.delete( endpoints[endpoint]) assert delete_response.status_code == 200 def test_endpointClass_GET(self): """Check non collection Class GET.""" index = self.client.get("/{}".format(self.API_NAME)) assert index.status_code == 200 endpoints = json.loads(index.data.decode('utf-8')) for endpoint in endpoints: if endpoint not in ["@context", "@id", "@type"]: class_name = "/".join(endpoints[endpoint].split( "/{}/".format(self.API_NAME))[1:]) if class_name not in self.doc.collections: class_ = self.doc.parsed_classes[class_name]["class"] class_methods = [ x.method for x in class_.supportedOperation] if "GET" in class_methods: response_get = self.client.get(endpoints[endpoint]) assert response_get.status_code == 200 response_get_data = json.loads( response_get.data.decode('utf-8')) assert "@context" in response_get_data assert "@id" in response_get_data assert "@type" in response_get_data def test_IriTemplate(self): """Test structure of IriTemplates attached to collections""" index = self.client.get("/{}".format(self.API_NAME)) assert index.status_code == 200 endpoints = json.loads(index.data.decode('utf-8')) for endpoint in endpoints: collection_name = "/".join(endpoints[endpoint].split( "/{}/".format(self.API_NAME))[1:]) if collection_name in self.doc.collections: response_get = self.client.get(endpoints[endpoint]) assert response_get.status_code == 200 response_get_data = json.loads( response_get.data.decode('utf-8')) assert "search" in response_get_data assert "mapping" in response_get_data["search"] collection = self.doc.collections[collection_name]["collection"] class_ = self.doc.parsed_classes[collection.class_.title]["class"] class_props = [x.prop for x in class_.supportedProperty] for mapping in response_get_data["search"]["mapping"]: if mapping["property"] not in ["limit", "offset", "pageIndex"]: assert mapping["property"] in class_props def test_client_controlled_pagination(self): """Test pagination controlled by client with help of pageIndex, offset and limit parameters.""" index = self.client.get("/{}".format(self.API_NAME)) assert index.status_code == 200 endpoints = json.loads(index.data.decode('utf-8')) for endpoint in endpoints: collection_name = "/".join(endpoints[endpoint].split( "/{}/".format(self.API_NAME))[1:]) if collection_name in self.doc.collections: response_get = self.client.get(endpoints[endpoint]) assert response_get.status_code == 200 response_get_data = json.loads( response_get.data.decode('utf-8')) assert "search" in response_get_data assert "mapping" in response_get_data["search"] # Test with pageIndex and limit params = {"pageIndex": 1, "limit": 2} response_for_page_param = self.client.get(endpoints[endpoint], query_string=params) assert response_for_page_param.status_code == 200 response_for_page_param_data = json.loads( response_for_page_param.data.decode('utf-8')) assert "first" in response_for_page_param_data["view"] assert "last" in response_for_page_param_data["view"] if "next" in response_for_page_param_data["view"]: assert "pageIndex=2" in response_for_page_param_data["view"]["next"] next_response = self.client.get(response_for_page_param_data["view"]["next"]) assert next_response.status_code == 200 next_response_data = json.loads( next_response.data.decode('utf-8')) assert "previous" in next_response_data["view"] assert "pageIndex=1" in next_response_data["view"]["previous"] # Test with offset and limit params = {"offset": 1, "limit": 2} response_for_offset_param = self.client.get(endpoints[endpoint], query_string=params) assert response_for_offset_param.status_code == 200 response_for_offset_param_data = json.loads( response_for_offset_param.data.decode('utf-8')) assert "first" in response_for_offset_param_data["view"] assert "last" in response_for_offset_param_data["view"] if "next" in response_for_offset_param_data["view"]: assert "offset=3" in response_for_offset_param_data["view"]["next"] next_response = self.client.get( response_for_offset_param_data["view"]["next"]) assert next_response.status_code == 200 next_response_data = json.loads( next_response.data.decode('utf-8')) assert "previous" in next_response_data["view"] assert "offset=1" in next_response_data["view"]["previous"] def test_bad_objects(self): """Checks if bad objects are added or not.""" index = self.client.get("/{}".format(self.API_NAME)) assert index.status_code == 200 endpoints = json.loads(index.data.decode('utf-8')) for endpoint in endpoints: collection_name = "/".join(endpoints[endpoint].split( "/{}/".format(self.API_NAME))[1:]) if collection_name in self.doc.collections: bad_response_put = self.client.put( endpoints[endpoint], data=json.dumps( dict( foo='bar'))) assert bad_response_put.status_code == 400 def test_bad_requests(self): """Checks if bad requests are handled or not.""" index = self.client.get("/{}".format(self.API_NAME)) assert index.status_code == 200 endpoints = json.loads(index.data.decode('utf-8')) for endpoint in endpoints: collection_name = "/".join(endpoints[endpoint].split( "/{}/".format(self.API_NAME))[1:]) if collection_name in self.doc.collections: collection = self.doc.collections[collection_name]["collection"] class_ = self.doc.parsed_classes[collection.class_.title]["class"] class_methods = [x.method for x in class_.supportedOperation] dummy_object = gen_dummy_object( collection.class_.title, self.doc) initial_put_response = self.client.put( endpoints[endpoint], data=json.dumps(dummy_object)) assert initial_put_response.status_code == 201 response = json.loads( initial_put_response.data.decode('utf-8')) regex = r'(.*)ID (.{36})* (.*)' matchObj = re.match(regex, response["description"]) assert matchObj is not None id_ = matchObj.group(2) if "POST" not in class_methods: dummy_object = gen_dummy_object( collection.class_.title, self.doc) post_replace_response = self.client.post( '{}/{}'.format(endpoints[endpoint], id_), data=json.dumps(dummy_object)) assert post_replace_response.status_code == 405 if "DELETE" not in class_methods: delete_response = self.client.delete( '{}/{}'.format(endpoints[endpoint], id_)) assert delete_response.status_code == 405 def test_Endpoints_Contexts(self): """Test all endpoints contexts are generated properly.""" index = self.client.get("/{}".format(self.API_NAME)) assert index.status_code == 200 endpoints = json.loads(index.data.decode('utf-8')) for endpoint in endpoints: collection_name = "/".join(endpoints[endpoint].split( "/{}/".format(self.API_NAME))[1:]) if collection_name in self.doc.collections: response_get = self.client.get(endpoints[endpoint]) assert response_get.status_code == 200 context = json.loads( response_get.data.decode('utf-8'))["@context"] response_context = self.client.get(context) response_context_data = json.loads( response_context.data.decode('utf-8')) assert response_context.status_code == 200 assert "@context" in response_context_data class SocketTestCase(unittest.TestCase): """Test Class for socket events and operations.""" @classmethod def setUpClass(self): """Database setup before the tests.""" print("Creating a temporary database...") engine = create_engine('sqlite:///:memory:') Base.metadata.create_all(engine) session = scoped_session(sessionmaker(bind=engine)) self.session = session self.API_NAME = "demoapi" self.page_size = 1 self.HYDRUS_SERVER_URL = "http://hydrus.com/" self.app = app_factory(self.API_NAME) self.socketio = create_socket(self.app, self.session) print("going for create doc") self.doc = doc_maker.create_doc( doc_writer_sample.api_doc.generate(), self.HYDRUS_SERVER_URL, self.API_NAME) test_classes = doc_parse.get_classes(self.doc.generate()) test_properties = doc_parse.get_all_properties(test_classes) doc_parse.insert_classes(test_classes, self.session) doc_parse.insert_properties(test_properties, self.session) print("Classes and properties added successfully.") print("Setting up hydrus utilities... ") self.api_name_util = set_api_name(self.app, self.API_NAME) self.session_util = set_session(self.app, self.session) self.doc_util = set_doc(self.app, self.doc) self.page_size_util = set_page_size(self.app, self.page_size) self.client = self.app.test_client() self.socketio_client = self.socketio.test_client(self.app, namespace='/sync') print("Creating utilities context... ") self.api_name_util.__enter__() self.session_util.__enter__() self.doc_util.__enter__() self.client.__enter__() print("Setup done, running tests...") @classmethod def tearDownClass(self): """Tear down temporary database and exit utilities""" self.client.__exit__(None, None, None) self.doc_util.__exit__(None, None, None) self.session_util.__exit__(None, None, None) self.api_name_util.__exit__(None, None, None) self.session.close() def test_connect(self): """Test connect event.""" socket_client = self.socketio.test_client(self.app, namespace='/sync') data = socket_client.get_received('/sync') assert len(data) > 0 event = data[0] assert event['name'] == 'connect' last_job_id = crud.get_last_modification_job_id(self.session) assert event['args'][0]['last_job_id'] == last_job_id socket_client.disconnect(namespace='/sync') def test_reconnect(self): """Test reconnect event.""" socket_client = self.socketio.test_client(self.app, namespace='/sync') # Flush data of first connect event socket_client.get_received('/sync') # Client reconnects by emitting 'reconnect' event. socket_client.emit('reconnect', namespace='/sync') # Get update received on reconnecting to the server data = socket_client.get_received('/sync') assert len(data) > 0 # Extract the event information event = data[0] assert event['name'] == 'connect' last_job_id = crud.get_last_modification_job_id(self.session) # Check last job id with last_job_id received by client in the update. assert event['args'][0]['last_job_id'] == last_job_id socket_client.disconnect(namespace='/sync') def test_modification_table_diff(self): """Test 'modification-table-diff' events.""" # Flush old received data at socket client self.socketio_client.get_received('/sync') # Set last_job_id as the agent_job_id agent_job_id = crud.get_last_modification_job_id(self.session) # Add an extra modification record newer than the agent_job_id new_latest_job_id = crud.insert_modification_record(method="POST", resource_url="", session=self.session) self.socketio_client.emit('get_modification_table_diff', {'agent_job_id': agent_job_id}, namespace='/sync') data = self.socketio_client.get_received('/sync') assert len(data) > 0 event = data[0] assert event['name'] == 'modification_table_diff' # Check received event contains data of newly added modification record. assert event['args'][0][0]['method'] == "POST" assert event['args'][0][0]['resource_url'] == "" assert event['args'][0][0]['job_id'] == new_latest_job_id def test_socketio_POST_updates(self): """Test 'update' event emitted by socketio for POST operations.""" index = self.client.get("/{}".format(self.API_NAME)) assert index.status_code == 200 endpoints = json.loads(index.data.decode('utf-8')) for endpoint in endpoints: if endpoint not in ["@context", "@id", "@type"]: class_name = "/".join(endpoints[endpoint].split( "/{}/".format(self.API_NAME))[1:]) if class_name not in self.doc.collections: class_ = self.doc.parsed_classes[class_name]["class"] class_methods = [ x.method for x in class_.supportedOperation] if "POST" in class_methods: dummy_object = gen_dummy_object(class_.title, self.doc) # Flush old socketio updates self.socketio_client.get_received('/sync') post_response = self.client.post( endpoints[endpoint], data=json.dumps(dummy_object)) assert post_response.status_code == 200 # Get new socketio update update = self.socketio_client.get_received('/sync') assert len(update) != 0 assert update[0]['args'][0]['method'] == "POST" resource_name = update[0]['args'][0]['resource_url'].split('/')[-1] assert resource_name == endpoints[endpoint].split('/')[-1] def test_socketio_DELETE_updates(self): """Test 'update' event emitted by socketio for DELETE operations.""" index = self.client.get("/{}".format(self.API_NAME)) assert index.status_code == 200 endpoints = json.loads(index.data.decode('utf-8')) for endpoint in endpoints: if endpoint not in ["@context", "@id", "@type"]: class_name = "/".join(endpoints[endpoint].split( "/{}/".format(self.API_NAME))[1:]) if class_name not in self.doc.collections: class_ = self.doc.parsed_classes[class_name]["class"] class_methods = [ x.method for x in class_.supportedOperation] if "DELETE" in class_methods: # Flush old socketio updates self.socketio_client.get_received('/sync') delete_response = self.client.delete( endpoints[endpoint]) assert delete_response.status_code == 200 # Get new update event update = self.socketio_client.get_received('/sync') assert len(update) != 0 assert update[0]['args'][0]['method'] == 'DELETE' resource_name = update[0]['args'][0]['resource_url'].split('/')[-1] assert resource_name == endpoints[endpoint].split('/')[-1] if __name__ == '__main__': message = """ Running tests for the app. Checking if all responses are in proper order. """ unittest.main()

"""Test for checking if the response format is proper. Run test_crud before running this.""" import unittest import random import string import json import re import uuid from hydrus.app_factory import app_factory from hydrus.socketio_factory import create_socket from hydrus.utils import set_session, set_doc, set_api_name, set_page_size from hydrus.data import doc_parse, crud from hydra_python_core import doc_maker from hydra_python_core.doc_writer import HydraLink from hydrus.samples import doc_writer_sample from sqlalchemy import create_engine from sqlalchemy.orm import sessionmaker, scoped_session from hydrus.data.db_models import Base def gen_dummy_object(class_title, doc): """Create a dummy object based on the definitions in the API Doc. :param class_title: Title of the class whose object is being created. :param doc: ApiDoc. :return: A dummy object of class `class_title`. """ object_ = { "@type": class_title } for class_path in doc.parsed_classes: if class_title == doc.parsed_classes[class_path]["class"].title: for prop in doc.parsed_classes[class_path]["class"].supportedProperty: if isinstance(prop.prop, HydraLink) or prop.write is False: continue if "vocab:" in prop.prop: prop_class = prop.prop.replace("vocab:", "") object_[prop.title] = gen_dummy_object(prop_class, doc) else: object_[prop.title] = ''.join(random.choice( string.ascii_uppercase + string.digits) for _ in range(6)) return object_ class ViewsTestCase(unittest.TestCase): """Test Class for the app.""" @classmethod def setUpClass(self): """Database setup before the tests.""" print("Creating a temporary database...") engine = create_engine('sqlite:///:memory:') Base.metadata.create_all(engine) session = scoped_session(sessionmaker(bind=engine)) self.session = session self.API_NAME = "demoapi" self.page_size = 1 self.HYDRUS_SERVER_URL = "http://hydrus.com/" self.app = app_factory(self.API_NAME) self.socketio = create_socket(self.app, self.session) print("going for create doc") self.doc = doc_maker.create_doc( doc_writer_sample.api_doc.generate(), self.HYDRUS_SERVER_URL, self.API_NAME) test_classes = doc_parse.get_classes(self.doc.generate()) test_properties = doc_parse.get_all_properties(test_classes) doc_parse.insert_classes(test_classes, self.session) doc_parse.insert_properties(test_properties, self.session) print("Classes and properties added successfully.") print("Setting up hydrus utilities... ") self.api_name_util = set_api_name(self.app, self.API_NAME) self.session_util = set_session(self.app, self.session) self.doc_util = set_doc(self.app, self.doc) self.page_size_util = set_page_size(self.app, self.page_size) self.client = self.app.test_client() print("Creating utilities context... ") self.api_name_util.__enter__() self.session_util.__enter__() self.doc_util.__enter__() self.client.__enter__() print("Setup done, running tests...") @classmethod def tearDownClass(self): """Tear down temporary database and exit utilities""" self.client.__exit__(None, None, None) self.doc_util.__exit__(None, None, None) self.session_util.__exit__(None, None, None) self.api_name_util.__exit__(None, None, None) self.session.close() def setUp(self): for class_ in self.doc.parsed_classes: link_props = {} class_title = self.doc.parsed_classes[class_]["class"].title dummy_obj = gen_dummy_object(class_title, self.doc) for supportedProp in self.doc.parsed_classes[class_]['class'].supportedProperty: if isinstance(supportedProp.prop, HydraLink): class_name = supportedProp.prop.range.replace("vocab:", "") for collection_path in self.doc.collections: coll_class = self.doc.collections[ collection_path]['collection'].class_.title if class_name == coll_class: id_ = str(uuid.uuid4()) crud.insert( gen_dummy_object(class_name, self.doc), id_=id_, session=self.session) link_props[supportedProp.title] = id_ dummy_obj[supportedProp.title] = "{}/{}/{}".format( self.API_NAME, collection_path, id_) crud.insert( dummy_obj, id_=str( uuid.uuid4()), link_props=link_props, session=self.session) # If it's a collection class then add an extra object so # we can test pagination thoroughly. if class_ in self.doc.collections: crud.insert( dummy_obj, id_=str( uuid.uuid4()), session=self.session) def test_Index(self): """Test for the index.""" response_get = self.client.get("/{}".format(self.API_NAME)) endpoints = json.loads(response_get.data.decode('utf-8')) response_post = self.client.post( "/{}".format(self.API_NAME), data=dict(foo="bar")) response_put = self.client.put( "/{}".format(self.API_NAME), data=dict(foo="bar")) response_delete = self.client.delete("/{}".format(self.API_NAME)) assert "@context" in endpoints assert endpoints["@id"] == "/{}".format(self.API_NAME) assert endpoints["@type"] == "EntryPoint" assert response_get.status_code == 200 assert response_post.status_code == 405 assert response_put.status_code == 405 assert response_delete.status_code == 405 def test_EntryPoint_context(self): """Test for the EntryPoint context.""" response_get = self.client.get( "/{}/contexts/EntryPoint.jsonld".format(self.API_NAME)) response_get_data = json.loads(response_get.data.decode('utf-8')) response_post = self.client.post( "/{}/contexts/EntryPoint.jsonld".format(self.API_NAME), data={}) response_delete = self.client.delete( "/{}/contexts/EntryPoint.jsonld".format(self.API_NAME)) assert response_get.status_code == 200 assert "@context" in response_get_data assert response_post.status_code == 405 assert response_delete.status_code == 405 def test_Vocab(self): """Test the vocab.""" response_get = self.client.get("/{}/vocab#".format(self.API_NAME)) response_get_data = json.loads(response_get.data.decode('utf-8')) assert "@context" in response_get_data assert response_get_data["@type"] == "ApiDocumentation" assert response_get_data["@id"] == "{}{}/vocab".format( self.HYDRUS_SERVER_URL, self.API_NAME) assert response_get.status_code == 200 response_delete = self.client.delete( "/{}/vocab#".format(self.API_NAME)) assert response_delete.status_code == 405 response_put = self.client.put( "/{}/vocab#".format(self.API_NAME), data=json.dumps(dict(foo='bar'))) assert response_put.status_code == 405 response_post = self.client.post( "/{}/vocab#".format(self.API_NAME), data=json.dumps(dict(foo='bar'))) assert response_post.status_code == 405 def test_Collections_GET(self): """Test GET on collection endpoints.""" index = self.client.get("/{}".format(self.API_NAME)) assert index.status_code == 200 endpoints = json.loads(index.data.decode('utf-8')) for endpoint in endpoints: collection_name = "/".join(endpoints[endpoint].split( "/{}/".format(self.API_NAME))[1:]) if collection_name in self.doc.collections: response_get = self.client.get(endpoints[endpoint]) # pdb.set_trace() assert response_get.status_code == 200 response_get_data = json.loads( response_get.data.decode('utf-8')) assert "@context" in response_get_data assert "@id" in response_get_data assert "@type" in response_get_data assert "members" in response_get_data # Check the item URI has the valid format, so it can be dereferenced if len(response_get_data["members"]) > 0: for item in response_get_data["members"]: class_type = item["@type"] if class_type in self.doc.parsed_classes: class_ = self.doc.parsed_classes[class_type]["class"] class_methods = [ x.method for x in class_.supportedOperation] if "GET" in class_methods: item_response = self.client.get( response_get_data["members"][0]["@id"]) assert item_response.status_code == 200 def test_pagination(self): """Test basic pagination""" index = self.client.get("/{}".format(self.API_NAME)) assert index.status_code == 200 endpoints = json.loads(index.data.decode('utf-8')) for endpoint in endpoints: collection_name = "/".join(endpoints[endpoint].split( "/{}/".format(self.API_NAME))[1:]) if collection_name in self.doc.collections: response_get = self.client.get(endpoints[endpoint]) assert response_get.status_code == 200 response_get_data = json.loads( response_get.data.decode('utf-8')) assert "view" in response_get_data assert "first" in response_get_data["view"] assert "last" in response_get_data["view"] if "next" in response_get_data["view"]: response_next = self.client.get(response_get_data["view"]["next"]) assert response_next.status_code == 200 response_next_data = json.loads( response_next.data.decode('utf-8')) assert "previous" in response_next_data["view"] break def test_Collections_PUT(self): """Test insert data to the collection.""" index = self.client.get("/{}".format(self.API_NAME)) assert index.status_code == 200 endpoints = json.loads(index.data.decode('utf-8')) for endpoint in endpoints: collection_name = "/".join(endpoints[endpoint].split( "/{}/".format(self.API_NAME))[1:]) if collection_name in self.doc.collections: collection = self.doc.collections[collection_name]["collection"] dummy_object = gen_dummy_object( collection.class_.title, self.doc) good_response_put = self.client.put( endpoints[endpoint], data=json.dumps(dummy_object)) assert good_response_put.status_code == 201 def test_object_POST(self): """Test replace of a given object using ID.""" index = self.client.get("/{}".format(self.API_NAME)) assert index.status_code == 200 endpoints = json.loads(index.data.decode('utf-8')) for endpoint in endpoints: collection_name = "/".join(endpoints[endpoint].split( "/{}/".format(self.API_NAME))[1:]) if collection_name in self.doc.collections: collection = self.doc.collections[collection_name]["collection"] class_ = self.doc.parsed_classes[collection.class_.title]["class"] class_methods = [x.method for x in class_.supportedOperation] dummy_object = gen_dummy_object( collection.class_.title, self.doc) initial_put_response = self.client.put( endpoints[endpoint], data=json.dumps(dummy_object)) assert initial_put_response.status_code == 201 response = json.loads( initial_put_response.data.decode('utf-8')) regex = r'(.*)ID (.{36})* (.*)' matchObj = re.match(regex, response["description"]) assert matchObj is not None id_ = matchObj.group(2) if "POST" in class_methods: dummy_object = gen_dummy_object( collection.class_.title, self.doc) post_replace_response = self.client.post( '{}/{}'.format(endpoints[endpoint], id_), data=json.dumps(dummy_object)) assert post_replace_response.status_code == 200 def test_object_DELETE(self): """Test DELETE of a given object using ID.""" index = self.client.get("/{}".format(self.API_NAME)) assert index.status_code == 200 endpoints = json.loads(index.data.decode('utf-8')) for endpoint in endpoints: collection_name = "/".join(endpoints[endpoint].split( "/{}/".format(self.API_NAME))[1:]) if collection_name in self.doc.collections: collection = self.doc.collections[collection_name]["collection"] class_ = self.doc.parsed_classes[collection.class_.title]["class"] class_methods = [x.method for x in class_.supportedOperation] dummy_object = gen_dummy_object( collection.class_.title, self.doc) initial_put_response = self.client.put( endpoints[endpoint], data=json.dumps(dummy_object)) assert initial_put_response.status_code == 201 response = json.loads( initial_put_response.data.decode('utf-8')) regex = r'(.*)ID (.{36})* (.*)' matchObj = re.match(regex, response["description"]) assert matchObj is not None id_ = matchObj.group(2) if "DELETE" in class_methods: delete_response = self.client.delete( '{}/{}'.format(endpoints[endpoint], id_)) assert delete_response.status_code == 200 def test_object_PUT_at_id(self): """Create object in collection using PUT at specific ID.""" index = self.client.get("/{}".format(self.API_NAME)) assert index.status_code == 200 endpoints = json.loads(index.data.decode('utf-8')) for endpoint in endpoints: collection_name = "/".join(endpoints[endpoint].split( "/{}/".format(self.API_NAME))[1:]) if collection_name in self.doc.collections: collection = self.doc.collections[collection_name]["collection"] class_ = self.doc.parsed_classes[collection.class_.title]["class"] class_methods = [x.method for x in class_.supportedOperation] dummy_object = gen_dummy_object( collection.class_.title, self.doc) if "PUT" in class_methods: dummy_object = gen_dummy_object( collection.class_.title, self.doc) put_response = self.client.put('{}/{}'.format( endpoints[endpoint], uuid.uuid4()), data=json.dumps(dummy_object)) assert put_response.status_code == 201 def test_object_PUT_at_ids(self): index = self.client.get("/{}".format(self.API_NAME)) assert index.status_code == 200 endpoints = json.loads(index.data.decode('utf-8')) for endpoint in endpoints: collection_name = "/".join(endpoints[endpoint].split( "/{}/".format(self.API_NAME))[1:]) if collection_name in self.doc.collections: collection = self.doc.collections[collection_name]["collection"] class_ = self.doc.parsed_classes[collection.class_.title]["class"] class_methods = [x.method for x in class_.supportedOperation] data_ = {"data": list()} objects = list() ids = "" for index in range(3): objects.append(gen_dummy_object( collection.class_.title, self.doc)) ids = "{},".format(uuid.uuid4()) data_["data"] = objects if "PUT" in class_methods: put_response = self.client.put( '{}/add/{}'.format(endpoints[endpoint], ids), data=json.dumps(data_)) assert put_response.status_code == 201 def test_endpointClass_PUT(self): """Check non collection Class PUT.""" index = self.client.get("/{}".format(self.API_NAME)) assert index.status_code == 200 endpoints = json.loads(index.data.decode('utf-8')) for endpoint in endpoints: if endpoint not in ["@context", "@id", "@type"]: class_name = "/".join(endpoints[endpoint].split( "/{}/".format(self.API_NAME))[1:]) if class_name not in self.doc.collections: class_ = self.doc.parsed_classes[class_name]["class"] class_methods = [ x.method for x in class_.supportedOperation] if "PUT" in class_methods: dummy_object = gen_dummy_object(class_.title, self.doc) put_response = self.client.put( endpoints[endpoint], data=json.dumps(dummy_object)) assert put_response.status_code == 201 def test_endpointClass_POST(self): """Check non collection Class POST.""" index = self.client.get("/{}".format(self.API_NAME)) assert index.status_code == 200 endpoints = json.loads(index.data.decode('utf-8')) for endpoint in endpoints: if endpoint not in ["@context", "@id", "@type"]: class_name = "/".join(endpoints[endpoint].split( "/{}/".format(self.API_NAME))[1:]) if class_name not in self.doc.collections: class_ = self.doc.parsed_classes[class_name]["class"] class_methods = [ x.method for x in class_.supportedOperation] if "POST" in class_methods: dummy_object = gen_dummy_object(class_.title, self.doc) post_response = self.client.post( endpoints[endpoint], data=json.dumps(dummy_object)) assert post_response.status_code == 200 def test_endpointClass_DELETE(self): """Check non collection Class DELETE.""" index = self.client.get("/{}".format(self.API_NAME)) assert index.status_code == 200 endpoints = json.loads(index.data.decode('utf-8')) for endpoint in endpoints: if endpoint not in ["@context", "@id", "@type"]: class_name = "/".join(endpoints[endpoint].split( "/{}/".format(self.API_NAME))[1:]) if class_name not in self.doc.collections: class_ = self.doc.parsed_classes[class_name]["class"] class_methods = [ x.method for x in class_.supportedOperation] if "DELETE" in class_methods: delete_response = self.client.delete( endpoints[endpoint]) assert delete_response.status_code == 200 def test_endpointClass_GET(self): """Check non collection Class GET.""" index = self.client.get("/{}".format(self.API_NAME)) assert index.status_code == 200 endpoints = json.loads(index.data.decode('utf-8')) for endpoint in endpoints: if endpoint not in ["@context", "@id", "@type"]: class_name = "/".join(endpoints[endpoint].split( "/{}/".format(self.API_NAME))[1:]) if class_name not in self.doc.collections: class_ = self.doc.parsed_classes[class_name]["class"] class_methods = [ x.method for x in class_.supportedOperation] if "GET" in class_methods: response_get = self.client.get(endpoints[endpoint]) assert response_get.status_code == 200 response_get_data = json.loads( response_get.data.decode('utf-8')) assert "@context" in response_get_data assert "@id" in response_get_data assert "@type" in response_get_data def test_IriTemplate(self): """Test structure of IriTemplates attached to collections""" index = self.client.get("/{}".format(self.API_NAME)) assert index.status_code == 200 endpoints = json.loads(index.data.decode('utf-8')) for endpoint in endpoints: collection_name = "/".join(endpoints[endpoint].split( "/{}/".format(self.API_NAME))[1:]) if collection_name in self.doc.collections: response_get = self.client.get(endpoints[endpoint]) assert response_get.status_code == 200 response_get_data = json.loads( response_get.data.decode('utf-8')) assert "search" in response_get_data assert "mapping" in response_get_data["search"] collection = self.doc.collections[collection_name]["collection"] class_ = self.doc.parsed_classes[collection.class_.title]["class"] class_props = [x.prop for x in class_.supportedProperty] for mapping in response_get_data["search"]["mapping"]: if mapping["property"] not in ["limit", "offset", "pageIndex"]: assert mapping["property"] in class_props def test_client_controlled_pagination(self): """Test pagination controlled by client with help of pageIndex, offset and limit parameters.""" index = self.client.get("/{}".format(self.API_NAME)) assert index.status_code == 200 endpoints = json.loads(index.data.decode('utf-8')) for endpoint in endpoints: collection_name = "/".join(endpoints[endpoint].split( "/{}/".format(self.API_NAME))[1:]) if collection_name in self.doc.collections: response_get = self.client.get(endpoints[endpoint]) assert response_get.status_code == 200 response_get_data = json.loads( response_get.data.decode('utf-8')) assert "search" in response_get_data assert "mapping" in response_get_data["search"] # Test with pageIndex and limit params = {"pageIndex": 1, "limit": 2} response_for_page_param = self.client.get(endpoints[endpoint], query_string=params) assert response_for_page_param.status_code == 200 response_for_page_param_data = json.loads( response_for_page_param.data.decode('utf-8')) assert "first" in response_for_page_param_data["view"] assert "last" in response_for_page_param_data["view"] if "next" in response_for_page_param_data["view"]: assert "pageIndex=2" in response_for_page_param_data["view"]["next"] next_response = self.client.get(response_for_page_param_data["view"]["next"]) assert next_response.status_code == 200 next_response_data = json.loads( next_response.data.decode('utf-8')) assert "previous" in next_response_data["view"] assert "pageIndex=1" in next_response_data["view"]["previous"] # Test with offset and limit params = {"offset": 1, "limit": 2} response_for_offset_param = self.client.get(endpoints[endpoint], query_string=params) assert response_for_offset_param.status_code == 200 response_for_offset_param_data = json.loads( response_for_offset_param.data.decode('utf-8')) assert "first" in response_for_offset_param_data["view"] assert "last" in response_for_offset_param_data["view"] if "next" in response_for_offset_param_data["view"]: assert "offset=3" in response_for_offset_param_data["view"]["next"] next_response = self.client.get( response_for_offset_param_data["view"]["next"]) assert next_response.status_code == 200 next_response_data = json.loads( next_response.data.decode('utf-8')) assert "previous" in next_response_data["view"] assert "offset=1" in next_response_data["view"]["previous"] def test_GET_for_nested_class(self): index = self.client.get("/{}".format(self.API_NAME)) assert index.status_code == 200 endpoints = json.loads(index.data.decode('utf-8')) for endpoint in endpoints: if endpoint not in ["@context", "@id", "@type"]: class_name = "/".join(endpoints[endpoint].split( "/{}/".format(self.API_NAME))[1:]) if class_name not in self.doc.collections: class_ = self.doc.parsed_classes[class_name]["class"] class_methods = [ x.method for x in class_.supportedOperation] if "GET" in class_methods: response_get = self.client.get(endpoints[endpoint]) assert response_get.status_code == 200 response_get_data = json.loads( response_get.data.decode('utf-8')) assert "@context" in response_get_data assert "@id" in response_get_data assert "@type" in response_get_data class_props = [x for x in class_.supportedProperty] for prop_name in class_props: if isinstance(prop_name.prop, HydraLink) and prop_name.read is True: nested_obj_resp = self.client.get( response_get_data[prop_name.title]) assert nested_obj_resp.status_code == 200 nested_obj = json.loads( nested_obj_resp.data.decode('utf-8')) assert "@type" in nested_obj elif "vocab:" in prop_name.prop: assert "@type" in response_get_data[prop_name.title] def test_required_props(self): index = self.client.get("/{}".format(self.API_NAME)) assert index.status_code == 200 endpoints = json.loads(index.data.decode('utf-8')) for endpoint in endpoints: if endpoint not in ["@context", "@id", "@type"]: class_name = "/".join(endpoints[endpoint].split( "/{}/".format(self.API_NAME))[1:]) if class_name not in self.doc.collections: class_ = self.doc.parsed_classes[class_name]["class"] class_methods = [ x.method for x in class_.supportedOperation] if "PUT" in class_methods: dummy_object = gen_dummy_object(class_.title, self.doc) required_prop = "" for prop in class_.supportedProperty: if prop.required: required_prop = prop.title break if required_prop: del dummy_object[required_prop] put_response = self.client.put( endpoints[endpoint], data=json.dumps(dummy_object)) assert put_response.status_code == 400 def test_writeable_props(self): index = self.client.get("/{}".format(self.API_NAME)) assert index.status_code == 200 endpoints = json.loads(index.data.decode('utf-8')) for endpoint in endpoints: if endpoint not in ["@context", "@id", "@type"]: class_name = "/".join(endpoints[endpoint].split( "/{}/".format(self.API_NAME))[1:]) if class_name not in self.doc.collections: class_ = self.doc.parsed_classes[class_name]["class"] class_methods = [ x.method for x in class_.supportedOperation] if "POST" in class_methods: dummy_object = gen_dummy_object(class_.title, self.doc) # Test for writeable properties post_response = self.client.post( endpoints[endpoint], data=json.dumps(dummy_object)) assert post_response.status_code == 200 # Test for properties with writeable=False non_writeable_prop = "" for prop in class_.supportedProperty: if prop.write is False: non_writeable_prop = prop.title break if non_writeable_prop != "": dummy_object[non_writeable_prop] = "xyz" post_response = self.client.post( endpoints[endpoint], data=json.dumps(dummy_object)) assert post_response.status_code == 405 def test_readable_props(self): index = self.client.get("/{}".format(self.API_NAME)) assert index.status_code == 200 endpoints = json.loads(index.data.decode('utf-8')) for endpoint in endpoints: if endpoint not in ["@context", "@id", "@type"]: class_name = "/".join(endpoints[endpoint].split( "/{}/".format(self.API_NAME))[1:]) if class_name not in self.doc.collections: class_ = self.doc.parsed_classes[class_name]["class"] class_methods = [ x.method for x in class_.supportedOperation] if "GET" in class_methods: not_readable_prop = "" for prop in class_.supportedProperty: if prop.read is False: not_readable_prop = prop.title break if not_readable_prop: get_response = self.client.get( endpoints[endpoint]) get_response_data = json.loads( get_response.data.decode('utf-8')) assert not_readable_prop not in get_response_data def test_bad_objects(self): """Checks if bad objects are added or not.""" index = self.client.get("/{}".format(self.API_NAME)) assert index.status_code == 200 endpoints = json.loads(index.data.decode('utf-8')) for endpoint in endpoints: collection_name = "/".join(endpoints[endpoint].split( "/{}/".format(self.API_NAME))[1:]) if collection_name in self.doc.collections: bad_response_put = self.client.put( endpoints[endpoint], data=json.dumps( dict( foo='bar'))) assert bad_response_put.status_code == 400 def test_bad_requests(self): """Checks if bad requests are handled or not.""" index = self.client.get("/{}".format(self.API_NAME)) assert index.status_code == 200 endpoints = json.loads(index.data.decode('utf-8')) for endpoint in endpoints: collection_name = "/".join(endpoints[endpoint].split( "/{}/".format(self.API_NAME))[1:]) if collection_name in self.doc.collections: collection = self.doc.collections[collection_name]["collection"] class_ = self.doc.parsed_classes[collection.class_.title]["class"] class_methods = [x.method for x in class_.supportedOperation] dummy_object = gen_dummy_object( collection.class_.title, self.doc) initial_put_response = self.client.put( endpoints[endpoint], data=json.dumps(dummy_object)) assert initial_put_response.status_code == 201 response = json.loads( initial_put_response.data.decode('utf-8')) regex = r'(.*)ID (.{36})* (.*)' matchObj = re.match(regex, response["description"]) assert matchObj is not None id_ = matchObj.group(2) if "POST" not in class_methods: dummy_object = gen_dummy_object( collection.class_.title, self.doc) post_replace_response = self.client.post( '{}/{}'.format(endpoints[endpoint], id_), data=json.dumps(dummy_object)) assert post_replace_response.status_code == 405 if "DELETE" not in class_methods: delete_response = self.client.delete( '{}/{}'.format(endpoints[endpoint], id_)) assert delete_response.status_code == 405 def test_Endpoints_Contexts(self): """Test all endpoints contexts are generated properly.""" index = self.client.get("/{}".format(self.API_NAME)) assert index.status_code == 200 endpoints = json.loads(index.data.decode('utf-8')) for endpoint in endpoints: collection_name = "/".join(endpoints[endpoint].split( "/{}/".format(self.API_NAME))[1:]) if collection_name in self.doc.collections: response_get = self.client.get(endpoints[endpoint]) assert response_get.status_code == 200 context = json.loads( response_get.data.decode('utf-8'))["@context"] response_context = self.client.get(context) response_context_data = json.loads( response_context.data.decode('utf-8')) assert response_context.status_code == 200 assert "@context" in response_context_data class SocketTestCase(unittest.TestCase): """Test Class for socket events and operations.""" @classmethod def setUpClass(self): """Database setup before the tests.""" print("Creating a temporary database...") engine = create_engine('sqlite:///:memory:') Base.metadata.create_all(engine) session = scoped_session(sessionmaker(bind=engine)) self.session = session self.API_NAME = "demoapi" self.page_size = 1 self.HYDRUS_SERVER_URL = "http://hydrus.com/" self.app = app_factory(self.API_NAME) self.socketio = create_socket(self.app, self.session) print("going for create doc") self.doc = doc_maker.create_doc( doc_writer_sample.api_doc.generate(), self.HYDRUS_SERVER_URL, self.API_NAME) test_classes = doc_parse.get_classes(self.doc.generate()) test_properties = doc_parse.get_all_properties(test_classes) doc_parse.insert_classes(test_classes, self.session) doc_parse.insert_properties(test_properties, self.session) print("Classes and properties added successfully.") print("Setting up hydrus utilities... ") self.api_name_util = set_api_name(self.app, self.API_NAME) self.session_util = set_session(self.app, self.session) self.doc_util = set_doc(self.app, self.doc) self.page_size_util = set_page_size(self.app, self.page_size) self.client = self.app.test_client() self.socketio_client = self.socketio.test_client(self.app, namespace='/sync') print("Creating utilities context... ") self.api_name_util.__enter__() self.session_util.__enter__() self.doc_util.__enter__() self.client.__enter__() print("Setup done, running tests...") @classmethod def tearDownClass(self): """Tear down temporary database and exit utilities""" self.client.__exit__(None, None, None) self.doc_util.__exit__(None, None, None) self.session_util.__exit__(None, None, None) self.api_name_util.__exit__(None, None, None) self.session.close() def setUp(self): for class_ in self.doc.parsed_classes: class_title = self.doc.parsed_classes[class_]["class"].title dummy_obj = gen_dummy_object(class_title, self.doc) crud.insert( dummy_obj, id_=str( uuid.uuid4()), session=self.session) # If it's a collection class then add an extra object so # we can test pagination thoroughly. if class_ in self.doc.collections: crud.insert( dummy_obj, id_=str( uuid.uuid4()), session=self.session) # Add two dummy modification records crud.insert_modification_record(method="POST", resource_url="", session=self.session) crud.insert_modification_record(method="DELETE", resource_url="", session=self.session) def test_connect(self): """Test connect event.""" socket_client = self.socketio.test_client(self.app, namespace='/sync') data = socket_client.get_received('/sync') assert len(data) > 0 event = data[0] assert event['name'] == 'connect' last_job_id = crud.get_last_modification_job_id(self.session) assert event['args'][0]['last_job_id'] == last_job_id socket_client.disconnect(namespace='/sync') def test_reconnect(self): """Test reconnect event.""" socket_client = self.socketio.test_client(self.app, namespace='/sync') # Flush data of first connect event socket_client.get_received('/sync') # Client reconnects by emitting 'reconnect' event. socket_client.emit('reconnect', namespace='/sync') # Get update received on reconnecting to the server data = socket_client.get_received('/sync') assert len(data) > 0 # Extract the event information event = data[0] assert event['name'] == 'connect' last_job_id = crud.get_last_modification_job_id(self.session) # Check last job id with last_job_id received by client in the update. assert event['args'][0]['last_job_id'] == last_job_id socket_client.disconnect(namespace='/sync') def test_modification_table_diff(self): """Test 'modification-table-diff' events.""" # Flush old received data at socket client self.socketio_client.get_received('/sync') # Set last_job_id as the agent_job_id agent_job_id = crud.get_last_modification_job_id(self.session) # Add an extra modification record newer than the agent_job_id new_latest_job_id = crud.insert_modification_record(method="POST", resource_url="", session=self.session) self.socketio_client.emit('get_modification_table_diff', {'agent_job_id': agent_job_id}, namespace='/sync') data = self.socketio_client.get_received('/sync') assert len(data) > 0 event = data[0] assert event['name'] == 'modification_table_diff' # Check received event contains data of newly added modification record. assert event['args'][0][0]['method'] == "POST" assert event['args'][0][0]['resource_url'] == "" assert event['args'][0][0]['job_id'] == new_latest_job_id def test_socketio_POST_updates(self): """Test 'update' event emitted by socketio for POST operations.""" index = self.client.get("/{}".format(self.API_NAME)) assert index.status_code == 200 endpoints = json.loads(index.data.decode('utf-8')) for endpoint in endpoints: if endpoint not in ["@context", "@id", "@type"]: class_name = "/".join(endpoints[endpoint].split( "/{}/".format(self.API_NAME))[1:]) if class_name not in self.doc.collections: class_ = self.doc.parsed_classes[class_name]["class"] class_methods = [ x.method for x in class_.supportedOperation] if "POST" in class_methods: dummy_object = gen_dummy_object(class_.title, self.doc) # Flush old socketio updates self.socketio_client.get_received('/sync') post_response = self.client.post( endpoints[endpoint], data=json.dumps(dummy_object)) assert post_response.status_code == 200 # Get new socketio update update = self.socketio_client.get_received('/sync') assert len(update) != 0 assert update[0]['args'][0]['method'] == "POST" resource_name = update[0]['args'][0]['resource_url'].split('/')[-1] assert resource_name == endpoints[endpoint].split('/')[-1] def test_socketio_DELETE_updates(self): """Test 'update' event emitted by socketio for DELETE operations.""" index = self.client.get("/{}".format(self.API_NAME)) assert index.status_code == 200 endpoints = json.loads(index.data.decode('utf-8')) for endpoint in endpoints: if endpoint not in ["@context", "@id", "@type"]: class_name = "/".join(endpoints[endpoint].split( "/{}/".format(self.API_NAME))[1:]) if class_name not in self.doc.collections: class_ = self.doc.parsed_classes[class_name]["class"] class_methods = [ x.method for x in class_.supportedOperation] if "DELETE" in class_methods: # Flush old socketio updates self.socketio_client.get_received('/sync') delete_response = self.client.delete( endpoints[endpoint]) assert delete_response.status_code == 200 # Get new update event update = self.socketio_client.get_received('/sync') assert len(update) != 0 assert update[0]['args'][0]['method'] == 'DELETE' resource_name = update[0]['args'][0]['resource_url'].split('/')[-1] assert resource_name == endpoints[endpoint].split('/')[-1] if __name__ == '__main__': message = """ Running tests for the app. Checking if all responses are in proper order. """ unittest.main()

c06b33ca0266576dd77b1443051d6971f9e82077

py

Python

environment.py

LCBRU/hic_covid

eb5a37339185ed71246235e307a81d91dc91f9ec

environment.py

LCBRU/hic_covid

eb5a37339185ed71246235e307a81d91dc91f9ec

environment.py

LCBRU/hic_covid

eb5a37339185ed71246235e307a81d91dc91f9ec

"""Environment Variables """ import os from dotenv import load_dotenv load_dotenv() HIC_DB_USERNAME = os.environ["HIC_DB_USERNAME"] HIC_DB_PASSWORD = os.environ["HIC_DB_PASSWORD"] HIC_DB_HOST = os.environ["HIC_DB_HOST"] HIC_DB_DATABASE = os.environ["HIC_DB_DATABASE"] MS_SQL_ODBC_DRIVER = os.environ["MS_SQL_ODBC_DRIVER"] MS_SQL_UHL_DWH_HOST = os.environ["MS_SQL_UHL_DWH_HOST"] MS_SQL_UHL_DWH_USER = os.environ["MS_SQL_UHL_DWH_USER"] MS_SQL_UHL_DWH_PASSWORD = os.environ["MS_SQL_UHL_DWH_PASSWORD"] IDENTITY_API_KEY = os.environ["IDENTITY_API_KEY"] IDENTITY_HOST = os.environ["IDENTITY_HOST"] HIC_CONNECTION_STRING = os.environ["HIC_CONNECTION_STRING"] HIC_HOST = os.environ["HIC_HOST"] HIC_USERNAME = os.environ["HIC_USERNAME"] HIC_PASSWORD = os.environ["HIC_PASSWORD"]

"""Environment Variables """ import os from dotenv import load_dotenv load_dotenv() HIC_DB_USERNAME = os.environ["HIC_DB_USERNAME"] HIC_DB_PASSWORD = os.environ["HIC_DB_PASSWORD"] HIC_DB_HOST = os.environ["HIC_DB_HOST"] HIC_DB_DATABASE = os.environ["HIC_DB_DATABASE"] MS_SQL_ODBC_DRIVER = os.environ["MS_SQL_ODBC_DRIVER"] MS_SQL_UHL_DWH_HOST = os.environ["MS_SQL_UHL_DWH_HOST"] MS_SQL_UHL_DWH_USER = os.environ["MS_SQL_UHL_DWH_USER"] MS_SQL_UHL_DWH_PASSWORD = os.environ["MS_SQL_UHL_DWH_PASSWORD"] IDENTITY_API_KEY = os.environ["IDENTITY_API_KEY"] IDENTITY_HOST = os.environ["IDENTITY_HOST"] HIC_CONNECTION_STRING = os.environ["HIC_CONNECTION_STRING"] HIC_HOST = os.environ["HIC_HOST"] HIC_USERNAME = os.environ["HIC_USERNAME"] HIC_PASSWORD = os.environ["HIC_PASSWORD"]

f0682b750b96bf2f312eb6ff9f2bea5aef2c5958

py

Python

nbd_app/migrations/0007_alter_socialamenities_hotline_number.py

Kevson102/Nbd-Phoenix

509a9cf026d24827dccc9a5ec67819ecd86fbf03

nbd_app/migrations/0007_alter_socialamenities_hotline_number.py

Kevson102/Nbd-Phoenix

509a9cf026d24827dccc9a5ec67819ecd86fbf03

nbd_app/migrations/0007_alter_socialamenities_hotline_number.py

Kevson102/Nbd-Phoenix

509a9cf026d24827dccc9a5ec67819ecd86fbf03

# Generated by Django 3.2.9 on 2022-01-03 14:32 from django.db import migrations, models

# Generated by Django 3.2.9 on 2022-01-03 14:32 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('nbd_app', '0006_generalposts'), ] operations = [ migrations.AlterField( model_name='socialamenities', name='hotline_number', field=models.BigIntegerField(), ), ]

a68bccebe211588992d72d7335645e18121c4bf1

py

Python

python/icp.py

nowtechnologies/ridi_imu

2d8a8e54d0491c44de7edac662b101db47ad3cfc

2018-05-27T16:11:40.000Z

2022-03-28T15:49:28.000Z

python/icp.py

nowtechnologies/ridi_imu

2d8a8e54d0491c44de7edac662b101db47ad3cfc

2018-07-16T20:59:58.000Z

2021-12-09T08:35:43.000Z

python/icp.py

nowtechnologies/ridi_imu

2d8a8e54d0491c44de7edac662b101db47ad3cfc

2018-02-14T03:53:51.000Z

2022-03-07T15:59:41.000Z

import numpy as np def fit_transformation(source, target): """ This function computes the best rigid transformation between two point sets. It assumes that "source" and "target" are with the same length and "source[i]" corresponds to "target[i]". :param source: Nxd array. :param target: Nxd array. :return: A transformation as (d+1)x(d+1) matrix; the rotation part as a dxd matrix and the translation part as a dx1 vector. """ assert source.shape == target.shape center_source = np.mean(source, axis=0) center_target = np.mean(target, axis=0) m = source.shape[1] source_zeromean = source - center_source target_zeromean = target - center_target W = np.dot(source_zeromean.T, target_zeromean) U, S, Vt = np.linalg.svd(W) R = np.dot(Vt.T, U.T) if np.linalg.det(R) < 0: Vt[m - 1, :] *= -1 R = np.dot(Vt.T, U.T) t = center_target.T - np.dot(R, center_source.T) T = np.identity(m + 1) T[:m, :m] = R T[:m, m] = t return T, R, t

import numpy as np def fit_transformation(source, target): """ This function computes the best rigid transformation between two point sets. It assumes that "source" and "target" are with the same length and "source[i]" corresponds to "target[i]". :param source: Nxd array. :param target: Nxd array. :return: A transformation as (d+1)x(d+1) matrix; the rotation part as a dxd matrix and the translation part as a dx1 vector. """ assert source.shape == target.shape center_source = np.mean(source, axis=0) center_target = np.mean(target, axis=0) m = source.shape[1] source_zeromean = source - center_source target_zeromean = target - center_target W = np.dot(source_zeromean.T, target_zeromean) U, S, Vt = np.linalg.svd(W) R = np.dot(Vt.T, U.T) if np.linalg.det(R) < 0: Vt[m - 1, :] *= -1 R = np.dot(Vt.T, U.T) t = center_target.T - np.dot(R, center_source.T) T = np.identity(m + 1) T[:m, :m] = R T[:m, m] = t return T, R, t

d22e0e78871a5ab8c68a346843dd049461897cb0

py

Python

codechef/long-challenge/may21/MODEQ.py

ramanaditya/data-structure-and-algorithms

8dcfeb011e76b2b38b54842e8ccc7a59728141f8

2020-05-22T14:22:04.000Z

2021-12-18T10:11:23.000Z

codechef/long-challenge/may21/MODEQ.py

techhub-community/data-structure-and-algorithms

8dcfeb011e76b2b38b54842e8ccc7a59728141f8

2020-08-06T21:08:00.000Z

2021-03-31T16:07:50.000Z

codechef/long-challenge/may21/MODEQ.py

techhub-community/data-structure-and-algorithms

8dcfeb011e76b2b38b54842e8ccc7a59728141f8

2020-05-22T14:25:21.000Z

2021-12-30T03:13:13.000Z

""" [Modular Equation](https://www.codechef.com/MAY21C/problems/MODEQ) Given integers N and M, find the number of ordered pairs (a,b) such that 1≤a<b≤N and ((M mod a) mod b)=((M mod b) mod a). Input The first line contains an integer T, the number of test cases. Then the test cases follow. The only line of each test case contains two integers N, M. Output For each testcase, output in a single line the answer to the problem. Constraints 1≤T≤1000 2≤N≤106 1≤M≤5⋅105 The sum of N over all test cases does not exceed 106. Note: Multiplier for JAVA for this problem is reduced to 1.25 instead of usual 2. Subtasks Subtask #1 (10 points): 1≤T≤10 2≤N≤103 1≤M≤105 Subtask #2 (40 points): 1≤T≤100 2≤N≤105 1≤M≤105 The sum of N over all test cases does not exceed 106. Subtask #3 (50 points): Original Constraints Sample Input 3 3 5 3 6 3 10 Sample Output 2 3 2 Explanation Test Case 1: The valid pairs are {(1,2),(1,3)}. Test Case 2: The valid pairs are {(1,2),(1,3),(2,3)}. Test Case 3: The valid pairs are {(1,2),(1,3)}. """ import sys # Brute Force """ if __name__ == '__main__': input = sys.stdin.read() data = list(map(int, input.split())) T = data[0] idx = 1 while T > 0: N, M = data[idx: idx + 2] res = 0 for i in range(1, N): for j in range(i + 1, N + 1): if (M % i) % j == (M % j) % i: res += 1 print(res) T -= 1 idx += 2 # Time : 0.58s """ if __name__ == '__main__': T = int(input()) idx = 1 while T > 0: N, M = list(map(int, input().split())) res = 0 mod = dict() for a in range(2, N+1): mod_with_a = M % a res += mod.get(mod_with_a, 1) for b in range(mod_with_a, N+1, a): mod[b] = mod.get(b, 1) + 1 print(res) T -= 1 # Time : 4.92s

""" [Modular Equation](https://www.codechef.com/MAY21C/problems/MODEQ) Given integers N and M, find the number of ordered pairs (a,b) such that 1≤a<b≤N and ((M mod a) mod b)=((M mod b) mod a). Input The first line contains an integer T, the number of test cases. Then the test cases follow. The only line of each test case contains two integers N, M. Output For each testcase, output in a single line the answer to the problem. Constraints 1≤T≤1000 2≤N≤106 1≤M≤5⋅105 The sum of N over all test cases does not exceed 106. Note: Multiplier for JAVA for this problem is reduced to 1.25 instead of usual 2. Subtasks Subtask #1 (10 points): 1≤T≤10 2≤N≤103 1≤M≤105 Subtask #2 (40 points): 1≤T≤100 2≤N≤105 1≤M≤105 The sum of N over all test cases does not exceed 106. Subtask #3 (50 points): Original Constraints Sample Input 3 3 5 3 6 3 10 Sample Output 2 3 2 Explanation Test Case 1: The valid pairs are {(1,2),(1,3)}. Test Case 2: The valid pairs are {(1,2),(1,3),(2,3)}. Test Case 3: The valid pairs are {(1,2),(1,3)}. """ import sys # Brute Force """ if __name__ == '__main__': input = sys.stdin.read() data = list(map(int, input.split())) T = data[0] idx = 1 while T > 0: N, M = data[idx: idx + 2] res = 0 for i in range(1, N): for j in range(i + 1, N + 1): if (M % i) % j == (M % j) % i: res += 1 print(res) T -= 1 idx += 2 # Time : 0.58s """ if __name__ == '__main__': T = int(input()) idx = 1 while T > 0: N, M = list(map(int, input().split())) res = 0 mod = dict() for a in range(2, N+1): mod_with_a = M % a res += mod.get(mod_with_a, 1) for b in range(mod_with_a, N+1, a): mod[b] = mod.get(b, 1) + 1 print(res) T -= 1 # Time : 4.92s

836b1d1ef0eb551fa9e79cfab6310f6344d73c36

py

Python

invana_bot/manifests/single.py

subhead/invana-bot

c60883986acd1baf279ccbe74dfe06435680fba9

2019-01-31T08:04:39.000Z

2021-12-20T15:55:27.000Z

invana_bot/manifests/single.py

subhead/invana-bot

c60883986acd1baf279ccbe74dfe06435680fba9

2019-02-13T04:59:38.000Z

2021-12-13T20:43:07.000Z

invana_bot/manifests/single.py

subhead/invana-bot

c60883986acd1baf279ccbe74dfe06435680fba9

2019-02-10T18:27:33.000Z

2019-07-12T17:52:36.000Z

import sys import os import yaml class SingleCrawlerManifestManager(object): """ """ required_files = ["spider_manifest.json", "spider_manifest.py"]

import sys import os import yaml class SingleCrawlerManifestManager(object): """ """ required_files = ["spider_manifest.json", "spider_manifest.py"] def __init__(self, config_path=None): print("Setting ETI path as: {}".format(config_path)) self.config_path = config_path def import_files(self): # print("self.manifest_path", self.config_path) self.spider_config = yaml.load(open("{}/spider_manifest.yml".format(self.config_path))) sys.path.append(self.config_path) import spider_transformations self.cti_transformations_module = spider_transformations # print("cti_manifest is {}".format(self.spider_config)) # print("cti_transformations_module is {}".format(self.cti_transformations_module)) def validate_cti_path_and_files(self): errors = [] try: files_in_path = os.listdir(self.config_path) except Exception as e: errors.append("No such path exist {}".format(self.config_path)) files_in_path = [] if errors == 0: for required_file in self.required_files: if required_file not in files_in_path: errors.append("{} file not in the path {}".format(required_file, self.config_path)) return errors def import_cti_transformations(self): for tranformation in self.spider_config.get("transformations", []): method_to_call = getattr(self.cti_transformations_module, tranformation.get("transformation_fn")) tranformation['transformation_fn'] = method_to_call def get_manifest(self): errors = self.validate_cti_path_and_files() if len(errors) > 0: return None, errors self.import_files() self.import_cti_transformations() return self.spider_config, errors

ba156beb04b99754b5ff29ec37e1bcc01509b22c

py

Python

swam/src/mavros_set_home.py

donghee/swam-indoor-flight

bad8adce428e0adacb0f4110ca5739f31a9a11f8

swam/src/mavros_set_home.py

donghee/swam-indoor-flight

bad8adce428e0adacb0f4110ca5739f31a9a11f8

swam/src/mavros_set_home.py

donghee/swam-indoor-flight

bad8adce428e0adacb0f4110ca5739f31a9a11f8

#!/usr/bin/env python ## # # Send SET_GPS_GLOBAL_ORIGIN and SET_HOME_POSITION messages # ## import rospy from pymavlink.dialects.v10 import ardupilotmega as MAV_APM from mavros.mavlink import convert_to_rosmsg from mavros_msgs.msg import Mavlink class fifo(object): """ A simple buffer """ def send_message(msg, mav, pub): """ Send a mavlink message """ msg.pack(mav) rosmsg = convert_to_rosmsg(msg) pub.publish(rosmsg) print("sent message %s" % msg) def set_global_origin(mav, pub, lat, lon, alt): """ Send a mavlink SET_GPS_GLOBAL_ORIGIN message, which allows us to use local position information without a GPS. """ #target_system = mav.srcSystem target_system = 0 # 0 --> broadcast to everyone lattitude = lat longitude = lon altitude = alt msg = MAV_APM.MAVLink_set_gps_global_origin_message( target_system, lattitude, longitude, altitude) send_message(msg, mav, pub) def set_home_position(mav, pub, lat, lon, alt, _x, _y, _z): """ Send a mavlink SET_HOME_POSITION message, which should allow us to use local position information without a GPS """ target_system = 0 # broadcast to everyone lattitude = lat longitude = lon altitude = alt x = _x y = _y z = _z q = [1, 0, 0, 0] # w x y z approach_x = 0 approach_y = 0 approach_z = 1 msg = MAV_APM.MAVLink_set_home_position_message( target_system, lattitude, longitude, altitude, x, y, z, q, approach_x, approach_y, approach_z) send_message(msg, mav, pub) if __name__=="__main__": rospy.init_node('set_home', anonymous=True) # Global position of the origin lat = 37.4933566 * 1e7 lon = 126.8339491 * 1e7 alt = 200 * 1e3 # x = 3.678 # y = -1.719 # z = 0 x = 0 y = 0 z = 0 set_home(lat, lon, alt, x, y, z)

#!/usr/bin/env python ## # # Send SET_GPS_GLOBAL_ORIGIN and SET_HOME_POSITION messages # ## import rospy from pymavlink.dialects.v10 import ardupilotmega as MAV_APM from mavros.mavlink import convert_to_rosmsg from mavros_msgs.msg import Mavlink class fifo(object): """ A simple buffer """ def __init__(self): self.buf = [] def write(self, data): self.buf += data return len(data) def read(self): return self.buf.pop(0) def send_message(msg, mav, pub): """ Send a mavlink message """ msg.pack(mav) rosmsg = convert_to_rosmsg(msg) pub.publish(rosmsg) print("sent message %s" % msg) def set_global_origin(mav, pub, lat, lon, alt): """ Send a mavlink SET_GPS_GLOBAL_ORIGIN message, which allows us to use local position information without a GPS. """ #target_system = mav.srcSystem target_system = 0 # 0 --> broadcast to everyone lattitude = lat longitude = lon altitude = alt msg = MAV_APM.MAVLink_set_gps_global_origin_message( target_system, lattitude, longitude, altitude) send_message(msg, mav, pub) def set_home_position(mav, pub, lat, lon, alt, _x, _y, _z): """ Send a mavlink SET_HOME_POSITION message, which should allow us to use local position information without a GPS """ target_system = 0 # broadcast to everyone lattitude = lat longitude = lon altitude = alt x = _x y = _y z = _z q = [1, 0, 0, 0] # w x y z approach_x = 0 approach_y = 0 approach_z = 1 msg = MAV_APM.MAVLink_set_home_position_message( target_system, lattitude, longitude, altitude, x, y, z, q, approach_x, approach_y, approach_z) send_message(msg, mav, pub) def set_home(lat, lon, alt, x, y, z): try: mavlink_pub = rospy.Publisher("/mavlink/to", Mavlink, queue_size=20) # Set up mavlink instance f = fifo() mav = MAV_APM.MAVLink(f, srcSystem=1, srcComponent=1) # wait to initialize while mavlink_pub.get_num_connections() <= 0: pass for _ in range(5): rospy.sleep(1) set_global_origin(mav, mavlink_pub, lat, lon, alt) set_home_position(mav, mavlink_pub, lat, lon, alt, x, y, z) except rospy.ROSInterruptException: pass if __name__=="__main__": rospy.init_node('set_home', anonymous=True) # Global position of the origin lat = 37.4933566 * 1e7 lon = 126.8339491 * 1e7 alt = 200 * 1e3 # x = 3.678 # y = -1.719 # z = 0 x = 0 y = 0 z = 0 set_home(lat, lon, alt, x, y, z)

c71a1bff4f3192e1d1ec8a670f9e040c334dc954

py

Python

tests/clims/api/serializers/models/test_process_definition.py

commonlims/commonlims

36a02ed244c7b59ee1f2523e64e4749e404ab0f7

2019-05-27T13:55:07.000Z

2021-03-30T07:05:09.000Z

tests/clims/api/serializers/models/test_process_definition.py

commonlims/commonlims

36a02ed244c7b59ee1f2523e64e4749e404ab0f7

2019-05-20T14:16:33.000Z

2021-01-19T09:25:15.000Z

tests/clims/api/serializers/models/test_process_definition.py

commonlims/commonlims

36a02ed244c7b59ee1f2523e64e4749e404ab0f7

2020-08-10T07:55:40.000Z

2020-08-10T07:55:40.000Z

from __future__ import absolute_import from tests.clims.models.test_substance import SubstanceTestCase from clims.plugins.demo.dnaseq.workflows.sequence import SequenceSimple from clims.api.serializers.models.process_definition import ProcessDefinitionSerializer expected_sequence_simple = { 'id': u'clims.plugins.demo.dnaseq.workflows.sequence.SequenceSimple', 'fields': [{ 'label': u'Comment', 'help': None, 'required': False, 'choices': [], 'type': u'textarea', 'name': u'comment' }, { 'label': u'Sample prep', 'help': u'The method used for preparing the sample', 'required': True, 'choices': ['microwave', 'mixer'], 'type': u'select', 'name': u'sample_prep' }, { 'label': u'Sequencer', 'help': u'Instrument where the sample will be sequenced', 'required': True, 'choices': ['iPhone', 'Android', 'Bang & Olufsen'], 'type': u'select', 'name': u'sequencer' }, { 'label': u'Sample type', 'help': u'The type of the sample', 'required': True, 'choices': ['DNA', 'RNA'], 'type': u'select', 'name': u'sample_type' }], 'presets': [{ 'variables': { 'sample_prep': 'microwave', 'sequencer': 'Android', 'sample_type': 'DNA' }, 'processDefinitionId': 'clims.plugins.demo.dnaseq.workflows.sequence.SequenceSimple', 'name': 'Android: DNA prepared with microwave' }, { 'variables': { 'sample_prep': 'mixer', 'sequencer': 'Android', 'sample_type': 'DNA' }, 'processDefinitionId': 'clims.plugins.demo.dnaseq.workflows.sequence.SequenceSimple', 'name': 'Android: DNA prepared with mixer' }, { 'variables': { 'sample_prep': 'microwave', 'sample_type': 'DNA', 'sequencer': 'iPhone' }, 'processDefinitionId': 'clims.plugins.demo.dnaseq.workflows.sequence.SequenceSimple', 'name': 'iPhone: DNA prepared with microwave' }, { 'variables': { 'sample_prep': 'microwave', 'sequencer': 'Android', 'sample_type': 'RNA' }, 'processDefinitionId': 'clims.plugins.demo.dnaseq.workflows.sequence.SequenceSimple', 'name': 'Android: RNA prepared with microwave' }, { 'variables': { 'sample_prep': 'microwave', 'sample_type': 'DNA', 'sequencer': 'Bang & Olufsen' }, 'processDefinitionId': 'clims.plugins.demo.dnaseq.workflows.sequence.SequenceSimple', 'name': 'Bang & Olufsen: DNA prepared with microwave' }, { 'variables': { 'sample_prep': 'mixer', 'sequencer': 'Android', 'sample_type': 'RNA' }, 'processDefinitionId': 'clims.plugins.demo.dnaseq.workflows.sequence.SequenceSimple', 'name': 'Android: RNA prepared with mixer' }] }

from __future__ import absolute_import from tests.clims.models.test_substance import SubstanceTestCase from clims.plugins.demo.dnaseq.workflows.sequence import SequenceSimple from clims.api.serializers.models.process_definition import ProcessDefinitionSerializer class ProcessDefinitionSerializerTest(SubstanceTestCase): def test_simple(self): process = SequenceSimple() serializer = ProcessDefinitionSerializer(process) assert serializer.data == expected_sequence_simple expected_sequence_simple = { 'id': u'clims.plugins.demo.dnaseq.workflows.sequence.SequenceSimple', 'fields': [{ 'label': u'Comment', 'help': None, 'required': False, 'choices': [], 'type': u'textarea', 'name': u'comment' }, { 'label': u'Sample prep', 'help': u'The method used for preparing the sample', 'required': True, 'choices': ['microwave', 'mixer'], 'type': u'select', 'name': u'sample_prep' }, { 'label': u'Sequencer', 'help': u'Instrument where the sample will be sequenced', 'required': True, 'choices': ['iPhone', 'Android', 'Bang & Olufsen'], 'type': u'select', 'name': u'sequencer' }, { 'label': u'Sample type', 'help': u'The type of the sample', 'required': True, 'choices': ['DNA', 'RNA'], 'type': u'select', 'name': u'sample_type' }], 'presets': [{ 'variables': { 'sample_prep': 'microwave', 'sequencer': 'Android', 'sample_type': 'DNA' }, 'processDefinitionId': 'clims.plugins.demo.dnaseq.workflows.sequence.SequenceSimple', 'name': 'Android: DNA prepared with microwave' }, { 'variables': { 'sample_prep': 'mixer', 'sequencer': 'Android', 'sample_type': 'DNA' }, 'processDefinitionId': 'clims.plugins.demo.dnaseq.workflows.sequence.SequenceSimple', 'name': 'Android: DNA prepared with mixer' }, { 'variables': { 'sample_prep': 'microwave', 'sample_type': 'DNA', 'sequencer': 'iPhone' }, 'processDefinitionId': 'clims.plugins.demo.dnaseq.workflows.sequence.SequenceSimple', 'name': 'iPhone: DNA prepared with microwave' }, { 'variables': { 'sample_prep': 'microwave', 'sequencer': 'Android', 'sample_type': 'RNA' }, 'processDefinitionId': 'clims.plugins.demo.dnaseq.workflows.sequence.SequenceSimple', 'name': 'Android: RNA prepared with microwave' }, { 'variables': { 'sample_prep': 'microwave', 'sample_type': 'DNA', 'sequencer': 'Bang & Olufsen' }, 'processDefinitionId': 'clims.plugins.demo.dnaseq.workflows.sequence.SequenceSimple', 'name': 'Bang & Olufsen: DNA prepared with microwave' }, { 'variables': { 'sample_prep': 'mixer', 'sequencer': 'Android', 'sample_type': 'RNA' }, 'processDefinitionId': 'clims.plugins.demo.dnaseq.workflows.sequence.SequenceSimple', 'name': 'Android: RNA prepared with mixer' }] }

6ce3cc168aa038a313b9bba66d4ce994e8318fc9

py

Python

app/__init__.py

x14119641/tracker

bd16897848c23c461d5d4b7a353eedd8f17367a6

app/__init__.py

x14119641/tracker

bd16897848c23c461d5d4b7a353eedd8f17367a6

app/__init__.py

x14119641/tracker

bd16897848c23c461d5d4b7a353eedd8f17367a6

from flask import Flask from config import Config from flask_sqlalchemy import SQLAlchemy from flask_migrate import Migrate from flask_login import LoginManager from logging.handlers import SMTPHandler,RotatingFileHandler from flask_mail import Mail, Message import logging,os, smtplib from threading import Thread app = Flask(__name__) app.config.from_object(Config) db = SQLAlchemy(app) migrate = Migrate(app, db) mail = Mail(app) login = LoginManager(app) login.login_view = 'login' from app import routes, models, errors class ThreadedSMTPHandler(SMTPHandler): """ Mimic SMTPHandler from logging module but seperate the actual emission (.emit) in another thread to avoid blocking the main process """ if not app.debug: if app.config['MAIL_SERVER']: auth = None if app.config['MAIL_USERNAME'] or app.config['MAIL_PASSWORD']: auth = (app.config['MAIL_USERNAME'], app.config['MAIL_PASSWORD']) secure = None if app.config['MAIL_USE_TLS']: secure = () mail_handler = ThreadedSMTPHandler( mailhost=(app.config['MAIL_SERVER'], app.config['MAIL_PORT']), # fromaddr='no-reply@' + app.config['MAIL_SERVER'], fromaddr=app.config['MAIL_USERNAME'], toaddrs=app.config['ADMINS'], subject='Tracker Failure', credentials=auth, secure=() ) mail_handler.setLevel(logging.ERROR) app.logger.addHandler(mail_handler) if not os.path.exists('logs'): os.mkdir('logs') file_handler = RotatingFileHandler('logs/tracker.log', maxBytes=10240, backupCount=10) file_handler.setFormatter(logging.Formatter( '%(asctime)s %(levelname)s: %(message)s [in %(pathname)s:%(lineno)d]')) file_handler.setLevel(logging.INFO) app.logger.addHandler(file_handler) app.logger.setLevel(logging.INFO) app.logger.info(f'{app.__repr__} - startup')

from flask import Flask from config import Config from flask_sqlalchemy import SQLAlchemy from flask_migrate import Migrate from flask_login import LoginManager from logging.handlers import SMTPHandler,RotatingFileHandler from flask_mail import Mail, Message import logging,os, smtplib from threading import Thread app = Flask(__name__) app.config.from_object(Config) db = SQLAlchemy(app) migrate = Migrate(app, db) mail = Mail(app) login = LoginManager(app) login.login_view = 'login' from app import routes, models, errors class ThreadedSMTPHandler(SMTPHandler): """ Mimic SMTPHandler from logging module but seperate the actual emission (.emit) in another thread to avoid blocking the main process """ def emit(self, record): #I am not sure of the best way to write the following line thread = Thread(target=super().emit, args=(record,)) #for Python2 : either modify super either : thread = Thread(target=logging.handlers.SMTPHandler.emit, args=(self, record)) thread.start() if not app.debug: if app.config['MAIL_SERVER']: auth = None if app.config['MAIL_USERNAME'] or app.config['MAIL_PASSWORD']: auth = (app.config['MAIL_USERNAME'], app.config['MAIL_PASSWORD']) secure = None if app.config['MAIL_USE_TLS']: secure = () mail_handler = ThreadedSMTPHandler( mailhost=(app.config['MAIL_SERVER'], app.config['MAIL_PORT']), # fromaddr='no-reply@' + app.config['MAIL_SERVER'], fromaddr=app.config['MAIL_USERNAME'], toaddrs=app.config['ADMINS'], subject='Tracker Failure', credentials=auth, secure=() ) mail_handler.setLevel(logging.ERROR) app.logger.addHandler(mail_handler) if not os.path.exists('logs'): os.mkdir('logs') file_handler = RotatingFileHandler('logs/tracker.log', maxBytes=10240, backupCount=10) file_handler.setFormatter(logging.Formatter( '%(asctime)s %(levelname)s: %(message)s [in %(pathname)s:%(lineno)d]')) file_handler.setLevel(logging.INFO) app.logger.addHandler(file_handler) app.logger.setLevel(logging.INFO) app.logger.info(f'{app.__repr__} - startup')

10521b97d7d1e032daf03879ad3e7c6638f79485

py

Python

Python Fundamentals/Regular Expressions/More Exercises/Task02_3.py

DonikaChervenkova/SoftUni

bff579c037ec48f39ed193b34bc3502a32e90732

2022-03-16T10:23:04.000Z

2022-03-16T10:23:04.000Z

Python Fundamentals/Regular Expressions/More Exercise/Task02_02.py

IvanTodorovBG/SoftUni

7b667f6905d9f695ab1484efbb02b6715f6d569e

Python Fundamentals/Regular Expressions/More Exercise/Task02_02.py

IvanTodorovBG/SoftUni

7b667f6905d9f695ab1484efbb02b6715f6d569e

2021-12-04T12:30:57.000Z

2021-12-04T12:30:57.000Z

import re data = input() total_income = 0 while data != "end of shift": pattern_customer = r"\%(?P<customer>[A-Z][a-z]+)\%" customer = re.finditer(pattern_customer, data) is_customer = bool([c.group(0) for c in customer]) pattern_product = r"\<(?P<product>[0-9a-zA-Z\_]+)\>" product = re.finditer(pattern_product, data) is_product = bool([p.group(0) for p in product]) pattern_count = r"\|(?P<count>[0-9]+)\|" count = re.finditer(pattern_count, data) is_count = bool([cnt.group(0) for cnt in count]) pattern_price = r"(?P<price>[0-9]+\.?[0-9]+)\$" price = re.finditer(pattern_price, data) is_price = bool([pr.group(0) for pr in price]) if is_customer and is_product and is_count and is_price: customer_dict = {} for c in re.finditer(pattern_customer, data): customer_dict = c.groupdict() product_dict = {} for p in re.finditer(pattern_product, data): product_dict = p.groupdict() count_dict = {} for cnt in re.finditer(pattern_count, data): count_dict = cnt.groupdict() price_dict = {} for pr in re.finditer(pattern_price, data): price_dict = pr.groupdict() total_product_price = int(count_dict['count']) * float(price_dict['price']) total_income += total_product_price print(f"{customer_dict['customer']}: {product_dict['product']} - {total_product_price:.2f}") data = input() print(f"Total income: {total_income:.2f}")

import re data = input() total_income = 0 while data != "end of shift": pattern_customer = r"\%(?P<customer>[A-Z][a-z]+)\%" customer = re.finditer(pattern_customer, data) is_customer = bool([c.group(0) for c in customer]) pattern_product = r"\<(?P<product>[0-9a-zA-Z\_]+)\>" product = re.finditer(pattern_product, data) is_product = bool([p.group(0) for p in product]) pattern_count = r"\|(?P<count>[0-9]+)\|" count = re.finditer(pattern_count, data) is_count = bool([cnt.group(0) for cnt in count]) pattern_price = r"(?P<price>[0-9]+\.?[0-9]+)\$" price = re.finditer(pattern_price, data) is_price = bool([pr.group(0) for pr in price]) if is_customer and is_product and is_count and is_price: customer_dict = {} for c in re.finditer(pattern_customer, data): customer_dict = c.groupdict() product_dict = {} for p in re.finditer(pattern_product, data): product_dict = p.groupdict() count_dict = {} for cnt in re.finditer(pattern_count, data): count_dict = cnt.groupdict() price_dict = {} for pr in re.finditer(pattern_price, data): price_dict = pr.groupdict() total_product_price = int(count_dict['count']) * float(price_dict['price']) total_income += total_product_price print(f"{customer_dict['customer']}: {product_dict['product']} - {total_product_price:.2f}") data = input() print(f"Total income: {total_income:.2f}")

3655fbbcf0aa34a78e5e06dada0505806b450706

py

Python

drawShapes.py

Shubhra1906/OpenCV

dc2b3291562bab54506380c52ac74ecb06adb8d3

drawShapes.py

Shubhra1906/OpenCV

dc2b3291562bab54506380c52ac74ecb06adb8d3

drawShapes.py

Shubhra1906/OpenCV

dc2b3291562bab54506380c52ac74ecb06adb8d3

import cv2 as cv import numpy as np from matplotlib import pyplot as plt if __name__ == '__main__': drawShapes()

import cv2 as cv import numpy as np from matplotlib import pyplot as plt def drawShapes(): image = cv.imread('./img/sea.jpg') # img = np.zeros([600, 800, 3], np.uint8) # line image = cv.line(image, (40, 100), (255, 255), (0, 0, 255), 5) # Arrowed Line image = cv.arrowedLine(image, (0, 255), (500, 350), (0, 0, 255), 5) # Rectangle image = cv.rectangle(image, (400, 250), (750, 400), (0, 0, 255), 5) # Circle image = cv.circle(image, (550, 350), 150, (0, 0, 255), 5) # Text font = cv.FONT_HERSHEY_COMPLEX image = cv.putText(image, "OpenCV", (10, 500), font, 4, (0, 255, 0), 10, cv.LINE_AA) cv.imshow('Shapes', image) cv.waitKey() cv.destroyAllWindows() if __name__ == '__main__': drawShapes()

57b3acfb96da7afb637bdd2beedbb9da791d379a

py

Python

tests/test_util.py

willkg/francis

c727fa5eaa77662277f95fbf000f7afc71cbbed4

2018-07-26T07:18:54.000Z

2018-09-06T16:47:18.000Z

tests/test_util.py

willkg/francis

c727fa5eaa77662277f95fbf000f7afc71cbbed4

2016-07-22T15:09:18.000Z

2016-07-22T23:25:39.000Z

tests/test_util.py

willkg/francis

c727fa5eaa77662277f95fbf000f7afc71cbbed4

import datetime import pytest from francis.util import ( parse_date, parse_rc, prettytable, ) # FIXME: test multiple > 40 columns

import datetime import pytest from francis.util import ( parse_date, parse_rc, prettytable, ) class Test_parse_rc: def test_empty(self): assert parse_rc('') == {} def test_key_val(self): assert parse_rc('foo=bar') == {'foo': 'bar'} assert parse_rc('\n foo = bar ') == {'foo': 'bar'} def test_uppercase_key(self): assert parse_rc('FOO=bar') == {'foo': 'bar'} def test_multiple(self): assert parse_rc('foo=bar\nbaz=bat') == {'foo': 'bar', 'baz': 'bat'} def test_comments(self): assert parse_rc('# foo=bar') == {} assert parse_rc(' # foo=bar') == {} class Test_prettytable: def test_empty(self): assert prettytable(0, []) == '' assert prettytable(100, []) == '' def test_single_row(self): assert ( prettytable(100, [('a', 'b', 'c', 'd')]) == ( 'a b c d\n' '--- --- --- ---' ) ) def test_truncation(self): # Total width is under 50, so no changes get made assert ( prettytable(50, [ ('1', ('a' * 39) + 'b') ]) == '1 aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaab\n' '--- ------------------------------------------' ) # Total width is > 50, so all the c are truncated assert ( prettytable(50, [ ('1', ('a' * 41) + 'b' + 'cccccc') ]) == '1 aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaab*\n' '--- ---------------------------------------------' ) def test_numbers(self): # Total width is > 50, so all the c are truncated assert ( prettytable(50, [ (1, ('a' * 41) + 'b' + 'cccccc') ]) == '1 aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaab*\n' '--- ---------------------------------------------' ) # FIXME: test multiple > 40 columns class Test_parse_date: @pytest.mark.parametrize('text', [ '2015-05-05', '2016-01-01' ]) def test_datestamps(self, text): assert parse_date(text).strftime('%Y-%m-%d') == text @pytest.mark.parametrize('text,expected', [ ('today', '2016-01-01'), ('tod', '2016-01-01'), ('tomorrow', '2016-01-02'), ('tom', '2016-01-02'), ]) def test_today_tomorrow(self, text, expected): # This grounds relative dates to January 1st which was a Friday start = datetime.datetime(2016, 1, 1, 0, 0, 0) assert parse_date(text, relative_to=start).strftime('%Y-%m-%d') == expected @pytest.mark.parametrize('text,expected', [ ('friday', '2016-01-01'), ('saturday', '2016-01-02'), ('sunday', '2016-01-03'), ('monday', '2016-01-04'), ('tuesday', '2016-01-05'), ('wednesday', '2016-01-06'), ('thursday', '2016-01-07'), ]) def test_day_of_week(self, text, expected): # This grounds relative dates to January 1st which was a Friday start = datetime.datetime(2016, 1, 1, 0, 0, 0) assert parse_date(text, relative_to=start).strftime('%Y-%m-%d') == expected def test_value_error(self): with pytest.raises(ValueError): parse_date('2016-06-40')

cd5c17b028d7e8b3a56676c62432707a97d11c80

py

Python

ML/newton.py

siriusctrl/UniPublic

9df7f8bb9d1209de2af8ac4b5f57ada38587ad50

2021-03-14T14:19:10.000Z

2021-07-13T12:35:26.000Z

ML/newton.py

Sirius-ctrl/UniPublic

9df7f8bb9d1209de2af8ac4b5f57ada38587ad50

2018-05-29T04:28:20.000Z

2018-06-09T04:55:19.000Z

ML/newton.py

Sirius-ctrl/UniPublic

9df7f8bb9d1209de2af8ac4b5f57ada38587ad50

2019-01-10T10:30:33.000Z

2019-05-30T09:33:20.000Z

import math

import math def newton(n, e): theta = n/2 error = 99999 count = 0 while(error > e): theta -= (theta**2 - n) / (2*theta) error = theta**2 - n count += 1 print("guess", count, "times") return theta

7eedddac8d7d9c1d55b9ac6e1b3b84147bbbd112

py

Python

storedisagg/example/__init__.py

mcsoini/storedisagg

3fa360234995cdee897122ea8d85bc8658229053

storedisagg/example/__init__.py

mcsoini/storedisagg

3fa360234995cdee897122ea8d85bc8658229053

storedisagg/example/__init__.py

mcsoini/storedisagg

3fa360234995cdee897122ea8d85bc8658229053

__all__ = ['example_data']

__all__ = ['example_data']

a39283f2f128ca0dec249a7d9063c5194c19d4e4

py

Python

loanPrediction3/src_old/ensemble.py

MayukhSobo/AnalyticsVidya_Contests

a21079f8d217a35e88e72e88233c7ef0b8dd348b

2017-09-22T08:12:04.000Z

2021-10-30T14:30:44.000Z

loanPrediction3/src_old/ensemble.py

Manish041997/AnalyticsVidya_Contests

a21079f8d217a35e88e72e88233c7ef0b8dd348b

loanPrediction3/src_old/ensemble.py

Manish041997/AnalyticsVidya_Contests

a21079f8d217a35e88e72e88233c7ef0b8dd348b

2017-10-03T12:09:11.000Z

2019-08-03T14:05:47.000Z

from xgboost.sklearn import XGBClassifier # from xgb_model import xgb_model_fit # from sklearn.grid_search import GridSearchCV from sklearn.ensemble import VotingClassifier from sklearn.metrics import accuracy_score import pandas as pd from sklearn.model_selection import train_test_split import numpy as np from sklearn.ensemble import AdaBoostClassifier from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import RandomForestClassifier print('\n\n') dtrain = pd.read_csv('../data/cleaned_train_v2.csv').iloc[:, 1:] dtest = pd.read_csv('../data/cleaned_test_v2.csv').iloc[:, 1:] # print(dtrain) loan_ids = dtest.Loan_ID dtest = dtest.iloc[:, 1:] features = np.array(dtrain.iloc[:, 0:-1]) labels = np.array(dtrain.Loan_Status) test = np.array(dtest) # print(features.shape) # Classifier 1 - XGBoost clf1 = XGBClassifier(learning_rate=0.1, n_estimators=1000, max_depth=3, min_child_weight=1, gamma=0.2, subsample=0.8, colsample_bytree=0.8, objective='binary:logistic', nthread=4, scale_pos_weight=1, reg_alpha=69, seed=42) # Classifier 2 - Random Forest clf2 = RandomForestClassifier(bootstrap=True, criterion='gini', max_depth=3, oob_score=True, max_features=3, min_samples_leaf=10, min_samples_split=10, random_state=42, n_jobs=-1) # X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.40, random_state=42) tree = DecisionTreeClassifier(criterion='gini', max_depth=3) clf3 = AdaBoostClassifier(base_estimator=tree, n_estimators=3000, learning_rate=0.03, random_state=42) eclf = VotingClassifier(estimators=[ ('forest', clf2), ('xgboost', clf1), ('adaboost', clf3)], voting='hard') eclf.fit(features, labels) pred = eclf.predict(test) # print(accuracy_score(y_test, pred)) # print("Random Forest Classifier.....") # clf2.fit(X_train, y_train) # pred1 = clf2.predict(X_test) # print(accuracy_score(y_test, pred1)) # print('\nXGBoost Classifier......') # clf1.fit(X_train, y_train) # pred2 = clf1.predict(X_test) # print(accuracy_score(y_test, pred2)) # clf1.fit(features, labels, eval_metric='error') # pred = clf1.predict(test) # print(pred) submission = pd.DataFrame({'Loan_ID': loan_ids, 'Loan_Status': pred}) submission['Loan_Status'] = submission.Loan_Status.map({0: 'N', 1: 'Y'}) submission.to_csv('submission2.csv', index=False) # xgb_model_fit(clf1, features, labels, folds=3) # Classifier 2 - Random Forest # Tuning ```max_depth``` and ```min_child_weight``` # param_dist = { # "max_depth": list(range(1, 8, 2)), # "max_features": list(range(1, 10, 2)), # "min_samples_split": list(range(2, 11, 2)), # "min_samples_leaf": list(range(2, 11, 2)), # "bootstrap": [True, False], # "criterion": ["gini", "entropy"] # } # gs1 = GridSearchCV(estimator=clf2, # param_grid=param_dist, # scoring='accuracy', # n_jobs=-1, # iid=False, # cv=7, # verbose=5) # gs1.fit(features, labels) # # bootstrap=True, criterion=gini, max_depth=3, max_features=3, min_samples_leaf=10, min_samples_split=10, score=0.870588 # # # print(gs1.grid_scores_) # print(gs1.best_params_) # print(gs1.best_score_)

from xgboost.sklearn import XGBClassifier # from xgb_model import xgb_model_fit # from sklearn.grid_search import GridSearchCV from sklearn.ensemble import VotingClassifier from sklearn.metrics import accuracy_score import pandas as pd from sklearn.model_selection import train_test_split import numpy as np from sklearn.ensemble import AdaBoostClassifier from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import RandomForestClassifier print('\n\n') dtrain = pd.read_csv('../data/cleaned_train_v2.csv').iloc[:, 1:] dtest = pd.read_csv('../data/cleaned_test_v2.csv').iloc[:, 1:] # print(dtrain) loan_ids = dtest.Loan_ID dtest = dtest.iloc[:, 1:] features = np.array(dtrain.iloc[:, 0:-1]) labels = np.array(dtrain.Loan_Status) test = np.array(dtest) # print(features.shape) # Classifier 1 - XGBoost clf1 = XGBClassifier(learning_rate=0.1, n_estimators=1000, max_depth=3, min_child_weight=1, gamma=0.2, subsample=0.8, colsample_bytree=0.8, objective='binary:logistic', nthread=4, scale_pos_weight=1, reg_alpha=69, seed=42) # Classifier 2 - Random Forest clf2 = RandomForestClassifier(bootstrap=True, criterion='gini', max_depth=3, oob_score=True, max_features=3, min_samples_leaf=10, min_samples_split=10, random_state=42, n_jobs=-1) # X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.40, random_state=42) tree = DecisionTreeClassifier(criterion='gini', max_depth=3) clf3 = AdaBoostClassifier(base_estimator=tree, n_estimators=3000, learning_rate=0.03, random_state=42) eclf = VotingClassifier(estimators=[ ('forest', clf2), ('xgboost', clf1), ('adaboost', clf3)], voting='hard') eclf.fit(features, labels) pred = eclf.predict(test) # print(accuracy_score(y_test, pred)) # print("Random Forest Classifier.....") # clf2.fit(X_train, y_train) # pred1 = clf2.predict(X_test) # print(accuracy_score(y_test, pred1)) # print('\nXGBoost Classifier......') # clf1.fit(X_train, y_train) # pred2 = clf1.predict(X_test) # print(accuracy_score(y_test, pred2)) # clf1.fit(features, labels, eval_metric='error') # pred = clf1.predict(test) # print(pred) submission = pd.DataFrame({'Loan_ID': loan_ids, 'Loan_Status': pred}) submission['Loan_Status'] = submission.Loan_Status.map({0: 'N', 1: 'Y'}) submission.to_csv('submission2.csv', index=False) # xgb_model_fit(clf1, features, labels, folds=3) # Classifier 2 - Random Forest # Tuning ```max_depth``` and ```min_child_weight``` # param_dist = { # "max_depth": list(range(1, 8, 2)), # "max_features": list(range(1, 10, 2)), # "min_samples_split": list(range(2, 11, 2)), # "min_samples_leaf": list(range(2, 11, 2)), # "bootstrap": [True, False], # "criterion": ["gini", "entropy"] # } # gs1 = GridSearchCV(estimator=clf2, # param_grid=param_dist, # scoring='accuracy', # n_jobs=-1, # iid=False, # cv=7, # verbose=5) # gs1.fit(features, labels) # # bootstrap=True, criterion=gini, max_depth=3, max_features=3, min_samples_leaf=10, min_samples_split=10, score=0.870588 # # # print(gs1.grid_scores_) # print(gs1.best_params_) # print(gs1.best_score_)

442885ee6cfa521582e9f66dbef3c77656fe3fce

py

Python

data/format_wikibio.py

KaijuML/dtt-multi-branch

a49850a95034e58d387b9d48c647cfc2b83c45b5

2021-02-25T08:19:55.000Z

2022-03-12T06:25:36.000Z

data/format_wikibio.py

KaijuML/dtt-multi-branch

a49850a95034e58d387b9d48c647cfc2b83c45b5

2021-05-20T19:11:58.000Z

2021-07-14T07:46:33.000Z

data/format_wikibio.py

KaijuML/dtt-multi-branch

a49850a95034e58d387b9d48c647cfc2b83c45b5

from utils import nwise import pkg_resources import re, time, os import itertools import argparse import numpy import json DELIM = u"￨" # delim used by onmt def split_infobox(dataset_folder, destination_folder): """ extract box content, field type and position information from infoboxes from original_data *.box.val is the box content (token) *.box.lab is the field type for each token *.box.pos is the position counted from the begining of a field """ bwfile = [os.path.join(destination_folder, 'processed_data', setname, f"{setname}.box.val") for setname in ['train', 'valid', 'test']] bffile = [os.path.join(destination_folder, 'processed_data', setname, f"{setname}.box.lab") for setname in ['train', 'valid', 'test']] bpfile = [os.path.join(destination_folder, 'processed_data', setname, f"{setname}.box.pos") for setname in ['train', 'valid', 'test']] mixb_word, mixb_label, mixb_pos = [], [], [] for setname in ['train', 'valid', 'test']: fboxes = os.path.join(dataset_folder, 'raw', setname, f"{setname}.box") with open(fboxes, mode="r", encoding="utf8") as f: box = [line.strip() for line in f if line.strip()] box_word, box_label, box_pos = [], [], [] for ib in box: item = ib.split('\t') box_single_word, box_single_label, box_single_pos = [], [], [] for it in item: if len(it.split(':')) > 2: continue # print it prefix, word = it.split(':') if '<none>' in word or word.strip() == '' or prefix.strip() == '': continue new_label = re.sub("_[1-9]\d*$", "", prefix) if new_label.strip() == "": continue box_single_word.append(word) box_single_label.append(new_label) if re.search("_[1-9]\d*$", prefix): field_id = int(prefix.split('_')[-1]) box_single_pos.append(field_id if field_id <= 30 else 30) else: box_single_pos.append(1) box_word.append(box_single_word) box_label.append(box_single_label) box_pos.append(box_single_pos) mixb_word.append(box_word) mixb_label.append(box_label) mixb_pos.append(box_pos) print(f'{setname} done') for k, m in enumerate(mixb_word): with open(bwfile[k], "w+") as h: for items in m: for sens in items: h.write(str(sens) + " ") h.write('\n') for k, m in enumerate(mixb_label): with open(bffile[k], "w+") as h: for items in m: for sens in items: h.write(str(sens) + " ") h.write('\n') for k, m in enumerate(mixb_pos): with open(bpfile[k], "w+") as h: for items in m: for sens in items: h.write(str(sens) + " ") h.write('\n') def create_tables(folder): """Here we create the tables.jl files used in PARENT metric We could optimize the code so that step is done in create_input but it's easier and more convienient to just add it there. """ for setname in ['train', 'valid', 'test']: input_filename = os.path.join(folder, 'full', f"{setname}_input.txt") with open(input_filename, mode="r", encoding="utf8") as f: # each line is a table. Each token is a value in the table. # We take the value/label of the token and discard the pos # given that they are written in the right order allvals = list() alllabs = list() for line in f: vals = list() labs = list() for token in line.strip().split(): val, lab, _, __ = token.split(DELIM) vals.append(val) labs.append(lab) allvals.append(vals) alllabs.append(labs) tables = list() for idx, (vals, labs) in enumerate(zip(allvals, alllabs)): table = list() for key, group in itertools.groupby(labs): size = len([_ for _ in group]) vvals, vals = vals[:size], vals[size:] table.append((key, vvals)) assert len(vals) == 0 # we exhausted all tokens tables.append(table) output_filename = os.path.join(folder, 'full', f"{setname}_tables.jl") with open(output_filename, mode="w", encoding="utf8") as f: for table in tables: f.write(json.dumps(table) + '\n') def preprocess(dataset_folder, destination_folder, args): """ We use a triple <f, p+, p-> to represent the field information of a token in the specific field. p+&p- are the position of the token in that field counted from the begining and the end of the field. For example, for a field (birthname, Jurgis Mikelatitis) in an infobox, we represent the field as (Jurgis, <birthname, 1, 2>) & (Mikelatitis, <birthname, 2, 1>) """ print("extracting token, field type and position info from original data ...") time_start = time.time() split_infobox(dataset_folder, destination_folder) reverse_pos(destination_folder) duration = time.time() - time_start print(f"extract finished in {duration:.3f} seconds") print("merging everything into single input file ...") time_start = time.time() create_input(destination_folder) duration = time.time() - time_start print(f"merge finished in {duration:.3f} seconds") print("extracting first sentences from original data ...") time_start = time.time() extract_sentences(dataset_folder, destination_folder, args.first_sentence) duration = time.time() - time_start print(f"extract finished in {duration:.3f} seconds") print("formatting input in human readable format ...") time_start = time.time() create_tables(destination_folder) duration = time.time() - time_start print(f"formatting finished in {duration:.3f} seconds") if __name__ == '__main__': dataset_folder = pkg_resources.resource_filename(__name__, 'wikibio') parser = argparse.ArgumentParser() group = parser.add_argument_group('Destination path') group.add_argument('--dest', '-d', dest='dest', default=dataset_folder, help='Folder where to store the resulting files') parser.add_argument('--first_sentence', action='store_true', help="Activate to keep only the first sentence") main(parser.parse_args())

from utils import nwise import pkg_resources import re, time, os import itertools import argparse import numpy import json DELIM = u"￨" # delim used by onmt def split_infobox(dataset_folder, destination_folder): """ extract box content, field type and position information from infoboxes from original_data *.box.val is the box content (token) *.box.lab is the field type for each token *.box.pos is the position counted from the begining of a field """ bwfile = [os.path.join(destination_folder, 'processed_data', setname, f"{setname}.box.val") for setname in ['train', 'valid', 'test']] bffile = [os.path.join(destination_folder, 'processed_data', setname, f"{setname}.box.lab") for setname in ['train', 'valid', 'test']] bpfile = [os.path.join(destination_folder, 'processed_data', setname, f"{setname}.box.pos") for setname in ['train', 'valid', 'test']] mixb_word, mixb_label, mixb_pos = [], [], [] for setname in ['train', 'valid', 'test']: fboxes = os.path.join(dataset_folder, 'raw', setname, f"{setname}.box") with open(fboxes, mode="r", encoding="utf8") as f: box = [line.strip() for line in f if line.strip()] box_word, box_label, box_pos = [], [], [] for ib in box: item = ib.split('\t') box_single_word, box_single_label, box_single_pos = [], [], [] for it in item: if len(it.split(':')) > 2: continue # print it prefix, word = it.split(':') if '<none>' in word or word.strip() == '' or prefix.strip() == '': continue new_label = re.sub("_[1-9]\d*$", "", prefix) if new_label.strip() == "": continue box_single_word.append(word) box_single_label.append(new_label) if re.search("_[1-9]\d*$", prefix): field_id = int(prefix.split('_')[-1]) box_single_pos.append(field_id if field_id <= 30 else 30) else: box_single_pos.append(1) box_word.append(box_single_word) box_label.append(box_single_label) box_pos.append(box_single_pos) mixb_word.append(box_word) mixb_label.append(box_label) mixb_pos.append(box_pos) print(f'{setname} done') for k, m in enumerate(mixb_word): with open(bwfile[k], "w+") as h: for items in m: for sens in items: h.write(str(sens) + " ") h.write('\n') for k, m in enumerate(mixb_label): with open(bffile[k], "w+") as h: for items in m: for sens in items: h.write(str(sens) + " ") h.write('\n') for k, m in enumerate(mixb_pos): with open(bpfile[k], "w+") as h: for items in m: for sens in items: h.write(str(sens) + " ") h.write('\n') def reverse_pos(folder): # get the position counted from the end of a field bpfile = [os.path.join(folder, 'processed_data', setname, f"{setname}.box.pos") for setname in ['train', 'valid', 'test']] bwfile = [os.path.join(folder, 'processed_data', setname, f"{setname}.box.rpos") for setname in ['train', 'valid', 'test']] for k, pos in enumerate(bpfile): box = open(pos, "r").read().strip().split('\n') reverse_pos = [] for bb in box: pos = bb.split() tmp_pos = [] single_pos = [] for p in pos: if int(p) == 1 and len(tmp_pos) != 0: single_pos.extend(tmp_pos[::-1]) tmp_pos = [] tmp_pos.append(p) single_pos.extend(tmp_pos[::-1]) reverse_pos.append(single_pos) with open(bwfile[k], 'w+') as bw: for item in reverse_pos: bw.write(" ".join(item) + '\n') def create_input(folder): for setname in ["train", "valid", "test"]: valfilename = os.path.join(folder, 'processed_data', setname, f"{setname}.box.val") labfilename = os.path.join(folder, 'processed_data', setname, f"{setname}.box.lab") posfilename = os.path.join(folder, 'processed_data', setname, f"{setname}.box.pos") rposfilename = os.path.join(folder, 'processed_data', setname, f"{setname}.box.rpos") with open(valfilename, mode='r', encoding='utf8') as valfile: vals = [line.strip() for line in valfile if line.strip()] with open(labfilename, mode='r', encoding='utf8') as labfile: labs = [line.strip() for line in labfile if line.strip()] with open(posfilename, mode='r', encoding='utf8') as posfile: poss = [line.strip() for line in posfile if line.strip()] with open(rposfilename, mode='r', encoding='utf8') as rposfile: rposs = [line.strip() for line in rposfile if line.strip()] assert len(vals) == len(labs) == len(poss) == len(rposs) input = list() for idx, (val, lab, pos, rpos) in enumerate(zip(vals, labs, poss, rposs)): vval = val.strip().split(' ') llab = lab.strip().split(' ') ppos = pos.strip().split(' ') rrpos = rpos.strip().split(' ') if not len(vval) == len(llab) == len(ppos) == len(rrpos): print(f"error at step {idx}:", len(vval), len(llab), len(ppos), len(rrpos)) raise RuntimeError input.append( ' '.join([re.sub('\s', '~', DELIM.join(tup)) for tup in zip(vval, llab, ppos, rrpos)]) ) input_filename = os.path.join(folder, 'full', f"{setname}_input.txt") with open(input_filename, mode="w", encoding="utf8") as f: for i in input: f.write(i + "\n") print(f'{setname} done.') def extract_sentences(dataset_folder, destination_folder, only_first=True): for setname in ['train', 'valid', 'test']: inputnb_filename = os.path.join(dataset_folder, 'raw', setname, f"{setname}.nb") inputsent_filename = os.path.join(dataset_folder, 'raw', setname, f"{setname}.sent") output_filename = os.path.join(destination_folder, 'full', f"{setname}_output.txt") nb = [0] with open(inputnb_filename, encoding='utf8', mode='r') as f: # Here we get the indices of the first sentence for each instance # The file .sent contains one sentence per line but instances have # more than one sentence each. (number is variable) for idx, line in enumerate(f): nb += [int(line.strip())] indices = numpy.cumsum(nb[:-1]) sentences = list() with open(inputsent_filename, encoding='utf8', mode='r') as f: for idx, line in enumerate(f): sentences += [line.strip()] if only_first: with open(output_filename, mode='w', encoding='utf8') as f: for idx in indices: f.write(sentences[idx] + '\n') else: with open(output_filename, mode='w', encoding='utf8') as f: for start, end in nwise(indices, n=2): f.write(' '.join(sentences[start:end]) + '\n') def create_tables(folder): """Here we create the tables.jl files used in PARENT metric We could optimize the code so that step is done in create_input but it's easier and more convienient to just add it there. """ for setname in ['train', 'valid', 'test']: input_filename = os.path.join(folder, 'full', f"{setname}_input.txt") with open(input_filename, mode="r", encoding="utf8") as f: # each line is a table. Each token is a value in the table. # We take the value/label of the token and discard the pos # given that they are written in the right order allvals = list() alllabs = list() for line in f: vals = list() labs = list() for token in line.strip().split(): val, lab, _, __ = token.split(DELIM) vals.append(val) labs.append(lab) allvals.append(vals) alllabs.append(labs) tables = list() for idx, (vals, labs) in enumerate(zip(allvals, alllabs)): table = list() for key, group in itertools.groupby(labs): size = len([_ for _ in group]) vvals, vals = vals[:size], vals[size:] table.append((key, vvals)) assert len(vals) == 0 # we exhausted all tokens tables.append(table) output_filename = os.path.join(folder, 'full', f"{setname}_tables.jl") with open(output_filename, mode="w", encoding="utf8") as f: for table in tables: f.write(json.dumps(table) + '\n') def preprocess(dataset_folder, destination_folder, args): """ We use a triple <f, p+, p-> to represent the field information of a token in the specific field. p+&p- are the position of the token in that field counted from the begining and the end of the field. For example, for a field (birthname, Jurgis Mikelatitis) in an infobox, we represent the field as (Jurgis, <birthname, 1, 2>) & (Mikelatitis, <birthname, 2, 1>) """ print("extracting token, field type and position info from original data ...") time_start = time.time() split_infobox(dataset_folder, destination_folder) reverse_pos(destination_folder) duration = time.time() - time_start print(f"extract finished in {duration:.3f} seconds") print("merging everything into single input file ...") time_start = time.time() create_input(destination_folder) duration = time.time() - time_start print(f"merge finished in {duration:.3f} seconds") print("extracting first sentences from original data ...") time_start = time.time() extract_sentences(dataset_folder, destination_folder, args.first_sentence) duration = time.time() - time_start print(f"extract finished in {duration:.3f} seconds") print("formatting input in human readable format ...") time_start = time.time() create_tables(destination_folder) duration = time.time() - time_start print(f"formatting finished in {duration:.3f} seconds") def make_dirs(folder): if not os.path.exists(folder): os.mkdir(folder) os.mkdir(os.path.join(folder, 'full')) os.mkdir(os.path.join(folder, "processed_data/")) os.mkdir(os.path.join(folder, "processed_data/train/")) os.mkdir(os.path.join(folder, "processed_data/test/")) os.mkdir(os.path.join(folder, "processed_data/valid/")) def main(args): make_dirs(args.dest) preprocess(dataset_folder, args.dest, args) if __name__ == '__main__': dataset_folder = pkg_resources.resource_filename(__name__, 'wikibio') parser = argparse.ArgumentParser() group = parser.add_argument_group('Destination path') group.add_argument('--dest', '-d', dest='dest', default=dataset_folder, help='Folder where to store the resulting files') parser.add_argument('--first_sentence', action='store_true', help="Activate to keep only the first sentence") main(parser.parse_args())

26cc0c3344909259086172abac7c8974633f63f8

py

Python

recalibration.py

DanielAndreasen/pymoog

1d4042705c5180a6e609d08e7455d3d893e86392

recalibration.py

DanielAndreasen/pymoog

1d4042705c5180a6e609d08e7455d3d893e86392

2015-01-31T15:36:38.000Z

2015-07-06T22:11:42.000Z

recalibration.py

DanielAndreasen/pymoog

1d4042705c5180a6e609d08e7455d3d893e86392

#!/usr/bin/env python # -*- coding: utf8 -*- # My imports from __future__ import division import numpy as np import pandas as pd import argparse from utils import _update_par as updateBatch from utils import _run_moog as runMoog from utils import Readmoog from interpolation import interpolator import os def solar_abundance(atom): '''Give atomic number and return solar abundance from Asplund et al. 2009 Input ----- atom : int The atomic number Output ------ abundance : float The solar abundance of the atom ''' if not isinstance(atom, int): raise ValueError('Atomic number need to be an integer') solar = [12.00, 10.93, 1.05, 1.38, 2.70, 8.43, 7.83, 8.69, 4.56, 7.93, 6.24, 7.60, 6.45, 7.51, 5.41, 7.12, 5.50, 6.40, 5.03, 6.34, 3.15, 4.95, 3.93, 5.64, 5.43, 7.47, 4.99, 6.22, 4.19, 4.56, 3.04, 3.65, 2.30, 3.34, 2.54, 3.25, 2.52, 2.87, 2.21, 2.58, 1.46, 1.88, -5.00, 1.75, 0.91, 1.57, 0.94, 1.71, 0.80, 2.04, 1.01, 2.18, 1.55, 2.24, 1.08, 2.18, 1.10, 1.58, 0.72, 1.42, -5.00, 0.96, 0.52, 1.07, 0.30, 1.10, 0.48, 0.92, 0.10, 0.84, 0.10, 0.85, -0.12, 0.85, 0.26, 1.40, 1.38, 1.62, 0.92, 1.17, 0.90, 1.75, 0.65, -5.00, -5.00, -5.00, -5.00, -5.00, -5.00, 0.02, -5.00, -0.54, -5.00, -5.00, -5.00] return solar[atom-1] def recalSingleLine(line, params=None, version=2014, maxiter=40, driver='abfind'): '''Recalibrate a single line and return the new loggf Inputs ------ line : list The line containing (wavelength, element, EP, loggf, EW) in that order params : list/tuple The parameters (Teff, logg, [Fe/H], vt) version : int The version of MOOG driver : str The MOOG driver to use (abfind or ewfind) Output ------ loggf : float The new recalibrated loggf ''' ewdriver = True if driver == 'ewfind' else False fmt = ('%9.3f', '%10.1f', '%9.2f', '%9.3f', '%28.1f') header = 'Wavelength ele EP loggf EW' np.savetxt('temporary.moog', line[:, np.newaxis].T, fmt=fmt, header=header) loggf_old = line[3] a, b = loggf_old-5, loggf_old+5 # extreme values of loggf c = (a+b)/2 for _ in range(maxiter): if c == 0: # Don't evaluate at loggf = 0 c += (abs(a) + abs(b)) / 10 fa = moogAbund(a, ewdriver=ewdriver) fc = moogAbund(c, ewdriver=ewdriver) if fc == 0: return c elif fa*fc < 0: b = c else: a = c c = (a+b)/2 return c if __name__ == '__main__': # pragma: no cover args = _parser() fname = args.input fout1 = 'rawLinelist/%s' % args.output fout2 = 'linelist/%s' % args.output.replace('.ares', '.moog') lines = pd.read_csv(fname, skiprows=2, delimiter=r'\s+', names=['WL', 'num', 'EP', 'loggf', 'ele', 'EW']) if args.parameters is None: params = [5777, 4.44, 0.00, 1.00] else: params = map(float, args.parameters) params[0] = int(params[0]) interpolator(params=params, atmtype=args.model, save=True) cols = ['WL', 'num', 'EP', 'loggf', 'EW'] fmt2 = ('%9.3f', '%10.1f', '%9.2f', '%9.3f', '%28.1f') header1 = 'WL num E.P. loggf ele EWsun\n' header1 += '------- ---- ---- ------ ---- -----' header2 = 'Wavelength ele EP loggf EW' x = lines[cols].values[0][:, np.newaxis].T np.savetxt('temporary.moog', x, fmt=fmt2, header=header2) options = {'driver': args.driver, 'damping': args.damping} updateBatch(line_list='temporary.moog', **options) newloggf = np.zeros(lines.shape[0]) for i, line in enumerate(lines[cols].values): print 'Wavelength: %.3f' % line[0] print 'Old loggf: %.3f' % line[3] zz = recalSingleLine(line, params=params, version=args.moogversion, driver=args.driver) zz = np.log10(zz) if zz > 0 else zz newloggf[i] = zz print 'New loggf: %.3f\n' % newloggf[i] lines['newloggf'] = pd.Series(newloggf) X = lines[['WL', 'num', 'EP', 'newloggf', 'ele', 'EW']] fmt1 = ('%7.2f', '%7.1f', '%9.2f', '%10.3f', '%10s', '%9.1f') print 'Saving results to: %s' % fout1 np.savetxt(fout1, X, fmt=fmt1, header=header1, comments='') X = lines[['WL', 'num', 'EP', 'newloggf', 'EW']] print 'Saving results to: %s' % fout2 np.savetxt(fout2, X, fmt=fmt2, header=header2) os.remove('temporary.moog')

#!/usr/bin/env python # -*- coding: utf8 -*- # My imports from __future__ import division import numpy as np import pandas as pd import argparse from utils import _update_par as updateBatch from utils import _run_moog as runMoog from utils import Readmoog from interpolation import interpolator import os def solar_abundance(atom): '''Give atomic number and return solar abundance from Asplund et al. 2009 Input ----- atom : int The atomic number Output ------ abundance : float The solar abundance of the atom ''' if not isinstance(atom, int): raise ValueError('Atomic number need to be an integer') solar = [12.00, 10.93, 1.05, 1.38, 2.70, 8.43, 7.83, 8.69, 4.56, 7.93, 6.24, 7.60, 6.45, 7.51, 5.41, 7.12, 5.50, 6.40, 5.03, 6.34, 3.15, 4.95, 3.93, 5.64, 5.43, 7.47, 4.99, 6.22, 4.19, 4.56, 3.04, 3.65, 2.30, 3.34, 2.54, 3.25, 2.52, 2.87, 2.21, 2.58, 1.46, 1.88, -5.00, 1.75, 0.91, 1.57, 0.94, 1.71, 0.80, 2.04, 1.01, 2.18, 1.55, 2.24, 1.08, 2.18, 1.10, 1.58, 0.72, 1.42, -5.00, 0.96, 0.52, 1.07, 0.30, 1.10, 0.48, 0.92, 0.10, 0.84, 0.10, 0.85, -0.12, 0.85, 0.26, 1.40, 1.38, 1.62, 0.92, 1.17, 0.90, 1.75, 0.65, -5.00, -5.00, -5.00, -5.00, -5.00, -5.00, 0.02, -5.00, -0.54, -5.00, -5.00, -5.00] return solar[atom-1] def recalSingleLine(line, params=None, version=2014, maxiter=40, driver='abfind'): '''Recalibrate a single line and return the new loggf Inputs ------ line : list The line containing (wavelength, element, EP, loggf, EW) in that order params : list/tuple The parameters (Teff, logg, [Fe/H], vt) version : int The version of MOOG driver : str The MOOG driver to use (abfind or ewfind) Output ------ loggf : float The new recalibrated loggf ''' def moogAbund(loggf, ewdriver=False): line[3] = loggf np.savetxt('temporary.moog', line[:, np.newaxis].T, fmt=fmt, header=header) runMoog() if ewdriver: d = np.loadtxt('summary.out', skiprows=5, usecols=(6,)) out = d-line[4] else: m = Readmoog(params=params, version=version) _, abund = m.elements() solar = solar_abundance(int(line[1])) out = round(abund[0] - solar, 3) return out ewdriver = True if driver == 'ewfind' else False fmt = ('%9.3f', '%10.1f', '%9.2f', '%9.3f', '%28.1f') header = 'Wavelength ele EP loggf EW' np.savetxt('temporary.moog', line[:, np.newaxis].T, fmt=fmt, header=header) loggf_old = line[3] a, b = loggf_old-5, loggf_old+5 # extreme values of loggf c = (a+b)/2 for _ in range(maxiter): if c == 0: # Don't evaluate at loggf = 0 c += (abs(a) + abs(b)) / 10 fa = moogAbund(a, ewdriver=ewdriver) fc = moogAbund(c, ewdriver=ewdriver) if fc == 0: return c elif fa*fc < 0: b = c else: a = c c = (a+b)/2 return c def _parser(): parser = argparse.ArgumentParser(description='Recalibrate the loggf for a set of settings') parser.add_argument('input', help='Input linelist from rawLinelist folder') parser.add_argument('output', help='Name of output file (saved in rawLinelist)') parser.add_argument('-m', '--model', help='Model atmosphere', default='kurucz95', choices=['kurucz95', 'apogee_kurucz', 'marcs']) parser.add_argument('-v', '--moogversion', help='MOOG version', default=2014) parser.add_argument('-d', '--damping', help='Damping to be used in MOOG', default=1, choices=map(str, [1, 2])) parser.add_argument('-dr', '--driver', help='Which driver to use', default='abfind', choices=['abfind', 'ewfind']) parser.add_argument('-p', '--parameters', help='Atmospheric parameters, Teff, logg, [Fe/H], vt', nargs='+', default=None) args = parser.parse_args() return args if __name__ == '__main__': # pragma: no cover args = _parser() fname = args.input fout1 = 'rawLinelist/%s' % args.output fout2 = 'linelist/%s' % args.output.replace('.ares', '.moog') lines = pd.read_csv(fname, skiprows=2, delimiter=r'\s+', names=['WL', 'num', 'EP', 'loggf', 'ele', 'EW']) if args.parameters is None: params = [5777, 4.44, 0.00, 1.00] else: params = map(float, args.parameters) params[0] = int(params[0]) interpolator(params=params, atmtype=args.model, save=True) cols = ['WL', 'num', 'EP', 'loggf', 'EW'] fmt2 = ('%9.3f', '%10.1f', '%9.2f', '%9.3f', '%28.1f') header1 = 'WL num E.P. loggf ele EWsun\n' header1 += '------- ---- ---- ------ ---- -----' header2 = 'Wavelength ele EP loggf EW' x = lines[cols].values[0][:, np.newaxis].T np.savetxt('temporary.moog', x, fmt=fmt2, header=header2) options = {'driver': args.driver, 'damping': args.damping} updateBatch(line_list='temporary.moog', **options) newloggf = np.zeros(lines.shape[0]) for i, line in enumerate(lines[cols].values): print 'Wavelength: %.3f' % line[0] print 'Old loggf: %.3f' % line[3] zz = recalSingleLine(line, params=params, version=args.moogversion, driver=args.driver) zz = np.log10(zz) if zz > 0 else zz newloggf[i] = zz print 'New loggf: %.3f\n' % newloggf[i] lines['newloggf'] = pd.Series(newloggf) X = lines[['WL', 'num', 'EP', 'newloggf', 'ele', 'EW']] fmt1 = ('%7.2f', '%7.1f', '%9.2f', '%10.3f', '%10s', '%9.1f') print 'Saving results to: %s' % fout1 np.savetxt(fout1, X, fmt=fmt1, header=header1, comments='') X = lines[['WL', 'num', 'EP', 'newloggf', 'EW']] print 'Saving results to: %s' % fout2 np.savetxt(fout2, X, fmt=fmt2, header=header2) os.remove('temporary.moog')

1b7145ed8bd88d7082cc74667eb42dcf2d744fe3

py

Python

armi/bookkeeping/db/database.py

celikten/armi

4e100dd514a59caa9c502bd5a0967fd77fdaf00e

2021-05-29T16:02:31.000Z

2021-05-29T16:02:31.000Z

armi/bookkeeping/db/database.py

celikten/armi

4e100dd514a59caa9c502bd5a0967fd77fdaf00e

armi/bookkeeping/db/database.py

celikten/armi

4e100dd514a59caa9c502bd5a0967fd77fdaf00e

# Copyright 2019 TerraPower, LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import Generator, Tuple from armi.settings import caseSettings from armi.reactor import systemLayoutInput class Database: """Abstract class defining the common interface for all Database implementations. Notes ----- This is a pretty anemic set of interfaces, since the different implementations can vary wildly. For now these are the bare minimum interfaces that should be needed to convert one Database format to another, and serve as a common ancestor.""" # Cannot annotate type, because cannot import blueprints, because blueprints cannot # be imported until after plugins are registered, and this module gets imported by # plugins as they are being registered. def genTimeSteps(self) -> Generator[Tuple[int, int], None, None]: """Get a sequence of tuples (cycle, node) that are contained in the database.""" raise NotImplementedError() def genAuxiliaryData(self, ts: Tuple[int, int]) -> Generator[str, None, None]: """ Get a sequence of auxiliary dataset/group names for the passed time step. Returns ------- Generator[str] A generator that produces **absolute** paths to the auxiliary data. Absolute names make it easier for a database version-agnostic converter to find the actual data. """ raise NotImplementedError() def getAuxiliaryDataPath(self, ts: Tuple[int, int], name: str) -> str: """ Get a string describing a path to an auxiliary data location. Parameters ---------- ts The time step that the auxiliary data belongs to name The name of the auxiliary data Returns ------- str An absolute location for storing auxiliary data with the given name for the given time step """ raise NotImplementedError()

# Copyright 2019 TerraPower, LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import Generator, Tuple from armi.settings import caseSettings from armi.reactor import systemLayoutInput class Database: """Abstract class defining the common interface for all Database implementations. Notes ----- This is a pretty anemic set of interfaces, since the different implementations can vary wildly. For now these are the bare minimum interfaces that should be needed to convert one Database format to another, and serve as a common ancestor.""" def loadCS(self) -> caseSettings.Settings: raise NotImplementedError() # Cannot annotate type, because cannot import blueprints, because blueprints cannot # be imported until after plugins are registered, and this module gets imported by # plugins as they are being registered. def loadBlueprints(self): raise NotImplementedError() def loadGeometry(self) -> systemLayoutInput.SystemLayoutInput: raise NotImplementedError() def genTimeSteps(self) -> Generator[Tuple[int, int], None, None]: """Get a sequence of tuples (cycle, node) that are contained in the database.""" raise NotImplementedError() def genAuxiliaryData(self, ts: Tuple[int, int]) -> Generator[str, None, None]: """ Get a sequence of auxiliary dataset/group names for the passed time step. Returns ------- Generator[str] A generator that produces **absolute** paths to the auxiliary data. Absolute names make it easier for a database version-agnostic converter to find the actual data. """ raise NotImplementedError() def getAuxiliaryDataPath(self, ts: Tuple[int, int], name: str) -> str: """ Get a string describing a path to an auxiliary data location. Parameters ---------- ts The time step that the auxiliary data belongs to name The name of the auxiliary data Returns ------- str An absolute location for storing auxiliary data with the given name for the given time step """ raise NotImplementedError() def writeInputsToDB(self, cs, csString=None, geomString=None, bpString=None): raise NotImplementedError() def readInputsFromDB(self): raise NotImplementedError() def writeToDB(self, reactor, statePointName=None): raise NotImplementedError() def close(self): raise NotImplementedError()

eb785d59f66731316d04178e89fe3c2b7adfe88a

py

Python

src/kogniserver/adm.py

aleneum/rsb-wamp-bridge

4c6401f9f1997294b5891fa814e700986612ebdb

src/kogniserver/adm.py

aleneum/rsb-wamp-bridge

4c6401f9f1997294b5891fa814e700986612ebdb

2016-08-05T13:52:45.000Z

2016-08-05T14:16:42.000Z

src/kogniserver/adm.py

aleneum/kogniserver

4c6401f9f1997294b5891fa814e700986612ebdb

import argparse from os import makedirs from os.path import abspath, exists, join, dirname, expanduser import re import subprocess import time import json import threading import socket import sys CONFIG_JSON = """ { "version": 2, "controller": {}, "workers": [ { "transports": [ { "paths": { "ws": { "type": "websocket" }, "/": { "directory": ".", "type": "static" }, "proto": { "directory": ".", "type": "static" } }, "endpoint": { "type": "tcp", "port": 8181, "tls": { "key": "server.key", "certificate": "server.crt" } }, "type": "web" } ], "type": "router", "options": { "pythonpath": [""] }, "realms": [ { "name": "realm1", "roles": [ { "name": "anonymous", "permissions": [ { "uri": "", "match": "prefix", "allow": { "call": true, "register": true, "publish": true, "subscribe": true }, "disclose": { "caller": false, "publisher": false }, "cache": true } ] } ] } ] } ] } """ if __name__ == '__main__': main_entry()

import argparse from os import makedirs from os.path import abspath, exists, join, dirname, expanduser import re import subprocess import time import json import threading import socket import sys def run_crossbar(config_path, keep_alive): ret = subprocess.call(['crossbar', 'status']) if ret == 0 and not keep_alive: subprocess.call(['crossbar', 'stop']) if ret != 0 or not keep_alive: cmd = ['crossbar', 'start', '--config=%s' % config_path] subprocess.call(cmd) def main_entry(args=None): parser = argparse.ArgumentParser() parser.add_argument('-f', '--force', help='overwrite config file if it already exists', action='store_true') parser.add_argument('-k', '--keep-alive', help='use existing crossbar instance', action='store_true') parser.add_argument('-c', '--config', help='location of the config file') parser.add_argument('-g', '--generate', help='only generate config file (with default options)', action='store_true') args = sys.argv[1:] if args is None else args args = parser.parse_args(args) pwd = abspath(__file__) elems = re.compile('[\\\\/]+').split(pwd) if 'site-packages' in elems: idx = elems.index('site-packages') elems = elems[:idx-2] else: elems = elems[:-1] prefix = join("/", *elems) serve_path = join(prefix, "var/www/kogniserver") config_path = join(prefix, 'etc/crossbar/config.json') if not args.config else args.config choice = 'n' if exists(config_path) is False: input_valid = False if not args.generate else True while not input_valid: choice = raw_input("config.json for crossbar does not exists. Should a default one be created " "at %s? [y]/n:" % config_path) or 'y' if choice not in ['y', 'n']: print("please enter 'y' or 'n'.") else: input_valid = True if choice in 'y' or args.force: input_valid = False if not args.generate else True while not input_valid: serve_path = raw_input("Please enter the directory from which " "files should be served [%s]:" % serve_path) or serve_path serve_path = expanduser(serve_path) if not exists(serve_path): choice = raw_input("%s does not exist. Should it be created? [y]/n: " % serve_path) or 'y' if choice not in ['y', 'n']: print("please enter 'y' or 'n'.") elif choice == 'y': makedirs(serve_path) input_valid = True else: input_valid = True protopath = join(prefix, 'share/rst0.17/proto') input_valid = False if not args.generate else True while not input_valid: protopath = raw_input("Location of proto-files? [%s]:" % protopath) or protopath if not exists(protopath) and not args.generate: choice = raw_input("%s does not exist! " "Do you want to ommit RST in your configuration? [y]/n:" % protopath) or 'y' if choice not in ['y', 'n']: print("please enter 'y' or 'n'.") if choice =='y': input_valid = True protopath = None else: input_valid = True if exists(config_path) and not args.force: print "Config file already exists! Use --force to overwrite." return else: if not exists(dirname(abspath(config_path))): makedirs(dirname(abspath(config_path))) ssl_cert = False input_valid = False if not args.generate else True while not input_valid: ssl_cert = raw_input("Location of TLS certificate (without .crt and .key) if needed. " "Leave empty if not needed:") or ssl_cert input_valid = True if ssl_cert: if not exists(ssl_cert + ".crt"): print("%s does not exist!" % (ssl_cert + ".crt")) input_valid = True ssl_cert = False with open(config_path, 'w') as target: j = json.loads(CONFIG_JSON) paths = j['workers'][0]['transports'][0] paths['paths']['/']['directory'] = serve_path if protopath: paths['paths']['proto']['directory'] = protopath else: del paths['paths']['proto'] if ssl_cert: paths['endpoint']['tls']['key'] = ssl_cert + '.key' paths['endpoint']['tls']['certificate'] = ssl_cert + '.crt' else: del paths['endpoint']['tls'] json.dump(j, target, indent=4) # In a dry generation run we can exit here if args.generate: return t1 = threading.Thread(target=run_crossbar, args=(config_path, args.keep_alive,)) t1.setDaemon(True) t1.start() while not check_server('localhost', 8181): time.sleep(0.5) with open(config_path) as crossbar_config: j = json.load(crossbar_config) ssl_cert = None if 'tls' in j['workers'][0]['transports'][0]['endpoint']: ssl_cert = j['workers'][0]['transports'][0]['endpoint']['tls']['certificate'] try: # async cannot deal with ssl yet and importing the runner # already sets the environment to asyncio if ssl_cert: raise RuntimeError from .async import main_entry as server_main_entry except RuntimeError: # will be used if a) twisted has been used before or if an ssl_cert should be used from .twist import main_entry as server_main_entry server_main_entry(ssl_cert) def check_server(address, port): # Create a TCP socket s = socket.socket() try: s.connect((address, port)) # print "Connected to %s on port %s" % (address, port) s.close() return True except socket.error, e: # print "Connection to %s on port %s failed: %s" % (address, port, e) return False CONFIG_JSON = """ { "version": 2, "controller": {}, "workers": [ { "transports": [ { "paths": { "ws": { "type": "websocket" }, "/": { "directory": ".", "type": "static" }, "proto": { "directory": ".", "type": "static" } }, "endpoint": { "type": "tcp", "port": 8181, "tls": { "key": "server.key", "certificate": "server.crt" } }, "type": "web" } ], "type": "router", "options": { "pythonpath": [""] }, "realms": [ { "name": "realm1", "roles": [ { "name": "anonymous", "permissions": [ { "uri": "", "match": "prefix", "allow": { "call": true, "register": true, "publish": true, "subscribe": true }, "disclose": { "caller": false, "publisher": false }, "cache": true } ] } ] } ] } ] } """ if __name__ == '__main__': main_entry()

d0fce2cb257f939d5dd37e46b222ea6b9fa3b758

py

Python

LeetcodeAlgorithms/315. Count of Smaller Numbers After Self/count-of-smaller-numbers-after-self.py

Fenghuapiao/PyLeetcode

d804a62643fe935eb61808196a2c093ea9583654

2019-08-20T06:54:38.000Z

2022-01-07T12:56:46.000Z

LeetcodeAlgorithms/315. Count of Smaller Numbers After Self/count-of-smaller-numbers-after-self.py

yhangf/PyLeetcode

d804a62643fe935eb61808196a2c093ea9583654

LeetcodeAlgorithms/315. Count of Smaller Numbers After Self/count-of-smaller-numbers-after-self.py

yhangf/PyLeetcode

d804a62643fe935eb61808196a2c093ea9583654

2018-11-01T16:10:34.000Z

2020-06-02T03:24:43.000Z

import bisect

import bisect class Solution(object): def countSmaller(self, nums): """ :type nums: List[int] :rtype: List[int] """ ans = [] bst = [] for num in reversed(nums): idx = bisect.bisect_left(bst, num) ans.append(idx) bisect.insort(bst, num) return ans[::-1]

8c8034428e29fd160cf8e41c0c7d98c8aa53bb4b

py

Python

10205/10205.py

Keilan/uva

4218328466c3ab2fdf34cdf45fc7a8dea90964bc

10205/10205.py

Keilan/uva

4218328466c3ab2fdf34cdf45fc7a8dea90964bc

10205/10205.py

Keilan/uva

4218328466c3ab2fdf34cdf45fc7a8dea90964bc

import sys shuffle()

import sys class Card: def __init__(self, rank, suit): self.rank = rank self.suit = suit @classmethod def from_position(cls, position): """ Given a number from 1 to 52, creates the card in the correct position assuming the deck is sorted by alphabetical suits and then in ace-high ranks. """ suits = ['Clubs', 'Diamonds', 'Hearts', 'Spades'] ranks = ['2', '3', '4', '5', '6', '7', '8', '9', '10', 'Jack', 'Queen', 'King', 'Ace'] suit = suits[(position-1) // 13] rank = ranks[(position-1) % 13] return cls(rank, suit) def __repr__(self): return '{} of {}'.format(self.rank, self.suit) def shuffle(): num_cases = int(sys.stdin.readline()) sys.stdin.readline() # Skip blank line for case_num in range(num_cases): if case_num != 0: print() # Blank line # Read input num_shuffles = int(sys.stdin.readline()) # Read shuffles shuffles = [] count = num_shuffles * 52 while count: data = [int(i) for i in sys.stdin.readline().split()] count -= len(data) shuffles.extend(data) shuffles = [shuffles[x:x+52] for x in range(0, len(shuffles), 52)] # Split into sets of 52 result = list(range(1, 52+1)) # Read shuffles to apply until we reach a blank line line = sys.stdin.readline() while not line.isspace() and line != '': shuffle = shuffles[int(line) - 1] # Apply the shuffle previous = result.copy() for i, new_position in enumerate(shuffle): result[i] = previous[new_position-1] line = sys.stdin.readline() # Print card names for idx in result: print(Card.from_position(idx)) shuffle()

4f1c141f04a988a04635609952303d8c99c330a8

py

Python

design-patterns/ddbreplica_lambda.py

weisisheng/amazon-dynamodb-labs

76b9b72b3d16bb7b93e313c48dc8efcd981e18f7

2020-01-17T05:24:10.000Z

2022-03-18T18:31:10.000Z

design-patterns/ddbreplica_lambda.py

weisisheng/amazon-dynamodb-labs

76b9b72b3d16bb7b93e313c48dc8efcd981e18f7

2020-01-22T21:18:08.000Z

2022-02-16T19:49:43.000Z

design-patterns/ddbreplica_lambda.py

weisisheng/amazon-dynamodb-labs

76b9b72b3d16bb7b93e313c48dc8efcd981e18f7

2020-01-16T23:07:46.000Z

2022-03-31T11:39:31.000Z

import json import time import urllib import boto3 import traceback from lab_config import boto_args from boto3.dynamodb.types import TypeDeserializer

import json import time import urllib import boto3 import traceback from lab_config import boto_args from boto3.dynamodb.types import TypeDeserializer class StreamTypeDeserializer(TypeDeserializer): def _deserialize_n(self, value): return int(value) def _deserialize_b(self, value): return value # Already in Base64 def get_table_name_from_arn(arn): return arn.split(':')[5].split('/')[1] def _lambda_handler(event, context): dynamodb = boto3.resource(**boto_args) dynamodb_table = dynamodb.Table('logfile_replica') ddb_deserializer = StreamTypeDeserializer() records = event['Records'] for record in records: ddb = record['dynamodb'] event_name = record['eventName'].upper() # INSERT, MODIFY, REMOVE if (event_name == 'INSERT') or (event_name == 'MODIFY'): if 'NewImage' not in ddb: print ('Cannot process stream if it does not contain NewImage') continue doc_fields = ddb_deserializer.deserialize({'M': ddb['NewImage']}) item = dynamodb_table.put_item(Item=doc_fields) def lambda_handler(event, context): try: return _lambda_handler(event, context) except Exception: print (traceback.format_exc())

803f3c5cd78d87ab3c97e23ccd0aab8fcc148c64

py

Python

networking_f5_ml2/plugins/ml2/drivers/mech_f5/agent/f5_agent.py

sapcc/networking-f5-ml2

03739c1362df80510739afbdc937b5ec68591c52

networking_f5_ml2/plugins/ml2/drivers/mech_f5/agent/f5_agent.py

sapcc/networking-f5-ml2

03739c1362df80510739afbdc937b5ec68591c52

networking_f5_ml2/plugins/ml2/drivers/mech_f5/agent/f5_agent.py

sapcc/networking-f5-ml2

03739c1362df80510739afbdc937b5ec68591c52

# Copyright 2016 SAP SE # # All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. import collections import signal import time import eventlet eventlet.monkey_patch() from oslo_config import cfg from oslo_log import log as logging from neutron.i18n import _LI, _LW import oslo_messaging from oslo_service import loopingcall from neutron.agent.common import polling from neutron.common import config from neutron.agent import rpc as agent_rpc from neutron.common import constants as n_const from neutron.common import rpc as n_rpc from neutron.common import topics from neutron import context from neutron.i18n import _LE from neutron.db import db_base_plugin_v2 as db_base from neutron.plugins.ml2 import db as db_ml2 from networking_f5_ml2.plugins.ml2.drivers.mech_f5 import constants as f5_constants from oslo_utils import importutils LOG = logging.getLogger(__name__) cfg.CONF.import_group('ml2_f5', 'networking_f5_ml2.plugins.ml2.drivers.mech_f5.config')

# Copyright 2016 SAP SE # # All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. import collections import signal import time import eventlet eventlet.monkey_patch() from oslo_config import cfg from oslo_log import log as logging from neutron.i18n import _LI, _LW import oslo_messaging from oslo_service import loopingcall from neutron.agent.common import polling from neutron.common import config from neutron.agent import rpc as agent_rpc from neutron.common import constants as n_const from neutron.common import rpc as n_rpc from neutron.common import topics from neutron import context from neutron.i18n import _LE from neutron.db import db_base_plugin_v2 as db_base from neutron.plugins.ml2 import db as db_ml2 from networking_f5_ml2.plugins.ml2.drivers.mech_f5 import constants as f5_constants from oslo_utils import importutils LOG = logging.getLogger(__name__) cfg.CONF.import_group('ml2_f5', 'networking_f5_ml2.plugins.ml2.drivers.mech_f5.config') class F5NeutronAgent(): target = oslo_messaging.Target(version='1.4') def __init__(self, quitting_rpc_timeout=None, conf=None, ): self.conf = cfg.CONF cfg.CONF.log_opt_values(LOG, logging.DEBUG) self.agent_conf = self.conf.get('AGENT', {}) self.polling_interval = 10 self.iter_num = 0 self.run_daemon_loop = True self.quitting_rpc_timeout = quitting_rpc_timeout self.catch_sigterm = False self.catch_sighup = False # Stores port update notifications for processing in main rpc loop self.updated_ports = set() # Stores port delete notifications self.deleted_ports = set() self.network_ports = collections.defaultdict(set) self.local_vlan_map = {} self.f5_driver = importutils.import_object(cfg.CONF.f5_bigip_lbaas_device_driver, cfg.CONF) host = self.conf.host self.agent_host = host + ":" + self.f5_driver.agent_id self.f5_driver.agent_host = self.agent_host self.agent_id = 'f5-agent-%s' % host self.setup_rpc() self.db = db_base.NeutronDbPluginV2() self.agent_state = { 'binary': 'neutron-f5-agent', 'host': self.agent_host, 'topic': n_const.L2_AGENT_TOPIC, 'configurations': {}, 'agent_type': f5_constants.F5_AGENT_TYPE, 'start_flag': True} self.connection.consume_in_threads() def port_update(self, context, **kwargs): port = kwargs.get('port') self.updated_ports.add(port['id']) def port_delete(self, context, **kwargs): port_id = kwargs.get('port_id') self.deleted_ports.add(port_id) self.updated_ports.discard(port_id) def network_create(self, context, **kwargs): pass def network_update(self, context, **kwargs): network_id = kwargs['network']['id'] for port_id in self.network_ports[network_id]: # notifications could arrive out of order, if the port is deleted # we don't want to update it anymore if port_id not in self.deleted_ports: self.updated_ports.add(port_id) def network_delete(self, context, **kwargs): pass def _clean_network_ports(self, port_id): for port_set in self.network_ports.values(): if port_id in port_set: port_set.remove(port_id) break def setup_rpc(self): LOG.info(_LI("RPC agent_id: %s"), self.agent_id) self.plugin_rpc = agent_rpc.PluginApi(topics.PLUGIN) self.state_rpc = agent_rpc.PluginReportStateAPI(topics.PLUGIN) # RPC network init self.context = context.get_admin_context_without_session() self.context_with_session = context.get_admin_context() # Define the listening consumers for the agent consumers = [[topics.PORT, topics.CREATE], [topics.PORT, topics.UPDATE], [topics.PORT, topics.DELETE], [topics.NETWORK, topics.CREATE], [topics.NETWORK, topics.UPDATE], [topics.NETWORK, topics.DELETE]] self.connection = agent_rpc.create_consumers([self], topics.AGENT, consumers, start_listening=False) report_interval = 30 # self.conf.AGENT.report_interval heartbeat = loopingcall.FixedIntervalLoopingCall(self._report_state) heartbeat.start(interval=report_interval) def _report_state(self): LOG.info(_LI("******** Reporting state via rpc")) try: self.state_rpc.report_state(self.context, self.agent_state) self.agent_state.pop('start_flag', None) except Exception: LOG.exception(_LE("Failed reporting state!")) def _check_and_handle_signal(self): if self.catch_sigterm: LOG.info(_LI("Agent caught SIGTERM, quitting daemon loop.")) self.run_daemon_loop = False self.catch_sigterm = False if self.catch_sighup: LOG.info(_LI("Agent caught SIGHUP, resetting.")) self.conf.reload_config_files() config.setup_logging() self.conf.log_opt_values(LOG, logging.DEBUG) self.catch_sighup = False return self.run_daemon_loop def _handle_sigterm(self, signum, frame): self.catch_sigterm = True if self.quitting_rpc_timeout: self.set_rpc_timeout(self.quitting_rpc_timeout) def _handle_sighup(self, signum, frame): self.catch_sighup = True def _scan_ports(self): start = time.clock() # For now get all ports, we will then check for the corresponding VLAN config on the device # Will not scale but should prove concept works, we are also using direct DB calls rather than RPC, I suspect this # is an anti pattern and done properly we should extend the RPC API to allow us to scan the LB ports all_ports = self.db.get_ports(self.context_with_session, filters={}) for port in all_ports: LOG.info( port) LOG.info(_LI("Agent port scan for port {}".format(port['id']))) network = self.db.get_network(self.context_with_session, port['network_id']) binding_levels = db_ml2.get_binding_levels(self.context_with_session.session, port['id'], self.agent_host) for binding_level in binding_levels: LOG.info(_LI("Binding level {}".format(binding_level))) # if segment bound with ml2f5 driver if binding_level.driver == 'f5ml2': segment = db_ml2.get_segment_by_id(self.context_with_session.session, binding_level.segment_id) if segment['network_type'] == 'vlan': # and type is VLAN # Get VLANs from iControl for port network and check they are bound to the correct VLAN for bigip in self.f5_driver.get_config_bigips(): folder = 'Project_' + network['tenant_id'] name = 'vlan-' + network['id'][0:10] v = bigip.net.vlans.vlan if v.exists(name=name, partition=folder): v.load(name=name, partition=folder) tag = v.tag if tag != segment['segmentation_id']: # Update VLAN tag in case of mismatch LOG.info("Updating VLAN tag was %s needs to be %s", tag, segment['segmentation_id']) v.tag = segment['segmentation_id'] v.update() LOG.info(_LI("Scan ports completed in {} seconds".format(time.clock() - start))) def loop_count_and_wait(self, start_time, port_stats): # sleep till end of polling interval elapsed = time.time() - start_time LOG.debug("F5 Agent rpc_loop - iteration:%(iter_num)d " "completed. Processed ports statistics: " "%(port_stats)s. Elapsed:%(elapsed).3f", {'iter_num': self.iter_num, 'port_stats': port_stats, 'elapsed': elapsed}) if elapsed < self.polling_interval: time.sleep(self.polling_interval - elapsed) else: LOG.debug("Loop iteration exceeded interval " "(%(polling_interval)s vs. %(elapsed)s)!", {'polling_interval': self.polling_interval, 'elapsed': elapsed}) self.iter_num = self.iter_num + 1 def rpc_loop(self, ): while self._check_and_handle_signal(): start = time.time() port_stats = {} try: self._scan_ports() except Exception: LOG.exception(_LE("Error while processing ports")) self.loop_count_and_wait(start, port_stats) def daemon_loop(self): # Start everything. LOG.info(_LI("Agent initialized successfully, now running... ")) signal.signal(signal.SIGTERM, self._handle_sigterm) if hasattr(signal, 'SIGHUP'): signal.signal(signal.SIGHUP, self._handle_sighup) self.rpc_loop()

1a75c8a8fd2beddcfde21f65f430272b0cbb7ae4

py

Python

lstm.py

KT12/hands_on_machine_learning

6de2292b43d7c34b6509ad61dab2da4f7ec04894

lstm.py

KT12/hands_on_machine_learning

6de2292b43d7c34b6509ad61dab2da4f7ec04894

lstm.py

KT12/hands_on_machine_learning

6de2292b43d7c34b6509ad61dab2da4f7ec04894

import tensorflow as tf import numpy as np tf.set_random_seed(5) n_inputs = 28 n_neurons = 150 n_layers = 3 n_steps = 28 n_outputs = 10 learning_rate = 0.001 from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("/tmp/data/") X_test = mnist.test.images.reshape((-1, n_steps, n_inputs)) y_test = mnist.test.labels X = tf.placeholder(tf.float32, [None, n_steps, n_inputs]) y = tf.placeholder(tf.int32, [None]) multi_cell = tf.contrib.rnn.MultiRNNCell([tf.contrib.rnn.BasicLSTMCell(num_units=n_neurons) for _ in range(3)]) outputs, states = tf.nn.dynamic_rnn(multi_cell, X, dtype=tf.float32) top_layer_h_state = states[-1][1] logits = tf.layers.dense(top_layer_h_state, n_outputs, name='softmax') x_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=logits) loss = tf.reduce_mean(x_entropy, name='loss') optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) training_op = optimizer.minimize(loss) correct = tf.nn.in_top_k(logits, y, 1) accuracy = tf.reduce_mean(tf.cast(correct, tf.float32)) init = tf.global_variables_initializer() states top_layer_h_state n_epochs = 25 batch_size = 150 with tf.Session() as sess: init.run() for epoch in range(n_epochs): for k in range(mnist.train.num_examples // batch_size): X_batch, y_batch = mnist.train.next_batch(batch_size) X_batch = X_batch.reshape((batch_size, n_steps, n_inputs)) sess.run(training_op, feed_dict={X: X_batch, y: y_batch}) acc_train = accuracy.eval(feed_dict={X: X_batch, y: y_batch}) acc_test = accuracy.eval(feed_dict={X: X_test, y: y_test}) print("Epoch", epoch, 'Train acc: ', acc_train, "Test acc: ", acc_test)

import tensorflow as tf import numpy as np tf.set_random_seed(5) n_inputs = 28 n_neurons = 150 n_layers = 3 n_steps = 28 n_outputs = 10 learning_rate = 0.001 from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("/tmp/data/") X_test = mnist.test.images.reshape((-1, n_steps, n_inputs)) y_test = mnist.test.labels X = tf.placeholder(tf.float32, [None, n_steps, n_inputs]) y = tf.placeholder(tf.int32, [None]) multi_cell = tf.contrib.rnn.MultiRNNCell([tf.contrib.rnn.BasicLSTMCell(num_units=n_neurons) for _ in range(3)]) outputs, states = tf.nn.dynamic_rnn(multi_cell, X, dtype=tf.float32) top_layer_h_state = states[-1][1] logits = tf.layers.dense(top_layer_h_state, n_outputs, name='softmax') x_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=logits) loss = tf.reduce_mean(x_entropy, name='loss') optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) training_op = optimizer.minimize(loss) correct = tf.nn.in_top_k(logits, y, 1) accuracy = tf.reduce_mean(tf.cast(correct, tf.float32)) init = tf.global_variables_initializer() states top_layer_h_state n_epochs = 25 batch_size = 150 with tf.Session() as sess: init.run() for epoch in range(n_epochs): for k in range(mnist.train.num_examples // batch_size): X_batch, y_batch = mnist.train.next_batch(batch_size) X_batch = X_batch.reshape((batch_size, n_steps, n_inputs)) sess.run(training_op, feed_dict={X: X_batch, y: y_batch}) acc_train = accuracy.eval(feed_dict={X: X_batch, y: y_batch}) acc_test = accuracy.eval(feed_dict={X: X_test, y: y_test}) print("Epoch", epoch, 'Train acc: ', acc_train, "Test acc: ", acc_test)

22230bd8b1f3797e8dc3f1afa43c1e2179290ce4

py

Python

dygiepp/dygie/models/dygie.py

feiLinX/SciREX

768c869af746f4a61b3d58b15897e03caa5e2d32

2020-05-04T11:07:00.000Z

2022-03-30T12:55:00.000Z

dygiepp/dygie/models/dygie.py

feiLinX/SciREX

768c869af746f4a61b3d58b15897e03caa5e2d32

2020-08-05T18:22:44.000Z

2021-05-06T21:35:05.000Z

dygiepp/dygie/models/dygie.py

feiLinX/SciREX

768c869af746f4a61b3d58b15897e03caa5e2d32

2020-07-09T13:37:42.000Z

2022-03-26T09:56:43.000Z

from os import path import logging from typing import Dict, List, Optional import copy import torch import torch.nn.functional as F from overrides import overrides from allennlp.data import Vocabulary from allennlp.common.params import Params from allennlp.models.model import Model from allennlp.modules import Seq2SeqEncoder, TextFieldEmbedder, FeedForward from allennlp.modules.span_extractors import SelfAttentiveSpanExtractor, EndpointSpanExtractor from allennlp.nn import util, InitializerApplicator, RegularizerApplicator # Import submodules. from dygie.models.coref import CorefResolver from dygie.models.ner import NERTagger from dygie.models.relation import RelationExtractor from dygie.models.events import EventExtractor from dygie.training.joint_metrics import JointMetrics logger = logging.getLogger(__name__) # pylint: disable=invalid-name @Model.register("dygie") class DyGIE(Model): """ TODO(dwadden) document me. Parameters ---------- vocab : ``Vocabulary`` text_field_embedder : ``TextFieldEmbedder`` Used to embed the ``text`` ``TextField`` we get as input to the model. context_layer : ``Seq2SeqEncoder`` This layer incorporates contextual information for each word in the document. feature_size: ``int`` The embedding size for all the embedded features, such as distances or span widths. submodule_params: ``TODO(dwadden)`` A nested dictionary specifying parameters to be passed on to initialize submodules. max_span_width: ``int`` The maximum width of candidate spans. lexical_dropout: ``int`` The probability of dropping out dimensions of the embedded text. initializer : ``InitializerApplicator``, optional (default=``InitializerApplicator()``) Used to initialize the model parameters. regularizer : ``RegularizerApplicator``, optional (default=``None``) If provided, will be used to calculate the regularization penalty during training. display_metrics: ``List[str]``. A list of the metrics that should be printed out during model training. """ @overrides def forward(self, text, spans, ner_labels, coref_labels, relation_labels, trigger_labels, argument_labels, metadata): """ TODO(dwadden) change this. """ # For co-training on Ontonotes, need to change the loss weights depending on the data coming # in. This is a hack but it will do for now. if self._co_train: if self.training: dataset = [entry["dataset"] for entry in metadata] assert len(set(dataset)) == 1 dataset = dataset[0] assert dataset in ["ace", "ontonotes"] if dataset == "ontonotes": self._loss_weights = dict(coref=1, ner=0, relation=0, events=0) else: self._loss_weights = self._permanent_loss_weights # This assumes that there won't be any co-training data in the dev and test sets, and that # coref propagation will still happen even when the coref weight is set to 0. else: self._loss_weights = self._permanent_loss_weights # In AllenNLP, AdjacencyFields are passed in as floats. This fixes it. relation_labels = relation_labels.long() argument_labels = argument_labels.long() # If we're doing Bert, get the sentence class token as part of the text embedding. This will # break if we use Bert together with other embeddings, but that won't happen much. if "bert-offsets" in text: offsets = text["bert-offsets"] sent_ix = torch.zeros(offsets.size(0), device=offsets.device, dtype=torch.long).unsqueeze(1) padded_offsets = torch.cat([sent_ix, offsets], dim=1) text["bert-offsets"] = padded_offsets padded_embeddings = self._text_field_embedder(text) cls_embeddings = padded_embeddings[:, 0, :] text_embeddings = padded_embeddings[:, 1:, :] else: text_embeddings = self._text_field_embedder(text) cls_embeddings = torch.zeros([text_embeddings.size(0), text_embeddings.size(2)], device=text_embeddings.device) text_embeddings = self._lexical_dropout(text_embeddings) # Shape: (batch_size, max_sentence_length) text_mask = util.get_text_field_mask(text).float() sentence_group_lengths = text_mask.sum(dim=1).long() sentence_lengths = 0*text_mask.sum(dim=1).long() for i in range(len(metadata)): sentence_lengths[i] = metadata[i]["end_ix"] - metadata[i]["start_ix"] for k in range(sentence_lengths[i], sentence_group_lengths[i]): text_mask[i][k] = 0 max_sentence_length = sentence_lengths.max().item() # TODO(Ulme) Speed this up by tensorizing new_text_embeddings = torch.zeros([text_embeddings.shape[0], max_sentence_length, text_embeddings.shape[2]], device=text_embeddings.device) for i in range(len(new_text_embeddings)): new_text_embeddings[i][0:metadata[i]["end_ix"] - metadata[i]["start_ix"]] = text_embeddings[i][metadata[i]["start_ix"]:metadata[i]["end_ix"]] #max_sent_len = max(sentence_lengths) #the_list = [list(k+metadata[i]["start_ix"] if k < max_sent_len else 0 for k in range(text_embeddings.shape[1])) for i in range(len(metadata))] #import ipdb; ipdb.set_trace() #text_embeddings = torch.gather(text_embeddings, 1, torch.tensor(the_list, device=text_embeddings.device).unsqueeze(2).repeat(1, 1, text_embeddings.shape[2])) text_embeddings = new_text_embeddings # Only keep the text embeddings that correspond to actual tokens. # text_embeddings = text_embeddings[:, :max_sentence_length, :].contiguous() text_mask = text_mask[:, :max_sentence_length].contiguous() # Shape: (batch_size, max_sentence_length, encoding_dim) contextualized_embeddings = self._lstm_dropout(self._context_layer(text_embeddings, text_mask)) assert spans.max() < contextualized_embeddings.shape[1] if self._attentive_span_extractor is not None: # Shape: (batch_size, num_spans, emebedding_size) attended_span_embeddings = self._attentive_span_extractor(text_embeddings, spans) # Shape: (batch_size, num_spans) span_mask = (spans[:, :, 0] >= 0).float() # SpanFields return -1 when they are used as padding. As we do # some comparisons based on span widths when we attend over the # span representations that we generate from these indices, we # need them to be <= 0. This is only relevant in edge cases where # the number of spans we consider after the pruning stage is >= the # total number of spans, because in this case, it is possible we might # consider a masked span. # Shape: (batch_size, num_spans, 2) spans = F.relu(spans.float()).long() # Shape: (batch_size, num_spans, 2 * encoding_dim + feature_size) endpoint_span_embeddings = self._endpoint_span_extractor(contextualized_embeddings, spans) if self._attentive_span_extractor is not None: # Shape: (batch_size, num_spans, emebedding_size + 2 * encoding_dim + feature_size) span_embeddings = torch.cat([endpoint_span_embeddings, attended_span_embeddings], -1) else: span_embeddings = endpoint_span_embeddings # TODO(Ulme) try normalizing span embeddeings #span_embeddings = span_embeddings.abs().sum(dim=-1).unsqueeze(-1) # Make calls out to the modules to get results. output_coref = {'loss': 0} output_ner = {'loss': 0} output_relation = {'loss': 0} output_events = {'loss': 0} # Prune and compute span representations for coreference module if self._loss_weights["coref"] > 0 or self._coref.coref_prop > 0: output_coref, coref_indices = self._coref.compute_representations( spans, span_mask, span_embeddings, sentence_lengths, coref_labels, metadata) # Prune and compute span representations for relation module if self._loss_weights["relation"] > 0 or self._relation.rel_prop > 0: output_relation = self._relation.compute_representations( spans, span_mask, span_embeddings, sentence_lengths, relation_labels, metadata) # Propagation of global information to enhance the span embeddings if self._coref.coref_prop > 0: # TODO(Ulme) Implement Coref Propagation output_coref = self._coref.coref_propagation(output_coref) span_embeddings = self._coref.update_spans(output_coref, span_embeddings, coref_indices) if self._relation.rel_prop > 0: output_relation = self._relation.relation_propagation(output_relation) span_embeddings = self.update_span_embeddings(span_embeddings, span_mask, output_relation["top_span_embeddings"], output_relation["top_span_mask"], output_relation["top_span_indices"]) # Make predictions and compute losses for each module if self._loss_weights['ner'] > 0: output_ner = self._ner( spans, span_mask, span_embeddings, sentence_lengths, ner_labels, metadata) if self._loss_weights['coref'] > 0: try : output_coref = self._coref.predict_labels(output_coref, metadata) except : output_coref = {} if self._loss_weights['relation'] > 0: output_relation = self._relation.predict_labels(relation_labels, output_relation, metadata) if self._loss_weights['events'] > 0: # Make the trigger embeddings the same size as the argument embeddings to make # propagation easier. if self._events._span_prop._n_span_prop > 0: trigger_embeddings = contextualized_embeddings.repeat(1, 1, 2) trigger_widths = torch.zeros([trigger_embeddings.size(0), trigger_embeddings.size(1)], device=trigger_embeddings.device, dtype=torch.long) trigger_width_embs = self._endpoint_span_extractor._span_width_embedding(trigger_widths) trigger_width_embs = trigger_width_embs.detach() trigger_embeddings = torch.cat([trigger_embeddings, trigger_width_embs], dim=-1) else: trigger_embeddings = contextualized_embeddings output_events = self._events( text_mask, trigger_embeddings, spans, span_mask, span_embeddings, cls_embeddings, sentence_lengths, output_ner, trigger_labels, argument_labels, ner_labels, metadata) if "loss" not in output_coref: output_coref["loss"] = 0 if "loss" not in output_relation: output_relation["loss"] = 0 # TODO(dwadden) just did this part. loss = (self._loss_weights['coref'] * output_coref['loss'] + self._loss_weights['ner'] * output_ner['loss'] + self._loss_weights['relation'] * output_relation['loss'] + self._loss_weights['events'] * output_events['loss']) output_dict = dict(coref=output_coref, relation=output_relation, ner=output_ner, events=output_events) output_dict['loss'] = loss # Check to see if event predictions are globally compatible (argument labels are compatible # with NER tags and trigger tags). # if self._loss_weights["ner"] > 0 and self._loss_weights["events"] > 0: # decoded_ner = self._ner.decode(output_dict["ner"]) # decoded_events = self._events.decode(output_dict["events"]) # self._joint_metrics(decoded_ner, decoded_events) return output_dict @overrides def decode(self, output_dict: Dict[str, torch.Tensor]): """ Converts the list of spans and predicted antecedent indices into clusters of spans for each element in the batch. Parameters ---------- output_dict : ``Dict[str, torch.Tensor]``, required. The result of calling :func:`forward` on an instance or batch of instances. Returns ------- The same output dictionary, but with an additional ``clusters`` key: clusters : ``List[List[List[Tuple[int, int]]]]`` A nested list, representing, for each instance in the batch, the list of clusters, which are in turn comprised of a list of (start, end) inclusive spans into the original document. """ # TODO(dwadden) which things are already decoded? res = {} if self._loss_weights["coref"] > 0: try : res["coref"] = self._coref.decode(output_dict["coref"]) except : pass if self._loss_weights["ner"] > 0: res["ner"] = self._ner.decode(output_dict["ner"]) if self._loss_weights["relation"] > 0: res["relation"] = self._relation.decode(output_dict["relation"]) if self._loss_weights["events"] > 0: res["events"] = output_dict["events"] return res def get_metrics(self, reset: bool = False) -> Dict[str, float]: """ Get all metrics from all modules. For the ones that shouldn't be displayed, prefix their keys with an underscore. """ metrics_coref = self._coref.get_metrics(reset=reset) metrics_ner = self._ner.get_metrics(reset=reset) metrics_relation = self._relation.get_metrics(reset=reset) metrics_events = self._events.get_metrics(reset=reset) if self._loss_weights["ner"] > 0 and self._loss_weights["events"] > 0: metrics_joint = self._joint_metrics.get_metric(reset=reset) else: metrics_joint = {} # Make sure that there aren't any conflicting names. metric_names = (list(metrics_coref.keys()) + list(metrics_ner.keys()) + list(metrics_relation.keys()) + list(metrics_events.keys())) assert len(set(metric_names)) == len(metric_names) all_metrics = dict(list(metrics_coref.items()) + list(metrics_ner.items()) + list(metrics_relation.items()) + list(metrics_events.items()) + list(metrics_joint.items())) # If no list of desired metrics given, display them all. if self._display_metrics is None: return all_metrics # Otherwise only display the selected ones. res = {} for k, v in all_metrics.items(): if k in self._display_metrics: res[k] = v else: new_k = "_" + k res[new_k] = v return res

from os import path import logging from typing import Dict, List, Optional import copy import torch import torch.nn.functional as F from overrides import overrides from allennlp.data import Vocabulary from allennlp.common.params import Params from allennlp.models.model import Model from allennlp.modules import Seq2SeqEncoder, TextFieldEmbedder, FeedForward from allennlp.modules.span_extractors import SelfAttentiveSpanExtractor, EndpointSpanExtractor from allennlp.nn import util, InitializerApplicator, RegularizerApplicator # Import submodules. from dygie.models.coref import CorefResolver from dygie.models.ner import NERTagger from dygie.models.relation import RelationExtractor from dygie.models.events import EventExtractor from dygie.training.joint_metrics import JointMetrics logger = logging.getLogger(__name__) # pylint: disable=invalid-name @Model.register("dygie") class DyGIE(Model): """ TODO(dwadden) document me. Parameters ---------- vocab : ``Vocabulary`` text_field_embedder : ``TextFieldEmbedder`` Used to embed the ``text`` ``TextField`` we get as input to the model. context_layer : ``Seq2SeqEncoder`` This layer incorporates contextual information for each word in the document. feature_size: ``int`` The embedding size for all the embedded features, such as distances or span widths. submodule_params: ``TODO(dwadden)`` A nested dictionary specifying parameters to be passed on to initialize submodules. max_span_width: ``int`` The maximum width of candidate spans. lexical_dropout: ``int`` The probability of dropping out dimensions of the embedded text. initializer : ``InitializerApplicator``, optional (default=``InitializerApplicator()``) Used to initialize the model parameters. regularizer : ``RegularizerApplicator``, optional (default=``None``) If provided, will be used to calculate the regularization penalty during training. display_metrics: ``List[str]``. A list of the metrics that should be printed out during model training. """ def __init__(self, vocab: Vocabulary, text_field_embedder: TextFieldEmbedder, context_layer: Seq2SeqEncoder, modules, # TODO(dwadden) Add type. feature_size: int, max_span_width: int, loss_weights: Dict[str, int], lexical_dropout: float = 0.2, lstm_dropout: float = 0.4, use_attentive_span_extractor: bool = False, co_train: bool = False, initializer: InitializerApplicator = InitializerApplicator(), regularizer: Optional[RegularizerApplicator] = None, display_metrics: List[str] = None) -> None: super(DyGIE, self).__init__(vocab, regularizer) self._text_field_embedder = text_field_embedder self._context_layer = context_layer self._loss_weights = loss_weights self._permanent_loss_weights = copy.deepcopy(self._loss_weights) # Need to add this line so things don't break. TODO(dwadden) sort out what's happening. modules = Params(modules) self._coref = CorefResolver.from_params(vocab=vocab, feature_size=feature_size, params=modules.pop("coref")) self._ner = NERTagger.from_params(vocab=vocab, feature_size=feature_size, params=modules.pop("ner")) self._relation = RelationExtractor.from_params(vocab=vocab, feature_size=feature_size, params=modules.pop("relation")) self._events = EventExtractor.from_params(vocab=vocab, feature_size=feature_size, params=modules.pop("events")) # Make endpoint span extractor. self._endpoint_span_extractor = EndpointSpanExtractor(context_layer.get_output_dim(), combination="x,y", num_width_embeddings=max_span_width, span_width_embedding_dim=feature_size, bucket_widths=False) if use_attentive_span_extractor: self._attentive_span_extractor = SelfAttentiveSpanExtractor( input_dim=text_field_embedder.get_output_dim()) else: self._attentive_span_extractor = None self._max_span_width = max_span_width self._display_metrics = display_metrics if lexical_dropout > 0: self._lexical_dropout = torch.nn.Dropout(p=lexical_dropout) else: self._lexical_dropout = lambda x: x # Do co-training if we're training on ACE and ontonotes. self._co_train = co_train # Big gotcha: PyTorch doesn't add dropout to the LSTM's output layer. We need to do this # manually. if lstm_dropout > 0: self._lstm_dropout = torch.nn.Dropout(p=lstm_dropout) else: self._lstm_dropout = lambda x: x initializer(self) @overrides def forward(self, text, spans, ner_labels, coref_labels, relation_labels, trigger_labels, argument_labels, metadata): """ TODO(dwadden) change this. """ # For co-training on Ontonotes, need to change the loss weights depending on the data coming # in. This is a hack but it will do for now. if self._co_train: if self.training: dataset = [entry["dataset"] for entry in metadata] assert len(set(dataset)) == 1 dataset = dataset[0] assert dataset in ["ace", "ontonotes"] if dataset == "ontonotes": self._loss_weights = dict(coref=1, ner=0, relation=0, events=0) else: self._loss_weights = self._permanent_loss_weights # This assumes that there won't be any co-training data in the dev and test sets, and that # coref propagation will still happen even when the coref weight is set to 0. else: self._loss_weights = self._permanent_loss_weights # In AllenNLP, AdjacencyFields are passed in as floats. This fixes it. relation_labels = relation_labels.long() argument_labels = argument_labels.long() # If we're doing Bert, get the sentence class token as part of the text embedding. This will # break if we use Bert together with other embeddings, but that won't happen much. if "bert-offsets" in text: offsets = text["bert-offsets"] sent_ix = torch.zeros(offsets.size(0), device=offsets.device, dtype=torch.long).unsqueeze(1) padded_offsets = torch.cat([sent_ix, offsets], dim=1) text["bert-offsets"] = padded_offsets padded_embeddings = self._text_field_embedder(text) cls_embeddings = padded_embeddings[:, 0, :] text_embeddings = padded_embeddings[:, 1:, :] else: text_embeddings = self._text_field_embedder(text) cls_embeddings = torch.zeros([text_embeddings.size(0), text_embeddings.size(2)], device=text_embeddings.device) text_embeddings = self._lexical_dropout(text_embeddings) # Shape: (batch_size, max_sentence_length) text_mask = util.get_text_field_mask(text).float() sentence_group_lengths = text_mask.sum(dim=1).long() sentence_lengths = 0*text_mask.sum(dim=1).long() for i in range(len(metadata)): sentence_lengths[i] = metadata[i]["end_ix"] - metadata[i]["start_ix"] for k in range(sentence_lengths[i], sentence_group_lengths[i]): text_mask[i][k] = 0 max_sentence_length = sentence_lengths.max().item() # TODO(Ulme) Speed this up by tensorizing new_text_embeddings = torch.zeros([text_embeddings.shape[0], max_sentence_length, text_embeddings.shape[2]], device=text_embeddings.device) for i in range(len(new_text_embeddings)): new_text_embeddings[i][0:metadata[i]["end_ix"] - metadata[i]["start_ix"]] = text_embeddings[i][metadata[i]["start_ix"]:metadata[i]["end_ix"]] #max_sent_len = max(sentence_lengths) #the_list = [list(k+metadata[i]["start_ix"] if k < max_sent_len else 0 for k in range(text_embeddings.shape[1])) for i in range(len(metadata))] #import ipdb; ipdb.set_trace() #text_embeddings = torch.gather(text_embeddings, 1, torch.tensor(the_list, device=text_embeddings.device).unsqueeze(2).repeat(1, 1, text_embeddings.shape[2])) text_embeddings = new_text_embeddings # Only keep the text embeddings that correspond to actual tokens. # text_embeddings = text_embeddings[:, :max_sentence_length, :].contiguous() text_mask = text_mask[:, :max_sentence_length].contiguous() # Shape: (batch_size, max_sentence_length, encoding_dim) contextualized_embeddings = self._lstm_dropout(self._context_layer(text_embeddings, text_mask)) assert spans.max() < contextualized_embeddings.shape[1] if self._attentive_span_extractor is not None: # Shape: (batch_size, num_spans, emebedding_size) attended_span_embeddings = self._attentive_span_extractor(text_embeddings, spans) # Shape: (batch_size, num_spans) span_mask = (spans[:, :, 0] >= 0).float() # SpanFields return -1 when they are used as padding. As we do # some comparisons based on span widths when we attend over the # span representations that we generate from these indices, we # need them to be <= 0. This is only relevant in edge cases where # the number of spans we consider after the pruning stage is >= the # total number of spans, because in this case, it is possible we might # consider a masked span. # Shape: (batch_size, num_spans, 2) spans = F.relu(spans.float()).long() # Shape: (batch_size, num_spans, 2 * encoding_dim + feature_size) endpoint_span_embeddings = self._endpoint_span_extractor(contextualized_embeddings, spans) if self._attentive_span_extractor is not None: # Shape: (batch_size, num_spans, emebedding_size + 2 * encoding_dim + feature_size) span_embeddings = torch.cat([endpoint_span_embeddings, attended_span_embeddings], -1) else: span_embeddings = endpoint_span_embeddings # TODO(Ulme) try normalizing span embeddeings #span_embeddings = span_embeddings.abs().sum(dim=-1).unsqueeze(-1) # Make calls out to the modules to get results. output_coref = {'loss': 0} output_ner = {'loss': 0} output_relation = {'loss': 0} output_events = {'loss': 0} # Prune and compute span representations for coreference module if self._loss_weights["coref"] > 0 or self._coref.coref_prop > 0: output_coref, coref_indices = self._coref.compute_representations( spans, span_mask, span_embeddings, sentence_lengths, coref_labels, metadata) # Prune and compute span representations for relation module if self._loss_weights["relation"] > 0 or self._relation.rel_prop > 0: output_relation = self._relation.compute_representations( spans, span_mask, span_embeddings, sentence_lengths, relation_labels, metadata) # Propagation of global information to enhance the span embeddings if self._coref.coref_prop > 0: # TODO(Ulme) Implement Coref Propagation output_coref = self._coref.coref_propagation(output_coref) span_embeddings = self._coref.update_spans(output_coref, span_embeddings, coref_indices) if self._relation.rel_prop > 0: output_relation = self._relation.relation_propagation(output_relation) span_embeddings = self.update_span_embeddings(span_embeddings, span_mask, output_relation["top_span_embeddings"], output_relation["top_span_mask"], output_relation["top_span_indices"]) # Make predictions and compute losses for each module if self._loss_weights['ner'] > 0: output_ner = self._ner( spans, span_mask, span_embeddings, sentence_lengths, ner_labels, metadata) if self._loss_weights['coref'] > 0: try : output_coref = self._coref.predict_labels(output_coref, metadata) except : output_coref = {} if self._loss_weights['relation'] > 0: output_relation = self._relation.predict_labels(relation_labels, output_relation, metadata) if self._loss_weights['events'] > 0: # Make the trigger embeddings the same size as the argument embeddings to make # propagation easier. if self._events._span_prop._n_span_prop > 0: trigger_embeddings = contextualized_embeddings.repeat(1, 1, 2) trigger_widths = torch.zeros([trigger_embeddings.size(0), trigger_embeddings.size(1)], device=trigger_embeddings.device, dtype=torch.long) trigger_width_embs = self._endpoint_span_extractor._span_width_embedding(trigger_widths) trigger_width_embs = trigger_width_embs.detach() trigger_embeddings = torch.cat([trigger_embeddings, trigger_width_embs], dim=-1) else: trigger_embeddings = contextualized_embeddings output_events = self._events( text_mask, trigger_embeddings, spans, span_mask, span_embeddings, cls_embeddings, sentence_lengths, output_ner, trigger_labels, argument_labels, ner_labels, metadata) if "loss" not in output_coref: output_coref["loss"] = 0 if "loss" not in output_relation: output_relation["loss"] = 0 # TODO(dwadden) just did this part. loss = (self._loss_weights['coref'] * output_coref['loss'] + self._loss_weights['ner'] * output_ner['loss'] + self._loss_weights['relation'] * output_relation['loss'] + self._loss_weights['events'] * output_events['loss']) output_dict = dict(coref=output_coref, relation=output_relation, ner=output_ner, events=output_events) output_dict['loss'] = loss # Check to see if event predictions are globally compatible (argument labels are compatible # with NER tags and trigger tags). # if self._loss_weights["ner"] > 0 and self._loss_weights["events"] > 0: # decoded_ner = self._ner.decode(output_dict["ner"]) # decoded_events = self._events.decode(output_dict["events"]) # self._joint_metrics(decoded_ner, decoded_events) return output_dict def update_span_embeddings(self, span_embeddings, span_mask, top_span_embeddings, top_span_mask, top_span_indices): # TODO(Ulme) Speed this up by tensorizing new_span_embeddings = span_embeddings.clone() for sample_nr in range(len(top_span_mask)): for top_span_nr, span_nr in enumerate(top_span_indices[sample_nr]): if top_span_mask[sample_nr, top_span_nr] == 0 or span_mask[sample_nr, span_nr] == 0: break new_span_embeddings[sample_nr, span_nr] = top_span_embeddings[sample_nr, top_span_nr] return new_span_embeddings @overrides def decode(self, output_dict: Dict[str, torch.Tensor]): """ Converts the list of spans and predicted antecedent indices into clusters of spans for each element in the batch. Parameters ---------- output_dict : ``Dict[str, torch.Tensor]``, required. The result of calling :func:`forward` on an instance or batch of instances. Returns ------- The same output dictionary, but with an additional ``clusters`` key: clusters : ``List[List[List[Tuple[int, int]]]]`` A nested list, representing, for each instance in the batch, the list of clusters, which are in turn comprised of a list of (start, end) inclusive spans into the original document. """ # TODO(dwadden) which things are already decoded? res = {} if self._loss_weights["coref"] > 0: try : res["coref"] = self._coref.decode(output_dict["coref"]) except : pass if self._loss_weights["ner"] > 0: res["ner"] = self._ner.decode(output_dict["ner"]) if self._loss_weights["relation"] > 0: res["relation"] = self._relation.decode(output_dict["relation"]) if self._loss_weights["events"] > 0: res["events"] = output_dict["events"] return res def get_metrics(self, reset: bool = False) -> Dict[str, float]: """ Get all metrics from all modules. For the ones that shouldn't be displayed, prefix their keys with an underscore. """ metrics_coref = self._coref.get_metrics(reset=reset) metrics_ner = self._ner.get_metrics(reset=reset) metrics_relation = self._relation.get_metrics(reset=reset) metrics_events = self._events.get_metrics(reset=reset) if self._loss_weights["ner"] > 0 and self._loss_weights["events"] > 0: metrics_joint = self._joint_metrics.get_metric(reset=reset) else: metrics_joint = {} # Make sure that there aren't any conflicting names. metric_names = (list(metrics_coref.keys()) + list(metrics_ner.keys()) + list(metrics_relation.keys()) + list(metrics_events.keys())) assert len(set(metric_names)) == len(metric_names) all_metrics = dict(list(metrics_coref.items()) + list(metrics_ner.items()) + list(metrics_relation.items()) + list(metrics_events.items()) + list(metrics_joint.items())) # If no list of desired metrics given, display them all. if self._display_metrics is None: return all_metrics # Otherwise only display the selected ones. res = {} for k, v in all_metrics.items(): if k in self._display_metrics: res[k] = v else: new_k = "_" + k res[new_k] = v return res

665c07f5066184b2d353592c7cd71270f8ccbf8a

py

Python

utils.py

vdumoulin/espresso-shot

b8113b52e8468c659bd762f7d22243269bef3984

utils.py

vdumoulin/espresso-shot

b8113b52e8468c659bd762f7d22243269bef3984

utils.py

vdumoulin/espresso-shot

b8113b52e8468c659bd762f7d22243269bef3984

"""Utility functions.""" import enum import json import struct import subprocess import time import numpy as np # Measurements contain 5 floats (elapsed_time, basket_resistance, # group_resistance, basket_temperature, and group_temperature) and an int # (state, for which 0, 1, 2, and 3 map to START, RUNNING, STOP, and STOPPED, # respectively). FORMAT_STRING = 'fffffi' def compile_and_upload(fqbn, port): """Compiles the Arduino sketch and uploads it to the device. Args: fbqn: str, fully qualified board name. port: str, upload port. """ subprocess.run(['arduino-cli', 'compile', '--fqbn', fqbn, 'espresso-shot']) subprocess.run(['arduino-cli', 'upload', '-p', port, '--fqbn', fqbn, 'espresso-shot']) def find_port_if_not_specified(fqbn, port): """Finds an upload port if it's left unspecified. If `port` is None, then uses `arduino-cli board list` to find all boards connected to the computer with the specified fully qualified board name and sets `port` to that of the first board found. Args: fbqn: str, fully qualified board name. port: str or None, upload port. Returns: port: str, the upload port. Raises: RuntimeError, if `port` is None and no board with the specified fully qualified board name is connected to the computer. """ process = subprocess.Popen( ['arduino-cli', 'board', 'list', '--format', 'json'], stdout=subprocess.PIPE) devices = json.loads(process.communicate()[0].decode('utf-8')) for device in devices: if 'boards' in device and any(board['FQBN'] == fqbn for board in device['boards']): port = port or device['address'] break if port is None: raise RuntimeError('no port specified and no board with the specified ' 'FQBN was found.') return port def read_measurement(serial_port): """Reads a measurement from the serial port. Args: serial_port: Serial, serial port to read from. Returns: tuple of (float, float, float, float, float, int) of form (elapsed_time, basket_resistance, group_resistance, basket_temperature, group_temperature, state). """ return struct.unpack( FORMAT_STRING, serial_port.read(struct.calcsize(FORMAT_STRING))) class MockSerial: """Mock serial port used to test the interface when no device is available. We simulate alternating between pulling a shot for 30 seconds and letting the machine idle for 30 seconds, but we have time run twice as fast for convenience. """

"""Utility functions.""" import enum import json import struct import subprocess import time import numpy as np # Measurements contain 5 floats (elapsed_time, basket_resistance, # group_resistance, basket_temperature, and group_temperature) and an int # (state, for which 0, 1, 2, and 3 map to START, RUNNING, STOP, and STOPPED, # respectively). FORMAT_STRING = 'fffffi' class State(enum.IntEnum): START = 0 RUNNING = 1 STOP = 2 STOPPED = 3 def compile_and_upload(fqbn, port): """Compiles the Arduino sketch and uploads it to the device. Args: fbqn: str, fully qualified board name. port: str, upload port. """ subprocess.run(['arduino-cli', 'compile', '--fqbn', fqbn, 'espresso-shot']) subprocess.run(['arduino-cli', 'upload', '-p', port, '--fqbn', fqbn, 'espresso-shot']) def find_port_if_not_specified(fqbn, port): """Finds an upload port if it's left unspecified. If `port` is None, then uses `arduino-cli board list` to find all boards connected to the computer with the specified fully qualified board name and sets `port` to that of the first board found. Args: fbqn: str, fully qualified board name. port: str or None, upload port. Returns: port: str, the upload port. Raises: RuntimeError, if `port` is None and no board with the specified fully qualified board name is connected to the computer. """ process = subprocess.Popen( ['arduino-cli', 'board', 'list', '--format', 'json'], stdout=subprocess.PIPE) devices = json.loads(process.communicate()[0].decode('utf-8')) for device in devices: if 'boards' in device and any(board['FQBN'] == fqbn for board in device['boards']): port = port or device['address'] break if port is None: raise RuntimeError('no port specified and no board with the specified ' 'FQBN was found.') return port def read_measurement(serial_port): """Reads a measurement from the serial port. Args: serial_port: Serial, serial port to read from. Returns: tuple of (float, float, float, float, float, int) of form (elapsed_time, basket_resistance, group_resistance, basket_temperature, group_temperature, state). """ return struct.unpack( FORMAT_STRING, serial_port.read(struct.calcsize(FORMAT_STRING))) class MockSerial: """Mock serial port used to test the interface when no device is available. We simulate alternating between pulling a shot for 30 seconds and letting the machine idle for 30 seconds, but we have time run twice as fast for convenience. """ def __init__(self, **kwargs): self._time = 0 self._period = 30 self._running = True def read(self, size=1): # One simulated second lasts half a real-time second. time.sleep(0.5) # Sample random basket and group readings. basket_resistance = np.random.normal(loc=10000.0, scale=100.0) group_resistance = np.random.normal(loc=10000.0, scale=100.0) basket_temperature = np.random.normal(loc=92.0, scale=0.5) group_temperature = np.random.normal(loc=92.0, scale=0.5) # The device displays the previous shot's time when the machine is idle. elapsed_time = self._time if self._running else self._period # Determine the device's state. if self._running: # The state is 'START' at the first measurement of a shot, and 'RUNNING' # afterwards. state = State.START if self._time == 0 else State.RUNNING else: # The first measurement at the end of the shot has state 'STOP', and # subsequent measurements have state 'STOPPED'. state = ( State.STOP if (self._time == self._period and self._running) else State.STOPPED) # Advance simulated time by one second, and reset to zero after 30 seconds # has passed. self._time = (self._time + 1) % (self._period + 1) # Switch between "pulling a shot" and "idle" at every cycle. if self._time == 0: self._running = not self._running return struct.pack( 'fffffi', elapsed_time, basket_resistance, group_resistance, basket_temperature, group_temperature, int(state))

d63c354c403a2f8213840fefd6b381f385a4e15a

py

Python

setup.py

jangroth/gtit-cv

6c912dcd11ced9f6613a36697a99eac86bf6480e

2021-09-27T17:55:54.000Z

2021-11-15T11:43:02.000Z

setup.py

jangroth/git-cv

6c912dcd11ced9f6613a36697a99eac86bf6480e

setup.py

jangroth/git-cv

6c912dcd11ced9f6613a36697a99eac86bf6480e

from setuptools import setup, find_packages setup( name="gitcv", version="0.1", packages=find_packages(), author="Jan Groth", license="MIT License", setup_requires=['pytest-runner'], tests_require=['pytest'] )

from setuptools import setup, find_packages setup( name="gitcv", version="0.1", packages=find_packages(), author="Jan Groth", license="MIT License", setup_requires=['pytest-runner'], tests_require=['pytest'] )

a573cae0a3b0113b03c43c3f97dc1f74f8754061

py

Python

sine_attractors.py

dcxSt/attractors

7df0fc593ca7bc2dbc05d488b1742fc359cf6c7f

2021-11-07T11:56:53.000Z

2021-11-07T11:56:53.000Z

sine_attractors.py

dcxSt/attractors

7df0fc593ca7bc2dbc05d488b1742fc359cf6c7f

sine_attractors.py

dcxSt/attractors

7df0fc593ca7bc2dbc05d488b1742fc359cf6c7f

#!/usr/bin/env python # coding: utf-8 # http://paulbourke.net/fractals/clifford/?curius=373 # In[13]: import numpy as np import math as m import matplotlib.pyplot as plt # In[65]: a = -1.5 b = 1.6 c = 1.2 d = 0.7 # In[66]: # In[ ]: # In[77]: sidelength = 8192 center = (sidelength // 2 , sidelength // 2) grid = np.zeros((sidelength,sidelength)) x,y = 0,0 for i in range(30000000): x,y = update(x,y) posx = int(x * sidelength / 5) + center[0] posy = int(y * sidelength / 4) + center[1] if posx < sidelength and posx >= 0 and posy < sidelength and posy >= 0: grid[posx][posy] += 2 else: print(posx, posy) # print(x,y) # In[74]: max(grid.flatten()), max(np.log(grid.flatten() + 1)) # In[88]: lovely_cmaps = ["YlGn","rainbow", "gnuplot2"] for cmap in lovely_cmaps: plt.figure(figsize=(20,20)) plt.imshow(np.log(grid + 1), cmap=cmap) plt.tick_params( axis='both', # changes apply to the x-axis which='both', # both major and minor ticks are affected bottom=False, # ticks along the bottom edge are off top=False, # ticks along the top edge are off labelbottom=False, labelleft=False, left=False, right=False) # labels along the bottom edge are off plt.axis("off") plt.savefig("convergence_orbweaver_{}.png".format(cmap)) print("convergence_orbweaver_{}.png".format(cmap)) plt.show() # In[ ]:

#!/usr/bin/env python # coding: utf-8 # http://paulbourke.net/fractals/clifford/?curius=373 # In[13]: import numpy as np import math as m import matplotlib.pyplot as plt # In[65]: a = -1.5 b = 1.6 c = 1.2 d = 0.7 # In[66]: def update(x,y): # takes floats, returns updated floats xnew = m.sin(a * y) + c * m.cos(a * x) ynew = m.sin(b * x) + d * m.cos(b * y) return xnew, ynew # In[ ]: # In[77]: sidelength = 8192 center = (sidelength // 2 , sidelength // 2) grid = np.zeros((sidelength,sidelength)) x,y = 0,0 for i in range(30000000): x,y = update(x,y) posx = int(x * sidelength / 5) + center[0] posy = int(y * sidelength / 4) + center[1] if posx < sidelength and posx >= 0 and posy < sidelength and posy >= 0: grid[posx][posy] += 2 else: print(posx, posy) # print(x,y) # In[74]: max(grid.flatten()), max(np.log(grid.flatten() + 1)) # In[88]: lovely_cmaps = ["YlGn","rainbow", "gnuplot2"] for cmap in lovely_cmaps: plt.figure(figsize=(20,20)) plt.imshow(np.log(grid + 1), cmap=cmap) plt.tick_params( axis='both', # changes apply to the x-axis which='both', # both major and minor ticks are affected bottom=False, # ticks along the bottom edge are off top=False, # ticks along the top edge are off labelbottom=False, labelleft=False, left=False, right=False) # labels along the bottom edge are off plt.axis("off") plt.savefig("convergence_orbweaver_{}.png".format(cmap)) print("convergence_orbweaver_{}.png".format(cmap)) plt.show() # In[ ]:

0f36567e3c2d414731c0ed12a4624896fc40e0c8

py

Python

bot.py

MistyScene/AutoForms

e5391e95acfc1aaeae5bec635845203f7ff6b027

bot.py

MistyScene/AutoForms

e5391e95acfc1aaeae5bec635845203f7ff6b027

bot.py

MistyScene/AutoForms

e5391e95acfc1aaeae5bec635845203f7ff6b027

# -*- coding: utf-8 -*- import discord from discord import Embed from discord.ext import tasks from datetime import datetime import os import requests import random import json TOKEN = os.environ["TOKEN"] client = discord.Client(intents=discord.Intents.all()) # 次回送信予定時刻を06:00-8:30までの間でランダムに設定 time_set = setting_time_set() # 起動時に初回の次回送信時刻を設定 tem_set = set_tem() # Embedの関数 @client.event @client.event @tasks.loop(seconds=60) loop.start() client.run(TOKEN)

# -*- coding: utf-8 -*- import discord from discord import Embed from discord.ext import tasks from datetime import datetime import os import requests import random import json TOKEN = os.environ["TOKEN"] client = discord.Client(intents=discord.Intents.all()) # 次回送信予定時刻を06:00-8:30までの間でランダムに設定 def setting_time_set(): setting_time_h = random.randint(6, 8) if setting_time_h == 8: setting_time_m = random.randint(0, 30) else: setting_time_m = random.randint(0, 59) setting_time = f"{setting_time_h:02}:{setting_time_m:02}" return setting_time def set_tem(): choice_list = ["３６．４", "３６．５", "３６．６"] choice_ans = random.choice(choice_list) # 36.4-36.6までの間でランダムに選択 return choice_ans time_set = setting_time_set() # 起動時に初回の次回送信時刻を設定 tem_set = set_tem() # Embedの関数 async def template_embed(message, title, name_1, name_2, value_1, color, description=None): ch = client.get_channel(message) embed_time = datetime.now().strftime("%Y年%m月%d日-%H:%M") embed = Embed(title=title, description=description, color=color) embed.add_field(name=name_1, value=f"{value_1}", inline=True) embed.add_field(name=name_2, value=f"{tem_set}", inline=True) embed.set_footer(text=f"{embed_time}") await ch.send("<@343956207754805251>") await ch.send(embed=embed) @client.event async def on_ready(): await template_embed(message=768274673984208926, title="起動ログ", name_1="次回の送信予定時刻", value_1=time_set, name_2="送信予定の体温", color=discord.Color.orange()) @client.event async def on_message(message): if message.content == "/reset": await reset(message) if message.content == "/now": await now(message) async def reset(message): global time_set global tem_set time_set = setting_time_set() tem_set = set_tem() await template_embed(message=768274673984208926, title="リセットされました", name_1="次回の送信予定時刻", name_2="送信予定の体温", value_1=time_set, color=discord.Color.purple()) await template_embed(message=message.channel.id, title="リセットされました", name_1="次回の送信予定時刻", name_2="送信予定の体温", value_1=time_set, color=discord.Color.purple()) async def now(message): await template_embed(message=message.channel.id, title="現在の状況", name_1="次回の送信予定時刻", name_2="送信予定の体温", value_1=time_set, color=discord.Color.greyple()) @tasks.loop(seconds=60) async def loop(): global time_set global tem_set now_t = datetime.now().strftime('%H:%M') print(f"現在の時刻：{now_t}／送信予定時刻：{time_set}／送信予定体温：{tem_set}") if now_t == time_set: # 送信予定時刻になった？ dt_now = datetime.now().strftime("%Y-%m-%d") # 現在時刻を2020-01-01の形で取得、dt_nowに格納 file_name = "cfg.json" with open(file_name, "r", encoding="utf-8")as f: cfg = json.load(f) cfg["output"]["ans_1"] = f"{dt_now}" cfg["output"]["ans_4"] = f"{tem_set}" params = {"entry.{}".format(cfg["entry"][k]): cfg["output"][k] for k in cfg["entry"].keys()} res = requests.get(cfg["form_url"] + "formResponse", params=params) if res.status_code == 200: await template_embed(message=768274673984208926, title="ログ情報", description=f"[URL]({res.url})", name_1="完了状態", name_2="送信された体温", value_1="成功しました", color=discord.Color.green()) else: res.raise_for_status() await template_embed(message=768274673984208926, title="ログ情報", name_1="完了状態", name_2="送信予定だった体温", value_1="エラーが発生しました。", color=discord.Color.red()) else: if now_t == "21:00": time_set = setting_time_set() tem_set = set_tem() await template_embed(message=768274673984208926, title="送信時刻更新のお知らせ", name_1="次回の送信予定時刻", name_2="送信予定の体温", value_1=time_set, color=discord.Color.blue()) loop.start() client.run(TOKEN)

5609448a8b557b5dc9ac459a5689d4f1819a5e51

py

Python

exam.py

mfshiu/dog-siamese

3e9077f2231d9cc9ac8eea5ac246901d2d5e6729

exam.py

mfshiu/dog-siamese

3e9077f2231d9cc9ac8eea5ac246901d2d5e6729

exam.py

mfshiu/dog-siamese

3e9077f2231d9cc9ac8eea5ac246901d2d5e6729

import os import sys import torch from config import Config from train3 import image_size from model import SiameseNetwork from evaluate3 import TestDataset from torch.utils.data import DataLoader use_gpu = False register_dir = "./data/ct0202a/" threshold = 50 siam_model = None log_lines = [] ## if __name__ == '__main__': register_dir = Config.register_dir dog_id = None dog_img = None exam_dir = None model_path = "./trained/DogSiamese-2.pkl" for a in sys.argv[1:]: if a.lower() == 'gpu': use_gpu = True else: aa = a.split("=") if "dog" == aa[0]: dog_id = aa[1] elif "img" == aa[0]: dog_img = aa[1] elif "exam_dir" == aa[0]: exam_dir = aa[1] elif "model" == aa[0]: model_path = aa[1] else: register_dir = a print('Use gpu：', use_gpu) print('Register dir：', register_dir) print('Dog ID to be checked：', dog_id) print('Dog image to check：', dog_img) if use_gpu: siam_model = SiameseNetwork(image_size).cuda() siam_model.load_state_dict(torch.load(model_path, map_location=torch.device('cuda:0'))) else: siam_model = SiameseNetwork(image_size).cpu() siam_model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu'))) siam_model.eval() if exam_dir: img_paths = [] sum_lines = [] for path, subdirs, files in os.walk(exam_dir): for name in files: img_paths.append(os.path.join(path, name)) img_paths.sort() for i, img in enumerate(img_paths): find_id, similarity = find_dog(img) if find_id: line = "%s = %s (%s)" % (img_paths[i], find_id, similarity) else: line = "%s = None" % (img_paths[i],) sum_lines.append("%s\n" % (line,)) print(line) elif dog_id: is_same = exam_dog(dog_id, dog_img)[0] if is_same: print("Yes, The dog is %s." % (dog_id,)) else: print("No, The dog is not %s." % (dog_id,)) else: find_id, similarity = find_dog(dog_img) if find_id: print("The dog is %s, similarity is %s" % (find_id, similarity)) else: print("Cannot find the dog.") with open("exam.log", "w") as fp: fp.writelines(sum_lines) fp.writelines("\nDetails ==================\n") fp.writelines(log_lines)

import os import sys import torch from config import Config from train3 import image_size from model import SiameseNetwork from evaluate3 import TestDataset from torch.utils.data import DataLoader use_gpu = False register_dir = "./data/ct0202a/" threshold = 50 siam_model = None log_lines = [] ## def exam_dog(dog_id, img_path): exam_count = Config.exam_count dog_dir = os.path.join(register_dir, dog_id) walked = [x for x in os.walk(dog_dir)][0] dog_paths = [os.path.join(walked[0], x) for x in walked[2]] dog_paths.sort() similarities = [] test_set = TestDataset(img_path, dog_paths[0: min(len(dog_paths), exam_count + 1)]) test_dataloader = DataLoader(test_set, shuffle=False, batch_size=1, num_workers=0) for i, data in enumerate(test_dataloader): img0, img1 = data if use_gpu: similarity = siam_model.evaluate(img0.cuda(), img1.cuda()) else: similarity = siam_model.evaluate(img0.cpu(), img1.cpu()) similarities.append(similarity) is_same = len([x for x in similarities if x > threshold]) > exam_count // 2 return is_same, similarities def find_dog(img_path): walked = [x for x in os.walk(register_dir)][0] dog_ids = walked[1] dog_ids.sort() max_avg = 0 hit_dog = None for dog_id in dog_ids: similarities = exam_dog(dog_id, img_path)[1] similarities.sort() avg = sum(similarities) / len(similarities) # avg = sum(similarities[Config.exam_count//2:]) / (len(similarities)-Config.exam_count//2) # avg = sum(x**2 for x in similarities)**0.5 # avg = max(similarities[]) log_lines.append("%s->%s(avg:%s,%s)\n" % (img_path, dog_id, avg, similarities)) if avg > threshold and max_avg < avg: max_avg = avg hit_dog = dog_id return hit_dog, max_avg if __name__ == '__main__': register_dir = Config.register_dir dog_id = None dog_img = None exam_dir = None model_path = "./trained/DogSiamese-2.pkl" for a in sys.argv[1:]: if a.lower() == 'gpu': use_gpu = True else: aa = a.split("=") if "dog" == aa[0]: dog_id = aa[1] elif "img" == aa[0]: dog_img = aa[1] elif "exam_dir" == aa[0]: exam_dir = aa[1] elif "model" == aa[0]: model_path = aa[1] else: register_dir = a print('Use gpu：', use_gpu) print('Register dir：', register_dir) print('Dog ID to be checked：', dog_id) print('Dog image to check：', dog_img) if use_gpu: siam_model = SiameseNetwork(image_size).cuda() siam_model.load_state_dict(torch.load(model_path, map_location=torch.device('cuda:0'))) else: siam_model = SiameseNetwork(image_size).cpu() siam_model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu'))) siam_model.eval() if exam_dir: img_paths = [] sum_lines = [] for path, subdirs, files in os.walk(exam_dir): for name in files: img_paths.append(os.path.join(path, name)) img_paths.sort() for i, img in enumerate(img_paths): find_id, similarity = find_dog(img) if find_id: line = "%s = %s (%s)" % (img_paths[i], find_id, similarity) else: line = "%s = None" % (img_paths[i],) sum_lines.append("%s\n" % (line,)) print(line) elif dog_id: is_same = exam_dog(dog_id, dog_img)[0] if is_same: print("Yes, The dog is %s." % (dog_id,)) else: print("No, The dog is not %s." % (dog_id,)) else: find_id, similarity = find_dog(dog_img) if find_id: print("The dog is %s, similarity is %s" % (find_id, similarity)) else: print("Cannot find the dog.") with open("exam.log", "w") as fp: fp.writelines(sum_lines) fp.writelines("\nDetails ==================\n") fp.writelines(log_lines)

e460f5ef73e060f6a64ca12aa77375e312b5b1cf

py

Python

auxiliary/auxiliary.py

burakbalaban/student-project-burakbalaban

1b58ab1896e65937c16d9f5b4ff1a8bddc2d2db7

auxiliary/auxiliary.py

burakbalaban/student-project-burakbalaban

1b58ab1896e65937c16d9f5b4ff1a8bddc2d2db7

auxiliary/auxiliary.py

burakbalaban/student-project-burakbalaban

1b58ab1896e65937c16d9f5b4ff1a8bddc2d2db7

"""This module contains auxiliary function which we use in the example notebook.""" import json import matplotlib.patches as mpatches import matplotlib.pyplot as plt from scipy.stats import norm import pandas as pd import numpy as np from grmpy.estimate.estimate_output import calculate_mte from grmpy.read.read import read def process_data(df, output_file): """This function adds squared and interaction terms to the Cainero data set.""" # Delete redundant columns\n", for key_ in ['newid', 'caseid']: del df[key_] # Add squared terms for key_ in ['mhgc', 'cafqt', 'avurate', 'lurate_17', 'numsibs', 'lavlocwage17']: str_ = key_ + 'sq' df[str_] = df[key_] ** 2 # Add interaction terms for j in ['pub4', 'lwage5_17', 'lurate_17', 'tuit4c']: for i in ['cafqt', 'mhgc', 'numsibs']: df[j + i] = df[j] * df[i] df.to_pickle(output_file + '.pkl') def plot_est_mte(rslt, file): """This function calculates the marginal treatment effect for different quartiles of the unobservable V. ased on the calculation results.""" init_dict = read(file) data_frame = pd.read_pickle(init_dict['ESTIMATION']['file']) # Define the Quantiles and read in the original results quantiles = [0.0001] + np.arange(0.01, 1., 0.01).tolist() + [0.9999] mte_ = json.load(open('data/mte_original.json', 'r')) mte_original = mte_[1] mte_original_d = mte_[0] mte_original_u = mte_[2] # Calculate the MTE and confidence intervals mte = calculate_mte(rslt, init_dict, data_frame, quantiles) mte = [i / 4 for i in mte] mte_up, mte_d = calculate_cof_int(rslt, init_dict, data_frame, mte, quantiles) # Plot both curves ax = plt.figure(figsize=(17.5, 10)).add_subplot(111) ax.set_ylabel(r"$B^{MTE}$", fontsize=24) ax.set_xlabel("$u_D$", fontsize=24) ax.tick_params(axis='both', which='major', labelsize=18) ax.plot(quantiles, mte, label='grmpy $B^{MTE}$', color='blue', linewidth=4) ax.plot(quantiles, mte_up, color='blue', linestyle=':', linewidth=3) ax.plot(quantiles, mte_d, color='blue', linestyle=':', linewidth=3) ax.plot(quantiles, mte_original, label='original$B^{MTE}$', color='orange', linewidth=4) ax.plot(quantiles, mte_original_d, color='orange', linestyle=':',linewidth=3) ax.plot(quantiles, mte_original_u, color='orange', linestyle=':', linewidth=3) ax.set_ylim([-0.41, 0.51]) ax.set_xlim([-0.005, 1.005]) blue_patch = mpatches.Patch(color='blue', label='original $B^{MTE}$') orange_patch = mpatches.Patch(color='orange', label='grmpy $B^{MTE}$') plt.legend(handles=[blue_patch, orange_patch],prop={'size': 16}) plt.show() return mte def calculate_cof_int(rslt, init_dict, data_frame, mte, quantiles): """This function calculates the confidence interval of the marginal treatment effect.""" # Import parameters and inverse hessian matrix hess_inv = rslt['AUX']['hess_inv'] / data_frame.shape[0] params = rslt['AUX']['x_internal'] # Distribute parameters dist_cov = hess_inv[-4:, -4:] param_cov = hess_inv[:46, :46] dist_gradients = np.array([params[-4], params[-3], params[-2], params[-1]]) # Process data covariates = init_dict['TREATED']['order'] x = np.mean(data_frame[covariates]).tolist() x_neg = [-i for i in x] x += x_neg x = np.array(x) # Create auxiliary parameters part1 = np.dot(x, np.dot(param_cov, x)) part2 = np.dot(dist_gradients, np.dot(dist_cov, dist_gradients)) # Prepare two lists for storing the values mte_up = [] mte_d = [] # Combine all auxiliary parameters and calculate the confidence intervals for counter, i in enumerate(quantiles): value = part2 * (norm.ppf(i)) ** 2 aux = np.sqrt(part1 + value) / 4 mte_up += [mte[counter] + norm.ppf(0.95) * aux] mte_d += [mte[counter] - norm.ppf(0.95) * aux] return mte_up, mte_d

"""This module contains auxiliary function which we use in the example notebook.""" import json import matplotlib.patches as mpatches import matplotlib.pyplot as plt from scipy.stats import norm import pandas as pd import numpy as np from grmpy.estimate.estimate_output import calculate_mte from grmpy.read.read import read def process_data(df, output_file): """This function adds squared and interaction terms to the Cainero data set.""" # Delete redundant columns\n", for key_ in ['newid', 'caseid']: del df[key_] # Add squared terms for key_ in ['mhgc', 'cafqt', 'avurate', 'lurate_17', 'numsibs', 'lavlocwage17']: str_ = key_ + 'sq' df[str_] = df[key_] ** 2 # Add interaction terms for j in ['pub4', 'lwage5_17', 'lurate_17', 'tuit4c']: for i in ['cafqt', 'mhgc', 'numsibs']: df[j + i] = df[j] * df[i] df.to_pickle(output_file + '.pkl') def plot_est_mte(rslt, file): """This function calculates the marginal treatment effect for different quartiles of the unobservable V. ased on the calculation results.""" init_dict = read(file) data_frame = pd.read_pickle(init_dict['ESTIMATION']['file']) # Define the Quantiles and read in the original results quantiles = [0.0001] + np.arange(0.01, 1., 0.01).tolist() + [0.9999] mte_ = json.load(open('data/mte_original.json', 'r')) mte_original = mte_[1] mte_original_d = mte_[0] mte_original_u = mte_[2] # Calculate the MTE and confidence intervals mte = calculate_mte(rslt, init_dict, data_frame, quantiles) mte = [i / 4 for i in mte] mte_up, mte_d = calculate_cof_int(rslt, init_dict, data_frame, mte, quantiles) # Plot both curves ax = plt.figure(figsize=(17.5, 10)).add_subplot(111) ax.set_ylabel(r"$B^{MTE}$", fontsize=24) ax.set_xlabel("$u_D$", fontsize=24) ax.tick_params(axis='both', which='major', labelsize=18) ax.plot(quantiles, mte, label='grmpy $B^{MTE}$', color='blue', linewidth=4) ax.plot(quantiles, mte_up, color='blue', linestyle=':', linewidth=3) ax.plot(quantiles, mte_d, color='blue', linestyle=':', linewidth=3) ax.plot(quantiles, mte_original, label='original$B^{MTE}$', color='orange', linewidth=4) ax.plot(quantiles, mte_original_d, color='orange', linestyle=':',linewidth=3) ax.plot(quantiles, mte_original_u, color='orange', linestyle=':', linewidth=3) ax.set_ylim([-0.41, 0.51]) ax.set_xlim([-0.005, 1.005]) blue_patch = mpatches.Patch(color='blue', label='original $B^{MTE}$') orange_patch = mpatches.Patch(color='orange', label='grmpy $B^{MTE}$') plt.legend(handles=[blue_patch, orange_patch],prop={'size': 16}) plt.show() return mte def calculate_cof_int(rslt, init_dict, data_frame, mte, quantiles): """This function calculates the confidence interval of the marginal treatment effect.""" # Import parameters and inverse hessian matrix hess_inv = rslt['AUX']['hess_inv'] / data_frame.shape[0] params = rslt['AUX']['x_internal'] # Distribute parameters dist_cov = hess_inv[-4:, -4:] param_cov = hess_inv[:46, :46] dist_gradients = np.array([params[-4], params[-3], params[-2], params[-1]]) # Process data covariates = init_dict['TREATED']['order'] x = np.mean(data_frame[covariates]).tolist() x_neg = [-i for i in x] x += x_neg x = np.array(x) # Create auxiliary parameters part1 = np.dot(x, np.dot(param_cov, x)) part2 = np.dot(dist_gradients, np.dot(dist_cov, dist_gradients)) # Prepare two lists for storing the values mte_up = [] mte_d = [] # Combine all auxiliary parameters and calculate the confidence intervals for counter, i in enumerate(quantiles): value = part2 * (norm.ppf(i)) ** 2 aux = np.sqrt(part1 + value) / 4 mte_up += [mte[counter] + norm.ppf(0.95) * aux] mte_d += [mte[counter] - norm.ppf(0.95) * aux] return mte_up, mte_d

375be2219b434f699514d5dfd0f487eacc8beb7f

py

Python

dataentry/models.py

louisdijkstra/kumbhmela-metadata

59c6f22d0d5b307617ff03e3700c089f209788bf

dataentry/models.py

louisdijkstra/kumbhmela-metadata

59c6f22d0d5b307617ff03e3700c089f209788bf

dataentry/models.py

louisdijkstra/kumbhmela-metadata

59c6f22d0d5b307617ff03e3700c089f209788bf

from django.db import models from django.utils.encoding import python_2_unicode_compatible """ Contains the models/tables for the kumbhmela_db.sqlite3 database. Every field has (when necessary) a 'help_text' that explains the meaning of the field. """ __author__ = "Louis Dijkstra" @python_2_unicode_compatible class Drive(models.Model): """ Table/model to represent (a collection of) drive(s). """ label = models.CharField(max_length=50, help_text="Label added to the drive, e.g., 'kumbhmela_5'.") external = models.BooleanField(default=False, help_text="True when the drive is external and false otherwise.") time_added = models.DateTimeField(blank=True, null=True, help_text="Time when the drive was added to the drive bay.") time_removed = models.DateTimeField(blank=True, null=True, help_text="Time when the drive was removed from the drive bay.") whereabouts = models.TextField(max_length=1000, blank=True, help_text="Whereabouts of this drive copy, e.g., who had it lasts, where is it now etc. (optional).") note = models.TextField(max_length=1000, blank=True, help_text="Additional notes on this (collection of) drive(s) (optional).") @python_2_unicode_compatible class DriveCopy(models.Model): """ Every Drive might have several copies. This table/model is used to keep track of them. """ drive = models.ForeignKey(Drive, on_delete=models.CASCADE, help_text="The unique drive it is a copy of.") label = models.CharField(max_length=50, help_text="Label added to the drive, e.g., 'kumbhmela_5II'.") number = models.IntegerField(help_text="Drive copy number.") whereabouts = models.TextField(max_length=1000, blank=True, help_text="Whereabouts of this drive copy, e.g., who had it lasts, where is it now etc. (optional).") note = models.TextField(max_length=1000, blank=True, help_text="Additional notes on this drive copy (optional).") @python_2_unicode_compatible class Person(models.Model): """ Table/Model to represent a person """ name = models.CharField(max_length=100, help_text="First and last name.") email = models.CharField(max_length=200, blank=True, help_text="Email address(es) (optional).") note = models.TextField(max_length=1000, blank=True, help_text="Notes (optional).") @python_2_unicode_compatible class Experiment(models.Model): """ Table/Model to represent the various subexperiments """ # every experiment is linked to a contact person contactperson = models.ForeignKey(Person, on_delete=models.CASCADE, help_text="Main contact person for this subexperiment.") name = models.CharField(max_length=100, help_text="Name of the subexperiment.") number = models.IntegerField(help_text="Number of the subexperiment.") description = models.TextField(max_length=1000, blank=True, help_text="Short description of the experiment (optional).") note = models.TextField(max_length=1000, blank=True, help_text="Additional notes on the subexperiment (optional).") @python_2_unicode_compatible class Format(models.Model): """ Table/model to represent a file format, i.e., the format in which output of a sensor is stored """ extension = models.CharField(max_length=50, help_text="Extension of the file (in small letters!), e.g., '.txt' and not '.TXT'.") description = models.TextField(max_length=10000, blank=True, help_text="Description of the file format (optional).") @python_2_unicode_compatible class Location(models.Model): """ Table/model to represent a (geo)location """ latitude = models.FloatField(blank=True, help_text="Optional.") longitude = models.FloatField(blank=True, help_text="Optional.") description = models.TextField(max_length=1000, blank=True, help_text="Description of the location (optional).") @python_2_unicode_compatible class Sensor(models.Model): """ Table/model to represent a sensor (e.g., camera/GPS device) """ sensor_type = models.CharField(max_length=100, help_text="Short description of the sensor, e.g., 'GoPro Camera'.") location = models.ManyToManyField(Location, blank=True, help_text="The location for this sensor (optional).") format = models.ManyToManyField(Format, blank=True, help_text="The format for the output of this sensor (optional).") note = models.TextField(max_length=1000, blank=True, help_text="Notes for this sensor (optional).") @python_2_unicode_compatible class Source(models.Model): """ Table/model to represent a data source (e.g., 'Local police') """ name = models.CharField(max_length=200, help_text="Name of the data source (e.g., 'Local Police')") note = models.TextField(max_length=1000, blank=True, help_text="Additional notes on this data source (optional).") @python_2_unicode_compatible class File(models.Model): """ The main table/model for this app. It is used to keep track of all files on the various drives for the Kumbh Mela experiment. """ # a file can be stored a several drives: drive = models.ManyToManyField(Drive, through='StorageLocation', help_text="The drives on which the file is stored.") format = models.ForeignKey(Format, on_delete=models.CASCADE, blank=True, null=True, help_text="Format of the file (optional).") experiment = models.ManyToManyField(Experiment, blank=True, help_text="The subexperiment this file belongs to (optional).") source = models.ForeignKey(Source, on_delete=models.CASCADE, blank=True, null=True, help_text="The data source (optional).") sensor = models.ForeignKey(Sensor, on_delete=models.CASCADE, blank=True, null=True, help_text="Sensor used to obtain the data (optional).") location = models.ForeignKey(Location, on_delete=models.CASCADE, blank=True, null=True, help_text="Location where the recording took place (optional).") time_added = models.DateTimeField(auto_now=True, blank=True, help_text="Time when the drive was added to the drive bay (optional).") size = models.IntegerField(blank=True, null=True, help_text="Size in bytes (optional).") start_recording = models.DateTimeField(blank=True, null=True, help_text="Time when the recording started (optional).") end_recording = models.DateTimeField(blank=True, null=True, help_text="Time when the recording ended (optional).") note = models.TextField(max_length=1000, blank=True, help_text="Additional notes on this file (optional).") def __str__(self): """Returns the file path""" filepaths = set() n_copies = 0 # the number of copies for storagelocation in self.storagelocation_set.all(): filepaths.add(storagelocation.path) n_copies += 1 if n_copies == 1: return ', '.join(filepaths) + ' (1 copy)' return ', '.join(filepaths) + ' (%s copies)'%(int(n_copies)) class StorageLocation(models.Model): """ A location where a specific file is stored. This model/table links files and drives together. (Each file can be stored on multiple drives under different names). """ drive = models.ForeignKey(Drive, on_delete=models.CASCADE) file = models.ForeignKey(File, on_delete=models.CASCADE) path = models.CharField(max_length=300, help_text="Path of the file on the drive.")

from django.db import models from django.utils.encoding import python_2_unicode_compatible """ Contains the models/tables for the kumbhmela_db.sqlite3 database. Every field has (when necessary) a 'help_text' that explains the meaning of the field. """ __author__ = "Louis Dijkstra" @python_2_unicode_compatible class Drive(models.Model): """ Table/model to represent (a collection of) drive(s). """ label = models.CharField(max_length=50, help_text="Label added to the drive, e.g., 'kumbhmela_5'.") external = models.BooleanField(default=False, help_text="True when the drive is external and false otherwise.") time_added = models.DateTimeField(blank=True, null=True, help_text="Time when the drive was added to the drive bay.") time_removed = models.DateTimeField(blank=True, null=True, help_text="Time when the drive was removed from the drive bay.") whereabouts = models.TextField(max_length=1000, blank=True, help_text="Whereabouts of this drive copy, e.g., who had it lasts, where is it now etc. (optional).") note = models.TextField(max_length=1000, blank=True, help_text="Additional notes on this (collection of) drive(s) (optional).") def __str__(self): return self.label @python_2_unicode_compatible class DriveCopy(models.Model): """ Every Drive might have several copies. This table/model is used to keep track of them. """ drive = models.ForeignKey(Drive, on_delete=models.CASCADE, help_text="The unique drive it is a copy of.") label = models.CharField(max_length=50, help_text="Label added to the drive, e.g., 'kumbhmela_5II'.") number = models.IntegerField(help_text="Drive copy number.") whereabouts = models.TextField(max_length=1000, blank=True, help_text="Whereabouts of this drive copy, e.g., who had it lasts, where is it now etc. (optional).") note = models.TextField(max_length=1000, blank=True, help_text="Additional notes on this drive copy (optional).") def __str__(self): return self.label @python_2_unicode_compatible class Person(models.Model): """ Table/Model to represent a person """ name = models.CharField(max_length=100, help_text="First and last name.") email = models.CharField(max_length=200, blank=True, help_text="Email address(es) (optional).") note = models.TextField(max_length=1000, blank=True, help_text="Notes (optional).") def __str__(self): return self.name @python_2_unicode_compatible class Experiment(models.Model): """ Table/Model to represent the various subexperiments """ # every experiment is linked to a contact person contactperson = models.ForeignKey(Person, on_delete=models.CASCADE, help_text="Main contact person for this subexperiment.") name = models.CharField(max_length=100, help_text="Name of the subexperiment.") number = models.IntegerField(help_text="Number of the subexperiment.") description = models.TextField(max_length=1000, blank=True, help_text="Short description of the experiment (optional).") note = models.TextField(max_length=1000, blank=True, help_text="Additional notes on the subexperiment (optional).") def __str__(self): return "%s (subexperiment %d)"%(self.name, self.number) @python_2_unicode_compatible class Format(models.Model): """ Table/model to represent a file format, i.e., the format in which output of a sensor is stored """ extension = models.CharField(max_length=50, help_text="Extension of the file (in small letters!), e.g., '.txt' and not '.TXT'.") description = models.TextField(max_length=10000, blank=True, help_text="Description of the file format (optional).") def __str__(self): return self.extension @python_2_unicode_compatible class Location(models.Model): """ Table/model to represent a (geo)location """ latitude = models.FloatField(blank=True, help_text="Optional.") longitude = models.FloatField(blank=True, help_text="Optional.") description = models.TextField(max_length=1000, blank=True, help_text="Description of the location (optional).") @python_2_unicode_compatible class Sensor(models.Model): """ Table/model to represent a sensor (e.g., camera/GPS device) """ sensor_type = models.CharField(max_length=100, help_text="Short description of the sensor, e.g., 'GoPro Camera'.") location = models.ManyToManyField(Location, blank=True, help_text="The location for this sensor (optional).") format = models.ManyToManyField(Format, blank=True, help_text="The format for the output of this sensor (optional).") note = models.TextField(max_length=1000, blank=True, help_text="Notes for this sensor (optional).") def __str__(self): return self.sensor_type @python_2_unicode_compatible class Source(models.Model): """ Table/model to represent a data source (e.g., 'Local police') """ name = models.CharField(max_length=200, help_text="Name of the data source (e.g., 'Local Police')") note = models.TextField(max_length=1000, blank=True, help_text="Additional notes on this data source (optional).") def __str__(self): return self.name @python_2_unicode_compatible class File(models.Model): """ The main table/model for this app. It is used to keep track of all files on the various drives for the Kumbh Mela experiment. """ # a file can be stored a several drives: drive = models.ManyToManyField(Drive, through='StorageLocation', help_text="The drives on which the file is stored.") format = models.ForeignKey(Format, on_delete=models.CASCADE, blank=True, null=True, help_text="Format of the file (optional).") experiment = models.ManyToManyField(Experiment, blank=True, help_text="The subexperiment this file belongs to (optional).") source = models.ForeignKey(Source, on_delete=models.CASCADE, blank=True, null=True, help_text="The data source (optional).") sensor = models.ForeignKey(Sensor, on_delete=models.CASCADE, blank=True, null=True, help_text="Sensor used to obtain the data (optional).") location = models.ForeignKey(Location, on_delete=models.CASCADE, blank=True, null=True, help_text="Location where the recording took place (optional).") time_added = models.DateTimeField(auto_now=True, blank=True, help_text="Time when the drive was added to the drive bay (optional).") size = models.IntegerField(blank=True, null=True, help_text="Size in bytes (optional).") start_recording = models.DateTimeField(blank=True, null=True, help_text="Time when the recording started (optional).") end_recording = models.DateTimeField(blank=True, null=True, help_text="Time when the recording ended (optional).") note = models.TextField(max_length=1000, blank=True, help_text="Additional notes on this file (optional).") def __str__(self): """Returns the file path""" filepaths = set() n_copies = 0 # the number of copies for storagelocation in self.storagelocation_set.all(): filepaths.add(storagelocation.path) n_copies += 1 if n_copies == 1: return ', '.join(filepaths) + ' (1 copy)' return ', '.join(filepaths) + ' (%s copies)'%(int(n_copies)) class StorageLocation(models.Model): """ A location where a specific file is stored. This model/table links files and drives together. (Each file can be stored on multiple drives under different names). """ drive = models.ForeignKey(Drive, on_delete=models.CASCADE) file = models.ForeignKey(File, on_delete=models.CASCADE) path = models.CharField(max_length=300, help_text="Path of the file on the drive.") def __str__(self): return "File %s on Drive %s"%(self.path, self.drive.label)

7cbb4eae4e6ffdbfc230e0a6bca2576aecfb51ea

py

Python

generalexam/plotting/front_plotting.py

thunderhoser/GeneralExam

95b99a16fdaa67dae69586c7f7c76e27ccd4b89a

2019-05-10T11:03:48.000Z

2020-10-19T08:04:09.000Z

generalexam/plotting/front_plotting.py

thunderhoser/GeneralExam

95b99a16fdaa67dae69586c7f7c76e27ccd4b89a

generalexam/plotting/front_plotting.py

thunderhoser/GeneralExam

95b99a16fdaa67dae69586c7f7c76e27ccd4b89a

2020-11-19T08:16:31.000Z

2021-03-04T02:30:15.000Z

"""Plotting methods for warm and cold fronts.""" import numpy import matplotlib matplotlib.use('agg') import matplotlib.colors from gewittergefahr.gg_utils import longitude_conversion as lng_conversion from gewittergefahr.gg_utils import error_checking from generalexam.ge_utils import front_utils from generalexam.plotting import narr_plotting DEFAULT_WARM_FRONT_COLOUR = numpy.array([228., 26., 28.]) / 255 DEFAULT_COLD_FRONT_COLOUR = numpy.array([31., 120., 180.]) / 255 DEFAULT_LINE_WIDTH = 2. DEFAULT_LINE_STYLE = 'solid' DEFAULT_GRID_OPACITY = 0.5 DEFAULT_WF_MARKER_TYPE = 'o' DEFAULT_CF_MARKER_TYPE = '>' DEFAULT_MARKER_SPACING_METRES = 150000. DEFAULT_MARKER_SIZE = 12 DEFAULT_MARKER_COLOUR = numpy.array([31, 120, 180], dtype=float) / 255 def get_colour_map_for_grid(): """Returns colour map for frontal grid (to be used by `plot_frontal_grid`). N = number of colours :return: colour_map_object: Instance of `matplotlib.colors.ListedColormap`. :return: colour_norm_object: Instance of `matplotlib.colors.BoundaryNorm`. :return: colour_bounds: length-(N + 1) numpy array of colour boundaries. colour_bounds[0] and colour_bounds[1] are the boundaries for the 1st colour; colour_bounds[1] and colour_bounds[2] are the boundaries for the 2nd colour; ...; etc. """ main_colour_list = [DEFAULT_WARM_FRONT_COLOUR, DEFAULT_COLD_FRONT_COLOUR] colour_map_object = matplotlib.colors.ListedColormap(main_colour_list) colour_map_object.set_under(numpy.array([1., 1., 1.])) colour_map_object.set_over(numpy.array([1., 1., 1.])) main_colour_bounds = numpy.array( [front_utils.WARM_FRONT_INTEGER_ID - 0.5, front_utils.WARM_FRONT_INTEGER_ID + 0.5, front_utils.COLD_FRONT_INTEGER_ID]) colour_norm_object = matplotlib.colors.BoundaryNorm( main_colour_bounds, colour_map_object.N) colour_bounds = numpy.concatenate(( numpy.array([-100.]), main_colour_bounds, numpy.array([100.]))) return colour_map_object, colour_norm_object, colour_bounds def plot_front_with_markers( line_latitudes_deg, line_longitudes_deg, axes_object, basemap_object, marker_spacing_metres=DEFAULT_MARKER_SPACING_METRES, marker_type=None, front_type_string=None, marker_colour=DEFAULT_MARKER_COLOUR, marker_size=DEFAULT_MARKER_SIZE): """Plots front with markers (instead of a line). P = number of points in line :param line_latitudes_deg: length-P numpy array of latitudes (deg N). :param line_longitudes_deg: length-P numpy array of longitudes (deg E). :param axes_object: Front will be plotted on these axes (instance of `matplotlib.axes._subplots.AxesSubplot`). :param basemap_object: Basemap used to convert lat-long coordinates to x-y (instance of `mpl_toolkits.basemap.Basemap`). :param marker_spacing_metres: Spacing between successive markers. :param marker_type: Marker type (any format accepted by matplotlib). :param front_type_string: [used only if `marker_type is None`] Front type (determines marker type). :param marker_colour: Marker colour (any format accepted by matplotlib). :param marker_size: Marker size (any format accepted by matplotlib). """ error_checking.assert_is_valid_lat_numpy_array(line_latitudes_deg) error_checking.assert_is_numpy_array(line_latitudes_deg, num_dimensions=1) num_points = len(line_latitudes_deg) these_expected_dim = numpy.array([num_points], dtype=int) error_checking.assert_is_numpy_array( line_longitudes_deg, exact_dimensions=these_expected_dim) line_longitudes_deg = lng_conversion.convert_lng_positive_in_west( line_longitudes_deg) error_checking.assert_is_greater(marker_spacing_metres, 0.) if marker_type is None: front_utils.check_front_type(front_type_string) if front_type_string == front_utils.WARM_FRONT_STRING_ID: marker_type = DEFAULT_WF_MARKER_TYPE else: marker_type = DEFAULT_CF_MARKER_TYPE x_coords_metres, y_coords_metres = basemap_object( line_longitudes_deg, line_latitudes_deg) for i in range(num_points - 1): this_x_diff_metres = x_coords_metres[i + 1] - x_coords_metres[i] this_y_diff_metres = y_coords_metres[i + 1] - y_coords_metres[i] this_distance_metres = numpy.sqrt( this_x_diff_metres ** 2 + this_y_diff_metres ** 2) this_num_points = 1 + int(numpy.ceil( this_distance_metres / marker_spacing_metres )) these_x_coords_metres = numpy.linspace( x_coords_metres[i], x_coords_metres[i + 1], num=this_num_points) these_y_coords_metres = numpy.linspace( y_coords_metres[i], y_coords_metres[i + 1], num=this_num_points) axes_object.plot( these_x_coords_metres, these_y_coords_metres, linestyle='None', marker=marker_type, markerfacecolor=marker_colour, markeredgecolor=marker_colour, markersize=marker_size, markeredgewidth=0.1) def plot_polyline( latitudes_deg, longitudes_deg, basemap_object, axes_object, front_type=None, line_colour=None, line_width=DEFAULT_LINE_WIDTH, line_style=DEFAULT_LINE_STYLE): """Plots either warm front or cold front as polyline. P = number of points in polyline :param latitudes_deg: length-P numpy array of latitudes (deg N). :param longitudes_deg: length-P numpy array of longitudes (deg N). :param basemap_object: Instance of `mpl_toolkits.basemap.Basemap`. :param axes_object: Instance of `matplotlib.axes._subplots.AxesSubplot`. :param front_type: Type of front (string). Used only to determine line colour (if `line_colour` is left as None). :param line_colour: Colour (in any format accepted by `matplotlib.colors`). Defaults to `DEFAULT_WARM_FRONT_COLOUR` or `DEFAULT_COLD_FRONT_COLOUR`. :param line_width: Line width (real positive number). :param line_style: Line style (in any format accepted by `matplotlib.lines`). """ error_checking.assert_is_valid_lat_numpy_array(latitudes_deg) error_checking.assert_is_numpy_array(latitudes_deg, num_dimensions=1) num_points = len(latitudes_deg) longitudes_deg = lng_conversion.convert_lng_positive_in_west(longitudes_deg) error_checking.assert_is_numpy_array( longitudes_deg, exact_dimensions=numpy.array([num_points])) if line_colour is None: front_utils.check_front_type(front_type) if front_type == front_utils.WARM_FRONT_STRING_ID: line_colour = DEFAULT_WARM_FRONT_COLOUR else: line_colour = DEFAULT_COLD_FRONT_COLOUR x_coords_metres, y_coords_metres = basemap_object( longitudes_deg, latitudes_deg) axes_object.plot( x_coords_metres, y_coords_metres, color=line_colour, linestyle=line_style, linewidth=line_width) def plot_narr_grid( frontal_grid_matrix, axes_object, basemap_object, first_row_in_narr_grid=0, first_column_in_narr_grid=0, opacity=DEFAULT_GRID_OPACITY): """Plots NARR grid points intersected by a warm front or cold front. This method plots data over a contiguous subset of the NARR grid, which need not be *strictly* a subset. In other words, the "subset" could be the full NARR grid. :param frontal_grid_matrix: See documentation for `front_utils.frontal_grid_to_points`. :param axes_object: Instance of `matplotlib.axes._subplots.AxesSubplot`. :param basemap_object: Instance of `mpl_toolkits.basemap.Basemap`. :param first_row_in_narr_grid: Row 0 in the subgrid is row `first_row_in_narr_grid` in the full NARR grid. :param first_column_in_narr_grid: Column 0 in the subgrid is row `first_column_in_narr_grid` in the full NARR grid. :param opacity: Opacity for colour map (in range 0...1). """ error_checking.assert_is_integer_numpy_array(frontal_grid_matrix) error_checking.assert_is_numpy_array(frontal_grid_matrix, num_dimensions=2) error_checking.assert_is_geq_numpy_array( frontal_grid_matrix, numpy.min(front_utils.VALID_INTEGER_IDS) ) error_checking.assert_is_leq_numpy_array( frontal_grid_matrix, numpy.max(front_utils.VALID_INTEGER_IDS) ) colour_map_object, _, colour_bounds = get_colour_map_for_grid() frontal_grid_matrix = numpy.ma.masked_where( frontal_grid_matrix == front_utils.NO_FRONT_INTEGER_ID, frontal_grid_matrix) narr_plotting.plot_xy_grid( data_matrix=frontal_grid_matrix, axes_object=axes_object, basemap_object=basemap_object, colour_map=colour_map_object, colour_minimum=colour_bounds[1], colour_maximum=colour_bounds[-2], first_row_in_narr_grid=first_row_in_narr_grid, first_column_in_narr_grid=first_column_in_narr_grid, opacity=opacity)

"""Plotting methods for warm and cold fronts.""" import numpy import matplotlib matplotlib.use('agg') import matplotlib.colors from gewittergefahr.gg_utils import longitude_conversion as lng_conversion from gewittergefahr.gg_utils import error_checking from generalexam.ge_utils import front_utils from generalexam.plotting import narr_plotting DEFAULT_WARM_FRONT_COLOUR = numpy.array([228., 26., 28.]) / 255 DEFAULT_COLD_FRONT_COLOUR = numpy.array([31., 120., 180.]) / 255 DEFAULT_LINE_WIDTH = 2. DEFAULT_LINE_STYLE = 'solid' DEFAULT_GRID_OPACITY = 0.5 DEFAULT_WF_MARKER_TYPE = 'o' DEFAULT_CF_MARKER_TYPE = '>' DEFAULT_MARKER_SPACING_METRES = 150000. DEFAULT_MARKER_SIZE = 12 DEFAULT_MARKER_COLOUR = numpy.array([31, 120, 180], dtype=float) / 255 def get_colour_map_for_grid(): """Returns colour map for frontal grid (to be used by `plot_frontal_grid`). N = number of colours :return: colour_map_object: Instance of `matplotlib.colors.ListedColormap`. :return: colour_norm_object: Instance of `matplotlib.colors.BoundaryNorm`. :return: colour_bounds: length-(N + 1) numpy array of colour boundaries. colour_bounds[0] and colour_bounds[1] are the boundaries for the 1st colour; colour_bounds[1] and colour_bounds[2] are the boundaries for the 2nd colour; ...; etc. """ main_colour_list = [DEFAULT_WARM_FRONT_COLOUR, DEFAULT_COLD_FRONT_COLOUR] colour_map_object = matplotlib.colors.ListedColormap(main_colour_list) colour_map_object.set_under(numpy.array([1., 1., 1.])) colour_map_object.set_over(numpy.array([1., 1., 1.])) main_colour_bounds = numpy.array( [front_utils.WARM_FRONT_INTEGER_ID - 0.5, front_utils.WARM_FRONT_INTEGER_ID + 0.5, front_utils.COLD_FRONT_INTEGER_ID]) colour_norm_object = matplotlib.colors.BoundaryNorm( main_colour_bounds, colour_map_object.N) colour_bounds = numpy.concatenate(( numpy.array([-100.]), main_colour_bounds, numpy.array([100.]))) return colour_map_object, colour_norm_object, colour_bounds def plot_front_with_markers( line_latitudes_deg, line_longitudes_deg, axes_object, basemap_object, marker_spacing_metres=DEFAULT_MARKER_SPACING_METRES, marker_type=None, front_type_string=None, marker_colour=DEFAULT_MARKER_COLOUR, marker_size=DEFAULT_MARKER_SIZE): """Plots front with markers (instead of a line). P = number of points in line :param line_latitudes_deg: length-P numpy array of latitudes (deg N). :param line_longitudes_deg: length-P numpy array of longitudes (deg E). :param axes_object: Front will be plotted on these axes (instance of `matplotlib.axes._subplots.AxesSubplot`). :param basemap_object: Basemap used to convert lat-long coordinates to x-y (instance of `mpl_toolkits.basemap.Basemap`). :param marker_spacing_metres: Spacing between successive markers. :param marker_type: Marker type (any format accepted by matplotlib). :param front_type_string: [used only if `marker_type is None`] Front type (determines marker type). :param marker_colour: Marker colour (any format accepted by matplotlib). :param marker_size: Marker size (any format accepted by matplotlib). """ error_checking.assert_is_valid_lat_numpy_array(line_latitudes_deg) error_checking.assert_is_numpy_array(line_latitudes_deg, num_dimensions=1) num_points = len(line_latitudes_deg) these_expected_dim = numpy.array([num_points], dtype=int) error_checking.assert_is_numpy_array( line_longitudes_deg, exact_dimensions=these_expected_dim) line_longitudes_deg = lng_conversion.convert_lng_positive_in_west( line_longitudes_deg) error_checking.assert_is_greater(marker_spacing_metres, 0.) if marker_type is None: front_utils.check_front_type(front_type_string) if front_type_string == front_utils.WARM_FRONT_STRING_ID: marker_type = DEFAULT_WF_MARKER_TYPE else: marker_type = DEFAULT_CF_MARKER_TYPE x_coords_metres, y_coords_metres = basemap_object( line_longitudes_deg, line_latitudes_deg) for i in range(num_points - 1): this_x_diff_metres = x_coords_metres[i + 1] - x_coords_metres[i] this_y_diff_metres = y_coords_metres[i + 1] - y_coords_metres[i] this_distance_metres = numpy.sqrt( this_x_diff_metres ** 2 + this_y_diff_metres ** 2) this_num_points = 1 + int(numpy.ceil( this_distance_metres / marker_spacing_metres )) these_x_coords_metres = numpy.linspace( x_coords_metres[i], x_coords_metres[i + 1], num=this_num_points) these_y_coords_metres = numpy.linspace( y_coords_metres[i], y_coords_metres[i + 1], num=this_num_points) axes_object.plot( these_x_coords_metres, these_y_coords_metres, linestyle='None', marker=marker_type, markerfacecolor=marker_colour, markeredgecolor=marker_colour, markersize=marker_size, markeredgewidth=0.1) def plot_polyline( latitudes_deg, longitudes_deg, basemap_object, axes_object, front_type=None, line_colour=None, line_width=DEFAULT_LINE_WIDTH, line_style=DEFAULT_LINE_STYLE): """Plots either warm front or cold front as polyline. P = number of points in polyline :param latitudes_deg: length-P numpy array of latitudes (deg N). :param longitudes_deg: length-P numpy array of longitudes (deg N). :param basemap_object: Instance of `mpl_toolkits.basemap.Basemap`. :param axes_object: Instance of `matplotlib.axes._subplots.AxesSubplot`. :param front_type: Type of front (string). Used only to determine line colour (if `line_colour` is left as None). :param line_colour: Colour (in any format accepted by `matplotlib.colors`). Defaults to `DEFAULT_WARM_FRONT_COLOUR` or `DEFAULT_COLD_FRONT_COLOUR`. :param line_width: Line width (real positive number). :param line_style: Line style (in any format accepted by `matplotlib.lines`). """ error_checking.assert_is_valid_lat_numpy_array(latitudes_deg) error_checking.assert_is_numpy_array(latitudes_deg, num_dimensions=1) num_points = len(latitudes_deg) longitudes_deg = lng_conversion.convert_lng_positive_in_west(longitudes_deg) error_checking.assert_is_numpy_array( longitudes_deg, exact_dimensions=numpy.array([num_points])) if line_colour is None: front_utils.check_front_type(front_type) if front_type == front_utils.WARM_FRONT_STRING_ID: line_colour = DEFAULT_WARM_FRONT_COLOUR else: line_colour = DEFAULT_COLD_FRONT_COLOUR x_coords_metres, y_coords_metres = basemap_object( longitudes_deg, latitudes_deg) axes_object.plot( x_coords_metres, y_coords_metres, color=line_colour, linestyle=line_style, linewidth=line_width) def plot_narr_grid( frontal_grid_matrix, axes_object, basemap_object, first_row_in_narr_grid=0, first_column_in_narr_grid=0, opacity=DEFAULT_GRID_OPACITY): """Plots NARR grid points intersected by a warm front or cold front. This method plots data over a contiguous subset of the NARR grid, which need not be *strictly* a subset. In other words, the "subset" could be the full NARR grid. :param frontal_grid_matrix: See documentation for `front_utils.frontal_grid_to_points`. :param axes_object: Instance of `matplotlib.axes._subplots.AxesSubplot`. :param basemap_object: Instance of `mpl_toolkits.basemap.Basemap`. :param first_row_in_narr_grid: Row 0 in the subgrid is row `first_row_in_narr_grid` in the full NARR grid. :param first_column_in_narr_grid: Column 0 in the subgrid is row `first_column_in_narr_grid` in the full NARR grid. :param opacity: Opacity for colour map (in range 0...1). """ error_checking.assert_is_integer_numpy_array(frontal_grid_matrix) error_checking.assert_is_numpy_array(frontal_grid_matrix, num_dimensions=2) error_checking.assert_is_geq_numpy_array( frontal_grid_matrix, numpy.min(front_utils.VALID_INTEGER_IDS) ) error_checking.assert_is_leq_numpy_array( frontal_grid_matrix, numpy.max(front_utils.VALID_INTEGER_IDS) ) colour_map_object, _, colour_bounds = get_colour_map_for_grid() frontal_grid_matrix = numpy.ma.masked_where( frontal_grid_matrix == front_utils.NO_FRONT_INTEGER_ID, frontal_grid_matrix) narr_plotting.plot_xy_grid( data_matrix=frontal_grid_matrix, axes_object=axes_object, basemap_object=basemap_object, colour_map=colour_map_object, colour_minimum=colour_bounds[1], colour_maximum=colour_bounds[-2], first_row_in_narr_grid=first_row_in_narr_grid, first_column_in_narr_grid=first_column_in_narr_grid, opacity=opacity)

8688bd25c08e7e8ca794f9c3a84a55b513d0107f

py

Python

propara/evaluation/evalQA.py

keisks/propara

49fa8fe0481291df18b2c7b48e7ba1dafaad48e2

2018-06-02T02:00:53.000Z

2022-03-13T12:17:42.000Z

propara/evaluation/evalQA.py

keisks/propara

49fa8fe0481291df18b2c7b48e7ba1dafaad48e2

2018-10-31T00:28:31.000Z

2020-05-12T01:06:53.000Z

propara/evaluation/evalQA.py

keisks/propara

49fa8fe0481291df18b2c7b48e7ba1dafaad48e2

2018-09-14T20:37:51.000Z

2021-03-23T09:24:49.000Z

import sys, collections, pylev from stemming.porter2 import stem #-------------------------------------------------------------- # Author: Scott Wen-tau Yih # Usage: evalQA.py para-ids gold-labels system-predictions # example usage: python propara/eval/evalQA.py tests/fixtures/eval/para_id.test.txt tests/fixtures/eval/gold_labels.test.tsv tests/fixtures/eval/sample.model.test_predictions.tsv #-------------------------------------------------------------- # Data structure for Labels ''' PID -> [TurkerLabels] TurkerLabels = [TurkerQuestionLabel1, TurkerQuestionLabel2, ... ] # labels on the same paragraph from the same Turker TurkerQuestionLabel -> (SID, Participant, Type, From, To) ''' TurkerQuestionLabel = collections.namedtuple('TurkerQuestionLabel', 'sid participant event_type from_location to_location') # Data structure for Predictions ''' PID -> Participant -> SID -> PredictionRecord ''' PredictionRecord = collections.namedtuple('PredictionRecord', 'pid sid participant from_location to_location') # Fixing tokenization mismatch while alinging participants manual_participant_map = { 'alternating current':'alternate current', 'fixed nitrogen':'nitrogen', 'living things':'live thing', 'red giant star':'star', 'refrigerent liquid':'liquid', 'remains of living things':'remains of live thing', "retina's rods and cones":"retina 's rod and cone" } #, 'seedling':'seed'} #---------------------------------------------------------------------------------------------------------------- #---------------------------------------------------------------------------------------------------------------- ''' Read the gold file containing all records where an entity undergoes some state-change: create/destroy/move. ''' #---------------------------------------------------------------------------------------------------------------- #---------------------------------------------------------------------------------------------------------------- #---------------------------------------------------------------------------------------------------------------- #---------------------------------------------------------------------------------------------------------------- # Q1: Is participant X created during the process? # Q2: Participant X is created during the process. At which step is it created? # Q3: Participant X is created at step Y, and the initial location is known. Where is the participant after it is created? #---------------------------------------------------------------------------------------------------------------- # Q4: Is participant X destroyed during the process? # Q5: Participant X is destroyed during the process. At which step is it destroyed? # Q6: Participant X is destroyed at step Y, and its location before destroyed is known. Where is the participant right before it is destroyed? #---------------------------------------------------------------------------------------------------------------- # Q7 Does participant X move during the process? # Q8 Participant X moves during the process. At which steps does it move? # Q9 Participant X moves at step Y, and its location before step Y is known. What is its location before step Y? # Q10 Participant X moves at step Y, and its location after step Y is known. What is its location after step Y? #---------------------------------------------------------------------------------------------------------------- #---------------------------------------------------------------------------------------------------------------- if __name__ == "__main__": main()

import sys, collections, pylev from stemming.porter2 import stem #-------------------------------------------------------------- # Author: Scott Wen-tau Yih # Usage: evalQA.py para-ids gold-labels system-predictions # example usage: python propara/eval/evalQA.py tests/fixtures/eval/para_id.test.txt tests/fixtures/eval/gold_labels.test.tsv tests/fixtures/eval/sample.model.test_predictions.tsv #-------------------------------------------------------------- # Data structure for Labels ''' PID -> [TurkerLabels] TurkerLabels = [TurkerQuestionLabel1, TurkerQuestionLabel2, ... ] # labels on the same paragraph from the same Turker TurkerQuestionLabel -> (SID, Participant, Type, From, To) ''' TurkerQuestionLabel = collections.namedtuple('TurkerQuestionLabel', 'sid participant event_type from_location to_location') # Data structure for Predictions ''' PID -> Participant -> SID -> PredictionRecord ''' PredictionRecord = collections.namedtuple('PredictionRecord', 'pid sid participant from_location to_location') # Fixing tokenization mismatch while alinging participants manual_participant_map = { 'alternating current':'alternate current', 'fixed nitrogen':'nitrogen', 'living things':'live thing', 'red giant star':'star', 'refrigerent liquid':'liquid', 'remains of living things':'remains of live thing', "retina's rods and cones":"retina 's rod and cone" } #, 'seedling':'seed'} #---------------------------------------------------------------------------------------------------------------- def compare_to_gold_labels(participants, system_participants): ret = [] found = False for p in participants: p = p.lower() if p in system_participants: ret.append(p) continue for g in system_participants: if (pylev.levenshtein(p,g) < 3): #print (p, "===", g) ret.append(g) found = True if not found: if p in manual_participant_map: ret.append(manual_participant_map[p]) #else: # print("cannot find", p, system_participants) return ret def preprocess_locations(locations): ret = [] for loc in locations: if loc == '-': ret.append('null') elif loc == '?': ret.append('unk') else: ret.append(loc) return ret def preprocess_question_label(sid, participant, event_type, from_location, to_location, system_participants=None): # check if there are multiple participants grouped together participants = [x.strip() for x in participant.split(';')] # check if there are multiple locations grouped together from_locations = preprocess_locations([x.strip() for x in from_location.split(';')]) # check if there are multiple locations grouped together to_locations = preprocess_locations([x.strip() for x in to_location.split(';')]) #print(participant, participants, system_participants) if system_participants != None: # check if the participants are in his list participants = compare_to_gold_labels(participants, system_participants) #print("legit_participants =", participants) #print(from_location, from_locations) #print(to_location, to_locations) return [TurkerQuestionLabel(sid, p, event_type, floc, tloc) for p in participants for floc in from_locations for tloc in to_locations] #---------------------------------------------------------------------------------------------------------------- ''' Read the gold file containing all records where an entity undergoes some state-change: create/destroy/move. ''' def readLabels(fnLab, selPid=None, gold_labels=None): fLab = open(fnLab) fLab.readline() # skip header ret = {} TurkerLabels = [] for ln in fLab: f = ln.rstrip().split('\t') if len(f) == 0 or len(f) == 1: if not selPid or pid in selPid: if pid not in ret: ret[pid] = [] ret[pid].append(TurkerLabels) TurkerLabels = [] elif len(f) != 11: sys.stderr.write("Error: the number of fields in this line is irregular: " + ln) sys.exit(-1) else: if f[1] == '?': continue pid, sid, participant, event_type, from_location, to_location = int(f[0]), int(f[1]), f[3], f[4], f[5], f[6] if gold_labels and selPid and pid in selPid: #print("pid=", pid) TurkerLabels += preprocess_question_label(sid, participant, event_type, from_location, to_location, gold_labels[pid].keys()) else: TurkerLabels += preprocess_question_label(sid, participant, event_type, from_location, to_location) #TurkerLabels += (TurkerQuestionLabel(sid, participant, event_type, from_location, to_location)) return ret #---------------------------------------------------------------------------------------------------------------- def readPredictions(fnPred): ret = {} for ln in open(fnPred): f = ln.rstrip().split('\t') pid, sid, participant, from_location, to_location = int(f[0]), int(f[1]), f[2], f[3], f[4] if pid not in ret: ret[pid] = {} dtPartPred = ret[pid] if participant not in dtPartPred: dtPartPred[participant] = {} dtPartPred[participant][sid] = PredictionRecord(pid, sid, participant, from_location, to_location) return ret #---------------------------------------------------------------------------------------------------------------- def readGold(fn): # read the gold label dtPar = {} for ln in open(fn): f = ln.rstrip().split('\t') parId, sentId, participant, before_after, labels = int(f[0]), int(f[1]), f[2], f[3], f[4:] if (before_after != "before") and (before_after != "after"): print("Error:", ln) sys.exit(-1) if sentId == 1 and before_after == "before": statusId = 0 elif before_after == "before": continue # skip this line else: statusId = sentId if parId not in dtPar: dtPar[parId] = {} dtPartLab = dtPar[parId] if participant not in dtPartLab: dtPartLab[participant] = {statusId: labels} else: dtPartLab[participant][statusId] = labels return dtPar #---------------------------------------------------------------------------------------------------------------- def findAllParticipants(lstTurkerLabels): setParticipants = set() for turkerLabels in lstTurkerLabels: for x in turkerLabels: setParticipants.add(x.participant) return setParticipants def findCreationStep(prediction_records): steps = sorted(prediction_records, key=lambda x: x.sid) #print("steps:", steps) # first step if steps[0].from_location != 'null': # not created (exists before the process) return -1 for s in steps: if s.to_location != 'null': return s.sid return -1 # never exists def findDestroyStep(prediction_records): steps = sorted(prediction_records, key=lambda x: x.sid, reverse=True) #print("steps:", steps) # last step if steps[0].to_location != 'null': # not destroyed (exists after the process) return -1 for s in steps: if s.from_location != 'null': return s.sid return -1 # never exists def location_match(p_loc, g_loc): if p_loc == g_loc: return True p_string = ' %s ' % ' '.join([stem(x) for x in p_loc.lower().replace('"','').split()]) g_string = ' %s ' % ' '.join([stem(x) for x in g_loc.lower().replace('"','').split()]) if p_string in g_string: #print ("%s === %s" % (p_loc, g_loc)) return True return False def findMoveSteps(prediction_records): ret = [] steps = sorted(prediction_records, key=lambda x: x.sid) # print(steps) for s in steps: if s.from_location != 'null' and s.to_location != 'null' and s.from_location != s.to_location: ret.append(s.sid) return ret #---------------------------------------------------------------------------------------------------------------- # Q1: Is participant X created during the process? def Q1(labels, predictions): tp = fp = tn = fn = 0.0 for pid in labels: setParticipants = findAllParticipants(labels[pid]) # find predictions be_created = {} for participant in setParticipants: pred_creation_step = findCreationStep(predictions[pid][participant].values()) be_created[participant] = (pred_creation_step != -1) for turkerLabels in labels[pid]: # labeled as created participants lab_created_participants = [x.participant for x in turkerLabels if x.event_type == 'create'] for participant in setParticipants: tp += int(be_created[participant] and (participant in lab_created_participants)) fp += int(be_created[participant] and (participant not in lab_created_participants)) tn += int(not be_created[participant] and (participant not in lab_created_participants)) fn += int(not be_created[participant] and (participant in lab_created_participants)) return tp,fp,tn,fn # Q2: Participant X is created during the process. At which step is it created? def Q2(labels, predictions): tp = fp = tn = fn = 0.0 # find all created participants and their creation step for pid,lstTurkerLabels in labels.items(): for turkerLabels in lstTurkerLabels: for x in [x for x in turkerLabels if x.event_type == 'create']: pred_creation_step = findCreationStep(predictions[pid][x.participant].values()) tp += int(pred_creation_step != -1 and pred_creation_step == x.sid) fp += int(pred_creation_step != -1 and pred_creation_step != x.sid) fn += int(pred_creation_step == -1) return tp,fp,tn,fn # Q3: Participant X is created at step Y, and the initial location is known. Where is the participant after it is created? def Q3(labels, predictions): tp = fp = tn = fn = 0.0 # find all created participants and their creation step for pid,lstTurkerLabels in labels.items(): for turkerLabels in lstTurkerLabels: for x in [x for x in turkerLabels if x.event_type == 'create' and x.to_location != 'unk']: pred_loc = predictions[pid][x.participant][x.sid].to_location tp += int(pred_loc != 'null' and pred_loc != 'unk' and location_match(pred_loc, x.to_location)) fp += int(pred_loc != 'null' and pred_loc != 'unk' and not location_match(pred_loc, x.to_location)) fn += int(pred_loc == 'null' or pred_loc == 'unk') return tp, fp, tn, fn #---------------------------------------------------------------------------------------------------------------- # Q4: Is participant X destroyed during the process? def Q4(labels, predictions): tp = fp = tn = fn = 0.0 for pid in labels: setParticipants = findAllParticipants(labels[pid]) # find predictions be_destroyed = {} for participant in setParticipants: pred_destroy_step = findDestroyStep(predictions[pid][participant].values()) be_destroyed[participant] = (pred_destroy_step != -1) for turkerLabels in labels[pid]: # labeled as destroyed participants lab_destroyed_participants = [x.participant for x in turkerLabels if x.event_type == 'destroy'] for participant in setParticipants: tp += int(be_destroyed[participant] and (participant in lab_destroyed_participants)) fp += int(be_destroyed[participant] and (participant not in lab_destroyed_participants)) tn += int(not be_destroyed[participant] and (participant not in lab_destroyed_participants)) fn += int(not be_destroyed[participant] and (participant in lab_destroyed_participants)) return tp,fp,tn,fn # Q5: Participant X is destroyed during the process. At which step is it destroyed? def Q5(labels, predictions): tp = fp = tn = fn = 0.0 # find all destroyed participants and their destroy step for pid, lstTurkerLabels in labels.items(): for turkerLabels in lstTurkerLabels: for x in [x for x in turkerLabels if x.event_type == 'destroy']: pred_destroy_step = findDestroyStep(predictions[pid][x.participant].values()) tp += int(pred_destroy_step != -1 and pred_destroy_step == x.sid) fp += int(pred_destroy_step != -1 and pred_destroy_step != x.sid) fn += int(pred_destroy_step == -1) return tp,fp,tn,fn # Q6: Participant X is destroyed at step Y, and its location before destroyed is known. Where is the participant right before it is destroyed? def Q6(labels, predictions): tp = fp = tn = fn = 0.0 # find all created participants and their destroy step for pid,lstTurkerLabels in labels.items(): for turkerLabels in lstTurkerLabels: for x in [x for x in turkerLabels if x.event_type == 'destroy' and x.from_location != 'unk']: pred_loc = predictions[pid][x.participant][x.sid].from_location tp += int(pred_loc != 'null' and pred_loc != 'unk' and location_match(pred_loc, x.from_location)) fp += int(pred_loc != 'null' and pred_loc != 'unk' and not location_match(pred_loc, x.from_location)) fn += int(pred_loc == 'null' or pred_loc == 'unk') return tp, fp, tn, fn #---------------------------------------------------------------------------------------------------------------- # Q7 Does participant X move during the process? def Q7(labels, predictions): tp = fp = tn = fn = 0.0 for pid in labels: setParticipants = findAllParticipants(labels[pid]) # find predictions be_moved = {} for participant in setParticipants: pred_move_steps = findMoveSteps(predictions[pid][participant].values()) be_moved[participant] = (pred_move_steps != []) # print(be_moved) for turkerLabels in labels[pid]: lab_moved_participants = [x.participant for x in turkerLabels if x.event_type == 'move'] for participant in setParticipants: tp += int(be_moved[participant] and (participant in lab_moved_participants)) fp += int(be_moved[participant] and (participant not in lab_moved_participants)) tn += int(not be_moved[participant] and (participant not in lab_moved_participants)) fn += int(not be_moved[participant] and (participant in lab_moved_participants)) return tp,fp,tn,fn # Q8 Participant X moves during the process. At which steps does it move? def Q8(labels, predictions): tp = fp = tn = fn = 0.0 for pid in labels: setParticipants = findAllParticipants(labels[pid]) # find predictions pred_moved_steps = {} for participant in setParticipants: pred_moved_steps[participant] = findMoveSteps(predictions[pid][participant].values()) num_steps = len(predictions[pid][participant].keys()) for turkerLabels in labels[pid]: gold_moved_steps = {} for x in [x for x in turkerLabels if x.event_type == 'move']: if x.participant not in gold_moved_steps: gold_moved_steps[x.participant] = [] gold_moved_steps[x.participant].append(x.sid) for participant in gold_moved_steps: res = set_compare(pred_moved_steps[participant], gold_moved_steps[participant], num_steps) tp += res[0] fp += res[1] tn += res[2] fn += res[3] return tp,fp,tn,fn def set_compare(pred_steps, gold_steps, num_steps): setPred = set(pred_steps) setGold = set(gold_steps) tp = len(setPred.intersection(setGold)) fp = len(setPred - setGold) fn = len(setGold - setPred) tn = num_steps - tp - fp - fn return (tp, fp, tn, fn) # Q9 Participant X moves at step Y, and its location before step Y is known. What is its location before step Y? def Q9(labels, predictions): tp = fp = tn = fn = 0.0 for pid in labels: for turkerLabels in labels[pid]: for x in turkerLabels: if x.event_type == 'move' and x.from_location != 'unk': pred_loc = predictions[pid][x.participant][x.sid].from_location tp += int(pred_loc != 'null' and pred_loc != 'unk' and location_match(pred_loc, x.from_location)) fp += int(pred_loc != 'null' and pred_loc != 'unk' and not location_match(pred_loc, x.from_location)) fn += int(pred_loc == 'null' or pred_loc == 'unk') return tp,fp,tn,fn # Q10 Participant X moves at step Y, and its location after step Y is known. What is its location after step Y? def Q10(labels, predictions): tp = fp = tn = fn = 0.0 for pid in labels: for turkerLabels in labels[pid]: for x in turkerLabels: if x.event_type == 'move' and x.to_location != 'unk': pred_loc = predictions[pid][x.participant][x.sid].to_location tp += int(pred_loc != 'null' and pred_loc != 'unk' and location_match(pred_loc, x.to_location)) fp += int(pred_loc != 'null' and pred_loc != 'unk' and not location_match(pred_loc, x.to_location)) fn += int(pred_loc == 'null' or pred_loc == 'unk') return tp,fp,tn,fn #---------------------------------------------------------------------------------------------------------------- def main(): if len(sys.argv) != 4: sys.stderr.write("Usage: evalQA.py para-ids gold-labels system-predictions\n") sys.exit(-1) paraIds = sys.argv[1] goldPred = sys.argv[2] fnPred = sys.argv[3] qid_to_score = {} selPid = set([int(x) for x in open(paraIds).readlines()]) gold_labels = readGold(goldPred) labels = readLabels('tests/fixtures/eval/all-moves.full-grid.tsv', selPid, gold_labels) predictions = readPredictions(fnPred) blHeader = True qid = 0 for Q in [Q1, Q2, Q3, Q4, Q5, Q6, Q7, Q8, Q9, Q10]: qid += 1 tp, fp, tn, fn = Q(labels, predictions) header,results_str, results = metrics(tp,fp,tn,fn,qid) if blHeader: print("\t%s" % header) blHeader = False print("Q%d\t%s" % (qid, results_str)) qid_to_score[qid] = results[5] cat1_score = (qid_to_score[1] + qid_to_score[4] + qid_to_score[7]) / 3 cat2_score = (qid_to_score[2] + qid_to_score[5] + qid_to_score[8]) / 3 cat3_score = (qid_to_score[3] + qid_to_score[6] + qid_to_score[9] + qid_to_score[10]) / 4 macro_avg = (cat1_score + cat2_score + cat3_score) / 3 num_cat1_q = 750 num_cat2_q = 601 num_cat3_q = 823 micro_avg = ((cat1_score * num_cat1_q) + (cat2_score * num_cat2_q) + (cat3_score * num_cat3_q)) / \ (num_cat1_q + num_cat2_q + num_cat3_q) print("\n\nCategory\tAccuracy Score") print("=========\t=====") print(f"Cat-1\t\t{round(cat1_score,2)}") print(f"Cat-2\t\t{round(cat2_score,2)}") print(f"Cat-3\t\t{round(cat3_score,2)}") print(f"macro-avg\t{round(macro_avg,2)}") print(f"micro-avg\t{round(micro_avg,2)}") def metrics(tp, fp, tn, fn, qid): if (tp+fp > 0): prec = tp/(tp+fp) else: prec = 0.0 if (tp+fn > 0): rec = tp/(tp+fn) else: rec = 0.0 if (prec + rec) != 0: f1 = 2 * prec * rec / (prec + rec) else: f1 = 0.0 accuracy = (tp+tn) / (tp + fp + tn + fn) if qid == 8: accuracy = f1 # this is because Q8 can have multiple valid answers and F1 makes more sense here total = tp + fp + tn + fn header = '\t'.join(["Total", "TP", "FP", "TN", "FN", "Accuracy", "Precision", "Recall", "F1"]) results = [total, tp, fp, tn, fn, accuracy*100, prec*100, rec*100, f1*100] results_str = "%d\t%d\t%d\t%d\t%d\t%.2f\t%.2f\t%.2f\t%.2f" % (total, tp, fp, tn, fn, accuracy*100, prec*100, rec*100, f1*100) return (header, results_str, results) #---------------------------------------------------------------------------------------------------------------- if __name__ == "__main__": main()

deae37deb80e55431aeffc4a40f5e13cb6af2f86

py

Python

peerlyApp/peerlyWS.py

z4m0/peerly

187275822dcb6f35cc798c277ec43287e778a049

peerlyApp/peerlyWS.py

z4m0/peerly

187275822dcb6f35cc798c277ec43287e778a049

peerlyApp/peerlyWS.py

z4m0/peerly

187275822dcb6f35cc798c277ec43287e778a049

from gevent import monkey; monkey.patch_all() import gevent from socketio import socketio_manage from socketio.server import SocketIOServer from socketio.namespace import BaseNamespace from socketio.mixins import RoomsMixin, BroadcastMixin from twisted.internet import reactor, task, defer from twisted.python import log from peerlyDB.network import Server import sys, signal from p2p import P2PNamespace log.startLogging(sys.stdout)

from gevent import monkey; monkey.patch_all() import gevent from socketio import socketio_manage from socketio.server import SocketIOServer from socketio.namespace import BaseNamespace from socketio.mixins import RoomsMixin, BroadcastMixin from twisted.internet import reactor, task, defer from twisted.python import log from peerlyDB.network import Server import sys, signal from p2p import P2PNamespace log.startLogging(sys.stdout) class Application(object): def __init__(self, port=8469): self.buffer = [] # Dummy request object to maintain state between Namespace # initialization. server = Server() server.listen(port) self.request = { 'queries': {}, 'kadServer' : server, 'inserts' : {}, 'port' : port, 'bootstraped' : False } gevent.spawn(self.startReactor) def startReactor(self): gevent.sleep(1) reactor.run() signal.signal(signal.SIGINT, signal.default_int_handler) def __call__(self, environ, start_response): path = environ['PATH_INFO'].strip('/') if not path: start_response('200 OK', [('Content-Type', 'text/html')]) return [open('peerlyApp/web/peerly.html').read()] #return ['<h1>Welcome. ' # 'Try the <a href="/chat.html">chat</a> example.</h1>'] if path.startswith('static/') or path == 'peerly.html': try: data = open('peerlyApp/web/'+path).read() except Exception: return not_found(start_response) if path.endswith(".js"): content_type = "text/javascript" elif path.endswith(".css"): content_type = "text/css" elif path.endswith(".swf"): content_type = "application/x-shockwave-flash" else: content_type = "text/html" start_response('200 OK', [('Content-Type', content_type)]) return [data] if path.startswith("socket.io"): socketio_manage(environ, {'': P2PNamespace}, self.request) else: return not_found(start_response) def not_found(start_response): start_response('404 Not Found', []) return ['<h1>Not Found</h1>']

9134aff78215faf06e32eb3662c9a17d4dfcc14c

py

Python

ppr-api/src/ppr_api/resources/searches.py

cameron-freshworks/ppr

01d6f5d300c791aebad5e58bb4601e9be2ccfc46

ppr-api/src/ppr_api/resources/searches.py

cameron-freshworks/ppr

01d6f5d300c791aebad5e58bb4601e9be2ccfc46

ppr-api/src/ppr_api/resources/searches.py

cameron-freshworks/ppr

01d6f5d300c791aebad5e58bb4601e9be2ccfc46

# Copyright © 2019 Province of British Columbia # # Licensed under the Apache License, Version 2.0 (the 'License'); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an 'AS IS' BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """API endpoints for executing PPR searches.""" # pylint: disable=too-many-return-statements from http import HTTPStatus from flask import current_app, g, jsonify, request from flask_restx import Namespace, Resource, cors from registry_schemas import utils as schema_utils from ppr_api.exceptions import BusinessException, DatabaseException from ppr_api.models import SearchRequest, SearchResult from ppr_api.resources import utils as resource_utils from ppr_api.services.authz import authorized, is_bcol_help, is_gov_account, is_sbc_office_account, is_staff_account from ppr_api.services.payment import TransactionTypes from ppr_api.services.payment.exceptions import SBCPaymentException from ppr_api.services.payment.payment import Payment from ppr_api.utils.auth import jwt from ppr_api.utils.util import cors_preflight API = Namespace('searches', description='Endpoints for PPR searches.') VAL_ERROR = 'Search request data validation errors.' # Validation error prefix SAVE_ERROR_MESSAGE = 'Account {0} search db save failed: {1}' PAY_REFUND_MESSAGE = 'Account {0} search refunding payment for invoice {1}.' PAY_REFUND_ERROR = 'Account {0} search payment refund failed for invoice {1}: {2}.' # Map api spec search type to payment transaction details description TO_SEARCH_TYPE_DESCRIPTION = { 'AIRCRAFT_DOT': 'Aircraft Airframe DOT Number:', 'BUSINESS_DEBTOR': 'Debtor Business Name:', 'INDIVIDUAL_DEBTOR': 'Debtor Individual Name:', 'MHR_NUMBER': 'Manufactured Home Registration Number:', 'REGISTRATION_NUMBER': 'Registration Number:', 'SERIAL_NUMBER': 'Serial/VIN Number:' } CERTIFIED_PARAM = 'certified' ROUTING_SLIP_PARAM = 'routingSlipNumber' DAT_NUMBER_PARAM = 'datNumber' BCOL_NUMBER_PARAM = 'bcolAccountNumber' @cors_preflight('POST,OPTIONS') @API.route('', methods=['POST', 'OPTIONS']) class SearchResource(Resource): """Resource for executing PPR searches.""" @staticmethod # @TRACER.trace() @cors.crossdomain(origin='*') @jwt.requires_auth def post(): # pylint: disable=too-many-branches,too-many-locals """Execute a new search request using criteria in the request body.""" try: # Quick check: must be staff or provide an account ID. account_id = resource_utils.get_account_id(request) if not account_id: return resource_utils.account_required_response() # Verify request JWT and account ID if not authorized(account_id, jwt): return resource_utils.unauthorized_error_response(account_id) request_json = request.get_json(silent=True) # Validate request against the schema. valid_format, errors = schema_utils.validate(request_json, 'searchQuery', 'ppr') if not valid_format: return resource_utils.validation_error_response(errors, VAL_ERROR) # Perform any extra data validation such as start and end dates here SearchRequest.validate_query(request_json) # Staff has special payment rules and setup. if is_staff_account(account_id) or is_bcol_help(account_id): return staff_search(request, request_json, account_id) query = SearchRequest.create_from_json(request_json, account_id, g.jwt_oidc_token_info.get('username', None)) # Charge a search fee. invoice_id = None payment = Payment(jwt=jwt.get_token_auth_header(), account_id=account_id, details=get_payment_details(query, request_json['type'])) transaction_type = TransactionTypes.SEARCH.value # if gov account user then check if sbc if is_gov_account(jwt): # if SBC staff user (authy, api call) then change transaction type to $10 fee is_sbc = is_sbc_office_account(jwt.get_token_auth_header(), account_id) if is_sbc: transaction_type = TransactionTypes.SEARCH_STAFF.value elif is_sbc is None: # didn't get a succesful response from auth raise BusinessException('Unable to verify possible SBC staff user before payment.', HTTPStatus.INTERNAL_SERVER_ERROR) pay_ref = payment.create_payment(transaction_type, 1, None, query.client_reference_id) invoice_id = pay_ref['invoiceId'] query.pay_invoice_id = int(invoice_id) query.pay_path = pay_ref['receipt'] # Execute the search query: treat no results as a success. try: query.search() # Now save the initial detail results in the search_result table with no # search selection criteria (the absence indicates an incomplete search). search_result = SearchResult.create_from_search_query(query) search_result.save() except Exception as db_exception: # noqa: B902; handle all db related errors. current_app.logger.error(SAVE_ERROR_MESSAGE.format(account_id, repr(db_exception))) if invoice_id is not None: current_app.logger.info(PAY_REFUND_MESSAGE.format(account_id, invoice_id)) try: payment.cancel_payment(invoice_id) except Exception as cancel_exception: # noqa: B902; log exception current_app.logger.error(PAY_REFUND_ERROR.format(account_id, invoice_id, repr(cancel_exception))) raise db_exception return query.json, HTTPStatus.CREATED except SBCPaymentException as pay_exception: return resource_utils.pay_exception_response(pay_exception, account_id) except BusinessException as exception: return resource_utils.business_exception_response(exception) except Exception as default_exception: # noqa: B902; return nicer default error return resource_utils.default_exception_response(default_exception) @cors_preflight('PUT,OPTIONS') @API.route('/<path:search_id>', methods=['PUT', 'OPTIONS']) class SearchDetailResource(Resource): """Resource for processing requests to update the search selection (UI autosave).""" @staticmethod # @TRACER.trace() @cors.crossdomain(origin='*') @jwt.requires_auth def put(search_id): """Execute a search selection update request replacing the current value with the request body contents.""" try: if search_id is None: return resource_utils.path_param_error_response('search ID') # Quick check: must be staff or provide an account ID. account_id = resource_utils.get_account_id(request) if account_id is None: return resource_utils.account_required_response() # Verify request JWT and account ID if not authorized(account_id, jwt): return resource_utils.unauthorized_error_response(account_id) request_json = request.get_json(silent=True) # Validate schema. valid_format, errors = schema_utils.validate(request_json, 'searchSummary', 'ppr') if not valid_format: return resource_utils.validation_error_response(errors, VAL_ERROR) search_request = SearchRequest.find_by_id(search_id) if not search_request: return resource_utils.not_found_error_response('searchId', search_id) # Save the updated search selection. search_request.update_search_selection(request_json) return jsonify(search_request.updated_selection), HTTPStatus.ACCEPTED except DatabaseException as db_exception: return resource_utils.db_exception_response(db_exception, account_id, 'PUT search selection update') except BusinessException as exception: return resource_utils.business_exception_response(exception) except Exception as default_exception: # noqa: B902; return nicer default error return resource_utils.default_exception_response(default_exception) def staff_search(req: request, request_json, account_id: str): """Execute a staff search with special payment validation and methods.""" payment_info = build_staff_payment(req, account_id) # bcol help is no fee; reg staff can be no fee. # FAS is routing slip only. # BCOL is dat number (optional) and BCOL account number (mandatory). # All staff roles including SBC can submit no fee searches. if ROUTING_SLIP_PARAM in payment_info and BCOL_NUMBER_PARAM in payment_info: return resource_utils.staff_payment_bcol_fas() if CERTIFIED_PARAM in payment_info: request_json['certified'] = True query: SearchRequest = SearchRequest.create_from_json(request_json, account_id, g.jwt_oidc_token_info.get('username', None)) # Always create a payment transaction. invoice_id = None payment = Payment(jwt=jwt.get_token_auth_header(), account_id=account_id, details=get_payment_details(query, request_json['type'])) # staff payment pay_ref = payment.create_payment_staff_search(payment_info, query.client_reference_id) invoice_id = pay_ref['invoiceId'] query.pay_invoice_id = int(invoice_id) query.pay_path = pay_ref['receipt'] # Execute the search query: treat no results as a success. try: query.search() # Now save the initial detail results in the search_result table with no # search selection criteria (the absence indicates an incomplete search). search_result = SearchResult.create_from_search_query(query) search_result.save() except Exception as db_exception: # noqa: B902; handle all db related errors. current_app.logger.error(SAVE_ERROR_MESSAGE.format(account_id, repr(db_exception))) if invoice_id is not None: current_app.logger.info(PAY_REFUND_MESSAGE.format(account_id, invoice_id)) try: payment.cancel_payment(invoice_id) except Exception as cancel_exception: # noqa: B902; log exception current_app.logger.error(PAY_REFUND_ERROR.format(account_id, invoice_id, repr(cancel_exception))) raise db_exception return query.json, HTTPStatus.CREATED def build_staff_payment(req: request, account_id: str): """Extract payment information from request parameters.""" payment_info = { 'transactionType': TransactionTypes.SEARCH_STAFF_NO_FEE.value } if is_bcol_help(account_id): return payment_info certified = req.args.get(CERTIFIED_PARAM) routing_slip = req.args.get(ROUTING_SLIP_PARAM) bcol_number = req.args.get(BCOL_NUMBER_PARAM) dat_number = req.args.get(DAT_NUMBER_PARAM) if certified is not None and isinstance(certified, bool) and certified: payment_info[CERTIFIED_PARAM] = True elif certified is not None and isinstance(certified, str) and \ certified.lower() in ['true', '1', 'y', 'yes']: payment_info[CERTIFIED_PARAM] = True if routing_slip is not None: payment_info[ROUTING_SLIP_PARAM] = str(routing_slip) if bcol_number is not None: payment_info[BCOL_NUMBER_PARAM] = str(bcol_number) if dat_number is not None: payment_info[DAT_NUMBER_PARAM] = str(dat_number) if ROUTING_SLIP_PARAM in payment_info or BCOL_NUMBER_PARAM in payment_info: if CERTIFIED_PARAM in payment_info: payment_info['transactionType'] = TransactionTypes.SEARCH_STAFF_CERTIFIED.value else: payment_info['transactionType'] = TransactionTypes.SEARCH_STAFF.value elif CERTIFIED_PARAM in payment_info: # Verify this is allowed. payment_info['transactionType'] = TransactionTypes.SEARCH_STAFF_CERTIFIED_NO_FEE.value return payment_info def get_payment_details(search_request, search_type): """Extract the payment details value from the search request criteria.""" details = { 'label': TO_SEARCH_TYPE_DESCRIPTION[search_type] } if search_request.search_type == SearchRequest.SearchTypes.BUSINESS_DEBTOR.value: details['value'] = search_request.search_criteria['criteria']['debtorName']['business'] elif search_request.search_type == SearchRequest.SearchTypes.INDIVIDUAL_DEBTOR.value: details['value'] = search_request.search_criteria['criteria']['debtorName']['last'] + ', ' +\ search_request.search_criteria['criteria']['debtorName']['first'] else: details['value'] = search_request.search_criteria['criteria']['value'] return details

# Copyright © 2019 Province of British Columbia # # Licensed under the Apache License, Version 2.0 (the 'License'); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an 'AS IS' BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """API endpoints for executing PPR searches.""" # pylint: disable=too-many-return-statements from http import HTTPStatus from flask import current_app, g, jsonify, request from flask_restx import Namespace, Resource, cors from registry_schemas import utils as schema_utils from ppr_api.exceptions import BusinessException, DatabaseException from ppr_api.models import SearchRequest, SearchResult from ppr_api.resources import utils as resource_utils from ppr_api.services.authz import authorized, is_bcol_help, is_gov_account, is_sbc_office_account, is_staff_account from ppr_api.services.payment import TransactionTypes from ppr_api.services.payment.exceptions import SBCPaymentException from ppr_api.services.payment.payment import Payment from ppr_api.utils.auth import jwt from ppr_api.utils.util import cors_preflight API = Namespace('searches', description='Endpoints for PPR searches.') VAL_ERROR = 'Search request data validation errors.' # Validation error prefix SAVE_ERROR_MESSAGE = 'Account {0} search db save failed: {1}' PAY_REFUND_MESSAGE = 'Account {0} search refunding payment for invoice {1}.' PAY_REFUND_ERROR = 'Account {0} search payment refund failed for invoice {1}: {2}.' # Map api spec search type to payment transaction details description TO_SEARCH_TYPE_DESCRIPTION = { 'AIRCRAFT_DOT': 'Aircraft Airframe DOT Number:', 'BUSINESS_DEBTOR': 'Debtor Business Name:', 'INDIVIDUAL_DEBTOR': 'Debtor Individual Name:', 'MHR_NUMBER': 'Manufactured Home Registration Number:', 'REGISTRATION_NUMBER': 'Registration Number:', 'SERIAL_NUMBER': 'Serial/VIN Number:' } CERTIFIED_PARAM = 'certified' ROUTING_SLIP_PARAM = 'routingSlipNumber' DAT_NUMBER_PARAM = 'datNumber' BCOL_NUMBER_PARAM = 'bcolAccountNumber' @cors_preflight('POST,OPTIONS') @API.route('', methods=['POST', 'OPTIONS']) class SearchResource(Resource): """Resource for executing PPR searches.""" @staticmethod # @TRACER.trace() @cors.crossdomain(origin='*') @jwt.requires_auth def post(): # pylint: disable=too-many-branches,too-many-locals """Execute a new search request using criteria in the request body.""" try: # Quick check: must be staff or provide an account ID. account_id = resource_utils.get_account_id(request) if not account_id: return resource_utils.account_required_response() # Verify request JWT and account ID if not authorized(account_id, jwt): return resource_utils.unauthorized_error_response(account_id) request_json = request.get_json(silent=True) # Validate request against the schema. valid_format, errors = schema_utils.validate(request_json, 'searchQuery', 'ppr') if not valid_format: return resource_utils.validation_error_response(errors, VAL_ERROR) # Perform any extra data validation such as start and end dates here SearchRequest.validate_query(request_json) # Staff has special payment rules and setup. if is_staff_account(account_id) or is_bcol_help(account_id): return staff_search(request, request_json, account_id) query = SearchRequest.create_from_json(request_json, account_id, g.jwt_oidc_token_info.get('username', None)) # Charge a search fee. invoice_id = None payment = Payment(jwt=jwt.get_token_auth_header(), account_id=account_id, details=get_payment_details(query, request_json['type'])) transaction_type = TransactionTypes.SEARCH.value # if gov account user then check if sbc if is_gov_account(jwt): # if SBC staff user (authy, api call) then change transaction type to $10 fee is_sbc = is_sbc_office_account(jwt.get_token_auth_header(), account_id) if is_sbc: transaction_type = TransactionTypes.SEARCH_STAFF.value elif is_sbc is None: # didn't get a succesful response from auth raise BusinessException('Unable to verify possible SBC staff user before payment.', HTTPStatus.INTERNAL_SERVER_ERROR) pay_ref = payment.create_payment(transaction_type, 1, None, query.client_reference_id) invoice_id = pay_ref['invoiceId'] query.pay_invoice_id = int(invoice_id) query.pay_path = pay_ref['receipt'] # Execute the search query: treat no results as a success. try: query.search() # Now save the initial detail results in the search_result table with no # search selection criteria (the absence indicates an incomplete search). search_result = SearchResult.create_from_search_query(query) search_result.save() except Exception as db_exception: # noqa: B902; handle all db related errors. current_app.logger.error(SAVE_ERROR_MESSAGE.format(account_id, repr(db_exception))) if invoice_id is not None: current_app.logger.info(PAY_REFUND_MESSAGE.format(account_id, invoice_id)) try: payment.cancel_payment(invoice_id) except Exception as cancel_exception: # noqa: B902; log exception current_app.logger.error(PAY_REFUND_ERROR.format(account_id, invoice_id, repr(cancel_exception))) raise db_exception return query.json, HTTPStatus.CREATED except SBCPaymentException as pay_exception: return resource_utils.pay_exception_response(pay_exception, account_id) except BusinessException as exception: return resource_utils.business_exception_response(exception) except Exception as default_exception: # noqa: B902; return nicer default error return resource_utils.default_exception_response(default_exception) @cors_preflight('PUT,OPTIONS') @API.route('/<path:search_id>', methods=['PUT', 'OPTIONS']) class SearchDetailResource(Resource): """Resource for processing requests to update the search selection (UI autosave).""" @staticmethod # @TRACER.trace() @cors.crossdomain(origin='*') @jwt.requires_auth def put(search_id): """Execute a search selection update request replacing the current value with the request body contents.""" try: if search_id is None: return resource_utils.path_param_error_response('search ID') # Quick check: must be staff or provide an account ID. account_id = resource_utils.get_account_id(request) if account_id is None: return resource_utils.account_required_response() # Verify request JWT and account ID if not authorized(account_id, jwt): return resource_utils.unauthorized_error_response(account_id) request_json = request.get_json(silent=True) # Validate schema. valid_format, errors = schema_utils.validate(request_json, 'searchSummary', 'ppr') if not valid_format: return resource_utils.validation_error_response(errors, VAL_ERROR) search_request = SearchRequest.find_by_id(search_id) if not search_request: return resource_utils.not_found_error_response('searchId', search_id) # Save the updated search selection. search_request.update_search_selection(request_json) return jsonify(search_request.updated_selection), HTTPStatus.ACCEPTED except DatabaseException as db_exception: return resource_utils.db_exception_response(db_exception, account_id, 'PUT search selection update') except BusinessException as exception: return resource_utils.business_exception_response(exception) except Exception as default_exception: # noqa: B902; return nicer default error return resource_utils.default_exception_response(default_exception) def staff_search(req: request, request_json, account_id: str): """Execute a staff search with special payment validation and methods.""" payment_info = build_staff_payment(req, account_id) # bcol help is no fee; reg staff can be no fee. # FAS is routing slip only. # BCOL is dat number (optional) and BCOL account number (mandatory). # All staff roles including SBC can submit no fee searches. if ROUTING_SLIP_PARAM in payment_info and BCOL_NUMBER_PARAM in payment_info: return resource_utils.staff_payment_bcol_fas() if CERTIFIED_PARAM in payment_info: request_json['certified'] = True query: SearchRequest = SearchRequest.create_from_json(request_json, account_id, g.jwt_oidc_token_info.get('username', None)) # Always create a payment transaction. invoice_id = None payment = Payment(jwt=jwt.get_token_auth_header(), account_id=account_id, details=get_payment_details(query, request_json['type'])) # staff payment pay_ref = payment.create_payment_staff_search(payment_info, query.client_reference_id) invoice_id = pay_ref['invoiceId'] query.pay_invoice_id = int(invoice_id) query.pay_path = pay_ref['receipt'] # Execute the search query: treat no results as a success. try: query.search() # Now save the initial detail results in the search_result table with no # search selection criteria (the absence indicates an incomplete search). search_result = SearchResult.create_from_search_query(query) search_result.save() except Exception as db_exception: # noqa: B902; handle all db related errors. current_app.logger.error(SAVE_ERROR_MESSAGE.format(account_id, repr(db_exception))) if invoice_id is not None: current_app.logger.info(PAY_REFUND_MESSAGE.format(account_id, invoice_id)) try: payment.cancel_payment(invoice_id) except Exception as cancel_exception: # noqa: B902; log exception current_app.logger.error(PAY_REFUND_ERROR.format(account_id, invoice_id, repr(cancel_exception))) raise db_exception return query.json, HTTPStatus.CREATED def build_staff_payment(req: request, account_id: str): """Extract payment information from request parameters.""" payment_info = { 'transactionType': TransactionTypes.SEARCH_STAFF_NO_FEE.value } if is_bcol_help(account_id): return payment_info certified = req.args.get(CERTIFIED_PARAM) routing_slip = req.args.get(ROUTING_SLIP_PARAM) bcol_number = req.args.get(BCOL_NUMBER_PARAM) dat_number = req.args.get(DAT_NUMBER_PARAM) if certified is not None and isinstance(certified, bool) and certified: payment_info[CERTIFIED_PARAM] = True elif certified is not None and isinstance(certified, str) and \ certified.lower() in ['true', '1', 'y', 'yes']: payment_info[CERTIFIED_PARAM] = True if routing_slip is not None: payment_info[ROUTING_SLIP_PARAM] = str(routing_slip) if bcol_number is not None: payment_info[BCOL_NUMBER_PARAM] = str(bcol_number) if dat_number is not None: payment_info[DAT_NUMBER_PARAM] = str(dat_number) if ROUTING_SLIP_PARAM in payment_info or BCOL_NUMBER_PARAM in payment_info: if CERTIFIED_PARAM in payment_info: payment_info['transactionType'] = TransactionTypes.SEARCH_STAFF_CERTIFIED.value else: payment_info['transactionType'] = TransactionTypes.SEARCH_STAFF.value elif CERTIFIED_PARAM in payment_info: # Verify this is allowed. payment_info['transactionType'] = TransactionTypes.SEARCH_STAFF_CERTIFIED_NO_FEE.value return payment_info def get_payment_details(search_request, search_type): """Extract the payment details value from the search request criteria.""" details = { 'label': TO_SEARCH_TYPE_DESCRIPTION[search_type] } if search_request.search_type == SearchRequest.SearchTypes.BUSINESS_DEBTOR.value: details['value'] = search_request.search_criteria['criteria']['debtorName']['business'] elif search_request.search_type == SearchRequest.SearchTypes.INDIVIDUAL_DEBTOR.value: details['value'] = search_request.search_criteria['criteria']['debtorName']['last'] + ', ' +\ search_request.search_criteria['criteria']['debtorName']['first'] else: details['value'] = search_request.search_criteria['criteria']['value'] return details

a7efbeae0499e8157828f5c76354ddc9e5702563

py

Python

airbyte-integrations/connectors/source-smartsheets/source_smartsheets/__init__.py

rajatariya21/airbyte

11e70a7a96e2682b479afbe6f709b9a5fe9c4a8d

2020-09-21T13:45:56.000Z

2022-03-31T21:21:45.000Z

airbyte-integrations/connectors/source-smartsheets/source_smartsheets/__init__.py

rajatariya21/airbyte

11e70a7a96e2682b479afbe6f709b9a5fe9c4a8d

2020-09-21T00:43:50.000Z

2022-03-31T23:56:06.000Z

airbyte-integrations/connectors/source-smartsheets/source_smartsheets/__init__.py

rajatariya21/airbyte

11e70a7a96e2682b479afbe6f709b9a5fe9c4a8d

2020-09-20T05:48:47.000Z

2022-03-31T10:41:29.000Z

from .source import SourceSmartsheets __all__ = ["SourceSmartsheets"]

from .source import SourceSmartsheets __all__ = ["SourceSmartsheets"]

0c5b8758ff761fbeb2d01ca908ca3c7ef55e1e5d

py

Python

multiplayer-rl/mprl/rl/evaluation/payoff_table_gen.py

oslumbers/pipeline-psro

479ca386bf43a99fe7db372ce0017b6d3c4b7949

2020-11-04T13:50:58.000Z

2022-03-11T08:09:00.000Z

multiplayer-rl/mprl/rl/evaluation/payoff_table_gen.py

JBLanier/distributed-rl-for-imperfect-info-games

e150e99e433cadae27aa3ae5f6c7134f7e5c6fda

multiplayer-rl/mprl/rl/evaluation/payoff_table_gen.py

JBLanier/distributed-rl-for-imperfect-info-games

e150e99e433cadae27aa3ae5f6c7134f7e5c6fda

2020-12-07T11:39:37.000Z

2021-11-04T15:59:17.000Z

from mprl.rl.envs.stratego.stratego_spatial_multiagent_env import SpatialStrategoMultiAgentEnv from progress.bar import Bar import numpy as np import dill from multiprocessing.pool import Pool from multiprocessing import cpu_count

from mprl.rl.envs.stratego.stratego_spatial_multiagent_env import SpatialStrategoMultiAgentEnv from progress.bar import Bar import numpy as np import dill from multiprocessing.pool import Pool from multiprocessing import cpu_count def run_dill_encoded(payload): fun, args = dill.loads(payload) return fun(*args) def apply_async(pool, fun, args): payload = dill.dumps((fun, args)) return pool.apply_async(run_dill_encoded, (payload,)) def _eval_policy_matchup(get_policy_fn_a, get_policy_fn_b, env, stratego_env_config, games_per_matchup): resample_policy_fn_a = False if isinstance(get_policy_fn_a, tuple): get_policy_fn_a, resample_policy_fn_a = get_policy_fn_a policy_a_name, policy_a_get_action_index = get_policy_fn_a(stratego_env_config) resample_policy_fn_b = False if isinstance(get_policy_fn_b, tuple): get_policy_fn_b, resample_policy_fn_b = get_policy_fn_b policy_b_name, policy_b_get_action_index = get_policy_fn_b(stratego_env_config) policy_funcs = [policy_a_get_action_index, policy_b_get_action_index] policy_a_state = None policy_b_state = None policy_states = [policy_a_state, policy_b_state] def policy_index(agent_id): if agent_id == 1: return 0 else: return 1 policy_a_total_return = 0 ties = 0 # with Bar('Evaluating {} vs {}'.format(policy_a_name, policy_b_name), max=games_per_matchup) as bar: for game in range(games_per_matchup): if resample_policy_fn_a: policy_a_get_action_index(None, None, resample=True) if resample_policy_fn_b: policy_b_get_action_index(None, None, resample=True) obs = env.reset() dones = {} infos = {} game_length = 0 while True: if "__all__" in dones: if dones["__all__"]: break game_length += 1 assert len(obs) == 1 acting_agent_id, acting_agent_observation = list(obs.items())[0] acting_policy_fn = policy_funcs[policy_index(acting_agent_id)] acting_policy_state = policy_states[policy_index(acting_agent_id)] action_index, new_policy_state = acting_policy_fn(acting_agent_observation, acting_policy_state) policy_states[policy_index(acting_agent_id)] = new_policy_state obs, rewards, dones, infos = env.step(action_dict={acting_agent_id: action_index}) player_a_won = infos[1]['game_result'] == 'won' tied = infos[1]['game_result'] == 'tied' if player_a_won: policy_a_total_return += 1 elif not tied: policy_a_total_return -= 1 elif tied: ties += 1 # print(f"game length: {game_length}") # bar.next() policy_a_expected_payoff = policy_a_total_return / games_per_matchup tie_percentage = ties / games_per_matchup return policy_a_name, policy_b_name, policy_a_expected_payoff, tie_percentage def generate_payoff_table(get_policy_fn_list, games_per_matchup, stratego_env_config, policies_also_play_against_self=True, return_matrix=False, num_processes=0): env = SpatialStrategoMultiAgentEnv(env_config=stratego_env_config) payoff_table_dict = {} tie_dict = {} results_dict = {} payoff_table_matrix = np.zeros(shape=(len(get_policy_fn_list), len(get_policy_fn_list))) payoff_matrix_i_names = [None] * len(get_policy_fn_list) payoff_matrix_j_names = [None] * len(get_policy_fn_list) if num_processes == 0: num_processes = cpu_count() pool = Pool(processes=num_processes) for i in range(len(get_policy_fn_list)): get_policy_fn_a = get_policy_fn_list[i] if policies_also_play_against_self: j_start = i else: j_start = i + 1 for j in range(j_start, len(get_policy_fn_list)): get_policy_fn_b = get_policy_fn_list[j] res = apply_async(pool, _eval_policy_matchup, (get_policy_fn_a, get_policy_fn_b, env, stratego_env_config, games_per_matchup)) if i not in results_dict: results_dict[i] = {} results_dict[i][j] = res print(f"submitted {i} vs {j}") for i in range(len(get_policy_fn_list)): print("waiting for and processing results now...") if policies_also_play_against_self: j_start = i else: j_start = i + 1 for j in range(j_start, len(get_policy_fn_list)): policy_a_name, policy_b_name, policy_a_expected_payoff, tie_percentage = results_dict[i][j].get() payoff_matrix_i_names[i] = policy_a_name payoff_matrix_j_names[j] = policy_b_name if policy_a_name not in payoff_table_dict: payoff_table_dict[policy_a_name] = {} tie_dict[policy_a_name] = {} payoff_table_dict[policy_a_name][policy_b_name] = policy_a_expected_payoff tie_dict[policy_a_name][policy_b_name] = tie_percentage payoff_table_matrix[i, j] = policy_a_expected_payoff print(f"got {i} ({policy_a_name}) vs {j} ({policy_b_name})") if return_matrix: return payoff_table_dict, tie_dict, payoff_table_matrix, payoff_matrix_i_names return payoff_table_dict, tie_dict def generate_single_player_payoff_table(get_policy_fn_list, play_as_agent_id, games_per_matchup, stratego_env_config, resample_policy_every_game=False): env = SpatialStrategoMultiAgentEnv(env_config=stratego_env_config) payoff_table_dict = {} tie_dict = {} payoff_table_matrix = np.zeros(shape=(len(get_policy_fn_list))) for i in range(len(get_policy_fn_list)): get_policy_fn_a = get_policy_fn_list[i] policy_a_name, policy_a_get_action_index = get_policy_fn_a(stratego_env_config) policy_func = policy_a_get_action_index policy_a_state = None policy_state = policy_a_state policy_a_total_return = 0 ties = 0 with Bar('Evaluating {}'.format(policy_a_name), max=games_per_matchup) as bar: for game in range(games_per_matchup): if resample_policy_every_game: policy_func(None, None, resample=True) obs = env.reset() dones = {} infos = {} # env.base_env.print_fully_observable_board_to_console(state=env.state) while True: if "__all__" in dones: if dones["__all__"]: break assert len(obs) == 1 acting_agent_id, acting_agent_observation = list(obs.items())[0] assert acting_agent_id == play_as_agent_id acting_policy_fn = policy_func acting_policy_state = policy_state action_index, new_policy_state = acting_policy_fn(acting_agent_observation, acting_policy_state) policy_state = new_policy_state obs, rewards, dones, infos = env.step(action_dict={acting_agent_id: action_index}) player_a_won = infos[play_as_agent_id]['game_result'] == 'won' tied = infos[play_as_agent_id]['game_result'] == 'tied' if player_a_won: policy_a_total_return += 1 elif not tied: policy_a_total_return -= 1 elif tied: ties +=1 bar.next() policy_a_expected_payoff = policy_a_total_return / games_per_matchup tie_percentage = ties/games_per_matchup payoff_table_dict[policy_a_name] = policy_a_expected_payoff tie_dict[policy_a_name] = tie_percentage payoff_table_matrix[i] = policy_a_expected_payoff return payoff_table_dict, tie_dict

abcc03631da5d5f6c8a14d3e8de962d1222734b2

py

Python

backend/todoList/admin.py

crowdbotics-apps/basic-todo-app-28878

c84718ed616ec508eeb948caa78413f964f560b6

backend/todoList/admin.py

crowdbotics-apps/basic-todo-app-28878

c84718ed616ec508eeb948caa78413f964f560b6